[ { "path": ".deepsource.toml", "content": "version = 1\r\n\r\ntest_patterns = [\"python/prophet/tests/**\"]\r\nexclude_patterns = [\"R/**\", \"notebooks/**\", \"docs/**\", \"examples/**\"]\r\n\r\n[[analyzers]]\r\nname = \"python\"\r\nenabled = true\r\n\r\n [analyzers.meta]\r\n runtime_version = \"3.x.x\"\r\n" }, { "path": ".gitattributes", "content": "docs/* linguist-documentation\nexamples/* linguist-documentation\nnotebooks/* linguist-documentation\n" }, { "path": ".github/workflows/build-and-test.yml", "content": "name: Build\n\non:\n push:\n branches: [ main ]\n pull_request:\n branches: [ main ]\n\njobs:\n build-and-test-python:\n\n runs-on: ${{ matrix.os }}\n strategy:\n matrix:\n python-version: [\"3.12\"]\n os:\n - macos-14-large # Intel\n - macos-14-xlarge # ARM64\n - ubuntu-24.04 # Intel\n - ubuntu-24.04-arm # ARM64\n - windows-latest\n fail-fast: false\n\n steps:\n - name: \"Set environment variables (Windows)\"\n if: startsWith(runner.os, 'Windows')\n shell: pwsh\n run: |\n (Get-ItemProperty \"HKLM:System\\CurrentControlSet\\Control\\FileSystem\").LongPathsEnabled\n $os_version = (Get-CimInstance Win32_OperatingSystem).version\n Echo \"OS_VERSION=$os_version\" >> $env:GITHUB_ENV\n - uses: actions/checkout@v4\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v5\n with:\n python-version: ${{ matrix.python-version }}\n cache: pip\n cache-dependency-path: \"**/python/pyproject.toml\"\n - name: \"Restore RTools40\"\n if: startsWith(runner.os, 'Windows')\n id: cache-rtools\n uses: actions/cache@v4\n with:\n path: C:/rtools40\n key: ${{ runner.os }}-${{ env.OS_VERSION }}-rtools-v1\n - name: Install and test\n run: |\n cd python\n python -m pip install -U --editable \".[dev,parallel]\"\n python -m pytest prophet/tests/\n\n typecheck-python:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v6\n\n - uses: astral-sh/setup-uv@v7\n with:\n activate-environment: true\n\n - name: Install\n run: cd python && uv pip install \".[dev,parallel]\"\n\n - name: Typecheck with pyrefly\n run: cd python && pyrefly check --output-format=github\n\n build-and-test-r:\n\n runs-on: ${{ matrix.config.os }}\n name: R ${{ matrix.config.os }} (${{ matrix.config.r }})\n strategy:\n matrix:\n config:\n - {os: ubuntu-22.04, r: 'release'}\n fail-fast: false\n\n env:\n GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}\n R_KEEP_PKG_SOURCE: yes\n R_REMOTES_NO_ERRORS_FROM_WARNINGS: true\n PKGDIR: \"R/\"\n\n steps:\n - uses: actions/checkout@v4\n\n - uses: r-lib/actions/setup-pandoc@v2\n\n - uses: r-lib/actions/setup-r@v2\n with:\n r-version: ${{ matrix.config.r }}\n use-public-rspm: true\n\n # Install dependencies - separating the steps to fix dependency conflicts\n - name: Install dependencies\n run: |\n install.packages('remotes')\n remotes::install_deps(dependencies = TRUE)\n remotes::install_cran(c(\"rcmdcheck\", \"knitr\", \"testthat\", \"readr\", \"rmarkdown\"))\n shell: Rscript {0}\n working-directory: ${{ env.PKGDIR }}\n\n # Install Stan packages separately if needed\n - name: Install Stan packages\n run: |\n install.packages(c(\"cmdstanr\", \"posterior\"), repos = c(\"https://stan-dev.r-universe.dev\", getOption(\"repos\")))\n shell: Rscript {0}\n\n - name: Check\n uses: r-lib/actions/check-r-package@v2\n with:\n upload-snapshots: true\n build_args: 'c(\"--no-manual\",\"--compact-vignettes=gs+qpdf\")'\n working-directory: ${{ env.PKGDIR }}\n\n - name: Upload package source\n if: always() # Upload even if the check step fails\n uses: actions/upload-artifact@v4\n with:\n name: r-source-package\n path: \"**/*.tar.gz\"\n" }, { "path": ".github/workflows/wheel.yml", "content": "name: \"Create Python distributions and upload to PyPI\"\n\non:\n release:\n types: [ published ]\n workflow_dispatch: {}\n\n\nenv:\n STAN_BACKEND: \"CMDSTANPY\"\n\njobs:\n make-wheels:\n name: Make ${{ matrix.os }} ${{ matrix.cibw_arch }} wheels\n runs-on: ${{ matrix.os }}\n strategy:\n matrix:\n os:\n - macos-14-large # Intel\n - macos-14-xlarge # ARM64\n - ubuntu-24.04 # Intel\n - ubuntu-24.04-arm # ARM64\n - windows-latest\n cibw_arch: [\"native\"]\n # Build one wheel (ABI = none) using first build spec, then test on all python versions.\n cibw_build: [\"cp310-* cp311-* cp312-*\"]\n fail-fast: false\n\n steps:\n - name: \"Set environment variables (Windows)\"\n shell: pwsh\n if: startsWith(runner.os, 'Windows')\n run: |\n (Get-ItemProperty \"HKLM:System\\CurrentControlSet\\Control\\FileSystem\").LongPathsEnabled\n $os_version = (Get-CimInstance Win32_OperatingSystem).version\n Echo \"OS_VERSION=$os_version\" >> $env:GITHUB_ENV\n\n - name: \"Checkout repo\"\n uses: actions/checkout@v5\n\n - name: \"Restore RTools40\"\n if: startsWith(runner.os, 'Windows')\n id: cache-rtools\n uses: actions/cache@v4\n with:\n path: C:/rtools40\n key: ${{ runner.os }}-${{ env.OS_VERSION }}-rtools-v1\n\n - name: \"Build wheels\"\n uses: pypa/cibuildwheel@v3.2.1\n with:\n package-dir: python\n env:\n CIBW_ENVIRONMENT: >\n STAN_BACKEND=\"${{ env.STAN_BACKEND }}\"\n MACOSX_DEPLOYMENT_TARGET=\"${{ matrix.os == 'macos-14-large' && 10.11 || 11.0 }}\"\n CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 # distributed cmdstan binary is broken with manylinux_2_28\n CIBW_BUILD: ${{ matrix.cibw_build }}\n CIBW_SKIP: \"*musllinux*\"\n CIBW_ARCHS: ${{ matrix.cibw_arch }}\n CIBW_BUILD_FRONTEND: build\n CIBW_TEST_REQUIRES: pytest\n CIBW_TEST_COMMAND: pytest --pyargs prophet\n\n - name: \"Upload wheel as artifact\"\n uses: actions/upload-artifact@v4\n with:\n name: artifact-${{ matrix.os }}-${{ matrix.cibw_arch }}-wheel\n path: \"./**/*.whl\"\n\n make-sdist:\n name: Make source distribution\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n\n - run: pipx run build --sdist\n working-directory: python\n\n - uses: actions/upload-artifact@v4\n with:\n name: artifact-source-dist\n path: \"./**/dist/*.tar.gz\"\n\n upload:\n name: Upload to PyPI\n needs: [make-wheels, make-sdist]\n runs-on: ubuntu-latest\n if: github.event_name == 'release' && github.event.action == 'published'\n environment: release\n permissions:\n id-token: write\n steps:\n - name: Download all artifacts\n uses: actions/download-artifact@v4\n\n - name: Copy artifacts to dist/ folder\n run: |\n find . -name 'artifact-*' -exec unzip '{}' \\;\n mkdir -p dist/\n find . -name '*.tar.gz' -exec mv '{}' dist/ \\;\n find . -name '*.whl' -exec mv '{}' dist/ \\;\n\n - name: Upload\n uses: pypa/gh-action-pypi-publish@v1.13.0\n" }, { "path": ".gitignore", "content": "# Compiled python modules.\n*.pyc\n\n# Persisted models.\n*.pkl\n\n# Setuptools distribution folder.\npython/dist/\npython_shim/dist/\n\n# test cache\n.pytest_cache\npython/prophet/tests/dask-worker-space\n\n# Python cache\n__pycache__\n\n# Python egg metadata, regenerated from source files by setuptools.\npython/*.egg-info\npython/build/\npython_shim/*.egg-info\npython_shim/build/\n\n# Notebook checkpoints\n.ipynb_checkpoints\n\n*.*~\n\n*.idea\n*.vscode\n*.DS_Store\n*.envrc\n\n# Development with Python\npython/**/bin/\npython/**/etc/\npython/**/lib/\npython/**/lib64\npython/**/pyvenv.cfg\npython/**/share/\npython/**/stan_model/\n.Rproj.user\n" }, { "path": ".travis.yml", "content": "dist: xenial\naddons:\n apt:\n packages:\n - libv8-dev\nlanguage: python\n\njobs:\n include:\n - language: python\n python:\n - \"3.7\"\n cache: pip\n install:\n - pip install --upgrade pip\n - pip install -U -r python/requirements.txt dask[dataframe] distributed\n script:\n - cd python && python setup.py develop test\n - python setup.py clean\n - rm -rf prophet/stan_model\n - wget https://github.com/stan-dev/cmdstan/releases/download/v2.22.1/cmdstan-2.22.1.tar.gz -O /tmp/cmdstan.tar.gz > /dev/null\n - tar -xvf /tmp/cmdstan.tar.gz -C /tmp > /dev/null\n - make -C /tmp/cmdstan-2.22.1/ build > /dev/null\n - CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=CMDSTANPY python setup.py develop test\n\n - language: r\n r:\n - devel\n cache: packages\n install:\n - R -e 'install.packages(\"devtools\")'\n - R -e 'devtools::install_deps(\"R\", dependencies = TRUE)'\n - cd R\n - R CMD build .\n - R CMD INSTALL *tar.gz\n script:\n - R -e 'library(prophet); library(devtools); devtools::test()'\n\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Code of Conduct\nFacebook has adopted a Code of Conduct that we expect project participants to adhere to. Please [read the full text](https://code.fb.com/codeofconduct) so that you can understand what actions will and will not be tolerated." }, { "path": "Dockerfile", "content": "FROM python:3.7-stretch\n\nRUN apt-get -y install libc-dev\n\nRUN pip install --upgrade pip\n\nCOPY . .\n\nWORKDIR python\n\nRUN python -m pip install -e \".[dev, parallel]\"\n\nWORKDIR /\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) Facebook, Inc. and its affiliates.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "Makefile", "content": "build:\n\tdocker-compose build\n\npy-shell:\n\tdocker-compose run package ipython\n\nshell:\n\tdocker-compose run package bash\n" }, { "path": "R/.Rbuildignore", "content": "^renv$\n^renv\\.lock$\n^data-raw$\n^libs$\n^doc$\n^Meta$\n^.*\\.Rproj$\n^\\.Rproj\\.user$\n" }, { "path": "R/.gitignore", "content": ".DS_Store\n\n# History files\n.Rhistory\n.Rapp.history\n\n# Session Data files\n.RData\n\n# User-specific files\n.Ruserdata\n\n# Example code in package build process\n*-Ex.R\n\n# Output files from R CMD build\n/*.tar.gz\n\n# Output files from R CMD check\n/*.Rcheck/\n\n# RStudio files\n.Rproj.user/\n\n# produced vignettes\nvignettes/*.html\nvignettes/*.pdf\n\n# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3\n.httr-oauth\n\n# knitr and R markdown default cache directories\n*_cache/\n/cache/\n\n# Temporary files created by R markdown\n*.utf8.md\n*.knit.md\n\n# R Environment Variables\n.Renviron\ninst/doc\n\n# Stan\nexec/*.rda\nsrc/*.o\nsrc/*.h\nsrc/*.cc\nsrc/*.cpp\nsrc/*.hpp\nsrc/prophet.so\nsrc/prophet.dll\nsrc/init.o\n\n# Development\n.Rprofile\nrenv.lock\nrenv/\n" }, { "path": "R/DESCRIPTION", "content": "Package: prophet\nTitle: Automatic Forecasting Procedure\nVersion: 1.2.2\nDate: 2026-01-25\nAuthors@R: c(\n person(\"Cuong\", \"Duong\", email = \"cuong.duong242@gmail.com\", role = c(\"cre\", \"aut\")),\n person(\"Sean\", \"Taylor\", email = \"sjtz@pm.me\", role = \"aut\"),\n person(\"Ben\", \"Letham\", email = \"bletham@fb.com\", role = \"aut\")\n )\nDescription: Implements a procedure for forecasting time series data based on\n an additive model where non-linear trends are fit with yearly, weekly, and\n daily seasonality, plus holiday effects. It works best with time series\n that have strong seasonal effects and several seasons of historical data.\n Prophet is robust to missing data and shifts in the trend, and typically\n handles outliers well.\nURL: https://github.com/facebook/prophet\nBugReports: https://github.com/facebook/prophet/issues\nDepends:\n R (>= 3.4.0),\n Rcpp (>= 0.12.0),\n rlang (>= 0.3.0.1)\nImports:\n dplyr (>= 0.7.7),\n dygraphs (>= 1.1.1.4),\n extraDistr,\n ggplot2,\n grid,\n lubridate,\n methods,\n RcppParallel (>= 5.0.1),\n rstan (>= 2.18.1),\n rstantools (>= 2.0.0),\n scales,\n StanHeaders,\n stats,\n tidyr (>= 0.6.1),\n xts\nSuggests:\n cmdstanr,\n posterior,\n knitr,\n testthat,\n readr,\n rmarkdown\nAdditional_repositories: https://stan-dev.r-universe.dev\nSystemRequirements: GNU make, C++17\nBiarch: true\nLicense: MIT + file LICENSE\nLinkingTo:\n BH (>= 1.66.0),\n Rcpp (>= 0.12.0),\n RcppParallel (>= 5.0.1),\n RcppEigen (>= 0.3.3.3.0),\n rstan (>= 2.18.1),\n StanHeaders (>= 2.18.0)\nLazyData: true\nVignetteBuilder: knitr\nEncoding: UTF-8\nRoxygenNote: 7.2.0\n" }, { "path": "R/LICENSE", "content": "YEAR: 2017-present\nCOPYRIGHT HOLDER: Facebook, Inc.\n" }, { "path": "R/NAMESPACE", "content": "# Generated by roxygen2: do not edit by hand\n\nS3method(plot,prophet)\nS3method(predict,prophet)\nexport(add_changepoints_to_plot)\nexport(add_country_holidays)\nexport(add_regressor)\nexport(add_seasonality)\nexport(cross_validation)\nexport(dyplot.prophet)\nexport(fit.prophet)\nexport(generated_holidays)\nexport(make_future_dataframe)\nexport(performance_metrics)\nexport(plot_cross_validation_metric)\nexport(plot_forecast_component)\nexport(predictive_samples)\nexport(prophet)\nexport(prophet_plot_components)\nexport(regressor_coefficients)\nimport(Rcpp)\nimport(RcppParallel, except = LdFlags)\nimport(rlang)\nimportFrom(dplyr,\"%>%\")\nuseDynLib(prophet, .registration = TRUE)\n" }, { "path": "R/R/data.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n\n#' Generated table of holiday dates at the country level from 1995 to 2045\n#'\n#' The data is primarily based on the Python package [holidays](https://pypi.org/project/holidays/)\n#'\n#' @format A data frame with four variables: ds, holiday, country, year\n#' @source \\url{https://github.com/facebook/prophet/blob/main/python/scripts/generate_holidays_file.py}\n#' @export\n\"generated_holidays\"\n" }, { "path": "R/R/diagnostics.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n## Makes R CMD CHECK happy due to dplyr syntax below\nglobalVariables(c(\n \"ds\", \"y\", \"cap\", \"yhat\", \"yhat_lower\", \"yhat_upper\", \"size\"))\n\n#' Generate cutoff dates\n#'\n#' @param df Dataframe with historical data.\n#' @param horizon timediff forecast horizon.\n#' @param initial timediff initial window.\n#' @param period timediff Simulated forecasts are done with this period.\n#'\n#' @return Array of datetimes.\n#'\n#' @keywords internal\ngenerate_cutoffs <- function(df, horizon, initial, period) {\n # Last cutoff is (latest date in data) - (horizon).\n cutoff <- max(df$ds) - horizon\n tzone <- attr(cutoff, \"tzone\") # Timezone is wiped by putting in array\n result <- c(cutoff)\n while (result[length(result)] >= min(df$ds) + initial) {\n cutoff <- cutoff - period\n # If data does not exist in data range (cutoff, cutoff + horizon]\n if (!any((df$ds > cutoff) & (df$ds <= cutoff + horizon))) {\n # Next cutoff point is 'closest date before cutoff in data - horizon'\n if (cutoff > min(df$ds)) {\n closest.date <- max(df$ds[df$ds <= cutoff])\n cutoff <- closest.date - horizon\n }\n # else no data left, leave cutoff as is, it will be dropped.\n }\n result <- c(result, cutoff)\n }\n result <- utils::head(result, -1)\n if (length(result) == 0) {\n stop(paste(\n 'Less data than horizon after initial window.',\n 'Make horizon or initial shorter.'\n ))\n }\n # Reset timezones\n attr(result, \"tzone\") <- tzone\n message(paste(\n 'Making', length(result), 'forecasts with cutoffs between',\n result[length(result)], 'and', result[1]\n ))\n return(rev(result))\n}\n\n#' Cross-validation for time series.\n#'\n#' Computes forecasts from historical cutoff points which user can input.If\n#' not provided, these are computed beginning from (end - horizon), and working\n#' backwards making cutoffs with a spacing of period until initial is reached.\n#'\n#' When period is equal to the time interval of the data, this is the\n#' technique described in https://robjhyndman.com/hyndsight/tscv/ .\n#'\n#' @param model Fitted Prophet model.\n#' @param horizon Integer size of the horizon\n#' @param units String unit of the horizon, e.g., \"days\", \"secs\".\n#' @param period Integer amount of time between cutoff dates. Same units as\n#' horizon. If not provided, 0.5 * horizon is used.\n#' @param initial Integer size of the first training period. If not provided,\n#' 3 * horizon is used. Same units as horizon.\n#' @param cutoffs Vector of cutoff dates to be used during\n#' cross-validtation. If not provided works beginning from (end - horizon),\n#' works backwards making cutoffs with a spacing of period until initial is\n#' reached.\n#'\n#' @return A dataframe with the forecast, actual value, and cutoff date.\n#'\n#' @export\ncross_validation <- function(\n model, horizon, units, period = NULL, initial = NULL, cutoffs=NULL) {\n df <- model$history\n horizon.dt <- as.difftime(horizon, units = units)\n\n predict_columns <- c('ds', 'yhat')\n if (model$uncertainty.samples){\n predict_columns <- append(predict_columns, c('yhat_lower', 'yhat_upper'))\n }\n # Identify largest seasonality period\n period.max <- 0\n for (s in model$seasonalities) {\n period.max <- max(period.max, s$period)\n }\n seasonality.dt <- as.difftime(period.max, units = 'days')\n\n if (is.null(cutoffs)){\n\n # Set period\n if (is.null(period)) {\n period <- 0.5 * horizon\n }\n period.dt <- as.difftime(period, units = units)\n # Set initial\n if (is.null(initial)) {\n initial.dt <- max(\n as.difftime(3 * horizon, units = units),\n seasonality.dt\n )\n }else {\n initial.dt <- as.difftime(initial, units = units)\n }\n cutoffs <- generate_cutoffs(df, horizon.dt, initial.dt, period.dt)\n }else{\n cutoffs <- set_date(ds=cutoffs)\n # Validation\n if (min(cutoffs) <= min(df$ds)) {\n stop('Minimum cutoff value is not strictly greater than min date in history')\n }\n end_date_minus_horizon <- max(df$ds) - horizon.dt\n if (max(cutoffs) > end_date_minus_horizon) {\n stop('Maximum cutoff value is greater than end date minus horizon')\n }\n initial.dt <- cutoffs[1] - min(df$ds)\n }\n\n # Check if the initial window (that is, the amount of time between the\n # start of the history and the first cutoff) is less than the\n # maximum seasonality period\n if (initial.dt < seasonality.dt) {\n warning(paste0('Seasonality has period of ', period.max, ' days which ',\n 'is larger than initial window. Consider increasing initial.'))\n }\n\n predicts <- data.frame()\n for (i in 1:length(cutoffs)) {\n df.c <- single_cutoff_forecast(df, model, cutoffs[i], horizon.dt, predict_columns)\n predicts <- rbind(predicts, df.c)\n }\n return(predicts)\n}\n\n#' Forecast for a single cutoff.\n\n#' Used in cross_validation function when evaluating for multiple cutoffs.\n#'\n#' @param df Dataframe with history for cutoff.\n#' @param model Prophet model object.\n#' @param cutoff Datetime of cutoff.\n#' @param horizon.dt timediff forecast horizon.\n#' @param predict_columns Array of names of columns to be returned in output.\n#'\n#' @return Dataframe with forecast, actual value, and cutoff.\n#'\n#' @keywords internal\nsingle_cutoff_forecast <- function(df, model, cutoff, horizon.dt, predict_columns){\n m <- prophet_copy(model, cutoff)\n # Train model\n history.c <- dplyr::filter(df, ds <= cutoff)\n if (nrow(history.c) < 2) {\n stop('Less than two datapoints before cutoff. Increase initial window.')\n }\n fit.args <- c(list(m=m, df=history.c), model$fit.kwargs)\n m <- do.call(fit.prophet, fit.args)\n # Calculate yhat\n df.predict <- dplyr::filter(df, ds > cutoff, ds <= cutoff + horizon.dt)\n # Get the columns for the future dataframe\n columns <- 'ds'\n if (m$growth == 'logistic') {\n columns <- c(columns, 'cap')\n if (m$logistic.floor) {\n columns <- c(columns, 'floor')\n }\n }\n columns <- c(columns, names(m$extra_regressors))\n for (name in names(m$seasonalities)) {\n condition.name = m$seasonalities[[name]]$condition.name\n if (!is.null(condition.name)) {\n columns <- c(columns, condition.name)\n }\n }\n future <- df.predict[columns]\n yhat <- stats::predict(m, future)\n # Merge yhat, y, and cutoff.\n df.c <- dplyr::inner_join(df.predict, yhat[predict_columns], by = \"ds\")\n df.c <- df.c[c(predict_columns, \"y\")]\n df.c <- dplyr::select(df.c, y, predict_columns)\n df.c$cutoff <- cutoff\n return(df.c)\n}\n\n#' Copy Prophet object.\n#'\n#' @param m Prophet model object.\n#' @param cutoff Date, possibly as string. Changepoints are only retained if\n#' changepoints <= cutoff.\n#'\n#' @return An unfitted Prophet model object with the same parameters as the\n#' input model.\n#'\n#' @keywords internal\nprophet_copy <- function(m, cutoff = NULL) {\n if (is.null(m$history)) {\n stop(\"This is for copying a fitted Prophet object.\")\n }\n\n if (m$specified.changepoints) {\n changepoints <- m$changepoints\n if (!is.null(cutoff)) {\n cutoff <- set_date(cutoff)\n last_history_date <- max(m$history$ds[m$history$ds <= cutoff])\n changepoints <- changepoints[changepoints < last_history_date]\n }\n } else {\n changepoints <- NULL\n }\n # Auto seasonalities are set to FALSE because they are already set in\n # m$seasonalities.\n m2 <- prophet(\n growth = m$growth,\n changepoints = changepoints,\n n.changepoints = m$n.changepoints,\n changepoint.range = m$changepoint.range,\n yearly.seasonality = FALSE,\n weekly.seasonality = FALSE,\n daily.seasonality = FALSE,\n holidays = m$holidays,\n seasonality.mode = m$seasonality.mode,\n seasonality.prior.scale = m$seasonality.prior.scale,\n changepoint.prior.scale = m$changepoint.prior.scale,\n holidays.prior.scale = m$holidays.prior.scale,\n mcmc.samples = m$mcmc.samples,\n interval.width = m$interval.width,\n uncertainty.samples = m$uncertainty.samples,\n fit = FALSE\n )\n m2$extra_regressors <- m$extra_regressors\n m2$seasonalities <- m$seasonalities\n m2$country_holidays <- m$country_holidays\n return(m2)\n}\n\n#' Compute performance metrics from cross-validation results.\n#'\n#' Computes a suite of performance metrics on the output of cross-validation.\n#' By default the following metrics are included:\n#' 'mse': mean squared error,\n#' 'rmse': root mean squared error,\n#' 'mae': mean absolute error,\n#' 'mape': mean percent error,\n#' 'mdape': median percent error,\n#' 'smape': symmetric mean absolute percentage error,\n#' 'coverage': coverage of the upper and lower intervals\n#'\n#' A subset of these can be specified by passing a list of names as the\n#' `metrics` argument.\n#'\n#' Metrics are calculated over a rolling window of cross validation\n#' predictions, after sorting by horizon. Averaging is first done within each\n#' value of the horizon, and then across horizons as needed to reach the\n#' window size. The size of that window (number of simulated forecast points)\n#' is determined by the rolling_window argument, which specifies a proportion\n#' of simulated forecast points to include in each window. rolling_window=0\n#' will compute it separately for each horizon. The default of\n#' rolling_window=0.1 will use 10% of the rows in df in each window.\n#' rolling_window=1 will compute the metric across all simulated forecast\n#' points. The results are set to the right edge of the window.\n#'\n#' If rolling_window < 0, then metrics are computed at each datapoint with no\n#' averaging (i.e., 'mse' will actually be squared error with no mean).\n#'\n#' The output is a dataframe containing column 'horizon' along with columns\n#' for each of the metrics computed.\n#'\n#' @param df The dataframe returned by cross_validation.\n#' @param metrics An array of performance metrics to compute. If not provided,\n#' will use c('mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage').\n#' @param rolling_window Proportion of data to use in each rolling window for\n#' computing the metrics. Should be in [0, 1] to average.\n#'\n#' @return A dataframe with a column for each metric, and column 'horizon'.\n#'\n#' @export\nperformance_metrics <- function(df, metrics = NULL, rolling_window = 0.1) {\n valid_metrics <- c('mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage')\n if (is.null(metrics)) {\n metrics <- valid_metrics\n }\n if ((!('yhat_lower' %in% colnames(df)) | !('yhat_upper' %in% colnames(df))) & ('coverage' %in% metrics)){\n metrics <- metrics[metrics != 'coverage']\n }\n\n if (length(metrics) != length(unique(metrics))) {\n stop('Input metrics must be an array of unique values.')\n }\n if (!all(metrics %in% valid_metrics)) {\n stop(\n paste('Valid values for metrics are:', paste(valid_metrics, collapse = \", \"))\n )\n }\n df_m <- df\n df_m$horizon <- df_m$ds - df_m$cutoff\n df_m <- df_m[order(df_m$horizon),]\n if (('mape' %in% metrics) & (min(abs(df_m$y)) < 1e-8)) {\n message('Skipping MAPE because y close to 0')\n metrics <- metrics[metrics != 'mape']\n }\n if (('mdape' %in% metrics) & (min(abs(df_m$y)) < 1e-8)) {\n message('Skipping MDAPE because y close to 0')\n metrics <- metrics[metrics != 'mdape']\n }\n if (length(metrics) == 0) {\n return(NULL)\n }\n w <- as.integer(rolling_window * nrow(df_m))\n if (w >= 0) {\n w <- max(w, 1)\n w <- min(w, nrow(df_m))\n }\n # Compute all metrics\n dfs = list()\n for (metric in metrics) {\n dfs[[metric]] <- get(metric)(df_m, w)\n }\n res <- dfs[[metrics[1]]]\n for (i in 2:length(metrics)) {\n res_m <- dfs[[metrics[i]]]\n stopifnot(res$horizon == res_m$horizon)\n res[[metrics[i]]] = res_m[[metrics[i]]]\n }\n return(res)\n}\n\n#' Compute a rolling mean of x, after first aggregating by h\n#'\n#' Right-aligned. Computes a single mean for each unique value of h. Each mean\n#' is over at least w samples.\n#'\n#' @param x Array.\n#' @param h Array of horizon for each value in x.\n#' @param w Integer window size (number of elements).\n#' @param name String name for metric in result dataframe.\n#'\n#' @return Dataframe with columns horizon and name, the rolling mean of x.\n#'\n#' @importFrom dplyr \"%>%\"\n#' @keywords internal\nrolling_mean_by_h <- function(x, h, w, name) {\n # Aggregate over h\n df <- data.frame(x=x, h=h)\n df2 <- df %>%\n dplyr::group_by(h) %>%\n dplyr::summarise(sum = sum(x), n = dplyr::n())\n\n xs <- df2$sum\n ns <- df2$n\n hs <- df2$h\n \n trailing_i <- length(hs)\n x_sum <- 0\n n_sum <- 0\n # We don't know output size but it is bounded by length(hs)\n res_x <- vector(\"double\", length=length(hs))\n \n # Start from the right and work backwards\n for(i in length(hs):1) {\n x_sum <- x_sum + xs[i]\n n_sum <- n_sum + ns[i]\n while (n_sum >= w) {\n # Include points from the previous horizon. All of them if still\n # less than w, otherwise weight the mean by the difference\n excess_n <- n_sum - w\n excess_x <- excess_n * xs[i]/ ns[i]\n res_x[trailing_i] <- (x_sum - excess_x) / w\n x_sum <- x_sum - xs[trailing_i]\n n_sum <- n_sum - ns[trailing_i]\n trailing_i <- trailing_i - 1\n }\n }\n\n # R handles subsetting weirdly\n if(trailing_i == 0) {\n res_h <- hs\n } else {\n res_h <- hs[-(1:trailing_i)]\n res_x <- res_x[-(1:trailing_i)] \n }\n \n res <- data.frame(horizon=res_h)\n res[[name]] <- res_x\n\n return(res)\n}\n\n\n#' Compute a rolling median of x, after first aggregating by h\n#'\n#' Right-aligned. Computes a single median for each unique value of h. Each median\n#' is over at least w samples.\n#'\n#' For each h where there are fewer than w samples, we take samples from the previous h,\n# moving backwards. (In other words, we ~ assume that the x's are shuffled within each h.)\n#'\n#' @param x Array.\n#' @param h Array of horizon for each value in x.\n#' @param w Integer window size (number of elements).\n#' @param name String name for metric in result dataframe.\n#'\n#' @return Dataframe with columns horizon and name, the rolling median of x.\n#'\n#' @importFrom dplyr \"%>%\"\nrolling_median_by_h <- function(x, h, w, name) {\n # Aggregate over h\n df <- data.frame(x=x, h=h)\n grouped <- df %>% dplyr::group_by(h)\n df2 <- grouped %>%\n dplyr::summarise(size=dplyr::n()) %>%\n dplyr::arrange(h) %>%\n dplyr::select(h, size)\n\n hs <- df2$h\n res <- data.frame(horizon=c())\n res[[name]] <- c()\n\n # Start from the right and work backwards\n i <- length(hs)\n while (i > 0) {\n h_i <- hs[i]\n xs <- grouped %>%\n dplyr::filter(h==h_i)\n xs <- xs$x\n\n next_idx_to_add = which.max(h==h_i) - 1\n\n while ((length(xs) < w) & (next_idx_to_add > 0)) {\n # Include points from the previous horizon. All of them if still less\n # than w, otherwise just enough to get to w.\n xs <- c(x[next_idx_to_add], xs)\n next_idx_to_add = next_idx_to_add - 1\n }\n if (length(xs) < w) {\n # Ran out of horizons before enough points.\n break\n }\n res.i <- data.frame(horizon=hs[i])\n res.i[[name]] <- stats::median(xs)\n res <- rbind(res.i, res)\n i <- i - 1\n }\n return(res)\n}\n\n# The functions below specify performance metrics for cross-validation results.\n# Each takes as input the output of cross_validation, and returns the statistic\n# as a dataframe, given a window size for rolling aggregation.\n\n#' Mean squared error\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of mean squared errors.\n#'\n#' @keywords internal\nmse <- function(df, w) {\n se <- (df$y - df$yhat) ** 2\n if (w < 0) {\n return(data.frame(horizon = df$horizon, mse = se))\n }\n return(rolling_mean_by_h(x = se, h = df$horizon, w = w, name = 'mse'))\n}\n\n#' Root mean squared error\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of root mean squared errors.\n#'\n#' @keywords internal\nrmse <- function(df, w) {\n res <- mse(df, w)\n res$mse <- sqrt(res$mse)\n names(res)[names(res) == 'mse'] <- 'rmse'\n return(res)\n}\n\n#' Mean absolute error\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of mean absolute errors.\n#'\n#' @keywords internal\nmae <- function(df, w) {\n ae <- abs(df$y - df$yhat)\n if (w < 0) {\n return(data.frame(horizon = df$horizon, mae = ae))\n }\n return(rolling_mean_by_h(x = ae, h = df$horizon, w = w, name = 'mae'))\n}\n\n#' Mean absolute percent error\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of mean absolute percent errors.\n#'\n#' @keywords internal\nmape <- function(df, w) {\n ape <- abs((df$y - df$yhat) / df$y)\n if (w < 0) {\n return(data.frame(horizon = df$horizon, mape = ape))\n }\n return(rolling_mean_by_h(x = ape, h = df$horizon, w = w, name = 'mape'))\n}\n\n\n#' Median absolute percent error\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of median absolute percent errors.\n#'\n#' @keywords internal\nmdape <- function(df, w) {\n ape <- abs((df$y - df$yhat) / df$y)\n if (w < 0) {\n return(data.frame(horizon = df$horizon, mdape = ape))\n }\n return(rolling_median_by_h(x = ape, h = df$horizon, w = w, name = 'mdape'))\n}\n\n\n#' Symmetric mean absolute percentage error\n#' based on Chen and Yang (2004) formula\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of symmetric mean absolute percent errors.\n#'\n#' @keywords internal\nsmape <- function(df, w) {\n sape <- abs(df$y - df$yhat) / ((abs(df$y) + abs(df$yhat)) / 2)\n if (w < 0) {\n return(data.frame(horizon = df$horizon, smape = sape))\n }\n return(rolling_mean_by_h(x = sape, h = df$horizon, w = w, name = 'smape'))\n}\n\n\n#' Coverage\n#'\n#' @param df Cross-validation results dataframe.\n#' @param w Aggregation window size.\n#'\n#' @return Array of coverages\n#'\n#' @keywords internal\ncoverage <- function(df, w) {\n is_covered <- (df$y >= df$yhat_lower) & (df$y <= df$yhat_upper)\n if (w < 0) {\n return(data.frame(horizon = df$horizon, coverage = is_covered))\n }\n return(\n rolling_mean_by_h(x = is_covered, h = df$horizon, w = w, name = 'coverage')\n )\n}\n" }, { "path": "R/R/make_holidays.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n\n#' Return all possible holiday names of given country\n#'\n#' @param country.name Country name (character).\n#'\n#' @return A vector of all possible holiday names (unique) of given country.\n#' @keywords internal\nget_holiday_names <- function(country.name){\n holidays <- generated_holidays %>% \n dplyr::filter(country == country.name) %>%\n dplyr::select(holiday) %>%\n unique()\n return(holidays$holiday)\n}\n\n\n#' Make dataframe of holidays for given years and countries\n#'\n#' @param years List of years for which to include holiday dates.\n#' @param country.name Country name (character).\n#'\n#' @return Dataframe with 'ds' and 'holiday', which can directly feed\n#' to 'holidays' params in Prophet\n#' @keywords internal\nmake_holidays_df <- function(years, country.name){\n country.holidays = generated_holidays %>%\n dplyr::filter(country == country.name)\n max.year <- max(country.holidays$year)\n min.year <- min(country.holidays$year)\n if (max(years) > max.year || min(years) < min.year){\n warning.msg = paste(\"Holidays for\", country.name, \"are only supported from\", min.year, \n \"to\", max.year)\n warning(warning.msg)\n }\n holidays.df <- country.holidays %>%\n dplyr::filter(year %in% years) %>%\n dplyr::select(ds, holiday) %>%\n dplyr::mutate(ds = as.Date(ds)) %>%\n data.frame\n return(holidays.df)\n}\n" }, { "path": "R/R/plot.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n#' Merge history and forecast for plotting.\n#'\n#' @param m Prophet object.\n#' @param fcst Data frame returned by prophet predict.\n#'\n#' @importFrom dplyr \"%>%\"\n#' @keywords internal\ndf_for_plotting <- function(m, fcst) {\n # Make sure there is no y in fcst\n fcst$y <- NULL\n df <- m$history %>%\n dplyr::select(ds, y) %>%\n dplyr::full_join(fcst, by = \"ds\") %>%\n dplyr::arrange(ds)\n return(df)\n}\n\n#' Plot the prophet forecast.\n#'\n#' @param x Prophet object.\n#' @param fcst Data frame returned by predict(m, df).\n#' @param uncertainty Optional boolean indicating if the uncertainty interval for yhat\n#' should be plotted, which will only be done if x$uncertainty.samples > 0.\n#' Must be present in fcst as yhat_lower and yhat_upper.\n#' @param plot_cap Boolean indicating if the capacity should be shown in the\n#' figure, if available.\n#' @param xlabel Optional label for x-axis\n#' @param ylabel Optional label for y-axis\n#' @param ... additional arguments\n#'\n#' @return A ggplot2 plot.\n#'\n#' @examples\n#' \\dontrun{\n#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n#' y = sin(1:366/200) + rnorm(366)/10)\n#' m <- prophet(history)\n#' future <- make_future_dataframe(m, periods = 365)\n#' forecast <- predict(m, future)\n#' plot(m, forecast)\n#' }\n#'\n#' @export\nplot.prophet <- function(x, fcst, uncertainty = TRUE, plot_cap = TRUE,\n xlabel = 'ds', ylabel = 'y', ...) {\n df <- df_for_plotting(x, fcst)\n gg <- ggplot2::ggplot(df, ggplot2::aes(x = ds, y = y)) +\n ggplot2::labs(x = xlabel, y = ylabel)\n if (exists('cap', where = df) && plot_cap) {\n gg <- gg + ggplot2::geom_line(\n ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)\n }\n if (x$logistic.floor && exists('floor', where = df) && plot_cap) {\n gg <- gg + ggplot2::geom_line(\n ggplot2::aes(y = floor), linetype = 'dashed', na.rm = TRUE)\n }\n if (uncertainty && x$uncertainty.samples && exists('yhat_lower', where = df)) {\n gg <- gg +\n ggplot2::geom_ribbon(ggplot2::aes(ymin = yhat_lower, ymax = yhat_upper),\n alpha = 0.2,\n fill = \"#0072B2\",\n na.rm = TRUE)\n }\n gg <- gg +\n ggplot2::geom_point(na.rm=TRUE) +\n ggplot2::geom_line(ggplot2::aes(y = yhat), color = \"#0072B2\",\n na.rm = TRUE) +\n ggplot2::theme(aspect.ratio = 3 / 5)\n return(gg)\n}\n\n#' Plot the components of a prophet forecast.\n#' Prints a ggplot2 with whichever are available of: trend, holidays, weekly\n#' seasonality, yearly seasonality, and additive and multiplicative extra\n#' regressors.\n#'\n#' @param m Prophet object.\n#' @param fcst Data frame returned by predict(m, df).\n#' @param uncertainty Optional boolean indicating if the uncertainty interval should be\n#' plotted for the trend, from fcst columns trend_lower and trend_upper.This will\n#' only be done if m$uncertainty.samples > 0.\n#' @param plot_cap Boolean indicating if the capacity should be shown in the\n#' figure, if available.\n#' @param weekly_start Integer specifying the start day of the weekly\n#' seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day\n#' to Monday, and so on.\n#' @param yearly_start Integer specifying the start day of the yearly\n#' seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day\n#' to Jan 2, and so on.\n#' @param render_plot Boolean indicating if the plots should be rendered.\n#' Set to FALSE if you want the function to only return the list of panels.\n#'\n#' @return Invisibly return a list containing the plotted ggplot objects\n#'\n#' @export\n#' @importFrom dplyr \"%>%\"\nprophet_plot_components <- function(\n m, fcst, uncertainty = TRUE, plot_cap = TRUE, weekly_start = 0,\n yearly_start = 0, render_plot = TRUE\n) {\n dt <- diff(time_diff(m$history$ds, m$start))\n min.dt <- min(dt[dt > 0])\n # Plot the trend\n panels <- list(\n plot_forecast_component(m, fcst, 'trend', uncertainty, plot_cap))\n # Plot holiday components, if present.\n if (!is.null(m$train.holiday.names) && ('holidays' %in% colnames(fcst))) {\n panels[[length(panels) + 1]] <- plot_forecast_component(\n m, fcst, 'holidays', uncertainty, FALSE)\n }\n # Plot weekly seasonality, if present\n if (\"weekly\" %in% colnames(fcst)) {\n if (min.dt < 1) {\n panels[[length(panels) + 1]] <- plot_seasonality(m, 'weekly', uncertainty)\n } else {\n panels[[length(panels) + 1]] <- plot_weekly(m, uncertainty, weekly_start)\n }\n }\n # Plot yearly seasonality, if present\n if (\"yearly\" %in% colnames(fcst)) {\n panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty, yearly_start)\n }\n # Plot other seasonalities\n for (name in sort(names(m$seasonalities))) {\n if (!(name %in% c('weekly', 'yearly')) &&\n (name %in% colnames(fcst))) {\n if (m$seasonalities[[name]]$period == 7) {\n panels[[length(panels) + 1]] <- plot_weekly(m, uncertainty,\n weekly_start, name)\n } else if (m$seasonalities[[name]]$period == 365.25) {\n panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty,\n yearly_start, name)\n } else {\n panels[[length(panels) + 1]] <- plot_seasonality(m, name, uncertainty)\n }\n }\n }\n # Plot extra regressors\n regressors <- list(additive = FALSE, multiplicative = FALSE)\n for (name in names(m$extra_regressors)) {\n regressors[[m$extra_regressors[[name]]$mode]] <- TRUE\n }\n for (mode in c('additive', 'multiplicative')) {\n if ((regressors[[mode]]) &\n (paste0('extra_regressors_', mode) %in% colnames(fcst))\n ) {\n panels[[length(panels) + 1]] <- plot_forecast_component(\n m, fcst, paste0('extra_regressors_', mode), uncertainty, FALSE)\n }\n }\n\n if (render_plot) {\n # Make the plot.\n grid::grid.newpage()\n grid::pushViewport(grid::viewport(layout = grid::grid.layout(length(panels),\n 1)))\n for (i in seq_along(panels)) {\n print(panels[[i]], vp = grid::viewport(layout.pos.row = i,\n layout.pos.col = 1))\n }\n }\n return(invisible(panels))\n}\n\n#' Plot a particular component of the forecast.\n#'\n#' @param m Prophet model\n#' @param fcst Dataframe output of `predict`.\n#' @param name String name of the component to plot (column of fcst).\n#' @param uncertainty Optional boolean to plot uncertainty intervals, which will \n#' only be done if m$uncertainty.samples > 0.\n#' @param plot_cap Boolean indicating if the capacity should be shown in the\n#' figure, if available.\n#'\n#' @return A ggplot2 plot.\n#'\n#' @export\nplot_forecast_component <- function(\n m, fcst, name, uncertainty = TRUE, plot_cap = FALSE\n) {\n\n wrapped.name <- paste0(\"`\", name, \"`\")\n \n lower.name <- paste0(name, '_lower')\n lower.name <- paste0(\"`\", lower.name, \"`\")\n\n upper.name <- paste0(name, '_upper')\n upper.name <- paste0(\"`\", upper.name, \"`\")\n\n gg.comp <- ggplot2::ggplot(\n fcst, ggplot2::aes_string(x = 'ds', y = wrapped.name, group = 1)) +\n ggplot2::geom_line(color = \"#0072B2\", na.rm = TRUE)\n if (exists('cap', where = fcst) && plot_cap) {\n gg.comp <- gg.comp + ggplot2::geom_line(\n ggplot2::aes(y = cap), linetype = 'dashed', na.rm = TRUE)\n }\n if (exists('floor', where = fcst) && plot_cap) {\n gg.comp <- gg.comp + ggplot2::geom_line(\n ggplot2::aes(y = floor), linetype = 'dashed', na.rm = TRUE)\n }\n if (uncertainty && m$uncertainty.samples) {\n gg.comp <- gg.comp +\n ggplot2::geom_ribbon(\n ggplot2::aes_string(\n ymin = lower.name, ymax = upper.name\n ),\n alpha = 0.2,\n fill = \"#0072B2\",\n na.rm = TRUE)\n }\n if (name %in% m$component.modes$multiplicative) {\n gg.comp <- gg.comp + ggplot2::scale_y_continuous(labels = scales::percent)\n }\n return(gg.comp)\n}\n\n#' Prepare dataframe for plotting seasonal components.\n#'\n#' @param m Prophet object.\n#' @param ds Array of dates for column ds.\n#'\n#' @return A dataframe with seasonal components on ds.\n#'\n#' @keywords internal\nseasonality_plot_df <- function(m, ds) {\n df_list <- list(ds = ds, cap = 1, floor = 0)\n for (name in names(m$extra_regressors)) {\n df_list[[name]] <- 0\n }\n # Activate all conditional seasonality columns\n for (name in names(m$seasonalities)) {\n condition.name = m$seasonalities[[name]]$condition.name\n if (!is.null(condition.name)) {\n df_list[[condition.name]] <- TRUE\n }\n }\n df <- as.data.frame(df_list)\n df <- setup_dataframe(m, df)$df\n return(df)\n}\n\n#' Plot the weekly component of the forecast.\n#'\n#' @param m Prophet model object\n#' @param uncertainty Optional boolean to plot uncertainty intervals, which will\n#' only be done if m$uncertainty.samples > 0.\n#' @param weekly_start Integer specifying the start day of the weekly\n#' seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day\n#' to Monday, and so on.\n#' @param name Name of seasonality component if previously changed\n#' from default 'weekly'.\n#'\n#' @return A ggplot2 plot.\n#'\n#' @keywords internal\nplot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0,\n name = 'weekly') {\n # Compute weekly seasonality for a Sun-Sat sequence of dates.\n days <- seq(set_date('2017-01-01'), by='d', length.out=7) + as.difftime(\n weekly_start, units = \"days\")\n df.w <- seasonality_plot_df(m, days)\n seas <- predict_seasonal_components(m, df.w)\n seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))\n\n gg.weekly <- ggplot2::ggplot(\n seas, ggplot2::aes_string(x = 'dow', y = name, group = 1)) +\n ggplot2::geom_line(color = \"#0072B2\", na.rm = TRUE) +\n ggplot2::labs(x = \"Day of week\")\n if (uncertainty && m$uncertainty.samples) {\n gg.weekly <- gg.weekly +\n ggplot2::geom_ribbon(ggplot2::aes_string(ymin = paste0(name, '_lower'),\n ymax = paste0(name, '_upper')),\n alpha = 0.2,\n fill = \"#0072B2\",\n na.rm = TRUE)\n }\n if (m$seasonalities[[name]]$mode == 'multiplicative') {\n gg.weekly <- (\n gg.weekly + ggplot2::scale_y_continuous(labels = scales::percent)\n )\n }\n return(gg.weekly)\n}\n\n#' Plot the yearly component of the forecast.\n#'\n#' @param m Prophet model object.\n#' @param uncertainty Optional boolean to plot uncertainty intervals, which\n#' will only be done if m$uncertainty.samples > 0.\n#' @param yearly_start Integer specifying the start day of the yearly\n#' seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day\n#' to Jan 2, and so on.\n#' @param name Name of seasonality component if previously changed\n#' from default 'yearly'.\n#'\n#' @return A ggplot2 plot.\n#'\n#' @keywords internal\nplot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0,\n name = 'yearly') {\n # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.\n days <- seq(set_date('2017-01-01'), by='d', length.out=365) + as.difftime(\n yearly_start, units = \"days\")\n df.y <- seasonality_plot_df(m, days)\n seas <- predict_seasonal_components(m, df.y)\n seas$ds <- df.y$ds\n\n gg.yearly <- ggplot2::ggplot(\n seas, ggplot2::aes_string(x = 'ds', y = name, group = 1)) +\n ggplot2::geom_line(color = \"#0072B2\", na.rm = TRUE) +\n ggplot2::labs(x = \"Day of year\") +\n ggplot2::scale_x_datetime(labels = scales::date_format('%B %d'))\n if (uncertainty && m$uncertainty.samples) {\n gg.yearly <- gg.yearly +\n ggplot2::geom_ribbon(ggplot2::aes_string(ymin = paste0(name, '_lower'),\n ymax = paste0(name, '_upper')),\n alpha = 0.2,\n fill = \"#0072B2\",\n na.rm = TRUE)\n }\n if (m$seasonalities[[name]]$mode == 'multiplicative') {\n gg.yearly <- (\n gg.yearly + ggplot2::scale_y_continuous(labels = scales::percent)\n )\n }\n return(gg.yearly)\n}\n\n#' Plot a custom seasonal component.\n#'\n#' @param m Prophet model object.\n#' @param name String name of the seasonality.\n#' @param uncertainty Optional boolean to plot uncertainty intervals, which\n#' will only be done if m$uncertainty.samples > 0.\n#'\n#' @return A ggplot2 plot.\n#'\n#' @keywords internal\nplot_seasonality <- function(m, name, uncertainty = TRUE) {\n # Compute seasonality from Jan 1 through a single period.\n start <- set_date('2017-01-01')\n period <- m$seasonalities[[name]]$period\n end <- start + period * 24 * 3600\n plot.points <- 200\n days <- seq(from=start, to=end, length.out=plot.points)\n df.y <- seasonality_plot_df(m, days)\n seas <- predict_seasonal_components(m, df.y)\n seas$ds <- df.y$ds\n gg.s <- ggplot2::ggplot(\n seas, ggplot2::aes_string(x = 'ds', y = name, group = 1)) +\n ggplot2::geom_line(color = \"#0072B2\", na.rm = TRUE)\n\n date_breaks <- ggplot2::waiver()\n label <- 'ds'\n if (name == 'weekly') {\n fmt.str <- '%a'\n date_breaks <- '1 day'\n label <- 'Day of Week'\n } else if (name == 'daily') {\n fmt.str <- '%T'\n date_breaks <- '4 hours'\n label <- 'Hour of day'\n } else if (period <= 2) {\n fmt.str <- '%T'\n label <- 'Hours'\n } else if (period < 14) {\n fmt.str <- '%m/%d %R'\n } else {\n fmt.str <- '%m/%d'\n }\n gg.s <- gg.s +\n ggplot2::scale_x_datetime(\n labels = scales::date_format(fmt.str), date_breaks = date_breaks\n ) +\n ggplot2::xlab(label)\n if (uncertainty && m$uncertainty.samples) {\n gg.s <- gg.s +\n ggplot2::geom_ribbon(\n ggplot2::aes_string(\n ymin = paste0(name, '_lower'), ymax = paste0(name, '_upper')\n ),\n alpha = 0.2,\n fill = \"#0072B2\",\n na.rm = TRUE)\n }\n if (m$seasonalities[[name]]$mode == 'multiplicative') {\n gg.s <- gg.s + ggplot2::scale_y_continuous(labels = scales::percent)\n }\n return(gg.s)\n}\n\n#' Get layers to overlay significant changepoints on prophet forecast plot.\n#'\n#' @param m Prophet model object.\n#' @param threshold Numeric, changepoints where abs(delta) >= threshold are\n#' significant. (Default 0.01)\n#' @param cp_color Character, line color. (Default \"red\")\n#' @param cp_linetype Character or integer, line type. (Default \"dashed\")\n#' @param trend Logical, if FALSE, do not draw trend line. (Default TRUE)\n#' @param ... Other arguments passed on to layers.\n#'\n#' @return A list of ggplot2 layers.\n#'\n#' @examples\n#' \\dontrun{\n#' plot(m, fcst) + add_changepoints_to_plot(m)\n#' }\n#'\n#' @export\nadd_changepoints_to_plot <- function(m, threshold = 0.01, cp_color = \"red\",\n cp_linetype = \"dashed\", trend = TRUE, ...) {\n layers <- list()\n if (trend) {\n trend_layer <- ggplot2::geom_line(\n ggplot2::aes_string(\"ds\", \"trend\"), color = cp_color, ...)\n layers <- append(layers, trend_layer)\n }\n signif_changepoints <- m$changepoints[abs(m$params$delta) >= threshold]\n cp_layer <- ggplot2::geom_vline(\n xintercept = as.integer(signif_changepoints), color = cp_color,\n linetype = cp_linetype, ...)\n layers <- append(layers, cp_layer)\n return(layers)\n}\n\n#' Plot the prophet forecast.\n#'\n#' @param x Prophet object.\n#' @param fcst Data frame returned by predict(m, df).\n#' @param uncertainty Optional boolean indicating if the uncertainty interval for yhat\n#' should be plotted, which will only be done if x$uncertainty.samples > 0. Must be\n#' present in fcst as yhat_lower and yhat_upper.\n#' @param ... additional arguments passed to dygraphs::dygraph\n#' @importFrom dplyr \"%>%\"\n#' @return A dygraph plot.\n#'\n#' @examples\n#' \\dontrun{\n#' history <- data.frame(\n#' ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n#' y = sin(1:366/200) + rnorm(366)/10)\n#' m <- prophet(history)\n#' future <- make_future_dataframe(m, periods = 365)\n#' forecast <- predict(m, future)\n#' dyplot.prophet(m, forecast)\n#' }\n#'\n#' @export\ndyplot.prophet <- function(x, fcst, uncertainty=TRUE,\n ...)\n{\n forecast.label='Predicted'\n actual.label='Actual'\n # create data.frame for plotting\n df <- df_for_plotting(x, fcst)\n\n # build variables to include, or not, the uncertainty data\n if(uncertainty && x$uncertainty.samples && exists(\"yhat_lower\", where = df))\n {\n colsToKeep <- c('y', 'yhat', 'yhat_lower', 'yhat_upper')\n forecastCols <- c('yhat_lower', 'yhat', 'yhat_upper')\n } else\n {\n colsToKeep <- c('y', 'yhat')\n forecastCols <- c('yhat')\n }\n # convert to xts for easier date handling by dygraph\n dfTS <- xts::xts(df %>% dplyr::select(.dots=colsToKeep), order.by = df$ds)\n\n # base plot\n dyBase <- dygraphs::dygraph(dfTS, ...)\n\n presAnnotation <- function(dygraph, x, text) {\n dygraph %>%\n dygraphs::dyAnnotation(x, text, text, attachAtBottom = TRUE)\n }\n\n dyBase <- dyBase %>%\n # plot actual values\n dygraphs::dySeries(\n 'y', label=actual.label, color='black', drawPoints=TRUE, strokeWidth=0\n ) %>%\n # plot forecast and ribbon\n dygraphs::dySeries(forecastCols, label=forecast.label, color='blue') %>%\n # allow zooming\n dygraphs::dyRangeSelector() %>%\n # make unzoom button\n dygraphs::dyUnzoom()\n if (!is.null(x$holidays)) {\n for (i in 1:nrow(x$holidays)) {\n # make a gray line\n dyBase <- dyBase %>% dygraphs::dyEvent(\n x$holidays$ds[i],color = \"rgb(200,200,200)\", strokePattern = \"solid\")\n dyBase <- dyBase %>% dygraphs::dyAnnotation(\n x$holidays$ds[i], x$holidays$holiday[i], x$holidays$holiday[i],\n attachAtBottom = TRUE)\n }\n }\n return(dyBase)\n}\n\n#' Plot a performance metric vs. forecast horizon from cross validation.\n\n#' Cross validation produces a collection of out-of-sample model predictions\n#' that can be compared to actual values, at a range of different horizons\n#' (distance from the cutoff). This computes a specified performance metric\n#' for each prediction, and aggregated over a rolling window with horizon.\n#'\n#' This uses fbprophet.diagnostics.performance_metrics to compute the metrics.\n#' Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'.\n#'\n#' rolling_window is the proportion of data included in the rolling window of\n#' aggregation. The default value of 0.1 means 10% of data are included in the\n#' aggregation for computing the metric.\n#'\n#' As a concrete example, if metric='mse', then this plot will show the\n#' squared error for each cross validation prediction, along with the MSE\n#' averaged over rolling windows of 10% of the data.\n#'\n#' @param df_cv The output from fbprophet.diagnostics.cross_validation.\n#' @param metric Metric name, one of 'mse', 'rmse', 'mae', 'mape', 'coverage'.\n#' @param rolling_window Proportion of data to use for rolling average of\n#' metric. In [0, 1]. Defaults to 0.1.\n#'\n#' @return A ggplot2 plot.\n#'\n#' @export\nplot_cross_validation_metric <- function(df_cv, metric, rolling_window=0.1) {\n df_none <- performance_metrics(df_cv, metrics = metric, rolling_window = -1)\n df_h <- performance_metrics(\n df_cv, metrics = metric, rolling_window = rolling_window\n )\n\n # Better plotting of difftime\n # Target ~10 ticks\n tick_w <- max(as.double(df_none$horizon, units = 'secs')) / 10.\n # Find the largest time resolution that has <1 unit per bin\n dts <- c('days', 'hours', 'mins', 'secs')\n dt_conversions <- c(\n 24 * 60 * 60,\n 60 * 60,\n 60,\n 1\n )\n for (i in seq_along(dts)) {\n if (as.difftime(1, units = dts[i]) < as.difftime(tick_w, units = 'secs')) {\n break\n }\n }\n df_none$x_plt <- (\n as.double(df_none$horizon, units = 'secs') / dt_conversions[i]\n )\n df_h$x_plt <- as.double(df_h$horizon, units = 'secs') / dt_conversions[i]\n\n gg <- (\n ggplot2::ggplot(df_none, ggplot2::aes_string(x = 'x_plt', y = metric)) +\n ggplot2::labs(x = paste0('Horizon (', dts[i], ')'), y = metric) +\n ggplot2::geom_point(color = 'gray') +\n ggplot2::geom_line(\n data = df_h, ggplot2::aes_string(x = 'x_plt', y = metric), color = 'blue'\n ) +\n ggplot2::theme(aspect.ratio = 3 / 5)\n )\n\n return(gg)\n}\n" }, { "path": "R/R/prophet.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n## Makes R CMD CHECK happy due to dplyr syntax below\nglobalVariables(c(\n \"ds\", \"y\", \"cap\", \".\",\n \"component\", \"dow\", \"doy\", \"holiday\", \"holidays\", \"holidays_lower\", \"generated_holidays\",\n \"holidays_upper\", \"ix\", \"lower\", \"n\", \"stat\", \"trend\", \"row_number\", \"extra_regressors\", \"col\",\n \"trend_lower\", \"trend_upper\", \"upper\", \"value\", \"weekly\", \"weekly_lower\", \"weekly_upper\",\n \"x\", \"yearly\", \"yearly_lower\", \"yearly_upper\", \"yhat\", \"yhat_lower\", \"yhat_upper\",\n \"country\", \"year\"\n))\n\n#' Prophet forecaster.\n#'\n#' @param df (optional) Dataframe containing the history. Must have columns ds\n#' (date type) and y, the time series. If growth is logistic, then df must\n#' also have a column cap that specifies the capacity at each ds. If not\n#' provided, then the model object will be instantiated but not fit; use\n#' fit.prophet(m, df) to fit the model.\n#' @param growth String 'linear', 'logistic', or 'flat' to specify a linear,\n#' logistic or flat trend.\n#' @param changepoints Vector of dates at which to include potential\n#' changepoints. If not specified, potential changepoints are selected\n#' automatically.\n#' @param n.changepoints Number of potential changepoints to include. Not used\n#' if input `changepoints` is supplied. If `changepoints` is not supplied,\n#' then n.changepoints potential changepoints are selected uniformly from the\n#' first `changepoint.range` proportion of df$ds.\n#' @param changepoint.range Proportion of history in which trend changepoints\n#' will be estimated. Defaults to 0.8 for the first 80%. Not used if\n#' `changepoints` is specified.\n#' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE, FALSE,\n#' or a number of Fourier terms to generate.\n#' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE, FALSE,\n#' or a number of Fourier terms to generate.\n#' @param daily.seasonality Fit daily seasonality. Can be 'auto', TRUE, FALSE,\n#' or a number of Fourier terms to generate.\n#' @param holidays data frame with columns holiday (character) and ds (date\n#' type)and optionally columns lower_window and upper_window which specify a\n#' range of days around the date to be included as holidays. lower_window=-2\n#' will include 2 days prior to the date as holidays. Also optionally can have\n#' a column prior_scale specifying the prior scale for each holiday.\n#' @param seasonality.mode 'additive' (default) or 'multiplicative'.\n#' @param seasonality.prior.scale Parameter modulating the strength of the\n#' seasonality model. Larger values allow the model to fit larger seasonal\n#' fluctuations, smaller values dampen the seasonality. Can be specified for\n#' individual seasonalities using add_seasonality.\n#' @param holidays.prior.scale Parameter modulating the strength of the holiday\n#' components model, unless overridden in the holidays input.\n#' @param changepoint.prior.scale Parameter modulating the flexibility of the\n#' automatic changepoint selection. Large values will allow many changepoints,\n#' small values will allow few changepoints.\n#' @param mcmc.samples Integer, if greater than 0, will do full Bayesian\n#' inference with the specified number of MCMC samples. If 0, will do MAP\n#' estimation.\n#' @param interval.width Numeric, width of the uncertainty intervals provided\n#' for the forecast. If mcmc.samples=0, this will be only the uncertainty in\n#' the trend using the MAP estimate of the extrapolated generative model. If\n#' mcmc.samples>0, this will be integrated over all model parameters, which\n#' will include uncertainty in seasonality.\n#' @param uncertainty.samples Number of simulated draws used to estimate\n#' uncertainty intervals. Settings this value to 0 or False will disable\n#' uncertainty estimation and speed up the calculation.\n#' @param backend Whether to use the \"rstan\" or \"cmdstanr\" backend to fit the\n#' model. If not provided, uses the R_STAN_BACKEND environment variable.\n#' @param fit Boolean, if FALSE the model is initialized but not fit.\n#' @param ... Additional arguments, passed to \\code{\\link{fit.prophet}}\n#'\n#' @return A prophet model.\n#'\n#' @examples\n#' \\dontrun{\n#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n#' y = sin(1:366/200) + rnorm(366)/10)\n#' m <- prophet(history)\n#' }\n#'\n#' @export\n#' @importFrom dplyr \"%>%\"\n#' @import Rcpp\n#' @rawNamespace import(RcppParallel, except = LdFlags)\n#' @import rlang\n#' @useDynLib prophet, .registration = TRUE\nprophet <- function(df = NULL,\n growth = 'linear',\n changepoints = NULL,\n n.changepoints = 25,\n changepoint.range = 0.8,\n yearly.seasonality = 'auto',\n weekly.seasonality = 'auto',\n daily.seasonality = 'auto',\n holidays = NULL,\n seasonality.mode = 'additive',\n seasonality.prior.scale = 10,\n holidays.prior.scale = 10,\n changepoint.prior.scale = 0.05,\n mcmc.samples = 0,\n interval.width = 0.80,\n uncertainty.samples = 1000,\n fit = TRUE,\n backend = NULL,\n ...\n) {\n if (!is.null(changepoints)) {\n n.changepoints <- length(changepoints)\n }\n\n if (is.null(backend)) backend <- get_stan_backend()\n\n m <- list(\n growth = growth,\n changepoints = changepoints,\n n.changepoints = n.changepoints,\n changepoint.range = changepoint.range,\n yearly.seasonality = yearly.seasonality,\n weekly.seasonality = weekly.seasonality,\n daily.seasonality = daily.seasonality,\n holidays = holidays,\n seasonality.mode = seasonality.mode,\n seasonality.prior.scale = seasonality.prior.scale,\n changepoint.prior.scale = changepoint.prior.scale,\n holidays.prior.scale = holidays.prior.scale,\n mcmc.samples = mcmc.samples,\n interval.width = interval.width,\n uncertainty.samples = uncertainty.samples,\n backend = backend,\n specified.changepoints = !is.null(changepoints),\n start = NULL, # This and following attributes are set during fitting\n y.scale = NULL,\n logistic.floor = FALSE,\n t.scale = NULL,\n changepoints.t = NULL,\n seasonalities = list(),\n extra_regressors = list(),\n country_holidays = NULL,\n stan.fit = NULL,\n params = list(),\n history = NULL,\n history.dates = NULL,\n train.holiday.names = NULL,\n train.component.cols = NULL,\n component.modes = NULL,\n fit.kwargs = list()\n )\n m <- validate_inputs(m)\n class(m) <- append(\"prophet\", class(m))\n if ((fit) && (!is.null(df))) {\n m <- fit.prophet(m, df, ...)\n }\n return(m)\n}\n\n#' Validates the inputs to Prophet.\n#'\n#' @param m Prophet object.\n#'\n#' @return The Prophet object.\n#'\n#' @keywords internal\nvalidate_inputs <- function(m) {\n if (!(m$growth %in% c('linear', 'logistic', 'flat'))) {\n stop(\"Parameter 'growth' should be 'linear', 'logistic', or 'flat'.\")\n }\n if ((m$changepoint.range < 0) | (m$changepoint.range > 1)) {\n stop(\"Parameter 'changepoint.range' must be in [0, 1]\")\n }\n if (!is.null(m$holidays)) {\n if (!(exists('holiday', where = m$holidays))) {\n stop('Holidays dataframe must have holiday field.')\n }\n if (!(exists('ds', where = m$holidays))) {\n stop('Holidays dataframe must have ds field.')\n }\n m$holidays$ds <- as.Date(m$holidays$ds)\n if (any(is.na(m$holidays$ds)) | any(is.na(m$holidays$holiday))) {\n stop('Found NA in the holidays dataframe.')\n }\n has.lower <- exists('lower_window', where = m$holidays)\n has.upper <- exists('upper_window', where = m$holidays)\n if (has.lower + has.upper == 1) {\n stop(paste('Holidays must have both lower_window and upper_window,',\n 'or neither.'))\n }\n if (has.lower) {\n if(max(m$holidays$lower_window, na.rm=TRUE) > 0) {\n stop('Holiday lower_window should be <= 0')\n }\n if(min(m$holidays$upper_window, na.rm=TRUE) < 0) {\n stop('Holiday upper_window should be >= 0')\n }\n }\n for (h in unique(m$holidays$holiday)) {\n validate_column_name(m, h, check_holidays = FALSE)\n }\n }\n if (!(m$seasonality.mode %in% c('additive', 'multiplicative'))) {\n stop(\"seasonality.mode must be 'additive' or 'multiplicative'\")\n }\n return(m)\n}\n\n#' Validates the name of a seasonality, holiday, or regressor.\n#'\n#' @param m Prophet object.\n#' @param name string\n#' @param check_holidays bool check if name already used for holiday\n#' @param check_seasonalities bool check if name already used for seasonality\n#' @param check_regressors bool check if name already used for regressor\n#'\n#' @keywords internal\nvalidate_column_name <- function(\n m, name, check_holidays = TRUE, check_seasonalities = TRUE,\n check_regressors = TRUE\n) {\n if (grepl(\"_delim_\", name)) {\n stop('Holiday name cannot contain \"_delim_\"')\n }\n reserved_names = c(\n 'trend', 'additive_terms', 'daily', 'weekly', 'yearly',\n 'holidays', 'zeros', 'extra_regressors_additive', 'yhat',\n 'extra_regressors_multiplicative', 'multiplicative_terms'\n )\n rn_l = paste(reserved_names,\"_lower\",sep=\"\")\n rn_u = paste(reserved_names,\"_upper\",sep=\"\")\n reserved_names = c(reserved_names, rn_l, rn_u,\n c(\"ds\", \"y\", \"cap\", \"floor\", \"y_scaled\", \"cap_scaled\"))\n if(name %in% reserved_names){\n stop(\"Name \", name, \" is reserved.\")\n }\n if(check_holidays & !is.null(m$holidays) &\n (name %in% unique(m$holidays$holiday))){\n stop(\"Name \", name, \" already used for a holiday.\")\n }\n if(check_holidays & !is.null(m$country_holidays)){\n if(name %in% get_holiday_names(m$country_holidays)){\n stop(\"Name \", name, \" is a holiday name in \", m$country_holidays, \".\")\n }\n }\n if(check_seasonalities & (!is.null(m$seasonalities[[name]]))){\n stop(\"Name \", name, \" already used for a seasonality.\")\n }\n if(check_regressors & (!is.null(m$seasonalities[[name]]))){\n stop(\"Name \", name, \" already used for an added regressor.\")\n }\n}\n\n#' Convert date vector\n#'\n#' Convert the date to POSIXct object. Timezones are stripped and replaced\n#' with GMT.\n#'\n#' @param ds Date vector\n#'\n#' @return vector of POSIXct object converted from date\n#'\n#' @keywords internal\nset_date <- function(ds) {\n if (length(ds) == 0) {\n return(NULL)\n }\n\n if (is.factor(ds)) {\n ds <- as.character(ds)\n }\n\n # If a datetime, strip timezone and replace with GMT.\n if (lubridate::is.instant(ds)) {\n ds <- as.POSIXct(lubridate::force_tz(ds, \"GMT\"), tz = \"GMT\")\n }\n else {\n # Assume it can be coerced into POSIXct\n if (min(nchar(ds), na.rm=TRUE) < 12) {\n ds <- as.POSIXct(ds, format = \"%Y-%m-%d\", tz = \"GMT\")\n } else {\n ds <- as.POSIXct(ds, format = \"%Y-%m-%d %H:%M:%S\", tz = \"GMT\")\n }\n }\n\n attr(ds, \"tzone\") <- \"GMT\"\n return(ds)\n}\n\n#' Time difference between datetimes\n#'\n#' Compute time difference of two POSIXct objects\n#'\n#' @param ds1 POSIXct object\n#' @param ds2 POSIXct object\n#' @param units string units of difference, e.g. 'days' or 'secs'.\n#'\n#' @return numeric time difference\n#'\n#' @keywords internal\ntime_diff <- function(ds1, ds2, units = \"days\") {\n return(as.numeric(difftime(ds1, ds2, units = units)))\n}\n\n#' Prepare dataframe for fitting or predicting.\n#'\n#' Adds a time index and scales y. Creates auxiliary columns 't', 't_ix',\n#' 'y_scaled', and 'cap_scaled'. These columns are used during both fitting\n#' and predicting.\n#'\n#' @param m Prophet object.\n#' @param df Data frame with columns ds, y, and cap if logistic growth. Any\n#' specified additional regressors must also be present.\n#' @param initialize_scales Boolean set scaling factors in m from df.\n#'\n#' @return list with items 'df' and 'm'.\n#'\n#' @keywords internal\nsetup_dataframe <- function(m, df, initialize_scales = FALSE) {\n if (exists('y', where=df)) {\n df$y <- as.numeric(df$y)\n if (any(is.infinite(df$y))) {\n stop(\"Found infinity in column y.\")\n }\n }\n df$ds <- set_date(df$ds)\n if (anyNA(df$ds)) {\n stop(paste('Unable to parse date format in column ds. Convert to date ',\n 'format (%Y-%m-%d or %Y-%m-%d %H:%M:%S) and check that there',\n 'are no NAs.'))\n }\n for (name in names(m$extra_regressors)) {\n if (!(name %in% colnames(df))) {\n stop('Regressor \"', name, '\" missing from dataframe')\n }\n df[[name]] <- as.numeric(df[[name]])\n if (anyNA(df[[name]])) {\n stop('Found NaN in column ', name)\n }\n }\n for (name in names(m$seasonalities)) {\n condition.name = m$seasonalities[[name]]$condition.name\n if (!is.null(condition.name)) {\n if (!(condition.name %in% colnames(df))) {\n stop('Condition \"', name, '\" missing from dataframe')\n }\n if(!all(df[[condition.name]] %in% c(FALSE,TRUE))) {\n stop('Found non-boolean in column ', name)\n }\n df[[condition.name]] <- as.logical(df[[condition.name]])\n }\n }\n\n df <- df %>%\n dplyr::arrange(ds)\n\n m <- initialize_scales_fn(m, initialize_scales, df)\n\n if (m$logistic.floor) {\n if (!('floor' %in% colnames(df))) {\n stop(\"Expected column 'floor'.\")\n }\n } else {\n df$floor <- 0\n }\n\n if (m$growth == 'logistic') {\n if (!(exists('cap', where=df))) {\n stop('Capacities must be supplied for logistic growth.')\n }\n if (any(df$cap <= df$floor)) {\n stop('cap must be greater than floor (which defaults to 0).')\n }\n df <- df %>%\n dplyr::mutate(cap_scaled = (cap - floor) / m$y.scale)\n }\n\n df$t <- time_diff(df$ds, m$start, \"secs\") / m$t.scale\n if (exists('y', where=df)) {\n df$y_scaled <- (df$y - df$floor) / m$y.scale\n }\n\n for (name in names(m$extra_regressors)) {\n props <- m$extra_regressors[[name]]\n df[[name]] <- (df[[name]] - props$mu) / props$std\n }\n return(list(\"m\" = m, \"df\" = df))\n}\n\n#' Initialize model scales.\n#'\n#' Sets model scaling factors using df.\n#'\n#' @param m Prophet object.\n#' @param initialize_scales Boolean set the scales or not.\n#' @param df Dataframe for setting scales.\n#'\n#' @return Prophet object with scales set.\n#'\n#' @keywords internal\ninitialize_scales_fn <- function(m, initialize_scales, df) {\n if (!initialize_scales) {\n return(m)\n }\n if ((m$growth == 'logistic') && ('floor' %in% colnames(df))) {\n m$logistic.floor <- TRUE\n floor <- df$floor\n } else {\n floor <- 0\n }\n m$y.scale <- max(abs(df$y - floor))\n if (m$y.scale == 0) {\n m$y.scale <- 1\n }\n m$start <- min(df$ds)\n m$t.scale <- time_diff(max(df$ds), m$start, \"secs\")\n for (name in names(m$extra_regressors)) {\n standardize <- m$extra_regressors[[name]]$standardize\n n.vals <- length(unique(df[[name]]))\n if (n.vals < 2) {\n standardize <- FALSE\n }\n if (standardize == 'auto') {\n if (n.vals == 2 && all(sort(unique(df[[name]])) == c(0, 1))) {\n # Don't standardize binary variables\n standardize <- FALSE\n } else {\n standardize <- TRUE\n }\n }\n if (standardize) {\n mu <- mean(df[[name]])\n std <- stats::sd(df[[name]])\n m$extra_regressors[[name]]$mu <- mu\n m$extra_regressors[[name]]$std <- std\n }\n }\n return(m)\n}\n\n#' Set changepoints\n#'\n#' Sets m$changepoints to the dates of changepoints. Either:\n#' 1) The changepoints were passed in explicitly.\n#' A) They are empty.\n#' B) They are not empty, and need validation.\n#' 2) We are generating a grid of them.\n#' 3) The user prefers no changepoints be used.\n#'\n#' @param m Prophet object.\n#'\n#' @return m with changepoints set.\n#'\n#' @keywords internal\nset_changepoints <- function(m) {\n if (!is.null(m$changepoints)) {\n if (length(m$changepoints) > 0) {\n m$changepoints <- set_date(m$changepoints)\n if (min(m$changepoints) < min(m$history$ds)\n || max(m$changepoints) > max(m$history$ds)) {\n stop('Changepoints must fall within training data.')\n }\n }\n } else {\n # Place potential changepoints evenly through the first changepoint.range\n # proportion of the history.\n hist.size <- floor(nrow(m$history) * m$changepoint.range)\n if (m$n.changepoints + 1 > hist.size) {\n m$n.changepoints <- hist.size - 1\n message('n.changepoints greater than number of observations. Using ',\n m$n.changepoints)\n }\n if (m$n.changepoints > 0) {\n cp.indexes <- round(seq.int(1, hist.size,\n length.out = (m$n.changepoints + 1))[-1])\n m$changepoints <- m$history$ds[cp.indexes]\n } else {\n m$changepoints <- c()\n }\n }\n if (length(m$changepoints) > 0) {\n m$changepoints.t <- sort(\n time_diff(m$changepoints, m$start, \"secs\")) / m$t.scale\n } else {\n m$changepoints.t <- c(0) # dummy changepoint\n }\n return(m)\n}\n\n#' Provides Fourier series components with the specified frequency and order.\n#'\n#' @param dates Vector of dates.\n#' @param period Number of days of the period.\n#' @param series.order Number of components.\n#'\n#' @return Matrix with seasonality features.\n#'\n#' @keywords internal\nfourier_series <- function(dates, period, series.order) {\n t <- time_diff(dates, set_date('1970-01-01 00:00:00'))\n features <- matrix(0, length(t), 2 * series.order)\n for (i in 1:series.order) {\n x <- as.numeric(2 * i * pi * t / period)\n features[, i * 2 - 1] <- sin(x)\n features[, i * 2] <- cos(x)\n }\n return(features)\n}\n\n#' Data frame with seasonality features.\n#'\n#' @param dates Vector of dates.\n#' @param period Number of days of the period.\n#' @param series.order Number of components.\n#' @param prefix Column name prefix.\n#'\n#' @return Dataframe with seasonality.\n#'\n#' @keywords internal\nmake_seasonality_features <- function(dates, period, series.order, prefix) {\n features <- fourier_series(dates, period, series.order)\n colnames(features) <- paste(prefix, 1:ncol(features), sep = '_delim_')\n return(data.frame(features))\n}\n\n#' Construct a dataframe of holiday dates.\n#'\n#' @param m Prophet object.\n#' @param dates Vector with dates used for computing seasonality.\n#'\n#' @return A dataframe of holiday dates, in holiday dataframe format used in\n#' initialization.\n#'\n#' @importFrom dplyr \"%>%\"\n#' @keywords internal\nconstruct_holiday_dataframe <- function(m, dates) {\n all.holidays <- data.frame()\n if (!is.null(m$holidays)){\n all.holidays <- m$holidays\n }\n if (!is.null(m$country_holidays)) {\n year.list <- as.numeric(unique(format(dates, \"%Y\")))\n country.holidays.df <- make_holidays_df(year.list, m$country_holidays)\n all.holidays <- suppressWarnings(dplyr::bind_rows(all.holidays, country.holidays.df))\n }\n # If the model has already been fit with a certain set of holidays,\n # make sure we are using those same ones.\n if (!is.null(m$train.holiday.names)) {\n row.to.keep <- which(all.holidays$holiday %in% m$train.holiday.names)\n all.holidays <- all.holidays[row.to.keep,]\n holidays.to.add <- data.frame(\n holiday=setdiff(m$train.holiday.names, all.holidays$holiday)\n )\n if (nrow(holidays.to.add) > 0) {\n all.holidays <- suppressWarnings(dplyr::bind_rows(all.holidays, holidays.to.add))\n }\n }\n return(all.holidays)\n}\n\n#' Construct a matrix of holiday features.\n#'\n#' @param m Prophet object.\n#' @param dates Vector with dates used for computing seasonality.\n#' @param holidays Dataframe containing holidays, as returned by\n#' construct_holiday_dataframe.\n#'\n#' @return A list with entries\n#' holiday.features: dataframe with a column for each holiday.\n#' prior.scales: array of prior scales for each holiday column.\n#' holiday.names: array of names of all holidays.\n#'\n#' @importFrom dplyr \"%>%\"\n#' @keywords internal\nmake_holiday_features <- function(m, dates, holidays) {\n # Strip dates to be just days, for joining on holidays\n dates <- set_date(format(dates, \"%Y-%m-%d\"))\n wide <- holidays %>%\n dplyr::mutate(ds = set_date(ds)) %>%\n dplyr::group_by(holiday, ds) %>%\n dplyr::filter(dplyr::row_number() == 1) %>%\n dplyr::do({\n if (exists('lower_window', where = .) && !is.na(.$lower_window)\n && !is.na(.$upper_window)) {\n offsets <- seq(.$lower_window, .$upper_window)\n } else {\n offsets <- c(0)\n }\n names <- paste(.$holiday, '_delim_', ifelse(offsets < 0, '-', '+'),\n abs(offsets), sep = '')\n dplyr::tibble(ds = .$ds + offsets * 24 * 3600, holiday = names)\n }) %>%\n dplyr::mutate(x = 1.) %>%\n tidyr::spread(holiday, x, fill = 0)\n\n holiday.features <- data.frame(ds = set_date(dates)) %>%\n dplyr::left_join(wide, by = 'ds') %>%\n dplyr::select(-ds)\n # Make sure column order is consistent\n holiday.features <- holiday.features %>% dplyr::select(sort(names(.)))\n holiday.features[is.na(holiday.features)] <- 0\n\n # Prior scales\n if (!('prior_scale' %in% colnames(holidays))) {\n holidays$prior_scale <- m$holidays.prior.scale\n }\n prior.scales.list <- list()\n for (name in unique(holidays$holiday)) {\n df.h <- holidays[holidays$holiday == name, ]\n ps <- unique(df.h$prior_scale)\n if (length(ps) > 1) {\n stop('Holiday ', name, ' does not have a consistent prior scale ',\n 'specification')\n }\n if (is.na(ps)) {\n ps <- m$holidays.prior.scale\n }\n if (ps <= 0) {\n stop('Prior scale must be > 0.')\n }\n prior.scales.list[[name]] <- ps\n }\n\n prior.scales <- c()\n for (name in colnames(holiday.features)) {\n sn <- strsplit(name, '_delim_', fixed = TRUE)[[1]][1]\n prior.scales <- c(prior.scales, prior.scales.list[[sn]])\n }\n holiday.names <- names(prior.scales.list)\n if (is.null(m$train.holiday.names)){\n m$train.holiday.names <- holiday.names\n }\n return(list(m = m,\n holiday.features = holiday.features,\n prior.scales = prior.scales,\n holiday.names = holiday.names))\n}\n\n#' Add an additional regressor to be used for fitting and predicting.\n#'\n#' The dataframe passed to `fit` and `predict` will have a column with the\n#' specified name to be used as a regressor. When standardize='auto', the\n#' regressor will be standardized unless it is binary. The regression\n#' coefficient is given a prior with the specified scale parameter.\n#' Decreasing the prior scale will add additional regularization. If no\n#' prior scale is provided, holidays.prior.scale will be used.\n#' Mode can be specified as either 'additive' or 'multiplicative'. If not\n#' specified, m$seasonality.mode will be used. 'additive' means the effect of\n#' the regressor will be added to the trend, 'multiplicative' means it will\n#' multiply the trend.\n#'\n#' @param m Prophet object.\n#' @param name String name of the regressor\n#' @param prior.scale Float scale for the normal prior. If not provided,\n#' holidays.prior.scale will be used.\n#' @param standardize Bool, specify whether this regressor will be standardized\n#' prior to fitting. Can be 'auto' (standardize if not binary), True, or\n#' False.\n#' @param mode Optional, 'additive' or 'multiplicative'. Defaults to\n#' m$seasonality.mode.\n#'\n#' @return The prophet model with the regressor added.\n#'\n#' @export\nadd_regressor <- function(\n m, name, prior.scale = NULL, standardize = 'auto', mode = NULL\n){\n if (!is.null(m$history)) {\n stop('Regressors must be added prior to model fitting.')\n }\n if (make.names(name, allow_ = TRUE) != name) {\n stop(\"You have provided a name that is not syntactically valid in R, \", name, \". \",\n \"A syntactically valid name consists of letters, numbers and the dot or underline, \",\n \"characters and starts with a letter or the dot not followed by a number.\")\n }\n validate_column_name(m, name, check_regressors = FALSE)\n if (is.null(prior.scale)) {\n prior.scale <- m$holidays.prior.scale\n }\n if (is.null(mode)) {\n mode <- m$seasonality.mode\n }\n if(prior.scale <= 0) {\n stop(\"Prior scale must be > 0.\")\n }\n if (!(mode %in% c('additive', 'multiplicative'))) {\n stop(\"mode must be 'additive' or 'multiplicative'\")\n }\n m$extra_regressors[[name]] <- list(\n prior.scale = prior.scale,\n standardize = standardize,\n mu = 0,\n std = 1.0,\n mode = mode\n )\n return(m)\n}\n\n#' Add a seasonal component with specified period, number of Fourier\n#' components, and prior scale.\n#'\n#' Increasing the number of Fourier components allows the seasonality to change\n#' more quickly (at risk of overfitting). Default values for yearly and weekly\n#' seasonalities are 10 and 3 respectively.\n#'\n#' Increasing prior scale will allow this seasonality component more\n#' flexibility, decreasing will dampen it. If not provided, will use the\n#' seasonality.prior.scale provided on Prophet initialization (defaults to 10).\n#'\n#' Mode can be specified as either 'additive' or 'multiplicative'. If not\n#' specified, m$seasonality.mode will be used (defaults to 'additive').\n#' Additive means the seasonality will be added to the trend, multiplicative\n#' means it will multiply the trend.\n#'\n#' If condition.name is provided, the dataframe passed to `fit` and `predict`\n#' should have a column with the specified condition.name containing booleans\n#' which decides when to apply seasonality.\n#'\n#' @param m Prophet object.\n#' @param name String name of the seasonality component.\n#' @param period Float number of days in one period.\n#' @param fourier.order Int number of Fourier components to use.\n#' @param prior.scale Optional float prior scale for this component.\n#' @param mode Optional 'additive' or 'multiplicative'.\n#' @param condition.name String name of the seasonality condition.\n#'\n#' @return The prophet model with the seasonality added.\n#'\n#' @export\nadd_seasonality <- function(\n m, name, period, fourier.order, prior.scale = NULL, mode = NULL,\n condition.name = NULL\n) {\n if (!is.null(m$history)) {\n stop(\"Seasonality must be added prior to model fitting.\")\n }\n if (!(name %in% c('daily', 'weekly', 'yearly'))) {\n # Allow overriding built-in seasonalities\n if (make.names(name, allow_ = TRUE) != name) {\n stop(\"You have provided a name that is not syntactically valid in R, \", name, \". \",\n \"A syntactically valid name consists of letters, numbers and the dot or underline, \",\n \"characters and starts with a letter or the dot not followed by a number.\")\n }\n validate_column_name(m, name, check_seasonalities = FALSE)\n }\n if (is.null(prior.scale)) {\n ps <- m$seasonality.prior.scale\n } else {\n ps <- prior.scale\n }\n if (ps <= 0) {\n stop('Prior scale must be > 0.')\n }\n if (fourier.order <= 0) {\n stop('Fourier order must be > 0.')\n }\n if (is.null(mode)) {\n mode <- m$seasonality.mode\n }\n if (!(mode %in% c('additive', 'multiplicative'))) {\n stop(\"mode must be 'additive' or 'multiplicative'\")\n }\n if (!is.null(condition.name)) {\n validate_column_name(m, condition.name)\n }\n m$seasonalities[[name]] <- list(\n period = period,\n fourier.order = fourier.order,\n prior.scale = ps,\n mode = mode,\n condition.name = condition.name\n )\n return(m)\n}\n\n#' Add in built-in holidays for the specified country.\n#'\n#' These holidays will be included in addition to any specified on model\n#' initialization.\n#'\n#' Holidays will be calculated for arbitrary date ranges in the history\n#' and future. See the online documentation for the list of countries with\n#' built-in holidays.\n#'\n#' Built-in country holidays can only be set for a single country.\n#'\n#' @param m Prophet object.\n#' @param country_name Name of the country, like 'UnitedStates' or 'US'\n#'\n#' @return The prophet model with the holidays country set.\n#'\n#' @export\nadd_country_holidays <- function(m, country_name) {\n if (!is.null(m$history)) {\n stop(\"Country holidays must be added prior to model fitting.\")\n }\n if (!(country_name %in% generated_holidays$country)){\n stop(\"Holidays in \", country_name,\" are not currently supported!\")\n }\n # Validate names.\n for (name in get_holiday_names(country_name)) {\n # Allow merging with existing holidays\n validate_column_name(m, name, check_holidays = FALSE)\n }\n # Set the holidays.\n if (!is.null(m$country_holidays)) {\n message(\n 'Changing country holidays from ', m$country_holidays, ' to ',\n country_name\n )\n }\n m$country_holidays = country_name\n return(m)\n}\n\n#' Dataframe with seasonality features.\n#' Includes seasonality features, holiday features, and added regressors.\n#'\n#' @param m Prophet object.\n#' @param df Dataframe with dates for computing seasonality features and any\n#' added regressors.\n#'\n#' @return List with items\n#' seasonal.features: Dataframe with regressor features,\n#' prior.scales: Array of prior scales for each column of the features\n#' dataframe.\n#' component.cols: Dataframe with indicators for which regression components\n#' correspond to which columns.\n#' modes: List with keys 'additive' and 'multiplicative' with arrays of\n#' component names for each mode of seasonality.\n#'\n#' @keywords internal\nmake_all_seasonality_features <- function(m, df) {\n seasonal.features <- data.frame(row.names = 1:nrow(df))\n prior.scales <- c()\n modes <- list(additive = c(), multiplicative = c())\n\n # Seasonality features\n for (name in names(m$seasonalities)) {\n props <- m$seasonalities[[name]]\n features <- make_seasonality_features(\n df$ds, props$period, props$fourier.order, name)\n if (!is.null(props$condition.name)) {\n features[!df[[props$condition.name]],] <- 0\n }\n seasonal.features <- cbind(seasonal.features, features)\n prior.scales <- c(prior.scales,\n props$prior.scale * rep(1, ncol(features)))\n modes[[props$mode]] <- c(modes[[props$mode]], name)\n }\n\n # Holiday features\n holidays <- construct_holiday_dataframe(m, df$ds)\n if (nrow(holidays) > 0) {\n out <- make_holiday_features(m, df$ds, holidays)\n m <- out$m\n seasonal.features <- cbind(seasonal.features, out$holiday.features)\n prior.scales <- c(prior.scales, out$prior.scales)\n modes[[m$seasonality.mode]] <- c(\n modes[[m$seasonality.mode]], out$holiday.names\n )\n }\n\n # Additional regressors\n for (name in names(m$extra_regressors)) {\n props <- m$extra_regressors[[name]]\n seasonal.features[[name]] <- df[[name]]\n prior.scales <- c(prior.scales, props$prior.scale)\n modes[[props$mode]] <- c(modes[[props$mode]], name)\n }\n\n # Dummy to prevent empty X\n if (ncol(seasonal.features) == 0) {\n seasonal.features <- data.frame(zeros = rep(0, nrow(df)))\n prior.scales <- c(1.)\n }\n\n components.list <- regressor_column_matrix(m, seasonal.features, modes)\n return(list(m = m,\n seasonal.features = seasonal.features,\n prior.scales = prior.scales,\n component.cols = components.list$component.cols,\n modes = components.list$modes))\n}\n\n#' Dataframe indicating which columns of the feature matrix correspond to\n#' which seasonality/regressor components.\n#'\n#' Includes combination components, like 'additive_terms'. These combination\n#' components will be added to the 'modes' input.\n#'\n#' @param m Prophet object.\n#' @param seasonal.features Constructed seasonal features dataframe.\n#' @param modes List with keys 'additive' and 'multiplicative' with arrays of\n#' component names for each mode of seasonality.\n#'\n#' @return List with items\n#' component.cols: A binary indicator dataframe with columns seasonal\n#' components and rows columns in seasonal.features. Entry is 1 if that\n#' column is used in that component.\n#' modes: Updated input with combination components.\n#'\n#' @keywords internal\nregressor_column_matrix <- function(m, seasonal.features, modes) {\n components <- dplyr::tibble(component = colnames(seasonal.features)) %>%\n dplyr::mutate(col = seq_len(dplyr::n())) %>%\n tidyr::separate(component, c('component', 'part'), sep = \"_delim_\",\n extra = \"merge\", fill = \"right\") %>%\n dplyr::select(col, component)\n # Add total for holidays\n if(!is.null(m$train.holiday.names)){\n components <- add_group_component(\n components, 'holidays', unique(m$train.holiday.names))\n }\n # Add totals for additive and multiplicative components, and regressors\n for (mode in c('additive', 'multiplicative')) {\n components <- add_group_component(\n components, paste0(mode, '_terms'), modes[[mode]])\n regressors_by_mode <- c()\n for (name in names(m$extra_regressors)) {\n if (m$extra_regressors[[name]]$mode == mode) {\n regressors_by_mode <- c(regressors_by_mode, name)\n }\n }\n components <- add_group_component(\n components, paste0('extra_regressors_', mode), regressors_by_mode)\n # Add combination components to modes\n modes[[mode]] <- c(modes[[mode]], paste0(mode, '_terms'))\n modes[[mode]] <- c(modes[[mode]], paste0('extra_regressors_', mode))\n }\n # After all of the additive/multiplicative groups have been added,\n modes[[m$seasonality.mode]] <- c(modes[[m$seasonality.mode]], 'holidays')\n # Convert to a binary matrix\n component.cols <- as.data.frame.matrix(\n table(components$col, components$component)\n )\n component.cols <- (\n component.cols[order(as.numeric(row.names(component.cols))), ,\n drop = FALSE]\n )\n # Add columns for additive and multiplicative terms, if missing\n for (name in c('additive_terms', 'multiplicative_terms')) {\n if (!(name %in% colnames(component.cols))) {\n component.cols[[name]] <- 0\n }\n }\n # Remove the placeholder\n components <- dplyr::filter(components, component != 'zeros')\n # Validation\n if (\n max(component.cols$additive_terms\n + component.cols$multiplicative_terms) > 1\n ) {\n stop('A bug occurred in seasonal components.')\n }\n # Compare to training, if set.\n if (!is.null(m$train.component.cols)) {\n component.cols <- component.cols[, colnames(m$train.component.cols)]\n if (!all(component.cols == m$train.component.cols)) {\n stop('A bug occurred in constructing regressors.')\n }\n }\n return(list(component.cols = component.cols, modes = modes))\n}\n\n#' Adds a component with given name that contains all of the components\n#' in group.\n#'\n#' @param components Dataframe with components.\n#' @param name Name of new group component.\n#' @param group List of components that form the group.\n#'\n#' @return Dataframe with components.\n#'\n#' @keywords internal\nadd_group_component <- function(components, name, group) {\n new_comp <- components[(components$component %in% group), ]\n group_cols <- unique(new_comp$col)\n if (length(group_cols) > 0) {\n new_comp <- data.frame(col=group_cols, component=name)\n components <- rbind(components, new_comp)\n }\n return(components)\n}\n\n#' Get number of Fourier components for built-in seasonalities.\n#'\n#' @param m Prophet object.\n#' @param name String name of the seasonality component.\n#' @param arg 'auto', TRUE, FALSE, or number of Fourier components as\n#' provided.\n#' @param auto.disable Bool if seasonality should be disabled when 'auto'.\n#' @param default.order Int default Fourier order.\n#'\n#' @return Number of Fourier components, or 0 for disabled.\n#'\n#' @keywords internal\nparse_seasonality_args <- function(m, name, arg, auto.disable, default.order) {\n if (arg == 'auto') {\n fourier.order <- 0\n if (name %in% names(m$seasonalities)) {\n message('Found custom seasonality named \"', name,\n '\", disabling built-in ', name, ' seasonality.')\n } else if (auto.disable) {\n message('Disabling ', name, ' seasonality. Run prophet with ', name,\n '.seasonality=TRUE to override this.')\n } else {\n fourier.order <- default.order\n }\n } else if (isTRUE(arg)) {\n fourier.order <- default.order\n } else if (isFALSE(arg)) {\n fourier.order <- 0\n } else {\n fourier.order <- arg\n }\n return(fourier.order)\n}\n\n#' Set seasonalities that were left on auto.\n#'\n#' Turns on yearly seasonality if there is >=2 years of history.\n#' Turns on weekly seasonality if there is >=2 weeks of history, and the\n#' spacing between dates in the history is <7 days.\n#' Turns on daily seasonality if there is >=2 days of history, and the spacing\n#' between dates in the history is <1 day.\n#'\n#' @param m Prophet object.\n#'\n#' @return The prophet model with seasonalities set.\n#'\n#' @keywords internal\nset_auto_seasonalities <- function(m) {\n first <- min(m$history$ds)\n last <- max(m$history$ds)\n dt <- diff(time_diff(m$history$ds, m$start))\n min.dt <- min(dt[dt > 0])\n\n yearly.disable <- time_diff(last, first) < 730\n fourier.order <- parse_seasonality_args(\n m, 'yearly', m$yearly.seasonality, yearly.disable, 10)\n if (fourier.order > 0) {\n m$seasonalities[['yearly']] <- list(\n period = 365.25,\n fourier.order = fourier.order,\n prior.scale = m$seasonality.prior.scale,\n mode = m$seasonality.mode,\n condition.name = NULL\n )\n }\n\n weekly.disable <- ((time_diff(last, first) < 14) || (min.dt >= 7))\n fourier.order <- parse_seasonality_args(\n m, 'weekly', m$weekly.seasonality, weekly.disable, 3)\n if (fourier.order > 0) {\n m$seasonalities[['weekly']] <- list(\n period = 7,\n fourier.order = fourier.order,\n prior.scale = m$seasonality.prior.scale,\n mode = m$seasonality.mode,\n condition.name = NULL\n )\n }\n\n daily.disable <- ((time_diff(last, first) < 2) || (min.dt >= 1))\n fourier.order <- parse_seasonality_args(\n m, 'daily', m$daily.seasonality, daily.disable, 4)\n if (fourier.order > 0) {\n m$seasonalities[['daily']] <- list(\n period = 1,\n fourier.order = fourier.order,\n prior.scale = m$seasonality.prior.scale,\n mode = m$seasonality.mode,\n condition.name = NULL\n )\n }\n return(m)\n}\n\n#' Initialize flat growth.\n#'\n#' Provides a strong initialization for flat growth by setting the\n#' growth to 0 and calculates the offset parameter that pass the\n#' function through the mean of the the y_scaled values.\n#'\n#' @param df Data frame with columns ds (date), y_scaled (scaled time series),\n#' and t (scaled time).\n#'\n#' @return A vector (k, m) with the rate (k) and offset (m) of the flat\n#' growth function.\n#'\n#' @keywords internal\nflat_growth_init <- function(df) {\n # Initialize the rate\n k <- 0\n # And the offset\n m <- mean(df$y_scaled)\n return(c(k, m))\n}\n\n#' Initialize constant growth.\n#'\n#' Provides a strong initialization for linear growth by calculating the\n#' growth and offset parameters that pass the function through the first and\n#' last points in the time series.\n#'\n#' @param df Data frame with columns ds (date), y_scaled (scaled time series),\n#' and t (scaled time).\n#'\n#' @return A vector (k, m) with the rate (k) and offset (m) of the linear\n#' growth function.\n#'\n#' @keywords internal\nlinear_growth_init <- function(df) {\n i0 <- which.min(df$ds)\n i1 <- which.max(df$ds)\n T <- df$t[i1] - df$t[i0]\n # Initialize the rate\n k <- (df$y_scaled[i1] - df$y_scaled[i0]) / T\n # And the offset\n m <- df$y_scaled[i0] - k * df$t[i0]\n return(c(k, m))\n}\n\n#' Initialize logistic growth.\n#'\n#' Provides a strong initialization for logistic growth by calculating the\n#' growth and offset parameters that pass the function through the first and\n#' last points in the time series.\n#'\n#' @param df Data frame with columns ds (date), cap_scaled (scaled capacity),\n#' y_scaled (scaled time series), and t (scaled time).\n#'\n#' @return A vector (k, m) with the rate (k) and offset (m) of the logistic\n#' growth function.\n#'\n#' @keywords internal\nlogistic_growth_init <- function(df) {\n i0 <- which.min(df$ds)\n i1 <- which.max(df$ds)\n T <- df$t[i1] - df$t[i0]\n\n # Force valid values, in case y > cap or y < 0\n C0 <- df$cap_scaled[i0]\n C1 <- df$cap_scaled[i1]\n y0 <- max(0.01 * C0, min(0.99 * C0, df$y_scaled[i0]))\n y1 <- max(0.01 * C1, min(0.99 * C1, df$y_scaled[i1]))\n\n r0 <- C0 / y0\n r1 <- C1 / y1\n\n if (abs(r0 - r1) <= 0.01) {\n r0 <- 1.05 * r0\n }\n L0 <- log(r0 - 1)\n L1 <- log(r1 - 1)\n # Initialize the offset\n m <- L0 * T / (L0 - L1)\n # And the rate\n k <- (L0 - L1) / T\n return(c(k, m))\n}\n\n#' Fit the prophet model.\n#'\n#' This sets m$params to contain the fitted model parameters. It is a list\n#' with the following elements:\n#' k (M array): M posterior samples of the initial slope.\n#' m (M array): The initial intercept.\n#' delta (MxN matrix): The slope change at each of N changepoints.\n#' beta (MxK matrix): Coefficients for K seasonality features.\n#' sigma_obs (M array): Noise level.\n#' Note that M=1 if MAP estimation.\n#'\n#' @param m Prophet object.\n#' @param df Data frame.\n#' @param ... Additional arguments passed to the \\code{optimizing} or\n#' \\code{sampling} functions in Stan.\n#'\n#' @export\nfit.prophet <- function(m, df, ...) {\n if (!is.null(m$history)) {\n stop(\"Prophet object can only be fit once. Instantiate a new object.\")\n }\n if (!(exists('ds', where = df)) | !(exists('y', where = df))) {\n stop(paste(\n \"Dataframe must have columns 'ds' and 'y' with the dates and values\",\n \"respectively.\"\n ))\n }\n history <- df %>%\n dplyr::filter(!is.na(y))\n if (nrow(history) < 2) {\n stop(\"Dataframe has less than 2 non-NA rows.\")\n }\n m$history.dates <- sort(set_date(unique(df$ds)))\n\n out <- setup_dataframe(m, history, initialize_scales = TRUE)\n history <- out$df\n m <- out$m\n m$history <- history\n m <- set_auto_seasonalities(m)\n out2 <- make_all_seasonality_features(m, history)\n m <- out2$m\n seasonal.features <- out2$seasonal.features\n prior.scales <- out2$prior.scales\n component.cols <- out2$component.cols\n m$train.component.cols <- component.cols\n m$component.modes <- out2$modes\n m$fit.kwargs <- list(...)\n\n m <- set_changepoints(m)\n\n # Construct input to stan\n dat <- list(\n T = nrow(history),\n K = ncol(seasonal.features),\n S = length(m$changepoints.t),\n y = history$y_scaled,\n t = history$t,\n t_change = array(m$changepoints.t),\n X = as.matrix(seasonal.features),\n sigmas = array(prior.scales),\n tau = m$changepoint.prior.scale,\n trend_indicator = switch(m$growth, 'linear'=0, 'logistic'=1, 'flat'=2),\n s_a = array(component.cols$additive_terms),\n s_m = array(component.cols$multiplicative_terms)\n )\n\n # Run stan\n if (m$growth == 'linear') {\n dat$cap <- rep(0, nrow(history)) # Unused inside Stan\n kinit <- linear_growth_init(history)\n } else if (m$growth == 'flat') {\n dat$cap <- rep(0, nrow(history)) # Unused inside Stan\n kinit <- flat_growth_init(history)\n } else if (m$growth == 'logistic') {\n dat$cap <- history$cap_scaled # Add capacities to the Stan data\n kinit <- logistic_growth_init(history)\n }\n\n model <- .load_model(m$backend)\n\n stan_init <- function() {\n list(k = kinit[1],\n m = kinit[2],\n delta = array(rep(0, length(m$changepoints.t))),\n beta = array(rep(0, ncol(seasonal.features))),\n sigma_obs = 1\n )\n }\n\n if (min(history$y) == max(history$y) &\n (m$growth %in% c('linear', 'flat'))) {\n # Nothing to fit.\n m$params <- stan_init()\n m$params$sigma_obs <- 0.\n n.iteration <- 1.\n } else {\n if (m$mcmc.samples > 0) {\n args <- .stan_args(model, dat, stan_init, m$backend, type = \"mcmc\", m$mcmc.samples, ...)\n model_output <- .sampling(args, m$backend)\n } else {\n args <- .stan_args(model, dat, stan_init, m$backend, type = \"optimize\", ...)\n model_output <- .fit(args, m$backend)\n }\n m$stan.fit <- model_output$stan_fit\n m$params <- model_output$params\n n.iteration <- model_output$n_iteration\n }\n # Cast the parameters to have consistent form, whether full bayes or MAP\n for (name in c('delta', 'beta')){\n m$params[[name]] <- matrix(m$params[[name]], nrow = n.iteration)\n }\n # rstan::sampling returns 1d arrays; converts to atomic vectors.\n for (name in c('k', 'm', 'sigma_obs')){\n m$params[[name]] <- c(m$params[[name]])\n }\n # If no changepoints were requested, replace delta with 0s\n if (m$n.changepoints == 0) {\n # Fold delta into the base rate k\n m$params$k <- m$params$k + m$params$delta[, 1]\n m$params$delta <- matrix(rep(0, length(m$params$delta)), nrow = n.iteration)\n }\n return(m)\n}\n\n#' Predict using the prophet model.\n#'\n#' @param object Prophet object.\n#' @param df Dataframe with dates for predictions (column ds), and capacity\n#' (column cap) if logistic growth. If not provided, predictions are made on\n#' the history.\n#' @param ... additional arguments.\n#'\n#' @return A dataframe with the forecast components.\n#'\n#' @examples\n#' \\dontrun{\n#' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n#' y = sin(1:366/200) + rnorm(366)/10)\n#' m <- prophet(history)\n#' future <- make_future_dataframe(m, periods = 365)\n#' forecast <- predict(m, future)\n#' plot(m, forecast)\n#' }\n#'\n#' @export\npredict.prophet <- function(object, df = NULL, ...) {\n if (is.null(object$history)) {\n stop(\"Model must be fit before predictions can be made.\")\n }\n if (is.null(df)) {\n df <- object$history\n } else {\n if (nrow(df) == 0) {\n stop(\"Dataframe has no rows.\")\n }\n out <- setup_dataframe(object, df)\n df <- out$df\n }\n\n df$trend <- predict_trend(object, df)\n seasonal.components <- predict_seasonal_components(object, df)\n if (object$uncertainty.samples) {\n intervals <- predict_uncertainty(object, df)\n } else {\n intervals <- NULL\n }\n\n # Drop columns except ds, cap, floor, and trend\n cols <- c('ds', 'trend')\n if ('cap' %in% colnames(df)) {\n cols <- c(cols, 'cap')\n }\n if (object$logistic.floor) {\n cols <- c(cols, 'floor')\n }\n df <- df[cols]\n df <- dplyr::bind_cols(df, seasonal.components, intervals)\n df$yhat <- df$trend * (1 + df$multiplicative_terms) + df$additive_terms\n return(df)\n}\n\n#' Evaluate the flat trend function.\n#'\n#' @param t Vector of times on which the function is evaluated.\n#' @param m Float initial offset.\n#'\n#' @return Vector y(t).\n#'\n#' @keywords internal\nflat_trend <- function(t, m) {\n y <- rep(m, length(t))\n return(y)\n}\n\n#' Evaluate the piecewise linear function.\n#'\n#' @param t Vector of times on which the function is evaluated.\n#' @param deltas Vector of rate changes at each changepoint.\n#' @param k Float initial rate.\n#' @param m Float initial offset.\n#' @param changepoint.ts Vector of changepoint times.\n#'\n#' @return Vector y(t).\n#'\n#' @keywords internal\npiecewise_linear <- function(t, deltas, k, m, changepoint.ts) {\n # Intercept changes\n gammas <- -changepoint.ts * deltas\n # Get cumulative slope and intercept at each t\n k_t <- rep(k, length(t))\n m_t <- rep(m, length(t))\n for (s in 1:length(changepoint.ts)) {\n indx <- t >= changepoint.ts[s]\n k_t[indx] <- k_t[indx] + deltas[s]\n m_t[indx] <- m_t[indx] + gammas[s]\n }\n y <- k_t * t + m_t\n return(y)\n}\n\n#' Evaluate the piecewise logistic function.\n#'\n#' @param t Vector of times on which the function is evaluated.\n#' @param cap Vector of capacities at each t.\n#' @param deltas Vector of rate changes at each changepoint.\n#' @param k Float initial rate.\n#' @param m Float initial offset.\n#' @param changepoint.ts Vector of changepoint times.\n#'\n#' @return Vector y(t).\n#'\n#' @keywords internal\npiecewise_logistic <- function(t, cap, deltas, k, m, changepoint.ts) {\n # Compute offset changes\n k.cum <- c(k, cumsum(deltas) + k)\n gammas <- rep(0, length(changepoint.ts))\n for (i in 1:length(changepoint.ts)) {\n gammas[i] <- ((changepoint.ts[i] - m - sum(gammas))\n * (1 - k.cum[i] / k.cum[i + 1]))\n }\n # Get cumulative rate and offset at each t\n k_t <- rep(k, length(t))\n m_t <- rep(m, length(t))\n for (s in 1:length(changepoint.ts)) {\n indx <- t >= changepoint.ts[s]\n k_t[indx] <- k_t[indx] + deltas[s]\n m_t[indx] <- m_t[indx] + gammas[s]\n }\n y <- cap / (1 + exp(-k_t * (t - m_t)))\n return(y)\n}\n\n#' Predict trend using the prophet model.\n#'\n#' @param model Prophet object.\n#' @param df Prediction dataframe.\n#'\n#' @return Vector with trend on prediction dates.\n#'\n#' @keywords internal\npredict_trend <- function(model, df) {\n k <- mean(model$params$k, na.rm = TRUE)\n param.m <- mean(model$params$m, na.rm = TRUE)\n deltas <- colMeans(model$params$delta, na.rm = TRUE)\n\n t <- df$t\n if (model$growth == 'linear') {\n trend <- piecewise_linear(t, deltas, k, param.m, model$changepoints.t)\n } else if (model$growth == 'flat') {\n trend <- flat_trend(t, param.m)\n } else if (model$growth == 'logistic') {\n cap <- df$cap_scaled\n trend <- piecewise_logistic(\n t, cap, deltas, k, param.m, model$changepoints.t)\n }\n return(trend * model$y.scale + df$floor)\n}\n\n#' Predict seasonality components, holidays, and added regressors.\n#'\n#' @param m Prophet object.\n#' @param df Prediction dataframe.\n#'\n#' @return Dataframe with seasonal components.\n#'\n#' @keywords internal\npredict_seasonal_components <- function(m, df) {\n out <- make_all_seasonality_features(m, df)\n m <- out$m\n seasonal.features <- out$seasonal.features\n component.cols <- out$component.cols\n if (m$uncertainty.samples){\n lower.p <- (1 - m$interval.width)/2\n upper.p <- (1 + m$interval.width)/2\n }\n\n X <- as.matrix(seasonal.features)\n component.predictions <- data.frame(matrix(ncol = 0, nrow = nrow(X)))\n for (component in colnames(component.cols)) {\n beta.c <- t(m$params$beta) * component.cols[[component]]\n\n comp <- X %*% beta.c\n if (component %in% m$component.modes$additive) {\n comp <- comp * m$y.scale\n }\n component.predictions[[component]] <- rowMeans(comp, na.rm = TRUE)\n if (m$uncertainty.samples){\n component.predictions[[paste0(component, '_lower')]] <- apply(\n comp, 1, stats::quantile, lower.p, na.rm = TRUE)\n component.predictions[[paste0(component, '_upper')]] <- apply(\n comp, 1, stats::quantile, upper.p, na.rm = TRUE)\n }\n }\n return(component.predictions)\n}\n\n#' Prophet posterior predictive samples.\n#'\n#' @param m Prophet object.\n#' @param df Prediction dataframe.\n#'\n#' @return List with posterior predictive samples for the forecast yhat and\n#' for the trend component.\n#'\n#' @keywords internal\nsample_posterior_predictive <- function(m, df) {\n # Sample trend, seasonality, and yhat from the extrapolation model.\n n.iterations <- length(m$params$k)\n samp.per.iter <- max(1, ceiling(m$uncertainty.samples / n.iterations))\n nsamp <- n.iterations * samp.per.iter # The actual number of samples\n\n out <- make_all_seasonality_features(m, df)\n seasonal.features <- out$seasonal.features\n component.cols <- out$component.cols\n sim.values <- list(\"trend\" = matrix(, nrow = nrow(df), ncol = nsamp),\n \"yhat\" = matrix(, nrow = nrow(df), ncol = nsamp))\n\n for (i in 1:n.iterations) {\n # For each set of parameters from MCMC (or just 1 set for MAP),\n for (j in 1:samp.per.iter) {\n # Do a simulation with this set of parameters,\n sim <- sample_model(\n m = m,\n df = df,\n seasonal.features = seasonal.features,\n iteration = i,\n s_a = component.cols$additive_terms,\n s_m = component.cols$multiplicative_terms\n )\n # Store the results\n for (key in c(\"trend\", \"yhat\")) {\n sim.values[[key]][,(i - 1) * samp.per.iter + j] <- sim[[key]]\n }\n }\n }\n return(sim.values)\n}\n\n#' Sample from the posterior predictive distribution.\n#'\n#' @param m Prophet object.\n#' @param df Dataframe with dates for predictions (column ds), and capacity\n#' (column cap) if logistic growth.\n#'\n#' @return A list with items \"trend\" and \"yhat\" containing\n#' posterior predictive samples for that component.\n#'\n#' @export\npredictive_samples <- function(m, df) {\n df <- setup_dataframe(m, df)$df\n sim.values <- sample_posterior_predictive(m, df)\n return(sim.values)\n}\n\n#' Prophet uncertainty intervals for yhat and trend\n#'\n#' @param m Prophet object.\n#' @param df Prediction dataframe.\n#'\n#' @return Dataframe with uncertainty intervals.\n#'\n#' @keywords internal\npredict_uncertainty <- function(m, df) {\n sim.values <- sample_posterior_predictive(m, df)\n # Add uncertainty estimates\n lower.p <- (1 - m$interval.width)/2\n upper.p <- (1 + m$interval.width)/2\n\n intervals <- cbind(\n t(apply(t(sim.values$yhat), 2, stats::quantile, c(lower.p, upper.p),\n na.rm = TRUE)),\n t(apply(t(sim.values$trend), 2, stats::quantile, c(lower.p, upper.p),\n na.rm = TRUE))\n )\n\n colnames(intervals) <- paste(rep(c('yhat', 'trend'), each=2),\n c('lower', 'upper'), sep = \"_\")\n\n return(dplyr::as_tibble(intervals))\n}\n\n#' Simulate observations from the extrapolated generative model.\n#'\n#' @param m Prophet object.\n#' @param df Prediction dataframe.\n#' @param seasonal.features Data frame of seasonal features\n#' @param iteration Int sampling iteration to use parameters from.\n#' @param s_a Indicator vector for additive components\n#' @param s_m Indicator vector for multiplicative components\n#'\n#' @return List of trend and yhat, each a vector like df$t.\n#'\n#' @keywords internal\nsample_model <- function(m, df, seasonal.features, iteration, s_a, s_m) {\n trend <- sample_predictive_trend(m, df, iteration)\n\n beta <- m$params$beta[iteration,]\n Xb_a = as.matrix(seasonal.features) %*% (beta * s_a) * m$y.scale\n Xb_m = as.matrix(seasonal.features) %*% (beta * s_m)\n\n sigma <- m$params$sigma_obs[iteration]\n noise <- stats::rnorm(nrow(df), mean = 0, sd = sigma) * m$y.scale\n\n return(list(\"yhat\" = trend * (1 + Xb_m) + Xb_a + noise,\n \"trend\" = trend))\n}\n\n#' Simulate the trend using the extrapolated generative model.\n#'\n#' @param model Prophet object.\n#' @param df Prediction dataframe.\n#' @param iteration Int sampling iteration to use parameters from.\n#'\n#' @return Vector of simulated trend over df$t.\n#'\n#' @keywords internal\nsample_predictive_trend <- function(model, df, iteration) {\n k <- model$params$k[iteration]\n param.m <- model$params$m[iteration]\n deltas <- model$params$delta[iteration,]\n\n t <- df$t\n T <- max(t)\n\n # New changepoints from a Poisson process with rate S on [1, T]\n if (T > 1) {\n S <- length(model$changepoints.t)\n n.changes <- stats::rpois(1, S * (T - 1))\n } else {\n n.changes <- 0\n }\n if (n.changes > 0) {\n changepoint.ts.new <- 1 + stats::runif(n.changes) * (T - 1)\n changepoint.ts.new <- sort(changepoint.ts.new)\n } else {\n changepoint.ts.new <- c()\n }\n\n # Get the empirical scale of the deltas, plus epsilon to avoid NaNs.\n lambda <- mean(abs(c(deltas))) + 1e-8\n # Sample deltas\n deltas.new <- extraDistr::rlaplace(n.changes, mu = 0, sigma = lambda)\n\n # Combine with changepoints from the history\n changepoint.ts <- c(model$changepoints.t, changepoint.ts.new)\n deltas <- c(deltas, deltas.new)\n\n # Get the corresponding trend\n if (model$growth == 'linear') {\n trend <- piecewise_linear(t, deltas, k, param.m, changepoint.ts)\n } else if (model$growth == 'flat') {\n trend <- flat_trend(t, param.m)\n } else if (model$growth == 'logistic') {\n cap <- df$cap_scaled\n trend <- piecewise_logistic(t, cap, deltas, k, param.m, changepoint.ts)\n }\n return(trend * model$y.scale + df$floor)\n}\n\n#' Make dataframe with future dates for forecasting.\n#'\n#' @param m Prophet model object.\n#' @param periods Int number of periods to forecast forward.\n#' @param freq 'day', 'week', 'month', 'quarter', 'year', 1(1 sec), 60(1 minute) or 3600(1 hour).\n#' @param include_history Boolean to include the historical dates in the data\n#' frame for predictions.\n#'\n#' @return Dataframe that extends forward from the end of m$history for the\n#' requested number of periods.\n#'\n#' @export\nmake_future_dataframe <- function(m, periods, freq = 'day',\n include_history = TRUE) {\n # For backwards compatibility with previous zoo date type,\n if (freq == 'm') {\n freq <- 'month'\n }\n if (is.null(m$history.dates)) {\n stop('Model must be fit before this can be used.')\n }\n dates <- seq(max(m$history.dates), length.out = periods + 1, by = freq)\n dates <- dates[2:(periods + 1)] # Drop the first, which is max(history$ds)\n if (include_history) {\n dates <- c(m$history.dates, dates)\n attr(dates, \"tzone\") <- \"GMT\"\n }\n return(data.frame(ds = dates))\n}\n" }, { "path": "R/R/stan_backends.R", "content": "#' Get the stan backend defined in the environment variables.\n#'\n#' @return 'rstan' or 'cmdstanr'. 'rstan' if variable is not set.\n#' @keywords internal\nget_stan_backend <- function() {\n backend_setting <- Sys.getenv(\"R_STAN_BACKEND\", \"RSTAN\")\n if (backend_setting %in% c(\"RSTAN\", \"CMDSTANR\")) {\n backend <- switch(\n backend_setting,\n \"RSTAN\" = \"rstan\",\n \"CMDSTANR\" = \"cmdstanr\"\n )\n if (backend == \"cmdstanr\") check_cmdstanr()\n return(backend)\n } else {\n return(\"rstan\")\n }\n}\n\n#' Check that the required packages for using the cmdstanr backend are installed.\n#'\n#' @return NULL if successful, and prints the current version of cmdstan being used.\n#' @keywords internal\ncheck_cmdstanr <- function() {\n if (!requireNamespace(\"cmdstanr\", quietly = TRUE)) {\n stop(\n \"Package \\\"cmdstanr\\\" needed to use cmdstanr backend. See installation instructions: https://mc-stan.org/cmdstanr/.\",\n call. = FALSE\n )\n }\n if (!requireNamespace(\"posterior\", quietly = TRUE)) {\n stop(\n \"Package \\\"posterior\\\" needed to use cmdstanr backend. See installation instructions: https://mc-stan.org/posterior/.\",\n call. = FALSE\n )\n }\n cmdstanr_version <- cmdstanr::cmdstan_version()\n return(invisible(TRUE))\n}\n\n#' Load the Prophet Stan model.\n#'\n#' @param backend \"rstan\" or \"cmdstanr\".\n#'\n#' @return stanmodel object if backend = \"rstan\", CmdStanModel object if backend = \"cmdstanr\"\n#' @keywords internal\n.load_model <- function(backend) {\n switch(\n backend,\n \"rstan\" = .load_model_rstan(),\n \"cmdstanr\" = .load_model_cmdstanr()\n )\n}\n\n#' @rdname .load_model\n.load_model_rstan <- function() {\n if (exists(\".prophet.stan.model\", where = prophet_model_env)) {\n model <- get('.prophet.stan.model', envir = prophet_model_env)\n } else {\n model <- stanmodels$prophet\n }\n\n return(model)\n}\n\n#' @rdname .load_model\n.load_model_cmdstanr <- function() {\n model_file <- system.file(\n \"stan\",\n \"prophet.stan\",\n package = \"prophet\",\n mustWork = TRUE\n )\n model <- cmdstanr::cmdstan_model(model_file)\n\n return(model)\n}\n\n#' Gives Stan arguments the appropriate names depending on the chosen Stan backend.\n#'\n#' @param model Model object.\n#' @param dat List containing data to use in fitting.\n#' @param stan_init Function to initialize parameters for stan fit.\n#' @param backend \"rstan\" or \"cmdstanr\".\n#' @param type \"mcmc\" or \"optimize\".\n#' @param mcmc_samples Integer, if greater than 0, will do full Bayesian\n#' inference with the specified number of MCMC samples. If 0, will do MAP\n#' estimation.\n#'\n#' @return Named list of arguments.\n#' @keywords internal\n.stan_args <- function(model, dat, stan_init, backend, type, mcmc_samples = 0, ...) {\n args <- switch(\n backend,\n \"rstan\" = .stan_args_rstan(model, dat, stan_init, type, mcmc_samples),\n \"cmdstanr\" = .stan_args_cmdstanr(model, dat, stan_init, type, mcmc_samples)\n )\n args <- utils::modifyList(args, list(...))\n\n return(args)\n}\n\n#' @rdname .stan_args\n.stan_args_rstan <- function(model, dat, stan_init, type, mcmc_samples = NULL) {\n if (type == \"mcmc\") {\n args <- list(\n object = model,\n data = dat,\n init = stan_init,\n iter = mcmc_samples,\n chains = 4\n )\n } else if (type == \"optimize\") {\n args <- list(\n object = model,\n data = dat,\n init = stan_init,\n algorithm = if(dat$T < 100) {'Newton'} else {'LBFGS'},\n iter = 1e4,\n as_vector = FALSE\n )\n }\n\n return(args)\n}\n\n#' @rdname .stan_args\n.stan_args_cmdstanr <- function(model, dat, stan_init, type, mcmc_samples = NULL) {\n if (type == \"mcmc\") {\n args <- list(\n object = model,\n data = dat,\n init = stan_init,\n iter_warmup = mcmc_samples / 2,\n iter_sampling = mcmc_samples / 2,\n chains = 4,\n refresh = 0,\n show_messages = FALSE\n )\n } else if (type == \"optimize\") {\n args <- list(\n object = model,\n data = dat,\n init = stan_init,\n algorithm = if(dat$T < 100) {'newton'} else {'lbfgs'},\n iter = 1e4,\n refresh = 0\n )\n }\n\n return(args)\n}\n\n#' Obtain the point estimates of the parameters of the Prophet model using\n#' stan's optimization algorithms.\n#'\n#' @param args Named list of arguments suitable for the chosen backend. Must\n#' include arguments required for optimization.\n#' @param backend \"rstan\" or \"cmdstanr\".\n#'\n#' @return A named list containing \"stan_fit\" (the fitted stan object),\n#' \"params\", and \"n_iteration\"\n#' @keywords internal\n.fit <- function(args, backend) {\n switch(\n backend,\n \"rstan\" = .fit_rstan(args),\n \"cmdstanr\" = .fit_cmdstanr(args)\n )\n}\n\n#' Obtain the joint posterior distribution of the parameters of the Prophet\n#' model using MCMC sampling.\n#'\n#' @param args Named list of arguments suitable for the chosen backend. Must\n#' include arguments required for MCMC sampling.\n#' @param backend \"rstan\" or \"cmdstanr\".\n#'\n#' @return A named list containing \"stan_fit\" (the fitted stan object),\n#' \"params\", and \"n_iteration\"\n#' @keywords internal\n.sampling <- function(args, backend) {\n switch(\n backend,\n \"rstan\" = .sampling_rstan(args),\n \"cmdstanr\" = .sampling_cmdstanr(args)\n )\n}\n\n#' @rdname .fit\n.fit_rstan <- function(args) {\n model_output <- list()\n model_output$stan_fit <- do.call(rstan::optimizing, args)\n if (model_output$stan_fit$return_code != 0) {\n message(\n 'Optimization terminated abnormally. Falling back to Newton optimizer.'\n )\n args$algorithm = 'Newton'\n model_output$stan_fit <- do.call(rstan::optimizing, args)\n }\n model_output$params <- model_output$stan_fit$par\n model_output$n_iteration <- 1\n\n return(model_output)\n}\n\n#' @rdname .sampling\n.sampling_rstan <- function(args) {\n model_output <- list()\n model_output$stan_fit <- do.call(rstan::sampling, args)\n model_output$params <- rstan::extract(model_output$stan_fit)\n model_output$n_iteration <- length(model_output$params$k)\n\n return(model_output)\n}\n\n#' @rdname .fit\n.fit_cmdstanr <- function(args) {\n # TODO: Replace with method to extract parameter names once implemented in cmdstanr\n param_names <- c(\"k\", \"m\", \"delta\", \"sigma_obs\", \"beta\", \"trend\")\n model_output <- list()\n model <- args$object\n args$object <- NULL\n model_output$stan_fit <- do.call(model$optimize, args)\n if (model_output$stan_fit$return_codes()[1] != 0) {\n message(\n 'Optimization terminated abnormally. Falling back to Newton optimizer.'\n )\n args$algorithm = 'newton'\n model_output$stan_fit <- do.call(model$optimize, args)\n }\n model_output$params <- list()\n for (name in param_names) {\n model_output$params[[name]] <- unname(model_output$stan_fit$mle(name))\n }\n model_output$n_iteration <- 1\n\n return(model_output)\n}\n\n#' @rdname .sampling\n.sampling_cmdstanr <- function(args) {\n param_names <- c(\"k\", \"m\", \"delta\", \"sigma_obs\", \"beta\", \"trend\")\n model_output <- list()\n model <- args$object\n args$object <- NULL\n param_names <- c(param_names, \"lp__\")\n model_output$stan_fit <- do.call(model$sample, args)\n model_output$params <- list()\n for (name in param_names) {\n model_output$params[[name]] <- posterior::as_draws_matrix(model_output$stan_fit$draws(name))\n }\n model_output$n_iteration <- nrow(model_output$params$k)\n\n return(model_output)\n}\n" }, { "path": "R/R/stanmodels.R", "content": "# Generated by rstantools. Do not edit by hand.\n\n# names of stan models\nstanmodels <- c(\"prophet\")\n\n# load each stan module\nRcpp::loadModule(\"stan_fit4prophet_mod\", what = TRUE)\n\n# instantiate each stanmodel object\nstanmodels <- sapply(stanmodels, function(model_name) {\n # create C++ code for stan model\n stan_file <- if(dir.exists(\"stan\")) \"stan\" else file.path(\"inst\", \"stan\")\n stan_file <- file.path(stan_file, paste0(model_name, \".stan\"))\n stanfit <- rstan::stanc_builder(stan_file,\n allow_undefined = TRUE,\n obfuscate_model_name = FALSE)\n stanfit$model_cpp <- list(model_cppname = stanfit$model_name,\n model_cppcode = stanfit$cppcode)\n # create stanmodel object\n methods::new(Class = \"stanmodel\",\n model_name = stanfit$model_name,\n model_code = stanfit$model_code,\n model_cpp = stanfit$model_cpp,\n mk_cppmodule = function(x) get(paste0(\"rstantools_model_\", model_name)))\n})\n" }, { "path": "R/R/utilities.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n#' Summarise the coefficients of the extra regressors used in the model.\n#' For additive regressors, the coefficient represents the incremental impact\n#' on \\code{y} of a unit increase in the regressor. For multiplicative regressors,\n#' the incremental impact is equal to \\code{trend(t)} multiplied by the coefficient.\n#'\n#' Coefficients are measured on the original scale of the training data.\n#'\n#' @param m Prophet model object, after fitting.\n#'\n#' @return Dataframe with one row per regressor.\n#' @details Output dataframe columns:\n#' \\itemize{\n#' \\item{regressor: Name of the regressor}\n#' \\item{regressor_mode: Whether the regressor has an additive or multiplicative\n#' effect on \\code{y}.}\n#' \\item{center: The mean of the regressor if it was standardized. Otherwise 0.}\n#' \\item{coef_lower: Lower bound for the coefficient, estimated from the MCMC samples.\n#' Only different to \\code{coef} if \\code{mcmc_samples > 0}.\n#' }\n#' \\item{coef: Expected value of the coefficient.}\n#' \\item{coef_upper: Upper bound for the coefficient, estimated from MCMC samples.\n#' Only to different to \\code{coef} if \\code{mcmc_samples > 0}.\n#' }\n#' }\n#'\n#' @export\nregressor_coefficients <- function(m){\n if (length(m$extra_regressors) == 0) {\n stop(\"No extra regressors found.\")\n }\n regr_names <- names(m$extra_regressors)\n regr_modes <- unlist(lapply(m$extra_regressors, function(x) x$mode))\n regr_mus <- unlist(lapply(m$extra_regressors, function (x) x$mu))\n regr_stds <- unlist(lapply(m$extra_regressors, function(x) x$std))\n\n beta_indices <- which(m$train.component.cols[, regr_names, drop = FALSE] == 1, arr.ind = TRUE)[, \"row\"]\n betas <- m$params$beta[, beta_indices, drop = FALSE]\n # If regressor is additive, multiply by the scale factor to put coefficients on the original training data scale.\n y_scale_indicator <- matrix(\n data = ifelse(regr_modes == \"additive\", m$y.scale, 1),\n nrow = nrow(betas),\n ncol = ncol(betas),\n byrow = TRUE\n )\n coefs <- betas * y_scale_indicator / regr_stds\n\n percentiles = c((1 - m$interval.width) / 2, 1 - (1 - m$interval.width) / 2)\n bounds <- apply(betas, 2, stats::quantile, probs = percentiles)\n\n df <- data.frame(\n regressor = regr_names,\n regressor_mode = regr_modes,\n center = regr_mus,\n coef_lower = bounds[1, ],\n coef = apply(betas, 2, mean),\n coef_upper = bounds[2, ],\n stringsAsFactors = FALSE,\n row.names = NULL\n )\n\n return(df)\n}\n" }, { "path": "R/R/zzz.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n.onLoad <- function(libname, pkgname) {\n # Create environment for storing stan model\n assign(\"prophet_model_env\", new.env(), parent.env(environment()))\n tryCatch({\n if (.Platform$OS.type == \"windows\") {\n dest <- file.path('libs', .Platform$r_arch)\n } else {\n dest <- 'libs'\n }\n binary <- system.file(\n dest,\n 'prophet_stan_model.RData',\n package = 'prophet',\n mustWork = TRUE\n )\n load(binary)\n obj.name <- 'model.stanm'\n stanm <- eval(parse(text = obj.name))\n\n ## Should cause an error if the model doesn't work.\n stanm@mk_cppmodule(stanm)\n\n assign(\n \".prophet.stan.model\",\n stanm,\n envir=prophet_model_env\n )\n },\n error = function(cond) {}\n )\n}\n\n# IMPORTS ----\n# StanHeaders - Used to prevent issues with Prophet dynload error\n\n#' @import StanHeaders\n" }, { "path": "R/configure", "content": "# Generated by rstantools. Do not edit by hand.\n\n#! /bin/sh\n\"${R_HOME}/bin/Rscript\" -e \"rstantools::rstan_config()\"\n" }, { "path": "R/configure.win", "content": "# Generated by rstantools. Do not edit by hand.\n\n#! /bin/sh\n\"${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe\" -e \"rstantools::rstan_config()\"\n" }, { "path": "R/data-raw/generated_holidays.R", "content": "## Copyright (c) 2017-present, Facebook, Inc.\n## All rights reserved.\n\n## This source code is licensed under the BSD-style license found in the\n## LICENSE file in the root directory of this source tree. An additional grant\n## of patent rights can be found in the PATENTS file in the same directory.\n\n\ngenerated_holidays <- read.csv(\"data-raw/generated_holidays.csv\")\nusethis::use_data(generated_holidays, overwrite = TRUE, internal = TRUE)\n" }, { "path": "R/inst/include/stan_meta_header.hpp", "content": "// Insert all #include statements here\n" }, { "path": "R/inst/stan/prophet.stan", "content": "// Copyright (c) Facebook, Inc. and its affiliates.\n\n// This source code is licensed under the MIT license found in the\n// LICENSE file in the root directory of this source tree.\n\nfunctions {\n matrix get_changepoint_matrix(vector t, vector t_change, int T, int S) {\n // Assumes t and t_change are sorted.\n matrix[T, S] A;\n row_vector[S] a_row;\n int cp_idx;\n\n // Start with an empty matrix.\n A = rep_matrix(0, T, S);\n a_row = rep_row_vector(0, S);\n cp_idx = 1;\n\n // Fill in each row of A.\n for (i in 1:T) {\n while ((cp_idx <= S) && (t[i] >= t_change[cp_idx])) {\n a_row[cp_idx] = 1;\n cp_idx = cp_idx + 1;\n }\n A[i] = a_row;\n }\n return A;\n }\n\n // Logistic trend functions\n vector logistic_gamma(real k, real m, vector delta, vector t_change, int S) {\n vector[S] gamma; // adjusted offsets, for piecewise continuity\n vector[S + 1] k_s; // actual rate in each segment\n real m_pr;\n\n // Compute the rate in each segment\n k_s = append_row(k, k + cumulative_sum(delta));\n\n // Piecewise offsets\n m_pr = m; // The offset in the previous segment\n for (i in 1:S) {\n gamma[i] = (t_change[i] - m_pr) * (1 - k_s[i] / k_s[i + 1]);\n m_pr = m_pr + gamma[i]; // update for the next segment\n }\n return gamma;\n }\n\n vector logistic_trend(\n real k,\n real m,\n vector delta,\n vector t,\n vector cap,\n matrix A,\n vector t_change,\n int S\n ) {\n vector[S] gamma;\n\n gamma = logistic_gamma(k, m, delta, t_change, S);\n return cap .* inv_logit((k + A * delta) .* (t - (m + A * gamma)));\n }\n\n // Linear trend function\n vector linear_trend(\n real k,\n real m,\n vector delta,\n vector t,\n matrix A,\n vector t_change\n ) {\n return (k + A * delta) .* t + (m + A * (-t_change .* delta));\n }\n\n // Flat trend function\n vector flat_trend(\n real m,\n int T\n ) {\n return rep_vector(m, T);\n }\n}\n\ndata {\n int T; // Number of time periods\n int K; // Number of regressors\n vector[T] t; // Time\n vector[T] cap; // Capacities for logistic trend\n vector[T] y; // Time series\n int S; // Number of changepoints\n vector[S] t_change; // Times of trend changepoints\n matrix[T,K] X; // Regressors\n vector[K] sigmas; // Scale on seasonality prior\n real tau; // Scale on changepoints prior\n int trend_indicator; // 0 for linear, 1 for logistic, 2 for flat\n vector[K] s_a; // Indicator of additive features\n vector[K] s_m; // Indicator of multiplicative features\n}\n\ntransformed data {\n matrix[T, S] A = get_changepoint_matrix(t, t_change, T, S);\n matrix[T, K] X_sa = X .* rep_matrix(s_a', T);\n matrix[T, K] X_sm = X .* rep_matrix(s_m', T);\n}\n\nparameters {\n real k; // Base trend growth rate\n real m; // Trend offset\n vector[S] delta; // Trend rate adjustments\n real sigma_obs; // Observation noise\n vector[K] beta; // Regressor coefficients\n}\n\ntransformed parameters {\n vector[T] trend;\n if (trend_indicator == 0) {\n trend = linear_trend(k, m, delta, t, A, t_change);\n } else if (trend_indicator == 1) {\n trend = logistic_trend(k, m, delta, t, cap, A, t_change, S);\n } else if (trend_indicator == 2) {\n trend = flat_trend(m, T);\n }\n}\n\nmodel {\n //priors\n k ~ normal(0, 5);\n m ~ normal(0, 5);\n delta ~ double_exponential(0, tau);\n sigma_obs ~ normal(0, 0.5);\n beta ~ normal(0, sigmas);\n\n // Likelihood\n y ~ normal_id_glm(\n X_sa,\n trend .* (1 + X_sm * beta),\n beta,\n sigma_obs\n );\n}\n" }, { "path": "R/man/add_changepoints_to_plot.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{add_changepoints_to_plot}\n\\alias{add_changepoints_to_plot}\n\\title{Get layers to overlay significant changepoints on prophet forecast plot.}\n\\usage{\nadd_changepoints_to_plot(\n m,\n threshold = 0.01,\n cp_color = \"red\",\n cp_linetype = \"dashed\",\n trend = TRUE,\n ...\n)\n}\n\\arguments{\n\\item{m}{Prophet model object.}\n\n\\item{threshold}{Numeric, changepoints where abs(delta) >= threshold are\nsignificant. (Default 0.01)}\n\n\\item{cp_color}{Character, line color. (Default \"red\")}\n\n\\item{cp_linetype}{Character or integer, line type. (Default \"dashed\")}\n\n\\item{trend}{Logical, if FALSE, do not draw trend line. (Default TRUE)}\n\n\\item{...}{Other arguments passed on to layers.}\n}\n\\value{\nA list of ggplot2 layers.\n}\n\\description{\nGet layers to overlay significant changepoints on prophet forecast plot.\n}\n\\examples{\n\\dontrun{\nplot(m, fcst) + add_changepoints_to_plot(m)\n}\n\n}\n" }, { "path": "R/man/add_country_holidays.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{add_country_holidays}\n\\alias{add_country_holidays}\n\\title{Add in built-in holidays for the specified country.}\n\\usage{\nadd_country_holidays(m, country_name)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{country_name}{Name of the country, like 'UnitedStates' or 'US'}\n}\n\\value{\nThe prophet model with the holidays country set.\n}\n\\description{\nThese holidays will be included in addition to any specified on model\ninitialization.\n}\n\\details{\nHolidays will be calculated for arbitrary date ranges in the history\nand future. See the online documentation for the list of countries with\nbuilt-in holidays.\n\nBuilt-in country holidays can only be set for a single country.\n}\n" }, { "path": "R/man/add_group_component.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{add_group_component}\n\\alias{add_group_component}\n\\title{Adds a component with given name that contains all of the components\nin group.}\n\\usage{\nadd_group_component(components, name, group)\n}\n\\arguments{\n\\item{components}{Dataframe with components.}\n\n\\item{name}{Name of new group component.}\n\n\\item{group}{List of components that form the group.}\n}\n\\value{\nDataframe with components.\n}\n\\description{\nAdds a component with given name that contains all of the components\nin group.\n}\n\\keyword{internal}\n" }, { "path": "R/man/add_regressor.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{add_regressor}\n\\alias{add_regressor}\n\\title{Add an additional regressor to be used for fitting and predicting.}\n\\usage{\nadd_regressor(m, name, prior.scale = NULL, standardize = \"auto\", mode = NULL)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{name}{String name of the regressor}\n\n\\item{prior.scale}{Float scale for the normal prior. If not provided,\nholidays.prior.scale will be used.}\n\n\\item{standardize}{Bool, specify whether this regressor will be standardized\nprior to fitting. Can be 'auto' (standardize if not binary), True, or\nFalse.}\n\n\\item{mode}{Optional, 'additive' or 'multiplicative'. Defaults to\nm$seasonality.mode.}\n}\n\\value{\nThe prophet model with the regressor added.\n}\n\\description{\nThe dataframe passed to `fit` and `predict` will have a column with the\nspecified name to be used as a regressor. When standardize='auto', the\nregressor will be standardized unless it is binary. The regression\ncoefficient is given a prior with the specified scale parameter.\nDecreasing the prior scale will add additional regularization. If no\nprior scale is provided, holidays.prior.scale will be used.\nMode can be specified as either 'additive' or 'multiplicative'. If not\nspecified, m$seasonality.mode will be used. 'additive' means the effect of\nthe regressor will be added to the trend, 'multiplicative' means it will\nmultiply the trend.\n}\n" }, { "path": "R/man/add_seasonality.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{add_seasonality}\n\\alias{add_seasonality}\n\\title{Add a seasonal component with specified period, number of Fourier\ncomponents, and prior scale.}\n\\usage{\nadd_seasonality(\n m,\n name,\n period,\n fourier.order,\n prior.scale = NULL,\n mode = NULL,\n condition.name = NULL\n)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{name}{String name of the seasonality component.}\n\n\\item{period}{Float number of days in one period.}\n\n\\item{fourier.order}{Int number of Fourier components to use.}\n\n\\item{prior.scale}{Optional float prior scale for this component.}\n\n\\item{mode}{Optional 'additive' or 'multiplicative'.}\n\n\\item{condition.name}{String name of the seasonality condition.}\n}\n\\value{\nThe prophet model with the seasonality added.\n}\n\\description{\nIncreasing the number of Fourier components allows the seasonality to change\nmore quickly (at risk of overfitting). Default values for yearly and weekly\nseasonalities are 10 and 3 respectively.\n}\n\\details{\nIncreasing prior scale will allow this seasonality component more\nflexibility, decreasing will dampen it. If not provided, will use the\nseasonality.prior.scale provided on Prophet initialization (defaults to 10).\n\nMode can be specified as either 'additive' or 'multiplicative'. If not\nspecified, m$seasonality.mode will be used (defaults to 'additive').\nAdditive means the seasonality will be added to the trend, multiplicative\nmeans it will multiply the trend.\n\nIf condition.name is provided, the dataframe passed to `fit` and `predict`\nshould have a column with the specified condition.name containing booleans\nwhich decides when to apply seasonality.\n}\n" }, { "path": "R/man/check_cmdstanr.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/stan_backends.R\n\\name{check_cmdstanr}\n\\alias{check_cmdstanr}\n\\title{Check that the required packages for using the cmdstanr backend are installed.}\n\\usage{\ncheck_cmdstanr()\n}\n\\value{\nNULL if successful, and prints the current version of cmdstan being used.\n}\n\\description{\nCheck that the required packages for using the cmdstanr backend are installed.\n}\n\\keyword{internal}\n" }, { "path": "R/man/construct_holiday_dataframe.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{construct_holiday_dataframe}\n\\alias{construct_holiday_dataframe}\n\\title{Construct a dataframe of holiday dates.}\n\\usage{\nconstruct_holiday_dataframe(m, dates)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{dates}{Vector with dates used for computing seasonality.}\n}\n\\value{\nA dataframe of holiday dates, in holiday dataframe format used in\n initialization.\n}\n\\description{\nConstruct a dataframe of holiday dates.\n}\n\\keyword{internal}\n" }, { "path": "R/man/coverage.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{coverage}\n\\alias{coverage}\n\\title{Coverage}\n\\usage{\ncoverage(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of coverages\n}\n\\description{\nCoverage\n}\n\\keyword{internal}\n" }, { "path": "R/man/cross_validation.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{cross_validation}\n\\alias{cross_validation}\n\\title{Cross-validation for time series.}\n\\usage{\ncross_validation(\n model,\n horizon,\n units,\n period = NULL,\n initial = NULL,\n cutoffs = NULL\n)\n}\n\\arguments{\n\\item{model}{Fitted Prophet model.}\n\n\\item{horizon}{Integer size of the horizon}\n\n\\item{units}{String unit of the horizon, e.g., \"days\", \"secs\".}\n\n\\item{period}{Integer amount of time between cutoff dates. Same units as\nhorizon. If not provided, 0.5 * horizon is used.}\n\n\\item{initial}{Integer size of the first training period. If not provided,\n3 * horizon is used. Same units as horizon.}\n\n\\item{cutoffs}{Vector of cutoff dates to be used during\ncross-validtation. If not provided works beginning from (end - horizon),\nworks backwards making cutoffs with a spacing of period until initial is\nreached.}\n}\n\\value{\nA dataframe with the forecast, actual value, and cutoff date.\n}\n\\description{\nComputes forecasts from historical cutoff points which user can input.If\nnot provided, these are computed beginning from (end - horizon), and working\nbackwards making cutoffs with a spacing of period until initial is reached.\n}\n\\details{\nWhen period is equal to the time interval of the data, this is the\ntechnique described in https://robjhyndman.com/hyndsight/tscv/ .\n}\n" }, { "path": "R/man/df_for_plotting.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{df_for_plotting}\n\\alias{df_for_plotting}\n\\title{Merge history and forecast for plotting.}\n\\usage{\ndf_for_plotting(m, fcst)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{fcst}{Data frame returned by prophet predict.}\n}\n\\description{\nMerge history and forecast for plotting.\n}\n\\keyword{internal}\n" }, { "path": "R/man/dot-fit.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/stan_backends.R\n\\name{.fit}\n\\alias{.fit}\n\\title{Obtain the point estimates of the parameters of the Prophet model using\nstan's optimization algorithms.}\n\\usage{\n.fit(args, backend)\n}\n\\arguments{\n\\item{args}{Named list of arguments suitable for the chosen backend. Must\ninclude arguments required for optimization.}\n\n\\item{backend}{\"rstan\" or \"cmdstanr\".}\n}\n\\value{\nA named list containing \"stan_fit\" (the fitted stan object),\n \"params\", and \"n_iteration\"\n}\n\\description{\nObtain the point estimates of the parameters of the Prophet model using\nstan's optimization algorithms.\n}\n\\keyword{internal}\n" }, { "path": "R/man/dot-load_model.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/stan_backends.R\n\\name{.load_model}\n\\alias{.load_model}\n\\title{Load the Prophet Stan model.}\n\\usage{\n.load_model(backend)\n}\n\\arguments{\n\\item{backend}{\"rstan\" or \"cmdstanr\".}\n}\n\\value{\nstanmodel object if backend = \"rstan\", CmdStanModel object if backend = \"cmdstanr\"\n}\n\\description{\nLoad the Prophet Stan model.\n}\n\\keyword{internal}\n" }, { "path": "R/man/dot-sampling.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/stan_backends.R\n\\name{.sampling}\n\\alias{.sampling}\n\\title{Obtain the joint posterior distribution of the parameters of the Prophet\nmodel using MCMC sampling.}\n\\usage{\n.sampling(args, backend)\n}\n\\arguments{\n\\item{args}{Named list of arguments suitable for the chosen backend. Must\ninclude arguments required for MCMC sampling.}\n\n\\item{backend}{\"rstan\" or \"cmdstanr\".}\n}\n\\value{\nA named list containing \"stan_fit\" (the fitted stan object),\n \"params\", and \"n_iteration\"\n}\n\\description{\nObtain the joint posterior distribution of the parameters of the Prophet\nmodel using MCMC sampling.\n}\n\\keyword{internal}\n" }, { "path": "R/man/dot-stan_args.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/stan_backends.R\n\\name{.stan_args}\n\\alias{.stan_args}\n\\title{Gives Stan arguments the appropriate names depending on the chosen Stan backend.}\n\\usage{\n.stan_args(model, dat, stan_init, backend, type, mcmc_samples = 0, ...)\n}\n\\arguments{\n\\item{model}{Model object.}\n\n\\item{dat}{List containing data to use in fitting.}\n\n\\item{stan_init}{Function to initialize parameters for stan fit.}\n\n\\item{backend}{\"rstan\" or \"cmdstanr\".}\n\n\\item{type}{\"mcmc\" or \"optimize\".}\n\n\\item{mcmc_samples}{Integer, if greater than 0, will do full Bayesian\ninference with the specified number of MCMC samples. If 0, will do MAP\nestimation.}\n}\n\\value{\nNamed list of arguments.\n}\n\\description{\nGives Stan arguments the appropriate names depending on the chosen Stan backend.\n}\n\\keyword{internal}\n" }, { "path": "R/man/dyplot.prophet.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{dyplot.prophet}\n\\alias{dyplot.prophet}\n\\title{Plot the prophet forecast.}\n\\usage{\ndyplot.prophet(x, fcst, uncertainty = TRUE, ...)\n}\n\\arguments{\n\\item{x}{Prophet object.}\n\n\\item{fcst}{Data frame returned by predict(m, df).}\n\n\\item{uncertainty}{Optional boolean indicating if the uncertainty interval for yhat\nshould be plotted, which will only be done if x$uncertainty.samples > 0. Must be\npresent in fcst as yhat_lower and yhat_upper.}\n\n\\item{...}{additional arguments passed to dygraphs::dygraph}\n}\n\\value{\nA dygraph plot.\n}\n\\description{\nPlot the prophet forecast.\n}\n\\examples{\n\\dontrun{\nhistory <- data.frame(\n ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n y = sin(1:366/200) + rnorm(366)/10)\nm <- prophet(history)\nfuture <- make_future_dataframe(m, periods = 365)\nforecast <- predict(m, future)\ndyplot.prophet(m, forecast)\n}\n\n}\n" }, { "path": "R/man/fit.prophet.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{fit.prophet}\n\\alias{fit.prophet}\n\\title{Fit the prophet model.}\n\\usage{\nfit.prophet(m, df, ...)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Data frame.}\n\n\\item{...}{Additional arguments passed to the \\code{optimizing} or\n\\code{sampling} functions in Stan.}\n}\n\\description{\nThis sets m$params to contain the fitted model parameters. It is a list\nwith the following elements:\n k (M array): M posterior samples of the initial slope.\n m (M array): The initial intercept.\n delta (MxN matrix): The slope change at each of N changepoints.\n beta (MxK matrix): Coefficients for K seasonality features.\n sigma_obs (M array): Noise level.\nNote that M=1 if MAP estimation.\n}\n" }, { "path": "R/man/flat_growth_init.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{flat_growth_init}\n\\alias{flat_growth_init}\n\\title{Initialize flat growth.}\n\\usage{\nflat_growth_init(df)\n}\n\\arguments{\n\\item{df}{Data frame with columns ds (date), y_scaled (scaled time series),\nand t (scaled time).}\n}\n\\value{\nA vector (k, m) with the rate (k) and offset (m) of the flat\n growth function.\n}\n\\description{\nProvides a strong initialization for flat growth by setting the\ngrowth to 0 and calculates the offset parameter that pass the\nfunction through the mean of the the y_scaled values.\n}\n\\keyword{internal}\n" }, { "path": "R/man/flat_trend.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{flat_trend}\n\\alias{flat_trend}\n\\title{Evaluate the flat trend function.}\n\\usage{\nflat_trend(t, m)\n}\n\\arguments{\n\\item{t}{Vector of times on which the function is evaluated.}\n\n\\item{m}{Float initial offset.}\n}\n\\value{\nVector y(t).\n}\n\\description{\nEvaluate the flat trend function.\n}\n\\keyword{internal}\n" }, { "path": "R/man/fourier_series.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{fourier_series}\n\\alias{fourier_series}\n\\title{Provides Fourier series components with the specified frequency and order.}\n\\usage{\nfourier_series(dates, period, series.order)\n}\n\\arguments{\n\\item{dates}{Vector of dates.}\n\n\\item{period}{Number of days of the period.}\n\n\\item{series.order}{Number of components.}\n}\n\\value{\nMatrix with seasonality features.\n}\n\\description{\nProvides Fourier series components with the specified frequency and order.\n}\n\\keyword{internal}\n" }, { "path": "R/man/generate_cutoffs.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{generate_cutoffs}\n\\alias{generate_cutoffs}\n\\title{Generate cutoff dates}\n\\usage{\ngenerate_cutoffs(df, horizon, initial, period)\n}\n\\arguments{\n\\item{df}{Dataframe with historical data.}\n\n\\item{horizon}{timediff forecast horizon.}\n\n\\item{initial}{timediff initial window.}\n\n\\item{period}{timediff Simulated forecasts are done with this period.}\n}\n\\value{\nArray of datetimes.\n}\n\\description{\nGenerate cutoff dates\n}\n\\keyword{internal}\n" }, { "path": "R/man/generated_holidays.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data.R\n\\docType{data}\n\\name{generated_holidays}\n\\alias{generated_holidays}\n\\title{Generated table of holiday dates at the country level from 1995 to 2045}\n\\format{\nA data frame with four variables: ds, holiday, country, year\n}\n\\source{\n\\url{https://github.com/facebook/prophet/blob/main/python/scripts/generate_holidays_file.py}\n}\n\\usage{\ngenerated_holidays\n}\n\\description{\nThe data is primarily based on the Python package [holidays](https://pypi.org/project/holidays/)\n}\n\\keyword{datasets}\n" }, { "path": "R/man/get_holiday_names.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/make_holidays.R\n\\name{get_holiday_names}\n\\alias{get_holiday_names}\n\\title{Return all possible holiday names of given country}\n\\usage{\nget_holiday_names(country.name)\n}\n\\arguments{\n\\item{country.name}{Country name (character).}\n}\n\\value{\nA vector of all possible holiday names (unique) of given country.\n}\n\\description{\nReturn all possible holiday names of given country\n}\n\\keyword{internal}\n" }, { "path": "R/man/get_stan_backend.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/stan_backends.R\n\\name{get_stan_backend}\n\\alias{get_stan_backend}\n\\title{Get the stan backend defined in the environment variables.}\n\\usage{\nget_stan_backend()\n}\n\\value{\n'rstan' or 'cmdstanr'. 'rstan' if variable is not set.\n}\n\\description{\nGet the stan backend defined in the environment variables.\n}\n\\keyword{internal}\n" }, { "path": "R/man/initialize_scales_fn.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{initialize_scales_fn}\n\\alias{initialize_scales_fn}\n\\title{Initialize model scales.}\n\\usage{\ninitialize_scales_fn(m, initialize_scales, df)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{initialize_scales}{Boolean set the scales or not.}\n\n\\item{df}{Dataframe for setting scales.}\n}\n\\value{\nProphet object with scales set.\n}\n\\description{\nSets model scaling factors using df.\n}\n\\keyword{internal}\n" }, { "path": "R/man/linear_growth_init.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{linear_growth_init}\n\\alias{linear_growth_init}\n\\title{Initialize constant growth.}\n\\usage{\nlinear_growth_init(df)\n}\n\\arguments{\n\\item{df}{Data frame with columns ds (date), y_scaled (scaled time series),\nand t (scaled time).}\n}\n\\value{\nA vector (k, m) with the rate (k) and offset (m) of the linear\n growth function.\n}\n\\description{\nProvides a strong initialization for linear growth by calculating the\ngrowth and offset parameters that pass the function through the first and\nlast points in the time series.\n}\n\\keyword{internal}\n" }, { "path": "R/man/logistic_growth_init.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{logistic_growth_init}\n\\alias{logistic_growth_init}\n\\title{Initialize logistic growth.}\n\\usage{\nlogistic_growth_init(df)\n}\n\\arguments{\n\\item{df}{Data frame with columns ds (date), cap_scaled (scaled capacity),\ny_scaled (scaled time series), and t (scaled time).}\n}\n\\value{\nA vector (k, m) with the rate (k) and offset (m) of the logistic\n growth function.\n}\n\\description{\nProvides a strong initialization for logistic growth by calculating the\ngrowth and offset parameters that pass the function through the first and\nlast points in the time series.\n}\n\\keyword{internal}\n" }, { "path": "R/man/mae.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{mae}\n\\alias{mae}\n\\title{Mean absolute error}\n\\usage{\nmae(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of mean absolute errors.\n}\n\\description{\nMean absolute error\n}\n\\keyword{internal}\n" }, { "path": "R/man/make_all_seasonality_features.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{make_all_seasonality_features}\n\\alias{make_all_seasonality_features}\n\\title{Dataframe with seasonality features.\nIncludes seasonality features, holiday features, and added regressors.}\n\\usage{\nmake_all_seasonality_features(m, df)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Dataframe with dates for computing seasonality features and any\nadded regressors.}\n}\n\\value{\nList with items\n seasonal.features: Dataframe with regressor features,\n prior.scales: Array of prior scales for each column of the features\n dataframe.\n component.cols: Dataframe with indicators for which regression components\n correspond to which columns.\n modes: List with keys 'additive' and 'multiplicative' with arrays of\n component names for each mode of seasonality.\n}\n\\description{\nDataframe with seasonality features.\nIncludes seasonality features, holiday features, and added regressors.\n}\n\\keyword{internal}\n" }, { "path": "R/man/make_future_dataframe.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{make_future_dataframe}\n\\alias{make_future_dataframe}\n\\title{Make dataframe with future dates for forecasting.}\n\\usage{\nmake_future_dataframe(m, periods, freq = \"day\", include_history = TRUE)\n}\n\\arguments{\n\\item{m}{Prophet model object.}\n\n\\item{periods}{Int number of periods to forecast forward.}\n\n\\item{freq}{'day', 'week', 'month', 'quarter', 'year', 1(1 sec), 60(1 minute) or 3600(1 hour).}\n\n\\item{include_history}{Boolean to include the historical dates in the data\nframe for predictions.}\n}\n\\value{\nDataframe that extends forward from the end of m$history for the\n requested number of periods.\n}\n\\description{\nMake dataframe with future dates for forecasting.\n}\n" }, { "path": "R/man/make_holiday_features.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{make_holiday_features}\n\\alias{make_holiday_features}\n\\title{Construct a matrix of holiday features.}\n\\usage{\nmake_holiday_features(m, dates, holidays)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{dates}{Vector with dates used for computing seasonality.}\n\n\\item{holidays}{Dataframe containing holidays, as returned by\nconstruct_holiday_dataframe.}\n}\n\\value{\nA list with entries\n holiday.features: dataframe with a column for each holiday.\n prior.scales: array of prior scales for each holiday column.\n holiday.names: array of names of all holidays.\n}\n\\description{\nConstruct a matrix of holiday features.\n}\n\\keyword{internal}\n" }, { "path": "R/man/make_holidays_df.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/make_holidays.R\n\\name{make_holidays_df}\n\\alias{make_holidays_df}\n\\title{Make dataframe of holidays for given years and countries}\n\\usage{\nmake_holidays_df(years, country.name)\n}\n\\arguments{\n\\item{years}{List of years for which to include holiday dates.}\n\n\\item{country.name}{Country name (character).}\n}\n\\value{\nDataframe with 'ds' and 'holiday', which can directly feed\n to 'holidays' params in Prophet\n}\n\\description{\nMake dataframe of holidays for given years and countries\n}\n\\keyword{internal}\n" }, { "path": "R/man/make_seasonality_features.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{make_seasonality_features}\n\\alias{make_seasonality_features}\n\\title{Data frame with seasonality features.}\n\\usage{\nmake_seasonality_features(dates, period, series.order, prefix)\n}\n\\arguments{\n\\item{dates}{Vector of dates.}\n\n\\item{period}{Number of days of the period.}\n\n\\item{series.order}{Number of components.}\n\n\\item{prefix}{Column name prefix.}\n}\n\\value{\nDataframe with seasonality.\n}\n\\description{\nData frame with seasonality features.\n}\n\\keyword{internal}\n" }, { "path": "R/man/mape.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{mape}\n\\alias{mape}\n\\title{Mean absolute percent error}\n\\usage{\nmape(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of mean absolute percent errors.\n}\n\\description{\nMean absolute percent error\n}\n\\keyword{internal}\n" }, { "path": "R/man/mdape.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{mdape}\n\\alias{mdape}\n\\title{Median absolute percent error}\n\\usage{\nmdape(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of median absolute percent errors.\n}\n\\description{\nMedian absolute percent error\n}\n\\keyword{internal}\n" }, { "path": "R/man/mse.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{mse}\n\\alias{mse}\n\\title{Mean squared error}\n\\usage{\nmse(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of mean squared errors.\n}\n\\description{\nMean squared error\n}\n\\keyword{internal}\n" }, { "path": "R/man/parse_seasonality_args.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{parse_seasonality_args}\n\\alias{parse_seasonality_args}\n\\title{Get number of Fourier components for built-in seasonalities.}\n\\usage{\nparse_seasonality_args(m, name, arg, auto.disable, default.order)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{name}{String name of the seasonality component.}\n\n\\item{arg}{'auto', TRUE, FALSE, or number of Fourier components as\nprovided.}\n\n\\item{auto.disable}{Bool if seasonality should be disabled when 'auto'.}\n\n\\item{default.order}{Int default Fourier order.}\n}\n\\value{\nNumber of Fourier components, or 0 for disabled.\n}\n\\description{\nGet number of Fourier components for built-in seasonalities.\n}\n\\keyword{internal}\n" }, { "path": "R/man/performance_metrics.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{performance_metrics}\n\\alias{performance_metrics}\n\\title{Compute performance metrics from cross-validation results.}\n\\usage{\nperformance_metrics(df, metrics = NULL, rolling_window = 0.1)\n}\n\\arguments{\n\\item{df}{The dataframe returned by cross_validation.}\n\n\\item{metrics}{An array of performance metrics to compute. If not provided,\nwill use c('mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage').}\n\n\\item{rolling_window}{Proportion of data to use in each rolling window for\ncomputing the metrics. Should be in [0, 1] to average.}\n}\n\\value{\nA dataframe with a column for each metric, and column 'horizon'.\n}\n\\description{\nComputes a suite of performance metrics on the output of cross-validation.\nBy default the following metrics are included:\n'mse': mean squared error,\n'rmse': root mean squared error,\n'mae': mean absolute error,\n'mape': mean percent error,\n'mdape': median percent error,\n'smape': symmetric mean absolute percentage error,\n'coverage': coverage of the upper and lower intervals\n}\n\\details{\nA subset of these can be specified by passing a list of names as the\n`metrics` argument.\n\nMetrics are calculated over a rolling window of cross validation\npredictions, after sorting by horizon. Averaging is first done within each\nvalue of the horizon, and then across horizons as needed to reach the\nwindow size. The size of that window (number of simulated forecast points)\nis determined by the rolling_window argument, which specifies a proportion\nof simulated forecast points to include in each window. rolling_window=0\nwill compute it separately for each horizon. The default of\nrolling_window=0.1 will use 10% of the rows in df in each window.\nrolling_window=1 will compute the metric across all simulated forecast\npoints. The results are set to the right edge of the window.\n\nIf rolling_window < 0, then metrics are computed at each datapoint with no\naveraging (i.e., 'mse' will actually be squared error with no mean).\n\nThe output is a dataframe containing column 'horizon' along with columns\nfor each of the metrics computed.\n}\n" }, { "path": "R/man/piecewise_linear.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{piecewise_linear}\n\\alias{piecewise_linear}\n\\title{Evaluate the piecewise linear function.}\n\\usage{\npiecewise_linear(t, deltas, k, m, changepoint.ts)\n}\n\\arguments{\n\\item{t}{Vector of times on which the function is evaluated.}\n\n\\item{deltas}{Vector of rate changes at each changepoint.}\n\n\\item{k}{Float initial rate.}\n\n\\item{m}{Float initial offset.}\n\n\\item{changepoint.ts}{Vector of changepoint times.}\n}\n\\value{\nVector y(t).\n}\n\\description{\nEvaluate the piecewise linear function.\n}\n\\keyword{internal}\n" }, { "path": "R/man/piecewise_logistic.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{piecewise_logistic}\n\\alias{piecewise_logistic}\n\\title{Evaluate the piecewise logistic function.}\n\\usage{\npiecewise_logistic(t, cap, deltas, k, m, changepoint.ts)\n}\n\\arguments{\n\\item{t}{Vector of times on which the function is evaluated.}\n\n\\item{cap}{Vector of capacities at each t.}\n\n\\item{deltas}{Vector of rate changes at each changepoint.}\n\n\\item{k}{Float initial rate.}\n\n\\item{m}{Float initial offset.}\n\n\\item{changepoint.ts}{Vector of changepoint times.}\n}\n\\value{\nVector y(t).\n}\n\\description{\nEvaluate the piecewise logistic function.\n}\n\\keyword{internal}\n" }, { "path": "R/man/plot.prophet.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{plot.prophet}\n\\alias{plot.prophet}\n\\title{Plot the prophet forecast.}\n\\usage{\n\\method{plot}{prophet}(\n x,\n fcst,\n uncertainty = TRUE,\n plot_cap = TRUE,\n xlabel = \"ds\",\n ylabel = \"y\",\n ...\n)\n}\n\\arguments{\n\\item{x}{Prophet object.}\n\n\\item{fcst}{Data frame returned by predict(m, df).}\n\n\\item{uncertainty}{Optional boolean indicating if the uncertainty interval for yhat\nshould be plotted, which will only be done if x$uncertainty.samples > 0.\nMust be present in fcst as yhat_lower and yhat_upper.}\n\n\\item{plot_cap}{Boolean indicating if the capacity should be shown in the\nfigure, if available.}\n\n\\item{xlabel}{Optional label for x-axis}\n\n\\item{ylabel}{Optional label for y-axis}\n\n\\item{...}{additional arguments}\n}\n\\value{\nA ggplot2 plot.\n}\n\\description{\nPlot the prophet forecast.\n}\n\\examples{\n\\dontrun{\nhistory <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n y = sin(1:366/200) + rnorm(366)/10)\nm <- prophet(history)\nfuture <- make_future_dataframe(m, periods = 365)\nforecast <- predict(m, future)\nplot(m, forecast)\n}\n\n}\n" }, { "path": "R/man/plot_cross_validation_metric.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{plot_cross_validation_metric}\n\\alias{plot_cross_validation_metric}\n\\title{Plot a performance metric vs. forecast horizon from cross validation.\nCross validation produces a collection of out-of-sample model predictions\nthat can be compared to actual values, at a range of different horizons\n(distance from the cutoff). This computes a specified performance metric\nfor each prediction, and aggregated over a rolling window with horizon.}\n\\usage{\nplot_cross_validation_metric(df_cv, metric, rolling_window = 0.1)\n}\n\\arguments{\n\\item{df_cv}{The output from fbprophet.diagnostics.cross_validation.}\n\n\\item{metric}{Metric name, one of 'mse', 'rmse', 'mae', 'mape', 'coverage'.}\n\n\\item{rolling_window}{Proportion of data to use for rolling average of\nmetric. In [0, 1]. Defaults to 0.1.}\n}\n\\value{\nA ggplot2 plot.\n}\n\\description{\nThis uses fbprophet.diagnostics.performance_metrics to compute the metrics.\nValid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'.\n}\n\\details{\nrolling_window is the proportion of data included in the rolling window of\naggregation. The default value of 0.1 means 10% of data are included in the\naggregation for computing the metric.\n\nAs a concrete example, if metric='mse', then this plot will show the\nsquared error for each cross validation prediction, along with the MSE\naveraged over rolling windows of 10% of the data.\n}\n" }, { "path": "R/man/plot_forecast_component.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{plot_forecast_component}\n\\alias{plot_forecast_component}\n\\title{Plot a particular component of the forecast.}\n\\usage{\nplot_forecast_component(m, fcst, name, uncertainty = TRUE, plot_cap = FALSE)\n}\n\\arguments{\n\\item{m}{Prophet model}\n\n\\item{fcst}{Dataframe output of `predict`.}\n\n\\item{name}{String name of the component to plot (column of fcst).}\n\n\\item{uncertainty}{Optional boolean to plot uncertainty intervals, which will \nonly be done if m$uncertainty.samples > 0.}\n\n\\item{plot_cap}{Boolean indicating if the capacity should be shown in the\nfigure, if available.}\n}\n\\value{\nA ggplot2 plot.\n}\n\\description{\nPlot a particular component of the forecast.\n}\n" }, { "path": "R/man/plot_seasonality.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{plot_seasonality}\n\\alias{plot_seasonality}\n\\title{Plot a custom seasonal component.}\n\\usage{\nplot_seasonality(m, name, uncertainty = TRUE)\n}\n\\arguments{\n\\item{m}{Prophet model object.}\n\n\\item{name}{String name of the seasonality.}\n\n\\item{uncertainty}{Optional boolean to plot uncertainty intervals, which\nwill only be done if m$uncertainty.samples > 0.}\n}\n\\value{\nA ggplot2 plot.\n}\n\\description{\nPlot a custom seasonal component.\n}\n\\keyword{internal}\n" }, { "path": "R/man/plot_weekly.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{plot_weekly}\n\\alias{plot_weekly}\n\\title{Plot the weekly component of the forecast.}\n\\usage{\nplot_weekly(m, uncertainty = TRUE, weekly_start = 0, name = \"weekly\")\n}\n\\arguments{\n\\item{m}{Prophet model object}\n\n\\item{uncertainty}{Optional boolean to plot uncertainty intervals, which will\nonly be done if m$uncertainty.samples > 0.}\n\n\\item{weekly_start}{Integer specifying the start day of the weekly\nseasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day\nto Monday, and so on.}\n\n\\item{name}{Name of seasonality component if previously changed\nfrom default 'weekly'.}\n}\n\\value{\nA ggplot2 plot.\n}\n\\description{\nPlot the weekly component of the forecast.\n}\n\\keyword{internal}\n" }, { "path": "R/man/plot_yearly.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{plot_yearly}\n\\alias{plot_yearly}\n\\title{Plot the yearly component of the forecast.}\n\\usage{\nplot_yearly(m, uncertainty = TRUE, yearly_start = 0, name = \"yearly\")\n}\n\\arguments{\n\\item{m}{Prophet model object.}\n\n\\item{uncertainty}{Optional boolean to plot uncertainty intervals, which\nwill only be done if m$uncertainty.samples > 0.}\n\n\\item{yearly_start}{Integer specifying the start day of the yearly\nseasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day\nto Jan 2, and so on.}\n\n\\item{name}{Name of seasonality component if previously changed\nfrom default 'yearly'.}\n}\n\\value{\nA ggplot2 plot.\n}\n\\description{\nPlot the yearly component of the forecast.\n}\n\\keyword{internal}\n" }, { "path": "R/man/predict.prophet.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{predict.prophet}\n\\alias{predict.prophet}\n\\title{Predict using the prophet model.}\n\\usage{\n\\method{predict}{prophet}(object, df = NULL, ...)\n}\n\\arguments{\n\\item{object}{Prophet object.}\n\n\\item{df}{Dataframe with dates for predictions (column ds), and capacity\n(column cap) if logistic growth. If not provided, predictions are made on\nthe history.}\n\n\\item{...}{additional arguments.}\n}\n\\value{\nA dataframe with the forecast components.\n}\n\\description{\nPredict using the prophet model.\n}\n\\examples{\n\\dontrun{\nhistory <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n y = sin(1:366/200) + rnorm(366)/10)\nm <- prophet(history)\nfuture <- make_future_dataframe(m, periods = 365)\nforecast <- predict(m, future)\nplot(m, forecast)\n}\n\n}\n" }, { "path": "R/man/predict_seasonal_components.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{predict_seasonal_components}\n\\alias{predict_seasonal_components}\n\\title{Predict seasonality components, holidays, and added regressors.}\n\\usage{\npredict_seasonal_components(m, df)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Prediction dataframe.}\n}\n\\value{\nDataframe with seasonal components.\n}\n\\description{\nPredict seasonality components, holidays, and added regressors.\n}\n\\keyword{internal}\n" }, { "path": "R/man/predict_trend.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{predict_trend}\n\\alias{predict_trend}\n\\title{Predict trend using the prophet model.}\n\\usage{\npredict_trend(model, df)\n}\n\\arguments{\n\\item{model}{Prophet object.}\n\n\\item{df}{Prediction dataframe.}\n}\n\\value{\nVector with trend on prediction dates.\n}\n\\description{\nPredict trend using the prophet model.\n}\n\\keyword{internal}\n" }, { "path": "R/man/predict_uncertainty.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{predict_uncertainty}\n\\alias{predict_uncertainty}\n\\title{Prophet uncertainty intervals for yhat and trend}\n\\usage{\npredict_uncertainty(m, df)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Prediction dataframe.}\n}\n\\value{\nDataframe with uncertainty intervals.\n}\n\\description{\nProphet uncertainty intervals for yhat and trend\n}\n\\keyword{internal}\n" }, { "path": "R/man/predictive_samples.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{predictive_samples}\n\\alias{predictive_samples}\n\\title{Sample from the posterior predictive distribution.}\n\\usage{\npredictive_samples(m, df)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Dataframe with dates for predictions (column ds), and capacity\n(column cap) if logistic growth.}\n}\n\\value{\nA list with items \"trend\" and \"yhat\" containing\n posterior predictive samples for that component.\n}\n\\description{\nSample from the posterior predictive distribution.\n}\n" }, { "path": "R/man/prophet.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{prophet}\n\\alias{prophet}\n\\title{Prophet forecaster.}\n\\usage{\nprophet(\n df = NULL,\n growth = \"linear\",\n changepoints = NULL,\n n.changepoints = 25,\n changepoint.range = 0.8,\n yearly.seasonality = \"auto\",\n weekly.seasonality = \"auto\",\n daily.seasonality = \"auto\",\n holidays = NULL,\n seasonality.mode = \"additive\",\n seasonality.prior.scale = 10,\n holidays.prior.scale = 10,\n changepoint.prior.scale = 0.05,\n mcmc.samples = 0,\n interval.width = 0.8,\n uncertainty.samples = 1000,\n fit = TRUE,\n backend = NULL,\n ...\n)\n}\n\\arguments{\n\\item{df}{(optional) Dataframe containing the history. Must have columns ds\n(date type) and y, the time series. If growth is logistic, then df must\nalso have a column cap that specifies the capacity at each ds. If not\nprovided, then the model object will be instantiated but not fit; use\nfit.prophet(m, df) to fit the model.}\n\n\\item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear,\nlogistic or flat trend.}\n\n\\item{changepoints}{Vector of dates at which to include potential\nchangepoints. If not specified, potential changepoints are selected\nautomatically.}\n\n\\item{n.changepoints}{Number of potential changepoints to include. Not used\nif input `changepoints` is supplied. If `changepoints` is not supplied,\nthen n.changepoints potential changepoints are selected uniformly from the\nfirst `changepoint.range` proportion of df$ds.}\n\n\\item{changepoint.range}{Proportion of history in which trend changepoints\nwill be estimated. Defaults to 0.8 for the first 80%. Not used if\n`changepoints` is specified.}\n\n\\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, FALSE,\nor a number of Fourier terms to generate.}\n\n\\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, FALSE,\nor a number of Fourier terms to generate.}\n\n\\item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE, FALSE,\nor a number of Fourier terms to generate.}\n\n\\item{holidays}{data frame with columns holiday (character) and ds (date\ntype)and optionally columns lower_window and upper_window which specify a\nrange of days around the date to be included as holidays. lower_window=-2\nwill include 2 days prior to the date as holidays. Also optionally can have\na column prior_scale specifying the prior scale for each holiday.}\n\n\\item{seasonality.mode}{'additive' (default) or 'multiplicative'.}\n\n\\item{seasonality.prior.scale}{Parameter modulating the strength of the\nseasonality model. Larger values allow the model to fit larger seasonal\nfluctuations, smaller values dampen the seasonality. Can be specified for\nindividual seasonalities using add_seasonality.}\n\n\\item{holidays.prior.scale}{Parameter modulating the strength of the holiday\ncomponents model, unless overridden in the holidays input.}\n\n\\item{changepoint.prior.scale}{Parameter modulating the flexibility of the\nautomatic changepoint selection. Large values will allow many changepoints,\nsmall values will allow few changepoints.}\n\n\\item{mcmc.samples}{Integer, if greater than 0, will do full Bayesian\ninference with the specified number of MCMC samples. If 0, will do MAP\nestimation.}\n\n\\item{interval.width}{Numeric, width of the uncertainty intervals provided\nfor the forecast. If mcmc.samples=0, this will be only the uncertainty in\nthe trend using the MAP estimate of the extrapolated generative model. If\nmcmc.samples>0, this will be integrated over all model parameters, which\nwill include uncertainty in seasonality.}\n\n\\item{uncertainty.samples}{Number of simulated draws used to estimate\nuncertainty intervals. Settings this value to 0 or False will disable\nuncertainty estimation and speed up the calculation.}\n\n\\item{fit}{Boolean, if FALSE the model is initialized but not fit.}\n\n\\item{backend}{Whether to use the \"rstan\" or \"cmdstanr\" backend to fit the\nmodel. If not provided, uses the R_STAN_BACKEND environment variable.}\n\n\\item{...}{Additional arguments, passed to \\code{\\link{fit.prophet}}}\n}\n\\value{\nA prophet model.\n}\n\\description{\nProphet forecaster.\n}\n\\examples{\n\\dontrun{\nhistory <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),\n y = sin(1:366/200) + rnorm(366)/10)\nm <- prophet(history)\n}\n\n}\n" }, { "path": "R/man/prophet_copy.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{prophet_copy}\n\\alias{prophet_copy}\n\\title{Copy Prophet object.}\n\\usage{\nprophet_copy(m, cutoff = NULL)\n}\n\\arguments{\n\\item{m}{Prophet model object.}\n\n\\item{cutoff}{Date, possibly as string. Changepoints are only retained if\nchangepoints <= cutoff.}\n}\n\\value{\nAn unfitted Prophet model object with the same parameters as the\n input model.\n}\n\\description{\nCopy Prophet object.\n}\n\\keyword{internal}\n" }, { "path": "R/man/prophet_plot_components.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{prophet_plot_components}\n\\alias{prophet_plot_components}\n\\title{Plot the components of a prophet forecast.\nPrints a ggplot2 with whichever are available of: trend, holidays, weekly\nseasonality, yearly seasonality, and additive and multiplicative extra\nregressors.}\n\\usage{\nprophet_plot_components(\n m,\n fcst,\n uncertainty = TRUE,\n plot_cap = TRUE,\n weekly_start = 0,\n yearly_start = 0,\n render_plot = TRUE\n)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{fcst}{Data frame returned by predict(m, df).}\n\n\\item{uncertainty}{Optional boolean indicating if the uncertainty interval should be\nplotted for the trend, from fcst columns trend_lower and trend_upper.This will\nonly be done if m$uncertainty.samples > 0.}\n\n\\item{plot_cap}{Boolean indicating if the capacity should be shown in the\nfigure, if available.}\n\n\\item{weekly_start}{Integer specifying the start day of the weekly\nseasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day\nto Monday, and so on.}\n\n\\item{yearly_start}{Integer specifying the start day of the yearly\nseasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day\nto Jan 2, and so on.}\n\n\\item{render_plot}{Boolean indicating if the plots should be rendered.\nSet to FALSE if you want the function to only return the list of panels.}\n}\n\\value{\nInvisibly return a list containing the plotted ggplot objects\n}\n\\description{\nPlot the components of a prophet forecast.\nPrints a ggplot2 with whichever are available of: trend, holidays, weekly\nseasonality, yearly seasonality, and additive and multiplicative extra\nregressors.\n}\n" }, { "path": "R/man/regressor_coefficients.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/utilities.R\n\\name{regressor_coefficients}\n\\alias{regressor_coefficients}\n\\title{Summarise the coefficients of the extra regressors used in the model.\nFor additive regressors, the coefficient represents the incremental impact\non \\code{y} of a unit increase in the regressor. For multiplicative regressors,\nthe incremental impact is equal to \\code{trend(t)} multiplied by the coefficient.}\n\\usage{\nregressor_coefficients(m)\n}\n\\arguments{\n\\item{m}{Prophet model object, after fitting.}\n}\n\\value{\nDataframe with one row per regressor.\n}\n\\description{\nCoefficients are measured on the original scale of the training data.\n}\n\\details{\nOutput dataframe columns:\n\\itemize{\n \\item{regressor: Name of the regressor}\n \\item{regressor_mode: Whether the regressor has an additive or multiplicative\neffect on \\code{y}.}\n \\item{center: The mean of the regressor if it was standardized. Otherwise 0.}\n \\item{coef_lower: Lower bound for the coefficient, estimated from the MCMC samples.\n Only different to \\code{coef} if \\code{mcmc_samples > 0}.\n }\n \\item{coef: Expected value of the coefficient.}\n \\item{coef_upper: Upper bound for the coefficient, estimated from MCMC samples.\n Only to different to \\code{coef} if \\code{mcmc_samples > 0}.\n }\n}\n}\n" }, { "path": "R/man/regressor_column_matrix.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{regressor_column_matrix}\n\\alias{regressor_column_matrix}\n\\title{Dataframe indicating which columns of the feature matrix correspond to\nwhich seasonality/regressor components.}\n\\usage{\nregressor_column_matrix(m, seasonal.features, modes)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{seasonal.features}{Constructed seasonal features dataframe.}\n\n\\item{modes}{List with keys 'additive' and 'multiplicative' with arrays of\ncomponent names for each mode of seasonality.}\n}\n\\value{\nList with items\n component.cols: A binary indicator dataframe with columns seasonal\n components and rows columns in seasonal.features. Entry is 1 if that\n column is used in that component.\n modes: Updated input with combination components.\n}\n\\description{\nIncludes combination components, like 'additive_terms'. These combination\ncomponents will be added to the 'modes' input.\n}\n\\keyword{internal}\n" }, { "path": "R/man/rmse.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{rmse}\n\\alias{rmse}\n\\title{Root mean squared error}\n\\usage{\nrmse(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of root mean squared errors.\n}\n\\description{\nRoot mean squared error\n}\n\\keyword{internal}\n" }, { "path": "R/man/rolling_mean_by_h.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{rolling_mean_by_h}\n\\alias{rolling_mean_by_h}\n\\title{Compute a rolling mean of x, after first aggregating by h}\n\\usage{\nrolling_mean_by_h(x, h, w, name)\n}\n\\arguments{\n\\item{x}{Array.}\n\n\\item{h}{Array of horizon for each value in x.}\n\n\\item{w}{Integer window size (number of elements).}\n\n\\item{name}{String name for metric in result dataframe.}\n}\n\\value{\nDataframe with columns horizon and name, the rolling mean of x.\n}\n\\description{\nRight-aligned. Computes a single mean for each unique value of h. Each mean\nis over at least w samples.\n}\n\\keyword{internal}\n" }, { "path": "R/man/rolling_median_by_h.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{rolling_median_by_h}\n\\alias{rolling_median_by_h}\n\\title{Compute a rolling median of x, after first aggregating by h}\n\\usage{\nrolling_median_by_h(x, h, w, name)\n}\n\\arguments{\n\\item{x}{Array.}\n\n\\item{h}{Array of horizon for each value in x.}\n\n\\item{w}{Integer window size (number of elements).}\n\n\\item{name}{String name for metric in result dataframe.}\n}\n\\value{\nDataframe with columns horizon and name, the rolling median of x.\n}\n\\description{\nRight-aligned. Computes a single median for each unique value of h. Each median\nis over at least w samples.\n}\n\\details{\nFor each h where there are fewer than w samples, we take samples from the previous h,\n}\n" }, { "path": "R/man/sample_model.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{sample_model}\n\\alias{sample_model}\n\\title{Simulate observations from the extrapolated generative model.}\n\\usage{\nsample_model(m, df, seasonal.features, iteration, s_a, s_m)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Prediction dataframe.}\n\n\\item{seasonal.features}{Data frame of seasonal features}\n\n\\item{iteration}{Int sampling iteration to use parameters from.}\n\n\\item{s_a}{Indicator vector for additive components}\n\n\\item{s_m}{Indicator vector for multiplicative components}\n}\n\\value{\nList of trend and yhat, each a vector like df$t.\n}\n\\description{\nSimulate observations from the extrapolated generative model.\n}\n\\keyword{internal}\n" }, { "path": "R/man/sample_posterior_predictive.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{sample_posterior_predictive}\n\\alias{sample_posterior_predictive}\n\\title{Prophet posterior predictive samples.}\n\\usage{\nsample_posterior_predictive(m, df)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Prediction dataframe.}\n}\n\\value{\nList with posterior predictive samples for the forecast yhat and\n for the trend component.\n}\n\\description{\nProphet posterior predictive samples.\n}\n\\keyword{internal}\n" }, { "path": "R/man/sample_predictive_trend.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{sample_predictive_trend}\n\\alias{sample_predictive_trend}\n\\title{Simulate the trend using the extrapolated generative model.}\n\\usage{\nsample_predictive_trend(model, df, iteration)\n}\n\\arguments{\n\\item{model}{Prophet object.}\n\n\\item{df}{Prediction dataframe.}\n\n\\item{iteration}{Int sampling iteration to use parameters from.}\n}\n\\value{\nVector of simulated trend over df$t.\n}\n\\description{\nSimulate the trend using the extrapolated generative model.\n}\n\\keyword{internal}\n" }, { "path": "R/man/seasonality_plot_df.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/plot.R\n\\name{seasonality_plot_df}\n\\alias{seasonality_plot_df}\n\\title{Prepare dataframe for plotting seasonal components.}\n\\usage{\nseasonality_plot_df(m, ds)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{ds}{Array of dates for column ds.}\n}\n\\value{\nA dataframe with seasonal components on ds.\n}\n\\description{\nPrepare dataframe for plotting seasonal components.\n}\n\\keyword{internal}\n" }, { "path": "R/man/set_auto_seasonalities.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{set_auto_seasonalities}\n\\alias{set_auto_seasonalities}\n\\title{Set seasonalities that were left on auto.}\n\\usage{\nset_auto_seasonalities(m)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n}\n\\value{\nThe prophet model with seasonalities set.\n}\n\\description{\nTurns on yearly seasonality if there is >=2 years of history.\nTurns on weekly seasonality if there is >=2 weeks of history, and the\nspacing between dates in the history is <7 days.\nTurns on daily seasonality if there is >=2 days of history, and the spacing\nbetween dates in the history is <1 day.\n}\n\\keyword{internal}\n" }, { "path": "R/man/set_changepoints.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{set_changepoints}\n\\alias{set_changepoints}\n\\title{Set changepoints}\n\\usage{\nset_changepoints(m)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n}\n\\value{\nm with changepoints set.\n}\n\\description{\nSets m$changepoints to the dates of changepoints. Either:\n1) The changepoints were passed in explicitly.\n A) They are empty.\n B) They are not empty, and need validation.\n2) We are generating a grid of them.\n3) The user prefers no changepoints be used.\n}\n\\keyword{internal}\n" }, { "path": "R/man/set_date.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{set_date}\n\\alias{set_date}\n\\title{Convert date vector}\n\\usage{\nset_date(ds)\n}\n\\arguments{\n\\item{ds}{Date vector}\n}\n\\value{\nvector of POSIXct object converted from date\n}\n\\description{\nConvert the date to POSIXct object. Timezones are stripped and replaced\nwith GMT.\n}\n\\keyword{internal}\n" }, { "path": "R/man/setup_dataframe.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{setup_dataframe}\n\\alias{setup_dataframe}\n\\title{Prepare dataframe for fitting or predicting.}\n\\usage{\nsetup_dataframe(m, df, initialize_scales = FALSE)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{df}{Data frame with columns ds, y, and cap if logistic growth. Any\nspecified additional regressors must also be present.}\n\n\\item{initialize_scales}{Boolean set scaling factors in m from df.}\n}\n\\value{\nlist with items 'df' and 'm'.\n}\n\\description{\nAdds a time index and scales y. Creates auxiliary columns 't', 't_ix',\n'y_scaled', and 'cap_scaled'. These columns are used during both fitting\nand predicting.\n}\n\\keyword{internal}\n" }, { "path": "R/man/single_cutoff_forecast.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{single_cutoff_forecast}\n\\alias{single_cutoff_forecast}\n\\title{Forecast for a single cutoff.\nUsed in cross_validation function when evaluating for multiple cutoffs.}\n\\usage{\nsingle_cutoff_forecast(df, model, cutoff, horizon.dt, predict_columns)\n}\n\\arguments{\n\\item{df}{Dataframe with history for cutoff.}\n\n\\item{model}{Prophet model object.}\n\n\\item{cutoff}{Datetime of cutoff.}\n\n\\item{horizon.dt}{timediff forecast horizon.}\n\n\\item{predict_columns}{Array of names of columns to be returned in output.}\n}\n\\value{\nDataframe with forecast, actual value, and cutoff.\n}\n\\description{\nForecast for a single cutoff.\nUsed in cross_validation function when evaluating for multiple cutoffs.\n}\n\\keyword{internal}\n" }, { "path": "R/man/smape.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/diagnostics.R\n\\name{smape}\n\\alias{smape}\n\\title{Symmetric mean absolute percentage error\nbased on Chen and Yang (2004) formula}\n\\usage{\nsmape(df, w)\n}\n\\arguments{\n\\item{df}{Cross-validation results dataframe.}\n\n\\item{w}{Aggregation window size.}\n}\n\\value{\nArray of symmetric mean absolute percent errors.\n}\n\\description{\nSymmetric mean absolute percentage error\nbased on Chen and Yang (2004) formula\n}\n\\keyword{internal}\n" }, { "path": "R/man/time_diff.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{time_diff}\n\\alias{time_diff}\n\\title{Time difference between datetimes}\n\\usage{\ntime_diff(ds1, ds2, units = \"days\")\n}\n\\arguments{\n\\item{ds1}{POSIXct object}\n\n\\item{ds2}{POSIXct object}\n\n\\item{units}{string units of difference, e.g. 'days' or 'secs'.}\n}\n\\value{\nnumeric time difference\n}\n\\description{\nCompute time difference of two POSIXct objects\n}\n\\keyword{internal}\n" }, { "path": "R/man/validate_column_name.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{validate_column_name}\n\\alias{validate_column_name}\n\\title{Validates the name of a seasonality, holiday, or regressor.}\n\\usage{\nvalidate_column_name(\n m,\n name,\n check_holidays = TRUE,\n check_seasonalities = TRUE,\n check_regressors = TRUE\n)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n\n\\item{name}{string}\n\n\\item{check_holidays}{bool check if name already used for holiday}\n\n\\item{check_seasonalities}{bool check if name already used for seasonality}\n\n\\item{check_regressors}{bool check if name already used for regressor}\n}\n\\description{\nValidates the name of a seasonality, holiday, or regressor.\n}\n\\keyword{internal}\n" }, { "path": "R/man/validate_inputs.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/prophet.R\n\\name{validate_inputs}\n\\alias{validate_inputs}\n\\title{Validates the inputs to Prophet.}\n\\usage{\nvalidate_inputs(m)\n}\n\\arguments{\n\\item{m}{Prophet object.}\n}\n\\value{\nThe Prophet object.\n}\n\\description{\nValidates the inputs to Prophet.\n}\n\\keyword{internal}\n" }, { "path": "R/prophet.Rproj", "content": "Version: 1.0\n\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSpacesForTab: Yes\nNumSpacesForTab: 2\nEncoding: UTF-8\n\nRnwWeave: knitr\nLaTeX: pdfLaTeX\n\nAutoAppendNewline: Yes\nStripTrailingWhitespace: Yes\n\nBuildType: Package\nPackageUseDevtools: Yes\nPackageInstallArgs: --no-multiarch --with-keep.source\nPackageRoxygenize: rd,collate,namespace\n" }, { "path": "R/src/Makevars", "content": "# Generated by rstantools. Do not edit by hand.\n\nSTANHEADERS_SRC = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"message()\" -e \"cat(system.file('include', 'src', package = 'StanHeaders', mustWork = TRUE))\" -e \"message()\" | grep \"StanHeaders\")\n\nSTANC_FLAGS = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"cat(ifelse(utils::packageVersion('rstan') >= 2.26, '-DUSE_STANC3',''))\")\nPKG_CPPFLAGS = -I\"../inst/include\" -I\"$(STANHEADERS_SRC)\" -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG -DBOOST_MATH_OVERFLOW_ERROR_POLICY=errno_on_error $(STANC_FLAGS)\nPKG_CXXFLAGS = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"RcppParallel::CxxFlags()\") $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"StanHeaders:::CxxFlags()\")\nPKG_LIBS = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"RcppParallel::RcppParallelLibs()\") $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"StanHeaders:::LdFlags()\")\n\nCXX_STD = CXX17\n" }, { "path": "R/src/Makevars.win", "content": "# Generated by rstantools. Do not edit by hand.\n\nSTANHEADERS_SRC = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"message()\" -e \"cat(system.file('include', 'src', package = 'StanHeaders', mustWork = TRUE))\" -e \"message()\" | grep \"StanHeaders\")\n\nSTANC_FLAGS = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"cat(ifelse(utils::packageVersion('rstan') >= 2.26, '-DUSE_STANC3',''))\")\nPKG_CPPFLAGS = -I\"../inst/include\" -I\"$(STANHEADERS_SRC)\" -DBOOST_DISABLE_ASSERTS -DEIGEN_NO_DEBUG -DRCPP_PARALLEL_USE_TBB=1 $(STANC_FLAGS)\nPKG_CXXFLAGS = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"RcppParallel::CxxFlags()\") $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"StanHeaders:::CxxFlags()\")\nPKG_LIBS = $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"RcppParallel::RcppParallelLibs()\") $(shell \"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript\" -e \"StanHeaders:::LdFlags()\")\n\nCXX_STD = CXX17\n" }, { "path": "R/tests/testthat/data.csv", "content": 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10:55:00,-4.4\n2017-01-02 11:00:00,-4.4\n2017-01-02 11:05:00,-4.5\n2017-01-02 11:10:00,-4.5\n2017-01-02 11:15:00,-4.5\n2017-01-02 11:20:00,-4.5\n2017-01-02 11:25:00,-4.5\n2017-01-02 11:30:00,-4.5\n2017-01-02 11:35:00,-4.5\n2017-01-02 11:40:00,-4.5\n2017-01-02 11:45:00,-4.6\n2017-01-02 11:50:00,-4.6\n2017-01-02 11:55:00,-4.6\n2017-01-02 12:00:00,-4.6\n2017-01-02 12:05:00,-4.7\n2017-01-02 12:10:00,-4.8\n2017-01-02 12:15:00,-4.8\n2017-01-02 12:20:00,-4.9\n2017-01-02 12:25:00,-5.0\n2017-01-02 12:30:00,-5.3\n2017-01-02 12:35:00,-5.5\n2017-01-02 12:40:00,-5.5\n2017-01-02 12:45:00,-5.6\n2017-01-02 12:50:00,-5.9\n2017-01-02 12:55:00,-6.1\n2017-01-02 13:00:00,-6.0\n2017-01-02 13:05:00,-6.1\n2017-01-02 13:10:00,-6.1\n2017-01-02 13:15:00,-6.0\n2017-01-02 13:20:00,-5.7\n2017-01-02 13:25:00,-5.5\n2017-01-02 13:30:00,-5.3\n2017-01-02 13:35:00,-5.2\n2017-01-02 13:40:00,-5.1\n2017-01-02 13:45:00,-5.0\n2017-01-02 13:50:00,-5.0\n2017-01-02 13:55:00,-5.0\n2017-01-02 14:00:00,-4.9\n2017-01-02 14:05:00,-4.9\n2017-01-02 14:10:00,-5.0\n2017-01-02 14:15:00,-4.9\n2017-01-02 14:20:00,-4.9\n2017-01-02 14:25:00,-4.9\n2017-01-02 14:30:00,-4.9\n2017-01-02 14:35:00,-4.9\n2017-01-02 14:40:00,-5.0\n2017-01-02 14:45:00,-4.9\n2017-01-02 14:50:00,-4.9\n2017-01-02 14:55:00,-5.0\n2017-01-02 15:00:00,-4.9\n2017-01-02 15:05:00,-4.9\n2017-01-02 15:10:00,-4.9\n2017-01-02 15:15:00,-4.9\n2017-01-02 15:20:00,-4.9\n2017-01-02 15:25:00,-4.9\n2017-01-02 15:30:00,-4.9\n2017-01-02 15:35:00,-4.9\n2017-01-02 15:40:00,-4.9\n2017-01-02 15:45:00,-4.9\n2017-01-02 15:50:00,-4.9\n2017-01-02 15:55:00,-4.9\n2017-01-02 16:00:00,-4.9\n2017-01-02 16:05:00,-4.9\n2017-01-02 16:10:00,-4.9\n2017-01-02 16:15:00,-4.9\n2017-01-02 16:20:00,-4.9\n2017-01-02 16:25:00,-4.8\n2017-01-02 16:30:00,-4.8\n2017-01-02 16:35:00,-4.7\n2017-01-02 16:40:00,-4.8\n2017-01-02 16:45:00,-4.8\n2017-01-02 16:50:00,-4.8\n2017-01-02 16:55:00,-4.9\n2017-01-02 17:00:00,-4.8\n2017-01-02 17:05:00,-4.8\n2017-01-02 17:10:00,-4.8\n2017-01-02 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21:45:00,-2.3\n2017-01-03 21:50:00,-2.3\n2017-01-03 21:55:00,-2.3\n2017-01-03 22:00:00,-2.4\n2017-01-03 22:05:00,-2.3\n2017-01-03 22:10:00,-2.3\n2017-01-03 22:15:00,-2.4\n2017-01-03 22:20:00,-2.4\n2017-01-03 22:25:00,-2.5\n2017-01-03 22:30:00,-2.5\n2017-01-03 22:35:00,-2.7\n2017-01-03 22:40:00,-2.7\n2017-01-03 22:45:00,-2.8\n2017-01-03 22:50:00,-2.8\n2017-01-03 22:55:00,-2.8\n2017-01-03 23:00:00,-2.8\n2017-01-03 23:05:00,-2.8\n2017-01-03 23:10:00,-2.8\n2017-01-03 23:15:00,-2.7\n2017-01-03 23:20:00,-2.7\n2017-01-03 23:25:00,-2.6\n2017-01-03 23:30:00,-2.6\n2017-01-03 23:35:00,-2.5\n2017-01-03 23:40:00,-2.5\n2017-01-03 23:45:00,-2.4\n2017-01-03 23:50:00,-2.4\n2017-01-03 23:55:00,-2.4\n" }, { "path": "R/tests/testthat/test_diagnostics.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nlibrary(prophet)\ncontext(\"Prophet diagnostics tests\")\n\n## Makes R CMD CHECK happy due to dplyr syntax below\nglobalVariables(c(\"y\", \"yhat\"))\n\nDATA_all <- read.csv('data.csv')\nDATA_all$ds <- as.Date(DATA_all$ds)\nDATA <- head(DATA_all, 100)\n\ntest_that(\"cross_validation\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(DATA)\n # Calculate the number of cutoff points\n te <- max(DATA$ds)\n ts <- min(DATA$ds)\n horizon <- as.difftime(4, units = \"days\")\n period <- as.difftime(10, units = \"days\")\n initial <- as.difftime(115, units = \"days\")\n df.cv <- cross_validation(\n m, horizon = 4, units = \"days\", period = 10, initial = 115)\n expect_equal(length(unique(df.cv$cutoff)), 3)\n expect_equal(max(df.cv$ds - df.cv$cutoff), horizon)\n expect_true(as.Date(min(df.cv$cutoff)) >= ts + initial)\n dc <- diff(df.cv$cutoff)\n dc <- min(dc[dc > 0])\n expect_true(dc >= period)\n expect_true(all(df.cv$cutoff < df.cv$ds))\n # Each y in df.cv and DATA with same ds should be equal\n df.merged <- dplyr::left_join(df.cv, m$history, by=\"ds\")\n expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)\n df.cv <- cross_validation(\n m, horizon = 4, units = \"days\", period = 10, initial = 135)\n expect_equal(length(unique(df.cv$cutoff)), 1)\n expect_error(\n cross_validation(\n m, horizon = 10, units = \"days\", period = 10, initial = 140)\n )\n})\n\ntest_that(\"cross_validation_logistic\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n df <- DATA\n df$cap <- 40\n m <- prophet(df, growth = 'logistic')\n df.cv <- cross_validation(\n m, horizon = 1, units = \"days\", period = 1, initial = 140)\n expect_equal(length(unique(df.cv$cutoff)), 2)\n expect_true(all(df.cv$cutoff < df.cv$ds))\n df.merged <- dplyr::left_join(df.cv, m$history, by=\"ds\")\n expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)\n})\n\ntest_that(\"cross_validation_flat\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n df <- DATA\n m <- prophet(df, growth = 'flat')\n df.cv <- cross_validation(\n m, horizon = 1, units = \"days\", period = 1, initial = 140)\n expect_equal(length(unique(df.cv$cutoff)), 2)\n expect_true(all(df.cv$cutoff < df.cv$ds))\n df.merged <- dplyr::left_join(df.cv, m$history, by=\"ds\")\n expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)\n})\n\ntest_that(\"cross_validation_extra_regressors\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n df <- DATA\n df$extra <- seq(0, nrow(df) - 1)\n df$is_conditional_week <- seq(0, nrow(df) - 1) %/% 7 %% 2\n m <- prophet()\n m <- add_seasonality(m, name = 'monthly', period = 30.5, fourier.order = 5)\n m <- add_seasonality(m, name = 'conditional_weekly', period = 7,\n fourier.order = 3, prior.scale = 2.,\n condition.name = 'is_conditional_week')\n m <- add_regressor(m, 'extra')\n m <- fit.prophet(m, df)\n df.cv <- cross_validation(\n m, horizon = 4, units = \"days\", period = 4, initial = 135)\n expect_equal(length(unique(df.cv$cutoff)), 2)\n period <- as.difftime(4, units = \"days\")\n dc <- diff(df.cv$cutoff)\n dc <- min(dc[dc > 0])\n expect_true(dc >= period)\n expect_true(all(df.cv$cutoff < df.cv$ds))\n df.merged <- dplyr::left_join(df.cv, m$history, by=\"ds\")\n expect_equal(sum((df.merged$y.x - df.merged$y.y) ** 2), 0)\n})\n\ntest_that(\"cross_validation_default_value_check\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(DATA)\n df.cv1 <- cross_validation(\n m, horizon = 32, units = \"days\", period = 10)\n df.cv2 <- cross_validation(\n m, horizon = 32, units = 'days', period = 10, initial = 96)\n expect_equal(sum(dplyr::select(df.cv1 - df.cv2, y, yhat)), 0)\n})\n\ntest_that(\"cross_validation_custom_cutoffs\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(DATA)\n # When specify a list of cutoffs the cutoff dates in df.cv1\n # are those specified\n cutoffs=c(as.Date('2012-07-31'), as.Date('2012-08-31'))\n df.cv <-cross_validation(\n m, horizon = 32, units = \"days\", period = 10, cutoffs=cutoffs)\n expect_equal(length(unique(df.cv$cutoff)), 2)\n # test this works ok when periods is NULL\n df.cv <-cross_validation(\n m, horizon = 32, units = \"days\", cutoffs=cutoffs)\n expect_equal(length(unique(df.cv$cutoff)), 2)\n})\n\ntest_that(\"cross_validation_uncertainty_disabled\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n for (uncertainty in c(0, FALSE)) {\n m <- prophet(uncertainty.samples = uncertainty)\n m <- fit.prophet(m = m, df = DATA)\n df.cv <- cross_validation(\n m, horizon = 4, units = \"days\", period = 4, initial = 115)\n expected.cols <- c('y', 'ds', 'yhat', 'cutoff')\n expect_equal(expected.cols, colnames(df.cv))\n df.p <- performance_metrics(df.cv)\n expect_false('coverage' %in% colnames(df.p))\n }\n})\n\ntest_that(\"performance_metrics\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(DATA)\n df_cv <- cross_validation(\n m, horizon = 4, units = \"days\", period = 10, initial = 90)\n # Aggregation level none\n df_none <- performance_metrics(df_cv, rolling_window = -1)\n expect_true(all(\n sort(colnames(df_none))\n == sort(c('horizon', 'mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'))\n ))\n expect_equal(nrow(df_none), 16)\n # Aggregation level 0\n df_0 <- performance_metrics(df_cv, rolling_window = 0)\n expect_equal(nrow(df_0), 4)\n expect_equal(length(unique(df_0$h)), 4)\n # Aggregation level 0.2\n df_horizon <- performance_metrics(df_cv, rolling_window = 0.2)\n expect_equal(nrow(df_horizon), 4)\n expect_equal(length(unique(df_horizon$horizon)), 4)\n # Aggregation level all\n df_all <- performance_metrics(df_cv, rolling_window = 1)\n expect_equal(nrow(df_all), 1)\n for (metric in c('mse', 'mape', 'mae', 'coverage')) {\n expect_equal(df_all[[metric]][1], mean(df_none[[metric]]))\n }\n # Custom list of metrics\n df_horizon <- performance_metrics(df_cv, metrics = c('coverage', 'mse'))\n expect_true(all(\n sort(colnames(df_horizon)) == sort(c('coverage', 'mse', 'horizon'))\n ))\n # Skip MAPE and MDAPE\n df_cv$y[1] <- 0.\n df_horizon <- performance_metrics(df_cv, metrics = c('coverage', 'mape'))\n expect_true(all(\n sort(colnames(df_horizon)) == sort(c('coverage', 'horizon'))\n ))\n df_horizon <- performance_metrics(df_cv, metrics = c('mape'))\n expect_null(df_horizon)\n # List of metrics containing non valid metrics\n expect_error(\n performance_metrics(df_cv, metrics = c('mse', 'error_metric')),\n 'Valid values for metrics are: mse, rmse, mae, mape, mdape, smape, coverage'\n )\n})\n\ntest_that(\"rolling_mean\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n x <- 0:9\n h <- 0:9\n df <- prophet:::rolling_mean_by_h(x=x, h=h, w=1, name='x')\n expect_equal(x, df$x)\n expect_equal(h, df$horizon)\n\n df <- prophet:::rolling_mean_by_h(x=x, h=h, w=4, name='x')\n expect_equal(x[4:10] - 1.5, df$x)\n expect_equal(3:9, df$horizon)\n\n h <- c(1., 2., 3., 4., 4., 4., 4., 4., 7., 7.)\n x.true <- c(1., 5., 22/3)\n h.true <- c(3., 4., 7.)\n df <- prophet:::rolling_mean_by_h(x=x, h=h, w=3, name='x')\n expect_equal(x.true, df$x)\n expect_equal(h.true, df$horizon)\n\n df <- prophet:::rolling_mean_by_h(x=x, h=h, w=10, name='x')\n expect_equal(c(7.), df$horizon)\n expect_equal(c(4.5), df$x)\n})\n\n\ntest_that(\"rolling_median\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n x <- 0:9\n h <- 0:9\n df <- prophet:::rolling_median_by_h(x=x, h=h, w=1, name='x')\n expect_equal(x, df$x)\n expect_equal(h, df$horizon)\n\n df <- prophet:::rolling_median_by_h(x=x, h=h, w=4, name='x')\n x.true <- x[4:10] - 1.5\n expect_equal(x.true, df$x)\n expect_equal(3:9, df$horizon)\n\n h <- c(1., 2., 3., 4., 4., 4., 4., 4., 7., 7.)\n x.true <- c(1., 5., 8.)\n h.true <- c(3., 4., 7.)\n df <- prophet:::rolling_median_by_h(x=x, h=h, w=3, name='x')\n expect_equal(x.true, df$x)\n expect_equal(h.true, df$horizon)\n\n df <- prophet:::rolling_median_by_h(x=x, h=h, w=10, name='x')\n expect_equal(c(7.), df$horizon)\n expect_equal(c(4.5), df$x)\n})\n\n\ntest_that(\"copy\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n df <- DATA_all\n df$cap <- 200.\n df$binary_feature <- c(rep(0, 255), rep(1, 255))\n inputs <- list(\n growth = c('linear', 'logistic', 'flat'),\n yearly.seasonality = c(TRUE, FALSE),\n weekly.seasonality = c(TRUE, FALSE),\n daily.seasonality = c(TRUE, FALSE),\n holidays = c('null', 'insert_dataframe'),\n seasonality.mode = c('additive', 'multiplicative')\n )\n products <- expand.grid(inputs)\n for (i in 1:length(products)) {\n if (products$holidays[i] == 'insert_dataframe') {\n holidays <- data.frame(ds=c('2016-12-25'), holiday=c('x'))\n } else {\n holidays <- NULL\n }\n m1 <- prophet(\n growth = as.character(products$growth[i]),\n changepoints = NULL,\n n.changepoints = 3,\n changepoint.range = 0.9,\n yearly.seasonality = products$yearly.seasonality[i],\n weekly.seasonality = products$weekly.seasonality[i],\n daily.seasonality = products$daily.seasonality[i],\n holidays = holidays,\n seasonality.prior.scale = 1.1,\n holidays.prior.scale = 1.1,\n changepoints.prior.scale = 0.1,\n mcmc.samples = 100,\n interval.width = 0.9,\n uncertainty.samples = 200,\n fit = FALSE\n )\n m1$country_holidays <- 'US'\n out <- prophet:::setup_dataframe(m1, df, initialize_scales = TRUE)\n m1 <- out$m\n m1$history <- out$df\n m1 <- prophet:::set_auto_seasonalities(m1)\n m2 <- prophet:::prophet_copy(m1)\n # Values should be copied correctly\n args <- c('growth', 'changepoints', 'n.changepoints', 'holidays', 'country_holidays',\n 'seasonality.prior.scale', 'holidays.prior.scale',\n 'changepoints.prior.scale', 'mcmc.samples', 'interval.width',\n 'uncertainty.samples', 'seasonality.mode', 'changepoint.range')\n for (arg in args) {\n expect_equal(m1[[arg]], m2[[arg]])\n }\n expect_equal(FALSE, m2$yearly.seasonality)\n expect_equal(FALSE, m2$weekly.seasonality)\n expect_equal(FALSE, m2$daily.seasonality)\n expect_equal(m1$yearly.seasonality, 'yearly' %in% names(m2$seasonalities))\n expect_equal(m1$weekly.seasonality, 'weekly' %in% names(m2$seasonalities))\n expect_equal(m1$daily.seasonality, 'daily' %in% names(m2$seasonalities))\n }\n # Check for cutoff and custom seasonality and extra regressors\n changepoints <- seq.Date(as.Date('2012-06-15'), as.Date('2012-09-15'), by='d')\n cutoff <- as.Date('2012-07-25')\n m1 <- prophet(changepoints = changepoints)\n m1 <- add_seasonality(m1, 'custom', 10, 5)\n m1 <- add_regressor(m1, 'binary_feature')\n m1 <- fit.prophet(m1, df)\n m2 <- prophet:::prophet_copy(m1, cutoff)\n changepoints <- changepoints[changepoints < cutoff]\n expect_equal(prophet:::set_date(changepoints), m2$changepoints)\n expect_true('custom' %in% names(m2$seasonalities))\n expect_true('binary_feature' %in% names(m2$extra_regressors))\n})\n" }, { "path": "R/tests/testthat/test_prophet.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nlibrary(prophet)\ncontext(\"Prophet tests\")\n\nDATA <- read.csv(test_path('data.csv'))\nN <- nrow(DATA)\ntrain <- DATA[1:floor(N / 2), ]\nfuture <- DATA[(ceiling(N/2) + 1):N, ]\n\nDATA2 <- read.csv(test_path('data2.csv'))\n\nDATA$ds <- prophet:::set_date(DATA$ds)\nDATA2$ds <- prophet:::set_date(DATA2$ds)\n\ntest_that(\"fit_predict\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(train)\n expect_error(predict(m, future), NA)\n})\n\ntest_that(\"fit_predict_no_seasons\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(train, weekly.seasonality = FALSE, yearly.seasonality = FALSE)\n expect_error(predict(m, future), NA)\n})\n\ntest_that(\"fit_predict_no_changepoints\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n expect_warning({\n # warning from prophet(), error from predict()\n m <- prophet(train, n.changepoints = 0)\n })\n fcst <- predict(m, future)\n\n expect_warning({\n m <- prophet(train, n.changepoints = 0, mcmc.samples = 100)\n })\n fcst <- predict(m, future)\n})\n\ntest_that(\"fit_predict_changepoint_not_in_history\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n train_t <- dplyr::mutate(DATA, ds=prophet:::set_date(ds))\n train_t <- dplyr::filter(train_t,\n (ds < prophet:::set_date('2013-01-01')) |\n (ds > prophet:::set_date('2014-01-01')))\n future <- data.frame(ds=DATA$ds)\n expect_warning({\n # warning from prophet(), error from predict()\n m <- prophet(train_t, changepoints=c('2013-06-06'))\n expect_error(predict(m, future), NA)\n })\n})\n\ntest_that(\"fit_predict_duplicates\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n train2 <- train\n train2$y <- train2$y + 10\n train_t <- rbind(train, train2)\n m <- prophet(train_t)\n expect_error(predict(m, future), NA)\n})\n\ntest_that(\"fit_predict_constant_history\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n train2 <- train\n train2$y <- 20\n m <- prophet(train2)\n fcst <- predict(m, future)\n expect_equal(tail(fcst$yhat, 1), 20)\n train2$y <- 0\n m <- prophet(train2)\n fcst <- predict(m, future)\n expect_equal(tail(fcst$yhat, 1), 0)\n})\n\ntest_that(\"fit_predict_uncertainty_disabled\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n for (uncertainty in c(0, FALSE)) {\n m <- prophet(train, uncertainty.samples = uncertainty)\n fcst <- predict(m, future)\n expected.cols <- c('ds', 'trend', 'additive_terms', 'weekly', 'multiplicative_terms', 'yhat')\n expect_equal(expected.cols, colnames(fcst))\n }\n})\n\ntest_that(\"setup_dataframe\", {\n history <- train\n m <- prophet(history, fit = FALSE)\n \n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n history <- out$df\n\n expect_true('t' %in% colnames(history))\n expect_equal(min(history$t), 0)\n expect_equal(max(history$t), 1)\n\n expect_true('y_scaled' %in% colnames(history))\n expect_equal(max(history$y_scaled), 1)\n})\n\ntest_that(\"setup_names_errors\", {\n m <- prophet()\n expect_error(\n m <- add_seasonality(m, \"3monthly\"),\n \"You have provided a name that is not syntactically valid in R, 3monthly\"\n )\n expect_error(\n m <- add_regressor(m, \"2monthsale\"),\n \"You have provided a name that is not syntactically valid in R, 2monthsale\"\n )\n})\n\ntest_that(\"logistic_floor\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n skip_on_os('mac') # Resolves mysterious CRAN build issue\n m <- prophet(growth = 'logistic')\n history <- train\n history$floor <- 10.\n history$cap <- 80.\n future1 <- future\n future1$cap <- 80.\n future1$floor <- 10.\n m <- fit.prophet(m, history)\n expect_true(m$logistic.floor)\n expect_true('floor' %in% colnames(m$history))\n expect_equal(m$history$y_scaled[1], 1., tolerance = 1e-6)\n fcst1 <- predict(m, future1)\n\n m2 <- prophet(growth = 'logistic')\n history2 <- history\n history2$y <- history2$y + 10.\n history2$floor <- history2$floor + 10.\n history2$cap <- history2$cap + 10.\n future1$cap <- future1$cap + 10.\n future1$floor <- future1$floor + 10.\n m2 <- fit.prophet(m2, history2)\n expect_equal(m2$history$y_scaled[1], 1., tolerance = 1e-6)\n})\n\ntest_that(\"get_changepoints\", {\n history <- train\n m <- prophet(history, fit = FALSE)\n\n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n history <- out$df\n m <- out$m\n m$history <- history\n\n m <- prophet:::set_changepoints(m)\n\n cp <- m$changepoints.t\n expect_equal(length(cp), m$n.changepoints)\n expect_true(min(cp) > 0)\n expect_true(max(cp) <= history$t[ceiling(0.8 * length(history$t))])\n})\n\ntest_that(\"set_changepoint_range\", {\n history <- train\n m <- prophet(history, fit = FALSE, changepoint.range = 0.4)\n\n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n history <- out$df\n m <- out$m\n m$history <- history\n\n m <- prophet:::set_changepoints(m)\n\n cp <- m$changepoints.t\n expect_equal(length(cp), m$n.changepoints)\n expect_true(min(cp) > 0)\n expect_true(max(cp) <= history$t[ceiling(0.4 * length(history$t))])\n expect_error(prophet(history, changepoint.range = -0.1))\n expect_error(prophet(history, changepoint.range = 2))\n})\n\ntest_that(\"get_zero_changepoints\", {\n history <- train\n m <- prophet(history, n.changepoints = 0, fit = FALSE)\n \n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n m <- out$m\n history <- out$df\n m$history <- history\n\n m <- prophet:::set_changepoints(m)\n cp <- m$changepoints.t\n expect_equal(length(cp), 1)\n expect_equal(cp[1], 0)\n})\n\ntest_that(\"override_n_changepoints\", {\n history <- train[1:20,]\n m <- prophet(history, fit = FALSE)\n\n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n m <- out$m\n history <- out$df\n m$history <- history\n\n m <- prophet:::set_changepoints(m)\n expect_equal(m$n.changepoints, 15)\n cp <- m$changepoints.t\n expect_equal(length(cp), 15)\n})\n\ntest_that(\"fourier_series_weekly\", {\n true.values <- c(0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837,\n -0.9009689)\n mat <- prophet:::fourier_series(DATA$ds, 7, 3)\n expect_equal(true.values, mat[1, ], tolerance = 1e-6)\n})\n\ntest_that(\"fourier_series_yearly\", {\n true.values <- c(0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249,\n 0.6874572)\n mat <- prophet:::fourier_series(DATA$ds, 365.25, 3)\n expect_equal(true.values, mat[1, ], tolerance = 1e-6)\n})\n\ntest_that(\"growth_init\", {\n history <- DATA[1:468, ]\n history$cap <- max(history$y)\n m <- prophet(history, growth = 'logistic', fit = FALSE)\n\n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n m <- out$m\n history <- out$df\n\n params <- prophet:::linear_growth_init(history)\n expect_equal(params[1], 0.3055671, tolerance = 1e-6)\n expect_equal(params[2], 0.5307511, tolerance = 1e-6)\n\n params <- prophet:::logistic_growth_init(history)\n expect_equal(params[1], 1.507925, tolerance = 1e-6)\n expect_equal(params[2], -0.08167497, tolerance = 1e-6)\n\n params <- prophet:::flat_growth_init(history)\n expect_equal(params[1], 0, tolerance = 1e-6)\n expect_equal(params[2], 0.49335657, tolerance = 1e-6)\n\n})\n\ntest_that(\"piecewise_linear\", {\n t <- seq(0, 10)\n m <- 0\n k <- 1.0\n deltas <- c(0.5)\n changepoint.ts <- c(5)\n\n y <- prophet:::piecewise_linear(t, deltas, k, m, changepoint.ts)\n y.true <- c(0, 1, 2, 3, 4, 5, 6.5, 8, 9.5, 11, 12.5)\n expect_equal(y, y.true)\n\n t <- t[8:length(t)]\n y.true <- y.true[8:length(y.true)]\n y <- prophet:::piecewise_linear(t, deltas, k, m, changepoint.ts)\n expect_equal(y, y.true)\n})\n\ntest_that(\"piecewise_logistic\", {\n t <- seq(0, 10)\n cap <- rep(10, 11)\n m <- 0\n k <- 1.0\n deltas <- c(0.5)\n changepoint.ts <- c(5)\n\n y <- prophet:::piecewise_logistic(t, cap, deltas, k, m, changepoint.ts)\n y.true <- c(5.000000, 7.310586, 8.807971, 9.525741, 9.820138, 9.933071,\n 9.984988, 9.996646, 9.999252, 9.999833, 9.999963)\n expect_equal(y, y.true, tolerance = 1e-6)\n \n t <- t[8:length(t)]\n y.true <- y.true[8:length(y.true)]\n cap <- cap[8:length(cap)]\n y <- prophet:::piecewise_logistic(t, cap, deltas, k, m, changepoint.ts)\n expect_equal(y, y.true, tolerance = 1e-6)\n})\n\ntest_that(\"flat_trend\", {\n t <- seq(0, 10)\n m <- 0.5\n y = prophet:::flat_trend(t, m)\n y.true <- rep(0.5, length(t))\n expect_equal(y, y.true, tolerance = 1e-6)\n\n t <- t[8:length(t)]\n y = prophet:::flat_trend(t, m)\n y.true <- y.true[8:length(y.true)]\n expect_equal(y, y.true, tolerance = 1e-6)\n})\n\ntest_that(\"holidays\", {\n holidays <- data.frame(ds = c('2016-12-25'),\n holiday = c('xmas'),\n lower_window = c(-1),\n upper_window = c(0))\n df <- data.frame(\n ds = seq(prophet:::set_date('2016-12-20'),\n prophet:::set_date('2016-12-31'), by='d'))\n m <- prophet(train, holidays = holidays, fit = FALSE)\n out <- prophet:::make_holiday_features(m, df$ds, m$holidays)\n feats <- out$holiday.features\n priors <- out$prior.scales\n names <- out$holiday.names\n expect_equal(nrow(feats), nrow(df))\n expect_equal(ncol(feats), 2)\n expect_equal(sum(colSums(feats) - c(1, 1)), 0)\n expect_true(all(priors == c(10., 10.)))\n expect_equal(names, c('xmas'))\n\n holidays <- data.frame(ds = c('2016-12-25'),\n holiday = c('xmas'),\n lower_window = c(-1),\n upper_window = c(10))\n m <- prophet(train, holidays = holidays, fit = FALSE)\n out <- prophet:::make_holiday_features(m, df$ds, m$holidays)\n feats <- out$holiday.features\n priors <- out$prior.scales\n names <- out$holiday.names\n expect_equal(nrow(feats), nrow(df))\n expect_equal(ncol(feats), 12)\n expect_true(all(priors == rep(10, 12)))\n expect_equal(names, c('xmas'))\n # Check prior specifications\n holidays <- data.frame(\n ds = prophet:::set_date(c('2016-12-25', '2017-12-25')),\n holiday = c('xmas', 'xmas'),\n lower_window = c(-1, -1),\n upper_window = c(0, 0),\n prior_scale = c(5., 5.)\n )\n m <- prophet(holidays = holidays, fit = FALSE)\n out <- prophet:::make_holiday_features(m, df$ds, m$holidays)\n priors <- out$prior.scales\n names <- out$holiday.names\n expect_true(all(priors == c(5., 5.)))\n expect_equal(names, c('xmas'))\n # 2 different priors\n holidays2 <- data.frame(\n ds = prophet:::set_date(c('2012-06-06', '2013-06-06')),\n holiday = c('seans-bday', 'seans-bday'),\n lower_window = c(0, 0),\n upper_window = c(1, 1),\n prior_scale = c(8, 8)\n )\n holidays2 <- rbind(holidays, holidays2)\n m <- prophet(holidays = holidays2, fit = FALSE)\n out <- prophet:::make_holiday_features(m, df$ds, m$holidays)\n priors <- out$prior.scales\n names <- out$holiday.names\n expect_true(all(priors == c(8, 8, 5, 5)))\n expect_true(all(sort(names) == c('seans-bday', 'xmas')))\n holidays2 <- data.frame(\n ds = prophet:::set_date(c('2012-06-06', '2013-06-06')),\n holiday = c('seans-bday', 'seans-bday'),\n lower_window = c(0, 0),\n upper_window = c(1, 1)\n )\n # manual coercions to avoid below bind_rows() warning\n holidays$holiday <- as.character(holidays$holiday)\n holidays2$holiday <- as.character(holidays2$holiday)\n holidays2 <- dplyr::bind_rows(holidays, holidays2)\n # manual factorizing to avoid above bind_rows() warning\n holidays2$holiday <- factor(holidays2$holiday)\n m <- prophet(holidays = holidays2, fit = FALSE, holidays.prior.scale = 4)\n out <- prophet:::make_holiday_features(m, df$ds, m$holidays)\n priors <- out$prior.scales\n expect_true(all(priors == c(4, 4, 5, 5)))\n # Check incompatible priors\n holidays <- data.frame(\n ds = prophet:::set_date(c('2016-12-25', '2016-12-27')),\n holiday = c('xmasish', 'xmasish'),\n lower_window = c(-1, -1),\n upper_window = c(0, 0),\n prior_scale = c(5., 6.)\n )\n m <- prophet(holidays = holidays, fit = FALSE)\n expect_error(prophet:::make_holiday_features(m, df$ds, m$holidays))\n})\n\ntest_that(\"fit_with_holidays\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n holidays <- data.frame(ds = c('2012-06-06', '2013-06-06'),\n holiday = c('seans-bday', 'seans-bday'),\n lower_window = c(0, 0),\n upper_window = c(1, 1))\n m <- prophet(DATA, holidays = holidays, uncertainty.samples = 0)\n expect_error(predict(m), NA)\n})\n\ntest_that(\"fit_with_country_holidays\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n holidays <- data.frame(ds = c('2012-06-06', '2013-06-06'),\n holiday = c('seans-bday', 'seans-bday'),\n lower_window = c(0, 0),\n upper_window = c(1, 1))\n # Test with holidays and append_holidays\n m <- prophet(holidays = holidays, uncertainty.samples = 0)\n m <- add_country_holidays(m, 'US')\n m <- fit.prophet(m, DATA)\n expect_error(predict(m), NA)\n # There are training holidays missing in the test set\n train2 <- DATA %>% head(155)\n future2 <- DATA %>% tail(355)\n m <- prophet(uncertainty.samples = 0)\n m <- add_country_holidays(m, 'US')\n m <- fit.prophet(m, train2)\n expect_error(predict(m, future2), NA)\n # There are test holidays missing in the training set\n train2 <- DATA %>% tail(355)\n future2 <- DATA2\n m <- prophet(uncertainty.samples = 0)\n m <- add_country_holidays(m, 'US')\n m <- fit.prophet(m, train2)\n expect_error(predict(m, future2), NA)\n # Append_holidays with non-existing year\n max.year <- generated_holidays %>% \n dplyr::filter(country=='US') %>%\n dplyr::select(year) %>%\n max()\n train2 <- data.frame('ds'=c(paste(max.year+1, \"-01-01\", sep=''),\n paste(max.year+1, \"-01-02\", sep='')),\n 'y'=1)\n m <- prophet()\n m <- add_country_holidays(m, 'US')\n expect_warning(m <- fit.prophet(m, train2))\n # Append_holidays with non-existing country\n m <- prophet()\n expect_error(add_country_holidays(m, 'Utopia'))\n})\n\ntest_that(\"make_future_dataframe\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n train.t <- DATA[1:234, ]\n m <- prophet(train.t)\n future <- make_future_dataframe(m, periods = 3, freq = 'day',\n include_history = FALSE)\n correct <- prophet:::set_date(c('2013-04-26', '2013-04-27', '2013-04-28'))\n expect_equal(future$ds, correct)\n\n future <- make_future_dataframe(m, periods = 3, freq = 'month',\n include_history = FALSE)\n correct <- prophet:::set_date(c('2013-05-25', '2013-06-25', '2013-07-25'))\n expect_equal(future$ds, correct)\n})\n\ntest_that(\"auto_weekly_seasonality\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n # Should be enabled\n N.w <- 15\n train.w <- DATA[1:N.w, ]\n m <- prophet(train.w, fit = FALSE)\n expect_equal(m$weekly.seasonality, 'auto')\n m <- fit.prophet(m, train.w)\n expect_true('weekly' %in% names(m$seasonalities))\n true <- list(\n period = 7, fourier.order = 3, prior.scale = 10, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$weekly[[name]], true[[name]])\n }\n # Should be disabled due to too short history\n N.w <- 9\n train.w <- DATA[1:N.w, ]\n m <- prophet(train.w)\n expect_false('weekly' %in% names(m$seasonalities))\n # prophet warning: non-zero return code in optimizing\n m <- prophet(train.w, weekly.seasonality = TRUE)\n expect_true('weekly' %in% names(m$seasonalities))\n # Should be False due to weekly spacing\n train.w <- DATA[seq(1, nrow(DATA), 7), ]\n m <- prophet(train.w)\n expect_false('weekly' %in% names(m$seasonalities))\n m <- prophet(DATA, weekly.seasonality = 2, seasonality.prior.scale = 3)\n true <- list(\n period = 7, fourier.order = 2, prior.scale = 3, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$weekly[[name]], true[[name]])\n }\n})\n\ntest_that(\"auto_yearly_seasonality\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n # Should be enabled\n m <- prophet(DATA, fit = FALSE)\n expect_equal(m$yearly.seasonality, 'auto')\n m <- fit.prophet(m, DATA)\n expect_true('yearly' %in% names(m$seasonalities))\n true <- list(\n period = 365.25, fourier.order = 10, prior.scale = 10, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$yearly[[name]], true[[name]])\n }\n # Should be disabled due to too short history\n N.w <- 240\n train.y <- DATA[1:N.w, ]\n m <- prophet(train.y)\n expect_false('yearly' %in% names(m$seasonalities))\n m <- prophet(train.y, yearly.seasonality = TRUE)\n expect_true('yearly' %in% names(m$seasonalities))\n m <- prophet(DATA, yearly.seasonality = 7, seasonality.prior.scale = 3)\n true <- list(\n period = 365.25, fourier.order = 7, prior.scale = 3, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$yearly[[name]], true[[name]])\n }\n})\n\ntest_that(\"auto_daily_seasonality\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n # Should be enabled\n m <- prophet(DATA2, fit = FALSE)\n expect_equal(m$daily.seasonality, 'auto')\n m <- fit.prophet(m, DATA2)\n expect_true('daily' %in% names(m$seasonalities))\n true <- list(\n period = 1, fourier.order = 4, prior.scale = 10, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$daily[[name]], true[[name]])\n }\n # Should be disabled due to too short history\n N.d <- 430\n train.y <- DATA2[1:N.d, ]\n m <- prophet(train.y)\n expect_false('daily' %in% names(m$seasonalities))\n m <- prophet(train.y, daily.seasonality = TRUE)\n expect_true('daily' %in% names(m$seasonalities))\n m <- prophet(DATA2, daily.seasonality = 7, seasonality.prior.scale = 3)\n true <- list(\n period = 1, fourier.order = 7, prior.scale = 3, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$daily[[name]], true[[name]])\n }\n m <- prophet(DATA)\n expect_false('daily' %in% names(m$seasonalities))\n})\n\ntest_that(\"test_subdaily_holidays\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n holidays <- data.frame(ds = c('2017-01-02'),\n holiday = c('special_day'))\n m <- prophet(DATA2, holidays=holidays)\n fcst <- predict(m)\n expect_equal(sum(fcst$special_day == 0), 575)\n})\n\ntest_that(\"custom_seasonality\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n holidays <- data.frame(ds = c('2017-01-02'),\n holiday = c('special_day'),\n prior_scale = c(4))\n m <- prophet(holidays=holidays)\n expect_error(\n add_seasonality(m, name=\"incorrect.fourier.order\", period=30, fourier.order=-10),\n \"Fourier order must be > 0.\"\n )\n m <- add_seasonality(m, name='monthly', period=30, fourier.order=5)\n true <- list(\n period = 30, fourier.order = 5, prior.scale = 10, mode = 'additive',\n condition.name = NULL)\n for (name in names(true)) {\n expect_equal(m$seasonalities$monthly[[name]], true[[name]])\n }\n expect_error(\n add_seasonality(m, name='special_day', period=30, fourier.order=5),\n \"already used for a holiday.\"\n )\n expect_error(\n add_seasonality(m, name='trend', period=30, fourier.order=5),\n \"is reserved.\"\n )\n m <- add_seasonality(m, name='weekly', period=30, fourier.order=5)\n # Test priors\n m <- prophet(\n holidays = holidays, yearly.seasonality = FALSE,\n seasonality.mode = 'multiplicative')\n m <- add_seasonality(\n m, name='monthly', period=30, fourier.order=5, prior.scale = 2,\n mode = 'additive')\n m <- fit.prophet(m, DATA)\n expect_equal(m$seasonalities$monthly$mode, 'additive')\n expect_equal(m$seasonalities$weekly$mode, 'multiplicative')\n out <- prophet:::make_all_seasonality_features(m, m$history)\n prior.scales <- out$prior.scales\n component.cols <- out$component.cols\n expect_equal(sum(component.cols$monthly), 10)\n expect_equal(sum(component.cols$special_day), 1)\n expect_equal(sum(component.cols$weekly), 6)\n expect_equal(sum(component.cols$additive_terms), 10)\n expect_equal(sum(component.cols$multiplicative_terms), 7)\n expect_equal(sum(component.cols$monthly[1:11]), 10)\n expect_equal(sum(component.cols$weekly[11:17]), 6)\n expect_true(all(prior.scales == c(rep(2, 10), rep(10, 6), 4)))\n})\n\ntest_that(\"conditional_custom_seasonality\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet(weekly_seasonality=FALSE, yearly_seasonality=FALSE)\n m <- add_seasonality(m, name='conditional_weekly', period=7, fourier.order=3,\n prior.scale=2., condition.name='is_conditional_week')\n m <- add_seasonality(m, name='normal_monthly', period=30.5, fourier.order=5,\n prior.scale=2.)\n df <- DATA\n # Require all conditions names in df\n expect_error(\n fit.prophet(m, df)\n )\n df$is_conditional_week <- c(rep(0, 255), rep(2, 255))\n # Require boolean compatible values\n expect_error(\n fit.prophet(m, df)\n )\n df$is_conditional_week <- c(rep(0, 255), rep(1, 255))\n fit.prophet(m, df)\n true <- list(\n period = 7, fourier.order = 3, prior.scale = 2., mode = 'additive', \n condition.name = 'is_conditional_week')\n for (name in names(true)) {\n expect_equal(m$seasonalities[['conditional_weekly']][[name]], true[[name]])\n }\n expect_equal(\n m$seasonalities[['normal_monthly']]$condition.name, NULL\n )\n out <- prophet:::make_all_seasonality_features(m, df)\n #Confirm that only values without is_conditional_week has non zero entries\n nonzero.weekly = out$seasonal.features %>%\n dplyr::as_tibble() %>%\n dplyr::select(dplyr::starts_with('conditional_weekly')) %>%\n dplyr::mutate_all(~ . != 0) %>%\n dplyr::mutate(nonzero = rowSums(. != 0) > 0) %>% \n dplyr::pull(nonzero)\n expect_equal(\n nonzero.weekly, as.logical(df$is_conditional_week)\n )\n})\n\ntest_that(\"added_regressors\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet()\n m <- add_regressor(m, 'binary_feature', prior.scale=0.2)\n m <- add_regressor(m, 'numeric_feature', prior.scale=0.5)\n m <- add_regressor(\n m, 'numeric_feature2', prior.scale=0.5, mode = 'multiplicative')\n m <- add_regressor(m, 'binary_feature2', standardize=TRUE)\n df <- DATA\n df$binary_feature <- c(rep(0, 255), rep(1, 255))\n df$numeric_feature <- 0:509\n df$numeric_feature2 <- 0:509\n # Require all regressors in df\n expect_error(\n fit.prophet(m, df)\n )\n df$binary_feature2 <- c(rep(1, 100), rep(0, 410))\n m <- fit.prophet(m, df)\n # Check that standardizations are correctly set\n true <- list(\n prior.scale = 0.2, mu = 0, std = 1, standardize = 'auto', mode = 'additive'\n )\n for (name in names(true)) {\n expect_equal(true[[name]], m$extra_regressors$binary_feature[[name]])\n }\n true <- list(prior.scale = 0.5, mu = 254.5, std = 147.368585)\n for (name in names(true)) {\n expect_equal(true[[name]], m$extra_regressors$numeric_feature[[name]],\n tolerance = 1e-5)\n }\n expect_equal(m$extra_regressors$numeric_feature2$mode, 'multiplicative')\n true <- list(prior.scale = 10., mu = 0.1960784, std = 0.3974183)\n for (name in names(true)) {\n expect_equal(true[[name]], m$extra_regressors$binary_feature2[[name]],\n tolerance = 1e-5)\n }\n # Check that standardization is done correctly\n df2 <- prophet:::setup_dataframe(m, df)$df\n expect_equal(df2$binary_feature[1], 0)\n expect_equal(df2$numeric_feature[1], -1.726962, tolerance = 1e-4)\n expect_equal(df2$binary_feature2[1], 2.022859, tolerance = 1e-4)\n # Check that feature matrix and prior scales are correctly constructed\n out <- prophet:::make_all_seasonality_features(m, df2)\n seasonal.features <- out$seasonal.features\n prior.scales <- out$prior.scales\n component.cols <- out$component.cols\n modes <- out$modes\n expect_equal(ncol(seasonal.features), 30)\n r_names <- c('binary_feature', 'numeric_feature', 'binary_feature2')\n true.priors <- c(0.2, 0.5, 10.)\n for (i in seq_along(r_names)) {\n name <- r_names[i]\n expect_true(name %in% colnames(seasonal.features))\n expect_equal(sum(component.cols[[name]]), 1)\n expect_equal(sum(prior.scales * component.cols[[name]]), true.priors[i])\n }\n # Check that forecast components are reasonable\n future <- data.frame(\n ds = c('2014-06-01'),\n binary_feature = c(0),\n numeric_feature = c(10),\n numeric_feature2 = c(10)\n )\n expect_error(predict(m, future))\n future$binary_feature2 <- 0.\n fcst <- predict(m, future)\n expect_equal(ncol(fcst), 37)\n expect_equal(fcst$binary_feature[1], 0)\n expect_equal(fcst$extra_regressors_additive[1],\n fcst$numeric_feature[1] + fcst$binary_feature2[1])\n expect_equal(fcst$extra_regressors_multiplicative[1],\n fcst$numeric_feature2[1])\n expect_equal(fcst$additive_terms[1],\n fcst$yearly[1] + fcst$weekly[1]\n + fcst$extra_regressors_additive[1])\n expect_equal(fcst$multiplicative_terms[1],\n fcst$extra_regressors_multiplicative[1])\n expect_equal(\n fcst$yhat[1],\n fcst$trend[1] * (1 + fcst$multiplicative_terms[1]) + fcst$additive_terms[1]\n )\n # Check works with constant extra regressor of 0\n df$constant_feature <- 0\n m <- prophet()\n m <- add_regressor(m, 'constant_feature', standardize = TRUE)\n m <- fit.prophet(m, df)\n expect_equal(m$extra_regressors$constant_feature$std, 1)\n})\n\ntest_that(\"set_seasonality_mode\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- prophet()\n expect_equal(m$seasonality.mode, 'additive')\n m <- prophet(seasonality.mode = 'multiplicative')\n expect_equal(m$seasonality.mode, 'multiplicative')\n expect_error(prophet(seasonality.mode = 'batman'))\n})\n\ntest_that(\"seasonality_modes\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n holidays <- data.frame(ds = c('2016-12-25'),\n holiday = c('xmas'),\n lower_window = c(-1),\n upper_window = c(0))\n m <- prophet(seasonality.mode = 'multiplicative', holidays = holidays)\n m <- add_seasonality(\n m, name = 'monthly', period = 30, fourier.order = 3, mode = 'additive')\n m <- add_regressor(m, name = 'binary_feature', mode = 'additive')\n m <- add_regressor(m, name = 'numeric_feature')\n # Construct seasonal features\n df <- DATA\n df$binary_feature <- c(rep(0, 255), rep(1, 255))\n df$numeric_feature <- 0:509\n out <- prophet:::setup_dataframe(m, df, initialize_scales = TRUE)\n df <- out$df\n m <- out$m\n m$history <- df\n m <- prophet:::set_auto_seasonalities(m)\n out <- prophet:::make_all_seasonality_features(m, df)\n component.cols <- out$component.cols\n modes <- out$modes\n expect_equal(sum(component.cols$additive_terms), 7)\n expect_equal(sum(component.cols$multiplicative_terms), 29)\n expect_equal(\n sort(modes$additive),\n c('additive_terms', 'binary_feature', 'extra_regressors_additive',\n 'monthly')\n )\n expect_equal(\n sort(modes$multiplicative),\n c('extra_regressors_multiplicative', 'holidays', 'multiplicative_terms',\n 'numeric_feature', 'weekly', 'xmas', 'yearly')\n )\n})\n" }, { "path": "R/tests/testthat/test_stan_functions.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nlibrary(prophet)\ncontext(\"Prophet stan model tests\")\n\nskip_on_os(\"windows\")\n\nfn <- tryCatch({\n rstan::expose_stan_functions(\n rstan::stanc(file=\"../../inst/stan/prophet.stan\")\n )\n}, error = function(e) {\n rstan::expose_stan_functions(\n rstan::stanc(file=system.file(\"stan/prophet.stan\", package=\"prophet\"))\n )\n})\n\nDATA <- read.csv('data.csv')\nN <- nrow(DATA)\ntrain <- DATA[1:floor(N / 2), ]\nfuture <- DATA[(ceiling(N/2) + 1):N, ]\n\nDATA2 <- read.csv('data2.csv')\n\nDATA$ds <- prophet:::set_date(DATA$ds)\nDATA2$ds <- prophet:::set_date(DATA2$ds)\n\ntest_that(\"get_changepoint_matrix\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n history <- train\n m <- prophet(history, fit = FALSE)\n\n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n history <- out$df\n m <- out$m\n m$history <- history\n\n m <- prophet:::set_changepoints(m)\n\n cp <- m$changepoints.t\n\n mat <- get_changepoint_matrix(history$t, cp, nrow(history), length(cp))\n expect_equal(nrow(mat), floor(N / 2))\n expect_equal(ncol(mat), m$n.changepoints)\n # Compare to the R implementation\n A <- matrix(0, nrow(history), length(cp))\n for (i in 1:length(cp)) {\n A[history$t >= cp[i], i] <- 1\n }\n expect_true(all(A == mat))\n})\n\ntest_that(\"get_zero_changepoints\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n history <- train\n m <- prophet(history, n.changepoints = 0, fit = FALSE)\n \n out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)\n m <- out$m\n history <- out$df\n m$history <- history\n\n m <- prophet:::set_changepoints(m)\n cp <- m$changepoints.t\n \n mat <- get_changepoint_matrix(history$t, cp, nrow(history), length(cp))\n expect_equal(nrow(mat), floor(N / 2))\n expect_equal(ncol(mat), 1)\n expect_true(all(mat == 1))\n})\n\ntest_that(\"linear_trend\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n t <- seq(0, 10)\n m <- 0\n k <- 1.0\n deltas <- c(0.5)\n changepoint.ts <- c(5)\n A <- get_changepoint_matrix(t, changepoint.ts, length(t), 1)\n\n y <- linear_trend(k, m, deltas, t, A, changepoint.ts)\n y.true <- c(0, 1, 2, 3, 4, 5, 6.5, 8, 9.5, 11, 12.5)\n expect_equal(y, y.true)\n\n t <- t[8:length(t)]\n A <- get_changepoint_matrix(t, changepoint.ts, length(t), 1)\n y.true <- y.true[8:length(y.true)]\n y <- linear_trend(k, m, deltas, t, A, changepoint.ts)\n expect_equal(y, y.true)\n})\n\ntest_that(\"piecewise_logistic\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n t <- seq(0, 10)\n cap <- rep(10, 11)\n m <- 0\n k <- 1.0\n deltas <- c(0.5)\n changepoint.ts <- c(5)\n A <- get_changepoint_matrix(t, changepoint.ts, length(t), 1)\n\n y <- logistic_trend(k, m, deltas, t, cap, A, changepoint.ts, 1)\n y.true <- c(5.000000, 7.310586, 8.807971, 9.525741, 9.820138, 9.933071,\n 9.984988, 9.996646, 9.999252, 9.999833, 9.999963)\n expect_equal(y, y.true, tolerance = 1e-6)\n \n t <- t[8:length(t)]\n A <- get_changepoint_matrix(t, changepoint.ts, length(t), 1)\n y.true <- y.true[8:length(y.true)]\n cap <- cap[8:length(cap)]\n y <- logistic_trend(k, m, deltas, t, cap, A, changepoint.ts, 1)\n expect_equal(y, y.true, tolerance = 1e-6)\n})\n" }, { "path": "R/tests/testthat/test_utilities.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nlibrary(prophet)\ncontext(\"Prophet utilities tests\")\n\nDATA <- read.csv('data.csv')\nDATA$ds <- as.Date(DATA$ds)\n\nbuild_model_with_regressors <- function(data, mcmc.samples = 0) {\n m <- prophet(mcmc.samples = mcmc.samples)\n m <- add_regressor(m, 'binary_feature', prior.scale=0.2)\n m <- add_regressor(m, 'numeric_feature', prior.scale=0.5)\n m <- add_regressor(\n m, 'numeric_feature2', prior.scale=0.5, mode = 'multiplicative')\n m <- add_regressor(m, 'binary_feature2', standardize=TRUE)\n\n df <- data\n df$binary_feature <- c(rep(0, 255), rep(1, 255))\n df$numeric_feature <- 0:509\n df$numeric_feature2 <- 0:509\n df$binary_feature2 <- c(rep(1, 100), rep(0, 410))\n m <- fit.prophet(m, df)\n\n return(m)\n}\n\ntest_that(\"regressor_coefficients_no_uncertainty\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n m <- build_model_with_regressors(DATA, mcmc.samples = 0)\n coefs <- regressor_coefficients(m)\n\n expect_equal(dim(coefs), c(4, 6))\n expect_equal(coefs[, \"coef_lower\"], coefs[, \"coef\"])\n expect_equal(coefs[, \"coef_upper\"], coefs[, \"coef\"])\n})\n\ntest_that(\"regressor_coefficients_with_uncertainty\", {\n skip_if_not(Sys.getenv('R_ARCH') != '/i386')\n suppressWarnings(m <- build_model_with_regressors(DATA, mcmc.samples = 100))\n coefs <- regressor_coefficients(m)\n\n expect_equal(dim(coefs), c(4, 6))\n expect_true(all(coefs[, \"coef_lower\"] < coefs[, \"coef\"]))\n expect_true(all(coefs[, \"coef_upper\"] > coefs[, \"coef\"]))\n})\n" }, { "path": "R/tests/testthat.R", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nSys.setenv(\"R_TESTS\" = \"\")\nlibrary(testthat)\nlibrary(prophet)\n\ntest_check(\"prophet\")\n" }, { "path": "R/vignettes/quick_start.Rmd", "content": "---\ntitle: \"Quick Start Guide to Using Prophet\"\nauthor: \"Sean J. Taylor and Ben Letham\"\ndate: \"`r Sys.Date()`\"\noutput: rmarkdown::html_vignette\nvignette: >\n %\\VignetteIndexEntry{Quick Start Guide to Using Prophet}\n %\\VignetteEngine{knitr::rmarkdown}\n \\usepackage[utf8]{inputenc}\n---\n\n```{r, echo = FALSE, message = FALSE}\nknitr::opts_chunk$set(collapse = T, comment = \"#>\")\noptions(tibble.print_min = 4L, tibble.print_max = 4L)\nlibrary(prophet)\nlibrary(dplyr)\n```\n\nThis document provides a very brief introduction to the Prophet API. For a detailed guide on using Prophet, please visit the main site at [https://facebook.github.io/prophet/](https://facebook.github.io/prophet/).\n\nProphet uses the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and `plot` on this model object.\n\nFirst we read in the data and create the outcome variable.\n\n```{r, results= \"hide\"}\nlibrary(readr)\ndf <- read_csv('../tests/testthat/data.csv')\n```\n\nWe call the `prophet` function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data.\n\n```{r}\nm <- prophet(df)\n```\nWe need to construct a dataframe for prediction. The `make_future_dataframe` function takes the model object and a number of periods to forecast:\n\n```{r}\nfuture <- make_future_dataframe(m, periods = 365)\nhead(future)\n```\nAs with most modeling procedures in R, we use the generic `predict` function to get our forecast:\n\n```{r}\nforecast <- predict(m, future)\nhead(forecast)\n```\n\nYou can use the generic `plot` function to plot the forecast, but you must also pass the model in to be plotted:\n\n```{r}\nplot(m, forecast)\n```\n\nYou can plot the components of the forecast using the `prophet_plot_components` function:\n\n```{r}\nprophet_plot_components(m, forecast)\n```\n" }, { "path": "README.md", "content": "\n# Prophet: Automatic Forecasting Procedure\n\n![Build](https://github.com/facebook/prophet/workflows/Build/badge.svg)\n\n[![PyPI Version](https://img.shields.io/pypi/v/prophet.svg)](https://pypi.python.org/pypi/prophet)\n[![PyPI Downloads Monthly](https://pepy.tech/badge/prophet/month)](https://pepy.tech/project/prophet)\n[![PyPI Downloads All](https://pepy.tech/badge/prophet)](https://pepy.tech/project/prophet)\n\n[![CRAN Version](https://www.r-pkg.org/badges/version/prophet)](https://CRAN.R-project.org/package=prophet)\n[![CRAN Downloads Monthly](https://cranlogs.r-pkg.org/badges/prophet?color=brightgreen)](https://cran.r-project.org/package=prophet)\n[![CRAN Downloads All](https://cranlogs.r-pkg.org/badges/grand-total/prophet?color=brightgreen)](https://cranlogs.r-pkg.org/badges/grand-total/prophet)\n\n[![Conda_Version](https://anaconda.org/conda-forge/prophet/badges/version.svg)](https://anaconda.org/conda-forge/prophet/)\n\n-----\n\n**2023 Update:** We discuss our plans for the future of Prophet in this blog post: [facebook/prophet in 2023 and beyond](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138)\n\n-----\n\nProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.\n\nProphet is [open source software](https://code.facebook.com/projects/) released by Facebook's [Core Data Science team](https://research.fb.com/category/data-science/). It is available for download on [CRAN](https://cran.r-project.org/package=prophet) and [PyPI](https://pypi.python.org/pypi/prophet/).\n\n## Important links\n\n- Homepage: https://facebook.github.io/prophet/\n- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html\n- Issue tracker: https://github.com/facebook/prophet/issues\n- Source code repository: https://github.com/facebook/prophet\n- Contributing: https://facebook.github.io/prophet/docs/contributing.html\n- Prophet R package: https://cran.r-project.org/package=prophet\n- Prophet Python package: https://pypi.python.org/pypi/prophet/\n- Release blogpost: https://research.facebook.com/blog/2017/2/prophet-forecasting-at-scale/\n- Prophet paper: Sean J. Taylor, Benjamin Letham (2018) Forecasting at scale. The American Statistician 72(1):37-45 (https://peerj.com/preprints/3190.pdf).\n\n## Installation in R - CRAN\n\n⚠️ **The CRAN version of prophet is fairly outdated. To get the latest bug fixes and updated country holiday data, we suggest installing the [latest release](#installation-in-r---latest-release).**\n\nProphet is a [CRAN package](https://cran.r-project.org/package=prophet) so you can use `install.packages`.\n\n```r\ninstall.packages('prophet')\n```\n\nAfter installation, you can [get started!](https://facebook.github.io/prophet/docs/quick_start.html#r-api)\n\n## Installation in R - Latest release\n\n```r\ninstall.packages('remotes')\nremotes::install_github('facebook/prophet@*release', subdir = 'R')\n```\n\n#### Experimental backend - cmdstanr\n\nYou can also choose an experimental alternative stan backend called `cmdstanr`. Once you've installed `prophet`,\nfollow these instructions to use `cmdstanr` instead of `rstan` as the backend:\n\n```r\n# R\n# We recommend running this in a fresh R session or restarting your current session\ninstall.packages(c(\"cmdstanr\", \"posterior\"), repos = c(\"https://stan-dev.r-universe.dev\", getOption(\"repos\")))\n\n# If you haven't installed cmdstan before, run:\ncmdstanr::install_cmdstan()\n# Otherwise, you can point cmdstanr to your cmdstan path:\ncmdstanr::set_cmdstan_path(path = )\n\n# Set the R_STAN_BACKEND environment variable\nSys.setenv(R_STAN_BACKEND = \"CMDSTANR\")\n```\n\n### Windows\n\nOn Windows, R requires a compiler so you'll need to [follow the instructions](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started) provided by `rstan`. The key step is installing [Rtools](http://cran.r-project.org/bin/windows/Rtools/) before attempting to install the package.\n\nIf you have custom Stan compiler settings, install from source rather than the CRAN binary.\n\n## Installation in Python - PyPI release\n\nProphet is on PyPI, so you can use `pip` to install it.\n\n```bash\npython -m pip install prophet\n```\n\n* From v0.6 onwards, Python 2 is no longer supported.\n* As of v1.0, the package name on PyPI is \"prophet\"; prior to v1.0 it was \"fbprophet\".\n* As of v1.1, the minimum supported Python version is 3.7.\n\nAfter installation, you can [get started!](https://facebook.github.io/prophet/docs/quick_start.html#python-api)\n\n### Anaconda\n\nProphet can also be installed through conda-forge.\n\n```bash\nconda install -c conda-forge prophet\n```\n\n## Installation in Python - Development version\n\nTo get the latest code changes as they are merged, you can clone this repo and build from source manually. This is **not** guaranteed to be stable.\n\n```bash\ngit clone https://github.com/facebook/prophet.git\ncd prophet/python\npython -m pip install -e .\n```\n\nBy default, Prophet will use a fixed version of `cmdstan` (downloading and installing it if necessary) to compile the model executables. If this is undesired and you would like to use your own existing `cmdstan` installation, you can set the environment variable `PROPHET_REPACKAGE_CMDSTAN` to `False`:\n\n```bash\nexport PROPHET_REPACKAGE_CMDSTAN=False; python -m pip install -e .\n```\n\n### Linux\n\nMake sure compilers (gcc, g++, build-essential) and Python development tools (python-dev, python3-dev) are installed. In Red Hat systems, install the packages gcc64 and gcc64-c++. If you are using a VM, be aware that you will need at least 4GB of memory to install prophet, and at least 2GB of memory to use prophet.\n\n### Windows\n\nUsing `cmdstanpy` with Windows requires a Unix-compatible C compiler such as mingw-gcc. If cmdstanpy is installed first, one can be installed via the `cmdstanpy.install_cxx_toolchain` command.\n\n## Changelog\n\nSee [Release Notes](https://github.com/facebook/prophet/releases).\n\n### Version 1.3.0 (2026.01.27)\n\n#### Python\n\n- Support pandas>=3.0 and numpy>=2.4.\n\n### Version 1.2.2 (2026.01.25)\n\n#### Python\n\n- Version constraints on pandas (`<3`) and numpy (`<2.4`).\n\n#### R\n- Update build requirements to C++17 to Comply with CRAN Policy.\n- Add .tar.gz upload for R package to CI.\n- Re-generated holidays.csv for R package.\n\n### Version 1.2.1 (2025.10.22)\n\n#### Python\n\n- Also copy makefile to fake cmdstan.\n\n### Version 1.2.0 (2025.05.30)\n\n#### Python\n\n- Use latest CmdStan.\n- Add null check to CmdStanPyBackend cleanup() function.\n\n### Version 1.1.7 (2025.05.30)\n\n#### Python\n\n- Enable creation of custom performance metrics.\n- chore: address pandas futurewarning from \"M\" being deprecated.\n- cleanup() for cross_validate.\n\n### Version 1.1.6 (2024.09.29)\n\n#### Python\n\n- Bug fixes: include predictions for dates with missing `y` the history, zero division error in cross validation metrics.\n- Changed `NDArray[np.float_]` to `NDArray[np.float64]` to be compatible with numpy 2.0\n\n#### R\n\n- Updated `holidays` data based on holidays version 0.57.\n\n### Version 1.1.5 (2023.10.10)\n\n#### Python\n\n- Upgraded cmdstan version to 2.33.1, enabling Apple M2 support.\n- Added pre-built wheels for macOS arm64 architecture (M1, M2 chips)\n- Added argument `scaling` to the `Prophet()` instantiation. Allows `minmax` scaling on `y` instead of\n `absmax` scaling (dividing by the maximum value). `scaling='absmax'` by default, preserving the\n behaviour of previous versions.\n- Added argument `holidays_mode` to the `Prophet()` instantiation. Allows holidays regressors to have\n a different mode than seasonality regressors. `holidays_mode` takes the same value as `seasonality_mode`\n if not specified, preserving the behaviour of previous versions.\n- Added two methods to the `Prophet` object: `preprocess()` and `calculate_initial_params()`. These\n do not need to be called and will not change the model fitting process. Their purpose is to provide\n clarity on the pre-processing steps taken (`y` scaling, creating fourier series, regressor scaling,\n setting changepoints, etc.) before the data is passed to the stan model.\n- Added argument `extra_output_columns` to `cross_validation()`. The user can specify additional columns\n from `predict()` to include in the final output alongside `ds` and `yhat`, for example `extra_output_columns=['trend']`.\n- prophet's custom `hdays` module was deprecated last version and is now removed.\n\n#### R\n\n- Updated `holidays` data based on holidays version 0.34.\n\n### Version 1.1.4 (2023.05.30)\n\n#### Python\n\n- We now rely solely on `holidays` package for country holidays.\n- Upgraded cmdstan version to 2.31.0, enabling Apple M1 support.\n- Fixed bug with Windows installation caused by long paths.\n\n#### R\n\n- Updated `holidays` data based on holidays version 0.25.\n\n### Version 1.1.2 (2023.01.20)\n\n#### Python\n\n- Sped up `.predict()` by up to 10x by removing intermediate DataFrame creations.\n- Sped up fourier series generation, leading to at least 1.5x speed improvement for `train()` and `predict()` pipelines.\n- Fixed bug in how warm start values were being read.\n- Wheels are now version-agnostic.\n\n#### R\n\n- Fixed a bug in `construct_holiday_dataframe()`\n- Updated `holidays` data based on holidays version 0.18.\n\n### Version 1.1.1 (2022.09.08)\n\n- (Python) Improved runtime (3-7x) of uncertainty predictions via vectorization.\n- Bugfixes relating to Python package versions and R holiday objects.\n\n### Version 1.1 (2022.06.25)\n\n- Replaced `pystan2` dependency with `cmdstan` + `cmdstanpy`.\n- Pre-packaged model binaries for Python package, uploaded binary distributions to PyPI.\n- Improvements in the `stan` model code, cross-validation metric calculations, holidays.\n\n### Version 1.0 (2021.03.28)\n\n- Python package name changed from fbprophet to prophet\n- Fixed R Windows build issues to get latest version back on CRAN\n- Improvements in serialization, holidays, and R timezone handling\n- Plotting improvements\n\n### Version 0.7 (2020.09.05)\n\n- Built-in json serialization\n- Added \"flat\" growth option\n- Bugfixes related to `holidays` and `pandas`\n- Plotting improvements\n- Improvements in cross validation, such as parallelization and directly specifying cutoffs\n\n### Version 0.6 (2020.03.03)\n\n- Fix bugs related to upstream changes in `holidays` and `pandas` packages.\n- Compile model during first use, not during install (to comply with CRAN policy)\n- `cmdstanpy` backend now available in Python\n- Python 2 no longer supported\n\n### Version 0.5 (2019.05.14)\n\n- Conditional seasonalities\n- Improved cross validation estimates\n- Plotly plot in Python\n- Bugfixes\n\n### Version 0.4 (2018.12.18)\n\n- Added holidays functionality\n- Bugfixes\n\n### Version 0.3 (2018.06.01)\n\n- Multiplicative seasonality\n- Cross validation error metrics and visualizations\n- Parameter to set range of potential changepoints\n- Unified Stan model for both trend types\n- Improved future trend uncertainty for sub-daily data\n- Bugfixes\n\n### Version 0.2.1 (2017.11.08)\n\n- Bugfixes\n\n### Version 0.2 (2017.09.02)\n\n- Forecasting with sub-daily data\n- Daily seasonality, and custom seasonalities\n- Extra regressors\n- Access to posterior predictive samples\n- Cross-validation function\n- Saturating minimums\n- Bugfixes\n\n### Version 0.1.1 (2017.04.17)\n\n- Bugfixes\n- New options for detecting yearly and weekly seasonality (now the default)\n\n### Version 0.1 (2017.02.23)\n\n- Initial release\n\n## License\n\nProphet is licensed under the [MIT license](LICENSE).\n" }, { "path": "docker-compose.yml", "content": "version: \"3\"\nservices:\n package:\n image: facebook/prophet\n build:\n context: .\n dockerfile: Dockerfile\n\n volumes:\n - .:/usr/src/app\n" }, { "path": "docs/.gitignore", "content": ".DS_STORE\n_site/\n*.swo\n*.swp\n_site\n.sass-cache\n*.psd\n*~\n.jekyll-metadata\n" }, { "path": "docs/CONTRIBUTING.md", "content": "This provides guidance on how to contribute various content to the Prophet documentation.\n\n## Getting started\n\nYou should only have to do these one time.\n\n- Rename this file to `CONTRIBUTING.md`.\n- Rename `EXAMPLE-README-FOR-RUNNING-DOCS.md` to `README.md` (replacing the existing `README.md` that came with the template).\n- Rename `EXAMPLE-LICENSE` to `LICENSE`.\n- Review the [template information](./TEMPLATE-INFORMATION.md).\n- Review `./_config.yml`.\n- Make sure you update `title`, `description`, `tagline` and `gacode` (Google Analytics) in `./_config.yml`.\n\n## Basic Structure\n\nMost content is written in markdown. You name the file `something.md`, then have a header that looks like this:\n\n```\n---\ndocid: getting-started\ntitle: Getting started with ProjectName\nlayout: docs\npermalink: /docs/getting-started.html\n---\n```\n\nCustomize these values for each document, blog post, etc.\n\n> The filename of the `.md` file doesn't actually matter; what is important is the `docid` being unique and the `permalink` correct and unique too).\n\n## Landing page\n\nModify `index.md` with your new or updated content.\n\nIf you want a `GridBlock` as part of your content, you can do so directly with HTML:\n\n```\n
\n
\n
\n

Your Features

\n \n
\n
\n\n
\n
\n

More information

\n

\n Stuff here\n

\n
\n
\n
\n```\n\nor with a combination of changing `./_data/features.yml` and adding some Liquid to `index.md`, such as:\n\n```\n{% include content/gridblocks.html data_source=site.data.features imagealign=\"bottom\"%}\n```\n\n## Blog\n\nTo modify a blog post, edit the appropriate markdown file in `./_posts/`.\n\nAdding a new blog post is a four-step process.\n\n> Some posts have a `permalink` and `comments` in the blog post YAML header. You will not need these for new blog posts. These are an artifact of migrating the blog from Wordpress to gh-pages.\n\n1. Create your blog post in `./_posts/` in markdown (file extension `.md` or `.markdown`). See current posts in that folder or `./doc-type-examples/2016-04-07-blog-post-example.md` for an example of the YAML format. **If the `./_posts` directory does not exist, create it**.\n - You can add a `` tag in the middle of your post such that you show only the excerpt above that tag in the main `/blog` index on your page.\n1. If you have not authored a blog post before, modify the `./_data/authors.yml` file with the `author` id you used in your blog post, along with your full name and Facebook ID to get your profile picture.\n1. [Run the site locally](./README.md) to test your changes. It will be at `http://127.0.0.1/blog/your-new-blog-post-title.html`\n1. Push your changes to GitHub.\n\n## Docs\n\nTo modify docs, edit the appropriate markdown file in `./_docs/`.\n\nTo add docs to the site....\n\n1. Add your markdown file to the `./_docs/` folder. See `./doc-type-examples/docs-hello-world.md` for an example of the YAML header format. **If the `./_docs/` directory does not exist, create it**.\n - You can use folders in the `./_docs/` directory to organize your content if you want.\n1. Update `_data/nav_docs.yml` to add your new document to the navigation bar. Use the `docid` you put in your doc markdown in as the `id` in the `_data/nav_docs.yml` file.\n1. [Run the site locally](./README.md) to test your changes. It will be at `http://127.0.0.1/docs/your-new-doc-permalink.html`\n1. Push your changes to GitHub.\n\n## Header Bar\n\nTo modify the header bar, change `./_data/nav.yml`.\n\n## Top Level Page\n\nTo modify a top-level page, edit the appropriate markdown file in `./top-level/`\n\nIf you want a top-level page (e.g., http://your-site.com/top-level.html) -- not in `/blog/` or `/docs/`....\n\n1. Create a markdown file in the root `./top-level/`. See `./doc-type-examples/top-level-example.md` for more information.\n1. If you want a visible link to that file, update `_data/nav.yml` to add a link to your new top-level document in the header bar.\n\n > This is not necessary if you just want to have a page that is linked to from another page, but not exposed as direct link to the user.\n\n1. [Run the site locally](./README.md) to test your changes. It will be at `http://127.0.0.1/your-top-level-page-permalink.html`\n1. Push your changes to GitHub.\n\n## Code Blocks\n\nThis template uses [rouge syntax highlighting](https://github.com/jneen/rouge) out of the box. We have [custom CSS](https://github.com/facebook/Site-Templates/blob/master/product-and-feature-docs/first-common-fb-template/_sass/_syntax-highlighting.scss) for when those blocks are rendered. Rouge has plenty of [supported languages](https://github.com/jneen/rouge/wiki/List-of-supported-languages-and-lexers). \n\n## Other Changes\n\n- CSS: `./css/main.css` or `./_sass/*.scss`.\n- Images: `./static/images/[docs | posts]/....`\n- Main Blog post HTML: `./_includes/post.html`\n- Main Docs HTML: `./_includes/doc.html`\n" }, { "path": "docs/Gemfile", "content": "source 'https://rubygems.org'\n\ngem 'github-pages', group: :jekyll_plugins\n" }, { "path": "docs/LICENSE", "content": "Attribution 4.0 International\n\n=======================================================================\n\nCreative Commons Corporation (\"Creative Commons\") is not a law firm and\ndoes not provide legal services or legal advice. Distribution of\nCreative Commons public licenses does not create a lawyer-client or\nother relationship. Creative Commons makes its licenses and related\ninformation available on an \"as-is\" basis. 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For\nthe avoidance of doubt, this paragraph does not form part of the public\nlicenses.\n\nCreative Commons may be contacted at creativecommons.org.\n\n" }, { "path": "docs/Makefile", "content": "notebooks:\n\tfor f in ../notebooks/*.ipynb; \\\n\tdo \\\n\t NAME=$$(basename $$f .ipynb); \\\n\t jupyter nbconvert --to markdown ../notebooks/$$NAME.ipynb --template=nbconvert_template.tpl; \\\n\t mv -f ../notebooks/\"$$NAME\".md _docs/; \\\n\t rm -rf static/\"$$NAME\"_files; \\\n\t mv ../notebooks/\"$$NAME\"_files static/; \\\n\tdone\n" }, { "path": "docs/README.md", "content": "## User Documentation for Prophet\n\nThis directory will contain the user and feature documentation for Prophet. The documentation will be hosted on GitHub pages.\n\n### Contributing\n\nSee [CONTRIBUTING.md](./CONTRIBUTING.md) for details on how to add or modify content.\n\n## Jupyter Notebooks\n\nMost of the `doc` pages are generated from [Jupyter notebooks](http://jupyter.org/) in the [notebooks](https://github.com/facebook/prophet/tree/main/notebooks) directory at the base of the source tree. Please make changes there and then rebuild the docs:\n\n```\n$ cd docs\n$ make notebooks\n```\n\nMake sure you have installed [rpy2](https://rpy2.bitbucket.io/) so that the R code can be run as well.\n\n### Run the Site Locally\n\nThe requirements for running a GitHub pages site locally is described in [GitHub help](https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/#requirements). The steps below summarize these steps.\n\n> If you have run the site before, you can start with step 1 and then move on to step 5.\n\n1. Ensure that you are in the same directory where this `README.md` exists (e.g., it could be in `/docs` on `main`, in the root of a `gh-pages` branch, etc). The below RubyGems commands, etc must be run from there.\n\n1. Make sure you have Ruby and [RubyGems](https://rubygems.org/) installed.\n\n > Ruby >= 2.2 is required for the gems. On the latest versions of Mac OS X, Ruby 2.0 is the\n > default. Use [Homebrew](http://brew.sh) and the `brew install ruby` command (or your\n > preferred upgrade mechanism) to install a newer version of Ruby for your Mac OS X system.\n\n1. Make sure you have [Bundler](http://bundler.io/) installed.\n\n ```\n # may require sudo\n gem install bundler\n ```\n1. Install the project's dependencies.\n\n ```\n # run this in the 'docs' directory\n bundle install\n ```\n\n > If you get an error when installing `nokogiri`, you may be running into the problem described\n > in [this nokogiri issue](https://github.com/sparklemotion/nokogiri/issues/1483). You can\n > either `brew uninstall xz` (and then `brew install xz` after the bundle is installed) or\n > `xcode-select --install` (although this may not work if you have already installed command\n > line tools).\n\n1. Run Jekyll's server.\n\n - On first run or for structural changes to the documentation (e.g., new sidebar menu item), do a full build.\n\n ```\n bundle exec jekyll serve\n ```\n\n - For content changes only, you can use `--incremental` for faster builds.\n\n ```\n bundle exec jekyll serve --incremental\n ```\n\n > We use `bundle exec` instead of running straight `jekyll` because `bundle exec` will always use the version of Jekyll from our `Gemfile`. Just running `jekyll` will use the system version and may not necessarily be compatible.\n\n - To run using an actual IP address, you can use `--host=0.0.0.0`\n\n ```\n bundle exec jekyll serve --host=0.0.0.0\n ```\n\n This will allow you to use the IP address associated with your machine in the URL. That way you could share it with other people.\n\n e.g., on a Mac, you can your IP address with something like `ifconfig | grep \"inet \" | grep -v 127.0.0.1`.\n\n1. Either of commands in the previous step will serve up the site on your local device at http://127.0.0.1:4000/ or http://localhost:4000.\n\n### Updating the Bundle\n\nThe site depends on Github Pages and the installed bundle is based on the `github-pages` gem.\nOccasionally that gem might get updated with new or changed functionality. If that is the case,\nyou can run:\n\n```\nbundle update\n```\n\nto get the latest packages for the installation.\n" }, { "path": "docs/_config.yml", "content": "# Site settings\npermalink: /blog/:year/:month/:day/:title.html\ntitle: Prophet\ntagline: Forecasting at scale.\ndescription: >\n Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.\nfbappid: \"1615782811974223\"\ngacode: \"UA-44373548-26\"\nlogo: /static/logo.svg\n\n# baseurl determines the subpath of your site. For example if you're using an\n# organisation.github.io/reponame/ basic site URL, then baseurl would be set\n# as \"/reponame\" but either set to \"\" or remove it all together if you have\n# a top-level domain URL as it is now set to \"\" by default as discussed in:\n# http://jekyllrb.com/news/2016/10/06/jekyll-3-3-is-here/\nbaseurl: \"/prophet\"\n\n# the base hostname & protocol for your site\n# If baseurl is set, then the absolute url for your site would be url/baseurl\n# This was also be set to the right thing automatically for local development\n# https://github.com/blog/2277-what-s-new-in-github-pages-with-jekyll-3-3\n# http://jekyllrb.com/news/2016/10/06/jekyll-3-3-is-here/\nurl: \"http://facebook.github.io\"\n\n# Note: There are new filters in Jekyll 3.3 to help with absolute and relative urls\n# absolute_url\n# relative_url\n# So you will see these used throughout the Jekyll code in this template.\n# no more need for | prepend: site.url | prepend: site.baseurl\n# http://jekyllrb.com/news/2016/10/06/jekyll-3-3-is-here/\n# https://github.com/blog/2277-what-s-new-in-github-pages-with-jekyll-3-3\n\n# The GitHub repo for your project\nghrepo: \"facebook/prophet\"\n\n# Use these color settings to determine your colour scheme for the site.\ncolor:\n # primary should be a vivid color that reflects the project's brand\n primary: \"#3b5998\"\n # secondary should be a subtle light or dark color used on page backgrounds\n secondary: \"#f9f9f9\"\n # Use the following to specify whether the previous two colours are 'light'\n # or 'dark' and therefore what colors can be overlaid on them\n primary-overlay: \"dark\"\n secondary-overlay: \"light\"\n\n#Uncomment this if you want to enable Algolia doc search with your own values\n#searchconfig:\n# apikey: \"\"\n# indexname: \"\"\n\n# Blog posts are built into Jekyll by default, with the `_posts` directory.\n# Here you can specify other types of documentation. The names here are `docs`\n# and `top-level`. This means their content will be in `_docs` and `_top-level`.\n# The permalink format is also given.\n# http://ben.balter.com/2015/02/20/jekyll-collections/\ncollections:\n docs:\n output: true\n permalink: /docs/:name/\n\n# DO NOT ADJUST BELOW THIS LINE UNLESS YOU KNOW WHAT YOU ARE CHANGING\n\nmarkdown: kramdown\nkramdown:\n input: GFM\n syntax_highlighter: rouge\n\n syntax_highlighter_opts:\n css_class: 'rougeHighlight'\n span:\n line_numbers: false\n block:\n line_numbers: true\n start_line: 1\n\nsass:\n style: :compressed\n\nredcarpet:\n extensions: [with_toc_data]\n\n# Gems\ngems:\n - jekyll-feed\n - jekyll-seo-tag\n - jekyll-sitemap\n\n# Set default open graph image for all pages\ndefaults:\n -\n scope:\n path: \"\"\n values:\n image: /static/og_image.png\n" }, { "path": "docs/_data/authors.yml", "content": "\nsjt:\n full_name: Sean J. Taylor\n fbid: 605889\n\nbletham:\n full_name: Ben Letham\n fbid: 605889" }, { "path": "docs/_data/features.yml", "content": "- title: Accurate and fast.\n text: |\n Prophet is used in many applications across Facebook for producing reliable forecasts for planning and goal setting. We've found it to perform better than any other approach in the majority of cases. We fit models in [Stan](http://mc-stan.org) so that you get forecasts in just a few seconds.\n align: left\n\n- title: Fully automatic.\n text: |\n Get a reasonable forecast on messy data with no manual effort. Prophet is robust to outliers, missing data, and dramatic changes in your time series.\n \n- title: Tunable forecasts.\n text: |\n The Prophet procedure includes many possibilities for users to tweak and adjust forecasts. You can use human-interpretable parameters to improve your forecast by adding your domain knowledge.\n\n- title: Available in R or Python.\n text: |\n We've implemented the Prophet procedure in R and Python, but they share the same underlying [Stan](http://mc-stan.org) code for fitting. Use whatever language you're comfortable with to get forecasts.\n" }, { "path": "docs/_data/nav.yml", "content": "- title: Docs\n href: docs/\n category: docs\n\n- title: GitHub\n href: https://github.com/facebook/prophet\n category: external\n" }, { "path": "docs/_data/nav_docs.yml", "content": "- title: Documentation\n items:\n - id: installation\n - id: quick_start\n - id: saturating_forecasts\n - id: trend_changepoints\n - id: seasonality,_holiday_effects,_and_regressors\n - id: multiplicative_seasonality\n - id: uncertainty_intervals\n - id: outliers\n - id: non-daily_data\n - id: diagnostics\n - id: handling_shocks\n - id: additional_topics\n - id: contributing\n\n# n title:, 1 items: per title:, n id: per items:\n# - title: A\n# items:\n# - id: B\n# - id: C\n# - title: D\n# - id: E\n" }, { "path": "docs/_data/powered_by.yml", "content": "" }, { "path": "docs/_data/powered_by_highlight.yml", "content": "" }, { "path": "docs/_data/promo.yml", "content": "# This file determines the list of promotional elements added to the header of \\\n# your site's homepage. Full list of plugins are shown\n\n- type: plugin_row\n children:\n - type: button\n href: docs/installation.html\n text: Install Prophet\n - type: button\n href: docs/quick_start.html#r-api\n text: Get started in R\n - type: button\n href: docs/quick_start.html#python-api\n text: Get started in Python\n - type: button\n href: https://peerj.com/preprints/3190/\n text: Read the paper\n" }, { "path": "docs/_docs/additional_topics.md", "content": "---\nlayout: docs\ndocid: \"additional_topics\"\ntitle: \"Additional Topics\"\npermalink: /docs/additional_topics.html\nsubsections:\n - title: Saving models\n id: saving-models\n - title: Flat trend\n id: flat-trend\n - title: Custom trends\n id: custom-trends\n - title: Updating fitted models\n id: updating-fitted-models\n - title: minmax scaling (new in 1.1.5)\n id: minmax-scaling-(new-in-1.1.5)\n - title: Inspecting transformed data (new in 1.1.5)\n id: inspecting-transformed-data-(new-in-1.1.5)\n - title: External references\n id: external-references\n---\n \n\n### Saving models\n\n\n\nIt is possible to save fitted Prophet models so that they can be loaded and used later.\n\n\n\nIn R, this is done with `saveRDS` and `readRDS`:\n\n\n```R\n# R\nsaveRDS(m, file=\"model.RDS\") # Save model\nm <- readRDS(file=\"model.RDS\") # Load model\n```\nIn Python, models should not be saved with pickle; the Stan backend attached to the model object will not pickle well, and will produce issues under certain versions of Python. Instead, you should use the built-in serialization functions to serialize the model to json:\n\n\n```python\n# Python\nfrom prophet.serialize import model_to_json, model_from_json\n\nwith open('serialized_model.json', 'w') as fout:\n fout.write(model_to_json(m)) # Save model\n\nwith open('serialized_model.json', 'r') as fin:\n m = model_from_json(fin.read()) # Load model\n```\nThe json file will be portable across systems, and deserialization is backwards compatible with older versions of prophet.\n\n\n \n\n### Flat trend\n\n\n\nFor time series that exhibit strong seasonality patterns rather than trend changes, or when we want to rely on the pattern of exogenous regressors (e.g. for causal inference with time series), it may be useful to force the trend growth rate to be flat. This can be achieved simply by passing `growth='flat'` when creating the model:\n\n\n```R\n# R\nm <- prophet(df, growth='flat')\n```\n```python\n# Python\nm = Prophet(growth='flat')\n```\nBelow is a comparison of counterfactual forecasting with exogenous regressors using linear versus flat growth.\n\n\n```python\n# Python\nimport pandas as pd\nfrom prophet import Prophet\nimport matplotlib.pyplot as plt\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nregressor = \"location_4\"\ntarget = \"location_41\"\ncutoff = pd.to_datetime(\"2023-04-17 00:00:00\")\n\ndf = (\n pd.read_csv(\n \"https://raw.githubusercontent.com/facebook/prophet/main/examples/example_pedestrians_multivariate.csv\", \n parse_dates=[\"ds\"]\n )\n .rename(columns={target: \"y\"})\n)\ntrain = df.loc[df[\"ds\"] < cutoff]\ntest = df.loc[df[\"ds\"] >= cutoff]\n```\n```python\n# Python\ndef fit_model(growth):\n m = Prophet(growth=growth, seasonality_mode=\"multiplicative\", daily_seasonality=15)\n m.add_regressor(\"location_4\", mode=\"multiplicative\")\n m.fit(train)\n preds = pd.merge(\n test,\n m.predict(test),\n on=\"ds\",\n how=\"inner\"\n )\n mape = ((preds[\"yhat\"] - preds[\"y\"]).abs() / preds_linear[\"y\"]).mean()\n return m, preds, mape\n```\n```python\n# Python\nm_linear, preds_linear, mape_linear = fit_model(\"linear\")\n```\n 01:19:58 - cmdstanpy - INFO - Chain [1] start processing\n 01:19:58 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nm_flat, preds_flat, mape_flat = fit_model(\"flat\")\n```\n 01:19:58 - cmdstanpy - INFO - Chain [1] start processing\n 01:19:58 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nm_linear.plot_components(preds_linear);\n```\n\n![png](/prophet/static/additional_topics_files/additional_topics_16_0.png)\n\n\n```python\n# Python\nm_flat.plot_components(preds_flat);\n```\n\n![png](/prophet/static/additional_topics_files/additional_topics_17_0.png)\n\n\n```python\n# Python\nfig, ax = plt.subplots(figsize=(11, 5))\nax.scatter(preds_linear[\"ds\"], preds_linear[\"y\"], color=\"black\", marker=\".\")\nax.plot(preds_linear[\"ds\"], preds_linear[\"yhat\"], label=f\"linear, mape={mape_linear:.1%}\")\nax.plot(preds_flat[\"ds\"], preds_flat[\"yhat\"], label=f\"flat, mape={mape_flat:.1%}\")\nplt.xticks(rotation=60)\nax.legend();\n```\n\n![png](/prophet/static/additional_topics_files/additional_topics_18_0.png)\n\n\nIn this example, the target sensor values can be mostly explained by the exogenous regressor (a nearby sensor). The model with linear growth assumes an increasing trend and this leads to larger and larger over-predictions in the test period, while the flat growth model mostly follows movements in the exogenous regressor and this results in a sizeable MAPE improvement.\n\n\n\nNote that forecasting with exogenous regressors is only effective when we can be confident in the future values of the regressor. The example above is relevant to causal inference using time series, where we want to understand what `y` would have looked like for a past time period, and hence the exogenous regressor values are known.\n\n\n\nIn other cases -- where we don't have exogenous regressors or have to predict their future values -- if flat growth is used on a time series that doesn't have a constant trend, any trend will be fit with the noise term and so there will be high predictive uncertainty in the forecast.\n\n\n \n\n### Custom trends\n\n\n\nTo use a trend besides these three built-in trend functions (piecewise linear, piecewise logistic growth, and flat), you can download the source code from github, modify the trend function as desired in a local branch, and then install that local version. [This PR](https://github.com/facebook/prophet/pull/1466/files) provides a good illustration of what must be done to implement a custom trend, as does [this one](https://github.com/facebook/prophet/pull/1794) that implements a step function trend and [this one](https://github.com/facebook/prophet/pull/1778) for a new trend in R.\n\n\n \n\n### Updating fitted models\n\n\n\nA common setting for forecasting is fitting models that need to be updated as additional data come in. Prophet models can only be fit once, and a new model must be re-fit when new data become available. In most settings, model fitting is fast enough that there isn't any issue with re-fitting from scratch. However, it is possible to speed things up a little by warm-starting the fit from the model parameters of the earlier model. This code example shows how this can be done in Python:\n\n\n```python\n# Python\ndef warm_start_params(m):\n \"\"\"\n Retrieve parameters from a trained model in the format used to initialize a new Stan model.\n Note that the new Stan model must have these same settings:\n n_changepoints, seasonality features, mcmc sampling\n for the retrieved parameters to be valid for the new model.\n\n Parameters\n ----------\n m: A trained model of the Prophet class.\n\n Returns\n -------\n A Dictionary containing retrieved parameters of m.\n \"\"\"\n res = {}\n for pname in ['k', 'm', 'sigma_obs']:\n if m.mcmc_samples == 0:\n res[pname] = m.params[pname][0][0]\n else:\n res[pname] = np.mean(m.params[pname])\n for pname in ['delta', 'beta']:\n if m.mcmc_samples == 0:\n res[pname] = m.params[pname][0]\n else:\n res[pname] = np.mean(m.params[pname], axis=0)\n return res\n\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\ndf1 = df.loc[df['ds'] < '2016-01-19', :] # All data except the last day\nm1 = Prophet().fit(df1) # A model fit to all data except the last day\n\n\n%timeit m2 = Prophet().fit(df) # Adding the last day, fitting from scratch\n%timeit m2 = Prophet().fit(df, init=warm_start_params(m1)) # Adding the last day, warm-starting from m1\n```\n 1.33 s ± 55.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n 185 ms ± 4.46 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n\n\nAs can be seen, the parameters from the previous model are passed in to the fitting for the next with the kwarg `init`. In this case, model fitting was about 5x faster when using warm starting. The speedup will generally depend on how much the optimal model parameters have changed with the addition of the new data.\n\n\n\nThere are few caveats that should be kept in mind when considering warm-starting. First, warm-starting may work well for small updates to the data (like the addition of one day in the example above) but can be worse than fitting from scratch if there are large changes to the data (i.e., a lot of days have been added). This is because when a large amount of history is added, the location of the changepoints will be very different between the two models, and so the parameters from the previous model may actually produce a bad trend initialization. Second, as a detail, the number of changepoints need to be consistent from one model to the next or else an error will be raised because the changepoint prior parameter `delta` will be the wrong size.\n\n\n \n\n### minmax scaling (new in 1.1.5)\n\n\n\nBefore model fitting, Prophet scales `y` by dividing by the maximum value in the history. For datasets with very large `y` values, the scaled `y` values may be compressed to a very small range (i.e. `[0.99999... - 1.0]`), which causes a bad fit. This can be fixed by setting `scaling='minmax'` in the Prophet constructor.\n\n\n```python\n# Python\nlarge_y = pd.read_csv(\n \"https://raw.githubusercontent.com/facebook/prophet/main/python/prophet/tests/data3.csv\", \n parse_dates=[\"ds\"]\n)\n```\n```python\n# Python\nm1 = Prophet(scaling=\"absmax\")\nm1 = m1.fit(large_y)\n```\n 08:11:23 - cmdstanpy - INFO - Chain [1] start processing\n 08:11:23 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nm2 = Prophet(scaling=\"minmax\")\nm2 = m2.fit(large_y)\n```\n 08:11:29 - cmdstanpy - INFO - Chain [1] start processing\n 08:11:29 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nm1.plot(m1.predict(large_y));\n```\n\n![png](/prophet/static/additional_topics_files/additional_topics_28_0.png)\n\n\n```python\n# Python\nm2.plot(m2.predict(large_y));\n```\n\n![png](/prophet/static/additional_topics_files/additional_topics_29_0.png)\n\n\n \n\n### Inspecting transformed data (new in 1.1.5)\n\n\n\nFor debugging, it's useful to understand how the raw data has been transformed before being passed to the stan fit routine. We can call the `.preprocess()` method to see all the inputs to stan, and `.calculate_init_params()` to see how the model parameters will be initialized.\n\n\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\nm = Prophet()\ntransformed = m.preprocess(df)\n```\n```python\n# Python\ntransformed.y.head(n=10)\n```\n\n\n\n 0 0.746552\n 1 0.663171\n 2 0.637023\n 3 0.628367\n 4 0.614441\n 5 0.605884\n 6 0.654956\n 7 0.687273\n 8 0.652501\n 9 0.628148\n Name: y_scaled, dtype: float64\n\n\n\n```python\n# Python\ntransformed.X.head(n=10)\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
yearly_delim_1yearly_delim_2yearly_delim_3yearly_delim_4yearly_delim_5yearly_delim_6yearly_delim_7yearly_delim_8yearly_delim_9yearly_delim_10...yearly_delim_17yearly_delim_18yearly_delim_19yearly_delim_20weekly_delim_1weekly_delim_2weekly_delim_3weekly_delim_4weekly_delim_5weekly_delim_6
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1-0.3614780.932381-0.6740690.738668-0.8955010.445059-0.9958270.091261-0.961479-0.274879...0.185987-0.9825520.528581-0.848883-9.749279e-01-0.2225214.338837e-01-0.9009697.818315e-010.623490
2-0.3453860.938461-0.6482620.761418-0.8713510.490660-0.9871960.159513-0.981538-0.191266...0.032249-0.9994800.375470-0.926834-7.818315e-010.623490-9.749279e-01-0.222521-4.338837e-01-0.900969
3-0.3291920.944263-0.6216870.783266-0.8448810.534955-0.9738920.227011-0.994341-0.106239...-0.122261-0.9924980.211276-0.9774265.505235e-141.0000001.101047e-131.0000001.984146e-121.000000
4-0.3129000.949786-0.5943760.804187-0.8161600.577825-0.9559790.293434-0.999791-0.020426...-0.273845-0.9617740.040844-0.9991667.818315e-010.6234909.749279e-01-0.2225214.338837e-01-0.900969
5-0.2965160.955028-0.5663620.824157-0.7852670.619157-0.9335420.358468-0.9978500.065537...-0.418879-0.908042-0.130793-0.9914109.749279e-01-0.222521-4.338837e-01-0.900969-7.818315e-010.623490
6-0.2800440.959987-0.5376770.843151-0.7522830.658840-0.9066860.421806-0.9885310.151016...-0.553893-0.832588-0.298569-0.9543884.338837e-01-0.900969-7.818315e-010.6234909.749279e-01-0.222521
7-0.2634890.964662-0.5083560.861147-0.7172950.696769-0.8755390.483147-0.9719040.235379...-0.675656-0.737217-0.457531-0.889193-4.338837e-01-0.9009697.818315e-010.623490-9.749279e-01-0.222521
8-0.2468570.969052-0.4784340.878124-0.6803980.732843-0.8402480.542202-0.9480900.318001...-0.781257-0.624210-0.602988-0.797750-9.749279e-01-0.2225214.338837e-01-0.9009697.818315e-010.623490
9-0.2301510.973155-0.4479450.894061-0.6416890.766965-0.8009800.598691-0.9172670.398272...-0.868168-0.496271-0.730644-0.682758-7.818315e-010.623490-9.749279e-01-0.222521-4.338837e-01-0.900969
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\n\n\n\n```python\n# Python\nm.calculate_initial_params(num_total_regressors=transformed.K)\n```\n\n\n\n ModelParams(k=-0.05444079622224118, m=0.7465517318876905, delta=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 0., 0., 0.]), beta=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0., 0., 0., 0., 0., 0., 0.]), sigma_obs=1.0)\n\n\n\n \n\n### External references\n\n\n\nAs we discuss in our [2023 blog post on the state of Prophet](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138), we have no plans to further develop the underlying Prophet model. If you're looking for state-of-the-art forecasting accuracy, we recommend the following libraries:\n\n\n\n* [`statsforecast`](https://github.com/Nixtla/statsforecast), and other packages from the Nixtla group such as [`hierarchicalforecast`](https://github.com/Nixtla/hierarchicalforecast) and [`neuralforecast`](https://github.com/Nixtla/neuralforecast).\n\n* [`NeuralProphet`](https://neuralprophet.com/), a Prophet-style model implemented in PyTorch, to be more adaptable and extensible.\n\n\n\nThese github repositories provide examples of building on top of Prophet in ways that may be of broad interest:\n\n\n\n* [forecastr](https://github.com/garethcull/forecastr): A web app that provides a UI for Prophet.\n\n" }, { "path": "docs/_docs/contributing.md", "content": "---\nlayout: docs\ndocid: \"contributing\"\ntitle: \"Getting Help and Contributing\"\npermalink: /docs/contributing.html\n---\n\n-----\n\n**2023 Update:** We discuss our plans for the future of Prophet in this blog post: [facebook/prophet in 2023 and beyond](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138)\n\n-----\nProphet has a non-fixed release cycle but we will be making bugfixes in response to user feedback and adding features. Please let us know if you encounter a bug by [filing an issue](https://github.com/facebook/prophet/issues). Github issues is also the right place to ask questions about using Prophet.\n\nWe appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.\n\nIf you plan to contribute new features or extensions to the core, please first open an issue and discuss the feature with us. Sending a pull request is fine too, but it will likely be merged more quickly if any design decisions are settled on beforehand in an issue.\n\nWe try to keep the R and Python versions feature identical, but new features can be implemented for each method in separate commits.\n\nThe following sections will describe how you can submit a pull request for adding enhancements, documentation changes or bug fixes to the codebase.\n\n## 1. Forking the Prophet Repo\n\nYou will need your own fork to work on the code. Go to the [prophet project\npage](https://github.com/facebook/prophet) and hit the ``Fork`` button. You will\nwant to clone your fork to your machine:\n\n```\n$ git clone https://github.com/your-user-name/prophet.git\n$ cd prophet\n$ git remote add upstream https://github.com/facebook/prophet.git\n```\nThis creates the directory `prophet` and connects your repository to\nthe upstream (main project) prophet repository.\n\n## 2. Creating an environment with dependencies\n\nBefore starting any development, you'll need to create an isolated prophet\ndevelopment environment. This should contain the required dependencies.\n\n### Python\n\n- Install either Anaconda [anaconda](https://www.anaconda.com/download/) or [miniconda](https://conda.io/miniconda.html)\n- Make sure your conda is up to date (``conda update conda``)\n- ``cd`` to the *prophet* source directory that you have cloned\n\n```bash\n$ cd python\n\n# with Anaconda\n$ conda create -n prophet\n$ conda activate prophet\n\n# with venv\n$ python3 -m venv prophet\n$ source prophet/bin/activate\n```\n\n### R\n\nDependencies can be managed through [``Packrat``](https://rstudio.github.io/packrat/) or [``renv``](https://rstudio.github.io/renv/articles/renv.html).\n\nFor ``renv``, you must first initialise a new project local environment.\n```R\n> setwd(\"path/to/prophet/R\") # set R subdirectory as working directory\n> install.packages('renv')\n> renv::init()\n```\n\nThis should also install the dependencies listed in the DESCRIPTION automatically. Any new R packages can be installed as they are needed in the project.\n\nYou can save the state of the project:\n\n```R\n> renv::snapshot()\n```\n\nor load the environment:\n\n```R\n> renv::restore()\n```\n\n## 3. Building a development version of Prophet\n\nThe next step is to build and install the development version of prophet in the environment you have just created.\n\n### Python\n\n```bash\n$ python -m pip install -e \".[dev,parallel]\"\n```\n\nYou should be able to import *prophet* from your locally built version:\n\n```bash\n$ python # start an interpreter\n>>> import prophet\n>>> prophet.__version__\n'1.1.2' # whatever the current github version is\n```\n\nThis will create the new environment, and not touch any of your existing environments,\nnor any existing Python installation.\n\n```bash\n# to view your environments:\n$ conda info -e\n\n# to return to your root environment::\n$ conda deactivate\n```\n\nSee the full conda docs [here](http://conda.pydata.org/docs).\n\n### R\n\nFrom the terminal, ``cd`` to ``R`` subdirectory and run:\n```bash\n$ R CMD INSTALL .\n```\n\nThis will build and install the local version of the prophet package. Then from the R console you can load the package: ``library(prophet)``.\n\n## 4. Creating a branch\n\nYou want your main branch to reflect only production-ready code, so create a\nfeature branch for making your changes. For example:\n\n```bash\n$ git checkout -b new-feature\n```\n\nThis changes your working directory to the new-feature branch. Keep any\nchanges in this branch specific to one bug or feature so it is clear\nwhat the branch brings to *prophet*. You can have many \"new-features\"\nand switch in between them using the ``git checkout`` command.\n\nTo update this branch, you need to retrieve the changes from the main branch:\n\n```bash\n$ git fetch upstream\n$ git rebase upstream/main\n```\n\nThis will replay your commits on top of the latest *prophet* git `main`. If this\nleads to merge conflicts, you must resolve these before submitting your pull\nrequest. If you have uncommitted changes, you will need to ``git stash`` them\nprior to updating. This will effectively store your changes and they can be\nreapplied after updating.\n\n\n## 5. Testing with Continuous Integration\n\nAdding tests is one of the most common requests after code is pushed to prophet. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue. Once your pull request is submitted, the Github Actions CI (continuous integration) service automatically triggers Python and R builds for Prophet and runs the tests. A pull-request will be considered for merging when you have an all ‘green’ build. If any tests are failing, then you will get a red ‘X’, where you can click through to see the individual failed tests.\n\n### Python\n\nProphet uses the ``pytest`` package for running tests in Python and ``testthat`` package for testing in R. All tests should go into the tests subdirectory in either the Python or R folders.\n\nThe entire test suite can be run by typing:\n```bash\n$ python -m pytest prophet/tests/\n```\n\n### R\n\n\nThe entire test suite can be run from the R console by installing ``devtools``:\n\n```R\n> install.packages('devtools')\n> devtools::test()\n```\n\nAlternatively the test suite can be also run from the terminal after ``cd`` to the ``tests`` directory\n```bash\n$ Rscript testthat.R\n```\n\nor for just running a single test script like ``test_diagnostics.R`` from the R console:\n\n```R\n> library(testthat)\n> source('test_diagnostics.R')\n```\n\n## 6. Generating documentation\n\nMost of the `doc` pages are generated from [Jupyter notebooks](http://jupyter.org/) in the [notebooks](https://github.com/facebook/prophet/tree/main/notebooks) directory at the base of the source tree. Please make changes there and then rebuild the docs:\n\n```bash\n$ cd docs\n$ make notebooks\n```\n\nMake sure you have installed [rpy2](https://rpy2.bitbucket.io/) so that the R code can be run as well.\n\nIn R, the documentation for the source code must also generated if new parameters are added or a new function is created. This is documented with ``roxygen``.\n\nRun the command below before submitting a PR with any changes to the R code to update the function documentation:\n\n```R\n> devtools::document()\n```\n\n## 7. Committing your code\n\nKeep style fixes to a separate commit to make your pull request more readable. Once you’ve made changes, you can see them by typing:\n\n```bash\n$ git status\n```\n\nIf you have created a new file, it is not being tracked by git. Add it by typing:\n\n```bash\n$ git add path/to/file-to-be-added.py\n```\n\nDoing ‘git status’ again should give something like:\n\n```bash\n# On branch new-feature\n#\n# modified: /relative/path/to/file-you-added.py\n#\n```\n\nNow you can commit your changes in your local repository:\n\n```bash\n$ git commit -m\n```\n\n## 8. Pushing your changes\n\nWhen you want your changes to appear publicly on your GitHub page, push your forked feature branch’s commits:\n\n```bash\n$ git push origin new-feature\n```\n\nHere origin is the default name given to your remote repository on GitHub. You can see the remote repositories:\n\n```bash\n$ git remote -v\n```\n\nIf you added the upstream repository as described above you will see something like:\n\n```bash\norigin git@github.com:yourname/prophet.git (fetch)\norigin git@github.com:yourname/prophet.git (push)\nupstream\thttps://github.com/facebook/prophet.git (fetch)\nupstream\thttps://github.com/facebook/prophet.git (push)\n```\n\nNow your code is on GitHub, but it is not yet a part of the prophet project. For that to happen, a pull request needs to be submitted on GitHub.\n\n## 9. Review your code\n\nWhen you’re ready to ask for a code review, file a pull request. Before you do, once again make sure that you have followed all the guidelines outlined in this document regarding code style, tests, performance tests, and documentation. You should also double check your branch changes against the branch it was based on:\n\n1. Navigate to your repository on GitHub – https://github.com/your-user-name/prophet\n2. Click on Branches\n3. Click on the Compare button for your feature branch\n4. Select the base and compare branches, if necessary. This will be `main` and new-feature, respectively.\n\n\n## 10. Making a pull request\n\nIf everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the GitHub community and can be reviewed and eventually merged into the `main` version. This pull request and its associated changes will eventually be committed to the `main` branch and available in the next release. To submit a pull request:\n\n1. Navigate to your repository on GitHub\n2. Click on the Pull Request button\n3. You can then click on Commits and Files Changed to make sure everything looks okay one last time\n4. Write a description of your changes in the Preview Discussion tab\n5. Click Send Pull Request.\n\nThis request then goes to the repository maintainers, and they will review the code. If you need to make more changes, you can make them in your branch, add them to a new commit, push them to GitHub, and the pull request will be automatically updated. Pushing them to GitHub again is done by:\n\n```bash\n$ git push origin new-feature\n```\n\nThis will automatically update your pull request with the latest code and restart the Continuous Integration tests.\n\n\n## 11. Delete your merged branch\n\nOnce your feature branch is merged into ``upstream main``, you can delete your remote branch via the ``Delete branch`` option in the PR and the local copy by running:\n\n```bash\n$ git branch -d new-feature\n```\n\n## 12. PR checklist\n\n* Write docstrings for any functions that are included.\n* Test that the documentation builds correctly. See “Generating documentation”.\n* Test your code.\n - Write new tests if needed. See \"Testing with Continuous Integration\"\n - Test the code using unittest. Running all tests takes a while, so feel free to only run the tests you think are needed based on your PR. CI will catch any failing tests.\n* In R, you can also run ``devtools:check()`` for carrying out a number of automated checks all at once, for code problems, documentation, testing, package structure, vignettes etc. This will take a few minutes to run.\n* Once you push your changes and make a PR, make sure you use an informative title which summarizes the changes you have made.\n* If the PR addresses an issue, please reference it e.g. fixes #1234\n" }, { "path": "docs/_docs/diagnostics.md", "content": "---\nlayout: docs\ndocid: \"diagnostics\"\ntitle: \"Diagnostics\"\npermalink: /docs/diagnostics.html\nsubsections:\n - title: Cross validation\n id: cross-validation\n - title: Parallelizing cross validation\n id: parallelizing-cross-validation\n - title: Hyperparameter tuning\n id: hyperparameter-tuning\n---\n \n\n### Cross validation\n\n\n\nProphet includes functionality for time series cross validation to measure forecast error using historical data. This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point. We can then compare the forecasted values to the actual values. This figure illustrates a simulated historical forecast on the Peyton Manning dataset, where the model was fit to an initial history of 5 years, and a forecast was made on a one year horizon.\n\n\n\n![png](/prophet/static/diagnostics_files/diagnostics_4_0.png)\n\n\n[The Prophet paper](https://peerj.com/preprints/3190.pdf) gives further description of simulated historical forecasts.\n\n\n\nThis cross validation procedure can be done automatically for a range of historical cutoffs using the `cross_validation` function. We specify the forecast horizon (`horizon`), and then optionally the size of the initial training period (`initial`) and the spacing between cutoff dates (`period`). By default, the initial training period is set to three times the horizon, and cutoffs are made every half a horizon.\n\n\n\nThe output of `cross_validation` is a dataframe with the true values `y` and the out-of-sample forecast values `yhat`, at each simulated forecast date and for each cutoff date. In particular, a forecast is made for every observed point between `cutoff` and `cutoff + horizon`. This dataframe can then be used to compute error measures of `yhat` vs. `y`.\n\n\n\nHere we do cross-validation to assess prediction performance on a horizon of 365 days, starting with 730 days of training data in the first cutoff and then making predictions every 180 days. On this 8 year time series, this corresponds to 11 total forecasts.\n\n\n```R\n# R\ndf.cv <- cross_validation(m, initial = 730, period = 180, horizon = 365, units = 'days')\nhead(df.cv)\n```\n```python\n# Python\nfrom prophet.diagnostics import cross_validation\ndf_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')\n```\n```python\n# Python\ndf_cv.head()\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dsyhatyhat_loweryhat_upperycutoff
02010-02-168.9545828.4628769.4523058.2424932010-02-15
12010-02-178.7209328.2226829.2427888.0080332010-02-15
22010-02-188.6046088.0669209.1449688.0452682010-02-15
32010-02-198.5263798.0291899.0430457.9287662010-02-15
42010-02-208.2682477.7495208.7418477.7450032010-02-15
\n
\n\n\n\nIn R, the argument `units` must be a type accepted by `as.difftime`, which is weeks or shorter. In Python, the string for `initial`, `period`, and `horizon` should be in the format used by Pandas Timedelta, which accepts units of days or shorter.\n\n\n\nCustom cutoffs can also be supplied as a list of dates to the `cutoffs` keyword in the `cross_validation` function in Python and R. For example, three cutoffs six months apart, would need to be passed to the `cutoffs` argument in a date format like:\n\n\n```R\n# R\ncutoffs <- as.Date(c('2013-02-15', '2013-08-15', '2014-02-15'))\ndf.cv2 <- cross_validation(m, cutoffs = cutoffs, horizon = 365, units = 'days')\n```\n```python\n# Python\ncutoffs = pd.to_datetime(['2013-02-15', '2013-08-15', '2014-02-15'])\ndf_cv2 = cross_validation(m, cutoffs=cutoffs, horizon='365 days')\n```\nThe `performance_metrics` utility can be used to compute some useful statistics of the prediction performance (`yhat`, `yhat_lower`, and `yhat_upper` compared to `y`), as a function of the distance from the cutoff (how far into the future the prediction was). The statistics computed are mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), median absolute percent error (MDAPE) and coverage of the `yhat_lower` and `yhat_upper` estimates. These are computed on a rolling window of the predictions in `df_cv` after sorting by horizon (`ds` minus `cutoff`). By default 10% of the predictions will be included in each window, but this can be changed with the `rolling_window` argument.\n\n\n\nIn Python, you can also create custom performance metric using the `register_performance_metric` decorator. Created metric should contain following arguments:\n\n - df: Cross-validation results dataframe.\n\n - w: Aggregation window size.\n\n \n\nand return:\n\n- Dataframe with columns horizon and metric.\n\n\n```R\n# R\ndf.p <- performance_metrics(df.cv)\nhead(df.p)\n```\n```python\n# Python\nfrom prophet.diagnostics import performance_metrics\ndf_p = performance_metrics(df_cv)\ndf_p.head()\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
horizonmsermsemaemapemdapesmapecoverage
037 days0.4933580.7023950.5039770.0583760.0493650.0586770.676565
138 days0.4991120.7064780.5089460.0589510.0491350.0593120.675423
239 days0.5213440.7220420.5150160.0595470.0492250.0600340.672682
340 days0.5286510.7270840.5178730.0598520.0490720.0604090.676336
441 days0.5361490.7322220.5188430.0599270.0491350.0605480.681361
\n
\n\n\n\n```python\n# Python\nfrom prophet.diagnostics import register_performance_metric, rolling_mean_by_h\nimport numpy as np\n@register_performance_metric\ndef mase(df, w):\n \"\"\"Mean absolute scale error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and mase.\n \"\"\"\n e = (df['y'] - df['yhat'])\n d = np.abs(np.diff(df['y'])).sum()/(df['y'].shape[0]-1)\n se = np.abs(e/d)\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'mase': se})\n return rolling_mean_by_h(\n x=se.values, h=df['horizon'].values, w=w, name='mase'\n )\n\ndf_mase = performance_metrics(df_cv, metrics=['mase'])\ndf_mase.head()\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
horizonmase
037 days0.522946
138 days0.528102
239 days0.534401
340 days0.537365
441 days0.538372
\n
\n\n\n\nCross validation performance metrics can be visualized with `plot_cross_validation_metric`, here shown for MAPE. Dots show the absolute percent error for each prediction in `df_cv`. The blue line shows the MAPE, where the mean is taken over a rolling window of the dots. We see for this forecast that errors around 5% are typical for predictions one month into the future, and that errors increase up to around 11% for predictions that are a year out.\n\n\n```R\n# R\nplot_cross_validation_metric(df.cv, metric = 'mape')\n```\n```python\n# Python\nfrom prophet.plot import plot_cross_validation_metric\nfig = plot_cross_validation_metric(df_cv, metric='mape')\n```\n\n![png](/prophet/static/diagnostics_files/diagnostics_18_0.png)\n\n\nThe size of the rolling window in the figure can be changed with the optional argument `rolling_window`, which specifies the proportion of forecasts to use in each rolling window. The default is 0.1, corresponding to 10% of rows from `df_cv` included in each window; increasing this will lead to a smoother average curve in the figure. The `initial` period should be long enough to capture all of the components of the model, in particular seasonalities and extra regressors: at least a year for yearly seasonality, at least a week for weekly seasonality, etc.\n\n\n\n\n \n\n### Parallelizing cross validation\n\n\n\nCross-validation can also be run in parallel mode in Python, by setting specifying the `parallel` keyword. Four modes are supported\n\n\n\n* `parallel=None` (Default, no parallelization)\n\n* `parallel=\"processes\"`\n\n* `parallel=\"threads\"`\n\n* `parallel=\"dask\"`\n\n\n\nFor problems that aren't too big, we recommend using `parallel=\"processes\"`. It will achieve the highest performance when the parallel cross validation can be done on a single machine. For large problems, a [Dask](https://dask.org) cluster can be used to do the cross validation on many machines. You will need to [install Dask](https://docs.dask.org/en/latest/install.html) separately, as it will not be installed with `prophet`.\n\n\n\n\n\n```python\n\nfrom dask.distributed import Client\n\n\n\nclient = Client() # connect to the cluster\n\ndf_cv = cross_validation(m, initial='730 days', period='180 days', horizon='365 days',\n\n parallel=\"dask\")\n\n```\n\n\n \n\n### Hyperparameter tuning\n\n\n\nCross-validation can be used for tuning hyperparameters of the model, such as `changepoint_prior_scale` and `seasonality_prior_scale`. A Python example is given below, with a 4x4 grid of those two parameters, with parallelization over cutoffs. Here parameters are evaluated on RMSE averaged over a 30-day horizon, but different performance metrics may be appropriate for different problems.\n\n\n```python\n# Python\nimport itertools\nimport numpy as np\nimport pandas as pd\n\nparam_grid = { \n 'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],\n 'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],\n}\n\n# Generate all combinations of parameters\nall_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]\nrmses = [] # Store the RMSEs for each params here\n\n# Use cross validation to evaluate all parameters\nfor params in all_params:\n m = Prophet(**params).fit(df) # Fit model with given params\n df_cv = cross_validation(m, cutoffs=cutoffs, horizon='30 days', parallel=\"processes\")\n df_p = performance_metrics(df_cv, rolling_window=1)\n rmses.append(df_p['rmse'].values[0])\n\n# Find the best parameters\ntuning_results = pd.DataFrame(all_params)\ntuning_results['rmse'] = rmses\nprint(tuning_results)\n```\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ​\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
changepoint_prior_scaleseasonality_prior_scalermse
00.0010.010.757694
10.0010.100.743399
20.0011.000.753387
30.00110.000.762890
40.0100.010.542315
50.0100.100.535546
60.0101.000.527008
70.01010.000.541544
80.1000.010.524835
90.1000.100.516061
100.1001.000.521406
110.10010.000.518580
120.5000.010.532140
130.5000.100.524668
140.5001.000.521130
150.50010.000.522980
\n\n```python\n# Python\nbest_params = all_params[np.argmin(rmses)]\nprint(best_params)\n```\n\n {'changepoint_prior_scale': 0.1, 'seasonality_prior_scale': 0.1}\n\nAlternatively, parallelization could be done across parameter combinations by parallelizing the loop above.\n\n\n\nThe Prophet model has a number of input parameters that one might consider tuning. Here are some general recommendations for hyperparameter tuning that may be a good starting place.\n\n\n\n**Parameters that can be tuned**\n\n- `changepoint_prior_scale`: This is probably the most impactful parameter. It determines the flexibility of the trend, and in particular how much the trend changes at the trend changepoints. As described in this documentation, if it is too small, the trend will be underfit and variance that should have been modeled with trend changes will instead end up being handled with the noise term. If it is too large, the trend will overfit and in the most extreme case you can end up with the trend capturing yearly seasonality. The default of 0.05 works for many time series, but this could be tuned; a range of [0.001, 0.5] would likely be about right. Parameters like this (regularization penalties; this is effectively a lasso penalty) are often tuned on a log scale.\n\n\n\n- `seasonality_prior_scale`: This parameter controls the flexibility of the seasonality. Similarly, a large value allows the seasonality to fit large fluctuations, a small value shrinks the magnitude of the seasonality. The default is 10., which applies basically no regularization. That is because we very rarely see overfitting here (there's inherent regularization with the fact that it is being modeled with a truncated Fourier series, so it's essentially low-pass filtered). A reasonable range for tuning it would probably be [0.01, 10]; when set to 0.01 you should find that the magnitude of seasonality is forced to be very small. This likely also makes sense on a log scale, since it is effectively an L2 penalty like in ridge regression.\n\n\n\n- `holidays_prior_scale`: This controls flexibility to fit holiday effects. Similar to seasonality_prior_scale, it defaults to 10.0 which applies basically no regularization, since we usually have multiple observations of holidays and can do a good job of estimating their effects. This could also be tuned on a range of [0.01, 10] as with seasonality_prior_scale.\n\n\n\n- `seasonality_mode`: Options are [`'additive'`, `'multiplicative'`]. Default is `'additive'`, but many business time series will have multiplicative seasonality. This is best identified just from looking at the time series and seeing if the magnitude of seasonal fluctuations grows with the magnitude of the time series (see the documentation here on multiplicative seasonality), but when that isn't possible, it could be tuned.\n\n\n\n**Maybe tune?**\n\n- `changepoint_range`: This is the proportion of the history in which the trend is allowed to change. This defaults to 0.8, 80% of the history, meaning the model will not fit any trend changes in the last 20% of the time series. This is fairly conservative, to avoid overfitting to trend changes at the very end of the time series where there isn't enough runway left to fit it well. With a human in the loop, this is something that can be identified pretty easily visually: one can pretty clearly see if the forecast is doing a bad job in the last 20%. In a fully-automated setting, it may be beneficial to be less conservative. It likely will not be possible to tune this parameter effectively with cross validation over cutoffs as described above. The ability of the model to generalize from a trend change in the last 10% of the time series will be hard to learn from looking at earlier cutoffs that may not have trend changes in the last 10%. So, this parameter is probably better not tuned, except perhaps over a large number of time series. In that setting, [0.8, 0.95] may be a reasonable range.\n\n\n\n**Parameters that would likely not be tuned**\n\n- `growth`: Options are 'linear' and 'logistic'. This likely will not be tuned; if there is a known saturating point and growth towards that point it will be included and the logistic trend will be used, otherwise it will be linear.\n\n\n\n- `changepoints`: This is for manually specifying the locations of changepoints. None by default, which automatically places them.\n\n\n\n- `n_changepoints`: This is the number of automatically placed changepoints. The default of 25 should be plenty to capture the trend changes in a typical time series (at least the type that Prophet would work well on anyway). Rather than increasing or decreasing the number of changepoints, it will likely be more effective to focus on increasing or decreasing the flexibility at those trend changes, which is done with `changepoint_prior_scale`.\n\n\n\n- `yearly_seasonality`: By default ('auto') this will turn yearly seasonality on if there is a year of data, and off otherwise. Options are ['auto', True, False]. If there is more than a year of data, rather than trying to turn this off during HPO, it will likely be more effective to leave it on and turn down seasonal effects by tuning `seasonality_prior_scale`.\n\n\n\n- `weekly_seasonality`: Same as for `yearly_seasonality`.\n\n\n\n- `daily_seasonality`: Same as for `yearly_seasonality`.\n\n\n\n- `holidays`: This is to pass in a dataframe of specified holidays. The holiday effects would be tuned with `holidays_prior_scale`.\n\n\n\n- `mcmc_samples`: Whether or not MCMC is used will likely be determined by factors like the length of the time series and the importance of parameter uncertainty (these considerations are described in the documentation).\n\n\n\n- `interval_width`: Prophet `predict` returns uncertainty intervals for each component, like `yhat_lower` and `yhat_upper` for the forecast `yhat`. These are computed as quantiles of the posterior predictive distribution, and `interval_width` specifies which quantiles to use. The default of 0.8 provides an 80% prediction interval. You could change that to 0.95 if you wanted a 95% interval. This will affect only the uncertainty interval, and will not change the forecast `yhat` at all and so does not need to be tuned.\n\n\n\n- `uncertainty_samples`: The uncertainty intervals are computed as quantiles from the posterior predictive interval, and the posterior predictive interval is estimated with Monte Carlo sampling. This parameter is the number of samples to use (defaults to 1000). The running time for predict will be linear in this number. Making it smaller will increase the variance (Monte Carlo error) of the uncertainty interval, and making it larger will reduce that variance. So, if the uncertainty estimates seem jagged this could be increased to further smooth them out, but it likely will not need to be changed. As with `interval_width`, this parameter only affects the uncertainty intervals and changing it will not affect in any way the forecast `yhat`; it does not need to be tuned.\n\n" }, { "path": "docs/_docs/handling_shocks.md", "content": "---\nlayout: docs\ndocid: \"handling_shocks\"\ntitle: \"Handling Shocks\"\npermalink: /docs/handling_shocks.html\nsubsections:\n - title: Case Study - Pedestrian Activity\n id: case-study---pedestrian-activity\n - title: Default model without any adjustments\n id: default-model-without-any-adjustments\n - title: Treating COVID-19 lockdowns as a one-off holidays\n id: treating-covid-19-lockdowns-as-a-one-off-holidays\n - title: Sense checking the trend\n id: sense-checking-the-trend\n - title: Changes in seasonality between pre- and post-COVID\n id: changes-in-seasonality-between-pre--and-post-covid\n - title: Further reading\n id: further-reading\n---\n```python\n# Python\n%matplotlib inline\nfrom prophet import Prophet\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport logging\nlogging.getLogger('prophet').setLevel(logging.ERROR)\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nplt.rcParams['figure.figsize'] = 9, 6\n```\nAs a result of the lockdowns caused by the COVID-19 pandemic, many time series experienced \"shocks\" during 2020, e.g. spikes in media consumption (Netflix, YouTube), e-commerce transactions (Amazon, eBay), whilst attendance to in-person events declined dramatically.\n\n\n\nMost of these time series would also maintain their new level for a period of time, subject to fluctuations driven by easing of lockdowns and/or vaccines.\n\n\n\nSeasonal patterns could have also changed: for example, people may have consumed less media (in total hours) on weekdays compared to weekends before the COVID lockdowns, but during lockdowns weekday consumption could be much closer to weekend consumption.\n\n\n\nIn this page we'll explore some strategies for capturing these effects using Prophet's functionality:\n\n\n\n1. Marking step changes / spikes due to COVID events as once-off.\n\n2. Sustained changes in behaviour leading to trend and seasonality changes.\n\n\n \n\n#### Case Study - Pedestrian Activity\n\n\n\nFor this case study we'll use [Pedestrian Sensor data from the City of Melbourne](https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Monthly-counts-per-hour/b2ak-trbp). This data measures foot traffic from sensors in various places in the central business district, and we've chosen one sensor (`Sensor_ID = 4`) and aggregated the values to a daily grain. \n\n\n\nThe aggregated dataset can be found in the examples folder [here](https://github.com/facebook/prophet/tree/master/examples/example_pedestrians_covid.csv).\n\n\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_pedestrians_covid.csv')\n```\n```python\n# Python\ndf.set_index('ds').plot();\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_4_0.png)\n\n\nWe can see two key events in the time series:\n\n\n\n* The initial drop in foot traffic around 21 March 2020, which started to recover around 6 June 2020. This corresponds to the declaration of a pandemic by WHO and subsequent lockdowns mandated by the Victorian government.\n\n* After some slow recovery, a second drop in foot traffic around July 9 2020, which began to recover around 27 October 2020. This corresponds to the \"second wave\" of the pandemic in metropolitan Melbourne.\n\n\n\nThere are also shorter periods of strict lockdown that lead to sudden tips in the time series: 5 days in February 2021, and 14 days in early June 2021.\n\n\n \n\n#### Default model without any adjustments\n\n\n\nFirst we'll fit a model with the default Prophet settings:\n\n\n```python\n# Python\nm = Prophet()\nm = m.fit(df)\nfuture = m.make_future_dataframe(periods=366)\nforecast = m.predict(future)\n```\n 02:53:41 - cmdstanpy - INFO - Chain [1] start processing\n 02:53:41 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nm.plot(forecast)\nplt.axhline(y=0, color='red')\nplt.title('Default Prophet');\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_8_0.png)\n\n\n```python\n# Python\nm.plot_components(forecast);\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_9_0.png)\n\n\nThe model seems to fit reasonably to past data, but notice how we're capturing the dips, and the spikes after the dips, as a part of the trend component. \n\n\n\nBy default, the model assumes that these large spikes are possible in the future, even though we realistically won't see something of the same magnitude within our forecast horizon (1 year in this case). This leads to a fairly optimistic forecast of the recovery of foot traffic in 2022.\n\n\n \n\n### Treating COVID-19 lockdowns as a one-off holidays\n\n\n\nTo prevent large dips and spikes from being captured by the trend component, we can treat the days impacted by COVID-19 as holidays that will not repeat again in the future. Adding custom holidays is explained in more detail [here](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#modeling-holidays-and-special-events). We set up a DataFrame like so to describe the periods affected by lockdowns:\n\n\n```python\n# Python\nlockdowns = pd.DataFrame([\n {'holiday': 'lockdown_1', 'ds': '2020-03-21', 'lower_window': 0, 'ds_upper': '2020-06-06'},\n {'holiday': 'lockdown_2', 'ds': '2020-07-09', 'lower_window': 0, 'ds_upper': '2020-10-27'},\n {'holiday': 'lockdown_3', 'ds': '2021-02-13', 'lower_window': 0, 'ds_upper': '2021-02-17'},\n {'holiday': 'lockdown_4', 'ds': '2021-05-28', 'lower_window': 0, 'ds_upper': '2021-06-10'},\n])\nfor t_col in ['ds', 'ds_upper']:\n lockdowns[t_col] = pd.to_datetime(lockdowns[t_col])\nlockdowns['upper_window'] = (lockdowns['ds_upper'] - lockdowns['ds']).dt.days\nlockdowns\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
holidaydslower_windowds_upperupper_window
0lockdown_12020-03-2102020-06-0677
1lockdown_22020-07-0902020-10-27110
2lockdown_32021-02-1302021-02-174
3lockdown_42021-05-2802021-06-1013
\n
\n\n\n\n* We have an entry for each lockdown period, with `ds` specifying the start of the lockdown. `ds_upper` is not used by Prophet, but it's a convenient way for us to calculate `upper_window`.\n\n* `upper_window` tells Prophet that the lockdown spans for x days after the start of the lockdown. Note that the holidays regression is inclusive of the upper bound.\n\n\n\nNote that since we don't specify any future dates, Prophet will assume that these holidays will _not_ occur again when creating the future dataframe (and hence they won't affect our projections). This is different to how we would specify a recurring holiday.\n\n\n```python\n# Python\nm2 = Prophet(holidays=lockdowns)\nm2 = m2.fit(df)\nfuture2 = m2.make_future_dataframe(periods=366)\nforecast2 = m2.predict(future2)\n```\n 02:53:44 - cmdstanpy - INFO - Chain [1] start processing\n 02:53:45 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nm2.plot(forecast2)\nplt.axhline(y=0, color='red')\nplt.title('Lockdowns as one-off holidays');\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_15_0.png)\n\n\n```python\n# Python\nm2.plot_components(forecast2);\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_16_0.png)\n\n\n* Prophet is sensibly assigning a large negative effect to the days within the lockdown periods.\n\n* The forecast for the trend is not as strong / optimistic and seems fairly reasonable.\n\n\n\n \n\n### Sense checking the trend\n\n\n\nIn an environment when behaviours are constantly changing, it's important to ensure that the trend component of the model is capturing to emerging patterns without overfitting to them.\n\n\n\nThe [trend changepoints](https://facebook.github.io/prophet/docs/trend_changepoints.html) documentation explains two things we could tweak with the trend component:\n\n\n\n* The changepoint locations, which by default are evenly spaced across 80% of the history. We should be mindful of where this range ends, and extend the range (either by increasing the % or adding changepoints manually) if we believe the most recent data better reflects future behaviour.\n\n* The strength of regularisation (`changepoint_prior_scale`), which determines how flexible the trend is; the default value is `0.05` and increasing this will allow the trend to fit more closely to the observed data.\n\n\n\nWe plot the trend component and changepoints detected by our current model below.\n\n\n```python\n# Python\nfrom prophet.plot import add_changepoints_to_plot\nfig = m2.plot(forecast2)\na = add_changepoints_to_plot(fig.gca(), m2, forecast2)\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_18_0.png)\n\n\nThe detected changepoints look reasonable, and the future trend tracks the latest upwards trend in activity, but not to the extent of late 2020. This seems suitable for a best guess of future activity. \n\n\n\nWe can see what the forecast would look like if we wanted to emphasize COVID patterns more in model training; we can do this by adding more potential changepoints after 2020 and making the trend more flexible.\n\n\n```python\n# Python\nm3_changepoints = (\n # 10 potential changepoints in 2.5 years\n pd.date_range('2017-06-02', '2020-01-01', periods=10).date.tolist() + \n # 15 potential changepoints in 1 year 2 months\n pd.date_range('2020-02-01', '2021-04-01', periods=15).date.tolist()\n)\n```\n```python\n# Python\n# Default changepoint_prior_scale is 0.05, so 1.0 will lead to much more flexibility in comparison.\nm3 = Prophet(holidays=lockdowns, changepoints=m3_changepoints, changepoint_prior_scale=1.0)\nm3 = m3.fit(df)\nforecast3 = m3.predict(future2)\n```\n 02:53:49 - cmdstanpy - INFO - Chain [1] start processing\n 02:53:52 - cmdstanpy - INFO - Chain [1] done processing\n\n\n```python\n# Python\nfrom prophet.plot import add_changepoints_to_plot\nfig = m3.plot(forecast3)\na = add_changepoints_to_plot(fig.gca(), m3, forecast3)\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_22_0.png)\n\n\nWe're seeing many changepoints detected post-COVID, matching the various fluctuations from loosening / tightening lockdowns. Overall the trend curve and forecasted trend are quite similar to our previous model, but we're seeing a lot more uncertainty because of the higher number of trend changes we picked up in the history.\n\n\n\nWe probably wouldn't pick this model over the model with default parameters as a best estimate, but it's a good demonstration of how we can incorporate our beliefs into the model about which patterns are important to capture.\n\n\n \n\n### Changes in seasonality between pre- and post-COVID\n\n\n\nThe seasonal component plots in the previous sections show a peak of activity on Friday compared to other days of the week. If we're not sure whether this will still hold post-lockdown, we can add _conditional seasonalities_ to the model. Conditional seasonalities are explained in more detail [here](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#seasonalities-that-depend-on-other-factors).\n\n\n\nFirst we define boolean columns in the history dataframe to flag \"pre covid\" and \"post covid\" periods:\n\n\n```python\n# Python\ndf2 = df.copy()\ndf2['pre_covid'] = pd.to_datetime(df2['ds']) < pd.to_datetime('2020-03-21')\ndf2['post_covid'] = ~df2['pre_covid']\n```\nThe conditional seasonality we're interested in modelling here is the day-of-week (\"weekly\") seasonality. To do this, we firstly turn off the default `weekly_seasonality` when we create the Prophet model.\n\n\n```python\n# Python\nm4 = Prophet(holidays=lockdowns, weekly_seasonality=False)\n```\nWe then add this weekly seasonality manually, as two different model components - one for pre-covid, one for post-covid. Note that `fourier_order=3` is the default setting for weekly seasonality. After this we can run `.fit()`.\n\n\n```python\n# Python\nm4.add_seasonality(\n name='weekly_pre_covid',\n period=7,\n fourier_order=3,\n condition_name='pre_covid',\n)\nm4.add_seasonality(\n name='weekly_post_covid',\n period=7,\n fourier_order=3,\n condition_name='post_covid',\n);\n```\n```python\n# Python\nm4 = m4.fit(df2)\n```\n 02:53:55 - cmdstanpy - INFO - Chain [1] start processing\n 02:53:56 - cmdstanpy - INFO - Chain [1] done processing\n\n\nWe also need to create the `pre_covid` and `post_covid` flags in the future dataframe. This is so that Prophet can apply the correct weekly seasonality parameters to each future date.\n\n\n```python\n# Python\nfuture4 = m4.make_future_dataframe(periods=366)\nfuture4['pre_covid'] = pd.to_datetime(future4['ds']) < pd.to_datetime('2020-03-21')\nfuture4['post_covid'] = ~future4['pre_covid']\n```\n```python\n# Python\nforecast4 = m4.predict(future4)\n```\n```python\n# Python\nm4.plot(forecast4)\nplt.axhline(y=0, color='red')\nplt.title('Lockdowns as one-off holidays + Conditional weekly seasonality');\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_34_0.png)\n\n\n```python\n# Python\nm4.plot_components(forecast4);\n```\n\n![png](/prophet/static/handling_shocks_files/handling_shocks_35_0.png)\n\n\nInterestingly, the model with conditional seasonalities suggests that, post-COVID, pedestrian activity peaks on Saturdays, instead of Fridays. This could make sense if most people are still working from home and are hence less likely to go out on Friday nights. From a prediction perspective this would only be important if we care about predicting weekdays vs. weekends accurately, but overall this kind of exploration helps us gain insight into how COVID has changed behaviours.\n\n\n \n\n#### Further reading\n\n\n\nA lot of the content in this page was inspired by this [GitHub discussion](https://github.com/facebook/prophet/issues/1416). We've covered a few low hanging fruits for tweaking Prophet models when faced with shocks such as COVID, but there are many other possible approaches as well, such as:\n\n\n\n* Using external regressors (e.g. the lockdown stringency index). This would only be fruitful if we a) have regressor data that aligns well (in terms of location) with the series we're forecasting, and b) have control over or can predict the regressor much more accurately than the time series alone.\n\n* Detecting and removing outlier data from the training period, or throwing away older training data completely. This might be a better approach for sub-daily time series that don't have yearly seasonal patterns.\n\n\n\nOverall though it's difficult to be confident in our forecasts in these environments when rules are constantly changing and outbreaks occur randomly. In this scenario it's more important to constantly re-train / re-evaluate our models and clearly communicate the increased uncertainty in forecasts.\n\n\n```python\n# Python\n\n```" }, { "path": "docs/_docs/holiday_effects.md", "content": "---\nlayout: docs\ndocid: \"holiday_effects\"\ntitle: \"Holiday Effects\"\npermalink: /docs/holiday_effects.html\n---\n### Modeling Holidays\nIf you have holidays that you'd like to model, you must create a dataframe for them. It has two columns (`holiday` and `ds`) and a row for each occurrence of the holiday. It must include all occurrences of the holiday, both in the past (back as far as the historical data go) and in the future (out as far as the forecast is being made). If they won't repeat in the future, Prophet will model them and then not include them in the forecast.\n\nYou can also include columns `lower_window` and `upper_window` which extend the holiday out to `[lower_window, upper_window]` days around the date. For instance, if you wanted to included Christmas Eve in addition to Christmas you'd include `lower_window=-1,upper_window=0`. If you wanted to use Black Friday in addition to Thanksgiving, you'd include `lower_window=0,upper_window=1`.\n\nHere we create a dataframe that includes the dates of all of Peyton Manning's playoff appearances:\n\n```python\n# Python\nplayoffs = pd.DataFrame({\n 'holiday': 'playoff',\n 'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',\n '2010-01-24', '2010-02-07', '2011-01-08',\n '2013-01-12', '2014-01-12', '2014-01-19',\n '2014-02-02', '2015-01-11', '2016-01-17',\n '2016-01-24', '2016-02-07']),\n 'lower_window': 0,\n 'upper_window': 1,\n})\nsuperbowls = pd.DataFrame({\n 'holiday': 'superbowl',\n 'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),\n 'lower_window': 0,\n 'upper_window': 1,\n})\nholidays = pd.concat((playoffs, superbowls))\n```\n```R\n# R\nlibrary(dplyr)\nplayoffs <- data_frame(\n holiday = 'playoff',\n ds = as.Date(c('2008-01-13', '2009-01-03', '2010-01-16',\n '2010-01-24', '2010-02-07', '2011-01-08',\n '2013-01-12', '2014-01-12', '2014-01-19',\n '2014-02-02', '2015-01-11', '2016-01-17',\n '2016-01-24', '2016-02-07')),\n lower_window = 0,\n upper_window = 1\n)\nsuperbowls <- data_frame(\n holiday = 'superbowl',\n ds = as.Date(c('2010-02-07', '2014-02-02', '2016-02-07')),\n lower_window = 0,\n upper_window = 1\n)\nholidays <- bind_rows(playoffs, superbowls)\n```\nAbove we have include the superbowl days as both playoff games and superbowl games. This means that the superbowl effect will be an additional additive bonus on top of the playoff effect.\n\nOnce the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the Quickstart:\n\n```python\n# Python\nm = Prophet(holidays=holidays)\nforecast = m.fit(df).predict(future)\n```\n```R\n# R\nm <- prophet(df, holidays = holidays)\nforecast <- predict(m, future)\n```\nThe holiday effect can be seen in the `forecast` dataframe:\n\n```R\n# R\nforecast %>% \n select(ds, playoff, superbowl) %>% \n filter(abs(playoff + superbowl) > 0) %>%\n tail(10)\n```\n```python\n# Python\nforecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n ['ds', 'playoff', 'superbowl']][-10:]\n```\n\n\n\n
\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dsplayoffsuperbowl
21902014-02-021.2203081.204992
21912014-02-031.9004651.444581
25322015-01-111.2203080.000000
25332015-01-121.9004650.000000
29012016-01-171.2203080.000000
29022016-01-181.9004650.000000
29082016-01-241.2203080.000000
29092016-01-251.9004650.000000
29222016-02-071.2203081.204992
29232016-02-081.9004651.444581
\n
\n\n\n\nThe holiday effects will also show up in the components plot, where we see that there is a spike on the days around playoff appearances, with an especially large spike for the superbowl:\n\n```python\n# Python\nm.plot_components(forecast);\n```\n```R\n# R\nprophet_plot_components(m, forecast);\n```\n \n![png](/prophet/static/holiday_effects_files/holiday_effects_13_0.png) \n\n\n### Prior scale for holidays and seasonality\nIf you find that the holidays are overfitting, you can adjust their prior scale to smooth them using the parameter `holidays_prior_scale`, which by default is 10:\n\n```R\n# R\nm <- prophet(df, holidays = holidays, holidays.prior.scale = 1)\nforecast <- predict(m, future)\nforecast %>% \n select(ds, playoff, superbowl) %>% \n filter(abs(playoff + superbowl) > 0) %>%\n tail(10)\n```\n```python\n# Python\nm = Prophet(holidays=holidays, holidays_prior_scale=1).fit(df)\nforecast = m.predict(future)\nforecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n ['ds', 'playoff', 'superbowl']][-10:]\n```\n\n\n\n
\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dsplayoffsuperbowl
21902014-02-021.3623120.693425
21912014-02-032.0334710.542254
25322015-01-111.3623120.000000
25332015-01-122.0334710.000000
29012016-01-171.3623120.000000
29022016-01-182.0334710.000000
29082016-01-241.3623120.000000
29092016-01-252.0334710.000000
29222016-02-071.3623120.693425
29232016-02-082.0334710.542254
\n
\n\n\n\nThe magnitude of the holiday effect has been reduced compared to before, especially for superbowls, which had the fewest observations. There is a parameter `seasonality_prior_scale` which similarly adjusts the extent to which the seasonality model will fit the data.\n" }, { "path": "docs/_docs/installation.md", "content": "---\nlayout: docs\ndocid: \"installation\"\ntitle: \"Installation\"\npermalink: /docs/installation.html\nsubsections:\n - id: r\n title: Using R\n - id: python\n title: Using Python\n---\n\nProphet has two implementations: [R](#installation-in-r) and [Python](#installation-in-python).\n\n\n\n## Installation in R\n\nProphet is a [CRAN package](https://cran.r-project.org/package=prophet) so you can use `install.packages`.\n\n```r\n# R\ninstall.packages('prophet')\n```\n\nAfter installation, you can [get started!](quick_start.html#r-api)\n\n#### Experimental backend - cmdstanr\n\nYou can also choose an experimental alternative stan backend called `cmdstanr`. Once you've installed `prophet`,\nfollow these instructions to use `cmdstanr` instead of `rstan` as the backend:\n\n```r\n# R\n# We recommend running this is a fresh R session or restarting your current session\ninstall.packages(c(\"cmdstanr\", \"posterior\"), repos = c(\"https://stan-dev.r-universe.dev\", getOption(\"repos\")))\n\n# If you haven't installed cmdstan before, run:\ncmdstanr::install_cmdstan()\n# Otherwise, you can point cmdstanr to your cmdstan path:\ncmdstanr::set_cmdstan_path(path = )\n\n# Set the R_STAN_BACKEND environment variable\nSys.setenv(R_STAN_BACKEND = \"CMDSTANR\")\n```\n\n### Windows\n\nOn Windows, R requires a compiler so you'll need to [follow the instructions](https://github.com/stan-dev/rstan/wiki/Configuring-C---Toolchain-for-Windows) provided by `rstan`. The key step is installing [Rtools](http://cran.r-project.org/bin/windows/Rtools/) before attempting to install the package.\n\nIf you have custom Stan compiler settings, install from source rather than the CRAN binary.\n\n\n\n## Installation in Python\n\nProphet is on PyPI, so you can use `pip` to install it.\n\n```bash\npython -m pip install prophet\n```\n\n* From v0.6 onwards, Python 2 is no longer supported.\n* As of v1.0, the package name on PyPI is \"prophet\"; prior to v1.0 it was \"fbprophet\".\n* As of v1.1, the minimum supported Python version is 3.7.\n\nAfter installation, you can [get started!](quick_start.html#python-api)\n\n### Anaconda\n\nProphet can also be installed through conda-forge.\n\n```bash\nconda install -c conda-forge prophet\n```\n" }, { "path": "docs/_docs/multiplicative_seasonality.md", "content": "---\nlayout: docs\ndocid: \"multiplicative_seasonality\"\ntitle: \"Multiplicative Seasonality\"\npermalink: /docs/multiplicative_seasonality.html\nsubsections:\n---\nBy default Prophet fits additive seasonalities, meaning the effect of the seasonality is added to the trend to get the forecast. This time series of the number of air passengers is an example of when additive seasonality does not work:\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_air_passengers.csv')\nm <- prophet(df)\nfuture <- make_future_dataframe(m, 50, freq = 'm')\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_air_passengers.csv')\nm = Prophet()\nm.fit(df)\nfuture = m.make_future_dataframe(50, freq='MS')\nforecast = m.predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/multiplicative_seasonality_files/multiplicative_seasonality_4_0.png)\n\n\nThis time series has a clear yearly cycle, but the seasonality in the forecast is too large at the start of the time series and too small at the end. In this time series, the seasonality is not a constant additive factor as assumed by Prophet, rather it grows with the trend. This is multiplicative seasonality.\n\n\n\nProphet can model multiplicative seasonality by setting `seasonality_mode='multiplicative'` in the input arguments:\n\n\n```R\n# R\nm <- prophet(df, seasonality.mode = 'multiplicative')\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\nm = Prophet(seasonality_mode='multiplicative')\nm.fit(df)\nforecast = m.predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/multiplicative_seasonality_files/multiplicative_seasonality_7_0.png)\n\n\nThe components figure will now show the seasonality as a percent of the trend:\n\n\n```R\n# R\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/multiplicative_seasonality_files/multiplicative_seasonality_10_0.png)\n\n\nWith `seasonality_mode='multiplicative'`, holiday effects will also be modeled as multiplicative. Any added seasonalities or extra regressors will by default use whatever `seasonality_mode` is set to, but can be overriden by specifying `mode='additive'` or `mode='multiplicative'` as an argument when adding the seasonality or regressor.\n\n\n\nFor example, this block sets the built-in seasonalities to multiplicative, but includes an additive quarterly seasonality and an additive regressor:\n\n\n```R\n# R\nm <- prophet(seasonality.mode = 'multiplicative')\nm <- add_seasonality(m, 'quarterly', period = 91.25, fourier.order = 8, mode = 'additive')\nm <- add_regressor(m, 'regressor', mode = 'additive')\n```\n```python\n# Python\nm = Prophet(seasonality_mode='multiplicative')\nm.add_seasonality('quarterly', period=91.25, fourier_order=8, mode='additive')\nm.add_regressor('regressor', mode='additive')\n```\nAdditive and multiplicative extra regressors will show up in separate panels on the components plot. Note, however, that it is pretty unlikely to have a mix of additive and multiplicative seasonalities, so this will generally only be used if there is a reason to expect that to be the case.\n\n" }, { "path": "docs/_docs/non-daily_data.md", "content": "---\nlayout: docs\ndocid: \"non-daily_data\"\ntitle: \"Non-Daily Data\"\npermalink: /docs/non-daily_data.html\nsubsections:\n - title: Sub-daily data\n id: sub-daily-data\n - title: Data with regular gaps\n id: data-with-regular-gaps\n - title: Monthly data\n id: monthly-data\n - title: Holidays with aggregated data\n id: holidays-with-aggregated-data\n---\n \n\n## Sub-daily data\n\n\n\nProphet can make forecasts for time series with sub-daily observations by passing in a dataframe with timestamps in the `ds` column. The format of the timestamps should be YYYY-MM-DD HH:MM:SS - see the example csv [here](https://github.com/facebook/prophet/blob/main/examples/example_yosemite_temps.csv). When sub-daily data are used, daily seasonality will automatically be fit. Here we fit Prophet to data with 5-minute resolution (daily temperatures at Yosemite):\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_yosemite_temps.csv')\nm <- prophet(df, changepoint.prior.scale=0.01)\nfuture <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\nfcst <- predict(m, future)\nplot(m, fcst)\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_yosemite_temps.csv')\nm = Prophet(changepoint_prior_scale=0.01).fit(df)\nfuture = m.make_future_dataframe(periods=300, freq='H')\nfcst = m.predict(future)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_4_0.png)\n\n\nThe daily seasonality will show up in the components plot:\n\n\n```R\n# R\nprophet_plot_components(m, fcst)\n```\n```python\n# Python\nfig = m.plot_components(fcst)\n```\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_7_0.png)\n\n\n \n\n## Data with regular gaps\n\n\n\nSuppose the dataset above only had observations from 12a to 6a:\n\n\n```R\n# R\ndf2 <- df %>%\n mutate(ds = as.POSIXct(ds, tz=\"GMT\")) %>%\n filter(as.numeric(format(ds, \"%H\")) < 6)\nm <- prophet(df2)\nfuture <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\nfcst <- predict(m, future)\nplot(m, fcst)\n```\n```python\n# Python\ndf2 = df.copy()\ndf2['ds'] = pd.to_datetime(df2['ds'])\ndf2 = df2[df2['ds'].dt.hour < 6]\nm = Prophet().fit(df2)\nfuture = m.make_future_dataframe(periods=300, freq='H')\nfcst = m.predict(future)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_10_0.png)\n\n\nThe forecast seems quite poor, with much larger fluctuations in the future than were seen in the history. The issue here is that we have fit a daily cycle to a time series that only has data for part of the day (12a to 6a). The daily seasonality is thus unconstrained for the remainder of the day and is not estimated well. The solution is to only make predictions for the time windows for which there are historical data. Here, that means to limit the `future` dataframe to have times from 12a to 6a:\n\n\n```R\n# R\nfuture2 <- future %>% \n filter(as.numeric(format(ds, \"%H\")) < 6)\nfcst <- predict(m, future2)\nplot(m, fcst)\n```\n```python\n# Python\nfuture2 = future.copy()\nfuture2 = future2[future2['ds'].dt.hour < 6]\nfcst = m.predict(future2)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_13_0.png)\n\n\nThe same principle applies to other datasets with regular gaps in the data. For example, if the history contains only weekdays, then predictions should only be made for weekdays since the weekly seasonality will not be well estimated for the weekends.\n\n\n\n \n\n## Monthly data\n\n\n\nYou can use Prophet to fit monthly data. However, the underlying model is continuous-time, which means that you can get strange results if you fit the model to monthly data and then ask for daily forecasts. Here we forecast US retail sales volume for the next 10 years:\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_retail_sales.csv')\nm <- prophet(df, seasonality.mode = 'multiplicative')\nfuture <- make_future_dataframe(m, periods = 3652)\nfcst <- predict(m, future)\nplot(m, fcst)\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_retail_sales.csv')\nm = Prophet(seasonality_mode='multiplicative').fit(df)\nfuture = m.make_future_dataframe(periods=3652)\nfcst = m.predict(future)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_16_0.png)\n\n\nThis is the same issue from above where the dataset has regular gaps. When we fit the yearly seasonality, it only has data for the first of each month and the seasonality components for the remaining days are unidentifiable and overfit. This can be clearly seen by doing MCMC to see uncertainty in the seasonality:\n\n\n```R\n# R\nm <- prophet(df, seasonality.mode = 'multiplicative', mcmc.samples = 300)\nfcst <- predict(m, future)\nprophet_plot_components(m, fcst)\n```\n```python\n# Python\nm = Prophet(seasonality_mode='multiplicative', mcmc_samples=300).fit(df, show_progress=False)\nfcst = m.predict(future)\nfig = m.plot_components(fcst)\n```\n WARNING:pystan:481 of 600 iterations saturated the maximum tree depth of 10 (80.2 %)\n WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation\n\n\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_19_1.png)\n\n\nThe seasonality has low uncertainty at the start of each month where there are data points, but has very high posterior variance in between. When fitting Prophet to monthly data, only make monthly forecasts, which can be done by passing the frequency into `make_future_dataframe`:\n\n\n```R\n# R\nfuture <- make_future_dataframe(m, periods = 120, freq = 'month')\nfcst <- predict(m, future)\nplot(m, fcst)\n```\n```python\n# Python\nfuture = m.make_future_dataframe(periods=120, freq='MS')\nfcst = m.predict(future)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/non-daily_data_files/non-daily_data_22_0.png)\n\n\nIn Python, the frequency can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases . Note that `MS` used here is month-start, meaning the data point is placed on the start of each month.\n\n\n\nIn monthly data, yearly seasonality can also be modeled with binary extra regressors. In particular, the model can use 12 extra regressors like `is_jan`, `is_feb`, etc. where `is_jan` is 1 if the date is in Jan and 0 otherwise. This approach would avoid the within-month unidentifiability seen above. Be sure to use `yearly_seasonality=False` if monthly extra regressors are being added.\n\n\n \n\n## Holidays with aggregated data\n\n\n\nHoliday effects are applied to the particular date on which the holiday was specified. With data that has been aggregated to weekly or monthly frequency, holidays that don't fall on the particular date used in the data will be ignored: for example, a Monday holiday in a weekly time series where each data point is on a Sunday. To include holiday effects in the model, the holiday will need to be moved to the date in the history dataframe for which the effect is desired. Note that with weekly or monthly aggregated data, many holiday effects will be well-captured by the yearly seasonality, so added holidays may only be necessary for holidays that occur in different weeks throughout the time series.\n\n" }, { "path": "docs/_docs/outliers.md", "content": "---\nlayout: docs\ndocid: \"outliers\"\ntitle: \"Outliers\"\npermalink: /docs/outliers.html\nsubsections:\n---\nThere are two main ways that outliers can affect Prophet forecasts. Here we make a forecast on the logged Wikipedia visits to the R page from before, but with a block of bad data:\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers1.csv')\nm <- prophet(df)\nfuture <- make_future_dataframe(m, periods = 1096)\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers1.csv')\nm = Prophet()\nm.fit(df)\nfuture = m.make_future_dataframe(periods=1096)\nforecast = m.predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/outliers_files/outliers_4_0.png)\n\n\nThe trend forecast seems reasonable, but the uncertainty intervals seem way too wide. Prophet is able to handle the outliers in the history, but only by fitting them with trend changes. The uncertainty model then expects future trend changes of similar magnitude.\n\n\n\nThe best way to handle outliers is to remove them - Prophet has no problem with missing data. If you set their values to `NA` in the history but leave the dates in `future`, then Prophet will give you a prediction for their values.\n\n\n```R\n# R\noutliers <- (as.Date(df$ds) > as.Date('2010-01-01')\n & as.Date(df$ds) < as.Date('2011-01-01'))\ndf$y[outliers] = NA\nm <- prophet(df)\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\ndf.loc[(df['ds'] > '2010-01-01') & (df['ds'] < '2011-01-01'), 'y'] = None\nmodel = Prophet().fit(df)\nfig = model.plot(model.predict(future))\n```\n\n![png](/prophet/static/outliers_files/outliers_7_0.png)\n\n\nIn the above example the outliers messed up the uncertainty estimation but did not impact the main forecast `yhat`. This isn't always the case, as in this example with added outliers:\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers2.csv')\nm <- prophet(df)\nfuture <- make_future_dataframe(m, periods = 1096)\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers2.csv')\nm = Prophet()\nm.fit(df)\nfuture = m.make_future_dataframe(periods=1096)\nforecast = m.predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/outliers_files/outliers_10_0.png)\n\n\nHere a group of extreme outliers in June 2015 mess up the seasonality estimate, so their effect reverberates into the future forever. Again the right approach is to remove them:\n\n\n```R\n# R\noutliers <- (as.Date(df$ds) > as.Date('2015-06-01')\n & as.Date(df$ds) < as.Date('2015-06-30'))\ndf$y[outliers] = NA\nm <- prophet(df)\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\ndf.loc[(df['ds'] > '2015-06-01') & (df['ds'] < '2015-06-30'), 'y'] = None\nm = Prophet().fit(df)\nfig = m.plot(m.predict(future))\n```\n\n![png](/prophet/static/outliers_files/outliers_13_0.png)\n\n" }, { "path": "docs/_docs/quick_start.md", "content": "---\nlayout: docs\ndocid: \"quick_start\"\ntitle: \"Quick Start\"\npermalink: /docs/quick_start.html\nsubsections:\n - title: Python API\n id: python-api\n - title: R API\n id: r-api\n---\n \n\n## Python API\n\n\n\nProphet follows the `sklearn` model API. We create an instance of the `Prophet` class and then call its `fit` and `predict` methods. \n\n\nThe input to Prophet is always a dataframe with two columns: `ds` and `y`. The `ds` (datestamp) column should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The `y` column must be numeric, and represents the measurement we wish to forecast.\n\n\n\nAs an example, let's look at a time series of the log daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning). We scraped this data using the [Wikipediatrend](https://cran.r-project.org/package=wikipediatrend) package in R. Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).\n\n\n\nFirst we'll import the data:\n\n\n```python\n# Python\nimport pandas as pd\nfrom prophet import Prophet\n\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\ndf.head()\n\n```\n\n\n\n
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dsy
02007-12-109.590761
12007-12-118.519590
22007-12-128.183677
32007-12-138.072467
42007-12-147.893572
\n
\n\n\n\nWe fit the model by instantiating a new `Prophet` object. Any settings to the forecasting procedure are passed into the constructor. Then you call its `fit` method and pass in the historical dataframe. Fitting should take 1-5 seconds.\n\n\n```python\n# Python\nm = Prophet()\nm.fit(df)\n\n```\nPredictions are then made on a dataframe with a column `ds` containing the dates for which a prediction is to be made. You can get a suitable dataframe that extends into the future a specified number of days using the helper method `Prophet.make_future_dataframe`. By default it will also include the dates from the history, so we will see the model fit as well. \n\n\n```python\n# Python\nfuture = m.make_future_dataframe(periods=365)\nfuture.tail()\n\n```\n\n\n\n
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ds
32652017-01-15
32662017-01-16
32672017-01-17
32682017-01-18
32692017-01-19
\n
\n\n\n\nThe `predict` method will assign each row in `future` a predicted value which it names `yhat`. If you pass in historical dates, it will provide an in-sample fit. The `forecast` object here is a new dataframe that includes a column `yhat` with the forecast, as well as columns for components and uncertainty intervals.\n\n\n```python\n# Python\nforecast = m.predict(future)\nforecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()\n\n```\n\n\n\n
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dsyhatyhat_loweryhat_upper
32652017-01-158.2126257.4563108.959726
32662017-01-168.5376357.8429869.290934
32672017-01-178.3250717.6008799.072006
32682017-01-188.1577237.5120528.924022
32692017-01-198.1696777.4124738.946977
\n
\n\n\n\nYou can plot the forecast by calling the `Prophet.plot` method and passing in your forecast dataframe.\n\n\n```python\n# Python\nfig1 = m.plot(forecast)\n\n```\n\n![png](/prophet/static/quick_start_files/quick_start_12_0.png)\n\n\nIf you want to see the forecast components, you can use the `Prophet.plot_components` method. By default you'll see the trend, yearly seasonality, and weekly seasonality of the time series. If you include holidays, you'll see those here, too.\n\n\n```python\n# Python\nfig2 = m.plot_components(forecast)\n\n```\n\n![png](/prophet/static/quick_start_files/quick_start_14_0.png)\n\n\nAn interactive figure of the forecast and components can be created with plotly. You will need to install plotly 4.0 or above separately, as it will not by default be installed with prophet. You will also need to install the `notebook` and `ipywidgets` packages.\n\n\n```python\n# Python\nfrom prophet.plot import plot_plotly, plot_components_plotly\n\nplot_plotly(m, forecast)\n\n```\n```python\n# Python\nplot_components_plotly(m, forecast)\n\n```\nMore details about the options available for each method are available in the docstrings, for example, via `help(Prophet)` or `help(Prophet.fit)`.\n\n\n \n\n## R API\n\n\n\nIn R, we use the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and `plot` on this model object.\n\n\n```R\n# R\nlibrary(prophet)\n\n```\n R[write to console]: Loading required package: Rcpp\n \n R[write to console]: Loading required package: rlang\n \n\n\nFirst we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n\n```\nWe call the `prophet` function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data and are described in later pages of this documentation.\n\n\n```R\n# R\nm <- prophet(df)\n\n```\nPredictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function takes the model object and a number of periods to forecast and produces a suitable dataframe. By default it will also include the historical dates so we can evaluate in-sample fit.\n\n\n```R\n# R\nfuture <- make_future_dataframe(m, periods = 365)\ntail(future)\n\n```\n ds\n 3265 2017-01-14\n 3266 2017-01-15\n 3267 2017-01-16\n 3268 2017-01-17\n 3269 2017-01-18\n 3270 2017-01-19\n\n\nAs with most modeling procedures in R, we use the generic `predict` function to get our forecast. The `forecast` object is a dataframe with a column `yhat` containing the forecast. It has additional columns for uncertainty intervals and seasonal components.\n\n\n```R\n# R\nforecast <- predict(m, future)\ntail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])\n\n```\n ds yhat yhat_lower yhat_upper\n 3265 2017-01-14 7.818359 7.071228 8.550957\n 3266 2017-01-15 8.200125 7.475725 8.869495\n 3267 2017-01-16 8.525104 7.747071 9.226915\n 3268 2017-01-17 8.312482 7.551904 9.046774\n 3269 2017-01-18 8.145098 7.390770 8.863692\n 3270 2017-01-19 8.156964 7.381716 8.866507\n\n\nYou can use the generic `plot` function to plot the forecast, by passing in the model and the forecast dataframe.\n\n\n```R\n# R\nplot(m, forecast)\n\n```\n\n![png](/prophet/static/quick_start_files/quick_start_30_0.png)\n\n\nYou can use the `prophet_plot_components` function to see the forecast broken down into trend, weekly seasonality, and yearly seasonality.\n\n\n```R\n# R\nprophet_plot_components(m, forecast)\n\n```\n\n![png](/prophet/static/quick_start_files/quick_start_32_0.png)\n\n\nAn interactive plot of the forecast using Dygraphs can be made with the command `dyplot.prophet(m, forecast)`.\n\n\n\nMore details about the options available for each method are available in the docstrings, for example, via `?prophet` or `?fit.prophet`. This documentation is also available in the [reference manual](https://cran.r-project.org/web/packages/prophet/prophet.pdf) on CRAN.\n\n" }, { "path": "docs/_docs/saturating_forecasts.md", "content": "---\nlayout: docs\ndocid: \"saturating_forecasts\"\ntitle: \"Saturating Forecasts\"\npermalink: /docs/saturating_forecasts.html\nsubsections:\n - title: Forecasting Growth\n id: forecasting-growth\n - title: Saturating Minimum\n id: saturating-minimum\n---\n \n\n### Forecasting Growth\n\n\n\nBy default, Prophet uses a linear model for its forecast. When forecasting growth, there is usually some maximum achievable point: total market size, total population size, etc. This is called the carrying capacity, and the forecast should saturate at this point.\n\n\n\nProphet allows you to make forecasts using a [logistic growth](https://en.wikipedia.org/wiki/Logistic_function) trend model, with a specified carrying capacity. We illustrate this with the log number of page visits to the [R (programming language)](https://en.wikipedia.org/wiki/R_%28programming_language%29) page on Wikipedia:\n\n\n```R\n# R\ndf <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n```\n```python\n# Python\ndf = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\n```\nWe must specify the carrying capacity in a column `cap`. Here we will assume a particular value, but this would usually be set using data or expertise about the market size.\n\n\n```R\n# R\ndf$cap <- 8.5\n```\n```python\n# Python\ndf['cap'] = 8.5\n```\nThe important things to note are that `cap` must be specified for every row in the dataframe, and that it does not have to be constant. If the market size is growing, then `cap` can be an increasing sequence.\n\n\n\nWe then fit the model as before, except pass in an additional argument to specify logistic growth:\n\n\n```R\n# R\nm <- prophet(df, growth = 'logistic')\n```\n```python\n# Python\nm = Prophet(growth='logistic')\nm.fit(df)\n```\nWe make a dataframe for future predictions as before, except we must also specify the capacity in the future. Here we keep capacity constant at the same value as in the history, and forecast 5 years into the future:\n\n\n```R\n# R\nfuture <- make_future_dataframe(m, periods = 1826)\nfuture$cap <- 8.5\nfcst <- predict(m, future)\nplot(m, fcst)\n```\n```python\n# Python\nfuture = m.make_future_dataframe(periods=1826)\nfuture['cap'] = 8.5\nfcst = m.predict(future)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/saturating_forecasts_files/saturating_forecasts_13_0.png)\n\n\nThe logistic function has an implicit minimum of 0, and will saturate at 0 the same way that it saturates at the capacity. It is possible to also specify a different saturating minimum.\n\n\n\n \n\n### Saturating Minimum\n\n\n\nThe logistic growth model can also handle a saturating minimum, which is specified with a column `floor` in the same way as the `cap` column specifies the maximum:\n\n\n```R\n# R\ndf$y <- 10 - df$y\ndf$cap <- 6\ndf$floor <- 1.5\nfuture$cap <- 6\nfuture$floor <- 1.5\nm <- prophet(df, growth = 'logistic')\nfcst <- predict(m, future)\nplot(m, fcst)\n```\n```python\n# Python\ndf['y'] = 10 - df['y']\ndf['cap'] = 6\ndf['floor'] = 1.5\nfuture['cap'] = 6\nfuture['floor'] = 1.5\nm = Prophet(growth='logistic')\nm.fit(df)\nfcst = m.predict(future)\nfig = m.plot(fcst)\n```\n\n![png](/prophet/static/saturating_forecasts_files/saturating_forecasts_16_0.png)\n\n\nTo use a logistic growth trend with a saturating minimum, a maximum capacity must also be specified.\n\n" }, { "path": "docs/_docs/seasonality,_holiday_effects,_and_regressors.md", "content": "---\nlayout: docs\ndocid: \"seasonality,_holiday_effects,_and_regressors\"\ntitle: \"Seasonality, Holiday Effects, And Regressors\"\npermalink: /docs/seasonality,_holiday_effects,_and_regressors.html\nsubsections:\n - title: Modeling Holidays and Special Events\n id: modeling-holidays-and-special-events\n - title: Built-in Country Holidays\n id: built-in-country-holidays\n - title: Holidays for subdivisions (Python)\n id: holidays-for-subdivisions-(python)\n - title: Fourier Order for Seasonalities\n id: fourier-order-for-seasonalities\n - title: Specifying Custom Seasonalities\n id: specifying-custom-seasonalities\n - title: Seasonalities that depend on other factors\n id: seasonalities-that-depend-on-other-factors\n - title: Prior scale for holidays and seasonality\n id: prior-scale-for-holidays-and-seasonality\n - title: Additional regressors\n id: additional-regressors\n - title: Coefficients of additional regressors\n id: coefficients-of-additional-regressors\n---\n \n\n### Modeling Holidays and Special Events\n\nIf you have holidays or other recurring events that you'd like to model, you must create a dataframe for them. It has two columns (`holiday` and `ds`) and a row for each occurrence of the holiday. It must include all occurrences of the holiday, both in the past (back as far as the historical data go) and in the future (out as far as the forecast is being made). If they won't repeat in the future, Prophet will model them and then not include them in the forecast.\n\n\n\nYou can also include columns `lower_window` and `upper_window` which extend the holiday out to `[lower_window, upper_window]` days around the date. For instance, if you wanted to include Christmas Eve in addition to Christmas you'd include `lower_window=-1,upper_window=0`. If you wanted to use Black Friday in addition to Thanksgiving, you'd include `lower_window=0,upper_window=1`. You can also include a column `prior_scale` to set the prior scale separately for each holiday, as described below.\n\n\n\nHere we create a dataframe that includes the dates of all of Peyton Manning's playoff appearances:\n\n\n```R\n# R\nlibrary(dplyr)\nplayoffs <- data_frame(\n holiday = 'playoff',\n ds = as.Date(c('2008-01-13', '2009-01-03', '2010-01-16',\n '2010-01-24', '2010-02-07', '2011-01-08',\n '2013-01-12', '2014-01-12', '2014-01-19',\n '2014-02-02', '2015-01-11', '2016-01-17',\n '2016-01-24', '2016-02-07')),\n lower_window = 0,\n upper_window = 1\n)\nsuperbowls <- data_frame(\n holiday = 'superbowl',\n ds = as.Date(c('2010-02-07', '2014-02-02', '2016-02-07')),\n lower_window = 0,\n upper_window = 1\n)\nholidays <- bind_rows(playoffs, superbowls)\n```\n```python\n# Python\nplayoffs = pd.DataFrame({\n 'holiday': 'playoff',\n 'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',\n '2010-01-24', '2010-02-07', '2011-01-08',\n '2013-01-12', '2014-01-12', '2014-01-19',\n '2014-02-02', '2015-01-11', '2016-01-17',\n '2016-01-24', '2016-02-07']),\n 'lower_window': 0,\n 'upper_window': 1,\n})\nsuperbowls = pd.DataFrame({\n 'holiday': 'superbowl',\n 'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),\n 'lower_window': 0,\n 'upper_window': 1,\n})\nholidays = pd.concat((playoffs, superbowls))\n```\nAbove we have included the superbowl days as both playoff games and superbowl games. This means that the superbowl effect will be an additional additive bonus on top of the playoff effect.\n\n\n\nOnce the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html):\n\n\n```R\n# R\nm <- prophet(df, holidays = holidays)\nforecast <- predict(m, future)\n```\n```python\n# Python\nm = Prophet(holidays=holidays)\nforecast = m.fit(df).predict(future)\n```\nThe holiday effect can be seen in the `forecast` dataframe:\n\n\n```R\n# R\nforecast %>% \n select(ds, playoff, superbowl) %>% \n filter(abs(playoff + superbowl) > 0) %>%\n tail(10)\n```\n```python\n# Python\nforecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n ['ds', 'playoff', 'superbowl']][-10:]\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dsplayoffsuperbowl
21902014-02-021.2239651.201517
21912014-02-031.9017421.460471
25322015-01-111.2239650.000000
25332015-01-121.9017420.000000
29012016-01-171.2239650.000000
29022016-01-181.9017420.000000
29082016-01-241.2239650.000000
29092016-01-251.9017420.000000
29222016-02-071.2239651.201517
29232016-02-081.9017421.460471
\n
\n\n\n\nThe holiday effects will also show up in the components plot, where we see that there is a spike on the days around playoff appearances, with an especially large spike for the superbowl:\n\n\n```R\n# R\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_14_0.png)\n\n\nIndividual holidays can be plotted using the `plot_forecast_component` function (imported from `prophet.plot` in Python) like `plot_forecast_component(m, forecast, 'superbowl')` to plot just the superbowl holiday component.\n\n\n \n\n### Built-in Country Holidays\n\n\n\nYou can use a built-in collection of country-specific holidays using the `add_country_holidays` method (Python) or function (R). The name of the country is specified, and then major holidays for that country will be included in addition to any holidays that are specified via the `holidays` argument described above:\n\n\n```R\n# R\nm <- prophet(holidays = holidays)\nm <- add_country_holidays(m, country_name = 'US')\nm <- fit.prophet(m, df)\n```\n```python\n# Python\nm = Prophet(holidays=holidays)\nm.add_country_holidays(country_name='US')\nm.fit(df)\n```\nYou can see which holidays were included by looking at the `train_holiday_names` (Python) or `train.holiday.names` (R) attribute of the model:\n\n\n```R\n# R\nm$train.holiday.names\n```\n [1] \"playoff\" \"superbowl\" \n [3] \"New Year's Day\" \"Martin Luther King Jr. Day\" \n [5] \"Washington's Birthday\" \"Memorial Day\" \n [7] \"Independence Day\" \"Labor Day\" \n [9] \"Columbus Day\" \"Veterans Day\" \n [11] \"Veterans Day (Observed)\" \"Thanksgiving\" \n [13] \"Christmas Day\" \"Independence Day (Observed)\"\n [15] \"Christmas Day (Observed)\" \"New Year's Day (Observed)\" \n\n\n```python\n# Python\nm.train_holiday_names\n```\n\n\n\n 0 playoff\n 1 superbowl\n 2 New Year's Day\n 3 Martin Luther King Jr. Day\n 4 Washington's Birthday\n 5 Memorial Day\n 6 Independence Day\n 7 Labor Day\n 8 Columbus Day\n 9 Veterans Day\n 10 Thanksgiving\n 11 Christmas Day\n 12 Christmas Day (Observed)\n 13 Veterans Day (Observed)\n 14 Independence Day (Observed)\n 15 New Year's Day (Observed)\n dtype: object\n\n\n\nThe holidays for each country are provided by the `holidays` package in Python. A list of available countries, and the country name to use, is available on their page: https://github.com/vacanza/python-holidays/.\n\n\n\nIn Python, most holidays are computed deterministically and so are available for any date range; a warning will be raised if dates fall outside the range supported by that country. In R, holiday dates are computed for 1995 through 2044 and stored in the package as `data-raw/generated_holidays.csv`. If a wider date range is needed, this script can be used to replace that file with a different date range: https://github.com/facebook/prophet/blob/main/python/scripts/generate_holidays_file.py.\n\n\n\nAs above, the country-level holidays will then show up in the components plot:\n\n\n```R\n# R\nforecast <- predict(m, future)\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\nforecast = m.predict(future)\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_24_0.png)\n\n\n \n\n#### Holidays for subdivisions (Python)\n\n\n\nWe can use the utility function `make_holidays_df` to easily create custom holidays DataFrames, e.g. for certain states, using data from the [holidays](https://pypi.org/project/holidays/) package. This can be passed directly to the `Prophet()` constructor.\n\n\n```python\n# Python\nfrom prophet.make_holidays import make_holidays_df\n\nnsw_holidays = make_holidays_df(\n year_list=[2019 + i for i in range(10)], country='AU', province='NSW'\n)\nnsw_holidays.head(n=10)\n```\n\n\n\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
dsholiday
02019-01-26Australia Day
12019-01-28Australia Day (Observed)
22019-04-25Anzac Day
32019-12-25Christmas Day
42019-12-26Boxing Day
52019-04-20Easter Saturday
62019-04-21Easter Sunday
72019-10-07Labour Day
82019-06-10Queen's Birthday
92019-08-05Bank Holiday
\n
\n\n\n\n```python\n# Python\nfrom prophet import Prophet\n\nm_nsw = Prophet(holidays=nsw_holidays)\n```\n \n\n### Fourier Order for Seasonalities\n\n\n\nSeasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an arbitrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:\n\n\n```R\n# R\nm <- prophet(df)\nprophet:::plot_yearly(m)\n```\n```python\n# Python\nfrom prophet.plot import plot_yearly\nm = Prophet().fit(df)\na = plot_yearly(m)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_30_0.png)\n\n\nThe default values are often appropriate, but they can be increased when the seasonality needs to fit higher-frequency changes, and generally be less smooth. The Fourier order can be specified for each built-in seasonality when instantiating the model, here it is increased to 20:\n\n\n```R\n# R\nm <- prophet(df, yearly.seasonality = 20)\nprophet:::plot_yearly(m)\n```\n```python\n# Python\nfrom prophet.plot import plot_yearly\nm = Prophet(yearly_seasonality=20).fit(df)\na = plot_yearly(m)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_33_0.png)\n\n\nIncreasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting: N Fourier terms corresponds to 2N variables used for modeling the cycle\n\n\n\n \n\n### Specifying Custom Seasonalities\n\n\n\nProphet will by default fit weekly and yearly seasonalities, if the time series is more than two cycles long. It will also fit daily seasonality for a sub-daily time series. You can add other seasonalities (monthly, quarterly, hourly) using the `add_seasonality` method (Python) or function (R).\n\n\n\nThe inputs to this function are a name, the period of the seasonality in days, and the Fourier order for the seasonality. For reference, by default Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality. An optional input to `add_seasonality` is the prior scale for that seasonal component - this is discussed below.\n\n\n\nAs an example, here we fit the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:\n\n\n```R\n# R\nm <- prophet(weekly.seasonality=FALSE)\nm <- add_seasonality(m, name='monthly', period=30.5, fourier.order=5)\nm <- fit.prophet(m, df)\nforecast <- predict(m, future)\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\nm = Prophet(weekly_seasonality=False)\nm.add_seasonality(name='monthly', period=30.5, fourier_order=5)\nforecast = m.fit(df).predict(future)\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_36_0.png)\n\n\n \n\n### Seasonalities that depend on other factors\n\nIn some instances the seasonality may depend on other factors, such as a weekly seasonal pattern that is different during the summer than it is during the rest of the year, or a daily seasonal pattern that is different on weekends vs. on weekdays. These types of seasonalities can be modeled using conditional seasonalities.\n\n\n\nConsider the Peyton Manning example from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.\n\n\n\nFirst we add a boolean column to the dataframe that indicates whether each date is during the on-season or the off-season:\n\n\n```R\n# R\nis_nfl_season <- function(ds) {\n dates <- as.Date(ds)\n month <- as.numeric(format(dates, '%m'))\n return(month > 8 | month < 2)\n}\ndf$on_season <- is_nfl_season(df$ds)\ndf$off_season <- !is_nfl_season(df$ds)\n```\n```python\n# Python\ndef is_nfl_season(ds):\n date = pd.to_datetime(ds)\n return (date.month > 8 or date.month < 2)\n\ndf['on_season'] = df['ds'].apply(is_nfl_season)\ndf['off_season'] = ~df['ds'].apply(is_nfl_season)\n```\nThen we disable the built-in weekly seasonality, and replace it with two weekly seasonalities that have these columns specified as a condition. This means that the seasonality will only be applied to dates where the `condition_name` column is `True`. We must also add the column to the `future` dataframe for which we are making predictions.\n\n\n```R\n# R\nm <- prophet(weekly.seasonality=FALSE)\nm <- add_seasonality(m, name='weekly_on_season', period=7, fourier.order=3, condition.name='on_season')\nm <- add_seasonality(m, name='weekly_off_season', period=7, fourier.order=3, condition.name='off_season')\nm <- fit.prophet(m, df)\n\nfuture$on_season <- is_nfl_season(future$ds)\nfuture$off_season <- !is_nfl_season(future$ds)\nforecast <- predict(m, future)\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\nm = Prophet(weekly_seasonality=False)\nm.add_seasonality(name='weekly_on_season', period=7, fourier_order=3, condition_name='on_season')\nm.add_seasonality(name='weekly_off_season', period=7, fourier_order=3, condition_name='off_season')\n\nfuture['on_season'] = future['ds'].apply(is_nfl_season)\nfuture['off_season'] = ~future['ds'].apply(is_nfl_season)\nforecast = m.fit(df).predict(future)\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_42_0.png)\n\n\nBoth of the seasonalities now show up in the components plots above. We can see that during the on-season when games are played every Sunday, there are large increases on Sunday and Monday that are completely absent during the off-season.\n\n\n \n\n### Prior scale for holidays and seasonality\n\nIf you find that the holidays are overfitting, you can adjust their prior scale to smooth them using the parameter `holidays_prior_scale`. By default this parameter is 10, which provides very little regularization. Reducing this parameter dampens holiday effects:\n\n\n```R\n# R\nm <- prophet(df, holidays = holidays, holidays.prior.scale = 0.05)\nforecast <- predict(m, future)\nforecast %>% \n select(ds, playoff, superbowl) %>% \n filter(abs(playoff + superbowl) > 0) %>%\n tail(10)\n```\n```python\n# Python\nm = Prophet(holidays=holidays, holidays_prior_scale=0.05).fit(df)\nforecast = m.predict(future)\nforecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n ['ds', 'playoff', 'superbowl']][-10:]\n```\n\n\n\n
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dsplayoffsuperbowl
21902014-02-021.2060860.964914
21912014-02-031.8520770.992634
25322015-01-111.2060860.000000
25332015-01-121.8520770.000000
29012016-01-171.2060860.000000
29022016-01-181.8520770.000000
29082016-01-241.2060860.000000
29092016-01-251.8520770.000000
29222016-02-071.2060860.964914
29232016-02-081.8520770.992634
\n
\n\n\n\nThe magnitude of the holiday effect has been reduced compared to before, especially for superbowls, which had the fewest observations. There is a parameter `seasonality_prior_scale` which similarly adjusts the extent to which the seasonality model will fit the data.\n\n\n\nPrior scales can be set separately for individual holidays by including a column `prior_scale` in the holidays dataframe. Prior scales for individual seasonalities can be passed as an argument to `add_seasonality`. For instance, the prior scale for just weekly seasonality can be set using:\n\n\n```R\n# R\nm <- prophet()\nm <- add_seasonality(\n m, name='weekly', period=7, fourier.order=3, prior.scale=0.1)\n```\n```python\n# Python\nm = Prophet()\nm.add_seasonality(\n name='weekly', period=7, fourier_order=3, prior_scale=0.1)\n```\n\n\n \n\n### Additional regressors\n\nAdditional regressors can be added to the linear part of the model using the `add_regressor` method or function. A column with the regressor value will need to be present in both the fitting and prediction dataframes. For example, we can add an additional effect on Sundays during the NFL season. On the components plot, this effect will show up in the 'extra_regressors' plot:\n\n\n```R\n# R\nnfl_sunday <- function(ds) {\n dates <- as.Date(ds)\n month <- as.numeric(format(dates, '%m'))\n as.numeric((weekdays(dates) == \"Sunday\") & (month > 8 | month < 2))\n}\ndf$nfl_sunday <- nfl_sunday(df$ds)\n\nm <- prophet()\nm <- add_regressor(m, 'nfl_sunday')\nm <- fit.prophet(m, df)\n\nfuture$nfl_sunday <- nfl_sunday(future$ds)\n\nforecast <- predict(m, future)\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\ndef nfl_sunday(ds):\n date = pd.to_datetime(ds)\n if date.weekday() == 6 and (date.month > 8 or date.month < 2):\n return 1\n else:\n return 0\ndf['nfl_sunday'] = df['ds'].apply(nfl_sunday)\n\nm = Prophet()\nm.add_regressor('nfl_sunday')\nm.fit(df)\n\nfuture['nfl_sunday'] = future['ds'].apply(nfl_sunday)\n\nforecast = m.predict(future)\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/seasonality,_holiday_effects,_and_regressors_files/seasonality,_holiday_effects,_and_regressors_52_0.png)\n\n\nNFL Sundays could also have been handled using the \"holidays\" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known.\n\n\n\n[This notebook](https://nbviewer.jupyter.org/github/nicolasfauchereau/Auckland_Cycling/blob/master/notebooks/Auckland_cycling_and_weather.ipynb) shows an example of using weather factors as extra regressors in a forecast of bicycle usage, and provides an excellent illustration of how other time series can be included as extra regressors.\n\n\n\nThe `add_regressor` function has optional arguments for specifying the prior scale (holiday prior scale is used by default) and whether or not the regressor is standardized - see the docstring with `help(Prophet.add_regressor)` in Python and `?add_regressor` in R. Note that regressors must be added prior to model fitting. Prophet will also raise an error if the regressor is constant throughout the history, since there is nothing to fit from it.\n\n\n\nThe extra regressor must be known for both the history and for future dates. It thus must either be something that has known future values (such as `nfl_sunday`), or something that has separately been forecasted elsewhere. The weather regressors used in the notebook linked above is a good example of an extra regressor that has forecasts that can be used for future values. \n\n\n\nOne can also use as a regressor another time series that has been forecasted with a time series model, such as Prophet. For instance, if `r(t)` is included as a regressor for `y(t)`, Prophet can be used to forecast `r(t)` and then that forecast can be plugged in as the future values when forecasting `y(t)`. A note of caution around this approach: This will probably not be useful unless `r(t)` is somehow easier to forecast than `y(t)`. This is because error in the forecast of `r(t)` will produce error in the forecast of `y(t)`. One setting where this can be useful is in hierarchical time series, where there is top-level forecast that has higher signal-to-noise and is thus easier to forecast. Its forecast can be included in the forecast for each lower-level series.\n\n\n\nExtra regressors are put in the linear component of the model, so the underlying model is that the time series depends on the extra regressor as either an additive or multiplicative factor (see the next section for multiplicativity).\n\n\n\n \n\n#### Coefficients of additional regressors\n\n\n\nTo extract the beta coefficients of the extra regressors, use the utility function `regressor_coefficients` (`from prophet.utilities import regressor_coefficients` in Python, `prophet::regressor_coefficients` in R) on the fitted model. The estimated beta coefficient for each regressor roughly represents the increase in prediction value for a unit increase in the regressor value (note that the coefficients returned are always on the scale of the original data). If `mcmc_samples` is specified, a credible interval for each coefficient is also returned, which can help identify whether the regressor is meaningful to the model (a credible interval that includes the 0 value suggests the regressor is not meaningful).\n\n" }, { "path": "docs/_docs/trend_changepoints.md", "content": "---\nlayout: docs\ndocid: \"trend_changepoints\"\ntitle: \"Trend Changepoints\"\npermalink: /docs/trend_changepoints.html\nsubsections:\n - title: Automatic changepoint detection in Prophet\n id: automatic-changepoint-detection-in-prophet\n - title: Adjusting trend flexibility\n id: adjusting-trend-flexibility\n - title: Specifying the locations of the changepoints\n id: specifying-the-locations-of-the-changepoints\n---\nYou may have noticed in the earlier examples in this documentation that real time series frequently have abrupt changes in their trajectories. By default, Prophet will automatically detect these changepoints and will allow the trend to adapt appropriately. However, if you wish to have finer control over this process (e.g., Prophet missed a rate change, or is overfitting rate changes in the history), then there are several input arguments you can use.\n\n\n \n\n### Automatic changepoint detection in Prophet\n\nProphet detects changepoints by first specifying a large number of *potential changepoints* at which the rate is allowed to change. It then puts a sparse prior on the magnitudes of the rate changes (equivalent to L1 regularization) - this essentially means that Prophet has a large number of *possible* places where the rate can change, but will use as few of them as possible. Consider the Peyton Manning forecast from the Quickstart. By default, Prophet specifies 25 potential changepoints which are uniformly placed in the first 80% of the time series. The vertical lines in this figure indicate where the potential changepoints were placed:\n\n\n\n![png](/prophet/static/trend_changepoints_files/trend_changepoints_4_0.png)\n\n\nEven though we have a lot of places where the rate can possibly change, because of the sparse prior, most of these changepoints go unused. We can see this by plotting the magnitude of the rate change at each changepoint:\n\n\n\n![png](/prophet/static/trend_changepoints_files/trend_changepoints_6_0.png)\n\n\nThe number of potential changepoints can be set using the argument `n_changepoints`, but this is better tuned by adjusting the regularization. The locations of the signification changepoints can be visualized with:\n\n\n```R\n# R\nplot(m, forecast) + add_changepoints_to_plot(m)\n```\n```python\n# Python\nfrom prophet.plot import add_changepoints_to_plot\nfig = m.plot(forecast)\na = add_changepoints_to_plot(fig.gca(), m, forecast)\n```\n\n![png](/prophet/static/trend_changepoints_files/trend_changepoints_9_0.png)\n\n\nBy default changepoints are only inferred for the first 80% of the time series in order to have plenty of runway for projecting the trend forward and to avoid overfitting fluctuations at the end of the time series. This default works in many situations but not all, and can be changed using the `changepoint_range` argument. For example, `m = Prophet(changepoint_range=0.9)` in Python or `m <- prophet(changepoint.range = 0.9)` in R will place potential changepoints in the first 90% of the time series.\n\n\n \n\n### Adjusting trend flexibility\n\nIf the trend changes are being overfit (too much flexibility) or underfit (not enough flexibility), you can adjust the strength of the sparse prior using the input argument `changepoint_prior_scale`. By default, this parameter is set to 0.05. Increasing it will make the trend *more* flexible:\n\n\n```R\n# R\nm <- prophet(df, changepoint.prior.scale = 0.5)\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\nm = Prophet(changepoint_prior_scale=0.5)\nforecast = m.fit(df).predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/trend_changepoints_files/trend_changepoints_13_0.png)\n\n\nDecreasing it will make the trend *less* flexible:\n\n\n```R\n# R\nm <- prophet(df, changepoint.prior.scale = 0.001)\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\nm = Prophet(changepoint_prior_scale=0.001)\nforecast = m.fit(df).predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/trend_changepoints_files/trend_changepoints_16_0.png)\n\n\nWhen visualizing the forecast, this parameter can be adjusted as needed if the trend seems to be over- or under-fit. In the fully-automated setting, see the documentation on cross validation for recommendations on how this parameter can be tuned.\n\n\n \n\n### Specifying the locations of the changepoints\n\n\nIf you wish, rather than using automatic changepoint detection you can manually specify the locations of potential changepoints with the `changepoints` argument. Slope changes will then be allowed only at these points, with the same sparse regularization as before. One could, for instance, create a grid of points as is done automatically, but then augment that grid with some specific dates that are known to be likely to have changes. As another example, the changepoints could be entirely limited to a small set of dates, as is done here:\n\n\n```R\n# R\nm <- prophet(df, changepoints = c('2014-01-01'))\nforecast <- predict(m, future)\nplot(m, forecast)\n```\n```python\n# Python\nm = Prophet(changepoints=['2014-01-01'])\nforecast = m.fit(df).predict(future)\nfig = m.plot(forecast)\n```\n\n![png](/prophet/static/trend_changepoints_files/trend_changepoints_21_0.png)\n\n" }, { "path": "docs/_docs/uncertainty_intervals.md", "content": "---\nlayout: docs\ndocid: \"uncertainty_intervals\"\ntitle: \"Uncertainty Intervals\"\npermalink: /docs/uncertainty_intervals.html\nsubsections:\n - title: Uncertainty in the trend\n id: uncertainty-in-the-trend\n - title: Uncertainty in seasonality\n id: uncertainty-in-seasonality\n---\nBy default Prophet will return uncertainty intervals for the forecast `yhat`. There are several important assumptions behind these uncertainty intervals.\n\n\n\nThere are three sources of uncertainty in the forecast: uncertainty in the trend, uncertainty in the seasonality estimates, and additional observation noise.\n\n\n\n \n\n### Uncertainty in the trend\n\nThe biggest source of uncertainty in the forecast is the potential for future trend changes. The time series we have seen already in this documentation show clear trend changes in the history. Prophet is able to detect and fit these, but what trend changes should we expect moving forward? It's impossible to know for sure, so we do the most reasonable thing we can, and we assume that the *future will see similar trend changes as the history*. In particular, we assume that the average frequency and magnitude of trend changes in the future will be the same as that which we observe in the history. We project these trend changes forward and by computing their distribution we obtain uncertainty intervals.\n\n\n\nOne property of this way of measuring uncertainty is that allowing higher flexibility in the rate, by increasing `changepoint_prior_scale`, will increase the forecast uncertainty. This is because if we model more rate changes in the history then we will expect more in the future, and makes the uncertainty intervals a useful indicator of overfitting.\n\n\n\nThe width of the uncertainty intervals (by default 80%) can be set using the parameter `interval_width`:\n\n\n```R\n# R\nm <- prophet(df, interval.width = 0.95)\nforecast <- predict(m, future)\n```\n```python\n# Python\nforecast = Prophet(interval_width=0.95).fit(df).predict(future)\n```\nAgain, these intervals assume that the future will see the same frequency and magnitude of rate changes as the past. This assumption is probably not true, so you should not expect to get accurate coverage on these uncertainty intervals.\n\n\n\n \n\n### Uncertainty in seasonality\n\nBy default Prophet will only return uncertainty in the trend and observation noise. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do this here for the first six months of the Peyton Manning data from the Quickstart:\n\n\n```R\n# R\nm <- prophet(df, mcmc.samples = 300)\nforecast <- predict(m, future)\n```\n```python\n# Python\nm = Prophet(mcmc_samples=300)\nforecast = m.fit(df, show_progress=False).predict(future)\n```\nThis replaces the typical MAP estimation with MCMC sampling, and can take much longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the uncertainty in seasonal components when you plot them:\n\n\n```R\n# R\nprophet_plot_components(m, forecast)\n```\n```python\n# Python\nfig = m.plot_components(forecast)\n```\n\n![png](/prophet/static/uncertainty_intervals_files/uncertainty_intervals_11_0.png)\n\n\nYou can access the raw posterior predictive samples in Python using the method `m.predictive_samples(future)`, or in R using the function `predictive_samples(m, future)`.\n\n" }, { "path": "docs/_includes/blog_pagination.html", "content": "\n{% if paginator.total_pages > 1 %}\n
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\n {% if paginator.previous_page %}\n « Prev\n {% else %}\n « Prev\n {% endif %}\n\n {% for page in (1..paginator.total_pages) %}\n {% if page == paginator.page %}\n {{ page }}\n {% elsif page == 1 %}\n {{ page }}\n {% else %}\n {{ page }}\n {% endif %}\n {% endfor %}\n\n {% if paginator.next_page %}\n Next »\n {% else %}\n Next »\n {% endif %}\n
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\n{% for item in {{include.data_source}} %}\n {% include content/items/gridblock.html item=item layout=include.layout imagealign=include.imagealign align=include.align %}\n{% endfor %}\n
" }, { "path": "docs/_includes/content/items/gridblock.html", "content": "{% if include.layout == \"fourColumn\" %}\n {% assign layout = \"fourByGridBlock\" %}\n{% else %}\n {% assign layout = \"twoByGridBlock\" %}\n{% endif %}\n\n{% if include.imagealign == \"side\" %}\n {% assign imagealign = \"imageAlignSide\" %}\n{% else %}\n {% if item.image %}\n {% assign imagealign = \"imageAlignTop\" %}\n {% else %}\n {% assign imagealign = \"\" %}\n {% endif %}\n{% endif %}\n\n{% if include.align == \"right\" %}\n {% assign align = \"alignRight\" %}\n{% elsif include.align == \"center\" %}\n {% assign align = \"alignCenter\" %}\n{% else %}\n {% assign align = \"alignLeft\" %}\n{% endif %}\n\n
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100%;\n\tleft: 0;\n\tposition: relative;\n\tz-index: 99;\n}\n\n.homeContainer {\n background: $primary-bg;\n color: $primary-overlay;\n\n a {\n color: $primary-overlay;\n }\n\n .homeSplashFade {\n color: white;\n }\n\n .homeWrapper {\n padding: 2em 10px;\n text-align: left;\n\n .wrapper {\n margin: 0px auto;\n max-width: $content-width;\n padding: 0 20px;\n }\n\n .projectLogo {\n img {\n height: 100px;\n margin-bottom: 0px;\n }\n }\n\n h1#project_title {\n font-family: $header-font-family;\n font-size: 300%;\n letter-spacing: -0.08em;\n line-height: 1em;\n margin-bottom: 80px;\n }\n\n h2#project_tagline {\n font-family: $header-font-family;\n font-size: 200%;\n letter-spacing: -0.04em;\n line-height: 1em;\n }\n }\n}\n\n.wrapper {\n\tmargin: 0px auto;\n\tmax-width: $content-width;\n\tpadding: 0 10px;\n}\n\n.projectLogo {\n display: none;\n\n img {\n height: 100px;\n margin-bottom: 0px;\n }\n}\n\nsection#intro {\n margin: 40px 0;\n}\n\n.fbossFontLight {\n font-family: $base-font-family;\n font-weight: 300;\n font-style: normal;\n}\n\n.fb-like {\n display: block;\n margin-bottom: 20px;\n width: 100%;\n}\n\n.center {\n display: block;\n text-align: center;\n}\n\n.mainContainer {\n background: $secondary-bg;\n overflow: auto;\n\n .mainWrapper {\n padding: 4vh 10px;\n text-align: left;\n\n .allShareBlock {\n padding: 10px 0;\n\n .pluginBlock {\n margin: 12px 0;\n padding: 0;\n }\n }\n\n a {\n &:hover,\n &:focus {\n background: $primary-bg;\n color: $primary-overlay;\n }\n }\n\n em, i {\n font-style: italic;\n }\n\n strong, b {\n font-weight: bold;\n }\n\n h1 {\n font-size: 300%;\n line-height: 1em;\n padding: 1.4em 0 1em;\n text-align: left;\n }\n\n h2 {\n font-size: 250%;\n line-height: 1em;\n margin-bottom: 20px;\n padding: 1.4em 0 20px;\n text-align: left;\n\n & {\n border-bottom: 1px solid darken($primary-bg, 10%);\n color: darken($primary-bg, 10%);\n font-size: 22px;\n padding: 10px 0;\n }\n\n &.blockHeader {\n border-bottom: 1px solid white;\n color: white;\n font-size: 22px;\n margin-bottom: 20px;\n padding: 10px 0;\n }\n }\n\n h3 {\n font-size: 150%;\n line-height: 1.2em;\n padding: 1em 0 0.8em;\n }\n\n h4 {\n font-size: 130%;\n line-height: 1.2em;\n padding: 1em 0 0.8em;\n }\n\n p {\n padding: 0.8em 0;\n }\n\n ul {\n list-style: disc;\n }\n\n ol, ul {\n padding-left: 24px;\n li {\n padding-bottom: 4px;\n padding-left: 6px;\n }\n }\n\n strong {\n font-weight: bold;\n }\n\n .post {\n position: relative;\n\n .katex {\n font-weight: 700;\n }\n\n &.basicPost {\n margin-top: 30px;\n }\n\n a {\n color: $primary-bg;\n\n &:hover,\n &:focus {\n color: #fff;\n }\n }\n\n h2 {\n border-bottom: 4px solid $primary-bg;\n font-size: 130%;\n }\n\n h3 {\n border-bottom: 1px solid $primary-bg;\n font-size: 110%;\n }\n\n ol {\n list-style: decimal outside none;\n }\n\n .post-header {\n padding: 1em 0;\n\n h1 {\n font-size: 150%;\n line-height: 1em;\n padding: 0.4em 0 0;\n\n a {\n border: none;\n }\n }\n\n .post-meta {\n color: $primary-bg;\n font-family: $header-font-family;\n text-align: center;\n }\n }\n\n .postSocialPlugins {\n padding-top: 1em;\n }\n\n .docPagination {\n background: $primary-bg;\n bottom: 0px;\n left: 0px;\n position: absolute;\n right: 0px;\n\n .pager {\n display: inline-block;\n width: 50%;\n }\n\n .pagingNext {\n float: right;\n text-align: right;\n }\n\n a {\n border: none;\n color: $primary-overlay;\n display: block;\n padding: 4px 12px;\n\n &:hover {\n background-color: $secondary-bg;\n color: $text;\n }\n\n .pagerLabel {\n display: inline;\n }\n\n .pagerTitle {\n display: none;\n }\n }\n }\n }\n\n .posts {\n .post {\n margin-bottom: 6vh;\n }\n }\n }\n}\n\n#integrations_title {\n font-size: 250%;\n margin: 80px 0;\n}\n\n.ytVideo {\n height: 0;\n overflow: hidden;\n padding-bottom: 53.4%; /* 16:9 */\n padding-top: 25px;\n position: relative;\n}\n\n.ytVideo iframe,\n.ytVideo object,\n.ytVideo embed {\n height: 100%;\n left: 0;\n position: absolute;\n top: 0;\n width: 100%;\n}\n\n@media only screen and (min-width: 480px) {\n h1#project_title {\n font-size: 500%;\n }\n\n h2#project_tagline {\n font-size: 250%;\n }\n\n .projectLogo {\n img {\n margin-bottom: 10px;\n height: 200px;\n }\n }\n\n .homeContainer .homeWrapper {\n padding-left: 10px;\n padding-right: 10px;\n }\n\n .mainContainer {\n .mainWrapper {\n .post {\n h2 {\n font-size: 180%;\n }\n\n h3 {\n font-size: 120%;\n }\n\n .docPagination {\n a {\n .pagerLabel {\n display: none;\n }\n .pagerTitle {\n display: inline;\n }\n }\n }\n }\n }\n }\n}\n\n@media only screen and (min-width: 900px) {\n .homeContainer {\n .homeWrapper {\n position: relative;\n\n #inner {\n box-sizing: border-box;\n max-width: 600px;\n padding-right: 40px;\n }\n\n .projectLogo {\n align-items: center;\n bottom: 0;\n display: flex;\n justify-content: flex-end;\n left: 0;\n padding: 2em 20px 4em;\n position: absolute;\n right: 20px;\n top: 0;\n\n img {\n height: 100%;\n max-height: 250px;\n }\n }\n }\n }\n}\n\n@media only screen and (min-width: 1024px) {\n .mainContainer {\n .mainWrapper {\n .post {\n box-sizing: border-box;\n display: block;\n\n .post-header {\n h1 {\n font-size: 250%;\n }\n }\n }\n\n .posts {\n .post {\n margin-bottom: 4vh;\n width: 100%;\n }\n }\n }\n }\n}\n\n@media only screen and (min-width: 1200px) {\n .homeContainer {\n .homeWrapper {\n #inner {\n max-width: 750px;\n }\n }\n }\n\n .wrapper {\n max-width: 1100px;\n }\n}\n\n@media only screen and (min-width: 1500px) {\n .homeContainer {\n .homeWrapper {\n #inner {\n max-width: 1100px;\n padding-bottom: 40px;\n padding-top: 40px;\n }\n }\n }\n\n .wrapper {\n max-width: 1400px;\n }\n}\n" }, { "path": "docs/_sass/_blog.scss", "content": ".blogContainer {\n .posts {\n margin-top: 60px;\n\n .post {\n border: 1px solid $primary-bg;\n border-radius: 3px;\n padding: 10px 20px 20px;\n }\n }\n\n .lonePost {\n margin-top: 60px;\n\n .post {\n padding: 10px 0px 0px;\n }\n }\n\n .post-header {\n h1 {\n text-align: center;\n }\n\n .post-authorName {\n color: rgba($text, 0.7);\n font-size: 14px;\n font-weight: 900;\n margin-top: 0;\n padding: 0;\n text-align: center;\n }\n\n .authorPhoto {\n border-radius: 50%;\n height: 50px;\n left: 50%;\n margin-left: -25px;\n overflow: hidden;\n position: absolute;\n top: -25px;\n width: 50px;\n }\n }\n}" }, { "path": "docs/_sass/_buttons.scss", "content": ".button {\n border: 1px solid $primary-bg;\n border-radius: 3px;\n color: $primary-bg;\n display: inline-block;\n font-size: 14px;\n font-weight: 900;\n line-height: 1.2em;\n padding: 10px;\n text-transform: uppercase;\n transition: background 0.3s, color 0.3s;\n\n &:hover {\n background: $primary-bg;\n color: $primary-overlay;\n }\n}\n\n.homeContainer {\n .button {\n border-color: $primary-overlay;\n border-width: 1px;\n color: $primary-overlay;\n\n &:hover {\n background: $primary-overlay;\n color: $primary-bg;\n }\n }\n}\n\n.blockButton {\n display: block;\n}\n\n.edit-page-link {\n float: right;\n font-size: 14px;\n font-weight: normal;\n line-height: 20px;\n opacity: 0.6;\n transition: opacity 0.5s;\n}\n\n.edit-page-link:hover {\n opacity: 1;\n}\n" }, { "path": "docs/_sass/_footer.scss", "content": ".footerContainer {\n background: $secondary-bg;\n color: $primary-bg;\n overflow: hidden;\n padding: 0 10px;\n text-align: left;\n\n .footerWrapper {\n border-top: 1px solid $primary-bg;\n padding: 0;\n\n .footerBlocks {\n align-items: center;\n align-content: center;\n display: flex;\n flex-flow: row wrap;\n margin: 0 -20px;\n padding: 10px 0;\n }\n\n .footerSection {\n box-sizing: border-box;\n flex: 1 1 25%;\n font-size: 14px;\n min-width: 275px;\n padding: 0px 20px;\n\n a {\n border: 0;\n color: inherit;\n display: inline-block;\n line-height: 1.2em;\n }\n\n .footerLink {\n padding-right: 20px;\n }\n }\n\n .fbOpenSourceFooter {\n align-items: center;\n display: flex;\n flex-flow: row nowrap;\n max-width: 25%;\n\n .facebookOSSLogoSvg {\n flex: 0 0 31px;\n height: 30px;\n margin-right: 10px;\n width: 31px;\n\n path {\n fill: $primary-bg;\n }\n\n .middleRing {\n opacity: 0.7;\n }\n\n .innerRing {\n opacity: 0.45;\n }\n }\n\n h2 {\n display: block;\n font-weight: 900;\n line-height: 1em;\n }\n }\n }\n}\n\n@media only screen and (min-width: 900px) {\n .footerSection {\n &.rightAlign {\n margin-left: auto;\n max-width: 25%;\n text-align: right;\n }\n }\n}" }, { "path": "docs/_sass/_gridBlock.scss", "content": ".gridBlock {\n margin: -5px 0;\n padding: 0;\n padding-bottom: 20px;\n\n .blockElement {\n padding: 5px 0;\n\n img {\n max-width: 100%;\n }\n\n h3 {\n border-bottom: 1px solid rgba($primary-bg, 0.5);\n color: $primary-bg;\n font-size: 18px;\n margin: 0;\n padding: 10px 0;\n }\n }\n\n .gridClear {\n clear: both;\n }\n\n}\n\n.gridBlock .alignCenter {\n\ttext-align: center;\n}\n.gridBlock .alignRight {\n\ttext-align: right;\n}\n.gridBlock .imageAlignSide {\n\talign-items: center;\n\tdisplay: flex;\n\tflex-flow: row wrap;\n}\n.blockImage {\n\tmax-width: 150px;\n\twidth: 50%;\n}\n.imageAlignTop .blockImage {\n\tmargin-bottom: 20px;\n}\n.imageAlignTop.alignCenter .blockImage {\n\tmargin-left: auto;\n\tmargin-right: auto;\n}\n.imageAlignSide .blockImage {\n\tflex: 0 1 100px;\n\tmargin-right: 20px;\n}\n.imageAlignSide .blockContent {\n\tflex: 1 1;\n}\n\n@media only screen and (max-width: 1023px) {\n\t.responsiveList .blockContent {\n\t\tposition: relative;\n\t}\n\t.responsiveList .blockContent > div {\n\t\tpadding-left: 20px;\n\t}\n\t.responsiveList .blockContent::before {\n\t\tcontent: \"\\2022\";\n\t\tposition: absolute;\n\t}\n}\n\n@media only screen and (min-width: 1024px) {\n .gridBlock {\n display: flex;\n flex-direction: row;\n flex-wrap: wrap;\n margin: -10px -10px 10px -10px;\n\n .twoByGridBlock {\n box-sizing: border-box;\n flex: 1 0 50%;\n padding: 10px;\n }\n\n .fourByGridBlock {\n box-sizing: border-box;\n flex: 1 0 25%;\n padding: 10px;\n }\n }\n\n h2 + .gridBlock {\n padding-top: 20px;\n }\n}\n\n@media only screen and (min-width: 1400px) {\n .gridBlock {\n display: flex;\n flex-direction: row;\n flex-wrap: wrap;\n margin: -10px -20px 10px -20px;\n\n .twoByGridBlock {\n box-sizing: border-box;\n flex: 1 0 50%;\n padding: 10px 20px;\n }\n\n .fourByGridBlock {\n box-sizing: border-box;\n flex: 1 0 25%;\n padding: 10px 20px;\n }\n }\n}" }, { "path": "docs/_sass/_header.scss", "content": ".fixedHeaderContainer {\n background: $primary-bg;\n color: $primary-overlay;\n height: $header-height;\n padding: $header-ptop 0 $header-pbot;\n position: fixed;\n width: 100%;\n z-index: 9999;\n\n a {\n align-items: center;\n border: 0;\n color: $primary-overlay;\n display: flex;\n flex-flow: row nowrap;\n height: $header-height;\n }\n\n header {\n display: flex;\n flex-flow: row nowrap;\n position: relative;\n text-align: left;\n\n img {\n height: 24px;\n margin-right: 10px;\n }\n\n h2 {\n display: block;\n font-family: $header-font-family;\n font-weight: 900;\n line-height: 18px;\n position: relative;\n }\n }\n}\n\n.navigationFull {\n height: 34px;\n margin-left: auto;\n\n nav {\n position: relative;\n\n ul {\n display: flex;\n flex-flow: row nowrap;\n margin: 0 -10px;\n\n li {\n padding: 0 10px;\n display: block;\n\n a {\n border: 0;\n color: $primary-overlay-special;\n font-size: 16px;\n font-weight: 400;\n line-height: 1.2em;\n\n &:hover {\n border-bottom: 2px solid $primary-overlay;\n color: $primary-overlay;\n }\n }\n\n &.navItemActive {\n a {\n color: $primary-overlay;\n }\n }\n }\n }\n }\n}\n\n/* 900px\n\n\n .fixedHeaderContainer {\n .navigationWrapper {\n nav {\n padding: 0 1em;\n position: relative;\n top: -9px;\n\n ul {\n margin: 0 -0.4em;\n li {\n display: inline-block;\n\n a {\n padding: 14px 0.4em;\n border: 0;\n color: $primary-overlay-special;\n display: inline-block;\n\n &:hover {\n color: $primary-overlay;\n }\n }\n\n &.navItemActive {\n a {\n color: $primary-overlay;\n }\n }\n }\n }\n }\n\n &.navigationFull {\n display: inline-block;\n }\n\n &.navigationSlider {\n display: none;\n }\n }\n }\n\n 1200px\n\n .fixedHeaderContainer {\n header {\n max-width: 1100px;\n }\n }\n\n 1500px\n .fixedHeaderContainer {\n header {\n max-width: 1400px;\n }\n }\n */" }, { "path": "docs/_sass/_poweredby.scss", "content": ".poweredByContainer {\n background: $primary-bg;\n color: $primary-overlay;\n margin-bottom: 20px;\n\n a {\n color: $primary-overlay;\n }\n\n .poweredByWrapper {\n h2 {\n border-color: $primary-overlay-special;\n color: $primary-overlay-special;\n }\n }\n\n .poweredByMessage {\n color: $primary-overlay-special;\n font-size: 14px;\n padding-top: 20px;\n }\n}\n\n.poweredByItems {\n display: flex;\n flex-flow: row wrap;\n margin: 0 -10px;\n}\n\n.poweredByItem {\n box-sizing: border-box;\n flex: 1 0 50%;\n line-height: 1.1em;\n padding: 5px 10px;\n\n &.itemLarge {\n flex-basis: 100%;\n padding: 10px;\n text-align: center;\n\n &:nth-child(4) {\n padding-bottom: 20px;\n }\n\n img {\n max-height: 30px;\n }\n }\n}\n\n@media only screen and (min-width: 480px) {\n .itemLarge {\n flex-basis: 50%;\n max-width: 50%;\n }\n}\n\n@media only screen and (min-width: 1024px) {\n .poweredByItem {\n flex-basis: 25%;\n max-width: 25%;\n\n &.itemLarge {\n padding-bottom: 20px;\n text-align: left;\n }\n }\n}\n\n" }, { "path": "docs/_sass/_promo.scss", "content": ".promoSection {\n display: flex;\n flex-flow: column wrap;\n font-size: 125%;\n line-height: 1.6em;\n margin: -10px 0;\n position: relative;\n z-index: 99;\n\n .promoRow {\n padding: 10px 0;\n\n .pluginWrapper {\n display: block;\n\n &.ghWatchWrapper, &.ghStarWrapper {\n height: 28px;\n }\n }\n\n .pluginRowBlock {\n display: flex;\n flex-flow: row wrap;\n margin: 0 -2px;\n\n .pluginWrapper {\n padding: 0 2px;\n }\n }\n }\n}\n\niframe.pluginIframe {\n height: 500px;\n margin-top: 20px;\n width: 100%;\n}\n\n.iframeContent {\n display: none;\n}\n\n.iframePreview {\n display: inline-block;\n margin-top: 20px;\n}\n\n@media only screen and (min-width: 1024px) {\n .iframeContent {\n display: block;\n }\n .iframePreview {\n display: none;\n }\n}" }, { "path": "docs/_sass/_react_docs_nav.scss", "content": ".docsNavContainer {\n background: $sidenav;\n height: 35px;\n left: 0;\n position: fixed;\n width: 100%;\n z-index: 100;\n}\n\n.docMainWrapper {\n .wrapper {\n &.mainWrapper {\n padding-left: 0;\n padding-right: 0;\n padding-top: 10px;\n }\n }\n}\n\n.docsSliderActive {\n .docsNavContainer {\n box-sizing: border-box;\n height: 100%;\n overflow-y: auto;\n -webkit-overflow-scrolling: touch;\n padding-bottom: 50px;\n }\n\n .mainContainer {\n display: none;\n }\n}\n\n.navBreadcrumb {\n box-sizing: border-box;\n display: flex;\n flex-flow: row nowrap;\n font-size: 12px;\n height: 35px;\n overflow: hidden;\n padding: 5px 10px;\n\n a, span {\n border: 0;\n color: $sidenav-text;\n }\n\n i {\n padding: 0 3px;\n }\n}\n\nnav.toc {\n position: relative;\n\n section {\n padding: 0px;\n position: relative;\n\n .navGroups {\n display: none;\n padding: 40px 10px 10px;\n }\n }\n\n .toggleNav {\n background: $sidenav;\n color: $sidenav-text;\n position: relative;\n transition: background-color 0.3s, color 0.3s;\n\n .navToggle {\n cursor: pointer;\n height: 24px;\n margin-right: 10px;\n position: relative;\n text-align: left;\n width: 18px;\n\n &::before, &::after {\n content: \"\";\n position: absolute;\n top: 50%;\n left: 0;\n left: 8px;\n width: 3px;\n height: 6px;\n border: 5px solid $sidenav-text;\n border-width: 5px 0;\n margin-top: -8px;\n transform: rotate(45deg);\n z-index: 1;\n }\n\n &::after {\n transform: rotate(-45deg);\n }\n\n i {\n &::before, &::after {\n content: \"\";\n position: absolute;\n top: 50%;\n left: 2px;\n background: transparent;\n border-width: 0 5px 5px;\n border-style: solid;\n border-color: transparent $sidenav-text;\n height: 0;\n margin-top: -7px;\n opacity: 1;\n width: 5px;\n z-index: 10;\n }\n\n &::after {\n border-width: 5px 5px 0;\n margin-top: 2px;\n }\n }\n }\n\n .navGroup {\n background: $sidenav-overlay;\n margin: 1px 0;\n\n ul {\n display: none;\n }\n\n h3 {\n background: $sidenav-overlay;\n color: $sidenav-text;\n cursor: pointer;\n font-size: 14px;\n font-weight: 400;\n line-height: 1.2em;\n padding: 10px;\n transition: color 0.2s;\n\n i:not(:empty) {\n width: 16px;\n height: 16px;\n display: inline-block;\n box-sizing: border-box;\n text-align: center;\n color: rgba($sidenav-text, 0.5);\n margin-right: 10px;\n transition: color 0.2s;\n }\n\n &:hover {\n color: $primary-bg;\n\n i:not(:empty) {\n color: $primary-bg;\n }\n }\n }\n\n &.navGroupActive {\n background: $sidenav-active;\n color: $sidenav-text;\n\n ul {\n display: block;\n padding-bottom: 10px;\n padding-top: 10px;\n\n .navListSubItems {\n padding-bottom: 0px;\n padding-top: 0px;\n }\n }\n\n h3 {\n background: $primary-bg;\n color: $primary-overlay;\n\n i {\n display: none;\n }\n }\n }\n }\n\n ul {\n padding-left: 0;\n padding-right: 18px;\n\n li {\n list-style-type: none;\n padding-bottom: 0;\n padding-left: 0;\n\n a {\n border: none;\n color: $sidenav-text;\n display: inline-block;\n font-size: 14px;\n line-height: 1.1em;\n margin: 2px 10px 2px;\n padding: 1px 0 1px;\n transition: color 0.3s;\n\n &:hover,\n &:focus {\n color: $primary-bg;\n }\n\n &.navItemActive {\n color: $primary-bg;\n font-weight: 900;\n }\n\n &.navItem {\n font-weight: bold;\n }\n\n }\n\n .navListSubItem {\n padding-top: 0;\n margin-left: 20px;\n\n a.navItem {\n font-weight: normal;\n }\n }\n }\n }\n }\n\n .toggleNavActive {\n .navBreadcrumb {\n background: $sidenav;\n margin-bottom: 20px;\n position: fixed;\n width: 100%;\n }\n\n section {\n .navGroups {\n display: block;\n }\n }\n\n\n .navToggle {\n &::before, &::after {\n border-width: 6px 0;\n height: 0px;\n margin-top: -6px;\n }\n\n i {\n opacity: 0;\n }\n }\n }\n}\n\n.docsNavVisible {\n .navPusher {\n .mainContainer {\n padding-top: 35px;\n }\n }\n}\n\n@media only screen and (min-width: 900px) {\n .navBreadcrumb {\n padding: 5px 0;\n }\n\n nav.toc {\n section {\n .navGroups {\n padding: 40px 0 0;\n }\n }\n }\n}\n\n@media only screen and (min-width: 1024px) {\n .navToggle {\n display: none;\n }\n\n .docsSliderActive {\n .mainContainer {\n display: block;\n }\n }\n\n .docsNavVisible {\n .navPusher {\n .mainContainer {\n padding-top: 0;\n }\n }\n }\n\n .docsNavContainer {\n background: none;\n box-sizing: border-box;\n height: auto;\n margin: 40px 40px 0 0;\n overflow-y: auto;\n position: relative;\n width: 300px;\n }\n\n nav.toc {\n section {\n .navGroups {\n display: block;\n padding-top: 0px;\n }\n }\n\n .toggleNavActive {\n .navBreadcrumb {\n margin-bottom: 0;\n position: relative;\n }\n }\n }\n\n .docMainWrapper {\n display: flex;\n flex-flow: row nowrap;\n margin-bottom: 40px;\n\n .wrapper {\n padding-left: 0;\n padding-right: 0;\n\n &.mainWrapper {\n padding-top: 0;\n }\n }\n }\n\n .navBreadcrumb {\n display: none;\n h2 {\n padding: 0 10px;\n }\n }\n\n}" }, { "path": "docs/_sass/_react_header_nav.scss", "content": ".navigationFull {\n display: none;\n}\n\n.navigationSlider {\n position: absolute;\n right: 0px;\n\n .navSlideout {\n cursor: pointer;\n padding-top: 4px;\n position: absolute;\n right: 10px;\n top: 0;\n transition: top 0.3s;\n z-index: 101;\n }\n\n .slidingNav {\n background: $secondary-bg;\n box-sizing: border-box;\n height: 0px;\n overflow-x: hidden;\n padding: 0;\n position: absolute;\n right: 0px;\n top: 0;\n transition: height 0.3s cubic-bezier(0.68, -0.55, 0.265, 1.55), width 0.3s cubic-bezier(0.68, -0.55, 0.265, 1.55);\n width: 0;\n\n ul {\n flex-flow: column nowrap;\n list-style: none;\n padding: 10px;\n\n li {\n margin: 0;\n padding: 2px 0;\n\n a {\n color: $primary-bg;\n display: inline;\n margin: 3px 5px;\n padding: 2px 0px;\n transition: background-color 0.3s;\n\n &:focus,\n &:hover {\n border-bottom: 2px solid $primary-bg;\n }\n }\n }\n }\n }\n\n .navSlideoutActive {\n .slidingNav {\n height: auto;\n padding-top: $header-height + $header-pbot;\n width: 300px;\n }\n\n .navSlideout {\n top: -2px;\n .menuExpand {\n span:nth-child(1) {\n background-color: $text;\n top: 16px;\n transform: rotate(45deg);\n }\n span:nth-child(2) {\n opacity: 0;\n }\n span:nth-child(3) {\n background-color: $text;\n transform: rotate(-45deg);\n }\n }\n }\n }\n}\n\n.menuExpand {\n display: flex;\n flex-flow: column nowrap;\n height: 20px;\n justify-content: space-between;\n\n span {\n background: $primary-overlay;\n border-radius: 3px;\n display: block;\n flex: 0 0 4px;\n height: 4px;\n position: relative;\n top: 0;\n transition: background-color 0.3s, top 0.3s, opacity 0.3s, transform 0.3s;\n width: 20px;\n }\n}\n\n.navPusher {\n border-top: $header-height + $header-ptop + $header-pbot solid $primary-bg;\n\tposition: relative;\n\tleft: 0;\n\tz-index: 99;\n\theight: 100%;\n\n &::after {\n position: absolute;\n top: 0;\n right: 0;\n width: 0;\n height: 0;\n background: rgba(0,0,0,0.4);\n content: '';\n opacity: 0;\n -webkit-transition: opacity 0.5s, width 0.1s 0.5s, height 0.1s 0.5s;\n transition: opacity 0.5s, width 0.1s 0.5s, height 0.1s 0.5s;\n }\n\n .sliderActive &::after {\n width: 100%;\n height: 100%;\n opacity: 1;\n -webkit-transition: opacity 0.5s;\n transition: opacity 0.5s;\n z-index: 100;\n }\n}\n\n\n@media only screen and (min-width: 1024px) {\n .navigationFull {\n display: block;\n }\n\n .navigationSlider {\n display: none;\n }\n}" }, { "path": "docs/_sass/_reset.scss", "content": "html, body, div, span, applet, object, iframe,\nh1, h2, h3, h4, h5, h6, p, blockquote, pre,\na, abbr, acronym, address, big, cite, code,\ndel, dfn, em, img, ins, kbd, q, s, samp,\nsmall, strike, strong, sub, sup, tt, var,\nb, u, i, center,\ndl, dt, dd, ol, ul, li,\nfieldset, form, label, legend,\ntable, caption, tbody, tfoot, thead, tr, th, td,\narticle, aside, canvas, details, embed,\nfigure, figcaption, footer, header, hgroup,\nmenu, nav, output, ruby, section, summary,\ntime, mark, audio, video {\n\tmargin: 0;\n\tpadding: 0;\n\tborder: 0;\n\tfont-size: 100%;\n\tfont: inherit;\n\tvertical-align: baseline;\n}\n/* HTML5 display-role reset for older browsers */\narticle, aside, details, figcaption, figure,\nfooter, header, hgroup, menu, nav, section {\n\tdisplay: block;\n}\nbody {\n\tline-height: 1;\n}\nol, ul {\n\tlist-style: none;\n}\nblockquote, q {\n\tquotes: none;\n}\nblockquote:before, blockquote:after,\nq:before, q:after {\n\tcontent: '';\n\tcontent: none;\n}\ntable {\n\tborder-collapse: collapse;\n\tborder-spacing: 0;\n}\n" }, { "path": "docs/_sass/_search.scss", "content": "input[type=\"search\"] {\n -moz-appearance: none;\n -webkit-appearance: none;\n}\n\n.navSearchWrapper {\n align-self: center;\n position: relative;\n\n &::before {\n border: 3px solid $primary-overlay-special;\n border-radius: 50%;\n content: \" \";\n display: block;\n height: 6px;\n left: 15px;\n width: 6px;\n position: absolute;\n top: 4px;\n z-index: 1;\n }\n\n &::after {\n background: $primary-overlay-special;\n content: \" \";\n height: 7px;\n left: 24px;\n position: absolute;\n transform: rotate(-45deg);\n top: 12px;\n width: 3px;\n z-index: 1;\n }\n\n .aa-dropdown-menu {\n background: $secondary-bg;\n border: 3px solid rgba($text, 0.25);\n color: $text;\n font-size: 14px;\n left: auto !important;\n line-height: 1.2em;\n right: 0 !important;\n\n .algolia-docsearch-suggestion--category-header {\n background: $primary-overlay-special;\n color: $primary-bg;\n\n .algolia-docsearch-suggestion--highlight {\n background-color: $primary-bg;\n color: $primary-overlay;\n }\n }\n\n .algolia-docsearch-suggestion--title .algolia-docsearch-suggestion--highlight,\n .algolia-docsearch-suggestion--subcategory-column .algolia-docsearch-suggestion--highlight {\n color: $primary-bg;\n }\n\n .algolia-docsearch-suggestion__secondary,\n .algolia-docsearch-suggestion--subcategory-column {\n border-color: rgba($text, 0.3);\n }\n }\n}\n\ninput#search_input {\n padding-left: 25px;\n font-size: 14px;\n line-height: 20px;\n border-radius: 20px;\n background-color: rgba($primary-overlay-special, 0.25);\n border: none;\n color: rgba($primary-overlay-special, 0);\n outline: none;\n position: relative;\n transition: background-color .2s cubic-bezier(0.68, -0.55, 0.265, 1.55), width .2s cubic-bezier(0.68, -0.55, 0.265, 1.55), color .2s ease;\n width: 60px;\n\n &:focus, &:active {\n background-color: $secondary-bg;\n color: $text;\n width: 240px;\n }\n}\n\n.navigationSlider {\n .navSearchWrapper {\n &::before {\n left: 6px;\n top: 6px;\n }\n\n &::after {\n left: 15px;\n top: 14px;\n }\n }\n\n input#search_input_react {\n box-sizing: border-box;\n padding-left: 25px;\n font-size: 14px;\n line-height: 20px;\n border-radius: 20px;\n background-color: rgba($primary-overlay-special, 0.25);\n border: none;\n color: $text;\n outline: none;\n position: relative;\n transition: background-color .2s cubic-bezier(0.68, -0.55, 0.265, 1.55), width .2s cubic-bezier(0.68, -0.55, 0.265, 1.55), color .2s ease;\n width: 100%;\n\n &:focus, &:active {\n background-color: $primary-bg;\n color: $primary-overlay;\n }\n }\n\n .algolia-docsearch-suggestion--subcategory-inline {\n display: none;\n }\n\n & > span {\n width: 100%;\n }\n\n .aa-dropdown-menu {\n background: $secondary-bg;\n border: 0px solid $secondary-bg;\n color: $text;\n font-size: 12px;\n line-height: 2em;\n max-height: 140px;\n min-width: auto;\n overflow-y: scroll;\n -webkit-overflow-scrolling: touch;\n padding: 0;\n border-radius: 0;\n position: relative !important;\n width: 100%;\n }\n}" }, { "path": "docs/_sass/_slideshow.scss", "content": ".slideshow {\n position: relative;\n\n .slide {\n display: none;\n\n img {\n display: block;\n margin: 0 auto;\n }\n\n &.slideActive {\n display: block;\n }\n\n a {\n border: none;\n display: block;\n }\n }\n\n .pagination {\n display: block;\n margin: -10px;\n padding: 1em 0;\n text-align: center;\n width: 100%;\n\n .pager {\n background: transparent;\n border: 2px solid rgba(255, 255, 255, 0.5);\n border-radius: 50%;\n cursor: pointer;\n display: inline-block;\n height: 12px;\n margin: 10px;\n transition: background-color 0.3s, border-color 0.3s;\n width: 12px;\n\n &.pagerActive {\n background: rgba(255, 255, 255, 0.5);\n border-width: 4px;\n height: 8px;\n width: 8px;\n }\n }\n }\n}\n" }, { "path": "docs/_sass/_syntax-highlighting.scss", "content": "\n\n.rougeHighlight { background-color: $code-bg; color: #93a1a1 }\n.rougeHighlight .c { color: #586e75 } /* Comment */\n.rougeHighlight .err { color: #93a1a1 } /* Error */\n.rougeHighlight .g { color: #93a1a1 } /* Generic */\n.rougeHighlight .k { color: #859900 } /* Keyword */\n.rougeHighlight .l { color: #93a1a1 } /* Literal */\n.rougeHighlight .n { color: #93a1a1 } /* Name */\n.rougeHighlight .o { color: #859900 } /* Operator */\n.rougeHighlight .x { color: #cb4b16 } /* Other */\n.rougeHighlight .p { color: #93a1a1 } /* Punctuation */\n.rougeHighlight .cm { color: #586e75 } /* Comment.Multiline */\n.rougeHighlight .cp { color: #859900 } /* Comment.Preproc */\n.rougeHighlight .c1 { color: #72c02c; } /* Comment.Single */\n.rougeHighlight .cs { color: #859900 } /* Comment.Special */\n.rougeHighlight .gd { color: #2aa198 } /* Generic.Deleted */\n.rougeHighlight .ge { color: #93a1a1; font-style: italic } /* Generic.Emph */\n.rougeHighlight .gr { color: #dc322f } /* Generic.Error */\n.rougeHighlight .gh { color: #cb4b16 } /* Generic.Heading */\n.rougeHighlight .gi { color: #859900 } /* Generic.Inserted */\n.rougeHighlight .go { color: #93a1a1 } /* Generic.Output */\n.rougeHighlight .gp { color: #93a1a1 } /* Generic.Prompt */\n.rougeHighlight .gs { color: #93a1a1; font-weight: bold } /* Generic.Strong */\n.rougeHighlight .gu { color: #cb4b16 } /* Generic.Subheading */\n.rougeHighlight .gt { color: #93a1a1 } /* Generic.Traceback */\n.rougeHighlight .kc { color: #cb4b16 } /* Keyword.Constant */\n.rougeHighlight .kd { color: #268bd2 } /* Keyword.Declaration */\n.rougeHighlight .kn { color: #859900 } /* Keyword.Namespace */\n.rougeHighlight .kp { color: #859900 } /* Keyword.Pseudo */\n.rougeHighlight .kr { color: #268bd2 } /* Keyword.Reserved */\n.rougeHighlight .kt { color: #dc322f } /* Keyword.Type */\n.rougeHighlight .ld { color: #93a1a1 } /* Literal.Date */\n.rougeHighlight .m { color: #2aa198 } /* Literal.Number */\n.rougeHighlight .s { color: #2aa198 } /* Literal.String */\n.rougeHighlight .na { color: #93a1a1 } /* Name.Attribute */\n.rougeHighlight .nb { color: #B58900 } /* Name.Builtin */\n.rougeHighlight .nc { color: #268bd2 } /* Name.Class */\n.rougeHighlight .no { color: #cb4b16 } /* Name.Constant */\n.rougeHighlight .nd { color: #268bd2 } /* Name.Decorator */\n.rougeHighlight .ni { color: #cb4b16 } /* Name.Entity */\n.rougeHighlight .ne { color: #cb4b16 } /* Name.Exception */\n.rougeHighlight .nf { color: #268bd2 } /* Name.Function */\n.rougeHighlight .nl { color: #93a1a1 } /* Name.Label */\n.rougeHighlight .nn { color: #93a1a1 } /* Name.Namespace */\n.rougeHighlight .nx { color: #93a1a1 } /* Name.Other */\n.rougeHighlight .py { color: #93a1a1 } /* Name.Property */\n.rougeHighlight .nt { color: #268bd2 } /* Name.Tag */\n.rougeHighlight .nv { color: #268bd2 } /* Name.Variable */\n.rougeHighlight .ow { color: #859900 } /* Operator.Word */\n.rougeHighlight .w { color: #93a1a1 } /* Text.Whitespace */\n.rougeHighlight .mf { color: #2aa198 } /* Literal.Number.Float */\n.rougeHighlight .mh { color: #2aa198 } /* Literal.Number.Hex */\n.rougeHighlight .mi { color: #2aa198 } /* Literal.Number.Integer */\n.rougeHighlight .mo { color: #2aa198 } /* Literal.Number.Oct */\n.rougeHighlight .sb { color: #586e75 } /* Literal.String.Backtick */\n.rougeHighlight .sc { color: #2aa198 } /* Literal.String.Char */\n.rougeHighlight .sd { color: #93a1a1 } /* Literal.String.Doc */\n.rougeHighlight .s2 { color: #2aa198 } /* Literal.String.Double */\n.rougeHighlight .se { color: #cb4b16 } /* Literal.String.Escape */\n.rougeHighlight .sh { color: #93a1a1 } /* Literal.String.Heredoc */\n.rougeHighlight .si { color: #2aa198 } /* Literal.String.Interpol */\n.rougeHighlight .sx { color: #2aa198 } /* Literal.String.Other */\n.rougeHighlight .sr { color: #dc322f } /* Literal.String.Regex */\n.rougeHighlight .s1 { color: #2aa198 } /* Literal.String.Single */\n.rougeHighlight .ss { color: #2aa198 } /* Literal.String.Symbol */\n.rougeHighlight .bp { color: #268bd2 } /* Name.Builtin.Pseudo */\n.rougeHighlight .vc { color: #268bd2 } /* Name.Variable.Class */\n.rougeHighlight .vg { color: #268bd2 } /* Name.Variable.Global */\n.rougeHighlight .vi { color: #268bd2 } /* Name.Variable.Instance */\n.rougeHighlight .il { color: #2aa198 } /* Literal.Number.Integer.Long */\n\n.highlighter-rouge {\n color: darken(#72c02c, 8%);\n font: 800 12px/1.5em Hack, monospace;\n max-width: 100%;\n\n .rougeHighlight {\n border-radius: 3px;\n margin: 20px 0;\n padding: 0px;\n overflow-x: scroll;\n -webkit-overflow-scrolling: touch;\n\n table {\n background: none;\n border: none;\n\n tbody {\n tr {\n background: none;\n display: flex;\n flex-flow: row nowrap;\n\n td {\n display: block;\n flex: 1 1;\n\n &.gutter {\n border-right: 1px solid lighten($code-bg, 10%);\n color: lighten($code-bg, 15%);\n margin-right: 10px;\n max-width: 40px;\n padding-right: 10px;\n\n pre {\n max-width: 20px;\n }\n }\n }\n }\n }\n }\n }\n}\n\np > .highlighter-rouge,\nli > .highlighter-rouge,\na > .highlighter-rouge {\n font-size: 16px;\n font-weight: 400;\n line-height: inherit;\n}\n\na:hover {\n .highlighter-rouge {\n color: white;\n }\n}" }, { "path": "docs/_sass/_tables.scss", "content": "table {\n background: $lightergrey;\n border: 1px solid $lightgrey;\n border-collapse: collapse;\n display:table;\n margin: 20px 0;\n\n thead {\n border-bottom: 1px solid $lightgrey;\n display: table-header-group;\n }\n tbody {\n display: table-row-group;\n }\n tr {\n display: table-row;\n &:nth-of-type(odd) {\n background: $greyish;\n }\n\n th, td {\n border-right: 1px dotted $lightgrey;\n display: table-cell;\n font-size: 14px;\n line-height: 1.3em;\n padding: 10px;\n text-align: left;\n\n &:last-of-type {\n border-right: 0;\n }\n\n code {\n color: $green;\n display: inline-block;\n font-size: 12px;\n }\n }\n\n th {\n color: #000000;\n font-weight: bold;\n font-family: $header-font-family;\n text-transform: uppercase;\n }\n }\n}" }, { "path": "docs/blog/all.html", "content": "---\nid: all\nlayout: blog\ncategory: blog\n---\n\n
    \n
    \n

    All Posts

    \n {% for post in site.posts %}\n {% assign author = site.data.authors[post.author] %}\n

    \n \n {{ post.title }}\n \n on {{ post.date | date: \"%B %e, %Y\" }} by {{ author.display_name }}\n

    \n {% endfor %}\n
    \n
    \n" }, { "path": "docs/blog/index.html", "content": "---\nid: blog \ntitle: Blog\nlayout: blog \ncategory: blog\n---\n\n
    \n {% for page in site.posts %}\n {% include post.html truncate=true %}\n {% endfor %}\n
    \n" }, { "path": "docs/css/main.scss", "content": "---\n# Only the main Sass file needs front matter (the dashes are enough)\n---\n@charset \"utf-8\";\n\n@font-face {\n\tfont-family: 'Lato';\n\tsrc: url(\"{{ '/static/fonts/LatoLatin-Italic.woff2' | relative_url }}\") format('woff2'),\n\t\turl(\"{{ '/static/fonts/LatoLatin-Italic.woff' | relative_url }}\") format('woff');\n\tfont-weight: normal;\n\tfont-style: italic;\n}\n\n@font-face {\n\tfont-family: 'Lato';\n\tsrc: url(\"{{ '/static/fonts/LatoLatin-Black.woff2' | relative_url }}\") format('woff2'),\n\t\turl(\"{{ '/static/fonts/LatoLatin-Black.woff' | relative_url }}\") format('woff');\n\tfont-weight: 900;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: 'Lato';\n\tsrc: url(\"{{ '/static/fonts/LatoLatin-BlackItalic.woff2' | relative_url }}\") format('woff2'),\n\t\turl(\"{{ '/static/fonts/LatoLatin-BlackItalic.woff' | relative_url }}\") format('woff');\n\tfont-weight: 900;\n\tfont-style: italic;\n}\n\n@font-face {\n\tfont-family: 'Lato';\n\tsrc: url(\"{{ '/static/fonts/LatoLatin-Light.woff2' | relative_url }}\") format('woff2'),\n\t\turl(\"{{ '/static/fonts/LatoLatin-Light.woff' | relative_url }}\") format('woff');\n\tfont-weight: 300;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: 'Lato';\n\tsrc: url(\"{{ '/static/fonts/LatoLatin-Regular.woff2' | relative_url }}\") format('woff2'),\n\t\turl(\"{{ '/static/fonts/LatoLatin-Regular.woff' | relative_url }}\") format('woff');\n\tfont-weight: normal;\n\tfont-style: normal;\n}\n\n// Our variables\n$base-font-family: 'Lato', Calibri, Arial, sans-serif;\n$header-font-family: 'Lato', 'Helvetica Neue', Arial, sans-serif;\n$base-font-size: 18px;\n$small-font-size: $base-font-size * 0.875;\n$base-line-height: 1.4em;\n\n$spacing-unit: 12px;\n\n// Two configured colors (see _config.yml)\n$primary-bg: \t\t\t\t{{ site.color.primary }};\n$secondary-bg: \t\t\t\t{{ site.color.secondary }};\n\n// $primary-bg overlays\n{% if site.color.primary-overlay == 'light' %}\n$primary-overlay: \t\tdarken($primary-bg, 70%);\n$primary-overlay-special:\t\tdarken($primary-bg, 40%);\n{% else %}\n$primary-overlay: \t\t \t#fff;\n$primary-overlay-special:\t\tlighten($primary-bg, 30%);\n{% endif %}\n\n// $secondary-bg overlays\n{% if site.color.secondary-overlay == 'light' %}\n$text: #393939;\n$sidenav: \t\t\t\t darken($secondary-bg, 20%);\n$sidenav-text: \t\t\t$text;\n$sidenav-overlay: \tdarken($sidenav, 10%);\n$sidenav-active: \t\tlighten($sidenav, 10%);\n{% else %}\n$text: #fff;\n$sidenav: \t\t\t\t lighten($secondary-bg, 20%);\n$sidenav-text: \t\t\t$text;\n$sidenav-overlay: \tlighten($sidenav, 10%);\n$sidenav-active: \t\tdarken($sidenav, 10%);\n{% endif %}\n\n$code-bg: \t\t\t\t\t#002b36;\n\n$header-height: 34px;\n$header-ptop: 10px;\n$header-pbot: 8px;\n\n// Width of the content area\n$content-width: 900px;\n\n// Table setting variables\n$lightergrey: #F8F8F8;\n$greyish: #E8E8E8;\n$lightgrey: #B0B0B0;\n$green: #2db04b;\n\n// Using media queries with like this:\n// @include media-query($on-palm) {\n// .wrapper {\n// padding-right: $spacing-unit / 2;\n// padding-left: $spacing-unit / 2;\n// }\n// }\n@mixin media-query($device) {\n @media screen and (max-width: $device) {\n @content;\n }\n}\n\n\n\n// Import partials from `sass_dir` (defaults to `_sass`)\n@import\n \"reset\",\n \"base\",\n\t\t\t\t\"header\",\n \"search\",\n \"syntax-highlighting\",\n\t\t\t\t\"promo\",\n\t\t\t\t\"buttons\",\n\t\t\t\t\"gridBlock\",\n\t\t\t\t\"poweredby\",\n\t\t\t\t\"footer\",\n\t\t\t\t\"react_header_nav\",\n\t\t\t\t\"react_docs_nav\",\n\t\t\t\t\"tables\",\n\t\t\t\t\"blog\"\n;\n\n// Anchor links\n// http://ben.balter.com/2014/03/13/pages-anchor-links/\n.header-link {\n position: absolute;\n margin-left: 0.2em;\n opacity: 0;\n\n -webkit-transition: opacity 0.2s ease-in-out 0.1s;\n -moz-transition: opacity 0.2s ease-in-out 0.1s;\n -ms-transition: opacity 0.2s ease-in-out 0.1s;\n}\n\nh2:hover .header-link,\nh3:hover .header-link,\nh4:hover .header-link,\nh5:hover .header-link,\nh6:hover .header-link {\n opacity: 1;\n}\n\n.videoBlock {\n text-align: center;\n}\n\n/* Social Banner */\n.socialBanner {\n\tfont-weight: bold;\n\tfont-size: 20px;\n\tpadding: 20px;\n\tmax-width: 768px;\n\tmargin: 0 auto;\n\ttext-align: center;\n}\n" }, { "path": "docs/docs/index.html", "content": "---\nid: docs\ntitle: Docs\nlayout: redirect\ndestination: quick_start.html\n---\n" }, { "path": "docs/feed.xml", "content": "---\nlayout: null\n---\n\n\n \n {{ site.title | xml_escape }}\n {{ site.description | xml_escape }}\n {{ '/' | absolute_url }}\n \n {{ site.time | date_to_rfc822 }}\n {{ site.time | date_to_rfc822 }}\n Jekyll v{{ jekyll.version }}\n {% for post in site.posts limit:10 %}\n \n {{ post.title | xml_escape }}\n {{ post.content | xml_escape }}\n {{ post.date | date_to_rfc822 }}\n {{ post.url | absolute_url }}\n {{ post.url | absolute_url }}\n {% for tag in post.tags %}\n {{ tag | xml_escape }}\n {% endfor %}\n {% for cat in post.categories %}\n {{ cat | xml_escape }}\n {% endfor %}\n \n {% endfor %}\n \n\n" }, { "path": "docs/index.md", "content": "---\nlayout: home\ntitle: Prophet\nid: home\n---\n## Watch Introductory Video\n\n
    \n \n
    \n\nProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.\n\nProphet is [open source software](https://code.facebook.com/projects/) released by Facebook's [Core Data Science team](https://research.fb.com/category/data-science/). It is available for download on [CRAN](https://cran.r-project.org/package=prophet) and [PyPI](https://pypi.python.org/pypi/prophet/).\n\n-----\n\n**2023 Update:** We discuss our plans for the future of Prophet in this blog post: [facebook/prophet in 2023 and beyond](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138)\n\n-----\n\n{% include content/gridblocks.html data_source=site.data.features layout=\"fourColumn\" align=\"left\" %}\n" }, { "path": "docs/nbconvert_template.tpl", "content": "{%- extends 'markdown/index.md.j2' -%}\n\n{%- block header -%}\n---\nlayout: docs\ndocid: \"{{resources['metadata']['name']}}\"\ntitle: \"{{resources['metadata']['name'].replace('_', ' ').title()}}\"\npermalink: /docs/{{resources['metadata']['name']}}.html\nsubsections:\n{%- for cell in nb['cells'] if cell.cell_type == 'markdown' and '##' in cell.source -%}\n{% for line in cell.source.split('\\n') if line.startswith('##') %}\n - title: {{ line.lstrip('# ') }}\n id: {{ line.lstrip('# ').lower().replace(' ', '-') }}\n{%- endfor -%}\n{% endfor %}\n---\n{%- endblock header -%}\n\n{%- block any_cell -%}\n{%- if not cell.metadata.get(\"block_hidden\", False) -%}\n {{ super() }}\n{%- endif -%}\n{%- endblock any_cell -%}\n\n{% block input %}\n{%- if cell.source[:3] == \"%%R\" -%}\n```R\n# R\n{{ '\\n'.join(cell.source.split('\\n')[1:]) }}\n```\n{%- else -%}\n```python\n# Python\n{{ cell.source }}\n```\n{%- endif -%}\n{%- endblock input -%}\n\n{%- block output_group -%}\n{%- if not cell.metadata.get(\"output_hidden\", False) -%}\n {{ super() }}\n{%- endif -%}\n{%- endblock output_group -%}\n\n{%- block input_group -%}\n{%- if not cell.metadata.get(\"input_hidden\", False) -%}\n {{ super() }}\n{%- endif -%}\n{%- endblock input_group -%}\n\n{% block data_png %}\n![png](/prophet/static/{{ output.metadata.filenames['image/png'] }})\n{% endblock data_png %}\n\n{% block markdowncell %}\n{%- set lines = cell.source.split('\\n') -%}\n{%- for line in lines -%}\n{% if line.startswith('##') %}\n \n{% endif %}\n{{ line }}\n{% endfor %}\n{% endblock markdowncell %}\n" }, { "path": "examples/example_air_passengers.csv", "content": "ds,y\r1949-01-01,112\r1949-02-01,118\r1949-03-01,132\r1949-04-01,129\r1949-05-01,121\r1949-06-01,135\r1949-07-01,148\r1949-08-01,148\r1949-09-01,136\r1949-10-01,119\r1949-11-01,104\r1949-12-01,118\r1950-01-01,115\r1950-02-01,126\r1950-03-01,141\r1950-04-01,135\r1950-05-01,125\r1950-06-01,149\r1950-07-01,170\r1950-08-01,170\r1950-09-01,158\r1950-10-01,133\r1950-11-01,114\r1950-12-01,140\r1951-01-01,145\r1951-02-01,150\r1951-03-01,178\r1951-04-01,163\r1951-05-01,172\r1951-06-01,178\r1951-07-01,199\r1951-08-01,199\r1951-09-01,184\r1951-10-01,162\r1951-11-01,146\r1951-12-01,166\r1952-01-01,171\r1952-02-01,180\r1952-03-01,193\r1952-04-01,181\r1952-05-01,183\r1952-06-01,218\r1952-07-01,230\r1952-08-01,242\r1952-09-01,209\r1952-10-01,191\r1952-11-01,172\r1952-12-01,194\r1953-01-01,196\r1953-02-01,196\r1953-03-01,236\r1953-04-01,235\r1953-05-01,229\r1953-06-01,243\r1953-07-01,264\r1953-08-01,272\r1953-09-01,237\r1953-10-01,211\r1953-11-01,180\r1953-12-01,201\r1954-01-01,204\r1954-02-01,188\r1954-03-01,235\r1954-04-01,227\r1954-05-01,234\r1954-06-01,264\r1954-07-01,302\r1954-08-01,293\r1954-09-01,259\r1954-10-01,229\r1954-11-01,203\r1954-12-01,229\r1955-01-01,242\r1955-02-01,233\r1955-03-01,267\r1955-04-01,269\r1955-05-01,270\r1955-06-01,315\r1955-07-01,364\r1955-08-01,347\r1955-09-01,312\r1955-10-01,274\r1955-11-01,237\r1955-12-01,278\r1956-01-01,284\r1956-02-01,277\r1956-03-01,317\r1956-04-01,313\r1956-05-01,318\r1956-06-01,374\r1956-07-01,413\r1956-08-01,405\r1956-09-01,355\r1956-10-01,306\r1956-11-01,271\r1956-12-01,306\r1957-01-01,315\r1957-02-01,301\r1957-03-01,356\r1957-04-01,348\r1957-05-01,355\r1957-06-01,422\r1957-07-01,465\r1957-08-01,467\r1957-09-01,404\r1957-10-01,347\r1957-11-01,305\r1957-12-01,336\r1958-01-01,340\r1958-02-01,318\r1958-03-01,362\r1958-04-01,348\r1958-05-01,363\r1958-06-01,435\r1958-07-01,491\r1958-08-01,505\r1958-09-01,404\r1958-10-01,359\r1958-11-01,310\r1958-12-01,337\r1959-01-01,360\r1959-02-01,342\r1959-03-01,406\r1959-04-01,396\r1959-05-01,420\r1959-06-01,472\r1959-07-01,548\r1959-08-01,559\r1959-09-01,463\r1959-10-01,407\r1959-11-01,362\r1959-12-01,405\r1960-01-01,417\r1960-02-01,391\r1960-03-01,419\r1960-04-01,461\r1960-05-01,472\r1960-06-01,535\r1960-07-01,622\r1960-08-01,606\r1960-09-01,508\r1960-10-01,461\r1960-11-01,390\r1960-12-01,432\r" }, { "path": "examples/example_pedestrians_covid.csv", "content": "ds,y\n2017-06-02,39230\n2017-06-03,35290\n2017-06-04,27083\n2017-06-05,28727\n2017-06-06,30315\n2017-06-07,31947\n2017-06-08,32678\n2017-06-09,42988\n2017-06-10,38413\n2017-06-11,33640\n2017-06-12,26918\n2017-06-13,30826\n2017-06-14,31877\n2017-06-15,32900\n2017-06-16,40287\n2017-06-17,35550\n2017-06-18,27752\n2017-06-19,30266\n2017-06-20,32907\n2017-06-21,31454\n2017-06-22,34480\n2017-06-23,38806\n2017-06-24,36749\n2017-06-25,29411\n2017-06-26,31695\n2017-06-27,32049\n2017-06-28,33322\n2017-06-29,32774\n2017-06-30,40760\n2017-07-01,36464\n2017-07-02,30679\n2017-07-03,33094\n2017-07-04,35595\n2017-07-05,38035\n2017-07-06,39145\n2017-07-07,48176\n2017-07-08,41213\n2017-07-09,33078\n2017-07-10,35946\n2017-07-11,36472\n2017-07-12,37371\n2017-07-13,38761\n2017-07-14,41629\n2017-07-15,38953\n2017-07-16,26256\n2017-07-17,29875\n2017-07-18,29426\n2017-07-19,30454\n2017-07-20,32189\n2017-07-21,38774\n2017-07-22,32602\n2017-07-23,26479\n2017-07-24,31699\n2017-07-25,30461\n2017-07-26,32349\n2017-07-27,33470\n2017-07-28,41767\n2017-07-29,38623\n2017-07-30,30904\n2017-07-31,30204\n2017-08-01,30472\n2017-08-02,31816\n2017-08-03,30143\n2017-08-04,42407\n2017-08-05,38091\n2017-08-06,26501\n2017-08-07,29673\n2017-08-08,33094\n2017-08-09,32863\n2017-08-10,33610\n2017-08-11,40093\n2017-08-12,39040\n2017-08-13,34350\n2017-08-14,31024\n2017-08-15,30195\n2017-08-16,33293\n2017-08-17,31748\n2017-08-18,35115\n2017-08-19,37146\n2017-08-20,33220\n2017-08-21,27667\n2017-08-22,30552\n2017-08-23,28746\n2017-08-24,32432\n2017-08-25,38925\n2017-08-26,45181\n2017-08-27,26646\n2017-08-28,29415\n2017-08-29,30051\n2017-08-30,31619\n2017-08-31,33335\n2017-09-01,41259\n2017-09-02,37216\n2017-09-03,25834\n2017-09-04,27306\n2017-09-05,29313\n2017-09-06,29931\n2017-09-07,31965\n2017-09-08,38163\n2017-09-09,33767\n2017-09-10,27184\n2017-09-11,29207\n2017-09-12,29790\n2017-09-13,30616\n2017-09-14,31900\n2017-09-15,36408\n2017-09-16,35226\n2017-09-17,29298\n2017-09-18,29681\n2017-09-19,29026\n2017-09-20,32496\n2017-09-21,32907\n2017-09-22,40738\n2017-09-23,41764\n2017-09-24,28092\n2017-09-25,31805\n2017-09-26,35380\n2017-09-27,37885\n2017-09-28,40143\n2017-09-29,37015\n2017-09-30,33680\n2017-10-01,29456\n2017-10-02,34470\n2017-10-03,34369\n2017-10-04,36744\n2017-10-05,35355\n2017-10-06,40236\n2017-10-07,35455\n2017-10-08,27599\n2017-10-09,27996\n2017-10-10,31668\n2017-10-11,31523\n2017-10-12,33734\n2017-10-13,39484\n2017-10-14,38312\n2017-10-15,31869\n2017-10-16,30904\n2017-10-17,32338\n2017-10-18,32756\n2017-10-19,31646\n2017-10-20,40941\n2017-10-21,35043\n2017-10-22,30059\n2017-10-23,31039\n2017-10-24,31402\n2017-10-25,32920\n2017-10-26,35068\n2017-10-27,43369\n2017-10-28,35711\n2017-10-29,27530\n2017-10-30,27907\n2017-10-31,30298\n2017-11-01,31988\n2017-11-02,34092\n2017-11-03,39169\n2017-11-04,43052\n2017-11-05,29028\n2017-11-06,42047\n2017-11-07,25589\n2017-11-08,36989\n2017-11-09,34504\n2017-11-10,42942\n2017-11-11,35870\n2017-11-12,30049\n2017-11-13,32196\n2017-11-14,32222\n2017-11-15,34197\n2017-11-16,35279\n2017-11-17,47399\n2017-11-18,37529\n2017-11-19,34185\n2017-11-20,35252\n2017-11-21,37347\n2017-11-22,36846\n2017-11-23,38951\n2017-11-24,50989\n2017-11-25,39334\n2017-11-26,36244\n2017-11-27,36761\n2017-11-28,36369\n2017-11-29,31836\n2017-11-30,35346\n2017-12-01,34484\n2017-12-02,24061\n2017-12-03,29718\n2017-12-04,35274\n2017-12-05,38288\n2017-12-06,42378\n2017-12-07,37749\n2017-12-08,47848\n2017-12-09,46207\n2017-12-10,39975\n2017-12-11,41462\n2017-12-12,41633\n2017-12-13,40192\n2017-12-14,44674\n2017-12-15,49985\n2017-12-16,48413\n2017-12-17,41197\n2017-12-18,41784\n2017-12-19,38603\n2017-12-20,45806\n2017-12-21,50222\n2017-12-22,50427\n2017-12-23,43355\n2017-12-24,40830\n2017-12-25,30544\n2017-12-26,50186\n2017-12-27,37422\n2017-12-28,37173\n2017-12-29,37661\n2017-12-30,35722\n2017-12-31,41683\n2018-01-01,33817\n2018-01-02,35434\n2018-01-03,36414\n2018-01-04,36576\n2018-01-05,37924\n2018-01-06,25824\n2018-01-07,30378\n2018-01-08,35708\n2018-01-09,36700\n2018-01-10,37463\n2018-01-11,34076\n2018-01-12,35581\n2018-01-13,31726\n2018-01-14,32373\n2018-01-15,35917\n2018-01-16,37575\n2018-01-17,40496\n2018-01-18,34703\n2018-01-19,35644\n2018-01-20,39008\n2018-01-21,31307\n2018-01-22,37255\n2018-01-23,38388\n2018-01-24,40662\n2018-01-25,41688\n2018-01-26,41773\n2018-01-27,34072\n2018-01-28,23554\n2018-01-29,26667\n2018-01-30,32726\n2018-01-31,33005\n2018-02-01,34156\n2018-02-02,38033\n2018-02-03,37296\n2018-02-04,30648\n2018-02-05,32913\n2018-02-06,33455\n2018-02-07,32373\n2018-02-08,35865\n2018-02-09,42287\n2018-02-10,39063\n2018-02-11,31862\n2018-02-12,34195\n2018-02-13,35202\n2018-02-14,37897\n2018-02-15,36978\n2018-02-16,43738\n2018-02-17,52074\n2018-02-18,45421\n2018-02-19,34939\n2018-02-20,34733\n2018-02-21,35917\n2018-02-22,38072\n2018-02-23,42577\n2018-02-24,36858\n2018-02-25,34237\n2018-02-26,35253\n2018-02-27,34319\n2018-02-28,34718\n2018-03-01,37669\n2018-03-02,42956\n2018-03-03,38090\n2018-03-04,29905\n2018-03-05,33333\n2018-03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8-12-09,33207\n2018-12-10,39235\n2018-12-11,39820\n2018-12-12,39274\n2018-12-13,33560\n2018-12-14,42199\n2018-12-15,44814\n2018-12-16,34865\n2018-12-17,41117\n2018-12-18,41669\n2018-12-19,42090\n2018-12-20,44257\n2018-12-21,43610\n2018-12-22,47510\n2018-12-23,49272\n2018-12-24,43094\n2018-12-25,29825\n2018-12-26,49699\n2018-12-27,33721\n2018-12-28,36877\n2018-12-29,34694\n2018-12-30,31234\n2018-12-31,44962\n2019-01-01,33237\n2019-01-02,34536\n2019-01-03,34299\n2019-01-04,26737\n2019-01-05,31750\n2019-01-06,28513\n2019-01-07,34676\n2019-01-08,34224\n2019-01-09,35649\n2019-01-10,36078\n2019-01-11,38363\n2019-01-12,34803\n2019-01-13,28452\n2019-01-14,31894\n2019-01-15,31812\n2019-01-16,35119\n2019-01-17,35089\n2019-01-18,40335\n2019-01-19,37222\n2019-01-20,32114\n2019-01-21,35029\n2019-01-22,34941\n2019-01-23,37656\n2019-01-24,32401\n2019-01-25,30321\n2019-01-26,42501\n2019-01-27,31260\n2019-01-28,25387\n2019-01-29,30543\n2019-01-30,28447\n2019-01-31,32327\n2019-02-01,39427\n2019-02-02,32085\n2019-02-03,22833\n2019-02-04,31301\n2019-02-05,33187\n2019-02-06,30881\n2019-02-07,34245\n2019-02-08,39506\n2019-02-09,34763\n2019-02-10,32387\n2019-02-11,32336\n2019-02-12,30034\n2019-02-13,34695\n2019-02-14,38642\n2019-02-15,40987\n2019-02-16,38791\n2019-02-17,28462\n2019-02-18,31984\n2019-02-19,33245\n2019-02-20,34548\n2019-02-21,37424\n2019-02-22,42070\n2019-02-23,41073\n2019-02-24,36773\n2019-02-25,33371\n2019-02-26,33921\n2019-02-27,36152\n2019-02-28,34304\n2019-03-01,36621\n2019-03-02,28577\n2019-03-03,22348\n2019-03-04,32704\n2019-03-05,35561\n2019-03-06,33644\n2019-03-07,38089\n2019-03-08,44357\n2019-03-09,42754\n2019-03-10,40649\n2019-03-11,38356\n2019-03-12,33990\n2019-03-13,35589\n2019-03-14,37318\n2019-03-15,44469\n2019-03-16,42267\n2019-03-17,31942\n2019-03-18,35232\n2019-03-19,34594\n2019-03-20,34644\n2019-03-21,37090\n2019-03-22,42828\n2019-03-23,36554\n2019-03-24,29791\n2019-03-25,29968\n2019-03-26,33747\n2019-03-27,35388\n2019-03-28,39825\n2019-03-29,42488\n2019-03-30,35803\n2019-03-31,30261\n2019-04-01,33065\n2019-04-02,35792\n2019-04-03,36002\n2019-04-04,37692\n2019-04-05,43533\n2019-04-06,44368\n2019-04-07,35346\n2019-04-08,39564\n2019-04-09,39317\n2019-04-10,43514\n2019-04-11,42678\n2019-04-12,49857\n2019-04-13,44067\n2019-04-14,36100\n2019-04-15,38150\n2019-04-16,41964\n2019-04-17,42012\n2019-04-18,46591\n2019-04-19,37029\n2019-04-20,44084\n2019-04-21,35649\n2019-04-22,33049\n2019-04-23,34909\n2019-04-24,37276\n2019-04-25,35869\n2019-04-26,37604\n2019-04-27,34933\n2019-04-28,25304\n2019-04-29,30553\n2019-04-30,32386\n2019-05-01,33906\n2019-05-02,33941\n2019-05-03,40779\n2019-05-04,34944\n2019-05-05,27852\n2019-05-06,30551\n2019-05-07,31278\n2019-05-08,32066\n2019-05-09,33546\n2019-05-10,33120\n2019-05-11,36305\n2019-05-12,26726\n2019-05-13,29322\n2019-05-14,31174\n2019-05-15,33114\n2019-05-16,32796\n2019-05-17,40446\n2019-05-18,37200\n2019-05-19,30127\n2019-05-20,29444\n2019-05-21,31274\n2019-05-22,32723\n2019-05-23,33467\n2019-05-24,39442\n2019-05-25,33729\n2019-05-26,26013\n2019-05-27,26811\n2019-05-28,30065\n2019-05-29,27257\n2019-05-30,32831\n2019-05-31,35962\n2019-06-01,37343\n2019-06-02,26064\n2019-06-03,26580\n2019-06-04,29828\n2019-06-05,30526\n2019-06-06,32586\n2019-06-07,39995\n2019-06-08,35239\n2019-06-09,33038\n2019-06-10,26402\n2019-06-11,31072\n2019-06-12,28128\n2019-06-13,33326\n2019-06-14,36681\n2019-06-15,35674\n2019-06-16,26931\n2019-06-17,29809\n2019-06-18,27695\n2019-06-19,30766\n2019-06-20,32323\n2019-06-21,40234\n2019-06-22,33788\n2019-06-23,25332\n2019-06-24,30364\n2019-06-25,31483\n2019-06-26,33352\n2019-06-27,34961\n2019-06-28,40281\n2019-06-29,30226\n2019-06-30,30784\n2019-07-01,31383\n2019-07-02,33033\n2019-07-03,34587\n2019-07-04,36184\n2019-07-05,43736\n2019-07-06,38334\n2019-07-07,28310\n2019-07-08,35429\n2019-07-09,35958\n2019-07-10,35919\n2019-07-11,36051\n2019-07-12,39751\n2019-07-13,32332\n2019-07-14,23822\n2019-07-15,30147\n2019-07-16,31042\n2019-07-17,31542\n2019-07-18,34179\n2019-07-19,41617\n2019-07-20,37166\n2019-07-21,27287\n2019-07-22,30519\n2019-07-23,28259\n2019-07-24,32704\n2019-07-25,34231\n2019-07-26,36603\n2019-07-27,39967\n2019-07-28,29906\n2019-07-29,30680\n2019-07-30,30224\n2019-07-31,31123\n2019-08-01,32415\n2019-08-02,39622\n2019-08-03,36526\n2019-08-04,28273\n2019-08-05,31092\n2019-08-06,31002\n2019-08-07,32742\n2019-08-08,33716\n2019-08-09,35269\n2019-08-10,33134\n2019-08-11,28079\n2019-08-12,31084\n2019-08-13,31787\n2019-08-14,32418\n2019-08-15,35081\n2019-08-16,37823\n2019-08-17,39567\n2019-08-18,27147\n2019-08-19,29278\n2019-08-20,31776\n2019-08-21,33601\n2019-08-22,38660\n2019-08-23,51992\n2019-08-24,47023\n2019-08-25,31045\n2019-08-26,31996\n2019-08-27,30174\n2019-08-28,27802\n2019-08-29,32906\n2019-08-30,41225\n2019-08-31,40434\n2019-09-01,27354\n2019-09-02,29706\n2019-09-03,32407\n2019-09-04,31267\n2019-09-05,34341\n2019-09-06,37791\n2019-09-07,34043\n2019-09-08,25249\n2019-09-09,27121\n2019-09-10,30613\n2019-09-11,32760\n2019-09-12,31040\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16:00:00,4100,3981\n2023-04-29 17:00:00,3639,3500\n2023-04-29 18:00:00,3030,3307\n2023-04-29 19:00:00,2687,2827\n2023-04-29 20:00:00,2072,2166\n2023-04-29 21:00:00,1933,1972\n2023-04-29 22:00:00,1591,1904\n2023-04-29 23:00:00,1054,1370\n2023-04-30 00:00:00,694,881\n2023-04-30 01:00:00,480,648\n2023-04-30 02:00:00,308,454\n2023-04-30 03:00:00,223,288\n2023-04-30 04:00:00,66,123\n2023-04-30 05:00:00,80,172\n2023-04-30 06:00:00,103,254\n2023-04-30 07:00:00,199,313\n2023-04-30 08:00:00,388,612\n2023-04-30 09:00:00,834,1177\n2023-04-30 10:00:00,1591,1951\n2023-04-30 11:00:00,2544,2708\n2023-04-30 12:00:00,3286,3481\n2023-04-30 13:00:00,3361,3300\n2023-04-30 14:00:00,3539,3400\n2023-04-30 15:00:00,3636,3494\n2023-04-30 16:00:00,3734,3928\n2023-04-30 17:00:00,2917,2949\n2023-04-30 18:00:00,2393,2396\n2023-04-30 19:00:00,1987,1795\n2023-04-30 20:00:00,1357,1514\n2023-04-30 21:00:00,1176,1107\n2023-04-30 22:00:00,612,732\n2023-04-30 23:00:00,275,399\n" }, { "path": "examples/example_retail_sales.csv", "content": "ds,y\n1992-01-01,146376\n1992-02-01,147079\n1992-03-01,159336\n1992-04-01,163669\n1992-05-01,170068\n1992-06-01,168663\n1992-07-01,169890\n1992-08-01,170364\n1992-09-01,164617\n1992-10-01,173655\n1992-11-01,171547\n1992-12-01,208838\n1993-01-01,153221\n1993-02-01,150087\n1993-03-01,170439\n1993-04-01,176456\n1993-05-01,182231\n1993-06-01,181535\n1993-07-01,183682\n1993-08-01,183318\n1993-09-01,177406\n1993-10-01,182737\n1993-11-01,187443\n1993-12-01,224540\n1994-01-01,161349\n1994-02-01,162841\n1994-03-01,192319\n1994-04-01,189569\n1994-05-01,194927\n1994-06-01,197946\n1994-07-01,193355\n1994-08-01,202388\n1994-09-01,193954\n1994-10-01,197956\n1994-11-01,202520\n1994-12-01,241111\n1995-01-01,175344\n1995-02-01,172138\n1995-03-01,201279\n1995-04-01,196039\n1995-05-01,210478\n1995-06-01,211844\n1995-07-01,203411\n1995-08-01,214248\n1995-09-01,202122\n1995-10-01,204044\n1995-11-01,212190\n1995-12-01,247491\n1996-01-01,185019\n1996-02-01,192380\n1996-03-01,212110\n1996-04-01,211718\n1996-05-01,226936\n1996-06-01,217511\n1996-07-01,218111\n1996-08-01,226062\n1996-09-01,209250\n1996-10-01,222663\n1996-11-01,223953\n1996-12-01,258081\n1997-01-01,200389\n1997-02-01,197556\n1997-03-01,225133\n1997-04-01,220329\n1997-05-01,234190\n1997-06-01,227365\n1997-07-01,231521\n1997-08-01,235252\n1997-09-01,222807\n1997-10-01,232251\n1997-11-01,228284\n1997-12-01,271054\n1998-01-01,207853\n1998-02-01,203863\n1998-03-01,230313\n1998-04-01,234503\n1998-05-01,245027\n1998-06-01,244067\n1998-07-01,241431\n1998-08-01,240462\n1998-09-01,231243\n1998-10-01,244234\n1998-11-01,240991\n1998-12-01,288969\n1999-01-01,218126\n1999-02-01,220650\n1999-03-01,253550\n1999-04-01,250783\n1999-05-01,262113\n1999-06-01,260918\n1999-07-01,262051\n1999-08-01,265089\n1999-09-01,253905\n1999-10-01,258040\n1999-11-01,264106\n1999-12-01,317659\n2000-01-01,236422\n2000-02-01,250580\n2000-03-01,279515\n2000-04-01,264417\n2000-05-01,283706\n2000-06-01,281288\n2000-07-01,271146\n2000-08-01,283944\n2000-09-01,269155\n2000-10-01,270899\n2000-11-01,276507\n2000-12-01,319958\n2001-01-01,250746\n2001-02-01,247772\n2001-03-01,280449\n2001-04-01,274925\n2001-05-01,296013\n2001-06-01,287881\n2001-07-01,279098\n2001-08-01,294763\n2001-09-01,261924\n2001-10-01,291596\n2001-11-01,287537\n2001-12-01,326202\n2002-01-01,255598\n2002-02-01,253086\n2002-03-01,285261\n2002-04-01,284747\n2002-05-01,300402\n2002-06-01,288854\n2002-07-01,295433\n2002-08-01,307256\n2002-09-01,273189\n2002-10-01,287540\n2002-11-01,290705\n2002-12-01,337006\n2003-01-01,268328\n2003-02-01,259051\n2003-03-01,293693\n2003-04-01,294251\n2003-05-01,312389\n2003-06-01,300998\n2003-07-01,309923\n2003-08-01,317056\n2003-09-01,293890\n2003-10-01,304036\n2003-11-01,301265\n2003-12-01,357577\n2004-01-01,281460\n2004-02-01,282444\n2004-03-01,319077\n2004-04-01,315191\n2004-05-01,328408\n2004-06-01,321044\n2004-07-01,328000\n2004-08-01,326317\n2004-09-01,313524\n2004-10-01,319726\n2004-11-01,324259\n2004-12-01,387155\n2005-01-01,293261\n2005-02-01,295062\n2005-03-01,339141\n2005-04-01,335632\n2005-05-01,345348\n2005-06-01,350945\n2005-07-01,351827\n2005-08-01,355701\n2005-09-01,333289\n2005-10-01,336134\n2005-11-01,343798\n2005-12-01,405608\n2006-01-01,318546\n2006-02-01,314051\n2006-03-01,361993\n2006-04-01,351667\n2006-05-01,373560\n2006-06-01,366615\n2006-07-01,362203\n2006-08-01,375795\n2006-09-01,346214\n2006-10-01,348796\n2006-11-01,356928\n2006-12-01,417991\n2007-01-01,328877\n2007-02-01,323162\n2007-03-01,374142\n2007-04-01,358535\n2007-05-01,391512\n2007-06-01,376639\n2007-07-01,372354\n2007-08-01,388016\n2007-09-01,353936\n2007-10-01,368681\n2007-11-01,377802\n2007-12-01,426077\n2008-01-01,342697\n2008-02-01,343937\n2008-03-01,372923\n2008-04-01,368923\n2008-05-01,397969\n2008-06-01,378490\n2008-07-01,383686\n2008-08-01,382852\n2008-09-01,350560\n2008-10-01,349884\n2008-11-01,335571\n2008-12-01,384286\n2009-01-01,310269\n2009-02-01,299488\n2009-03-01,328568\n2009-04-01,329866\n2009-05-01,347768\n2009-06-01,344439\n2009-07-01,348106\n2009-08-01,353473\n2009-09-01,324708\n2009-10-01,338630\n2009-11-01,339386\n2009-12-01,400264\n2010-01-01,314640\n2010-02-01,311022\n2010-03-01,360819\n2010-04-01,356460\n2010-05-01,365713\n2010-06-01,358675\n2010-07-01,362027\n2010-08-01,362682\n2010-09-01,346069\n2010-10-01,355212\n2010-11-01,365809\n2010-12-01,426654\n2011-01-01,335608\n2011-02-01,337352\n2011-03-01,387092\n2011-04-01,380754\n2011-05-01,391970\n2011-06-01,388636\n2011-07-01,384600\n2011-08-01,394548\n2011-09-01,374895\n2011-10-01,379364\n2011-11-01,391081\n2011-12-01,451669\n2012-01-01,355058\n2012-02-01,372523\n2012-03-01,414275\n2012-04-01,393035\n2012-05-01,418648\n2012-06-01,400996\n2012-07-01,396020\n2012-08-01,417911\n2012-09-01,385597\n2012-10-01,399341\n2012-11-01,410992\n2012-12-01,461994\n2013-01-01,375537\n2013-02-01,373938\n2013-03-01,421638\n2013-04-01,408381\n2013-05-01,436985\n2013-06-01,414701\n2013-07-01,422357\n2013-08-01,434950\n2013-09-01,396199\n2013-10-01,415740\n2013-11-01,423611\n2013-12-01,477205\n2014-01-01,383399\n2014-02-01,380315\n2014-03-01,432806\n2014-04-01,431415\n2014-05-01,458822\n2014-06-01,433152\n2014-07-01,443005\n2014-08-01,450913\n2014-09-01,420871\n2014-10-01,437702\n2014-11-01,437910\n2014-12-01,501232\n2015-01-01,397252\n2015-02-01,386935\n2015-03-01,444110\n2015-04-01,438217\n2015-05-01,462615\n2015-06-01,448229\n2015-07-01,457710\n2015-08-01,456340\n2015-09-01,430917\n2015-10-01,444959\n2015-11-01,444507\n2015-12-01,518253\n2016-01-01,400928\n2016-02-01,413554\n2016-03-01,460093\n2016-04-01,450935\n2016-05-01,471421\n" 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02:55:00,10.4\n2017-05-01 03:00:00,10.2\n2017-05-01 03:05:00,10.0\n2017-05-01 03:10:00,9.9\n2017-05-01 03:15:00,9.8\n2017-05-01 03:20:00,9.6\n2017-05-01 03:25:00,9.4\n2017-05-01 03:30:00,9.3\n2017-05-01 03:35:00,9.1\n2017-05-01 03:40:00,9.0\n2017-05-01 03:45:00,9.0\n2017-05-01 03:50:00,8.9\n2017-05-01 03:55:00,8.8\n2017-05-01 04:00:00,8.6\n2017-05-01 04:05:00,8.5\n2017-05-01 04:10:00,8.5\n2017-05-01 04:15:00,8.4\n2017-05-01 04:20:00,8.3\n2017-05-01 04:25:00,8.2\n2017-05-01 04:30:00,8.2\n2017-05-01 04:35:00,8.1\n2017-05-01 04:40:00,8.0\n2017-05-01 04:45:00,7.9\n2017-05-01 04:50:00,7.8\n2017-05-01 04:55:00,7.7\n2017-05-01 05:00:00,7.7\n2017-05-01 05:05:00,7.6\n2017-05-01 05:10:00,7.5\n2017-05-01 05:15:00,7.4\n2017-05-01 05:20:00,7.4\n2017-05-01 05:25:00,7.4\n2017-05-01 05:30:00,7.3\n2017-05-01 05:35:00,7.3\n2017-05-01 05:40:00,7.0\n2017-05-01 05:45:00,7.0\n2017-05-01 05:50:00,7.0\n2017-05-01 05:55:00,6.9\n2017-05-01 06:00:00,7.0\n2017-05-01 06:05:00,7.0\n2017-05-01 06:10:00,6.8\n2017-05-01 06:15:00,6.8\n2017-05-01 06:20:00,6.7\n2017-05-01 06:25:00,6.7\n2017-05-01 06:30:00,6.7\n2017-05-01 06:35:00,6.7\n2017-05-01 06:40:00,6.6\n2017-05-01 06:45:00,6.4\n2017-05-01 06:50:00,6.2\n2017-05-01 06:55:00,6.1\n2017-05-01 07:00:00,6.0\n2017-05-01 07:05:00,5.9\n2017-05-01 07:10:00,5.7\n2017-05-01 07:15:00,5.5\n2017-05-01 07:20:00,5.4\n2017-05-01 07:25:00,5.3\n2017-05-01 07:30:00,5.2\n2017-05-01 07:35:00,5.1\n2017-05-01 07:40:00,5.0\n2017-05-01 07:45:00,5.0\n2017-05-01 07:50:00,4.9\n2017-05-01 07:55:00,4.9\n2017-05-01 08:00:00,4.8\n2017-05-01 08:05:00,4.7\n2017-05-01 08:10:00,4.8\n2017-05-01 08:15:00,4.8\n2017-05-01 08:20:00,4.8\n2017-05-01 08:25:00,4.7\n2017-05-01 08:30:00,4.6\n2017-05-01 08:35:00,4.6\n2017-05-01 08:40:00,4.6\n2017-05-01 08:45:00,4.6\n2017-05-01 08:50:00,4.6\n2017-05-01 08:55:00,4.4\n2017-05-01 09:00:00,4.3\n2017-05-01 09:05:00,4.3\n2017-05-01 09:10:00,4.2\n2017-05-01 09:15:00,4.1\n2017-05-01 09:20:00,4.0\n2017-05-01 09:25:00,4.0\n2017-05-01 09:30:00,4.1\n2017-05-01 09:35:00,3.9\n2017-05-01 09:40:00,3.8\n2017-05-01 09:45:00,3.8\n2017-05-01 09:50:00,3.9\n2017-05-01 09:55:00,3.9\n2017-05-01 10:00:00,3.8\n2017-05-01 10:05:00,3.9\n2017-05-01 10:10:00,3.9\n2017-05-01 10:15:00,3.8\n2017-05-01 10:20:00,3.6\n2017-05-01 10:25:00,3.6\n2017-05-01 10:30:00,3.5\n2017-05-01 10:35:00,3.4\n2017-05-01 10:40:00,3.3\n2017-05-01 10:45:00,3.2\n2017-05-01 10:50:00,3.3\n2017-05-01 10:55:00,3.4\n2017-05-01 11:00:00,3.3\n2017-05-01 11:05:00,3.3\n2017-05-01 11:10:00,3.4\n2017-05-01 11:15:00,3.5\n2017-05-01 11:20:00,3.4\n2017-05-01 11:25:00,3.3\n2017-05-01 11:30:00,3.3\n2017-05-01 11:35:00,3.5\n2017-05-01 11:40:00,3.6\n2017-05-01 11:45:00,3.4\n2017-05-01 11:50:00,3.4\n2017-05-01 11:55:00,3.4\n2017-05-01 12:00:00,3.7\n2017-05-01 12:05:00,3.8\n2017-05-01 12:10:00,3.9\n2017-05-01 12:15:00,3.9\n2017-05-01 12:20:00,3.8\n2017-05-01 12:25:00,3.7\n2017-05-01 12:30:00,3.7\n2017-05-01 12:35:00,3.8\n2017-05-01 12:40:00,3.7\n2017-05-01 12:45:00,3.6\n2017-05-01 12:50:00,3.6\n2017-05-01 12:55:00,3.6\n2017-05-01 13:00:00,3.5\n2017-05-01 13:05:00,3.5\n2017-05-01 13:10:00,3.7\n2017-05-01 13:15:00,3.6\n2017-05-01 13:20:00,3.6\n2017-05-01 13:25:00,3.8\n2017-05-01 13:30:00,3.9\n2017-05-01 13:35:00,4.0\n2017-05-01 13:40:00,3.9\n2017-05-01 13:45:00,4.0\n2017-05-01 13:50:00,4.0\n2017-05-01 13:55:00,4.0\n2017-05-01 14:00:00,4.2\n2017-05-01 14:05:00,4.2\n2017-05-01 14:10:00,4.3\n2017-05-01 14:15:00,4.3\n2017-05-01 14:20:00,4.2\n2017-05-01 14:25:00,4.4\n2017-05-01 14:30:00,4.5\n2017-05-01 14:35:00,4.7\n2017-05-01 14:40:00,4.8\n2017-05-01 14:45:00,5.0\n2017-05-01 14:50:00,5.1\n2017-05-01 14:55:00,5.3\n2017-05-01 15:00:00,5.6\n2017-05-01 15:05:00,6.0\n2017-05-01 15:10:00,6.4\n2017-05-01 15:15:00,6.6\n2017-05-01 15:20:00,7.1\n2017-05-01 15:25:00,7.8\n2017-05-01 15:30:00,8.2\n2017-05-01 15:35:00,8.6\n2017-05-01 15:40:00,9.5\n2017-05-01 15:45:00,9.9\n2017-05-01 15:50:00,10.7\n2017-05-01 15:55:00,11.2\n2017-05-01 16:00:00,12.1\n2017-05-01 16:05:00,12.8\n2017-05-01 16:10:00,13.6\n2017-05-01 16:15:00,14.2\n2017-05-01 16:20:00,15.0\n2017-05-01 16:25:00,15.7\n2017-05-01 16:30:00,16.4\n2017-05-01 16:35:00,16.9\n2017-05-01 16:40:00,17.5\n2017-05-01 16:45:00,18.3\n2017-05-01 16:50:00,18.7\n2017-05-01 16:55:00,19.7\n2017-05-01 17:00:00,20.5\n2017-05-01 17:05:00,21.2\n2017-05-01 17:10:00,21.6\n2017-05-01 17:15:00,22.1\n2017-05-01 17:20:00,22.9\n2017-05-01 17:25:00,24.1\n2017-05-01 17:30:00,24.6\n2017-05-01 17:35:00,25.1\n2017-05-01 17:40:00,25.2\n2017-05-01 17:45:00,26.0\n2017-05-01 17:50:00,26.1\n2017-05-01 17:55:00,26.7\n2017-05-01 18:00:00,27.5\n2017-05-01 18:05:00,28.3\n2017-05-01 18:10:00,29.0\n2017-05-01 18:15:00,29.4\n2017-05-01 18:20:00,29.9\n2017-05-01 18:25:00,30.7\n2017-05-01 18:30:00,31.1\n2017-05-01 18:35:00,31.5\n2017-05-01 18:40:00,32.3\n2017-05-01 18:45:00,32.4\n2017-05-01 18:50:00,32.4\n2017-05-01 18:55:00,33.1\n2017-05-01 19:00:00,33.7\n2017-05-01 19:05:00,33.8\n2017-05-01 19:10:00,33.7\n2017-05-01 19:15:00,34.2\n2017-05-01 19:20:00,34.4\n2017-05-01 19:25:00,34.8\n2017-05-01 19:30:00,35.2\n2017-05-01 19:35:00,35.3\n2017-05-01 19:40:00,35.2\n2017-05-01 19:45:00,35.7\n2017-05-01 19:50:00,36.0\n2017-05-01 19:55:00,36.3\n2017-05-01 20:00:00,36.5\n2017-05-01 20:05:00,36.4\n2017-05-01 20:10:00,36.0\n2017-05-01 20:15:00,36.3\n2017-05-01 20:20:00,37.1\n2017-05-01 20:25:00,37.0\n2017-05-01 20:30:00,36.2\n2017-05-01 20:35:00,37.0\n2017-05-01 20:40:00,37.3\n2017-05-01 20:45:00,38.0\n2017-05-01 20:50:00,37.1\n2017-05-01 20:55:00,36.9\n2017-05-01 21:00:00,37.4\n2017-05-01 21:05:00,37.8\n2017-05-01 21:10:00,38.2\n2017-05-01 21:15:00,37.5\n2017-05-01 21:20:00,37.6\n2017-05-01 21:25:00,37.8\n2017-05-01 21:30:00,37.9\n2017-05-01 21:35:00,37.5\n2017-05-01 21:40:00,36.6\n2017-05-01 21:45:00,36.4\n2017-05-01 21:50:00,37.0\n2017-05-01 21:55:00,36.8\n2017-05-01 22:00:00,36.9\n2017-05-01 22:05:00,36.5\n2017-05-01 22:10:00,36.2\n2017-05-01 22:15:00,35.8\n2017-05-01 22:20:00,35.4\n2017-05-01 22:25:00,35.2\n2017-05-01 22:30:00,35.8\n2017-05-01 22:35:00,35.6\n2017-05-01 22:40:00,35.5\n2017-05-01 22:45:00,35.6\n2017-05-01 22:50:00,34.8\n2017-05-01 22:55:00,34.5\n2017-05-01 23:00:00,34.2\n2017-05-01 23:05:00,34.0\n2017-05-01 23:10:00,33.5\n2017-05-01 23:15:00,33.3\n2017-05-01 23:20:00,32.6\n2017-05-01 23:25:00,32.2\n2017-05-01 23:30:00,32.2\n2017-05-01 23:35:00,31.3\n2017-05-01 23:40:00,31.2\n2017-05-01 23:45:00,30.5\n2017-05-01 23:50:00,30.7\n2017-05-01 23:55:00,30.1\n2017-05-02 00:00:00,29.4\n2017-05-02 00:05:00,28.9\n2017-05-02 00:10:00,29.3\n2017-05-02 00:15:00,29.1\n2017-05-02 00:20:00,28.3\n2017-05-02 00:25:00,27.5\n2017-05-02 00:30:00,27.2\n2017-05-02 00:35:00,26.5\n2017-05-02 00:40:00,25.6\n2017-05-02 00:45:00,25.3\n2017-05-02 00:50:00,24.6\n2017-05-02 00:55:00,23.4\n2017-05-02 01:00:00,23.0\n2017-05-02 01:05:00,22.5\n2017-05-02 01:10:00,21.7\n2017-05-02 01:15:00,20.7\n2017-05-02 01:20:00,19.9\n2017-05-02 01:25:00,19.1\n2017-05-02 01:30:00,18.0\n2017-05-02 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11:55:00,6.5\n2017-05-06 12:00:00,6.5\n2017-05-06 12:05:00,6.5\n2017-05-06 12:10:00,6.4\n2017-05-06 12:15:00,6.3\n2017-05-06 12:20:00,6.1\n2017-05-06 12:25:00,6.3\n2017-05-06 12:30:00,6.4\n2017-05-06 12:35:00,6.4\n2017-05-06 12:40:00,6.4\n2017-05-06 12:45:00,6.3\n2017-05-06 12:50:00,6.3\n2017-05-06 12:55:00,6.1\n2017-05-06 13:00:00,6.2\n2017-05-06 13:05:00,6.3\n2017-05-06 13:10:00,6.4\n2017-05-06 13:15:00,6.5\n2017-05-06 13:20:00,6.5\n2017-05-06 13:25:00,6.5\n2017-05-06 13:30:00,6.5\n2017-05-06 13:35:00,6.6\n2017-05-06 13:40:00,6.6\n2017-05-06 13:45:00,6.4\n2017-05-06 13:50:00,6.6\n2017-05-06 13:55:00,6.6\n2017-05-06 14:00:00,6.7\n2017-05-06 14:05:00,6.9\n2017-05-06 14:10:00,6.8\n2017-05-06 14:15:00,6.7\n2017-05-06 14:20:00,6.5\n2017-05-06 14:25:00,6.4\n2017-05-06 14:30:00,6.4\n2017-05-06 14:35:00,5.9\n2017-05-06 14:40:00,5.9\n2017-05-06 14:45:00,6.1\n2017-05-06 14:50:00,6.1\n2017-05-06 14:55:00,6.0\n2017-05-06 15:00:00,5.2\n2017-05-06 15:05:00,5.3\n2017-05-06 15:10:00,5.8\n2017-05-06 15:15:00,5.7\n2017-05-06 15:20:00,5.4\n2017-05-06 15:25:00,5.3\n2017-05-06 15:30:00,5.5\n2017-05-06 15:35:00,5.8\n2017-05-06 15:40:00,5.6\n2017-05-06 15:45:00,5.4\n2017-05-06 15:50:00,5.6\n2017-05-06 15:55:00,5.6\n2017-05-06 16:00:00,6.0\n2017-05-06 16:05:00,6.3\n2017-05-06 16:10:00,6.5\n2017-05-06 16:15:00,6.5\n2017-05-06 16:20:00,7.2\n2017-05-06 16:25:00,7.0\n2017-05-06 16:30:00,6.8\n2017-05-06 16:35:00,7.0\n2017-05-06 16:40:00,7.5\n2017-05-06 16:45:00,7.3\n2017-05-06 16:50:00,7.8\n2017-05-06 16:55:00,7.5\n2017-05-06 17:00:00,7.0\n2017-05-06 17:05:00,6.4\n2017-05-06 17:10:00,6.4\n2017-05-06 17:15:00,6.7\n2017-05-06 17:20:00,6.6\n2017-05-06 17:25:00,6.6\n2017-05-06 17:30:00,6.7\n2017-05-06 17:35:00,6.9\n2017-05-06 17:40:00,7.5\n2017-05-06 17:45:00,7.4\n2017-05-06 17:50:00,6.7\n2017-05-06 17:55:00,6.6\n2017-05-06 18:00:00,6.6\n2017-05-06 18:05:00,6.5\n2017-05-06 18:10:00,6.3\n2017-05-06 18:15:00,6.1\n2017-05-06 18:20:00,5.8\n2017-05-06 18:25:00,6.0\n2017-05-06 18:30:00,6.0\n2017-05-06 18:35:00,5.9\n2017-05-06 18:40:00,6.0\n2017-05-06 18:45:00,7.1\n2017-05-06 18:50:00,7.6\n2017-05-06 18:55:00,7.1\n2017-05-06 19:00:00,6.7\n2017-05-06 19:05:00,7.2\n2017-05-06 19:10:00,6.8\n2017-05-06 19:15:00,6.3\n2017-05-06 19:20:00,6.6\n2017-05-06 19:25:00,7.0\n2017-05-06 19:30:00,7.3\n2017-05-06 19:35:00,7.2\n2017-05-06 19:40:00,7.6\n2017-05-06 19:45:00,9.6\n2017-05-06 19:50:00,9.0\n2017-05-06 19:55:00,9.1\n2017-05-06 20:00:00,9.2\n2017-05-06 20:05:00,9.8\n2017-05-06 20:10:00,10.1\n2017-05-06 20:15:00,9.8\n2017-05-06 20:20:00,9.1\n2017-05-06 20:25:00,8.5\n2017-05-06 20:30:00,8.2\n2017-05-06 20:35:00,8.5\n2017-05-06 20:40:00,8.2\n2017-05-06 20:45:00,8.0\n2017-05-06 20:50:00,8.1\n2017-05-06 20:55:00,8.1\n2017-05-06 21:00:00,7.7\n2017-05-06 21:05:00,7.5\n2017-05-06 21:10:00,7.3\n2017-05-06 21:15:00,7.1\n2017-05-06 21:20:00,7.0\n2017-05-06 21:25:00,7.0\n2017-05-06 21:30:00,6.9\n2017-05-06 21:35:00,7.2\n2017-05-06 21:40:00,7.3\n2017-05-06 21:45:00,7.4\n2017-05-06 21:50:00,7.3\n2017-05-06 21:55:00,6.7\n2017-05-06 22:00:00,6.6\n2017-05-06 22:05:00,6.8\n2017-05-06 22:10:00,6.4\n2017-05-06 22:15:00,6.3\n2017-05-06 22:20:00,6.3\n2017-05-06 22:25:00,6.1\n2017-05-06 22:30:00,5.9\n2017-05-06 22:35:00,5.9\n2017-05-06 22:40:00,5.6\n2017-05-06 22:45:00,5.5\n2017-05-06 22:50:00,5.3\n2017-05-06 22:55:00,5.0\n2017-05-06 23:00:00,4.8\n2017-05-06 23:05:00,4.8\n2017-05-06 23:10:00,4.9\n2017-05-06 23:15:00,4.7\n2017-05-06 23:20:00,4.9\n2017-05-06 23:25:00,4.9\n2017-05-06 23:30:00,4.9\n2017-05-06 23:35:00,5.4\n2017-05-06 23:40:00,5.5\n2017-05-06 23:45:00,5.3\n2017-05-06 23:50:00,4.4\n2017-05-06 23:55:00,4.4\n2017-05-07 00:00:00,4.4\n2017-05-07 00:05:00,4.4\n2017-05-07 00:10:00,4.7\n2017-05-07 00:15:00,5.0\n2017-05-07 00:20:00,5.4\n2017-05-07 00:25:00,5.6\n2017-05-07 00:30:00,4.9\n2017-05-07 00:35:00,4.9\n2017-05-07 00:40:00,4.9\n2017-05-07 00:45:00,4.9\n2017-05-07 00:50:00,4.7\n2017-05-07 00:55:00,4.6\n2017-05-07 01:00:00,4.5\n2017-05-07 01:05:00,4.3\n2017-05-07 01:10:00,4.2\n2017-05-07 01:15:00,3.9\n2017-05-07 01:20:00,3.3\n2017-05-07 01:25:00,2.3\n2017-05-07 01:30:00,1.6\n2017-05-07 01:35:00,1.3\n2017-05-07 01:40:00,0.8\n2017-05-07 01:45:00,0.6\n2017-05-07 01:50:00,0.5\n2017-05-07 01:55:00,0.5\n2017-05-07 02:00:00,0.2\n2017-05-07 02:05:00,0.1\n2017-05-07 02:10:00,0.0\n2017-05-07 02:15:00,0.0\n2017-05-07 02:20:00,0.0\n2017-05-07 02:25:00,0.1\n2017-05-07 02:30:00,0.1\n2017-05-07 02:35:00,0.2\n2017-05-07 02:40:00,0.1\n2017-05-07 02:45:00,0.1\n2017-05-07 02:50:00,0.1\n2017-05-07 02:55:00,0.2\n2017-05-07 03:00:00,0.2\n2017-05-07 03:05:00,0.1\n2017-05-07 03:10:00,0.1\n2017-05-07 03:15:00,0.0\n2017-05-07 03:20:00,0.0\n2017-05-07 03:25:00,0.0\n2017-05-07 03:30:00,-0.1\n2017-05-07 03:35:00,0.0\n2017-05-07 03:40:00,0.0\n2017-05-07 03:45:00,0.1\n2017-05-07 03:50:00,0.1\n2017-05-07 03:55:00,0.1\n2017-05-07 04:00:00,0.2\n2017-05-07 04:05:00,0.2\n2017-05-07 04:10:00,0.2\n2017-05-07 04:15:00,0.3\n2017-05-07 04:20:00,0.3\n2017-05-07 04:25:00,0.3\n2017-05-07 04:30:00,0.4\n2017-05-07 04:35:00,0.4\n2017-05-07 04:40:00,0.5\n2017-05-07 04:45:00,0.5\n2017-05-07 04:50:00,0.5\n2017-05-07 04:55:00,0.6\n2017-05-07 05:00:00,0.6\n2017-05-07 05:05:00,0.6\n2017-05-07 05:10:00,0.6\n2017-05-07 05:15:00,0.6\n2017-05-07 05:20:00,0.8\n2017-05-07 05:25:00,0.8\n2017-05-07 05:30:00,0.7\n2017-05-07 05:35:00,0.8\n2017-05-07 05:40:00,0.9\n2017-05-07 05:45:00,1.7\n2017-05-07 05:50:00,1.8\n2017-05-07 05:55:00,1.8\n2017-05-07 06:00:00,1.8\n2017-05-07 06:05:00,2.0\n2017-05-07 06:10:00,2.0\n2017-05-07 06:15:00,1.8\n2017-05-07 06:20:00,1.7\n2017-05-07 06:25:00,1.6\n2017-05-07 06:30:00,1.6\n2017-05-07 06:35:00,1.5\n2017-05-07 06:40:00,1.5\n2017-05-07 06:45:00,1.6\n2017-05-07 06:50:00,1.8\n2017-05-07 06:55:00,2.1\n2017-05-07 07:00:00,2.1\n2017-05-07 07:05:00,2.2\n2017-05-07 07:10:00,2.2\n2017-05-07 07:15:00,2.3\n2017-05-07 07:20:00,2.1\n2017-05-07 07:25:00,1.8\n2017-05-07 07:30:00,1.6\n2017-05-07 07:35:00,1.5\n2017-05-07 07:40:00,1.5\n2017-05-07 07:45:00,1.4\n2017-05-07 07:50:00,1.4\n2017-05-07 07:55:00,1.3\n2017-05-07 08:00:00,1.3\n2017-05-07 08:05:00,1.2\n2017-05-07 08:10:00,1.2\n2017-05-07 08:15:00,1.2\n2017-05-07 08:20:00,1.1\n2017-05-07 08:25:00,1.0\n2017-05-07 08:30:00,1.0\n2017-05-07 08:35:00,0.9\n2017-05-07 08:40:00,0.8\n2017-05-07 08:45:00,0.7\n2017-05-07 08:50:00,0.6\n2017-05-07 08:55:00,0.5\n2017-05-07 09:00:00,0.4\n2017-05-07 09:05:00,0.3\n2017-05-07 09:10:00,0.2\n2017-05-07 09:15:00,0.2\n2017-05-07 09:20:00,0.1\n2017-05-07 09:25:00,0.1\n2017-05-07 09:30:00,0.1\n2017-05-07 09:35:00,0.2\n2017-05-07 09:40:00,0.1\n2017-05-07 09:45:00,0.2\n2017-05-07 09:50:00,0.1\n2017-05-07 09:55:00,0.2\n2017-05-07 10:00:00,0.4\n2017-05-07 10:05:00,0.6\n2017-05-07 10:10:00,0.8\n2017-05-07 10:15:00,0.8\n2017-05-07 10:20:00,0.9\n2017-05-07 10:25:00,0.9\n2017-05-07 10:30:00,1.0\n2017-05-07 10:35:00,1.0\n2017-05-07 10:40:00,1.1\n2017-05-07 10:45:00,1.2\n2017-05-07 10:50:00,1.2\n2017-05-07 10:55:00,1.2\n2017-05-07 11:00:00,1.1\n2017-05-07 11:05:00,1.1\n2017-05-07 11:10:00,1.2\n2017-05-07 11:15:00,1.2\n2017-05-07 11:20:00,1.4\n2017-05-07 11:25:00,1.5\n2017-05-07 11:30:00,1.6\n2017-05-07 11:35:00,1.7\n2017-05-07 11:40:00,1.7\n2017-05-07 11:45:00,1.6\n2017-05-07 11:50:00,1.6\n2017-05-07 11:55:00,1.6\n2017-05-07 12:00:00,1.6\n2017-05-07 12:05:00,1.7\n2017-05-07 12:10:00,1.7\n2017-05-07 12:15:00,1.7\n2017-05-07 12:20:00,1.7\n2017-05-07 12:25:00,1.6\n2017-05-07 12:30:00,1.6\n2017-05-07 12:35:00,1.7\n2017-05-07 12:40:00,1.6\n2017-05-07 12:45:00,1.6\n2017-05-07 12:50:00,1.6\n2017-05-07 12:55:00,1.6\n2017-05-07 13:00:00,1.6\n2017-05-07 13:05:00,1.6\n2017-05-07 13:10:00,1.7\n2017-05-07 13:15:00,1.7\n2017-05-07 13:20:00,1.7\n2017-05-07 13:25:00,1.8\n2017-05-07 13:30:00,2.0\n2017-05-07 13:35:00,2.5\n2017-05-07 13:40:00,3.2\n2017-05-07 13:45:00,3.8\n2017-05-07 13:50:00,4.0\n2017-05-07 13:55:00,3.8\n2017-05-07 14:00:00,3.8\n2017-05-07 14:05:00,4.3\n2017-05-07 14:10:00,4.5\n2017-05-07 14:15:00,4.6\n2017-05-07 14:20:00,4.7\n2017-05-07 14:25:00,4.7\n2017-05-07 14:30:00,4.3\n2017-05-07 14:35:00,4.5\n2017-05-07 14:40:00,5.5\n2017-05-07 14:45:00,5.0\n2017-05-07 14:50:00,4.8\n2017-05-07 14:55:00,5.0\n2017-05-07 15:00:00,4.4\n2017-05-07 15:05:00,4.4\n2017-05-07 15:10:00,4.1\n2017-05-07 15:15:00,3.7\n2017-05-07 15:20:00,3.5\n2017-05-07 15:25:00,3.6\n2017-05-07 15:30:00,3.6\n2017-05-07 15:35:00,3.9\n2017-05-07 15:40:00,4.2\n2017-05-07 15:45:00,4.8\n2017-05-07 15:50:00,4.9\n2017-05-07 15:55:00,5.0\n2017-05-07 16:00:00,4.8\n2017-05-07 16:05:00,4.8\n2017-05-07 16:10:00,5.0\n2017-05-07 16:15:00,5.0\n2017-05-07 16:20:00,6.6\n2017-05-07 16:25:00,7.3\n2017-05-07 16:30:00,6.9\n2017-05-07 16:35:00,6.6\n2017-05-07 16:40:00,7.0\n2017-05-07 16:45:00,7.7\n2017-05-07 16:50:00,8.6\n2017-05-07 16:55:00,9.8\n2017-05-07 17:00:00,9.8\n2017-05-07 17:05:00,9.2\n2017-05-07 17:10:00,10.7\n2017-05-07 17:15:00,12.5\n2017-05-07 17:20:00,14.0\n2017-05-07 17:25:00,15.5\n2017-05-07 17:30:00,14.0\n2017-05-07 17:35:00,12.7\n2017-05-07 17:40:00,14.1\n2017-05-07 17:45:00,13.8\n2017-05-07 17:50:00,13.5\n2017-05-07 17:55:00,14.1\n2017-05-07 18:00:00,11.1\n2017-05-07 18:05:00,13.8\n2017-05-07 18:10:00,16.5\n2017-05-07 18:15:00,17.8\n2017-05-07 18:20:00,13.1\n2017-05-07 18:25:00,12.1\n2017-05-07 18:30:00,11.3\n2017-05-07 18:35:00,12.0\n2017-05-07 18:40:00,13.0\n2017-05-07 18:45:00,11.5\n2017-05-07 18:50:00,11.4\n2017-05-07 18:55:00,11.8\n2017-05-07 19:00:00,15.2\n2017-05-07 19:05:00,14.7\n2017-05-07 19:10:00,17.4\n2017-05-07 19:15:00,16.7\n2017-05-07 19:20:00,18.6\n2017-05-07 19:25:00,20.4\n2017-05-07 19:30:00,21.5\n2017-05-07 19:35:00,22.0\n2017-05-07 19:40:00,23.3\n2017-05-07 19:45:00,22.4\n2017-05-07 19:50:00,23.3\n2017-05-07 19:55:00,23.7\n2017-05-07 20:00:00,23.9\n2017-05-07 20:05:00,24.6\n2017-05-07 20:10:00,21.8\n2017-05-07 20:15:00,18.6\n2017-05-07 20:20:00,19.1\n2017-05-07 20:25:00,22.5\n2017-05-07 20:30:00,23.1\n2017-05-07 20:35:00,20.8\n2017-05-07 20:40:00,20.9\n2017-05-07 20:45:00,18.5\n2017-05-07 20:50:00,16.4\n2017-05-07 20:55:00,15.9\n2017-05-07 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02:35:00,13.2\n2017-05-11 02:40:00,12.8\n2017-05-11 02:45:00,12.6\n2017-05-11 02:50:00,12.2\n2017-05-11 02:55:00,11.8\n2017-05-11 03:00:00,11.6\n2017-05-11 03:05:00,11.3\n2017-05-11 03:10:00,11.1\n2017-05-11 03:15:00,10.9\n2017-05-11 03:20:00,10.7\n2017-05-11 03:25:00,10.6\n2017-05-11 03:30:00,10.4\n2017-05-11 03:35:00,10.3\n2017-05-11 03:40:00,10.2\n2017-05-11 03:45:00,10.2\n2017-05-11 03:50:00,10.1\n2017-05-11 03:55:00,10.1\n2017-05-11 04:00:00,10.0\n2017-05-11 04:05:00,10.0\n2017-05-11 04:10:00,9.9\n2017-05-11 04:15:00,9.8\n2017-05-11 04:20:00,9.8\n2017-05-11 04:25:00,9.7\n2017-05-11 04:30:00,9.6\n2017-05-11 04:35:00,9.5\n2017-05-11 04:40:00,9.4\n2017-05-11 04:45:00,9.3\n2017-05-11 04:50:00,9.2\n2017-05-11 04:55:00,9.1\n2017-05-11 05:00:00,9.0\n2017-05-11 05:05:00,8.9\n2017-05-11 05:10:00,8.9\n2017-05-11 05:15:00,8.9\n2017-05-11 05:20:00,8.8\n2017-05-11 05:25:00,8.9\n2017-05-11 05:30:00,8.9\n2017-05-11 05:35:00,8.7\n2017-05-11 05:40:00,8.6\n2017-05-11 05:45:00,8.5\n2017-05-11 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12:30:00,6.0\n2017-05-11 12:35:00,5.7\n2017-05-11 12:40:00,5.6\n2017-05-11 12:45:00,5.7\n2017-05-11 12:50:00,5.7\n2017-05-11 12:55:00,5.9\n2017-05-11 13:00:00,6.1\n2017-05-11 13:05:00,6.3\n2017-05-11 13:10:00,6.4\n2017-05-11 13:15:00,6.6\n2017-05-11 13:20:00,7.1\n2017-05-11 13:25:00,7.1\n2017-05-11 13:30:00,6.9\n2017-05-11 13:35:00,6.9\n2017-05-11 13:40:00,7.1\n2017-05-11 13:45:00,7.5\n2017-05-11 13:50:00,7.9\n2017-05-11 13:55:00,8.0\n2017-05-11 14:00:00,7.7\n2017-05-11 14:05:00,7.7\n2017-05-11 14:10:00,7.7\n2017-05-11 14:15:00,7.8\n2017-05-11 14:20:00,8.1\n2017-05-11 14:25:00,8.4\n2017-05-11 14:30:00,8.6\n2017-05-11 14:35:00,8.9\n2017-05-11 14:40:00,9.2\n2017-05-11 14:45:00,9.6\n2017-05-11 14:50:00,9.3\n2017-05-11 14:55:00,9.8\n2017-05-11 15:00:00,10.2\n2017-05-11 15:05:00,10.7\n2017-05-11 15:10:00,11.0\n2017-05-11 15:15:00,11.5\n2017-05-11 15:20:00,11.5\n2017-05-11 15:25:00,10.3\n2017-05-11 15:30:00,10.0\n2017-05-11 15:35:00,10.4\n2017-05-11 15:40:00,11.6\n2017-05-11 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01:15:00,17.7\n2017-05-12 01:20:00,17.8\n2017-05-12 01:25:00,17.4\n2017-05-12 01:30:00,17.6\n2017-05-12 01:35:00,16.7\n2017-05-12 01:40:00,16.1\n2017-05-12 01:45:00,15.6\n2017-05-12 01:50:00,15.1\n2017-05-12 01:55:00,15.0\n2017-05-12 02:00:00,14.6\n2017-05-12 02:05:00,13.9\n2017-05-12 02:10:00,13.9\n2017-05-12 02:15:00,13.4\n2017-05-12 02:20:00,13.4\n2017-05-12 02:25:00,13.4\n2017-05-12 02:30:00,13.5\n2017-05-12 02:35:00,13.6\n2017-05-12 02:40:00,13.2\n2017-05-12 02:45:00,13.3\n2017-05-12 02:50:00,12.8\n2017-05-12 02:55:00,12.7\n2017-05-12 03:00:00,12.0\n2017-05-12 03:05:00,11.1\n2017-05-12 03:10:00,10.5\n2017-05-12 03:15:00,11.2\n2017-05-12 03:20:00,11.5\n2017-05-12 03:25:00,10.9\n2017-05-12 03:30:00,10.6\n2017-05-12 03:35:00,10.4\n2017-05-12 03:40:00,9.9\n2017-05-12 03:45:00,9.6\n2017-05-12 03:50:00,9.3\n2017-05-12 03:55:00,9.1\n2017-05-12 04:00:00,8.8\n2017-05-12 04:05:00,8.7\n2017-05-12 04:10:00,8.6\n2017-05-12 04:15:00,8.5\n2017-05-12 04:20:00,8.4\n2017-05-12 04:25:00,8.3\n2017-05-12 04:30:00,8.2\n2017-05-12 04:35:00,8.1\n2017-05-12 04:40:00,8.1\n2017-05-12 04:45:00,8.0\n2017-05-12 04:50:00,7.9\n2017-05-12 04:55:00,7.8\n2017-05-12 05:00:00,7.7\n2017-05-12 05:05:00,7.7\n2017-05-12 05:10:00,7.7\n2017-05-12 05:15:00,7.6\n2017-05-12 05:20:00,7.6\n2017-05-12 05:25:00,7.5\n2017-05-12 05:30:00,7.5\n2017-05-12 05:35:00,7.4\n2017-05-12 05:40:00,7.3\n2017-05-12 05:45:00,7.3\n2017-05-12 05:50:00,7.2\n2017-05-12 05:55:00,7.1\n2017-05-12 06:00:00,7.0\n2017-05-12 06:05:00,7.0\n2017-05-12 06:10:00,7.0\n2017-05-12 06:15:00,6.9\n2017-05-12 06:20:00,6.9\n2017-05-12 06:25:00,6.8\n2017-05-12 06:30:00,6.8\n2017-05-12 06:35:00,6.7\n2017-05-12 06:40:00,6.7\n2017-05-12 06:45:00,6.7\n2017-05-12 06:50:00,6.6\n2017-05-12 06:55:00,6.7\n2017-05-12 07:00:00,6.7\n2017-05-12 07:05:00,7.3\n2017-05-12 07:10:00,7.7\n2017-05-12 07:15:00,7.8\n2017-05-12 07:20:00,8.0\n2017-05-12 07:25:00,8.0\n2017-05-12 07:30:00,8.0\n2017-05-12 07:35:00,8.1\n2017-05-12 07:40:00,8.3\n2017-05-12 07:45:00,8.3\n2017-05-12 07:50:00,8.3\n2017-05-12 07:55:00,8.3\n2017-05-12 08:00:00,8.3\n2017-05-12 08:05:00,8.3\n2017-05-12 08:10:00,8.4\n2017-05-12 08:15:00,8.4\n2017-05-12 08:20:00,8.4\n2017-05-12 08:25:00,8.3\n2017-05-12 08:30:00,8.3\n2017-05-12 08:35:00,8.3\n2017-05-12 08:40:00,8.3\n2017-05-12 08:45:00,8.3\n2017-05-12 08:50:00,8.3\n2017-05-12 08:55:00,8.3\n2017-05-12 09:00:00,8.2\n2017-05-12 09:05:00,8.2\n2017-05-12 09:10:00,8.2\n2017-05-12 09:15:00,8.1\n2017-05-12 09:20:00,8.1\n2017-05-12 09:25:00,8.1\n2017-05-12 09:30:00,8.1\n2017-05-12 09:35:00,8.1\n2017-05-12 09:40:00,8.0\n2017-05-12 09:45:00,8.0\n2017-05-12 09:50:00,8.0\n2017-05-12 09:55:00,8.0\n2017-05-12 10:00:00,7.9\n2017-05-12 10:05:00,7.9\n2017-05-12 10:10:00,7.9\n2017-05-12 10:15:00,7.8\n2017-05-12 10:20:00,7.8\n2017-05-12 10:25:00,7.8\n2017-05-12 10:30:00,7.7\n2017-05-12 10:35:00,7.7\n2017-05-12 10:40:00,7.7\n2017-05-12 10:45:00,7.7\n2017-05-12 10:50:00,7.7\n2017-05-12 10:55:00,7.7\n2017-05-12 11:00:00,7.7\n2017-05-12 11:05:00,7.6\n2017-05-12 11:10:00,7.5\n2017-05-12 11:15:00,7.5\n2017-05-12 11:20:00,7.4\n2017-05-12 11:25:00,7.5\n2017-05-12 11:30:00,7.5\n2017-05-12 11:35:00,7.4\n2017-05-12 11:40:00,7.4\n2017-05-12 11:45:00,7.4\n2017-05-12 11:50:00,7.4\n2017-05-12 11:55:00,7.4\n2017-05-12 12:00:00,7.4\n2017-05-12 12:05:00,7.3\n2017-05-12 12:10:00,7.2\n2017-05-12 12:15:00,7.1\n2017-05-12 12:20:00,7.0\n2017-05-12 12:25:00,7.0\n2017-05-12 12:30:00,7.1\n2017-05-12 12:35:00,7.0\n2017-05-12 12:40:00,6.9\n2017-05-12 12:45:00,7.0\n2017-05-12 12:50:00,6.9\n2017-05-12 12:55:00,6.6\n2017-05-12 13:00:00,6.5\n2017-05-12 13:05:00,6.5\n2017-05-12 13:10:00,6.5\n2017-05-12 13:15:00,6.5\n2017-05-12 13:20:00,6.4\n2017-05-12 13:25:00,6.5\n2017-05-12 13:30:00,6.4\n2017-05-12 13:35:00,6.5\n2017-05-12 13:40:00,6.7\n2017-05-12 13:45:00,6.6\n2017-05-12 13:50:00,6.4\n2017-05-12 13:55:00,6.3\n2017-05-12 14:00:00,6.1\n2017-05-12 14:05:00,6.0\n2017-05-12 14:10:00,6.0\n2017-05-12 14:15:00,5.9\n2017-05-12 14:20:00,5.8\n2017-05-12 14:25:00,5.9\n2017-05-12 14:30:00,6.3\n2017-05-12 14:35:00,6.7\n2017-05-12 14:40:00,6.5\n2017-05-12 14:45:00,6.4\n2017-05-12 14:50:00,6.7\n2017-05-12 14:55:00,6.5\n2017-05-12 15:00:00,6.5\n2017-05-12 15:05:00,6.9\n2017-05-12 15:10:00,6.7\n2017-05-12 15:15:00,6.4\n2017-05-12 15:20:00,6.7\n2017-05-12 15:25:00,6.8\n2017-05-12 15:30:00,6.5\n2017-05-12 15:35:00,6.1\n2017-05-12 15:40:00,6.0\n2017-05-12 15:45:00,5.9\n2017-05-12 15:50:00,5.8\n2017-05-12 15:55:00,5.6\n2017-05-12 16:00:00,5.7\n2017-05-12 16:05:00,5.7\n2017-05-12 16:10:00,5.7\n2017-05-12 16:15:00,5.6\n2017-05-12 16:20:00,5.7\n2017-05-12 16:25:00,5.6\n2017-05-12 16:30:00,5.5\n2017-05-12 16:35:00,5.5\n2017-05-12 16:40:00,5.4\n2017-05-12 16:45:00,5.4\n2017-05-12 16:50:00,5.4\n2017-05-12 16:55:00,5.8\n2017-05-12 17:00:00,5.5\n2017-05-12 17:05:00,5.8\n2017-05-12 17:10:00,5.5\n2017-05-12 17:15:00,5.5\n2017-05-12 17:20:00,5.5\n2017-05-12 17:25:00,5.5\n2017-05-12 17:30:00,5.3\n2017-05-12 17:35:00,4.9\n2017-05-12 17:40:00,5.1\n2017-05-12 17:45:00,5.1\n2017-05-12 17:50:00,5.5\n2017-05-12 17:55:00,5.5\n2017-05-12 18:00:00,5.2\n2017-05-12 18:05:00,5.2\n2017-05-12 18:10:00,5.0\n2017-05-12 18:15:00,5.1\n2017-05-12 18:20:00,5.3\n2017-05-12 18:25:00,6.4\n2017-05-12 18:30:00,6.9\n2017-05-12 18:35:00,6.7\n2017-05-12 18:40:00,7.3\n2017-05-12 18:45:00,8.7\n2017-05-12 18:50:00,9.9\n2017-05-12 18:55:00,9.1\n2017-05-12 19:00:00,9.7\n2017-05-12 19:05:00,9.8\n2017-05-12 19:10:00,8.7\n2017-05-12 19:15:00,9.6\n2017-05-12 19:20:00,9.5\n2017-05-12 19:25:00,9.6\n2017-05-12 19:30:00,9.4\n2017-05-12 19:35:00,9.3\n2017-05-12 19:40:00,10.3\n2017-05-12 19:45:00,10.5\n2017-05-12 19:50:00,10.2\n2017-05-12 19:55:00,10.3\n2017-05-12 20:00:00,10.3\n2017-05-12 20:05:00,11.4\n2017-05-12 20:10:00,12.1\n2017-05-12 20:15:00,13.2\n2017-05-12 20:20:00,14.4\n2017-05-12 20:25:00,13.3\n2017-05-12 20:30:00,12.7\n2017-05-12 20:35:00,12.7\n2017-05-12 20:40:00,13.0\n2017-05-12 20:45:00,14.0\n2017-05-12 20:50:00,14.7\n2017-05-12 20:55:00,14.4\n2017-05-12 21:00:00,13.7\n2017-05-12 21:05:00,15.4\n2017-05-12 21:10:00,17.6\n2017-05-12 21:15:00,15.4\n2017-05-12 21:20:00,19.0\n2017-05-12 21:25:00,21.3\n2017-05-12 21:30:00,19.1\n2017-05-12 21:35:00,20.2\n2017-05-12 21:40:00,19.5\n2017-05-12 21:45:00,23.3\n2017-05-12 21:50:00,24.3\n2017-05-12 21:55:00,23.8\n2017-05-12 22:00:00,23.4\n2017-05-12 22:05:00,23.0\n2017-05-12 22:10:00,20.3\n2017-05-12 22:15:00,24.1\n2017-05-12 22:20:00,23.6\n2017-05-12 22:25:00,20.2\n2017-05-12 22:30:00,21.9\n2017-05-12 22:35:00,19.4\n2017-05-12 22:40:00,16.0\n2017-05-12 22:45:00,15.9\n2017-05-12 22:50:00,16.7\n2017-05-12 22:55:00,18.2\n2017-05-12 23:00:00,17.1\n2017-05-12 23:05:00,16.4\n2017-05-12 23:10:00,18.8\n2017-05-12 23:15:00,19.5\n2017-05-12 23:20:00,21.3\n2017-05-12 23:25:00,21.0\n2017-05-12 23:30:00,19.3\n2017-05-12 23:35:00,20.2\n2017-05-12 23:40:00,19.7\n2017-05-12 23:45:00,18.3\n2017-05-12 23:50:00,20.2\n2017-05-12 23:55:00,20.0\n2017-05-13 00:00:00,17.5\n2017-05-13 00:05:00,15.8\n2017-05-13 00:10:00,14.7\n2017-05-13 00:15:00,14.1\n2017-05-13 00:20:00,13.5\n2017-05-13 00:25:00,13.4\n2017-05-13 00:30:00,11.8\n2017-05-13 00:35:00,11.9\n2017-05-13 00:40:00,14.9\n2017-05-13 00:45:00,14.9\n2017-05-13 00:50:00,15.2\n2017-05-13 00:55:00,15.1\n2017-05-13 01:00:00,16.2\n2017-05-13 01:05:00,15.9\n2017-05-13 01:10:00,15.2\n2017-05-13 01:15:00,13.8\n2017-05-13 01:20:00,11.3\n2017-05-13 01:25:00,10.0\n2017-05-13 01:30:00,9.8\n2017-05-13 01:35:00,9.5\n2017-05-13 01:40:00,9.1\n2017-05-13 01:45:00,8.7\n2017-05-13 01:50:00,8.3\n2017-05-13 01:55:00,8.5\n2017-05-13 02:00:00,8.5\n2017-05-13 02:05:00,8.4\n2017-05-13 02:10:00,7.9\n2017-05-13 02:15:00,7.8\n2017-05-13 02:20:00,7.7\n2017-05-13 02:25:00,7.0\n2017-05-13 02:30:00,7.0\n2017-05-13 02:35:00,6.1\n2017-05-13 02:40:00,5.1\n2017-05-13 02:45:00,4.5\n2017-05-13 02:50:00,4.0\n2017-05-13 02:55:00,3.6\n2017-05-13 03:00:00,3.5\n2017-05-13 03:05:00,3.2\n2017-05-13 03:10:00,2.6\n2017-05-13 03:15:00,2.4\n2017-05-13 03:20:00,2.2\n2017-05-13 03:25:00,2.2\n2017-05-13 03:30:00,2.1\n2017-05-13 03:35:00,2.3\n2017-05-13 03:40:00,2.7\n2017-05-13 03:45:00,2.8\n2017-05-13 03:50:00,3.1\n2017-05-13 03:55:00,2.9\n2017-05-13 04:00:00,2.7\n2017-05-13 04:05:00,2.4\n2017-05-13 04:10:00,2.2\n2017-05-13 04:15:00,2.0\n2017-05-13 04:20:00,2.0\n2017-05-13 04:25:00,2.0\n2017-05-13 04:30:00,1.7\n2017-05-13 04:35:00,1.7\n2017-05-13 04:40:00,1.6\n2017-05-13 04:45:00,1.3\n2017-05-13 04:50:00,1.0\n2017-05-13 04:55:00,0.7\n2017-05-13 05:00:00,0.4\n2017-05-13 05:05:00,0.2\n2017-05-13 05:10:00,0.0\n2017-05-13 05:15:00,-0.1\n2017-05-13 05:20:00,-0.3\n2017-05-13 05:25:00,-0.3\n2017-05-13 05:30:00,-0.4\n2017-05-13 05:35:00,-0.5\n2017-05-13 05:40:00,-0.6\n2017-05-13 05:45:00,-0.7\n2017-05-13 05:50:00,-0.6\n2017-05-13 05:55:00,-0.4\n2017-05-13 06:00:00,-0.6\n2017-05-13 06:05:00,-0.6\n2017-05-13 06:10:00,-0.8\n2017-05-13 06:15:00,-0.9\n2017-05-13 06:20:00,-1.1\n2017-05-13 06:25:00,-1.0\n2017-05-13 06:30:00,-0.5\n2017-05-13 06:35:00,-0.6\n2017-05-13 06:40:00,-0.7\n2017-05-13 06:45:00,-0.8\n2017-05-13 06:50:00,-1.1\n2017-05-13 06:55:00,-1.3\n2017-05-13 07:00:00,-1.5\n2017-05-13 07:05:00,-1.6\n2017-05-13 07:10:00,-1.6\n2017-05-13 07:15:00,-1.8\n2017-05-13 07:20:00,-1.9\n2017-05-13 07:25:00,-1.9\n2017-05-13 07:30:00,-1.9\n2017-05-13 07:35:00,-2.0\n2017-05-13 07:40:00,-2.1\n2017-05-13 07:45:00,-2.1\n2017-05-13 07:50:00,-2.1\n2017-05-13 07:55:00,-2.1\n2017-05-13 08:00:00,-2.2\n2017-05-13 08:05:00,-2.3\n2017-05-13 08:10:00,-2.3\n2017-05-13 08:15:00,-2.4\n2017-05-13 08:20:00,-2.5\n2017-05-13 08:25:00,-2.6\n2017-05-13 08:30:00,-2.6\n2017-05-13 08:35:00,-2.7\n2017-05-13 08:40:00,-2.7\n2017-05-13 08:45:00,-2.8\n2017-05-13 08:50:00,-2.9\n2017-05-13 08:55:00,-2.9\n2017-05-13 09:00:00,-3.0\n2017-05-13 09:05:00,-3.1\n2017-05-13 09:10:00,-3.2\n2017-05-13 09:15:00,-3.3\n2017-05-13 09:20:00,-3.3\n2017-05-13 09:25:00,-3.3\n2017-05-13 09:30:00,-3.4\n2017-05-13 09:35:00,-3.4\n2017-05-13 09:40:00,-3.5\n2017-05-13 09:45:00,-3.5\n2017-05-13 09:50:00,-3.5\n2017-05-13 09:55:00,-3.6\n2017-05-13 10:00:00,-3.6\n2017-05-13 10:05:00,-3.6\n2017-05-13 10:10:00,-3.7\n2017-05-13 10:15:00,-3.8\n2017-05-13 10:20:00,-3.8\n2017-05-13 10:25:00,-3.9\n2017-05-13 10:30:00,-3.9\n2017-05-13 10:35:00,-3.9\n2017-05-13 10:40:00,-3.9\n2017-05-13 10:45:00,-4.0\n2017-05-13 10:50:00,-4.1\n2017-05-13 10:55:00,-4.1\n2017-05-13 11:00:00,-4.2\n2017-05-13 11:05:00,-4.2\n2017-05-13 11:10:00,-4.2\n2017-05-13 11:15:00,-4.2\n2017-05-13 11:20:00,-4.2\n2017-05-13 11:25:00,-4.2\n2017-05-13 11:30:00,-4.1\n2017-05-13 11:35:00,-4.1\n2017-05-13 11:40:00,-4.1\n2017-05-13 11:45:00,-4.2\n2017-05-13 11:50:00,-4.2\n2017-05-13 11:55:00,-4.2\n2017-05-13 12:00:00,-4.1\n2017-05-13 12:05:00,-4.1\n2017-05-13 12:10:00,-4.2\n2017-05-13 12:15:00,-4.2\n2017-05-13 12:20:00,-4.2\n2017-05-13 12:25:00,-4.2\n2017-05-13 12:30:00,-4.2\n2017-05-13 12:35:00,-4.3\n2017-05-13 12:40:00,-4.3\n2017-05-13 12:45:00,-4.3\n2017-05-13 12:50:00,-4.3\n2017-05-13 12:55:00,-4.2\n2017-05-13 13:00:00,-4.2\n2017-05-13 13:05:00,-4.1\n2017-05-13 13:10:00,-4.1\n2017-05-13 13:15:00,-4.0\n2017-05-13 13:20:00,-3.8\n2017-05-13 13:25:00,-3.6\n2017-05-13 13:30:00,-3.5\n2017-05-13 13:35:00,-3.5\n2017-05-13 13:40:00,-3.4\n2017-05-13 13:45:00,-3.2\n2017-05-13 13:50:00,-3.0\n2017-05-13 13:55:00,-2.8\n2017-05-13 14:00:00,-2.6\n2017-05-13 14:05:00,-2.4\n2017-05-13 14:10:00,-2.0\n2017-05-13 14:15:00,-1.8\n2017-05-13 14:20:00,-1.6\n2017-05-13 14:25:00,-1.5\n2017-05-13 14:30:00,-1.2\n2017-05-13 14:35:00,-1.1\n2017-05-13 14:40:00,-0.7\n2017-05-13 14:45:00,-0.3\n2017-05-13 14:50:00,-0.1\n2017-05-13 14:55:00,0.3\n2017-05-13 15:00:00,0.4\n2017-05-13 15:05:00,0.8\n2017-05-13 15:10:00,1.1\n2017-05-13 15:15:00,1.6\n2017-05-13 15:20:00,2.0\n2017-05-13 15:25:00,2.4\n2017-05-13 15:30:00,3.0\n2017-05-13 15:35:00,3.4\n2017-05-13 15:40:00,3.8\n2017-05-13 15:45:00,4.4\n2017-05-13 15:50:00,4.9\n2017-05-13 15:55:00,5.7\n2017-05-13 16:00:00,6.5\n2017-05-13 16:05:00,7.2\n2017-05-13 16:10:00,7.6\n2017-05-13 16:15:00,8.3\n2017-05-13 16:20:00,8.8\n2017-05-13 16:25:00,9.6\n2017-05-13 16:30:00,10.4\n2017-05-13 16:35:00,11.0\n2017-05-13 16:40:00,11.9\n2017-05-13 16:45:00,12.5\n2017-05-13 16:50:00,13.0\n2017-05-13 16:55:00,13.8\n2017-05-13 17:00:00,14.3\n2017-05-13 17:05:00,14.6\n2017-05-13 17:10:00,15.3\n2017-05-13 17:15:00,15.6\n2017-05-13 17:20:00,16.2\n2017-05-13 17:25:00,16.8\n2017-05-13 17:30:00,17.3\n2017-05-13 17:35:00,18.6\n2017-05-13 17:40:00,18.7\n2017-05-13 17:45:00,19.3\n2017-05-13 17:50:00,20.0\n2017-05-13 17:55:00,20.0\n2017-05-13 18:00:00,20.4\n2017-05-13 18:05:00,21.6\n2017-05-13 18:10:00,22.0\n2017-05-13 18:15:00,22.6\n2017-05-13 18:20:00,23.1\n2017-05-13 18:25:00,23.1\n2017-05-13 18:30:00,24.0\n2017-05-13 18:35:00,24.3\n2017-05-13 18:40:00,24.7\n2017-05-13 18:45:00,24.7\n2017-05-13 18:50:00,24.7\n2017-05-13 18:55:00,25.4\n2017-05-13 19:00:00,26.1\n2017-05-13 19:05:00,26.8\n2017-05-13 19:10:00,27.4\n2017-05-13 19:15:00,26.9\n2017-05-13 19:20:00,23.6\n2017-05-13 19:25:00,22.0\n2017-05-13 19:30:00,24.8\n2017-05-13 19:35:00,26.8\n2017-05-13 19:40:00,25.2\n2017-05-13 19:45:00,23.2\n2017-05-13 19:50:00,23.0\n2017-05-13 19:55:00,27.6\n2017-05-13 20:00:00,29.0\n2017-05-13 20:05:00,26.1\n2017-05-13 20:10:00,28.6\n2017-05-13 20:15:00,29.4\n2017-05-13 20:20:00,29.0\n2017-05-13 20:25:00,30.3\n2017-05-13 20:30:00,27.5\n2017-05-13 20:35:00,30.1\n2017-05-13 20:40:00,23.6\n2017-05-13 20:45:00,22.7\n2017-05-13 20:50:00,28.9\n2017-05-13 20:55:00,23.5\n2017-05-13 21:00:00,25.7\n2017-05-13 21:05:00,24.9\n2017-05-13 21:10:00,28.6\n2017-05-13 21:15:00,28.4\n2017-05-13 21:20:00,23.0\n2017-05-13 21:25:00,20.8\n2017-05-13 21:30:00,17.9\n2017-05-13 21:35:00,21.3\n2017-05-13 21:40:00,20.1\n2017-05-13 21:45:00,25.4\n2017-05-13 21:50:00,26.5\n2017-05-13 21:55:00,20.6\n2017-05-13 22:00:00,19.5\n2017-05-13 22:05:00,20.1\n2017-05-13 22:10:00,23.7\n2017-05-13 22:15:00,27.5\n2017-05-13 22:20:00,25.3\n2017-05-13 22:25:00,25.1\n2017-05-13 22:30:00,26.2\n2017-05-13 22:35:00,20.0\n2017-05-13 22:40:00,22.2\n2017-05-13 22:45:00,18.5\n2017-05-13 22:50:00,25.2\n2017-05-13 22:55:00,25.7\n2017-05-13 23:00:00,25.8\n2017-05-13 23:05:00,26.3\n2017-05-13 23:10:00,20.9\n2017-05-13 23:15:00,21.6\n2017-05-13 23:20:00,25.4\n2017-05-13 23:25:00,22.2\n2017-05-13 23:30:00,21.1\n2017-05-13 23:35:00,20.3\n2017-05-13 23:40:00,21.8\n2017-05-13 23:45:00,23.9\n2017-05-13 23:50:00,23.7\n2017-05-13 23:55:00,21.3\n2017-05-14 00:00:00,20.8\n2017-05-14 00:05:00,17.7\n2017-05-14 00:10:00,18.4\n2017-05-14 00:15:00,20.9\n2017-05-14 00:20:00,22.4\n2017-05-14 00:25:00,20.5\n2017-05-14 00:30:00,19.6\n2017-05-14 00:35:00,20.1\n2017-05-14 00:40:00,18.7\n2017-05-14 00:45:00,16.6\n2017-05-14 00:50:00,18.0\n2017-05-14 00:55:00,17.6\n2017-05-14 01:00:00,17.0\n2017-05-14 01:05:00,16.5\n2017-05-14 01:10:00,16.0\n2017-05-14 01:15:00,15.4\n2017-05-14 01:20:00,14.5\n2017-05-14 01:25:00,13.3\n2017-05-14 01:30:00,12.2\n2017-05-14 01:35:00,11.4\n2017-05-14 01:40:00,10.7\n2017-05-14 01:45:00,9.9\n2017-05-14 01:50:00,9.4\n2017-05-14 01:55:00,9.0\n2017-05-14 02:00:00,8.7\n2017-05-14 02:05:00,8.3\n2017-05-14 02:10:00,8.0\n2017-05-14 02:15:00,7.7\n2017-05-14 02:20:00,7.2\n2017-05-14 02:25:00,6.7\n2017-05-14 02:30:00,6.2\n2017-05-14 02:35:00,5.8\n2017-05-14 02:40:00,5.3\n2017-05-14 02:45:00,4.8\n2017-05-14 02:50:00,4.5\n2017-05-14 02:55:00,4.1\n2017-05-14 03:00:00,3.8\n2017-05-14 03:05:00,3.6\n2017-05-14 03:10:00,3.3\n2017-05-14 03:15:00,3.2\n2017-05-14 03:20:00,3.0\n2017-05-14 03:25:00,2.8\n2017-05-14 03:30:00,2.6\n2017-05-14 03:35:00,2.4\n2017-05-14 03:40:00,2.3\n2017-05-14 03:45:00,2.2\n2017-05-14 03:50:00,2.0\n2017-05-14 03:55:00,1.8\n2017-05-14 04:00:00,1.7\n2017-05-14 04:05:00,1.6\n2017-05-14 04:10:00,1.5\n2017-05-14 04:15:00,1.5\n2017-05-14 04:20:00,1.5\n2017-05-14 04:25:00,1.4\n2017-05-14 04:30:00,1.4\n2017-05-14 04:35:00,1.4\n2017-05-14 04:40:00,1.3\n2017-05-14 04:45:00,1.3\n2017-05-14 04:50:00,1.3\n2017-05-14 04:55:00,1.2\n2017-05-14 05:00:00,1.1\n2017-05-14 05:05:00,1.0\n2017-05-14 05:10:00,0.9\n2017-05-14 05:15:00,0.8\n2017-05-14 05:20:00,0.7\n2017-05-14 05:25:00,0.6\n2017-05-14 05:30:00,0.5\n2017-05-14 05:35:00,0.5\n2017-05-14 05:40:00,0.4\n2017-05-14 05:45:00,0.4\n2017-05-14 05:50:00,0.3\n2017-05-14 05:55:00,0.2\n2017-05-14 06:00:00,0.3\n2017-05-14 06:05:00,0.3\n2017-05-14 06:10:00,0.2\n2017-05-14 06:15:00,0.0\n2017-05-14 06:20:00,0.0\n2017-05-14 06:25:00,-0.1\n2017-05-14 06:30:00,-0.1\n2017-05-14 06:35:00,-0.1\n2017-05-14 06:40:00,-0.2\n2017-05-14 06:45:00,0.0\n2017-05-14 06:50:00,0.0\n2017-05-14 06:55:00,0.1\n2017-05-14 07:00:00,0.2\n2017-05-14 07:05:00,0.2\n2017-05-14 07:10:00,0.2\n2017-05-14 07:15:00,0.2\n2017-05-14 07:20:00,0.1\n2017-05-14 07:25:00,0.1\n2017-05-14 07:30:00,0.0\n2017-05-14 07:35:00,-0.1\n2017-05-14 07:40:00,-0.2\n2017-05-14 07:45:00,-0.3\n2017-05-14 07:50:00,-0.4\n2017-05-14 07:55:00,-0.5\n2017-05-14 08:00:00,-0.5\n2017-05-14 08:05:00,-0.6\n2017-05-14 08:10:00,-0.6\n2017-05-14 08:15:00,-0.5\n2017-05-14 08:20:00,-0.6\n2017-05-14 08:25:00,-0.5\n2017-05-14 08:30:00,-0.5\n2017-05-14 08:35:00,-0.5\n2017-05-14 08:40:00,-0.4\n2017-05-14 08:45:00,-0.4\n2017-05-14 08:50:00,-0.5\n2017-05-14 08:55:00,-0.6\n2017-05-14 09:00:00,-0.7\n2017-05-14 09:05:00,-0.7\n2017-05-14 09:10:00,-0.7\n2017-05-14 09:15:00,-0.6\n2017-05-14 09:20:00,-0.5\n2017-05-14 09:25:00,-0.5\n2017-05-14 09:30:00,-0.7\n2017-05-14 09:35:00,-0.8\n2017-05-14 09:40:00,-0.8\n2017-05-14 09:45:00,-0.7\n2017-05-14 09:50:00,-0.8\n2017-05-14 09:55:00,-0.9\n2017-05-14 10:00:00,-1.0\n2017-05-14 10:05:00,-1.1\n2017-05-14 10:10:00,-1.4\n2017-05-14 10:15:00,-1.5\n2017-05-14 10:20:00,-1.6\n2017-05-14 10:25:00,-1.7\n2017-05-14 10:30:00,-1.7\n2017-05-14 10:35:00,-1.7\n2017-05-14 10:40:00,-1.8\n2017-05-14 10:45:00,-1.9\n2017-05-14 10:50:00,-1.8\n2017-05-14 10:55:00,-1.8\n2017-05-14 11:00:00,-1.8\n2017-05-14 11:05:00,-1.8\n2017-05-14 11:10:00,-1.9\n2017-05-14 11:15:00,-1.9\n2017-05-14 11:20:00,-2.0\n2017-05-14 11:25:00,-2.1\n2017-05-14 11:30:00,-2.1\n2017-05-14 11:35:00,-2.1\n2017-05-14 11:40:00,-2.2\n2017-05-14 11:45:00,-2.1\n2017-05-14 11:50:00,-2.1\n2017-05-14 11:55:00,-2.2\n2017-05-14 12:00:00,-2.2\n2017-05-14 12:05:00,-2.2\n2017-05-14 12:10:00,-2.3\n2017-05-14 12:15:00,-2.3\n2017-05-14 12:20:00,-2.4\n2017-05-14 12:25:00,-2.5\n2017-05-14 12:30:00,-2.5\n2017-05-14 12:35:00,-2.5\n2017-05-14 12:40:00,-2.6\n2017-05-14 12:45:00,-2.7\n2017-05-14 12:50:00,-2.7\n2017-05-14 12:55:00,-2.7\n2017-05-14 13:00:00,-2.7\n2017-05-14 13:05:00,-2.6\n2017-05-14 13:10:00,-2.6\n2017-05-14 13:15:00,-2.6\n2017-05-14 13:20:00,-2.5\n2017-05-14 13:25:00,-2.2\n2017-05-14 13:30:00,-1.9\n2017-05-14 13:35:00,-1.7\n2017-05-14 13:40:00,-1.4\n2017-05-14 13:45:00,-1.2\n2017-05-14 13:50:00,-1.0\n2017-05-14 13:55:00,-0.8\n2017-05-14 14:00:00,-0.6\n2017-05-14 14:05:00,-0.3\n2017-05-14 14:10:00,-0.1\n2017-05-14 14:15:00,0.0\n2017-05-14 14:20:00,0.3\n2017-05-14 14:25:00,0.5\n2017-05-14 14:30:00,0.7\n2017-05-14 14:35:00,0.9\n2017-05-14 14:40:00,1.1\n2017-05-14 14:45:00,1.5\n2017-05-14 14:50:00,1.7\n2017-05-14 14:55:00,1.9\n2017-05-14 15:00:00,2.2\n2017-05-14 15:05:00,2.4\n2017-05-14 15:10:00,2.7\n2017-05-14 15:15:00,2.9\n2017-05-14 15:20:00,3.5\n2017-05-14 15:25:00,3.7\n2017-05-14 15:30:00,4.0\n2017-05-14 15:35:00,4.8\n2017-05-14 15:40:00,5.4\n2017-05-14 15:45:00,6.0\n2017-05-14 15:50:00,6.6\n2017-05-14 15:55:00,7.2\n2017-05-14 16:00:00,7.8\n2017-05-14 16:05:00,8.4\n2017-05-14 16:10:00,8.6\n2017-05-14 16:15:00,10.0\n2017-05-14 16:20:00,10.6\n2017-05-14 16:25:00,10.7\n2017-05-14 16:30:00,11.6\n2017-05-14 16:35:00,12.4\n2017-05-14 16:40:00,12.6\n2017-05-14 16:45:00,12.8\n2017-05-14 16:50:00,15.3\n2017-05-14 16:55:00,15.1\n2017-05-14 17:00:00,14.9\n2017-05-14 17:05:00,16.5\n2017-05-14 17:10:00,17.1\n2017-05-14 17:15:00,16.2\n2017-05-14 17:20:00,17.6\n2017-05-14 17:25:00,18.9\n2017-05-14 17:30:00,19.0\n2017-05-14 17:35:00,20.0\n2017-05-14 17:40:00,19.9\n2017-05-14 17:45:00,20.6\n2017-05-14 17:50:00,21.4\n2017-05-14 17:55:00,22.0\n2017-05-14 18:00:00,23.3\n2017-05-14 18:05:00,23.8\n2017-05-14 18:10:00,24.0\n2017-05-14 18:15:00,24.3\n2017-05-14 18:20:00,22.8\n2017-05-14 18:25:00,23.1\n2017-05-14 18:30:00,23.9\n2017-05-14 18:35:00,22.7\n2017-05-14 18:40:00,24.0\n2017-05-14 18:45:00,26.7\n2017-05-14 18:50:00,27.8\n2017-05-14 18:55:00,24.4\n2017-05-14 19:00:00,25.6\n2017-05-14 19:05:00,28.4\n2017-05-14 19:10:00,28.0\n2017-05-14 19:15:00,29.4\n2017-05-14 19:20:00,30.3\n2017-05-14 19:25:00,30.8\n2017-05-14 19:30:00,27.9\n2017-05-14 19:35:00,28.2\n2017-05-14 19:40:00,28.2\n2017-05-14 19:45:00,29.7\n2017-05-14 19:50:00,30.4\n2017-05-14 19:55:00,31.0\n2017-05-14 20:00:00,26.1\n2017-05-14 20:05:00,27.4\n2017-05-14 20:10:00,30.6\n2017-05-14 20:15:00,30.2\n2017-05-14 20:20:00,32.8\n2017-05-14 20:25:00,33.6\n2017-05-14 20:30:00,26.0\n2017-05-14 20:35:00,22.0\n2017-05-14 20:40:00,23.3\n2017-05-14 20:45:00,22.2\n2017-05-14 20:50:00,19.2\n2017-05-14 20:55:00,25.3\n2017-05-14 21:00:00,20.6\n2017-05-14 21:05:00,19.8\n2017-05-14 21:10:00,17.7\n2017-05-14 21:15:00,15.5\n2017-05-14 21:20:00,18.4\n2017-05-14 21:25:00,19.3\n2017-05-14 21:30:00,16.2\n2017-05-14 21:35:00,21.2\n2017-05-14 21:40:00,18.0\n2017-05-14 21:45:00,18.2\n2017-05-14 21:50:00,24.0\n2017-05-14 21:55:00,20.1\n2017-05-14 22:00:00,18.6\n2017-05-14 22:05:00,16.3\n2017-05-14 22:10:00,21.5\n2017-05-14 22:15:00,22.6\n2017-05-14 22:20:00,25.0\n2017-05-14 22:25:00,23.1\n2017-05-14 22:30:00,20.5\n2017-05-14 22:35:00,16.9\n2017-05-14 22:40:00,20.1\n2017-05-14 22:45:00,17.2\n2017-05-14 22:50:00,18.5\n2017-05-14 22:55:00,20.8\n2017-05-14 23:00:00,18.4\n2017-05-14 23:05:00,17.0\n2017-05-14 23:10:00,19.1\n2017-05-14 23:15:00,17.7\n2017-05-14 23:20:00,21.2\n2017-05-14 23:25:00,22.4\n2017-05-14 23:30:00,22.0\n2017-05-14 23:35:00,22.1\n2017-05-14 23:40:00,19.1\n2017-05-14 23:45:00,18.8\n2017-05-14 23:50:00,19.9\n2017-05-14 23:55:00,18.6\n2017-05-15 00:00:00,16.3\n2017-05-15 00:05:00,15.6\n2017-05-15 00:10:00,15.1\n2017-05-15 00:15:00,14.6\n2017-05-15 00:20:00,15.5\n2017-05-15 00:25:00,15.2\n2017-05-15 00:30:00,14.8\n2017-05-15 00:35:00,15.0\n2017-05-15 00:40:00,15.3\n2017-05-15 00:45:00,14.9\n2017-05-15 00:50:00,13.7\n2017-05-15 00:55:00,13.1\n2017-05-15 01:00:00,12.4\n2017-05-15 01:05:00,11.7\n2017-05-15 01:10:00,12.1\n2017-05-15 01:15:00,12.0\n2017-05-15 01:20:00,12.3\n2017-05-15 01:25:00,12.4\n2017-05-15 01:30:00,11.9\n2017-05-15 01:35:00,11.4\n2017-05-15 01:40:00,10.8\n2017-05-15 01:45:00,10.5\n2017-05-15 01:50:00,10.0\n2017-05-15 01:55:00,10.2\n2017-05-15 02:00:00,9.8\n2017-05-15 02:05:00,9.4\n2017-05-15 02:10:00,9.0\n2017-05-15 02:15:00,8.7\n2017-05-15 02:20:00,8.3\n2017-05-15 02:25:00,8.0\n2017-05-15 02:30:00,7.8\n2017-05-15 02:35:00,7.4\n2017-05-15 02:40:00,7.2\n2017-05-15 02:45:00,6.8\n2017-05-15 02:50:00,6.1\n2017-05-15 02:55:00,6.0\n2017-05-15 03:00:00,5.9\n2017-05-15 03:05:00,5.6\n2017-05-15 03:10:00,5.5\n2017-05-15 03:15:00,5.6\n2017-05-15 03:20:00,5.6\n2017-05-15 03:25:00,5.4\n2017-05-15 03:30:00,5.3\n2017-05-15 03:35:00,5.1\n2017-05-15 03:40:00,5.0\n2017-05-15 03:45:00,4.9\n2017-05-15 03:50:00,4.9\n2017-05-15 03:55:00,5.0\n2017-05-15 04:00:00,5.0\n2017-05-15 04:05:00,5.0\n2017-05-15 04:10:00,4.8\n2017-05-15 04:15:00,4.7\n2017-05-15 04:20:00,4.4\n2017-05-15 04:25:00,4.4\n2017-05-15 04:30:00,4.2\n2017-05-15 04:35:00,3.8\n2017-05-15 04:40:00,3.7\n2017-05-15 04:45:00,3.3\n2017-05-15 04:50:00,3.0\n2017-05-15 04:55:00,2.7\n2017-05-15 05:00:00,2.5\n2017-05-15 05:05:00,2.3\n2017-05-15 05:10:00,2.0\n2017-05-15 05:15:00,1.8\n2017-05-15 05:20:00,1.6\n2017-05-15 05:25:00,1.5\n2017-05-15 05:30:00,1.5\n2017-05-15 05:35:00,1.4\n2017-05-15 05:40:00,1.5\n2017-05-15 05:45:00,1.6\n2017-05-15 05:50:00,1.6\n2017-05-15 05:55:00,1.7\n2017-05-15 06:00:00,1.8\n2017-05-15 06:05:00,1.7\n2017-05-15 06:10:00,1.8\n2017-05-15 06:15:00,1.8\n2017-05-15 06:20:00,1.8\n2017-05-15 06:25:00,1.7\n2017-05-15 06:30:00,1.6\n2017-05-15 06:35:00,1.5\n2017-05-15 06:40:00,1.4\n2017-05-15 06:45:00,1.3\n2017-05-15 06:50:00,1.2\n2017-05-15 06:55:00,1.1\n2017-05-15 07:00:00,1.0\n2017-05-15 07:05:00,0.9\n2017-05-15 07:10:00,0.8\n2017-05-15 07:15:00,0.8\n2017-05-15 07:20:00,0.9\n2017-05-15 07:25:00,1.0\n2017-05-15 07:30:00,1.1\n2017-05-15 07:35:00,1.1\n2017-05-15 07:40:00,1.2\n2017-05-15 07:45:00,1.1\n2017-05-15 07:50:00,1.0\n2017-05-15 07:55:00,0.8\n2017-05-15 08:00:00,0.7\n2017-05-15 08:05:00,0.6\n2017-05-15 08:10:00,0.6\n2017-05-15 08:15:00,0.9\n2017-05-15 08:20:00,1.1\n2017-05-15 08:25:00,1.2\n2017-05-15 08:30:00,1.2\n2017-05-15 08:35:00,1.0\n2017-05-15 08:40:00,0.9\n2017-05-15 08:45:00,1.2\n2017-05-15 08:50:00,1.5\n2017-05-15 08:55:00,1.9\n2017-05-15 09:00:00,1.9\n2017-05-15 09:05:00,2.0\n2017-05-15 09:10:00,2.2\n2017-05-15 09:15:00,2.4\n2017-05-15 09:20:00,2.5\n2017-05-15 09:25:00,2.7\n2017-05-15 09:30:00,2.8\n2017-05-15 09:35:00,2.8\n2017-05-15 09:40:00,2.8\n2017-05-15 09:45:00,2.7\n2017-05-15 09:50:00,2.7\n2017-05-15 09:55:00,2.5\n2017-05-15 10:00:00,2.2\n2017-05-15 10:05:00,1.9\n2017-05-15 10:10:00,1.9\n2017-05-15 10:15:00,2.0\n2017-05-15 10:20:00,2.1\n2017-05-15 10:25:00,2.2\n2017-05-15 10:30:00,2.4\n2017-05-15 10:35:00,2.5\n2017-05-15 10:40:00,2.6\n2017-05-15 10:45:00,2.7\n2017-05-15 10:50:00,2.7\n2017-05-15 10:55:00,2.7\n2017-05-15 11:00:00,2.7\n2017-05-15 11:05:00,2.7\n2017-05-15 11:10:00,2.7\n2017-05-15 11:15:00,2.8\n2017-05-15 11:20:00,2.8\n2017-05-15 11:25:00,2.8\n2017-05-15 11:30:00,2.8\n2017-05-15 11:35:00,2.8\n2017-05-15 11:40:00,2.8\n2017-05-15 11:45:00,2.8\n2017-05-15 11:50:00,2.8\n2017-05-15 11:55:00,2.8\n2017-05-15 12:00:00,2.8\n2017-05-15 12:05:00,2.7\n2017-05-15 12:10:00,2.7\n2017-05-15 12:15:00,2.5\n2017-05-15 12:20:00,2.5\n2017-05-15 12:25:00,2.4\n2017-05-15 12:30:00,2.5\n2017-05-15 12:35:00,2.4\n2017-05-15 12:40:00,2.4\n2017-05-15 12:45:00,2.4\n2017-05-15 12:50:00,2.4\n2017-05-15 12:55:00,2.4\n2017-05-15 13:00:00,2.4\n2017-05-15 13:05:00,2.4\n2017-05-15 13:10:00,2.5\n2017-05-15 13:15:00,2.5\n2017-05-15 13:20:00,2.5\n2017-05-15 13:25:00,2.5\n2017-05-15 13:30:00,2.5\n2017-05-15 13:35:00,2.6\n2017-05-15 13:40:00,2.7\n2017-05-15 13:45:00,2.8\n2017-05-15 13:50:00,3.0\n2017-05-15 13:55:00,3.1\n2017-05-15 14:00:00,3.4\n2017-05-15 14:05:00,3.6\n2017-05-15 14:10:00,3.8\n2017-05-15 14:15:00,4.0\n2017-05-15 14:20:00,4.4\n2017-05-15 14:25:00,4.8\n2017-05-15 14:30:00,5.0\n2017-05-15 14:35:00,5.3\n2017-05-15 14:40:00,5.5\n2017-05-15 14:45:00,5.7\n2017-05-15 14:50:00,5.7\n2017-05-15 14:55:00,6.0\n2017-05-15 15:00:00,6.0\n2017-05-15 15:05:00,6.0\n2017-05-15 15:10:00,6.7\n2017-05-15 15:15:00,7.8\n2017-05-15 15:20:00,7.8\n2017-05-15 15:25:00,8.3\n2017-05-15 15:30:00,8.5\n2017-05-15 15:35:00,8.7\n2017-05-15 15:40:00,8.5\n2017-05-15 15:45:00,8.1\n2017-05-15 15:50:00,8.5\n2017-05-15 15:55:00,8.6\n2017-05-15 16:00:00,8.9\n2017-05-15 16:05:00,8.7\n2017-05-15 16:10:00,9.4\n2017-05-15 16:15:00,8.7\n2017-05-15 16:20:00,9.0\n2017-05-15 16:25:00,9.4\n2017-05-15 16:30:00,11.1\n2017-05-15 16:35:00,11.4\n2017-05-15 16:40:00,9.4\n2017-05-15 16:45:00,9.4\n2017-05-15 16:50:00,9.4\n2017-05-15 16:55:00,9.2\n2017-05-15 17:00:00,10.2\n2017-05-15 17:05:00,9.9\n2017-05-15 17:10:00,12.7\n2017-05-15 17:15:00,13.8\n2017-05-15 17:20:00,11.6\n2017-05-15 17:25:00,11.2\n2017-05-15 17:30:00,13.5\n2017-05-15 17:35:00,19.0\n2017-05-15 17:40:00,15.1\n2017-05-15 17:45:00,12.4\n2017-05-15 17:50:00,12.2\n2017-05-15 17:55:00,15.4\n2017-05-15 18:00:00,17.5\n2017-05-15 18:05:00,14.5\n2017-05-15 18:10:00,13.1\n2017-05-15 18:15:00,16.7\n2017-05-15 18:20:00,16.1\n2017-05-15 18:25:00,15.9\n2017-05-15 18:30:00,15.3\n2017-05-15 18:35:00,12.2\n2017-05-15 18:40:00,11.0\n2017-05-15 18:45:00,10.0\n2017-05-15 18:50:00,10.1\n2017-05-15 18:55:00,10.3\n2017-05-15 19:00:00,10.6\n2017-05-15 19:05:00,9.7\n2017-05-15 19:10:00,8.7\n2017-05-15 19:15:00,8.2\n2017-05-15 19:20:00,8.2\n2017-05-15 19:25:00,8.1\n2017-05-15 19:30:00,8.3\n2017-05-15 19:35:00,7.7\n2017-05-15 19:40:00,7.3\n2017-05-15 19:45:00,7.5\n2017-05-15 19:50:00,7.2\n2017-05-15 19:55:00,7.0\n2017-05-15 20:00:00,6.5\n2017-05-15 20:05:00,6.4\n2017-05-15 20:10:00,6.6\n2017-05-15 20:15:00,6.7\n2017-05-15 20:20:00,6.4\n2017-05-15 20:25:00,6.3\n2017-05-15 20:30:00,6.2\n2017-05-15 20:35:00,5.8\n2017-05-15 20:40:00,5.9\n2017-05-15 20:45:00,5.3\n2017-05-15 20:50:00,4.8\n2017-05-15 20:55:00,3.4\n2017-05-15 21:00:00,2.3\n2017-05-15 21:05:00,1.7\n2017-05-15 21:10:00,2.5\n2017-05-15 21:15:00,2.4\n2017-05-15 21:20:00,1.2\n2017-05-15 21:25:00,1.6\n2017-05-15 21:30:00,2.2\n2017-05-15 21:35:00,3.7\n2017-05-15 21:40:00,4.4\n2017-05-15 21:45:00,4.4\n2017-05-15 21:50:00,4.8\n2017-05-15 21:55:00,4.1\n2017-05-15 22:00:00,2.8\n2017-05-15 22:05:00,3.0\n2017-05-15 22:10:00,5.4\n2017-05-15 22:15:00,6.0\n2017-05-15 22:20:00,6.3\n2017-05-15 22:25:00,6.1\n2017-05-15 22:30:00,5.8\n2017-05-15 22:35:00,6.5\n2017-05-15 22:40:00,6.5\n2017-05-15 22:45:00,6.3\n2017-05-15 22:50:00,5.6\n2017-05-15 22:55:00,5.2\n2017-05-15 23:00:00,4.3\n2017-05-15 23:05:00,3.9\n2017-05-15 23:10:00,4.3\n2017-05-15 23:15:00,5.0\n2017-05-15 23:20:00,5.3\n2017-05-15 23:25:00,5.2\n2017-05-15 23:30:00,5.3\n2017-05-15 23:35:00,5.3\n2017-05-15 23:40:00,5.1\n2017-05-15 23:45:00,5.0\n2017-05-15 23:50:00,5.0\n2017-05-15 23:55:00,4.6\n2017-05-16 00:00:00,3.2\n2017-05-16 00:05:00,3.7\n2017-05-16 00:10:00,3.8\n2017-05-16 00:15:00,4.0\n2017-05-16 00:20:00,3.9\n2017-05-16 00:25:00,4.4\n2017-05-16 00:30:00,4.6\n2017-05-16 00:35:00,4.7\n2017-05-16 00:40:00,4.8\n2017-05-16 00:45:00,4.4\n2017-05-16 00:50:00,4.4\n2017-05-16 00:55:00,4.6\n2017-05-16 01:00:00,4.8\n2017-05-16 01:05:00,4.8\n2017-05-16 01:10:00,4.8\n2017-05-16 01:15:00,5.0\n2017-05-16 01:20:00,4.6\n2017-05-16 01:25:00,4.5\n2017-05-16 01:30:00,4.3\n2017-05-16 01:35:00,4.2\n2017-05-16 01:40:00,4.3\n2017-05-16 01:45:00,4.2\n2017-05-16 01:50:00,4.2\n2017-05-16 01:55:00,4.1\n2017-05-16 02:00:00,4.1\n2017-05-16 02:05:00,4.0\n2017-05-16 02:10:00,3.9\n2017-05-16 02:15:00,3.9\n2017-05-16 02:20:00,3.9\n2017-05-16 02:25:00,3.9\n2017-05-16 02:30:00,3.8\n2017-05-16 02:35:00,3.8\n2017-05-16 02:40:00,3.7\n2017-05-16 02:45:00,3.4\n2017-05-16 02:50:00,3.0\n2017-05-16 02:55:00,2.9\n2017-05-16 03:00:00,3.0\n2017-05-16 03:05:00,3.1\n2017-05-16 03:10:00,3.2\n2017-05-16 03:15:00,3.2\n2017-05-16 03:20:00,3.3\n2017-05-16 03:25:00,3.2\n2017-05-16 03:30:00,3.3\n2017-05-16 03:35:00,3.2\n2017-05-16 03:40:00,3.3\n2017-05-16 03:45:00,3.2\n2017-05-16 03:50:00,3.0\n2017-05-16 03:55:00,2.8\n2017-05-16 04:00:00,2.9\n2017-05-16 04:05:00,2.9\n2017-05-16 04:10:00,3.1\n2017-05-16 04:15:00,3.2\n2017-05-16 04:20:00,3.1\n2017-05-16 04:25:00,2.8\n2017-05-16 04:30:00,3.0\n2017-05-16 04:35:00,3.1\n2017-05-16 04:40:00,3.0\n2017-05-16 04:45:00,2.9\n2017-05-16 04:50:00,2.9\n2017-05-16 04:55:00,2.7\n2017-05-16 05:00:00,2.6\n2017-05-16 05:05:00,2.3\n2017-05-16 05:10:00,2.2\n2017-05-16 05:15:00,2.2\n2017-05-16 05:20:00,2.0\n2017-05-16 05:25:00,2.1\n2017-05-16 05:30:00,2.6\n2017-05-16 05:35:00,2.8\n2017-05-16 05:40:00,2.9\n2017-05-16 05:45:00,2.9\n2017-05-16 05:50:00,3.0\n2017-05-16 05:55:00,3.0\n2017-05-16 06:00:00,3.0\n2017-05-16 06:05:00,3.0\n2017-05-16 06:10:00,3.1\n2017-05-16 06:15:00,3.1\n2017-05-16 06:20:00,3.1\n2017-05-16 06:25:00,3.1\n2017-05-16 06:30:00,3.1\n2017-05-16 06:35:00,3.1\n2017-05-16 06:40:00,3.1\n2017-05-16 06:45:00,3.0\n2017-05-16 06:50:00,3.0\n2017-05-16 06:55:00,3.1\n2017-05-16 07:00:00,3.1\n2017-05-16 07:05:00,3.1\n2017-05-16 07:10:00,3.1\n2017-05-16 07:15:00,3.1\n2017-05-16 07:20:00,3.0\n2017-05-16 07:25:00,3.0\n2017-05-16 07:30:00,2.7\n2017-05-16 07:35:00,2.6\n2017-05-16 07:40:00,2.6\n2017-05-16 07:45:00,2.3\n2017-05-16 07:50:00,2.0\n2017-05-16 07:55:00,1.7\n2017-05-16 08:00:00,1.5\n2017-05-16 08:05:00,1.2\n2017-05-16 08:10:00,1.4\n2017-05-16 08:15:00,1.7\n2017-05-16 08:20:00,2.0\n2017-05-16 08:25:00,2.3\n2017-05-16 08:30:00,2.3\n2017-05-16 08:35:00,2.4\n2017-05-16 08:40:00,2.4\n2017-05-16 08:45:00,2.4\n2017-05-16 08:50:00,2.3\n2017-05-16 08:55:00,2.2\n2017-05-16 09:00:00,2.2\n2017-05-16 09:05:00,2.0\n2017-05-16 09:10:00,1.8\n2017-05-16 09:15:00,1.4\n2017-05-16 09:20:00,1.0\n2017-05-16 09:25:00,0.8\n2017-05-16 09:30:00,0.7\n2017-05-16 09:35:00,0.6\n2017-05-16 09:40:00,0.6\n2017-05-16 09:45:00,0.6\n2017-05-16 09:50:00,0.6\n2017-05-16 09:55:00,0.5\n2017-05-16 10:00:00,0.5\n2017-05-16 10:05:00,0.4\n2017-05-16 10:10:00,0.4\n2017-05-16 10:15:00,0.3\n2017-05-16 10:20:00,0.3\n2017-05-16 10:25:00,0.2\n2017-05-16 10:30:00,0.1\n2017-05-16 10:35:00,0.0\n2017-05-16 10:40:00,-0.1\n2017-05-16 10:45:00,0.0\n2017-05-16 10:50:00,-0.1\n2017-05-16 10:55:00,-0.3\n2017-05-16 11:00:00,-0.4\n2017-05-16 11:05:00,-0.3\n2017-05-16 11:10:00,-0.3\n2017-05-16 11:15:00,-0.2\n2017-05-16 11:20:00,-0.2\n2017-05-16 11:25:00,-0.2\n2017-05-16 11:30:00,-0.2\n2017-05-16 11:35:00,-0.1\n2017-05-16 11:40:00,-0.2\n2017-05-16 11:45:00,0.1\n2017-05-16 11:50:00,0.4\n2017-05-16 11:55:00,0.6\n2017-05-16 12:00:00,0.7\n2017-05-16 12:05:00,0.4\n2017-05-16 12:10:00,0.4\n2017-05-16 12:15:00,0.6\n2017-05-16 12:20:00,0.7\n2017-05-16 12:25:00,0.9\n2017-05-16 12:30:00,0.8\n2017-05-16 12:35:00,1.0\n2017-05-16 12:40:00,1.3\n2017-05-16 12:45:00,1.4\n2017-05-16 12:50:00,1.5\n2017-05-16 12:55:00,1.3\n2017-05-16 13:00:00,1.3\n2017-05-16 13:05:00,1.2\n2017-05-16 13:10:00,1.1\n2017-05-16 13:15:00,1.0\n2017-05-16 13:20:00,0.9\n2017-05-16 13:25:00,1.2\n2017-05-16 13:30:00,1.4\n2017-05-16 13:35:00,1.7\n2017-05-16 13:40:00,1.8\n2017-05-16 13:45:00,2.0\n2017-05-16 13:50:00,2.2\n2017-05-16 13:55:00,2.4\n2017-05-16 14:00:00,2.5\n2017-05-16 14:05:00,2.6\n2017-05-16 14:10:00,2.8\n2017-05-16 14:15:00,3.0\n2017-05-16 14:20:00,3.2\n2017-05-16 14:25:00,3.6\n2017-05-16 14:30:00,4.0\n2017-05-16 14:35:00,4.3\n2017-05-16 14:40:00,4.3\n2017-05-16 14:45:00,4.8\n2017-05-16 14:50:00,4.9\n2017-05-16 14:55:00,5.0\n2017-05-16 15:00:00,5.0\n2017-05-16 15:05:00,4.9\n2017-05-16 15:10:00,5.0\n2017-05-16 15:15:00,5.1\n2017-05-16 15:20:00,5.3\n2017-05-16 15:25:00,5.4\n2017-05-16 15:30:00,5.7\n2017-05-16 15:35:00,5.8\n2017-05-16 15:40:00,6.1\n2017-05-16 15:45:00,6.3\n2017-05-16 15:50:00,6.5\n2017-05-16 15:55:00,6.7\n2017-05-16 16:00:00,7.3\n2017-05-16 16:05:00,7.7\n2017-05-16 16:10:00,8.2\n2017-05-16 16:15:00,9.1\n2017-05-16 16:20:00,9.1\n2017-05-16 16:25:00,9.5\n2017-05-16 16:30:00,9.4\n2017-05-16 16:35:00,9.3\n2017-05-16 16:40:00,9.5\n2017-05-16 16:45:00,9.4\n2017-05-16 16:50:00,9.6\n2017-05-16 16:55:00,9.7\n2017-05-16 17:00:00,9.9\n2017-05-16 17:05:00,10.0\n2017-05-16 17:10:00,10.0\n2017-05-16 17:15:00,10.4\n2017-05-16 17:20:00,10.7\n2017-05-16 17:25:00,11.3\n2017-05-16 17:30:00,12.1\n2017-05-16 17:35:00,12.3\n2017-05-16 17:40:00,12.3\n2017-05-16 17:45:00,11.9\n2017-05-16 17:50:00,11.9\n2017-05-16 17:55:00,12.0\n2017-05-16 18:00:00,12.5\n2017-05-16 18:05:00,13.0\n2017-05-16 18:10:00,12.8\n2017-05-16 18:15:00,12.0\n2017-05-16 18:20:00,13.1\n2017-05-16 18:25:00,13.4\n2017-05-16 18:30:00,12.8\n2017-05-16 18:35:00,12.1\n2017-05-16 18:40:00,11.8\n2017-05-16 18:45:00,12.6\n2017-05-16 18:50:00,13.2\n2017-05-16 18:55:00,13.6\n2017-05-16 19:00:00,13.0\n2017-05-16 19:05:00,12.6\n2017-05-16 19:10:00,12.8\n2017-05-16 19:15:00,13.0\n2017-05-16 19:20:00,13.6\n2017-05-16 19:25:00,14.2\n2017-05-16 19:30:00,15.4\n2017-05-16 19:35:00,17.0\n2017-05-16 19:40:00,16.3\n2017-05-16 19:45:00,17.5\n2017-05-16 19:50:00,17.3\n2017-05-16 19:55:00,17.5\n2017-05-16 20:00:00,16.1\n2017-05-16 20:05:00,15.0\n2017-05-16 20:10:00,13.1\n2017-05-16 20:15:00,11.8\n2017-05-16 20:20:00,10.6\n2017-05-16 20:25:00,9.6\n2017-05-16 20:30:00,9.1\n2017-05-16 20:35:00,9.0\n2017-05-16 20:40:00,9.0\n2017-05-16 20:45:00,8.7\n2017-05-16 20:50:00,8.8\n2017-05-16 20:55:00,8.6\n2017-05-16 21:00:00,8.2\n2017-05-16 21:05:00,8.2\n2017-05-16 21:10:00,8.0\n2017-05-16 21:15:00,8.0\n2017-05-16 21:20:00,7.8\n2017-05-16 21:25:00,7.9\n2017-05-16 21:30:00,7.8\n2017-05-16 21:35:00,7.5\n2017-05-16 21:40:00,8.3\n2017-05-16 21:45:00,7.8\n2017-05-16 21:50:00,7.5\n2017-05-16 21:55:00,7.3\n2017-05-16 22:00:00,7.2\n2017-05-16 22:05:00,7.6\n2017-05-16 22:10:00,7.2\n2017-05-16 22:15:00,7.2\n2017-05-16 22:20:00,7.4\n2017-05-16 22:25:00,7.5\n2017-05-16 22:30:00,7.7\n2017-05-16 22:35:00,7.7\n2017-05-16 22:40:00,7.4\n2017-05-16 22:45:00,7.7\n2017-05-16 22:50:00,7.3\n2017-05-16 22:55:00,7.9\n2017-05-16 23:00:00,9.3\n2017-05-16 23:05:00,9.0\n2017-05-16 23:10:00,8.8\n2017-05-16 23:15:00,9.1\n2017-05-16 23:20:00,9.2\n2017-05-16 23:25:00,10.1\n2017-05-16 23:30:00,9.5\n2017-05-16 23:35:00,10.1\n2017-05-16 23:40:00,10.2\n2017-05-16 23:45:00,9.1\n2017-05-16 23:50:00,8.2\n2017-05-16 23:55:00,7.8\n2017-05-17 00:00:00,7.3\n2017-05-17 00:05:00,7.1\n2017-05-17 00:10:00,6.9\n2017-05-17 00:15:00,6.7\n2017-05-17 00:20:00,6.8\n2017-05-17 00:25:00,6.7\n2017-05-17 00:30:00,6.8\n2017-05-17 00:35:00,6.8\n2017-05-17 00:40:00,6.8\n2017-05-17 00:45:00,6.8\n2017-05-17 00:50:00,6.9\n2017-05-17 00:55:00,7.2\n2017-05-17 01:00:00,7.2\n2017-05-17 01:05:00,7.0\n2017-05-17 01:10:00,7.0\n2017-05-17 01:15:00,6.7\n2017-05-17 01:20:00,6.5\n2017-05-17 01:25:00,6.3\n2017-05-17 01:30:00,6.0\n2017-05-17 01:35:00,5.9\n2017-05-17 01:40:00,5.8\n2017-05-17 01:45:00,5.7\n2017-05-17 01:50:00,5.6\n2017-05-17 01:55:00,5.6\n2017-05-17 02:00:00,5.8\n2017-05-17 02:05:00,5.7\n2017-05-17 02:10:00,5.0\n2017-05-17 02:15:00,5.0\n2017-05-17 02:20:00,4.8\n2017-05-17 02:25:00,4.7\n2017-05-17 02:30:00,4.6\n2017-05-17 02:35:00,4.6\n2017-05-17 02:40:00,4.6\n2017-05-17 02:45:00,4.5\n2017-05-17 02:50:00,4.5\n2017-05-17 02:55:00,4.4\n2017-05-17 03:00:00,4.3\n2017-05-17 03:05:00,4.2\n2017-05-17 03:10:00,4.2\n2017-05-17 03:15:00,4.2\n2017-05-17 03:20:00,4.2\n2017-05-17 03:25:00,4.1\n2017-05-17 03:30:00,4.1\n2017-05-17 03:35:00,4.1\n2017-05-17 03:40:00,3.9\n2017-05-17 03:45:00,3.9\n2017-05-17 03:50:00,3.7\n2017-05-17 03:55:00,3.7\n2017-05-17 04:00:00,3.7\n2017-05-17 04:05:00,3.6\n2017-05-17 04:10:00,3.5\n2017-05-17 04:15:00,3.5\n2017-05-17 04:20:00,3.3\n2017-05-17 04:25:00,3.3\n2017-05-17 04:30:00,3.3\n2017-05-17 04:35:00,3.4\n2017-05-17 04:40:00,3.4\n2017-05-17 04:45:00,3.4\n2017-05-17 04:50:00,3.3\n2017-05-17 04:55:00,3.3\n2017-05-17 05:00:00,3.3\n2017-05-17 05:05:00,3.3\n2017-05-17 05:10:00,3.2\n2017-05-17 05:15:00,3.2\n2017-05-17 05:20:00,3.2\n2017-05-17 05:25:00,3.2\n2017-05-17 05:30:00,3.1\n2017-05-17 05:35:00,3.2\n2017-05-17 05:40:00,3.1\n2017-05-17 05:45:00,3.1\n2017-05-17 05:50:00,3.1\n2017-05-17 05:55:00,3.1\n2017-05-17 06:00:00,3.1\n2017-05-17 06:05:00,3.1\n2017-05-17 06:10:00,3.1\n2017-05-17 06:15:00,3.1\n2017-05-17 06:20:00,3.1\n2017-05-17 06:25:00,3.1\n2017-05-17 06:30:00,3.1\n2017-05-17 06:35:00,3.1\n2017-05-17 06:40:00,3.1\n2017-05-17 06:45:00,3.1\n2017-05-17 06:50:00,3.1\n2017-05-17 06:55:00,3.1\n2017-05-17 07:00:00,3.0\n2017-05-17 07:05:00,2.9\n2017-05-17 07:10:00,2.8\n2017-05-17 07:15:00,2.9\n2017-05-17 07:20:00,2.9\n2017-05-17 07:25:00,2.8\n2017-05-17 07:30:00,2.8\n2017-05-17 07:35:00,2.8\n2017-05-17 07:40:00,2.7\n2017-05-17 07:45:00,2.7\n2017-05-17 07:50:00,2.7\n2017-05-17 07:55:00,2.7\n2017-05-17 08:00:00,2.8\n2017-05-17 08:05:00,2.8\n2017-05-17 08:10:00,2.8\n2017-05-17 08:15:00,2.8\n2017-05-17 08:20:00,2.8\n2017-05-17 08:25:00,2.8\n2017-05-17 08:30:00,2.7\n2017-05-17 08:35:00,2.5\n2017-05-17 08:40:00,2.5\n2017-05-17 08:45:00,2.5\n2017-05-17 08:50:00,2.7\n2017-05-17 08:55:00,2.7\n2017-05-17 09:00:00,2.7\n2017-05-17 09:05:00,2.7\n2017-05-17 09:10:00,2.7\n2017-05-17 09:15:00,2.7\n2017-05-17 09:20:00,2.7\n2017-05-17 09:25:00,2.7\n2017-05-17 09:30:00,2.6\n2017-05-17 09:35:00,2.5\n2017-05-17 09:40:00,2.2\n2017-05-17 09:45:00,2.0\n2017-05-17 09:50:00,1.7\n2017-05-17 09:55:00,1.5\n2017-05-17 10:00:00,1.3\n2017-05-17 10:05:00,1.2\n2017-05-17 10:10:00,1.3\n2017-05-17 10:15:00,1.4\n2017-05-17 10:20:00,1.5\n2017-05-17 10:25:00,1.3\n2017-05-17 10:30:00,1.0\n2017-05-17 10:35:00,0.8\n2017-05-17 10:40:00,0.7\n2017-05-17 10:45:00,0.7\n2017-05-17 10:50:00,0.9\n2017-05-17 10:55:00,0.8\n2017-05-17 11:00:00,1.3\n2017-05-17 11:05:00,1.7\n2017-05-17 11:10:00,1.9\n2017-05-17 11:15:00,1.9\n2017-05-17 11:20:00,1.9\n2017-05-17 11:25:00,1.9\n2017-05-17 11:30:00,2.0\n2017-05-17 11:35:00,2.1\n2017-05-17 11:40:00,2.1\n2017-05-17 11:45:00,2.2\n2017-05-17 11:50:00,2.2\n2017-05-17 11:55:00,2.3\n2017-05-17 12:00:00,2.3\n2017-05-17 12:05:00,2.3\n2017-05-17 12:10:00,2.4\n2017-05-17 12:15:00,2.3\n2017-05-17 12:20:00,2.4\n2017-05-17 12:25:00,2.3\n2017-05-17 12:30:00,2.3\n2017-05-17 12:35:00,2.3\n2017-05-17 12:40:00,2.3\n2017-05-17 12:45:00,2.3\n2017-05-17 12:50:00,2.3\n2017-05-17 12:55:00,2.3\n2017-05-17 13:00:00,2.3\n2017-05-17 13:05:00,2.3\n2017-05-17 13:10:00,2.3\n2017-05-17 13:15:00,2.1\n2017-05-17 13:20:00,2.0\n2017-05-17 13:25:00,1.5\n2017-05-17 13:30:00,1.2\n2017-05-17 13:35:00,1.1\n2017-05-17 13:40:00,1.2\n2017-05-17 13:45:00,1.7\n2017-05-17 13:50:00,1.8\n2017-05-17 13:55:00,1.8\n2017-05-17 14:00:00,2.0\n2017-05-17 14:05:00,2.0\n2017-05-17 14:10:00,2.2\n2017-05-17 14:15:00,2.3\n2017-05-17 14:20:00,2.5\n2017-05-17 14:25:00,2.4\n2017-05-17 14:30:00,2.4\n2017-05-17 14:35:00,2.4\n2017-05-17 14:40:00,2.5\n2017-05-17 14:45:00,2.3\n2017-05-17 14:50:00,2.7\n2017-05-17 14:55:00,2.8\n2017-05-17 15:00:00,3.2\n2017-05-17 15:05:00,3.3\n2017-05-17 15:10:00,3.0\n2017-05-17 15:15:00,2.9\n2017-05-17 15:20:00,3.3\n2017-05-17 15:25:00,3.3\n2017-05-17 15:30:00,3.3\n2017-05-17 15:35:00,3.4\n2017-05-17 15:40:00,3.3\n2017-05-17 15:45:00,3.3\n2017-05-17 15:50:00,3.4\n2017-05-17 15:55:00,3.5\n2017-05-17 16:00:00,3.9\n2017-05-17 16:05:00,4.4\n2017-05-17 16:10:00,7.9\n2017-05-17 16:15:00,8.4\n2017-05-17 16:20:00,9.4\n2017-05-17 16:25:00,8.4\n2017-05-17 16:30:00,7.4\n2017-05-17 16:35:00,7.6\n2017-05-17 16:40:00,8.9\n2017-05-17 16:45:00,9.4\n2017-05-17 16:50:00,12.2\n2017-05-17 16:55:00,12.5\n2017-05-17 17:00:00,11.9\n2017-05-17 17:05:00,10.5\n2017-05-17 17:10:00,9.5\n2017-05-17 17:15:00,14.7\n2017-05-17 17:20:00,12.9\n2017-05-17 17:25:00,9.9\n2017-05-17 17:30:00,13.3\n2017-05-17 17:35:00,15.5\n2017-05-17 17:40:00,15.5\n2017-05-17 17:45:00,12.6\n2017-05-17 17:50:00,10.9\n2017-05-17 17:55:00,12.4\n2017-05-17 18:00:00,15.5\n2017-05-17 18:05:00,12.0\n2017-05-17 18:10:00,12.3\n2017-05-17 18:15:00,17.3\n2017-05-17 18:20:00,12.5\n2017-05-17 18:25:00,13.0\n2017-05-17 18:30:00,17.9\n2017-05-17 18:35:00,17.3\n2017-05-17 18:40:00,14.1\n2017-05-17 18:45:00,14.2\n2017-05-17 18:50:00,13.9\n2017-05-17 18:55:00,13.0\n2017-05-17 19:00:00,15.4\n2017-05-17 19:05:00,12.6\n2017-05-17 19:10:00,17.1\n2017-05-17 19:15:00,15.8\n2017-05-17 19:20:00,19.6\n2017-05-17 19:25:00,14.9\n2017-05-17 19:30:00,19.7\n2017-05-17 19:35:00,16.6\n2017-05-17 19:40:00,19.2\n2017-05-17 19:45:00,19.8\n2017-05-17 19:50:00,15.8\n2017-05-17 19:55:00,19.3\n2017-05-17 20:00:00,21.6\n2017-05-17 20:05:00,23.0\n2017-05-17 20:10:00,22.4\n2017-05-17 20:15:00,23.6\n2017-05-17 20:20:00,24.4\n2017-05-17 20:25:00,23.7\n2017-05-17 20:30:00,21.9\n2017-05-17 20:35:00,18.0\n2017-05-17 20:40:00,19.8\n2017-05-17 20:45:00,22.0\n2017-05-17 20:50:00,23.3\n2017-05-17 20:55:00,20.2\n2017-05-17 21:00:00,19.5\n2017-05-17 21:05:00,20.7\n2017-05-17 21:10:00,22.7\n2017-05-17 21:15:00,19.0\n2017-05-17 21:20:00,21.9\n2017-05-17 21:25:00,20.6\n2017-05-17 21:30:00,21.6\n2017-05-17 21:35:00,20.3\n2017-05-17 21:40:00,20.8\n2017-05-17 21:45:00,23.9\n2017-05-17 21:50:00,24.8\n2017-05-17 21:55:00,24.0\n2017-05-17 22:00:00,23.7\n2017-05-17 22:05:00,24.7\n2017-05-17 22:10:00,24.3\n2017-05-17 22:15:00,24.6\n2017-05-17 22:20:00,19.4\n2017-05-17 22:25:00,23.4\n2017-05-17 22:30:00,21.4\n2017-05-17 22:35:00,20.0\n2017-05-17 22:40:00,17.1\n2017-05-17 22:45:00,17.7\n2017-05-17 22:50:00,23.2\n2017-05-17 22:55:00,24.3\n2017-05-17 23:00:00,23.6\n2017-05-17 23:05:00,24.1\n2017-05-17 23:10:00,21.0\n2017-05-17 23:15:00,22.9\n2017-05-17 23:20:00,23.7\n2017-05-17 23:25:00,23.6\n2017-05-17 23:30:00,23.5\n2017-05-17 23:35:00,24.0\n2017-05-17 23:40:00,23.9\n2017-05-17 23:45:00,22.7\n2017-05-17 23:50:00,21.7\n2017-05-17 23:55:00,21.8\n2017-05-18 00:00:00,22.0\n2017-05-18 00:05:00,21.5\n2017-05-18 00:10:00,20.2\n2017-05-18 00:15:00,20.5\n2017-05-18 00:20:00,17.3\n2017-05-18 00:25:00,16.3\n2017-05-18 00:30:00,18.0\n2017-05-18 00:35:00,17.3\n2017-05-18 00:40:00,18.0\n2017-05-18 00:45:00,17.6\n2017-05-18 00:50:00,16.9\n2017-05-18 00:55:00,16.3\n2017-05-18 01:00:00,16.3\n2017-05-18 01:05:00,16.2\n2017-05-18 01:10:00,15.0\n2017-05-18 01:15:00,14.2\n2017-05-18 01:20:00,13.8\n2017-05-18 01:25:00,12.4\n2017-05-18 01:30:00,11.5\n2017-05-18 01:35:00,11.9\n2017-05-18 01:40:00,11.5\n2017-05-18 01:45:00,12.0\n2017-05-18 01:50:00,11.1\n2017-05-18 01:55:00,10.3\n2017-05-18 02:00:00,10.5\n2017-05-18 02:05:00,10.4\n2017-05-18 02:10:00,9.8\n2017-05-18 02:15:00,8.7\n2017-05-18 02:20:00,8.1\n2017-05-18 02:25:00,8.4\n2017-05-18 02:30:00,7.8\n2017-05-18 02:35:00,7.6\n2017-05-18 02:40:00,7.2\n2017-05-18 02:45:00,6.8\n2017-05-18 02:50:00,6.4\n2017-05-18 02:55:00,6.1\n2017-05-18 03:00:00,5.9\n2017-05-18 03:05:00,5.6\n2017-05-18 03:10:00,5.4\n2017-05-18 03:15:00,5.2\n2017-05-18 03:20:00,5.0\n2017-05-18 03:25:00,4.9\n2017-05-18 03:30:00,4.7\n2017-05-18 03:35:00,4.6\n2017-05-18 03:40:00,4.5\n2017-05-18 03:45:00,4.4\n2017-05-18 03:50:00,4.3\n2017-05-18 03:55:00,4.2\n2017-05-18 04:00:00,4.1\n2017-05-18 04:05:00,4.0\n2017-05-18 04:10:00,3.9\n2017-05-18 04:15:00,3.9\n2017-05-18 04:20:00,3.8\n2017-05-18 04:25:00,3.7\n2017-05-18 04:30:00,3.7\n2017-05-18 04:35:00,3.7\n2017-05-18 04:40:00,3.6\n2017-05-18 04:45:00,3.6\n2017-05-18 04:50:00,3.5\n2017-05-18 04:55:00,3.5\n2017-05-18 05:00:00,3.5\n2017-05-18 05:05:00,3.4\n2017-05-18 05:10:00,3.3\n2017-05-18 05:15:00,3.3\n2017-05-18 05:20:00,3.2\n2017-05-18 05:25:00,3.2\n2017-05-18 05:30:00,3.2\n2017-05-18 05:35:00,3.3\n2017-05-18 05:40:00,3.2\n2017-05-18 05:45:00,3.2\n2017-05-18 05:50:00,3.2\n2017-05-18 05:55:00,3.2\n2017-05-18 06:00:00,3.2\n2017-05-18 06:05:00,3.2\n2017-05-18 06:10:00,3.2\n2017-05-18 06:15:00,3.1\n2017-05-18 06:20:00,3.0\n2017-05-18 06:25:00,2.8\n2017-05-18 06:30:00,2.8\n2017-05-18 06:35:00,2.7\n2017-05-18 06:40:00,2.6\n2017-05-18 06:45:00,2.5\n2017-05-18 06:50:00,2.6\n2017-05-18 06:55:00,2.5\n2017-05-18 07:00:00,2.6\n2017-05-18 07:05:00,2.5\n2017-05-18 07:10:00,2.3\n2017-05-18 07:15:00,2.2\n2017-05-18 07:20:00,2.2\n2017-05-18 07:25:00,2.1\n2017-05-18 07:30:00,2.0\n2017-05-18 07:35:00,1.8\n2017-05-18 07:40:00,1.8\n2017-05-18 07:45:00,1.8\n2017-05-18 07:50:00,1.8\n2017-05-18 07:55:00,1.7\n2017-05-18 08:00:00,1.5\n2017-05-18 08:05:00,1.5\n2017-05-18 08:10:00,1.5\n2017-05-18 08:15:00,1.4\n2017-05-18 08:20:00,1.4\n2017-05-18 08:25:00,1.5\n2017-05-18 08:30:00,1.5\n2017-05-18 08:35:00,1.5\n2017-05-18 08:40:00,1.5\n2017-05-18 08:45:00,1.3\n2017-05-18 08:50:00,1.3\n2017-05-18 08:55:00,1.3\n2017-05-18 09:00:00,1.4\n2017-05-18 09:05:00,1.4\n2017-05-18 09:10:00,1.3\n2017-05-18 09:15:00,1.3\n2017-05-18 09:20:00,1.5\n2017-05-18 09:25:00,1.5\n2017-05-18 09:30:00,1.4\n2017-05-18 09:35:00,1.5\n2017-05-18 09:40:00,1.5\n2017-05-18 09:45:00,1.4\n2017-05-18 09:50:00,1.2\n2017-05-18 09:55:00,1.1\n2017-05-18 10:00:00,1.0\n2017-05-18 10:05:00,1.0\n2017-05-18 10:10:00,0.8\n2017-05-18 10:15:00,0.7\n2017-05-18 10:20:00,0.6\n2017-05-18 10:25:00,0.5\n2017-05-18 10:30:00,0.4\n2017-05-18 10:35:00,0.3\n2017-05-18 10:40:00,0.3\n2017-05-18 10:45:00,0.3\n2017-05-18 10:50:00,0.3\n2017-05-18 10:55:00,0.4\n2017-05-18 11:00:00,0.4\n2017-05-18 11:05:00,0.4\n2017-05-18 11:10:00,0.4\n2017-05-18 11:15:00,0.4\n2017-05-18 11:20:00,0.4\n2017-05-18 11:25:00,0.4\n2017-05-18 11:30:00,0.3\n2017-05-18 11:35:00,0.4\n2017-05-18 11:40:00,0.4\n2017-05-18 11:45:00,0.3\n2017-05-18 11:50:00,0.3\n2017-05-18 11:55:00,0.3\n2017-05-18 12:00:00,0.3\n2017-05-18 12:05:00,0.3\n2017-05-18 12:10:00,0.3\n2017-05-18 12:15:00,0.2\n2017-05-18 12:20:00,0.2\n2017-05-18 12:25:00,0.2\n2017-05-18 12:30:00,0.2\n2017-05-18 12:35:00,0.1\n2017-05-18 12:40:00,0.1\n2017-05-18 12:45:00,0.0\n2017-05-18 12:50:00,0.1\n2017-05-18 12:55:00,0.1\n2017-05-18 13:00:00,0.2\n2017-05-18 13:05:00,0.2\n2017-05-18 13:10:00,0.2\n2017-05-18 13:15:00,0.3\n2017-05-18 13:20:00,0.5\n2017-05-18 13:25:00,0.7\n2017-05-18 13:30:00,1.0\n2017-05-18 13:35:00,1.3\n2017-05-18 13:40:00,1.7\n2017-05-18 13:45:00,1.9\n2017-05-18 13:50:00,2.2\n2017-05-18 13:55:00,2.5\n2017-05-18 14:00:00,2.8\n2017-05-18 14:05:00,3.0\n2017-05-18 14:10:00,3.3\n2017-05-18 14:15:00,3.5\n2017-05-18 14:20:00,3.7\n2017-05-18 14:25:00,4.0\n2017-05-18 14:30:00,4.2\n2017-05-18 14:35:00,4.4\n2017-05-18 14:40:00,4.5\n2017-05-18 14:45:00,4.7\n2017-05-18 14:50:00,4.8\n2017-05-18 14:55:00,4.9\n2017-05-18 15:00:00,4.0\n2017-05-18 15:05:00,4.3\n2017-05-18 15:10:00,4.6\n2017-05-18 15:15:00,5.0\n2017-05-18 15:20:00,5.5\n2017-05-18 15:25:00,5.8\n2017-05-18 15:30:00,6.4\n2017-05-18 15:35:00,6.8\n2017-05-18 15:40:00,7.1\n2017-05-18 15:45:00,7.6\n2017-05-18 15:50:00,7.8\n2017-05-18 15:55:00,8.5\n2017-05-18 16:00:00,8.9\n2017-05-18 16:05:00,9.4\n2017-05-18 16:10:00,10.2\n2017-05-18 16:15:00,10.5\n2017-05-18 16:20:00,11.1\n2017-05-18 16:25:00,11.7\n2017-05-18 16:30:00,11.8\n2017-05-18 16:35:00,12.4\n2017-05-18 16:40:00,13.2\n2017-05-18 16:45:00,13.5\n2017-05-18 16:50:00,14.0\n2017-05-18 16:55:00,14.6\n2017-05-18 17:00:00,15.4\n2017-05-18 17:05:00,16.5\n2017-05-18 17:10:00,17.2\n2017-05-18 17:15:00,17.3\n2017-05-18 17:20:00,17.6\n2017-05-18 17:25:00,18.4\n2017-05-18 17:30:00,18.7\n2017-05-18 17:35:00,18.8\n2017-05-18 17:40:00,19.1\n2017-05-18 17:45:00,20.1\n2017-05-18 17:50:00,20.7\n2017-05-18 17:55:00,20.9\n2017-05-18 18:00:00,21.7\n2017-05-18 18:05:00,22.8\n2017-05-18 18:10:00,22.8\n2017-05-18 18:15:00,23.4\n2017-05-18 18:20:00,23.1\n2017-05-18 18:25:00,23.3\n2017-05-18 18:30:00,23.7\n2017-05-18 18:35:00,24.6\n2017-05-18 18:40:00,25.0\n2017-05-18 18:45:00,25.8\n2017-05-18 18:50:00,26.0\n2017-05-18 18:55:00,26.4\n2017-05-18 19:00:00,26.8\n2017-05-18 19:05:00,27.3\n2017-05-18 19:10:00,27.7\n2017-05-18 19:15:00,27.8\n2017-05-18 19:20:00,28.3\n2017-05-18 19:25:00,28.0\n2017-05-18 19:30:00,28.4\n2017-05-18 19:35:00,28.5\n2017-05-18 19:40:00,29.0\n2017-05-18 19:45:00,29.1\n2017-05-18 19:50:00,29.3\n2017-05-18 19:55:00,30.0\n2017-05-18 20:00:00,30.0\n2017-05-18 20:05:00,30.5\n2017-05-18 20:10:00,30.2\n2017-05-18 20:15:00,30.2\n2017-05-18 20:20:00,31.0\n2017-05-18 20:25:00,30.9\n2017-05-18 20:30:00,30.8\n2017-05-18 20:35:00,31.4\n2017-05-18 20:40:00,31.9\n2017-05-18 20:45:00,31.6\n2017-05-18 20:50:00,31.8\n2017-05-18 20:55:00,32.3\n2017-05-18 21:00:00,32.7\n2017-05-18 21:05:00,32.6\n2017-05-18 21:10:00,32.8\n2017-05-18 21:15:00,32.7\n2017-05-18 21:20:00,32.3\n2017-05-18 21:25:00,32.5\n2017-05-18 21:30:00,32.9\n2017-05-18 21:35:00,32.6\n2017-05-18 21:40:00,32.2\n2017-05-18 21:45:00,32.3\n2017-05-18 21:50:00,31.9\n2017-05-18 21:55:00,32.0\n2017-05-18 22:00:00,31.7\n2017-05-18 22:05:00,32.0\n2017-05-18 22:10:00,32.0\n2017-05-18 22:15:00,31.8\n2017-05-18 22:20:00,30.8\n2017-05-18 22:25:00,29.8\n2017-05-18 22:30:00,29.5\n2017-05-18 22:35:00,29.6\n2017-05-18 22:40:00,29.5\n2017-05-18 22:45:00,29.5\n2017-05-18 22:50:00,28.1\n2017-05-18 22:55:00,28.6\n2017-05-18 23:00:00,28.0\n2017-05-18 23:05:00,27.6\n2017-05-18 23:10:00,27.3\n2017-05-18 23:15:00,27.3\n2017-05-18 23:20:00,27.2\n2017-05-18 23:25:00,27.8\n2017-05-18 23:30:00,29.0\n2017-05-18 23:35:00,27.3\n2017-05-18 23:40:00,26.4\n2017-05-18 23:45:00,26.7\n2017-05-18 23:50:00,27.0\n2017-05-18 23:55:00,26.7\n2017-05-19 00:00:00,26.3\n2017-05-19 00:05:00,26.3\n2017-05-19 00:10:00,25.9\n2017-05-19 00:15:00,25.7\n2017-05-19 00:20:00,24.9\n2017-05-19 00:25:00,24.8\n2017-05-19 00:30:00,23.8\n2017-05-19 00:35:00,23.5\n2017-05-19 00:40:00,23.0\n2017-05-19 00:45:00,22.8\n2017-05-19 00:50:00,22.2\n2017-05-19 00:55:00,21.7\n2017-05-19 01:00:00,20.9\n2017-05-19 01:05:00,20.5\n2017-05-19 01:10:00,20.0\n2017-05-19 01:15:00,19.6\n2017-05-19 01:20:00,18.7\n2017-05-19 01:25:00,17.6\n2017-05-19 01:30:00,17.1\n2017-05-19 01:35:00,16.2\n2017-05-19 01:40:00,15.6\n2017-05-19 01:45:00,15.3\n2017-05-19 01:50:00,14.5\n2017-05-19 01:55:00,14.2\n2017-05-19 02:00:00,13.9\n2017-05-19 02:05:00,13.5\n2017-05-19 02:10:00,13.2\n2017-05-19 02:15:00,13.1\n2017-05-19 02:20:00,12.7\n2017-05-19 02:25:00,12.4\n2017-05-19 02:30:00,12.0\n2017-05-19 02:35:00,11.7\n2017-05-19 02:40:00,11.3\n2017-05-19 02:45:00,10.8\n2017-05-19 02:50:00,10.4\n2017-05-19 02:55:00,9.9\n2017-05-19 03:00:00,9.6\n2017-05-19 03:05:00,9.3\n2017-05-19 03:10:00,9.0\n2017-05-19 03:15:00,8.8\n2017-05-19 03:20:00,8.4\n2017-05-19 03:25:00,8.3\n2017-05-19 03:30:00,8.1\n2017-05-19 03:35:00,7.9\n2017-05-19 03:40:00,7.8\n2017-05-19 03:45:00,7.6\n2017-05-19 03:50:00,7.4\n2017-05-19 03:55:00,7.3\n2017-05-19 04:00:00,7.2\n2017-05-19 04:05:00,7.1\n2017-05-19 04:10:00,7.0\n2017-05-19 04:15:00,6.9\n2017-05-19 04:20:00,6.9\n2017-05-19 04:25:00,7.0\n2017-05-19 04:30:00,7.0\n2017-05-19 04:35:00,7.0\n2017-05-19 04:40:00,7.0\n2017-05-19 04:45:00,6.9\n2017-05-19 04:50:00,6.9\n2017-05-19 04:55:00,6.9\n2017-05-19 05:00:00,6.9\n2017-05-19 05:05:00,6.8\n2017-05-19 05:10:00,6.8\n2017-05-19 05:15:00,6.8\n2017-05-19 05:20:00,6.7\n2017-05-19 05:25:00,6.7\n2017-05-19 05:30:00,6.7\n2017-05-19 05:35:00,6.6\n2017-05-19 05:40:00,6.5\n2017-05-19 05:45:00,6.4\n2017-05-19 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09:10:00,4.6\n2017-05-19 09:15:00,4.6\n2017-05-19 09:20:00,4.4\n2017-05-19 09:25:00,4.3\n2017-05-19 09:30:00,4.4\n2017-05-19 09:35:00,4.4\n2017-05-19 09:40:00,4.3\n2017-05-19 09:45:00,4.5\n2017-05-19 09:50:00,4.6\n2017-05-19 09:55:00,4.5\n2017-05-19 10:00:00,4.5\n2017-05-19 10:05:00,4.5\n2017-05-19 10:10:00,4.5\n2017-05-19 10:15:00,4.3\n2017-05-19 10:20:00,4.3\n2017-05-19 10:25:00,4.3\n2017-05-19 10:30:00,4.3\n2017-05-19 10:35:00,4.3\n2017-05-19 10:40:00,4.2\n2017-05-19 10:45:00,4.3\n2017-05-19 10:50:00,4.2\n2017-05-19 10:55:00,4.1\n2017-05-19 11:00:00,4.2\n2017-05-19 11:05:00,4.3\n2017-05-19 11:10:00,4.2\n2017-05-19 11:15:00,4.2\n2017-05-19 11:20:00,4.0\n2017-05-19 11:25:00,3.9\n2017-05-19 11:30:00,3.9\n2017-05-19 11:35:00,3.7\n2017-05-19 11:40:00,3.0\n2017-05-19 11:45:00,3.5\n2017-05-19 11:50:00,3.5\n2017-05-19 11:55:00,3.4\n2017-05-19 12:00:00,3.4\n2017-05-19 12:05:00,3.4\n2017-05-19 12:10:00,3.3\n2017-05-19 12:15:00,3.3\n2017-05-19 12:20:00,3.3\n2017-05-19 12:25:00,3.2\n2017-05-19 12:30:00,3.2\n2017-05-19 12:35:00,3.2\n2017-05-19 12:40:00,3.1\n2017-05-19 12:45:00,3.2\n2017-05-19 12:50:00,3.2\n2017-05-19 12:55:00,3.1\n2017-05-19 13:00:00,3.2\n2017-05-19 13:05:00,3.2\n2017-05-19 13:10:00,3.3\n2017-05-19 13:15:00,3.5\n2017-05-19 13:20:00,3.6\n2017-05-19 13:25:00,3.9\n2017-05-19 13:30:00,4.2\n2017-05-19 13:35:00,4.5\n2017-05-19 13:40:00,4.7\n2017-05-19 13:45:00,4.9\n2017-05-19 13:50:00,5.2\n2017-05-19 13:55:00,4.5\n2017-05-19 14:00:00,4.4\n2017-05-19 14:05:00,4.7\n2017-05-19 14:10:00,4.9\n2017-05-19 14:15:00,5.2\n2017-05-19 14:20:00,5.5\n2017-05-19 14:25:00,5.7\n2017-05-19 14:30:00,6.1\n2017-05-19 14:35:00,6.4\n2017-05-19 14:40:00,8.4\n2017-05-19 14:45:00,8.8\n2017-05-19 14:50:00,8.8\n2017-05-19 14:55:00,8.9\n2017-05-19 15:00:00,9.0\n2017-05-19 15:05:00,9.5\n2017-05-19 15:10:00,9.8\n2017-05-19 15:15:00,10.1\n2017-05-19 15:20:00,10.6\n2017-05-19 15:25:00,10.9\n2017-05-19 15:30:00,11.4\n2017-05-19 15:35:00,11.9\n2017-05-19 15:40:00,12.5\n2017-05-19 15:45:00,12.7\n2017-05-19 15:50:00,13.4\n2017-05-19 15:55:00,13.8\n2017-05-19 16:00:00,14.2\n2017-05-19 16:05:00,14.6\n2017-05-19 16:10:00,15.1\n2017-05-19 16:15:00,15.8\n2017-05-19 16:20:00,16.3\n2017-05-19 16:25:00,17.0\n2017-05-19 16:30:00,17.3\n2017-05-19 16:35:00,17.5\n2017-05-19 16:40:00,17.9\n2017-05-19 16:45:00,18.5\n2017-05-19 16:50:00,18.9\n2017-05-19 16:55:00,19.8\n2017-05-19 17:00:00,20.3\n2017-05-19 17:05:00,20.6\n2017-05-19 17:10:00,21.2\n2017-05-19 17:15:00,21.7\n2017-05-19 17:20:00,22.1\n2017-05-19 17:25:00,22.3\n2017-05-19 17:30:00,23.1\n2017-05-19 17:35:00,24.0\n2017-05-19 17:40:00,25.1\n2017-05-19 17:45:00,25.5\n2017-05-19 17:50:00,26.1\n2017-05-19 17:55:00,26.5\n2017-05-19 18:00:00,27.1\n2017-05-19 18:05:00,27.9\n2017-05-19 18:10:00,28.0\n2017-05-19 18:15:00,28.4\n2017-05-19 18:20:00,29.0\n2017-05-19 18:25:00,29.3\n2017-05-19 18:30:00,28.1\n2017-05-19 18:35:00,29.1\n2017-05-19 18:40:00,29.6\n2017-05-19 18:45:00,29.4\n2017-05-19 18:50:00,29.5\n2017-05-19 18:55:00,30.8\n2017-05-19 19:00:00,29.7\n2017-05-19 19:05:00,30.4\n2017-05-19 19:10:00,30.2\n2017-05-19 19:15:00,30.6\n2017-05-19 19:20:00,31.0\n2017-05-19 19:25:00,30.9\n2017-05-19 19:30:00,31.2\n2017-05-19 19:35:00,32.2\n2017-05-19 19:40:00,32.9\n2017-05-19 19:45:00,33.4\n2017-05-19 19:50:00,33.2\n2017-05-19 19:55:00,33.2\n2017-05-19 20:00:00,34.0\n2017-05-19 20:05:00,34.0\n2017-05-19 20:10:00,34.2\n2017-05-19 20:15:00,34.9\n2017-05-19 20:20:00,34.8\n2017-05-19 20:25:00,35.1\n2017-05-19 20:30:00,35.0\n2017-05-19 20:35:00,35.0\n2017-05-19 20:40:00,35.7\n2017-05-19 20:45:00,34.8\n2017-05-19 20:50:00,35.5\n2017-05-19 20:55:00,34.7\n2017-05-19 21:00:00,34.8\n2017-05-19 21:05:00,34.8\n2017-05-19 21:10:00,35.0\n2017-05-19 21:15:00,34.1\n2017-05-19 21:20:00,34.6\n2017-05-19 21:25:00,34.3\n2017-05-19 21:30:00,34.3\n2017-05-19 21:35:00,34.6\n2017-05-19 21:40:00,34.3\n2017-05-19 21:45:00,34.4\n2017-05-19 21:50:00,34.3\n2017-05-19 21:55:00,34.2\n2017-05-19 22:00:00,34.0\n2017-05-19 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01:15:00,21.7\n2017-05-20 01:20:00,20.8\n2017-05-20 01:25:00,20.3\n2017-05-20 01:30:00,19.4\n2017-05-20 01:35:00,18.6\n2017-05-20 01:40:00,17.9\n2017-05-20 01:45:00,17.5\n2017-05-20 01:50:00,17.2\n2017-05-20 01:55:00,16.8\n2017-05-20 02:00:00,16.3\n2017-05-20 02:05:00,15.8\n2017-05-20 02:10:00,15.5\n2017-05-20 02:15:00,15.4\n2017-05-20 02:20:00,15.1\n2017-05-20 02:25:00,14.8\n2017-05-20 02:30:00,14.6\n2017-05-20 02:35:00,14.4\n2017-05-20 02:40:00,14.0\n2017-05-20 02:45:00,13.4\n2017-05-20 02:50:00,12.9\n2017-05-20 02:55:00,12.5\n2017-05-20 03:00:00,12.1\n2017-05-20 03:05:00,11.8\n2017-05-20 03:10:00,11.5\n2017-05-20 03:15:00,11.3\n2017-05-20 03:20:00,11.0\n2017-05-20 03:25:00,10.8\n2017-05-20 03:30:00,10.6\n2017-05-20 03:35:00,10.5\n2017-05-20 03:40:00,10.4\n2017-05-20 03:45:00,10.2\n2017-05-20 03:50:00,10.1\n2017-05-20 03:55:00,9.9\n2017-05-20 04:00:00,9.9\n2017-05-20 04:05:00,9.8\n2017-05-20 04:10:00,9.9\n2017-05-20 04:15:00,9.8\n2017-05-20 04:20:00,9.7\n2017-05-20 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23:55:00,30.0\n2017-05-27 00:00:00,29.8\n2017-05-27 00:05:00,28.2\n2017-05-27 00:10:00,27.1\n2017-05-27 00:15:00,27.7\n2017-05-27 00:20:00,26.8\n2017-05-27 00:25:00,26.4\n2017-05-27 00:30:00,26.1\n2017-05-27 00:35:00,25.4\n2017-05-27 00:40:00,25.1\n2017-05-27 00:45:00,24.3\n2017-05-27 00:50:00,24.0\n2017-05-27 00:55:00,23.4\n2017-05-27 01:00:00,22.7\n2017-05-27 01:05:00,21.9\n2017-05-27 01:10:00,20.6\n2017-05-27 01:15:00,19.8\n2017-05-27 01:20:00,18.9\n2017-05-27 01:25:00,17.6\n2017-05-27 01:30:00,17.2\n2017-05-27 01:35:00,16.6\n2017-05-27 01:40:00,15.7\n2017-05-27 01:45:00,15.7\n2017-05-27 01:50:00,15.5\n2017-05-27 01:55:00,14.9\n2017-05-27 02:00:00,15.1\n2017-05-27 02:05:00,15.1\n2017-05-27 02:10:00,14.8\n2017-05-27 02:15:00,14.4\n2017-05-27 02:20:00,14.2\n2017-05-27 02:25:00,14.0\n2017-05-27 02:30:00,13.8\n2017-05-27 02:35:00,13.6\n2017-05-27 02:40:00,13.4\n2017-05-27 02:45:00,13.3\n2017-05-27 02:50:00,13.1\n2017-05-27 02:55:00,12.9\n2017-05-27 03:00:00,12.5\n2017-05-27 03:05:00,12.5\n2017-05-27 03:10:00,12.4\n2017-05-27 03:15:00,12.2\n2017-05-27 03:20:00,12.0\n2017-05-27 03:25:00,12.0\n2017-05-27 03:30:00,11.9\n2017-05-27 03:35:00,11.7\n2017-05-27 03:40:00,11.7\n2017-05-27 03:45:00,11.5\n2017-05-27 03:50:00,11.3\n2017-05-27 03:55:00,11.1\n2017-05-27 04:00:00,10.9\n2017-05-27 04:05:00,10.8\n2017-05-27 04:10:00,10.5\n2017-05-27 04:15:00,10.5\n2017-05-27 04:20:00,10.3\n2017-05-27 04:25:00,9.8\n2017-05-27 04:30:00,9.3\n2017-05-27 04:35:00,9.3\n2017-05-27 04:40:00,9.5\n2017-05-27 04:45:00,9.4\n2017-05-27 04:50:00,9.1\n2017-05-27 04:55:00,9.0\n2017-05-27 05:00:00,8.3\n2017-05-27 05:05:00,7.8\n2017-05-27 05:10:00,7.8\n2017-05-27 05:15:00,7.8\n2017-05-27 05:20:00,7.7\n2017-05-27 05:25:00,7.3\n2017-05-27 05:30:00,7.2\n2017-05-27 05:35:00,7.1\n2017-05-27 05:40:00,7.0\n2017-05-27 05:45:00,6.9\n2017-05-27 05:50:00,6.9\n2017-05-27 05:55:00,6.8\n2017-05-27 06:00:00,6.8\n2017-05-27 06:05:00,6.7\n2017-05-27 06:10:00,6.7\n2017-05-27 06:15:00,6.4\n2017-05-27 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09:40:00,4.9\n2017-05-27 09:45:00,4.8\n2017-05-27 09:50:00,5.1\n2017-05-27 09:55:00,5.4\n2017-05-27 10:00:00,5.7\n2017-05-27 10:05:00,5.9\n2017-05-27 10:10:00,5.9\n2017-05-27 10:15:00,5.8\n2017-05-27 10:20:00,5.8\n2017-05-27 10:25:00,5.6\n2017-05-27 10:30:00,5.5\n2017-05-27 10:35:00,5.4\n2017-05-27 10:40:00,5.3\n2017-05-27 10:45:00,5.0\n2017-05-27 10:50:00,5.1\n2017-05-27 10:55:00,4.9\n2017-05-27 11:00:00,5.0\n2017-05-27 11:05:00,5.2\n2017-05-27 11:10:00,5.1\n2017-05-27 11:15:00,5.0\n2017-05-27 11:20:00,4.8\n2017-05-27 11:25:00,5.0\n2017-05-27 11:30:00,5.1\n2017-05-27 11:35:00,5.0\n2017-05-27 11:40:00,4.8\n2017-05-27 11:45:00,4.8\n2017-05-27 11:50:00,5.0\n2017-05-27 11:55:00,4.8\n2017-05-27 12:00:00,4.5\n2017-05-27 12:05:00,4.3\n2017-05-27 12:10:00,4.3\n2017-05-27 12:15:00,4.3\n2017-05-27 12:20:00,4.4\n2017-05-27 12:25:00,4.2\n2017-05-27 12:30:00,4.1\n2017-05-27 12:35:00,4.0\n2017-05-27 12:40:00,4.1\n2017-05-27 12:45:00,4.3\n2017-05-27 12:50:00,4.4\n2017-05-27 12:55:00,4.4\n2017-05-27 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12:40:00,7.8\n2017-06-09 12:45:00,7.8\n2017-06-09 12:50:00,7.8\n2017-06-09 12:55:00,7.9\n2017-06-09 13:00:00,7.9\n2017-06-09 13:05:00,7.9\n2017-06-09 13:10:00,8.0\n2017-06-09 13:15:00,8.0\n2017-06-09 13:20:00,8.0\n2017-06-09 13:25:00,8.0\n2017-06-09 13:30:00,8.1\n2017-06-09 13:35:00,8.2\n2017-06-09 13:40:00,8.2\n2017-06-09 13:45:00,8.2\n2017-06-09 13:50:00,8.4\n2017-06-09 13:55:00,8.5\n2017-06-09 14:00:00,8.6\n2017-06-09 14:05:00,8.5\n2017-06-09 14:10:00,9.4\n2017-06-09 14:15:00,9.6\n2017-06-09 14:20:00,9.2\n2017-06-09 14:25:00,9.4\n2017-06-09 14:30:00,9.4\n2017-06-09 14:35:00,10.1\n2017-06-09 14:40:00,10.0\n2017-06-09 14:45:00,10.2\n2017-06-09 14:50:00,10.8\n2017-06-09 14:55:00,11.5\n2017-06-09 15:00:00,10.4\n2017-06-09 15:05:00,9.5\n2017-06-09 15:10:00,9.6\n2017-06-09 15:15:00,9.9\n2017-06-09 15:20:00,9.9\n2017-06-09 15:25:00,10.5\n2017-06-09 15:30:00,11.1\n2017-06-09 15:35:00,11.8\n2017-06-09 15:40:00,13.0\n2017-06-09 15:45:00,12.6\n2017-06-09 15:50:00,13.2\n2017-06-09 15:55:00,12.3\n2017-06-09 16:00:00,12.4\n2017-06-09 16:05:00,12.3\n2017-06-09 16:10:00,13.0\n2017-06-09 16:15:00,12.7\n2017-06-09 16:20:00,14.1\n2017-06-09 16:25:00,13.7\n2017-06-09 16:30:00,12.4\n2017-06-09 16:35:00,13.7\n2017-06-09 16:40:00,14.2\n2017-06-09 16:45:00,14.8\n2017-06-09 16:50:00,15.1\n2017-06-09 16:55:00,14.5\n2017-06-09 17:00:00,13.6\n2017-06-09 17:05:00,14.7\n2017-06-09 17:10:00,14.5\n2017-06-09 17:15:00,14.2\n2017-06-09 17:20:00,13.8\n2017-06-09 17:25:00,13.7\n2017-06-09 17:30:00,13.8\n2017-06-09 17:35:00,15.5\n2017-06-09 17:40:00,15.7\n2017-06-09 17:45:00,16.2\n2017-06-09 17:50:00,14.2\n2017-06-09 17:55:00,13.4\n2017-06-09 18:00:00,13.9\n2017-06-09 18:05:00,13.1\n2017-06-09 18:10:00,12.6\n2017-06-09 18:15:00,13.6\n2017-06-09 18:20:00,12.9\n2017-06-09 18:25:00,13.2\n2017-06-09 18:30:00,13.4\n2017-06-09 18:35:00,13.1\n2017-06-09 18:40:00,13.4\n2017-06-09 18:45:00,13.2\n2017-06-09 18:50:00,12.7\n2017-06-09 18:55:00,14.8\n2017-06-09 19:00:00,14.5\n2017-06-09 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08:00:00,8.1\n2017-06-10 08:05:00,7.9\n2017-06-10 08:10:00,7.9\n2017-06-10 08:15:00,8.0\n2017-06-10 08:20:00,8.0\n2017-06-10 08:25:00,8.0\n2017-06-10 08:30:00,7.9\n2017-06-10 08:35:00,8.0\n2017-06-10 08:40:00,8.0\n2017-06-10 08:45:00,8.0\n2017-06-10 08:50:00,7.9\n2017-06-10 08:55:00,7.9\n2017-06-10 09:00:00,7.9\n2017-06-10 09:05:00,7.9\n2017-06-10 09:10:00,8.0\n2017-06-10 09:15:00,7.9\n2017-06-10 09:20:00,7.7\n2017-06-10 09:25:00,7.5\n2017-06-10 09:30:00,7.3\n2017-06-10 09:35:00,6.9\n2017-06-10 09:40:00,7.2\n2017-06-10 09:45:00,7.1\n2017-06-10 09:50:00,6.5\n2017-06-10 09:55:00,6.4\n2017-06-10 10:00:00,6.7\n2017-06-10 10:05:00,6.5\n2017-06-10 10:10:00,6.6\n2017-06-10 10:15:00,6.7\n2017-06-10 10:20:00,6.4\n2017-06-10 10:25:00,6.4\n2017-06-10 10:30:00,6.4\n2017-06-10 10:35:00,6.6\n2017-06-10 10:40:00,6.8\n2017-06-10 10:45:00,6.8\n2017-06-10 10:50:00,7.1\n2017-06-10 10:55:00,7.2\n2017-06-10 11:00:00,7.4\n2017-06-10 11:05:00,7.3\n2017-06-10 11:10:00,7.0\n2017-06-10 11:15:00,6.9\n2017-06-10 11:20:00,6.9\n2017-06-10 11:25:00,7.1\n2017-06-10 11:30:00,7.0\n2017-06-10 11:35:00,6.5\n2017-06-10 11:40:00,6.4\n2017-06-10 11:45:00,6.6\n2017-06-10 11:50:00,7.2\n2017-06-10 11:55:00,7.4\n2017-06-10 12:00:00,7.4\n2017-06-10 12:05:00,7.5\n2017-06-10 12:10:00,7.5\n2017-06-10 12:15:00,7.6\n2017-06-10 12:20:00,7.6\n2017-06-10 12:25:00,7.4\n2017-06-10 12:30:00,7.4\n2017-06-10 12:35:00,7.3\n2017-06-10 12:40:00,7.3\n2017-06-10 12:45:00,7.3\n2017-06-10 12:50:00,7.3\n2017-06-10 12:55:00,7.1\n2017-06-10 13:00:00,7.2\n2017-06-10 13:05:00,7.1\n2017-06-10 13:10:00,7.0\n2017-06-10 13:15:00,7.3\n2017-06-10 13:20:00,7.4\n2017-06-10 13:25:00,7.4\n2017-06-10 13:30:00,7.3\n2017-06-10 13:35:00,7.5\n2017-06-10 13:40:00,7.6\n2017-06-10 13:45:00,7.7\n2017-06-10 13:50:00,7.7\n2017-06-10 13:55:00,7.8\n2017-06-10 14:00:00,7.7\n2017-06-10 14:05:00,NaN\n2017-06-10 14:10:00,NaN\n2017-06-10 14:15:00,NaN\n2017-06-10 14:20:00,NaN\n2017-06-10 14:25:00,NaN\n2017-06-10 14:30:00,NaN\n2017-06-10 14:35:00,NaN\n2017-06-10 14:40:00,NaN\n2017-06-10 14:45:00,NaN\n2017-06-10 14:50:00,NaN\n2017-06-10 14:55:00,NaN\n2017-06-10 15:00:00,NaN\n2017-06-10 15:05:00,9.7\n2017-06-10 15:10:00,10.1\n2017-06-10 15:15:00,10.2\n2017-06-10 15:20:00,10.5\n2017-06-10 15:25:00,11.3\n2017-06-10 15:30:00,13.1\n2017-06-10 15:35:00,12.5\n2017-06-10 15:40:00,12.9\n2017-06-10 15:45:00,13.0\n2017-06-10 15:50:00,13.3\n2017-06-10 15:55:00,13.2\n2017-06-10 16:00:00,12.7\n2017-06-10 16:05:00,14.3\n2017-06-10 16:10:00,13.8\n2017-06-10 16:15:00,14.9\n2017-06-10 16:20:00,12.6\n2017-06-10 16:25:00,11.5\n2017-06-10 16:30:00,12.6\n2017-06-10 16:35:00,14.5\n2017-06-10 16:40:00,14.2\n2017-06-10 16:45:00,14.3\n2017-06-10 16:50:00,13.3\n2017-06-10 16:55:00,13.6\n2017-06-10 17:00:00,13.1\n2017-06-10 17:05:00,15.1\n2017-06-10 17:10:00,15.8\n2017-06-10 17:15:00,17.7\n2017-06-10 17:20:00,15.3\n2017-06-10 17:25:00,16.1\n2017-06-10 17:30:00,16.3\n2017-06-10 17:35:00,15.5\n2017-06-10 17:40:00,15.1\n2017-06-10 17:45:00,15.4\n2017-06-10 17:50:00,15.3\n2017-06-10 17:55:00,18.7\n2017-06-10 18:00:00,18.6\n2017-06-10 18:05:00,16.2\n2017-06-10 18:10:00,15.4\n2017-06-10 18:15:00,15.2\n2017-06-10 18:20:00,17.0\n2017-06-10 18:25:00,17.2\n2017-06-10 18:30:00,15.8\n2017-06-10 18:35:00,17.0\n2017-06-10 18:40:00,17.0\n2017-06-10 18:45:00,19.6\n2017-06-10 18:50:00,19.2\n2017-06-10 18:55:00,17.8\n2017-06-10 19:00:00,17.3\n2017-06-10 19:05:00,18.1\n2017-06-10 19:10:00,17.3\n2017-06-10 19:15:00,16.5\n2017-06-10 19:20:00,17.6\n2017-06-10 19:25:00,16.4\n2017-06-10 19:30:00,15.8\n2017-06-10 19:35:00,17.1\n2017-06-10 19:40:00,16.8\n2017-06-10 19:45:00,15.9\n2017-06-10 19:50:00,17.6\n2017-06-10 19:55:00,18.0\n2017-06-10 20:00:00,16.8\n2017-06-10 20:05:00,17.5\n2017-06-10 20:10:00,20.9\n2017-06-10 20:15:00,19.5\n2017-06-10 20:20:00,17.6\n2017-06-10 20:25:00,17.0\n2017-06-10 20:30:00,16.4\n2017-06-10 20:35:00,15.6\n2017-06-10 20:40:00,16.2\n2017-06-10 20:45:00,15.8\n2017-06-10 20:50:00,17.7\n2017-06-10 20:55:00,17.5\n2017-06-10 21:00:00,23.9\n2017-06-10 21:05:00,23.1\n2017-06-10 21:10:00,26.3\n2017-06-10 21:15:00,23.4\n2017-06-10 21:20:00,21.8\n2017-06-10 21:25:00,19.3\n2017-06-10 21:30:00,18.3\n2017-06-10 21:35:00,17.8\n2017-06-10 21:40:00,17.1\n2017-06-10 21:45:00,18.6\n2017-06-10 21:50:00,18.3\n2017-06-10 21:55:00,19.6\n2017-06-10 22:00:00,23.1\n2017-06-10 22:05:00,23.6\n2017-06-10 22:10:00,22.0\n2017-06-10 22:15:00,25.5\n2017-06-10 22:20:00,21.5\n2017-06-10 22:25:00,19.9\n2017-06-10 22:30:00,23.3\n2017-06-10 22:35:00,17.8\n2017-06-10 22:40:00,17.1\n2017-06-10 22:45:00,17.7\n2017-06-10 22:50:00,17.7\n2017-06-10 22:55:00,19.1\n2017-06-10 23:00:00,21.3\n2017-06-10 23:05:00,18.6\n2017-06-10 23:10:00,18.6\n2017-06-10 23:15:00,22.2\n2017-06-10 23:20:00,19.4\n2017-06-10 23:25:00,19.7\n2017-06-10 23:30:00,18.5\n2017-06-10 23:35:00,18.0\n2017-06-10 23:40:00,22.3\n2017-06-10 23:45:00,19.5\n2017-06-10 23:50:00,18.6\n2017-06-10 23:55:00,21.3\n2017-06-11 00:00:00,22.8\n2017-06-11 00:05:00,22.6\n2017-06-11 00:10:00,22.2\n2017-06-11 00:15:00,22.5\n2017-06-11 00:20:00,22.6\n2017-06-11 00:25:00,23.4\n2017-06-11 00:30:00,22.4\n2017-06-11 00:35:00,22.3\n2017-06-11 00:40:00,21.6\n2017-06-11 00:45:00,18.8\n2017-06-11 00:50:00,19.7\n2017-06-11 00:55:00,19.5\n2017-06-11 01:00:00,18.9\n2017-06-11 01:05:00,18.1\n2017-06-11 01:10:00,17.4\n2017-06-11 01:15:00,17.1\n2017-06-11 01:20:00,16.0\n2017-06-11 01:25:00,15.0\n2017-06-11 01:30:00,14.0\n2017-06-11 01:35:00,13.3\n2017-06-11 01:40:00,12.8\n2017-06-11 01:45:00,12.0\n2017-06-11 01:50:00,11.6\n2017-06-11 01:55:00,11.2\n2017-06-11 02:00:00,10.8\n2017-06-11 02:05:00,10.4\n2017-06-11 02:10:00,10.2\n2017-06-11 02:15:00,9.9\n2017-06-11 02:20:00,9.4\n2017-06-11 02:25:00,9.1\n2017-06-11 02:30:00,8.7\n2017-06-11 02:35:00,8.3\n2017-06-11 02:40:00,7.8\n2017-06-11 02:45:00,7.6\n2017-06-11 02:50:00,7.3\n2017-06-11 02:55:00,7.0\n2017-06-11 03:00:00,6.7\n2017-06-11 03:05:00,6.6\n2017-06-11 03:10:00,6.3\n2017-06-11 03:15:00,6.1\n2017-06-11 03:20:00,5.7\n2017-06-11 03:25:00,5.4\n2017-06-11 03:30:00,5.2\n2017-06-11 03:35:00,4.9\n2017-06-11 03:40:00,4.7\n2017-06-11 03:45:00,4.6\n2017-06-11 03:50:00,4.4\n2017-06-11 03:55:00,4.3\n2017-06-11 04:00:00,4.3\n2017-06-11 04:05:00,4.1\n2017-06-11 04:10:00,4.0\n2017-06-11 04:15:00,3.9\n2017-06-11 04:20:00,3.8\n2017-06-11 04:25:00,3.6\n2017-06-11 04:30:00,3.5\n2017-06-11 04:35:00,3.4\n2017-06-11 04:40:00,3.3\n2017-06-11 04:45:00,3.1\n2017-06-11 04:50:00,3.1\n2017-06-11 04:55:00,3.0\n2017-06-11 05:00:00,2.9\n2017-06-11 05:05:00,2.8\n2017-06-11 05:10:00,2.8\n2017-06-11 05:15:00,2.7\n2017-06-11 05:20:00,2.6\n2017-06-11 05:25:00,2.6\n2017-06-11 05:30:00,2.7\n2017-06-11 05:35:00,2.7\n2017-06-11 05:40:00,2.8\n2017-06-11 05:45:00,2.9\n2017-06-11 05:50:00,2.8\n2017-06-11 05:55:00,2.7\n2017-06-11 06:00:00,2.6\n2017-06-11 06:05:00,2.6\n2017-06-11 06:10:00,2.5\n2017-06-11 06:15:00,2.4\n2017-06-11 06:20:00,2.4\n2017-06-11 06:25:00,2.3\n2017-06-11 06:30:00,2.3\n2017-06-11 06:35:00,2.2\n2017-06-11 06:40:00,2.2\n2017-06-11 06:45:00,2.1\n2017-06-11 06:50:00,2.0\n2017-06-11 06:55:00,2.0\n2017-06-11 07:00:00,1.9\n2017-06-11 07:05:00,1.9\n2017-06-11 07:10:00,1.8\n2017-06-11 07:15:00,1.8\n2017-06-11 07:20:00,1.8\n2017-06-11 07:25:00,1.7\n2017-06-11 07:30:00,1.7\n2017-06-11 07:35:00,1.6\n2017-06-11 07:40:00,1.6\n2017-06-11 07:45:00,1.6\n2017-06-11 07:50:00,1.6\n2017-06-11 07:55:00,1.6\n2017-06-11 08:00:00,1.6\n2017-06-11 08:05:00,1.6\n2017-06-11 08:10:00,1.5\n2017-06-11 08:15:00,1.5\n2017-06-11 08:20:00,1.4\n2017-06-11 08:25:00,1.3\n2017-06-11 08:30:00,1.3\n2017-06-11 08:35:00,1.3\n2017-06-11 08:40:00,1.3\n2017-06-11 08:45:00,1.2\n2017-06-11 08:50:00,1.1\n2017-06-11 08:55:00,1.1\n2017-06-11 09:00:00,1.1\n2017-06-11 09:05:00,1.1\n2017-06-11 09:10:00,1.1\n2017-06-11 09:15:00,1.0\n2017-06-11 09:20:00,1.1\n2017-06-11 09:25:00,1.1\n2017-06-11 09:30:00,1.0\n2017-06-11 09:35:00,1.1\n2017-06-11 09:40:00,1.4\n2017-06-11 09:45:00,1.8\n2017-06-11 09:50:00,2.1\n2017-06-11 09:55:00,2.4\n2017-06-11 10:00:00,2.7\n2017-06-11 10:05:00,2.9\n2017-06-11 10:10:00,2.9\n2017-06-11 10:15:00,2.8\n2017-06-11 10:20:00,2.7\n2017-06-11 10:25:00,2.9\n2017-06-11 10:30:00,3.0\n2017-06-11 10:35:00,2.8\n2017-06-11 10:40:00,3.1\n2017-06-11 10:45:00,3.0\n2017-06-11 10:50:00,2.9\n2017-06-11 10:55:00,2.5\n2017-06-11 11:00:00,2.1\n2017-06-11 11:05:00,1.8\n2017-06-11 11:10:00,2.0\n2017-06-11 11:15:00,2.2\n2017-06-11 11:20:00,1.7\n2017-06-11 11:25:00,1.8\n2017-06-11 11:30:00,1.7\n2017-06-11 11:35:00,1.5\n2017-06-11 11:40:00,1.6\n2017-06-11 11:45:00,1.2\n2017-06-11 11:50:00,1.4\n2017-06-11 11:55:00,1.3\n2017-06-11 12:00:00,1.6\n2017-06-11 12:05:00,2.1\n2017-06-11 12:10:00,2.0\n2017-06-11 12:15:00,1.5\n2017-06-11 12:20:00,1.7\n2017-06-11 12:25:00,1.6\n2017-06-11 12:30:00,1.4\n2017-06-11 12:35:00,1.1\n2017-06-11 12:40:00,1.6\n2017-06-11 12:45:00,2.4\n2017-06-11 12:50:00,2.8\n2017-06-11 12:55:00,2.7\n2017-06-11 13:00:00,2.3\n2017-06-11 13:05:00,2.7\n2017-06-11 13:10:00,2.9\n2017-06-11 13:15:00,2.9\n2017-06-11 13:20:00,3.2\n2017-06-11 13:25:00,2.9\n2017-06-11 13:30:00,2.5\n2017-06-11 13:35:00,2.1\n2017-06-11 13:40:00,2.4\n2017-06-11 13:45:00,2.9\n2017-06-11 13:50:00,3.2\n2017-06-11 13:55:00,3.7\n2017-06-11 14:00:00,3.6\n2017-06-11 14:05:00,4.1\n2017-06-11 14:10:00,4.5\n2017-06-11 14:15:00,4.7\n2017-06-11 14:20:00,4.2\n2017-06-11 14:25:00,4.1\n2017-06-11 14:30:00,3.7\n2017-06-11 14:35:00,3.7\n2017-06-11 14:40:00,4.0\n2017-06-11 14:45:00,3.9\n2017-06-11 14:50:00,3.7\n2017-06-11 14:55:00,3.6\n2017-06-11 15:00:00,4.0\n2017-06-11 15:05:00,4.2\n2017-06-11 15:10:00,4.0\n2017-06-11 15:15:00,4.1\n2017-06-11 15:20:00,4.1\n2017-06-11 15:25:00,3.9\n2017-06-11 15:30:00,3.8\n2017-06-11 15:35:00,3.9\n2017-06-11 15:40:00,3.9\n2017-06-11 15:45:00,3.7\n2017-06-11 15:50:00,3.7\n2017-06-11 15:55:00,3.7\n2017-06-11 16:00:00,3.5\n2017-06-11 16:05:00,3.1\n2017-06-11 16:10:00,3.3\n2017-06-11 16:15:00,3.2\n2017-06-11 16:20:00,3.4\n2017-06-11 16:25:00,3.3\n2017-06-11 16:30:00,3.6\n2017-06-11 16:35:00,4.6\n2017-06-11 16:40:00,4.2\n2017-06-11 16:45:00,4.4\n2017-06-11 16:50:00,4.4\n2017-06-11 16:55:00,4.7\n2017-06-11 17:00:00,4.2\n2017-06-11 17:05:00,4.1\n2017-06-11 17:10:00,4.2\n2017-06-11 17:15:00,4.3\n2017-06-11 17:20:00,4.2\n2017-06-11 17:25:00,4.1\n2017-06-11 17:30:00,4.0\n2017-06-11 17:35:00,4.0\n2017-06-11 17:40:00,4.1\n2017-06-11 17:45:00,4.2\n2017-06-11 17:50:00,4.0\n2017-06-11 17:55:00,4.2\n2017-06-11 18:00:00,4.1\n2017-06-11 18:05:00,4.0\n2017-06-11 18:10:00,3.8\n2017-06-11 18:15:00,3.7\n2017-06-11 18:20:00,3.9\n2017-06-11 18:25:00,3.7\n2017-06-11 18:30:00,3.5\n2017-06-11 18:35:00,3.5\n2017-06-11 18:40:00,3.9\n2017-06-11 18:45:00,3.7\n2017-06-11 18:50:00,3.6\n2017-06-11 18:55:00,3.9\n2017-06-11 19:00:00,4.0\n2017-06-11 19:05:00,4.3\n2017-06-11 19:10:00,4.1\n2017-06-11 19:15:00,4.1\n2017-06-11 19:20:00,3.8\n2017-06-11 19:25:00,3.8\n2017-06-11 19:30:00,4.2\n2017-06-11 19:35:00,3.9\n2017-06-11 19:40:00,4.0\n2017-06-11 19:45:00,3.9\n2017-06-11 19:50:00,3.8\n2017-06-11 19:55:00,4.1\n2017-06-11 20:00:00,5.1\n2017-06-11 20:05:00,9.5\n2017-06-11 20:10:00,10.9\n2017-06-11 20:15:00,10.9\n2017-06-11 20:20:00,9.2\n2017-06-11 20:25:00,7.8\n2017-06-11 20:30:00,7.2\n2017-06-11 20:35:00,6.5\n2017-06-11 20:40:00,6.0\n2017-06-11 20:45:00,6.0\n2017-06-11 20:50:00,7.8\n2017-06-11 20:55:00,8.4\n2017-06-11 21:00:00,10.2\n2017-06-11 21:05:00,10.9\n2017-06-11 21:10:00,14.2\n2017-06-11 21:15:00,18.7\n2017-06-11 21:20:00,13.1\n2017-06-11 21:25:00,15.2\n2017-06-11 21:30:00,20.2\n2017-06-11 21:35:00,24.0\n2017-06-11 21:40:00,23.2\n2017-06-11 21:45:00,26.3\n2017-06-11 21:50:00,22.0\n2017-06-11 21:55:00,17.9\n2017-06-11 22:00:00,15.4\n2017-06-11 22:05:00,20.8\n2017-06-11 22:10:00,16.8\n2017-06-11 22:15:00,16.3\n2017-06-11 22:20:00,17.9\n2017-06-11 22:25:00,17.2\n2017-06-11 22:30:00,17.8\n2017-06-11 22:35:00,18.9\n2017-06-11 22:40:00,18.4\n2017-06-11 22:45:00,23.3\n2017-06-11 22:50:00,24.8\n2017-06-11 22:55:00,20.8\n2017-06-11 23:00:00,21.7\n2017-06-11 23:05:00,22.0\n2017-06-11 23:10:00,17.3\n2017-06-11 23:15:00,14.3\n2017-06-11 23:20:00,14.7\n2017-06-11 23:25:00,12.1\n2017-06-11 23:30:00,11.4\n2017-06-11 23:35:00,11.5\n2017-06-11 23:40:00,10.9\n2017-06-11 23:45:00,11.7\n2017-06-11 23:50:00,11.3\n2017-06-11 23:55:00,9.5\n2017-06-12 00:00:00,8.9\n2017-06-12 00:05:00,8.6\n2017-06-12 00:10:00,8.7\n2017-06-12 00:15:00,9.5\n2017-06-12 00:20:00,13.4\n2017-06-12 00:25:00,13.8\n2017-06-12 00:30:00,10.8\n2017-06-12 00:35:00,9.0\n2017-06-12 00:40:00,8.7\n2017-06-12 00:45:00,9.1\n2017-06-12 00:50:00,8.4\n2017-06-12 00:55:00,7.9\n2017-06-12 01:00:00,8.1\n2017-06-12 01:05:00,7.9\n2017-06-12 01:10:00,7.6\n2017-06-12 01:15:00,7.3\n2017-06-12 01:20:00,8.0\n2017-06-12 01:25:00,10.3\n2017-06-12 01:30:00,9.1\n2017-06-12 01:35:00,7.9\n2017-06-12 01:40:00,6.5\n2017-06-12 01:45:00,6.3\n2017-06-12 01:50:00,5.5\n2017-06-12 01:55:00,5.0\n2017-06-12 02:00:00,5.2\n2017-06-12 02:05:00,5.3\n2017-06-12 02:10:00,5.2\n2017-06-12 02:15:00,4.5\n2017-06-12 02:20:00,4.1\n2017-06-12 02:25:00,4.0\n2017-06-12 02:30:00,3.9\n2017-06-12 02:35:00,3.8\n2017-06-12 02:40:00,4.0\n2017-06-12 02:45:00,4.0\n2017-06-12 02:50:00,4.0\n2017-06-12 02:55:00,4.1\n2017-06-12 03:00:00,4.0\n2017-06-12 03:05:00,3.1\n2017-06-12 03:10:00,1.4\n2017-06-12 03:15:00,-0.2\n2017-06-12 03:20:00,-0.7\n2017-06-12 03:25:00,-0.7\n2017-06-12 03:30:00,-0.8\n2017-06-12 03:35:00,-0.9\n2017-06-12 03:40:00,-1.1\n2017-06-12 03:45:00,-1.1\n2017-06-12 03:50:00,-1.2\n2017-06-12 03:55:00,-1.2\n2017-06-12 04:00:00,-1.2\n2017-06-12 04:05:00,-1.4\n2017-06-12 04:10:00,-1.4\n2017-06-12 04:15:00,-1.4\n2017-06-12 04:20:00,-1.4\n2017-06-12 04:25:00,-1.4\n2017-06-12 04:30:00,-1.4\n2017-06-12 04:35:00,-1.4\n2017-06-12 04:40:00,-1.5\n2017-06-12 04:45:00,-1.6\n2017-06-12 04:50:00,-1.6\n2017-06-12 04:55:00,-1.5\n2017-06-12 05:00:00,-1.6\n2017-06-12 05:05:00,-1.7\n2017-06-12 05:10:00,-3.3\n2017-06-12 05:15:00,-4.6\n2017-06-12 05:20:00,-5.2\n2017-06-12 05:25:00,-5.9\n2017-06-12 05:30:00,-6.0\n2017-06-12 05:35:00,-5.8\n2017-06-12 05:40:00,-5.8\n2017-06-12 05:45:00,-5.4\n2017-06-12 05:50:00,-5.1\n2017-06-12 05:55:00,-5.0\n2017-06-12 06:00:00,-5.1\n2017-06-12 06:05:00,-5.2\n2017-06-12 06:10:00,-5.2\n2017-06-12 06:15:00,-4.9\n2017-06-12 06:20:00,-4.8\n2017-06-12 06:25:00,-5.0\n2017-06-12 06:30:00,-5.0\n2017-06-12 06:35:00,-5.4\n2017-06-12 06:40:00,-5.3\n2017-06-12 06:45:00,-5.5\n2017-06-12 06:50:00,-5.9\n2017-06-12 06:55:00,-5.6\n2017-06-12 07:00:00,-5.9\n2017-06-12 07:05:00,-6.7\n2017-06-12 07:10:00,-6.9\n2017-06-12 07:15:00,-6.7\n2017-06-12 07:20:00,-7.1\n2017-06-12 07:25:00,-7.2\n2017-06-12 07:30:00,-7.4\n2017-06-12 07:35:00,-7.6\n2017-06-12 07:40:00,-7.8\n2017-06-12 07:45:00,-7.5\n2017-06-12 07:50:00,-7.7\n2017-06-12 07:55:00,-7.8\n2017-06-12 08:00:00,-7.1\n2017-06-12 08:05:00,-6.9\n2017-06-12 08:10:00,-7.2\n2017-06-12 08:15:00,-7.6\n2017-06-12 08:20:00,-7.7\n2017-06-12 08:25:00,-7.4\n2017-06-12 08:30:00,-7.9\n2017-06-12 08:35:00,-7.8\n2017-06-12 08:40:00,-8.2\n2017-06-12 08:45:00,-8.2\n2017-06-12 08:50:00,-8.0\n2017-06-12 08:55:00,-7.8\n2017-06-12 09:00:00,-7.7\n2017-06-12 09:05:00,-7.6\n2017-06-12 09:10:00,-7.9\n2017-06-12 09:15:00,-7.6\n2017-06-12 09:20:00,-7.3\n2017-06-12 09:25:00,-7.1\n2017-06-12 09:30:00,-7.2\n2017-06-12 09:35:00,-7.7\n2017-06-12 09:40:00,-8.1\n2017-06-12 09:45:00,-8.2\n2017-06-12 09:50:00,-8.4\n2017-06-12 09:55:00,-8.3\n2017-06-12 10:00:00,-8.2\n2017-06-12 10:05:00,-8.0\n2017-06-12 10:10:00,-7.8\n2017-06-12 10:15:00,-7.3\n2017-06-12 10:20:00,-6.8\n2017-06-12 10:25:00,-6.6\n2017-06-12 10:30:00,-6.2\n2017-06-12 10:35:00,-6.3\n2017-06-12 10:40:00,-6.2\n2017-06-12 10:45:00,-5.7\n2017-06-12 10:50:00,-5.5\n2017-06-12 10:55:00,-5.2\n2017-06-12 11:00:00,-4.1\n2017-06-12 11:05:00,-3.4\n2017-06-12 11:10:00,-3.1\n2017-06-12 11:15:00,-3.3\n2017-06-12 11:20:00,-4.2\n2017-06-12 11:25:00,-4.7\n2017-06-12 11:30:00,-4.1\n2017-06-12 11:35:00,-3.2\n2017-06-12 11:40:00,-2.6\n2017-06-12 11:45:00,-2.5\n2017-06-12 11:50:00,-2.6\n2017-06-12 11:55:00,-2.5\n2017-06-12 12:00:00,-2.5\n2017-06-12 12:05:00,-2.6\n2017-06-12 12:10:00,-3.0\n2017-06-12 12:15:00,-2.9\n2017-06-12 12:20:00,-3.5\n2017-06-12 12:25:00,-3.2\n2017-06-12 12:30:00,-3.0\n2017-06-12 12:35:00,-3.1\n2017-06-12 12:40:00,-2.7\n2017-06-12 12:45:00,-2.6\n2017-06-12 12:50:00,-2.6\n2017-06-12 12:55:00,-2.8\n2017-06-12 13:00:00,-2.5\n2017-06-12 13:05:00,-2.2\n2017-06-12 13:10:00,-2.1\n2017-06-12 13:15:00,-2.0\n2017-06-12 13:20:00,-1.9\n2017-06-12 13:25:00,-1.8\n2017-06-12 13:30:00,-1.8\n2017-06-12 13:35:00,-1.7\n2017-06-12 13:40:00,-1.6\n2017-06-12 13:45:00,-1.6\n2017-06-12 13:50:00,-1.5\n2017-06-12 13:55:00,-1.4\n2017-06-12 14:00:00,-1.3\n2017-06-12 14:05:00,-1.2\n2017-06-12 14:10:00,-1.2\n2017-06-12 14:15:00,-1.1\n2017-06-12 14:20:00,-1.1\n2017-06-12 14:25:00,-1.0\n2017-06-12 14:30:00,-0.9\n2017-06-12 14:35:00,-0.9\n2017-06-12 14:40:00,-0.7\n2017-06-12 14:45:00,-0.6\n2017-06-12 14:50:00,-0.4\n2017-06-12 14:55:00,-0.4\n2017-06-12 15:00:00,-0.2\n2017-06-12 15:05:00,-0.1\n2017-06-12 15:10:00,0.0\n2017-06-12 15:15:00,0.0\n2017-06-12 15:20:00,0.2\n2017-06-12 15:25:00,0.3\n2017-06-12 15:30:00,0.3\n2017-06-12 15:35:00,0.3\n2017-06-12 15:40:00,0.4\n2017-06-12 15:45:00,0.5\n2017-06-12 15:50:00,0.7\n2017-06-12 15:55:00,0.8\n2017-06-12 16:00:00,0.9\n2017-06-12 16:05:00,1.2\n2017-06-12 16:10:00,1.1\n2017-06-12 16:15:00,1.2\n2017-06-12 16:20:00,1.6\n2017-06-12 16:25:00,2.1\n2017-06-12 16:30:00,2.3\n2017-06-12 16:35:00,2.1\n2017-06-12 16:40:00,2.2\n2017-06-12 16:45:00,2.4\n2017-06-12 16:50:00,2.1\n2017-06-12 16:55:00,2.0\n2017-06-12 17:00:00,2.4\n2017-06-12 17:05:00,3.0\n2017-06-12 17:10:00,3.3\n2017-06-12 17:15:00,3.6\n2017-06-12 17:20:00,3.9\n2017-06-12 17:25:00,4.6\n2017-06-12 17:30:00,5.9\n2017-06-12 17:35:00,5.2\n2017-06-12 17:40:00,7.4\n2017-06-12 17:45:00,9.1\n2017-06-12 17:50:00,9.2\n2017-06-12 17:55:00,8.1\n2017-06-12 18:00:00,8.1\n2017-06-12 18:05:00,7.2\n2017-06-12 18:10:00,6.5\n2017-06-12 18:15:00,5.9\n2017-06-12 18:20:00,6.1\n2017-06-12 18:25:00,7.1\n2017-06-12 18:30:00,7.6\n2017-06-12 18:35:00,6.6\n2017-06-12 18:40:00,6.1\n2017-06-12 18:45:00,5.9\n2017-06-12 18:50:00,7.0\n2017-06-12 18:55:00,6.6\n2017-06-12 19:00:00,7.3\n2017-06-12 19:05:00,7.4\n2017-06-12 19:10:00,7.5\n2017-06-12 19:15:00,7.0\n2017-06-12 19:20:00,6.9\n2017-06-12 19:25:00,7.7\n2017-06-12 19:30:00,10.5\n2017-06-12 19:35:00,10.5\n2017-06-12 19:40:00,10.9\n2017-06-12 19:45:00,11.0\n2017-06-12 19:50:00,9.9\n2017-06-12 19:55:00,10.5\n2017-06-12 20:00:00,9.8\n2017-06-12 20:05:00,11.6\n2017-06-12 20:10:00,12.7\n2017-06-12 20:15:00,12.3\n2017-06-12 20:20:00,11.4\n2017-06-12 20:25:00,14.7\n2017-06-12 20:30:00,18.8\n2017-06-12 20:35:00,17.5\n2017-06-12 20:40:00,14.6\n2017-06-12 20:45:00,16.4\n2017-06-12 20:50:00,13.8\n2017-06-12 20:55:00,15.8\n2017-06-12 21:00:00,18.2\n2017-06-12 21:05:00,17.9\n2017-06-12 21:10:00,13.9\n2017-06-12 21:15:00,14.7\n2017-06-12 21:20:00,20.6\n2017-06-12 21:25:00,21.4\n2017-06-12 21:30:00,20.2\n2017-06-12 21:35:00,22.9\n2017-06-12 21:40:00,17.8\n2017-06-12 21:45:00,15.1\n2017-06-12 21:50:00,21.5\n2017-06-12 21:55:00,22.0\n2017-06-12 22:00:00,16.3\n2017-06-12 22:05:00,14.2\n2017-06-12 22:10:00,13.3\n2017-06-12 22:15:00,14.4\n2017-06-12 22:20:00,19.7\n2017-06-12 22:25:00,21.6\n2017-06-12 22:30:00,21.3\n2017-06-12 22:35:00,22.2\n2017-06-12 22:40:00,22.6\n2017-06-12 22:45:00,17.1\n2017-06-12 22:50:00,15.4\n2017-06-12 22:55:00,15.5\n2017-06-12 23:00:00,14.9\n2017-06-12 23:05:00,14.5\n2017-06-12 23:10:00,14.1\n2017-06-12 23:15:00,14.8\n2017-06-12 23:20:00,19.5\n2017-06-12 23:25:00,21.4\n2017-06-12 23:30:00,17.1\n2017-06-12 23:35:00,15.0\n2017-06-12 23:40:00,13.4\n2017-06-12 23:45:00,13.1\n2017-06-12 23:50:00,12.9\n2017-06-12 23:55:00,12.9\n2017-06-13 00:00:00,12.4\n2017-06-13 00:05:00,13.1\n2017-06-13 00:10:00,12.9\n2017-06-13 00:15:00,11.3\n2017-06-13 00:20:00,9.9\n2017-06-13 00:25:00,9.6\n2017-06-13 00:30:00,13.4\n2017-06-13 00:35:00,14.0\n2017-06-13 00:40:00,12.1\n2017-06-13 00:45:00,11.2\n2017-06-13 00:50:00,12.3\n2017-06-13 00:55:00,13.2\n2017-06-13 01:00:00,11.8\n2017-06-13 01:05:00,12.1\n2017-06-13 01:10:00,14.5\n2017-06-13 01:15:00,14.8\n2017-06-13 01:20:00,13.1\n2017-06-13 01:25:00,13.8\n2017-06-13 01:30:00,13.6\n2017-06-13 01:35:00,12.4\n2017-06-13 01:40:00,11.3\n2017-06-13 01:45:00,10.5\n2017-06-13 01:50:00,9.8\n2017-06-13 01:55:00,9.3\n2017-06-13 02:00:00,9.0\n2017-06-13 02:05:00,8.1\n2017-06-13 02:10:00,8.1\n2017-06-13 02:15:00,7.5\n2017-06-13 02:20:00,7.5\n2017-06-13 02:25:00,7.5\n2017-06-13 02:30:00,7.2\n2017-06-13 02:35:00,7.3\n2017-06-13 02:40:00,7.5\n2017-06-13 02:45:00,7.2\n2017-06-13 02:50:00,6.8\n2017-06-13 02:55:00,6.5\n2017-06-13 03:00:00,6.1\n2017-06-13 03:05:00,5.8\n2017-06-13 03:10:00,5.4\n2017-06-13 03:15:00,4.9\n2017-06-13 03:20:00,4.6\n2017-06-13 03:25:00,4.3\n2017-06-13 03:30:00,4.1\n2017-06-13 03:35:00,3.9\n2017-06-13 03:40:00,3.7\n2017-06-13 03:45:00,3.6\n2017-06-13 03:50:00,3.4\n2017-06-13 03:55:00,3.3\n2017-06-13 04:00:00,3.1\n2017-06-13 04:05:00,3.0\n2017-06-13 04:10:00,2.9\n2017-06-13 04:15:00,2.8\n2017-06-13 04:20:00,2.7\n2017-06-13 04:25:00,2.6\n2017-06-13 04:30:00,2.6\n2017-06-13 04:35:00,2.5\n2017-06-13 04:40:00,2.4\n2017-06-13 04:45:00,2.4\n2017-06-13 04:50:00,2.3\n2017-06-13 04:55:00,2.2\n2017-06-13 05:00:00,2.2\n2017-06-13 05:05:00,2.2\n2017-06-13 05:10:00,2.1\n2017-06-13 05:15:00,2.1\n2017-06-13 05:20:00,2.0\n2017-06-13 05:25:00,1.9\n2017-06-13 05:30:00,1.9\n2017-06-13 05:35:00,1.8\n2017-06-13 05:40:00,1.7\n2017-06-13 05:45:00,1.7\n2017-06-13 05:50:00,1.7\n2017-06-13 05:55:00,1.5\n2017-06-13 06:00:00,1.4\n2017-06-13 06:05:00,1.3\n2017-06-13 06:10:00,1.4\n2017-06-13 06:15:00,1.4\n2017-06-13 06:20:00,1.3\n2017-06-13 06:25:00,1.3\n2017-06-13 06:30:00,1.2\n2017-06-13 06:35:00,1.1\n2017-06-13 06:40:00,1.0\n2017-06-13 06:45:00,1.0\n2017-06-13 06:50:00,1.1\n2017-06-13 06:55:00,1.1\n2017-06-13 07:00:00,0.9\n2017-06-13 07:05:00,1.1\n2017-06-13 07:10:00,1.2\n2017-06-13 07:15:00,1.1\n2017-06-13 07:20:00,1.0\n2017-06-13 07:25:00,0.8\n2017-06-13 07:30:00,0.7\n2017-06-13 07:35:00,0.6\n2017-06-13 07:40:00,0.4\n2017-06-13 07:45:00,0.4\n2017-06-13 07:50:00,0.3\n2017-06-13 07:55:00,0.2\n2017-06-13 08:00:00,0.1\n2017-06-13 08:05:00,0.1\n2017-06-13 08:10:00,0.0\n2017-06-13 08:15:00,0.0\n2017-06-13 08:20:00,0.0\n2017-06-13 08:25:00,0.0\n2017-06-13 08:30:00,0.0\n2017-06-13 08:35:00,0.0\n2017-06-13 08:40:00,0.1\n2017-06-13 08:45:00,0.0\n2017-06-13 08:50:00,0.1\n2017-06-13 08:55:00,0.2\n2017-06-13 09:00:00,0.2\n2017-06-13 09:05:00,0.3\n2017-06-13 09:10:00,0.3\n2017-06-13 09:15:00,0.3\n2017-06-13 09:20:00,0.1\n2017-06-13 09:25:00,0.2\n2017-06-13 09:30:00,0.2\n2017-06-13 09:35:00,0.2\n2017-06-13 09:40:00,0.2\n2017-06-13 09:45:00,0.2\n2017-06-13 09:50:00,0.2\n2017-06-13 09:55:00,0.2\n2017-06-13 10:00:00,0.3\n2017-06-13 10:05:00,0.2\n2017-06-13 10:10:00,0.1\n2017-06-13 10:15:00,0.1\n2017-06-13 10:20:00,0.0\n2017-06-13 10:25:00,0.0\n2017-06-13 10:30:00,-0.1\n2017-06-13 10:35:00,-0.1\n2017-06-13 10:40:00,-0.2\n2017-06-13 10:45:00,-0.2\n2017-06-13 10:50:00,-0.2\n2017-06-13 10:55:00,-0.2\n2017-06-13 11:00:00,-0.3\n2017-06-13 11:05:00,-0.2\n2017-06-13 11:10:00,-0.1\n2017-06-13 11:15:00,-0.1\n2017-06-13 11:20:00,-0.1\n2017-06-13 11:25:00,-0.2\n2017-06-13 11:30:00,-0.2\n2017-06-13 11:35:00,-0.2\n2017-06-13 11:40:00,-0.3\n2017-06-13 11:45:00,-0.3\n2017-06-13 11:50:00,-0.4\n2017-06-13 11:55:00,-0.4\n2017-06-13 12:00:00,-0.4\n2017-06-13 12:05:00,-0.3\n2017-06-13 12:10:00,-0.5\n2017-06-13 12:15:00,-0.6\n2017-06-13 12:20:00,-0.8\n2017-06-13 12:25:00,-0.7\n2017-06-13 12:30:00,-0.7\n2017-06-13 12:35:00,-0.8\n2017-06-13 12:40:00,-0.9\n2017-06-13 12:45:00,-0.8\n2017-06-13 12:50:00,-0.8\n2017-06-13 12:55:00,-0.9\n2017-06-13 13:00:00,-0.8\n2017-06-13 13:05:00,-0.8\n2017-06-13 13:10:00,-0.7\n2017-06-13 13:15:00,-0.6\n2017-06-13 13:20:00,-0.4\n2017-06-13 13:25:00,-0.3\n2017-06-13 13:30:00,-0.1\n2017-06-13 13:35:00,0.0\n2017-06-13 13:40:00,0.2\n2017-06-13 13:45:00,0.3\n2017-06-13 13:50:00,0.5\n2017-06-13 13:55:00,0.7\n2017-06-13 14:00:00,0.9\n2017-06-13 14:05:00,1.1\n2017-06-13 14:10:00,1.3\n2017-06-13 14:15:00,1.6\n2017-06-13 14:20:00,1.8\n2017-06-13 14:25:00,2.0\n2017-06-13 14:30:00,2.2\n2017-06-13 14:35:00,2.4\n2017-06-13 14:40:00,2.6\n2017-06-13 14:45:00,2.8\n2017-06-13 14:50:00,3.1\n2017-06-13 14:55:00,3.5\n2017-06-13 15:00:00,4.0\n2017-06-13 15:05:00,4.3\n2017-06-13 15:10:00,4.9\n2017-06-13 15:15:00,5.4\n2017-06-13 15:20:00,6.0\n2017-06-13 15:25:00,6.5\n2017-06-13 15:30:00,6.9\n2017-06-13 15:35:00,7.5\n2017-06-13 15:40:00,8.1\n2017-06-13 15:45:00,8.6\n2017-06-13 15:50:00,9.2\n2017-06-13 15:55:00,9.8\n2017-06-13 16:00:00,10.5\n2017-06-13 16:05:00,11.2\n2017-06-13 16:10:00,11.8\n2017-06-13 16:15:00,12.1\n2017-06-13 16:20:00,12.7\n2017-06-13 16:25:00,13.2\n2017-06-13 16:30:00,13.7\n2017-06-13 16:35:00,14.3\n2017-06-13 16:40:00,14.6\n2017-06-13 16:45:00,15.0\n2017-06-13 16:50:00,15.8\n2017-06-13 16:55:00,16.6\n2017-06-13 17:00:00,17.0\n2017-06-13 17:05:00,17.3\n2017-06-13 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\"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame({\\n\",\n \" 'ds': pd.date_range(start='2020-01-01', periods=20),\\n\",\n \" 'y': np.arange(20),\\n\",\n \"})\\n\",\n \"m = Prophet(weekly_seasonality=False)\\n\",\n \"m.fit(df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\",\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: n.changepoints greater than number of observations. Using 15\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\\n\",\n \"df <- data.frame(\\n\",\n \" ds=seq(as.Date(\\\"2020-01-01\\\"), by = \\\"day\\\", length.out = 20),\\n\",\n \" y=1:20\\n\",\n \")\\n\",\n \"m <- prophet(df, weekly.seasonality=FALSE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Saving models\\n\",\n \"\\n\",\n \"It is possible to save fitted Prophet models so that they can be loaded and used later.\\n\",\n \"\\n\",\n \"In R, this is done with `saveRDS` and `readRDS`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"saveRDS(m, file=\\\"model.RDS\\\") # Save model\\n\",\n \"m <- readRDS(file=\\\"model.RDS\\\") # Load model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In Python, models should not be saved with pickle; the Stan backend attached to the model object will not pickle well, and will produce issues under certain versions of Python. Instead, you should use the built-in serialization functions to serialize the model to json:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from prophet.serialize import model_to_json, model_from_json\\n\",\n \"\\n\",\n \"with open('serialized_model.json', 'w') as fout:\\n\",\n \" fout.write(model_to_json(m)) # Save model\\n\",\n \"\\n\",\n \"with open('serialized_model.json', 'r') as fin:\\n\",\n \" m = model_from_json(fin.read()) # Load model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The json file will be portable across systems, and deserialization is backwards compatible with older versions of prophet.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Flat trend\\n\",\n \"\\n\",\n \"For time series that exhibit strong seasonality patterns rather than trend changes, or when we want to rely on the pattern of exogenous regressors (e.g. for causal inference with time series), it may be useful to force the trend growth rate to be flat. This can be achieved simply by passing `growth='flat'` when creating the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: n.changepoints greater than number of observations. Using 15\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df, growth='flat')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"m = Prophet(growth='flat')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is a comparison of counterfactual forecasting with exogenous regressors using linear versus flat growth.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from prophet import Prophet\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import warnings\\n\",\n \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n \"\\n\",\n \"regressor = \\\"location_4\\\"\\n\",\n \"target = \\\"location_41\\\"\\n\",\n \"cutoff = pd.to_datetime(\\\"2023-04-17 00:00:00\\\")\\n\",\n \"\\n\",\n \"df = (\\n\",\n \" pd.read_csv(\\n\",\n \" \\\"https://raw.githubusercontent.com/facebook/prophet/main/examples/example_pedestrians_multivariate.csv\\\", \\n\",\n \" parse_dates=[\\\"ds\\\"]\\n\",\n \" )\\n\",\n \" .rename(columns={target: \\\"y\\\"})\\n\",\n \")\\n\",\n \"train = df.loc[df[\\\"ds\\\"] < cutoff]\\n\",\n \"test = df.loc[df[\\\"ds\\\"] >= cutoff]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fit_model(growth):\\n\",\n \" m = Prophet(growth=growth, seasonality_mode=\\\"multiplicative\\\", daily_seasonality=15)\\n\",\n \" m.add_regressor(\\\"location_4\\\", mode=\\\"multiplicative\\\")\\n\",\n \" m.fit(train)\\n\",\n \" preds = pd.merge(\\n\",\n \" test,\\n\",\n \" m.predict(test),\\n\",\n \" on=\\\"ds\\\",\\n\",\n \" how=\\\"inner\\\"\\n\",\n \" )\\n\",\n \" mape = ((preds[\\\"yhat\\\"] - preds[\\\"y\\\"]).abs() / preds_linear[\\\"y\\\"]).mean()\\n\",\n \" return m, preds, mape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"01:19:58 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"01:19:58 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m_linear, preds_linear, mape_linear = fit_model(\\\"linear\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"01:19:58 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"01:19:58 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m_flat, preds_flat, mape_flat = fit_model(\\\"flat\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m_linear.plot_components(preds_linear);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m_flat.plot_components(preds_flat);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 124,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(11, 5))\\n\",\n \"ax.scatter(preds_linear[\\\"ds\\\"], preds_linear[\\\"y\\\"], color=\\\"black\\\", marker=\\\".\\\")\\n\",\n \"ax.plot(preds_linear[\\\"ds\\\"], preds_linear[\\\"yhat\\\"], label=f\\\"linear, mape={mape_linear:.1%}\\\")\\n\",\n \"ax.plot(preds_flat[\\\"ds\\\"], preds_flat[\\\"yhat\\\"], label=f\\\"flat, mape={mape_flat:.1%}\\\")\\n\",\n \"plt.xticks(rotation=60)\\n\",\n \"ax.legend();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this example, the target sensor values can be mostly explained by the exogenous regressor (a nearby sensor). The model with linear growth assumes an increasing trend and this leads to larger and larger over-predictions in the test period, while the flat growth model mostly follows movements in the exogenous regressor and this results in a sizeable MAPE improvement.\\n\",\n \"\\n\",\n \"Note that forecasting with exogenous regressors is only effective when we can be confident in the future values of the regressor. The example above is relevant to causal inference using time series, where we want to understand what `y` would have looked like for a past time period, and hence the exogenous regressor values are known.\\n\",\n \"\\n\",\n \"In other cases -- where we don't have exogenous regressors or have to predict their future values -- if flat growth is used on a time series that doesn't have a constant trend, any trend will be fit with the noise term and so there will be high predictive uncertainty in the forecast.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"jp-MarkdownHeadingCollapsed\": true\n },\n \"source\": [\n \"### Custom trends\\n\",\n \"\\n\",\n \"To use a trend besides these three built-in trend functions (piecewise linear, piecewise logistic growth, and flat), you can download the source code from github, modify the trend function as desired in a local branch, and then install that local version. [This PR](https://github.com/facebook/prophet/pull/1466/files) provides a good illustration of what must be done to implement a custom trend, as does [this one](https://github.com/facebook/prophet/pull/1794) that implements a step function trend and [this one](https://github.com/facebook/prophet/pull/1778) for a new trend in R.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Updating fitted models\\n\",\n \"\\n\",\n \"A common setting for forecasting is fitting models that need to be updated as additional data come in. Prophet models can only be fit once, and a new model must be re-fit when new data become available. In most settings, model fitting is fast enough that there isn't any issue with re-fitting from scratch. However, it is possible to speed things up a little by warm-starting the fit from the model parameters of the earlier model. This code example shows how this can be done in Python:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.33 s ± 55.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\\n\",\n \"185 ms ± 4.46 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"def warm_start_params(m):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Retrieve parameters from a trained model in the format used to initialize a new Stan model.\\n\",\n \" Note that the new Stan model must have these same settings:\\n\",\n \" n_changepoints, seasonality features, mcmc sampling\\n\",\n \" for the retrieved parameters to be valid for the new model.\\n\",\n \"\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" m: A trained model of the Prophet class.\\n\",\n \"\\n\",\n \" Returns\\n\",\n \" -------\\n\",\n \" A Dictionary containing retrieved parameters of m.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" res = {}\\n\",\n \" for pname in ['k', 'm', 'sigma_obs']:\\n\",\n \" if m.mcmc_samples == 0:\\n\",\n \" res[pname] = m.params[pname][0][0]\\n\",\n \" else:\\n\",\n \" res[pname] = np.mean(m.params[pname])\\n\",\n \" for pname in ['delta', 'beta']:\\n\",\n \" if m.mcmc_samples == 0:\\n\",\n \" res[pname] = m.params[pname][0]\\n\",\n \" else:\\n\",\n \" res[pname] = np.mean(m.params[pname], axis=0)\\n\",\n \" return res\\n\",\n \"\\n\",\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"df1 = df.loc[df['ds'] < '2016-01-19', :] # All data except the last day\\n\",\n \"m1 = Prophet().fit(df1) # A model fit to all data except the last day\\n\",\n \"\\n\",\n \"\\n\",\n \"%timeit m2 = Prophet().fit(df) # Adding the last day, fitting from scratch\\n\",\n \"%timeit m2 = Prophet().fit(df, init=warm_start_params(m1)) # Adding the last day, warm-starting from m1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As can be seen, the parameters from the previous model are passed in to the fitting for the next with the kwarg `init`. In this case, model fitting was about 5x faster when using warm starting. The speedup will generally depend on how much the optimal model parameters have changed with the addition of the new data.\\n\",\n \"\\n\",\n \"There are few caveats that should be kept in mind when considering warm-starting. First, warm-starting may work well for small updates to the data (like the addition of one day in the example above) but can be worse than fitting from scratch if there are large changes to the data (i.e., a lot of days have been added). This is because when a large amount of history is added, the location of the changepoints will be very different between the two models, and so the parameters from the previous model may actually produce a bad trend initialization. Second, as a detail, the number of changepoints need to be consistent from one model to the next or else an error will be raised because the changepoint prior parameter `delta` will be the wrong size.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### minmax scaling (new in 1.1.5)\\n\",\n \"\\n\",\n \"Before model fitting, Prophet scales `y` by dividing by the maximum value in the history. For datasets with very large `y` values, the scaled `y` values may be compressed to a very small range (i.e. `[0.99999... - 1.0]`), which causes a bad fit. This can be fixed by setting `scaling='minmax'` in the Prophet constructor.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"large_y = pd.read_csv(\\n\",\n \" \\\"https://raw.githubusercontent.com/facebook/prophet/main/python/prophet/tests/data3.csv\\\", \\n\",\n \" parse_dates=[\\\"ds\\\"]\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"08:11:23 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"08:11:23 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m1 = Prophet(scaling=\\\"absmax\\\")\\n\",\n \"m1 = m1.fit(large_y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"08:11:29 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"08:11:29 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m2 = Prophet(scaling=\\\"minmax\\\")\\n\",\n \"m2 = m2.fit(large_y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 119,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m1.plot(m1.predict(large_y));\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 120,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m2.plot(m2.predict(large_y));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Inspecting transformed data (new in 1.1.5)\\n\",\n \"\\n\",\n \"For debugging, it's useful to understand how the raw data has been transformed before being passed to the stan fit routine. We can call the `.preprocess()` method to see all the inputs to stan, and `.calculate_init_params()` to see how the model parameters will be initialized.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"m = Prophet()\\n\",\n \"transformed = m.preprocess(df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0.746552\\n\",\n \"1 0.663171\\n\",\n \"2 0.637023\\n\",\n \"3 0.628367\\n\",\n \"4 0.614441\\n\",\n \"5 0.605884\\n\",\n \"6 0.654956\\n\",\n \"7 0.687273\\n\",\n \"8 0.652501\\n\",\n \"9 0.628148\\n\",\n \"Name: y_scaled, dtype: float64\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"transformed.y.head(n=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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    \"\n ],\n \"text/plain\": [\n \" yearly_delim_1 yearly_delim_2 yearly_delim_3 yearly_delim_4 \\\\\\n\",\n \"0 -0.377462 0.926025 -0.699079 0.715044 \\n\",\n \"1 -0.361478 0.932381 -0.674069 0.738668 \\n\",\n \"2 -0.345386 0.938461 -0.648262 0.761418 \\n\",\n \"3 -0.329192 0.944263 -0.621687 0.783266 \\n\",\n \"4 -0.312900 0.949786 -0.594376 0.804187 \\n\",\n \"5 -0.296516 0.955028 -0.566362 0.824157 \\n\",\n \"6 -0.280044 0.959987 -0.537677 0.843151 \\n\",\n \"7 -0.263489 0.964662 -0.508356 0.861147 \\n\",\n \"8 -0.246857 0.969052 -0.478434 0.878124 \\n\",\n \"9 -0.230151 0.973155 -0.447945 0.894061 \\n\",\n \"\\n\",\n \" yearly_delim_5 yearly_delim_6 yearly_delim_7 yearly_delim_8 \\\\\\n\",\n \"0 -0.917267 0.398272 -0.999745 0.022576 \\n\",\n \"1 -0.895501 0.445059 -0.995827 0.091261 \\n\",\n \"2 -0.871351 0.490660 -0.987196 0.159513 \\n\",\n \"3 -0.844881 0.534955 -0.973892 0.227011 \\n\",\n \"4 -0.816160 0.577825 -0.955979 0.293434 \\n\",\n \"5 -0.785267 0.619157 -0.933542 0.358468 \\n\",\n \"6 -0.752283 0.658840 -0.906686 0.421806 \\n\",\n \"7 -0.717295 0.696769 -0.875539 0.483147 \\n\",\n \"8 -0.680398 0.732843 -0.840248 0.542202 \\n\",\n \"9 -0.641689 0.766965 -0.800980 0.598691 \\n\",\n \"\\n\",\n \" yearly_delim_9 yearly_delim_10 ... yearly_delim_17 yearly_delim_18 \\\\\\n\",\n \"0 -0.934311 -0.356460 ... 0.335276 -0.942120 \\n\",\n \"1 -0.961479 -0.274879 ... 0.185987 -0.982552 \\n\",\n \"2 -0.981538 -0.191266 ... 0.032249 -0.999480 \\n\",\n \"3 -0.994341 -0.106239 ... -0.122261 -0.992498 \\n\",\n \"4 -0.999791 -0.020426 ... -0.273845 -0.961774 \\n\",\n \"5 -0.997850 0.065537 ... -0.418879 -0.908042 \\n\",\n \"6 -0.988531 0.151016 ... -0.553893 -0.832588 \\n\",\n \"7 -0.971904 0.235379 ... -0.675656 -0.737217 \\n\",\n \"8 -0.948090 0.318001 ... -0.781257 -0.624210 \\n\",\n \"9 -0.917267 0.398272 ... -0.868168 -0.496271 \\n\",\n \"\\n\",\n \" yearly_delim_19 yearly_delim_20 weekly_delim_1 weekly_delim_2 \\\\\\n\",\n \"0 0.666089 -0.745872 -4.338837e-01 -0.900969 \\n\",\n \"1 0.528581 -0.848883 -9.749279e-01 -0.222521 \\n\",\n \"2 0.375470 -0.926834 -7.818315e-01 0.623490 \\n\",\n \"3 0.211276 -0.977426 5.505235e-14 1.000000 \\n\",\n \"4 0.040844 -0.999166 7.818315e-01 0.623490 \\n\",\n \"5 -0.130793 -0.991410 9.749279e-01 -0.222521 \\n\",\n \"6 -0.298569 -0.954388 4.338837e-01 -0.900969 \\n\",\n \"7 -0.457531 -0.889193 -4.338837e-01 -0.900969 \\n\",\n \"8 -0.602988 -0.797750 -9.749279e-01 -0.222521 \\n\",\n \"9 -0.730644 -0.682758 -7.818315e-01 0.623490 \\n\",\n \"\\n\",\n \" weekly_delim_3 weekly_delim_4 weekly_delim_5 weekly_delim_6 \\n\",\n \"0 7.818315e-01 0.623490 -9.749279e-01 -0.222521 \\n\",\n \"1 4.338837e-01 -0.900969 7.818315e-01 0.623490 \\n\",\n \"2 -9.749279e-01 -0.222521 -4.338837e-01 -0.900969 \\n\",\n \"3 1.101047e-13 1.000000 1.984146e-12 1.000000 \\n\",\n \"4 9.749279e-01 -0.222521 4.338837e-01 -0.900969 \\n\",\n \"5 -4.338837e-01 -0.900969 -7.818315e-01 0.623490 \\n\",\n \"6 -7.818315e-01 0.623490 9.749279e-01 -0.222521 \\n\",\n \"7 7.818315e-01 0.623490 -9.749279e-01 -0.222521 \\n\",\n \"8 4.338837e-01 -0.900969 7.818315e-01 0.623490 \\n\",\n \"9 -9.749279e-01 -0.222521 -4.338837e-01 -0.900969 \\n\",\n \"\\n\",\n \"[10 rows x 26 columns]\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"transformed.X.head(n=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ModelParams(k=-0.05444079622224118, m=0.7465517318876905, delta=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\n \" 0., 0., 0., 0., 0., 0., 0., 0.]), beta=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n\",\n \" 0., 0., 0., 0., 0., 0., 0., 0., 0.]), sigma_obs=1.0)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m.calculate_initial_params(num_total_regressors=transformed.K)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### External references\\n\",\n \"\\n\",\n \"As we discuss in our [2023 blog post on the state of Prophet](https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138), we have no plans to further develop the underlying Prophet model. If you're looking for state-of-the-art forecasting accuracy, we recommend the following libraries:\\n\",\n \"\\n\",\n \"* [`statsforecast`](https://github.com/Nixtla/statsforecast), and other packages from the Nixtla group such as [`hierarchicalforecast`](https://github.com/Nixtla/hierarchicalforecast) and [`neuralforecast`](https://github.com/Nixtla/neuralforecast).\\n\",\n \"* [`NeuralProphet`](https://neuralprophet.com/), a Prophet-style model implemented in PyTorch, to be more adaptable and extensible.\\n\",\n \"\\n\",\n \"These github repositories provide examples of building on top of Prophet in ways that may be of broad interest:\\n\",\n \"\\n\",\n \"* [forecastr](https://github.com/garethcull/forecastr): A web app that provides a UI for Prophet.\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Edit Metadata\",\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.17\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"9f764ff66e3236555d51a00749f5293db82082e341e153963b8b2deea93b52fc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/diagnostics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"from prophet import Prophet\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"19:38:36 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:38:36 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n },\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"35e541741f9c4809af8a301e3ae44e7b\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \" 0%| | 0/3 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(facecolor='w', figsize=(10, 6))\\n\",\n \"ax = fig.add_subplot(111)\\n\",\n \"ax.plot(m.history['ds'].values, m.history['y'], 'k.')\\n\",\n \"ax.plot(df_cv['ds'].values, df_cv['yhat'], ls='-', c='#0072B2')\\n\",\n \"ax.fill_between(df_cv['ds'].values, df_cv['yhat_lower'],\\n\",\n \" df_cv['yhat_upper'], color='#0072B2',\\n\",\n \" alpha=0.2)\\n\",\n \"ax.axvline(x=pd.to_datetime(cutoff), c='gray', lw=4, alpha=0.5)\\n\",\n \"ax.set_ylabel('y')\\n\",\n \"ax.set_xlabel('ds')\\n\",\n \"ax.text(x=pd.to_datetime('2010-01-01'),y=12, s='Initial', color='black',\\n\",\n \" fontsize=16, fontweight='bold', alpha=0.8)\\n\",\n \"ax.text(x=pd.to_datetime('2012-08-01'),y=12, s='Cutoff', color='black',\\n\",\n \" fontsize=16, fontweight='bold', alpha=0.8)\\n\",\n \"ax.axvline(x=pd.to_datetime(cutoff) + pd.Timedelta('365 days'), c='gray', lw=4,\\n\",\n \" alpha=0.5, ls='--')\\n\",\n \"ax.text(x=pd.to_datetime('2013-01-01'),y=6, s='Horizon', color='black',\\n\",\n \" fontsize=16, fontweight='bold', alpha=0.8);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[The Prophet paper](https://peerj.com/preprints/3190.pdf) gives further description of simulated historical forecasts.\\n\",\n \"\\n\",\n \"This cross validation procedure can be done automatically for a range of historical cutoffs using the `cross_validation` function. We specify the forecast horizon (`horizon`), and then optionally the size of the initial training period (`initial`) and the spacing between cutoff dates (`period`). By default, the initial training period is set to three times the horizon, and cutoffs are made every half a horizon.\\n\",\n \"\\n\",\n \"The output of `cross_validation` is a dataframe with the true values `y` and the out-of-sample forecast values `yhat`, at each simulated forecast date and for each cutoff date. In particular, a forecast is made for every observed point between `cutoff` and `cutoff + horizon`. This dataframe can then be used to compute error measures of `yhat` vs. `y`.\\n\",\n \"\\n\",\n \"Here we do cross-validation to assess prediction performance on a horizon of 365 days, starting with 730 days of training data in the first cutoff and then making predictions every 180 days. On this 8 year time series, this corresponds to 11 total forecasts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" y ds yhat yhat_lower yhat_upper cutoff\\n\",\n \"1 8.242493 2010-02-16 8.958128 8.498452 9.455592 2010-02-15\\n\",\n \"2 8.008033 2010-02-17 8.724605 8.226165 9.226773 2010-02-15\\n\",\n \"3 8.045268 2010-02-18 8.608332 8.079648 9.108332 2010-02-15\\n\",\n \"4 7.928766 2010-02-19 8.530238 8.038721 9.024928 2010-02-15\\n\",\n \"5 7.745003 2010-02-20 8.272207 7.761112 8.778963 2010-02-15\\n\",\n \"6 7.866339 2010-02-21 8.603539 8.127878 9.087186 2010-02-15\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Making 11 forecasts with cutoffs between 2010-02-15 and 2015-01-20\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"df.cv <- cross_validation(m, initial = 730, period = 180, horizon = 365, units = 'days')\\n\",\n \"head(df.cv)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"5adcf2eb7cd94e14a4ba3d84d414f595\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \" 0%| | 0/11 [00:00\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    dsyhatyhat_loweryhat_upperycutoff
    02010-02-168.9545828.4628769.4523058.2424932010-02-15
    12010-02-178.7209328.2226829.2427888.0080332010-02-15
    22010-02-188.6046088.0669209.1449688.0452682010-02-15
    32010-02-198.5263798.0291899.0430457.9287662010-02-15
    42010-02-208.2682477.7495208.7418477.7450032010-02-15
    \\n\",\n \"\"\n ],\n \"text/plain\": [\n \" ds yhat yhat_lower yhat_upper y cutoff\\n\",\n \"0 2010-02-16 8.954582 8.462876 9.452305 8.242493 2010-02-15\\n\",\n \"1 2010-02-17 8.720932 8.222682 9.242788 8.008033 2010-02-15\\n\",\n \"2 2010-02-18 8.604608 8.066920 9.144968 8.045268 2010-02-15\\n\",\n \"3 2010-02-19 8.526379 8.029189 9.043045 7.928766 2010-02-15\\n\",\n \"4 2010-02-20 8.268247 7.749520 8.741847 7.745003 2010-02-15\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_cv.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In R, the argument `units` must be a type accepted by `as.difftime`, which is weeks or shorter. In Python, the string for `initial`, `period`, and `horizon` should be in the format used by Pandas Timedelta, which accepts units of days or shorter.\\n\",\n \"\\n\",\n \"Custom cutoffs can also be supplied as a list of dates to the `cutoffs` keyword in the `cross_validation` function in Python and R. For example, three cutoffs six months apart, would need to be passed to the `cutoffs` argument in a date format like:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"cutoffs <- as.Date(c('2013-02-15', '2013-08-15', '2014-02-15'))\\n\",\n \"df.cv2 <- cross_validation(m, cutoffs = cutoffs, horizon = 365, units = 'days')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"49bd5a3e78c04c848a47966f18a69fbf\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \" 0%| | 0/3 [00:00\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    horizonmsermsemaemapemdapesmapecoverage
    037 days0.4933580.7023950.5039770.0583760.0493650.0586770.676565
    138 days0.4991120.7064780.5089460.0589510.0491350.0593120.675423
    239 days0.5213440.7220420.5150160.0595470.0492250.0600340.672682
    340 days0.5286510.7270840.5178730.0598520.0490720.0604090.676336
    441 days0.5361490.7322220.5188430.0599270.0491350.0605480.681361
    \\n\",\n \"\"\n ],\n \"text/plain\": [\n \" horizon mse rmse mae mape mdape smape \\n\",\n \"0 37 days 0.493358 0.702395 0.503977 0.058376 0.049365 0.058677 \\\\\\n\",\n \"1 38 days 0.499112 0.706478 0.508946 0.058951 0.049135 0.059312 \\n\",\n \"2 39 days 0.521344 0.722042 0.515016 0.059547 0.049225 0.060034 \\n\",\n \"3 40 days 0.528651 0.727084 0.517873 0.059852 0.049072 0.060409 \\n\",\n \"4 41 days 0.536149 0.732222 0.518843 0.059927 0.049135 0.060548 \\n\",\n \"\\n\",\n \" coverage \\n\",\n \"0 0.676565 \\n\",\n \"1 0.675423 \\n\",\n \"2 0.672682 \\n\",\n \"3 0.676336 \\n\",\n \"4 0.681361 \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from prophet.diagnostics import performance_metrics\\n\",\n \"df_p = performance_metrics(df_cv)\\n\",\n \"df_p.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    horizonmase
    037 days0.522946
    138 days0.528102
    239 days0.534401
    340 days0.537365
    441 days0.538372
    \\n\",\n \"
    \"\n ],\n \"text/plain\": [\n \" horizon mase\\n\",\n \"0 37 days 0.522946\\n\",\n \"1 38 days 0.528102\\n\",\n \"2 39 days 0.534401\\n\",\n \"3 40 days 0.537365\\n\",\n \"4 41 days 0.538372\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from prophet.diagnostics import register_performance_metric, rolling_mean_by_h\\n\",\n \"import numpy as np\\n\",\n \"@register_performance_metric\\n\",\n \"def mase(df, w):\\n\",\n \" \\\"\\\"\\\"Mean absolute scale error\\n\",\n \"\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" df: Cross-validation results dataframe.\\n\",\n \" w: Aggregation window size.\\n\",\n \"\\n\",\n \" Returns\\n\",\n \" -------\\n\",\n \" Dataframe with columns horizon and mase.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" e = (df['y'] - df['yhat'])\\n\",\n \" d = np.abs(np.diff(df['y'])).sum()/(df['y'].shape[0]-1)\\n\",\n \" se = np.abs(e/d)\\n\",\n \" if w < 0:\\n\",\n \" return pd.DataFrame({'horizon': df['horizon'], 'mase': se})\\n\",\n \" return rolling_mean_by_h(\\n\",\n \" x=se.values, h=df['horizon'].values, w=w, name='mase'\\n\",\n \" )\\n\",\n \"\\n\",\n \"df_mase = performance_metrics(df_cv, metrics=['mase'])\\n\",\n \"df_mase.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Cross validation performance metrics can be visualized with `plot_cross_validation_metric`, here shown for MAPE. Dots show the absolute percent error for each prediction in `df_cv`. The blue line shows the MAPE, where the mean is taken over a rolling window of the dots. We see for this forecast that errors around 5% are typical for predictions one month into the future, and that errors increase up to around 11% for predictions that are a year out.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"plot_cross_validation_metric(df.cv, metric = 'mape')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from prophet.plot import plot_cross_validation_metric\\n\",\n \"fig = plot_cross_validation_metric(df_cv, metric='mape')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The size of the rolling window in the figure can be changed with the optional argument `rolling_window`, which specifies the proportion of forecasts to use in each rolling window. The default is 0.1, corresponding to 10% of rows from `df_cv` included in each window; increasing this will lead to a smoother average curve in the figure. The `initial` period should be long enough to capture all of the components of the model, in particular seasonalities and extra regressors: at least a year for yearly seasonality, at least a week for weekly seasonality, etc.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Parallelizing cross validation\\n\",\n \"\\n\",\n \"Cross-validation can also be run in parallel mode in Python, by setting specifying the `parallel` keyword. Four modes are supported\\n\",\n \"\\n\",\n \"* `parallel=None` (Default, no parallelization)\\n\",\n \"* `parallel=\\\"processes\\\"`\\n\",\n \"* `parallel=\\\"threads\\\"`\\n\",\n \"* `parallel=\\\"dask\\\"`\\n\",\n \"\\n\",\n \"For problems that aren't too big, we recommend using `parallel=\\\"processes\\\"`. It will achieve the highest performance when the parallel cross validation can be done on a single machine. For large problems, a [Dask](https://dask.org) cluster can be used to do the cross validation on many machines. You will need to [install Dask](https://docs.dask.org/en/latest/install.html) separately, as it will not be installed with `prophet`.\\n\",\n \"\\n\",\n \"\\n\",\n \"```python\\n\",\n \"from dask.distributed import Client\\n\",\n \"\\n\",\n \"client = Client() # connect to the cluster\\n\",\n \"df_cv = cross_validation(m, initial='730 days', period='180 days', horizon='365 days',\\n\",\n \" parallel=\\\"dask\\\")\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Hyperparameter tuning\\n\",\n \"\\n\",\n \"Cross-validation can be used for tuning hyperparameters of the model, such as `changepoint_prior_scale` and `seasonality_prior_scale`. A Python example is given below, with a 4x4 grid of those two parameters, with parallelization over cutoffs. Here parameters are evaluated on RMSE averaged over a 30-day horizon, but different performance metrics may be appropriate for different problems.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"19:39:23 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:23 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:24 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:24 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:24 - 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cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:40 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:41 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:42 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:42 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:42 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:42 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:43 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:43 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:43 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:43 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:44 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:44 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:44 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:44 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"19:39:45 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:45 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:45 - cmdstanpy - INFO - Chain [1] done processing\\n\",\n \"19:39:45 - cmdstanpy - INFO - Chain [1] start processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"import itertools\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"param_grid = { \\n\",\n \" 'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],\\n\",\n \" 'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0],\\n\",\n \"}\\n\",\n \"\\n\",\n \"# Generate all combinations of parameters\\n\",\n \"all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]\\n\",\n \"rmses = [] # Store the RMSEs for each params here\\n\",\n \"\\n\",\n \"# Use cross validation to evaluate all parameters\\n\",\n \"for params in all_params:\\n\",\n \" m = Prophet(**params).fit(df) # Fit model with given params\\n\",\n \" df_cv = cross_validation(m, cutoffs=cutoffs, horizon='30 days', parallel=\\\"processes\\\")\\n\",\n \" df_p = performance_metrics(df_cv, rolling_window=1)\\n\",\n \" rmses.append(df_p['rmse'].values[0])\\n\",\n \"\\n\",\n \"# Find the best parameters\\n\",\n \"tuning_results = pd.DataFrame(all_params)\\n\",\n \"tuning_results['rmse'] = rmses\\n\",\n \"print(tuning_results)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"best_params = all_params[np.argmin(rmses)]\\n\",\n \"print(best_params)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, parallelization could be done across parameter combinations by parallelizing the loop above.\\n\",\n \"\\n\",\n \"The Prophet model has a number of input parameters that one might consider tuning. Here are some general recommendations for hyperparameter tuning that may be a good starting place.\\n\",\n \"\\n\",\n \"**Parameters that can be tuned**\\n\",\n \"- `changepoint_prior_scale`: This is probably the most impactful parameter. It determines the flexibility of the trend, and in particular how much the trend changes at the trend changepoints. As described in this documentation, if it is too small, the trend will be underfit and variance that should have been modeled with trend changes will instead end up being handled with the noise term. If it is too large, the trend will overfit and in the most extreme case you can end up with the trend capturing yearly seasonality. The default of 0.05 works for many time series, but this could be tuned; a range of [0.001, 0.5] would likely be about right. Parameters like this (regularization penalties; this is effectively a lasso penalty) are often tuned on a log scale.\\n\",\n \"\\n\",\n \"- `seasonality_prior_scale`: This parameter controls the flexibility of the seasonality. Similarly, a large value allows the seasonality to fit large fluctuations, a small value shrinks the magnitude of the seasonality. The default is 10., which applies basically no regularization. That is because we very rarely see overfitting here (there's inherent regularization with the fact that it is being modeled with a truncated Fourier series, so it's essentially low-pass filtered). A reasonable range for tuning it would probably be [0.01, 10]; when set to 0.01 you should find that the magnitude of seasonality is forced to be very small. This likely also makes sense on a log scale, since it is effectively an L2 penalty like in ridge regression.\\n\",\n \"\\n\",\n \"- `holidays_prior_scale`: This controls flexibility to fit holiday effects. Similar to seasonality_prior_scale, it defaults to 10.0 which applies basically no regularization, since we usually have multiple observations of holidays and can do a good job of estimating their effects. This could also be tuned on a range of [0.01, 10] as with seasonality_prior_scale.\\n\",\n \"\\n\",\n \"- `seasonality_mode`: Options are [`'additive'`, `'multiplicative'`]. Default is `'additive'`, but many business time series will have multiplicative seasonality. This is best identified just from looking at the time series and seeing if the magnitude of seasonal fluctuations grows with the magnitude of the time series (see the documentation here on multiplicative seasonality), but when that isn't possible, it could be tuned.\\n\",\n \"\\n\",\n \"**Maybe tune?**\\n\",\n \"- `changepoint_range`: This is the proportion of the history in which the trend is allowed to change. This defaults to 0.8, 80% of the history, meaning the model will not fit any trend changes in the last 20% of the time series. This is fairly conservative, to avoid overfitting to trend changes at the very end of the time series where there isn't enough runway left to fit it well. With a human in the loop, this is something that can be identified pretty easily visually: one can pretty clearly see if the forecast is doing a bad job in the last 20%. In a fully-automated setting, it may be beneficial to be less conservative. It likely will not be possible to tune this parameter effectively with cross validation over cutoffs as described above. The ability of the model to generalize from a trend change in the last 10% of the time series will be hard to learn from looking at earlier cutoffs that may not have trend changes in the last 10%. So, this parameter is probably better not tuned, except perhaps over a large number of time series. In that setting, [0.8, 0.95] may be a reasonable range.\\n\",\n \"\\n\",\n \"**Parameters that would likely not be tuned**\\n\",\n \"- `growth`: Options are 'linear' and 'logistic'. This likely will not be tuned; if there is a known saturating point and growth towards that point it will be included and the logistic trend will be used, otherwise it will be linear.\\n\",\n \"\\n\",\n \"- `changepoints`: This is for manually specifying the locations of changepoints. None by default, which automatically places them.\\n\",\n \"\\n\",\n \"- `n_changepoints`: This is the number of automatically placed changepoints. The default of 25 should be plenty to capture the trend changes in a typical time series (at least the type that Prophet would work well on anyway). Rather than increasing or decreasing the number of changepoints, it will likely be more effective to focus on increasing or decreasing the flexibility at those trend changes, which is done with `changepoint_prior_scale`.\\n\",\n \"\\n\",\n \"- `yearly_seasonality`: By default ('auto') this will turn yearly seasonality on if there is a year of data, and off otherwise. Options are ['auto', True, False]. If there is more than a year of data, rather than trying to turn this off during HPO, it will likely be more effective to leave it on and turn down seasonal effects by tuning `seasonality_prior_scale`.\\n\",\n \"\\n\",\n \"- `weekly_seasonality`: Same as for `yearly_seasonality`.\\n\",\n \"\\n\",\n \"- `daily_seasonality`: Same as for `yearly_seasonality`.\\n\",\n \"\\n\",\n \"- `holidays`: This is to pass in a dataframe of specified holidays. The holiday effects would be tuned with `holidays_prior_scale`.\\n\",\n \"\\n\",\n \"- `mcmc_samples`: Whether or not MCMC is used will likely be determined by factors like the length of the time series and the importance of parameter uncertainty (these considerations are described in the documentation).\\n\",\n \"\\n\",\n \"- `interval_width`: Prophet `predict` returns uncertainty intervals for each component, like `yhat_lower` and `yhat_upper` for the forecast `yhat`. These are computed as quantiles of the posterior predictive distribution, and `interval_width` specifies which quantiles to use. The default of 0.8 provides an 80% prediction interval. You could change that to 0.95 if you wanted a 95% interval. This will affect only the uncertainty interval, and will not change the forecast `yhat` at all and so does not need to be tuned.\\n\",\n \"\\n\",\n \"- `uncertainty_samples`: The uncertainty intervals are computed as quantiles from the posterior predictive interval, and the posterior predictive interval is estimated with Monte Carlo sampling. This parameter is the number of samples to use (defaults to 1000). The running time for predict will be linear in this number. Making it smaller will increase the variance (Monte Carlo error) of the uncertainty interval, and making it larger will reduce that variance. So, if the uncertainty estimates seem jagged this could be increased to further smooth them out, but it likely will not need to be changed. As with `interval_width`, this parameter only affects the uncertainty intervals and changing it will not affect in any way the forecast `yhat`; it does not need to be tuned.\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Edit Metadata\",\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/handling_shocks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"0893f116\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"from prophet import Prophet\\n\",\n \"import pandas as pd\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import logging\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"plt.rcParams['figure.figsize'] = 9, 6\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f84512d1\",\n \"metadata\": {},\n \"source\": [\n \"As a result of the lockdowns caused by the COVID-19 pandemic, many time series experienced \\\"shocks\\\" during 2020, e.g. spikes in media consumption (Netflix, YouTube), e-commerce transactions (Amazon, eBay), whilst attendance to in-person events declined dramatically.\\n\",\n \"\\n\",\n \"Most of these time series would also maintain their new level for a period of time, subject to fluctuations driven by easing of lockdowns and/or vaccines.\\n\",\n \"\\n\",\n \"Seasonal patterns could have also changed: for example, people may have consumed less media (in total hours) on weekdays compared to weekends before the COVID lockdowns, but during lockdowns weekday consumption could be much closer to weekend consumption.\\n\",\n \"\\n\",\n \"In this page we'll explore some strategies for capturing these effects using Prophet's functionality:\\n\",\n \"\\n\",\n \"1. Marking step changes / spikes due to COVID events as once-off.\\n\",\n \"2. Sustained changes in behaviour leading to trend and seasonality changes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"36216eb9\",\n \"metadata\": {},\n \"source\": [\n \"#### Case Study - Pedestrian Activity\\n\",\n \"\\n\",\n \"For this case study we'll use [Pedestrian Sensor data from the City of Melbourne](https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Monthly-counts-per-hour/b2ak-trbp). This data measures foot traffic from sensors in various places in the central business district, and we've chosen one sensor (`Sensor_ID = 4`) and aggregated the values to a daily grain. \\n\",\n \"\\n\",\n \"The aggregated dataset can be found in the examples folder [here](https://github.com/facebook/prophet/tree/master/examples/example_pedestrians_covid.csv).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"ac3aa527\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_pedestrians_covid.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"284f936b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df.set_index('ds').plot();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d6ed9e13\",\n \"metadata\": {},\n \"source\": [\n \"We can see two key events in the time series:\\n\",\n \"\\n\",\n \"* The initial drop in foot traffic around 21 March 2020, which started to recover around 6 June 2020. This corresponds to the declaration of a pandemic by WHO and subsequent lockdowns mandated by the Victorian government.\\n\",\n \"* After some slow recovery, a second drop in foot traffic around July 9 2020, which began to recover around 27 October 2020. This corresponds to the \\\"second wave\\\" of the pandemic in metropolitan Melbourne.\\n\",\n \"\\n\",\n \"There are also shorter periods of strict lockdown that lead to sudden tips in the time series: 5 days in February 2021, and 14 days in early June 2021.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ea888620\",\n \"metadata\": {},\n \"source\": [\n \"#### Default model without any adjustments\\n\",\n \"\\n\",\n \"First we'll fit a model with the default Prophet settings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"f94487ef\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"02:53:41 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"02:53:41 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m = Prophet()\\n\",\n \"m = m.fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=366)\\n\",\n \"forecast = m.predict(future)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"ef22e986\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m.plot(forecast)\\n\",\n \"plt.axhline(y=0, color='red')\\n\",\n \"plt.title('Default Prophet');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"bd0a2b76\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m.plot_components(forecast);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"709a0e93\",\n \"metadata\": {},\n \"source\": [\n \"The model seems to fit reasonably to past data, but notice how we're capturing the dips, and the spikes after the dips, as a part of the trend component. \\n\",\n \"\\n\",\n \"By default, the model assumes that these large spikes are possible in the future, even though we realistically won't see something of the same magnitude within our forecast horizon (1 year in this case). This leads to a fairly optimistic forecast of the recovery of foot traffic in 2022.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ff5f2833\",\n \"metadata\": {},\n \"source\": [\n \"### Treating COVID-19 lockdowns as a one-off holidays\\n\",\n \"\\n\",\n \"To prevent large dips and spikes from being captured by the trend component, we can treat the days impacted by COVID-19 as holidays that will not repeat again in the future. Adding custom holidays is explained in more detail [here](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#modeling-holidays-and-special-events). We set up a DataFrame like so to describe the periods affected by lockdowns:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"e566cf71\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    holidaydslower_windowds_upperupper_window
    0lockdown_12020-03-2102020-06-0677
    1lockdown_22020-07-0902020-10-27110
    2lockdown_32021-02-1302021-02-174
    3lockdown_42021-05-2802021-06-1013
    \\n\",\n \"
    \"\n ],\n \"text/plain\": [\n \" holiday ds lower_window ds_upper upper_window\\n\",\n \"0 lockdown_1 2020-03-21 0 2020-06-06 77\\n\",\n \"1 lockdown_2 2020-07-09 0 2020-10-27 110\\n\",\n \"2 lockdown_3 2021-02-13 0 2021-02-17 4\\n\",\n \"3 lockdown_4 2021-05-28 0 2021-06-10 13\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lockdowns = pd.DataFrame([\\n\",\n \" {'holiday': 'lockdown_1', 'ds': '2020-03-21', 'lower_window': 0, 'ds_upper': '2020-06-06'},\\n\",\n \" {'holiday': 'lockdown_2', 'ds': '2020-07-09', 'lower_window': 0, 'ds_upper': '2020-10-27'},\\n\",\n \" {'holiday': 'lockdown_3', 'ds': '2021-02-13', 'lower_window': 0, 'ds_upper': '2021-02-17'},\\n\",\n \" {'holiday': 'lockdown_4', 'ds': '2021-05-28', 'lower_window': 0, 'ds_upper': '2021-06-10'},\\n\",\n \"])\\n\",\n \"for t_col in ['ds', 'ds_upper']:\\n\",\n \" lockdowns[t_col] = pd.to_datetime(lockdowns[t_col])\\n\",\n \"lockdowns['upper_window'] = (lockdowns['ds_upper'] - lockdowns['ds']).dt.days\\n\",\n \"lockdowns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"338f8e50\",\n \"metadata\": {},\n \"source\": [\n \"* We have an entry for each lockdown period, with `ds` specifying the start of the lockdown. `ds_upper` is not used by Prophet, but it's a convenient way for us to calculate `upper_window`.\\n\",\n \"* `upper_window` tells Prophet that the lockdown spans for x days after the start of the lockdown. Note that the holidays regression is inclusive of the upper bound.\\n\",\n \"\\n\",\n \"Note that since we don't specify any future dates, Prophet will assume that these holidays will _not_ occur again when creating the future dataframe (and hence they won't affect our projections). This is different to how we would specify a recurring holiday.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"61ac656c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"02:53:44 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"02:53:45 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m2 = Prophet(holidays=lockdowns)\\n\",\n \"m2 = m2.fit(df)\\n\",\n \"future2 = m2.make_future_dataframe(periods=366)\\n\",\n \"forecast2 = m2.predict(future2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"6ea726f7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m2.plot(forecast2)\\n\",\n \"plt.axhline(y=0, color='red')\\n\",\n \"plt.title('Lockdowns as one-off holidays');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"27f83233\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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MFPxERj/GrMwcQGmTjwQ17zC5FxGvtKT6Es8HwytuxtYWCn4iIh+gWaOPGcwby4ffFfLb3oNnliHid3NJqvi+uItyHJnMcS8FPRMSD/PbcQQTZ/Lj/vUyzSxHxKgXlNWzPK6dnsD9+Fu9epPlEFPxERDxIj64BXDexP+u+LeKbggqzyxHxCoXlNWzLLaNHsLkzeEur68kurTbt+KDgJyLicW47z44FuPfd78wuRaTTKyyvYWtuGRFd/PE3KfQ5Gwxe3pHP5f/awoPv78EwzLtTj/ffm0REpJPpF96Fn42O4oUv88kprSImLNjskkQ6pQOVtWzNKSMi2LzQt7Ogggfe301GYSVjY0K5bkJ/LCYONavHT0TEA/1hShx1jgbueVu9fiKnovhQ4wLNYSb19JVW1/OX9Zlcu3o7hRW13PfTeJ6aNZzYCHP/kFOPn4iIB0ro052LhvbhhS/zeejioYT7yH1ERVwht7SaHfnlpizQ3GAYvLazkH98kkVlrYOfjY4mZWJ/ugV6RuRSj5+IiIe6KzmOiloH96/XDF+RtsopqebLvHIiurg/9O3aX8mC//uS+97LZGBEMP++cgw3TxrkMaEP1OMnIuKxxvcP5ycDI0jbksP/+2k8XQL0I1vkRPaVVPFVfgU93Tx7t7ymnif/9wMv7cgnPNife6cOYfoZvU29lq816vETEfFgdyUP4cChOv760fdmlyLi0Yoqa/k6v4KeXQPcFvoaDIPXdxYyK20rL3+Vz5xRUbx8TRIXJfTxyNAHJge/mpoaxo8fz8iRI0lMTORPf/oTAFlZWUyYMAG73c7cuXOpq6sDoLa2lrlz52K325kwYQJ79+5t2tfSpUux2+3Ex8fz9ttvN21ft24d8fHx2O12li1b5tbzExE5XRcM6cmIyO6kfrYPh7PB7HJEPFJRZS1bsksJ7+KPzc89geu7okque3EH9777Hf3CgnjuZ6O57bzBdA/y7J55U4NfYGAgGzZs4Msvv2T79u2sW7eOjRs3smTJEm666SZ2795NeHg4K1asAGDFihWEh4eze/dubrrpJpYsWQJARkYGq1evZufOnaxbt44bbrgBp9OJ0+lk8eLFvPXWW2RkZPD888+TkZFh5imLiLSLxWLhruQh7Cut5umNP5hdjojHORL6woL8CXDDNX2VtQ4e/mAPV//3C34oqeLu5Dj+OWck8b27dfixXcHU4GexWOjWrfGNqq+vp76+HovFwoYNG5g9ezYA8+fPZ82aNQCkp6czf/58AGbPns369esxDIP09HTmzZtHYGAgAwcOxG63s3nzZjZv3ozdbmfQoEEEBAQwb9480tPTTTlXEZFTddnwSAZGBPP4J3tNXfhVxNMUVdbyeXYpoW4IfYZh8OY3hcxK28L/bc/j8uGRvDI/iRmJfTvVLeBM7490Op2MHTuW3bt3s3jxYgYPHkxYWBg2W2NpMTEx5ObmApCbm0u/fv0AsNlshIaGUlxcTG5uLhMnTmzaZ/PXHHn+ke2bNm06robU1FRSU1MBKCgoIC8vr2NO1kcVFRWZXYI0o/bwPG1pkxvG9uS2d/fx9PtfM+OMHm6oyrfp+8TzHNsmJdX1fFNQQbdAG9U1FjryRmjfl9Tyj8/389X+as7oEcT/mxTJkB5BNFQepLSyffuqrHaQl+fomELbwPTgZ7Va2b59O6WlpVx22WXs2rXL7TWkpKSQkpICQFJSElFRUW6vwdvpPfUsag/Pc7I2+U3vPjz0WQErvizh+vOHu6kq36bvE89zpE0OVtWxr6yEqKjuHbpkS2Wtg9SN+/i/7bl0C7Rx5wVxzEjsc1o9fI5DdURF9XZhle1jevA7IiwsjMmTJ/PZZ59RWlqKw+HAZrORk5NDdHQ0ANHR0WRnZxMTE4PD4aCsrIwePXo0bT+i+Wta2y4i0pkE2qzcdp6d297I4O1dhUw9o4/ZJYmY4mBVHZt/KCEkyNZhoc8wDN7+tohHP86i+FAdlw7ry+KzYwnr4t8hx3MnU6/xKyoqorS0FIDq6mreffddEhISmDx5Mi+99BIAaWlpzJw5E4AZM2aQlpYGwEsvvcT555+PxWJhxowZrF69mtraWrKyssjMzGT8+PGMGzeOzMxMsrKyqKurY/Xq1cyYMcOUcxUROV2/OnMAIUE2lm3YY3YpIqYoPlTHph9K6B5kI8hm7ZBjfF98iEUvf8Wd676lV9cAVs4bxR0XxHlF6AOTe/zy8/OZP38+TqeThoYG5syZw8UXX8zQoUOZN28ed955J6NHj2bhwoUALFy4kJ///OfY7XYiIiJYvXo1AImJicyZM4ehQ4dis9lYvnw5VmvjF8Tjjz/O1KlTcTqdLFiwgMTERNPOV0TkdHQPsvGbcwZy/3uZfL6vhHH9w80uScRtymvqySor6bDbsFXVOXlm0z7++0UuXf2t3H6+ncuG9cXqpuVh3MViaIrYUZKSktiyZYvZZXiVvLw8XSvjQdQenqc9bXKgspaY//ceyXE9ef2XEzq4Mt+l7xPPUnyojve3f0dUVJTLQ59hGLyXeYC/ffQ9+yvrmJnYh1+fHUt4cMfcH/vAoTqmntHx1/i1lmc85ho/ERE5uZ7dAlk4vj9Pb/yBb/dXEN+7u9kliXSo8pp6Pt9XQrdA11/Tt/dgFQ9+sIfN+0qJ79WVZdMTGBEV4tJjeBrdsk1EpJNZcv5gAO55+zuTKxHpWNX1TrZklxEcYMXf6roh1+p6J8s/3cu8f28jo6CC308ezKqfjfb60Afq8RMR6XT6hwczd2QUL3+VT15ZDVGhQWaXJOJy9c4GtuWUAQZdA/wpdcE+DcPg/T3FPPLh9xRW1HJxQm9+c85AenTtmGFdT6QePxGRTujO5DhqHQ3c+863Zpci4nKNoa+UqjoHoUGumU27r6SaG9fs5PdvfENIoI1/XjGCe6bG+1ToA/X4iYh0Sgl9unNRQm9Wf5HHAxcNJSzYO5aaEGloMNiRV05ZtcMloaym3snKz7NZtTWHAKsft04axOyRUdi8bLZuW6nHT0Skk7oreQjltQ7+sj7T7FJEXMIwDL4rOkRhZe1phz7DMPhwTzFzntvKis3ZXBDXi5fnJzFvdLTPhj5Qj5+ISKc1YUA4Z8eG86/Ps/nzT4cQ5K8f6dK5fV98iD3FlfTuFnha+8kprebhD7/nk6yDDOoRzNOzhzM2Jsw1RXZyp/VT4pJLLsFygvvVvfbaa6ezexEROYm7LxzC1NRN/O2jLP4wJc7sckRO2Q8Hq9i1vzH0neq9cGscTtI+zyFtSzY2Pz9+d+5A5o2MwmbVAOcRpxX8br31VgBeeeUVCgoKuPrqqwF4/vnn6dNH95EUEeloyUN6Maxvd5767Ad+P9nudXcZEN+QX1bN1wUV9Ox66qHvk6yDPPTBHnLLarhwSC9uOncgvU6z59AbnVbwmzRpEgC33HLLUatDX3LJJSQlJZ1eZSIiclIWi4W7LxzCnFVbSd24l0VnDTS7JJF2KT5Ux/a8cnoE+5/StXd5ZTU88uH3fPh9MbHhXXhy1nDG9QtzfaFewiUXhBw6dIjvv/+eQYMGAZCVlcWhQ4dcsWsRETmJy4dHEhvehX98vJfrz4w94SU4Ip7kYFUdn+9rvP+ufzuHY2sdDTy3NYeVm7Px84PfnBPLlaOj270fX+OS4Pe3v/2N8847j0GDBmEYBj/88ANPP/20K3YtIiInYfWz8McL4kh5cQcvfZnPFaN0j1nxfIdqHWzNLqV7UPtvxfbZ3hIe/GA32aU1XBDXk9+dO4i+3TWs2xYuCX4//elPyczMZNeuXQCcccYZBAaqAURE3OWapBjufGsXD32wW8FPPF51vZPN2aX4+/kRZLO2+XUF5TU88tH3vL+7mP7hXXj8smFMHBDegZV6H5fN/d+6dSt79+7F4XDw5ZdfAnDNNde4avciInICgTYrt543mN+/8Q3vfbefC4b0NrskkRbVOpxszS7FMAxC2nhXjnpnA89/fZD/fL0bA1h8VixXjYkmoJ09heKi4Pfzn/+cPXv2MGrUKKzWxuRusVgU/ERE3Oj6M2P5f+9m8pf3div4iUc6cv/dWoeTsC5tW6B5074SHnx/Dz+UVDN5cA9unjSIyBDdn/pUuST4bdmyhYyMDF1QLCJiou5BNn59dixLN+xmy75SkvqHmV2SSJNah5MvcsuoqGnbrdgKK2r520ff817mAWJCg/jL5GguHDnIDZV6N5f0kQ4bNoyCggJX7EpERE7DTZMGEWjz48/vfmd2KSJNauqdbN5XyqHak4e+emcDq7bkMHvVFj7+/iDXnzmA//v5WMZHd3VTtd7NJT1+Bw4cYOjQoYwfP/6oSR26c4eIiHv16hbIgnH9SN20j8yiSuJ6dTO7JPFx1fVOtmSXUudoOOnw7pbsUh54fzdZB6s5d1AEt0waTHRo47ButTuK9QEuCX733HOPK3YjIiIusOR8O6mb9vGnt7/lv1ePNbsc8WGHah1sPjyRI6xL6xM5iiprefTjLN7+tojokCD+NmMoPxnUw42V+g6XBL9Jkybxww8/kJmZyQUXXEBVVRVOp9MVuxYRkXYaEBHMnJGRvPpVAfnlNboQXkxRVl3P5n2lBFgtdGtl9q7D2cDqL/NI/WwfjoYGrpvQn/njYtq1xIu0j0uu8XvmmWeYPXs2v/rVrwDIzc3l0ksvdcWuRUTkFNxxwRBqHA38+R1d6yfuV15Tz6YfSuji70e3wJb7mLbllHHVf7/g0Y+yGB0dwgs/H8uvzhyg0NfBXBL8li9fzqeffkpISAgAcXFx7N+/3xW7FhGRU5DYtzvTzujN81/kUlZdb3Y54kN+DH1WuvgfH+IOHKrjrnXfkvLSDqrqnDx8yVAenZlITFgXE6r1PS4JfoGBgQQE/HjBpsPh0NIuIiImu/vCIZTVOFi6PtPsUsRHHKyqY+PeEoJsVoIDjg59jgaD1V/kMittC+9lFrFgfD9evGYs5w3uoczgRi67xu8vf/kL1dXVvPvuuzzxxBNccsklrti1iIicookDwjkrNpyVn2dz79R4AlvofRFxlaLKWrZklxESZD1uuHZ7XhkPbtjDdwcOMbF/GLdNHsyA8GCTKvVtLunxe+CBB+jVqxfDhw/n6aefZvr06dx3332u2LWIiJyGu5OHsL+yjkc//t7sUsSLFZbX8Hl2KWFBtqNC38GqOu595zt++cIOymrqeeCiBP5x2TCFPhOddo+f0+kkMTGRXbt2cd1117miJhERcZEL43uR2Kc7T/7vB249z47VT0Nq4lq5pdVszysnoot/071znQ0GL3+Vz5P/+4HqeifXJsWwcEL/Fq/5E/c67R4/q9VKfHw8+/bta9frsrOzmTx5MkOHDiUxMZHHHnsMaFwTMDo6mlGjRjFq1CjWrl3b9JqlS5dit9uJj4/n7bffbtq+bt064uPjsdvtLFu2rGl7VlYWEyZMwG63M3fuXOrq6k7zbEVEOheLxcJdyXH8UFLNs5vb93Na5GQaQ18ZPYN/DH1f5ZdzzfNf8OD7e0jo3Y3VV4/h1+cMVOjzEC65xq+kpITExETGjx9P164/3lLlRHfusNlsPPLII4wZM4aKigrGjh1LcnIyADfddBO33nrrUc/PyMhg9erV7Ny5k7y8PC644AK++65xmYLFixfz7rvvEhMTw7hx45gxYwZDhw5lyZIl3HTTTcybN4/rr7+eFStWsGjRIlecsohIpzF7ZBQD3vyGRz/6nl9O6K8L6eW0GYZBVnEV3+yvoGdwADarH6XV9Tz+6V7WfF1Ar64BLJ1+BhfE9dTXm4dxSfCrqanhjTfeaPrcMAyWLFlywtdERkYSGRkJQPfu3UlISCA3N7fV56enpzNv3jwCAwMZOHAgdrudzZs3A2C32xk0qPHGzfPmzSM9PZ2EhAQ2bNjAf//7XwDmz5/PPffco+AnIj7H6mfhD1PsXP/SV7zyVT6zRkSZXZJ0Ys4Gg137K/ihpJpeXRtv0/ryjnyWf7qXQ/VOfj42ml9O6E/XAJdEDHExl7SKw+Fg0qRJR22rrm77XfX27t3LF198wYQJE/j00095/PHHWbVqFUlJSTzyyCOEh4eTm5vLxIkTm14TExPTFBT79et31PZNmzZRXFxMWFgYNpvtuOcfKzU1ldTUVAAKCgrIy8trc+1yckVFRWaXIM2oPTyPO9rkwmgbPbrY+Mvb33Bmzw4/XKen75OW1TkayCw6RHltPWFB/nyeU8PfNxfy3cFaRvbpwm/GRRMbFkh9eTGlLj52ZelBF+/RHJXVDvLyHKYd/7SC35NPPskTTzzB999/z4gRI5q2V1RUcPbZZ7dpH5WVlcyaNYtHH32UkJAQFi1axF133dV4Xcpdd3HLLbfw7LPPnk6ZJ5WSkkJKSgoASUlJREXpr2FX03vqWdQenscdbXLb5GpuX7uLb6sCmGxX+jsZfZ8crbymni3ZZfiFBBJmwBP/28urXxXQo2sA9/00nqnxvTp8WDesZ58O3b87OA7VERXV27Tjn1bwu/LKK5k2bRp/+MMfjppU0b17dyIiIk76+vr6embNmsVVV13F5ZdfDkCfPj826nXXXcfFF18MQHR0NNnZ2U2P5eTkEB0dDdDi9h49elBaWorD4cBmsx31fBERX7To7FjuX7+b+979TsFP2mV/RQ3bcsro4m/lvcxi/vFJFpW1Dn42OpqUif1bvS2beJ7TmtUbGhpKbGwszz//PAMGDGj615bQZxgGCxcuJCEhgZtvvrlpe35+ftPHr776KsOGDQNgxowZrF69mtraWrKyssjMzGT8+PGMGzeOzMxMsrKyqKurY/Xq1cyYMQOLxcLkyZN56aWXAEhLS2PmzJmnc7oiIp1aSJA/N5w9gPd3F7Mtu9TscqST+OFgFZ9nl1FQXsuvX/2a+97LZGBEMP++cgw3Txqk0NfJmNZan376Kc899xzDhw9n1KhRAPzlL3/h+eefZ/v27VgsFmJjY3n66acBSExMZM6cOQwdOhSbzcby5cuxWhunhj/++ONMnToVp9PJggULSExMBBoXlp43bx533nkno0ePZuHChaacq4iIp7hl0mAe/SiLe9/9jvQF480uRzyYYRjsPnCIbTmlvPhlAa98lU94sD/3Th3C9DN6u2RYt8EwcDgN6pwNVDucWACrxUJ9g4EBWIAuNisBNj9q6huorHUcrg3qGxpoMA7XSuMHXf1tx90qTo5mMQzDMLsIT5KUlMSWLVvMLsOr5OXl6VoZD6L28DzubpPrX9zBis372LXkPAb37Oa243Ymvv594mww+Dq/jH99nkPalhzKauqZMzKKX00cQPegtvUZ1TkaqHM2UO9swGEYYFjAYsCRD2lcZzLY30rXACuR3QNpMOBQnZOe3QLoFmCjrKae3LIaKmsd1JUdoEfvvlgs4GeBLv5W/P388PMDf6uVeqeTHw5WU1bjwM+PH4+HhR8P2vi/gUGA1Q9/qwWrxdIUYv2tFvw6+DrFA4fqmHpGx1/j11qeUf+siIiPuX2KnX9u+oE/vf0d/75qjNnliIepdTh5+cs87l+/m4zCSkZEdufxycOI7936Hwm1jgZqHU5qnY19SRYgOMBK2OG7eXQNsNIt0IazwcDPYiHA6kcXfz+sfpYT9hz26hZIr26NS8bk5dUSFRV6wtqjQ7tQWevEYoFAmx/OBoMGw6DW0YABBFgbj3mo1kHRoTqq65zUNRg0HO46LKmux9kAYDS93s/SGA5tVgtBNr9Ovy6hgp+IiI+JjQhm9sgoXvkqn8LyGvqEBJldkniI7NIqfrdmJ2u+LiAkyMbdyXFcPLTPUb1g1fVODtU5aTAMjmzuFmCjV7dAenYNICTIRoDVD5v1tG8O1m4Wi+WoHskjNwsJDjj6eV38rfQ8HCiba2gwqHU2UF5dT2FlLd0CbNQ1NFDvaKCqvoGDVfU00Di0HGj1o2uADVsnuw2igp+IiA+6K3kI/7c9jz+/+x3LZ404+QvEqxmGweOfZPGnt7+jtLqe2SMiWXTWAEKC/HE2GFTUOqhxOjEMCO3iT3zvboQG2Qjyt2Lzs+BvQsjrCH5+Frr4Wenib23xD6KGBoMah5PKWicFFTXsr6yjvrGLkCCbH4E2q1uGi0+Hgp+IiA9K7Nudn8b34j/bcll2UQLdg/zNLklMsiOvjF++8CWfZ5cxtE83/n5pIvG9u1FR62B/ZS1WPz96dwugb/duhATZCPbhO3L4+VkIDmh8D3p3b+wxrKx1cKjOSVFlLeU1Dkqq6zGMxokrPYIDsHpYj6Dvtp6IiI+7+8IhnPWPT1m2YTf3T08wuxxxs/Kaeu5e9y2Pf5JF1wAbfzh/MJPtPXE0wMGqeiJDAokO7UJokM2UYdvOolugjW6BNvp0/3HouKHBIOvgIXbtr6RHcAA19Q0EB1g9IgQq+ImI+KgzYyOY0D+MFZv2cXfyEAL9tQyGLzAMg9Vf5HHzazsprKjlgrieLJzQjx5dA4kKCaRP9yCFvdPk52dhcM9u+Fv92LX/ED2D/SmsrMMDcp+Cn4iIL7tnajzTntnEPz7J4tbJdrPLkQ6WUVDBr1/5ivf3FBPXsyu3nTeIKXG9iAnrQlgXf4/okfIm/cOD6RfWBYvFQmWtg9LqOsxeRE/BT0TEh02N78XQPt1Y/ulebp40GD/94vdKlbUO/vzOd/zto+/p4m/ltz8ZyMLx/Ynr1ZUg9fR2qCPLvxwZEjab+RWIiIhpLBYLdyUP4Wf/3sa/Ps9mwYT+ZpckLmQYBi9+mc/Nr+0kt6yGGYl9uOGsWM6z9yDQpsDnizSALyLi42aPiKR/WBB//fB7dDMn7/Ht/koufHojc5/bSrcAK8/OGcmqn41m6hm9Ffp8mIKfiIiPs1n9uP38OHYWVrDm6wKzy5HTdKjWwR/XfsPwhz9g4w8l/H7yYF5fOJ5fTOhPaBct2+PrNNQrIiL8Ynw/7n77Wx7YsJvLhkeaXY6cAsMwePWrAn6X/jXZpTVcnNCbG38ykPPsPb1mgWU5ffpKEBERgvyt3DxpEJv2lfLhngNmlyPtlFlUybRnNjErbQtBNiv/vGIEz101huT43gp9chR9NYiICACLz46lW4CV//duptmlSBtV1Tm4661dDHvoQz7JOsitkwbx+sJxLJw4gDAN60oLNNQrIiIAhAT5s+jsWB5+fw9f5pYxMjrU7JKkFYZh8PrOQn675mv2llQz7Yze/PYnAzk/TsO6cmL66hARkSa3ThpMgNWPP739rdmlSCv2HDjEJSs2M3Pl5/j5WUidPYL/XNU4W1ehT05GPX4iItKkd/dArkmKYeXn2WQVVzGwR7DZJclh1fVOHtiwm2UbdmO1WPjduQNJmTiAhD7dzS5NOhH9aSAiIkf5w5Q4DMNQr58HeTOjkGEPfcC973zHeYN78PL8sTxw0VCFPmk3BT8RETnKwB7BXDY8kpd25FFUWWt2OT5t78EqLn12Mxev2IxhGDw5azj/vXoMP03oQ4BNv8Kl/fRVIyIix7n7wiFU1zfw53e+M7sUn1RT7+S+d78j4YH3eee7In5zTiyvLRzP9WfFEhEcYHZ50onpGj8RETnO8MgQkof05LmtOSydfgbdgrQ0iLus27Wf37z6FbsPVHFBXE9+d+4gkof0Ug+fuIS+ikREpEX3XBhPWY2DB97fY3YpPmFfSRWz/vU5057ZRJ3T4PHLhvH81WO4aKiGdcV11OMnIiItOmtgBOP7hfHMpn3cfeEQLRXSQeocDfz1wz38v3czcRoGi8+K5VdnDmBYZHcsFovZ5YmX0XexiIi06k9Th1BYUcs/Ps4yuxSv9N53RYx4+AP+sHYXE/qH8dI1Y3lk5lCGR4Uo9EmHUI+fiIi0atoZvTmjdzce/zSL3507CD8/hRFXyCmt5ubXdvLil/nEhAbx2MxEfjYmml7dAs0uTbycaT1+2dnZTJ48maFDh5KYmMhjjz0GwMGDB0lOTiYuLo7k5GRKSkqAxtvT3HjjjdjtdkaMGMG2bdua9pWWlkZcXBxxcXGkpaU1bd+6dSvDhw/Hbrdz4403YhiGe09SRKSTs1gs3HlBHFkHq1m1Jcfscjq9OkcDD72/mzMeeJ/Xvi7k+jMH8PrCcfzmJwMV+sQtTAt+NpuNRx55hIyMDDZu3Mjy5cvJyMhg2bJlTJkyhczMTKZMmcKyZcsAeOutt8jMzCQzM5PU1FQWLVoENAbFe++9l02bNrF582buvffeprC4aNEinnnmmabXrVu3zqzTFRHptOaOiqJfaBAPf7BHf0Cfhvd3H2DUXz/k9298w9iYUF64Zix/m5nIqOgwDeuK25gW/CIjIxkzZgwA3bt3JyEhgdzcXNLT05k/fz4A8+fPZ82aNQCkp6dzzTXXYLFYmDhxIqWlpeTn5/P222+TnJxMREQE4eHhJCcns27dOvLz8ykvL2fixIlYLBauueaapn2JiEjb2ax+LDnfzs7CCt7IKDS7nE4nr6yGK/+9jfOf/Izyagd/mzGUF69JYsawvgT5W80uT3yMR0zu2Lt3L1988QUTJkygsLCQyMhIAPr27UthYeMPmdzcXPr169f0mpiYGHJzc0+4PSYm5rjtIiLSfgsn9KdHsD/3vP2dev3aqN7ZOFs3ftkGXt6Rx3UT+vPagnH89txB9O6uYV0xh+mTOyorK5k1axaPPvooISEhRz1msVjc0v2dmppKamoqAAUFBeTl5XX4MX1JUVGR2SVIM2oPz9NZ2uR3E/ty14Zslq//isuH9jS7nA51um2yMaeCO9bvY9eBGsZHdeXX43rxk9hwAqxV5OdXuahK39JZvk88nanBr76+nlmzZnHVVVdx+eWXA9CnTx/y8/OJjIwkPz+f3r17AxAdHU12dnbTa3NycoiOjiY6OpoPPvjgqO3nnXce0dHR5OTkHPf8lqSkpJCSkgJAUlISUVFRrj5Vn6f31LOoPTxPZ2iTJX368uz2Yv66aT/XTx6GzcvX9TuVNikor+G2NzL499ZcIrsH8vAlQ7lqTDR9Q4I6oELf0xm+Tzydad+1hmGwcOFCEhISuPnmm5u2z5gxo2lmblpaGjNnzmzavmrVKgzDYOPGjYSGhhIZGcnUqVN55513KCkpoaSkhHfeeYepU6cSGRlJSEgIGzduxDAMVq1a1bQvERFpP3+rHw9fkkDWwWoe2LDb7HI8isPZwN8//p74B97n/7bnsWB8P15bOJ6bJw1S6BOPYlqP36effspzzz3H8OHDGTVqFAB/+ctfuP3225kzZw4rVqxgwIABvPDCCwBMnz6dtWvXYrfbCQ4OZuXKlQBERERw1113MW7cOADuvvtuIiIiAHjiiSe49tprqa6uZtq0aUybNs39Jyoi4kUuGx7J+H5h/O2j7/n1OQMJ7aJ7+H6adZAbXv6KHfnlTOwfxm2TBzMtoQ9dNHFDPJDF0FW6R0lKSmLLli1ml+FV8vLy1D3vQdQenqeztcmmH0qY+PdP+NWZA3hq9gizy+kQbWmT/RW1LHnzG/71eTZ9ugdyy6RBXD0mmsjQLm6q0rd0tu8Ts7WWZ0yf3CEiIp3LhAHhXDqsL2mfZ7Pk/MEMjOhqdklu5WwweOp/e7lz3S4qa51cmxTD9WfFMq5fmO5sIh7Pu6/MFRGRDvHIjKE4GgxuTs8wuxS32vhDCeMe/Yhfv/o1Q3p2Y/XVY1g+azgTBoQr9EmnoB4/ERFpt0E9unL9mQN44n972fRDCRMGhJtdUoc6UFnLH9bu4p+b9tGrawBLp5/BNUkxRGlYVzoZ9fiJiMgpufen8XQNsHHzazvNLqXDOBsMnv5sL0OWvc/Kz7P5+dho0heM4/eT7Qp90impx09ERE5JRHAAd15gZ8mbu3h5Rx6zRnjXhfef7yvlhld2sCW7jLExofx+8mAuSuhD10D96pTOSz1+IiJyym78ySCiQgL549pdNDR4xyIRxYfqWPLuD0z4+8fsPVjNfT+NZ80vkpgzKlqhTzo9BT8RETllQf5WHrx4KN8VHeLRj743u5zT0tBgsGLTPuKXbeC/Xx3gZ6OiefXaJP4wJY6YsGCzyxNxCQU/ERE5LT8bHc2IyBAe/GAPh2odZpdzSrbllHLWPz7hly98Sf/wLjw1fQBPXzGCcwb10Gxd8SoKfiIiclr8/Cz8/bJECitquXPdLrPLaZeSqjoWv/wVSY9+TOaBQ9w7dQjpvxjHRWf0pJuGdcUL6ataRERO26TBPZka34tnNu7jtvMGe/yM14YGg1Vbcvj9GxkUV9Uxd2QUi86M5exBEVj9LORVl5pdokiHUI+fiIi4xN9mJlJT7+SmdM9e3uXLvDLOXf4pv/i/7USGBPHcz0bzzJyRnGvvgVXDuuLl1OMnIiIukdCnO9eO68e/tuSwPbeMUdGhZpd0lLLqeu5++1se/ySL0CB//pQ8hF+M78eACE3cEN+hHj8REXGZ+6cnEGj186hFnQ3D4Lkt2cQve59/fJzFrOGRvHLtWO66cIhCn/gc9fiJiIjL9OkeyG3nDebed7/jrW8KmZbQx9R6vsovZ/ErX/Hx9wcZ1rc7D1+SwMxhkXQP0q8/8U3q8RMREZe6bfJgenUNYMkb32AY5izqXF5Tz83pOxn914/YkVfOnRfE8dqCcVyd1E+hT3yavvpFRMSlugba+Mv0M7juxR08/dkPXH9WrNuObRgGq7/I45bXd1JQXsulw/qy+OxYJg3ugc2qvg4RfReIiIjL/WJ8f+J7deO+9zLJLa12yzEzCiqY8tRnXPmfbYR38WflvFH862ejmDKkl0KfyGH6ThAREZez+ll4+orhHDhUx9AHP+C5LdkdNuxbWevg969nMPKRD9maXcYfzrfz2oLxzB/Xj5Ag/w45pkhnpaFeERHpEJMG92TL787hyn9/wTXPb+f/tuexct4oenULdMn+DcPgxS/zufm1neSW1TAzsQ+/PjuW8+w91cMn0gp9Z4iISIcZFhnKF7dMYslkO+98V0T8svd5+cu8097vrsIKLnx6I3Of20q3ACvPzhlJ2s9Gc0F8b4U+kRNQj5+IiHQoq5+FZRcnMHtkJFf/ZxuzV23lihF5PH3FCMKDA9q1r0O1Du57L5NHPtxDoNWP308ezHUTBmDv1bWDqhfxLgp+IiLiFkn9wvjqtvO4OX0nT/5vLx/sKeZf80YxfejJ1/ozDINXvyrgd+lfk11awyVD+/CbcxqHdf3VwyfSZvpuERERt/G3+vGPy4fzwQ1n0T3QxkUrNnPt819QUeNo9TWZRZVMe2YTs9K2EGSz8s8rRrDqytEkx/dW6BNpJ/X4iYiI250zqAc7f38ev37lK57dnM273xXx7ytHMzmuV9NzquocLF2/mwff34O/1cKtkwbxy4n9ie/d3cTKRTo3BT8RETFFkL+Vf84dxdxR0Sx8YTvnP7WR688cwCMzhvLudwf47Zqv+aGkmmln9Oa3PxnI+XEa1hU5XQp+IiJiquT4XmTcdh4pL+3gqc9+4L9f5FJe42BQj2BSZ49g9sjIdk8CEZGWmfqn04IFC+jduzfDhg1r2nbPPfcQHR3NqFGjGDVqFGvXrm16bOnSpdjtduLj43n77bebtq9bt474+HjsdjvLli1r2p6VlcWECROw2+3MnTuXuro695yYiIi0S7cgf/579VjW/GIcseHB/O7cgbyxcDzXnTlAoU/EhUwNftdeey3r1q07bvtNN93E9u3b2b59O9OnTwcgIyOD1atXs3PnTtatW8cNN9yA0+nE6XSyePFi3nrrLTIyMnj++efJyMgAYMmSJdx0003s3r2b8PBwVqxY4dbzExGR9pk5rC9f3jqJv85IJKGPruUTcTVTg9+5555LREREm56bnp7OvHnzCAwMZODAgdjtdjZv3szmzZux2+0MGjSIgIAA5s2bR3p6OoZhsGHDBmbPng3A/PnzWbNmTQeejYiIuIrFYjG7BBGv5JFXyT7++OOMGDGCBQsWUFJSAkBubi79+vVrek5MTAy5ubmtbi8uLiYsLAybzXbUdhERERFf5XGTOxYtWsRdd92FxWLhrrvu4pZbbuHZZ5/t0GOmpqaSmpoKQEFBAXl5p387IflRUVGR2SVIM2oPz6M28TxqE8+jNnENjwt+ffr8uIL7ddddx8UXXwxAdHQ02dnZTY/l5OQQHR0N0OL2Hj16UFpaisPhwGazHfX8Y6WkpJCSkgJAUlISUVFRLj8vX6f31LOoPTyP2sTzqE08j9rk9HncUG9+fn7Tx6+++mrTjN8ZM2awevVqamtrycrKIjMzk/HjxzNu3DgyMzPJysqirq6O1atXM2PGDCwWC5MnT+all14CIC0tjZkzZ5pyTiIiIiKewNQev5/97Gd88MEHHDhwgJiYGO69914++OADtm/fjsViITY2lqeffhqAxMRE5syZw9ChQ7HZbCxfvhyr1Qo0XhM4depUnE4nCxYsIDExEYAHHniAefPmceeddzJ69GgWLlxo2rmKiIiImM1iGIZhdhGepGfPnsTGxppdhlcpKiqiV69eJ3+iuIXaw/OoTTyP2sTzqE3aZ+/evRw4cOC47Qp+0uGSkpLYsmWL2WXIYWoPz6M28TxqE8+jNnENj7vGT0REREQ6hoKfiIiIiI9Q8JMOd2SpHPEMag/PozbxPGoTz6M2cQ1d4yciIiLiI9TjJyIiIuIjFPxEREREfISCn7RbdnY2kydPZujQoSQmJvLYY48BcPDgQZKTk4mLiyM5OZmSkhIAdu3axZlnnklgYCAPP/zwUfv629/+RmJiIsOGDeNnP/sZNTU1bj+fzs6V7fHYY48xbNgwEhMTefTRR919Kl6jvW3yn//8hxEjRjB8+HDOOussvvzyy6Z9rVu3jvj4eOx2O8uWLTPlfLyBK9tkwYIF9O7du+nOUnJqXNUmre1HWmGItFNeXp6xdetWwzAMo7y83IiLizN27txp3HbbbcbSpUsNwzCMpUuXGr///e8NwzCMwsJCY/PmzcYf//hH46GHHmraT05OjhEbG2tUVVUZhmEYV1xxhbFy5Ur3nowXcFV7fPXVV0ZiYqJx6NAho76+3pgyZYqRmZnp/hPyAu1tk08//dQ4ePCgYRiGsXbtWmP8+PGGYRiGw+EwBg0aZOzZs8eora01RowYYezcudOEM+r8XNUmhmEYH374obF161YjMTHRzWfhXVzVJq3tR1qmHj9pt8jISMaMGQNA9+7dSUhIIDc3l/T0dObPnw/A/PnzWbNmDQC9e/dm3Lhx+Pv7H7cvh8NBdXU1DoeDqqoq3YD7FLiqPb755hsmTJhAcHAwNpuNSZMm8corr7j1XLxFe9vkrLPOIjw8HICJEyeSk5MDwObNm7Hb7QwaNIiAgADmzZtHenq6+0/IC7iqTQDOPfdcIiIi3HsCXshVbdLafqRlCn5yWvbu3csXX3zBhAkTKCwsJDIyEoC+fftSWFh4wtdGR0dz66230r9/fyIjIwkNDeXCCy90R9le63TaY9iwYXz88ccUFxdTVVXF2rVryc7OdkfZXq29bbJixQqmTZsGQG5uLv369Wt6LCYmRr/QXOB02kQ6hqvapPl+pGU2swuQzquyspJZs2bx6KOPEhISctRjFosFi8VywteXlJSQnp5OVlYWYWFhXHHFFfz73//m6quv7siyvdbptkdCQgJLlizhwgsvpGvXrowaNQqr1dqRJXu99rbJ+++/z4oVK/jkk0/cWaZPUZt4Hle1yYn2Iz9Sj5+ckvr6embNmsVVV13F5ZdfDkCfPn3Iz88HID8/n969e59wH++99x4DBw6kV69e+Pv7c/nll/O///2vw2v3Rq5oD4CFCxeydetWPvroI8LDwxkyZEiH1u3N2tsmO3bs4Je//CXp6en06NEDaOwVb97rmpOTQ3R0tBvPwru4ok3EtVzVJi3tR1qm4CftZhgGCxcuJCEhgZtvvrlp+4wZM0hLSwMgLS2NmTNnnnA//fv3Z+PGjVRVVWEYBuvXrychIaFDa/dGrmoPgP379wOwb98+XnnlFa688sqOKdrLtbdN9u3bx+WXX85zzz13VNgeN24cmZmZZGVlUVdXx+rVq5kxY4Z7T8ZLuKpNxHVc1Sat7UdaYeLEEumkPv74YwMwhg8fbowcOdIYOXKk8eabbxoHDhwwzj//fMNutxtTpkwxiouLDcMwjPz8fCM6Otro3r27ERoaakRHRxtlZWWGYRjG3XffbcTHxxuJiYnG1VdfbdTU1Jh5ap2SK9vjnHPOMRISEowRI0YY7733npmn1am1t00WLlxohIWFNT137NixTft68803jbi4OGPQoEHGfffdZ9YpdXqubJN58+YZffv2NWw2mxEdHW3885//NOu0OjVXtUlr+5GW6ZZtIiIiIj5CQ70iIiIiPkLBT0RERMRHKPiJiIiI+AgFPxEREREfoeAnIiIi4iMU/ERE3Oiee+7h4YcfNrsMEfFRCn4iIiIiPkLBT0Skg91///0MGTKEc845h2+//RaAv//97wwdOpQRI0Ywb948kysUEV9hM7sAERFvtnXrVlavXs327dtxOByMGTOGsWPHsmzZMrKysggMDKS0tNTsMkXER6jHT0SkA3388cdcdtllBAcHExIS0nSv3REjRnDVVVfx73//G5tNf4OLiHso+ImImODNN99k8eLFbNu2jXHjxuFwOMwuSUR8gIKfiEgHOvfcc1mzZg3V1dVUVFTw+uuv09DQQHZ2NpMnT+aBBx6grKyMyspKs0sVER+g8QURkQ40ZswY5s6dy8iRI+nduzfjxo3DYrFw9dVXU1ZWhmEY3HjjjYSFhZldqoj4AIthGIbZRYiIiIhIx9NQr4iIiIiPUPATERER8REKfiIiIiI+QsFPRERExEco+ImIiIj4CAU/ERERER+h4CciIiLiIxT8RERERHyEgp+IiIiIj1DwExEREfERCn4iIiIiPsJmdgGepmfPnsTGxppdhlepr6/H39/f7DLkMLWH51GbeB61iedRm7TP3r17OXDgwHHbFfyOERsby5YtW8wuw6vk5eURFRVldhlymNrD86hNPI/axPOoTdonKSmpxe0a6hURERHxEQp+IiIiIj7C64PfunXriI+Px263s2zZMrPLERERETGNVwc/p9PJ4sWLeeutt8jIyOD5558nIyPD7LJERERETOHVkzs2b96M3W5n0KBBAMybN4/09HSGDh1qWk0OZwMfZx007fhmOHCggp5Vx88sEnOoPTxPR7XJ6OhQwrq4dhbk7gOHyC6tPuFz+nYPJKFPd5cety1qHU42/VCK0zBOe1/taRMLMK5fGDsLKzhU5zztY7dF1wAr4/qFYbFYAMgoqKCwstYtxzaLt/zsCgvyZ3RMqGnH9+rgl5ubS79+/Zo+j4mJYdOmTcc9LzU1ldTUVAAKCgrIy8vrsJrKa52c/+T2Dtu/iMgRFw4KYeVlcS7bX4NhMOof2zlU33DC51ktsP364UQEB7js2G3x1OcF/L+Pct16zCPGRgazNb/Krcf8z+WDOW9gGBW1TkYs347z9POuuMHovsG8cVWCacf36uDXVikpKaSkpACN0587crp4b2cD76QEsrekChf8UdopVJaV0C003Owy5DC1h+fpiDa5771MqrG59OdZeU09h+obuGJEJBcM6dnic97fXczq7XnUBYYTFeXmXo3AcgCenj38tHfVnja5cc1Oiqobf6D/4Xw7sRFdTvv4J/J9cRUPvL+Hgw1diIqKorCiFqcBPxsdxXmDe3Tosc3kLT+7egQHmLosjVcHv+joaLKzs5s+z8nJITo62sSKwGb1Izm+t6k1uFtenrlf5HI0tYfn6Yg2efJ/P4CL/7gsq3YAEN+7Gylnxrb4nDqnwerteTSY8Jets8HAaoEZiX3pGxJ0WvtqT5vc8npG0/kOi+zOlWNiTuvYJ/PZ3oM88P6eps+Nw8ceENal1XbxBvrZ5RpePblj3LhxZGZmkpWVRV1dHatXr2bGjBlmlyUi0uEsFpfnPspq6gHoFmBt/biH/29w+dFPrsEAPz9L03Vv7mLB0nS2fm449pHzMw4f1Thmu8iJeHWPn81m4/HHH2fq1Kk4nU4WLFhAYmKi2WWJiHQ4C5amniBXKatp7PHrFtj6rw4/v8OhxIRLWZwNBn5YcHf8sVh+7HVz57GPvMdH/lfuk7bw6uAHMH36dKZPn252GSIibtURPX6H6hqDX5B/64NFTT1+Zgz1GgZ+fuYEoAY3hq9jD9FgQuiUzsurh3pFRHyVxYLLk5/zcLqx+bX+q+PIUGeDCT1+DYaBn8WEHj+a9fi5Zai38f8jb/GPPX6KfnJyCn4iIl6o+XVnrnJkuRCbtfWAcSR7mDO5ozF4uv0aP0uzHj93HO+YoxgmXE8pnZeCn4iIF2p+3ZmrNBxON9YTBKsjj7j62G3R2OPn/qHe5iHbncc+8h7rGj9pDwU/EREv1AEjvU13xLD6tZ4wjgz1mja5w4yhXjdP7jg24DWFTjccWzo/BT8RES9ksXTAUG9Tj9+Jjnv4uab2+Lk/Ah0Z6nXLci6H/z/uGj9FP2kDBT8RES/kB6ZO7jDjqrOma/zcfFwLzWbWujF0Ni3nwpFju+3Q0okp+ImIeKHG5VxcfI3f4d2daKi3aXLHiW/n2yEaDOPwAs7uPa7FYmnW6+aO4zX+f/ysXjccXDo9BT8RES/UPIy4ypHh2xPkvmbr+Lk/+TkNAz/cP+TZ/HpK96zjd+ys3pa3i7REwU9ExAt1yOSOw11+/tY2DPWatY6fKT1+7l1Euen8Dh/zx2FmNxxcOj0FPxERL9Q409S1+2ya3NGWoV5TZvU29kaakX8MN07uaDrmMcdW7pO2UPATEfFClg64dceRoV7bCRLGj5M7zJrVa0KPHxa39rodt5yLGd2r0mkp+ImIeCFXx75v91eS8uIOAPxOMKv3SCZxmjC5w9lgYDXpzh3uzF5HruVr6vFrVofIySj4iYh4oaaZny5KJDe++nXTx7YTDvWalz6choHFhKHexpB98okvLjteU9se/b8md0hbKPiJiHghC66d1ds9yNb08QnmdjQFHzPu1dvQ1OPn3uMefa9ed17jd/iWbc3qEDkZBT8RES/UuI6f63QP/DH42SwnGOo1c3KHweEeP/cnoCM9q35u6PI79gjuvF2cdH4eF/zuueceoqOjGTVqFKNGjWLt2rVNjy1duhS73U58fDxvv/120/Z169YRHx+P3W5n2bJlTduzsrKYMGECdruduXPnUldX59ZzERExi4XGIUBXdbz96/Pspo+tJ7hn24/LuZgzucNq0gLODW6cWds0nG4c9Z+pw+zSeXhc8AO46aab2L59O9u3b2f69OkAZGRksHr1anbu3Mm6deu44YYbcDqdOJ1OFi9ezFtvvUVGRgbPP/88GRkZACxZsoSbbrqJ3bt3Ex4ezooVK8w8LRERt2nMAB0Tvk54r97D/7v7Xr3bc8tY+81+DlbVm3SNX7NP3ER37pBT4ZHBryXp6enMmzePwMBABg4ciN1uZ/PmzWzevBm73c6gQYMICAhg3rx5pKenYxgGGzZsYPbs2QDMnz+fNWvWmHsSIiJucuQav46IX/4nmtVr0r16l3+6F4CCilpTZvUe4Y51/I49gjsXj5bOz3byp7jf448/zqpVq0hKSuKRRx4hPDyc3NxcJk6c2PScmJgYcnNzAejXr99R2zdt2kRxcTFhYWHYbLbjnn+s1NRUUlNTASgoKCAvL6+jTs0nFRUVmV2CNKP28Dwd0SZ1dbXUO+rJz89zeRg5eKCAPEdwi4+VlJQCUHbwIHl5p3fcC9IymB4Xxs1nRZ30uV0tP17KU5Cfd9rhrz1t0uB0Nn1cVnyAvOCOvayoqLgagMryMvLy8ti/vwqAqooyr/79pZ9drmFK8LvgggsoKCg4bvv999/PokWLuOuuu7BYLNx1113ccsstPPvssx1aT0pKCikpKQAkJSURFXXyHzLSPnpPPYvaw/O4uk2CgvZhrTaIjIw64Z022m5r00e9ekcS1bNri8/qWWoF9tAtLPy0z+mbA1v55kA1D89OOuHz3t99gLCQEKDx90pUVJRLev3aWr/VmgHUAxDRsydRUT1P+9gnUm6tADII7h5CVFQUBQ2lAHQNCfP6721vPz93MCX4vffee2163nXXXcfFF18MQHR0NNnZP15cnJOTQ3R0NECL23v06EFpaSkOhwObzXbU80VEfIGBcXiShfsGAV2xnMt/t+XQP6xLm55rGAbnP/nZUdvMHOp155GPX8dP5OQ87hq//Pz8po9fffVVhg0bBsCMGTNYvXo1tbW1ZGVlkZmZyfjx4xk3bhyZmZlkZWVRV1fH6tWrmTFjBhaLhcmTJ/PSSy8BkJaWxsyZM005JxERd3PlkibHztA9UaQ7ctTTmdtx1X++4CfL/9em51bVOU/+pA521Dvtjmv8jjmG1vGT9vC4a/x+//vfs337diwWC7GxsTz99NMAJCYmMmfOHIYOHYrNZmP58uVYrVag8ZrAqVOn4nQ6WbBgAYmJiQA88MADzJs3jzvvvJPRo0ezcOFC085LRMSdjtxGzBWTLJzHLMp3ot68I6GkpXX8KmsdFFTUYm9lmPhUHPKE4OfuyR3H3av38PYOP7J4A48Lfs8991yrj91xxx3ccccdx22fPn1607IvzQ0aNIjNmze7tD4Rkc7AlffqPXZplhMtznxkqLeldfwuXrGZD/cUU/PAdAJt1jYf3+FswNbK7UKODX6vXnvi6wE7QvPeVXf2uv14r97Ds3rV5Sdt4HFDvSIicvosFovLFlF2ONvR40frPX4f7ikG4Kv88nYdf/X2XPaVVLX42KE6x1Gf9wj2b9e+Xc0dv1SPHU43Ya1s6cQ8rsdPRERO35EeP1eEgiM9fgvH92NaQm8GRrS8lAucuMfvyPDzodr2Dc/+/L/b6RHsz4H/99PjHqs8psfP7wRrDHaUoyZ3mHiNnxvuFideQMFPRMQLWVw41nvkGr+wLv7MGnHi5TSarvFrdvCy6np+/epXTSG0xtHQ7hqKq+pb3H7s5A4zwo+llY87+nhHhngbGo4s4KzkJyenoV4RES/U1OPngvTnOBws2jJx4chTGpplu6c++4F/b/1xAf2aetdNyDh24onVhOvcmvfAufUav+Pu1eu+Y0vnpR4/EREvZLFYXBL6tmaX8s53jXdMaMtC0M2fUl3v5Or/bKOL/9ETOarrW+7x25pdSpB/2yd9wPHXG5ow0nvMUK97jwctD6uLtEbBT0TECx25nu50JT36cdPH1jaEmiPDjQ9+sIeKWievfHX8XZpqHC33+DU/VlsdG/ysFnMHstwx3HrsMdTjJ+2hoV4RES/kyskdR/i1o8fv++Iqbnsjo8XnlNU4Wtx+MscO6wIcM+GYVlZ96VBHX+PnvvSlO3fIqVCPn4iIF7JYLK5byO+wtlw/15ZZrbllNad0fEdDA1a/xqFgh7OBhAc/IDb86Fu7mbGWnbuv8TtyjCND+VrHT9pDPX4iIl6oscfPtcmvLdf4tSV6PPTBHvxufb3dx69v1r23s7CC3QcO8V7mgaOe05bhaFdrfkh3zCo+9hDq8ZP2UPATEfFCrrxl2xFt6fFry3AwNNb2s+e2MvSB99t8/L0Hq5qGe+scLZ9ZW8Kpq5k1uePHO3eItJ2GekVEvJAFi0sCQe9uAeyvrAPaNmO2Pbln9fY8ANI+z25TYBr+8IfMGxXF8z8f2+rdQ9xxr9wTMfMaP7PPXToH9fiJiHghV13iZ2vWg9amHr9TCB/Xrt7O/Oe3E9yGpVyOhMXWgp9P9PgdEy6PvBe6c4e0hYKfiIgXsgAYp7/GW/OJtO1ZwPlUBNra/iuppRm+YNadO5pN7nDH8Y5bx8+NB5dOT0O9IiJe6HQXcN5fUYvNajkqYPm3YebE6WSPitq2LfPy7KZ99Oke2OJjZvf4uXMB6SOh/kg7+yn5SRso+ImIeCELhyd3nGL263PPOwD0CPZv2mZr0zp+px4+HK304h1r4QtfEhrU8q8vM8LP0T1+bljA+djJHcbR20VOxJSh3hdffJHExET8/PzYsmXLUY8tXboUu91OfHw8b7/9dtP2devWER8fj91uZ9myZU3bs7KymDBhAna7nblz51JX13gRcm1tLXPnzsVutzNhwgT27t3rlnMTEfEErrrGr3kWs7WhO8td4aO1RaDbOqu4o7hjgkXrd+5Q8pOTMyX4DRs2jFdeeYVzzz33qO0ZGRmsXr2anTt3sm7dOm644QacTidOp5PFixfz1ltvkZGRwfPPP09GRuOK8EuWLOGmm25i9+7dhIeHs2LFCgBWrFhBeHg4u3fv5qabbmLJkiVuP08REbNYjty64zTVOn+8r25H9/i5ghlHP2pyhxuP9+OsXsNtx5bOz5Tgl5CQQHx8/HHb09PTmTdvHoGBgQwcOBC73c7mzZvZvHkzdrudQYMGERAQwLx580hPT8cwDDZs2MDs2bMBmD9/PmvWrGna1/z58wGYPXs269ev142sRcRnNC7ncvpLOFfV/Xhf3TZd4+eD6cPS6icdywCyS6r556Z9gHuvL5TOy6Ou8cvNzWXixIlNn8fExJCbmwtAv379jtq+adMmiouLCQsLw2azHff83NzcptfYbDZCQ0MpLi6mZ8+exx03NTWV1NRUAAoKCsjLy+uYE/RRRUVFZpcgzag9PE9HtEl1dRVOp5PCgnzK27BMSpv2WV5CXt6J91V0oNolxzpVB/YX0lDpf/InnkR72sThqG/6uKSokLz68tM+/onsP9R4vOrKcqanfsrX+xvf88rSgydtn85MP7tco8OC3wUXXEBBQcFx2++//35mzpzZUYc9JSkpKaSkpACQlJREVFSUyRV5H72nnkXt4Xlc3SZdgwux+B2id59IurcyEeLEth63JbxHz5PWWepXAWScwvFco3efvvRuZcZve7W1TQL8dwON4atnn75E9ejqkuO3xlpRC+ygS9fu1DYcatoeEt7D67+3vf383KHDgt97773X7tdER0eTnZ3d9HlOTg7R0dEALW7v0aMHpaWlOBwObDbbUc8/sq+YmBgcDgdlZWX06NHjNM9KRKRzaFzOxbXaco2f2UO9Zh/fnXNLDCCsy4+9m2ZfXymdg0ddETBjxgxWr15NbW0tWVlZZGZmMn78eMaNG0dmZiZZWVnU1dWxevVqZsyYgcViYfLkybz00ksApKWlNfUmzpgxg7S0NABeeuklzj//fM14EhGf0biA86lHv7AWegn9Nbmj5WO6/c4djQwgvFnw0284aQtTgt+rr75KTEwMn332GRdddBFTp04FIDExkTlz5jB06FB++tOfsnz5cqxWKzabjccff5ypU6eSkJDAnDlzSExMBOCBBx7gr3/9K3a7neLiYhYuXAjAwoULKS4uxm6389e//vWoJWBERLzdkeVcTnV6R9fA44OfzXryXxlt6RX0NkfP6nXfOn5wdI+f+jakLUyZ3HHZZZdx2WWXtfjYHXfcwR133HHc9unTpzN9+vTjtg8aNIjNmzcftz0oKIgXX3zx9IsVEemELFhOp8OPhhYWU25LqDPjzhnNmTGy0zzsuWcdv0aGAWFdfvw1rlEtaQuPGuoVERHXsFig6FAdT/3vh3a97vviQ/ztwz04W0iNbQl+Zvf4mZF93L2O3xEGBqFBza7xc+OxpfPS14mIiBc6ktuWvPlNu1435q8fcfNrGeyvrDvusbb0Zpke/Ew4ZknVj8u5uKPX7cgxsoqrjg666vCTNvCodfxERMQ16prdcaM9WroV2hm9u9HQYBAefPL18XxxqDfzwI9LqrhlcsfhY7z8VQHTz+jdtN3siTXSOSj4iYh4oVpHY/Brbw7rEexPcbMeLIAp9p48emmiJnd4oB35Py4WrXde2kLBT0TECx3p8WtvEOvTPfC44GextG1Gb+PxzLmC6MGLE6isdRBk8/4rmJq3aNGhH4fk1eMnbaHgJyLihY70+LU3iDVfF+6I9gSKNuZDl7tydDTRYV3MOXgz7ohezYezj7Rz43Y3HFw6vXZ/i5aUlLBjx46OqEVERFzkSI+fK665a88uzOrx8/OhIebWztR33gE5HW36Dj3vvPMoLy/n4MGDjBkzhuuuu46bb765o2sTEZFT9GOPX9vjwJ4Dh1qcFNKeCROumtzR3r14Sugxcy09DfVKW7Qp+JWVlRESEsIrr7zCNddcw6ZNm07pXrwiIuIe7b3G7+f/3YZ96QY+zy477rH2ZDlXdby1d+1pX1q82IdOVTpAm4Kfw+EgPz+fF154gYsvvrijaxIRkdPU3h6/l3fkt/pYe3qSThTA/t9P47n53EFt3ld7+FIWau22cA2neHs+8S1tCn533303U6dOxW63M27cOL7//nvi4uI6ujYRETlFTT1+1rZFoilxvVp9zFW9eHcmD2FibHibnntBXE+g7cHVU3rBzCwjwKyZNdKptOmr5Pzzz2fHjh088cQTQOP9cV9++eUOLUxERE7dkR4/axsT0YkCliuGUQMPL7PS1nrmJ8VQdv9PcbRwz+CW+PtQ6GntLYwNN39Ws3i+Nn2nTJw4kSuuuIK1a9dinM5dv0VExC3qHO3r8Ws4wc/20+3xu+ncgfxr7iig7ZM/ugfZCAnyZ0ivrm16vqcsHO2WO3e0sO2NheMY3LNbxx9cOr02Bb/vvvuOlJQUnnvuOeLi4vjjH//Id99919G1iYjIKap1tm8dvxN1rJ3qbNGIYH+uTYrhrzOHMW9M9OF6TryvJZMH8+Ss4Zw5IAKAjxafzbNzRp70WP5tDLjeoKXmmDS4p+m3y5POoU0/ESwWC8nJyTz//PM888wzpKWlMX78eCZNmsRnn33W0TWKiEg7tXdyx4l6/E7Vt0sm84/Lhx+17WThpGuAjevPiqV390Cg8U4iv5jQ/6TH8jdp/UBPocgnbdWm75Ti4mIee+wxkpKSePjhh/nHP/7BgQMHeOSRR7jyyivbfdAXX3yRxMRE/Pz82LJlS9P2vXv30qVLF0aNGsWoUaO4/vrrmx7bunUrw4cPx263c+ONNzYNOR88eJDk5GTi4uJITk6mpKQEAMMwuPHGG7Hb7YwYMYJt27a1u04Rkc7qyFBvW3uBThT8TvUSHz8/C90Cj75B1MmC6Kl2WvnUAs4tdPl5yuQW8XxtCn5nnnkm5eXlrFmzhjfffJPLL78cm81GUlLSUeGsrYYNG8Yrr7zCueeee9xjgwcPZvv27Wzfvp2nnnqqafuiRYt45plnyMzMJDMzk3Xr1gGwbNkypkyZQmZmJlOmTGHZsmUAvPXWW03PTU1NZdGiRe2uU0Sks6pt5zp+Dcev29whThZE23ut3jNXjOCpWcNP/kQ3cbZxMsrpaOkd8qV1DOX0tOlevd9++22rX1RLlixp90ETEhLa9fz8/HzKy8uZOHEiANdccw1r1qxh2rRppKen88EHHwAwf/58zjvvPB544AHS09O55pprsFgsTJw4kdLSUvLz84mMjGx3vSIinc2RTjpXDPVGhwadUg0tHfmkPX7tDH6Xj4gkIjigXa/pSPVOcyZAKvZJW7Up+B04cIAHH3yQnTt3UlNT07R9w4YNLi8oKyuL0aNHExISwn333cdPfvITcnNziYmJaXpOTEwMubm5ABQWFjaFub59+1JYWAhAbm4u/fr1O+41LQW/1NRUUlNTASgoKCAvL8/l5+XLioqKzC5BmlF7eJ6ObJMGR12bfqZV1dQet21eYjhV9Q0MCa4/pZ+L+fn5VHfxP2rbweLKE74mpOFQu45VvL+AmoA2/Sprl1Ntk8KCfKzVpxaU2+rI9ZvHHtfbl7TRzy7XaNN3y1VXXcXcuXN54403eOqpp0hLS6NXr9YX+wS44IILKCgoOG77/fffz8yZM1t8TWRkJPv27aNHjx5s3bqVSy+9lJ07d7alRKCxq/tUurtTUlJISUkBICkpiaioqHbvQ05M76lnUXt4Hle3yWT7Xt7fXYzVFtCmffsH7D1uW0JML+6+ML7dx/7k10Gs/aaQftHRdA86+tdM3/oS4Nvj6x3cg/unn0Fi3+6EBPkf9zhsbfFYUZFRdA10ffCD9rRJY213JccxeEAM4R3cA9l4/eYXR22LjIwiwObdwQ/0s8sV2vTdUlxczMKFC3nssceYNGkSkyZNYty4cSd8zancyzcwMJDAwMaZXGPHjmXw4MF89913REdHk5OT0/S8nJwcoqMblwbo06dP0xBufn4+vXv3BiA6Oprs7OwWXyMi4u3eWDieQX/ZcMJlWppraaj3VJdxOXtgBGcPjGjxsWN3GWj1a7oe8czYll9zIp60hMkNZ8V2eOiDlidy6BI/aas2/Xng79/411dkZCRvvvkmX3zxBQcPHnR5MUVFRTidTgC+//57MjMzGTRoEJGRkYSEhLBx40YMw2DVqlVNvYYzZswgLS0NgLS0tKO2r1q1CsMw2LhxI6Ghobq+T0R8RnCAjbieXdu8TEtDCwmxIzLVsdfAHbm/bFsXmp7QP4yQZj18nhT8zJxg4Tnvgni6NvX43XnnnZSVlfHII4/wm9/8hvLycv72t7+d8kFfffVVfvOb31BUVMRFF13EqFGjePvtt/noo4+4++678ff3x8/Pj6eeeoqIiMa/AJ944gmuvfZaqqurmTZtGtOmTQPg9ttvZ86cOaxYsYIBAwbwwgsvADB9+nTWrl2L3W4nODiYlStXnnK9IiKdkdXP0vbg18LTTrXH70SO3EP4iCNBsGfXtvWU/eeqMUQE+xPz5/eob2ho8y3gvIlm9crpaFPwu/jiiwEIDQ3l/fffP+2DXnbZZVx22WXHbZ81axazZs1q8TVJSUl8/fXXx23v0aMH69evP267xWJh+fLlp12riEhnZbVYcBqN6/CdKBi8+20RxVV1x23viOBXf0zwiwoJJK+8loTe3dv0ej+LhfDgAIr/31Ryy2o8av0+d1XS4jp+bjq2dH4nDH6/+c1vTvjD4u9//7vLCxIREdew+jUO4RpG69eAbcg8wIWpG1t8rCMy1eAejffevX3yYHp0DWBKXE92FlRw7qAebXr9kYmrQf5WBvds23183cVdnW4KeXI6Thj8kpKSAPj000/JyMhg7ty5QOOdN4YOHdrx1YmIyClrHOqFEw32ZhRWHLetW4CVyjonw/q2rReuPQb37ErBPcn4+1mI6No4mW90TNhJX7dy7ijSdxbQtQOWbvEGntTzKZ7thN9B8+fPB+DJJ5/kk08+wWZrfPr111/PT37yk46vTkRETlnjUK9x+JZrLQeD/PKa47aN6xfG2usmtPsuGm3Vp3v717m7dnw/5o+L0bVsaAavnJ42zeotKSmhvLy86fPKysqme+KKiIhnCrT5UedsOOGSLsVV9cdtcxoGQf5WbB62ILCnhz4zr/ETaas29ZnffvvtjB49msmTJ2MYBh999BH33HNPB5cmIiKnI8hmpc7RgPMEM3tburesG243KyImaVPw+8UvfsG0adPYtGkTAA888AB9+/bt0MJEROT0BNr8qHc2tBjujmhpuZe2LgEjR1NPnHQGJ+zH37VrFwDbtm0jLy+Pfv360a9fP/Ly8ti2bZtbChQRkVMT5N94V4wTB78fPw48PLTb0mLOcnLKfdIZnLDH75FHHuGZZ57hlltuOe4xi8XChg0bOqwwERE5PY09fsYJe/CaP+ZvtVDr1FCviDc7YfB75plnAFyyaLOIiLhXkM1KrePEPX7NHwsP9qeyzklp9fETPuTkzOrw66jZ1+KdThj8XnnllRO++PLLL3dpMSIi4jqBNj8cDcZx98dtrnkmjAoJIru0psW7eIhnumZsDBcM6Wl2GdKJnDD4vf76660+ZrFYFPxERDxYkK3xmr2qemerz2k+1Ns90MbC8f34ycCIDq/NG5kxuePJ2cMJ1qLW0g4n/GpZuXKlu+oQEREXCzwc/KrbGPwaDIN/zh3V0WV5nf9cNZrP9pY0vd/upJnE0l5t+iotKyvj5ptvJikpiaSkJG655RbKyso6ujYRETkNQf5W4MTBr/k1flrF5dRcOSaGf1w+nC6H3293UuyT9mpT8FuwYAHdu3fnhRde4IUXXiAkJIRf/OIXHV2biIichiPLs9Q4TtTj1/LH0jmow0/aq00XBuzZs4eXX3656fM//elPjBo1qqNqEhERFwjybwx+h+oaWn3OUUO9KPl1NlYlP2mnNvX4denShU8++aTp808//ZQuXbqc8kFvu+02zjjjDEaMGMFll11GaWlp02NLly7FbrcTHx/P22+/3bR93bp1xMfHY7fbWbZsWdP2rKwsJkyYgN1uZ+7cudTVNc5Gq62tZe7cudjtdiZMmMDevXtPuV4Rkc7oyDVnNSe8xu/HjzXU2/lYtZSLtFObgt+TTz7J4sWLiY2NJTY2ll//+tc8/fTTp3zQ5ORkvv76a3bs2MGQIUNYunQpABkZGaxevZqdO3eybt06brjhBpxOJ06nk8WLF/PWW2+RkZHB888/T0ZGBgBLlizhpptuYvfu3YSHh7NixQoAVqxYQXh4OLt37+amm25iyZIlp1yviEhnFGRr3zV+umNH56PJHdJebQp+CQkJ/P73v2fBggVcfvnlXHrppaxZs+aUD3rhhRdiszWOMk+cOJGcnBwA0tPTmTdvHoGBgQwcOBC73c7mzZvZvHkzdrudQYMGERAQwLx580hPT8cwDDZs2MDs2bMBmD9/flNd6enpzJ8/H4DZs2ezfv16DP05KyI+pKnHz9HGoV79jBTxem0KfjNnzuT1118nKCiI6OhounXrRteuXV1SwLPPPsu0adMAyM3NpV+/fk2PxcTEkJub2+r24uJiwsLCmkLkke3H7stmsxEaGkpxcbFLahYR6QyOrOP32d6SVv/wbR72bFb3L0ciIu7VpskdOTk5rFu3rl07vuCCCygoKDhu+/3338/MmTObPrbZbFx11VXt2rerpaamkpqaCkBBQQF5eXmm1uNtioqKzC5BmlF7eJ6OapPy0kMAPLNpH1FBTlKS+h73nKqa2qaPDUetfv4d1lm+T3ypvTpLm3i6NgW/s846i6+++orhw4e3ecfvvffeCR//17/+xRtvvMH69eubrlGIjo4mOzu76Tk5OTlER0cDtLi9R48elJaW4nA4sNlsRz3/yL5iYmJwOByUlZXRo0ePFmtJSUkhJSUFgKSkJKKiotp8ntI2ek89i9rD83REmxRbyoFdAHy+v557WjiGv//epo+7BHXR10Yznv1ebAU8vUbX87Xz7QgnDH7Dhw/HYrHgcDhYuXIlgwYNIjAwEMMwsFgs7Nix45QOum7dOh588EE+/PBDgoODm7bPmDGDK6+8kptvvpm8vDwyMzMZP348hmGQmZlJVlYW0dHRrF69mv/+979YLBYmT57MSy+9xLx580hLS2vqTZwxYwZpaWmceeaZvPTSS5x//vm6CFZEfEpAs6FbxzH36121JZv5z28/aptmiHYeD12cQFRIkNllSCd0wuD3xhtvdMhBf/3rX1NbW0tycjLQOMHjqaeeIjExkTlz5jB06FBsNhvLly/Ham2clfb4448zdepUnE4nCxYsIDExEYAHHniAefPmceeddzJ69GgWLlwIwMKFC/n5z3+O3W4nIiKC1atXd8i5iIh4KluzIFffcPQEj/WZB074fPFst062m12CdFInDH4DBgzokIPu3r271cfuuOMO7rjjjuO2T58+nenTpx+3fdCgQWzevPm47UFBQbz44ounV6iISCfWvAfPccxSLX26BR73fAU/Ee+nKVwiIl6qeZBzHjPU62xhlq+/ZvWKeL02Te4QEZHO50Q9fs2XcRkZFUJYkD+/nNDfbbWJiDkU/EREvJTtqOB39DV+zXNg90AbHyw+y11liYiJ1K8vIuKlmvf41R8z1Nv89mxVda3f0k1EvIuCn4iIl7JaTjTU++PHdc7Wb+kmIt5FwU9ExEvZjrnGz9nQ8n15dY9eEd+h4Cci4qWOHuptOOo6v+Zh79jeQBHxXgp+IiJe6tgev+Z372g+uqsOPxHfoeAnIuKlmvf4ldc4uHb1djIKKoCje/xaWtNPRLyTgp+IiJdqfn/y8loHL+3IZ+n6TOCYa/w01CviMxT8RER8yJHr+ZpnPeU+Ed+h4Cci4kOq6p3kllVTVlPftE336BXxHbpzh4iID9l7sJqYP7931DYFPxHfoR4/EREfsiO//LhtNquCn4ivUPATEfFx/lb9KhDxFaZ8t992222cccYZjBgxgssuu4zS0lIA9u7dS5cuXRg1ahSjRo3i+uuvb3rN1q1bGT58OHa7nRtvvBHj8Iy0gwcPkpycTFxcHMnJyZSUlABgGAY33ngjdrudESNGsG3bNrefp4hIZ6ChXhHfYUrwS05O5uuvv2bHjh0MGTKEpUuXNj02ePBgtm/fzvbt23nqqaeati9atIhnnnmGzMxMMjMzWbduHQDLli1jypQpZGZmMmXKFJYtWwbAW2+91fTc1NRUFi1a5N6TFBHpJBT8RHyHKcHvwgsvxGZrnFcyceJEcnJyTvj8/Px8ysvLmThxIhaLhWuuuYY1a9YAkJ6ezvz58wGYP3/+UduvueYaLBYLEydOpLS0lPz8/A47JxGRziYqJJAewf78auIAs0sRETcx/cKOZ599lmnTpjV9npWVxejRo5k0aRIff/wxALm5ucTExDQ9JyYmhtzcXAAKCwuJjIwEoG/fvhQWFja9pl+/fi2+RkTEV1wxMrLVx0KC/Mn9UzI/T4pp9Tki4l06bDmXCy64gIKCguO233///cycObPpY5vNxlVXXQVAZGQk+/bto0ePHmzdupVLL72UnTt3tvmYFovlqJXq2yo1NZXU1FQACgoKyMvLa/c+pHVFRUVmlyDNqD08T0e2yd+mRHJh/yCuez3ruMfq6+spLCjQUG8L9H3iedQmrtFhwe+999474eP/+te/eOONN1i/fn1TWAsMDCQwMBCAsWPHMnjwYL777juio6OPGg7OyckhOjoagD59+pCfn09kZCT5+fn07t0bgOjoaLKzs1t8zbFSUlJISUkBICkpiaioqFM8a2mN3lPPovbwPB3ZJnE1gcDxwa+mwUJUZCQ2zeptkb5PPI/a5PSZ8t2+bt06HnzwQV577TWCg4ObthcVFeF0OgH4/vvvyczMZNCgQURGRhISEsLGjRsxDINVq1Y19RrOmDGDtLQ0ANLS0o7avmrVKgzDYOPGjYSGhjYNCYuI+JJAm7XF7VV1zlMaJRGRzsuUO3f8+te/pra2luTkZKBxgsdTTz3FRx99xN13342/vz9+fn489dRTREREAPDEE09w7bXXUl1dzbRp05quC7z99tuZM2cOK1asYMCAAbzwwgsATJ8+nbVr12K32wkODmblypVmnKqIiOlaG8p1Nhgo9on4FlOC3+7du1vcPmvWLGbNmtXiY0lJSXz99dfHbe/Rowfr168/brvFYmH58uWnV6iIiBfwb+XOHE7DQB1+Ir5FF3aIiHg5m1/LP+oNAw31ivgYBT8RES/X6lDv4TsgiYjvUPATEfFyrQU/Q8FPxOco+ImIeLnWgl+Dcp+Iz1HwExHxcv6trNPXoB4/EZ+j4Cci4uXU4yciRyj4iYh4OVsry7mIiO9R8BMR8XKt9fjpFr0ivkfBT0TEy7UW/Fq79k9EvJe+60VEvFxrAS9AwU/E5+i7XkTEy1lbuTtHa7dyExHvpeAnIuLl/DTUKyKH6bteRMRHdQuwml2CiLiZzewCRETE/X41sT/TE/qYXYaIuJmCn4iID3rssmEE2tTjJ+JrTBvqveuuuxgxYgSjRo3iwgsvJC8vD2i8afiNN96I3W5nxIgRbNu2rek1aWlpxMXFERcXR1paWtP2rVu3Mnz4cOx2OzfeeGPTjccPHjxIcnIycXFxJCcnU1JS4t6TFBHxEE/PHsFFCb2bPregiR0ivsi04HfbbbexY8cOtm/fzsUXX8yf//xnAN566y0yMzPJzMwkNTWVRYsWAY0h7t5772XTpk1s3ryZe++9tynILVq0iGeeeabpdevWrQNg2bJlTJkyhczMTKZMmcKyZcvMOVkREZOlnDmAxy4d1vR5KxN9RcTLmRb8QkJCmj4+dOgQlsM/hdLT07nmmmuwWCxMnDiR0tJS8vPzefvtt0lOTiYiIoLw8HCSk5NZt24d+fn5lJeXM3HiRCwWC9dccw1r1qxp2tf8+fMBmD9/ftN2ERFf5Ncs7Sn3ifgmU6/xu+OOO1i1ahWhoaG8//77AOTm5tKvX7+m58TExJCbm3vC7TExMcdtBygsLCQyMhKAvn37UlhY6I7TEhHxSM1Xb7Goy0/EJ3Vo8LvgggsoKCg4bvv999/PzJkzuf/++7n//vtZunQpjz/+OPfee2+H1WKxWFr9QZeamkpqaioABQUFTdcbimsUFRWZXYI0o/bwPO5qk/0VdU0fF+TnKfydgL5PPI/axDU6NPi99957bXreVVddxfTp07n33nuJjo4mOzu76bGcnByio6OJjo7mgw8+OGr7eeedR3R0NDk5Occ9H6BPnz7k5+cTGRlJfn4+vXv/eGFzcykpKaSkpACQlJREVFRUe09VTkLvqWdRe3get7RJWQ3wVdPxFPxOTN8nnkdtcvpMu8YvMzOz6eP09HTOOOMMAGbMmMGqVaswDIONGzcSGhpKZGQkU6dO5Z133qGkpISSkhLeeecdpk6dSmRkJCEhIWzcuBHDMFi1ahUzZ85s2teR2b9paWlN20VEfJG12R08FPpEfJNp1/jdfvvtfPvtt/j5+TFgwACeeuopAKZPn87atWux2+0EBwezcuVKACIiIrjrrrsYN24cAHfffTcREREAPPHEE1x77bVUV1czbdo0pk2b1nSMOXPmsGLFCgYMGMALL7xgwpmKiHiGVu7cJiI+xLTg9/LLL7e43WKxsHz58hYfW7BgAQsWLDhue1JSEl9//fVx23v06MH69etPr1ARES/hp14+EZ+ne/WKiPgIq7r8RHyegp+IiI9Q7hMRBT8RER+hoV4RUfATEfERGuoVEQU/EREfodwnIgp+IiI+QkO9IqLgJyLiI6yHg9+s4X1NrkREzGLaOn4iIuJefn4Wyu77KTX1TrNLERGTKPiJiPiQkC7+hHTxN7sMETGJhnpFREREfISCn4iIiIiPUPATERER8REKfiIiIiI+QsFPRERExEdYDMMwzC7Ck/Ts2ZPY2Fizy/AqRUVF9OrVy+wy5DC1h+dRm3getYnnUZu0z969ezlw4MBx2xX8pMMlJSWxZcsWs8uQw9Qenkdt4nnUJp5HbeIaGuoVERER8REKfiIiIiI+QsFPOlxKSorZJUgzag/PozbxPGoTz6M2cQ1d4yciIiLiI9TjJyIiIuIjFPxEREREfISCn7RbdnY2kydPZujQoSQmJvLYY48BcPDgQZKTk4mLiyM5OZmSkhIAdu3axZlnnklgYCAPP/zwUfv629/+RmJiIsOGDeNnP/sZNTU1bj+fzs6V7fHYY48xbNgwEhMTefTRR919Kl6jvW3yn//8hxEjRjB8+HDOOussvvzyy6Z9rVu3jvj4eOx2O8uWLTPlfLyBK9tkwYIF9O7dm2HDhplyLt7CVW3S2n6kFYZIO+Xl5Rlbt241DMMwysvLjbi4OGPnzp3GbbfdZixdutQwDMNYunSp8fvf/94wDMMoLCw0Nm/ebPzxj380Hnrooab95OTkGLGxsUZVVZVhGIZxxRVXGCtXrnTvyXgBV7XHV199ZSQmJhqHDh0y6uvrjSlTphiZmZnuPyEv0N42+fTTT42DBw8ahmEYa9euNcaPH28YhmE4HA5j0KBBxp49e4za2lpjxIgRxs6dO004o87PVW1iGIbx4YcfGlu3bjUSExPdfBbexVVt0tp+pGXq8ZN2i4yMZMyYMQB0796dhIQEcnNzSU9PZ/78+QDMnz+fNWvWANC7d2/GjRuHv7//cftyOBxUV1fjcDioqqoiKirKbefhLVzVHt988w0TJkwgODgYm83GpEmTeOWVV9x6Lt6ivW1y1llnER4eDsDEiRPJyckBYPPmzdjtdgYNGkRAQADz5s0jPT3d/SfkBVzVJgDnnnsuERER7j0BL+SqNmltP9IyBT85LXv37uWLL75gwoQJFBYWEhkZCUDfvn0pLCw84Wujo6O59dZb6d+/P5GRkYSGhnLhhRe6o2yvdTrtMWzYMD7++GOKi4upqqpi7dq1ZGdnu6Nsr9beNlmxYgXTpk0DIDc3l379+jU9FhMTo19oLnA6bSIdw1Vt0nw/0jKb2QVI51VZWcmsWbN49NFHCQkJOeoxi8WCxWI54etLSkpIT08nKyuLsLAwrrjiCv79739z9dVXd2TZXut02yMhIYElS5Zw4YUX0rVrV0aNGoXVau3Ikr1ee9vk/fffZ8WKFXzyySfuLNOnqE08j6va5ET7kR+px09OSX19PbNmzeKqq67i8ssvB6BPnz7k5+cDkJ+fT+/evU+4j/fee4+BAwfSq1cv/P39ufzyy/nf//7X4bV7I1e0B8DChQvZunUrH330EeHh4QwZMqRD6/Zm7W2THTt28Mtf/pL09HR69OgBNPaKN+91zcnJITo62o1n4V1c0SbiWq5qk5b2Iy1T8JN2MwyDhQsXkpCQwM0339y0fcaMGaSlpQGQlpbGzJkzT7if/v37s3HjRqqqqjAMg/Xr15OQkNChtXsjV7UHwP79+wHYt28fr7zyCldeeWXHFO3l2tsm+/bt4/LLL+e55547KmyPGzeOzMxMsrKyqKurY/Xq1cyYMcO9J+MlXNUm4jquapPW9iOtMHFiiXRSH3/8sQEYw4cPN0aOHGmMHDnSePPNN40DBw4Y559/vmG3240pU6YYxcXFhmEYRn5+vhEdHW10797dCA0NNaKjo42ysjLDMAzj7rvvNuLj443ExETj6quvNmpqasw8tU7Jle1xzjnnGAkJCcaIESOM9957z8zT6tTa2yYLFy40wsLCmp47duzYpn29+eabRlxcnDFo0CDjvvvuM+uUOj1Xtsm8efOMvn37GjabzYiOjjb++c9/mnVanZqr2qS1/UjLdMs2ERERER+hoV4RERERH6HgJyIiIuIjFPxEREREfISCn4iIiIiPUPATERER8REKfiIibnTPPffw8MMPm12GiPgoBT8RERERH6HgJyLSwe6//36GDBnCOeecw7fffgvA3//+d4YOHcqIESOYN2+eyRWKiK+wmV2AiIg327p1K6tXr2b79u04HA7GjBnD2LFjWbZsGVlZWQQGBlJaWmp2mSLiI9TjJyLSgT7++GMuu+wygoODCQkJabrX7ogRI7jqqqv497//jc2mv8FFxD0U/ERETPDmm2+yePFitm3bxrhx43A4HGaXJCI+QMFPRKQDnXvuuaxZs4bq6moqKip4/fXXaWhoIDs7m8mTJ/PAAw9QVlZGZWWl2aWKiA/Q+IKISAcaM2YMc+fOZeTIkfTu3Ztx48ZhsVi4+uqrKSsrwzAMbrzxRsLCwswuVUR8gMUwDMPsIkRERESk42moV0RERMRHKPiJiIiI+AgFPxEREREfoeAnIiIi4iMU/ERERER8hIKfiIiIiI9Q8BMRERHxEQp+IiIiIj5CwU9ERETERyj4iYiIiPgIBT8RERERH2EzuwBP07NnT2JjYzv8OPX19fj7+3f4cXyJ3lPX03vaMfS+up7e046h99X13PWe7t27lwMHDhy3XcHvGLGxsWzZsqXDj5OXl0dUVFSHH8eX6D11Pb2nHUPvq+vpPe0Yel9dz13vaVJSUovbNdQrIiIi4iMU/ERERER8hIKfiIiIiI9Q8BMRERHxEQp+IiIiIj5CwU9ERESOsiHzAAPve4+U1/aw/rsiGhoMs0sSF1HwExERkSbPbcnmp89spN5p8NEP5Vzw9EYG/WU9y9ZnUlhRa3Z5cpq0jp+IiIhgGAZ/WZ/JnW99S1JMKA9fMpRIWzWv7a3j+W25/GHtLu5c9y0XJ/TmhrNjuSCuF35+FrPLlnZS8BMREfFx9c4Gbnj5K/65aR/TzujNg5ckMKxvCHl5edx63mBuPW8wm34oYfmnWbyeUUj6zkJiQoO4/swBLJjQn8iQILNPQdpIwU9ERMSHVdQ4mPPcFtbtKmLh+H7cM3UIMWHBxz1vwoBwJgwI51BtPc9s2sd/t+Vy57pv+dPb3zItoQ83nDWAC+N7Y1UvoEdT8BMREfFReWU1XPTPTXyVX86dF8Rx06RBRAQHnPA1XQP9+d25g/nduYPZkl3C45/s5fWMQt7IKCQqJIhfTezPwon9iQ7t4qazkPZQ8BMREfFBOwsqmPbMRooP1fO3mYksGN+froHtiwVJ/cL518/Cqa5z8s9NP/Cfbbn86Z3vuPfd75ga33gt4E/je2Gzai6pp1DwExER8THv7z7AZSs/x+ZnIfWKEVwxMooA26mHsy4BVn7zk0H85ieD2J5bxt8/zuK1nQW8tWs/fbsHkjKxPwsn9Kd/+PFDyOJeCn4iIiI+5N9bc1jwf9vpF9qFxy5NZHpCH5fOzh0VHcqz80ZRU+/k2c3Z/GdrDv/v3Uzuey+TC+J6ccPZsVyU0Fu9gCZR8BMREfEBzZdrGRsTyiOXDGWSvWeHHS/I38oNZ8dyw9mx7Mg70gtYyKUrP6dPtwAWThjAdRP7ExuhXkB3UvATERHxcg5nAze88hXPbNzHtDN68eDFQxkWGeK244+ICuWfc0dRW+/kX1uy+ffWXJZtyGTp+kzOj+vJDWfFckliH/zVC9jhPPoddjqdjB49mosvvhiArKwsJkyYgN1uZ+7cudTV1QFQW1vL3LlzsdvtTJgwgb179zbtY+nSpdjtduLj43n77bfNOA0RERHTVNQ4uOTZzTyzcR8Lxvcj9YoRbg19zQX6W/nVmbF8/Ouz2XHrJH45oT9f55czK20LUfe+y+1vfsP3xYdMqc1XeHTwe+yxx0hISGj6fMmSJdx0003s3r2b8PBwVqxYAcCKFSsIDw9n9+7d3HTTTSxZsgSAjIwMVq9ezc6dO1m3bh033HADTqfTlHMRERFxt7yyGiY98SnvflvEH6fYeeiSoS2u0WeGxL4hpM4ZSfZdyfxzzkiG9unGQ+/vZvBfNjD5if/x4pd51DkazC7T63hs8MvJyeHNN9/kl7/8JdB4bcKGDRuYPXs2APPnz2fNmjUApKenM3/+fABmz57N+vXrMQyD9PR05s2bR2BgIAMHDsRut7N582ZTzkdERMSddhZUcObfP2HX/kr+OiORP06JO+kafWbwt/mxcEJ/Plx8NjtvO4/rzxzArv2VzFm1lch73+G213eSWVRpdplew2Ov8fvd737Hgw8+SEVFBQDFxcWEhYVhszWWHBMTQ25uLgC5ubn069cPAJvNRmhoKMXFxeTm5jJx4sSmfTZ/TXOpqamkpqYCUFBQQF5eXoeeG0BRUVGHH8PX6D11Pb2nHUPvq+vpPT3ap/sq+OVre7BZ4K8XxDBtYCBlxfspa+d+3P2+hgB3ndWTP0yIIH1XMS/sPMhfP/yehz/4ngnRXZk/qjc/tYcReBpLz5jN7K9Vjwx+b7zxBr1792bs2LF88MEHHX68lJQUUlJSAEhKSiIqKqrDjwm47Ti+RO+p6+k97Rh6X11P72mj/2zN4RevZLpsuRaz3tff9IvhN8mwu6iSv330PWu+LuCGN7MI6+LPL8bF8KszY4nv3c2U2k6XmV+rHhn8Pv30U1577TXWrl1LTU0N5eXl/Pa3v6W0tBSHw4HNZiMnJ4fo6GgAoqOjyc7OJiYmBofDQVlZGT169GjafkTz14iIiHgTwzBYun43d7y1i7ExoTx0cQKT43qZXdZps/fqxvJZI3hs5jCe355L2pYc/v7JXv72URZnDghn8dmxzBoRSZC/1exSOwWP7CtdunQpOTk57N27l9WrV3P++efzn//8h8mTJ/PSSy8BkJaWxsyZMwGYMWMGaWlpALz00kucf/75WCwWZsyYwerVq6mtrSUrK4vMzEzGjx9v2nmJiIh0BIezgV+9tIM73trFT+N7sXLuKK8Ifc3ZbH78PKkf711/Jt8tmcyvz44lu7Saq//7BX3veYffvvo1GQUVZpfp8Tyyx681DzzwAPPmzePOO+9k9OjRLFy4EICFCxfy85//HLvdTkREBKtXrwYgMTGROXPmMHToUGw2G8uXL8dq1V8EIiLiPSprHcxZtZW3du3nF+P6ce/UIfTz8lujDerZlX9cPpxHZybywpd5rPw8myf+t5e/f5LFhP5h3HB2LFeMjKKLegGPYzEMwzC7CE+SlJTEli1bOvw4eXl5uh7FxfSeup7e046h99X1fPU9zS+v4eIVm/kyt4wl59u5edJgenR13czdzvS+7i0+xKMfZ/HqVwXsK60mJNDGz5Ni+NWZAxhu0rqFLXHXe9panulUPX4iIiLSKKOggmnPbKLoUC2PzEjklxP60zXQd3+tx/boyqOXDuOvM4by0pf5rPw8m9SNP7D8072MjQll8dmxzBkZ5dPvESj4iYiIdDof7D7ApSs/x+Zn4ZkrRnLFyCgCOvESJ67k5+fHnNHRzBkdzb6SKh77OItXduSz4P++5MZXv+aqsTEsOmsAI6NCzS7VFAp+IiIinch/t+Vw7ertxIQG8fdLh532ci3erH94MI/MSOShixN49esCVmzKZuXmfTz92Q+Mjg7hhrNimTc6mm4+1AvoO2cqIiLSiRmGwbINu/nj2l2MiQ7l4Uu8Y7kWd/Dz82PWiChmjYgit6yaxz7K4uWv8rnuxR38ds1OrhwTzaKzBjAmJszsUjucgp+IiIiHczgbWPzKV6Ru3MfU+F48dPFQhkd5zoSFziQ6tAsPXjKUZRedwesZ+/nnxh94bksO/9y0jxGRISw+O5afjY6me5B3RiTvPCsREREvUVnrYO5zW1n7TeNyLfdMHUJ/L1+uxR38/PyYOawvM4f1paC8hr9/nMWLO/L41Us7+F3618wbFc2is2JJ6heKxeI9Q+kKfiIiIh6qoLyGi1ZsZntuGX84384t57l2uRZp1DckiL9clMD9089g7Tf7Sd34A89/kcvKz7MZ1rc7i86K5aox0YR28Te71NOm4CciIuKBMgoqmP7PTeyvrOWvWq7FLSwWCxcN7cNFQ/uwv6KGf3yylxe+zGPxK19xy2s7mTMyikVnxzKhf1in7QXUV5CIiIiH+XBP43ItVouF1NkjmDMqWsu1uFnv7kH8v2ln8OefxvP2t/tJ/WwfL+7IY9XWHBJ6d+OGs2O5emwMYZ2sF1DBT0RExIP8d1sOv1j9JVEhgfz90mFcNFTLtZjJYrHw0zP68NMz+nDgUC2Pf7KXF7/M4zevfs2tr2cwe0Qki86K5azY8E7RC6jgJyIi4gEMw+CBDbv5w9pdjIkO4eFLhmq5Fg/Ts2sg90yN556p8bz7bRFPf7aXNV8X8J9tuQzp1ZXrzxzA/HH9iAj23OswFfxERERM5nA28OtXv+bpz37gwiG9ePgSLdfi6ZLje5Ec34uDh+p4/NPGXsCbX8vg9je/4fLhjb2APxkU4XG9gAp+IiIiJmq+XMu1STHc+9N4LdfSiUR0DeDuC4dw94VDeD/zAE9+tpfXMwpZvT2PwT2CWXRWLPOTYujZLdDsUgEFPxEREdMUlNdw8YrNfJFbxu3n27lVy7V0apPjejI5rielVfU88b+9/N/2PG59PYM/rP2GmYl9ueHsWIZ0MUytUcFPRETEBN8UVjDtmU0UVtTyyCVD+eXEAT51z1hvFhbszx8viOOPF8Tx0Z4DPPm/H3jzm/28tCOfcVFd2XxLtGm16StMRETEzT7aU8zMlZsbl2u5YgRztVyL1zp3cE/OHdyT8pp6nvzfXg4eLDG1HgU/ERERN3p+Wy7Xrt5OVEggj106jIu1XItPCAnyZ8n5ceTl5Zlah4KfiIiIGxiGwYPv7+H2N79hTHQID10ylPO1XIu4mYKfiIhIB3M4G7hxzdc8+b/G5VoeujiBEdGhZpclPkjBT0REpAMdqnUw79/beCOjkPlJMfxZy7WIiRT8REREOshRy7VMHsytk+1arkVMpeAnIiLSAb4prGD6M5so0HIt4kH0FSgiIuJiH+0p5tKVn2OxoOVaxKMo+ImIiLjQ6i9ymf/8diJDAvm7lmsRD6PgJyIi4gKGYfDQ+3tY8uY3jI4O4WEt1yIeSMFPRETkNDVfriV5SE8eujiBkdFhZpclchwFPxERkdPQfLmWa8Y2LtcyIELLtYhnUvATERE5RYUVtVy8YhPbcspYMnkwt543mJ7dAs0uS6RVHjnFKDs7m8mTJzN06FASExN57LHHADh48CDJycnExcWRnJxMSUnjjY4Nw+DGG2/EbrczYsQItm3b1rSvtLQ04uLiiIuLIy0tzZTzERER77OrsIKJj33M1/kVPHzJUO5MHqLQJx7PI4OfzWbjkUceISMjg40bN7J8+XIyMjJYtmwZU6ZMITMzkylTprBs2TIA3nrrLTIzM8nMzCQ1NZVFixYBjUHx3nvvZdOmTWzevJl77723KSyKiIicqo+/L+asf3xKea2Dp2ePYPHZA7VGn3QKHhn8IiMjGTNmDADdu3cnISGB3Nxc0tPTmT9/PgDz589nzZo1AKSnp3PNNddgsViYOHEipaWl5Ofn8/bbb5OcnExERATh4eEkJyezbt06s05LRES8wP99kcsFT20kJMjGyrmjuGpsjNbok07D4/882bt3L1988QUTJkygsLCQyMhIAPr27UthYSEAubm59OvXr+k1MTEx5Obmtrr9WKmpqaSmpgJQUFBAXl5eR54SAEVFRR1+DF+j99T19J52DL2vrueO99QwDJ7aUsh9H+UyrFcX/nxeFEkRDRQW5Hf4sc2ir1XXM/s99ejgV1lZyaxZs3j00UcJCQk56jGLxYLF4poFMVNSUkhJSQEgKSmJqKgol+z3ZNx1HF+i99T19J52DL2vrteR76mzweDGV7/mif/lkhzXk4cu8Z3lWvS16npmvqce2zddX1/PrFmzuOqqq7j88ssB6NOnD/n5jX9Z5efn07t3bwCio6PJzs5uem1OTg7R0dGtbhcREWmrQ7UOLlv5OU/8by8/HxvDM3NG+kzoE+/jkcHPMAwWLlxIQkICN998c9P2GTNmNM3MTUtLY+bMmU3bV61ahWEYbNy4kdDQUCIjI5k6dSrvvPMOJSUllJSU8M477zB16lRTzklERDqfwopaJj/5GW9+U8jvJw/mrzOGao0+6dQ8cqj3008/5bnnnmP48OGMGjUKgL/85S/cfvvtzJkzhxUrVjBgwABeeOEFAKZPn87atWux2+0EBwezcuVKACIiIrjrrrsYN24cAHfffTcRERGmnJOIiHQu3+6vZNozm8grr+Ghi4eScuYAzdyVTs8jv4LPOeccDMNo8bH169cft81isbB8+fIWn79gwQIWLFjg0vpERMS7ffJ9MTOe/RyLBZ6ePYJ5o6MI/P/s3Xd4VGX2wPHvpPeekB4SEiAJKSSEJPQqCBosNBsoICs2xLqrrspv7QXLWuMqxV1lFRUQpffei9QESCAV0gsJKZP390dgFqQIZCYzSc7neXwkd2buPfdkcufMe99iYW7ssIRoMpMs/IQQQghj+X5PLuO+2007R2s+ui2SWyK8MTfTz2BCIYxNCj8hhBCCxv7l7605zjOLDhLj68R7t0YwsKOnscMSQq+k8BNCCNHmaRsUU+fv55ONmQw6N11LrIzcFa2QFH5CCCHatDM19dz9n10sPHCK++L9+L+hnWjvZm/ssIQwCCn8hBBCtFmnKmq49att7Mwu5Zl+HXi2fwc8HKyNHZYQBiOFnxBCiDbpwula3r4lgr/IdC2iDZB3uBBCiDZnw/EiRszcjlIyXYtoW5q0csc///lPSkpK9BWLEEIIYXA/7M1l0BdbcLC2YObYGO6N95eiT7QZTSr8Tp06RUJCAqNHj2bJkiVXnHRZCCGEMLbG6VqOMXrOTjp5OvD16BhSImWOPtG2NKnwe/XVV0lPT2fixInMmjWLsLAwnn/+eY4dO6av+IQQQogm0zYoHv95P0//cpCBYR7MGhvDwI6eaDRS9Im2pUmFHzQul+bt7Y23tzcWFhaUlJQwcuRInn32WX3EJ4QQQjRJVW09d87azscbM7k3zo9/jY6mq7+LscMSwiiaNLjjww8/ZM6cOXh4eDBp0iTeeecdLC0taWhoICwsjLfffltfcQohhBDX7XRFDbd+vY3tJ0t5ul8Iz/UPlelaRJvWpMKvuLiYn376iaCgoIu2m5mZsWjRoiYFJoQQQjTFkdOVDPvXVnLKzvLOrRFMTgrC0UYmsxBt2w39BRQXFwMwderUi34+z83NjfDw8CaGJoQQQtyYjRnFpHy9rXG6ljujGBvnJyN3heAGC7/4+Hg0Gs1lR/FqNBqOHz/e5MCEEEKIGzFvby73frubdg7WfHRbJLfIyF0hdG6o8MvIyLjiYzKlixBCCGNQSvH+uuM8/ctBorydmJESwYAwDxm5K8QFmjSq96WXXrro54aGBu69994mBSSEEEJcL22DYur8Azy18CD9O7jLdC1CXEGTCr+srCzeeOMNAGpqarj99tsJCwvTS2BCCCHEtSiuqmXyL8f554YM7onz46sxMTJdixBX0KThTV9//TX33HMPb7zxBqtXr2bYsGE88cQTegpNCCGEuFROWTXrjxc3/pdRxP78ClDwdN8Qnh0QiqdM1yLEFd1Q4bdr1y7dv6dOncpf/vIXevbsSZ8+fdi1axdxcXF6C1AIIUTbpZQireAM648XsT6jsdjLKK4CwNbSjCgfJyYnBhLjCvf26iTTtQjxJ27oL+Spp5666GdXV1cOHjzIU089hUajYdWqVXoJTgghRNtSr21gb275uSKviA0ZxZyurAXAxdaCrr7O3NbFm1hfJ5Lbu+LvYoutpTm5ublS9AlxDW7or2T16tX6jkMIIUQbVF2nZeuJEl2htzmzhMpaLQC+TtZ0C3Chq68TXf2c6R7ogreTDZbmTV5tVIg2q0lfj06dOsXzzz9Pbm4uixcv5uDBg2zevJmJEyfqKz4hhBCtSElVLRszSxpv3R4vZkd2KXXaxmnAOrjbMbSzF7G+TnQLcCHWzxkPeyuZg08IPWpS4Xf//ffzwAMP8NprrwHQsWNHxowZI4WfEEIIALJLGwdibMj430AMpcDCTENEOwfuivUj1s+JxEBXOnk54GZnKVOwCGFATSr8CgsLGT16tG5KFwsLC8zNZUkcIYRoi5RSHDldqRuEsf54EZkl1UDjQIxoHyf+khRErK8TSe1dCXGzl355QjSzJv3F2dvbU1RUpPt2tmXLFpydnfUSmBBCCNNWr21gT265bsTthuPFFJxpHIjhamtJrK8Tt0f50NXPiaSg/w3EEEIYT5MKvxkzZpCSksKxY8fo2bMnBQUFzJs3T1+xCSGEMCFVtfVsO1l62YEYfk42dA90oaufM7G+TjIQQwgT1aTCLy4ujrVr13LkyBGUUnTq1AlLS0t9xSaEEMKIiqtq2Xj+tm1GMTvPDcTQAB087Li5sxexfo0DMWJ8ZSCGEC1Bkwq/qqoqZsyYwYkTJ/jyyy9JT0/nyJEj3HLLLfqKr8mWLFnC1KlT0Wq1TJo0ib/+9a/GDkkIIUzS+YEY6zMaR9zuz68ALh6I0dXPie5BrnT2csDVVgZiCNHSNKnwe+CBB4iPj2fz5s0A+Pn5MWrUKJMp/LRaLY888gjLly/H39+fhIQEUlJSiIiIMHZoQghhVEopDp+u/N+I2wsGYthZmhPt48hDyUG6/nnBMhBDiFahSX/Fx44d47///S/fffcdAHZ2diil9BKYPmzbto3Q0FBCQkIAGDt2LAsWLJDCTwjR5tRrG9idU65rzduQUUzhhQMx/Jy449xAjEQZiCFEq9Wkws/Kyorq6mpdU/+xY8ewtjadxbFzcnIICAjQ/ezv78/WrVsveV5qaiqpqakA5Ofnk5uba/DYCgoKDH6MtkZyqn+SU8NojrxW1zWwK+8M23Iq2Jpdyc68M1TVNQDg7WBJ13a2RHm6EuVlS4y3Pe4O1lic759XV05JQTklBo9Sf+S9ahiSV/0zdk6bVPhNnz6doUOHkpWVxT333MPGjRuZNWuWnkJrPpMnT2by5MkAdOvWDV9f32Y5bnMdpy2RnOqf5NQw9J3X4qpaNugmSr54IEaohz3DI9rpVsSI8XXG094Ks1Y2EEPeq4YhedU/Y+a0SYXf7NmzGT58OCNHjiQkJIQPP/wQDw8PfcXWZH5+fmRlZel+zs7Oxs/Pz4gRCSGEfmSVVOtu264/XsyBUxcOxHDk7q5+uvVtO8lADCHEOU0q/CZOnMj69etZvnw5x44do2vXrvTp04epU6fqK74mSUhIID09nYyMDPz8/Jg7dy7ffvutscMSQojrcuFAjPPF3okrDMRIDnKlvQzEEEJcQZOuDP3796dPnz5s376d1atX8/nnn3PgwAGTKfwsLCz4+OOPGTJkCFqtlgkTJhAZGWnssIQQ4qrqtA3szinTLXu2IaOYoqo6ANzsGlfEGBntQ1c/ZxKDXPB3tsVGBmIIIa5Bkwq/gQMHcubMGZKTk+nduzfbt2/Hy8tLX7HpxbBhwxg2bJixwxBCiCuqqq1ny4lS3dJnW06UcOb8ihjONiQHuRLr59w4h16gC+0cZUUMIcSNaVLhFx0dzc6dO9m/fz/Ozs64uLiQnJyMra2tvuITQohWp7i6nh3783VLn+3MLqO+4X8DMYaFe9HV15lugc5E+7TOgRhCCONoUuH3/vvvA1BRUcGsWbN44IEHyM/Pp6amRi/BCSFEa3Cmpp51x4tYnlbIivQCfs9rHIhhad44EOPeOD9i/ZxJPDcQw0UGYgghDKRJhd/HH3/M+vXr2blzJ+3bt2fChAn07t1bX7EJIUSLpG1Q7MouY3laAcvTCtiUWUytVmFlriHW15kHYtxJDvMjKchFBmIIIZpVk642Z8+e5cknnyQ+Ph4LC7lwCSHarsziKpanFbDsSAGrjhZSfG4wRkcPe8bE+pIY6ErPYDfCPOwpKzotc6MJIYyiSdXa008/ra84hBCiRSmtrmP10UJdq97RwioAPO2t6BHkSmKQKz3auxLj64yb3cW3bsuMFbQQos2TZjohhLgGddoGtpwoOVfoFbLtZAkNCmwtzYj3d2FEpDeJgS4kt3fFx8kWcxmMIYQwQVL4CSHEZSilOHK6kuVphSxLK2DN0UIqa7WYaSDcy5EHEgJIDHSlV4gb7d3ssJV59IQQLYAUfkIIcU5BZQ0r0v53+za77CzQOJfeTZ08dYVeRDtHXGwtjRytEEJcPyn8hBBtVnWdlg3Hi3WF3p7ccgAcrS1ICHDmvnh/EoNcSAx0xcvBWubSE0K0eFL4CSHajIYGxb68cpYdaSz0NmQUc7a+AQszDdE+TkzpEdTYqhfsRoCLLVYWsjqGEKJ1kcJPCNGqZZdW6wZkrEgroOBMLQAhbnbc3sWbxCBXegW70tHTUebTE0K0enKVE0K0KhVn61lzrJDl5/rqHT5dCYC7nSXdAxtv2yYHudLV3xkPeytZIUMI0aZI4SeEaNHqtQ3sOLdKxrIjBWw5UUJ9g8La3Iw4f2ee6B1MYmDjnHq+zjZYmMvtWyFE2yWFnxCiRVFKcayoSjcgY1V6IWVn69EAnbwcuCfOj8RAF3qHuBHibo+dlVzmhBDiPLkiCiFMXnFVLSvTz02zcqSAzJJqALwdrenXwV23HFoXH0fc7KyMHK0QQpguKfyEECanpl7L5swSlp1r1duZXYZSYG9lTjd/F0bH+pJ4rr+et5ONrJIhhBDXSAo/IYTRKaU4kF+hG3279lgRVXVazDUQ6e3EpO6BJAa50Cu4cZUMawtZJUMIIW6EFH5CCKPIKz/LigumWcmrqAEg0NWW4RFeuvn0wts54GQjq2QIIYQ+SOEnhGgWVbX1rDterJs8eX9+BQDONhZ0D3RhYmIgSUEudAtwxdPeSlbJEEIIA5DCTwhhENoGxe6cMt3o240ZxdRqFZbmGmJ9nXi0Z3uSghqnWfF3scVSplkRQgiDk8JPCKE3mcX/m2ZlZXohxVV1AIR52DM6xrfx9m2IG6Ee9jhYy+VHCCGam1x5hRA3rKy6jtVHG1fJWJZ2mqOFVQB42lvRI8iVxHMtejG+zrjZWcoqGUIIYWRS+AkhrlmdtoGtJ0p0y6FtyypF26CwtTQj3t+FEZHeJAa6kNzeFW9HWSVDCCFMjRR+QogrUkpx+FSFrtBbc6yIipp6zDQQ7uXI+G7+JAa60CvEnWA3O2wtZZoVIYQwZVL4CSHQNiiySqtJK6gkveAMaYVnSCuoZG92KXmVjf30/JxtGNzR49wqGa5EejvhYivTrAghREsihZ8QbYRSirzyGtILK0krONNY4BVUkl54hqOFZ6jVKt1zbS3NCHCxpbO7Dfd3DyIxqHGVDC8Ha5lmRQghWjAp/IRoZYrO1JJ+rsWusbg7Q3ph478ra7W651maa/B3tiHQxY54fxcCXWwIdLUlzNOBUHc7XGytKC86hZ+fnxHPRgghhD6ZXOH3zDPP8Msvv2BlZUWHDh2YOXMmLi4uALzxxht89dVXmJub89FHHzFkyBAAlixZwtSpU9FqtUyaNIm//vWvAGRkZDB27FiKioqIj4/nm2++wcpKFnAXLV9lTf1FLXZpBf8r9Iqr63TPM9OAr5MNgS623BLRjgAXW4JcbQl2t6OzpwNu9lY4WVtcsRWvQkbhCiFEq2Jyhd/gwYN54403sLCw4LnnnuONN97grbfe4uDBg8ydO5cDBw6Qm5vLoEGDSEtLA+CRRx5h+fLl+Pv7k5CQQEpKChERETz33HNMmzaNsWPH8tBDD/HVV18xZcoUI5+hENfmbJ2W40VVly3uzi9vdl47BysCXGzpF+pOoIstga62BLvZEd7OEU+HxuJORtgKIYQwucLvpptu0v07KSmJefPmAbBgwQLGjh2LtbU1wcHBhIaGsm3bNgBCQ0MJCQkBYOzYsSxYsIDw8HBWrVrFt99+C8D48eN55ZVXpPATJqVe28CJksZBFY23ZP9X3J0orUb9r9sdrraWBLra0i3AhUAXWwJcbQhysSPSxxFvR2ucbSyxspDiTgghxJWZXOF3oa+//poxY8YAkJOTQ1JSku4xf39/cnJyAAgICLho+9atWykqKsLFxQULC4tLni9Ec2poUOSUnf1fUXeu9S69oJLjxVXUXTCowt7KnCBXWzp5OTC4oyeBrrYEutgS3s6BABdbnG0tZcoUIYQQN8wohd+gQYPIz8+/ZPtrr73GiBEjdP+2sLDgnnvuMXg8qamppKamApCfn09ubq7Bj1lQUGDwY7Q1xsypUoqi6noySmo4XnKW4+f+n1FSQ0bpWc7W/6+4szLX4OdoiZ+jFV07u+DnaIW/kxWhrjb4O1vjaGOBjYXZH1a5qKahspqSSihpxvOS96lhSF71T3JqGJJX/TN2To1S+K1YseKqj8+aNYtFixaxcuVK3Yefn58fWVlZuudkZ2frRhtebru7uzulpaXU19djYWFx0fP/aPLkyUyePBmAbt264evr26Tzu1bNdZy2xNA5Lauuu2TE7PlWvLKz9brnmZtp8HO2IcjFlq4Bbuda7mwI87AnzNMBVztLHKyuPKjClMj71DAkr/onOTUMyav+GTOnJnerd8mSJbz99tusXbsWOzs73faUlBTuvvtunnzySXJzc0lPT6d79+4opUhPTycjIwM/Pz/mzp3Lt99+i0ajoX///sybN4+xY8cye/ZsXWuiEFdTXafl6Lni7o/z3Z2urNU9TwN4O1oT6GrLTeduywa42BLibkd4Owfc7axwsrHEvAUUd0IIIdoGkyv8Hn30UWpqahg8eDDQOMDj888/JzIyktGjRxMREYGFhQWffPIJ5uaNfZ0+/vhjhgwZglarZcKECURGRgLw1ltvMXbsWF588UW6du3KxIkTjXZewrTU1jeQUVx16Xx3BZVklZ296LnudpYEudqSHORKoKstQedGzUZ6O+LlYI2zrSWWMmJWCCFEC2Byhd/Ro0ev+NgLL7zACy+8cMn2YcOGMWzYsEu2h4SE6Eb+irbn/DJkf5zvLr2gkoySarQN/+t352RtQaCrLVG+TgyPaKebEiWinQN+zrY421pgbSGDKoQQQrRsJlf4CXG9lFLsyytn+b5CCnaV6vrdHSuqoqa+Qfe888uQBbvZ0afDufnuXGzp3M6B9m52ONtYYGclfxJCCCFaL/mUEy1SVW09q44WsejgKX49eIrsc7dnL16GzFnXcnfhMmQO1uZ/GDErhBBCtA1S+IkWI6ukml8PnWLRwVOsTC/kbH0DtpZmJAW68kD3ADra19EjPORPlyETQggh2iop/ITJ0jYotp0sYdHBxmJvX14FAH5ONtzWxZtewW706+BOBw97bCzNyc3NxdfD3shRCyGEEKZLCj9hUkqr61h2pIBFB0+x+PBpCs/UYq6BGF9nHu8VTO8QN5KDXPFxspEWPSGEEOI6SeEnjEopRVrBGV2r3oaMYuobFM42FvRo70qvYDf6dnAnyscJJxtLY4crhBBCtGhS+IlmV1vfwLrjRbr+ekcLqwDo4G7HvXF+9Apxo2+IB0FutjI/nhBCCKFHUviJZnGqoobFh06z6NAplh0poKKmHitzDd0CXLgzyodewW50D3TF08FKRtwKIYQQBiKFnzAIpRR7cspZdK5Vb3tWKUqBp70Vg8I86B3iRt8Qdzp5OWBvLW9DIYQQojnIJ67QmzM19axML2TRoca59XLLa9AAkd6O/CUpiF7BbvQOdsPPxVbWrxVCCCGMQAo/0SQniqsaJ1E+dJpVRwupqW/A3sqcxEAXHkwMoneIG3H+zrjaWRk7VCGEEKLNk8JPXJd6bQNbTpTw66HTLDp4iv35jXPrBbjYcEeUN72D3egb6kEHdztZ21YIIYQwMVL4iT9VUlXL0gvm1iuuqsPcTENXXyee6BNM72A3ktu70c7BWubWE0IIIUyYFH7iEkopDp+u1N3C3ZBRjLZB4WJrQc/2jf30+nRwp4u3E4428hYSQgghWgr51BYA1NRrWXusqPEW7oFTHC9unFuvo4c94+L9dcVeoKvMrSeEEEK0VFL4tWH55Wf57YK59c7UarE2NyMh0IXRsb70au9KYpAr7vYyt54QQgjRGkjh14Y0NCh255Q1Lo926BQ7ssoAaOdgxdBOnvQOcadvB3c6etpjZyVvDSGEEKK1kU/3Vq6ypp4VaQUsOnia3w6dIq+icW69KB9HHu7ROLder2A3fJ1lbj0hhBCitZPCrxXKKKrSrYO7+mghtVqFvZU5PYJc+UtyEH06uNHVzwUXW0tjhyqEEEKIZiSFXytQr21g84mSxlu4B09x8FQlAIGutoyK8W2cW6+DO8Eyt54QQgjRpknh10IVV9Wy5PBpFh08zZLDpympbpxbL97PmSf7hNAr2I3k9q60c7SWgRlCCCGEAKTwazGUUhw8dX5uvVNszCimQYGrraWun16/Du5EeDviYC2/ViGEEEJcSioEE3a2TsuaY0X8eu4WbmZJNQCdPO2Z0D2AXu0b59YLcLHFQubWE0IIIcSfkMLPxOSWneW3cwMzlqcVUlWnxdrCjMRAF+6O86NXsBsJAS54OFgbO1QhhBBCtDBS+BlZQ4NiZ/b/5tbbld04t563ozXDw70ab+GGuhPm6YCtpQzMEEIIIcSNk8LPCCrO1vNbegmb15/m14OnOVVZg5kGonyceLRne3oFu9Ez2BVfJ1vMZG49IYQQQuiJFH7NLLu0mpDXV1KnVThaW5Ac5ErvEDf6hLgT7eskc+sJIYQQwmCk8Gtmfs42PNc/FFdNNcPjQgl2s8fKQgZmCCGEEMLwTLbieO+999BoNBQWFgKN05k8/vjjhIaGEh0dza5du3TPnT17NmFhYYSFhTF79mzd9p07dxIVFUVoaCiPP/44SqlmP48/0mg0/OPmzoyNbkcnL0cp+oQQQgjRbEyy6sjKymLZsmUEBgbqti1evJj09HTS09NJTU1lypQpABQXFzN9+nS2bt3Ktm3bmD59OiUlJQBMmTKFL7/8Uve6JUuWGOV8hBBCCCFMgUkWftOmTePtt9++aMWJBQsWMG7cODQaDUlJSZSWlpKXl8fSpUsZPHgwbm5uuLq6MnjwYJYsWUJeXh7l5eUkJSWh0WgYN24c8+fPN95JCSGEEEIYmcn18VuwYAF+fn7ExMRctD0nJ4eAgADdz/7+/uTk5Fx1u7+//yXbLyc1NZXU1FQA8vPzyc3N1ecpXVZBQYHBj9HWSE71T3JqGJJX/ZOcGobkVf+MnVOjFH6DBg0iPz//ku2vvfYar7/+OsuWLWvWeCZPnszkyZMB6NatG76+vs1y3OY6TlsiOdU/yalhSF71T3JqGJJX/TNmTo1S+K1YseKy23///XcyMjJ0rX3Z2dnExcWxbds2/Pz8yMrK0j03OzsbPz8//Pz8WLNmzUXb+/Xrh5+fH9nZ2Zc8XwghhBCirTKpPn5RUVGcPn2azMxMMjMz8ff3Z9euXXh7e5OSksKcOXNQSrFlyxacnZ3x8fFhyJAhLFu2jJKSEkpKSli2bBlDhgzBx8cHJycntmzZglKKOXPmMGLECGOfohBCCCGE0ZhcH78rGTZsGL/99huhoaHY2dkxc+ZMANzc3Pj73/9OQkICAC+99BJubm4AfPrpp9x///1UV1dz8803c/PNN//pcTIzM+nWrZvhTuScgoICPD09DX6ctkRyqn+SU8OQvOqf5NQwJK/611w5zczMvOx2jTKFye3aoG7durFjxw5jh9GqSE71T3JqGJJX/ZOcGobkVf+MnVOTutUrhBBCCCEMRwo/IYQQQog2Qgo/Izk/fYzQH8mp/klODUPyqn+SU8OQvOqfsXMqffyEEEIIIdoIafETQgghhGgjpPC7Aa+99hqRkZFER0cTGxvL1q1bm7zPV155hXfffVcP0bUsGo2Ge++9V/dzfX09np6e3HLLLXrZf1vLa1FREbGxscTGxuLt7Y2fn5/u59raWr0dZ82aNXr7HRnbtGnT+OCDD3Q/DxkyhEmTJul+fuqpp5gxY8af7iczM5MuXboYIkQdBwcHg+7f0K70/nRxcSEiIsLgx581axaPPvqowY9jaszNzXV5j42Nvew0H8OGDaO0tPSS7W3tGvpH1/N5P2vWLL0s+dq+fXsKCwubvJ8raTHz+JmKzZs3s2jRInbt2oW1tTWFhYV6/UBta+zt7dm/fz/V1dXY2tqyfPlyWWGlCdzd3dmzZw/QeMF2cHDg6aefNm5QJq5nz558//33PPHEEzQ0NFBYWEh5ebnu8U2bNvH+++8bMcLW40rvz8zMzCZ9kaivr8fCQj7OrsTW1laX9z9SSqGU4rfffmveoFqA6/28nzVrFl26dLmu5diM8d6VFr/rlJeXh4eHB9bW1gB4eHjg6+t7UYW+Y8cO+vXrBzRe3CZMmEC/fv0ICQnho48+0u3rtddeo2PHjvTq1YsjR47otn/55ZckJCQQExPDnXfeSVVVFRUVFQQHB1NXVwdAeXn5RT+3ZMOGDePXX38F4LvvvuOuu+7SPVZcXMxtt91GdHQ0SUlJ7Nu3D5C8Xo/777+fefPm6X6+sNXonXfeISEhgejoaF5++WUAzpw5w/Dhw4mJiaFLly7897//BWDJkiV07tyZuLg4fvrpJ90+tm3bRnJyMl27dqVHjx66nPfp0+eiD5tevXqxd+9eQ57qDenRowebN28G4MCBA3Tp0gVHR0dKSkqoqanh0KFDaDQa+vbtS3x8PEOGDCEvLw+AnTt3EhMTQ0xMDJ988olun7NmzeKOO+5g6NChhIWF8eyzz+oeW7ZsGcnJycTFxTFq1CgqKysB+Otf/0pERATR0dG6Yj0jI4Pk5GSioqJ48cUXdfuorKxk4MCBxMXFERUVxYIFC4DGCewvbL184YUX+PDDDw2TOD3TarU8+OCDREZGctNNN1FdXQ1Av379dHOeFRYW0r59e6AxxykpKQwYMICBAweSl5dHnz59iI2NpUuXLqxfvx6AmTNn0rFjR7p3787GjRt1x/vll19ITEyka9euDBo0iFOnTtHQ0EBYWBgFBQUANDQ0EBoaqvu5tcjMzKRTp06MGzeOLl26kJWVddFnmFxDG13p8/7//u//SEhIoEuXLkyePBmlFPPmzWPHjh3cc889xMbGUl1dfdW64L777qNnz57cd999FBUVcdNNNxEZGcmkSZO4cOjFbbfdRnx8PJGRkaSmpgLw9ddf88QTT+ie8+WXXzJt2rRrPzElrktFRYWKiYlRYWFhasqUKWrNmjVKKaWCgoJUQUGBUkqp7du3q759+yqllHr55ZdVcnKyOnv2rCooKFBubm6qtrZW7dixQ3Xp0kWdOXNGlZWVqQ4dOqh33nlHKaVUYWGh7ngvvPCC+uijj5RSSt1///3q559/Vkop9cUXX6gnn3yymc7acOzt7dXevXvVnXfeqaqrq1VMTIxavXq1Gj58uFJKqUcffVS98sorSimlVq5cqWJiYpRSktdr8fLLL6t33nlHjR8/Xv3www+67fb29koppZYuXaoefPBB1dDQoLRarRo+fLhau3atmjdvnpo0aZLu+aWlpaq6ulr5+/urtLQ01dDQoEaNGqX7HZWVlam6ujqllFLLly9Xd9xxh1JKqVmzZqmpU6cqpZQ6cuSIio+Pb47TviHt27dXJ06cUJ9//rn67LPP1Isvvqh+/fVXtWHDBpWUlKSSk5PV6dOnlVJKzZ07Vz3wwANKKaWioqLU2rVrlVJKPf300yoyMlIppdTMmTNVcHCwLneBgYHq5MmTqqCgQPXu3VtVVlYqpZR688031fTp01VhYaHq2LGjamhoUEopVVJSopRS6tZbb1WzZ89WSin18ccf6353dXV1qqysTCmlVEFBgerQoYNqaGhQGRkZqmvXrkoppbRarQoJCbnofW9Kzr8/lVIqIyNDmZubq927dyullBo1apT65ptvlFJK9e3bV23fvl0p1XiuQUFBSqnGHPv5+amioiKllFLvvvuuevXVV5VSStXX16vy8nKVm5urAgIC1OnTp1VNTY3q0aOHeuSRR5RSShUXF+vy/eWXX+r+7l955RX1/vvvK6Ua/0bOv59bMjMzMxUTE6NiYmLUbbfdpjIyMpRGo1GbN2/WPef8Z5hcQ//nSp/3599zSil17733qoULFyqlLn6vKnX1uiAuLk5VVVUppZR67LHH1PTp05VSSi1atEgButedP1ZVVZWKjIxUhYWFqqKiQoWEhKja2lqllFLJyclq375913xe0uJ3nRwcHNi5cyepqal4enoyZswYZs2addXXDB8+HGtrazw8PPDy8uLUqVOsX7+e22+/HTs7O5ycnEhJSdE9f//+/fTu3ZuoqCj+85//cODAAQAmTZqkW6pu5syZPPDAAwY7z+YUHR1NZmYm3333HcOGDbvosQ0bNnDfffcBMGDAAIqKinS34SSvTbNs2TKWLVtG165diYuL4/Dhw6SnpxMVFcXy5ct57rnnWL9+Pc7Ozhw+fJjg4GDCwsIu6ZdZVlbGqFGj6NKlC9OmTdPlddSoUSxatIi6ujq+/vpr7r//fiOd6Z/r0aMHmzZtYtOmTSQnJ5OcnKz72c/Pj/379zN48GBiY2N59dVXyc7OprS0lNLSUvr06QOge5+eN3DgQJydnbGxsSEiIoITJ06wZcsWDh48SM+ePYmNjWX27NmcOHFC97yJEyfy008/YWdnB8DGjRt1LeAX7l8pxfPPP090dDSDBg0iJyeHU6dO0b59e9zd3dm9e7fud+vu7t5MWWya4OBgYmNjAYiPj7/iclMXGjx4sG6JzoSEBGbOnMkrr7zC77//jqOjI1u3bqVfv354enpiZWXFmDFjdK/Nzs5myJAhREVF8c477+jetxMmTGDOnDlAY8tKa7genL/Vu2fPHn7++WcAgoKCSEpKuuS5cg39nyt93q9evZrExESioqJYtWqVLg/XIyUlBVtbWwDWrVunu6YOHz4cV1dX3fM++ugjYmJiSEpKIisri/T0dBwcHBgwYACLFi3i8OHD1NXVERUVdc3Hlk4RN8Dc3Jx+/frRr18/oqKimD17NhYWFjQ0NABw9uzZi55/vpn4/Gvr6+uvuv/777+f+fPnExMTw6xZs1izZg3Q2BcpMzOTNWvWoNVqDd6RvDmlpKTw9NNPs2bNGoqKiq7pNZLXa3Phe7OhoUHXR0Upxd/+9jf+8pe/XPKaXbt28dtvv/Hiiy8ycODAiy7+f/T3v/+d/v378/PPP5OZmam7nWFnZ8fgwYNZsGAB33//PTt37tT/yelJz5492bRpE7///jtdunQhICCA9957DycnJ/r160dOTo7udvB5l+sIf6HLvT+VUgwePJjvvvvukudv27aNlStXMm/ePD7++GNWrVoFNA6A+qP//Oc/FBQUsHPnTiwtLWnfvr3uujNp0iRmzZpFfn4+EyZMuN5UGM0f83X+Vu/Vrq329va6f/fp04d169bx66+/cv/99/Pkk0/i5OR0xeM99thjPPnkk6SkpLBmzRpeeeUVAAICAmjXrh2rVq1i27Zt/Oc//9HXKZqUC3N3rdriNfSPn/dffPEF+/btY8eOHQQEBPDKK69c8r4871rfu1eyZs0aVqxYwebNm7Gzs6Nfv34X/Z2//vrrdO7c+boLbWnxu05HjhwhPT1d9/OePXsICgqiffv2ug+2H3/88U/306dPH+bPn091dTUVFRX88ssvuscqKirw8fGhrq7ukovOuHHjuPvuu1vNN6rzJkyYwMsvv3zJt5bevXvrcrBmzRo8PDyuejGXvF7qwvfmwoULdX1vhgwZwtdff63rY5aTk8Pp06fJzc3Fzs6Oe++9l2eeeYZdu3bRuXNnMjMzOXbsGMBFhUtZWZluQM4fW78nTZrE448/TkJCwkXfYk1Njx49WLRoEW5ubpibm+Pm5kZpaSmbN2/mrrvuoqCgQFf41dXVceDAAVxcXHBxcWHDhg0A11QgJCUlsXHjRo4ePQo09qdMS0ujsrKSsrIyhg0bxvvvv6/rC9mzZ0/mzp17yf7Lysrw8vLC0tKS1atXc+LECd1jt99+O0uWLGH79u0MGTJEPwkyogvfvxf2Vf2jEydO0K5dOx588EEmTZrErl27SExMZO3atRQVFVFXV8cPP/yge/6F79vZs2dftK9JkyZx7733MmrUKMzNzQ1wVqZLrqH/c7nP+06dOgGN/f0qKysvek86OjpSUVGh+/la64I+ffrw7bffArB48WJKSkqAxveoq6srdnZ2HD58mC1btuhek5iYSFZWFt9+++1F/eKvhbT4XafKykoee+wxSktLsbCwIDQ0lNTUVA4dOsTEiRP5+9//rmvxuJq4uDjGjBlDTEwMXl5eJCQk6B77xz/+QWJiIp6eniQmJl70Rrrnnnt48cUXr/sXber8/f15/PHHL9l+fhBHdHQ0dnZ2l1yg/0jyeqkHH3yQESNGEBMTw9ChQ3XfNG+66SYOHTpEcnIy0Hhb49///jdHjx7lmWeewczMDEtLSz777DNsbGxITU1l+PDh2NnZ0bt3b13+nn32WcaPH8+rr77K8OHDLzp2fHw8Tk5OJv9hEBUVRWFhIXffffdF2yorK/Hy8mLevHk8/vjjlJWVUV9fzxNPPEFkZCQzZ85kwoQJaDQabrrppj89jqenJ7NmzeKuu+6ipqYGgFdffRVHR0dGjBjB2bNnUUrppo/58MMPufvuu3nrrbcYMWKEbj/33HMPt956K1FRUXTr1o3OnTvrHrOysqJ///64uLi0iqLl6aefZvTo0br335WsWbOGd955B0tLSxwcHJgzZw4+Pj688sorJCcn4+LioruVDI3XllGjRuHq6sqAAQPIyMjQPZaSksIDDzxg8u9bQ5Br6P9c6fPexcWFLl264O3tfVF+7r//fh566CFsbW3ZvHkzL7/88jXVBS+//DJ33XUXkZGR9OjRg8DAQACGDh3K559/Tnh4OJ06dbrk1vzo0aPZs2fPdX+plpU7Wph58+axYMECvvnmG2OH0qpIXg0jNzeXfv36cfjwYczM5AZDc2hoaCAuLo4ffviBsLAwY4fTIu3YsYNp06bpRgaLPyfX0OZ3yy23MG3aNAYOHHhdr5MWvxbkscceY/HixTLfkp5JXg1jzpw5vPDCC8yYMUOKvmZy8OBBbrnlFm6//XYp+m7Qm2++yWeffdZq+/YZglxDm1dpaSndu3cnJibmuos+kBY/IYQQQog2Q76GCyGEEEK0EVL4CSGEEEK0EVL4CSGEEEK0EVL4CSHaLHNzc2JjY4mMjCQmJob33ntPN+GqoTzzzDNERkbyzDPPGPQ4mZmZrWoiXSGEfsioXiFEm3V+KSuA06dPc/fdd1NeXs706dMNdszU1FSKi4tbxRx7QoiWR1r8hBAC8PLyIjU1lY8//hilFJmZmfTu3Zu4uDji4uLYtGkT0LhCwfz583Wvu+eee1iwYMFF+1JK8cwzz9ClSxeioqL473//CzRODFxZWUl8fLxu23lRUVGUlpailMLd3V23Xuy4ceNYvnw5Wq2WZ555hoSEBKKjo/niiy90r33nnXd0219++eVLzu348eN07dqV7du36yVXQoiWS1r8hBDinJCQELRaLadPn8bLy4vly5djY2NDeno6d911Fzt27GDixIm8//773HbbbZSVlbFp06ZLVpT56aef2LNnD3v37qWwsJCEhAT69OnDwoULcXBw0LUyXqhnz55s3LiRoKAgQkJCWL9+PePGjWPz5s189tlnfPXVVzg7O7N9+3Zqamro2bMnN910E+np6aSnp7Nt2zaUUqSkpLBu3Trd7P9Hjhxh7NixzJo1i5iYmOZIoxDChEnhJ4QQl1FXV8ejjz7Knj17MDc3Jy0tDYC+ffvy8MMPU1BQwI8//sidd96JhcXFl9INGzZw1113YW5uTrt27ejbty/bt28nJSXlisfr3bs369atIygoiClTppCamkpOTg6urq7Y29uzbNky9u3bp1sbtKysjPT0dJYtW8ayZcvo2rUr0LjMVHp6OoGBgRQUFDBixAh++uknIiIiDJQpIURLIoWfEEKcc/z4cczNzfHy8mL69Om0a9eOvXv30tDQgI2Nje5548aN49///jdz585l5syZejl2nz59+OSTTzh58iSvvfYaP//8M/PmzaN3795A4+3jf/7znwwZMuSi1y1dupS//e1v/OUvf7loe2ZmJs7OzgQGBrJhwwYp/IQQgPTxE0IIAAoKCnjooYd49NFH0Wg0lJWV4ePjg5mZGd988w1arVb33Pvvv58PPvgA4LIFVe/evfnvf/+LVquloKCAdevW0b1796sePyAggMLCQtLT0wkJCaFXr168++679OnTB4AhQ4bw2WefUVdXB0BaWhpnzpxhyJAhfP3111RWVgKQk5PD6dOnAbCysuLnn39mzpw5fPvtt03OkRCi5ZMWPyFEm1VdXU1sbCx1dXVYWFhw33338eSTTwLw8MMPc+eddzJnzhyGDh2Kvb297nXt2rUjPDyc22677bL7vf3229m8eTMxMTFoNBrefvttvL29/zSexMREXYHZu3dv/va3v9GrVy8AJk2aRGZmJnFxcSil8PT0ZP78+dx0000cOnSI5ORkABwcHPj3v/+tGzVsb2/PokWLGDx4MA4ODle93SyEaP1krV4hhLhOVVVVREVFsWvXLpydnY0djhBCXDO51SuEENdhxYoVhIeH89hjj0nRJ4RocaTFTwghhBCijZAWPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINkIKPyGEEEKINsLC2AGYGg8PD9q3b2/w49TV1WFpaWnw47QFkkvDkLzqn+TUsCS/hiF51b/myGlmZiaFhYWXbJfC7w/at2/Pjh07DH6c3NxcfH19DX6ctkByaRiSV/2TnBqW5NcwJK/61xw57dat22W3y61eIYQQQog2Qgo/IYQQQog2Qgo/IYQQQog2Qgo/IYQQQog2Qgo/IYQQQog2Qkb1CiGEEEJcoE7bwGebMtmTU46DtQXT+oQQ7G5n7LD0Qgo/IYQQQohzTpZUcetX29mXV46XgxVl1fV8vjmTT++IYlJSkLHDazKTvdX7/vvvExkZSZcuXbjrrrs4e/YsGRkZJCYmEhoaypgxY6itrQWgpqaGMWPGEBoaSmJiIpmZmbr9vPHGG4SGhtKpUyeWLl1qpLMRQgghhKmrrKkn5evtHCs6w3u3RrD7yb7seaoPsb5OPPTj7yzcn2fsEJvMJAu/nJwcPvroI3bs2MH+/fvRarXMnTuX5557jmnTpnH06FFcXV356quvAPjqq69wdXXl6NGjTJs2jeeeew6AgwcPMnfuXA4cOMCSJUt4+OGH0Wq1xjw1IYQQQpioR376nd/zynljWGce6x2Mr7MNnds5suKhZELc7Bj33R4KKmqMHWaTmGThB1BfX091dTX19fVUVVXh4+PDqlWrGDlyJADjx49n/vz5ACxYsIDx48cDMHLkSFauXIlSigULFjB27Fisra0JDg4mNDSUbdu2GeuUhBBCCGGiNmcWM2dHNuO7BfBA90Aszf9XIjnZWPLD+HgqauqZumC/EaNsOpMs/Pz8/Hj66acJDAzEx8cHZ2dn4uPjcXFxwcKisVuiv78/OTk5QGMLYUBAAAAWFhY4OztTVFR00fY/vkYIIYQQAqChQfHEggN42Fvxl+QgHKwvHQIR4+vMAwkBfL8nlz05ZUaIUj9McnBHSUkJCxYsICMjAxcXF0aNGsWSJUsMdrzU1FRSU1MByM/PJzc312DHOq+goMDgx2grJJeGIXnVP8mpYUl+DaMt5HVVRhnbTpbyVFI7/C2qyM2tvuzzHo93Zc6ObF5atJfPbw294eMZM6cmWfitWLGC4OBgPD09AbjjjjvYuHEjpaWl1NfXY2FhQXZ2Nn5+fkBjC2FWVhb+/v7U19dTVlaGu7u7bvt5F77mQpMnT2by5MlA46LGzbUYtSx6rT+SS8OQvOqf5NSwJL+G0drzOmvhCTztrXigd2f8vJ2u+Dxf4K64Yr7fk4vGwRUfJ9sbPqaxcmqSt3oDAwPZsmULVVVVKKVYuXIlERER9O/fn3nz5gEwe/ZsRowYAUBKSgqzZ88GYN68eQwYMACNRkNKSgpz586lpqaGjIwM0tPT6d69u9HOSwghhBCmZW9uGSvTCxkT60u4l+OfPv+pvh04W9/AW6uONUN0+meShV9iYiIjR44kLi6OqKgoGhoamDx5Mm+99RYzZswgNDSUoqIiJk6cCMDEiRMpKioiNDSUGTNm8OabbwIQGRnJ6NGjiYiIYOjQoXzyySeYm5sb89SEEEIIYUI+3ZiJtYUZo2J8MDPT/Onzo32dSA5yZf7+fBoaGpohQv3SKKWUsYMwJd26dWPHjh0GP05ubm6rbzpvLpJLw5C86p/k1LAkv4bRmvNaXafF55Vl9Ax248fx3bCxvLbGoS+3nGDyD/tYPCmRoeFe133c5sjpleoZk2zxE0IIIYQwtAX78yk7W88t4V7XXPQBjIz2wdJcwzc7sw0YnWFI4SeEEEKINmnW9izaOVozpPP1tdq52lkxpJMXS4+cpra+Zd3ulcJPCCGEEG1OYWUNK9ILGdbZi/audtf9+nvj/CiqqmPhgXwDRGc4UvgJIYQQos1ZcOAU2gbFwDCPaxrU8UdDO3thYabhlwOnDBCd4UjhJ4QQQog258d9efg52dC3g9sNvd7Z1pJewW6szyiiJY2TlcJPCCGEEG1KaXUdK9ILGBDmjm8TJmG+PcqbjOJqtp4o1V9wBiaFnxBCCCHalF8PnqJOq+jf4cZu8553a4Q3AD/sNfxSr/oihZ8QQggh2pRfD53Gzc6SfqHuTdpPsLsdoe52bDlZoqfIDE8KPyGEEEK0GfXaBhYfPkXP9m4EuNz4bd7zburkya7sMsqq6/QQneFJ4SeEEEKINmPziRJKq+vpGeyKhXnTy6CbOnlxtr6BRYdaxuheKfyEEEII0Wb8evA05mYa+nXw0Mv++nVwx0wDq9ML9bI/Q5PCTwghhBBtxtIjp4n1dSKinaNe9udsa0m8vzNbT5bqZX+GJoWfEEIIIdqEojO17MktJyHABUcbC73td3BHTw6dqiC7tFpv+zQUKfyEEEII0SasPVYEQLcAZ73ut3eIO1oFy9IK9LpfQ5DCTwghhBBtwuqjhdhamtG3Q9Omcfmj5CBXNMDWE6Y/rYsUfkIIIYRoE1YdLSTW15kgVzu97tfZ1pJIb0f25Jbrdb+GYLKFX2lpKSNHjqRz586Eh4ezefNmiouLGTx4MGFhYQwePJiSksbKWinF448/TmhoKNHR0ezatUu3n9mzZxMWFkZYWBizZ8821ukIIYQQwohOVdRw8FQl8f7OWOphGpc/6tvBnd/zyik5U6v3feuTyRZ+U6dOZejQoRw+fJi9e/cSHh7Om2++ycCBA0lPT2fgwIG8+eabACxevJj09HTS09NJTU1lypQpABQXFzN9+nS2bt3Ktm3bmD59uq5YFEIIIUTbseZo43Qr3QNdDLL/XsFuVNc1sOqYaU/rYpKFX1lZGevWrWPixIkAWFlZ4eLiwoIFCxg/fjwA48ePZ/78+QAsWLCAcePGodFoSEpKorS0lLy8PJYuXcrgwYNxc3PD1dWVwYMHs2TJEmOdlhA3rKFBceR0JSVVpv1NUgghTNXqY0XYW5kzIFQ/8/f9Ua9gNwA2ZZh2A5P+xjLrUUZGBp6enjzwwAPs3buX+Ph4PvzwQ06dOoWPjw8A3t7enDrVOEt2Tk4OAQEButf7+/uTk5Nzxe1/lJqaSmpqKgD5+fnk5hp+seWCAtMf+dNStOZcKqX49vdCXl+fQ+lZLeYaGBDszIyh7XGzNeyfb2vOq7FITg1L8msYrSWvSw/lE+VpA2eKya3S6H3/ZoCfoyXbM0+Tm+t61ecaM6cmWfjV19eza9cu/vnPf5KYmMjUqVN1t3XP02g0aDT6+cVNnjyZyZMnA9CtWzd8fX31st8/01zHaQtaay6fmL+fD9efJN7fmZs7e3GipJr/7slh+LdprH04mfbu9gY9fmvNqzFJTg1L8msYLT2vOWXVZJbWcHu0H35+fgY7Tv+wfBYfPo2bpxc2llcvsYyVU5O81evv74+/vz+JiYkAjBw5kl27dtGuXTvy8vIAyMvLw8vLCwA/Pz+ysrJ0r8/OzsbPz++K24VoCWZuO8mH6zMYE+vL9/fF84+bOzPn7q4sfjCRoqpabv16O3XaBmOHKYQQJm/10cb5+5KCrt4S11S9Qtwoqqpjmwmv4mGShZ+3tzcBAQEcOXIEgJUrVxIREUFKSopuZO7s2bMZMWIEACkpKcyZMwelFFu2bMHZ2RkfHx+GDBnCsmXLKCkpoaSkhGXLljFkyBCjnZcQ1+po4Rke/vF3uge48OrQzoR4/K9lb0CYJ6kjo9mfX8GTCw4YMUohhGgZVqUX4mxjQb8QN4Mep1ew+7njFRn0OE1hkrd6Af75z39yzz33UFtbS0hICDNnzqShoYHRo0fz1VdfERQUxPfffw/AsGHD+O233wgNDcXOzo6ZM2cC4Obmxt///ncSEhIAeOmll3BzM+wvXQh9eG7RQTQa+L+hnQj1vPR27t3x/szfn88XW07waK9gOnk5GCFKIYRoGVYdLSTO3xlPR2uDHifcywEXW0t255YZ9DhNYbKFX2xsLDt27Lhk+8qVKy/ZptFo+OSTTy67nwkTJjBhwgS9xyeEoWw4XsRPv+fzUHIQ/a8y+mzGiEgWHMjnmV8OsnBi92aMUAghWo6MoipOlFQzOsZXb2MDrsTMTENykAv78ypQShn8eDfCJG/1CtGW/d/yNDzsrZiYGIiVxZX/RP1dbJnSoz2/HjrFrqzS5gtQCCFakNXn5u/r0d6w/fvOSwpyI6O4ihPF1c1yvOslhZ8QJuT3vHKWpxUyNtaXOL8/X0T8+YFhmJtpeHP10WaITgghWp7Vxwpxs7Okt4H7953XPdAFde64pkgKPyFMyIy1x7GxMGNktA9mZn9+i8DL0ZpR0b78evA0BZVnmyFCIYRoOZRSrEovJN7fGTc7q2Y55vmVQXZlm2Y/Pyn8hDARxVW1fLsrm1sj2pF0HbckpvUNoapOy7trjhswOiGEaHnSC8+QW15DN3+XZutv52ZnRbCbHQfyK5rleNdLCj8hTMR3u3Ko1Spu6+KNtYX5Nb+uW4ALsb5OzP8934DRCSFEy3O+f1/P4Obp33deUpAL+/MrqK03vblWpfATwkTM3J5FR097BoRd/zqSExMDSSs8o1uEXAghROPEzV4OViQGNnfh50rBmVr25Zne7V4p/IQwAfvzytmZXcatEe3wdrK57tePifXFwkzD19uy/vzJQgjRBiilWHW0kHh/F9ztm6d/33ndzxWaa48VN+txr4UUfkKYgG9352CugWGdvW7o9Z4O1gzq6MGSw6dN8taCEEI0t4OnKimorCUhwLnZ59OL9XXC0kzD73nlzXrcayGFnxBGppTih725xAe40O3caLAbMS4+gIIztfxyQPr6CSHE+f59ye2bf8UuG0tzuvg4meQADyn8hDCyvbnlHC2sYlCYB042lje8n+ERXliaa/h5vxR+Qgix6mghvk7WdPP/8zlRDSE5yJVDpyspr64zyvGvRAo/IYzsh725mGtg4A0M6riQk40lA0M9WH20EK1WbvcKIdquhgbF2mNFdAto/v595yUGuXCmVsuWkyVGOf6VSOEnhJH99Hs+cf7OdAtwafK+RsX4kltew/K0gqYHJoQQLdS+vHKKq+qadf6+P+p+7pq+McO0BnhI4SeEEaUXVHL4dCV9O7g36TbveSmR7TDTNBaTQgjRVq06178vKah5p3G5UEdPBxytzU2un58UfkIY0S8HTwHQJ9hdL/vzcLAm3t+ZzSdM69aCEEI0p9VHiwh0taWrn5PRYjAz0xDv78LBU5VGi+FypPATwogWHjhFqIcdiXr8Vjo8vB0H8itILzCti40QQjSHem0D644V0c3f2Wj9+85LDHQlvfAMBZU1Ro3jQiZd+Gm1Wrp27cott9wCQEZGBomJiYSGhjJmzBhqa2sBqKmpYcyYMYSGhpKYmEhmZqZuH2+88QahoaF06tSJpUuXGuM0hLis4qpaNmQU0yfEHU8H/V2chnb2QgE/7svT2z6FEKKl2JVTRnlNvVH7952XEOhMfYNi/XHT6edn0oXfhx9+SHh4uO7n5557jmnTpnH06FFcXV356quvAPjqq69wdXXl6NGjTJs2jeeeew6AgwcPMnfuXA4cOMCSJUt4+OGH0Wq1RjkXIf5oZXoh2gZFz/Zuer04dQtwwdXWkg0m1qFYCCGaw+qjRQAktzde/77zEs4N8NhqQiN7Tbbwy87O5tdff2XSpEnAuaVXVq1i5MiRAIwfP5758+cDsGDBAsaPHw/AyJEjWblyJUopFixYwNixY7G2tiY4OJjQ0FC2bdtmlPMR4o+WHSnAwcqc3iH6nVzU3EzDkE6ebDlRQnVtvV73LYQQpm710UJC3O2I8jFe/77zAlxs8bC34qAJDfCwMHYAV/LEE0/w9ttvU1HRmKyioiJcXFywsGgM2d/fn5ycHABycnIICAgAwMLCAmdnZ4qKisjJySEpKUm3zwtfc6HU1FRSU1MByM/PJzc316DnBlBQINNt6EtLzKVSisUH84ltZ4vV2TJyc/W7rE+StxVz99Tx49bDDOhwY4VlS8yrqZOcGpbk1zBaUl5rz/Xvu6mDE2dLC8gtM+6tXoBoLxt+zy0lJydHd3fHmDk1ycJv0aJFeHl5ER8fz5o1awx+vMmTJzN58mQAunXrhq+vr8GPCTTbcdqClpbLI6cryamoZVxCIEEBfnrf/1hHd55YksmW04p7e994blpaXlsCyalhSX4No6XkdcPxIqrrG+gZ5oOfn/6vrTeiT1gFq5elUW/nSpCrnW67sXJqkoXfxo0bWbhwIb/99htnz56lvLycqVOnUlpaSn19PRYWFmRnZ+t+qX5+fmRlZeHv7099fT1lZWW4u7vrtp934WuEMKZlRxq/7fUMNkwflHaO1sT4OrHphPTzE0K0HcvTCjHTQE8jrM97JQmBLihg3bEi7utm96fPNzST7OP3xhtvkJ2dTWZmJnPnzmXAgAH85z//oX///sybNw+A2bNnM2LECABSUlKYPXs2APPmzWPAgAFoNBpSUlKYO3cuNTU1ZGRkkJ6eTvfu3Y12XkKctzytAH9nG5KCDHdxGh7uxb7cck6UVBnsGEIIYUpWpBcQ0c6R8HYOxg5F5/wAj53ZZcYN5ByTLPyu5K233mLGjBmEhoZSVFTExIkTAZg4cSJFRUWEhoYyY8YM3nzzTQAiIyMZPXo0ERERDB06lE8++QRzc3NjnoIQ1NY3sPpoIUlBrgadY2poZy+0CubLKh5CiDag/GwdW0+W0j3QBVc7487fdyFPB2v8nW1MZoCHSd7qvVC/fv3o168fACEhIZcdlWtjY8MPP/xw2de/8MILvPDCC4YMUYjrsuVECZW1WpICXQx6nMRAV2wtzdhyooSpBj2SEEIY35qjRWgblG6NXFOSGOjClhOlNDQozMyMO+CkRbX4CdEaLEsrwFwD/UM9DHocKwszerZ3Y3tWKUopgx5LCCGMbUV6ITYWZvQwof5953UPdCWn/CxpJrCikhR+QjSzZUdO08XHic7N0AdlYJgHx4qqOHTKNG4xCCGEoSxPKyDOz5n2bsYfQPFHCefu8Kw9XmTcQJDCT4hmVXSmlh1ZZSQFumBnZfieFudbFRcdPG3wYwkhhLFkl1Zz+HQl3YNccLQxvV5s8f7OaIA9Ofqds/VGSOEnRDNadbQQRWP/u+YQ7++Mg5U520xouSAhhNC3FWmFACQGGH+ZtstxsrEk1MOeAyZw90UKPyGa0fll2vqFujfL8SzMzegd4s727DK0DdLPTwjROq1IL8DNzpKkIBdjh3JFiYEuHMyvoLZea9Q4pPATopkopVh2pICEQBcCXGyb7biDOnpwsqSafXmmMYeUEELoU0ODYkVaId0DXPBrxmvr9eoe6EpRVR2/G3lalyYVfk899RQHDhzQVyxCXLff88r5eGs+j/+8n3dXHyO7tNrYIV1RWsEZTpZWkxToioV5833n6t+hsZ/fr9LPTwjRCu3MLuNUZQ3J7V2xbMZr6/U6P8Bj3THjrqjUpB6Q4eHhTJ48mfr6eh544AHuuusunJ2d9RWbEFdUWl3HkwsOMHN745J89lbmnKnV8rffDvFYr/a8c2sk5kaeK+mPzi/T1qN98/ZBifF1wtnGgu1Zpc16XCGEaA6/HMzHTAN9OzRPF5obFevrhIWZht/zyhkTZmO0OJpUGk+aNImNGzcyZ84cMjMziY6O5u6772b16tX6ik+IS5yuqKHfp5v4Zmc247v588PIEI4/P5Atj/fi5s5evL8ug4Gfb+ZsnXH7UfzR8rQCAlxs6N5MAzvOMzPT0C/UnZ1ZZdRrG5r12EIIYWgLD5wixteJaB8nY4dyVTaW5kS0czT6AI8mt4lqtVoOHz7M4cOH8fDwICYmhhkzZjB27Fh9xCfERSpr6hn0xWYOn67kgxGRfHJHFD2CXPFytCYxyJWFE7vzzi3hrD1WxJ2zdtBgIgMazi/TlhjoiqdD8y8lNDDUk5zys+zILm32YwshhKGcLKlib245vYPdTWqZtiuZPqQj47v5GzWGJt3qnTZtGr/88gsDBw7k+eefp3v37gA899xzdOrUSS8BCnGeUoqJ/93LgfwKPhzRhUlJgVhbmPPHIQtP9w+lslbL9GVpPP/bId68JcIo8V7owmXaNJrmvwU9IKyxn9/iQ6dJCjK9We2FEOJGnJ+jtE9Iy7iu3RblA0Bubq7RYmhS4RcdHc2rr76Kvb39JY9dbk1dIZri621ZfL83l0d7tuf+7gFYW5hf8bkv39SR3TllvLf2OLdFeRu92Dm/TFuvEOP0QYlo54CHvRXbs2RkrxCi9Vh4IJ9AF1u6Bcj4gmt1Q4Xfrl27AIiJieHIkSOXPB4XFyeDPIRe5Zad5amFB4jzc+apfiE4WF/9ravRaPh6TCyd3lzF+O/2cPDZ/kYd7LHsSEHjMm1ehl+m7XI0Gg39O7iz9ngRdfVaLK9SNAshREtQcbae1UeLGBXjg7+L6S3TZqpuqPB76qmnrviYRqNh1apVNxyQEJfzxIL9nK1r4MVBYbR3u7SF+XLc7a345I4oxv57F2+sTOfFwR0NHOXlFZ2pZUd2KZMTA3G2tTRKDAADO3rww748tpwsoXeIh9HiEEIIfVieVkCttoHewW4mN4uDKbuhwm/16tU0NDSwefNmevbsqe+YhLjI+uNF/LA3j78kBTIswuu6Xjs61pf31x3nvbXHmNKjPe72zd/5d9XRQpRqvmXaruT8ur2/HSqQwk8I0eItPJCPo7UFfUx8GhdTc8Ojes3MzHj00Uf1GYtOVlYW/fv3JyIigsjISD788EMAiouLGTx4MGFhYQwePJiSksb1R5VSPP7444SGhhIdHa27FQ0we/ZswsLCCAsLY/bs2QaJ93pV12mpMrGpRkxVQ4PiyYUH8HKwYlJS0FX79V2ORqPh0zujKK2u54XfDhkoyqs7v0xbzxDjFn5hHvZ4O1qzU+bzE0K0cLX1DSw8cIpewa50cL+2u0CiUZOmcxk4cCA//vgjSul3ygwLCwvee+89Dh48yJYtW/jkk084ePAgb775JgMHDiQ9PZ2BAwfy5ptvArB48WLS09NJT08nNTWVKVOmAI2F4vTp09m6dSvbtm1j+vTpumLRWHLKqnF+YTHf7is0ahwtxc/789iRVcbDPdoT53dj/Ubj/F0YHu7Ft7tzKays0XOEV3fhMm1Brsbtg6LRaBgY5sGO7DKjrxUphBBNsTytgJLqOm7q6ImVhemu1mGKmpStL774glGjRmFtbY2TkxOOjo44OTV9AkUfHx/i4uIAcHR0JDw8nJycHBYsWMD48eMBGD9+PPPnzwdgwYIFjBs3Do1GQ1JSEqWlpeTl5bF06VIGDx6Mm5sbrq6uDB48mCVLljQ5vqbwdbLBzdaSNZkyuvLPaBsULy9No72rLffE+WPWhD4c04d0oqKmnleWpekxwj934TJt19taaQiDwjwpqa4z+pJBQgjRFHP35OBkbcHgTp7GDqXFadJ0LhUVhp99OjMzk927d5OYmMipU6fw8WmcA8fb25tTp04BkJOTQ0BAgO41/v7+5OTkXHH7H6WmppKamgpAfn6+wefXSfC1Y/PJcrKyszE3k28qV7LgcDEH8it4oZcPtrWl5OZevlguKCj40335mEOfIEf+vSOLR2KdcbZp0lv/mn27o/E9GumkNeq8TedFONUDMH/XMSIc66763GvJq7g+klPDkvwahqnltbqugZ/35dEnyAHH+gpycyuNHdJ1M2ZOm/zpV1JSQnp6OmfPntVt69OnT1N3C0BlZSV33nknH3zwwSUtiRqNRm8T4U6ePJnJkycD0K1bN3x9ffWy3ysZElnDorT95Dc4kODvYtBjtVRKKb74Lp1gN1sm9gnH70/6cFzL7+yNW23o+fFGvtpfwbspkfoK9ao25Jygg7sdg2LDaOdo3SzHvBpfXwhyPcb+4oZrypmh/xbaIsmpYUl+DcOU8jp3dw5n6hq4NTqQQH8/Y4dzw4yV0yY1N/3rX/+iT58+DBkyhJdffpkhQ4bwyiuv6CWwuro67rzzTu655x7uuOMOANq1a0deXh4AeXl5eHk1jvD08/MjKytL99rs7Gz8/PyuuN3Y+pybxHfpkdNGjsR0LT1SwN7ccsbFBxCsp467PYLd6B3ixqztWZypqdfLPq+mrLqOdceL6BXshpcRlmm7kkFhHuzMLqW6Vvr5CSFanq+3ncTH0Zqhna9vlgfRqEmF34cffsj27dsJCgpi9erV7N69GxcXlyYHpZRi4sSJhIeH8+STT+q2p6Sk6Ebmzp49mxEjRui2z5kzB6UUW7ZswdnZGR8fH4YMGcKyZcsoKSmhpKSEZcuWMWTIkCbH11RdvB1xtDJjZ7b087uSt1cfxcvBijFd9fuN6OXBHSmqqmPG2mN63e/lLE8roL5B0TvYzSjLtF3JwDBPKmu1rDpqWrdvhBDiz5wormJFeiG3RrYz+oC5lqpJhZ+NjQ02NjYA1NTU0Llz58uu5HG9Nm7cyDfffMOqVauIjY0lNjaW3377jb/+9a8sX76csLAwVqxYwV//+lcAhg0bRkhICKGhoTz44IN8+umnALi5ufH3v/+dhIQEEhISeOmll3BzM/56fmZmGrr5OrA7Rwq/y9l+spTVR4u4u6uf3le6GBDmQRdvR77elkVDg35Ho//RooOncLK2oEewcadx+aP+oY0tzsuOyMhyIUTLMmt7Fii4NaJdkwb8tWVN6uPn7+9PaWkpt912G4MHD8bV1ZWgoKAmB9WrV68rThGzcuXKS7ZpNBo++eSTyz5/woQJTJgwockx6VtygCOrM3NIL6gkzNM4y3iZqrdWH8XR2oJ74vz03lKm0Wj464BQ7v12N3N2ZHN/94A/f9ENaGhQ/HboND3au17zSiPNxdvJho4e9uyQ+fyEEC1IbX0DX2w5QVKQq9HWPW8NmtTi9/PPP+Pi4sIrr7zCP/7xDyZOnKibYkVcXaJ/Y7G3+LD087tQWkElP/2ex8hoH7oaaODL6FhfvB2t+XhjhkH2D7A9q5SCM7X0CnbD1tL407j80aCOHuzOLaOs+uoje4UQwlTM25dLXnkNY2N9cTHi8pctXZPnEtmwYQMzZ86kb9++JCcnX3a6FHGp6HZ22FiYsSXTuBNKm5r31hzD0kzD3V39DLb2oqW5GU/2DWFndhmr0w3Tz+3XQ6cw00CvYON3LbicgWGeVNc1sES+eAghWgClFB+uzyDQ1ZaULu2MHU6L1qTCb/r06bz11lu88cYbQONI3HvvvVcvgbV2VuZmJAS4sDu33NihmIz88rPM3pHNLRHt6N3BsAXT5KQg7K3MeWu1YQZ5LDp4imgfJ6J9mz6huSH0D3XHTAMr06WfnxDC9K0+WsS2k6XcHesrgzqaqMm3ehcuXIi9fWMfJl9f32aZ1Lm16BfqTlpBJVkl1cYOxSR8uD6D2voG7o3zN/gqF862lkxKDGRFWgHpBfqd/PNkSRW7c8rpFeyGq53pTONyIVc7K+L9XdgkLc5CCBOnlOKVZUfwcrBibFdfk5oloSVqUuFnZWV10UTKZ86c0UtQbUWfEHcaFPx2+JSxQzG68rN1fLYpkwFhHgzq2DxL8DzZNwSA/1uu32Xc5u1rnGtyUJiHXverb8PDvTh4qoKMIvm7FUKYrlXphaw/Xsz4bgFE+dzYmu3if5pU+I0ePZq//OUvlJaW8uWXXzJo0CAefPBBfcXW6vUMdsPK3EzWTQVSN5+k7Gw94+L9cWym5dQCXe24LcqHn3/Pp+RMrd72+/2eXDp7OdDDRPv3nTe0sxeK/xWqQghhauq1DTy58CDejtbcE+crU7joQZNb/AYNGsSdd97JkSNH+L//+z8ee+wxfcXW6tlamtMz2JUtJ0uuOH1NW1BTr+X9dcfo5u/MsPDmnYn9+YGhnKnV8vYa/fT1O1FcxdaTpQwK88DTwfhLtF1NtwAXXGwsWH9cvngIIUzTl1tPsi+vnCf6BBPtK619+tCkwu/06dP87W9/48SJEwwaNIhBgwbpK6424+bOXhwvqmJ/Xtsd5DFrexa55TXcnxCARzMXS3H+LiQHuTJz20nqtA1N3t9/9+QCpn+bF8DcTMOgjp5sPlGCVg/nLoQQ+pRVUs3ffj1EN39n7ov3l759etKkwu/VV18lPT2diRMnMmvWLMLCwnj++ec5dszwy2G1Fjd1auzP9vPv+UaOxDhq6xt4fcVRung7clsXb6PE8PzAUE5V1pK6+UST9qOUYtb2LKJ9nOhp4rd5zxsW7kXhmVpWHysydihCCKHT0KC4f+4e6rSKFweF4etsa+yQWo0mz+On0Wjw9vbG29sbCwsLSkpKGDlyJM8++6w+4mv1oryd8LC3ZNOJtjm68pud2ZwsrebBxED8XIzzhz0svB0h7nZ8tCGjSbfct2eVcuh0JbdGeDV7y+WNGtKp8db6wgNt84uHEMI0vbT0CKuOFvJk3xBuDpd5+/SpSYXfhx9+SHx8PM8++yw9e/bk999/57PPPmPnzp38+OOP+oqxVTMz0zAsvB2bMks4U1Nv7HCaVZ22gddWpBPRzoGUSOP9YZuZaXi2XwfSCs40qQCauS0LawszhkW0nIuUr7MN4V4ObGmjXzyEEKZn5raTvLYindu7ePNIz/ZYWTS5jUpcoEnZLC4u5qeffmLp0qWMGjUKS8vGJVTMzMxYtGiRXgJsC27r4k1FTT3z97etVpf/7Mwho7iKSYmBtHc37nq24xMCcLOz5O0bnNC5/Gwd/9mVw4BQD7r6tawOyMMjvNidU05++VljhyKEaOO+3HKCid/vJTHQhdeGdcbbycbYIbU6TV65Iygo6LKPhYeHN2XXbcrgjp5Ymmv47VDzLZ+llGLLiRKe/eUgw77cSq9/bmDk7B28t+YYGUVVBj9+bX0Dr65Io5OnPXdEGadv34VsLM15vHcwmzJLWH/8+vu7fb0ti4qaesbG+prk2rxXMzy8HfUNirm7c40dihCijaqtb+CJ+fuZ/MM+koNc+fSOKMLbORo7rFZJ2k9NgIO1Bf07eLDueFGzjK7cmVVK3083kfzRBj5Yf5zjRWc4W9/A9pOlPP3LQULfWMmds3YYtAD8fHMmx4qqmNKjPUFuxm3tO+/xXsE4WVvw3KJD1/U6bYPio/UZxPg6MbRz805How+9gt1wsbVguYHWLRZCiKtZd6yIuPfX8eH6DO6K9eXL0THEBbgYO6xWq3lmyhV/6o5ob5bNK2DV0SIGdzLMyhVKKd5ZfYwXFh/G2caCZ/t34LYu3kR6O+JobUGDgt3ZZXy8MYPv9+by66FTvDGsM0/0CdHrMPrS6jqmL0uje4ALY2J99bbfpnK1s+LFwWE8u+gQP+zJZdQ1xvbfPY23rN8aHo6XY8sY1HEhC3Mzbolox8L9p6iqrcfOSi4LQgjDamhQLE8rYMba4yxLK8Db0ZoZKRHnut2Y5lKXrUWrb/FbsmQJnTp1IjQ0lDfffNPY4VzRnVE+WJhp+M+ubIPsv17bwKTv9/Lcr4fo28GdH8bF8/qwcJLbu+FkY4lGo8HcTEO3QBdm3dWV35/uR0KAC08uPMhtM7dTqceBJ8//dojS6jqm9g42uf4bj/cOxt/ZhmcXHeRsnfZPn19Tr+WF3w7T0dOeUTE+zRChYdwR5UN5TX2bnVZIiOamlOJAfgXrjhWxP6+8zUziX1xVy4y1x+j01iqGfrmVHdmlPN4rmIUTuvNEnxAp+ppBq/5qr9VqeeSRR1i+fDn+/v4kJCSQkpJCRESEsUO7hIeDNf1DPVh6pID6+gYs9DiKqV7bwN3/2cUPe/N4MDGQl2/q+KdTp3TwsGftwz14cfFh3lp9lNj31rL4wUTCPB2aFMvmzGI+33yCsTG+3Bpp/L59f2RtYc6Xo6O5+cttPLngAJ+OjL7q8z/dmElmSTUf396FYCMPUGmKmzp6Ym1hxoID+dwT72/scIRotbQNitQtJ3h71VEyS6p1270crHhuQCiP9gxulaNYM4ureHv1UWZuy+JsfQOxvk68OrQTt0a0I9LHCXNZiq3ZtL531wW2bdtGaGgoISEhWFlZMXbsWBYsWGDssK5oXDd/8itqWHBQf60uSikmfb+XH/bm8UTvYN68Jfya58szM9Pw+vBwFkxIoPBMLd3eX8+KtBvvB3ampp4J/92Ll70Vj/UObrY1ea/X0M7tGB3jS+qWEyw+dOqKzzt0qoIXFh+mR3tXRsaYzi3rG2FvbcHNnb1YlV5Ibb2s4iGEIRRW1tDv0008/OPvuNha8uKgMD67M4qXB3ekvasdTy08SNyMteSUVf/5zlqIrJJqHpi7h7A3VvGvrScZ2tmLb+/pytLJSbwwuCPRfs5S9DUz0/zk1ZOcnBwCAgJ0P/v7+7N169ZLnpeamkpqaioA+fn55OYafnRjQcGlBVR3d4WNhYavNqSTrKcVv97fnMfsHbncF+XG5ChHzpYWklt6ffuIc4Ff7+7E3T+mMzR1C68OCGBc7PUPYpi2JJMjpyt5e5A/gZbVesvz5XLZVNN7e7Els5Cx3+xg0d2d6eB2cbFcXqNl1H+PYGWmYWqcK9qKInIr9B5Gs7o52I75+/P5au1+RoR7GCSvbZ3k1LBMOb/5lbWM+j6NrLJanu3hzV1dPC7oE2zFg1EO/HCgiOdXniTmndX8PLbTJdcdY7mRvDYoxde7T/Pm+ly0SpHS0ZlR4a4kBbpgZWFGfSu4ZjaFMd+rrbrwu1aTJ09m8uTJAHTr1g1f3+Zpvbncce6OK+I/u3Iwc3Brcv+37/fk8u6mXIaFe/HW7dH4NGHJG19f2Bvsz7B/beNvK7PIqjbjo9ujrvmb2ntrjvH9gSImdg/gL/276L21T9+/M19gxRRXEj5Yz63fpfH9uG665fVOllRxz393cLjoLO/dGsGdycGt4hvrvR5eTFuSyfKTNUwZ2JjP5vpbaEskp4ZlivmtrKnnlrkbya+s59M7o7g33h+by0z79ISfH30iAhn4+WbGzDvGtid64e9iZ4SIL3U9eS2pquWe/+xm8eHT9GzvynP9Q7mpsyfWFi1rqitDM9Z7tVXf6vXz8yMrK0v3c3Z2Nn5+fkaM6M892jOYmvoGZqw73qT9bDtZwvjvdhPj68Q7t0Q0qeg7z8XOinWP9OC+eD8+3XSC/p9tIu8aJv1N3XyCp385yMAwD54fGGayt3j/KMzTgS2P98LZ1pIhqVuIemcN/T/dRIfXV3Egv4L3bo3gwaTAVlH0AdhZWXBrpDerjhZSWVNn7HCEaBWUalxzdl9uOW8M68y4bgGXLfrOi/N3YenkJMrO1nPzl9taXNeL40VnSP5oA8vTCniufwf+e188t3bxlqLPhLTqwi8hIYH09HQyMjKora1l7ty5pKSkGDusq+rq70y8vzNzd+dQV//no0ovJ6ukmhFfb8fNzpJ3bwknwlt/k2BamJsx5+443r01gm0nSwl/azWfbcyk/jLzD1bW1DN1/n7+Mm8fPYJcee/WcEI8WtYAiM7tHNn/dF+eHxiKpbmGojO1jIr2Yd64eB7qEdTqpj55ICGAsrP1fLUt68+fLIT4U//aepIf9+XxSM9gJiYGXtPAje6Brnw9Nob9+RVM+n6P4YPUk6OFZ+jzySbyymv49I4oXh7SyWhrsIsra12fWn9gYWHBxx9/zJAhQ9BqtUyYMIHIyEhjh/Wnnh8Yxp2zd/DRhgye6hd6Xa+tOFtPytfbqKip5+sxMQzsaJg5AZ/q14FBYR5M+O8eHv7pd/5veRr3xvkRH+CCBtiRXca/d2aTX1HD2Fhfpg/tSEfPljkLu6ONJa8NC+e1YeFoGxRmGvQ6r6EpGdTRE18na/67O5dRHUKMHY4QLVpGURVPzD9A90AXpvUNxt762j9yx8T6seZoEZ9vPsGILt7cGW16t7AvlFVSTf9PN3Gmtp4vRkYzMsYHC/NW3bbUYrXqwg9g2LBhDBs2zNhhXJfbungT2c6BD9Zl8FivYKyusYm8TtvAyNk7+D2vnPdHRHJHlI9BC5QYP2d2TOvD7O3ZpG45wfvrM9A2NM5FZWGmoXuAC28O68yIKB9cbC0NFkdzai23da/E3EzDxO6BvLoinbTCKkywu5QQLYJSiod/2gfQOGr3BlYoen9EJMuOFPDIT/sZ0MEDV3vTnOOutLqOm/+1lZLqOr4cFc2oWN9Wf61syaQcN0FmZhreGB5OdtlZXlmWdk2vOT9ty7K0Ap4fFMaE7oHN8m1Lo9Fwf/cANj3ei7yXBvPrpO4seKAbW6f2YulfkhjfPbDVFH1txYTEQDQa+HLnlaeyEUJc3Y/78lhyuIApPYIYdIN3Xmwszfn23jhOV9Tw6M/79RyhfiilGP/dbg6fruTdWyMYGSNFn6mTws9E3RLRjps6ejBjzXEO5Jdf9blKKf7262Hm7MjmoeQgHut1fbcU9MXT0Zph4e1I6eJDnL8LDkaIQTRdezc7hoW345e0UsqrZZCHENertr6Bv/56iFAPOyYnX1u/vitJDHLlgYQA/rsnh60nSvQYpX68u+YYCw+c4onewYzr5o+l3N41efIbMlEajYaZY7tiZWHGHTN3UHqFD+CGBsUTCw7w1uqj3BHlzQuDQvF0aHnrxQrT8lz/DlTUNvDe2mPGDkWIFufLLSc4VlTFYz2D9dK3+e1bI7C3suDRn343qaXd1h8v4m+/HWZAqDvP9OvQ6ga7tVZS+JkwX2cb5t4bx/HiKvp8vJHs0otnc88urWZI6hY+Wp/BXV19mZESaTJzPomWrWewG108bflqaxY117BmsRCiUcXZeqYvSyPe35m7uuqnk6y7vRWvDevMjuwyPt90Qi/7bKqiM7WM/WYnfk42vHZzZ9qZ2Lrr4sqkPDdxwyLaMefurjwwdw+d31rNXV39CHG3Y39eBfP25WKm0fDCwFAe6xUsf3hCbzQaDc/19uO+n47y+qqjTB/SydghtRnHCs+w+mghh05XUlFTj7W5GUGudnQPdCExyEXmQzNx7609RsGZWt69NQJPR/1dk6f0aM+nGzOZvjyN++L9cTDyfKjTFhzgVGUtc8bGktTezaixiOsjhV8LcFdXP2J9nXjs5/18tzuHM7VanG0sGNHFm3vi/Li5c7tWuai3MK7+7Z1IDHThkw0ZPNknBGc9DNI5U1PPivRC1hwrJLO4Go0GAl1sGdLJk0EdPdts/yClFD/9nsfbq4+x7WQpANYWZjhaW3C2TktlbWOrq5ONBWNifHmybwid27XM6ZFas1MVNby75hiDwjy4Lcpbr/s2N9Pwxaho+nyyiecXH+aj27vodf/XY8nh03yzM5tJiQHcHu1jtDjEjZHCr4UIb+fIioeSOVNTT1ZpNS62lrjZWUnBJwxGo9HwwW1d6PHRBqb8+Dvf3ht3w/s6XVHDO2uO8a+tJyitrsfGwgxfJxvQwOJDp/lwfQZBrra6UYFtSVpBJQ9+v5d1x4sJcLHhid7B9A5xI87fGWcbS8w0GrLLqlmZXsiSwwXM2ZHNv7aeZGxXX94f0YV2jtKn11S8u+YY1XVapvQIwslG/7MZ9A5x55YIL77aepJn+nUgwLX5J0eurKnnLz/sI9jNlqf6hmJ7lVVIhGmSwq+Fsbe2kG/6otkkBbkyOTmQ1M0nGdfNj6Gd213X62vrG5ix9hivrUinqk7LwDAPbu/iTd9QD/ydbbCxMKesuo7v9+by4foMRs3Zybj4U6SOjm4TtzR/2pfH+O92oznXZWN8QgChHvaXzL/pbGtJpLcTj/cO4URJFS8tPsK3u3P45cAppg/txNTeITKFhpEVVNbw6cZMhnTyYnBHL4MdZ0ZKJOFvr2Hagv3Muz/BYMe5khcWHyartJp/jY6hczuHZj++aDppLhJCXNW7t0bi52zDvf/ZTWZR1TW/blV6ITHvreVvvx0m3t+F7++L58fxCUzpGUxEO0ecbCyxsjDD09GaR3oFc/DZfjyUHMScndkMSd3C2VY+qOSf6zO4c/YOglzt+O7eOF4Z0okwT4c/nXQ9yNWO2Xd3Zee03nTxduSphQfp9fEGcsqqr/o6YVjvrztOdZ2Wid0DDLoeeZinA5MTA5m/P58dJ5t3epfNmcX8c0MGo2J8uCvOtNe9F1cmhZ8Q4qocrC1Y8mAiZ+sbGPjF5ktGl/9RekEld87azsDPN1NRU88HIyKZNz6eO2N8r/qBaGFuxmcjo/lgRCRrjxUz7MutupVgWpsZa4/x+Pz99Ovgzr/viWV4RLvrnnA92teZTY/34oMRkezJKSfy7TUs3J9noIgNRylFVkk1mzKKWZVeyJ6cshZX9BdX1fLxhkwGhXlwUyfDtfad939DO2Frac6TCw8a/Fjn1dRrmfT9XrwcrHlugNzibcnkVq8Q4k9F+jjx8/3duH3WDrrOWMfnd0ZxR/TFSwIeLTzDR+sz+GxTJpbmGh5KDuLBxEC6+jtf19KBU/uEUKdt4JlFh3jkx318PirGEKdkNLO3Z/HUwoMMCvPg4zu60MnrxrtuaDQapvYJYUCYByNn7WDEzB08PzCUV2/ubPLrSW85UcKs7Vks3J9PXkXNRY9ZmmnoGezGg0mBjI7xNfk1Xz9an0FFTT0TEwMN2tp3noeDNc8PCuP53w6zYH8eI7oYfoDFGyuPcvBUJR+OiCTO38XgxxOGI4WfEOKaDO7kxfpHe3LHzO2MnLOTQBdbktu7YqbRsD+/nN/zKjDXwK2R3jyUHETfDu7Y3GCrwNP9QzlwqoIvtpykZ7Ab93UL0PPZGMfW7Aom/ZBO9wAXZqRENqnou1CUjxN7n+7L2G928vrKo+zNLef7cfEmOaHu+uNFPP/bYTZkFGNjYUavYDfujfcnwMUGawszSqrqOHiqkpVHC7nnP7v5++IjfHpnFEM6G74l7UaUn63jw/XH6dfBvVljnNYnhI83ZPDsokPcGuGNmQH7eB4urOb1lenc3NmT8Qmt42+xLTO9q4IQwmR19XMm7a/9+XBDBr8ePM2G48WggQBnGx7t2Z7h4V70CnHXy3J9X4yMYfvJUh77eT/9Qz3wd2n+EYz6lFNWzeRfjuPrbMNbw8OJ8nXS6/5tLM35+YEEXlmaxj+WpxH//nqWTU4yysjPyymtruPRn37nP7ty8HKw4um+IYyK8SXa1+myXxC02gZmbs/i1RXpDP1yK5MSA/jkjmiTm8ng4w2ZlFbXM7F7QLOuS25jac7bt0Rw77e7+eeG40zt08Egx9E2KJ5edgJ7K3Oe7heql2mdhHFJ4SeEuC6WFo0fAE/3C6W2voFabQOW5hq9j8K1sjBj3vhuxM5Yx73/2c2aR3rodf/NqaFBMf67PVTWavlsZAx9Q90NchyNRsP0oZ2I9nVk/Hd76DpjLfMf6E6vEONOsLv6aCHjv9tNbtlZHkwMZHJyEHF+zldtpTI3N2NSUhD3xvvz0Lx9/GtrFntzy1n8YBLu9lbNGP2VVdbUM2PtMXq2d+Xm8Osb8a4Pd3X14+3VR3lj5VEmJQYZZI32f27IYHfeGV4d2ol+HQzzvhXNy7S+OgkhWhQrCzMcrC0MNvVK53aOvHxTR9YeL2LmtpMGOUZz+OeGDFamFzIl3pOUSG+D97+7M9qXzY/1xNbSnAGfb+LLzcZZ5qumXsszvxxk4Geb0Wg0fD0mlo9u70K3AJdrvjVpY2nOrLu68uWoaPbmlpP44XpO/aFPoLF8vukERVV1TEoMNEoxamam4Z+3R3GqspYXFh/W+/6PF53hhd8O093XnomJgQa9nSyaj8kVfs888wydO3cmOjqa22+/ndLSUt1jb7zxBqGhoXTq1ImlS5fqti9ZsoROnToRGhrKm2++qduekZFBYmIioaGhjBkzhtra2uY8FSGEHjzdrwOdvRz466+HqDxbb+xwrtuB/Aqe+/UQvYPdmNC1+VbZifJ1Zs9Tfejq68zkeft4+Md91GsbmuXYAL/nldP9g/W8u+YYd0b7sHhSIuMSAm643+ekpCDmP5BATtlZeny0gZIq417Pq+u0vLvmGN0DXYzS2ndenw7uDA/34vPNJzh8qlJv+9U2KO6fuweNBp5I9MJblgRtNUyu8Bs8eDD79+9n3759dOzYkTfeeAOAgwcPMnfuXA4cOMCSJUt4+OGH0Wq1aLVaHnnkERYvXszBgwf57rvvOHiwcYj7c889x7Rp0zh69Ciurq589dVXxjw1IcQNsDQ3I3VUNKcra3l+8SFjh3NdausbuPc/u7CzNOfFQWG4NXOrkLu9NRsf68m4eH8+23SCAZ9tNnjB1NCgmLH2GN3eX0d22Vk+GBHJ7LtiifBu+kCWm8PbsWBCAlll1Qz8fLNRp335cssJTlXWMKl7oNFXT/l8ZDQWZhrun7sbpfQzBdIH646z/ngxz/TrQM/2rnrZpzANJlf43XTTTVhYNPZTSEpKIjs7G4AFCxYwduxYrK2tCQ4OJjQ0lG3btrFt2zZCQ0MJCQnBysqKsWPHsmDBApRSrFq1ipEjRwIwfvx45s+fb6zTEkI0Qe+QxlaNr7ZmcaL42ieRNrYP1h1nT245LwwMZVBHT6PEYGFuxuy7uzIjJYLNJ0qIeHsNGzOKDHKsrJJqBn+xhacWHiQ5yI0f7ovn8d7Beh1dfFMnL/41OobdOeWM+Ho7DUaY67GmXsvbq4/R1c+J4RHGa+07z9/FlteHdWbryVI+2ZjZ5P0dyK/ghcWH6RvizsTEACzkFm+rYtKDO77++mvGjBkDQE5ODklJSbrH/P39ycnJASAgIOCi7Vu3bqWoqAgXFxddEXnh8/8oNTWV1NRUAPLz88nNzTXI+VyooKDA4MdoKySXhmFqeX02yZPFh0/z2A87+PzWUGOH86dyymt5ZekRevjbc3OgJfn5eUbN6ZgwW4JHhfHwrxn0+WQTjyd6My3ZVy8f6kopvvu9iH+sy6ZW28BTSe0YH+uFu30deXn6n1R6kK85f+3ly5sbcrn/m828Obg90Hzv2W/2FpBTdpbHu7nDmWJyzzTLYa/qjhBr/uVly/O/HqRXOw1eN9i6XFPfwF3fHcHGXMOjcc6YVZWa3LWgNTBmTo1S+A0aNIj8/PxLtr/22muMGDFC928LCwvuueceg8czefJkJk+eDEC3bt3w9W2eReKb6zhtgeTSMEwpr76+8GBSJf/acoI8rR3xAS7GDumqHlu2nQbgbzdFENHBW7fdmDm9zRf6RwUz5ptdfLAln6XHK0kdFU2vkBsfrXnkdCV/mbePtceKiPd35sVBYQwLN3xfxtdv8yG/xoxZ27PpH+7PA90DAcPnt07bwOc7D9LF25G7kjvj62pn0ONdj7njnYidsZYnluWy+uHkGxpE9PCP+/j9dBXv3hrBbd2DdZNnm9K1oLUwVk6NUvitWLHiqo/PmjWLRYsWsXLlSt0b18/Pj6ysLN1zsrOz8fNrXCvwctvd3d0pLS2lvr4eCwuLi54vhGiZ/jG0E//emc3Tvxxk9cOmO73L4kOn+On3fB7p0d7kJh52trVi8YOJfL0tixcWH6b3J5sYHePDqzd3JszT4Zr3c6zwDK+tSGfOzmxdH8aJ3QNo725vwOj/R6PR8MXIGPbmlvPIT7/T1c8Jr2a4IzlzWxaZJdV8MCKSABMq+gAivB157ebOPLvoEH9fcphXbw6/rtd/ueUEn206wbh4fyYmBpr8iinixpjcb3XJkiW8/fbbLFy4EDu7//1RpaSkMHfuXGpqasjIyCA9PZ3u3buTkJBAeno6GRkZ1NbWMnfuXFJSUtBoNPTv35958+YBMHv2bF1rohCiZfJ0sObJviGsOVbE2qOFxg7nsmrqtTz2837au9oypWcQlib44anRaJiYGEjaX/szKTGQ+fvz6fzWaoZ9uYXv9+RSWl132ddVnK1n/u95jJ6zg05vrebbXTmMjvFh3rh4XrqpY7MVfedZWZjxy8Tu2FqaM2LmDsoNPOq7sqael5ceIcbXiTujvf/8BUbwdL8ODAv34o2VR/lu9+W7N13OooOnmPLj7/QIcuWFwaHNOhm1aF4m18fv0UcfpaamhsGDBwONAzw+//xzIiMjGT16NBEREVhYWPDJJ59gbt44LcDHH3/MkCFD0Gq1TJgwgcjISADeeustxo4dy4svvkjXrl2ZOHGi0c5LCKEfT/btwAfrjvPKsjRWh3oYO5xLfLIxk2NFVfzzti5Eeut3dQ59c7Kx5MvRMbwwKIz/W5bGLwdPsfjwTsw0EOJuTwd3OxytLaiq05JZXEVawRnqGxQuthaMjfXlvnh/egW7GWTi4Gvl52zLvHHdGPzFZh5ceIx1UwMMNk/ie2uOkV9Rw+s3d8bfxbRa+87TaDR8f188Pf65gfu/242dpdmfruX7475c7v73bjp62PNeSiQdPfWzlKAwTRqlr7HfrUS3bt3YsWOHwY+Tm5srfSb0RHJpGKac15cWH+YfK9JZ83AyfTuYTvFXXFVLh9dXEu7lyIIJCXg6XDzNhynnFKCmTsv8/fmsTC/kaOEZTlXWUF2nxcbCHE8HK0I97Onm70y/UA/CPOxN6lbgW6vS+euvh/nrgFDeGH59tzivRX75WULfWEVSkCvf3Rt3ye/W1BSdqaXHPzdwtPAMr9/cmaf7h2L+h4E8ddoGXl2ezqsr0uji7cT7IyIYEHbp6HNTf9+2RM2R0yvVMybX4ieEEH/myX4d+GD9caYvS2PVFNMp/P6xPI3ys/VM7R1s8oXB5VhbmjOmqx9jujb2h1ZKUXtu0mdLMzOTXrnh2f6hrD6cx9urj5Ic5EpKF/3ein1lWRpn6xt4pEf7FvG7dbe3Yue0Ptz61Tb++tthvtmZw9Q+wSQEuKBtUGzMLObjDZmkF57hlnAvXrqpEwmBLsYOWzQDKfyEEC2Oi60lU3uH8OqKdNYfL6J3E0al6svRwjN8sjGTlEhvbjGBud30QaPR/xrMhqLRaPh4eDDDvk1n3He72TGtD6Ee+ulzuDe3jH9tPcnIaB8GdzLOfIw3wsHaglVTkvl80wneXHWUyT/su+jxiHYOzEiJ4L54fzxaQDEr9EMKPyFEi/Rk35DGvn5Lj7ByivFH+P7t10NYmGl4KDnIqH3e2jI7S3MWP5hI1xnrGP7lVnY/1afJk0drGxQPfr8PJ2sLHkwMxKGF/W41Gg1TerbnL8mBrDpaxJ6cMhoUdPKyp3uACz7OtsYOUTQz0+mgIYQQ18HVzoqpvYNZdbSIjccNsxLFtdqYUcy8fXmMi/dnQJjp3Hpuizp42POfe7qSXniG22fuQNvElT0+WHec7VmlPNU3hH4mOJjoWpmZmTGooydP9w/l2QGhjOjiI0VfGyWFnxCixXqyXwfsrcx5eVma0WJQSvHUwgN42lsxKdE0p29pa26N9Oa1mzuzLK2ACXP33PD6tbuyS/nbb4foE+LGhO4BlwyOEKIlkiuUEKLFcrOz4vHewaxML2RTRrFRYvh+Ty5bT5byUI8g4vydjRKDuNTfBoXxcI/2zNmZzd9+PXTdry+uqmXMNztxtbXkpcEdpXVMtBpS+AkhWrSnz7X6vbT0SLMf+2ydlud+PURHD3vGxQeY9KjXtujjO7owMtqHt1Yf4/+WXfv7o6Zey20zt3OipJrXb+5M/xZ8i1eIP5LCTwjRormd6+u3Mr2QTZnN2+r34foMTpRU80SfYEI9m3fVCvHnNBoN390bx7BwL15emsbk7/dSd256miupOFvP8H9tY/3xYl65qSP3xPtLQS9aFSn8hBAt3vlWv78vPtxsxzxdUcPrK9PpHeLGHdEyua2psjA345cJ3ZnQPYAvt56k2/vr2J1ddtnn7sgqJemj9aw5WsgrN3XkkZ7B2Fi2jOlshLhWLWtcuhBCXIarnRVP9A7htZXpbDheRK9mmNfvlWVHOFNTz9RewbRzlDnQTJmZmYavxsTSK9iNaQsOEPf+OgaGeXBbF2/8nG3Ir6jh14On+O3waTzsrPjwti7cnxAg0/KIVkne1UKIVuGpfiF8sP44f19yhNUPG3Zev4P5FXyx+QQjo324qZOXQY8l9OeB7oGkRLTj+cWH+fXQaR77eb/uMXc7SyZ2D+DeOH96h7jL7V3RaknhJ4RoFVztrHi6XwemL0tj8aFT3BxuuNUznll0EDtLcyYnBeFoI5fRlsTdwZovRsWg1TawK6ecEyVVuNhY0MnLAV9nW5myRbR60sdPCNFqPNOvA14OVjy76NANz932Z5YePs1vh04zKTHQJJaKEzfG3NyMhEAXRsb4MqiTFwGudlL0iTZBCj8hRKthb23Bazd3Zn9+BV9uOaH3/VfV1vPwT78T6GrLAwkBWFnIJVQI0bLIVUsI0ao80D2QTp72/N+ydM7W1et13/9Yns7xoiqeHxBKlK+TXvcthBDNwWQLv/feew+NRkNhYSHQuCzS448/TmhoKNHR0ezatUv33NmzZxMWFkZYWBizZ8/Wbd+5cydRUVGEhoby+OOPG+zWjxDCdJibafjwti7klJ/llaX6W8ptX24576w5RkpkO0bG+KLRyG1BIUTLY5KFX1ZWFsuWLSMwMFC3bfHixaSnp5Oenk5qaipTpkwBoLi4mOnTp7N161a2bdvG9OnTKSkpAWDKlCl8+eWXutctWbLEKOcjhGheQzp7MSjMg482ZJBeUNnk/WkbFJN/2IuTtQVTewfjbm+lhyiFEKL5mWThN23aNN5+++2LvlEvWLCAcePGodFoSEpKorS0lLy8PJYuXcrgwYNxc3PD1dWVwYMHs2TJEvLy8igvLycpKQmNRsO4ceOYP3++8U5KCNGs/jU6BqXggbl7mtza/87qo2w9WcqTfUPoIwM6hBAtmMnNQ7BgwQL8/PyIiYm5aHtOTg4BAQG6n/39/cnJybnqdn9//0u2X05qaiqpqakA5Ofnk5ubq89TuqyCggKDH6OtkFwaRkvPswIChQAAFMhJREFUqyXwbE9f/m9tNi8v3M1DCd43tJ9deWd4cfFh+gY5MKK9FadP5d9wTC09p6ZO8msYklf9M2ZOjVL4DRo0iPz8Sy+er732Gq+//jrLli1r1ngmT57M5MmTAejWrRu+vs2z/FJzHactkFwaRkvP68u3+LD6ZBVvb8rj9vgQuvq7XNfr88vPMuXXA3g5WDN9WBRRoR5Njqml59TUSX4NQ/Kqf8bKqVFu9a5YsYL9+/df8l9ISAgZGRnExMTQvn17srOziYuLIz8/Hz8/P7KysnT7yM7Oxs/P76rbs7OzL9kuhGg7zMw0/HdcPA5W5tz61XZOV5y95teeqann9lk7KDxTy7u3RtCng9ziFUK0fCbVxy8qKorTp0+TmZlJZmYm/v7+7Nq1C29vb1JSUpgzZw5KKbZs2YKzszM+Pj4MGTKEZcuWUVJSQklJCcuWLWPIkCH4+Pjg5OTEli1bUEoxZ84cRowYYexTFEI0Mx8nG36Z2J3CM7UM+GwLJVW1f/qaqtp6bv16G9tOlvCPoZ1kFK8QotUwqcLvaoYNG0ZISAihoaE8+OCDfPrppwC4ubnx97//nYSEBBISEnjppZdwc3MD4NNPP2XSpEmEhobSoUMHbr75ZmOeghDCSJLbu/Hve7pypKCSpI82cKK46orPzS6tpu+nm1hztIjpQzrxUI/2MlGzEKLVMLnBHRfKzMzU/Vuj0fDJJ59c9nkTJkxgwoQJl2zv1q0b+/fvv8wrhBBtzcgYX6wszLj737uIfGcN/ze0E5OTgnCwbrwMllXX8fW2k/zf8jTqtIp3b43gwQseF0KI1kCuaEKINiMl0ptdT/bhnn/v4qmFB/nbr4fo7OWIBjh0uoJarSIp0IVn+nfg1khvLM2lpU8I0bpI4SeEaFM6ejqwfVoffj14irm7czheVIW5uYbRMb4MDPPg5s5etHOyMXaYQghhEFL4CSHapOER7Rge0c7YYQghRLOS+xhCCCGEEG2EFH5CCCGEEG2EFH5CCCGEEG2EFH5CCCGEEG2EFH5CCCGEEG2EFH5CCCGEEG2ERimljB2EKfHw8KB9+/YGP05BQQGenp4GP05bILk0DMmr/klODUvyaxiSV/1rjpxmZmZSWFh4yXYp/IykW7du7Nixw9hhtAqSS8OQvOqf5NSwJL+GIXnVP2PmVG71CiGEEEK0EVL4CSGEEEK0EVL4GcnkyZONHUKrIbk0DMmr/klODUvyaxiSV/0zZk6lj58QQgghRBshLX5CCCGEEG2EFH5CCCGEEG2EFH5/wsHBwdghXNWECRPw8vKiS5cuxg7lumg0Gu69917dz/X19Xh6enLLLbfoZf/X8ns7fPgwycnJWFtb8+677+rluKbIkLkuKiqif//+ODg48OijjzZ5fy3Nn73P+vXrd11TNrzwwgsEBASY/HXnal577TUiIyOJjo4mNjaWrVu33tB+1qxZw6ZNm/QWV/v27S87p9mNGjp0KC4uLnq7Zl0LjUbDU089pfv53Xff5ZVXXmm2419In+9RU76OmPrf4o3UAFL4maD6+vprfu7999/PkiVLDBiNYdjb27N//36qq6sBWL58OX5+fte1j+vJ0+W4ubnx0Ucf8fTTTzdpP6ZOH7m+EhsbG/7xj3+06sK5Od16661s27bN2GHcsM2bN7No0SJ27drFvn37WLFiBQEBATe0L30Xfk1xuWvNM888wzfffNOscVhbW/PTTz/ptYA1hj/mU64jFzN0DSCF3zWorKxk4MCBxMXFERUVxYIFC4DGWbHDw8N58MEHiYyM5KabbtJ9uF74Tb+wsFC3GkhmZia9e/cmLi6OuLg43YVtzZo19O7dm5SUFCIiInjppZf44IMPdDG88MILfPjhh5fE1qdPH9zc3Ax49oYzbNgwfv31VwC+++477rrrLt1j27ZtIzk5ma5du9KjRw+OHDkCwKxZs0hJSWHAgAEMHDiQyspKHnjgAaKiooiOjubHH3/U7eOFF14gJiaGpKQkTp06dcnxvby8SEhIwNLS0sBnanw3kus+ffqwZ88e3fN69erF3r17L9qvvb09vXr1wsbGxvAnYaLWrFlzUavPo48+yqxZsy56ztdff80TTzyh+/nLL79k2rRpl+wrKSkJHx8fQ4VqcHl5eXh4eGBtbQ00roTk6+sLwM6dO+nbty/x8fEMGTKEvLw8oPFaOXXqVGJjY+nSpQvbtm0jMzOTzz//nPfff5/Y2FjWr19PQUEBd955JwkJCSQkJLBx40YAXnnlFcaPH0/v3r0JCgrip59+4tlnnyUqKoqhQ4dSV1eni+/tt98mKiqK7t27c/ToUYCr7ve+++6jZ8+e3HfffZec68CBA3F0dDRcMi/DwsKCyZMn8/7771/yWGZmJgMGDCA6OpqBAwdy8uRJysrKCAoKoqGhAYAzZ84QEBBAXV0dx44dY+jQocTHx9O7d28OHz4MNBYSU6ZMISkpiZCQENasWcOECRMIDw/n/vvvv+iY06ZNIzIykoEDB1JQUABw1f0+9NBDJCYm8uyzz160H1O/jrS6GkCJq7K3t1d1dXWqrKxMKaVUQUGB6tChg2poaFAZGRnK3Nxc7d69Wyml1KhRo9Q333yjlFKqb9++avv27brXBAUFKaWUOnPmjKqurlZKKZWWlqbi4+OVUkqtXr1a2dnZqePHjyullMrIyFBdu3ZVSiml1WpVSEiIKiwsvGyMGRkZKjIyUv8nb0D29vZq79696s4771TV1dUqJiZGrV69Wg0fPlwppVRZWZmqq6tTSim1fPlydccddyillJo5c6by8/NTRUVFSimlnn32WTV16lTdfouLi5VSSgFq4cKFSimlnnnmGfWPf/zjirG8/PLL6p133tH7OZqKG831rFmzdLk9cuSI7r16OTNnzlSPPPKIYU/EBNnb21+US6WUeuSRR9TMmTOVUv+7DlRUVKiQkBBVW1urlFIqOTlZ7du376r7bYkqKipUTEyMCgsLU1OmTFFr1qxRSilVW1urkpOT1enTp5VSSs2dO1c98MADSqnGHE2aNEkppdTatWt117I//l3eddddav369UoppU6cOKE6d+6se17Pnj1VbW2t2rNnj7K1tVW//fabUkqp2267Tf38889KKaWCgoLUq6++qpRSavbs2brf2dX2GxcXp6qqqq54vn/83Ruavb29KisrU0FBQaq0tFS988476uWXX1ZKKXXLLbeoWbNmKaWU+uqrr9SIESOUUkqlpKSoVatWKaUa8z5x4kSllFIDBgxQaWlpSimltmzZovr376+UUmr8+PFqzJgxqqGhQc2fP185Ojqqffv2Ka1Wq+Li4nSfd4D697//rZRSavr06bq//6vtd/jw4aq+vv6K52eK15HWWANYXF+Z2DYppXj++edZt24dZmZm5OTk6FqQgoODiY2NBSA+Pp7MzMyr7quuro5HH32UPXv2YG5uTlpamu6x7t27ExwcDDT2R3F3d2f37t2cOnWKrl274u7ubpDzM5bo6GgyMzP57rvvGDZs2EWPlZWVMX78eNLT09FoNBd9ax88eLDuG86KFSuYO3eu7jFXV1cArKysdK0w8fHxLF++3NCnY9JuJNejRo3iH//4B++88w5ff/31Jd/2xbVzcHBgwIABLFq0iPDwcOrq6oiKijJ2WHrn4ODAzp07Wb9+PatXr2bMmDG8+eabdOvWjf379zN48GAAtFrtRS2b51ug+/TpQ3l5OaWlpZfse8WKFRw8eFD3c3l5OZWVlQDcfPPNWFpaEhUVhVarZejQoQBERUVddE0+f5y77rpL1+J6tf2mpKRga2vb1LTolZOTE+PGjeOjjz66KLbN/9/e3cY0df0BHP+2FefE2cUXdMYSWaITKW0pDA0qsaDgG61BQgnzmWBiCDrdhEXjY+JTjJubbJGYKCJCZLKxvZuZT1E3DYKBFxLQxGBMDFmjUIc6A/T8XxDun6KAIojS3+cVvZx7zumBe/rrufecc+0av/76KwDLly/XRtXS09MpKysjISGB06dPk52dTWtrK3///TdpaWna+c+fP9d+XrRoETqdDqvVislk0v5XLRYLjY2NREVFodfrSU9PB2DZsmUsWbKk33zT0tIwGAxD0CpDa6TFABL4vYKSkhI8Hg/V1dUEBQURFhbGf//9B6Dd0gAwGAzaMO+oUaO04fWutACHDh3CZDJRW1uLz+fzG9oODg72KzcrK4sTJ07Q1NREZmbmkL2/4eRyudi0aROXLl3i4cOH2vFt27aRkJBARUUFjY2NOJ1O7Xc92+llgoKC0Ol0QOff5U2fBxwJXretx44dS1JSEr///js///wz1dXVw1Tzd1v3ax38r/fusrKy2Lt3L+Hh4axevfptVe+tMxgMOJ1OnE4nVquVoqIiYmJisFgsXLt27aXndF2rvb0G8Pl8XL9+/aW3A7v6Yb1e73ft6/V6v2u/e75dP/eV76v0NcNhw4YNREdHv9L/kcvlYsuWLTx69Ijq6moSExN58uQJH3/8sd+jHN11b8/un3E927M7nU6Hz+frM993tT37M9JiAHnG7xV4vV5CQkIICgri4sWL3Lt3r99zwsLCtA/K8vJyv7wmTpyIXq+nuLiYjo6OXvNISUnhjz/+4MaNGyxYsODN38g7KDMzkx07drww+uH1erUJCD2fl+ouKSmJn376SXvd3Nw8JPUcCQbS1llZWaxfv57Y2FhtNFX4mzx5MnV1dTx//pyWlhbOnz//0nQzZ87k/v37lJaW+j1jOZI0NDRw584d7XVNTQ2TJ09m2rRpeDweLfBra2vj1q1bWrqysjIArl69itFoxGg08tFHH/Hvv/9qaZKTk8nPz/fL+3V1lVNWVkZcXNyg5fu2TZgwAbfbzbFjx7Rjs2bN0u5+lJSUEB8fD3SOwsbGxvLll1+ycOFCDAYD48eP59NPP+XMmTNA54hWz+d3++Pz+bTPttLSUubMmTMo+b6LRloMIIFfH9rb2/nggw9YunQpVVVVWK1WTp48SXh4eL/nbtq0iSNHjuBwOPxmYGVnZ1NUVITdbqe+vr7Pb0CjR48mISEBt9vd6/B4RkYGcXFxNDQ0YDab/TqC94HZbGb9+vUvHM/Ly2Pz5s04HI4+R+u2bt1Kc3MzkZGR2O12Ll68+MplNzU1YTab+e6779i9ezdms5nHjx8P6H28DwbS1jExMYwfP77PkYWwsDC++uorTpw4gdls9rttNpJ19Q+hoaG43W4iIyNxu904HI5ez3G73cyePbvXIDovLw+z2czTp08xm83DtlTHQLW2trJy5UoiIiKw2WzU1dWxc+dORo8eTXl5Od988w12u52oqCi/GbtjxozB4XCwdu1arQ9btGgRFRUV2uSOw4cPU1VVhc1mIyIigoKCgteuX3NzMzabjR9++EGbIDHQfOPj40lLS+P8+fOYzWbOnj372vV5E19//bXfZ0t+fj6FhYXYbDaKi4v9JgKkp6dz6tQp7dYsdAaHx44dw263Y7FYtAkLryo4OJjKykoiIyO5cOEC27dvf6N838V+ZKTGALJlWx9qa2tZs2bNsC2v4PP5iI6O5syZM0ydOnVY6iAC24MHD3A6ndTX16PXy/fE7gbSPyxcuJCNGzcyb968IazZ+8XpdHLw4EE+//zz4a6KEH5GagwgPXkvCgoKyMjIYPfu3cNSfl1dHVOmTGHevHkS9IlhcfLkSWbOnMmePXsk6OvhdfuHlpYWPvvsMz788EMJ+oR4D4zkGEBG/IQQQgghAoR8jRdCCCGECBAS+AkhhBBCBAgJ/IQQQgghAoQEfkKIgGcwGIiKisJisWC32/n222/9FmUeCrm5uVgsFnJzc4e0HCGE6E4mdwghAt64ceO0bbr++ecfvvjiC2bPns2uXbuGrEyj0cijR4/eyhZW7e3tjBolGzUJIWTETwgh/ISEhHD06FF+/PFHlFI0NjYSHx9PdHQ00dHR2sLDK1as4LffftPOW7p06QuL1SqlyM3NJTIyEqvVqu0c4XK5aG1tJSYmRjsGnet2TZ06FY/Ho72eMmUKHo8Hj8dDamoqsbGxxMbG8tdffwFQWVlJXFwcDoeDWbNm0dDQAHTuwuJyuUhMTJQlZIQQ/6eEECLABQcHv3DMaDSqpqYm9eTJE/Xs2TOllFK3b99WMTExSimlLl26pBYvXqyUUqqlpUWFhYWptrY2vzzKy8vV/PnzVXt7u2pqalKhoaHqwYMHvZaplFI7d+5Uhw4dUkopdfbsWbVkyRKllFIZGRnqypUrSiml7t27p8LDw5VSSnm9Xq3cP//8U0tfWFioJk2apB4+fDigNhFCjEwy9i+EEH1oa2sjJyeHmpoaDAYDt2/fBmDu3LlkZ2fj8Xj45ZdfSE1NfeF26tWrV8nIyMBgMGAymZg7dy43btzA5XL1Wl5mZiaLFy9mw4YNHD9+XNsu79y5c37bWD1+/JjW1la8Xi8rV67kzp076HQ62tratDRJSUlMmDBhMJtDCPGek8BPCCF6uHv3LgaDgZCQEHbt2oXJZKK2thafz8eYMWO0dCtWrODUqVOcPn2awsLCQSk7NDQUk8nEhQsXqKyspKSkBOi87Xv9+nW/8gFycnJISEigoqKCxsZGnE6n9ru+9gEVQgQmecZPCCG68Xg8rF27lpycHHQ6HV6vl4kTJ6LX6ykuLqajo0NLu2rVKr7//nsAIiIiXsgrPj6esrIyOjo68Hg8XL58mRkzZvRbh6ysLJYtW0ZaWpo2+SM5OZn8/HwtTU1NDQBer5dJkyYBnc/1CSFEXyTwE0IEvGfPnmnLucyfP5/k5GR27NgBQHZ2NkVFRdjtdurr6/1G0UwmE9OnT9dux/aUkpKCzWbDbreTmJjIgQMH+OSTT/qtT9fkj+75Hj58mKqqKmw2GxERERQUFACQl5fH5s2bcTgctLe3v0kzCCECgCznIoQQA/T06VOsVis3b97EaDQOWr5VVVVs3LiRK1euDFqeQggBMuInhBADcu7cOaZPn866desGNejbv38/qamp7Nu3b9DyFEKILjLiJ4QQQggRIGTETwghhBAiQEjgJ4QQQggRICTwE0IIIYQIEBL4CSGEEEIECAn8hBBCCCECxP8Ak1fjCM60awoAAAAASUVORK5CYII=\",\n 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m2.plot_components(forecast2);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"227a7af2\",\n \"metadata\": {},\n \"source\": [\n \"* Prophet is sensibly assigning a large negative effect to the days within the lockdown periods.\\n\",\n \"* The forecast for the trend is not as strong / optimistic and seems fairly reasonable.\\n\",\n \"\\n\",\n \"### Sense checking the trend\\n\",\n \"\\n\",\n \"In an environment when behaviours are constantly changing, it's important to ensure that the trend component of the model is capturing to emerging patterns without overfitting to them.\\n\",\n \"\\n\",\n \"The [trend changepoints](https://facebook.github.io/prophet/docs/trend_changepoints.html) documentation explains two things we could tweak with the trend component:\\n\",\n \"\\n\",\n \"* The changepoint locations, which by default are evenly spaced across 80% of the history. We should be mindful of where this range ends, and extend the range (either by increasing the % or adding changepoints manually) if we believe the most recent data better reflects future behaviour.\\n\",\n \"* The strength of regularisation (`changepoint_prior_scale`), which determines how flexible the trend is; the default value is `0.05` and increasing this will allow the trend to fit more closely to the observed data.\\n\",\n \"\\n\",\n \"We plot the trend component and changepoints detected by our current model below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"f3675c69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from prophet.plot import add_changepoints_to_plot\\n\",\n \"fig = m2.plot(forecast2)\\n\",\n \"a = add_changepoints_to_plot(fig.gca(), m2, forecast2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b6cd90ff\",\n \"metadata\": {},\n \"source\": [\n \"The detected changepoints look reasonable, and the future trend tracks the latest upwards trend in activity, but not to the extent of late 2020. This seems suitable for a best guess of future activity. \\n\",\n \"\\n\",\n \"We can see what the forecast would look like if we wanted to emphasize COVID patterns more in model training; we can do this by adding more potential changepoints after 2020 and making the trend more flexible.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"85ccc556\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"m3_changepoints = (\\n\",\n \" # 10 potential changepoints in 2.5 years\\n\",\n \" pd.date_range('2017-06-02', '2020-01-01', periods=10).date.tolist() + \\n\",\n \" # 15 potential changepoints in 1 year 2 months\\n\",\n \" pd.date_range('2020-02-01', '2021-04-01', periods=15).date.tolist()\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"6df44e0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"02:53:49 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"02:53:52 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Default changepoint_prior_scale is 0.05, so 1.0 will lead to much more flexibility in comparison.\\n\",\n \"m3 = Prophet(holidays=lockdowns, changepoints=m3_changepoints, changepoint_prior_scale=1.0)\\n\",\n \"m3 = m3.fit(df)\\n\",\n \"forecast3 = m3.predict(future2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"effcdd7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from prophet.plot import add_changepoints_to_plot\\n\",\n \"fig = m3.plot(forecast3)\\n\",\n \"a = add_changepoints_to_plot(fig.gca(), m3, forecast3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab8ce52d\",\n \"metadata\": {},\n \"source\": [\n \"We're seeing many changepoints detected post-COVID, matching the various fluctuations from loosening / tightening lockdowns. Overall the trend curve and forecasted trend are quite similar to our previous model, but we're seeing a lot more uncertainty because of the higher number of trend changes we picked up in the history.\\n\",\n \"\\n\",\n \"We probably wouldn't pick this model over the model with default parameters as a best estimate, but it's a good demonstration of how we can incorporate our beliefs into the model about which patterns are important to capture.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6dd2dc42\",\n \"metadata\": {},\n \"source\": [\n \"### Changes in seasonality between pre- and post-COVID\\n\",\n \"\\n\",\n \"The seasonal component plots in the previous sections show a peak of activity on Friday compared to other days of the week. If we're not sure whether this will still hold post-lockdown, we can add _conditional seasonalities_ to the model. Conditional seasonalities are explained in more detail [here](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#seasonalities-that-depend-on-other-factors).\\n\",\n \"\\n\",\n \"First we define boolean columns in the history dataframe to flag \\\"pre covid\\\" and \\\"post covid\\\" periods:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"75559257\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df2 = df.copy()\\n\",\n \"df2['pre_covid'] = pd.to_datetime(df2['ds']) < pd.to_datetime('2020-03-21')\\n\",\n \"df2['post_covid'] = ~df2['pre_covid']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b75a9e13\",\n \"metadata\": {},\n \"source\": [\n \"The conditional seasonality we're interested in modelling here is the day-of-week (\\\"weekly\\\") seasonality. To do this, we firstly turn off the default `weekly_seasonality` when we create the Prophet model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"cd65ad72\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"m4 = Prophet(holidays=lockdowns, weekly_seasonality=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29b63e33\",\n \"metadata\": {},\n \"source\": [\n \"We then add this weekly seasonality manually, as two different model components - one for pre-covid, one for post-covid. Note that `fourier_order=3` is the default setting for weekly seasonality. After this we can run `.fit()`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"538a2914\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"m4.add_seasonality(\\n\",\n \" name='weekly_pre_covid',\\n\",\n \" period=7,\\n\",\n \" fourier_order=3,\\n\",\n \" condition_name='pre_covid',\\n\",\n \")\\n\",\n \"m4.add_seasonality(\\n\",\n \" name='weekly_post_covid',\\n\",\n \" period=7,\\n\",\n \" fourier_order=3,\\n\",\n \" condition_name='post_covid',\\n\",\n \");\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"10614e70\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"02:53:55 - cmdstanpy - INFO - Chain [1] start processing\\n\",\n \"02:53:56 - cmdstanpy - INFO - Chain [1] done processing\\n\"\n ]\n }\n ],\n \"source\": [\n \"m4 = m4.fit(df2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ee8bed18\",\n \"metadata\": {},\n \"source\": [\n \"We also need to create the `pre_covid` and `post_covid` flags in the future dataframe. This is so that Prophet can apply the correct weekly seasonality parameters to each future date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b65e2ec7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"future4 = m4.make_future_dataframe(periods=366)\\n\",\n \"future4['pre_covid'] = pd.to_datetime(future4['ds']) < pd.to_datetime('2020-03-21')\\n\",\n \"future4['post_covid'] = ~future4['pre_covid']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"b7dc89f0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"forecast4 = m4.predict(future4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"4dc6c2d4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m4.plot(forecast4)\\n\",\n \"plt.axhline(y=0, color='red')\\n\",\n \"plt.title('Lockdowns as one-off holidays + Conditional weekly seasonality');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"47921893\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m4.plot_components(forecast4);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e1234edf\",\n \"metadata\": {},\n \"source\": [\n \"Interestingly, the model with conditional seasonalities suggests that, post-COVID, pedestrian activity peaks on Saturdays, instead of Fridays. This could make sense if most people are still working from home and are hence less likely to go out on Friday nights. From a prediction perspective this would only be important if we care about predicting weekdays vs. weekends accurately, but overall this kind of exploration helps us gain insight into how COVID has changed behaviours.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2b44dbd7\",\n \"metadata\": {},\n \"source\": [\n \"#### Further reading\\n\",\n \"\\n\",\n \"A lot of the content in this page was inspired by this [GitHub discussion](https://github.com/facebook/prophet/issues/1416). We've covered a few low hanging fruits for tweaking Prophet models when faced with shocks such as COVID, but there are many other possible approaches as well, such as:\\n\",\n \"\\n\",\n \"* Using external regressors (e.g. the lockdown stringency index). This would only be fruitful if we a) have regressor data that aligns well (in terms of location) with the series we're forecasting, and b) have control over or can predict the regressor much more accurately than the time series alone.\\n\",\n \"* Detecting and removing outlier data from the training period, or throwing away older training data completely. This might be a better approach for sub-daily time series that don't have yearly seasonal patterns.\\n\",\n \"\\n\",\n \"Overall though it's difficult to be confident in our forecasts in these environments when rules are constantly changing and outbreaks occur randomly. In this scenario it's more important to constantly re-train / re-evaluate our models and clearly communicate the increased uncertainty in forecasts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aa9f4101\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3.8.6 ('python-P8zs_nH7')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"b89c51048a83bff423ddf9419a4921628c4e382d20a75037c2900b180e09c6be\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "notebooks/multiplicative_seasonality.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import logging\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"logging.getLogger('numexpr').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default Prophet fits additive seasonalities, meaning the effect of the seasonality is added to the trend to get the forecast. This time series of the number of air passengers is an example of when additive seasonality does not work:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_air_passengers.csv')\\n\",\n \"m <- prophet(df)\\n\",\n \"future <- make_future_dataframe(m, 50, freq = 'm')\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_air_passengers.csv')\\n\",\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\",\n \"future = m.make_future_dataframe(50, freq='MS')\\n\",\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This time series has a clear yearly cycle, but the seasonality in the forecast is too large at the start of the time series and too small at the end. In this time series, the seasonality is not a constant additive factor as assumed by Prophet, rather it grows with the trend. This is multiplicative seasonality.\\n\",\n \"\\n\",\n \"Prophet can model multiplicative seasonality by setting `seasonality_mode='multiplicative'` in the input arguments:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"m <- prophet(df, seasonality.mode = 'multiplicative')\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(seasonality_mode='multiplicative')\\n\",\n \"m.fit(df)\\n\",\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The components figure will now show the seasonality as a percent of the trend:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 6 -u in\\n\",\n \"prophet_plot_components(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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6YFBUOdiqC3Z3wd3PEw0nTurOG6Nik8BNCCCG6ufpmIyv3FfDR7nx+Ol5Go9GMj9ae6+MCGRXhQ7i3MypUuDpq0Lk74ePigJuM1+uUpPATQgghuqFmo4mvDhbx4Z58vssoob7ZhLuThqv7+DOqpzdR/q7YqcFH64DOo2UWrtZByobOTv4LCiGEEN2Eyayw/mgxH+zOY83hYqoajbg42DG2lw8je3oTG+iGo0aNv6sjOg8n2TWjC5LCTwghhOjCzGaFzdllfLArj1UHCimrN+CkUTMi3JtREd4k6NzROmgIdnckwM1Jxut1cVL4CSGEEF2MoiikFdTy0rb9rNxXQGFNEw52Kgb38GR0hA8De3ji7WRPiKcTvi6OMl6vG5HCTwghhOgCFEVhf0ENH+7JY/lePScrG9CoVQzQuXPrQB1De3gR7OHUOjlD1tfrnuS/uhBCCNGJHS2u5eM9eSzbqyejtA61CuIC3ZncU8uEfuFE+GgJdnfES+uAk+yH2+1J4SeEEEJ0Mtll9SxLy+eTvfnsL6hBBfTxd+XuIT2YEOVL30A31PUVRIUHyn644gxS+AkhhBCdQF5lA5+m6/l4Tz678qoAiPZz4fZBoSRH+zEgxJ0AV0fcneyxU6vQ62ul6BNnkcJPCCGE6KCKa5pYua+AT/bmsyW7HIBePlrmDNQxsbcfQ8O88HVxxN1Jg0olkzPExUnhJ4QQQnQg5fXNfL6vgGVpen44VopZgTAvZ25N1DEx2pdRvXxbJmc42EmxJy6bFH5CCCGEjVU3Glh9oJBle/V8l1GC0awQ7O7ETQnBJEf5MSG6pdiTnTNEW8k3SAghhLCBuiYjaw4Xsywtn7WHi2kymglwdeD6uECSo/2YGO2Lv5uTzMQV7cpmhV94eDhubm7Y2dmh0WjYtWsX5eXl3HTTTeTk5BAeHs6KFSvw8vJCURTmzZvH2rVr0Wq1vPfeeyQmJtoqdCGEEOKKNBpMfHOkmOVper48WES9wYSP1p6pfQMYH+XLlD7+BLo7yTZpwmJs2uL3ww8/4Ovr2/r3BQsWMH78eObPn8+CBQtYsGABCxcuZN26dWRmZpKZmcn27du577772L59uw0jF0IIIS6NwWTm+4wSlqXpWbW/kOomI55OGib19mNcpC9TYvwJ9XKWGbjCKjpUV+/q1avZtGkTAHPnzmXMmDEsXLiQ1atXM2fOHFQqFUOHDqWyspKCggKCgoJsG7AQQghxDiazwo9ZZSxLy+ezfQWU1xtwdbBjTKQPE6L8mNTbj16+LlLsCauzWeGnUqmYOHEiKpWKe+65h5SUFIqKilqLucDAQIqKigDIz88nNDS09diQkBDy8/PPKvxSU1NJTU0FoLCwEL1eb6W7sa2SkhJbh2BzkgPJwWmSB8kB2CYHZkVhV34dXx4t5+uMCkrqjThpVAwPcWVsuB+jw9zp4aXF3k4FphpKimosGo98D1pIHs5ks8Jvy5Yt6HQ6iouLSU5Opk+fPme8r1KpLnuaekpKCikpKQAkJSURHBzcbvF2dN3pXs9HciA5OE3yIDkA6+RAURR25VaxPC2f5Wl68qoacbRTMzLCm+RoXyZG+9E30A1HjW0maMj3oIXk4Vc2K/x0Oh0A/v7+XHfddezYsYOAgIDWLtyCggL8/f1bP5ubm9t6bF5eXuvxQgghhDUpisL+ghqWpeWzfK+e4+X1aNQqhoV7ce+wMJJ7+xEX5I6zzMYVHZBNCr+6ujrMZjNubm7U1dXx3Xff8eyzzzJt2jSWLl3K/PnzWbp0KdOnTwdg2rRpvPHGG8yaNYvt27fj4eEh4/uEEEJY1ZGiGpan6VmWpudIcS12KhgU6smtp3bRSAj2wMWxQw2dF+IsNvmGFhUVcd111wFgNBq55ZZbmDx5MoMGDWLmzJksWbKEsLAwVqxYAcCUKVNYu3YtkZGRaLVa3n33XVuELYQQopvJLqtneVo+y9L0pOurUQGJIR7MHxdJcrQvSaGeuDvZ2zpMIS6ZTQq/iIgI0tPTz3rdx8eHDRs2nPW6SqVi8eLF1ghNCCFEN5dX2cCKdD3L0/TsOFkJQHyQG4+NjmBClC9Dw73xdJZiT3RO0iYthBCi2yuqaWJlup7l6Xo2Hy8HoI+/Kw+N7ElytC/Dwr3xcXGwcZRCtJ0UfkIIIbql8vpmPt9XwLI0PT8cK8WsQISPlnuHhTEx2o8RPb3xd3O0dZhCtCsp/IQQQnQb1Y0GVh8oZFmanu+OlmA0K4R6OnH7oFCSo/0Y3cuHADfHy15OTIjOQgo/IYQQXVpdk5GvDxWxPF3P2sPFNBnNBLo5csuAltm4o3t5o/NwlmJPdAtS+AkhhOhyGo1mVu1v6cb96mAR9QYTvi4OXN8vkORoP8ZH+RLi6YxaLcWe6F6k8BNCCNElGExmvs8oYVmani/26alpNuPprOHqPn5M7O3H+Cg/wr212EmxJ7oxKfyEEEJ0WiazwqZjpSxP1/PZvgLK6w24OWoYEerKNfE9SO7tS4S3Cxo7ta1DFaJDkMJPCCFEp2I2K/ySU86yND0r0wsoqm1Ca2/H6F7eJEf7MTHaD1djDWGhsrWnEP9LCj8hhBAdnqIo7MqtYllaPivS9ORVNeJop2ZkhDePRkcwMdqXmEA3HDUt++Pq9bU2jliIjumKCr+pU6decPbTl19+ecUBCSGEENBS7O0rqGZ5mp7le/UcL69Ho1YxLNyLe4eFkdzbj7ggd5zt7WwdqhCdxhUVfo8//jgAn3/+OYWFhdx6660AfPLJJwQEBLRfdEIIIbqdI0U1LE/TsyxNz5HiWuxUMCjUk1sH6kiO9mOAzgMXR+mwEuJKXNG/nNGjRwPw2GOPsWvXrtbXp06dSlJSUvtEJoQQots4XlbX0rKXpiddX40KSAzxYP64SJKjfUkK9cTdSfbHFaKt2vQrU11dHcePHyciIgKA7Oxs6urq2iUwIYQQXVteZQMr0vUs26tnZ24lAPFBbjw+OoLxUb4MDffG01mKPSHaU5sKv3/+85+MGTOGiIgIFEXhxIkTvP322+0VmxBCiC6mqKaJlekt3bhbsssBiPF35aGRPUmO9mVYuDc+Lg42jlKIrqtNhd/kyZPJzMzkyJEjAPTp0wdHR9nQWgghxK/K6pr5fH8By9P0/HCsFLMCvXy03DssjInRfoyM8MbPVX52CGENbR4du3v3bnJycjAajaSnpwMwZ86cNgcmhBCi86pqMLD6YCHL0/R8d7QEo1mhh6czdwwOJTnaj1ERPgS4Ocr+uEJYWZsKv9tuu42srCwSEhKws2uZTq9SqaTwE0KIbqiuycjXh4pYlqZn3ZFimoxmgtwcuWWAjom9/Rjdyxudh7MUe0LYUJsKv127dnHo0KEr/kdsMplISkpCp9Px9ddfk52dzaxZsygrK2PgwIF88MEHODg40NTUxJw5c9i9ezc+Pj4sX76c8PDwtoQuhBCiHTQaTHxzpJhlaXq+OlhEvcGEr4sD1/cLZGJvP8ZF+hLi6Yxa9scVokNoU+HXr18/CgsLCQoKuqLjX3vtNWJiYqiurgbgiSee4JFHHmHWrFnce++9LFmyhPvuu48lS5bg5eXFsWPHWLZsGU888QTLly9vS+hCCCGuULPRzPeZJSxP07NqfyHVTUY8nTVMifEnOdqX8VF+hHtrsZNiT4gOp02FX2lpKX379mXw4MFnTOq4lJ078vLyWLNmDU899RSvvvoqiqKwceNGPv74YwDmzp3Lc889x3333cfq1at57rnnAJgxYwYPPPAAiqJId4EQQliJyayw6Vgpy9L0fL6/gPJ6A26OGsZG+jAx2o8J0b708nFBY6e2dahCiAtoU+F3uhi7Eg8//DAvv/wyNTU1AJSVleHp6YlG0xJSSEgI+fn5AOTn5xMaGtoSsEaDh4cHZWVl+Pr6nnHO1NRUUlNTASgsLESv119xfJ1JSUmJrUOwOcmB5OA0yUP75cCsKOzKr2P10XLWZFRQUm/EWaNieIgrY8P9GB3mQaiXM/Z2KjDWUFxU0y7XbQ/yPZAcnCZ5OFObCr/Ro0dz4sQJMjMzmTBhAvX19ZhMpose9/XXX+Pv78/AgQPZtGlTW0I4Q0pKCikpKQAkJSURHBzcbufu6LrTvZ6P5EBycJrk4cpzoCgKO3MrWZ6mZ0WanryqRhzt1IyM8GZitB8To32JCXTDUdPx98eV74Hk4DTJw6/aVPj95z//ITU1lfLycrKyssjPz+fee+9lw4YNFzzu559/5ssvv2Tt2rU0NjZSXV3NvHnzqKysxGg0otFoyMvLQ6fTAaDT6cjNzSUkJASj0UhVVRU+Pj5tCV0IIQRgNiscKKzhp+Nl/HS8jM3HyymsaUKjVjE83It7h4WR3NuPuCB3nO07frEnhLiwNhV+ixcvZseOHQwZMgSAqKgoiouLL3rcSy+9xEsvvQTApk2b+Mc//sFHH33EjTfeyMqVK5k1axZLly5l+vTpAEybNo2lS5cybNgwVq5cybhx42R8nxBCXAGDycyevKpThV45W7LLqGwwAhDg5kiizp3BoV4k9/YlIdgDF8c2L/cqhOhA2vQv2tHREQeHX7fWMRqNbSrIFi5cyKxZs3j66acZMGAAd955JwB33nknt912G5GRkXh7e7Ns2bK2hC2EEN1GfbOR7Scr2Xy8nJ+Ol7E1p4J6Q8uQnDAvZ8b08mWAzp2BIR4khnji7+oos3GF6MLaPMbvb3/7Gw0NDaxfv54333yTqVOnXtY5xowZw5gxYwCIiIhgx44dZ33GycmJTz/9tC2hCiFEt1DVYGDD8SoO7qlic3YZO3MrMZgUVECUnwvTYgNIONWq1y/IDR+tg6yxJ0Q30qbCb+HChbzzzjvExcXx9ttvM2XKFO666672ik0IIcRFFNc0sTm7pdv2p6wy0guqURSwU6uIDXDllgE6Bug8GNzDk97+rng528tQGSG6sSsu/EwmE7GxsRw5coS77767PWMSQghxHifK69mc3dJt+1NWGUdL6gBw1KiJD3Lj7iE9iHY1Mz4+gggfLe5O9jaOWAjRkVxx4WdnZ0fv3r05efIkPXr0aM+YhBBC0LK0ytHi2t8UeuWcrGwAwNXBjgSdBxN7+5Go82BImBc9PJ1xcdSg1+sJDvawcfRCiI6oTV29FRUVxMbGMnjwYFxcXFpfv5SdO4QQQpzJZFbYp69uWVYlu6XrtqSuGQAfrT0JOg9mJgSRqPNgcA8vdB5OOMkSK0KIy9Cmwq+xsZGvv/669e+KovDEE0+0OSghhOgOzGaFdH01m7JK2ZTV0nVb2diytEqwuyODe3gyQOfBAJ07g0I9CXR3wl62RBNCtEGbCj+j0cjo0aPPeK2hoaFNAQkhRFdlNivsK6hmU1YZm46V8tPxcioaDACEejoxJtKXRJ3HqaVVPPCTpVWEEO3sigq/t956izfffJPjx48THx/f+npNTQ0jRoxot+CEEKIzM5sV9hdWs+lYGZuyyvgxq6y10AvxcGJUhDcDQzwZGuZJgs4DXxcHmXErhLCoKyr8brnlFq6++mr+9Kc/sWDBgtbX3dzc8Pb2brfghBCis8mrbODboyV8c6SYjcdKKa9vKfR0Hk5cFeHNwBAPhoZ5MTDEE2+tLK0ihLCuKyr8PDw88PDw4JNPPmnveIQQolNpMpr4ObuCb44Us+5IMQcKawDwd3VgRHhLoTeohydJIZ74uUqLnhDCtmQTRiGEuEzZZfV8c7SYdYdbWvXqmk1o1CoG6Nx5aGRPhod7cVWENwFuTjJGTwjRoUjhJ4QQF9FgMPFjVllrq17GqUWTg90dubqPP8PCvBgX5UtMgCuOGlleRQjRcUnhJ4QQ55BRUsu6w8V8c7SYTcfKaDSacbRTMzDEg6mjAxgW1tKq5+fqKN23QohOQwo/IYSgZR3SvflVfL6/kM/3FXC4uBaAMC9nrusXyLBwL8b08iHKz1UWTRZCdFpS+Akhui2zWWHriQo+31/A5/sKyKloQK2CRJ0Hj4+JYGRPb4aFecukDCFElyGFnxCiWzGYzGw6Vsbn+wtYdaCQwpom7O1UDOnhxW1JIYyP9GNAiDvuTva2DlUIIdqdFH5CiC6vwWDiu6MlfL6/gC8PFFLZaMTZXs2IcG8eHBnOhCg/+gW5oXWQR6IQomuTp5wQokuqbjSw5lAxn+8vYO3hYuoNJtwdNYyK8GZspC8Te/sR7eeKg0b2vhVCdB82KfwaGxsZNWoUTU1NGI1GZsyYwfPPP092djazZs2irKyMgQMH8sEHH+Dg4EBTUxNz5sxh9+7d+Pj4sHz5csLDw20RuhCiAyupbeKT/aVsXJvL9xklNJsUfLT2TInxZ2ykDxOj/Qj31qKxk2JPCNE92aTwc3R0ZOPGjbi6umIwGBg5ciRXX301r776Ko888gizZs3i3nvvZcmSJdx3330sWbIELy8vjh07xrJly3jiiSdYvny5LUIXQnQwuRUNrDpQyOf7C/jpeBlmBXTuTszsH8zYSF8mRPsS4uGMWhZSFkII2xR+KpUKV1dXAAwGAwaDAZVKxcaNG/n4448BmDt3Ls899xz33Xcfq1ev5rnnngNgxowZPPDAAyiKIrPshOimMktqW5Zd2V/AjpOVAET4aLljcChJPiqmJUUT6O4kzwghhPgfNhvjZzKZGDhwIMeOHeP++++nV69eeHp6otG0hBQSEkJ+fj4A+fn5hIaGtgSs0eDh4UFZWRm+vr5nnDM1NZXU1FQACgsL0ev1Vrwj2ykpKbF1CDYnOejaOVAUhUMlDazLrGRtZgVHyxoB6O3jyJ0JvowMdWVwqDuezvaUlJSg1FVQUGfjoG2oK38XLpXkQHJwmuThTDYr/Ozs7EhLS6OyspLrrruOI0eOtPmcKSkppKSkAJCUlERwcHCbz9lZdKd7PR/JQdfKgdmssP1kReuCysfL61GrICHYg8dHBzMm0ocRPX3wdD572ZWulIcrJTmQHIDk4DTJw69sPqvX09OTsWPHsnXrViorKzEajWg0GvLy8tDpdADodDpyc3MJCQnBaDRSVVWFj4+PjSMXQrQ3g8nMT1llfL6/kC/2F1BQ04RGrWJwD09uSdQxLsqXpBBP3Jxs/ugSQohOySZPz5KSEuzt7fH09KShoYH169fzxBNPMHbsWFauXMmsWbNYunQp06dPB2DatGksXbqUYcOGsXLlSsaNGydjd4ToIhoNJtZnlPD5/kK+PFhIeb0BJ42a4eFe3D8inAnRfsTJGntCCNEubPIkLSgoYO7cuZhMJsxmMzNnzuSaa66hb9++zJo1i6effpoBAwZw5513AnDnnXdy2223ERkZibe3N8uWLbNF2EKIdlLTaGTt4SI+31/I2sNF1DabcHWwY1QvH8b18mFCbz/6+LviqJE9cYUQoj3ZpPCLj49n7969Z70eERHBjh07znrdycmJTz/91BqhCSEspKyumS8PFvL5/kLWZ5TQZDTjrbVnYm8/xkX6Mj7al14+LtjLGntCCGEx0ncihLCY/KoGVu1vKfZ+PF6GyawQ5ObIDXFBjIv0YWykL2HeWuxkjT0hhLAKKfyEEO0qq7SOL/YX8tn+AradqACgp7czc5NCGBfpy+he3gS7y4LKQghhC1L4CSHaRFEUDhbWtC6onK6vBiDG35U/DA9jbKQvV0X44O/qIJOyhBDCxqTwE0JcNrNZYVdeJZ/vayn2MkvrUAEJOnceHRXBmF4+DO/pjY+Lg61DFUII8RtS+AkhLonRZGZLdnnrGnt5VY3YqVUMDvXkxv5BjIv0JSnUE49zLKgshBCiY5DCTwhxXk1GExsyS/l8XyGrDxZSWteMo0bN8DAvUoaGMS7Kh/7BHrg6yqNECCE6A3laCyFamc0K6fpqvs8s4fuMUjZnl9FgMOPiYMdVPb0ZF+XLuEhf+ga64Wwva+wJIURnI4WfEN3c8bI6vs8oZUNmKRsySyirNwAQ4aPl2thAhoZ5MT7Kl0g/F1lQWQghOjkp/IToZgqrG/npeHlrq152eT0Afi4ODAvzYnAPL4aEeTJA54Gvi8zEFUKIrkQKPyG6sKKaJnbnVbIrt+rU/1eir24CwMXBjqQQT2bEBzGkhydDwjwJdHNCIztnCCFElyWFnxBdRFm9gX1HitmVW8nuvCp25VaSV9UIgAro4eVM/2B3bkpwIz7IjRE9venh5Szdt0II0Y1I4SdEJ1RW1/w/LXlVnKxsaH2/h6czsYFuzIgPIibAlUSdBz19XPBytpcdM4QQohuTwk+IDq68vpk9p1rwduVVsTu3kpyKX4u8UE8n+vi7MjnChcSeQQwI8SDCW4uX1kH2wBVCCHEGKfyE6EAqGwzsPt1Vm1fJ7twqjp+afAGg83Aixt+VqbEBxPi7kqDzINLXBW+tA0WFBQQHB9sweiGEEB2dFH5C2EhVg4E9+VXszj1V5OVVcqz01yIv2N2RPv6uTInxJybAlYTgliLPx0Va8oQQQlwZKfyEsILqRgN786tOTbpo6bbNLK1rfT/QzZGYAFcmRvvRN8CN/sHuRPu54q21l1m2Qggh2o0UfkK0s9omI3vzq86YXZtRWoeitLwf4OpATIAb46N86RvgSv9gd3r7u+EjRZ4QQggLs0nhl5uby5w5cygqKkKlUpGSksK8efMoLy/npptuIicnh/DwcFasWIGXlxeKojBv3jzWrl2LVqvlvffeIzEx0RahC3GGuiYjafrqM4q8IyW1rUWen4sDMQGujI30pY9/S5HXx98VHxcH7KXIE0IIYWU2Kfw0Gg2vvPIKiYmJ1NTUMHDgQJKTk3nvvfcYP3488+fPZ8GCBSxYsICFCxeybt06MjMzyczMZPv27dx3331s377dFqGLbqy+2Ui6vvqMJVQOF9dgPlXk+Wjt6Rvgxt0RPYjxdyU+2J2YADd8pcgTQgjRQdik8AsKCiIoKAgANzc3YmJiyM/PZ/Xq1WzatAmAuXPnMmbMGBYuXMjq1auZM2cOKpWKoUOHUllZSUFBQes5hGhvDQYT+0615O3Kayn0DhXVYjpV5Xlr7Ynxd+XOwT3oc6rI63uqyHPQSJEnhBCiY7L5GL+cnBz27t3LkCFDKCoqai3mAgMDKSoqAiA/P5/Q0NDWY0JCQsjPzz+r8EtNTSU1NRWAwsJC9Hq9le7CtkpKSmwdgs21JQeNRjNHShtIL6xnX1Ed+4rqOVragOlUS56nox1RPo7M6utFtI8TMb5ORPm0rJNnb3d6dq0B6sr5zXwNq5PvQQvJg+QAJAcgOThN8nAmmxZ+tbW13HDDDSxatAh3d/cz3lOpVJe9OXxKSgopKSkAJCUldas1zbrTvZ7PpeSgyWhif0HNGbte7C+owXiqJc/DSUPfADfmJLVMvIgNdCMuyA0/V8dOsbWZfA9aSB4kByA5AMnBaZKHX9ms8DMYDNxwww3Mnj2b66+/HoCAgIDWLtyCggL8/f0B0Ol05Obmth6bl5eHTqezSdyi82g2mjlQWN066WJXXhX7C6oxnGrKc3fUEBPgyq2JOmIC3Oh3qsjzd+scRZ4QQghxuWxS+CmKwp133klMTAyPPvpo6+vTpk1j6dKlzJ8/n6VLlzJ9+vTW19944w1mzZrF9u3b8fDwkPF94gwGk0Ja6zp5lezKq2SfvprmU0Wem6OGGH9XbhmgI8a/pSWvf7A7fq6OONlLkSeEEKJ7sEnh9/PPP/PBBx8QFxdHQkICAH/729+YP38+M2fOZMmSJYSFhbFixQoApkyZwtq1a4mMjESr1fLuu+/aImzRQRhNZg4V1f66hEpeJWn5Va1FnouDHTH+rtyU0FLk9Q10ZUCwB35ujjhLkSeEEKIbs0nhN3LkSJTTC539jw0bNpz1mkqlYvHixZYOS3RARpOZI8W1vy6hkldFWn4VjUYz0FLk9fZzZXq0J/3D/Okb4MaAEHcC3JykyBNCCCH+h81n9QpxmsmscLS4ll2/mXixN7+KBkNLkae1t6OPvwvXxwXRN8CVPv6uJIZ4EODmSGVpsQzeFUIIIS5CCj9hE2azQkZJLbvyft3abG9+FXXNJgCcNGr6+LtybWwgMQFuxAS4kqhrKfJcHM/+2lZaOX4hhBCiM5LCT1ic2ayQWVp3xhIqe/KqqD1V5Dlq1PT2c+WavgEtY/ICXEkM8STAzRHXcxR5QgghhLgy8lNVtCuzWSGrrK51du3uvJaZtjVNRgAc7dRE+7kwJcafGP9TLXkhHgS5O0mRJ4QQQliY/KQVV0xRFI6X1Z8xu3ZPXhVVjS1FnoOdiihfVyb39qNPgCt9/d1IDHUnyM0ZNyf56gkhhBDWJj99xSVRFIWc8gZ25VWyO7fq1P9XUnmqyLO3UxHl68KEKN+WMXn+rgwM9SDYXYo8IYQQoqOQn8jiLIqicLLiVJH3my7b8noDABq1ikhfF8ZGtmxrFhPgRmKIBzoPJ9yd7G0cvRBCCCHORwq/bk5RFPIqG88o8nblVlJ2qsizU6uI9NEyKsKHGH/XU2PyPAnxcMLdSXPZ+ykLIYQQwnak8OsmzGaF3MoGDhfXcrio5tT/13KoqKa1Jc9OBb18XBjR05u+/i0teQNDPdB5OOMhRZ4QQgjR6Unh18UYTGaySuvOKOwOF9VypLiWeoOp9XMeThoifLSMjvAhys+FvqcWQw7xdMbT2V6KPCGEEKILksKvk6pvNnK0uI5DRTXsyCogrz6fw0W1HCurw2D6dTu8ADdHeno5Mz02gHBvLT29tcQEuBLh44KX1l62NRNCCCG6ESn8Orjy+mYOF53ZPXu4qIYTlQ2c3u5YrYIQT2d6emkZ0sOTcG8tET5aYgPdCPFoacFz0KhteyNCCCGEsDkp/DoARVHQVzeeKupqOVxc01rgFdU2t37O0U5NmJcz0X6uTO7jT7i3Mz29teg09cREhOHhZI+dWrpohRBCCHFuUvhZkcmskF1e39J6V1TL4eKWMXhHimqpPrWzBYCrgx0RPlqGhHnR81T3bKSvlhh/N3xdHXBzPHOihV6vx1vrYItbEkIIIUQnIoWfBTQZTWSU1J0xueJwcQ0ZJXU0Gc2tn/N1caCntzOT+vi1Fni9/VyI8nPBy9kBF9nCTAghhBDtSCqLNqhuNHCkuJZDhb/pni2u4XhZPeZT4+9UQLCHEz29tczsH9Qy/s5bS98AV8K8XfB01uCokQkWQgghhLA8KfzaYPZHe/n6UBHQsptFD6+WMXdjevm0tuDFBbkT6OaIp7M9GjuZYCGEEEII27FJ4XfHHXfw9ddf4+/vz4EDBwAoLy/npptuIicnh/DwcFasWIGXlxeKojBv3jzWrl2LVqvlvffeIzEx0RZhn+XR0RGMj/Ih3EtLvyA3fF0ccXfUoJYJFkIIIYTogGzSBPX73/+eb7755ozXFixYwPjx48nMzGT8+PEsWLAAgHXr1pGZmUlmZiapqancd999tgj5nMZG+vLwqF5cGxdEpK8rns72UvQJIYQQosOySeE3atQovL29z3ht9erVzJ07F4C5c+eyatWq1tfnzJmDSqVi6NChVFZWUlBQYO2QhRBCCCE6vQ4zxq+oqIigoCAAAgMDKSpqGTuXn59PaGho6+dCQkLIz89v/exvpaamkpqaCkBhYSF6vd4KkdteSUmJrUOwOcmB5OA0yYPkACQHIDk4TfJwpg5T+P2WSqW6or1iU1JSSElJASApKYng4OD2Dq3D6k73ej6SA8nBaZIHyQFIDkBycJrk4VcdZpppQEBAaxduQUEB/v7+AOh0OnJzc1s/l5eXh06ns0mMQgghhBCdWYcp/KZNm8bSpUsBWLp0KdOnT299/f3330dRFLZt24aHh8c5u3mFEEIIIcSFqRRFUax90ZtvvplNmzZRWlpKQEAAzz//PNdeey0zZ87k5MmThIWFsWLFCry9vVEUhQceeIBvvvkGrVbLu+++S1JS0kWv4evrS3h4uOVvpgMoKSnBz8/P1mHYlORAcnCa5EFyAJIDkByc1h3zkJOTQ2lp6Tnfs0nhJ9pXUlISu3btsnUYNiU5kBycJnmQHIDkACQHp0keztRhunqFEEIIIYRlSeEnhBBCCNFNSOHXBZxewqY7kxxIDk6TPEgOQHIAkoPTJA9nkjF+QgghhBDdhLT4CSGEEEJ0E1L4CSGEEEJ0E1L4dUB33HEH/v7+9OvXr/W19PR0hg0bRlxcHFOnTqW6uhpoWavH2dmZhIQEEhISuPfee1uP2b17N3FxcURGRvLQQw/RmXr12yMH9fX1/O53v6NPnz7ExsYyf/58m9xLW7TXd+G0adOmnXGuzqC9ctDc3ExKSgrR0dH06dOHzz77zOr3cqXaKweffPIJcXFxxMfHM3ny5POu89VRXU4eAPbt28ewYcOIjY0lLi6OxsZGoPs8G+HcOejsz8b2+h6c1hmfi22iiA7nxx9/VHbv3q3Exsa2vpaUlKRs2rRJURRFWbJkifL0008riqIo2dnZZ3zutwYNGqRs3bpVMZvNyuTJk5W1a9daPvh20h45qKurUzZu3KgoiqI0NTUpI0eO7FQ5UJT2+y4oiqJ89tlnys0333zBz3RE7ZWDZ599VnnqqacURVEUk8mklJSUWDjy9tMeOTAYDIqfn1/rff/xj39U/vznP1s++HZ0OXkwGAxKXFyckpaWpiiKopSWlipGo1FRlO7zbDxfDjr7s7G9vgeK0nmfi20hhV8H9b8Pb3d3d8VsNiuKoignT55UYmJizvm50/R6vdK7d+/Wv3/88cdKSkqKhaNuX23Nwf966KGHlNTUVMsEa0HtkYeamhplxIgRysGDBzvlA649chASEqLU1tZaPlgLaWsOmpubFV9fXyUnJ0cxm83KPffco7z99tvWCb4dXWoe1qxZo8yePfus47vTs/F8OfhfnfHZ2B456OzPxSslXb2dRGxsLKtXrwbg008/JTc3t/W97OxsBgwYwOjRo9m8eTMA+fn5hISEtH4mJCSE/Px86wbdzi43B79VWVnJV199xfjx460Wr6VcSR6eeeYZHnvsMbRardXjtYTLzUFlZSXQkofExERuvPFGioqKrB53e7rcHNjb2/PWW28RFxdHcHAwhw4d4s4777RJ7O3pfHnIyMhApVIxadIkEhMTefnll4Hu9Ww8Xw5+q6s8G68kB13tuXippPDrJP773//y5ptvMnDgQGpqanBwcAAgKCiIkydPsnfvXl599VVuueWWM8Y2dCVXmgOj0cjNN9/MQw89REREhK3CbzeXm4e0tDSysrK47rrrbBx5+7ncHBiNRvLy8hg+fDh79uxh2LBhPP744za+i7a53BwYDAbeeust9u7di16vJz4+npdeesnGd9F258uD0Whky5YtfPTRR2zZsoUvvviCDRs22Dhay7jSHHSlZ+Pl5qArPhcvlcbWAYhL06dPH7777jug5TeYNWvWAODo6IijoyMAAwcOpFevXmRkZKDT6cjLy2s9Pi8vD51OZ/3A29Hl5iApKQloWbwzKiqKhx9+2CZxt7fLzcPOnTvZtWsX4eHhGI1GiouLGTNmDJs2bbLVLbTZ5eZg4MCBaLVarr/+egBuvPFGlixZYpvg28nl5kA5NYGhV69eAMycOZMFCxbYIPL2db48hISEMGrUKHx9fQGYMmUKe/bs4dZbb+02z8bz5eB0615XejZebg5cXV273HPxUkmLXydRXFwMgNls5sUXX2ydqVdSUoLJZALg+PHjZGZmEhERQVBQEO7u7mzbtg1FUXj//feZPn26zeJvD5ebA4Cnn36aqqoqFi1aZJOYLeFy83Dfffeh1+vJyclhy5YtREdHd/qH2+XmQKVSMXXq1Nb73rBhA3379rVJ7O3lcnOg0+k4dOgQJSUlAKxfv56YmBjbBN+OzpeHSZMmsX//furr6zEajfz444/07du3Wz0bz5cD6HrPxsvNQVd8Ll4yG48xFOcwa9YsJTAwUNFoNIpOp1PeeecdZdGiRUpUVJQSFRWlPPHEE62DWFeuXKn07dtX6d+/vzJgwADlyy+/bD3Pzp07ldjYWCUiIkK5//77W4/pDNojB7m5uQqg9OnTR+nfv7/Sv39/5T//+Y8tb+uytdd34bRLnQjTkbRXDnJycpSrrrpKiYuLU8aNG6ecOHHCVrd02dorB2+99ZbSp08fJS4uTrnmmmuU0tJSW93SFbmcPCiKonzwwQdK3759ldjYWOWPf/xj6+vd5dmoKOfOQWd/NrbX9+C0zvhcbAvZsk0IIYQQopuQrl4hhBBCiG5CCj8hhBBCiG5CCj8hhBBCiG5CCj8hhBBCiG5CCj8hhBBCiG5CCj8hhGhHzz33HP/4xz9sHYYQQpyTFH5CCCGEEN2EFH5CCNFGf/3rX4mOjmbkyJEcPXoUgH/961/07duX+Ph4Zs2aZeMIhRCihezVK4QQbbB7926WLVtGWloaRqORxMREBg4cyIIFC8jOzsbR0ZHKykpbhymEEIC0+AkhRJts3ryZ6667Dq1Wi7u7O9OmTQMgPj6e2bNn8+GHH6LRyO/YQoiOQQo/IYSwgDVr1nD//fezZ88eBg0ahNFotHVIQgghhZ8QQrTFqFGjWLVqFQ0NDdTU1PDVV19hNpvJzc1l7NixLFy4kKqqKmpra20dqhBCyBg/IYRoi8TERG666Sb69++Pv78/gwYNQqVSceutt1JVVYWiKDz00EN4enraOlQhhEClKIpi6yCEEEIIIYTlSVevEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3YdXCr7KykhkzZtCnTx9iYmLYunUr5eXlJCcnExUVRXJyMhUVFQB89tlnxMbGctVVV1FWVgZAVlYWN910kzVDFkIIIYToMqxa+M2bN4/Jkydz5MgR0tPTiYmJYcGCBYwfP57MzEzGjx/PggULAHj99dfZuXMn99xzDx9//DEATz/9NC+++KI1QxZCCCGE6DKsVvhVVVXx008/ceeddwLg4OCAp6cnq1evZu7cuQDMnTuXVatWtQSmVtPU1ER9fT329vZs3ryZwMBAoqKirBWyEEIIIUSXorHWhbKzs/Hz8+P2228nPT2dgQMH8tprr1FUVERQUBAAgYGBFBUVAfCnP/2JCRMmEBwczIcffsiNN97IsmXLLniN1NRUUlNTATh8+DCRkZGWvSnAaDSi0Vgtjd2C5NSyJL+WJfm1DsmzZUl+LcvS+c3Pz6e0tPSc76kURVEsduXf2LVrF0OHDuXnn39myJAhzJs3D3d3d15//XUqKytbP+fl5dU6zu+0999/n/LycoYOHco//vEPvLy8eO2119Bqtee9XlJSErt27bLU7bTS6/UEBwdb/DrdieTUsiS/liX5tQ7Js2VJfi3L0vm9UA1kta7ekJAQQkJCGDJkCAAzZsxgz549BAQEUFBQAEBBQQH+/v5nHFdfX897773H/fffz5///GeWLl3KyJEj+eijj6wVuhBCCCFEl2C1wi8wMJDQ0FCOHj0KwIYNG+jbty/Tpk1j6dKlACxdupTp06efcdzf//53HnroIezt7WloaEClUqFWq6mvr7dW6EIIIYQQXYJVO/Bff/11Zs+eTXNzMxEREbz77ruYzWZmzpzJkiVLCAsLY8WKFa2f1+v17Nixgz//+c8APPjggwwaNAhPT8/WSSBCCCGEEOLSWLXwS0hIOGef84YNG875+eDgYNasWdP69xtvvJEbb7zRYvEJIYQQQnRlsnOHEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEIIIUQ3IYWfEEJ0YpUNBgqrG1EUxdahCCE6Aavu3CGEEKLtGgwm/rPtBP/anE1WWcu+5R5OGu4c0oP54yLxc3W0cYRCiI5KCj8hhOhEjhbXMmPpLg4U1jBA586DI8Nx1KhJz69m0U/HeX9XHmvuHMzgMC9bhyqE6ICk8BNCiE5i58lKkt/eCsCi6bHMGqAjwO3X1r1fssuZsXQXo978hW9ThjC6l6+tQhVCdFAyxk8I0e2YzQq7cytZd7iIX7LLaTCYbB3SRaXlVzExdSuuDho+uHkAD4zseUbRBzC8pzd7Hh2Fv6sD1727i2OldTaKVgjRUUmLnxCi2zCYzPzzx+O8tvk4+uqm1tcdNWrmJoXwTHI0IZ7ONozw3Ipqmpi6ZAeOdmr+PSOO3/UNQKVSnfOzge5OrL9nKIMXbeHa/+4k7bFRaOzkd3whRAt5GgghuoXcigaGvLaZJ9YcpoenM3+Z3Jt3b+rPq9P6Mqm3H//dkUvMwh/4ZE+erUM9g9Fk5sb3d1FS18wr02IvWPSd1tvfjTdviONgUQ1PrTtipUiFEJ2BVQs/k8nEgAEDuOaaawDIzs5myJAhREZGctNNN9Hc3AzA66+/Tr9+/ZgyZUrra1u2bOGRRx6xZrhCiC4io6SWEW9sIaOkjr9fE8PqOwbzdHI0vx/cg0dG92L1HYPZ++hVhHtrueWjvTyz7kiHWR7lL+sz2Xy8nKcnRDGjf9BFi77TbknU8bsYfxb9dJwjRTUWjlII0VlYtfB77bXXiImJaf37E088wSOPPMKxY8fw8vJiyZIlAHz00Ufs27eP4cOH8+2336IoCn/5y1945plnrBmuEKILKK1t4ur/bKe2ycTbM+KZNyoCf7ezlzvpF+TBnkdHMTU2gBe/z+SxLw/ZINoz/ZxdzovfZ/C7GH/uGRaGo8buko9VqVS8fWM8KpWKh1YdsGCUQojOxGqFX15eHmvWrOGuu+4CQFEUNm7cyIwZMwCYO3cuq1atan3PYDBQX1+Pvb09H374IVdffTXe3t7WClcI0QUYTWZuWLqLvMoGXp3Wl1kDdNhfYLybvZ2aVb8fxKyEYP7503EWbMi0YrRnajSYuHN5GgGujjw54crW5tN5OPPo6AjWZ5Ty3dFiC0QphOhsrDa54+GHH+bll1+mpqaly6GsrAxPT080mpYQQkJCyM/PB+CBBx5g6NChxMbGMmLECKZPn86333570WukpqaSmpoKQGFhIXq93kJ386uSkhKLX6O7kZxaVnfK72vbCvjpeDn/NzyQMYFqigoLLum4hWMDKaxoGR8XZG8gOdLzkq/ZXvlduCWfoyV1LBinI8y+8YqfZ7f3deWtn+146qv99HOLufgBnUR3+h7bguTXsmyZX6sUfl9//TX+/v4MHDiQTZs2XfTzt912G7fddhsAL7zwAg899BDr1q3j/fffJzQ0lFdeeQW1+uzf2lNSUkhJSQEgKSmJ4ODgdr2P87HWdboTyalldYf87s2r4tWtBUyM9uOxifHn7N69kK/vCaD/Kz/y8Hcn2N03jAhfl0s+tq35TddX8ebOIn4X48+94+LwcLZv0/n+OLaRp9Yd4VCtPROi/dp0ro6kO3yPbUnya1m2yq9Vunp//vlnvvzyS8LDw5k1axYbN25k3rx5VFZWYjQagZauYJ1Od8Zxer2eHTt2cO211/LKK6+wfPlyPD092bBhgzXCFkJ0Umazwr2f7cPDScP8cb0uu+gDcHHUsPbuIRhMClP/u4Mmo3XW+jOazNy5PB13Jw2Pj+nV5qIP4IGR4bg5avjr97bruhZCdAxWKfxeeukl8vLyyMnJYdmyZYwbN46PPvqIsWPHsnLlSgCWLl3K9OnTzzjumWee4YUXXgCgoaEBlUqFWq2mvr7eGmELITqp/+44yY6TlTw8KqJNu1dE+7mydFYCh4pqmftxWvsFeAGLfspmd14VfxzTi1ERPu1yTncnex4YGc6PWWWk51e1yzmFEJ2TTdfxW7hwIa+++iqRkZGUlZVx5513tr63d+9eABITEwG45ZZbiIuL4+eff2by5Mk2iVcI0fHVNhl5cu0REnXu/H5QCGr1pS1/cj439A/moZE9WZ6u57/bT7ZTlOd2rLSOZ789wqgIb+4YHNrm2H/r/hHhqFXwyo/H2+2cQojOx+o7d4wZM4YxY8YAEBERwY4dO875uQEDBrQu7wItk0MefvhhK0QohOjM/rU5m5K6Zv5+TQwhntp2Oec/pvXlx+NlzFt1gKsivInyc22X8/6WoiikfJqOWqVi/rhI/N2c2vX8Og9npsYG8OXBQqoaDO3ShSyE6Hxk5w4hRJdR2WDg75uOcVWEN9PjgtrtvPZ2ar74/SAU4Pr3dmEwmdvt3Kct2X6SH46VMe+qnhabgPHgyAiqGo28viXbIucXQnR8UvgJIbqMVzZlUdlg5N6hYXi2c4tWTx8t/5nZnwOFNTz0RfsuiJxb0cDjXx0iUedBytCwC6412BZjI32I8nXhk735Fjm/EKLjk8JPCNEllNQ2sein4yRH+TKtX6BFrnHzAB23DdTx760nWH2gsF3OaTIr3PbJXppNZp5JjiLMu326p89FpVLx4MieHCqqZe2hIotdRwjRcUnhJ4ToEl7+IYt6g4mUYWG4Olpu+PK/Z8TTy0fLncvTyK9saPP5Xv7hGD9mlfF/YyKZEhPQDhFe2JykEJzt1fx76wmLX0sI0fFI4SeE6PQqGwz8+5ccJkb7Mam3v0WvpXXQsOr2QdQ1m/jdkh00G698vN/Ok5U8+81RkqN8uXNIKA4ayz+SPZztuXmAjvUZJZTUNln8ekKIjkUKPyFEp/efbSeobTZx68AQ3Jwsv1hBvyB3lszsT7q+mtkf7UFRlMs+R3l9Mzd/uBsfFwf+ND6SUC/LdfH+rzsH96DRaObfv0irnxDdjRR+QohOrdlo5rXN2QwK9WBKjGVb+37rloEhPDY6gpX7Cnj8q0OXdWyz0cwN7+3iZGUDf7u6T5sWmb4Sw8K96OmtZVU7jVMUQnQeUvgJITq1Fel68qsamZ0YgpfWwarX/vvUvtyUEMSrPx7nyTWHL6nlr9loZtaHu9mUVcazydHcnKhr14WaL4VKpeKOwaHsya9iT16lVa8thLAtKfyEEJ2Woii8simLCG8tN/Zvv3X7LpVKpeLDWxK5rl8gL208xh3L0mi6wJi/srpmpv13B1/sL+TxMRE8MLInzvZ2Voz4V3OSQlABb8skDyG6FSn8hBCd1sbMUtL01dySqCPYw9kmMWjs1Kycm8Q9w3rw3q48Jr5/iG+PFJ/R+mcyKyzbm0/CKz+yIbOUpydE8dSE6HZfa/By9PDSMqqXD2sOF2OywILUQoiOyepbtgkhRHt55cfjeGvtuSUx2KZxqNUq/j2jP6N7+fLIF/uY/J/tBLk5MriHJwqw/UQlRbVNRPho+e9N/ZnRP9hmLX2/defgUOZ8ksYXBwqY0V9n63CEEFYghZ8QolM6WFjDuiPF3DssjD7+brYOB2hZ4HmIt5nlxxpZn1HCgcIaFCAxxJ3RvXyY2T+Ynj4utg6z1fVxQdy7cj8f7s6Xwk+IbkIKPyGsTFEU1h0p5utDRZyoaCDaz4WbEnQMDfOydWidyqs/ZuGoUXNLog6VyrqTIy7Eyd6OP42P4k/jozCazBjNCvZ2auysPIHjUrg4arghPpDP9xdS1WDAw4Zdz0II65AxfkJYUXl9M9e+u5PfvbOD93flkVlSx1u/nGDYv7Ywdcl2KuqbbR1ip1BY3ciHu/OY2jeAIT06bsGssVPjZG/XIYu+034/qAd1zSb+u+OkrUMRQliB1Qq/3Nxcxo4dS9++fYmNjeW1114DoLy8nOTkZKKiokhOTqaiogKAzz77jNjYWK666irKysoAyMrK4qabbrJWyEK0q5pGI1f/ZzvrjhQz76qe7Hh4JOmPjybnqfE8ODKcb46UkPDqT2SX1ds61A5v8c85GEwKtwzQWWW3i65sTC8fgtwc+Xy/rOknRHdgtSemRqPhlVde4dChQ2zbto3Fixdz6NAhFixYwPjx48nMzGT8+PEsWLAAgNdff52dO3dyzz338PHHHwPw9NNP8+KLL1orZCHajaIozP5oD7tzK3np6j689LsY+ga442xvR6C7E/+6Lo51dw+hqsHAqMU/U1DV9j1gu6r6ZiNv/pLD6F4+TIj2s3U4nZ5areLWpBC25pSTXVZn63CEEBZmtcIvKCiIxMREANzc3IiJiSE/P5/Vq1czd+5cAObOncuqVataAlOraWpqor6+Hnt7ezZv3kxgYCBRUVHWClm0QVWDga8PFfGfbSfYebISs/nyt7TqSj7cncdXh4p46KoI7h0efs4ZnROi/Vh79xBK65oZ9+9tNBhMNoi043tvZx7l9QZuTdRZZXu27mBuUigmBd6SLdyE6PJs8tTMyclh7969DBkyhKKiIoKCWhZeDQwMpKioCIA//elPTJgwgeDgYD788ENuvPFGli1bZotwxWUwmxXe+iWH+WsOU9v8a+EyLMyLD2cPIKIDzWi0ltLaJuatOkj/YHceHBmOi+P5/9kND/fmo9mJ3LB0F3M+3sunc5OsGGnHZzIrvPpjFv0C3ay6PVtXFxvoRlyQG18fKuLlqX1tHY4QwoKsXvjV1tZyww03sGjRItzd3c94T6VStc7OS05OJjk5GYD333+fKVOmkJGRwT/+8Q+8vLx47bXX0GrP3NQ8NTWV1NRUAAoLC9Hr9Ra/n5KSEotfo7MwKwqPfXuCFQfLGBik5eZYb3p4OrHlZDXv7C0l8ZUf+eqW3vTyvvBCu10tpy/+mEdVo4GHEoNxbKpCr6+64OeH+sI9A/15e3cBf/8mndnx7dud2Znz+3VGBVll9Tw7KgilrgJ9XYWtQzpLZ83vDb09eG5THl/tPMpAXcdYHudCOmueOwvJr2XZNL+KFTU3NysTJ05UXnnlldbXoqOjFb1eryiKouj1eiU6OvqMY+rq6pSxY8e2HltbW6u89957Smpq6gWvNXDgwPa/gXPIz8+3ynU6g/tWpis8+qVy9/I0Jbei7oz3DhRUKR5PrlV6vLBeKa1tvOB5ulJOC6sbFe0Ta5SrU7cp1Q2GSz7OYDQp/f+xSfF8aq2ir6xv15g6a37NZrMyeNFPSugL3ykZxTW2Due8Omt+C6sbFbvHvlR+//EeW4dySTprnjsLya9lWTq/F6qBrDbGT1EU7rzzTmJiYnj00UdbX582bRpLly4FYOnSpUyfPv2M4/7+97/z0EMPYW9vT0NDAyqVCrVaTX29zHzsSJbuzOWtX05w20Adf5ncmxDPM1tjYwPd+fKOweRXNXD3in02itL6XtmURZPRxN1DelzWeDSNnZqPZidS12xi7rI0ywXYiWw+Xs6Ok5XMHqAj0rf7DRmwtAA3R8ZH+bHuaAnGC+w3LITo3KxW+P3888988MEHbNy4kYSEBBISEli7di3z589n/fr1REVF8f333zN//vzWY/R6PTt27ODaa68F4MEHH2TQoEH8+9//5pZbbrFW6OIiMkpq+cNn+xkY4sGzyb0JcHc65+dG9fLh/hE9+eJAId8dLbZylNbXYDDxzvaTjI30vaLZp7GBbjw1IYr1GaX8d7ussfb3TVl4OmuY0T+4Qy3Y3JXcPjiUopomVu63/DAZIYRtWG2M38iRI8/YtPy3NmzYcM7Xg4ODWbNmTevfb7zxRm688UaLxCeujKIo3LdyP2oV/GVybyL9LtwS89cpfViepufR1YfY/0e/Lv0DfPlePRUNBmbEB13x7NMnx0exIk3P/319iKmxAfi5OrZzlJ3DocIavj5URMrQHvQPdr/4AeKKTO8XiIuDHR/v0TNrQIitwxFCWICsfCra5OM9+Ww8VsoDI3uSfAmtWq6OGv48MZqDRTV8mt61WxXe2ppDT2/nNs0+tT/V5VvZYOCeT7tPF/n/+uv3mThp1MzsH4zGTh5bluJsb8cN8UFsyCylrK7J1uEIISxAnqDiitU1Gfnj14eIDXAjZWiPS/6BfPvgUHxdHHhl03ELR2g7Bwtr2HGykuvjggjxvPAs5otJ0Hnwh27URf6/DhXW8ElaPjclBDMywtvW4XR5KUPDqDeYePNnWdNPiK5ICj9xxRZtPk5BdROPjO5Jz8tYn8/J3o7Hx/RiR24lGzO75pIBH+/JQ62Cyb3926U7+29T+hDo5sgfPtuPwdS9Bt4//10Gzho7bhsYgqPm7IWvRfsaHu5FtK8Ly9LybR2KEMICpPATV6S0tomFG7MY08uHa/sFXfbxKUN74GCn4t9bu16rgqIofLJXz+BQTwaHebbLOV0dNbx1QxxZZfU8s+5ou5yzM9hfUM2n+/TMGhDMiJ7S2mcNKpWKP4wI51BRLT8cK7V1OEKIdiaFn7gir23OprbJyB+Gh+Hj4nDZx3tpHbguLohvjpRQ3WiwQIS2s/1kJdnl9Uzq7Y+7k327nffauCAm9/Fj0ebjZJbUttt5O7Lnv8tAa2/H7EQdTufY5k5YxpykEBzt1LyxJdvWoQgh2pkUfuKyVTcaeH1LNmMjfZjU58onLqQMDaOmycjbXWx/0BVpeuztVEzq0747bgCkzuiPnUrFHcvTzztLvqvYm1fFZ/sKuGWATlr7rMxL68AN/Vt+Mauob7Z1OEKIdiSFn7hsb/1ygqpGI7cPCm1Ti9aYXj6EejrxxYHCdozOthRF4cuDhQwK9SQ+qP2XHQn1cubPE6PZkl3O0p257X7+jkJRFB5efQBPZ42M7bOR+4eHU28w8bq0+gnRpUjhJy5Lg8HEqz9mMSzMi2n9Att0LrVaxc0DdOzIreREedfYieVQUS1ZZfWMjvDBxdEyy2Q+OjqCGH9X/u/rw1Q1dM3WmM/2FfDT8XLuHRbOcGnts4lh4V709nfhk716q7Yu/5xdzgOf7yfx1R+JfmkjE/69lX/8kNXlhoQIYStS+InL8t/tJymubW5za99pM/sHYzIrvNdFWq++PNjSejm6l+WKFY2dmndn9ae0rpn7Pz9gsevYSk2jkUdWHyTSV8vtg0OwU3fdRb47MpVKxcNXRXCkuJYv9hdY/HrZZfVMTt3GyDd+Zsn2kzjaqQnzcuZERT1//PoQ4S9uYPlemWksRFtJ4ScumcFk5uUfsugf7M4N8W1r7TstMcSDUE8nvsvoGsu6rD5QSN8AV5JCvSx6nSFh3tw1tAcf78nn64NFFr2WtT259jD5VY08NT6KaD83W4fTrc0dFIqXsz3//Mmy3b2f7dPT/5Uf2ZJdzsNX9WTzAyPY+IfhrL93GBnzx/FtyhB0Hk7M+nAPT6093OXHtwphSVL4iUv28Z58TlY2cPugULxd2mfrMJVKxU39g9l+spK8yoZ2OaetlNY2sSO3kqt6el/RTOfL9c9psYR7a7l9eRoltV1jl4VNx0pZ/EsOMxOCuT7+8pcJEu3L2d6OB0eGsyW7nK055Ra5xiubspixdDfhXs58MjuRl6f2JSnUE+dTs7hVKhUTe/uz65GrmN4vkL9tOMbz32ZYJBYhugMp/MQlMZkVXtqQSbSvCzclBLfrua+NC8JkVljWybtxvs8sRVFgWJhlW/tOc3HU8Pnvk6hqNDBj6a5O3wpSWtvE7I/2EOrpzCOjItp1KRxx5R4c2RNnezV//rb91498aUMmj391iAlRvrx/SwJT+wVif54dgBw1dnw+N4lr+vrz/PoMPj8kawwKcSWk8BOXZNWBAo6W1HH74FAC3Z3a9dxDw7zwdNawKausXc9rbd8eLcHdUcPICB+rXTNB58Hfru7DT8fLeXF9ptWu294MJjO3fLSHkrpm/nZ1Hwb38LR1SOIUX1dHHhjZk+8zSvklu/1a/V776ThPrj3C5N5+vD0jngSd50WPUatVfDonibhAN/60IZfssrp2i0eI7kIKP3FRiqLwtw3H6OHp3O6tfQB2ahWTe/vzS04FTQZTu5/fGhRF4bujxQzu4UloG/fmvVyPjelFcrQvz6/P4OtDnW9pHEVRePCLA6zPKGX+uEiujw9ql23uRPuZPy4SrYMdT6490i7nS916godXH2RspA+Lb+hHhO/lbfn4+e2DMJkVbnx/d6dv6RbC2qTwExf17dES9uRVMXdQyGXtyXs5psYGUNFgYN2RYouc39IOFdWir25iaJgXDhrr/rNSqVSsnJtEhLeWWR/sYcfJCqtevy0URWHeqoO8vfUEv08K4eFREa1ju0TH4a11YP64SH48XsanbdzD94Ndudz72T5GhHvx1g3xRPi4XvY5In1deHZ0KLvzqnjtp+NtikeI7qZDFH7ffPMNvXv3JjIykgULFgAwe/Zs4uPjefLJJ1s/9+KLL7Jq1SobRdl9/W1DJgGuDhZp7Tttch9/1CpYe7hzFn7rT81KHtpOe/NeLncnezbeNww3Rw3J/97GNgsNxG9PtU1Gbv5wD69vyebWRB0vXt0Hb63lJ8WIK/PHsb0I83Lm8a8P02gwXtE5VqTp+f2yNJJCPHjzhjh6+19+0Xfarf19GaBz54X1mZR28slNxTVNLN2Zy5+/OcqL6zP4PqOEJmPn7P0QHZ/NCz+TycT999/PunXrOHToEJ988gn79u3D2dmZffv2sXPnTqqqqigoKGD79u1ce+21tg65W9l8vIzNx8u5bWAIMf6WW1rDW+tA/2B3duZWWuwalvTDsVJCPJwsvozLhYR4OvPLgyNwdbRj7Ftb233Ns5pGIyW1TRRWN1JY3YjBZL7ic31zpJjEV3/i03Q9D4wI5+VrYtBZuYtcXB5HjR3/nhHHyYoG7v/s8tePXL43n1s+2kP/YHdevy6O+GCPNsWjVqn4700JVDYYeOzLQ206l61UNhh4ZPUBdC+s5/fL0nhhfQbPfHOU5Le3Ef7iBpZsPyld2aLdWWZrgcuwY8cOIiMjiYiIAGDWrFmsWbOGhoYGzGYzBoMBOzs7nn32WZ5//nkbR9v9vLThGJ7OGm4aoENt4YV0J0T58c+fjlNS24zl2hbbn8ms8GNWGWMjffGzwjIuF9LTx4WdD1/F1f/ZzqwP9/DloSL+dW2/y1pepsloYk9eFb/kVLD9ZAWZJXVkl9dT1Xh2K4+31p5gdycifV2I9nMhyteFKD8XonxdCXJ3bB2rZzSZySqrZ0NmKe/tzGVnbiU9PJ158/o4Zg8MwdVCu5yI9jW5TwB3DA7lvztyuTrGnxn9L+1f6rK9+cz+aA8Jwe7869o4hrTTzPcEnQc3DwhmWVo+TydHEeV35S2I1pZZUsvv3tnBsdI6ru0XyA3xQQwP98KkKHxzpIR//3KCu1ak8/n+Aj65NVFmuYt2c9Gn7WOPPcYdd9xBbGysRQLIz88nNDS09e8hISFs374dPz8/EhMTue222zh27Bhms5nExMQLnis1NZXU1FQACgsL0ev1Fon5t0pKusbCw+dyoKiedUeKuSPBhwBVHXq9ZbdV6++twmhW+HpfLn6unafLb39RS1EU4wGFhZbf4eBSrJoZyZ9/OMlHe/P5fJ+eWf18ubaPN/0DtVSV/zp7utlk5nhFE0dLG0grrGOXvo79RfUYzC2tDIEuGnp4ODKmhwv+LvY4alSoVSoUBaqaTJQ3GCmtN7LnZDlrDhW1HgegUatwc1CjADVNJkyn3urh4cCDg/y5oa83kT72VJcVU23N5FhYV34mADw51JcfM4uZ+8lenAy1JAafv9hSFIWlaSU880Mu/fyceeEqf3o6NbbLs/l0nh9O8uHT9AIe+nQ3S66NavN5reFYWSPXLT+Kyazw6sQQrunti9ZBDU1VAFwf4ch1PaP4xy/5/Gt7EYNe/YEvZvXB08l6vyB19e+xrdkyvxf9FsXExJCSkoLRaOT222/n5ptvxsOjbU30l2LRokWtf546dSpvv/02f/3rX0lPTyc5OZm77777rGNSUlJISUkBICkpieBg67QbWes61nbvuh24Otjx+xG9CQ2x/BIl03380azK4kC1qlPl9JOMLADGxIYRHOxv42h+9f7cEB46WcGfv8vgw30lvJdWgp1KRYCrBnfnMuqaTRTWNGE4VZE52qmJCXBl1gAd8UFuDAr1JDbQDS+tw0W3TTOZFSrqmzlYVMPBwhpOVDRQVNNEXbMJFeDqqCHE04mEYHeGhnkR5O7UpWfudqbv75X48UEfhizawq1fZPHJrQO5Oubs7315fTMPrzrIB7vzuKqnN3+f2rfdWvpOCw4OJjgY7h5aw9vbTlBt50afgI6924u+qpHblmxBpVLx7qwEpvULPO+/r3/eqGNktJ6bP9rDjE+z2PHwVVZtHe/q32Nbs1V+L/oNuuuuu7jrrrs4evQo7777LvHx8YwYMYK7776bsWPHtjkAnU5Hbu6v+7Tm5eWh0+la/7569WoGDhxIbW0tWVlZrFixgkmTJjF79my0Wm2bry/ObduJCr46VMR9w8MYaqUFiV0dNQwM8SCtqAFFUTpNYfDDsVJ6eDmT0MYxS5aQ1MOLNXcNQV/VwLI0PQcLazhZUomicURrb4eviwO9fLRE+roQH+xOqKfzFf1gsVOr8HV1ZLSrI6N7+VrgTkRHovNw5sf7hzEpdTtT3tnOrIRg7h0eRqSvCwXVTXx1sIg3f8mhor6Zu4f04MkJkYR7W2ZFAIAnxvXi7W0neO67DJbdNtBi12kro8nMrA93U1LXxH9u7M/0foEXHUJzQ/9gPlHBzPd387t3trPxvuGyf7Vok0t6wptMJo4cOcKRI0fw9fWlf//+vPrqq7z99tssW7asTQEMGjSIzMxMsrOz0el0LFu2jI8//hgAg8HAokWLWLNmDZmZma2FgMlkorm5WQo/C3pq7RG8nO25NTHkvCvpW8KEaD9e2pBJdln9Za3tZStms8LPOeWM6eWLr43H911IsIczj47uBYBer5ff5EWb9fJ1Zd/jo/nDZ/tZka5nWdqv3bcqYHi4F/ePiGVabCAuFm6l6uGl5eYBwXyaXsCJ8jrCLFhktsXz32Ww+Xg5f5ncmxv7B1/yuOkb4oP5x9QGHv3yEPNWHeCN6+MsHKnoyi76r/GRRx7hq6++Yvz48Tz55JMMHjwYgCeeeILevXu3PQCNhjfeeINJkyZhMpnOGE+4ePFi5s6di1arJT4+nvr6euLi4pgyZQqenp5tvnZHUN1oQF/VSJPJjIuDhkA3R5sPdN+YWcrGY6U8OiqCQVbeQWFsLx/++n0ma48U88DInla99pU4WlJLZYOR+CA3i09+EaKj0TpoeO/mAbx8TQwr0vXkVzbipXWgX6AbI3p64+FsvQkJT42P4qPd+bywPpMlNyVY7bqXalduJX/bkMnUvgHcMyzsstf7fGR0L3bmVvLmzzlM6u3H1NhAC0UqurqLVhjx8fG8+OKLuLic/RvUjh072iWIKVOmMGXKlLNef/jhh1v/rFKp+OSTT9rleraUW9HAuiPF/JhVxo/Hy8ivajzrMz29tSRH+zIjPpjxUb5WLSgUReGpdUcIcHXglkSdVVv7AIb39MZerWL7iYpOUfhtzWlZLLl/B+zmFcJa/N2ceGBkhE1j6BPgxrTYAFak6Vn4uxh8XR1tGs9vGUxm7l6RjrfWgcdH98LvCmNLvbE/W3MquGtFOvsf98LfrePco+g8zlv47dmzB4D+/ftz9OjZm3MnJiZaZZJHV2AwmfnqYBHvbD/JN0eLURTw0dqTGOLBdf0CCXBzxFGjpsFgoqC6icNFtXywO4/UbScJ83LmyfGR3D64h1WKsFUHCtl2ooKnxkeSoLP+f19nezsSg1zYlVfVKcb5bT1RgbujhiE2WrhZCPGrZ5KjWX2wiBe/z2TRtf1sHU6rxT/nkKavZuHvYhgZ4X3F53F11PDZ75MY8toWbvpgNxvvG9bhn5Gi4zlv4ffYY4+d9yCVSsXGjRstElBXklFSyzvbTrJ0Vy7Ftc34uThwx6BQJvfxZ0S4FwFuTudtzausb2bJjlz+u+Mk96zcz8IfsnhvVgJXRVhudm19s5FHVh+kl4+WWweG2GwA8cgebry6tYCs0joiO/i6XFtzyukX5EZAB2pdEKK7GhjqyagIbz7Zm8/C38Xg2AG2/yutbeK5b48yLMyLOUkhbe7BSQzx5C+Te/OntUf4x6Ys/jg2sp0iFd3FeQu/H374AbPZzNatWxkxYoQ1Y+rUapuMfL6/gCXbT/LT8XLsVHBVhA9Pjg/kurhAQj2dL+k3NE+tA4+N6cWjoyNYujOXJ9ceYdTiX/j9oBAWTe9nkbEzL204xomKBlJnxNt0SYThPdx4ZWsBaw4XM68DF36VDQYOFtVy77AwNFbuEhdCnNsT4yL53Ts7WPxLTuuEJlv687cZ1DYZeWRUTwLdndrlnP83NpKvDhbx/HcZXB8XRK8OOhGutsnIVweLWJ9RQrq+mhMV9TQazTjbqwl0c6JfoBtXRfgwqbdfh7qHBoMJfVXL7kRmBVQq8HVxwEfr0CXGcl9wjJ9areaBBx5g79691oqnU6ppNLIpq5QV6Xo+31dIvcFEqKcTD4wIZ3q/QEb09L7ijedVKhW/H9yDGfFBPPjFAd7flcc3R0pYMWdgu7b+7cmrZMHGY0yJ8WdG/6B2O++VGBDogr2dil0dfPu27SdaxvfFBXXsdcOE6E4m9/YnwkfLku0neWRUhE27QrPL6knddoLr44KYEhPQbudVq1V8ODuR2L//wOyP9rD1oZEdqsu3oLqRBRuP8e6OXGqajHg4aegb4MaYXj442dvRYDBRXNvMD1llrbPBw72duTUxhN8PCrVqEXi8rI4t2eXsOFnJztxKskrrKKs3nPOzdioV/m4OxPi7EhfkTmygG/0C3YgNdOtUO6tcdHLH+PHj+eyzz7j++us71BerI3jrlxze25bNnoI9GM0Kbo4aJvfxY0qMP8lRfoR6XVrr3qVwdbLn3ZsHMHdQKLd9vJcxb/7C/HGRPD+pd5tbmxoNJuZ8koaXsz2Pj47AS2vbZUkcNWoSdR7sK+jY+zlsPVGBCiza/S6EuDxqtYrHR/fiD5/vZ/WBQq6Ns90vsi9+n4FaBXcMDm33JW16+mhZ+Lu+PLTqQIfp8jWazPzzp+M8920GTUYTk/r4c12/QMZG+hLq6XzWTGaD0cSO3Eq+PFjEj1ll/PX7TF78PpOhYZ7cNSSMG/sHtXtB1Ww089PxMtYeLmbN4SIySuoA0NrbERPgyqgIHwLdHfF3ccTh1E5FJrNCZaOB8noDRTVNZJfX80vOCRqNv+5XHuLhRFyQO3FBbkT5uhDk7kSQe8t6qQoti9yX1TdTUW+w6XcSLqHwe/vtt3n11VfRaDQ4OTm1Drivru7YP5StYZ++msoGA7cm6hgS5sVVPb2J8nO97Gn6l2NMpC8H/jia2R/t5W8bjrE+o4SVc5Po4XVlaxoqisK9K/dxsLCGf10b22EW3x0V4cOizccpqm4koJ26R9rb1pwKevm2LH4shOg45g4K4U9rD/Ovzdk2+yF7rLSOpbvymNk/iHFRfha5xv0jwlmWls9z32ZwfVwgvXxtNzQmr7KBG9/fzbYTFYyO8OHhUT1Jjva7YMFrr7FjRE8fRvRs+eX5SFENi3/O4atDRdy1Ip0HPt/P9H6B3D4olPFRvlfcyFFU08Taw0V8faiI746WUNtswsFORVKIJ9PHBDIwxIMhPbwIdHfE6RJ752obDewvrGFnbiWZJXUcK2353/qMEoy/2bryf9mpVdT+9eoruo/2ctHCr6amxhpxdEqLr48jT+9DqC7Yqq2hHs4OfH3XEN7Yks38NYfp9/cfeWdmPDMTdBc/+H+8+uNxlu7K4+4hPZiTFNphxi8MD/fi75sUNmaVcvOAEFuHcxazWWHbyQrGR/pecTe+EMIytA4a7h0exssbszhQUE2/IHerx/CX9Rlo1CruGNzDYo0BarWKj2YnEvvyJm75cC/b5tmmy3fTsVJu+mA3tU0m/np1b+4aEnZFS830CXDj9evjeO3aWNYcLub9XXmsO1zM8jQ97k4aJkb7cU3fAAb38CTS1+W8K10U1TSx7UQF209WsCGzlB0nKwHwd3UgOdqPqyK8GRvpSx9/10su9P6Xq5M9w8K9GRb+6yxtk1mhrK6JjNI6csobKKxupMloRq1SoVaBm5OGQDcnbP1j9pLanisqKsjMzKSx8dc150aNGmWxoDoLtVqFRq2yWRf4AyN7Mi7Sh5nv7+GmD/aw5lAxb94Qd8ldCou3ZPP4V4cYF+nDUxOirLrY6sUMP/WPaWtORYcs/A4X11LdaCTeBj9QhBAX99DICF7ZdJy/fZ/Jx1bexu1ocS0f7s7jlgE6xvSy7FCQcG8tL/2uD/NWHbRJl+/bW3O4//MDhHg4sfj6OK7tF9jm4UdqtZqpsYFMjQ2kptHA0l15rM8o4YdjpazcVwCAvVpFL18XPJw0rb98F9Y0kV/VQE2TCWhpXevr78q9w8K4qqc3w8O9CPF0tthkPDu1Cn83J/zdnOjIy9BetEJ45513eO2118jLyyMhIYFt27YxbNgwWc6lg+gb6M6eR6/iD5/v57/bc/khq5TXr+vHtNjA8xakzUYzf/z6EP/anM2oCG/+dV0/wrw71vZ3/m6O9PTWsk/fMVuct+aUAzDYyjubCCEuTbCHEzfEB7H6YBFltU34WHHJpb+sz8DBTs0dQ3pYZcb/gyN7smyvnue+zeCGuCCrbXf5yqYsHv/qECPCvXj5mr4M73nlaxSej5uTPQ+M7MkDI3vS2Gzix+Nl7DhZSVZZHScrGmgwmKhuNGBSINjdkYRgd4LcW2YMJ+rcCfdxwbMDNWp0BBct/F577TV27tzJ0KFD+eGHHzhy5AhPPvmkNWITl8hBY8c7MxOYHhvIQ18c4Np3dxEX5Ma8qyL4XYx/6xICZXXNrD5QyN82ZJJVVs8tA3Q8kxxl06VbLmRUhDdfHiyiodmIs4Ntt7H7X1tPVODhJAs3C9GR/d/YXixP0/PyD1ksnNrXKtc8XFTDx3vzmTMwhKssUAidi0ql4pNbE+n78iZmfbibbQ9dZdFhO4qi8MJ3GTz3XQbJUb68fn0/evtb/ueIk4Mdk/r4M6mP/xmvm8wKiqJgZ8MeuM7koj9NnZyccHJqKRyampro06fPOXfyELY3NTaQyX38efmHY/xn20nuWpEOgIeTBju1iop6AwoQ7evCv66N5daBITafwXshw8O9Wborjx25lR1m0slpv+RU0C/QDe8OnD8hurvEEE+Ghnny/u48/jqlj1Va357/LgNnjR23D7JOa99pYd5aFl7Thwe/aOny/b9xlunyVRSFJ74+zN83ZTG1bwBvXN/viicXtpeWzQak4LtUFy38QkJCqKys5NprryU5ORkvLy/CwsKsEZu4AvZ2ap6aEM2fxkWyPqOUb44Ut+4H7OvqwJAeXoyN9LH5P9RLMeLUb8ubjpV1qMKvor6ZI8UtCzfLb5dCdGzzx0Vx7bs7WbLjJPcMC7fotfYXVLMiXc/vk0IZ0dPLotc6l/tH9GR5mp7nvjvKdXGBRLXzAvhms8KDXxzgzV9yuDE+iEXXxhLs4dyu1xCWd9HC74svvgDgueeeY+zYsVRVVTF58mSLBybaRq1Wn7NJvDOJ8XfF3VFDur5jLR20/dQMsXhZuFmIDu+avgH08HRi8ZYcixd+z3+Xgdbejt8PCrHJbj4qVcss374vb+L693ax65GrcNS0z6oDJrPCXSvSeW9nLrcNDOHv18R02KW2xIVd0jdzy5YtvPvuu4wePZphw4aRn59v6biEQK1WMTTMi/SCaswXWBfJ2rbmVKBWwWgLz9YTQrSdnVrFY2N6sb+whvUZJRa7Trq+is/2FXDLAN0ZS3xYWw8vLe/M7M+BwhruWJ7eLuc0mMzc8uEe3tuZy73DwvjHVCn6OrOLFn7PP/88Cxcu5KWXXgLAYDBw6623WjwwIQCuivDmeFk9x8vqbB1Kq60nyunl40KEjyzcLERncMfgHrg5avj7xmMWu8az3xzF1cGO25JCzru+nLXMGqDjgRHhfLwnn5e+z2zTuWoajVz77k5WpOt5eFRPXry6D/5uUvR1Zhf9dn7xxRd8+eWXuLi0/JALDg6+7EWdP/roI+Lj44mLi2P48OGkp//6W8g333xD7969iYyMZMGCBa2vz549m/j4+DNmEL/44ousWrXqsq4tOrfT6/l9n1lq40hamM0K205UEhfkdsULfwohrMvVUcNdQ3qw8VgpR4tr2/38205U8OXBIuYkhTA0zPpj+85l0bX9SI725al1R3hvR+4VnSOvsoGrFv/Mt0eKeWp8JM8mR+PjIhPaOruLFn4ODg6oVL9Oka6ru/yWl549e/Ljjz+yf/9+nnnmGVJSUgAwmUzcf//9rFu3jkOHDvHJJ59w6NAh9u3bh7OzM/v27WPnzp1UVVVRUFDA9u3bufbaay/7+qLzGtzDE7UKdudV2joUAA4V1VDTJAs3C9HZPDIqAoAX12e0+7mfWnsEb609tw60fWvfaXZqFatuH8QAnQd3rkjjP9tOXNbx6YV1DHltM8dK61g0vR/zx0d16FUgxKW76Dd05syZ3HPPPVRWVvKf//yHCRMmcPfdd1/WRYYPH46XV8tvQUOHDiUvLw+AHTt2EBkZSUREBA4ODsyaNYvVq1djb29PQ0MDZrMZg8GAnZ0dzz77LM8///wV3KLozFwdNcQGuHWYhZy3nqgAICnU07aBCCEuS6iXM9fFBfH5/kIKqhovfsAl2pBRwsZjpdwxKJRBHey5oHXQ8NP9w0kK9STl03089MV+DCbzBY9pNpp5aUMm0z85ilmBJTP7c/ewHrhe4o5QouO76H9JBwcHJkyYgLu7O0ePHuWFF14gOTn5ii+4ZMkSrr66ZYPi/Px8QkNDW98LCQlh+/btxMTE4OfnR2JiIrfddhvHjh3DbDaTmJh4wXOnpqaSmpoKQGFhIXq9/orjvFQlJZYbLNxd/W9OBwQ48unBMrJycnF2sG336oZD+bg7qglzaLDK98sS5DtrWZJf67iSPP9hgBef7Svgsc93849Jbd9TS1EU/rj6CH5aDVf3sKe4qLDN57SEZdf15PFvc3h9Sw5fHdDz9KgQJkR4Ym/363JUDQYzX2VU8K9tBWRXNjE8yIHHR4Yw2B/KiotsGH3XZMvnxEULv+LiYv71r3+RmJjIHXfcwYQJE674Yj/88ANLlixhy5YtF/3sokWLWv88depU3n77bf7617+Snp5OcnLyOVsdU1JSWruRk5KSCA4OvuJYL4e1rtOd/DanE/qaeD+9lGONDkwKD7BhVJBWcoT4YE/6RoRZdGV8S5PvrGVJfq3jcvMcHAzXxZWz6kgx/7jem2CPtk1S+GJ/AXsL63kmOYox8VEd+pnw2d0hfLQ7l/lrjnDXl8fxcrZnYIgHXlp7imqa2JVbRb3BRISPlkXTYxkfpKZfVLitw+7SbPWcuGhX74svvkhmZiZ33nkn7733HlFRUTz55JNkZWVd8LjFixeTkJBAQkICer2effv2cdddd7F69Wp8fFqWwdDpdOTm/jroNC8vD51Od8Z5Vq9ezcCBA6mtrSUrK4sVK1awcuVK6uvrr+R+RSd0emmEH7PKbRpHeX0zR4vriAty69APeCHE+b14dR+ajGYe+/Jgm87TaDDx+FeH6OntzOxEXad4JsweGErWk+NIvTGeEeFe5FU1svNkJRX1BqbFBvDvG+L44d5hzBsVgbdM4uiyLqnTXqVSERgYSGBgIBqNhoqKCmbMmEFycjIvv/zyOY+5//77uf/++wE4efIk119/PR988AHR0dGtnxk0aBCZmZlkZ2ej0+lYtmwZH3/8cev7BoOBRYsWsWbNGjIzM1snmJhMJpqbm9FqO/7uE6Ltevlo8dHas7/Atgs5bz81vk8mdgjRecUEuHH7oFDe25XHE/lVJOg8rug8r/54nONl9Sy20j617cVBY8fdQ8O4e2gYiqLQYDChVqlklYJu5KItfq+99hoDBw7k//7v/xgxYgT79+/nrbfeYvfu3Xz22WeXdJEXXniBsrIy/vCHP5CQkEBSUhIAGo2GN954g0mTJhETE8PMmTOJjY1tPW7x4sXMnTsXrVZLfHw89fX1xMXFMXDgQDw9Pa/sjkWno1KpGBbuxb6CGhTFdgs5bz3RsnDz8PCOsVyDEOLK/G1KDE4aNfd/vv+KninHSut48fsMxkb6cFOC7uIHdFAqlQqtg0aKvm7moi1+5eXlfP7552ftz6tWq/n6668v6SLvvPMO77zzzjnfmzJlClOmTDnnew8//HDrn1UqFZ988sklXU90PSPCvfn6UDHHSuvaff/JS7U1p4JIHxd6esvCzUJ0Zv5ujrwwuTePfXmIt37J4Q8jLn2ih9mscMfyNOxUKv44ppesayc6nUvaueN/i77TYmJi2j0gIc5l2KlWtu8tuOXShZjMCttPVhAX5IabkyxrIERnN++qCAbo3Hly7RFyKxou+biFPxxj8/FyHh/Ti+RoPwtGKIRldIyVJoW4iKQQT+xUsDO3yibXb1m42UScjO8TokuwU6v4aHYizSYz1yzZQbPxwuvbAaw/WsLT646QHO1LytAeaDrIYs1CXA751opOwcVRQ2ygu80meGzNaZnYMeAKB4ILITqemAA33rmxP/sKqpnx/i6MF1jceNuJCm5Yuotwby1/mdyHIA9nK0YqRPuRwk90GldFeHOwqIb6JqPVr731RAWezhoGhkjhJ0RXcsvAEJ6ZEMVXB4v43Ts7qGwwnPWZz/bpmfj2NjydNbxxXT+GdJD9eIW4EjJYSXQaw8O9WPxzDj8dL2NyjHUXct6aU05ckDv+bo5Wva4QwvJeuLoPjho1f/72KJF/28C8qyIY3MOT0rpmPtqTz7ojxfQNcOUfU/syuY+/rcMVok2k8BOdxrCwloWcN2VZt/Arr2/maEkdE6L8OswG7EKI9vVUcjRjIn146IsD/Pnbo5xe5MXXxYH7hofx4MiexAR0nvX6hDgfKfxEpxHu7YyviwP7Cmqset1tpxZujguSh74QXdmInj7sfnQ0R4tr2HaiEid7NYk6DyJ8XLDrBDtzCHEppPATnYZKpWJYmBd786tQFKV1JxdL25pTgZ0KBveQcT1CdAe9/d061W4cQlwO6bcSncpVEd7kVTWSWVJrtWtuPVFBpK8LPX1ki0AhhBCdmxR+olMZdmo23fqMUqtcz2RW2H6igvggdzxk4WYhhBCdnBR+olMZGOqJRq1iZ26lVa53sLCG2uaWhZut1bUshBBCWIoUfqJTcba3Iy7IzWoLOW89UQ5AfLCM9xFCCNH5SeEnOp2RPb05WFRLTePZC622ty3Z5Xhr7UmUHTuEEEJ0AVL4iU5nRLg3TUYzP2aVWfxam4+XkxDsTqC7k8WvJYQQQliaFH6i0xkW3jLB48fjli38cisaOFHRwACdhyzcLIQQokuw6k+znTt3otFoWLlyZetrS5cuJSoqiqioKJYuXQpAU1MTkydPpl+/frz55putn01JSWHPnj3WDFl0QKGezgS4ObLfwgs5b85uKSwHSDevEEKILsJqhZ/JZOKJJ55g4sSJra+Vl5fz/PPPs337dnbs2MHzzz9PRUUF3377LSNHjmTfvn188MEHAKSnp2MymUhMTLRWyKKDUqlUDA/3Yn9BNSazcvEDrtDm4+W4ONgxKNTTYtcQQgghrMlqhd/rr7/ODTfcgL//rxtcf/vttyQnJ+Pt7Y2XlxfJycl888032NvbU19fj8FgQFFafrA/88wz/OUvf7FWuKKDG9nTG311k0Vn927OLic+yB2dh4zvE0II0TVYZUXa/Px8vvjiC3744Qd27tx5xuuhoaGtfw8JCSE/P58bb7yRDz74gKFDh/LHP/6RL7/8ksTERIKDgy94ndTUVFJTUwEoLCxEr9db5oZ+o6SkxOLX6G4uJaex7i2/EHy6IwP/Ibp2j6G8wcjBwhpu7+9DVVkxVe1+BduR76xlSX6tQ/JsWZJfy7Jlfq1S+D388MMsXLgQtfrSGhg1Gg0ff/wxAAaDgUmTJrF69WoeffRRTp48yZw5c5g2bdpZx6WkpJCSkgJAUlLSRQvF9mKt63QnF8tpQKCC+8pMDlQoFsn/rgOFAAyL0nXJ/75d8Z46EsmvdUieLUvya1m2yq/FCr/Fixfzn//8B4CqqipmzZoFQGlpKWvXrkWj0aDT6di0aVPrMXl5eYwZM+aM87z55pvMmTOHbdu24eHhwfLlyxk3btw5Cz/RfdipVYyK8GF3bhWKorT7rhqbs8uxt1MxuIdM7BBCCNF1WGyM3/33309aWhppaWlkZ2eTk5NDTk4OM2bM4M033+Taa69l0qRJfPfdd1RUVFBRUcF3333HpEmTWs9RUVHB119/zZw5c6ivr0etVqNSqWhoaLBU2KITSY72Jb+6kbT89u+I3Xy8jL4BboR7u7T7uYUQQghbseniZN7e3jzzzDMMGjSIQYMG8eyzz+Lt7d36/gsvvMBTTz2FWq1m0qRJbN68mbi4OG677TYbRi06irGRvgCsOVzcruetazKyO6+KAcHueDjbt+u5hRBCCFuyyhi/33rvvffO+Psdd9zBHXfccc7P/vOf/2z9s5OTE999950lQxOdTGyAG17O9mw7UdGu591+shKjWSFB1u8TQgjRxch2BKLTUqtVjIn0YVdeFWazud3O+8OxUtQqGBrm1W7nFEIIIToCKfxEpzYhyo+imiZ25bbfOL/1GSXEBrjRx9+13c4phBBCdARS+IlObWykDwBfHypql/NVNhjYmVvJkDBPGd8nhBCiy5HCT3Rqffxd8XN1YEduZbuc74djpZgVGBwq3bxCCCG6Hin8RKemUqkYF+nLrtxKDEZTm8/3fUYpzvZqRkZ4X/zDQgghRCcjhZ/o9K7u409ZvYH1GaVtOo+iKHx7tJhEnQfh3tp2ik4IIYToOKTwE53e1X38UQFftXGc39HiWrLK6rmqpzfO9nbtE5wQQgjRgUjhJzo9fzdHEkM82Hy8vE3nOV04juwp3bxCCCG6Jin8RJcwvV8gh4pqyCiuueJzfHWoiGhfFwbL+n1CCCG6KCn8RJdwTUwACvDxXv0VHV9e38zP2eVcFeGNr4tD+wYnhBBCdBBS+IkuIUHnTg9PZ747WnJFx68+UIhZgVERPqhUqnaOTgghhOgYpPATXYJKpeLmAcHsOFlBTln9ZR//yd58dB5OrQtCCyGEEF2RFH6iy5g1QIdJgSU7T17WcUU1TWzILGVitB86D2cLRSeEEELYnhR+osvoH+xOhI+WdYeLL+u4T9P1mBWY1NsPtVq6eYUQQnRdUviJLkOlUnH7oFB251WxK7fiko5RFIWlu3Lp5aNldC/p5hVCCNG1Wa3w27RpEwkJCcTGxjJ69OjW17/55ht69+5NZGQkCxYsaH199uzZxMfH8+STT7a+9uKLL7Jq1SprhSw6obuG9MBOrWLRT9mX9PntJyvZlVvFjPggAtwcLRydEEIIYVsaa1yksrKSP/zhD3zzzTf06NGD4uKWrjiTycT999/P+vXrCQkJYdCgQUybNg2j0YizszP79u0jOTmZqqoq6uvr2b59O08//bQ1QhadVKC7E9NjA/jqYBHVjQbcnewv+Pk3tmTj4mDHdXGBMptXCCFEl2eVFr+PP/6Y66+/nh49egDg7+8PwI4dO4iMjCQiIgIHBwdmzZrF6tWrsbe3p6GhAbPZjMFgwM7OjmeffZbnn3/eGuGKTm7eVRFUNxlZsOHYBT+XX9XAinQ9U/sGMEDnaZ3ghBBCCBuySotfRkYGBoOBMWPGUFNTw7x585gzZw75+fmEhoa2fi4kJITt27cTExODn58fiYmJ3HbbbRw7dgyz2UxiYuIFr5OamkpqaioAhYWF6PVXtpjv5SgpubJ148T5tTWnvZwUBge78NbP2czp64K747m/5o99kwMK/C7ckdLiwjZdszOR76xlSX6tQ/JsWZJfy7Jlfq1S+BmNRnbv3s2GDRtoaGhg2LBhDB069ILHLFq0qPXPU6dO5e233+avf/0r6enpJCcnc/fdd591TEpKCikpKQAkJSURHBzcrvdxPta6TnfS1py+fqOWIa9tYdHuSv49I/6s99P1Vaw4VMatiTpuGBqDs71dm67X2ch31rIkv9YhebYsya9l2Sq/FuvqXbx4MQkJCSQkJBAcHMykSZNwcXHB19eXUaNGkZ6ejk6nIzc3t/WYvLw8dDrdGedZvXo1AwcOpLa2lqysLFasWMHKlSupr7/8RXpF9zG4hxc3xAXyzrYTfHfkzOVdahqN3PbxXtwdNdw5uEe3K/qEEEJ0XxYr/O6//37S0tJIS0vjuuuuY8uWLRiNxtZJGjExMQwaNIjMzEyys7Npbm5m2bJlTJs2rfUcBoOBRYsW8X//9380NDS0Dr43mUw0NzdbKnTRRSy5KQGdhzO3fLSHX7LLgZY9eW/6YDeHCmv425Q+jOjpbeMohRBCCOuxSldvTEwMkydPJj4+HrVazV133UW/fv0AeOONN5g0aRImk4k77riD2NjY1uMWL17M3Llz0Wq1xMfHU19fT1xcHFOmTMHT09MaoYtOzMPZnjV3DSb57W1ctfhn+ge7k1PeQFWjgSfGRjI3KRSNnSxlKYQQovtQKYqi2DoIS0hKSmLXrl0Wv45er5dxEO2svXNa1dDMQ18c5FhZHS72dtw5pAe/6xuA63kmfXR18p21LMmvdUieLUvya1mWzu+FaqDu+ZNPdCsezg4svWWArcMQQgghbE76uYQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugkp/IQQQgghugmrFH5VVVVMnTqV/v37Exsby7vvvtv63tKlS4mKiiIqKoqlS5cC0NTUxOTJk+nXrx9vvvlm62dTUlLYs2ePNUIWQgghhOhyrFL4LV68mL59+5Kens6mTZt47LHHaG5upry8nOeff57t27ezY8cOnn/+eSoqKvj2228ZOXIk+/bt44MPPgAgPT0dk8lEYmKiNUIWQgghhOhyNNa4iEr1/+3de1CU9fcH8PfugrdFMTLIWAY0LWDZu6ioq1xEyMtaMMBQCkrkmJKXEpoyb1+1LC0VsxgbBUUNyjQbazRJKEoNQcEpB7AUu2qrwgpCubDn9wfj83NFUJHltuf1F7v7POfzeQ67nz37eW4iVFdXg4hQU1MDFxcXODg44NChQwgNDYWLiwsAIDQ0FAcPHkT//v1RW1sLs9kMIgIALFmyBGlpae3RXcYYY4yxbqldCr+kpCQYDAY89thjqK6uRnZ2NsRiMf788094eHgIy8lkMvz555+IiopCZmYmRo4cieTkZHzxxRfQarV47LHHWmxny5Yt2LJlCwCgtLQUw4YNs+l2AYDRaMQjjzxi83bsCefUtji/tsX5bR+cZ9vi/NqWrfNbUVHR7GvtUvgdOnQIarUaR44cwa+//orQ0FDo9frmO+XggN27dwMAzGYzwsLCsH//frz88sv47bffEBcXB4PB0GS9WbNmYdasWTbbjjsZNmwYCgsL27XN7o5zalucX9vi/LYPzrNtcX5tqyPza7Nj/DZv3gy1Wg21Wo3NmzcjIiICIpEIQ4YMwaBBg1BaWgp3d3f8/vvvwjp//PEH3N3dreJ88MEHiIuLw/Hjx+Hs7Izs7Gy8++67tuo2Y4wxxli3ZbPCb+7cuSguLkZxcTG8vb3xzTffAAAuXbqEsrIyDB48GGFhYfj6669RWVmJyspKfP311wgLCxNiVFZW4sCBA4iLi0NtbS3EYjFEIhHq6ups1W3GGGOMsW6rXXb1LlmyBDNmzIBCoQAR4e2338aAAQOE1/z9/QEAS5cuFU70AID//e9/WLx4McRiMcLCwrB582YoFArMnj27Pbp9T9p717I94JzaFufXtji/7YPzbFucX9vqyPyK6OZps4wxxhhjrFvjO3cwxhhjjNkJLvwYY4wxxuyE3RR+Tk5OHd2FFiUkJMDV1RV+fn4d3ZVWEYlEmDZtmvC4vr4ejzzyCCZPntwm8e/l/1daWoqAgAD07NkT69ata5N2OzNb5vzKlSsICgqCk5MTkpKSHjheV3a3915gYOB9XZZh8eLF8PDw6PRj0r1YvXo15HI5lEol1Go1fvzxx1bFycvLw9GjR9usX15eXrh8+XKbxQsPD0f//v3bbDy7HyKRCK+88orweN26dVi+fHm79wNo2+/RrjDGdPbPaGvrBrsp/DpCfX39PS87Y8YMHDx40Ia9sS2pVIqffvpJOOP68OHDTS7Nczf3k687cXFxQWpqKhYtWvRAcbqKtsh5c3r16oWVK1faRQHd3qZMmYKCgoKO7sYDO3bsGA4cOICTJ0/i9OnTyMnJsbog//1o68LvQdxpHEpOThZuH9reevbsib1797ZpIdsRbs8rjzF31h51g10VfjU1NQgJCYFWq4VCocD+/fsBNF7h2sfHBy+88ALkcjkmTJggfJne+mv+8uXL8PLyEtbR6/XQarXQarXCoJWXlwe9Xg+DwQBfX18sXboUGzZsEPqwePFibNy4sUnfxo4da3VGc1c0ceJEfPnllwCAjz/+GLGxscJrBQUFCAgIgEajwahRo1BWVgYAyMjIgMFgQHBwMEJCQlBTU4OZM2dCoVBAqVTis88+E2IsXrwYKpUKI0eOxKVLl5q07+rqCn9/fzg6Otp4SzuP1uR87NixKC4uFpYbM2YMSkpKrOJKpVKMGTMGvXr1sv1GdAF5eXlWsz1JSUnIyMiwWmbbtm1YsGCB8Pijjz7CwoULm8QaOXIkBg4caKuutpu///4bAwYMQM+ePQEAAwYMEO6uVFRUhHHjxkGn0yEsLAx///03gMbxdP78+VCr1fDz80NBQQEqKiqQlpaG9evXQ61WIz8/H0ajEZGRkfD394e/vz9++OEHAMDy5csRHx8PvV4PT09P7N27FykpKVAoFAgPD4fZbBb6984770ChUGD48OH45ZdfAKDFuNOnT8fo0aMxffr0JtsaEhKCvn372i6ZLXBwcMCsWbOwfv36Jq9VVFQgODgYSqUSISEh+O2332AymeDp6QmLxQIAuH79Ojw8PGA2m/Hrr78iPDwcOp0Oer0epaWlABoLiBdffBEjR47E4MGDkZeXh4SEBPj4+GDGjBlWbS5cuBByuRwhISEwGo0A0GLc2bNnY8SIEUhJSbGK01XGmG5ZN5CdkEqlZDabyWQyERGR0Wikxx9/nCwWC50/f54kEgmdOnWKiIiioqIoMzOTiIjGjRtHJ06cENbx9PQkIqLr169TXV0dERGVl5eTTqcjIqLc3Fzq06cPnTt3joiIzp8/TxqNhoiIGhoaaPDgwXT58uU79vH8+fMkl8vbfuPbgVQqpZKSEoqMjKS6ujpSqVSUm5tLkyZNIiIik8lEZrOZiIgOHz5MERERRESUnp5O7u7udOXKFSIiSklJofnz5wtxr169SkREAOiLL74gIqLk5GRauXJls31ZtmwZrV27ts23sbNpbc4zMjKEHJeVlQnv3TtJT0+nuXPn2nZDOjmpVGqVVyKiuXPnUnp6OhH9/xhRXV1NgwcPphs3bhARUUBAAJ0+fbrFuF1ZdXU1qVQqGjp0KL344ouUl5dHREQ3btyggIAA+ueff4iIKCsri2bOnElEjblKTEwkIqJvv/1WGO9u/8zGxsZSfn4+ERFduHCBvL29heVGjx5NN27coOLiYurduzd99dVXRET09NNP0759+4iIyNPTk1atWkVERNu3bxf+dy3F1Wq1VFtb2+z23v4eaC9SqZRMJhN5enpSVVUVrV27lpYtW0ZERJMnT6aMjAwiItq6dStNnTqViIgMBgMdOXKEiBrz//zzzxMRUXBwMJWXlxMR0fHjxykoKIiIiOLj4ykmJoYsFgt9/vnn1LdvXzp9+jQ1NDSQVqsVvhsB0M6dO4mIaMWKFcLY0FLcSZMmUX19fbPb15nHmO5aN7TLdfw6CyLC66+/ju+++064V/DNmaNBgwZBrVYDAHQ6XYv3uQMabyWXlJSE4uJiSCQSlJeXC68NHz4cgwYNAtB4rMnDDz+MU6dO4dKlS9BoNHj44Ydtsn0dTalUoqKiAh9//DEmTpxo9ZrJZEJ8fDzOnj0LkUhk9cs8NDRU+NWSk5ODrKws4bWHHnoIANCjRw9hxkWn0+Hw4cO23pwuoTU5j4qKwsqVK7F27Vps27atyS961jpOTk4IDg7GgQMH4OPjA7PZDIVC0dHdshknJycUFRUhPz8fubm5iImJwZo1azBs2DD89NNPCA0NBQA0NDRYzXDenJUeO3Ysrl27hqqqqiaxc3JycObMGeHxtWvXUFNTAwB46qmn4OjoCIVCgYaGBoSHhwMAFAqF1bh9s53Y2Fhh5rWluAaDAb17937QtNhEv379EBcXh9TUVKs+Hjt2DHv37gUATJ8+XZhVi4mJQXZ2NoKCgpCVlYU5c+agpqYGR48eRVRUlLD+f//9J/w9ZcoUiEQiKBQKuLm5Ce9duVyOiooKqNVqiMVixMTEAACmTZuGiIiIu8aNioqCRCKxQVbaR3esG+yq8Nu1axeMRiOKiorg6OgILy8v/PvvvwAg7K4AAIlEIkzZOjg4CFPmN5cFgPXr18PNzQ0lJSWwWCxW09VSqdSq3cTERGRkZODixYtISEiw2fZ1BgaDAYsWLUJeXh6uXLkiPL9kyRIEBQVh3759qKioQGBgoPDa7fm6E0dHR4hEIgCN/58HPR6wO7nfnPfp0wehoaHYv38/PvnkExQVFXVQz7uOW8cBwHosuFViYiLefPNNeHt7Y+bMme3VvQ4jkUgQGBiIwMBAKBQKbN++HTqdDnK5HMeOHbvjOjc/x809BgCLxYLjx4/fcTfgzbFaLBZbjQtisdhqXLg17s2/W4p7L+NQR1qwYAG0Wu09va8MBgNef/11XL16FUVFRQgODsb169fRv39/q8M8bnVrXm/9Prw9r7cSiUSwWCwtxu3seb2b7lg32NUxfiaTCa6urnB0dERubi4uXLhw13W8vLyEL8Y9e/ZYxRo4cCDEYjEyMzPR0NDQbIxnnnkGBw8exIkTJ6xuSdcdJSQkYNmyZU1mOkwmk3Diwe3HRt0qNDQUmzdvFh5XVlbapJ/dSWtynpiYiHnz5sHf31+YVWXN8/T0xJkzZ/Dff/+hqqpKuAXl7UaMGIHff/8du3fvtjresjsqKyvD2bNnhcfFxcXw9PTEk08+CaPRKBR+ZrMZP//8s7BcdnY2AOD777+Hs7MznJ2d0bdvX1RXVwvLTJgwAZs2bbKKfb9utpOdnY2AgIA2i9tRXFxcEB0dja1btwrPjRo1SthDsmvXLuj1egCNs7H+/v6YP38+Jk+eDIlEgn79+mHQoEH49NNPATTOZN1+bO/dWCwW4Xtw9+7dGDNmTJvE7cy6Y91gF4VffX09evbsieeeew6FhYVQKBTYsWMHvL2977ruokWL8OGHH0Kj0VidVTVnzhxs374dKpUKpaWlLf6q6dGjB4KCghAdHd3slHdsbCwCAgJQVlYGmUxm9eHuSmQyGebNm9fk+ZSUFLz22mvQaDQtzta98cYbqKyshJ+fH1QqFXJzc++57YsXL0Imk+G9997DqlWrIJPJcO3atVZtR1fSmpzrdDr069evxdkDLy8vvPzyy8jIyIBMJrPaRWYvbo4dHh4eiI6Ohp+fH6Kjo6HRaJpdJzo6GqNHj262oE5JSYFMJkNtbS1kMlmHXZrjQdXU1CA+Ph6+vr5QKpU4c+YMli9fjh49emDPnj149dVXoVKpoFarrc7Y7dWrFzQaDWbPni2Mc1OmTMG+ffuEkztSU1NRWFgIpVIJX19fpKWl3Xf/KisroVQqsXHjRuHEiNbG1ev1iIqKwjfffAOZTIZDhw7dd3/awiuvvGL1PbRp0yakp6dDqVQiMzPT6gSAmJgY7Ny5U9g1CzQWh1u3boVKpYJcLhdOVLhXUqkUBQUF8PPzw5EjR7B06dIHituZx5juXDfYxS3bSkpK8MILL3TYJRQsFgu0Wi0+/fRTDB06tEP6wNit/vrrLwQGBqK0tBRisV38/muV1owdkydPxsKFCxESEmLDnnVNgYGBWLduHYYNG9bRXWGsRd25buj2I35aWhpiY2OxatWqDmn/zJkzGDJkCEJCQrjoY53Cjh07MGLECKxevZqLvhbc79hRVVWFJ554Ar179+aij7EurLvXDXYx48cYY4wxxuxgxo8xxhhjjDXiwo8xxhhjzE5w4ccYY4wxZie48GOM2QWJRAK1Wg25XA6VSoV3333X6qLMtpCcnAy5XI7k5GSbtsMYY/eKT+5gjNkFJycn4fZc//zzD5599lmMHj0aK1assFmbzs7OuHr1arvcsqq+vh4ODnZ1MybGWCvwjB9jzO64urpiy5YteP/990FEqKiogF6vh1arhVarFS44HBcXh88//1xY77nnnmtycVoiQnJyMvz8/KBQKIQ7RhgMBtTU1ECn0wnPAY3X5xo6dCiMRqPweMiQITAajTAajYiMjIS/vz/8/f3xww8/AAAKCgoQEBAAjUaDUaNGoaysDEDjHVkMBgOCg4P5EjKMsXtDjDFmB6RSaZPnnJ2d6eLFi3T9+nWqq6sjIqLy8nLS6XRERJSXl0dTp04lIqKqqiry8vIis9lsFWPPnj00fvx4qq+vp4sXL5KHhwf99ddfzbZJRLR8+XJav349EREdOnSIIiIiiIgoNjaW8vPziYjowoUL5O3tTUREJpNJaPfw4cPC8unp6eTu7k5XrlxpVU4YY/aH9wswxuye2WxGUlISiouLIZFIUF5eDgAYN24c5syZA6PRiM8++wyRkZFNdqd+//33iI2NhUQigZubG8aNG4cTJ07AYDA0215CQgKmTp2KBQsWYNu2bcKt83JycqxuW3Xt2jXU1NTAZDIhPj4eZ8+ehUgkgtlsFpYJDQ2Fi4tLW6aDMdaNceHHGLNL586dg0QigaurK1asWAE3NzeUlJTAYrGgV69ewnJxcXHYuXMnsrKykJ6e3iZte3h4wM3NDUeOHEFBQQF27doFoHG37/Hjx63aB4CkpCQEBQVh3759qKioQGBgoPBaS/f7ZIyx2/Exfowxu2M0GjF79mwkJSVBJBLBZDJh4MCBEIvFyMzMRENDg7DsjBkzsGHDBgCAr69vk1h6vR7Z2dloaGiA0WjEd999h+HDh9+1D4mJiZg2bRqioqKEkz8mTJiATZs2CcsUFxcDAEwmE9zd3QE0HtfHGGOtxYUfY8wu1NXVCZdzGT9+PCZMmIBly5YBAObMmYPt27dDpVKhtLTUahbNzc0NPj4+wu7Y2z3zzDNQKpVQqVQIDg7GO++8g0cfffSu/bl58setcVNTU1FYWAilUglfX1+kpaUBAFJSUvDaa69Bo9Ggvr7+QdLAGLNzfDkXxhhrQW1tLRQKBU6ePAlnZ+c2i1tYWIiFCxciPz+/zWIyxtjd8IwfY4w1IycnBz4+PnjppZfatOhbs2YNIiMj8dZbb7VZTMYYuxc848cYY4wxZid4xo8xxhhjzE5w4ccYY4wxZie48GOMMcYYsxNc+DHGGGOM2Qku/BhjjDHG7MT/AW1Ow1QjJEA9AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With `seasonality_mode='multiplicative'`, holiday effects will also be modeled as multiplicative. Any added seasonalities or extra regressors will by default use whatever `seasonality_mode` is set to, but can be overriden by specifying `mode='additive'` or `mode='multiplicative'` as an argument when adding the seasonality or regressor.\\n\",\n \"\\n\",\n \"For example, this block sets the built-in seasonalities to multiplicative, but includes an additive quarterly seasonality and an additive regressor:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(seasonality.mode = 'multiplicative')\\n\",\n \"m <- add_seasonality(m, 'quarterly', period = 91.25, fourier.order = 8, mode = 'additive')\\n\",\n \"m <- add_regressor(m, 'regressor', mode = 'additive')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = Prophet(seasonality_mode='multiplicative')\\n\",\n \"m.add_seasonality('quarterly', period=91.25, fourier_order=8, mode='additive')\\n\",\n \"m.add_regressor('regressor', mode='additive')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Additive and multiplicative extra regressors will show up in separate panels on the components plot. Note, however, that it is pretty unlikely to have a mix of additive and multiplicative seasonalities, so this will generally only be used if there is a reason to expect that to be the case.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.17\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/non-daily_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"import pandas as pd\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"logging.getLogger('numexpr').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\",\n \"R[write to console]: \\n\",\n \"Attaching package: ‘dplyr’\\n\",\n \"\\n\",\n \"\\n\",\n \"R[write to console]: The following objects are masked from ‘package:stats’:\\n\",\n \"\\n\",\n \" filter, lag\\n\",\n \"\\n\",\n \"\\n\",\n \"R[write to console]: The following objects are masked from ‘package:base’:\\n\",\n \"\\n\",\n \" intersect, setdiff, setequal, union\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\\n\",\n \"library(dplyr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sub-daily data\\n\",\n \"\\n\",\n \"Prophet can make forecasts for time series with sub-daily observations by passing in a dataframe with timestamps in the `ds` column. The format of the timestamps should be YYYY-MM-DD HH:MM:SS - see the example csv [here](https://github.com/facebook/prophet/blob/main/examples/example_yosemite_temps.csv). When sub-daily data are used, daily seasonality will automatically be fit. Here we fit Prophet to data with 5-minute resolution (daily temperatures at Yosemite):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_yosemite_temps.csv')\\n\",\n \"m <- prophet(df, changepoint.prior.scale=0.01)\\n\",\n \"future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_yosemite_temps.csv')\\n\",\n \"m = Prophet(changepoint_prior_scale=0.01).fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=300, freq='H')\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The daily seasonality will show up in the components plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 9 -u in\\n\",\n \"prophet_plot_components(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = m.plot_components(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Data with regular gaps\\n\",\n \"\\n\",\n \"Suppose the dataset above only had observations from 12a to 6a:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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},\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df2 <- df %>%\\n\",\n \" mutate(ds = as.POSIXct(ds, tz=\\\"GMT\\\")) %>%\\n\",\n \" filter(as.numeric(format(ds, \\\"%H\\\")) < 6)\\n\",\n \"m <- prophet(df2)\\n\",\n \"future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df2 = df.copy()\\n\",\n \"df2['ds'] = pd.to_datetime(df2['ds'])\\n\",\n \"df2 = df2[df2['ds'].dt.hour < 6]\\n\",\n \"m = Prophet().fit(df2)\\n\",\n \"future = m.make_future_dataframe(periods=300, freq='H')\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The forecast seems quite poor, with much larger fluctuations in the future than were seen in the history. The issue here is that we have fit a daily cycle to a time series that only has data for part of the day (12a to 6a). The daily seasonality is thus unconstrained for the remainder of the day and is not estimated well. The solution is to only make predictions for the time windows for which there are historical data. Here, that means to limit the `future` dataframe to have times from 12a to 6a:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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XU5zn3Ud3A58+fR60wderUnj17sk4oqtwKfLCMGDFi+fLlcXFxfn5+/fr1Y/N8bicAPHjwgM273qyM3qDzAIASJUqw+bCwMMzOaN++Pe584sSJc+fOZeO+ffuy+ZiYGFQbABAREdGlSxf8MfJOSt2SmepefPnll1OmTNm1a1fv3r3Hjx/PPxJlWRYEgT99s9kcFhbGutoeOHCACQgAYGqDsXnz5m7dukGWsiIPHz7EnQ8ZMsTLy8toNHbs2BEn0dTBLgXO8yEvkiSxeVZ6lXH+/Pnr16/XrFkT8h5ZLzgA6PU24G4SflI1H3knhamQJ9kYJ/l5rdYmimLW+byD2x9bGzdu9PDwiI6Ovn379o4dOzw9PZn3Lj093dvbm23TuHFjdjenpaWpKgHnCkajUaPRYC0/wk14eXmhd5lwEwULFkxPT6dazu5AdQNfuXKF/+3Vq1fZCzofegkAd+/eZYEaixcvjouLA4CkpKSlS5diXkmrVq2w+0loaCh7xMXGxgIA6D1BowWAc+fOseWBP4DExER8HvLfeEJCAptX5YD4+vryz0+TyaR6mqanpzMBlJKScv/+fV9fXza/cOHCTz/9FABmzpw5dOhQnGRqAwA+/vjjNWvWsHHTpk0xbaRAgQLsL6pCVRwOB5tPTExE+8SWLVt+++03JikwehQA9Ho929hut69duxbnV69ezd4/VeXCvLy83LFMPD1ZLzibBADVPC5G/Hy2k4/dGOc1Go3VauXvE/ddpCJFivy7D7o9hmPmzJlTp0595513qlev3qFDh4oVKzKv58WLF91RF48gCCJX4Gtz+fr64vOqYcOG/Ga41vLhF3xbE1QbwL2su8wkBi8QNMClgPJBo19++SWOWdA9A4t5qAxdjy38NXv2bJZWs337dkzovXTpElMbADB58mRMOuVPhz8F3gqC9htVzXKUTXxG6/79+1HD/fbbbzi/ZcsWNmDv9whGmNauXXvkyJFsPGvWLNVq9x+MhMi/POu02EaNGt24ceOzzz7z8/Nzh/eEIAgiV+AtHMnJyViqS1VCg4XDQ+aO88OHD2cDV5WqgsWhWBAAMBMIADRp0gQAQGcEUQQuTAFLjv69DQAAdOzYEcdoHsCwSgbuPCew6xsAYPMRXlgAlwPCJ+DwURS8gQcPgHeRAKeEVAGeGKKHFw04BaMKzsXTTEtLW7RoERv/+eefZN7LvzwjwVG0aNGxY8cCgFarfe+99yZMmMAipwiCIPIFGOfIZ6ICF76AtTSAW1BFUezduzcEN4M6LwNA586d2bzBYACtHjRaEDXA9W3X6/UgaqDzOACBr1z+wQcfVAiuCgCNGjUaNWoUmyxXrlyLFi1wm+bNmz/6FPiwVlzd69Wrx+8BwyOuX7+O83wPtjfffBPHrVu3ZgNVCRC0cISEhAwZMoSN58yZg0GjgwcPxo3btGnDBhqNpm3btjiPGotvzBYeHs4XaSXyF1T4iyAIIhtUqRAYz6h6a0dZgBEPAPD999/jeMCAAQWKBoKo7dixY9WqVdlkXFwc6I0AwAQHGicSExPBWAAqh4JWh4UoAGDv3r1XL50HgGPHjh0/fpxNiqLIBwk+Ft5QgcLC29t7z5495StVqVat2scff4yRhjklDPLdTLDgemBgIL89/qHU1NQffviBjf/880/UVXyfWz4gg0/GOXXqVLZH4o5MxjzLC+YwIsFBEASRDdWrV+dtBtiphG/hUbduXQy95BdCbHECAC+99FJqug1EzZ9//vm///2PTdapUwf0ngAAgggAtWvXxo1d86J22LBhuJMjR47geN26dWxw586dbNvZ5wQfNle9enU2MJlMr7/++rWYC+fOnevevTtKgf79+2N24axZs/CDfEYr6qSkpKRshQhm4QLA/v37MaeGL8KEoSe8n4XfJigoCP3v5cuXV7lviHwECQ6CIP4xy5Yt8/f39/f356P/XjAMBsN33303ceLEcePGnTp1CnMlMJgRACIjIzFwcvDgwf5lykPzwcD1UnGh92SWjD///JNNeHl5ffLtcgCoXKXq0aNH0R3j6+sLBi8AAFHDm1j4+Ax0WKDWYTz21T86OhrHGKq5adMmdIgkJyevWrWKjf/44w+sPDZp0iT8IB80ii1gMOVQtQ0fkgJcVXJ0owAAq8bEPsVXcUV5FBkZiaGs165dQ8sHke8gwUEQxD/j2rVrEyZMYONRo0Y9NlYx/3LkyJG5c+fOnz9/27ZtOMkLDuDCFzZs2JBgUaBGewDAWhouDC7BgRLBbrev/HkpAFyMiZk4cSIW2fz9998zBIeIkZLASnMWrQANuvN79fHxwb4qtWrVatmy5aNP59q1a1nPAtd7Blo1VKeZbf93DDhVBXLi6agyWlGI8EVU+SLrfFkRDBxR1TJRFSQl8hEkOAjixUSSpG3bti1fvlyVy/D0qHp28DF9LxJJSUkY8Dhp0iRWDRm46AcAKFq0KFr4b968CXojq6vB55FWCK4Cei8mOLB81pkzZ2IuXwEAEMTw8HC8hk6nEwzMpaLhS5parVYoVgHK1AbOB2G323/88Uc2jo6OVkWzZoWPPkHbQ2hoKNoVSpcujbk2fMP3bt26YQgLX78SK5SrakzhznlhAQC8oQIneTHHg+YQ1c5Vnpf/IPk3sIMEB0E8C27durVixYrHLgm5yLvvvjto0KCxY8dWqVJFlfr4lKi6d+bUEDW/46rNlQFmbfDpsvfv38c80rZt24LeE0QtAPTq1Qu3uXrpAhiMLFYD+7AUKlSIj+EoVKgQm9dqtS4Lh6DhV2uj0Yg7x6RTV7xFoQDw8IHMeSXZgjGbwJkNkpKS0K5w69Yt7A7j2rhMbdDqT58+jR98//332aBevXpo6NLpdF9//TUbd+7cGVVL3bp1+QPAwBEePneG7y6LQTN8gi5kDn3NLfLvEp6/IMFBEG7n3Llz9erVGzNmzGuvvTZ79uxn8BfT0tKwNCRkfolMSUlZs2bNtm3b+OXnH6Gyk/+jRIl8RMWKFfkfMcFEdfoYvlCiRAnQG5klg9dkJcuUB70nExYYflG+fPkGrToCAAji0KFDcSUuVqwYulT4v1KkSBH0y2Dlq+LFi7dq1Qqa9ofKLQCAT5HNFj7IA507OekqVwnztu9A0fJXr17FXBJUJCdPnuSFCDZ63bZt271799i4UKFCx48f7xM2/L333ouMjMQiabzR4saNGzjmK41u3bqVDVTOHTQ1EfmOF/NJQRB5Cn7tX7BgwTP4i6q+TbhGJicnBwUFvfPOO4MGDeratetj93PhwoX+/fv7+/tPmzYN8z9VeaEvTPdOFarYFCz/0K5dO9ZABABmzJiBboX169eD3giCAILwww8/4OW6c/MaGLyYgMBJSZLuPkgCABDFc+fOYbv2QYMGeRcpDgAgaljPM8aECRNA7wWiWLdu3XHjxrFJQRDavNbPULAIaHRhYWGs1csj4GukonMnJ13lUiQGT9DogCuj/vvvv+PGeGPbbLbly5fjPB/CEhgYWL1ug7p162KEKQBUqlQJx3Xq1MExn92Dok1lUVP9SDBOxaU9fqPnDQkOgnA7fDPuZ4Ner3/vvffYuHLlypg08fPPP+M2x48fj4mJwR9v3LixevVqbFXKmDBhAitPuXjxYkxIKVeuHO78rbfeUpnNXxhUhcPxrd1sNmPJDSyJAexb5rwkaPgJadYSLRxonDh//vztuLts48OHDx84cIDN79ixw2ST2DxfGPTatWvMwhEZGYk2BlmWp4x6yyaLIGqWLl3Kp8hmy6BBg6rVqgsA9evX7927N5vMSVfNnj3b6OXFjrxTp054NfgaX5hDq7pWaPWx2+0DBgyYPPLNgQMHDhgwAP04/B+NiorCcffurqjYwMDAsLAwNlYl46g6yDx7ntz/Qp4aFSQ4CMLthIWFYQYB/yLoVrATx8WLF3EpUgWQ4uJx5MiRBg0ajBw5slWrVitWrGCTiqLwC+rGjRtx3KhRoxYtWjRv3hwLSORrrl69+sYbb3h6en788ceYjqFyOWHmKh8QunnzZgyE7N+/f4lylQAARA3GcgJA14FvgagBUQwJCXn77bfZZFJSkqvwlyACwOXLl9n85cuXM2I4xH379qFFZPXq1aD3ZI1XVq9ezSZdX1+Gq4WXj9ny2WefnYuOBIC//voLXXuq9Rt1UlRUVLrNAaIGRM327dsxVJPveII6QxXHg63FoqKi8HLt3bsXM1r5NFreqoFpw7GxsehewSvMUDXVI/IRJDgIwu34+fn99ttvJ0+evHnzJt8Uw32oeiNjhiEazBm42Pzyyy84iRUa+IoLwHUpS05O7t27d3h4+MGDB0eOHImLZf5l7Nix7Ky/+eabX3/9lU2WLl2arzGFYRaq9vSoCZKTk+MTHgAACCL/1n7jdhybPHHiBK6dQUFBoPcEp40Jjlq1arH5Fi1aYAxHly5dcPnX6XRg8GR+GazP4UoGMXixnahKo2YFQ1aBC4xw6aqgRlC+PnBJp5cuXcooQaYBrj0bHwiCiSSqVFW0zagSTNDTx9fh4Gt14L0HXLAI738BLpg0n/JfNnuQ4CAIt6Moyrx588aPHz9q1Cg+IdB9eHt7820psGImn2kJXGwH3weEDwLld4IVNlVBfHzkYD6Fr+OJ/VqTk5P5EhEYq6iy8GMcqCuGAwBE8ZtvvkEDyTFWbVMQgXNpBQYGlqxUDawmEMVevXo1a9aMzdt9SrjSYgWR7+H+0oBhKCxYXyrANi4ZFg6VyswKLwtQNrl0ZJnaEFgNuDuhadOmmKDbqlUrzKMZPnw4+BSBQd8AAHo9VMIC82z5djD8Ee7cuRMnsXAqAJQpUwbHmP1rMBiOHTvWpGW70NDQ7du3v6gxQ/8FSHAQLzI3b97cunWryiT77FmzZs2CBQv27dv3xx9/TJ48mf9VTEzM1q1b+YUtt+CTKTBer3Hjxpix+e6772LFaP6Nk3+g8x3PMZ1Btbrk90JMqroOuOKquqXjfIMGDfj5ChUqsIHT6cyI4dAA52WQtQYAV9YJWokAwMO3iFFUAgICmzdvjiJvz9aN6FL57rvv0L+TnJSE5UpR8Pn4+DRo3hq0BiZEULVADq/R3t7eULAYNBsI3C3hCjDSq0uT1ahRo+UrPQGgaLHifJH1rVu3gpcfeBcGprEymDt3Ln4QK5OqbpVss1R4eC3LC9958+Yd2b/rwIEDs2bN4mur58SztyL8l+0WTw4JDuKFZceOHfXr1x88eHDdunUfG0/nVvhEvt27d+MKt2LFiqZNmw4ePLhatWq5aydwOBx8NsGMGTPYQBTFTp06NW3aNDQ0lM+i5Ita8lEmfOEQ3KEqTQDfffMpgiDwrpOAgAA2KFCgAB8P26hRIzYoWbLkwuW/+w37rsvgt48dO4ZuAo1GA7q/wzIwQLJOi/YAwB62WEnMarVeuXot/eH9uLt3R44ciXeI0+lAwQHcortl3a9o4UCnDwAMnzQTAAJKllq6dCmf7pEtjRs3Br9SUKEhcNUsXMGwBleRD6zJcfz48f379gDA/QeJvXv3RukTExMDBk/QaCBza/uwsLAvlq2ZM2fOunXrUOyqrBGoZt544w28/ZYsWYIb8M1rUA3funULlc3Bgwe3b9/+6NN8BHlWFrjjwPLgyZLgIF5Y+IVz2bJlz+9AoGnTpjh+9dVXMTaCzx506xFirOitW7cGDx7M0iJef/11tKujh/4RYHcuVWyHqqx1foS3MOFppqamRkZGQtXWENwcMnsBHiY+SNMXvJtq5VfcAgUKgMHlUgFOK5w5exZkiU1u2rSJTe6OvAR6T7CZmYbA3BBRowW9J8gSCAJwqkWr1WIMB58SsnHDOgCIu3s3LCzssXayESNGtHi5u97o+fbbb3/44YdsMiAgwL9YcbRwYJTP2bNn+aQbvCwhISFg8GJWHL4G18KFC8cM7vnBBx8EBwdjyTJVLTJUM35+fk2bNq1ZL+Tll1/mVR1/apjJwreThf9AHY48KBRyCxIcxH+C51sOuVq1ajguX748jlVSsnAAACAASURBVDHTEgBSU1Nz8S/qdDq+7MFLL73EBhg9ysAoQr4yZk4tOTClUxWcqHI95HdweXMtfiWCoWgF4LxLN2/enPbeMIegPf7Xya5du+L2PXr00Pv4AgAI4ujRo3HtPBUdjcICs45LlCwNBk+wmtg8BkJ6eXmDwQusaSoLR5d+YaDRscnBgwezSYfDsZmlDgkiZK6QAQCDfr+UlJ4p0SYqKir8wAG7U1q8eDHmg5hMpoR7d1HNoCbw9fXNiOEQgYsPdQkOUQOZK4d++umnUKM9hIYBwMqVK9mkKt4Tc5o2bdo0e/bs0ydPbNmyBaUPZBYceJsFBgbyO8EAW3ih1+YXEhIcxAsLX/zq+ZbfxqbkAPDll19iJB1/VNk2x3oaeDWDL+K89AFuPRg1alTr1q19CvoGBwfz9aZ69uyJYxQlhQsXxgDSWrVq8YGl+RTeko8Bj67FPqN4KAaBHjp0CEQNaA1sPjIyks2fPn3armgAAATxq6++wh2WqVILrCZmsUB/jVan03sXKhVQvEzZsuvXr8eaXb4lSoEATIiUL18eF2BXWKsgApfKIYoiRphCZiEoKUqcyZ5izSQ4li5dihVBsKqK609kWDjwL6anp/MWDryFli1bBnpPEAQAgY/3BADwKQKevsAF0np5ebVvzzxKUL9+fVQtvANx165d+D7Qt29fnEf3k5+f3+jRo9m4Y8eOzybPi3AHJDiIFxa+xxifhvDsUbVQQW3Bhw64tX4iKhtVlSe0eK9atWrv3r1pKcmXLl2aOXMmbjBnzpyRI0d26NBhxowZAwcOZJNOpxObmEdHR2P6Yv6Fdw20bt2aDVSCA+McdTpdRr5opufn9oMnMkprCMDVOJFEHVhNzAeBjgmnrNhlRbCbnZLM59leu3kb7FaQnCCK165dQ/PJ1es3AFxrP+YwazSaDr0GsSNp0aIF34jEYpcVBRxSJsOeRqMBg+t00C/m6ek5cMRoFiDSsmVLbINSr149vlIImhk0Gg0m7qqca9ijDsXZjRs3du7cCVVago//X3/9FR4ezuZ5S4a/vz/uh4+14mM1XnvttZBmLRs1atSlSxeVRe2Hv+6mOzLlwuQiz9eC8uLZb0hwEC8svJOCj2y/fPnysGHD+vTpw1cczy0URYmMjIyOjuYnVWICDwZt4wCAJaFyixo1auAYg/XUK0QGWJEJMkeW+Pj4TJs2bcWKFcOGDUPzvqoVHF8cLJ/CF6jAFh56vb558+agM7IASaxPFRoaysd1Yqhm1Vp1XUGjohgaGoqxtLLOA2wuCwcqv9R0OyjyrYtnYuPi33rrLYzhKBVcHWxmABkEoWXLlhh9WSq4BoDCJA62pAeAWk1aA0Cjxk2WLVvG5yWZ7BIASJk9iR06dMgaqwEAklNi806nEyNyBEHgdRWq5A4dOmC6rLoZW0bkKZbWdd0ztV+GEpX40+fvw4SEBBQofCVctIJIkhQaGnri0P5jx46NGDGCv1cBYN25hAeWTP3riTwLCQ7ihQWj3IGLPwCAiRMnrl+/fvfu3e+88w52nMoVZFl+6623OnTo0LZt2xEjRqCh+M0338RtSpcujQtDhw4dLly4sHXr1hs3bqAlP7c4c+YMjnFBVZWgxtWFL6bE91hJSUn58MMP+/TpM3/+fFwVVJXaczf65LmQbR87h8Nx8OBB0BuZcQJfuIsVK9Z7+DgA8PUr/Pnnn6OYC65ZF7R6UXZ26vzy559/jvsJqFAVbGa2bOOdEBt/F2wWUGQ2j4XCSlSo4mPUa0SxWPESo0aNwp007tBFp0garS4sLGzKlCk4f+jQIQA4dvxE+fLl+TKyZocEAE45k+DYs2cPulQwmsRms6383xIQNSCIhw4dwqrtp0+f5v01mLC6Z88e1Fu8Z+Srr75CNYNKukyZMi/36MsqhXTq1AkTd/nU4nbt2uFtyf+n1KtXjw145yAAHDt2DMc2pywpal3FePHMAy8AJDiIFxa+Tdr06dPZICkpiXdwoAM+Vzh58iSWjVq3bh2+tvK1FPlYTgAoUqRISEiIO9pDoO8cuC7hKgsHSqJevXp98sknDVu0HjRoEB/DMW3atB9//HH37t1z5szB9EVVQEzJkiVz/eCfMXxMsdq3pVf7IBITE39b+TMAJKekjh8/Hpd5i10SBNBIDllR+Iice4lJYDUBCACwe/duNunhWwTsFlBkNo9f0IWYy2n3YyWH4979hC+++AIP7MbtWEdqoiTLS5cuxRtYluUjRw4DuDQB+rkAgDVkUQkOgL9jNTLBGWzwNGvVqsXHcGBJrqR0Z7b9bPv27Rtcr3FwlSqXLl3iwzw1Gi0YvEDUOp1OPJ22bdvOmzevau16r7zyyrx583DjqVOnvtKrf7169UaNGvXOO++wSdU3wietmB0yAEju8aiQZMl1SHAQLyz169c/derUqA8/OXjw4Msvv8wmMWyN8YSNx/7dowcfrydPnsTJiIgIVW1sN8HLAnyDV3WRxaxLQRCio6Njzp0+fvw4xhkAl24AABEREWzg7e2NL9+hoaF4bfMvvAbFbuk6na5NmzagN7IVF2M7Lly4gD4F4LI0z52/oFgtDrt1x85d48ePZ99+dLwpzWoHq4ltjGYnqyQUK1LYx6eAh9E4atQodHBcvnINbGZQZGZvQDVz6GA4prrwfgdXfXRRhMyyia3EzswxHEOHDgUDi/f8OzzTYDD4lw4CcAkIrJwRHBxcuW5jAKVGzZr79u3DyIlufQdjfbPXXnsNd77gxxWXLl+5FHOZT4uNj4/f9NsvLAVm165de/bsYfMPHz7cvHnz+VMnN2/ejDYVAIiMjNy8esXJkye//vprvFYqlcwLXItLcDzPHLRnTL6WQSQ4iBeWS5cu1a5d++vZ05o3b84XUTh8+HC3bt3atm27aNGikJCQXPyLZcuW5X9EY4aqIpM7KmVlfQzxMXeoFVTpwfjjtGnT1q9f/zDxwcWLF/nYQ37M92n76KOP1q5dO3/+/F9++YVvxJVP4fuH4du5LMt79uwBvZGtxJhHGhwczL/6V6lShc2fv3jBZbQQhEOHDmFVDI1nAYzhwCwhs8N570ZMWkqy1WbnF1ezQwabGRSFbY+JJw5Bi6kuiCiKles1xtxabAALGTEcqjd/Dw8P1E+4ilut1oSHyXg6fJULQwE/D43Qtl17PlbjfHQkeLgsIrxQm/3hGDSfYO86WZb5jB5MLV63bh1+dvr06SiI+SJgixcvZgMvL6+RI0dCaBhUatqiRQtsfQwAluxCVdhMbNqTlofJ10t4/oIEB/HC8u233+KYT0ytVKnSjBkzJk+e3K1bN9VHnvLRo2rXic/uhg0bLly4sH379l26dDl06BD/xpaSkhIdHf0k1Zr/KZ06dcIxli6oXLkyZrE2aNAAq2fyK8fdu3dRiORUgvqrr77q0aPHuHHjBg8ejDmQ+QKLxRIdHf2IQmdoVFAUBUAAnculgiVM/P39u4W9AwCVgiv//vvv2MysWJmKYE8HRWErN87rfQqBzcy0AtY4uZ/40BXDIQgAgLFEHr5FwGYBRWI7wQzqirUaYnEOzEkGAFnPinkITZo04WuBsxgO1av/kiVLUBNgWTwPD49KtUMAXIIDZWXkneSrN25YkxO++PIrLFUOAOdO/QV6l0sFc2tdZNRCRY+S0WjM8Ndo+HlVEjjeb7wg5vuwvPnmm34Va5euWmfAgAG8h8XslAAgq4Hj9F3z9P031bPE84YEB/HCwj/U+AfZqlWrqlWr1qpVq549e6ratT8lqjJHfLJlr169pk6d+umnn/LBoeHh4UFBQW3bti1TpoyqJNfTw2cPonvFarViGEFERATmm2CAHgAULVoUJRFfrRXTjO12O9ZK37dvH28Sz+PExMSUKVOmbdu2FStW5LvM80s1vm2Logg6AwgCW0T5OJvzFy4CQMyVK6tXr8Zbq1jZoIBiRfR6XcNGjQ8cOID7TEqzoBECX/0Fg3eGOUQE7lbxK1HGIEhMtdSsWRM9gJLWADbXTlAFRsebYi5fYTs/cuTIihUr8AhNNhmyvPrfS7WiJuBVb/segwCgiL//3Llz0dgTeeywyeZkO//hhx+wIZGrZBkACELDhg1xJ1Vr1cXqYX8ftiTx1cPwj3bv3h0/OHbsWDTkNGnSBMrVh87jgTXO5bZJSjXfuhM3ZMgQNNcBgMWevUsl1ZbrdW2IXIAEx1Nx6NCht956a+zYsaoKvkRegH/F59f+d999lw0OHz789ddf5+Jf9Pf3X7NmTfv27du3b7969Wp0h9tstgEDBjRv3rxmzZpffPEFbv/999/j+JtvvsnFIwGuijZwmat8/idw2bBz585t0aKFT0HfwMDApUuX4ga8lR7jDFTP8nxU2hwbjAHArFmzcMyXzMIcIkEQ6jZvC+B69W/cuDGbv3jx4qWYy2x+zZo1qMNSLXbLwwd2a/rxEycwtxZYHY6M8Au8dGlWB9gtaA7B5fz23Xu21CTmUjl9+nRiYiKbP3fhAlhdO8EvSFEUMHihEOG/CLM9GwvHKz37gd7IanbxMcWKzgMABFHDt56/cvG8q/i6KAJXssVitmCQKZ/0dP7sGdDomCUDWyL7+/uXqVyLbVy4cGE08Pj7+28+dm7mN0v379/PVxqNiIgAnyLgWRAAsGhHWlravn37sIELn4btiuHI4lIx2aVsU1eI5wsJjmx4Qrv61atXu3XrtmnTpuXLl48dOzbbzDri2SDLckxMjCoYk3cToI9ctVjysR25QvHixQsXLuzr68tHp27duhX/0KxZs9AHwdtdcn3Z5mM50QfPt/8GLv4gLS1NFMW0lOTY2FhelPAZsLgqG41Gvn0on0abx8G8IeDWeMicFstXGek1fCwAFCladPLkyVgRy263824C/OLOnjuXfPcOyDKI4rx581gyhaKArDVg+MWgQYPYxma7E+zp6FLB4IYUiw2sZrR8YD93O2jBmsa0Ah6eIAgFiwXiznlXC3OpqN78r1++BDoD+ySfn7V18yaQHAmJSWFhYSzPFgDKBlUCvRF3jlrEJisgiOC0gyAePnwYd1K8XCUAlzjDBsJms/lmbBybT0xM5Ot6nf7r2KYde1euXMlfcEliFUG0wP2r/t3PVlC3kslWVwGAyS7J2UWSplilh+lP9aB+Sq/r03w8Ot6U7cdzms+DkOB4UpKSkq5fv86vELxl79ChQ8+9B/p/FrPZ3KdPn6ZNmwYHB/OxGnwFzMuXL7OBShfyCatPj8lkatGixapVq9asWdOuXTtM9+CbvAO3RPEhmWFhYfw2OT1BIu+kZDufFWynDpwR3sfH5/XXX2fjBg0aYEXtX3/9FReD0aNH433Om+6wVgQAzJgx4+sV61esWHHjxg1V4k9ehk/X5F0kWHEcACpWrIhjrZcPAGi1+rJly6I7oFy5crybADWcxSGB3QKgsOcq+5bTnbKsQLfXe4ga7ejRo9FNYAexcqVKTJ0AAAuEjI43OTV6sJtRiKSnp7Pt/UqUBpsJAEAQ+KhJqySiJuDllMmezav/yagol14RRax653Q6b9y6g4m7qCFqhTQFrZ7ZZsqVK4euwOYvvQ4OK0gOEMRp06bhzu8mPmSHx//Fv8WZKAL3jxAZGTl53KgTFXr88NNSLFsOAAMGDACDq9IaVrZ1yReDK/qED3iy5BDDkWbLPlX219P3v4+Iz+43uU9+EQHPEhIcT8SSJUuCg4NDQkIGDhyI/zOq5yxfppp4lqxdu3bv3r1s/P777+OLER9cFh/vesqoakjwmRdPj6rINzos+G4jtWvXxiyVbt26HT169Keffjp16hTvDs8V+DqquIrcv38f+19EREQcOHCAjXFhY2C8XokSJXCSX4kBoFS5CpUrV+ZlTd6HdxnwR86HD2OjOwBY/tMSALh7//6QIUN27drFJq9evQp6T0wkweJXRcsEodGid+/e7G9ZHJIW5A2//CAr8NVXX3322WdsY4tdbtWmTY9BQ6pUqXb9+nV8mIhGb7BZQHZZOEqXLg0A0fEmxeD53oQPAWD/gQP9+vXDI7SBiHXT0QcBAGabBABy5lW3RPnKIEsAAIKIp6nVaouVqwg2E9MEmEdz/tIlkCWwp4MgXr9+/cqVK2z+5p04V+KuKPIpLQ1adgRwCQuscluoUKEq9Zuyvwicl/Po0aNg8AZBAI1u7969WDuuZMmSoPdkp4PXxCWXM+b5GqkshkNVbiQ63mSyS3J2LpVUmzOb2iTEs4IEx+Ox2+2TJ09m4z///HMja8/IWTsZZOF4XqSlpfE/og2DFxNY2VBl4UDLR67Ad4IFLoaUN4adOnWKL80ZFBT06quvqvph5gpBQUE4RhM3Dhj4ssj7X9q2bYuybMqUKSyMY8CAAePHj8dtVqxY0bVprfr16/fr108lVvIyvIeIj9Llw1a+++47HJ86FQ3gemtHcVamTBkweILdwhZRfPWXRG3PvgMKF/GfPHXqwoUL2aTFLguOv10nWN/T7JCvREesXfb9hYsXhw8fjt+LQ9T3HTjIt5BftRo11q9fj6rI7JDuXbsIANt37MQLLikKcHXTcSWOjje5slQyL7qN27+sk+0AEFy5ygcffIDzwXUbFzDofAv5ffjhh3gnpJptGfpJBO5WOXP2rCu/BoS1a9fiTqo1CgUAEMSAgABetLESZEwrxMbGsskqVaqAwRvAVcsEv5cvv/wS82jwGgLAotXbQdSUKVdu8uTJfPM2c3aGHABIy86lEh1vMtvlbGM7Pjt053aKLev802OXlGGbL2erfv6DkOB4PCrnOrZvVlnjycLxvFCV6+YLDOAkrqCqPE++/PnTU7hw4QULFpQqVSowMPCjjz7629ieOev12azQ+ErKU6pUKYxFaN68OaYkoOceAHbv3o0WjgIFCtSpU6dXr141a9Y0Go1sUpblMWPGsPGuXbvc0ZLGTfAhLHxiDu/z4sdBNesCqOttXE1VQO+FiSd4Wcx2KTJ8V+K9uzNnzcZusWaH5CEquGzjU8Jsd+76fSXIEojijh07sLO82Sa3btO2So2aw4eP4HvYPky1rFr8OQDMnTsPS5ubbZIoQJGCPiAIHTt25Futmuyyp05UrbknT0Y50pIA4FLMZT5cWtF5FPDQaXV6vv9OYFAVjWRjUql06dJ/Gy0Cy2RUChE7dOiA2x85ehhkCQQxLi4OS2gkJSXdZsZFUQTuNmvduvVL/d8CgFZt2u7atQv/Z202GwoOrEoHAEXLVACA0Fat33vvPf6MLNll/7LvQgIBsmCyS6piaIyIONNd078PonqE9yTV5ryaaHW6q7tcPoMEx+NR1bnD5U1l4bhz586zOyaCg08xAE4g8ss833/BFTkhagAAAxpyBZvNNnbs2Nu3b8fGxk6fPh17QPAule7du6uqm7vJ18tnHuJiKQgCGn6KFCmC8ktVSwPtQF9//fXEiRNXr149YcKERYsWqX7L4P8RDh06NGbMmDlz5jybaqr/FKwiCpnLvxYqVAhEDRQKhMyuloZtXmJaoVOnThiSGXvrBhg80a6AJTRux929cvovtv3MmTPZFTDb5eJFCrfu9AoIYo8ePT766CO2sdkuge3vLBW8hiaH5K3XCIKgZF4vLU4FrGkAAKKI3WLNDlnjtD+4FweC+Oeff2JpMgAw250+Bo1qJT57/hxYzWwnvEw8d/7CnZgzDxKT+vbti5rgxu07UrqJHeGtW7fwRatqg+bFChcCRWkeGoodAwDAKegxGQdvJ19f34wiaQJw1iAAKF4uGACCKgXzlUP79u0LBldwKB/htGfPbgD4ZfmKvn378veqJbvYWGAxHNm5TnLKXjHbJDfVR88prJXnvxPtQYLj8aj6XGDVyAoVKvDzufuuTDw5vIk1JCQEC1/yQft86t2DBw8gqBG89D6wTlS5h6rwF3pSfH19r127NmnuV7/++iu+/LmbKVOmVKxaHQCaNGmCmcApKSm4SGzYsAFPn880GTBgANbw4FMQUbTp9XreVICRp9HR0d26dVuxYsX8+fNHjBjhjpN6Svigb96N9fDhQyhZDV5+HzJ76P6KiGCL6Pbt27F+iX/xEuAqtyUCF9qS7pCB856wddHskHyMulEffAyCsHjxYmYWjY43pTuVps2aouWDmZ2csmJzyl46jSgAb4SXFEUStWB1BY2iw8LskBzmZFQtfGURk13y0WtVlvyCxUqCzQSgAAht2rRhk7IsJ6WZMfIUY4eTzel8pRC8LMlmq8P0EGTp4KFDvEeydJWaYHUFgvTv359NiqLYuMOrAAoI4uTJkzEt9uLFiz999w0A/PDTUr5Pcrt27fxLVTB4eo4dO5Z3833/9RcAAIK4a9euX3/9FefNOafFZqsfTI5sln6HpNik7F0tT09OTp//JiQ4nogJEyawQUBAQGhoKBtXrlwZbZ5du3Z1R71q4kngX1VPnDiBnpRq1apFRkaOnz5vy5YtY8eOxW327dsHnr4seF4V/5GWkqyKcngEWd9LVKEYfBTF7du3L5yOOnHiBF9qTJKk5cuXfzVjytq1a5XcfiQtX7788vmzAHDkyBF8nVWZ5TCahG+IxTfn5OexJoTT6cTeocClHPMe/b179+bBCqS8MQzjCYC9Leg9QauHzI2FL12+jEVCUZx5evmIRu9CXh4gCPPmzcMoS32BQmi0aNu2LXsDsdglT614aPc2APjfsp/ROGSxy9WqVmWRp23atGFPD7ND1gjgoRXtVmsKF+hjscuiIHR+5VUAaNe+w8cff8zmzXbZSyegJsD7Ld0hSwr4GDQq74Gnnz/Y01lqDFpqz9yz8PU2MObaL6BMQU8DO8JWrVrhi9bJkyeTYq+zZBwUFgBQpnKtsqUCfAv7Hz16lK/ZpS/g56XXjhk7jveGHDp0CIt57Nu3D/8pZi78PiE5xWZzLFiwAJu6uXJlweWXwZs2Ot6UU+Ev0z+xcJiewAjxJCSlZxORasrB6fPf5D8tOJ7ckIWx5XFxcVg7KCoqCis9bNy4kbfWEs8SflGEzJ6UXRHnrlw4GxUVxcuIvn37gt4TNFoAQCe0LMujR4/u1rxOqVKlfvjhh393JH5+fpiX+9lnn2G1sdjY2NDQ0A0rl3355ZcjRozAZ/qCBQvGjh27ec3Kt99+m49bzBX41uGYxRMYGMhXfMLXXL5Aws6dO1GH8X1SMOtHFTSDpj6VelMlA+cFWNIHA8u9g0twuEqY87FZJcoH40qMy7nVKcsKFC7oFdKwMdaGBwCzzZlh4RCtVisLQbA45Yfxt+ZPmwgA70+ciIuoxSktmfMRM4fs2bNn9erVAGCySV4G7Zvd2p04uHfax5/wlgxPndisdXsBoGGjxpg6ZLZLfj4+bCcBAQEYnWN2SAataNAKKgvHlWs3sNrYjh07/v4F5yFCSWqVhFo1azZt1aZN23a//PILGr1SrQ5XHk3mbrFmhxOsJofDcebMmUxt5GySj0Gbbs10M1SoUIFPl8W3tYjD4aB3lStFIavRaELadAZwBdPwHeMsDtmoE1XWDEWBbLNUJEVJd8jZFu2A3NAEMw/c2nNVXTI/J5fKfZPj2O00yIF8VFrjH/GfFhxPiOqVNzo6mg1U7Rge0Z2ByC3u378/d+7cTz75hI+IbNeuHY7r1KmD2XTh4eEThw3YuOqXqVOnoo0KWGaKwbW64GN37969aKqdNGkSL2K2bt26aM7HK1eufJLabg0bNpwwYcLIkSObNm2Kk0eOHMFxeHg4alO++BJvD88VfH19ccx7QHgdgKepKgiGOgPdJZA5RXbz5s2lywUBQJcuXXAB4HMTVAeQR+C/QfxHBuYt0hlZvSkMVgCAoqUrgM3CMi9wOU+zOUTZceVs9ImIiBYtWqClJNVszeilIhw6dIjVTzPbpYdxN4EtfoLIFlFZUaxOOaOXigaYT4dV67KZb127wjTBiRMn2CuN2SZrZfukt99QZGnGzJmYNGd2yGkPYtnGcXFxP/30E5s32SVvnUYEQbXMGQsWwQYufL86weAFVhNbDuLi4tikxSlpFWdKUlJi0kNsRAcATo2eby/HXc8zN85Gms3moUOHYpRJdLwpPiHx7tUL3y1ZMmTIEIyvatWqVdWQZgAQ0rDxhg0bMKa7RMnS2F6Ov3+adOgCAMVLlFi6dCl/T5odTm+9RuU9YQaerLGhZpukKNn0ss+20d0jsNjlfVcfZp1PtUn2bMJX5Wx3HhmftvFCbvZVyBeQ4MgGS2ZTsCqvAZc3voMiZOkUSuQ6DodjxIgRn3/++aJFixo3bowPQX7liIqKQks+Fk4AAL7LVEREBEbCY4EKV+ifjz8z3qLLYM2aNYMHD97468/vvfceXxjb9aczv4U4nc7hw4d/9tlnixYtatKkCfomVPcGljzn3XC5HgM0derUZm07AkC3bt0wozU+Ph7TO4FL9XStxL1mg4c3cAkCAwcOLFS4CAAUKVKkV69e+MGff/751vUrALBp0yas7c0Hw6p6ieURVDYYfBHnLRxY2hwAbsfdZYXD+eX8xu1YOd2Er/jsFoqONzlFLd93jekzs10u6GUERQYAEERmJbI4ZACh08uv4LLdpUsXADA7JNmSBgAYCPLHH3+45tNN7IhBELE9vdkuJcffxiPBQF2zXfIyiELmQBAACKhQuVLFCqDIzZq3wPBVRQFF54HhF3gfplpsezf+djbyxKno6Hr16qE2lTSGbNvLJaaasb0cxrvIshR77z6b37hxI7bdkWU5Nd0OACf++ou3DXfq3gd0BrYTPup529Y/AODuvYSwsDBVaXMfvTo21iUgsrpOHNkLi2wtHI8wMGyJSfrfqftZ5012KZt8mRxcKjmVJnuxyXNPhOfLlStXXnvttVcbV3/99dd5L/Wnn37KBiVLlsTixKpuW6qiT0Suc/XqVb600dGjR9mAz0CBrOlC5esDt8YDM+dmmG3xrd312tRiMFRpCVyqAm95RhtvTly7do0/n6ZIkgAAIABJREFUQjRsNGjQACPjFi1ahGHIU6ZMYVaBLl268DaYXCEgIODjBd/evHlzyZIl+LKoyuVGJfTw4UMAAQKqMsGBibs//vjjw8QHAPDgwQNccRVFwephwJWHx2UGAI4cOZIHe6zwNpgOHTpgAlrFihVB76FqPAYASSlpzMIB3HKu9/bFNvTAGYHSJQDH391imWvP4pRDGoa0aNcRADp07MgyWi122agT3pn4UYPGzTx9Cvzyyy9sJ2abVMjbCACoIerXrw8AJofkqRMAXEIE33nMdjm4YhCA60h69uzJ5k122VuvEQW1hSNdUlq3aWc0ei78ehG+L6U7ZRBEDFXBncQnJPKuExYpFR1v8vEPQAvHm2+++bes5PwymHhit9kyUogF/hru3Lnzzt37AACCOGbMGLzf1q/5DUBgG6OuUhQl5vJVAJf/Be83h6Q4JMU7SzJOml3Sa4SsMRymnAI+/qFLJc0uObNTC2ZHdoLD1UVPvbHJnm2QyQsOCY5MzJ07l9kwDxw4gN5WAJg6dSob3LlzB/tn8tlcWX8kch2+TCRwZn/VIoqBgVqtFjy8oetHoNHdvXsXX8X69OmDVQtRB7hqIHp4g1bfunVrLOHFFwN4bJiO6ghRWDx48ACTU9asWYP7DAgIaND2lZ6Dh2QNOlYU5SkX7KtXr054q1+ZMmV69OiBVen0ev3IkSPZuHr16s2aNWPjatWqgd4DBAFEXbFixfAtn3ct4aqgShRHMacy0vDtavMIfAU2XkqGh4eDzpWNiY3HgJV/yFiJMUvTqdEHFvNn3pBJkyaxmnKSojhkQJcKZCyuFrvkISiW1FQASDWZ2PW0OGUvrbjgk0kRh/dbzOaBAweysvEmu1yyRLGyZcuytI7y5cu7LB92uXRAiQZNQwGUoIqVsGaX2eGsV6fu4HfGlCxZOjw8HANKzA6JpbqoV2KrY++m39LNprr16mFYj8UhGbTisPfe9/UrcuTIkbp167q2ZsXNMtQMfrmyzth30GAPD2OHjp3QTBIdb/IpUsJDUEAQ/f39sSKIh9FT4+mDGT0o+JKSkjBoFLg40KQ0V9YucP4vQRBA7wkOKyu+joE4FoesFQWjVqOKAzXZpYIGtZ8FcginYGVJIbtEEruk7MoSk8H2k1UryIqS7pCzBo3maOHIodtLVLwpNi3PKfXcggRHJnijBcbcqdIHsEi2ijwYk/+CoQoOxTAOleDAdbpMmTKu4HaNFrgW5KNHjw6q3cC3iP/evXsxT8/X1/fixYvFylZ6pUvXn3/+GTfmV01Vq9Ws8CVEIcMxD5wxBgDCw8PxyP/3v/99PGb4mmU/vPHGG3zn2NWrV7fr9FJg695hYWGy/HjLa7bm39mzZ0edOAIA+/fvR/XscDiwnMbZs2e3bdvGxhcvXgS9EQBAFO/du4fOoICAACgWBD1nP+KvY5Ru9+7d8b05b6bF8vYn4NSkw+FAl8qtW7dwgwL+Aa3btClarHh4eDjWnjfbJZ3iZMLi3r177PlgcciCIIDdyoJGg4KCgoODWcXPqIO7/zp8AACOHjvOvHJmu9NTp9m7bROaQ1gJDbNDKuCh27dvX0iD+i+//MquXbuYP9fikJyWlIjDB0CWr1y9xrlUZE+9WLlqzcL+/lhqhR2ht16T1aVyKzY+JvI4M5Ogg8xslwyC/P38GckPH3766ad4D8safas2bUBRAMSJEydi9m9SmuXXr+dZLaYdO3dh/TcASEhJsSbfB0FMSEjAm1lSFEnUNW/etFHjJlFRUagVSlRvhIKjc+fO6Iyr37I9mwQAPgqqSftXtU4riGKHDh1QzZgdTk+9RiOo63Ck2aQCRq2iqE8/J2HBaqFmXf3vpNq+OxGnns1BcJgdsqJks5Nsu+ixg5HUcwAAmy4mRtzJMZg0v0OCIxO81xlD8QVB4NMdUXfz+Y0AwMdVEe5A1egVL7jqhRu/xFq1amU02dLyIXLnz5+/HRuX/PDh+PHj+S9x+udf30t8uHnrtpUrV+Kka+c9Z4GHDx+tli18RiVwNShV6bL4eN28eTNOoqHYbrePHDkSStWAcvU3b978r1NmLBYLBFaF5oOBi2hWpcWisHD1sABggZNobomPjwdvP/AsAFmuM9TrCrVfAi5dNjIyEkt3f/vttzifd+CzVICLP9Dr9RiryNeWSLGkXzp57H7Cg1mzZqF8TLFYb5w/xZbtn376iZkKLHbZICoArKiocOXKFfZmYrbL1pREAIVVv2BPD4tD9tRrAP6O1WD2A7Nd8tJp7Ha7w2ZLt9pQyZkdUnL8bQAWwyFwhb8kS9L9H7+YHX36dP/+/fEIzXbZ2yBmDRpNdygZziANZPxDWRyyPe0hkz7bt2//9ttv2cYmm/PG+dPsCOfOnYu3UIrFih6l9evXo753gh6sLmsQbmy2SaIgCA6r1WbjA2gKFfH3LFwMAHgPEQAE1QrRCYrBYFyzZg2a4gDgxp07TnMKS67BUGuLQ/bUiqKoth+Y7FIBvRay2BWYsMguaDTH3NrsqpIyb0g2tU0hOzWT03zOibuyDNnMvxgtYEhwZILPlONbBPEp+xjFXbNmzSZNmrBxSEgI87YS7kNlscciKK6o3uKVmLzANXX37t2Yvr9v3z58nR02bJhNFkHQREZGokslMjLy1x+/AYMXiJoPPvgA1YxerwcQILAaeHjxrVMZjswPJJWFA3+sW7cuvq717NkTowT4/jv4Yu3yc2ck7vKhr/+Itm3bgl9JKFYBuFYphQoV6ty5M26DWcGtW7du3bUPAIAoLlu2DO06AQEBYPBiKoSPnn7zzTehUCAU8IeMgEfIXMADALBZbt6B152BgYEGg4GNy5Urh4KDvz7JJkvs5XMgCH/++ec333zDJpNSzRm5HiJknHW6U9YLMgCg0YLdQhaHHNIwBOdDQkIAwGKXvfTiH3/8wSb79u3L7A1mu+yhVYYMGRIVGbln374aNWqwW8hilwt6GgBAlWBitssRe7dduXgWBGHHjh1ouzLZJS+dRhTVL9Y67wIZabFCv379WG6I2S5bkx9grAZ+iclplutnI/F0MFLKIepczdsEETgToKI3gjWNCQ6838wOWXRaw3dsORV9ukWLFhjoFnX8iMUhg6KAKI4ZMwb/Z812yceo1+j1/DcFAHH3HmAgyJYtW9gku4xiluXcZJcKemgAQM5crdVkl0RByLEOR5YFPdWWfQc4s9355MU8cppPy6G9nMkmZWvT7L3m4sP0x2fJ5XFIcGRi2LBhzA1fuHBhvk4lD3pY+Be+EydOPEnOJPE0eHh4oD6oUaMGVmBzBW2EhkHp2sDZyUVRzBAcGuBiO+7eT8CgUXwCup6zBi82j4ulKIqgN7LgBtX78YUEy5jtmZwsqgbCaNg4ffo0psOsWbPm7t27bMzvEMeu1+6MxN1/nVwaFhb2Up+wUmXKbdiwgU8w4e9bfD0VBKFTr4EAsG37Dj6ysnbt2pjRw3cL6t69Oxg8QdS2bNkSy42g/gaAVq1aqbrL5gX4UrCxsbHoBh01apSXX1EQNdWrV+eL0jpAA3ZXuW781gQPr+JF/HAlbt26NQBY7JKPUQ/g0gS+vr6sbofZLjVq2GjMR7MEgCFDh7JW7Gan7KkVBUHAoh3sSzc7JFtyYnh4OEZOsOQps0OqVq1qsRKBzDjBJBHz11w+dQJkl8UCj9DskLx0opA5UyM63uQQdCPHTtRptV26dp09ezZuXLigN4aeMHtDdLzJLmhdlgxRhIybWVIUJ4hNGjdmp48F5SRFcYA4+v0P9AbP9evXs4Z/AJBisTrNKSrPEQAkJtwHgzfLAAIumDQpJc2adM9isfTs2RP/N6PjTWDwwo0DAgIyjlz21ImiKGQJGnUWNGRn4XBIPnrxyTWByZ69hcPsyMbPac7BTMLP865PU3Z+mZzmnbKSbHW+AHktJDgysWLFCrbSJCYm8oWY2DOFgVaQ8+fP8/UVsKUCkVuoQhPsdjuGXp45cwZ9EK7CU3pP0OiAW+ZdWgFcnSqxnoqo0WC/BlzOGzVqBBotaPUgaIKCgrDRq06ny/DLiLx3HwAepjtZZWXEYDC88847bMxLIlwJGPgSyb9MY4d0lzsjY5nHZ3fWC/L3lZGUQesvZX1Q2UAjCwLvijKbzVu3bsUf+cDJjb+vA4DOL7+C/ZCBldc0eLEj4f8jPvvsM3aE+/fv//HHH9mkn58fnlG7du2wuELegS8lApzPKyoqymxzgiCePXsW+6hFxqWB1oD9QbD6u8Uut27TpmHz0AYhDSMjI1nMrMUpa5w2ACY4xOTkZNa63eKQYqKOfzF9kiI5f/jpJxbDYbFLnnrtK6+8go4JFopusktFfH0AANvTu3JrbfLtC2fuxbOSGwImNJntcvsOHVEr4BGabLK3IZsslTSbc9EnExx266Y/No8bNy7jdCTF+neaKzosrLLQpGlT1hW2WbNmrHmb2SYJAEd2bmFHjtnmzHXSoH4DrV7H95yzKRq+aAde/+oNmoCoYcGkrVu3RuNlePgBS9J9Vn501qxZf39x5YOZhaNr165vvPGG68gdkqdOqxEEXhawlrDMwpHFpSIX8NDmJDiya8jiVBkh2D9gtkIkp2IeGW17s9k+B/NJNvMZqiXr5vkMEhyZ4Itk8y9D77//Phs0b9586NChbKxKSeATLwl3oHJYoJ+rbNmyro7hoqZChQoYSOFyB4Ar6B3t5yXLVMBcf3wCJiYm4sbogHdt4JrX8F4zSZL+OnkyJSWFrxPlcDjQ8H7mzBn0hjBDOqNixYoY3/fqq69WqVUXSlSqU6cOqhOj0fjdd9+xPJqhQ4diMVDG3P3XIu5kug4AkGZ3xqbabZmfaitWrNi9c2ds3N3XX38dU6tcS2zDHlCuHnA37cGDBw8fOshOf8iQIRg9UKxYMXZhIXPPmn379qE1CK/Vt99+i1GokyZNwpCCvMOrr77ar18/Nl6xYgWG+2zcuNGVpAPCl19+ySbTHTKAAvZ0tliiCdPskAp4elSpXqtmrVq4Ulocsl5QAP52qSQmJsqKku6U/9q3HQBY4smCBQsAwOKQvHQiAKBjIi4uLjreZLbJxYsUmj17NtvJxIkTWd10s0OypyVlbC9Ahp3VbJeat2gRWKo0CEKTJk1YvgxkxIKoXv1lRbFJMlYaXbt2LbNvWZxyUtx1PGz8Ni0255E/N2EdM/ZsNDtkneJ0haqI4vr161nuksUpe+rEmDOn7A4n33nYbJfKlS7FDrt3797YK1Ff0E+jSOC0gyC+8soraHVLNqVjSgvfi86mCMX8fEHQiKKINmZ2GcUssbFpNqkAs3BkcbUU8NBmW4fDqBOz8ZI4sg+zMDuy1QQ59a2VBUFtg2EHmXVSURSTI5uuLq4KIvm/IYv28Zs8Q7RarTuKBel0OlEU8W0GMRgcqkl+SXvw4AH+FqtAHjx48Ny5cyy1ISgoqHfv3kzjt2rVCv/b/7NotdqsF/lpUH1BpUqV6tSp0/bt7PENL730EvvtyZMnb968CXojiOLVq1fPnz/PAib69++/4HDcNQAQxN9++w1LaxQqHoi5/oGBgWwnu3fv5vP09u/fP2zYMGDJt670DY0gCHg8o0eP/iPyHtTr2qxZz8OHDzO7lypT6eLFi+ylkzeNXL58OTU1lS1UCxYsuHAnAXrOjvq2/+zZs5ctW8a2qdbq1Vppl4p4ahcMqqO6IBfum+LNUqPM19kBaQCg1RuMHn//R+/cuRMMFZlW2LVrFwrl5s2bHywcDA4bXD9Zr149dkb379/PsAa5mpey3J/3339/3dRf7wliy5Ytp02bhqffre/gDVojMx11796dzWNZJ0ZkZCTffysvoCgKxsN6enpmiuo1sdMXOnbowOYlwayRnZIssVtl06ZNzAqV7oQHt66sW78BvAv7RBhmzJgBAA5Z1Eho4RAA4OrVq8XrtQWA4v6FAQBkGUDo1KmT0Wi0SoKPUT9w4MBfdhxhoq1bt246g8HilH09PbyLFAHlGggCHqHFqTSrVfOoa+dix44dPT09DQan2SFvXfm/2JvXoYp45MiRRYsWzZw5Myo21SLJvl4eWk26hvuXlAQrCALY01HlFChQQBAEqyRUrljpYoTLCMG+Ta3ebpUUsLM6HBoASEhIMBqNdkVj1IANgGWvAEChQoUEQbArWo3TOn3cCAhb0q3b64sWLRo8eDAA2BTR26BhasbDw8NoNLJg/PPnj0mWVFAkFsPRs2dPloldKrj65cPH2ZGMHz+eHbzB4Eix2KXrl6B4xfXr19euXZu1RrLJoo9RZ5cUUaPD0zQYHBanUsjbQxQEvcHDaHQ5UqNiUy1OKOShU0DgnyoGg8Nslwt66ESNVjVvcQoyZwZjk7KspDtkENQ7sSoCAOBOomJTDQaDrCgWh+ytE7V6vdFoNBhc3l6rQ3bKipKxE5w3/5+99w6sosrix8/cmTfv5ZUEkhBICL33KoIKCqKIUuyKgoDi2sCGuGLDumvDVbGgoKwVxYIoSJEiRZAiAtJDDyQQkpAyM3nT7vz+ODMn817i97ff77os6+75Sy+TeTPvzdx77jmfYliO4whMpN0Rhl5hAYAUCCSN+0MQBMZYErL7952Q//k4sxIO27b/Ff4LgiCIopikUH7kyJFPP/t6d8tGQ4cOJUKKW3yO1AX1lCRJ9Cd+icnZs2cj372kpIQqiitWrNiwYUPnzp3psK2FSpfsah+K/4ZgjP3jzmf/SBiGkXTC119//bZ7HrDVsnvuuadBgwb4r+5L5fUgZFnG8cOHDx84chiye4PAPvvsM/KIv3zMnSvXKsBY9+7dJ06ciAdXVVV5PtoiAKiqiuMFBQVeIiJu3LgRBx3Heffdd+Hs6/ATP//8c7/vNoWmaXi8X9oB/xfLY5s3bwY5gp0gv+2cqiiH8vOPGeqKFWV9+vTxfyGKbuumlfS1lKs6AChaVRCqzclKS0shK0L4A/wTwzBWr14N116KONCvv/4aLbXOOeccmLUMALDEnZWVhccLgoB8UUmSOOf0uYOvvPbbZcUWY/379+/YsSOOt2vXDvUkMNq2bfv7Pg//fCxcuJCQlVdeeeWJEyew73Pu5SNf+ny/AwCM3X///XjZ5VpctE3b2/pnZGTgeEXcXPnu61AnGwT2yiuv9O7de+DAgRXxeAxxnR76sqKiokyNp0isfeduAIAAyfT09Hg8Xqmb6SF2xY03rtx19LDALr744rZt2+4qMSp121LKbr31Vjj/ZhDYlClT+vbt69Rrruj2KeWIexKBLVq0KB6PV+l63LJ/+n4+ZOSiQMWhQ4fi8bhhGJVxOyhwh3PDNOknKFPjKSI7Z/CwZQ4HQZg9ezZOtpVVRs+ePfrV//P7peFP587t27dvPB4vU+OCIHTr3nOz5iYiZ511VjweL9PiGWmRnkOuWOpwEIQPP/wQT1KmxU0fVuObb77BVmB5lf7rupWY4vz973/v2rUr+r0dKTgOugmOg1d++PBhbGLWadD43HOlH0vZ9OnTr7zySrx4XTdsFiAMx8GDB90fosoIiWBYCbdpGEZ53ExhDhNAq4pHmE3jlbqZmypX6rb/sTQMo9Kw60cCumEmjVdUGY4DWlUV81ZxPNhxwLJ50sHlmgEAunclWD1STdsBB61k8KfB40s1EwBs7yQ0XqIYAGBadpIGzylFBwDdMAzjN9uUjDFJkpL+8F/0AiY5qP/jcWa1VJzTFQcPHuzevfsbzz916623XnvttTTep08fCITg1veAia1bt6ZxPxouNTUVB5Ma84cPHz5t139mxu/4Cx49enTUqFEXdmp68803l5SU0PikSZOWzp+7YsWKyy+/PD8/Hwc1TQMpCEzEXIGOX7RoEYFG586dW1lZieO/7twJACCwzZs3r1mzBgevv/56L7FwVZ5wvG3btkR1adOmDd0pQDWuMzMzE8eTVPDr1q2L42737YJbIZYJAGlpaTheUFBADYsjR47Qbd5y+UXlSry4pHTo0KErVqzwfzOKYZmWnfR1qYYFABZPGDQFCYIuNjYcDuOgm1sHU0BMuPLs7OyLrroRAM4577xp06bRT/nGG2+cKC0DgS1duvTpp5+mk7837SVLlEEQV6xYMX36dBwkzgtGVlZW7b/u6YotBZVJIy4tKLsNrnNlZWU4vj9vr8MkfCq+++47HFRNu24sjIvlgAED7rnnHjxnZZVOjiT04muGw0yc3N2t/9q1axXDigTYT6uWAwCCJD7++GPHcTTDDgfY888/f/jQQRDYkiVL3nvvPcdxVMPS3daJu3LjyVXTqjxZCFDdgjFNU9VtAOHG0bfQwZdddhleuaLbEZkxQbB59SupmnZEFic/90pObqNFi5cMHDjQHbe4pVVuWL1c1/UPPvigoqLCcRxVt8MS+/OzL7Zu3z4rq/7SpUszMjIcx6nU7agsTX32iaZNmtx513g6iWJYMliUbEWjUXc8bnmUFsH/kNfNbQq6iuWT9PT0Jk2a4HhlPF6QtwNA+OKLL/Py8nBQM20QBFJgu/TSS2k8LIkCOJad8Ogrhh0JCEwAiyeO63ZqUEx+eRxHNezUUC3jFboNAMkn1y0A8B+Mj5lS68FxO0VikgiJw06lq/whJH2iErcBwK5xKe6H2v/oBLvzpJpXov2DB/8/xD+0nNcWZ1bCcdqC2rQAsGrVKiKIP/roo6179AEmghggD2ioqUAAAAAtW7b0uzD7d6L/i38ynnnmGWydfPPNNyRaVVlZ+dVXX9Ex1FvJyMggXCf4xDDS09P9rtaUDWz46SewDMxOSAkjOzt73INPAkCDnJxly5bRSQ4dOkQYjj179tDLNmfOHJAjILAhQ4bcdNNNOJjEVKJynStZ1qoPpGaBT0EkEolQYYY+sby8/Fj+YQim4GWTLgKGonPzN9QMk5j6mVkNiOpJ2miCIDzxt+kgR0AQr7jiClLPPHXq1PeLFwHA2nU/3XfffXSbW3bvp6zF31P/adUKCLhXSCYASVI0ZyCGwyVbXvMspNa76KKLiFWE9w4AWLTA/6wyeFok5d2vFtXNrPfZZ58RZivOhfbt2hCu02WpmHaKxACqKxz5+fmqYYdlKa1OOoCbQyAiRzXscEBcvny5P2sBANXkrZo169evH43jrKLq3IUBORwE4YorrpAkCakoo++655JhV8spKc899xzhjlXTjspiErhBNaywzE6eOF6lKmt+XEtEC1W3fv5h0Zb1a0Fgc+fOfe211/B2wjJ75alH9m7fVlRcPHDgQCSSqKYtcbNLly6HDhx48623SCpDNXij7AZpdeqCIGRmZt588804rlm8aW4upUSUkuYXHCcwaWlpKUnq5e07cHD7ZgBYunw5GUqoJg9JQq/e59ZJz3j99ddJrE817HCAMSHZFFfR7ZgssRrICcWwU4NSEvQS5UFT5drApJgWJHJrXTO2Wt5BDgDcSTwYUSY10CRewvFbbi+1j9u16XPUGkv3la06mIz0OhPivzThSLLbIJmavLw8V7RflPzuncRpBB/jQJblt95666Y77rn77rvXr1+fpHf5v/hngvSjAICq9G7GkNUc7VFIaDwWi/npr0SOGD58eL3GLQAABPb2229T40zRLdot+YW/CotLAOD4iSK/iWvdunW9VgsDX+rZv3//C4ZdE0mtM2vWLGqUEvMWg5Yot9cWdKU1unbtiuNjxozxKhwCCVm6WBNPfN2PTdYtbvFacGy1UvKuGnWzGE4FJmZmZhKwHwAy6zeQIu44fSerVq2CgAtiWLJkCX23fS4YiHkVABAKBAAuHTEWBAHHCUxKuFcAuPDCC/002jMkUlNTgYkgySAG/D9WSloGIMdEYKQAoZlc4uYbzz1xqrT0tttuI4CXZvJIQAJwMLFAIQrNshs1zAaoxnCkpaVpFo/IbPI9t+fk5GBxAg3JNIuHZXbjjTdSxWLQoEFxi1vcSYsEr7nmGlyh77nnHqwraBbPSk8DcLMZfBFUnUdktuCL2YvmfmYYxkMPPYScI4s7cYtHAslKo5rJA4494qI+p4qLnnn2WSTo4sXkbd1IV4JiMyg/um7lMsoVfvjhBwBQTVspRlSpAwL78ssvcbemmfapgiPlp4oBhOLiEr8Wav8LBwy8dFjzFi0XLFhQrZsuR/xiHgSsqTRsiCsAgNon7hUalmDqG9asKCsrHz9+PPEBNZOHZUw4Epb5SsOOBmuRIXFBo0mDJg9JTJZqw3UaFtR4rdTfYKMohh2SkpGnqskjsiTWIO4qhh2Wmc15kg39b9rO/QaP5rdCNe1a1cP+7fFfmnAkKUJSvPHGG9TIRywYBsk/QCJWPysr66Y77n3sscfIeuN/8buE38GEygbuYLMe0Po88JFL8/Pz/YBHou8HAoHs5m0B4PEpT5A4FQAIoQjorjcm5QonT55c8O03eJJJkyYRR2n06NG5rdvjOPnXY6hm8sTAGHvkkUegbg6kNWjRogX5tkej0VnfLIdAuEfPXj/++GMsFsPxvXv3Ug2G9M4DgUC9ho1BDODtEM8WvMmupuagZtWiZrjkm69sMQgCKy4u/uijj2h8/A1XWEwGgc2YMePzzz/HwTp16rhVIiHBMfXsvv3lWF0QWPv27YnfAQDX3z4RAJo1b/Hpp5/StrVZs2bT58y/atQtkydPTirMnCHx/fffU2763XffUbrZsnN3iLsuHlT2qLJ4Uf6BzetWIx0DCSYAoBj2xhXfIV8UAJCYoxk8NSUEUN31GDJkiGrYYYnNnTu3oKAAd/Nz587dWqhoBg9LrH79+lQOicViqmkHRWZUaRMmTMCs5dVXX92yZYtmcQBn0wokMDsAbPbs2YZhoGHKTyuXE6P1s88+AwDV4KIAIYkxSBC50gxuVJQAuLWWTz/9FAkmqsH7nn8+5Uk4v2kWjwRF/+1gTqDoPBaUvHEBvG2AatqmWg5YNWGMiluayVNTQmf3Pb99hw5+rlZag9ymjXLxJI99QaY/AAAgAElEQVQ//jixpUKp6aBjwsHGjRuHg6rBq04V0SeS+Idm8JosFd3mpu1EZVFMvH3Mw1Jl0UrU0FB0KyqLNYsQgNlPbWIeUGuFw7BjQbGgQk8ctCJBsdZaS1pQsiG5cO4pgiQNexWRGh9aWGm8s6kWq43f4tz+2+O/NOFo0qQJAECLXhCpCz4bMLenDgCJEgJ+jUu/ydb333//0O031atXj6QITk/8H6yT/xjh14wiKK67HQ+kIMqSJDSqpTIEBj4EVlFR0bZftwHAU08/M2HCBDphSp1M0FVcLejXzM/PBzmM+zbwlU8ikUjHs88HgIceftivwYyySzXp9c8++yx0vQw6Xbx//36SRDRNc9pLz4EAP2/ZMnnyZEJyrVmzhigwRK8tLS09WVoO4KJJiNEKv63BrNVG0y88egSZO+CzobFtGwJBYCKOE5q1X79+6TlNAAAYmzJlCi26X89+3xAkYGznzp1+XYRjJ4oA4ODhw/Pnz/cD01q27XDHpEfvv//+JC86OO0P7SuvvDKwc7P+/fv7PWzT0tLoCwcf9u3nTT+DoaH6OOlwaKbtoPKVB8kEAIs7hu2ArpG2BD6HVSaPpch9+/alksAll1yimTwii24qyW1gDJnSCKd4+eWXaTmfM2eOotuRoFhSdBygGsNx4MABVecpEnOTVC9BkSRJMe2IzDjndDCu/YphR4KSIABjgn9tVS2eIgr+k8iyjI/xFVddM+7uSUwKzJo1C5mrmsFjQfmZZ57BZX7kyJHYyNBMu3XLFsOHD8cPnTZtGhaKNIPnNsgChwMACAIRyFXDVkuPz5n1zvwF3z3yyCMkCaOa/OxevTp06T5j5rv0bm4tVFhK7JrrRwDAzbeMe/LJJ90fwuIRWaTLphdftexwQEzKqxTDDopMFgUmJLwRimFLTIjIDDMG3ziPyrWohwEVDpOKFgaXxWSBE7zT1JCYJAimGDyKKREk1WCs1KBYEwzhiXnU3jOtWVY5VKZvPFbLa6Ua9pkp2vFfmnC4CjlnXQ0N23fu3Jkmxz59+tBeubr6h/JH9ZpDzysAgLa5iqLccMMNm9auBoDJkydv3br1tN7DHzr8Xz41IFwR5WAy6CEzM9NtBzAGPtveGZ9+TUzXzz//nBpnXc8bkBqU8SQPPPAADrZr1w6CETDifltwADhw4MCihQsA4Lnnn3/kkUf8F1lSrlq2TVL3QDWYYARbJ9SZ3rFjx+ZfNgMACOKqVavWr1+P4y1atKB+DV12aWkpBKtvhyQx4DewGgCg1eZI2ePc8yEQwqWIkjZRFHtfPBQ/EVD+HM+gaXEbuY7CTz/9RA3+/COHIRBEFrFfJWz2h+/j7Xz00UekxgYA+Qf3/+2ph19++WW7dmuq0xQrV6589tlnAWD79u0jRowgn9vevXtTwtG5c2eqb0mRVDCqaEnDqDJ53WiEBl34hclFASZOnoIr7iWXXOIaupp2isRWr15NOUReXp5q8EiAuT0+h4PAUCVTM3g4IPbv359yhdTUVDw4p3GTvn370kl69+6tWTwcEFNxlfV2+YIgqAaPyGJ6NESpjydVbkcDIgAIkLCgqbrVtHGjNm3aoIzpxIkT8XjN4KkpwZZt2wkCI9FYzbQjsjhu3Lg/3TquTdt2Tz75JDYTFYPHgtLMmTPP79f3tWnTSJVONe0uXTpPnPJXAHjhxZeQ+rS1UFFNvvKbz/N2bgOBvfPOO7Nnz8bjFd0u2L/zyIG9r742jfJyAKjQ9M/ffgUA3ps1i/hEqmHnNMjC7+qCCy5AX8CthYpm2OEaHnWVuh2VRfyKeEIiwiMyEwWhRk6AeJeEvszWQkUzucUdoYYznGrYMVmqCadQDJ4akJJyBVcQpWaFQ7fTglLN7Yrbr6np9qLXDhz5LblS9TcMWf7t8V+RcMQtjj8khTsBBcPApG3bttF8NHHixCZtOwMAMPHhhx+m423bhvotoGl38GEPk2xj/5dw/I7hB82Qoqi7CnooS2q7NGjQoN+QqwAABDZq1CiiFEUQ2+E4Sd0TRw5HZAHBd4SQSElJ6Tfk6ogELVu1/vHHH2mLP3fuXMpa3nnnHVpH//7GyydKSm2bd+nShUyGA4HAkCFDSJqTBGpTU1PJi9X9XwAAmDp1aqxeDp4cuSEA0KxZM+wE4Srir80gMO03tZkTN0z9h10HAMGU8AsvvEDalAAwasJDAJDTMHfmzJkkHrNmzRrNtMHQgLHFixeTgVnnPhfg/h4EhoRGjG2/bsdB8Nn2btmyZezwgQuqcv/65iy/3OTpDz++FXyu9D/88AP9ENu2baNksdt5A2LhEC7ztKPQDN61a9cxd90nBuS33noLb181rLAs7tn1K679ixYtQm3iKpOnIB7GS1Dy8vLQztQFizgOCKygoMBxXAzHVVddRVnI2LFjEekpopCDl1hEIhFFtyKyePzYUYDqBoeiKLiY2bZNg4TrjMgMAJJ8QzSLq6Un9uzZg3DXqVOnkp76hqULHrptlM15nz59Vq5cCYjHlMU77rjjnbff3rM3r0WLFtglUQ07EpSmTZu28ocVb78zgwxWVJ1HZXHgZcMB4LrrRxCOSjWsvC3rKSWi5+pkWfnq+V9Wlp3avmPH2LFjqbelGhboKgCAwHzi67xBRvrtEx9u3KTJ1KlTCS2nmTwSYCJLgGoqBo8GRQBIarUohh0NiIwlFycUBHwIUKPVYqcEmFgjV1ANHguy2qTNrdSQaNfITiLBWsonis7TQmLNBKLSsCUm1AIaNWoHjVYaVq3OK4pZuyHLvz3++AmHbduTPlx6xetLKKsAmiK9pYvmnczMzMEjxgLAj2vX+RFwoiiShRWZFSUhBAk68L/452PLli3039RrYIxlZWWBnOL3usTock5/ALhk8KXPPPMM4TrrZtQDOYXwoaQpt/nnnwv378KG+lNPPUUn4YFQTr30Ll27kq45uMJf1f0aOslHb08jQxbsnWOMm/xsesOmDXIa+pfz5s2bX3TljXiSW265pVs3V84rJyenW98LAWDn7t3nnXceDoqieOtDTwFAWp30pUuXusLtAFANm08OzUXIJw5aHABSIrGxY8cSOBQA5n71JQAUHD8+btw4qsGc0EWQwyTjTSiTjr37MQEA4OFHHvW3VHqcfzH+JODLq1x+R25HiGbk5eUlZeSnM1CgkwJtTQBfZJ/aCmnBOYGUjh06yLK86efN1DjTLDsiSx26dGdM7NGjBz5XqsEjEpv/+Se0zM+bNw/cpSXQv39/skHp37+/ZvJwQHSFZR0OAmvbtm2VxR3HCQfYokWLUK+zoKDgyy+/VAw7EmD1nIolS5aQg8nSpUtVk0dkdvE5PQDcrKVhw4aI+YjIYps2bSg7wbKc4lU4ktxiNYPrFQmc27KyMu44cYtvXrWE8iSE+2gWZ6Y+d+5cus3vv/9+a6GiGPbJI/ueeuopcJwdO3cNHToUX0PVtANgv/r0wwBw9z33EF9JNeyze51NV0iPyqlKzbOcre5gcsexBJEwHNXoXcMuPnpg+otPHck/2qNHD0ofVZOHZSZAYiVDt72EI+H2cZzV8JdRdDsqi2JNGXi38lGjpWJaNZGn4CJSk3MI1eSRgCjWZKkYVmpQqgUcqtu1i6/rv1X5sJMAs+7n6v+rcPw7wnGcW2+9ddbMmavXrhs5ciRVp1G5C4vzffv2dSEdAABQUq4AwN59CaZcpmlSJZ+ykySNsn+veRvnfPny5QsWLDjTpJb+kajZ3SfMASRq1xQVFdEy70845n/zNQAsWrJk5MiR5DyZf/gQBKq9MGgSLFOqIK5im8BvoY67tOQNjRiFYDW2o5ogLTCQQ3gSWrcAYM6sd0or1eMnTo4bN87vLjvgihEAMGbszf4UB7y0QJQSktdApI4gCHXqNfDbFwP5MiTtlhRlx45dAPCXvz5HJlgAUKZWAUBZ2ak77riDALaO4yxdssi9fgACjXbuebaYEkNtpfvvv7/aH0vnqBJ9x513+lUL+wwaBuA0yM6ePn066VW7WKhgBCFQSaokpzOSbAeI915tr5MIbdFMHg4wkTHJt4uoMvmx/bsm3XK9aVm9evVC2prPWd59JKLR6NZCpcriYYnZtk102SNHjqCdqcuJczgIbPfu3aphhSQmCsL8+fOBu3jPPXv2qAaPBEX3S/ZOEo1GERzqgoocDgI7duxYZWWlZvCILFZWVpLdPGZ4uKvukh3NTpX9j4pq8qaNcr0rFwYMGJCeno6pagAsxK8AuA+zZvBU1Oj0cgX8cVXTrjxx1D+Ob5Bi8o3Lvls0dw4AzPvmG4JfqCa/bfw9PXufVyc944033sBd3NZCBUJR0BX6DtGaWzO5IAjdu3fHKyRWlGbap44doYMxJbK4o1s8LNVoqRh2TMZXNSEFVw07KosCS25MYEtFrM3jPibXjvdMDYpJBYS4xW0HYgGpRnZiRwKMMeFAaVUSG6VmOQQAVIOnyqxmDqGYtlCD/Qu/YTmLtKbafef+3fEHTzgOHTr07bffAreAiatWrcJqIQA0btx45O13QyClbkbmyJEjaf+3e/fuz2d/BABjb76ZStxARhuJPfXWrVs3a9YM2g+A7DYA4GcenubAvOq6664bM2aMP6/6zw3aYYOvqAC4c5VduS3atubn5x88nA8AIIirV68mPnPLzj3BAXT77NevHy2isawcKtuSrcnWQkUx7dSgmLS7KDx6BOQI6BpOdpRltunem3oNdIWO43wy8w0IRvFRITg95/z9d98BgL9/8OHIkSPpJL8cKz94+CgA3HvffaRmAQA7d+8SzaoThQX+2gn8Bv31xRdfPFlWDgBLli71ZzPzv54Llg4gfPHFFyQqIwiC31+GUiVd120mQlzFRbFapMG0wxIAwJat2/wfekrRJNs6fqJo06ZNdDtDhgwBSUYDPPDpc5z+IBg4Bt1mLBbzE5poXDOsI3t+NeJVD0x6kKo+msn3+/zZP/jgA/DWrf79+1NJAFt4SNFctWoV7dp37dqF6437Kd4KXWU6mLL07t2bDm7SpAlWJurWrdu5c2f60J49eyL9NekksizjyYn/Agh8dlGQtfQUNMNq3DB73LhxckDKqt/g1ltvBQDV4CkBdt3Y24lggvAL1eRpkZTHH38cb3P48OEXX3wxAKi63aFdewC3c3TBBRdkZWUhnKL02CEXNCIwTHzRSuaHb+ZsWruyrKz8rrvuIvKXztmkR5+sV7/B+Rdc8NNPP1GWkxJgf37mJSbARx9/TAAj1eAi1+ne8eAqkzNBwNTN/0Youh2TRQBIHkdtEhCcGp71LoYj8RGq1Gvn1qoGT5XFpJxAMeygxIISSz5Y5xEURKkBHEmrtcLhEndrGY8GWK0VkZrkGtXkjuOckR2VP3rC4e6xPDsA2qLt2rXro/dmgiCcKq+47bbbaE/8ySef0Hzkn7uzsrKoMU/IdkEQvv766wbnX9Vh4JXz5s3DJP33jX8Q2H/w4MFvvvkG/3vlypWUV/3nRosWLSCcBsMfATKDBQCAHTt2kJgVMSxSUlI8looAvl85PadJSILU1LTBl1722muvUXEirX7D3OwsAOjcpcstt9xCJ1cNHquRcHTt1QeCYYhXgiAMGjSIvAz25O0DbgMACIwoLe5HeK06upK9e/fuxsaQwFasWEEIle++/KykohIAFi5a/Oijj+KgoiizP/i7pZbH4/Hx48ej6ah3hbVgNTbv2E1NH6pkAEB+4QmIu+zfL774gsYHjxiDB19wwQUkWfbzz5sBAAwNBOG7776j/uCh/KMFedsBYOiw4SgJhfHjmtWWegoENnPmTAL3ua8JQNLtw2mnqCS5wtI7e/755zdo1goAQGDXXnstFUJ27d6dt3WjbRpLly2nH6LKsgOORTmBx//kYVlYsWIFrX8LFixAX7SwJBJ9AwAadeqlGjwsS679HufA2KBBg1TLdW4LhUKUtVA5pLS0dNu2bf5GhmbZkYDoEkedapaKavCIzOrWrUuDmKZjRQQAmJCwuGoWLz56aObMmYYeLzp5csSIEbZta6YdkViT5i37D7oMAEbddBOqp2iGFZbFCRMmvPXmm126dps5cybuyhTTbtWi2ezZszMzMzp37vrqq6/iyVXT7tC+PYCDt48sM9XkAMIHrz1H31U1aNSw27Rs0aJ1m2uuvY5ecNW0g+Bcd2FvbtsjR93kOzlv36YNgAOCkJOT46ZKph0OMEFIrmQohh1xQaPJGI6IXIvTG0JnavqrKYYVq8GtxeNjNXIFFyAiODVPHkHObWKDo1K3a8VwoDRZ7YlIsJaKSKVRC1aj1m3JGRJ/8IQjOzv73nvvBccGQbz88stJGHT79u1+bUpaMILBoL/FS9G5c2cSYvKzyUtKSuKWoJu23zL09EeSEGqS5vp/YgwePBiiGZDbERK/cACglgqBaTIzM5t16AZmHAQ2fPhwn3CTHXBs2zROFhf7uST5x44f3fMrAGz7dbsfl6AaVlQWk972ho2bghyGuAJMrF+/Po337D/YazYLBL8AgNsfeAyCYRBYkyZNRo0ahYMpKSl+IAj1Gg7s3U2yEIRzdAm6nnMEOboBseMSrzCtbgaxgv1rfE6LdmS86ZfQqFO/MQBk1c/u0aMHwVe5FAQjDraVpMq6fsMGqFIAABgj5UcAOHTkGNqFA2aBAADQtWvXxu264sHXXXddrf4ypyeS3kfCKu7fv/94USkAABPnzJlDrZaTp8rBqEItr6VLlyJfUTN5/wEDqX5w9913AzYspASBilAohI2JlIDw0ksvtW3TOqdh7iuvvHLWueejnamLenYcENjixYtV3QwHWJfs6MmTJ6mRUVFRoZh2RBbdp9qT1giFQpiIuLAzzzLtxIkTCBpVFIWIu7hxohU3SYJTNXj5iXy6EgA4efKkatgRWfry7akrFs4DgA8/+tj1s7V4RGIAkJYWk32GYarBBUMbMWJEcVHRtu3byWRK1fnQYcPGTnhAAGfM2LHI51INTK0cyqsoX1dNe/fPa/ds2/rW9OnLli3zTm7bVZX03ZISkmZYR/ftwHsvKChAj0AsKeFtJlQyDDtWDRoF3zjHCgevsfZH5VpyhUrdjgXFJG4t3mksKNbgyrrg0OR+jcmRpZKUQigGTwvW5ltr2qk1EKm6xU3bSQ1JNXOLytrYKN4sUePsZ0D8wRMOwL4m58BYSUkJoQEcx/EvAMSHliSJhJj8dvOXjLozu0W7cCzt6aefJjVfx3EGDBhQpsX3HTjw4IMPrl279vTdVWLwxCcxqXf+nyja0bNnT2qdUNcDANp16gpBN/Pzl82zm7VJj4VvuPHGmTNnEkJ+d96+ypMFamXFpp83X3rppaTPoRqWp2bIqOsBAKrJU4PJXdhlSxaCKCEQ5IMPPqBHqMp26CR+lMnmzZtAYMDEw4cPE4+0SZMmTdu7K3Hv3r1dCJFbPnEpMKhBCQDNmzcHOQxxFedovxuTYtgBljwfVZaXgRwGSweB+XFFmskpa6EaHgCsXr0SHF5UXDx16lS6/dzWHQJgo67U4MGDadPJpRDoircuVkejNh0pmyFCyhdffHHkWCEAgCD60SSnP5JScMJX7dmzx99SoZ1Gar1s10YVhGHDhuGfa4bdtHHji4deDgBjxt7cuHFjwD1rUBo0aBAt2/369asyeUhigiB88sknu3ftLCgsvPfee48ePqgadlhm7qd7CYpmQjjgugxSI4NzjglENBp9+OGHccq6+OKLL730UtW0wwHRpe57pYICM4RgUlEUqYKLgZUPAGCsOjdFnmebVq3pJN26datfvz7iLo8fP07dEKRcqQYPy2zbtm2vvPzynr15CJvQbW5xZ/u6lXSSTz75pLi4GEEDxw7snjXtJYfzv7//ARZc8UoefWEafld9+vTBgqLtOFUmf+nPd50qKdqxc+f111+PTB/N4rJg+28TQ7P4ro1rKWtBGWLN5GGJdcmO5qTK9MZuLVQQ7Am1JCKW2yL5DVqsnTzOY8HaREJNu+ba72UzyYBu9Tf6MpWGnRqSHAecGpWStFAgqRyimHZQZCGR1ZKg6HatZRL4X8Lxb4mCgoLPPvsMuA1MXL169YIFC3C8YcOGfqkoWrp++OEHKs4fP368OhEJBIJpGeHU1OHDhxPgw1XT81otfjXu0xw4FVKQSPZ/bsyaNQvkFKQF+fU9d+34FaSgX4MZ42RZefzUyTU/riUUJAAcOnLUj4RH7N7WQoWlRD0Mh0AtFRSW/vCVZ5YtW/HnP/+ZTsKlENgmmHoS1WXHzt2UcPh7WBs2bsJBAPj1119xUFXVQ/lHcfynn34iHul5Ay8BOSSAc9PoMc899xwOBoPBrKYtKVcgZduthQq5TPm/K1EMgJyC9QY/c+p4cSliMsBjUmAcLSwi3bNt21xkRuGJYlMpwzl94cKFRFNs0fmszDqpOP7222/TSRq26tC4Yf266Rl//etfR48ejYPbt2+HUBQASOHq3xVJoFGCBHXr1s3VOBEY+PTlIun1Bw8ZLgfk60fc8OKLL+JglcWXfP7BknlfAMDf338fd/OawcOSsHjxYnquFixYgC5iXbKjU6ZMoXVx4VefaSaPSKI7YxCjNW5iQpCWluZ25ZhoSSmEvTh58iSu0EuWLNE0DRMRF5jlnfzkiUIsTtSpUydJh0OhFTexrq6hv5p3Jb/88otlWZpphyXWunVr0uzq1asXZicpknDhhRduWL++olK57777Nm7cqOq2LAqhYAKYVJIk1EL9/vOP6OQonobNnYryU8jcWbduHWrhqLrNBAAj7tcswe+2Yf16XXv1wdufPn064GOv87NdvIsAAOeeey5gUUGWAEBkicJfHoZDEBLWbUXnMRTtSHxUFNOO1qC6ALJUAqJQs6Vi2KkyS9bbMO1IQKxJf1UNKxJgLPHkCHdNDYoA4O+ScMepMnmqzJJTH92OuKlSraDRpDHP7eV/SqOnP9ySqfdY0/Tap0+fph26AQAw8bLLLmvWrBmON2nSpFbjjJ/XrTmUf6y4qKhz586ESUxPT+/e+1xir5yeZb64uPjee+99ZPwtf/nLX2jfFggE1q1bN2j41UOHDv3qq6/+ADrr+/fvJyQEAc0A4IJh1wC4jTByyzNNM2//Aa30+JH8o3feeSf9yvUaNfO7elJa5gRSOrRrAwDtO3Qk4fCiU+VgGVUVZZZtv/fee1i2BYBufS+SuIkl7ieeeIL6ONHMbDJ98LuU9ew/CMB9fugKjxw54t9YEyxDM7kAEJGl8RMmkH4iALQ/69ymjRrKodC0adP8UvqqwVNDgaSJ5PLRfwKBga4CY34d9PScJqC7XQ+/hEb9Zq0I20GaJYfzKTkT/N+5Ewjl1K8nAAy6ZDCxfAEgzoWObds2b9Fy3LhxlIT16VNdsBkyZAj8+yIJN02VjNzc3JadegBARmbm66+/TrXAKsvu2LFjKCU0ZOhQkmDRTF56/IhXhBDdRdGyPZaKb8X1yvveOAMAKRBQDR4JephiL1fQbI4VDlEUvWWehVLCqmFFZFFRlBkzZtBJvv32W6wTuFdFQuOZmbiYnTx5kgZxQsAEBWrwQlWLnziUB1DdUiksLERqzMMPPzzpgQcA4KHJk/ER0gzLViv8n7ht2zbV5BFZPOeCgV6BR7z//vvr1KmDWqhyoFryvJpCLLPXnn2cOkEIvFVMOxwQCPABXhVTM+20cKhH7/NEJgy86GLqpaqW3aZVS7oSVLHTTDscYAAgJPJFE/ItX4EHO02ikNz1UHUeDYpiDdCootuxIPMXJ7BUrBo8FpSST2LUzq1FWmxSrlCp2xITEMeTRNwVQKjZ1VUMOyrX4lEHAIpu1SZBhkgvOAPjD55wuGKUHveMOtaFhYWHXF4DW7BgAS0YN9z9sBhJxXE/aPSbzz7Exjzg5tuL+6f8lYWiLVu1+eCDD/wMxn9dF+PPf/7zxx9/vH7V8r/97W/vvPMOjbds2fLKG8eOGzeOyvX/v3EmtFocxxkxYsRV5/fo1auX3zItIyMD5BQQBBCEY8eO0fgNd04CgHAk+sQTT1x77bU4WFhYCIFqvQ2qK6TUrdepU6e0tDrnntd33bp1uNF0HFANa8dPqwBg567dJC60ZvUa100qUVrDloL10+t069Xn8SlT/Mt5MC3D7TUIzC/jndOyA4CbEnXs2BEHmzVr5vetJR0OVbcDjh3XKp986mn/bZogndWzZzStLik5usebdiyYTNxt2Kq9LEK9zMw33njTnxPktGzXuUMHEMQJEyb4H+Ym7brWjQQbZOe88sorlBbUyc4Fo4r2ytTG2rv/wLYflzvcXrzk+8mTJ+Pg1kKlMm7s2bwub9++Z555hpo+gwcPHnjVSADoP+BCqhP8n+Nf9BC6GUaP4dC4M/hE+fLz8/cdOAQAJadOjR8/nsRnK7T4i3++q6K0ZORNNyE2dmuhUmXyZk0agzuhC/iCI3f60UcfxfWvWbNmo0aN0kyeEmCAZkweSfWya25EJKkn/EUVDgtXymXLllHCUVR4rLpF4jvYNE3EdriX6o0XFRTgYpaTkwOOC14WBAHJVlGZAYDIqpcox3GqTN6pYwf/SRo2bKiZPCKxYDA4ZvRNADD88ivxNVEt3qBepl/ztFevXriWB2T5mmuuyaqX2ap160svvRQQ8BFgV980jk6OhUPVQ5PQc+We3OCxYOC1j76qm57eqnXrJUuW4BukGnZFUcG7r71oW+bSZcseeughvHhV51vWVNvIvfvuu+DDcIiJKzE6t0FNHQ7DBYcmdTEQwyGwGhgOL4fwC/uatqPbvCaXTdHtaJAJidquSNKJBJkAQkKtxWviQGIdArXqJTGZjaIYnvlt4ofqNjdspzbQaO0OcGdC/METDleqwXFwr0yV1d27d/tbKoQpS6ubHkrNkAT+3AsvoIAuhsBEwir6WZrpDXK5IF46ZMjgwYNPyw0BsVEg0WVj8uTJt1172fDhw2+88cb/IFrsQ3pTZpQAACAASURBVA89tHTp0vJTpQcPHiTGBKCNuKtWLt5www00vmHDTwCgVVU98cQTtAsvZqkQDNOunWTRVYM3bFAvq0H2wIsvJg5t3OIOCLSbpxZMg6atPb9sd3rFcc2wwwHB4baqJQicOMGQZ6jB/FZz33z9FYDLlyHPzFAo1LH3+YLDmzVvPmfOHNJ9WfHDMqPylGUYC75bSNMrAGgGj8qs1u5sWiiQNLxhw0aj4tTJwmN3TbiblB8BQDVsZlWBAOXlFZQTbC1UdIc1yq7fvEXLYcOG0cFCMNy5U6cG2TkdO3WhBQAAissqqEq0ePFimqwLT5bs37qhQlFeffVVYqkAQKN2nQHg6muv/S1/xNMTbq+kYQfIbAK+4tbOnTv9pSbqbZ0s9UCjwJ5++mnOuWE7Fnd69jzL63owlNVC3sGmTZvwpz948ODKlSs100WSXnvttXfdeeewyy8/evRoKC0TeZsIUCAcaJXJsQLRoUMHKp/Eq6rcdSixHHKouBLpr+5U5sE1bA64mFVUVHjwC8HV4NJ5pJoW634hKPcSkgSA6tqMbduohbpy5coOHTqAw/ucc+7atWsxO4kExebNm+PBHTp0aNy4sWrySIAd2rd33LhxRSeO5+3bP3DgQMMwFN0Ky1Ju02Y3/mm8yNiQocPwHcRGw5gxY9BHBvC9xpU1wL766L1TxUV5+/a/9tpreOWaxbVTRd4VsiVLlrjoXcuWfLRY/IpQgQ1vM7GlYsVksUt2NJJYKvARSaqfE0SfuGDSGo2MWDBZn0M1bYkJKQFmJ7dUeARdXcB/MAcQwhJjLNnVJRYUsXpeC6y1Fg4tj8pMrMGv+S1rWdW0U2rj0J4J8QdPONxWrveq0+6/Q4cOXutEAACC05cWF6uGZVWeemjyw34djqE33owe4gBw++230/i+g4cBYN6385P0Ek5PUKO9tLSU3ONWrFiRpOt8JgeJBgLAiRMnaD2rFmgSmB81+eOaNTgIAO+99557sMBclUxg4BMKU+LGltXL83b++uSTT02cOBEHNcuWwAFu44JBhq6x+jkRWcSZrnXr1khJAIDSSvX4gb1b1q+d+vLLfgPhuC2AruLC4C9OUE8BEqGLBSdOOvHKg4eOvP322wTt3LfvIFl40K+2tVBRLTsq1+LtpBq8phDh94u/A8P1EvPDLA4cPrJl9fcA8MGHH/p1+gtPlmxbs2ztup+aN29OUhmaxetnpjdv1eacc88l8xoACKZlguHmYZdeeindkWZyiLslJb8srHpmANZc7xg5BVgAAIhG1KlTJ5BTaNdOL74pSH4vFUEQqkwuCrDoi49pOScdjnBAXLRoER38888/ayZP8VoqabFoRma9YDCIy7MgeAaB3vHlmo5b88zMTAAHLyYQDODy7H7D3hXKwSAKfLkqMpQrSAFczERRJLynm3D4UJPVCYfBQxLbv2uH/+SHDx9GLVSsGeAjNGPGjCrTAQDR1t9//3287B07dsybN0/V7Ygsxgvy3JMwBgD5+flYm9n03ZyP33ndtsz533332GOPgZecHT58GBmtALBw4UJADIel/7BoPj5X8+fPx+qmZvCcrHrelbjPG2qhDr38Krps9O7WTLe3xVgC+7fSJ0OSkCtUe6ZUPyeKyQEgEmBJMvAAUGlY0RrCX3inDKpZJ26fxRX4Aoc75DiPJB1BEBgIji8R8WonAInpgneFyeZt2AyqieGo1G2xhtULnic1mGwjd4bEHzzhEARh3bp1Z/XsGY3FZsyYQTNpgwYNLr5mFAA0btp00aJFZMa9cP7XIDCE3z/11FPE/kCyX2p65t69e/09i7envQwAh4/kjx8/ft26dafhjqjBDD73sqRIguifyeHXS5Blma58zZo1xBf1a0jYTAZwl3OKkyeLQJIRxAAA1Jo5eOTo8YN7cHf1wQcfoCySZiQg4QkHoxq8XZtWdz74WOcuXdasWUPQ4F+2bKksLiC7cFVVcVwzOZVJqFUHABdeeSPNjKQFt3zb/tIKBSGcy5YtI//S7BZtwVDxJJdddhmdRIkba+Z/qarqO++8Q2mH4ziqiaJDCXOMLQZdhoXva9laqJQpmmu5LghfffUV/VNRaRlhO6g/qBr2rk1r165Y8s6MGX/605/wyd9aqMipdbt07iSA07hJU0raAICFYhCvxAnE77SHGsyHj+RDYhRr5h3f7oPTFS5HJhBCzVOqZOTk5NRr1Ey09XbtOyxZsqRaXy4QAtNN2gYNGiQIgmba4YAokAOawMBzI4vIrFOnTiRh3qJFiyqTY0sefOucZrg1f1S2oFaLxQJhSQTSOeU2MKYpWpXJo0Emy3KzZs3o10zPzFJNHg6ILs7Mq8BJ4bSwxARBqFevHvVl8N9Vs5YVVzXtsCS26dAJoDr1adKkCVFa6EoAQLV4SPKgJ96VCIKA2lnuLOrBMho1aoSpkvveORxAQPYTpkr+gzEUg4dFxzt59WSlmrxtm9YXXjacCUKjxo3vu+8+8OoEPXv0uGDQkIAcGj9+PKaJaIAHiQJf6EFP+ZY/EXGREIlrP+UEAtTSUokFpSRBMNRBr4XqonNXN903iIptUJOgq9sx2c0s/Z/p1WBq8mXsWikwCm4/agONxuRkG7kzJP7gCQcAzJ07d+P6nxRN++ijj4h2zznftXsPABzJPzZz5kya0yvjFlgG2Ca4T4P3uho2AIiS5O/WV1ZW7ti1G8B91Tdt2uT/3JWHymu6ev7zQZ6iAEDSmenp6QQLSE9PJ7mRMz9IkQkADMMgtXJFUajC4U9KWnc7G0zX0PXmm2/GwWAsAxwOuoa/Wo8ePXBcd8Db+lc/55rJQdcAcIsmUotKMWxmVq35fmH+0aO4D/NOIoKuJfmIAkDDlh28XgPz2+40ad85IrNWbdru3r27UaNG1X8QDNMyT89V807dG+dkB2X50suGEEsFAIrLKn9Z/b1l80ceeaTaLd3iAE4slEzc7XH+IBd9Igj+PqAUSSMeDXFuAQCCEYhX4u3QlRScKDq6dwd+V19//fUvv/ziXsmpyq1rljvcPpKfT6ILhu1wgZ177rmRWGzSpEkEbdlSULlx8y8A8NLLL99+++1+tnaJZhVUVFeqTlN4tjt0m1uOVZRruq1W7Nqzh158JGV4pAlh8eLFtm1j0eLKyy4mbCN22XBx/fXXX0l9/OjRo6pppwREACgtLV28cOGyFSumT5+umhauiG7JzVvOy1QN1yGv8mEDE4N1MrFioev6wYMH6eCDeXtU3YrKoqss4hVr9x86HAkyADh27Bg1fVauXFllcgccAjfQo4LZT07jpp5wOBs+fLgsy6gUglJa+DDfdtttmmGFJTEYDN13332YJ/Xr12/48OHoZ9umTZsPP/ywcaNGbdu1W7lypSzLuLK67x3nqMLifVfs2muvpdfHxXaYdlZG3f79++MnnnPOOfi3qmGfOLR32YJ53DLzjx174YUXwJUfFWZNm/rDwnmmZb3++uto2aNZrkedP1dAD3rE0/gZrYbtGLYTCYoCS0BCVCNMa5i6qQaPyiwpEXHdX2s4yGMRQkhC6VYrsCUcr9RWggJiowjJmYJi8JiMtnPJFY60oOQ4yZVQxUV6wRkYf/CEQ9f1F154AR/rlStXfv311zi+devW/MLjCCb94osviNfgSEE/kZJ+SUW3RCaYpkUoMwCIxWJN2yPVJXmfBwDPrc4vqPxHZ9h/HDqXnZ0NwQgMuA0AiNegKAqtEKWlpbSB/o8LSjjatm1LCQdxiADJFJ78w6pVq3CwijsBcD0z27dvT1zHlDpZzRs3xvGRI0fi8q+5CHnXip1s6BXD2rDsu22bfjpVVj569GgiOWc3b5MWDiE49KabbqIMKZrZgLIZ5Olh7NqdpxYV5OXta9u2LTUsMurVD0TqIMpkwIAB1fZgJm/SKDcaS71p9Bi/7ovhMMpOqq2qdJ4iMUlMbjbXb9qyfkZdkQmZ9er75x4pknbZ0KEAcMONI9GrHYOFohB3209Ug4lbAGYV7dopV1AMk96IJUuWUNFeYsI1N4zKaJDz4IMPEhc3b9f2k6fK8bX68ssv/f7JimFbp7GpnJubO2rUKAiEgIkDBw4kz7Dd27caDkNW8Jw5c5AuZNoOB8FfJXIcp8riYUl0nzGHgyAgPkajkoCXyCLiAXGgkydP3rhh/bGCwscee2zNmh8jAdYlO+qT0GAAYIKEiYhboXQ4CCwYq4sVi2AweM4559DBzTt0tR2IyMwVnfMwHFI0DRezbt26UQ3myiuvVAw7RWKii3WoLsIjrrM8b/PmzZvx5PPmzcvPz0eP+759+65fv14OyPO++bZ3796Ex3z44YdffP751PSMDz/8MDU1VfXwmKFQSGSCILrbdFxZx44dO3HiREmSBl96GYKUq0XZvUoGPp+qYQfBXrFiBX6Ha9euRa1CzbRPHjkIAAim+f7775G4GwmIx44copNgC081eFhKTiwqdZsKNoKvYaEYdkAUgiJjQsL6XC3a4TO6I2/6qCwm6XAoJo+4yNOE582lxQoJuhqKz7bXf7wP1lrDzxabPjXcWyK1K4XYqSERamQ/qm6nhpLroGdI/METDnfe9N5eWs/Ky8td+UhBAB8YAoIRQgKCb9rdu/+ArZQpitKqVSu/wJclBgDcCodfc0m3uWk7hvX7J5ljxoyB1CxodwEA3HbbbQm36QVpfp/5cc0119B/9+nTh0inDRs2JJMtvyverzt2EBtl/fr1OFimVplKGf5qO3fuJM2JKts5t+95qWl12rVv7+7hAFSDM8sAcFcRqksVn6oEXaMVl8ZPniovP3EUt7M7duygXkNBUbH3qAgoGICx78BBgq8SsMZxwBblnj2633DDyI8//pj0FkvKlZ0bfywpKrx+xA2kKGo7DgRCdJvVlBbsE0Myf2/N6tUnDu+zLau4pAR3lhiayetEQgCQEg4T0jlucQ7CuNvv6ta9+/79+5s2bYrjUiStVfNmeJtDhgyh7NkSg4SlHThwIHIosK2w8KvZRcePv/LKK4SZ5ZxDMEIZod9dTzHs06y1PODq0VIktWFuo1tuuYWeK9OyXT8/n/+fZnFRALB0vM3JkydLkqSZdgpBNR0OArMsCxsWkYDYvXt3Wv8yMjJohf7qq6+oCHHg0GFsKPgkNEQAqPAwHK5/NbeBiarpRINeU4YxmrKqOARFJjGPeu2NF54owSZOJBIBh2NCzDnHhwTP46d6ot28/3bABY3a4YD4888/n3322YZeNfzyK9avX4+VjC7Z0TfeeGPSAxMrystHjx5dUVGBBx86dOiaa645uH//rt27+/Xrp+s6JmHHjh2bOnWqZegLFy1es2YNuOrj4vvvv0+fiBgj1eCWWuZ9Jww8Zplm8IYN6tF4q1atJEnCLzwrO4dKSm3btgU/hsNXJ1BMV4QDAESf7hm2QqCGg67qWcsm66DrdjjAJCYIycdbEVlkLJkw4mOu+g720CRCYq6AFQv80KQEJSozoSY4VLejQVGsge2oNKy0oAQ1XGRVE3k0/0s4TnukpKT06tWLHmtyA5Jl2a8eTQt2h17n0X5u4sSJNE+tWLEc4pV48FtvvYWDtm0fLTgB4CYcfqiBqtsA8K9oqfTr16//8OuYFLjvvvsIkpKUcAR9UsRneFx00UU33HBDap06jRo1QjlkjMOHD5MgyoYNG2i8Race9KvR7au67dfboASlXI1/+NrzFadKdu3eff755+O8r5m25FgAkNQl4YGgm0AAA0IdAhzKP0ZYjZ9//pkk0uO2AIaKqwuKM2KcLKtA4xX/lcQtzh2nQWZ6o8aN/U7xv/66veToATz5pEmTSFFbAMjNzhaYOHjwYMohFMMzgkqcSnbs2k23r6oq2alU6ubHrz0HAO++N6uaYWjaQZGlRlLEQMD/nFRZ/MrrRvQbeMkNN4589913MbGwHcd0hFvuGB8IBC66+GJqqWiGbauVi+d+Ho/Hn3322TfeeAPHW7fvGKqbBfEKzFooVQIA1eS/izDAP1gLtCxr7PCBFojHCo+PGDGCCE1N23cCANSNHTZsGPKWq0w7JSBOnz49nBI6r28/pCJXGTwcYB7qnAMTGWNVJgdwwjKjOgEA7Nu3D4W/AOCss86ixCKcXg+LEJ06IXLCTUQMQXTF0TE4ByZaYgAPtm17zZo1vpV4J+6ScZWlVks0s0FElrpkR90CmNcLUwg2kQwatSMBsXXraqVRADhuhzXDDsvM/QUdDoy9+eabmsXDsmia5hNPPIG3+cMPP3zxxRe4BXeLqR4k5cCBA5plhwOiy9J3OAhs1qxZWwsVFPYWkC3qUwpRDLtBvQy8ebyS9u3bI1baUsu92xHy8vJs20a11tyMVBSeBw+jRgAR0dcNUXQ7Jkt0+7TM/2YXwyAKcQLCtLoIkcxS4VGZMYCkNka1SVtCwuFpzIOTABrV3Q9lgmD7USY6RzBpDawGVT4Sx3U7DSscNdgrqXIt2I4zIf7gCYdlWRs2bKDHnXIFq06uS6T0mSMDAAtFOnfq1LRFy7/89a9+mqIJEm1baXW3LMvPrXVNqAEcx5n/7bcAMP2dGYQa+b3i+eefX7FsKQf2t7/9berUqTjo9lbaD4DzbgKa4P4TYtWqVZ988klFWVl+fv4999xD4507d6aWytChQ2k8q0lLspsnzxSLSRlpqbQA4PFbC5VyNU4wCyA1Q5M3adQQAHBTOGXKFDyJZjnX3TiybYfOkVjs1VdfpS2+AaKfLkt1LCeYQsUwP2i0SfuulJuOGTPGO7ktM2H7xnVfz5vnlyWNO4Jf/cJNOAwucvPowTwHhIULF1Iiqxp2BFFsiVOJI6d4yFMGHjPLtB3bEVxBVcYINKrqPOCYLz/x0KafN+fm5iKQFj+08mThoX17ftmyldpzmsEBhJx69QSA1LQ0kslSTdvRFVq3qOnDmBiI1W2W2/CWW2+lrAVD0W3HScbl/evi6NGjIEogBvCdpXqVzsWgKNStW7d1m7YTJkzAwo9mcsmxbr/9dk1V1vz4I9qlaqadEhDd/NJxAITvv/9etXhQZKIgDB48mL7wTp06kQ6HIAiUK5SpcaxktGnTplu3bpi1DBxyBZVDMjIyANwKhxNIwcRCFMV27dqBY2PWUje7IS5a2dnZAwYMwA/t378/9453KdacA2O9evXChwRv1g8a1SwekZmrOOdd4eEDearJowHRndM4xzKJZljhgEeS8g62LAtXUHe29PncIrLBQ5lwEAS3wqFb7vHewbjlUww7IxaZNm1aJCUlGk19/PHH3XanwZldXX0EAK9mI+7duxe4axmzfft2zE7CHi2WnitKFMAFjbqBcFc8mNb+rYVKpVf5SKpkoDc9ADBIqCp4iqIAiSqurhVtDfMaF8OR6LHiy37AqTEuCIJTo3USk5lQ48WvNGyscCTlFqrJY8FanOHOhPiDJxwuozJxyQGAtLrpKXXqYWX1vPPOI1iianJTLS8rPrn8hx8KCwvpPK26nU3bVhJtlGUZghFifBF/5LPPPvvgvRkA8PHsT/10xN8l5s+fTyYjtIq4O+m0+hDLBF/qc+aH20eI1QMQ9u/fX1JSguOjR4+OZtQHgCFDh/nxB/sPHiS5bmpkqCaPBEVc+y+44AKqS1ks4AeNIhZENWkXxQEEwhkohl10aO/ubZtVTZsxYwaBdSLp9f2tFsKBCnKUoJoEBNlaqITq1GvWODcaS33yySfdDSWAGre4rhzK270nL+/qq68ma7F6jVvQFd59993e+mdbajnNudTCU007ivy9xKmkbgPS7KoGHqmmzQDAjKMNCgl8qaZtaeXkjULacYpuvfn0Q0f27921e/cll1xSVFSEB4cYf3rSXUa86suv5iJrAABUg4fE6teK8nXHAdXg2Zl1GjduwhIxtgi7/lfU/GqN7OxsV8cl0bvgZOkpveLUqeKivXl5F110EZJZqkwbjCoA945WrFihKIpm8XDAa+dxDoy1bNlSNSxcQvxGr8FgsMpwWSobNmxAq0gAOHKsEIEdK1eu/OWXXzCxWDp/rma4vQC3FuXYIIgmk6Oy2CU7CgC7du2i9sHJU5V4kh07dixfvpyu8PCxArwSlzblcBCYJEnIdAAAXddPHC80TCJh2WHJcx/0ihPpGfUQTOpK4DgcBDZixAgk9MqyPH78+GoK8bmDsNXiUnu4+3wKgoBUF5fr5yscYutt9+7dNIiC96rJg8yeMGGCqiiKpj311FNILNdMuymC4h0OAmvatGkgEMArLCsrIyIu/mpEAvKn4EQJhkRsB63xWG2pbrWYtYNGKRFhibrpiEoRIEGzC6XKo6ibngAadZ8WIbENWm0vl+gk5+lwJNvLeRiO5Hxd0T0MR1K5xbBrul6fIfEHTzjC4XCjRo1o7r7iiitw/NiRw1U2IIZjzZo1hAY4dPjoro0/lpUUL1223L/hTstq2KJJI4GJEyZMIACaIAj1GjUDI44vJM3pP/74I4RcMYZPP/30972j0tJSCIZRgpP8sdwNiufq4heuOMPDZQxe/RQ0aAW+ZtD2oioxHAOAJ59+2s9SsZlM4IZqMzbdPLJ7m7/2i+NVtuOX68b5XTN4+YmjAO7MiDhitClZMe8zTB+3b9/+5JNPuh8ph68ecUNGZr3mzVsuXLgQsxnHcSwx0LZNa3Ccjp06+/mix44XHfx1k6KqU6ZMITPuo8eLLAWXeW9ZAgCAskqNajCUhaDSAyUQJFfqIeFrWmA7xKOJxWIuzMLg0aB4w7i7BMcZMmwYKYioJo+XFdN3ggWbrYVKhab7sxbMw1SdM0sHcHecS5Yswe9cNe0WzZqcN+BiENi4cePuv/9+PLlm2Q44MTnZ7QX/BE6jPkdJSQkEQgBuwkFvyv4DB/2ocIQOVFlOLIQerQ4lKIgDdXf5jg0C27dvn2Zw5IbMnTuXTrJkyRLVclkqANVdD8MRIgEJklgqUtB2ALMT98G2bRBFk8lYyfDqCm6FQ7N4RBa7ZEfd0pq3cquGhRUO/55q7dq1qmlHZBarKsrNzX36qSd/WLly9erV4JmxJcmVFhYWoBaqV/mwgbGNGzeq3lo+ZcqUv896NyUS/fanHXXSMxJaKuCeZO/evaiF6nXQHACG7U6sB9i2DeAWJ3Rd31qoKLqtlZzwvnABAH755RcE6hYdqRZfP3ToUGVlJWqhxmIxumzMbDQfB6QaNOpLOPyVD782CfiKEwiz6JIdbVInmIjftGIutiOhkYF36kpoeN0Q1eCiACEpWZtLMWx8WsREGi0qfCRdIVCZpMZrQgCRZI1Uw04NSoKQoM/hGbVINSXPz4T4gyccAPD222/Xz8pionTFFVeMGDECBw09DnIKoQFIKuogOkqAAyCsWLHCJcoD/Lpz5/6tGxwQpk2b5jc0b9+rb0iwGmRnP/jgg8Q87NChg6v+xNjvrkBabXTORNrKy7I8evRoCIbR7YzyqjMtajbgJ02aBAAQjIAkX3755X4D2CqTA0ClkvAnobRM0JUkRI6SiOHAqZk7jsHB31LBX1mzbBkxHI4DAiPH+QrNcJdtQQQANHpFoegD2zaXFJ04cPAg0X80izsO371pLTh8+44d1KoDlP/zWio41wNAIFoHjGpuLZUETpZVUEq0ePFiXDw0izdsUK/fhYNAEO6++x7qy+D0Ldbg7ylxAwwN5/TKykr3Nk07IovNWrcRAHIa5pLghKrbudkNaAM9atQoHDcF0U/TQKkD1dv/4ThVj1TTTg3JObm5wZSUZs2aEUtF0XlKgNlGvLwioZOIThaQWIX+l0ZWVhaBjgHfGgAAiLmusG5igYQmzeSZ6XWefPLJtFisV+/e2PPCloo7AzgcHxXVtCOS2CU7Wr9+faoTHDp2nFgqKSkpNA7BKOYE7qfjeDAcYCAxAaoTEQuYaLEAAQyHDx9OJ2nUtjMuq+FwmK4EAKpsN2tx9zncBiaOGDECcwJXtJBzYCI+nJrFwwmtEwEAKuMmrpSuQK3DEZBUZbm3U1JSsnzpUtOytmxcB94T6KFJ3IdZSamHSFW31sI5MLZlyxZXrlRmDGUrmAheGVg17UbZDbxPdKUXUQu1TetW/tuMxWKqaUWCkq7r9KuVlJS4NKLqCkc1ODTmFYqYr9eA0uNdsqP4T6TPUavxCrgVDgkAWG20WMFt9ySXSZLYK6onKSv4+jjexYgAICRKliGCVWAJ4FCsGqKNXBK2oxLpskniHwYXBQgHkj2lz5D44yccb7755onjhdyBuXPnEpGyUfNWEAihVGL//v3JwiqcnulNuwL4vFdOlJQR4MPvJeYEUnKyMrp0637//feTaJUkSZ7cpOi39YL/e+eIbSdUI7FBl5qaSuAGvzXoL7/8ArJb4SAzkTM/MjIyvln7qxRJffjRx2bMmEHjxcXFhu2AZVzQ/8LvvvuOxiurdMJwkIuezoWWzZvSfDR8+HAgvaCzzsLxwYMH4zKvGlxmuG3lIAgkb1rFwcNqCOATCClT45tXLcZpdOrUqShTreqc2SY4HGdG7FhjsHBqTWyQzqFVixYNGjZsmJv70ksvoerA1kLFEgIEBMnIyMDlXDN4WiQ08cm/AsDDjzxCSAgq5yYt2ywl1d85wnxLNWww1GcfvJvb1jszZriJHYBqcVstB8fxO30DQJXFH3ri2ZyGjRo3afLuu++iFrhi2lnpaTff/UAoGDr3vL5kyKIZ/Oie7XNmvaPrxiOPPELS5ophQ1xdOv+rV1977YEHHvBfpGco9S9JOGq+VuXl5SBXVzho5yCF03JzGoRCobQ6dZ5++mlUstEMOxxgHTt2lGUpq359ZKtWmU5YElyKNefAWOvWrVWvwlG/fn1aceukZ2qGu0K3bNmSxlPqZOKgp0puA2Mgh1NEd6Jw2T3cBiY5gZSIVyOZN28enWTP3n1Y+fAdLAKAZjo47j57DgfGZs+erRp2NCC6qYzDQWAeMMiu7nqAe3JblMMBURA8JXjHAYFlXjpKuQAAIABJREFUZWWphoW1mXvvvfeD9/9u2fyxCeN+3bxR0XkkyFq0aPH22283adwop2HunDlzorFUhEy6NRgvV1A9EU/vzAKgiyGAYth1o4gHcmt+oVAIWd8ZGR5K1+tUIv3V49YyAMjMzMTsxKXF+lCWlQb3Yzh8tFhO5RDw9SAqSQaeJVYyDB7zDFkSWioIGhUAfA+zqru+tUwQqnlZ7jgDAAZJfracspya2A4xUWT9/2PvzcOsKM798bequ8/W58wKMyAIDKvIpoAsAopLFI0ajSQ/9P4SE+UajXviviT33hg1McYoubmJJl5JjIkr7gtEQVYVFwaQnWGAgRmYGc7MmdN9Ti9V9f3jra7uc2bMk5vliY9P6q+xaOv0WvXW+34WzBqm45rWh9oYS8e1sqJP3mVmXO+9LfmMtM95wME5f+WVV1QAESo3+IIQcubZZ5108twnnnhCZfIr+h81bdq0qqrqiZOOW7ZsmYohjHR1gOEgUX3PA4fbmxo/eHPpsgULFqi07W9/+1uV4Yh6W/xfW2Nr/v7VLWWT6ejRoxV9Q0EEXNfduHGjspeLsjQ/483zvB9//1Zf0Ht+9OOoLPdzT/0BBAe3AIQo93MA8GlMYThUXsp2+YmzZn/1koVjjx23Y8cOTFqgXtC/X3dzv/79xxxzzB3Bym17rK4G9UsEAD322GPxKeddrjAZADB37lwcvBimSQgEFEfLY8RF9TABhCq4j88FJ9rEcWOBkIULFyresuVx3+5u27/vwMHWFStWqGKQS6jK8Hd2dioeTUqnvuMCgBehBFteoHwcmV8aW/OZfvXgFFRJBaNk2+NeT+j2GQGN+q1N2xQkZcWKFXjaLhMjhzdkKjPpTIVyG0aRhosXXtW/vv7Ou+5Sjm62x3raD6pVRFWImva12J1tyPRZvHhx1I0PFUh7ixF9cth6cWtn3y/H39Cqq6ujGA65KQfoyhdadmwp2lZ3rueuu+5CzFDB48zuufDCC9sPHXrlldeuvvpqACj4LGlo0m5GcCB05MiRKo3v+75a/zhnSNMANIANYgKmGdgpg1fOgOoQS0pDEwCpLct9oFqyur9ilwQHawDQ47i4ZsvNT5AqKHIo59yiM5zLzDiVn4wQQAheguWylEGPO+64oUOH4sGDBw8e0HAM1mu++93vzpw5EwQfPeaY2267zfZ4yqCu677xxhvqTD56bw3WdwBg3bp1e5v3HGxrwxkV0yfSaFBwIOTWW2+1XJ5EYe8Iy7d///5CgO2y3KEDweXIIiMO0vjhe+qGf/DBB0eOHEGM7dixY9U3WF9fb7s8qVP8chWGA4s1YcARkSGJ4jQhkmlTNFra2wEuiBVK5EoDbS4oFfkIWCclIh9orgsovs7lIFwI2w8wHL1LML1EPjBrKMVQy2mxLB2jGiFcRNIkLksbWm/h1M9I+5wHHIE6rygTBrA8ltLpwKMG9+vfXxUmAMB2+YwZM8aMm/itK69UrMvG1nzVgKOPmzAeAC6cP/973/ue6t/dtBchBStWrFDWHslkEuJpAABC/0YLK8vjbmmkeu6556qSiqruS6Zl3MSvN0qaaGzNr92fO5T/R6E6/ka3z9dee231yhUAAES78847pTIBQL7oBcqPJa+okalSGA5VDrB91rJz69P/+6ut27ZdffXVatlOauRbX/1ix6HW7Tt2zp49GxlDtssJIgQ5Bypx+0KIgi+i1f0zzjgDAHwuPE4mjR8PggPQ8847D7f+lsfjREm8EOUMh0KHX7v08pGjRt99993q1crZzp7NH+Pgr7zyyquvvor9RUaiKBMli1To7rzwpOMB4KJ/+/9REwnk8k97SyIWBVX66D09PUissDxekUKTUgGEhlJjPh80oK5c0sBllJCF587dvqlxy9Ztp5xyCuJdlNE5Z75lF9Qv5l2WScXVaStrtC6roPxlQOEZ8eQ9Dn1hOD5utZbtzpb3AnR0dKi0xF/RKKVDRoe2vUoir6W1VfnOQKCCb/ssfySAFFC6fPlyuc4ZVCJ2BQeiNTc3512OeM90Oh0GHIIXfclSSaVSSoeDaQlMh5QkJ+KpBOWY2F+zZg0AYjj0LstRilXDhg1TyFNX6Nify2H4KPt7ihKQGFnm6Xe+8x1M48+YMePDDz/81uULR46dgHVkXM737du3d+9ePLilpWXn7t2Yg3nmmWfWrVsHgu/YueuXv/wl4jHl2xtcZu2gYQDCNOhHH330+OOP4yv085//vGXvHtRCra+vnz17Nh4/atQopbMphFD3JJfLFXzOBAw9WoFDZUnFcv1UjA4dHuZa8MGhFuqAAWEdMJVKocIH3i6Nku6ijxNR3mGZ4DZGZUjypRiOEGQaBChlZFQszUAftFhmouB9JHpGyxjAkkpkFKWJQiPaXOhBjyTqKNxVirIblBJRlrFQCiJlCcK842fi5R4rCOIp48V8dtrnPOCQTWn8BQpFlsvBsx5f9JPnl7xwww03hBLmHs+27m/etfWll15WjAkAKDD46oKLAeCnDz4oqfnY4ilVsM8HaIPvf//7ysFLFeD/uma7zCvdFT755JOqpKJkuSmlp551HsRSuBeJCloAwB83tb/X8ndm5/69WnNzs4qfILJETTvtLHBtzEIrTGJja14YiRtuud2IxdeuXatosZbL3n7hDwrYiEBdy2U69wDCyQsrTZbPhKOSE9IzE1XDp06dqg6W6RCXUUJ+8OAvZp582rlf+tIjjzwipRVdvzqdUoMrMXXL43HKb1p48a7dTV/5ylcUXain4EYDC/WquKBFUSaI4bd9vm/bJqTjrV695he/+AUEMAgTbS1LZxIPjOggiMO1XHb0wAEDjhqMlxneK4endKouE0UwbZ8nNBFoeIdMV8tjSQ0euOXbB/bumf+VryoMrOXxk0+aO3LMMUDoCSecoOo16f5HVaeTOMjcuXOnT5+uTvLTSip5l3mlRUMhxHXXXTd27NiRI0c++OCDn/rq/NnW09Oz78BBAJnhUFTkWLoK3IJa/zBSLHi8trICIFz/qqqqUIdDTgKcYyBiB0tIoRA6vbmcAgCu3Js3b8YAAgCKDFBvI0hO+EA1iKWSQYZD6g1yD6juCJoOVtDm5mZEkgKARzVcWeWmIsg3OJyaUaoLZ0Bpc3OzQkcOGTLk5JPmxJJJ9BJD1Qr54ikbOaugeDRqkO3bt1t+aA2jDj7c1YNbbTlIgDLpyuVRC/V3v/tdoCCiXXbZZVbA3LGsMAbNZrOWxxM6NZOJCRMmRLEatsdNXZszYYR6EMcffzwhBOs1K1asCPiAZNOmTYqiAqWKomW02DDzEdBcUYOVid6ZD9JdDLOJapzo4BCUVAgBEuXcOkpvo8QtNh8Kf5XwYswYhj0QZbUoUfYycKgV0I6iaRIIvOllCaYMZRLTyhIzn532+Q84Jk2apKZRxWlsa++wOg7htPvEE08o5mG2J/+7h+89dKDlzaXLorYUuaK7+Kd3A8DQhuFRz5RkdZ1CniqBJrNh4pgpMwBg3tlnK9PRv6I5jPtclHGsm5ubFRpOiSgAwJHOdoibeCZbt26N/i+Wy73PpgqMxMCG4D6ZHy61C49epuWwB++4zvPZLbfcokjOlssCvGe4F7R9HlAP5CqCtFjb5UMGh8abw4YN0zQN85YfrF6hDn7rrbdAwr7o4baWzsOtBw4eVJpalsu9HqX8SBQSIu8wFjBaV61apQyHfc3oV1WhlnmEuzIhhBaLwi8UhuNQ8w5lkCG3tmp3VZqGFQIcTgbW9cdB7rvvPkT22D5r37+n7WALLq6Knm15bGfj++oyA4BkoBYVLAy4Etsub9+3+/XXX8f+a6+9VkpzunzXpvW7tn4ChK5fvx5tVAHA9sXUyZNnzJl7zrnn/u53v4vmDq2+rLQbW/N5l5eF1KtWrXryySfx73vuuQcJuv/Xlk6noyUVVXzkRmLW7Nn96wYAIYsWLcJXouDx2ioVcNDJkydTSm2fJQ0tamA2fPhwtbGOZjiYHo9rFNeQnp4elYQQsYQZ10Cx8WVJJcVsGW7KwRkDTfeoYQYrZUNDgwqJPGHgsh2QUSUFpigIZj4kkklwINrzzz+fd0IzNi0ikm15LGXQcePGBY7z2qBBg2qPHo7gA5mi4xyoVllZaTl+ytADhCkDIEC1nF3EjIWsrAUk1Uz9UaiFKjWHgtuSdzxUBHFdV3W6roshUdvBlk2bNqk3f926dRhDyKyP4EDpxx9//PbG3ZbPUobW0dEhIQyEtLS02D5H9C5IDkgkgDBUwAFRWmyQhIAoraMnFP4qSU6oDIfi1ja25oVAZZFyMGmJrkYUNBr8qBbVQnV4RJosrJLkXeX0VlIiUbwbAqLMkCWu0bhGCS3n3CLK5F8llX9Oa2xsVF+vElfI5u0oOFT1H8lZyqV6+fLliBAEgFzB3f7RGgAAINFdFzcSZ3/xnHHjx3/88ceqwv3B2lXbd+4GgDfeXHrzzTeXn09fNYhlu7te6FXJtl0OAH5prDBr1iylNjZu3DjVv+H9dRBP4RSglB+xWb3SJJ+dFhX4AsTiAQDAxk2b1IN45plnsNNhnAmBJZVVq1Yp3bOCJ6bPmIFCXhCQdGwXd+1hJqOzs7OxNW+7rH3fbgA5MzY3NzuOg2v51BNPUjMjTsGWxxI6LLzgzB2bGz/6eMPVV1+teKHgFdTgYanOZZrvqEFUUSDv+B0te1Q/sldslxMA5RkGgbyp5fGB/fspV/RQJMblplE+lRR8LkC0Nm3DQd5++23st10eRZlARJ9jzKiRZZdpe9yMGYsXLx4+fBgQ8sADD2CpxfIY4CAB5kNd/oGdW9RpKwxH3mUVCWNIQ8PIUWOUShgAcCEKPiekD5ZK3mVlwbAKsLD9ddJ5hJCpc88AAEzvK0ROweOjR46aNvuk73z3JlmMALA91nlgj7pXH330UVtbG5ZUZF5BcCC0qalJGakUCmGaxOUkZVDJjKAUmI/JCYiZiGoMbFAww5GUVj4AEvTNPNAMFzSVVAjM23RAWmxch1IddABwOAlEpSiAjBRr+vUPiUWlJqW2y01Da21tbWpqwqjlwIEDe/e34I9ef/31CxYsAMEnTp0x/4obUYOEUvqtb30rCHy1CTNOwYOlrm4gnLp9x268J2effba6V2eccYbti7QhMZ7REgkuzzX9lYMuBYARI0ZYPk8ZmsQPBZuHqpp+tsvMmCbjJACgdMSIESg/ipemU8oieYJMPHr5YSEjzHwEtA6lnwEABCCanOhx/Yjkuews+lxRmmlE5MMKvIKjv4iDhywVoUZmoYZ9hHOrQqVykfXQeKUUZeIwM94H8hRramUKH5+d9vkPOADCOTecvAwzQAhSCLRyG1vzXI9HEYJI4mJC+ESDQg8AACGh0igXjs9HNQzt178uauK6fvUKheH4C3U4thy2N7ZZZZ2IxC7TSrrvvvtS1XUAMGXqCcp9AwBAj4Fm4NYqOtcDgOWVz+nwN2Mv/l6tUCgEGY4IAg7ArOkPnpTgVCrjBY8TzsBz8KmF+QafTRiHaobklFNOwZqC7TFD0l+D3G9XFwDYPrePHAYI3wpd13EtT5mm6kT+guVy6hXVwatWrcLdquXyBpQrFRwIVdIseY8HNQUKAIqGnS/60fcKAxHLY3EqAER5bsb1W5u2KUculcjBSbNMcND2mMY94AzvlXo5LY+NHN4QnDmZN28eIaSxNW85fN68Lw4eMhQIPfHEE1FsBqfvqVOnDho48Nhx42fMmCEHcfk4hFsKAUAuvvjiZDLZ2JpHMKn6fFQmo8dhZkwjUG5rabmcAEnotHdZ2XJY2Rs+c+ZM9ffEiRMVgvX/1IQQH6x/H3wXKN25c+dTTz2lbtfWj9a++syTP33wQZX1Kfi8rrYGIEyG1dbWFjyeVAGHQqUEJZW6urpQ+CtdnQpihTFjxshYQY+BpuOiGAQWDDQd4inKpFit5B8xDzSdpirS5aBRCgDMSOAvRmS1NCDEIxLbEYREDIh2pKNdwW4Ad+cgAEAIsH2eitGyJES37SDKJJ1Oz5s3r7qqeuSxE6tr+6m46vbbb/+3iy8CgFt++GCqth7PUPrvBPDVdO0A3MdLwVnOgGpLly7FM5k0MD1nzhz1izNmzLBc34xp8Xhi4sSJiqDbv39/5NEEVJeAclUs4HI+cuRIGXAQyhiz3QiGg8gSCRfC8ngk3irxWFH96gvCKkYikCstM3XLxPRJA9NVCSMsnXg8oVOkNEcrm1YoYxrtlOIcsj/MZJRGhGpwhSYpVwMLMRwl8NXAkKVsByL9bHtRWj4j7fMfcHzlK19Rr/W8efOws9su9GYSAoCvxc6/cD4IPnr0Mc8995zUUHIYJURu9QgNy+EepwAPfe+776xcedFFF6ntbK47CwkTfBeh4H/JSdpeH0kImYUu7dye9c3aegD49W8eU85bAHDq+Qvw9ADgzjvvVP1MiKLP+1R47JOjyDmPYv1U+wcFKOvXr49mOPbs2YP9jkA0JQNClZevhcrHQcZCiVbZLv/1/f+JT3P58uW///3vASDv8n4yTy6PHzlypMeEx8SgUANAPn38qlcue111ouSG5bGKpBEcTCAIRGyXEz8MRFAnEQAsxx825Ojvfv/eiuqaVatWKdt6R9Dx40KBZ6QnWC5PYm41IKkiWyrY1EpXdGXUYnnMjOkUyueXpI5fsYgypS2Xd7W1qDN/44031CCfvL+ypXkPELJ27Vo0uit4PKmTK6+8ctXKlVu2bps1axYGc3mX1fWr+uIXv4irizLFtV1f80NdhFCBzWWZWHnRB8cxDaqTctQbnk/Zy6kQtQCwcePGcu/tv6x5ngexJLg2nqFip3dZ9rtLJW3t0UcfxTXS9vgxY8bcfPPN+ICeffbZLR2O7bOUrkUzHCCZyRogdjvonDjzZLX4XXDBBQqrQQSPaxRCWqzslyGsSqwyDzSDa3GV4Rg5cqTKcAg9IdPyGJ0Eg0DAOJVl4iDzoRgTIEsqAAAFjwshUgaVashBCO6ChujF55577hvf+Ea289Dzf1j82MM/sT1pxHrzzTf//neLAeBH37t567bt6N4iFdkVZtZIYLwlLyeIw3ocD6OZxsZG9fl89NFHeZenY7QOejZu3KgIuu+88w4ygOQMEAze092NpZZ9+/apgOPdd99FpXZ5mQEv1HIjRNxSjxX0mg9vixAQqWIEB8vlHKUvgpJKVCXMV8EcpbC5zVJIVbxSWvKLvhnXEatBCUQzGZloSBQOHpROKET1ziMYjpJMRo8S8ygNRCyXp+OU9DJk+Yy0z3/A0dnZCYHrjxJi0syKWGBoDoH+DwDYLst3tIIQO3btUtABTFI98syrAPCHP/7x8ssvVwcTv4hz8Z/+9CelEt12oEV6ZlKqKAZ/vtke83vNqzaqQfcKRFARq+zwmWd8CQCA0lmzZkU3hZbDhOgjle34/MI/bC2b65cvX15fXz9s2LBLLrlEJRv+oW3YsGHRWrtUN5IsldBkBDfutscM7qnJS7p+A9heifCX1Kv2GbMVL5QAwMaNG22P65SYiZIYQtd1y2VmTP/CF76gOjGYs1xeW5lRW7T58+fjj1oeO7xnlxpE1bDyLksb9MjhNp+x5uZmdZm2548bOxZX6LPPPhtxx7bH43JaEKjDKDMfLncjABFknWDgaBqUlvpl2x73UKmdcwDy+uuvq3sSXH6Jb5blsbambaocsHTpUjyYuIWVK1eqy1+7dm1ja97y2J7NH7/66qv4IK666iqEXVseF8UwQYgNoXnpuEaEUIra6raYMY30pQ2Qd8vR9Aqaii3qMPCXt1gs1m9wAzg2Xo7S38sXPPBCvKekxbo8ZdDzzjtveMOw8750/qRJk4SAgieSMRrmDzDDEdBfe3p61OUfznYng0Xuiiuu+MJppwLVj5tzqqK5Dho0CACA+aDpUdCoRF/6LgrfKQzHrl27MO0BAH4QiES4tRrETUMwEkCeg34KesxlQo2jVlzL5wmdaiSAWQTpEySGQCBzh5m2p3+/mAuBZJynnnoKhAAhgGpbtu/ANVsiloLL37l7N3Jrox4rQ4cOLfiAy3N7e7u64R0dHbgFD3xkwiQZypvK7KzSN3Mch3EpV8plSWXIkCGWy1OBAZ4eSGjg16e0DNTWH3dckQyHnA9LAR9hWq7gc58LjEWioFErUA7FQVQeAoW5AGEWJcofQemEhnUZJZoOvcCkkTRJKYYjHtRrSjMfyMcp49Hgt6b1kiX9jLTPf8AhDQhAVvGxsyObc3uyanpFIWcEaf5pyR+x//rrr8e6KT7CI+2HAWDf/pYot5bZObVaqMG7skcgbiJ7xff9Mghnn6+B5ZVD5wDA9jj0MubpyeUs1wcQk6dOjdqoLnnuGQAAQtesWRPV4bD6GgQAci7Pu6xMVeyrX/0q/vHaa68tXry4jxP925rv+4oohO3MM89UoNGRI0cqcF+6doBShGxoaMBCdcHjFWZcPk1C0KQNNQcHDahTKDbpmeLw2oo0QLjlGjJkiOX5KZW4VttWy0Iu2V133TVj+nQgdMGCBQsXLgQAy2Xc7lm1ahWeybPPPovEE8tjNRWh8mPEqYTv27bxf3/+E9uyvva1r6mCmuXyp371IC7br7322nPPPQcAtufXVJpDhw7FBaChoWHChAmNrXnb48wKJTTwJbQcphFI6n1YYIMk3ZSY31oumzhxfDAIOeWUU3DtzLtM88MsEfJFbY9XIekmwMGgJZjlcj9fIoZt2zYCMqZNP6EMegIAPQ5rXPWnP/z6vx959NdKJQyCmZr2pTSa98pt66PsMFDCVv/31pHtUhkOCUUE4EZixPAGpTeFORvb5073kdmzZzft3v3Sy69885vfLHhMCJHUA2pooD9rBXyEFStWqKTC7j3NauPruu6+PXtA0zZs2Ej9QCcGY3fmgWZALFnslhcolwTmQqoamB+jcpmZO3euynAwLYbrlnR1QURqPCXLhSWIVA1iKZ0STKsA1howUnflaz9ixIhjjjlGnXmqph7PPHCAY0C16aefgyDQ8FZyBlRzQcNrlzS94LMSMRMHiZi30b179/YUPYxmpGYJECDEsixUB49QXSgA5PN5y+MpnWIGUYVEsXS1RiCu0VgsFmQ4tH379tleFMMhMxxRigpEQKN5l8X18IoUtCUK+NAo4UIgo0d504MEjcoBFesEsPBRQjyRRJIoSjdMh0QPjpZUIsmJvKtUwkoDi2gmI/Kh9Li+KqlE9Tmw6kfIv2ix/6R2/fXXq3Lj/PnzsZPp8WhJBYUEbJdrIMAPMdXBjpOBa916xddB8FtuvU1Jm1suz8R19eGdeeaZ2D/ztLNAM5TZm/yYAQBge0fh+tebep+k7fZR9ZCxQmn/0088LoCAWwBCFWoSALZg4p0QUHQ7OUjfdESs1/wZMGl0kL9Lu//+++dNn3DU0OEPPfSQ6mxqalIllV27dqmEUEdXt1La3rNnT4BV5NWZ9Oix4wBg4qTjUIiw4AkAOLBrq3KWR2kN22e1VRXz58/H1WXOnDnHHHMM5orj8QRAWIROJpM4C9i2/e66dUBINpuVy7zH5LIRvBK4HFoumz3npFmzZoEQQ4c1RDkgucMH1MhYyECjFlUhUvfWcrnme3v37sX3cM+ePXv37gUA2/UDBxMBhGIuPe+xVEwjBEhpcsv2ef9qVTmiCPbEqKVjf5idXr58eSDywTXmqKS60kGvSCYeffRR3HHecccdaJZruWzaCScEg5Dzzz+/srIS4+B92zarz0cVfbpt542nHseDFy1apFxn8y5PG+WSZXjKluOXvZxRFRmIyLv9mfb+gZ4rX9oZ7fF9H4wkOHb0hgNAwWPUK6pvVl3+Jx+uDV4Junr16n0tLbjoSiAkZ0C1abPnKnhgd3d3WDsoOCrDce+9927fuhmoDvFU7tCB9raDoKTNuQ+aAfEUL8rqpCxw+B6YVeBaamtOCAHmAyFgJDiRy7zMtcg0iUk9KYsii2j4ysXNkM4aKRPYwWn39PRs27ZNvczt2W4c/Gtf+9qAAQPwrTjh1HlqLZeN+9GAo0wR5EiPhf3BmchopsAgNLoLqiGpVAoFc+VTjuCobNc3YyUaqQDQ0nYYtVDlzRFSsqwEw0EBQWqKWoKNRrAaKpNR0h9BkkYhHL24tX3EEFFVcqsvmIUVoQtFma6fBmsNfWvLMRlclVSis7U6+TLbeqz6kV61+M9I+/wHHLfffvujjzxCKH3nnXcUGq7I4AtnzsPX9xvf+MacOXMAtXGkc4QAQk8//fQBAwYAQN7lTldH0E8efvhhHMRyWZTqokRFL1x4DWEeYhtvuukmVSYAgA7byzkl2WYIlofemz/bY9CrdHIomwXfBd8DQhUlAQBmnXWBwo0rVxcI6pp97CwdH3plPi699FL19/nnn9/rXv71bdu2bT/+8Y9h5gI44ct33323Kjd0dHQo4VSILAy6WREVrpB8UY8dOdi845ONALBx0+Z7770XANAxHARXCwDiZiyXC8d+9tlnsX/VqlXr169HCeq0CAkmANDV1YUqBffffz/+4ptvvikdKFw+GEkiggMhI0aMOPbYYxE12b5315o1a0Dwvfv2PfHEE8FdZQnK1Mgylw5QYGLs2DGqH1XFLJdrPLRGU5dv+by6wlT9yHFQVtdamRKR44cZDkIV8dtyeef+puAyJdICc3ia8NSZyLjK5akYPfXUU7949lnHjhunvHgsjw0ZNPCFF16oqqw8cdbs+++/Hw9OGrTj0MHgtInKSeRdBtK2ngLm0oP+wLy75K1ADaiyl/Okk06K/ieGPn++Hc57h/IlmTNd12sHDQVXasRhMqyxNd9T9HY2rlc3HDMfRY/LrENw5l1WEWMIaSYsOBD6/uoVSodD13XFF4VYQq3Qzz77rCqdgGvff+s1oEIoHzMcqbpqqUImcaCeA2YNFC3G5MzQ0tIC3AcASFYQweMo4C0xHAyjGZU+CVICPgYcUeSpTgk6PCBhAAAgAElEQVQTHB8Z8mgiMAsNAPJFD+EXixcvbmtrw1hh+VtvpSQqSH5KeLwLeonnXJCEMMxK7I/GBACQs91Q+CugunR3dyPEMggsmILzozRZWcBRYFLBPUw1UdrS0mJFMByKpZJX7O7g8n3OAePdSOZDreiKKwtlCNMIzCIqCFYSQ0S4IWFJJWoX54WDEwgpt1F7uahtilWieapOtsTtRZTwaFgIGoXI8R5Lx/rQQf+MtM9/wCGEKBZsUUq3szzetOlDnHYff/xxhGtYLktheVUIIMTzPMUkNEMLK6LqwZbHe9oPqAldCQZYrqhOJ6fPOeW/f/GLMlpsj8P6zCnYXh+2VnZfflddOTu6Eqs2/bQvxoElTPOhhx6K4vytTzHNwvRJWVrl3nvvvfPHi+66667Vq1dPmDChjxP9a5u8OfE06HGI3Ktx48YFGQ5SXV2t0CfxTI0qqUSwilwU7WC3RKSiqOunYtrDv3sO78mCBQtQgdT2GDjKuZvgj1oeTxkBsjKiDYCoxrfeektt/fEMbZ/xIpa9OVC6e/duxIdKNQuQhQzU5sK7esqppw8dPgIImTZtmnKRtV229f216lWRWEWfDehXO3z4cOwfNmzYcccdh+iB2/7jhynTBMETyRRig1T9uJyl4nO5YxYcCEHpMAgIvdHL9DzPcrmhkVTMUPcESyoFjyV1unDhwldfeXnL1m1Tp049dOgQokbynW3nn39+V/bI2nXvXnXVVaBcOgVRD0KxygucjD92rLrMkO3isd7m3RCKc5R0RqPe+vr66Hv4achl2+Ner/m19qihgwcOqKntt3jxYiVt7nCImrcNGjSosTVve2zatBnqHgJAqqp/ytAmDUzLmAl37ZrhButfR0eHWkTzDlN4AkJIUDpJgWtjKCZ1Tn0X9BjEUpmkBPbKRdQvglkNrs18Wav1fR+YBwCQqizPZGA0EzdVSSVUy6AaxE0JQwYAzHBwAYHMKITVEJmEcEHDUEnisjkDqn2yZYsZ+J9J5TruA9U9ouPKV1NTE5BxNAAQRgqXYRleK6WQQHzdNE2V4Rg0aFDeZaahBVkfuUloaWnBqFfeK0ULcnwMiWSeWHAgdO7cucq8BqSiKGY4/EwkN6NTwnplMqC01JKOMHp6JxugV4ajTyva0BUWPoWNEkEvRdMwUduUvMODjEVJ2V2lW8rEQ0szHCUZEdRCFf+ixf5T2g9/+MNrrr4KgJx73nnK7TNnO7s3fajmF0wV2G6gOiAYEPrOO++g1ZDlsVEjGmafPg8EP3H2nDB/7rJhgwerCV2lBALYTh9CzpbHmOgj4rA91heAH+EXJf3t2e6oq6dqy99+2+k6XCw61113nbpMNUjvn8S5vizgoJTOnXfOtddei2ahf3XrvTBIQFk8BVSvqqpSKN0dO3YoDEc2m1V4l9b2DpU9WrNmjYwtfDZ4YL2avJCMqtLFCvGgxKmGDB44d+5cda9mzZqFHqqBcpHcXdXW1mKy9Nprr1Vbf3yalsOFTICXhJWWy/tVZQAQkkOxmoaoyX3bGvfu2g6Evv/++wjJdJAlFAkTsXJku1xjTlNTE/Y3Nzc3NzcXfC6EWPna87ZlgRBFx73lllsgqEpMGpge1c8sy7gOG6JUokNVVttlY0Yq400CKAjtMtPQisVwxZUVIp8zO7d8+XLVv2LFCtvlAOST91apQZYuXYpBm2loSdNUID5Mq/hcFH2RievqFyM6aczsq6SS7yvgiNZQDh061NTURwmyrFme311kZZ0HDx1q2bH5SLbrkksuweKOEFDw4crrb8R7dfnll59wwgkO40xAKtxRUADIO24yRkC9t5wBpXVDRuqUYL4hHo+DYAAAhEIsqdL7Z555pgw44ilwbKQjya2O72IgcmCnfMklLMMrQqYWHEsxjBhjgFmBZKWU4Q8BHx6mT8xA+SOQK5WZj3Q8jDgoAAMCAHbgZR9kOMqrHhEZUwrxlMI5Sa1YPD5h4iA7d+7cvn27SpO0HGor4dEEIbvigJxxxhnqVWloaEDjVomEC274wIEDsdwp7RoEB0KrqqrMmnozRicNTB9//PGqP5lM2h4vLakA/FkMR1lJZethW8qP9inaEXjTQxnxJHB/hQg3RAihPFOiBBPLjaZDSjAcYeaDhEGB6i/T28iHwl+ER9goSNyFPkowvikd3eAz2D7/AcfDDz+sJA2QBNjYmnc4kdqUVIMgSWh7HFXk1KqDUOq8w6vMxIJLrzB0/YILvqwWY8tjDUMGDzhqEBAya9Ys5b2SD/dz5c+8x1HbmLChdVZfJZU+qiGDRo0Lli4tatTy8YaPwckjfFUpZUFomtU7mvnH2oWXxRy7d+8GAIilQNO6uro++eQT7B8wYADEkgACJymsYQEAGMnoThT3QygSOnbCcQAwYeIkDDgwXbzksZ/jwS+++OJjjz3W2Jq3fJaJxxYtWjRm9KghQxvefvtt9GVIGVpgEhHSN9AF9I477rj/xz/SY7E333wTGa2Wx4YPC7ZWQObMmTNp0iQAsFx2/pe/gnzR8RMm3Hbbbequ7tvSiKoVAIA+t5bHDY0cddTAMld0y2MizMFQAGhtbbU9ltDp0iVPB/0Es/oqzaaVs1RYw9Cj58+fj4Mj6hYViiaMH6sGnzp1KqUUIQgSfxepVRVcXlOZjp7JwIEDEX3Wr66EQlxVVYU6Ct+8aL76rALAB9cpWbfsFXWw4uLmHZ6OU0rKfW7zbh8htQpTsJXxvDYd6oM8ZXu87FvbcLAnX/QUSwWptkWfCyHWv43kZ/LII48cPny44HGdksOIdwk21m3tnVhWiEIyD2e7FFYjKKkAUE0YCYxOAJlTzAOqQTwNro2bfsm8YC7oMYgn45o8VVlScYugx8GxRlbKwWXNgnmQqjRAlopkwYL7GFh4liT6luimx80EkUYtgFt8ziGyUsoZQ0mWCQnLCKTJGFC9om6QSh7IH2UeaLordDwYdW6iGQ68V1GRdQDIu14IJhUcv/H9+/fj8hyLxYYOHapelXj/IYjhkF7cggPRurq69h44kDJ0CFT/8QG9/vrr+BKGl4mgUYelg9wMyJJKEHBEMh9aQM/GtVzKlUaMV5Q3PWDQpuRKnZClorgh6IqghL9YEBPYUcBHqYR5JMohUS1UJVdaigqX0ZJGy5RCeJ/6HJaHeCnxL5bKP6edccYZKver1jPb55ddeTUSTBYuXIjWVpbHJWMt+Awkk9BlPYcOXn3x+Z7r3HTzzT/84Q/lIC7fueH9tpZ9yA2JmnR3tuxZ89Yb111/w9NPPx09mbzLegvA9QkOhQDDUdZfPeDowNCcRHGdiap+yhUdEzPB4J8ScPQ11/u+/9arLzz88MPys//7NTmjxZIYWChF0WOOOWZgwxh0hb3zzjsVBCFeUX3CCciDoE8++aR0MPHYjo/f39r4IQBs2rwZHwSmi5cvX66emnQvc3hSJ9dee+32bdv27d9/6qmndnZ2YnSybt06gHB1eW1dI2Y4duzYseT553yfLVq0CHeleY9Xm8mtW7eeNW/exEmTfv3rX8fjcSHA8ni/yvTjjz9+4swZ373pJiX5ajk8ZYTemBiwomblwf37VD8SCy2XSR5NEIiYpmm7PBXTIptFSZQIjaBKA1nbZd2tB5599lk8+IorrrAsy3ZZTCMfrlmpBv/ggw+y2azlMjOuaZquzgQ3mpbP0zEjek9838eKtVEanDHGsCwVj8dVqgmZRz0BzF4drPyTEcZPe+X88n3V++ReFgAA5syZE/3PvMtueH2325vP5fKyPkIIxJLgSGNh/PBtn8Uo+WB1aSLHY0mDNtQq0IwG0rA+EgHL/EEJQkLxRRk1VEll7dq14MtqCDg2hhoydeE5WFJRi5nMK2AOw7EVgUvKAjEPB1ErqOzUDYilOvc3Y4dMCAVnmKDhzQxpsYHziMy1qJKKoIFuJlX9OdtRyFOJS2M+aLrlyZUvqEhK0GjBF6iFirE4DlJd27/gAb4PktjMOVD6xhtvYL6BMbZ37171vu3esRWVRoPQR/ZryQyejCwGBaESaqHK2xhoeysYRNAvsR2Wy6OBiEZBBRyRjEVYyFDe9FD6uZUQTwJuiHJzBVz7w4NLcjARt5cSabLIj4Z6G6qTCVHw+zBkASzN9LKcLQaE3t6OS5+R9vkPOC666CJ8gPUDBip/UcvlM2fMvGDB14c2NHz729/G9cz2GAn1qiP5c48f2PkJgPwMFMnC8ljLrlDgWTl1bdy0+XDzDhzkqquuUk5dmMTrzVay+xL4AoD2rhyA2Lh5M4/Up7usQhSpqv6pyAkqf0A0T6Cill7RbjDXl3RefPHF9952ww9+8INZs2Yh1+Pv1YYNG/b9738f7eXuvPNOVVIpFos520E2wTvvvKPS6QWPp2IaxlU0YHsWPO72HAEhsMCBgEdMF49HQ9eoZ4rP8p2ty5cvV5PXypUrUUJRTmrBohuLx3EPdM8996xdvRqo9sorryxatAh10M2Y1q9fv7knnTRi1GikFRR8zoVUO9AoLVXdYWd98ZxMRSVQ2r9//29+85sAYLvMNJR+VJg5sz0uF5ugP5vNWj4zDfpvl18FIN9D5NyqEq+SOZK/6HPP7ooO0tHREWwiS2KIdDqNO10nUlLBRc52mXBL8C4HDhzA6bXjcFt0kI6ODgxElixZouJ4FPvPuyyBhlZBghCXKyTpmIYWlTTAlndZQqdlnalUasuWLQuvv+Wuu+565JFHZLFf3l4OAG6vZLHl8d5vOI2nkBZ79NFHI9/bdqWnq3r09fX1BVekUMgy0q+bFQmDgoKscgaUDh93HEpnghKMCegbqgbR3d0NzAPBIVUFro26F3KF9h1IZEAzZM5MVUmwjOvklWCr1JHzHEhVuT1dJVeFyNO46duBzCDGEKinHjcN4WHnpZdeOm3qFNu29zXtsj2Gy7PkxWBmV48xILgoBskJH6gWr6hNRQoQQb/BtDgGEBFcCwUU+DIoKFos50C17JEOZdATqJUzoNq446ZYHjdjNAip5XtYUV1b8LgZo0OHDh02bBg+iIaGBhFLYlKhzLq2BMNBCbJUopkJANCILBlHZUYBQCOUKd2OSMait8EKlCYUrdJYAafekigkArMoEVMPYBZClFNjImIeYekk4i3HCZCktJwtiSHUSUaRp2iwolPSG6D9GWmf/4Dj2WefRarHoUOHlRa47bHf3P8fS37/2N59+yZPnoygUdtlR9VjHM2BkLlz544YMQIALJdVpFAsXLJXcBDL5VOnTAbpYSiR8ACwu3lfoJtOAGDDhg3qZHqcPtjRtiSSlHRu3LixsXEjOIU//PEplbEHgIq6o8DFYlAoUAEAR4+eCMU8/mKUpZJ3GSEQVZJRJw+lC4DrutIICgAA/mqjzk9rl156aUXdUZOnTlVhHwBs2LDBcn1wLCSSoF04AHRbxXdeflbhQHmQGR6DQh1CAKEnnHACBDuJzZs3q8lox44dXIiiz+tqkA4gH0RdXZ3ts5ShYUSiHlCmshq5ZK+/HiqNKr2NlEEXL158y803L3nhhd/85jf4iwmd+k7xyiuvXLXynQd++iBWiDDzsfq153u6s0BIe3u7PN6X030oXSDByLyuplJdDgAkk0lcFOd//d/v/Z/Hk/HEf919N6qqqAxtLxYcGzNyhBr8lFNOGTJkCEoaBLNQqIdmOb4Z00YNHaREO2KxWGNrvuDxgXX9jjvuOHX5c+fORVvLydNnyUGAnHrqqYMHD8YZ1vM8leHA+5l3WFKa18htqAIlYLo4arApT95lFQmt9xfRv3//BZdece2110aLhgBQ6EvsH/rCXLtMcCBYUtm/fz+KiRXUpjN49EKIAuNJnQYBhLwtdmDShh5+mD9o2teSCKohUlpD+rElFa0jwIG6YFYrDIcsTHhFSFUCZ4RJYVbpx4YkI7egjBekcqvvQqoK3FLZX+YrgAh2hJxbqkPc1IUHAIsWLXr55ZeBMybIbx6+X4W8gVtsoHkKIqZFqPucgaY7CtGCSu3yR3VmSM3TYcOG1dbWRpRCEqX6HKgIkgQiCw0yfhIcCP1kw4dWFMKpgNscACBl0MbGxubmZnxAe/bs2X/wEGaPJLpLPiBiR+AUSto8Wq2ASKwQhU0AACVSnivvsEwAeSkxZHFKxEBDqYzSDAd+hVFwaJSMqlKSwRkCoOQrCBXNUFrqpSLN28LSSeBmEPxi5CVHb3ooR576pkKe/gs0+k9pr776qjLexMDCY8JlYt3Sl9VEj0J7lser06bU7SD0nHPO0TQN0xInnXzSjJNPxUL+jTfeiCNbLssk4kr+QU2vqZr6qFGLxJ0BAMoc9dqKoWdK2ZT50ksvQSyFVrSPPfaYegU7sqGNqhKoAIB+QxpmzzoxaWaefvpppUMFAWi5N5qkN3ulTFpU5cP/Ls33/Qsv+lrOLn708YYxY8aoubWyshJiSXDyeA9RTrGxNV9kAryCSnHj8bbHqkwloSFVIAseTxl0+vTpUYMohL/U1VTfe++9+DS//vWvz5o1C01Dyhwlsp3tqBp+zTXXqN25VB93uJ09fOONN+IieuuttzY1NaHC8ZLfP46FjE+2bMHiDs4mdrZd8ZOx5hWx0pavSmtrK6ZPRITRCgDd3d22x0xdG12lffjWKwU7/+prr6HKhRIu1AjBrJV8aj6v71f73nvvTTvhhJknznr00UcJIZiYGVEfuG8QircazbWPHDmCbzJEzNgSmtiwYYM6k23btqFhyunHj7riiivw3l544YWEEMwSGYahUk0oR513WaWUgZf3MGIxw9IxqhFSFlVbLs/Etd5fRGNr/uO2/JFCOYHccn3oFZrjnSx7wws+B87AL2IAIXcUHotTHj3DlpYW22OpmCbTk0ptpTuHMUSJKXwslYnLbzwScGgQS6ndtrRY8yTxBDf9EiDpFYFQcO21KySTKCipWAAATl5iSANZIGAemNV6EJ0ABi6+A1oMYqkAyxzkWmQgYhq+AyG7hAHV1ry91AoKEBHLWR3iZpzIX/zSl74kB6H60aPHq5VSckaYB7EkD1gqbW1tnZ2doZi64+Px6TTWBxkQDWJmInDQlYrAnAPVxk+dgcqhckILbnhbx5GETikhMsQM0HUioBwHEBYGVAMjzoVIRkoqLAggoqDRT2OpaIpGq4qAEaoLlIJPo+wVjNfxb5W0KNGSj5BRS01tqJImSxpUC7VQJZcEveZDMzYIB0lHYKrqDJU3PZQiT61glkC7uM8gjOMfHnDYtn3XXXfddtttd999t+u6vu//9Kc//a//+q8S47F/ZLvuuuuU8SauIrbHNQLgO2rzh2+57fFid+fPfvYznEZvvPFGieH3WL/KzLe+c3sqmXzgwQenTJkCmCj2+MFdn6hdkYJrdNsOOJZa/9AgA1ve6cNHzfZQEqMUOpd3IZZSSYtQFCiRVhkOiEQ5lssnjR+fTGeU1UtwkiwT1z4NNBqduuXkErRoXebT2qfRFNtt/8lNJa7iO3fu/GDtSoiloFT5cVNLFmJJcGy8HIxyOLrlSWU2MmDAAITXFVy+9YO1AHKHilAM3PoXi6Gak+u6tsuTOiWEvPPOOxi1/Pa3v+3s7LQ9ntIDsG3wgFJmRdHnaYPecccdP7rvHgD441NPn3POOUKIUqc3AgD79+9HhmrbwXCJQkRb3mVJnU4+IQx98Fkgj2aslDYnoKxoXTagn7KlIACQSCSw3P6rX/0Ko5n33l9/9913y6pEXIdS2j36yJgxraamprIik0im8GWLpnnVyXR0dOC9sixL3Susrxc8Lgq4gsqJfu/evThjrl+//pe//CUej/VBzBJJ+QchFF0o77KKZDz6i+oVkmJEvVkqHquI6SjVX9b+96NDa/flyjptyeXu5TrklWM4Ch4zgKnNAFLZbY9nEiWZeU3TCi5P6lQCIYN+ocWTRq91Lp4yiAz1Sp1NkiolIJnAXhHSNeDYGOjINCQGl47dnZVVEjk4hg6OLeMP5Y7mOWBWV6RiJReG3NpIhkOCRpG9Ek/pwgdl3IoKtqPGWI6UMJfxkKS6JA3OsEIk4avMB03nelzFT1JPnXmQqiSCoxVZYCPnB9xaisuw9IJgDDQN4qlkkA2SORjBgFCXGOiXRgg588wzVV3mmMknYiZD/mKQJCsG+ujB+8ZRaDWqHKpTWSKJ0lkBQNfIobyDyqGZEtCoYq/wdC+qC5RmOAiIgzkHJzorEluol1kaGcqDQzJqVAedEhkl95Imk0kLy2ExTUrEEkoQHQSl0QyNUGDybnh8KfLUl0AQIPCZ1P76hwccy5cvP+644+69994RI0asXLny3XffHTJkyPe+97329nYFeviHNmmODACE4tucd5kZ12+956c4H5177rmYErA95uVLyuEB/JC179/9zS+dbufz373xJgUaDeQj5cEY4De25rsLDhTDkoqqegBAT6+tGCjmaml3wbYgHqLeVCZjQMOoYUOQi0sWL16sfG4tj6XjWu+CjeXwTLyPDEf+U5Cq0H84TDkfSuMkAPjksLW/2/nUu1za9mQLb+3ujvZ4ngd6DKgm+XgFSfbLVFYF4L4w8iv4XAjl207b2towELF8uehiP2LrcIVubGxUy/mKFSsQSdre1vrGG2+o/rfeegt359JmT4FM2zvR1/HgwYNvLVsGAE898wwqEQGI8ccqrocGAJMnT8Z07sy5pwPIxRWxGphEvfBrl1102ZWE0jvuuOOyyy4D1HcxtK1bt6rpFZMWts95IRc9k5aWFttnKYOi5Cj2I7dWZWi1Ujq+5THqFUeNGrVs6ZvLV6z4xje+wRjDe1JmslVZWYlUvVgspmKCTCaDamD1/Wul1iRIOijyYiQAOcJ0xUDksssuO+qoo/DefvnLXwaAvMsM3I4HByvhL0sKf5UEHJg7rOjr5cTR+gSHQp8lFbecpVLweKWZmnnyqemKiv/5n/+REuYej4HKcFAAyOfztseShlZmPNZtFbGkEuhN+UB1iJkVCbn8B8s8w5U7GSwiP/7xjw3DkMQT10YCdlBScUBwcO3sYekOI7+CYg+AAMdS86HEGHlFMGsMXprm8RF5agpHxvqyGhJkOCpTMQA499xzpYQ5kD27d+0/2IYvT1A5khxaA+Qa+8ILLwAAcA+ofqCtPRW4vci5y3chVWVwiWlNp9MTJ06U164ZviA4+OzZs9W9Gn389IqkvFcB5oMD1Xbs2q2SCo7jqJezae8+DImC3IwMfLNWoURoFR9QLKXOEALUpBC9SiokUCAtw3BESi3pXlQXiHjTQ6kMV5QWq/IQVqneBoscHEYzyi6u9ExoKEFWIpoOgbp5VMosmvnocZjiP5cgT51AHz0o4X7W2j884Bg1atTcuXMBIJ1OG4axc+dOZFqPHz9e+W53d3dv3bp169atlmXpf++2cuVKCIw3n3nmGV3XHQamQTe8vw5f35dffrm9vV3XddsTIxuGAsj5aOzYsePGjdM0zfL5smd+CyBn0p/97Ge6rmuaZrmMOCH+zvd97NfNqqjeoud5eCb4v3AOZWdY9IFSIqCkf8+O7UGGQ9basd9hpKYyA4IfP3lKfX19OLLHu1v3Ok5x586dahBN0yyPVcR1AbTsR7GkAqSkHwBgwEhomBI9bWzPb+lcuru7bBBN0x776JAn5CCUUnVFjIvokZzzqCtsVVUV9sdSGSBUxVWJRELXdYeTGBUguFq6KioqdF23PZ6Oq6Q9FULoul5gIiAzM7WIFpkwY1pFZZV6angPbZ+n44YkPgSrS/+hDamYpuvaD37wg6VvvgkAS1548eGHHy4ySOhaRUXFpk2b5p15RsPwEY2NjdXV1QUfzJg2bfbcL3zhCyDEUYMGXXLJJbqu2z6k4/qqZa//4dGfCyA//OEPn3vuOV3Xi0zI2SFS9MGXoSqdiPYzxgq+SMcNuUMVAghduHChpmmWyysShq7rGqXqVcHnu39HaKb11ltv7d69u8iEGdfKAg7DMPDy6+vrVWcikXA56JQIr9jW1gZC2hxu2bKl4EMmrkf1D2pra4cPH255IpPQX3jhhYMHpdjoiy++qOu65fGKZMllrly5Utd1SrW8xyuTMUpJ9H0LLkrnovyLYL5/JGevWfdesViM9hdwOqda2Uto+7xsEIeBZ3eve3tp3rKuvPLKjRs34oOoTKvdNgGAeDxeZGDGNMmwCF4h3awwY7qu65IwwhGSmYwJH8cPyBQ+aAYYyUwqjv2GYXieB4g9d+3Vq1eHXxYIcGxw7Y6OjvCjAADmQzEPjqUuVu7yvSIk0pQ56qIIIeA5YMQxw4GdAecW1cZMg8vPdtu2bVKulGrtR7rS8VjJL2o6xExDuHiwzPlJVbFkJhkLfxEAfAfMauoXNU3Tdb2rq2vjxo2KoEsJpOKGruuBZhcDqolYyozpeLyUveEMqHb0mPHpoH/FihUqsGjr6DRj2pSjq8rMb31qpOO6rusyapH1mpQZ03AQXdcTCI6mNO/yirihbpehaYIQTdPyLqtIxMIXhhCg1BPE56IqJfvjhs4BNE3TNC3v8apEbMrRVbquG7omgGiaRggt+LwiaeAxlFBCqaZptifwiqYcXTV5cCXjQg7isopkDP82dMoF0TTN8kQmpmtBo4QSQjVNs32RlsdqOjraUy2YvdXh8kw0TbN9yMTUIECo6hfphK5pmq6hLkj5tP/3an92zf9z7a//P//ChsIA69evX7t27V133bV582aMspPJpCrkf/DBB6gXfu2112J08ndsEftB0q9fv0wmw/QuM6a98cIzMGQSzjtvvvnmDTfc4DARR1IZ56AbW7du9TzPNE3L4Qd2bAkGkdVK0+S2z/n+JtX53nvvZTIZ0+RFTqMlle7ubtyFGAnm+JyQoJIaNA+0dEwDQqP91IiBZqiVWDmjHmg7vGXFUph83scbGufN+y3WfU2T9xS8X959K5x/14knnvjqq6+iFJVpctsXVWZcN4zo4B+1dOXsm7QAACAASURBVGN2OpFKRfvnzJmzqmAiFmzgwIHRfypyQnWj7MxNkz+1uf37Z42ty5gAQANchU80BiR68PTp06WEOZWYRPzX9mwXMA98VwlRTJs2DTSrIhmfev5X3hACCP3Vr37Vv3//j1q6Cx53uo+oB7F69epMJuMwqMmk5s6du2JTEw5y0kkn+dTIJIzJQ6qjT41zXmSittJsk77tktfXvO9gRSJhmuaSJUtAMwAACN2/f/90LZ6Oa6Zpbl63pmX//j2+/d573rhx4zyiVabinyx/admyZXDe1IOtbffdd98rr7zCaEdFMrbizZcVfeOVV17593//dxe0SlObMWPGu53yTFKpVCyZdJkYPKCEBOh5nie0qpR+wQUXnHXWWa8LPmLkqG9/+9u+adoe71eZzmQyei4ngnubTLGiJwC1UIP3rbKy0m3xq1KJ999fqd58AFj24dYiM2vSyVhNTTT9xrWEGddramqDe0Lx6a/nZGBlvKVFmeLSzs7OQ4cOOUzUZMwmlfmg2rJlyzKZjMOp3NQGg9fV1WUyGYi5AKKuKqNrWiIZvm+myQtMjEgnAfLRV4Ux9h/Xfyt/3Ld/+8QTza899vLLL8sFGMAnOgAkkqlMxlTHJ5Os6LN0TIsOIvQj3VJ8nQLAkiVLTj75ZB+0iJSwjPB8olWm4lL6Nji+q8eqzhydyWRkP5MZjjjl+CuyWsQ8SKSBkOp0AvuD6gnCMuyNGzeWfDKOBU5BCIGdiEkHANi0FI60VFRUYL/ciXlFAGjZvjlyx8yeYhE0A5IV4FjYL5ETzAU9AQkzBm74ixK7pnMj0a/SjPTLSlCcyDOZN2/ebbfdBsyHuGnW1NdUpLF/4sSJy5YtA9+FVHVF0jBNM5PJ6KGrCyp/MOwPpDJ80OM7d+84fo6P/VEKzP7Ww2ODcSZNmtSYD8T3jhrqJmOZTEZmfYIHYTl+vwozk8mUIFK1eMrQ5ow5Cq9GizMA4Hqcg6ivyYS3K5UgNG+apuXx/lXh5RuGZsRiXIsnDFqZkVda4Wmcg2maQkDe4XXVcpxEPKa7YJpmrugTIP2rKjAGM3QtFkuYpukArU4n8IqKxOUApmm6THhM1FVlUCYulXQEJaZpuiRfZcbU+2zouh6Lm6bpa25FUvbrPseXPK5TV3RVmQnsTyVsvBwA8MCpSsnjDV03YvKYIhNVqbhpmjHOgy+lZMb+p7e/NeBob2+Xr8Knt0cffTSfz995552pVCqVSuErVSgU5KcCcNppp5122mkA0NPTk81m/8ZTKmvXXHPNyy+/DILPmHnilVdemc1m27O5wCFaqPxBNpvNFb3Og6gyJBPLGzduFAPGeJybBumAcJ7q6urq7u6xPabb3SBiOL06jpPNZnO5vEeNKEulrq4OL+pgRxeOceRINqpLfiRvpQ3N8bzotY+cNG0DZ+AVFGES+7N5O0qLPXDgQCqVas/mGBAoWriCPvroo9OmTQOAXC7f43gd+3bt6DKPHBmggCC5XD7v+HGddnXnsukwF10sFiFu4t7CMIzo+eRsN2fZZU+n/UiOcdGZ7a6hLgCYpmlZFgB05izf98sOvufRP96+rnvosIan33uPEIL/KmIpcA+qqgelNJvNHurqTmgwfdKxbzTysePG1dbWZrPZ7u4e2+MD6hF+wYGQCRMmZLPZbtshvnv48GE1Sa1ateq0WRcnNCGFDYJd1EGL9Tg+OEWikaCfAkARtKRGcrnc1Vdf/fNf/A8AAKFz5849dKQ7pdO9e5ouuugiGDMHxp9++eWXT5w4sTOnxYEFJt0CCHnjjTey2ezhrnyCgq4rW0uqaVo2m832FPqn9XfffRdGzsDL3L59+4TObo2A3XVEvm/BhjubLwzMxB588MHXX38dLjln9549t9xyyy0PPNLj+OAWstms4IRxgXfvcGdOgNBJCf4um81mLT1GiQy1A9BosVjssnXKvX37QkWQt99++yvZ7qRGRlbS7373uw8seQcoHTVq1Omnn754SdPRGeoxL3qvhBDdBY+wokQ4cg6EDhw4cMWW/Z1W4eh6c/z48ZuDwa+55ppsNtvW0Z3UaT7fA4x39/Rks5p6CbttNwa+z3n0Vdm0adO6tath6vVAtZUrVy5ZsgShVwBwpMcGgGxXd1YLoZRtR7qFAFY6SGd3T1wDJ/hgs9jyhWRMO+OMM5Yekq/EjBkzPthla5QSEuSgKQUAR1DwnWw2G9n6GxBPJTT5JcoSJ/MhWQG+29PVnc3qoPChknhiHzhwAI8naN9V7AHXVi8/fiwAAKsWA8Du3bvRd1cuuq5Mk6jrqqur62lqAiHASIBrt7W1xeNBqOS7kMhAzOzctzWbzUq0IGcAAmGtwi1ks9lIhsOAeMq3unHwZ599FgCAuaAZlstYIS+nrIMHAQA8ByoH8EK+weTZbDbwuPeBGhBLGdzP5XLZLJe4lgDDkTIoHh+xQdEgbiYo4PGNjY3QMBUfxL6DrWa/sdlsNiIrogGA7QvhFbPZbJBqklqocRI+biy9tbRnkzq183n1FrjFYtHxurtzeYeBU1DHmzr05O1W6pu6hmcCAFaP43Oey+VsjzPBhSNvu+c4BcfJ5XJteTdl0J4eiSsSgvVYVi5Hsj324MoEjpMv+pyL7u5cV9HXKXHsvCPPxHY9P5fLtXflE1Qokw3B/bxl53K5w109SU0ynD0mAKA7l0votDNnxwnH/mKx4Hke/n24O5/U5DicM8u2cjmdUtpluTFguVwOy0O5XE82W85P/Lu0MvrYX97+1pLK1KlTv/zlL7/00ktltuOqrVmzRtf1G264AUOwUaNGIcFp27Ztkvv+D27HH3/86tWrdU2/7fY7MF+HKgU///nPcbr8whe+MH/+fLRUqKtVgGoKKHrtsphGpkxXVguyeiK9SPJd0Xow/iJJpGXAQenkyZMVGCLvMAx4y/ChtsfTMVpWmD7tywt07uKZIKkPm5GuVD638+bNw3SR5XLCffAdRYDEgw8dbnN8seGdZUuX/en6669Xg6CeTEWvH12/fr0KOKIKjyjc2dsFpk99dACwfc6g/EVPVNYCwKjRo5VhCmCGIxI/Ia2j4HLiFf/zP/8TgG/dum3+/Pmu6xY8ASA2rV2hHsSmTZsggGS2t7erqOXgwYO2x1I6lXdeqRkmkrbLUjE6fPhwxfUfPHgwyicD7jgDDea6ujoUDIiCQwGgqanJ8nwzrkt2tOBAtAULFoAUA6W33nC1wgyhLYjtsZSuDRkyJCQBuq7l+qmYVtO/bsCAASomOPbYY22PmzG6a1eYV1CI1HSgNBq6Rvk8rtGJx08NDiZz5swZMWKE5fqpGJXJwqBKMnT4SASCDB8+XL3hM2fORJoPADzwwAN4Jjt37ly2bFkUZq9C83XbWxDCIre5QgAhiDy1XJaJ6/fcc0//fv2Q0oJqK8q8m9DeOhw8E9fLKs3ZbBYSabyBeMPD96ovUZk+JewKvnB6suqVkGr3PkvqtLa2VvVnMpmCJ1I6kUDIIOnlCoIsFblBDATFNV/CmGTmn3mQrADXnjJEJiDlSo8AC8eSuhcKEVXIgRuCQ8uIYC+99FLJjXBtAIh6o1RVVYEQ4BWxOoPRg9wu+y4YCYgnWbFHXS8AAGOgGxBLSk41JmCkephJAqMWebwU+QjJOLKQ4TtgVrNCruRaJEw1dG8JFEjLbeSiWqh1w0YrDa4RI0aoL6LIANGR8nICtJPDJSI1YtSiQ6xEwR1JH92lVBRAWAYI5I6ZJewVyoToRV0hiiubjCBSCQDSYpVz26SB6UkD0xqJAkGo+kUAYFLsPFxbQ0pLuSIICZCnEW4t7oYEgBTTUwCREOenvOkBgSCKFuv5yH9G0Cj77DFj/9aAo6mp6Vvf+tbTTz89evTo73znO9JcMdI2bdq0YcOGW2+99dZbb121atWMGTOam5vvv//+mpoaJSv5D21PP/307Nmzfc/50vkXIKsesfoKZrhs2TJM+lku51YJiK+1tdXyeDqmVVbXBP0EAHRdR89Mxnx1sIzBAQqcyJIK0I8++ijqpVkR16A3PtTjZqxcjYDEzdqKzHHTZl5wwYVK+QMAGI2dOGs2CDFg4EBlcxX61tKSOfrlF14CmSahTz75pAqJ0CbDjOlloNGzvvz/qYBDcRrlTzi8T3YA9KIp4sGs18F9KrVX1A6IWsOgd0OoiBWsi4cOHbJ9FteoW0QeqVz/8GDp+RSUA+rr6y2XpWK63NOozMfyZUWfI35Tcv0pbWlp2b5zN06Cr776qoL7/P73v8eo5bzZk6ODTJkyxXJ5SqennXbaokWL+tXUTJ8580c/+hEEMUFEEYv+8Y9/BHzfEH0ZPCCQcZJm5/NtbW1q8C1btqAChLz5ggGlCxYsQFynkjZXy7bt+qah9a8fcPvtt1ek02Ymc+GFFxqGgYNLOXkQmK5rad5jOcyMaa2trUEnWbduHSqHlqWyV360GePygEgp+810GlWlA+EmBkQKqeEM/tBDD7UfPgyEPPfccxgoh2YQpQoiAGC5fkVCKxMMGDVqFMRNAMD3MOoWK+0MS78UjEJ6fVOstjKjXgkJRvZ4QhN/+MMfVP+LL75Y8FnS0CTDIrRR9VPS/xO1vSUkM67JCwh28z4kK8AtKBC0rutTpkyBQg/4LnB2zTXXYL9c6a0j4NpKrE/mD4K2evVq/EMiWF0bAApH2tUBckV3C4inxiyLxIF6LqSqQIAo9EA0d8J9iKeB0BLHeRlYJEVAdZExkESkpgyQSNWpU6cCALhFMGv6V1ViZywW+8lPfoKDjD5+Rn1tDQYQ8n0LvOtEUZ6D1OHgDKh+uDOrOLe7d+8usZHTNVD+MkHk54HWWwsVYqkYCZnhGiWEQHfRT5fqlWmUMC65Y1okpazLfl7OoeWSuVoeiPQdK8gYN4okVQFHmXuLRhRBl2ci0mTKcR79ZdTIAIoCE1EEiRqylKiKRYS/AtAoIUBIyGr57LS/NeDQNO3MM8/87//+75tuuunXv/713Llzp0yZsnbtWnXAFVdc8dBDD91333333XffnDlzdF2//vrrb7rppksuueRv/Om/sEkAtuBACNqIWx5L6uTRRx9V887zzz8PALbHjjlmDEC4nk2YMKFENEaE2WlkHhp6aLyJc5DDuM8hwHAQAFBW7D0uq0hgwFG+RcvEyvUWDx7uOLR354Z3Vy958UW074KAArP2zRdB8LZDh7/61a8qCSmCfBkgEMhHAsD2Hf+PvTcPt6Qqr4fX3jWcc+qc2/f2RPft0Qa6G1HsBkVQEHBARRkCMYlK4qOiRg0QYzQ/Y5LHGIeIJGowojEGEsfgFIdGMSCDCEIL3fQAPdDYMz3d+Z6qOjXsXd8f7967dtW5RL/PfIHwc//BQ++urlOnqs5+373e9a61U/XQWvgBdMuD07fjvPJ9H1i44qRm0Ln22msNjk0jykU/ktHNCOHoyy3y/nxDB4Yq8pEUfNXKE5cuX7Fk6bIf/OAH5CIR53Le7FIR65xzzlmyZAlpfr/ofOr3k+B8+fLl1J4TeHxwcNBuFY5ybRqCMms5NjbJGGu5vNxFMQeAcBv0iDUxTYLxiYmJMJXthhMEwfr1689/6UvnLFh49913z5kzhxredu3addVVV40cO3r/+vWf+tSnAFCHqjJjA4z2PEX0FStWmC7NJElIKvG5y+eYrwnl6mJLkxVgfOHCheTyShpNFa3lVAY+P7hv70c/+tGpyYkwit/1rneNjIzQvCpZ6s1iq90mL5U9e/YYIGfevHlxJtq+02q1FOrDOYBnnLg6TEXb42vWrFm0aBH9UlasWDF/4TCdxHbcPXLkSCElNSVqx10OLT0ZppK2gE71zae2glkNt/ZaLVy4cPbwMwCA8cHBQdstNswl+rj3hEHWJuNcnnj8CfQbPOecc97xjndAdTBWzJLSNI0y2fKUB02p7M49aotVr5AKoi1fr+zqVVEIR6zyDwDATTfd9Ny1z/EhPnr9jaZHXSEfW3+MR39m4Aejt0vD7FgU6YGIIGlk9DlUHScJ6a9o/6ARjgTtOUhjijH/9E//pE5KV5gndsSFSEnMw6xsiiJGdRnuzAoUKKuEv7IYjjubJHAAAA8//DBEBsZ27t7jaTEPlQ/pes1QR0nE2i3EzaH5JlKuWrXKJBwZXEqJtI+MqGmhqvdNS5Z1mpVuYY+z8TjvRzikLAzAZgb9gvpzAiPkNVBradGxv+3NlIhYAYJyBSGpJ1ZNrhnurJofWNlMGXONq8t0WmqkMsaYSVCqyIeFcNQSETUf2ojI07It9qtf/eoll1xyyimnbN26dd26daOjo1/4whfe8IY3/Ldc3H/LcE0/FePGA8yym1c4HjmozR0ceOELX0jzZ555ZrPZpOW10SBye2G4/SSxMm/BwlJtN4oAhInwOJCnZvEyy0o3FYMNF327/CgTHb8ulfHzn//c9NzecMMNRvO7l1d8R2kzFKbClZm5PFNgG1iw2BR3YLYg+j3uN9O69fv/cXhsvJck733ve8lCnQYJdz6RRmp/IhKmsh/NI/mT2lmiXIwc2LN/964DBw/+7d/+LZWHw1TMmdW5+uqrmw1/7rx5b3zjGxljYS4Cl0VhacVOvaNxJtuee+TIkUpzaSoDn9fkkP2B2S2PMaaUxc0DEo5P2wKbX8w5N52oy5YtO+3UtZ2BISoG0Z5Gya4UEox/8pOfLIqCPO4XL15swjlty+hidu/ebT4RqtDA1WZXX+G+0ZAqROrkUoLxT33qUzbiartMUV7lTT0OlC/n7t276STPec5zFi1aRCdfsWLFgkWLCWsZHh42GAxlJy2P53muEhHmANj72M5uKjoN94EHHlDdKJzv3r1728OP5LIIPEfbqEpwfsIJJzDOaQt42WWXmZeTwq1ZkRmrFD7IYa7j85o80UMPPTQexXR5k5OT3/rWt2rvW70imctOw63BJFEmli5Z9Mkb/33B4qVf//rXKRbGubDMktR+Ok5l4HHl92H8QTIR2K8EGb022o5QOYECfrIErUEjYU5jcHDwVa98xXFzBp9/9nlmUqUFBx/GoR1m8tJLL7Wv2WQtKvwrfY7QJBwKbklCAj8IOlUUujzBwFwk3WCgAyOQCkDkZMgyMao2G4sXL0aewvVsj3uVGOUJ2kNMiqangp96D9MYgLJ3AHq93o033ggi97SGpo8epHm1n9HlJxmpEowSSRMCjtuTzHS0+r5vsuGUOZRYKDy4kHAc+IGjtVA11CSoj8ax9NAA+A4fjbOBamJBIIQds2mQIFjd0Y2jKCCLoi7mwZVaXa1K4uh5G2xwuaqY27AHLAmyMJ0ZJumzl9NKIVklm5kR4WBW464Nw7CnpLr5r5tw3H777VdeeeXu3buvv/76c845h3N+6qmnfvSjH/1vubj/lnH11VcDAApwTjwG2rn+wz/8A63RL33pS1/3uteFmXQY9u/cdu+999KKed999z3wwAP0aPfveQwoN0aTk5NRlrcb7sLFS81CT5zzbiqVf5JedlU93iqp1FfMVHYa9ZJK4bVg9dyq7g9Z5ODavI1D10fDTDa5NPHMCC6tPedlSCJTgFerqtrqcdZnpvXjm7+DRptO8sUvftHMh5ksipkSi5kURACE2YwIh2w33Np3PzoyNvb4XgqKd9999+233w4gzkQWTlx33XW9OBodG3/zm9/c6/UI4QiPakYFQQVC5rIIPK6UthkHEMcxaXtrnoFWeWq22y4HoFUuVHQ5dHSUfqVlHyl35syZE6Wy4ztSyre85S3XfOxv9+3ZffFrfm/9nhHa6ygoW5+cMRZmouM74+PjhsOR5/mmQ90ok23XOfHEE20OR5TKwHNdW10ACDodqhBprSR1fNeWSbY5HKkIypZODb8NDIepaPvuhg0bdOcq5QoPp6Jo+3zNmjUm4VizZg0pf9h2cQAazRblW1o5W3f6+E3CWlQNopBg/LHHHhMiJyTj6quvXrRwod9ovvrVr1aOu9qTc1bDtX1nuplsutzjvPamHD16FI0OoEoqNtFhxqpclMoB36nVWXpZgTT+6uf/8cihxz/wgQ8QNkBvoP018zyPctFyHf01BUnFZAVruVaUolpDI3Bylfdv3rxZzQdDSGNVBNHjpHnBS46vyOipjXt1KOKIHgbhIM1+xFMoCqQ9c4AqEfa6REq96667YGCPNEazgyQaHx2BpRNoWCbr77mTJg4ePIg8JfqFCduk9UJSY0USxlp0WJ0njQG4WofDNeqrUiAYDBz1ZpYEEcdHo21E0jR3OyN5U/Mmj4yMlN0o2sxdu+XlYA4a7YZ+O9QdJkzaD1qVFAK+w8ajvJZYkN5GN6nEcmgTXdsYBSZXkBVveqicQIJivC2hoRkStCPVt0uFeVuTlA6WJZIxA0xSuxhHJyjT1sUzS2/DeNMDsI1hTUkFgMPYU1Dd/NdNOL7whS+cf/75NqIIgKySniLjec973rFjx2bNGrzrJ3cTcEocjte//vXf+fa35y1c9NWvfrXdbpM/ZKPZAMoAQJ3Qbc9ZOo8QfpVDKENzlw/PmVXDD7qpCEwLDDiM0B7QTeSshotqhCY+ZqdPDHTFyWvnDg5Qv8w111zjahlvBq0gRBrMpIWa5E0SrmAMlvzo6Pg0ktBEC6MlSiHTAaut9ft+sQuNNh1sVJtQur30q4rlmFmIaQZ9sygTAz6vnSQtnBls6PMimSL6hZo/ePBgnInSTFITIcNe7jnMc1gQBKYA32q1ojRvN9xK3RdoDs0LfHfNcKds3yAEQoAYgqTTRbfrrW99a5iJwHN27dq1bt06OvjBn/1008/vIy/7N7zhDeeeey7Nf+5znyNBQyWrpYkgyos1ywPf2bVrl3lAUC8hd1333HPPNU9z7fPPpAqR1kKVYPzZz342KXXSP7SViOilJScX83U23ncPnaT2NaXbcDlrOPzIkSNG7H/Tpk3EGmGMXXDBBebgtaef1ctl23NOPfXUyy+/nK7kiiuumD28nLZuFhrkAEhy5LLo+M7HPvaxxx8/mGb5zTff/JWvfAVVuqv99I0aWO1NecELXqA5HByA7bxD0ua1V4sAwhpMEmdi409v//lP76C2auVHoz1gS3BLCPLyHR4eVkpZnA8tXMoYIyxE0Z6Ikum1uiOKSa1y1jxBewhpdNttt9mfvqDjvWHtAtvldcZWPsYq5UVTCVXLaTiBNAIKw/lQK0lvikoqlPMpCmfWAwx1DC9/+cvVSZUhS/y8VctpYtmyZcgTgCEYYmlcOXPWQ7Nj01q120sIwC1UdlLKMOQp2rM7TfVmqmVHKOYp2cihDk60zdb88OHD5rfJGkoeXuWXUpKimivUSbSCu1DsXdmPcFS86WEhGTZtAtrZZDrN7eqGoV/UHOBc47hbLc043HKAq4EWsiAPgfIkDjNaqDWl0bJ0UqnXQCci5cUbIgj6LGetGqvlf8vqxcenwvh1E47/FePo0aOFyEf1VokCyWOPPfbJT3xiamqSJEAsw4sqVyOVbY+TWFlFtDETbd9ZsWKFYU684AUvoJPP7gTnX6SA5Te/+c1K9wboZmLAd/qRriiVHc+pxeeMucPHzT1u4fCy5c+gHlcozwsGqNzi3HPPpYUszOT00ccNE/D++++n4w+PjNsNuqYtiJiwta6BNE0nJ8ZNwqH0lfXnYibLWeUCU50n0fd+2GPHzl1Hdj/64IaNb3/728tZv3nS6pV0Yy+66CIiS8aZWLJw2L7hy5cvJyaEYrwXEozPnz8/lqB1SpUJtMpFlMu22/c0JetzlKBI7NPG94c//KE5/tvf/jYZr1iRlYr6DcpB58yZc9NNN11y8UXvee+fkVsesSxVVqePh+6jOemkk2x+cZTJdoPneX7XXXcZLtFPf/yfVCFS8UxKcL5169YwzcxiajzH9XO0ywEcwOjRw1Eq255zyimnLFlCorR82bJlQ4uW0Vas0WgYsX+6vMBziqL44Q9/aFCf2++4DWAtjwshRkdH6UoOHDjQTVLaQikis5Tg/LzzzgsFmi53ObvlllsMZ4gyIYNFMxQVf21d16slELNmzVp28loAAD/ttNNUpAGgzdtqrxYJTtdJo3nx2JYNJmF9+OGHNx3qkv6sfjqK7US+xAcPHty+fTvFv4np0IOkMKl0PLME7SEwzJmlFBQUySZPEMxGGpuK5xMN1byqx4wmFyb/uPPOOwFgdC++/zEAn/70p2leFWdVIqISDnV/VA9tOOgz2PaNhHAkkULvgH379kHkkALt2W6hMBXVA5L2AIY0Om2p2pko+jlRRo4pfNSoHlP3SjSmijUq9aF6jd/2pUI4SvE0spfTSMmqVavMJkG6DXo0WlYkIy15R6b02qusi2ASP/Cr/ATPYeNxNlDlcBBDgmqXlXnOZFGQrnnpWU9JUVHY3vSwsuRalcThELKw2dzl8UXVW0ClPhIzZCfclFQqJ4F9vAGQYFvRDtglFWMvZ8ue9gHYT4Xx9E84br311mc961nTk+OX/fZrCISMUtn2+Pvf//677rw9TdOPfOQj3/ve98JcBJ5zyuJKtGg0GmEqOg1Hc/jV+nXkyBHCANVabHmmdFPhI7v1+9+mg03L9aZDXcLHeHXZhd6i1V6OX+zdv/X+nxw9eGDfgQMvfvGLaYWKMtlpun/0R3/ke968+ce97W1vo0UqzEQ2PWa4iqbjvDk41/TQ0q2gedqL17oGfN8/bvESeE1ai+2ydPREpZM+Bzg1n8kaxJ3n+c8fWJ+HE7mQ3/rWt8hGlc78vOed/vor3nHxJZfccMMNtHkKUzF3TufUU0+lwHDBBRe4rktPzW6mOHbs2FSc0joVBIGOImzbtm1hIgOfayMMtZ11WwOawk2FfBVcoxz0K1X0Xs1XoCVm2bJlb3/72+kTX/KqS9Y87wyz9DiOM2f2bL+hyHS0TVHtxIUEY3fddVcmikwUgc8XLVpkrsT3/TCTgesoDEY/oLiXUYVIJamFBOOrVq0KoBbbNwAAIABJREFUs6JSUjFdKpkIXIP66MqR34hyGfh8y5YtBw4cIBBi3759Dz+8jSiTL3/5y/Wr4lx22WVkC167V3v27W97nDFs2rTplltuoZP86Ec/2r5zF313tacvJBi/8847J8Ie3cMLLrjAYH6Ugne1VWnNMrubiHbD6V8Wjx07tu/gYQDgfMOGDQrt18YxDbeeW0S57Ec4okyuWbvG3BNS3Y6Np7meF0LEmWy5vKIV4bcaGvtTqIOiZPYmRhTsp3b5WYJGgDRWFZYnHqZVjYbpaqmBHDRU94oU2LcJwE033UTzqmFn/2bs3wy9JdAe96SSHq5btw6mAAGDcETKbdVsJNIYwVDRU6UTJSqTkdtLXGrSU7dLbxpAOq1WFd/3VfqSp2jP9jVptNLq0giM7ZxaA5WMaeBrPfWdO3eqLASQmritGabU/to2pRN1EiXK3kqmKh3FvsNG+0oqCuFIZKfRh3zIvtjPGUEC0/10CkI4qlUS6mglDxRimejzQEhpemjtTwQtEY0akgH02ctR90oqCuPoBothClQu0hDjiqKIrOyqP5V/Koynf8KhuAhFAcZvvPFG09dw++23m3Vny5YtRBE4/vjjlRgDdxYuXLh27VpaLi3DQ8XKpKgTRZGB92mZ6CZy8tA+/Ynsm9/8puoKI2edPoSjKIo4L9p+3VHi6OiY5nCUri5RKlyZfeYzn0mT3sjo2OWXX06EsjCRLY+bbauCAYCjE5O2b62hkoWZ4HkSTk6MVbW5pBcASv/A1uFQLp19r6/2uK/f8ygVRVEJAbt27YLXMl50d999tzoyE9HYsXtv/9GDGzZ+4xvfoMk4l8f2/WLjxo301H74wx/u2bOHPMNqnhdRqlyphBCGqrn8+BONI/wpp5xiwbZtKp1oxnup8tQ2B9M8c9asWWPwyYsvvvjUNWua7c7ZL30F4zxM1NJ2/fXX33brf/7Hd75LmQpt5dWCXkhw56yzzooySYUM1aXCOIDTTjuN4BN939WVxLKgCpEKOYUE4zt37pxO8raxTkAhSgRVlua3xgekGwFFYBpJjKcd82gxUnmnBeS0Sqc3BZMIJ6CDazyYnuRtz1kz3KnNh5nyriyKwnzN8fFxqjTRYlorqVB+xvpKKo7joNkGilp9sCgQZaLj1d1lFdWm+gbGuVixfLlBMp71rGfRyxZU2eJHppM4ly2X6cyPlLNbHpMUokoSQzCINBobVZFYATxam8vuy5tx1CCQnTt39h9jfjDKqEwPgyi89rWvnTNnDvZswOYfBUHwwQ9+ECZCK23TkFIEVWUDkBHLpCz6qKw664GxZGq08hFar8yUyBX1JJ4CEOiwqkAvKFPcbFolNwoLzBK4DTTaTMSVk6tSSzvXx7/85S83KGPhtYxjHGDE11tcC58oICfP4LrwA5ZV7qfP+UQvqyccDpOiCPtJow5XZNIqIsIJ4ahWPVzOZ+Z1cqje2jpTlQlZr79QX0ySy0wUA9WsRZ287vZCSiG5px3aYDFMYXnTQ7nFKgwGgE0o+Q3C8SQMW4xBMR5S2fad8847z6yMZ555ZpSJtst379792GOPUZg/fPjwPffcQ2pOWtlCrV+7d++mgOF5nlkZaREJM0GAp1m7zeJCYF3Nwor0rNoejy3PcQBeewhpZD5x48aNoBJGarb4DJoHGmbiuDmDhquocGDg0NFjSMqWlkceeYTmH9uz90df/9c9u3Zc/cfvMt3/aZqOTEwDiqynWIEADDm0X28jm6GkIooiEfX5VatWoREgjehKjPZXmIpv33j9nl07Dh469M53vpN0XKJMNkr1TNVyHKWy5XHFeNfzU3Hadp01wx2FcABgfO8vdkW6AXLLli0muuw/dDjwXfQhHOSNAuA973nPVVdd5Tr8lb/9e+e//g+JqxHH8ate9aqNGx7s9eK/+dN3PvroLlGg7fN169Z94AMf2L9377YdO973vvcBCBPRaeiUqCgA5rpuN1VBTkpprmR6epqIRPpKdDgvOC27OldQy3FkrUcOL3EpqhEQN9Oc5AWvuKTpcocxy2CTA+BBh76mBdepkkpZftIQdyxKFEfPcwDdXkZ50u/8zu+sXr2a5q+66qpYCzdpOIQDIAqwafbhjNkvCq3g3Nq30dif+AtWrEYSgfFTTjmFRPoBJEKKAv3Wx2EmOw1eFJXEpZfJHRvuM5d9ww030D+njLPMNfNCFkXg6wdBct1+i2tyqFa5iME4kqiRK0hAda7SLz2NaxJe/cN2cITlljyjLcWHP/xh+4+RpnAC2LFjx/s++omr3v/BvXv3EiqjzpBGQIFU6Z2fdNJJ6h/kCdqzkUTf//73aULlEGkMoDehEg71e9SNuCbhUCePp1BIV9+TkrGX9uA180glEKqbJuvBa8IPeuOqilTCIa6HRrvlqcTl2LFjCrEA0sKh901rpJJ6WGBSIlXU1vKmi4+rKF16LhMFBhqVm+w7LC1kf5eKwyBkHVSA7oCt6XC4uuO6VlKh9zbs67lVyEe9pMKFLKZT0XC4V4NDVPdKhfPhoJAFuqmsED40TGJ706sr0TBJYJaUav/8U2c8/RMOzRyU4Ao3jnKNd2nsd2pqijaLtpwR/VvqbC5PopFY2v4ODQ0ZDgeNbiq0v2gp5kF/NZ3mumhdXh6JRXoOl0UFXHVJUVR/ommwVN3tek2noBJmouO7JuIa9nuUweZwmG3WzkcfQxLTPTHVDQCKrGdViNRFJjMkFgAiQj7qXb7qPbenkyTRrrDl1wEw2Y2RxeYeEjQdp7JVhb7jOI5y0fZ4rXzQE4zinybTAIyfesYLSbNLfbZGOKbihCKflk9WwTXSv94HH3zw05/+dJ4mt3znWxvX30vyWbWWlm07tjcc7nKmNO4KAeb8+Mc/TvM8EVKZv6uT86NHj9KVrBnubN682Vz2fffdR73ZtYQjkZyqD0uWLDFXuHLlyjArzNdxGDM6P1Eq2qalRX/NkbFJ0lBSWbKe33fgcNvja4Y7fdmMbPn2veIAZi9aRr8Rda/0FY6HEa2wW7du3bFjB80/9NBDZr/IGDMH/+QnP4FVV66tgMTM56xOaCikPDI6gXgKjG/ZskWh/UCUKvi6XpHUltz2+xnnksvU9GdxzqNUupyp5V5zXR8/etRzmMuZVmonJL/laYpApWCRhkaqiyATDQnE+GXDZkTBqrDY/HqlY6H9p8rvElfO/7ILL73ktX26A1IgjZFE1K5sfDGR9dCZgzQy4h/qp5dGAGRPlU0VJUUKZD2kkTH7UJ2uUuAHf999fFf9Q7MYgIy7lTNT5w5Dd+QIza9duxYgt5cG/GB2R5UgDxw4AJnDcQEmHJ9+yBXmqd8e1IogiqikGakHdmkIBwDgcwagllg0HJ4JalafISeo8TSh9EDrDvIGhLClNWD33NZOwhkRQTq1kkpRL+JA5QoyziQRrsuTcy5kUSd2WD20voV8GNo1MfNqXxNPsfH0Tzgsz0xGlZEwydue9irUKyM1B6qmLNP+6s7qpqLtc0Uy1+uU6l4hSQMdt+h31U3F8Lw569atG164cMHChTfffLNRxQgT2Wk4nFUidJTmge+wvmw0Y+6soEFZy+zZs2kXG2VCEct1NkPV1jCRswfMFp8Zunt73gLN4XBg7Xtyt4Gkq0mmavi+v/yZa5HGtoiCusicxBx/pbbYMBVE+baP37dvH/wAPZVwGEWQ3PFUXsU5AOIuRLkselpvgzbW3S7VvOz2DQAT3Yg4HBMTE6bzgnEnzmegAEdZQZPa1jKH4wDImU+JyOc//3lARejv3/RlktTUa7G6kkUnPotOohIyKUk+K8rhctas9OKybdu2RZoyOT4+XlY9Jie7mSgxZFP10LRWdX8KCcYfffTR6ThtGyFCzkgwAJqUbjnIcwA7H/tF4PM1wx3FBDSNu41WpUyuX+YolZTNnHbaaSavctuDVVtwrQhZOPR1HnroIQCkhXr33XePTnYHfGfNcKcoym4pipRhmXBUU+1MVxirWnBjo8fQ7CCeNsmZedkCbwbOR5Qrlp9534jt8aJzzjM/8Le+9a1RJoNSwU/9wBsDsykd1DziHNyB1+Jab0MluNqMzZAbFKqkIYFfapH1J3/yJ/YfX//619P/GO1zWMRS9eD0UKWKJx5qgxFPIwkpRTBig8gSMl4xB6v0NAmRJ2b1J5cAAIinkMTqjbIvb8fdI4cruqiAyrQMObSkrzousiRN4srl5Rlas8B0lka5hXLFa4Ep9rcFNblotAwGowkiqqTiity+EN/h6E84XJ7mM+hw+C7PZiq1UG5B3vQlmdSIgaYV8un8ti8kKSzw6km08FdfrlCjo0LlBKo/3IipQyMf/XUW1VtredMDYFBKo7ReVT8UT7Xx9E84SGSQlt0//MM/THIpCljcMQZgfHycQHi9nVVr8WPbt4Wp7HiO2nzodeqxiZw4jHv27DGTDz300KZDXVJ/+uIXv3jo4MEjx4599rOfNXsLkpRhqADL1MLAdR3OjKOj41NHDtJlj4+PK7prJhtVkQ9DGg1HDpuSCnWpbDrULbzA1jx9zWteQyefu3gFkghSgLF3vvOd5kOHT3wmemqht/edUSqdvoV+06EukUbrASBTvys7r2KMwW8hURwOs5BFSW63xZJmSZTmA8p3VLUW53lO3ZuarK6SvJ4ErQLdbtcgHOPTXehaZqfTMRFXOg2KrOV2ljlwfcmUX0NpecW5EII67C02ZQUmUaOQJJ81Eap5q0vFOeuss0KdWKxcudIgZ3OHl87YFZUWPPC4qhCpK3FmDQ7FgtFGaseOHe94+x8CuO7fvkHdQIGvK2j6JPOXn0AH6+2pymbGpqMKLcPwYHK1OG7YsMF8zaNjk8TVqGUtptautvhSgvGlS5dm3KeTcM5NmkgfZLBru0tFdxErhMNOZdvtDpod9KZMckbzhtxa7yrXdXR7PhZi15YHzc/ktttui3MFNdmmNoXbbNkS5hrhMJ4p2v01QlEgjQzCwTk/7rjjlGdKGitFzicer3jFK974xjf6fqPRbP7Zn/2Z6RerlVpo1FQGDN7wREOReKIJ9Lr0Cypbe8gtJSm1UNXBSYgkSkJFaS8bT+IpspejP9mJ1MLFfU4UaQRAxOoBVfplkrCp5QDUyFO0ZyONWaHQo8HBQVM6YXlW0UKVOemjT42o1hi1YuQpGh1wJxo7Yp+bPKpqwl++wwjhqIX5hsMSIbtZH/3CYUJWJC6gRTtA+EG18YRgkv6SiiiKMK0KfzlMyoqohjqYQxQzpD6EfMzUcAugXmqxNU+rzNPfIBxPxli7du273vWuRqNx4UUXr127loSQLWSVg8y0MhG4vLQI0s4RpPVmCzkDyLOUEA5bzYlKJ91ExBNHv/71rxP9Yt26dRT+ZVFEuej4Dud1YDnwuHH3obHpUHcy6tmuZj/72c8ARJlYMjw8f/58ihbPfOYzFyxYQDRY5fQGgHOzQ8qYa3M4jHkNb3X+6iMff+Ypa/7uE59UKkMAgMMjY+hN00JforJUsumrnQMIM+oaqJdU+gNA1F4IPzAlFSNNFmWF7aVC3z3KiuHjyI26wtINPF6Lf3FeUPnAcRxQGZ870m20XJUozJkzx+zO/YEhWh3KcO64aLR5IYhnrqjBhQTjzGvmsmh7juL86q3/zl2/oJM897nP1fP8BS94QVJw+rVrkEyC8SiKSOBLXYnprW00SA1M3R2diBybmKIsxHaUmJqcmIyTtu9IKc8+++xvfeMbAD7y53+y85EtUSYDl1vEfgdAmCgireZwqCsvvCYtghqDUR8a6QLWGWecYUoqzcF59DVLUJ3Cf9Sjoo/upJDgzv79+02Xiq3gTqUBo4w0A2nU57Svs18hxjkabcTTBL+ZlzbMRct1OK/L4/a/b0WBXlbc9u2vmRu+fv36SLeo7Nu3z+qI9mhSpTyaq2hEq1ScKyTSCElFUTRNU8TTAJDGJaLwxOPaa6/9wQPbb16/7b3vfa+ZtB0ePvnJT874D+3G4BmHysL/8zrsfpDeHMV4gGGZRB//+MdpQnGMetNII8VEseun0yNIQgORliZwwOGDJalLjYQSDnVPSn10KZBETANXardGnT5J6OnPklKCgIrmLKYJIirX0dKuXNvLqTtPTTcie3xfaekHqA2kve8H0HR5SmHbqyciEz0BFLV2WQ4IWYS1cK6FfUnpv5znisNBJ7fba0VRhGletYtjOV1JHeHg/f0y+iT14o5hO03XWlq0hHktJaKTP9XG0z/huO666z71qU8lcbTu5h98+MMfJmBW/Z1ejzpa4VEtr3qLduzIYWpwUhC3Xqf273mM2mLHxsZQCADQ5qVhKhWdWy+7yu4klQys7XFedTuLcxF4ROyoAMtJwe2OViKNhqkMxw4fO3YMKAC+bds2onZ2U8HzBCioSmL6Ylizg7TsUtmwYQO0TsZxcwdnDc5uVdey8TA2UHatS6UBMTXdrd3bMJOz+rS8Qi3VZ9fUiwKKw0HxTKPEPVFc8OqLjKzIhRdeWBSIc5lNT9oP6ODBgwQJ6EKG5h/oXg+7aSJ3GuYRDwwMWEz4Js2rZVQoW0uznVWAuRTgTgqHElOdg2oD4SUrKBLbJZWiKExLy/bt2838xo0bI02ZHBkZMSfpxXFlSdK4gnB8ukLVACkFOD95zXN7BWt7XEHuGsjZtnljlOZt36RE6uS79+2nT9RfR81PRElgJxzqQ1mcq92Y7/sG25uKE/qainygsxlvYDYt0yom6QfUzdQ+cvXq1SbBXbx4cS8v69P1tthMtn2HwpKdcLiNJvwWJb7nnHPOxRdfTPP0AvR3lYd6HTfvYSqkLIq5swJzedu3b48y2fIdAL/1W79l5odXrGrZT1PLcgvNSzA9Moinbd81EJczUi5rpvudht2E+V+PpUuX7tmz5+//5WsbNmz4/d//fTOvXNMA/Oo6iqP7od9kcg0EgCQGcMllrzF2EwrMiKeQRCaxKBGOn9yA7XcZ06Xa96qPJERRTB9TC0VpGJ6ESMNDBw/Qn1TLcZagMwdJJLRS+6JFiyBzAGgNuFUhL5Vw+C3TjaIwIa20NmSVogC0fV5rT4VBOJJ61aPpOuNxFrgOr/YkO4x1UyGKSiJCehu1DlUY5mkq6yIfDhOiqMV+32GiwFSSD9SRjEIWBfWHV04ykwuMYZh2UzFgW9EaudKk7/inXpvK0z/hoChLxeZdu3aZmvrSpaUqued5FBhKb0zuABgfG41y2fYdxX7Q69RxCxdTgAnD0NZQKoqim4klCyu7c4pY02lOgDCrcnkoceEoHf9oSKdhd7Rqf22p7A+MfCTBfalMJkfteRpJUdFBN5T4biq2/fyeRx/e9KUvfdnuRpmMUlNSMeXqNE0f3LDx6O6d/3nbbdSJZ1286PSZbxGdm1VtU9ICYBxJSNtWQoM2HerGmXzVJb914uqT58yde/nll3c6nURIWRQnrjxB33AGYNWqVXEu2q7NsnSgPVdRad93Dh46YqoVU1NThgnfk6xCTFM9/QHTFe4SEuCOif2+719++eU0ufKZz24OzqVag/KaKSQYv++++0anFZtSlQBK4ogqnfR6PTM5euwIia+DxOmtLl96OUtBcc6LQpKWQLnbBsD4rKHZYS4Dz1GXrfkKs45bTM04lqM3ByC4T4humbgwDq8hi6JlWmOE4rWkzKWspdVqveY1ryGY5KRT1kinUYFJdCLSTVS+NTQ0ZMuNdFPR0PVpW7IMmkyqvDEtGkeYCM74xb912asvvOhrX/ua6fHWxcc+3Txd6rbkSaTLGUReCpzEcZQrHrHv++YHnkhlQ68yCTIe81t52Bdo4ykkkc3fFEIgmkCeIin91X7p6M9F2u32mtPPrFlnf/GLX7z00ktPevaaP/3TP333u9/9X59TRXQAGiEolU/TEMDpzz+9dgC6Yzaxo8yrJg6j1zWpg33mGUZvGmk0PaVKKiVM0usiicJRlYgotdash85cJN2JcdXR47ouhAAKBIM8Syp3Jk/Jt7aIleWsqlSS0loSzxqsKMe3XKcGbwBouk4qCpJpsecbLhuLKzKj6nocjPfyVpVOQS6yxEszPE1oLa+wr6TCgSgnNbDy4KbrABiNsr6+GK7IpH20VkI4KqIdGgW3venNwUDd/9ZWJn3qjKd/wqF17iQYP3z4sNmJ7t+/3w7PYSoD31WZvg4Mh4+NAkXH0/6iev0yBX7XdXUhQz3pbiqU/rEOlkSzNx3bNUw4ykXL5TXSaC4LyV27pEKGsZHJefW8EILU7pYOG5ZJ+UzDVLzo7BeZg43gcbeXXf83/2fs2JH71q+3l7NgzgL0pulgs9DfeuutY1Mhki7A/vEf/9HsfnJZJDnZWNR1EQKfMzDbOOPwsVHkKfKUbjiZ2lAD7b9c84Fd27aOjU+87W1ve+CBB6JM+g6LJifsB3H7xh3kMVbTlojygtBUdbXavZp6ZfVTVkERXouiS4WY5rdlrKKLAnWkBHNGpyOKuJOTk1/5yleI8PHotq0Pb9tGj0Bx76UAd04786ycuTRva3ZBW8VCCVSoPGn37t25VAzWOI5NdWMyTKhC9MADDwCqGrJt88ZukrWNpJgunEVRRLQGx3HWrl1rvmZPoMRUUCZnU72sbYs5UiLSCHyH0Qp70kknoRAAA+ODC5a0dXbyzW9+kyL09i2bjo5N0GXXZONNRdw2yOj1eqG1mPZ1qYiO7tOxc9PpVAYe27F5w5atDxvVLzo+8Bxe1eMviiLO6yWVOBetqoHwvHnzYp35ff3rXy9b1h/dRRLmOmElIcuWXxf9B6JxJKFNNvF9H0WBL16J6WM28vHfMubPn//5z3/+H7/6nfe9730z8jzsoR4oAI2nlmBDrwtg3qySiqFy2cfux/1ftz/OPqEhfLRqPIzaiKeQhCbPKK8z6SIJVZ5hti7ULptE8eOPWZ9SIM/QGjR9yGrkKYl5eKgWd7IE7dlII5FXbnjg8xp4AMA0NFVIV0DDYaNxXSUMgO/wsajfcpaLolTLNVkRqZXPUA1x2FRPUCObmSQm2HhcRzgIhKgpfMBopFY5H6ak0p+IqD6aLO94ZdZFEmR4io2nf8Jh+2Bt27aNxH/WDHcuuugive6wNWvWUPtiTfR6/tITWh5njKkfj9ELGh2VRdFuOGp5hWolRVGEqVi8YL59MOXmpiDHUYEyqD2S80p4DjPJWcUz5TnPeQ4d7JcCFQrZVmp3biURAZRQ3d0/+JbJQm655RYAmSiyQtsukEzkxASATYe6QwuXGoRDdWcQn8BvoddVzt16MxelgjMW+LxuAKtpsHYiMt1LkZXkULqfUSYZYw/de5e57PXr15NdeJ6T4ZM0HxpZBmamjTbSlEy1QZQCnA+vWG0QjpNPPlkR0ywbes0QVCUV09enFFOUvnVEOKda6fQrEetwfu65515zzTXDCxfMW7Doqj//YFdblOkahATnJ510UqR1Nqenp81J5i55htkwZVlmNtwpc+jKFbGfnCPA4hxtj2sDMJW4LF11MoDA40eOHHnooYfK8tPho7Q4WkkYXbnChK2s2oEluLRz507Ql3Xcw8fGKDux+kUdALFUX1878arIHWpq3sknn2xSnKVLl3ZTYQDtWoMJrZv9HI4wFdnU+I6tD+3bv/9Nb3qTcS0m5TfGK9kJiesHVeQjzorA4ytXrjTv1ZIlS8JU1VJtFXyjkqKpAzlcD14rj/oQjnu+jB13w2Ahxut14jCAF77whfXj/weHYUSZUXbh9roAAivOKaggHMf+LWbSMHNpmC6VJ0qk1CdOjyCe8nXCUSIcUaVeo7I0RV8tW4vVVlBkCAbNbxBEks0TeA34rVSrhKmkKuvBbyGNtm+8z76etsdriQKAhsvor2pyrk3PmYyzdl/C4XE23uuznFVtrnUQwuVc9pFDATiMTWmcEjpBYQweZ6Nx3geH6Abd2rzOZgbqJRVCOKrzDqNfrf1bw28QjidrUKimCvfrX/96Q2FrNptms3gkElRqsQzNHQCRVK1WdW1KyRsudxgbHBy0E44wSVNRZGHFeIwQbKpYo08AjtSpeXWTF6XCESlQijaSDxZJm9snL8gK2XearYp5KbEKXM6Qp6ZLhZaAbiqYFMhTsxwb4DSW3HA4zGZuwbNf4A3MRtIFcy688EKzutGdtN3S9Xze9t3adrYxaw5Le+ZKXve614H0NlymI6jKq6gb5RWnP8v+OvMWLkpFEfhO6WxC8S8rKIrs3r0bUJDAoZFRw+FotVqlX4PF7QA0ft4Iil6X1gVdmxDg3J01RKWTOXPmnHbaaaphZGj20AIlUDExMXHttdceOnhwZOTY9R//kHmvdEerAOPbt28PU5USGad4AO25Cy1BQGbxOlu0fmltLiV8WaBoNxzHcWwKS5yh6XLOmCLl6a/pDQwFdueFPrlwGvShlkq0g0bQ4AV9fSGEqqlzJ2Wuce887rjjTNaSM48U1bTkq0Y49Ir8rne96zmnnMId99RTT33Pe97TrbpMVczbMtnxVMJh56aj02Eyecy8KkSXhkrNOa8eHKWSboL9vsW5bLmOqmGBgbGpqalYl1Qcx7FeCb9VEbhM4fjwW/FERYkcAEb2EmPDXM+11177xje+cdWzTnnxBRfdeOON9eP/B4dSbbFGCVtGE6j2ixoHRxqUUtQacQ26WevINWuCWg8PbMU3/9Jswsu4Ho0jDQ2TTJ1Nd68YHTNtgJciGMzDMuNxXdfIlRqlEJX6aGnXGkv3+Nmt5y+qdyb7DmeMzYRkMFHUW1rwBProDoOUdREOAJwVQtY7VwF4nE/G9ToLFHzSn81AwSS13lpAFoUJGfpgbgS++kijiiZY8bP9jfDXkzIuueQSQJVUzj//fNOm+I1vfMPkCpseuJ+gb2s/xwHs3ruPAkCtOcLrqH6H+fPnq5NwPmfOnBSu57BlS5bqgxloz0ditD6H0r6tdqmQUzwqy6hyHNAnITWRMM0bqCAcSZLwl3pwAAAgAElEQVSQd/niZc8AVCKycuVKxhjBgKtOfrbZhhqnN57H9kkMaOF2BrX6ODNuTxNjIxlc9EJqujH8MiNd1afDIQO33r4YpWLZ0kVLli3jjnvFFVcQF4T2rGeccYbJnxqNRpzJwOPU3Wcyv60Pb+PMqqGWJRUlrmAJYjrtOceZH+qhQ4cq4kJ2dNFuUqaYrYBlKcBdp6mMV+I4Nv2iUxPj+x5/nE7+oQ99aGRkhCLuz++5a1xzOOyGpnnz5kVaEcTzPBOec+aaTaeqynEOgDc7gd1bq6sepBx6ww03qE1hIcGddd/7D6q/aPtvdfLJsEcnt4ggxFfgfXwXDr+lGq3VPRFAAe4WfpMSi4MHDx49erRsgdF5le70kbqkUtAi+KUvfWnzQw9JFBs3brzuuuvskoptBkFiR22fa9JoeQkp8zR7yYGlm0dsmD4xD0E3wS5KxplouVzVsAAwnue56UNO09R8nUSywLahzxK4HvyW7IVmrr4/1jHY9/1rr732+q997y+uuW5OlcP4ROOJyKS/Osl0xmHASDNUSQ5Ad5RJYSMcJpmgQQCerfIHqzhSa5AxC0VJMk2iGjoCAJt/hO0/MR0uy5YtA8qEQ73DdkNKMNsmlCRJgiwGY2i00ynlvaATDnJ7iWoXvGpe6/I18/tvo++wdh9Xo+ly9Il2QDnA5QP1kgp1o9SRDJI8D/vkSn3Oxnp5DQ6hKxnrzaRtKiXZyFXm2QxCYY6mS9f8bw2SEVYlzkyC8pQaT/+E45//+Z8BFbq+8pWvUJgEcMIJJ5j1KO31olQEvltziODNgbZtmqznj46O03yapihUb8jY2NjIxHTHdzzfP+WUU8oeSN+Hstupr4ygAoTPHcbsZDTMxbzZg51Oh0LRwMDA7/7u72461I1zqYSBdSIyNjZG3uX7dpsIzR999NEsy6hGvvORrSacU6drmIqmqssUYHzFihVmDRJuk4yawJiBPW7/4ffQCAy3g+oyUKa73GEzKI0Gfn0+yuRgq/nXn/zs0LzjPvaxjxnh1KbL7r///pKrcfvtZBdeU9Ted/DxwOOM4ayzzhoeHqZd+5Llz0jhqJZLdXwO7hpQASRBrcQVmmDMlFqazaZRLTQMeaX/KHNwpycZ/Xp1O4aqvpmSikqJdOga60a0TbFd0EZGRsJEVYKM5yqIT66dKmfPnl016XZg1KwVCNFu8gK2I5cUYHxsMqLFVCccqnIU5QVJlZfzjgOgV3A6vnR7YQ78oKU1TO+44w5A1XGoiINKl68D7uTglRScPpTxRIK+/r333msS3Mcee4y6x+mbcl7m1GGmyKSMocYv7qbieaedumLl6oXDwx/+8IeNAJ3CAnlNN0+lEdwCkOO8aHpckUMBcMdxnCiXrbIP2UizVBEOog74rSIpEw7j1UxDtUNb49dMF/7/GCaoo9ct/umNNp/A6JnSoByihnCYzUatpGISEdsaxlZeV+PwThz9hUnCVDUkUbmCes2ANWvWAKrTNY/KrEW1y4oMgND8KpXnaS35X2rPSyPweH/sb8ykEgbAc/hYnHWqXvauKanUkQxkogjTvEYQ8V02Hmf9WY7nsOmkHz5h+UwKpI4DIWU3qSqH6gRiqopkOA6EoESkRhqdQRv6SR9P/4TD0rHmyk/Pc0DQt9amjHu9RMi2y+tUuFw5DaryuUY+WLND77FWjzbmW6zjO4cPHtyyZYsJ8+vXr4f1NvB6SUUGqqfDmkxlp+mtWrWK2l+np6fJBiVK5ch+qh0owHnHjh20pnt+A1CJyNy5cz3Po4T3mc851SyvtKyUYjWFBGNHjx41SGmYCpNwfOlLX6LJgntwPCOhYWwnyfe5XyOVyBb9+mYNjnU3fbk7NW1M2kyPSZnJkRa451iERw7g6Pg4xZUtW7YcOnSIHsSBvXvCRPQ1nvDmrLmmhiqEUO2vfoA8KXJVV/Y8z/hom/qxYvAIAcc124VZs2adccYZSjDAcRqzlEDF+eefD5Qkhpx7dLzaexUC3Jk7d26sLdGbzaahZE7GiUl9XNc1Jyl8ZZmmzQKpj6Y90PBgM/ukAHcG5i8kbqxaiw2SUbAK/Ev6Zo4rdK5gnZzDDxpcvXlWDuEKp0GLmtrUaoEKFKCUVwHmOiViAEEFNkNCStlNSrjYxvbsojhHxWt8OhGz282hocFGs3XCCSeUrxAp1tTeq1xqo5Yya4mNiqvZUaSpofs0m82SR9yoeoblCbiD5oCNcKxbt27p0qWMMcdx/+Zv/gZP9ujPb1Qnth5CCNMEC+CqP6k0udQAG8rUjcgpDWO6pMAJ68z9J7GTEmOqAKvzRTvDdem/xstatf5mPbSH5lq4i+Z89JAn0rBfaWhp11qW80Q5X+Dx/sSi6c6ggw4qqfQhHA6HEEXNKhZA03MyWfSrh3kOn4jz9gwIBy/66jjUuNsPk5ADXK1eQwJORYEwqUiZWeLreWcm5OMpNZ7+CccFF1wAqNC1evXqUG+JlHwWAO4cHZsAWMtjlvGKAyARao1We1+9ko5MddsNvma4U0M+Dhw62vGc+QsW2pMnnnjipkPdru5lqlWyoywPPMfU4fSkcLLehg0bTGJB24IoE8997mn65A6AM85QbunDA4Z9wkdHR+M4phLg+NFDtXJ4mMmpIwfMwWEYKgIEIMjQFQDjRpZ0MoyUyQJZoOk2WvLAq30dAIS41HeimTjw6MPf+eq/pln6zne+8zvf+Q50N4G+EgZg2bJlpEOlhCz1PQyG5gWus2a4o8gNFES5k0ol/KUVSAW42yu4cbVmjBk1QyTRvXfdrq4nipAr+0qpGYI6EufgTmv2fIMf3H///To3dUcmp2ZkU45348rSI9WD6CY5Adq9Xs9whuAHZmMkhDAnMUohBw4cMFdiMJgSRqb6TpxWVMK003daOFVTXEHNOKyQBCZroxbq6GnNaqnTqqxaZHB96fr0NXWTl2r/8SAp2CggnVKiZtuHpBiUJInJv7Ms61qsOjvVtuvQtepbNxWPrL9743337N2373Wve50hjYaZUKl5lWEa+M4t//H1LOn92xe/SLEtznRDk0o4mOd5xpteJxwcALTSqBp5gqJAa1atLXbDhg23bvrFjzY+qmSLn9ojz/NTTjll8+bNn//85++8885LXvsGOx4bVwEahHrWkAzzG6+1xdaSFfNx5v9t4MH4xShqFC0sSWS2N+TtR5azowd3m3+oPjQJkURSlspj5UnSqPjV9u4t1xmoetMDoBev49fnPc7H47y/YUQqm5LKfMNhcSZ7eb3U4jtsvFeHPWgefdJkvsNSIfuJIEquNKm0zDhUfERRUyzlXHM4soooCMEkM9+XJ288/RMO5RwtBbhDeACFqIJKIUUBxnsCLY80MgzPjgNAs0OvglqLjSCmJveV8kfcATA6Od32NRFEB1HqZiQpBSg34XJQBYHVSiqpVHssfZIgCHq5FAX0fGFOTtm3ZlMqWKXVatH84cOHzUmIpRUmuVpP9cEEm4uiEMx52cteCuDiSy7567/+a7qYVDpII/PdjTEmQRGcwe6v2XSoG5KUGepAzqFf7DBXovMnGfjOO97xDhOiLrvssjiXpe+aPj6DR8FV9ydLOA78gDNVkVWrm3b7rGxHCJxozUIaHTtk+UGIFK6PRlv5tpghc3AvzpWzaFmYAMCdRDA6OfnMGZGPSS15rjblRgtVFDQfx7GOfw78lqGvCiFMgmuEwxWMpBt3idZa9ihKAe5kzAtsTEUHUWN3ol4JobOWQm0WtUiokvE2AE+pRREMokCrwtVQfJcGU9HFgkkcNDot7Q//yCOPmJ/PI488ElqLqZ2bVucrOcRUnB189GHzcpKoP3TxsZ6vp2Lq6ON/94H/k2fZp/7hug996EMgDgf1JmiEI4oiel1BeZjO1wuvZSpKnHMUBbIeuBONH8OvN/4n6yxKJk4PWsSGh4cvvfRSlbhbo1aMoOWxxtUwqa3pgadhqBslh6N6QluZ1JR1NHCYIY3t1mKV+mQ9OJ6dQ6j/SUKb2KG+V54iT5BEM6Y+/SPweD/YQASI/q4Wz2G5LAb6EItcFqF+ecwg9TAG1qqVVBw2HuftPlEQn3MAA9WsxXNY1mfSBsDlmOrlEqhYyHIAimRaRziEaZetIhxPuXzj/4KEwwbnx8bGIq0NZ6+Y7bkLSodulAiHPzBE76uCAY1oY9ijKKLin9ZcGpkMOw0nTXr2wdQCSgW8NcOdwHPMgklOEIHHOa/AX1Eu5g627StZvXo1CVQsWLjAPvnQ0BChytoHpLQHo6x83rx5RoOSEotuKlhGJu8qa6FI1k0lB0b27Qbwve+vMzuhgQWLbMtZg5qSBTNDX0klFYHyvKjU2hcvPM4s9CSkRvzQ5z73uRRdzj333NmzZ1PfsvUgOICJKK6Gf7U7b3DVBawTEdV50dRF69NPP52KwWgNIo3v+L6q5qjFy/XRCMy6poQuTNbScAEEQaA04qSA4+aaeWrhBBxACo+WMOUWq++5QdRUWyxANQizxERRZOgX8FslNwhlSUWEkzBVDCjkozNvQduueujUJwEn4S+roddFo821qakFWnD4gStUIlLmFq1BR6R0Y9UNV6lPoHTnTPOXbi0eaCr8/MCBA+alHR0dtb06bWlz207TRjg2HepGokAvNI/eOA5Gqeg3b4tyOXFon7rhjH3uc58DEGdFy+Xj4+OKX8X41NRUpAVRpJSlGJrjmZKKbQwrLQ7HU3DUshlbH/2KK674r9W6agkH1TeVR50eRiFwx44d9rwRA3yieG8nHDO01E4eQThm/qQSYtIStfxs1fvcC43NW+Wqwgmk8a+KcHhOf+mEonVNIRTGAa5GAiUQIqk3kjRcNh6TlmPlJB7nqd5j2IPs2ZpuZd7XfrY1+MR3+XgvJ6q4maSGrjiXiai7wooCuSxq87/pUnlyhuWNyaenpw3upAJnIcG40xoIfJd+xgsXLixVBwQIV9fmbWpeus2gYqihm1GbQcd3zn3W8lNPPZXoFwBe8pKXgKjFDRdUsbYjcS4JD6iZpc0dpKJmQYITGzZsIIGKaWIO6k3kkSNHSHz9+OOP11eiZLuIrTIyMmJE1inHinI5i8JDIcD40NAQLR/dRDgyfWj9PQDA+Kc//Wl1A1sDtsj6i170InORbZ87M3pbeHXPiygXz3/+6cuecTwYe/GLX3zFFVdAiZ45b3nLW+gG3nXXXV/60peoVK+fmmrpHJ/s1gW+mING0NQJvZamJvPJwAg3rVmzBlKgkGgNIolMtZsxhiwhhANJSGucjsRqNx+4DECv11MIs8zheLEoKMy/+tWvVleiGk8Uh0NJtpiij5CEqCkkHwXVJpr2L49yAseF61OBWYVzkcP10AgCD7BNvKQEd0Ynp/sIsw64I5jTdu151erCtC2F4oFqhKOlFymV5eQpgiHVx1S2utA9CVTzVL2k0vGEJU1tgEC16yo5HCYE0a5uzXBnzXCH95FGr3j7O45ftQpg7373u+k+bzrUjXLCAutWRFm3lHYlJDIWouU6tkfdwYMH6eejXhWdnBVes+XXEo4Q1DT+v2csXrx4+/bt/+cjn/jmN7/5sY997L8+WFn96UG277UsxEhlGL4FDdOMWuut/aVDJShffhdG9prJKpk0Lnvl6MH1pmknYK5NvYoTjyOsXNV/MTo+n9WcOeEY8OsghMcZ+hzgmi7P5AxdKg2Hj/dm5GqQ+Ed9vuXPwCbxHJ4IGaaydjE+Z2N90mS0h5qMc9ubHtqGPkxlLaHpX5mfCuPpn3DY4MSuXbvCNCczLbUTlQLcCdPcbHQOHz5sdpyTcVJHOBgHMBUrWwrVkKa3sznzOr6TpunGjRsNfnDPPffAcjfmvCYkINLu+Jc++0kAW7ZupckwEwNN31w2gBNPPJEkpNYumwuU1RDTCKB+lnqtT9OURNMvu+wycxIy0+qmYtXKE/VJ2Nve9jb1oalw88SUvU0CNHfp8UhKB/mXvexl5iJJVGeGtlifc9QrR7s2P7jvF4+CsTvuuOPmm2+GogFWXPS2bt0a5bLlctupBABrdmjXbsU5Dj/wpNqdV7mNgdlw69pEhmCWjdA6jqOkDxttI0ak5bYyHeatwgSd3G8J8NKfFgZXYLFQjiGvfOUr9ZU7J516JmdqFVALPe3+Gy2zkfJ9X2UtXguFzHthebDICD9wZQ67XaIQYA5vdQg7sRILF35gYF5L38yD3/ZkRim1KopJxaXtakNOtaDnGdpDjo64GuFQJiNuoU6i9sTKRrw90FT5+qWXXlo6lSxZGlZ0OMpcwWbm18RDpxOxdNHwoiXLnrHi+LPPPttsprWXSrXdOhPqWRcFGCfHDeJwqB12IcH40Jy5sV1S0Wmi4L6p36kP0kj+T37yEzzZ41evy8ydO/f8iy4999xzf+mRr3jFK+w/0stTK6mY4p1NAoXOTlB1ke0/W/8ot0PWUCtwqv5rshz1IKIJehB/9Ed/RPOKKPrtD2LvRtidOE883nnGogtW1juW257DGOv0IRzEhu5UOR/kcV/rOKX5qI8xCsB3OWaCT9oeH+gv4nA2GedFn42c5/B+RRDOGGOYSPIaEYSTmEc2o28tnmrj6Z9waHKD2itHuUI4FPAoBTh3WrMqj9zoAnkBPXW9sVbz072sbXByWCWVqW7Hd6yeRg7dzWhcj21j2FQUuSz+4q2v+/aXbwDwkpe+jDbT1Fn63e9+d9HwQjB2ySWXvPrVrw5zQbKSQBmhDx48SKUNlRLpRCSKItpEnnXWWQR7+L5/5plnAghTg0IXYPzBBx+ki+mmYs6stkGhzzvvPJpvDc0/YcVyQIKxL3zhC6atLkxl4Du8KsREyB61L9Y4HFvuu8tc3q233goqqfg6WGql9jgTge+o3U+plHUc7eYtCw8XfilapfZexgtDd9ldddVVAJCnaA0ijRR/jZbUPAHjCIaQRPfddx9MEM1JEKxNzcMlJ07kaA3yoqDthX70qv4iCyVVrrx7pAB3tu/a1fIU6FqmBQ5hMEV5JSpsB0ii8ZGjMA0punH3wM6tAFavXl3SMhynM29RYCO0VAlqBB5EBebVJzd6Gzo5oytpGaMyNZ+nCAZV0c38TLRImlcohEOh6zrhEJp4+7KXvcz8TFasPKmrm8KKokiTXsnheGLJ824ivvdvn/3pbT/cs3fvZZddRqRReq8233Pnzoc3/+y++4yKZZQKR2jvAq5wPko4VNJWSHBnYmyUABK6EsgcjIFxZpFpVJyLu1RVufLKK1EdT8H21/8P4w/+4A/sPxI3vKaebqQ1aj20Rq08qzaPmM1JLRFRKQUwoy+dqqHodlnDVFVI2/QIUUTVD6r8MJW1/Cpy8oMNp+boBoAxrJrbnBN4tXnqmq6BDQ2H5bLO04SRMe2vy/CZW2ACz5lRnGMqFS2P1wozvsvH46w/JXI5m0EfXTFM6ymRLXvz1BlP/4SDoiyF/9PPPi9M1LpTti8ypydRWbuNGDb323124VBMQGKAMn1yB8BEmLQ9Z2ho6PnPf74JluRgEuqita1cFGXCYTi6f7d2gOMf+MAHoDpLnU984hOPHzwA5nz3u9+9+eab41QGPlclbWNeOnculcMt0iijb0cIxN/93d/RlaRp+r73vQ9AmArl9FZIMLZx40blAJeJ+YOzbvzubQzsK1/92tvf/na6yDAVPiTVZW677TbzOydZX1tcAVrvvOlyjlozjpg7OGAqPtRsEmkCL1AYvgvV2mtiVr0cFM5LZ3nuwG8ZSoESY5YqLu5+eBPNv+QlLznhhBMoiPI83bRJza9cuVL5aLs+kpDyGB2JMzg+/GBW4MOqZ5MGs6/NHVRKJFT9xYN0tDKKdYVB4KpApftocjAOP2hoA/ROp2PABqTR4/v2op4/taeOHATAOdc93gLc3bv/AO1ptLV6BjA0B1xZBoOlS5eWCYf+RE2/IN+QYN6QiqM6m0kRzHbr0FGmEwsVijTLJAN30ehwjR7dcccd5s2/945bSQxm27Ztxx133Gevv/5731+3f//+TYe61KUyPT193XXXpb14y9aHzTV3U7HhzltquWmUCsbwkXf/4YE9u777ve+Z9tQwl3u3bwbUy0xoYpzJlsvLJh3GFy47PhPKdqfVamkFd69wG4b0p4JuPEkJR6l68vQa7Xb7z//8z+n/Tz/99L/8y7+E/ZIDAMwmpCY6YvQ5asMk5TWJd1MEtJMDk9yo9EU1nsT/9m//RvPz5s0DgI3fx0/+FUZCvuqgi1/q8/Jfjs9ceOJgH95Ab0ItnDdcDmC819cW+wTqYQ2FcPQlHL4zE02VF8VMDbqcjfXqrSsAfOqjqffQQhRFmPUZsvDf6HA8GWP16tWACgCu60W6kUnBg1KCOz3BAjux1SVe4Tb6zUgBHB6doKer2hf1Crtn/4F2gwsh1q9fXy67995LuorapNuSP0ql2ndqOj05vVE15K677jInueaaa0gLVW0LLGkNcixUUUdzO+7fdZjaU6enp83aTehINxWzO6SDXhCRlqI1uSQvXXGCw9mpFu99Kk62PXAPpTL//u///qMf/QiK7pqTvbi90yAlSqXmZL3ucSpHD+61q/sATFmd/NIALFiwgHailsY8B+ANDLVsRUjdL2o23HojnhHCcWzvTnPsfffdt3jR8JpzX/nay0txAoXxpjFQII3oDqj9WZ7o3XwGm1sjMgRDrk5xyoSDDtYR2kLUqP1VvVdKB4kQjkbQdNRplyxZohOLAGk8NT4Os+aSSbemtRrMmb5+Am4kzFevXq36aILBwGVmL75//359T0purJXMufBb0bjqRFBRIUvQnm3k3tWbr6/QlFqUaAeZjzQV3wXUz0V4FWMrn/lsIsRdc8019DQPPn7o4x//OHSf4ZVXXvmhD30o6cVvvuItpP1QFOimAr3QpM70pKJcWmL/DpFDAYSJEXkrACVQG+ei5Tm24+7hYyMOU8EgDEN1r5ptsLLLQLHIw3Fy/SjtSJ52493vfvexY8du27z7Bz/4Ac2UbrEALC2v2k0wCoE1JMO42NcIIiYRsdVISZcIhnZNwj9pZBKR97///QCQp/RX5FsJ4Bvf+AZZJjVbrc9+9rO/+vf9FQcxhGqgBdkLTvZEbf6J1MPosH7kI3CZncoQe0nBITOJdvSrhAHwSCmknlgwKdHf6sIZ+xUwoP/p8fRPOL72ta8BKrH42b0/LTT0bftrF37T8IpbrZZBOKTbrIorqCqJ1DZgtY144Qedkk5RmObAbiablkm3STyjTCjFCM2coLAUZkIx7fSyOzIyQn196jdvlD8me4Q0qK2/kTdtNqnUoroxtXsn9cUc27/LPpiA024qGhDvf+eb8jQ5+dnPNvoHk2GCJLK1TfXFz2DSVio/VkstYS7WrnmOuSfUO0ptscuXLzdXIoSIUkHSCOedd54B53mj07YFvoQuTGgOo8J+RYbWLAC7H9lkvwMNh4/FeWD9UNXtSiNkCaTYsmULSuZphkYLbsMvctiE/DxFMBg41TMo/KBj5Ep1cyklFi0jq6UWZU1r7Y4eoflSh6PRQho9eNctAM4++2z1ia6HRqfJBTQZyHyouSegbgJyY28N2iqZ8+fPNwlHNq2odiXB1nHRCKaOqTxGJW1ZD5055saqMJAncH1bglMDNoRwtPNQ4QG2oPijO3ekomg3HPUuSQnOCSHvpsIrchXwCgnGVJUtl6LAO/74T+iVWL58OZFGo0yq0klRgDHiahCTVGHylvNzlMuWy4ixRI1RS1aebEonSqkdBZoDyNPTtAfHX/zFXwDA1BE64U033YSn9bArRLWSiqmk1LxUTDes7U8LKyn/1re+Zc8rpQ3gta99rZlUVU7gd3/3dwEgmkCeQAqVZAOvfe1r3/zmN9P/33jjjaZHt9lsbtiw4bbNu9fd/4hRCfpvHCfND2re9AA4Yy5nsig69bZYDk0Ote8kpb/9pNEXLpv1qlWza5NrF3XwBCLrRTFD4y7JmA70JRaiKMK0nhL9Rmn0yRkViLvRNr1Gaj2SObjjtYdMNMrzXO3nAOm1qEvFcpGl9sU2vVJ//Md/DJSkUTTaHa9CjwdQFIUxUgHAUIqKRplUHGotCfXKV75y06FulIqBVimdCSBJkigVbZ9rgog6+bHDh8I0bzccLUGmIvrUxDiRSaWU9loMIMxEOHpYXbYW+QDQzcTBR7eu/+mdxOH4+7//ezpeev682YPmO1LTDXRbLBkelifPFTWP80p+HaXyd17/hue98Gwwdumll1199dXQipB79+41V7h3717qlZVS3nnnneaGHxkdpV+yfhCqBsG0q7XaWuUpgkFkyfixI/Y70PT4WJTbP0glrJLGpF3IbSPcPEEwBCk6rcpqS+0bptagEXvVvgEdiXXmR75r7bZGzqz3zYUfPEMbbg0NDRlKJtLYbBbVlbgNNALyzCxlH0UOxyv8UiVTKTGIjBAOc4Jjx44ZDiyfgQfqwQ8O71ZokPa2SNCeLWp2qQprac/SlW8lfJJncH00Oo/vVDWRiYkJu/uXTHGVkGUhwDiJwXRTMbujIfFCgnHa74apaHn8sx/7a3o59+7d++Uvfxkq4UjNSZSuPBClUmlmFAUYIwy/l8mW52gVYAHODxw6bLpR1M9T5GgOQLcKA7j88svf9KY3YftPcOcXPvrRj2pv3v8149ekmNjuJKYCUnkbgYcfVk+51hZrHkfNqMXkK8973vN+/vOfv+O9f/mRf/yXv/qrv6JJ5fA8fhCP74AlFAbgmmuuuW3z7ts2777wwgt/nS/1/2p0GjNUPaAlNGrmbdT80umTMKfVr78tdvlQc23fAyJ0bUbN0xnnPcbGor6SCqeSiqyRSR3Of5NwPAlDu8Uq402j8VBqVnK3m+SGwyGlVJwytwHX79icMr3hRkOdJwgCxhjViQGg0e742tJT79qHhoam01KSxabIhZmYPaCrGwAYV52rmQycSvtGr9eL8iIwzcUolT4AACAASURBVH66dDI+OkJVDM2B1SKPadpNRafhMMYAZeZOo5uKlcc/w5xclZxIbYwq9FIC7Mc//jHNT/fykX2Pmd5a4wsVprLdqLcpRqmSmrb1zTYd6ka5eMbSJX/18esAfPZzn6OFiXai9tfcu3cveV7UNOYLT/WM6KemulHMrr2EBFqDSKKpyUrjXNPlNeketeVKupQoUJuuKipnCdqzkUYTYxXVI+Qp2rPTaQXwlHJGjodGm6cq9bH6ZRz4gQ+FKmsdT0HUzqyrznPllVfaVQ9TsVaf6Prwg7w7AeD5z3++1XjiSrdhshk1LzKS0KhftuOh0TZXrirxuvwkQmXIqaJLGsPxpCWGtnjxYuQpuIPWLCPaoei3IoXjodlmul7zzGc+0yQcC56xSvsHlU+Z7k9FpKiQYA4RDI3lkEmdSUeHEhE9z0wcCjOhSkWFAONZlm061CUOhzp5SaZR3QdaO0egNYA0tukFn/nMZ2574JHb7rz7rW99K/4vG3bTh2Km99nWGwqULTkP824DL33pS+15+96uWrXqt//girf93sUmWVHpSHcM3/xLAETcfhLHcKexZFajf95zGG1T7ZSOYIZ+rkbbdxirZydPNKhfZiZFkCdIOBw22uu3nOVC1J3bAHicpflvEo7/8aGr+wKOA7/d1vu/SkiztCmbzabSpgwGIUXDJpNq0ij8dtvWCyqtGdqditKoqhnb0i41z0xf+UgozVMiE4SZbDdcJX2oHeBI+MiWFQEw1Y1zWbQ9RwUAPT84e06YyrbH165da/KkRqNRFIhSceW7/uycc87B/8Pem8fJUZXr4885tXRXdU/Plj0hJIFsGBKQJYGwyRUEhRhUEJArInwBkYsiCIhcBGVRlosXQf25wBV/3qt+DYQdAQEJIEQvq7KGgCQkzGSbpbu6a//+cZaqrp7AJExmJpnz/DGfmdM9Vae7T1e9532f93nieMzYcb/85S8BvLC2XPbC3WbNZC8JlJ577rlskr1VXyiNEoiLURxzJ5RMJqMiurPS7YtxjKofs55GpEotVS+0Da2trU0eHLrppHODIq5yI7Bm5pR6poGcFYr7Iq8HBx6LFXyvjkXPgpV0wDFu3LjrrrsOtTJc56QzzmHM4hSJoQ1etVatpg+CwIXdqoUeu+7wzZxo0zAEmTRRCqE6clZRNOgKzhCPLVwREu2yyy7pLhUuksHP6EHPIV9kUdH1118v+KEsw2HLKJmz/AIPhRanK1HJ5M04mgnTrm7kfhm8BSbwYeRg5LSo3r3TrwHo7XxXHuTdd99F4AIxCi1SgpPftgOp1sqjlhUrVkjHgI5NXeyjTKvSGYYJoOyHFk2tD0LY+8PaZXfeZYZcyazz3PHDyRPGH3XUUYijabtMZ1RHAI4nSipxDEK4tHkQWaaIWdlxTIu7Hkpdh9BHvgm+k5GaUECKw8GTEAKcmt3QIfLcc8+xXxK7HwD1OmAfiIyq6eBjzlj7msOzvrsATL0Pj3vb0CghjaWTgkFlEv0Dc06EwNJJoyJIbjOMVFOnmxoyHKx4XXaz4uumTvzh1xe74wccmdS39K5MKzdohZK8G1FKEzHsjOAgCyx0E5peF9vGYlw3CzktxQ7hu/aymxgKU0KkFrjjh9Uu8TWLIxBSrVbDOHaDyGalGelbSylzn8oIVGiFkqkRQyOiLTYSjierWSCSy+UkEcR1XaaPPqq1tGTJkq9/7WvHHneczHCUvXDm9Ok//u1dOdO89rrrJJXdjcmsGTPkGVkffzWIWCMoQV3aTgpI19mFB1Ecx5ZOn37sYQBPPPkUWNrDjy2DlkqlhE0ycecgihO1YNmlEoqggTMnfGg6DEvu5nnzjl9DoRWuQ+oLsX3mOU8++eQjjjhy3332ueGK+hyvX4PdDLeiCeo+r2X4Lopt0qWFB7LijtuUN9i6Eo0kvOgjZ8i1kkIfuQKopgmh8XXr1iHwAMIad2Viic8kZ8PIwXXK5TJnI4HbncDIZU0pfRd2C02VCVzXlTOs9fCUNV+fgQerCYAvfEOE8pUDEaIliGMEPgrtmqhh8So+F08rsj4acFUxEYKbBXbR5BbzcQRCd99rXwAVL2q2hSBmFIHyskvZDZtM7Z9vvi5jUKak4vhRycrdeuutp5166rHHHcd22HEMJ4h4WSSOQLS5c+fGcVwLIksnXDeWRdtGsqNIIkWrlMlwvA92jJ7Y90Fan1RSOjJkUqk0mgkO/i4EhHjdUCAj75FBxoY30bXbKmy7D8jStcZMBiHMGS57Ax3fZP7rvLHoNyxd21xdpk+hsK5amAlQNErC1E5PwtSIN/xYozt+wMHBpaIsS2Q4kuQ81UJqSg4HZ1lGIQotDQEHF3JG6Mv2bl3XEXL1aIR+ThMq49Lpbf36Xi8smJTT/lMCcBUvaiuJ70kcgdD999/f8SJCiKVTXdfTRJCKH9lmnZwRgMjg9FVBVRHcjq5uFoikoxYAvV4o6auUxOnO1bIbFk0NMQhi1wvYq3hhbdnxo7UrX5W5lmXLlgGo+BEL5Gm98Bfrr5k3vmgbVAYcjh/mdPqHW3965QVnA/j88ScsXbo0jsFsU4S3BQXQXa5phDPAJ0yYIIkjPtETEQUkvSFda1clnwI4/wCe01Pf08j7IRsKq215Pf2t5rUGZlPnOqEQG+AbO7+KQqsu8gE81yIaSXoF9ZIHFkIppGTx6ziPZoIAdjMCryQurzwmAGA3w6tKo85isQi/inwRcQyv2t3dnYgiMKoKkl5ukbRwUWhJy3LHcQzfhZFLs0z47ST0YLfAdyOxDeWvyHUAOBsTEgxvSPGrsEp5kZbgmTaWg8kViFep+yAEZYq9vVdeeWWpVEIU6rn8KWefVwuiIIqTL1ccghCWru/1wmJO23v/g+R6Y6k7JvoCgCDOBLL8HY5jxrmuBnEcY/5OJf4FZ0mynC2v6jwZxvjFXrU/+lEjAen7vaRiTJ8+Pf0cGX9kYgXpbs+FEAUmCaJSn9A0Ld2Om2mpHT7o03IWgG3SxtJJXqfHzRm1BQc3aR91mc1xOwiJ4rjBn5YEYdRXwEFVSWUIwAlrwlFCGvimHCV0pLLTosvAh1UXcBiGgSji5L6UyL9hGIiZenSBPT9dsQYwatSoipeEpekrpuNFE8eNXrRoEXv+gv0XHnXUURU/tHVKCNKxgqZpVS8qGLQuUQy4scZWZ0ZkvTRmElt/F154odwsHn744Y6f8raoV4Ype2HHytfOOmFRrepc8u//zhRBAJTdoPu91ZIIct111wFwPC7rS0GyXSpMbzsVVzEe6EvP/lU249x5551uyHIkWhzHKaU1U6aa1qxZwyM5ICA6u9+IZlRemOhZX69O6NfAhNjrwb6idkP+c99Jxf0nJzQ3fhNlEuBeZeYYfgnml1G/BqoRwdXg64cZmlulsgg4eOQnuBpSVotHLaHPXF3++iT3JOMpgdCH3ZJuDtx///3h1QAC12GqrTfeeCOfKNfmcmU5nG89fTedhADbtrIu33yJeJkKkQe7Ga4jAw5e9GFrPrXCuYeLVwMhK1763/qDuLCaoJvNwnKWi8cz05x8wdLJvPHFJUuW9PT0II6CIPz9r35W9sKcTpsKYvsbxyCUpehYSeVfzzyHhc6HHHIIa1ioBiH7hqYXreOHOZ30bGKiMiEofeyxx2p+aGpEp6ROrC+10+C5wMCDVYJfk7vzEQ7eFQwg1fWaSVHIwCLTvfLEE0+wX2bOnCk5oVdddVWmItOItNOsZJ5+IAY521QwaSMJFMBRM9umNOcbx7cIVl/RDNv99tEuqxMIafZkkBIvjMteVvbU0Ig3/NzbdvyAg6vzihS3Ses/Ay6GnRD+k61boQVuZYrNL8dTp06VUtPpQETXdWHtUYBbKfd0g23LRLIhMAps38aPn7oTO0FoGxr3Xoo4pY7JjM4bXywUCkmbq2lWgjCx0paap1WfBRZ1+zngvQ0bC9J8RFimtba2VrywkKOuW/v85z9/7TXX3H7H0ueff54dsuKF/3jmzwDSPlgAnCCGl0ibM4VN6UlGaJw2wqj4IVOeSGc+Kn5k67SyaZ1smFyzZg3zotMpsW1bvkzdLslugkmTJvFPTc/FhNYpjbJ+UdN2NnTWfZpeFSCNAcfm7JrmjC0cMjURb+bKDW4VAKtisPG33npLHByaaH/ltS2enGiVd+ikX1QzYFp6yDfQQprTg90Mz1n9T+7HzXeWvotCC1xH0vGmTp0Kn8uEAKjVatOnT+cMDy6cmgQQfCvp12CV4CUBByEEfg2IYZXiTLrOd2G3wq/2dHEyKS8GucxGvMG9zKsC6O1IuB35fB5eFcV2BJ4m2mhTuh0acgXWRSy6VCIQWmWUJkOriLeXrU+2rspe2GRqrz/zKFO2feyxx9j6lJ6Lac6Q40W2nvpGEEoprYa8JMcTQmwJGVY2bx247D2U/iBbhx2m1CLb3ZGyCcx0o8igJEN8eeaZZ+Tv55xzTkdHR0dHx5YSbyVxZHDQ/w/O1vtIQgA4cfcxrVaWfrGlmNycG9+UNdtjN6M+hcJQbyELFlgEUSNp1KTUU14qgw/BEeOBhZVcoEQmQ9ORs7WY30iE8IDH7iLcjIO7enKtJHhOFPEr7Kc//WlhkFFArVJoKoHtzsVNdMO6zrSFVd2d2Attk372s58FgDh++plnLrzwQkcYBYmtP2eeMhdW/p+yBlHz2JdBZPJFB6won99zzz1y8He/+11PzS+a2k+u+d4jjzyCONqwcaO8LpT9aHRLIvLB2l8ZORRuRaa42R2l4odFQ5s3vjihKVeX4fC5EFPaqYuplXd2dkrd9DVr1lSDjJkWBRBohq3z2tPq1atFXsqmccjKQEnrplYnC8EfYvdgt5rRFSgx39eGHUMGnOPG4hXPkVdA3gLjOQBkiST5IEIfxTZf9IDwMCXkOp5emfP8udlb4MFugVuZ/pHd2bhIq9Rgt8JzpNTBhAkTWDGF3ftZaidJqxRaYy957fy//CoICZyknOT7PlhVhZA41XjCz1hohetIEgMPfaq9ANLj4hwOgNhNDlKr1bgfjVuRBFsRb7FvSqEpryPxlwlBtbbRY5jM6JrVnIHI1idriGD6RTfffLPsirrjjjvAYlYmwZdaV5UgzS+OQahhGI4XMo241Ew0mJZsiuGO7YHPalibs/8YaZDhNaRsfwNkUDJv3rz0eIbqQSnNfAE3h6TTmxk7bwN8+IjQNvugWQwULjpwp7ljs+QVtmdrPKnVd4aD+nFcERG5hKERX5VUBh8JiU8z0lJRyVZMN2FYhpDo51lEluHwKkuXLmXjnZ2dsiUBrhOufYONX3PNNc1NBRZwTNt5UrInkAZRgV9OlVQ0mpY2j3JyCxGHIPTee++tCM24crmctGl4nuNHhWQ/x+/Q1SAuGnTe+CLvaRSByNp1Gxg9dvTo0UkPCPO/NbS/P/c3+WS2wwvjuBZEnzn283vO359du7/xjW8AqAVRFMeiS0XEB6InFvUcWLAbg/CoS7p/vcg2JLUlZu2LrOlm3vhiZ2enzMF09Tp2ejMh8lI0rC+0S15Capd/ySWXEGF1zaUtBVrzuk5Jo6tCBjzCEDUFaVXFUasASAuHi8nUUGyT/TJi/QQsByMLGUnmzG6BV22mfv24y8bltZ6ntV2HzYfdj7mEBqOq1CryYspT014VgNedbFU5vCriCF6tKsICnvnQzTRrkj/K4hWvIrW9hauZg8DjfGoATDuVveFupbWd161FQjEE1ZAvmHGYvJYoAqX33/47JlIUJS0MEQiJoohJnjeZ2tSpU2WUzD4UR9grprvKHS8qmBoXjIojENLe3i4D2XTfO9M4Yf+V6JvZLfCqiZzayEa6o2Rz5u9S61YaLTEw94atwM9+9jP2yz777MMkaAcWA5J/KhhaYzfKNkVTji6e3W7r2ZNafZFJTZ14QVz2s1Lopq5KKkMBocHM9RLkRidhA1glEFBR+RYOXi4KbahVXn+dyyJpmpaoPHmVu+++m40bhrHvXnudd8X1X/veDZMn8n70BQsWyDv0lF1m9LrJVqy+CB2VLLE5iCO+RRNJAkKIzCuEotTCnyxd0bVcIadDWj+Ik67r6mE1iEmTJtWRRh2/YNJdZs6WT+bapm5IQDa8+8/nnnmKXbvvueceAE4Q5nTCHd7TUtM+r6lTUif8JdMzaX2zahAVDI3fFKMIlMZxzDxTwEgM4u5it42Rta22tjZpMEZSZQIACDwYeRhWOg9xzjnn3L/0/wI4/eyvf+lLX0o/fVyT2ZL/4OQnr0z7NYQBPEdy8nmJ2nMAVDclTad8G+e7sErUrU8IC9+1XsEy4SUYIfIhM/n8du5VQQhc56CDDuIvkb1dtTIr1rC4meuji4PIs/EMh1sB4JeTDIcQVK0yIoiUVYjjGCI4k3cXXlHqXY/XlqXtwvkd2i2nKSYc3HmrvF4orfF7kogUjdgDcPPNNwOQYWXnpp6iobW0CmOOKAKh7H1gxce33npLrjcWDzkBz3BQgqQi6Ye2ofGMRRwzUZmqyIWkQh8K08oLDkdC97Fb4FU56WTHxdbddKWVWhoyw5HJfr355ptbcQoAe+2117p16+5d/vJ9992XJpEMIRrfrs/NGf2Z2X2byGwjUELOnj+BNOyPbINmvOnBSipRVHGzbi+qpDI0EF1wTMwxaaQUxhkerGb47qbO99g4J2CLfgdpeDh+/HhZmYbrvPHGG/IUhkZAtIofFUXPLSFE3uZjxOXNkUb90ECYVibdb7/9ZGAxZ86cJFagWi1IVaylFjg1WWDBk5OJNJnNApF//OMf6YBjfW9v0dTO+vIXgcRLpaenp+yHBZM+9se75cEZh6Pi8gBChj5cltSL2PHTrS7PvdsjFXbTcRVTbUopiBAwIxWdd/zKRI4XU+mZsnHjRqHLacF35IVA13XW/wkCeFWmSs7QkjfQF1djUsm8ZfEMfBCSMnatF27lL3/5C/uL7+eqPQDGtLXI5/MPzq8CRKpZiMZdj2XUpCqGEPmoodgGtyL108BUtL0qgI9+dE8p6syDg1ovCyMYDYIXd/wajLw0roNMk7DcTCrrw6Nnz2HRCedSsPXp8sqRfDIPwaMQ914LxDIk4s07tTLcSnof3NXVBb+KOEatsqHjveTISCu++xCtrTJKXt/dWzS11lFCsyGOQXgCjLVKTZ8+XS5aFvdIzlCmIskth8DX1aOPPlrzIya5kQh8UR2mlROlGK4E6Neg6fCqQy7/MDzBdVrrIU1iM9yOPqOT/iOX78OGbfiQY0bbeqO17JDAMmhGThSAqdGqH4UNJnCmrkoqQwHeji8UIVliHHJTyEl81Y41nCd18MEHA4BXQ9MouBVOGAQOOuigRBHSddJt64ZGAmafIzJvf/nLX+S9v3PtmrIXNTWQRlnHacfbK4RQWAxC33rrrYoXFfQU35NSACRnE0LYldS2bXntjowc28/xjb64TG/qdVgg8tJLL6Wlzb1YLxgaVxEWB1+7di0jOZeaW+RM2PMrQVgwtX0WHiyPLGxQwoLRZ4YjlDvRpH3RD22jznE+n89XxTNLpZJ8r/LNbTJcaG9vF8wbm3pJScWyLPgO8kVmg8I8OBgYhyurIQEAsEVb8vsgEd1yuuEmsSa/nVd7AHS/9458Ps+IMDKpyJAlgaxmwLQ0Ic3JSTZ+DfkivKpMnwA49NBDWU/gNy+5XHpg8kC52s3CCLZc+aL1qgD8cqICyW+utTIAkyQxAX8JboUdpM4fxEuiE3Yj4TMUkLtYngKp9qC+zyWKIskykU/mrnhRAGogz4lE/EYllpBH9IKpLdhlTKafq1LuYd+UlpYWuQh5hsOPbJ2uWLHixReeXy0ojZW0oqhIn1T8MK+nvhFCNj4WX/w999wTEL1IfnVz5YORht122y395+zZs9kv6dhC/p6++qFBYHSgMHxijkEDM3Xb3KO2rjU19spqJI6Ra3CByeu0pkoqgw9eFBexgivEsPn1yGc1dWfFs7yUyy/0fhW6CdeRNfVPfvKTKQnqCt8nAQBMjQZRnUHwtGnTEiKk76et/DLtG+3NdTocr7/+uuOHjMfwpz/9ife/ALFh5TX+hRfpExZwWIWsFx0F0OvyttWNGzcijgDC8gq9blDI0f322w/gqeyDDjpol112YTbiRx930rgJrARDuKunG9kG3Wv32VI9jNl2SKVwjZL0qq6k2mLrulQMKlLfEQidPXu240esIWXChAly2rGel2oZhBBJ6eW3BwAs8cMaSbwqUkZTAFrymkb60BvuJxIK26uPoXMltwKWiYHKRgBuT+L4IJSyqgCkQDi/cAceTAtGrr2ZdxgmIh8gcJ0MJ5/NOW0vxzNtm9ay8zJ9Nl7g49zYirxZ8k+/1gsgrCaMP645LeoykhhYKBTS6RBWr5H3GAaZzODhQmVjH60rYPLwZYhUROJRx+zsK12QNyqZ4ejqrWPgS1GZMGJtscLimLCzv7C27Pjh2pWv7bfffg89+Md777v/hhtugOBw8Dc2ikDpxIkTayHXy+c9mSLDURLWLbzGKpZQev2MZCxdulQ2u+67777ybUkX0dLBGVMoBjB//nwp/KrQH2x1IGUZtK/WFYI+RdZ1zfG3QOl1cLDjBxw8MuACTRb3LpGaNgFvR5TbWV5E9zgbTpLYFy5cOH70KCbaCNf5whe+IE9hUOJHcdmN5GpYuXJlIhJqGLUgkuZtdaRRL5w5Y0Z6n+e6rryXh2GY6jqxjMhnKzWKoiTDoeVYYMH3uHKz6IWM1Dlv3rzETAvorbkFQxs3btyyZcsW7r/frNmzf/zjH+u6zjoVlz18/3trVrNr/f333w9RO//HP/4hqx533XUX0m2K9RmORNo8FVdV/cg2KS9MRBEonTx5siNq7Rs3bkQcAgSE+kSXZSPP83hJxbCiamIidf7558tGEva5yIcoIW220WfTfH+QVJH/ejs2rpKJYr6fK2/EfdfHmaZTAG4FUQiR4eBJCLauYkhBMB4TeNlCBkMxpxGSCHkBmDNnzle/+lU89f9j+ZLzzjuPJZZ4akocRJJAef3F6UYU1lI+MjyyrPawfIb04rrwwgtZOgSeUywWWVYmYwEqvxH8PrTiaTz5m/QT+Muv9rCsITt4Sgo24cwK+g5ftBt6yiw0F4EIX+SdHWt7vbBo0qamJll6Yzc5x4v+9tgDAE+/XXXVVQCcICoYGr/zxZFhmFdeeaXkcPBckWhPkzQg/o1mCR6/Kn1DRjhaWlpWrVr18MMPv/LKK/fee68cT3egpHW6Fi1atGbNmldfffWee+55f0XREYVtmpWZ3mYt3KmUGTQpJaQPuzjbpI6nMhyDDp6xCDwYOZgFaabFN9zcOMORnHwe0fNrekWmuA3D+M/rr7VLreOmzTz5tNN5dRwAkNOpH0XpNMYhhxwiL6/NEybrlOTFvUTWGqI4dsOooNO000Qcx/JePmrUKHmbh2mZGr+Bx3HMSXlAZNh1bdnipE7ALZI3bdok1S8AOF7MxmfNmvXpRUfPmr0beyFlNyzkNC4OEUcg2qOPPhrHMeMirVixQqa4WY2pIkoqGoUMLPww9sNYlFrqyX269uKLL7Lpg9A///nPUsrJcRzpvuG4gcxw1Go1BNzpVMpnATjooINOOO5zCDy4zqdP+CL/HAWuOHTKrNFbefnLbHblfk5SH/Dqn9cLsgKSW5cDt5KWMAISlYs3X325bryBOcFQMnVLp5nS+GWXXXbnky/c9ZeXLrroIjbCKxf8IA38g951cMu+n5i3MY8YOF3sX6TIx5lnnnn5v3+bBO6k8eOeeuopNpi5bciAg2c4qj1Y+ypExAl5+6n2sAwHsxIVciM+NAP54kt/eRSJvQDPYxXHTGKlFs6BFVFyteq5QVQ0Na72Syg7O5Mwb7Yt+WS2i6h4gW1STlONIz8Mv/SlL1V93qUicn6MvmpJsxuOGk/wpNtBMSLT+GnMmzdP2sQzSOsDNHxBDMPg6aV+Hnwg3tuR/AHt0p5vlDElBAbty+3F1Bx/2PnF7vgBB6+J+Nzpu3c9v2Hwgr1fZR4WWaI1u6bXKmnD5Unjx+SKpYm7zp7MnNIEDEqCKC57SWMSpRQx5yW8t747vRrkndjxI4BYBuEb6DgEoaZpMpkssGYzSQI1bT3gV8y0Pgdy3CSCB0CJ+QgPLLgiCHjA0VV1i0Io7KUXX1yz9j3u3umFRVPbe+FB7AQg5JhjjiGEOEFUMGihUJAp7jiOX1hbrrhRKpORkEN1SnJ6tn2R9crybVMUgdCVK1c6Hr8xcGlzAISSvC05HHEcI/QB0ihadeONN7YVrblzd//FjddnPu5d2vNaI727f8jICUDc4NN3Yk1v6HapleFWZJOn4Gq4TMezVk5yMx/96Edlz20mTmrOa33KhBSKTXZKc5rfoV2enJB1Gd4Cs/6f+J9vpv+dianglcfw/L0ATjvtNPnQWWedNa616QtfOlVafWbMLOTBM7dkKUHLtahfeQyrXoIoYXDNqDCAkYORX/H8cgBHH300IKobwKaeClNR5Na4UrGmp8xC82q1KgPcdevWMQnz/Q9YCPC4/MUXX9y0aZPjR7aucYVKcRDHCxjvmL8nItdioH63x9zmMuqrCg249dZbpUOKFANUkBgOAZBl9KGFWjBoGKMaDK8kx44fcAhbS5fV1EORnJ8yZQrAjDNI5rozb948UeGucA4pACCvUy+IK16YkdDPG9QLYlaVYCMbNmyQ9IveWpBOQsj2DceLLIMQQlJFaM22bcnhEFJjLMNhu0JaihAir92xYXOJcSl5TjUAHnRWUjn66KNF/oCClWBMrVqtnnjiib++7VfL//q3U045JYqish8WTXryosMWL16MOJy4006nn346wF2PmS41CzhY5rwSCHIoIC0Jy14gVTTqpM29yDaoiKv4cSRpdNSoUUnRx7Bkl4qmaQhcALCa4da3xQKlvNGUzyr0bQ79vCg0qhWxm266FTqdTwAAIABJREFUdB2nGgL5rbrakyY3CJmHKgjNJCEeeOCBM770BQCHfeKIBx98MH2idtuwG5prGsEzHDWuzSVT34khSNd7SJVOTNN89tlnF33iXw7fZ86jjz5aZ0UL7NySG1VI4qdMhkMGHOmYO33wW2+91bIsvPIYOlbstttu7Av1mc98BhBGLVHYZFkAuLicSMvVYi7dyJIiMkr+8xPLWDQs1hsPOJgXT7krY0+4iuUCd911Vzl+1FFH1ULkDZK8J6EPIw/dLK9PVFUMw2AUYHjVRs+w4XALGT7YaaedHn/88eeff37NmjXz588f6ukMGYbzqigYeqOri07JpJJZ9YdXwPFhlVkHFpqmZVT6Pzx4TYS13cfYsPptdoo5c+YAkn/nTJo0SZ76iiuuOPpbNwKYO+cj3/72t+V4a6jVgsgJ4mY7l55nIWeEvX7Fi1oLeTZerVZ5Dy3Q6/rNecM0TfaQaehU0/P5vE9owdBM0+Qb6zgCobquV4OIHX/GjBm470HO9zQtAwE7gu/78hqNnN1SzDMASEimptVi5/P5/KGHHoo4AmI2Xo1ISyH35JNPPvHEE/jIx0HpAw88cN8zL1WDYqtl3HfffUuXLsVJB7/77pprr732zjvvrIVosgzf9xETdqEvl8umaTpexI6fM01CKX/VQZm9onw+r2uabvCXXA3jZjt3wgkn3HDDDexlTp8+vRahZBn5fL69vR1RCMSgWqjlShb/ryAIwJIchZa4c0VmYTRbRlNeb1wtpumhweuBjffnydOmTUs3BLa2tmqaNnr0aCnHYhea5PNPO+20b3zjG3C64VamzZhVd3xBswjDMD3+5ROP+/9uXr5o8TGZu/heO7VGoHKRbG6GgghSReCxtFzyXqVw8803X3755ez36dOnn3/5DwDsOTFb/b3myBnpg2eEEP75z3+yh2bNmpXWQDvggAPYeD6f7+zs/PFv7hg3fuJnDuVuXrxqw3XznHWda/L5PBemDGW9L2op5PL5/KxZs5566imZrmsZO6mU103T1HUdIQ9MdV33Y61gaHP32hdIAtZ41NRq+HbJzv32t78FeOajp6eHhCjlzXw+LxTVfFjNiOPKxvXylR5//PG/vu8x9k6e+pX/I8e3xfVnx4Asxm0dKKWmaW5OfrTP7+Y2xebO2M+rRH/GMw/JwfcZlw9lBj8QhRxtzpmN7/B/H7/7xLbhFScNr4AjiiLZ6j1Q4MQ6rwqqwXPeW72KnSJtxg3PeeWVV+SpFy5ceOkV47/7+JprbvrZmDGtctwkURjHmxw/T5GeZ05DzQ/LXpDXYjbe1dUliZA9NX+iqQVBwB4iiD0/9H2/p+rZBp0zxpo8efI777zD3KeKxWLFDdnxTzrppB/8x38CANWQs3eeNDGZudgsIlcw4yCZjMyI5AqWFvu+z2UqpBNKLbAoVrD6kZQlXbWqF7tOaDL+8dw/xJO1Rx55pFqtlt2gzdI1TYPLz6hpWhAEZS9kLzaOwyAI2QTKXmgbhL1SCnjiJVe8IEdjzqaMIxDtjTfeaPOCHCW+7/NtbhhC08uun9P4e8u7JLjHWHX16tVp3kxLjuZ12rha2K23z/H+PHnZsmX777//P//5TwCXXnppFEVRFNW1QJuGn3GRXfE01r21smNF3fFFI0nm+C0mAOQ1kpmMrcUHTynNGWNlxjMznD17Nv9Ae9ej1vvkk0/2+X1ZsmRJunHgfd6T9HhG0Po3v/nN1VdfDakpJ7BgwYL0ofY54OD0QbhvLaNGuRXfdX3fP++88wCm9qsDWLep19Lg+/4ll1xyyy23yHW4x4EfX/FGJQiCyZMnr13ZwQYnTpzYW/Nsk87fZeyiRYvuWhU1lZpvf+ghqmkVN8zT+PHHHwf4Yn788cf3XuSZGnzf5y8n8NDUDr/6xpsvyUlef/31ubE/+l1vx8nnX3jJuWfJcV3XB/z6owAgn8/3+QVkeJ+HthE2d8Z+XiX6M77HhKb0Q+ktwebG5UNZQtgHwdI1y0AQBLqu93nA4YPhFXDEcZxWFhoQ8AKkyGQUikV2Ch4M8tKJUy6X06ceVbTyOtUpTQ8aBISQXi+0jTol4JxGet3AC2PbIGyce9YD0PQ4VygYdPexNnuIxHEQRWEYlt3A1rUwDBNbS6JpObvbi/I6wjAcN27czBnTXwNANKtl9LhR7fwIhHDuvZEH1Z5+5L6Dp4mWGZ75IDBtS6dhGNZJHRDqxbAM4fUlLvRWsVDeGBQMOnN2okA6bdo0QkjZDWydTJw4cf3rb7Od5ejRo6MocvzQ0hCGIeI4ivm7UXH9gqHxVxpHQRjycT/KayTJwTB3GDfM6yQMQ6Egwj0v8hqpWwN+jbF6P/vZz3KJJwDAuKJpGbRxtbCiQ5/jjYO7j7VfWFv3uZumuXz58gf++nKppfWAmRMb/4WGnhzkvJ9aL+9HFeOEkFjwOmn9EmrJUUMjlp6d4ftMOz2eSGX87kI43b1NTeyhzFWmVqulD9XPg2fEFeQ3YsqUKc8995wcb21tfZ+D8xkGHlonwK3kbTv5iEUdMDIsWydhGBYKhT333PO5KATVJk+e7EIvGFoURVOnTn3mzffY4hw7dmzZDWyDXnfddXfddRfmfbK34vzyl7887eKrK35gSdVF0Sju+HFeQxiGnPzIbXir1Uol/QEd/8UvHy/y5HJ8W1x/FADEcdznF5DhfR7aRpBX4/7MpJ9fn/cfT2uzNn59Mg9lbYw+CLZBbINGAo0HHD7Y8TkcokvFRRTCc3q7u+seZmR1N6v/M7kl96kZbZlDEcLtczLS+nmdbnB8IyU629zcjDgEAEL1QnOaNCrJDY4QT+RNEFEESjeu73QCLiC2atWq1155GQCoVg2i7k4uC2GapnQ1Q+C99uxf2TillJNMjRwot61PNQhoyNmIYes00ZSkFEDoB2UvLJiUN4LGMQhZuXKl7/usLbatrU1GJ01NTX4Ye2HMyvBpRdFyyl5OE66eL6wtV/3QNvVTTz31wAMPZC/z1ltvrQZCsYOb2oSgGskXpGwXn7lfY6xe6aLHcMpHx54wdzQ+NBpLs5TSiZOnNJUSF9k+tY8ASKNdBilUms/nEfoI3LRwOD84ITcftevYQn/ZJxlwY2EATjdSSmV6PZU1U6/pJzgZogFnnHFG+s8jjzzyfQ4ivA9dFNtQq4SBD+D73/8+AOn9C00vCg7Hc889x5bWO++88/jjy5iuUVdXl8zJdXV1MTW5lPE9ZWUU+Q0CksVcCyJGA+IBh3CFLZh1VOLhXJIfaVCfxYdEW15v/9C+tYODHT/gSNwgPQeu43RvqHuYk9UbdBFM7Sv7jm88mqVrGkFer3vfbIOud4J0VMH5ngCoVovr5OGSgEOwJnkWOg5BtA3r19cCfhnN5XKyXxSmZYLfuo444gihLlCAWxk7YSIbJ4QIt5cCApcJz/FChvCzNUlECJnAumzEprClrZ0pjYqqB+/FpZQyE0JCiIyfarVaxa/rRpHk0IoXyVYLKgKRKI5rQWQbZPTo0b///e/HTZj4+//7h6OOOsoR7YtCgtoH1WM9L28h3BGKmYx41UzLqE7JVnejbCnSxM+0bENGvEuuNO4XWCszz1VpAMswrTW/1RPPlNJ5nwiw9957p8e5xswWYkJ975WsB++1117XXXfd2PETJ02Z+tvf/pZ7lLw/fBeFdngVr1aD/IjZos0XtDhkakXLly8HEtLo319+mTkOCi8VCmDFihVMOI5rjUif5E2bKn5km5pogRFmbx7vrOYFOGaKm3LGUVDY7vD+CqRf23/iEdOz2+PhiR0/4EjkjGoVeA6vuwNgd+haL5gbal9o/Ixtk9qNHc8G3VD10wEHqzUgjqDpkWGl5eGkv7YTcL2NVL5BG7PzLgAXgCKEALFwn7LlJA877DB2e0bOhufkRXNBHIsn52y4ThgGkM2BvGBRyJEQUj9KXKPXd3awtlhBX43Z+P+u2lTxQ5vZoLCdOiGEkIoXyS6sevfOoNCgb8b6smydxnF80003dW9c/4Mf/vjB/31NxlucZBOG0HPQTdmlwgWdhMfYV7/61T4/o0YMeLt/muSVpk/KhtLMQyktcAcAJxlsA0hdhFtuuSU9vkXqCBK5XC5tO/6rX/1K/n7yySf/5o9P/Nddj/RXxNqvImdLLVSe1w0DZi8gjRJ5nJTI44ZNUp9DBBajR492vKhg0AwbaeVbb0VxXDDo1KlTgSR6rgYxM9UUBNsarBK8aiKmoqCwY2HQtl4fHjt+wMHvuABqvfCcNImGm5JUu/uwwdwMCgZttCoumJofxsXUeLKlo3ps2vUBB4IoBlARQhR8hlEESjs3dFkGF4DiykvCX7vrPS58GQQBwgC6gVwBbuWtN7jdpQg4eObjhb89g7SFFdGQs7XAg7xTikxGqaWl4oVFQ+O7wEhopBLK1KPXrVsncy2dnZ0V0biL+vbXSspNRqNJ929ep4SQJUuWXHnlldVyz/8+8+RNV3+n6ke2zrWrASAKYDUhjvPCRJ5S+tBDD7EJH3HU0Zdeeml/PqBtgXRyJd1ZymUkGh7iz69sYswhrrE9EMisUplTSRY5AODZZ5/duuM//PDD999//2lfv/C111474ogjtu4ggBDqrVXYW2Hb9qhRo6SfUSnP9U54h62IFZAvsm+KZVmJZm6lwkRped5IjI+ZPC2nUZ0Sns4RUUs1iJnlUOJRQEjGvEZBQWFIsOMHHMndotqTdpmC3Ik++nN0rsxwbTa3Sy7mNKZvUTdoagDS4/zIYQBN96mZ4XB09HrMHoLxFXiePA5B6JSP7FkQ9Rqhz8HpFxPGcsqC7/sIfRaFwHWeW85NTS3L4mXyfAFupVatQho58sxHwevZkBrkm8U/3X93NYiK0pYiMdPqrfhhQXI4ABC6du1aNshfDiER+DtcTtXUKZBUjnQNzNBOHPwvyx7zQr4T5RYeoY98E/xaOljfY489Dv2XQwF85Zxv9PlxDA7SlMy0CtYZZ5zB1VyARYsWSUYnb0n9+0NY9SKlNK3V+CFBCEkMX5jbDoB03RAAwLpstg5777338V8+kyuMpbBleSORl2LpqyiK1q9fj9AH1ZBv0kKe4RBOb0lrFQs48vm8XJzSXYh/p6TximGxxbZo0SLe+03oyV89zwsjS0tnOHiT2pYy/xVGIBSbZFtjxw84EjibMtcdTst/bVn/BQeLptaY4WB54LR9juCB+qA68sWmlKaTnmz9w4KhQ5INoxBUe/udVbaps3XP7yKsmdCw1q3iWqhtbW3y2g23UunmXYszZ86U9qpwK5veehly282jlkJY7Ya8IYmd5bPPPk9ALIMKNgm/phebmxmhpFgsyoBjypQpFTcqCPPV+gxHIJ1Q0oKqzKmVCzGxmZg2IVzufdOmTQAQBrBK8Jw4rov8CmbWZGTwkdjW1wccK1eulB7ud911l+RqfOUrX7nuuuvaNr4+c0xxq5MNfSKO43RNULJWM6TRRAfsg7AtrrCEEF7+c8tJXxUAZpxrt5Q7ee1JcImkqEyR6ZBFUZR2J3aCsGBQLqgjCB9l12erwrbtXXbZJWcadrE0ftp0QjB/clMyG9GGNuAvU0FhYDESwp2RFHC88me8+Uy6ayDh/AO8EvxBaM7pRUPLrIxSXgeQHhfW2Mwzs9jQpQIAFdGm8eSTTwKS12nLNo3EeJNqyNkvPsWpAAsXLgSTObeb4Tlyd/uxj31MdK8U4Dksc8A3xKKrJaj0zBtf5G2x0u1l/E4FkxIilBjE5nL12k6A2DrlNnIAKH377bedVIaDEvTUeAwnndvAORwi4DAkJYVpoVKYlq1TNsCFtkKf1dpntNQ3XOT6MBkZZGyuS+2FF15I/ynN3gCcfPLJv39k+c3/fSf3Jq1H45Xlfa416Ycy78Pmmuy3jsMxUKCU8ht8rVLXpBP6QIxCS62LVzcS8zZeUikwDlC6pPLmm2+y4iM3opMS5n7EGKa33377z3/+c7dadarV73/n2zmN0PS7lDLFVVAYcoyEqOJ9MCICDn5de+cFrP5Hevzb3/72hRdeOGXXGQcediQzR/1AtFp6oUGCupTTIAorDKmSioFcQQsSZW6NIIwjpJr6RCMJc5lKAg6eohf+2rUe3itRKpUQ+ogjFFrgViRz8LzzzrNzBjQDOTtP48WLF0M2B7KD5wqR0wNgr732ApJr927zD2KTFzvjCCAA3Igy8fUNGzYAsbS3qAh7ObCSShy/sLb8wtqy44WSTKpREsUEqZJKQlUhFKZlG/yuwLseQh92M7xqpqejOa9JGulQIVOwkDdR6THBwMO4bQy90cwFAJC2L/7Rj340CDORyFxD586dK6zRHBZviVgzRuDDbo1qveNoyhwnCpkmL3JFza+BlVpE1LvffvsxfjF3aRHjr7z+BiMS8boS51ZbBklCHMMwUGVWSirgUFAYeoyIgGNzIIScf/75v7j9j9+5/sf93BR+fJeWz34k69eX16mpkbRhCm88CX1mZ+/1Jjn55E4c8F4PbukZhaC6XijJezZvjhBVEuLxPLmmaVOnTkXgodAKr/qVr3yFjZum+b3LLp0x96MLj1i8aPEx7CovTLpDdpCoVkYdHZWzK1jAkaavAuh1PZbJSImEUtRnMihNTLHKXiijJQqEcVJSgYxm2EkNyzJ4UYbfvwOPZTgyDqjtlmGL8s1QISO1KSssv/jFL9Ljt91224CfuvGFZ+S5JH74wx8+9NBDX73w0pdeeumQQw4Z8Jn0H0uXLp07cxqAcaNas3F84KLQilrl/PPPTwajQJJGX12+DMDq1avlYtu4cSPrUuGlN8F0Xr1mLVtsH//4x8W4BiNfzCXvD6WUt6G5Dho+RwUFhUHGiAg4+tmB0h+MK5q7tlmN4805Ld2KklI5bAbQ/V6SbNdlrcELbUOHlHaIQlAtIIaV4StEPnIFUC2qJcrTt99+u0Fja/Sk/fff78QTT5Tjpkbbxo4ftdMujB2SOgjPcLCtpyiH88AiIAYLIHicJDIZm8oOCzhS/lh1SQuwDEeqS8U2tc7OzrPPPvuOJX946i9PgyVyjFQihxFHcnZevFs8w8EFmrJKWXPG2sc1RHiDjIyLrHQv493FAqtXrx6EyWTEP9LYY489jvnCKRmHNnxQE/+Aw7btpf/zawCXX/0fmc5h+C6K7XArslXYtm0eDRMC0yZhFcArr7widV9ee+21ShDaBk3ScpQCGDdlJgvNefcKq0ialhYl/BVOJnW64FU1TUt0WhUU+o0RXgQZWIyIgCNzD1uzZk3mCR9+SbVYRpo0ypMTgQe7FZ7z12WPyIfqhL90CnZ5hYwJbJqqv3AVc6uEMECQ1BomTZrU1lwqTZy2cOEB6WnkdBpEccUPs3WfhNtRAVAqlSZMmMA3hcCeB36cpWe4DIMYt1pGszI59xEVVrSVdDcKSdxiK15YMLUvfOELv/vd7zasX/fEU38544wzWE8jgN12203MRINpWYIFIqRgmd1XNXMXb8nrRzZIvm4FPsxHnMl+cUEI4MILL0yPH3fccVt9iq1Do9PpNkX/38OmnJbTaVODgyVXIHUrsnC2ePHiJBoGxo8aBaCpqSltcVz1YtvU9txzTyAhfATUYOuK64WwgxhW77r35Nluu+22OXPmkJ4OI/Z+/OMfb/ULV1BQGBCMiIAjY+j34osvDvgpvjB3zLxxydWfX08DD8VWuJU/P5gklnVCAiFQwW7bnK8qSielQjJbSqlgUzoZJoGlaxucINMyk9OpF8Tsxl83PxHNsNwyWKE9CkFpLm/Z7eNZSSXR4aAUwLqNXSw5wXmdQp+j7CXnlQkbsEBEJ7x1Io5A6J133un4IZMb4ZkMWVIRMvC8QsEUIf3qI48kwdlQIXNnzfi2v/HGG+wXaSHLsGTJkm09sQx4pmpY4pbFM8YUk8wQp3EIUzeZdDz22GPTTOfxEyaBJQhFVxSAih8UTK2pqQlI6oA9NdeWKmFy3MxrUV3766OPPrrk3GPu/e1tn/nMZ7b5a1YYYVDJjy3FiAg4pFgCw7aQADpg51K7lVxeuYRi4KLQjlq5J1U81jXepeII+SzeBCEEvryehPBh2zZCn9UaMuJOlkGDKM6IgliG5odxWgkU7LYk6KuMtB/H8QMPPMDO6Naqjz/+54KhzRtf5D3Donxedjk5lCtJpBsExHllwsYNoyCKCqYo5UQRKA3DkPU0QrIdOTfWkiUV3hrju8gX4VVvuummLXzvtznSLrUAZCtsJhmzbNmyQZsSA68mDEuMLdbVoUTAUYWmw63IbNCCBQtm7DqNNXPlSTh99kfA22KZsi1fb5ZO+XdKLEIYNltXhx12GCAD2XyTXfc1AVDK63T7kWJUGIZQgcVAYUQEHJxRIcD369sSX/7ylwFwXmetHEWJZZ9OSRTFtSAKY7ArJq/98wyHter1v8sna5rG/dkbyA3ciCRrI0fcMEqTOgGcddZZCH1oOnKFMa3NqAtxNAA+4WZavO1ClMmRs1iGg28iY2lXkQQ0iVK7FxmUmEInVNZlHC9icZWwkeO1dkv2ubDMjVcFCDyn/wJNg3YV4NJkAkuXLmW/ZNSxMvEHtp3KhUAjn2Pwr4z9PCPrmYJbBUC8yg033MDGdV0/6YTj91p48JmXXDVp3BhCRWzKrYgoNMMP44JB58yZc/HFF7e3tuhm7oIrr6+FqCONimautuamPiegoDBoUAHK5jAiAo7vfe976T8z7pfbAnPnzgUSilz6IZ2SIIodLzQ1wvzVeK6YxwR2T0dCMcnlciLgqGZKKizUaPStdYOo7IVpnfULLrhg4sSJYydNMYot53/7MgCaph1wwAE82QBM+chHWcDx8ssvA0np5L31G9h44ixPNABlP5SZDErASKNOEBXSNXtppiU8U4ToAs9w5ET74rXXXgsIaaZhqZfA+yMEZF6B3+oE9tlnn0GYTHoZZNisA4Vtcbn86U9/umDBAhq6AL5xUZ1KfU7XSm1jxk6d2SQWVUdHB+IIiEE0mLZGkNNpd3f3VVddtWFdZxDF13z7vG7HZesqkcanGgwrqlWy51ZQUBgeGBEBx1577fXzn/+8ffSYMePG//rXv26k8W8rBDU0tcMtp8cMSoI4doJYGqvyik8UQDNg2tVNScVn1qxZCDzYLXCrO++8c/o4vK/EzBA7qMtKKqlSC6V0xszZn/8/Z5N8ccok3jXwq1/96uADDySGefa3LtOLzazFhpdOROZDs0pskp/97GcBHkMs+vy/9qm3UXG5cGoi8EV4OoTtRHk/Z8QadC3ZQMtf1wAJNG2Lm+Xxxx/f5/jo0aOlXyuAq6++esBP3Yh0y+sXv/jFQTjjgIAQcvfdd3/yqKMBLJi/b/ohU6d+FJfdUAasnDTDYlaRZuOarWJxdm7aZKeZ0TxzljdJXSJQQUFh+GBEBBwATjrppN/96Zn/fvCpD2VJtSWglMKrMfXxtHaCzHDIRhKebQ48FnCMaU+a9775zW/Cd1mry+mnn54+PusryXSjFEyt6kcVP8pkPkyNOH7khbEstVxwwQV/fuThGOSmqy9bs24DOw4XKhCxQlfZYdd0LqcdhyDas08/UQkSG3om/AWgEvA6Dt/oS9GOgItzJBLmVINh0TBpuqGUolYGhmmGY4899kj/mS5q3HnnnXfdddett9761ltvNfqPbBH6GSrdd999P/zhD/dZePB//dd/XXXVVYNwxgFEscEEAICpET+Me72oKce1evmWgC0hw2aSLbzWJgKOWsjTe1wfnSUIDavJ7lunREFhO8WOVKDpW7VQ4cNjn332eYZZWNXKH/vYx9ig53ndmzYEYVwJQkvnl1feLxoG0E3k7Bnjp8uDzJ8//xOfNB9ZVd1/1sTjj6/bFzblNOaWmR60Da3ih4TArg9ExpfMTdVApyQnRD6WLFkCqwSqA9hUrrE7AecESLsKj+tt8Ip7FIHS1e+8SfxEaVTXSBhx4VRmsJLpJqh6UX3qm9faqZ8QHqdNm7ZCBBz9VGAb5C+hpmncY72hN4RLbg8iTjnllDmHfnr4X4YaZ1g0NVNLEX0AADmN+lFUdsOmDK2HGwBZlkYg5XFFHRCGzRYhp86wzJmRD5xeKCgoDEuMlAwHw2Beo2+77bYpO00CsOuUnW+++WYAy5cvnzhx4tf+7ey3V7655r11MibgTRCByzIcbcW6Jsy2lpIfk7GjWjPHb871IbLORvI61epp+ZZON1T99PMPO+wwRAEIAaGxYbGAIxUrUAC1iMunppRJKUwbQIo0ihAEXIRDR0IE4QfpqXl2qmVGcmNZOZ/h7rvvHt9iA2gp5G+//fb0zIfJbTWdGFMNlluNUl7PFAEB5HTiBVE5xXROJDeoBtMu5g0AbW1tN9xww/ixY6lunHrON11QO91uHQYskA3KKuBQ2P4wTC502xojK+DYInzIFdDW1nbMcccDOO6EE5m21WWXXQYAoe/Uag8+cL+8Z/NaQ+DDyMHMr3r9pfRxLIMAsBssattsPSu2wUONLJOUjW90gvS1/rvf/e7h//IvABafdGqg5xjJlKuPCxdZqdklaBkhI/GZJJZlBY2IDIcfsfkIBzie4ZDS5sK31mfdv4VcQngcNWrUQ0t/D+C6n97C8z3DDP/xH//xuc99DsDxxx/PP8chxXZ6eWrN97Focxr1orjshk3iISFKy42FjThgg+eee+7ad1dFoL+88dqKMFvmmafQh2bAtJqLeTSgz7drO30PFRS2X6iAYxuC7cCk5Plbb70F8C3+mvc6ZQzBU/RM2xt4+a9P1h1E14BsiQTA3LGFUz7aB/vVNrTGTaRt0A2Onw5EJk2atN++ewPYbe/9akFU1xYrkhMB5fKpKUEwDTmbpJgWtWolDGNwgxUK2UTKnky1EJS5r3V3dwPJjaGyviM9wzEFs5TXS7lhWuNra2v7yU9+sm7duh/96Ed8S62w5dh9rH3aXtlFmzeoH8ZlL5TrlouFpj3FAAAar0lEQVTHRzzAZYuQy6wxpzc9F0Yx+35xuVW2roy8jhAKCtseWxGwqhhXBRxbhn7aiDPwRhJxm+e2ZKEPzci3tMmAo1qtAlJq083obbCD2A2OqUVTO2jnUuM0CqbWuIm0TW1DNUiPX3zxxZdf9h3E0VWXX0qEqgfX0BQSGr21gF3TueQ5r6kXcpT7tV122WUfO/jgcqVy64+udzyu+sU1pOMQRKP5IiGEucNw4bIggG7AtCK/TkOCEPz+uFlt1jANOAYQI/miUzS1hZOzi9bSqRdGvV4oQ/NUkowiZzGxfz7ISic5m0SBoRHIdAgnjeaNWAUcCgrDFCrg2IbYc3wRgHSUEFS4AJrRuaG7kOFwsIDDzSqK7j62gIZuFIm+ohxaMLL2qgVDqwURM0ZhI7/+9a8BZi/XooUeq5DstNNOgEhOAG5MCtnMh4acXdnQMW988bnnnrv55pvZ4G9+flPnpk3syX//+98BlsjRIj1nkogdnLfqhD40E6ZV2dCZmXmGALu9YyQHFlsEy6A1P06Lx4wePRoQ3j2mzUJzXjrhOi625ADZtj1p0iSZOZvEmlYUFIYT1NWAYQQFHAP1kff/OK2WgZRUBr9iBj40I9RNS3A4uO6n70Iz4DlvvFzH4WixdABWQ4Zjcyj2leFgQU8xQxplk7Fb8jSumyELLDQjiDg5lPvZxpw0GlZ7AZx99tlA0jWw+t0OVrJJealo6W4UUWv3oJswrN71icmWwohC1qrG0NwwKnuhDM25oFnIff700AcwceJEAAgDALBK0g+5VqutXr0agQfdhGmtXvF3KCgoDEuMoIBj8DGmYOiUZD0z+ZXRlowKbmTFiBGew9tHBViVYXMZjkY057TGJ3P9gxSHQ3D7fdjNRRH6iA5D4VtLwNpoTz31VIDdAHSYBSPyIcU5eDqEbKpU2Xm5Z4roRvErXewGM2bMGP7y80UQ0vXeO/18RQo7NiyD1oKo1004HO+++y6QkJSrXZ0ApkyZMnr0aIQ+ANjNJctk64rL4PoudBNGvuu9rBe0goLCMIEKOLYhmnLa746bna0UCIGvumZRgBnHs7Bj1apVcnh0wYDgn/YHrZbRSBplQU86EPnDH/4AMHu5VreX28XV253YOcLL4ZmSihkHSBuyAKDUJ1rRTCuNhiAsHcKlpnkg4ruwWxB4a97e5o42CtsFCoYWRHEQxXLdiupbAE2Dade6NwCoVCrr1q1DFCKOYJVYmg0AFwQLPOQK0AzW0qKgoDAMoQKObVtdaxamqAkVNHBBNeSL2U5XZhzvOgBeeOEFOdyS13dqzrVY/XXNOHbOqEUzs9pZzTmWJknOyOWqAg+FNlvjc+NJCFEml4auIrbg41G1F7K5hrmBU61cC5hJ2ymnnAIAIZcwj4WVzOTJk/nLL7TCq5b70ktQlc4RiLxOKSE5nRpCEIzzQLltvb1x7TuQ3SjgabnKRt7lxKlRfg1GHnGMctZCT0FBYZhABRyDBEII37cFLhCj0JrONxiGAb+GKGQeZlKZlOHWY2Y05/rL4RhXNMcUs9EJi3vSJRWehwg8FNv00GV3et6OyGKFnG3EPhvnRZ+INwg0N1kAWlpaAJnh0EM9x14R9+ZlTzZtHkhJb1W/xrzoqpU6i5n3gYpCdhj0+VESAkunabtBnlETVbnIKUNGIQACD1ZzT8e74t+Z8X2N/Vy7VpVUFBSGKVTAMXg49NBDASCOEfgotKYzHE888YRpmqj1wnUuv/zyZDM3QGi1dELqSKNPPfUUwFpj2vxyFxssFotAos1F/cTupKmpSSQtbD3wIY1XohCIGbmPZVB4dMLaFE2L28DKjIhXA6HwHc9NlEYVRjhso47qlIpZWUllPdI2uczOMGO747uIQnhVLs+voDACMG98cfvaj6mAY/CwYMEC/ptfg5FP0zKmTZv27rvvTpkw7qRTTj3rrLP6c7QtWmemRoumlraP55YlvotiW23TejbIFUF4KrsAL0lC9Pb2igxHobJ+LdJFIr4TtYtmSkVDDDbcGBg3tpqRG1EYySiYenpxzpo1C2BLyIDJff44Bwg84DBiv/4YMTwHfpXryykoDBG2rwhgkKECjoFBfwTB9t57bz7kVdGXeGirpTdKmA8UDt+ldadSovAhenFrsJqqm9axQW4LLlLZaQ2llpYWmbRosnNIx09hgHwRVLPTZNUoADVYhqNWS3zaeIVlWLrCKgwVCjmtKfV14I0noipX3sA7qA8//HAA8F0UWg/7xJHZo9Qq8Gs8aFZQ2CGwg4UvKuAYPDz44IP8N88BYGnZN3/h5NLMURb6wodfdl/Zd/woO+F28Iq4XwOIX67vUmEBR67glzfJ5++8885ynHE1uC4CgCiAVULgPXbfUjZACBHRiQ2vesghhyTzSDXj8MZahRGPoqE1paJVbiLI/JPNvNvN1+fBBx8MMN5xS1BpyGTUeuDVeCSdwg52yVZQYNgeF7YKOAYPvAcEgOcgjqyGZMbi2e3zxg0we2Nz4MkMrwrA793EChxCC5WJNtpeT5d8fkdHBxdzzNm9nWsgRTsABD6sZnjO984/OzlB4DMhJniODCwIIfCqiCN23meffXbbv1CFocEWXQ2b81paBjcRpbWaAFQ2clHaxx57DAD8GvJNb/3jOfnvfN063fCqCbdUQWE7wfYYOmwdVMAxeDj55JOPPvpoAHAdSydkSIW8eeaZlTbcCtsXij4aFlhYBpIyeT6fF+rRtha6APhrAW9ThOtwLVHgwgsvlE+GV50yZQobnz9/PuIYtTI776c//enBeKkKwx7TWvM7Nze4vAYerGb4bhRyaQ0RJTsAmlJuw5wPVN4IvyoXoYKCwnCDCjgGD/l8/tJLL73sssvm7f6Rprw5tJPhvArWQuJW2GWa7xR5rFCQHa0AHMfhGqm5grOpE8DixYuFqCjPcMgY6rzzzivkdBa1EL/KN6bA3XffPX/+fFR7aOjefvvtQxxzKQwbfO4jo46e1ZYdDTwWyMqBiy666KCDDmLR6iePXiTHecDR8Qa6OxQZWWF4ov9pjB044aECjsHDa6+9ts8++1x22WUvLHu42rV+aFcVvy4zVS63wkKHRJxDN2DanlAgBeC6Llclp5rsavnP//xP/ny7GV417dv+9+efhaZPmjn32pt/wRMnAIB77rlnj4/MOu2srx144IHb+jUqbN/g7a9JwLHrrrsuWbLk6EXHAJgwbkz2+S89iCduG8wJKigobBFUwDF4uPPOO/lvTlfv+rVDm/vl3LpaGQDcMvf+Zgg86DnkbGfjOjmm6zoCD3YrAo+C8/IWLVrEn2+3wK1wpQ0AALOk7w1gN+isL9xps9xYBQWGOXPmMHIoPIebGAs0F3Kol83NIONGpKCgMEygAo7BA5faBNDdAaeH362HFrVexDHc6tq1a5NB4fZCUs2rlmVJVXIqSiE8TSJuDFwKDACgUWJqpMcNGxt9j9lt8LixCtspHn744Y/O/QhtGjWqtfmvf/1r+iEmEWbrybWLa80JPPDAA4MzSQUFhS2CCjgGDyeeeCKnT772BO69Zvny5VdfffVgTiBdxOFlDqcLbhmIX3755eR5fg1GDjmb+Il+RhAETP8AbqWnmwcWPKURuLBb4VXHjRuXPp1l0DjeAts5BQUJTdOOOepIWM2zZkxPNEYBAKWcjnoZG66QK/D0008PziQVFBS2COpmMHh46KGH3n77bQBAjMCDbPPbWmyOBdIfdghXIdvwDpZ8B0xIFACjcfg1aAbyTUxSmmHdunXwq6ymHgSBK4TJ58+fz+RK4VZ+8IMfpE9RMHQABRVwKKTQf+pSwaBRHKczZBs3bjzzzDN/cO6pJI7d3kSHY8KECel/fP755wdkqgoKCgMLdTMYPDz33HOZkYxJ22CC+2PFMTpWQHp8syoJs5dLGb0CCMMQXg2EsK4BSUD5/e9/P2PXXWHkj/zUUUcddVT6FAWTagQ5Xa0xha0Bc6tPZ8iuvPLKJUuWoHddXO2+9abr5fgnPvGJ9D9mMm0KCgrDBOpmMHiQN3WGuXPnfvOb38w858O3rvTzCJlr9LRp05I/4hi+CyT6HGCkUW6DUoFURABs2959990BzNh1euYURUPbdkrtCjs8irmsxTFnGm1YhV9/rfO9hHX05S9/Of2PJ5988iBNUUFBYUugAo7Bw+zZs9N/vvPOO0Po+5DuKEGK2M87Y70qAg9RWC5z/7aLLrqIKyK4TiJqDkBU0wsN1jBNOWo1DCoo9BPNDVyNxI2osunjB+4nx8ePHy9j9xNOOEF1XCsoDE/oH/wUhQFCIm0OAOjq6rriiiu+//3vD8lkEu9NAKmAY8yYMR0dHVL8YM2aNaVSCcDXvvY1e+zki9/C9F2mPfjTJ9L/y/oFGpMZzZZu61TJ3ShsHUq8GyVZV+ecc06pVPrTk8/Mnrvnv/3bGeknX3DBBQuOPsFz3Y/vsetgT1RBQaF/UAHH4OGee+7JjDzyyCNDMhNInzYBSQJtbm7u6OiAYG+8+eab3CscOPEziy6+fvn8+fMzTQGs1t6oi9CaNxpFOBQU+olWK5vh0HX9tNNO2+dTx0Oq4qbQ3NI6mNNTUBhCbKfbs8EOOIIguPHGG8vl8uTJk7/0pS8N8tkHH+ll8corr2Qezdy5BxOZkoq0vOKSBrUyQAD88Y9//NSnPsUeKpiaoZHGTAbXRWjoRplYMl/foAIOha1Em6UT0kfmTEFhhGA7jSreB4NdYn/66acnT5586aWXrlu3bvXq1YN89qHF+PHjMyNXXXXVkMwEqZQGQ0dHB/uFFVBQK7OqyooVK9JPK5l6sYGWwXQRGjkch05t/tZBO0FBYatgaLRkao1t1TveVVhhJGNErefBznC88cYb++23H4A5c+a88cYbkyZNAuD7PqNPhmG4LQy9iMCAH3mLkLl5A2BvhcTmJjkgk88cJFNSWbVqFXv0hBNOWL58Oard0AwAu+++e/q/2m29YGaNblstA0DR1DOnoJQWTdo4c0LIiPqODTiGw2IeWOwxoanP8S/uOXZqa16+WMdxrrjiipUrVxZGjf/h1d9taqr7rw+/rna8N3b4QL23feJ9rvnyZ2Zwc7/3eZsbbu/5YAccjuOwjkrLsqTY1P333//d734XwHe+853E9HygkcvlttGR+4l8PmvA3d7env6z2dHa21sa//HQ+qdtHTIHb2mpO9Hq1avZZM4888xzzz0XvRug5wBcfPHF6Ul+/eBdZowuZqZ97L4tFzz41vhRrfl8Pv0amx0NQOMr2tzLVOgnMsHiDoyTF9QtoWuuueZnP/sZ+31Se+mmm25KP3lA1lXjl1RhQDByFu0WYXOLts+LJxtkSD+UHs/UyofblXawAw7btlkyo1qtSgbDokWLmLFIb2/v+vXr3+//twqWZWmaJjs8hwrXXHPNEUccIf+klGZe7M4WtsXLZ+jqKq+3EiGQjP3E3/72N3nqZcuWffOyq9zaa99ZurS5uTk9pRklAreyfn02av6fY2f29nRXq/lKJdEK6+oqA0iftM+ZKGwRmpubq9Wq5Nzs2MgsobR03muvvZb5snz4dVUoFNILWGGgMKIW7RZhc4u2z4snG2RIP8TGKaW6rnuex/J8L6zt+/I7IBg1atTW/eNgczimT5/OuJOvvvrqrruOrAa2vfba64ADDpB/PvTQQ0M4mcxOLu0WO2vWrCtu+PG1N/9s4cKFgz4vBYXNIt2Z0tnZOYQzUVBQ2AoMdoZjwYIFN91007XXXjtmzJiM6/RIwB133LFhw4aVK1dG7TvPnTbmg/9hm6G5uTn9ZybhoaAwDPHkk0/K3+vsBhUUFLYHDHbAoev617/+9UE+6bBCe3t7e3s7y3cNITJkog+fbVI8UIVtDcdx5O8ZowCoFaigMOyh9rVDg8G/ODae8aKLLpK//+Y3vxnc6Sgo9AsqjFDYgTHSlrcKOEYuzjvvvGefffb6X/7P22+/PXny5EE770j7jikMFOI4Tv/50ksvDdVMFBQUtgIq4BjR2Gmnnebts6BQKAz1RBQUthinn376UE9BQUFhC6ACDgUFhe0DGSuANKVDQUFh+EMFHApbBlUQURgq/OQnP0n/KV1+FBRGFLbfi7AKOEY6tt+1qzDScPjhh8t2qlGjRl1yySVDOx8FBYUtgrKnV9hWmDe+OOTdvwo7GJ566qnly5e/+M66kz99uGmaQz0dBYVthR1yK6gCDgUFhe0GhJD58+fPnz/U81BQUNhyqJKKgoKCgoKCwjaHCjgU+saAJPR2yKygwtCiq6tr7dq1Qz0LBQWFLYYKOBT6gAoUFIYnfvKTn0yfPn3u3Lmnn356o7q5gsIIxzC/dKuAQ0FBYftAuVy+9NJL2e933HHHH//4x6Gdj4KCwhZBBRwKCgrbBzJKX729vUM1EwUFha2ACjgUFBS2D4wZM2bx4sXyz8MPP3wIJ6OgoLClUG2xCluAYV4gVNjh8dOf/nTx4sXd3d1HHnlka2vrUE9HQUFhC6ACDgUFhWGKxgBX0zSlaK6gsJ1CBRwKCgrbDbq7u3/1q19t2rTp2GOP3W233YZ6OgoKClsAFXAoDABUqUVhcHDmmWc+/PDDAG666ably5dPnTp1qGekoDBIYGYR2/XFVpFGFRQUtg9s2LCBRRsMjz766BBORkFBYUuhAg4FBYXtA6VSKf3nu+++O1QzUVBQ2AqogENBQWH7gGEY6T/vv//+oZqJgoLCVkAFHAoKCtsHenp60n9u3LhxqGaioLBNsV0TNd4HKuBQUFDYPlAoFNJ/Tpo0aahmoqAwzDE8QxYVcCgoKGwf0DQtXVXZeeedh3AyCgoKWwoVcCgoKGwfCMPQ933559/+9rchnIyCwnDDnhNLH/ykIYUKOBQUFLYPZMzbPM8bqpkoKChsBVTAoaCgsH2gqakp/efxxx8/VDNRUFDYCqiAQ0FBYbvBLbfcwn459NBDv/Wtbw3tZBQUFLYIStpcQUFhu8HRRx/d0dHhuq5lWUM9FwUFhS2DynAoKChsT6CUqmjj/7V3PyFRrnsAx58ZZ7TEG0Fonv5ARgZB1CWiiULBoEXtbNMfLCKoaFO0aVEYtVFalUREQa2KoBYlQUhGEdE/ihYhKUTiIgiMEDQkcJy5Cy+HTpzr6dzj42v6+SwGxOHlx+ODfHne8RV+RYIDAIhOcADAL2BqPs7r5wkOACA6wQEARCc4AIDoBAcAEJ3gAACiExwAQHSCAwB+bf9e8K+/flPSBAcAEJ3gAACiExwAQHSCAwCITnAAANEJDgAgukzSA/xBOp3OZrMTftmSkpJIV+Z7FnkSpFKpTCZTLBaTHmQasoEjsWmjymQyY68lJSXfb+BMJjPV9rMTDgAguql1wlEoFEZGRib8smMBGOPKfK+0tNQix1YsFvP5vHWOwQaOxKaNKp/Pj72mUqnvF3kKrrkTDgAgOsEBAEQnOACA6AQHABCd4ACAX9jq3yqSHuGnCA4AIDrBAQC/ql/leCMIDgBgEggOACA6wQEARCc4AIDoBAcAEJ3gAACiExwAQHSCAwCITnAAANEJDgAgOsEBAEQnOACA6AQHABCd4AAAohMcAEB0ggMAiE5wAADRCQ4AIDrBAQBEJzgAYLpZ/VtF0iP8SHAAANEJDgAgOsEBAEQnOACA6AQHABCd4AAAohMcAEB0ggMAiE5wAADRCQ4AIDrBAQBEJzgAgOgEBwAQneAAAKITHABAdIIDAIhOcAAA0QkOACC6TNID/EE6nS4pKZnwyw4PD4+OjpaVlU34lflBjB8f3/v8+XNpaal1jsTCxmDTToJ0Op1Kpab4IqeKxWLSM0R37dq13t7ekydPJj0I/FMHDx7cvn37pk2bkh4EftaBAwd27tzZ0NCQ9CAkzC0VACC6qXVLJZLa2tq5c+cmPQVMgLq6uoULFyY9BfwN9fX1Ni1hhtxSAQCS5ZYKABDddAuOJ0+eNDU1FQqFsS8vXrzY0tKS7EjwDw0NDZ04cSLpKeDv+dN929HR8ezZs0TmIXHTLThCCKWlpd3d3SGEYrHY29ub9DgAwHQMjlwu9+LFixDChw8fli1bFkL49u1ba2trS0vLhQsXQghnzpz58uVLCKG5uXl4eDjZaeEn3b179/Xr1yGEGzdudHd3d3Z2Xrp06erVq62trSMjI0lPB3/uh32b9DgkaRoGx6JFiz5+/BhCePny5fr160MI/f39jY2Nx48fHx4e7u/vz+Vyr169GhgYmD17dnl5edLzwv+prKxs3759S5YseffuXdKzAPyFaRgcIYSampq+vr7e3t6ampoQQnl5+b1799ra2vr6+gqFwtq1a9+8efP8+fONGzcmPSmM5+3btyGEH/6U7PePKC1dujSEMHv27NHR0cmfDf6X8fctM9b0DI7169e3t7fPnz8/lUqFENrb2zdv3nzkyJF58+YVi8WKiop8Pv/06dNcLpf0pDCeW7duDQwMDAwMVFdXp9Ppr1+/hhB6enrGvju2vWGqGX/fMmNNzwd/1dbWtrS0HD16dOzLXC53586dzs7O6urqzs7OPXv2rFmzpqura9asWcnOCeNrbGw8f/58Op3etWvXnDlzzp49++zZs8rKymw2+/t7stmsO4NMKT+zb5mBZuiDv27fvl1VVeWWCgBMjul5S2V8jx496urqGvs8KQAwCWboCQcAMJlm4gkHADDJBAcAEJ3gAACiExxAFGfOnGlra0t6CmCqEBwAQHSCA5gwIyMjhw4dWrJkSS6XG3u+9eDg4N69e1esWFFfX//o0aOkBwQSMz2fNAok4sqVKz09Pe/fvx8YGFi1atW6deuuX79eKBS6u7sfPHjQ3t7e0NCQ9IxAMpxwABPm8ePHu3fvzmazVVVVW7duDSFs2LDh4cOHzc3NFRUV586dS3pAIDGCA5gw6XQ6nf7vb5VMJhNCWL169YsXLxYsWHDq1Klt27YlOh2QJE8aBSbM5cuXb9682dHRMTQ0tHLlymPHjg0ODubz+dOnT3/69Gn58uWDg4P+yS3MTIIDmDAjIyOHDx++f/9+ZWXlli1bampq6urqduzY8fnz58WLF+/fv7+pqSnpGYFkCA4AIDqf4QAAohMcAEB0ggMAiE5wAADRCQ4AIDrBAQBEJzgAgOj+A4zc0YxydzSPAAAAAElFTkSuQmCC\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"future2 <- future %>% \\n\",\n \" filter(as.numeric(format(ds, \\\"%H\\\")) < 6)\\n\",\n \"fcst <- predict(m, future2)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"future2 = future.copy()\\n\",\n \"future2 = future2[future2['ds'].dt.hour < 6]\\n\",\n \"fcst = m.predict(future2)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The same principle applies to other datasets with regular gaps in the data. For example, if the history contains only weekdays, then predictions should only be made for weekdays since the weekly seasonality will not be well estimated for the weekends.\\n\",\n \"\\n\",\n \"## Monthly data\\n\",\n \"\\n\",\n \"You can use Prophet to fit monthly data. However, the underlying model is continuous-time, which means that you can get strange results if you fit the model to monthly data and then ask for daily forecasts. Here we forecast US retail sales volume for the next 10 years:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_retail_sales.csv')\\n\",\n \"m <- prophet(df, seasonality.mode = 'multiplicative')\\n\",\n \"future <- make_future_dataframe(m, periods = 3652)\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_retail_sales.csv')\\n\",\n \"m = Prophet(seasonality_mode='multiplicative').fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=3652)\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is the same issue from above where the dataset has regular gaps. When we fit the yearly seasonality, it only has data for the first of each month and the seasonality components for the remaining days are unidentifiable and overfit. This can be clearly seen by doing MCMC to see uncertainty in the seasonality:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling weekly seasonality. Run prophet with weekly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 1).\\n\",\n \"Chain 1: \\n\",\n \"Chain 1: Gradient evaluation took 0.000184 seconds\\n\",\n \"Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.84 seconds.\\n\",\n \"Chain 1: Adjust your expectations accordingly!\\n\",\n \"Chain 1: \\n\",\n \"Chain 1: \\n\",\n \"Chain 1: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 1: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 1: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 1: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 1: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 1: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 1: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 1: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 1: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 1: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 1: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 1: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 1: \\n\",\n \"Chain 1: Elapsed Time: 11.649 seconds (Warm-up)\\n\",\n \"Chain 1: 17.3646 seconds (Sampling)\\n\",\n \"Chain 1: 29.0136 seconds (Total)\\n\",\n \"Chain 1: \\n\",\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 2).\\n\",\n \"Chain 2: \\n\",\n \"Chain 2: Gradient evaluation took 0.000176 seconds\\n\",\n \"Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.76 seconds.\\n\",\n \"Chain 2: Adjust your expectations accordingly!\\n\",\n \"Chain 2: \\n\",\n \"Chain 2: \\n\",\n \"Chain 2: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 2: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 2: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 2: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 2: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 2: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 2: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 2: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 2: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 2: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 2: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 2: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 2: \\n\",\n \"Chain 2: Elapsed Time: 10.7148 seconds (Warm-up)\\n\",\n \"Chain 2: 14.0044 seconds (Sampling)\\n\",\n \"Chain 2: 24.7192 seconds (Total)\\n\",\n \"Chain 2: \\n\",\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 3).\\n\",\n \"Chain 3: \\n\",\n \"Chain 3: Gradient evaluation took 0.000114 seconds\\n\",\n \"Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.14 seconds.\\n\",\n \"Chain 3: Adjust your expectations accordingly!\\n\",\n \"Chain 3: \\n\",\n \"Chain 3: \\n\",\n \"Chain 3: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 3: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 3: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 3: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 3: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 3: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 3: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 3: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 3: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 3: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 3: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 3: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 3: \\n\",\n \"Chain 3: Elapsed Time: 10.1486 seconds (Warm-up)\\n\",\n \"Chain 3: 14.4732 seconds (Sampling)\\n\",\n \"Chain 3: 24.6218 seconds (Total)\\n\",\n \"Chain 3: \\n\",\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 4).\\n\",\n \"Chain 4: \\n\",\n \"Chain 4: Gradient evaluation took 0.00012 seconds\\n\",\n \"Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.2 seconds.\\n\",\n \"Chain 4: Adjust your expectations accordingly!\\n\",\n \"Chain 4: \\n\",\n \"Chain 4: \\n\",\n \"Chain 4: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 4: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 4: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 4: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 4: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 4: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 4: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 4: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 4: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 4: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 4: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 4: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 4: \\n\",\n \"Chain 4: Elapsed Time: 11.4876 seconds (Warm-up)\\n\",\n \"Chain 4: 16.2248 seconds (Sampling)\\n\",\n \"Chain 4: 27.7124 seconds (Total)\\n\",\n \"Chain 4: \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 6 -u in\\n\",\n \"m <- prophet(df, seasonality.mode = 'multiplicative', mcmc.samples = 300)\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"prophet_plot_components(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:pystan:481 of 600 iterations saturated the maximum tree depth of 10 (80.2 %)\\n\",\n \"WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(seasonality_mode='multiplicative', mcmc_samples=300).fit(df, show_progress=False)\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot_components(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The seasonality has low uncertainty at the start of each month where there are data points, but has very high posterior variance in between. When fitting Prophet to monthly data, only make monthly forecasts, which can be done by passing the frequency into `make_future_dataframe`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"future <- make_future_dataframe(m, periods = 120, freq = 'month')\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"future = m.make_future_dataframe(periods=120, freq='MS')\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In Python, the frequency can be anything from the pandas list of frequency strings here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases . Note that `MS` used here is month-start, meaning the data point is placed on the start of each month.\\n\",\n \"\\n\",\n \"In monthly data, yearly seasonality can also be modeled with binary extra regressors. In particular, the model can use 12 extra regressors like `is_jan`, `is_feb`, etc. where `is_jan` is 1 if the date is in Jan and 0 otherwise. This approach would avoid the within-month unidentifiability seen above. Be sure to use `yearly_seasonality=False` if monthly extra regressors are being added.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Holidays with aggregated data\\n\",\n \"\\n\",\n \"Holiday effects are applied to the particular date on which the holiday was specified. With data that has been aggregated to weekly or monthly frequency, holidays that don't fall on the particular date used in the data will be ignored: for example, a Monday holiday in a weekly time series where each data point is on a Sunday. To include holiday effects in the model, the holiday will need to be moved to the date in the history dataframe for which the effect is desired. Note that with weekly or monthly aggregated data, many holiday effects will be well-captured by the yearly seasonality, so added holidays may only be necessary for holidays that occur in different weeks throughout the time series.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/outliers.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"import pandas as pd\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"logging.getLogger('numexpr').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are two main ways that outliers can affect Prophet forecasts. Here we make a forecast on the logged Wikipedia visits to the R page from before, but with a block of bad data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers1.csv')\\n\",\n \"m <- prophet(df)\\n\",\n \"future <- make_future_dataframe(m, periods = 1096)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers1.csv')\\n\",\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=1096)\\n\",\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The trend forecast seems reasonable, but the uncertainty intervals seem way too wide. Prophet is able to handle the outliers in the history, but only by fitting them with trend changes. The uncertainty model then expects future trend changes of similar magnitude.\\n\",\n \"\\n\",\n \"The best way to handle outliers is to remove them - Prophet has no problem with missing data. If you set their values to `NA` in the history but leave the dates in `future`, then Prophet will give you a prediction for their values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"outliers <- (as.Date(df$ds) > as.Date('2010-01-01')\\n\",\n \" & as.Date(df$ds) < as.Date('2011-01-01'))\\n\",\n \"df$y[outliers] = NA\\n\",\n \"m <- prophet(df)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df.loc[(df['ds'] > '2010-01-01') & (df['ds'] < '2011-01-01'), 'y'] = None\\n\",\n \"model = Prophet().fit(df)\\n\",\n \"fig = model.plot(model.predict(future))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the above example the outliers messed up the uncertainty estimation but did not impact the main forecast `yhat`. This isn't always the case, as in this example with added outliers:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers2.csv')\\n\",\n \"m <- prophet(df)\\n\",\n \"future <- make_future_dataframe(m, periods = 1096)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_R_outliers2.csv')\\n\",\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=1096)\\n\",\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here a group of extreme outliers in June 2015 mess up the seasonality estimate, so their effect reverberates into the future forever. Again the right approach is to remove them:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"outliers <- (as.Date(df$ds) > as.Date('2015-06-01')\\n\",\n \" & as.Date(df$ds) < as.Date('2015-06-30'))\\n\",\n \"df$y[outliers] = NA\\n\",\n \"m <- prophet(df)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df.loc[(df['ds'] > '2015-06-01') & (df['ds'] < '2015-06-30'), 'y'] = None\\n\",\n \"m = Prophet().fit(df)\\n\",\n \"fig = m.plot(m.predict(future))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/quick_start.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings('ignore')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Python API\\n\",\n \"\\n\",\n \"Prophet follows the `sklearn` model API. We create an instance of the `Prophet` class and then call its `fit` and `predict` methods. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The input to Prophet is always a dataframe with two columns: `ds` and `y`. The `ds` (datestamp) column should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The `y` column must be numeric, and represents the measurement we wish to forecast.\\n\",\n \"\\n\",\n \"As an example, let's look at a time series of the log daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning). We scraped this data using the [Wikipediatrend](https://cran.r-project.org/package=wikipediatrend) package in R. Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).\\n\",\n \"\\n\",\n \"First we'll import the data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from prophet import Prophet\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    dsy
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    \"\n ],\n \"text/plain\": [\n \" ds y\\n\",\n \"0 2007-12-10 9.590761\\n\",\n \"1 2007-12-11 8.519590\\n\",\n \"2 2007-12-12 8.183677\\n\",\n \"3 2007-12-13 8.072467\\n\",\n \"4 2007-12-14 7.893572\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"df.head()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We fit the model by instantiating a new `Prophet` object. Any settings to the forecasting procedure are passed into the constructor. Then you call its `fit` method and pass in the historical dataframe. Fitting should take 1-5 seconds.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:cmdstanpy:Chain [1] start processing\\n\",\n \"INFO:cmdstanpy:Chain [1] done processing\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Predictions are then made on a dataframe with a column `ds` containing the dates for which a prediction is to be made. You can get a suitable dataframe that extends into the future a specified number of days using the helper method `Prophet.make_future_dataframe`. By default it will also include the dates from the history, so we will see the model fit as well. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    ds
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    \"\n ],\n \"text/plain\": [\n \" ds\\n\",\n \"3265 2017-01-15\\n\",\n \"3266 2017-01-16\\n\",\n \"3267 2017-01-17\\n\",\n \"3268 2017-01-18\\n\",\n \"3269 2017-01-19\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"future = m.make_future_dataframe(periods=365)\\n\",\n \"future.tail()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `predict` method will assign each row in `future` a predicted value which it names `yhat`. If you pass in historical dates, it will provide an in-sample fit. The `forecast` object here is a new dataframe that includes a column `yhat` with the forecast, as well as columns for components and uncertainty intervals.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    dsyhatyhat_loweryhat_upper
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    \"\n ],\n \"text/plain\": [\n \" ds yhat yhat_lower yhat_upper\\n\",\n \"3265 2017-01-15 8.212625 7.456310 8.959726\\n\",\n \"3266 2017-01-16 8.537635 7.842986 9.290934\\n\",\n \"3267 2017-01-17 8.325071 7.600879 9.072006\\n\",\n \"3268 2017-01-18 8.157723 7.512052 8.924022\\n\",\n \"3269 2017-01-19 8.169677 7.412473 8.946977\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast = m.predict(future)\\n\",\n \"forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can plot the forecast by calling the `Prophet.plot` method and passing in your forecast dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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eMsAtZxPAgy1zU+KUtDbmGmujE62/dXikgspmQpKvRbq2m9ydki1KJRBILUef/ncgW1PGJtXC54447mDhxYqvn9VhNdOIZKwXE7Xa3aQpIVxsHiUR6kFuJ9AJJJJJY1Kg6IMz/xLCQu0Iu6v5IW3tQu1oTnfZKAelq4yCRSAO5lciKZIlEEot12yoQgvB/61vI8ZaLKvNDTdp64dIVm+i0RwpIVxwHyf6NNJBbifQCSSSSugQCAV55+0PGZI4lLSu7wSK9eMlFlZGwCO2xcImX37mzkeMg6UpIA7mVxJsXSNJ2SI+apCVYxmYwGMLr8/LEq29z9Ji0zj6sfdJekbCueg+1lyHXVcdDItkfkQZyGyBXxb88pEdN0lIsY9MwdFQVinOXcVScG8jtEQmTqgXRyDlFIulaSBULiSQGUmFA0lIsY9PlduP1eknNGdewEHKcYEXC7r777jYz3KRqQTRyTpFIuhbSgyyRxEDmlktaSnJ6Jk++9jZ5y5aQlDWWxNRMqlWdos17SR/Sr7MPr0HaOhImVQuikXOKRNK1kAayRBKD1uaWy1zD/RfNEIxOy2R4Ujq6EZF521e76V8iUrUgGlmvIpF0LaSBLJE0QEs9ajLXUCIAw2ozLcy/u7J53NIFn6zPiMYaD6u5lDSUJZL4RRrIEkkbI7Wx928UFFv72DKUhYjdLKQrsL8t+No7+rO/jadE0lWRRXoSSRvTXq1aJV0HZ0qF1STEECJmw5B4Z38qLmtJm2nLG9zUltT703hKJF0Z6UGWSNoYmWsoEQgMAS4EKErYYDYNZ0Xp7KNrHvtTcVlzoz8t8QYPGDAAl8uFEOIXP54SSVdGGsgSSTsgcy/3bwwBetgYDqpGuFBPdMk85LZa8FWHNDbvrWXEwb3b+AjbjuYuBlpiUN9www3ouo7L5WLGjBlynpBI4hRpIEskEkmbIzAMAS6Fd96YRe5n73PyGWeRc8ufgYgLuauonbTFgq+8VqMiqLbREbUPfr+fGTNmMG/ePKZMmdLoObfUoDYMA0VR2LVrVxsevaSr0VXu//0VaSBLJBJJG2MIMBC889psnv7XzQCULP+Sowf05A+/vwbYv4q1/rezir01qq3sEa9YHt5QKMSSJUsYM2bMPn+T5nrX96d0Fcm+2Z/u/66KLNKT7Nc0t8BGImkKVi3eR/Nei3p9/vz59r8XL15MMBhE13WCweAvulhrT03I1oOOZ1pSQOf3+7n11lubZNwcNiKZJ197m6tvvE0aROzf868s1ox/pAdZst/S1iv4X3q47Jd+fm3JmuJ83pv7Bt+uWxX1+uTJk+1/DxgwAMMwADAMgwEDBnToMXYk8W4YW7S3h7cqpDMmPZNRKZnkHHlgm267q7G/e1BlNCH+kQayZL+lLfWKnZO92+3m8ssvZ9q0aV12wq9rDP/SH2ZtZfwXFhby9rvv8crsWWiqGiXrNu7k07niqqvsv3ft2oXL5cIwDFwu1y86H9UahXjvJhgrZaKtF4aGgFXF+XzxevF+vdjc3/XipdpR/CMNZMl+S1uu4J2Tva7rPPPMM8yaNatLGpKxjOHmPsxWby3nsD7dGNi7WwceectoK+M/EAgwdepUgsFgPb1jt9tD5vETozypEyZMoFu3bvuFB8k67zi3j4HogsS2XhgqwKrCfP500WQ09Ze52Gwq0oMq1Y7iHZmDLNlvsVbwd999d6sfUtZkr4RFboUQXTavLJYx3NzmJ0FNpzyoocd7VRZtlwtobcc2joeMgRsXwhl/Re97KP+99+/k5kZyLS3FhPT0dE477bQ2OJP4IxAIcP3f/sHqonzA1IfuSrRHnmhR7jJUmXvapvOvRNIeSA+yZL+jbsi0LSZmv9/Px598ysP/fZ4P5r2Grmld1isSy7PT3HCgIWBPtcqe6jLSh/TrmANvIQ15svYVWhdCsLMqhBBwcJ9u9na8Ph+aquJyu+n/m2vZDjDyBDhkGMGXruXVl19mwnHj7e3/8Y9/JBQKAfDhhx/yxRdf/GIMhcLCQs4//3yCoRBer48nXl3AyJSMzj6sZtEeXs7UnHF4fV40lS47R7QV0oMqiWekgSxpNoFAgIULFzJp0qQuN7m1Vy6tbgh6Dk3kpn8/xJW/u5RVBcu7bF5ZW+RhCgEoXcNjOHxMGm8t/DDqN2vsOin+qQwwz88ykP1+P489O5sVq1ZzbGo2z39jsL0s/AXFBUIw66UXufx3l+H3+1m8eDGqGtEF/qXlYQYCAVPzV9dRCVGUu4wRXchAtq75GTNmsGvXrhbdz7HmysTUTB6ePZ+1hQEmn37KL+b3lkh+aUgDuQXsz9X8TsPh0Ucf7XKhsfYsDDGEQCDIysnh1yef0Cbb7CzaKg+zC2RY8FNZLYeMSOJWx2/W2HViCIGiROfU7q4OMWJMCqPHTWRXdYiK/I/AZSlTmKk3mqbZ25owYQJutxtN0wDTK92ZShZWaoiVJtQaAoEAJSUlALhcLrxeH6k54zCEoGjzXlIP74vLFb89t9tiIR1rrux39GjWFOezIm8ZOeOOIzun68ydEsn+hsxBbibWpHfHHXcwceLE/U6/satrNzY3l7Y5GML87+NPPcOEiacwc+bMNtt2Z9KS37wrFWVBfUO+setE1Pl/gO92VaEZZltpzRD0PbB/5M2w0enclt/v5ze/+U3UdlesWNH6k2khm/fWUPJzeau3EwgEmDBhAh999BG6roOi8Oc77gEBr/53BiuL8uI+rtAW81zdbXz06SIK8nL588WTeeGR+7juwsnk5e5fzw+JpCshPcjNZH+XpunqlcftJa0jhNkE4Z3XZ/Gfv/8VgC8//wyAq6++uk320Vk09zcv2rw3LOcVvx5CMA25dz/6jOGp2SSkZka919h1IgT1jDzLyDYEaLqIOn1fj55MPG8af732yqhtHXrooW15Si1m445KKoNam8iw1U0dEYbBxrWrePTu21BDKrOffJhjP/uM8ePGtnpf7UVbzHN1tzE0KZO8ZUtQQyqGoaOqIZYu+YqTJxzX9icgkUhajTSQm0lXNxBbi2U4dNUcZGifwhCBGXb/8qN3o16fN29elzKQY6UPNb9AL5J5HK8eZCsS5CwgO+yA4zjiwJ72Z/x+P9nZOTFTAYQAUSfFQtgGsuCDpYWs3hv5Xo9evfnDP+4n65iBUdtJTU3d598dRUVQRUFBYCqQdPO4W7ytCRMm4PV67eJDj9eHArZhqKkhvly8OK4N5LZYSNedK12DRiIEeH1eVBW8Xh/jjzu+HY6+9ezPaYQSiYU0kJuJFPc2x2Do0KEMGjSosw8lrhAIjjvtNxQuXWy/NmXKlM47oGayr7zL5iwqhON/47UxhBUJMnQdLVxAlpKZFWUg16o6a7dVxFThMNsmRxvO1rmuKS7gpe+j3wuFQnaOupN4aRZiCHCHDf41W2Ofc1OxChD/+9//orl8HPebc3Ap8OH8N1BV02A+/oT4z9Fvi4W0c67M3bSHpPQsHp69gKLcpWSNjc8c5F96UyCJpKm0m4F8+eWX895773HwwQezZs0aAHbv3s3UqVPZtGkTRx55JHPmzOHAA7teu03nxFm0eS8ph/fFHccFJ5L2Rwjzv2ecN43uHheff/gul190fpfyHrdV+pAQkXSD+DSPoyNBHq+PtJxx9bzdmiFiGvhlNSqGAFfYoCzavJf0If0wBKxbuYKb/vA7uHZO1He8Pl/MgsUJEybg8XhQVRWPx9MpEalAIMDLCz4kc+x4RqZktElhpd/v5/AhR7BD9CQ/N48Vecu4/u/3sHHNKjzu/XOu1IXAhQKI8LUWn3fH/p5GKJFYtJuBfNlll3H99dczbdo0+7X77ruPiRMncsstt3Dfffdx3333cf/997fXIbQLztBTZlY2AoFuCGkgS+wivaOPTWDv7j2MGTOmsw+pWbQmfaiwsJC1a9cyYcIElMNGOozN+DQCrEjQ7AUfkuEfR0JqZpRhKISIShWx0A3BNzsrMc8r3BTG8pkLQUlRPmpIpS5uj2kgl9Vo9PZ5otI2LNWItlCPaC52qkkwxAuP+3j0lQWkZma1ybYVYGVhPjdOm4waUnGH0zZ0Tefj+W/ud55JIQRvvzaL+/9+E4au88qT3Tjm40/jLgd5f08jlEgs2s1APv7449m0aVPUa++8845dDXzppZcyYcKELmUgW8L31sTx5Gtvk5iW2fgXJb9odlWF2LS7GiEEa4vzufXyc1FDIV564qEuZQS0NH3IarGsqio+n48ZLy9gREp63Gsg+/1+OGwELkUxO/457NMf99TYjUDqYi2EzEI9gRFOtTAEJKdn4fV5Cdb5jsA0kLZX1tLN47K1k2fPnm1333NKwHUUdqpJODd4Re5SUjLaxkAGKAoss3OPhWqYV4Sjy2RXuTfaglVF+bZxDKCGgrz+6itxZyDLNEKJxKRDc5C3b9/OYYcdBpjV29u3b2/wszNnzrRlsrZt28aWLVs65Bj3xWeffRYVelr62UccPmQoW5UqvO79SzGvtLS0sw+hVRQWFhIIBPD7/WRktLx5QWlpKVp5LXuqVWpUncIvP0UNBTEMg1AoxMKFCxk6dGgbHnn7MnToUC699FKAJt9zCxcuDBtZ5jkv//xjDhs8BM3nJqgZbOlW11yMH/buLMetKGb4W4Et3hoAtuypoSqooYvIa2DmGe8trUTVDTxuF0IIXIrC/0Ql5TsrOOKIofzz0ee4bWP0flzCQK/cxR7dR49gd7QKL4WFhbzwwguR1tSA2+3u0LkuMTERr9eLADweL8NGJrB3546oc26Mhu6l0p07GT4qAY/Xi6aBy+VGAXRDx+v1kpiYGBfz+r5oi3nCmivzvvjENo4tqqur4nIMhg4dSmJiIgsXLqS0tJSMjIxWj0XdZ4bVjfKg3t3a6rC7DF39+dlWxPs4dFqRnqIo+wwpXn311Xb+ZkZGRlwUhJ188sk899xzhEIhvD4fYyeexgEDDubQww5oVdV3VyUefpOWEAgEoiIBrfXyqt37oVUEcYc0Uo8/mXmzZqKq5rYnTZrUZcepqUyaNIkZM2aYebReHzknnkrv/gfRu5uHWs1g0KDOa37RGN8Hu+FxudAMAwWFrbqL5EEHUOGpxlWjohuCQYMiesaabvCTWkZQ0/F5XOHiNoU9LujVvzua183ocUfBxoKo/eyu1Zlb9AMH/ZTPr0+dSOLJJ7B27VpTJziMYRjceeedHHfccR3mtZs0aRKff/45s+Z/QPa44zh6TBpelyvqnIUQFP8Uu2X4vu4lVRdkTziVh2bPY01BgOGpObgU2Ficy4WTTuOEcMvteKOsRmVreS17vlvTZvPEoEGDyDrxVF5//inUsLqH2+Ph8quuicv5oe7vOmPGDG644YZWj4V1rpVBjdLKEKoRZNCgrleH1BbE4+/eGcTzOHSogXzIIYewdetWDjvsMLZu3crBBx/ckbtvNRkZGXboKSlzLAOHJ8Vtlb6kYdq6CMVa5xnAiOQM/nzHPSz+6F2uuPj8X1x4siEZuDfffJO84pUcnZzF8KSMcMOM+MdOlXC0xq7VDFvXWgA7KoIM7OWjrFblx701dm6y3QwlnGZhhF8LakbMfc39QeCacS8vPvEQny9aZOd61tbWhvfXOakHfr8f7ZBj6eF1URXUY+o7N5Qu09i9JBCMSskkOT2bsloVt0shIyuHrCP6td8JtZKyWpXKkNaqecJ5n1gRpMTUTJ54bSHz33wVl6JwxpSpZGbntOeptJi65z5v3rw2nTN3VAbZXR3qEp02JfsvHWogT5o0iVmzZnHLLbcwa9YszjrrrI7cfZtgKViUVgb5387qyMNV0mVojyIUI2xNrVtRwKN3344aCrG6MJcxY8b8Yozkfck/ZWRkkHPiaXy/u5pazfSKdoV7w8ojtnJjDaGwcUel+R6m0frj3mp8HhfltRqqboQN4kgvPUMouAR2PWJpZajB/RmGjho2MG699VazUHD2bF588UU0Teu0oijrt7Jypd/9bDGr8peTkjWOQ0ckxzRkAoEAP/74Ix6P+Ripf+wi6howDDONJd5z0xUUDAHjjzu+RfNE3fvkjTfeYNKkSQghSEzLYMDwJNwK9OkevyqrdefIKVOmsGTJklbPmdbCYXhqNkMTUuN+fpDs37TbHXrBBRewePFidu7cyeDBg7nrrru45ZZbOO+883j++ecZOnQoc+bMaXxDcYylayrv8a5FWxehWA9UQ8CaggBquOgpXguRWtoEoKkeNcvQiqX7G2+IsOvY9iALMDDzig37PJyfdxbomf+vKKYmsmkjC66dv3qf+3Q75NysBfe0adM6vSjKPC/B6uJC/nLJ2WiqmUr2xKsLGJ2WhRDCTotzGoFut5urrrqKadOm2ce+uzrEd7uqcffpYV8LOuB2jF08IwT0HJrYonmi7n0SCARMAxlYXVTAV19+yah0P2PH+qPyz+OJWHPkmDFjWnWNOovcvV4fT7y2gMTUtisIlUjamnYzkF9//fWYry9atKi9dtlh6IYp7eYMs0q6Fm3dTU+Ew+6JmX7cHjdCNXC73XEnkdSaJgCNed4VxTSELG96VwifGnYqhYh4Om2NY2G//92uKts4Xl2UT1HuMrLGjscQUJK3jPSc8Rw1Oo21K1cAvfe5z4mnnFpvzNuju2NTCQQCvDLvfTLHHscxY9L4aP6bhIJm2gchs4lKYmpWeDFgfsdpBAIcccQRUcdfXquhGwJXOHVEN6I91PGOEFBSmMeODcVNNggrajV2V4fq3SfWd9euKOCmS6eghlRcbjennX0Bf7zmck47MT676dW9Jp1/t2SRHQgEHNdMiKLAMhJSM9lREaR/Ty+e/azQXRL/xG+MJ47ZsKOSWlUPP0C7xoQvaR9U3cAwIteB80roDF3bxmhNXmVTPO+WdxXC90Wc3xp2ZoRtHJveY/PfhA198/dVUFhdlM+N085GDam8/IQbUNA1jVk+L7+/9d/8996/w3X7jowddPCh7XhGzcOpgzz7yYe59ra7+XDea/acprhcpGWPqxcpq9toZVRajEgCke6CmmEaykFN0Mvnjss50zL6EtL9bK8I8qeLJ6OpTV9I7qoOsbMqWO8+OeKIIwgEAsx6/EFT0s8wMAyd9+fM5vOFc7qUFCS0fJHt9/vta8br9ZGaMw5DwI97q4GetvShRBIvSAO5BQQ1PVKog7An/3g0iCRtS13Pybc7q+g3sLftgVxTEEDX9E7TtW2M1uZfN+7pDBuTitk5LP7MoAhVQc0+PsuzaaVYgGLnGlvpM24FinKjdX0Jf09VYckn76GpDecfg9lR77wLLmrX82oOixcvJmilBAVr+XDua2iqZr8vwsWWEUeAOcdZRqDVge/wUclR21Ww0lVMNMPAEBDSdATeuIu8OY0+r8/HryZPbXaqVHFBHp99/gXqb0+375M91SFemTOfv117ObW1QYQwzJsjnJwdr2lY+6Kli2yryH3hR58xIi2bEckZZiRWyOemJD6RBnILsdIrhIBvSqs49IBuHN63R2cflqQdieU5Ed1MOSwhQBcGI1Jz8Pi8aGqsoqXOxzJsPv7scxLScxgyKqXeZ1oSPtUNgcsIG5jhlrohTbBxZQHL3izhpJNOjCsjQAjB+h0VtvtY2AveSOEewmk4K6BAavY4vD4vqmpqFgPouqnrO/6UX7OmMJd9mcj3vzQ/rpQLJkyYgNvtxtDNRd0361ahuBREWH1OCEFRYCkJqRls3lvLoL7dbElLv9+PcthIvG4FLUY+jXOODOrC9hrHm3EMdVJGQiEE5mLG8iBb93HR5r2kHN63XufU9z5bzIW//bXZIOjxSIOgoGZQUpQfbgZjoLhcHD0qiR++Xodh6HjjcI5ojNYssv1+P97BI1FQCGqRxZf0LUniEWkgtwJLmskQgpAWh7O+pE1ZvHgxwaDZBCQYDLJ48WLSTz0bgNXF+eQtW8qwlGz+OXMOm1bm8bspv44ro9DCekgB7KlRGdwvsrBrSvg0lgG9aXc1PUWvsLfVTFMoKsjlzqvOQ1VV7rmn9XrTbYlTtcGy7UTYS2o+rBU7/9iZNpKYlsmDL82jOG8ZGX5Tx7ckdxkpOeM4enQaBx16KHd83fB+R6S0vClNe+D3+znz3AtZ8Nosez7zn3gquYs/RRgGXl83OxS+qzpId6+Lww4wDWQrkmYVOdaoOj28bjbtrmZXVSiSgoZA0wWW8J9VvxFP1E0ZOfW3Uznj7PPZvr6QkyeeRHZ2DmU1KgLB16WVHHZAd/r18Nrf//DTz1HV+h5nRVFISs/C4/UhCOHxeJn2f3dhCMF3K/M4/8zT4uaeaCqtLXJeWZjPh/PeRBeCU357HulZ8bNglEicSAO5BUQ9XOkaxUgSk5YqOAAMGDAAwzCDxoZhMGDAAARmC1mr+Mbr8/GPZ97gwt/fgH/YwHY4g7bBqcLgpLHwaUMGtGoYtiyapf5gKnqocavoMf/Vl/j0g4VM+NUkzjjv4mgPssN4XlOcz+qC5WSNPY7hSemMTMnk2OQMNEPQ0+tiZEoGLqBGMzh6VBJ8/UOD+9TjcLI4/ezzeX/eG+FGL17Ou/J6Lvn9nynJW0bm2HEcm5Rhp5k4HX2rt5bbmcmGEKzbVkH6kH7srAqihFNUIl5jQWlliN4+d1zK/9kpI29/SKZ/PEePTsPjVkjPyuaIA3uwsyrEj3urMQRUhcxiPKeBnJYzDq/Xh0p9r+qoMak89uoC3n3rdbZt387sr1aTk5LIuVf+kdQjDjTl71xdy4Xa0qLSQCDAH86fZDdLeX/uazzx+kKOPnVCGx+hRNJ6pIHcAqy5Xe8CRUiSCK1RcADYtWsXLpcLwzBwuVzs2rWLoZghaMsQ1NQQawoCcWUIxqIhO62x8KnTgK6trWX27Nn4/f4oo1I3BG5FkJCegzdO001mPvss9912IwBFSxdjCINfnTstShdZYCoP3PI7c/Hz4uP/4ZGX53P06DQMIQjqBj28LtPLbEnENWL5rVtRQM7QU9v/BJvBmPQsHpo1j5K85QxLzWZkSiY9vS6S0jPxuBRqVLNxyqrCfN5emc+ZvzoZv98fXZDp0Ia21C4MAapm8HVpJTe9tx6ApMP6MP3Xo+JS/s/v9+MaNBKPS6EiqNnREM0QeFxK1DVel9FpWfz39bfJW7aE7HHRnRAFpgb0x/NfNw3DG6/lfz/A6JJCBvc9jhpV5+gBvTrsPJtKa5wJDbF48WI0VbX/1lSV4sBSJse5gdweYyGJf6SB3EQCgQALFy5k0qRJeAePsifLdSsK+GLhW/T0ebj+6svlzRPH2AVJLewGNWHCBLp16xZlPJYDaf5IXqrL7aZ028+sWVHAicPPaL+TaSWWYaPqBlvLaznsgO5A4+FTK2dVD+esPvvss6SmpnJM5glR2sdVqs7wpAweeGke64tymTb59Li6N+bPnxf191cfvcdp51wSlScrBKzMXxa1+FmRu4yjR6exuqiAlQXLyR47npEpmZhJB6LRaNLqggDirPgykIUwu7wNSUglqEXyscFS+RCsWVHAX6dNQQ0FefDef3HDX26kim6MzhxLdk6O7WGGiEweAjQhKNhcZu9r1dYKuxgyHolI/Qlclra1vQiILjys+72k9CyOTU7HV0+uTFCcuxTV1weuf9F+dXVBgInHjyfUQOfFzsTpTPB4fXz22WeMHze21dudMGECHq/X9iB7vF5Sc8a1ervtSWsdK5KuizSQm4DzBpkx41Eef3UBiWmZrCku4OZLJ9s3+7zXX+aLL76QN0+cMmHCBLxeHxqmzFBzPJplNWpM4/Gjwg2MScvi38+9xSdvz2Hxwjl8Pv81lrz3FumDFzF2bOsfKu2BQNhh8J/LamwDGfYdPvUNHsW0Sy/juWdnAmaB2vXXX89/Zs4m68TTTGNCmB4zAYxKySQzOwf/kf074KyazuTJZ/PpJ5/Yf+ec8msALJ0Gy0BOyhxrL348Xh8p2WNZs6KAO646Fy2kMnfmI9z3wlxGp2VRWhnid+/+uM/9jkrLiTvfqaXIowuHkke4MNEahxW5ywgFTRUGwzB46MEHUFwufD4fN/7zXnbu3EXW2ONIG3yKvV1bKk6pu7/4DrxZEYQ1xfmsKQjwq1NOIifHb0cXlPB/LIo277VzsWOdmyFgTOZYXO9/FWVcJ6TH37VgEa1zbToT2sJA9vv9PPHaQj5Z8CaqYXDyWeeRmJrZ+gNuR1ojjSnp2kgDuQnUnSyKc5eRmJbJyvxlUeGi/fnmqQ5p9PTF9+Xk9/t58rUFFOcua3ao7JudlYw57IAo4/HrUqslMQxLSuelgh9Rrz8XHp5EKCS48867uOuuO+PyejC9fPUllkKamUtsKRXU/57g3Asv5qUXX0DTTDkwXdcpKco3DWTH56B+J7p44aqrrmLT7moWfbCQjJPO4OSzL7KbnJhd9Mx/j0wxveDrCgNk+MczLCmd5594GM32KsPK/OUkpmXxzc6qRvc7LCk97jSAbW+xYf1mwlbwANPQTc4ei8vtQnd4O4VhEAqFeODvN2EYBi897uX7u+5n9+7dZI0dz8DDBsf0FBuGiLsxsDDzphXWFhdw82Vmas0Ljz/Ef19/m1EpmXYetpVfrYSvFeu7q4sKWFOwHD0qYmKmadRdKAxPyohbT/qECRPw+nwQ9iAfd3zbNTNJTMskPSubiqCGqjcedelsWiuNKem6xLdFEyfUrXBOyxmHCHsFnOGi/fnmWbe9ghEH9aFP9/i+pEanZTEmPYv+PX3N+p4hYM3WCob062EL2pfXqnbYNagZrHUPMT/sciEMg0WLPmPp0iVxGZIzw+jRIWQwm+CoukH6kH6xvwekpGfx2ONP8Oc//RFd1+nWrRsp6WbLWMuDZoXmLWWEzmBfeYMCOPP8aZx09kVUBDXTe0pE4k0gKK/V6OF1MTLFfKB7XC6qQxoJGX48Xi+aBh6PlzFZYxEi0mBkX8SnNrRg7YoCcpcuYUSan/SsbIdxbKYUjErJ5MY7H+CRO2+202tcLhcoit1NTw2FuP/v/4cQ8OLjPu56/FmGZZ1Ub28G8e5BhpK85VGpNYWBpYxIzsBKo9lTrVJeW07K4X3t631lYT43TpuMGlJ56YmH+OTTzxg2Ji2cqrMcQ9Oj9yNEoznrnYXf72f+ux/y7ieLSMseR06bzl8iMjeE/4pnmqPaIXOVf1nEtzUTJ1g3yMKFCzkmcwKj07LQhWBEcgb3vTSfL999i97dvPt1DnJTCpTigZYeohAgFEFlSONgTAPZ9HyYDzlVj3jWRmcdx9r8JRhhD1s8RhWcBpATy3NatHlvTCNZCNi8t4YTJ1/Il8lJ9sOgqlt/WyatKqSxcks5yYP62uH7jqYpeYOrigooCCzhmORsRqVkRGkhWwVaqiHwOnJmDeDYpAzueOZN1hYGGJPp59ik9PD7jZ+pEYfustVFZgvkUDCEoihkTTiVC6+5nrTM7ChVj9+cP42RiQkUBpbRt9+B7N69m3UlRSz97EN7W5axrKkh1hQVcPCY4/huV7Rnfe5zj9PjrF/xq5Pir8Wyda5jMv1RqTVpOePs68ISnCjKz+WD9UUcPDKdUakZUU1kQqEQbyz8iN8dkWDmeGf4cX34JU4TWRfx7T3NzM7h57JainKXMfygXpxwnClr2BojsCpoRp2EcKb0xL+CR1NUO1qTqywbjcUn0kBugLqTgN/vZ+jQofwYsjRjBZphMDIlg/TMbI44sCdHDejZqcfcmcTzRO/EDB8rzfZqGkKg1JnIIwU90Z8984ob+LokD11V4zaqEN1SOfoERIzXwCzosxZCuog8NH7YXc3G7zebereGwSNffU/RT2U89tsE+vXwdsrCqVG5uuUB29vn8Xr5x8w3SU7PMhueuCNhc1UXaO7owj2A4UnpDEtKtxUPTMmvxh9wB/XuFnORtqc6REgXHNLB7XaFEKzIW0YoaDayEAJyP/+QoqWLeOzVd0hKz3JIAgrGpGWRmJoVbv6hs7oon7yvPkdTQ7jcHlwuF7qu4fH6SEzN5I6PN/JzWW3UPt944gHmPzuDz+MwsmKRkJrJ/S/OY11RgOxx40lMzTLbyguBSzGlHa+7cDKqatYzPPrKfNIcTWQ8Xh9pfqtFt7noFEZ0MZ4Q8Sn7Z1GYn8f1F5k1NrOeMJufAK0qWFu/oyJK7cboxAhTW9OaXOXin8pIPbxvl5P7+6UjDeQY7GslaAhhSxjVqAZuRw7a/oiqG3xTWtWg/FG8YdbSNP1Iy2tVvimtMj1Hdb4WMZgEb6/ZZr++qdsQ7nz2LX5anc/Fv40v9QYhBGW1WvjHMguLnJevrTBQZ56uDmms315pd8lzPtRKq4Lh70KtJmyDaEBPX6cpFjSWN/jVV1/a3j7V3Zt1hQE8LoX1Rbmk+8cxOjXTNgqtxhYiHF7fXRXk213VpBx+AITHQjcEumiaGsGW8hqqVZ1hA01pr+qQxo97a1B1o8MNZIDkrLEobhcibQqs+ghqK9BUlRV5yxiTnuXIsVUc6hbm75qQmsljr75NYWApaTnj8boVigJLyRw7ngMPHczPG7+rtz/D0FFDIT74JP4MZMtjbgjBqNRM0rKy6e5xRYrvhDn3m9KOZmMQNVyX8rvrbuTBWfNYW2Dmq49Jy7KLVvMKijCSTo/a15ayGgYd0PG/d1NZtuSreu22gVYVrNmKICIsk0o45aYrPDwaoTW5yp0VaZPsG2kgx2BfK0HLA6npRngF3DUMw/aiVjWoVrUus0iwfr+6rN9eQQ+vmyH9ekS1kQ1qhsPLEXl96bLlvPrOhwwbkUDWiacyb3XEQH69ZAuutMO58qo/xV2zkBpV59udldEqBcCLb3/CD6sLOGJMhu0hdBIIBJj/wackZY0lJSM76j2nDFZQ09FCtYDCvR+s5KbTkujli13w1540ljd43PHH4/V5CR00CnHuvZT6fuIfV56Hpqq88bSX6S/M5dCRyabSh63sADUhjb9/tJEdlSHGDhQcviWPhLRMkjOymzwRGMJceFms3x4u9uyEW0gIU+Lt7NseZW7lEDh0OCy8F7fbQ0r2OECwbkUBK/OXk+4fR1a23x4L66GemJbJ8OR0PIqC1+0iITUDn9vFj5u3xNyny+3G4/XFqbxXZE63coSdRrMVSUr3j7dbUXu8XlKyTW9xQkomKRnZeF2m1JshwECQ2ycdhkbv6eb3N/DpNfGrZDE+fI+odXTMW1OwFhlb04NsKcZ0BRpLLWlNh0HLo163hbmkc5EGcgwaWgkWFhby/pIC0nPGMzQxFbCKneCXEiZqCV3FewwNe5ArgxrVIZ1dVaGo3NuwnGvEIBDwxVdLOP2001BD5sPxkZfn19teMLyAikeswiuwKu/zueGSs+1w8eOvLmB0Wqb9QBgwYAA33HADwWAIr8/L46++TWZ2DpVBjd7dPHbeLgi+WVXEzq1boP9g1u4VPPRRCY9e0Dlewn3lDWbn+Jn+wlxey/+OQqB40w7UkJlmoKpQlLuMM0YkgyIceaOCoC7YUWkW5S7fqUDxJnzPPsw/n53DUcOSYh/Ie/fDb/4GREvIWUSUM9rirJuPIQRpx53C3A83QLde5lGGG2OsLirglsvPQQuZC4cnXn2bxLRMnKVVTsWSiDEJIUOPub9zfv9/nDrxRNIzs2O+39lY3nGDOhObw3BOSs/isVcXUJK7jDFZYxmdlhll/K0qzqckbxlp2eM46PDBGO5uQLDevnQjfjWhM7NzeOTlBZTkLeP8M0+176XWtJm2EGFlDwF443SedNLU/OKWdBg0wtfA6q3lDO7Xo1OiSJLYSAM5BrFWgoFAgKlTpxIKqczy+bjr2TcZPCoFLTwrdhUPaltjpZsIIdhabobWnS1Y4w0rTFpeq7FxRyUjDu4NRIpvLG+xs2DCKj4TQmFvjcq89z9FVc3Qo6bBh/PfhEFTovcj4jMvWwkbYmuLC1hbGCA9Zzwr8paZ56PraOFwsRBwwyWTCYXMwi3DMPVvVRWKc5eSlpnNxh2VpA/pFxU2ffuzJXDICfb+9u7ZbY9fvBWhjEjJ4LCtBvwMu7b/DMJAcbnwer0kpOfYnxNGxHO4vqQAcDSCcHlQ1RCvP/MYR57zl/o7CdUwOus41oT/VOxrzHF9tc/pNQlr8RedHiIwdJ2SvGUoCraknfnbLyMhNdPuoqcQzqMNG5SWo2B1UT6vvfQcDJ9Wb58nXXgNow/v1xGn12ysxX5lUKNPd4+9KLbziB0BqMTUTJLSsgjqur3wFsLUT75x2hTUUAivz8edjz2LSzko5v40I/6ahABs3lPDzqoQCakZJKZm0svnZlt5LYce0B2/309GVjYKxGyTLYQgqBl099aPHFljaAgzOudSFFSXEfcOlvbSQt5dHeL7XdXh6IQZgZPED3Vb/kjC+P1+br31Vvsm+OKLLwhZOWdqiFX5ywGzEjekx+ck15EIoCqksaVOQU68oOkGW8pqo7zdZbVqPS+vIcyCCQtL59TqEAeQmjMOr9eHy+3G5XLz8fzX6+8wjnPKVhflc/NlU3ju4en86eLJ9Ol3IF6vD7cj9F2cu5Rg+IFgtdZ2ud14vV5Ss8fbBgNE8rBXFxdQ4jCOAXr3619PSi4eEMA3pZV89GPYq6eYU+ExCUn8+/m5HJucYXt6rYf5O6/P5u6rzonekKIgDIM1A7N5b3P9h9udad2ZMPlCx47NB6FzreBcYHQGtarBhh2m0oTicodTILykZI8lOWscHq838tuH1RxMQ7CA1555lMK8PIdEHqwuLuDaCybxxbtzY+7v9/PWmI6FOKUqqJtyfI572Iq6WM6A8KtENKMjqRkr8pZF8pPVEKuLCuppIFusLS6My3liZ1XIXDQ5FgfVqnl9BzWdVVvKWbmlnP/tqq/9vbMqxNptFTG3G/nZI/OpNdbxjBVVdrvdbVp4HUnh6zpR2P0J6UHeB1VBjV7dzCE6/gSzC5uqqXi9Xnr3O5B3XniCMZnjSMrI3K8vbtuAjONBKKvV2FJeE/XQMwwo+bnM9oJa79Q1bZ2hU4Ax6Vk88eoC8pcv5afvv+bjt6PbFltYGsDx5DlVgKLcpZECNTVE+Z7dPP7qAlbkLiPdP45RKZnohojqOviXf97D7l27GZM1lsS0zKgJ3QpJr8hdCkSHzXsd0C9u8/Rv/WADqjs6nHnUyDEMT0ojqAk2762mt8/LQb19rCjMY8adN6OHm6NYHDToCEpRYMARMfdRt+vw5rIgww/yREfuw390dBQqEAjw+edf8K5rDHk7zdcGHJ3A+GtuJjM7hxEpGbgVhVueep1vS/JIyR4XTiUQrCnO55bfmY00PD4vD7w4j8TweyV50Q2UYqEZRoNGY2ciMB0e1uLI+u/q4gLyly9hdIaf5Iwse46wUq9citV1UJCUZXZf1MJqFiNTMvhqR+yTXV24nDMmxp/cHTg6Hjru36qgxoYdlfa1mp+Xy9zVBVHpFtaYxMZS9VDsOVcIha3lQVyKYmvMxxutyS9uDDM1qWvI3e1vSAN5H2zYUckxA3vRr4cXv9/Pff99kdVr1jBwwABm/Os2NFXlnecf467n5jDq1BM7+3A7DaenLZ6xJnwlPDHrQuAWZqtYa9J2yrltLa+ltDIERK/whTAbjgxPSqfgy09Y9P5CQnX2Vbp9K0IMNcPQcTbvpWaPxxN+gHu9XpLCOZSj0zJxKQqaIUhMzeTJ1xawZV0hw1KyOWZMOiHNsPWerQXDyi1l9u8+Jmss5NXxoorIQqEpMmgdQWVQY9Pu6ujoQcqvcVXt5oQzz0EXpuzarR9sBOCNi9NYUxDA0A24eEbUtkoPz4Ts8xrc17rCAANOPMr++4o5K1n8B3+Ux8w0tpQOXUQ4cyqNCx6Cg48BYGcQlvQ/nguTkghpBh6Xi+FJ6YxOy8LrNn8/ISwvaaSb4Kdvz2Fl/nIy/ONsr7P627sa3H+tGp9RNxcKmjBNlqBm4HW7WF2czx8vPCuco+/lPy8vsFtPK4rAwFS4sYzmhJRMHp49n9UFAVKyx9L7oEEopdtj7i8xwx8nd0VdIi3HnYV0gUCABR9+Smr2OBQFrg9L3Vl5uUclprK9MtjgtWzdcnbhs6JgYKb4/FxWG7cGMrQsv7gpOBdikvhCGsj7wOr2oxtm84NRSamMHjeRt559DE1VMfoeitqtB2sKApy9PxvIxH8OtqJEKtHd4TzcNcX5ZnV+juk1tYx8V9io21YeRBdG2CPiKEYSEa/5yNEpXPfYmzyyMXp/Sz98m3VHuDl1xBmdcLYNI4DEtAymvzCX9YUB+vUfQEnecnxuN4lpmeEmCOZ1Pzoty1as0KIUWyIKGKpusKY4n8Dnn5A8fmKM/cVf+HBPtUqtpoc9+5EjM8ZdwrFJ6Wwpq+WPC9ZEXkeQkOHH281H6OCj629w3EWwd1v914Gd235G/fF/QCQfc1VxPjk55oO2rEYNj2vHjpCVMqbruhlKcbCr2uwQWaMZeF2Wp9TsvGhdA8lZY+1Flsvl5pMFr6NrOq/918vDsxfw2Kvv8IdAw17keJwuftpbg9etIIzINasZgg/mv4EaMlNx1FCITxbMMdU8ILwABBTQHPm1iamZpGXmoBkGX2/ajFpbQ6wF4rHJGXF1b9RFEFkMF+Tlctk5ZxIKmVGlM6ZMJRSuXbDycn2DR9n3vFXEC2YL+293VkGvbo5ix/A+wnOLOz5XCu2Odc3Eb1Le/os0kPeBNUluKavlsy+X8NWnH5Fy3MkkZ43DddgwjKkPIoCElPgyADoS07sg4t5IdqoHCMVsC/vni81GEV6fj0dfWYBbgcLAUjL848kccqpZUGU4NCqd3o/wn5UhnUc21p/ZDcNgZf5yxNT4MpDBHIORKRkowK2Xn4MaUnnlqf/w6CsLbIPYeoDVDR87PR0CwfxXZ/HAHTdj6Abu556E66PzTgVhNYA4wiosjaWoJIDtFdFqA0LAiKR07nx2DretaGCj/Q6N+fJn815FWb0Jfn2T/dr/XTqFp15/m8wjTjONBgQrCwsoWL4U19lnRHmpdEOwaks5qYP7Nu8kG+GYlGw8Xh8QMovr6ry/saSQhNQMQo43DMc1MCo1k7uffYuV+cso3fozi+a/ZhfyleQt5ZJr/wKB5Q3u30CgxJnvdGt5LTWa7vByNqwyIRyfsZ4ThXm5rCvKJSVrLCnhNAzrPgpWV4Grd/3tGPFpFjnSrAFzHljy5ZdmxEHXCYkgG9eswuP2oBORgbMXxAI27KggZVBfPG4XNaqOqht4wnNnUDNVfhTFwO1yR6W4dUVa013Q8qbLFIv4QxrIjSAEFObn8ocLfosaCvL6C//lgZfmoU190P7MCLvV7P5FWY1KaVUoUqAT52PgFGM3hf4jebgfzn+Dj+a/iRoK8ZLPxwHz3uXoMWmEo63hRUA4xEzEUKjWYp+0y+VmTObYuEotAPO4LQPA2RrXUihITs92aNw6z1lEjZ8QpgTYA3dE8nK1UN1EE6IM63hCCOxOgtGvi3q/1rP33opXUZhw5jk097cUwkCUbop6TdV0ipYvY+rpEzGEYFVRAX+55GzUUIjZT/4nSkJKM4wmNyBpDiOSM3jq9QXkL1vKxz2OZVOdWqu1RQFGpWaghH84I+wqNW8H8/cckZLO0WPS2FBSyJfvvoWmmSk7KdnjGpU4jMeiLOuQfyqrpW93N163CyEEp589lQ/mvoamqri9Xk797XmOyIh5X6xdURjR0fZ5eWjWPNIzc+wC1h69erGnpv4+LS3geCOyQA5H38DUfvb6CIkgwjBYv7oEr8fDVVddxbRp0/D7/RT+uBdr5qhr8FnziqIo1Kg6l725kkEHdGPGWaPrKbt0JVraYvqb0krKa7WIl574f4bub0gVi31gGUK5y74Ky3oZqKpKYWBp/c914dVvS9lSXsvempAtURPvI2B6uaMLaazqfAWiKs+//PJL+3t2lbGdYhHxICsNdOUbnT2eUSnp7X5OzcVpsCZk+KPGIDlrnOMzEe8YWA0PIt4ygaA4d6mZl2vh61F/f/Z34uvqMIRA6PVVJwTUSxpfPPQ8Pp37MndedW7zdvJD2N28ezN89qT9sstjqkFs3FGJAFbkLrNlA50dyyLH2rzdNgVFMfPoL/rDDfTu06fe+6PSc+zUIrAiKeEokfW6MD3NawsDXHbTXZw25SJOPmsqAMUFefvcfzzWK+iGoDqoc+PCtczM/ZGNK4uY+9zjCAGPvPI25113M3c/P5dRlsydw1takmsWJpo52Soleab33Lrue/Ts1eA+43HmVBTF9oAbYSM/KVycnDnuBBSXC2EY6LrOEUccEWUQWnOH8ze2UtysZ6oefm9LedCeI+LdPK4OaTFfjyUB1xTKws2C7IY00jqOO6QHeR9Y12vOuONNBQvMIo2R6X4oiXxuY0khy74u4rzf/CruWqe2J6YGKraH1cpdjUcUxeqIZnpFR6Zk8PDs+azKX05y9jgUTD1jq1lGWs64iKEItjfVEKac2YrcpSRkjKXfoYfH3N+a/GWsTzoo7nKQIZJHPCIpnWtv+zdfffweE04/k8TUjIj3J/yZiFcnUrRjjUtK9jh83boRCgVRUDCufzPGzkSUoR0PKAqsKiqguqIMekanLry7bhuvFcfuANeYMoOTC1IH0a37Bl72eDAMA6V6j92cRXG57JQkcxzH2vNLLAmpthy7ddsqSDi0j71dM4Wm/ueGjUm3i6geXPw/evrc/OX4YwhpwszTFYINKwv51zVT0VQVl9sNQqDpGh/NexWXy1Uv3cZJPKZjGULYkp2rf97Dir+Z5/bWzEd4aNY8RqX7WVMQwOt2kZOT44gsCZKyx+LxetE08Hi9ZvQIbNu3oUJdXcSXyo1FJJ0qHEEIn8fotCwu//PfWFkQQFVVPF4fRyZlArBhe4UjnSpcp+DcJthFyyGH3q9pMMfbEjqaGlVn/fZKhh/UiwO6R+v8t7TFtBlcdHRubPOjlrQWaSA3ggDSs7J5/NUFLPnsI1KPO5kho1KgpNj+zL+umYqmqTz9yIN83sTwyi8BJZyb6ixei+eb3Gmo6YZZSJOckYWmm2L3j76ygMLlS0j3j2d0WlZUOHntikI2FAU4aOAAHvnXbeHcZS83Pvw8UN8DZxgGqwviLwfZMswAPpn7Cs/fdzu6rlOStxQhBFMu+l2UB8h8xJm/szNsLjBbDM94eT5FgaWMTEjk/9bV35+1MIm3roIrcpeCGFbv9YaMYwBXj940Vcb/kN7dyDnnQo4dNZJVecv5fm8Qy6eqG4LiwFIzT1VAQmqmLRvoH39cvfmjrWxJIQTVqkbR5r1R245toAlCumDa6yX1vn+Ay4MQsLIgF7Xf4Yjt/8MQhn2guqY1Ok5xaB+jOw5K0zTbI6yq8PGCOXyy4M2wwexlxssLGJWSYWsjH5uUwR3PvMnqguWk5oxjVEoGIFhdnM8XH39IcNApxPKRvj3rv3DqSSSeeXKHnWdTqBvyj0SSBKPTIs6F7HHHMTzJjJRVhrSo6JMr3DhlT3WI0ipTDcgQZvHeha+uiNqXbkSW5fGGU9ou1jTWXAm4QCDAwoULGZY1gaT0LPsZE4+Lxv0daSA3wo/hxLHRaZkcfPhguvc9iD3V0Z4keyJtww47bUVrigf2tc2FCxeSPP5kjkhIDT8k4nV6MzH1CkxPRVAzC4SEO2z4CYELhdFpGQxLSsPndtmeDiHgvTdm88Tdt9gNMwzDQIS7ym1YsxK6j6+/QyHo069/h59nU1hTXMAH897g0/mvYYTTDHRN49G7bmHEqNEMT0qLeJCUiAdJCFhXUsCGogDp/vGMSskkITWT4cnp1O7dCeu+rbev7l4XPq+bDTsqGXVIb3r6Om/KKa9V6ROuqk/OHgefxpbeisUJ51zGl0ec3eTPGwLcLoURyRkMG51OXmEReevN9zzdupOWM95OPRFCMCY9i+HJ6fSM0X1sVVE+n79WxIknntiqe9jOKUVEQt5CxFQPuPOTr/nrCdGKHZF0GfOe+frgHMRFOShfvYgo+RC0SJMgl9vNvjKn41HkzbnADyleXCdfi2vRU3i9XnQhIvN80ODD+W8wMtmqPTEXE8OT0jlqdBrdPWbm4qqiAv56qdlRj/MGw6CEevuc/9wTvP/cDI75PB4dKyKsV61E0svCRm5CaiZJ6Vl097qi0rDsbwqrQ6NgS3mQGlULp+UIdlVH1yq8/cLjjB1/PGPHxtv5m9jRnn18pqkScM58ZY/3UW785z2U7tzF6AyzsFMSX0gDeR9EQpBKVLHW6jpdgiKhtbbrsNMWtLR4oKnb9M54lBmvzOfo0emOwFr8YhkEQd0KEyvY0lXWw1FEfndFKKwuzufxf/3NNiQNIXC53QhFweP1MiwhGb6LsTNFoXzvblZvLSf18L712rF2FrmBADddNgU1GKyXDiMMg+LcpQxLSnPkKlu5iIK1JQXcdvk5aCGV2U96eXj2fBLTMsM5qQ388mGDzBCCzm44+U1pFT6PC90QJKRm0K8owN76dYUxMSZcCd/tbvK+rGIkBVMtIzEpCdavAuBvM16MNFtxeOaIEYEJBAL86eLfoqlqm9zDZlg3smBs6Cf5dmc1/1u7CmeZiuXjW7uigNylS/ix91jz9eN/Z6p4fPZf84MeLwf+4Vl27es44iyiAISv0ciIGKNP4YJje5E97jiqVJ1P57+BYegIIfho3uuc8tvzGJFs/o4hzfqeaUBqhmBF7lK0kIowDIiR725+GlQ1vhwr2yuCaLq5QNYMgccVuS7tudK6kMKvGQ61D2vRB2ZKD2AvJISoXxz71lMP8fZzj/Lk62+TMeS09j/BFuD0preGBx54gJoa0+kmRJAH77gZwxB4fV7+M3s+R048ofU76YLohmBreS2D+9WvY+lMZJHePrBWy+a/zYngq+92c++ib6I+d9vTb3DlX27liVcXxM0kBy0vHmjqNlU1RHHuModMDfWMrnjB8npZ/9aMiDSdU9bJlrIKPxI+nP+mbRyDmT96w533c9mfb+Ge5+dy6FEjYu7P5faQmj3O9lrHC0uXfGU+tOsck6IoeH1mm2m7AIlor9Cit+cQCgbDYWeVFXnLIukYDVha5tc7tglGQxhCUJAX4LnH/sOa4gK6+XxN/u6SZhjHAGu/+c7O7VVcCt09Ec/wUaOSsUbYCmXX7dYIsKsqxPwPPkUNqW1yD9ueMIdhHstgsXhoQ/TjYUdFkPUlhdx06RTeePJBdm7dHHmz1wDHv/uzy7vv6Il53vFwVUQwBNSo0cd01uXXMywpjRFJ6Rw/6Tw7mdhMS1pu30chXXDL++tZvbWCjSsLeeOZR+lzYH+zTbfL3WABmuL14o0zx8rmvdWOaJupA7+6OJ8Xn3iY1UUFZlEZ0dfrip/L7OvKcjY4tePtuRVRb64wLngIVVUpDizryNNsFm0xhc+cOZO333476jXdMCLzaW78nn97UxHU2FZR2/gHOxjpQd4HjkUyljxWXY1UMAtajh83lh7e+BpOZ/GAx+vjhBNavzq1thkMBlEUhT79DnRMjPH1wKuLc+K2DBO70EYIUOrn3u0q3RG1jTFpWRx17Ch2B5ZRowk+3LC3ob0BVkg7PhBC4B9/HB6fF1VzoRyVwcSUYRybkER1+V6Ss8aSkJpJea2KooS7CoY1oxe+9TqfzH/NHhi320Ny1lg79BgyYnvI8n7cyxfflnL2mEEdeKb1UXWDVUX5XBfu/OX1eun9lznttr/SLZtxkYU1hTiNUC286nDeM9Y16PTE761RzQK+cEOO5hQA1WVXVYhNu6ttRQIrKmZq0TbtCr3hnXWc+uNbtjRglKXj3EYT5gGnkd7ZCCEoq9Wo1Qw+/bq03vvvvfkygU/fp1ffA3G73RiGgcfrJSVrLELAd7ur+b+F61ANwTNLv6XykXPRVBWv18vEGx/Gs/t7vh+cxNq99U/4gqv+xOknjI0rx4pz8aQZgvUlBfzzqvNQw+f0n9nzGZGc4ZhPhW38QuQ1V50iPcJGslZXtvCQY/B4vaTljOuAs2sZwnF+LWXevHlRfx9x9DC2/rQZVQ3h8XpJzh7bqu13deIwqCQN5H1h3ehOD1ish8nFr63gnd9l0s1TP3+wM7GKB7744gsOHplud+9q7TZnzJjBtdddh24YPPHv2zn0qBGMTE6P60pka4FjhGOElvqG4SiQULCULsAId4rrP/DgqO306Xcgf512NmpIhdP+iDHqpAb2B6sKljN2bPxMepv31jBwWBK/f2wOs9ZVU+bqSfpJx5B1xIH09LpNo9gQkbCqgLXFBdw4bQqhYK1tzCmKwulTzmdUaqb9MF1VshLoG3O/D3zxHZNHd56BXKvqrN1WQVFgmS3lFzx8NMH6a902w9e9hzlXhBdebkeKzQevv4hxfA5j0rMiCzKiF2YWo9OyeHj2fHZ/vYLU7HEMH5PWouOpUfVIpCc8nwlgW0WwWZ6bRe/Mwe1xY7h7QY8D7NcVl9tx7zc+C8RTQVJ1SOfbnZU88n4+n9dpiPjpvFd4/p5bzD/OuxeumIz7uSu4/OZ/MTIlE9UweLNkC2r46V5bU23rgauhEB+FjqB736F4ql0Qo3Rx0sVXMvLg+g1EOhPrOiwpzCew9Cv27tgS0UvHlCU8Nikj8nmH48GlWBE47LkUR747Ya90Xf717BzGpMdvDm5bXK9Tpkzhk08+sf8+//Lfc9SIBHKXLiEhw8+I5MxW76Or4mxnHk9IA7kRrJWjdYM3lEq6raKWgb2aHrLtKPx+P1nZORRu3suWcvMYu8coBGoOu3btQtiFamaofXhSmm10xiNmWoVhV53rQrB2RQGr8peTkOEnOT2LkKbjUhQMwBW+W0+bfB4fzH0NXVNxe7z0H3gQoVDIzCt0eRvcn8vlYkzmWHvf8UBQM9CF4LENgKsnAHtrNFRdsG5NAasLAqTljOXwUalmDqIHSvJMo9JpHLs9XrNgr7iAkSmZrC3OZ/pN18HvX2lw351pEFltwdNyxuH1+VBDQYxz7m7XfZYs/5Kvkw5heFI6HpcSNfkvfOU5Pnr2fma8vICE1Ez7GOumHVj2dUJqJgPHj6NW0/mprJaBvbu16Jgihrgl3ye47I2SZm3DUCBt3EkUpPwelEgKxjGjU0k77GYO7D+A7XureLsp24qTG8OKkixevBhGToh6b/Hbb0T+GDwaMMevfM9uDCGoUaONvVinVKuLBnOQ403dBcxrbnVxAX/73RTUYAhFUXC5XCiKWW8TiRwJ2zi2axasxVfY+WA5mAwhMBDoukHJlvJ6+4z3ZltNObTGCuKvvvpqwPQkT5w4kROmXoYhBEMTUqlVdWpVPe6jsO1JPJ66NJD3Sdh7bOVO0XAagR6nLUMthMD2FLU2EX7ChAl4vT40TcXj9TIqLYd1JYVsKAxw4okTSD781NYfcDsQ1AzbSNhQUsB9116AGlLx+Lw88OI8jhmThuIy8yyEYv7miamZ/H3mHDYU5TIm08/OH78xjWOAOmkFSt6biGyzUcJJZ5xlykDF0V1vyfI5MYQZQr33D+ejBkPMdru48tZ7OWHyBfTweUjODhuVagiXy03q+JMoWfY57895hU/efpPpL8ylqCAX/aTf73Pfgoa1YDsCgSlL9/irC3j+0fvZdxuLNtjf10tZXTiE4cnp+NxuMyXBek8Ie2E5KiUDBNSqRj1D2vys+Rvl5QYoyl1G9rjjSDn8lGYfj2VsWwYLmJ0Qm4vH251+Aw6KMo4Bvq1UGDt2KicnHsqOiiBvL1izz+3oVlpTnFAUWIah1W8EsWlD+DzGRIrHPB4vCRl+KoIa61cU8tN3u8A1EICgpxfufodglO1AcXkaVeswwh7VshqVvj0aXnB3JEKYMohqMIQQhu0FPuv8Szhp0rmMSM6MlnwU0Vq+q4sKKAwsJTVnLOmZOeZnwv+dXfQTb6+prx6jx8+lUA+7l6io//wvrQzSr4eXwvy8JhXEX3311Vx99dV8u2kzpeFxsyOvIraM3P5CPFpQskhvHwjHTS+EmcfYUGNQEc7tizdCmsHOqki3u7bi5N+cxVnnX8KDs+YhBNx55Xm88vgD/P783xIIBNpuR22EEOZYCMN8MG9bV2SHDTVVpSR/WXiiEnZxyeqiAh664/9Y8t5cEtL9DE/KYNfOXUQS06Mff2edd2Fkf8CbMx8NF7V03Hk2Rl2D3TAEX703l1BtLUIY6JrGc9Nv47O5r/D60zMAeOyVBVz251v4x8y3OCYxBV3TzTSF2lqenn4H33U/Co6NIXXnoLONISvndUxaFiedfma77kt5+hK8ZVtJzjIjCD6PwterCiMfcLlRFBfJWePs+aVG1esVRYL50Fi7ooDrLpzMMw/dyzVTz2rx/WWNgXlt53PDJZObvY3/e3Q2J/zmnJjvzS762c5bbQzdMOJqtjzgwAPrLXgB9PMfNP9xynX2a8Z1bzA0wWyx/e/fT+XHr6MFwPXLn+X86/7GiZMvaHS/hhDUagbf7Kxs3Qm0IQJBas54s9mL9ZoQHHzYYBJSM23vsfUD2h5jIVhTXMCN0yYz69H7uOnSKawuzre3ioAtZbHzmtQ4djBZ96UAvttVzabd1fZ7P+ypZldVqMGC+Ia67/2wx9zGmuJ8Xv3vDDasLMQAdlaH+MGx/f0FRYmfiJITaSDvAxH+Xzt8JBqux+9sA6AhdlQGw1XJbRPCsGTePlzwFu/PMzunrSsKONqsto1aRnthGSSZ/vG4PWberdvtZkzmWAwBqh6Wsiou4PoLJ/HeG7NYNO8V/nXNuXw67xW2b/kJt5VrXqcc+7DDB9v//vzj93n5sfv508W/JRBY3mHnty8UTG1WJ+tX5PPVwuhiNV3XmfXAHcx+7H7+Ou1sBIJzr/wTxyank5DuN7umhfl69Qryvvi40X03xWhqTyKLXcHe3XvadV/Tfv9n/jHzTRJTM1FQcCkKqwsdRq3LhRBmPpKlcKILR5haCEorg1QGdQwBJXnL7dzplt5fihXmDg9Ece4yM4++mQxNSNqnIfPNzir+/M7aRrejGfGTh6woCnt37yamOvMhx6C4owOtGi7yCotZW2jOe7EM699e8UfGnzGl0X1f+sZK8vPz4so4EAJGp2Uw4abHcA0YguJy2Qo3a1cU8MYzj7J2hbngW1WUz8tPzTANYcvzbOUrqyrFucvsVAwjEryoh2bEV0TBojKosbMqFLW4rApFfm/rd7OK191ut11MW1GrsW57RcztGsL0tP/54sm88eSD/POq89iwshAhoLSqHYsj4pr4+/2lgbwPrJsCzKlzTWkNz+VvjvlZg/jMoQGiDPzWYq2UDcNAU0OsyF1OQrrflDNyu+NOCxrMBhF7alQ2761hzbYKympVM+SMOS6apvHW80+yvqTQTMMQgpL85VGthbVQiBfuu51P572Gy+UiY8KvGHRkdCMF59xvoNgPia+++rJjTrQRFAWKc5dGvbZxZWGUjB0ouF1uDN2IeshphhmJUA4fyYRJU6O2ESs0XRejja6/5hAIBJg+fTp5uQEigmq0e7V8UmY2I5MzUBSF7l43LgVSshz7VMy2zCvylmEYkfvTykMur9X4YU81ajipPynTVLJwt/L+skbAEILUnHF4fc0P6f9+7mruuufeBt9/e83WJm1HM4xOny93VgZtD19qzvgGH4YjL7613mv/W1diLhYPHQbDcuq9L4Tg6NGpTTqOW++8m1WF+Y1/sIOwfpZFocFw6ROcd+1N3PnsHAwBN106hdmP3c/tV5zD26/P4o8XTeb5R6bz54sns7o4n74H9kdxKaZR7fWSHL7uLS9sVdnemPs0GtKJ7GR+2lvDzqqgvXCte8kKASHdID0zm0WLFnH33Xfb6RXWwiAWQphzsTOKuabAXETH02KpI4nH85YG8j6wdUrDhvL9gR0NftYKwcQjdppIM44wEAhw77338uLbn0S9bq2ULWM4MdPP8OR0/v70m5x/7U088eoCMrKy2/YEWsmm3TXsrQlx03vrueuTr/n93NV8FchHV82HozAMchd9yJ1XnsO6EjMlIilzLB5v2ICYej9c9zq6pmEYOrquM2x0CqNSoquu1zgayLg85oLB6/Vy3PEndNi57gsFxX5gWewcfgrKwUfZv+fJUy7m8lvvwevzobhcKIrCAf36E9QMbnhnLXd8tJHxv5mCy+Uo9Kwr2xSDtcXNz3dtDVak44477uDXvzqNVUUFdpOD0WntWy1/y5Xn8/XqIhQId1VTSMt03BMuN25PtEye5Z0yhOCbnZVR2twjUzJ4cNY8rrrxNp58bQFDE5pmeDlRlMgi2cBMNXl49vwWnZ925u0NvlewuazB9044OqKNrOtmR7W122J72DqCTXuq+d+uanZXh0hMzWDcyafH/Nz6AfXVBXr07suxyelo5z8Ivvo1HT+X1XJpAwWQp488KOpvXdMpiiMNXBFOLwNzoX/C+ddw7Jh0VoSjDpZBt/jDd21d9FAoxMcL5vDo3bdh6GbH0WtuuZvE1AxWFppe5nWrVlC+Z2fMfWpG/EZhVxbm88rTM1izon66nBBQWmlex36/n1tvvRW/34+mG7aG9JayWlM9yfm9cBqL1xd2LHm8JKTnxJX8YUehh5WT4vG8GzWQH3/8cfbsaduQ5KOPPsro0aNJTExkxowZbbrtNsXOsWo8f7ezQ8gN4SzOMQRUhXQqaiMeP003PaaWty0QCNjGxT/+8Q/+cEF0TrHf72fWvPe4+Jo/8sSrCxg2Jo2SLWWscx/O5Cv+yIiUDFZuafgh2RkoCvW6uA0ZkxmVYwdmy/B1haa3cVRqJo+++g6nnHMJHD4KuvWyP+d2u0lI99crOuvlaKOcOv5kLr7+Zh6ePZ/sNpDXay1bymqpCGokpmZEv+HtjmvqdKb+4Sb+PvMtXKf+gaxfT+XCv/zTbqv9+L9vY+2KQvsaH56UzpW33WumWihKk4rvVuQt79AijLo5gStyl0b0Xdv5OLRQiHVhb5CimNJXTpk3wtedwCzQ0QzDNo6dC3KIpCGMTM5k2rU3MDotiy3lNS06Lue2BcJW0OgozhgVkUw8sKcPIRrO0ewIDGHWJWwtr8UQ0P+gQ5r83YWznmLjyqIG3//ru+safO+cpMOi/va4XaRmRy9cVd1gZ2XnhNpXFxdE5adfO38NAmxNbnMx7eXIkaPDqUKmk+GHbzeihtRwYZ+gvGwPa8L5888/Mp1/33AlXm/sqMXXq0vi0oNYUmhqp7/wyH38ddrZrCqK9vRb+dh6HSfB/3ZV8+Me8z7dUl5DZZ3r3EpjefCleUy55q/845k3OTYpY59e518q3+6s4vtd1baXPp5oVMVi+/btZGZmkpaWxuWXX85pp53WZGH5WKxZs4Znn32W/Px8fD4fv/rVr/jNb37DsGHDWrzN9sL5QNENA9c+EsmNOF0BQfhhGw7jVgY1NpZWkDHkQABWbinnx3UruGjyr+0K3EsvvdQ2LiDEF198EVWRe9ToVM4dcCgDDz6MzXtruP/z/wFw9pjDIrlmcUQsjcVjRiTwu1vu4YX7brNTDNxeL6PSc1i7opC1hcvJ9I/nqtvv49OXIw9CRVE44cyplOyBz36K9oasWVEIPY4AQHTvw7lX/YlevrbVxm5MSqghtpTX2HmodVHcHoQOBbvgky07+WTjTg6p7GX+/mHFhXVFy8FnPsTv+ewbbp50AUOGjWR9UYCl2w1+bGT/YzL9HXp/OJvkeL0+ksNdDQVKi9QbmoPH7WZ0pt82jl2KEi0PqbjsTmxDE9Ls0G2NatDN47Y93QCI+rm6zR3HPdUhKoJauIgqn9UFAQ4aOIDdu3YBHRPt6V+zjS8WFIPP1HF2OZrydBaWOoO1YHA3ox28jivqnmgOdfdz6Y1/JzEteuFaWhliS3lNiyX9WsOK3GWE6uSnbywpJC0rm/tenEfesiUkZY9lbUEg4oHBbEHu9nhAN5U+xmSOpSR3mSN/3lw8EmNKfPhvf+DIZ55n/NG/6ohTbDL5y5fYx6+qZlpEWmY2q7aUkzToAPv6rXtP6oawF72KqH9dWYv0o8ek0WtoIl6PO5KGpsTZA7Sd0RxjJUTnqh3VpVED+d///jd33303n3zyCS+++CLXX3895513HldccQXHHHNMs3e4fv16srOz6dnT1GE94YQTmD9/PjfffHPzj76dEUKE5b6gWjX22RHNUkCIR0RYj9JlCdaED3NHRRBdCJYu+TLK2wbg9fkg3IEvOWscqm7gdZueL8MyuBFRXZGsNI54WyhYXeGcCAEnT7mII4aN5Kv351LR7UBOP/kkhBDcfsU5aCGV1//7MMFrHQVsFz6EZ8GdHDlyNM/9VP9q2PzD9zDSNJC37zG1PmNJA+6uDnFgD2+zF5qWZ78xKSELTTfYURmib3cPhgB3Awu8ULCWOf99ECX9LDjuMvP4ex9pXyhutxvtGD+E0+/Xba+k+Ke95CSloSjwxqPPw+B9q1h0BHUXD4sWLWLu+x+TmOFn2JgM+3cw87DbL83itqdeY1RKZvhuq/8bKx4vXq+XpMyxCASqIdi4spCNRblkjzuO7Bx/lH6s8yEsgBjP233yv11VKCisKS7g1svPIRSW71JcLrjh7dacapMpf/56Pu5zGFxqGsjnvVzMJ1dnc0D3iEdR0w1UQ1Cr6hzYs/015SNeevPfzbCPEec/QO3hBvzc/P1660Suhhw1POZ92VnzaEr2WLzdfIQcr/3rmqn867k5pGRkc9iIZDxuF0KYc4Nu1SAIwelTzqf3wMMYlpLNyJQMfG7Flol0H3gYVT0GErXhMJphUJy7DM6JLwM5e9xx9vF7vV5Ss815LqiZThXT6xm7dN/Ku3bFeNO65jRdcP3ba6hRDd64OC3cpCmOLMQYhDQDr1tplaO0LkY49ctpp8QDTdJBVhSFQw89lEMPPRSPx8OePXs455xzOOWUU3jggQeatcPRo0dz++23s2vXLnr06MEHH3xARkZG41/sJIR9IRu4FMWuOq/LonfmcMBxaZxwTOw8ts5EOC4+iHhtftxbjRCQNfY429vm8/mYNm0ap/12Kp8s+pzUnHEcMiKJ0soQg/p2t7cH5kW9p1p17EdgGCKml7JT2cfi5djkdL71DWF20U8c5R5EMHcuml2FXefDhx7L1Q+/yq61AYj5/I7c2Huqg9au6/HdripGHNSHPt2bJ0MeS0poXwZyWa3GlvIatpRHvGXBurkmYQwhUDz1vVWKonDSWVOZt9lV7/NCQEFBEcbQlEaP/ZbLzyHxsE84dcJxjX62JTS0eBg4bAx7alS7kxwIUrLHQaD5Cg5NxaUozHvuMTL848nIzrG9pRann3MRJ6YlMDIlgxpVY21xIff8cRp6TRXzn5vBY6+8TWJauIEI4UJSK/0C84Fco+r0aGLDH2txVBLOIXWGxTsKTVVBRKs9lNVq9OkWMZBXhhtICAQZ7WwgF23eG+mSKkwHh7uZD/x3fm5+Cc9fR9ZvNmVFKuvSWZG4hNRMHnxxHn/Od6TiaSqrCwKRtBwhODYpnRvuvJ8Zd/4NYRh4fT5OnTyVg4cnE9R0DMNM43ns1QXkL1vC82SzK4ZxDODxdSMlDlstp2Zm8+grC8hbtoQxmX4S0zJxPk1s/ecGFzhhAzr8vm4ISn4us6NGOtjNZgRmwZ/bFV8deZ1UBTU27KjkiAN7cFAbRTesCK/lAIgnGn1CP/roo8yePZuBAwdy5ZVX8uCDD+L1ejEMg+HDhzfbQB41ahR/+9vfOPXUU+nVqxcpKSm43fUviJkzZzJz5kwAtm3bxpYtW5q1n7agYvceVN0stKmsqG1Q4g3gswVv8NXMfzO425txZfDvrAxSVl5LSDNwKwqaz41uwBZPDXtLK9CF4IihR/LGG28QCATw+/0MHTqU7hVBzpp6EUII9pbuwFvTDarMh9benRVUle3B5VK4bv639r5q9u6kPOih1uNmi6/p7Wvbm727qthbG50DVlG2G8ULoPD9DjPH/o2SLQw7MAOP14OmgsfjqefsOOSww+injYLvY+zI8YCtxsea739ieP8ebO1WG2XM7C2tYLteSUUz0y8SExPtHD6v10tiYuI+74uyGpU9ZbUomDJiHhfkfh8jP7xbLzjr74ij61y34a552RNO4eP/Rb8VrKqgZo/BFz3S4KjGDQUtpPLR++8y+thI1Gn99goG9upG727uJht7DbFw4cKoxcPChQsZOnQoX36xjOWBAKNSMjh2TDJel4shQ46AwP8a32gzOLSbwYADerK2tJa7rz4HXdN47SkvDz4zizEpqVHGTlG3EXT/chGhyjIKGcKCrxW45hV4/yHUb5ax/POPGTL0CGrdLiqCGtVhWSlXTy8uRQEUlu/ewciDezfJi7O3tAK3C4aNGoXH60UNCdNI7shYZgwLonLPTso0H1t8Zq7mntIKO2K/xd2yPOumsqe0AtUw8LldaIagOqSjBdtff/aH/M9IGj6U80b1Y876vQDUVu6lfJfOlh7WolpQWhmirDLEFm/7jkMsynft4fAhQyA/Msl5PB6GjRhF2c7taAa4w63oj5t4GgMPOoS1KwoYlZrJ4UOOoGxPKZphUFnjQfe5GHLEUA4ZNJjn3/quwX3+7m93c8TQoZ3ynN8Xe/bUMOSIIxh42AUgoHzXdrwul1l856mhfNdegh63ec06fqs9u6qpDOlohtkAaLNWTnl3DwLYU1pJ1d49KIpCtcPBVL2nlLJqL2o3F1u6xc/z08IQgo07KhECvLXdUNugc3BpaSmVrmpznlN1tvQI4XV3IQ/y7t27mT9/PkOHDo163eVy8d5777Vop1dccQVXXHEFALfddhuDBw+u9xmr4wxARkYGgwYNatG+WkOfKi/lQY3ePbx0c9Xgcv3c8LJeUdA0lbVr1zJp0qSOPdB9UVZLhaeKGtXA44Le3cxw+w7hos+AHmiGoH8vH/7EY6KOW99TQ42vBoHA43JxUL8etgd5U3A3QU2jd/+DgYiB3K3fAHr07EZPr4tBgwZ09Jk2yF5XBXpVCOex/nfFHq4dO5SDenfD260KqAJgm96DO56Zw4biXBIy/NxeEr2tLVu2sGfjuti5hy43zPojXPo4AK+sr+Sh3xzBIYf2p3e3yK22Wd2Dt083+vX20dNR2NdYfvGkSZP4/PPPm5yD3L06RLnHPC9DCLxuhbtf+Tr2h+sax0T84d1696333s6dO/kkbyHl3ZqWWuHxeTntjN9E3cc/a3tQFShTFI4Z1K9J22mISZMm8eijj9oe5EmTJvHDDz/wxyvNfHqvz8uDL80jNTM77LFpWwO5R69ePHVuCjf/7W/khUMPmhriq88+4coLz+Hb0irAHPvtQRcLVv7Mey8/Tfc/O1J4hmXh+SGfnBNP5YABB+N1u9CqVdRaDY9boXcvH+5wUaTHpTDg4APo5nE1aiR/kPsxK/KWMSIth38//xaLF75F2e5Stm/b6rgjOoA6x9mzb3/69OvJoEGmusVP2h5zEW8IBg06sF0PZbO6B1U36OF1U6sZENTo1qMWaN8C4/6HDabngQdxbtZBzFlv1jZ069WXPv0HMmiQ2Y1v9dZy9J4GfboZ9ti0J5pumHrwLoWfy2roeaCHop+ix+G2p99kdGqmmQIAuBXTXeTyukg74VTSTjgVIQQh3aB7Nx1VN+jWzUPvbh66e11UhwygYQP56JQc+vQ/0B6DeKHKW0moIog7pKMLQZ9uHrp5XOFrtD9fV/vo4TUN5L4D+tArPM/vdVXgqlVRdTO3vdaj4PV5OXpAT7bqZVSpGn36H8wuqoEfAOjWdyDde3jo090bV89PC90QbNX3Ygg46MAeHHZA9zbZrs/dB6pVlKDGIYcOoHsrnSVtSaOun7vuuquecWwxatSoFu10xw5TLu3HH39k/vz5XHjhhY18o3NwuxSzFaQhKP65jFqtYQ+ylVcYbxrAENE+dQZULR1Sq3Dv69L6nZxi5RMbjo5HddMW3lq5FTUsbRNPCMyORU427Kjk+fzN7KgMsmFH5Nx1XWNtYYCE9ByGj0mrt61n776Zt556qIE9KbDrB/g2FzBzjRvqF1ZaFeSHPRGPg1OWbOLEiQ12S3NKCTWFunmWzUEMTcUwdNYW1j+WdxYtZc5zTzV5W/96dg4ZWdF6sQ2FJluClXPs1CFdvHgxITVk6zmX5C8PhzHrN3ZoLUKY84W6LTq0sHvndrp7XHjcChcdFTEQBWbagRaKKBUcMvhIJp51nv1bCRG+T3EUDBMJ667dVsHW8n0rHXyw6Euuv2gyT//nXm6+bArffb2ezxbOIfD5J3y/ueO8dYrLhbd6d9RrddsL2yojwox+tCfRxcSCWk1n57b2H4+XHvgnX68sxONcLNRJwalVrfzWdj8cAFZtLefbnVUYhmBreS1Lv9vDLe9viPrM8KR0+zrcXR3iu11VCCHQww0+LBmzoGbYc55umAazIWBl0b6bu+vh50pFbeepmsTCql8xREQH2boHIVJfIhCs32FKFgY13VaNsvWThSPF0doIREkBXvzaClOHv2NOrUW017NdCLNpUmPzWUfTKTrIU6ZMISEhgTPPPJMnn3ySfv36dcZhNIpCuEAvpPPIV7Fi6hHG/+ZcHpo1r1nKAh2FnRPsyJcyH7LCNp7LalT7oRTSDFuX0HDc2GU1Kisc+VNqnSfcgtXbyP1xd9xJtSiYncPq4gJuXLiOLY6bsqaqijlPPchdV09lXUl9GSf9gocwblgQe0cuFy632/bo7awKxVQ3idU0o6FWpa3FKtBskbjZ2XficrnZsbV+JZIx4nj4TezC2r5KkHODyzjhoMiS7NjkjHrXhdMgagvqLh4mTJiAz+uz9ajHZJo5jvta6LaU2tpaPl28hD27orXSBxxsSputLMpnzn3/F3lDUfB4vHTrFsnjK93yEx+/9So3TpvMmuICu4hHCGG2SHcqmofvS72RJ9b7nywyq/B1c5EQ+PR9O8dev+K5tjr9fdJbUZl67c3887+vcd2IyOshPfqajExTgm93VtXbTtHmvW02t1gGj/lveGjxd3zStB4nrUJVQ6wryo1yptdbKBCZmzsCy5AFcyy2xZCXeyH/x7ARK7hx4Tpu+WADb7/whN39zUDYi7qb3l3P4v/tCi/wzG0XBfat86yFByGWo6azEcJ8Jqq6cMzd4WvH8Rnr0lyztYKgZjb5sYpsrds0sDzAS08+zPrVJegxagDK42yB4CRyv5gLqS1lbZcGIjDHON7qlzrFQF6yZAnr1q1j5cqVTJw4sTMOoUkoioIwzCrzxsiceGaH64o2BSVs5UdprBIxfp1eqW92mpPTqq1lpvfT8VmBCK9uI5rQNWp9T1xIj78iPSEEh4ypv3DxuJX6BoZidsDTNJWVzZQDO2L4KC687mZGpJoKCeaEWt80jVWM4GxV6vW1XTdC6zdeVVTAS08+0uzvCyFY9F4DC4IGivN69jmAyVdcT/9DIukUsRrpNFaUEdJaV0Tm9/uZPf89rvzLrUx/YS6jUjIQAj7eEG3EDj2wfqOHxuheuwe+jyygyvbuYfofLuD7jWH9W0XB6+vG76+4HI/bRcHypWjBiOFx6JAjueWp16MMZEMI29u9Is9s0VurRQp4dCNihDQ2dppuULR5L2k54/CGryuP10vOyb+2u17W5fyUtk9jyxjcl0fPTScxw8+X783lmTv+aL+3fk0JzrOw7hPnotyJQLBpdw1VwZYbEVVBzTS07eWGOaZLv9/d6HfbitGZfhRHQa8Ro82yFd1raAGk6ka95hMtxTJOVm4pb1DNY9E3u9i8twYhCBt/MOepB/nnleeFmyuZI1oV0vm5rJbn8zezcWUhc2Y+xoqCfHr33XfKTJ/uHqxuknFFeCGlGSKqW6499HWeoZ9/uYQXnniYlUV5jtfND+cGApx26ik8+/B0/vnHK1nw+uwYu4s/FSgn1gJBCFqsx+5k445KKoOaLSTQt5mF6+1NfB1NHFEV1MjNy2dF4CtGZzVeXauL+A2NWDevc5XrXAWLOu9ZVe+RkJL5hqIQZfCF6ro+iHwnnlAUuHd1fWMrZtevbr1QUn+Na/dPLPA1XQrMrcB1p2cx4uCT2PjZN1Bubts54emGYEtZra1E4HwOWSkC7370GcNSs1odifg+3CEMBKuLC/jLJZNRQyo05P1uAL3XALhiZrO+IwT1pHqEEPy4p4agZjC4n2mQmleKEvOBUF6r8k1pFelD+jVr33VJzczm0BHJVAV1hICqkMazedGqzUP6do9Kd2mMtMP7wntPU9w7yX4tWFuLCIVACBSXi8xxJ3DFn//GKWHVjsyx4/E++7xd9JmYOY7hSakoP663t6G4XGZhpNtNSvY42wPncSlsLCnk6xW55Iw/nsTUTPsaagg17O0bnZbFY68soCRvGYclZJCQmsERw0awtiiX1+p8pzk6wE3l4D7dyH3vTV6473ZTV7tHX/u99StXcOaEyNzqDFuLGNePIWBXdRC3CzvXs7nUqHrUIt8yRDuEmnKzocY3GxjtcKZY6QUWwrwtEEDJz2WkDe5bL8981ZZyDuzp5egBvWgt1v50Yabd7fgptqp54NP3+abKBZ4jATDOfwB1wZ2U5C1jaEIqG0uruP3DSGrG3ddMRVNV5sz0cvyvz4WhDadkfrermmSHrnC8oCim4aYZAo9Dxcqw/9+ax8zug9dfNBk1FMLr8/HIy/M5NindfrYWBZbaKV+aCks++RCyj43an/N5G29EOdhacIzbK4L07+m15WIBewEYj/KwIFtNN8jFz33BTV/t5I0nH+TOK89r9PNV2zZH55XFCQqRUKxTjNt6SAgEq4rymfXUw3aXoIihHMmf2lYe5Mc9pgdhTXEBc196lvUr6ntYBfv2bHUGzT0eceI1HHniOc36zmsXpXL0gF71pKJ0x/4rgxrbK2ujFiNO/H4/f/zrTYxpg1bIVmMIQ5jpJVaL2GYzYEizvxIxOCInaYVnKxzeP2cEoy4NeRGbQ1VQMz1tQpghYBrwUDXztt21dTOrln4W9SMahoGlLez1+bjyhr8xJj1iBKVkZPPvx56x/x5wqOmtbWjKEI4H79cri/jXNVN55fEH+Mslk1lTnG+PXWVIazBf1/pMYlom0679C0eHc+qPTc5g8uXX1/v8oD5t35Ri986dvDD9dlMrVwgIRZQijklMrXMfCPt/Y/1MVtSrNZg5pY65jQ6ar3b/BM9eDkDup+9H/e6aUd/csFJoYqVjrd1WgRH2arYFTsfIqqJ83nk59oJ4/rMzWB02jgE4dDiuozM4JiUbAazZVh71eU2NtKVWlX0XXj2xbBNf/m93HBpJSqQLq8N7GkkHst+iKHcpqm0AhyjJW2bnIRtCkO4fjzec8uXxesk8qb4kbAyfU9zgjD5bfzcF3RAUbd7L5r3VUbKwELEXREM3fScjDeQG2LblZ1Dcdri9MV568mHWxDAYOxvrgaAaIqrgx3pIrC0u4I8XTWbmf6Zz3YWTCQQCEQOasA5reFuGMI3pv047m1dnvcAdgb319ifiyEIOaQYbd1RSUdt8w/B/qxtuIxsLQ4DLVV88fd6zj7Hg4y8or1VtD3ystIu622otkX0JkrPG2S1im81v72j2Vyoryvl8/qtsLIncD9Y156Qxz50hTA+a2oB2c2Os31FBdcj0HFteOi3GE+jc5MMY3LfpFdl7SrebBt/OTZEXw797QmoWdz07hzFpWWFJtgjp6dEpWHuqVXY7dcRRzLC6rlOSZ7bGXrutgqXFq2xjQ1VVSvKWYflxqkO6nRpVF8vIgkge+oaVhcx77nE2lhTW+/ymj2eTue2zJo9DU9hZWorhyLVUHFrIQ49NqG8YNrCIqWuYtAZnikqHeZDVWtDM+IH/lF/jXJX99L2pqFIvncKeK6KpUbU2m2JXhNUqLMOnOHcpej0B+DATf1/vpQlnnsuwMekxP+46JsM0Bj1evjzi7EaP5aeymrizkRTMLrrW/GU5moQQFG7eExUhTM12GsBmgy3bAASS0rN44tUFXH7DLfzzsec44bdT6+1Pj/WDxxH286vOYZb8XMbXpZUx85KjHXPRRO7r+DxpmWLRAEMHH05u2W77Bm9A39xGN4T54Do3fhqFVNSaGqqWUWAV/CiK+SBGQEneMkcrTbOt9IkXjjDDtyKSQ2V5nIsCS81mA0NHQ6/6eWXxomChG4LVW8tb7IUUiSc16/Pz3nqTbtW7SMsZB/SxX3/jyQeZ9+yj9H37fU4+YbxjFV7fdWhNvm2FtVhJTMvg4dnzWZW/nGc6oDdE1ecvMXPlB+C/EPxmZ8FYnrCI96Xhc1Z1g6BmRIXlmoozVSioGfTwuuycXicDenbjlpOGcf2CNU3arsfXDSEMyHsLyrbD6TcCZvfJtONPZm1BgIE9fYxJj0QCPC6Fbt7IOQjgT++sjdquoihRKRaGgOmf/w+8yfi8XjTN1L9Oyhob9bAxhJlz7AmP0Z7qENsqgg4vtPnZr1cWcd+1F6CpKvN69DK1lx3M/e9DuLr3gj+c3KRxaArdDjjQ7kTmcrn4w9+n80TY0RhLxcK6ZmNdK23VZSuycKiv7tNuKOZvkzHhV/zq3EvYuDKyQJn73GOMO9RDv+5jo1KKIvmeAqdB3VYLBbBUUiLNLJKzx+H6tJCYLoXDE+q99Pk7bzLUVc6vzr2k3i+jnXk7F6rLSUj38/dVTTue1qYXNCaV2WwUWFtcQGHuMkZn+EnJyDKvRSJF6lbCQWJaBk+8uoDCwFIyx45nWFI6taoRnuvNTyWmZXLMmDR279jBrmD9Ua6bbhNPOOuRnA7fWtWU9auoFZTXqrYcbPR3G4gKhf/f6YiLJ6SB3ABDBh+O97tazvrDTcz1NZ6D7Pb6SMqMr05AG0srUFCo1nQQAiPsTXYrEfWK5GzTu6iq4PX6OCzB1MONeDqjvTepOePx+ryElAbaa4rOv8F3VATZvLcmIrXTkiMaeGSzPj7/mf9A2Q7mzvRy9C1vYj3QrHBb7tKvIgZyA8e0bnuFXcn7dWklwwf2anE7T2tKXl2cz8r85aRkj+OSa2/gmSeWt2h7Td7vgrsQdvFa5BydnhYhBD/trbUNgLqnaEkjGXUMg+ZiGOByhyMhAlYXF/Dxe+/A4WdFH7PSQP7tI7+Fv7xd7+XdpdvDB2rAz2YOcc8+B3DJ3/7NCw/cga5qvPn0fzjz3Av5v+uuxu/3c8yAnrZ8l3mOMQ7Y7UUoTiM68qHbnn6Dr4tzGXfc8YxMyYjKVRTC7EJnGVc7q0JUhSIpNuEG86wvCqAOSUGccROGJ9LBLnJMBnqobRtT9OrXn7uem8Pq/OWMyRpLSno2TzxnSn5pjmtibzhNxJqXYhfptT7txoysCDvv1rDce+2NouDr3p1Jl/4BlwKr85eDy5Q9NAxBSf5ycvx+M0fauk+IPU9YHrzi/DzmrcpvlTEYXagtSEjN4MjhI5usEi6E4KX7b+foY0eBu34/g4R0P8OTM2BV4xE5K8+8pTTUTbOl/LinmqXLlnP7leeihVQWeL3c9dwcjhtnLlBXFORREFhK9tjjSEjNDBvJmYxMycDrdtm/pdM4NIRpDK7cXs3dy+rrQrdV2kx7Eev5vnZbRdRi3cnu6hA/l9U2uKATzos9DpEGcgN43QqqAWWjJ8HXO2N+pr9PsDtkPn56/OpaDhmRwjellQw/qHdHHmqDWB40VTNwuxR7whVCsVUtElIyeHj2AkrylnJg/wEUBpai6oLkjKxo3cbwdxNTM5j+wlzeXVrEFzH2GQ+RklA479T2fnfAMRkCCLenLt+7G1ym0LsVbjuw/wAefvABBiVkMDwpLaYHuVbVcYUXMeW1qv37tQQhBKuKCvi/S6eghlS8Pi8zXl7QijNsGi63J+J9cli+Pbxuu8BFMwTbK2sbnBQ37a6xVVSaUzdW13tk/vamZ+zrlYXcdfW5aJ6e8PuIgTxM32YayLEWIqJx36LLZT7wDjv4ICq3m6kQwjAwQjrzX32J9+e+zhdffIHf74/qFrhiRTG4D4ve3ZFpcNbt6AvvoSRvGcOSIs1bhielk5iaycBePjvSYJV5Oj2iFs7FLYp5T2wbMhYxqZHGLu3cfjpK3iycl16r6ny7szKyGMc6dmEvEK1FU2u6/+2sDLKjMmSHy81rSzTYfr0t6X/QIdz07ByOSkzD7VJIyhoLheZ+XV4vyVljEcC6bRVRC5+GIu6rivP580WTUdXWGYPOy0Zgptxt2rAajmhqBE3B0A3WFAZw55xb791/XTOVaf93F9B4zwTzWFo+WceSymyNgbyjMkju0iW2LKKmweqCAP26e3lv7ht8suB1dE3n9f8+zMOz5zM6LQtFRIxEu/ZBKFHpPELA6tLYC1FVr69oEi9EjP3IM3XznpqoBWfdQy+r0QhqesMGMlYkJz5sh7pIA7kBPOEn86cNGMcA5QsfgV+Z4dUKFZ5c9j3/mVQ/DBWLNg8FxUAIEIpZzOGyJOt0gduj2GFMRYGE1AxAcOO0s1FVFa/Xx+OvLmDwqBQUFDv8YU3Wo1IyWbutgi9+irFPOr8aVVEixoERK/GpHXB7vYiw3u4B/frzcziMPO1Pf+OQgwbyr9tvRg2F8Hh9PDhrLikZ2VSGNDbvqWGIQ2bMKhsy9qFQ0DQUVuQutYvzVNXSgm59AeC+sAw1xeVi6MgxbAq/3qebJ/IeDgMufK6VQc3uNmh5Py2PUlOup7reo4UffEyPoQkIFEK6wZfvzkULhcAXWby6C+cz7XeTzWOqM9zKspfNy0YNgtdRvFa1B5a8ZP95xllT6JlyGNlD+vHNFwfiUlzojqB9MBhk9uzZ+P3+KC/1/+oYxzZHZeDxeknOHkdIi/Y42w9YAGGFJa3/RLZtplBFjEpFUQhqBktKG0hT+Xk9fPJYeEdtayyOdO3hn1dNRQupzH3Wy/0vzrPf03XzIli7rSIqShU2D+tFFwwBLsw2zLoBRw3o2axj+bm8FlU37OiES5iL0dbKCTaFfv0HMio5gaAu6OXzkJiWBYVmQ6FfT/sDo1IyzfmqrnEcKzVJQLGliNBKYzBK/lPAitxlGHozZPRGn4J763r+13MYBcX19dI1TSVv0fswtnEDeceWnxDiiGYcfTSWVKY1B7RWKtMQkJYzDnHxI7D7J1wl79G7bz9uuGQyoWDQNmRV1UxVTEwzawys11cVFbAibxkpWWNJz8qxn6H7Wvhb+c7xiO1wsv4WmEXnOJxoMQ7euYB3Gv+mrrn5fizJ2HhAFuk1QFNyHrU6HblCmkFTnBFN7ZrWFlircssrEwx3urMnRsx/r8gLKx3oZi5yUWCpGe7HfGKtKszn5aceYXVRAQLBpm82Nri/eLjBrZvSENAOfSHqMeDQwZw25SIenDUvSvPz/Kv/zN7du23PhqqGKMmLpDlsr4wUNUSM+ogx1BJKK4MU5ueybctPuD1uR6OM9m9iI8IeSGEYVOyJaMt++b9dUbqt1u9jpZtsCHehgrCBR2RR1hTqeo8WfPipPflWh5z3aeS+TkxOYWRKBgpKvYlQ5L1l/uPxOl6xN2+BXZvtP3v3OYCzRx9G+fdreeGBf6AbeoNezpVF+TFfr8udz84hMdzW1+KdFx5n48pCdOEYOysn0LGI2F0digrtWmMcqzjRZucm2FPfuGkttw6vIvR1IOKBU1VW5UeufadkltPr5jwfC2sxZb28q7plHbfqemQNIVjXAcXVNTU1oJiGkdulRBlIAwYNrX/v4/xv9GAYQpCaMw6f19S3bokxqOkGK7eUOZ4M5r6TssfWKy7dJ0ck0f/KxyjQDo75tuuoDIZMiK0IlHxYn6i/P35rNquaqT3vJFY3zdYgBIxJz0IMPBKOHY923n1U7Nlj1uBYi31FwRte0Fq/H8D8117i/y45i5cfu49bLz/HbvzjXLTGQm/hnN9RCPt/wn875iPzrfrHbziu5+htReb/2jjtICgN5AbwNBLbTfCWwbctM2zbq2taLCKrvbCxaBVl4NQdheSssbbSgdfrJTVnPLWaKUa/doWp7/jsw9O5cdpk1hYXMOSY4TH319mX+frtFWwrD2IIU5fy5aceYXVxOz8Av17GjjW5fPr2GyhEZ80KAak54/CGH2ZeK5xax7CxP4/TAGoZ7362mD9c8FvefeNlAE4/52IenDWPo0bXb53d5rgiaQS7NkTG/Z5F36KLiLRW3fOL5SWLSHE1PhLORis+n8/0/IS3oxmC439zDvQfDFc+a39n945tuBTz9+rui63w4fZEB9lcjr89Ph+jM/woisKGYtMQxPHgVBQFn8/HtGnTAMhf9lWj5wFw7Jh0Vhfn89azj9mvzXv6P9x9zVRKCvMjY1fnwVMd0vhuVxUhzWBVkbmgtZR19tXsqL3EKR/8x9844MADcXnc9niUbo2EnSx5s4hR6PBmOs6rrEZlVbiJhVN/trkoYc3t1UX5vP7Mo6xdUWAW8xa0n4PCYmDtNpRwUkzdOcJZMBi5DyJ5+LHmiMTUTJ56/W2u+ettLTIGQ7oRlkAMbzM8/gkpmYzJGtesbVUZDZsR2qTb+SB0ZMz3hm5aFPW3EVZvaQ11u2m2Bku1ycmodL/9nPT4fJxyzsX8+/m3SEjJsH+vNSsK+M8/bkbXNIRhEAqF7POyf88GDGHdiA8HU10Mw0yFiqRZWNG/aKdYQxEZc0FRd6EXcQLFa1qJTLFoAK9r32uHzOB61ht69EOephWRtHUoqCGsQ7n5vfUoisJ/zkzAVqewVrMo6AISUjNtpYOU7HEkpmbw494aNENQkrfcoXQBKwuWM/T4s2DLjpj77MyLvTKo4VIUVhcV8KeLf4saUvH06AnXvNp+O33vfgC0UIhP3p7DiRfeTP7mveZb67czNTWT/77+NoWBpSRm+BmelBHJM3RsJhKmMtMOWjqMhYGIMgk6HDTocBJSMzumjanTM7LhSw488WL2dDe9S86HcZTsj4g2eCzt7jXFBazMW45//HGcMuE4u8FILCzvkZW25Bo0MhwtMc2uY5PTOeL8W/nRYcAfPXwEisus1OrudTP7/BSmvVFiHoPLhUtx8ZtLruYdx35+e+UNVPyvhIpdO+ndf6DtFUxMH8sCnxc1KBDCQCgKHrebxx9/3H5YH3f8BO7/MForNhYbSgr51+/PJ5R1PoTTkK0cyJV5yxiTFgnHWxEiQ5hFnoaAtcX5/Oliq2GBl4dnz+eg4ckN7k80oo/tDryG7r+w0eOui6aG+H79GrNgVAh0TeP9OS/DDVMAqzNgtOfJ1iZ2XA9VId1eLNklhy28N1YV5ZupZCGVN5728u/n3mJUuh9WRz4zbvunLDvklJbtIAaeRU/xx3/cAIDiUgiLldgc2MNrLwRtr7FjAV3/VM3fOyk9i9FpmWQese8udQ1hCNPwUVwKQhG2BzQhPYeS75s+wNVK0yUSnbw7679wdSTlyxVWb3EihCll2NLGMK1hVVEBf754Mlz7pv3a8OR0/v38W6wpCHBsWg4jkjNwWSl9gEtASe4yDEco2eVy2edlSqeKBlelmohPC3lLeS3bKmrtol87lVKx/m274li9tTxKjcW6rpynbKY6EXWvx6ORLD3IDeBppDrK3bNPvR/0+w1rmxQiautQ0L4QAraUB81KUqJDdxZG2JOTmJrJJdfeQGJapn3h6gLGZPrx+nwoLpcZGjrgEOZuiNGFjtZXmbcWa+/FzvzbDijCsVCA448ZwIRjzCK9J5dtQtMNktKzuPS6v5CQmukIR8V+0JuvN81zGou0bLO9sBUNSMocS1AzuOX99Y1/uZX0OXCgHT50uVzsLfrEfm91UT5VIZ0NOyqjcotjneXqogL+Ou1snn9kOn+44LcsXda4+obTexTZtjmR5/24hx+7Rzc9GZOSbk+ACtCzm4fbJh7DFUcLzvn9/3H3S/Pp1adv1HeqKys4adK5FC//gs/nv8a/rp7K1ysLOTY5nXuen0tierbZES9s0O3atcv+7tixTbvP1xQGUPscChmTo153ud0kZvod6izhcxTR/zWlGENRusk7KhtOSVAOOSb6hUfOivpT/7qFyieaRtnuUnRnLrWjCNBZSGupSUTybiMPXaumwGz1axnSzT8cIUT0vKCqlOQv55ARSVGfu+2223nzktjavi1h0umnkpaZjaJEHrjOotCt4c51kRC9CKsdmB7J73dVUxnUIm2yHdd2a0QPLANlTXEBs558hNXFZnTi4MFHtnyjzdq/QFkekRo87bxpjErJiPpMWa3Gekf6VUdi1nBEC7wKBMPHpHPe1X9iRLLlNY6O/CVnj8XXzXxeut0errrtXrP7ZfgaV3WjwajNPlOhOhF7zqmzgLWuWUstyhnlcX7Xei+kG2wtr2XVlvLI68TlmgCQHuQGaSzForJsL4riQrz3APzmZgB2bPmRP154A0mLzap1TTeo1Qx2V4c44sDoghK/39+uhjE4VnmOvyOdfcIXvWEWMXnd7kjFrYjOJRqWlM6f77iH//zzZgzD4NXvQRwc+4HrrNTvDIQAoQhSHPJ1Hp+vUR3rtmJ4YjI9vW68jgWWHjWZRKzCWCkWYE1GLfcgj04zBenzli+hR59+rMxfzg9V8HVpOy8UdnxHxcrPQQhcLpfZIKI20sRiZf5yxo0bS0jXHWNhnmTdB73TkNHUEPnLl3D+ryc2+VAi2t0KVSGdR776vt5nXC7YuLKQkrzlJGePZURyBgmHHEDSYemo2akRr9VXO6HPQAD27NzB4oVv2Xm1qiqY8/TDZE88g2BlGRnHn8w3a0rQ1PrRoaZK9iWk+3HPn4fT368oCsefeZ75UA6fn8u+V63olXk9pfvHO6QbvSRmjt2nxrMYNIpTz70EQ8CxCUks+/Q9VlpvLnsFV9mWfWsFr/kMRsfQTg6nr3h8XkJBA4Sw88shImkVK9Ra96FpXSqG5bFi37mcDZGaHRkbj8dLQoaf389dHfWZvTVqi3S3G+LnzT8w89H/MCw1m4RUM+d9vaNRy9xXZ5E9QJCelYMLgaKAakQ8bNWqxu7qkF3o6jRGWuOPEAJWF+dzk0Pp5ozpb7FgcweZK1oQV8F89LEXA3DQYYPrGUqxihQ7iuSwo8H5pDMMx0LNPjaBpotwGlEkGrvkq68YmZ7D6LSs8PUcTnPURYN53pYO8rptFSQc2ifmZzoDKz0pEnk23cfWHB7JJzbv5817amw95IhBLdhRGTQL/6MWFlaZcXsle7UcaSA3gLcRD/LObT/jcrvRv14KmAYyAlQ1UlH8U1ktO6vM26uugdwR1DV0L3u9hJcvSLUvSkOY1aPRBXvWl8P/IwSGAXv37EYYwvQAuRo2gnv5PKYXa/PeqDBLRyJEZJIqDCwjIXMsNxV2TJXsN+tWoSjRCwXLgx2rAMn5+zg9qi33H5vfHZ2WRa2m89dLp6CFVNxDEmDKPS3cYhN55Qb7n3b3NIeu7uhwkaDTIIqE7EwCgQBz3v2IA/r1jxgyXh+ZYxuRJwsjhNnOWmDOvgK4+q3YXQq2/7yZZ246Dy2kMv85L3c9O4ehian2g04BRiZn8A+1gK8+/4SvNleTv2QWbo8bt8eN0Mz7YWXuV6wMfGm2mfZ6+eud0ynbs4eLf3t6ixbBw5PTue7O//Dohshr3u49OOi4KeG0BGckKFLoYsk3JqVn8fDs+RSFmxsMH5MO+fsoEFSDHD1qDCdNvojuXheDh49g5QrzLV/JQi695R7WlG0g4B0Z9TXF0BHPXQljTm1gwwr9BhzENbfczVP/vtXsPugwDCJ56NFeJOshvHZbBSMO7m2/ZqpYRBaWxT+VNXmOqajV0A2zUcN9L86jKHcpI9NyGJGUDiXRGr0bVxaSlN52ai8FgWUUbFqBx+floVnzGJjtN4sVv9VhWI6Z65q/nLSsbIQZs0bVwr9uvfkC+9oGWjxLmB1WrfS5iNJNRxnHnlnXogWrohqSlG7ZjBDRWspWJ9iOxjAEo9MyefSV+fx+eaSz4MIXHyc5ezyJKVa/ANOwU3UDj8uFW1EQihmN7T8sKeyEMpWkvOGf7bGPCvlqxz4MZGEuiuINs715uAhbuFEU06yNtVDbXlnLzqqQbUAbIqykJQDF8kKbX9LDNkYjWa2dgjSQG8DTyK/1+bxXYr7ucrmivEYiMpd1OIoSLWsa0gWqEZl0DSHQhAjnCEZWc6Zuqogy2Kx2xaGQM9+oPla71Jabd63HeogkpGYyPCmD6pAGhdGtdQd2h531u2K2GhfmattpIFueMjscRaQRQixj2f59WvhksPSTS/KW255O0VD72HbC4/FiCAOEansfj03KqJdWYU6Y5rlb6i7BUAiv18vvb/03FWV7yB53HKkZ2U3a754ale92VUVSEPZxrRYVFaOGQgjDQA0J1hYGOCoxzVYYUIANKwv5dkWAbsEyWPIawtAxdDhtykVs2fwDq/KWRCl3aKpG+d493PS3Wziyf/SiuKn+kY0rCzl6xBjYEOm0d/aDb/HGdzqHfb+bk48dGB5TJeJNRHEYnOYCcVhSBrphsLakkSYNNeXMfOpWhBDUlO9lVIafE30/s/7rbzj9xn9y6jkXM3hFIYFoRysHdndRqVahKrHnOLevGxPPOo91hQE7J9OZYqGFfyQrmgVQmJ/LmoIA6f5x+P1+KoOanZNedxHfnHSujaUVdvrCqJQMjkpMNee4GJ+979oLuPO5OU3etk3xQkibVO9lwzDA0NFUWFMQICvbT7p/HL4npxAaloMYdwkHjsLOK3crCpphoBsCnztyvpZ8H457qFUpFkBKuDjbijao0CGdBY09W+u9tm3zJgT1F5SWw2X0YX3o5umYCOWKn8tsRwvLIylGc559nHlPP8zvbrmH08692J5jdEPgcpkRK5diFaBaTgBBVVCjh9fF2uL8Bo1jgLwf93L8MQM7ZVGwL5RwUx3Lwx3SDXxuF1A/9cKKbmnCsI1ip7MuYn+Y29as/CrpQe46/PB9U3sJRXPDnfdHeY0ihUnNDwe2BSsK86L+3rCymH45WeFqWWeifUSORQFqw3mDAnPCTEzL4Lq/38Ojd/4NXTTsjdXt0HY7nVAjROdGmX9Uheofb68ePdhZ27Zdw9weL5dMmxb2IEcWWE4JHMuLXKsZdPdGJlFNN+wFjXXsP+6t4Yh+PZpdoCKEQCimMonH50VTweVWaG+fhMvt5uhRY/jVORcxdPgo3nnpSb4P9sASEHPmjzor912YYffFixdTGwyaBqsQlO3dzYXX3EBPn5uqkM7abRUkNiHs6PRo1K1Cd1Id3heYhtsBffub3mMAFDasLOSfV5keZpfblMtDAY/Xyym/PY+QbrCuKGDnKbpcLjxeL2ecMrGecQxN73Fx7+/P59rHI90YAVx9DwV+ZneNimYIPK6Ip9VyKArF6Zk3F7LrSgq57Q+XwhUv7mPADAxd59l7bwOBqToB6JrOq14vw0YmsOS9uTA0WvLO4+vGHc+8yRtFP7I2xmanXnoFCakZeFyKWQhW5zbcsuk7ROKhttG3pjif2y8/F001C+iefO1tlPDCyFq82x4rmud4sK6Hd16bzWcfvEP2yb9mwm8vjBnWVUNBvlz4Fgyp3/hin/y8NqaBjGLWbni83nArekjOyOKBF97khnzz+pu7tTtnhidjETawVF3Qvc4iwOoy6NS6bglWjvfIFDPStjJ/Od179+WJMh2U9jdCPT4vakhELZgOHXJkzOeGtTjSdEFH1eo556corn8T/aXrePH+2xl0zEj6HZPIoX26oQMeEdHXrgxqVId03IpCd6/bNh5XFSwHJafB/S75fjcDev3ALSfFVonqLKwOlNYtHNQMOw0pWn0mnKbp+K6Vo4xjIW0bzMJU7ujpdcfdogBkkV5MAoEA902/t+EPzLo+5stHjkzk11On1Zu0OqqbmxM13E2uKLAs6vV1K/IJ6YKgZkTyiQwzDGS3eBWmVMs3q4qY/9zjbCgpJKQJynebaRb76ra1tbzWDhN1BlHV8MJsu3vRayvqfS5U23buY5fLTc5Jp/PCvPc5+7QTcdVJsYhMHKbMnqYbhKwq3vA4rdxSbhrHRK6XqpDGrurmZU+X1aj2ZDU4IZV/PP0mJ599IZnj6+fv9jMq62+gFZxxzkXc9+oH/Pq8aWz+Zj1LP/2Qn7+OuB2jJtHwv6tV3V5QDRgwwH5gGobBAX0PtEPwADXhsKPewEy6aXc13++qtj0aa4rz+b/LpjR4vN6efVHCrZ0Vl4uKst2m7FtYaWBtYUTD1zB0jj/zXKb+4SbufWEuo1Iy8bld5oJSUXB7PJx+7iVc/ue/sXzJV63SNlcPOZavV5VEvVb4015zJMJjZkmk2f8NGzz2AwjzOlqdvxytMRH+cLTM0HVbrzhS5Bdi8XtzWbzwzXpfE8CI5AzOOSNG/vHDZzHn8ems34f3ev2K/PA5mfPOyrzlaGqkgK4osMwRaYncP3beYzPmGEPAgldf4sG/38iK5V/y9L9u5rN5r8Y0MIUQfDqvBao3DRyP4nZz8pSLuef5txiTlmUvlJLTI1ERqyGTdb66iGjimuct0MJzRlTOdvOP0q6Nsa6dxLRMkrPG8dS9t2OoHVOtcc9zb3HBdTfzh388YL82cNCQqN9je0WQH/ZUt9pT3hxqVZ2vSyvtyEzM1s8HHYlhGDy9qpwb31lnOzWcUVfNEFw5ZxW3LyxhwfOP8//snXd4VNXWxn/nnJlJ6B0FFbDQAqQnMIASxe4Vla6CXbl2vV77tffeFbErKlXsHUVaII00OoqI9BZISJmZc/b3xz5tWhKK5XseXx+EOXPqnn32Xnutd71reUmBfF/S4xvHFnbXhP7yZPdIWLxha85yz1/WgtWyHSL58ZZjiLD93W1lMPydQh77cc1f9nzx8I+BHANz5sxBr4uf9a3t3hhzeygYjOLjRGac/1mQuqGCXw8NfyG7p2RjGIJlxfnMeP0FVhbLF3evpXFo3uiK4gIevepcpr78BPddPpqi/EUk9x+Ix+ulvmpb7xVuYGLuupjf/VlqEjJUKQfVhet2xtyntqb64F1QgR7JqaTYvEWFJq5QoDXAC6BOl6twt3j6yq1VrgExPNlhX7F6u3Mu3ZCUmjmfTmfR7K+i9g0cZA96xinDURVJS/hw4jNyY9BZiCwvLrCNYWuQDIYMe1D9dt4ie19FVdldscs2jEC2R+H6Coo37GZ3TTRlZE+tM7HYBlcgPrWkaduOeM1sc1VVadnG8SArCvTLkh54VdPweLwMPmMkIy67lj6pWSgKLC/MxQjp0sDTdSp2bOXN5x7j0Qfvi1kAqLFJKGLkg3TulRq2bdW2vfI7IdB1YRYMcRa1dh8TzgLLENA3ayAen6/+CyoqmsdjK9W4BzBhGCz+/ktCMQynYF0dq0oLWPbZW9zdL7KzCkKhIOX5C02KRbSRnmjmK8iSvNjji6PFPsgeNy1qUiBkhE3AuiHYsTfA9qo6ttej1AHw41efOR9UjR8XF3LL57GVXQzDgF3h47zy5ZNxzz0skE//7p1h1YKo75S6veQMG0Vvs99IHWQlIqIgwg0OIdDNxYAhBLuqg6zbVRNmtNmRpl37Npb9uqvGpCEJOwJRvHg+ejBY79h+MBESguGXXcepo8bb2yzjeG+dXAhvq6qTv3c9DqaaoE5d6ODkl2zeU8uKrVXsqZXz+NaqOk6cuCh6R0XF6/WxVW0FwIPfr2JXdYCSgjymTJL62tbtbqxRmPHKk9x72WhKChZzZJ+GteiNvzACGw8yuik7p/v3cKvNWHOaexySO7nzDdwUCycyAJC/PrYy1l+JfwzkGMjJyalX5u3e12eSmXOqnExcaNmmbVTozxrsYhmHq7ZVxZzoDwYMIagJGvwUIVXctXcyK0sKePjfY/nwxce5f8IYVhYXENIN+8UUmFJTqWdipJ5BKBSkLD+XpLQs7pg4hfaHxCmTa2Lhz1udwdeF0o174nr/DgYcIXNhv5RanLi252BlqX/2GAoKLVu3DeOuNnVRLKSHz/QE6Y4BbE2KlXVBV0Zv9Aq8sXCXZi4ryuej119g3hcz5MItcolWsZHqyoY1efcF3ZPTWVVSwC0XnsO2zaZx4Yo23HPFGLNoi2PAWWXMy4vy+XSa47XzeDw0b92GKa8+R1lRvtMmpjci1vtky4G5DC6t9SFx77cmEOLy2x6wFTdee/QuVpUW2AZM79RMHnpjBuddcwsX/Pc+VhTlsrK00DZsUvsPRjUXQkIIFv7wDYG62oNSAOj1X2L3260bfzevZ/Ic7X5jeZDDuYC9UzP4zxOvxTyXBUXzcNb4CTwz+WN69U2N+n7Prh3RBwEtdqzm/ivGMHPiUzxyVbROsscjKQWt2rYN/2LmPQC06ZUtPcLm/fZJy+J/E6cy6sr/8sibM+iTZpbuNf/TDWEvfK3txRt2s3bnXtbtquHXOIaiJSl1/GlnOhuPu5ifk8fxW0U9i8S3rgz7qHk8DP79s5i7dmzVnIJvP7E10W18/ADG+nLmfiYrM7oN40glA2dcCPfMOYsEEWZYWONcfRJ+seAUq3DGmJT+g9A89Ts/Dia+mzUVVQkvuWz9rsu3Vkp9ZjNBr74pY+nmPazYenAiYZsr68wIhWzbX3fE7hv9Tz6T217+0P68YuteXvl4Ng9eMZJ3n3+UWy4awZxcJ+dFapgHKVy0oHGV8oT9v78NVEXBqv/hOHfMhTnY92wZz26apeUkdL+3bg/y37XMNPxjIMeE3+/nofvvj/u9okD3vqlRHqHmrdpgCCjesNuuJmN1oqWbK9kZES7fUxtk10E0kPfWhdhaKQfLeINK0DBk6DgYxPAmEuw+mPIC6emyPFIAXVL8iEHjIecyPB4vfbL81AR0uidn0P7QzvXeR6CuNqZxF8toPphYulnqZTphyPg17317tx2Ua6o/L8IwDCY9ehclBTJkrCoKCS4DecWS8DKj9iBiewGticrxBBqu36KxsM5ZXpTH9ePOYerLTzD302mmy8q5n45bS+GDWzjYg/Dq0iK7b9lwFaEIGQZFufPtAdZK+DCEoHjRAlsvV1EUso8bymuP3sW7zz/GjePPoaQgz/ldkRzuWJ4j92/f9uh+BC+aGP+GfYkUzvvBrngVCgZZZlZVs7zIfdOyaNW6LW89difTXnqCuy8Zzjcz3sOnKfTLyOLU4efa5GI3n9Lj8UQVADoYiaubf19nPqfMKK8zPfB2eN5lWAUNQXVQZ2OTw+s9pxDw0VsvseD7r1mzIr4cHD87fG51zmv0qCi1KRGhQLiRdsLwcdz57Ot4VIXn7789/Dw75DPM3mQwo3STuZiVXMQeKRn0zpAqD2V2eV7HW+xOFLLeE8ebFevZhC0reNa5F3HTg0/Rb8BxtM+IUwhklVPJzevzhn11yZXX0qv7MTEPe/2h29D1GCz/X6QuvlUcRFHAp2moihIxNrmlr5yesmxJPu++/AylhXm2kREWMWD/Ik3OeeS5jumXwZ2vTkVrQN70YGHuZ9NZWVIQ5kXXjXDamZ08LizedfR5DvZ04ng/46sqpB17Et2Tw3Wyl8yfY48jda2P4KXVzs0qGWfJOTTT36j73d/f9I+Egik9CA6VSzhjDrgcHvY7abmBnLnf/T47kcS/L/4xkOOgb5/ecb+79/JRtHCVULVgVYwSCGpDuv2CW8ZOMEIEXAgZFm4oNNhYbNxTy28V0otiCBFztbppTx1JGX5JlTj1BsTJ19Guj1t+S5asfXy581z/mziVHsmZ1OmCh75f3eCKPSEh0fZiueH2hvwRcGe6WyvceB7kjUU/HaSrCoSQxtX8ebKUsKLAhp9X2Xvcd8UYSovyIrxDrnu1BhoIG1gMYSXrNOzVqawNsXxrFULAElflw1AoiKFo0DXN3ndr2QKo3QNfPgV7Kw68CUw8cuVYSVNwzywuj5THm0DagMFOCFk4CYvJ/QfaxU18CYm0btfRpTUsvS9uz/rmytqofmi9b5aHevve+hef61YuDaeeCMHiH77i2xmTWVNayLTXXuCrae8y8SFTogxZDe65e29lRUkhCnDyOaNJSEgIHwcUhYsvvvgP0TnveHgX61bRhcXhDvc2Wu0b0gUzSzbxXtGG+k4JFVJRYO63n4cV9WjfKcKw/uRBWC0XEH369OX4M0fYFBQtIiLTtWcfuvdNkUoqEQoq7ldy1ba9Nqc2oBusMKNb7z7/GNePO5uyQvne2GOZcPIn3AtKiD22OIsq+d+/xlxAl0sfZ7uIrsqori3g8tR2DB0xntNGX8D9b8ywv3v+7D50bqrw7pP3xGnE2COblZw39KzRaKZ8YKeWCRzaMoGlRQWorvfDfi8MweJ1OyksLOTOS0fx+tOyWE5pYZ5tZMiwtGN07K4J7pPqjbu/6EJQE5TOj4SEhEafY78x+QZCwQBzPp0e9t6UFyxiWXG+pMbl5vLa80/JZya2IWyVyj4YRvK2Kuk9djsu4s0dE3PX8f3q7WHb3ItftWXH8O+GXMpdr07lmH4ZNHYG/LtxkAUOFUKAlHwlvB9ZxrK7MIj1t24IO4HfvbiV5/t7Pasb/6hYxMHqFfGrjoUCAX6Y9SEYsX9a9zjlXpHG2i+oG/y6q5r2zQ9sYLIGR0PIJIx4oalHf1jD1YOO5PZXPuTF0mp2AIcc1Ut2WkNgqJKa4UaPlAxWFOezrCCXZd6BDd6L1+eL6Smu753fWxeisi7EoS33r2wpWM8rX8ilRXkU5y1EPzqGkfLWlYjuB8d4URQVRcXkTDpavb+uLAe1LwChUJCi3Pkc0y/dvE9TucEylM2BpKwwn6JF8zkmdQDJGdkIIbmHu6qDtGnq5bBWiXFljvbUBakJSg6uW7pJGAJxwr/DCzkYOoqq0sZrsPPrZ2DEfQfeEB/eTCgUZO2yCC0wl1f1luffo296JiFd8igNe5AVJKVm8fz7syjKnU+mfzA7qgN89/FUQiHZtun9B2EIR9fB7WkCqAvpDr3CHHyl8dsl9v3+Vgrfvxxxqwary4tZXV6M5vEiDANFVdAj+LOGrvPNrKkkZ2STnJHNY2/N5NuPp/LdrCnouo7P5+OCCy6IedmmGlQfQERxXagFFTVBOrbQZMKsqhDSBStKCijLX0j6gEGkZfanLmSwqqSAwiVbQDs0/gnLvoV57wCQc8q/mPHOawSDATxeL8Mvu5aieT+wZ8dWOnc9inlff4JuGnTtOh+Ooigcf+YoQoZAUxXsmonb1vLuh3fR9fDDSBswCI/Xayt9aJrGhJvv4uUKuavlHRRCUo3uLVNRumQgVi8gGITCRQvol5Etx1CsRaOBQCWymE7k+FIX0tmx15UXokhj8IvlEbwzEymZ2Zx4ehpCCFomegiEDI5ct5S1O2to18zHgsJFUcZ+JDp1ORK3gNlFN9zOEcf0JCkti0SPKssSCylnd924czAueR0SW1BXW41AaurWhQyenruWpkbQXiSGggE5NphjSEA3MISjjrB6exWHtkikQ3Nfg1JoUnfcWWyGbE478n39I53IehC2/gLAdzPfp0efZKAnAEvzF3L7pJv5z90P89wDd9pyj0+8M5P0rOjkttKNkiJmCEFdSD8gCbh1LnqORQ+oz5k+q2Q9YQ1lRuhUTeOkEefzTYTPq3tyBrohuO/TJTSqgf9GNqNuJvAH7CRR2VPdVQStfh22SMexfXThlJe2l6vmPvXk/P/l+MdAjoPS0mLgqLjfr1laEtPii1SssDiT9RmHByOcUrxxtz3glZgJevH4vqu3VVHbuhs71PUA3P3NSu49pQfJnVrKATPiuI/mFvDxrWPl5HDDrEbcjRLlLW6oItKWqjp2VgcOyEAWQnJZv/poKl/O+AA9pKMkHQcn3+jsVFuFumczqGrj9T4NPW5xFF0PoSgqoy+6gn7pkjOpKNCrTzJfL5dX8HjMhCMBq7fvZWtlHYOPaucKlQpKC/O5cfw5BANBPD4vD785g6xsOSkYQrCzOsCu6iBJh7aIW63Q8pT1Tsvi6XdnMXfuT1TsruQbtWfYforHh9frY+QVN/D6hzMOSjuoW9bg8Xql/JS7/7g8ZEcmpTjhf3MRB5iSg5KDmpSaiUdV+X13DXdMnMKaJYvxDz6WfhnZNj9Qco2dSSaoG5RvqrS9E0LAl1PfZcqrr8L5T8d+ltwPoLoi7qPqIWkIKahommZ6kB2sXlrKpx++w66dO+mX5ef6+57kjBFjKfjpO0acfVZM73HeokWEXrsMLnk97nUbwurte3l49hqeOasPBgqqkNXQ7rpsFMFAkPd9Xp6bPIudNUHuu3wUoWMvgdTT456vyZbltOt8KGeNv5tzL7yEnJNPJ3eBrMD42qN3EQrK/nj5bQ/QO70/r06eitFjED9NvJ95e3ciDAOP18slt96P+vRFGNV7wAhhqCrLSwo44da7efGDT/lm1lRaN/Fy2ogxbNpTC4sdGUmQiUkL85fIbf6xqL8skgsjUxatziU76ebxW4tLiK48ua0qwObKWidkLqi3lG+bNm1QgJUlhfxSspheGQO47+RU9gZCKAr0yfAzU9PqfV/OvuhKphledlTL/nPuhOvZumWzLCKhKigo+DwqufPmEQwGICQXDnXV1fYCcG9A9rVqNRGf10soJIvlpPYfbI+rAV1QVphH4aIF9B94LGlZ2WyurGVLZR2HtkzgsFbRHnI3LMNmeXE+pXkL6ZU+gC5JaZJiYTaR9tlDKOuWELpmRr3n2ie4qF6GYfDyg7fDdR8BIBSFUDDID199RiAQwNB1gkg99/Ss/lG/rzsZuXxT5QEVpnIFKOyIRrxqdwB7K/eAr3nYeKgoCkPPPo+OnQ+DtdHHrCgpYHVV41YffycPssXhrwtJ7WPHqeP2ArtULgg3ji0niCEU2zBGyHce8+i/K/4xkONgYGY6r8+uiNru2bMJXVER5sTvLp0KTsewPwv3IB4OQ8Qe2PcHuuFUqrG9aHEMZAG8kbc+bNvUJRvpe0gL0KA6gts57TcVJRhCGI10fSluvrEzIAgEv+6splPLRJrHELQ80IVCaaHk3gbq6mwPthKRfZ/TpSna2efxW6uerG7sib98Ev51a9yvhTD48PWXGXzi6Qw88hRUBXokJcFyyee8c+JUktKyqAka3PX1SgAGHdlWLmDM+WhJWGllKM1bSGaWWwNWLjvqC6M6gzv0Scvkp9r2fFMcLcifcsKZjP7PFXTvl0Ftx168+2vEDr8vhcP7RF8gb4bkVJ4Xnc0//JKrSB50Iq2bevh21oe2x9BtIOuuBYEunGHR4WE7iR1CSK9LckY2rRM9YV5AYRo71m9sDeByPpP9bv63X1BvwpEQeHw+zhl/Ob+tWoZAIW/eD2G7qKqK1+djwm0PsLy8lB8+nmIbzstKilhWUoSiqng8Hk4dfi4TLr2I7pdMILl7t5iXnDfvJ0J7dsCWn+GQo+PfWwPYXFlrt6MiZF9xV0MrWrSAtWvXSeWJBpKuaqqr2fTbr3Tr3gsF6JeRzdH90nn75WccybWA4MOXnqTjYV0Qa5fA08NkaN48RygEVRW7uOyGW3nj0TsxkFn+fdKyzHNmkZHdnz6HtuDz2T9x84Uj4CpZiKNy104MISgryuPlia/C8RNAUTh5+PmcOWosfdOzTNqXgaoqOFrIImyCBjm+1AZ1El0LSDfXFpTYsl0mNEVhVUkB918xCj0URPN6uWfSdI7sm46CQq/UTE44ewzfLp4G/Uc7By6QRaM0j5du3XvzSt9kCjdUsHNv0B79NFUu7NIOa4WqKviPPRav10ed2Z8SmjaV/VlRCLiM+P9NnMqvZXkMOvZYeqZkUmtG91YU53P/hDGEAkHef/lpXnx/FimZUkJu057auAbyb7uq2VMbQsog5nPnpaMIBYJ4vF56j7+Dypap9r7XjhtFu653cFfsQpT7h4gFtnvhqWgePF4vOaeeSUn+IkBGMpKzBsaNwAoBQjlwg9JZeEvrTXpF4xuzekgHr+ua2SPR1hfTrXdf3nrhUfhXOO9eAHM+mwFdGtbWjnQu/dUIGYbZFjKnobouRNtmKoGQsKMeKBAy5JhsVWm0EG5Q4xT+EYKqgMGDcxo9E//p+IeDHAfnnRyDSvDrEo7dtdDmSnp9Ps4Y44RS3RxkZ0UansHphrWPIQQVB5isZ3tiXMZGvIzZJRui1QtkGUmptRkKRR/nSUiQhRJiYGxqjKQ91ykCIYNNe+okV642yPa9sbU2D3ShUGQambZxrChoLo/F4//qzQmHKMz7fDprypY0+ryqxwfvXlfvPoZhsGSxTPBRUPC52qp7SrrNE7QgMLN3zd8qpb+sVGhJivXL8ofxkq0Bpj64aQfVQZ1pMYxjgCMOP4yl+bl8N3Myv61eFv5l+ffwxeMxj0NRYPMq2BXNae2RkkGPlAxSMvpzxkiXooFrUWVxjoWAL5Zv4eqPylhZXMD0119gaVFeGFd0ZUkBn7z5IsuX5JsVz5x+bbVEyBBs2F3j6vPWIkEw8OQz6u1Q55x7AQ+/OZNLbrqby2+8lZL8CEk2VSVz0BCem/wxZ4y9kAl3PcYltz0YpVwjq/AF+Hzqu4wadjorSuP3q8HHDkHzaFB3YFn3AoXiogJ74dQ7Y4DNBfZ6vaRkD3T6SkOqBIr0lpcVLLS5wcuLC9m2cYP9rMIwKMr9iW8/ej8qOqwoCh6Pl75Zfk4eOY67XpvBmKtu4baXP6Rnv1TzEgoZR7Qm0auxeME8gi7pvd07t2MIeK9gA/rxE+zt7TodRu/UTNu5EBJSbFbgKJ84f+TTGsJJ1JX3Fh7qFUhqQjzsqA7y0+czCAUDCCEIBQLM/WyGS3UCjh82Cl/hDBmBAFg0BRZLY18Ig3KzHVM7t+Lknh3YXRtCxamwqZpx+8zsAbz84SxatGoNQEJiUwwBAV3n86Vb7HvqnpLB6Muvo196dtgCuDw/18XRD1C4aEHY4jIW1u2sthUvBFCStyDsHCUu4xhgz+5d9DJLKh9M9E7vH7NqTmr2IB56YzrDzr2Alz6YxYSb7uDpdz+id1pWveNfY8bGhhDpQXYWVY04yERoxINSEjEQPb8VFRYw55NoLfFYkKbo3wfue3nshzVcNUs6ftyKHwIZ5akK6LIfmn/cuVDuecxARnPyNlSxevveP/eB9gH/eJDjIBafSQnVcsLw4Rz/r+GsWrKIY4cMoUdyBp+/YqpA4LxgYGoH2ttF5Nzi8qDBmu1VZB7RZr/v1/au4XAwV5QVx9w3loEqO63U6I1lWN/y4gf8vCSXKTHO54uQxLNecOssO6sDbNxTYy4cogfGsk17CISk5ufyLZX0PqThammxkGoamcGgLN5xwtlj6DB4JO+bssyqorCsMNfUq2488alrzz78uuKnegctRdVINznIGUe0pnSTswhZWVzIj0vz6dIvG8vCEAiXRxV6p2by9LuzWDDvJ45J7U+P5Ew7lGsIYSumrK+o4dCWibRuEp5hb+1nedbqMwS++Gg6Yv57sg26pMDIB5wvv30emraOeVzLtu05LL0/a3wJRC7nnvjvBO5+dRrdco7l1OFj+WTKu1LVwdXOQZeh+5YZwbjnyzL44hVmTHqGp9/9iKS0LMqK8nlgwhhCwSAfv+7loTdnMNDvN49VnGQ801vm/l2swffUUeP44od5sSKdAPxr5Lkc0jIRRZEe12DEpObxeLj8hlvpl5FNbVCnSpFe0shOICuayXYPBAIUF+Zx4djYxUmy+w/gzFHn8VHtgU9/jy1TuZ0CUjOy6JGSyYOvT2dpgSzR3DMlk+P2BvlxcSF68qkNnEk1F2QDUVAoL8rjPxcMN6Mwrj4kpNZzpGGTffwpDLvoKnqlZqIL6JWSSVJqFgHdQKvdbSs3WOg/6Fi8Ph8WRbNV2/Zycd76MLBeGUWlR9qAsPFRCGyedXL2IPoPGGC3u+smMYT0Iu+uDbn4x85+kYnSbhRt2E2ktoV171IIRqF3Sib3vTaN579YHMY1lpEE6e1UzP0tWohMzgvXPW7TxEv/AQPx5c2BOgjUViOE4KOyzXxY7OgvC4TJuXZC2gIhE63NKpmWZnRZYR5LFi8gfcAgsrqcEvV82/bWYQhZht4Qgj6ucygoRMYHU7MHsqqkgINNSj78qB7s2bWTDWvDPYdJaZn0Tu2MENA3XfL7q4M6tUGjngisvPcDda5Yc5N7MVVPVzElLqLH2NKaZjD8pqjtTyxXoWU9uQCR9/M3spCtsfa+b1fbxqxuGr5mmSX0kE7QyisRtl/ZzrOybCAr/8YqivV3TtCDfzzI+4SOnQ+n4KdvmPHq07Ro3Ya0zOwoWRwrUcyCPUBHnMvpSA5vcn+Rm5vL2y89HSEFBOVFBQ0fHIFNu2u586sVUdu79Unh92ZHxjymsi5EcifHqLW8eBYsPUv34sENm18oHP7dvmJ3TZAkk3s7/tpbueDm+2jf6fCwIUxToG+W3+bKRmJAu9hG5dqS/DAJLxuLXB4BYYRNI+4M6IevOpc3nnmUByaMsbedN3kJ26qkUWZFHPqkZ3L2JdfKhA7hjkI4+1QFQmypjK16Yk2gUD/X0hCGY/zUVMbdLxJ7du1kedFigjGK6OjBEMsKclEUSM7I4sJ/X2tezGk3wzAkf9R9z0ckI/qeRDAYZMniBQghKMqdT9DTBEPVCIWClOYtcE1crnbBnQQrz1tWmM/7rzzH0qICOh7WNe6z/LJyGR5VQVMVqvbsMc9mWUPhtCkU0IA+WX58CQkoqiysMf7f13HWuRfh9cnois/n46yTcmiZGL14sdCzb3LM7Uc12/cB4JFlqv2O90jOYNTl19rG6TEp6eijHwVPA0VCVJUJ/3tY0iFci4W4C8gYA1Xv1EwUJD3h9Ydu57WHbmNNWSEeDxBRECM9qz8vfTDL/tyiTVsCuqBFq1b2tvadDuOY5HSbmy6AVSWF3HfTNUzLXcF9l49mWVF+mCcZnHFv6eZK1ldU29VELfkpefv1t3POmSNlwRRFwevzMWTYKMeDDHYhmS3li+XG3VvtxKx7Jk2ld1oWiqqwurSQT996idWlRfYiwR2yT/RqbF5ZzI6tmwHY8vs6ypfkUxUx/hmGi0rgGkO7p2Rw72vTuPC623j63Y8QAq45/xwmPvUwV593TsxKju4IkxCSwnTPpGmMuvJmTh93RdT+7bsns6wwRqGMA8R3M95j47qfnQ3mOGol7obJf7meO6gbYRQzEdEuIOfCRx55ZJ8qWa7Zvtc1LznKIPVp9vsSm8SetGOVGrdw4YsxN9/UN9pPadkNfwdYt+H29NpCAIZcwAWNiLwA4SRgW0n/1vxu5WVB/TSWvwP+8SDvA7Zs3Mgnn8rM99JFP9G6iSeMYgHhE7i1so2labtkw26XYSxXXPuD3Nxchg4dSl1dgLdeeIqn3/2I5IwsDAHNWrWG3Y07jzXITC/dxM7qaLrH5dNKgdgFQgRw54k9+M+nS9mwuxYijH5bzzLOtS2vhuUh3FfUBnVWb69CCEHf9CwqaoPcd/logloiyuAq6Cv9QiFD0Cs1i7snTeWjonUUuU/y3AhqxlwHhw4JP/l3L8Evi6MvumgqLHQKWwghWLJ4AQyXnhv3wikY0sHQicjz4ped1XRt09RZOLjaKKRLmS5buk6YHOR62kcIYRsCq+oLW7loJ2z7BWbeDSPuN6veKbRo3oyYZvMKSxov+pf0eD30zfLbRTbeeulZOjZP4PFnnnOed/lSjmrXH19kkRZFQdM0kk1qQNqAQQgjG7b8jGfG7fTJ9GNNXKCAYtGYTG+82XfLCvO4YdxZBINBPAmJJF1wV9wmWPjDN+T4M/lq2ru8O/G58C9Nb2nRYqmgoCoysapXahYvfziLvAXzyRo4mJTMbHQDho0ay6IF87ho+Ol07RrfKC8tzOPp++6E02+Lbr/dG8BTv15xLJQtyadfeiYhAYoBewO6PVk1BoqqUlWxC82kU6QPGITX5yMQqDNVPGSVQSGErC4XceLFP3zNdzMmc2SP3tx16Ug7vPzjp1N5/JW3ad+xU5gaQNumPlIz+8P8efa2kG6gusY/ry8RJ8lYXu/tku0EL3gJgOCKnyjOW0hSWpYdXXGPu9Y7oykuL5U5aV8+vX5Cbc+UTO5/YwbrSvPpkdafrn3TCYQM2wOsKrKIkrF0NuzZBr+VIMxJflnhIhK8GkHdsCMgHq+Xu557jUMOjR47pSyklAQTQlCWn4uRPjxsn/z1uzjhmA724lAXTli7R3Im6Zn9SfCofPjqc055cGSRmshEUYc6IK+3tLiAbwqWM9c3ENpFt4UhBH2z/LCk3ibbN5hJiWEOh4Xv480+m6nFG6kJ6vznuKNsT6NARjaFEPyyo5q2TX0c2a6pPAfObw/OXBgIBPD5fMyePbtBqUXDEFTUBEyjWAkz8uqLNshwbYxFZJv6awTEQklBHi+dewFXm9QFq9cLEZOJ8qcjFtkkaCoQhYQ0Ig3zhh2nn+NB1g0hpxxFKu7g6od/d/zjQd4XRHTWH776LGqj20sqkCL1sbQawzI+Y3zfWMyZM0dm/JpSQMWL55ve4zzefSlOBn8MrN6+l49KN+2XmX5IiwRUxWkJRVHsAd1CvIWC9R0Qs50aC7stESzNl9qr4qw7Mfo6QVNDCFQFeqVm0f6wCPkvPcj6n2NI+5V9A0j5nsSQq1JXxMjl9SWQ4R9kf/5lebjcmRWCjXnP5kMLYMx7hUwv2WRztxxeYcORBoH0OBWs38UdX0ZHAdz3fsRR3R0+7bpieP0yWsy8FRBUbt0QVUWMF0bDdpOr8t0LYV8doW/nlicn0SvNUfEA2LNnTxgHeWXubCy921j3bi0QkszzcMjRUoM7RXJRrWIYkZ4wK+z8xcwpkiohBKHscylt3jduE/y6ZiUrivP5avoHMZpHhsRbt2lDeVEek195ljVmBT2L6qIqCr06tjA95tlccNUNDU7G8+fNjVmyGaDtIfs+sQJ07ZeJwJJQEoTMSap8U+MiA5rHR2r/Qfg0xfZ0nj5iLGeOGc9xZ4zgkMO7csIZZ3P6qHH06JuKokRPGYu+/4Ly/IWyVLEJPRikvCjfXjBZaN3Ey+GtwxPIDES4YpYCMqHO8Tyt0xxtWY/XR99MPy772R5vjIj3xJ0gJJDRrnqhKPRNy+Lia24kKS3LniAV5G+uKAr9MgfKd/m3EkCODT9+MpUPX3ycOy4ZyZxPpzsFVEJBli8psAvPuDFkyJCwjb3SB0RJiz0371fb6EBAbUjwe0UNT835hYAdeRMmvcyHpml4vT6O6JsV8/Gs8eSzqe9y9yXDmbsydp6CbApJKelVc5CSqCo2wfs3ynNHcPkN03vw6dIt9jhueyKFEx0Lmn3C9jTjzLnWXLivlSyt84BzHUFsqqGFQDAI6sHxLxblLYqSeY3lPW6MHv4fhSj7xRAIQ9hFe3DNVe553J0z4nbgWPv+3Q3QfzzI+4KIyaFt2w5hA9rGtatZtmQXgwbJBD8FKcfjVeVLt21vAFWB9s0TbMPIbSTvD3JycvD5fNQFAni8PumFE0Jmtof27YWaWbaZE46J4UqoB1f10jj2qHZhlIXI51GsLOM4BrDb82PxaGNRIOqD5R3RFMUuhBJoe0TYPmt/XkPZJ/No3roNs79eDIMvDPt+5+aNxIKqaVxx5yOkndKfJ+b8zJod1bhn9IFDT+PG//6XYSfm2Nvmffs5dDjVOgHH9E7i15PvC3O+WosFK1RsDf6fLtvCeemHuRYTziAkvRyxO4vVflsqYxthNhSVjb+tJSvnZPJ++Fpu27OVsMrTEYl4Q84agxeD5i1a8svycjp4f2N2sAsDu7Xh+mMzqNm1zQxBRywZXZ4iYVIX6qL6pYKh63z78TSK8xba0l4AX+5pQx/k77usKJ/ivIWk9R9IWpYjgWf3N7d3vVOPeptg7arl/PfCEfROTovRPgqGYfDkPbch+XUhFFWhU5cj2bz+Vwzd4N2XnqLX7Nl4D+9lP0NDsN7VGveuoQDaB/+BG59v8PhYeG6linrILjIOb4WOs9h+5Ic1jTp+3JU30Dc9C6+mUlaYx9XnnUMwGEBRFFthYPNva+2CFx6vh2AwGPYiH3/amXQ6siea12t7kFVNIzUzG5DKDW5EGoGRnrKNe+p4bXYxN56eETPyNOCkYdSZvEZr0WtHYNyTM04+BjRe6SDBozkGMdjGrVUauXdaJuP/ex/zPptK6/aH0q5DB76d+b6ZsCmv4bHk2Txe+qRngRL93Irr/9Ydxxr35HPKv0O6wau561i+tYqVW/fSv6sXA4WktEyef38WxYsWkNJ/EL1SM+xxVDdkErgVZfnu42l8/OE7GL5m9Xo8V5cWkpbZn9C89+Dk+JVlG4skYwPNkvvQpn0O3Xr15a3H7iZoVmDU62rA10w+r3AiZQIZIUz0qLZRFdQNSjfukb+zIrCyXqz3y/IgR1ayjAf3ItvttAjVZ5AmNNufJoiJY1Iyw/pGrIhrIGRQtmnPAUnZ7Q+Wba6M+Q429WoyqTSk49U8YYsMkCpFqnDpa+PyjOOM2/s6z//Z+MdA3hdEGMjfff4R54y/hDMOV/jid8H6n1dz36tX8/S7H5Ha+USbx2RJS9YEQ6zbpUsD2b1qjfC27gv8fj+zZ8/m7Y++JHvgYI7sm44hBMnZA/FMnRqVTNUQShvpebLw+k3j6PTC2/RJz7KfQIZWpBere4dmYUl7sZ7TkmCyJrf9CS3ZE6PpTRh8xgjmejy4/UWvPfckYvlPsvhD8hn2dnXvTgwU2L0l6rwnjBjH0LNGk2QmIZ3YvT1rdvzGwMGD2Wv8RsbA4xg34VqObt8Mn8fpHxU7d0AH88OAsbT2bCIYQY9wPCRye3XA8bZKb7CjoS0NAaccbSBksDcQok1TF8dUSK9Hg22nqNLoA3wJiQQCdQ2u0C6+5X5+X15sS0upFTqcfgs1Qd0VOTD/BpsDqHk0O/nnu3YnMrY6SIIn/AYVVUXVNL6a8T6GrvOe1wum9uqidRVyYi/K4+aLRri0fj8mJSPb8k1gCDh9xBg+n/G+mYTZ8PsUDAbpdkxPygrz7GIgiil9IIQwE/fM3mvA7784njTLQ3Xx1Wl8/eM8Fi+Yi3pO/RQLv9/PrC++5uJP1zkJXtvXIXZtYOeWjftFsQBZ2j7j8FZ2f9oXyStLPUcBlixaQDAo9WcjIYRA13WGnnMeHTodRnVVJb+tWor/pDM4+7yL2FMbZMIdD/HKA7dh6Dqq6SFUFGzlBguFeQ5lqXLXTj5583P2dhyI21j8aYvg1OICBg8cGPU887/6mMWfTubRt2aQnJmNSrRRbNHbHE9k/bx8C4oCiV7VVRrabcYqrCgu4LuPp/LdrKkYuo7Hu4LTz7vUpgwIw+Do3skMGTaKksUL6ZvpJ7Ov1CFv6g2fbufO/Qk41GpgVhQuRs2K7gNWHxdCFjlZblaQNJC0Ma/5fH3Tskhx6YVbqKgJsnbnXkqL8rhh/DnU1Znv+7hnoWWHqOtZWF64iG2/rubnZaVwsrPdK0IElXpMh5KvIOW0qM3HnXgaJ/adgKrKnJTstBQmPfMY+Qt+QoScRX35knySM7KtZiFkhM8auuE4UsBJrLPmwjlz5pCTk9OoSpbC9Y/96S8HA8PGjo9aXlu0EndS9/46jw4E1UE5g0YOKR+Vb+bsvofaNApHDxnqdIFHlfdeZUZsLM1jYQhqg4bN71b20+75s/B393D/rdCjXXgRC103WLJoAZ03LIJta6FbKsHmh1C8aIH9s+uGMPUB5Z+SwsU8/PDDlJhlQ8NLDO8f/H4/4668QVacQg6cXZLSOPKKxlMsLMSTYIuHYKCO5YW5UR3JGmwcgn78ginhNIL9WypYE+NnU9/lvstH8ENeCSEtPEHJMAwMQ8cwDJQK6S1Wyr7h3HbbZentik3w6kVhx1xy+yMyCcn0AA3s1oabc47mmtGnc/erHzJ02Ei8mhr1XK3aujzxmeeQ0DHacAqXcRPsDRhh31mVw4R8QHvVXRPUKd20m5937I06X0hvBJu9aicA7Tp0lAUeXOFOVdM4pm8qV94dLvX2c/kSlhU40lL6RqnnnHN0e+n9UKC5z4OCQllRHkOHDuW1116LSsJYvX1vVFv1yRpExuCh6KGQyzB1QQgzecwpPb1k0XxzMnPk3/plZPP0e7MYffWtdO7Srd4mUDQPXq+X00eMZd68eZxw2r/QNKsymfsGY/dGy0O1dukSrhx7FpOeeoShQ4dSUFB/YuyAAX4OPdxF7zGpN+0O3T+KhXWHq0sL+Oj151mxpICZpfHD5pH47N1XWL4kH1VROOf0k9A0D4qiREk6KoqC1+vl+LNGMfyy67jwxjt5+PWpnDX2Qtt4dKt86LpOcWFeGL0C5MJp+L8cZY3yvPnMeOVJluUvjLq3svzcMBqF+3ll8uZCe+VtjRth4V2c8LsAakON03FXTSpNmAKHorCypIDbLxnJV9MmEwo6pdyXFiwIO/6XFWX0SsnknEuuoWeKNXYoJB0ars4z5Lgc5wIdj+LnzoOIBbdM6HuFvzvbDYPqgG73f3DmGSGg6Pfd7KwO2NuLcqUMpt1A9RjHAC1atWHiQ7dHJSjrBZ+gmFSJmCj8JObmVWt/BdMrryqycuFlN9yKz+eTFfZM3HzhCJYW5YcZx5YRVh3Q2b434HghzcHRah+/38/tt9/e6DLv1rxkuDqNIWBHdYApi1c16hwHgmt7KRzdrlmYR8jqt5EwzN90T+2BScLuC9w0Fjc+KttsV6R0UwQFgkDIsH83SzHW7pfIpPzakMHWyjpm/3pgkpd/NP4xkOMgMgv2mt4e+rExrCNrmkr6gEGk9h+MIgT4miIueIHk/gNtzqhurvpqgzqlhXlcde453H333Vxz7jCevvtmSgvz2X+z0IHbgyoEvF+0gVWVf/xK0+vR6JM1MGpV6+bPulUsYj+l3GobhPuwWqisDbFhd63MXl+Sz7P33ore9xQY/XDUvoqmoWoamseD+lsxTL4B9afX6Nm9O/e+Np3MnFNQa3eHHaOq8iWxw62KQtrhLVEVxdQ2JUrJBCDnlDPDPmefEF3NzHpMyzOwepszWHy5fGsYDcehqDgTodtLVF0nJ0q9IQ/i/Peg8GNUVeXUc8ZQ6TJqFEXh+LPP49H3v+TUUePDDnv0yrG0bNPW0dutq+SBZEFWl1Yo5vN7NZUEj8qvpfk2F9DQ9TDuthDw8sJ1YefumZZNq3bt497yiuICkrMG2jrRXq+X1P6DHOPHNYgnpWVx9iXX0K5j/ZJKnY/szjX/e4jkjGz8fj8nHjvQprG4oSgKXY8Jp2sMOeV0OwFozpw5BExjqa6ujqeffrreDHqLx2qhbcdDuee1abTpcEi991sfKnZs55Erz2XKS09w3913MH0fDGRjfTnFeVK7t4lXsz06mqYx9opryTr2eC749/VccdOdPPveLJJMg88y+nwe+TxeTSUle6DdPzweL6kZ2VHRDIsnakEYBkavIRg9oo3DpIwBGEJQF+nJ0zQ0TaNf9kB7ArfGDdsLiPPeWEfXxtB3j4TbX+xRFWksmwl6ZXkLCdQFcI9iUu0i3HFSsX2rw112RVUi4R/op007x0gt2CGYt3Zn9I72c4XDLSFq/Rucd8EQgrU7qvltVw2GkMmvXp83bEFcH6p274qqIAlmm275GSo2xz4wFFtlZ87nH7G6VC4gvWZVwX7p2Tw3eRYJruIucgEsFx1SIsxRt9AN4VRHdD2rtVjeH4SdC9mej/+45k+ZP/91bLa9YIi4q6jf2/pNA/tIndxXbNpTy/ItlfY1IXZNhdqgoz5luGwYWVPBcYxV1oXYW6fbzgwdSRV6ePZqyrbVhp3zzzT+G4N/KBZxIAn+TpLP9pVF9EnL5LOEBIKBAKqqcssDT5CSmU1tyODw0iDrTTugd2oWeYsXMfuHH2l6VAp90rLRhaDQlFAyDB1d1/li6rt89/FUU3ki+4Du152gYgiHz/pHY/j5l9DL9LDahhbO6v73ihq7QpGdRS0kfcAderUMQCs5rTEd89ed1ezYG7AHx5K8Beh44IQJMfcXQqF/zsm0atue72Z+AFt/QWgaywtz6ZPl5+i+aRx+5DF87DpmdUkRKZlZkudoblMJ1zVVYgSKevZNhqJC+3O3Xv1gRXnYPob5m6mKwuw123lyzi/2d+8XbeCcfp3sVbkhH8DM7BZhxvGu6gCrtu/FoyqsKCkgP381eLrHbrRV80EY9MkYQHJGNnuDuq2F6vF6GfKvkba3vK++nvLfd8DeXeihIFUVu3jo9eksyVtIr7QBKAp8+uZLpPQfSOfDu6Aq4POonHbSUJ589GECgQCqphH88mkY9j8Afl+7mtx1EVQTwH/aCH6YNUVWOhtycdj35QW5XHjVjTzy5gxK8xaSnD1QVljDWTC4Q3yC+svEAmxYu5oXH5xD76Q+9O96Cjk5OXg0jUAEvUDTNP732POsXrGMH7/+jJNOH8bZ511IVpc2gMMrDtTVYRgG8+bNY+jQoXEz6FUlbI1N89bt6JXal+r1FXy5fFu99xwPq3fVEUxoiajbGluOMA7UZ8/G4/WRPmAQigI//fQTuq7bdIoWLVryzDvTSfRoBHWpoLCzOohhCFYWF7JqySIy/VLNo7lPIyktiwdfn05p3kL6ZPlJ6tMjykC2edjWht5D5J8Y6JGcSXVQj86lSD6VYztKxQmrGqllUNhqBMjNYX2kkW2jIBVHFi+YR8/0ARzWOw0FSM4eiKqp6K77EULQsnWbsOPbtO/IipICihcvpF/2QLL79IgZFo/VQzfHkHC0jeCIQWb9z6v45atc+g+UpditNrD5tOYVdGEghCzl/uQ7M1lasAitaUteisOm69JM4Z4z+rHoc1fS8p6t0LIjXr0OVv2EXk/JbWXvLrL2FJLXMiNsuyEES/Nz6Z2SSROfNIithMy6qj3QSu6naRrJ/SW1JmAqsuiW9wfHULQGXdOGo+j33fvE0RVCmGoMrnOZ89O2in2jGu4vpkx6jqNT+kueeti9OWv1qroQ26oCWMoefzR21wTZGwix2+StQ+wEQWvM/WblVt7IW8+ZSR05M+kQmvokJ1lWbISrPipHU+DtsalhtJG9geiIzq4DLJh2sPGPBzkOIgn+vVIyUFAYOmw0p44ax8SpnzP8/ItsYyLBFZEsK8zj7DNO5alHHuCRK89lZUkBQkC6mWlsDZZCCHu1LIDC9RW2JvD+wNHLFDG9mn8Epkx6nveeecjkWTsjuGOwONQKayDbWR0IK6RhewDN78s3VTaqsuD2vXXmteSxyVkD8SXG130VwOIfv6FixzY0j2Z7ulq2acN9l49i6ouP8snbL9v7Ky+fK+kjiiINLhcv0QrDEmHwWDi8VXgxj3jqHRafbv2u2hjfC2cicHmQrMHTOmdQF2iq9KDfe/locr/9LH6jmcd0ObonmqqQlJrFQ69P58Th55E2MIe5n89gVUkBigLnph+O7/OHUb973q6W1jstkxGXSY3j+68Yw7SXn+Cey0ezZmmJrV5icQEfeOABzhx1np2hDvDb6mh1DavPCgR090NauPc9KcNPyDBISsti1BXX0TvV4rsLl9dHfrLG8QZpenU1BOrq+HKm1GD1+/2MOu+CqN0mXHM9yZnZnH3ehTzzzgz7nbfg9/t5f9YXZA0egqqqGIZRbwa9pobrAgvkIJx0aAtGN/k55jENYa+SiLjwRRTNg9ayY8MHmFAUlYtuvo++6XIBaBmvlhJC2oDB2D5V05BRTarBXZeP4q1nH+WG8cNZWpRvvxdJZv/olSIrsEUOQ36/n8+++qZR92cIQVA3ostDZ40gkDkccJQO3JEEd1jYek9qg3r9hR+A/xx3FAryPbry3LN57elHuP2SkawulSoU6Vn9uf7ex8IUGIRh0KZdB5d2cgLdk/px9+WjmfbyE9x3xWiWlS6Jadbk5uaya+eORrQDhMy2cOPDNyYy7eWnuO2SkSwtyrf3tWgCToTKcVD0Scvigqtu4IQR58e9XuuWLWie4JGUGauzTrkF9bNHuCPFw12PPc/Yq26mbccYUY+nzwIEhW89FPWVoqj0y5IJ7JbUo6JA8eL5YV7nk84ZS580+Y5bPOCwMvQ4ETT3b+zwkhuHLZV1dhKaYU0+SMdFXfWfE/p/57nHuH/CGFaWONQsj5nMbBXQ2lxZx47qOjsq8mfAELB6e5U9d8eSvGuR4MHnUW2qxWfLtvJ2gaQACRzVIZBFV45p30z+duYPGenE6Na2CV3bNP3Dnml/8I+BHAeR3p/Ne2p55D+X882M9/nhk2n2dq8mxfp/Ls23t333yTTpKdYtjlouBtAvI5sX3p/F2edehOaRxSqs1bL1ktevvVgPbCPDUXP4cyD45J1XWF7seEur9lSwvLggjGNrDV6OUefywhA90DVG0sYaLKzjeqdmceeLk+s5wEAYBot/+BrDMDiqdz8uvPk+fl1RbvNehRCQNwO+fhav0EnK8Lsy2iXZVnUZySCNgMiBS1EUrujvcE13xwgdWSLqlrcr1vMZLg6e1UbWb2yFlUOmRVCyeAFBQ4E2sfWqrTYAaNZCSpRpJk/kh0+msviHr/l+5nvcfelIVpQU0jM1i/9NnMqICTdx96tTSUrLshd3VkVCw9AJ1tWx4NvPzOxr2RAWF/D0EWPNhA2JI47pFXlHfL5sC19/+50Zzo3db62kDncp97CJ0WyjmmCIH9fsoHhjdDl1C+qse6FiI0IIPpv+gU2JGDH2PDSPE7tQVJVWrVqFKRq0SPSQFFHpsf+AAUy48TYSEhLQzIIh8TLoNVWJ8gJZn2r27I4+AOimxn8WG95Ekq5+ltBp0VW84kEgqNq9S1Z6Q7EXNrf+7x5efH8W/dKzwhIwVUWOd0sjyhwXLV5gP4dVSMPnUdFUJWbRlGVLy6O2xcKqkkKEgB9/jjYitRbtqNMFVo01y8hxlwi2jMKgLqjTDd538XcjMajpTjbPnsyKkgInWdHkuy8rXGQ/07CxF3DimY5WsRCC7kn9ePCNmVxx0528/OEnVO6uIFhnHh8IUFKYFzOiIZP0GsbK4gJqg0aUPKI+9CpE/1EE6+r49uOpdjhc4HhCcbWNm6ddXwKapsr+3ifLT4JVGKdmDxeOHEaPlEx6pmYy/LLr8CUkxjhamBGIGHJ6ekDa22ZfKi3M47E7bmJZSRGKtb8wOOGs0a5nEM79u57HiZg6BEWrHzQGubm5PPPk4xQXLA6n6SDH3GbNmjfuRAcIi8deXuDQsponePCqqj0PynE1vA0isXF3LWt3VMf+ch9hOTqsPgNERdZAjgWJpuKLhZqgDkgJuMh7fW7eWhcRI3qkH963nnnrL8I/BnI9cFdQLpr3vWMQmFW/mng1endsQfHi+YhaV+cUAq/PZ3sokzL89mTeNz2L00eMicrsjlVtb19hGUyGgF1b4/DD/ggIQXn+QnZtkyvJyt0V3HPZaMoK88P4oW4+oPX3ss1urpPDQ64LGQRCRr0edcM9YApZG35bi26xdy7/zlXoAvRQiDVLS3jniXuo2BER2p7/Lv1ahrj9lSn0TM20dXHBkTGzPGZWMk8kVCWcm3zPN9EJH6qiEDSkfu3OLdEScwIIGYQZgI4UkZwSDCHLSmuqQs/0AXD6f8F/btw2s2TXZr49kaVFBSR4NMrzc8M0bEPBIOUmL7VHSgbDLr6GnqlSikg1lwl9M/2oZjl2IQRzv/qUpUvyoy6XnJHNDf9zSll37nZ01D5BQ5Df2dSrjiG+v7RwITom/cYIn+wj+1dQF7y26Lf4zw90by5sz5ihh2xv76CBA7n1gSfweDyoqkpiQgIDBx/nUjQAr6rS1BdOAOrcMpFxZ57I7Nmzufnmm+stUKAqCse7pBSPad/MNBgUeianRx/w4yT6bGtcVbClni4N7wRSz3rPVhkVyB6IomCHpv1+P9ffdAv9MrLNRaBi3reMmPg0RVajtPngPjL9g1CshaN5iUSPSpsmXnp0CDc0Jk2axHVXX9Wo23zw32P4avpkXlrwa9R3ApnsY7gWTuEa2Y53WReCH9bs4HPT0xULi36azZSXZDSkVZs2eL2WJ11GThRFVl1cWlTAD1987ByoKFTurjC1sG8kOTOb1m3bYFUiFIZBq1atY44ROUNyGiXX8+C/x1BWlEdZYfT7xZGZCCH4btYUyszENrejJKzF7PFS2NG3WKiuqmTWGy+gAE+8M5MLrruNB97+iBOGn29G02R/6NlhH4zIBZMRS76gvEAmYy4tKmDCmDP56P23mPvtlwjDNJAVhdK8BSwryg+bD8MWPsIZ++3omst50BCsgiJPPXw/V593DuVFefIahhw/9gZ0mjY/eDJu9cFKiE02PevO9vBncZwisZ9w+95Avb/p/sDdpjNi5DV8vXIb00s2sslNC3L/DkKwbpdNpmJK8UbctYYibaA/Kei9T/jHQI6D3NxclEkXwtTboPgL8qZNtOWovF4vGf5BCGRHbt2mLQSdjnBkr2See28Wl994O8q1U3nj96ZhYcDCRfPRQw7Xr3jxwn1a/caC22sQ1A22b97Q4DEHjGfOAqTyQXn+Qmr2mrwtRSUQqKNo0fwI7441njmry+pgyOaYmfMaQsjwV9mmPXELHux1Cf5bA8fH5Zt5McZkCsDct3EXrQB5Y6FQkDbtO4R5DjWPh5ETbqJHSoZtDGsuKoWiKmbinvzcoVkCh7QIp3YoipSLqg/PzlvLii1VGAK2blgf9f3K4gJpQOMYAM4zOwui2pAuDayUTBKOiV0gwHlmZ+Jesng+HlUhJXsgmtfx9Hm8Xsm3xAmD2d5B0wjqmZrJ0LPGuAxN3aYKRbZDcj+nvHK8Li4UDa/PF9NAzmszkNqATkA3CJp/wsLH5r8DIYOVxQ2XVz91xHl4zXLjHo/H9vZ2bJHAvTdfx8Rpn/Pggw/y7LPPUpy3gB1ryigrzOO9l5+lKD+6qqKVpOb3+7n22mvrzaBXgFN7deSD89J46LSeXJJ1BFZsokesMtRrFvHle680+EyNxvoyePdaehVO4s6JU+ltUiHCnwfb2FXMhV7LRK/92/dOzeLRN2dw6Q238eL7szj+2GNdOsFyp1jKLgAzZ85s9K0GgwFef+zumN9Znj7dcCXjifBx0DIqBFJxqD7ohrCjIauWlvHKhx8z4aY7ePa9WfROySTBo6IqyDwH16Jd0zRSsgfSLEGz26eqosKmYaiqyu7dFTHt4IED/bRq3bbBdgiFgpTn57K0IP5CSc4jC+S4gGNQOsmK4Qvtqz+K78VfuVvIhM8rxqApCmOvuB4F+PStF1ldUmhrQ+c0205SqP7FqI3F0/CoCsnZcjG1ZPF8Qq5FuT0wGILJzz/OzReNkJrNq7axZnuVmYNB2DOB80z7Mn+6C4qEggG7xH1tyKAqoBMyDOr21lOJ9CBC4LxnFnQR/oxgPps9f4ajNqjb0RIrce5AYc2phpDG99v50dGXLZV1TC4KtzMso1cIySe+9Yvw4ltCGPY8FqUL/qdFvRuPv8RAfuaZZ+jTpw99+/bl3HPPpbY2mn/5V2POnDnoVRWwYRn88CoYOsIQnDryfJ6b/DEn5xzHkW1lRajdu3ZB0FlF7anYydH90hl5+XXUGQrrdtWEhb9S+w/G6/OiqCqKotCydZuwkNj+wDKgQoZBQBcc0umwA3n8Rl5U/mXoOoUL5jjbFRWErO7k3jVkGC5OmXms+bec6BzPh1vcv3zTnihu2fKtlU7IzTzf1qp6JOpiGF7SQPIy9KwxXH7Hw7a0lao6QXDF9Jq6DQZLvUJBGkaHtkyI8ioe0jyR7esaLtbwyrxV1AQNOnQ+Iuq7pYW5MkHJ5SFx/9sQgk176thZHcBjjjYejxZ1nljt4PX5yBgw2DZ4HnlzJqeNHs9JI8fz4Bsz6ZMekV2tKGY7OAbz8cNG4UtIkJESryeskqAFVVH4fZVTVfCpn36J2sfCxbc8EDMUvXavwvKtVQRCUnjeQIbyIg2j6qAeFqqMC5d3JrJfqYpCSmY2OTk53HDDDdxz990MO/Ukrhx7Fq8+9TDjzjmjXpWKxkABPJrKke2a4jN/L1VRYtOiTFrQQUPhxwAkD8yhZ0pGXAem5TEHaJnooVOLRCdqokB6dn/GXXkjKZnZdulfn0ezJzlNia1w2qFD/dJibqiKUxwiEntM7q6O/N2rg7qZ9Apu8lZdSC6mIr1VUdcyDVqLdgNwyTX/cUqNm+HE1m3a4jZRzrlwAn1MKooVTcrwD8ZnRhC9Pp9U84hxTUVRuHTgUQ22g4xCDqBj3xgLL0WRVTq9Xlq0bsvkV55hWVF+hFfVNJKtVmnERGOF/kvzFrK8OJ97Lh/NzIlPcf8Vo5j00G18O2Myj/x7DMu/+jD6fn0+zr74ahTdGY+P7pPK+OtupU9qFj5NJWPAYLyuRbnD6xZ2lLZ48QLeyFvPvd+uliF73FQD+UyVdTohw50A3vCzubn2VnEtMGl/5vE1lRXhBy2exkFH7hQQMg+pPN8ZUySdTAlzHIl6nm/p5kpCumyPpZsPLLmwLqQ7ifRmO+9LaDvRo9KheQKWJF8kWjdxHEmR78T+qpD8kfjTDeQNGzbw/PPPU1BQQHl5ObquM2XKlD/7NhpETk6O1MZ1QSDo2OlwUjKyaZnooanPgwJk+AehugaDpCw/IUOEJZpJA1FaOH3TM7nyjgftpJ5XHv4f5UvyMYRgc2UdWyvr2LGPesRLl+QzeeJzLMmX+sp/QrIr1ptjd2xrRjUNf+vFFgJ0Q05UAdP7ZwjBUrtKj6uEaMT7KJD6pZsr68JWxw7NwKGV1DsHRhoZisLJI8dx3+vT6JOWGSZ3FgqFmPHq06wuld4SVZWGoWryR93eVJ9m+f/CcUSbJpTO/67BFty+eSOrSgro2ClaB3f7pt9ZVlIgJwAcvqX1zELAht21dmKaIDrxIRKqpnLGmAt59r1ZdhhdUaB3WhZX3/M4OWeOZGnBQpYvyZfGjuU1Nj2JPk2GmlEUktKyuP+1aYy/9lbuePo1UjKzoxZ5lWvLuevSUQ22A8Ceil2ISC+/CUMIQsKwn7s2ZLi8Y/KiuiE4Jq1h/dP5336B4YrguBPqLCqB28sUDAZt3dtgsPElbGPB4mCGLbwUi5IT68EPonH87rXwSz4en4/eGdIo0KwbcqFFgofmPo8dTrfv3bz/BI+GR43QCQa8moJXxaRbKK7e6mDbtsYrdZx+wQS8voSY31Xs2CZ/e0Ny8AO6xVd1e5KhppFJz4d3cwxVQw/xc/FiurRpIpVZNBXVlCXbvWunbcwpqkrzlq3kvxWHatUvXapFjLvmFp6bPIuklLS47+XY1M6kdG5Z770Frp7GkX3TeX5VTDMbVVE58/xLefHBO3jjmUel97UoL4KCJmRXcr0vDUHTNLIGDqbMxTkPBQN8O2Mykx6+g2AwgBCu9q3cDkD3PmlcdOOdTLlogP3V2uVlvP/i4ywvzsfnUeiXkc2UT7/m4ssuZ/i4i0jK6O88jxml7eeiHcx8/QXKivKsEd/JZTEEdea7HMu7GgsW1/6/d9zN8+/PIilVRt0CLiPc+l1tBA8ufQGAXLPMvRDsrdxt94NvVm5j1TaZILd6216pJkG4lJ8b7mjBvhQJioWyTXuoDujm2CrsKHljseDXXbwwf23c79s29SGAOT/vYOOe8DYN/EmFWfYFf4kHORQKUVNTQygUorq6ms6d918k/4+C3+/nzemfS21cTUNRVbzmatPdYRRFITWzPyef7RgB3ftlUBUIMupdJ3FNlq60vKLS6ywMIcuTBoM2zaKiJsD6ihrW7mx8iCc3N5f/XjiCt599lAf/PYbvZ77Pj7OiV/YHG+6JwuvzYU+0igqKwpJF8wFpyO6pDclQp7naF8iEKkulIRhyOIPOYOesZDfsrmFrlfNCucXzLe9BvSGaCA+yqigcP2wUvVIyURWTV+mVXn1hGJQtnsfD/x7LytJCWflNwfHwWSFoRcGjxb5mbm4uC75sRDhZwPSJT7NjUzQlZvbHU7n3stEU5C1ycSuxB0IhBHV2BbjoUF0s3PTkG9z4wBOmZ8y1plFgeXEh910xhskvPM6tF49gVUmBaSDJJUBTnwqK9Ci6w+2jr7iOvqnpKCi0TAz3pC+cP49QXQ2NQVKGX+qJx8D6n1c7z2+FzS2JJnObATy4tOHRPHvo6Xh83pgJdYqikHFE6zAvk9frDVN3cO+/r7B569Zn6281vge5a88++309N7oc1Z2TRo7n3knT6ZGSYWsyRybEJno1epmJiO5bshIVm3hVPGaUpVfH8P1UVUFTTQ5ljJ9yxIgRjb7fz9qcwq3Pvxvzu1amfrA1DhgCli0pYMqrz1HuUnQwTA5OQ73i999+tf/t8Xg445QTSfRoqIpCgseJnKT7B+PzJdje4ZTsQTblQFGwf9l+6ZKT3C89m0SvRnIcI9haZDcEdxGhSAgEPy8rDyukU7x4YZjR6I7KVcXw7MW6saFnjyE5I5u0/lLf2h5fhcAwDBlps7Zt+VkuwIDlSxbz7Yz30VSVlNCvULfXNK5DlOXn4lElXSU9qz+vvPIKdzz8DC1atZHnUVXGXn0Lj701k6P6OTJxU19+gv9eOILSgjzbG24ZcZb3VAjBiq1VrN1RzeY99UelWx/Vl1GXX0eftCwniqk7UdwWLgM5NaGCE474Y9UVflmxlP8N7U7rJnL8vP7jpWEGr0Uli6mGRDhf+EAQtqjC7Df7eNYvTA3/WMNZUJcUizfyoimFB56FdfDxp+sgH3bYYfz3v/+lS5cuNGnShJNPPpmTTz45ar9JkyYxadIkADZv3szGjdFJTH80uh3ZjWvufoyV5UtYU1ZE88QECn76DrWukjZDBlKVIJuveTBI+7ZtYOMuAPbu2kZ1MPzHrq3YTkW1hyY+yc07pldvPF5Tf9bjpXvP3uzevgWfptqTy0atcYbFp59+auorG4SC8MPM99Bb9Dy4jREDx51+DnsrdtG2fXuOP30YM1fsoQCgWRuUU26gR68kdm/fiiEENZV1BE2vU0UgAUMYeFSVkCHQdYPKQAjdgCYejZBXxavJQVQ3neGqAmq1F0+NzJzevb0SXRjUaio1QZ3aoEGotp4s3hgUiyXzvqdL167omka3rt24/elJfPzORErzFyGEQSgYpGTe92QkdSekS/pKXW0IoamoKgRVFaN6N5s3b7IpDhY+/fRT9OpGhLsUKC3Kp7zFN9Dv1PDvrp9J8MUxFM/7nqOO7AYC9AQNr0uv1zAsySRBTU0QIxigvnXvEV26ULNrGyFNxaMq1IUM6Y0VUDj3O9NTahAMQuG87+h8RBfqQgZ4VCrrfLZgfG1NkJAmw4CaoqDv3U1royVqdTUbq3fZ19M0rdHkwCO6dqVjp87ESi/9ZWkRNUldbM+lqigoPo2gISTlRVHMDOr6caTYTsZxJ9K5cyd+W7qEf504hK5du0aNL127dmXKlCnk5ubanOKvf5jLcYMHxtzfQmM8pJU7d1NTHZBqAqpCSFUwDEEgGIMGpKqc+K/hvHEQ5o71v/3KRReOp2u3rtTt3o6uKqgeD8KrsHFjdMSqckclHZr7wPCwp1aleUhn605ZCdGnqQQNwZ7EOvYAe7bLvl6nGwhDUFmrodTuYePGcKWDf/3rX/z731cxsZH33OnwI2BpNP+xtEJh9fqNHNU6EVWFNUtLePQ/VxAKBvF4fTz00pt075tMbVWANZVB3imIn6AH2Ml+KAojRo6ia9eubNm8iT07qwmEDBQFAh6Vbl27MeE/tzJv9rdkDjmRbt26UrVzKyGvB6vSfMiAoK6DgJBHRVTvZtuW2AaWUVlDoGIbEF+eEqCmYnvsLxQFzeMhfeBxLCta7MwnvXqzZ8dW6jwaupAKGMGQwKM55X/rhRB063YUe3ZspWu3rvzvmdeY/cXHLPjmM7PEtpfzr7mZzXtq+Aro+PsittY5Tp3Jzz9Mh0MO4czurVj234vQVVmgqUfv3uzdtY2gpuCp8eGtTeAQVaC7PLQnnz2GFokedm1zRgLD0AkFYOEP39Dp8C4ETbre3qCOR1UIaiq6T6NWU9lqDsVGhNqMGxs2V8rfVDeoMUsfW5SA7wvKKN7sGMg3nJzK59tiJEgeRGQPGoJetYO2CSoV5rS/e/tWex607tOnKpRV7qB9swTaN5N9pmKbfJaQIdWrNnoaZzfEQsW2SjTVjNAJyfGvrNl3XnP1rm3UxTgu55VcbvfHlqKsrapi65bNBPZEK9/8VfjTDeRdu3bxySefsHbtWlq3bs2oUaOYPHky48aNC9vviiuu4IorrgAgMzPzL/Ey71Yr2RDag695a3ZXVPDxx1PRdZ2Z70wi6atvOCnnWECuinxNdwHSMEhs1Z52Pg1wVkm+1u1ITPDQItGLYQjSjj2Rx96eSd6CefTrP5C0zP408WokeKThEjIEnTu3adR9Dhs2jKeefhoRDKIoCuvWrICUY+zvlV/yaN4ji8rQweVd5H73hSyaoqn0zcjmwcvHc/WHi/m5CozeOaQNGUAzrwddCHZp1fhU6Vpq0SIBgcCjqrbOad3eAAHDoGmil6ZejUSvhqpIiSZJb4D2LRJo3yKRX3bspUW7RHRD0MSrodWFMGpDeBJrgDiSWBEGckJCAunHnkSzNh1J8Mj7yMo5iVZt2nLbJSPlROvxknbcibTpcIhdPjO4N0CCKWHl01R0n4dOnTrhjYiPDxs2jKeff5GGiTIKDL8Ho3PvmN96mrUkZfCJKM3bsXZpEWuWLCbLP5juyRkIZN9rZlJ9ShYsoLJiJ7SIX5Wuedt2tOnQHI+q0qtjc8o3V+IL6uiGIHPISXz0zquEgkG8Xi+ZQ06maZsOqCGDRI9KS5NbBmDUBNFUaSCrCgifxiGHdqJ5QviQouu6TbdpCFXeFiQMGEmsCgTdklJJbN1Oei7N0L/Pq+ERUvNbUxRW5C2iPm5RamuDm884CcMQZBx3EmcPG0bfTvHD28OGDWPYsGFhnxuDhsaqtXUJ1FYGEELgURW81kIjqOMeMyzUBUNc1P433q5opEpFHAhD8MnkNxl95U0c2TcNn6rSpqkXr6bSuXO7qP036rvo3akVPo/Tt3ewE0MI850RdO4sk8w2hOTYV2cW0Wji1fDU+GK2xWGHHwYxCsbFQnFF/MXe/fO38uG4dFRFYfXy5YSCIekkCAVZtXwp/QYNJUGr46GvSwjGIzPbcLyjgwYNonPnzlTVhajQKqkNGiiKQqJHZUn+Yl59+jGCgQDLSopIy/LTKyWDpj6PvUiWlA/DXkgYCZ64faJ81TzWLS2CLgNifm/B16otEB1latPhEK59eQrJmf3pnZ5Nad5C0voPom96NpoKzXwegrpcBNeFdOmUqAsBDSXXKQRDIVq174gnoJN67IkcnZ3DqWMuZHlBLqn9B9G1bzqBkMGlmspXTZbxxk9T7aP3VOziwesv5Z7XZnD7y1NYvWQxvfv0IWvIyTTxavTt1IJmPg+aqrBgwUI2b9wAyPnO26odvgQPNbXOfSpJx+P5JRf/CafQvF0HaoMGuhDotSG8HpVEj0ozn0aiSf/RhaA2wcdR7WKrUawP7sKjykW1qAsR0gVGUGdVSSGTXcYxQMu27Uk99kRmlDTQZPuIYRddxa8rljL0jDM5Y/QFGAKaJe4Ac9Zo3rajbQ/UBHXUgI5PU2iR6MFQVTp3lvf5W2CXdNjoBpqiNtpuiIXfArvwagp7A7qZ0yQIVAeAdft0Hl/LduwJxNaSXlMdO0/G16w5HQ85lPbNY9Oq/gr86RSL77//niOPPJIOHTrg9XoZPnw4Cxcu/LNvo1FQVFhdWsgjV57LdzMm29qYoWCABfPm2vtF8stmvfkiK8vD3ybJw5X/NsyVWY+UDM6+9FqO7itDSY0NMVgJbZGQIRED/cRrYcglznMsnkb2zzMade59QTAQQAgDPRTi2Xtv5aX7b8Hr0rwN6Y5CZVhyBe5EM8mfnvn6C6wqKbTpEtbzRGYt7w2EqAqEHJ6hGYYKGg2EUV1czqSUdL74+ht6p2XavE9VVWjqldXAHnpjOudefQv/c2X5W3xLS+fVHSKPdV2/38/YCy5tuBEVBeIYxwDXPzaJ7skZrCwp4N7LRvP2s49yw/hzWLok3+xTTl94ZkllvcYxyMQn645bNfGiICkTqqLQOzWTp9/9iMtvvJ1n3ptFn9QsJ3yM6/kVyUO2PPsyOSl26+fk5DS6tO0TP/7MOiO21+d3rSNVdSGz7wibz24pGZQV5nHfDf+Oe+6Te7Tnv6dbtAL5zu6ubYQn7Q+BEtaXQCa4xaJYqKrK9k0bqFhRIBOGD+iyCqWL5nLfFaNZXVIEikJTrycud79d0wS8EeF/5z0IH/fC3g8Feh/S3PZwReK444agfvlEo275tcX1G3IW1SgpU1KkZMKoLEVuU0diRI+ioKj2g+wwEwCbejUObZFoL8gAlix2qqEGgzKJTVNl1C+lc0sSzZLJzvsS/5K5ubmcecqJVGyIz9m0EE+3eJfSjJDi4aPXn0c3BMMvvZZeqZl2ohVY84Kpf2wIVjRC6UXzeGSFRRQ8qip52IpC75RMRl9xPX3Ss+1kZVWBk0aM4/Cjwqt36qEg8z6fwfLCXFq0acOy4nyWLclHVeRYm7d4EVdeeSVDh57A+l+chGYhBN8tzGfcB0ucbafeyH2vTaNvelZ4ojKO5m4YDU3IglQNwTDHUN2kEiwtjLZFPCp24ZuDiXE33MldEz9g9LhL8GgKHhW7wiA445x1n+D8lm64qYgHSlOI5DNb7bOv+HbVNh74LnaS+vSSaMk4+Hsm6f3pHuQuXbqwaNEiqquradKkCbNnzyYz8+B3voOBkvw8pk982jQE5Y+nKAo+n49TTjzB3s+t/wkw/eUnmPnBO3DRq/a2n7dXk9yphW3Y6UKYvFuLY+ROOFLqJdsXb9jN4a2bcEgLudL69vsfpPSQOVmQdHzY/pqmsXvHNuh6YO0RCVVVbfkkQ9f5fMq7KGccAj0GA9jhePdAjctINoSgvCiPmy4YTjAQxOP18tCb00nL6h820BnINt5WFaCixjKOXYMiNKKMrDDvWeP6ux7G7x/Ii9O+otz0hvRKyZSJOAYkpWXRvV8GtSEDr8dStHASquwQP/VPgKuWlULm0HrvytvmUIL1jAtHJfUDYFmBqzBHEIoXL6RnSia6maSU4FHYrtaf7AOOtrdT5EQ+h6aCbij0S88mNVMmzFTVhRxauWK1gnxmj6qiG4ajahGHc+o+tiH8VhGfN/jTLzuorAvx3+OPAhTzNxcYioJqCL7NKyV48atxj1dVGYWwniLB82cUbY0Ne3GlWG0jea61EcZo3515rKir4tuZ70uFleH3H9iFzfEhGAiwvDCXvhmWJGDslrDUKcLvXbH/VhQRtl0g7G+9mhr3dx/g93Nyr0P5evs6aH9gg1JNIESzBC/dkzO4c+JUVhTmktx/ID1SMqmsC1ET1AmKRvzS5r16vT6GHJcDyD5zeOsm/Larxn7uDL+shhoMBvC6ynTLSqKOwejOCYj3es+ZM4dgMAi1DVdtu2ZWfFm2h8sF6ktPMOM1L/e+No1kU4EmjJtqzi+lRXnc8+qH4D+v3uudcNZYUjKzZdMgEzAV84OdimH9bR7zr3GXM/H+W+xzaJrGnM+mEgqGEMJAUVRmvTOJF9//mODvzTnl5JOora2NMoxmz3yfdz//AU66Nmx7j5RMe46sDekowJY9dRzWqgmYfGRb9s10SBVv2E2/Ti3DNOkL11c4cwhygf3rzhr+8+lS8EUr8cx4axK90tJ5Y3Qyl08rxjhYfkUzb0ZTQdHl/OJxvTMhIfCZvcrJO1Hs39OCO0F9P2zZ8FuyerHlgBKiwQqUsfBe4b7LzB7ovf8R+NM9yP3792fkyJGkp6fTr18/DMOwqRR/J+Tm5nLRyDMpXTTXfLkVPF4fZ469IKoQgJV4Y0FK5ITzb+7+ZmVY4hnmymxzZZ3LqyrCDMN4MIQIK6AxYPCx4UkUEbj94ado3b7x8kqNwX3HHspFtz2I5vHY1xVCYLh4aIa5si8ryufjN1/k2+mT+eiNFxxBe5BVq6wMaVNWyD2jGKZVbbVHQNfDtG8to/v5eb8yq7zh4ihnjh1Pv4wsFi/K5c7LRvHe849x28UjWFFSgLvYgfVMTb2OvqkCNPVoWFOCpir2xBgLJ51xVoP309DkbQ0avTMcD5nM8JbFZ3SkykdjoZkJMm7DVlHMqkhe6ztI8Cj2pGJ5XS11AulVcowlmagY+znmzJlz0DwDRRt2c97kJVw3q9xWLpEaoNC0W9+4x/X0VTIquZNdwU5RILEhObw/EFZ/srS0rQWXV1PDioiktdIxQjqGIf80OZDqXgvfh62ylLWqqnaFSMu429d7t/5t4dCWCU7/aOB8ixfl8t3nsxplGDaEy6aX8V7h7+iGoHtKOmddcg09UjJNuSq4YkZZwyf5rQSl8GP8Q0/jxQ8/YUCEjrX7mU4cciwvfyg17p+fPItkU8LNpzkRAMWyJGPq2zjIycmR40zdgWvuysimlAurMSXvLGNSIGxt3R8LyjAaMI4Bhgwbad+9JUFovfuW+oqV5Gn131NGjuOKux6je99U/ENP4+QR55t6/6b2upAJ6UWLFzB37k8EXI4nN5bO/xYjFF151FKyMJBz5w8/7+DGT5dRbpWLxqVsZD6/lD0Nd564K7xa81DJxt1x2+K9V57i3stHk//lNIyK+rns+wLhWm1YSd9u7fxIj7gVAYh1Jmff/R9rl22utOdW9+JKP5hKOvXg7+hB/ktULO677z5WrFhBeXk57733HgkJfx/OiYU5c+ZQF6jD6nBCCI7odhSnjxgbsxCAe1Kw5LEiYf38hpDe1a/nF3DDJ0t574ciOwtf2PvKf/1eUROVZR7ZjVIysrln0jT6j74SLSHa69PqiB7knDmyEU/dOHRp04RurX0ccUwvTh5+LseedBpeM7NbCYUrTZQV5XPj+HOYXtWFN/Z0Y+pLj3Pj+HMoL8pHCEFy/4Fopn6qqmr0Sh8QFiqyBNDd2bzW3+7Q0vxfGyY1erxeThs+FgWFeXPnusrlBinLX4iqYMtbWd49T5iRKAcwa0KwDMh4Zb2T+sQ32hqLLVV1GELQPTmdu16dytirb+HB16fTOzVLto0hqA01nKUP8OxZSXg1hdaJXtIOawU4mfdOSNhSWbAWC4rpMQ43qDXXxOgxvdCxwnu2EXAQsW1vwM7Ot/5/6OHd4u7fdPH7NE9wFpCqouDR6vf+/5FwipaHe+a9msoNxzpyYy1btbUXRaqqUVN1ABqnixx+6FnjJ8gKka77afS9uz2Iru2HtWrCYa0Sw6Ir8TB/3lxZVrzuwA1kgC+Xb2Xl9iqbRSUE1Lqicw3h0q4BHnjxTe576R3pfY24eeuZm3g1DmvVhJSM/oy/6kb6mR54RVFI7twSVVU4pEWCXX7XMiK9cVYgfr+f0884s3EUkEZAURT6ZAwwtZ+d8dNyxiCgS4/GjUk9UzLDPcWK0w7WosqOolmUIeDkEeN44sMvufeldzjhrFHScaM69BVN08gYMIghQ6RKjBJ2EYktAyfE1K2e8uLjXD/uHKnzDKzaKvvPb7uqqTX14m0DT2BTBCzohuDn7Xtd1L3GSd4JPUSwro7vP/qgUW3XGPTvLOdpJaJd3Xx/t+SpVCwybFvAfdv2XGjNlfvpirWKdjma0i6H3p+AfzzI/4+Qk5ODFsGdXLt6BdecOyxmoQC3cZA6/hZUT7TRr5tLM0MIXvi6kDd+kYPAlz8t4Ktpk236ATgvwObK2jA95XU7q6M6bHVAp0W3Piw67FT0q6M1pZ++/WrWr1nRuAdvBIQQ/LyshIf/PZavp7/Pgh++5fjThtGzbwoEnAza5cX5LFk0n2AgCO2OAF8T24tQvHg+hmPlINocji4U1q5aYXOe3LwyZ6CILjsaq8RxLBiGYRuCQ4YMcZXL9ZLaX8r3LV2Sx5RJz7GypMAxGl0TgII0Dr2aNCB7H9IibhGCIlPm7kDwwHer+XGNqS+anME5pofMGgxlGwgmF0Vn+kfi8FZNHI+PZfCa31mGjVv6zfGIKTSxPOmK4+1QFQWfR+pD12ds/hGegSnFGwiGdLt/1Hf9wSecbHvGFdek3pBm9B8FxyZw9StzMbaqxOGHTnr0Ti6+5X7GX3srQ88ew4F4h8K1eyUVJ5YnuMHz4KJZxDiuMedq1870kh8Ez6mFR2evYd2uajOiJDtFYyfc39f/ZlMhYt2/1XcEMmrUr1NLVAXKi/J556VnWFrkjD/tmvk4onUT29t6dPtmHNYqMfqkJm646SZXkYwDg2EYzPlsBqtLCwjoDodUIKtM6kCXYxpWN1Kn3srKkoKwKJPtLcbtUXYbd04eQhOvB02VVLVLbrmfI3sno2oWm1O+dwNNLeIJEyaYBUOchl8XbMqIy2+I8XymhF3ewrDfSkcWhLENO3N/y7irqgsR1GWS4q6aQNS8sqc2RP6q6ORY14URQrB2RTlR72CoHp6zWZQnEskJezh8xSfMnjmZj15/gdLCPLNdLEUi8/6Fs8gzkDJ0ttybdQmTLmI+MELAkg27CYT2fdEV7phyFhj6n2Uh/w3xj4EcB36/nwcefyZqxAwFgzELBWzZ6BgoS9oPsquyhR0bssoGw6INrslBwCsP3kZpYZ79+rn1CN3YtrcualtVIERlPQlHG35ZzasP3Br3+31FbW0tK4oLw5L0vv1kOstLihC1znOV5eeSNmAQXl94xSQriUYgWLJ4IUFfC7joJUTOpbz12J2UF+W5QkvWIBFuNH865V1uvmgULz56L7dd3EhtVSFYsng+igKDBg7kyXdmctH1t/H42zPpl55NeVEBV593Du8+9xh3XjqKVaUFuAduq4yuT5Mc1ni0Agv+wcc17r4awGuL11O2qdLuO45nRLZJIGQwMbfhkq81QV2G01237VTGcyY/20B2FYNIMD0b7kmxiVeVnmTih+kPJsXCjU/KtzB7jUym2rKnlu9WxZdXO2PEWNsItW4zwaPaHP6/AraH0Wbtyvb+4dPp9j7BQIBfV5Rx7oTrOfHsMTTU3+qDx+OxtXv7mdq9Dr98H+5bcSg5SREyWlYyW0OUja+++kr+4yBQLCwEdMGbeevthaMA5v+yo1HHfjPtXe686Gzuu/rCsLHHgqLIAgdHtJaGrs+jUl6Uz7Xnn8OrTz3MdePOiXKahEda4jfIQaP6ZI9CGAbfzZzMI1eey/LiApl4ZvHOTbpbg7/1mlzExhUsc1WktIYMmw6kYnJnVVckxCx0o0i1j2ZeDyuKC3jz8bv5ZWkJhh4CIdD1EJuXF6KqCn6/n1deeYUzzjgj6sZ2bI7msLoLiKzetpevVsh33qIWGELYtD5p4Mnv1u2qZt0u6bhxRyOtY+/4pJhle+prGCeKHEZzWrMInh8Ja2JX1lTmx9bwLi8pYuYbLzLpwVv54MXHuOrcc0wnT3i0IczYd8+HJm96T23Q9hzbtAizHfbHqLUOsWoTWKiqOzjlqxvCXxXRqw//GMj1IKlPX3qnZ4dt83i95OTkhG3Lzc3ly+nvh20LxeDtWPwhg4iQuKJg6AYleQvDkgdAvtBBXRCykuFcqzwLs8o2c+fXK+M/iJmhrVXF0dKMg76tYq9Cq6tr6Z2aGVudwOVBTsr00ycti0fedBQ0Rv77v9z32jSS0rIQAvpl+VGbmIPOYX0wDIOS/IVhYTDLMAT58n7y4Ts8/b+byJ8/h/dffZ5AXeOqHKmaZpdXVhTIzB7ABVfeQFJaFqqiULx4vq1UEqyr46fPZ4R52SxPis+j2QZlfcjI7t/AHo3HYz/+zKzyTa72kMl5VuGVxmB1aSGRZn2/Ti3DvMPgGDjucLnbkD66XTOZUGJm7yuKwqEtEmnui875zcnJwfcHUagsbuF9363i5x3xNbAXLlqMojpKBKqi0K6Zj25t/1jx/3hwe5BVVS62IqvWRaJPWiaJTff/fg3d4KTh5/HQGzPonZoZFhnZFw5y55ZNTGNIiSqv3synNYqDbGtIx6mauL9wewVrgwYvLPi1/gNm3Q8vnYvlels4+yuuHHsWubnhSgZJh7TgqHbNaJnoLPRl7oSjahTLadKYCX/u3J8QByO2PHg8+M8FRSUUCrI0fyG6IQjqwubmbtpTyys/1TNPACAdGH0y/eGJvIplLCgkmFr9CZqCYlHQcCIiAB4NyvIXEqwL4La2NM0TNn/m5uby+eefR93FDzPfi9o2+sqbeeKdmfROzeSOr5yIqG0UuzyrlsFsGY+7a4L8vrs2rI9YdIJN1fW3v9eljiILYplo4AcefeVNnOpbF7XdKl4DIAyDUDDAkkUL7AiXvZ+L5mAt+twzsqy2F2E8h9u2+wTLD2W1i7XwiORw/xG4bnA3co6Olpr8q/GPgRwHubm5jDn7dFYU5aMoKocedgQDhgzllSmfRXGQ58yZI1UkXFBieJBXli4xE+wMmjR16TMqCh6vl75Z8rxucj7Apj21lGzcw/ItlXbndWNmWWzZFPf5AYz3rufwJo1/fZa/fFPM7YYQLC8uYOApMXRhXQZyj+QMDAFH9k23t42dcD09UzJtz3BSWhajJ9xo3qesVtgnM7wd7Mxk02D+8evPYj5fQ+iR1M8sr+xQBFSXXFn6gMGysAVysJnzyVSbvmEbja5//9kh+mnFm+yETilNZJZgbmQSxfLCXFCk8WDB51EdrrEZ5kvwWFJVMuxvDRKKAhlHtKZNU1k10YoGKkCbpt6YVBO/38+MT7/a/4euB1V1IVPIvv4FUnlhrkyII5xD+1dBVbBpKV4VuzgQKAw9ezTa65fAqxfi8fo4ftgo03hWOKZDwyol8aDrMsLUJ80yjs3rAk29jRczr97uGQAAmIFJREFU6twqMe7CsHmCh/TDW9OuaUKYakAkLr20EfKH+wlrXNEb4vVW74a1BVE0j2AwwHyXhCcQtRAAR81C0zQ8MaorWm3cENq1a3cggYFw+M9F6TsUj8dLUobfLn5RaxbQeW7uL2yqrf9i7Toewv1vzKBnapYdOTq6fTNAkYo+1sIYJC+Z8Ge13zEUUrIHobr14RWFM0aeGzZ/zpkzB13XYWc4RUyceXvUvZ19yTX0SI5WvPpgyUY+nV8oK7656RMivOLqntqgi55nGc8iLkXOwvgb72bctbfy0OvTyQ6shFozF2DLz/Ue1ydjID88c3O9+wComocMU1LPDXe+TU0wZP9bRP1xe5r3nzMcOc8apqd6+daDF+mJhzN6HxJFaf074O93R38TzJkzh0BdHUIYCGGwfcsmRo6/1Ja+cSMnJ8deSVvokZQStd9jN15KcUEe1QGdhETHq9a6wyHc+epUaTjanmPnb+vfe039X1zE+ZBuNH58rduLp7LxWbjWpBqJmuq9zHjjRRZ+82n0l4ZzzMriAgSCimqHQz3inUK77LSBVCFISJJUhOZt2nL7Kx/SIyUTcHmPcS8aBENOOTPskmeMn1D/gxTJ+xw2ZpztFQU5qHtUWS5aVRSSM7IZNvp8exWv6zpLFi0w91XsFb7NSf4LDC0BCENQG5KVpDo08zU6nNY7w49HVamJVbHN5SE/qm1T2jfzyaQRTQnzDFlwuMoNewwz+9dfBGF/8cnSLdz5aTE1ev030Dt9QBjFwvrzV0FBkWWMsdrWacM+qVnc+/wbjL14AndMnEpSWpZJaVAYldQWZe5b+39d03DRFOn5a+JVaeLVSDo0fsWxeOepD0e2a1ovreCKK67ghv/896C/QL/srGbeLzJZt8E1Y5xra6rKCccf3+C1kjOyeemDWUy46Q5e+mBWhKqRk8Sm1TPD5ubmcvXVV0fz6A4AYujVjH/qQ7qnZNo8UpmwSKM6ffNWremVkommQNc2TenZsblU8kEq+qiusc9JxgznJ1uX6ZeexYQ7HpFKR6pKgi+BM0aMDbteTk6O5CDPfRtq4hR5sp4NOV/EUnKYvlbny2nvxUjAc/a1jGW34VkXMupdzAHs3bOLMVdcR++0TI5q5YWXz4f3rofF05wbi4FlhVKaMwquvqcoCsNGnUdyZnbUnOL2gNeFbDexQ6sg3Gtuzan7250s20M3pJc7pAtmlm7izRhloQ82LF39vxv+MZDjICcnvMCBYRiUFeXFHFf9fj9djuoRtu2wrt2i9gsJwzS4BKE6R/N1986dtqcUK7xhfhfOLXLCLhbqQka9kxGLpsJmKditeTyy0pqJJprg2PWfoKwvjX1s5Xb49OGYXxmGgSEM2+Nqw+W5KS/IJWQILp5WErZLtSnNJQS8kbeedwqk96BKacKuuvCVsSM7I+z2+NfY8dxw/5NkDs7hPw88SWbOKfGfH2DO6xx7xnDOOf8i6Sk+vLXcrliGr5OwdcaIsfgSEmy+W4Z/EAkelX6dWroSqszDG3ijC/MW17/DfuCXHXtxD/ttmvriSP9Eo0dKhl2q2Q23VFPGEa1pZlbDszzszXxalFFpJSGBk7T1V2BDTf3XblKzg6TUDJsfi6JELWb/bPTt1ELK5CkubidO3+qVmsk5l15rF4iZ8drzrCguYP4XMxAbl+/XNT1eHycMG23TIxSkPN/+tET39s05sm2T/boPC61at479RcFHB3TeVxf9RnUoxEfl9UfVfDt+jdqmahovv/xyTJWiSCiKNJIvuvrGKKeJZr4baYe1iul9tvDuu+8SCsV2Qlx41H72UUXhvXUeVhYXMOP1F1heUsCK4nyee3sqa7bHpyFZ8DZrZY8FiR7N1LN2aBZN7UIWbuUbp2CQzFtQKC3M49l7bmbtijIuve0hxl93K4+88hbJEW3l9/uZM2cOffqlwLol9d7b8uICBMQpla0w8cHbWVqUH6FkESnrFi6JVhPUG5g/p1BbtcdezFbt3oWiqLBtrTPXLfsh6rD3z0ulj1m8JupONQ8erw9V0/AlJHDGyLF2W7rV2d32vS6MsPnQ+t55Nsdg3t9iIVb76IZBpZnY+Ouu/S9bvS/QlH2jev1Z+NMLhfx/gd/v57nnX+S6667BMAy8vgSSM7I5onVsHmCrtuH8mZqa6KIHHm8CSSaNorJiFzSRZVrFUVm8XrqDl7JcOwvpHbaFu3GMZbc9FNCN+jvWQsmNPuKo7mz8bS07Nv8O7WXltpq9Vcyf8SYi/Sw4Ijn8uKclfUL5eXHU66bOfhlUDY/Py033PMzK8jI2/bqaxbkLwwp2JGX4w8L5FgzDWQhsrgxvp+dXaxzSNJ8Og6QAv5S/U8K5VQJOG30BZ469gKAuuPPl98F7TNR1LPRO78/1D78on8c1ANlhQQW7iERyRjbPT/6Ygtx5pPYfbE9+Po/KMe2bs2KLE25q6IVetGAukF7/TvuIO79ayVNnJtGpVYI9UDZU6UhV4KHT5G/u0WLftEJsR4iCVOyQv0Xs4/5qykJ9uNvfzpaksxc2f+0tkeDR7KI0gL1AswpvWEbz6tJCHrv6XIKBIJpHk+9W+6PinzgOkha/wKhXp5GUlukk0tlX2ne0ahI96e8rhgw5DvXHX6Orim9bd8Dnfjd/A3N+jp+gpxZ8RGBhuNrPgKGnMW7CtVwx6rRGXePIts1Yu1PSMyKXGc0SPKSYsm/7i/cfuQXGPLZfxxqGwYP/HkMoGJTSmwoEr2lcJdWmLVrG5MNb0QdLZcGiWPg0lWXFBZTlLSQleyApmdksLcrnmvOGEQxIhQevz8ejb39Enx7HxOxxfr+fx596in899Um9pt0jV5/PfZM+5Mg+McZURUb8vvt4GhnZ/R3ersugtFU9hEAoipxjjXoisNUVsPADPlkIXbsdyWmjL6BPpl8WignUOcnHPy/miPnPs37wdfahXk2lV2omF99yP69GiLUkZw9ixBn9WLF8GccddxzJGXKOEYhwD7L1t3AiAAKpKqEKxXGUWE4116JgX1AdCLFme7XtmNMNp63+LBqhgL/lRPKPB7keXH7F5Tz/waeMu/ZWXv5wFr2T0+Jmvu/YuiXsc+68H6P2ufqRV+iZksnsme9Tuzc8nLRRa2frIOuGNIuLN+6Owzdy3oCALurtxFaBk8OOPEZqj7qtnHnvNqgwEGUcf/Ukl543ilGXXs39r01j+PkX0axFC9s4dt9K95QMgjGKWDhJErHvvTw/187GBYdX6CwQHPmb2pBOYT3GMcARR/dAUeDQ5gmkmvq/VttYA79b3qxfRhbjrpQ6p+2aJtC1jVwUtW7iDXu+hvxv/QcdHBWLSOysDoT9jPXpXp7YvT2vjEjmmPZNAUfX2Y1EM0ks2rMc7h1yfx+ZwPO3hYJtFFq/t6b8lWWmrdsyw+9m+1r9UnV5uFcU5TpFdIJBydXcD+/Q2MuuoUeK9KInaLJ0sMUz/6t+vAH+gfTLjq5aljLohBh77xvqM44BxNa1EApfmO/cunmfjIF2zXz2+5/SOZob7qmPW2HiggsuiFsDIBRsXOJxLBiGsKtu6qGgrNa3D7AScC1YrWKNBw5NTWFteRF3XDKS955/jNsvGcnX0ybzxnOPhV0zZOrMy3PHbuNEj9qg7F/wyg8pyM+P4yGVK/hvZ31IaWG+aTSKsPnSMo7dmvqiPiNQcdrgm48+QFGkPvQdE6dw4vBxtva/LyGRcy4Jr/q3urQQgKrdu6DCKWDVQ9nO1Sel0LNfCiMuu5Z+6dm0TJQLKo9ZxMmCMCyqCPy6s5rNlbLqYCDk4gqbnuOaoC4LxJhu5901wbBiYvVhb0AnoOv2nOrOafmzhocNu2v/lh7kfwzkeqAAfdOzGXPF9aRk1K9IkNYrwrOjRntZuvTsixAwY1MCtO8W9b2VzewugBFeg12EaQcD7K0L1duxLrzhdi67/UHy5nwrNyyaAr+Vwkvnoi77XhYhiJFQCKZhEUGcz/Qfx0kjxnHW+Evpm57FCw/fx3sTn7c9x8L1cq0sKWDaGy9GnfeX5WV8MPE5li3Jp2p3RdT3SZn+sAQ96/nBbVzLle7SJQVRx0fi+GGjUM1zuPlm7oG/V8cWtG+WYG53Kuq1beqlRaITaHFPEhlHtK73uhlZMfrM/Pfg3euit+8j3AZyi4TYgSC19Cv6+7bSKtFjP2ssA7lLmyZ079Asqh85VQXl53ZNncncTTM5oHFtz8GrTBULS/NzzedS7PClV1Ma5B3+0VAUzAIril10JlJvOylzIF6fz8mg93pRKjbWr70aA9I4tn5LeY0mPvUvnZAUIBhDfWbAyWdG73ywESMZaFXZEm4cHy3XVh86t0qkc8smjTKGY8Hv9/Pjjz9yZI/e0V/uiS9b2BAMRUU9vDeqpqF5vGiHxTh/vGPNccWjKmGGqLVQjvxcvHihXXApEAjw9L23kD//p7AByuP1kuUfDED64Y6Dwo158+aGSYTGQ+semfVybHVdp3jxfBfdwC2ZaiWfOVSEt/LXUx2MY0h6HMWKNUvLWFFcAIpCz5RMJtz1KK9O/ZTzr7mFOydOpVuvPmGH3nfFGFaVFNAvy4/3g+ulNxq4NKMTLRM8uGU1BXJBJRfJTiNbJZ4NIbjzq5Xc8MkywFLvCVeskDrPhj1v/r67hrU7qhulh6zg0DoFUjXL+nfk4iEzzu93oHBLcP6d8I+BXA8sz1Oip+GA5J0Xj6BFjTOoqQnR4vBvLP6NksJ8KtvG9niuKC6gOqijG+aLbYRzb61Vo1sfcW8gRHVl/OSGlKyBrF1ehqGbg0DldpjxP5RANf8aP4GxV9/CiSPHx3p4ju6TEsXPqgnprC6VZZlXlRQyedILca/98L/HMuWVp6O2v3jX9bzz3KPcdslItm+KLnDRPTmDqrqQ/Zx2dq3lQTcHt7qQztL8hVHHR6JrnzRURWHb3vAJ2Xo0VVFonuChW9umkm/r8jZGNQsHaBCGArD9V6hqnEZrLCzbWiVX/Obnrb/E4aX+OInVRbnyWVSrIl70K28XAnE9WVOfKWVneooOb92EI9s59KJeHVvY/MTGeBkG7/gp9hc7Gy5wciDok+V3eZDlb+fV1LjVD/cXubm5PPLIIxQUNLxgA+c+VNdCy/bOmR96pmTy4vuzuOI/t/PiB5/y2Nsfce7l1zJsTzTnseHrOTx7a2JWUBqMIP1R0FSFjp06R2//E1xW8Z45GEfjPh46tUykcz1FQBoDv9/PcaecEf1FbSU8fdZ+nzc08mFGX3kzd0+ajj760UYdc4hWyxUDugCy7LkFm7/vchzIT4qtcy+rPaoYhkxqV1WV3inpnHnuhTzwxgxSM/vb54qF4447DjXUMN/1sKN62N7ZMCiKvZBMzh5oe47dOTtO9NH8LOCHNfWMw17ntzUMnfL8hY5DQJFJ3aMuvw4FmPHKU2GHBoMBlhYuoldqJvdOmkqTJpKz36ZbL9sYtKhf4d3R+WBFTt0RQgOpWmTNgdbzBF3ONcuxtjegU7ap/sTHoG4Q0EWYM6ouZNh2R+T7+EeNFgfsaPmD8I+BXA8s75lV/rE+Mv/SJflUlbloFVq0B3nplioeeu3DuOeY89kMAmaZUCsU5H55LONQ1qKXK8OqgE6VK/EuErdcNJyKHbH1j7/+8E1atm6D/ltZ1Hc+XwJtOxwqaRlfP2tvL89bKFfH5SWUF+RGTTaq6nijZSGR6FcqpAs7bKzEeOVWlRSaGr+mxiU4ITOc1W7IECRlNJxQA9JrGLlGjUcPcLx40V/aZagbdVV45qyk6JMfIGaVbeayaaWUmoNfaVFso8zj8ZKU6Q/zlGtq7FvwamqYR7xD8wT6dmph82Ej0TzBY7YptEpsmJPaPrhDSmtZ2P4bvPbHyX1ZsN5hJ6HQWfAeLOTm5jJ06FDuuusuxowZ0ygvpKJICa3I38Krqi7qD6RkZnPhVf+hX0YW/TKyGX7ptfy6Yik817jiOG13rrInZJ/mcLHdkYG/Apqi0P6QTmHbrhnUjawurRl6TPvYB+3eEnv7fkJRVY7qk2p76b0x5Nr+DBzeLR6v/MDMkaQMP91TMhq9f7f1czmkhTQKvZpizz3WWGi/S64//dKlzv1F19/G1f97BJ8vAU3T8PoSuPHuh7npgSfpa5bvrs/RPsA/kHPOu6jBexTAq8UVUdsVVWPUlf/l3tem0TMlK2yecCtDOdtlgl5j4fH6aNW2LR+9/gKrSgrtMXFlSQEP/nsMC790cbx/yUdVVfpm+VEVhd6pmZzXXzrFPC49do8WPf+4f3HLTA26DOSft1eDK4pq86sNJ+psRaCtZ64PK7dWsWlPreN0C4vaiqj7a0xp7n3F++en2U6pvxv+MZAbhOJaMcdH0aIFCJcBYMRp2voI9Fb4xy4oImKvgEHSC0CuEn2++AZKKBikbfv2tsC5JSslhCBQV8ukB29j9kt345l4XthxJ42+iMJ538sPy36AD6Weo1hfSigUZHlxPtkDjyUhMdGmYhx33HHhQ3rWcBRPjCxej2ZX1OvQ+Yio75cWLsQA6kK6GTZyNH+dhYPUaDykZ0r8BrWuZ3pEYoXVYxmAtoctxg+ecUTrBrnHFgSQ0imCn3iQBgFDwCOzpTpJUmrsSfCOiVPoleLo3qpELxLqg7MYiH1MM59Ggkeje4fmMb+3IISgR59kwob/zStR9u6/F72xKMvPdekfWwmHB3cgnjNnDoFAAF3XG+2FjKcA4tMc6oM7iqEqJn9cUfCfdDrowYbD8MVfUjH5Nlab5aubeD3OVRvp+f+joKrhiWCaAjt+/IC15UUMahKnoFEDMmCNhskt9fp8XPDfe3nwjZlcfuPtvPj+rEYpWBxstGvqC9/w8YMH5bxLCxdS2xge6u9LYdFUmgZ32UawO7Suqgqph7WyxwL3KKIokoaYMWAQlbsruPHuh5hw0x288uHHJGdkoypSTtAt/xYLCnDM0Uc3eKtCCDZpbWN+N+xiqZMcMpyy0xZXwK42Z0YhETSOfqCqDDjhNP5z7yO8+OCdfPji4zz47zGsNN+p8nxTyq1yG3z5FLwyDu2zRzjt/MtZmp/LyuICVKBb7W+Mqp3Pb8uLWVVayKz3Xg8ztGPBMCTfOOgq1HHX1ytt49hdWtv+Yz6b22ZYsz0+dSVkODaG+xjrc+T9DeoWu+0PBF5VseUD/274R8WiHlg8q8YoDWf4B6F8vsAxAWJ4kBvCsf8agRBSBs2rquh25xcglLBVXtCQ1fVCukAPxuckapqHo5KSOU1TCRmCzsf04e3H7yJkHmNxh0PVLjHwTx/hy58XhXt/N62Ep4fJpL+EBJLSskjOyGLGp19Rmr/Q9rwcd/HNWOlPYvAFDOnSksjguoGKpqhccNN9rGjTFiI84Hsr9yAEBEy9X4+qoBs6iR41bHDYUR1gwoxo73ckLK9hLP5t5Etpe+8UQMT+3ffFgxyVya4c/DXpMUn9oLAoanvPlEzTyA2fnPaFe2rxYmNFArq0aUqXNo07T2XFLhBuz6CCMAw8Xh+x0uVymmyjxe51fOaLLgywLzg6JTvMO5Hg0VAVx6t0MJCTk4PP5yMQCOCNUWkzFtwcbvc2TTVl2AzheO5c6haKAieNHI8h4LvP32Bj2nkE2nSJex0jFOKnz2fQPSUTr6YQMuR5hBku/2sIFtHQQyGmvvwEHq+X484aC4cPj94pGK0MtF9QpJf+klvup2dKJs18GoMGDcQbR+Hlj0bWEa259Gh4w6o78UveQTnvVN8gpk4paXjHabejaR6GvD4jrIqmG5oqKTqdWyWiKgrrK6ptJZRflxbxnwuGEwwE8fp8vPzhLJIzstGF7MMWrau+vqYqiktCLj7imbQCeGHeWm7KOZqQIVCQ5bVtL7KLnmcIQUgIGuM/1jweRl92NauWLHYSZkMyt+GI3mn0yx6Ix+slFAL151xyho2hSfMWfPn+JAzDYLrPx2W3PMDrj91FKBhk1mvPoiiyv89851Vmff41yUOOjf1MQlIaI8drR/fY7SmWf1vlxaUjTUYBKmoC1AQTaeKN3b4OddM6lzxv2aY9djlvgDdHJ9MswYuqKDw/f20jWm/f8Hcajyz840GuB4qiUF6Uz3svP0NpYV69hkXXNk2hrtLZEMNzCqC0iF9OsXtyBgj5YteG9IhVnWsFDFTWhiRZX4G27eKEJIGUQcfz+qN38dmU9/hmpszEPeGsMfW7j9ZEUydADhZDh4/j/tem0aNvCgoKmf0HcPvtt+P3+/H7/ZwxJpzPPK82xvMqMgGkcvfOmG365U+5rCjOl1m85kBmK1kgQ0+GIdhV3XB2tpVU4I0R3+vRoRm9Oragd0enUELnlom0aeKzjeRYL+yhLRI5tEXjuIdRGdKWgRwvVLUfCXwLf90Vc7vlHXd7IaHxldPcIdUDReu2bcJXFeZJQ3EWdwufuZ5DWjWL+d2+oEdKhhMexqQ1HGS2m9/vZ/bs2TzwwANMnTq1cTq65j24+4dlEGtmpbLwkt/Y7beqtICqil1cdsOtDM5Ki30BQ4eSL+2PkV5pVZEe5UPjqPL8GVAi+oM0PoIosargrVkEXz55cC687RdQFKoqpMd0ZXEB7738DGWFB8cw3VcsXZLPezeNbvT+GkZYxdKDgdRjT6BnSqbMVcBaREVDATo083FM++a2o2DJ4gUutZUAq4oWmzkNDn+5oXFEKmc0/F7W1kOLWPRbBUJIr2hdSCcYMmyvql2q2ZxDQrpoVInvUDDIj59OJ2PAICdh1uOlX9ZANFUhKTWTu1+dytirbua+N2Yw5MyRfDl5EnoohDAMgoEAC7//3ClLbqqKGIZBMBgkL3e+TeGMxK6aoJ0054bFP0bIRD5H5UkQMsJpJZYzadnmSipqwufLylpZidSmZghBbVDaHdWBEI/9GF4p0HK0HHtUW9IP2/+qnrFgOXIORtX1g4l/DOR6kJuby7Xnn8NrTz/CleeezfLSJXH3zVs4H1HtMpDjeJBFv/hFLT6ePs2WnqkNGehGuJaj9aID7KkLUWe+DW3a1Bf2UAjUBRDCQA+FeP3h22nSoiW+hAQUK2FLUfBYNebXxuazqqrGxbc+xOX/e5SktCzrMMBJUJo0aRJfTJscdpzRI8bqOPMc8Dbhl9oE5v4SzZ82ht/PT5/OCFOuMICyojw+mPicFIMH1q2qv2jCEU0FNx9/TNi9utHU56F5gscujAHQItHDIS0SwgzLSHRulcjhrRtXKCFqzG/I2qzYCLs317+PC1ur6nh2XvRqXpv5P1aXFoZNeIoiFSt6H1I/JcK5VYcLeyDj1urSQp6857bwRUED7RCoq2XOx/H5+o3BMbtKWV1ahKpITqXP4/AoDzaVzu/3c/vtt5OZ2TiPdzzuOzjqIVb0wR32XlVSwL2Xj2bqy09w/4Qx7N4Rh2bx4ljY8RuqppFz5sjwc5n/HdYqkQ7N/zoDuYebmmNOkKqqccKZI6N3zpsBe2MvBPcJL50LW39BVVVatG7D6tJC7rxsFK89/QhXn7dvKhYHC0sWLbA1gxsDIQSeOA6Y/UWrdh3tcQIa6J+qIiUvzehShpmop6gywaHzIR1okeBBVRSWFuUz+ZVnG1x8qIpCwU/fNXifz8yt33NpmEltAV2YeSzOYsv9ygd0o3HGmBB8NfMDAJ5/fxaX3nAb90yaSlJapm3Q90rNZPhl19ErNZNlBblhtQBUVeXo3n0R1qJPCDyaB1WVnPdjjz0u8nI2rvt4KYZuMKV4Y/gzyuz9MLnT8s2VGELSMcK4yS6veSR/eOW2ynB+toCqQIi7v17JFTEis6riVjY6eE4Gt/zmH8FxPhD8Q7GoB3PmzHFWfsEAq8uKgLEx983JycH7/OvYa7SkfdfznB7oTnJJAb1S5STrrpBm8ZOXFuVRlp9L3yw/yZ2GgiLY8NtaIHa52JKFP6KoCsJceAvD4PP3XuWKOx7mlxVlVGzfRqt2HTh+2CgO6Z7C3E+X8fasGCdSoHL3rjCvpKJAweJFjBp2GoFAAEVRCHVrhIHQYzBGTSX5LePvO/vjD2jSogUJTZuT0n8Qqqpwz6UjCQWDaB4vF9/2IG9+OhuGXh3/Mod1sCky+2Lk+TSLrXtgL2uCqS8chs2r6z9ID8IbV8B/YpTxjoFtVbEnVn1dKXM/n0Hf9CzXpKfs8+BmGWcHgg/enywNgDAD2VycGXE8QrrO2pXl0ECRxLioqeSXd+7hkQ+8PPTmdAb6B8rQaxxu+Z+NWBSL5j4PewNOSNVr/qOZz0Mzn8b6ihqW5ufaslqhEFRs2wKeTkRDtvWwC/5Nz9Qs2xPyd9KtHnpMe1o38XLTp8tAkfQpFEuKMc679+HNcO4T+33NMRdczIy3JmIYBm88fjdD/jXKac9ggDlz5vzpPORWbdqGyWM2BMMwMEJBiCPvuE8omIXH5+PYM0bKAiHIBdSRbZvRMuL8kd0m44jWLPp1F8kZ2Vx/18M8dc8tCMPgtptvIiM1hZqgznXjziEYCPD2iz6enPgWJ6T2jHkbixbl8sk7E2FU7MqtjYX0Eju9x+05tri5lgH99crGSUzqoRBFixZwwVU30jMlg+17gzKfwZJmNN9TFama4/X5CAYDqKrK1f97hIqKXSiqijAMFFXlpOFjadOmLceffBr9B4T3tcg8mV931TCzNLwqpGF7ieVzFazfzYPfr+b8tMM4rXcHQobAa3qZHeqEc14hBEW/78YQpvPDPJkuDJZvqeKXnbErLqoo5kK28RTDxkLaFf8YyP+vkJOTg9fnIxQM4PX6OHHI4Lj7+v1+bnv0eR4oNQe6Q7vv1zWXFuTSMyUTYcq5aNbEgWBZUT63XzKSYCCIx+ely6wvaHVkX7Zu/B3axda6NAydY4eeytzvv7IHYcMwWLuijB8/nU4oEET1yHBY157LKV84J+ociqLg8Xjpk+FndWkBq5Ys5phevTnyiMNZMG+unaCkqqqU+mnMgybGNujt+9Z1Pn37ZUDho1efpkdKhu1lCQUDTF+6Hb0e4/jEQw3GZRwmjUEh0FQlOhkmDnweqehQsP7APFadWiayu8Zh2KpvXIaxu/5Buf2hh8WUvouHXWbYrKlXi6nnGcZxbfRZw48/rFWTRrdd/YjhQf7+ZRh+TwxdcBHmidlnKLLvB4OC8vxcBg0cGFbS+a8ehq3fwj0f9jqkBRt311JZFwJFcjcPaZFgRyt+311L3yy/rKoXkL91v6Z7+TnWGkkPgaLQvEVL8zqmB9llmP9VEm82FOjijsQIgaFLOS20CCPVuvFNK6H0K0huXMW7SLRo0UpStwxDJlcp4PF50YPg8/01KhZ7Knai2ON8I/D9y+BrAsdfcUDX7bK1iEHJh5B0yXSO6pduF9MBWQglErEKaliOkt27diIM+c4GAgHmzv0J3RBhDqbfli0BxsS8l59++gkjTtntfcHKkgJ6pViOF3exKcvJJJO7q+qCvJm3vlHn9Hh9pA8YZEdxrGe29K+beTWzZDX0Ss3intemUZa3kAz/IJIz+lNelMcUr49QKIjH4+WEs8ZwZLduHHF4tMzhxVlH8H7RBvtzLC930KZUSAPZki/duKfWRcN0KJkGJr/XdS6bv6xY/GNhG8xx4TKOD+YiW55LQVUbrgr7Z+MfikU98Pv9vPzhLCbcdAfvzfq8wfBpr77J9X7fGKz/eRUfvSGlZHRDhoKsl2HJ4oVhlbXm/vQTXlWlU+fD457P4/FywZXXcvMDT6J5PKBIxYmK7dsdz0kgwDfT32PSg7dSMOfr8BMoCqeMGs8dE2Vp1nsuH8W7zz3CfddeSnlRPoOOPQ6fz4emaSQkJDDs3Asb96ANvWE+a+IU6HqIFUvCQ3SBVvGf+bC9a7nohAw7IUsBjmzbdJ81S1skeGkaJ7GhsfC4Dv/3DTc7RVni6P927dkHbR/Cp0+YPDGPHp7ApHk85Jw5Ek1x9G/39WXXVOlJ6tQyMS5PrjG44IIL8Hgjn8n8/at2wHcvxTzuwMJ48lhhGLRq3cY838HjVB8orIm2TZNoQ8QqGhKZNGZ9shYOhq7z+VO3cWefiEnl6WEgDLxeL32z/CgKtIosdnNQn2b/oCoK1iMq23+1+Z0tYlLGJAWjd0Z/WraOn8fREJq2bI3H1O31eL0MHTaKp9/9iMv/czuzZ8/+S1Qs0gcMxptg9oNfo5Ntw/DN87B0Niz5HH6YeEDX3b17F32z/JJ7rICiKnFlIEFSYjrGoOQoKPIZzHnAWmgcd9wQe5vX52PwoIFx7yUnJwftICRJPnTbjawskTrJlgFpcXKXFuUzddJzLCspYFVZcaPOl5lzCi9/+AnJmTLZd9mSfGa9/gIrigtsHWNL7tKinPRKzeTsS66hd2oWy5fkU5a3kEtuvZ9RV/6Xe16bRp80aUfEWqg3T/BwRu+O9udYLaLrlkqHjDLbVETzX7ot0xZePbAuZNgUDFvlA8eIbmi9rKmmpzfOnWU3UDirPiiKPO/fzD7+x4PcELL7+0nJ6C9X1LUV9e4bKxFsX7Hgq48ABV9CAve/Ps30Jkv0y/Lj8XkJBWV1oj6ZMvO6ZetWsCW2t+2OiVNIzsimV4rkDT91zy0YhkHB/NloHg0RNOr1JI29/BrGXX8HlXU6T910GSGXF3f+FzN5fdJEZs+ezZw5c8jJyWFPm6P5+LXFB9wOXDMVKjbBD6/C+lKEHkLK00mPT2KTJsQOBMGITgGbH+amF+wrenZsHFe3PriF1geeOYY1y0r5dvp78PljMPRK6BXOQevU9SiK581u9Pk37JGG8R4j3ABNHTyUnqlZtgddQcGjhWtqNgaxPEn7Cr/fz7gLL+Jtdz/b5uISxqBZaJqHjCEnst9pU5bHVFWpq9odlhTXsXnCQfKI7z9SD2sVxjN2Q0FqpHoiDWRFRpjsoj+AHgqyasli8A2I2Ffh+LPGkJSWRdAQ9nXiqRT8FbDCqnef3J269YLfW99C74wB8nkih1JVRfN6WVO2hGDrNIgv3FEvNm/bxiW33E/e91/iP+kM+mVk0yrRS0bWAAZ0a3Ogj7RfSM7I5uE3Z/DlzOnM+ezD+iNw5grvrIuuZH3zY2jAnK4XuysquPfyUdzz2nSO7pdhyiHG7xjuiqLu2wHol5HFSx/MYuuKInJycvD7/QR1g5c+mEXhogWccdJQ0pLiR1X9fj+Djz8pSvFoXxE8/zmmTXqG0VfcwFF90zAE1AYNVpcUcuvFI2T01eslafApkNKwDvvRfdNIzswGoLwoj+vHDScYCDB9kpdH3pxBcma2pEUoJvXQ5YVdt7TIuabPyx2vTKF3aqa9OI4lfacoEdVeY/wcISFs+qVl7AKs2lZNRU2Q1k28Np1EeocVFAEb99SwqyZI747N7cWDKiSR0DCk7F19knD2wjrGPT1yek8WrauIedynF2fy9cptvLxwXdR3L556OCFd4PMoqOrfj2Lxjwe5AfTt1JK+nVpwRCOSsva35GgYTr0BVI1gMEB5fq7NMzKEICkti/tem8a5V9/Cw2/O4Mi+6awoLiB3zvcxT6VsWWMn5KiKYofBhGFg6Donnj2WU0aNkxrJdghWQdM8HN0nlVseepqrbr3HTA4qpCgiicLSlLUSlPx+P+uXLUGZ+2bDz9k2Wv84Cq07wfB74ZppgFwRax6NC2++nzbtO8Y97NVH75IJaq5w2F8FryZ1QCUUcoaNku1dtxfWRlSE2ryKdSuXYsTj5daHX/LDPrZu18FWK7AmvUSTSvNX4Lzzx9sDq+eLx8ho5hqIY1ApLrrtQYZffBXqu9fu3wUV1S7+kGmWubXmnY7NE8ISM/8KaKoS0zgGM3yrqnhUNcyroyoKydmD0FzeeM3rJSk9vKS5ahZqOGHYKPuZ3YaxokD39s3lRPoXQjH5Lr07tiAtI4MRl11Lr9QskrOjPY1tOnZi6Flj0EP6AWVYLi9cxJuP303xonm8/thd/FxW+KdU76sPigJJqZkcf+Zwjh82hsOOqoeep6h07d6bZs1b0qlrvAIjjb4yoWCQZQW5TiIoSkwqRfwzWH8rpGb2t+cBkP01NbM/l1xzI1n9B8Q/iYmBJ5y6H88QjaW9RnPff67ih4/exzBk4lrRIkdpIxgKsq0RCkja10+RlOG3aVllebk2ZSQYDLKsMFcW9rH+M72gsi0VSvPCI77LCxfZixBNhaRDW0SNQ6ri5B4AMY3RkOEk49UEdX7bJV1FG/fUcvuXK8I42AiLSiWj0LVBnbU7q8MMa8v3PHHROj6MSAh0IxgybMP+9N4d7O0dmvno0qZp3OMABh0ZHRXq1bE5iaqTayU5yPWe5k/HPwZyI5Dg0WIWmYiE72DoaCadAGfeCk1b0yfLb4dRrKzVnimZnHPptSSlZmIYgiWLF8TlbilTbmZ5oczKVhRI9w9G82imEazRPakfHTsdztnjL0fTNJN+oXHpHQ/x8OQvGDnuItvLs7wwXPpN1TTGj78AcFQscnNzmfzq84hAI/RK2++DC0gzBxEhOW65lc35uSp+WwfNQV8u7B0Vh78CHlXlqTOTuGpgV1S1fv6rZ8YdZA89PcwAaiy08m/CPh/7r5H2b2cZRQkHuYLcvmDAAD+t20g5wgfu/h+dwyb3iBYp/46q3RX0Ts3iiquulouJfcRhrRI59+pbuPvVqfTLMLWQ+fskqNUHBQWfFl5Iw0K/9Cwef/sjThs9nlNHX8Bdr06nb3omNx53FE/+qzd3JAlGTLiJu1+dSu/ULPNs8v9NfRpHtG6KgkKrJt6Dmom+P3AXnbDuxTIWI+E/5SxOGDYaj8/LgWiJly2aS7CuzjZyyvNz//JFtAIsLy7ggQlj+HHWB2xZv07S4QBm3BWxs8Jvq5fz4UuP8/UHbxzghRUzEukYtB5N2adxws1pT+4cLv2lqQoZR7SmQ7MEWjVpeEHavXefxt97fWhzGMapN/DOi0+woqQAA+iX7bepNWq/U9jg/3f959i9mROSutDTVYlw9JmnuKouekkfMNh2QsiqmNLFavXldP9gRxrOK3N4UMCrynkhli6xgkIzlx70x2XRikYhQ3qQDQRPz/2Fz5c5eS1VAT2ady2c8tMAu2oCNu/YrZC1dke8mKzEjuqA7fXu2bE5L57TF4DmCVqDhWBiLboM4SRNW3PV382D/A/F4iAiFq9J+f5FxInX7NuJju6Pr3V7uidnghCUFeazNH8hR6b2p3dqJsIUYEeBDP9g1JIvYoblVE1zDX6WkWiFVAxeefhO9JCOoir/1955x8dRnH38N7PlTjp1WbItuVfJ6tWWMSBjWkILYDAtEAJxIIWUN4GEJJSXJAbCmwAhzZAQEgimhZIQSIjBFCOwLXdjTC+u2LJsy7KkKzvvH7Oz5XSnetLdyfPlYyTd7e3Ozs3OPPNUM3UMj3o9fKDVlamCEoJZNWHRudf+GHPnNlhldv1+P3Rdx5hx4wESXbs7WJhh4C21Z+GaUoX7XgKWb128UCjBuKwUpHtUEBBsWd0EQ1S3Cps0fvKHxzCjogaaQvH7wxFO1gMNxzbiNcffPP8vn6wpA0LoXjZ0OFEocNmcybjr1Q+RN6UYH2+7NfqxL/4eJRfyogUTpxdBOdDZp6T+grGdO1Hv347S2Q2YVlZjpXUTi1miI5658EVF+DvOqqrDTHOD3O4PgQBomJiNIDMwrroGxRVVPA8tcQswusKD/gbrVx8rbJ9wYqe2MzcyM/N82LbX3hjt+vQjkNLJ+Nl9j+H3q3fg/ein7dOFKaXQNA1Vs48Z7G0MGkKATateRzDAtY0gvPjPO5vWA59s6HYwM6WaUGhwQW2+rBxc+oOfYmZlHUIGs9Ih9ucZyUnV0XokYI7NyIL1xJyetYsWsXw2x86EccEd2LK6CUWVdZhcUo0bfv8INq9pwtqUWXintwmFUDSeeR7/1WxXQ0MD7nnoSaxueg1pWdn495OP4N9PPoLPnbMIU8tqYIQYqOkGSAlQXl2P3/ztSby58jVMrazH5NIaiKqu0W61oiADKQ4BeWWEHPftXUErv/Nbu7svFLwKrQFdoXbtAIfgKVLHCr0EM4/vbX0QxYXEFzXKp+PSmnGom8DdxU6cMQobd7V1y4IRNfiPOYKHwfssLwYufbFEapBjyOY13XNosg8G5iXWBQ0vPP5XvL1hDa67/Fz86c5bcfNXzsfbG9aY0acEYNx/7ZgTTo54jsYzFmGmGdFLCcG6N1byyG3GEAoGLXNRyPRpFOWfS+rmminB+OB9e8NqbG1uwuXX3owvXnMd7v7bMzjtnAtACHGV2fX7/SiaORPo6qd01xe++wygqH3XehF3+p14CYcelUJX7LCGsrq5ds7QsIIIM02h9vDB/mfPWPnSf6zfr5s/1dLMWRsdswXx2p9TQnDGrNF48KIqfLp1PTwp0V2W5p91AWZW1uLtDWvwkysWIhTs3RzqZPfKp/H0b5fgxq+cj3c2Njv80Lnw1ZeCBPEkmqabEpiBQXZpVmpKmUKwfHdjM565/x5s28jL2FJrHNgnjORLGg9cY9R8TVMoCAFuO92RlWfrCqx7+G786MvnAgSYUjzAYOhfngkAOOfyq3H5t36AX/31SZTV1A3qHmKBplCU1891WfdOPfdi7orVDft7HKwFoL2tDX+8/Qa8s3ENbDGlf3PllFyfVRRksEQ6R+6+twZ+wvRRKK5pAGMMm9etxqY1TZhU0YD9gd7Fnty8fBQJf2FHyypq61E1+xj8+n9/iKf/9mc8/bc/45sXfQFb16+2tKiE8NgCQoCymnpc+rVvo6iyFu9uWIMn/ng3Nq9d3WMf91bI6bpn38a2vYcRNDc14YRCZqEx03VCCMOu7BZCg2z+7AiEenetcW1m+bGnFuch16eDEIJMr4afnNTdPaitKxhxrBpgVn+B8JLr2XGODQlHCsgxpLreEQHdto//HPDMQdD0wr/C8p4GsGV1E95evwaP/OEubF3P/U53BCNnZ2g8cyEo4YUtpo3yISsn205Y7sRMG0MpxRXX/i8A4Mk//hobm1dhy9o1+P5l5+KR3/4C999+I8rrj0FJlb2oNDY2WlksdF3HF844HTQQ2ypPFp603lNT/emrMIyQ7VeH+LlXAFygsQIGCcGsqlrcev8TqJh9bPeWmX+W182F8pu+V9YCAGZucnwHP0Vay3vW9TwqtdyD4tkPQlv43qZmLLn6AjS//AKooiB39FhUHbvAdexxZ3D3kM2rmxAyN3T9gTFm+f29tcY2oRPw3K0xiRUYQmyTox2ZDgjB2a5MBjgqJRKCdzc045avLsITv/8/3Lz4fLy9odmlQU4s4yVc3wtM4cLeTDpG63O/BAy+oX/x6cdQNrbnFJE9UVQ9G1f8z09w0VXfQll1XUK43VQUZAKApRUMBUPYs+NjABGEYKFxoxTHnHLWwC/60Vqg6WEEA3xNseIV4BYI+4IoQz1Y3tu6pdtrLQ/9eFDnnFFRg23r1+DGr5yPx377C9z65l7so32oAqdoIODBsmkeFVNyeVVPQoD1b5qKJpNgwI9Nq1433zfHsWPO3bJuDf5wyw9w8+Lz8ODdt+PbXzwbWzeuj3hZSgjSPL1beN7Z246OgBHVdcEWiJ0VBB2uF87MFcwWVnvEEBtu2y2KhI0Yjbrn1gcuqLSe8blhQbAizRyQuO5vib1SJBnl1Q7fOSGIDtRfjhDUL/g8imts3ylV1bDJn4EbNhH8+Z478MMvL8SGNW/igyi+Q9PLeenQQMiAphAcbG21q+eFYwrJH769CTcvPh8P33M7rr7wC3juyWWOGvQBbF71OhhsU7WzzO7y5cvR0tIC1jEEGmSAVyc84wc9HkIO7YGq8vRWQnNMaf8n/VhiCSngk0ppdR0u/eb3oahhEyGzhegTTfNen69h/mxvO4RbvroI75rCka5Qs4xrhKIlw4j4BrY2N1mmZCMUQstnu7HpzVdcx04preY+jfVzB+aPTZjl91deN9f67uN5//1BuB2EN7dmfBYIAVI0xbwXYgmWlABvOfo2GAhYi7ZIQ5Vo8Pvjlh4xn+gKNQWuyJ/571PLkLX/A3y3aGDi/vjJM6BQaqXosq0r8WXzmiaEAtxlwjBCePSPv0UwGIyoEFBUFV/98a0oCgvO7A/68ntAA0egWqkA7Tmyv4/J1NxUlA5i0yLYtmld9xdDIeCZJQM+5wctR7B5TRMCXdxaioy+uf91HDmCJ+77Nd7f2IzsFA05pmZzaq4PVXPmudJWqpqOytnzXEV4hCVk09pV+NYlZ+P5x/5qWWz9fj82rV0d6bKglCBN74MLFGMIhiK7RdhCMczYJVsYtqvquY8VLmg94fOobrctxz8rGDjswc0yYx0oAb45bzJqxmVa74nnXAjHiTg9J4atbYSgOIVPa2Ib2LdOFBV7tn+ME8+9GDfd+yi2rG6C7svA/a1m/l/Ni0DXYaxsehNAVeRzgEfEdgS4sF7TcAx03QO/vwvMMEAp5RV+TOFY1Xj5UGdlKcIINF1DwEwtV2UmTHcO5oaGBlf+UO32X6HvhVP7wVf6EJTCGE5edDmKK2ot7YtHVVyauOFELMCiz7wqhUoJqupm4+wvfxOPh+WqF0L01OJyoB/7jOKyCghjZCDgx1vNTaiu5wuoQghSdWr54sYLQghm1cyFqmkI+A3blzLQ3YWCACipqsOS+5/A9as60Vcni4XlY1BS+jW8t74aZXUNKDVN6JTw1GrJgFh0fLraLdMEJTwYuNOhGRaPYmldA/6uaQgG+bNaUT8XlBBoCheovXEM0oyE0/3HlJWhUEBl7s3BlJIKfLBlAwAgFArhmQd+h7WrXgeuejDSaaNfT1Fw/JkL4VGFdm/gQmGsqZlzDB5QKEJBuyQxpdTle1+l78e0eeWo/95VmFZegx0H+xAMHYWr/ufH2Ld3N6ob5mFKaTWE157lttMPVIXGRJAoqajG4+vDn3QGvDfw8t/XP/c2rsyMYjntgSOH2/DwfbfjcV1D8T+ex2knHg8AyE7VUV5Th98/8gyeeWwZKAFOO/cCzKyoRWfQAAUBIwxelfsZNzfxMuLOjQ4zDGRkZkW9tk/vvTeFgBvpu2JgpkDMLKsEGDHfsyvsiQwXwj+5J5edpQvLkJWiWdZQK/gb9kREzOfXCYHtBkYcWuqGidm4qLoAJHTYOi7B4vMASA1yTHEOjuOzTTeDIweAeyKXp+4Jxhj+8cBv8eBdP8OMshrMqpmDl59ZZo8is/JSYVl91HO8s3GNpZHRFYq62Q1mPfkf4odLfonTF30RlCpgZgnemmPmIzU9nWt0KIWq6fj8uYvwq78+iYu/cS1uvvdRVNTWgxKCyVECLxoaGrDkdw/0+35jBWMM/3poKbZZ5mUCn07j9vAR02dUTCUqJZYZriAsTZPQGlIQfPj2xn5dZ/wk81yElzRNy8w2I6ttv7F4IhbfGRU1uOEPj+Dk8y6BonKfctre0u1Yy5RMCI5hfQ/JKjF2YFZVLRZe+U0UVdZCoxRpHhVTc319ykSTCAghZfooHzK83QusWOZw4kwrxat43XTfo1h41fdwy72PYZbpCqVRnlKpsJ+FcoYaywcZTgGVmAGG9nd1yXdvgu7x8gwEioK1r74Aw9/Vv4t9tBaX/+BnKKqohcfcMFga5AQYFjX1c3DVj5bwZ4JS6B4v5p3orrM+7dBbOO+KbwCE4In7fo3W9zfhsctqopyxZ97dugnnXHkNSirrrO8g3haWk+fV4Qvj3W0oq+09PVxvrF/5Ev9l/lf6/qH9261MJ6+/5rZwERCU19Tj+z/9P1z3s1+ivKbemtPDNci1ZiYL53imlKLt4IGol9b74AImlrNIU5rQFAcMZq17IvWbnbXCWUgE+LDlCN7+LLpGpjAzhVsiFe4y6EwfKf4BJGLKRKFhdlrFFlUWICdVtzbIKbqCQ52Dr6QYa6QGOYY4DZk3f/VC/OmeX+IvQT8G7P1X3IjnOsfj1QWVONiyF2i4EPBlmRfj19r8wacAxkT8+Ftr7BK7qkJRNjYD7dX1mFVZC49KsW/fLxEKBsGYARYCmpY/Z51bURT8z40/R0VtPbqCBiaXVKMrGIJHURA0DHh7iIQPrwA23BghA5vXvI6SajtAMZ5wNw/+i6qIXI8EE7JsgUUIL/Yk2782/+eZJ4Dza60THD7YihSNurTX8ewGsUUgAGZW1kJXKV586hFe9KLrCLR7FiLwjccBAO9ubEZ5TT3eXr8G1395IYL+AJTykxE64eper7O1+Q2UVttp3VJ1igyvmnDBH9HISdUQCBnY3dYVUaMj+pFSHqinKQRBg5lmSp4GcuKsKqR7VEsIVM1NcrzTuoVTVZiJF97ZyzXHhLii3QmA/z1lJna3dWLGpGzcfN+jeH/9m9i1fTv+9ehfAaUfc+r2zShc9xAW/PhFS9NljUdh4o2zh7auUnzu/EswcXoRNqx6Hccdfzw+fmcrXv73v4BDnwEZ+UjPysG7G9fg5q8uQqCLZxP6yo9+DqCo39d7/u9/w3GfPwtVtdzKRAigq24hb7ihhOKqU+tdhabqTj4Lm5tXDerb2b/XTJVWdUbfPrDhOWDlX0HMTCdz57mLOTnHjNjMUrNCiFCGUEKgUorymnrc89CT+OcTy/Cvx/8GIxSCqukor4mu2OpLiITQSEda2zbvakPNuEy0dwWRoquODBbc6VdU1+Ov8IC+Hz+/rcfrEQLTFYo/PF5NQWfAtm84m3zyjDz85529AICDnQFe0RZcSD63bCz2HwmgqjDDKnIFcMtqvNfpSEgBeQg4YRovhVpWNxeKogwsHQ+hwOe+ixDAhWOAC8jW+wqYYeD1tZuAaREE5FAQJbUNZqoXx8ccwlLVnGOg6Tr8XZ1uXzfT5eJg635LA5miU4SYAYUCIRZ9IDc1NeF7XzofuOqh/t9zjFB10/eU2OageC1/tmDoNOvyyXRSrg/3L6pAuz9kpoGDuYATnHDW+Xiuue9mQXbAXATeXw0whvSsbMC6prgy4uZqAjh81wjB5tWvIxQMcRNfyMyk8o/bQPzt2Oo/FRU19dyn1nT3QR81hhlZOZZZjzH3pjUZ8GoKJmSnYM/hrogtp44x7dUoggZByB/iWhxTKySi6SkFVBKbAKqhgLrMtbzstO3fSDBrTDqm5aXCMIDiilo0zGnAlnWr8cLTj8Df1R8nLoLPXXSleS3zFTEPwv473oj4g2nlNRib7sXGN1dyF7iHvgtkjcHhc87GluY3EOjygzEDoZCBe39+PXDN3/t9LRYMYMUzj6GqdjYIeBGhFE2BP2jEzdoU6Tv4yy9uxFlfuhpPDeK800+/Ah9s6WPBoeW/4wIyAKIo+NYNP0dtlCInYvyUF2Sg+dOD0BWKLmZYY6sw04uQwWDU1qO4qhYLzjoPW9e8gcrZx2Dy5ElRm9DXirxrPj2Ave3dn4MlL76HR79Y43KzAGzjM2PM5ZPcGeh9nRFWBvG7V6XoDEsNJ5RAV8yegPMqxuCj/dyKTvkiCAJgXJYXN508A5+0dljB0nawX+IhXSxizLNX1OPL9bxKXFFlLa64/mcDm317+4zCNbghX263t9LRie+WKJhZURvFrMxfKzfLg5514WXQdI8VwMd3zjrqjjkWGV7NEvJUSnsNalmxYgUCfSkUMkTUNp6KG/7wGIqr6mzfRgC9Zr8YQkQaK+JYnIWJPDtVQ45PQ6quWK8rFCipqkVG1/6+X+RwC/Dbi4HVT/A/D7Q6tMfENIPFbxqysy/wn2X1x0Azg08VTYOmaaAfvAFt99tW9aq6hnnWMfTd14E1TwH//W30izQ9jL/ccSPe2dTsyuOdiL5tPSHMtZHfM3+CB7lolLjuVYx5QoAUVTEDVBNDAIyEJcwTmKZbt+uD7SPMb66sph5L/vQ4Tl54MehdZ/Mx0QszJhbi5IWXhC3Cdpo8QoDRafF1P+ECiO1+RQjPca/rOqi/Hfr+T1BSOxdldQ2gDgHKiFCFsk8wZs0NqkLNwE93nw83kcZoMOBHx+FDgzrvvwMTeZrQXtDv+5IlHAPcV/hQa2u3tcN2L+BjR7hTpGoKNMp9/Z3zjrAIVtTMxpe+8R2U1dT1uAnpiyuYwYC7Xvso6vsi+K4jELIC8wA7SE9Ize3+UJ9UJsTcaNtrinu+AeHj1/wVKZqCWWN44KbQPBNTUg7fnIq1KRGnaalBjiG8hKS7hOzJC7+I1/71JKJlc5yba+D1lgj7FOdsMWoSMCEs9yflX52SU9D9nDPHoaF+onUO58Bzmhdh+lKVVNXjtHMvwJuvvwpfRjYOHWjF3HnH4thj5mKUT8f+9gAoYVAVYp0jGo2NjdBUdWiC9PrAtNJKXiSD2JV5eLqs+GBPJsT2tWR8MlBgmsvNhZExhhRVQbpHxf5gAN+qycYtT78JTI0Srf7RWmBSNb8OpWCdbQAATddRWmcHTYrve2Z+WpyLRNg+p2XVdfjGj3+OV//9DzSceBrGTZuJ5qaVKK0TxT0IymrqcccDT+CNla/Bm56Jv95xE4LBAIySBcDYmd1P3/QwgoqCt1Y3oby6HgBLWO1pb0RrtlMLb6rIrfFFwC0Ett+7e2OWiGgKgT9ErCpuAut5AW+/kAkpAcqq6+EPGXjp6UdgvPInkK42sGO+GPH8nysguOC4RlDiLlggNq2zxqSbAk18O0nME5TZaSHLa+rxiweewD8fX4aWzz7Dq/98HNNKyjD7+JPwxor/gDHGLYD9vdjGf4MQYMFZ54MASNWoK7dtzfisWN5an+HfteN7MPNW98iOt4DCWTG5fqj9EBRVRcisTKtqGmrmdC8k42yiCKgTS/6WdWuw/s2VmH3MPEw6sRFZKRra/SEsf+U1NDe9hvq586zYgGhofRCQn4xQYc8JYwzMAAIMUClDyEr5xmzfYwB+s2R1b/ANuf2XJSwDDnnCHsdCEBafdSqJGCPWnCXcUuzPJRZSQI4h4gF37sDf2bgG72xaCxwf+TM5+WOAls+6v+FMD3fp3d3fV1RexjJrLNDBR/jVDROxq60TpxWPdrgXuD/mzKHKBy6BQRjKaupRVMkjcTVKrGo+zhQsusKzIPb0/DY0NOAPy57C5a+EmcSX/w5Y0LsP6WAgH69HyRkNEIUUAFMAJQSeOOW+deaJFN3GhWQ4/JFhLeCaQqwUVBXVtSjZ2oIt0ao+te6wBORzL78arQcOQqEEJ551PmaU19i+coRri3y6EjdBwJpIzQlx24Y1uOen1yPgD2DD6ib89I+P45wrvglKCYIhwyrDWlZdj3HFVegIhjB5RjFWPPM43nrjXuycfSVQ4Pa9FKkQS+sa+KYoEVUSfWRmXnq3lEmAYwwBACEgDNAUBSHDsO7ZuRiJ8Z+oiMptIsuG2MC75initH4QKBTYsroJIVGRctXjmFFUjHdyu5eo3tv8Hzz30UqUz56LKSXV9oaC8X6JVO43Hoi1gzFmaXMBft8vPv0Ign4uBv/3CVPrq2k46ewLcPzp5+G6df0b6PTF3+PLP/y5JaippgbUbkl8iFSumCpKzxl9jhyM2fUZGOafdZHlnrTgzPNRXtvdVzg7RceBDl49sHg015KO8nmw4tXX8N1Lz0bAH8BffqNj3L+exynzj8PubRtwzcVnI+D344F7dNz90JM4pa40ajvUPsTwHAn0XArQmf84aC4yPL0bsfMjm+ne+qZBdmaucM/nAlvDTEAp1yiHwCAym1Mw97kc5wbia+WNhnSxiDHOqlAEwMvPPOZKKh4rzr/8Kvx+2TPY77dHaH7bh7igshA+j11mlhIC3amZcRgaKSEoG8uTpgtTkWZqwAnCBYy+L7Qi8MPJvOoyYNfb0T/0QeS8kH1lUsdHuOmMCsysqHWYnPm91IzPgs8Tv70g/x7szZMQWlRKrU2KeBCdRUUIJbj6zOMxgbZFO7P125MP/AEEBPPPPA9FlbV80XNsEuItIzknRUqATatft/JrBwIBbF79ukuLoCvEJUw//affYPWKf+Olpx/Gzg/fBYzuC8TCq76HG/7wCIor63ifmu4H8fS7HijRqt05fZDFAuVRhWuCrZFxVm8EEnezIJ4FazOHcCsXJ83MwSqE6dK6BlAzjzhjDO9tjFyxdO0ry7HsN7fhxisW4p0NzfCYrmlcuEkb6tvrF2Le0lVbYN3w5uvd0iAysxJqwbjxmFVVi2WXVPfrOo1fuAAnL7zE2og7tcfxnCZmjUl3zVOKqoIZDPfd/pPoH/rvPcDbr0R/vx+oqobjT1+IxT++FVf95DYUV9WaVhk3U0f5kKrbmxgAmJybiream+yaAQE/1r6xEgDw8ssrrBzIAX8X/njnbdiwrjlqO+hAayc4sCvm8TLSIoMFwFxBeiLeqNfzMVjWR6fFigcoOseROdfDYcUixPZDJvbzLcadSuy5PtGQAnIMsSZ284veun4Nlj/9SOTVyQhhoaeH9FUeX4/Xml5aicLiKnSFbB+0JVdfgHc2NIPCFrimjfJhfJZd1rdsbIZLM6Or1C3UE1ujk+FV3QPfPKY3C9DG5lXdXlt0+VdxyykRzOKCba/2fNJemN0wFyVVda7vQCGAJ84ZNYRgCGJr3sUkoVpCIKyZRWgPRJ9nejV8pTQN5PEIFaUcE0ooGMR/n/grbvzK+di2nqf3c37PznEZL4h5j4QAVbN5+iNFUaCZOXuFWR2AFcCxdf1q3PCV8/Hob27H0/f/1jR/MsDvLo7jVSnOvPwbmFlZa21GdJdmbGRQXpDhEBZF6kAR7MKPoYSbfp3CZ6JjLazWoureVDor7BECFFfWYcFZi6ybZqHISgijvRVgDAG/H6/883F4HZldUvuQb3a4cGrjFEtbztOERSqWQyhF1Wyek16lBBdVFfb5WvNO4xVW7bzYxDF24jdYwktWM4OBMaNnBVNHG7D+2UFfO9s4jOt/vwwzKmr4+me+Hq07MrwqUsJKQs8+5lhu1TWryjY2NgIA5s+fz1O9UQrDMLB65ctYtGgRmpoi53eORTpK4WvsdKkIGVw4Npid/o0B+Mdbe3o/H2BmriKmUAwu8MIR+EptFzqBU1EnrEBECM9m5worUiJu4qWAHEOcuyMA2LSmCUYwiilk74d46o7roidB13oOGnlr80bsOuQOhgsGA9ja3GT79ADd0js5tRPh7QbsfIalY9ORl+Zx35dD4OqJNW+81u21V/77ArY2R0/4Tg/s7PmkvcDMoBOAa8I9KoGm0ITJfevsQ/FPMSVjEQBhHgkA1iRNCPDMA78D+yRSXuTu9xbw+7Fp9es8xZvj7XgLSbY2gf9eVlOHux96El/9n+vxq78+iVlhmxuxZ1j3xkoE/YHuif7/fRfwPt+IjfJpuOecUsfnwTdHKoVHVZDh6X81vkRFMysjAuHPLbHKwDrzCjvno0TEKRRTQjAjL828R7c5V+RatTTNBJh/5nnQdQ93rXl3JeaOcq+wc/csBw7scr3G85AnXp8QQqwyvZQQFI9OQ2aKhoraetz+57+jpvEUUEUBCIGiqvifm29HaXU9AG5pOrNkND5f0HvAXrm6D9PLa0Aoj5ex+9d+PhOBTI0XrhIVMSNyuCXy6/2kJIvgmnIftjY34b2NzVzQM+egrBQd2Sndr1+YmYKSMe7qgdV1s3H3g09i8Xd/iOXLl1vFsxoaGvCbvz2JumOO57EihoFAIIAVK1ZEbI8ag+/g4ofWoa0raAudpqsFY0DQYAgxA/6QAYMBD63d0ev5ggaLuJ44c/wrgENotu0/1nwV9k88z16VIt2jYWKOrchLFBJnCz0CEDXpxYAor5sLVdcQDPBSjy4IRTDgx7/u+B6Qkg0s/lO/rjVtVrlVSUl7/HoE97wPVdVQUtfgME9Grq/uTPIN8EU3ELLT0zhlylRNwZh0L7Yf6LSOnZKbikMtHVHbVtdwLLDC/f5jf72XB5V95+mIn/nKlVcgfQLDHVsHNjsc6AhYD55HpUjVFDMNTfxnfEL45GFp+sTr5k+XacqpyTF/3/+ZGZDR2QZ47Ul5yowifNC2D0gfZb3GDAPp2TkuIYAQYEZe/M3JQugXzh/l1fU4df6x+Li1Ax0Bg+/WHX0B8Ej+P+sa/H4G5ozY7zgEvPYAMLUe03J9SNNVGLxWt+1qAKA4P83SRo8kROBpeHAMIbxypHiNH5u4WmQrpsFsHzddE3hUiiOB8EYTW1AG1yLfdB+vMjq1oh7ltXV4/YE11tGnnXwCVj36O4SCASiahsYzFvZ5kx8PeE5r93dFCVBZW4+xRX/EB5ua8e76N1HXcCwqa+sRMgOQCYB3NjQj7aM3kPvmBrQcGyXWo7MNb//xa3h35sOorJvtcllJFBRKcEMpw9a1byDjezehs+0gcnJy8PtffxWBy/5gH/jJRuBfv+C/D7L5+/fsxJKffQvBQABPahpuvu9RlJj+2ROzU/qcdo2Ab/wrausxe2K2673K2tm48tvXYcPqNxAM+KFpmqVhDidWSp0dhzowLTcNjDAwEHQEQvCq1NQkO6rs9YHWI36MTtcdLlBCE8y7P8SANK+KI2ZWDEqYWZ2RP9yWC5iJ02qtKRSjfLpVzjuRGHYBedu2bVi0aJH19wcffID//d//xbe//e3hbkrMmTU6Has+OWDNvrOqarH4B7fg9ReeRTcPOfMYIxQCWM8O95EIMWDXIR4Id9cv78DLK1ZgZtVsFFXW8TKlxC5HGc4onwct7X5rIq4oyMS67QfBmGEPfKHJpATjslIsYVyhBGkeFT0l3qmsrQdWvBx+wz3aUPa3tKDt4BuA2hD1mJ4IGjwYwCDENNkIrdSAThdTKAE0lSLNo5h/C6HGoSUjYkNjCzzUnFxK6+fi/S3rgd9fBnz9YUDjmv3W/S2gT18Pw+GzRihF24FWy7wO8PN3r8g2/BACV5YXQoAJ2an4pLXDJRQL3zRCeCT/L//yd7z6yivoam/DUw/8zhaUWz7FAv1TXNRQDpHVgRBHuqEEWvhjiVWRiu8HwBjfFHYFDeig3I2A2AF7JWPSLaE5EaGu793WQPG0kiHrPl3p7AgBCVMAhH/dE2ZV4aZ7H8PWtW+gsn4uJpdWAyBQCMWE7MTTVlFianUdU6WYBwiAmRW1mD2nwa6Qaeb53rZhDW756nkIBAIghSU9XIFYVsbqOh4nQqntPyquF0+2bWjGkqv4vaiahit/8FOsfOFZBFv3AP4OQDe/t5aPQbvaAUUBVXUMpgZbl9+PYMD0HQ7yANCy6p4zTUSje5ghp7wgA8Gaevzu4aewpuk1nHJMraVhDidWAvJN/34XVzVMwHFTc61gvJBZWjo0AHcGK+AbMF2UFARDfM41COPWGX4gCCVmhV6YbkCAK92yQ8OcyPP0sAvIM2fOxPr16wEAoVAIhYWFOPvss4e7GUOCplB4VVsD8Pb6Nbj31p/A3+UHUSaD1Trvk5vKACAUIeCoN+6+7RYcc8X1yE7RUF8/GzlTSi0tsKVFA4s44U3KSUWmV8UnB2wtr6YQGMzOeBBOf8ZwxEN7OUFxTQO8ugKs6/t1Il6YuTW10Sas4UL4E/o0BQQ8Ar8oPw0GAzbsPGjvyAlcpnFnEJYvLQOEEDAjCIQCtoDcsg/osn1xCSHQdB1ldXNBCE9U3xEw0HKkn2V5hwgCAo+qQKVu9wBL6BHfFXEPl9LqeuTPqICuUNSfcAqevP+3aNmzG/O/cCEWnHuWZa4zmEObCufGY2RRmOlFmq5gx8FOc0Fi8CoUwZAB1axIZT4K3ISZIJkaIkEId/sKGe7vXCyqQliG+bM4Px3rdx6CSgk2rV2NG79yPoJ+LkzdfN+j1ufnmhq8mZW1KKmug6YQdIW44kBXKXJ9iaetAoSvJv89P01HMGTgiN+0hJmadSE0G+ZcveKZxxAws1ywaGtJoAv4+41QVQ3FNQ0OzZ77WYy3QuE/Ty6z7iXo92Ppz35o+SK7IBQAw4KzL4JnWh3+OYi8olTVoagKWMCAoigoreP9w1hkC2xPhFtgBUILfey8Y3DhaQuwf290v18lBkF6gt83fYJdh7pwYVUhDEbgDzIEQgyUErR3BXDnqx/263x2QJ49dgA+x4jAaNFvBLa1UFUoaJBZ87EVX2DO1YmYwQKIs4vF8uXLMXXqVEycODGezYgpIjMBAGxc/Tr8ZtUjvHI/kDcJmFgFAMjIzcP19/8dLzz1KJY/+1S/rxMyGD7Y04qx+Tw4w5X3FLaQEI3sVN1Vfndmfhr2tHXxRZd0j/zvz0SxIUKQ3vGN8/Hqx2u7u5oAoE1/A0oWobiiFli3JsIRvWNVECPMoQ2Jv2lZZNF45f0Wc2KhVkYNl6kKsLRHAE+pp6sKiD+Ikrq50HQPAv4u9/fimEg1XccJZy3CSWcvQnFlLQiIJQhkpSSGJ1VlYSZe+6DFNUkCsO5fuA04X6fENiMTAhRX1qL4zj+hM+geSbYGXmgX468NGypSNF71LC/NgzWfHgDMjYGlmQcsDfPYjPgWwOgLmkJhMAMeVbE3TXAIxlSMEAafR7XcAjavtistCs3fw1/6OoIhA4pCEAoxxxzA00wRkIRdjMU4z/N54NUUKJTnRN7X7reEEU3hJnJxT845AwCw5z1kde7FAW+e69wz9zShauG5KK1rwKSSqjBrDVCQkYK2riDa/YPRxQ6ecO2pEeICP6EUoNS1KjHGkDe2EJ+1twGDMJB5fGl2tTnY/vz9V1s5LX+R39Mo6XXDGuu4mae37MGFVYUA42Wlu4IheDUFL77XgnU7ei/C8v3GKS5FQ0FGCj490AGFEgQNnh9ZaI9TNQXt/hAomLXWi6dXUxx+ybDjnRJ5no6rc96yZctw4YUX9n5gEqErFBTcRFZeN9dV9cjpYpCenYuiilocd/p50LT+CzCEqvB7s1BgLoCWmRL2zo1nSejb6NMU2i01lOt6xK3d6InVr78G8uj11t8L2Tpc/Y1r8PM/P9n94GAXsOoxHlzYp5Z2J0MDzpw12iUYA7CErkRACMPOxVm4EYg2elU7ipsQnuqMgqCoshY33/coTlx4Ccjudx0n5T8mT5+J2/78d1z9k9tQUllnfR7g32t2gvh2iYBRxXQ6FVoV22zOj1Mdi771OrHTCoHAIWQ704TB3GjYQlWCfP1DgnMhFeNL+PqJsVWYmXiuBOGI55Rn2CGuinogBCLZgnPjpFCgop7HeDjzXyuEQFEcLmIOgcU5xhINu4IgwcScVOu7JWFj2zoWdr8tOPN8qLpu9d1360Z1O3/98QtwzhXfRFEFzxVtn4+fP9enYUaeD5UFmUN+rz2xcNHF1r0oIte/okDTdWi6Yx6jFJquo6S2AZOLoucU7gsd7e0IBgJW2XuRcpK7KvVvwBDAqiAX6b2+oA6BVGaIbBbm3/3ZI9aMy7LGIyVAQabXcoOzgsnB5x2RkUJYPJyVMa0AWXPwOefwRCVuqiW/349nnnkGS5Ysifj+0qVLsXTpUgDA7t27sXPn4LIcxIK9e/f2esyh1g4cOdAOqquYMHESvnHtT3D3rTdzv0nHqDRCQRw5sBcTJ07E939+N37eP0sHoGrY12kgVwvhwL496DzUia6gAUXnpvygKXzs3hWEp49PXGtHAIcOdoISYDdpdwUnhNo6MMqnQwtQ7NzZ1mNfFM0qgbb3NqvC0/TJE9F5cB8mTZoErPso7GgKVVUxragYHQf29f3+Hdx7xhR0+A/jSGsHgiEDbV06OhW+u1UpsFONHlA4WPoyJgDg8P6DUAjl7VG4W0Tb/kM43BVCiDEoIPDqFIeO8D5nDOgMhtB5JIAgJZgwYSIu+8b3ULVhPf66/h3sypoBgEBVVXzn+ptQMHESOg7sQ9DMcnCoQ8VnIS86h8n3uK/90Lb/IDRKYTCGyQUZ2LmzHYda2hAIhtARNGAwBpVSHOriqczSdBWHOgLoPOJHyEyBx/uGT/Uiix8lBIEQg6EQpPo86AwZ6FIodmlHhtXNoq/9ECsOtbSBMb4p6BJ9Qgk6KS8IMJRjvzf6NF+2tIESwB9i2OnhrkA+gyHYFcTu/UfQ2RlAmkfBgQBfqnaqHXy8hAxMmDgJP7nzXmxuXo1p5TWYMHEiug7uhch8GTQMUE3higJTPDnk19ClUuxk0SpPxJ6+9EOawRDoCGDfoU7s9NjZifxBA20th9F5xA8C4KCfu1cplMddBA2GCZMm4Ue/ug9b1q3GzIpaTJw0Edj0iev8gcOt6Dy4F5QQdAYMaF4FaocGQoAxo3xo+Wzox0lf+mHylEn48Z334a11azCjvAYejeKDTWsxrbwGt7xPrcCaSTNL8aWzl2LCxEnQDvuB93f1cubotO77zFqbKVUwvXgWOg/shapQ7Nrl77MA13KwE4c6A2hVO9Aa4f1DLW2gRzRond4e++JQSxvuOnkcvvWf7dZrV1TkYtYoD/5n+cDkoPc+2YUJmToUQtAVDGHVZ514YE3f1ttOc10+YFZrEc9gZ9BAMGRYrhUK4RVij3SFEGQGAiFerqSzS4VhWj46O3iazpBCoRCCwOEDOLhPw56gF/5D8Y+TCSduAvJzzz2H6upqjB49OuL7ixcvxuLFiwEAtbW1KCjoXlI5HvTWji7PEexhh5DmUdAVZLjoq9+Ex5eO/7vxWpfJ5pTiMfBl50MNGqiadwLwYeRE99FgBcUwGMHk0TnIGjUaB5QOkEAQPo/Gg8IUCp+uYOzYrD77IGqHu9ChH0Fxfnq3whqRbjtaXxx74qm444EncM0qfsezGk6AV1OQolEAH7mOJaqCG/7wKCaVVsPnVbu93xe8mTkgQYZ0D89kkJWimxHhDNPzfEMeHduXsflhlxeqaTItKMgCAHziTwHtCvKFnPDgx/KxGdjX7sf+I360+0Po9HRZO2/GgLrjToRn2kHc8t/3UJjCsODKr2Pb1rcQ9KRjenkNPKoCXSVI1RTkjxr6e3fSl374oNMDr0bhDzIUFOQAAD4NtCIQMqB2hRA0DKTqCjK8KnRFQcmYdKz6pBWhdr/p3wYADNTPk9+rCn9NpUBnwIBXo8hM98IfMuBRKQoKsobdD3k456odwVYYjGtnOoMhAHyh0hQCBoaCguxha0skeuuLHcFWEAIEQvZ4AADfET9aaRv87X5kpWooyPBick4qVIVie7AV/qABLWCg+riTUDZ3ATqDvPpiwGAIGbz4QdBg8HkU6Ao3zXsUiswUFR5VQYFZIGm46MuYSDvix5HPDrv6oTMQwm7jEDrauOIiM90LQoDKgkys3X4QAEA6/Kg69kSUzF0AgzFT22cLyOSR6/DZnBo8v30zyurnYnxxFXwpGjJTNVBCMHXi8I2R3vqh09OO6uNOQsW8E9EVNDDKp+Pkz52Ggx1BkA9WQZTtmDSrEpXHTUbQACZnMXyraBfu6qEOVY9sfRkA12gu+MIiVB93Erwq5fPH2JyIFS0jkTfawBF/CJkR0sIBQGZu0HKdAaL3xaeBVhSnBQHYAvIZVZPRFWIABiYg3/DKbiy7pJpfOxDCb55b3+fPerNGgRCCosnZGOXT4VEV5I028ObHrVAogT9oQFV48KvBmLWmdYV4Wrm8dI9V1c/f7gczGDwqNWO2KLLy8pGfk2qllU0k4iYgP/zwwyPOvQLgC7WVgsk0jZ154WUYN20mfv76XuwG8MszZ2GcafocaJodVnYKAG7ugLiWw5zBz92/86Z5ePLzWFSdK62uA1a9EdY2gsapuVjxviN/JaGYWVkDf8gYsDl8T1uXJQh6VHeBjERJHWNai7sFIulmcKQ4RqE8tVB2qoZNuw45TKkEhgh+2LUN2l+/jR37PsVfmAFCKXRdx0//+Bhq6+dYZq1EdLW0/K4Jc72mOMxvukJRmJmC3FTdCrRM0RX4g4bpH+/I4gDb/cdZ5t2ZRmgkI8YFxxHYqSsYkwT+x4Dto9j9dbf5VnW4gAnXJAr+RTureRmw51/hgsMYD87L8GrIS9AAPcuFKPx1lxmbH+fMZy/6wHomCIGXMnQa/ADls/fxyj/ehxEM4fF7NVz/u2Womz3HOl+iQQlgmGuGV6PY1LwKTz36MPxjzgSoED6ZFXPywuMP4b4l1wPX/L3f17qpUsXPf/Mqgqabzvwzz4OzBml/0BSKzJTo1tq+rquRXIF2HOpEfgwESDs7St8XCFE8TKXUyoajmUWYdIWammJ3RiYrywzhSoxAiK9dHoWg0xyjqkIQon1z24wXcfFBbm9vxwsvvIBzzjknHpcfUqiZxmr/kYCdWxhAWXU9xoyfBADYfqDD5VvZ1x1qJIQPsiWAweGDG+FB64kUTemW/HyguCd6c/ECwTXHTsJJMxw+csxeGAgBLgxGLyYSDQZwH1US5r86iPbHGvHdOOckQvhCJ3AGJXlEhUMqBADbr2trcxNCLdsBM7JbJJ7fsrrJEhKm5voiJriPP2LpIa5XVCr85blAw4MUbR9l1RR+RQCNeJ3nVXb7H/NutH06RzLTRvncAVfm95/uVZGVkN+/m/AqgE4IIValTyci/aGY35yCowj+EcoCam5ChW9kfponYXzy+4oYx5pVZdMe/9b7jieKEuCBi2tRl8Mnm/xxExE0Sx0HAwFsbX4DGSJQeFjvpHeYKTyJ+9m8dg2uvuBM/HPZAwi17QcATExlOK+8AATAtg1rcN+S680Km/1HHzsVNyx9BJd88zrcuJSXqVcVu55BPKYPsZa76X9GjXAM8BRvbV1BhKLlgI3ankibWIecAbjWXWssOv4mhGe7EM+tKHVOHPEoiUZcWuXz+dDS0oLMzPgGBAwFlHATZ8hgaHNEBBMCTMlNBQBkp2oOB3U+QB6+pHpA1xuTYe8qraAvU+MiNCfxwCmYWBoQwlPY6I6HwdZ98f+X1/UvD/JFVQVI96qOXKrOhSJxpn9nsIL9ml1cRizo1g7f/L9TgyY+P6u2gVfVcqAqKsrr5wodIrJT9UFtvIaKSPlWbU2f/bvzPVMecltJ4NgMOgTDFE2xrjOyQ/Q4mSlaWNYPe9FJBnjFTr2bQCC+3xRN6fZeyZh0vqlyjRNijxMrmwc3+/KcyskRshn+yIpxrSqEm/0VijLTPcQ5z1FTAnFWoTy9EFB/vRA7P3zXcX6KkpoGM8d0Ys2RAsUx161/8zW71PTjPwZW3Ic5B15Hro8H8m1Z3QQj1HsFwWh88u5beKv5DZTVzUVRVR0USqBZSom+B7nHEjG/OaGEWuN6oIi15Wt/39znz1xeN95qUzjODZkYp1bgnhiH4rmEKQibWb6Elp6AoKowM2E384kpticxBHxweDUKnVJbaCUEC8vG4mefK8KUXJ9rkQeAlAGErqbCD6+ommWdK/5ms8m5qaAEOGPWaIzL9FqTnbNwhQtipysqNqOs+8rnivIdJjHbZWV0ugflBcPrZ9gbHlWxXGsAXrAFMBdA2n0SEr7kYtMj5sfp5bWYf9Yi6wOEEJx30SUoqa63PpeoRBJSRBETjZr5oomdF9Ne8N1VKok5K7uyFFCuiRDH14zPGoY7ij/ObCAAUJyfjjHpiefPFwlNoa7y2U7sDaU7+4uIqRCaPudx3MJgz4eqIjZQ8Z8XeyNSGzXTrO1VKVRh1nZYVux/9twqhJWtzU0IBQOu8xnMcGlGq8clnpLKKeRXz5lnl5o+uAd0/bNIz8yxvvfSugZoHp2ngRsA995yLf529xJcd+lZeOHxBy3LVTw3DuMyU7rNkqoyeGHt4ofWYcehvgVj/voLpXj16w04ZWaepeAKhWnb7D7iz51P54HV/Jnm76V5FNvCQ+y1SVXstSsRFTkCKSAPAdTMCyh2RUJ3oSoUU3NTXRrV8AmuP4xNd2qPnVo4kRILiIcCOUVTkOlVcWntONx+RrEl3Cimf3b4008cbR5IP4TvUkvGpGNcVt9LhA4HhACj0zzId3xnk3JS4VUpvJpiCXbO74uAwKcrDvOVLV42nrEQuu4BoRSUKqisqrYWvkTWlInvyTknzshLQ4ZXNat6dW+7GNeU8rGlKlzLYz07/MymeV1MvEN9J4lD6ZgM5KVxtwFKzHLNCbzohJNqFtEJRxjaownPQrDmGil7LNjPC5+HxfHcopaAjvkmabqC0Wluv3FKCcrGZkCl1Cw6ZJOdwr9z5zpiae8oQUltAxTVrZljjOGttW9YfyeiC5LITEMoQXlNPb5/822YOG0GKKVgjOEvd9yI5X9/EI8tvRsAcMu9j+GL37xuQNcKBXkguREKYenPfoh3N67hyhrET9EwJsOLFD3MX5nFZoN3/bPbej3m2sYpKB6dho9bO6zxoZuVOp3Yay6neDR3z/Q4ihWJDFpu65Zp6SNI4JWKkzgSxAiCT9bEHhyOCYy/bw92S+M1gJEyrcCd79Jp4nBeLx6EC/1i4tYduX4BYPooHwA+KQ4kaOTdjc32okBIXHf+PRGtVWJBF/egWTtr+5/IH0wJ97l75v57AELwpWtvBqUUhmHghh98D+9sWGN9NtEJb6O4P6eWQVA8Og2E8CwEABd60j2qw4THP5AM+W6HAuGmYz/3yXXzmSlaRG2/02zb7T1znlMVcxMphGPHeBB5gZPDuYIrUMZHKYEtvluneD91lA86pVCJ7ZcPh/Z1ZkUtfvSHR1DbeCqoooCYuYPL6hoS+vmw5j0CbF67Cv938/X45P13YRgGGDPg93fhviU/wl/uvg03fuV8UAqU1R8zsIs5KvQZhoENq163xlE815JwwUxUZI3GFfXj+3Terj64o9RPyMbutk6XJt8TwcpjKzvc67Y1/zqeXVuZQczNLRnQej/cJEaJrRGG8EN2DRpwU4JwjheBJj5NQVtXcEATeIEzSt3UqKRoFJTyyPaJ2VxDGQ8UQu0HArbwolF7AZ89IQtXNUwCgSOLAYArZ0/AfW9+EvXcAvLoD7D1zNNRXlMf0bc1kQhf3CIc0U1IEP3nVSkMBmxZv8YqrfvUH+9C4+nnWWVY/X4/Nq9uQnlNfcL2AWB/P3m+SC4AvHJeeF+l6ioIzKhng1dDA7HLmYrj0z2OQitDeRMJCiHAzLzYBNkmCmJhDX92nIszVQjSdBVBg4HBrrwofNKPBEJJ4WLRE7ZliHR73Tl3CncSavbD9PJafO+Xdfhoy1q81dyE0tq5KKqstY5NRBTzi/KqCta+sRKBgN+t+TcLejDGEAwAm1fzwG7yp/vArryvz9c5Id/AigPbreIZmqajeg4XtAlg+XrHA6fx8/vHT7EqSEZjwbRRyPSq+OUr/S2o0B2eAYa4BF+RTtOJNdc6lBrieCHQu2Ug/n6KJqphJuwQtJAC8hBgDS4IzRZPW6MQIEjc5htKuf+twYDcVA0tRwLRTxzG2Ay3kOFRqSspfjzzCirU9gUEuBkVgKXhAIAJWSmmj5KdxYAQgpNn5CE3VcNtL70f/QKBLmh7P0BxTYMZvEBcwlKiMTnHh0xv98fNGXTW7T3YpmFNIdjkLK0b4AeouoZgANB1HQ3zjouogU0kCIBcn46JOand3qMEMBhBpldDelhKJOKYgMVxYuPFGC+NrlB7UwaSiKNg6Mj0amjrCiI9whhLVkgvUpxTp6YpvLgtCwGMMD7PiOIPDoE5WUdFNG2bqEypKwRHAoACRxYPalsUiivrUFZTD8NgVknvRPU2EdlsdIVg3nHH4Y933wG/0WkLyYSAKgoYY9A0DbUNxyAQYlB/ewf6vnoCly+oxvwxT+D1fz0BQoDTF16I0up6a1Omx0m5BADU4VNdXpBhrQVVhRkRy0MbYJg6Ki0m1+4MhLi/O2xllhbBx9s5Jzu12+JzqbpiKcacAdgadXwugdcqQLpYDAm81G34ok545DDsSUss9qlmYNJvzy1HuqdvRT0AW4MstGqamaw7EYQkTXH4BROergnmA3H8lFwolKBhcrZrgfOotpm4vCADD15UiYcurop4fqpquGHpI5heXgMKvmPVzVROiehnmOvTrTyu4Qj/LCcifyv3veXBZ1VzHKV1NZ6z8+d/ehwXfv1aPPOvf6Nu9mwAxAp6S0ScWUbCEQLP9Ly0bsVtnAKClZfT6Y4Ce6KmhGB6jBaLZCHdq1o+gCMFp2AbDSvnKgFSNOpyMfGKldgcE2m6Cp/e9/k1kbBym0cQ8Xl8BwWFmR8Z7k236EPh2ucRxwznDfQD7jLE14RT5x+HR5/5F75w0ZesstO67sFXr1+Cy675AX711ydRVsODkxkAPPQdINDZ4/mdFFXW4hs33Y5v3vgLlFXXWX0Sb1c9NezyYtNz40kzMDW3u3IBIJbvdiwQygenpSZ85E0bxedpp7bdOeZ0xV2TYEYen5MVauewSPQ5a+SoGxIIAmL67PDRoVKKkGGYGQlCpgmMgNcCIzxCNcBNyF8/ZhJufbEHzakDISB3BkNQFWKl7gGL/+SnOTTZBKYZMMT/Lsj0Ytkl1QgazNIoa9TWuts+lNHNokRRUFRRjY5AyDIzUkowKTvVSvWVLETaSWsKRXlBBlZ9cgDU1IaWVdfjlnsfw7o3V6J6zjGYXl6DFE3B5NJqzJmRhx1mmXAxESUiRflpEUufW9ryKCPXEoJNNwwDgE9X4Q+ZWkLHOVSFRK1mJUkOxFxg+d6y8Pftn8zMpc6VAwYon1asogbiHEUJvhj3RE9KDzF/pOgqL0EdYqZAbL8nNhEqsX1JE1V7pynELITChbLa+jkoKKrAgrPOw2uvvIKsnBwcPrgf1XPmobymHiGDYdPqJhjBELDnfeAPlwHHXQ6Un9rjdd7d2IzymnoohK+9diYQgnjbGhRK8M1jJ6MzYNbfFYoFx8Yn0mdihRUTIq4Z4dRZKRoOdwXRGQi5MquIn9QxZrNSdGR4NVcGlXgr8fqC1CDHGOFekZvKc3vm+nRMMTNXmEpUPngckbJix0/Bd2I/OGFa79cBrFROgRDr5kQf9x0wpZYflcsUQxw5EuFO++KshmUdC4LvNU7pdv5TZ+bxScPsP8Xs21FpHoeAnTxEarEI4BQbBYUS1M+Zg3Ov/CaKK2tt/0PHZ+L9vfeGz6NG16RHmYid79nmPGKNI4U6XXQS13Qs6TuZKRpmjUkHIXwjJLJ0CKw5RYwDay6w37MCXuOuLhg8hPCsFZMjuCaJudSr8oA94vwHe51RqJ1Oc5RPj6uPbU9QwnNci7Uh3aOCEoLSqjrMqmnAn26/AQ/++nZcd/m52LJ2NSgBKuq5dY0QAvg7gFcf6PEa5N4rcOvXLsS2jc1QqQjmdK9V8YQSivlTc3HCtFFWe5xjPRxC3HnBB8qdZ5WY1weIw00y2rxakOHtNo6cGSsyvRpm5qdhmhmM79zoJcNzKQXkIYAQoNhMZm+9BlN4IXzQCRnBzuMphEOCktFpuG5+d6HQycLysdauLWgY5g7YLUjEE4UKjbZbGLYmIWsCh7krDkswDrtfasdluc59cXUBLqkuNP8i1uQf73seKOJ7j/aeeEf03Tsbm/HYfXfjnQ08g4dXHXgmlESC9jBundkMnNoJTeGLG/e9j65dkSQfwhI0ISulW8l4EeRpb5o41AwCJrCDvXrSviYLlACZXrWb+5RXoy7FgtAwivmAz6+2Nt5rulfoql02OJHITtXMdZI/74xx96HSMRkghFcRFXEYgUAA695cCUKA0uo63HLvYzjxnEugajofCD3A2vYiGAzgrTVNppKhe9xQPFEpXDE8zjSG4ZxVMpp//4P0sSjI8FhKN2LKKc6CZpGglLh8tVM12ymBgGBSTirSHPEkE7JSAcc8nuhIF4shQHzxHlVBmq5Apba2izgmMK8qXCJsAVqMmfKCnhO4X1o7zvqdQfiw2saheA8+xbErtxtjC72AQ1MMe3PgFO7FpG6ETQsEBFRUgjJ9vZOlUlYkKEHUohaUcFccPnYItjSvxs2LeSYLVdfwq7/8nUelE/tcyUh+mgeEAC3t/ojvO4VjAoAxAkIYUjSFuxhR99iRjAyifZ8Tc1KhUIKdhzpdx3kUig7DsPJkO8/T1NSEFStWoLGxEQ0N/avYGW9SNBU+T/flelquD3vb/Xh/XztAGAgj8GoER/zMcs2y51lbEZOo5KTq8CgUa3cc7NZOQoCSugYrMFnTNNTMOQZCSTKrqhZTy6ox97Rz8Peld2FT+wHAl9X9IjvfBiEEqqqhtK7BivVwXs8b582DGpbiwZIPwr6/6sJMXFQ9DiFmWBvCgWIwe20W44UHRPPvZXR67yXai/LTsLutC9sPdkQcZ/npHry3r926RqIjBeQhQAgpzkpulYWZWPnhfnuSAlxVv5wTGIB+D3aXdpbFX1ByVT0L0/KoBAgB5sbAfCBdApAt6oqH9OzSMXhy824A4EUlxANs92BSPHDhpOpKtwpF4Ti1GhtWrXRlstiw6nUrSCU8H2Uyke5V4VEpWqNkcakel4nNu9vQFQyBgMAgDGDOEsJ8k5jp1eKavUUSW3oO0HP6INubbF0hABTXc7P9rQ246OzPw+/3Q9d1LF++PKmE5JIxkf2nKeXBWXze5/75HpWiI8CTl4m1Rvj9C0tMIrsh2Wuhs6Imb3tRRS1uufcxvLv+DdTMmYfyWu6DbMX3UILp5bU476vfwdbF5yH4zcfdJ3/nNZD/3IPaxlNw2qVXobiy1jq/+OlRKUrj7H6imIVh7NzuPAjf6BZfxKwVkA4ycw+PiArfTHGBYky6B6nhxUsiQKmwBAOMRFZZRa2om4BIF4sYk6I5Cx/bOHNV2oKju1QqcQyonvxof3F6cYQJzhnkBkzN9cXidgaFs3IOYAvKljmcuouaUMI1QE4XVWJqoS+oKsSfF1Xga3MnYt7kHLf7ikPjnGxMzklF2ZjokzFx9A8BQfWcY6CZmSw0TUNdwzwrwnlidgpmJXEgkq7SqIIAIQQFGV4UZqagsjDT7cvmGGO5Pt2qYDmcNDU1YcmSJVizZs2wX3skQ0CsbCXh5Kd5MDnH55pnxDzqUd0R9FvXNsHv9yMUCsHv92PFihXDdg9DDXHM+865VGhWCeHuKiIzTjJsosPdCSw1CiUorqrFZV/7Dspq6i3h0GMG9QlXhJmVtZh/1qLu521vBQKd2PD6CqFWMfvDEZSWAFDqdhukBJYrmRMzzh0EPBvW1XMnDviajDGrL4ScMpA+yUzResykZN9TovR2dKSAHGMyvJGrQgG2a4XYmTlN6/bkzo+lBLh/UUW3c0zKTsGEbDtQgzF3wQRxruzU3s0hQ4l1n+J3hwCvEOrqC+cmQaUUKhURseaER20/42Mm50CUUuUPMbHSFyXB89aN3mrRO98iBCitrscvHngCl13zA/zyL39HVd1sa2OgKbRPu/xEJjy9m5Ncn46xGV4rMNGpIYznwt/U1IQFCxbgJz/5CRYtWoSmpqb4NGQEUjM+K6qvrKZQ5Pp0VI/LdG2ShYBIwIPSJmSlorGxEbquQ1EU6LqOxsbG4buJYUBsoC1rmngddp84NwyJjMiD7HaRCYtlIe7nnhC4KtQSAMefsbDbuRkIGDMQDAawtbnJETAmzpk41VidLpdinQwX2ERRD34QcPKMvAFfjzEhmzg3Wnx96k/ge4qmYEZeWo9ZV1J1BZWFPbuRJgLJvZomGU7h17nAA46gNoQsLUikKGMGd8L7oMFcOYcBLkgkAsScddwTnZ3BQ0x2CqWOICxmpdyxhWt7ErD8sBznc+bDHWmIPhEmNEp4ujeRnmhCdorl03U0QYnpZgFALKbC3D7crFixwtJOir+TyXyf7FhzRZiwQwiQ5lGRn+5BfkMDli9fnrQ+yL1h3bs5/i3tI7HL1zt9kRMdSoDCzBRXukZbQLT0yagZn4VVn7S6XGzEd19UWQdssC06HspgvPUCQooCVdUwq6bBGjuUEIzN8CI7RYMWJcvOcCOC+glxusi4hVVb62vHqwwUf8hwWSvFdaxYon4SbT0Wa3os09INFVJAHkYI+Oxk+xXB+unTFQRCQSFVRk36fXndeFNk4g9CIMSQ5lHBTL/j0ekejMtKGfqb6QNiofKZWk1xv4ojPFdU1nH6IDujzwkhoIyBORc/2KYPQmw/1MR/3PpPVWEmmrcfAADXAqBRCgaGvDQPPmg5Et9GxoGa8Vl48+NWAPaCT2ALA8OJ0E76/X5omjbitJPJgD2nMmthD1+gGxoaRpxgLCDm/8Q9q9QWitM8qsOKZ2vWExXR1rFmnn/rNeK0MLm1xYDQssJaC97ZuAbztj+L18adBgBYuqgSH5b8Cu+sexOzauZgWlmNy02rMDMx1k2BHThoz2uE8BSygpDhWAsxOLeFSTmpjnXVVkylDrCi4OQcX8SUnkIrnQxIAXkY4S4Eph8RceYDtM1g4rhIw+cH86eiKD/NLK3MCRgGcj0a2jpDCadBFffhdQSIMOau0y6q7JWMSce7+9phmIU/rCA8wgP6qFVlVPjVETDGNaqpugLDSA6fpv5CHdkZZualY8vuNstdJWRXXk3I6oFDjXgMREBoVWFmXCbeBod2sqSkZMQKYYlMRUEGtuxpQ9BgLveCo+G5EK4GDAzMYWnzqBSBoOEQIIGCjBSM8ulx2Uj2h/Cp3FauEEv4d77G1xUgAP69b9vYjBuvXIig3w/M6wDqF+KDTevNtccW/jRHtbdEwykPaNSukLt4zgRkelX86+3PwMCsdZRrwwc23i+sKsCC6aMcTjq2i4caZqHuK9Es2aVj0wedcWO4kALyMEJAoCsKvKqCkMG6aZKdlY/EQn/9gmn4+fL3AMCqLkQBy8ciEGJI11Uc7goh06vFJUApGgTuXIpcgy5SltkCMDW1wKmaAn/QcJl3CAEI4zpzMUGA8MIgQQOuROZF+YlbQW4wlIzJAAGswCPLVCoE5Li2Ln64NpSOZyYeCO3kzp0749aGoxlVsUssC2ExWlGFkUZWCi/GsO2zwxALg5hTnfnxKSEoyPT2fLIERgSNeVRqWUwBvjF+d187DnUGLF/dt9Y0IRQwM+K89hdg5V+x8pxLsOIfjyAYCEDTNNy49DHMO2YugMR0z7OEecKF1JDBLP/dL9YUwudRcPzUXFOQZWZKtoHdyLRcn2XpFVZKsfGItW02EfNvRyMxnG2OEvgiDnhUrvEqHcsj9kXSe1eRDABH/CFUFGTi3vPK8aW6cZiR5+sWVcrAzU6UANNG+VxJueONEFoIuAtJ2diMMOHXITgDmJKbikk5qW4h0KExFkcSYn9WdQhFkfKEjgRSNIXXvCd2VLoTvhgefY/y+KxUa5NZkJFY5lFJfLADuAgm56ZiUvbIHxeEEKR5VNSMzwqzQnKlQzJuocP3uiJ4zJn6TLhgiLVAVxUuVBKgtK4BimYri1RVw8H9e7lGmTEE/H68/M/HzSIXpNv1EgHLPxq2XABz/SSE4JyysZa7BU+vNvBA5ZYjfstNhxJetl3kEhdj6WhkZEoUCQohdjlIQuyd1MScVGgKtRNogz8cnxw4Ao+qwOdRcOL0PDPwIlxCZtCVyKnl4ol4qNJ0xdLk6CpFzfgsvPpBi0uL7DQRCS2BSFBOQcAIbNORqUE39UVmtT5gELEJSYUzNZGYDHkUf2J9/8PB6HQPDnUG0NphJLVmTBIbfLqCQIcBQhgY4xvLaGXNRyrOWYASgDhy7Q82T+5w4ZzbnGR4NRzx24Fk4T7DBLDWyOKKWiy5/wn896lHsX/fXqRnj0Jb6z6Ef0BTCPxBlnjrJ4iV7tRZHMRKvcb96sxjAY/CBeSBehQZTCjo+HU1lVsfgowNSvBOdqSAPIwIwTdaSJlilpd0lZ8O+3y3na5pcqoqTDwhiZtpaDcfWUvLA3uzEPZJKIRYEboE3J1AVM2zhWo+wYlMD0cDNeOz0PTRfve4SLDvfbhJRO2PZPiZkpuKlnY/Ptx/JBmVpjFBaFctSxNzuPAlyTzBszl1b+uMvDTsaeuK+jkRi8Dd8IBZVXUIGQw3XHk+goEAqKJAUVUYoRAUTcP8M88zlQ0MFQXxLQwSCdsXGFY7wQjSPSpajwTAYGcr8aoKPCpFuz+EpQvLsPjxTf27FrHnUY0Ky4MdMH8UuPJHRArIw4hzZxx5ruJa1oARsp3kKQEx+MNhpz6zXfEJAF2hCRkVaplnIgjAEEGJJII5DaZrhsHvmTKAEWZNGITwKGxq8HsHji4hydIgH61SgIN0j2pVDZMc3ViBSkfPfrkbRflp+Ki1Ax2BIOCYJ3SVYmZecsRoKJRErSUQKUUqYCtNFNPCyONfCLasaUIwwCuPggAnnXMRxhSOx8yaOSiuqDXPSRLS0qCYN+tc2/k92gomy9VQsZVqOT4PNEoQMPr+EOSkaID57GgKtbTRJML6fDQhBeRhZFJ2Kt7+7DCAyLt5Ah6IdyRgQGQqMGVIl1+uODYYMqCrSkIKxwCstjvzNsP826tRS1vOH0jH5wivCtRm8MADJjQhjvPxjYHiCuY7Wuh5k3V0MSbDizEZ0r1CwslK0TApOxUft3YcldtHn0fF1NxUtBzx45PWDkvIAWD62yY3zmxPUQ6w8+wToKxuLlRdQzAAqKbWuLymHp1BI+HnT0qANF3FYX8QgHPet9c8vm46SlKbn5ue58Nbew736To/WjANZWMzrDVY5FwWvvyJ3k9DiRSQh5FRaR6QvdGLOhBiJuUm9uC3cxzaqVcEAYPB10PlsXhilzUl8KoKRjlSvhAQeFWKkKn4qyjI7PZZQrh2ELD9o1xZPwigmyWrUzQFo9M9w3FbCYGtPT5K1WQSSRQUSjAqzQOvGdh6NOLVFGR5NXyKzqj+vMmK8JMlEfyphUAn1gmFEhRX1uLmex/FujdeQ9WceSiurLPe95oxQInYP0IZplDispKKDQ9xHAeIv+1y2d87fir+ve0zPLJhV6/XKhmbbhVhIYArBSABD4BO9LSAQ4UUkIeZ8IFtvU7sh1+kRqOOlBUEsEouC8tJIGQgOzVx0ro5SfOoKBmTjrXbD2JSTqoruwY3gfJggGh5SkVKuBRNQagzCF1T0O4PWS4WAKyI5QyvmjDVA4cDZ3J7iUTSnUTK5hMviEt4Ghn0NOfZlkVukRQBe0UVtRhfXIVUXbFzp4OnSxOfSzTyzU3ehp0HLc2/nSbV3vV0i1cy30rVFZw+azRmT8zGd595q9fr2ZmizCtZwnhypwYcLMlvc0kywnMfd3+fV55TCCxfKjsdGiB0h4TYOZATFZ6arPvrYiIT2vJwLO054cF6Tv8qEEfaG4e24Gjig01rcf89v8Tmtavi3RSJRJKAOKdF4ao3EojmXuZVFcv9Qld5kRRnERBKYa0ZIoZFnGKUL/Gsj9QMlOO1EyjGZHhQUZCBwswUt4XZqUAjdhEuO/1lz8LtJdWFvN+onULU8vN2aKqPVhJXuhqhEMJ9fKbkpkZ8T7wfMoxuDvLcZxcwABzuCkFNIQlvRiTonnrGFawYHmxBHLl+zc+JgAE7mld89uijqakJX1p4Brr8fuiajuIXl8vKbRKJpBti7SjI9LrKEyczJOynYFyWF1kpGjbtOmStH5pCHJXziEvo48IzQXaqhkk53dfiREEIvQAvhjM2w4sPWux0sOEKN01x58rvLcPR54tHO+Kc7KBOsf4ejQooJ1KDPMwIP+PUCJpf60EmXPuqWAPW3vmKAZvmUcBY4gdeRHq+xqR77UwMYe+r1JrKHOmJ4OgH25wkPn80paBZsWIF/H4/jFAIgYAfK1asiHeTJBJJAuJUuCT6OtFXdEcAmROnYsVpb3UGr/EqtHxdSdGUhFewONc89xpnB+9bbpnmT6E1F4IxJcAjX6yJeg3DPLGo4ivqCgjF1VEuH0sBebhxKEa7v+cynXAfZHtXB8fAJ9AUrjnWk9B5flxWikP4dbc/zaOioiDD0hZz+OSnWUF5FAqlI8p02FcaGxuh6zoURYGu62hsbIx3kyQSSYIhikdlejWkJbAbXn+pKMjEuKwUjE6L7DpAnesl7JzIVhEqSqBRYq2xiUw0N0IrNMkhJwC2tlkh9ibC6oMIfG3uRFsAJtx1UaVwrc0J3kVDzsh5cpIEXaVWEm4nWSkaDncF0dIesF5L1RQYjOGIP2T5GykECJoDn7HEr2veUwBEtM2CKioDmpHKosZ8iqagM2hAVygCZgqMo+0BbmhowPLly7FixQo0NjZK9wqJRNINVaFRcwknO9EyFjnd9oSyyXIZAM+prytC8ZQc1kexvDnb6cxoZQUdEjt437bM8l9IhJusH5+FeVNyrEA8IQxrZuXF0ekeKJRg+4GOobu5JEAKyMNM+diMiH5BKZqCaaN82H/kAIgjlYvwoYLpYqFSAkoor6JDKE8mnsAQArAoOnNCopswuA+UMI/xCU6lFOkex+6YANNHpcGnJ/YmIdY0NDRIwVgikUjCoAQwHEsiAXcxCYQM0wpJzcxJia8dFdbjiBmvHLmexU+xlioOjTKvI9D9RoMGAwWBQZilbVco99mmhAcGqsrRGQTvRLpYDDM9Oc3bD4QzZYu5CyT2blKhfJeXmgSpjIRfVLfXiftnpPedx1Bn35haAUp4irdErIIkkUgkkuHF1hjbvrm6WXpONddNpwtGIsPbyO/BqQgjxHShMYVZSglyUz2u9VLcpzjPT0+d6Tp3bqrmkkWcPtyzxqQjL01HTqqO8gQswT2cSMkiwbB9jW0TitgdUnPkC/8pn5b4X1/1uEykRHApEQ//jCjlT52RuIC9GxYTmzOQQCKRSCRHN15VQU6q7lKuKNR2qUjVFYdyhZhVXBN7DSEEKB6dhjEOtxICmNpd2+94cm4qxmWmuAp+WOlQAczIS8Ok7BQAwEVVBbi4ptCVFcr2V+ZFvITwHEm5dTRxdN99AuLUkApBMFVVuqV0oZTAlwTBF9E05uKhzPBGLnRCHOKvvXl2ZrYgqCzM7DWNzUimqakJS5YsQVNTU7ybIpFIJHGFUoIpub4w66NdfEvkCBbvF+WnYXxWSlzb3BOinR5VsQLxAL72qcTOXKFSLsaNyfBilE93HetUuJ1RMhoA0DgtFx5VsdZOIUzb1z1619RwEl/COsqoHpeJNZ8eAGAP1BSNwh8ykKorCBm8ko5GAV8SuFhEY2Zeeo/v14zPQrPVD3YOaPG38Jk6WmlqasKCBQvg9/uh6zqWL5f5kCUSicRVO8D0SVaoXR1OKJp4AarEXUOsdKZhr5eOTccbH7e6fI2dCBdNYXEWm4K5k3JQPz4LiiLeF9kwbPfFo3hJjYjUICcYwg8ZcFTKITzFmUe1B7JHpUmZ4k2Q7lWR7u1ZwK8szMSMvDTLv0rsmIHE9x8bakQ+5FAoBL9f5kOWSCQSgAuEIrWdiFXRrfzA5jFJsoBEaqfHrBqoKcR1TwKF2OlhnW6aQptMhYuJ4xqqKS0nS78MF1JATkCK8tOtCFLhL6WZqc/EIE/RFHhGSPL3aCiUIN2rYkJ2imVGcvqQHc00Nsp8yBKJRBJOzfgsFI1OR36aCFwz/WrN94XmNNFTvAHRcxFTIsprR85y4TH9iLm8QLoJelxo5h+k4OlnCQhyRkjFxViRvDb6EUyaRzXTrfCy0gSwggnEjnFGXprL12gko1Dub7zqk1Ywxk1jFQWZ8W5WXJH5kCUSiaR3hDueU4uaLAHe1eOyIr4utL5BA1ZhEOs98MwdhNgWaadXMiUEhNqCNwHXsPtDBqbk+oboTpKTuAjIBw4cwJVXXonNmzeDEII//elPcoEPI82joitogJkJzi1zCOyKQEcblPDCIsmw8x8OZD5kiUQiiY5wMzAzHwNgmJidCo9K4Y2QXSlZsF0NCarGuZVFlmsFbJcKYrldMFdMDxF+yi4hWiKIi4D8rW99C6eeeioef/xx+P1+HDlyJB7NSGgm5aQi06vivX3tLjMKIUCqdnQq/seke7G7rVP6SUkkEomkV3ihDIiirKAEyEuLXIkvmejJl5qYcUu2cOxIkSp+8iOtVG/RznW0M+yS1sGDB/HKK6/gz3/+MwBA13XouvR7iYRlHiGwqspRQjBrTM8ZIEYq47JSMMqnywdZIpFIJD2SqiuW1pgQHhg+egQIx4At1IbH4rjzGSu29txxHHevEEIxr5pHHOeU2Ay7gPzhhx8iLy8Pl19+OTZs2ICamhrcdddd8Pncvi9Lly7F0qVLAQC7d+/Gzp07h7up3di7d++wXs9gDHpnAPsPdYEB6KC83vpOLf710Ye7LxIV2Q8c2Q8c2Q82si84sh848eiHMZRh697DMBiQ6vegPaCjvXXYm9GNwfbFoZY2KBQIGW55YF9bFw4d8aMraMBgDAZjCBlAyGDoDIYQMgBDIVAp90X2hwx4UzQc7KAIMYadns7B3lq/SPRnY9gF5GAwiLVr1+LXv/41Zs+ejW9961u49dZbccstt7iOW7x4MRYvXgwAqK2tRUFBwXA3NSLD3Y5xADo+5k+0SgkMxlBQkD2sbYhGonwn8Ub2A0f2A0f2g43sC47sB85w9wNjDLuMAwgZQF5WCgoyvcN6/Z4YTF9sD7ZCIQShMHkgrTMApbUDhzqDvGYCAfwhA4EgA/EHETQYvCq1ckBrQQOZPh1ejSIYQlxki0R+NoY9T9i4ceMwbtw4zJ49GwCwcOFCrF27dribkVQ4cx0e7enNJBKJRCLpC8R0NRhpyyYl7p+CDK+G4vw0y99YpQQehfKgfodbhUgB50wjN9L6KBYMu4A8ZswYjB8/Htu2bQMALF++HLNmzRruZiQVomCIT1dQWXh0pzeTSCQSiaSvlI7NMD2RR076I14ohGJ0WneNODHLUIsAPIUSeBTieM/OWJHhVc2zSek4EnFJh/DrX/8aF198Mfx+P6ZMmYL7778/Hs1IGggIxmelIN2rHtXllSUSiUQi6S+UjKziUpWFma5iH+E4A/v5C7DrKph9wZiZH9r5GYmLuAjIlZWVWLNmTTwunZTUjM+KdxMkEolEIklKikenwasmb97jcHpSlLnSuZmCMiUECiEwCLPcMoRw7CwYInEzsmsVSyQSiUQiOapJ1dWjpriWSzgWrwG8MIrlfiGq7dmadalB7o4UkCUSiUQikUhGAMQhBIsAf0K4H7KosqcrBJQSV5BfhleLX6MTFCkgSyQSiUQikYwQygsyXH8T838UgFejVhYLYlbT86gUM/LS4tDSxEYKyBKJRCKRSCQjBE2hlqsFINwuCHy6Co9KLa2yM4WspDtxCdKTSCQSiUQikQwNlvsEE/mOGTTFdqsQfsoFGSnI9Un3ikhIDbJEIpFIJBLJCIKI/xxFRYQmWQjHlAApGoVnBGX4iCVSQJZIJBKJRCIZQZSNzeCaYwhtsl0QRATrjc3wIt0jHQmiIXtGIpFIJBKJZAShW77G/G936jf+S2FmSjyaljRIDbJEIpFIJBLJCCMnVYNHVaDSsKA9yOC8viA1yBKJRCKRSCQjjAnZqdbvb37cCoMwAHAJy5LoSA2yRCKRSCQSyYiHi8XUUUFPEh0pIEskEolEIpGMYMK1xkX5sjBIb0gBWSKRSCQSiWQEQ83MFQAXln0ye0WvSAFZIpFIJBKJZATjzIksnSv6hhSQJRKJRCKRSEYwlYWZ1u/S/bhvSB27RCKRSCQSyQhGoVyD7NUUjM/yxrs5SYHUIEskEolEIpGMcAgAj0qR4dXi3ZSkQArIEkkS0dTUhCVLlqCpqSneTZFIJBJJEiFdK/qHdLGQSJKEpqYmLFiwAH6/H7quY/ny5WhoaIh3syQSiUSSBEzKTkWqrsS7GUmD1CBLJEnCihUr4Pf7EQqF4Pf7sWLFing3SSKRSCRJwqg0D1J1qRftK1JAlkiShMbGRui6DkVRoOs6Ghsb490kiUQikUhGJHIrIZEkCQ0NDVi+fDlWrFiBxsZG6V4hkUgkEskQIQVkiSSJaGhokIKxRCKRSCRDjHSxkEiSCJnFQiKRSCSSoUdqkCWSJEFmsZBIJBKJZHiQGmSJJEmQWSwkEolEIhkepIAskSQJIosFpRSEEOTm5sa7SRKJRCKRjEikgCyRJAkNDQ248847oSgKDMPAt7/9bemLLJFIJBLJECAFZIkkiWhpaYFhGDAMQ7pZSCQSiUQyREgBWSJJImSxEIlEIpFIhh6ZxUIiSSJksRCJRCKRSIYeKSBLJEmGLBYikUgkEsnQIl0sJBKJRCKRSCQSB1JAlkgkEolEIpFIHEgBWSKRSCQSiUQicSAFZIlEIpFIJBKJxEFcgvQmTZqE9PR0KIoCVVWxZs2aeDRDIpFIJBKJRCLpRtyyWLz00ksYNWpUvC4vkUgkEolEIpFERLpYSCQSiUQikUgkDuIiIBNCcPLJJ6OmpgZLly6NRxMkEolEIpFIJJKIxMXF4rXXXkNhYSE+++wznHTSSSgqKsJxxx3nOmbp0qWW8Lx7927s3LkzHk11sXfv3ng3IWGQfcGR/cCR/cCR/WAj+4Ij+4Ej+8FG9gUn0fshLgJyYWEhACA/Px9nn302Vq1a1U1AXrx4MRYvXgwAqK2tRUFBwbC3MxKJ0o5EQPYFR/YDR/YDR/aDjewLjuwHjuwHG9kXnETuh2F3sWhvb0dbW5v1+3/+8x+UlpYOdzMkEolEIpFIJJKIDLsGec+ePTj77LMBAMFgEBdddBFOPfXU4W6GRCKRSCQSiUQSkWEXkKdMmYINGzYM92UlEolEIpFIJJI+QRhjLN6N6I1Ro0Zh0qRJ8W4G9u7di7y8vHg3IyGQfcGR/cCR/cCR/WAj+4Ij+4Ej+8FG9gUnUfrho48+wr59+7q9nhQCcqJQW1srq/6ZyL7gyH7gyH7gyH6wkX3Bkf3Akf1gI/uCk+j9IAuFSCQSiUQikUgkDqSALJFIJBKJRCKROJACcj8QeZklsi8Esh84sh84sh9sZF9wZD9wZD/YyL7gJHo/SB9kiUQikUgkEonEgdQgSyQSiUQikUgkDqSALJFIJBKJRCKRODiqBeRPP/0U8+fPx6xZs1BSUoK77roLALB//36cdNJJmD59Ok466SS0trYCABhjuOaaazBt2jSUl5dj7dq11rmuvfZalJSUoLi4GNdccw2SzXMlln1x3XXXobS0FKWlpXjkkUficj8Dpb/98Pbbb6OhoQEejwd33HGH61zPP/88Zs6ciWnTpuHWW28d9nsZDLHshy9/+cvIz89PypLyseqHaOdJJmLVF52dnaivr0dFRQVKSkpw4403xuV+Bkosnw0ACIVCqKqqwumnnz6s9zFYYtkPkyZNQllZGSorK1FbWzvs9zJYYtkXBw4cwMKFC1FUVITi4mI0NTUN+/0MlFj1w7Zt21BZWWn9y8jIwJ133jn8N8SOYnbu3Mmam5sZY4wdOnSITZ8+nW3ZsoV9//vfZ0uWLGGMMbZkyRJ27bXXMsYYe/bZZ9mpp57KDMNgTU1NrL6+njHG2MqVK9ncuXNZMBhkwWCQzZkzh7300ktxuaeBEqu++Oc//8lOPPFEFggE2OHDh1ltbS07ePBgfG5qAPS3H/bs2cNWrVrFrr/+evaLX/zCOk8wGGRTpkxh77//Puvq6mLl5eVsy5Ytw39DAyRW/cAYYy+//DJrbm5mJSUlw3sTMSBW/RDtPMlErPrCMAzW1tbGGGPM7/ez+vp61tTUNMx3M3Bi+Wwwxtj//d//sQsvvJCddtppw3cTMSCW/TBx4kS2d+/e4b2BGBLLvrj00kvZvffeyxhjrKuri7W2tg7fjQySWD8bjPG1dPTo0eyjjz4anptwcFRrkMeOHYvq6moAQHp6OoqLi7Fjxw48/fTTuOyyywAAl112GZ566ikAwNNPP41LL70UhBDMmTMHBw4cwK5du0AIQWdnJ/x+P7q6uhAIBDB69Oh43daAiFVfvPXWWzjuuOOgqip8Ph/Ky8vx/PPPx+u2+k1/+yE/Px91dXXQNM11nlWrVmHatGmYMmUKdF3HBRdcgKeffnpY72UwxKofAOC4445DTk7OsLU9lsSqH6KdJ5mIVV8QQpCWlgYACAQCCAQCIIQM340Mklg+G9u3b8ezzz6LK6+8ctjaHyti2Q/JTqz64uDBg3jllVdwxRVXAAB0XUdWVtaw3cdgGYoxsXz5ckydOhUTJ04c8vaHc1QLyE4++ugjrFu3DrNnz8aePXswduxYAMCYMWOwZ88eAMCOHTswfvx46zPjxo3Djh070NDQgPnz52Ps2LEYO3YsTjnlFBQXF8flPmLBYPqioqICzz//PI4cOYJ9+/bhpZdewqeffhqX+xgsfemHaETrn2RkMP0wkohVPzjPk6wMti9CoRAqKyuRn5+Pk046KWn7YrD98O1vfxu33347KE3upXiw/UAIwcknn4yamhosXbp0qJs7pAymLz788EPk5eXh8ssvR1VVFa688kq0t7cPR7NjTqzmy2XLluHCCy8cqmb2SHI/lTHi8OHDOPfcc3HnnXciIyPD9R4hpFftxnvvvYetW7di+/bt2LFjB1588UW8+uqrQ9nkIWOwfXHyySfj85//PObOnYsLL7wQDQ0NUBRlKJs8JAy2H0YKsh84seqHns6TLMSiLxRFwfr167F9+3asWrUKmzdvHqrmDhmD7Yd//vOfyM/PR01NzVA2c8iJxXh47bXXsHbtWjz33HP4zW9+g1deeWWomjukDLYvgsEg1q5di6uvvhrr1q2Dz+dLuvgVIHbzpd/vxzPPPIPzzjtvKJrZK0e9gBwIBHDuuefi4osvxjnnnAMAGD16NHbt2gUA2LVrF/Lz8wEAhYWFLm3o9u3bUVhYiCeffBJz5sxBWloa0tLS8LnPfS6pHOsFsegLAPjRj36E9evX44UXXgBjDDNmzBjmOxkc/emHaPTUP8lCLPphJBCrfoh0nmQj1mMiKysL8+fPTyo3LCA2/bBy5Uo888wzmDRpEi644AK8+OKLuOSSS4a87bEkVuNBzI35+fk4++yzsWrVqqFr9BARi74YN24cxo0bZ1lUFi5c6AqATwZiOUc899xzqK6ujpvL6lEtIDPGcMUVV6C4uBjf/e53rdfPPPNMPPDAAwCABx54AGeddZb1+l/+8hcwxvDGG28gMzMTY8eOxYQJE/Dyyy8jGAwiEAjg5ZdfTjoXi1j1RSgUQktLCwBg48aN2LhxI04++eThv6EB0t9+iEZdXR3effddfPjhh/D7/Vi2bBnOPPPMIW17LIlVPyQ7seqHaOdJJmLVF3v37sWBAwcAAB0dHXjhhRdQVFQ0ZO2ONbHqhyVLlmD79u346KOPsGzZMpxwwgl48MEHh7TtsSRW/dDe3o62tjbr9//85z9Jl/EmVn0xZswYjB8/Htu2bQPA/W9nzZo1dA2PMbFeNx5++OG4uVcAOLqzWLz66qsMACsrK2MVFRWsoqKCPfvss2zfvn3shBNOYNOmTWMLFixgLS0tjDEeff21r32NTZkyhZWWlrLVq1czxniU5eLFi1lRURErLi5m3/nOd+J5WwMiVn3R0dHBiouLWXFxMZs9ezZbt25dHO+q//S3H3bt2sUKCwtZeno6y8zMZIWFhVbWjmeffZZNnz6dTZkyhf30pz+N5231m1j2wwUXXMDGjBnDVFVlhYWF7L777ovnrfWLWPVDtPMkE7Hqiw0bNrDKykpWVlbGSkpK2M033xznO+sfsXw2BC+99FLSZbGIVT+8//77rLy8nJWXl7NZs2Yl3VzJWGzHxLp161hNTQ0rKytjZ511Ftu/f388b61fxLIfDh8+zHJyctiBAwfidj+y1LREIpFIJBKJROLgqHaxkEgkEolEIpFIwpECskQikUgkEolE4kAKyBKJRCKRSCQSiQMpIEskEolEIpFIJA6kgCyRSCQSiUQikTiQArJEIpGMEG666Sbccccd8W6GRCKRJD1SQJZIJBKJRCKRSBxIAVkikUiSmJ/97GeYMWMG5s2bZ1XguvvuuzFr1iyUl5fjggsuiHMLJRKJJPlQ490AiUQikQyM5uZmLFu2DOvXr0cwGER1dTVqampw66234sMPP4TH47HKOkskEomk70gNskQikSQpr776Ks4++2ykpqYiIyMDZ555JgCgvLwcF198MR588EGoqtSDSCQSSX+RArJEIpGMMJ599ll8/etfx9q1a1FXV4dgMBjvJkkkEklSIQVkiUQiSVKOO+44PPXUU+jo6EBbWxv+8Y9/wDAMfPrpp5g/fz5uu+02HDx4EIcPH453UyUSiSSpkLY3iUQiSVKqq6uxaNEiVFRUID8/H3V1dSCE4JJLLsHBgwfBGMM111yDrKyseDdVIpFIkgrCGGPxboREIpFIJBKJRJIoSBcLiUQikUgkEonEgRSQJRKJRCKRSCQSB1JAlkgkEolEIpFIHEgBWSKRSCQSiUQicSAFZIlEIpFIJBKJxIEUkCUSiUQikUgkEgdSQJZIJBKJRCKRSBz8P+frAqWKeNteAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig1 = m.plot(forecast)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you want to see the forecast components, you can use the `Prophet.plot_components` method. By default you'll see the trend, yearly seasonality, and weekly seasonality of the time series. If you include holidays, you'll see those here, too.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig2 = m.plot_components(forecast)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"An interactive figure of the forecast and components can be created with plotly. You will need to install plotly 4.0 or above separately, as it will not by default be installed with prophet. You will also need to install the `notebook` and `ipywidgets` packages.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from prophet.plot import plot_plotly, plot_components_plotly\\n\",\n \"\\n\",\n \"plot_plotly(m, forecast)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_components_plotly(m, forecast)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"More details about the options available for each method are available in the docstrings, for example, via `help(Prophet)` or `help(Prophet.fit)`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## R API\\n\",\n \"\\n\",\n \"In R, we use the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and `plot` on this model object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We call the `prophet` function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data and are described in later pages of this documentation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function takes the model object and a number of periods to forecast and produces a suitable dataframe. By default it will also include the historical dates so we can evaluate in-sample fit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" ds\\n\",\n \"3265 2017-01-14\\n\",\n \"3266 2017-01-15\\n\",\n \"3267 2017-01-16\\n\",\n \"3268 2017-01-17\\n\",\n \"3269 2017-01-18\\n\",\n \"3270 2017-01-19\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"future <- make_future_dataframe(m, periods = 365)\\n\",\n \"tail(future)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As with most modeling procedures in R, we use the generic `predict` function to get our forecast. The `forecast` object is a dataframe with a column `yhat` containing the forecast. It has additional columns for uncertainty intervals and seasonal components.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" ds yhat yhat_lower yhat_upper\\n\",\n \"3265 2017-01-14 7.818359 7.071228 8.550957\\n\",\n \"3266 2017-01-15 8.200125 7.475725 8.869495\\n\",\n \"3267 2017-01-16 8.525104 7.747071 9.226915\\n\",\n \"3268 2017-01-17 8.312482 7.551904 9.046774\\n\",\n \"3269 2017-01-18 8.145098 7.390770 8.863692\\n\",\n \"3270 2017-01-19 8.156964 7.381716 8.866507\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use the generic `plot` function to plot the forecast, by passing in the model and the forecast dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"plot(m, forecast)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use the `prophet_plot_components` function to see the forecast broken down into trend, weekly seasonality, and yearly seasonality.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 9 -u in\\n\",\n \"prophet_plot_components(m, forecast)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"An interactive plot of the forecast using Dygraphs can be made with the command `dyplot.prophet(m, forecast)`.\\n\",\n \"\\n\",\n \"More details about the options available for each method are available in the docstrings, for example, via `?prophet` or `?fit.prophet`. This documentation is also available in the [reference manual](https://cran.r-project.org/web/packages/prophet/prophet.pdf) on CRAN.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.17\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/saturating_forecasts.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"import pandas as pd\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Forecasting Growth\\n\",\n \"\\n\",\n \"By default, Prophet uses a linear model for its forecast. When forecasting growth, there is usually some maximum achievable point: total market size, total population size, etc. This is called the carrying capacity, and the forecast should saturate at this point.\\n\",\n \"\\n\",\n \"Prophet allows you to make forecasts using a [logistic growth](https://en.wikipedia.org/wiki/Logistic_function) trend model, with a specified carrying capacity. We illustrate this with the log number of page visits to the [R (programming language)](https://en.wikipedia.org/wiki/R_%28programming_language%29) page on Wikipedia:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We must specify the carrying capacity in a column `cap`. Here we will assume a particular value, but this would usually be set using data or expertise about the market size.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"df$cap <- 8.5\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df['cap'] = 8.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The important things to note are that `cap` must be specified for every row in the dataframe, and that it does not have to be constant. If the market size is growing, then `cap` can be an increasing sequence.\\n\",\n \"\\n\",\n \"We then fit the model as before, except pass in an additional argument to specify logistic growth:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df, growth = 'logistic')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = Prophet(growth='logistic')\\n\",\n \"m.fit(df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We make a dataframe for future predictions as before, except we must also specify the capacity in the future. Here we keep capacity constant at the same value as in the history, and forecast 5 years into the future:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"future <- make_future_dataframe(m, periods = 1826)\\n\",\n \"future$cap <- 8.5\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"future = m.make_future_dataframe(periods=1826)\\n\",\n \"future['cap'] = 8.5\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The logistic function has an implicit minimum of 0, and will saturate at 0 the same way that it saturates at the capacity. It is possible to also specify a different saturating minimum.\\n\",\n \"\\n\",\n \"### Saturating Minimum\\n\",\n \"\\n\",\n \"The logistic growth model can also handle a saturating minimum, which is specified with a column `floor` in the same way as the `cap` column specifies the maximum:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"df$y <- 10 - df$y\\n\",\n \"df$cap <- 6\\n\",\n \"df$floor <- 1.5\\n\",\n \"future$cap <- 6\\n\",\n \"future$floor <- 1.5\\n\",\n \"m <- prophet(df, growth = 'logistic')\\n\",\n \"fcst <- predict(m, future)\\n\",\n \"plot(m, fcst)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df['y'] = 10 - df['y']\\n\",\n \"df['cap'] = 6\\n\",\n \"df['floor'] = 1.5\\n\",\n \"future['cap'] = 6\\n\",\n \"future['floor'] = 1.5\\n\",\n \"m = Prophet(growth='logistic')\\n\",\n \"m.fit(df)\\n\",\n \"fcst = m.predict(future)\\n\",\n \"fig = m.plot(fcst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To use a logistic growth trend with a saturating minimum, a maximum capacity must also be specified.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/seasonality,_holiday_effects,_and_regressors.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\\n\"\n ]\n }\n ],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=366)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"m <- prophet(df)\\n\",\n \"future <- make_future_dataframe(m, periods=366)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Modeling Holidays and Special Events\\n\",\n \"If you have holidays or other recurring events that you'd like to model, you must create a dataframe for them. It has two columns (`holiday` and `ds`) and a row for each occurrence of the holiday. It must include all occurrences of the holiday, both in the past (back as far as the historical data go) and in the future (out as far as the forecast is being made). If they won't repeat in the future, Prophet will model them and then not include them in the forecast.\\n\",\n \"\\n\",\n \"You can also include columns `lower_window` and `upper_window` which extend the holiday out to `[lower_window, upper_window]` days around the date. For instance, if you wanted to include Christmas Eve in addition to Christmas you'd include `lower_window=-1,upper_window=0`. If you wanted to use Black Friday in addition to Thanksgiving, you'd include `lower_window=0,upper_window=1`. You can also include a column `prior_scale` to set the prior scale separately for each holiday, as described below.\\n\",\n \"\\n\",\n \"Here we create a dataframe that includes the dates of all of Peyton Manning's playoff appearances:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: \\n\",\n \"Attaching package: ‘dplyr’\\n\",\n \"\\n\",\n \"\\n\",\n \"R[write to console]: The following objects are masked from ‘package:stats’:\\n\",\n \"\\n\",\n \" filter, lag\\n\",\n \"\\n\",\n \"\\n\",\n \"R[write to console]: The following objects are masked from ‘package:base’:\\n\",\n \"\\n\",\n \" intersect, setdiff, setequal, union\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(dplyr)\\n\",\n \"playoffs <- data_frame(\\n\",\n \" holiday = 'playoff',\\n\",\n \" ds = as.Date(c('2008-01-13', '2009-01-03', '2010-01-16',\\n\",\n \" '2010-01-24', '2010-02-07', '2011-01-08',\\n\",\n \" '2013-01-12', '2014-01-12', '2014-01-19',\\n\",\n \" '2014-02-02', '2015-01-11', '2016-01-17',\\n\",\n \" '2016-01-24', '2016-02-07')),\\n\",\n \" lower_window = 0,\\n\",\n \" upper_window = 1\\n\",\n \")\\n\",\n \"superbowls <- data_frame(\\n\",\n \" holiday = 'superbowl',\\n\",\n \" ds = as.Date(c('2010-02-07', '2014-02-02', '2016-02-07')),\\n\",\n \" lower_window = 0,\\n\",\n \" upper_window = 1\\n\",\n \")\\n\",\n \"holidays <- bind_rows(playoffs, superbowls)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"playoffs = pd.DataFrame({\\n\",\n \" 'holiday': 'playoff',\\n\",\n \" 'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',\\n\",\n \" '2010-01-24', '2010-02-07', '2011-01-08',\\n\",\n \" '2013-01-12', '2014-01-12', '2014-01-19',\\n\",\n \" '2014-02-02', '2015-01-11', '2016-01-17',\\n\",\n \" '2016-01-24', '2016-02-07']),\\n\",\n \" 'lower_window': 0,\\n\",\n \" 'upper_window': 1,\\n\",\n \"})\\n\",\n \"superbowls = pd.DataFrame({\\n\",\n \" 'holiday': 'superbowl',\\n\",\n \" 'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),\\n\",\n \" 'lower_window': 0,\\n\",\n \" 'upper_window': 1,\\n\",\n \"})\\n\",\n \"holidays = pd.concat((playoffs, superbowls))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Above we have included the superbowl days as both playoff games and superbowl games. This means that the superbowl effect will be an additional additive bonus on top of the playoff effect.\\n\",\n \"\\n\",\n \"Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df, holidays = holidays)\\n\",\n \"forecast <- predict(m, future)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"m = Prophet(holidays=holidays)\\n\",\n \"forecast = m.fit(df).predict(future)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The holiday effect can be seen in the `forecast` dataframe:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" ds playoff superbowl\\n\",\n \"17 2014-02-02 1.219646 1.211218\\n\",\n \"18 2014-02-03 1.913081 1.359259\\n\",\n \"19 2015-01-11 1.219646 0.000000\\n\",\n \"20 2015-01-12 1.913081 0.000000\\n\",\n \"21 2016-01-17 1.219646 0.000000\\n\",\n \"22 2016-01-18 1.913081 0.000000\\n\",\n \"23 2016-01-24 1.219646 0.000000\\n\",\n \"24 2016-01-25 1.913081 0.000000\\n\",\n \"25 2016-02-07 1.219646 1.211218\\n\",\n \"26 2016-02-08 1.913081 1.359259\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"forecast %>% \\n\",\n \" select(ds, playoff, superbowl) %>% \\n\",\n \" filter(abs(playoff + superbowl) > 0) %>%\\n\",\n \" tail(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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    \"\n ],\n \"text/plain\": [\n \" ds playoff superbowl\\n\",\n \"2190 2014-02-02 1.223965 1.201517\\n\",\n \"2191 2014-02-03 1.901742 1.460471\\n\",\n \"2532 2015-01-11 1.223965 0.000000\\n\",\n \"2533 2015-01-12 1.901742 0.000000\\n\",\n \"2901 2016-01-17 1.223965 0.000000\\n\",\n \"2902 2016-01-18 1.901742 0.000000\\n\",\n \"2908 2016-01-24 1.223965 0.000000\\n\",\n \"2909 2016-01-25 1.901742 0.000000\\n\",\n \"2922 2016-02-07 1.223965 1.201517\\n\",\n \"2923 2016-02-08 1.901742 1.460471\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\\n\",\n \" ['ds', 'playoff', 'superbowl']][-10:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The holiday effects will also show up in the components plot, where we see that there is a spike on the days around playoff appearances, with an especially large spike for the superbowl:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 12 -u in\\n\",\n \"prophet_plot_components(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Individual holidays can be plotted using the `plot_forecast_component` function (imported from `prophet.plot` in Python) like `plot_forecast_component(m, forecast, 'superbowl')` to plot just the superbowl holiday component.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Built-in Country Holidays\\n\",\n \"\\n\",\n \"You can use a built-in collection of country-specific holidays using the `add_country_holidays` method (Python) or function (R). The name of the country is specified, and then major holidays for that country will be included in addition to any holidays that are specified via the `holidays` argument described above:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(holidays = holidays)\\n\",\n \"m <- add_country_holidays(m, country_name = 'US')\\n\",\n \"m <- fit.prophet(m, df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = Prophet(holidays=holidays)\\n\",\n \"m.add_country_holidays(country_name='US')\\n\",\n \"m.fit(df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see which holidays were included by looking at the `train_holiday_names` (Python) or `train.holiday.names` (R) attribute of the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" [1] \\\"playoff\\\" \\\"superbowl\\\" \\n\",\n \" [3] \\\"New Year's Day\\\" \\\"Martin Luther King Jr. Day\\\" \\n\",\n \" [5] \\\"Washington's Birthday\\\" \\\"Memorial Day\\\" \\n\",\n \" [7] \\\"Independence Day\\\" \\\"Labor Day\\\" \\n\",\n \" [9] \\\"Columbus Day\\\" \\\"Veterans Day\\\" \\n\",\n \"[11] \\\"Veterans Day (Observed)\\\" \\\"Thanksgiving\\\" \\n\",\n \"[13] \\\"Christmas Day\\\" \\\"Independence Day (Observed)\\\"\\n\",\n \"[15] \\\"Christmas Day (Observed)\\\" \\\"New Year's Day (Observed)\\\" \\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m$train.holiday.names\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 playoff\\n\",\n \"1 superbowl\\n\",\n \"2 New Year's Day\\n\",\n \"3 Martin Luther King Jr. Day\\n\",\n \"4 Washington's Birthday\\n\",\n \"5 Memorial Day\\n\",\n \"6 Independence Day\\n\",\n \"7 Labor Day\\n\",\n \"8 Columbus Day\\n\",\n \"9 Veterans Day\\n\",\n \"10 Thanksgiving\\n\",\n \"11 Christmas Day\\n\",\n \"12 Christmas Day (Observed)\\n\",\n \"13 Veterans Day (Observed)\\n\",\n \"14 Independence Day (Observed)\\n\",\n \"15 New Year's Day (Observed)\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m.train_holiday_names\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The holidays for each country are provided by the `holidays` package in Python. A list of available countries, and the country name to use, is available on their page: https://github.com/vacanza/python-holidays/.\\n\",\n \"\\n\",\n \"In Python, most holidays are computed deterministically and so are available for any date range; a warning will be raised if dates fall outside the range supported by that country. In R, holiday dates are computed for 1995 through 2044 and stored in the package as `data-raw/generated_holidays.csv`. If a wider date range is needed, this script can be used to replace that file with a different date range: https://github.com/facebook/prophet/blob/main/python/scripts/generate_holidays_file.py.\\n\",\n \"\\n\",\n \"As above, the country-level holidays will then show up in the components plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 12 -u in\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"prophet_plot_components(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Holidays for subdivisions (Python)\\n\",\n \"\\n\",\n \"We can use the utility function `make_holidays_df` to easily create custom holidays DataFrames, e.g. for certain states, using data from the [holidays](https://pypi.org/project/holidays/) package. This can be passed directly to the `Prophet()` constructor.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    dsholiday
    02019-01-26Australia Day
    12019-01-28Australia Day (Observed)
    22019-04-25Anzac Day
    32019-12-25Christmas Day
    42019-12-26Boxing Day
    52019-04-20Easter Saturday
    62019-04-21Easter Sunday
    72019-10-07Labour Day
    82019-06-10Queen's Birthday
    92019-08-05Bank Holiday
    \\n\",\n \"
    \"\n ],\n \"text/plain\": [\n \" ds holiday\\n\",\n \"0 2019-01-26 Australia Day\\n\",\n \"1 2019-01-28 Australia Day (Observed)\\n\",\n \"2 2019-04-25 Anzac Day\\n\",\n \"3 2019-12-25 Christmas Day\\n\",\n \"4 2019-12-26 Boxing Day\\n\",\n \"5 2019-04-20 Easter Saturday\\n\",\n \"6 2019-04-21 Easter Sunday\\n\",\n \"7 2019-10-07 Labour Day\\n\",\n \"8 2019-06-10 Queen's Birthday\\n\",\n \"9 2019-08-05 Bank Holiday\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from prophet.make_holidays import make_holidays_df\\n\",\n \"\\n\",\n \"nsw_holidays = make_holidays_df(\\n\",\n \" year_list=[2019 + i for i in range(10)], country='AU', province='NSW'\\n\",\n \")\\n\",\n \"nsw_holidays.head(n=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from prophet import Prophet\\n\",\n \"\\n\",\n \"m_nsw = Prophet(holidays=nsw_holidays)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Fourier Order for Seasonalities\\n\",\n \"\\n\",\n \"Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an arbitrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 3 -u in\\n\",\n \"m <- prophet(df)\\n\",\n \"prophet:::plot_yearly(m)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from prophet.plot import plot_yearly\\n\",\n \"m = Prophet().fit(df)\\n\",\n \"a = plot_yearly(m)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The default values are often appropriate, but they can be increased when the seasonality needs to fit higher-frequency changes, and generally be less smooth. The Fourier order can be specified for each built-in seasonality when instantiating the model, here it is increased to 20:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 3 -u in\\n\",\n \"m <- prophet(df, yearly.seasonality = 20)\\n\",\n \"prophet:::plot_yearly(m)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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QZ0eWSGuRo6xDtbQeFvH+NjWi+3VlRywVtiUiaBEHAj+cLED/EARGev69DOTfMDSM8bPHRkUwYDJyhTgMHQ5oR7b5UDMC4m6qbi6NVU88ft4YiIqlKKqhCemkNJgY4wVr9+7w3mUyGB8b7I62kBttTi0WskMi4GNKMaPelEjhbqzB6iIPYpfRYS08aQxoRSdQP5/MBABMDnNo8tiTaC2qFDGsSssxdFpHJMKQZiSAI2H2pGCN9HODRD5ewsLNQQgZw/04ikqxtyYUIdbNBnK9jm8ccrdWYF+6BbcmFqKpvFKE6IuNjSDOSlKJq5FXWI87XHipF/2tWuVwGeysle9KISJL0BgEnsrWI8rKDcwcTs+4c5QttnQ5fnMwxc3VEptH/0oRE7UgpAgDE+TiIW0gfOFqp2JNGRJJ0sbAK1Q16hLnZdHjM9GBX2Fsqsfl8gRkrIzIdhjQj+elCIYY4WGGMf9tu+P7CyVrNnjQikqTj2eUAgOHuHYc0tVKOBSM8sDetBBW1vOVJ/R9DmhFU1+uw61IxrhnqBHcbC7HL6bXmnjSdnlPYiUhajmdpYaWSY2QXdytujvFGdYMen5/MNk9hRCbEkGYEO1OLUa8zYEKAE+T9bH20qzlaN22yXq9jSCMiaTmeVY5QNxt42HX+QXhqsAscrFT4gbc8aQBgSDOCH5MKoFErMDHQWexS+sTRSoXKeh3q2ZNGRBKi0xtwKkeLMDfbVuujtUelkGNhhAf2XS6Flrc8qZ9jSOujRr0B35/Lx3h/JwQ6W4tdTp84WatRUadDXSP3vyMi6bhYVI06nQGhnUwauNrSGG/UNOrxGW95Uj/HkNZHv14sQnF1A2aFusJCqRC7nD5xtFJBZxCg5dZQRCQhFwoqAQAB3fwgPDnQGU7WKmw+l2/KsohMjiGtjz4/mQN7SyVmDnMVu5Q+a951oKiam6wTkXQkF1YBAEZ42HZxZBOlQo4bIz2x/3IpymsbTFkakUkxpPVBVb0Om8/lY1qwC0Jcu9cNL2WOVs0hjS9qRCQdyYVV8LC1gJd993dzWRrjjTqdAeuPc2Fb6r8Y0vrg85PZqGnUY26Ye7/cZeCPmkNaaTUH2xKRdCQXVsHf0QoOv71GdcfEAGe4aNTYfJ63PKn/6v/JQiSCIODdAxkY5qrBrND+f6sT4CbrRCQ9giA0hTQn6x59GFbIZVgU5YkD6aUoruIQDuqfGNJ66WhOFc7lV2JxtBe87K3ELscoHK2a9sPj1lBEJBU52jpUN+jh79jz19mlMd6o1xmw+kimCSojMj2GtF764HgB7C2VuGGEh9ilGA170ohIaponDfg79XyJo2uGNi2N9OUpjkuj/okhrRfO5lXg1zQtbo72QqSnndjlGI2dhRIyAJX1HJNGRNLQHNK6u0ba1WQyGR4Y74+z+ZXYlVpk7NKITI4hrRde2nkJVkoZbo7xhnIATBhoJpfLYG+lRAVvdxKRRKQUVUOjViDQpXeLhd8R7wsLpRzvHMgwbmFEZjBwEoaZVNXrsCu1CNeHOGCcv5PY5Rhd89ZQRERScLmkGt72lrC3VPfq+Y7WaiyP88GWpAJcKKgwcnVEpsWQ1kM2FkpcenIqnrjGB2rlwGs+Rys1tOxJIyKJuFxSA287S9hY9H5Hl7/PCIFcBjz5U7IRKyMyvYGXMszAxlIFd9vefaqTOidrFSrrdDAYBLFLIaJBThAEpJfWwNveEjKZrNfn8XGwworRftiSVIADl0uMWCEZS662DvdvPIOY1/ZixCt78MbeNNH3kS6raYBObxC1BoY0asXRWoWKeh0aRP7FJCLKr6xHnc7Qo50GOvL87GFwslZj+YZE0d/8qbUfz+dj2Mu78NHRTFirFZAD+OsPSZj7UYKoIemRzecR+epe6EXstGBIo1aax6QxpBGR2NJLagAA3nZ9D2lO1mqsWxqNy6U1WLj2uOg9JNTkm9O5WLj2OIY4WuGb20dix31jcebxyXhpTih2XSrGnV8milLXldIafH4yB9FedpD3vhO3zxjSqBVHKzUq6nT8pElEortc2hTSfHuxkG17Zoe549lrQ7A1uRDXfZyAshruUyymz05k4+b1JxDuYYu1N0djfoQnrFRNYw+fmBaMlWOG4POTOdiXZv5b1K/uSQMA3DrSp0+32vuKIY1acbRWQWcQUF7LyQNEJK7030JaqJvGaOd8ZuYwPD9rGHakFCHk5d344HAGGtmrZnYfHrmCZRtOIdbbHutviUb8EMc2x/z7uuGwt1Ti8R+TIAjmu+VYXFWPj45mYnaoG6YEOZvtuu1hSKNWmjdZL6rmXndEJK7LJTVw1ajhorEw6nn/b0YI9t4/Dh62Frjv27MIeHEnPj6aadZboHWNemw+l49fLxYOuh69dw+kY+U3ZzB2iCPW3hyNCE/7do9zsFLh7zNDkJBVjh/PF5itvo8TslCnM+DWWG9Yq5Vmu257GNKoleatoYqrB9eLBhFJT/MaabYWxn+jHB/gjDOPTcJnt0TD1kKJFV+fRsC/dpplZ4KfLxQg+KVduGHNMVy7+igC/7ULG0/nmvy6YhMEAf/edQl/3nQOkwKcsXpRJMK72LXn3jF+0KgV+ODIFbPUqDcIeO9QBuJ87DE9xNUs1+wMQxq10tyTVjLIPtkRkfRcLqmBl70lLFW9XyOtMzKZDLeO9MX5VZOx/pYYyGUyzPzgCN7Zf9kk1wOA3ZeKsXDtcVipFHhzfjhWL4qEu60Fbvr0BD45OnA3gm/UG/DAd2fxxE8XMDPEFe/fFNFlQAMAjYUSt8Z6Y0dKEXK0tSav86ekAlwpq8XiaC84a8RfaoshjVppDmllNRyTRkTi0ekNyK2og5edcW91tkcmk+G2kT4489gkjPV3wkPfnzdJYMrR1mLBmmPwtrfE+zdF4OGJAbhnjB8SH52IkT72uH/jWRzKGHjruF0qrsb4dw7ivUNXsDzOBx8sikCou223n/+ncf5o0At4e3+6CatssuZYFpytVZgX7m7ya3UHQxq10ny7s4pbQxGRiHIr6mAQAHcb04e0ZnaWKmy/dwzifOxx38Yz2G/khW8f2nQOdY0GvHb9cEwN/v1WmoVSgW33jIaDtQq3fX4K9bqBMbteEAR8ejwLMa/txcXCKvx7bhhenx8Of6eeTQSJ9rZHuLstfrlo2lvRxVX1+OlCAWaHuiHIxcak1+ouUUPatm3bMGzYMAQFBeHll19u8/jatWvh6uqK6OhoREdH46OPPhKhysHFwYohjYjEl1VeBwBwtzVfSAMAS5UCv9w7Bk7Watz2xSlUG+m1cOuFAnx3Nh/3jBmCWaFubR53sbHAJ4ujkF5ai0d/SDLKNcVUVa/D8g2JWL4hESGuNvjitlg8MikATta9u4W4JMYLZ3IrkFxQaeRKf/dlYi4a9QLmhrlDIebiaFcRLaTp9Xo88MAD2Lp1K5KSkrBhwwYkJbX9xVyyZAkSExORmJiIFStWiFDp4GL32wDdCoY0IhJRVnnT+CNj7DbQU07WaqxfGoPMslo88N3ZPp9PEAT845eL8LazxH3j/DocYzdnuDsWRnhg9ZEruGDCMGJqZ3IrEPfGPnx+Mhv3jhmCr5eNxNzh7lApeh85FozwgABg/Yls4xX6B+tPZCPERYPJIi+7cTXRQlpCQgKCgoIQEBAAtVqNm2++GZs3bxarHPqNUiGHjVqBSoY0IhJRZllTSAt0Nt4aaT0xY5grlsf54LOTOUjM0fbpXFuTC3E8S4s7R/lieBdjsd66YQTkMhke3nSuT9cUy8H0Ulzz7kGU1DTifwsj8Pr8cAS69P3/MNzDFkOdrLErtdgIVbZ1pbQGCZnlmDnMFR5G2OHCWERbACQnJwe+vr4tX/v4+ODo0aNtjtu4cSP27duHkJAQvPHGG62e02z16tVYvXo1ACA/Px+5uaafylxUZPpp2mKxVctRqq1iO/ZTbFPjY5uaRmftmpxTDI1KDmVtOXJzq81Y1e8eiXPC14k5ePCbE/h6cWivziEIAp7ekgwPjRLX+amQl5fX6fFyAPeOdMPbR/Ox4WASJg116NH1xPxdPVtQg4VfXYSjpQKvzfDGNX4qlBcXotxI558VYIvVJwpwOiUDrjbGnXm59kTTOmxxzmjz3idmm4q7SlsXrr/+eixduhQWFhb44IMPsHz5cuzatavNcStXrsTKlSsBAHFxcfDy8jJLfea6jrk52aSgXqZiO/ZjbFPjY5uaRkftWtqYDQ87Swz19YbGBOukdYcXgKen1+P/tl3EkWJgYWTPfwe2JRciMb8GT08Lwujhgd16zr/mu2HDuZ148WAhlowNg7yH46PE+F0tqqrHyo+TYGuhxEdLonFtqJvRt1O6caQK7x0vwKEi4E8hxv0Zd3yXjiAXa8yODYFbO+Mgxfr7F+12p7e3N7Kyslq+zs7Ohre3d6tjnJ2dYWHR1FgrVqzAiRMnzFrjYNW8yToRkViyyuvgbmsBa7Vp1kjrrr9ODoSXnQWe+jkZBkPPtiYSBAHP/ZoCD1sL3B7X9i5QRzQWSrw8NwznCyrx3qGMHlZsfoIg4M4vE1FQWY9Xrx9ukoAGAOOHOsFCKcf+dOPOus2vqMPBjFJMDXIxeg9dX4kW0uLj45Gamor09HQ0NDTgyy+/xLx581odc3W38A8//ICwsDBzlzkoNYc0fQ9fkIiIjOVKWQ3cbSxE3dwaAKxUCvznuuG4WFSN1/em9ei5O1KKceRKGe6I90WIa8/GZd0R74twD1s8vyMVNQ3S/tD85alc/HShEA+M98dNUV4m+z+zVClwzVAnJGSW9zgwd+anC4UQBGByoLPov29/JFpIUyqVePfdd3HttdciLCwMixcvRnh4OP7xj3/ghx9+AAC8/fbbCA8PR1RUFN5++22sXbtWrHIHFQcrFarqdWjgpsNEJILaRj1KahrNvvxGR5bGeCPayw7/2Z2GyrrGbj2nqRftItxt1Lgt1rvHb/5yuQzvLhiBgsp6PL01uTdlm4W2thEPf38O4e62+NM4f5PtDtFsZogr0kpqcDavwmjn3JpcCDcbNSYEOBntnMYi6jppc+bMQUpKCtLS0vD0008DAP75z3+29Ki99NJLOH/+PE6fPo3du3cjNLR3AzepZxysVKis16NBx5BGROaX/dvyGx4SCWlyuQzvLoxAUXUDnvq5e4FpV2oxDmaUYXm8L4Z7dH91/atNDnLB7FA3rD6ciUtF4kye6More9JQVN2AJ6YGIqiHvYW9MeO3/TR/MNKG6416A7ZfLMI4fyd42VkZ5ZzGxB0HqA3H33rSahsGxqrXRNS/NC9kK5WQBjSNh7p+uDs+OpqJzNKaTo8VBAFPbU2Gm40at8T07fbffxdGQICAW784CUGQ1hCUvIo6vLH3MmaEuOC6cA+zXDPKyw6OViokZJYZ5XyHMkpRUa/DOH/HHk/QMAeGNGrDwUoJAUBpbfe69YmIjKl5IdshjtLq2Xhjfjh0BgEPdrGG2Ten85CQWY77xvohwtO+T9cc6myN/1w3HAmZ5Xjml4t9OpexvbH3Mup0evxprH/LbjWmJpfLMD3EBUczy9FghO2ztl4ogkIuw9QgFyNUZ3wMadRG8x9bSU29yJUQ0WCU+VtIC3SxFrmS1gJdNLh/nD+2JBVgRwf7SFbV6/DETxcQ5GKNZXG+RumdeWC8P2aHuuKF7anYcDKnz+czhup6HT48egVTglwwY5hr108wopkhriiqbsD+9NI+n2trcgGivewQ5i6NvTr/iCGN2mgOaUVVDSJXQkSDUVZ5LRytVHC2ls7tzmb/nDUMnnYWWLbhFMpq2r5GPrL5PDJKa/C3KUFGWWkfAGQyGb5dHodwD1vcvuEU3tibJvqtz/UnslFeq8PSaC/YmHkdu+m/jUvbllzYp/PkaGtxJq8S4/wdYa2W5rKx0qyKRNUc0ni7k4jEkFVeC3dbC2hEXiOtPfZWKny7PA4T3j2ImauPYNd942Br2fRW+u6BdHx0NBN3xPlgSbR3F2fqGWu1EvsfHI/rP07AX39IwodHMrEszgfBrhoorhrzVqnVYrqNEzxNuLWRIAh4e386Qt1ssCDCPGPRrubvZA1/RysczSzv03m2XmgKeeP9pTersxlDGrXhYNkU0rQ1DGlEZH6ZZbXwsLUw+XIOvTXW3wnv3xSJ+749g6jX9mDFaD+czq3A16dzMTHACaumBrUEN2NysFJh7/3j8Oa+y3jvcAae7Gim6aZLGOfviFevH46xJggg21OKcKGwCs9dGwInjTi9nTOHueLzkzmoqmuEjWXvxsNtTS6Eu40a1wxlSKN+pLknrbKeszuJyPyyyusQ4WkndhmdWjHGDy4aNf720wU8vTUZ9pZK3D7SB/+YEWLSpSjkchn+OjkQf50ciNSiKpzLq4BOAGQA5DIgv7AYSVoZvkzMwTXvHsS/54bhsSlBRq3hrf3pcLZW4ZYYH6Oetyemh7hi9ZFM/HKxGDdGefb4+Y16A3akFGNasIukNlT/I4Y0asPBqunXgltDEZG5aWsbUVmvg7uN9Maj/dENEZ64IcIT5/MqYKVSwMfBCmql+YZ6B7vaINi19YD33Fzgfi8vvDgnFDesOYbHt1xARb0O/5xlnHVGU4qq8POFQqwcM0TUiR1TAp0hA7AjtahXIe3qpTcUElx6oxknDlAbdpbNPWkMaURkXs3Lb7jbSmsPxc6Ee9ohwEVj1oDWFXsrFXbcNxbXDXf7bVZotlHO+87+dKgUMiyN6fkuCsbkYmOBEZ62SOjluLSfLxRCKZdhYqCzcQszMun8RpFkKOQy2FooUcWQRkRm9ntIk35PmtQp5DJ8vaxpVui9357FlS4W4e2KtrYRa49lYWaIK8ZJYLD9zBBXnM2vQEl1z1ci2JpciBhvO4S6SXPpjWYMadQue0sle9KIyOyadxvwc5TWGmn9lZVKgU13xqNBb8Ctn/dt14JPEjJR1aDH0hhvSfQazghxRaNewI/n83v0vKyyWpzNq8Q4f6eWO0dSJX4rkyQ5Wqs4cYCIzC6rvBZyGRDgLK3dBvqzIBcNXpoTioMZZXjvUEavzqE3NC27EeNth1mhbsYtsJeuGeoEpVyGXZdKevS8bRebl95wNEVZRsWQRu1ytFKxJ42IzC6zvBauGjXsLfvPmLT+4KEJAYjwsMWzv6SgohdrYP54Ph8ZZbW4Odobzhpp/N9oLJQYPcQBx7LKe/S8ny8UwsPWAvG+Diapy5gY0qhdzZusGwzS2tCXiAY2KS9k258p5DKsXhyFouoG/GXz+R4//6396fCwtRBl8drOXBvqhouFVbhcUt2t4xt0BuxIKcJ4f0f4OEi/t5YhjdrlYKVCRb0O9XqD2KUQ0SCSVVYLdxvpLmTbn43xc8Stsd5YfyIbiTnl3X7e8axy7EkrwZJoLwQ6m24NuN6YFuQCAcD3Z7s3Lu1AeimqGvQY5+8EpUL6EUj6FZIoHH673dmgY0gjIvMQBAHZ2jrO7DSh1+aFw1Ipx5++PdvtSQQv7UyFrYUSN8d4GWXDeGOKH+IAGwsF9l3u3ri0rcmFUClkmBAg/uzU7mBIo3Y5WKlQ3aBHTQMnDxCReRRXN6BOZ2BIMyF3Wws8N2sYjmSW49PjXa+ddqGgEt+dzcfiKE+M9HEwfYE9pFLIMTvUDfsul6K+sfP3K0Fomgka422PMHdbM1XYNwxp1K7mraFKanq+/gwRUW9wjTTzeOiaoQhx1eDJny6gpqHzCWJ/23IB1ioFbo72gkqitwdvjPBEWW0jfkgq6PS4M3kVuFhUjWlBLrCx6B8bLkmzxUl0zZusF/dikUAiot5oXiOtP2wJ1Z8pFXKsXhSJvMp63Pft2Q6P236xCD8mFeCuUb6YGOhixgp7ZnaYG1QKGTaf63xc2pencqGQATOGuZqpsr5jSKN2Ne/fyZ40IjKX5p40P0fpz7rr7yYFumDlmCFYfyIbXyfmtnm8vLYR9393Bt52llgx2lcSi9d2xM5ShSmBLth9qRi6DsZRC4KArxJzMWqII+IkeNu2I9JtdRJV8+3O0uqer6dDRNQbWeW1UMplGMKQZhZv3TACw1w1uOurROxLK275fqPegGVfnEJGaS2evTYEkV72IlbZPbeN9EZuRT2+PtM2cALAoYwypJfWYEaIC2wt+8etToAhjTrgaN0U0rR1DGlEZB5Z5XVwt7GQ/FY9A4WlSoGdfxoLZ40a164+iie2JOHT41kY984B/JhUgEcmBuCWWHE3Uu+um6K8YG+pxJqErHYff3PfZdhZKHHdcGmt89aV/hMnyayax6Rx1wEiMpemhWzVXMjWjLztrXD4z+OxbEMi/rM7DQKa9m7+99wwrBgzBNbq/hETrFQK3BHvi3cPZuBycTUCXH5fzy29pAbfnc3D7SN9EO1tJ2KVPdc/Wp/Mrvl2J/fvJCJzyS6vRZi7DReyNTMveyvsuG8szudVILO8Fv5O1ghxtYFCYmuideX+8f5450A6Ht+ShI13xLd8/6VdqZDJZFgcJd0Zqh3pX9WS2dhaKCEDUMWeNCIyA4NBQE5FHWd2iijc0w6zw9wR5m7b7wIaAIS42uD+8f747mw+tl1o2kR9S1IBPjySiaXRXpgU6CxyhT3HnjRql1wug52lEhUMaURkBgVV9WjUC1wjjfrkpTlh+O5MHhauO4YbRnjgx/MFCHHRYNWUIGj6ydpoV2NPGnXI3lLFMWlEZBZcyJaMwcZCiQMPjscoX0d8czoP4/yd8Or1wxHh1b/GojXrf7GSzKZ5/04iIlNrCWm83Ul9NNRZgz0PjENpdT3USkW/2V2gPf23cjI5R2sVKrgEBxGZQfNuA0OdrUWuhAYKJ03/D/y83UkdcvytJ02nb38FZyIiY8kqr4WFUg4vu/7/xkpkLF2GtEcffRTnz583Ry0kMU23O/VoYEgjIhNrWiPNArZcyJaoRZchLSwsDCtXrsTo0aPx/vvvQ6vVmqMukgAHKyWq6nVo0Atil0JEA1xWeR08bCxg008WTyUyhy5D2ooVK3Dw4EF8+umnyMjIQGRkJG655Rbs3r3bHPWRiByt1Khu0KOakweIyMSyypp60qS8kTeRuXXrr0Gv1yM5ORnJyclwcXFBVFQUXn/9ddx8882mro9E5GDV9Im2uLpB5EqIaCDT6Q3Iq6yDu61a7FKIJKXLfuVHHnkEP/74I6ZNm4annnoKo0aNAgD87W9/w7Bhw0xeIImneWuo4hqGNCIyndyKOhgELr9B9EddhrTIyEi88MIL0Gg0bR5LSEgwSVEkDY6/hbSiKoY0IjKd5uU3uJAtUWsdhrSTJ08CAKKionDx4sU2j8fGxsLe3t50lZHomkNaCXvSiMiEmhey9bS1FLkSImnpMKQ9+uijHT5JJpNh165dJimIpMPJuml8SEUtJw4Qkek0h7RAFy5kS3S1DkPa7t27YTAYcPjwYYwfP96cNZFEOFo39aRxk3UiMqUrZbWwUSvgYceeNKKrdTq7Uy6X48EHHzRXLSQxzbc7K+sY0ojIdNJLa+BlbwnbfrzHIpEpdLkEx7Rp07Bx40YIAhc0HWwsVQpYKuWoqOf+nURkOuklNfC2s4RGrRC7FCJJ6TKkffDBB1i0aBEsLCxgZ2cHW1tb2NnZmaM2kgB7KxUq2JNGRCYiCAIyypp60mQymdjlEElKl33LlZWV5qiDJMrRSsUxaURkMgWV9ahtNMCL49GI2ujWAICysjKkpqairq6u5XsTJ040WVEkHc7WKo5JIyKTSS+tAQB42zOkEf1RlyHto48+wltvvYXs7GxER0fjyJEjGDt2LJfgGCScrNW4WFQFvUGAQs5bEURkXM0hzYchjaiNLsekvfXWWzh27Bj8/Pywe/dunDp1Cg4ODmYojaTA0bppTFqD3iB2KUQ0ADWHtGFuNiJXQiQ9XYY0S0tLWFo2fcKpr69HaGhouzsQ0MDkaKVCZb0O9TqGNCIyvvSSWjhbq+DGfTuJ2ujydqePjw/Ky8txww03YMaMGXB0dISfn585aiMJcLRSobpBj+p6XcuG60RExpJeWgNPO0vYWnKNNKI/6vKvYtOmTQCAZ599FlOmTIFWq8WsWbNMXhhJQ/PWUEXV9fB2sBK5GiIaaNJLqxHsYgMrFddII/qjbn10OXDgAFJTU3HnnXeiqKgIOTk5GDp0qKlrIwlo3hqqqIqbrBORcdXrDLhSVotpwS5il0IkSV2OSXvuuefw73//Gy+99BIAoLGxEbfddpvJCyNpaN4aqqiaIY2IjOuKth4GAfBz5MbqRO3pMqRt2rQJP/zwAzQaDQDAy8uLC9wOIs0hrYQhjYiMLK20ae1NP0cOpSBqT5chTa1WQyaTtWzXUV1dbfKiSDqab3dyaygiMra00noAQLiHrciVEElTlyFt8eLFuPfee1FeXo4PP/wQ06dPxz333GOO2kgCmnvSKrk1FBEZWVpZHZytVfC2Z08aUXu6nDigVqsxffp02NnZ4eLFi/jnP/+JGTNmmKM2koDmnjQte9KIyMjSSuvg52gNBysuv0HUni570goLC/Hkk0/iypUrmD59OqZPn26OukgiLJQKWKsUvN1JREZ3uawOfo5WsFBy+Q2i9nQZ0l544QWkpqbi7rvvxtq1axEcHIynnnoKaWlp5qiPJMDJWgVtXaPYZRDRAFJS3YCyOj38nHirk6gjXYY0AJDJZPDw8ICHhweUSiXKyspw0003YdWqVaaujyTAWaPm7U4iMqqLhVUAAH8uv0HUoS4HArz11lv49NNP4eLighUrVuCVV16BSqWCwWBAcHAw/vOf/5ijThKRi0aNwsp6scsgogHkTF4FACDYVSNyJUTS1WVIKy0txXfffddmv065XI4tW7aYrDCSDheNGqlF1WjQGaBWdqvzlYioUydztLBVyxHB5TeIOtRlSHvuuec6fCwsLMyoxZA0OVuroa1rRJ1Oz5BGRACAyjodHtx0FgfTSzHe3wn/vi4MHnaW3X7+yWwtgpws4WpjYcIqifo3Ud9xt23bhmHDhiEoKAgvv/xym8fr6+uxZMkSBAUFYfTo0cjIyDB/kQRnjQoVdTrUNOjFLoWIJKCmQYex7xzAZyey4W5jga9O52LSfw9BW9u9CUaNegPO5lUg2MkCltxYnahDooU0vV6PBx54AFu3bkVSUhI2bNiApKSkVsd8/PHHcHR0xKVLl/DII4/gb3/7m0jVDm7O1moIAMelEREA4I19l3E+vxKvXT8cv9w7Bt8uH4m00hosWHusW89PKqhEg15AsBN70Yg6I1pIS0hIQFBQEAICAqBWq3HzzTdj8+bNrY7ZvHkzli9fDgC46aabsHPnTgiCIEa5g5qzRg0AyK9iSCMa7PIr6vDSzkuYEuSM2+N8YWOhxHXDPfD3GcHYfakEG0/ndnmOk9laAECwc/dvjxINRqIt85yTkwNfX9+Wr318fHD06NEOj1EqlbC3t0dJSQlcXFxaHbd69WqsXr0aAJCfn4/c3K5fJPqqqKjI5NeQjLpKAEBaVj5ybY27FMegakczYZsaH9v0dy8fyEFtox53hNuiXluM3Ka8hdtDNVh9UIXHNp/FKGcBCrmsw3Psv5gLK6UM3spas7xeDyb8XTU+Mdt0QOzFsXLlSqxcuRIAEBcXBy8vL7Nc11zXEVuIzgrAJdSrNCb5mQdLO5oT29T42KaA3iBgU/J5jPFzxA2jQmFnqWr1+BsLZFiy/iS+TavHI5MCOjzPiYIUhHvYIcDbk+1qAmxT4xOrTUW73ent7Y2srKyWr7Ozs+Ht7d3hMTqdDlqtFs7Ozmatk5rGpAHcv5NosNt9qRjZ2jpcF+beJqABwE2RXhjmqsFb+y9Db2h/aEqutg5n8iox1t+RkwaIuiBaSIuPj0dqairS09PR0NCAL7/8EvPmzWt1zLx587Bu3ToAwLfffoupU6dCJuu4C51Mo3lMWjm3hiIa1NYdz4KNWoG5w93afVwul+GF2aG4UlaL/x5Ib/eYXy4WAgDG+TmZrE6igUK0kKZUKvHuu+/i2muvRVhYGBYvXozw8HD84x//wA8//AAAuPvuu1FSUoKgoCC8/vrr7S7TQaZnb6mEQiaDtpY9aUSDld4gYEtSAaYEuSDUreMFaBdGeGKYqwav72u/N21bchFcNGqM83c0ZblEA4KoY9LmzJmDOXPmtPreP//5z5Z/W1pa4ptvvjF3WfQHMpkMjtZKbrJONIidytGivFaH0UMcOl3UWi6X4Z+zhmHJ+pP478F0PDTh97FpOr0B21OKcM1QJ3jZW6KwzhyVE/VfXD6eusXJmpusEw1mO1KaZrhNCOh6XPBNkV4IcdXg1T1pqGn4/XXj85M5KKttxJQgZygVfPsh6gr/SqhbXDTqbq8mTkQDz47UYgS5WCPWx77LY+VyGf63MAJZ5XV4YONZAE29aM9vT0GIqwY3RniaulyiAWFALMFBpueiUeNCQRX0hs7XPyKigae2UY8Dl0txY6QnbCy697YxLcQVd47yxdqELHjbW6K4phFpJTV49frhGOJkbeKKiQYGhjTqFheNGuW1jajX6WGt5q8N0WByKL0U9XoDRg1x6NHz3r5hBPK0dXhx5yXIANwU6Ymbo7mGF1F38d2WusXNxgJltY2oaWBIIxpsdqQWQyGXYVaoa4+eZ2OhxNaVY7DtQgEq63W4NtSt3fXViKh9fLelbnGzUUNnEJBfWQ8XG26KTDSYbE8pQoSHLQKdNb16/qwwdyNXRDQ4cOIAdYvbb8Est4Jz5okGk7KaBpzM1mLUEAfOyCQyM/7FUbcwpBENTrsvlUAAMLqH49GIqO8Y0qhb3G2bQlphZYPIlRCROe1ILYK1SoEZIT0bj0ZEfceQRt3iZtO0f2dZDUMa0WCyPaUYsT728La3ErsUokGHIY26xeW3TdZLarigLdFgkVlWg0vF1Rjl6wA510ckMjuGNOoWpUIOJysVyrjrANGgsTO1GAAw2o+boROJgSGNus3VRo1S3u4kGjR2pBTDyVqFCUMZ0ojEwJBG3eZha8GeNKJBQhAE7EwtxihfB7jZWopdDtGgxJBG3eZua4nSmkY06g1il0JEJnY+vxIFVfUYNcSB+/USiYQhjbrNzUaN0ppG1DbqxS6FiExsx2/j0cZwPBqRaBjSqNvcbC1QWa+DtlYndilEZGI7UoowxMEKI30cxC6FaNBiSKNua14rLUdbK3IlRGRKjXoD9qaVYNQQB7j+9ndPRObHkEbd5s6toYgGhYTMclQ16BHv6wCZjOPRiMTCkEbd1rx/Z46WIY1oINuWXAi5DBjvz/FoRGJSil0A9R/e9k3T8AurpLdWmiAI2JFSjM9PZkMpl2NpjBemca9Bol7ZklSAKC87jPC0E7sUokGNIY26zdOuOaTVi1xJa4Ig4Kmfk/HyrkuwtVDCIAj4OCETL8wehqenh4hdHlG/kqOtRWJuBR4c7w97K5XY5RANagxp1G1qpRwuGjWKJNaT9uKOVLy86xIWjPDAP2aEwNfRErd8dgr/t/UiXDRq3DvWX+wSifqNn5IKAQATApxEroSIOCaNesTb3lJSPWlHr5Th2V9TMGuYK966IRzRPvZw1lhgy4pRiPW2w5M/JaNYQvUSmcOZ3Aos++IUrvvoKNYmZMFgELr93B+TCuBlZ4Hx/gxpRGJjSKMeGeJghaKqhh696JtKg86A5RtOwUWjxpPTguHraN3ymEohx8dLoqGta8SDm86KWCWRee1LK0HsG/uw8UweTuVocedXiViw5hgadF3vFFJUVY9tyYWYGuTSMryBiMTDkEY94m1viYKqeknsOvDR0UxcLKrGE1MC2701E+1tjzvifLHxTD7SiqtEqJDIvIqr6nHL5yfhbWeJH+6Kx6Unp+LJqUH4IakAC9YegyB0/uFqw6kc6AwCrhvuDjm3giISHUMa9Yi3vSW0dTqUVIs7Lq26Xod//pqCWG87LI3x7nAtpyenB0NvEPDSzktmrpDI/J79NQUFlfX415xQTA12gZVaiX/NDcMzM0Pw84VCPL01udPnrzuejVA3G0wOcjFTxUTUGYY06pHmZTjSS8XddeCt/ekoqKrHA+OHwr2T2zJBLhrMCnXDxrN5qKxrNGOFROZVWtOANQlZmB3qhgURHq0+uDwzMwTzR7jj5Z2X8N2Z3Hafn5BZhpPZWlwX5gZ3WwtzlU1EnWBIox7xsbcCAGSUVYtWQ0l1A/696xImBjjhpijPLo9/fEogymt1+N+hDNMXRySSD49koqZRj6UxXrBWt564L5PJ8MWtsRjmZoPlGxKRXFDZ6nFBEPC3LRfgaKXCjd34myIi82BIox5p7knLLhdv14GXd11CZb0OD4z3h51l1+s4TQpwhq+DJTadzTdDdUTmJwgC3juUgXhfe8wc5tbuMdZqJbauGAWlQobZHx5FxVU9y1uSCrAnrQR3j/ZFvC93GSCSCoY06pHmkFYk0pi07PJavHMgHXPD3DAnzL1bz5HLZVge54tjWeW48IceBKKB4FhWOa6U1WJumDucNR1viO7vrMHXt49ElrYOE/97CMezyrH5XD6WfnYSAU7WWB7vCwUnDBBJBkMa9YidpRIatUK0tdKe+zUFBkHAyjF+sLHo/lrMy+J8YBCA9w9dMWF1pmUwCHhtTxqiXt0D/xd24JHvz0Fby3F2BGw8kweFXIbpIV0P+J8xzA0fL45CekkN4t/cjxvWHIO7rQX+d2MEwt1tzVAtEXUXdxygHpHJZE3LcFSaP6Ql5Vfik4RMLInywvRhPduXM9jVBiN97LE9pchE1ZmWwSBgxdenseZYFiI97TDE0QrvHMzALxeLsPeBcXC14UDvwUoQBHx3Ng/xPvaI9LTv1nOWx/tiZogL/nfoCuwslZgU4IxRfrzNSSQ17EmjHgt01iBbW9flmkvGJAgCHvr+HGzUStw9egisVIoen2NxlBcuFFbhVHa58Qs0sRd2pGLNsSzcM3oItq0cjX0PjMfXt4/E5dIazFp9RBKLC5M4zuVX4lJxDaYGu8DWsvufuz3trfD87FA8PiWIAY1IohjSqMeCXTXILq9DZZ3ObNfceCYPO1OLcd84P0wKdO7VORZEeAAAvjiZY8zSTC61qAov7kjFzBBXPD87tGUl+IWRnnj7hnCczKnACztSRK6SxLItuWmvzUkBvfu7ICLpYkijHgty1qCmUY/LpeZZhiO7vBb3fXsGoW42uHu0L5SK3v3aBrvaINRNg12XSoxcoWk9/P15qBQy/HVSQJv1q+4Z44epQc54aeclZJWJu3YdiWNHSjECnKwR49O9W51E1H8wpFGPBbk07ZF5Lq/3Wy0ZDAIuFVcjtaQWjfqO9xSsadBh6WcnUdOox4uzhyHEtW8DmxdFeSExV4uLhf1jm6hT2VpsTS7EnfG+mBrcdlC4TCbDezdFokFvwJM/XxChQhJTvU6P/eklGDXEAS6dzOokov6JIY16LMhFAwBIK+ldT9rXibnwem47gl/ahclrk2D31FbcvP4EdqYUtRpblautw8wPjuBQRin+Pj0E1w336HPtC0Z4wiAAn5/M7vO5zOG1vWmwVimwONoLqg56EENcbbAk2hvfnslDRmmNmSskMR3OKENtowGjhjh0uDUaEfVfnN1JPebvZA2FTIas8p7fXvv71mS8sCMV4e62uGfMEOhqKpFSCfx8oRBfJebCVaPGWH9HNOoF7LpUDL1BwL9mh+KBa4ZCrez7Z4pobzv42FtiTz+45ZldXouvEnOxKMoTo4d0PrD7n7OG4avEHPzz1xS8MLH9xUxp4NmRWgyFDBjv7yR2KURkAgxp1GMqhRxDHC2R1cNdBzaczMELO1Jx/XB3vHJ9GIa52SI3NxdeXl6orGvEh0cy8WtKEc7kVkCtlGNumBuWxflg5jC3Xs3mbI9MJsNNUZ7478EM5FXUtQzCl6J1x7OgMwhYEu3VZUANctFg1jA3fH8uH0+P5QDywWLPpWKEudtimJuN2KUQkQnwdif1SrCLDbK1td1ehiNXW4d7vjmNGG87vDQ3FMPcWo8ts7VU4a+TA7Ft5RhcemoaTv11Ir5ZFof5IzyNFtCaLYzwRKNekPQsT0EQsO5YNmK97TChm7P2/jIxAGW1jVifWGji6kgK6nV6HM/WItrLDvZWXW+PRkT9D0Ma9UqwqwaZZbWoqu/eMhxP/HQBDXoDnpkRgnAPu06PVchlsFYrITfR9jTj/J3gbK2S9MK2RzPLkVpcjbnD3eFk3b0B4dOCXRDobI1vkkpNXB1JwYksLep1BkR5df73RET9F0Ma9UqIqwZVDXqklXQ9UP14VjnWn8jGbbE+mBkq/ngphVyG68PdcSC9FFVmXOutJ9Yfz4aFUo45PWgvuVyGe8YMQVJxHRKulJmwOpKCgxlNYTzO10HcQojIZBjSqFdivZvWZDqQ3nWvzb92psLWQol7xvRupwBTWBjhieoGPTaezRO7lDb0BgEbz+RhwlAnRHv3bO2rW2J8IAPwcUKmaYojyTiYXgpfB0uM8OB+m0QDFUMa9UqMtz3kMuBMbkWnxyUXVOL7c/lYHOWJeAl94p8e4gorlRw/JRWIXUobhzJKUVBVj6lBLrDsYaj1dbTCaB8bbE0uhL6T9eeofxMEAYcyyhDlaQdnro9GNGAxpFGvaCyUCHWzwfn8yk6Pe2VPGtRyOW6J9e71TgGmYKVSYEaIK/ZeLpFcmNl4Jg9qhQzTQ9ouXtsdi8OdkVVehx8kGEDJOFKLq1FU3YAoLzuuj0Y0gEnnXZP6ndF+jjhfUIm6xvbHdeVoa7H+RDbmjXDHNUOltyzEjZGeKKxqwC8SmkAgCAK+O5uHMX6OCO/lbazZwY6wUMqx4ZR0Z69S3xz8bZgBJw0QDWwMadRro3wdoK3T4VRO+7c839yXDoNBwG2xPkZZiNbY5oa5QyEDvjsjnXFpiTkVyCqvw5RAF1ire7eMoZ2FAtcPd8evF4tQ0yDNiRHUNwfTy2BnocRov84XOSai/k1675zUbzSPMdub1nb1/tKaBrx/KAMzQlwxrZ09J6XAWaPGWH8n7Ekr6fZ6b6a25UIBZAAmBfVtBfllcT7Q1unw2Yn+sf0V9czBjFJEetnBw1a6izETUd8xpFGvRXjawdZCif3tzPB8c99lVDXocUe8LzQW0t3Y4sZIT6SV1OBYZrnYpQAAfkoqwHB3W0R79WxW5x/NCnWDo5UKG8/kG6kykoqS6gYkF1Yh0tNWkj3URGQ8/AunXlMr5VgU5Yndl4pRXFXf8v3y2ka8tT8dU4Kcca0E1kXrzIIRTZu2r5dAj1NhZT0SssoxIcAJjt1cwLYjKoUcN0Z6Yn96CUqrG4xUIUnBoQyORyMaLBjSqE/uiPdFbaMBHx39fV2u57enoKJOhxWjhsBB4tvV+DlZY6SPPXakFotdCrYmF0IQgGuGGmez7KUx3qhtNODT41lGOR9Jw8H0MijlMo5HIxoEGNKoT8b7O8HXwQpfnMxBvU6PvWnFeGPfZdwY4YG5w93FLq9bbon1RnJhFY6KvEr/lqQCuGrUmBRonJA2KdAZLho1fuRSHAPK/vQShLnZYKiTtdilEJGJMaRRn8jlMjw7MwRn8ysR89o+XP/xMXjbWeLxKUH9ZtPnRZFeAIB1IvY4NegM+PViEcYPdYKnnZVRzqmQy7A4yhMH08tQeNXtaOq/ahv1OJZVjmhve9hZ9o+/LyLqPYY06rO7Rg/B2zeMwOXSGozzd8Q7C0Zg1BAHscvqNl9HK4zxc8C25EIYDOIsbHsgvRQV9TpcM9QJCiNuLL80xhv1egPWJvCW50BwLLMcjXoBMd4cj0Y0GDCkkVH8ecJQlD1/LTYuj8MNEZ79bhX0u0YNQXppLbYli7Ow7U8XCqBSyDDZSLc6m43zd4KHrQW2XOAtz4Fgf3rTcjexPn2b/UtE/QNDGhmNlVop6eU2OrMoygtqhUy0W55bkgow0tseoW7G3SxbLpdhSbQXDmeUIa+izqjnJvPbf7kUAc7WRv89ISJpYkgjAuBgpcK8cA/8crEI1fWNZr12SlEVUoqqMSHA2SQh95ZYb+gMAj5JyOz6YJIsvaFpU/UYLzu4cFN1okGBIY3oNyvH+EFbp8N7h66Y9bo/nm+6FTkxwLi3OpvF+zrA18ESPycVmuT8ZB5ncitQWa9DjLe9UcctEpF0MaQR/WZasAuGOlmZfWHbLUkFCHKxRvwQ06x7JZPJsDTGG0ezypFZVmOSa5DpNY9Hi+YitkSDBkMa0W/kchkevGYozuRVYleqeSYQlNU0YH96KSYMdYabjeluYd0c7Q29QcDHRznLs7/af7kUHrYWiOakAaJBQ5SQVlpaihkzZiA4OBgzZsxAWVn7i4gqFApER0cjOjoa8+bNM3OVNBjdGe8LK5Ucr+5JM8v1tiUXQW8QMDHAyaQzYqO97RDgbI2fOcuzXxIEAQfSSxHjbQd3GwuxyyEiMxElpL388suYNm0aUlNTMW3aNLz88svtHmdlZYXExEQkJibihx9+MHOVNBg5Wqtxz+gh+PViEc7lVZj8ej8mFcDRSoXxRtoKqiMymQzL43xwPFuLM7mm/7nIuNJKapBfWY8oL3soFbwBQjRYiPLXvnnzZixfvhwAsHz5cnz//fdilEHUrsenBEEmk+G5X1NMep1GvQFbkwtwzVAn+Dmafouf5XG+kAH438F0k1+LjGt7StPt9zje6iQaVERZ1KqgoACenp4AAA8PDxQUtH8Lpq6uDnFxcVAqlXjiiSdwww03tHvc6tWrsXr1agBAfn4+cnNzTVL31YqKxFn0dKCRYjvKAdw03AnfnsvD3jOXEOximgB1OKsS5bU6xLrIUVyYb7TzdtSmKgCjfWyw+Vwenh7rDIWcPTLdJfbv6feJmfDQKBFkVW+W1zdzEbtdByK2qfGJ2aYmC2nTp09Hfn7bN54XX3yx1dcymazDsThXrlyBt7c3Ll++jKlTpyIiIgKBgYFtjlu5ciVWrlwJAIiLi4OXl5cRfoKumes6A50U2/HVhY7YlLwLLx8pxtaVY0xyjUPHz0OlkGFuTCC8vIw7s7OjNn1wogG3fXEKx0rlWBgpvXaXMrF+T+t1ehzMSsTsUDdEBPlDPsCW35Di339/xzY1PrHa1GQhbceOHR0+5u7ujry8PHh6eiIvLw9ubm7tHuft7Q0ACAgIwOTJk3Hq1Kl2QxqRsXnbW+Gha4bilT1p2JVahKnBrkY9vyAI+PZ0HkYPccRwd/OtHr8gwgM2agXWJGQxpPUTBy6XorpBj3H+jgMuoBFR50S53zFv3jysW7cOALBu3TrMnz+/zTFlZWWor68HABQXF+PgwYMYPny4Weukwe3/ZgTDVaPG/RvPQqc37sbrR66UIbO8FjNCXMy6lZa1WonF0V7YkVqMkuoGs12Xem9rciFUChkmBjiLXQoRmZkoIe2JJ57A9u3bERwcjB07duCJJ54AABw/fhwrVqwAAFy4cAFxcXGIiorClClT8MQTTzCkkVnZWarwzoIRuFhUjWd+uWjUc3+VmAu1QoaZIcbtoeuOFaOHoE5nwHuHMsx+beoZg0HA16dzMcrXAcPcbMQuh4jMTJSJA87Ozti5c2eb78fFxeGjjz4CAIwbNw5nz541d2lErSyO9sKaY1l4dU8a5od7YJRf38eO6Q0Cvjmdi3H+TogUYfX4MX6OCHbR4IuTOXh6erBJ12ejvjmQXoqs8jrcO8YPNmbscSUiaeD0LqJOyGQyfHZLDOwsVViy/gSq63V9Puf2lCLkVtRjVqgrrNXmf+OVyWT4y8ShuFBYhZ+SuLitlH1xKgeWSjlmDjN/jysRiY8hjagLLjYW+PzWWFwpq8WdXyX2+XwfHc2Eg5US1w1373txvbQszhe2Fgq8cyBDtBqoc7WNenxzOheTA50R4cn9OokGI4Y0om6YOcwVD08Yim9O5+GDwxm9Pk9hZT02n8vHdWHuGOYq3hgjGwsl7h41BDtTi3A+nzsQSNFHRzJRWtOIBREesFQpxC6HiETAkEbUTf+5fjjife3x0KZz2JtW3KtzfHj0CnQGAfNHeIi+vc+jkwObdlb4xbQ7K1DP1ev0+PfuS4jxtsP8EZ5il0NEImFII+omlUKOrfeMgZuNBRasOY7LxdU9en51vQ5v7kvHeH9HTAt2MVGV3efjYIXbRnpj8/l8pJfWiF0OXeXNfenI0dbh7lFD4G7LDdWJBiuGNKIecNao8eu9Y6AzCJix+gjKarq/1thHRzNRXN2Au0YNgaO12oRVdt/T00OgNwh48qcLYpcyKBRW1uNfO1Lx4Hdn8cL2FCRklkEQhFbH7E0rxtNbkzE1yBk3RrIXjWgw45xuoh4Kc7fFN8tG4vpPEjD+nYM4+OfxXYau0poG/GtnKmK97UWdMPBHQS4aLIvzxfoT2TifX4FwDw5QN5WvTuVg5bdnUFGng62FElX1Ovx920UEOltjSbQXIj3tcDqvAq/vvQwfe0s8d+0weNhZil02EYmIPWlEvXBtqBu+vH0kLpVUY8K7h1Be29jp8at+vICS6gY8NjkAbhK7ffXC7FAo5DI8svm82KUMWHsuFeP2Dafg72iNb5eNROqTU5G0ajKenRkCe0sl/r3rEm7+7CRe2nkJkwKc8cFNkRg/1EnssolIZOxJI+qlhRGe+PzWWNzy2UmMe/sAflk5Br6OVm2O23AyBx8nZOL2kT6YF+4hQqWd87K3xGOTA/DijkvYdDYPCyJ4i82YymoacNO64/Cxt8Rb88Mx+bfxiO62Fnjm2mF45tphuFJag9O5WjhZqxHuYSuZ2+FEJC72pBH1waIoL3x1e9MaalGv7cVnJ7JhMDSNMRIEAWsTsrBswynEetvj0UkBZt2nsyeenh4CXwdLPLL5PGob+r5gL/3uXzsvobSmES/ODm0JaH/k52SNeSM8cU2AMwMaEbVgSCPqo4WRXjjy8DVws1Hj9i9OYcgLO3DDJwmIeHUv7vwqEeEetnhzfjiivO3FLrVDVioFVi+KxJWyWjzw3TmxyxkwrpTW4O396bhuuDvmSmgsIhH1DwxpREYQ4WmHpFVT8Ob8cIS4aJCYWwGNSoG/zwjGd3fEYUKgs9gldmlWqDvujPfF2mNZ2JKUL3Y5A8LbB9KhFwTcO9YPdpYqscshon5GmvdeiPohuVyGhycG4OGJAWjQGSCTNa2t1p+8s2AEdqcV444NiUj86yT4tDPGjrqntlGPtceyMCXQGZP6QUgnIunpX+8gRP2EWinvdwENADQWSmy5axRqGvWY89FR1HB8Wq99ezoXpTWNuCnSEzYSHYtIRNLW/95FiMikwj3tsO7mGJzLr8TsD4+iUW8Qu6R+6cOjmRjiaIXZoW5il0JE/RRDGhG1sSjaC2/MD8e+y6VY/OmJlhmr1D052lrsv1yKOaFu7S7LQkTUHQxpRNSuhycG4OlpQfj+XD6Wf3mKQa0Hvj2dBwCYHuwCmUwmcjVE1F9xoAQRdej52aHQ1unw7sEMVNXp8fXykf1yrJ25fX06F8EuGkwI4IQBIuo9vtoSUYdkMhneXjACT08Lxvfn8zHzgyOcTNCFHG0tDmWUYVqwi+S2ACOi/oUhjYg6JZPJ8MKcULw2bzj2ppVg9FsHkFdRJ3ZZkvXzhUIAwJQg9qIRUd8wpBFRt/x1UiC+uDUWaSXViH5tL05klYtdkiT9fKEQHrYWGO/PDdKJqG8Y0oio226O9cbe+8dBJpNh/LsH8VVijtglSUq9To8dKUUY7+8ITztLscshon6OIY2IeiR+iCNO/mUCQlw0uHn9STz10wXO/PzNgculqGrQY/xQJ8jlnNVJRH3DkEZEPeblYIWEv0zAvOHueGnXJUx57xCKqurFLkt0PycXQqWQYWqQi9ilENEAwJBGRL1iqVLg+7vi8Z/rwnDkShlGvLIHO1OKxC5LVD9fKMRIb3uEuNmIXQoRDQAMaUTUazKZDI9PCcKe+8fBUqnA9A+O4N5vTqO6fvAt05FeUoPkwiqM83eClUohdjlENAAwpBFRn431d0LSqsm4NdYbHx7JROi/d+PH8/kQhMEzVm1rctPSG+OHOopcCRENFAxpRGQUGgslPrs1Fj/ePQpyuQzzPjmGae8fRlJ+pdilmcXPFwrgY2+JsX4MaURkHAxpRGRUc4e7I+VvU/DE1CAcyyxH5Kt7cMeGU8jR1opdmsnUNuqx61Ixxvk7wt2WS28QkXEwpBGR0VmoFHhpbhguPjEFi6O88PnJHAS+uAuP/nAeZTUNYpdndHsuFaO20YBruPQGERkRQxoRmYyXvRW+uH0kzj42CdNDXPDG3svwe2EnPjiUMaDGq/18oRAWSjkmBnCXASIyHoY0IjK5UHdbbFkxGof+fA1CXDW4b+NZTPjvIaSX1IhdWp8JgoCfkwsxytcBQS5ceoOIjIchjYjMZoy/IxIenoB/zw1DYo4WEa/uwfdn88Quq09SiqpxuaQG4/0dobFQil0OEQ0gDGlEZFZyuQyrpgbhzKOTMMTBCgvXHsdre9LELqvXfr5QAAAYP5S3OonIuBjSiEgUAS4anPjrREwJcsZjPybhhe0pYpfUKz9fKESAszVifRzELoWIBhiGNCISjZVKgW0rx2B2qBv+vu0i/ncwXeySeqSyToe9l0sw3t8JHrYWYpdDRAMMQxoRiUqlkOP7O+Mx3t8RD206h22/3T7sD3amFqFRL2C8vyOX3iAio2NIIyLRqZVybL1nDHwdrHDbF6eQXd4/Fr79ObkQGrUCEwOdxS6FiAYghjQikgRbSyW2rBiN6gY95n+SAJ3eIHZJndLpDfj+bD7G+TkiwNla7HKIaABiSCMiyQj3sMUHN0XiZE4F7tt4RuxyOrU3rQRF1Q2YMcwVFkqF2OUQ0QDEkEZEkrIs3hd3xPvi46NZ2Hg6V+xyOvTV6VxYqxSYEewidilENEAxpBGR5PzvxggEuVjj3m/PoKCiTuxy2mjUG/DdmTxMDHBCmIet2OUQ0QDFkEZEkmOlUmDj8nhU1OuwaP0Jo+3zWVWvw7enc/HG3jR8fiIbeb0MgD+cz0dJTSNmhbrxVicRmQz3MCEiSYr0ssOLs0OxassF/GfXJfxtWnCvz9WoN+ClnZfw2p40VNTrWr6vlMtwz+gheGluGOytVN0+33uHrsDT1gJzw9x7XRMRUVcY0ohIsh6dFIgtSQV45pcUzAp1Q5S3fY/PkV9Rh3mfHMOxrHJMDXLG0hhvRHnZIb+yHmsSsvDBkSvYmlyIX+4dgxDXrjdITy6oxM7UYjwwzp+zOonIpHi7k4gkSy6X4cvbR8JKLcfi9SdQ16jv0fPTiqsx/p2DOJtXgZfnhOKr20dixRg/xA9xxPXhHvjuznj8dPdolNc1YtSb+3E8q7zLc7665zJUChnmj3DnArZEZFIMaUQkaZ52lli7JBopRdW4uQfj005lazHunQMoqWnA+zdG4K+TA+Fi03brpllhbjj60ARYqRSY+t5hHEov7fCcZ3Ir8MmxTCyO8sI1AVzAlohMiyGNiCRvfoQnnpwahM3nC/DkTxe6PH73pWJM+t8hyGQyfLw4CrfH+UKl6PjlLsTNBof/PB52FkrMXH0Eey4VtzlGbxDwl83nYKtW4u7RvrBSccIAEZkWQxoR9QsvzA7FwggP/Ht3Gv7v5+QOe9Q+PpqJWauPwlWjxieLo7Aw0rNbtyX9nTU4/NB4OGvUmLn6CD48fKXlGoIg4M+bzmL3pRI8NGEoJgxlLxoRmR4nDhBRvyCXy/DV7SNx47rjeHFnKk5kl+PdhREIdNEAAC4VV+PJny7g2zN5GDXEAS/NCcXUYNceXcPX0RonH5mA2R8excpvz+D9w1cwI8QVvybn4VReNZaN9MHDE4ZC2UmvHBGRsTCkEVG/oVTIsemOeDz760W8tPMSgl7ahaCrQpqlUo77xvrhscmBLeGtp5w1Fjj052vw6p40vHf4Cl7bmwYXayWemhaEP43zb3dcGxGRKTCkEVG/IpfL8M9ZoVge74tXd6fhUnE19AYBc8PcMGuYK6YEu/R5gVmlQo4npgVj1ZQglNY0IDc3FxHB/pDJOJuTiMyHIY2I+qVAZw3euynSpNeQy2VwsbFAg40FAxoRmR0HVhARERFJEEMaERERkQQxpBERERFJEEMaERERkQQxpBERERFJEEMaERERkQQxpBERERFJEEMaERERkQSJEtK++eYbhIeHQy6X4/jx4x0et23bNgwbNgxBQUF4+eWXzVghERERkbhECWkjRozAd999h4kTJ3Z4jF6vxwMPPICtW7ciKSkJGzZsQFJSkhmrJCIiIhKPKNtChYWFdXlMQkICgoKCEBAQAAC4+eabsXnzZgwfPtzU5RERERGJTrJ7d+bk5MDX17flax8fHxw9erTdY1evXo3Vq1cDAPLz85Gbm2vy+oqKikx+jcGA7Wh8bFPjY5uaBtvV+Nimxidmm5ospE2fPh35+fltvv/iiy9i/vz5Rr3WypUrsXLlSgBAXFwcvLy8jHr+jpjrOgMd29H42KbGxzY1Dbar8bFNjU+sNjVZSNuxY0efnu/t7Y2srKyWr7Ozs+Ht7d3l8zIyMhAXF9ena3dHUVERXF1dTX6dgY7taHxsU+Njm5oG29X42KbGZ+o2zcjI6PAxyd7ujI+PR2pqKtLT0+Ht7Y0vv/wSX3zxRZfPKy4uNkN1TT12nc1Mpe5hOxof29T42KamwXY1Prap8YnZpqLM7ty0aRN8fHxw+PBhzJ07F9deey0AIDc3F3PmzAEAKJVKvPvuu7j22msRFhaGxYsXIzw8XIxyiYiIiMxOlJ60BQsWYMGCBW2+7+XlhZ9//rnl6zlz5rSENiIiIqLBhDsO9FLzRAXqG7aj8bFNjY9tahpsV+NjmxqfmG0qEwRBEO3qRERERNQu9qQRERERSdCAD2k2NjZil9Cpu+66C25ubhgxYoTYpXSbTCbDbbfd1vK1TqeDq6srrrvuOqOcvzv/Z8nJyRg7diwsLCzw6quvGuW6UmPKdi4pKcGUKVNgY2ODBx98sM/n62+6+h2bPHlyj2ZzPf300/D19ZX86017XnzxRYSHhyMyMhLR0dEdLhrelT179uDQoUNGq8vf39+os/VnzZoFBwcHo71OdYdMJsOjjz7a8vWrr76KZ5991mzXv5oxfzel+voh9b+/3rzfD/iQJgadTtftY++44w5s27bNhNUYn0ajwblz51BbWwsA2L59e7fWsLtaT9qoPU5OTnj77bfx2GOP9ek8UmaMdu6IpaUlnn/++QEbcM3t+uuvR0JCgthl9Njhw4exZcsWnDx5EmfOnMGOHTta7fTSE8YOaX3R3uvL448/jvXr15u1DgsLC3z33XdmWxrKVP7Ynnz9+J2p3+8HRUirqqrCtGnTEBsbi4iICGzevBlA0wJyYWFhuOeeexAeHo6ZM2e2vCFe/Um6uLgY/v7+Lc+ZMGECYmNjERsb2/KitGfPHkyYMAHz5s3D8OHD8Y9//ANvvvlmSw1PP/003nrrrTa1TZw4EU5OTib86U1jzpw5+OmnnwAAGzZswNKlS1seS0hIwNixYxETE4Nx48bh4sWLAIC1a9di3rx5mDp1KqZNm4aqqirceeediIiIQGRkJDZu3NhyjqeffhpRUVEYM2YMCgoK2lzfzc0N8fHxUKlUJv5JxdWbdp44cSISExNbjrvmmmtw+vTpVufVaDS45pprYGlpafofQqL27NnTqlflwQcfxNq1a1sd88knn+Avf/lLy9cffvghHnnkkTbnGjNmDDw9PU1Vqsnk5eXBxcUFFhYWAAAXF5eWldVPnDiBSZMmYeTIkbj22muRl5cHoOm18eGHH0Z0dDRGjBiBhIQEZGRk4P3338cbb7yB6Oho7N+/H0VFRbjxxhsRHx+P+Ph4HDx4EADw7LPPYvny5ZgwYQL8/Pzw3XffYdWqVYiIiMCsWbPQ2NjYUt9//vMfREREYNSoUbh06RIAdHre22+/HePHj8ftt9/e5medNm0abG1tTdeY7VAqlVi5ciXeeOONNo9lZGRg6tSpiIyMxLRp05CZmQmtVgs/Pz8YDAYAQHV1NXx9fdHY2Ii0tDTMmjULI0eOxIQJE5CcnAyg6Y3/T3/6E8aMGYOAgADs2bMHd911F8LCwnDHHXe0uuYjjzyC8PBwTJs2rWWro87Oe99992H06NFYtWpVq/NI+fVjwL3fCwOcRqMRGhsbBa1WKwiCIBQVFQmBgYGCwWAQ0tPTBYVCIZw6dUoQBEFYtGiRsH79ekEQBGHSpEnCsWPHWp7j5+cnCIIgVFdXC7W1tYIgCEJKSoowcuRIQRAEYffu3YK1tbVw+fJlQRAEIT09XYiJiREEQRD0er0QEBAgFBcXt1tjenq6EB4ebvwf3kQ0Go1w+vRp4cYbbxRqa2uFqKgoYffu3cLcuXMFQRAErVYrNDY2CoIgCNu3bxcWLlwoCIIgrFmzRvD29hZKSkoEQRCEVatWCQ8//HDLeUtLSwVBEAQAwg8//CAIgiA8/vjjwvPPP99hLc8884zwyiuvGP1nlILetvPatWtb2vXixYstv6PtWbNmjfDAAw+Y9geRII1G06otBUEQHnjgAWHNmjWCIPz+919ZWSkEBAQIDQ0NgiAIwtixY4UzZ850et7+pLKyUoiKihKCg4OFP/3pT8KePXsEQRCEhoYGYezYsUJhYaEgCILw5ZdfCnfeeacgCE1ts2LFCkEQBGHv3r0tr11//FtcunSpsH//fkEQBOHKlStCaGhoy3Hjx48XGhoahMTERMHKykr4+eefBUEQhBtuuEHYtGmTIAiC4OfnJ7zwwguCIAjCunXrWv6vOjtvbGysUFNT0+HP+8f/c1PTaDSCVqsV/Pz8hPLycuGVV14RnnnmGUEQBOG6664T1q5dKwiCIHz88cfC/PnzBUEQhHnz5gm7du0SBKGp3e+++25BEARh6tSpQkpKiiAIgnDkyBFhypQpgiAIwvLly4UlS5YIBoNB+P777wVbW1vhzJkzgl6vF2JjY1ve3wAIn332mSAIgvDcc8+1/N13dt65c+cKOp2uw59Paq8fA/H9XrI7DhiTIAh46qmnsG/fPsjlcuTk5LT0zgwdOhTR0dEAgJEjR3a6PQMANDY24sEHH0RiYiIUCgVSUlJaHhs1ahSGDh0KoGk8hbOzM06dOoWCggLExMTA2dnZJD+fGCIjI5GRkYENGza0WctOq9Vi+fLlSE1NhUwma/XJeMaMGS2fJHbs2IEvv/yy5TFHR0cAgFqtbunhGDlyJLZv327qH0eyetPOixYtwvPPP49XXnkFn3zySZtP09R9NjY2mDp1KrZs2YKwsDA0NjYiIiJC7LKMxsbGBidOnMD+/fuxe/duLFmyBC+//DLi4uJw7tw5zJgxAwCg1+tb9RQ29+hOnDgRFRUVKC8vb3PuHTt2ICkpqeXriooKVFVVAQBmz54NlUqFiIgI6PV6zJo1CwAQERHR6jW4+TpLly5t6cHs7Lzz5s2DlZVVX5vFqOzs7LBs2TK8/fbbrWo7fPgwvvvuOwDA7bff3tJbtWTJEnz11VeYMmUKvvzyS9x///2oqqrCoUOHsGjRopbn19fXt/z7+uuvh0wmQ0REBNzd3Vt+R8PDw5GRkYHo6GjI5XIsWbIEAHDbbbdh4cKFXZ530aJFUCgUJmgV0xlo7/eDIqR9/vnnKCoqwokTJ6BSqeDv74+6ujoAaOnmBwCFQtHS/alUKlu6nJuPBYA33ngD7u7uOH36NAwGQ6vuXo1G0+q6K1aswNq1a5Gfn4+77rrLZD+fWObNm4fHHnsMe/bsQUlJScv3//73v2PKlCnYtGkTMjIyMHny5JbH/thG7VGpVJDJZACa/k/6On6tv+tpO1tbW2PGjBnYvHkzvv76a5w4cUKkyqXt6r9xoPXf+dVWrFiBf/3rXwgNDcWdd95prvLMRqFQYPLkyZg8eTIiIiKwbt06jBw5EuHh4Th8+HC7z2n+++zoawAwGAw4cuRIu7fEml935XJ5q793uVze6u/96vM2/7uz83bn9UUMf/nLXxAbG9ut35958+bhqaeeQmlpKU6cOIGpU6eiuroaDg4OrYYxXO3q9rz6Pe2P7Xk1mUwGg8HQ6Xml2p6dGWjv94NiTJpWq4WbmxtUKhV2796NK1eudPkcf3//lje3b7/9ttW5PD09IZfLsX79euj1+g7PsWDBAmzbtg3Hjh1r2fpqILnrrrvwzDPPtOlZ0Gq1LQPc/zjG52ozZszAf//735avy8rKTFJnf9ebdl6xYgUeeughxMfHt/RQUmt+fn5ISkpCfX09ysvLsXPnznaPGz16NLKysvDFF1+0GhM4EFy8eBGpqaktXycmJsLPzw/Dhg1DUVFRS0hrbGzE+fPnW4776quvAAAHDhyAvb097O3tYWtri8rKypZjZs6ciXfeeafVuXuq+TpfffUVxo4da7TzmpuTkxMWL16Mjz/+uOV748aNa7mT8Pnnn2PChAkAmno34+Pj8fDDD+O6666DQqGAnZ0dhg4dim+++QZAU2/RH8eZdsVgMLS8l33xxRe45pprjHJeqRlo7/cDOqTpdDpYWFjg1ltvxfHjxxEREYFPP/0UoaGhXT73sccew3vvvYeYmJhWM3Puv/9+rFu3DlFRUUhOTu70k4ZarcaUKVOwePHiDruMly5dirFjx+LixYvw8fFp9UcsdT4+PnjooYfafH/VqlV48sknERMT02kv2P/93/+hrKwMI0aMQFRUFHbv3t3ta+fn58PHxwevv/46XnjhBfj4+KCioqJXP4fU9aadR44cCTs7u04/ufv7++Ovf/0r1q5dCx8fn1a3kAay5tcFX19fLF68GCNGjMDixYsRExPT4XMWL16M8ePHdxh4V61aBR8fH9TU1MDHx0e0ZRZ6qqqqCsuXL8fw4cMRGRmJpKQkPPvss1Cr1fj222/xt7/9DVFRUYiOjm41c9PS0hIxMTG47777Wl6zrr/+emzatKll4sDbb7+N48ePIzIyEsOHD8f777/f4/rKysoQGRmJt956q2XwfW/PO2HCBCxatAg7d+6Ej48Pfvnllx7X0xePPvpoq/eSd955B2vWrEFkZCTWr1/faqD5kiVL8Nlnn7XcngSagtzHH3+MqKgohIeHtwyI7y6NRoOEhASMGDECu3btwj/+8Y8+nVdqrx8D9f1+QO84cPr0adxzzz2iTY03GAyIjY3FN998g+DgYFFqoMEpNzcXkydPRnJyMuTyAf1ZrMd687pw3XXX4ZFHHsG0adNMWFn/MHnyZLz66quIi4sTuxSiFgP1/X7Avnq///77WLp0KV544QVRrp+UlISgoCBMmzaNAY3M6tNPP8Xo0aPx4osvMqD9QU9fF8rLyxESEgIrKysGNCKJGsjv9wO6J42IiIiov+LHbCIiIiIJYkgjIiIikiCGNCIiIiIJYkgjon5LoVAgOjoa4eHhiIqKwmuvvdZqgVpTePzxxxEeHo7HH3/cpNchIuLEASLqt2xsbFq2BCosLMQtt9yC8ePH47nnnjPZNe3t7VFaWmqW7XJ0Oh2UykGxMQwRtYM9aUQ0ILi5uWH16tV49913IQgCMjIyMGHCBMTGxiI2NrZlMdZly5bh+++/b3nerbfe2mYBT0EQ8Pjjj2PEiBGIiIhoWfl+3rx5qKqqwsiRI1u+BzStkRQcHIyioqKWr4OCglBUVISioiLceOONiI+PR3x8PA4ePAgASEhIwNixYxETE4Nx48bh4sWLAJp2j5g3bx6mTp3KZT+IBrtub8VORCQxGo2mzffs7e2F/Px8obq6WqitrRUEQRBSUlKEkSNHCoIgCHv27BHmz58vCIIglJeXC/7+/kJjY2Orc3z77bfC9OnTBZ1OJ+Tn5wu+vr5Cbm5uh9cUBEF49tlnhTfeeEMQBEH45ZdfhIULFwqCIAhLly4V9u/fLwiCIFy5ckUIDQ0VBEEQtFpty3W3b9/ecvyaNWsEb29voaSkpFdtQkQDB/vRiWhAamxsxIMPPojExEQoFAqkpKQAACZNmoT7778fRUVF2LhxI2688cY2txQPHDiApUuXQqFQwN3dHZMmTcKxY8cwb968Dq931113Yf78+fjLX/6CTz75pGVLrh07drTaMqeiogJVVVXQarVYvnw5UlNTIZPJ0NjY2HLMjBkz4OTkZMzmIKJ+iCGNiAaMy5cvQ6FQwM3NDc899xzc3d1x+vRpGAwGWFpathy3bNkyfPbZZ/jyyy+xZs0ao1zb19cX7u7u2LVrFxISEvD5558DaLr1eeTIkVbXB4AHH3wQU6ZMwaZNm5CRkYHJkye3PNbZHoFENHhwTBoRDQhFRUW477778OCDD0Imk0Gr1cLT0xNyuRzr16+HXq9vOfaOO+7Am2++CQAYPnx4m3NNmDABX331FfR6PYqKirBv3z6MGjWqyxpWrFiB2267DYsWLWqZWDBz5ky88847LcckJiYCALRaLby9vQE0jUMjIvojhjQi6rdqa2tbluCYPn06Zs6ciWeeeQYAcP/992PdunWIiopCcnJyq94pd3d3hIWFtdyS/KMFCxYgMjISUVFRmDp1Kv7zn//Aw8Ojy3qaJxZcfd63334bx48fR2RkJIYPH473338fALBq1So8+eSTiImJgU6n60szENEAxSU4iGjQqampQUREBE6ePAl7e3ujnff48eN45JFHsH//fqOdk4gGL/akEdGgsmPHDoSFheHPf/6zUQPayy+/jBtvvBEvvfSS0c5JRIMbe9KIiIiIJIg9aUREREQSxJBGREREJEEMaUREREQSxJBGREREJEEMaUREREQSxJBGREREJEH/D9/3rrTbXAcpAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from prophet.plot import plot_yearly\\n\",\n \"m = Prophet(yearly_seasonality=20).fit(df)\\n\",\n \"a = plot_yearly(m)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Increasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting: N Fourier terms corresponds to 2N variables used for modeling the cycle\\n\",\n \"\\n\",\n \"### Specifying Custom Seasonalities\\n\",\n \"\\n\",\n \"Prophet will by default fit weekly and yearly seasonalities, if the time series is more than two cycles long. It will also fit daily seasonality for a sub-daily time series. You can add other seasonalities (monthly, quarterly, hourly) using the `add_seasonality` method (Python) or function (R).\\n\",\n \"\\n\",\n \"The inputs to this function are a name, the period of the seasonality in days, and the Fourier order for the seasonality. For reference, by default Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality. An optional input to `add_seasonality` is the prior scale for that seasonal component - this is discussed below.\\n\",\n \"\\n\",\n \"As an example, here we fit the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 9 -u in\\n\",\n \"m <- prophet(weekly.seasonality=FALSE)\\n\",\n \"m <- add_seasonality(m, name='monthly', period=30.5, fourier.order=5)\\n\",\n \"m <- fit.prophet(m, df)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"prophet_plot_components(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(weekly_seasonality=False)\\n\",\n \"m.add_seasonality(name='monthly', period=30.5, fourier_order=5)\\n\",\n \"forecast = m.fit(df).predict(future)\\n\",\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Seasonalities that depend on other factors\\n\",\n \"In some instances the seasonality may depend on other factors, such as a weekly seasonal pattern that is different during the summer than it is during the rest of the year, or a daily seasonal pattern that is different on weekends vs. on weekdays. These types of seasonalities can be modeled using conditional seasonalities.\\n\",\n \"\\n\",\n \"Consider the Peyton Manning example from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.\\n\",\n \"\\n\",\n \"First we add a boolean column to the dataframe that indicates whether each date is during the on-season or the off-season:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"is_nfl_season <- function(ds) {\\n\",\n \" dates <- as.Date(ds)\\n\",\n \" month <- as.numeric(format(dates, '%m'))\\n\",\n \" return(month > 8 | month < 2)\\n\",\n \"}\\n\",\n \"df$on_season <- is_nfl_season(df$ds)\\n\",\n \"df$off_season <- !is_nfl_season(df$ds)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def is_nfl_season(ds):\\n\",\n \" date = pd.to_datetime(ds)\\n\",\n \" return (date.month > 8 or date.month < 2)\\n\",\n \"\\n\",\n \"df['on_season'] = df['ds'].apply(is_nfl_season)\\n\",\n \"df['off_season'] = ~df['ds'].apply(is_nfl_season)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we disable the built-in weekly seasonality, and replace it with two weekly seasonalities that have these columns specified as a condition. This means that the seasonality will only be applied to dates where the `condition_name` column is `True`. We must also add the column to the `future` dataframe for which we are making predictions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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6+zMb2KE6TmBFCBpP5nd9uHcyv3TwpPsnX5f7tSq3RkoNzq1U5lY051Y0KjTHSUxjjLYr3pmO9RXHeohA3ga1jbuUXSIVSu+xs6dpL5YM6ub/ZN/jBJ8IKSMx248yJGS6XaoLTl0uZzObjd+SrL1UcvV3vSVP+Yp6vmBcg5vmKef5iXoCEb/mHg1RYavPlUtWN+m/OlGzKqp6YIH0jLcD3cR0+BnH0cimhUKjRaJr9Yll3pfKT48Wv9PZXaI25Vaq8GlWpQhfsyo/3oaM8hXHeohgvYScPgf33NCVJUiwWy2Sy1ty5TqVfebFi9aWK5ACXN9ICewSIbR3eo8DlUnZj58ulIDGziaOJ2cwXLTuW9+PlSjMyz+jmOzrGU6kzlil1FUpdmVJXptBWNOhKFbpypa5eY/CgKR8RFSjh+4l5vmJe4F8Jm/IT8yR8+y1xaENilmsMy8+VrbpUMTzS462+QfafXHS+xIwQOl2sWHG+rJOHMMaLjpUKo71oIcX+TzerErOFUmtcfbniu/NlsVLRvNSAfqGutgvvUSAx2w1cxwwcV1ZF449XKn/OqUkJEn82NGxQuOv9i0Rjm5t1U+tNpQptRYOuTKkrU+oqG/RZFY3lSm25Ulet0tMUESDh+Yn5fmKev5jydeH5i/n+Ep6fmMfuBSpqvWnlxfLl58pSgiQHpiXEOMaEonNIC5akBUvYjoIBYj7xekrArGS/9VcrX953M0DCn5caMKSTO2wAB9oPEjN4PJ3RvCe3Zs3lylt16imdfa7N7++K1K15oJDCIzyFzW4xoTeaKhr0ZUpduVJXrtSVKrR/3FWUK3VlSl1Vox5DZj8xz88yJO7C85fw/MV8HxEV6MqXiijSZns96oymn65UfnOmNNZbtGVibDd/licRgYOjKfzFHn4zuvluzqr656E7/+YXv5EW+HS0R4fdNR0wAhIzaEmJXLvuatWGq5UBEt7z3f3GxnmK+JSbG11b26rE3AKKwINc+UGuzYwPm8zm6kZ9qUJX2aArVerKlbqr5Y378+ssQ+V6k9lbRP2VsMW8+/nb8o82XJBjYTSZt1+v/vKPEh8X3nejI/uGsDAyCTiKR2DTu/pM6ey980bN5yfvfX7y3qu9/cfHe3WoAqCAQZCYQTPMZnSiqH7N5crjd+pHxXhumBDd3d9+K1xwDPNx4fk8YplVndpQ0aArVWgtXe2CWtXJu/Iyha5MqVVqjV405Sum/MX8/8/clJ+Y5yfmR/jwHvVeN5vR/oLaz0+W4Bj6dHDo8ChHrIwEHB+JYxmJ0gkJXvvy6745U7Loj5JXegdM7uzd5h+LoMOCxAz+Rmc0r71SseZShc5ont7V55vhnTxpx3qTeAhJDyEZ19y8b6POWKrQWeawy5T6igbd5XJlqUJbodTXqPQSPmmZwPZ1oe73sA1G89dnSuvVhrf7BY2L84QRSNBOOIaNivEcGe35+y3Z4j9LvzpdMre3/7NdfGgHWOMGuMKxvnMBu1R60/Sd+Uqt8cNBIUM6uTtC2T6riHhElJcwyquZKW2c4hfVKMsUWktXu0ypzatWVzToGnXG57r5TukshVFHwCAMQ0Mj3IdGuJ+6K//mdOniMyWze/o/183HnlciAO6Cdwn4i0xtmLw9z1tEbZgQ43wlafkkHuouCHblIcTaVaegA+ob4to3xPVCifKbM6XLzpY9393nxR7+HkL44gUtgV4CQAihygbd6I03IjyFq8dGOV9WBoBdPQLFmybG7J4cX1ijTl5x+f0jRZUNOraDAo4LEjNAd+u1T6+/0TfEdemITra7EgmADi7Bh14zLvrXZxNrVYZe3199+9Cde3It20EBRwSJuaPLrVY9vf56RqL00yGhsPIJAFuL8hIuHxlxcmZno8ncd9W1V/bdvNnuiw+Bk4HE3KFdKlOO2XjjtdSAN/sEsh0LAB1IsCv/P0+G/zmri6uAHLI2+4XdBTnVKraDAo4CEnPHdeJOfcbWvH8NDp3Z3U7lkgAAD/IT8/41OPTinG6h7sKR629M25F/qYxjJfKALXA7MZvNyGDi0gbujmNffu1zPxcue7rThAQp27EA0KF50uR7/YMuv9S1i59o8vb8CVtyzxRzrKwqYFbbV+2rVKoVK1aoVCpfX9+ZM2diGIYQMhgMS5cubWhoCA4OzszMZCzMR9iZU/Pj5YrvR0WGesLlB1bYnFX9/pGin9KjnaOcAABOwFVAzk8LnN3Db+2Vyll7CkPc+G+kBT4R7sZ2XIAFbe8xnzp1KiwsbOHChQ0NDYWFhZYbz549Gxwc/P7771dXV5eUlDAU5CONjvHoGSh+Ym32vrxaW5/Laaw4X/7RsaLtk2IhKwPgaEQ8Ym4v/0svdRsX5/WPX28/8WPWvvxaE6cKO4L2a3tHUyqV5ubmymSympoaN7e/ftYVFhampKQghBISEgoLCwMDAxFC9fX1lkqWAoGAIAgmwv4LQRAfDw7vH+b+0t7Cs2WqBf0CuHUNLo7jOG7X2YRPj9/dfK1y/7Odm90eqzUsATP7OtoBhmE4jmOcWnduCZiLTU0QBLeKBBME4VBNTRNoVs+AGd39tmVX/+t48RenSualBY2L82qyGR8Xm5rT72rbHbzpLU1e1CVLljz8MHd392effbbJjQqFYsGCBSKRiMfjLViwgKIohNDy5ctHjhwZHBx8/PhxpVI5cuRIhNDixYv37t2LEFqzZk1ISAiDz+e+coXm2c1Xa1W6zVO6RXtDqb5mmMzmV3ffOFxQ/dus3iHubczKFhjW9G0DbASa2m4ctqmNJvP2a2WfHb2l0hnfHtTp2eRAHse3j3XYpmaLwWCwJND7mjbQF198gRDKyso6cuTIiBEjSJLcv3//rFmzPvjggybHWrVqVZcuXZKTk3ft2iUWi4cMGYIQ+vHHH1NTU6Ojow8ePCgQCAYOHPjgQ2QymdFotMUTI0nSRSz+YP/15efK/vVEyKQkb1uchXECgUCv19uoTR6kN5pe2X8rv0a1LSNOKqIe/4BHIwjCzc2ttpZjcwdCoVCr1ZpMJrYDsQJFUSKRqL6+nu1ArCMUCjUaDbe+eUmSFIvFMpmM7UAeyWxGvxbWfXOmtKpRP7eX/7Qu3gISp2larVZzq6n5fL5AIJDL5WwHYh2RSGQZ97UFmqZp+m9VeZoOZb/99tsIoV69emVnZ0ulUoRQXV3dU0899XBi1uv1ljeEyWQyGAyWGyMjI3Nzc6Ojo/Py8tLT0230NJqFY9i81IDUYMnsvYWn7iq+HBYm4nFstMRGNAbTc7sKFFrD7snxrgJYJQcA92AYGh7lMTzK49jt+m/OlH5zpmR2D7+X+4bDd5xTan5IpKKi4v60sVgsrqqqevg+48eP37179/vvv19QUDBo0KCCgoJly5b17t27qKho0aJFHh4eQUFBNgz8EXoFio/OSFLqjE/8mJVdaasfOByi0Bombsk1mc3bJ8VBVgaA6waGu+2dGr9mbPTpYkXMf/748tQ9mdrAdlCAYc2P9b/44osFBQXTpk3DcXzDhg1RUVHffvtt+09m06FsiURSV1dn+dNsRqsvV/z7RPG7/YOf7+brsCt+bD2ULVMbJmzJCfcQLn+6E1NlDWEo225gKNtuHH8ou1l5MsPnx26dLJJndvWd09OvnbNU9gFD2Q97eCi7+cSs1+tXr1594sQJHMf79+8/Y8aMJlPTbWO3xGyRXdn4wu6CKC96yYhO7g5ZZ82miblWZRi/+Uaij2jxiE4MVlaGxGw3kJjthqOJ2TLHnFPVuOTP0l8LZc8kSl/u7R8g4bMdV0sgMT/s4cTcfC+KIIhOnTqNGTNm1KhRrq6uljXVnJPoIzoyI0nCJwauyTpX0rE2uqtu1I/ddKO7v3jJU0xmZQCAo4mV0t+Nijz2XJLaYEpdefX1A7fuyDRsBwXapfl+5OTJk9VqdXh4uOVPHMfHjx9vx6gYI+IRy56O2JpdPWV73ks9/V9P9ccddlybORUNunGbcvqFun42JKwDPF0AAApzFywe0enNtMDl58sHrsl6MtL99ZSAGCn9+EcCx9N8YtbpdHv27LFzKLaTkShNDhDP3J3/x135t6MifF14bEdkQ6UK7bhNOUMj3D9+Aso4AtCxBLryPxsS+kZqwLfny0asv94v1HVeamBnXxHbcQHrND+UHRERwbmZrZZ18hD8Nj0pWkoPXJ115LZTPbUH3ZNrR2248XSM5yeDISsD0EFJRdQHA0MuzekWJ6UnbMnJ2Jrb0ebyuK75HnNpaam/v39KSoqHh4fllu3bt9sxKpvgEdhnQ0L7hkhe+uXmM4nSd/sHcX0DnSaK6jVjN+VkJEjf6cfChWoAAIfiLiTf6hv0Ui//NZcqMnfmR3kJX08JGAhVMbig+VXZp0+fbnJLWlpa+09m51XZj1Ii187eW6g3mVeOjgpxY3MFI4Orsm/VacZuupHZ1feNtID2H60FsCrbbmBVtt1welV2a5parTdtuFa17Gypr5g3LzVgWIQHWyNqsCr7Ya1dlZ2WllZQULB27dpevXrhOM5IVnYcga783VPi+4e6Dv4xa08uxxJMswpq1KM33ngh2eZZGQDARUIKfyHZ98KcrlM7ey88fHfA6ms/59QYoZi9o2o+MX/wwQfbt28/ffo0hmELFy5cuHChncOyNRLH3u0fvHps1HuHi944eEtj4FJHqoncatWYTTde6e3/Sm/IygCAR+IR+LQuPn++2OXl3v5fnS5N++Hq5qxqvZHD337OqvnEvGXLlm3btgUEBBAEcfDgwfXr19s5LPvoF+p6/PmkUoVuyNrsvGoV2+G0RXZl49hNN95MC3yxhx/bsQAAOIDEsQkJ0pMzkxYMCF51qbzX91fXXKrQGqH37ECaT8w6nU6v11v+rdFoBAKBHUOyKy+a2jIxNiNBOmL99fVXK9kOxzpXyhvSt+S+1z/4ue6+bMcCAOASHMOejvY8MiNp0ZPhO3Nqun97+dtzZY06m1e6A63RfGKeO3fu0KFD79y5s2jRor59+86ZM8fOYdkThqGXe/tvnxS7+EzpC7sLFFpu7Ah/oUSZsTX3o4HB07r4sB0LAICrngh32z8t4fvRkUfvyLutuPLV6RK5hhvfgU7skQWrjx49evz4cZqmBw8enJyczMjJHGRV9qMotIZ5B25fq2hYOTqqm78LU7G1oM2rss/eU07dkffF0LDx8V62CKwFsCrbbmBVtt04/arsVrpUpvzmTOmfxcrnuvnM7unvSTNfYgBWZT/s8fWYEUJ1dXUrV6585513Bg0aZKM4HJOET64eG/XT1coJW3LnpwXM6envmHt0nLorn7Gr4Ovh4aNiPNmOBQDgPLr7izekx+RUq745XZK84vKUzt5ze/n7iZ15q0TH1EyP2Wg0du7cee/evff3ymaKWq1m9oD34TjO4/E0Gma2bs+papy2JTvIlb8qPcHLlpXUCIIwmUxW/eA9XFg7bWv2yvHxI2OltgusBRiG8fl8pprabkiSNBqN3OrG4ThOUZRWq2U7EOtwtKkZ/AKxG4qi7i8GYlxBdeNXp+7+fKMqI8lnfr/QUHchI4clCIIkSc69q23a1DiO8/l/21Gj+aHsjIyMn3/+OTk5OSDgrytwGNn5y8GHsh+k1pve/f3O4Vv1K0ZF9AlxZeqwTVg7lP37LdnsPTe/Gx0xpJO7jUJ6LBjKthsYyrYbGMp+lHty7X/Plm27Xv10tMdrKQGRnu1NzzCU/bDW1mN27p2/Wu/nnJq3frvzfHffN/sEkjYonmhVYj5QUPfq/ls/jIkaGGarHwqtAYnZbiAx2w0k5pZVNOi+PVe2/mrVoHC3eamBCT5tL1oFiflhrd35a9OmTWkP+OGHH2wUkIMbG+d1eEbS0dv1YzfdKFWwOfbyS37dq/tv/TiO5awMAOiAfF14Hz8ReumlbhGegjGbbkzelncBqmLYUtPFX7GxsQih4uLio0ePWm4xGAxubh133/MQN/6+afH/Ol48aE3Wkqc6PRnpYf8YduXUvP3bnfXp0Y4Kk7IAACAASURBVClBEvufHQAAEEIeQvKf/YLn9vJffaly2s68OKloXlpAX5vN9HVkTRPzH3/8gRB66aWXvv322/s3duTEjBDiEfjHT4T2C3V7Zd/Nk3HyDweF8gj7LdfeklX1/tG7mybE9AgU2+2kAADQLAmfnJca8GIPv5+uVr6092aQG39easDgcHfHvIaFox55HbMtcG6OuYmKBt2cvTcVWsPK0VGdPBjYDe2xc8wbrlV9crx4y8SYrn72uK66NWCO2W5gjtluYI65bXRG86ZrVUvPlroKiDfSgp6Kcscfl59hjvlhrZ1jBs3ydeHtmBQ7PNJj2Lrs7derbX26NZcq/nX87o5JsY6TlQEA4D4egWV28zn3YpcXk/0+O1Hcd9W1rdnVBiha1W6QmK1D4NibfQLXp0f/63jxK/tv2W5r2e8vlC/6o2TX5PhEH5GNTgEAAO1HEfikJO8/Xuj8Vp+gFefLen13Zd2VSh1UxWgHSMxtkRIkOfZckkytH7w2+0YV82Wp/nu29L9ny3ZPiYuTtv2yBAAAsBscw0bHeh57rvO/h4Rtya5OXnH5+wvlaj2XJo8cByTmNvKgqfXjY2Z08xm54frqSxUMHvnr06U/XKzYMyU+2guyMgCASzAMDYt0P/hswrKnI34tlHX99tLiMyVKLRStsg4k5rbDMDQr2W/35PiVF8ozd+XXM1GS5fOT99Zfq9w7NZ6RxWUAAMCKfqGuP0+OW58ec65E2e3by5+fvFenstWWls4HEnN7JfmKjjyXJCTxgWuyLpS266L7T44Xb79evXdKfKgbZGUAAOf1CBBvnhi7a3Jcfo0q+bsrHxy9W6Hk2C7ZrIDEzAAXHrFiVOTbfYMmbc1b/GepyfqrF8xmtPBw0S95tXunxge58h//AAAA4IhEH9GP46IPPptQ3ajv+t/zr+3JuSeH9NwSSMyMmZQo/XV6wu6cmolb86oarRi0MZvRu4eLjtyu3zs1PkACWRkA4ISivehvR0b8OaeHzmjqu+raq/tv3qrjWDkvhNC18gY7nIX5OtgdWaSn8LfMpPePFHVbfgkhpG31BQOxUnrPlHipLUtMAgAA60LdBSvGJbza02f52bLBP2YNiXCflxoQ6/CXn1wpVdiubPHDIDEzjE9gXwwNm5caoDWYEUI8AhNSjxyWuL/zl4jCKQJGLwAAHYK/mPfpkNB5aQHfnS9/ev2NtBDJ6ykB3fwdaCelJj1joZCZctSt1PbEvGPHjnPnziGEGhoaevbsOWPGDIRQTU3N/Pnzvb29EULz5s3z9/dnKlBu8XXhteZuAgGpJ8xGI2wyCwDocLxoasGA4Jd7+6+6WPHM9rzOvqJ5qQEsluqxzzB1a7Q9Maenp6enpyOEli9fPnz4cMuN1dXVI0aMyMjIYCY6AAAATs1NQL7ZJ3BOT7+1Vyozd+T5S/gLB4YMCrdf5STHycf3tXcou7i4WCgU+vr6Wv6srKwsKytbtmxZfHz8wIEDLTdevXr19u3bCKG0tDSRyCYbTOI4jmGYQMCxq4woisJxnFuVFXAcRwhxrqlJkkQIcauyAkEQOI5zsakFAgG3mpqjXyBcbGqSJJt9VwsEaH7/8JdSQ9ZfqXh1/y13ITWtq2/fULeuAQx3oK+UKprcwuM9foCTIIj7d2P8fWL5Un1QexPzjh07Xnjhhft/0jSdkJDQtWvXJUuWeHp6JiUlIYSKiorOnz+PEOrVqxdF2WR9E4ZhCCEbHdx2CILAMLsW+Go/DMMwDIOmtgNLtoCmtgPuvqsR135utvyupihqblrorN7Bnx27/d3ZktUXy57t5j840gt/YLqvW6B1FaAvl/ytkpXlN7q1cBy//0A7vE/a9flpbGxcvHjxe++99/B/HTt2rLa21jLWfR/Xyz4y7rFlHx0QlH20Gyj7aDdQ9tFuWl/20WgyL/2zdHNWldZofiZROjjCjXqoZ2k3QqHw/qrszkyX+2O47OOlS5csfeL7Nm/efO3aNYRQcXHx/fFtAAAAwCoEjs1LC1w5OurFHn6/5NdN31mwO7dWZ+TSb+s2a9dQ9oULFyZOnGj5d0FBwaFDhzIyMlatWrVr1y53d/fU1FQmIgQAANBBdfF3wTCUFiy5WNaw8WrlxmuV6fFeI2O86EdfhuoE7DoVBEPZTcBQtt3AULbdwFC23Tj3UHYTlrXT2ZWqjdcqCmo0Y+K8xsZ5inmEDWJsBpeGsgEAAAA7sKTDRB/686Hhnw8NvVWnnro9f9WlCpmagbJ+jgYSMwAAAA6431WN8qI/GhSyeER4VYPu2Z35y8+VVTtXTUlIzAAAALjhwWHkMHfBu/2DvxsVqdabZuzM//pMSZlSx2JsDILEDAAAgDM6+7k8mJ4DJLw3+wSuGRtF4fiLewo/O1lcJONe0aomIDEDAADgmCYrsLxdeK/09l83PtpTSL164NZHR4sLa+1XDIpxkJgBAABwz8Oroz2E5KwefhvGR4e689/67c67h+7cqFKxEls7QWIGAADASc1euSQRkNO7+mxMj0n0FX1wpGj+wduXy5T2j609IDEDAADgqkddVUzz8GeSvDdOiEkLkSz6o+SVfbf+LFZw5ZJvSMwAAAA4rIUdP/gkPi7O66f0mCej3L89X/binoLjd+Qmh8/PkJgBAABwW8u7cVE49lSUx9px0RMSpT9drXz+58JDN2UGB94HEBIzAAAAznvsTpkEjg3p5L5qTORz3X135dRM31mwN69WZ3LE3nN76zEDAAAAjqCzn4tlS+0W4BjWN0TSJ1hyoVS54VrVhmtVExOkT0d7CEgH6qZCYgYAAOAkWpObEUIYhnoGinsGiq+WN2zKqtp0rWp8vNeYWE+RvapitAwSMwAAAOfRytxs0cXPpYufS06ValNW1fYbNaNiPMbHSV0FLKdnu5Z9vF82i3E4jvN4PI2GYzuxEQRhMpm4VbUNwzA+n8+5piZJ0mg0cqupcRynKEqr1bIdiHU42tRc/AKhKEqv51jxBoIgSJK0w7v6SqnC2ofcrFWtu1R2tlg+MtZrchc/LxHv/n+RJGkw/FXGqmuAhLEoEUII4TjO5/MfvAXqMbMJ6jHbDdRjthuox2w3Haoecxu0vt/8oGK5dkt29aki+eBObhmJUl8XHoJ6zAAAAED7tS2DBrvy3+oT+MOYSITQC7sLv/yjpFhu71ErSMwAAACcU5t7t74uvNdSAtaOixLz8Jd/ubnw0M3bdfab6YDEDAAAwGm1Z+TZk6bm9PRfnx4T6Cp449fbCw4X5dilKgYkZgAAAM6snbPCrgLixV6BG9KjY6X05yfvqfQ2X6oCiRkAAICTa/+KLRceMaWz99rxUTRl87wJiRkAAIDzY2Q1NY5h7T/I489ih3MAAAAArGP8SicbgcQMAACgo+BEbobEDAAAoANx/NwMiRkAAEDH4uC5GRIzAACADseRczMkZgAAAB2Rw+ZmSMwAAACAA4HEDAAAoINyzE4z2eZH7tix49y5cwihhoaGnj17zpgxAyFkMBiWLl3a0NAQHBycmZnJVJQAAACALXT2c2lbgUjbaXuPOT09fdGiRYsWLUpISBg+fLjlxrNnzwYHB7///vvV1dUlJSUMBQkAAADYiqP1m9s7lF1cXCwUCn19fS1/FhYWJiQkIIQSEhIKCwvbGx0AAABgew6Vm9s+lG2xY8eOF1544f6fKpWKpmmEkFAoVCqVlht379599uxZhNArr7zi5eXVzjM2C8MwDMPEYrEtDm47BEHweDyz2cx2IFbgblNTFMWtpsZxnCAIaGo7wDAMx3HONTVJkiRJcqupHfldTcsfWTaKJEns/3fJtkPw7UrMjY2NarX6wShpmlar1QghtVrt4vLXD5DQ0FCTyYQQIklSr9e354yPguM4RVE2OrhNGY1GS+NwBXeb2mAwcOsrjCAIgiCgqe0Ax3HbfTvZDoZher2eW01NkiSO447Z1Ik+9JVSRbP/heO4wWCw/Jvx4EmyaSJuV2K+dOlSUlLSg7dERkbm5uZGR0fn5eWlp6dbbuzSpUuXLl0QQjKZTKPRtOeMj0KSpEAgsNHBbUqv1xuNRrajsAJBEEKhkHNNjWGYVqvl1m8giqIoiuJiU2s0Gs5lCz6fz7mmxnGcc03N5/MJgnDYpo715DW7EIwgCJ1OZ/k348Fbhpkf1K455gsXLlgyLkKooKBg2bJlvXv3LioqWrRokYeHR1BQUHsODgAAANiZI0w2Y/b8tSWTyWzUOyRJUiKR1NXV2eLgtiMQCLjYY3Zzc6utrWU7EOsIhUIu9phFIlF9fT3bgVjHMqDCrW4cSZJisVgmk7EdiHUsU4fcamo+ny8QCORyOduBPEaTfrNQKLTM0iIbZG6appt0mmGDEQAAAOBv2O03Q2IGAAAAHAgkZgAAAKApFjvNkJgBAACAZrCVmyExAwAAAM1jJTdDYgYAAAAeyf65GRIzAAAA0JKuARJ7ng4SMwAAAOBAIDEDAAAAj2HPAW1IzAAAAMDj2S03Q2IGAAAAWsU+uRkSMwAAAOBAIDEDAAAADgQSMwAAAOBASHueDMMwDMNsenzbHdxGbN0mjLNEy62YLaCp7YNz7Yygqe2Oi2HbM2a7JmYej2ej0qE4jmMYJhAIbHFw2yFJkiAIbhUJtrw7OdfUFEVhmF2rj7cfQRA4jnOuqUmSFAgE3Gpq7n6B8Pl8tqOwDkmS3H1X2+jgON506NquiVmr1RqNRlsc2fIGvV/ImisEAoFer7dRm9gIQRAP1gznEK1Wy63fQBRFkSTJxabWaDTcSswkSfJ4PM41NYZhnGtqPp+P4zjnmtqmMdM03fR0NjoTAAAAANrAroN7crncRl0Wk8mkVqtFIpEtDm47PB7PYDBwqxtnMplUKpWLC2uVStuGi01tNBp1Op1QKGQ7EOvw+XydTsetbpzRaNRqtQ93XBwcn8/XarVsR2Edo9Go1+s5N5QtEAg0Go3tDt7kY86xWbdHKSwsfO211w4cOMB2IM6vtLR06tSpx44dYzsQ53fx4sWvv/5606ZNbAfi/HJyct59993du3ezHYjzO3LkyI4dO1asWMF2IA4NhrIBAAAAB+IkidnV1XX48OFsR9EhiESikSNHsh1FhyCVSgcNGsR2FB2Cm5vb0KFD2Y6iQwgICOjTpw/bUTg6JxnKBgAAAJyDk/SYAQAAAOdg1+uY20mlUn322WcGg0EkEr311ls4ji9durShoSE4ODgzM9NgMDz4p0qlWrFihUql8vX1nTlzJhc3mmFRy01tuc9XX301d+5cgUDQpOXZjJuDrGrqJnfm8Xisxs4xVjW15c9r164dPXp03rx5rAXNTVY1tdls/uGHH6qqqiQSySuvvALf1Qgh4sMPP2Q7htY6dOiQl5fXK6+8UlpaWlNTU1FRQRDEnDlzfv/995CQkOvXrz/458WLF0Ui0ezZs0+ePOnp6enp6cl2+FzSclMTBPHJJ5+cP39+woQJJEmeOXPmwf+VSCRsh88lVjV1kzuHh4ezHT6XWNXUCCGVSrV8+XKRSJSSksJ27BxjVVNfunRJq9XOnj1bp9Px+XzOXYppC1wayo6MjBwwYABCyMXFhaKowsLChIQEhFBCQkJhYWGTP6VSaXFxsUwmq6mpcXNzYzdyzmm5qUUi0UcffZSYmGi5c5P/ZS9qTrKqqZvcmbWgucmqpkYIrVu3Lj09na1oOc2qpr5x4wZCaOnSpWq12tfXl72oHQiXEnNUVJSnp+eFCxfOnDnTo0cPlUpl2RBAKBQ2NDQ0+TMiIuL27dtffvklSZLu7u5sx84xLTc1+v+9hS13fvh/QetZ1dRN7sxm3BxkVVNfuHDBx8cnODiYzYg5y6qmbmhouH379qRJk86dO3flyhU243YYXJpjRgj98MMPDQ0NCxYsoGmapmnL5qVqtdrFxaXJn9u2bXv22WeTk5N37dp1/PjxIUOGsB07x7TQ1E3u2fL/gsdqfVM3ubPdI+W81jf17t27BQLBtWvX7t2799tvvw0bNoyNeDms9U1tmSzw9vbu27fvrVu3unbtyka8joVLPebTp0+TJDlv3jzL1puRkZG5ubkIoby8vIiIiCZ/6vV6y5VgJpPJYDCwGznntNzUTe7c8v+CllnV1E3uDKxiVVN/+umnCxcunDt3bkJCAmRla1nV1BERETdv3kQI3blzx8fHx/7ROiAuLf46cODA9evXT5w4cfjwYT6fn5KScujQoVOnTnl5efXr18/f3//BP0NCQtatW3fixAm5XD5lyhTLag7QSi03teU+x44d69evH0mSTVqe3cg5x6qmbnLnkJAQdoPnFqua2vJnY2NjVlYWLP6ylrVfIAcPHty/f7/JZMrIyHi4BmIHBBuMAAAAAA4EfpsAAAAADgQSMwAAAOBAIDEDAAAADgQSMwAAAOBAIDEDAAAADgQSMwAdyxdffLFkyRK2owAAPBIkZgAAAMCBQGIGwPnp9fo5c+aEhob26tUrKysLIaRQKDIzM2NjY/v163fs2DG2AwQA/A/shwWA81u9enVeXl5hYaFMJktKSurZs+fGjRtNJlNubu7hw4f37NkzcOBAtmMEAPwFeswAOL8TJ05MmzaNoihvb+8RI0YghFJTU48ePbpw4UIXF5fFixezHSAA4H8gMQPg/HAcv78FsWUj6M6dO589e9bf3//DDz8cN24cq9EBAP4G9soGwPmtXLly27Ztv/76q1KpTEhIeOuttxQKhcFg+Oijj8rLy6OiohQKxf36uAAAdkFiBsD56fX6V1999dChQ1KpdPjw4WFhYX379p00aVJ1dXVQUNALL7wwdepUtmMEAPwFEjMAAADgQGCOGQAAAHAgkJgBAAAABwKJGQAAAHAgkJgBAAAABwKJGQAAAHAgkJgBAAAABwKJGQAAAHAgkJgBAAAABwKJGQAAAHAgkJgBAAAABwKJGQAAAHAgkJgBAAAAB0Kycla1Wm0ymRg8II/H0+v1zlGQgyAIo9HIdhQMsNQANhgMbAfCDKd5XRBCGIaRJKnX69kOhDHO9OoghAiCQAg52TNypqdDUZTBYGAq41AUxePxHryFncSs0WiYfZFomlYqlcwme7aIRCK1Ws12FAzg8/kCgcA5ngtCiKZpjUbjHD/+SJLk8/kKhYLtQBjjNJ8aC5FIZDabnewZOdPToWm6sbGRqSyGYViTxAxD2QAAAIADgcQMAAAAOBBIzAAAAIADgcQMAAAAOBBIzAAAAIADgcRsNZNTrMsFAADgmNi5XIqLCmrUW7KrD9+S3azT6I0mdyHZJ1gyvatP/zA3tkMDAADgPCAxP16pQvvJ8eJDN2Xj4rw+fiIkwUdE4lhlg+73m/Uv77sVKxV+OyrSi6bYDhMAAIAzgMT8GFuyqhYeuZueIL0wu5sn/b/mchOQ0V70zGTfBYeLnliTtSUjNlZKsxgnAAAA5wCJ+ZF0RvM7h24fuyPfMCGmV6C42fsISPw/T4avvFg+YUvuvmnxoW4COwcJAADAyUBibp5Ca5i+s8BkNh/OTHqwo9ysWcl+Sq1x0tbcwzOSXHiEfSIEAADglGBVdjPq1IbRG29IRdT2SXGPzcoW89MCY6Wit3+7Y+vYAAAAODfGEvNXX32l0Wju/2kwGL7++uuPP/547dq1TJ3CPmpVhjEbb3T1c/luVASPwFr/wG9GhJ8uVuzNq7VdbAAAAJweA4m5sbFx4cKFJ0+efPDGs2fPBgcHv//++9XV1SUlJe0/i33INYb0LTd6BIi/erITjlmRlRFCbgLy86GhCw8XNeqcp7oZAAAAO2MgMYtEoo8++igxMfHBGwsLCxMSEhBCCQkJhYWF7T+LHaj1pme25cVKRYueDLMyKf/lyUiPOG/6mzOlTIcGAACgo2Bm8ReO49jfU5lKpaJpGiEkFAqVSqXlxi1btlg61u+99563tzcjp7bAMEwikbSnVq7BZM5cf1kqFq6b3JXE25SWEUIILR2X1HPpmTcGRfmJ+W07AkEQJOkMi/JwHMdx3NXVle1AmIHjOEU5ydXqGIY500uDnOhTY0EQBELIad5vyOleIAzDxGIxU9XZHz6OrVqKpmlLWWy1Wu3i4mK5sUuXLl5eXgghHo/34IR0+5Ek2c4i9vP2F1QqtPszuxh0WkM7IgkU4WPivD47XPDl8Mi2HYHP52u12naE4ChIkmT8hWYRj8fT6XRsR8EMgiAEAoHTvDTIiT41Fnw+HyHkZM/ImZ4OSZJardZkMjFytId/gdkqMUdGRubm5kZHR+fl5aWnp1tujImJiYmJQQjJZDJmXyQXFxedTtfmZlp2tuxIYd2v0xNxk779cb3W22/A6qy5PX19XXhteLjlJW9vEI7BmZ4LQRA6nY6p38jsIknSKb8o2Y6CMSRJms1mJ3tGzvR0LBnHaGRmOZFlgORBzF8uVVBQsGzZst69excVFS1atMjDwyMoKIjxszDoYEHdf8+VbZ4Y08orox4r1E3wdLTHDxcrGDkaAACADgVjpQcgk8mY+q1h4enpKZPJ2tBjzqlWjdpwY/WYSGZrUWRXNo7fnHNtbnchZfVPH5FI1NjYyGAwbOHz+QKBQC6Xsx0IMyyzM07TYxaLxTKZjO1AGOM0nxoLkUhkNptVKhXbgTDGyV4gT0/P+vp6prIYTdOWJVn3degNRurUhqnb897pG8h4hahEH1GMF73tejWzhwUAAOD0Om5iNpjMz+3K7x/qOjPZzxbHn9XDd/UlGM0GAABgnY6bmD84cldnMn8xLMxGx38y0qNWpb9YqrTR8QEAADilDpqYt1+v3pNXu2ZsFI+wVQuQOJaR6L05C0azAQAAWKEjJuYbVap3DhWtHhvVtsuZWu+ZJOnu3FqVnplr3QAAAHQEHS4xK7SGzJ357/QLelSJZQZFegqjvYT78qGsBQAAgNbqWInZbEav7b/dxU/0QrKvfc44Pt7r55wa+5wLAACAE+hYifnb82UFtarFIzrZ7YyjYz3/uKuoU+ntdkYAAACc1oES88VS5VenS1aOjhLxmu5/ZjteNJUSLPklv85uZwQAAMBpHSUx16kNz/9c8NmQsHhv+vH3ZtTYWK/duTDNDAAAoFU6RGI2m9HcXwoHhLllJErtf/bhUe4XShS1qvbUrAIAANBRdIjEvPxcWYlC9/lQW+0l0jI3Adk72PXQTefZlxgAAIDtOH9ivlLe8PWZku9HR7ahngRThke67y+A0WwAAACP5+SJWaE1zPy54JMnQuOk9p5aftDwKI+TRXLYaQQAAMBjOXlinn/wdvcA8ZTO3uyG4S/mxXjRR2/XsxsGAAAAx0eyclaBQMDsATEMo2m6Sa3ctRdLr1aozr7cS8Rn52k+6KlY72N3lRndgh57T4qiRCKRHUKyNYIgCIJwjueCEKIoCsMwtqNgBo7jOI47zUuDnOhTY0FRFELIad5vyOleIISQUChkqjo7jjftIbOTsTQaDVMlpi0EAoFKpTKZ/jdWfLNW/c7Bwi0TYwiDttGgZfBcbTMgxGXytnsNDY2P/aw5TUVxPp+PYZhzPBeEEE3TarWaqY8iu0iSJEnSaV4a5ESfGguRSGQ2m1UqFduBMMbJXiCBQKBWq5nKYjTddKbVOYeydUbT7L2FL/Xy62H7DbFbKclHhGHYtYoGtgMBANiQ2YzuybXZlY3VjbDfH2gj9sd4beGLUyVCCn89JYDtQP4Hw9DgTm6/36rv4ufCdiwAAObJNYalZ8t2XK+u1xi8aKqiQe/rQk3r6pPZ1cdN4JzftMBGnPDt8sdd+fqrlUdmJBG4Y83QDO7k9t+zZf/oE8h2IAAAhh27I5+zt6B3kGTV2Kju/i44humNpjPFilWXKn+4UP7Z0LBRMZ5sxwg4w9kSs0xtmPvLzc+GhAW58tmOpal+oa4v7imsU+k9aIrtWAAAjFl7ufLTE8X/fbrTk5Ee92+kCLx/mFv/MLcjt+tf33/r7D3Fx0+Ekg7WWwCOydnmmN/89XZKsGR8vBfbgTRDwie7+LmcvKtgOxAAAGO2X6/+7GTxnqnxD2blBz0R7nZ4RuKV8sbMXfk6ozMsHgS25lSJeUtW1ZXyhi9Y2nqzNQaGuR2Dq5kBcBZnihX//L1o44SYlrcw8nHh7XwmTmMwT92eC7kZPJbzJOYimWbBkbvLR0a4OvA6iwFhrsfvQGIGwBnUqQ2z9xZ+MTQsOeDxV3/QFL5xQowJYS/uKTSaIDeDljhJYjaazHP2Fs7o5psSJGE7lpZ09XNR6U35Nc5zeSIAHdbrB24NDHNr/cQZn8DWjYsqU2rf+f2OTQMDXOckifnfRwq1BuNbfRzo+qhmETjWN0RysgimmQHgtn35ddmVjf8eEmrVo0Q8YuOEmKO36r89V2abuIAzcJLE3DPYfeWYaIrgwNPpF+p2skjOdhQAgLZTaA3/PHTn8yFhIh5h7WO9aGrTxJjFf5YevgWzWqB5HMhkrTEsWhrhKWQ7ilbpF+Z6plgBk0wAcNeSP8u6+bsMi3Rv28OjvegVoyLn7C0srFUzGxhwDk6SmDkk3F0g5hNXYW9OALjpnly75lLFB4NC2nOQJ8LdXk3xf3ZnvlxjYCow56MxmG7Wqgtr1Y6zv2mpQmuH/fIddwGzE+sb6nqqSN7d31H28QYAtN5nJ+89kyQNd29vibyXewVcr1TN3lu4cUIM7kSFpNqvTm346UrF/gJZVkWjm5AkMCTXGDxpalC42zNJ3r3Yq4BQrzE8veHGV0+GT/Cy7T5ukJhZ0DdEsu16zeupbMcBALBSQY3618K687O7tv9QGIYWj+j09Ibrn564t3BAcPsP6ATUetPiP0u+v1AxMMzt9ZSAvqESCZ9ECOmNputVqgP5dc//XBDmzn9/QAgrBYrmHbiVEiQZFO5m6xMxkJgNBsPSpUsbGhqCg4MzMzMtN9bU1MyfP9/b2xshNG/ePH9///afyGn0CXGdf/C2zmjmEfAzGQAu+ep0yfSuRgtNyQAAIABJREFUPl4M7aorpPCfxkcPWZsdK6XTbbxfYVWj/nadRmswCigixI3v68Kz6ena4Ep5w4t7CkPdBb9nJkb+fc0QReBd/Vy6+rm82Tdo7eWKydvzxsZ5fTgohKbsNxu7+lJFTpXqyHNJdjgXA4n57NmzwcHB6enpixYtKikpCQwMRAhVV1ePGDEiIyOj/cd3Pv5inp+Yd6lM6eBXXQMAHlRQo/79luzC7G4MHjNAwl83PnrS1rxgV35PG/QCL5Upt2RXH75VX9WgC3MXCkhMYzDfrdf4uFDDIz0yu/l28mjvmDwjNlyr+uDI3YUDgjO7+bRwNz6BvdjDb0ys5xsHbw/+MWv12KjYFvdcY0pWReO/T9zbPSXOxfp1+G3AQGIuLCxMSUlBCCUkJBQWFloSc2VlZVlZ2bJly+Lj4wcOHNj+sziZ1GDJ6bsKSMwAcMiyc6VTkrw9aYZnAHsEiBc9GTZ9Z/6+aQkMpsnfb8m++qO0WK7JSJSuHB3Zzc/lfsE9vdF0qaxxZ071kLVZwyM93u0fFCBhreqP2Yw+PVG8Jbt61+S4zr6i1jzEx4W3IT3m+4vlIzfcWDwi/Olo28741msMM3blLxgQlOjTqvDaj4F3mEqlomkaISQUCpVKpeVGmqYTEhK6du26ZMkST0/PpKQkhNDx48ezs7MRQunp6W5uTA7TYxhG07TZDqvlGDIoUrr+crlI1MzLTFFUs7dzDkEQBEE4x3NBCJEkiTnLCh0cx3Ecd5qXBtnlU1Om0O7Nq7vyWopIxHwXc1oPUZXanLEt7+isZD8xn6IohFCb329Z5Q1vHci/I1O/1T9sWjc/XnMbPDwhET8R4/vxMP2/jt7qvzrrs+FRmd1tOOH4qBfIZDa/sS//xO36U3N6Brpa17DzB0R0D/aYtiW7tNH0Rt9QZgJ9iMlsnrbzWlqYx8t9Oz14u1AoZCrj4HjTF4iBAXqaptVqNUJIrVa7uLhYbuzZs+eQIUO8vLwGDRpUUFDQ/rM4mX7hHufuybUGE9uBAABaZdmZ4jHx3tYmj9ab3y90TLz38DWXK5TaNh9EoTHM35c/dPXFJyI8rr2e+nyPgGaz8n1eImrxyJhd07r850RR5rbrKp2xzaduA7MZvf5L3pmi+kMzu7etYQeEexx5IXn1hdJ5v+SZbNMx+/jw7TKl5r+jYmxx8EdhoMccGRmZm5sbHR2dl5eXnp5uuXHz5s1xcXGdO3cuLi7u1OmvHxoDBgwYMGAAQkgmkzU2Nrb/1PcJBAKVSmUycSbPuRHIR0Sduln58Gi2SCRitnHYwufzMQxzjueC/v8HKIdGZVpAkiRJkk7z0iDbf2oadMYfL5b+MjXepmd5r4+/SqMduuriL5ndgt0EKpV1m+rvy6/956Gibv4uJ59PCpDwjVp1Y+tSfJIXdWh6/Eu/3Bz4/flNE2NssS6s2Rdo4eGiU3fku6fE0Ujf2NYrlQNotH9qfMbW3Kmbri4fGdHyDxFr7cqpWXOh5FBmolmveTBAgUCgVquNRmZ+x1iGnB/EwHPo3bt3UVHRokWLPDw8goKCCgoKli1bNnjw4AMHDnzwwQcymSw1FS4MakZaiOTPYiXbUQAAHm9LdnVnX5Gt1xlhGPp0cNioGI+BKy9eLLFiR/279dop2/MWHL775bCwdeOj2zBh7Cog16dHpwZLhv903T77kX1zpvRgYd2OZ2Lbv8RdKqL2TImvbtRP2Z7fyFyn/+w95T9+vbN2fHSQq70n4DFWegAymYyp3xoWnp6eMpmMQz1mhNDW7OrtN2p2TIptcrsz9ZgFAoFc7iQbgztZj1ksFstkMrYDYYxNPzUmsznl+6sfPxHa5j04rbUzT/7WgYJ5qQFzevrdX7HVrHqNYdnZstWXKp7t6vNWn8A27N3dxPJzZcvPlW3LiEvwYfJXSJMXaMO1qs9OFO+blhDW7n1a7tMazbN2F1Q06DZNiG3/Ar3catWYjTc+Hxo2Nq6Zy9g8PT3r6+sZ7DE36TTDlpysSQ2WXChR6I1c+jEBQAdkqTYxJMLm20rc92x3/yOzknfl1Axak/VrYV2zW+sXy7UfHytOXnHlZp36UGbiR4NC2p+VEUJze/m/3Tdo/JacaxW2+qHzW6Hso6N3N0+MZTArI4T4BLZmbFS8N/3U+uyiek17DnVbppmwJfcffQKbzcp2ADt/sSbIle9BU9cqGltTZR0AwJbVlypmdPe1866Zcd6iwzMSN2dVf3Ss+J1DRQPDXOO8Re5CUqk13KrTnC9R5teoRsV67Z4cz2zXFiE0vasPRWATt+Zuy4ht5fVLrXehRPnSLzdXj41MYvrICCECx74e3umr0yXD111fNz66bdeF59eo0jfnzkr2nZnsx3iErQSJmU2pQZI/7ykgMQPgsO7INOdKlCtHR9r/1DiGTensPTnJ+2Kp8uRd+ZXyhjqVXswnQtwE81IDBoS5CW2279XkJG8MYRO35m7PiGUwgxbUqKfuyPtiWNiAMBsOP8xPCwxzFzyzLe+x25U87Ow9ZeauvDfSAmexl5URJGZ2pQRLDuTXvdKb7TgAAI/w4+WKcXGergLWvioxDPUIFNt/a+hnkqRmZJ6wJWf7pDhGcnOJXDtxa+7rqYG23nwUITQuzivCQ5i5K//UXflXw8PdWvHymc3ox8sVn56499Xw8DGxtt2x5LFgjplNKUGS86VKqM0MgGPSGExbsquf785m54lFk5O83x8YMmFr7tXy9paprWzQTdiaOyFBOqennRozyVd0/PkkHoGlrry6Nbu65auci+o1z2zLXXmxfNfkONazMoLEzK5wdwGfwHKroVg6AI5od05NhKcw3tseuzE7pimdvT8aFDJxa+75krZf21mrMjy15tKAUNf3+gcxGNtjSfjkilGRy0dGLvmztN+qrJ+uVtY/VP06q6LxzV9vD1ydFSOljz3XmfE59baBoWw2YRjqFSg+W6JgfPkGAKD91l2tyuxq3SSl85mUKBWQ+OTtecufjmjDBWMVDbrxm3P6hXv8exA7pS0Hhrn+8ULnvXl1P12p/OehokhPQbi7QMQnaxp12ZUqg8k0IV56/PnOIW6s7Rb+MEjMLEsJdj1zVz6zuy/bgQAA/ia7svFWnXq0Awxssm5MrKebgJi5u/CdfkFWfVnlVasmb897OtrjPyNjVSrWtmfAMWxMrOeYWM96jeFKeeM9uUalN3kKJf/sRyf40HZeb98akJhZlhIkWXymhO0oAABNrbtSmZEgFZAw34cQQgPC3PZOjZ+2Pe9qecPnQ8NaU/1wX37dvAO3/tE3cFayn4PkPjcBOTDMFSFXtgN5DHjPsSxWKtQYzLfq2nU5PACAWQ06466cmmc7/Dj2g+Kk9O+ZiQ0644DV1yybrjxKrcrw6v6bb/92+4cxkexed8RR0GNmGYFjPQNdzt5TOEi5cgAAQmhXTk2ijyjSU8h2II7Fg6bWjovefr36jYO3wt0Fs3r4De7kziP+1x2+J9f+dKVy3dXKoRHuJ2Z2bv8+2B0TJGb29Q6SnC1RTunszXYgAIC/rLtSObeXDesTc9qEBOlT0Z6brlV+fvLei3sKE31E3i6URm+6LdNUNOiejvbcwdClzx0WJGb2pQRJ1l+tZDsKAMBfrpQ3lCq0T0fDsq9Hoil8ZrLfzGS/MqUuq6KhVmXgk3iwK7+Ln4jZwosdU/OJubq6WiqV2jmUDquLn0uFUlem1PmLmS+DCgCw1rorlZOTfB4coQWP4i/m+Ys92I7C2TT/0yY5OXncuHF79+7V69tYvBq0Ho/AuvmL23P9PgCAKQqtYXdu7dQuMLUEWNN8j/n27duHDx9ev379a6+9Nnbs2OnTp3fu3JnBswoEDC90wjCMpmnu1srt18nzYrlqSnIwQoiiKJHIGaZnCIIgCMI5ngtCiKIozEGu+Wg3HMdxHHealwYx+qnZcL2kV7BbYiCb49gURSGEnOb9hpzoa+0+oVDIVMbB8aY9ZKyFQ8vl8o0bN77zzjsEQYSHh//3v/9NTU1lJA6ZTMZUiWkLT09PmUxmMnG1tvGxO/IPjxSdmNkZ2bjkuz3x+XyBQCCXy9kOhBk0TavVau7++HsQSZJisVgmk7EdCGMY/NT0XXXt7b6B7E4wi0Qis9msUqlYjIFZTvO1ZuHp6VlfX89UFqNpmqb/tvlj80PZmzZtGj16dGJi4vXr1/ft21dbW7tq1apn/4+9+45vovwfAP7cXXaaNk26N92lZRZKWzZUlgwRlCEKCIgMBQEV/X4pm69acQD+HAxRBJQhexdakLJByijdixZaOpJ0ZI/fH9Fa21BKm+Qul8/7D15wJHef6/Wezz3PPeONN8wSBGiup7dDTrWy+TyuAABruvywRqbUDguBl6aATKabss+dOzd//vxBgwYRxF/Tu3Tr1m3dunVWDMy+OLCIjq7cqw9r2zAVLQDAXH68Vf56V3cGTp82ZGCLmibmSZMmGf+ybdu2bdu2NWzfvXv3q6++ar247E+sr+OVkhpIzACQpbxOfTKnetXgbmQHAuxd08Q8c+ZMUuIAsb6Cb64+JjsKAOzX9j/Lh4WIPBxg1CIgWdPEPHjw4Orq6h9++GHp0qWkBGS34v2cZh/KlWv0tOq5CICN0Oj0v9x+snVsKNmBAGCq85eTk9Mvv/ySn59v/WjsmYjLCHBm3yiF0cwAkOBQZrUrnxnjIyA7EABMdf4iCCIyMjI8PLxHjx7e3t7GjXv37rVuYPYoztfxysOa4ZEwQy8A1vb99ceze8I6SIASTPfKfvfdd999910rhwLifB1/SX9CdhQA2J1LxTVlteqxHWFybEAJpscxx8fHu7i4ODg4ODg4GAyGVatWWTks+xTn53ijtFaltdVpUgCwUd9ff/xmtAesvgAownSNefbs2ampqWVlZV27ds3MzJw3b56Vw7JPXgKWhwPrZmlNFxdYxBQAK8muVFwolH05PJDsQAD4i+knxCNHjty/f3/hwoXr1q27cePGvXv3rByW3YrzE1wspM9EiQBQ38YrpVO6uIl48DQMqMJ0Yjau+RgVFXXt2jU/P7+CggLrRmW/4v2cLhZKyY4CAHtRWqM6nFn1dgx0+wIUYrope/jw4aNGjfrxxx8TEhKePHni4uJi5bDsVm8/x4/OFGp0eia87gLA8r6+/GhsRxdvRzbZgQDwD9Ol/yeffLJ+/XpPT8/Nmzfz+fxvvvnGymHZLV8ntpjHTC+jzzIsAFBWiUy1517Fot4+ZAcCwL+YTswYhl27dm3WrFkxMTEJCQlBQUFWDsue9Q90vlhEk6USAaCyLy+Vjuvo4ucE1WVALaabspcvX379+vXCwkIMw5YtWxYXF7d69eqn7UKr1W7YsKGurs7Pz2/atGktbASt0TfAeeet0oXmWfkaAGBaTpXiwIPKP2Z2ITsQAJoyXWP+9ddf9+zZ4+3tTRDEiRMnduzY0cIurly54ufnl5iYWFFRUVJS0sJG0Br9A52vltSodTCaGQALWplSNDPaA94uAwoyXWNWq9Uajcb4d6VSyeFwWthFTk5OXFwcQigqKionJ8fHx+dpGzMzM41JukuXLi3vsw1YLJbBYDDvPknh78j2cGDfq1TH+TmRHUu7MBgMHMfZbJoUfARBsFg0WXeIIAgMw2hzaRBCBEE81+mcL5DcelS/bXwUm01YLqo2IwgCIWTPF4j6WCyWXm+e6hOON60hm07M8+bNGzJkiEQiSUpK2rlz55w5c1rYqVwu5/F4CCEul1tbW9vCxtu3b1+4cAEhFBERIRQK23cu/4JhGIfDoUdiJghiQLD40sPagaHuZMfSLjiO4zhu9icwshhPh+wozAPDMOMtQ3YgZmN81Gjlh1Va/ZLjuWuHh7o6UXQtN2Nibv0ZUd9zXSDqMz7XmivjNN+P6cQ8derU7t27p6am6nS6LVu29OjRo4Wd8ng8hUKBEFIoFA4ODi1snDhx4sSJExFCEolEJjNn/yaxWFxTU2Ou5xdy8fn8OC/uj7fK5/dwJTuWdmGz2RwOx7wXmkTGX2l6PPwxGAyBQECbS4MQ4vP59fWtHcvwRVqpMwd/KZi6PwE+n28wGORyOdmBmM1zXSDqE4vFtbW1Op3OLHvj8XhNWuNM1wB69OixadOmHj16LF68uOWsjBAKCQl58OABQigzMzM4OLiFjaCV+gY43XxUK9fQ4TkDAErJqJBvulqaNDSQRvU3QDemE3N+fv7s2bP37NkTGhq6aNGi9PT0FnYRGxtbWFiYlJQkEol8fX2zs7M3bdrUZKNlgqctFx4zWMS9/LCG7EAAoBW1zjDnUM4HfX3DXXlkxwLAU2EtNM3JZLKdO3cuXbqUIIjAwMCNGzfGx5tnEI9EIjFXI4CRWCyWSCS0acqur69PPFuIYdjKQf5kh9N20JRNWcambImEPrOyt7Kl9L3jecUy1d6JETi168vQlE1xYrFYKpWasSnb2CWrgeka865du8aMGdOpU6d79+4dPXq0qqpqy5Ytb7zxhlmCAK3Rv4PwfAFMmg2A2Wy9WXa+ULb5pVCKZ2UATHf+Onfu3Pz58wcNGmTsHIgQ6tat27p166wYmL2L83XMrVI8qde48WHRGwDa60BG5ScXHh58LVLENV3oAUAdpn9Ht2zZ0nzjq6++auFgwD94TDzWzyklXzqhk233zQaAdPvuVy49XbDrlfBIN3i1DGwATcZl0tLADk7n8qE1G4C20+oN/7tQvCy5cPcr4TE+ArLDAaBVoFWHugYFCjdcLtUbDPBKDIA2SC+r/+BUvsGATk3rBCtVABsCiZm6Ilx5HAZ++3F9dy8HsmMBwGYotfqUfOkv6U+uldS+E+s1t5cXA4dHW2BLIDFT2sBAYXKeBBIzAAghgwGV1KhKalRVcm2dWtew0ItGZ9AgorpOUVanzqlS3imrC3Xhjo90/WZUsJADRRywPfBbS2lDgp3Xp5V80Nd6M7Skl9Wnl9XJNXovAWtgB6GAklP8A7tSKdccelCVnCe5WlKr1RsChBwxj+nIJhrqwTiGRHwOG9eHufBGhIq6ewlgLAOwaZCYKW1AB+HsQzmPatVeAouva5SSL117vvhRrbqnt8CRwziSWfXusbzJnV3/09+Pz4L0DEiQL1F+mVZyOLMq3s/xxTDxmoQOgc4ckz0uaDZ/BbBzkJgpjcfE+/o7ncqpnt7dw3JH0ej0ieeKf79fsWJQwLhIMYv4q69+kVT13+TCvlvSf3klvCNMYQisSKnVf/ZHyY+3yqZ0cbs8u5sVHkwBoA5IzFQ3NMT5RLYFE7NSq59xIFuq1F6Y2cXd4V/Fn7+QvWN82JabZWN3ZfwyLqwnjDYBVpFRIX/rYI6ngJUyo3OAkD5rUwLQSjCOmeqGBDunFdfUqsw5tXgDjU4/bX+WVm/YN7Fjk6zcYGa0x5rB/lP2Z+VWKSwRAwCNHcmqHrXj/pSubnsmREBWBvYJEjPVeQpYnT34p3Itst7AohP5tWrdT+PCuMyWfhNeiXKdF+M5aU+mVKm1RBgAGG29WbbkZP72caFv9/SE0fvAbkFitgFjIsSHM6vMvtsfbjy+WlL7y/hwDuPZvwbvxnn38HZYdDzP7GEAYLTxSumXl0oPTu7Y19+J7FgAIBM575jZbLZ5l8/DMIzD4dBmST4ul9t4y/gu3mtSH2pxlhkHL10pln16oeT0zO5eotYOkt74UmT8t9f2PpC80d2rNZ9nMBg4jjc5F9vFZDLp8QuGEMJxHMMwSl2ab6+UfH+97NSM6GBxW6JqftfYNAaDgRCi2RnR6XQQQhwOx1wLDeN406oROYlZrVabdz1mHo+nUqnosR4zjuNKpbLxFjELdfbgH7z76JUo8yxoIVNqp++9978hHYKdGE2O1QImQpteDHptb2Y/PwePp7yQbozFYjU/F9uF47hKpaJHbmYwGCwWizqX5veMyk9SCw6+FunDx9oWFZ1+0xBCBEEYDAY6nRHNLpAx45grizV/ZCEnMRsMBrMXcJbYJ1man8jLEeI99yrGR7qYZf9LTub39Ba8GuXyvD+xGB/By5EuS0/l//hyWCu/QqeLQpvfMeNZUORcLhXXLDmRv/vV8HAXbntCosjpmAWdftka0O90LHdGMFzKNozt6LIipcgsM438nlF5vbT2/IzObfv6f/v7xf9wOzlPmhAkbGck5nL7cd1vdyvSimue1Kt5TCLMhTsmXDwmwqXlHm2ACoplqum/Z30xPBCWfgKgAZRctsGZy3ghyHnP3Sft3E9ZnfqjM4VfDQ90ZLfxmUzAJhIH+i07W6jRkf/ioKxO/ebvWa/+9oDFwNe9EHB0StS2saHDQ0U70p/Efv/ngYxKsgMELZFr9FP2Zk7v7jEmQkx2LABQCCRmmzGps9uvdyva2Xay6Hj+SxHi/h3aVdkdH+kq5DC23ipvVyjtllZcM2jbHRc+69rb3VYO8u/j7xQs5nb1dHijq/ux16OShgWuSi1+52iuUkv+AwQwafGJPF8n9gd9fcgOBABqgcRsMwZ2cFLrDOcLpW3ew693nmRVyhMH+LUzEgxDqwf7f5FWQuKw5hPZ1W/sy/rfCx0+G9rB5ApCQ4Kdz07vXF6veeXXjBoVDL+mnG03y66X1n4zKhiWGwegCUjMNoPAsRnRHptvlLXt649r1Ynnir5+McgsK1L08Bb09nP8+nJp+3fVBmfyJO8cy/vx5dCWm0BFXMbO8WGeAva43Q8sNHUaaJu75fVrzhdvHRsGyzIC0BwkZlvyWhe3S8U1BZLnHnVgMKCFx/PGdXTpY76pG/47wH/7rfLSGpW5dthKtx/XzTmcu/ml0H4Bzz4XJoF/Oyo4QMh+Y3+WWkerTqG2q16tm3kg++P+fl08+GTHAgAVQWK2JUIO49VOrpuuPHreL/58u7xQokwc6G/GYIJEnHGRLuvTSsy4z2eqqNe8vi9r5SD/gR1a+4RB4Ng3o4JxDC2Eacuo4cPTBRFuvBmWXDANAJsGidnGvBvrtT+jskj6HPXUnCrFqpTib0YFm3340KLePgcyqvKfvwbfNlq9YdbB7OGhote6uD3XF1kEvnVs6K1HtW14pgHmte9+5cWimi+HB8GbZQCeBhKzjfF2ZE/q5Pp5q+upap3h7cM583p59fA2/zhRLwHr9a5un/3x0Ox7NumLtBK5Rr8mIaAN3xVyGD+PC//qcmlacY254wKtVSRVLT1d8H+jgp258GoZgKeCxGx73uvtcyK7+uaj2tZ8+MNT+UIOY0Fcq2a3boMFcT6ncyUPKuQW2n+D66W1P9wo+35MCItoY1Ur1IWbNLTD24dyKuUa88YGWkOrN7x9OOfN7u7xfo5kxwIApUFitj1ufOZH/XzfO56vftYUH9tulqUWyja/FErglmo3FPMYM7p7fPaHZd80yzX6uYdzVw7y7+DcrgV6x3Z0GRriPP9ILr0mB7QNSRcfGgzo/T4wahmAZ4DEbJOmd3d3ZBNrz7fUhnw0q/p/Fx7uGBcusnCz4bxYr4tFsjtl9ZY7xOrU4mAx53lfLZve1eCAkhr15puP278r0HqXH9ZsvVn+3ZhgJgFlDgDPADeJTcIx7IeXQg9kVP70p+npt37PqFx4PG/7uLAod56lgxFyGHNivNadL7bQ/i8V1+y/X/HF8CCz7I3LxL8bHfLJhYdWaH4HRhKFds7h3HUvBAQI29XgAYCdgMRsq7wErF2vRKy78PDziyX6Ri2zCo1+VUrxR6cLdr4S3ttaL/Pe6ulxu6zuakmrXns/F6VWv+hE3opB/p7tXr2jQZQ7771477lHcp75LgCYxaIT+fF+jq+aadFSAGjPDI2cWq12w4YNdXV1fn5+06ZNM26srKxcvHixm5sbQui9997z8rJU5yN7FuXOO/lG1JsHsg5lVr0S6SLkMvKrlQcyKsNceaendfYXsq0WiQOLWBjvsya16MiUKPPuOeliibcje1InMzRiNzavl9eZXOlnf5T8t90TlIKWbb1Zdq+8/tybbVzNDAA7ZIbEfOXKFT8/v/HjxyclJZWUlPj4+CCEKioqRowYMWHChPbvH7SggzPn9NROp3OlJ3Kqa1U6b0fWN6OCzTi9V+tN7+7x/fXHp3MlQ4KdzbXPu+X1P94qS3mzi9nHvOIYtmlU8KBtd4YEO8OCg5Zzt7x+7fniA5MjBWwzTAQLgJ0wQ2LOycmJi4tDCEVFReXk5BgTc3l5+aNHjzZt2hQZGTlw4EDjJ6VSaX19PUKIw+EQhJlvVIIgMFrMWYBh2HP9cAiCGN3RdXRHktsJeQRa2s9vTWrxkBCxsRM4juPG8Nq2Q63e8N6J/A/6+gWKLfKavIOItzqhw/yjeRdmdW3N/OHG60KPxd6NN4vZ78EmalTamQdz/jvAv7u3xV+pWOF0rAnHcYPBQKczotkFQn+Xb2bRPHOZITHL5XIej4cQ4nK5tbV/vWXk8XhRUVHdunX7+uuvxWJx586dEULbt28/fPgwQmjbtm3+/uacHhLDMEdH+gyOZLOt1wptRrP7On13vexgTu2MXn+1D2MYJhS2cYnJT8/lshiMpUM6Wm6s1/wBwjMFNasvPPpufKfWfJ7DoU/fpfZcmtYwGNCbP92I8Re9/0JHyx2lMRu9a0zCMMxgMNDpjBDtLpAZM45W23T5O6zNNYDk5OT79+/HxsZmZGTEx8eHhYWdOHGCw+E01I+NUlJSqqqqxo8f33ijRCLR6cy52o9YLJZIJHo9Hfry8Pl8Y7uCLUrJl84/mnv17W4OLILNZnM4HJlM1ob95FYphvx099jrURGulu1VXlGv6bcl/esXg57ZAs/j8RQKBT1qzAwGQyAQSCQSyx3ii7TS3zMqT02NMstqZs9k03dNc3w+32AwyOX0GThAswskFoulUqm5shiPxzNWbhu0vTKekJCwYMGCXr16hYSEPHjwACGUmZkZHBxs/N/du3enp6cjhIqLiz08YLZ6ezEwUNjFw+GrS+05tTxuAAAgAElEQVRaDlKnN7x7LG9OjJelszJCyJXP/HJE4MLjeRX1MB2Y2ZzOlXx/4/HP48Osk5UBoBkztJLHxsYWFhYmJSWJRCJfX9/s7OxNmzYlJCQcP358+fLlEokkPj6+/UcBtmJ1QsC2W2U5VYo272HT1UcqnX6hxaYRbWJYiOjFMNE7R2E6MPPIqJDPO5L73eiQwPZN0waA3Wp7U3Z7QFN2C2jQ5vPZHw/TimtOTO/O5T53U/a9cvnonfeOvx4VbvnqcgOFRv/C9ruTOrvO6/XUpwFoym6Nx7Xq4T/fezfW681oq7aT0eCuaQyasimOuk3ZADzNwnifJ/WaH28+9xqL9WrdrEPZS/v5WjMrI4S4THzLSyFfpJVet8AcKfZDqtRO+O3BSxFiK2dlAGgGEjMwPxaBbRoZlHgmP7fq+R75l5zMDxFzZ0V7WiiwFoS78tYm+M84mP0EXja3SZ1aN3lvZid3/vKB5hxwAYAdgsQMLCLaSzAvzmfKrttKbWvfL3xz9dHNR3UbXgwiazj6xM5uI0JFb/6eBVN1Pq86tW7SngcefObX5F0+AGgDEjOwlA/6+bs6sBcez2vNa9mjWVVfXSr9ZXy4kGPZtbBatnqwPwPHFp3Ip8WrZCuRKrXjdz9w5TG/HxPCsNigcwDsByRmYCkEju2Y2Pluef3yc4Utf/J0rmTh8fyfxoWFunCtEtpTMQl8+7iwPx/X/e+CpRbLopmHMtWoX+6HuHA3vxQKSzoCYBZwIwELEnKZ+yd1PJMn/eBUwdPah7fdLJt7JHfrSyHx1loLq2VCDuPXVyP23KvceKVdo7HtwfXS2mE/3R0e4rxhRJDlJmgDwN5AYgaW5eHAOvRa5IOK+pE77t989K8+zwUS5ev7sjZdfXTotcj+HSw4PeTz8nVi75/U8YfrZV+kQW5+qq03yyb+lrl8kP/H/f3gvTIAZkTm+zxgJ9z4zAOTI7+5+mjynkw/IaebpwOGofvl9XfK62dEe3w7OtiBevNDBYk4R16PHLcro7xevTYhAF6dNlYl1753Ii+rQn5gcsfOHnyywwGAbiAxA2tg4NiCOO9ZPTwvFErvlctxDIuNFiQEOVN5NcAAIefE1E7T9meN353xf6NDgnlWHVpNWfvvV/4nuWBYiOjsm50p+EQFAA1AYgbWw2Piw0JEw0JEZAfSWm585sHXOq48Vzxga/q6YSHjwoX2XHHOqpR/fKawUKL8dnTowA4krPkNgJ2Ad8wAtIRF4GtfCNg2NnRDWvHgbXeOZFXr9HY3lKqiXvPh6YJhP93r6S1Ie6srZGUALApqzAA8Wx9/pyvzem2/VvTJheL/nCl4KUI8OFAY4+PIZdL80Vam1H5z9dGWm2Uvhon/mNnFx4k+S+oCQFmQmAFoFQaOvdbFfVInt+ultYczq/6TXJRbrQgScYJEXB9HliufJeYxRFymkEOIeUxXPtOFxyQ75HapUWm/v172/fXHffwdrbAwNgCgASRmAJ4DhqEYH0GMjwAhJFNqMyrkBRJliUz1uFZ9t6xOqtRVKzTVCm2lXIsjQ6CIG+bC7ebpEO/n2NmDj9vIoKI6te6H62XfXX/U01vw+yTodw2AtZGTmBkMBo6buQ2QyWTSY9lHHMeZTNuubBkRBIFhGD3OBSFEEASD8a/7xYXJ7Cfg9jP1Yb3BUF6nya2S3y+v//Nx3bfXHusMaFSEeFJn954+5M+j8rRLo9Tqt954/MXF4m5egv2vderuJSAlvDagzV1jRBCEwWCg0xnR7AIhs2YxrNkjO9SYATA/HMM8BSxPAatvwF8Tp9x+XPf7/Sev7cnwcGAtiPcZG+lKqQq03mD47c6TNSmFfkL2zlc7xvpB9y4ASIORsvC7RCIx1xLTRmKxWCKR0KPGTJsVxdlsNofDkclkZAdiHjweT6FQtPN+0ej0v2dUbbzyCMPQf/r7kjVyjMFgCAQCiURi/OeVh7X/SS5QaQ3LBvgNDXEmJaR2os1dY8Tn8w0Gg1z+fKumUhnNLpBYLJZKpebKYjwej/fvaRKgxgyA9TAJfEIn11eiXPbeq1x6unDbzbJPhwZ2cOaQFc+Tes3ys4XnCmQf9PGZ2s0dJjgDgApoPtgDAArCMWxCJ9dLb3Xt4ikY/OOdDZdLtVYfG603GLbdLOv9w20uE788q8uMaA/IygBQBNSYASAHj4n/p7/vyx3F7x7LO5ZdvWlkcIjYSqteZlbIF/18TypX7XolvKePzfTwAsBOQI0ZADJFuPJOvBE1NFg09Ke7W248tnSXD41O//nFkiE/3h4e7np2eifIygBQENSYASAZA8cW9fYeHCScczjnTJ5048hgN75FBpbcKat/91guj0mcndGte4BbQ+cvAAClQI0ZAEro4sE/O72zv5DTb8vtY1nV5t25UqtfnVo8dlfGpM5uR1+PtFqbOQCgDaDGDABVcJn4Z0M7DAl2Xng873h29doXAoQcM9yhFwpl75/M93Fin3uzs78QJrsGgOqgxgwAtSQECS/M6KzVG3r/cPvQg6r27OpRrXr2oZzZh3Lei/feN7EjZGUAbAIkZgAoR8Rjfj8m5MsRQStTisbtzrj/5LknmqhRadedL+6z+bYzl3Hpra4TO7tRaZ4xAEBLoCkbAIoaEuzcL8Bp05VHo365NzhQ+E6sd2vWk3hcq952s+zHP8vjfB2Pvx4VDqtCAWBrIDEDQF0cBr6kj8/07h7fX380/teMACHn5UiXQR2EoS5Ne289lKkuFMqOZVVfLJYNDxEdmNyxkzusCgWATYLEDADViXmMj/v7LertcyK7+khm1ecXS/QGQ6AzR8hlIoTq1boCiUKpNcR4OwwLEW0cGSzmwX0NgA2DGxgA28Bh4GM7uozt6GIwoGKZKr9aUavWIYS4TNzfiRMo4sCcmgDQAyRmAGwMhiF/IRu6WANAV2brlb1+/XqlUtnwT61W+8UXX6xatWr79u3mOgQAAABAe2ZIzPX19cuWLbtw4ULjjVeuXPHz80tMTKyoqCgpKWn/UQAAAAB7YIbEzOfzV65c2alTp8Ybc3JyoqKiEEJRUVE5OTntPwoAAABgD8zzjhnHcezf8xfI5XIej4cQ4nK5tbW1xo1fffXV4cOHEULbtm3z9/c3y6GNMAxzdnY24w7JxeFwyA7BPDAME4vFZEdhNlwufaaYptmlQTS6axBCGIYZDAY6/b4h2l0goVBorr1ptdomW9qemJOTk+/fvx8bG9urV6/m/8vj8RQKBUJIoVA4ODgYN06bNu2VV15BCHE4HKlU2uZDNycUCmtqavR6vRn3SRYul2v80dk6FovFYrHq6urIDsQ8OByOSqUyWHpdRqtgMBh8Pl8mk5EdiNnQ5q4x4vF4BoOBTmdEswtk3ozD4XCYzH8tKNf2xJyQkJCQkPC0/w0JCXnw4EFYWFhmZub48eONG4VCofEpQyaTmTeJlpSU8Pl8jBazDtKj6EcIKRSKmpoa2jwmGwwG2lwalUpVU1NjbNOiB9pcGiNjvYXFYpEdiNnQ7AKVlJTweDwct9Sc1uYfLpWdnX369Om3335706ZNSUlJbm5uvr6+TT7j5ORk3oOOHj1637597u7u5t0tWejRhHXmzJkDBw783//9H9mBmA1tMllGRsbHH3988OBBsgMxJ3rcNUaff/65o6PjW2+9RXYg5kSnCzRu3Liff/7Zx8fHQvs3W2JevXq18S+hoaGhoaEIoYULF5pr5wAAAICdoMkEI6NHj6bT4xg9+Pj49OnTh+wogAlCoXDIkCFkRwGeqnPnzrR5B0RLI0eO5PMtOBc9RrOmfwAAAMCmwXrMAAAAAIUQK1asIDuGf9TW1q5atWrw4MGW2LlWq/3qq6/OnDlTUFDQtWtX48b169d3796dwaBJk77VJCYm/vnnn3FxcS185uDBg4WFhbm5uU+ePDF2AIRLYDUmb6WTJ082XIvG4LpYwt27dzdu3Hju3LkLFy506NChNcNef/75Zz6fLxKJWrN/uGrtR9mMQ88as8kBc01mCTU5kyhojdraWrlcnpub23xcfAOFQvHSSy8NGzas8Ua4BNQE18XsysvLf/rppw8//HDNmjVz5sz58ssvGy8lgBBKT0/fv39/G/bcULjBVaMOs2ccatWY1Wr1xYsXe/funZSU9Mcff9y5cycmJubMmTPJycl37tw5efJkbGzs8ePHa2trvby8du/ezWAwBAJBkw/v27cvPT39/PnzERERPB5v2bJlsbGxTCbz7Nmz0dHRLi4uNTU1NTU1oaGhAwYMyMjI6NevHzxgPpc//vjDxcWFy+WyWCxPT8+ff/756tWrt27dOnfuXHR0dEpKivES1NbWPnr0SKPRsNlsYy0NLoHVGG8luVze+GaRyWRsNnvXrl1wa1ja4cOH4+LigoODEUIODg4ymUwqlQqFwjVr1ly8eLG6uvrmzZsZGRn+/v5fffXVqVOn7t6926tXrzt37ty8eTM1NTUlJaVnz55qtfqzzz5LTk6+e/duz549k5OTjXdWbGwsgrvJHCibcahYY37y5MnYsWM//vhjuVz+5MkThBCbzX7zzTcDAgIyMjKe+WGhULhgwYJevXpdv35dIpFwuVzj8NPGs4Qap6NqPpMoaI20tLTY2NiePXteunTJuMXV1XXWrFnh4eHJycno70vQ/ItwCagAbg0rKC8v9/b2bvinj49PeXn577//PmrUqMTERLlcPnDgwPj4+Bs3bgwePPjTTz/l8/nXr19HCPn7+3/88ccdO3Y8e/bssWPH+vfvv2rVKi8vr3PnzqF/31lw1cyFghmHKon5zp076O/ZYXg83vHjx7/++uvCwkLjBGGBgYEIIS6Xq9PpGr5i/K/mHzaOou7Ro8etW7cuX77cu3dv4+dNzhIKnld9ff29e/d27tx56tSpq1evNr5AgYGBZWVl6O9L0BxcAitofCs1aDzRHtwaViAWiysqKhr++eTJE7FYXFpaarw1XnvtNYIgEEKPHz8ODw9HCEVERDx+/Bj9fe9EREQ8efLk8ePHaWlpmzZtKisrc3R0RP++s+CqtQfFMw5VEvPevXslEolEIvHw8Dh06NALL7ywYMECsVhs/ME1fsrAcdz49JGZmYkQav5hYyuBg4ODVqtNS0trmMrbOEuo8YvGJibQBlevXn3ppZc+/PDDxMTEqKgo4xOl8VpkZmZ6enqivy9Bc3AJrKDxrdTkZjGCW8MKBgwYsH//fmO5XFVVdf78+V69erm7u+fl5SGEdu7caZx33d3dPTs7GyGUlZXl4eGB/r5SDx488Pb29vb2jo6Onj9/fvfu3Y2TTDW+s+CqtQfFMw5V3jELhcJffvnl7t2748aNc3d3P3To0I0bN4RCYUlJiUAgYDAY/v7+eXl57u7uoaGhO3fuvHr1qrOzc3h4uPHH2vzDCKHa2lqZTDZgwADjIby8vE6fPm18P9qvXz/jxpSUFHgl81x27tw5cuTIhi6mt27dYjAYeXl5qampVVVVU6ZMKS4uNl6C/Px8BoPR+B0zXAIraHwrBQUFNb5ZqqurjdcCbg1Lc3JycnJy2rx584ULF65duzZz5kxPT09/f/+tW7dev37dw8MjIiJi3759L7744sGDB8+ePctgMMaNG5efn19SUnLmzBmZTDZ58uTAwMDffvvt4sWLdXV1AwcONN5QDevywVVrD4pnHDpPMHLgwAE3N7eGhgVgIT///HN8fDw8s9sQuDUAMDsz3lZUaco2u5SUlHv37hm7LwIAGsCtAYDZmfe2onONGQAAALA5tK0xAwAAALYIEjMAAABAIZCYAQAAAAqBxAyAbVCpVBiGeXp6enh4eHt7z5gxo7a21ix7nj59+vjx482yKwBA+0HnLwBsg0ql4nA4xhtWoVAsWbKkvLx837597dytXC4PDQ0tKSlpf4Q6nU4mk7VycSQAwNNAjRkA28Plcr/44os//vijtLTUYDB8+OGHXbp08fT0nD17tsFgmDlz5q5duxBCOp3O39+/8dyQCKEVK1YEBweHhoauWrUKITR//vyqqqo333yz4QPNv75+/frAwMDw8HDjfETNj5iWljZhwoQuXbr88MMP1vw5AEBLMEEMADaJzWZ37NgxKytLKpWmp6ffuHEDIRQZGZmdnT1hwoRNmzZNnjw5OTk5Ojra1dW14VvHjx8/ePCgcaLggQMH9urVa+PGjSkpKdu2bWv4TJOv371799dff71x4waTyZwwYcKPP/4YExPT5IgIoQMHDjx48CAoKMjaPwgAaAcSMwA2DMOwyMjI7du3Hzx48N69e2VlZUqlctCgQTNmzJDJZDt27Jg2bVrjz6empo4fP9646M2UKVNSU1P79OnTZJ9Nvp6amiqRSCZMmIAQKi0tTUtLmz59epMjIoR69OgBWRkAs4CmbABsklqtzsjICA0NvXz58uDBg4uLiwcPHty1a1eEEEEQI0eO3LlzZ1pa2vDhwxt/y2Aw4Phfd71UKm28eE6DJl/n8XizZ88+c+bMmTNn7t69+9133zU/IkKo8SqHAID2gMQMgO1RqVTvv/9+nz59vL29k5OTR40atXjx4oCAgMzMTGNX7YkTJy5dunTs2LFMJrPxF/v3779//36lUqlQKPbt29cw4X4Tjb8+ePDgXbt21dbWajSaoUOHXr9+3eQRAQDmAokZAFvi6+vr4+MTFBRUU1Ozfft2hNDkyZP//PPP6OjoRYsWzZs3b/Xq1QihPn36EAQxderUJl8fOXLkiBEjunTp0qVLl7Fjx44YMcLkURp/vWfPnlOnTu3Zs2dwcHBsbGxcXJzJIwIAzAWGSwFAQ3/++efMmTNv3rxJytcBAO0BNWYA6GbPnj2vvPLKxo0bSfk6AKCdoMYMAAAAUAjUmAEAAAAKgcQMAAAAUAgkZgAAAIBCIDEDAAAAFAKJGQAAAKAQSMwAAAAAhUBiBgAAACgEEjMAAABAIZCYAQAAAAqBxAwAAABQCIOUoyoUCr1eT8qhnwbDMCaTqVaryQ6EBDiOEwSh0WjIDoQEBEEghEwuS0x7DAZDr9dT7U60DiaTqdVq7XBCYijoKFjQMZlMFovVeAs5iVmpVFKtKMRxnMvlymQysgMhAYvFYjAYCoWC7EBIwOPxMAyzz3N3dHTUarUqlYrsQEjA5XLr6+upVgpZARR0FLzZMQxrkpihKRsAAACgEEjMAAAAAIVAYgYAAAAoBBIzAAAAQCGQmAEAAAAKgcQMEELI/oaNAAAARZEzXApQypbrjxYfz8ExzJFNcJk4i8CEHAabgXOZhICFsxk4n0k4sAkWjgnYBI9FsAnMicNgEziHgTlxGGwGzmMSDiyCzcAcWATZZwMAALYNErO9y6yQrzxbcH5OL0+2vkalU2h0Kp1BptSqdHqFRl+r0qm0+jq1rl6jU+sMRVKVQqtXafUylU6l1Sk0+hqVTq0z1Kt1dWqdVm9ACPGYOIdJCFg4l/lPCucycQGb4DBwPovgMwkWgTlyCC4DZzNwJzbBYRIcBu7IJlgExmcRDiyCgWNk/2AAPVUrtLlViqpCRYQzEeDEevYXALA6SMx2TaUzzD6Us6SvX5y/s0wmc+a26/dBpzfUqnUKjV6l08uUOpVWr9DoatR6lVYv1+jqVDqVzlCn1lXINUqNrkalU2r1fz0EaPVyja5WpVPpDPVqHUKIwDEBi+AxcRaBO3EIY/XdkYUba+cCNsEiMAcWYXwIcGQTHAbOJjAhl8kiMOMH2ATGh+q7fVPrDHnVirxqRW6VIq9amVutzKtW1Kn1fk6sALHD1SKJnxN7VLh4ZJgo3JVHdrAA/AMSs11bda7Ihc98J97HLHsjcEzIYQg57d1PvVqn0upr1Xq5RqfWGaQKjUpnUGr1NSqdSqs31s6N1Xe5RqfS6mvUeqVGp9TqZUqdWqeXa/S1ap1Ob0AI8VkEm8AEbILHJFgEJuQy2QRmrJ1zmASPiTvzOSIe04OL+Tmx/YQcFgE1dVtVWqPKq1bmVStyq5S51Yq8akVJjVrEZYSIuUEiTrgrb2SYOFjM9XNiMQlcJBI9qZaezak6ml096pf7rnzmi6GiUeHizh58ss8DAEjMduxcvnTf/YrUGV1wjFrZiM8i+CxC1L46jFZvqFPr5Bq9Sqs31s6VGp1MpVNp9QqtsYneUK/RldWq7pXXFVTVF0mV1QqtO5/lJ2T7Czl+Tmx/IcfXieUv5HgKWNC0Til1at3fleC/asN51Qq9AQWJOEEibpCIMz7SJVjMDRJxHNlPLeLYBDY0xHloiLNmWIeLxbVHMitf/S2DzySMdehoLwHFbgtgRyAx26kqufbdY3mfDQ30FNDzNRujddV341zZ9fX1CCG1zvC4Vl0oURTJVEVSVXKepEiqLJSqZEqtkMPwF7L9hZwAZ46/kO3vxA5w5vo6saj2TEM/Wr2htOavi5JZIc+uUhZKFMUylRObEerCDXfhdXLnj490DXfltflyMAl8YAengR2cPh8WeL207tCDyjcPZGMIJQQ5Dwl2HhwkhMcyYGUYKeurSCQSqk0fj+O4s7NzVVUV2YFYg8GAXt+f6c5nrR8eiBBisVh2O69948T8NFKltqxWXV6nNibsQomySKrMrVZqdAZPATPAmevvxPYXsgOcOe4OLA8Hlr+QbbX428PR0VGlUlFqEQupUlskVRVJlZkV8qxKRZFUmVWpYDPwgL+fikLF3HBXXrCI084OBCKRSCaTtVAKZVbID2dW7b9fKVXpEgKFo8PFAwOdWITNjy+1q4KuCcoWdDwej8f7Vwsh1Jjt0dabj7MrFd+PDiE7ENsg5DCEHEbz/kENWaRQoiySqi4U1RRKFKU1agJDHgJW44TtL+QEOnMEbOiM9g+1Tv+4VpNZIc+qlBdJVUUy1YMn9RKlzkvADHDmhoo5/Ts4+Tu5BThz/ZzY1m+YCHflhbvyPujra8zQq1KL5h/VDAl2Hhri/EKQM5dp8xkaUBkkZruTVSlfd+HhvokdodNyOwk5DKEHo0uz7kImE/ZDmdqRTRjbw90dmB4OLGPCDhFzeXZQykuV2qxKhTEHF0qUWZWK3GqFgEX4C9lhLrwwF27/Dk7+Qk64K49Nsf53jTP06VzJ99cfzz+a29ffaXS4eGSYCG4iYAnQlP0XO2nhUesMQ7bfGR/pOj/Wq2EjZVt4rKA1TdlmYawgNrzALqtVl9drGl6XNn+B7e1o8R5nFmrKVusM+dWK7CqFMQFnVcrzqpVqncFTwAxz4YW78ozJOMKV20LPLEt7ZlN2C4plqpM51YceVN0trzdm6BGhIltpDrGTgs4kyhZ00JRt71aeK3LiMObEeJIdiN1hEbi/kN389bNKZyhr1OPsfIGsSKoskKgUWr2XgGl8aU3ZHmdP65nlxmeFu/L8ndhhLtzR4eL29MyiID8n9ls9PN/q4VkiUx3Pqf75dvmSk/m9fB2HBAtf7ujiwmOSHSCweZCY7UhKvnTv/Ypz0zsT0MuUMtgEZjJhN7SHl9Wpy+s0xoSdU6XQ6g2ezV5gG9vGLR3qM3tmvRLpEu7Ks5PGeYSQz98ZukquTc6THM6sWnWuqIunw5gI8ZhwsbvlrwigK0jM9qJKrn3nWF7S0EAfJ9voM2znnusFttl7nLW+Z5atdEG3KDGPMaGT64ROrtVyzZk8aeMMPSpMTNcRicByIDHbBYMBLTieOzTYeUyEmOxYQLuYTNganb5KoS2v07TQ48zYHu7OZ3kIWKFibuN+xbbbM4tqRDymMUNLldpTOZLTuZK1qcW+TpzR4aJxka5BonbPigfsA3T++gu9+0RsvVm25WbZ2emdTbYxUrZPhBVYrfMXKZRafZFU+VCmLpIqi2Wq4r/+VNWqdZ4CVoCIJ1NocirlOIaME2YFi7nBIq5x/ixb6c3UNu3p/PVc6tS607mSo5lVZ/OlIWLuyDDRqHAXEjM0vQu6llG2oIPOX/Yos0K+7vzD3yd3tJM3f6ABh4GHufDCXJqOwK5RaYul6ioNzsL0fgLC2xGaoy3FgUW83NHl5Y4uCo3+bL7kaFb1hu13fBzZI8NEo8LFEbB4BjAFEjPNqXWGtw/nvBfv3fxtJbBbjmxGlDuDgjN/0RiXiY8ME48ME6t1hsvFslO5kpd3ZfCY+NAQ5zERLjHeMDU3+Ac5Tdn19fWkHLcFGIbx+fy6ujqyAzGzD0/k3imrPTq1aws9sRkMBpPJVCgU1gyMIlgsFoZh9pmcOByOVqvVarVkB0ICPp+vUCj0ej2JMej0hmslNQfuPzlwv4JJYCPDXcZGuvXydbTouDK6FnStQdmCjsFgcDj/ertBTo1Zq9WSe0s0h+O4wWDQaDRkB2JOKfnS3emPz8/sptdp9S2+TWMwGDQ791ZiMBgYhtnnubPZbJ1OZ5/nbjAYtFot6T1denjyengGrB7sf62k9tCDyml77qn1hsGBzmMiXAYHCZkWmJobx3GEkH1edETVgo4gmvbnICcx6/V60m+JJow1eKpF1R5Vcu3cw9lJQwM9HRgtnxdBEAaDgU7n3np6vR7DMPs8d4PBQME70Wp0Oh11zr2nF7+nF3/NYH/j1NzLkvPnH7XI4hkGg8Fub3bKFnTN24/hHTM9GQxo4fG8IcFCGB8FgA1psnjG6tTi+UfVxsUzEoKcof+mnYDETE/bbpVlVcq/Hd2Z7EAAAG3RkKGLpKpTudWNF894MUzkAItn0BokZhrKqpSvPV+8b2JHuHsBsHX+wr8m/nwoU534e2rufgFOo8PFw0OdSVwIBFgOXFS6aRgf1d3LgexYAABm4/v31NylNapj2dV771d+cCq/p4/jkGDh2AgXVz4snkEfkJjpZlVKkYDFmBvj9eyPAgBskLfjXxm6WqE9kwuLZ9AQJGZaScmX7rkH60cBYBdE3L8Wz5AotKcbZeihwc6jI8QBQpia21ZBYqYPWD8KAPvk/HeGlim15wtlp3Ik/bekw+IZtgsSM33A+Jf0QGEAACAASURBVCgA7JwThzE6XDw6XLx+eOD5AtnhzKqEH+94O7JHh4vGRIibz5oOqAkSM01su1mWW634fgyMjwIAIA4DHxriPDTEWanVn82THs2qGvHzfS9H1qhw8bRYphsU/NQG14cOsirla2H9KABAMxwG/mKY6MUwkVpnuFAoO5pVHbvh4rHXI2FhKyqDxGzzjOOjFsZ7wfpRAICnYRFYQpBwSIgoytt5wbG8E29EQRdRyoIKls2D8VEAgNZbPCBIZzBsu1VOdiDgqSAx27bUAumeexX/NyoYHn4BAK3BwLH1wwL/d6G4WGaPq53aBEjMNqxaoZ1/FMZHAQCeT1dPh9c6u31wMp/sQIBppt8x6/X6s2fPVldX//UhBmPcuHFWjAq0yoJjeS8EwfgoAMBz+7i/X98ttw8+qHoJChDqMZ2YJ0+erFAoAgMDjf/EcRwSM9Vsu1mWVSn/9k0YHwUAeG5cJv7Z0MC5R3L6BTiJuNALmFpMXw+1Wn3o0CErhwJaL6tSvuZ88d6JEbB+FACgbQYFCgd2EK44V7jhxWCyYwH/Yvodc3BwsFQqtXIooJUa1o+K9hKQHQsAwIatTQg4kys9XwClPbWYrjGXlpZ6eXnFxcWJRCLjlr1791oxKtCS1TA+CgBgDiIec8Ug/0Un8v+Y1RWmJ6IO04l57ty5c+fOtXIooDVSC6S/wfpRAAAzmdDJdX9G5RdpJf8d4Ed2LOAvph+RevfuXVdXd/To0UOHDslkst69e1s5LGCScXzUZzA+CgBgPuuHBf54q/xueT3ZgYC/mE7MK1euTExM9PLyEggEH3300cqVK60cFjDJOD4KhjcAAMzI14n9Xrz34hP5Or2B7FgAQk9LzDt27EhJSXnnnXeWLVt2+fLlXbt2WTks0JxxfNTqhACyAwEA0M2cGE+9wbD5ZhnZgQCEWpj5C8P+eoWJ49AjgHzZlYo154u/HR0M46MAAGZH4NjXLwYn/VFSJIV5OslnuvPXlClT4uPjx40bh+P4/v37J0yY0MIutFrthg0b6urq/Pz8pk2b1sJG0DZqnWH24eyFcTA+CgBgKZFuvNe7un1wKv+3CRFkx2LvTNeGV6xYsXbt2pqaGqlUunLlylWrVrWwiytXrvj5+SUmJlZUVJSUlLSwEbSNcXzUvF4wPgoAYEEf9vXNr1buv19JdiD27qkzsY0YMWLEiBEIIYPhGd0BcnJy4uLiEEJRUVE5OTk+Pj5P23jlypWsrCyE0LBhwxwcHMx3FmaAYRiGYVwul+xAmjqXV/3bvYpLc2Ic+BwLHYIgCBzHKXjuVsBkMhFC9nnuBEGwWCz7fFeFYRiHw9Hr9WQHYm0tF3RcLvq/sRGv/3ZvWISHC59p5dgsjbIFXfN70HRiTklJOXTo0FdffTV27Njk5ORvv/12ypQpT9upXC7n8XgIIS6XW1tb28JGqVRaWlqKENLr9QRBrXelxnfqVIuqSq556/cHG8Z0DBDzLXcUHMcxDKPauVuHsZyy23PHcdw+zx39/WtPdhTW9syCbmCwy7Awl/+czt0yPsqKcVkDZQu65r+HphPz8uXLExMTL1++rFQqs7KyJk2a1EJi5vF4CoUCIaRQKBrqwSY3Dhs2bNiwYQghiURSV1fX7tMxJxzHWSwW1aKatT8rIdBpWAe+RQNjsVhcLpdq524dPB4Pw7D6enscweno6KhSqVQqe+zsw2Kx5HK5TqcjOxBra01Bl9jPu/fm9CN3SgYGCq0WmBVQtqAzVmIbM92KVVFRkZCQcOzYsdGjR3t5eT18+LCFnYaEhDx48AAhlJmZGRwc3MJG8Fx+vFX24AmMjwIAWJWIx1ydELD4ZL5cY3dN/RRhOjHHxMRMnTp1+/bto0ePXr58ubu7ewu7iI2NLSwsTEpKEolEvr6+2dnZmzZtarLRMsHTWXalYnVq8XdjYHwUAMDaxke6RLjyki62VCUDloOZ7Nslk8l27doVHx/fpUuX1atXv/766wEBAWY8qkQioVojEo7jzs7OVVVVZAeCEEJqnWHoT3fGRri8G+dthcMZW3hkMpkVjkU10JRtn03ZIpFIJpNRrRSygtYXdA9lqoHb7uyf1LGLhwU7uFgTZQs6Ho/XpDXbdI3Z0dFx0KBBCKH09PSBAwfOnj3bGtGBv61JLXJgETA+CgBAFl8n9uLePguO5Wp00KBtbaY7f82ePTs1NbWsrKxr166ZmZnz5s2zclj2LLVA+utdWD8KAECy2T09jmRV/XCjDCoJVma6xnzkyJH79+8vXLhw3bp1N27cuHfvnpXDsluwfhQAgCJwDPt8WOAXaaWFUiXZsdgX04nZ1dUVIRQVFXXt2jU/P7+CggLrRmW/Fh7PS4D1owAA1NDRlTetu/t7x/OfNdEUMCfTiXn48OGjRo3q27fv1q1bly5d6uLiYuWw7JNxfNQaGB8FAKCM9/v4lNao9t6vIDsQO2I6MX/yySfr16/39PTcvHkzn8//5ptvrByWHYLxUQAACuIw8C9HBC1LLqyUa8iOxV6YTswYhl27dm3WrFkxMTEJCQlBQUFWDsveqHWGtw/nLID1owAA1NPbz3FoiHPi2SKyA7EXphPz8uXL9+7dm5aWhmHYsmXLli1bZuWw7M2a1CI+C58PXR8BAJS0clBAaoH0dK6E7EDsgunE/Ouvv+7Zs8fb25sgiBMnTuzYscPKYdkV4/iob0eFwPgoAAA1OXMZaxMClp4uqFfb3aws1mc6MavVao3mr9cJSqWSw7HUgoMAxkcBAGzC2I4ukW78T/+AeTotznRinjdv3pAhQwoKCpKSkvr27Ttnzhwrh2U/YHwUAMBW/O+FgJ3pFTdKa8kOhOZMz/y1ZMmS7t27p6am6nS6LVu29OjRw8ph2Yntt8ofPJGnzOhMdiAAAPBsPk7sD/v5LD6Rnzy9E5MwXa8D7Wc6MSOEBg0aZJwu2+QqF6D9sisVq1KL9k6MgPFRAABbMTPa42BG1bfXHltniR37ZPqRJyUlZeHChQihsWPHOjo6/vLLL9aNiv5gfBQAwBbhGLZ+eODXlx8VSGCeTkt56nCpkSNHXr58WalUZmVlbd682cph0d7a88UwPgoAYIsiXHlvdnd/73geNKdaiOnEXFFRkZCQcOzYsdGjR3t5eT18CN3wzCm1QLr7zhMYHwUAsFHv9/WtkGt+uwfzdFqE6XfMMTExU6dOPXv27NWrV5cvX+7u7m7lsGisWqF952jep0M6wPgoAICNYhFY0tDA6QeyBwcKXflMssOhG9M15g0bNsTGxh47dszb25vBYOzevRshdPv2bevGRk8Lj+cNChSO7QjrggAAbFi8n+PwEOf/JheSHQgNmU7MTk5Oc+bM6dKlC0Jo2bJlAQEBCKGdO3daMzJaMo6PWvtCANmBAABAe60a7H/5Yc3JnGqyA6EbGIhmPcbxUbB+FACAHhzZjDUJAR+dKayDeTrNChKzlWh0+vlHc9+N9YLxUQAA2hgdLu7kzv/feeggbE6QmK1kzfmHbAb2TiwMyQcA0MpnQzvsuVdxvQTm6TQbSMzWcKm4ZvedJ/8H46MAALTj4cD6sK/P4pP5Gp2e7FhoAhKzxUmV2rlHcj8d0sEXxkcBAOjozWgPAZv45tpjsgOhCazJVNgffPDBZ599tmLFihUrVjT5aH5+fmBgoFmOqlAozLIfM8IwjMPhWCKwKb/eFbAZ346NMPuezYUgCAaDoVKpyA6EBEwmEyHUsMipXWGxWDqdTqezx247HA5HrVbr9XZXw7NcQZdVUd//++upb/UMd+ObfedmQdmCDsdxNvtf1bamiXnQoEE4jv/5558xMTGNt584ccKMcUgkEqoVBziOOzs7V1VVmXe3P/1ZvunKo5QZnancE5vFYnG5XJlMRnYgJODxeBiG1dfXkx0ICRwdHVUqFQXLKSsQiUQymYxqpZAVWKigM/rkwsPLD2sOTo7EKPnKjrIFHY/H4/F4jbc0nfnr9OnTd+/enTVr1po1a6wYGD0VSJSrU4t/fTWcylkZAADMYlFvn0Hb7uy68+S1Lm5kx2LbmibmiRMn7tu3TyAQREdHkxIQbWh0+tmHcub38uzhDeOjAAD0xyKwz4cFTtmXOThI6OHAIjscG9Y0MTs4OPj4+FRVVXXo0KHx9oKCAitGRQcwPgoAYG9ifQVjwsX/TS7c8lIo2bHYsKaJefv27XK5fP78+Rs3biQlIHowjo86O70zjI8CANiVFYP8e2++fSK7enioiOxYbJWJ1aV4PN62bdtOnTqVmpqq0+kGDBgwYsQI60dmu2B8FADAbgnYxLoXAj44VdDb39GRbXoBQ9Ay0+OYV65cmZiY6OXlJRAIPvroo5UrV1o5LJu2+ER+vwBHWD8KAGCfRoaJo70d/nehhOxAbJXpx5kdO3bcuXPH2IF78eLF3bp1W758uXUDs1U//Vl+p6w+ZUZnsgMBAADSfDqkQ98t6WMjxDE+0Pv1uT115i/s75FoOA6zg7WWcXzUt6Nh/SgAgF1zd2B91M934fE8tc7w7E+DfzNdY54yZUp8fPy4ceNwHN+/f/+ECROsHJYt0uj0bx/OmR/rBeOjAABgajf3/fcrN14pXdzbh+xYbIzp2vCKFSvWrl1bU1MjlUpXrly5atUqhNDt27etG5uNWXv+IYvA3unlRXYgAABAPhzDvhoR9M3VR1mVcrJjsTFP7TI3YsSIJp2xd+7c2bVrV8uHZJMuFdfsgvFRAADQSLCYOyfGa8nJ/MOvRVFznk5qgvfHZgDjowAAwKQFcV5SpW5HejnZgdgSSMxmAOOjAADAJBaBfzUicHVK8eNaNdmx2AxIzO318+3y9LK6dS90ePZHAQDA/kR7CcZ2dPn4DMzr3FqQmNulQKJcnVL83egQGB8FAABPs2yA361HdceyqskOxDZAYm474/ioeTA+CgAAWiRgE58PD/zwdIFMqSU7FhtgOjFXVFQ03zhnzhwLB2NjYHwUAAC00gtBzj29Hdaef0h2IDbAdGLu0aPHyy+/fPjwYY1G07AxMDDQWlHZAOP4qP8bFQLjowAAoDU+HRp48EHl1ZJasgOhOtOJOT8/f/bs2Xv27AkNDV20aFF6erqVw6I4qVI77yiMjwIAgOfgxmf+d4DfwuN5Kpins0WmEzNBEEOHDv3mm2/ef//9LVu2DBgwIDo6+tKlS1YOjrKWnMzv4wfjowAA4Pm83sXd04G14XIp2YFQmunEvGvXrjFjxnTq1OnevXtHjx6tqqrasmXLG2+8YeXgqOnn2+W3H8P4KAAAeG4YhpKGBX537XFmBczT+VSmp+Q8d+7c/PnzBw0aRBB/jQLq1q3bunXrrBgYRRnHR+1+NVzAhvFRAADw3IJEnHm9vBYezz/+RiQOE3WaghkM/2rrnzRpksnP7d6924xHlUgkOp3OjDtsPxzHnZ2dq6qqWviMRqcf+cv94aGihXHeVgvMClgsFpfLlclkZAdCAh6Ph2FYfX092YGQwNHRUaVSqVQqsgMhgUgkkslkVCuFrKA1BZ0VaPWGF7bfeaOr+/TuHlY7KGULOh6Px+PxGm9pWmOeOXOmFeOxMesuPGTiMD4KAADahYFj64cFvfrbg6EhIi8Bi+xwKKdpYh48eDBCSKfTNTRiI4SuXLnSwi60Wu2GDRvq6ur8/PymTZvW+L/Wr18/b948DodjtnjJc6m4Zmc6rB8FAABm0N3L4ZUo1yUn8ne9Gk52LJRjuvPX5MmTtVotQqiysnLWrFkTJ05sYRdXrlzx8/NLTEysqKgoKSkxbqyvr1+2bNmFCxfMHjEpjOOjPoHxUQAAYCbLBvhlVsqPZpHcrk5BphNzZGTkuHHjvv32286dO7u5uWVkZLSwi5ycnKioKIRQVFRUTk6OcSOfz1+5cmWnTp3MHjEpjOOjXobxUQAAYCY8Jv7Z0MClpwulME/nv5nulZ2YmPj5559/+OGHV69ejYiIaHkXcrnc+OKay+XW1v4zpQuO49i/e9x98skn+/btQwjt2bOHmvOIubiYSL0/XCm6+0Tx56L+jhzTPy56MHnudoLL5ZIdAjlYLJZAYKczvTs7O5MdAmmoc7NPdHE5kC1Luvzk+1c6W+eI1Dn3Bo1n2DRqmmnef//9hr+7ubnNmzcvOjoaIZSUlNTkk8nJyffv34+NjeXxeAqFAiGkUCgcHBxaOPzChQvnzp2LENJqtaR3C2wCx3GhUFhd3XTxkwKJ8sMjGb9OiNDUy6po2nWXsp0VrcCee2ULBAK1Wm2fvbKdnZ1ramrss1e2yYKORCv7e8f/8OfwIIe+/k4WPRBlCzoul8tkMhtvaZqYjY3Szf/eXEJCQkJCAkJIo9E8ePAgLCwsMzNz/PjxLXyFw+EYO4JJJBK9Xv+80VuUcdhYk8FjGp1+9qHsub28or0cmvwXnRj+RnYgJDB53e2H3V53ZK/nTsFfeBceY/lA/yUn8s7P7MomLNi11oYKuqbvmKf+Ta/XX7x48bXXXgsNDZ06dWoLu4iNjS0sLExKShKJRL6+vtnZ2Zs2bbJkzNZjHB/1biyMjwIAAEuZ3NnN25H9ZVoJ2YFQRdMJRoyWL19+/fr1wsLCu3fvDh06NC4ubvXq1WY8qk1MMHKpuGba71lnp3emfU9syrbwWIE9N2XDBCNUK4WsgCITjDSXL1EO3nbnyJSoKHfesz/dJpQt6JpPMGK6V/avv/66Z88eb29vgiBOnDixY8cOq4RHITA+CgAArCbQmbMg3nvxyTyd3gaami3NdGJWq9UN/cSUSiU9Zgh5LjA+CgAArGl+Ly+NzvDjrTKyAyGf6cQ8b968IUOGFBQUJCUl9e3bd86cOVYOi1w7YP0oAACwLgaOrR8e+MkfJaU19vh6pTHTA3OXLFnSvXv31NRUnU63ZcuWHj16WDksEhVIlKtSine9AutHAQCAVXXzdJgQ5brkZP7uV58xfwa9ma4xI4SKiooeP368ZMmS5mOfaUyj0799OGduL6+ePnY66wIAAJDovwP8sisVhzMp1z3Nmkwn5uXLl+/duzctLQ3DsGXLli1btszKYZHlfxdKYHwUAACQhcvEPxsW+NHpAnuepxN6Zf/jQn7VL+nl344OgfWjAACALIMDhX38nVacKyI7ENJAr+x/JJ7M/hTGRwEAANnWvhBwIrs6rbiG7EDIAb2y/3HyrV5jYXwUAACQzYXHXDk4YNHxPJXOHoc1Q6/sf3AYuD3O/wQAANQzsZPr/vuVn//x8D8D/MiOxdpMJ+bXX399+PDh77zzjqurq5UDAgAAABBCXwwPHLD1zugIcSd3PtmxWJXppuyEhISzZ8/27t07JiYmMTHx0qVLVg4LAACAnfN1Yi+I81p8It/e5uk0nZinTp26devWS5cuvfHGG9u3b+/Tp4+VwwIAAADm9vLSGQxb7WyeTtOJee7cuZ06dRo0aFBWVtaXX35ZXl5u5bAAAAAABo5teDH40wslxTI7mqfTdGJOT09XKpV9+vSJi4uLiYmBN80AAABIEenGe62L6wcn88kOxHpMJ+a0tLS7d+9OmjQpPz+/d+/e/v7+Vg4LAAAAMPqon19uteJARiXZgViJ6V7ZN2/ePH/+fGpq6u3bt6Ojo4cOHWrlsAAAAAAjLhP/ckTQrIPZ/QKEYp7ptEUnps9w9uzZQ4cOXbJkSXx8PINB/58CAAAAKuvr7zQo0HnFucKNI4PJjsXiTCfdGzduWDkOAAAAoAVrBvv33pyeWiAd0EFIdiyW9dRlHwEAAADqEPGYKwf7LzqRL9foyY7FsiAxAwAAsA2vRrkGi7nr00rIDsSyIDEDAACwGeuHBW6/VX6njM4rG0BiBgAAYDN8ndiLensvOJ6rpe88nZCYAQAA2JK3e3oSGLb5Bm3n6SRnKBSbzTYYqPWwg2EYhmFcLpfsQEhAEASO4/Z57kwmEyFkn+dOEASLxcJxe3w6xzCMw+Ho9TTvQ9QcbQq678dFDtlya2wnzw6i1p4LZQu65vcgOYlZrVbrdDpSDv00xgumVCrJDoQELBaLwWDY57njOI5hmH2eO5PJ1Gg0KpUdTUHcgMvlqlQqqpVCVkCbgi7YifFGN7cFhx/smdixlV+hbEHX/FmBnMRsMBioVmM2xkO1qKzD8DeyAyGBPV93RMk70Wrs89zp9Av/YV/fvpvT996rGB/p0prP21BBZ4+tWAAAAGwdh4F/OSLwP2cKquRasmMxM0jMAAAAbFIff6chIaLl5wrJDsTMIDEDAACwVasG+Z/Ll6bkS8kOxJwgMQMAALBVzlzG6sEBi0/m16vp05UPEjMAAAAbNi7SpaMbL+kifebphMQMAADAtn3yQodf0p+k02WeTkjMAAAAbJuPE3tJH58Fx3I1OjpMGgOJGQAAgM17q4cHj0V8f50O83RCYgYAAGDzcAxbPyzwy0ulhVLKze31vCAxAwAAoIMIV9707u7vHc+3hdm9WgKJGQAAAE180Ne3vE69514F2YG0CyRmAAAANMEisM+HBSaeLayUa8iOpe0gMQMAAKCPeD/HoSHOy5ILyQ6k7SAxAwAAoJU1CQEXi2pO5UjIDqSNIDEDAACgFUc2Y3VCwNIzBTY6TyckZgAAAHTzUoQ4yo3/yYWHZAfSFpCYAQAA0ND/XgjYdafiekkt2YE8N0jMAAAAaMjHib20n++Sk/n/3959xkVxrQ0AP9O2wcLSO4iAVEUFFWxBxYCKuTHgJRojGturmGJMMcUWU94brjdvDIk3ajSG2PXaYrkigmLHAojSFAmidJe6vbwfJpdLYK3AzuzO8/+QH05md59nZnefPWfOnGNy83RCYQYAAGCe5oQ5WfCIHy5XMR3Is4HCDAAAwDzhGLZ2Qt//O3+/tEHOdCzPAAozAAAAsxXoIJoX7vzecVOapxMKMwAAAHP23kiPepl6W67JLDwFhRkAAIA5o+fpXH6yrKZFyXQsTwUKMwAAADMX6WE1OcBuT75pNJrJ7j+FRqNZt25da2urp6fnrFmz6I0ymeyrr77SaDQWFhYffPABj8fr/gsBAAAAz+cfk/qJLURNTU1MB/JkPdBivnjxoqen54oVK+rq6iorK+mNmZmZAwcO/Oqrr3x8fM6cOdP9VwEAAACeG4ljTIfwtHqgxVxaWhoZGYkQCgkJKS0tdXd3Rwj5+fnZ2dkhhCwtLSmKovfMysq6ceMGQighIUEikXT/pXsQhmEYhllYWDAdCAMIgiAIgpu5029ObuZOEASfzyfJHvgSMDkYholEIp3OxOad6D74omNh7jjeuYXcAy1mmUwmEokQQkKhsLW1ld7Yr18/Ozu7nJyc8+fPDxkypPuvAgAAAHDB8/9YPnny5M2bNyMiIkQikVwuRwjJ5XJLS8v2HTZu3Nja2vrpp5/SZRshFBUVFRUVhRCSSqVtbW3dCryn4TjO5/PZFpVx8Hg8oVDIzdxFIhGGYdzMnSAIpVKpVJrGONWexefzZTKZVmuSSw91B3zRsTD39hLZ7vkLc3R0dHR0NEJIrVYXFhb6+/sXFRUlJCTQ//fcuXMkSS5ZsuS5nx8AAADgoB64vBQREZGampqSkuLo6Ojh4VFSUnLixAmSJAsLC5ctW4YQmjRp0qhRo7r/QgAAAIDZw/RMTFPW1NTEtmEXer2+ra2tY1c8d2i1WpVKJRQKmQ6EASqVCiHEzdv5FAoFSZLcHPzV2toqEom6Droxe/BFx8IvOoFA0CkqZgozC9XW1k6ZMuXcuXNMB8KAM2fObN269aeffmI6EAZs2LChubn5vffeYzoQBrz77rtjx46Ni4tjOhAGTJgwITU11cfHh+lAjK2+vv6ll146f/4804EwIDs7e8uWLZs3b2Y6kCfj3A9GAAAAgM242ItlkFAo/Mtf/sJ0FMxwcXF54YUXmI6CGQEBAQqFgukomBEREeHp6cl0FMyYMGGClZUV01EwQCAQcPmLjr4tiP2gKxsAAABgEejKBgAAAFjEzLuy9Xr9+vXr79+/r1QqIyIi2m+zfqJTp05RFGXSd3llZ2f/+OOPv/zyCz30dP369VKp9OOPP36ax5p0+qmpqTU1NXfv3nV3d6coav78+R4eHs/0DCaR/ueffz5t2jQfH58TJ05kZ2evWbMGIbRw4cK1a9d2na8AdSMp9h+NTmfc29vbz8/v+QJmf7KPV1JS8uWXX7a/4T/88EN6APaBAwcEAkFsbCy93dTTpD3313snLDwaZl6Yr1y5gmHYF198gRD65JNPRo4c6ezszHRQxsPj8QoLC4ODg/V6fVlZmY2NDdMRGcPixYsRQmvWrFm0aBE9YbtZCgoKKikp8fHxKSgokEqlarVapVJRFGWwKpu3Tmc8PT39iQ+Ry+UsvG2mR4SHh9MHpJ1cLn/55ZeZiqf3PPHrPS8v7/bt2/Hx8V0fy/I3gJkXZolEUlpaWlhYGBAQQJ+/w4cPu7i4hIeH79ixY+DAgZWVlWVlZRRF1dTUvPfeew0NDSkpKRKJRKPRREdHKxSKb775Rq/XW1tbJycn/+1vf5s7d66dnd3y5cs/+ugj9n8DDhs27OLFi8HBwXfu3PH19W1oaGhqakpJSVGr1U5OTu+8805GRoYZp0/rdMY9PT2/+eYblUplb2+/ePHiBw8epKWlabXakSNHBgQEmFD6QUFBx48fnzBhQn19fWRkZFFRkU6nCwwMbGtr65hgbW1tx6TS09M7nnGVSmUeR6OT7Ozs06dPazSaTz/99NixY50+8teuXRMIBPHx8eaR7GOkp6fTyQYFBQkEAj8/P3NKs+vXe6eMTp48+eDBg/r6+rCwsK5vgMTERNYeDWLVqlXGfD0js7Oz8/LyOnXqVFpaWm1tbXBw8O3bt8Visaura0FBgbOzc3Nzs0wmmz17Nr1g5dGjRydOnDht2rScnBwPDw+Konx9fadMmXL69Gk/Pz8+n19RUWFnZ5ebmzt27Fimk3uCiooKPp9fVFQUFRV10DlFFAAAIABJREFU7NixwYMHl5aW1tTUhIaGzps3Lz8/X6fTKZVKc03/zJkzQ4YMEYlEJSUlHc/42bNnQ0JCZs2ade/evaqqqqKiogEDBkybNq2qqurkyZMmlL5EItm9e3doaGhlZeWwYcPy8/Obmpq8vLxyc3M7JpiVldUxKY1G0/GMX7hwwTyOBupwxsvKypRK5bvvvltRUUFRVGNjY6ePvFwuT05OPnr0qOkm+ygNDQ27du26evVqZmZmbW0tvZBBcnJyWVkZSZIZGRnmkSat69d7TU1Nx4wcHR0tLCwkEonBN8DmzZtZezTMvMVcU1Pj6emZnJysUCi++OKLy5cvt/+v9qnH+vbtixASCoVarbampiY4OBgh5O/vjxASiUR79+7997//XV5ertPpwsPD161bp9PpRowYwUQ2z8Pb27u8vLysrGzy5MkIoaqqqnHjxiGEAgMDq6qqLCwszDv9jugzXlVVVVZWlpeXhxDy9PQMDQ1NS0s7fvx4bGysaaVPkqRQKKQrq7+//+7du0Ui0ejRo/Py8jom2Ckp9Oc3vNkcjU7omK2srDouU9H+ke/Xrx9CaNy4ceaRbCcdu7LT09PpZGnmlCYy9PXu7+/fMaNO+3d6A7D5aJj5qOzLly/v378fISQQCHx8fNRqNY7j9NqURUVF9D4Y9t/Vs52dnQsLCxFCJSUlCKGDBw+OHz/+7bfftrOz0+v1lpaWGo3m3Llzw4YNYyCZ5xIREXHw4EEnJyc6TScnJzq14uJi+nqMeaePEOp0xt3c3MLCwhYvXjx48GB3d/eSkpKZM2cuX758z549Jpd+YGDg4cOHQ0JCeDwejuO1tbWOjo6dEuyUFPrzGTeno9ERQRDtf3f9yNOzkJpNso/XccpVM0uz69d7p4wQQnq9/lFvADYfDTPvyvb29s7MzNy1a1dWVhafz09ISLCzs9u2bdulS5dsbGwCAgIaGxtJkvTy8rpz546Tk1NYWNiPP/54+vRpsVjct2/fPn36HDx48MqVKxKJpLKyMjQ0tKWlpampySTuUq+oqKAHK/7zn/9MSEiwsbG5fPnyjBkz0tLSMjIySJKMj4+/e/euuabf3rFpa2vb8YwPGjRo165dZ8+ebW1tHTNmTFtbW1pa2tWrVwMCAuLi4kwrfbVanZeXl5iYiBCqra3VarUjRozw8vLqmKCXl1fHpOj5sTuecbM5Gh27sukci4qKbGxsAgMDDX7kTTrZR2loaLhz587QoUPpf7YfCvqPUaNGmUeatK5f73w+v2NGISEhe/fuHT169KFDh7q+ATp9NFh1NGCCkWezf/9+R0dHU+nq6XGQPpfT74RTR4MjyXIkzafE4NEw867snpWZmVlQUBAREcF0IMyA9LmcfiecOhocSZYjaT4lZo8GtJgBAAAAFoEWMwAAAMAiUJgBAAAAFoHCDAAAALAIFGYAWE2pVGIY5uLi4uzs7ObmNmfOnJaWlh555tmzZz/3vP+dbN269f333++RpwIAQGEGwARUVVVVV1ffvn1bIBDMnj27+08ok8nS09P37t3b/acCAPQsKMwAmAyhUPiPf/wjOzv7/v37er3+ww8/DA0NdXFxWbBggV6vnzt37vbt2xFCWq3Wy8urrq6u42NXrVrl6+vbr1+/zz77DCG0ePHihoaGN954o32H8PDwa9euIYSGDx++aNEihNDWrVtnzpyJEFq7dm3fvn0DAgLa5yPquoW2evXqxMTEjhNhAgCelZnPlQ2AmeHz+UFBQcXFxY2NjXl5eVeuXEEIBQcHl5SUJCYmpqamTp8+/eTJk2FhYQ4ODu2POnr06IEDB/Lz8xFCY8aMGTZs2HfffZeZmbl58+b2fWJiYrKysoKCgurq6rKzsxFCZ86ciY2NPXXq1M6dO69cuUJRVGJi4pYtW7y8vDptodf8Xrt27bVr1/bt29dxRkwAwLOCFjMApgfDsODg4J9//vnAgQOff/55dXW1QqEYO3bs9evXm5qa0tLSZs2a1XH/rKyshIQEkUgkEolmzJiRlZXV9TnpwpyTkzN+/HgMw+rr67Ozs8ePH5+VlSWVShMTE1955ZXy8vJz58513YIQOnDgwOrVq2NjYztOzgwAeA7wEQLAlKhUqlu3bvXr1+/ChQtz58594403xo0bl5mZiRAiCCIuLm7btm3nzp3bsmVLx0fRU/nTfzc2Nhrsah4+fHhubu7p06dHjhxJEMTOnTslEomDg4NIJFqwYAE9tkur1er1+r///e+dtmzbts3Dw+PQoUPjxo2bOnWqvb19rx8IAMwXtJgBMBlKpfL9998fOXKkm5vbyZMnJ0+evHTp0j59+hQVFdFDtV999dVly5ZNmTKFoqiOD3zhhRf27dunUCjkcvnevXsNzstPkmRYWNiGDRtGjRoVFRX19ddfx8bGIoTGjRu3ffv2lpYWtVodExOTk5PTdQtCKCwsLDAwcPbs2R999JExjgUA5gsKMwAmwMPDw93d3cfHp7m5+eeff0YITZ8+/fr162FhYe+++25ycvKaNWsQQnRjNykpqdPD4+LiJk6cGBoaGhoaOmXKlIkTJxp8FboSe3h4jB49urKyMiYmBiE0ZMiQpKSkIUOG+Pr6RkREREZGdt3S/gyffPJJenr6xYsXe+cwAMAJMFc2AObj+vXrc+fOvXr1KtOBAACeH7SYATATu3fvnjp16nfffcd0IACAboEWMwAAAMAi0GIGAAAAWAQKMwAAAMAiUJgBAAAAFoHCDAAAALAIFGYAAACARaAwAwAAACwChRkAAABgESjMAAAAAItAYQYAAABYpAeWfdRoNOvWrWttbfX09GxfBXbv3r2XLl1CCLW2tg4dOpReBsfR0REhtGTJEldX1+6/LgAAAGB+emBKzrNnz1ZXVyckJKSkpEybNs3d3b3j//3+++/j4+OlUml+fn5iYqLBZ5DL5Tqdrpth9CyCIBBCBpetNW84juM4rtFomA7E2DibOIZhJEmq1WqmAzE2DMMoilKpVEwHwgAej6dWqzk4HzNFURqNhm2JUxTF4/E6bumBFnNpaSm97ltISEhpaWnHwlxRUSEUCp2dnYuKih48eJCamhocHDxmzJhOz6BQKNhWAkUiEYZhcrmc6UCMTSAQ4DjOwcR5PJ5QKORg4iRJ8vn85uZmpgMxNhzHRSJRU1MT04EwwMLCoqWlhW3NISMQCoUymYxtv78xDOv5wiyTyUQiEUJIKBTSq7W327t377x58xBCIpEoJCRk0KBB3377rZ2d3YABAxBCBw4coNdtffPNN+3t7bsfSQ8iSRIhhOOcuwZPEASO42KxmOlAjA3HcYIgOJg4hmHcPOMYhiGEOJg4zdLSkm0NRyPAMEwkErE/8R4ozCKRiG5nyOVyS0vL9u1tbW1yuZx+3w8dOpTeOHbs2JKSErow9+nTh/7JxsKeNAzDMAxjW1RGw8HESZLEcZyDieM4zsIPoBHQzRQOJo4Q4vP53OzK5vF4Go2GbV0FdDvwT1u6/6R+fn6FhYX+/v5FRUUJCQnt269evUoXYITQjh07goKCQkNDKyoqfHx86I0DBw4cOHAgQkgqlSoUiu5H0oNwHMcwjG1RGQ0HE+fxeARBcDBxuiubg4njOG5hYcHBxBFClpaWSqWSbfXJCEQikUqlYltXNt3l3FEPdNVGRESUl5enpKTY2tp6eHiUlJSkpqYihHJycui6ixCKjo4+evToypUrpVLp8OHDu/+iAAAAgFnqgVHZ3SeVStk5+KutrY3pQIxNIBBQFNVprAAX0IO/ODgUiCRJsVgslUqZDsTYcBy3tbWtr69nOhAG2NvbP3z4kIMtZltb2+bmZha2mDs1mjk3uAk8EQt+qgEAAHdBYQb/VdOqiv81f1jqhfvNSqZjAQAAjoLCDP7wW3HD6E15fWwEL/azj91acKOGc934AADABj0wKhuYulaVdmXG78dKH34X5/tSiAtFUR6W+JTtt76P843xs2E6OgAA4BYozFx35X7LosO3gx0tzs4baCv84/0wfYCjm5g372Dph6M85oQ5MxshAABwChRm7tLo9P84V7nhSvWKMZ4zBzp1+r8veEsOvhY8fU/RnYfyz6P74BjGSJAAAMA1cI2Zo0rq5TFbb2TebUqf1b9rVaYFOoiOzwy5VNkyZ3+pQsO5OysAAIARUJg5R69Hv+TWTPilIMbX5rcZwd42gsfs7GTJO/RasEqre3nbrXoZFycvBAAAI4PCzC11berX9hT9mFO1f3rQB6M8CPzJHdQWPOKXeP9QF4vYrTdKGzi3+BIAABgZFGYO+a344ciNuS5iXvqsAQOcLZ7+gQSO/e1F7/lDXOLSCs5XcG59QAAAMCYY/MUJLUrtqlO/Hy99mDrZd7zPc94BNT/cxVXMn7mv+H9f9E4IZtcynQAAYDagMJu/nMqWRYdvhziJsueG2oqo7jxVnL+tsyX1+t7isofyD0Z59FSEAAAA2rGiMPN4PDaspdERSZIYhgkEjxsYxX5qrS7lzO/rL1Z+9qLP7DDXp3kIRVEEQTwm8ZE+glPzLeN/za9u0347uR9FmMnVEHo9ZlM/48+BXuGUg4ljGIYQ4mDiND6fz7ZvXSOgF+Huuv4xs3C887coK+Jj7crVJr2IenG9bMGBEj6JZcwJ9bYRPmUu9Nf043d2tySPJw14fU/hlLS8rQkBVnxWvIu6jyRJkz7jz4cgCGTib/XnQ38bcjBxmlqt5mBh1uv1Go2GbYsZ0p/BjljxlarT6dh2pHQ6HYZhbIvqKen1KC2vZvWpigVDnJeOcCfwZ0hEp9Pp9fon7m/Fw/a8GvjWkdsTfs7fPjXA3Zrf7agZRhDE0yRufjAM42bidFniYOI0nU7HtuaQcbCw3HT9hWQm/ZCgXW2betruwg051Qdee9obop4Pj8DWT/aL87ed8EtBfjWseAEAAD0DCrNZOVTUMGpTnpsVP31W//5Oz3BD1PPBMPTBKI9loz1e2XHrxG1pb78cAABwASu6skH3NSs1q09VHC99+MNk33F9JcZ86ddCHd2sePMPli4b5fEGrHgBAADdAy1mc5D9e9PoTfmNCs3ZeQONXJVpUd6Sg68Fr7v44OP0uzrujSgBAIAeBIXZtKm0+jVZFW/8q+TdEW4/TelnI2SsCyTQQXQ8KeTivZY5+0tgxQsAAHhuUJhNWFGdLGZr/vUHrVlzBjxqhShjcrbkHZ4RrNTop2y/1SDTMB0OAACYJCjMJkmvRxuuVE1Kuzkl0H7vtEA3K7bcrWTBI9IS/Ps7WcRuvXEbVrwAAIBnB4O/TE9lk/LNI3fqZeqDrwWHOImYDqczAse+jvHecKVqUlrBz/H+kR5WTEcEAACmBFrMJuZQUcO4n28EOggzZg9gYVVuNz/c5e8TfF7fW7zvZj3TsQAAgCmBFrPJaFZqlp0ov3Cv+aeX/UZ6WTMdzpNN9rd1tqBm7iu+AyteAADAU4MWs2k4U940amOeUqM7NXuASVRl2hB38bGkkH/dqn/n6B21FoZqAwDAk/VAi1mj0axbt661tdXT03PWrFn0xvr6+qVLlzo6OiKElixZ4ujo2HUf8DSUWv3X2fd+uV7z5fg+U0McmA7nmfWRCI6+HpL0r+Lpe4o3T+kn5neerh0AAEBHPdBivnjxoqen54oVK+rq6iorK+mNdXV1EydOTElJSUlJcXV1NbgPeKLCOlnMz/m5Va2n54aaYlWm2YqofdOCbYTkpLSC+81KpsMBAABW64HCXFpaGhISghAKCQkpLS2lN9bU1Dx48CA1NTUzM/NR+4DHoG+Iiku7+UqQ/Z5XA13FPKYj6hYegf34kl+cv23s1oIbNbDiBQAAPFIPdGXLZDKRSIQQEgqFLS0t9EaRSBQSEjJo0KBvv/3Wzs7O4D7Hjx+/du0aQmj27Nk2Njbdj6QH0esxW1paMvLqFY2KuXsLHsrU6fPCB7iIjfnSBEHgON5LiX82IdDbweqV7bc2/zVkgj+7OgBwHCcIgqkzziAcx3vvjLMZhmGIuc844ywsLDi4HjOGYSKRiG3rXdJvxY56oDCLRCK5XI4Qksvl7e/yoUOH0n+MHTu2pKTE4D4SicTNzQ0hhOM42xbIpFeuZiSq/Tdr3zpU9Gqo8+cv+vJJYx8ZHMd7dXXemYOc3cS8pN0Fq8b7zB3i1kuv8nw4uywxRVEcTJz+NuRg4jStVsvBwowQ0mq1bCvMdLnpqAcKs5+fX2Fhob+/f1FRUUJCAr1xx44dQUFBoaGhFRUVPj4+zs7OXfeJiIiIiIhACEmlUrpssweGYRiGGTmqJoXmwxN3L1e2/PyK/3BPK51aKVcb8/UR+s/XdK8mPtxNeHBG8LRdhTermj6P7oN3+bXICB6PRxAE296HRkCSJI/H42DiOI63Nxi4xsLCQqFQsK0+GYFQKFQqlRoNuyYMpruTO+qBa8wRERHl5eUpKSm2trYeHh4lJSWpqanR0dFHjx5duXKlVCodPnx4p326/6LmJ+tu46hNeWqt/tQbA4Z7mvlsWUEOouNJ/S/ca54LK14AAMCfYWzozZBKpWzrUBKJRBiGtbUZY5gSfUNUWm7NV+O944PtjfCKjyEQCCiKah8H0KvaVNo5+0uaVdq0+AA7EcNz3fB4PKFQ2NTUxGwYxkeSpFgslkqlTAdibDiO29ra1tdzcWY6e3v7hw8fcrDFbGtr29zczMIWc6dGM0wwwrDCOtmLW/Lzq9tOzwllvCobmQWP+HVqQLCjKHbrjTsPFUyHAwAArGC4maLT6TIyMh4+fPjHTiQZHx9vxKg4QafXb7pa/bczlW9Hui6OcGXJpVYjI3EsJabvhitVE3+5sTU+IMLDqEPQAQCAhQwX5unTp8vl8r59+9L/xHEcCnPPutekTD58u1GhOfx6cJADe9eiMI754S7OlrwZe4tSYrynBHGr2wAAADoxXJhVKtXBgweNHAp37LpR93F6eWJ/+1VjvXgEXE1ACKGXAuxcxLzX9xaVNsCKFwAATjNcmH19fRsbGyUSiZGjMXsNMs27x+7cqGn7dSosVNzZEDfxsZn9X91VWNWiSontS+Jc7NsHAADDhfn+/fuurq6RkZG2trb0lj179hgxKvOUWdb41pE7o/tYn5kbasmDtRwM8LYRHJsZMnNf8fQ9RT+9DCteAAC4yHBhXrRo0aJFi4wcihlTaHSfZf7+r1v1/5jgM7GfLdPhsJqtiNo3PXjx4dK4tIIdiSY/STgAADwrwxc4R4wY0dra+ttvvx08eLCpqWnEiBFGDsucXK9qHbM5//ZDRdacUKjKT4NPYBv+0m+Sv23s1hsFNTKmwwEAAKMyXJhXr169YsUKV1dXsVj80UcfrV692shhmQeNTr/uwv2EHYWzBzvt+mugsyU0/p4WhqEPRnm8P9J9yvabGWWNTIcDAADGY3jmL19f3/z8fHouEplMNmjQoOLi4t4Lwixn/qpoUiYfLlVq9D9M9vW1E/ZgbL3KmDN/PY3Mu03zD5R88oLnrMFOvfpCMPMX04EYG8z8BTN/scQzzPzVvhAVjsP9PM9s1426sZvzh7pb/fZ6sAlVZRYa42194LXgb85Xfpx+V8eC6WMBAKC3GR78NWPGjOHDh8fHx+M4vm/fvsTERCOHZboaZJolx+7crGnbNjVgmDvMY9UDgh1Fx5P6T9tdOO9A6Q8v+fEJuI0KAGDOHrmIxdGjR7OyshBCo0ePjouL69UgWltb2bCWRkd8Ph8hpFQqn+lRGXekCw8URXlLvonrZ2GaN0RRFEWSJAvXwmtVaWfuvtmi1O6cFmInonr8+enVD2Uyzo01IwhCIBAYZ70WVsEwzNLSkj1XbYxJLBaz8FvXCCwsLORyOdv68CmKEggEHbc8eXUpvV6P9fI0zs3NzWw7UgKB4JnWY1ZodKsy7v7rZv3/TfKd6G/Xq7H1Kj6fTxAEO+uTRqd//9idM+WNu6cF+9j28AUCiqL4fH5ra2vPPi37EQRhYWHR3NzMdCDGhuO4lZVVYyMXhxZKJJKmpiYOFmYrK6u2tja2DWni8/lC4Z++0Ax3ZWdmZh48ePD//u//pkyZcvLkyfXr18+YMaP3wtJqtWw7UjqdDsOwpxwjcO1B66LDpZ7W/Mw3+jtZ8tg2suCZkCSJ4zhrU0iJ6bPhSlXMltxf4gOG9uiVAhzH9Xo9axPvVdxMnB49w8HEaVqtlm3NIePQarVsO+k8XucbdgwP7Fq5cmVcXNyFCxcUCkVxcfHGjRt7PzaT9McNUTtvvRHmvCsxyAluiOp988Nd/vZi3+l7ig4UNjAdCwAA9DzDLea6urro6OhPP/30pZdecnV1vXfvnpHDMgkVTcqFh0o1On36rAE+toInPwD0kL8E2rmIeTP3FZXUy2DFCwCAmTFcmIcOHZqUlJSRkXHp0qWVK1c6OfXuLaSmaNeNuk9OlicNclo2yp2CFaKMbqi7+OjM/q/uKqxuVX0dAyteAADMh+GKsm7duoiIiCNHjri5uZEkuWPHDiOHxWb1MvWMvUUpZ+9tnxqwPMoTqjJT+toIfns9pKBGNn13YauKXWMUAADguRkuKlZWVmPHjkUI5eXljRkzZsGCBcaNir0yyhqjfsq35pOn54T27OAj8BwcLaiDrwULKCIureBBi4rpcAAAoAcY7spesGBBVlZWdXX1wIEDi4qKkpOTjRwWC8nVujVZvx8obPhmgk+Mnw3T4YA/CCl8y5R+yzPKY7fe2PHXwGBH0ZMfAwAALGa4xXz48OGbN2++8847X3755ZUrVwoKCowcFttcfdAS9VNe2UNF5hsDoCqzDYFjX473Xhzh+pdtN0/BihcAABNnuDA7ODgghEJCQi5fvuzp6Xn37l3jRsUiGp3+6+x7CTsK54Q770wMhBuiWGt+uMu6ST5zD5T8klvDdCwAAPD8DHdlT5gwYfLkyVu2bImOjq6trbW3tzdyWCxR2iBfeKiUxPGMNwb0tYEbothuYj/bfdOCXttTVFQn+yLau5cnrAMAgF5huMX8v//7v2vXrnVxcdm4caOFhcX3339v5LAYp9ejzVfux24teNHX5sjrwVCVTcUgF8vjSf1PlzfNO1ii1HJuxkEAgBkwXJgxDLt8+fK8efOGDh0aHR3t4+Nj5LAYVy9Tbbte9a/pQR+M8iDgHlmT4mnNP/J6SH2betquwiYFu+beAwCAJzLclb1y5cqcnJzy8nIMw5YvXx4ZGblmzZpHPYVGo1m3bl1ra6unp+esWbPojTKZ7KuvvtJoNBYWFh988EFzc/PSpUsdHR0RQkuWLHF1de2FXHqSgwUvY144B5fcMQ8SAbn71cAlR8smpd3c8dcAD2s+0xEBAMDTMtxi3rlz5+7du93c3AiCOHbsWFpa2mOe4uLFi56enitWrKirq6usrKQ3ZmZmDhw48KuvvvLx8Tlz5kxdXd3EiRNTUlJSUlLYX5WBGeAReGqc70sBtrFbb1yv4tyaUQAA02W4MKtUKrVaTf+tUCg6LRXZSWlpaUhICEIoJCSktLSU3ujn5xcVFYUQsrS0pCiqpqbmwYMHqampmZmZPRg9AI+BYeiDUR7Lozyn7iw8VvKQ6XAAAOCpGO7KTk5OfvHFF6VSaUpKyrZt2xYuXPiYp5DJZCKRCCEkFArbVx3v168fQignJ+f8+fPLly8vKCgICQkZNGjQt99+a2dnN2DAAITQ8ePHr127hhCaPXu2jQ27bg4mSRIhZGlpyXQgxkYQBI7j5pT43OGWvs6S6dvzahUoebjno3bDcZwgCHNK/CnhOG5mZ/wp0cvMczBxmoWFBQfXY8YwTCQSsW29S6zLDSSGC/N77703ePDgrKwsrVa7adOm8PDwxzypSCSSy+UIIblc3vFdvnHjxtbW1k8//VQkEg0dOpTeOHbs2JKSErowSyQSNzc3hBCO42xbj5kgCIQQ26IyAnpZYjNLfJSX9Yk5YfG/5t1paPvfWL9HjeYzv8Sfhl6vpyiKg4nT34YcTJym1Wo5WJgRKxeipstNR4YLM0Jo7Nix9HTZTzx5fn5+hYWF/v7+RUVFCQkJ9MZz586RJLlkyRL6nzt27AgKCgoNDa2oqGgf4x0REREREYEQkkqldGlnDwzDMAxjW1RGQH9Nm1/ifcT4sZkhr+0pmrY9758v+QmpzhdxeDweQRDml/gTkSTJ4/E4mDiO4+2NCq6xsLBQKBRsq09GIBQKlUqlRsOumzXoLueODF9jzszMfOeddxBCU6ZMsbKy+vXXXx/zpBEREeXl5SkpKba2th4eHiUlJampqTdu3MjNzV22bNmyZcuys7Ojo6OPHj26cuVKqVQ6fPjwnsoHgKdHr3ih06O/bLtZ16ZmOhwAADAMM9ggHj169IoVKywsLD777LOffvpp2rRpp0+f7r0gpFIp2zqURCIRhmEcvF1KIBBQFNU+VsD8aHX6T0+Wn7gt3fHXwH72wvbtPB5PKBQ2NTUxGBsjSJIUi8VSqZTpQIwNx3FbW9v6+nqmA2GAvb39w4cPOdhitrW1bW5uZmGLuVOj2XCLua6uLjo6+siRIy+99JKrq+u9e/eMEh4AvY7Asa9e9F4w1GXytpvnKpqZDgcAADozfI156NChSUlJGRkZly5dWrlypZOTk5HDAqBXzQ93cRPzk/YVfzm+z19DHJgOBwAA/stwi3ndunURERFHjhxxc3MjSXLHjh0IodzcXOPGBkAvmuRvuzsxcNWp37/OvsfJ0akAAJYyXJitra0XLlwYGhqKEFq+fHmfPn0QQtu2bTNmZAD0tsGulseT+h8obHjr6B2VlnPX2wAA7GS4MAPAEZ7W/KMzQyoaFfG/3miUw1BtAADzoDADrpMIyD2vBjmJeSO+v5BTabbD0QEApgIKMwCIR2AbpwQuGe396u6ipcfKZGro1gYAMAYKMwAIIYRhaO5Qj6w5A35vUr6wKe/CPbiTCgDADCjMAPyXhzV/T2Lgm5GuM/YULz0b2qcjAAAgAElEQVRWJoemMwDA6Azfx6zT6TIyMh4+/GOlPJIk4+PjH7/GFADmAcPQzIFOY7wl7xy9M3pT3ro4n0gPK6aDAgBwiOHCPH36dLlc3rdvX/qfOI7Hx8e3/xMAs+dhzd/7alBaXs1re4qmBNp/Ht2n67oXAADQGwwXZpVKdfDgQSOHAgCrdGo6fxfnG+EhZjooAID5M1yYfX19GxsbJRKJcYKgKIokH7kAJSPoePh8PtOBGBtJkgRBcDNxHMe7Ju7ryD+cNOjnaw+m7yl8baDzZ+N9BKRZNZ0JgsAwjINnnF6PmYOJ03g8HjfXY6Yoquv6x8zC8c5fKYbL4f37911dXSMjI21tbekte/bs6b2wdDod29Y50el0GIaxbc0rIyBJUqfTcTBxHMf1ev2jEp850Cmqj/Wbh0tH/fPK9y/1C3Mzt6YzN8844mTiNK1Wy83CzMLvt67tUsOFedGiRYsWLer9eP6g1WrZdqTowsy21cGMgG44cjBxujA/JnFXS3Lvq4FpeTXx2wuSBjl9OMqDR2DGjLD3PD5xc0UXZg4mTtNqtWxrDhmHVqtl20nn8XidthjulBsxYkRra+tvv/128ODBpqamESNG9H5sALAdfdU5840B1x+0jt2cf+1BK9MRAQDMkOHCvHr16hUrVri6uorF4o8++mj16tVGDgsA1vKw5u+bFjR/iPNfdxWuyapQabnYHwgA6D2YwcsMvr6++fn5IpEIISSTyQYNGlRcXNx7QUilUrZ1ZYtEIgzD2tramA7E2AQCAUVRLS2cmzKax+MJhcKmpqanf0hFk/LtI3fqZep1k3wGuVj2Xmy9iiRJsVgslUqZDsTYcBy3tbWtr69nOhAG2NvbP3z4kINd2ba2ts3NzWzryhaJRHS1bffI8aX0kEVkaMAYAAAh5GnN/9e0oHnhzlN3QtMZANBjDA/+mjFjxvDhw+Pj43Ec37dvX2JiopHDAsAk0Fedo7wlbx+5M25Lvkk3nQEALEGsWrWq69aoqChPT8/i4mKFQjFr1qwFCxYghHJzc52dnXsjCIVCwbaB+xRFYRimVnNugV76PmaVSsV0IMZGEARFUUql8jkeay0gE0McSAJb/NudJqU20sOKwE1mwDZ997ZCoWA6EGPDMEwoFMpkMqYDYYBIJJLL5Wz71jUCoVCoVCrZ1odPURRFUR23PHJaj4kTJ06cOLHjlm3btg0cOLC3QgPAlLU3nd/67fa4LfnfTfIZCE1nAMBzgevHAPQYT2v+/unB88Kd43fAVWcAwHOCwgxAT6KbzqfeGHD1fkv0lvzcKrjXGQDwbKAwA9DzvCT8/dOD50LTGQDw7KAwA9Ar2pvOV+63RG/Jz6vm3D3xAIDn0wNrOmk0mnXr1rW2tnp6es6aNcvgRoP7AGD2vCT8A9OD0/JqXtl+a9Zgpw9HufMI+DUMAHgcw98RdXV1XTcuXLjQ4M4XL1709PRcsWJFXV1dZWWlwY0G9wGAC/7cdL4BTWcAwOMZLszh4eGvvPLKoUOHOt7I27dvX4M7l5aWhoSEIIRCQkJKS0sNbjS4DwDc4SXh758eNDfc+ZXtt9ZkVai07LqTEgDAHoa7ssvKyk6ePJmWlvb2229PmTIlKSkpNDT0UU8hk8noeT6FQmH7HMudNhrcJysr68aNGwihhIQEiUTSo3l1F327t4WFBdOBGBu97CMHEycIgiCI3k584QjLmEDn//lX4Ytbb26MDx7oyvy6zjiOc/OM07MOczBxmkgk4uAEI/SsMmybYKTrvNeGW8wEQcTExHz//ffvv//+pk2boqKiwsLCzp8/b3BnehIZhJBcLre0tDS40eA+AHBQX1vR8TmDF0Z6xP50dfmJ29B0BgB0YrjFvH379l27dl2/fj0uLu63334bOXJkXl7e1KlTb9++3XVnPz+/wsJCf3//oqKihIQEgxspiuq6T1RUVFRUFEJIKpWybR0njq8uxcHE6dWljJZ4YqBkmEv/t367E5lamxrnO8CZsXYbSZIkSXLwjOM4LhAIOJg4Qoiei5RtDUcj4PP5crmchatLddpiuMV86tSpxYsX371794cffhg9ejSO44MGDfryyy8N7hwREVFeXp6SkmJra+vh4VFSUpKamtppY6d/9nBaAJigPhLBgdeC5oY7T4GrzgCADjqvxzxt2jSD++3YsaP3goD1mNkD1mM2/kuXNyre/O12s0L7HRNNZ1iPmelAGADrMTMdyJ90XY+5c1f23LlzjRgPAAD1kQgOvhb8a17tFLjXGQDQtTCPGzcOIaTVagmCaN948eJFowYFAMfgGDZzoNMwd/FbR+6cvC1Nnezb34mjo4UBAIZ/mE+fPp1u7NfX18+bN+/VV181blQAcJG/vejo6yHxwfaTf70JV50B4CzDhTk4ODg+Pn79+vUDBgxwdHS8deuWkcMCgJsIHHsr0u3fSSFnf28av+XGjRrOjXIAABguzCtWrBg1atSHH36YkZHxxRdfdB3MDQDoPf72oiMdms5qaDoDwCWdrzG///777X87OjomJyeHhYUhhFJSUowaFwDcRuLYW5FuL/ravHXkTsYdaWqcX4gT/D4GgBM6F2Z6RuuufwMAjC/AQXR0ZsgPlx7E/VowJ8x52Sh3CgZsA2DuOhfmpKQk+o8tW7acP39+/fr1OTk5kZGRRg8MAIAQNJ0B4B7Dv75Xrly5Z8+ec+fOYRi2fPny5cuXGzksAEBHdNP5lSD7uF8L4KozAObNcGHeuXPn7t273dzcCII4duxYWlqakcMCAHRCN52Pzww5U94U9+vNkno50xEBAHqF4cKsUqnaV2JWKBQCgcCIIQEAHinAQXRsZsikfrYTfilYd+G+Vse5lfsAMHuGC3NycvKLL7549+7dlJSUUaNGLVy40MhhAQAehW46//Z68OHih5PSoOkMgLnpvIhFu1OnTmVlZYlEoujo6PDw8F4NoqWlhW3TqdOdBAqFgulAjI3H45EkKZPJmA7E2CiK4vF4prVsiUanT71Q+Y+z994d6fFmpDuBY8/xJARBiEQiDi5bgmGYlZUVI8uWMM7a2rq5uflRX/5mTCwWy2Qytq2ZRK+g03HLI2+9+P3336uqqt577732Pm0AAKuQOPbOCI9/vzHwYGF9zJa84jrO/ZwCwCx1vl2KtnLlypycnPLycnpUdmRk5Jo1a3ovCI1Gw7afMBRFYRjGwR8lBEHgOM7BxDEMoyjKFBP3lVBHXw/+4dKD8Ztz3450TR7m+kxNZ71er9frTTHxbsJxHCHEwcRpGo2Gbf2UxqHRaNi27CNFUZ22wKhsAExe+1XnQ0UNcb/eLG2Aq84AmDAYlQ2AmQh0EB1P6j/BzyZ2KwzYBsCEwahsAMxHe9P5IDSdATBZhq8xv/fee4MHD87KytJqtZs2bertUdkAgB4U6CD6d1L/Hy49iN1a8BxXnQEAzDJcmF9//fUJEya8+eabDg4ORg4IANB9dNN5vK/N4t9uH78t/Xaij5+d8MkPAwCwgOGu7Ojo6IyMjBEjRgwdOnTFihXnz583clgAgO6jm86xvnDVGQBT8sgJRhBC9fX1O3fu/PrrrysrK3t1YL1UKmXb7VIikQjDMNOabqJHCAQCiqI4ON0EfY+/uU43catO9uZvt/kkvm6ij++fm84kSYrFYqlUylRsTMFx3NbWtr6+nulAGGBvb//w4UMO3i5la2vb3NzMttulRCKRSPSnJeMMt5gXLVrUv3//sWPHFhcXf/PNNzU1NUYJDwDQK4IcRMdnhsT62sRA0xkA1jN8jTkvL0+hUIwfPz4yMnLo0KFwpRkAU0cR+FuRbi94S9787Xb6Hem6Sb7eNnAbJCfI1bqqVlVtq/p+s7JOpr7fpFTj98f3sRzjbYVjMCqQjQwX5nPnzikUipycnNOnTy9btgzDsN9//93IkQEAelyos8XJ2f3/frZy3Jb8j0Z7zglzYjoi0ANUWn1Nq6q6VVXTqq5qUVW3qqpb/vvPZqXGRkg6WfJcxDwnS56LJeUsFn504i6O6eeGOb86wNGSRzCdAfgTw4X56tWrp0+fzsrKys3NDQsLi4mJMXJYAIBewiPwj1/wfDnIfvHh2/tv1f/wF/9BYjHTQYEna1RoqltUNa2q6lZ1exn+vVFR3aqua1NTOHIW85wsec6WPCdLyt9eOLqPtbMl5WTJc7Pii/l/Kr329vbJYXany6QbrlR/eebelED7/xnqAuP22cPw4K/w8PCYmJiYmJjhw4eTpOHi3YNg8Bd7wOAvpgMxHpVWn5J9b/O1mndGe7sKkbWAtBYQVnzCWkBa80kh9cgVbswDCwd/1bWpa9vUD1qUta3qB3/UYLrt+9/S62zJcxbznCx4rlY8RwvK1YrvaEG5inkWz9Lq7Tj4q6hOtvFK9b5b9aO8rOeFO4/uY91r+THPVAZ/PW5U9lPSaDTr1q1rbW319PScNWsWvVEmk3311VcajcbCwuKDDz5obm5eunSpo6MjQmjJkiWurq4dnwEKM3tAYWY6EGPLr5X/dK22pknWrNI2KTRNCm2TQqPQ6HgEbsUnrAWEtYC0FpBWfMKaT0qEf/xBb/+jigtIawHJJ0zsaiUjhZlu9f5RbtvUNS2qqlZVbZv6QbOqrk2tR8hBRLpZ8x1Ef1RcF0uqvQvaRthjbaSuo7Klck1abs2W6zViHjEv3HlqiIOANMNfZhwqzGfPnq2urk5ISEhJSZk2bZq7uztC6MiRIwqFIj4+fseOHQ4ODm5ubvn5+YmJiQafAQoze0BhZjoQY3vU7VIKja5RoWmUa5qU2ka5plGhaVJq/vhDoW1UdPhDrlZq9QghPoFJhJREQFoLCImA/O8fQtKaT/7nD0IiJG2FFI/pQt5LhVmp1f/n+u4fvc3VLaqaNnV1i+pBi7JFqZUISKc/Wr2Us5jnZEk5W/7RBe1uxTPOHG2Pul1Kp9en327ceLX6+oPWxP72C4e6eljzjRCP0XCoMG/ZsiUyMjIgIODYsWMCgWDMmDEIoZKSEjs7Ozs7u8OHD1tZWen1+uvXr1MUFRwcTO+AEMrKyrpx4wZCKCEhQSKRdDOMnkWvw8XBJeFIksRxXKVSMR2IsREEQZKkUqlkOhBjw3Gcx+MpFIruPIlcrVVq9XK1tlGukcrVf/xXof7zPzX0H1K5WqnRCUhcIqRshJSAxAQUIRGQNkJKIvzjvxIB1fGfdiKKR/RwAw7DMIFAIJc/83TiSo3uoVxd3aKqalZWtyqrmpVVzcqqFmV1i7KqRVXdohSQuIsV31nMdxHzvW2FzmKei/iPf7pb86meTuQ5CIVChULxmC//vKqWjZcqd+VXj/a2SY70GOtrZ8zweo9AIFCpVGy7gRvHcT7/Tz+AeqBvRCaT0dVeKBS2t7T69euHEMrJyTl//vzy5csLCgpCQkIGDRr07bff2tnZDRgwoPuvCwBgCSFFCCkkEZAu4qdqYMnV2vY6rVBrFRp9x0J+96G8YyFvkKnV2v8WcomQlAhIIUUISPxRhdxeRHWz/jUqNFXNyqpmRXmjolPprW1V8QisvfQ6i3netsJIL4mLmOdiJXC35ov5vT4up7eFuohTXw5cOd7nl6sP/md/oZ2ImjfMfVqos5CC8dvG8Pwt5pMnT968eTMiIuLWrVvDhw/39/fv2GJGCG3cuLG1tXX+/PkWFhbtj8rMzGxoaEhISOj4VNCVzR7Qlc10IMZmEjN/tSi1jQpNs5K+Cq5pUmiaVVr6cnhT+3alpkmhpf9GCP3nEjh9Rbzj5XDCikdYC0iJkHK2tym9X9fp5qKaVlWdTM0ncBcxz8mSchXz6TFWDhaUmxXP0YLnKuaZ+si4Z5r5S6XVHSp6uCGnqrJZOWuQ06zBzo4WVG9H2EtMpSv7+X/ZRUdHR0dHI4TUanVhYaG/v39RUVF7xT137hxJkkuWLKH/uWPHjqCgoNDQ0IqKCh8fn+d+UQAAB4n5RKcbfh7vz1Vc26zUNik1TQqNVK7+vVFBV/RmpVahvWsrwJ0seS5iyksiGOZu5WxJ0SOfrQUm3+rtKTwCTwi2Twi2z6ls2XClasj6a5P87eaHOw90sWQ6NLPVM6OyU1NT1Wq1o6NjUlJSSUnJiRMnSJIsLCwUCoUIoUmTJgUEBGzatEmhUNjY2Lz11ls4/qffm9BiZg9oMTMdiLGZRIu5N7Dwdimj6c5c2bVt6p+vVW++Vu1uxZ8X7vJKkB0brpo/JVNpMfdAYe4+KMzsAYWZ6UCMDQoz04EwoPuLWKi0+v236tdfflDXpk4a5DQ33MW25+7m6j2mUphN5pcOAAAAluARWGJ/h6w5oZtf8S+ulw/6/mry4du36mRMx2UmTOA3DgAAAHYa5i4e5i6ublX9cr3m5W03/eyEC4a4TOxnSxrlhmxzBS1mAAAA3eJsyftglMeNN8NnDnRKOVs57J/X1124L5Wzq8fYhEBhBgAA0AP4BJbY3yF7bugPL/nlVbeFr7++9FhZEfRvPzvoygYAANCT6P7tu1LFr3m1L227OcDZcuZAx0n9bI0z4agZgBYzAACAnudtI1ge5Xlt0eCXAmz/98y9yA256y7cp6d/AY8HhRkAAEBvseQRMwc6nZ0XmhLjfamyZdAP15YeKytteOYpyjkFurIBAAD0LhzDXvCWvOAtufNQ8dPVqvE/3wh3E88Lc37R1waD7u0uoMUMAADASHxsBV+O985fPPhFX8lH6XeHb8zdcKVKrmbXck+Mg8IMAADAqKz45PxwlysLB60a43XidmP/1Ksfp9+tbOLcoquPAoUZAAAAA3AMi/Gz2ftq4P7pQUqNfsTG3Nf2FJ2+28h0XMxjxTVmkiQ7LWvBOIIgEEIUZaqrmz03giBwHOdg4iRJYhjGwcQJguBm4hiGIU5+xmkkSbJhoQTaYHfJYHfJ8nHe23Jr3jpyx8GSNzvM5dUBTgKy5+sC/Unv8aftjq7xsKscAgAA4CYHC947Izxy3xq6ZITHjrzaAesur8q4W9WiYjouBrCixazRaNi2uhRFURiGqdVqpgMxNrrFzMHE6VYjBxPX6/V6vZ6DidO9dBxMnKbRaLqzulTvwRCa5CeZ5CfJq27bkFMVlno52sfmf4a4DHEX98jzazQatq0u1bXbBlrMAAAAWCfU2eL7yb6X/2eQv71wxt6i6C35u27UqbVs/DHR46AwAwAAYCmn/yyPMS/c5YdLDwb9cP3r7HsPzX15DCjMAAAAWI1e/vn03NCfpvRrX/650HyXx2DFNWYAAADgiejlMSqalFuv1/zlP8s/m9/yGNBiBgAAYEo8rfnLozyvLxqc2N8h5WzlsB/NbflnKMwAAABMjwWPmDnQ6cyc0LWxfS9VtoStv7b0WFlxvTn0b0NXNgAAAFOFYYheHuOuVLHxSlXs1oIw018eA1rMAAAATJ63Db08RthLAbarMn+P+PH6hitVMtNcHgMKMwAAADMh5hMzBzqdmxf6dYz36btN/b+7+nH63XumtjwGdGUDAAAwK+3LP99ukG++Vj1qU94IT6v54c4v29gyHdpTgRYzAAAA8+RrJ/xyvPe1RYOHuIvfPHInaWcu0xE9FWgxAwAAMGe2QvKdSLfkoS4K0gIhE5gdvQcKs0ajWbduXWtrq6en56xZs+iN9fX1S5cudXR0RAgtWbLE0dGx6z4AAACAcVAE7mQjbG7mRmG+ePGip6dnQkJCSkpKZWWlu7s7Qqiurm7ixImJiYn0PmfPnu26DwAAAAA66YHCXFpaGhkZiRAKCQkpLS2li25NTc2DBw9SU1ODg4PHjBljcJ+LFy8WFxcjhGJjYy0tLbsfSQ+i1+ESCoVMB2JsFEXhOM7BxOn1LjmYOI7j3EycXp2eg4nTBAKBXq9nOgpjwzCMz+d3XWaRWfQKpB31QGGWyWQikQghJBQKW1pa6I0ikSgkJGTQoEHffvutnZ2dwX0aGxvv37+PENLpdARBdD+SHkR/aNkWlRFgGIZhGAcTx3Gcs4kjrr7VEScTpxEEwcHCjBAiCAJj2cwjXeN5/sJ88uTJmzdvRkREiEQiuVyOEJLL5e0N36FDh9J/jB07tqSkxOA+sbGxsbGxCCGpVNra2vrckfQGkUiEYVhbWxvTgRibQCCgKIptp8MIeDyeUCjkYOIkSYrFYg4mjuM4n8/nYOIIIYFA0NbWptOZ5OQb3cHj8WQymUbDrlm16VZrR89/u1R0dPTbb789bNgwPz+/wsJChFBRUZGvry/9f3fs2JGXl4cQqqiocHZ2NrgPAAAAADrBut+bodFoUlNT1Wq1o6NjUlJSSUnJiRMnEhMTN23apFAobGxs3nrrLZ1O13GfTs/Q1NTEtt9uarVar9fzeDymAzE2jUaj1Wr5fD7TgRibVqtVqVQcvOKo1WoVCoWFhQXTgRibXq9va2tj2+gW42hra6M7BZkOxNja2toEAgHbrl8IBIJO3zw9UJjN0g8//KBSqd555x2mAzG2PXv2XL9+/csvv2Q6EGM7c+bML7/8smnTJqYDMbbCwsIPP/zw0KFDTAdibA0NDXFxcRcuXGA6EAYMGzbs2LFjtramMQ1WD5o8efLf//53f39/pgN5Apj5CwAAAGARmPnLsODgYLYNEDAOHx8ftt1LYBzOzs6jR49mOgoG2NjY0GMwuUYgELz88stMR8GMl19+WSAQMB0FA2JjYyUSCdNRPBl0ZQMAAAAsAl3ZAAAAAItwqytbr9evX7/+/v37SqUyIiIiISHhKR946tQpiqJGjRrVq+H1nuzs7B9//PGXX36hZ5NYv369VCr9+OOPn+axppt7ampqTU3N3bt33d3dKYqaP3++h4fHMz0D+3P//PPPp02b5uPjc+LEiezs7DVr1iCEFi5cuHbt2q43R6JuZMTyQ9HpXHt7e/v5+T1ftCzP9GmUlJR8+eWX7e/2Dz/8kB58fuDAAYFA0H7lwtQzfe7v805YeBy4VZivXLmCYdgXX3yBEPrkk09Gjhzp7OzMdFBGwuPxCgsLg4OD9Xp9WVmZjY0N0xH1usWLFyOE1qxZs2jRIjs7O6bD6RVBQUElJSU+Pj4FBQVSqVStVqtUKoqiDFZlM9bpXKenpz/xIXK53IzvjgsPD6ePSTu5XG5m19Sf+H2el5d3+/bt+Pj4ro9l+dnnVmGWSCSlpaWFhYUBAQH06Tx8+LCLi0t4ePiOHTsGDhxYWVlZVlZGUVRNTc17773X0NCQkpIikUg0Gk10dLRCofjmm2/0er21tXVycvLf/va3uXPn2tnZLV++/KOPPmL5V+GwYcMuXrwYHBx8584dX1/fhoaGpqamlJQUtVrt5OT0zjvvZGRkmGvutE7n2tPT85tvvlGpVPb29osXL37w4EFaWppWqx05cmRAQICp5B4UFHT8+PEJEybU19dHRkYWFRXpdLrAwMC2traO2dXW1nbMKD09veO5VqlUZnAoOsnOzj59+rRGo/n000+PHTvW6WN+7do1gUAQHx9vBpk+UXp6Op1vUFCQQCDw8/Mzj0y7fp93yuXkyZMPHjyor68PCwvrevYTExNZexyIVatWGfP1mGVnZ+fl5XXq1Km0tLTa2trg4ODbt2+LxWJXV9eCggJnZ+fm5maZTDZ79uzKykqE0NGjRydOnDht2rScnBwPDw+Konx9fadMmXL69Gk/Pz8+n19RUWFnZ5ebmzt27Fimk3uciooKPp9fVFQUFRV17NixwYMHl5aW1tTUhIaGzps3Lz8/X6fTKZVKs8z9zJkzQ4YMEYlEJSUlHc/12bNnQ0JCZs2ade/evaqqqqKiogEDBkybNq2qqurkyZOmkrtEItm9e3doaGhlZeWwYcPy8/Obmpq8vLxyc3M7ZpeVldUxI41G0/FcX7hwwQwOBepwrsvKypRK5bvvvltRUUFRVGNjY6ePuVwuT05OPnr0qIlm+ngNDQ27du26evVqZmZmbW0tPSNycnJyWVkZSZIZGRnmkWnX7/OampqOuTg6OlpYWEgkEoNnf/Pmzaw9DtxqMdfU1Hh6eiYnJysUii+++OLy5cvt/6t96rG+ffsihIRCoVarrampCQ4ORgjRN6SLRKK9e/f++9//Li8v1+l04eHh69at0+l0I0aMYCKbZ+bt7V1eXl5WVjZ58mSEUFVV1bhx4xBCgYGBVVVVFhYWZpx7R/S5rqqqKisroyeO9fT0DA0NTUtLO378eGxsrAnlTpKkUCikK6u/v//u3btFItHo0aPz8vI6ZtcpI/Tn97l5HIpO6ICtrKy0Wm37xvaPeb9+/RBC48aNM4NMDerYlZ2enk7nSzObTLt+n/v7+3fMpdP+nc4+m48Dt0ZlX758ef/+/QghgUDg4+OjVqtxHKdnsS8qKqL36ThNnbOzMz3Fd0lJCULo4MGD48ePf/vtt+3s7PR6vaWlpUajOXfu3LBhwxhI5tlFREQcPHjQycmJztHJyYnOq7i4mL42Y8a5I4Q6nWs3N7ewsLDFixcPHjzY3d29pKRk5syZy5cv37Nnj2nlHhgYePjw4ZCQEB6Ph+N4bW2to6Njp+w6ZYT+fK7N5lB01HHaxa4fc5IkEULmkenToPOlmU2mXb/PO+WCENLr9Y86+2w+Dtzqyvb29s7MzNy1a1dWVhafz09ISLCzs9u2bdulS5dsbGwCAgIaGxtJkvTy8rpz546Tk1NYWNiPP/54+vRpsVjct2/fPn36HDx48MqVKxKJpLKyMjQ0tKWlpampKSoqiunMnqCiooIeuPjPf/4zISHBxsbm8uXLM2bMSEtLy8jIIEkyPj7+7t27Zpl7e/emra1tx3M9aNCgXbt2nT17trW1dcyYMW1tbWlpaVevXg0ICIiLizOh3NVqdV5eXmJiIkKotrZWq9WOGDHCy8urY3ZeXl4dM1IoFJ3OtXkcio5d2XSCRUVFNjY2gYGBBj/mppvp4zU0NNy5c6d9ib/2o0H/MWrUKPPItOv3OZ/P75hLSEjI3r17R02t4oYAAARSSURBVI8efejQoa5nv9OHglXHASYY6Zb9+/c7Ojqyv8+nN0Du3My9E+4cCsiUaxg8Dtzqyu5ZmZmZBQUFERERTAfCAMidm7l3wp1DAZlyDbPHAVrMAAAAAItAixkAAABgESjMAAAAAItAYQYAAABYBAozAKZBqVRiGObi4uLs7Ozm5jZnzpyWlpYeeebZs2c/9wIAnWzduvX999/vkacCgLOgMANgSqqqqqqrq2/fvi0QCGbPnt39J5TJZOnp6Xv37u3+UwEAegQUZgBMj1Ao/Mc//pGdnX3//n29Xv/hhx+Ghoa6uLgsWLBAr9fPnTt3+/btCCGtVuvl5VVXV9fxsatWrfL19e3Xr99nn32GEFq8eHFDQ8Mbb7zRvkN4ePi1a9cQQsOHD1+0aBFCaOvWrTNnzkQIrV27tm/fvgEBAe0TE3XdQlu9enViYmLH6TABAE+JW3NlA2A2+Hx+UFBQcXFxY2NjXl7elStXEELBwcElJSWJiYmpqanTp08/efJkWFiYg4ND+6OOHj164MCB/Px8hNCYMWOGDRv23XffZWZmbt68uX2fmJiYrKysoKCgurq67OxshNCZM2diY2NPnTq1c+fOK1euUBSVmJi4ZcsWLy+vTlvoBb/Xrl177dq1ffv2dZwXEwDwlKDFDIAJwzAsODj4559/PnDgwOeff15dXa1QKMaOHXv9+vWmpqa0tLRZs2Z13D8rKyshIUEkEolEohkzZmRlZXV9Trow5+TkjB8/HsOw+vr67Ozs8ePHZ2VlSaXSxMTEV155pby8/Ny5c123IIQOHDiwevXq2NjYjvMzAwCeHnxyADBJKpXq1q1b/fr1u3Dhwty5c994441x48ZlZmYihAiCiIuL27Zt27lz57Zs2dLxUfSc/vTfjY2NBruahw8fnpube/r06ZEjRxIEsXPnTolE4uDgIBKJFixYQI/t0mq1er3+73//e6ct27Zt8/DwOHTo0Lhx46ZOnWpvb9/rBwIAswMtZgBMj1KpfP/990eOHOnm5nby5MnJkycvXbq0T58+RUVF9FDtV199ddmyZVOmTKEoquMDX3jhhX379ikUCrlcvnfvXoMT9JMkGRYWtmHDhlGjRkVFRX399dexsbEIoXHjxm3fvr2lpUWtVsfExOTk5HTdghAKCwsLDAycPXv2Rx99ZIxjAYDZgcIMgCnx8PBwd3f38fFpbm7++eefEULTp0+/fv16WFjYu+++m5ycvGbNGoQQ3dhNSkrq9PC4uLiJEyeGhoaGhoZOmTJl4sSJBl+FrsQeHh6jR4+urKyMiYlBCA0ZMiQpKWnIkCG+vr4RERGRkZFdt7Q/wyeffJKenn7x4sXeOQwAmDOYKxsAM3T9+vW5c+devXqV6UAAAM8MWswAmJvdu3dPnTr1u+++YzoQAMDzgBYzAAAAwCLQYgYAAABYBAozAAAAwCJQmAEAAAAWgcIMAAAAsAgUZgAAAIBF/h893ifFy+sV1gAAAABJRU5ErkJggg==\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 12 -u in\\n\",\n \"m <- prophet(weekly.seasonality=FALSE)\\n\",\n \"m <- add_seasonality(m, name='weekly_on_season', period=7, fourier.order=3, condition.name='on_season')\\n\",\n \"m <- add_seasonality(m, name='weekly_off_season', period=7, fourier.order=3, condition.name='off_season')\\n\",\n \"m <- fit.prophet(m, df)\\n\",\n \"\\n\",\n \"future$on_season <- is_nfl_season(future$ds)\\n\",\n \"future$off_season <- !is_nfl_season(future$ds)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"prophet_plot_components(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(weekly_seasonality=False)\\n\",\n \"m.add_seasonality(name='weekly_on_season', period=7, fourier_order=3, condition_name='on_season')\\n\",\n \"m.add_seasonality(name='weekly_off_season', period=7, fourier_order=3, condition_name='off_season')\\n\",\n \"\\n\",\n \"future['on_season'] = future['ds'].apply(is_nfl_season)\\n\",\n \"future['off_season'] = ~future['ds'].apply(is_nfl_season)\\n\",\n \"forecast = m.fit(df).predict(future)\\n\",\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Both of the seasonalities now show up in the components plots above. We can see that during the on-season when games are played every Sunday, there are large increases on Sunday and Monday that are completely absent during the off-season.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prior scale for holidays and seasonality\\n\",\n \"If you find that the holidays are overfitting, you can adjust their prior scale to smooth them using the parameter `holidays_prior_scale`. By default this parameter is 10, which provides very little regularization. Reducing this parameter dampens holiday effects:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" ds playoff superbowl\\n\",\n \"17 2014-02-02 1.21154 0.9481949\\n\",\n \"18 2014-02-03 1.85564 0.9925750\\n\",\n \"19 2015-01-11 1.21154 0.0000000\\n\",\n \"20 2015-01-12 1.85564 0.0000000\\n\",\n \"21 2016-01-17 1.21154 0.0000000\\n\",\n \"22 2016-01-18 1.85564 0.0000000\\n\",\n \"23 2016-01-24 1.21154 0.0000000\\n\",\n \"24 2016-01-25 1.85564 0.0000000\\n\",\n \"25 2016-02-07 1.21154 0.9481949\\n\",\n \"26 2016-02-08 1.85564 0.9925750\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df, holidays = holidays, holidays.prior.scale = 0.05)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"forecast %>% \\n\",\n \" select(ds, playoff, superbowl) %>% \\n\",\n \" filter(abs(playoff + superbowl) > 0) %>%\\n\",\n \" tail(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
    dsplayoffsuperbowl
    21902014-02-021.2060860.964914
    21912014-02-031.8520770.992634
    25322015-01-111.2060860.000000
    25332015-01-121.8520770.000000
    29012016-01-171.2060860.000000
    29022016-01-181.8520770.000000
    29082016-01-241.2060860.000000
    29092016-01-251.8520770.000000
    29222016-02-071.2060860.964914
    29232016-02-081.8520770.992634
    \\n\",\n \"
    \"\n ],\n \"text/plain\": [\n \" ds playoff superbowl\\n\",\n \"2190 2014-02-02 1.206086 0.964914\\n\",\n \"2191 2014-02-03 1.852077 0.992634\\n\",\n \"2532 2015-01-11 1.206086 0.000000\\n\",\n \"2533 2015-01-12 1.852077 0.000000\\n\",\n \"2901 2016-01-17 1.206086 0.000000\\n\",\n \"2902 2016-01-18 1.852077 0.000000\\n\",\n \"2908 2016-01-24 1.206086 0.000000\\n\",\n \"2909 2016-01-25 1.852077 0.000000\\n\",\n \"2922 2016-02-07 1.206086 0.964914\\n\",\n \"2923 2016-02-08 1.852077 0.992634\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = Prophet(holidays=holidays, holidays_prior_scale=0.05).fit(df)\\n\",\n \"forecast = m.predict(future)\\n\",\n \"forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\\n\",\n \" ['ds', 'playoff', 'superbowl']][-10:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The magnitude of the holiday effect has been reduced compared to before, especially for superbowls, which had the fewest observations. There is a parameter `seasonality_prior_scale` which similarly adjusts the extent to which the seasonality model will fit the data.\\n\",\n \"\\n\",\n \"Prior scales can be set separately for individual holidays by including a column `prior_scale` in the holidays dataframe. Prior scales for individual seasonalities can be passed as an argument to `add_seasonality`. For instance, the prior scale for just weekly seasonality can be set using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet()\\n\",\n \"m <- add_seasonality(\\n\",\n \" m, name='weekly', period=7, fourier.order=3, prior.scale=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = Prophet()\\n\",\n \"m.add_seasonality(\\n\",\n \" name='weekly', period=7, fourier_order=3, prior_scale=0.1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"### Additional regressors\\n\",\n \"Additional regressors can be added to the linear part of the model using the `add_regressor` method or function. A column with the regressor value will need to be present in both the fitting and prediction dataframes. For example, we can add an additional effect on Sundays during the NFL season. On the components plot, this effect will show up in the 'extra_regressors' plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 9 -h 12 -u in\\n\",\n \"nfl_sunday <- function(ds) {\\n\",\n \" dates <- as.Date(ds)\\n\",\n \" month <- as.numeric(format(dates, '%m'))\\n\",\n \" as.numeric((weekdays(dates) == \\\"Sunday\\\") & (month > 8 | month < 2))\\n\",\n \"}\\n\",\n \"df$nfl_sunday <- nfl_sunday(df$ds)\\n\",\n \"\\n\",\n \"m <- prophet()\\n\",\n \"m <- add_regressor(m, 'nfl_sunday')\\n\",\n \"m <- fit.prophet(m, df)\\n\",\n \"\\n\",\n \"future$nfl_sunday <- nfl_sunday(future$ds)\\n\",\n \"\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"prophet_plot_components(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def nfl_sunday(ds):\\n\",\n \" date = pd.to_datetime(ds)\\n\",\n \" if date.weekday() == 6 and (date.month > 8 or date.month < 2):\\n\",\n \" return 1\\n\",\n \" else:\\n\",\n \" return 0\\n\",\n \"df['nfl_sunday'] = df['ds'].apply(nfl_sunday)\\n\",\n \"\\n\",\n \"m = Prophet()\\n\",\n \"m.add_regressor('nfl_sunday')\\n\",\n \"m.fit(df)\\n\",\n \"\\n\",\n \"future['nfl_sunday'] = future['ds'].apply(nfl_sunday)\\n\",\n \"\\n\",\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NFL Sundays could also have been handled using the \\\"holidays\\\" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known.\\n\",\n \"\\n\",\n \"[This notebook](https://nbviewer.jupyter.org/github/nicolasfauchereau/Auckland_Cycling/blob/master/notebooks/Auckland_cycling_and_weather.ipynb) shows an example of using weather factors as extra regressors in a forecast of bicycle usage, and provides an excellent illustration of how other time series can be included as extra regressors.\\n\",\n \"\\n\",\n \"The `add_regressor` function has optional arguments for specifying the prior scale (holiday prior scale is used by default) and whether or not the regressor is standardized - see the docstring with `help(Prophet.add_regressor)` in Python and `?add_regressor` in R. Note that regressors must be added prior to model fitting. Prophet will also raise an error if the regressor is constant throughout the history, since there is nothing to fit from it.\\n\",\n \"\\n\",\n \"The extra regressor must be known for both the history and for future dates. It thus must either be something that has known future values (such as `nfl_sunday`), or something that has separately been forecasted elsewhere. The weather regressors used in the notebook linked above is a good example of an extra regressor that has forecasts that can be used for future values. \\n\",\n \"\\n\",\n \"One can also use as a regressor another time series that has been forecasted with a time series model, such as Prophet. For instance, if `r(t)` is included as a regressor for `y(t)`, Prophet can be used to forecast `r(t)` and then that forecast can be plugged in as the future values when forecasting `y(t)`. A note of caution around this approach: This will probably not be useful unless `r(t)` is somehow easier to forecast than `y(t)`. This is because error in the forecast of `r(t)` will produce error in the forecast of `y(t)`. One setting where this can be useful is in hierarchical time series, where there is top-level forecast that has higher signal-to-noise and is thus easier to forecast. Its forecast can be included in the forecast for each lower-level series.\\n\",\n \"\\n\",\n \"Extra regressors are put in the linear component of the model, so the underlying model is that the time series depends on the extra regressor as either an additive or multiplicative factor (see the next section for multiplicativity).\\n\",\n \"\\n\",\n \"#### Coefficients of additional regressors\\n\",\n \"\\n\",\n \"To extract the beta coefficients of the extra regressors, use the utility function `regressor_coefficients` (`from prophet.utilities import regressor_coefficients` in Python, `prophet::regressor_coefficients` in R) on the fitted model. The estimated beta coefficient for each regressor roughly represents the increase in prediction value for a unit increase in the regressor value (note that the coefficients returned are always on the scale of the original data). If `mcmc_samples` is specified, a credible interval for each coefficient is also returned, which can help identify whether the regressor is meaningful to the model (a credible interval that includes the 0 value suggests the regressor is not meaningful).\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Edit Metadata\",\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.17\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/trend_changepoints.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"logging.getLogger('numexpr').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"\\n\",\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"m <- prophet(df)\\n\",\n \"future <- make_future_dataframe(m, periods=366)\\n\",\n \"m <- prophet(df)\\n\",\n \"forecast <- predict(m, future)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may have noticed in the earlier examples in this documentation that real time series frequently have abrupt changes in their trajectories. By default, Prophet will automatically detect these changepoints and will allow the trend to adapt appropriately. However, if you wish to have finer control over this process (e.g., Prophet missed a rate change, or is overfitting rate changes in the history), then there are several input arguments you can use.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Automatic changepoint detection in Prophet\\n\",\n \"Prophet detects changepoints by first specifying a large number of *potential changepoints* at which the rate is allowed to change. It then puts a sparse prior on the magnitudes of the rate changes (equivalent to L1 regularization) - this essentially means that Prophet has a large number of *possible* places where the rate can change, but will use as few of them as possible. Consider the Peyton Manning forecast from the Quickstart. By default, Prophet specifies 25 potential changepoints which are uniformly placed in the first 80% of the time series. The vertical lines in this figure indicate where the potential changepoints were placed:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"input_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=366)\\n\",\n \"forecast = m.predict(future)\\n\",\n \"fig = m.plot(forecast)\\n\",\n \"for cp in m.changepoints:\\n\",\n \" plt.axvline(cp, c='gray', ls='--', lw=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Even though we have a lot of places where the rate can possibly change, because of the sparse prior, most of these changepoints go unused. We can see this by plotting the magnitude of the rate change at each changepoint:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"input_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FqamqSy+XSI488IqfTqZaWllDUBgAAAISc3xXkdevWqaWlRUVFRbrssst05MgRvfTSS6GoDQAAAAg5vyvII0aMkCQNHDhQP/3pT4NeEAAAAGAnvyvIb7zxhrKysnTNNdcoOTnZ91931NfXKysrS6mpqcrKytKJEyfajDlw4ICmTp2q9PR0jRs3Ts8//3y37hMAAADoCr8BecmSJXrooYf0+uuva9++fb7/uqOwsFCZmZkqLy9XZmamCgsL24yJjo7WH//4R3344Yfatm2bHnzwQX3++efdul8AAADAH79bLAYPHqwbb7wxoHdaUlKiXbt2SZJycnI0ffp0PfHEE63GXHPNNb6vExISFBcXp7q6Ol1++eUBrQUAAAAwdRiQ33nnHUnSjBkztHLlSt16662Kiory3T5hwoRLvtPa2lo5nU5J0vDhw1VbW9vp+LKyMjU1Nenqq69u93aPxyOPxyNJqqmpUVVV1SXXFmx1dXV2l4Ag6U5vnf3OBLAStbkGgj1/X8C12zt19bE/rN/ZLo3ri4/9cNBTr1+em7svWL3tMCA//PDDrY69Xq/va4fDob/97W+dTnzDDTeopqamzfmCgoJWxw6HQw6Ho8N5qqurdc8996i4uFj9+rW/IyQ3N1e5ubmSJLfbrYSEhE5rs1tPrw+X7lJ7W90SHdQ6gj1/X9FXf+7e7GIe+10Zy2Og9+qJveO5OTCC8XN3GJBfffXVbk1cWlra4W3x8fGqrq6W0+lUdXW14uLi2h138uRJ3XTTTSooKNCUKVO6VQ8AAADQFX5fpPdf//VfrV4cd+LECf3P//xPt+40OztbxcXFkqTi4mLNmzevzZimpibdcsstWrRokRYsWNCt+wMAAAC6ym9A3rp1a6sXxl1xxRXasmVLt+40Ly9PO3bsUGpqqkpLS5WXlyfpy20cS5culSS98MILeu2117R27Vq5XC65XC4dOHCgW/cLAAAA+OP3XSyam5t17tw53wv0Ghsbde7cuW7d6dChQ7Vz5842591ut9asWSNJuvvuu3X33Xd3634AAACAi+U3IN91113KzMzUfffdJ0l67rnnlJOTE/TCAAAAADv4DciPPvqoxo8f73vR3apVqzRr1qygFwYAAADYwW9AlqTZs2dr9uzZwa4FAAAAsJ3fF+kBAAAAfQkBGegB4mOj/A+yYS4AAPqiLm2xaGxs1OHDhzVq1Khg1wP0STX5M4M6f3xslGpPde/dZ8y5AAAIZ34D8ssvv6wf//jHampqUkVFhQ4cOKDHHntMmzdvDkV9AAIg2AEcAIBw4neLRX5+vsrKynwfFuJyuVRRURHsugAAAABb+A3I/fv31+DBg1udczgcQSsIAAAAsJPfgJyenq6//OUvam5uVnl5uX74wx/q+uuvD0VtAAAAQMj5DcjPPvusPvzwQ0VFRenOO+/U4MGD9fTTT4eiNgAAACDk/L5I75VXXlFBQYEKCgp851588UXddtttQS0MAAAAsIPfFeRf/OIXXToHAAAAhIMOV5C3bt2qLVu26NixY3rggQd850+ePKnIyC69fTIAAADQ63SYdBMSEuR2u7V582ZNnDjRdz42NlZPPvlkSIoDAAAAQq3DgDx+/HiNHz9ed955p/r37x/KmgAAAADb+N0rUVlZqZ/85Cc6ePCgzp496zt/6NChoBYGAAAA2MHvi/Tuu+8+/eAHP1BkZKReffVVLVq0SHfffXcoagMAAABCzm9AbmxsVGZmpizL0ogRI5Sfn69XXnklFLUBAAAAIed3i0VUVJRaWlqUmpqqoqIiJSYmqqGhIRS1AQAAACHndwX56aef1pkzZ/TMM89o//79Wrdunf74xz+GojYAAAAg5PyuIGdkZEiSYmJi9Nxzz6m5uVkbNmzQ5MmTg14cAAAAEGodriCfPHlSv/jFL7RixQpt375dlmWpqKhIKSkpeuGFF0JZIwAAABAyHa4g33PPPbriiis0depUrVmzRj//+c9lWZY2btwol8sVwhIBAACA0OkwIB86dEh///vfJUlLly6V0+nU4cOHNXDgwJAVBwAAAIRah1sszE/Pi4iIUFJSEuEYAAAAYa/DFeT33ntPgwYNkiRZlqXGxkYNGjRIlmXJ4XDo5MmTISsSAAAACJUOA3Jzc3Mo6wAAAAB6BL/vgwwAAIDAi4+N6pFzoQvvgwwAAIDAq8mfaXcJ6AAryAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAIDBloBcX1+vrKwspaamKisrSydOnOhw7MmTJ5WUlKQVK1aEsEIAAAD0VbYE5MLCQmVmZqq8vFyZmZkqLCzscOyqVav0ne98J4TVAQAAoC+zJSCXlJQoJydHkpSTk6NNmza1O27//v2qra3VzJkzQ1gdAAAA+rJIO+60trZWTqdTkjR8+HDV1ta2GdPS0qKHH35Yf/rTn1RaWtrpfB6PRx6PR5JUU1OjqqqqwBcdIHV1dXaXgCCht+GN/vZOzn5nujRuWL+zXRrXk/99Qce4fsNXsHobtIB8ww03qKamps35goKCVscOh0MOh6PNuNWrV2vOnDlKSkrye1+5ubnKzc2VJLndbiUkJFxi1aHR0+vDpaO34Y3+9j7VLdEBHctjoPeid+ErGL0NWkDubNU3Pj5e1dXVcjqdqq6uVlxcXJsxb731lvbs2aPVq1eroaFBTU1NiomJ6XS/MgAAANBdtmyxyM7OVnFxsfLy8lRcXKx58+a1GfPnP//Z9/XatWvl9XoJxwAAAAg6W16kl5eXpx07dig1NVWlpaXKy8uTJHm9Xi1dutSOkgAAAABJNq0gDx06VDt37mxz3u12a82aNW3O33vvvbr33ntDUBkAAAD6Oj5JDwAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAAMBGQAAADAQkAEAAAADARkAAAAwEJABAAAAAwEZAAAAMBCQAQAAAIMtAbm+vl5ZWVlKTU1VVlaWTpw40e64w4cPa+bMmRo9erTS0tJUWVkZ2kIBAADQ59gSkAsLC5WZmany8nJlZmaqsLCw3XGLFi3SypUr9Y9//ENlZWWKi4sLcaUAAADoa2wJyCUlJcrJyZEk5eTkaNOmTW3GHDx4UBcuXFBWVpYkKSYmRtHR0aEsEwAAAH2QLQG5trZWTqdTkjR8+HDV1ta2GfPRRx/p8ssv16233qrrrrtOK1euVHNzc6hLBQAAQB8TGayJb7jhBtXU1LQ5X1BQ0OrY4XDI4XC0GXfhwgXt2bNH7777rq666ir9+7//u9auXaslS5a0GevxeOTxeCRJNTU1qqqqCtBPEXh1dXV2l4Agobfhjf72Ts5+Z7o0bli/s10a15P/fUHHuH7DV7B6G7SAXFpa2uFt8fHxqq6ultPpVHV1dbt7i5OSkuRyuZScnCxJuvnmm/X222+3G5Bzc3OVm5srSXK73UpISAjQTxEcPb0+XDp6G97ob+9T3dL1rXldGctjoPeid+ErGL21ZYtFdna2iouLJUnFxcWaN29emzEZGRn6/PPPff9n8Le//U1paWkhrRMAAAB9jy0BOS8vTzt27FBqaqpKS0uVl5cnSfJ6vVq6dKkkKSIiQr/61a+UmZmpsWPHyrIsLVu2zI5yAQAA0IcEbYtFZ4YOHaqdO3e2Oe92u7VmzRrfcVZWlt5///1QlgYAAIA+jk/SAwAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAAAMBAQAYAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAy2BOT6+nplZWUpNTVVWVlZOnHiRLvjHnnkEaWnp2v06NF64IEHZFlWiCsFAABAX2NLQC4sLFRmZqbKy8uVmZmpwsLCNmPefPNNvfHGG3r//ff1wQcfaN++fdq9e7cN1QIAAKAvsSUgl5SUKCcnR5KUk5OjTZs2tRnjcDh09uxZNTU16dy5czp//rzi4+NDXCkAAAD6mkg77rS2tlZOp1OSNHz4cNXW1rYZM3XqVM2YMUNOp1OWZWnFihUaPXp0u/N5PB55PB5JUk1NjaqqqoJXfDfV1dXZXQKChN6GN/rbOzn7nenSuGH9znZpXE/+9wUd4/oNX8HqbdAC8g033KCampo25wsKClodOxwOORyONuM+/vhj/eMf/9DRo0clSVlZWdqzZ4+mTZvWZmxubq5yc3MlSW63WwkJCYH4EYKmp9eHS0dvwxv97X2qW6IDOpbHQO9F78JXMHobtIBcWlra4W3x8fGqrq6W0+lUdXW14uLi2ozZuHGjpkyZopiYGEnSjTfeqLfeeqvdgAwAAAAEii17kLOzs1VcXCxJKi4u1rx589qMueqqq7R7925duHBB58+f1+7duzvcYgEAAAAEii0BOS8vTzt27FBqaqpKS0uVl5cnSfJ6vVq6dKkkacGCBbr66qs1duxYjR8/XuPHj9fcuXPtKBcAAAB9iC0v0hs6dKh27tzZ5rzb7daaNWskSREREfrd734X6tIAAADQx/FJegAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAYCMgAAACAgYAMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwACFvxsVE9ci4APVuk3QUAABAsNfkzuzSuqqpKCQkJQa4GQG/BCjIAAABgICADAAAABgIyAAAAYCAgAwAAAAYCMgAAAGAgIAMAAAAGAjIAAABgICADAAAABgIyAAAAYCAgAwAAAAYCMgAAAGAgIAMAAAAGAjIAAABgICADAAAABgIyAAAAYHBYlmXZXUQgDRs2TCNHjrS7jA7V1dXp3/7t3+wuA0FAb8Mb/Q1v9De80d/w1d3eVlZW6vjx423Oh11A7uncbre8Xq/dZSAI6G14o7/hjf6GN/obvoLVW7ZYAAAAAAYCMgAAAGAgIIdYbm6u3SUgSOhteKO/4Y3+hjf6G76C1Vv2IAMAAAAGVpABAAAAAwEZAAAAMBCQQ2Tbtm0aNWqUUlJSVFhYaHc5CLCRI0dq7NixcrlccrvddpeDblq8eLHi4uI0ZswY37n6+nplZWUpNTVVWVlZOnHihI0Vojva629+fr4SExPlcrnkcrm0ZcsWGyvEpTpy5IhmzJihtLQ0paen6+mnn5bE9RsuOupvMK5f9iCHQHNzs6655hrt2LFDSUlJysjI0Pr165WWlmZ3aQiQkSNHyuv1atiwYXaXggB47bXXFBMTo0WLFumDDz6QJD3yyCMaMmSI8vLyVFhYqBMnTuiJJ56wuVJcivb6m5+fr5iYGP34xz+2uTp0R3V1taqrqzVhwgSdOnVKEydO1KZNm7R27Vqu3zDQUX9feOGFgF+/rCCHQFlZmVJSUpScnKwBAwZo4cKFKikpsbssAB34zne+oyFDhrQ6V1JSopycHElSTk6ONm3aZENlCIT2+ovw4HQ6NWHCBElSbGysRo8erWPHjnH9homO+hsMBOQQOHbsmK688krfcVJSUtAaCns4HA7NnDlTEydOlMfjsbscBEFtba2cTqckafjw4aqtrbW5IgRaUVGRxo0bp8WLF/Mn+DBQWVmpd999V5MnT+b6DUNmf6XAX78EZCAAXn/9db3zzjvaunWr/vd//1evvfaa3SUhiBwOhxwOh91lIIB+8IMf6JNPPtGBAwfkdDr18MMP210SuqGhoUHz58/XU089pUGDBrW6jeu39/t6f4Nx/RKQQyAxMVFHjhzxHR89elSJiYk2VoRA+6qfcXFxuuWWW1RWVmZzRQi0+Ph4VVdXS/pyH1xcXJzNFSGQ4uPjFRERoX79+mnZsmVcw73Y+fPnNX/+fN1111269dZbJXH9hpOO+hvo65eAHAIZGRkqLy9XRUWFmpqatGHDBmVnZ9tdFgLk9OnTOnXqlO/r7du3t3p1PMJDdna2iouLJUnFxcWaN2+ezRUhkL4KT5K0ceNGruFeyrIsLVmyRKNHj9ZDDz3kO8/1Gx466m8wrl/exSJEtmzZogcffFDNzc1avHix/vu//9vukhAghw4d0i233CJJunDhgu68807628vdcccd2rVrl44fP674+Hj97Gc/080336zbb79dhw8f1ogRI/TCCy/wQq9eqr3+7tq1SwcOHJDD4dDIkSP1u9/9zrdnFb3H66+/rmnTpmns2LHq1+/LNcCf//znmjx5MtdvGOiov+vXrw/49UtABgAAAAxssQAAAAAMBGQAAADAQEAGAAAADARkAAAAwEBABgAAAAwEZAAwREREyOVyacyYMbrtttt05syZDsfu2rVLb7755iXf1+eff67Vq1f7jquqqrRgwYJOv6eysvKi3uNz+vTp8nq9l1xjqF1//fV+xzz11FOd9gUAuouADACGb3zjGzpw4IA++OADDRgwQL/97W87HBvogJyQkKC//vWvlzxfOOjK75OADCDYCMgA0IFp06bp448/Vn19vW6++WaNGzdOU6ZM0fvvv6/Kykr99re/1ZNPPimXy6U9e/aorq5O8+fPV0ZGhjIyMvTGG29IkvLz87V48WJNnz5dycnJeuaZZyRJeXl5+uSTT+RyubRy5cpWq8OVlZWaNm2aJkyYoAkTJnQpOD7xxBMaO3asxo8fr7y8PN/5F198UZMmTdI111yjPXv2dDr/rl27NH36dC1YsEDXXnut7rrrLn31dvlbtmzRtddeq4kTJ+qBBx7Q97//fUlffoLk4sWLNWnSJF133XUqKSmRJK1du1bz5s3T9OnTlZqaqp/97Ge+mn7zm99ozJgxGjNmjJ566inf+ZiYmE7reOaZZ1RVVaUZM2ZoxowZF99UAOgKCwDgc9lll1mWZVnnz5+3srOzrdWrV1srVqyw8vPzLcuyrJ07d1rjx4+3LMuyfvrTn1q//OUvfd97xx13WHv27LEsy7I+/fRT69prr/WNmzp1qnX27Fmrrq7OGjJkiNXU1GRVVFRY6enpvu83j0+fPm01NjZalmVZH330kTVx4sQ2Y0xbtmyxpk6dap0+fdqyLMv67LPPLMuyrO9+97vWQw89ZFmWZb3yyitWZmZmp/O/+uqr1qBBg6wjR45Yzc3N1pQpU6w9e/ZYjY2NVlJSknXo0CHLsixr4cKF1k033WRZlmX95Cc/sdatW2dZlmWdOHHCSk1NtRoaGqznnnvOGj58uHX8+HHrzJkzVnp6urVv3z7L6/VaY8aMsRoaGqxTp05ZaWlp1jvvvNPq999RHZZlWSNGjLDq6uq62lIAuGiRdgd0AOhJGhsb5XK5JH25grxkyRJNnjxZL730kiTpe9/7nj777DOdPHmyzfeWlpbq4MGDvuOTJ0+qoaFBknTTTTcpKipKUVFRiouLU21tbad1nD9/XitWrNCBAwcUERGhjz76qNPxpaWluu+++xQdHS1JrT5G99Zbb5UkTZw4UZWVlX7nnzRpkpKSkiRJLpdLlZWViomJUXJysr75zW9K+vLjmj0ejyRp+/bt2rx5s371q19Jks6ePavDhw9LkrKysjR06FBfHa+//rocDoduueUWXXbZZb7ze/bs0XXXXdfqZ2qvjm9/+9ud/h4AIBAIyABg+GoP8qVoaWnR22+/rYEDB7a5LSoqyvd1RESELly40OlcTz75pOLj4/Xee++ppaWl3Tm76qv7Nu+3s/kvtlbLsvTSSy9p1KhRrc7v3btXDoej1bmvH3el7q7WAQCBwh5kAPBj2rRp+vOf/yzpy72xw4YN06BBgxQbG6tTp075xs2cOVPPPvus79hf0P7695u++OILOZ1O9evXT+vWrVNzc3Onc2VlZem5557zvXitvr6+0/EXO/+oUaN06NAh3wr0888/77tt1qxZevbZZ317ld99913fbTt27FB9fb0aGxu1adMmfetb39K0adO0adMmnTlzRqdPn9bGjRs1bdq0Tu/f1NnvDQACgYAMAH7k5+dr//79GjdunPLy8lRcXCxJmjt3rjZu3Oh7kd4zzzwjr9ercePGKS0trdN3wJCkoUOH6lvf+pbGjBmjlStXtrrtP/7jP1RcXKzx48frn//8p287Qkdmz56t7Oxsud1uuVwu33aHjlzs/N/4xje0evVqzZ49WxMnTlRsbKwGDx4sSVq1apXOnz+vcePGKT09XatWrfJ936RJkzR//nyNGzdO8+fPl9vt1oQJE3Tvvfdq0qRJmjx5spYuXdpme0VncnNzNXv2bF6kByBoHNZX/8sPAEAnGhoaFBMTI8uytHz5cqWmpupHP/pRh+PXrl0rr9eroqKiEFYJAN3HCjIAoEt+//vfy+VyKT09XV988YXuv/9+u0sCgKBgBRkAAAAwsIIMAAAAGAjIAAAAgIGADAAAABgIyAAAAICBgAwAAAAY/h9lu+sjQjrX1QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"deltas = m.params['delta'].mean(0)\\n\",\n \"fig = plt.figure(facecolor='w', figsize=(10, 6))\\n\",\n \"ax = fig.add_subplot(111)\\n\",\n \"ax.bar(range(len(deltas)), deltas, facecolor='#0072B2', edgecolor='#0072B2')\\n\",\n \"ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)\\n\",\n \"ax.set_ylabel('Rate change')\\n\",\n \"ax.set_xlabel('Potential changepoint')\\n\",\n \"fig.tight_layout()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The number of potential changepoints can be set using the argument `n_changepoints`, but this is better tuned by adjusting the regularization. The locations of the signification changepoints can be visualized with:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"plot(m, forecast) + add_changepoints_to_plot(m)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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g7SHK82x5SQZZIYoB8AEqq05KW2baA1+uVSkIAqTB1LKRvf/tAKsgdFSs4aNq0+LYRC2IlRzPasR6AD+o6jqoq2z8AW3W71y5zzS7zFewlSwuRnZ3dMCWhPY1tLXJIhaljIcuJtw+kgtxRiUXggF2CD2IlRzPasR6A4yorURQFt40fgK1u7bbLXLPLfAV7ydJCpKenN0xJaE9jW4scUmHqeHT0HaT2gFSQJZIYYD0AB111Fb1796aXjW+McrtX0lpIJSGE7AuJpG0h07xJJDEiOzubocccQ6+ePeMtSp1Y1u54V3Hr6BSXlMg0UBKJRGJTpAVZIulgyO3e+FNcUsK6deu4f/16GdQpkUgkNkQqyB2VwYPt0UYsiJUc7alP6qFVt3vt0q82GpsdQrBDCHTDkG4u7Wls7SKHRCJpNlJB7qi8/bY92ogFsZKjPfWJnbBLv9pobEpfeIEbJk/GKbMatK+xrUUOWSREIml7SAVZIpFIWhnp5tJxaHDBlDaCVPYlHQWpIHdUbr3V/P300/FtIxbESo721Cd2wi79aqexufVWsoFsO8gSb9rT2EaRoz0VTJEFkSQdCakgd1TWrrVHG7EgVnK0pz6xE3bpVzuNjZ1kiTftaWyjyNHggiltAJkiUtKRkAqyRCKRSCQtRIMLprQBZEVASUdCKsgSSQsg/fQkEolFeykSIn3nJR0JqSBLJDGmuKRE+ulJGoVcUEnaCu1F2ZdI6kMqyB2Vk06yRxuxIFZyxKhPfly7VvrphWOXuWaX+QoRsnT4wKf2NLZ2kUMikTQbqSB3VKZPt0cbsSBWcsSoT6q8XjxhCk+H99Ozy1yzy3yFCFk6fOBTexpbu8ghkUiajVSQJZIYI/30JI1BBj5JJBKJ/ZAKckflxhvN382xeMSijVgQKzli2CfZ06dLxdjCLnMtjvO1ho9xNVmuvfZaAKZOndrx5k0bH1tbyiGRSJqNVJA7Klu32qONWBArOdpTn9gJu/RrnMYmqo9xQJbq702dOjUuMsaVNjK2DQqklNe/RNJucLRUw9dffz39+/dn9OjRwdc++OADEhIScDgc5OXltdShJRKJxDZE8zFuyHsS+2AtZO6//34mT56M1+uNt0gSiaSFaTEF+brrrmPhwoURr40ePZq5c+cyceLEljqsRBIXvF4v06ZNo7ikJN6iSGyG5WPsdDpr+BjX9Z7EPsiFjETS8WgxF4uJEyeybdu2iNdGjhzZUoeTSOJG+Db5eCFISkqiV7yFktiGuoI2ZUBn20AGUkokHQ/pg9xRSU62RxuxIFZyNLGdcOvSGkWhS5cuZMRGovaBXeZaHOdrjeIKYbJ0+MILbWBsG7yQscs9USKRNBtFCCFaqvFt27Zx7rnnsnHjxojXJ02axL///W/S09Nr/e706dOZHogE3rt3Lzk5OS0lZoM5cOAARx55ZLzFsAWyL0wOHDjA9u3bufzyy1FVFbfbzezZs+uc2+0ROR9MZD+EkH1hIvvBRPZDCNkXJnbphylTpkSNi7OtBfnGG2/kxkDKnPT0dAYNGhRniUzsIocdkH1hkpSUxJdfftnht8nlfDCJ1g8dtZS0nBMmsh9MZD+EkH1hYud+sK2CbHfa/APv6qvN32+/Hd82YkGs5GhGO8Ft8quvhuefj3+f2Am7zLU4zdeoad6efz4ustiSNjy2tpVDIpE0mxZTkK+88kqWLFnCwYMHGTx4MA888ABHHHEEf/zjHzlw4AC//OUvSU5O5vPPP28pEVqMqA+8tqYk79xpjzZiQazkaE99EgdqXTTapV/jNDZRS0l34HlSA5uMbUyMHnJcJZJ2Q4spyLNmzYr6+oUXXthSh2w1oj7w2pqCLJHEkLy8PK644oq2vWhsIaJmQGiDhoH2TFONHm1+J1EikdSKdLFoAjLlj0QSidfrlYvGKPg0nWFjUnl33qdsKfC2OUWqoyiATTF6RFWqW0leiUTS8kgFuQnI3KUSSSTZ2dly0RiFjXtKARg8KokLzpoUX2EaSbtwJWsg1Y0effv2Zdq0aXXe36Mq1a0st0QiaTmkgtxE2nzu0ljIbpfzj5Uc7alPWpn09PTaF4126dc4jI0hBA5FwaieTLMNzJNWcyWzwdiGGz369u3LrbfeWu/CIOpOoqykKZG0G1o0D3KsSE9Pj5qjrrXZvXu3rVOStCayL0xkP5jIfjCp3g85PxfiVBQ0Q5A1tE+t37OjK0NzLchtdU5MmzaN+++/H13XcTqdPPjgg9x9991RP9uQcYvWD3Yc75amrc6HlkD2hYld+qE2HVNakCUSiaSFEAJQAr9rwa6uDB3VlawxMSZN2Um063hLJJJIpILcUbn4YvP3nDnxbSMWxEqO9tQndsIu/RqHsbEUY0E1DTlMFjtnxWkVVzKbjW2zFgYNkMPO4y2RSEJIBbmjcuiQPdqIBbGSoz31iZ2wS7/GYWyMgAm5hg9ymCwdPiuODce2yQuDBshh9/HuiO4fEkk0pIIskUgkLYBhhNuNo/tYWMrI008/zaFDh6RS0k6prnTa1XWlpd0/pPItaUtIBVkikUhagDW7ihFCIBBRfZCLS0qkL6pNiaUiV1sRHTuOdUu6f8hiQpK2hiPeArQHvF4v06ZNw+v1xlsUiURiEwwRsiBHsx8XFRXVUEYk8ceyot5///1Mnjy52ff1aEV07Irl/uF0OmPu/tGW+kEiAWlBbjZtNiJ58mR7tBELYiVHe+oTO2GXfm3lsRGYQXrWT3VZlB078Lz9tm19UVsNm41ts6yoUeRoS0V0WtL9oy31g0QCMg9yo4iWs68xOTPbE3bJXxhvZD+YyH4wCe+H1dsLqVJ1unicVKo6p5zQr8bn7eSTGWtZ2uqciLXRY/fu3Wzfvt024xwvZD+EaKvXRqyxSz/IPMgthN0jkiUSSevj9XqZMe8zxmRkk5Y5ttbP2cUXtc3uhLUALWFFtcs4xxvZD5K2hFSQm4mdI5Lr5JxzzN+ffRbfNmJBrOQIa6fJ1jS79ImdsMtca6WxsZRNn8+P2+Pm2Xfmc1JiGpWqThe3s1VlaSgtFZzVoOvIhmPbZEWuHjnstGMgkUjqRirIMaBNroorK+3RRiyIlRyBdpplTbNLn9gJu8y1VhobS9k0DB1VhXzvck5MTGPz3lLShvRuVVkaSkvshDU4a0EbGtt6qUMOaaWXSNoWMouFRFKNaNY0iaShZGRPwOX24HA6cbvdpIydgBAiUDTEnlg7YQ8++GDMFDeZtSASeV+RSNoW0oIskVRD+pVLmkNiWiYvzJrH6hXLGZORTUJKOqpu1KymZzNivRMmsxZEIu8rEknbQirIEkk1YuFXLn0NOzajUzMZNiYdzTAVY5vrxi1Cenp624zPaCHabLyKRNJBkQpyR+Xcc+3RRiyIlRxh7TTZmnbuuWzbvl36GoZjl7nWivPVzH1sFgoxa+kR6WJhl2ungTR1wdeg66iNjW2d1COH1ReWe0WHvi9IJDZHKsgdlb/+1R5txIJYyRGjPpk1bVqLlWttk9hlrrXSfFUU87elEAtTS450sbDLtdMAWjy4zEZj2+ydn3rkkIF6EknbQQbpSSQxpiXLtUraBoYQGAACqlQDMC3KbZGOElzW1BLTXq+XadOmNejzHaUvJZL2gLQgd1Qspa05N+hYtBELYiVHjPokG6SvYTh2mWutOF8tqzFAuarTy+mK9EO2y7XTAFo8uMwmY9uUXNDVLcJ7RoygV8+etcoxadIkXC4XhmHgcrnk4lkisTFSQZZIWoA2mRtbEjNMn+OAu4UQCIGt07zVRUcJLmvKQqC6Ul1UVGQqyHUggq43bXM+SCQdBakgSyQSSYyx8h47UPjiw7fJ/+ozTjnnPE6597aIz7WVbCcdYcFnLQRmzJjR4O9UV6p79+5d5+eXLFmCrusIIdB1XcYndHDayvXfUZEKskQikcQIn2agqbqZvULAJ7PfYvpDdwKQv2IJw4/szo033ghAcUlJhwrYUnUDn2bQvZO9HztvvfUWfr+ft956q94xqW5d73X33XW2PWnSJNweD8hcyB0eGbBpf2SQnqRD05gAm4agGaJD5ryVmPx0uJz9ZT4zzRuChXPejXh/zpw5wX8XFRXh8/nQdR2fz9euA7Z2FFayfncJ3+wvjbcoddKUILrs7GzuvvvuBik3aRlZvD33v/zuL/dIhYjY33/bEjJg0/7YeykvaTkuu8webcSCJspRfQW/8eabOf7445sshtfrZXvnbowZOZyEJrdiX5q8HWiXudZK81UI2FiQw8cfzuL7zesj3rvwoouDsuxYvhzjp58AMAyDvn37top88WBfWRUKSvRqgjYa22YHJNYjx/o9xRybkMLUkclkDe3TdEHbAR3dgiorK9ofqSB3VG6+2R5txIImylF9BT+7b1/ubmJb1s2+yufD+dX/eD5hZHArvS1SXRlu1sPMLnOtjjZi5Qu4ae0avlr0BZ988A6aqkYEYo0//RxuuOGGoCzLiotxzJ6NYRg4HA4OHTrU5OPaHUOAUwlk96iOXeYHNV0mAKZNm9bweVGPHCIQuLkhP4cv3y3o0L6nTcka0p7oKMGvbRmpIHdUKirM3127xreNWNBEOaqv4E8bO9Zsqwnns2TJEnw+H50NAwyDW265hTFjxrTJm140ZbixD7Myn0ZXtxOHQ7HPXKuljVhZsrxeL7f//tf4/b6QYuxwgaHhdLpIP3ly4HWzT04bO5ZOnTq1ewvShj0lWKcdVUG2y/wIYAUkNmle1COHEdhduOWqC9HUjmk5tZAW1I4R/NqWkT7IHZVf/ML8iXcbsaCJclgr+AcffJDFixeT9cADTT6fSZMm4XA4+BT4FIIR6m2RaMpwY4uffLu/jL2lPvMPu8y1WtqIlS/gkiVLUFV/SDnOvBRunQt//AD9yON5adp9IV/LX/yCrAce4OmnnyYtLY2zzjqriScVO3RDoEf1gWg6Xq+Xl57+N+vyc4BaUt3ZZX5Uo0nzogFy5HmXo0rf0xr3X6koSuyGtCBLOhzVt9NjcWPOzs7m+eefR/nd7xBAp06d2qxFJJplp7HbgYYQVGk6lapOl1aSu6nUZslqrNvFpEmTcLs9aJqKw+nEnXUBFQDuTpB5Cb6PpzFz5kxOnjAeMLNY/PGPf8Tv9wPw2Wef8dVXX8VNUfh2fxmaIUgcVHce34aSl5fHFVdcgc/vx+328OKs+ZyUlBaTtluDlrJwpo4dj9vjCVqQ2+p9IhZIC6rEzkgFWdJovF4vCxYs4K6SknqT4tuNlgwMufHGG9n9zHOUl5bw1suvt9kbfzRluLHKogAKK1QOV/hJb3mRm0VqRiaff/E/li/7usE+14YhTPeRMLKzs3n8pTfYvHkzJ6VkMW2DQUXAiI5i+he8+cbr/Pq6a8nGzGKhqmrw+/H2w/RpBpphxKw9r9eL3+/H0HVU/OR7l3NiYttRkAGuvfZaAKZOndpkt5sFCxYwZcoUElLSEQLGpGXy9My5rM9dydXnn91m7xMSSXtHKshNoCMn9w5XHM4RgqSkJHrFW6hG0NKBIV2796B7z55tylIWjXDLTlMWFXX6nNqMDXtK6H3CGO4OWHah/nmyZlcxAP27d2JIn5CNfFRSCumTzqSwUsWfuwQUyxfVVKY1TTPbAnr37o3T6UTTNMAsLhLvTBaxGi6v18vatWsBcDgcuN1uUrPHYwjTlcNZbXFhN6rP+alTpzarjWeeeYbn3p3HqORM1uflsGb1CjKyJzB2bMd6fkgkbQnpg9xIrJve/fffz+TJkztc/sZwxUEYBkVFRfEWqVE01pe2oai6Qf6OIgAO7tvLTb+6iOnTp8ek7XjTFF9MSzFuA/oxRpQy0PXNEyNQKa+4KmQBLvdpGIZ57pWqQecuYYFaiqkQhrfVq2dPzj333Ih216xZE7sTaySxyuDt9XqZNGkSCxcuRNd1UBT+eN8jCAFvv/gUMxb8LybHaUli4ZdevY187wrWBwL0Xn1yGrdcdWGHe35IJG0JaUFuJO0mNc111zXpa+F+ee84HNzxq1/FVq6m0IhzqdOXtol9AqZVzBCCz44ewuc5K/H+9APepV8CtOl0b9A0X0wRULeEoFn9GqQF2rB2go4cnkpaZlbEe/X5XEdTJbfsLw2+7td1FAJmdKBX3yMZd9lU7rjlBrOtgCwDVq9u/nnFgHKfhm6ImFj8zWDF0MJBGAbfbV7Psw/di+r3M+P5JxnxZdguhF3mRxhN9j8Ok6N6GylZ48lfZQboGYaOqvpZsnQJ48ePi6nsEokkNkgFuZG0m9Q0TXygWIqD5Vc3zA6Lg0aeS62BIc14yCqKaYl84sA+8sNenzNnTptRkGtzHWpMgJ4QgrW7zLReQWXLLgpQWBvh298ut4eXZs0nZfCZER/PzBrLCaNT6d+jU42moimSRpjV/LX/5bO3KrRBd+TRQ7npskvJOunICFlSAgF6FikpKY0+rViwJYYV7sxgRXcw+NDl9iAEQcVQU6sZFuwyP8Joco7aMDmq3ysdg0ZgCHB7PKiqGbg4ceIpMZU7VnRkN0KJxEIqyI2k3ST3PnjQ/N2vX6O/mp2dzdChQxnk8ZjtNKGNmNKMc4lFO1WqTlGlihBw1rhT2bZ8CVbJh4svvrh5MrUS9fkZNybaXBdGwIIc8DONxfjEuI3wnSDwk+tdznUXRCrIhRV+fi6qiFCQhRAUV2kYQqCg4NcNiipVendxB8pLm3luF++L9F4rKymKdOMIyHLo0CEcDkfci4VYssfCgpydnc2SJUt4/oUXwdWJcb+4GBB8Pm82qmouSCIMC3aZH9VoUoaFanIE75WDBuHddpiE1AyeeXseuSuXkTHuZFs+Pzp6hTuJxKLFFOTrr7+eTz75hP79+7Nx40YADh8+zOWXX862bds49thjef/99+nTp+2V22wXqWkuucT83ZwcnLFoIxbESo4mtrOzuIqiSj+GEPzlq8+5/rgTuHrIUH591eVtxnocK9chIUxLatAHWWCfuRbWRvhOkMvtIXXs+BofVxSlhsJY4df5/mBZIAhRIITC9wfLSB/SB0MINq9fw903/xpufj/ie9179gYBm/eWkjCgB50DskyaNg2Xy4WqqrhcrrjsSHm9XmbO+4zUseMZnZpJ/o4i0ob0blab2dnZ9B1wNCWuHuSsWsXa1Sv40/0Ps3nDOtzOagF6dpkfsaAOOUJzyfL2jpXXd2xpN26EEkkzaTEF+brrruOWW26JiP599NFHmTx5MnfddRePPvoojz76KP/6179aSoQWwdp6mjjxFEanZtCrizveIklsgGmBMx93Xbp0JT17AmPGjImzVA2nOa5D4amsMrPGxtQa2VJYO0Ez531GckAx3BRQXsOJVjcjONZCMS3lIvTZ9fk5qH61xnf6HtEHj8uBIQSqLugc9p4SCOCzfrcmlrXQ5/Pj9nj4zzvzSEnPjFn76/JyuG3qRah+FafLCYCu6SycO7vDWSYFggWzZvDvv92OoevMfK4ToxYvDubFtgvtxo1QImkmLZbFYuLEiRxxxBERr3300UfBvJLXXnst8+fPb6nDtwh5eXnBDBann3E6cz7/Mt4iSWyAgpUJAcrLSvh+yyZe+PfDbSrLSVOrWlkK1uOPP87kyZNZuXJlwIpsZxuZSXZ2NlNv/jOjUzIQAir8WvC9Mp9GSZX5d/6OIjQ9lB/YGmsIWczNfwsSUzNwe2oumgWBVMjVXp8xYwZ+v1l9z0oB15pY1kLLN7jAuzxmI6YokO9dgepXA+2raKr5bztXkCus8LO7uCrm7W7Izw0qxwCq38fMmTNjfpzm0loV7nyazg8Hy1ukbYkkFrSqD/K+ffsYOHAgAAMGDGDfvn21fnb69OnBNFl79+5l9+7drSJjXSxatCjkt+j3s2LR55yamkAnV9vLltc3EEBzqIn9euDAgWa3ESuaIkdeXh5er5fs7GzS09Ob3M6BAwfwuSsprtTw6zqlRYUYwsDQzcIPCxYsYOjQoY04m/gxdOjQ4AK2odfbggULIrZjP/nkE069pC+ablDlciKEwBeDeRKLuRatjaKDJYCpzCko7HZVAvDdAbOqnGYIXA6FnaIMj8th+psfrEDTDRwOBZdDwRDgLTtMcbmfY4YO5YFnX+WuLZHHVv1+yg8fxFHuYr8ow+P3U1payuuvvx4qTQ04nc5WvdclJCTgdrsRgMvl5qSRoyg6sD/YDw0h2rUEcPjQQU4cOQqX242mgcPhRAF0Q8ftdpOQkMDu3btbbGybyk+HKqjSdDiqR63n1hg5Dhw4AID3y8+DyrFFeXmFLZ5t1Tlw4AAlJSUcOHCA3bt3N7ofamsznMJKlb0lVXTx96jlG+2X6n3RUbF7P8QtSE9RlDq3FG+88cag/2Z6ejqDBg1qLdFq5fTTT+fVV1/F7ze3I7NPO4tDSjeSjuqF29nGlGSPB6BZ/dopBm3EhEbK4fV6ueKKK2oGoTTxfCo9vdDLfPg0g+69+nBw726cDgcej4cpU6bEv39akClTpvDMM88E+zLr1DPp0fdI043A5UQXIjbzpIXa+MnXCVM1BqdDYdAgMybiAMVmZTld4HIqDBjYk85uJxV+jX2iBL9m4HIquBym24TbqdC9k47DoZA0fhhsiUzftmZvBf9Z9iOjDuVyyS/P4ASPh73l5YEgQRPDMPjHP/7BySe3XvDWlClT+PLLL3lr7qekZU9gVEoGDgUGDTJ3/3YWVVJYqTJmYPSKmbVeS5j+2lmTjuWxN+ewMXclw1Ky8DgdfFOwiqkXnBNKb2aX+RGgyFFKmV9j+/Zvaj23xsoxaNAgsk49k1mvvYAaUKKdLhc3/u5G290fqo/p008/za233hqToL3wc/WU+Sh3VwSvuY6G3cY9Xti5H1pVQT7qqKPYs2cPAwcOZM+ePfTv3781D99s0tPTgxksEjPG0XdYYkRAUpvippvs0UYsaKQctQahNPF8AlWEMYRg7/V/YEf+SjL27OTXV13R7nwsq6d/Ck9llTXpLAaPSqJKNTCECLlX2GWuRWnDEOBQzIwbllvIN/tK0XQR9DUWQqnxHVHt7/BiI75qVkKLr/fD8qcf4a3n/s3au+8GwDNtGlVVVQghEELEJSgqOzsb/aiTcDqUQAGU0PmW+TR8ms6hcj99u3lqfLe+gC4BjEzJIDEtg8IqjW5uJ+lZY8k+Nsz9zi7zIwwhmhisFpAj/DqxdpBGpWTwzDsf8fEHswD45SVXkGXDSnrVz3vOnDktFrTXJp+dkg5DqyrIU6ZM4a233uKuu+7irbfe4vzzz2/Nw8cESynYX+rjh0PlNR6ebYbLL7dHG7GgkXLUGoTSjPOxgrUWDTiaf370Aaqqsi53FWPGjGk3SnJt6Z+sVFZ6lz7sKa0KLhrNh5+wz1yL0oYhBA5FAUKKYZnfKv0c+QAXQgTOzSwKE7r2zX8bgX+v21VSqwiW/+0cl4u7776bxaefzowZM3jjjTfQNC1uQVGGACfm+SqKCCp4xyZmcMKYNH46XF5DQfZ6vWz94SdcLvMxUl12RQFhmIq/QAn2p7l4CsMu86MaTQpWu/zyGtfJe++9x5QpUzCEICElg8Ejk/FrBn262jPAu/p5X3zxxSxbtiwmQXvhC4cTx6QihOnjnzSoJ662tgsrafe0mIJ85ZVXsmTJEg4ePMjgwYN54IEHuOuuu7jssst47bXXGDp0KO+//379DdkYI6AA2DkQqVZ27DB/DxkS3zZiQSPlqDWXdTPOx7Ii7vlyIUf5/PwsDNumSGpqEYCGWNSCVtewv20z16K1EaEEC1TdCFiVzfOwLMO7iquoVHV8mmFm6LB+wtLaGYE4vr9/sbVOMVwuF2eOHAk7dgQXGFOnTo1rbnXLUi4wg8n+MvUi05XM7eHZd+aRlJ7JnpIq+nfvhNOhhLJf+P04nU4uuHIqf7n5hhqyRyyWrF2FwCLDqjJom/kRwCz6I5qW837HDvLnz4+4TrxeL1OmTEEIM0e2d/kyhqVkMWH8OFs+OaKd95gxY5o9P/Py8iJcN2Yv+JS+JyQiAF0IWZRBYjtabE7OmjUr6uuLFy9uqUO2GlZQjRACg5q5UtsE11xj/m5OJHks2ogFTZAjai7rJp6PEpgDhhD81fs1lyA4VVFwOp22S5HUnCIA9VnULFeToNJoKcp2mWtR2jCEwBFQ1ISA9btLItLUWYpjYaUfJeCGsSE/h/xVK0jPnoDToZC3cjnp2RMYmpDC5nVrge51inHGmWeT9vTTEbLEM7e61+vl7Tn/JT37ZEYmp7Nw7ntBtw/wU7BqBWPSMtlVXElXt5NeXdyh7Be6jgIcNWhwhPw7iyrZdrgCV88uGAiqtNBiw6h+w7TL/AhjQ34uX76bz2mnnsrdAXeYhspxTUkJd4RdJ1a/bCzI5c5fX4zqV3E4nZx98ZU4b/4tk06eEBOZY0n1+Vj976Yssr1eb8TCYeH/vuRXJ4yJcOmRSOyEXLQ1ge8OllNapZkP/+rbhZIOxb5SHxhqwIc08r145LWtj+pW4K+++qrBD7iGWNRMq6vZEW3BPz9cvJDrBAglXEkOuCAokXl9Zz7nBBR0TWPG825+d9eDvDTtfvhD3Ttj/QccBSVFLXdSjSA8D/LM55/klvse4rM5s4JGAMXhIDlQRCV8LOsrtFJSpQUXS0KAXzeC/WhZ3+2GpfT53d2Ydv+dqKrKw2GLSCEEe0t9DOzZOer3Cyv8dNMNevXsGRGr0rP/IN7+eBEznnvcTOlnGBiGzn9nz2DxR++3uXzQTV1kZ2dn15gz1d2YJBI7IRXkJlDm04KKgGk9lFd4R6G65eRwhZ/eXUVwm7C8tDjgc0kwr62dHn7VFZvhqY2TrT5Lp+VqIqJZCm1I+APaECErpyJMv2QreE4IBRQoWBXK6yvUgNexEKgqLP/iv6iqv87juT0ervzV1fCP+1v4zBpGeB5kv6+K/77/DpoaygctDAOCAYihBV94oZW07PEkptUsLmL1pRACPeCLbBjClkVkwl1GFEXBMAyEEekmpeqCXcWVtSrI/138NZnbf+aofkcEr5PvD5bz2Scfc/tN1+P3+RDCCG61xCsos7k0tdJeeJD74IQMTkpKw6cZ9X5PIokXUkFuIiHrmMCvC7oIYUuLoSR2RLOciE5mNL4RUAK69uiJoijBNG92c7GwFJtPPl/E8UmZJKTUzGnaVB9lExEsnKHpgm/X5XHMtu0ccUQfejVf/JgT9JVGRFiMCfzbILIqYPLY8bg9blTVzFkMCrqu4Xa7yT7zl2woyKFmHb0Q/3pzrq0yF0yaNAmn04mu6wgh+G7zehSHgggk4hBCULBqOaNTM8L1Y8CcSwwcjtOh1LAIK9Us8Fb/VWoG3WymHEPYQkHXcTgcOBwOUJQa13Btiv3MBYu44fIpLKyqZO+uHbgDOYMB1uTloPr9CGGgOBwcPzKR7Vs3Yxi6Le8R9dGcSnvWwmHdrmIqVL3NLKQlHROpIDcDy1L4w8FyjurRicG9u8RbJEkLsmTJEnw+H4Zh4PP5WLJkCWlnXgTApjW5rFqxnCQUjh2ewA3nXcDUC39hS8tQdnY27sEjopZRbsj2aW0KtBncFPLRz8tZxd9vuIxRVVXs2PFzhNJgFyxfaStYz7J4QkDps9wEzFp4jE7J4LE351CwagXp2eNxORzkepeTNnY8x4xKof9RA/n7d7Ufb2Ryhq2CerOyxnLuZb9i3jtvBa3l2aeeyaol/0MYBm5PJ5KzxkcE8YUTboEXYUYChVDWDwhY5xGh79ioD6Dmzsof73uYsuLDNa7h2qReteLr4O6BMIwIq2piWiZujwdV9eNyubnmr//AMATbN+Ry9fln2+6aqI8mBS8GsO4dlc6u7D9wkDGZ40hJz2pBaSWSpiMV5CYQDEQK/G0Igarb64ZfL3/5iz3aiAWNkKNO62g97fTt2xcjkKrAMAz69u0LwPr8HG6/1gy+qXQ6ueIPtzP15j9H5nq1GbX5B9e3fVqbAm0IgTBCQViqLlif60X1qzyBmUIsqzlbyTGer8uWr+CZl16jUtP55cVXMColo0Z2CivYcGNBDutzVpI1/mROSkpjRLKZ+szjdNDZ7eCEMWm4HFDm1zl2xGj47udaRQgqhna5doBfXHQF//3wPVRVxe12c+lvbmHq7//E2tUryBg3gWGJaQG/7MgJs6/UF1w4ABTsLCZtSG/A9L8XCNP3OPC1cr9OF7ejpouFDe5F1V1GTkxMx+lQyD7OvIbD/dNLqzS6eZw4HCGTekrWeNxuD08JH06ni9vDrKqjElN4auZcPvlgFvsP7Gf6yp84K/FYLr3hj2Qd369ZcseLpgSVWlksqnw+hGFa0z0eD8+8PZ/Uwae3kKQSSdORCnITCGY/DVpH4iZK0znvPHu0EQsaKEe91tF62jl06BAOhwPDMHA4HBw6dIihCPK8y4N+qQsU6Kzp3GLzOVFbcEx926fhCnRVVRUzZswgOzubHw6W0/2IbhAIyNIFjEobi9vj5jPVzJF7Z3O2kmM4X71eL6dPPg1/oKLZ/+a9xxMz5zE8KSOkCAX+27Qml7sCmQfefO4Jnp45l+NGp2IIgV/X6eQyFT4Dsz/Vem4Gm9bkMe7YM21z7QhgdGomj74xh3zvcjLGTWBEcgbdPA4S0zJxOaHCb9p+N+Xn8vH6HM4+/TTGjctmR1FFxByqvlVuCPDpBmt2FvPQItOsfnHiAG7IGhppibXJvSg7OxvHoBEoClSqOkJAcaVKJ5eD/WU+DpT5EQK+PVDKMb270r9Hp+B3R6dm8uKs+axesYyMcROC9xXTk93smy/mv4dmKJByM29s9TGyTx4nH39Ws+VuKZrnahW9PStIEUxLu6qqrFm1nKlT7Ksgx7ofJG0HqSA3EK/Xy4IFC0gYN5mTktKC1qUta3JZ+vGHdPE4ueXG69vOBfTtt+bv4cPj20YsaKAcS5YswRfwM4waXFJPO5MmTaJTp04RymOxgLTskF/qCIeDTt9tZn1+DuOOOzsWZ9ciWNvcmiEo92l062TeCurbPk1Iz47wWX3llVdISUnh+PRTgIAvtjALRJyUmM7jb83h0BefcP7pE0lpzrURw/m6ZMkSVDXkKaypKvneFZyUmB5pWRewLicUlKepZsqzYxNS2bwmlw25XsZNOJkTxqQF3A8EJb7oVfQsNuSsgAvPtM+1gzlmo1IyGDIqhe4eZzAji4HlaiLYVJDLrVdfgKaqPPHoQzz+5NN88/MeRqePIzXD3CIPz9YVqL2Crgs+3bI/+Prq7UX8NnNoZGCzje5FwbR/gR3C7w6WBQqRE1o4RUlLJhAkpmWS1KMXzmqxKKYf9wq0vsfBlY+bLyqwPmcl5eedTnePK8IabQeakw6yNqwsFpabmsPhwO12k5Q1vv4vx4mW6AdJ20EqyA0g/CJxuZ/huXfmkZCawaaCHO687iLUgCVqzqyZjUqbFVd+9zvzd3PyhsaijVjQQDkmTZqE2+1Bw487WnBJPe1EUx4/y/uGMamZPPz6hyycM5v75r6N+HwBZy/9gqSjv7TtXLCqmfk0g2/2lwW3xqHu7dNBI5KYeu11vPrKdAB0XeeWW27hsZdncPLpZwfdjiyr2ajkDNKffIRe32+GK65ousAxnK+Tpk3D7XYHLcgut5sRaWNNuYNZK0z5EzPGBRc/LreH5KxxbFqby99uuAzNr/Lh9KeY9vqHJKRmsnFPKfct2l2nCAnpgeIQNrl2wqsDCiGoUHW6uJwYwqyoJwL57hbOmx28z/n9fm770x8RApwuJ+de+ivOvugKkgKZLNbsLEYXRjDwMVxfNIKKdxg2uhdZyToEsKkgh415XtLGjic5PSsiTV04hyv8QV/2YffeZirUq5aH2gTGZIxDWXco9F3FQUJ6NruKKxECBvWKnhkjXjQ1U0VdWFksZs77jF59+nDo0GFGZ2STkJJh20xQLdEPkraDVJAbQPhFYiXOH5WSwfqclWhhlqiOfAGFB+jYlezsbJ5/dx4Fq1Zw2qmnNmqciitVenVx11AerUDNkxLTeGP1dsTAPNi9Bb/fzz/+8Q/+8Y9/2HI+hAdJNSaKXAj41dVX8+Ybr6NpZjowXddZn5/DhNPPwjAi3Tc0G/ofZWdn88WixTz10muousH4X1zMSUnpQf/pcCV/eHI6j785hw25K8meMJHjRqfyxvNPogWsyqpqWgJHpWTwzf6yeo89PCnNVsqA6Q8cCp6zFNhgarvgZyK/Z94LwfDrzHvnTRbMfpu//vMxisamsWDhItKyx9N/4BDzvlDtiHbOfWuJtbEgh78G4grcHg8vzprPqJQMiGJB/vFQebC/yktLKSstZs78LzjntIlUqDog8GtGwBZv4nC6ODExPbgQsxvVgxZjlWnDynziUBSqNB2fZu9KtM3J2CFp+0gFuQFES4ovgNGZ43C63WgBy0pHvIAEUFTh58dDFSQM6EFntzPeItXJ6NRMxqRlcmS3TvV/OIzvDpaRcnRvnFG2QoUAwxBscg4Bh3n+wjBYtGgRy5Yts+W2XPgzOfzxdLjCj08z8GsGQ4/oWuN7hoCk9Cyefe55/u+Pt6DrOp06dWJMWmbEw97KWmAqyPF5ANblO5g1NpvbB4+kuEqj3KeHCoIQVkEPU3kckZxBYlomXT1OSn0aI9OycbndaJppfR6TOc7cmm/ANrmpLtmLDQW55KxcxvGJWYxKCSltSiBrhRCCMy+4jM/nzkJTVRxOFw6HA031B5VrXdN4/P7bedLpQtc03B4PDz77KseknRKxcBYitKi0I1YGk7WrV0a41uR7lzMiOT2o3GuGQNMNXE5HcGGxPj+Hyi0bMYTgpisv4IVZ80hKy0IIWJe7EkPXw44Tst7b0a6QnZ3Nu/M+ZdGXX5IydnxM71+GCMTxBPoAYd9qtI3N2CH9ldsXUkFuANZFsmDBAo7PmMSYtEw0w2BEUhqPvjmXZZ98SHePiz+0JR/kGKEbgh8OlQOg6oLO7jgL1ACi3Yy/P1jOMbqBx+mo9TuFFX56dnbjcZmfyd9RFHzQ+8KymHTr0QulvBSjWqEBO2E9mAWRFQB/PFRuFr9BRCjImm5woNwMUvrpUAVnXHwVSxPHBB8GJZ4+QUtjYYWf7w6Uk3R0TzSjxoZ6qxDVdzDsfQGsyzMVw2HJWYwMU34gEGCmGfh1gcshEIG0bwI4fnQa9788m015XpKyxnFSYlqDFR1h1Ky4GE+8K738+ZoLg+WPUyacRr8j+3POxZeTGFj0CGBkSgZPvT2fjbkryRx3Mroh+GTOe3w8e2ZQ8ROGgWaYSrOm+tlQkEPvhPEcKPMFj+f3VfH+K/+h8wXncPqkk+N01rVjzeGkrEjXmrTsCSELuxDsKali8dLl/LBuNYMT0jkxMY38VSs4ITC4muonz7siqCAnpI3FseHj4O4EimL74O4xaRkcqvCR713BqAE9g/ew5iqBQoiISpWGjRdM0PCMHU31V1Z1g+8OlDNqQI9YiCuJIVJBroXqN4Hs7GyGDh3Kdn/n4INUN2BEUhoZmWM5pk8Xjo1icesIWA9Ru2NlKFCoqckUVfoZqAs81Qzgqm5QVKliCNhWWEH/7p04po85zpa1VAjzcxYDBg/B/dP36Kpq210FyyWmurImRLDQVwQlPs30lyRUbS78wfF5/jfmNSEED3yxlZ3FVbx1RRK6cMZlckT1HQx7f/UqL7dNNRVDl9vN36bPJiktM2jdMgxBpV9HNQRdA7si1nUvhODExDROTEzD5XRQqRq4nApKA85Tt6xmNuHrr5cGLaWGoZP71UIAFs1/j6ffnk9qxtig7+2o5HRS0rPo5HKg6oLhyekcN2I0z/7zLoRh4HS5sAqnuNweElIzuPWjTQE3A5ND+/fyzpuP8eH0p229szIq2cx3vTnPS2r2BBLTMqnS9MDOgsLavNX84VcXoqp+3G4PT8yYQ2rWeByKgiGEudMYCD6z1D8re4P5GvTo7LSle4VFQW7gHP1+3nzu3yxevBigyUFrQogIo4IIZouxoQm9CTTVX7lS1akIq14psQ9SQY5CXStBcwfOvOX5dYNOisPWPlS1ct99MWmjyqfF3xLSiHOxxq+oUmVfqY+jAqmaDAEH/3w73fpELnIOlvuDgTQ1lMnA/wwhWPiNGan/UNZlXDh6AI+cNJDKn9Yz+bTG+Tq3FuF+pWbeYoP1u0sCVlIRdUwtC1r1tzbsKQn2T5Wqs6ekCiC4/bz95tvofXSv5gncyPka1XcwIyP4/tKlIcVQVTqzKdeLoih8V7CK5KzxJKSaeZF1w0APBLBZeXB3FlWyu8RHxjG9QQh0IXCIhlkEhRBsL6yEP99Bv24eAAp2FtG7i5senVwc2b1xrj/N5eSJE3F73Pg0J2RfCatmg1qFpqqsXb2C5PSs4FZ4yIpuZXOAcy6byohRCRSsWkFy1nicDoUC73LSsydw5NGDqdjyY41jGoapQCz+MhDQHKN7USwIKW0wIjmD1IwsHIGtARH0FjKzUqiqmRFHxc/aVSv4zR//wv57H+KHbzbx7JVTGZ0aKL8tYFneesToMyKOtbfER89O9t1yW71iGapVhjyg8AHNCloz7y2mv7YRdj+x8TqhwTTHXznuz1BJVKSCHIW6VoLmY8K0vPk03Syz2hYn9+kxyDt5+ukYVSrsK4tvHzTwXCzfUoRp5fm5sCKoIAsBB8dOxN2zM4Oqfc+KUBeExtrr9TJz3mccP3wk2aedydsFuwBYPDSZxaVwW7fjeOiOc+hiM59sTTfYWVxlWtLDtjnf/ngRed7lpGSNJznDfLAX7Cwi5eheKIpC7upVfPTZIpKyxgUj+i18mh5UnKo0y8tW4cmF67jl9DEUjj8FhjWzIEIj52t9voMnn2wqhv5jshDn3kVlp5944IbL0FSVWR43017/kMEjUwDTmmz1k2oY/PWTLQAcq+/j2qT+nJSUCkLQv7snujC6Ck5TEdIDCtj+rJPpF9hSNYTgcIWfokq11RXkrLHZPP7WHF78PJ8tvZPBMGDFTJxOF0lZZqzFxoJc1qxeQdrY8aRmZgWVGyvzxaiUDLPQigCXQ2FUcjpup4O9e6Jk9FAUHE6n6bYwNpDeK0b3olgQ2vav6SNs7ZwYQiF17PhgRhyX20NilumH3vPaG0kF3E5HwDqsYCBY6E6qcazrZ69jyc32WzxbjJ1wcrACYLjC15ygNSuNooBAcS0Rui/bnPpcS5pTYVAI2HqgjOOP6IqrFjc/SesjFeQo1LYSzMvL47/LckkbO4HhSWmA+fBsc1X0ANauNX8nJzerDYdPRRw1LL43uAaeS9BSEdgLD1+1CwFdNm2gEMGgsycGX1cIPTAdYcrx5MmT8fn8uNxunn57XvDzSftNi1mp7xjKfZrtFOQyv87Bcl/YNqcZpPWnq0Pbxc+/O49RKRmszcth4Yx8+vXrx6233orP58ftcfPsO/NJzchid3EVg3p1DisnDFvX5aOrOrg85B8yeGXxWp4a1R3Kjmj2XAMa1UYN38GwNrLGZvPYm3N4btUuvgNytvyA6vcjhIGqQv6qFQwakRwcezAf4hX+kLvANudRPPTUs9z35z8wJjUDd20PtpwPIPtXAMEUap02roe93SA5OejWEa8raFRyBuPEILbk7AB3J0BBcSgIIdiQn8OdgUIp773k5umZ80jLzIKAJRAIui1Zltfw+VCd3v2O4uybbuf00yaRkWWm1ovVvajZbRCyIJf6tGBO6Mj3zGtmdGomz787j3zvCpKyxnFiYhoCQbdN61EUhXzVz5pVy8kafzJHDDy61uPZMcuLRWpGFs+8Pc8s5HHhOcFrqclKICGfY4BKTccQ1HBrsyMN9S9ubIXB4kqVfWU+DCEoqVKpUHV6SgXZNkgFOQrRVoJer5fLL78cv1/lLY+HR17/gP4nJpor4TDfsjbDrbeav5uTN/TWW+lsCIy35iGA7YUVDOzZmb7darGktRSNOBdDgIPI4DTTL04w5IG7TSXl7OUR3xGBbVUrvdOXX32FP7D1qKqC1575F2TeBsDTS14F4J3zTuGHQxX0a2WLYH0omH2wqSA3GHC1ZtXy4Haxhp/8VSvQDfi/qy9EU/0oioJhGBhGQHn0LjcV5JJKBvXqHNY/grlfrYb+44LHO7RvDyfOnwFd3M2ea0DM2hDA8KR03AUHQIPd234AYZa/dbvdjArkRQbT6qsZ4BGwZU0ehPlMGobOsv9+wJo1a9jaL73GIZUtX3HsiER+CvzdxW26nQz4212mZrBkSTD3bjywFNrIBa5A13XWrV6JoighVxQV1qxeQWpmViAdnIlfE3hcoYXkxoIcNuZ68RsAGRHHK1YVTvvV7xk9qGfoxRiPbXOpVA0MQwTT/llFYCwPC6uvRqdmMiolA90Q+AIltYc9cj/lZaX86cfvgtUXH/jPK0DPqMfS9OgxEfFmT0kVxZUaCSnpJKSkkzy4VzBuoSllpi1CrmrC7DdNDyxEYip+zGmpfMi7iquoUDVbpz7syMilSi1kZ2dz9913By8C8wKxHhR+1q1eAZgPBFU3bBV409qIwP+rND0iYt1OCCHM8rFhVq5g8Ex4Jodqw6goSkRgmmoIEjPH4XJ7UBwOhGFQsPLrKMczsyDYDUWB9Xk53HHdxbz21KPcctWF9Oh9BG63J7j1nZo1noJVy1EDDwSr6pXD6cTtdpMydgIQuqFbN/f1+blsDFOOAY44amBrn2KDEAI+XZbDZn/AN1pRQFFIyjqZaa9/yImJ6cGCFoYh8Gs682fN4IEbLo5oR3E4+XrBByxQh/FNceTk6aGV8sBlk0g+ObT9X1tgZDwekAfLfGzeW0pxpcrmfWYOZ4fTFZgHbhKzxpGcNQ6X2x0c++Ss8UFZNxXk8t70Z1iXtzrop7whP4fbpl7ES/9+mNeeeLDGMQVw44frA8Gg9lMMBaYffUgZDrwuAu4x1cbJ8qMlbKehrKQ4IkXchoLcWo+3aU3t78WTXcWVgQWC2Qdb9pVRWBlWfVKPfm/7Zl8pPx2qiPocsFwpBGaAe3gMhN2fntaustPpjGngtaKEnkU23kzosEgLcgM5eeIpuN1uVA3cbjdde/VhwRvPMSZjPIlpGR16cltbj3bug0PlfjM4SliFEEIZCaoHnuXvKCJtSG9+OlQRrJJlCIFDUSiuVBk4PInn353Hy0/9i7zlSyOi0y1MHzv7Kchgug+oYYu94sLD/OedeRR4l5OWPSGQxlDg9njQAv6HD//r33z78x5GZ2QzOtW0lIa7Hghg7erlQFbEsXr3O8p2lpFyn8be0ir+840I5q0GBafTyaU3/YVRKRkUV2ls3V9Ov65uOrudbFyTy9P/uANdi4w2H546li0bF0PXXjWOU1pagkPpHjG51u8pZtyxR0R8LhjXEOsTrYdlK1ay6Muv+NiTwY4K87VhE84hYXhvsrLHMSolA6eicN9Ls9mc7yVj3AQSUkyL8MYCc5Gl+lVcHjdPzpjLiOR01qw255YQdc993W6TAthRWAmYKRtFmDIsMHMcr1y+jDEZ40hKC7eKhwpdWEaS7j164fbsD6aIG5mcDlujH3Ndzkqu+MXkFjyrpmEIcCrh93ZhWtUN0xCyZV8ZqYN7sWrVqoid1jK/Rrlf51CFiOpPH7xnhLmrGALKfBrdPM7a3ZTiTHP8i+sj3I1LYi+kgtxAsrOzeeylN1i/YSN9+/bl6X/eg6aqfPTaf3jg1fcZPfC0eIsYFyzfSbtvEYUCbAQOlKDVomBncchFAIIPA4BDFb7A9qelQVsPQnN79df/dwfrcrxomkp1dWDfrh0YybX7HsYLBYWUrPHBHK/uQPnk0SkZJKSk4whYOEelZPCfd+axf0s+J6ZkMWxMGhmqjl83Im7mWtjfiZnjYLUe9bhx9CKowZ5SH8VVauSLp97AuDPO4aTENDTD4FC5j398sZXeXVxMvySJjbleDN2A2xZEfG1L3wzIuqyWIylszvdCykXBV+745BuW3hxpZTcEOJSQgtUaeL1efnXhL01XoT9+GFwobC1VMAafzlVjRgYXkyclpXFiUhpd3c7gboulCJtWUlg4dzYFq1bQq08fM/jRLxCXPlLr8TXdsF2BjH1lVYASHAdLiduQn8utV18Q8NF388TMeWRkmgpSeNCZ5U/ctXsPnpwxl7WrV5I5bgKd+w6ErdujHnNMuj2D9Cy3n9D90jzHTxYv5fNFX5I6djxVP/fgjDNOj/DLdR49AqcSXeEL7tcJ0AIBnuaxBHtLq/DrBsf37dZ6J9lImuNaUhtWjEu4b7bEPkgFuQ6qVD2iMtyIMSkkjJvM+6/8B01VMbr2QfV0YmOul0vO7ngKsk83K65Zlli7X9+W36Tlh7uxIJf1OWZ6qlEpIf/R8G1VFBGmXCvBdgBGpaTz4HOv4l2/lfnVjrXwvTc5fZCTM4b/omVPqgkkpKbzyOsfsiXPyxF9+7J29QqcDoVRKRmmEquYVrHRKRlknH8GW/aVhTJVhC2EhIB1u0vYWJDDysWfkz7pjBrHsutNP9oDPIdjEAIKdpXwt4XfAlBUqWEIQUJGNu5OHvzRGsu4ONqroCgc3LsL377dhHuzrc9fTVrEB00/12gKo2EIDlX4Y57d4quvvjJTeOm6mbnCEbrPfX+wwiyUohv4tFChl2DebwRJmeNwedxoKjicTj6fOwtd03F73Pzh3ocoKy5iujGq1uPbMThNNyLdrSzl9/N5s1H9psuA6vfzxbz3Sc80fdQtxUYIzDzJge+OTE4nITUDj9PB1p921HrME5PSan0vnlgZOMLdTD5ZtJSbrrwgGMx75VVX1/DLPe1XIyCQR72wwk+frqF4lGB2EKDSrwfbjea60pEQRJZ2l9gHqSDXwaa9pQzr141eXdysXLmSWe/NIeXk0818n32PxrjmOQQwKklwqNxPZ5eDwb27xFvshvFI7dadhrLrjr/h07SIbbi40IBzsYpfWH5w6/NzzApiqorb7eHpmXM5POViNm9YR9+8HDKPOQsIW90TqRia/4B+J4xm/pZQAM49E64BwPjey7qcFXCFvRRkqx+GJ6XjVJRghoIZz7t5csY8ktPNFG9WPx0q96MGrMSWpczKFGsIwZx33uSx++/A0A1mvfYC3PJhxPGEgG9uvZusY/rQrGD1GMxXqw3LalMdS9nZfriixnsnjUnjH6+8zz1rorTrrkV5VRT+9+E7sEuHsVcEX/7rtRdz4gOPctaEjODCcv2aHNauWoEeli0AzAIt2wsrYq4gDxmdicvtQeAnWq3Db9bmMSbgOma5EBgQ8DU2K+s98Mr7bMz1cmDPThbNeTcYyFdcWMjUm//M9BdW1nr8CP04hmPbHERAG7SUwnJVw+2smV8k0vc+pFBvXJPLmuOGceKoMfSHoF9yXXdFuyqFQX/hwC6CIQR53lAwryp85OTnB4rDUMMvVwj44VA56V09NdoFMz5DN4yQK0trnVgL0ZzqgpYBxv6e2B0PqSDXgaUE/HfRUi4+7xxUv49Zr73IEzPmol7zXPBzJyWZ1se9pVVtR0EeN67+z9RDZUYW5X4N4dfje4E38FysG75QMDM3WH64+Fk4bzYL585G9ftxz3ufnnM+5tjRqQFlSgQfhuHtGEJQWBXpUuAdNBIA5485JGY0v49jTcjfTZAX3geq6UOclJ4ZDLgSQmFncWUwyt7y17ba2VCQy2P3h/xyVX9U+yqFyRmI4Uc2T/AYzFerDeVQeS2Kifli9awCLz1yL24MJp13CY1zFHGYvrhbV0QoyKqq8lnRYU4fm83ancWsz8/ltqkXofr9vP7sv/nqy8gUUrFWooQQjEhO48VZ88lZuYzXhSeQcSLE5nwvCWkZYdvgpjUbQi5VJyamc+KYNL5Zl8eSjz9AU834jKRATuC6MFPDBYjh2DYHy+1q2+Hy4H3cEIKzLrycz+bMQlNVnG43k8+/NGyxGMgKsyaXv99wGVrAJ/vfI0aRmJZJuI9y1GPSsBLlrY11XuG7aVbuZ7/wYRgGW9avxe1yccMNNzB16lQz29O2w2Cl/AuziJZWaewoqkR06RQ0qFz97lomHNuHP086wbYLhYbQ1BLTYAXthvyxJfbCnh7xNsG6CX799RJz5WwYqKpK3srINGBB61pbmuArV5o/zaBr7mq65a0ObjHG7fwbcC6WW4X14B6TOQ63JxSdL4B0n4+sQODa0qVLg+MfPL9AW8HXMX1Hw8nevYXs3VtIm3Q2o1IiU1zZBUvhH5mWHdEHiZmmkhGe0ipcIRaB71pWtjWrlpt+uRZdetU8FoI+a3KaPddiMV/D24imwAXHuNqYLjnmYv734Uz+ccOljTqcstkszcvB7bD4xeDrTqeTs3sdgbZ8OYYQrF29PFixTFNDFcuCcjXqqHWj6Ybpdw8kpmVy9U1/xuWsadsfkTa2RnaN0PUTWih9sy6PTblerr/jn5x18VVMPt/0x16fn1OnHBH9H+OxbQpW/uNvD5Rx53+/4aNNe/lufT6zp/8HgP+88xGX3nwH/5j+AcePTg3601pxDWtXr0Dzq2QZOhl+P2tzVoaUzDoGUNjQ1cQifPwFgjFpmbwwax4Z408JZvDRdZ1jjjkmstIsYdk9AhRVqlSpeshNJ/De8m2FCMP+7nl1ES0FXEOoVHX0sCJEUkm2H9KCXAcCOFyhkphprpxVzCCNk9LGQthW67dr8/hqcy7p2RPICGzN25577jF/NyNvaP9pD6Abgr0z54VKhsZEuEbSwHMJ384bnpTBUzPnsW71CrNimIDRs2ZgAGe7PaQGqnxZ7hUhJVmwoSCXglXLSUjPxtE7Mo3ZI8tnAnD60SPYnDyQM4f/wlbprKxdEfMPwaQpl+J2ODj7ossZkZyOFUQT7lspFBHxPetGnpQ1Hk+nTvj9PnB2Qtw0s8bxDAOGPz0NR1cPLF3SdMFjMF+tNpQ5n7KhoKYCpxqCD9btZu6GvVG/rqlq1Nej8dSUUWzqkswbuR9gGAaK7o8I5Eya+Rr++R8gZn9MUtb4YMUyl7tmCqlYPTSrVJ1Ne0uD42ouBESNRR5gFr4IKH93/3cLCQN6cG36EPy6wO00r6Vv1+byzxsvR1NVHE4nCIGma3w+510cDkcNd5twIlwPYji2TW1DCNMHeX+puQuy/sfdzJ92BZqq8v7LT/HkjLmMTMtmU54Xp1Ph5PGB+wPmdTIibSwut5tHfDqKEKwN7B5pgfzItWHfGlOh7D5B32EBY1Izuf5Pd7Iu1xt0T0vPNtM+HizzBa3l1X1qrXRm1is+LbTzpouQG09bpKklpjftLQlW5Q033kjsg1SQ66Go0s+IpHSee2ceyxYtJPnk0xkyMhnWFAQ/89DvzYfEGx4Po75s+PZKeyB0AzUfuvZRBSMx8xmbN2K/Zt6IElIySEzLRA+YOoaNTKC0pJinn3qZ0amZwe8KAZvX5LIxz8tRR/blyQfuQfWruD1ubn78daB7jePpus66nJW280EG82a8ZW0e/7jhUlRVxRmwIOqGIDUjK2Q5DrN+hSvN1lgnpGTwzNvzyF25jGNOGsXfo6ayMh995X6NLrphmzKqBatWAJk1Xq9NOQZw9DqS6Dk6aqIAZ116NcNGjGTt6pV8W2wE19S6rlNaUkK3Hj1NS35yOs+8PZc1q1aQnj2hxv0jVs/N4NiFtWkIcETRkIUQHK5Q+d2H6wH4uaiKa9IH49MMugYKO6zLW43atS+icBeGCCW21TWt3n6yW954a3fE6oqSwsNmIHbA/eizubP5Yt5sNFVlzitunnl7HqNTzfLamiE4KSmdv708m/733EKPnr2oSk5nY0EOBatWMuC4YUDfqMed99qzdJ5yFlPOmNRap9ogLMNAuIsNmP00OjU9aFzIGn8yfU4YDcC2woqIzwV916u55mzZV8afF2wKO5a9cwBXqTqFlSoDe3aO+n5jU8B5vV4WLFjAsMxJJKVnhowPMkjPdkgFuQ4sa6OiwJi0TAYMHoK7Zz8KKyItSaEbaewq7MSK5gQP1NXmggULuLW0hM7dekRsw9nsuReBpSBousDlVILyioBm361HT7p078GoFPPBZwW0ffLeDJ578K5gwQzDMBCBqnLfbFoPXaP4PwpBj9598OsGnVz2qaUqhJm945V/3R/0GdY1jU/ee4v/zZ/N0zPNMtNWkYBQJTHzWti8No/NeV6yxp3M8OQ0ElLSGTYmlf379sLWbTWOZ527wIxWj/cNp8yvcbjCT1LmeFjVcItw9uW/w3v0Lxv8ectaNjI5g2MTUvlydQFrvjPfc7nd9OjZM/A5EVA6MhidmoHLUXMBsb4gh69mFcTkGo4orx6Y/9EsyI8v+ZGzq/mNG4Y5hoYQ+HWDnN5ZiF+PRfnvvxBbV0GYWuxwOmukPqwuh53uFZa13Nrt2e3si3Ps5ThWz8bldqPqRug+7zdYOG82CQEF2TAEwhCcmJROv4GDcSjmgvru6y9B9as43S74wwdRj/v+C48z/7Vn+LIRfqutRXgAYoQvujAz+CSmZdLZ5TALKAUs5daQhmf92bSvNJDtyLynbN5XGnGcua89y6mTJjFg0smtdWqN4mC5n72lVewt8ZEyuFfUzzQ0BVy4v7LD+STnXforTjv/Mk4YnRa/HVhJrdjDnGNjrAeKEXaz2BZIKG8RqjQVuwo7scC6GO+//35OPW0yXq83Zm0+/vjjfLtpI+WlJebNMOCYatcLXAkEo2u6qZBogf3DyChzy9IRspKuL8jh2X/eia5pCMPA0PVgVTmX282xJ42OfkCHg5KiQjbuKY3+fpxYvXoVd/z6Yr7bsLbGe6qqsmb1itA2adgPCDYW5HLP9Zfw5tOP8n9XX8CmgtyIbdhodHI5oipg8cK6nhNSapaFrou9Yy5p1OcPVfhRAifuUBRGjAilPHtg+vt0694zNP+q/YSTs3oV/3fVhdx///1Mnhybazh8MWtaTWsO0LrdJez+6ftq3zMVnE0Fubz78jNsKw8Eb/7yTpTEsBR/ns70v+mlumUglAfXLpgKfUgmfewVXHHz7Tz4ygdMPO9S040Esx8+/fBdNuTnBhc4hrXQDlw963JWBgNgdVVDqeVcTYW74X6rrYV1TlWabhZWInzXITJo2RCwfndJWKYb66ZqjnGVqgfaNP+uPt0+fPHf3HrNhRTkrm6t02s0hgBdNL9i7mOPPUZlZSW6bo77vHff4i9TL2LT2sBcstk10dGRCnIdhM9Vc8tJ4N1WyN2fbon43N0vzuI3t97Nf96eZysrQHjwQLTgn+a2KYRBaUkxYAW52NuPKvgww/J7M2/auggp9+HWcIDP5rxn5ooNoDgc3PqPx7j+1rt4+LUPOPqEk6Iey+FwkpQ5znY3veVfL0Xz17ScOhwOMwNB5riA4mY9FEOWoy/mz8bv8wV2S1TWBpRpQ0BVLc6U5quKbVZO5aUlvPHck1F9kOvip8OV9X8o/Dh+DQVzYeZQoJM7dKs9MTE8921YZhAifTENQ7Bs6dJgye/GBADVhrWLYh3PqEVBBpi5LfL1bh4n367N5c9TL+KDFx6PeE907RP649g09nqOqlMOG10SQOjar57B5Pzr/8iJSWkMT0xj4nmXBVNO6LoeqBxpcrjCz69nr6VKNSgvLWX/7p04XU4cTidOt4soGwOAaWl3x7B0cSwRQlCp6Qgh2FCQw1svPMWG/Jzg/SF8NyL8vm+Evb/1QHmYVbla9hLr9bNvRVVVVq9Y1uLn1BSsncTmuoFMnz6d+fPnR7wmhEBVVdbnrLDLLVISRrx3PG1NyGVACd5Afy6q+aAcNiaNkyeMp7ONttIhMnjA5fZwyimnhN58+ulmtenz+bhNUbj6/EsZEbAohBcVaFUaeC5mSiUlEDVtKa4KhgHCCd/f+xBqsMysglAEhw/sj2gjMTWT408aQcGqFVSogv/9EGkhvnXSb81/fL8yYsvRDmw/XMFxiZnmlrFwowxN4fSkYYwYnUhJcSGJGWZ5YdUQQWuQIWB9Xg6fzHmPL+a8G9RsnE5nMLhRCIGqR/c6nb9xL5Ouvo1Thx3J8OYI38T5Gs66X/+aG278HflPPILb7Yab3292m7UhUILWB4dDwRmmhGpCsO1vjwQXF0CN3wA/Hq7g+KRM3IGCHI0JAKqOT9Mp9emh9q3xJbqLRTQueiufM37+EC1gGY0gXMmup8w0hBaoQEzGtrltCGFmWli5/XC11wWff/AOqxb9l269+uB0OjEMA5fbTXLWeDRDsGp7If/66gcAbh9/JT+8cjv5WzfhdDoZcdXdXJA1nCe2OdGjlJ6/+Hd/4YopZ9vKsGIFFlqW4i1r8rjvt5cG4i48/PutDxmZEsjhbe1EKKFcviG3DCj1qcGAUCEEmiFqut6MOAXXl8+TNd6eLhYQGYvR1JjrOXPmRPytKAoOhwOX283o9HG2e160JqpuUKUa9OhsL5XUXtLYkNB2pPkwi/Yw+b/5m5h5ZYrtFGQreODLr77iqBFpZI0NuwknJze5zaeffpqb//AH1hgGm157nofHn8rwxLT4WY8bcC6B3c9AMIQIptex/u12KpSNHINPM3BhKipOoM+R/SPa6dG7TyBnrYo49w7EsMgH27r+xwOgfLeCDbkrmTB+XLNuqrGkzK8zOjWD65+azZvfqVQpHiadM4Kko3viDJSY9uuCSlXD5VAC/so53Db1Yvy+qqA1WVEUzr7oSkYlpwcfolu3bAJ6RT3urds9rJxSiytKQ2nifAXzwVxcpTF3+w7yNQ3D0PEd3Ux56mH39h9gWF8UoKvbGWGl3bK2gGMmjTctaoFiCaph4Kp2c6lSzfF6csY8ir9fw5DRGQwdldIkebYXVlJSpYKlwATk+elQJfvLouevjsZXH83G6XJiOCNLAitOV+jh3oD7QETlzWaMbZBmtFHm0/h2fxn3zc/n25LI9xbNeYdXH77T/OMPs+CIk3HOvpPr7/gnCSkZFFaqvOwNlZH+WnexN5AXXO81kM39MinZ78anR/d3v/6W2zjpyJpBvvFk3W6zEzauyWX5119Tsn9PWL50P2tWr2REcgZCiVQcI/5NzeA7IUzXtmjT46FXPyA1I6tlT6wZmG4jChv2lnDcEV3p2dnd6DYuvvhivvjii+DfV97wR3r16sXItLEcMyrFfCZ1UA15R1Elhyv8pA/pU/+HWxGpINdBzQs+uqJTWKmyr8xH7672687s7Gwys8aS+3MRa3cVc1zfrhzR1QOLFpkfOP30Rrd56NAhhGFwmmHg8PtZu3oFw0anmql84hGO3MBzsbwAjMCNetOaXNauXsmo9GxS0jPptXwp3YSgeMKkoBX5rAsv578fvIuumUUC+hzZH7/fjzAM8HStcYzJ29cCsMTlYUyGvawCVi7ol75XQDErXJX6Nao0g20bC1ifs5KRaWMZlpiOphu4nU7WrF6B6vdHKMdOl5k3euOaXEYlZ7B5bS7P/P0O+PXLUY87eftaeixT4crzmy58M+ZrUaXKD4fKyRZOznI6+RyBccmDTZelAcx95RnS+97GqJR0PE6Fn75ZH3zvkT9eS8ZttzN02HAOZk8EQNUELrcS8YBUFAVDmKXBT7n0bPJ2FFLq05osU3huWus2dsMH6xrXhqGTdsoZ5CbfHPH6hF9czIAxA+h9RF9yth+ivlYjfP+bMbZBmtFGuV9nbd5qvv15N/QeFPHexzNC+avp1A0GnIhAUFpUiAAqVD0ijeOEXd9QCCwGcJpK1M7S2oNBa9bpiz+GEGwsyOGv116M6jN96R0OByjgdntIzhwXCuAlpDxavschv+xQykiBGadS5tP4/mB5jWMOT2pcTEBroqAEgm7NYMRSnxZVQa4vIP7GG28ETEvy2ImTOO2KG+jscnKgzIdPF4HFg91mQ+thx0wm9tPobET4eJmr39pzWuoN2FaMF9ZWmCGgSg3I+dBD5u8mPFAmTZqE2+3hfl8VihAsTR7L5nV5bMnzctqpkzip/2S6uFvRml7PuWi6gRr4sR7Me75ZG9w2dHncPP7mHK568UkUFPLHTwqOfUJKBve+PJvv1qxmdOY4Dm3/zlSOAaptMyvLZ3Df7m8A6PbXfzE8OdVWPtmKUtO4J4RgY34uD990OarPj8Pp4Ld3P8zJ5/+K7kKQmDkumKPX4XCSMuE01q74kk/ff5v/zZ/Nv96Yw+qcXPSzb6v1uPetfp8hO3sirmiGgtyM+QrmeY9b+F8STxzOxf36sarpktTP3u8wfsxjU95KElLTcTocfLcuH5ym77Gq65z4+kv0P3ow+8ea28oVmk5ntyM47w5X+AOR/4CApcuWM/uTL5g48RSG/3Jyo0Wyymtb89GhwPr83Ea343K56XNEzaqIyw4o/OWsqWQM6U3fXSWs++r7KN8OEXJxotlj29w2FCB/1QrQB9R4b9+OgHU4M1QkxuVyMyo9GyEEW9fl4avQQDFLgd+6YSma082XhoaiOOrM5AGhHOuGIaKm24sHhoC1q1eg+vwIYSB0wAlTLr+Gsy+6guNHp4ay21iWYyWUY35DQQ45K5eTnDWO9MyxAMHPPrzoO749UFNBjpgPNkQErGO1idjQano33ngjN954I4vXfAuYY+/TDFM5diq2Wyy1JnYcfhmkVyfB9XHQf6q2QQzfYrIT5vatiLTYxIDJ555P3/5HccJIc6v6H7+9jHeefYzfX3EB7/13cQyP1Hy27C9jV3EVmm7mahVC8MO61cFtQy2QvcGKRLcCmTYU5PLv+/7Csv/OYVRaNieNSePwIbOUKlDD1/LSa34d/Lcu4IPp/2FDQa6tbvzVFXYh4OtPPsBfVYUQBrqm8eq0e/ly7tu89/IzADz7zjyu/9Nd3Df9fYYlJKNruummUFXFC4/cx8ZOJ8DA+j2M49ULliUWAd169ODUc85r0eM5Zt+B21AZE6hM6FCgR89w9xMFXdeDfsBCEPSLt8bnx0PlQZ/OjWtyOevMM3n534/w60vOa3ImC0vZRsCG/Fz+eNWFjW7jnpfeY+J50TN6PLH0x0DGg/pHOk7RCrXSq3ef6L7TF/0d+gyCCdcEX3L+32yGJ6aRn7uaaTddSUVpcfA91QBl0HCu+MOdZJ5Wf9EowxAcKlfZtM9e2W6Ss8abVuMAQgiOGjSY0akZYUWTIndZrZLbf7r6Qt58+lFuv/ZiNoQtwgwBPxyqiHq88LbsSDSXEYD8HUVouhG1ml65T+PnwlrON+w6/PDVZ/lmXR66EEFXg46GVTDFbkgLch1Ud7EAaq2EodtxdIG9pT52F1dh+h4230JhrZR9Ph93Cujdrz+b870RuaDzvMv59QVnNl/4GGFajgVa2BZz2tgJOF1OhGrgdDoZkzEOln0VtPhsKsjlL1MvDOYK/vrj97n+zgfZs+tnnC4nuqaZiWHDGDxkaPDfCz+ai/j6TWa//BSj/reIkyeMb41TrRMFamRvKFjxFUsXRAar6brOW4/djzAEs15y85935nPl7//EvjIfTkUxc9wGrOdbN6xBGZADY+oe73hfHsFrGSguLGzRY133p7s4PjEzLJ+2QnlpIWFRe+zfs4uefY4ILFxNldKvCzorCpWqHnTnMgSsWbUiWIq6qbnWzUVCSAG3XGcay7Axafy0saDW9zfsLgkGrNWFYcTXtcCqKnjikd1QFCgqLATRr+YHj03F6XJHFD6p1JWAlXWlWV2x2uQ2BJx//S2szMlnVdTiOSEufn0lT4/rSpqN/G8NIUhIySDrr8+z6oW7EeVFuD0eUsZOYGNBDqtWLGN0xjgcwLcFq0gZO4HRqekIBGtXrQjzV4aCVcvNgipYWUJqOWYrnl9j8GsGmmEEsm9EKaiDYN3uEhIzxtWopnew3M+Bch/H9KnpimcI00XtL4F4FpfbzQOvvs/Rp0xgX6nPdIPsYNhpt9VCKsh1EB6UZwjBD4U+Plh3MOpn7eRrGo6mWxbR2Cgp1krZMEwbUFlJMSPTsnG53WgauN3uYJlmOyGArfvLqNIMBvXy4LKMwEKgaRofvPY8F5eV0qVbDwwhWGM9/AJofj+vTbsXYQicLicpp57N9oFHcyDsGOGpnHSAwENi6dKltlCQoWYFufWrvo5IYwcKTocTQzcQwiyGUuBdzrEJqewp8eE4egSTplzOojmhstJCb5hfbGtb0i2fwPTsCXQ/NsFKzWrOz+UtZ6VJzhzHoBHJ5uNUMZWChJRMWGc5ADuD147VI0KYC7lOLgeb95aG712RlDkOt8eNqtKsXOuCkCKSHChv7WtkG1e/uwb3u7fBr56K+v70VT83TBZR+25cS6Mbgk17SxEIDpb7cSoKKVnjUeZ/E/Ue3v/0a9hT7TUBjErPxnFcKkav6CntjjlpFGzdEvU9Cx9u/nTdlTz/1ntkHBOyOOuGGTzscbX+Jq8hzKp4K9WBHHfrG2QcXkny2PGA4E9XX4jqV3G4nCiArum4PW6enjkPXQj2BdLboZvPgqSs8UHlWA9uYdTErs/P9XuKTcVYgFBqzllTRxCMSE6PqKY3eEQy+8t8dfrVrl0dWkxoGmzM9XLaxAkte0I2xo7lxqWCXAemZSeQDF/A37+uvQxtXILTGoh1g2rMCq22gINgmje/H8UQdOvRk5OS0rj/5dlsKVjFxIkTI8o02wUh4J7Pvgn+/RtnAZpqKnbCMFi1+DO2KQpDT0rAEDAmw1T6Vb8fLnsEOvdAn/FH88s6nJCQhGNYAgd+Lgq2uaOoKvhvp8uDcDpxu91MnHhKq5xjQ6heQW7fiWfh3LgKR+EuHA4nk6ZczrEjRjPj339HVf0oikLP3kfg13T+smAzAA+cewlfzn83TLGuf16VlZXSpxUvkeo+gU/NnIdVqmNMWiYsX17n95vD7dddzN+mv09aRhYup4JDURidnArr8s0POJwoikL3nr0wDIFfM8IWsaEiNQQsyCOSM/jXG3PYku9tcjU90wc59IAfnZrB0zPncpO34dUELdRalGOAQxW1t3d0r87sKjavkZ6d3WZ56jhhBZIVVqgIBAmp6Qxe62dHlB3xPQNr3s+2rMkjITUd7fz7o7afu6OIJ5b+GPW9xIE9WB9WQEjVVHPhelFIQd52uIKiSpW0Ib0bdV6xQfDNpg0A7KuCU6/8HUd278QXb78YVOiEagSzAPl9Bu9Of5acZV8GleezLrmKM86/jISUDDYW5LJu9Qr6H3siQu8BSs34FDu5oYUjhLnrtnLZ1ySPHU9KelaN9w3MO2B4Nb28HYVBtwHdEDir+ZeLagtfl8vNqLSxdVrZ2ytFlSqVql7nYiJe1KsgP/vss1x99dX06RO79BvPPPMMr7zyCkIIbrjhBm699daYtR1TRLhyWfdHdZv6IIPl5xhS9gF4OTLjQLhCDNQacGCljnvp7ffZOyKB8iEnUbCziN3OwVx4/S0M6NGp9fvh5ejZE4JEkWdYylgcDkdEbtLfCcE56WM5HcGolAz+885HzJ39Lv8bHJkSzOl0MjItm+3VUkK5HQq/O92M7h874hiOG9Wb7Aknk5U9tkmnFUvydxSZikBKOqwK82HtcSRdrniIc9nMsNRsVvmOJDN5IJpuMPPf92MYBv956B56DT4B69Z9YlIqN9wzjVceuRvDMOqd+L87/WYuZj33NucE6hvjalT3CSxYtZyMh/+Nprf8daqqKpvzvKRnjsXjdKAoZqq3IA4Hf3C5uPOGW+hjGGgBeSKrsoV+DGFaqNKzsujXrVOj5dlX6qPcbz6AdMMspywEJKRmgHdlbE66AZw7sj8vByzMnVwONN00LDgaObZRaWQb1n3dsgoaArp27wYVNQPIovHwH67iby++Q3V1xrr+t9aiHANMSTgqQkF2ezw1dt00Q8Rty3l9fi5P3PtnuOIJKnSFP8zbyOxr0kjKHB9U6BxOJ8IwYxaEECxf9BkQUHR16NN/EKNSMgJpIi8Kltx2/OG9qMf8Zm0eYwae1pqn2SDW5eVwy1Wmq53b4+aZt+cz9PSJEZ+JNkyWi5QQsHZXcXCh49N0dhf7EAJGpWTw77fmsHzZ14xIzebExHRbuhlUJ9YBpd8fLAtkCrHfuderIO/bt4+MjAxSU1O5/vrrOeussyLS2jSWjRs38sorr5CTk4PH4+Hss8/m3HPPZdiwYU1us6UIGHEifPdqQ7Pj8ofQRWoFGai6gV8z8Aw3g6p8mk7e6tWcccbpQYX42muvrRFwEG618gweyWXX3sgR/Y9iR1EVj880LWPnjx4QtjXcigyvO0DMVAgiZRqekMhv7n6YV6fdE7SE/uDx0Pv0c9m8Jo/NeV4yxp3Mb+99lP8Fzs9q65TzLmd9IazaWRTR5qplS/n+CHMeH9WtN5f89v/o2Tm22TzqSyVUG9b81aLMY3enzuCDr/bCioMHWfTdQY4u8QQCycxKTxvzvOAxg87+vnArfz//So4+fjib8718VdSVfXUce+sRgzkqY2jzZkU9Y1yd6kVykrPGU37ciRiGYGt+46roNRa3283ojGycDtNFS1HAHb5VrjjYYhgs2fEz51lFa6gW9BTYvbIKL0TLQNJQdhRVBKxZgs1rcln00fu4HGYKw9aid9V+1nz9LXgC4ygE+8t9VGk6I8LGVgtkmmm0a0Ej5odlSQ/+mOJEFHOpD83dmU1h14TF1iMGN7gNiweenxl11y1eT5Q1q5ajhfmnW+kBE1LSeXLGXJYsWUJy1niWfvIhX3xgulpZ91er8MXw1GxE0H8+UFhGBZcwolqQH/zd5Qyb80mTMrS0JAWrVqCq/qBP9ZpVy0nPzKJKNTihX7dafZMt98zwYkAAxZUahyp8wWfkcaPT6H7caBwowV2k5uhXrcGaXcUkDeqJyxkb9x/r/mZDD4v6FeSHHnqIBx98kC+++II33niDW265hcsuu4zf/OY3nHDCCY0+4JYtW8jKyqJrV9Nx/ZRTTmHu3LnccccdjZe+hRFCmMnQA1slDqV2S7JmNCx6Ox6YF6n5wD1Y7udguZ+0tWZZzw3JE5i1YCE+vx8joBCDadUgoFxU93m0yif3WbQQrcwP9A0cp2HW9pjz8cfm7/OiZycwrXKi2muCyRdfzTHDRrDkkw8pdffi1v69EGtzuO7lJ9H8KrNeehLtD7MJWonOvxfX509y7IjRvLqz5s3h+282cW5vsxLXlh5ZweCrWC2MG5pKKBqWCFqUktBFB/fz/szHYdJvIPlcAHZ1Pz4ouNPppPK4bNhlfv67g+X8dKiCE5PMtGWz3/oY+ifXeuxzf8jhWEVBNCdws54xhpqLh8WLFzP744WMTh/HiUlp9Fm0ECEg//tvCPfDjjWPvzWHY0al4MCMzlYAV5iDuuJyc77DwfkOZzDDzLfr8vhuzWoysieQOXZsaM4IM+ODUm2R627Ew8kQ4FTMoKA7rrsoqPx8NmcW3PJhzM67VnSNsldvJnfYBDjbVGTPez2Xr/8wnkrViBjb7w6WU+HXG+9a0ID5EY6pGJsmEGuHrbaS21G//9vX6XK8gGqG4nN/MBdfn5xQ+/yqfpRjRyXXeHY0Z0HUXJKyxuOc+Q7hkQXfrs2je1YWw5PS6XncaDoHdkQWz5tlBiwDKAop4yZy0Y23cfzoVASCxKyQG4Gj/3H4legFNjRNJWflMq6wmYKcOnY8brcHFX/QpxqgsNIPdAumuKuNaNmjgjtEgF83+PV76zjpyG7886zhaIZdwxVDVFf6m4u1mGiTFmQwVzQDBgxgwIABuFwuCgsLueSSSzjjjDN47LHHGnXA0aNHc++993Lo0CG6dOnCp59+Snq6fZOEG4ZAKGalq7pun19+NJseE1I55YRzWk22hmJdpNYENISAJ56g1KdhzJ5AWvYE3G4PGqbiNXXqVM644DIWffkVqVnjGTs20kXAsnYNfPUFevo0OM3cPBdCoBsGTkcrB5Y88YT5u5aHY7TFqfVQPCExjfXKIBat20Pqwn+iFu0LltL1q0pk5o8Tsvjzs+/x8zovRAsyVhT+kj8fgAtOSAxZp6p9rLEKjkW0VEINVpADq/RoOx2mjAq4O0eci/lL4dTzL+ejXZGzXxem+r8qfy3i2LQ6j/2X/Pk4Fmxi9blncPqkJpaTrWeMa1s89DxuNKVVGlWazuDXXgAg5Y77YWXjfW8bimEI5rz6LFnjTyYtYywORQkGhQJcdM0NPLPwPbosXcSnl/+W3Pw1vPDwvRi7vuHD6U/xzNvzzcj/wPzxaQbuQGXDcr/O+t0lDOzZmUG9OtcqQziWP/OaVSvQwwNP1ZbrgwgcjkC2h9DDXzVEIHe8EjG2TXYtqGd+hGPtcoUHhhlC4Gyk4e7jfZ2AyGBP6/qvTUG+Y6SIWCwB+Gop0x4vRianM/7Mc1ka9tqmfC9j0jKCVnYh4KSkdP74t0d59p93IQwDt8fDtbfczqARyfgNcydgZHIGT82cx7rVK3jZqH3R4HS5yRhnvwC10amZPPfOPFYuX0Zi5jgSUjKAkBEoYtdHiKD1N/w1Mwd05HtWvKIVu7T1QDkGIuAC1Vpn1zSszDuxwuorOy4N6lWQn3nmGWbMmEG/fv347W9/y+OPP47b7cYwDE488cRGK8gjR47kzjvv5Mwzz6Rbt24kJyfjdNbccpk+fTrTp08HYO/evezevbtRx4kFpYcL8WuCLm6z2o1DCVVjq84Xc2ex5OWHGNxptq0U/kNlPkpKqswSyoqC5nFiCCitqEIzDAoP7OeYoUP514uvs/fbdWRnZzN06FA8JVVccNlVCATrv9tG7y4e3IEnSNGBUiqKC9FUP1sPVgaPVVV0kKIqF51cDnZ3amx8fNPpG7CIHapljhQeLMWnRV5+JYWHcAgDA8F3e4sA2F1SRTd3D1xuF5oKTk8nqqsQg44ejKdqJPwU5UBK6MFXTGe279rDsX06sbeTj05hW8Zb9pUyrF+3RivJCQkJuN2mBcbtdpOQkFDndVHq09hZZI6PZphbgct/Kqr5wZ794crH4KgwNyfFgeJw4Ha7GTvpDL6olrnLV1pEpbuSTz0pcET9shtC8NmnnzDqpNCu05Z9pRzft1tE39RGfWO8YMGCiMXDggULGDp0KMu+XMFqr5eRKWmMVs02Bh8zFFbWXciisST11NA6dWfTgSr+MvVCDE1j1gtP8PjLb3HZWRPZsD/ksD6HMTxQ+CKapvKPhZv5oVCBS6fBx/9C/WEV3i8XMuSYY3A5HFRqGsUVGp1cCnRyobqdCAFFB4GjejRItqKDpbgccOLIkThdrqBi7HA4aTXVLIrSW3hgH36PE19gbFet2xpQkGG3q7LG5+uivvkRjmYIig6WAgKnYhZnqdI0jFrKQdfGwfLGZ0L5YfUizjluCOMHd2XFTjMisOzwIYqVCnZ7QkG+hwsrKa5S2e1uXD/EgtJDhSRnjmPpttBrJwwfQXnhAfxOBV+Vhu4wfesnnHY2AwcOYEN+LiOS0+k3aDAVRQdQdYGzkxOfw8ExQ4cydOhQXn6v9mvutsdeZOjQY+PynK+L4kOlDBl6LL8cYLrOlBzah+p2ohuwy1lB8cESFAWqihzs3KUEy4YXHyrB5VCo0gxcDgfrfcX07uKmzKdRVFxFWfFhNMOgIqwyZmXhQYqr3HT1d2a3WhJVnngjhKDoQBl7lfImGXmqc+DAAYq1TrgcCqU+jd3dWmnR3kDqVZAPHz7M3LlzGTp0aMTrDoeDTz75pEkH/c1vfsNvfvMbAO655x4GD67pt2VVnAFIT09n0KBBNT7T0vSq8FBUqdKti5tOzkocyk5qd5RR0DSVTZs2MWXKlNYUs05EcSUlrgqcqoHbAd07uczVr9OF0wk9jjiSTi4HY089i6zrrgh+T+tSgb+4CkOA6lLo3LsrR/XohBCCn/1dqNI0nG43ELqBe3r3pWsXD13cTgYN6tt6J+kxzbm1zZF9RhHlfh0I3aCfKzjMb7OG0quLC5enBOs8VFz87eX32VKwiuFp2fytWs3cHTt3svPbLTV8DwHT6rr/R+h/PAAffl/B384cwlED+tDVE7rUdmmFdD+iG706uyL8uOrzL54yZQpffvllg32Q95ZUUeKsRFHAr5mR1I++GyUxq7tzpHIcwKE4+PUd/yRx/GT4IT/ivVVfL6ZTagINrTWkKApn/+LciDHarRfSp193eneJvu0aQT1jPGXKFJ555pmgBXnKlCls376dP99g+tO7PW5+dfyJdOvegx5H9Cd8LsSCI448ikd+MYK//t/NrAgooJrq5+vFX3Drr69gu78z8F3w8wcKSzi4dxc/FIaULKXvYFw73GSdehY9+vY3A/yqNCo9fjq7HPTo4qarxxmMjRg0qGGB0594P2ddzgqGp47lH698yCczXqTwwH569etPy3pjB7AWjtVMY136HEmvzm46Bca22xFHoqCgGaLB5xaknvkRjl8z2KF2RghwOx2m9div43QdBKrq/X5z6HPUYLoecSRXZ/Zmxc6NALh79qFH354MGhRaaZa5yzDK/OzRHa2WycIwBIcr/Hh6OTEGj4Zt24LvjcqeTGe3E5cD/J20oHLkdDlIO+VMUk4+kypNRzUETl3gNAy6dnLhdjrCqqrWfs2dlHUqXXr3olufbvRqyP2gldjuN13mjEoVBYXObgfdO5kK8sCBvfnRZ+7idHU7MURo3m7zdcbtVHCpOh6nA9Wp4OnVhSMVhRJXOX5Np0ufIykprgICgau9+tGpq5s+fbsyqH/DFr+tSXGlyvcHy+nVrysDBvakkys28TXlvs54XApGhcagQTWrdMaTehXkBx54oNb3Ro4c2aSD7t+/n/79+/Pzzz8zd+5cVq1q0cKvTUZRTLN/uV9j895SfLWZjzH9Ct1ud5NzlLYk0bJYiMD/rO3M2iJxzcABa2tIUBVISRVqJMSH6/bwm8xjbOeLLYANBZFldQt2lfB2wU4uSxrI9wdDuZ0Mw2BjnpcxmeNQo+z5/Oeum9CL9sGt82q+qTjAX4FLmHa5wgo1ar5XQ8BPh8vp3cXDsH7dgIb7F4enEmrouYf7WzYGY8gYSgoLo/qbfbVpB1+/9i/4/dsNauu44Qn0yop01TEE7CyqRNMN+nVvfHaGcCyf4/DFw7Rp0/CHBdiUlRTTrXuPFvHzE8D+Mj/lhyPDFQ8fMP9WULjuBIU3fwg5F1cPHD1+VBInHdMpIoBMD8Q2hEtsuQbsL/XRt5unRgqpcLxeL/939QWoqorb7WbqXx+gYOUSNL+K0m8IpDT/3BuC4nDg2rMlYkfGrxqIMC8Ry1e6JV0RDUNQpenBWAmrn1XdoKKs5a12M/79d4YMG8FxY0Idb1hlHgNs2FNilhmndQOeS30a2woreD1nBx+ur5n5WQiBLhT2lfpQHHB0zy7mvKz2HLHueT5doCgG4GR9/uo6jz2gRycqVIPvDpaRPiR2GbNigTVXFEVEBHha71ljVMONL/C/8BoEVvEfA8HOokp+PTtkgbltwSZeuTTJttmw/LqVjjK2Aprun7aM0YtPHuSLL76YQ4cO4Xa7ef755+ndu3c8xKgXy3fVpwse+bLu6lAnn3cZl0xIbFKO0pbEil6vHrFtzcZwHzzLN1Y3RPDCt3KyVqk6O4uq2F/mCwafVVec5m7YS8rRvTj5uAbsubciiqKwZtVyIDKHpUOBWz/aHPGapvp5/4XHmfuKmwlTroQhkeV41eteAqOWjWlFQVGUoMtQYaUa9WYSfoO1aI5/cV0E/buiHLPe7170AN27bmHua8/WtJgn/wK9U80KUQDHuytIL1vDt0dls67ItDR17d4jqh94laazr8zfbAUZai4eJk2ahMftwR8IsOneoxcAah0L3aZSXFRIQe4qig8diHi9X3+ziMTGghzeeeqfMCWQNzcwV8LZ9u0mfvK+x5cL3ufJGXNJzRgb8NPFLEVN6PpVFPi5yFzYHdndU2vk+xeLvgxVNgNWL/rU9LF3uuHq/8Ts/OviSKWCM265kxOTs/ihHN4OuCephhGhAFq+0nUphZv2lnJC367BILHGsrukin2lvgg/SgH8af4mvmuFXW1V9bMl33T5Cb5W7UZapeqBgMHaq59+d6CMo3t1jtiZaipFlWowDaG5eK+ZDHrR1oOcM/IohC746ydm8ZMHxphpI624BiGgwq/xx3kb+cP4oYwZ2AuX4kAgyF+1ApTa010aAU07FtVeY40Aynwa3Ts5gzNTIFizqzhikSsElPs0Sn1axHOVQEaaSlVnbV4OXyz6kuNOGkHlkMjYjf1l/hqLZjth6UPRnl/NI+SnbTfioiAvW7YsHodtNFa+0IaQecZ5jBrRv2UFaiICc8VqiND5fP/ki0GlyQgEXK3fXULakN6s310SUX3PECLob2e9DpD3yH/4zfuRPgiqHgdX+5kz635fQN/R2bAhUrbqwTLXnPNnKD6A8f5daBroSi3uA47oD+dBx53Exqde5f09XUGFskDu2ZqWhWo5qYlMS2aVKm0Oe0qqApWczKNvyM9lQ+5KIKNR7bzx2P3ozi5wUxSXkpHRZew7YCAXnjKB51f8BEWFXHPOn3n10kQSVZ0enVxBi2ejMp7UN8ZROGF0Ki/Oms/K5csYlZ7N9/0H4nIpLPxmf8TnOjkd+Bo5b/sc3kqh0Rn6HQPAzz/9yLR/3oHfH/C9VxTcbg9//v1vATNdlOYLbd/fM/YcrpxyJuwItWkYBiJg7V67egUpGVlourlA1QVUqjpdAlu5CuAQCjuKKimqUoO+j9UZMiYdt8eNpoLL7SZz8i/4Zs1qVGenGvMye2hvvNuLGtUP9XHeqKO4cPQA9mztyuKP3mdpwSa40NyV3LQmj4GTJgTHVui1K8e6IfhmfxmVqkaFqtdUkBs4P3RDBO93BJVkwXcHG5b/uD6uOefPdb6vKAoJ6dm4whY01XPoh5Sr2q+P4iqV7p1cMVGQvz9YxpHdOtGnqzuUcqsab+bt5OwR/dm6Ph9rJ+rh31/O36bPJjk9C003Fzvf7CujuErjw/V76bT/e7auWcXY8RPo3KM3lNUug24ZcZp9NrHFVAhF0Dc+XEYzUNn6nCn/7M8Wk+9dQWLmeJLTMzEIGSeWLlvBTVdegOr343K7Of2vTwKDax6v1c6u8cR6jEp9GhDaNYtWVCWeyEp6dbAhP4fly75mVHo29W1P6zbNg2wGFppbQ8FtMAS+QUebF24g2thShAE0w8ChKMF0buHbSOGWyNL+g9jZIzKowhB12X9aiCFDan3rm32lVGk6j26oqQAt+eFQxN87exwJPY5EGTERh7+cpYPPb5QYt17xSwb378a+T7bAAfOBW5uLhaPaa9FcBJrDruLKYCWnjQW53DbVLBEb1TWkDrQ+x8BVTzbqO5WqmfHFumJ29jiSigFHs6u4kirV4Li+puVZNMBitHFPCaOO6oGjjjGujZ+LKhiVmsFxY1KpUg0qhGBXcRVPL1sf8bnBvTvzw6EoJdRq4Zq0o9n8ygvkH/PL4GslxYVm+jQhUBwOMsefwvV/upPx482FRVr2eFyzPkQFeleV8bu+vRjUpTsRGonTGdyBSMw0v1elGQhhptn6bu0qJk48hZEpGcGtWxBU+GsPtRuTmsmTM+axIXclw1KyGDwimWNPHMHa/BxqJniL/YOpe9VB3n38OZZ+PBtNVRFHHh98b33+ak4/ZQIcY46t8dPhgCXJdDMYM7Bn8LM+TadS1WpfUDVwfihKuBUscrs8FuzsUYcP5U+mcqlARKEF3Yi8ZwYt6XXIFUuZhTCzozgd5j2/srQ46uf+9+E7zP+xEgalAuCfch8bc72MTsmgSjP4/Nv9vJG7E4CKsjIe/v3laKrKB9Of4pRzL4VjRtQqw/cHK+gdUNDthFkHAQhkmAi3oBqG+bxzBAxp6/Nz+L+rL0RVzR2r596Zz3GjU4Ofz/MuD+ZU1lT4Zk0OjIhUkA2wpSUVzMVdyCIeG3YWVdKpVzeEYro+lfk0W/mgSwW5Fi56/n/M23AAxyuP43K74Q/v1/n53dt+hJFHtZJ0DceyglsPFstlou8n8xBCsO8XF7A+P4c873Iyx00g45izQknOhVVGM6TImApXDt4vP+dCxc1lPyu8PzyUuisu1/bs2ebvy2sWPij1aTVeq43LvjV3Nt7/xV85uvgbtjdChA+mpmIIhcELF3DO+p18OzAzYK0K3U403eBguT+Y+qc62dnZjB07NiYuAOZD1rSUFaxaHkrW31gG1f5Qq/3YImLL/7JvlzHos52IK66IcDkxRO0qWYVfY3thJVWaji4EjtmB6y/KGNdGUJkKPKCO+u98XBV+4NiIzzU2rdIPWzaybsWXEKYgq34zjZmZ+cPDb2+9M6L4Q2JaJvf/7UEqZnzAnblz6e0rhw+ns7HvEF5I+gUzR54aYWALPoiB79bnM+2mK9BUlfdffpqnZ841y2VTuzVH0w32l/kxhFlWOiUjk93FVWiGYFhiGgOGJ/PhB5G7PyP6d8e7vbBxnVEP7894HSN3buih7w8tREakZJnyB65fkXlG8Jx8Ws25WqebUB33gOpYyrFVIjiWt6zgPWR4tXSG6z6DL19GINic7yUxPTQ3DCO0iDZL7poasmWVjHoOMZK3tMpcdFSqOhWqxvr8XLYUrIbjs2p89rVH74lcYA9J5PgEEXSxWLktNHcqykvRVDWoDNaW/9jir59s5v1rUhnSO7rbVrwQCDTDCO7iWIYwAejCiDAemfdZf9CdKX/VCoYmpAZdFcNzKrtcbkYkpfNjtWRPNtWNg4R892sKWlKlVgvKrJ+QXmLVkrAXrZywtu2wZ/cucDjNC1yrP/XInFefYcu6vFaQrPEYYRe2teU/4J3XGfDOG2xck8sfr7qQV5+cxh9+dSHvf7o4zG9VhIIMMH/WB0qHvjPjTY6Y/wk3rfss4ljWllSr8uKL5k8AwxBUqeYDtjG+tzet+yx4Ptu/3dgoEfaWmGkAj33/La5cZRYt8Pv9bFmby0+HKyj3aRRXaewsrqwz+GZPiY8Ne2LjDGlZLpKyzBKxjijpFOvl1Bsb/ZXd23/g1YfvoqTQjAC/ad1nHP/+zECgiyWb1QvR+6HUZ1oTgkputTFuCOF+9waCobPfImHeOzU+d0PW0GAKw4awdetWszjCtoLQi4ETG5Waxd9feZ/E9MyQ4q2q9H/7df56z81MWz6TZUePYufxIzlw9HFUuDrx/Jcvs3v6dbzw3WpGC4Gu66xdvRKB4PNv9/N1wcagsqGqftasXhEZVxBlghdXaewuqQyLJTB9Xb9dl8+8159j68a1Nb5Ttvw9UvZ93eB+aAiGbgTlUxSF8MRyx49KNv8RGFuroiDULERgLfRrvZYbMT+shX8wDiOGt6vwe0gEmh+EgcPpIiE9O6IoyY9bNwfkEGzeW1rNP7qmcOU+LWYyf3ugNCJOoWDVcowoixMAzvy/Gi9Z15cgsgLcYUdPHEePwOF04uzclaVDLqhXlqIqzXbFIhShBA0WZpC6HugvgW5EPhuTs8bj9nhwOJ243W5SssZHLHTHpJk5la+/9S7ue/oVkidEKa0djx3YBqIoIVeSLfvK2FsSmfFl64EyfoyyE6cbguLK6DpUyGXDtNDbbSdeKsi1cMzgo8HhwuF04nLVb/LXDcHaVSvwa3Hwwa0HM/o4VAEv/La7dtWK4KpXU/0sXbo0oLaI4GoxPEK5wGtaI8Xg0eCsuQFhh+m9u6SKTXtLgw+SJinsJzbOxWH7NxuY9/pzVJSVmiWaAU3X+NsNl5Gfs4rCwA3C6s/aKzIaMXlIWDdtQ8ColHQef2sOv/3z3c1utyEULnyF/304k/WrQqUGgsZcYQYhFewsDssiUPN8ra3N5nRF+EKvLt/4oX26cPukhlcFNXQdIQxY9hYsCillbo+HjImnsznPy4b8HBRhwKxZMHIkQ+/9K1VDj2P85Y8y5YL7qerSjU1KNzKvepKMXz3BByeN59oDP7Ie+NowuMzvR1T5eDN3J195EnG53YEHr4ekzHGBsa29c6xo+XAvyW/X5fHw7y/ngxce58m/16xc+uELj7N+4ewG90NDcLgC91CPhzMuvpr7n341+J7ld2phKRMQfdwtZb85c0IJlPQNd1FpFZUkoDyedsEVjEjJ4NswY8qr0+5jY0FO4JoI3J8DF3AN9yxDsGV/acwU5HALqBCQlDkepbb+GHVqjZf+det1fLs2L0pUBWiXTuPym27nzmdrLkprlafBn4yO1+tl2rRpeL3eZrZkWtcFgg0FuSx44znzPMPmoB52rxYCElIzePadedzw57t5auY8ElIzAgvZ0JiOTs3kyt/9iWGjEqOOYXPveS2NdU8xhKBCjVxI1Sb3gTIf3x0s49v9ZTXu9dZfwcIrNjt56WJRC8cMPhrPj1VMuekOPu48tt4yL063h9EZ49iwp6TV8lbWx47CSgor1aCbQZVm0M1jplKyZmZSWClQl9tD6lizlGZwKwUibtTJAWtkbSnyhQ22SaxAnC37SzGEQGuKQH2ObtTHn/nrb9HLCvmdMHAMPMl8UTEriOV5V3DmpInB8rGWBaI6Px2q4HCFuS2+eW8powY0PRemNV4bC3JYu3oFYzLHcfVNtzL9+ZVNbrNBzPg/OLjNlCEinVrIB96qliYE1OaCHItSu2Z1NPNxX6kaVJSVcvDw4VqPV4MXroKbaz7cD+/fa/5DGFBmZq3oO2Awl971EK//6340v0rxC49zWo8eUFgIiYlUzV/AulFZrHypZrqrvAEncv2AP3H74NFcu+h5btI1xj/3OP5Zb/LQCZOYnngWv37xPb5bu5qJEycyIik96PsINfupStWp8IdSmVnK4OY8L2riLxEnXxv1diaEge6PbR7gUy++igFZwxidkc2JSemomg7r1wCgVbOWRaYKiyJfFMtyU7AWjuGLthZHceLp3JlJ512CAmbAbCCrgy4Ea1evICNrbFA2S87qd9OQRVKQu3oVm/K8zYpZCA8ENISZleKIzzZxqO6vhb5vCD5660X+/MQrRIutGpWezZBRSbBxXc03q1HbfbGhNDRVZkP59kAp6/Jzuff6S9BUlY9e+w8PvPo+R443LcMbC3IpWL2C1LHjycjMNpXklAxGpaQH3RCs3YrqLo4ff1/CrE2FNY5pLZDsxv5SH4cq/KGFG1EWb7WIbfouQ6lPRTcErojduuYteFsaqSDXgsuh4DfASLsQ34a90T+jCLTAE77/ebdwzKhkW20R7S2twqEo+HUDj9MR8HULy2sMjErJ4MkZc1mzegVHHNGXfO8KdAOSMzKD5xI+8RNSM3j0jTnMW74GPnqvxjEtv754E1TsiX3exmhoqg9h6BiEbcs4zN2H1Kzx5OesJs+7nCFjMjhxTGpwDMI5VOELBtZVqA33na6NDfmmO4zqV3F73Dzx1pxmt1kfDkdoLak4HMG54HI4ECLMxYKQtaD68FT4NSr8Ad/jRijK1QutWL7zhgHfrstj4LebEM7IGuEj9N0oSlrjwtPCtGmXoaMBoxJGUfbdp2T4/TwiBBM1gx8KC7nW5eL3L7xA9vjx9NxRFPzewaISUCJlOZQwmSd7Hskzc/7GYxdcxjVFRdz11Rzuyp3D7q2T2XbFdShJaahC4CCUBiy8e/aWVLGruCo47y23BUMI1vYbhzh5fN3nJmK7A3bkwMGMHuhgY64XAZwwOpTaSjdCc6DMr4dcHoiiGAZ3XQS7S6owhODIJqQGNBdeIjj/avOnjDWjU9O5+vpfMmyMOdeSMsdDrmmBc7o8JIdvx1vGCVGzTDGY723Iz+VP11yI2gxlMBRAHLrPbyjI4fC+PdDnpIY14nByeP/ewLVd8yp68HeXc8VfHwSGN0KephHrVJmGMLPJBH2pNdiQ66VPFzefzpnNZ3PeRdd0Zr3o5j/vzGd4UhoCJSLbhZktJZQ9yloALdleGv2YNnMxsNhVXBXwuQ4ZN8KpVOuObwm6NVXDrpZjC6kg14Llkzi3FuUYgM+egrNvA2B3heCFldt55BcNC2qqr2paLAgF24VS6OhCoFR7qI5INi1Sf732ElS/H7fHw3PvzGPwSDOZvTWxzRu3YGRyOrk7o/vJ2mUBbJ2vebNqebcXh9OJcDpxGAZOyyVHcfD3V95HAFdc8ItAeh8Pj7/1IUlpNYNgrEIJ4cVZmkL+jiKEMINEgjlwVVizeiXVc0HHGusGrzgcnJCQzPfB+2bkVnbwASJqTpct+8oCyq3ACAQr1Uc065E46iSEIijzayz75ENOqXYTdi5/i2tuudbMuFHt4a58NR3hrwC1yqw0aLGtAHIC+R8Uham/uhxX8jGc5d/LsIXzSRSCvcDNwKuAqml0ffttssePj0hfVFVNOQ4yJBGnx4PrsmsoSErnhic+4cYNn3P7xi8ZcvP/qDzmOHZeeS17L7oCo18/nIppkf/pUAXH9e3KzrDsJeHWQU0XbC2tZU59txK+mh74I7YXb6/SXfzttsvR/Couj5u/v/JB8D1dNzCMkCIf3FUQ0a1R1kPUp+nsKfE1WkG2dmiqL86iFQSKNcNHJzEyeQiqYVr+x6RmQK5ZIOvK2/7GqJSMMJ/5gGzUHA3rXm4FhDVHGbTu5+FKyprVKxF6Ixbn6Rdy8riTePy/BWwsqjm/NE0lb+liyKxfQW6uISPWqTKFgDEZ4zB+PxPWL8S5dSk9evXmT1dfiN/nC84hVTVdD4cnpgXnsYLCxoIcclYuJyVrPMkZmcHFiPV+NDQRm12SliA4X6g5Vpv2lkRd3Owv9QXTjTqiOO+Yi1VRZxG2eCIV5FqoniM3GpoeuWoq9zcseCLWW0F1EfIlNgXTDXA6BBv/8xqapZwIWLd6ZSgCV/VTsGoFg0Ymm3YqobA+P4eCVcsZnTGOoaNS+PmHrVxy3l01jycaos7EmA8jE1ZZ2/NKQNlsaIrbaOfTUPoPGkJScgpbzzibV7d3hkCsQmp6Jh+98VzwYSbws3b1ShLTMmu0IQTBQgnRLEcNxfSby2Hvrh04XU7Qwe12k5CRDbmt4yMvDIPC/Xuhb28uOe8u/jzhWE4JTAzTv9jKRxs9QbylJDmVwHsf1kxKFk5169H/Fn/JpCtPwkkoQPUSgF6hTDOpmWPNxaEhagRjiDWfmP949jK4bUHojU8eC2ViENB150/8Zd2DDP10LsVCcC/wNMHhj2DzmlA1x7rm2sOvfsDI5AwqVI0dPY/k/vFXs3/cCZxa6efU3OWc+K9/cPzTj3Lg3AvZf831VCancajCx3F9u4bNe1NAyweyTgvPgW1Qdjh4TrHioRGlbFy/wSxMEshmsCV/FTjMRZqVR3b9f14PPnjD4x/CCV9QRVUg6pkfAAfLfWHpK0PW5DW5savkWtu4HjywH0UxU9E5HZEuPYOOPTHSMieIWCxU33kxhCBlrBkQRjOVQas9y30lOXMcyqbPGz4NUqfwfXfB2oPR71OOIWPoP/58NkeJ0RrZC7aEZZT7/ptNjB7Y9OdgrFNlGsLM+MLq1ZB5CWrmJZQWrTRjcAITVFEUXG53aAcA0/Xii3mz+WzOLHRN5z2PmydmzCU1I8v0XQ7sjEVDE4Yd7Es1sNIjWsGtQpgFZtbtLkbTRUSsUjhWMSMrY5GqG7gcZjn5Hw9VBM81WtYaOyAV5FqoL6p9kLOC3T9Vy1oham4HCyHYW+pjYM+QFaqlqqZFI3xrRyGgLAgFf5++qLrAHXjgJIb5IrvdbpLHjscwTAVtY0Eud/3asi67+ecrHzD0hBNZvqdmcQKDmn3Q4vTrV+MlgWB9fi65K5cxKr2ebeUAh7r0rP9D0fh5HXu2buTAtm8575IrKOrWCSoChQcUheSsCbjcHsBMEJ+YMQ6EWdt+3e5ikgb1MmUOWnJqPhgbw7q8HP54lbn96nQ5OeeSqznzwss4NiEVcnPrb6BZhEWyb1wBp4zgUJee3Jd/mKVhxmsRdq1UaQbbD1cw9IhQiifLymcEStse2atPnemDqluPBo9OD9uihpPPvYTFS5bCxQ8Ev3Ngz66gtNGskYqi4HS5CbenORwODKA/cL9D4aZ3XgOXi49TMrl+TS6HArsVDocDIYRZ3nnqVADyVy0HTBeDuubaiUlpbCjIId+7HNxmPuTnn3mMl91uHn79QyZ078bgWW8ycMEHDJgzi7LEFPZfcz3cfH1w12hDfg653uWMycgmISWjRrW2iPNskI2+8Txw6+/59V/uw+FyIlQDRVE4uGcnHB1QkA1znNU+R5hKr18L+kxX30CxFDmo5f4S5R5QHetrmwpyyVm5nKTMcYxJy2R9zspgPzeX2sbVvf87HEoaimIaX5xhF7aV3ip8N8XKJx8thFAgSEjJ4IV357M2ZwVXnHd2k54fQUt6mOV6RHIGRw/dys5GtLN7335wRk9xql30T5bUkgTKveQlSPl98O9vN6xFTGrec7B6Nc3mYAjYvDbyGT8qLTv4nHQ4nUw+/3ImnncpCakZaLrBhvxc/jL1ohoW5rWrVpCSbqY11I3aFWRdt6ebRWTWnNAuh5WUwDAi3eYivxv4vmLuDg7o2YnuHhelPjU4/+q6P8UTmcWiFlz1VHM5W1uPolYLaIlSJlUzBLuKKyNesx7mTqczJltBtWFZW+769Bvu++wbDESgvCsMmPseA+fOAsybsemLbEbgPj1zHqNTMoLp4dauXhFmXVbZkLeSo4cex7WbFnPtpsURx4xLoZA33zR/wtiQn8v/XXUhrz/1KHf9/poGNRPtfBrEh/cDAs3vR33iYe78eXnwraXfHyIhJZ0XZ83nd3+5hydnzGVEcnrw4ReeXcGynlmr8ab2Y743NF66rnPkoKMZlZJBub/5fs31ElZ9UKz5mF6+g8F+1QP5RIHgeVp/HygPJQS1bsIbCnKY+cLTLFq6jPKXX60xxuFY1qMHH3yQxYsXMzo1M9h/Ahg2Jo0/pJzJtdvXBr8zPGEMDkyV/sjunbh3dKg9l9tD2qSzaixQ/v7Yy7w9cgw/OZ3cJARrJ0xm4ccr2PB/91DsDtkbHA4nv/vd7yIWvxNOPiX4fl1zbdOaPO666VreLT0m+JqVbnJdzgpKhyew5R+Ps3L5Rn78x79wVFZw/O1/RD96MMc8dB8/fTyXWwKpG/967cVsXJNbZ/okEZ4fu6qm65Rr4RO1frcuNNXPT1s2BnahBLqmsfCDUMU7K4vFEe+/S78P3g0oaiGf6eDnrLSNUQwQQaLcA6ojhFnM4bapFzHzP//irusvIT9nVaAQVIjUfUtraaF+oo2r+4O7+dX4UViLx86u/2fvveOkqLL3/3dVh0mkIQfJAsMMkzNJghEVBUXMcc266q5p17S6a85ZEVEwkRFUjMCQZoYJTCAHyTlNDt1dVff7R4Xunu4JYPz8fj6v18BMdVV11a1b9557znOeI/slKvkWcPLev/8iEnRu/t7yOivBMC45jatvu/fUjGMhcCneSp9GwAFNCHr0abmiC0BdxKlVkC3N81ea6BcdH7CPpok/zLu4rjCPf916td+2AfEpPPnBbG6452Ge+nAutz32PIPikq3xWp8rAz3Mcele5RlVC1T8MGGqGP0hFWlbgIYqTCaNy/d9bVh+WzTYz6MKJMnMQTDO8yc1kP/yIDcCh63ptUObdpEBq6UdmzdQ2qOWzD7n+G1vOKj/2qGgxmAmCOyv0A15TTOqNglBt/kz0YTg6KQrjbCHjZikFGKTU830H31SA2JSMnE4nbjdLj0jtXVnFmypYIoxEUyPGWt9p14a93fu7ObEeP31VLsU6hXNX7S9hQPs9UHu52Qxdud2uqgaqVeNIn9vBc8t28646M7EJacRnZhKnUfFpWh+vHBfWKtti3pw8i5kM/xqVnSKSx1KtUvhH4s2nvJ9tRThbdtRd0xGoHPOKtet5Podute6dO3ZtBk6lPI6j0X7MZPJJF+XoQTrCvK4/7pL8Lg9zHjbyYUDBkL7dnD99Y1+t+k9EkKQt6fc4rcJIVi7r5xLN+sKEubzTU7P1CksEkhCkJySwpVVazi6fR1D35/N1rW5rF3+IwAhips7i7/hoakLCKksZ7Ys8zgSO3OX85/D+xmUkMLZEy5n8exPQAiE0OjVq5ffu52RmQk/6Odrqq+ty8tG6REP7f2rbMk2G0NSMvX2kiQ8rVpx8Nq/cfjam2iTl0u3T6fS5eMpXDpVoQ3wNvCtW1C8Jpu6jo0nXUmR3bz9UFXg1YvgvoXW5+qmlXDuPxs9vjHIGBOh7/vno2yianp54k5zv0AAO8dfZiWn+XI0d5eZ6i5eD3LAGOMzBjQGIaAwx1s0R/HoSVdnXH4rlHirK/7rgX9QUefhb3NKGz1XYwj2XB977DHiktM4Uu2yquj5zi+H9+xARHfx4wJbhqsQ1jhwrMatc6iFDxXlpK9Qx7EaN3vK6li/No+i3NWkDxvB6XHJeFSN9h07wbGjLT7XwfpTzJeoKYN1P0Ds2QD0PD0q4I4OVNZzuMr1hyhDrc1djacBQV0IwaD4FJLTMjhW47aS0s0qe3Fp3kisLNsYOf4yxl1yOYMTUo0TgEvRGqXOqaqgzqNReuDPo4alQ/Lpi/7RHJPKKEl6st7afRUk92yHS1G9kRFrEei9b28/blwB44/GXwZyI2jOg3yirAxJkhE/vg1n3QnAkX17uPvKe4nLWuY3MQZ7+L9mKKgp+CdGGQR7n89dimZVQrIMNkMGzm2s9AbGJXPPY0/z8hMPomkan+8LgfbBhd7MF8elqITYT6EwxS/E5iNVSEiWHJ3HA3ZnSKOydL82wiJaIUv40QE8qgbG3yb3FiNhwXee9662vZ6kU8GQpFRe/3QBRbmrcLZqS0leNkUnBIeqfuNR6Oc11O4oBaHpVARNg3pvfbii3NUMGzrU8kA0pJT4wjfBUPG4qawop1P7ds1egqYJatyq3wCuqBqvrNjJhQ32lSWJrSUFFOVlE52cQUxiKucPT8E2IhVVCBw2iRCHnSuKvuY/+fPpWX2M4l59mZo2lHeXfo8mVCSPm1nvvEzGmecjIWG3OxCaisMRGBmyNTOmmBicnImclY/vsk6SJEZeeBkD41OM+zKVZ/UZqiItk5rMoUgHDyK/9hzRMz9hIbBX09i8aw9Xz86GiMig33dawnCi25ajadA/OpacH7/BEuVa+j6yTabJJeaxPdCxV8BmTVXpNzgWh9OBy6W7iyTJm6hjOsmE37/C4Dn6e5DNd8OMvJzshFrvUREIkjJ9xgWHg+iUTG6ZG2gItyQHpaWQgY/eeoX+CekMjE9BlnT5RROz33+NjM524lPSrfHad6FccqDSknz0Vd0I9t60FKomKC5YY6ncfPLOK7w0fS6LK9rz7eaWG8e/BJIkIS99D9UwkJUgD9WU7DxUWU9XH5ri74GE9KE4Pnw/YO4Q6IV4TJ64JsCt6ONFdGIqz06by8bCHPrEptFnSCLtwswS2vrTVTTRKHWuS+uQgAjKnwnWdQnv36ZmvYw/FWr9wSq/4zSD7nai1o1L0fyipH+pWPwfQ3Mc5H17dyPbbKjrvrcMZACP57flFJ8MGnoo7/5yPR9Mivfb6DZmKctbAZjGm0fTcBicy/KyE7rGsaaB3Hi3aRViRwj95fgjVsCmEkRMkk4ZKcxdRd/ETB4r+n1CVnU11bTv1IVQu3eCdSkaZuKR30TfYIIzt5m6mVuP1tC3fRjhzpN7TTWhy0oJIbjnmgkobg9y1PBT8gKeFBY+7b0G01Po8dKLYlKHeqkVCDT0xvBdKOTk5PDpl9/SJjLST5+7Tdt2LbqEw9Uu9lfU+Sw24OovioPuu3/3Tl64b5KusOBw8NTU2fSOSUKSJCShMeboYW5r35EOy6awtXtvzuwxmGXbspEP7cdmtyEU/X0oyV1BSc5yJFnGZrNx0RXXMHHyVQFjQEvngNPjkph089+Zudu7zdGmA86MS/0klvwWGOh8a0/nLjief53FEy7H/ek0Ltz5M2fN/4S98hfMG5DJO/HjWNUj2o/cvm9zKXdPuoy+Q5Jw2CR6D4iipEj/zLlpCdc+9DQlVdvJt5/ud50h1UdwffYAnHVXUAMZBNXlZdz+7//x5lMP69UHfbm3QrNi+37vgx74ovRAJdFdWlk6qoqGxd082el0wyG9uEZsUhrPTJtLYc4qYlIyGRSXDMWFfvuuK8yzFiK/Bv7zz7vQastwOBy8+PE8ktIyKMxZDdX9oVV7VE2jOM+buCvQuZ0eVcNus6EJnWLiVSfxX2icKnQqlrdCY/GabL79jVVuTNg+ugVVCFQfxYxd27cwtE/gvKkJ2FdR97sayC5FJToxlcff/IhHfeaOBdPeIj59KANjk42+Kiy6it1mB6FLpyakpHGk2o1H1bxjvrG4eeqrtexsRFHGpB392cxFc2HqUQU2WbL+9vMQG+92Q81n//vXf6pcHmtONL3vp67b9NvhLw5yI2jOg7B03meoASWoBbIsW54jYfCNvBXsfn/4fm95nYKmCSMpxAyV+PDd8L6YlkFnbIs3QkeSLNNU1ZQQu2yFzf8ICJ+XMyYphctvuYc+UXEB+4X/hktDCX8Psrd9A3VXg/mJzX3rPHp56pOHfs6iNastBQHV83v50HXY7XrlN1nzviMD4/QENd+B0lyYaUKQnZ3N2LFjee/lZ3j9qUe49eH/cv09D/PGZwsIjWhFjbt5qozwaWsI3r4m1hXm+im3bCjIQQK65q3k7GsvJPP+WxCSzCvjJnJnxgiWbctG01Q0VeWsCZcTnzFCfx9Mj56moakavXv15rwxI0+57baWFATwQOMe+pjvDspsPFSFKvTFhd9769OmADGp6US99C4bZ3/HU0++y9sJ4zhv11pWzv4XpTP+zm0li2llqHEITWX+tLfZWlLAvKlvIgHDHAc4bes3XPWPJzj70qu5KCmQlxrXpytOpS5guwm7w0Fs6lAqy8vQzIW4D8XCSuwBa+ApLczj8/dfZ8PafNyq6l3AC52fad6jR9VOqiS7l+cL/YYkMfGmuxloFFxpiOfuuIKf160N2N4s9gQvhqF46tFUI38jPxtZgpTM4Ti/fEK/pvP+Se/4DD+PWq1Hxa0FGkvmAto3aepUkZThLUHvcDiIT/91EhVbAlER6KXevnlDo2/r7zmduBSV9Qf1IlP9YxL8Ppv1zks8fuOlbC4pAJ9IhpmcbvJs3ar3WarClDGE9UX5jcstAt9vOYYmtN/1flsKIcxkOuHT/3wkGvEfgwJsCRFod2hCT9L+AzKXWoS/PMiNYPfOn0/hKIn7nnzB8hwdqnJxoKLe6jSnokjwiyBBaYG/asHG4kISU1O9G3wmDn3w1cM/iplODghNT+K769Gnee0/D6FqjRvI+oLgj1sBC+9baV1DsISHEIeD2oAFzi+DzW6nQyc9YcXXQBY+g4cm9AVKnUcj3OFdbVfWe/SL9hGaP1VelimpE582DLvDgaLoqgq/dYqezW6nb9QQzrv0KvoNimbWB2+yS2oPx/cD3kndSzPBzyPxxcLvcLlcaJqGR7iorCjjqtvuJdyHntIUDlXWc7TG5V30oRtcjaGqosIy2ISmMaSmhjNuv4Kua1ZS07UHX998D5NmvIf7u4W6sW+zgaQbfmddPBlNCDYU5OBx64sPWZaxO+yMO2ss7cMb0TluAZ69/QoueWEWvj4VOawNUI5HFSiaLpVk9isJb2Knb3KXELpiw3+nvYNyydM8Muwarti8nDtLFvPukvd4YcV0ZkSP5t3I7qxZ8QOFq5aiKiqyQY3SFJXPfnTQPyqa5V/PhV6T/K4zom0kj70/i3eLyzgQ5D6uvPNBohNTCHPIFm3LF7u2bWZkv6HW9g1F+Tx0wyUoRmGbtz//kujzx3plATWBJnmn0pNJ3jL7w4LPPmbxooVknDWOsROvDmqIeNwuVi6eCz0nBX7YFI7tDr5d0zmnDoeDxIzhSEjEJafx0Atv899Svf/luTsxHO9YoWkCtcFCUpK8xoZmedJPfaQ1iz6ty88mJXM4yu+YGGZ3OPB4hN+CqfeAaD/ngUfVrH59KnKXpworSVoQ2Cb3zkd59WKWL5pLpwGxVAuNNmE2n6ig/kTcqkatR8EuSdR7NGySRKhDZm3uaquCYjB8lL+XuG6tyejd/re8xVOAt18qKmh2b4RO/9TXMBbsK6/zG4d82xS8c6HpQdY0vcjUnw1/GchBkJOTw3PPPgNn/j34Dl88GHRz36gYLpjsTdDz8mwEblUjVP79OLk7jtcghKB4zSp8i0OsX7uG2KQU1rzzqZ+8kEtRCbXbrJBCnVtla0khW4vWMCQ1k4yMTCoNmoVZbWvchCcCvvdART2K+juvgBcvBvQB1Uv61//fV1HPjbMDOYaB3v/g99MSyLKNtFFnc81tf+dQQhw1LpXw9d5irQ09fQhz8PcuQrYerfYZUPw5ui2FqgmK91dYi7GYpBQefucLcr+bz9HQrhT57qyphEgaLslxSvccDC/M+JK+Q5IId9j4fu4n5C79DnonWu36Od5QnTDchnUe1Yo6tImMtKgZmqbRum2kZdhsmz4bTQiaCnz7F8kQbCzK5/7rLoE7ZgNBnm9YWyRJZqDQ+C8waeobuCLbU/LgU2yZeBWLPp+K26OgaSpIMOqiy+naoydJmcOJTkxlS3G+0dYSss3GuEuvonOPXqxauYKIEHuTNKum+pqnbxr7tm8Gx2BrW55Rhc/X89LQC2+2LWC9A6X52aiGAV/nCGFa7NlMG3IWaYe2ckfJYm5a/yN3qh6WA++43SwAPD6eekWB5V/PZdnCWXC3v8FY41YYNDSFi1sd453sBsbhG5fxhU0wJDmdAz9vblB2XEdJThbinKGUTv0CIQSlH39gRTw8Hp0CcPm4scb9CFQhsPu8E34LSGMMaAqLvpjOC4/ohZ1Kc5ejaTBqwpUB+wkhWDLvM7i3gYFctr/xEvSeetC0oM9VstkYfeFVXHDJ5cQlp1qOkpi4BChda9yL8U5I3nvz1UbeedyrJ2vyPX8pNAGDE1KITUplS0kB/7x2AtzVvJ70r4F/vTuTLWtziWzfnneNNIVufQf47VN6QI8Q/FJPeUtReqCSjhFO2oc7rHYOKj9msyMkuGvBBjyq4LOrEvV50YAQUFmvcNOsUjI7aPQ6mEtC+lAyMjKJSsrAfyAORLVL+dN6VE3terdFHTHGGz/DV6/ia0L4/Xj3N+e6ynoP13xRzPUppzF2QPNyjb8n/oQ2+x+PrKwsFLer0c9tR7cH3a54vPIuJsyJbMOhKo7X/H5hbj3bWbCji/9qdWCCrsVYunkDsz/7kM3FBQj0gcAy14QeCnrujiuY/c6LPHXLZIoK8ohP1z2SpoFc5wihzuGvH/txwT4+Ltj3+xrI4eEQHk7pgUrvahW93Zf/fDzoIUoQykGw+2kRJBgYl0hyWhqDendGhIcT6vC+Wl7Pp6BOUa3MZ9NgXruv3I8a4huuOhlYx5peDEU/QdaiORSvbCApJtt+ddpP35gkJGBTcQGfvv2KvtFTb7Xr5pICP6MO9MpR5vVuWe9dyEiyTEV5mRXCE2HhqKG6TnJFXeB7Bv4eDCGgeE02itu7EGr4fAeHhjBdgg3AucDiUWfx3Te57Lz6bwhnCENSM7EbIWi73cHw8y9l4t/+TkyCrvSyLj8bVVF0Q0bTQIJP3nqR/z35BGPHjiUnx1/GyhdN9TUx7n6cpwWvyGl6UlXNq2Pg25d8JyqAISlDsTVU5JEk8roN4vpz7+O0Wz7igZix9ARmAXuApwBTP0MIQeGKn4K+L1UV5WwrLeB41hekH/jB/0OlHsWjsCE/mxU/fB28ESQbmgA1NAwlNJzY1EzsDm/IPzFjGJuPVPuonmDdre8EC1hjQGPQhCDru6+8G+xOluUV88BXwZVdhKaBp8EcMOfRoPu2kz1MECWktaqmbk9xwHOVhMaoCyYRk5QGSJaBHOH0jTL5L6B9e7fpZDH2tBbQpvF86pJgXo9ekVFS+feCJMElf7ubcyd5JTgVVQTct3GV+v9B3vk9ZbVUu36d2JhbValyKVayYNH+cm6cFUibkR0hjDz/UjxG9bfXVuzAowlKC9bwxfuvs6k4n1qjME/O3krmvPMiT9x8GevW5nF6XPPc9j+klkAzMJ+ESYZQDOooAr93UzTsn9b76j+n+c5zx2r0frdk+7E/4taaxF8GchCMGjUKRxMZ5098MI+UUecafFwv2kS2t7w5YEQVLY+PaFKL9NeCpullZ4XQM22XHPL/zj7RcWwpKeDADRNp++Zz/PfWyWwpLkDTvB0bdKkpT/yFaEPO1vVX16wmOiGFf707k8gO+irv9uLF3F4c6LnJ2X7o910Bv/MOvPOOn3IBwpuwFwy2IPGcxu6nSXz9AhISbdpF0irETtgH79P902kWLQC8A54QevKNn4QTPqFx/A3nk2lDk+9uHr9+bT6fvvcqq76Zpy/cGpaP3bcBd32wem+njhq3wpaSAh68bgJHDxlBd0212vWJv13GusK8AA+oKgTrCvP4as7n1rnsdget20Yy8/3XWbc2n04zptL5kw8B2HasmmpXYHjd9FCY71t82lBsEW2tz83rOK3qKO/9+Dazpz7GZFnmdUmivyQxIXclm37ebBkwg+NTeG7aXK6660FuePApNhXmsLW0AEnSJ/g2kR0wy5gLTWP39q243S6/AkCNobm+tuRw8I67b8c2VKFTU1RNWIsgL71CWH1HE4KohBRu/vdzjX7P8bA2vD5kDA/deAfvXXUTRXYHjwC7gPnAWE2j7PDBoMequ0p48tbJzHnnRQqW+t+LZNBN4tKGcvrgIf4HZn8GQPKIMWgCun02je6fTdOTot6fxeV3PMCz0+YyJCnVeqc1gXG/Xs+Vvris0MdbYwwIhmqXXuH0jHN8dEwuepSfY6/gYFXjjhDevMzvT9lmhw/+FrCbXXPRoW0bCr+fz+3fvOj/XD//J1p9DasW655ZWfImGjYsfONLrfI1KMw+be7j2yYCr6e1pThR6+ZErcdwJOjfE5s6FJvj14smNYcVX8/VJe98urlZWdGEtSTwWfg1xJFqF0erf7njyaQGVrsUthypRhOClTtPBN33oTc/ZUB8svV3/t4K5sydx8PXT2DGG8/x75smsWataVhLhhKPh6Lc7BY5JUyZ1T8TTKUKX3vGXNT5cucDolsN/zb28+3bVpXIP9ctA38ZyEGRmZnJ/556stHPNQSnD0kIqKfeqm2kNWib/DhVeL08vwfqFZXjtS4jDBc4yXpUwYb8HCYqCpOQ8PTPZENhjk+n16+1d3wmYvg1cObt2O0OolMzqXGrDIhLIiw8AoDLtq7isq2rAr7DVVcblD/7m4mBz54Ns2f7cZ/N+2lMWiusPnDwa+x+GoWrBnl7Dpqm8cFzj7F6dQ7SnDl0XLyQEB8VC91L7+uhFwhNNJgQzd+FsVg5OQ7ygcp6K1N/3do87rtmAp++8QIrvpodmB5csAC+fKrlJ28htpUWsqEgx98TJTSrXRXFQ2Huassr7A0j6x4sUy9XkiTSzxjDB88/xow3nue+aybg+OITOnz9pX5KoVNS9pYFJojpg6zefs6e0Xhu/tj67OpNy/j3mtlsn3YrN2xYwtyE0UzKGME/heCoECgeDxvyc5CQLH3k6MRU2rRrz9Rn/82cd17ksRsm8u3sT5AlqCo/4V0kS5Iu3WWM8na7vckCQCfd1wzs3rbRCr0rmsCtqv7UHKsh9L5W5fLws9ypyXN6PB7mTnuHu2fPYJyi0A94ARgO/AhsBu4F2h3cah0jz3+CnlXbvEmgmv8CbNLt9/PwK1OwyRLzp0/x/8JdOq1g6laV4v0VdPl2EV2+XYQqBAPiU4hOzqQkL5vSwnwvx9HoJyoNk14Fa/dV4Plilj4ONECNS2HzkSoEgguvuJYHn36FIekjoXdC8MZY69V/djj9Dcar77wfu6s84JBjhw/x4TMPo6oKl6mK/3M9tA3QF1QSICHRIVz3MDfU2jefq7nQ3VpSyMwpr7OuML+B5827Lwb15GSw43gNdR5dClFRdQWGvkOS+M+UOSd1nl+CFV/NZktpoR+3WDUGcNMj7L1nf/6qieL9Faecp9EQJjVN/z79u+RGeM99o+MC8iG+/OhtPZqkaXgGn8WHPxvHhrdFGjgUu91BbGpmi663qQXBHwUJyTsvad5FuHchZ06+Pgl8xrGmJ9mc33T4Jxmb+/3Z8JeB3AhiY6Ib/ex/t15G63aRyHab3wtu5qEIhCEG7uPRaeTZ7y2rY8uR6uAfniKsFz2IWX6k2kV0ilEgoUNPxLn30XlIhh/frdql8OxG7339+72ZDIxLwa0JHlpQxIG6phMmQsLCMV8UXxTtr/jVw/q+8DWOvINc8H2Prs/9xd+nl48WCKHh8XhYm+udGA/s8tJwnrz5Mkr9Jjm9n5iFEnwHDk3zrWuvb6uqVyg0OKiNwaMKK2xVvCbbqyGseNBsTujlo+Rx5Gdw18Hcx0H59Wg/z91xBW3atfePrPhwT+12PWwu0BeOivAmaegqKU5kmw1nSCiRHTv78FE9VFdW6OG4apfVPtVuxa8/eT0a+v8/G7zNDnWVPL/iI9IPbqVHzQmmR4/h9Bvf56o2fVm0apn3eE1j2/oitpQUsK20gPlT3+Tb2Z/w3tP/0iXK0KvBvfnUQ2wuLiQpYxhO45ptNi9lRZIkbrjhhkCZt1+hjU87fbB3QgFLkca7+PJ6dOo8Gm+u2sUPW5sJXZYfAjAWNoLdksSbvfpyxbkXcxVwFHgV2P/F/Uz9+gWSDm8nLT2NURdcit1UtmnAMR6cnMmAmITABRP+67UtR6qt98KjamwpLuCpWyfzyRvPc8/VF7POSLL0KxKC8HvOumqAZoW8TWia0KljPuPvhZdfS4drngnaDPK677l1xEDGXnI150y6hqc+9PJx354whNMHDAz+ECXZ2w8bjG9mJbUzL5ps/A1d24TQOtTOpmL/BGrzUEVTmbmsgP/dfT2fvPE8914zwZtsKryJYw31xFsKsw0xDG23KiyK3W+O2nKYdiuK283yr+b4Ja6vWfYdG4ry2XykipycHD5++1VKC/MadTCZqia/Brx5EV66gK0RA/mWuesC5mxf54/UzZ9LLS54mMffn0W/IUktmv/0anoneQO/MST0ugjgHV+8kVD/97Mh1c9crHt/95/zTMHCP6F9/FeSXmPYunlTo58pbjdLF3wBDQYVb2fR/5YsRYLA1S/o4fayOs+vXkrTS+kI/OypH7dx74h+9BoYzf5qfYeu/fVkIJcqCLULjgXhSs+f+gYxqUPZ34xxDBAaFmZNSr5jjGm0Bht3ymrdVNQr9GnfOI+wOZhPw0zQKsnLprJ3kESpD29GxI875e+xINuQJBlJBofDSVLmcMhfiQTs2boRbPoiS1E8FOWuIioh2RjsJRCgCkNc3Rg01q/NozB3NQMS00lMSbf6UZ1R5EDTBHIT1B+z/8Wleqs5CU0gzn8A+vpw34ys+u5tHOxf+j6cffcvb4uP70RRPOzYtK7BRXk74WPvzyImMRWPqlmhctMTGp3oLW6SlDEcl6ry/fxZKAo4HA5atWkLwK6yWutea93eqk31BufPPOf6tfnkLlrKf3fs5Z61XxGuuDga3pZdbTpz61l3wd5SyJ3pf6lCsGbpdxSs+AlZllEVFUmWUFX/91NTVX74chYP/O9lXpkxn+I1qwlr3Y73n3sMj8dDiNPJtddeG7SZIp1Q9gvWJAW1rRla46ZjhFO/V6Mx1q/NZ31+NnFpQ0lOS0cIvT/9vLsGpNaNnzDnC8jTDUG7w4GmaTgcDu575g1UIZhXX8cNx44wMrI9aauWctWOPG7auopt3XqxqXUo546bwIatm/h5p0+S3r4NPPv2k/zrlSkkZw7nI4fDUvqw2WxccevdfGrUEPDlmZbVenilpAZPREfEiX16ol7uKhJT0y1pM4wFlR6gEQihu2Y1IahXVHx9vrvKaikzqQSm/Qos3xE8dD76nHGcdVYiZ6gqEQ47QgicpYW4VUGbUDurCnPQ1KbH6og2banx+fvS2x9gcEwMSalpugfZeH2L8tdwz9UT4Y5ZAJQfO4JGXyQBm45Us3C/DGf8Db55EY9Hr+qWmJqmvy9CHxMcNglJltCEoHBvOfHd22BvpgKsCcuJgLdtNhTkgLOB1FvZAYjs3qJztggHt0K5Ttn5cd5nRMXEAYMAyP1xMWunPMY/nniG1556BJfLzUdvOnl5xjwSUtJoOIX+mp5WL79dss7bVF2fbwu34OdfNB6uTbbRf0giWxsw2k6PS0YRcNecQppT/FWFdy77M0DVBEjCkFzU5y9zrvEzlIP8SPgYy8I41sewxuf/P9uiAP4ykBvFutJioF+jn/+8sSSItyAwFGSGPYN1+NKDFZYR/WvgWLWLA5Uu43sbD1lsPVaNWw5FkfR9H/l2C8+cF0W/DuE4ZCmgotH/nn8JbWuOnqB3Z2AYMxga3pM3y76Ra69xU1Hv+UUGsiZgXWE+3y2YxeK5n+sGTuI4OONm704Vh5CrjyHkXy60r3g8oCpIksxl199MbJJXPq/voGgwnMh2u4P49GEgYPuxGmpcKkmntUPVBA5ZQiBYV5hvVbWyOxw8+9FcUtIyKK/zIBuFEor2VzQ5CZqG9uDEVF6ZMZ+VK1ZQVlnF9639oyGSzYbDGcIFV93MB4uWNqFq3QBVR6F18HC9XH5A7x8SfhndaF6Don9skjVQelQv90zRNAQyQ5JSiE5MQZYkyus8PPzOF+woySNt2HBavfqMZehoQi9jjeR9rzYc8lZt2rpqOeU3TeYrAe00lZmDRvCfzCt47ycfjuqS95HctUH7gKooVuU4Cb34h+5B9mLrhlIWfTGdshPHSc4cQb8hSUQPGcKaZT9w5aUTgipYFKzJpeaNK+C2Lxpv42aw9WgNb67ayZPnDDLGFon1Rfk8fINeltvhdPDaJwtwKRr/u+UylIlPQY/Go2ERR7cQ2bsPE669hSFDYinMXcXg5EwUVeOJmy/Ti6g4HUR9OJei0efw4IIfuaZjX+7I+oDxU19nGPCxLPOBzcaWD2+GqmOgqSg2G5uKCzjn30/w5ucL+WHBbDq2ctK1fzSbDpUBffyuQwhYuLqYcrkVUtqlyD++icPhICF9OAKdCgAmFxl8Q7TWOdDHwI6tdAqDGVURhpMimNScLzp36YIswbaStWwvzmVIylCmXBqH6ZiOSc1EttkC3xdJfx9tNjudu5/G3lqvATT+hruQasssOoVsRBXzsldaiwaA40cOYRZbqHPr3yC16qC/q0bCorUgQv9/XWE+GwqySc4cQVJqWovmkbJat18EYktJIaV5q4lOySQ6ORN81rcXh+3mm7cfwNPCMb9F8PGOCE3j7f/9C/4+X/8bne6zdPFXuA19csXjpjh3NfFGERVfmGPBiVo3Hk1jYKdWp3xZvo4t0zPfGMUCDAdIRx9uvSQTmz6SSbf+gy/3AWUNzg9sKc7nmKuFcnV/ImOxeH8FblWzIheqplMEzQWr6QBUhUAWkvW+Cd1CRgNq3RqtQ2QfSobXk6yY0dM/oQv5LwO5EWQkJzJ1aUXAdlttOZokYybn+FU0Ev6rWl+DOdizN5PIfq1+ccLwRpucn8a4aUIQkJwyu+QAD4053Qrx+0IZ9yBsHo9yEsnCDcNiTa326z2qlSTxS7CuMI97r5mA2+XyhrsbhHfPi+qC++Ir+Tkyil0tPfGcR2HS/wK3GwOoEBpfTH2HEWeNIxNAgv4Do2D7ZgAefU/3nGpC8Ph3Oo9z5tVJlmGoCd1D5KVFQGleNslp6dS6VW9IisbbyDchVAjB4IQUfqzuwPfrDwfse8b5Ezkr7p9EJaRwsN1AFu1vsMPRXdCpT+CX5M7Sywpf8ULARxNvvIMhw8bSJsTOTwtmeid/Hw9yQ08Dxj2ZHcVc1MmSLh4/IC6ZuOQ02oU7zCCcdY+SzzmrXXrZV6m2li4zphL92vO0VlW+jGjP4xOfYF2nvoHtJcGtjz5HqF3m+3mfs2VdiS7n5gNZlnE4ndz68H/ZsqGUnxbMtOQBN5WsZVPJWiRZ5uM3X+SciVcwYfKVXHHjraRFB19YZ69agaeuTqe3/AJUVFVbk4yEID97lU9FNL0v7du7R1eeEE0vf2oqy6k/sZe+AwcTl5xGTFIKdR6NT959zUtxcQu+ePslOvXoSeX6JbwlNN4CRgF3APdoGv/UNPJDbTxdI/ENMrLdQXRCChISQ5LSSE5Lp37PJs4680xccgjcNsN4EPpYUVNdxVdzZ0LGFSBJnDXxSsZPuoKYxBSE0HMrQO8qblUjTMg+VAFveb1dZbWWgWzub3pKJbyTcTDYJIktJQX895ZJqIoHm8PBE1PmMCAuGY+qEZWQypiLJ/PDhiUQM9Z7YK6+4JEMI3hQp1bcN7Kv34Rveo8lJOyyzHlnjuXtl1/AHIXbd+7q59UF6DlgMCPvepC0ocMZkphqvTMIwebiAp68dTKK28OMt1/m7c+/pG/7UbQNcwRwm33x8/EaaxxeX5jP4zd7K0mefv1TEOFVT8mI6k3Se7N4fF0jJzsVSP7X5rvwlOwOHA4Ho867kOL8XARu7A4ncelDjds2n6IXZlvpOvK/FML6VyCa9CAfPrDX30AeeztnjuiDAIpXL4XosX77CwRZXwVqiTd9JX8OWGopmqCqXlf56Nom1DuWGx3TowjsDh/6hdAjPKqmy8i2CrH5eJB1j/S+KjcvrtETuv9MXnMTfxnIjeDKs4fxtwaZ2ezfyAh1C9lOJ4riwWazcc7EK7BEjCTTiPE+aKuzBHn4QoCQfr0UPt9MUyEaT4orOVDJ6Mv8eXimUW2FMhue25C68o0Om+e4MLozX2084re/5bXxGdCCbQOv908IOF7jpkPESRZZMNQC1t7/GB63VwJMkiRkWbK8ga9dFEPljvX87+s5uNNskOovpdWwTUzIDgfaFw/AFS/6f+DjYdA0TReBz8piy54yInZ7XQgD4nVqha+KicDgjwp9mElI99Ii7HYHQ1Iz/VbZwrADqt0KbSR7UC+yb/TCo8GXQYxjgN79BrCxYCV7t2/mYJkNHD6cufx5sC0HrnwpyJESHNwMigfs/glMUfEpDIxPplvrUM6/9Eq+/Pxj40Y1q12na15pn8/W7iN3dxl39K1nW9Ea0oYOJyEl3br+zcX5lKxZTXzaMNIyMtk86ys8qobNGIyNJ4wQsHXPMTp99jHd334V57Ej7E5I5uz1JeR2jwIf49j3+T752vvExScQEWInOmYId14xHs1H/1eSZVKGncEt9z1Er+gkxngUeg4cwofP/stP01doGh63m29mzeCnhbN54Z1ppA0ObiAPHT4SSZYQ7rpG+1pLcKjaQ35BIWkpyYBEVHIGdqcDxYPhdR3Knj0G5aGJoj7m56rioTQvm6FDh4KQ2FScz9ED+5BlGU1TEZrG2pzl2Gw2/fqNZsoClksSvRxOZpw/kdRVy/hSUTjaug2lZ4+n4jRdLE42DMMVy7Nwe9wIvMaMANZMX8Dfp3yNKutFdoSADl17MDghxRoZTQ+yHn3QLINJExKyJNgy+ytUDWTjgDpj0e0NAwtsSNQ1UY3RpWhkfTPHkrRT3G5WfD3XKjstSTB6/CSy/nYZ7tYdoVc8zHmM9ntL6A70UBS+O603SSPPZJRcQW2P3iiaQMYcKiSSe7YD4MxRI5j55TdM+EHnsrbr2BlN6Pq5P23TOeOhEa245LK7aR1qt9rFlw7RUDM6PiUNZ6WN7m1C/BYJfo/bZ2gvzlvtcw7Bxgj/8XBDQQ7nX3cnnEpVwUYg2WwMTk5no09Cq4nhZ53PhH/9nfSMTAYOjiZv9SpShw6nd0xSIw4mb57LL3WumF5j8Ho/m6KzBdAkOvfj803lDNn7I5raNmDvksJClgbREm8Mf1Tl3WAwr0QTcMf89Qhg9jXJVllszZij3JpGiN1rAJvH1XkU63jLUSbApQhmrDvBEUOF5C+Kxf8hNJTgAZDqKjnzkosZe8HFbF6by+hRZ3B6bDJfv2vonZqrJp9j/MINDaD5hCQK95Zbg+epoPRApU/dd/1l37w+UMMRsGgYvvB9CYKFOibd/iAxyRk8EcSb0CrEvxvp3kzvOdyKxolaN5oQbD1aQ892Ydagr3+nQJb0dth5oubkDWQDvkamLNsYfdFk2gy7lHl79c9lCTYVGklDzXjWfNE/JoHt33wUZBnjk6Ap20jOGGZ8j+SX4LGlpICl6/MZmOQNu1uGL3qbRyem8vKM+axesYKBSelExadY/UZY+0vsOF5Dh/AQ+nYIpKJ4vSxNe1Q+nfoOWtFiPQoSPQbOvdf74crp0HVA0OPad+lOF0c6m4JMHC/cfwuPvDebPqOGc+7EySyaOUPXBvYx0BRNo96jYbdJLNqgG+9P/rQD5r3F3Cmv8sqM+UQlpLCusICnbpmM4vGw8MM3eGrqbEaNGB7gRZfcbjrP+oweb72E8+ABKjKHs+WdjzkUl8IlhXlse2cawVWwYVBMHJIkYZOgKNeroGHCbrdz0z0PEZ+SZpX7rq4oC3DtSJLkpY243WwqLiD8Gn95MBMp6RkMH3sOKxq5phbD7uSVzXCfKCQ9NZmB8Sk8/eFc1udnE582lP6xyYyo8/Bj6Q403+TMYBAadruDuDSde7phbT4PXHepEYXxeUeEnqDYe2A0u7d6tYMzxpzLBdfexrHEVL7zeOi89HsGzJnO2Hmfoi6cSdn5F3P4mhuJmXgu7tGjcTqcuDzeML85Zh5wdMZcyco2G4OSMv3K9yqaYEtJIRsLc4hPH8qwzKGWv08TXt6oecUbDlVaRWN8w70upfH3ftHGw4zy+bs90LeyjK7ZWdgPH6Jd2VESjh3m4thEKlZMo0t9Dd0qj2KZopoGP32j/wA13U7jUHImR+LisY+7EKXHaX7f19AAU4Tg9ZU7KNhXYTY5LkUjXPMdn/X5ZXBypt+iKDFjGCUFeRTlriYpcxg3XnwOwWAOy0JAjM85JLyOBBNDUoayvbR5zuzJoE37TiT1GENF2Qn279jm99nQ0WcSfXpHBIIhSWkMik/BLstU1itBIpLeaJLvfZ0qrHbBqxLSFMVCttkDqDaH9+/lSHkExJ8dsP/zm2SCJgUFu5aWXvTvBHMOum1uqXVtAqxS8S5FQ5ZA0QIT9wQS5itnUuu8Ov/CTyXiL4rF/3G079qd/Kzv2blpA0PPPp+45DS/AVeSfL2kOhpKmfjCP+R86p0jJyeHT7/8lpTM4QyKT7GM5A1FhUBC0GP+WbAAgJdTJgDeld2Bijr+++O2gP3Pv/Z23p/3LdAt4BwHEu8kpksrNhzWvSHC5wd0fvGByjoEglqPQnmdx89A9hv8xSkMxi/p3s7oi6/nlRnzyVm1ktDW7aitLPPzeMuSRIxRjMAd5GteWj+fg/WS1SYmthWtCe6JW+PDzTONiZdeIsajUnDGZOujZ267HMWjcznNqm5XflrE9CsSaB1qtwaTmMRUOg2Ix62o+kIF72AjfAaexvuKN3rRlOa2qgrv9dYET1gKhhNHD3GiZA0M1wL0bxSPwqbCHM4bM4IhSWncf//9vPDCCyBUq58ol8aiaAK7j2EgupwOvRPwbM+maM1qBiWkULRmNR5na4RagaJ4WJ+fw+SNRWhCcPCWu9A8HroumkuvN18iZO9uqpLT2PbS21RkjmTd2nxy33+NqKQMevUb4Gcg+/b5bRtLSUhMxCbLVFdV4PeGmhOj4fmU0f8fkpLJghAnbrcbWZa58m93UFlZyeK5X6CqCk6nk3PHjGwyxD101Jms2BX4/nWVajgkIlr8LABe3SLxlLOQ6KRUBickExWfjCRJ1HpUBsSloJ3/UAvOIrj1kWeITkxFlqBozSo8Hre/cewDpUEFysgOnYhK1AunbNlYwru5K5F69+Oii67grKIc+n6/iI4L50J8PJl33MFH02exLD+PKeakCfT+8B3+UXyApxMuAiAmfQT9Y5N049lYSG4uKeDph+5FadOVBR+8xquf6IspfbwQdHn/LTQBh2/VE06F8JZmtrRZ0d+LDnWVdKs+QfeaE3SvPkE34//ergqSlGN40Ee5EIAfvtZ/DLhbt6WydRv2uRW29ojmgKeIQ/WVdEofQffR5zL08AFQFH7WNDpkZzFk2bf0/3oOPPMo9X36wVljYcwYClq3ZvKkSXC7N0mv3nOatRgDbwKrXghE74Xm1DIgLpknpszm5+I1JKYPRQi486oJeDxuHA4ng5cu8ePBa5owVITMdhd0H5zAE1NmU5y3mrqqSm8k1MDghBRmTnkdHA0S934BKo4f5bP5zyH5Ui32rYfThqAKX864cZ2mw0ZIlB6oZGCnVtaYGWzezMnJISsri1GjRjVZybIh/MZZoUdSmzKQY5PTKKkP3N5k8ve984Nu/u7mNM79IM/625T58zuv0JWxQoM47n5rmM+irM4n8iP0kveyJOFWNBx2WV9Em+3n8wzNX1Qh0HyiP+Dfxs0Fu/4I/GUgnwSOHzrAwkV6ok9p7nIiw+ycP9mbrW56Ay0Pl2Rsa0C7sPa3DELdC3IqyMnJYezYsbhcbhxOJ69/Op/oxFSEgPC27SCQRg3ABTt0iSFfY1AI+LzoAEeDqFjcMLMIzcc49j3HFODJcwZx69xSjtd6rHP5wlfztyFM746vUX0y0L76ihq3inbRdcQkpVJer/DkzZfhcbZGGiVggOnZhaiEVP4zdTYz8vey2fckb1zKiA5dqXW08TeQl32gD+ANsWwKFHmnFCEEa9eshjUrCAUY5TWQPW7d4GhY3fpART092oT6eIq9IUO14USBmeQYvJ/ofHfdqNY0wbajNUH303f2+X13Mcx7HC55Ss9YR6KVXSKo8OCOAvNuAz6yO+zEpGQaA57g+eef52BlPZ98+pnVTxavL6b70IwAOS4kCZvNRnyaPtEnpA9DaGmwuwj7188SnZpJ5FvPI4TA3aUbvd54gfCdP1MTm8DPT75A1egzUYTu/bz7yovweNzYQ8IYfP3jfl/j2+dXfPcVyUnJfDNrBp+894b/9QiBqqoU5a4mOTUdmywjSSpRCSm88emXFK9ZTVLGMBJS0/CogosmXcGa1Su5buI4evfu3Wizby0p4NWnHoFr3wl4/6pPHIHIQK50c9hQmM3gxBQq6lXCHDZkhN8E1DwkqirKkCW9wltSxnAcDidu4dKpJOaq33hGB3fv8Dv6h/lfMOLCSwF47KZLUQzu+dKFs/jf29MY89RzdFw0l74zp8OttzKpdRtGTZjMSmkPmzr0AgGdl//IuUdrLAO5ddv2CGOytRsG0ifFh/FcrT8n5fUJFK9ZzaD4FJ2PLkG7n75DcrupTs2Aolo6bthByNHDyEcOYz9yGPvRw4QcPYLt8GEu0wKTKcpCIjjWtiPOvr0o69KdZe56QqOGEBGXTE3HLlREdsLevRsiPJxZU97g07degLA2UFeBJEmc06s37aoqGZOdBcDFO7ejeDw47HbefOR/nAm0W7OK0Fmz4IMPSAEKgGVL3mNZzzj2du+IpiX68V5/Pl7LhkOVDO3THtAfgzUuAIPikklLz0CSYM6UN/CYiW3oRWp8DURTj18TwnqkW0sKWVCyj1LnMOgQ2DMqXQpD0oY2Wxr5pKC4jfHOx1+98Gm48wteytqBEDA+pqtPREDvxyYFsMat+FBOfKXZhDUXut1unE4nS5YsOQkjWfiMsfo/jRWZAigvK9Ofvy96xrbwu/zx0avP8OLld/PA17pyVjAD+Wi1m73ldb8oynyq8JUcNSHQ80Ssv40+6VZUHDbZ+hufIxUNbJKP/Bv+U9FfHuT/62iwolz67VdcMPk678cYL61PZ9K1GoO7kH2NxVPtG1lZWX4Zv2tzV+th6rV5fPrua3Dlay06z/pDVXy98XCjwTStiTBbpwgnSJKfJrS5IgfvHNuYZqfXW3BqHmSvN0H/Z0N+tl497upnEG27WPvtKauja5tQBsen0GmfYPNRn5Mobtz1deBoMOgV6eVpZZsNITSE5fnwv06HM0SnWKzRA+g7NnvD0EJoemWxBhxur/FrDDiqxuWfruWa5B5cEN3Vz4tiho/lJvqJud/yHcd5ftnPje8oyfTsN4D9u37WaRC7i2HqzXTuEMkRBNX7tsK02+DG97zHvDLe+/uCJ2GSl0M7WN3HpS9PISpB52qak7xw1fqpWBQtW8ywzHS0hqFGo03NPhOTmAo52dA7kUfe/oxMRSVk7y4cJ47TLi+b6kGD2fjuDGrHnY9LBRsSQmh8O38mHqNEvDLyb6wLb1y9YcfmDexYX8hXsz8N0j56X24XGUlpYR65q1fqHumYJOveJAnahjo4UeshLlkPB2f2bc+BAwca/c5VK1cYiXMNHuKx3bTr3InqU/CgdBqS6aURAIrRiI2VWG8Iu9NJfNpQnHZJL5ACXHDJ5bhUjerqajaXrqVXn766/FtIGLlLv/M7XlUVNhXohYZUn4RY1eNhY1EBo84ax/Err6Pvg/dAbi689TadZ05no9vNstNi2aBc7a964nduFXtFBWEnjtJn32EyazbTtaaMHsC43FV0XLWckGNHcB49jKNS9wQMmahTC/oCQpLwtO+Iu1Nn6jp2prz36cw8pHEgoj0HW0Xq/0e052BEJPWOEAZ0jOClC6OxydAuzEm1S6FS0fCoGvWKRttQO5Ik6SXIbTaUg1sAfWxY8uUsVEXlNqHpGt4eg9urQtaxo8Q89DjHbrmD1O6toaiI3R99xP733+eGDUu4q2QxGhLly6ahdIlmXocoVpwWQ2VIBM8u/ZmFN7TXn7EkqFc01h+qIuvnY9w9vK+utoC+qHQ4nSgePbEtWJEa04suAQu/mM7LTzyIesMUaEQFUAhBVHwKfXPmsTO08YVfi7EtB356B0mSkCTZmxTrM0Ys236cC6O7+Bir/lQ069rwNcD0fZYbc6FvJcuWGsgNPdaagCDFVi3s2b0Lon6FNgGWLV7ExNu90R5FDbQXTO/67wkhBNuP1QR1Wpmyo5avQ3gT+eyy91n5Jt9rmkC2eVW7BA1lYH+jG/kF+MtAPhk0yMBt376T3wPet2Mbm9qXk5HhnbQ8qh6GMF9oIQSSJKGo3iIRvp7Ck8WoUaNwOp243PrAGJ82FAQUr8lGOUl95ZnFBxjWJ/KkjukeDkP7tA/QNm5IBfCtptNwfWzmmJ+K6L3PSSxPdHSyQaPwMY4B9u/cyvwf82jdLpLVWRsg5RK/zxV3oOcc9Anw1keeZfCZsTzx/VYj1OS94WFnnsdNd95LclqGtS0n6yfoONr4S6J/dBwHznsS3xwhs7SqGZIqN7zvX64/xLjBXajzqLQOsVuThUTTCwjzPHvLm1FJkGQO7NlJ6qizWWMaPJWHOVLlk2hZ7m/onXXpNQBEtG7Dzs3rCbUdZI3ajbMGdOSm9CTclcesBZJZYbJViB1frrfTYffzCvlcOKqq8sOC2RTlZjMsajDXbshn3M5Cxr1bTOs6XbXBHR7Oh+dfjOvqm4hPzSTMMIwtKorvOX11n4Ng+6Z1/OPaiQyOSwxsHklC0zRe/s/DgISqKEiyRLdefTm0dxeaquFwOlm2dAly9yjjmCa/DjBK2Dud/lzPHfnYvn+V0Ac+I7jbvmm8tVWmbc9Kojob9AyjT72bs7tFx9/y+MvEJqVhkyTc+zZxlxGmlyTJUhg4tGenVfBCbiB5Z3c4SBk6nFq3is3hsN4h2WYjISUNmywxuHMrvYEyM7FlZnL4v8/yynWPcVvpt4x+4yFUuwM1rA3v//gWXWvK6VZzgn6uciKrK5CNsMtPPtdcZ7Ph2r4V0aMntQMGUT50BB2W/YjmcLLr0f9B1y7UduiE1LkLdULCbRi4tS6Ff33SeMJZbW0tkgQOm80og6x71TH+N38GJ6Ry/YP/ZemXX9Chc1ciO3bix3mfITRvzza5vXa7g7iUVCTJCCfb7ZCaSu/UVBbExHLBti6kHtrGmPx53GCr44rchVyvzEWVZAo792dZz1g6nV6FGD4M0ao19R6Vp37UlXBuH9oHU1s2JimVNz5dQGneauLShxHROwZV868kKoSu9PPdgll8NXMGWngktO7YaHtsKy0kKTUd24oP4exfXnkzumYj/S+7ish2bQlvG8mHzz2mL2g93pyYmqoKv4Q5VTN9u5IRyAhM+jYNf3MuND3ITVWy9IV5DvPhmb82RbEQUWec1L03hbQx5zZZchu8Cfi/J4SAinoPZo6FLzQNhOyNVJm5S2aE09y7zqNa+wi//XT4NvFfKhb/5+H/wvz49XwmXnMjo7pKZB0S7P15K0+8fycvT59HQo+zAF2L0yHrnWJfeR0V9R4GdmpFyYFKb1idU+8cmZmZLFmyhI/nLyZj2Aj6DklCE4K41KHYZ86m8VSt4Nh4+ORm6YPbN7G9tJAYHw1g/WXW70dRNWsVafHJGsC7ovxlq2RfT/Lw8y9haYPPv3jjObQdBbq4ftrl1nZbxUFUJFACkxfHTLyasRdNIiYpFUUVnBfVic+LDjDq7HMpC60gZdhIrrv9Hvp1CKebWg9lZVBVxaQjVSR2qaLO7qS2Vxw9gNLta6m1h+jbHCG0OxCOI9SNiAgDe4juzcXXi+FtD00IXTPbWGTUeVQ2HqqyQm66nrawFmBNwsf4sdnsqKribcBGcN3DT7N7YzFP3qJr40qeSBh5I2AYEnhDkpKkU38AbA6v2sXijudxuVsh1ObPo7NJMhmSRPqsGZwrBCnATcDBiEjm9Etn8PWX0Wfq62zbuonbv/0Kx5LveGXGfFLTM6znrQnBeRMns3ju53r0oLkkTFXB4/HQ9/Qo1hXmWcVAJCPcYSbdWeK5mr4ANqF4dA/V2KuiKC3MIy97FUw4r0mKRWZmJrMXLWbyYp/KdnVVCHcdNZVlILdv+pobwd6KOqI6R+japJzkIlOgF5yQJLKysvB43EGLYQhjETN24pUIDarKjtKxUxfGXjSJ+OR0yurc3Prvp3n3vw+jqapRTVHvFxENkngLduzghbRLeCnlYh6pXMsDn71I16rjXLgjn0Ph7TgY0Z51HXsT3a897eMT8HTuwm2rjnIoIpKDEZHUvXMl9ooynnv1AxJS0pAkidZbNyMQnBh1JiF2GY8qCLHLCI+3FHd9M0lStbV6EZoQu7HQk/CLGEiSxJbiAn74chY/fTkLTVHZt30z4668yU/ZJCw8gidf+YB1+TlEJWYwJEZPeo3t5h+dKjtxHI/tNLJ7RJN9ZAfbY9qz9ZqXaV1cwJg9pYzet4771i7CWTAfzW6nOj6JXV2iGR3en5xuUaiqZlAm9Hc/JilFl9NTNNyqXsY7qnMrwp023IpGqSGD6ap36Q/+lo+abI9NhTkc272dnzeWgk/eWXv3cU44g3AyTBzZAZ0DlVzufeRJ2rXvRLhTRlEFiXFxfPTG86xZtdya/Q7s3sH6IhtJqemWAVyvaDiNsD3olUW3Hq22oib6dmHNhSfDQa73qGw4VGXNw5oxiai/YzW7K+55JLDkdiMo3FtOYo+2zShs/HoI5r0H+HHrUcYN1h1Qft58AR5NwynJSJKERxU+n+knq/doFqHlLw7y/4fQv1NrfAPXqqpLew3QNLKOdoFuA/GEtqVoTTZXXXAmmqZrdsqyzTKCs7OzmbexkM5RSfSPTf7FHmTQJ15P54GEO2Wq6hUE0DM6gQG3vszGRjjIdfbgShG+RPzmUGd3gtDYWJjDkKTUgFVmvUdjV1mtIdEk/FaO/jBWmIGRpWaxr7yO1jYHWqgDTQgWzZzBG08+jNorqWEtAr20s6YiISOd2KN7ZYu+4orMQcx0OKjz1OuVnnxww7+eIcxp141AGc4Z2ImubUJJ6RZLl9SBtMpdTu+H/k77dWuRt3hZzXezEzY18FatL/b/e4b/n5rNxnjZSZ0jhPAZrdFCQ5HDw1FDw1BDQ9HCwhGhYUgR4VSFhtI9NAy6REJ4OK2w00WR8ThDSdpXw5G9NdQ6DGPcxyivszuprSlDAyI7dOaCa29h4UfeAhrDx02k8sRxMs86n/d9aMzbSteyvXiNJQsl7ciHkTcyop83emAOduvX5nH3VRNwu91Irdr79bUjVW56tA2lQ10l5+xay7idhZy/K592RnGOHOARSWbxVS9T0qkvQpKZdWYyrd9+nloh0IQua1W8ZjUp6RlG1EBY1IzXPl3I8uXL+Ca0NTUNOpPvdUiyhMPhYNwlk3ngrpt5+D9Ps3rJd6ia1uBlDN4jTQ/V+rV53HHFBDxuN9PfeomZM2cyfvz4oMcAjBg2jPDlS7zXYlBvWrWJPCUPMuiXu620kO3Fa+ifmMnKusa9gg0x5ekHien4Kgkp6YwaNQq7zY5H0ylBvoayJOntNXr8JPrFJBHmsBHhtKEK3Xi0yxLV5WVWc2mqSmlhHmecea7f9+Xk5HDp+PPg9lloso2n121gqKse0SOGcZOf9dv3ajWHG6+9GQ1YtcunNLymoXh0abq4lDQkIdBCQy1Poq+spq83sikFCwBHaBgS4JBlH+NYQpL0wjSbiwv4942Xeg1MQFFgQ8Fq6xx1QIWrnsHxKQyMT8GjaNi1aiQknHb/COSIEWfAV+X6H4kXcLQreHCyvGcsy3vG8gQQ7qnnu8EKvUvWEJm7ksnfzuBKoVFvc1CWnUxlxgjKM0dQn5iMCDX1NLzyd6oQ7K+o51BVPYU5ul52S0fZVm0jee/ph/2Mf4DKb95AUhTEpKeDH7j+Rxhza8Dm7Vs2kza0k9GmEJecxi33PUxRXo6lB11VXsYD113Cq58sICYxFbeqV0hs5bRbz7TWo/rNJb5OlczMzJNKzqszzmX1W+PXXSdq+Wn9Hn5NBY9guH2A8FuIgZdn3hCW+sNvekVe+Hp8G9ZUmF6wj/hubejWNlTf1ycaWuvWsIfqyc1KA8qpQLeJhBDsOlFLyWFvtNOlao1K0/5R+MtAbgSmB8zEjQNslBfu5WeTUAvYbDLJmcNwKQLpq62I1p0QN35AQprNb9WkaQJNg5KCPO68cgKKx41ss3HuxCs4/9LL9WzsX3i9ljwN+uV9nL+3UeMYYNzE//zCb9TP4XhnMv9JHRowjmgGf8mUcPNem/+dmiWHMT4Thqe9a+uQFpVNrXGrHJoxB5ci2Jibw+tPPoyWdhlkXB6wr2S3I9lsyDYbbM9B+fRebOX7GHTdHP4zdQ6Pf/QOhSt+9D/GGLxCjx+lY3EhrYsKOGd9Ee03luCo1S1IT/sOKKlpOK65GjIyICWFf89ezrvFNYQpLsI9Lu7r42LGZjfhHhdhiptwxcWVg9oxqLUdW30dUl0tR46Uk7/lIGGKi8R2dk4LlQjz1GOrq8NWXYXz2FFs9XXY6uuR62qR6+vBpadSRxo/APHAP5ppNxcgvplHnSRxAqg1floX5xPZdwBadhbDDiy2jGvX64u5bMRYesgS1ULCVXmY8+pW0mnjcdgVRq3HRcdePQlp05odPyymlctFtabhqjrOxde/jUd2knJoGxnTfyJiyY8c2bsZGcGRsLZsTh/D0vpyXlqzSi9AZbND5/7WtW4pLuDwc2/ywPWXIHs8htbvMJ8Qq4Sprx2TlEKbvjEsW7SRmgbJpr59fnBiGuefN464lDTSekUyJCGJVUu+C1ipSpJEr/4D2L3du3A645xxPPvEo2RmZrLgsSd1r6um4nK5eOWVV+jUqVOjk7RshNrNa+mlHuGWy2cx92hbqK5s5qkFx5EDe5j16BUoHg9S/LmoowONk8agHt9H8ZrVJKamG1vMsc3G5JvuZPumdUTFxNKqdVuGpGbSLSrBqoJokyUkoRdUCLHrSZZf+FALglU/y8rK8qsgp6kq552eCeP/FbBvTEomAq8GsgXZhs0mE2vQyQSw6ePZhq54A4eDFbUSzapsScaixbeghyR5sy/W5WXjdrnxNVEkScLhDLX+HgdkRMfzL2OekA0pwWBBnYzMTPjqW+vvJYcAqvz2qXWEcigzGXH22ewCLnj1J0bs38CYvaVcV7Wdge+8hPT2i6hh4VQmpVEzfCTH0obhik9Ec9itZF1NQGLGcBxOB263CDB6g2H3lvVBowmqqiL2NlE9pLY86OZXnnqMp158mbikNJw22TKSX5kxnztzzSiWhsfjoSh3NdGJKbhV1Yqo+b6aprfXnO9O1cEkNZibzMS/O+av57c2jgGGpSYhG0o5JoJSLAwZ1D/EfhTBI78e42J8CIAIIfldv9AElXUKIXYZm00gCT3aoWqCfy/eEnDOTUeq6dwmNGD7H4W/DORGkJWVBXgr5exfn0d6YjJfh4TgMWSeHvzviySkpFPnUelR6mGfHqEjKiGVvNxcli5bRni/eKIT06hXVApzV1shTFVV+XrWDH74chavzJhPXHIaR6tddIxwNh8iDwZzYkB/idwNJ5XfCGdPvp6ohJQAvpbvAAbeAU6vfKZSXuehXZiDdQcrrX1NLumhqnrCnTbah7dMD9nUrSzJy0YLbRvUOAadDps+6mzadejED3M/gyM70Gw2NhXmEJ2SSdsOnZFlGzbFQ8LRHaQf3MqwDR/SaX0REfv36N9ls1M+KIZdF15GZUIyB/v0pWN6Bj3bh9GjbZj1XX2GxFG+eR3l6OVPW52bQF5Fsd/1xJx5OmG92yNLEgs3HOLd7N2+KnrMvS6FjhEOZEmyuOw2WSLcYbPKgaf3bAv19Rw8dJxvFy9hY14OR2xd2V4fQbjHRbhiGOSmYb7yY8I99fTq2p1zzz6fY3t2s3HFT4RpGuGyTNfWbQgpP4G9vo5RR49hd9UTrrgJU1WcWT9g+UYVBd5tUDjFwNPGj95gGnVvXI5mcxChuNAkibwuA3gy83IW902hsEt/bs7sQ/eaXVQVrgHJBmfe4Xe+jYU5XHPHvbz48TzWrllNXOpQPZEP74LQ7Dtmh2suArmpIIefC1cRFRNNWq9zSMkcjs1mCzAIbDYbjz7/Bts2b2TZd19x1rjxTLjqOlJ66suRCePO5s2XnsctXGiaxsqVKxk7dmyjGfQWr9VAj36DiErozyWHqyg+cGoGcu7+OjyhbRGuw0hBDJrGIL12MQ6Hk4T0YciSxNKsLN34MegUrdu04dXpc3EY3BmXonGsxq1LupUUsK1oDQnpw4hPTiPELhOdmMrTH85hXV42g5IziY4eGGBjmDxRy2806qZGr29AbDICqPE0uKcBmZwR14eohBRUYZZv9uWkCsuLbPYP0CNITcHSUF6bT2HuapIzhtErJgkX+rsXlzYU2Saj+mXvC9q088/baN+pE1tKCijJyyY6OYP02EFBv6+lo7xuUGi4VUFVSDiL+6WyuF8qhwdrVOZnMS4inMRjR2ifu4rI55/iNEBp1YqqtKFUZQ6netgZVEXFEJOYwovT57EhPwd7RFvergr+fYkdZO49K443Hnw74LNWdoHrxF7UYCW3QZeN3JZDYlUJRa3j/T5SNJUN+TnEJ6cR7rR51RQbzB02m42EdH3x41G9hrE5m+jJ8N733XzOhyrr6XqSxpVJB9QpbF4P8u+FhdPeJDZ1GHEpXoqiqnkdRae1884ppgEarNjWrwlVE5bggNk2wYIvZl7Rx/l7Wb79BJfGd+OC6C7We4iRe3XbvHWk9mzLvSP76Y48JBozTwZ3PvVy4b8F/jKQG8GoUaNgrpcr2D8mEUQlY8dfhgAmTr6SoUOHclrbUNYfqsTpswRcvzaPf980Cbfbjd3u4NH3ZhGXnEpS+jBdQkmrt1arHo+HojWriU1OY3dZLeEOWwBnr6WwNCTBr1BFMDyaOxOA/zViTLYEj+bOhOzP+USWuemfj/oNLGbig6lFahrHAkGdR2X7sWpSekb6XbNl5J/kCNXt9RdRNUFs2nAc0z9slHctgDXLvidt1DnY7DZQdU9X63aRvP63S/nM42Ea0OXtyYQYvNyaLt0pj09mz5U3cCw2kf39orFF6AlRYQ6ZmrKjdJK9+qT8978ARF91p993e4JMzpFhTmrcKhFOW1BZNqvWPeaE4E3Ss75PlqmQHHxbuok7Hrgdt8uNSBoPZ9wYvBFyvgBPPReMOotBTzxLRZ2HA8UF/LhwNhXHj9KmfSdGjZ9ETGIK69fm6wU70LCHhPL0e18QFzMEraaWzXm5fPTo3YQoCq0ddq6/+e+kDImlg6TS2aayc+NGdm3cyJEDh+ixbQdyZHfeiR+H5zQns9sMC7hP8/5IvBAGj/L7PCo5g75vv8wgm8yA2/6h87DNNhECIfkYQcYvwRJsfPu80FTciovvF8zmxovP4bLzxvDt5KuZ88k0v2P+dsffiUtJY3BiKuOvuJZubULp7FOlLDMzk2lzF/H6C8+Qv2o5mqY1mUFvGu7mtfzU+zZkCQZ0jODCkF185eoT/Lk1gQo5AiY9jfTJ35E69WnxBC9LMjc8+JSVP+Cb5GR3OElMH+Y3DcsGlWJjUT5PGWWOHU4Hb3z2JdHGIjk6MZWo+BQ8mkDyBBr8mZmZLPjmO8792hveamws0oSepBUQdj3/Abr2EdYYqiFx2psvoQrB7jv/afEbLYPZ+L05D/Idmb3ZXFLAozddhsety2a++PFcekYnIkmQnpHJP/7zAi89fr/lgRWaRmSHToaChIfHZZm0I4eZ8DdvCeeX3vuItNGBhTvW5OYEbAsGAVS71QCT6NUn/412YAsfOR28+PE84h5/lrYVxwldvZLI3NW0XbOKyKU/AKC0bUd52lA6pQ9n2Jhz2NGtL283krDYtVNHwp022nXo5N348R0knnsp/7jvH+wc/DGb1+bwecMD66uRpv4NgJJpT8I9/rq/smwjNlV/J2yy910oWbMa8EYbzppwOdHGAth8ZOY8sa+8zqfegH/y2r6KupMykD2qhtvgcZs2p291vt8D8957mYUfvsH/PpxjbTP7+6Gqek5rF6ZHoH2oQr811h2sJNxpo1/7cGveDkZ9MNvthy26nfTZ2v10bxNCfPc2hoPOe835eyusNhZa8DYeH93ld+NWtxR/GciNIDMzE+Z+5d0gwXP33YziUXA4HVwy+UokINQhs2FtATvXFULfZAB+/HK2Lr2mqijoHrAhyanEJqfx9ucL+GbuLBbN/hRNVbDZbMSlDQ0IIZ0sTM+xOSk0JVEDMHZPKfDLDGTzHE9Pf5dhZ56H4tGHM1ddrTfxzvJqmxOav4FnhWJ8w1zGNkXVqPWotAn1L2tsYl95HdUuhW6rV6AJweBb7uWfL07huY1BdwejbO6apd9is9vpNziWi8eeT+rs6bzo8dAaXTb69Z7x5AmFoff8nW6jzqaV047TJuNWNdR6D3ZJH5gt1Qbfd3rJEgAc197FXcP68NbqXQDUBilxG+awGfI9wQ06IXSDzyYZbQjIRsOZeqag88aXLluml9m2O6GBeof/SfVnNCA6Fglw2vWEuaULZ1kKBFmLZvG/D+cRlZDKo+/NYl3+amJThxGVmoYmy7jCIsjZs4P1qoomNCSPB/bu5vT7/0Vo61DoEE5fdKmtDxd8j3rVZSg2B59Fj+b20wVs97+kWcUHid7xnZ86QkN0XLMKmyRR97d7dI+hXfYLsZp936MKVu08wcGqwIRLs78+c2QHmqceASyc9Rk5t/2NzMxMJk6+kvlfzLCuQ5Jl2rRtY5RKBoHuvW9YOfLCM0fRLtTBJRee12wGvZnQaF7LT8Jbc6W+uhKCdPWRjv2s8PRotG0AbG070fn2dzh4Eol+AkF1RZku7yZ5E34XLP6BgYkZDE5MNegGJvT739igzHFx7mpiElOsfU1OsoRX0cQXmzZuALxV5Robi7aUFJCQkkbOrrKAc4R37Y2iCou+0DZ7OULAzjv+oWvKm+MJJi9SZf66g422xeVt97P1m2zKjxywKDOKx01JXja9ohP1qouyxEVXXktRfjY/LZyrt6EQDBoSx3mXTGZDfg63LF5I5bZNeFy69rnHIyguzCN9TKCBvHLlCqCZSodGOwSjqyiXPQeLnsWzYw3Fa7KJTUrF06kzledfzPELJmCXZZyHDxCZu4rw1Stpk7OSjj8uBiC6fUdmdoxiWc9YlvaKY1u77tZAZjOe4xkXTGLFV7NRPB5sVUe46KxRAEQlpBCdlMrn0wsCrkkYjgURbDUiVKv/S0hsKipk4ezPOXr0MAzR70+SZc686DKLmicMz4k5v4FvArNuZGuasKgSLUVOTg6zvvqeuLRMTo9NseTyXC2sdvdrQdNUFAXW52eDTV88mPdt3s/mI9WGGoR/VNYXe8pqaRVib3HUtSl4VI0al66Dbb5DShAHT7AKwSbPX9UEbjS/qM3yn48xekBHGqv5cNbATkG3/5H4y0BuIQqX/2hoW2p4PLB2zWqGDh2KhETRmlUIl7esjkDgcDjwoHsoBydnWhN4bHIaQsDXcz/3lX80eD6nfn0Wfwr9JSo/dqTZY341CMHShbOpbDcaWnXg4J6dbCyS6Dh8mFfiTngHNmhoGPtUUDK3CThY6eJwdb0Vzm6IQ1X1ejlZ42+XR2NfWE9gX+DO+fNhp3dA760o/HN9MdevL8YBzAIGADXAQ7sKOfOSa+g26my9kpqRwW6Yw/oAL0veDPcg12ZyNE3cs3BDwD6qj+u84sTRgM8FutfLJmEN4ObYYqpaAFTUeRg6fCRzpryG+6L/QI/G9X9NA3nbxnX6NUqwriDbT8NW8XjYUJDNgPhkBsYn0y82UZe+8qEHRKdkIttsaJoekl/x7ULWX3cTXc4Y7vd1cclptOvZi/0G/ei0AVGw3Z97VulSKOk8EniPYCXANxbkGO0hcCsaTrvsNX7w9/rUKyrv5+5p/P6BPiF17DDcUIqiWN7exNR0HnjqBV56/EE0TSMkJISRI0dZfaCxyalViJ3zxp7BkiVLWLRoEePHj2+UgyxJcMewPmA4qlN6tjPaVaL/4Fh+bLB44JN7GHDLrc0ayKqQWm4c7ykBVw12u4MhqZmGpJn+UWZmJt0HxXO42oWiCosSYnrrbLLEkLShOD7QS7o7HE6SMoZhJl7JhqKCmejWsMWmTJnCfXfdAf9Y1Oxl/vfWyVx8/3PMrgtURNCEXqjAYZfxjfX7RqDM10vRBB/l72VZE9rQ8z75CHVHIbLdht1mRwXdi26Ujzcl3tavLWDZN196D5QkqsrLiE9OJyUtg4iVy6itrcFUURGaRtu27YJSfkaOHAkLy5tth2duu5wnp84OPj90H4TYnkPrtu2se0aYlCOBq0s3TkyczP4LL0XRBLbdu+hSkI1Ytozhq1YweesqAPZHtGdprziW9YylypHA/NIviUrK5PmP51OSl82g5Az6xCQaxqjeX7q3CeFAZeBCNCiWvo92ZCfrC7KJTUpjQ1E+d10xHrfJRx98E9gcnNZvICV52dhtErFJadZ7bdYX8NVFNsP4iiaw27zPvd6jYpelRnNYrOJabr3q4Csz5tE/NtmgJmrNJnP+mpAkSfespw2DQv1724Q6rPsEqPUYiw5hUiwCz3Ok2kWtWyUyzHFqFE0fmIl5O47XIIRAUTVmFO4P2K9ofyXrD1U3ONYnDwDYX+G1iz5Ys5dRp3e0IoAN8Qsv+zfBXwZyI8jJycH+0c0oHU+H6NGs+WYKNllGliQcDifJGcN0aroE7SLbg9urb9E3KpbzJl5OYe5qvg1LZ96xNkSbPCchUZi7ClVRLK5f8ZpshiSmNn4xLYTJXRLoVf+Qu//iczYJo8KcbLNRsmYlnDNa3y4EM157huHDvrEmBt9yr8Yufv97M9AbJGM0sWjQhFdaTACfrt3H/HWHgu+c8zkIjVjgYWAyoADTJYmcc8fzyY/f8JMpfWa3c8aFl1qFEyyjWDLE4yU9qUKWJOvH3mAGlIAQe9Nu/Dvnr+e5cVEM7tKasiOHQO7q9/mW4gLSMjLQNUCFVazFTMi0SYKNh6oor9eLVTz5wWz+VdTkV/pp6ZiGX2zqMD8NW7vDQWzaMEsL1vf+9YWCRFR8CmMvnsz3cz8FIdCMynNjGxjIsiQREREBZkJjI+QzR3gEitOJJ8gDz2s/FCGWW54HYZzHaROWp1A/t8aGtfk0x887Y/zl7N22SS//bbdb3l5JgglXXU//QdEU/vglAO0jnMR2a8OMRT9SmLuacWeP4cIzRwU9b2ZmJr1796Z798bfOwmJEX3bM7hzKwSCkf06WO9Iv0HRsH2T/wHH9/Lx84/C34OXqT1prF0EWVOJSkrnsne+YHBCCqEOOcDTaz5v85qRvMZyVHwKz06by5a1uSRnDic+Ra8oGGqXqdE0QCLUIaPW6zQiX8ybN6/Fl+rxuJn78Xsw+YWAz/RSt0YZZp8J2TcKpXN39b5S25DH3PC7VEWPMCmCG2++GVvbziRnDGNAXIquBWtYp0W5q1B99OVNvqweZdAbTNfNlhGGGoirpopBnQKrcaRnZMLCbwO2N4RZbl0IEbTssyTLVJaX+ed9CD151XyqptHj6tGLg336MOpIX4i+gQHlBxi9dx1j9pRyzq4irtmUBT/ATmC5bKPnnf8g8ZKryN27h4XT3iIqKZPY5DRk4IZe9UwpqeCorZ15IY3fRPE3hk7/MKMdV+PxWZSbA/2+n7fyycLnmfmegxc/nku2uyupPdvqUUThGy3yGmKq0MvXa0JQuLcc0Betgxrhs1rFtVS96mBR7mpDHlWfQ5Vf4qk6SZg2hK9x6BsZw/d/6yfw+jShJ6yv3Vfxq1XbM+fkbzcfZV5pYPRlXpC51rcISM7OE7y03L/yphC6kyfYPTSlO/1H4Q8xkF999VWmTp2KJEnExsby0UcfERr658lcBNg5dSrXlR1mVtlhqrfpMj5CtnHupKu4aNKVJKSkE+bQB4SKsjLweFdKleVlRCWk0iMqgRkzCjl6sMrLsUQiMd2bTSxJEm3aRVqD2+Yj1fRoG3rSyQYCcBkTgUOW6Ni1O/xOTmRNVTmwe6d3gySxa+tG46okP5qFW/EWSAGv7qTXAy75vTwCqHEpQXnZJqfJNJGO1wYv9AEwVFP5F3ABep74K8BrwLGQEP575d+4OXUYPPUgALIsWxOLJEmG8gBgmMzeULKE0y4R3bU1oQ2MYVmSOL5vJ83hveWbeH5iCh27dGVrAyfyxsIc4pJTcdp8Qo3WQkOfACvrFbOZ6R2TCEXNWMhCRZZlzps42TD4JaITU3h22jyWLpqDKgRjDA6yW9X8Bm4Zyc8TNmr8JJZ9NccyNJMzhwUsaGK6tuZQXa31939+8JfRM+FwOLn8of/ywXdrAobOXTW6nqbDZkQjDMNIEza/iEOdR2V9fg44A42IgGYw/29wwaZhOH36dNxuN9OnT+eVV1/j3nvvxeN28/FbL7H0pErYBkIyOpAkJJ9FRyOVu4TmlxT2i1H6PQAJQ0czIC5ZX/DbZBJ6tLV2CXXYjIWfjw6wIXcmGRn3SanppGVkYpcla1+7TaLeo6FK4LTJtGsTSt8O4X5f36lTy8OosiRb+tQNcXjfHji9A4rmn5BpGoJm1r+iaVbSUZMwJ3ZNIzExieTzJgF6yDjUbrOSuNpGtvfuDEy47lZiTCqK8SWt27bFeewIHkNxJSMz0yqP3BCXJ3RnZnHj1RdBj0JGp2RyoEbAtoafStjtdoakDWVDUT4b8nMYkpppJLF6x1O/ssymFSNJbIvswbbIHkyJOxeEIPr4XsbsLWX0simM11Tav/kivPkiPSSJSCHIkmRq73uEDompvHj7ZNwjboK4c8xL0a/X4eSCq2/mS5+r7BeTwMhzLrSSa5Mzh+FwOLweZPMxCF2K0+OB1XmFzCKRFTuO884lsX5KCl4pP92DLHy2mdHKxtCQax+XPhRN6AvsxgxQvngArgielHzKWPIeCD0PaV1eNsh6oSnFcnTpCljm1TTGjz5a7fLye4P6ZluODYe8torZjidDO/Ea9YI9QQpW+WtX++NPRj8GoBmm6q+P/fv388Ybb1BQUMD69etRVZWZM2f+3pfRLMYePcpU4CAwFRiK/vJ27nYacclpnN4xgr7t9cE/OXMYsuZdDUenZPp1bPDqcQoBMUkp3PnI/5BlGU3TePeZR9mwNt/qmBX1jXMxG8OGonw+ffdV1hfm41I05GZelOOhrTke2kiN0RbiuPET8NLKMnXV1ZQW5vu9MIomLFk3s3XMI1VDosmkmhyqcnG81o0QOgcrGMxze9q1x902ErnhNCgE5+4sZPmsh1mtqWQAjwG9gAclidhJ1/DkB7OJTkyhqryME8b9KIrCnPdfYVtJoeVF9aVTmMaxTQK7JOOQfcpsd+gAHTowpFtrNmQva7YNd2/dyObiAiI7dg747OjBfWwsKbAGbV8KiulNcRuDl4R+Lc1BkmX+/p8XGJKUZpr7AAxOTOWu/7zAqPGT2FiYw+biAsNj6K0i5rBJhqSXHkkZHJ/CUx/M5tq7H+Kx16eSkJIe4EkvKshj7fYtzfY1AVSWleHPO/Kirk07XGYYGXBr3knfnAw9mmBQcuOGq9nn85d+i6Z41Rp0xRpvRKAod7VfydoF8+dZvFSPkYB3qjDb0t02Ene7SKt9TRpPYMP8isbxO1fBib3YnU6ikzO9C70Gu3VpHUJc9zbW9fZoG8pp7cKs65RliVCHt9hL0mltfaTRJEvCKxiOHg2kEjU2Fp131c2NhskP7NlpvAf6SFLXph2edpFGpMUbkveY70dz3inj3ZEkiRPHj1n3LkkSTptEqF2/p8ryE0YRFP1datW6rRVVkSVQIiMJ7dmbl2fM56q7HuTVTxYQl5DU6Nf+44x+RIYFz7Ew4b5zNj2jEnh7W5B7MBYCO7ds4oHrLuGDV57l/usuYX2ROfbq74Z/dbpG5gZJYmPHXryVeAGXAN0cDha8+BYzR57JFiG4AvhCaLzwyn+ZfOe1vOxycfHh7UTWGXIYe0oAiB86iuvue4TZ1yZbp961aR2fv/0Cm4oLkCSJ+JQ0vvr+J2677TZu/Nst2BymxJ6MbLPhcDgYFKe3W1VtPXM+eFO/J7y0PFUTlo6xMAbFlqhQmFz7ux58lDc+W8DghFRLeqzRaq7qyc/JTSGi/gSU6HxwhKC2qsL67LO1+zla40YTULS/wo+yEMy43F1W6xeJ/SWodSvWXKMajdlcwr8v3s3Z7ZMzEHjc6R0j0DT4Moj32fYntJD/EA+yoijU1dXhcDiora1tMiT5R6HLwoV89+Lr1L/6MpMP7eMmYAtw5PABnEcPI3VtZQyMgviUNM4c7+YHIwoxIDaZOo/KLXNK/c6pGURSIaC8rEzP5tR03cfivGyS0tLRhES1S68UNLBTyyRPcnJyeOC6S/AYWdPX3v8kK1dsgNRLGz3m0iC6oyeLST5hRLvdjseKycog6WG0hJQ0EBKKplkvuPkS17oVa0XpUjTqFRWbbAcEdR5F5xcL78rZd5I7Vu2yPAgb3v6Ieo+GtEwncEpC45Jt2fx7zRwSj+5kb6uO3IO+0DF9mbIkMXr8JKLiU5BliSGpmVwRGorH7UZoGqVrVrJp7RoenzKLlLQMZNnLsfQLPzd8p40Q8tqcHAp+WgRXnNFkGwohmPv+y7Qe/08aDihL53/Oyq/n8vS0OQxOSEWS9Pv1DaMqQlgDWEvGxn+/8wVnnjHcMMiwEtAkCTYXF/Cfm/VKebOcDv43dQ794/RJTkL3LHpUjVC7RLVhWUUnphKfnI6n8hind4ygdQNPf1ZWFv+GoDq3DRGdnAnZwTMs37ryDjLTki3PuaR5C89YE6aq8Z91jQ+yZp+/IWQDW0vyUD34JdT1iQxDaRvK5ePPZdqbL1kJdxMnXsLyFStR0JUNGkvAawlMwsrqlz5AAhxm+8tSo0Zl70HR7D7lb/Ti9NhE+vbuzRkXXsrpsckoRnJTsJCy2Se6tQklMtxBeZ1ibQtz2CzjuqFhryezYeUeNMQll1zCDz/84LetsbHopw5juPXRc3k7wGsKXXv1AYw+LyD3lans3VhE0fuvk5QxjJikVITQixVBy/mNQgg6dOjoQy/Bu0hEIjFjOE5nCB6PG7vDQVzaUB/6EWx/bwayBEM8KlEJKXpkydOInhrehMbm4GnC8tE0ldU/fo3HN3FyTTYJKeleWgJe+lpzFQVx14EkMXbC5XSfdCV5PftydXYWKApJwFhJ4tKwMG6sLOeudd+jrfuB4lbtWVZTxjJg9aqlbC4pYFB8Cv3UQ+w4XG4kPerJaPHJqfRsF07iyOGcPWoEtW6FGY/oGuQD4pJI7fkgyRnDiOwfC6UlVFWU8cUHLzB3yqu89dmXRCemWgacCsjCO68Ia25RKdpXQeJpbYPeYnJqOq37xFBRr1BjJFDr03Ng4llc3WbWn9gdXNbuFFFT469a9PPmDXz+xsNc+VkRVS6FZ5dsZ9rkeP9oqo+h7AuvkyA4P/lk4EuRMOXzTtZunVVygBH9OwR95xw2GSEEc4JQNv6iWAA9evTg/vvvp1evXoSFhXH22Wdz9tlnB+w3ZcoUpkyZAsChQ4c4cKDpMNRvgdbnXcDhtNG8VJBLl6/mcOa2zYz44mO0WTOoHTOW41ddSf2oUVSUuejUIRIO6iun2rKjHK7x+FWlqy0/SoXbQYjdhqIKBkQNxu4wRfXtnD5oMBXHjmCXZWwylAOtPC3z8C5atAiP242maSgeWDrvE7R2Q5o/8Bdi5LgJVJeX0aFjR8acP545m6spFECHXkhn3c3AqMFUHj8CSJRV11sTabk7BCEEOWVHqVf06jnVLgWPJlCddjwOGYdN1j0yms593KZWEe60WS/RpsNVuFWB0yZR51b189TXMmnLKh7PncmQ43vYHNmDG87+O58NPgPP65cEXH/Ryp/o1bs3TpuNXr168++XpzB/+nusy89FGNW6SlYtIXZQfxRJpt6j4VIUFEnCZpPQ7DKe6goOHToY8HIvWrQIta7xidELiXWFecgdV8HAEX6fiPu+6f8/fAAAyB5JREFURHltAvlZP9K5e09CHTK1SESE2PGoKhKS1QZ2m0RddeMUExO9e/eipuyoIdklo2iCWpcKEhQs/xHF6EceNxSu/InuPXvpFY5kmWolBEXVKTL19QqypK/63ZKMVlvOiaOHqWlANbHZbKA0f11C0+jVuzftO3YiWCrVprWrSRjYG81YEEgSSLV2Pw9yjbt5L09vTjBs7Dn07XUaBzYXc96YkfTu3dtvfOnXtw8zZ84kJyeHzMxMkpOTcYe0obgwjzOGDw/Y3xfBPKS+UDVB1YlK6qvdSBKosoSw2XCpKq7KwHay2eyMueASPvqFEx/Azg0lXHn9TfTu3Yf6imOomqDe5eBIrcwBW63fvpoQVB2vRrK35ngNlNd5qKysx6MKPaPdrmtxO2wSB+x1VB2vQpL0xDkEuG0yHrWaAw28oxdccAF/u/U2prbgeuulELp26wrbAr1NpcdVxhw+bOiEw/YNpTz3j1tQPG4+f9fJU29NZUhcEnU1bjYdq2fZ9sYT9AC/Fe+uXTuJOnEMRdNQNGF5XN12mT69+3DbPx5m+ZLvSR4+lv79+lB1/AhOu40+7cPYdaIOWdLVEIQQeGQZRanmwIFAdQEhBDUnapE8dYAt4HO/tqg4FvwDScZmt5OYOZKNhWtQFJ2SMWDQYCqOHTY84DI1bgVF1Skylc29J5IEQtCrTz8qjx+hV5/e3PiPfzPt5WcoEBolDicRT77AYpeHQ0uW0/6YhzF587hTaPwTUFSFDbdcjmPCZB5r14H75r9BpWzDZrcTFR1NXfkx1AgPh43hsd6jWlHIsLAwxoy8jFYhNo6WmfcsGdEbyF76PT169catqtS6VFTDcSKF2LBJkp5MjC4jB3BADpTPBH3+AD0CV+vW8KgaLlVDlmBR7nrAWxZ8iLafdb92HeQG80XasDOw1ZVZf1fVuSg/dsQqsS3QHUoOWWIH1bQJsVse1/JjVSiaPg8oGhxwBFIbWoqKY1WWgohH01Uo3LUnWeJTaNSWHUWtD2z7lNdWsmjy6UEPq684wZHDAndl0xGV3xO/u4FcVlbGwoUL2blzJ+3atWPSpEl8+umnXH311X773XLLLdxyyy0ApKSk/CFe5gq5inC1CqljV77s0p17clZyuiRzkyRzV2EhrX76Ebp2pds117Ck21DMQS60XUcGdpTRCRo6Qtp2JKJVCGEOGx5NI3HEWTz30VzyslcRm5pJUloGYQ4bTpuMw6ZXo+nePTLodWma8NMLHD9+PC+/8grC40GSJHZv3wxJg63PpZ35OPun4NK8xzyzcjoA/x5x3Sm3z7hvv0RTVR6124lNyeC5m6/i5s/WsKsGtJixJJ+RQYhd9zadkGtx2vT2adMmBE2gG3YeFU0TuOsUZFWjVZiDcKfeDnZZwqVo2GSJShkiWoXS3SjGsU8pw6NqhNhlTv/fEzi3byN50xb6HtzJxvY9uXzc/cwZOAxNDj7xhISEkDzyLFq170yI3YbdrXDV2jzO69qdESFOg1frIHHkmXTp2h23qhGiaEguBRtgt8mEOmQUu41u3bp7w0P/+pf1TF559wOaNQ0lCSY/h9Y5MFMfwOZwEj/iTGytO7BjUxEb8nMYPnIkAwzPrk3RCLPLOO0yhRtW01yCWkT7zkRG6tQgh033Hsr1CpIkkXLGWcyd/j6KwZ1MPeMsItp3QvKohNpttGsTapQJBaXWrScn2iRskoTmlOnWrZtf6B30qlvPAmLl9Kb7miRzTGoFsecEdYX/c/fPnPbJBxTd+TA2g/fqcMjYZL2IytaSQvJzs8HZOMXi44LppPXvxtp2Y0gaeRYXjR9vUQkaYvz48X4lo+++/nIOTLiYDhH6IrcpNDVWqZpgtzuUvu8+gYTE1n/8mxCbDaemEUYtvmMG6F4ct0fh1s67ef9E7ya/tzmo7noWfjqNyXf8k9OHJKMIjY6tQ7HbJLp37+D/vUJwWKuge/d2AHQTgj4elZIDlXhUQbhTxqMKOrUKoXvHCA6oulxcnZEMF2KXCfeEBm2Lhrzipsaive7gslX7XA5eLSjj5fHRyJJE8rR3eNLl4l8IFMXD1k2byBh1DlUOFy98HVzv1w8mPUkI+vbty7Ah/TlU5WJ/Rb0hcaXTLIoL1vDeK8/hcbvZWLKW1MxhDE5IIcQuM7B3JK1evA9Jgp8NXXi7LBHuDmm0TxRvWk7lkf3QrleTl+do3R4IVBIYEJfMFdecT1xyGlEJaWwozCY5YzhRCamE2CRkg/Ii6hWDbiKor1OAJpReJAlJllEVhbYdO+P0qFx03R30ik5mQ0EOyYaH3qMK+g07kx3rijg7fy52IAMYA4yuqyXji+lEC41rbHa2dunGkYRk2oWEU9O6rV97ZK1cZXlAbY4QQtp2JMRpQ4h6/Z4jIpH6JGI/tImhY86hTYdO1Hs0hEuxoiCtQnWD0UyMdtrkRufQQ5X1tFH0ecSlaOBS9OqQHpVtpYXMPeI/JqSccRZzP36/+bH8JCDJMhfdcCc/b1rHyHMu5OIrrzMWYrsAkGU7rdp3ItRu8zoBXHpFujqnjdYRIXQ3xvG9Hn0uDHPYcKsa3bu3XOqxIXa7T6AJvd/WKyqKJghvJYBGFmjBbw576w4odje6cKo/5Ijgdk2ryA507tKZjj468380fncD+aeffqJv375WssbEiRPJzs4OMJD/DJBk2FpawDO3XY7H7UIIwSbgISTq7rmHRxPiYdo0pFde4Rn1RcZ1H8yHQ85i8XtZDBnt7xU3C2fov+s//WOT6R6VYOkeCiuo0XSopGh/BT3bhdG5td6RFCNkpoeZNMSZd0PMWO8B2Z8zXN7Fkt6TrE2ZBwPLPJ4s0oyJTlUUXn3iAbZuKME50OupNW9BCJNe0lDPUt9pQ1E+OatWMigpk9T0dJ9Qkm9BCDhUqScjdGkdop9T1Wj37UJO+2Qqtro6dnft06xhDBAdn8Tbb75OdfvT/fifkaWFtEfi6Q/nULImm0FJGQyOT7GSlRyyURLUCKfqpmiD/H+jRHnms89y6RVXB4rpN4AkSYhGjGOAf70zk0Fxyaxbm8ezd1yB4vYwZ8qrvDh9HtGJqRbXXULileIakJum5fgmE5lh41C7jEcTRCem8uqM+RSvySYpYxh9YpLwaJoPU9moWoaXLyYHbwULo0aNwg2IZvqbQPDIt5txieDJqV137iDy6H5rXz0BS+e1byrK58lbJ+NxtoG/BTeQL0/oxrnL9yOVHgD0xLS6ZpQNGqJ7218nkViSJDqWFiIhsd3whtvk4C0oyzaOHdxH3fGD0K4jhESc+hcLjdLcFWwqWsPj78+if2wyoT5VGRteo282vCRJOrXCJ1lPQucUAsR0bcPuE7XUKyoYiayNc34kKFwIyRcBTY9FU5qQ7Dte67ZoW/E11bSV9PbSS5EPpdrgU7YIPi/FsWPHsBsLdP1qvU9mrVkN1aAMlObpJZHN9XGrtfkWdxnhq+UbiJycHCaefzbuC/7drIHcGMOivkN/Kj01zP/wTWLThnLxjXfTymk36EeSdywVwoj+wObStTS9kNYpc6lDR+i5DYak5cD4FAbEJdMqxG5wrvWRZ1BCCo9PmcOzd19NVnUVWcZZ+vfoyVOTrydi2ffEHtzHiO8WIX+7EM0ZQsWQGIrbt6c8MZFr33wTcdsXYPR16MesrAK+2OW9IjHxSZ5KhCFJaSiaZhSh0ulmMl7VEk1470wIPfHO0YDHvq+iztJQFhj5L0Y/2VAQmOQ7OCGFJ6bM4pGSJh/RSaFD565cd+V4XIpGqxCbfi0NKQY+lBHvXBrIk/byk39ZLQUgwEZBnDw3WADvZu8id0950M8f+HpT0O1/Qgry75+k16tXL3Jzc6mtrUUIwZIlSxg8eHDzB/4BKMnPY857r+i8VKPnSZKE0+lk7NlnwUUXwcKFSHv3Mu/Ke+hUV8lHP7zBW1NeocP1E8k4sNnqyQcq6jFLM+qlGzU/XplZ7clXDL0xaEL4aTX+tHSZLj0kdE6zn3EM2GSZiuNNh35PBb79WVNVvp45g23FudY2I9rqwxP1DtbmK79ubR73X3cJM99+kadvm8zG4gKrrU05H98Ek0NV9ZTuKyfyqwUkjRvJ6XfeBAIq+p7O9f+ezqyokU0ax7Js457HnkFGYs7UN9hcUghg8SYBBiekcunNdzMoPkUveCCZWfwSTrtsZPKbk2fj2L6xtIlPdUT2iWry876xCQhgU6FvcQYPxWuyvTw8Tb+OsmaMY/AatLIk0SE8BEnSucWmXF5schrX3nEvianpfklc3rFbbw+nTfbhXkp+hvepoLxOaVJ/9ESthyNVLkCgaV7OoVvR2FCQg6dHPOJvjQfuHeb1Gn87bX/MaOy9Br1xzUkh1C7T8JLSDv4EEvw473O+mtPcUqsFMLj8HrebjQU5Vii6pS3hm1Co/+39LMxhM9pYN6b6d4igbVgjyg1XXY1t1ce/5E4Aw7gROpUhLKI1fQbFcOVdD/LMtLlEJ6TiVoXFQW4Wxs04HE5Gjx7tt9m3b6cPG0GI02klkqVkDjcW0D6kZbzc5aaQlZWlS525apvcD/RyvcGwt6Ke14qrmPn2izxx82VsKi6wojxmoqJZXVLRBCVrC3j602+a/jJZZsxFk4lNTrXyLvRxz/9dt36AQQkpnD3pGr/T9ExI4Ya3nmdCUR79D+6jvYCJDgfZqRnsLirijJ9+4qIXX+RAfT3fLniKB/Pm0TnvJ7LmTmfWj6sDLmtAfIr1u0A3fk/UuH0WAfonvo6Z0iDl2/2S3oQuj7Zq5wku/3QtM4Mo4Mz64A2QJN69JLbpdjsJ2B1On7HIRojdhiz78NElbylnw02kz4MEJun5GsanqmLhUTUK95Zbc7NppzRWxKopnKj1NGocA2xqJOn+z1ZFD/4AD3J6ejqXXnopSUlJ2O12EhMTLSrFnwk5OTlcf+mF1NfXA3oYx2Z3cP6kK3norlv8ZZ66deO7i67n0i5jGHpgEzfNepjLXHXcOPNBNrbvybQhZ/JSzSg+uucc6+U0pVzK691EhoVgypyZhmNTK8GGH6UPG4Hd6cDj0gIVJYBbHn+RrUtarj/aEjwzqitd5/bl0J6dhqSScf0uL+/Io6qAjfVr8/hp6TLaRXaguqKMESPPID5FzxwuWpNtJZcoiu6RSU5NN156CW+LAJpGu2++pPvrLxK+dTM1/Qey/c0P6DBjGtuO1ZK1KzCc0xAXXn4NSHDuOWfhcrmxOx289PE8+sclYzq9vJqmgjCn12smSxBik1E11fL6aU0YhmeffxF5h5u+nhPupgcFRdMnu6ikTJ2zroDD4SA+TZcmUoFqt0IrZ9Nh/2Do2yGcYzUuPaRmk3GrqmX02mTvZAjeQVIvmKF7nRVNp7/oi5fg95GVlUXzomstQ1mdh6s/L6Z3ZBjPnh8FQi8jHJ2SibTuaKNTw0BHFaNP72Bdo2ng/xFouJAwjSi7LNOjXSjXppzGjAK90E2/DuEUKCqapoKqL+5OmQn5/eug6dxTWZaJSfGOXycz/+n7evtCw8/MxWRkuJO68sZPLABUD9hOnW/oUjSu/qyYG1J7MhpBeERrJtx0N6F22dJdv3FWC9x+O/KRdhcz7KxxXHPb3/3GdlkCzVgUdoxwcvUFZzJgyRKmz19MXNow4pLTqFc0Yrv5h+XNgilNzfmjRo3Sx04fidBTQng7y6O9IT+bAbHJRITY0NAjXr4SbwuKdiMSLmj6fJKewKwnJOsjocn7R+iLbL3fSpY2NgKuve9RZCRylyxm6FnjiGjdlhWLv8QsmlKBYKGmccAmUyDLtFVVRgGjgdHVx3h+1XQAqlbbGN++N0srj7GsZyylnfogJJl5H7zBGWeMYnBiCh5V4+P8vXyz6QhTJ8XRKkRfoOkGndew9PW813tUDle5fOTgJGu+XLypcU3UGa8/j93p4MYHnwKadmi0FIrh6pYliQinTaeNoS+Uq42kQdOAB68msXch4IWvvN2pepBN+VWrIJX5Xfx+yXMBKlR/AvwhKhZPPvkkTz755B/x1S1GVlYWLrfusQJ9kOnZpx/nTbw8qAaqZMTds3tEkyNJ3NO6E5elX8ZN63/ipRUf8eyqGRzbdhbHJ1/NoWFjEEKwNKeAd7dJTDhNcOPZaX6rw6ZWggFZtsmpPPH+LL5Z/C1rFnxMw8Bx36gYuodL/PQrhYgGdYqgZxsnIWHhRHbqwojEZHKylqCqCsLjsq5c1WD92jzuuXoCrmvfg/IQpHf+xYKpr/HaJwuIS04jPm0oNrsN4dGQbTZiU4Z6B3TJeEk1lchvvqLnmy8StnUzdacPZMvrUzh0znhCQ510+OSjZgsBgF4A47yJky0ZL3NSKc7LZmC8XirXJplyX3rFKIch6SRLEpoksNskJA8WD7apsWPQ4Gg4/MsmvxM1Cm1CnAyIS+bx92exsTCH9GEjLGkiUzu6JWPYqwZf0/TygdcDZJN0OStTk9d3H1mSaB1i1/cVunKLLnOnh6HNbPJgMCkWvyZ2l9WhaaAZ/WNgXArDzoGVjQRJnMs/IHzyGZi2nSRhaSr/EfCWn/H16IPDZmPikK6WgRzWuq21KJJlG0ojEngtwoYl1q8XX3srgxJSDX1gqUkjriF8tZHbBpEnk1rQFwuyV6KpKtTXQES7ln95I/gofy/PGuOFR9FwyhIKWouthct6uImdMpMRw4b6cehDfSglJmRZIjMzE9F1kBXlkyVwGtxX89YlyTQkG//ezMxMzh13AYtdv04CmCRJRCdn+htSQlewMFWEIrv1alYf3664iEoY4U/FMsYJX3qZl2Zm9Avgun88wk33PwpIbCkpYKbTgcdtRDYlCZvNxpjzLqQ0P5cKt5tFksR8RYHr3qZLTRmj9q5j0oECYreWcOHyDwFdBjDrtCFk/ZzLO++/wi2ffEmngfHkG0VBqt2qkasi+80b/oWpBMdr3Ryt0Sv/+VIsmjMC9QRBQe5P30DGr2Mgj+7dymo3XyeE0y6DW3dUmDRDU83JvCez36ma4FiNG8tGgVMe0yTJG7FFkqwIna4e9fuMlL+THX5S+KuSXiMYNWoUNln2q0G+c9tm7rriQuKWL2+yUMDw6+5n9dezmBZ7NtNizybq+F5uXP8TdxevpNvS7xjQsTMLYkfwU58zIbIHC35aQbfyrUy46jqLS9QY52ztvnLrxTFR7VZp3WcI2T0kuGtcwDFvPHgb4849B9/V775WHQL2aynqPRrbN5Qib9uEpmmsXvoDZ14wgT07f2az25tBu6k4n50leXjcHmilJw4IocvaFa1ZTWxyqpfz1OV01GN72Ll1M4lp6Xo7qBodv1lA77deJmKbbhhve+MDTpx/MR4kXSZPwGGns0X3Iww+bXLGMKY7nUapUYdRCUvC1bU7lWXHmTnldWJShtInJtHPeDC9KaYhLQFRnVv5h1JPO836tTBnNeDVAj0VPPTNJu4/ox/x3dtwelwyA+P1ymf6AKbPBpqme1Oaw2ntwrz3YlzywE6t2HKkBtATPyWfz0xvoWwYlOY22TAanHYJh003AkxjNRj2SRLiF/Q3COyv80oPMCm+u+Xl6NClKxwN7q5PHX0OkiRR16Wbfg+G5+uPgOmVr+vSTe9DkndbiF1m/do8a99PXnqCmx7+H3VV5Rzav5cffgHB0LeyW0SbNn5UiZOB02ZDUVU6Rjjp3T484POWnLdjx476L65qiGj3i8YiE8VSa07vGKEXBhEyqtryYPPxQ/sb0Ih0tAtzkNyzHfl7ygLOtaEon8KcVdZi1YS9V09dycM438BOrag83jiF4p5/3s/iZ+e08EqbhqZpZH09F01AYmqaD59UUOVWsEuyUUAqUGLLwuGf0b57hS1p79B9pE+ZbcMylgxetU3WI0mW7KUPhz7MYcOlaEQnpHLjg0/x47zP2bl5vR4JQWJAVAxLliwhKyuLDh06cPfdd+MGDkdEMitqJLOiRpJ4ieDInuN6lb+9pYzZU8olQoDbTcUNkygfPoZrnP1Y2GkwmhjsrSIrdM+wcecIIVnV9cBrYApj7BRCcLzGzc6jlTRFNhKaRv+oIRQ3/MBdB86wIEcAR3ZAkPySSW0OouX9yA/7elFRfoIzzjiDuOQ0S3HE91q9Br/wcYjoqHIp7C2vtXJ5hGiamtkczON1opSX1+1upPrpr41fyp/+LfCXgdwIMjMzefKFV3jkn/f4PTnF4yErKyvAQD50YJ/1+8r2I5Al76C3uUNPHjzjBvq8+yqDC5bTefanTMqaz+ViHit6RDOtQ09mfPcqAwZHk5Sa7tfR1x+spH/HCMIMz4bJS/JFrUvlhI+kXEPs/XkL7/93CfxjkbXtmnH/PIVW0VFfX8um4gKeMlfoisL3Xxr3m+odENbl5zB8xEgcTgcuY5sky9gdDuLThqEJKMlbjSeiE1z5MqL4G95/5l8MGBTFuSeO0OuNFwjftoWa/rrHuPLCCWiyXtXqy8+ms/y7RQyKiWNuQS6uO2Y3e91CCIrWrOaGu+5jyZIlTJ37DfFpQ4lOTAUBC675G/dcPQHPqhXYnQ6eeH8W3Ubok4RsGIEmJ89hFEMI4Bl++qn1a8awEbC0eX5hc3hp+Q6eOGsAg7u0NvjrApumy+N5VI1jNW7ebyKZyYR5pa1DbbQzvH9tQh04ZF0eyFsl0Aj7S7pChSSbPmXD02C0h12vu617QhuZW7KysnhUktB+QX+DwP66YP1hBnVuxZCubThQUc/qnWWNHnvmpVcjS5D/7Fv6/Ska3dqE0jEiuELCbw1JgoJn3iTELhMiSciy3ooOWSJr0VzoqeuXKx43uzav454nX+SbWZ/ww9FTn0HsdjuqqhravabhE5wq0RTiurdhze5Ag9H33ppbfHz7rVFe2aBj/ZKxyMS5Z9zNuKjOXCFAFSA0waqdJ1p07JJ5n7Jk/meMOPM8rrv976T3Psfvc19PKej0u7uvmmBUVnyZtz5bQEYf/ZiwWV9QVlGPXFGHAFo57VQ30R72X2udFnsOYt33/DTvU1Z8NYf/fTiHuOQ0BLoikmloNft1639EnNjH+oJsq2y8+d6biWThThlVA5ssqHbpH+r76Ge3GVGqTcX5THvhcdz13kisqioc3FjADRf/25pDv1n8LYsaXEZtZQX7W3fk0+jRfBo9GoSg/+sTOFOW+UdSGl0KcnnpxFe8BFR93ZXjacOoyhxB9fCRqD17+XhAvc6khp5Yc3F914L1NNcy5sLSD9+9BhuX+s2rfscsn4qY9EzA9gUzZ6BsyQYhkGSZuVNe4+3PF1g5LybMe7BJ+h1UuRTahzn9aJS+fGrzoMK95adcbtqMYutRCGHkuPz/14P8xxDx/o8gKnoIg5PS/LbZHQ5GjRrlty0nJ4fFcz/z26YF0U302GwcPfM88t/+hNNveJuHh19L15pyPi79nv2aRsLTj9CqKF9/AYw+Wa+o1Lh07qCZwdqwv3618XCjJXwBTAkjua6iBXftRXy74C9GfW0t0QkpyMFq43q8HuTolEyiE1N5dtpca9uk2x/gqQ9mE5OUghAQlzoUOdyo2tW5H5eoKpPuvoGou28CIdjwyvsUfLOCIxdMBJsuefPl59N5+dF/ULAqi8/efwO3yxVwGcEg22wkZwxDQl8A3XjXfcQmpVoGXvGaVd6KaS4Xy7+eiy9v1fSWmAlJzQ2qSWnpLbquluDJH7fxg1GLWqCHkusNibdgvPNg2FJagCRLVLtUerT19yaboXZf2ojDJhNi17lxvqFj4zdLecGXitEQo0aNwhny28j2eFTddXL/Vxs4WtM4kWNbaSFmyXATDptkhcV/b0hgFJ7RZamcNr0yoe6kM57lMe+CR5YkqioaXwC0BJqmcdbEK3lm2lxiElKDRApO4vqb2F8C4huRzjNhaUirjS/qTwWmU0Fogi1Hq3l95c6mD5jzKLx1uf67EKz8cTG3X34ROYYSjYle7cKR8EqI6cl1JkXLzdo1gQllpkHZXNuuXrni1OPivjjrTkiZAEKXuSvNzzYMY0GNS0UI2HK0mrmlzdQTkCTsDgexqUMZ0q21X9lxfZkMNkn2jhlmASW89ytJuvxjaX42HpeXAgC6rvfo0aOtv3Nyclj8zdcBl7GlOM9/gySRcseDRH+6kAMfzabddVMZdP073Db2dvZHJ9I1O4vYR+8lc1QSyaOS6fPA3XT6cg72wwf8DGXr/xbk+piQbTYcTiexqcMaXFPT48elF45jlCNQmk8xEuoBQ2/fTWHuaj9niyR5kyxBH+s0Tc+5sCqF41ty22vYnqoX2SwkA94FhOmZ/63xn7MHWk6bPxP+MpAbQU5ODldNOJ/Na/ORJJkuPXqSecZY3pv1VYD3OCsrS1eR8EWQl2dT8VpUAdUuhfLOp/F82qUMuuFdRmRewZeSzLBtm0iadB5DzhpKj6lvw5Ejfsawb6aud5tgZnFzg55+LWL6XXQJ1Y99ddkHvLrsgyYPW//GnUG3q5rGpuICPu/Zm1cbfuhDsRgYp1c+Oz3OSzNImnATg+JTjJCQYGB8CpdcfxuTtqxi3bevMEsIQkLD2PzaFIq+XcXhCyagyV6xdIEg67uv/L7yVSGavReAgTGxxKekeQ0DJEPBQR/o79i4nlfNgUsIli2cxYa1+YQ6bPTroMtZSZKXVhB07rv3Xv3nN8C0vL262okhb6Zogm5tWi49VrImG3uQGdscmBsaTF5vuTdzvWe7cGuytBQ+jFByMIMzMzOT9eeMa9HzaQrB+mu1W2FLcSHNRQA3F+YiSRD3/OPEPf+4kUD1iy7nF0GSIOGFx4l+7jE/Q0pCYsxFl2F76zL49B7sDiejx+vSjPFpQ5G25zRz5sahKvoiOyYh1Xq+NiDCaSO6y8mVnJdo3LCQJBotD23ipptu8vu7JWNRc3h12QfcMksfjQRQ626G17t9DewtBbd/hEfxBJYS79w6hMQebelqvGujRo3C4dCVLOwOJymZPobTvffS5bGH6GkY1c2hQ4eOze7TYoy8AanDadjtDmJSMlGMF0MxyMiPfbcF0cw1nT54CP/7cB5RCSlISAzsFGHIG0qE2v2LcJh8dAmMxZ13HAlz2EhIG4bs0xckSWLcpVf4zZ9ZWVm6LvaRHf4XEhtYPGz8DXda+u9IElvbn8b78ecxOPl2HvjfVFYsWMaWR5+mJiqG9t99TdQ/byclYwixo9Pp9cg/ifxqAbajR/wM5paMA6MuvoJnp80lOjGFdMWnrGNl02TuwcmZZL92X7Pnl212kjOGBZR0NjnkQoDLsC80w2A9VuPm5+M11n6mMe2r+HSy8PK29fO4VJUaj8re8l+YRNoCXBDd5Tf/jlPBXxSLRpCVlYXL5bIycI8dPsiDTz2vl+9sgFGjRiEtKvFzBEg2W4Bj4Pn7bqTde9PpOjAOp9MJ7v/H3nnHR1Wlffx7bplJT+i994T0AgGUqLj2DqKuva9bdIvurruW1VW3N9117WsXUbGuq74oKhBK6EVEUQEp0hICpEy59/3j3DbJTBqBDJifH0xy5865p91znvOU3xMAIVgzupgNV5/P7Kwshvzfm/R9+TmG//5OzD//luEnnIx69VXUnnYKO2rDzinP3qCC4eaWPA/q9qEf2AVqD/J2NqNdgcZCv4X91XuZ+dQDfM8w6NnwQ8PN0vTp8gq6T5zADk+Gt1/8dx3PfTefjEQdMxym65uz+NMTf2Pg1i/5tGtfZl7/U3zX3UTXtERp0gsajg+W3e7JJ53Boo8/cMo8vldfdjfVno//A8C0iy6jW7KPyhqpubIFQJteZtCunRzfvQdi5w5pXgqHWbZwHpOPmUiSJ72u6bgVROn55ctj16Md4GoJsHwBWx5slllUihaF3swJwjEjabykNlmQ4pPLhE9V6JniY3NVTYO2iya5Mvvv3EHezoPTgEabrw+Vb0JsqIBhTWvqMwtLUYD0T9fIgBxFEGiCUu5QIyPRR/qna60AHI9ri5Cpu+98eAZrKsoZXTieMXnF6Kogu6CEaz5Zy8PvPgMT28YZ74yzZQnRVJloJcnXum0glla0b1oCaf7my7r22mtZvW4991vKtZasRc0hb+eXsBOW7NhPbt90mlXLxmiEoiiNLITyunt/aWkpb779Dq/89z2KJ0zi5OOPdW9cvhwVydXeK7Vpy0l5eTk3/ugHcNz1Tde1Fci45A9cNiaJ4dmFhE2T+pA8ULcUvfsNYEx+kbOnSCuSYlnOFOrDYU+QnnTBMi2/ZA9DGaoQZBUUc/2v7uPf9/wSwzDw6T5OPe+CiOfJw4ZO4KXb4IZIK2xDNKXRfOB/izALunLWxdfwzeXX4cNEW72CbgvnkbFgLt1nzaTXM08wDDgwcgx7xk/iwIRj2JnZfIxI9979yLLSW+emh1h491nQewRst6y2phFdIbaknFAwipUkQlMsOGPaReQUljQSbG1BVUU4FJhhjw+yYZrOP1eb3HqEwga1QcNxqwBBTTBMMGzyxw82sDwKVV57wzBNgu2drbAd0KlBjoGyMhmkZ8M0DFYuWRzVt6e0tJSBw0ZEXMsfP6nRfSHDYLlljgsFXLeAKoujuD4phW3TL2HNK++y4K2P+Obya0mtWED6tHPQhg7Bd/uv8X35RcSLFLDSY8bEopdg11cAqJrGgb1VzkeqgGM2vw6xiPprquCN30f9yLCioqN84Py6uqKc2mCYq19cEXFLIBSm+1uvUnz6ZEp//j3214WYftrNZF76Tz7KnYRhZdxzfaE80cjA6Rdcyo/v+hOFk8r46W//TGpGlyY6AFj8Csecei5XXX01Q7slNxpDh8dUQLcePfH5Exye08LxbqCKq+nDudYUli1e2PQNbcDXVbURGgOI7s4TDSNyCtEUJWa9nTYiIq4J4YQnOr87whatY0Fob5jNCMe+wD5G5RV5rAagKR3r7za8e7LTd84hzT6UIBiVW8S5V/0QgJcf/QcbVi1FEbBx3So40IaDRk0Vmu7jhDPPB1xTuc8SkFtf/xT6RUmakqCrLc6ClZ6eEf2DgxSW75n9OdX1IT76ohn/4z2Ng1oVVeVf//pXkwHYNqaUHcPlP/gxEyZMaJUVx4unnnqKUCh62ufx3domLFQqKby1VfDSo/9g3YoK6kJh1q2o4A9PzmrR9/sNGiLXQyWSgcNeB6QW2bWg2W5WtsJAri+C1UsX8fc7buaLT1Zx9S/v5aIf/Jw//vsJcgsjXRZLS0uZ/f4HZI4aCds/oymsX1FBOKaG1OTf9/zSDXJVVaozc9hy9fdZ9/gLLFm+gTWz3uGrn/2a+h696PviM4y+/lLOmDyWimd+zB8/fJxTvqggpYFFgfcfomZ/teN6tr/Kev+2e1waF7/SqDZPXZDHmEJJzdkQiuZDsywQPr+f06ZOj7oeGablE0xkci3bhcLRhDv7o/yltS4WX+6p4cs9B1z2KFNSKIYMk5XbDr1wDLBjXz0H6g+CpecQoVODHAOlpaXceu+fuefWn2IYBrrPT05hccz707tERmL7ExpHeGu6n6yiUkwTDlTvhYQMAMzhpTy9Yju5VvGGaVI7fDQbf/1bvvzZbfT48F26z3iW3g/+nT7//CtVxaWELr8CLv8u9abWOFDMi7lPATBg6Ei2bvqCyh3boKsU5sOhIHNfegzGnQ99RkV+7y8yza74fH4jQVj54CFQVIRpMGDwUM6bcCw7Nn3OvHnzMEx3Yc8sLOVAwN0AFCPM1M/mc+YFP6XLl5+xf9hIfnrRbfytZ4GT3OOBz6BPcgWTJkwA4dLxuC+9FAxPOf9STrvgUkwTdjz4bxCxN+YxBeP40b33N1qEXEHQCkYDUlLT+Mezs6iY/zHZJRPIthZ0TZGxvdKcKCngmjuuL5z/MQfLYtEQP39rHQ9NzSE9QXOer0bzBfcgPUHjV1NGSPN3jFsVAaYpW5jTN42v9tRQVRt0fFW9fVc4IIOFGytdP1azaSGrA70ZuK0opZG2X1M6jsUiGoSA3qkJfLWnxjmkfLpCZvAMBYPMeOhv3PCru5n92gwYObl1hdcfIHv545z70AzG5Bc7VH66ppCoKy1yA2iI9vAVnDDpGMQn5Y3nxpa10GPIQZX914++YM32fTE/V9//N+GV70RcG3/8KVzx/Ru59tyTYnyrMdpvBjV+Qxa/9jRMuqxNpX2+ahkbXv0Tsx75O1f+/C4e//3tBL7ffBAzQO/+A7DXQy/szHmJDg2efIc+XbGEJeVzyR0nOaFVAWuWLuYHF51JMCAth7rPx28fe5nMrEglko3S0lJ+csc9XDNzZZNrxe+/fxF3PjKD/ChWXBCEw2H+79UXKSgZ53gZOwoWXWd/fjG7swsxrrsRo76ObquXo86Zw7433+GHy9/kZ0teJSQUKnoN5/2BOXyg+Zm3/C3eXA4jhw/jO1MvYWzJBFRVJRy29jUhKNZ2UBf8klW6O299msLI3EKuuOUuHjoQWdNp1/2ETF81G9avZeIki8ECOzOh3ZpIq6mjNTY8SUE8biKqcDXILj1by2aoVwllOOwV0pXS5vM+1Ai1or6HE50CchM497uX0WPQcBbNn8txZWX0HRA7HejunTug3zDn7/KP58AFp0Tc8/37HmRkbhFvPv8k9QfSHAEZ4EutN6ZhUbngTlrD52PPyWew95QzYcsWur78Ar1mPkvG96+FX/yUhPPOJze1kNVav6hqMSEEqq7Tf8gwNn+xnojFeO+OiIDAaGgkHL9xD1dfchF7d2xhyEfvk5GeTkpaGrPmzcMwDITiupaMyC0kZLiC8e0LXiBr92YqBw9n1V8fZtt3zuDdt9ZhNNjM1iyez8TSUkA4PL92f4BnwTChJhBGa0I4Bug/dKRjyo7sm+jBNDkFJYzOLcLEtARHE1URHsHQ9bVrCuMmHAOzD57FoiH21wdJS3Bf3b5NaLCmjOzORXn9SPKpGJhoihLDD1n+s7XruhU41tA/2YbTZ2bHamObgyPgYwe+yCCjjobw/E8IScG3sbJG9rcQrFmygFAw6ASCzX3nTQxPcE+LYZpM/96PGZxVILmuNZWQYeDXFDTFTtJ7+JFbNI4+72xnawOl0aBRWWw8yLKbEo4BjE2roAGn9J6d2x2f3ZbCDqRqKy699FKeeOIJooUYh2NollsCEzCtxEvl770V3cwf67umzVDjVR941krrmiIkx/0vrjiPYCDIiw/r/OnJl9EVhSf+8QeZIdBCKBhkzZJyCrNGRLU22QdYs5mMgvU3zGD14nKy8qMoqqREyXuvvsBpUy+QrpC2cGmlxJZ9Y+2xup+9RaVsG5nPBUmTSQjWM2HbOkkrt2klN1fM4lYjTABYAHz6yAN0GzSczMxcLvv53Tz5h9swDQPN5+PsK77HSvqyaoVLn/fZyiUMGVvA/r2VEVJWlrmVs445HaVmD4WTp9AtyY+mKI6bhBfegPz1O/bTPVnHp7n7q8vIIanZgp4Misu3VKMqgvQELSodo42w4WbwtadyTdDKymtKC4E3Ff2wbklsaIK28GAQj9tIp4DcBEZ0T6a6aBzDsgvpmeJn147YadGyRw1jizeDotK4a8eXSOaGWZsM6DWg0eeG6Z7+TBMnCtk0BaYwCfTqw6brb+Sra37I6E+X0eel50ic8RzP1D7GL7oN5LGxJ/LMmDJ2JaU7ZV520y9JTM3goXt+KS/Mfw5SurN+6ycIUybnQI2eoUvysyoRnxVMKOM7Uy8mtG83lZ99yor1a3nSE8Xt3XDXL1tI2sxnWLm4gqzdm1nbdQDTT7uZIWeWsvPTJYxdsYTa/Y2fnFlYyv5AmBSfZK0wkKdLOwrZ4bG0THndu/SNOS4Ak8+YCh5hL6KNSMFZVSB57BipNfW4D8R0R0AQ1ft35Ejn1/zicTD7g8jPZ/9bmpEviO660hI4mZMM++/om7S68r9MHHsKSb6BcgMyiaARsjGoSyLf7Kun0kMVODAjkYxEnbXb93l8rz0aDscVw2xWQA4NG8766sbR3ACEQ6A2vwytb2aMY2FNRbnlYiE4MGgYhmlG9cM+3KgZMszR0rlD4v6dXVzKS1aSEE33cezJZ7KiYgHBLxbA5jLMATkteo6q6YzOLaI+bFgaYzlumujYg41fUzAsLZx3bEfkFLHxs12tLq8180MRjRPbrF+1jJ9ceg75/d9vkYsFQP/0RI9G1YLn/W8OpaWlfPDBB5z5rw9o1OKvo6eWbhEG5iG6D0Lb9w3jTjiVNdv3tTgDY3VdyFoPoygTEGBpOQWCpQvnuVlQg/DurBd579UXJauQZ03SdJ28kglOOQ2hKIINyxdCXfQUxF6MKRgfI35AFhwOh1m+cB65RSWOT63MmOcekF1Fi8kf52wAoE738/7AXN4fmMttEyE5UMukilc4fsEMjgOu3PQV6qVnk+9PYFxOARefOZ0lCTqceBZD8opZuTxyfbv7uunc+u8XGFtcin7dhQSvfx6A88b2QlMEprBd1qSCI9mn8tmuSFWzy+tscts7nzIoI5Hfn25xPuNmCpS+2a7W2DBlCvZwGCprTQY10Z/Lt+xt8DyoD7vukw23i/Nz+3Lf+583UWLbEYeZpjsF5Kbgt6J27UWwqfG748pzmPOn/1HnkxHhis/faFF6bOFmJibspKpX9Jzun66ooHjceM8JMdLfyqGpEYK6CcfAGSex5hd3M/Pnf+C0xe/x1w8f4/cfP8nrw0p4PGsK7wzOJ6dkAu++OkNmrgKo2gYv/pLrheCMy77HhWnpbO07jg8aBuQKwbCsXL5ctzriciBs8OnKJYQP7OWy8g+pr2sc4WprjKf+9U9kmiZruknB+KUREzAUFf3W7xGu3I7m00m98h+Q0Dvi+yNyZSrRupDAwESxFjhvuk3D0iyvqSjnuRN/0MTIwPDsQnyqaJRtz/FLFTC6ZyrKI4+wdVu1k8kIpGtFlySXL9fOviR/jzIjHn64ybpQvx+2fiI1WErr00MDrNu5n16pfkfA+mRN9M3U/OARPh2dQVZ+EYqQ/LCq0ljoT0vQCRsmVR4BWVHszHq2j6yweI8lUv06++tDsdk8PKj82z956se3Nf6gapsczBYIN9c1M8axMKpwvKWhgjV3/YlA2KCr1v6a0/LycubMmUNWVhZnnnlms/dvuPev+FQFYZj0tPx2vVRao3OL+PW/Z7BlzWKKSicxOq+IHoOGs2zhfOr2b+U1I6tF88d0hHCBX1Ndq4jl93wYrKdR0SVRJyU1DfZFjm1bhfbWzI9wKLpGNRiD4z4Wekez3DT3/jdAaWkpIz/aza4dDQZixxfw0GVw3ZOtKg8ARcG89H5+nW3Sa1QuD9eMadHXMn3VTBzSBSGwmGvcwWjoTiUEFJVO5HGfTjAIuuVrGwwEME0DRVEYnZ3H8MxsJp02lbEFJSi1lQzMiK7NPGbyZET5zGbfyuHZhaxdVkHDVUeoKkJVJcf+uIkRrgluwhQcCy1IBdSSr/cSDQd8ibwz4bu8s2AGACdMmMSfL76WlPkf03XhXI5bPJ/jgNCsF9k4YjSf6934fNTJLOs5FENRCQYDfLKknAuvvZG7HnqeXy6T5Y7MLXL2EFsRYwLJfo2s3qlO3YQQhKw/7MQzG6skt3bIMDztMwmbJuGwQdiQB8+G7Bxhw4wZa2BYAZbefvEeIhrucfvq25ea0QsRhxJyp4DcBOSiIBy/zabGb+2yxdSveAeKJck/WuMkBO99tovZc5+ESZdHLePDN14iu6DYyZQWtmapa0pxX3DbtLcvIYUnRozj7hEnkbVrI1eu/j8u+eQDpn42n69TuvH0PypJLmkcMAjwv+cf58qf3wVfrwFfpNDu8/np2rM3n69eDu/eD9+RQUOrFnzMupfu59iTzpAZ8jxQgGn7dnPb0z8ia/dm1gDTNT8vXfIPx8cYIBQ2pQkwSFT3jk+XVzC2sJi6YBhNVTCFHe9vNlr0xhSWwsqozYuAX1MbEZ57ze8pnuh7r8/tyB7JEVH+9v2KEIzplcKenU2bm35z0ijueMcTBNkO5v3HFm7m5ZXb+NGkwYzqmcKaFcuBrEb3aZrO2KJS2RYRQ+NtwRaCG8LWovdLj0ysMapnCiu27sUM25wesWFi0i24O/LihkVyXl3wuya/e7AYkVNotd3a2KP4Vx4sysvLOeGEEwhYmRnff795LaQjcAADuyQ5dYuYe7mFTCmbJM2cYZO8onEMzirgvu9/F8r/FTNBgRfD9q4FMQFhafptTRqmHPNGGtDDCCkgu0FAV5UMpKBfGku+2smeQJQx2rQSBrZMc94krHdQKApDx+Sw6bO1GOEwmu6jrKzs4MtvJbp06wE7olCGHUxqcQv1MZiIoqGgZg3J/snWnhe5TtkHZVu3KAQcM3Eif316FgvmfkT++EloCrz36ouEggE03cePb7+XUXlF7KsP4dcUEnwqPWMwe4wbX0ppWRXzmzEeBA2Dv6yoASU54vqInCLy+v6U3HGTGJlT5FGmmJjCtX7Z+6giYF9dy91YSs44l4+6dWNxt+4Mu/U+cgcNI3NdBQdeeYmE2f/l56bJz5e8R5U/mQ9TuvEhMKpHH4RpMia3iLLqL5lnJ66xrJmqiBLgbLo/6kNhEjSFmqBbz+376kjQkiKURWBSFzKxDWNeATdsmCzfsjdm4pBIoVg6d3qz9TWsX3vTYyoC5txQyp6aYFwyRnQKyE3A0Y55NI2xsLR8HuYB9zRqxBAaDGJvSCYQxp2sdspM0yKEiiTylr/UBMP4dB2CsKb7IH5adhW/OOZSztiwmKtWv8ctX+1GXfARc4TgMdPkFaBWCP5tmlBfx/d++wtpdk5OJ3TtU05dTjz/cv73/KPyj9XvQeUWOP9ezC1rHZ+2hwUYQvB9ReHmwYO55ptvGLz1E0dj/PI3GzA+eR+zgaZLaBooCpqm061nL3Y3cBlcs6SczIJiEFJj7VMVVLwcjfLF318fInlQFg/9+QYgthbJe8iJvC5/OovAtdfStybIhnv/QmOPZes7CLok6WQk6iREEy6uvVb+tDRJEwd3aVRCe6CqNsRd733OmVl9GJWdB8sbn+x/+eDzjnuB4pnD0VxN0hN1MntH8uHaKZBlqmmlEb9ttyQfwbBJ33Q/u3fEPih0vekH3Lp+Ha97DQXb1yPq99FSksKH3nsAaL0m2XGXEZB5+08xTJNtf7y/VWU0hzlz5hAIBCSfq/V3cwLy8F/9BEVITbK3rl4WEZc9RGr+7UQV46ecyoryD6G2GhKbSMrx3j8ZXTAsYsy9/vNZvVOjz+HDAK/l4aH3HiBNF7weyGdn4XguHiz4R7S8R5VfxxSQWzc/5JN1n4/LfvYbVAW+XlNBXsnEFmuPY6LB+98S9G/ICDLjF/Kn2TYmCxtrlsyneHDjg3MjfLEYsXklw75/OQp2wiB3fFL9Gr1SEthaXeccohUBI3qksKOwmMFZ+Xy+cgkrF83nx7ffQ3VVJYWlE8nKLyEYNtAUxUlV3xT69O0Lu5pIg40M5qpqIBwDJKemc8bpPyBRV609Q8avGAgU0z0Y2kKzQaRvbSwIoXD25d9jxOgsvnfhWQQDQTRd57aHZtDtO2fw2PrPePr9/9HTNCgblMdxG5dzfP0BzgK44yZq/ngbVaXHcnPv/uSHQny+MoRPV1kzbzYTjj+ZkccfS0aiFMMsFZB8sOVaETbMCN/4H7+2llcuL7L+MnFpX00MIVwtudNeV2aIBtNyzzYxHYu3Kyw31iAP7hojrXYboQrBxj0yd0JqQvyJo/FXoziCLSBgCtITdbp3T4l57/QzTuJf//VEZasxIr2bkLKPOf08TFNyG9sTXeA5EXpOvyaSvzAUNjEamAyDqs4rIyfwysgJDPr7udw/eQpFC+fy9IH9POT383QwSKFpsg/AsET2/VXo4SDCBPHWH3l/wwJ0wIel7dqyFvHXs1EAn9/PdyafwKnr1qDs3sGFySmkbNhAzZAhXNRlMDPOu1tqjEcdw7FDuvJRg3YaCFShcMnP7mR5egbsizRzHdi3F6xFrC4YRvHZLAuKFXghe3ljZS0/eWMtH1Q2kygFGmlEnOFAji0A69fjD3h5PhuboJtN4bk+cmdvpCE4BI6fw0ZlwvIVja6PyClyhVz78cQ2q0fTJgoBPZL9EUGBNvpntGyx1DZ8TpdvGoyREJiGgaIqUf0jT0rYhr96C6/75GYwsgVjHA0OQZ0QpGz8AtOE7e08BGVlZfh8PkeD3BItZOKXG6JS6iXqKjWBMCbu3LEUvo6g+51pl2CY8N4b97N5/PWEk7tFewQA/332YUqnnMzwsYXugdD6LF6YPEZWboVADTP+9X9ouk7ROVdA31Ma3Wf3Q8wyWgohUFSVq2+5m1F5RWiqYErZMe2TUnd9NMm+aVyQ3491yxbyYX0fSa25Za38IJYGuWYveOJMYmGGbyIz3ljb9E27vkK8dg+X/+IeRuYWWcqESDcsTVUY0CWR7fvqnP53qe0EX65exs2Xy2A93efjwedfJaewhLAprRZJutLsXFOFIKEFmS2D4eiHhmVbq3ltzXbOz+1HyDDYV2+iq4oTwwKu9ti0fHRbMtzX/vo+zrjgMt5+8p+uv3UI1laUM2RsAbklE3jep7MjGOSlrWvYed53+W9qGiuefojJYYPjaw5w+qJ5nLq3ilOBbc8/xoeKwlemyQOPP0je++8zZKKb+t3R6Dr1NRsFjzamd7N+tzLtyaB2adnz3hOtL12FW2TQnwl8/MUe9nq07LMuL6I+ZHBGZk/eWNt0kpSWwrTji8zmLZEdgXjUascVVi9dzNMP/pUVFYuaPwXXeVShMQRkkdD49GtjeHYhpmlyIBByXmCbBxE87hXAnpoggbBMopHRpWvMMvtMPpFp8+fQ/8ABTlAUVo7I5HIEhUAZUmMdAoJA4O/nUf+P86jbsIAaoBaoAQ4A+4F9wF5gZ309N//kevp8upZeu3aRkpwEL7xA0mefsffyH0W4U3xc3yiVCFgv8L6qyqgL53/nVrBuRYVzKggjeRlt04/te7WnJnZ6YRvfGdkdgQxOaxhxPrJHCiN6JDO8uzsmCbpCzxR/TJeD1qJRGc25WMy6q9XPmL+xMup1R3sKICLTSLcUAhjUNclKrd026KpA0xq+D7Iijm98A8z5y430TI/9rrQGAtCt6tvtb0/f29LSUmbPns3dd9/NjBkzWqSFtIfBOx62646dyMM+pDXULK9fUcGBvZVc9eNbGTooBrPO5lWwfi5GOMyaxeURGkGB6PDNSERxXpfCRxAlmub0q6WY855pn4db6/T+vXv4bEUFrzx6P6uWLOqwA8OnKyr4+J+/tv7y1CEGv3n/lPbctgVCEQwYPlomHrKGZXCXpIjkKOCuJakJmpOqXhGwYpE3WC/A1jUVHn5kd81p6pVTlZYFadU2kSHxpZXbnf0h6GVnwP5nWtpjk2DIaBF92bJ5H6ApgsLSSeg+K3uippNVXIquKmTmF3PXIy8y9fqf8ZvHXmLy6dN48+mH+Soc5klMLgemZOYwHMHVwPvAZMPgIdNkbSBA1mmnwWWXwZNPwqZNTidtrqqjLmRgQONMoaaH7SqifW6gXcMMvNGwcmt1hOuJfUA0gQOBEA8v3BRxv23BuriwMcFAW+G4fNEZpHfEoby8nB9+9xyCgQD/uf/PzJjxQswAnI8++hCzxkOqHcUHGcAsPCfm8958+QXOPf8i+UKETTTFdq6X7hYGOOwNtcGwzK5jQkaXLrBnd9Qyg2GDQH0AE5P3DZMJa1dwwYVXcOezj+EDHrfuE6pKeNwFmHu2YK6bE/nSAQhB0fGnMCqnEF1TCNTVMPq1mWjJSSx+6CHmfPwx3R57jHdfnQkn/sht7/DxjSs1fjq89wDrAyks31zV6GPjrNv48PWZjMotck+1AlYuXcTKhfMZXVTKsLEFfL1hPU25LIxIMbl63CCAqBnn0hIaH2JUIUhL0NlWHY18qfVo5M7Q3Ca8bR0E60FvWcKFr/bU8PCCTY2uqy/dymfZvyWnsMTVHgtI0FS6JUefmw1hC2wHi/379rH5qwapZJspN1Bfz9vPPQrn/LnNzx1WtYrPVpjkFBWT5NOiCqXthdLSUkpLS9m6tW2abpD1Ui1hOGxGBq3YgvJnKyq46/rphCxTb6+bZxD1HZj5KwAUTWNscWmjsuIBQ7sms2yLvWbKg4GiqJROOZW5nzS4+d0HWsRy0Cz+9V2o349QVQ7sr+au66YTCgZ56eG/8cCzsxg3qOU8yO2F/70yAyNoHfa9g2NGPzzWHtgPia1LDx4TlvbukyXlZOYXI6x1MlrCl/QEneq6EEkeS5MiBAXjJ6L7fAQC9Qgh6NGjuzNfVy9bzNLyuRRPmMTYUcNjVmPxwgUsfP9DSG/ax/zG19Y0+bmXFrQuFCZRV11XRWsvCYYMQk1oVr1Y9P7/eO/lZ7jw0iu5/9lZLJ7/MYOyJQ2oTKYiGJNXzMCsfPyaysyH/xGRuElRFIZmZvNy+UdsAB4DNFVjhGkwRVG5o6AA/vtfeOopVGB+lz681y+bDwZkc9sTe/jzdVOY0YAhwxWGpSAcMgw+37mf4T1SCIZNfKq8S7JfScVatEA9b1pq0zQJGQbb9tXxpzlfsH5nA/JmsHymW69kaQrSUiYcpqp4Q6eA3ATmzJlDMBjAMMIEgwHKy8tjCsiTJ5ehP/A4jrPD4IJWP++F+lFkLq9gTH4R9WFTpvi0GBxMIU+Ga5YtZvXicnJKJjD61ONBmGzbvBGI7v6xYv4HctGz1lrTMHhxxpPcOWgo1TUHeDe7gIxu3TnuzGn0HJHLR68/z3/undOoHEVRmDYmh6mXXU+ST2X/np0MXTSfA7X7OeE73yEQCMjI2xHRAwIjMHIixv7dLE/NjXnL7FefIzE1FX9SCnnjJoIQ3HH1VMLBIKquc9nP7+HJD1ZAyfkxyxg7uM9BvcxexoqDKSMClTHozmzUH4D7p7UoAAtg54HoWvTwptV8/ObL5BWWgIgMtvOycjSFBF1lVM/YbkUtxTfbtzfS3jua9HD0qGjTNNm6cUPbH7rtU76ccTu/fVbnnsdfovfECY4PYlwsw1G0Jpm9UtlUWctuESSMiYKttRMM6ZbE2u37WLuknJDH1Fu7fx8osf2QT7/kOkbnFrnBf6arCewoDmQbN0wcRKKugJXDQh7EaaS5lB9aQsf//gYn39TmZ5574aW89vRDGIbBq0/Kn6ZhEAoGWLpwHrQiUUh7oLy8nLdeeg6UKAfiGBrk3d9sg8F92qcCS15D132MKSyVVrMmbh3RI8WhEvNibEEJP7njHv5w2y0YhsFNN93E0wNHUF0X5Ee2gukBHw889iTjMoc1Kre8vJxTTvoOtdmnwaSDC8I0sbSnpkm9IfdQw3Rnu52qORw2eGPt9haV+fE7b3LhpVeSU1jCyNxCdu4PgJAsP94MmAIYW1TKK34fwUAARSh8/9f3sbeqEqEomIaBUBROPO9CenTrRvaUk+l27klynFevxnz/fdb9+0WmfTaPa1a/C8CWNwZT1XMMyQNymDMgm92JaRE+wqZp8lTF17y6+hv+cNoYBnZJJMlnu2K6FHfeQL1g2GDr3jpLOLYJ4yBkmDy/bGtU4Rg8Wv52FGQNM7o1LV7QKSA3gbKyMnTdR4gAuu5r0nQ6cUIpP73nb/xutbXpDI2dda8prF1Szui8IkndYphOdh3TlO4ev7xyqtQe+XT6z3qLtMFj2bl9K3SNzr9pGGGOOeFkPvq/tzGtBdcwDNb4fHz99UYWz3kXRZMagUGjP2H1/DmNyhBCmshT07sw6/EHKBo/kf4DB1GTNZYNa1Y4AUqKoiCE0rJt1xebvByk6f31//wLELzy0F8YmVtIyMrMFAoEePGzWsKWcLw8SuatqQNMzsru7fg3tYjSKi/P0+Y2vrCeMgZ2SWSXR4BVnvw+xm4rxW2MynTv3Y9d275u8eNsNxOZbTDyM69rhX1Pa0/pXnaPtuLr7t1Zua1B8I0tIL92L5x7B3Tp1+h7XqE62hg3B3mwNVlTMZ9jJk7gwJhsx5e3o1GTORaBINXTv0k+DV1VHL/xBItmUhGCNL/MmJlVPAFVUzEC8sR7bOIu3gqmUm94GmWllkcIUlLTEECirlmHJDOmf/3hhk9VyOqdKse2pgp2bMAIh/l8xWLQGqyftoC89n0oOAN6RgpaLZ0famIypmFiGtJ8rSgKphBouo+i0okH3yjP+98SzJkzh3AoDD5rMLwuWLGC9BbNlAft/DPaVkcLF6VvwSgrJPvmHzAwM99hqmhqWjRcP2zXnb2VldIH1jAIBAKsX7aAA/UhR8EUCgZYv2IJXDStUZl2kGt7TMhPV1QwNr9YCspWEJthunvo/voQPk1hwaZKnlvaMmtP2Smyn+3+sd9PVSioQroe1IdkWP7ovCLueuRFVi0uJ6u4lIKicaxZthjd5yMUDKJpOsefNZ0xw4fSvZcVtawokJMD2dlUHjuNbk8vJm/nlxy/aSUXVq3nkrVzuGHF2wCs7D6IhG1TCE6eTGXJBEL+ZL6wEndU1gUZQKLjZmHDTo7y5e4aeqX6qA0a7DxQH+GKYZqmtFo10Q92EqP2doWw55QSH6qLCHQKyE2gtLSUfz0/iyXl8zjuuDKKckbHvFcIweisHFi94qCeuXnDp7zy2P2MKhhPflEJquL6Ha9YNM/VHgXh4w8/5Oxh2fTq04c9MTwCNE3nsu/9iHGTT+DPd9ziCLK/HzCYxRs+wzDCGIEw78x8OlbDOHnaJfQbkcVTf7qDYCDAjH8pXPWTWxl8x31UrV+J78xTCAQC+Hw+Bo7N5tPoJbUOielQuxcwCYdDrFu2KOLjQKpLifDj466J+Gxo9TrOO/ZCcIIs5cIWiwvSwd/+Jn9aKZZHdE8h2dfKKH+7DCS1XIbHjePGm27k73f+XPrdVm2Dbo19uQaNzKRy5zeNEhnEwgNzvwLAR5g6D0OKqmkce/pUuaBjmbKgsSb3MCDxoYe4efLkyIv2Rrt3u0yHfsYvG31PUdwAvoZj3Cys8k3DoEtX6aP/5e33AtA/2d9iN5NDhS13/o4eKT5Gd2l8UFSFjAnwaSJi3kphGceEa4TDvP7nW7jsLy/y2Beeuf2UdHHSdZ2xxaUgPD7YyHdhWPfkDmOwsCEE+FWFHx93DeKrpSifz7cO4uky8MELIyzX2IISNvgTaWg3aen8SEnvgubTCVncvVfcche11VVMnixT/h40PO9/S1BWVibdE4ygFExXvdv0F576IezaKKk5k7vCyLYL9Zu++pIf/PBH6JpCbSDksN20ZomQAqNJYelESwiU+0BZWRlVNQFHweTz+Zg0cULUMuwg19p2OLne89Pvccdf/82IHBnca1OjrlqymKUL5tI3q4jconFs/PILWmJLOuuK73POhZeDdfeapYuZ+/FHjC0upaB4PIoQ6KqH9UMIRucVyYBH4JPlFaxaNJ8rb7mLyj17yC4qJTO/CKN6d6ODuhCCod2SGNsvg6XKcJb2Gs6X2b15bfnXFH3zOcdtXsnxm1dR9sozaM8/hqkoVI4ey096jOGFrqPR9vfGJDWSBUu4rhi7a+rRVWG5nRBxcJBa9aYD5ez1pz094LsnqhGxFvGGTgG5GZx14nHkFJbQNckH9XubvPdgAplszHt7FiDw+f3c+eiL5BaWOGwWOcUTnMVd03WyiiegqwrpaemwM7q24ZcPPk9OYQmjLDPrn26XZrAlc99H1VTMoNGk0HT+Vd/n0h//mmce/JvMkIRJOGTw2J/vZVzpBE4/oYzZs2czZ84cysrKmLdxLzcvbgGZeHNvw/eelub3t/8KXy7BDNZa2mDZdp/PFzVFK8Ap/YTUDJnSTB1GZotrqWuBXT2H3eIg4G1mwcnnM2XFCt596WnpI3rGL6FfJIl/74FDYe77LS5/2z7ZC3VmpLCTP2kKI3MLHQYHTBO9BVHihwKlpaVcePFlRBzBbC1nDKiqRtHkKSxs60M9XLcH9lY5gW6mKdk3mj0sHWJk9U7FF2O9UCzVv64q9EtPoGuSz9FarVuyICKwMRwM8tWnq0CPdFcSQnDcWdMZkycFBZuiL8mnMbJHcrusVQcLgWBg1ySuGTeQbmNMtgy4hVH541m5dHGUnclE1XU+X7WM4OjpkN62Z278dDXHnTENVRFMOet8sgpK0BTomeJnbJ8mKPMOEUpLS3ng2Vl8+NGH+BLX8Z/5zzad9c4wEEJw5uU3sCRtFC23NTXG/A9nc/LkiZJSE5uD2z5OtwwF/dNZuLGKnMIS/vncLHasW0pZWRmlpaXsPhDgX8/PomL+PM4/4zuMHDY0ahl2kOuZ9z3fOKNgKxG8/CFefPDPTL3uJwzLKSBsmKxbXsFtV09zKNrufPRFPl22CLqPa7a8lNQ0Zw1fvXQxN11yjizHp/PXp2YxtqAYFcstyBIe7cQbG9csc1Jxaz6dXz04Q7KmKArBGJpYIYhYmxQhCKkaC/qOZkHf0dw37nxmnJ/F8A2r6bpgLknzPuaiuS9zWThE+LXfsjsrl+rSY9g/4RjqSsZjJiZFMFTYz3CysXoCGfcHQnyzv+nYG+H8z8XAjAR+NnkY98z+jG/2N3b5W/SjiTy8cBOPLtwccb1kQAbnj0h05l08+iB3/CoZ5+iW7GNQlyQPrU1sqO2RwrbsahCCYDDA6kXzZVINawKPyivizkde5ILv38J9j7/EsLEFrFtewaK5c6IWJXZ+iT2bhYBqywxmGgb/CQZ5p08/Tp52MbrP50hyQghUVWP42Dx+dOcfuebm21GE1Lx4F07DCJN/+y10v/5KSktL+eUvf0lpaSkZPgFzn2y+nT2jL5YRUHU4/Rb4ocxmZJqgqiqX3nwXaRldnNue/u+fefq/bjDXI7+/jc9WVLgBBULg05TmhaKLL4aLL5a+320191hl2PCmNRZCUHbWNNnfNVWwoYH4980GNq5fi9GWBAG7IxefjG49nEXHPp0nai10f2lvXHwx9276yvlTf+GnFPfxaE6jVOryX/yWc664AeWZm4DGY9wchKKgqCq67qOwVPrFD7/pekb8+Pq2tKDdkXDFZSiXXhL1M5+moAgZFOPTFHya4mh+c0smoOruwU3TdcZkuUl+xLZ1st0+P8edOc2yIEhNdJ+0BDJ7pcSFcAyu1ujW//yG62c9ybRrfsTovCLGFjTW5Pbo058Tzpou3RGiHOhbOj/mvPYC7738HLNfe9HSzktLT7sdmBq8/y1BTlEJ067+EUNHjGLo6GZ4i02DY049l5TUNNKaYC9qCYywwdol5REUkK3tBi+/dm5RibMPAHRN0pl68vFc/oMfM35808wupaWljD/uO61sQXSs7H8id15/EbNffpawYbJy0XwPRVuQlYvmE4jm890A6iu3k1UsEy2l+DW+WrnIw9YRZPXi+db+ArricsPY2uQ1FfMj7v9kSbnLqCOgS2JjhY1AMi45f0cZj5DuY0tuCV/+8Gbe+OcLjP3lLKacdxe/KzqXnfvqGfrYA+RdPpWSvKFkTj+dgf/4AykL52HW1VNVG2S35fZns0HJP+DW/65j9fZ9jR9oIRg2HTeTSwpcl7j8ful0TdZj7i1VdSEmD21MRTlxSFe6WBzQtqUz0Iiyo2PRqUFuAXpYEb2Vzdynt8ciW3Am+FPg48fJLCwlbJrUhwyZXc+EUblFjMguJMWvEjIMli+chxEj0Ek89xPWpf+MM6ZMdqKNba1xf6CfEPTo05+zL7mGV558yHK/ULn61ns48byLyUjSnY15/95K7NAeAEVVGaqpKFu2OGl2y8rKeOahf0CwBRtwG3xKwcQwDWYf6MYWj+DZf38kg0coGHR8uW0HgxYJvF9LfUyirjbPd9xMGTZ8isKfzsjks537Xcq1GNBm/pySn/2GdcsW0PIcTxLqstcIT3GTJBx7+lTPYi0IY+LT1I7xO/36a3p6+EvvuPdPLP7wHc8NDSq18EX25/cnM7+Ya6+/nn/vbzzGzWHUsCEU/0BqJLMLihGAf7v0OYwLPcXXsXV/ApkUJJqfcE5hCfc98QrvvyYj2yaeeh7DcgpJH1DNsO7JbF5jsqbrz8gpnsCYXDfFuGvGjIvWAzgCRdKO7S6tnSIYlZsPK5dF3HvqhVeSOWYMH7wxk2hJ9lo6P2ztezAIqxfPJ6+oJIIC76DRxLjGgkD6zt513XSCAQ+bhWnC6/fBmZHuR3MtKyOn3QwjorsttOi5qkpW8QT3EA2oSus9Qe0plaRrDa5LRqCWbou9+/aHrzc3f2NzGJiDUXg2//nzXQweMYbM4lJ0KyW2puls6F3KZ3ozlVr6OicUZ0u2Csv6VFY2md/f5yMYDKDpOgXjJzpUdsk+lX314QgtfMH4STzlc+/PLCx1lDV1AoZ1b0xjqQhBsidz6/yvGksdpgmGIUXbm15bw/6AyfpBecwelMejXRJ58KTB9F5eQY/F80gv/5hB9/8R8Y8/EE5I5EDROKonTCJp/CSqxuSCrjmUcdv3Na093nmgnlS/lAlOz5LxPU8t+dqNm4jxPcOMvu7ItPcupaVPFVTWBunbMHFOB6JTQG5HfB7F/1h8/B/MYy5vXUFZx+Pv1pvhOYUYJqxasoj1yxYwJG8cI3OKnKQhpoDC0kkoK9+OapZTVJXMQncBteeoDF412bLxS565/w8IRUi/RtN0+IkRghSfRjBsIDAYW1SKz+8nGAygKAo33Pxr0ud/wN7qaifNrs/no3e/AaD3bV17WwS56JiGwUa1V5N3Kopc+O2Ago6UCXRNYVi3JHql+FCEYE1FudSCQaNd+faHZjI0uwC/pvJgKxmt8ovHU+H5e2RuoSMcy/W6/YMrWgMBXD1uAI8v2szgsfnMfOgvMe9VF84g6+qXUAQMGjEalsW8NSoG1W4kM7CdseMmMCy7wD0cxY9sGBO2hkZVInm4bW2rIiAzv4ix+cWETZPaYJiwYZLbN52QaTAmv4jhuQXSfcMxXZpRhe2ORmMfTGnS1JXovtGZ+UXc89hM7ltczZ62PDDkmn91XaegdBJ2tsKO7BpFCFYvnk8oGMQ0JdNBr/6D2L75K/i8PPJmIVwasYPMtDdsbIEzBqolqKT4VMKtnChCSPfChpk4nc9pQfyHXVB7Yfx0jLSerF1SztRrfsQf/vMyC+fPZXhuCX9a1/zXVU3nOIuxyg6mHTdeusO88dLzBMMmX6xfS0X5XHLHTWRMXpHDOGMfMbILi/nX87OY9/HHjCkcz4AxMhBSJq2K3tah3ZIiaPS+3lvX6J6QYWAilR37A5HWRhMIJaWw89gTODDlREAQ3r2b7ksW0GWhFJj7/+G39AdCycnsLRpP9YRj+aZwAooRjshh0Bj2fip/Th7Wlb11QU7P7IkiBNdNGMRd737W6FuVNcGoQ2vaQYHWAS1BV52sgvGC+KrNEY5Pli4ECiOumZ98CK0VkIE6JYH/e/lZBo8cLbk6Lf+pOx55keHZhY6GIaewhNLjgsz7pvFiWXbGdEblyvooApYumEcoGLIEYSzC8TCYlileURzfZluwFELwyfIK1i4p54pb7qJuXxUlEyYxdMgQmP8BVVVVDotFIBBg4NARfPnZN63vvObwk9fgr2cjWkhOZQsTcgM0O0xIVi1hx9bQ5BRPYKZPJxAwG/l+j8orImQYUlvfyvipJQvmwknS3/RXJwx3Io69Gg1FNE6WctggBOdm92Hy0G58vmop/kRPFr4GdTrurAsYmVvIuhVLuO2qqfCDl1r1qM1vP8bmTat481GdOx5+kf7HTnL6P15YLJqCvdGCG1Rpa2gUOwGDJUzaTVEEKKZg/coKVi2eT07xBLILS1As60G8ZM1rCFt75JxhhPTBfO2KYs56YrG8uO1Tnnz+56T4BKdMu4QuX61mz+7Yqc1j4h9TARh/wilcev2PyCkstrRb7dGStmNotySySyY41j1N0zjn8u/x6O9+7WqUbQi3oxQRPQtlS7FhzQrufPcpfvvoTAZlFdDWc6Sluoj5eUutcdGe66+rpD6hSytrZKH3CDIL+4FpStO9abIvCDXh5ls4+aTTGJNbgAmM7plCsl+jJiDteu/MmkGgPsDbLz6FUBR8Ph9/eeoVBmbmy3YI9x3OLRzHoKwCAiGDJYsWsm7pAo4rm0z/gYOiPldTpEtVU7j8hRU8+938qAxDdkC/m27aJJiWwa4pp1L1ndNkso+dO0ku/5jU+R+TsWAe3e67gyHALn8yH/YfywcDsnl/QA5rug/E9LCqKIq3bZKFZlpuX8clJLNnKjcdO4S/ffRlRJ1ChhHV/dQ0cQZdEZJGrk9q/GiPoVNAblfklYyHhZa7Q201JKbRZiZdIVg4+y0O7K2M4D1ds3g+4bDJFysXklM8gR6TJrIjnEjjsG8oO2Oq6yMmYHC/XpjRtA6WwKyqKlfdchdCwCuP3k9Z2WTCBvzk0nOdQIPfPf4SWfnF1O2VJs2MjAx827c7GuRpZ5/Ox/c+cFALd0woCma4GeeDf1+KYcjsYZm5UmBUO3AHVERk0EVmfhF/fPJlnvj7H1gWhcJYCClEq9dcQPh7z7f4OaZlOu6yax3+HSb0LXKEY4Hr19ZRmjLbfPvZyiX87oYLCQWDKKpKl+496VU4Hm9C3Emnn4cQglWL5hMOtiDgswEMA7CYXtZWlHPCsZMcgTM+xcRIeLXFXtj+xLZPccR3FMH6ZRX89nqZ+GKWrnPP4zPJLiiJ2zY7LkdOBWWEfSAUjhRan78ZgFmP/4uhI8eQ06cnG9oiIAOj88dx+/1PWPR68vDcWuaG9oZMWCScTGjhcJgdWzYC8v2NqJolsIwcm4teVMqavW186Nr3Mb9cQkiYrF48nyFjCxxrU2vjL/ya6mRZOxh8s7Wxe0r9m3+BqXe3qbxuvfsxPFfG6djJdYybZrXouyKlK04KZE93LFsoswba+6hpGASDQZYvnMegrHzsyZzi05zvfbp8CW+/8gLvvPI84VCYlx7+G/f963FOKmzMiqUISGoBc9KBQJi6YJRYFYvWLmQYgOrJJOgeYYJdu7PrlLPY8p0zZIrvHdtRPvqQFc++zvGbV3K2FRuzMzGNOf3H8v7AHD4YkIMw811XCqtxipOhNLry5f5zsqzDvCBJV6nx1NkmHxDWIjAgo3H2xo5Gp4DcjsjJL4SFC+QfIdufp40LhxAUH38Kg0dkouk6oZD0n1obSOW5pQH41x/w6xr/eu5VPtsVPVnESIu5omuSj/REjfcOVDuE5eUNbzalRvPLdat47A+3EwoEmfnw3zjl3OmeQANYtbicgpJx8iUpLSUdmP3Pfzo+yHPmzMGsbYeMV9GQ3BXOub3R5fI+o5zfRV01mu4ju7jU8a3T1BYu+S1IEdzaMlRFWAK6HTAnyMov5uIf3MzKv/8ngs7Nuosx+cVMOf1c3qHlEEIuhJW7d/Lb62/n9odmkFVQLFPHCukL3WFrT2kp4ZCBKuCTJeWEgkEnEHH3ju3sXVEB/U9wbh9lUSTZAWkhIse4OWiaiqGqaDbNGXIOpJYdI2/oaJUhNDnXbJ9Ye0xtjOmVwtKv96IoAkUBI4xjJVCI7NtQSL6ruYXjHGG7oxODNIQtHO+x2FYAEnWFYNgg2lFu2+avuPXKqdz64AyMfiavbXHX1pbOj35DR0TwghuWUNhuQXptWEMUYSk+gvLwb4TDvPLEg0A0WkaB5vNxzS9+y8u70mBvVZuq6fvg34SEiabrZBdPcOIV5BNah6zeqe1yCNv29UagAfXlphXw/kNw/HWtLm93QPBNdT2rK8oJ1geiK4diYMsX63l53RtkFpXynVGnONclnZ1lATQMFEVB133kjZuE1wVBVxUUIVi5ZBE/ufRcAvV1zlgGA/WsWrIIrrig0XMVRZDcEvpF06Qu1Lg9JhAKS6lYHriIYLGw04LY1xBQ37M32086m2t3yrT1A6t3cNzmVZJWbtMqpn02H4DaN39D5biJ7Bk3kS1FE6jt3lc6lHiF5gbTVQYiyouPnJ/Dd591/eVkH8k22H7e8YZOAbkdEaGptBc2pW0CsqJq7N32NSOmXsLtD89gbUU5/pR0nqgeItMQqz6CgTrmli8E8qOWIYSblaprkuSm9Pl8BAIBbrVebtWiQzNNuVgKhIdrWQreToCDrlMyQTICKAK47z4ASiEiiYr+p/sb8ZS2C65+NOrlW4+5zP3DNPnO9MsZnVfkCBqq0kL2Bqs9B4UGZShCoHlcLGx+jLyiEqacF+YdTzInIUCYsm+HjsmGVpwzho0czefW78FggLVLyhlbUExagkZNMOykCe0QBoP77iNUH0LZVElmUSmv6TrBgEUvaGnMvLCFw6yCEu574mVuXmJGjnETuDC/L6PG3s5nyxeQN26Ck0XOrkfcIEZd+qQloCsKm6pq6ZniJ93Do53kk8lCfJa5MixsIU/219jiUl720EDmlkyQZlGjfdPDthdsf+tPbrxVvifWO5KgKdR7gjp79R/Mji0bMU2TYDDIm08+yJKvtsO5dzn3tGR+KKrKsadPI8EKgLS9jwd3SSKjHSgdgTbNMSEEueMmoqgKYVvoMU2pzPDcV6jvYsQFF1BYOonM/CJqN1exYGNVm6r53Z/cQd2+vRSMn+hkbnX6pJVzpb0OF4MHDWLxV1GE2BVvt0lABvjx62s5Y191q4RjgHXLF7N+9r/RfDrjBr1H2TGTSPJpTDv5BILPvMqyBXPp2q0rVXsqyRs/kcy8IqrrQ66lyuqSJeVzCQYCEQcdwzBIy8iI+lxVCFJaoEF2BNwGME0IGAaKonp4jk2LD1o499gUb6ZlPTY8M21TWk+ezDqBJ7NOANNkslnJr7UtjFi1iN4LP6bvW68wFtjfux87iyeys2QCu4oncKBXP+eZkbDSyHuuXFUygBNGdKNq1w5rX4xPdArI7Qivn834jAALDKB2Hzx0GVz3ZKvKMgyDl5/8J/WGwSU3/RpMePyPt8Pkn8sbLNNb37ElsCK6GfrTFRXkFZU4QRelpaX8/ZlXWbpgLt26dWXNypW8/fLzhIJBhCIonHgcSampCEWgoKDpPk477wJOPvcC5s/9kMzCUgpLxlEbNGIuoqWlpdz590e4ddkhcbJoFqZp8t9nH+GYE08jq0Bm4/K1B/1eG9HQVK6rrnm8Z9/+sH1Lg/vltv3lulXQP3Zimobo23+gFJCFtBCkZHRBU5WITS+/X3qHmbBsd48ROUXc/vAM5r31Eu++8jxGOIyy4/NGiVEEdnILwaTQOuZqLdMQjghsZkxBCblFxeiqwG9lo4u3ALVYSNRVuqf42FxVy8AoSUQUIX3/wqaJYrjpqBUhGJVXzG8eeZEVC+eTO24CWfmSvSNZV+mVkkD3Dk6O0hBpCTrf7AtgCNPVXgpBgq4Q8AjI1/3mr/zu+9ItR1VVlnz8HkbfzNY9bM1srv7lvYzKLURXFUxM59Dq11SHJ7qjkF1YzPW/uo9/3/NLDMPA5/Mx7tjj+ejd/zr3DN23jvOuvZFNa5fx4sP/YHjeOP521lhuem1Nq5+3cf1qbrjtD1ayGFNy+YpI4e5w44ZTxrF5ZjkLGpIhH2Qw4ltPPyR/OfVnLf/Svl2O1fSjDz+k7BipGEpN0MgpLGZsQTF+TUjB04CwaTgHe2kJlO9qYekkmQjGo0FWFIV9MTT/qiJaxFcfNokhjLqJPzBlam3JUiFcoRhbsyyvhwxY900MbYwQVPYdxJ4pJ7KQy+mX5oN1n5Iy70O6LJhL34/fY8gbklFn34DB9BtdyDf6EOYMyOab5C7OXGroxnRJYX++3lvr/J3k0wjGGcUbdArI7QqvX+A/fjCd+//6J54O1kGwtolvxYBQYPh4/rcnlY+n5LF3904oOAsSUqyHyVPmmi82A72jFrG2opxx40sjNNvy5S5izA2X89WGz3kzFMI0DcwwlM9+23q2QFVVfnrHvWRbiUoGZ+VTZ7EvCAHDu6fAeefJ+19+ObIfDvNe89LrUmsz1aJDMsIGayrmk11YjE2Z1SLEaE+r0KAMYZ+eLVN4kk91FoKBXdxAtcFdE51F1fbJag0+/r//wWkWH64Q7K+qJMWvRvgVdph/13nn4TdMxB8fRggYnVdMgq7yf6/OkLRbNVXoD0wlaAXjrV+xhMyCYj5btZRfXSVTq7/ctT9mRl9njGPhkyXljHX8boVzIFEV0T7j215ooi6uX15jyHEEDHnYStBVQoGwM3fG5BYxJKsAv6agqcIJEu2bntDhyVEaYkT3ZL7YVUP+j69GIFj+j8esgC85ZteXDsLEZMTgrtz1yItsWL6QbVu+5q0ZTzc68TRcAyJQMYtCZSsnTr0F05T9FgjbgY7t3CdtnGOaIjjl/IsZPHIMqxbNY8Ixk/ly/doIATklvQvrVy7hrmvPJ1Av2YSm/vwPwPBWV3POqzOYcvZ08gpLMEzJB+2zD9QdNE38msL3T8xjwfPLnWua7nMsmW2FYRhyPx19bMu+8O79iE8/BEVB13WObZAF1LY8OD8FYEr6NsOQwWb2odVOoPLGSy/w35eeIxwOo+s+SsZFd8VRRCQPciyEjeiHhq/31vF1VS3DuqcQNEzqQ2FAeFwsAMyIdNQ1gRB3vdeYfcJGVW3IeUs0VWXf8JHsGjCU1edeil+BLhvW0WPRfLovmkvR/Hd54YDkUl7bdQDa18fzTdEEqkomUJvelWvHD+SjL2wOGuEEGvs1Je5cwKBTQG5X2OaV8QMzqAkaMthKVQk3F1gWC2feSgjY+8Yf5N9lV7mfKRqmYTB/+WoYFkVADgfJKp5Aoq5GLHh2HfWqSnrreqPTLeD4I++t3GOd/ITz0toaUV0VsLsx92h5eTm3X38xXPNU29rcBnSriyQ313w62SUTnbZK9oYWFBSlPa1GgzK8Ub+2dlgIWZncvuk8eF42NYEQGUm69Ce17jvhrGn8b0nLFwxz9yb5y6aVYJqkZnRxNUJ0sNvt7t0I04w4KKxaNJ9wKCzNf+GwdLN464+IfTtZGzibsYXFrFjoplbvGqiFBuMcDbLdOMGpuqLQPyORrkl6+4xve6GZusTymnfmk7UBJ2gKB4JhD1uLO+YJmkLIcK/FG4SQhzZ/VaUnml04mrGyYTK5QDBsMDqviEkTJ7C8YhHvvjqDQNXWiLIargERzwHOvOwG65l2sKPpcES368bcxjmmOvEJRWTlF5GeoLNi4TzpZvHIVeBPZP/5F7C2opyA5U8bDhvMfPCPcMVDrX6eEQ7x4eszKSgah2GaaIriKBI6arZEo4MzDYPMwnERQbythdpvNOFtsQXACLz9F/hkDiYCVVO5/pe/bZTkxAku9fwuLV4KppDcZQLBqJ4pbKqsJbeohDF5xZxw1jTWLC6nrKyMY/PHNHyyVV7jANxoCBsmH2yIPtd+/tY6Zl5ayIFACEzQVBx3CrC0x9j+yKLZgPpNVbWRMoTVYCFAqCrVo7LYPyqLz757NYFAkG7r1+D7cA7DVy2i1xszGfmitJ5Xjcwkp6iUq0qPJbBvEIri87z3zTa5Q9ApILcjhICZlxZSY3ETjskv5ppf3cu/7/55GwprRutpaZDDyY0z1CSbtVyTmcAoi+TcKxzK0651X2oaDzw7i/++PIM3X3qOUCiIaUgeTjsDmSIEhjBRFUGCpuLSekfHnDlzCNY3TTh+KFFy3Mmcdun3yMwv6lih0ILsLTejnXPNOmj0TPVTG9CcA4gqbK7bYvwLP6ReS4xZdgR2bYR/Xyoz9AH79lZ5FvGWLbqHGt7NJLdkAi9Yvu2KKt0gwp/PR9N0sopKEUBR6STH/519u+Q78d4/4cTvR39A+Qs8s/QVho3OIqeg2BGGdLWDfK8PArGGy7uRuIeuSMFZWB2tKQqGabRvIox2hmJvtriHJ0x3ztrCsmLtyjmFJdzz6EzenfUis/9+Dsa0+6Bv065IU04/i1GeQECQ/aQKBVN0jCtYNCi2tcdqe8H4ifh8PoI1e9ACOmMKS0n0qRG+ykYMTWJLIYRAFeDT3DWio9YKIRok26rZSzgcYsDQkQclIIen/x5/qIYmdyUjDA9M93BlSyXRgb2VUetpj9WgjCS+3FNjBXoCKJKdQUiN/OCuiezcH2BbdT05hSUUlYynS6IO4dgHupYIiyHT5KHyjTE/N003QVB92ERTTAxLk2z7LxsmhA0wjeYPiO57Kf2FNUVBiLDzt73moKlUZubiz8nnw0AILRym69oV9F1STo9Fcxn+8jNozz2GqSjsGZ3NjrwCqiZNgWMmYXZt7FLW0egUkNsZKT4twn/upKkX89Fbr8R8wcd3M1iwO8rm7V2k0nvBwLzIz1U5dFqXxkk5JmcOYnxhf8e03rBY75XswhKyC0s45bzpVJTPJTktgz179nDMscdSdswkMhI11u88gBDSPxKafoHLysrQtXsPTZBeCzA8O59RuYXWAubS9HSk+caO0HUFGUu7afuSKjgCtK0VDIYNfpaXyD2zN0CvYdELXvsBZB4HIDVNlnCs+3xSyPQI5PEAgZtStKB4HD/89b189M4bFB9/KoNGjGbJgnlkF5cyMkcebnIKS/j9Ey9TsWAuvWe9wDebv0JZ+38Y46ZBWs/GDyh/jpCqsrainNxCma44Ds4FrUZTZm5FCEvvY7vOmFaQi3D6VvG89/YBLV7hzFFrfXG145GCiP3+KALGFpQQCBvMeWMGxgu3wJm3xix/2iDBGRMKXGuK4gqB+f3TqdjcWADqCHjH3DbRZxeW8McnX+bNl15gz86dzH3rZUaNzWF82YmUf/CuDKzWNFpNhLhgBoqqcvyZ56Mq0qRvc7WrCuT1S2/v5rUIQki2GQf/jp6K3cGujdA9Op9wQ9RrzQhfoYD8J4QTIKnrOsUTj2lcT6SOSVcVuib5+KqyBjsr5Jpli1lSPpfC0kkU9j+RJJ9G1yR4b87HLJo/l5IJk5g4sZnshy14XZ9dsqXJzw3TRDEARUhWCx1HMWazWBimSTAcbvHOaLvoaapCArC/3mavcN9X+6B7IBCS+6+uszu3iAOFJay75kaMmhrSVyyld8U8ei9ZwMgXn0Z97gkMTSNYUATPPg3DW+8ydKjQKSC3M2whB+Q8/3RFBetXLYXJ0e/v0qMX7N4ZrST316seafyxqqH7fJDRG2rlFL+0sB+VtUHOzu4dEXDhFQ5tIc3+3RZ2cwpLyCksoTYozd6pCXJqyIxekVqFpjQMpaWlPPTcy1zxUYPz+tt/gVN+EvN77QFl7WzGfvcERxPl1wSG0bHmmwgXC/uadd2vKdQGww6zBUBagoZfUzgQEBQUFTFk7V6+jKUk2u2mZT33su9RWb0XTQhOPPt8huYUukKSiAcTlu2LLdu/bsUS7v/trQQDQVYsLue3j87knCt/gKYqGIabGnxsYTFDxhbQ5eMPSExM5vix+ayd/Qe2nnQbJEVu5Iqqomk62cXS794OxDpSAvS8iDVcY/uk8sk3+wmFDWdO2fPJG+hmC1nef/EI1bNW2gdJw7ZTec1djsAvhbk1Sxa4GSnf+B2+3kMJqI2TDOz86EXe+mwuOcUTGJVX3KhfuyT6IjKXdRzc98NvWTvsdeP912YQshKGvP+K7BdN15ly9gUUn3wed61u3ZOUhS9w5S/uJaugyPFRl1nNRFQ3h8MFOf6RyiJFURk2JicGo4+AYL1kdTpYCAWhKGQVT2LCiadxoLqKwvETnYN2ZD3ddFVCWIoOw2D10sX86LvnEAwE+I/Px6h33+PYYyaycEE5P7CuP/VPH4/PfJ2ygthBpi3p/eVbq5u9xw3UM5yAPPsD0w7eM2WgYXNQhHCyLSoi8r21LaTYh3XhWkGcPc/SNIf8CewoHM/OovGsU1XUXV/T54svGLRiEd0Xz4deTWfJPdzoFJDbGQ1zks95/SVCbUh20NyONv3K73FiaQE3lLtlD67bxGmFJY7W1CsMe4sVCKonTnZMQYCzDwnkCTEtQad7si8i2rtRlU44gWjIKyqBjz6OuFZWUsCcLWugX1b0Bn1ZAUOKmmxzLMwemEOPwE7unH48o3KLHJORIgSKKtAUhQStBZtgjPa0ClHLEAjFdbGwDxiaKhBBIgRHhxLOmkc/PH08d7+xjEozSoYhz4C8+vRDlJ11IZPPnMaY/GLqw0bE8zpUQDrhBEvotTR4ApYtmOvwaweDsGrxfAZmFTjCnG5pkhTkIXN3bQ31gTo+fGOGzAZpNCbJn/a9mxlTOJ7RVtpX07Sjp02nHnGDZuoSa7z8muqMp4J9CLAPQtJioggc9g6p0XGtFvEGAewZf6x8F6x6h42wI/DbxwA7gAxAUySlnaKpGIEwmAb/S+mJ2WNwo/I/ev1FjFCAV3Sd3zz2kpM8yJYBh3VPbt8GtXGOuYKFux4ArFxU3ihZjmmahEMhevcbQE5hMTPyDKY/s7TFzzru7IuYct53UYXiPDceHE3suWpD1TSMsMGjf7gNbngxyhcEPHsTnHc3pHY/yIcLdN3Hudf+mJzCElRFOBbThsjuk8bKbXutrwly+6azaFMlyxbMIxgIOBSpH330IcceM5G5H39EMCivB+rrePOlFygruCtq2dA+Fj+bqQJMDKMhc4WrNDDMxhldY8HV7ts+/K7AHOHGJVxaUxuKsza5a5UiwExMYueEyagnnYSRlsCA1Ba6FB4mdArI7YiGmrpPllfwf6+9EFOFdY7/C+roEb2wZk7Fo7Lz6Tc6j8DcRc61391wIbc/NIPhOYXOhtjQB7lbkp8d++vZfuPNhE2TUd1T2B8I8XVVndUGy7eqS6K1WZn0SPZH5IR32njbbVHrtmrJ4kbXzr7yBo7/Yg23r4rRoC8Wt1lA/u34C7h63ABOGtVTvpSKiPDNbLHJMEZ7WoUoZTjuFUQKqhaLmfzcEwRpf0cBeqYkcNNYP3f+9m7Ms2LXLxwKMfvlp/n4rZnc+/hLjMkrijCt90nrwBSet92GGTYQm6usqGVBwXhJfxQKBmSygpIJTpsN3GxSnyyv4NYrz3M0aA7qD0BKV+fPnik+zjn7B5hYByMhtWKJukaynZK1Pca3vdBEXTRFQbRgmxQChGlriwWKCWFAwSRBc7WQ8eB/HgtCCL644Sck6Ao6guw+aSzcWBlpcRHufLDHdnRuESecNZ13XnoGTJO7h5VA5vGNyjeC0pIVDASY8/pMxuYXO2UeErRxjnktTS6TjfTDf0LXG81/oSgUlk6UbhGqwojwVj5TG7vbRcPk06daVgZbihEowmwk1Bxu5PVL55v9ruXRNExM04itYBIK7NkCn8yBkqkH9eyhopKrHp4h00U7e4ewLLCR8GkKqX6dRN3VdgugcPxEdJ+PYDCApvs49lhpNj5pyvHc99u7CISldfaVF57hgnPP5Mwzz4xal/aweNlZ6hTLIcQRji3tcX0ojIGkgZu5YmszpblaY9f9ydrLnHnr9pkiXJcvbwyBIUzP/uY6gSlOP8efqe/Iilw5AuDdkFYtno8RaqzpAmDbet740y2wYVH0z5MymnzOmlUr2Fod6cYQCgVZu6TcmXC2UJbsIR4f0i0pQghLT9Tpl54YaY51prt0sRjUNSnCHaO5DbdiwdxG1957+03WLmmUv8+BsvOrJstsDqbpLu6JmuIsXvHgfymQdDZ2nJjb1zarhRv6KLy9b933xpMPYm5ofOiItssHAwFWLZqPX3M1bkJAv/SOPZnL+SOctmcXFvOPZ2dx/U9v5a9Pz2JMXnFEP/hVOWdXxko3Pes3sPNLAEb1SOZ3p47xPMelGszqnRpTExSvUBVB4YCMmJ83dJ2wNxt78/JpirOBCUSH+ZS2BN73wOsWJjxzxUm2g2dTBo47cxo+nx9FVdEXPMfwlMgNtuSb2Y2e51MV0hN1MnulHobWtRz2GmGnE0/xaQgEuUUyWU5x2ckymFUIVE3jZ3f9gbEFxc58vzivD8qq5nNvntrXYKQVsKgprmDTsaKxhC1YAYxKlYmrFCsjZlTs3tguz73p2CFcktuTNUsW8NnKJREWl1jeJqN6pkTwlMs1rYQHnp3FdT+5lX89P4vxVvKs0tJSrrzySqfMUChEeXnsvbA9rD3XzlwJuEKxYZrUhcJOyunakEwN8uk3+3l51fYmywI5T2RGWvc9tN9bPIofKZR7rKJC7m+qZ6+z77OtiZoiYvZzR6NTg9yOcIQca7BzSiagWZmtGpmwhCAUDPDOn34CSd3gmuhZ4mJhRFYu26qlVld/6VeEdn6JpumMLZngEY5letokX+QwC2D4pVPxayq8945zTWrfopvk7c0rUVcZ2i2JPTtr4BQrBefbb0fcWzT+GPgwkvv5racfhq2fwI9fi9qeyy67lB7DTP6wtvVvyn9fuZN+/5fA58+8JIUrTcGnKoSM2AlNoiJGe1qFKGUIIQMcdIutwb7mFWoUcLSfYPt+y98rd0ZfwDJzC1gbDoLqbiCmYZCW0QVdcQXkDl98TjkF1QQeei6Ckzu3sITLzpzC4k1VMguVszGZjoBbWDoJTdd5zdKgnWp/uXoHzH0azrmd4d2TSfFrjrZCCIEmBCHRQCPRHuPbXjjIuthjaiAZOmSvCXRFsnZ451m88R97IYCCay5AEYLPnprpXLPnv60J895vr09j8or5zaMvsmpxOXe+/w7+d//CoAk/de49+fgylr74IOFQEFXXOe7MaST7VHyqaLQmthvaOK5CuEG7QkgBbPmWahRhkplfzM1/fYy1yxezZXUFhaUTySsaR33IcMcZOC6tmnmr/ktd9qnRH/J5OeOzxzvP82ky25pX09eRcrJN+/erLJNPly1g8i2/obqykh7du/Hvf99I8KK/uzcveAEWv9Iuz63cupl//Xg6oWAQTdf5zaMvOkl2BCIyQ24MJPs09tWHyC4sIbeoxLJ0uJ9feumlPPGfJwkFA/h8vojMs43QDmNwICCD7+yiglZSIZcLWWroW+J/DFLA9qleq1QDa6fnkO5QTioCEZYCuaIIFMP9jpueWh5ae6Uk0CMlvhIZQQcIyJ9++inTp093/v7iiy+46667uOmmmw53VdodjjbHmgVj8oq47hd3M/+9t1gS4ztGOAxm63mSQwZssQTkf/zlj3w0Zw4j8scxOrcIw3RNA9FOo0KAVl9PghmpWbPPdtHWA+/LkGBr5GqjJ0DJKyqBDz9sXEATtqP9eyupWbkQtPEx74mFxFAAUWc6pOO66hUOW7HaxGhPqxClDFtI8Zq9/ZpKTcATaGX1j11dW8gRArKKJ7BhzXL4x1T40UtOuTt3bEd5556IRU4oCvuqKqXZ3b7W0dohq0/sgwK4dfJrqrNYSg2avNEbPPq3Z16l7w1XEAoFUPZVy3cG4MsKyvQtnJ+X62hLTVugEq5ZvmE94gIHUZckXSUQMtBUQdgwnI1LtdruMlwIRMNDQpxBCIFWX+dYS0AKGyEjGBF0aH/m0xTqQ0bEey0APViPVh+paRyRU8idj8xk7dIF5I+byPDsAsf15pDhIMZVBu26h3q/pmAETce0PTK3kFOOP5agFV/gBrxWcNd102RG1PEXxH6AafLJknJG5RVZ66QgEJJrjhJj3T/c+HT5Yv7wvWkEg0F0XeeKW+5m3rtvEdrRQFu85RMUIwSqitkgJXdrsXLpYkJBGQ8RCsGainKyrSysBf3TW6TRHdMrlYpNVY4bQcPvlZaW8sCzs9i8ejEnn3gCgwbFZt9oL7P+hc8s5U9nZNIrxUc4bCJU4VC82RzILR3z+pDRIIGJQMVjcbbWdmGYEUofmabEkzzF+balFLIOhr1S/fhamtDrMOKwC8ijRo1i+fLlAITDYfr168c555xzuKtxyODVvq5bXsHDv7uNQH0AkZaPOeY490ahoGqy+2NlxWkKf7/vN5Re82u6JukUl4yjx/BsixFBTjohREyfnuHdUxxhzamOcN0UmhKQW7IQRX3pmuF1HlNYik9XmbG8BQ9o6rl2G+SVDl/0bVOTHS2uCMGgLol0T/Hz8Re7PWZki7fSI9irllk5OSVNjmco0g9x5/btMpW551m6z0dOyQTHZJ3i1xjWLT74JRWBRWFnOn8DkUKdNYjDuiWzuybInpoAYwtK6DNoKELAfTfeystP/JM9O7Zz7JnT+c7UM5wywO5vYZUfBzv+IcBQazyFECzaVImw+rUmGMYJyosbw3nTEMiN1fRoisf0SuGrPTVU1QaxaeysV5ucPmnM/6oSRUj/9DuuOZ9QIMg1psGgkW4A8DFDpH/66LwiMguKSYgiWMcTvJY7+8A8qmcK26rrqKwNOu+IYp3+nHVaWIHgloXFjJWWecta1HlPMXbqg/JvR+MXaR7v6MP0e6++SNBqSzAQ4NH7bnV8kSNgrRPFk09kURvzcNmoCRqomooZNFAUleyiCR4Xn5b3h73/jOyREvV7OUUlTDvleNITdbZubcLvtx2H4GdvrOXGYwZTOqgrhinfs5pAGNOEbdX1PLZwU4vKcd0p3INZok8jEAp7DnSq1FQL6T7h1ShrTlyQy/6hKDJJSTxT1Heoi8Xs2bMZNmxYk6epIwnu5iSxcvF8J+sRb/8VkrrAoDwAMnr04udPvML/vfois//7equfFTJMvvimkr69+lsPd1c5xZSTLmREf9cyEvVGdneBAOGe/hq3LYbgGwXLKxr7VR9/4knMeXJt1Ghp5f2HIOtqsvKKYHlFyx4SBaoQhK2o/nhxL7B9J/2aNIP7VEH3FBmAaYswtq+Wpkb6eWuWn0FWUSm630+wvj7ygOIZEM3n44SzpjPl7Oky1bKQ/ubpCXrcmNgFUug3THvhlNcdVgbnpwxUU4VnkbXKyMwvYmTu44RNqLMyyJmma84DdzGPk2a3O7ybrxNB7tHkNOzbeIZtmvW6Fwkh3UZUK6e20x7TNc0KIVi1uNzJtGgCNfv38sT0XDSLuSYUNhxB0GsCjkedunuoc8dOVUQj/0w5s20GAduv09Oile+QUnQa+30ZEeUP+Pxtrv7zg4zOL5baxAihWPZJPEyVhnWwrUWioZbYeu/ra2sx1YPj4Ojaqw8bGtShLV1hu6qkJUT3mW6FqN2Gp8fGv+ZtpHRQVzBNDgTDhCwTyv1zv+SryuYtHnd+Z2TE/m+v1ZoCQeG+k35NEAxb6coddh2TBE2gKwoKhmUpst9JMOwDXxzMvWjoUNn9hRde4MILL+zIKrQrBndNkr5H1t85xRNQvMcjj20vrWt3RucWcczpU9H11p9ThKoR8KfTJ80StrybJG6wR0uRqKuO1jHZpzXyuxICuib56JfePBtCRflcxMxfO39PM5fyvR/9mHv/M6vxzQeqYNX/WLe0vM3LQpIG3ZJ9juDgaCSB0T07NhhHVxUKB2R4AtTcVnq1xzY118CMJCfrm2a5SYzOK+I3j7zIiVMvdgLTvBgyYhT3Pf4y19/2e7ILivGpchx9qhJHwjEUDsiIEFwzEqXPWcPDjGJd82uKG+3saNa82mIaCT+SR9YWNuKj7YcSImK2R2rjNVWyQsQz3CFqHJioOmPuCdDDsq4IQa4V42EHryWnpsu1S3FXPi91mm4FrsYjL/bYPqmkJ+qAoHuyy2DkWESEiJj3Wb1TnTXl+LPOR/PJ9U8L1nBzYeOgzHOu+bGVQMkqA8+eYR+yEHRLjhEQd5hw6nkXOG1RLa5/RVUl578HQijoPh+lJ56Goh5cEO7uHdsJBYOSG9gIs2rxfHyq0qYDdlNLjm0VbA692tkX1wAMy54cDBmWRr5l3504uAtjrIBW91Al551qmTB8qqvg8FnxELa7nAASNFW6DHoO8YjIgL14RYdpkAOBAK+//jr33Xdf1M8ffvhhHn74YQC2b9/etEniMGHnzmgJPSIhamqpqTqAUqMxcNBgfvjz2/j7fb/BNGT2cxvhcIjaqp0MGjSYn93zN+5rLPc0DU1nd71JNz1M1a5vqN1XT13QQPOrmCaEVIWwafDN9lDUNLvJxx4LwAGrX9NNk1BdkN3V9Qzok8r27ZGpMLsaYRLqVfbXS872nTt3NirDxujMLPQdv3ey6eWPHk5d1S4GDRoMy75q0GECTdMYNiqTmqrm+zcaEk8/ia1Bg7rKXQQNg331PmoVqWWv9tfRPKW6RKz2NIWGcyJWGfv27EVTFIK6wlZTst5X766mpj4ktWcIdA1qEurZX11HTSBMXTBMfW2QoBAMGjiIy37wM3JWrODx1V+zJ6W/03c/vvVOeg4aRG3VTtBVQopCvarwDfupPkwMDk29G94+2bdnL6ZhEtJUBuipbN26l+rd+zhQHyJscSWHTZNvtodI0BQyDIMde/azuVimn66p3EXYMAmbJoGwgaFIo50qhAxwQlAf8hM0DOpVha16bdR6dEQ/eNFedekhTD7Zs486RVBnuRBIDbygVlHYxYGDKv9g0JK+qN69j+3jJ2CYkX2xa189+6tqqAmEMUyoCvsxTdiq1VK9ex+1wRCDBg7mtr89wpqli6nZ8Q27evejfu9OwgYgIBQ2UHRVWi1UBV0R7KtTUQ7o+OsPDe1htHFt6Zyo3lfHvn0BfP40tm6tAmBPTZD9ew5QWxvCME2q2E/IgD2+Oqp37yMUCDFo0GB+/ddHWbu8gmHZ+QwePBBWbY4svHYvdXt3YSiCkBWYtzfkJ2TAgIwEtlXX0Ts1AU3V2Vpb1R5d0Qgt6YfBgwdz298eY/WyRYzJK0ZTBBtWLWFETiG/Xu/ed/xpZ3HM6B8yKjuPrxP688auttfr81XLnFOToqiMGJ1JbdVOBIKtW1uewyDdykq3dWt0rWwPYVK9u4Zqmu6LQb56fjmhF/fN/8a59oOiHvRL0fjlnG0tro+NsGGydds39EjSsJOcvLthH5/tan5tCATqCezdiQlUGwn4NYUh3ZL4ZOd+AiGD2mAY1a8RNkzqFQiEDUIhCBoGigKBkIm/TgOfyoG6EIGQQSBkYKoCNJXA/kr2aSrb9dq4UeZ40WEC8ttvv01BQQG9YmROufbaa7n22msBKCoqom/flnE8Hmo0V486/wF2sY8Un0p9yOS71/0QX1Iqf77jFryEb8cO70lSlx6oIZO8icfDly0negcwe43AMAVDenUjo3svqrValFCYZCs6W1cVNFXQu09XyVbREHdJonKvrkHbV0+dv4a+fbu0qA7pUcoAmHTCSfzxyZe5cZFscdHkE9kXCONTBPBVxL0JSUnc9shMhozNJ8WnARtb9GwvttxwM9X1YdITNMKmSUaSD00RBMNmi9sCRO2TliBiTsQo48t6P5qikORT6Wtp9b6qT0DUhTAtk2mirtKnbwZ1/hp8gRAHAmHq9gdQFMnKYGIy/tgTCA7Yzd8//oqBXZIou/r7rFmzhj0kUlA8jlS/5gQE9uyZ6mREPByI+W54+sTuB3m/HJstoUrU+hCBkAwd8akKvXt3IdmvUR8Ks93Yy+bv/QwhBCmqIGyY0tctFMZnaY80BUJh6WrRNSOBuqBBgq5Ejn8bx7e1aNFa1Y512RqqRFeFjEGwNI7dU3z0SPZZWsmOQ3N9sSVUSfVPbiUYNhnlGSujqpa96gGoC6IIQUZaAooQ9O2bztehSvT6EIGwScGxJ5J/zBSq68PUagpJWPMAqA+FSU7Q0BSBT1XxawpZvVNJT9DQDpXjY4xxbcmcCFXWEPDXR8xZdV89lcp+AgcCmIZJRtckQoZc174OVaLVh6gPGeQdM4W8Y6awrz5Eok8DXAFZPPcT1kwsZeeGT8gbN5Gh2QVoiiAjI5GQYZI5qAvqjv0M7JJ4yCkRm+uHjYEECo6dQtbEExACMhI0TjjpVKrrQrB+oXNf4YnnkD+kK0LABSdNouv8ZTz5ZdsELHPrOkBq6487czoFx04h3XKT6Nu3W5vKbAli9cXmYCWFWip4BOTT84eyty4EtF5ABvjZ7K28cHGBkzXxmTUt8z2uM1SSuvbEMEy6dE0iyafSp0cK28JV1IUMkg0Tv6YQMuQeFgwb1IcMAmF5WNfCBmlJPkk1WxuUcRKBMD5NIcWnUaNCRvde9OmTfujeyYNAhwnIzz///FHlXmHDNs24CVIFZ114GQOGj+Ke8l1sB35/2hgGdUm0zM1mm0zBZr4MTuprJYDwmlZtJGpqq9wsbL+g9sDYgmJYtACQmlybG3Hy0K58+MUe5z7Nl8Co3HwZmd3GZ32zv54ky00l1a9F+ErFD6JT50kmAtk39gm6V6ofcx8yoh13XAxTIBRI37MB/dmfsnnHVzxlGghFmhvveWwmJeNKW+UvfvghGpm5hYAkXSMYDoIpSNCVBt9wqcrcOW467hqu/6pMkGHzbfbvYO7nwwV3vrs+yF2T9A4XjlsCL/dxo88s/32fFeXeO9X13VesqFYFT9wBRLwvrknX7ZdknxqXG7GNqP1gt61BMiGBtJzYzElO1tMGZai7N/HhG5swQmFefvRv3PHQDMYWRKZQHtUzpf0b0wZ4XT5MpKJnzbLFvPric9DPTQZiZwr9dHkF78x6kTnvvgnXPNnq5/2lWOUX/9xIyOJbLjtzKu5a3TGLqO0eFXGtnWrTUlo3L2wK0gEZiXRN0h23togEV7guFRDpLuey0bhlyjXd6y4XnxtWh6wUBw4c4L333uPcc8/tiMcfUkj/OEUKAMLNIpNdUELv/jIYcce+OjcoQxEO52tb0Dfd469m/d8m525S8C4rk/88SPKprRPWo5Rhw1vO7gMBR2i76ZghfGekmxbU5v20P/9uKDaBeiycdP10yq6eijdlM7RBSGyiPQdbhl2vhoKhrri+WLYfYJckH/3TE51N0e4bu4xPlpQT3rnRiew2DYNwMMiqxeXOBtM92d+Y5qyj4OmTaGOT2SvV8Tcm4i5XIJ5wxXmUXnFuRD84whAuPZzD1Qn0bpg9sD3Gt73QjnXxCkwND8nxjuw+aYy54AzGXHBGo88cdxFVHh5twdYJUBOuIFN29VSOufI8d354hEk7+cRhOTi28xyz57gqRIT/MERSikYcBoCZlxYyJNkkKVhNz/6DCDkpkIOsqSh3kkfFm1Xb2wYBrFm6mO9feCb/nfGUc8+wFJPcfql8uqKCX109jdmvPEOori5mmU0hr6CY2x+ewSU//Dl3PfIio/OLHTqzjuobm/Uo2vWDgUwxbbK3tuVuI97gzQRdQVMVFEWQ2TsVEBHvomJt5Lb8Ye9F9hzVFAW/pjoHPl1V4oZeMBY6REBOTk5m9+7dpKfHb4antkIIl+v1QCDsXEPgBLilJWiOhkNu7gpPXpDXpuf1TvVokO1J6alLa5Di1yjon9GmejREw2cLIWT6SUWJOMU6/PTWSzW2uAkC9Si4vnSg079WSa5Wqk01PzSwAyAbLnKK4m4GDbX9wtoUG47tmMLSRoEpqqZZ9G6ynMFdk+LSp8s7P20k+TR0VR4Uvdoj5zu41GXeg1/kfLcC0xpo2b4N8L7vdv8dKfBZ7C7RqixwmRy8B8u8fulWEKdovObJHdqdE1jvmGejjlfEOtxIukZVsg/hWlMKB2Q4wVHOfuIRLi8bahL495Vs/fIzT1kKWcUT8Hs42eMN3vmwfOE8N9X08zfDO3+ncPd8kjSN1RaLiWmaYMbIWNsMnnvo7wghuODaGxmTV4yCZBry7iOHG10SfVEFZHGQL7adRe/al1a2+DtnZPV0gkS9SNRVTwCse/iUCkKLVxsi9jxVcQP6EEhmC8/+F4+IX1vTEQzbOhJyaKfkRLmooD+/mjKc4T1SnEU8UVcRAtITW+/tkkTAWejA1azZi6QUNA5/yHa/dOkveMroHgy1Ult7/xme3c7O9mPXfUxuUaueNXFwV8/mKMsZ0yuF1AQtrlgMhHVA8poyhbBNpO7fEd9Bzg/sBcca15G5hRx31nTnC0IILrr4UrLyiqOWE0/wcr16IcDDlel+6l2bVUWgKp4kKrhaNLsPXe1PHHdCO8M9ELqR4r44diNoCJ+qRKxjNoSQArTWwMSmWtRn9iYtb5Y/HNo7cA5cflXSTEU7hMYbolJsCtlmIQTdkn3k9XUVS7ZgEsngI4WpT5aUEw5FagsN04igkou3/vC+0wIoGD/JTTW97VOUT+aQmtEFRQgnU62iqqjR4mxagMf+eh+3XXEOd/3wctYtr0BV3MyUHbWEDOue7CjZbATCxkELaxc9s4xguGWUeLeUDeWZi/LI6ZPuzrGGChw87Dme905TRMSaHUFhCBFrfLOW7g7GkbOKHiGwJ5Nhmuiaa+4VSHN6Vu/UCC2Pz9nsWz9J+qa57hXYk88qR1GsQL2D8d9oI7ok+chI0LisaAC/PWUU2BrAKBpUb2IPr8mwdfDSOskFbli35LiiuLI3ey+jiL1R2VynDZsu2+JSXAlwTFhlZ0zF5/OjKAqKqpKfn+85zcfvgtMUNEXgV6P7KAvnfZH8yHg0F7Z22fZz/7Yhq3cqST7V6Y/CARmk+DuU4r5VkJqlxuuUIiz+1KhDaifgsTTNnsOiXZItEutWXEg8b8QAyf7GLm72n15NnFd4itDgYc1/ITf2rMIJqFqkH7ppmqxZXI5AWBbD+LTi2sJUdmExP/vN7xg0bCSKomCaJk//6U5mv/IsqxbP5/pf3M13f3ALdz08o03PMU0DIxymfPbb/Pqq89i4ZqmzZ8bTbKmqDbaLwP7g/I3N3vODiYPJ7pPmuroIV+nnhbc+ihAkaHINStBVRwj2a25WW4G7biuen5m9UxsdCOIFnQLyIYAiBDsPBCzWhsgECHIjtzdz1/zXlvkxvE/3iL+FInPZK0KyHuT1S+8wM7uqKM5LYms4omlKM3tJjWrDAJSW4rOVSyJMzO7zRVylroyqGbL+sxlHBK7G39747DSzdl+uW17B60/8E4TgilvuQigKhmHwi5t/yoZVS5zvxitsAb6RttyyuylK40OU90/dmlearRG0tU1CapZjaeOPZiToqqO1OdrabQu2DS1h9lqqqQo+TYkcd+HOGTvtvPdfvKJrki+qi5ureWv8HVsXF6mlk9dG5RVxxyMzKSw7SXJFW8G8Y4tLXaElDjtEEcIRmNYsXcxffvMrNn3xGYZhYJoGgUA9j953K0/9/fc89LvbyCmZGFWAay1CwSArF5U7loeOUC7ZaDguTSk+knSVgn4tO+iUb6xs9p6JVhZKV2El8Efhmm74Po3tk+as7fb76Fcj301XkWFdQxxy5pSDwZGjZjiCIJNtqKQn6nRP9pHi1/h0x34ZcGW42b7se4kiMLQEfT1JOwQCVZjuyaw5wfj881v/wFaUoSqWi4eVNc1m9/CaV6bl9OH0zF4RGmRFwI8mDeYfc79q9vHi6R/xybTz+fqkMyICK9qcCOAQ9omuKo0Wgpy+aSz9eq88MAk34MGGvdlrivTrWrN8MbdbqXVnPaJTduY0SfpuGAQCAVYvLie3aFz8CQGePrGrlhDFJOpqwSJdg4SAXaee7SzI0ryuOJHsTpm6y9oSdfq3x/i2Fw5BXQREJJk4YhCjL+z1IhpcL2N5aNp56lnUhgxnHTEs6kQ7acxhs6ocinF11s3Ga5sQMqFOMByO8AWVLTYZlVvET/78KJvXLmNtRTl54yaSmV8ct9p0+7CrIEjQVZYumEcwGJB+xjZMEyMcxjRNgkFYUzGfkGEinnkW87IHWvys8dpWFqmqk61P03UKxk+U9UCQ34Hade/o3HrCcGyLSTQYpsmPjhnMc0u38O76gyCEbvBs4fld1xo/3CKSidhvXIuHO2+7JPrYWxeMsHYkWHthnE5DB50C8iGAImyzufD4j4EKBPGayyJN635VbvotRd8GUfq2EOrXlOaFxBtuaEWLWl+GZmn6bDoum60hQXPb2yvVZVpQsHKyhwXHDu1Gil/jd+9/HpuW5sAe9OrtjCks5avicfhUhQRcbX17t+dgy8jt23ixtX3dbI1Xdp+0CK23dXTCp0m3Am9q3VBIfq75dEJB8Pl8jJt0jPO9uIKnTxQh6JueQK+UxoKcrSkcmJFkcWK7fbDj0qsczZit3bEDGO3N3q+65ryo2p/2GN/2QjvXxfblG9w1qV3LPSyI0hcRrgPRNKceDaiCydaLr6Q2YAvIJoZprcMeF6WDOjy3FO08rukJOt2T/Wyrjs7SYGcvqw0ahAwasd7YLASZ+cWMLShBFa7GzjzkndF62HW3Bf/C0onouo+AUefWVwgUVcU0TXRdp2DcRGqDBtqDf6bl/Azw/WmncuqYVyh/+yVUReHU8y4gu7AkLtZPrzCc1dvOZCechEhehA2TRF1rV1pLr9VXCHkIbXQPAuEwdXmuIWWesMwsTe9UP0K4bFaaqjgKkjj1rHAQPzboowheE7BzDZfn1utaAcKi8BE8Nj3XiqBtGfqkeVOSygXFr6moQmleSKypkf8OBk2UYfvVAs4LY7sMnDyqB2l+jZE9khyfuWS/JoUea4Uc2yeVJy/I49nv5kctPyEphdsfnsHI3EK02hr0uloQUisbLXPgwbbnUJXhpWnyNQi4tIV9zfLRzB/nBqVous5xZ07jnsde4qLv38Jb/3uHPGtxjzvtkKdPMnun0jvVH9XCYb8PPT2fa6rCmF4pqLW1qHU1ES4afg/nsZPKVEg2lrxoJsf2GN/2QjvXZVCXREbHCZdtqxGlL9ITdJJ0zVEmRNOcgi1QCbS6WlKN+gh3JCksuu/XYXkt2nlcfZrC4K5J0jQdxdXEhmIdGlU8hwvbqua4YbifxbNgIg+6cr/MLSrhwedf5ZyLLnfSTvt8fq7/1X1cedMv+Pszs8gpLLGsTsCsu1r1rFF5Rfzgzj/y47v+SE5hcUQfdSgaaGXtfzMvLWh0qwERirb2qoC9F4PcgxrOvcIBGa7Vr8F7pikCn2bPWWkxtPezZJ/qHIRS4zxWIr5rd4TC1iAL4aUx8yY7kIuWNAPKQL1A2CRBV/nBxCH85aMvWvScfpYGuS4YRhFSI+n4rJrNvC2nnip/zpnThhY2X4at9RRCYJommqIQMgyEEPTPSOKR83Otv637VcXyIfVSepkxX3pfQhKjc3OpCxmUfv8SFCFY9dxrbReOm2nPoSpDEZK8vaEJOMmnkd0njcWbKwEpAI4tKOaeR2eybOE8sksmMCavGL+mMCK7gPGje/LZTpnCenj35LbX/1DA0yfN+ZtF27iTfBqjr5gOwLoX37BMsK5/qeZ8KbYZsmE9OhztXBddVQ5u7nckovRFil9jdM8UFm+uiuqn6Iy4pWXOvOoCTGDRk68iLG51XYgI4UJTFM9cOXxtaQ/EWgft5qiKcJKGBCzXI8Uj0tgWBq9WMB4Do4S1j2lW4qhkn0Z+8ThyCksoO3Mqr7/4AgCDR46muGQ8uiqzuK1ePB8jFIYvK+Dx6+Ds26Frvyaf9dnKJYwtKMFVZXnd3DpWuy6As8f2YlCXJOtvmya18ZiZptluWu+0BM1j+XXfr1hKF1vGsYM9bQVGQ+t4nzQ/GYkay7ZUywQh1vvYOyO+kzkdoStqfEMRgpE9kj30S/ap3QoosoLppAO7NKHbvlclAzP46bFDmn2GQLoogKSA0VX3VAaxF9TDBanNdgOp5EvhDVa0XE2wXn5v/2BvgHJR+NUJwxuVf/bY3lJD5GlvPPL+NgfvBt4QUqPscStQFXKKxnHeNT9idG6RcxCzV0f7hH4kZFBrCHdexBhDj8bQ9ttXhdRMqNamZs+h9MOYXrsThw72nCjon063ZF/EZ25QmkdLTORmnuKL9EnP65cel0JhSxBrjbAPRTZFok9zGW+E9UV73ZUJVwAEQ7slMzRO3XG8e8Gwbsnk9k1jWPdkVCH46M2ZvD/rOX511TTWLFvs9Eu2RfkmhICqbbCwCVaLmr2Ihy7ndzdcyPqVFRH9FS/7pyIElxcPYNygjAjXsmjro+nN5nEQKOiXzr2njgb7WYonOY2I7p7kDcKXf+P8VIQ3DklaBOzrIvZKH1foFJAPAYSAAV0iFx+BtYhZv+uKa1K3N3dbmMztm8aNxwxu8hlnZvVyFsdg2LQIuF2BsaPN7PZG5Sw+wn1pRIPr4L5gHnnPefFyGvjv3vmdkZxmB/c57e34Ra0tcDfwGCd03IVREXLerF9RwcuP3c+6FRWO5kg0KO9IREtNm7bgpCjCsdTYi+6oHimNM+h14oiEV+vZELl905xscPJjeY+uulpSxXOATvUfeYdGL7zChhc5fdLokqTjaOwQEemn7e+qQr4rmrVW+FUlLlNuexUG7j4g27Z84XxCwaCTEXD5wnmOxXFMnrSunXDuxWg+X9MLyb5dmAf2EAoFWVtRjk912Z+8boEdCVuhpnhW9Kg0qcBVJQPaxS0kp08q3ZJ8rkIP130iVtldknQyEt3Da6T8YTFqeQJl7b1M3hd/PvANEX9vyFEAr4BnT4GIbEfONY9zu3XdFiBLB3Vt8hnXjB/k/G5gOqczr9DYkYhG62ZvZN7DQCMh2hGg3esNF4Ute2udPnYXkCPlTNoYTSm1IrUGgk+WV3DHNefz3P1/4FdXTWPtsgp8Fv+k94B0pKFvWgJpCXrM+tsykKvtacz4caS2vROxUTggI+p1TXWtbu77I10xVFu4sNcQAaN6plBeXs59991HeXnr09l3NHqm+OmS6GusSVdc64ndF95skvb74ve4vMXze5KRqDvvtjfgWiAoGD8RTZcxGLqukzdukrOnCAFj8ou47rbfccfDMxnVrYlD8pY1CCHQNJ2xJRNItHzdhYDRPVMPLtC7nWAnQ7Lh5ST24ufHDePEUT2dNfFgYCftcoRYIjXD0cTZod2SI1z6RvZIceKNbOuP1z0qHvq2Nei0RbYzvBPAXrjTEjSGdkti5dZqz0bvnrCEMFHs67hCYmvgnPqEjHKNh0kYoQWwXzjL79oQMvrc8cEVHi27Z1G0+/DCvL48v3wrABmJvoiyHfN7HLS5teiR4uObffUoIlbwjfRVB9nM5Qvnu0wWQVi5aB5Z+W72wSOxD0C6hQgBe2ujx6En+VQndbsiBIZFaSjhCgSd+PbAazmwN3BVsfxxTTeFtQmUl5dzwgknEAgE8Pl8zJ49m9LS1qW170gM7BLbHcJ7iBZCUmjVBMKOIsIUMomDHfMhEHGruxvaLZlvfPV8tnN/o/c5u7CEOx55kTWL53Ps5DIy84scBYquKAStGJdRuUVMv/xa7r1+KqEfvhRZyOwHUda+z5TzLmbCaecxOrco4pCVpKuM7pVCOCZ90uGBTLoRlGudKde6FL9KZW0ky5XN1iKQQvXBQJblVTp4ktMIETX9dUOkJej4VIX6kBHT+uPKSPG/YHcKyO2MHik+UvyW6Q/JPiCEID1B+kcJD/2bIiTrwpLNex1NgH1Oa0qr+LvTRjewtdm+u6LZ7zq4/PI2ta81ZTjZrbzsHZYJMCzkeVURMojP7hO/qqKIsKsNt757dnYfThndgzU79pPb183yowjB12dPJ9nXOAtVe7fnUJTRPyMRv6awsTJ25LvX37Jg/ESe8ukEg6DrOoWlkxwqK3lPm2t+6NCKPok1hOoVV7Br9wHZB/0zWLSpMkJrbM+X9qpHa1FeXs6cOXPIysrizDPPbP4Lh7AuRxza2BddE3UUIRjcJZGNUy90UtgrQqAppqstNWHOnDkEAgHC4TCBQIA5c+YcGgG5g8bVtkB6g8LtYCtFyIND2M5aGo9rRANE03QrAkbnFTEqp5D+GYmEDEsIQ/Kih+oNZ00YmVvI8WdP592GBVduRREmZWdNY3BWvseE7gqD8ZC4wpsfQVj7u64KlAarnIkboDd+UBe2Vdcza/X2Nj3TtBRr9lrqdYdsLTuUrfhqCO96fSSgU0BuZwghSLI4XPP6pUe4GXhdBmwTkh2Jb7NP4LwYgn+dO5YbXlkdUf7wbkkMbqhN8Cx8uqrIk2ZNM4yQh0EYFFajHS2y9YuuKgSNsKvxbpAUwqcq1Iqw0y6wfU4V8vqmRyzyioBvzruIVL968P5CHSAgg6S6ieUj6d0khJCpV//45MtUlM8lf9wkxo0fz/56qVntmqSzvz4Ol54W9kmqX2N49xhUZZdfzp7NlRHuSd7gLPuA1aTe5xAJL17tpK7rvP/++80LX50Csos29kX3FD/dLT7t3edfRNgw8VmCjs0xLrWlJmVlZfh8PkeDXFZW1m7Vj0AHjav0T7bfgUhBJxJHhok7Wt3toG5DcfMIFA7IYOHGSselRgHH3lZ25jTeXda4bCMcZu3icgZn5TsPiRt6NwuudURCcwTlyPvqg4bDfa2rChcW9GuzgFxZG/RofE1HgywsOaXV9Y92HdfikagrEG5TVQ8bOgXkQwgvq4Iz2fD49Xgme0SAgPVylAzs0qhM0/N9kCThts+PENAtWadfeiJ0a6Zyu6yMO927N33fQZRhLzqOT5z1P7+mUBt0zeUJuuoReMxIH2WrLCOiXOG4pCAEyXv3oGsqRvfmGn1w7TlUZSToKqNicNg2XK8FgpyCEoZlF+KzsvPVBKTmpE+8Bqe1sE+EEGTEYuDYtYvkvfswrDK8ga3yu9I8awduHUw9WguvdtL+u1kB+RDV5YhEO/SFvme3zFLao0cjf02fplBcWsrs2bOZM2cOZWVlh869ooPGNUK4MwVJukooLEVFe3/A887EYY4QB/aB1/7X8DPh+R0aKBE810bnFsGyCvfD2mrEzi+k73FxqaOkkt+LvxgWgbQ4i7CJbgX0N+yPytqAyzSBFKTbirDhdXm0rdwCrZWap95pfnbsw3GJ80II8GsqQ7omkaSrbN++r+0VPgzoFJAPKyy3AMd04kJVXH/kWKcvgAvz+0V8L2gYJFp0Rq16N6ZOlT8Phq+zmTLsNtpZeGxzZ4Rp3BKYvRrhCLO546bRUAPv3j/2R1chgPUz32h7W1rQnsNWhgeKEFZWItt32zrRKwo+VbFceOLcZNVO/ToaMN7/AMDRHHnnUtckX+zvt1c9osCrndR1vWXayUNUlyMS7dAXw793OaYJn81807M2CHql+ult0WGWlpYeer/jDhjXXql+NEWwrbrO5T1GOKxJmmKLf2ZULWQ8QojGAZrSVURgGqYjvNnX7b/txFNCSLaf8VvfYUHfkwC4vSiZT9Xvk11cypi8YmqD4QiBOlZAaEfA1bS6VlWQbfWpCgEr467pGU876L2t6Jbskxp4S0GnWAeIqFlJm0DXJB9dk3yOEsyGrQTL7pPW5joebnQKyIcRApd6yLD8bsHVGHtPs9Eknn+eM1ZOYuGakoMhk4wklfpQfATmRcL1DxMC0hN8VNUGnNO61wyYnqBTXRdy3E2ka4bptFV4cr4LxXX+Fx4fLC/dzNGCrN6pJOgqX+2pYdeBeucQleRT0RRB/4xEvtlf39HVPCwQNLTKuJ91pO91qUc7mZWVdUQFfx0tsAODQK6jdooMVRFxSWfWnkjUVXqn+tm+rw6B5Wss3Lb7venrhRRgEvX47pOGe5lfUxxhLWyGI+7pk5bA5so61y1BwGcrlnL3ddMIBQIwrQ8MyAEgq7CUtRXl6IrCoKz8CCrAeIJzyEN6Ifg1V0D+69lZ/Pbd9WzbV4+qEBHI11ZNyS+PH8bY3mkoljLGEcw1mzu79Wjoy62pipNQ5EhBp4B8GKEIQVqCZgWmuSdWOa9FBFWbLSz/bPJQ/vShzKy3Y3+99LnzvNFBwyDFpxEIB0nSNdLiiO/TcbFQFdISdIZ3T6aq1seqbdXOYoalER3aLZnKmgDb99VTE5Abna1BFphOQhHb7cLuAr/n7Y27DHLtgARrkemSqFMTCLOvPiQFRcuMbM+ZOLaYHjLY7429mHckbO3k1q1bO7Yi31L4NYV6S6tm+zh29Jw4nFAVQapfR1cFO/cHANkPyT7VYTew/fSHdov/dbLhgdenKeT2TWPel3skW4Vn1euXnugIyPaYf7KknHDQisOZeRsIwdxzL+SjN2YSCgaZqevc8fBMJk6cEPV58QBHG45HW64I+qX5+c1JI5m1ejsnjOjh+J8riq2Saj2OHdaNvbUhK+jRjOiQ9nQ9aS07V0cjvo+RRxnshTvB41JgXwdQPYpj+2fJwC78e2o2F+b1ZUSPZLzcyQBhA5L9GgIY0yuF1DjKIibbqlovsFzMMhJ1j4uEayIDGXTTO9XvulkI27c6UqMuPN9J8qlx7l/QPkhP1K3sjI2FwWi+et8G+DU3UcS3sf2diA6Xmqpj63E4IYRgVM8UhnZL9ljmIum0hDgy+iTFrzrvtheqZTlMT5BKIG9bXHcEKc5lFpai6rayyETTNfbu3iU1yqZJMBDgwzdfcoI5e1rBnvECRVHcuBzhiqg+K6V8oq5ySVF/JxsvYPF/t+15Nke2rXiRSijBt+2g2RDxI019C2CfcBUReSbrmeJn5/6AkwY1giBdSB7lU8f0RFUUZwHw0vnYPrzxdjoTAhJ0JcoJVEQGLXrusJOEqEBYSD8oE9PpM+G5R3jcVL4NQrJLfRS5aBX0T4+7sT8cyOmbxqpt1YQCYQr6Z3R0dTrRwXDM0paLhUAGq33b4PYDGGbk6pukx/+Wn+TTovqpNqR+a7jk6apCICTdA0bnFXHfEy/z3qsvsmfXTtK7dmfv7p0NCpQ/fKrSJM90R8F7oHG3Odcn2PYRtt3ufIpK2DRilNY0tuytI8WvRfXtjrfgxcOJ+H9bjiJ4XYS8L/fALkkoQlCzJ+wxEwm276uPuN/WnArhmtRNpJtBQpQTd5P43vfa3pAWluE1dUayNrvMFvZ9NPg8QVepCYadMuxEGs4hA5lAQwA7L74CEzho1//D0CcHA1URjOqRyurt1fKCs3jG+QJ2CPvVMjQcvnq0F+KpLh2Ndpofoj7EyB4pJOoKy7fu7ZigqzgYV69yRWZndTXImb1TO65i7QBv4oqGrD0+VaFWMZz7xuQVETZMbr/6fELBIIqqomoaRjiMquscd+a0uHDPigVHiytMR0iVvuXuPbZiTCAThYRDgr+cmclPXl/bqmd5+ZRVRSYqSdAVJ4nXtxWdAvJhRGRmmcazTqYJVQiFpWBYWROUDBWGR7NMwxOdiU9TGNvayNDp09vUhpaWYZpmhEDvhR1AIpkZhOdQIKEIgU8V1AWJ6C+7/U7UsmVyqzzz3PahLTrEfdIeSE3QIgIcjwgc4n5tcT8c4rFpFeKpLh2NdpofNlGiXHs66O2Ig3G1uZ+led4N3j5i1osmIVuR5FPp7RGQ7fVQU6w7pI8eqyvKCQVl5lEEnHjuRfTq15/s4gkMzy6Ma+HPpn5VaXzocQRjIVCFdDezs9C2RRuuCuEk5VKEwKcpTuBrPPfRoUangHwYMapnCiu3Su1fdF8hmXmvPmxIQVBxtc6uQGhpkE0TwzRRRetJvAHYvFn+HDCgbY1pQRl2XaM4WEh/a0U4i0DD7ymKQFGkv3bIMAgZrl+y7aKhWwKzb+sWGbUehTe6Pdtz2MpoBkeUcAyHvF9bvIAfhrFpMeKpLh2Ndp4fQoiOi5aPg3Ed1CWR7fvqCYZDmKb7chwNfvoynsWNabFhbw2qkMHuhkUFl1M8gRd9OqEgaJrUGo8tKJHfsayT8coJbdO2uTzWrouhrSazr0ttr2m5XbTuOXefPBKfpjoaa10REYqtI3/WtB2dAvJhhO3jA439emyhR1cFmuUcr+AmyrD9kbxph4Nhk2RfG4fwkkvkz4Ph62yijFS/Rq+UBDZV1Vr0PJEuFZoqX2ghpF+c103AXriSdGnmqQ2aThCB4vSD7CshYMhN18vy533c9rY0057DWkYLcETtdYewX4d3TyYULafpoapHeyGe6tLROATzo8PcjuJgXLun+AmbMlGDnT44PVFnQEacJhJqBWIFjTmueIog0adi1EuO48z8In7zyIusWDifzMJSMvOLHerQZF2Ny9gdG/aWqSlugmnHiqq42mSEfa9wrt1/dhYPL9jEqhYk4hjcJSlC2NZVh4j1Wx8A3SkgH2Z4/cGifSa5K2Uaxv0BV1UoLGHZPkGHDJNg2IideayDoakKA7oksrmqlhE9kknxCPK2sO/XVIJho5FfnP2yetPFampkUJ8qpB+zIgR+yxz0bYGj+fhWkrtFIuFbGITViU60FK7rgYjKDHGkwd43Y618irDbqjia4TG5RQwdW+hx+7N4gy0JNB7X0b5pfqpqg9LCagnDibpK3/QEVm6tdgLbXTnComdDCvw9UxO4+bhhzPl8N48v3tzs8yIYLISXFUmQ0/fISezR3vj2SBVxhtinYBls4NdUx8TipqZ2f68PGQTDBsn++F70bO2x4tUge/7f0IBja8ntl9/uJ81zTVPtfrAXuG8XVi9dxBMP/IVNa5d3dFU60YlOxCkcrWoDC96RjIbxKjYye6VayiLZXptKVbF9dYncPzWbRi1Ou6VLkg9VyDTPCbpK92Q/AzIS8ame+BPRMC7Hlhdsa7TSrNXg2vEDASyttLsze90r2uTCeZTg29vyDoLimXjRIIRL22bzGtqioD3xhQBdUwgaB+FicZgQS1MOOC+2FxmJOqN7priLm21SE+7JVpqAXFeLbxPKy8u54cJz+Pef7+Xic06jvLy8o6vUiU50Ig5hC4BjeqXQN+3Id68Adz9siCSfRqKuOp8rigz0jqBMxVXAaJZvQjxvH5oqLbGKgB7JPhmgHSHse+UJV4Hk3Rd7psTOLpuoKxw3vDvgppZWFbdseT2ee+jQo1NAPsxwXtYG804gXIHQuhbJeuH1QRYkW6ZlbxrRIwUjeiR79MeRHSGEsBKfuD3hmn1cE9q3lcR8zpw5hIIBwuEwgUCAOZ0+rJ3oRCcaID1Bo0uiDyGkRVI5SjQJPVP8VpB69M8FApf2zE3PLIQV+G0Jx7oleErGpPjcQ4d2S3bqGEGT6lGcea2t7mFANlgA6Yk+nv9uQdTyTdNz2BDSjVFVRISA/W3bXxsivtWPRyGGNZHm0zYf2YKhqshTnX3NNRGZCCEwTbPtL/dPf9q277WyjGiLWVqC7rY1xgvove760dkLnsCw6ItG90xF/9nP2keVfJj65GBQVlaGz+cjEAjg8/koKys7pM9rF8RLvx7isWkV4qkuHY2jaWzjpB4Jusqw7sns2RTo6Kq0KwZ2SaJbkg9Nbbze2/uJAhgIR7i0BWY7sFvxKFgGZCTSLTm2lrUjITz/b3jNbVOkllxXFALCiBBwo/UVwE3HDolICmIr2wSCMT1T2VhZy/5AqJ1bdWShU0A+zOiSFP1lTPGrjpbUjjxO0FTCpuVbhKRwUQWErEXANGm7gHzGGW1vRCvKiCW3eonPY33umsbkCdqneRcF2Ucpfg3OOrP1dY+Gw9QnB4PS0lJmz57NnDlzKCsro7S09JA+r10QL/16iMemVYinunQ0jqaxjZd6WBDi6IvRSPbHFluE5SpgWllWbdcBkHtmkq4RsGhUhZB8/Goca9cbuiF63SztvdDW+hpYBwAhHMo2TDMqS8cvjx9OTt+0Ru4npukGyMdyZ/k2oVNAjhOkJejk90tn0aaqiMhjxTStlKHWaVARGKaCooCG9MdtEz79VP4cNartlW5BGYLoC5CjFY+xfEfTINusFg6lTSvq0SIcpj45WJSWlh4ZgrGNeOnXwzA2LUY81aWjcTSNbbzUw0Kirh01AXotgSKwlEru30IgOfcVgaZCIOy6IzTkU44neIPuvNUUQiYPQdhUp5KhpD4UxrTje4TcW71+yl5s31dHLmmOv7HwlC0PVaYleH975k40dArIcQSb59eZrgKH+Nuw3SuE1KQaJiTpSts5HK+7Tv48GB/WFpSR1y+9yRN6Uy+gNwBB/u2mm/ZyQrdLW9qrnPaqy9GEeOnXeBqbeKpLR+NoGtt4qYeFrCM8tXRr0C3Jh2nClr21mKYU/HyaSsgIoyCtrU4W0hiCY7xBCOiblkiyz2WrsjPdBcOmozTK7pPG11W1bNlbF5GdVphSm/zjY4fw14++dMqQbo5en2X506uxHtI1qeUc80cp4tM7/VsMbxCfYgnEmmVCUYVL26NA3DNYADGFY8XyjSockBH1c69/svu7AEdY7sBsWZ3oRCc60Ym4gk9T6JueEEF1pggcarQEPTJor0uiZIaIZwiBbFODfdT2F1aFq+Xtn5EoDwENhX8BpYO6On/eUjaM4gHpzm4qM/a6Lo+qUNAUBU1VvvU8850CcpzBfbndIANVyFNiopX5RxEyIjfFd+RO3uYU34qA3qkJTppNb+Su+zIfASqAQ4jy8nLuu+++Tqq3TnSiE52wUDggIzLgXbhJpSTkDjKse3LcJ0+JtsNFMlY0cEfEVigJVwC27p0yUlK65fRNRbV4oMFLBCB7Jr9/01bfbxPi+/j0LURB/wwWb6oEXB9dvyaoD0uNazhkAjJILfkIFpD7pyc26RsnEHRP9lETDBOsNRBCRiZjuVl829/f8vJyTjjhBIfNYvbs2UeWX3InOtGJThwiuIoU06J286ZrPjL4fW1hN9Z1Vxj2fCYkk0UQ2xHbdJKIXF0ykMsK+zvCs00FBx5f7fjvlsOKTg1yHMKlXpF+x6qikKhrHu5DO9vekTt8vdMS6J7ij/n52D6pJOg2s4cgw+L0bI794tuCOXPmEAh08iF3ohOd6ERDOMmorD0zwROvIxCM7RP/vtlpCRrDu6c0uu4EuNsa4AZ7oU9VrGyztpXV+7tXvrCFbOFQqH7b99WG6NQgxyHG9kll/c4DhM0wRlhO5ATNDTBQhMw1f1DmoV//+uAr2h5lxIDdtsFdEgmlJ5Cgq+ypkZyemioY3s2zcLRXPeK8T7woKzuC+JDjpV8P09i0CPFUl47G0TS28VKPbzlG9Uzh810HCFs0ZwqRTA3x7loBUnCV6bNd+FSFrkk+aoMyGC9RVyTVqec7NtuVgidJCp724yYYQbgcyN4kZZ2Q6BSQ4xB+TbWc793TomLRodlTuE9aAkkH42IxZcrBV7Q9ymgGmqpgr2X2C60KEcmF2V71OEL6BI4wPuR46dfDNDYtQjzVpaNxNI1tvNTjW44Uv6S3C4Zdq6tXq3ykQgjB0G7JbKuuI8WvYZgy416j+/BwJTuKtWiB7y6lrH29Ey46RECuqqri6quvZvXq1QghePzxx+N7g+8AjOiRzK4DATZV1gImhnXdJj/v0YR7QouwfLn8mZfXsWW0AooQGKbZmDm5vepxhPXJEcOHHC/9epjna5OIp7p0NI6msY2XenSCLkk69dUGQpgO7RscLckvRNREHk5wouWaKJTIYEXpbuFmEgQQisxSKwT0SD5IueIoQ4cIyDfeeCMnn3wyL730EoFAgJqamo6oRlxD9/gYK0I4ROFCtFOAwU03yZ8H47vaHmW0Arl901ixtRq1Yfvbqx5HYJ8cEYiXfo2nsYmnunQ0jqaxjZd6dIJ+6Ykk6Srrdx4AK2kGHBkBes3B1hBHy3Nif+b1MfYG4Xk/N8zIYMBBXZMOWxuOBBx2AXnv3r189NFH/Oc//wHA5/Ph88VnLvR4gCJkqlCvQ35u37QOrVNHQVMVxvZJPSoWuE50ohOd6MThge1zm+RTGRbFJeFIQ1PuIsIjGOuKR0jGk2gLmwrOxG9xJ8dxUsEOw2EXkL/88kt69OjBFVdcwYoVKygsLOTvf/87ycmRk/bhhx/m4YcfBmD79u1s3br1cFe1EXbu3HlYn1ddF6R6bx0AYQNUi7Rih1Z70GV3C8iAt91t7NedO3cedBnthfaqR1vKaTgn4qVPDjeaejfao0/ipYzm0NI14tswTw5nX8RLf0arx+HeN+IVHdEP++pDVFfWYppy/wz7NHaFEw97PRriYPuievc+AOo1wVZxwLmeFAxTVxNg34EA9UEDRcABIBQ2CYYN6sMGhiJQhSCkKoRNkwRNobpWwwS26gcvW7QG8f5uHHYBORQKsXTpUu6//37GjRvHjTfeyO9+9zvuvvvuiPuuvfZarr32WgCKioro27fv4a5qVBzOevQMG6Tsq0cI+LqqjvQEjSHdktonAtfS2h9Me/ztUEa7oL3q0cZyIu6Plz7pAMRsc3v0SbyU0QK0qPxvyTw5bH0RL/0Zox4dXq84weHuh6raIPu0/ZimTJ2cnqijq/FBj3owfbE5WClTaasKfftGZpTNOBAguGM/SYbhMF/VBMLUhcKIoIGuKuiKZMIKhA3S/BrJfhXDhL59uxxss1qNeH43DvtM6d+/P/3792fcuHEATJ06laVLlx7uahwR0FSF/hmJ9EtPlGYRRRwR9DSd6EQnOtGJTnQ0bPoyRQiSfGrcCMcHC0W4+RAawvY1Vi3KN5l+WjhuJkJIbboQUp7QVNFJ8RYDh12D3Lt3bwYMGMCnn37KqFGjmD17NpmZmYe7Gkcc2j1z3L33xkcZ7YH2qsfR1CfxhHjp13gam3iqS0fjaBrbeKlHJwBI1FUKB2RQFwyToB89yiUhZBBizxhsVlJzbCKQwrAtUMvPsBKQ2fRu8oOjqX/aCx3CYnH//ffz3e9+l0AgwNChQ3niiSc6ohpHFLon+0lLaMfhmjAhPspoD7RXPY6mPoknxEu/xtPYxFNdOhpH09jGSz06EYGjTfjrmeKnW5IPNYbmTAgQppAB/hFZ94TDjaxYnykCeqf56Z2acLiqf8SgQwTkvLw8KioqOuLRRywGtzf9yvz58ufBLOjtUUZ7oL3qcTT1STwhXvo1nsYmnurS0TiaxjZe6tGJoxoDuzQtDziJQCxmCl1RqBeGQ/2mKMLJqWC7oMQStr/N6Myk923FrbfKnwfD19keZbQH2qseR1OfxBPipV/jaWziqS4djaNpbOOlHp34VsNOJ21YErKqWD7JloSsK25a6k6Kt9g4OjzWO9GJTnSiE53oRCe+5bCFY/m7+1MG48nf/ZpquV2IqFzKnZDoFJA70YlOdKITnehEJ44CZCTqDO+e7LpZIAVmzZNRT1WEE7gnk5F1qpCjodPFohOd6EQnOtGJTnTiKIAQgvREXaaSdq4BQrJWJDhUscL5L9nXKQpGQ2evdKITnehEJzrRiU4cRZCirye9tJDCsV9TPOmoIb9fOkpngF5UdArI31b87W/xUUZ7oL3qcTT1STwhXvo1nsYmnurS0TiaxjZe6tGJbz0KB2RQsbkSsIRhkH7IXso3jxtGJxqjU0D+tiIvLz7KaA+0Vz2Opj6JJ8RLv8bT2MRTXToaR9PYxks9OtEJICPRR20wTCBkSC2yE6aHo0XuRGx0CsjfVvzf/8mfU6Z0bBntgfaqx9HUJ/GEeOnXeBqbeKpLR+NoGtt4qUcnOgEM754MQE0gxPIt1QhMRzjGtPmSO8XkWOgUkL+t+O1v5c+DWcjbo4z2QHvV42jqk3hCvPRrPI1NPNWlo3E0jW281KMTnWgAIRpm1YMuib6OrVSco5PmrROd6EQnOtGJTnTiKIadZhpcQXmYpWHuRHR0Csid6EQnOtGJTnSiE0c5bC0ySP/k/2/v/mOqrPc4gL+PEHQV0JkeoKPJCJTDjwPyQz2uMdEg+3HxkjYhnd7SsVUbs7Zsqz+qrYaVbtbWvbvWH9l1C7e7JSpXplP8ORoloMvKlZMNkAhQCEw5HPjcP84FjyUmnO85z/c55/3aHM702/v7fp5z+Pr4Pc9Dd8ctFkRERERByvL/h4SMfiwvInzK2P5kGh+vIBMREREFsdG9xxYLIHxw3j3hFeRQ9a9/6TGGCqpyBFMnOtGlV52OjU5ZjBZMx1aXHEReIsOmID7mflzpu2l0FFPhAjlULVigxxgqqMoRTJ3oRJdedTo2OmUxWjAdW11yEHmZMsUC2/S/oOPXm7DAgjA+Oe+ecIEcqg4c8Hz961+NHUMFVTmCqROd6NKrTsdGpyxGC6Zjq0sOojuIjrwPtun34/5w7q69F1wgh6odOzxffXkjVzGGCqpyBFMnOtGlV52OjU5ZjBZMx1aXHER3sMAaZXQEU+FfI4hMpL6+HpWVlaivrzc6ChERUdDiFWQik6ivr8eKFSvgcrkQERGBo0ePwul0Gh2LiIgo6PAKMpFJHD9+HC6XC8PDw3C5XDh+/LjRkYiIiIISF8hEJrFs2TJEREQgLCwMERERWLZsmdGRiIiIghK3WISqf/9bjzFUUJVD806cTieOHj2K48ePY9myZebZXqFLr7qcr4BeWYwWTMdWlxxE5DMukEPV3Ll6jKGCqhwm6MTpdJpnYTxKl151OV8BvbIYLZiOrS45iMhn3GIRqvbu9fwwegwVVOUwQSemvIuFLr3qcr4CemUxWjAdW11yEJHPeAU5VP3zn56va9caO4YKqnJo3olp72KhS6+6nK+AXlmMFkzHVpccROQzXkEmMgnexYKIiCgwuEAmMonRu1hMmTIFFosFDzzwgNGRiIiIghIXyEQm4XQ6sXPnToSFhWFkZARbtmwx115kIiIik+ACmchEenp6MDIygpGREW6zICIi8hN+SC9U/ec/eoyhgqocJuhkdJvF6Af1TPGwEF161eV8BfTKYrRgOra65CAin3GBHKpmzdJjDBVU5TBBJ6Z8WIguvepyvgJ6ZTFaMB1bXXIQkc+4QA5Vn33m+fr3vxs7hgqqcpikE9M9LESXXnU5XwG9shgtmI6tLjmIyGfcgxyqPvvs1pu5kWOooCpHMHWiE1161enY6JTFaMF0bHXJQUQ+4wKZiIiIiMgLF8hERERERF64QCYiIiIi8mLIh/QSEhIQHR2NsLAwhIeH45tvvjEiBhERERHRHxh2F4u6ujrM4i1xjPPf/+oxhgqqcgRTJzrRpVedjo1OWYwWTMdWlxxE5DPe5i1UTZ2qxxgqqMoRTJ3oRJdedTo2OmUxWjAdW11yEJHPDNmDbLFYUFRUhJycHOzatcuICPSPf3h+GD2GCqpyBFMnOtGlV52OjU5ZjBZMx1aXHETkM0OuIJ8+fRo2mw2//PILCgsLkZKSgvz8/Nt+z65du8YWzz///DOuXLliRNTbdHV1GR1BmQf27AEA9Pztb5P6811dXT6PoYqqHJMZ5/fnhC6dBNrdXhsqOtFljD9zr+8RoXCeBLILXfq8U45g+r7hC/ZwC7vw0L0HQxbINpsNAGC1WlFSUoKGhoY/LJDLy8tRXl4OAMjNzcWDDz4Y8Jx3oksOn0VEAPBtPpEKxlBCVY5JjnPb79elEwOMO2cVnegyxj24p/FD5DwJWBe69DlODsNzaYI93MIuPHTuIeBbLK5fv47+/v6xnx8+fBjp6emBjkFEREREdEcBv4Lc2dmJkpISAIDb7cazzz6LlStXBjoGEREREdEdBXyBnJiYiHPnzgX6f0tEREREdE8sIiJGh/gzs2bNQkJCgtEx0NXVhdmzZxsdQwvswoM9eLAHD/ZwC7vwYA8e7OEWduGhSw8tLS3o7u7+w6+bYoGsi9zcXD717//YhQd78GAPHuzhFnbhwR482MMt7MJD9x4MuQ8yEREREZGuuEAmIiIiIvLCBfIEjN6XmdjFKPbgwR482MMt7MKDPXiwh1vYhYfuPXAPMhERERGRF15BJiIiIiLywgUyEREREZGXkF4gt7a2oqCgAKmpqUhLS8OHH34IALh69SoKCwuRnJyMwsJCXLt2DQAgIqioqEBSUhIcDgcaGxvHxtq6dSvS0tJgt9tRUVEBs+1cUdnFa6+9hvT0dKSnp2Pv3r2GzGeyJtrDDz/8AKfTicjISGzfvv22sWpra7FgwQIkJSVh27ZtAZ+LL1T28Pzzz8NqtZrykfKqehhvHDNR1cXNmzexaNEiZGZmIi0tDW+++aYh85ksla8NABgeHsbChQvx1FNPBXQevlLZQ0JCAjIyMpCVlYXc3NyAz8VXKrvo7e3FmjVrkJKSArvdjvr6+oDPZ7JU9XDx4kVkZWWN/YiJicHOnTsDPyEJYVeuXJGzZ8+KiMivv/4qycnJcuHCBXn11VelsrJSREQqKytl69atIiJSU1MjK1eulJGREamvr5dFixaJiMiZM2dk6dKl4na7xe12y5IlS6Surs6QOU2Wqi4OHjwojz76qAwNDcnAwIDk5uZKX1+fMZOahIn20NnZKQ0NDfL666/LBx98MDaO2+2WxMREuXTpkgwODorD4ZALFy4EfkKTpKoHEZETJ07I2bNnJS0tLbCTUEBVD+ONYyaquhgZGZH+/n4REXG5XLJo0SKpr68P8GwmT+VrQ0Rkx44dUlZWJk8++WTgJqGAyh7mzZsnXV1dgZ2AQiq72LBhg3zyySciIjI4OCjXrl0L3ER8pPq1IeL5XhobGystLS2BmYSXkL6CHB8fj+zsbABAdHQ07HY72tvbUV1djY0bNwIANm7ciH379gEAqqursWHDBlgsFixZsgS9vb3o6OiAxWLBzZs34XK5MDg4iKGhIcTGxho1rUlR1cV3332H/Px8hIeHY9q0aXA4HKitrTVqWhM20R6sVivy8vJw33333TZOQ0MDkpKSkJiYiIiICJSWlqK6ujqgc/GFqh4AID8/HzNnzgxYdpVU9TDeOGaiqguLxYKoqCgAwNDQEIaGhmCxWAI3ER+pfG20tbWhpqYGmzdvDlh+VVT2YHaquujr68PJkyexadMmAEBERARmzJgRsHn4yh/nxNGjR/Hwww9j3rx5fs//eyG9QPbW0tKCpqYmLF68GJ2dnYiPjwcAxMXFobOzEwDQ3t6OuXPnjv2ZOXPmoL29HU6nEwUFBYiPj0d8fDwee+wx2O12Q+ahgi9dZGZmora2Fr/99hu6u7tRV1eH1tZWQ+bhq3vpYTzj9WNGvvQQTFT14D2OWfnaxfDwMLKysmC1WlFYWGjaLnztYcuWLXj//fcxZYq5vxX72oPFYkFRURFycnKwa9cuf8f1K1+6uHz5MmbPno3nnnsOCxcuxObNm3H9+vVAxFZO1ftlVVUVysrK/BXzrsz9qlRkYGAAq1evxs6dOxETE3Pbf7NYLH96deOnn37C999/j7a2NrS3t+PYsWM4deqUPyP7ja9dFBUV4YknnsDSpUtRVlYGp9OJsLAwf0b2C197CBbswUNVD3cbxyxUdBEWFobm5ma0tbWhoaEB3377rb/i+o2vPRw8eBBWqxU5OTn+jOl3Ks6H06dPo7GxEYcOHcLHH3+MkydP+iuuX/nahdvtRmNjI1544QU0NTVh2rRppvv8CqDu/dLlcmH//v145pln/BHzT4X8AnloaAirV6/GunXr8PTTTwMAYmNj0dHRAQDo6OiA1WoFANhsttuuhra1tcFms+HLL7/EkiVLEBUVhaioKDz++OOm2lg/SkUXAPDGG2+gubkZR44cgYhg/vz5AZ6JbybSw3ju1o9ZqOghGKjq4U7jmI3qc2LGjBkoKCgw1TYsQE0PZ86cwf79+5GQkIDS0lIcO3YM69ev93t2lVSdD6PvjVarFSUlJWhoaPBfaD9R0cWcOXMwZ86csX9RWbNmzW0fgDcDle8Rhw4dQnZ2tmFbVkN6gSwi2LRpE+x2O1555ZWxXy8uLsbu3bsBALt378aqVavGfv3zzz+HiOCrr77C9OnTER8fj4ceeggnTpyA2+3G0NAQTpw4YbotFqq6GB4eRk9PDwDg/PnzOH/+PIqKigI/oUmaaA/jycvLw48//ojLly/D5XKhqqoKxcXFfs2ukqoezE5VD+ONYyaquujq6kJvby8A4MaNGzhy5AhSUlL8lls1VT1UVlaira0NLS0tqKqqwvLly7Fnzx6/ZldJVQ/Xr19Hf3//2M8PHz5sujveqOoiLi4Oc+fOxcWLFwF49t+mpqb6L7hiqr9vfPHFF4ZtrwAQ2nexOHXqlACQjIwMyczMlMzMTKmpqZHu7m5Zvny5JCUlyYoVK6Snp0dEPJ++fvHFFyUxMVHS09Pl66+/FhHPpyzLy8slJSVF7Ha7vPzyy0ZOa1JUdXHjxg2x2+1it9tl8eLF0tTUZOCsJm6iPXR0dIjNZpPo6GiZPn262Gy2sbt21NTUSHJysiQmJso777xj5LQmTGUPpaWlEhcXJ+Hh4WKz2eTTTz81cmoToqqH8cYxE1VdnDt3TrKysiQjI0PS0tLk7bffNnhmE6PytTGqrq7OdHexUNXDpUuXxOFwiMPhkNTUVNO9V4qoPSeampokJydHMjIyZNWqVXL16lUjpzYhKnsYGBiQmTNnSm9vr2Hz4aOmiYiIiIi8hPQWCyIiIiKi3+MCmYiIiIjICxfIREREREReuEAmIiIiIvLCBTIRERERkRcukImIgsRbb72F7du3Gx2DiMj0uEAmIiIiIvLCBTIRkYm9++67mD9/Ph555JGxJ3B99NFHSE1NhcPhQGlpqcEJiYjMJ9zoAERENDlnz55FVVUVmpub4Xa7kZ2djZycHGzbtg2XL19GZGTk2GOdiYjo3vEKMhGRSZ06dQolJSWYOnUqYmJiUFxcDABwOBxYt24d9uzZg/BwXgchIpooLpCJiIJMTU0NXnrpJTQ2NiIvLw9ut9voSEREpsIFMhGRSeXn52Pfvn24ceMG+vv7ceDAAYyMjKC1tRUFBQV477330NfXh4GBAaOjEhGZCv/tjYjIpLKzs7F27VpkZmbCarUiLy8PFosF69evR19fH0QEFRUVmDFjhtFRiYhMxSIiYnQIIiIiIiJdcIsFEREREZEXLpCJiIiIiLxwgUxERERE5IULZCIiIiIiL1wgExERERF54QKZiIiIiMgLF8hERERERF7+Bzl6rG2eMeWWAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from prophet.plot import add_changepoints_to_plot\\n\",\n \"fig = m.plot(forecast)\\n\",\n \"a = add_changepoints_to_plot(fig.gca(), m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default changepoints are only inferred for the first 80% of the time series in order to have plenty of runway for projecting the trend forward and to avoid overfitting fluctuations at the end of the time series. This default works in many situations but not all, and can be changed using the `changepoint_range` argument. For example, `m = Prophet(changepoint_range=0.9)` in Python or `m <- prophet(changepoint.range = 0.9)` in R will place potential changepoints in the first 90% of the time series.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Adjusting trend flexibility\\n\",\n \"If the trend changes are being overfit (too much flexibility) or underfit (not enough flexibility), you can adjust the strength of the sparse prior using the input argument `changepoint_prior_scale`. By default, this parameter is set to 0.05. Increasing it will make the trend *more* flexible:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"m <- prophet(df, changepoint.prior.scale = 0.5)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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IYiwsiiaQrsBQIumGweV8No4f26eom2UgBWSKRSDqAbeW1YGuQGyZWNbUHqytZSxYGrVkUxuqCSCLpCgTmGKnqsedjIQVkiUQi6QAMERr8hRANRmnHsqb2YLQKdPTCIFYXRBJJV2EIUA0RjNmIHaSALJFIJB2ANdYbQtiCcl0sU/ujjz7Knj17DipNbSzT3guDcJeKWF4QQce6f0jXEkl9BEIoaLpBjxjyPwYpIEskEkmHYGmOBUFNch1HC2lqPziIliM8Vl1XOvKZlLnSJQ1hWdf6eGJLQHZ0dQMkEonkQMQK0BNBJ+S6GuRopnZJbODz+Zg9ezY+n69djhXNpeKOO+6IOQGxI5/JaP0gkVhBerGI1CC3A9JsJJFIwjF9js2/LV/kulksYt3UfrDS3lrU7pQjvCOfye7UD5LORTe6ugXRkQJyG5FmUolEEo2Qe4UpGdfVkhysWSJinfYOoutOOcI78pnsTv0g6TxsK1sMIgXkNiIjkiUSSV2Wr8hl/pKPGJ/qZUJKetAf2ZwIFEWx94ulLBHSEmbSEVrUWLrPTdGRbe1O/SDpPBrK8NPVSAG5jUgz6YGJFBYkreXjL5bxq7PORA0EcHvczH7hbY6dkEw/R+wOt9ISFkJq9iWSzmHr/lqEJw4DQGlq784ndkfsboIcTA88pLAgaQvLvvwSVQ1gGDqqCkV5ORw1LgkDU4usxOBE0FGWsO660OwoTWd37Q+JpCPYX6vi9pg+yDE4LEoBuT2QZqMDC+k2I2kLk044Ebfbg0oAt9vNuJRMM0CviYp6XUlHWMJkWq9I5MJbIonECCszHYtIAVkiqYN0m5G0hbSMTJ6YvxhfznImpmVxxNhEDBGcDIQgFnUlHWEJayi92cGKXHhLJJFYrsdVAT1qIaWuRgrIEkkd2iIsfL2zkqMH9aS4IF+aUg9SFCA+OY2jxydjCCirUe2MFrFMe1vCZFqvSOTCWyKJRATNan5Nx+WMvbIcUkCWSKLQWmGhvFbli2XFnHfOmdKUehAjghrjnRW1XPH6Km6bcixTRx4S80JyQ7TGd1am9YrEWnjPmzevq5sikcQEVm74owf2ojKgd21joiAFZImkHTEE5CxfJk2pBzGKophV9ITgq52VAHz+7R5OOf6QmDQjNkVbfGe7W3xGZwTRvfzyywQCAV5++WW5eJYc1MS6ZS32dNoSSTdGCJh84ol4PB6cTqc0pR6kCAE1qs53e6rtzwYCNVZLRjXCwVIS21oI3HXXXUydOrXZZaZbUpb6YOlLiaQxalWdop/KQgqD2AvLAKQGWSJpVwwhSM/IlKblgxhFMS0Jj6/4gc+/3QMQ1CjDhp2VjBnah95x3WfoPVh8Z1sTRNdS7fqUKVNwuVwYhoHL5Tpg+1IiaQy/ZiAQGELEbOAySAFZImlXBGaltO5mWpa0L0IIvt5VZX/evnkTX5VUMPSESejdzM/iYMn13pqFQEuF6r1VAbusbqyW15VIOgNDgB50sVCkgCyRHLhousHqreUYAlTdQNONmIzKlXQ8Cma+Y0fYmL9t8w/c8fR1PP36EkafdYq9vbsUjjgYFnytCaJrqVD9wadL0XQdIQS6rsv4hIOc7vL+dwRCgK5DhV+jh8vZ1c2JihSQJZJ2QDNMc5EhBJv31bCzMkD8YX27ulmSLsIQAkd4yTxFQQsE+GDhAi4KCsiycERs0pIgupZq15MzJ+F2e9A4sN1VJE1zsL3/m/dVc+SAnuysDLCtvNaOy9BFrDpYyCA9yUFOSwJsmkJgpfcSaDFaGUjS8VjPgSNi1Dc/CEXY5sTs7Gz8fj+6ruP3+2XAVgzQmiA6r9fLHXfc0SzhZnxyOk+//g7X/eGvB7xA1Bzac/ztbhxsAZs7K/3ohmB3VQDNMEz/Y8yxUolRCVlqkCUHLe29gl9TmE9eznK8k08gNSOzHVsaGxzI5sD2ujYhBIYhWFdSwO7tu8Ez0Pzi6CQcw+P5xfSL7X0HDx6MEVxIGYbB4MGD23QNsY4QZqCiwxGjsyGdE5A4ISWNsYlpZB49sN2P3Z042DSodTlYgl8twl3uRbDEdL0vYgwpIEsOWtqz9OvKPB83XjadgN/PS4/9k78+8C8Sbr+lnVvcedQVGA/kyaw9r+1/2bl88uGHLHlrPuoFs+GwkBBkXDSb+ORU+/OePXtwOBwYhoHD4WDPnj1tvpZYJaAZbK+oZVdlgJQjB3R1cxqkrssEwOzZs9tlUVjl1wBTHlhTlM8XrxcfkIvN5nKwl94+WIJfLYT1X5jm2BKTY3XJLAVkyUFLe67gl335JQG/HyEMdM1g9p1/4hcnpHfLQS+awHggT2btdW0+n4+br55JIOA3MxSI+m42RpiyZMqUKcTFxeH3+3E4HAe0BnnttnLAdD+KdayAxPZeFH61s8IUjovzufny6WjqgbfYbAkHmwY1GgdD8KtFXQ2yNRYaMTwkSB9kyUGH5fcGsHTpUu677742T1InnHQSjrCsFYZhdFufsmgCozWZHYjFTxq6tpb6R2ZnZ6OqwTReA4+AIUfX2+fd11+2//Z6vdx0000A6LrOrbfeesD5Ylp9uKpwJUBMV82qS3v7iBpBs3KRLwf1IPI9bQhLg9oe468kdqn0a6zZWm7/rQddrazc8CKGnZClBllyUBFNK3THHXe0+bgZmV7+cM9DPHLP7QjDwO2J67ZCZEOanSuvvBKAmTNnHlCTWTRTZ2u0h1OmTDEzFGgq+q+fjrrPf+6+nTMnpXHqlBPw+Xw88sgjth+yFah3oPRtYWEhF198MYFAAJfbw9Ovv8O4pLSublazGTx4MA6HAyFEuywKLQ1acuYk3B43msoBt9hsKQeTBvVgpdKvEdB1DCH4bk8VCootHMc6UkCWtBifz8eSJUuYNm1atxvcOspVQAjBORfPZOgxo/huVR4Zk07sdn1jEc0PM1xYnDlzZtc2sAOoO1G35jnxer3869mXWLduHc/o0fcxDIPly77k1CknkJ2dbQvHFgeKsOTz+bj77rupra0N+hwGKPKtYGxi9xCQfT4ft956K7qu43A4ePTRR1vtdmONlcrhY4KaMzhj+gxcToU/Xn9ttx0nJJLmYoiQW4VDEXa2J8MwPZNjU38sBeRWcSBH8zdFuGZtzpw53c401hF+bwHNoLxWQwjB6IQUdm3ayLOPzCaw4weuu+66tje6CwgXGGfPnn3A+h83RGufk/EJSaScdBrPPJ4T9Xu3283kE0+yz2EF6YEpPK9du7bL+lbVDYQAj6ttnnc+n48pU6YQCATsbU6nixTv5G7hgwyhBZJhGCiK0qoAyrpj5aOvLEYAt1w+HTUQwO3x8Mfrr23/xkskMYa1MAyvKi0AA1BieEiQAnILOZCj+ZtDdw/W6ojI4c1lNZTVBDCE4MM3X+Hpv98OQN6yLwC6rZBscTAG07T2ORE0HnTyz5cWkRZMAej1eklOTiY/P9/+fuHChV32vHy1oxJVN9qcZcL0xVYjtp11wSUAzHvqPyjnnRXzY0Z7PPN1x8rivBUAqIEAhqGjqeb4mZWV1c6tl0hiC3NhLEwNslDMcdIICsxCxKoLshSQW0p3FxDbyoEgLHWE35tZFQhyP/1fxPauFHhaSkOWkQM1HVFTlqDWPSdWMqPojE5MNYNSgkyZMiVCQE5MTGzh+doPPVgNsq2YvthuW4Ps9sQxOn4i119yLmogwLwn/x3zioX2eObrjpUJGZNwKOD2eFBV0y/7xJOmtH/jJZIYQ4jQHGmNMJbQHMtGJSkgt5ADQUBsC9bE0V19kDsKy8cq89SzWO370t5+/vnnd2Grmk9TlpGWCovFP5dx7ODeDOjp7ojmtpmOsgQpitLogG8YIkJ8HjBgQPA3AkVRGDBgQJvb0FoEol2yTHi9XrKzs3n66afp3bs3KaedS6FvBapqak67i2KhrQvpumNlYOhInIrCnFcXkZ+7goysE8jMjM2CQgezG6Gk/RGW9tjOeizMbBbAyCG9ccSoCrnDBORZs2bx/vvvM3ToUEpLSwHYu3cvM2bMYNOmTRx99NG8+eabDBzYvaoJHajatJbg9XoZMWIEw4YN6+qmxAQKoRXy1PMup1+cm2WfvMesyy7uNtrj9raM6IagKqDFrIDcYcGawNrigga/txZSFpa2VVVV3G53ly64rWd4d6WfIX3i2nSs8DFi7qKP2b71Z5xOl70QOJBzPocT3g9ffrsblFA/t89ypP052N0IJe1HQDMIBGMbTCE5pEAwDFN77HbGbrbhDmvZVVddxUcffRSx7cEHH2Tq1Kl88803TJ06lQcffLCjTt9hyJW1JBrmy29OeUePGktyxmQmTJjQ1c1qNm3Jc+zz+Xj88cfx+XwIIVi1ZT+GgBrVsKuHxRodldd5/eoSfn/F9Aa/F0Kwq9JPRW1YvwS1J0oXaVF8Ph83/vlvdq7iTfuq2/XYN182nffeeAXD0FEUBcMwDsicz01hCMG7r8/jhovP4cX/PMBNl54bk33Q3vmfJQcvX+2sYGeln2CyCtu9TAhTiRKbS8QQHaZBPvHEE9m0aVPEtnfffdd+2a688kqmTJnCP//5z45qQrsTntfTWlknpKTRyyM9VQ52LM3ghlWF/OP6GagBlZee+Fe30b601jJSN1L/008/I+6osQDsr1EpqwmQemTsWYk6yhK0qiAfNaA2+L0BVAY0ft5fw9gefZk3bx5qwCwuomlap7seWPfPHwjgdnt4fP5iElPT2+342dnZdlCaIswFgBCi27hZtARVNzCEIM7ljPp9aUkBj9x9G4Zu5gBUA35eeeUVTpg8qTOb2SQHuxuhpP0wDIFumO+8JkSERlaHmK8c1KmS3Y4dOzj88MMBOOyww9ixY0eD+86dO5e5c+cCsH37drZu3dopbWyMzz77LGJlvWTJEqp7DGLkIX1wOWLTh6aj2LVrV1c3oU0UFhbi8/nwer2kpqa2+ji7du0i4Klhf2WAqoDO6uWfBgUCw35GRowY0Y4t7zhGjBhhFwNp7vu2ZMmSiHfi/fff45QLB2MIgcthmtO2Oms6stmtZsSIEVwxcyZb99dS8vUPHNq3bW4Fu6sCHDdqNC63m0AD+1Tt3cU+NQ6/x8mSlV/wwgsvRATtOZ3OTh3rrPtn6DoqAXI//5gRI45mq6v596yhd2nXrl3Ex8fjcrvRNHA6TMFRN3Tcbjfx8fExMa5HQwhBpV/j69LVzR4nfthTTa2mM/bQvhHbrbEy/4tPbOHYoqqqKub6YMSIEdxzzz188MEHnHXWWYwYMYIlS5a0ebyMNmeousDtPLjmTuj+82dzKd9TSY1qoOoGAV3gckLA5UIzDCqrAtRWlFG2uwcAlTUaO901BMpjxy2vy1SfiqI0alK87rrrbP/N1NTUmPB3PfXUU3n++eftlfW0adNwDR7K4Yf3j2k/mo4iFu5Ja/D5fPUsAa3VZJXVqDh6DaRvnIpaFWD8pKn877XnUVXVfka6az81h2nTpjFnzhy7L88+55e4Bw9FMwQ9XA50IRg2LPY0yBZFP5Wh9OmFw+Vg2LD+bTrW1p/2kZR1ItfMeZOnNkbf54O3X+eUk6cwaVIWy957Cz1MYNJ1g3vuuYcTTjih0zSr4ffP5fbgPfkM+g8Z2ux71tS7NHJsPP95ZRGrVuaQ4p2MqhusL8rjinN/EbPa450VfraW11K8Jo8bL23+OLFXKcej6lH7btiwYaScdDoLXngaNZjdw+lyce1vfhNz44PP5+Oee+4hEAiQn5/PgAEDuPXWW9tlvAy/1r3VAbbtqSZl2IB2ann3Itbue0ewi/24/Bp+zcClGXicDnp7XPg1nUqXH10IBgw5FACtKsDQQ/u3Of6hPelUAfnQQw9l27ZtHH744Wzbto2hQ4d25unbTGpqaj2zbP7mfTGdpkRSn/YM0NpWXssAj2ladTgUTjphEnv+fB8Fn3/A1ZdfHLNCQGup64NfN1I/LT2Dwp/2B6unxXYKHwhWc2qnNurBvJ4NCccAC556mEXPz+HFt5fYpmyr2hx0vuuBdf/mLf6A5MzJxCelYQjR7FiLxt6lqoDG1h0VjE1MJT45DaeiENAN0jK8ZIyI3UXT7qoAmmFQ5Mtp8ThhPUtW/6VnTeaQYUcyDBibmMqjr77Du2+9jgKcfcHFZGbG3vhQ954uXLiwQwJa2yutoCQ2MAwzn7Gl+CyvVe1cx0awcl4wsRu6EBjdYH7oVAF52rRpvPzyy/zlL3/h5Zdf5le/+lVnnr5dqJv6J9ZvsKQ+7eljZ02IQoDb4eCHtcU898+70AIqawvzmDBhwgEjJDcU3R4eqa8F/TDtqkkxTvj9ayvNOYZh6KhqgPzcFVzywD0sXbqUh56Yy//efh1D17rE59Pr9SIOG41DUdCFoLSogFuvmG7f548+/pTeR8fXKyDi8/nYvHkzLpc5jdRtuz0xAo6wHKjd5blIzpzU7HHiu91V+DUzWv+jz5dx3jlnEgj6dT/09AscfeQRAIxLSuPQUQmgKAzo4YpJF8y64+P555/P8uXL22W8DF94HT8hOSavX9I61m4vp2+ci2MH9wZg465KdLNIaHAxhP1fbfBdifWxoMME5EsuuYTs7Gx2797N8OHDuffee/nLX/7CRRddxH//+19GjBjBm2++2VGn7zRi/QZL6tOeAVp2yhohQIHilTmo/gBCGPj9/pgMRGptJpbmaN4tDYGlGYr19yMy5ZBoUyYJQwjWrykB+jS6n9PpIj1rMmA+i7cdNppzLriYkrwcLusi14NQDmRBcd6KiPu8+KNPufQ34yL2Dw/uczmdXHvttcycOTOi7Up4SrNgLkQh6BZaQyFgfHJ6xDiRmZnJD3uqOWZwr3r7760O4FAUBIL3Pg7FqkCAVYX5ZJ5yBkJAScFKCnJXMDolk0lZWRH+57FCtPFxwoQJEZ9bM4aEB7m7PR6eeu0dxiWldfDVSDoLVTeoUQ37s618IPTO64YBOFAtyTnGl0gdJiC//vrrUbcvXbq0o07ZqQghqArowYkltm+ypD7tVU3PuvNCQN6mffhcoxDCfPkNw4i5fK91tcCffPoZkyc1r9RtU5p3IQR+zbAzeljCcixjtU8TUPzz/maVWQ4XDgD77zVb9vOX382CGxpZ+PcaQOakLJJSM8LaYApjCSkZpB7V9PnbG5/Px7xFH5CSOZmxSan0HTAIRVFwOBy43R4mpHnruaFYiyVD19GBo446Kur7ZGmRIaQ9Nv9t22Kko1ldlE9JXg4zp/+CO+64AzBzuu6p9tcTkEt+NtMaKoq5AAjXPLvdHiakpFNSmM+7C17j03feQNd0HE4nZ5x3CY7rr+aUk07oiktslLrjY/jn1uZJ9vl8oYVDIEChbwVjE6WAfKAQPkb4Nd1WPliLYiEgoJvzg2bEvvIEZCW9VrO7KsCP+6rbzX9R0j2xfG11Afd+uhHoj6I4EMLA4XCwZ8+erm5iBHW1wO98+GmzBeSmNO/bK/xs2V8TMSDGOoYIFXoxmiHMhwsHTqcTRVHQNNM14rRzZ6BpTeR9/u08Bm5bRGVA46d9Neys9Ae1iEqXLCVsTbA/wMseDzf+3/08cf+daLqZs/iiq65jfHI6QpgBjfGH9aWH2xmxWHI3YHZXUILPgTU5CrtqoBB2+ueYwVr4BNy9mX3XX1ADAV4OpmpMz8ikWtUxBKzZWs5xg3vRO86cPvXggtgS/sM1z8cnZrBl+05uvuxcAn6/rTE2DJ3/vTmPz5e82W1SQVq0NobD6/Xaz4zL7SE5c1LML6Alzcca7zXdoHRbRZiALNAMERwPzLlBDwpORw+qb42JJaSA3EpCmpHuIQhI2oe6pkVLK6aGZSNwezxomkpcDOYQDRdsXG4PKd7IHKw/7auhpHAlawtyOeXkk+tNfEOOn8DtGZk4o6Q1tPzMRJhwHOvvRrgmszlNDRcODMMSjMzgOhC4PD1oOAuyyenTLwJgR2Vts8/bUdiaYEMn4K/lgzfnE/AHgu4Qgteef5ITTvsF8clpGAL8mkEPt9NeLL2y+EO8k0/k2PHJEcet8mtU+jWEJ/Qs1Kg6Pd1O8xnpgmttjPCFj6Io6IaBCKZqXPr5Fxw+OoHdVf6goK9TFdBtAdlaEJYWF1CQu4IU72SuPe8MMjIy+X5vNR/ccxdqQA25U0z8BfxYDPt3dMt80K2N4QgPch8Wn8qYhFQ0qWE6YBDW2I8V/GwG7elCoBngcgi7YIi5qIz9ey8F5DYQKhkqORiIZlrEPQAB+PXQU/DXZ97gxzUFXHvh2TE38YULNsmZkyJM/QCffrmM310yHVUN8EAU8+n+WpVlK1aQl7OinhZZUcAwwqKVhVl2+cs3imO28qQhQr6y4VhCvqPOQiBcOKirQT713IvIPPkM/rqh8XOOSUgNLSAIjSMonT+STJkyBafTia7rCCHYuH4NikNBBNd7hmFQlJfD2KS0euOc1+uFw0fjdir8VFYdkUd6074a9lQH8Lgt/2YFA3OyjEWtYfjCx+Fw4HA4EIqCy+0hc9IJtkUk3GUEzOfEELCmqIBbLp+OGgjwksfD+M+X4hluFsyZmJyG2+Mm4B4Ap92EODoZKvfg+O813bIQR1tiOCxXjZKfywjoQWlJ0u0p+qks4r223euEqTXWdAOn4gAhCOiWBSnGTEhRkAJyK1B1Ay2YokYugA8OqgNmlTO/349hmAF489/5kLMuuAQh4MPl+ViV21eLYVx19Q1kjD2saxvdAF6vF+XwMQBUBXRWbdlP4hH9ASjy5aCqpm9puHZLCEGNqrO6MJ+bLpse1f/QMqlb4RdrivK55fJz0YI5oWPJlGxZAvofn0hCanq97ArfBrMSHD+kNyWF+RHCgCUcZE0+gf21Gp989jkp3skcNS6Jvbt3wobvI87lHWzg2xPKk64LIs5nl1/t4GuOhtfrZdasWTz77LPBRYHAe/Lp5GV/ijAM3J44kjImBb8LTWhCCL7bUx0RiW4Ywl5QKIRSO9k+h4bpo97L7bTdSmKFupaVm/7vAcrL9pKeNZnRCamU+zX7ehRRv93FeSvsioGqar43p142BoeiMGZiIrNfeJtHszeyuc/xALj6DuLSm/7MFb86M2beiZbQ2hgOn8/HZ0s/p9bVm3179zIhPYtJx5zZAS2UdCbWArJW0/lmV5W9kHQqpmVRMww8ODCEOZfaY23sDAFRkQJyK9iwsxK/pgc1ZSImI5El0WltBof1OyoYNGiwbVY3DIN+A81crquL8nn+u5AAtLh0O0cO6MGZow/FE6MVFo2wESoUUWwGGLndHjQizaf7azW+3V1JkS8HfzA4q7a2lnnz5uH1etENgRI0q1lCkyk0qKb5PoZMySG/Wz+Kw8Ef7nmIcy+9EgFs2FHBmEP7Uq3qBDSD1/+3lBsuPbfegmDw8RMor1Xpg8KVoxMQQFmNFjVDQ03ZbiCU810zwiO9Q7lBu2q2uPjSy3jxpZdQVRW3282Ma2/kit/ewqqVK0jPOoFRCanohmBNUT4flBZw5mlTSU3PoKwmYGuHAUq2RAY5hoRnQY2q2eZVywwbS4RbVlK8kxg50awWF+dysKOyFoWQG0641dByr0hIn4Tb40FVA7jdbkYlZUbsA/Dz99/AxOOD2wQXXnMTacfGVhBvOAHNwK8Z9O3RPmKClcWi1u9HGAaKw4HH4yHxiM9jYlxoiNbOGQcT4e9zVVAAtrLX6NacENxPtQaCWJeOkQJys/H5fHYxhLgjx9orpHXFBWS/Vszpp54iX54Yp7XR12C+7Lv37MbhcGAYZgBe2d59CKDYtwJIj9j/p80/ootxUY8VC4g6/7+r0s/eapXxyek89fpiCnNzSMuaTGZmprlPUBOYmDkJp9OJETTJv/jii8ycOZPqHoMYIHqbCeCDo2VixiTcHjeaWj9HblcSbgnAMHjknts5bsw4xienUhnQTL/kYEBJoW9F1ICkFTk5FPpySPNOJj45zfa/M6KYlFatWAreS+zPmh5KdxYR0NgFUqNhCHofHc9DLy2ktCCXsSlexiam0cPlYHyy+W+NqlNanM8fZp6PpgZ4+MF/8PEnnxJ31LjISPXgBXy3u4oaVQ/zRQxeM8E+IjZ90y3LiqKYmjALK6DQCjg0gJ2VfhwKDO7lQQiIT0rlifmLyc9dTmLGZEbEJ5o5YIPuO6vyczDCAjh1XWd9SSEnHHtG519oM3n302yys7O5vJ1SD1pZLITlu28YqKrK/Hc+JL2BuIaupi1zxoGGYZiZu3p5nBH3StONsOw0BGMMQopDKyBPBFNpChRcDgcDe7qhpqorLqXZSAG5GYS/JHPmzOGp1838jWuL8vnDzPMIBALc//d7ePLJJ+3y2JLYozXR10IIVD0oHKZPIi4uzh4skzPNALekzEmQGxma9d78/5KX1J+TT4y9FE4QMuubf8OP+6rtzxOS0xmXmI7LGdrfzD4A8Ulp/PLCS1n82st2cNq8efM479pbgwcLBb6NS0rjkXmL2f9tSUxpX6ZMmWIvdMCcqEvyVjAuKRVDmKmILB1nUkb9YhE+n8/00w4EeMHj4Yn5i4lPTkMXgkAzfK7Cc0Rb1Qa7Ul4UAsYmphGfnEYgmMDfEOAIW0CV5OUEszCY7kXP/PdF+h8yjF79B1K9fx/JmZOYmGouEvfVBFAIZasA7OBNA8W+7ljEEAKnopjPMda9UnAooclfUcw0Vj+V1TCol8feb3xyOiMnpoBiXa95TIFgdLIXSt4NnUhxsLYgF3F+bArIPp+PmeedQ0AN8OLj/2oXwdDKYmEtTs00gm6SMiehGQZOh7Ppg3Qy7Vl1tbuzpzrA5n01AEwc1g+300F1QOOrHZWhMY2Q5UgXAhdKUGMc0iAbQjCkt4cBPd2U1XTNtTQXKSA3g7ovSWGumb+xZGUOgWBRCM0wuPHGGw+oymkHGhGpqdzN02jurgoNCoeOnsj8xf9jQ3Ge+dvDR7N35w7GJKZCri/id5qu89e77uaRBx+IyefBDk4j5BJhEdL8KRHpuCwN2i/Ou5j333rN1AYFtcij00/ihFPPNFP4CIEwBG4HxCenkjn9NOJcsTP5eb1e5jz2OLfcfBOGYeD2eEjMmGRrcku3VdjFTiakpNcLSJo9e7btb6qpAYryVjA2KQ3dMPjs+4rIk/mrYFNJhAbZ0qCa5wjPE9z5QqN5r81//ZpBrWbQ262EhNng89F34EDC83u/OX8euqYjhGUqj+OJ+YvRt/Tj1Xc+JNU7mcOGH4kA1m4t555PNzIjcRgnHTuYgT3dMalBhpCriwBKi/MpLfCR6p1MUlqGnfPefF8UDARb99faWmVDCEpLCli1MoeMrBOCRTDMd2hnjYCRYekUFYX4NC/ltSp941wxpz3Nzs4mECUWoS1YWSxeWfwh/QcOZN/evYxP8zI+Ob3pH3cR7Vl19UCgbgEo3QizgGHGKYRcT4MZnrRQGkR7AdpNkAJyM6j7kiR7zSpYE9OzcDgd6MEHQNf1g3qFGet4vV4++PgT3nz/E049pX4Ks2iEgjHN4JzktAymn3EyADk/7EUg0PQos70QrFzxJVOnTo1Js5ydezc4aDkszV6E4GwOZD/sqWZvdcAWpMYnp3PeJZezYN6LCCHQNI01RflMPvUMDMwgViGgGkEfj4vSbRXNKsDR3jTmOzjrmmuJO+wYVixfZrtJGGGBt1ZmCwNRLyBpypQptr+py+0hKWMyQgg++Xo3r5butfdThIGYexWoflh0D5x3D2CaHK0JxHq2COvvzmZNUT6+FcsZlZxh+94aQthaU4Fg/759KA6H6TuqKOiaFgouNAwC/lpeeeZxClZ8jt9v5kZ+4In/MmHyqby9dhuqLni1aAurt5bz8Dmx63pkaX5Li/P505XnowZUXvJ4ePr1dzh+YkrwmQh5UO6orLWXNWuL8/njzPNQAyqvPPkIc15dTGp6BgaCOV8r0O+Q0IkUByMnpgZdNRSGD+jZ+RfbCFOmTMHj9qDSvoKhlfnEoShohmGXHI5VWpqx40D3V7Y0xBZBY0udDBYhq5gR5nplu5Qp3SduSwrIzcB6SSwfZNcRY9ANGJeYxo13PciT9/0FwzCIi4s76FeYsU6fo+O58nfxESmpmsJ60R0Cyv0ae6oCDO7twTKMR83lqTjsPKqxtmgKBUlZAWJ1NMhhGuVqVWd/rWr7lFnagXMvupR333zdXjROSEm3hQtdmMKDqodyYnY2zfEdtPx+dSNcWLXcC+r3i4XX6+WxVxdTnLeCtKzJjElMRTegwh9ZJMQtNIQi0J1OHLof69vQ+bB9KwwBXaFEzM3N5abLTHcRh9NJ8uRTGDJ0KGeeN4OJyel2GyemZeHxeNBUFYfTCShomhryJxWCFZ9/BEJgGAaaGmBNcT7xWVNxhGmMtu8t5425c4ib/gumnDC58y+4Caz7vWplrh1gqqoBinwrOG5CivnuBC9ndWE+RXk5jE/NYnRCCsV5ORG/Kc5bQWp6RoP+M5Zffywq1LxeL68sfp/5r7xST7vdViEwuDa3379YLz3e3IwdB4O/cl1rY91t4Zm9hDBLS0fOLV2nCGgNUkBugLqDgNfrZcSIERx22OEU/lxmm0fPmjGTcePH801JHueddfoB90IcaER7wcGM2HY6lHqTgVVlzdLwBTSDH/ZWsbsqYAsP4VkgLJwuJ8LpjEmznG3qsrXG5rVZYrMVoCYEfL2zEsAWHC3BLiktw9asHDY2hWFHjggJfYTnF+6aVIhN+Q6uzPNxY1AwfO0ZN/+Zt4jRiakhzTpAUBOyZms58Yf1xekw7//abeXEJ6cSn5yGy2FmAbGS4ocTKNuJ0zA4ZswEUqdfxYKgm7cZsGZVYDTPFNAMHG4Hm/ZWd2p1qS+//NJ2FzEMnfwvPgLg08Vv8J9X3yE1PRNDCMYkpvLIvEWsLcglPesENENQ6FtB6aoifEs/wkoR53Q4IJg/eEJyGrowImLVd23bwrx5/2TBM/+JSQHCcq1JyMjC7XGjquB2e0jxTrZdKRQBawrz7efH7XHz0EsLSUgP/4075LbTwLneLd3O7yYd3YlX13I+XLQAVQ3wwcI3zLzv0GYh0LJOhdx7DoxiWwe6v7JdcTSoJNF0M8tJuG++lQrRQheheBcBjBzSm2/3VMVoBEJ9pIAchcZWgiVb9mMYoQcFzMCl0046gWMGx3bZREloQLbKXVoC8dpt5fTr4WLkIX3q/cYeACz/RAEVfhVDCGpUg5nziur95oyLfs0RJ8Tz6/PPislBUggzBU/IFBYUjsPM6pY2wKGEBGnLHQBCmpXCn/axZ+eO4HGtjAxmtHJXDYRN+Q4uCxMMNRVWrcxBNwSlhT5SvJNJTE23+yag66i6GURkCcNmv5jPgzUp1POtMwx0TeO79avZvOs/cPnjgJnmzVqoWf/5dYM4l4Ndlf5OFZBPOukks4iF34gwe2qqyqqVOSSnZdj3cGxiKompGcS5HAR0gzGJqRTlr6RoRbad3uwPd/+DvXv2kpY1mUOHHYkaxf0o1tL+RRC85+MS03jwxYWsL8wlxTuZCSlpVPp12+3G1BZbeY9hdX4uV1x/Kw+/tJCSlbmkZU1mfHKaLQRG49XiLfzGO8JOIRdrrMxZbl+jdb+ANgmB67dXBMceYS8erPesu3Mw+CuH++F/v6+a8jDrYkQZeYLzihEeqCtwOR0IAT1iKCalMaSAHIXGVoL1ojXpPv40kpCwu69aZV91KG+rIUTUydz6zvLJND9DaVE+eTnL6TliPLVa73q/OWTYcC79lRfv8UM66lJajTVpOwkJwutLCinJW0Fy5mQmpoSEQ6tH1hTlk5+znInpWSSmZVAd0Nld6cfjctiaZdO9QvD1qgLWFfoYl+ol0+vtEhNqU76DJwQFQ1UFl8tN34GDuO0q0+f05WBmitGJqVGLARkC1hblszo/l1TvZEYFfVMddQVk67MQaGrA3qwZYSnPrMnDiAzY6ywyMr08Mm8RC959n2WHngmv3Aq7vsfpdJGQnmWnsizKyyHFO4nktMyw4ELB2MRUHn1lESUrc0jKmERSWgaaIXA7FbZv28rfP91IyZby0AkVB44YtawAEffFXBCk43aGfPUtTVlyZmTe44lpWQR0wdikNEYlpNLD7QgtgOobmGxiudRyxqQToqZpbK0QKISgOpgP24k1ZoTK08c6TbmWtKXCYHfBmj8DuoGmh8YsK0bHCM4nprbZtJAdNbAnZTWa7YI29tC+XXsRLUAKyFFobCVoaSCttVRXTGqS1mH70AYH4/DJyRBQo+p8taMi4gVWlKAWVIRcM9YWhcyrjsNHwoyHopwrNn3rAprB+h0VdsCECApAfwgGF7k9Hh6bv5iRE5IpLc5n9cpcBg4axL/v/attTn5k3mIyMjPZFEwNZ12mQFBaVMA9v5uJ1qMf7rmP8q+XF5KSntkl19qY72BGppf/vLKYlTnLievTn+wP3rNztJr+ozmMSki177t1JxXFXCxY/fWyx8O/5y1k1MTU+r6kwQ2KouByOrASAVoaZCvzgeVqEZ4WzcJKo9SRQY7jktIYaQxjWf5miD8ZvvzBLDeNGaxmLRzeeMbNnFcXk5yWgeV+AjA2KTWYsSHkjmTZWyKEY2DwoYdz1o23M7Odcut2BLoQVAY0erqcET6T4e5I45PTeWL+Ygp8Kxif6mVMYhqVAQ2HAnFOJ6XFBaxemUNa1gn0PWRYw+cy6rvmxAopaZk8Mm8xawtymTk9dL+WLl3K/Hc+5NJWVAEMuVSEtI1GlOc+1miuf3FrKwyGWzNjGUuhsnFXZTCVo7nVwFz02PNk8FL0COEoxm9yFKSAHIWGVoKFhYV8uKKQpIxJjA3mTEVqkLsNtsafhv3eqgIaqm7gdjoIaEYwI0MoiwVAUVhZWUP1Rz8XsblwqlFNd4H1JQWsL/SRmnUCJXWDi3wr8Gs6twWj+BWHgmEYQeERs8JaZkbQ7SQkQAoBS5e8iXr6rXBMCupj57MqP5fk9Iwuvur6CCEYl5TKutJSnnngDnRdByEicrOG3EwE1QEdl0NhZZ6P5x/9Z4Qw/dGiBRT6VlB2ZJ2FgMOBy+Ph5GkzSDvrQv5Ram62MqNYAUr2YgVsn2SLgC46dKFlXWPYEgCEQNd1Vq/MxelQwp4NMx9yUlpGhNZPMwSuYCFJQwjWFBWwNj8XHA4gJeJ87rgenH/ZTaTHoGUFzGvSDDPANM5lprqDUCo8QwgcQX/98cnpjEpINf0wMb9XdcG3awu4c9YFqAGVeU8+wp8efgYYGPV8miFi1MHCJD7J1KJnHh1qv9frxXXEGFKGD2jRsWyrq2U1IWSBjcGhMoKO9C/ujEVwe2HdP6uMtGVVCZ9brSBnCAnIQ3p76O3pHm4V4UgBuQHqrgR9Ph8zZswgENSyPTJvEYeMnEicyxGTmkJJdAwR9lIHh+Win8qwCgIYwgzKSjlyAOu2V9h+cgbgCN7npIzJuD0eAgE/QteinieaNjBWWFOUz19mXYAWUHnlqX/zuzvvjwguSsqcRH5uqEy0AwcOhwOhKHbwkT3RKSGLytrifD5/ZwHcsAAAh8vNhLSsmO2HtUX5pnBsVThTFJKyTmTWzbczITndLCeP+cz8sLeKJa/PY/adf0ILE6adTicfL3odXdNRJp4Op1xvH79n737c/d+3OX5CCpUBDUpXA6DrpjuPVZ1N1UN5dKMttjuy+6zJzsLhcILTicvtJiEjC6fiwBU0s1v33vpdaXEBxStzGJfiJT3DLK28tjif318x3c4Pzx+WRJwvPFNILCIIFooJs7BYTQ03J5v7hjK7KIoSrBQGa/IjF5xfrSqA/qdHPd/6kgIShk3tnItrARt2VFAV0G0LR/HP+xkztA+941yNCrR6cPGnGYKe7voCkZ0nHUImeqIHTscSHeVfvLvSz47KQLeQISxrqq3xDyuIEwrOFvZcCiEBOc7lIM5aRXcjpIDcTLKzs1HV0KD38eI36DHoCxIzJ5PeRSbkWMKv6TFVDCIa1suNACPML7Buahorb6NmGDgUxR4QjOAaeXxyGnNeXcRz//knRRu+b/BcsTjkKQoU+XLQwrSC+/ft45F5i1i1MocU72TGJaVRqxoRUfy33PUAO3ftZnx6FvFBc7qVP9kaJIvzctB79IdgRazJ51zE2MTUmBSGhICSlbnouoAjJ8JPa3A6ncy88TbGJaWFLA1BTenqwgL+ceefbGFaURRSsk7kiKOO5t3X55nCYB1rQk1NNQp9URwKTiU0ORwzuFewcpiDWk1EtClcg6yGRYl3BLm5uXz82edoRyXx0WYPABNPPovjxgwgM8t8DhTg/55ewMaSPJIzJ9lltcNdL1weN4/MW8S4xFQ71ZloxPHWMATf7a7i+CG96d/T3TEX1wpqVbO8tJWVJlxIXl2Uj2/5Msalem0ffQgJy7oR0iKPT43MZjE6IRU2RT/n6vxcLjk79gTkCr9mBucScherUXV6up1Uq2awYmVAY01RAcuXfWlbWldt2Q+Yi4fUIyO15vaiOng8v2bgdIS0j7FMR/kX76wMUBP0y+4OWAoj3RC4HGYonh2HJcKel6CFtoGwnm6DFJCbyZQpU3C73QjA6XTy4UJTa/TO84/xjxff5pjTpnR1E7uUtdvKGTu0L73jYvORqlF1vttdhREciayJzTBCQ7NlblbCnAIFlimcsMIJZuaSy274E6v/+Hui6ZB3bv0JIYZ37EW1ksTMSZFawfQsxiWlMS7JTFumCzOt17/nLWJtfq6d63dXZQBn0DfVMqVZwRgACemTEEaoKlbq6edC2PexQnmtytb9tSSkZ+FMOxd98pU4lszm+svPY0xiWkhDaPtIQlFejl2aGsDhdHLVzbezaeOGkDCo6xHnUXJfpbS3l1EJKYQrTwxhTjAOxZQYLH2LLsAZ1lXf7q6iKqB1iHbJ5/Mx9dRTCQQCGNe+BL36A7Bqn4LjuF8wM2E0RrBK1qiEFMYlp9HDFXovSlaGtKSaCh8vWkBJXg4DBg02s2IEhJ0jOaJPgtdpas4biV7rAkq3l9uaTTAXK4YQlBYXcOvl59oBeQ++tIiMzGDQNua1WAUvhBCMmpjCgy8uZF1hLhPTs+g1+HDY9HPUc45Pi00/bHu8I+ResrMywEdfLMe3YhmJGZNY54AbLjVjMSy/XNcRY4JKhejHtc3whkATAl0HjzP2NcjQev/ipuguwjEQFpMRisGyFlChktJWJgsFvbEI1W5AbEozMYjX62XBggW8vyyfXdu2sOSNV8zJQTNTRJ17kAvIdU21sUZFrUZt0GRurXtLiwt57ZkcEoM+5WaZTLMgCGAH51mm71DaMnNgGJOYyu/+ei+P/VD/fJ8vms8vhrs4Y8zZnXSFzUNBIT4pjQeef4uSlTkcOnQIq/JzURTs0rjWIDguMZWJKelmWi/NMN0CCNesmUUT1hYXkPfFx2RMiTQj/3uDg/5H7+OEYwZ3ybU2xM7KAJUBjWGjEznqjJn8UAVnXf8Xzjwzww7CshZClok5OWMSHk8casCP4nBww//NJj4pjVUrc0MV5pwue7HleOc+HD+tZtf4o/h6dSHHTUi2z/+7BUU8en4S/Xs6UHWDSr9O/55uM5NFmCBtFRTpCOEhOzvb9KPXdegRmdqweEt50ExuEAj616q6gUsJZWaYmJ5lL7IcDicfBd1M3B43N9x5P9Xl+9mk9+GjOufdURlgw85KRgyMrcpxEOrnQND9pUY1iHM6+XjxG6gB0zqgBgJ89s6bpGdk2ikRVV3gV3XmFf3MeRMOx+VwMyYhlYSUNAK64OctWxo856hg5cJYw9bqitD7npeXy+8umR5cKHg4+/wZqHX8ck+5dIytSKh3TEJzhCYEwhD4DYNebmeM6487DrtiZzdYIShhPsfhmYusTCTVAd3M+IK53aF03PjVWUgBuZnk5uby0RfLSMiailNR+HDRG3aKqPhU70H7glvEsnBsYQRdKxwOWF0YzNwQHOwffWURbqdCQe4KUryTyRhxRoSflWGZkMIGBd0Q9Bt2PPywvf65DMGa/Fy4JLYEZDDbPjYpDb9mcN9vZ6AGVOZ73Dz80kIS0zLw6yLoThJ2vZh9oIlQbmNDwHuvvcy/774dQzd4/fmn4Ma3I85VuHk/k44e1OnX2BSGgJvfLWVblakVHTrsKLCi6wlpusD0tS0t8PHHu//B3r17iE/1mlXVgMSMLNMffdKvEQm/sI/vPekU8uev5uO3XuXTRa/xqyuvh/6nAbCtRvBeTjGXTk3noezvKNlSzn0TBd+sWklq5iT698gieXh/lKAmriPMz1a5bAKBoJYn0j1qXUkBE5LTgynpgtYWu18E45LS+Ptzb7K2wMeurT/z2aLXIlx2Zt34ByY9kRP13Hd//DVnjD4k6nddiSHg57IarnpjNTdMGkHW0YPQomjB7eIfQUFhd5Wfx5ZvYvW2cjasK2VmwiGkpmcQh7NJLbkg0mIVKxgi/F0wPxf5clBVc1EVEH42lK7B6TJFiPrZnuo/s6Gx03TJszTJ1njanWlLdUFLubSnKsCgXu6YfB4sTH/xsBzW9uhgWgTQwekAvy7o4XJ0+/sqBeRm4PP5OPXUU/H7/bifeZJHX1nM3c+9yYq8Ao4bn8qohNRu/yC0llpVZ10w+XusY63UHUKhOCwThUqAjxYv4KNFC1ADAV7yeOj11hKOn5BiD17hPsrWhLFo7TZeLKgvHAMoThcT0rOwEqjHEtbqv7TQF5mhYGUuiWkZBDTdDsiwhz/r/gozyt/yQ/333bfbfrlqIFDvXFZATqwhBGwrD/kM60JQHTDoE6dgBWsi4H8L5vHU/XdgGAYeTxxzXl3M8ROT8WsGumG6ovzjhbf5U2GkIDRo8BA0VUMIA10zWPTyM3Dzafb3m7/dQO1JqXYatHvvfwD9+yIWPOPmqdffIfGI0+12dsTi0+v18vriD/h46ef813Cj1jlHaYEv6G5CqB2GpVE0/zt+YirHT0hhw+pCst9/y3bZScjIalIjFosaMyHg+z01AKz8sQzviEHoQnDG9Bl8uPB1NFXF6XJz0rQLg7lezVRlD3z2Ld/vNVMe/rDxK2Y/cxP3Pf8WqRkZ+LUmBOTY6wYgFENhjZmGCOZ+dnsICD/CMPhq7SrcLhdXX30NV111JV6vF9+mvVjvT9FPZaE884YgoOt2gJ9fD2kXY3WMaC5tLTFt+fD+sLcKt7MP/XrEjl9+OCKoYDKECBUACdMQCwEawSwvhjCtTwJcztia/1pC9wsr7ET2VgcwDGGneDEMA1VVKVmZg37oaD6IS+Pxb0JatoMRM8WRCEUnx2hHhMxDZvsmpJmBNE6nE7fbY5qR7cpYAXKXL7N/G3KxwPZJfPWZOSzfEOlXeHjPsIHA4UQBfiqr6fiLayFWH/TpPxDFodipzSammxknNCOUkzc0+IVS+NSoGgJBSV4ORpMasu7xbggBmjBCPnWGYE1RPk/db2a5EIaBGvBTnLfCnhQqAxqqLhg9MaXe8Y4fE4/DGe4vEemfPPy40dRqoW3atP9DjD4BVVUp9JmaVzsEpoP6LzUjgyuuvxWns/40MC7Fa/tHf/jVTtZsKzd9p8OeA4Rgw+pC1hX4mHX73znz/Ms47dwZgJkppTFi0eIUXgyo8Of9fJ5XxFvPPY4AHnllMSefdylTps2ws1VY+av31agRx9E0lTUFuQB8snEXiuKgoRS3sRvEFEo9aGkIxyen89Tri0mbdJLtVqTrOu4BQ22BMHy8CF8Eba/ws3lfTci/3xD8vL+W6mCmDN2I3bmjKaKlgGsJIaVLhzSvXQllgDIJX0ghQjEbAvPZHnto325TNS8aUoPcCD/sqebYwb3sFC/+QMBOdfS7j76299uwqoCcr4u58JwzYjb5fUdimYG/213FoF6emC25bWlDBDAqIYVHX1nM6vwcEtInYQgz0MgKxEkKS2clBKiGwAjofLe2mN9fMd2M1D//HjgywT7+Mf7NbONIAAwUVufncsLkLI4aGDv9YU3q60ryeflf92DoBopDIWXSyUEh2Ew5phvBUtxKeL+ZbheWEDkxIwtPnJn6CBTECVfWP1+MLh5X1xHgtm7ZjDZqCJbOPKALSlZGLgAcDgcJ6Vm898Y8ln38PhmnnsXZF11BZSBS+AXoefjR/PbO2TzzwB0Iw8DpchEhRkXpF6X/UNxuN8kZk9hTFSCgWwJ7x3WggKgFCo5PSCagGTyT9yPLvt8LwIIrUqhRzWw1QghKSwp54A+/Rd+/E4fLCYZA1zU+fHs+DoejnrtNxHljzLJiPfvhbXrheweOJx7irbn/4fo77mdZbj56bTXL3n+LB198m+S0DFTNiCz0ccQ4nP0PZVxKJht3VTE3bzP5h/a0s+FEPW8MviDhrmRWbAbAhOR0Zt3yZ1YX+FBVFZfbQ1KmOVau314RcsepY/mw/OktE70A/rBkHUcO6MHj545n874aArrBEf1jzze9KVqbAm5nhZ9aNaRgisHHIAKrMp51/6x5QTeEXR3UWlRb+3d3pIDcCJZ/jdfr5eNPPuWFNxaSOPlUxiWlgs9n7/f362agaSrP/OehFptXOpq2+EY1F2swNVP31BcWYgWBQA+6B+iGID45jQkp6XauxsfmL6YgZzkp3slMCEvlVKvpbFhdyPoCHxW7t9luCXW1gr169oSq4AfFQd8BA+1JJlaEASGgVjV4t+hb1BsWwHOzEBW7yf38I4pyvuDhlxcybHQiNYYgzuXEEg8tS/HXqwrZUJLHiSeeyLikNB6Zt4iivBxGjBrH3RvrlxANr14YK30AUOxbAYQybny23YGnZAtXZ45AUcwyqmNSvLjjPGbFRIeD2+97mO83fsWcu/8EQEluNgCTp11S7/h+TecXF13O0aPG8t2qPLZv+Zl3wr7f/O3XiEnJEb+ZmHEiV948k/HJ6Wwuqw5u7dj+E0LUL5ENfLOrihrVsIVja19VN9sS0A3mri5D/fWz8NgFGKpqS/y6ptHUKBBrJZYtzVfdtYKVpWPZx++hXfJvALQ50ynOyyExNR3VqNN//YYifv00oxJSWbayCIBVOxq2Ii18/nHifnUGvzx1SntfUpuxM/2ISJeY+KRUHpm3iNX5uWROOoH4ZDPtY3VYujLLAlXl1+jlcQYteKEAaGu/n8pq7UwgTbmjdBVm8RiBO4qlBVqeAs7n87FkyRJGZkwhISUjYv6MVb7bXUVZjRpcEIZKrwvMd1kX4VmNQlrx7o4UkBvButGabhCfnMZFPQbj7Du43kpPC+ZHbu8KO22lrb5RjR13yZIlTJs2jfik1Hp+urGIQkjTbSWyt8yk1pp3fFIax01Ixu2ILP7y33/fx/9eeRaEwOV243Q5QY8M5AKorakCghXCHE4qyvbZ2tYYkg1Z5ltJoX646WA1aDhU7AYhUFWVYl8Oh41KRNMNMx+0MDVfAd1g4+oi/vHbi9FUlYVz/8PDLy1kYko6x09MZd/O7bBxU4PnjKU+UMDUeuVGmsaXfruHWelHoRtmCrLRE1O48+kF/LBmJWneyWRkevntpdMjfpP7yf+YPO3ieucQKCiKwvikNE6YlMXqonzeu/8x9JN/C5guFh9/vTPiN2NTMhmTODxoxlTsbAClJQX43lrFySef3C7vb3mtSpzLEazoV18oBLj7441ccbTArhlL0AyMmbVkxbIv2erOMr+4+W146//gpzX2vg6Hg8bEnVgTkIGgX3EkjmDRlLRTzqIkuGZRUOgzYCCqAaph1HuuNaGwYVUhT//9dpjxcKPnfPOph3nnv3P4PMYUK6bpXARznYMdoBxM/TY2MZXxyen09jjte1m/oppgw85Kjg1aFC2t4ze7qrjpnVL7XG8/9xinnDyFk0+c3LkX2Ux2Vgb4Oegq11C1u+amgAufkx3OR/jlRZdy6q9mMDohJShgxsggWYcKv2ancnM5ghYBAEuwD9MsW0a3A0GDLH2QG8F8oQVby2v5fk+VfcNr1Ej9iMvtDg6k7Vdhpz1oq29UNKwX/OGHH2bq1Knk5+XZQQaxjlUkxK8Z9qBu+08RNK7bQrS57Z3XXub9l59GGGZ1LU1VOfO8S7jipj8zJiHS93TI8GPtv51uDxPSsjrjslrEypV5PLReQXX3NjcEC1goioLL7WZkcmaEicyaKGtUnfeKvyeQcWnQT1tlVX6urd1sSBiyJspYwbqmccFiJ5FfBn3nDOu64bgJSVx47c3EB4uHnHDGORE/yTj1rAZNow4U3E4FRYGJyenc8rtQlb03Niu8UhSZ/iu8v63F+driAm678nz+9re/MXXqVHxhlquWEtAMin4qY+OuStZuK2dPVaBBDTLAK5sit2/dX8OGVYX8/orpvPFkHcHvyPGRn5tYDekxJiALTL/zulr6i66/jbvnvol/XCjAUr/mBV74598oLsijvEalOoqLzVvPPIIWiF6GPhzD0FHbaWxuT4QwFzHVqhHpYhX828poYu1r/Rt6dq08ucIeby1r0ppt5RHneuPJh7nh0ukUF6zs5KtsHqpu2PErbZ3nHnroIWpqatB1874vnv8yv79iOutKCmJ+Dg3XFgd08wkwM0OJCOWYXw8VzenuSA1yIwhh5u3UjFDwjhCCqkBkaYi/PvMG29YVkpY1Oaa0AB1RHjNc6K6treW1+a9w9V8fjAiAi0UssxCYOTh1IzTg60LgsnxrIegfavrbZn/0Xt0Dcdr0ixg+NokNn38L+8vsr77/aQtwGAATM8ziGnZKqBjAMATLsrOBxNBGxYHL7eG06Rcz5ZcXctyEZHvxYLmjrC40q2UVebIg9XiUFS+Z2QrSs8IKJUS/SsvUFit98PKSzyjMXc641PqLF3tyJ2QuhJAmxBBw9oyZ1KoGn+UW8u3EyxhwFNz19HwYEF/nYGYuXZczqAlWYGJCAny1qsG2ReYWBRQoyQsV5Girhcocx0J3wgpcbcByXI8/vvcVp21+K+Ri1BD9D8W4+rlGj2UE+ycWUHWD7eV+irfs584PN0R896tZN7K70s/L76wLbew9AE1TWVvg47WS6Mdcu3I5DDyiyXM7nE7c7Vi6uD3RdNOSYrgdlJYUsr7QR0rmZI6fmGL70Idb4OwqpWHvkAB+3l9jZvwIrkDrLsiswOiVOcu54MxTOv9Cm4EhQjmLW/vczp07l3feeSdimwha7latzCUhJaPtDe0g7KDtoDJMNcKC8bAWTZELpliWB5qLFJCboEbVgul8zIekVtV5KufHiH2On5DMqSdNxuWILYV8R5THnDJlCk6nEz2Ysmf+vHlknHkeIyemxHyQgRWBLwyzCEKE5kOxslUEJ29hav6OHzuB/OVf2Mc44RfTQcCCuY9RdUikkLWmMA9SzgVg9dp1rC7qz5TJkzrp6prm+73VHJeYActDmq2ESSczfer/MTElnZ0Vfn6/ZB0/ldUyK+1Izhp3KN8U53PnrAvwDxgBl5rX61AcXPPn+xibmGYLlH41usAUrlGii82HPp+P3178KzsQk9+9GfG9YViaIoVaVaeH2xkRlW09LydPv4zSQ7L45oe9/GvpOsQR8fXOtWXT97jGHYrb4WDEwF58v6eaPp7Gh1vLpmH5fDpQgnmWzYIc7bHIDdf0WJXSWnJfvnh3AU6XE3FEQp1FT9gxhh7frHbEynixt1plR2UtD37yVb3vPtqwk5rSL4HjIrY7XW7iUzJhdfRjislXQNr5TZ77vN/+kUt/eWZMKVbAfG/XFOWzMnc5Q4YM4fkH70INqLg9Hv718tuMTkhDESGBqNKv1dEkh8ZTAbYPsoIAJfLGO5xO3G43mZNO7NyLbCFtfV4XLlwY8VlRzAxCLrebiWlZttY9FjEVTMKO37EUTgFNt6vThvrHyoIUm9fSEqSA3AjW5GhH8iN4b/0OvvhuT739RAwN+OG0d3lMr9fLrFmzePbZZ82oVl2jOC/HzBkcoy+3haaLoMYTDu/Xg9LifFatzCU+1UtSWkaYKVGxBbrefftB36FmdZHyHfTo1Ys/zpyOqrhRzu0PEcJRSEgwPL1YvTKHkyZnxYRwCKZryfjkNFi+wt5WE38mQ44/Bk03n+2fymoBeLHwJ84cM5RVQQ0ml4ZM6oYwyPnkfY4fM852Vcj++H0iNNNBLKEvFp6MT5d+HlbooH6LdF1n4+oiPEeO5S8fbOC4wb24JHEYqUcOsCcHhJm6r/DnMoCowjHAwkfuZPKwh0hNy+SQPnH8sLean78qbrR9ERUKg4vy+KQ0Zr/wNhtLVpKeNZkR45Jaff2W+4ClBROYOa0bSkEWDcPQmXrepXx65IWRx3a6QFFwOJ0cMz6Bb5s4TqxNoKsL89mzfSsMHBax/eXCn+G//4Sr50Zsn/mnexk5MRVWF0U/YDOEY4CzrvgdSSNjr2jKuuIC/jLrArSAiuJQMAzDTHWoBihZmcvIialmvtugALxhZ0WE7zFhc6JBcDEmBKpm1ItVOe6GJ7lp0pEkp8WuBtVOT9eGY5x//vl88skn9udLr7uJfv37E5/q5fgJKbGdyUKEudSAGbOiG/h10xprEGq79W93Tu9mIQXkRjC1X0qwmlrDk/ze6gBHDewVI2JAxzNz5kxeeuklO81PQvok1pUUsK7Qx5QpJzHm7FO7uon1EMEXWgT9S79dU8yfrjwfNaDi8rj598sLGZVgCnv2II9gYroXhJntwPPkRZTt2WUWxPjN84jeAyJPEmZ7U3r2JSUzMaaiea1AxXA27qrisRU/cMXhlaxd+Q30G2V+EVwsjEvz4vK4CS8BIm59h1VPX8ZXvz6f2S+8zboywYIowjGAMGJj4ajpBsPHp+J2e1CFHyNK+IVwuPj73Xdy5s33Awrf7anm/qXfsuiq1JBGDPjje+sbP9mrv8fYs4nVK3NIS88EYG1RPrddeV7jqc/C/rUWVQKzpLnLqVDoW0FPt5OLz57a4uvfVl7L1v21IbOnUFhdmM/fl29jp9L8UuAul5tjxowPZWsJcuzJ5zF+wjDSMzLZP/B4Hsr+rtHj6LHwUARRgKK8HIRxaPQd6gjHAOX7y9rlvR7Uy2Nr4GKF73ZXsSY/F81ypTFMLa8SzBmfEMyXHlwvAgLdsNI/CtYW55Ofu4KkjCyS0zJxhi3MArrBvurI4NhvXMMYnRAlJiCGCM/O0ZCyo6mMUddddx1gapLj0yZx3qwbcDsdVAXzqZtevbGJZUGzXRIdCrWabscSmAUnw4Rk4OhBsZPetLXElk9ADBK64ebNjxbQcsu769lV6Y/pLA7tzYUXXsj5l13JE/MXA4Lbrzqf5x+ZzawLprUpkKgjiKjeFPxckrfC9qXUVJWSlblmAYwwrcfaogI+XLTAPs7/PbOA/oOC2p66wjGgOFz01SsB6HfYUZQW+lhTVNDh19cSosklu3bv4e6rz2PzxlBkuQDeeeEJhCG4e+6b9X804AgCfj+fvvMmX36zo7EzRrgndAUVtRqrt5YzITmdx+cvZszERLh1UdR9tQseIPfnyohtbz//OGuLCiKCNxtD2bsZVzCXtqWd/WDRG1ErDYYTXmwnfEJeV2IG6j3zr3/w6wt+2ar3y4pCt7LNrC3O54ZLp7O5BcIxmPEWFfv21dv+XaXCqsNOYeTE1Gb5aMbSwhGg/4CB0JhfdR3e8njJWVnY5vN++f0edlUF+GZXZdM7dxL7agJMSM9COWIcOM2qboqiMG3GFTz26mLTtSoskNQeM4PP6i2XT+elRx/kT1eez9riAtt31UDw2PIfeHvNtnrn1KOU9I4tQlkawtGCKRus4PW77rqr0WDa6667jo8//pizzpthvodF+cx/Zg7rSgrs2JdYRAm6Y/VyO+ntcWII0yIb6hPBYX17MOqQPridkVmgujNSQG6E8CAEy92iocG/buBerNFeA7A1ELz22mssefN1BFCyMiesZHHsRWRv3FXF5n01fL+niq93VlGlaqR4J+N0OVEUBafTSUJ6FpoR9FMWUFpUwM2X/YoPFsyzj/PqJjjhnAtwOKKbjlJP+QUvXHUiiQMM9htuXn3qUf4wczq+3NhYMCgKrCmuX+Fs366dGCMnQep5EdsXzHuBe6+bEX3QDvqkffTWK1Ttry8wWXhc5hDTldYVzfYtNl0WxsRPbHT/vY7IfM5vPPEQt//6fNYWFVCjNv2e//rm27n3uTeZmJJujxeKaFpqNLUz2BkCLCFydX5uu7xf1uJQCGGXWm8pm3ocRe/+A6N+92NZjelv2gx3IrNgRItP32GU7dtnmjtawGMbW+c29ZvMo+y//7PsBwrzV1JWpxpfV6IbMPi4CegXPgCn/g4wn5tDhw1HAK89+yjrSgojFr568GFdtTL8WVUpyVthP8sIyPuxLOo5YyloM5wdFX72Vqt2HEJVQI/Q+K/eWk5AM6JmjKr0a/ywpzrqcQ0BpcX53HiZuZi4Y9YFlBYXxNSiMRwRvIE9PQ4O6xuHEAIrLlsgOGpALwb0dON0KLY7zYGAFJAbIdysYOezbWBf852JzYfCMAT7a9tnAA4fCFQ1QHHeCiamm4FEjqAJLtYisqsCZmnk29//iv/7aANXL1hDdfDtFkKgaRpvPPcEX68qoFYzhanV+bloqgpn324fZ2OFQnFhIWdf8Zuo5+k/5BDi3E6+2WcKHmLSZaiqypfLsjv8GpuLWSCjDn0GwVl/qrdZXPM8mqbyZeHa+r+xzaaCbZsaNqf/+PU6vl3TgJ9mB+Pz+fjz3+7lnU+yI3K0nnV+/bzFjWGl4nphzkOUFjdtEUjMyGJsYioup4Nxh5rC9m+v+TVOV+Mebe+u28H/1u2w/TfBFFZGJ2Xiaof3K9wsnpQxGbfH0+JjvFTwM//9qeFqZz/ureaZFd803ZYYMrcpCiRlTAo6tHQ8h/aN7Pc/zLqkydLcnYkQpiAIQPxUlENG4HK56TdgELdcfi7z5vyTO6++gP+98TIvPflIUEtsulbs2PozTpfTDrxLyJhUJzNM9D6Oocchgp/Kqk3XvKCG/NvdlWwtrw25FgStPlbGKKfTicfjITlzEpv31bCnOnqqPwGsWpmDGgjYVsxV+TmdeGUtRDFdaKr8ekgGCmqPFaCXJ6Q0GtonjkP7xHVBI9sf6YPcKMFgGUKal4ZeZDUsK0IsYvmHNbcaV0P+VOGp49xuD4kZkzl+QjL/eOFtNhT5OPGkjqvY11qsBU44KwuK0ILaQGEY+JZ+SOHypdw99y1SMzKZkObF6fagjY5MXv/OS0/hUavghjOjnMgMRunVI44qPzBoOG63mxNPPKmDrqxlKEBixiTw1Vks9ejT8I+OSuRTdxSNqxK+tm74wV+/q5q/3HA1ScPfZsoJnVcIwLJ0+IPP6ePzF5u5jAVMSEmHFVEWCg3hjkOofgpzv2R1gQ9uiOJyEsZtV57P3597iwGZmfQKZq44dcoJXHDpTBY0+kt4oeAnLkk+ws47W6vpHDcxmfuee4vvV69sdTYaq8KVZfocn5zGY/MX85uclmuRxZCjG/zugaXfsF9teozRY8zFIj45jSNXBdgcXeHXbpw6cogZ1xKGqmsU+XK4evoZHXvyZmIIwXcbwtytrnicuxMEm9YUhLTDAcGT99+BoRs4nA4umnU9i+Y9hxpQcbicnHnBZZx27gzik9IoLS5g1cochh8/GmH0BqW+BS7W/LAtDAHrivPxrVhGQsYk0jIy2VHhZ091gIRh/TGEWWJ7xLikiIxR7uFjqFF1DAElP+8naXj/OkcWJGRMwu3xoKoBXG438aleDCP2qo5aBDQD1TBM6yHWYrv+i9wn7sARK7vkSubMmcNzzz2HEIJrr72WW2+9tSua0SRmtLcZqGcIwe5qlfU7qqLuq8ewC1W4Jryh9y5cIAYarMBnpY5bsmQJEydP5ahxSVQHDMYkppKSlsHg3i3XSnUGdRcv45NScDgd6GHlTTVVpbTQR3J6BmMTzTLKN+fX9UsUqGp0ocLsX4VB/fqwa1cVg0aM4p6XFpKRGTsLhvjkNPDlNnt/o090H1XvGb/C99/gJNqY7+aQowlc8A/efv8TJmdl4Wpu0t02Ylk6DF1HI0CRbwXjktJa5w9901soj55rRvE7m36+VVVlXZGPDG9mxPbExCQW/Nz06SxXLkOAGhQcRiWkkJXl5ZA2aGbCo+QFprsJOe2rtdpf2zxXs1jIYlGj6vRwWc+joHefPlDdsb7A12QcxfodFRHb3J44kjNjJx3k2uJ8/vXXW+CS/9jbRk5MpX9PN26PG1U1xzld10EIdM3g9eeeCC7CBOgw5PAjGJuYSmlxPn+YeT5qwBQCHTe8HrUM+VerC0k4ouXBpx3NmkLTDUINBHB73Dwx/x2S0jIIBOcNa9FZreoRGaMKNu+z8ybrdVx3LEXbuMRU5ry6iOzsLxmX4mXUxFR+3FdDZUCPuQA3RYCqh4oKWQkMYlkp2B50uoBcWlrKc889R35+Ph6PhzPPPJNzzjmH449vOndmZxPuNWEA/8jdwU/l0V0VdMOIKY2IhWEIqgO6bTbr7XHiqJPXqW5J6iuvvLKeP1W41srr9TJixAjKHH2C/lkhf8lY7INojB4/kev++g+efeAODN0csp1uN2NTvHZqv+MmpEB+HdPnqTcgjo0ecV29bD5PfllBzcjzAQVHn0GMTkxu13Y3FSndEAHNaJ1wOOXaqJsPnTSNKw/th8+Xx/djp9CoWNT3EJIyjwgWZOkcwi0dLreHpMxJdhq1ta0wZ7vdHjRNxfjtq83Y101iela9gN7ysn3AiCZ/b2ewECIYCIMd4V7p11i3vYL4w/o2cZQox8UUfj5d/CZOh8KZ581o8THai1gYLddtL6dvnNtejDhbku+uDdRVUtz71DzTqhEjFOfloAUi5zkBjJqYysMvLWTF8mUMHDSY5//xV3Q9+OYHze0OhwOXy824FC9CWPEplhsBOBvwU7z72osYufh/HB1jhUKK83LM1JCGjqqa2U6S0jLsp9fKmV8Xq4S7IQSOOt+LMMFiXFIaA46dEEyTZhbbqlupt6vZXl6LX9fRDAOHYkYYGAYIZ2zFEXQEnS4gf/XVV2RkZNCrl7lCOumkk1i0aBG33357E7/sfMI1rkLQoHAMwQcmBh+WreW1bK8w0zt9vauC4f17cli/HhH71A0wAJqswLe3WsUfZ05zgaCPlhAiZk1lde+NEDBl+mUcdsxoPvnkY9YNSuV3SYMZmZDMV6sKWFvg4/jEzPoHakA4dr30G3x7g9HZF8fDsLHBfmm/8LS6C5lwzX5TrN1WjiEEV7zWeC7eerijayzfKd3BlOMmszFpbMuO14HUXTwsXbqUBe99RHxqVrCqoan5KMrLAVqWVupvzy5g2f/e5pOmd+WuZxcwMSW9Xn7hSSecCB+WR/9RGGYe2VDaLDB9di0f6uYECobzc1kN5bUaa4vy+eMV0+3gvP+9/Trc+FaLjtVeGEbXJ8UUIljgIhhE5uwks/axg3pHfB4xNiGm5o6E9Cycr8yPWPR+vaqQhNR0jopP4pfHTaCH2wkI5j5wB8LKQKEoJGWdyPRrf8/R8ckYCDs+RVXB4XLhcjmJJv9pqkreiuWcH2MCcnLmJDM1JGZxoaQMU9MfbomJdu+sbVbWmHC3CStgWBD5Hlg5+mONn/fXmFkrIhpnWYBisMHtSKcH6Y0fP57ly5ezZ88eqqur+eCDD/jpp586uxnNwkp8Hs2HtS4fLXyN0igZAroaOw1PWCaOutQNMJg5cyZLly7lvvvua1AI2xEUukXQDKwLQYU/tla+FuHlUCO2C8HIhGT86TMoixvCot19KSws4s6rL+TVx/7JfTde2exzaOEZAYImNbNGfX1TckUzzdB1iRYp3Vws39PNwUIg7cGW/c0/1g2XTievA9P/RUuz5PV6ueamPzI+OS3iPliTXEswBGQvaZ4wqeqCBXPnUFocmQYsNSPKgisK60rM3wkhWPb9Hr7fU21mtxANv8ONsb3CvE/FeTlm4GkQrQFXoQ7jw0fsP4WArftr+bmspnPbEIYRNi4KBJ1RCPWbNUXEuRzM+VWowIxf1eqNT6puUN1FmZHGJqaSddo5EdvWFflQDbOwDJh9N/W8y7nlnodwulw4HA48cXFcccNtZgEVzHs8LjGNR19ZzLW/v4O4mxZQo0dfhLhcbjImndCh19Uaxien8+Rri7ny5r/wzxcXEp+cat8ry00o6txib4v8rqxGZfXWcizXXdUIy5IVfLFjzfvYkiF02z86LOD3wJaPO1+DPHbsWP785z9z+umn07t3bxITE3E66zvtz507l7lzzQTt27dvZ+vWrZ3dVCr2lKGg4HQoVAXM6M2GnocP35zHZ88+wPC4BaSmpnZmMxtld4WffZUBNMPA5XDgqY2Dqkg/yhEjRvDGG2/YQsWIEaYZ+MorTQExWt+X79uLEGZ1torqAE7FLM28T+/BViW6n3ZXsX93BaoeeecumFfEHd6hjBwchxEUFDbsrGTH1gq0QMCsHKU3X+B3KKYGALAD2HTDoGLvLrb3CgQ1LiZf7ajguMG97RRozSU+Pt4skYxpxo+Pj2/2e1G2u6LpnVrIN7ubf59VNcD/3n+fkccd2+7tAFiyZEnE4mHJkiWMGDGCZZ8tp2DlSsYlpTJ6QgIOFIYfdRTkNlXrLZI1OZ81W6C8/zcXYmgaL7ncHP72W/Z4sLe6eZlkir78hOOPPRqHAk/lmmXtXzznKPZUuVB7ukzh0tV8wXLfrgocCowaOxany21fh8PhjOoP2iG88WfoHyrE0TNQzr5d1ZTvUXAMbSRItAMQQvDjvhoq/LpZJRWoVXWE1vGp1lav+IwjjxqBMxDq+Yp9eyh31rDVE1pwbt5XQ1VAY+yhLXelaS2GEOytVtm3u5IjEidB2NBy3Ogx1OzbCcC3e2txKA5GDPDgPeV0Dj/8MFYXFjA2KZXDjzySmn27zHnS7SDgdHDkiBEcNWIEz77R8Dt3+7+f5agRI7pknm+M/XvKOWrE0Qw4dDgAZbt30sPlQDdgq6ua/bvLcTgUqvcpbN3q4PghpnWgfE95sJiGgcuhsNVVjaIo7KtR2be/lsqyvWjCwK8Z3PThTyQM7cmVEwdRpsWhx7nYqrf/eN1aynZXoBuCygo/igLlogeHINhaVksvt5P+PdyU7W5ddGtl2d7Q3zUaO901BMrd7dX0NtMlQXpXX301V199NQB//etfGT58eL19rrvuOrvyTGpqKsOGDau3T0fzTbUHFHA5HPir/Dgd36M1EIwnFAeaprJu3TqmTZvWuQ1tBH1fDf64WgK6gcfp4JCBPRnWv0e9/aZNm9aidvfeVkG/IUOpCehUuv1m/kOHQv9BvRg2rH97XkKb2aqX4dcMIDL9VPZWlcMOPYTN5dvtbUqvfrjcHlRdxzHhtOYLEMGltMPp5JDhI9hhgIFCrwFDOPSwIfQOi+zdou3j0MP70dMduTBsyr942rRpfP755832Qd5fo/Lt7iomDutHP9VKzbWxuVfUrrjdHs4+55wOe4+nTZvGnDlzbPeTadOm8eOPP/LH3/zazLjicfPwywuZmJweVNG0TEDuk3AqjkM/pDmxuHpQS6uqAT788EP7vSr57MtmnSvxhFPpPXCoGTHO9wDEDRiM2+2iX984hIBhw6LnIo7GB3kfs2plDuNSvTz08iLefP5J9u/ewcBDDqX54ZptwNBh61cw4DB7U++BQ+jby0Ocy9Hp44VuCLYZZfTTzYpghgBHQMPt2QN0kEa7tgLHwrsZdN219Bx4CD2EALYA4Ok7iL6D+jFs2CB79wpXJc5atUX3ua2U1ahsNyp594d9vL41Mkhs/7Ak4voPQVHg3iVm2sY3Lk/G43KQetLpTJx0GqphUBPQeX3dT5w1digD+vekp9tJL4+TtU0UTBqfNZVBh/Rn2CGdu1hqih8DPXAoClp1AIei0NPtpKfbFJAPP3wA39bE4XSY2w0h7Pu1yd8Dt1PBpeq4HQ4OO3wgToeCp9JPpauaalUz3/EalT01m/j8x0quO2EUfQb0ZHBvD8M6edHYGD+p+9AMQZmzBqcCAwabi4A9jkoG9PIwpI2B+QOGmAtnrSrA0EP7MySGUsR1iYC8c+dOhg4dyubNm1m0aBF5eXld0YwmMUNlzL/8mhXBGV2HrLjcuN3umMsBbFbACQUACEw/4bqBenWpDmjsrVbZUeEn5cgB9b4PuWxY5ibTT7I6oLNhRwVjOlHz0RQK0QOz3E6FW95ZV2/7iedcyE/9RvL1wMRmn8NQQ/kuDU0DhzkRW75m4UR7gprrXxweKd0UNaqOQLBma3mjGUw6g0dfWdSh2Twsn+PwxcPs2bMJhAXYrFqZy4Tk9Fa5zb30gwKXPNL0jnXYvt1cfPl8Pi6Y9gu4vqlEb6Hm1aghcdzy8QfznVu1ZT+JRzQtWPp8PjMKXzV9KK/9y30U536BFkzHRcL1Lb6mFuNwojgcOJwue8F58avFfHRt81xOOgLbNzT4ed22ClZvKeu4E67/AmPnd7z00N0cdfwYxieFfOD1KP7YVuWyzsRKBbhuR33t5ZM5P3LisUMiGmV5oOqGGXQZ0Aw++noXH3+9ix/31XDP6aOIczkQAlatXAFkNHhuvR1jNdoTy5VAMwRuhxKKCbDcLwkrQx3+O0IuCALTInB4v7iIYwJsWFWIJWVYgXqxiiFEROBxDDe13eiSQiHnn38+48aN45e//CVPPvkkAwYM6IpmNIkIPsmmg7rRqG/QlHMv5d/zFsVcDmCwJgPzld5e7uernaFURmU1Kn6tvp50Z2WA7RW1Uf2rIPRy1GgG+2pUftpfY3lmUeGPnaqC5bUqQsC/fPXLm1ZGaefeyhqWfvAu33z/Q8tOFAzCcLvd9OpjLg4sv9doAYJ1aYt/cWNY976rS3+OTUrt8AnQ6/Vyxx132O/glClTcLs9dtGCCWlZCCGi3vf24NRhToZ/+e+IbYcdZmpNs7Ozm1257u5rLmJV4cqIyVIIYVefMyfs5uWVtM5r6GZlsxWfvI8WzGWrN6O6X3txyQ2388urbrA/76/VovrndwbWO2n9awjBLe+uI9CRqTqDjpuqGmB9oS8iY4ZWx6l8465KKmrNsve1nZzRwMy+EP25MAyDXy9YZX++5NVi/JoR9E2Flwp+4sUCM57IEvoDuoFA0HdA45pwPYbrCAgE5bUaBpaSyaTSH/kMW371a7aW25+t7/dU+1m3vYL3PvuSl558hK/WrGJVUT53LwzFZXy9qqjesxALWFk36t+f2Gtre9OkBvnxxx/n8ssvZ+DA9jP1LF++vN2O1dGYK0TB5n01+PWGH4isM89l3MghndewZqIEyz6GO9SHB398u7uSgT09HDekd73fhq90630X/EbTBTcuLkU3BG9ekdLob7qCjbsqUQ3Bd9S/N8VbomQU8PRC/OZlROGiFp3H6fZw+rkXMO3CS3j0Ww9UV2EI0KHeIiPaRBCelqyhzCEtRVEU+36sLS5g9cqWZ29oL8I1oJ2F1+tl7oJ3yVm+jHEpXsYkmr7Azc3V21I+f+0ZRMky84Oi4HabAa8QFNY9nma57GiaSn7+Sp78IeQKZQfF2Nag5gm31nm1YDGCzFPPZn3xSlTDjbgxsuCJ06HYFcLam74DBlKyugCGhLSIa4vzSUvvGi1yeLBjpzyWwbgERVGIT8uKFJDrZAPYX6PiUMzc++u2V0S14LUn1QGNOJfp7tWYpemr1cVUBSK/LF1VRGZ6OoaA78LKKgsh2Li6iI0leWRNPoG9e/c2Gn0WY5nNbIKyYWgxRShobeOuSoIJZ2yL6kdffEmRL4eJ6VkkpmbY761VcOT6S861c0JPuPj3GCOz7HOtKFpDWkYmsTWDRgYchhe56RPnok9c/fixA4kmBeQdO3aQlpZGcnIys2bN4owzzojJKi8dwbqSAkpW5pKSOYk78hqfVH/87htEDArIEDL1RJv7bA1jFLeLhubK7eW19neaEBFlN8MF8a4mmva2WTicOHr0aZa/KcBQUc4NT7xMSnoGA3q64btVoTYYZuBLT7cTd7BIRjStfDQXgfZACFMQueXyc1EDKty6uF2O21JaqsG2/KfbIhx8vbOS0QmpHDM+2c4FvrsqwOWvlbT6mI1hbP3atCQ4HGSeMIWbb/trRIGdt5d8yBUfbmcfjRcBcDidKMem8+PmkD9s7eb1fJidzUknTTG18c3szoSUNOa8uojV+bmMSc7k8NGJjBg5hqKCAt6us2/GUQPI3bSvJZfcPAoX8+yyF2H4eLgoJCAX5+WQntH5FjdL6RHKYNEJ2JUnFRxKpJbW0sCGtw9CGsiOZv2OCob2iaN/DzcCQXV5WdT9/l5af97/qriAhKRUnE4lQrDeV1HF/Tdcjl5bzdtz/8Ovb/87NFKDRRdGTOTGro8IW0gJalWDPp6Qa0n4vVpTlM/Nl4eKijw+/x2OGZ9sC9SFvhV2TmVNrV92e2lcIjc10zLUmVjtH9TLQ/8eoQC64f0bLjl/oNCki8X999/PN998w9VXX81LL73EyJEj+etf/8p3333XGe3rMnw+H3+68nxeevRB/jDzvCb3f+OJf7KupPFAhK7CzrtoTQph3wkBK3JyufmOu/GFpeFSwr6vq/n74PMvefPFuawpymd9ceQ1d9ag3hSGISj+eb+5AGiFVsyY+Itm7/v4zCmMS0rFoZj91jcsIG/z+hJ2V/nZUeGnOqBR9FMZQpg+aWU1kVHzdV0E2gOBoMi3wi4R2xn0ctfXKliDbHOp1YwG3XuaS3mtGnFuA8Guqg5Kbbb9G9i+0XSz8Xj4/Z//ysVnR1YGS83I5N0bTmVAj8b1EkbqBdQV2268bDpvPPkwN18+nbVF+QgBW/bXoDZRwnP9jgrik9K46obfMzYxja9XF7K2wMeYxPqWhNEdECClZD8Py140P/xcCsVL7O8mpHnr3eGyGpVdlX46ir3VAdMnP8Jk3AnjlVWBzNBZV+CLEJC//3p9xK5Wu6InEDMpr1XbRdtvjUe1mkGNqrOmqID1hc1Px9iz3wDTlUKLdEHcE1BQL3kk6P+vUry/8cwEui4QsScbAiEXGM0QEYsZzTDC5lUozlthF0VRVZXivBWh7wWkeCfZLl8ut5vEaOkmWzhOdgZWc3q5ncS1MPNSd6dZV6soCocddhiHHXYYLpeLffv2ccEFF8RkcY/2wvTdU+2HvSl0IVi1slNiwluEQsjMble8E7Cr0o9hCFYXmWafp//9Dzt/rEW4lsXC5/Pxu0um8+qzj3HDJdO4e/ZD9ncbVhfawnhXUqPqbC2vtQX1Rjxj2oWAZrBxdSFvPvcY60sK+MWA/eYXu37gjlkXsLowP9guA2uJUuFX2d1Rwhqg6YYdJJiUORm3x40SJZ1iR9CjfAtHlkdmy4gWrNgUbX2OQtod6zkWiI7KMvp9AU6Xm1N+eQH3P/8Wx45PibqboxnBV0bmxWz5PrL/QhNvgFUrcxAItpXXUlajNuq6YvW7EFBanM99v5nBG088xD9/f3W9fX98+98k7Whepo3m4nC5UYIJhhWHA8eWtfZ3oydG9tGuSj/f7a5i096GU0ZpTSwImqIi6PscHpfRKQJJwEzh5nC6mJiexVerQoqF5x+8i7VhigZbQG5EWNq4q5Kd7bCQEEENaUWtxs/7a0yhTm++C9KbT/+bjcFxv55luf+hOJxOnL37ke88rtHjDO0bF5v646BmeMmLT7CuuCAs77Gw8wJb71dixiTcnlDMQ2LGZPt7QwgmJKfzxPzFzLr1L/xtznMMP3ZkvfNZFTNji9gMoOwMmnSxmDNnDvPmzWPIkCFcc801PPzww7jdbgzDYOTIkTz00ENNHaJbooz0onhn4MhbgMvlpilRxun2kJie1cRenY+ihAWLhQkHP+6rZvO+Goots0+UstIhv8cQdtCPYSBUFXH2n+3v1hf6mJSVhcvRta/Tzgo/u6r89sDU0RrtzxfNZ/5/7kULqLz+9H847dyLoOpwGHgEqqpS5MvhjJNPBKyqSh1fMemrnZUENHO4jU9K5ZF5i1i1MpfnOuHW7N21g71v34Vy5q2IcWZlLGtx1hKEMBcfLc0XHf77SB/AQpYVrAJXQquO1xgjkiZx3fWXUFNexrpCH4f2jSPlyNPq7ddQAFRdjjx2FGwOfXZ7PHYmioSMSfZztHlfDftrNTv/qoVf09m0tybUByJUKMQw9Kg5vj9f+CoMOBxmndSyi2+EqRdcwdDMUfQZMJCyvXsZEJ/FM8Fsi2od69SP+6pRUBp9N1ZvLWfkIb3p16N1uVKt7BDh/dLhr8Sq/0HufACmTr+Y9IxM5j39KFZWB90wKMnLoSgjk5QjBwTHKyWqu5oQpmVMCCjMz6O0wNcml6zwvjaCQp6y/rNm94mqOFlX6MM4bDSl2+tnv7jo+tsYmZLFfWuj/DiMVwp/5o9TjuPb3VX1nuWW0FSqzJawZms5a4sLuO2q89ECKu/+9zHuff5NhkyahBCmRtm+VwLik9N4bP5iinwrSEifRHxSKlUBPSKwb0JKOsdNSGbH9u1sqKrfy7EoiMaaRrszaVJA3rt3L4sWLbKLR1g4HA7ef//9DmtYV7NZGUTPSRczLfko4lO93Lm68f2vuvl2xqekd07jWoBCSCgLnwzMvwUJGabZRyOA0+lk8+bN+Hw+ho1OsNeNX+2o4NjBvejlcdlBP6oaQFGUiKCjN+Mm8cvyWo4a2LiPZWcQfs1GB9vuvlzyhp0ZQFMDpo8hAmPICMSVT5GUGXp37EVHBw86VmaSNUUFlOTlMDHdy2W/vYXnnu64anY2VfsAQiVoie52U1Gr2fm5+0ZxOxDCLJGdMKwfLmfTQnLdydE6pyEEa4vzufvaiwj0HgpXPhHxu+MG94oIMGoNx09MIaDv5KE/XYemarz+1L/wzZrFzJkzIyZqh6I0K93emrLIz0++tphPP8/mxBNPYkxiCqZvpJl2sipKxbVKv06FXw2ldhSCUUmZuNxuNA2UsJRrEejtWyxj8OHDGX+4g9ICH+NTvfQ7Nh6+MVMrarqICNwyMyg0vpAyhGhzpL/lNhY+FnYoBW+j6CruHj04edqFKIpCUkYWrDTvgNMdR2JGlj3eWv9a965u2w0hWF2Yz01Bf9eWlp2PdjxLyBNCRLy3TXLVU3xbXswbn0TPrz4uxctR4xJgbeMT6DvrdnDexMPp4XYArROQm5sqs7n4NZ1VK3NQ3X0RgX1omkppgY8BPdyUrMyhR5/+7C/bR4p3EilpmaaQnJTGmAQzGNgQ4eO9qaAyXb1gd43KknV7651TGLGV6q2sRrXnq4Mk9CyCJgXke++9t8Hvxo4d266NiSXcTgc4nfxq1k3mGL66qNH9RVDVGl5zvTHac6XbEJpu2Pk+DcwyoSF/TJP4pDSeen0x7731Bh8ufJ3nnnuOl19+mfmL/8chIycigFpNp7xWo5fHRXpGJo/NX8ziV19g6f/erTfJfr+3hiMHdJ3z/vbyWsqCqd2sRUBHa2s3bSjF6XKi6ODxeDjzvIvZulGlcA+IAYcTn5RGUf5KCn0rOGpCGqMSUjp8UrZMg7+/YjpqQMXtcfPQSy3LzNEalMLFCN8b5t8Ohz29W2V9w/l6l6lx6ul2EX9Y/bzZLbl30SZH47DRpkuDgNUrc1H9AejVMR0vhGBDsQ9VVRGGQSCg88wzz/Diiy/yxRdf4PV6cTkUSovzqamoAFfj78hX5ZFjSEJKOj1HxDOsXw/0MDemhq7GtByF52gVjEpI4fo5C3i3YAOb+jcwdrfzg7lr28/87W8XowVUXG43f3o6lDmjbnrJcFewaFguQ23B1FCLUFwG7WhA/rEERiTV23zBNTfRX1GJT/VyzPhkHApmPu6V5mL1st//jfjktIisGgKzcXWv17LoFefloNZJC9nSeSSU7i503lUrc8zCLi3g+2oX9Iv+3f2/ncHFt90PjGryOOHPdWuIliqzLXOrAMalZiEMU/EltqynT38Ht14xnYA/gBAGisPBG894mPPqYsYkpoZidhRYU5hPUV4OyZmTwjJamPf10ZW7+K6svl3aur+xgGEIvt1tZurQDSMi88rBwsHlcd0CXMGUR6pu8LtFTdiHgI3ffo8WDAxrCmsyv+uuu+r5/bYna7aVs628FiEEmt6w5mV8cjqHDhuOqmn24JKXsyzCHaAgP4+77r2feUs+JT4pjSGHHY6u1ddchZuTuoKf99eg6cIejMyXu2NbZOg6yZOncuPtd/Lpp58xMSWNww8dan+/triAi889i9n33cNNl01nXXFBh+eAXVOUz3OP/pNAeNBIfidoj7/6AtQaUBSOODY0KTaUD7qhW2NZPpp76+pOjh99tjQkAAB9+g9ECANc9U3z7aEZEQL69h+EQ4kcUv1+P/PmzQNg45oifnfxNPw19Ut0D/Y0fqFmyj5hVoS038vGta32ZB3sx5It+5mzUYkuHH/yuPWrRtvRUnb+/FPIuqKpfLMm5GsbCE8DABFCa9FPZRHH8Ws6q7Y0PbY2B0NY6bqC4nF7XfKen6JufueFJxg+Po1xSWkomM9buBLlsGOOt+/pt7urQi4gQLT7IQQkZpr+rk6ns01pIcPDtg0hmJCehaK0rEN6DDykwe8CZ/yBL1Z90+D34XyzvrRN98JKldnWPrEQAkYnhPzkxRHjqCjbixpQzbEE00pmBuTlQNgCb11xAX+66nxeeeyf3H7V+ZQW59vHNARUN1CStzG/867AGkMCHR3IE6NIAbkB3A4HAc0gd9PeBoOplK++sP9eLkbw0dc7m+Xv2lFFIepiCYaaYaALYRcXsEyu4UEqiUFXC6fTidvtYUyy1w5sWlOYz4W/PIt/3HcP119yLqXF+YxPTsPtqS9sdHUGC+v0hhCsKSpg3lOPUrqquMPP+926VQwYNJisLC+g0CMsk0Nx3gr7fqtqgJK8nAa1Vn5NZ3cbg298Ph83XDqd/OVfIgwDh8OB2+1mdFLDlazaC/v2C8HP339tb28oxWDjj4v5ZblfazJbQ93JccSEkEbOrxlU7N8LE86Ayx+N+J2iwJmjh9Y/YAspK9vHvH/dbWYKaUDizs7ODgb8hi66n1HFM+dPZPiQ/o0e//nH/8MXeUXUanrIZBum+auLucAIaeQMIfjnFw1kHvK9DqWfBg/WaDNazMjeGi6P247cn5AcckOru2APfawfN9BeBW8UxQxWfO2ZOawvKcAQ2EG0Ns9dDa/f1vKDV+6JulkPBPi62KwWa7oKKRHZAKyiGkJAWU0gwixvZUGwsCqixiel8fTr73DbnX9ro3tFSGMtgDEJaRwx4pgWHWdr3OENf3l8Jj+NOrtZx9mwpqRNwqGVKvO+++5rs3sFmM/b+lWFEdvGpXrNgOewwFOX201CepYtHJcWF/DSYw9FZLQoWZkDhDTEDcUi6DEkHZvXY/7rb0CgP9DpklLT3QG3U0EX8FTujw3uo2wqRow92f6c+8NeZiQOi9hHN8yysOH5XDuiKEQ0TJ8+0x/R5VBQHEqE1ilMliE+OY0nX1tMcdAkNDEl3a6IV5QXCuTTCFCcl8NZF1zMAy+8zZ8KjTrn7FofKuua1xYX2Ll/Xf0Gwaz/duh5d+/Yxr233cKwfj2YcPoF9HSFBOQJaV7bz9vldjMxI6vBiWBHhZ+dlf421aO3AiktE2BS1on84po/8deSTh7kwq7xq1UFnHjcmZFfWybHBlhTVMCa/BzSsiZzyokncPSghn3b6+aRdh4xJlhqW6FG1Tk20QsVR0b85lD/Du6cMZVD+8bRv6eLBz77tnXXCZTt3YOmhjJKOBwOhBC43e7IYiFuN4GwSx4xsBeDezcdcPZM/s+gp9LHU8jZJ2bargFCKBhGZB7z3ZV+PslezrIvv2RiehbjktIaF3zDffTV2npfu95/EO2cvzTZxrooT87Afe0tXPuX+8j55H2OGT2er4rzwWGmmNP0SB9bU+hXGsx4Eh5X0Bp+3FvNF8tXcMvlIbejR+YtonjlCnCEhKmE8eMYPmYi/2vugSv3wis3Q20l6BqcfG3E1y6XkwlpXhRFoafbzIMc7lIfnkfeEIqttLA1ySJyzWVev5kVYXxyGqlHtqWIlwhbqApUw2D40cfz8+42HLKVjJqQ2OYFkNfrbTeXxTVFBdzxmyvgmpfsbSMnpnL/f9/i6+I83L37U162j/g0L2MTUxFCsK6kkNuuOj/CBcPtdjPRDuA358eGvBV0PbbyWFga5KYUFAcqUkBuAFcz/G2M/TsjPn//9XrWFFaTftQZoX2iaD06qihENASma4XLodhVgKztwT9soXl8cjoTUtIjtLAAiRmTcbs9qMIPimKWDRXgPGIsFK6LPF8Dk1tnY+akDKbpixKx3170LHyTmrDPCxcuJOGMCyM0yGMSTW1PgW8F41OzGJWQ0qjrQFvXF+GBlG63mytuuI3d/Y8FmmfqbBNhLgaKEkqF/7drLyLxiI/tbB7QuK/pypU+23/6xcc9vPbO/zj6jJOj7m9hTY5CCPI3l5n5rx1mfz79gwcGHxWx/6njjmBYvzgMYfqmtgV3nwE4XE6MgPmsORxOrrnm6oggPa/Xy7W/u4knK0PXveWHb9i4GhQa1yCTci4Aa9et56wTM4PvrIJQBFrQfWJY/x6U12rk5ubaFbvcHjf/enmhKSQ3gENRQkVxAtXw0g1w1ZP298a3K1vcHwBOFPoNHMhzD96F6g+wKvdLiOsFN5g+6poQBDTBz2U1DOrltv3UGxKCm1pQNcXOSj+FuWHjgmr63I5J8UJJaL/zrvs9oyam8L/5JQ0fLIzDB/Ti5Gt+R4++AyjJ91H3VzffN8fuf4/TgRL8n8XPP3yLGHeoveCB8KJLdfLWE/LNb0uKOiEEtZoeOg5m/+6p8qP26A+0jztLozx7JfzmZfvjsaPHx8TcYVGctwK1juZUCMGoiamkpGWwvdKPJ6hJ1gxz0bMqL8d2wVAcDsanT+bKm29nXGJaKP5HNJxsUhMdn+GouVjWCkOYqVJjSLndaUgXiwZwNxE1f3GfTbD1q4htO7ds5neX/KqeT3G056ojikJEw9IM2NqIsL/Dg1TCJyZLcLE+xyel8oe7H8DhcGAYBk/cfyef5q/h9++uq38+ulZAtl7ihPRJuIOmXbe79drYxvCgkxS3L2JbYmIiigI93aHnR9MFE1PSufJ3vyc+ObWe2dTCCkJq6wDp9Xp5Yv5irv39HVz/1/tZlZ/D1k11zOu6CtUdMAlqpnvIMWPi6dM/pNnSVJWcZcsidrWelbp9sWFnJR9/+rktyGhqgLwVkb9tinAz9YsFP1FWU99f/sijRrBxbRGL//s4W76LHoXfEo4ZPS7s/AZHHXVUvfd73ZrVETPN3l07uPc3M6gqb969GDFqnO1eEZmiS7Blfw0VfjVYGCZk3l29MpfyRsprnzD1DM648ArSTj4Tt8eDUrbF/s7x0X9whllD6rExxyySEoWUDC+V+8vQwnw2Ccuxa7kgb6+o5asdlbbw25DgZ03Wm/ZW8/2e+n7cTSGAFO/ksHHBzYS0LArVwyL2O25CCg5H/fHf9d4/oh53+9YtxKd6OWrkGFavWBrx3UhjOz2cgsX/fZyvSgpQgllM1hSF3DoWPf8YpSUFoYWBdW+jKBsi3CLaoIzYXRVg/Y4K1hYV8OrTj7KmMB8B/PbtUoqaEUfTHih1+lizLjpGSMyYhDvOU2+7IQR+LRjfIiwXBLNS58T0rLDny8N51/2BMQmp9kLEEKbvfUMuFqoRW/UEBTQ4Xx0MSA1yA7idjWuUNn4WLSOAQFXrR8829mwFNNM/uGeU6mNtRQgzGKVG1RFCoYfbEUqlREgQtqw6ZoStEgoACh7HEFC2b5+5LRiUsGbjDxA3od45jS62EFkLgPhkM/dvUV4OxyZN4v9KIgUEBwZGG9eHisNB3tIPI7aVl5fjUKB32P3URciULIQZHSycCpV+jd1h7hRrtpa3mw/3hJQ0dIHtZuIY5YWzwgr7KA7a/UYt+QfsM4WrTRu/Qhy9D44wv3K53ByTkE5ZjUq/OBfbK/yYGV8iNck+n49X3/mQ/gMH4va4UVVwuT1kTj4x2hnrUavq7Kz028+vIeDDDTuj7rv1px955A8XoQVUHCMmwvSGM/Y0xTdri6F0DUDQ5zu669S06dPJnvs2nHZDcIuCpqpUlpeBo2lT+RHHjAxb3Aa1yALT6ViEC4EhC8LEjCzWbCtv8JjLNm7jwWkXceyEZL5bW8QbT/+bVdaXXy9jyvRLcamr+NCdGPnDDcvgg3/BuXdFPW6hbzlJU87A7XHj9wdT6IRlSNCtmIi6mmMR3boQWtgLKv0ttwoJARNT0vnXywsp8OUwMT2Lo8YlseilSD/Tb9YUMSGlvsb9ggsvYM2XT7A+48Y6xxW8+cwjuON6YKiR8QP9Vi3kn09+gaHrLHj2Pzz26jskpqVT5FuBMu8VxMzHMIRZZCohJSNs3A2N0T/srWbEwJ64HEqweltIMm7tcGH5Xv9hpmmleeXJR3hk3iKq1c6ptgkw4rhRbAr7/PVXpUwc1vmlxxtiXFIq9zz+In8tDvXJCy++wJT0RMYlmqncrPRtWtCPfExiGg+88DbrC30cPSGdYyck2VYA6znP9RWwfV8AHPUVN5ouWlX5tb2p8mtsDQb4QywtWzoXKSA3QFMuFsW52VG3OxyOiInRXumL+unfNu6qpCKo2Qn3UW4vLIH32rfW4hAGf09QGDp5ElhCiQiZ8CxhQiHys3kNguRgEF9A+E1ful69iJ5ItWtfpVB/w7ikNEYlpJrappLITCS93Q4q2pjyVdM0jCg5Qx2KQlyYBjm8gqEhBJaOQDMEm/ZV2wKyZpiahbYKyUU/laEbUBLmZiLUOoGmVsWElrDnJxh8ZMPff5sX+ruOeuuuZxcwISWNPVUBXA6FreU1BD0gqNUM1mwtp+rHdUydOhV/IIDb7eF3d95PZVkZ6VmTSU5rXoDhvhqVnZV+e0HSWF+WFuebvtqGgaG17WEws0UYKIqDk04+hfvuvTeqdejqa65l055q3n1nNj+mX4dStBiX202ffgOgsnnnMRA4wipkmtsBJSQEPjZ/MQU5yxmflsW2asHslQ1ryIXqZ9GLTzLtyuvZUJzHpNPOYc1b/8EI1OBwOJky7UIUBT4sifzdwEMPp8zhaNDtQeg6lWX7+O0d9/P43/+MoevBFbSJppsjjRF0KwivOlY//68I/dd0N0VvT/CHY5PSOGJsEg6iD2Gzr7+Ye59/s972t57+F2L/LqgjIKM4WO0LViB0RCo6ir/8xBYyVBVKVuaQlJZuLmJeepkAIM78A0ckOAi/altLrJiBe4N7ufFrBlv214bc4lrZDxZFvpwwd5MAq1auwCpe0tE4Ct7ix9V5UPMvOPtPAGwsXYM4JXYEZCHg+PgEKA4FeX/iTmTpNTP4+3MLKNYPZ2eVnxsnHWO7LxpCMDYxlfjENN5bv52B1Sq9PC7QdITHQWlxAQ9tUKIKxwBGsHx1V7OnOsD+WrX9UyF2M6SLRQP8vLnh4LwIHrsg7IPCH+99OKwSnbBX/NHm6fJac1LuyMwPlinPUBz8/TczWF20MkJLY09IEZOPsIV6a9uElHR+/7eQm8Xn7y2Mer7WlBRub6xrMoSZdu83b9dP0+dxt64SV8R5hOlrauF0mQFZycMHRLjo2C4rhNLOhXwMI9ttHbespvUCm3Vss/Spae6rl8Ny5/ccuzM/6u8bpOS9Rr92Ol0cPz6R86++AXdcHOFPwoSUNCJdAsIWZwICuk52djZ+vx9D11EDfvbv28dlv72F+OS0FmnK7KAjAWuLG77GqvLyUFGEVsxKPUTYoqNyD4qi4Pa4+ev//Y1Jk6JX1XQqCr+85EoefOEt7ssaxCUXns99z71F7379m3VOAaESt8GLtC0+llCFYHxSGhf/9hYMIfjXvXc2flC1lvzsT7jn2guZ//hDPDv7TpSNK+A70/dYUeDLJW/V+9nIhDTuf3Exw44+PuphFaeD8WleKvbvM4Vj+wpMvtuwrt7zX1qcz/xn5rC2KJQObm91gO/3VNuZLIRoXWo+gWDh/Bf586yL+PitV9EhqrZODfjJXvIWM7RI32u9Yq+ZpaTehYZXO9Hhm9zQOYMXqCgKbreblMxJKIq5iPnrw0/Z+61WD4lQphiGVco4NEYEdCMs+xAR37UUhcjxwe32kJAxqXUHaw05r5l983XIderoMRO6fO4AUHWDop/KMIQgWnYzXdfJXvIWC9duY/n3e8n7cS9CmAoO657sqqzlqdwf+deX36PpwnafK8nLafTcWliwbVexrbyWPVWWcBxUNBycMXpSQI6Gz+fj8TmPNm9nLTRJHj16HOdcPNP+vLXc8q2LTlPJ/tuKEGaggYWmqRTmrgilTLLaQaQ/qDXhrisp4I1n59hCRtm+fWalH8NAV6MLcK0pKdxeFP1UFmbKMv/bsDO6Wk5toP0twRAGQhg4HE5OOuMsnlnwnr046hPnCtsv3Dxs/lurGfX8SIX9PJgJ2lvfLvPfcUmmue/08y8lMWNyaIfVH6IsvofM4X24JiOKRvi1P0U/sMPBmL0NF8y5c+5bPDj/A67549+YdtnVsD2ktfx84fxIYSisT6w2Dx482NbIG4ZB3/4D7X32Vgf4drfpd1q3wERdrH5dV1LAbVee3+B+jrieKFZQYaDlPq0zen4Pb99luhl88TzeU87k8htvZ8XyZQ3mNnc6zCwGG1cXsb7QR3yql3FJqYhm+oMbQeEpPIUbhIQlW1AOCs5rCnLRmnrWFTPjhhosQ60FAuiaCkJgGDpfLnmLpe8uqP87AaMT0zgtfWL0w6LgUJQG/asLvvwsYjG7rqSAWy6fzouPPsitV0y3+3BPVSDoJlZ/UdkS3nntZWbf8QeKVmTz7H2388lbr0bNkS6E4NOF8/FXlIU2PjLNHOujSearPwptVxScH/7LbqSiKDidTs6eMZNH5i1mQko6Rw3oRZzLwcSkUFGRusVLArqgStXtZ3l/rRYsW0xEsHVbiE9O5eGXF3LVLX9hzquL+P7r9W0/aDNxuYOp/zwhH9/hx4+ut1+VX+Pnspp62zsSq4R0rWrw1uqt9XeoYyX4z7If2FJWg18zbOugPyhZ7yqvZtF/H+er1YUYQpCQHn3hbHFo346Jl2kJW/bXRC64wxbiBxtSQI5CdnY2mtryXLSaqkaYIgKasLWz63dUUF2nJKwtEHXgg5cYphVwDD2GsalmfuPSkgJee2YO66zgECIT9a8pKuCOWRcw77F/ctuV57OmKJ+kzJDGoSENzpYfvmuVwL+70s/GXa0XCiFSE2+93A0FQ2iBtuUaNjEFCxSIT0hhQlipcVd4WibCBEJM4aVudLTV5vC2txbDGtAQaLrB50veomhZKHhI2bYBR6CaqspyVq34PPLHb91JQ0s2V6CaK05ORhHRBdRRCSk4FDOlW/b7i2D1h/BzKQBz11XZ1gyFkDAXfr0lJSWhNjoc7C/bG2FKLqsxF6Ol2yqo8kcPOrPy/wohWJ2fS2BIdO0mwODDj8Qd50FxOHDu/Zmze4QKPRzeL47bxzR+E/zle2HzGtMXVw8wcMhQXn3iIe6/9+4GCwApCny1qpAHfjuDBU89zL2/mcGG1YUcvzMfflzV6PnA7Ccr0j0yh234/0J9OiEtC2dTZbqDuVzdbnc9AVAIQeGyz9ACUXLBV+/jmzWF1Oa+yZmezZHfvfp7DF2ntCCXbzeURr8WRQmbhGHVytwwk79q54e3sqFECv8te0Gq/BrZH0VaQPI++1+DRYQMw2DJvGciNyqK6Sa3JSRInhQo5bppU7ji5r9wwtnnoSgKhq7jfPYKlKcuDY4PCudccDEJqWk4FOjbw4XH5aCPJ2wRTUhBAeYiUAtqjAF2V/mpDuhhC6HQvW9NCi6r0uLYxDQu/e0tGALm3NvyVH6t5Yo/3cOM62/jvv++bW/TjPpC2K6qANsr6qce7Agq/Rp7qwPsqPAjgFeLt7Bo7fZ6+zncPTjxnAsittWoOutXFfLGs3NY/GU+175lxiNUV1bw5lMPc/e1F7GmKJ+jxyc32oZdlSqGIQh0Yd7h8IqOoTk1Mnd3e7N6a3lM5lqWAnIUpkyZgqsRG965RwhSp5xZLwq3/8BBES+4NQgJzHLNdQNLOlJA3lsdQABjg8EEANoljzByYgrrik3N2kuPPshtV55PaXHQnBmmeSrJW2FXwDIrBa1gQnIaj8xbxOU33k5CanRftbeee5T1JQVRv2uMfTWq7XLSWkT4/wcnkYbKYx7iasG5lr0YfXvwGVEUhQEDB0V8FX5eYQ/8oYk9XIgJtd/8X0j4ad2DYQgoLTaj0z95583ILAJmg9A1jXdffIrCLz6K/PFPayHK4nAq33DXn37PmKS0RkqpK2xcXcifrzqP3Tu2/T977x0fRfX9/z/vzO4mBAgdBAsonYT0BEIzoGJHehF7fdvL+23B3nsXG3ZF6aLYC4hICOkJSeigotIhEELKlpnfH9OzuymA4uf39fV++wg7O3tn5s7Mveee8zqvo23a9QsANccnccPUMRTkZlvXajPuVuXn8M4775gtuVxuolu14aPXXzSrUCkqbNp9MMh7aqDaF9C8OPrAHtk7HXViaOUBgJ07t3P1nQ9rtCFV4fvn/mt+98y5/egRG9ozaqBl6zaYiwlVZcumdQ0WABIIirMz8RveWp+P0twsElPScBV+Vu/xAP74ZQP+gBJE07EbTWAtePskpPCfu8P3AYA7ohnnXXglN973OC1btXZ8pyoKe4x7acfKObQoWsj9V05k3qtP893bzzi/37UZl9tF/7TB9OgbG/K4cUNGmOeqopLgUABwB+VyKBy6FvKanQfIOONcx7YTM8Yy9eNC545+69lXFIW0bd/jWnCvTlPSPYdz7zIXfsf36kfl/nKiW7dlxTeLUBUFVVUJVFWg1mgL/oDfzzefzNHE3YT1XnvsBodp8Gr/GR5Ie1QAnE4MM0qwNXwCZijYnx9DOKJwZSbK36h1++vaMsZecQN94635yZ7MbIeiwu/lf70Xee3OA/yyp4rdB2tRVcIuwmNuf49j+yQ4tq1c/DX3XTaWmS89ybtf2hR3mkWjDJiI3+ejKHtFgxXpbvi0lJLtFZTUk1T7V8MeyTTOtneH5s7n9Qhie0UNl88t5sWfN/8l7R8O/k3SC4H09HTunnYnD+QcCPl9cjuIjE2gYNn3jte5Ras2pvfKbkSEm9C1x0+EHBQOBTW6gdCqmVuXQVKprPOS+wIqhdmZDi3Q4uxM+ieloqBq+pxCJX7AYFxuN34/uN1uEgcOQVW1sP26cj+rldAhaUVVKc7JhMlnNfn8Dzc5wTmJaB9C2ceugBdRsRWiewV/GQo7NhFT9jFlMec7t/9ZhiTJKIrCsw/exUm9+zKgq6aBbTeQ1xbn0Xmo1n9Glrh9gqt7DeFkrhqDWn+AkvwcbtY1hCVZRpUkaGEZ8KpdFzoUp3LPFvjmBTjjZnPTJVMmEOGSNf1O7bENwsaSfDYVZTtD+l5rYvP5vGRn/sxZI4aZiwCwqEAB/byEEAw8+RRef+Je/F4fH73q5rVZn9EvMZXyam3ht35XJce2iuSY6Eiz/bLtFbq+rEYfat75RFgXpnocsHHtajylW8yS6cZ5d2vTTN8j3EJA5ZEz+1D22QqEkLTFhxCUFeaaD6HL5QqpYiGEpivucrvx+bRxIrp1G/okpHDJf+/jrfCnC8CsX6B/TBUntW0OQJRH1o1hkIUaZECqqkrXXv2gnvC5z1vLgrdf0d53X3g5ON67Fi7ReLPSup9wHTvcXETXfY5GjJnK4FNOQxLwyfszQjbX5cTejme9X2IqT7y7gO8/nYMsBJv3HCRNUW1RAYtf79V5oo1NblZVGDXlIi3ZcNFn+Iddyqe1zkUt65bBDxYv2O12M+r0Uzln5KmU5GbStl173nnyHu05UbSKhh+/+CjKplyzFLgJIRxWvIoWzeraJspULLJHt4yFTsC6cawrzmdzcTaDhgwjITVN389aZNurJDYFq3dU4gsompqOquloJwwYhCfCw5GIqzUGP376MT36xXLWRIuSGMo+N6JNOyprON58L/8a6M5+c4EdzrlSsl8wf5Vz0fjZ+6+h6uOIGqjzDqVPwVWwkNiU9Eat7P7YV0NMp5aHdhFHANY8+tdGtw0Yc+KG3U2nuf3V+NeDHAb9+oQ3nh68agLRbdogueQgb1rd58lSiwhux24gNMSrbAhbyqso236ADTp3VdHbPOcdpzc3oGg8KLunpm9yuoODDJrEzbTXZjP5utt55O35xCamoqIlPb2Uu5Nad/OQ5yG5IohLHRREJ6kP/oByWNxCA9Ykqn3WKBZ1dqraR+C1C/ltQ7CGc1hIEqu/c2a1x4idjBCbAI2T7fdpFQYNrFtttf/QVZMoKcjVJnbdw2mGltGu3zhf0yN4CEayP6BQsq3CUSTF7/ehDr0EzrzV2lG2rYsP7nW0ISRJS7aT6mwXGndWgtBJJG9eymP/mUx067bOyIrNQHa73QwYPFT/pFrvBprRaJaKjoikXYeOjghGftbyoNByeZ1ERmO7sU84eo0BRXJTmmcpb6iKQnLR61x0bBUbVuXz3cehDTuRswCxbR0xqem4PR7NuyjL2BOyLr300pAqFgItYfGS2x40E17feOJe1pfkc2KvPvWeL2jJtq+v+M18V006jUE/MCMU2nd+pRF0BN0z6jfKYAtB5xNOZNjZdfjbe/+AfZphMODUcxg+agIuo+xunWMMPWccvWLiKc3NCsuBtt9/wyvqVxQWfzaXL+fO5PIJo1i2fLnNaDEOYyjtNP4FMcaE86ZczJ2vzmS1v23QPpK3iv/c8QCnjruQMydexGPvfkKfhBT6JKQw6tLr6darj3lE6ccZtCnfiPKr5oE2+1gIZJeL8y65BpfbgxACl9vDWWMnIwR0aBFhzhmrC53ycqanWIW1Og3nw5ee5MYLRrMqz4qiqLZ+gNDOl/rgDQS050P/XOtX6BmXwqW3PxTcJz+91bTGG4PfilACAd56/G7WF1t9sPybRZTp0cysrCwee+wx8nOzHWP6Xwn7vGEYy+FQt0y6astiC/Wz+96YQ/f+9ReIMtsK41D7O2G+YeZj/dclDhrj9NG+5lD414McBps2hJdF8nu9LFk4C5TgIbruy2XPvA6GxRks3XbgsKTedlbWItDKtG7cfRBVhWpf8JLcH1Dpl5jK4+/MpyhnBUkDBnNCTKJlnAmQ9H/3jEsiNimVTSX5fPDq8yQOHExR9gpQu4Y9j3Muvpa+Cams2VEZdD27KmvpEKKEcrEeIjxcNQ9TQUNVWV2YS3HOCg6eWMdIqalE9VY3LS1XxUlRAEYMSsMbtZefhIQkaVq9w4dnmN9vKC0GtNCy369RVPompGhJHEZoXG/cuP6SglyKszPpmzKQpNSBTY4rGMZ1vJ6d7vPp9I4eA507ujzILhe941NYnb8SFtwPkgTlWzmpbxy/bVjN5tUlMNL6ycaSfNyyxM9fzEPpMjYoUYUDe/DLMpvX1FEMsRnIz33wiSnX5kxQVYlJTOHb777n+yU/ckJsKn5F5atPZuP3aYZ1cvoQfdGgyeAJ1Tlga2WltQYVVaWsIJdvv10BbeuRrfI4PVKqqpK/5CuKln2HkCQUfwCp7bEoF00P6ud3n7qPEWOmMPScCcgSnNS3P+8+dR8+n48Ij8csL10XWVlZfPTZNxTm52kec1XF7/NRlptFdL+QPwmCaRwjOOgNEB3horQwl+LsFcQPGERy6kBQ4au5H7IofxNbejYQzanUFkMutxtFUXC73dz86EuoqFQdPMj+PTs4tutJ/PzNZwTW/Ajp59Oje3cEcMqoiWxYvYqNvzu5mo9fM4U7n5tB6qAhzHzFjU/nMEuSZBpmrSLdJhXEcCIU23jIfp+XpUt/4sJ+SRiqHUYSlEG5aAi+gCYhaNJQgILfQycNjjj9bM4YmUjJSblsLMrWFtdCIND0ustys1D8WrKgsvcPmi19jfI63sKE9GFMve5/nNQ/mQEjzqBoZSZx/WPpn5zqMLgKc7O56YIxcK2W/Fi+aweqeiIBRVvUzCjcja95e9S9f+DzQf7KTBJTB+heZiPBVzaNMX9AwdUQ11yHzUlNQFXxqypVPj/79u4FW12Mrite4vfCH+HkKxrVbqOwKQc+ewTQqCdLFs2D4zQ+77KvP2XljPtxlb/IzTffbMo9vjRzIfEpaSGb+3N/Na0j3TSPOHKmjLHQlusxCvfu3onDFB55A3z6MPLBvRx7Uk/qMPLpEZdMQFW5YnYe4SNT+vH5ezy3IY+t01yMku+aksdfezJGN/8Ti5H8ayCHwZqyEqBb2O83lhWHfIoNQ9P6rOoJGKH31f91RIxDWY/q7av2ah60EC+iT9dZ7JOQQvf+yRpfDsuLI4GlkwysK8rjof9Mwu/14fa4ufrOh2Ff+HPt1bOHzbPlxG/lVbSL8gR5IBVV1fV/D6MD0Pq6tCCXbz6ZzdcLZhHwBxDxp8Pw/wBwXHPBjplP4Zck1KasiAPBHrCc7CwKX7qPQMCPkCQuv/oaxp8xwvy+d/94KNGiAi6Xm4QBg4P4x4b3R1E1Du5NF4wxywO/OHMhA04YSUODaV0Y9/a5DxaS+fNP7NxfxQ/NWjn2Sc44ncl33ohfUbj3svH4f9O8YC6PhxP7nsova0qC5KweumIcQgiNjnB6O4gZ4fhekmRcLrcWWbbfSJ+VYNM7PoV91T4qavwOGozxzkR1jWHUpf3wBVR8AZV7XpvDpuJs0gYNIS45jRq/YoZcVRWqvAF+21tF17ZRlG3XjCCExr++7eIx+LqlwjnhDeRjI/38GWK7QbkAEHv/CN5BCDaWFrGxtAghJGS3CwFcf89j7N32O+ePHxPSe5yVlcXpI0+juroG+yghyzLRrdvw5iN3wMQnw55vXaiAz69QUpbLfy8ai09/R1/+6FPWri5l+gO3weSn6m+k8Avab1rC+Q88TUz//hRkLad/2iCqvQHuvkIrouLyuLlm2sP0TRrAG4/dhZL7KR8pPtMDLskyLgH+b16AZtFQvhW/38faojxOn3Y/L3/8Gd8vnIu/ci9ff/0NvmXvoQ67hN83ryfl+FQ0ExRWF+ayY+sfWuW+gLbo7BaXxv5qnxVZUZ2evoagRWzsbEqVZ34KzXXs2OkY1hbn8eCVEwj4fbjcbh58ez49+ycDgtjUdGS9nLiqqmzfstlBpZBdLqZe+z/6JaZS61foE59Ct5hEIms0g9w+7OWs+BmfTZt8787t2jmqgpJtFWyROyBOuw5p/j243W6SBg7Wr1l7N1SgJD+H4pwVJKcPwSUNIL5LdING8uY9B82eUFQozc+lOCeTmJR0jolNB5tf6JyRp/Nm4XJCpGceOuqMu9/M+xBu0QxkFYHP52PBggV4vV6UQAA/XgpWLicuOS2IugiaJJkvoB62gfzLnip9PBKmQ6s+tbVf15VBBxu3vn1X2lzwGKNb7+H9VVugV7A60LfzZlLhazhKBE2LjhxJFPyx3xnJVHEkge6t8uINqBxzBNU2DA/yP9A+/pdiEQ4JceETdISwQop1X1iDgwxmcSvdqxXcTt2kmsOB3StgrvxCtOpXVGp8AUoLcpn35kusLs7Tk8gsr6aKFnZTVSjNy3KEuvfvK6dDp85hz+PppZuYX7It5PWEewGsvlHJ/31fo6/Zjiqvn5KCXG65cAyfz/7ALLWr2Cah207tx6VXXaOVkW3k2yh+nAF/6nSJ2XeY2zeUFuGr1dpWFYUZr7zkUC3obis7fNfrszUpL+CLsh28kfWblWyjD0b5KzMd5YELVmayo7K20dnMtf4A5VU+c1CLTUplyKSr+aH9qeBylks9pmt3SnM1rdYH3pzH6RMu5JRxF3LZHQ+zf/cuELYysAd2w2ytyINpOH7/MuQ4dXHHXnYt974xh1PPm4jLbZusbB5kIyzpVxQztF7rV0wvoqNPVJWT+icy/oob6JdoVDVTTWPHuH27DmqsSbvRVLRSS4JDDd938if3cf3Usdz04DP0jU9y6FkbEJKE2xNcatY+yauqgt/r5dv5M5n+yF0kpKSRNnBg8G/A1Hmu+7anDD2F2sr9+Gsbl61fU12lHVvRFt+FKzMd6g8FK5ez7Nsv9BNs4PlZvYS9f/5Ct159iUtK48JrbyE2KZUS23vvra3lmWk3ULD8R9SAAv4ak76jKAEUJcDwUZM4LeZYXCVfIf2Wj8vlpl9CCkJA/6Q0Zr//FgMGDMAf8KP+oUUZXl4T0Kp1oVG37rh0HIs/+RgVOGP8VF7+aCGxSdq9NwrsmJXksF7hVVsr6k3wNRb/xt9wkCX48fN5+H1ezRD1evlp0Tx0RzL9ElM5bcxk8/4regTAgCTJSJLGgjf+kxDIkpac2dymWnH6qafgdlvPVttOx6CoBgdZa/P4nv04//rbee6DT4hNSkVF1RPZYE1hLrdeNJa3nnucG6eOoSgvu1EOhr1VXrPvygpyuPfKCcx+5WkevGoSL6x3zmXdevXhgRAFUw4Lwmly2L2GQnbhdrsZN26cSbdyuT2acyEE5ax46/4jxpPdU1XrmIsVNQyVTMfu7cHyb527nsQXVV1QegXrSZcU5vHWY3c16lz+LkpJKDiVhbTnzT4FjZyRzTlvN1E/vwEYSjJ/taf6UPCvgRwG8bExYb8zeIduj8eRZGA4E4q3VpiGjZWgF8KDjP1hPLzzNX5vl0JaXVwYtJ9fUSgrzOWeyyfw8fSneOCKiawtzsMXUBzcp2q/wm/l1XSISTc1K91uNxEtWuGtqar3XBZv2B10PUbb9RnOhmHfVFR5/azZUWlWjrNzQe1jnCSgcr+m5dxYisWohBOQZN142roG5kwD4Bh1P8LWeEAvdGHCZkSdFJtkhv4/LtzKDxt2a9erWPc/ySHa7yY+bZAm2F7VOP/NtopatuyrshmZKtd+ElwgBeCbWe/y4UtPcu/l4/npi/kMHzWBE/vE8tZjd5G79BuUQAAhBK0+vALevFy7bjuUAFK1M4G1b0IKveNTiEtJ46zxtmRGh4Gsc62xFpJFednMfO0FynSOtnH7i/NzWPTudMoKc83sduO5Nv6CleFuXyDGDRiE7HbXe4/ve+gx+sSncNaki7j53sc0r6UNQpKIGzCUh98MLpDhGDat+CA+n4/i/BxHkRg7MjIyQnL52nXoSPLAwQ0bszp21Ag+XZZvLmYTBjhzChIGDGHQqWfr59VAm6qCoiisyl2BENo7srowj11b/9AWkvq1bf3tF7KXfB0U0NA4tm4yzh3PNfc+yX1vzGPytbdx1+uz6dU/3uwil6xVGPW4PY4mftqkSfnZJd6UQICOnY8jJtEqLqMoqlZwQcUqoKEvqL2BAAfCKA7opw/6c1OfJNrG3VVB1B3jArS+EZw2ehKeiIiQ9zEQ8LOuYCWSvr/RP5EuzTDua0u8GpkxlM+//tb83KZ9J0ArKGEw4yKbt2DM5doC0ZgjjAV1WV6WowpeflYme6u81DRQKtpuUBbnrLA5P4LHmdys5U2MXzUCksSAEWeE7L+0kaN4+v0FjL/gEhYvXsxVt97Fa7M+pV9iakiDUZuzjsxp2ecmg85Tbw5DiO92bfuTXVKrkLs/WiaZScgNwacroRwNWMsz8Ku6hKB+Li/+/MsRP17BtirGf6Bp6/8D7eN/KRbh4JZDvxytRS0pOu9w3OSpdO+fxJev64k+Kii6l6tGT7pT1XpULFTrgTxsAxkVQxHD4A+tyssFEh37bSgrYc/qbEtmyg+luVnEJKYiS6o+9QsUReWeb9YBgqtuf5DAwQpat23Liw9Owz/5GejQrf7zCTKQrVVp3VnWEdo6hGs3DGs791Yg6BqbSJszppCvO5eEEMSmDcI1w423kRSLrz9+xyl/9GcZ0otjaDPyPFt1MO262rVrZ7tg65/rV+Xz/aocUgdZxToU3Rtk0GJik9J44cOFFGVn0jtpoMM4aEo/GFnu9ckJKaqKqgRQvAG+n/8hP3zykXbKNoNSCQSo2LPbeSEAQnDciT047pQzWWnYJFtW8eQrD3P/jLkMHjyIM8dOYtHsD7T2bBSLgGIZuAqaHvB9V2gSSG6Pm+kfaZNhSUEut10yDr/Xx6dvvcQT785n8OBBjrCfsBnEOyprLG9iQQ5F2Su48s5HmPPDCpyphhb6xMZphgxQmL2cQJ0kWdnlYuI1/6VvYgrYZAulzdkoufORXS7OvfBqDlZUsPTzOQQCAdxuN+edlmGqFNRFeno6gwcPZtmyZY7ttdUHaSqVZvYWQWxRHn0SUojR1R9KcleQkDaImMQUuvSJ56AvwOztLag3N1znHCcN0Cq8leVrURhvrTeIdw/Q7pgu7N5q0U5ikgdwwU130SMuhfXFefz8xXyEBL1A71/LWExPT+fNuYv4ZGk2C43D6+98XOogXB43vk6xyNvX0z9tkGNcVNAoBasLVjJg0BBSBww0jWRF1dR5vH4lSIpKCOf4Gyovw0De7/t4ZNQEFn82G79Po1iMGDURIYyiJ1pk5tG35/POMw+zptCqtGdUyjOoEOuL81mVs4I+yQMZENs7ZMJX3U2KCgV/7ufB7zSeg6piKk3Y91FV6JucjsvjNjn6SQMH89WSnyhYmcnIU0Yw6rSMkNdoX0jGplhtCERQ2e0+SQMpy80C1xEs/ywErdt1YMiZY/j5q08cXyUMPZV+8V3YvOegRlHq3BuPLFGpJ3zXHdGOhGPJ2Za16FapP8lXkl1BHPhwxrGJtPH1f287l6PlQTYWk4qqjdeGtCLAh/kh6GaHiXlrrBH6r6wofKj410AOg+NahZCU2VLMvk8f5ge/F7fHw4QpU4O8RaanVLUGZ217cHNW5vnhPRxZWVm8/8lXDBg8lB5xyZqZrKJP7s5h76X1Eue16KLJTDXrAP1Pp29SOgH9pRRoHtkJH1gV09576n4efXsey76cj9/nbYRXKphDZXgNy3YcoFubKFo1c9u+U63fHUI3aDJQmhfzuQ8+4dO5s/jxszls7nEu+DqY++2t8tI/PoUH35rLB7m/4/CLvjaVAZffS7bHyRHz1gl7y7KLQaePChrchRDs2bPH9tn67on//gelYjdz3ngertVClsYgKNu8ojFJqcQmpVJZG6gnsTM87ANrRU14j5qQZSdPPoSnVbKpMtQ9yB+/bGTbD59DxlWw+kf4/mX8qsrq/JWccvJQYpPSuOCq6/nw9Zccz4q9SqOiQEnOCnw+L6qi4POq5K/MpG9iikOD2+/XPF2DBg1yeI7rXrOiasVvrp9yHn6fFzFoCsrAy8L2weLPP+HccROQBPy6YZ2Tcy0EqJo3tW6U9Y6zU/jluGtJTh/CSf2TUBSVkWMnUZq7gkFDh5GS0j/sMbOysli5cmXQ9h8+/4So5i1Ch1fqwUNXT+K+GXNIT0+nb0IK/ZNSzQW3N6CSctYkvvpuPQcPhBfvEgKuvutR4pLTkAQUrMzE5/OFNI4BOnQ+1mEgrynKwy1LrF+Vx32XjzcLiiz5bA73v/Q2XY87zvH7hJQ05C69WfhxEWA5B/okpHDRc3N5ayMMP0ald3yK9rwIAEFZQS4PXT0Jv8/H/BnP8/LMT4lJSkFRtWIjew562XPQ60gM/nN/Ndsrah2RKX89HuRWzdz0TYznhZmfsipnBTGp6ZwYk4RXl5qThMAt65UQVzkrSvaMTaBf/3hAsLYoj3uumGDyt5957V3Shp/u2D8rK4tRZ50O11hJel7/8XxcYPWtquolio1+wrqOnnHJPPjmXDYXZZMwYDAKcP2UMfh8Xt568Rl+XLLYwYNXFJXCP/eb85KKVqkv/bwL2BXRCbVZa+rEiegZl0xzjwtp7nKUnkeqDLXg23kf4nJ7TA67dY4EzQHmgjqEc8VMgLe9OFlZWSxdupSMjIyQeQDhYDirjGMquvMiHE6KT2NjU8nZQy5s/PkcJQ6yaY/gjPj9VRB17uc/Df9SLMKgW9sQBnLtQbO0tM/r5atPZof0AhheEYEwDYK6xkbhH1rihrmiP8TzzMrK4pRTTuHN5x7nmimjKcnPNb2I4aaCdQddTHttFm0veRYldRxR3frp4X5tsNlfRz7L7/dRnLNCO8m+w6HjSfWflLAPaNrgfNDr1yZuvxJkvJmhcw5voaCqEJOYSofOx2re3a4Jju+rfAGEgD7xKbTv7Jy4qT7Azj/q5h5bkF0uTp9wIfe8OY/9e/cEfe/xeBy6t5vWWrqz/sveREE45K6K/txPrT9geUX17YqtL5rqIbEvtOoLJasqdDo2RIlpAATRbdox7MzRuD2eoGI4RgNK6fecULEed+Z7SKi43C5iU9Nx6aWUb5j2AGeMnuDQx/1m/kyT36eoKi1btzWNc0VRiG7dBkWFxIGDrVK0LjcxKenWRKjaQ80qdkrR1wtm4/PWas9+/zPq7avspd8jhOCtZx/mm0/r0ChUrcRyWW4Wa4ryGRjYaH5leJ0lYfFM+yWmMOXqm0hOrUcxA42DHDLMqt8zV0QIvvP304O36fAOvYyy3Cwtacs0IgxVCJUbF5ayrR7j2Dh2VcU+XLLm7U1OH4zbbd13IUlIskzHY7ow4pxxrC9yGoZKIMDa/CzKcrMc5ecDPh/rivNM2oYddROqjPOO7NQNgNrm7U1HgzFpFmWvcBRXKVi53EE70NpxviwH9GRQsF4wfz3vU5RH8/zHp6Rx8XW3EJecpnuPNV3zSJeESxKU5mY5DDtJlvllXRmLZn/ITReM4YfP5loLPJ+PovxgzubSpUvxeu1Jetuo8Qcc1INtB2rZU+XFbysa4rWFvHvGpTD1PzfTLzGVgqzlWg5DwFD/WOo4nklT0g3OkvxcHvvPZH6SerO6/QDWNA8u8wyak2VU+8OrcOpAxS4A/D6vow9BM8iM8eHXvVUO55GqwpZ91UFyqCoq5VU+/txfbc6F9957b9hKlqEQUEJQuFRN2CccNnpDy5weCqJ2rnN8riOh/bfCcuipFpXpLzwZ+yLkn+hB/tdADoOQun91tm3esM65n3B68eyV9OreemMwsITeVLaUV5m81MbCGGgNWaSi7OUoqpb08sh/rwn5G3dEJD36J9OyXUdAE+j2BxT8AT0UGXTtEi1at6FHTH8485ZGnpl1HTv0MtIWD9n6bldlrTkoGt/n/74vqMBJOOyqrGXTHhv3Fj386HYH7auiGTjrivPIrOMBBvhlTXHY44wcez7X3f8UPeNSGHiaUzZr6NBh/Pjjjw6Pxa7V2c4GXG5EnGW0Pb10sz4JWPffGIwM49gYMKp9AdbsCF20xoCWEKq1cdDr57Ws38LuqwI7t/0ZRttSpaJ8D0u/WMDZUy5nyvV3cPndTzBywoWkn3ImssuFEAJZVbl6aE/ufuldJl93O3c+N8OsimWE1cv37nZ4kLO+/9K6RhX279urJbyil5Yu1/ioMYmpnPn4HE77zz3c/focesUlY9g5RjKf5e3Bce9NNMAxVyUX64rz+Pm7L8P0p+DggQruvnw8OS/fZl7HE9dNZs6rT3PXZeNZV5wHQiAhTO9ifcjIyDCTj+wLD1VV6R0Tx60PPxf8o5Lv4dWpoRuMO4O+yelU+wIo9rdK7wtfI8YSl8tF4oBBSEKQfHxrhIBzxk3m3EkXkXHOONp16IRAsHvnDn765jMCddRNJFkmceAQTeHB9s7JbjdJerXNuo+ZPVnN4HuWbN/P9MxfAfht/RrWFOWZC30VlZhU7Z0WkqQXV2lrLrSM98QYO+ywL8j8foUbwvDyAWqqqnSHh7ZYcEkClyTpSXqCCJcMCJIGDkLWecWSLJM+fCR+v1/j8uol7A1OuMvtJkH3ztthPAsG3J5IzSizddZBb4DrPyl1JO5V+xQ+Ld3O5JkFYI4VKgkDBuO2JbYNGTos6PoUFVbl5/L+q8/x0YyX8HprHQWE6mLDqnwtwlKnVHjEsrdwBxEybFi9JPT2rFmw5HVz0VUX1bu3msbp7oO15rxpXOPeKi/lVdoizFBKMmyqbRU15lxYXyXLujhQ46foz/2OeckYX+qTeTuS6PPHYsdnRflrjdL6YDmqbDkyBJ/PnoNHRtvEvgj5J3qQ/6VYNAnOF6YkL0ernqXjz83rWdN2H4MHDUKTpLG8x4qq8vu+aqp8Abq1jXJk3aMb1Tsra2kb5aFFEyRrjIG21uu1Mn5VKMxegS+MAoK3tpaPCv7kV71854yVW9h90MvE+C40l4SZVWpAuWkB7zw5keGjJsDxjas+Z6eZWJ6A4JXxb+WWp8B4ERVVpdavEEIyOQh7q3x4AwHLc6+31WvCTZTW2fePzRuY/302O7b+gWpPSsmcqf3dlAObc+GkVMfv3B4Pp46eaGazjxx3AZIQ5PzwFQOHncwLj9wXRLXp2LYVzHkDRlwNQPLQU8mPv9Kxz0Gv04NsZqnbPm+rqGFbRQ1GQlLCsa3CVngy+mBhyXaWbQ7Dvv2tCIq/QVUU4lPTWZW3Muxg/Mu6Uu59fRZev0KES+K3skLyfl6seUEFCEnQp38ycclpHNy7C5duIAoBnVpEcsqZo8he/YLZ3sBTtYWFilZ6vW+yVmjD7/fhdrs5UFHB/y6ZQMYZo/hsbw+ISObcHrFmX5QV5pK/MpOEtEEk6Jqwhh6woqqcOXYyX83/GH89FAEDJSt/4q7Mrzn59HP581dL9ksICSEJAkqAT99/TTNCVBU+/h/0HkpAp9z4fJoubu94TakhwiXRu2ML9uwMz/hNT09n8eLFfPHtD5Su38znsz9AVRWEJFGxby+pp/SHslV1fqVCTfjFUa/4ZAKqFpmR3QKEcBgODeHcS68nNikNSQiysrK4/vwx1Hq9lqSfDQJJC4vrXnBJlrnhvifon5TKniovj7/7CfPfns7endvJGD2FfvFa/kOH5taL3KFFBMszV9ivDkVVeei7Dea2X9eVcc8b1/PcB5/o8l5wXJ8ELvrfg7z75N0oisJLj9xFt1599JL3VphdRaXK69cX/RalRwX2Vvvq9ajX1GjjoaR1I25JQpYUc5skQSCAlYQnBLIsc8JJPcj84WtAi4T0i4vntNETyctazsDBw+jd48SgRUJ6ejpPPvMsN+qVE9cUZLGhTysk0cqxnwoOp4kvEGBmgSZOaBSBUdHoWS/NXMiqnEziBwwmqquVZG6MqavycrjpgtEWv3z8I9CyA+GwtiCLlpFuvF4nzSwxvj/dvNnM9gwK/cPtG6DfiKDN5yWfROfTHqGqopzm0W1464l7tQXFgvtg3EPMnfk+Fcvncd7EKSTousfm3BDCq2p3RimqNRd6vd6giF44GKoopgSnal9UNfjzI4JzL/oPGW1P4rll2jhkKJUcDRh9bOSxSMKI1Dn3O/3NbL69cgDtmoeIejUBdorFvzrI/9dRJ+aiKCpFKzMBzaD6feM67nvjep7/4BNizj7F9JT5FWugXvrzcraW5dG+TzLd+yc5wumH8kIak+67C74kbZDGQVZVrVqe6+NZhBI/2te8M9+s2+XYtmlPFQoqPl2ovi58fl+jhPkN2J91y5MerI9sD4/aPQZNgaESoqqQn5vDfSUCOgZz5uZMfwplYzaSS0Z2ReBv1hJ55WwSBw6hQJZR/LXw6cNw6yIATvh9Gb3GXchpoycSk5iKX1E0D48QnD7+AkZPuRh/xZ6Q6VWb15Q6vJgt2wVPRE4PibbNr2j0CLcs2eg3qs4P18Je4Qxk43mrF9++AD7NEDixZ296xSUx7+1XQu46+LRzWF+cR1H2ChIHDGJTUY6WzKaqKAGNgtA9Ntm8fo9slNCFP9YW0UKt5uxxUzB8tCPGTDWNtwM1fjr2iOPu1+ewqXglNZUH+HjGSwDkLV9q3oPrF5ay4JIUVuVrslaG1u9zHywkZcAA7VnR24xNSuGFmZ+RtXwZ82lGfaJpasCHz+fjxJ69uf3223n3g5m0bNMGl8vD+tIiyzA2sGOj9h8AWkJW/IDBSGg83I2FK3GPPpOuXbvW2/3p6emkp6fz2vxv+HbhHHw+rRhC8sAhnNg2iqEd4eed9TbhQHm1jw7NPZQU5LGpOJv4tEGc1D+Jl5b/0qjfL3rvVU5J7E3KgIFWNCpMtr3H4+HS2x9iY1kJHpfgjDGTbDQEjVpTuGIpfq+P3zaspe9JJzDs1DPo2jbKbGNzaQG3XDjWLJCxrjifOdnzUdxOaTy/z0fRykz6J6WZ4+KvFT6U/meiFn6Oz+ejMDuT+JS0OjQLWLOjEhXVpLiZIfMGPIKeyGamWoUBg2Jh/FdWkMv0R+4xC58EAgE2ri4xPQFCCCr3lROTlMqJsUm0jXITWbufmGOig463d89eQKN6KREtKM3PRnQdGbSfsWhWVJXXV1iRIb/ijDbFJKWSnDaAWr/iGGe3VdSyraJGl5K0LR5PCC9lCtCiVRvuvHi0tlA62dqe99VccnZshpsXhv5hmAXdSb1jGHpyBh5ZQgiIj4vjvZeeZGWO5mRSBl/IV99PZ/HUMUz/aCF9E1OwF6xQVCuyq6pGxEz3+irWXNhUDrI556jWWLytooZ8nQb5V6NvQgouSfCcnrsbav79O3Cw1m++azV+BZ+i4NHnoVD0h/JqH+2ae8x74wozL9X4ArhlKeS8Zd/0T6RY/Gsgh0Eo/lLbTl0cWfGyLJGcPhjP8iq8elf69YF78tmnANrg5vWr1EgKv+bncN35Y/D7NG/v0+/PJyFlgCOsbg9B16fDaEd6ejreDj2JcMlU+zSub5+EVM7/z828XxG8/46a4HatBCooW1VEXW+5yx3ByeeM5/sQLIS+0SprKpz7O1MnbNdWx/y1DGdhhtBVVUuoc0nCkcwXDvbFxReLPoNjzw25nz+gaJzYAKQNGUx19QbS/nsvp427gMWfzGTGo9McCWvpnVyMuexxmnuscKDhRTYm0nA8tTZRHlAtQ0NRg3dUzOiBaq4Kqr1+avwKzdyqrW90A6Cex0HPKyOg1l8i1TDaJUnizLGTyV7xM0JIpiczNWMk/poahp5+Dsf16M3dl2vJRgvfcnPrfY+ZKiEul5vY1HTT22Z4UUEr1HHd+aPxer1I7U+AqZqnaXVRPsdmaBXxpn29lj/31wCC1yZcxZu3X2q/Gscpry3K49fibJusFRRlLyc5bQCKquoVJDX1lpikFDr1jufLWbnU1BsFVJFlmaT0IVw19nR6DBjBDVM175p9lSGEoG3HY9izY5u5rWf/eK6961ESUtLIzV7JvVdMwOf18c7LzzB79mxGjRpV34EBiEtO5ZWPF5K7Yjkp6UPon5xGfs5Ksh+YaCZyNgbXLCjhiu4qH9yqJbDN8ri5f8ZcGmkfE1AUirIzSRs4kIyMDFwuF4qiPQt2Q/nkkWcx6cob6Nw7nhFjVKIjXTT3yAidkx3plimxyYb5/ZrqxLBTnVzwpUuXOiTFVv7wJdkFn6Nc9S5EtdY2ShIul5u4AYPM8eKD/D/4wR0Hw+MQxV+aUoimbjY2IxkjRG4tyg3/cn1weSLNf5vcY/0fshCUFORw/fmjTONY20/Qo19/cpcv1Y6gqrRq0wZZGHrIGlUjMoSyybCTh8Fn+7QPfU4mN0qldYiX1359y38tN7f7Awq+gCZHZswbxlXar9Qb0AzmRF3lp7amYbmx9PYqv+aXWVGEzblwYjInls7ltx2bgwoJmfjoVtixCdzN4LTrHF+99Oh9dGr9HLFJaXhkif5JqVx1y50UXDDeKkbSIx3f6sVmwq7h3VVDDH62YdN8TowFaFNgjzIY89RV84rrVQI6khDGAKpDUf5+D3KV18+anQfMfvQrloSeqtavUXzzZ2Ws+LWcMbHHcFtG9yAlmSGvrOCcvh154HQnz33TnoMU7bAkY/+JFIt/OchhEIq/1KZ9JzyRkQhJQna5uP3hp4lPSePuBH1gbX0MruatiE/TDAJFgYpanxlGzM/KxOczOFK1vP/y03pSnTUArt9VScEf+yn8s+mrV+MlV4CKGh8/VLRu9G8rD2iWdOm2Cp5ZEzwY3fnKLHrFpYT8bWL3Y4O22QdsgcUVDJk7ZiwKsAao/TU+fiuvDrGzhRpfwJZIAKuLcln+zWfhfyCEpo0sy+QvX0JJ9nLef/p+NqzK44QefUgeNtLihm5fT7/kdM0AEEL3KgnTg2F4zIRtmx0XXXQRss1ATjst2Gg3J27bQO9X9Mz1Oh5M4xr3VfuCElUc7alqSBF7qyHtBsQkpRGXnEbywMF4IjRdb4/Hw5hLr+XZ9+dxzuSLKM3NciQb7S8v58WZC7n85ju5f8YcBLDw7ZdZX6wlbrkkQXSkiz/Kck0uoMHJBHjsuqmU5OeAim4ca1i/q5Ihp51tnWOdVUdZ7gqdjmHX+tUjBLZnB7TJpdYfIDIiknqhKNgDfIVh1BtcbjeTr7kVl9uja/56uPKOh4lJTNX47AUrTcO9traW5557rpHJQYK45DQuuvYW4lI0jqqmBdx0bl/+ul8dCWxluSsa/pEO2eUiddBQkxdsPHOyLHP+VTeSNnQ4dz72HE+9OZO45FRaRrh0reRcTb+6MFdfHAni0jSpNoN7G5ccXB44IyPDUXxFVbWESHu05YSe/bj79Tn0TUg1F5BfrbHc6pKQuO6eR3V9XNVc3NvNYLtH2VhA15egZ127oLQgh/dfeZ6SghzTbmnmlinIynQk2hq/+f2XTY5t60pLWF2Yx4K3X3ZQ8OpiYLqTovB7lWDtzuCEOMNOq2s2rS0pYuZrL1BakINdw96Ipv26t4o1Ow6w56AXRdVoGM998AnnnX+JyaEOeV4dJP57Voou86jj04cRL45leGp/M4E2CDUH9CiLili9OOjrQCBAaV4WQkAzt4QktHfg2jvud+wnyy4SdaqgeU9tUQKtTyy993Ayqo2BqENJMsbkv8s4HufLYUNxnsOT6g9RMGB/te+Qi2g1FnYKpEZPMvpFDZnSYXh8V+iLtoWl2/lpc3ACO8AXa4LDYpM+LKCi1qZi8q8H+f8OMjIyYP5ux7Z+Lbz0GjWRgKoybvJUklMHEB3p4pQhaby8agm7O/XEfeVb9E1MJTd7JUuW/EhU9wT6JmgDedJALUPcp9aiKAoFK5ZRmreSp99foOveai+6pL+0TYFmZFtG5nu5v/MnrRv9+51//Mb6YpXvDrQL+f2Bdifx5I8bQ363oaSQB0YO5YHvrDqljgpJwtBnDqYBBE9khnGojRiKoob1pJdur7B5D1UKc7JQuiWHv0jd+EkZego5P35nerp++mI+P342xzJOpk/Wql+dNsv0IrkkjZ9a7QuYKgaIMMmcaJ6My66/hTfXa8fc3fJ4wKkjqSpO74W93LaxqFJt/SKALfuqaOv1cFK74Czq0oJcvpw/m2+31MCgC8L0gXaAE3v2QQhNf/nJdxdQkL2chLTBnNQ/CVkSBFSNpjNL10l16VqrfRNT6ZOQQlZWFvddqZUinv/mC0x7dgbdTzgeWRKcOmIEjz/6KF6vV/Pg6Yf2KwqF2Zn0jnfeIxXo2qsvkixrXkvZOSz1S06ne/9knn5/AQUrM4lLHUSMXjxB0cPahjep2q/wXu7vjVJv8Pu9fL1gDleOPZ3k9MHIxvFtSBt2CmdNvJAuJ/aiKDuThAGDzcp+koCkgUOY6fHg9Wrv9M8//8wpp5zC4sWL6/VkSTankfF4t2rT1im5N/+++q9BR6fju+Fyu/H7QXZHsOO4dNjR8O/aVW3lhoefIjE1jb6dWvL4O5rKhqpqFJqW0dE8//58k1uO7g1dX5LPg1dO1OkuHqZ/tJBe8SnEJqXx6NvzWZWTSeKAwfTr1SMk93b+oq8552ttQS7LLlRZRrFZBFvkDhxoe5KZeFnX+FFR2bd3r/Yo272IquY9lnRqhbG3YUj5GjB6RvfvzJqiPO65fIJJfXnhw084MTYJtyxpJZ3dbsciRlUU9uzY7mhn7+6d3HrRGHxeH7Nee5433v2Q+F4nBh0ve2XwQipUYqUZ3auz/dn/XUmgYg/zZrh5+v0F5oLEGEt361UmjWWgqqr0TUghPiWNoeeM5395oUlzbdu3B6B1HVpY8rCRjBh7Ad1792N1/ko+qvM7SXaB/g6JIHMeZJebuNRBpmfdeDYO7i+37aVy5rjJuoSfkbBm5VcAlFd52bynypzvDIpd/u/7HDJ/jYV9fLWrL/0dWPjK43zudvOQrWKhP4QHucoX+EsNSGMMVVQV2aaJrQKdoyNDHjvU6dTdZv/dxt0H6dE+vPqHN6Cyac9B2jcm+ehvwr8e5DCoO7n9ty98+8AlfDv/I5Z+Pg8JzatwQptmlBXksWe7ljhxUHXxxewPGH32GTzz+MM89p/JrCvK0zmSaUz/aCGpQ04GIVAVBa/XS1H2CoeBCw0FA4NhehAMb0kTl9SVrbvxyIuvs/OPX0N+/8KyX8JysjatLia2czSt3Noxa6urTO1O6/xs2eSK6qg0WNe4N2gFvoBC4Z/7w0qWmSEgvZ2tXQagnhJauYO8hbA5F1VV+fOXTcgu2ZQR27dnl9Nz561C8dawOj/LolNImkqByUc0PcnhmQ8xfS1N5fdyg0XWHbxrFcoKc/n47ddZV5xnedSNa8W5wldVlX02Ob6CnBz+e9FYvpn3IYo/fNldY5HQOybOvLaE1DTi0gbzw2dzefvRaZQV5NIywkW/xFQefWsek665jUffmmd6OiUhKLN5l321tfz87edmQqbBBXz44Ye5+pY7zUO7PBFaWLxuPygqP3w612acOnu0R1wyiqrSLyGViVfeSO+EFPOZUbD4iQA+v8L3650L25AIaOWEv5j/MVlZWcSnpDFqYrBaRIeOnZAQ9ElMZdSl19MnMRVJaGWJhdCoEq/OWkjq4JNNXdfGZNAL438CIlwy7aIiqNi313ntW4oavg6gfZfjuPPVWUy+9jbOfXwWi3c0jpp14N2bcctaOWRwqmy43B4SBw4xZe3si8KyXKuKm9/npWBlpvksxSSmMPHKG4lLSQtL9Vm3psz898gJFzHu6v/Suo1zYf7MT5spK9Dk0eoaLC6Xm77J6TZuqi2x1bagBGvMUdX6NZAnRW+l8ufZLFk0V5MgCxiqQJlEuiUkAXHJabw6axE9+1pa16qqkjhwCG6PVl3P7YmgXYeOeGu9pqpFXk6w9jXAsp9+Cns+dqwpzNOvxdkPvkveQGnZwZpD9LHTcjTYk9is6KKqQo/+SWGPJ+s38+Rzx2tSj0Lg8ngYdfE1mkxmYirjr7iBl8fE4lascVOxVYALxWM/a8ql9EvUopCS0Dz1705/jla2ey8kidPHTKrj0bWSEUEzpOxe37r3vDHIysri8ccfJyd7pdlnWlt/rxfTKNlempdl2xZ8DobxHk49Ys2OA2yraFyp+nAw3iFAqzaLThmKdIekWLyS+WtQ2Wn7PajxB3hFV6YBNOWVBtDpH2Qcw78GcqOx/Iev6+hwZgLaRFeYvRzVZ3msln37halJ6WvWivz8PN1gVOmfnMbwM841LR1VUWjZqo2Nf6y1cSjvqcWjUg9JoiZwyrVsLitq8u/Kd+3khwUz2a+H5Lb//hurC3LNazBX5mgD9L5qHyXbNA+Scb6mdxnntSuqyqqtIYjUWIO/ofkcaB7a+w3Aio9M4/D3zetRFIXufftz2R0P0aZ9cAKdJEkaxQKIjnTp1yEQUh0d3Hq6uXUD/Gn7tZcU5HLrZVP41JPKI7O+5+u5Mx3PhCGHZVxzZW2AjbutcGxW5jKrzHZ9Emf6d+tXrzKvY21RHtMuGctXcz7g89nvc+PU0ZQV5CKEoG9iCuOuuIHE1AGW5we0BDW9PLOqqiz7+jNKC6zBMj09nWnTpnHGmInmtluefccsmxvUD/YNdaIA64u1sso1pm60ZfxYhpFuBDXivTnp23vNZ0EJ+Fm6dCltmnk4e9xkR/jf5XZz7oTJSJJFxdD6TOgJJ5qBG588gMtuuoOIiAhknarSUAa98dwIBP07R3NiuyiSBg5xlC83dhx32XXBDdjwyartRJ7Qj9GX3UC7Ll0b7gAdfr+P1fmW8WYsbG6ddi+vfLyQ/kmppuELVmW8/qnpNhkzD8npgxGAR5bMBDe3JBHq1ZgxYwb/vdG6nvLdOxh16fV4QmhA33nZeD6f/T4fvPq8c/trs+nZP0kzArGPF6q5WLZ7lQ3D0VuPgfzJzHeYNf0pflg4G5fLhSzLuD0ekoxFgrEgFrB5g1VSQwhBi+ho3v/kCx599FFen/MZvWPiTKqOoii0bRtaSm3YySeHPR87HvnPJEoLcigtqEPXkGSIO8M2h1jjgxWx0z6bHlj9c96WfWGPt2f3Lha+/TJCwKuzFnHJzXfxyNvz6RWfrDkL9H44vlUkxyu2kLqox5zI+4SvXn6AtcX5CJ3Gct35Y3j1mUd5/oHbzd2OO6kXhSszWZWvzR/egGomuTsoZ1jzhaH60Ngp066XPPqs0ynJz3VEK+rj2x5pGJS/uFSLbmMkZYbCL3sPBqlMgSbtuXV/DetCUHQaCyfVRPtrjLvPLN0ctP+K38rZXidSZz+z2YVbeT+v8dX3ErpEN0nB6+/AvwZyGNTlEWYv/tLUb3S73SSnaxxIIbTQKF6LbH5SnxhTk1K97E0+86SiKtbgta98r0OIf8+ePeZq375ybgrqek/272lCKry9nT6NG7RNbF2LuuobZr/6jK0RhTefedgRZDMMPIKuT/tba3qU7YaPbcKrJ8RjXHu9OY11jMaA38+GsmLeeeo+evSNc3DyhCRxxbRH6Z2QAkILKxucYxlAWJnu9RnJDRnIAVWl2qcZfcXZmXgVPRTaI53XH51mToiGR918fqp9bK2ocQyi6YOH4fa4EcfFwOAw9ArASBw0kgwFgsKVTm6l3+clP2u52Z9CErj1zHOh00oSU9M4bfRk8+KVQMBcNNphV79LSU01Jzs7FBUydG8V/UbAuXc4vl9SUIaiQm1ARVFULK+gaobZwfaMNYCTR03G7XZres6yi4yMDLq3b86gQYN4+eNFjJpyMcNOO4tzJkxFIJAFrC/O4/P3prOuOA+PrHHSDQ46QP+kVBYvXsxtt93WIL3C6H/7X6ONjp2PhX3btKQo7aKoqqxgWKfwQ3WVL8DDP2wgoKhU+RpOwDJgJFrajfL09HRuuPV24lPSLDkzsN176JeYyisff8qVt0zjxyWLide9xZFuydzXLYuQ78WCBQscn39bv4bc3/exszLYM+bzennxwTt5+/knHNsDdbzF2iNgX2Sr1l9UFFT2VPl46edfwvaFz3B++P1ccsklPPzww8xb9BX9k1MdFRULVmY6ir3IskzSwCFMOftUpk2bRuqAdPbbxndJkvAeDL3AHzCwcclkfr/mkMlbsTz4S6FriO/b64jA1XW4YHqWtf56ZHFouhzAip0Ks6Y/xUNXTQLg/P/cRL/EVMciQSBYW5xHbZVN1nDLKlRFMbXNHVj2HgG/l9KcFQgBBdmZeL21qIrikBP8Y9N63nnhCW69aAylBTlU+/xUev1m4uVBr18rOqXqlBOb4dzYKbOuXnLBykxzUeVXlNDyqN+HVvo5XKgEzyEGxcIo2GUsBOqTbzTmhgO19UQPGzwX1bZQMJwQmmzo12sbZ0/kbLFqGNRXrCoULk87oUn7/x04Kgby888/T0xMDLGxsUyZMoWamsMLDfwVCAqRqiqqonLG+Kk8/+FCTZ9T/6qivBxsWpFRLaOZv+hrrrr1LuvnWF6uhAFD8OhVyiQhEdWqtc1D6HzZD9T4HZP+r3stQ9yOssJcPnhVS5pRVJW9O7eF3O+IY/49UF3Bvt07HdHhzetW88e+Gqp9AT2hwsomN15EsF7sg74ANb6AY2KzDH5Nuqlu8RD7fgFFrd9ADqWLq6r4fT4q95dz5V2PmYknLpeLrr36mrsJYXBGNc+hQBDhkhwGRCj8vraupq0Ty75cyOqiPBQVYtMGWVxrIVB0dQH9NM1J35RJ05M/DSSmalxideLj4K4nTKUqCCE4a+xk4rtEIwQmt9KA5hUcol+bQEKnFOgeUwG4hOCU0RPxRESYSVkDBg8NWhRItsnyneceYVV+jlnC1ICiqPSIS+Gaux5FtOoYdMpL3bGUH/QSCKgc9Ab0JEZbVMK2UCotyKu3z81uCPEv0IzU08dMJvvnH1k0+0OumTKaL+d+wH1XTmT+a89y/5UTWb+qwPSmSzajIT09nRtuuKFRWfRSCANZEoLufWLhnas1uUEd387/mMy7x2iVPMPAF1B5J3cLH+kaufVi+3ra7Chh2muz6JuQEkQ96NIqkl4dWljPvfk/7XOESyYhZQAXX3cL6enpREe6AYGsJ7MaSa2tIt307tDC0XaHDs5ozbYTh/PS8l9DnqZBWamrmPBomURmTp41odueBctIxFw8qarKO9lbKNvRsHdNVRSSkpKYNm0aKWkDreRcfXBr3aatwxI7/4rriEu2dNP7dWrJaaeMwOPR3ouIiIh6n4eXx8Q2eE4ul5ueiQPomTQwxLcCl8tFXOogygpzee+V5ygryLUWkRiGjj1C17AlaYT+C7IzcUlacaW3HrmTtx+9i/XF+awvzuO+Kyfy5y+6fnXh5/DV08YpMWDEGXT6/nFHm7IsE6cnsKekD0YOIQGk6vfb5/NRlJ2JX7FH0KC8ysfeKm+Qd9y5GKgfdalECQO0Mva1foUDtX5qQ3HVd4VfXB0WVBWfz0dpruWQC+j61hv0COG6nZVs3V/jcJKEaAY4dCUIs4iXbZ42tjfFV/f56h3c/fVaoPEqXAbqq1x4tPC3n9Kff/7JSy+9RF5eHqWlpQQCAWbPnv13n0aDyMjIAL8tfBAIoKLSsfNxxCVrGeyybiwkpw9GClj7xiQPol9iKhOvutHcZg3iKjGJKVx958PmBPDeU/dTVmipWQDmwLZu1wH2Vlmrwt0HaynZVuHgn2ZlZXHbxeN4/8UnuPPScRTl5eCJ+Ju4PGEKMlRVVvLT8uWs3n5Ae7mxTVr639JtFY7JrMavOFbJ9pVslc/P/hpnqWaHp1ltQOc0xHlqqgSaXFTlvnKz8/1+P3NffZYNeqlcbV/tXhueVMNA1gyJ0MddsmheyO0Gln25gAevnEhZQQ5d+yUSe51eSU1oBRl2bP2D4vyc4EkfZ7ThYK2fNTsO0DcxNdRhnFAUzr/6BuKS03DJVib5ix99xnnnX8KwkWdxzoQpgGYEmxxsm8dc0jsjJiGVx9+Zz8U33sljr7zDhDNHBAnHrym2eGffdDiLO66cSkGus8qgimYkV+wrhzDyUdXGs4HmObLKw1qLBr+i8GBpw4Ny7pKvUfzaYiwQCDgWw5IQFOdkahQpnWO7/NsvHGoehSuXOzyqWr80bTJo5jZoOxaEgIlXXB8UklCUAIH6eOVoXqdGca8LFiHPncYtp8bSMy4ZlyTM6I0BWRJE6bJkvTq0oH/naLq3b24ai80MT7F+9r06tDANfklAhP6ONPPIQWWld+1y6q/Xh3MvvBqPxxNSMWFZyaY6xp7Nawp1xpBGhN/NhY5g925nPxoJle2be4hSqpBs3uGW0dGOey9LgtQBA5n+0UKuuvUuvvv+B1JSQqv/AAw4oTXtouqPNF330ix6xaewJTKEh01Prvpl/Rpuu3gcM559nFsvGqspW+DkIGt0FLVRFRZBM2iTBw5mXXEe0y4dx/cLPmTxgg+5+7JxLFk0D7/XZ1XNK/wC/F79lATjL7uOVz+yRwu0hZNbj74MGTyYF1+ajsvl0vpTH58NyoGhVKOqKuuL85k74yX9mizPprUo0iMFqpaoV9NAFMWgEl1/+92a3nJCCgpaXoxGsQgxp5U3YuHZVHytj/eqysED+83Nhqyo8QxXev3652CqiQH7vNpU/Lm/mjU7Kk0nVrXP0tFu4XE12XDdtEdz4oUmWWkIpdbSkE750cBRsdn9fj/V1dX4/X6qqqro0qXL0TiNepGens4rJ0cTs3oW4ofpiIrtuN0e4tIGIRB0axtlZmTGJacxcJhVOah3fDLbD9Twg23CsnuQFRX27yvXMnTjz8SHoDhnhf5QWit+wPFCGKu8Gl+AAzWWN/XbH5bYNGJ9LF00n5KVP/+1HWRA10x1eTw4pnsBeVmZFpXCdh3GQqFGN1KMazMG9LoJGOZgaGtD80Lqw6WKIwu+sUgbfgaPvj2PfompxOq8SiFJqIpCcfbPPHT1JM1IxqBYCCJdmuC54T0OV7QjKyuLz+fWzfN2Qh1yEV53c2ZOf4YXv8lnlU/nULsiCKiCb+bP5MapYygtyLWt7I3+skUZav2N9gxdPu1xrr/jgSDPd3xyGmeMmUT2sh/5TPecri3OwyVJRLpkc/Ayrtv4G5OYypSrb6JffGLI4XBtvpOq5I9qw/sFzqz/Sq8ff0AhNm1Q2BF+8xqtRLCKFsmp8gU0vVDb7pW1jaAXVO0ndcSZuDzukHxhIdDUZmweppPPGOWQL0saOASDfxyKKtEYxBzTkiiP7JgUhNCkuIadNbbO3vo+hzL72VvxVRMbUcGDb2l8Uk2VIvwUIAmIjnTjcUm0buYm8dhWjqRU4Xjdde6xLGmLSAhplY4bN67R5xvVoiWPvj2Prpc8GvRd564nmRO5MW6UFuQw87XnKcnPsdGSNFpOg7dHvxhVVWmvqzgYmw06DcCI4cNNrrnbY4+0OBGXksYl193CoEZQbRoyDJ5fJ1NR4+eNlVtCfq8oATK/+9IxBxRmZ+rjgX381f5ZX7IiAN5qEIIzxk0hLjmNouxMAg4KlvZvl8eNtHMj8otjNVqQDlVR+HXjWiTgDF8RYt49gIoSUFiVs4JIt0SvDi249pqrmfvFd1z9v7tNb3KvuCQuuvEOnnpPU3ZaV5TH49dMZubLT3HrRWMpzsuxLYI0ScyAbikbDpWG1EpAm9+vvPF/xCalaYm+ikpAdToeDMQuewzJf4Sj3Hu2wJql5sfNa63EVUNH2GEDmPSHYIULbV9rXmgqDnoDjkRXv55s2TLCxXGtm+Fq4uBmnEKooWX+Ku05ueDjwqDvmuhw/lvwtzOijz32WP73v/9xwgkn0KxZM0aOHMnIkcHVg2bMmMGMGTMA2L59O1u31qPt+hchpns3brv9DtaVFrJhVW9aNPOQ99P3UHOAjsMHU+uRUVSVij2VdGzbGnZrq8DK8l3s2qXy6GJrQKsu38XuKhdRHplAQKF7777IvdLxj7ga0e54evTuy4E9O6mRJWpcEjU+ha2RtZTvOkBzXyTeCjeqqrJvl7bykqvdyNWa1mvvPn01mSefRg/Yu+MPFPW4v6WP4gedTLt27Rl+1igeX98Mbe2o4nZ76NWnH/t270QSUFHjp8YXIMIto3pcKKpWEa7GF0BBpapaq+Ij3BJ+t0StLCMLoa/mtfDtgT2wXmgV26p9AbwBrdJPjS9ApddPwNv4QUwIwYk9etK1azcOlu+iW7duPDL9bT6c8QoluStRFQW/z8eq5YtJiemFX9EKUvgCCn5VoaLahV9R2OUOnVW8aNEiAr4GNG07nAgjb6Bw4UOIVonQd7i23eWG/3yA+soUfD4vWUu+4ZjjjgMV8/kwuLybRSXlVV6qymuCqAuhMPS009m/ZycuCba6qjmw5wAqmkd25Y/f4tPlynx4Wfnjdxx73AkIIaislmjucXFMywg27DmIL6BQ41dM/lzg4H52bN8WVHI7KkKGT1+C07VoipAkNsidHfvMzP+T3eUVXJpwAm3btyeUkuaa/BUk9OyKgopfp7kobhlV1e7L2tJCCvLzoOUp4S/+t0Iu7upn8CljOPGE49i6togzRwyja9eubN26lfLyaipq/JzQtRtPv/YOhXk5JKak0SM2geh2HViVn0N8Shpdu3XlwJ6dGjddH9QVFba6q5vkIW2uqESqKlu3am/NgT0HOFjr55guTl3xAcOGk5+1HH+oRpoA8c7VdBhxKrUH91GzbzcBVaXG70aRJbZGOpNtjHFtq8vSIldVlQN7D2h8cFnzXG51a99X7DmALGE+E363hBqoYutWp3f0nHPO4dzzRvN5I873pF59qWlxDL+0Cp71fTVV7N+9g0CEC1kIVq0p5u7rLjeLMD06/W3iEpOp9vuprA0Q8DU0Nliz8/LlyznnnHPYc9DLgcpaNOlNiKiNoGvXrjzwwAPMXbiIwcNP44Su2rNg9APArkovFZW1qMA2V1WQR9ren9791ZpBSghdYRuqysNEB4SmyZ88ZBil+Svx+3VKRp++7N+jjb01ssRBr9+kKzTIDRUSqCrdup1IxZ6d9OrbD9nlxq+PZy6XmyEjRjJoxGkU5eWwd+c2fly0AMzFu8qrD95Bh46dSO3dlSU71+GXZGSXi159+1G5d6f5vHXt1pWxk6by1qwNBFSVyGbNOOe085Ek2L1zO/esEtA9HVb/iM8HKxZ/ywldu+FXFA7WavQ9SQgiXNpizy1LbKguJzrCjVsWQeORHfv2VHGg1k+NP0CtXzH75ePMNYAlRxaXPIDVBTlhWjlUOK3BtMEnU6C/4AcrD1C5R4JIF1vd1ezfrVUmrPEFkGWJrXI1btn6/YZdlVT5FDyywK+obPXUXz+gLsrLq9lf46fa76fWr3nSm3tkIlWZfbsrqK0KX+o+FJRAgFkr1/PyymCtySeWbCShdWhHRk1FOTt3CLwVDRcH+7vwtxvI5eXlfPbZZ/zyyy+0bt2aCRMmMHPmTC64wJlYdNVVV3HVVVcBkJKSclS8zLvZz3alEk+L1uzft4/P3p9DIBBgwfsz6P/1t5yaMRSA5m28tFhzENAM5MhW7amp8QGWgRzRuj2RES6im7nw+hW6JA7l2AnR/HYQYoedSfyQNKI8MhEuiUiXhMsboEuX9vzuK6djuygiXDJb9lUT3S4KFZX2rSLp0kYr4Tr0tDO47I6HWPHdl3TtHcPXH78FqeP/lj5ateInJFkiJimNyKhYqqp8tGnfiWlvz2PA4EGm96X2oBfVr+CRBC2be1BVzfsqe/2U5ufwdtYvjIw9nkED02ge4aKZW8IlSeagJUsCjyyh6d6CW9GyayNdMi6vn1d+3MSSXxufwRsZGUnysNNo2bYjsiTwBhTSMk5HimrFtMvG4/f5cLncDBwxktbtO2kGsgCvX9GTkCT8AZUOETUhn81Ro0bxwgsv1lvuWLswD6qqoAbqmD8R2gDtdrsZMPx0mrfpyOrCXEpzs0gfMpRe8VrYdo8QKFHQWvazcmUW4UXnNES17kDnjs1p7nHRpX1ztgbKdXqCyqARZzD7nTdMI2PwiNNp1aETAuh3TEtaR7oRAnaxD29AIcKnmNQTr9vFMZ07E+FyTvQ+n98MvdaH3O01XNe6A33jU1geQsK4e+IgIlq3R1FVXJImu+WSBC5ZYm1xHg/dfCU+RcD1oQ3k7oFtTDkrgfjkFGp8AZKGncqU8aPp0d7iyFa6K5GrffgVlRFnnM3AEacjCY2CkDTsVPqlj6B1lJsot4xA4JLh2FbN2F5RS0BV6NKlDcAhj1Ut2/op3rqfpGGn8umHM7Rn0O3msptuJ3XEmUw/tLxbE4rPy49fLCDzuy+49405dI9LomObKBRVpUsXpwKMqqrsUPbTpUtrx/bffeUoqkqES8LjkujSOdrc7pIENXoRm/Subdm+fVvIvqisbNx7Gjv4FH7bVw0E51Ps2L6diFZDaBbpQhKC9WtW6ypDCn6/j/VrVzP41DOQvAFqq3wcrPyVet8NG1e+efPmdOnShRY1Pry7DqKoKvFdWuGWBStXruSBBx6g1uultKiAhLR0+ienmvceQFTU4NtfjapCly6tEUKEfSZK1i2jYuef0Kb+BKXIVu2oq6MO0Cs+hckXnk1S2gB6xqVSlpdF8sDB9EtKxSVpjoUIlwS1fr34AxTkN2DsCYGQJLw+P63bdyJ9+Bm88NGnfDZvFqhw5thJ9EtMJaCq9EwbzqaSfH7+epFpQAOoqsLX8z/mtufe5p7X57L0i3kIXy1R0W1p1a4Tv/22jqVLlxKXOoidlbUElAAg4XJH0Kxte2QhdErdLzD4QqR1y3C73Qw65XSi23XEG1DwV/vwBlRcAlpEunBJEhEuCUWC/fq9Tq7z/IKmo+xXVKKU5uDzI2oDyAGFGp/C+uI8vt7j1OpNPvk05r/3Bt6dm6HjSfX3XWMhyYy+9Do2ryll6BnncN7ki3l9hqYo445sTlTb9kQ3c9O5cxv+8DcDwOUN4JIEx3Ru7RhntwbKkX0KzdwyNX6FLl1Cq6aEw0F3JepBH7LXj8sfQPIpHBMdaXqyW+5QgcYPPruq/Dwbwjg20Lx1e+CXoO0tW7ejY6dO/ygd5L/dQP7hhx848cQTzWSNsWPHsmLFiiAD+Z8ASWgSU49fMwWft9YK7/u8/PzzT6aBHOWR8dhWdAveepnj451Vkj7I+51rBnUzs28f+WEDvx3UftO8dVuTRgBWsglYoZOKWh9VXr8jMcXAz8tX8M6T9+H3+SjNWY7Spgt0D5XQceShqgoBv8IrD9+J+7qPwdWMVu060CsuRjtHoV1LtTcAQuBTrFCOoqqUFeTyv9tvwzfhSUq+X0UrjyBNz+426ChCODmFBu3CFj3k23WN99z1i0/i9ekvUdG2u+kBlHXKQD+dV1uck0nvpHTikweYvzO4iKY2bD3zbXp6OlMvv5q3GzgXIUkIWQYRXAggbcQZXPyfm+iXmEJRbg53XDoOn9fHB688ywsffkJsUioBReheFCjJXQGewfUeTxLa89pdpwf169SSdTsrCSjQPzmV12Z9ypbSXPokDaRbTJLuldZVGySLZgFawqIsaZ7EcF1x8skn89pnS21JH6H3U4Fqn0LvhGSWZ/8e9P2rGwTetrtJODYaf0ClS3QkAUBSVVblrMDX+njU858Ne93DBqbRv09H877JQqK8OpjXa2ToxxzTkuKt+/EHtIWRS5IQkkqkSzaTFY9t1YxjWkbg0T10h4uWkS5aRrjom5DKg2/OpSh7BZ3at6NoZSZbtmyBdsEVK5sEvTKN3+9jdV4WPXQOsjeEQ0cIEbLoghAat7B9cw9d20aZ263wqDD3C4dzR4/hx+D5MQgK4RNvI5o1B1S8ARVJqGalRZ9PW1TGDxiMomplln/KyiN/W5W56AwJ/YRdbjcXXXQRoNFLko9vTf7v+/RogTAVEJRAAB9eVuWsYMrZzkVZpxYRtIxwNSi5lZWVxXlnnob3nLsaNJDXlxYR6i3bIrWnbMNmNhbn0D81nTGXX0+kS7b42MIaM1UV1hTlct//boKpL9TTFxIul0unEmldE5uUyjG9E1BVVU/K1BqVhKB3QioPvDWPp26+jIpyK/5TvmsH61bls+yL+Sz7fC6K38/y7z7nlvse48WH78br9SLLWrly5T8fgyyxa9sfqGovVhflkZuXByTRsk07zrrmNgYPGUpsUppxaH0OUFGEsLjI+pwhi/BKUJv2WMmuZj/p00lpXhZ4nHN3x55xPPjWXEpzs/iII2Mgt+nQiYsvGEWtX6FtlAdZgncmxXHZnFW8sXILbpfE5ATrfTeuNxRZyCnt1zSOxa7KWrMIiUExqUtRP9Lc4HPfCV1d8p9IsfjbOcgnnHACK1eupKqqClVVWbx4MX379m34h0cBxfm5vP/M/Xhra8wHzyg3O2ToyeZ+srCynAHmvPo0z93xH0dbX6/dpScWASoODrGKamauGi+rnVO0taKWnQe8To6ure2fly0zNZoVJQAXvwIduh3h3qgfSiBAbY0W2qkxC4VY1+BTVNYV57HgrZcpKcgB3dAvzF6BX+eMqe5mlOVlWf1gS76oy701EzRCcMbqgyTJ3HLfYwghmPvmy6wtyrMSrgCPSxCTmMqkq26id3yKyTGWBBzTMtLK6G8Ed3BtWf0qFgAn9OzLxGtuI2XYaUHf3f7c2/RLTEFVoSgn01GcoTA7U6NGqAoBRaOrdI2rf1EUe0xLzTtke1ajPC6T/ycJwaWjR3L/PXeTlDrAUrEQTn5zfJdoBFqioqxHCOyLGDuObxOFUKxn3RWqRK0OFdj5Z7BxbOCt7C1c/0kpN39WZnLmfIpKTHI6os+wsL8b1kkwvEc7vdCFZtxHuESQXPSx0ZG0bx5hGnf9j4mmXXOPHsKVkMGhL945OhIhBO2aezihTRRHAh49wa1vQgoxyem88cS9vPHsY3y3cBaNSDWrH3rykSzLxKSkm4uFpsxLxrtS90ysZEVL8SUcLrnsikYda8Oug4gwl5z9w5dsWKWVOFdU6BabxNPvL+CSm+7kaZ2/qqoqVd4Ar24U9RvH+gX0jUvijbmfB6lOJB/f2lwcZmRYCghut4cBg4cS6XY+05IkaBHhIrZzy3r7YenSpfh8vnrVSQwsWRValq1GeFhQ1ZVZ05/k/ismsqYoD19AqZO7YhSPUliUvZpAfcaxdgGMGD3JrMxn52ELoGWEjCQg0iWZ736fhFQuvPFORzP90wbx4FUTWfzJTPxer0bd8vn48ZvPrVL0Ph+1tVbIaMvGNXw9dyZ3Xjae2W9O184fOPeS68yomXVtwkrAtM2bxjhUn+KDXUNdQSV7SzmTPsxndh3jGGDRu9MRwPgrbqi/35oAl0fzkgohiPLIuGWJTi0jze8Xlmw350HTEVTn+uzXozr+Nn6c+K28SossoOILKHj1EtP2x9b1N1muTVW9+DvwtxvIAwYMYPz48SQlJdG/f38URTGpFP8kZGVlcdm4s9lkK5whJIlzp1zMW3MXcfIQy1NneNQMGHqadWF5P53GlRHitj/Xqgr5v+9DRaXWbyPRq2rQyrh30gDkVh3h1kVwUtphX3tjcU75t6bep37WAFRXVZl6taoKJfk5vPnonTxwxXhmTX+Kmy4YQ0m+lnjWPzUdWQ8XCUmib3K6qQNpX90bCQpgDZCWJFzjcd7kixACRp52KjNfepI7Lh3HmsJcTb5NCJp7tKCKZBrB2u+EgONaN3MkZjWEkWef1+A+zVq0ZPRl19MuRLESFU2+S1FV4lIHOYozxKcNxqhKqKJ5cp5aWz+P8cHTe4U0iEJdk9AnRLvWrwFD/cKUfavHyFq6dCmqzQDolRD6+TTu6Ubd6GkI+2u0oii1/gDd+ycx5IxRYfdtnj+PCJdsGYRCkyqre83NI1x0sKlwSJJw3H+XbLvev2gsd8vCXEgu+2K+uSgKBAJaKd/DgaKAEIwYPUnT+NbRFA9RuMhJu6gIs62GFD1yskNXlquL+79dH9bADHTpS1FejjlAKKqmzzzpqpvok5BqJv2GKqoQCu7y37np3kcdEaNQsFeJfOXjhSSmht+/Lt2oLjIyMrTra5AfDTmunuG/dHlQFQWfz0tpbhY+RbWpVmi7qKqKV1ERXfqEb8eAkBhx7sTgRQ+A0HTh3bLQoyqW/N+ZEy/g2vueJmlQBjc/9AwtWrbCbxQvQhtT3G43I84811xkyOaCWRgnSub3Xzh+V+kXLM/J46PXX7AlYGq+VBVQzfnCkv00ULx1v+PSLDkz83D4/AqflToTh+1Y8Pqz3HflRL6bP7PhvmskVP38jb5zSxLuOlltNts4yKi3w55gp6gqBWEq3oY+D6MNKzmvbvt/l7rEvyoWOh588EHWrl1LaWkpH374IRF/lyRZE2Cu7m1QFYWWLaO54NxTTX4OaBOpsLk6hBCEqipkGDMGj9ZA7q+7+Dk7V68IZOkQ2suF2rfbBwBfQKFMOhbfpW9oG0bfE3TcEXt+OKQ+aAjfffy2VpJSaAUXjIvaLzXn+xV5KKrKqoIcrp86hiWfzsbnVxxZ1ooKkcfHII9/BNCMRbAmNaMP7FJelmfZ+F4zwBsDl9vNWeMmUbAyUwuTGnqbOSvMCaAkP4c5M15iTVEuMtpL65IF3XQPoSV3Vr8GMkDvfjENnpMxIO3bHczxUlWo9QdQVeiTkMKj78zn0pvuZPpHC4lJSgEVM4O7Maj2aQlUoUKP9mIIAG2j3LpRKcJ6DK3rD+9Rz8jIQPispJHV7cJLXmVvKWdN2+Sw39tx9fwS/txXY04c7Tp2DrtvbUBPJjQnc4HHFd6Mq+vIMMprN3PLDqPhr0BhXg53Xz6Bj15+iqWL5iDrVd1kWUapy1NvKnSKRY++cYA2TjXmObbDWAzVfYRObBdFmyh3oxYPmcuWNfp4K77/IvQXPQdR2HagOQage4vtDoS6kbZwmFCznAeef424lLSQtJK6SE9PJyMjg4KVmRTkNs7YD9fO2eecG9yZhwhVUdhQUsiawlzHWKkpOyioikr7Y8K/JwZ6bPqKPkELKO05cZkLYv2dRzfyABCcPuECnv9gPuMvuIT4tEHILtmUbuvZN5Yb73mM22+63lxkXHGFHk0wHhpVpWvvWOTISEQPa/Hx+kaZj15+iuunjqFE13neW+3lu3U7LceT8dd2/+smJNoNY20OUUPrHtuglQz3kvXDl84vvnsZnqvHCbLw4bBf9e/YDGwOCCHqqD6o1tyn0RStRcCOA7UOKTv7NaGGHt/DXptpIKv4FcvWsDfxd9mt/0AH8r+V9MIhIyMDtzs4m3LWm9ODquzVxfCxUxGu4N8GAor5ANcctPhpiuTmlQ2yVqrX5jGtu9I1ZYtsT2+tX2HZ5r31ns+SLxYivXTkk/Z8Xi/GCQcCfjwVVjLNGxsFpQW5FK5cru135btw3SzTixCfpmlcPvV9CbV6BndVVEce+Phb1hTlmpJ3WgjM1g86NaMkP4ePX3+Rzz5+nzsva9y1aVWeYOQpI0x9VbfbQ/LAwchCUFqYy/VTx/D+i08w7bLx/FJWiBAQ6ZLNxAG7V7mhFzpnRcNSe1UHK3no/UVk7QlubG1RHn5F8xJ7/Qp94lM4/z83EZuUZg7w+iPVaINNErC3DvfW7lk10DbKQ4/2zR3XGwpGhcFeHVo4Fo0O1NhCyB27h23r+WW/NOIKLBjVBHcf9FKyPXym9bBzxlseMLS/nVpEEqsnmNVFsDdd+41sKzn9V3k7sjN/NjWYAwE/Z42fwtX/vYszx58PmxtXBCUUxPPngapJMh7Yv9f0XDUVnaMjaeaWzfLrIY/VQLtt2rWFP1c36nifVQQXjjGw0yvbPGfoOraWN01VMYst1IcvP3ydNx+dxtP3/LfBsR2sUsWvP/sYl00Y1ajfhMOZZ57JYVNnbMhZ+i0PXjWJsqJch0xYjV9p3Dix+DV++fJt1hXnIQlo19zD8a2b6UmxkmPRbH8njIiTMY7IuoPIMOqUQIANa0p56ZG7WFOUZ5aiv+iiizQvsvnQqBysqGDYA7NQRzhpippDw0tB1nICKjz2w0bezvmdXZVek2Zh9xcYc2ZdmEWrdINy1db9DRaRURVFK+Lj3Ei99277+pCbT2uxi7aln/LtvJksfGe6TjnU6FuybZDSHCSKjWKhzYe7D9ayw1bi2R5HtXudmwJ7VLYu9aypVX0PFf96kP8PIT09nfsef4b2XZxyaaqqBlfZA3ZutYTE1ZYdQ/KADOpAQFGpqQp+IY1qOs4CCKrtRVeN/5uo8QUafrAUBaURSgJNhVQnJORd8BDYvFyFKzNJHDBEKyEc1QrcEZwy9gIeeWseMYkpmij5AWcZVrX/GZTkZjnoFajW4GBQNm6+YDTvPP8Yz95/G77axl2boigUrszkvJEZLF68mKtuncb0jxYSm5SGEIKilZn4fJpx4q2tZclnc4P4t4YHEqB98/ojH2mDhjZ4Tr9v3cYaObQk3+r8LBS0QbLab4W/7M9HQDegG2MhC32Sq4ueHZrTs30LWkUGL+oMz7Bc5xmzG9RCT9gLhaVLl6I0gmMZKmGuIRiLqGlfrWHj7vDH6B2vlQw3vV06p7iZOzgEHuGSHNJQ7Zt7aBvlsZIz9evu26lF0G+PBNq0bYuqz1CqotA3No5LrruFPjFx8P3LkF1/8ZlwUFUVIUm4PR7i0jR6mEFfaIqhfGyrZsR2jg4qCGOgbiQiFBZ/+y3Mmdao4/k94fvZHwiYuRuW1xCzLHGtX+G+b0IbKXZUHajg13VlLPzoPYYPH96gwetI1PN6Q84HjcW+8r0ckXjElXo6sKolYa7KWaGNW7pH0KcoVHsD7D7YwHsmJJSAn5LcFebi2CVrkYYWETIelxFB054fWV95GlEWyWYol+RmEfDZcm1UtVH9teTTj/ltdzBNwHBoJA4cgi8QoEKvrOoNBCxKnzVCBnlCK2v9lO044KApqMCj9ZTdtrpFonl0K0Z0sjW4X6dlhCmWJXkrcb0c7LxZMvsdFrz5Eq89dDsfv/wEN0wdQ0l+jjYu6WOSEelbu7PSVgMgtNGv2DzHTal8V6Mn5wGWnRHCxD7U6nz/f8C/BnIYZGVl8cjdd7Bn21a08JL2nycikoyMjKB9v5j7ofn5x7YZISkWa1YVoqqaQRMVFZw00jd5IH7bA644DCLLg4yqCZuDJnV2tEITl9z5iNNd5K2GLUXmx7i0QcQkpfLizE/MbZff/Tg945JNz3CLlsFevH4p6Y4CAIpqKTyowNcL5+Dzek3PBK2Pqf9EazTvoiRJjD9H09xOT0/n4mtvpX9yGpLQSiknDRxscuJUVeWbT2azZXURx7VuZjYlCcFJ7aKI7xJNt7b1J2bVx0800Tq8JNhPkfEaR07nFAaMe28LJ/oVBW9AYU9DEx+wYVW+lo1fZ3uES6ZVMzc9OwQbI5KAuC7RtKpTQtp0dDQQUs/IyCCCRhTwOATMLPiT7RW17K+pn3qwoTjfTBwzPKfhztklS8R3aWV+jvK4OK5VM4xkRYNhG+X5awSAyvfuRehjh5Ak9peXIwRU7NurVRmsCC+f1BB69Ivj0bfnEZOQqtGijAXOERw/jPB7fdixYxua5dawbnd98KuCKR8VmMUHtPHCGj8bqqgGQMEix0dvIwy4jAwrUa9uoZmmol27dkfmBrTsALcuQjRrgcvlpr8+hoIViXzyx40s2dhAtUWhVReNSx1EdKSbtlEeY7NGN9MX2Ma75JIkWy6CVkjJGB0SBgxCqqNDLCTJ0V9Lly5FURRrvuyahNK6CxtLCoNObfK1t/HizIXEJqWYdDHQri1gc6A4jUnLuquo8WuUNSy1h8YktAlJwuPRioSd2kUgvzwePrgB/igL+5vWNbsYd/V/ufzOYJpFIFB30VBLQXammfhofoctWR8tIdu4l8Yj49cTMsHynjfW41u63ahka6MsKsEOj8by+A8Xf5enuin410AOA81LUIuqh1GEJHHGmIm88vHCoCznpUuXotQVXw9hID996xWUFOQSUFRaRLcK+r5nXArV3kAd/rGl6GAkXoC1xjtQEwjrvavvXI4UgpJoaqvMfwb0pW0/WwnkS2YXmQafqkKL6GADuVdcsm3VbCl/mO9PnRdJvfT1+k/y1akAnHrGWWQMHRLiGrTBPy4ljVETp5rXFNA9KS1s5XKTj29NmygPrnoE6M12G9yjfmxTmhNQNN6oNglo/xmeIUXV+nhnpZfz3g0tnWPHmvwsXHLjzypClmgXFRFSbN+QuoP65/f09HQ+nPdpo4/ZFGw/UMv/vljT4H6r87P0cLD2WTRxRWnRTIT5+a/CgMFD8UTo9B+Ph+T0wQgEyQOHaJSvw5hETurbn36Jqeaixig20KZZaG9wU9E2ykObZh6Sjgse2+y40uCd1jRet7w+zF+1DW9A4+pX6VXBav1aRn69+OhWWPqWY5NUx4ALBXui3uLFi4Pmg8YiKyuLm2+++bAXCnYMuPIB7nptDj2MMRTLs9gQjQDgmGNP4NF3FtAvKZUT20bRIsJlLirtC0szEiNbxrEkNH144/WKSUzlmnseR3a5zDyV/z74lKO/gqiM7ghNhSlEyfkxl9+gRx7hp0272a7TDOa+9gzTH7idsoIcm1PJWhgU/bmfKl2G0dhm/G1E0T1i04bwxLsLiElIZXVuForfD7t/s3b4/Img39ya2p5+Kem8+/T9wQ3WmY8lSSJ54GAtN8I+1hrnqRv01T7FYQAfqPFTvLXCFl21FglbyqsaLAhjGsf6cxLpknBJgt4dW5g2Rc6Wch74ruEozOHi0tTjadPsn1MgxMC/BnIYZGRkOCkEqkrHYzoTnxKchZ+RkVFHzQF6xyUG7edXVApWZvLqil/5Y39w5rKKZvD4AxqXziila18ZGxQL473eX+tvWIblL5rR33vinuAVuC0hqyhnhbbJ9qLW+hUqzUVA6HCsqht+ZmKiamlEg8rpYybh9ngQQuglrhuGJLuYetWNjm2GoWCUkRbA2eMm44mINMN5Q4eGlw9rCIW52Yf8WwOBgMUuUxVV94DYZO6Ag7WNS97qm5yOR1egaAwkSXBiu9Be8t4dWzQ6YS1lwN+jyR0K4u0riE1Jt5IIBUTIWmGYRreBxbVu5pbNhM2/AkmpA3j2g0+44IY7eOrdBabUVv/kNM4YO+WQ23W5PQwfNUFXZ9HumEuSaBPlNjWxDxetm2lt1SdtBloRqOtu/u8RM5ABrv2klK0VNQ7O/vYD9ahDbMyGHc7QuiTLvPrqq40yeA0O7aEax6A5VmpqjmwJ45WuXpSIzhp3VTesVuXn8Nk70xv1+5NPP5t+iSkhKVVGFMX4rOfuOdQYDKqF0N+VcydfzMPvfsLUG+7k+bc+ZPT5FzvaTU9P54fFS0KcSbDlurYozzRsp2daBmpW60F88/0P3HHJWMoKNUeB5RlVtap7uti3aRzr7Wv9VA9y5tGtdyyxSdrCsmXrNrrTzIaNK+Hn9xybeieksCY/C783RGRPkrQokRDILhe3PfwUcclpSEI4KuQ5cpD0c7XPhQGdWmT3MhuUmp2Vteyv9lHrDzj4ynbUzXHyyIIedcaC6z4prb9/jhCuG9ytwXHjaOBfAzkM0tPTefSZF5BdLp27F0FccpqZfFB33yGnnOHYz6HBUgAAjmpJREFUNigjWNfW5YmgT/JAlmwMVUzXWgHW6N6PgPkyWMai3bMKGq+qMR7knv0TGnHVTUD+ZyiqgizLSJKkFbwQAmotAzkmOd1MEHHAMPxVFSmEeWV4gByLA6xFQt+EFJ56fyGX3nIXD741v8FT7ZM0gMfe+4S4lNSQ3wvbwB6XnMb0jxZyxS3TePmjhQwYeOgTYHYjkvQagteWMaFi0S0Mj0FjcWV3ld7xKXhkqeHnpRFoEeGyhVvr3/doDnspSUn0Tkgx+ZGgTdwN0QDqwlhItYly/+WVnvonpXH+1TcR4ZJ475XnWZWfgySgZ2xck73fBu5+Yw59E1LNeyULaO6pX4bsr0TrVq0apf/bWFTWBvRiQdoY+fu+am78NHwIvO5DO/CUM3l51ud/q+Rou3btwr/Dh6FYsurXbSx852XKCvIoKcjlfxePY155sIxkKLTs0EVzPMjWG9Iq0s1J7Zo7oijGu7S6MI95b73E2iItgdTo1pL8HJ6+53+8/MBtAEy5+kb6xSeGfO8GDQrWHu7ao3fQtoevnkRRXrZp3Jpo3hqGXIjf56Moe0WIxHbYUl7NNj2p1/KaBqtcOLA5F5Z/yOfvv8p382YihKByfzl1RzRJluke43SICaBv8iCkEFJ/stvD5f+7h4tuvJNXZn/BmPMvCaKqCWFQLA16pbbYCehesn3VfnbqZdA1up1d/criDe8+6OX3fVVB52DvH2NuDWWgHom54v8y/vZKev+XcNGll6O0O4HS3CyGZ5xMl+O7khgmfKjWeY4+mDEdJjpDL2PvfUUTOy8MnY2uoFEofAHNA+IzCmjYXngV7eXbW+XDF1DwK0qDHkFZgv4pg9nQ0AU3Fjs3I/38Hi63h+vveYS8ZT+SueQbAqqK8FWbw1fveE2y60CdBKyAar30odgf64rziU1OpbLWjxACVWhXbQ+f9U1IITYplX3VPiiqXzv3uBN7EZuUFtRPRia2JATHtIykc3QEa3dWEpeSRt/E1MMWSB8waCjisqtRpz5/yG2sXVVEUnKKxavTRzRFabwG9Kk92zMyvetfwiUTQkvcUiurw+5zNAXgR19yjR7+FXrGvQhJGakPxsTxdzk4PLJEcX42t108Hr/PyztuD7fc/yivPHI3ardD88b3jEs2veiS0Iyeo+mwGXryyYi1Kw4p4z4crMIKKrsONpC4W8d7vHfndv7updyePXu08S3Ul8s/gJMvO6R2N5YWsmnhMyx860VOPW+i5sXsHGxwhkKlN4CEcGjySpKgbZRHHz+tsy0tyOGWC8fg8/qY43Hz3AefkJAygNKCHK6ZPEpXOYJvP5nFsx8upF/P7mGfuf5tBCXlVtu/HQTqrEP9fh/5K5ebURUHhFYeu2WrNpYBbHMoaVUZhUntM/766xsTbZ7iZd99zsgJFxCXOgiX222W1ZZkmavvfpzyHiPYVLTV3H99cR694pM55bxJfJv/KSSPNr+76vqbyOh/Es3bdKBNM48VibP3jX5aRv61kXNkXJNW1MPyihui0Hb+dUWtX9fRh427Dzq8wz49Sm2nWITiAMuScPTRuf068fnqQ8+D+L+Gfz3I9SDCJRGXnMb4K24ISa2w45cN6xyfAyFWpnO2SGT9El6S7bXM38yEAwXNi+o3MtqN/3Qj0a+o1PisMsDhMFTZwOVXX8NnH74BpUdGD/mEE7sz/vLruOh/D7JxdSk/L/6aQCCgcWW91mp1dXEeH7z6ApM/KnT8fl1JMR+/8QIFudlUVewPaj83Px9VUfEpKpW1fn2QC52Esa6ocdJXblkKDhva/n18m2a4ZMlMPhEQUv+3KUhMHcCtN1x3GC3AE6sFXy7PMyWsFP35CKhacp5bEvUmN5x2jMKlqcfb5MkOi8bqgOF57xwd2fDORwk+RTXP0x4Gbgrsv/ur80gMT1JJzgp8ZrUxL1nff4XP64N1yxBZHzepzXcmxWOkzkV5ZL1sMhxN3/7Agel0Pv7EI9qm3xZdqW9ik795DrLnOratLynk1ovGHJZkW1ORkZFBZGQkIe/DYTxoKhKKouD3+QioKq4QcqX1QZKEXtExlEPBWiwWrLSqe/p0721pYS5vvvCko4aA3+ejJCfL/F0opFfkmsnUALQ9Pmgfl8tNv+T00F53IaGqKq8/fg9lBblWvo6DpmAYhFby+5dr6jH2bAZy735xmnpNYir3zJjLqeMuZPT5l/Dk+59y+vgLnNdVU8lDV09iw6p8TjlvIp5s6329q5/KyQNSMGYZs+CSvsW8NGF5jVWgWo/Cmon6tn8bXmCDo2wsFPdWeams9aOqsK/aa/bb/mofq7ZWONwrfkUJmexcd84c3K1N+P76/yH+NZDrgUeWzNKvDdWHOrFnnRV6mJK6objHBn7+Za9j1auqql4G0h4u0h5qf0Chxq+tICtDGJkGRqf1pnL/PgJ+P3z3EqxbXs9VOBG9vST0F+4I+iWk8MEz97No1numLBXgKArxwBUTeevFp4J+/sK0a/ngxSd56KpJ7N72R9D3X3hSzFCRtkCwVs8qes14VLZW1HDPqoYn+WHnjMcji5CGkahjMGnlUy1O8uFAEoLRUy5ybsz8qMntvP+L4PJ5q7h8bjGKqlJR4yeggD+g4pIEG1eHuU9A1JY8PC7JDM0fSa9hpEvGU0/p6H8C1ugJegb/WPNmi0b63i3UDYHWRVZWFo8//jh5eYeuVWw/VoIuj2goJUycMF6rpCgJ5PxPGm5Eh+yrJjrCpRcisDRrDU710YIsCQKBpkv71YcfNuzmtRW/EVBUftsUPrFI2bGJY0/sjlTn2fX5fIcl2dZUGMl+IYt37PkteFtj0TUBxj2Iy+Vm2DkTOPWWpxv1s06RKhnd22lyiCLYbDecBqCNmaeMGI7bpiffqk1bbpw6htzlPzkMfJfbTdLAwebv6kIAAwcNRuyv3zN51+uz6R2fEvbNVfVS1kXZmSYf19Q8xnKqGLOVosJ7ucHzj4nulgrRwcoKPLKexBafwlX3PMH4Wx+mb2Iqa4vz2JutF7RZ8TF8eCN+v481+Vn0S0zhwTetxVi/hERtftEjWUJAmyg3naMjHY4uw1dvOG9rTAPZUmkxKRKqii+gUu0POIx/pc5+oGmFV3r9Dh1oRdWK7IRCXYrF/2uSb/9SLOqBW38hWuiSTvVNkB07dITfrIdMcnkIxW7aWFYEhBe/V1Vt+gbLa2xIu9mzVQ/6FKq8AQS6BBStQ7Z35yVjOXvqlfYjhL+IOmi9/gcqjukftL2mupo1RXn4fcETXJdjj8dQhPb7fKghKwqCqgTwHZ/CzqjQGsBGPxhhMJdkJSEouvbp5j2huVV10TshBbcsBV256RGxVUE8qV0Ue6t8bNx9MChpoqno2qYZe6vqhHordsD8+2D8Q01u76CR3Ii+eFJUWka42FBaCJ7gCnSeVyaS/Poc0ysOmnZz+zAatk1Fz/bNj2iI/K9A3+R0s6ABaMocTfYg689J744tQmonG4UjvF4vbrebJUuWHFbyliQgLjmV6R8t5NdVOZx9+qmkpA1AtO/Kz8t+YmNZEY2rHQlCdmmLo4B27db1NCzH9ldCEtAiuhU7wtd3OSSs3LKP5+d8TbavU9h9VL+PPzf/jsvjIWnoaRSvWIISCOByH55k26EgPT2d3sv2sHtnnTfptyKYOw0mPn5oDXdN4JLbHqRjzzi+KmrcT67pqdImyhM2yiIJa2HpliXGnj6clz9aSF7WclLTh5CbtVwvcqMgSRJ94hLo1S+OEaMm0D85lZp9u+kQQjtekgTnnHoyXQtq+bUeWvrvzbrSHyjICU5+FpKMkGVcbjfxAwabc6WeqoiqCt0ra8wtjZAVE05FiTWFeeRm/cwJsWn00eeUdcV53HflRE2L3+Zxlj0e+iUPQghB3/gUzqj6jRW/lVsFVSSIcGmRnOYeF51aWoWozEOqlhxdIOD0GmOev7YA8AYUcyw2vMqSCqrQK/eqQou47qrUo5FOWThf3eogOuo+B/9EKba/Ev8ayPXA8BpFuIxCmuEnlA5/ZANWeU5FCu1ZyzvYAuqxT/bX+GgV6XasdLE9yFrpSe1M9tf48MgCj9sNYSh3fp+PX9eW6nwBW7ypEfh983oIIeKwa+vvtGjfCpfbjc+n6hXqBJLsYvuWzdBX209yyahCDjZMZRkkCXXU3WGPrZjEKs2grvUr5n1QUOvnjtU9HoTkE7eLikBFdVAEDPF7IWhU2dn64JIlouomQglJ04pWAhDmGakPa3YcIK5zNCqa0RfplunWNwE2Be877bVZ9ElI0byHaNcWHelyyNYdDo4mt7ix6B2vXb9Lp6LIckOxoGAIIep9FozCEQGdE7h06dJDNpDbN/fg9SvU+r3Ep6QxemQG7VtE4A8o9E9OpWPveGY8fAcseQNGXN1ge8aEZpRMF/p60BjbjtZ8J4SgeYuWcMBpIae0UcgrP7zAZn3GsQbdwAgE6BGbwJhLr+GP0jwSBw4+rIXNoaJN+w6wM7jUPHt+P6x2K/aVU+trpFrLn2tY4y0nJjFVK8ke4hVJPr41eb+X0zLSRS9dMz0uOY1+iZqxGFBV3B4Pfp8Xl9vDf+9/jN7xqbqRp409XcNox7tliQ5tW/HrwfDR0LdzfufMvp00dSSX8z71ik8h4bj/kTJwCP0SUkwDUAaHJ9XwrMrC4vU2BNnlondsHDdeMAaf14vL7ea+GXOISUylJHcFfq/PoWwhhCBh8AjW5GfRMkKme/9khkTsJHp/JhtWefG4JFavWMygEWeQOsDyUndo4XH4r4wqqaqqJeNLwsg9sa7HuM5AHeqhka1kT8Ir/FPrW1M61jC+FSNJL/ja686bCce2IjrCZRZpORKYeX7iEWvrSONfA7keGF6WxiTpnH/uSGbc9QZq2gRtgxzGCvbULxF15bxVdI6O4NpBXenbKRpUFW9ARdY9qP6AiiRUBIKK2gAeWdITBkJLNcmyi5P6xFKSm6lN4PaLqNwDP7wGo+8J/uHGlajl22Dx63CKs+SniqDywH7uf3Muv5fk0r59O6ICVeSVrWdhnmWpde3Zj5P6J/F9naYVJGQhNVg+wl7lx6dAtS9ApEsioKgEAmqwdzYEbh6q8RxDGQPhJMzgyLEzg1Q6DpPj8ND3G7itr0pKirUYG5yWzGubioL27RmXonmDwCHH9P8SjHfYLUv4AopZ1OBIGoYZGVrhCMODfDheyOhIN25ZYm+VL8SjIoiQJYaPmsDiy8bjb4yBvG4564s9nBiThNtW1OFowyi6UhcFP30HcWeE+MURhO4ZlGWZmJR0eiekcmrG0KO2WLgo5Ti+W70NL/qC+dsXtb+HqY+stOzInxUNyMjt2QIf3IjL5SJmxjzN0QH07tAy5AI4OtJN66CKm4KS/BwKV2Zy6/2P0kKpJmngEI7vl8j+ap9GbWrgmZOEVrCoIQQUhX7JA6HYuV0Fzr3kOprr0V6zbDLCmeRuRGKFaFgnG0gbfgZTrrqBjYUrzRLwfj+szssiISWN+LTBuDxufF7dUSRJSJJE/rLvyVv6HQvfeoEr7niYN5+4F7/PhyS/gBAQ8PuZ9+4bvDv/c/qdlqH3os0pJoSpWuFCcNncYgZ3a8MNQ060SdjZeMq6sW/s75RwU1GFIasqbBQM6zfGIsaObRU17KmyosTLrxtEpFtm8X8Gkvpi46ma9cEtCfp0/Guqkh4J/MtBrgdCCFYX5jLzNU1qqb53XAicSQbyoYteb6uo5a3s300+cq0/YBaFCKAT9oXGG3LLEm3btgvbVurJp/LFrLcJ+AMIIejSrbtzh805sCIEL3bRY9oAXfxV0FdCVeiXkEq/hBQuue4Wxp5/MdOmTePs8ZMd2tGbV69i6VefBrd90Usox/artw8mzyzgnZzf2VlZa2amG6W6a3wKHxX+yfTMX+tto3NgL0NObBvEM24ILqn+xMemIKiZI1C05YV7b2VdUZ4Z7gynHykEpjfoaHNOjwb6rNe4urIkzMS0hrjEhwJ74Yg5c+YcthfS4Hr26djSLOls0GQMBY7hoyc13FDmR/D9dMpys0wVD6P9Q0lWPJIwtHLrQjmCRTPCQ3N6nKr34cK3X6YkP+eoLRz2bSrF/47uhDi4D8oWa/8OUSwDQPzSOJ773NoePL6kgTLKqopA5cL/PajLIWqbW0aG9p316tCCji0tmoQkoKwgl+vOH8Obzz3Ocw/ezfDhwzljxDA6tfA4ktDqW4BIQjRKdjAQULm7OPhGrd/nJys3HwXNm2pEGA1KouU9Vk1DuTIM79YOFUhMSSM5fYjJt3a53MSkDkIWErFJqTzz/gImXXs7/7nvKU4dOxWjwquqKvi8XlZ8/4XNuPbh8/lQdL70L8U5IatyGsayqqJLu0Hmr+WogDegmPKvRnTZ4Cv7FYM+YWkc2ekUYOMu65/tpaztOPcdZ/EpY048knrFTc0F+bvxr4FcD1ZmZXHzhWOY8ezjXDNlNL+uWRV232U//YRaZQsPHYaBDPaXWnvZvQGFCj3L1JL6UpAlQeu2bcO24/UrGj8KbYW7dcUX1pfVFU0+rxbecm4efAK9YuNx6yHb4vxcHn/8cbb9st6xCFVVBX84ncm+wxs81nfrd/Hfz9fw6opfUVQoK8zlw9deoKww1wwX1YezerYxk9MkIWgb1bh70iLCRVyX4Ap/h4IjZWjb4Q8EWJ2fZY5wgTAzz8ZVBUhYxohmLP/9VkB+CM5gY9Dp20cO/aDb1nFCswDri/NNao4hcfZX9IBROMLu2T8cSEJoVcyE3agVrCvK5b4rJ7J4wceIHQ0YP1X7kAXEpKabVdDM/46yJznceyEOQ/+38QeXkGSZqJbRPHz1JGZNf4rrzh9DSX5jmd1HFl9/MhvFqxdzsPdLGAM5um3jNI0bBf2BOFhRDuiLpiY8F5qaxXJ8Ps0A9NbW8MEHH5jflxbk8OFrz1NaUH/f5mZnsS63Ya+kNwxXFtnNKxtkFEXlQK2fal9Ac6iYSXqWYayic3YbETLI/fEbvpr7IXHJabzy8UKuvGUaD7w116SuyQLiktIYd8UNnD7+Ajp0Ptb05gIISeKkvrEWBUNVcckuM7Fx+HBrHhQ22SQV3SGmR0sNFG3dj1evEVBX0cKIMBuVVvXD6dfpTO6z2xdGsn+ES1MC2bj7IO/mBNN7/gpK3T896e9fikU9+OmnpaaMjd/nZVNpATAh5L4nZ2QgvbfICpG4DtdAtj38CP2lUFlblEdZ7gpS0ofQVy/hvHXLL0DLkO0Ur/gRIQlUY6wt/AJpSzGDr7gbtbYU34gz2Nktnl8aeV5Xn5bEwK5tCRzYg1uWKS3I4bopY/D5vAghCPRwes9kl4tQU15Tilws3bSHU5rv5sGrJhDw+ZBdblrf9CHQLPQPaqsYvuMbTrvgESS0QaB7u+a0akIpy6Zq5YaDx2VrZ/t62Hj4MlLqhS/xW3uVLfuq6dG+eWgu3U/v8PNvLYlPSdW4a+rRWw0vmPMRtDkv9JcVOyE6RNLqc6PYKUlw+iEeVAi+nz+TJZ/OYfqsRSSkpBkCVUhCEOn65/oGwhmuQsCqHI3zqCgBxJw7aHP9e5RLod99qvZxzoVX0Tc+xUFnMozTVpFujfd4lBB0mUvfQlq3lO4nn8v6AyE6ofALSDznsI4Z7T9A5cHdKIrCp++/gaIoqIqC3+elcGUmjDnUB+7QkJWVxRfzPgZZz4NwGMihjcH9e3dDq65H5gR+fg+320Pf5EFaMqsQTTJaurWJIil9CPJLsu41VXn33Xe56KKL2Ffl5fqpGm/X7fHwytvvk9ave1AbWVlZnH3G6VQnnAfp9es1N5R7oqgax96raEahLGTTE2t4WQ3VhjezG8fxXvrN50y88FLiU9Lok5DKvmovvoCq56rY/gNi0wbhjvDg83qRJImbH3iKPbt3IyTJpGCcNnYyHdq1Y+ipZziiTQLLqyuwZAt9NgP5ySWb+OQSSxffvDZ9PvWrRj9YXnPjuq3PqulVloQWke3QIoJ2UW7mFm/j6aUhElrQFgNHGv/0nL9/7izxD8Dw4cNNqSW3x1Nv6HRQejo33G5xeSN6H16YVUFLRlPRPMYBRWXDqnweuP5SZn32FdMuG88avazmzq1/hm9HCTAwY6SjFLZa/icRGzPJ+exDcpd+z29rwsuE1UX2D1+yfpVWmKOZW2JjYQ4+n5agpCgKwk4hGPsAfn+YMFZ007wgH734GH6vpuXo93nZv3tX2H1dMy7ilHPGOKTajpa3LMJmiMlz7rTK6x7myJC1W3D7F2sA8IdqK/9TwDCGhOmBPCrdULeKjh31eAybsogKhjCfla8/ma07yrTrj+8S/ZdXwztc1HXWGM9vwsDByDpXU6gK7nC8zYUPweYcmrdsBUJLVrI3KQQc37oZ0UF80r8HQU/E1jVQsAi15iA9y4vC/Ojwn97T/cXg86Eqiqm2IMkyLreHief8vcYxaAmdhoY80CgPMjUH4M/Vh33sKa3+5PxzTuP+GXPoFZ9sSgE25b1r3yKCuKQ0Rk2cakY7/H4/S5cuZcXyn03Pst/nJT83dCTJSHLF33BOye4GCsCsK8ozOblGMSXLm6oZnP6Ays+/lvP9+vBziB0jzjyXKI9M1zZRlBXksuCt6awrziPKLZvROZfuee8Tn8Kjb8/n/Ovv4OkPPmXs+RcTlzbIIYd36uhJTLj4yqDaCpoDWTtZWRIoeuT4oM/5HNgpFIZxbKhYmLxknCpYhsPNwUu2tacdX7BuV/jy7/VFH6ckduHYVqH18Ef2Cj/X/8Pt438N5PqQnp7O9I8Wcs/9D/LD9z/UGz6VJEFsf0sSrfa4hMM6tremmt/2VvPjxt3mCnBNfha+M29DHf8IXlWwKncFAMd0OTZsOy6Xm4uvuYnbHn4G2eUCIZCExP69u/D7NE+Usqn+EPhxldaKcvlXC7nvsrH8sGg+koChw4bh0RcRERER9IqJt37YLQkumxG60RPiQ28Pg7Vum3D8qLvwtT0h7L7xgzLok5Ci6b7+FaTTJsBts3QuufMRS3v1CJ2TL6DwRAieoexykXHueAfXNMIlHRWZnnGTzw/9ReZH1DdE2vnsTYZdU9SWoGgYyv9kCEJz4I3rUHTPohIIsPOP30I38ksebreb/qnaQt2la64CfxnNpCkQdUL5wjBUXW6io8PQmyoaZ9DUh6oDFRrVSJLweDxcdddjXHzjnbw9bxHDhg4+7PabioyMDNxuj82BYb8zId6N4q+1xOl598CvBYd17N9//YXxV9xAHz0SKdCkAF1NfO+EgLPHTcYTEWnqdmdkZDD05JNxu7W5weX2kJIWugqkkeTaGNz55dp6v3/oqgm88cidrC3OI6BY3tOSglw+ePUFVhfmogDbfmtczHT0pdcxduoluGWJkvwcbrxgDO+/+AT3XTmRNUW55jsZqZevl4B+iSmMv+IGElLSWF2YS0luFlfe8TATrrmN+9+cS3xKmu51dh7LPi5t3lNFrU6lOFhbZ6GkWoa/osI363Zx0awiLV/HcKrpHGWTSmHjJBvedIOKYZ8TGksJjLBFWC9PO57/ntw97Jjy2Fl9+O/JJzWq3X8a/jWQG0BcShrTpt3J4MHB9eLrwnMEw7a7tv3JtK/WMmPlFpNf1C0hHfTkNldEJMm6+Hqb1q3CtjPttVkkpKYxaspF3PrAU8iyjKIqFC5fguyStZdy52Z4frT1I7+VuTrusut4+fpJxPi3aBsq9xDw+3n3uccoK8gjZcBAM0Fp8eLFDBox0nkC7iPkqRtykc7rFtCj/nK7xSuWsm5Vvsm7hPol+v5KeFwSr43rz38GnsAp46Zy6tgwxuIhYtOeKjbuDtaDThxyKr0TUjDKLAuhVVE7Giv25LQBTGzpjHKIzA8he07Y38iyi5STTzv0gwqtEpjb4+HMsZO1TeKv4YQfaXhcEjHHOGkThve7KHsFSsA2Ye4ObSALIcg4bxJ94rWqXS0jXCbv2OiHf1JfdDmxF+dfdzt3vz4nZHVNFr+OVPRF8PYm4uuP30QJaJ7jq+98mHFTL+Gy628hOXVAwz/+C5Cens5rsz5l6pXX0kPej7To0fp/UPQlwlfN6Iuv5rjjg6vNNQUrflrCuuI8qwCIEDRzy4eUvBmXnMarsxaa80B6ejojM4by7XffcfV/72LR198ycljoqGp6ejo//PADJ3RvXDns+uCPasMPC2by0FWTWFecj6qqlBbkcNMFo3n7+ce587LxrC3OY21BI/IiqvbTvGW0rnqj8vOyZVaync9H/spMc47xGBKKQisCIoC1xflcP1UzqN988l5iktPpk5BChL5Y7dsxmBpl919cNrcYv6Ly0+Y9zn2wvL6KopL5q1add5uuWOKoOovFTzYNYyxvcsBysOP1KxxspHzbB+cn0LqZxtBtDCVnSmJoJ96F/cPnT/0T8K+B3ACaMla4j+h8YzVmSNY8vdYKp9754vskpQ5kVX4OK35aUm872oAnqNi3FyWgce4CAT/Dz5vEyAkX4PZ4sHsrpFcmc1JMAtfd/zSX/vceBJDWohJm3gJ6YpCiBCjM1pIqjASl9PR0dm7bGnQGRwxCNMqrqCgBVudl6QlqR9WBjEDQo31zhpzUFoFg+KgJWn8fIU9uOKm71u06BMm6yaL+TPK/EjddPNHxueOx4SMAABff8QhjLr0WseqbQzre6KQTueDGO3nw7fnEJaU5jON/jlkYHpEhCpJIQpA0cDCyrXSwnPk+GR3rcFWLv8btiWD4qAkOrzFYsnf9O0cf0QV9UyGEYHycVUEuMqo546+8gd7xyfSNTwrav331Vq6482HE7+ETpRsDQ11AVVUO7N+na0Mf3Sfi/HNOYeKVN3JJbDQntmqA8qIqxKefTIuW0US3aX9Yx1UUhTJ9nLSXkG5qdxjPVtoAax4wcPLQITxy/z2MzBha74Js0KBBnDp6YtjvG43L3wRPFD5vLcu+mIeqwqrcFbZcIh8luVnsKg+xCLPjzzW4Zv+X2NR0OkdHcnzrZgwdNsxGlXCTPHAw9pLRRrTOMJSLszNtFBMfq/WqnkYFveYh9OiHneQ0GL9au5O36yTMWXxiCKiKOZ5pBbQslQptX9X2n9GCTrlApcYXwCi/ff3CUr7fsLtR3dy9XXNeGBUDwCC9/PRp9VApQuHWYSdxcfzhPcN/Nf41kBuAJERQPfJwcB2hxC7AMUot27yHzXudJYYGDxxAhEsiP2s5Sp+MsM2sLcjCKI3Qqk1bM5tWVRSiWkTTofNxnDX1SmRb2dUrpj3CQ+9/zpkTL8CQRKqqKNc8zTokSSZp4BDAKrOblZVFYXbm4V97OHRLRumaWO8uYvkHuFxuYlPT9YlPWB6SowSncaIvRdYsDblvsitEwYB6sLcqdLneYeeM1+k0wuQhHy2PYetm7qBje331eyoq95fTNz6Fqwd1a/LxMrwlnD88hQlX3qh5UO2h/P8L1nEYdG/fnP7JqTz13iecOfEizphwEQ+89jHDE/s49pvYpyX3vjGHvvEploKJsAyg5ONbH1Xj2MDJ3dvz2FnauasYyhqC3naalo6E9AxGjr+Ax0bF0VoJz5NsFITQDJz0webYeDS5kALB2qI8HrpqEptXrzLPMSQUhaIVS/lo+pOszT+8hF8hy/RPGYShkW6NU017SYQAlyyI7RyaGtMmqnH0iSP2al43C7VVZ376fC6lhbnEpAzSyrTrFJ6iyD7s6Dmy/jZ2bmTEaafTJz6FNs3cRLplBqan88rHC7nilmm8OHOhTpWwK39YCXsuWZA0cAhuty4N53YTk5Ju0bzCXO2tJ3fnOBuXt6I6eHw3lTlUldqAao6t7+X9wfaKWo2jbBQUUe2eY6cBbUjIGbmgBY1QhrIjtnM0eTcPJfFYLYJ9zaCu3H1Kj0b/Xjmqb13j8K+KRQNIOq5Voz0MG8uKgze+ew1c+lrTD2xLdnt1RXAY1cjm7Zs6GLJCexGl16fS75WZ2r+F5kG2V0n4/MM3NEkaoXlWWP4h4o8SKseNdiQ1rSnOY/e2P3F53AT8fiRJ4rJb7yIuOY3c7JWMP/dMvF4vHo+HDiOmNv1aG4tR0xrcpb+0i9T/PaAbB1b462jB8lxq51CWl4XiD8D306F6P6SOc+w/YvBA8n/aHKKl0PhxVWipr966DJEQAkkCoRi3/u8flKIj3bRr7vSMRTbXw4tbVkGb4PBbjG3ibipqqqv49J2XSRwwmF7xWt6AxutV/xId5L8LrZtpFKPYpDT6JqTiVxSqfQrrdgYbjNZEbBif0DbKQ6eWR0+1oi4ElodGVa3zDVVg8seFMxlx6gj6xqfQ+tcy9pVXH/Jxe8UmcNO9j9E/OdVMXj2aEAJKc1fg92kV2YQkccxxXdn++6+oL46DmxY4d0ZzcKiHqRndPSZJ93pakRWB0GQgmoBWkW48R8A5dERvw2WvE/jhFYqzVzD5qht57oNPyMvK5PjYFJ4sVRocWCTZxYhRE4J2i09Jw6+oFK7M5LcNa9i3t5zEgYO1nBebIyTCJdE/OZXXZn1KdubP9E4aSJUvwIK3XiYj42SOO6FbyOP27tiCDi08/LFfo0uEsz0CijaW+fyK2W87K728kvkrT56jlbKNUmXNc4xWats+9BsGdkCXwgtVabapkIQI0s9+c0Jc2P3/6QoW8K8HuUE0Jfy2uiA3eKOv9giejQW/vuxr0TV8wY3Lb72H3vHJgDb4tGrT1vFUBvx+LUnP4DTmzEPevVlb6QqtCtzaojzuvmICPy6cBSqcNm4qT33wKaePmogQsOLnZWaZXa/XS4dOnUOdyt+G0pzlfPjMA6wrzjfvXaRLPmphddNQ0U8gLlXzZggBVB8I2r9rmzDSdWHw03btORA/vx98XDTvhksIXJKEJI6ep6yuEbJtyy/ILhfdd2bT5+C6oP17xyezvjifGY/d1eRj5Sz5mtmvPM3dV0xgU0m+xUOvx3PzfwWSAFmywrhCQOdoW+GGvE+Y/9qzPHT1JNYV5ZuLXEkIIt1SyKIERwtCYBZCSTte80Jpz2vwtBTw1vLTonmHnWQpu9xceefDpnH8T1gwyUKQMMCmTiIEMYnJ2mI2UMeDaEYCDj8itGnZIu6+YgJri/IcSWNNbbdnhxZhS0g3BTu2/nHYbdihJp1Hv+R0vXiIVTnO5W74HUjOOIMYI3lR749mbpmd61dx0wVjmfHsYzw+7VZef/ZRbpg6hrKCXCJdkjm+RLokJCGIS07j4utuweOSeOiqSXysa26vWVUY8rhynfsa6k6oQK2uWvF52Q4K/7TqGSi60evQR7Yl6hm/V0zjGHq0b95oL39DqDu+Gt7lUPjXQP5/DAmpaSG2HuIqvwGu7dwP3mVVfg6/lQcnaBk4bdwF5ssmCagoL3fIvdWFEILh52rVpT575xU2lORRkptl6q4qSoAOnY8jJjFV80oC6UMsFQuPx8OJJ4RX1Pg7YFQrKstbYWbrH4nV8aHC8CAbE3u/pFSeeHcB8QOHhpyZW0e5eTSh6cdRK/ea/05vr408Am2gRkCESxt4I45SaD3UHVAUhRYtWhLXt6dje3e9BHhp3gqUcIVm6oFSVWHx/vKyzGNHR7qbFBH6J0Kg3UOD8whaYZt5FyUzyZsJyz8MegfAETj6x0ASgrbNPbw1IY7R/TtrBqskkENFfFSFzWtKWF+cf+hLnK+fJ3HICGITDeNYmImLR7NvJL3So12dZMkXC0Nb7rpFn5B+Mv1SGk4cDwfxwljUfdvw+3yU5q4wPclHc8Gw/fctR7S9dsccS6/4ZNYW5/G/i8fx4UtP8vDnBRz0N+4ChbCqTxqfi3NW4PN6HVRFv89LUXYmsiSZ/Wc3FMsKcnn/pafwemtRlAA+by2r6ilKY9doD3UvFFXF61dAhY8KncnPZhIeGqUioFqycIZmsqKqVPsVAnpb8t80P/50rTNJ82goKjUV/xrIRxBxicnWh1rDcJVgQ3iuWJfIMA9JqOIJNnz87ptcM2U0uatDi3ob0GrcS3hcEkkDB+PxeIIMBEmWtcSDiAhO7BfLg1dNYt6rT3PbxeNp1aYNLht/q39qOi5J4NZL96bWUbFof/BP2NU4CZ2/CpIs0z9tsDlIuWTpKHMMMTObJQT9k1KZeu3/kEOGJQV9Eppejc28pWuWknv/hP+vvfOOj6M69/7vnCm76pLVbLli3NS7bJlmQwyk0IIJNZAErkm7CUnecJPckECaSSfJ5SaUhJCbhB5auMmFGEwVuGEbDJgSm2CMjZusrt2dOe8fZ87MrLSr5pV2V36+fIyk3dkpZ2fOec5zfs/z4PWtG9ylPsAJ8JqRl7S8t4M6Ya5B2Da2vvA0/nLTz6Le+sGHFoExhpqW42AERufZOM3YCbN9l6v7a1p6PLijCVxQnJ3WxjEgv2eNcegcckCGJ4WqbFwK3fA9qy1LpbSEJ7dq3lAwSANfc4wzjjgFCWwbb76yFd+84jx0HDoQY4MR8OoTyCss8WW2URPYse0ukWxZF52dxLasmAHJTNNhBgK4+PP/D4WFhWM+nmnqbrBZdcvSKI90sppj2szZCd3foRBwqCeMl9c9h3C/DJYTc5tH9Nndb/8T99zyS+zYtimqPZYtWyYD9ZzvhnMO3TDRsOR4b5IBb2Vi68Z1+PzF52D9M0+6szDbtpGfnx/zuIzJbEOKR14dHJMStrw8xwNx8xw73uGwZbmeYpkZTgqSw04KuZGOix+rHdnK8FD9TNaA1avcOOXMU4nUP8M0ImppKtQNBDLlHfPwauDLD8X8TEH4AHZjDJGcnKO/rw+v7toHIHaqlNe3bkBFfTOyTB3F2SZCTS34+f/cjwfv/jMef/AuWJYFrmloOuEU5BcW46QzVuKJB+9BKNQPOEUWOtsP4frf3Yt1zz2DyqZWlNc1AwzQNIbS7CDyMwy0tra6kcuv7OmAtvomWMXHjP6aEsS8ynpU1DvBWYIldQBUQRuKDJPDtgVqmxbjlI/aeHSPt+2cggxwPrZZa9nMWXhXHhCRcAhPPnwvahpbnA47+cKCgZUJi8tmYb9TXcqyovN8Kq9eRV0Trvzad3H7zjfRMWVkwR+XrDwbJ1TMxmubnkdty3GobmjBtNzgIA10uqJuJTnpk8JyJgQYZ1hQ24hv3XwXXlr/HGoXH4eK+mZpUKfA9x8LtcLj94Zr8WodM+Z468LobD8A5M0Y9fE0XcdJH1npS8kF9/nIy0juUFjvZCeJhGQ8iWEG0LrsFDz16P9GbffRj12EpUtbUV7XhCkd/eiN2Fj3r/ZRH++siy+HnpWLpiXHYUFtI0KWp0NNlia7tGwGkEAvsg2Gz/zlJZzV1TFqvfbunW/if9b8Bnf+xkD1tMew7AQZkK5qI2x64VkUFRbBCHdiemUTyuuaIQRgOPeWuqc3tsky3FFZojhHX3dHzONyFu1B7g4NLhbziTs3408X1w8yOAE4VfIEBJj0NFsCfRHLcZQwN+WbrXIkQwzbN9RMy8XVy0fW/y6ZVYDGGXnYuOvwkNtds2I+PlJeis6DowtKn2jIQE4g/qX8BbkMrwsMWx3olXXPAo1njf5gXAcg0MmzgDgFl17Z0Ia6phZXr8wYUNPYjLJFtTj1nPOx5oG78dgDd2LdE4+C6xoO7X8fG576hzsN5ZqOhiXHY15NI2aU10PAX2SAYUZ+cJBHrq5pMc6/NIw/70iez/b1rRuwffMGVDa0QAgZ5ZvM1RxlBDAm9dB9YQuGBhSXzQD2eEtk13+43A2oGi26ph5lzwtkKC8HG2yoTzQDPcgfPqEZf3xAhxUOg/mqOgHy+1tU24TXNm/Azddfg1BEgJ9yJezKDwx7nDdf2ojqxsXe5IABQYMjEK/iXJrBIAc4jTHYziAsfIbmorpmHFvdKGUYkEvEyrNlJDFYNRZuUJ5yuUGWAA/bDNd/aBG+9r9eUQhD12BZcqWrv7cXiC9tjMt1v70P86obkWHosIQX/T+/OCtpKyuKqoZmrL7tL3j8wbsAMJz1sYuw4/VXogzkTwa24UOXfhbvvPIifnXt1bBsgeNPXYl1Y+gx7rv9Jnzvtr+gurEFEUvA1Gw3mLlxZn6Crmp0xOyeHlo9ouDsoXj49pvkL4UjzxstXn8WwrYQDgNPP/WkayADMlCvrrkFAV1D1dQcbNp1WJaDZgIBnbsB9AwMja0yk0VI9EM4lRtNM4DTl58Y87iMsSgPcjy6+yPIjhNPoDJVdPRFYAvAsrzS0qp4iNpOZbD46sNDVWYc+eCZaWq4aWUN1v3rEEpzBtdAaJieh03vHsZZlVNHvM9kQgZyAtF9A9BtnzsL9/7jWfwq0ouhElqJ8BijsbkGgONARItrUVU2tcLQNGjc0y2q/yrrm/HS+udgRaS22A5ZeOFxL+csYwxnnHeRjNq1bHDm1Ll3g35iB8s0zsxH1hjuqibrn9igJabajhACW9c/h+rGliGrHE8UrrbP+Vu1YaxTY4jzxjC8/eZ2YBHcUeaY8mpkmNw5XvKWTRV++/imldXY/+ZLvvRa0R3wy+vbUF7fjJfWtyEcCkPYFsRb64ARGMhPP3IfapsWe/lIkdzJUaLhjEmvj/P8aYzBdrxAA3N+Ky9pQOeompaTcpMEN/cu95alTZ3BDjNU+Aql/Obcahyo+xPeePF5vL97F/722lMQU+fH2Wt8FtY2wRYyyFEtWqSCvAKQ32tlfRMq6psgBDAl08CWF54F4548rOvwIby+dSOuvWIlwo6n+fEnHgcuG32WJMuysG19GxYvXgLD4LAFg6lrSZXixDz0m23AU78HTvzEmPertN247MaRfeCX54GLCATnMAwTJ554UtTbnDHkZRgozDTAGEPVtBxsfrcDarBhEOAMqJ6Wi8azT8XMv/0f7n3kUWTnT0FX+0GctOwkNNXHDq7nbLAUIeY1AeiPDPaKqwp6MgjP/7pA2LZhC+5pkR2hBmMMT7wVX7a09b3BweTD0TKrIObrvzy7Eu0xUtelKqRBTiCaL/o6oHMsb23GyWedH/8DG/4CY/PDWKC3j+FgBpA9BRaL/TBNMQUW1jUj05DqV0PjaJiR7+asZIyhtuU4J6NCdNfEGIMZCOAjKy9w/4bSCLqbxu5J29ra8PvVXxv15Rwz5wj1Z1YYuOOrYIzBMEzUNC9FQOfOxMCrcZ8MlDGjRh+3/HOMdpfvj2WUcq7PuQc7Dx9yVzTilS6eSPwe5ExTwysb2mBFLFk2dYAFW9Eo84UuPu4EN3+ptmMDcMsVwM/OjH+QR3+Fpx6+G69v2eC2Zapqb8dK/fQ8GJy7MhRD855NMOYUffCCUzXHMzva8sETQdW0HHAuz1mdnfJ0+w3X4mwT5XVNuPDKq/CBsz4Gfdv/AQ98d1THOmVeUdQkQv2unAbJxpWaQAbsMTAvZuT+a6E99kuUN7bilQ1tiIQ9A8P/+6gQArn5U5x+SerZOUOS75PY30PZgZePaK9ayWwgRmaUQbz0f8BvVwGRftiOt/dL3/o+Fi+JDi6rLcuNyvwQcCYWrrbdeR7VmLN4SSsu+9yXcPaFl+Kyz30JdU3xqzZyxpA9Ag9yPP3wrsN92NspM2fZvm3CtkA4YrvFRBQz8kaXNelICRoapuYGh98wRUi9XjONUQ9Is5OyiDFg2VnnxfS0Ttf7cFHtVHzjv/4HXzn7hNEfjGtAQVnUS9pTvwUAHH/MFPzo7Dq52cCAPMaQ5ZQTrW5sxs//536cccFlboUg3TRx6spL8JPb73OWqb1AFq9scfwhZe3atYh0to/6crL2vgb+6C9H/TmXdfeC7X0DNUtOxHW33o26phafvjG5gyDztaH82wtIitpOvTeWg6hejzGvUIry0LHkG4oaYzh5ngwq0jlHdXOrW+pcGzAoL6xrAgPQ0NyCn9x+Hy76/NW49Orvwgx1gGsacNNlsQ/y8mOwLcupDpb8AjHjAeee8cggU08pT7knf3IymDjv+V9PJXTOoTneYzDPsJeVxrwz7glZjmRILm9fe8vdWP7hc0Z8nDMqSrCqVU7A/ZkrGAPKS7MH5W5NBmoSzZicTDIG1DS14Me334eLzjgN3/7yv2N+TSNy8guiMhGNJGXZIF55AgDQ1XEwSv4lV1uS50g4o7IEhZmDpS5VjUdWBty65Jc49hPDTKhCvcBjNwKHnaAQZ+J+uP3QoE1jFQRTd+vLm9bhf/7759j92pao3NDbNq3HH399A7bFSgXrQ6ZjHN5AVvmLY/GVh191U7mp6nkRS2axCNsCIct2PcjZMar5ER7UOgnmtvNrYdlSGA84nT4fnEVh9653UHn6UsyprEdfDCH+sHANyI/W8Xzj059Ab/GxaJieC0PncQ0jjTP3fKobmlHV0IyTzliJreueQ2VTKxbUNKIoO+CcP3OXjAQc7zPiGx7Lli2D8eMbMLTyejB/+NE3YWsacOoXRvlJByaXw87/7FdQWd+MTFNHxJZlOCum5iBjBJ3OeMEAqKMrg0WI2JpQZSSPluMXzsSGA//E/PAOXHr7X3BsdaP7HakBMJlonOHq5fNwQV2ZYwj5Tqg7ehBS95fUzC/G7MoGdPSFceyiCqx96F689uLz2NV1EMiODk71Mq0sdQM0GZJTHGU8UYYUY55R7JdAyY2kB9kWqeEhjYXmeLgjPgNNfm+DDXopv9AQsW1U1DXhjcMCGGGynNe3bsIbxl4sqmv2ZbCQq2qpkxeauU4IU/XdAKrqm9EdsrDmgXvwp1/+AG9s3SC9m5qG1uWn4sSLPoPVo3Ww/v3n0E0TtS3HOUf2nrdk3imFmQHccFYlPn7HZvc1zjXMragGjrB44lsF1UNv4Ll/ASHAOIdhGGhqPW5Ea4+MAS87GSvCoRD+cONPsWbNGrS2tuKF59vc139vmvjdPQ9hWUNsiYXG2YiKrli2wP7u+KOsrTT2vtzHAkDIksWFgNRL+5iKkAc5wUzLDSJoaIhY8u5b+9A9ngbKh4AMomMM4GOpQqQZ4IUzEeDAT1tMnBVaD8aAJbPzoTseGGWI+OUFyiOcYWhRHsv6phZceOUXUVGvKo9J1EBsajKlVJahu96eWLS2tuIH//37UV+ObVtgR/DEzimvxrdvuRsLa5vkEqVrHCLpxjF3Bz3Ho+tIHlYsLMbZVaXetsyTX9xwVuWojvP8o/fjuK6NOP+Ci1Be1xxVIUudRzLhTMoBMk3pNX55g9S/CyEg9ryO0yIvAQCmO2VWdV+A4RtbN+Kh227EO2++hqf+eg/e3fEWYi0wfuwzX8U3f3MXKuqbHHmNbOeJyvM50SjD2H2OfSs97uoBUtuLrrtBhOoVLwvA0jkFWDI7H4AvfZYTePj269tGfIztm9fjO6vOx+tbN7gp5BgDasvGEOk3TqjvEiw6VdirmzfimstX4h/3/Q+2v/iCTAXnRGGV1zagpakJnztuzqiO1bTsdHzrpntkIQzXiZL8m0R5z/0IIfC7H30r9gcSmU6UMTDOUdVyAlZd80Nc/Pn/wOrf3YuTTzwBgRGMz9PzMrDp+WcRDsl0cqFQCGvXrgUAPPP0UwiHndf7+/Dg3XcMua+R5O2PCBufvz/+zEjlO/b+FhC2QNiyYdnSXJ5sjoPxIFWmz5MC/zIZALy2eQPWPHhX7KkaY8jNLwADw9MP3wVgwaiO1XLSKeiZsxgdIeAbn5Cz078ZBq699W7M93kPBw6Oquyx7oyszNkoaGgI25ajSfMivJVXIcPQ0B+xoWtA2B7aIxnrAb/4WA13/exaRM66ZvAH9rwBAFhY24DXBr87ImqWLkd5nYxSNnXuMw+SS9DQsKA4G4d6w7LUMbzvJCeg49KmmXjt/W6cONfJaeoYN3OmjE4bFgmFsOYvf8TTf70HP7jtXlTVe/k+BxrLyUJ9J5wBNS1LcbdpIBwGdMPAyc01WDmv2pUMKO38thc34BufWolQv/SWyDyeAvjLdcBFPwZ0udLxgflFOHvx56GydTAAmpaawWmJQGavkJPfoK7BhvfMqtzn/v4oBb7+mEg5gXeO0oEsT/bLJ85FxBnU/QVPDM5xUn0lnri/DZjfGn/nirdeQDgcwrb1bahtbHH3k0owJmVIllABl/K73bruWVgxdMaMc9QvPg4MwPJjC3HjsztHfKx5VXVYVNck092p/+KMFxNJbAPZjq+zZgzoaQcy84/84Ad3wTBNnHvll1DduBgGl2PinBFWCJyWG0RT6/EwTBORcAimaWLZsmUAgOXLTsIPvqfBtqRD4IG7/4SLV56FM88cIp5iGA50D6c9d8pLM/WXqqDn/C281Jtv7u8e83lMdsiDnECUF0ANVC+tfw52JJ58guGW67+JNff9CY/88eZRHyu3oAiH7AACPfvd2WkkEsYr69vk0is875HfPq8py3XNRk+fq6QUspNWSfsBX1CLb5DljGFRSXbcc3t5/fODXtP378SyukWxP/Dnr0DTdMyeO7pJgh8hPA1jQONR559scoI6dM5cT7bymGlO21976gJ8YH50ENGYDFohEA6H8NK655xSp/JlxuT3nkw8/br8jirqm/Gj39+HK770dVx3y90or29CXtCQull4nfeLzz+DUL+sXCWE7d3M+3dKzSCApXMK8G9LZruGsZoeSUNx8hnH/ueRQZaQ5u4SvWzroO554FPgEYiL22c6nmF1f/ABVj3z3TuGxlDZ0IQr6wrA/7p66AP8ciXw7isQto2c/IKolYlUQxmHnDFUTs2Bxjialx4PzYjW5XJNw1eu+xGqGlqkk4MzfKtKgO0eKlWXhL29SQbBMiDT1KOyvTCWvFL0gPPd+r4Y489fcgv+xP4AB/70ZWD/keVObiniOLH/ZSw/4zx4Kxijf27qmhbjxj/fjyu/8g1XXgEAJ51wPC7/1KfcFUQrEkFbW/ziYSPp+n+w5s0h35+WG4TwldcWEG5gnxAChsYxrygLbx/qwQV/3DSyCzwKIQ9yAhn4YFW3HAfdNBAJA7YVATQ9auNIKITfXf8N2GL085R/2HPBO/rwkZkl+JdhIoyQF5zFPMN44EBg+IxHZUD7DQrhG6g4Y8g0dbT3hsEZQ4ahO/IAMaROtqm1Fbc+G62Puv3XNwB73wS+dN6g7bmm4RNf+57UXh2B1kxdsxuIA5Ey+kvlHVGDkYBwotU9LSmUgcdGVtwk2HsQfRnROlxh28jNL3BkNtKbfsyUzEGFOiYa5vxPGT4MQGVDMxpblqCrP4KwZbvPj9TSys81tB4PrnFYMVIa4Z/rkdG1ByurK6I+p2vMO+AkZHZBBvZ29uNQb3iQsSwEoHMhv394hk/KVhD0TQYNLidRMkexL8uEM5kEfM8RgNPOuwQzjl2IJze+hH8YQ2tMGefobD+ETIMjw9AwNXdwjtZkIjXRzH0O1ApAbVMLfnL7/Xjk3jtx6MD7KC2dijPOuwA1jS2I2MLt50wOLI+8hmf+eRihuXG86m88h09VF2BBbaNvMj7Ag5zEZ2agHOqTV6xC+6GDKC4uwo0x62owoHM/sGM9UDRrzMctz+e446E/IRIO44mH7sF1t96N6oaWMe2rtqkFdU2LB+WSvvTSS3Hb7293vcvKeI5JAmYpb+zrRtPMfLz2vhxQbRuwuQCXFardb/lQT/qkXEsGEz5qbt++HXV1de6/3Nxc3HDDDRN9GuOC0paqwaiirgmrvvZd1C85AZfl7EBBhm8m7KSdsS0LsIfKlBwfWwAtFfPw6zsewIWfuxpf//UdKK9rdo0sNUAOLOnol0+on2rg4Qyu3jg/w8C0XBWsB2QYPOrz8WhoitG5MB5batLdDgDoOtyOzsODI4ZHQgHrw4cWlfiuOfq6UgHloQ9oHPXT8zC/KNvVHEpbzvtOpPNv+BPP2DHYC8E4R0f7IXAmvQjHFmamRKSy66Vyfjc17qQrg1fZDE6HxDwNbW1jC6669ofQdH3wl9nfjY+XHML0/Ayf5l6WKlZZHSYjuUEDhVmm+3yrPkdNdAOa9Jqr+78wM7WMQT/SOJOGkcojr86bw/vKlRxAefigVskYUIJO1OTFrpRm6E5JZdN0nAdSBz8lc3QlzMcb+dzLsuCKeUVZmJJpoqqxGauuuR5f+dlv8dXv/wRVDS3w65Rf37IB1606D4/fdzvCe3fGP4gAejoOYWDKSb+8JZksKsnG9i0b3b9v+9E1yMkvQNujj8T+AOfgmhaV1WMsbN20HpFw2F2F3bahbVBO8ZFQmhNASXYA1dMGr9a1trbi13c8gG9++zqsWbMGTU1N8XeUgC/i0/fJmA5PZ+yUuR6gTU7ZiXOKMOEj58KFC7F582YAMln59OnTcc4550z0aYwLswsysG1vpzswv7ZlA265/hqE+kPY/MIzKL3qDzgER5rAmBz0IZdc8Nh/ASs+P+pjzirIQMWiFuQfW43esCUHFsdzbGO4DA7MzU4hhPRaWCELQYNHdQ7KA6VyXwz3TBkxLZM4H2IMmqahsrFVlsN8cfTT5+V9L6I4+zhXl+n3hqTK8+9f/uZcJppX6ec482UJ4TKyvm+YCowAcOjA/qi/GWMwTBO1LUvBwJAXNFIoSt/vsVK5uP1yEgamll9E9CTuzAsuw5RZ8/Hqxjbk5U/BxqfXYP/7e7DsrAtx6spLXM8xY3K52dS5I7lJkS9/nPAbNpkGR7eTDi3D4O6VMwYcUzgyHWUyUKsGOmfuKgcTTiS/6/n2Ul/5veKvbdmA76w6X2pUT/siUL5s0P6/fuOf8daWF1DXshTzahpT9o7w91nqOckO6OgLW64US363mqzaBu+7X/vQvW7hkKFKKvND76DqvI+5hrVfWsGd35MZ0Bo0NDzx8D3A9JUAgHAohNuu/08IWwBLvjxoe7bxfjSdtALrx5AEyo8tAE3XIMIyO0h109IxTbDL8obO73vhh09B8OxTAQC7d++Ou12ivoHL796CH3xwIRhjONAdcnXISmphC4HO/rE5544WkrruumbNGhx77LGYPfsIi0SkCEXZAeQHDfcGf2n9c65+0opEsHvHG+62x+f1YvXv78cp51woe6qXHh3TMWfly2CuKK+x8ijFecIHSizUT407wXtgmFuYhZn58oF3I+V9Uoyh2Boj12Prhz4qc9jGOBlVwrq8vgk/qB/uigdT0bBEeh0Zc9LQecZVqngR1WAU/ZrnDfMGQO5ue8PZlUOXB/Ylv1f5q79/6z2oqPfSWaUK0QOxnAi4en2foefqlH33KGfAoromnHfFF3D6eZfgmv/6PX705//FB869WHoSlbfR+Wdq8VMcThYKMgzMK8p2J11Bg7uFQbhPypMq9388/H2Ql5FDSsH894L6XcmPGIBtTtEM27Yg3h6so/zmB+ZhUX0jVl7x7yiv91bWUjF43x8vMPC+9a/YKemJ+n6lbMt3Qa89FXP/pVvvxrcu/xgWOX2D6jlU22eZOuYVZaG8NH5syXjDAE9esEtmKZGBbTbw9xuiN773W8DL/0CotxciRpao0ZBRWOqpGoTXvomWpY0kv3Ei2bK7Ax/+7Xo8/sZ+HFOYCdvJ7SyEgMaB29a/g6seHD4bzI8+Uj4BZ5uaJNW9dOedd+LCCy+M+d7NN9+Mm2+WwWt79uwZcsY1Uezbt2/YbToPdqPvcC86+3TMX1gBzjksVfPR6Zk/VtKBM5acCkPjaF22Amsfugeh/v5Rn0+ubiPSeQDtnUBPZz/ClgA3pfdMZxwCAnvfi8DUox/0zgOydGTEBoqyDBzuDbuzylDEhsYZOvVe9GkM/REbhw90Q0pABWxTx/S8IPbti1+a8pl//B0wlkW9duLpZ+C0xkW4dmBsAeOwIxa2PPsPzDv2GPR3tAPIH1U7zJk9C33t+wBNA+NAR48OjUvPAGfAbm2M5bxHwEjuCQDoPNABzhhCBsduIXVhnQcPo6c/gogtwCE9/sFMAzbn6O6LoFhYuLAiH394aXjpybd/8VvMraiBrnF0HdwHLahjj+hC5giqMiWC4dohYgt0HuhEX3c/NMbQ2W9CQGpLw7aN3pCFsGVD0xjClkBHOOAaDSHLRrgvgjCkMaiBod+yELYEdE0aPBqXuUE5Y+iIBOR7nGG3MX7ffSxGej8kis4DnQhbAn0aQ1/YhhACXRqDxjg0DhRnB7B798S2gWJk/WUHhBDotQVmT8vFWwe6Ydnyue3pCSHiFDboQLdr2FrCRk9PGAvKK6AbOiJhQHurDZdPP4zfvOulbptj9iHUHgI0hojGEbZsdIQC0HsNBPonrprXSNohz4rg/c4QuvrDCGnc7bPae8PoOtiD3p4wbCFwGEFYtrzfI7ZAb8jC0pNPxWP33wkrEgHv3o+vzW3H9/+ZH7X/j35wBY45Zjb6Du2DJQRMjeNwxHRiNoDppTnoPtSJ8cxnMFw7CCFw/Cmn4tGvfxpW50FomgbGZfYH/a3nEMJV7rYqT3Hj0hOxdcMuHImJvH/3O3IVQgjYloWNTz+GRfPnoosz7A6OflweCUO1RV9XTMH1mPnF0ztwchlH7+F+aJwhN6Ajm+lY+/reYT/bUpaFhnwL7fuH33YsdLUf9H7vjeB9oxehjjhBmUkgaQZyKBTCQw89hNWrY0chr1q1CqtWrQIANDU1oaysLOZ2E81w59Ghd2I/upAdNNBw4gpc/b0f4fr//H9yluv08O+9uwuZU06GyRkaT1qB7//uXvzs6/+O90Z5LtOyNOQXyTy6XXov+i2BLFMDINwqVVOnTRk0c+0P9OCgM/g0zi5AR18Y2x0xf3/Ehs45ysry3Bn0MbOAF96WRlpeUMcxpTnYrfO4bXHCB07HLU/2Rb1mZuWjoXwF8OYA7zJj0E0DdSesQPaUYry1/VVAH0HqJh+ZBUXINDQEDQ2mxpAV0KExGQjXNLNgVPsaCyO5N98OBaE5mSzKHI3ajv4A0BtG2BLQOINlC+RlB2DqDHZPBHo4ghWZBWhdMAP5GQYuHBhtzBi0338a3/nVb1FbtwIR24bpeN7yswyUFudMaJWwodohbNl4N5KBnkA/NAbkOYVoNM4Qitjg/RGELHnvhSwb+XlBZBgaZuZn4OU9neD9EURs2828ooUt9EdsBHUOG1LW0x+xkWlqyMs2EbakEVBWlj8xF+9jIvuq98Vh9IYtmBpHT9hyJgtyJSjT1GLqISeS4dpiZ38QhsbQE7YwZ1YRIpndONgTgsYYQl39CEVs6BEb+YWZXlUwW6A/0I/6E1bgO7feg5fXt2Fe/WJMW1gL3PuSu+9AXhFMXUPQKTnfH7GRnxtAYVYAZRMsOxnJPWEe7MGuw3047hgv8Nbs6sch3o1QVz8sWyB/SiYitpz8hS2BYMRC44kr8P3f/QUvrX8Oc2tbUN3UAvxzY9S+9+/fj//7y52oaVmKedWNyA7oyM+SOuzqabmDnCjjxXDtsGT5qfjeL3Kx5YXnUN0sx4GdW9dhQcMSfHWj5yk/7aMX4KT6RSiva8L2ovV45gjmpf98dZs7Nmu6hsYTT0VBcSkYGMrKpgzz6bETry0yc7oA7EnYcQSA3MISHGQ94JwhPzuAvKABw9gDoG/Iz+qm6doY44Xaf6Q7hJLSPLdIWSqQNAP5b3/7GxoaGlBaOr6NP9GozAQAACZwzkWfgGUDP/321VBSqVnzyt0lrkxDQ01jCz76qc/GidSNj9YRPavzAguk3MDgsTW4xxRmgjNgj1OzPTdogDFfBogYn1GvjWR1srZpMfDkk1GvFWaZ4Az43HGzceOzb7uv62YQ1zkFPgCGquZW4MURHMTh47MtmbrJWWo2NE9/mUopvjzhR/Sr8ruSnk/LyWXr6hCd71EFd84vysIbvpyVM+bMxcn107B7y3MwYWFBTaMbsLWwOAfZgVS6fk8C5C/97V8u9mcs4IwhoMuMA5zJ1F4RO7rUOXPWh7mqIMeYUwBHvp/M5eKJom56njt5HShXSEUpwUBc+YRzP8wrysL6f0k9reb7nhmDrLDnSAxUQHF5XTPK65rRHY4gFPEuWMnDNFeSwNwqoKlMLEmMul7h+7t+eh7Wv9MOlT6xvL4JC2sb0RWy3Iwffu77+beAUB/uvcXAt2+6C0uXLkV2QMfCIdJ1JoNMQ0N5bRPmVTe633tRVgDPPfMUwJe421Wd9CGUHzMFDAyBI7Dtg1YP+rc/A0DeI8vOPB+V9XIsSoXc8YkgYgs8/sZ+1JTloqtfWiE/XfsWtr43vMGRDn3IeJI0DfIdd9wRV16RzgScKnYCAgd7ZBqmsy+6DL/880P4yHSgJk/g9KX1rtbM1OVS6Ic+9nGcHhhdPseqOdO8P5gXAKVMxFg14/34n383HVS8bTFyPe/Azf5wYR2m5wXBGMOpC0pw8rxC9z1NN1Be2+TmXh7tENa3vc03yCptK0Nxtpl075kff0CVwp/z2DMGvPc07v+b4fsfjM4jvevtnfjDb36B//7x9/Cfl6/E61s2gDGGgMaQE9RTKkJZnYs/DaIbJOS79xikMTyvKAtzC7Pkds5gpQrQaM6GahLh/s6ig/tSKUBxIvCq6aVOcOpw+OMF3Nd89zyg0rr57xEGw3nOue8+yDQ0/PgjFbj9gjr8+COylC/j3oQqyylCIxKRR2sciNf/Mch7X/XmhsY9nTnzpYWM0ccAAPv52RD9PTJLQziMbRufHzq2IYm4hX6ce/iNLRvwhUvOwR9+cX3UdsLZ9rXNG7D3gV8AGx8c0/GuX5INI9wlM50EAlh+1nnuBDt5z1DiD/yt/3sdM/IyoFxcd2wemWT1aK+2l5QRpLu7G4899hhuuummZBx+XDE0LnWRUB4Q+bBVNbSgcF4NAEdH6TMYlHF0/KKZWHPVKoQbzgFqPzjssaZmRD9IrsHhdJLq+PHwG7wz8jPwXkcfIsyOaQgzJgM5SnNGv/zheoEw2MheUJIFMLj157dteA7s/kcgVn5vRPsub2yN2i9jMl1QMstLx4IxWY600JdeyktcBTdIUnXKpsYRigg3CCtmZ+28KIRAOBTCSxueQ/OSJSnr+VDZCgBvwtA4Mx9tOw96gyKkp0/nMgDJFsLdNqBzd3lZYwwRiCgj258WLEWbYFxwr9n5P2PAlEwTxVmps1QZj+hzl1RNzcXujj6094YBBgTUPQPvvgkaUjLhlW+Xr88uyIjK8+q/J3SNoTAzgJLs1Erx5mfgfSug+gYOXROYkZ/hnr/qO2Tz+FdngBvPqcLnnFLEKrMFYwy6YaCyqXXUjoiJQ7irBALAa5ueRyjUPzg7B5Pp7a75t/MQ7g8B5gtA41mjPtqzTz+FT3z1OoS6D6OisRXzahoRdNKZJqsPObZo/OQ/03KDyBlF2s+j2zxOkgc5KysLBw4cQF5e3vAbpxnK+BUCMHTvQfMbhmrJz1tilx3bKxufh3V4H7Dm18DvPzfssQ5s9zSpfgPBX7o1nrckoPOoTrI0J4CpOYGYUdQAsLA4B4tKskeUP5Qx4L8/Gp2435/0X+3+7KqpuHr5PACAoTNkGBzVLUthvP868KcvycIiwzC/phHweRzkcjyPm8EjWXDHwPNr/bwJjfyn5ASA4wmPMgzUNrEfWc45alqOc+QJ4301Y4MxOJXMvMmh/MmiKv8ZvuVk9TkGWUhBFlBwpBTcm2AAXgEJ/76PBvztqCZH+RnGhOrPx4o3ofG+MBlL4KWaVNfBGFCc5fVRqsCO2+chOhMGAHdFTd1D0/OCyEqBvODxiNX3qtUkmeLNNylg3sSa+64XDJiaF8Rp4c3AXV9z9yMAnHnx5Siva0rZCaSSl6leorn1ePAYeY6ZAF7a8BwiobBjPI/NlLvvNz/F73/8bdS2HIfKhmYYTpzIwDF7IplbmIk/XFgX9dq3VszHHZc0HPG+84IG9nUNn0JUcVbl1Ki/I/bRZTJTqelxgDOG9zr6oPt6oailI0jvrs6ZazgCQHVzK7jSzR58Z+iD2BZOWuIlG2dMdqAaZ+7S/FCdYGlOAHXToycoXjnXwfqr0SzZM8Ywd0AQDIc3KVD7yQvqyDKlZtTUOAK6hor6Zlx3y92oOWYG2J//H/CzOPXqhcB/lMuHVXnhVSWuVOz8Gdggr/ZAr3FA565umrHoghfq+/zhh8tRmCk1yYw723KOT1z5eVTUNblHSzU0zlCSHYjKdxv0acR13zK6PmD51+8lMzh3t/E8ivJz2abmtmVRGnhPE0VOwLkf2ODJeKrjGfeD3/NW4BiKskzUTMvD7CmZUZ5S1U9xn3uZ++4Jv+wgFfsFP+pZ96M5fZrG5HNj+QyU0myps/Y/O14VUeD4xlpoe173diYE7r/9Jry1dSMYWMoun6txEgBqGltw0RWfHVQMJC/DQI1TqZZrGowxxpvYtoVQfx9uWn0Ndr68yet/IfPIJwMG2Zf5mV+UdcS9+nM7ZcaID/923bDbXrF4JtZ/8XicurDYfa2jL4w39h1Bqds0hAzkcYBzGSCmkq4rr4ahBnUuvWFBg3szZqfDH+lE+Jh3n0ZD82L3b0PjCBocpsYR1DVHoxdfZK+qVw06dyaXvY80YbzfwFYdt7J71FtCCPcGdAc6MJTXNWHllV+GYZqxcycDQE87fv758/HmSzJaWxaeYKgty016WeVYNM7MH2QgK8+Q8uUzxrCgOEtOGphPr8uY61Hp3PEyqtrlysFpJyyB7hSb+dPvbsJrmzektIE0qyATOmdOphWgytGID5wo6Jy7961rDPmMHPWamnS5RpZjKOQEdcyZkrrFMRLNwpJs5AZ1n2c1fYJrXM14LO0tk/2kWhVSqy/+KnL+gE2lR4Y7SZb3g8rqkaqTZ4UaD/xMyTSxoDg7SnagmFmQAYBFPTuuFh8yd/jlX/9+lHFp2za2bWxL3XYY8Jxve3E97v79LbJYSJ9MT6rd+0288/QDeHndc/i3r30XF33ualz76z8d0WFf27oJV11yDt58aaPbNvOLkxPAKEtuDx7DjlQ694UHtqE3PHxVlc8unY1Pt84Z5BCzbDFiJ9lkIXXXmtIYzVkKH6i3NJxUXkoK4XbYQnbu2za0ydLT6kMPrwbO+HrMYyxiA6qoAa4n1hbCDVwZjbGYn6GjL3zknjfhHN9/dhqXRqxmMTTPzMdjr+/HotJsn9fa7wFjWFDbiG/85k68vul5YI7An3cOfDAZIpEwXt3YhrqmxdKLwoYPTEwlcoMG+iM2ws537r9CNWnyF0vYvnkDrrvyfERCYRiZOYicdoZM/m7bCIdCeGVDG1qWLElhfaGjg+TRKxScAZZTPc92ktgraZCucVROzcHGXYejl+OFcAxiz7hKdQNovFHP0JRMc1Q6w2RSPz0Pb+7vxsGe8KD3NK4kNdGvLyjOxtb3OpBhcPSGbViIDv70xyNwJovvpMPKcHF2AEVZgyVsGY7hHOu59qRr0XIjVfn0A+degogtcPuPvgnbtmGaJppbT0jp50T2ewI2gBeffxbhcAiAAH73aSAjF9ahd3HrD7YBAjBMA9+79Z6ELP1HwiFsfuFZNLUsTmr71JTlYm9ndP7lRDk+TrjxuWG3+VTLrLjvMQb0Rayo1b/JTHr0ommG8ohlmhqyA1JG8O7hPhiaE1gCeP9cLyFQ07wUdxkGIhHIGvNvb8LgYQPQ7vgyTv7JDe7fYct2csRqnrEJ6bUcDQFdw+wEed78D7N/6S9ocNRMy8VdH2+EJWzXK+ItlXoeoYW1TahqaAEA/HnnhugD9LZDNwyUNy51tK0p3OPHYUFxNg72hHz6adlGUzJNdIesqOVhzpisHBaSlcPQ2ylXJQwTYYRgmCaalp4AILWNRGXs+31hcnlYOLIShqKsADJ93vago81WBoLyqhuc+/YZbSwfbWSZOjr6ItA15mb/SAf8GQsGvQc4K1ksKpYiJ6g7hiGHwQUilnQMRCxbPjPC61MCukr7J4ZcUUsVYnnoPO/44CqAzHE+uDIsNWlUnnQInHLuxVhUUYltG9pQ27IUtU3Ng7zRqYLfwAcEGlqPh/FfJvrtPulBdrzIypEUDgPbNrZJ6Yk2uvz5X11k4eeGiUhYanJ1w0TDkuMdp1WCLmgMyCqSA191O86kwhiw40AP5hVlpeRKbaIhA3kc0JwlEoNzBHXuenN0jTnLf8qz7HVmGmMor2/Gt2+5G1teeBb1S45DaXYAn3o6eiY5xe7El1b/FOW1je5rvWELhubpL4fOXTExcMbwjVPmYXdHn3t9AHznqIJnZCeuDCfTyXertonVJ8zOEqgN7ceSW+7G7Ip6V6uYih3+cDDItGwRJ1sDAJTkBBA0ODa/2yEHR8i2qGleirtMA5EwoOsGTjpzJT547vnY9PyzuOTs05E/txoRW6RCPxoXv6dPUT89D9v2dKKbRcAYiymPcI1jNU4IIMPk6Avbjlwo/eQFiaQsL4iyvImrDpdoYuf/ZTF1uYCn1+VcZj0xGUOnU0jGhnBKBjPo6r5I8YnjUPhX1wYGXatr05jMIRy2Iq68hDMBy8kdXVHfhOqGlijpSWaKZfoB1GRAriSBAbWNLfj1HQ/goXvuwF/v/hMsKwKuaU6FWguGYaC59Xh0hywYnzoX4c/fO+Jj1TY04tpb78Gzj9wLnXN8ZOWFqGpodifsyWTgRGkoD3KWqeHKJbPws6d2jPt5cTDYR1EfSwbyOCCLdEQbd25krtPRqeVhL3+v7PAX1TVhTmU9skwN/REb5nMvImTJuzGgcVz9oWYcWxTtIQpZwtG3MtdASHaqL8YYGmbko9opsx00uNuZe5pSVRBDvScnEYbNIctmeCL5stwAdnfIycLpNbNxyrwm6E4pbH8hgXSDMekxgG0PGgDdTtHxkJbXN+E7t9yNTc8/i9qWpVhY14yQZeHsyga0LijC9ve7opZZUxP5/fuvU+V0NfX4S+H+29kNSIJcggc8TyORfkzNCQwKSgK8vjK2Ptm7Jww324syppkrM1OSKwaZZjFV8/8OhWsE88GGiWoD3ZHWBZxnyM3mAdV+UqtsOpruBcXZKVVIyI/ylgec+JOaxhZU1DfjpDPOQ9szT6GiqRVBnePNF19AY+vxqKhvxrrn22SPsv9fQFF8iYCfN7ZuQnVDMxqaFyPD0KIyOyX7Lhk4fquYi1hYtkDjzHxcuWQWbnp+dLUUxnRuk99x7HIUXerEoTGG5lkFjsEjUcvAQHRaprrpeY5EgA+QGchSor85twZnVJQAAH56ZgWOKcyCLETiIaA6Ff+xxv864xFwip/45R6ZhjZIVqImCP70XBrj0ambnG2v/3AFViwoAiBTPfnTGvnbMx2J5ynjTA7+XiYLhvK6Zpx9+eexsK5ZGpVOMKaqPKekGqkKZ1IyMbsglpQnfoo6/8qDazgxmblFBXLFW3EgUpvsgI6pudHe74Hf90B41PvMXYGOLprB3NUlzhim5QZjShhSHX9FuYF56NXVKAlSpqFFByT6VimlNz0640Wqwdz/AabOosa0msZmVDS14tWNz4Mzhos/cxWqG6VX/KX1z8GOWMAfPg/86cvDH+j9t7D6Mxdg+9aNrifdH8NQnORyxwP7Qfc+j4FwVgnC1pF7iD64qGTI9+WKZurdN+MFGcjjAGOy01e3UaapIcvUPcPQJzdQEciG5hmKyujjHMjNMHBBfRl+d34tipxyzTGXHFXH5zuHZHFsYRbyM4xBj5Fn9MrRTBl2rnfZN8gBngHNGEN2QMPHG6fjO6ctlBXy3I6fuQZmCvb3w+If4PKChqurVe9mmJoXsOdco/cTbmlt1VYAUqqC4EAYGAozjUEDkJvFJU7nO9Awlp/x75V5xhGR9gjlBWXeqpgf5VVVqyyq71P9BaCyxHh9bTrDGUNZbhDZA4IvvbzqntGrCl14VSVl36FrnvMhleFQ8hgvQJMBeG3zBvzg0xfgnv/+Mb55xXl45cX17lhZ3bIUumnIjB173wT+/vP4B3j/n8Afv4xIJIxXNrTB9AWCMiZToCY7C47/O/rvj1YBkBmPynIHy6hsCHdMOBI+0TQD3zltQcz39nT0YU9nv9u/puFi7ZggA3kc8D9sgDRiyktz3E58oBHreU/lTzWTdQ0hxt0CCQzRX5rMZSmiDAj/PpMBd6LPo0w93yDmnqe6TsZwbGGWr0OP9jQreQpnHAuKs9zXVFumoidkpPiLuswvzvYVB3GkN8wJNPJ5N97YuhF/ufVX2L5lg7tkzHz3jr8YSaox1FeljJ1477nPDdR2LOpZU/dYMIWvnxg5/ud/IPOLsp182QwqiE0GNzkTSbCouAfGgLa2NqxevRptbW0Teh2JgLHYRZ+qp+aiINNwHSecwfUguynfmMqEkfzVxZHAOQPn8jqmZJpuLujNLzyHSDjslsze/MJzzrUwVNQ34zu33I0LP/8fWPXNH2LG3NiGHgDAiffQdQNVzbKqoMqClCpNo/m+pPxMA6qGwI2OsezHtge9NGbijaWH+yKe/cFYzHtxMkIa5HFA3WLHTMmK0nl5HX60ryw/w0BHXwSaE9TXY1uA8ohhgCzBsQyVRyVkCQR1LcpAYEwkfZld59y9QL90BACCuioT6w2AuUEDGmeI2M65MwYmVN5F4S6hAoNTOik5Rzp6Dz2jb/DJK29Y0JBaawGG7Zs34LtXno9IOIx7TAM/vO0+HFNZL/eR6iMfhjGQhxie5hVl4419Xa7XUKhJIaKfqVTWVhIjpzjbhBDArsO9YGBuKXpFXoaBQAdHD7NkX8jkSl3IsmELb3VO8dLGdfj3i89BKBSCaZpYs2YNWltHl/UgmcR7MlTubxnYFj2pUH2nzCfNnWdGPmWpGmTFHANfBVnOzM+AxhgO9YbRsOQ43O5keTIMA/VLjnOee4EMnaOivhnza5tgWQKFc9bh+odfgDh28eBjHHwHK1ZeguM+tBLltbK4khcPkxoTbHXv5gR03+pI7P7TRmLyEw+VKc+1LzgDt8VR40JO/p0wyZAlc+XNWphlupV5AC/gSuPRkoD5xdkozQk4s38uU/WwaEPQe4CjNaahiI28oO5bopbbjDbFW6LRuS8/p2u4y4csQ+euJ1y9p/BrSVU7uDIK9wKVvk5uowbPNLAPBzFwtWHge2rAMzRZNGHbxrYoL8pL659zJg9qEjJx5z4WhkovNZR3Kz/DQNDQYGgcC4qzMa8o2wv29P3jvraYSJR3csOGDcNvTAxLQNcwLTcABobqabkoyRmsCVWyMu50BpzBKbUuv3/Dt0T/4gvPIhQKwbIshEIhrF27duIuJgEoGVks/P2E6jvlqptnWOm+laZU7ycNjUetmHEuPbzVjc341s134fzPfBU/vv0+VDc0A5Djh0qNpsaUyromrDBjBKw9fyf4kzdj+ZnnYWFto+t4UWNyw4w8FCVZfwxI6cyN51Thx2eUA1Dfaex74OzKqVG2wlgZyivsXwEGS8+MUWOBDOQEk5dhDGGcSl2VSvu2YEClHjl7BmQ+ChXd73RszgZ+jzIA9Fs2CjJM9/OA471NMoam+QqlRHfOrjGvfnce7sJM0zGkGbKcoD5/J6/eU5pcZxeubjfbTL8FEU9HGbt7U16DTEOuEjS1Hu+WV9UNA41LjoPp6JADvslZqpIXNJAXjP09DTd4V5TmoHJqDnKCutS4+7ZVy8vJ6Ljb2tpwyimn4JprrsH555+flkv4qYgykoaq6umfGAFyW40DpsbdyqUAcPFZH4RpmtA0DaZpYtmyZeN+/omkYmrOkNIp7utbAS8zDIMq2Q2oVcmCDBMZRvLHiFjkBQ15vgP6AmWgLaptwkev+HfUNLa4Y4FyoviLpXAOLDvzvMEHeOdliP5ebFsvn1HVMpxJp1KqrMIZnGNGfgbyg4Zv4jM4xuJry4/FeXXTwAC3QulYGdKDjOj4qHcP96GrP3JEx0sH0s+iSGOUFkw91Lm+Wu+el1VqSL1SugO8g6rjEPI1IQSyAhp4N1CaHURBppESS0Q6Z04AmeU+1JzBXQ5VxjHgdYQz8jMQ1Dle39eNgM7RG7bcdrGFt6TO3X0xmJo3SC4qzZn4Cz1C1CA2sG/yvELMLXrAwFDV0Izrbrkbr25oQ+3i41DT2IKwk0pv7pRMmT80hYlXvlW1w1DEMpRUxx3UZVrEIy2RPhbWrl3reifV3+m0fJ+qsAE/Y27jvMmc/jCgM9i2gKazqAC91tZWrFmzBmvXrsWyZcvS7vsZWKbeT0GGgd6whY4+x2AR0dUqM01fjnyGQWlCU4lji7Kwt7Mfb+7v8sYLB7WqCCGfd8uR4ym5lcqrD6ffLK9tAl6MXtFhug5dN1DdvBSAHEcyDY5giuWE9lbFmIwz8k0G/NiQ1w3G0DQzD6uWzMLNY0z1NtTYEeXkEkDEFmjvCw8KGp1sTO6rSzEYmJu1InYmCs9baGgcIct2l06UXcm9EcH9UIbjXZxZkDFBVzI83nK3NGhVvtr+iA1VQtmd/SOWtyB6cgB4cg1lXNvM01+nK6bGZRWoGL5P5SlXHZeaGJXXNqG6oRk6544R4KR34+mbgkcF4owm+MOfAqpqWi7Clp2U6k7Lli2DaZoIhUIwDCPtvJOpzHDPtts7OAO3X2IzMANGa2tr2hnGIyEvw0CmqWHTrsPgTBpNGjgYs5FhaAjqKmxRpFHvwLCgODvKa84ZgwbAYt5YWV6ajW17ZHU9v9eZAdi+ZbDc6SMf/giWNH8bi+qa3DiYDFPHvBSbNChPrTv2MxXEH/0N2kL47neOk+cVjdlAHqp8dFQqVYajJhcyGcgTiKfjia0lcjVGTBkMXicPXwCGNBadXl/IIiHFWcnXTQ1EGfO6k8KuvDQHXf0RbH73sGvs244XwL+0pdpA5xwGByK2z+AGXAMbEG6as3RlYUk29nX1Y9fh3qjXPZ26cI1ed/BzlhKYMznqDqX/UtfcKZmI2MLzgo0Av1cMQNJKn/q9k5WVlZPSCEsGsSbPfvxeUqGqcYJFGUnKMJzseA4FyAA3Lg0qY0AKs1SXYClU4PZAdI1BWMIdBzJNJ/7GCUxX7fD6lo34zpXnAeEI8KUHAADfrrKhsUZs2/g8DI3jmKoG5AWNlOw/lWSEMyl9YAAyDB19kVD0hsJzRAFDy5GG4sL6MlzeMnPoc4JPwhPHhplskIE8gUTNcOPcW34dpca8AV91bq4hKRhCERtZAR0aZ5id5LyNsVADXIbh6ZGzA3qUFlnjHLVlub7PeJrcoM6Raequ1ok5O2VqOwz2sqYbGmcozDIRGSAAyzA0VE/Lxebdh32vyvKxAV2DLQQ0JoNCe0Lp31npGoeuYVRLnXlBmf0lFa5ceSd3796d7FOZVAxlz82ZkomIbUPnHJt2tfuy5Qz/2ckIA1A1NRdb3+sAgKiUkepnxdT0kKHFdiBJp0nEkTIpGmbk4YW32z0tMgRe3dgGKxyWywevPQnMacDTj/wNTz18t8wAZBi49tZ7Me3441Ki/xgIU/84A3MMZKmpjz7bsOXL5hO14jo6/m3xrCH7XuZ4r73UianYaonnKHGUpwbKoAMGz+TdLBXM2yagDy4eoozLrpCFkGUjN0U1QO5sk7FBwWMy7ySHqUljWB/g+VOGtfQ8w2sDX1YLjUtjSj2omWkYoKcwNBmQMRCpRY9eNgRkewYNjgxDQ05Qj5p4HU3ML85GQaYRlSmGmDx4Vd9i39walznCNZ8n2fkklO8YSB+v6ZGgMv0EfRVL1Uqb3ykzlJY5lYj1lTEGZ8yIDuKLKgzjOE6qmluhGY4H+n9/Cv3WT6LjwD5EQiFACIRDITz58D1SwpCivhV/eWn1vQ1sl33d/VKXDRzRODDcM6JWZtRqMGeyxHV/JIFJmFMQMpAnEH+alngdgDKHlabU1Dyr2K2IxDmyAxr6IjbyMgYvQ6UKrlE/YJFTdXSmrsXWYiNajiIj07mbwUNJLUwntU9ZXhDlJbGDv9KdbNOryKiWEDlT1y6rax0NBkA8jpmSiao08YoRo6embGRVIRcUZ7t9qspowhhQlGWiatrkvz80zlDpPAfeCl30sni6dBOq//eTE9AxuyATnDG3OJJ/7GCOcazS2S2qb8bq2+7Daed9HM3LT8dJZ5wf9zip2C6eN1yep4rh4Yzh6yfP823ny2x1BOuIQ0kz3mnvRciywSC/B80pWd4dimDHwW4AcAPFJxvp63ZLQyqn5mDzu3L5a+DtqMpI7zjYo+piSOkAhCu7UJX1AhqDEAyWEMg+wtQu4wljDIYKvvJN02Unx6FzhlgTUGUcc0dnzR0pQV9YZbOIllcweMuJk41FpTl4Y1+XlBIweQ+ohP9+jfrkvPrh8edLJSYfI9WV5wR1T1rhc0KYOj9qVhgGLpHLWA7h+zt9GHiujDEUZZmA4wWPZQrqGkNEqIwWQHldM2wbuOaK8xAJh8E1DZquw7YsaIaB5WeeB88ETS1c49gRlSuJImdA44w8/ObcKty1+T2csqDQi1HC2K9kKCdLdygi6zPAJ40c4L1/c383ZhdkIjOF7ZGxQAbyBBI0vIwLWQMkAabOUZIdwNuHet2oVXeZDDI4izE4JTFV5SSRculp/DDAzbAx6D2fl8NPXlDH9LwM7DzY4z7sKlhN44CwnWwNvmW2FF0hSxjzi7Ox82AP9nb2O51hdKM5/SNBHNVMz8vAex19AyRJRx9FWQEc6A65AV7ppsn257n34y8YlWVqg+oI6JxDcwS7yph7eYNXnhoMWPHRi1BaNgPVLcehvK4ppdvEL6n0y2QYZ8gydVyxZJa7Ki2canoMwGVNM3D7hl2jPlZ85Juu1JF5HmfOmJOONX3jgIaCDOQJRj3csXL2Rj0ESnsnBDTOIJxsD6pmvMaAsEBK5DyOhwzCY4OMWHmdjpdnwDCmaxzTcoN4+2CvZyAzlftYbm1wT584SR3HMXFuB7fjVG3qZfUgiKOXablBhC0bezr7AaSPQZho5kzJRFDnePtQr2vQZAd0lOUGk31qI6Iwy4zrifTLL/yrhsqhpMZO0ymtXdOyFHeZBiJhQNel17iqocUdh2U/mpqGnZIKSTyDVLWM50QDLLUFA05fVDIqA/nnZ1YMuVKjUrop20ON6+qs3j7UM2mD9shAnmCGMuj8aV3UtjaADFNDV1/E0SM7BhJnyDS1QQFuqYQ7+x3wutLG+Tu7mJ91t2ewncmALWSuW1U8oyw3A1MyU1eHnUiU1EYIbwkZkFHck7WDIojRovrYWfmZyE/hGI3xRhVTWlCcjSxTT0oRnbHAGIsbdO13rkS9Dm/5XwUx94ZslNc14zu33I3NLzyHyqZWVNQ3ux5qnae2g0VlrVJGsP81QMrt3G0EwJknPxwNJ8wtjPm6EAL+QFllRDN4jjn3+CncjkcCGcgTTEl2YJC8wo/ylgJwtLuy7GRA1xA0NCdqlIFDIDcNOn/l3fSXwcwNGjjQHRpSO8sY3OpYcj+AxjlyAhymzhFykrxPyw0cFcZhblDHoZ4wLNiDAkuOhusniJFQlCXL1b/f1Y+SnNTLDT9RqMweNhPINLS0MY6Hw5/nOtZ7slqtl/GBA6iob8YxVY1RcSsql3ymoaM425yo0x8VusbBwpYM0HdGSkPzAhEBT6ZoOX+oScJfP9WMv2/fh/96dueYjn2oJ4Q9nf0oL81xx2iVZs6TunjHmxx312BS1/04SZlVkInCrPgP5NzCrGiphfMvw+RQzmIGubyUl6Ip3hTKU1xemo3peV4aswXF2e4DH6/fjlpKY8rQ9nmfXQ3yZH00o5mSaaJuei50zvHypnW4/caf46WN65J9WgSRUmSaOmYWZKBxZn6yTyWpFGWZqJiaM+k8e0pCMVAU4enOvdiUgFNB0MvmocYNGWTGGTA1N4Di7NScSBkacwPivLFQ1gdgvmtRumzNJzPpt2xcVD8dv/tY7ZDHKB0wOdh9uA+9YQvdYctnhyhpo/IaO4Y5jw4MjNhi0umQU9vCOgqZkmkibAns7w6hL2zJ4DyoKkHejE3nLOXroPsf4pjvDTHv9HuXGYNbSdB9H8MFFkw+GGPo/dcr+NxF5yAcDsEwTFQ8voYqtxEEEYWqsul0nZOGeB7khcU52NPZh/3dIdfrZzorru5nmCx0YXDpnU31uA25asrcFG/e69HSCyXNDJg6wpYMmOsNR9ATtpAViB/En2Vq+OvlLVGvdfSH0dEflrEunOHVvZ0IGpoT+6J0x078D6SlrMbhPZ196OjTMatgcE7/dIU8yClIaU4AFaXZmF2Q4RmZzntqhqgKRaQquUEDM/IyHB1c7C5a11hcT8/M/Ex3ppqfYbr5lAG/tjm1O7jxYO3atQiHQ7AtC5FwCGvXrk32KREEkaJMtuXvgfIyRU5Ql1VaXRWuGitYlKNFSf7cEuUpOnuYW5iJ4qwAdM7cglGAfwXV+dt5LcPQZIySO4GQGxxbmIXrP7wo7nEGO6/8bee1n7/dvZVt5bDz/o7YkysfMhnIKQpjDFxVjoN3I6ufcwuzkD3E7DDZaJxham4wrhE7nPe3NCcAzhhKcwIoyw24nZs/+8XRuIy6bNkyBEwTmqbBMEwsW7Ys2adEEESKUlOWm9KB3KNlqLgV933fNlG58gFkGNwNdB/KeZNsMgwNuU6VVFPjA4xT5tZEUAasqlardNecA7aQkocPzC+OeYxZMaq3RhnCPs87EJ0uMEru6KR/U2WoJxOpvUZPuA+4SkWjbsxUl1coGIs9S5eaqaEfqPrpXnaG8pIcvLyn06lLL/MiH420trZizZo1eOKJJ7Bs2TKSVxAEEZeRFlpJF6SsgMUNOpQGIvNqCSijzjHiggZHT8iTXaR6DAtzMlXkBg1k6Bp6IxZ6w/2DslsAzMli4a2u7u3sR0dfBHOmZA7a7/JjC/GNU+YNel151m0It61VZi1/TJBav1X/bM7ABTC5BD1kIKc0BueeMSjULC69UqrE8xTXTMsb9jr8nVdWQB+0zHO00traSoYxQRBHHZwxHFOYibxg7AxOSnOtvMgaY4g4xp4sdMFcScCikpyo7EqpiPLSTs8NIiugozds4f3Ofjmuqvge5qWEZe5/TsxK2MKrezsH7XfpnAIUZA5OFuAfVlUdBvXa7IJMZJkaOvst/PNAtzvpUJ+bjGPy5JpeTjJygrrjRY02DNPJKxDvoTF1Purr8OuQj7YAPYIgiKOdiqk5mJJpDu1B9o2XGpdp0fyrrko7mx3QU9+D7K4gO38jWhcclR/ZDeL3rlHt42O106L2O5yf15NRePsJ6ByZpo4pmYZrhCsXMnfPK7Xbc7Skj6V1lMI5i3rgGYDasrykntNomF+UnbD67FMyzSh91NFOW1sbVq9ejba2tmSfCkEQxLgzVGB6YaaJstwMX9yOrP6mc4ZsU48aQ9Nh9GBOzM1AG96v++U+Yzl24Jx87erl0XKK+umxbQgv65Qn4eADIiNd4xyeBMQ1yNOhYUcBSSzSAOboi4D00R4r8hJYzGRuYRb2dYVg6hwVMUp1H020tbXhlFNOQSgUgmmaWLOG0r0RBHH0YuocZXlBWLbA3q4+18vJGYOuefpafxaLVIdH26auRzmgMYQtAV2TqeukxIJB50DEVvpg5n4GAKZkGjjYE8aGq06IeayekBWtN1aJAaByLKv9OUa4r9w05wyWJVLeIz9ayIOcBjTOzEeWqWN6XhDlR7lh6GrLjnKNxdq1axEKhWBZFkIhSvdGEAQBADMLMjC3MAuAN174F/8ZA+rieFBTDaWZjnqNATrnYAAyDQ2cM/k3A4K65nqY1bbcufI/X9yA2y+oi3mcvrCFf7X3eIF3rsRCvq8POIlo7zVz3w9FbPzrUG8iLj0lIAM5TSgvzcHU3GCyTyPpcDbZ4mTHxrJly2A66d5Mk9K9EQRBKKZkmm6BjYG6ZMbSx8HCGFCaHRwkLeG+XHbcMfjVdbmGK7xrfnVvJ4qyTFROje9gY46UYqAURRnkA4+vMa9qIeAZk70R60guOaVIr/V64qhH53xQZaGjEZXube3atZTujSAIIgYq5RmYSLsAb2WczhxQmY4zVeY5OiiOM8ASKk+yf3upERYitgSioy+Mgz3h6JRuA7TPuub9oXGGhSXZaO89BFhqEsLcfxpkDuZ0kbEMRVIM5Pb2dlxxxRV4+eWXwRjD7373OxrgiRFRPS13ssUBjBlK90YQBBEfZWQK4Rl809JoJTaWMe8GyDHvd8CrD+Bml4Bn6DJn5TXW2HmoN4y+iOXKNtTH/UncAjp3azEAMhZKZQcJWSpnspdlY/v7XSgvzcGejj4ASNvV76S44r74xS/i9NNPx2uvvYYtW7agvLw8GadBpCEaZ25wAEEQBEHEwq0up3IfgyEnqGN63uAKcqlI0OCDpA26UxvBH2iofjbOzIfJuS89m6cRHs6byxhz8kTD1UxEpcuL8XkZ/Mij9qGC95Snur0vjEO9YYSt9CxBPeEG8uHDh/HUU0/h8ssvBwCYpon8/PyJPg2CIAiCICYpxxZmYXZBhiuzSLcV/4CuDQom1DhD48x8MMZiFjnhMvdaVCo7JVeOVdG2sy+CsCXcdHDSuPWMXeWBj9V2KlDP1Jhbn8HNeMGAiGMUMwa8ub977A2RRCZcYrFjxw4UFxfjk5/8JLZs2YLGxkb84he/QFZWVtR2N998M26++WYAwJ49e7B79+6JPtVB7Nu3L9mnkDJQW0ioHSTUDhJqBw9qCwm1gyQZ7dDZH0HHoV4IJ3WZ3aVjd7hjws9jIEfaFl0HD4MxIMSkx3i3ITNHtO/vRk9vGL0RG31hCwKAxRnCtkC7HcTA2lw7D/YAADTOYWsMQuPot2xYtgAzNAgIaIxD79GxR3Shy5dmtvPgYVeG0W9Z4GDo7YtA40AoIvDyoX3wizratZ7B19F+0Pu9N4L3jV6EOhKXGvZImXADORKJYNOmTfjVr36FxYsX44tf/CKuv/56fPe7343abtWqVVi1ahUAoKmpCWVlZRN9qjFJlfNIBagtJNQOEmoHCbWDB7WFhNpBMtHt0N4bRofW5epvc4MGykqyJ/Qc4nEkbbGjX2p6DScPcllZPgDgAOsA7w1D64+AhywIAZiaNJDfD1uYmhlATtBwZRgZogu2LWBoHEGdI2hoCFo2+iI2skwNAoDBOXKDOkpLsqPqGuzsD7qe5ZBlg4Eh3BOCzhh42ELEli5rtU1+Uex2zy8qBQBEukMoKc1DUXZgzO2SaCZcYjFjxgzMmDEDixcvBgCsXLkSmzZtmujTIAiCIAhiEqMxL+0Z9+XrTXdUQJzJ+SAZhqFxmYJNbeertrensx9v7OvC9ve78OreTleb7FXQA0xdc3XLDE7GjDjBgu7vqsw1oiv6uYGE6aZvcZhwA3nq1KmYOXMmtm/fDgBYs2YNKioqJvo0CIIgCIKYxOQEdVRPywVnDPOKsjBnSmayTykh+DXVA3M6cwYvFaoyVH2Bc54B6+Wp4AzQuArWA3RfFgxlbA+UMKsASDdThto3fIVEfDmV05GkpHn71a9+hYsvvhihUAhz587FbbfdlozTIAiCIAhiEmPqHDVluTAGCnDTGMYACDbI8swL6ghFbLdUtFsRD57H128oux5k3+s6Y8gwdSdv8tClud0iIc5PnTMIATefspS2SGP81b2dWFiSnVbe5KQYyHV1ddiwYUMyDk0QBEEQxFHEZDKOAccAxWDpw6yCTGSZOl7Z2+kardJTLMDBYEF4WT3gGbaMM3BI7zHnDIYQsAWD7Ri784uzkBOINhdlfmn3L9drbEN4nmvh8yg7xnM6uZMn111DEARBEAQxiVHyhVip2+T7gOmkXePwqtwBfuOYeYYsZMCfzmXOaF3jCBrc1R/nBo1BVfj8yg6VM1lz8sy5RUN872uMDZJppDpkIBMEQRAEQaQJjTPzh3HEMmng+oPl4P1UZaVdPTJkOWn1H2dMpndjfjM3Go1xn8ZY/qdyIkd5p50T0PgQFn2KQgYyQRAEQRBEGlGcHUBhpjnodaU71jlDpqkyUkR7fFXFO/U7g6zSJ4P1nO0duUU8Q7x+Rl5UIZEoTbN6A56xzHn6eZCTokEmCIIgCIIgxsZQGTlUYJ3BgZAlXC+u8gYzxsAhdczcKb3HIN+W6dr8aeLinwNnDLaQQmNVjEUehYEzLw+y2kWaOZDJg0wQBEEQBDGZUJkpDE0G4PklD9H5kb0sFQx+6YV6ZfioOuUw5ioAkHnHd9+DLChip5GVTAYyQRAEQRDEJME1gBmQHdDi5Cn2fjc17hrFNdPyPEMXQ3uQ/dIKT1ARvW84+ZLBgHfae7GvKzRoP2HLRlcoksgmSAhkIBMEQRAEQUwCoqsHyteUsaxQwXlwttE19TuDqXMcMyXLM3yHMJBnF2RiTkEmastyPQ+y2rd7bLilrZmSZPgIWzbe6+hHxEo9zzIZyARBEARBEJOAvAwDVdNyoirdaT5jN8rwdVzEOouWUhRmmcgwPM9zPAqzTBRlB6Br3M1+AcCRdMhjBDQeJbUYyJv7u9EdiqRkAREykAmCIAiCICYJAV3z6YhloRSdM1c2wZ1APE1V2+OeN1kxkiA9P40z8wEAWaYWJeUwNO4cZ3BFvp0HezxPNicDmSAIgiAIghhH/GnXNC6LeLiaYJ9hzAHXUPbbr0FDc73QI4UzWdrbL83QuC8VXJzzVJ9NNSjNG0EQBEEQxCRClaOWMMdQZa73mDEGLlQFPGmdzivKdj8xuyADM/MzYNkj1wYzBkAoY5e58g3BATumxpi55alT0VubiudEEARBEARBjBHDKTUNKEmFL7uECpzjDIbGXe9tfobhfp451e9MfeRm4ryibJl/2dmn8lJnm7qbmxkAbCFwsCcU5VUmiQVBEARBEAQxrtSW5SHPMXiZGzTHXOkFZ0BAY8hygvESQX6GgYAu9c7+TBqGkyVDHacnZOH9rv7ozBqJOYWEQgYyQRAEQRDEJGNBcTZyg7qbek3nUm8c0KV3mXPmyisSZSSrYDxlhCvTV+PMDchztozKfMESdQIJhDTIBEEQBEEQkxQlqcgO6NAYg84ZbOFV0pO/JS4PsZRWyAIkXi7maCPcX7wESM0gPfIgEwRBEARBTFKUIao5pac1zqK0yEDiDFR3v3Aq9CkPsiO5sAVgCeHmYfbOI/UsZPIgE0Qa0dbWhrVr12LZsmVobW1N9ukQBEEQKY701jIw5nmJZVCcl8YtqGsJO54KANTgpXtTRnNHXxid/WHonPt0x6lnHANkIBNE2tDW1oZTTjkFoVAIpmlizZo1ZCQTBEEQcZmWG4TOOfZ29jvGMHM9x8p7W5oTQFluMCHHm5obwO7DAr1hGwbnXj7kqPLTPt2zW/I69YxkklgQRJqwdu1ahEIhWJaFUCiEtWvXJvuUCIIgiBQmO6Bjam4gSk4BKI+yJ29IVJq1KZkmZuRnOF5kzzfsr6SnKut575EGmSCII2DZsmUwTROapsE0TSxbtizZp0QQBEGkAf5qdjL9msoiAYgEBui5x3P2rRzDOvcMYo17GS6UNjoVVRYksSCINKG1tRVr1qwhDTJBEAQxKrijQ1Yow3VqThCFmWbCj8cYML8oGz1hCwGdo6s/gkM9YScXMwNUoB6kwawzhvAoqvZNBGQgEwRBEARBTFJMjcPQOMKWBcALmLMFUJYbTHgVO+Whzssw3GIlgOM55qoMNnO92Rpn0DWOUMSOt8ukQAYyQaQJFKRHEARBjBaNM9SW5eGFtw9BOE5azhgEPC9uohm4X40xaBxQid+4T+4R0Dk0xpBiDmTSIBNEukBBegRBEMSRYmoaastyx032GyvoTuMsKjhPFdljAIK6zHZhidSykMlAJog0QQXpcc7BGENhYWGyT4kgCIJIE/xBc7L08/iUeM4J6JhflB31mqrax/0FQsCcgD3pXbbJQCYIYiy0trbihhtugKZpsG0bV111Fdra2pJ9WgRBEEQaMDDXMBsnHzJz9MfRr3myCq48yQwwNJkLLtvUMacgc1zOZ6yQgUwQacSBAwdg2zZs2yaZBUEQBDFiKqfm+CrbMVROzZmwY6vsFcxJ96ZxaSjrmvQgZ5k6TD21TNLUOhuCIIaEciETBEEQYyFoaFE+46CRuPLSw8H8/xhgaBo4WFR+5FSDslgQRBpBuZAJgiCIsZKsis5uxT5Ib7LOZaU9nXNgHAqVJAIykAkizWhtbSXDmCAIghg1xxZmOUbpxKOCAjlkRgvdyWxhi/Gp5nekkIFMEARBEARxFFAwDlXzRoIqPW1qXKZ8c7JocA6I1KoP4kIGMkEQBEEQBDGuMAYYOoPBpZGsjGY7RVXIFKRHEARBEARBjBumzjGnIBMG59Cd3MeMAcVZgaTpooeDDGSCIAiCIAhiXCnKDkQVK2EMmD0lM2UN5KRILObMmYOcnBxomgZd17Fhw4ZknAZBEARBEAQxQTAm9cjqJyBTv6VYET0ASdQgP/HEEygqKkrW4QmCIAiCIIgJRBnHgOdJ5oylXJEQgIL0CIIgCIIgiAnANY7heZBry3Kh8dTTWSTFQGaM4dRTTwVjDFdeeSVWrVo1aJubb74ZN998MwBgz5492L1790Sf5iD27duX7FNIGagtJNQOEmoHCbWDB7WFhNpBQu3gcTS3RceBTrfUdFf7wZSw7eKRFAP5mWeewfTp0/H+++9jxYoVWLRoEU488cSobVatWuUazk1NTSgrK0vGqQ4iVc4jFaC2kFA7SKgdJNQOHtQWEmoHCbWDx9HaFu+EDzlV9aTuOJXbISmij+nTpwMASkpKcM4552DdunXJOA2CIAiCIAhigpDBedFa5FRlwg3k7u5udHZ2ur8/+uijqKqqmujTIAiCIAiCICYQBhmUl2lqmFWQkezTGZIJl1js3bsX55xzDgAgEongoosuwumnnz7Rp0EQBEEQBEFMIIwBM/IzUJJt4r33OpN9OkMy4Qby3LlzsWXLlok+LEEQBEEQBJFEjpmShfwMAyzV9RWgNG8EQRAEQRDEBFCYZSb7FEZM6mVmJgiCIAiCIIgkQgYyQRAEQRAEQfggA5kgCIIgCIIgfJCBTBAEQRAEQRA+yEAmCIIgCIIgCB9kIBMEQRAEQRCEDzKQCYIgCIIgCMIHGcgEQRAEQRAE4YMMZIIgCIIgCILwQQYyQRAEQRAEQfggA5kgCIIgCIIgfJCBTBAEQRAEQRA+yEAmCIIgCIIgCB9kIBMEQRAEQRCEDzKQCYIgCIIgCMIHE0KIZJ/EcBQVFWHOnDnJPg3s27cPxcXFyT6NlIDaQkLtIKF2kFA7eFBbSKgdJNQOHtQWklRph507d2L//v2DXk8LAzlVaGpqwoYNG5J9GikBtYWE2kFC7SChdvCgtpBQO0ioHTyoLSSp3g4ksSAIgiAIgiAIH2QgEwRBEARBEIQPMpBHwapVq5J9CikDtYWE2kFC7SChdvCgtpBQO0ioHTyoLSSp3g6kQSYIgiAIgiAIH+RBJgiCIAiCIAgfZCATBEEQBEEQhI+j2kB+5513sHz5clRUVKCyshK/+MUvAAAHDx7EihUrMH/+fKxYsQKHDh0CAAgh8IUvfAHz5s1DTU0NNm3a5O7r6quvRmVlJcrLy/GFL3wB6aZcSWRb/Md//AeqqqpQVVWFu+66KynXM1ZG2w6vvfYaWltbEQgE8JOf/CRqX3//+9+xcOFCzJs3D9dff/2EX8uRkMh2+NSnPoWSkhJUVVVN+HUcKYlqh3j7SScS1RZ9fX1oaWlBbW0tKisr8e1vfzsp1zNWEvlsAIBlWaivr8dHPvKRCb2OIyWR7TBnzhxUV1ejrq4OTU1NE34tR0oi26K9vR0rV67EokWLUF5ejra2tgm/nrGSqHbYvn076urq3H+5ubm44YYbJv6CxFHM7t27xcaNG4UQQnR0dIj58+eLbdu2ia9+9ati9erVQgghVq9eLa6++mohhBCPPPKIOP3004Vt26KtrU20tLQIIYR49tlnxdKlS0UkEhGRSEQsWbJEPPHEE0m5prGSqLb461//Kj7wgQ+IcDgsurq6RFNTkzh8+HByLmoMjLYd9u7dK9atWye+8Y1viB//+MfufiKRiJg7d6546623RH9/v6ipqRHbtm2b+AsaI4lqByGEePLJJ8XGjRtFZWXlxF5EAkhUO8TbTzqRqLawbVt0dnYKIYQIhUKipaVFtLW1TfDVjJ1EPhtCCPHTn/5UXHjhheLDH/7wxF1EAkhkO8yePVvs27dvYi8ggSSyLS699FJxyy23CCGE6O/vF4cOHZq4CzlCEv1sCCHH0tLSUrFz586JuQgfR7UHedq0aWhoaAAA5OTkoLy8HO+++y4efPBBXHbZZQCAyy67DA888AAA4MEHH8Sll14KxhiWLFmC9vZ2vPfee2CMoa+vD6FQCP39/QiHwygtLU3WZY2JRLXFK6+8ghNPPBG6riMrKws1NTX4+9//nqzLGjWjbYeSkhI0NzfDMIyo/axbtw7z5s3D3LlzYZomLrjgAjz44IMTei1HQqLaAQBOPPFETJkyZcLOPZEkqh3i7SedSFRbMMaQnZ0NAAiHwwiHw2CMTdyFHCGJfDZ27dqFRx55BFdcccWEnX+iSGQ7pDuJaovDhw/jqaeewuWXXw4AME0T+fn5E3YdR8p43BNr1qzBsccei9mzZ4/7+Q/kqDaQ/ezcuRMvvvgiFi9ejL1792LatGkAgKlTp2Lv3r0AgHfffRczZ850PzNjxgy8++67aG1txfLlyzFt2jRMmzYNp512GsrLy5NyHYngSNqitrYWf//739HT04P9+/fjiSeewDvvvJOU6zhSRtIO8YjXPunIkbTDZCJR7eDfT7pypG1hWRbq6upQUlKCFStWpG1bHGk7XHXVVfjRj34EztN7KD7SdmCM4dRTT0VjYyNuvvnm8T7dceVI2mLHjh0oLi7GJz/5SdTX1+OKK65Ad3f3RJx2wklUf3nnnXfiwgsvHK/THJL0fioTRFdXF84991zccMMNyM3NjXqPMTasd+PNN9/Eq6++il27duHdd9/F448/jqeffno8T3ncONK2OPXUU/GhD30IS5cuxYUXXojW1lZomjaepzwuHGk7TBaoHSSJaoeh9pMuJKItNE3D5s2bsWvXLqxbtw4vv/zyeJ3uuHGk7fDXv/4VJSUlaGxsHM/THHcScT8888wz2LRpE/72t7/hxhtvxFNPPTVepzuuHGlbRCIRbNq0CZ/5zGfw4osvIisrK+3iV4DE9ZehUAgPPfQQzjvvvPE4zWE56g3kcDiMc889FxdffDE++tGPAgBKS0vx3nvvAQDee+89lJSUAACmT58e5Q3dtWsXpk+fjvvvvx9LlixBdnY2srOz8cEPfjCthPWKRLQFAPznf/4nNm/ejMceewxCCCxYsGCCr+TIGE07xGOo9kkXEtEOk4FEtUOs/aQbib4n8vPzsXz58rSSYQGJaYdnn30WDz30EObMmYMLLrgAjz/+OC655JJxP/dEkqj7QfWNJSUlOOecc7Bu3brxO+lxIhFtMWPGDMyYMcNdUVm5cmVUAHw6kMg+4m9/+xsaGhqSJlk9qg1kIQQuv/xylJeX48tf/rL7+plnnonbb78dAHD77bfjrLPOcl//wx/+ACEEnn/+eeTl5WHatGmYNWsWnnzySUQiEYTDYTz55JNpJ7FIVFtYloUDBw4AALZu3YqtW7fi1FNPnfgLGiOjbYd4NDc344033sCOHTsQCoVw55134swzzxzXc08kiWqHdCdR7RBvP+lEotpi3759aG9vBwD09vbisccew6JFi8btvBNNotph9erV2LVrF3bu3Ik777wTJ598Mv74xz+O67knkkS1Q3d3Nzo7O93fH3300bTLeJOotpg6dSpmzpyJ7du3A5D624qKivE78QST6HHjjjvuSJq8AsDRncXi6aefFgBEdXW1qK2tFbW1teKRRx4R+/fvFyeffLKYN2+eOOWUU8SBAweEEDL6+rOf/ayYO3euqKqqEuvXrxdCyCjLVatWiUWLFony8nLxpS99KZmXNSYS1Ra9vb2ivLxclJeXi8WLF4sXX3wxiVc1ekbbDu+9956YPn26yMnJEXl5eWL69Olu1o5HHnlEzJ8/X8ydO1d873vfS+ZljZpEtsMFF1wgpk6dKnRdF9OnTxe33nprMi9tVCSqHeLtJ51IVFts2bJF1NXVierqalFZWSmuu+66JF/Z6Ejks6F44okn0i6LRaLa4a233hI1NTWipqZGVFRUpF1fKURi74kXX3xRNDY2iurqanHWWWeJgwcPJvPSRkUi26Grq0tMmTJFtLe3J+16qNQ0QRAEQRAEQfg4qiUWBEEQBEEQBDEQMpAJgiAIgiAIwgcZyARBEARBEAThgwxkgiAIgiAIgvBBBjJBEARBEARB+CADmSAIYpJw7bXX4ic/+UmyT4MgCCLtIQOZIAiCIAiCIHyQgUwQBJHGfP/738eCBQtw/PHHuxW4fvnLX6KiogI1NTW44IILknyGBEEQ6Yee7BMgCIIgxsbGjRtx5513YvPmzYhEImhoaEBjYyOuv/567NixA4FAwC3rTBAEQYwc8iATBEGkKU8//TTOOeccZGZmIjc3F2eeeSYAoKamBhdffDH++Mc/QtfJD0IQBDFayEAmCIKYZDzyyCP43Oc+h02bNqG5uRmRSCTZp0QQBJFWkIFMEASRppx44ol44IEH0Nvbi87OTjz88MOwbRvvvPMOli9fjh/+8Ic4fPgwurq6kn2qBEEQaQWtvREEQaQpDQ0NOP/881FbW4uSkhI0NzeDMYZLLrkEhw8fhhACX/jCF5Cfn5/sUyUIgkgrmBBCJPskCIIgCIIgCCJVIIkFQRAEQRAEQfggA5kgCIIgCIIgfJCBTBAEQRAEQRA+yEAmCIIgCIIgCB9kIBMEQRAEQRCEDzKQCYIgCIIgCMIHGcgEQRAEQRAE4eP/AxIm2f5UDRE8AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(changepoint_prior_scale=0.5)\\n\",\n \"forecast = m.fit(df).predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Decreasing it will make the trend *less* flexible:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"m <- prophet(df, changepoint.prior.scale = 0.001)\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(changepoint_prior_scale=0.001)\\n\",\n \"forecast = m.fit(df).predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When visualizing the forecast, this parameter can be adjusted as needed if the trend seems to be over- or under-fit. In the fully-automated setting, see the documentation on cross validation for recommendations on how this parameter can be tuned.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Specifying the locations of the changepoints\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you wish, rather than using automatic changepoint detection you can manually specify the locations of potential changepoints with the `changepoints` argument. Slope changes will then be allowed only at these points, with the same sparse regularization as before. One could, for instance, create a grid of points as is done automatically, but then augment that grid with some specific dates that are known to be likely to have changes. As another example, the changepoints could be entirely limited to a small set of dates, as is done here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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EAgLS0NCLbIBBLqADA9+/fqYdlZGTINCyF9fLly1atWnl4eFhaWp44cYLpcBBinowSDj8/P+pSk1KbSGmFhoZu27bt2bNnTAeCQF9fn7pJlp/q0aMHubNXr14mJiayjEpxHTx4kGxfuHCBwUgQkhO0Jxw8Hs/b2zsiIqLETaTMtmzZMnz4cB8fnz59+gQHBzMdjrLjcDgXL160tbUFgEWLFpF5Rp06dR48eDBz5syVK1fu3buXyRAVCk7nQUgKSyKR0H0OsVi8bNmypUuXqqurF98EgO/fvxNLETZo0IDuGQpqamoAwOfzaT0L3dTU1BT9EnR0dPr06XPz5k1i09nZmfqNUCGoqqoKBAKmo6gQdXV1sVis6Fchn3+Ijx8/kjN6Tp482bdv398dyWKxtLW1s7OzZRUaLarAm5KGhoZQKCwoKGA6kPJjs9lsNlsoFNJ6FnIU+d+SxaBRNptNXWpSahMA4uPjz507BwDu7u516tShNRiiMLOiL33J4XAU/RLg16+ANWrUIBNQRUH8bzMdRYVwOJwqcBXyeQmtW7fOzMx8/fp1Gb9HKdzrX0oVeFMiLkHRi/ez2Wy5XRpCLsLq3Llz586dASA7OzsrK4vWc2lqarJYLKnSlgpHS0tL0S/B0NBwzpw5RAlLGxubKVOm0P2nr3Sampq5ublMR1Eh2traIpEoLy+P2JRIJFevXn38+PE///zj5OQkh5/ixbFYLHV1dfIS5A0x06f01zaLxapevbrCvf6lVIE3JV1dXT6fr9D9NBwOh8vl0n0JhoaG5XugXCQcjMjIyHjz5k2jRo1MTU2ZjkVJmZubp6amZmVlVatWjelYEADAoUOH5s+fT7QTEhK8vLyYjQchVJUowDcYOrx+/bpp06aDBw82Nze/evUq0+EoNcXNNiQSyZ07d86fP//z50+mY6kcYWFhZPvBgwcMRqJwMjMzZTAeDiGFJqOEw8fHh3qHUmpT9shCRgBw9OhRBiNBimvy5MlDhw6dMGFCw4YN09PTmQ6nEhgbG5NtPT09BiNRIN+/f3d2dm7SpImxsfGrV69+dxifz583b56RkdGECROkSpsgpCSUtIcDoQrKyso6cuQIuXnt2jUGg6ksCxcuJFbOs7OzW7JkCYOR5OTkvHjxQiG6jjZv3hweHk60//e///3usF27dh06dAgAzp8/v3z5ctnEhpBcUdKEY+LEiWR7xIgRDEaCFJRUeW8NDQ2mIqlExsbGR44cSU1NDQoKql27NlNhREdH169f397evmHDho8ePWIqjDKill5NSUmZNWvWunXrMjMzpQ6LiYkh21gHDCknJU04WrVq9f79+9OnT798+bJ///5Mh4MUj4aGxpo1a4h23759HR0dmY2nKtm+fTvZ3rVrF4ORlIWzszPZfvbs2dGjR/38/IqPt7WysiLbY8aMkVFwCMkT5Z2lYmBgQK3ZjNDfmjFjxqBBg7Kysho0aKDoFQhK9+3bt40bN2ZmZjo7O8sgtaKWLRKJRHSfroL69u1748aNBw8exMbGHjt2jNh58+bN3NxcTU1N8jB3d3eRSBQREdG8efOpU6eW/pwFBQXfv383NTXFdfJQVSKLSqNll52dTfcEYqzDIScMDQ0zMjLEYjHTgZRf1avD8TsuLi7kum5Xr17t2LEjrVFFREQMGTKEaAcGBvbr16+Ug+WnDseVK1eoXRepqallfCBRhyMtLY3Y/PjxY5cuXYh2SEhI27ZtKzdOmlSBNyWsw1FG5a7DoaS3VBBCZZSTk0NdRVYGgyqsra2fP39+8ODByMjI0rMNudK/f//p06cDgK2tbUUGEW/evJlsb9mypRIiQ0g+KO8tFYRQWWhra1M3zc3NZXDSOnXq0L3KQaVjsVje3t7e3t4VfB5qt5ks14jh8Xh8Pt/AwEBmZ0TKBhMOhNBvff78edeuXf369cvOzlZRUbGzs8ORT3TIy8vz8/N78+ZNly5dhg0bRlYjdHJykk0Au3btIibrurm5bd68uRxV7UUi0ebNm588eWJmZjZ//nx9fX0awkSKDRMOhFDJ8vPzO3XqRG6+evXKxMSEwXgUQn5+PpvN/tvBnosWLdq6dSsAXL58ee3atREREZGRkW3btpVNf1JmZiZZGiQoKMjJycnGxuZvn2Tv3r3kvK2cnBzqVCOECDiGAyFUso8fP1I3nz59ylQkisLHx6dOnTq1atX627EXRLZBuHv3bvPmzT08PGSTbQBAdnY2dbN4EZGyePLkCdk+ceJERWNCVZGyJxyRkZFDhw41MjKaN2+e/E/AQ0iWpEZRNGnShKlIFEJ0dPS2bduItq+v77dv38r+2JEjR5JtYoFZWapTpw5RYZZQju4NALCwsCDbLi4ulRAWqnKU/ZYKOQb+0KFD7dq1Gz58OLPxICQ/9PT0goKC9u/fL5FIBg0ahAlH6aTW00lLSyv7uNcNGzbk5eWdOXNm1KhRM2fOpCG60rBYrAMHDly6dInH4w0YMKB8wy8mTZqUnJwcHR1tamrKbF18JLeUug6HSCSi3pOeNWvW4sWLaT17ZakCU96xDoc8KGMdDrplZmbOnz//woULAwYMWLt2bY0aNcr+WPmpw8Hj8czMzIh2jx49jh07VsaRHFJ1OBRUFXhTwjocZYR1OMqDw+EMGjSI3LSzs2MwmCopOjr60qVLKSkpTAeCfovP5y9cuNDIyGjy5MlJSUmMxLBhwwZieZHLly+vXr2akRgqTktL6/37976+vhs2bDh48CAWCUVIirLfUtm8eXOrVq2+f//u6OjYoUMHpsOpUvz9/ZctW0a0iYrOzMaDSrR169YDBw4AwJkzZ8Ri8Z49e2Qfw9evX8l2Ob7o5+bm7tu3LzExccCAAe3atavU0P6OgYHBhAkTyv3whISEgICAxKy8uVM869evX4mBISQPlD3h0NLSIooDokpHZhsAcPToUV9fXwaDQb/z9u1bsn3u3DlGEg5ra+vg4GCiXY6MYfLkyWfPngWAHTt2hIWFyWxyR+XKy8sjq5ifOrT3w4cPWMoCVTHKnnAg2VBXV2c6BFQyKyurM2fOEG2mVjEdP368trb2o0eP2rVrR52vURYFBQVEtkG4efOmgiYcb968oW4+e/asV69eTAWDEB0w4UB0OXjw4NixYwGgW7dunp6eTIeDSjZ27Nj8/Pzw8PAWLVoUX1RdNthstru7u7u7ezkeq6KiQt1UuILopNq1a1M369aty1QkCNFEqQeNIloNGDDg/fv3t2/fPnnypKmpaekH//z5c/r06UZGRl5eXuWrO4TKh8VijR07NjAwcMGCBdTl1BUIuVLaiBEjmnTrU/rBcsvExISszunq6pqQkCBXUwgRqjhMOBCNDAwMWrVqVZbh+uvXrz9+/DgAnDhxQnHnKaDK5ePjY2RkZGRkdOPGjVIOs7a2Tk1NTU1N3bx5M4ejwL22w4YNO307EgBOnjzp4uIi+4IcCNEKEw4kF758+UK2v3//zmAkSE7cvn2bLNw5cuRIWa6bWg6pqak+Pj4zZsx4+PBhRZ7n/q1gsh0UFCRVdBwhhYYJB5IL1DnJrVu3ZjASZfYyMYfpEIpIlQaXquMpb6ZOnbpt27agoCBHR8d379797rDc3NyrV6/ev3//d7dL1DV+uauFxTxQVaLA3Y+oKvHy8lJXV3/8+LGFhcXEiROZDgcxr3v37mTb1tb2j8OAGJSRkXH79m1yk1h9rfhhWVlZjRo1Itpubm7UBdtInax7GNYwSUtOAoDp06erqanREzJCDMCEA8kFLpc7efLkyZMnMx2IUsvO+pHCyTU2NmY6EACAevXqhYeHnzx5Uk9Pb/To0UyHUxo9PT3qZuPGjUs8jDoSJSgoaPny5QYGBlLHXD51jMg2ACAmJqZSw0SIYXhLBSEEALBy5cpBVv+0bNnS3t6eXOPm8+fP48ePd3d337t3r+xDatGixcqVK2fNmiXnJbDYbLa/vz+xwESbNm26dOlS4mFS3RVcbgnf95L+LbqRRBZDQ7J06dKl4cOHe3p6lnJrDJUPJhwIIUhPT9+xYwfRfvHixYYNG4j2woULL1y4EBwcvGTJEvn//GNwDMrFixeJouwvX778XXLWr1+/3r17E+0FCxbo6uoWP6azTdGKTuPGjaMhUlSa6OjocePGhYaGXrx40draWiQSMR1RlYK3VFCliYmJyc/PNzc3Z7MxkVUwWVlZ1E2i6qVAIKAOTYiKirK3t5d1ZH/vZWJOG1NtGZ+UerskNja2xGNUVVUPHz78/v17XV3dWrVqlXhM5+49fXcejLx3p3PbliNGjKAlVpmIjIz89OlTly5d6tWrx3Qsf+HFixfUze/fvytuKTk5hAkHqhwLFy4k1gDr06fPwYMHpeo/liIzM/PkyZNisdjV1bV69ep0xlghqampmpqaWlpaTAdS+Z4/fz5v3jzqHkdHRwBQVVXt0KFDZGQksTMnR47msMgbDw+Pw4cPE+1SSpKz2ew/rmLYycqmk5WN7HOmSrRjx46VK1cS7Zs3bzK7ot5f+eeff6ibNWvWZCqSKgm/iaJKkJCQQGQbAHDjxo07d+6U8YF8Pn/cuHHe3t7Lly93cnIqx0qhNImJiZk1a9b06dNfv34tFAonTJjQokULMzOzgwcPMh1aCbKzsydOnGhkZDRixAhqRZPSpaWlrV692tHRsXfv3tHR0eT+0aNHDx06lGhT33A/fPhQiTFXMb6+vsuXL3dzcwsICHBwcHj69Gl4eHhBQcFfPQlxS0hYUHD/VvC1a9fkvPRIKchsAwCCgoIYjORvmZub79+/387OztHRMTw8nMPhMB1RlYI9HKgSkGMMCWW/8RkdHX337l2iHRMT4+LicvnyZcZ7EbKzs62srIj28ePHt23bdv78eWJzwYIFw4cP19DQYC66Emzbtu3cuXMAcPPmTRUVlYCAgD8+RCQSTZw4MSIioviPevToQbap8y9YLFYlxFpFqaurT5s2jWjPmDGD+JS1sbEJDAz8q1eLUChcNmPCk3t3AKBnz55Hjhwpe2eh/LC1tb116xbR1tZWsK6agQMHDhw4kOkoqibs4UCVoE6dOsOGDSPatra2NjY2ZXygkZERdfPVq1fUQQNMoa7YDsWW8czNzS3xUQyOWIyPjyfbV65cKctDvnz5UmK2AQCjR48ms8ApU6aQ+3k8ntxW3yooKIiNjeXn5zEdCHz//p38Tn/79u2wsLC/evj71y+JbAMAwsLCoqKiKjc82SBL6VhZWU2aNInZYJD8wIQDVcj169d9fHyuXLmybdu2a9eunTlz5ujRo2WvVmRmZjZr1izqHnlYP6x+/frUzSFDhpBtJyenEgeaMFujkzqWc+rUqRKJJDAwcPz48Zs3b87Pzy/xITVq1CjlCWfPnk3kVYaGhuSRDx8+XLNmTeVFXWm+fv3az2mIpaVl/44tXjx5wGwwUp3wf9snr67+S3eIvPWllZGtrW1cXNyjR49OnTplYmLCdDhIXuAtFcUjEAgePnzI4XA6dOjAbC93YGDg3LlzibaPj0/5vsosXrxYLBYTVRcHDRpErS/JFGNj44CAAGIMoKura1RUFDGbcdCgQfLZ1+rs7Kyurh4REdGiRQt3d/ejR48Sf5cLFy4kJyevW7eu+EN0dHQOHDhATLycN2+enp7ekiVLyJ/GxcXNmTPH39//9OnTycnJ5H75XOZm69atUU8KVzA5E7j/n44ll8GQjcePH9etW/fr168AYG9v37Nnz796eMNmLRyGul05HQQAo0ePbtWqFS1R0k9LS6thw4ZMR4Hki/ImHHl5eYr47SE/P9/Dw4O47+Dq6krWTmBESEgI2Y6IiCh33+nSpUvHjRuXn58v1bXAIAcHBwcHBwAIDAycP38+sbN27drU3g65QgYMANR7JQcOHCgx4QAAR0fHvLw8kUjE4/GKd3icOXPG399fathj586dKzXqinrx4sWWLVuePn1K7mF2SXeiigO5eejQoT+OwLh27dqoUaMAYMmSJTaunumpydo6ur0cBtn2Ha+Xu0gAACAASURBVDDeZQC94SIkW8p4S+XBgwdGRkZ169b18PBQuMUY79y5Q45yOHnyJPFFiinUwswlVjEqu/Dw8JkzZ86aNYv6fVoekKMZAICciSPnmjZtSrYHDRr0x+NLvO1CDMTp168fdWdeHvODJEh8Pt/e3v7atWspKSnkTsvutgyGJFXF4Y8vZh6PR2QbAODr6/sm6tm6xXNOHNwdeuX84qlj//33X7oCRYgJyphwkF/4rl+/vn//fmaD+VsVvENcuRYtWkSUHOjVq9fixYvL/Tw7duzw8vJ68ODB0aNHyfdfOdGsWTOyPXjwYAYjKbtp06Z5eHgAgJOTE3WC4u98zBJb9epL3WNlZeXt7Q3FXmBPnjyp1EgrRGo5WQDo1rP3Nt9lvczr/+9//2MkpLZt21I3/7jgnFRG8vbl8xeP75ObFVzpHiF5o4wJR2hoKNmmfjdSCD169OjTpw/R9vT0/F29QtkwNTU9fvx4UlLS8ePH69atW+7nOX78ONl+9uyZVNVLWcrPz9+5c+ecOXOuXbtG7Jk2bRqRAw0ePLgsH96ylJGRMWbMGCMjIzc3N+pEFXV1dT8/v9TU1H379pVlkdWzRw/eDb0OAA2btnj48GFqauq5c+dat24NxQYtduvWrbIvovyKv+Tuhd0kGhs2bCDrlclSmzZt9uzZY29vP2DAgLJUcahXrx510FLnHr9UDGvQoAEtUSLEEGVMOLy8vMi2k5MTg5GUg4qKSmBg4N27d+/du7d27VqmwwGojF4WjtovM1MqeHemIhYvXrxixYrDhw+PGjWKKFatrq6+cePG1NTUPXv2yNt4ez8/P2ISbEhIiI+PT/meJCcnx39D4WNj37/9+PEj9afPnj2jbsrDkF6SqqrqnTt3yC6oVu06UH9KzcBkafDgwceOHTt48GCLFi3+eDCHw9m1a9fs2bOnT58eERFRu159n+2Ffa4TZi+2sLCgOdgi7969GzFihJGRkbe3d8UXEGF20haSW8o4aHTt2rXm5uaxsbF2dnbm5uZMh/PX2Gy2hYUFj8djOpBKM3b63HnjC5eN8Pf3Z3DqzZEjR8h2SEgI2ZkknxISEsh2+V4Pd+/eXb16NXXP668p1JsrtWvXpv60WrVq5TgLfVq2bHnv3r2wqI+pKckNmzZbNHkMOV2F6KGRT9TVXoyNjRcvXly9evW0tLSXiTmdrHrMWrbm/Zto/erVJRKJzP4XVqxYQZTq2r17d/Pmzd3c3GRzXqRUlDHhYLPZCtexUbX906nr+/fvX7161axZs9LrQ9Ctd+/eN28WdsvLf4d27969r169SrSl1oAoC5FIVHwxtk7Wvwy6bN68uaur68mTJ4nNzZs3V+7EqJ8/f/r5+X369MnKymrChAnlW/avZp16BobGm1ctJrMNAOjWrdu9e/eo42fpVpFF4559+/HjRw4A7Fy38uKJIwBw7ewJPbbA09OzMkP8PbIwKAC8f/9eNidFykYZb6ncuXPHxcXFyMhoy5YtTMdSCapG76X3Wj9nZ+dWrVoxe5/I29ub+Ax2c3MbP358KUe+ePFi+PDh7u7uV69eZepPMHz48ICAAE9Pz+3bt8+ZM+dvH56ZmUnd7NHb4VzE82p6+lKHUddsIzOPyuLt7b1r167g4GBvb29y8bNyeHT39rVz0rEdOnSoQsHRifqaIduH/bcQ2Qbhb6uUVgT1Oxi1tj1ClUi+ejhYLBbdXYgikYic6efr69u+fXty1QzFQv6iZPBLowkZ+ecPMacO7SV2btq0ycXFpVGjRoyE1KxZs7KsNZWfn0/2DQQHBwdcDK3boBEwsdrIgAEDBgwoZ7UGIyOjXr16kWOoR06aXk3fAH69igMHDpCdKADQu3fvyr1G6m/70aNHY8aM+eNDhELh27dvDQwMiNs9xKsoK6OEmutcLleWfxEWixWdxAMAqX6O4j0fRFTUf2EAEAmFh/23Ug/Lzs6WWfx+fn4NGzZ8/fp1hw4drKyshELh9evXeTxev379yn4frSq9KSko1n+YDqRk8pVwcDgcdXV1Wk8htRhEXFycnZ0drWekA5fLJX9RqqoCun9ppZBIJAcOHLh7926rVq2mTZv2V7XU1NTUiDJNebm/9BDweDwGr6gspAYkfv4Y06hZCwCQ87CLCwgI2Lx5c0x8Qr9Brk1aFBa1pF4FtaddS0vLzs6ucq9x6NChp0+fJtoWFhZ/fHIej+foPIxYbWT58uXz5s1jsVgcjqirrf3G5QukDp46daos/yKqqoWLu546ders2bMcDmfSpEn29vbF/0OJI4mdLxJ+qqqqAoCKqqrUE5blF1JZ1NXVuVzuzZs3b968ef/+fXV19evXrwOAl5dXQkJCWXIO+XlTKjcOh6OioiK3n9ZlwWaz2Wy23F6CfCUcQqGQz+fTegpDQ8OePXuSfZWdOnWSq1pGZcRms8mw+Xw+g5dAVjc/e/ZsQkJC2W+IaGlp5efnE8vMNmreqm0Hy6jIRwDQo0ePZs2ayeEfRSAQXLp0KTc3d8CAAVIDTRo2bUG8buUw7NIZGhquWrXq0edUACAuISM15cLeUwKBYMSIEVKjWHg83ty5c3V1dctSTKyMVqxYwWaz09PTDWo3GD169B9/gSdOnCDXNlu5cqWnp6eGhoZQKNSppnc8+MGt65d0q+n906lrWkrSYJtOGhoasvyLEL/AuNgP5Ip3wcHBsbGxfD5bKgzqq4XP57NYLHV1dbFY7DF55mH/LdDCFkybQpi/eY9+Mos/Ly9v/fr1RFtqAcWrV6+W5S8uP29K5aaioiIQCOj+DKIVh8Phcrl0X0K5F/SWr4RDNvbt2+fn55eZmenu7t64cWOmw1Fs1Cra+/fvL98IjMcRt4lso0WLFjt37lQt9lWPcWKxeMyYMcHBwQAwZ86cT58+3b59e/v27Twez8rBpXY9eanIXhFCofBb3Ocda5e/jHwEANu3b4+JiZkxYwY5ipYQHBxciQmHsbExMQr1ZWIOUQU8Ly/v7NmzeXl5Tk5OUosJA4BUqXXqBE4jE1PXMYWLlJrUqs3UwgVfP8dSN799+wYG9ah7qKM3Xr9+7X/giLau7sgJ04DN8Zg8Q7td75DoOCFHZcm0m2YNm8go6FJLwv/tNHXiAisyhBZVVcqYcJiYmKxYsYLpKKoI6iyAchTiJN6bVs0p/Eb49u3bc+fOyeF61nFxcUS2QQgPD3d0dDxy5Ehubq48DNqt+Jv7v/FfRg+QLgr+6NGj/v37JyQkjBw5kry3Qut6N2KxeOzYscSwksWLF79//55aPn/nzp3U/9zx48eX+5sWfZqb/zJdqFGjRjEZBSUeGR8fT9SPB4D3r6N9tu9nsVjRuRq5BvUbGGiYNSx/Jb1y0NTU9PLy2r59OwBYWVlZWloS1VpdXFzIIEuXnZ29atWqDx8+1G5mPmzsJA5HGT9cUOnwNVEVMPhlwsvLKykp6ciRI05OTuVL4yRiMXVTlmVGr1+/fuTIETU1NS8vr3bt2pVypNSXvN/d0laIb3U8Hi8lJaVu3bocDic0NPRe9Ie2HTqfOOBf/Mh69eoBgKqq6tq1a729vYODg52dnadNm0ZTYC8TcxK+xlMLAYeHh5O9KQ8ePKC+wC5fvmxpaUlTJBVhVMNkR9CFi8cD1dQ1ho4aT2QbJb4wqMv0PIq4lZaSrKdv8PnTxzSJlig1K9uimo6uTKueLFu2bMiQIampqZ07d1ZTU5swYUJ+fr6JiUlMTMyXL18sLS319aVnMFEtXbp03759AAAhIVyuCtnbhBBJ2RMOhfiEKAXjX681NDQ2bdq0adOmcj8Di822tu8bEXyd2JRZQbPY2FhiwREAuHLlytevX0vphDc0NPT29iaqeY4YMcLa2lo2QVaiGzduBAQEZGRkREVFAYC1tXX9+vUDAwOJn5o1ku69NzExad68OdFu0KDBsWPHaAosKSnp9OnTGQUcO4dBZwL3UX9UvXp1si1VApWsa/4yMYe8B5fKK3ibmpuYLRjW2oiRf+2IkOtEd53L6PETZv9hdSGp0uyigoJVc6d+1+sCpo2TEhMnOPdt28Fy/UrvOnXq0Bjxr1q2bEm29fT0AGDv3r1Lliwh9jx69KiUFecLsw0AAHgT9ex3hyFlpuwJB5IHWtpF/Qf+/v6rVq2SwUlfvXpF3YyLiyM/X0s0ffr0MWPGFBQUUDv5FUVqaurIkSOpeyIiIqjjb+I+fZB6SFJSUnx8PN3VzzIzM8mSoDcvnP747jX5o+HDh1OnrHfu3Jlst7PsWuIaMbueJH7Lyq+vL+vRG69fv96yZUtSxs/HdwuHW546tK+dpVX7LqVNube2tp49ezaRrHv/b/uIftYAAIO7AUcVOJzUpMSQy+dZfB59qV5ZkNkGABw5cqSUXszRo0eThU+atJDfMq+IQZhwINlJSUk5dOhQQUHBiBEjDA0Nyf06lBsWPXv2lE0wUlXtqUMTxGLx169f9fX1pW6d6OjoSD0J4z1MZfTly5dyPKpmzZqVHomUBw8ekG1qtgEAcXFxIpHoTWo+0VHRpEmTixcv7jxwWFtH12XMhBIn/gnFYr5IIv79+Ec6CASCEkc5pCR9L/H4h+Fh3l6eALBy5cpFixb1G+3FYrEun/yv3hebA1xVYBWWZAwODpZlgfPSEaN6f8fX11csFicmJurVNMP7KahEylhpFDFCIBBMmjTpf//735YtW9q3b08tiOLsMf6fTl2JNn1DBKQQtwns7e2tevUNDQ0lywbk5ua6u7t36NChUaNGR48eLfsTFggEF4ICFy5cSB2FICdK7LwhFqAHAOpsiPqNmwGAjY3NqVOnZFBKwdjY+Hc/evjw4adPn6h7unTpMn/1xikLlhkal7yKnljCEgglQrFME46vX7+WuL9951+6N4jclMfjEdkGACxfvpxcG69wuEbN5sDmAJsD7MKvgn379mU229i5cyfZLr0sm76+vp+fX1BQ0MQ5i4vXFEEIsIdDoSnK12vCrVu3qKPk7t+/361bt/evo18+fdygSdMNew4bQbaRkZEs58Ta29vb29tL3ew/efIkmTHMmjXLzc2NWOBDIpGsWrWKmMN57ty54gVqd21YdfnUMQA4cOAAkcrI6DLKQEdHJyIiYteuXek8vkOvHiwWq1u3bnXq1OkxdExGWmo1PYNA/60f373W1tEdN2N+jZq1ZDb6oUOHDp6envv37y/xp0ZGRv8KSnv4y8Qc6uexWCIpkEhEsu3hkBpj4TzKk8tV6e04xNi0hP6hpKQk6uad5+961WwKAH0HDb1148r9ZqNAJAQA07r163W31a9utGHlUjpj/zMXF5fu3bt/+/atVatWf0xAAwMDDx8+nJCY3Lh5y/GzFrYx/ev1fVDVhgkHKr+0tLQ1a9YkJSV17dp1ypQppX8VCwgIoG7Wq1cvNDR0qpsrsTl+1sI1i/96NRA6SI1aFQqFRA5048YNct2ywYMHJycnSz2QyDYIN27ckEHC8enTp8mTJ0dFRRmZmO7b7d+1a1epA27durV7924A6NOnz9ixY7dv3y6VXWloapnUUufz+T37D2xobgFCwbljAfUaNm4xwaP0/vNKtHbt2q9fv5Kzjge4uBO/yZnevv8K1P7qqcQSiUAo/nXaE+3U1NRCQkJWrtsoEgrtBw6x6tW3lIPr1av3T6euLx7fJzbNLToSDVU19fZdrO5ncoDDAoBstkZSzwWrnZqYmDA/pL1GjRplWVLx1q1bRA1AAEhPTX4UcSspKYnD4dAcHVIkSpdwJCYmSlUTUvSJKgxauHDhxYsXASAkJKR69erDhg0r5WBqkex27dq1adNmzpLl5J7op4/pi/OvODk5rVy5kmiPGzeO7HGJi4ujHhYfH08d0g8Alt1tH4UXXqOZmRndcQLAwoULiSknqUmJTk5OV5+87Vi/6LXN4/FcXQvzudu3b7dv315i1ABKesGfOxawa/0qcJgPX57BuzsgFuV8/+zr6yuDSyDs3bt3/c79WZmZPfsPrNew8dSFKzhsNovNBoDw4Gtrr59TUVGZOnXqH+fBisUgEMm6hwMA2rZtu2Lz7j8eRnRJLvDdeO7owbzc3H5DhpG9IHm5uVtXe4PXKWBxASCnACCbgUqdUVFRHz9+7Nq1azmG7zx//lxqT1jUJ3sL2a3WSzhx4oSXlxcALFiwgEyAkJxQojEcGRkZQ4cObdSoUbVq1cLDw18m5rxMzLl6Jmj8kD7t2rUjy/qisiOyDUJkZGTpB1PTEWKgRjW9ouken2LeEGs3MK527drv3r3bvn37qVOnqIVTpZbQLF5T1XPGgg5duwOAi4vLhAkT6I8UwsPDqZsJX39Z4SUhIYG6+eHDhyO7t/Uyr9/LvL7Uoq+71q8CAOCqwj8OYDsJAPbu3UtTzCX69FMyZOS4sdPn1mvYGIhF19hsAPgW99ln7tSwsLAbN24MGDDg2rVryd8Tfvck0658SssrEEskIlnnG3/H0NhkwuzFM5aubty8Fbkzl5cDAMBiA0cFAIDF4gtKvZlEg927d9vZ2U2ZMqVNmzbR0dF/+/BOnTpJ7TEyKXmoDX0yMjKIbAMA1q9f/+LFCxkHgEqnRAnHnj177ty5Q7SJkVCxMW83r1ry5eP7b9++bdy4UarPH/2Ro6Mj2S69cBYArF27ds6cOU5OTjt37iQWOB3uOcWiczfip+mpKR4eHuQYOmYZGhoOGzbMxsaGepOoefPmHTt2JDfPnz9PlGUkmTVqstb/UGpq6s6dO2WzcpVUl1KtemZEGk18k5YqCaqlpRW4azPRnjZtWkZGhvTTsdigpg2qGgDQu3dv+sIuu88fYqibo0aNcu/TbaJL/8VTxhRfy/5bliCLLwIAkYzvqfwq4WvcxROHnz64++dDKaobGXfo1gNYbCBfcmxZ34wgBxEDwF8NlyZYWVkt8N3UqHlLoxqmHbp233PqKlvmlyB1o/Pff/+VcQCodEp0S4VawvL27dtLAOI//1JK6PLly2VZHVtOyMOI0TVr1gg46sLsjE6dOg0fPrz0g7W1tRcuXEjdU93IeJnfroFdiqanenl5tW3bdvHixcTK4/LGvIvNkydPyM2FCxcG93GW/bsqyc/PT0NDIzQ0VMfAyGvxSjW1X7IcFRWV58+f79q1i8fjjR49WqrDIzk5+RtfVVNTEwDmrFjnt2Jh4YRMNsfYtCa1+gKDqhdbSwUAYmPexsLbJ/fu8Pn8a2HhWto6o6fONjIxFUkkYpEEABjs4fjw9tWUYYVZuJvn1LHTS+7Sz83JOX7Q/1vc547devQbXHjbi8vlUpMMroqsJ3rY2NiQy7aVbyUauwGD7AZU2jo75dCoUaPu3buTPX/U2i1IHihRwuHs7HzgwAGiTdzba9HWgnpAkyayWyqpaqhRo8bclesrMgJGS/uXyhYfP378+PFjdnb2kSNHfvcQBrmMHp/48fXVq1fJPWKRWDYJR0ZGxqpVq1JSUho3brxkyRJiZImqquqGDRtKST0zuPrkrR9qXcvu3bs3atTobVrhkpJ9B7uGXb0YRfTns9ntLLuVXgOtcvF4vOP7d6UkJXbr2Zvs8SLUrGNWygMXLy4s5ZmalLh+z2GJBApEYgAQiSW8ApGWCgOJYNjVopuMQft3/i7h2LFuRfClswBwL/SGmppaL4dBAPDwTii0m04eI5bIejbsuHHjiISjbcfOEycqZCENFRWVPXv2BAYG5uXlubu7U4v9EAQCwdKlSwMCAoilGGrVqsVInEqLI1fLmAkEAuryj5WrZs2agwcPbtasmZeX17Bhw5JzBNo6umYNm7yOepbH49nY2Kxdu5apFSb/lqqq6reMXyZTmOgwM/E9OUdQvlNramrGpWZJJJIuNnYZaakCgYCX/ZP40adPn+bPn1+pYZam7JeQwhMO7t1j165dxOaIiV7tLAs/IA+9SJawwFRbtdy/kNLNnz//+PHjnz9/joyM1NDQIIdPXrp0ybmv7WH/rRwul5zyAADJOYLkHAFQXhiampqtuvfX0NTs0MV6nc9ybW3t5ByBioqKRCL5kZkR/DE9jVMNdAzhZ4rX4J6tG5tV+iX8jpeXV9ChAx/eRIdeOX/7xhVNLa0GjZsRN7M0NLUKCgpeP//D8KDEhG/9Bg87G5tL9G1k5QouvfxqqZPbrF4J1UhpQvy2Y15FEWvtAoBF5252AwqXM8xIS925buXVsycS4uPMLTqumFW0PKG2brWuNnbq6uqvXjz/17RoGASLBSPaGMvy/7pRo0bdBo3UraYnFotjv6d0bf8Ph8N5+PDhtm3boqKiWrZsWfqNwuJvSsDE+5Kmpmbnzp2tra2J0uxStm/fvm3bNgCIiYlJTEyk3hQGADU1NZFIRN9nkAyw2Ww2m033JRA9o+VQ9Xs4Hj58uHPnTqFQ6OrqOmjQIHNzcxaLRU59tLbvZ23fD2epMKhRs5artu4ND77mM3cqscfBwYHZkEpEdCQYGxt//vz5/v37NWrU0DRrnZ+fT/w0NiPPWEvlH9oGyZ04caIokpcvicbPnz/HjRtHtAO2+7Xr1FVqqVIpterWGzd9HgBUr170gs/L5a2cPfldh+kgFABHtW6DJk1bmsts6pZEIjl37hy5+e1L7Ialc7/FfSbiBIBx0+cNGOrOz8/TNzSKjXk7Z1zJd+6qGxuLJYWl5ITAzsnjew7ubf7kCa1r2xbnNNzjTdRTYgDHkJHjyP07168Mv3kVAB5H3NKvbtitV597oTeIHzVo0gwAeDnZD+/ehnZe5ENkW72s0KPwsL2bCrvEhJlJw4YNIz+SX716JZ/9jn/l/fv3ZPvixYu/KwCDaFLFB43m5uY6OjrevHkzLCxswoQJHz4ULhghDwMgEFV3+37r1q2zt7f38PCQ5Ywh4pVADrQEymvjdy8SHR2dPn36/PPPLx/tIjHQOhtz1KhRZJucDiA1RC7x369xsR+kBlr+UfSzJ1FPHgKbAxwVYLG+xn9ZMXuSRFbjLkus3XJ8/y7qprFpzTr1G2rr6LbpYDl1QdFU6olzFrfvYt3Vxm7vmWtsNueXiuZsNgCEhYXRFfdv6FTTW+sfeDzk4fWn7zt260HuJ7INwvs30ZPnLu3RxwEAhowc5+g6EgCinz0hy5kTxBKJzKf3wuO7d8j23r17ySEdAHDjxg1qiRoFfQul1usbPXo0c4EoqSrewxEf/8tEwTdv3rRt25apYBDh2LFjV65c0dXV7T3cs2HTorEC48aNI7+vM0IsFl+5cuVpzBftIQ7UVTFv37799u1boyb/NG1V9NX/ZWIO2cN8dM/2J4m6b2NMoj8YLhlgUfKzV8yKFSu0tLQ+fvzYsWPH8ePHEzvr169PHSL3MDxszcKZAGDb13HR2s3EzFLig4GIWSQSPrwdyufnN3AdSK4Lwyc6aVgs4KoCALC5j8Jv/Rv/pW2tNnRcSHGhoaHD3NzTUoqSJ8vuPUUi4bWzJz9/iGnbwbJ77/7kjwa5j+5oZZOa9L1Z6zbqGppDR41nsViqqqp5/3U1FWJxAICRO/QsFsuohnRPl22/gbeuFQ7vaNmmXY2atZZu2L50Q9Esp+qGxlIJBwCIQdYZR+16ZmTbwcFBaum+0jvSXyT8pCmqckhISDh//ryenp6zszP1TtDw4cOFQmF4eHizZs2mTp3KYITKqYonHFL/MBYWtHwYlJsS1hyLjIycOXMm0T537lxIVCzxuSgPPL1mEzUu/f+3OiIiIokHJ7YdfvbsGTmbf/X2A5bdbaUe9eLx/UM7N4GzTw5L/fb9hwv60vI5ra2tTZYjI3G5XH9//7Xbdse8fikRS25fv0zsv3X90sDhHi0pY6JfJuZIJBKfudPuhd0EgLWLZsXGxhIdnAnxXwAAWOzC9TtYbADQ1Jbdy7JNmzYHL4ROdXf69iWW2GNoXCNgx6YTB/wB4PKpo3l5uX2chpLH16pbr1bdetRnyMoX7nuc+MuTstmD3Ef36dOH9ugpfuYLT75OG9++hPtqUxcu19LWTklKbNO+U59BLuT+f+O/RN4Lr2PWoFd/R5exk079+iiRzKf3uoye8P1bfOiVC1169Fq2bJmZmdm0adN27NhhY2Mze/Zsqe4ouX37SkpKIr9Y3rhxgzrFl8VieXh4eHh4MBSasqviCYeamlpERMTWrVsFAsHYsWPr1q3r4+Ozbt06AFi4ZnMvByemA1QW5HuT1KLwb6NftGjTLimnoJo6l5H3r+yfWZdOHsnOyurV34lam/zs2bNbt26VOjj06nmphEMsFgVfPAvum4CrClwVYLGzMtOhTgmj1WhiZGSkqaUTeS9car9IKKRupqcmP7gdSmQbhOkLlg52H9O8dRttYtkwsgIEmzPGa051oz+Xsq5EmtraZLYBAD8yM1KTihKIRxG3qAmHFF5O9pcfgpBPv5QVUdPUnjphuYyXPUvNFYbEZpaYcFTT05+xdLXUzg9vXk0ZXjhCYtTkmcM9p506/pZ6gIyXvQUAdQ3NhWs2L1yzGQB+AkQn8ZYvX758+XK5zS1KRL0TdPPmzcTERFNT2Q0fRqWQly+X9GnevPnu3bsPHjzYrVu3yMhIItsAgHWLZ+Vky1EfoCL6q/u4xMEdOnSg7pzh4bxi1qSdjxLuxmf95nH0WrtwZsB2vzOH909y/WWkakpOCXUeqaVRCYd2bA65ch70a4GGLnBV9Y1qyPijGgAuHD8ktceyuy11ynfM65euPS23rv5lGbCrZ46PG2T/5F5474HOXW3t4b9+psatzN3Hy2jBXirqEEsLy27qlN77mrWLJvQKCwrItkQi2ea7zLFz6xmjXXPz+dRnk/1HNXFS0d8M9Qy7VjSHNtB/S/GHyr5Ge6VjZKiH1GzYatWqyT4GVKKqn3BQSRWey0xLZSqSqoQ64vKPWrdufejQIerCZvdvBSenpcp4SXFCenr6k3t3yM0BLu5Eo6utPfXzj2DRuZvb+ClSO2PfvwUAYLOBYZs6OQAAIABJREFUowIclUYtzEHmqNW+DWuYrN9zZOWWPVxuYeflu1dR09x+25MXdGDnqnnT7t8OAfivM4DFTB2zcdPnec5cYNvXcfbytQ5D3cbPXNClRy8AsOkzwG38VADIy81dMXtyH4smvczrv4l6BgCvnkdeOnkEAIDFBu4vS82JZftyIl7/YgmIJJAvLLoRkpWZkcuTnilKKlySHgAAOlnZFE8vUlNSKjvSchKJhFevXj19+vTPnwrwJa1Xr17kTZOtW7eWbw7nX72toTKq4rdUpFCX0zSqYTJmYC8A2BRwso2D9I15OVd8fJac9HmWJYz+/ftXa9SWXB0UAFKTU84ff5RdW0UyZmLbWro0x1hE6qtP5x69pi1akcvjEZ8Ea/0PnT1y8OmDiBlLV1nbO1TT0y/+DKpEcU+iZBaLzeYyUA2lfRcrspC2veMQqdpZR/x/uTHUrlPX5MSEhK9xxOarZ0/zBAXQrDt5gEgsEUkkHPpvRiQnJy9btuzcuXMeHh6u0xaqqWsMG1tUncKkVp1V2/ZRj794PJCcSnrYf+usZb4/MgrnwQKLXZQwAQAAI7XNxRKJSCIZfe7Dtn4NjTQ5G5cvuHnxDABMnLN46KjxxY8fONzj9YunTx9EAID7+KnFe2WyGe2Czc/LPXP4gH9SvK2t7YnzV+6GFi519OnTJznvM2CxWH5+fr6+vlwul8y8kTxQrh4OY2Pj6Ojo+fPnd7fvl5qcROycPcaV2ajKQs5zbYlEEhcXV8LyHL+ho1vNwfm/ggq1WuTk8eM+f9q/dcPl08dKfVwl43K5vjsPEu0GTZp9/xovEUvI750dunZftzswNPrLAJeRJWYbAOA5c35HYgkMrgowtIrHpLlLiUXj7AYMcp8gfTeE2oUDAEM8xpErlAJAXi4P9GuDTdHHYcJPwfiLv5T8p8mKFSuIChyHDx8+fsAfAPLzcnk52b87Pi21aBrLs4d3R/S1XjXnvw6nYuOOJRIG7qqIxBKhWJJXIM4Xiq+dO0lkGwCwx29NdtYP6pFZfNG/PwU6utXW7Q48GfZo2cXnL9Ua3rp2WeoJTUsttEq3netXHdq56ezZs1OnTiWzDfh1hARJ6g0qLbfgYzoDq91SqaurY7Yhb5Qr4RCLxQUFBRMnTqzfuBl1v0DmCzPC3+QQcp5tCAsKVs6e3KFDh6ZNm54O3AcAfD6/xCOpF7Lcb+e63YGL120Bp2WgXxN0jaB63bcvpVe4plsnK5v5qzcCwOcPMTvWrdi/dcMfH0L0tUaEXJ8/YeTeTWtdxkxkcTjEN2xGvlgTi8aFRn9Z4LtJajkVAHD28CTbK7fs6WRl09Wm6H5W3foNC+8H/YcvEiemZvQyr79s2TIxnfnTmTNnyHZc7Mf9BwMcNl0a2MV8x7oVJR5vbdev+E5La9sZS1fbD3Qu/iPZT/EQS0AkBjFIRBLJldNB1B9JJVIn77/ddONF1o9MAKhuVCM9X/Q1M/9YinTfnljCluX/vtS5rp87WeJh2sVmMBUP8l78z1Ov0yoxtorLzc0NDAzcsWNHYmLin49G9FCihIPP548cOdLCwqJhw4Y/Mor+Gbrb9yNWplBQr5Jz0/MK/nwcbe6G3SCnP+zxW+Pq6lq7dm37/gPfvHlTyqNYLFbHbj1s+w3kqKiAqgY07wEdBjdtKaPaD1SP7xZ9YztzuEyVB3dv9F01Z8qDO6H3bwXP9XQjv0v/1ZhBGYh++jjl+7+t23UY4OK+/dj5rrb2ADBw2EjPGfM7WdsOGTlu+5GzDi4j4dc7QQUFQgDw9/e/cOECsUcoFG7ZssXd3X316tW83w9K+CsTJkwg201atDoRdBT0agLAhaDA1y+eFj/e3KLjjqALIyZ6NWnZmtwpAXB0HdF/qFvx42XcwxGdxLv4Pl0skQjFErEYPr57XfgDgzo1TGvXqFm0GOHUgzdOPXwbHfViiHW7xH+/AoBYwsrmC3ma/w03FhfWpWZ20Gi3niWsGOzs7GxjY/PHNEgkkTC7Zq8UiUQyduzYuXPnrly50tzcPC1NvpIh5aFEPU6XL18mxw1cOH544/5jD26HGtYwJYcKMq584zBOvk7pVrdan8Yl9/nLQF5uLnXz1q1bAPDiyYP169cXX0O8ODZXRSQGFldNz6hG38GOfzy+0tWuW1T9urt9CV+jpbyMfPRLXqJrTDaJhEPG42mePribmvS9fRdrI5OiuX8SsXjX/3zOHztEbL56HjlpXuEsFRabPWzcZGJhe01Nzf7OblcuvvvlGf+7QxEbWzhVdffu3b6+vgAQHByck5NDTvWqiGXLlhkaGr569apBm46t2rY/ePICuVbqz19vQJCatWpTx6xBTvbPD28KJ1c3adEaAEr8aJPxmrGfMvOef88BAKFYIpJIHIa6FXZyuK51rpdDTtCVSCTvP38DIzPIywKAmxfPjJ46WySW/KB+ZxAVELOUGVz2FgAmzlkMANSp1ACwZcsWDucPw4ovxaS/SeYxG7yUb9++UcvO3rlzx9m5hF4xRDclSjik+vkbNW/VtmMXpoKRUpGOUzHNRbX/qKuN3aYVhevOm9SqnZRQOBUoIye/+HVFRkaeP38etKuPHF9Y5k8klgCwJBxuZmZm4K4tnTaukVnkhKGjPJ89uhfzKqpZqzaeMxf88fivlIoRYFgPhqwit2T/Jrts2TJ/f3+iHXAxtE79wgKp1y+cJrMNwr9xX6h1XUkllLP8b6KKtbU10YiMLFo+7cCBA5WScKipqc2aNQsAXibmiMWiFm0t3v6XcLRtb1niQ3JzcgZ2+WUe0JHdW18mZCVUbwkGTaUOlvE/hUQCApGEaExxHwSJ76169eFyVe6qaYhZuWKxiFhVmMViAZsNHFVgc4DFUlFVBQAxQGbxhIOrKigoAGCs89W0dt0Vm3dfO3eS/O8GgPT09Jo1a5byKAB4npjzKT3fTF+N5gD/gtRCbkZGRkxFouSU6JZK3759yXZ/5+HaOroAIODzt/gsMTIyGjdu3Ldv35iL7i9QP8UPvUjOLRAx25NfTd/g/L2oRWs3r9kVMGluUbEHqYXEhAUFb6Ke9evXb9++ffs2r1vx31JtYmJ+AYcLLM7Xz59kGHihEwF7Yl5FAUDM65f7Nq/P/s3Xa1Kb9kVLegJHBVSKhk3IuBtfIpGQ2Qb8Wtfh49vXUgffvn7Zc3DvuZ5u1JpaUOIM0v96OB4/fkw0WrZsSf7Qza2E+xcVxGZzxs6Yr6KuMWPp6vN3X/yuzmlU5MPiO6MTMtK165CbZF+CjHv0ReLChAMAiK6au3fv3s7UFIpE/ls3/29Z0Wd2g6YtgKsC+rXUJx1wcHYDgJ9ZWT9yqQmHEERC8nmYZWFZNOmpY7ceUtlGiXNHxWIQiMRCeeri0NXV9fPzI9qenp5kJo1kTIl6OAwMDL58+XLv3r0sllbT1oWFb88eOUD0fF66dEkikRw8eFAGkfD5/EePHqWLNRo2ayH170pd+UJKiR31Z9+mVVPjyn58nBQd3Wo9+xcWe9h86NSLx/cbNGlOjBggXAgKlBoMeOvapfk+GzlF5RNYwGZ//SLrhCPrRyZRQpsQEXItL5e31v9Q8SOFBQUvnjxQU9do3a7D5kOnbl44XU3f4NmXlE+UEZcy/kNIVdLU1NIh263+sbh8qrCic3WjGla9ep84WHiZXiMHnwgp+uQuqYejMOF4/rxwDO+MGTN+/vy5Z88ed3f3ZcuWVeY1AADArc8/Tr/JYLE551U7dFXR+t1hWtrEBbKAzSYHOgCLDZyibgAVNghEAAAisViW97bEEspia5bD4MlpyM+Btv2AxYbe00Men57JzyeG9BoYmSSIeSo6Oiocrm41vf95z7uZzIUubgDAYoFEAiAqIHK+JcfDffo0aWPa+venpV2NmrUCLobevHRWt5p+f+dhZXmIGCQCkUSe8g0AAA8PDzc3N5FIpKZWctfL58+ffXx8rly54ubmNmLWMtXfHIYqQokSDgDQ1tYePHhw1PfsvLzCKVvUT7jLl6WnpdHh58+fY8aMiYiIAIAhI8dNnre0+DGlv1Emf0+4dOJwTk62w1A3sZglEInlqiJh63YdWrf7pZzowe1+Qft2FD9SVU2tQCQq2mZxEv/9FhcXZ2ZmRnOMhaKioqaOGSu1M/J+eEZqioGRMXUnn5+/fMYEotxFz/5OC9dsat2ug7q6esjzmJVPiiYgyP4P4f2/HT7zpoF2dX3nxQ7ORRlez/5O2VlZTx9G1DCt7TF5xqo5RetUpSUn/chI1zOoXhhz8STpvy/W7dq1IxpqamqrV69evfqX4tz+/v537txRVVWdPXu21Nq5fyszX5jKKxCJJek84avXr1vXr0X9/fP5+WFXLvzITO9u39/M839xqT+A9wNCdxb+mMUGDuV9TCQE4ACACGgvJUL9P/0lbzNtCvq1IekDsDnA5oCqJqioJX77ataoCQCIAQrEwGGzRQAvnz6+GZ8HDQr/X7gsKJBAy7o13nyMBY1qqZo1J40Z6fgmUkVFpdjJZadO/YaeM+aX/Xiih+NT/Nde43fbGuTv8FsnJ7cwSi/LsWLFiuvXrwNAUFBQUFBQ5+49x06f18ZUvpbfUnTKlXAU17CjTciN61CQD7+uAE6fa9euEdkGAJw9cmDU5JmlLJRVvLtSIBBsXDb/xZMHAHD1zHHunMsCkUSuEg4pb1++KDHb2HvqSnJOwYa7X4t2sdkAILMZQ/n5+XZ2diX+SFdfuoT580f3yeJaYVcvjJoys2adegCgb2gMUJRwyL5eavfe/Tta2bz8mrr5ZZ7UC8nJbZSTW+FL2qxxk5dPH5E/0tDUfPcq6vShvfkaBjpdh0rdWmWxOR2tbLq0bzt58uTfnTc8PJzs6rhx40ZKSkpFFi4RiyUCsVgkAV5ers+COZD+dYHvJrsBg14m8VrX0Fy7cCYxdPHgto0W664nqabkx1NuGP1ahEOQl8PV0hOKaZ8lQfxvkv+h1LOxVdWbdu7R9R+vgLc8EVEUjs3xHNw7JCr2yXeeQCgWSyQCEbBA8iHmDdRtAyaNiQdyWKwCkNixY98kvgejBsTVxcfHN2rUiNZrqVxikAhEYgFoQNv+t/aMWlVNc/v27X9+GKPCwsKIbIP0MDxMJBLVbxwgDwUVqwylSzheJPykvjNGa5v3XbA1/+kVy39alfL2SqO/fJv+8uULkW0QRCApEElScgrifvDl8x/jbth1qT3n777Qqaanp6f39EtyTHJ2UY1INqfvYNc/DkmrLPHx8SXuX739gNTXIKFQeO3Mceoe7n93gqQGQGTzRadep7m0+mUpBzpkZmb6+/snJia26d6nfRfr7wnf8vja2T+zqNWyqabM937z4umnmMK1wfp3bFH4g3aO8DUP1H65iyEBWL3jYNuapb2c3r37ZWJLZmamgYF0llZ2YgCBUAIAxGczAKxfMttuwKDlYV8Gc95SJ0qkp/2fvesOj6J6u2fKJpueEEhCCSX0TiD03kGK9CIqVUWkqSAoCkhVQKSJCkgR6R3pgdBDJyQQWijpIYW0TdndKff7Y2ZnZzeVQKKf/M6T58mUOzN3dm5571vOm6Q3chbODVZZ3QWepcGLJW3eUrvviKDSvBodiwOl0YBQYG2kCifGv/jhYipLUwB4kfCGzHVZleCcqfCg2LK0XhCqVK2OANPPS9EVKlSwfljxQ7GBzv5pbbuuPQsqbgYvEkEAQIG1gcAB+PfHoBqNxmHDcrEWWZHm/Q+vj7dO4FBj/rnINIPQwrfpO0O6ldhs3adPn3379p07dw7A8HET7B3ytFjnCqv5mBAYRXL6aWo2L75b2/0N1vNNwZBtQTg4fcGy/ds2+dSoVbdh46Xzfshu+L6ZBIKikxNLLnlEpUoWKc5HT5o2fOx42tJNL+Jp2Lqff7h2IUB9cMCI0QpZZ0x0pPpUqp4/cD+xBASOqVOnHjt2DMDOnTur9/04zMYbHj792zTae+6mYitRg2HY33YfTX6ZNKSjhbVLJmXPgQJ1Zi1btlS2O3bs+DrSBgBRJLJyiKJlYcLGft35+xmZmX9ut1CPUTQLluQucIgCaMbBXkvTtB5CCTvwZmfr1buJL5M4IwcHV1AAo5ECf0p7eopiqlFU+ZZqnQCKYm2kQzYMRVGoVqtW42ZJt40A8OW8ZVqtNZlb8SE1+eWTh/cZhlE8ruZ9OUFaIUi7BcbTjdr/WF5D0Yz0aerVq1ds9X0ziImJyfV41z4DSrgm/3m81QLHtWhdKTuNULLeTQ4ODtu3b79z5048Z6MEMeZEXh3byclp9k9rZVJnigZACDEI4r+NckqBoPbSAJZ+O03aqORTPSKbQWPVbEcz1y6eFQShwED/NwKtVrt25+EdG37lOMM7A4dLqcKssH6FtbQBYMIM2ZTww6wvj1y/rw6LRYm4DhiNRknakBAWE4/GLaQ5+HLAqV4KZ3wOMDkowEFbpD1jKDmyd9fmddfc7IYPH+7mlju/S8OGDSVTt4eHx+TJk4v+MgAAkah+NJoGMGzq3INPs0AzUDWGWvUbwt6e0RsFmoVHVSQ8VcoDkq8l07VuxUdJ2Q8Ss0qsWxsM+oVfTQ40eqH5YOUgJxCwGlkYoiiX0p4LdhylaUYE4dUCB82A0RCTzKTVMOu6VrFl6Ko1aty+lwigaq26KCmE3rk1ZfRwVPHDE4tooIS4WEXgKBCZRnMPYGxsP57+7fSp/4Tm+FXg7e2t3h06dGiaQSxXsVL/90b/U1X6r+JtEThydcMUCflHPC41Gk3Tpk2LzL3h26wlANjYo66cc44XCcfz/2D+tpBb1/f+uQGE9B8xyrd5a/WpmvUaHjXZI1xLlVY4XiOehaF8HQuLEk23aN/pXkJ2ib1FjTr15yxfq+y+iIk6cXCPQa9v0qJNo+atWJa9et5a2gCwbPZXU2cvunvr2pFjx1GzrdXZEiBYtHZzkTK50AyAI3u21ahbv3rt3NeUlxNRr/vQeydlyupSZTySLdOeaRha4EUAm35ZAU5/5syZHTt25OVV07Vr165du4aFhQUFBVEU9aq2MHVzVffBMVO/aVOzvN6l/MHD90FRcC2LtHgYswE8vBuMmiFMpQZ2leoR74Vflk84lqR9mEZJOjQNTXHAmMZea6/HPUjMyrmQKKYOcvLgnsCz/mhr6QHGaNRtW1+97dFUl4oZOp4XeHNiXtoqz63Ac1XctFD51Ag5Y4iKDfu3bYJbOXQerxY4fJu1qujzCh4kAoiyxGA1tgM/GPvv4XFOTU1dunRpRERE27ZtP/74Y8W2zrLsrl27vvzyS47j2rdvv3r16pAXb4ZO93+wwtsicOQKkcAo/qs9Lq0gyShOLq69Bg47eukmWpjtjhdOn1xz/cX6lctKvlZpqSlKArzAc6d3nbnqXsZTOduz3+DkxIQHd++4lXK/fS3Q4kpL67tLqTJTRlvEQRQ3MnTpz8MeeVX0eZylqevIvd9Tjs7fs2V9k5ZtPpn2bdc+A/z/3m911YmDe2rVb5SdlYmabdHCOvNfyWia/P39J0+eLDtSSHGhFIM+X4f5r/l0aJ/TIc+lYjzHMSyrDKxXo3Vle31SulZj9+THXfv037T6p2tGC2WShqZkwwBNA7hw4cL9+/cbNWqUVzX27NkzYYKcQe3YsWNNmzbNq6QVguMyDPrsDz6eceLA7jZdenj0/1I5dSTT04sqXY6AsdFSRp50+AhXdlQX4sIe3MOQxbBzFghYrT3LUB17Nj8bEMEwmXIgrMiDsWUoMBQArLsVN7tjxULW53WQnpoK5HAlsRQ4iI1dii7r3SHjMXCeRUwNqwExS6hZpvSw3au5hSZk3kvIsnISKvZFBcUo5qr6jZvVqt+w/3ujNDkkhoinYdvW/8IZjb0GDfNrZcFpIar4SP5tQ+usWbN2794N4OTJk46OjiNGmDmmO3XqFBQU5OzsbDAY8koF9T+8Pt4i4q+cEAjhBPEfJ7F4VRzcvuVlYkKtBo1t7FRDD80c3L4lMDAw7+veMBQNjRVb17PHD6UNXVrqohmTuzaquvmX5TY2NqMnfeldqYrFLSw1/I6ubhtWLlm1cHZkpIVjRDEhJCSkX+uGn3/++fBp3y+/8Hz1ojnqs7euXPp4YA//v/c3b9fpnQFD5/78m/rsHyuXGPR60AxY62D9kuGbatSo0RdffCHv0HIcBCo1hJ0zAIM+mxCy8MclPZrU6NrQJ8BECCYSEvrSkOVZ5/1PJlWrVbdS1eqqT0AAsIyy+JZnnfwTkUvpXiW8KofNrk2/nziwG8Cl0yduXr2sHNcZhLPPUneEJIgAaNqxVJnxM+Y6SdUoUwX2LgBlFIlAwItEJMTAywtqLiMV5zfGhT+lKQrArdiMlGwh53PfCNTqSS8pT4q1wMGqHU0IzT58kYp3puVIbEuZfWhEgTbJKBVdbWu428PK2FTMGPTBWNA0aAb958DexdnVdfTEL7V2dlbFeI4b279bwLFDF08fnzl+ZFT4M/VZQRAVPpJ/ioojVzoyAJK0IeHq1as5C0jYuXNnlwZVujSosu+vjfjX5878/4W3V+CQeHqkELV/ui6vgEtnTq75Ye7VCwEP74Vw6orTNP4hh3ArTxR3D1m9sfmXnwOOy9QmF0+f+P2nxT6W1Nr1GjdT78ZERZ05evDwrq1Tp0618vwoDsgcnT5+aPhOVlbW+VNHcy127ULAhBmz23Tu3rVPf+WgLj1t8y/LK1T2yelxmQuPVvHAx8dH3pKIKCgKNCuZpSKjon7YvPesSxu4eAJYNHOq0WAAkJ2tj42Nux54sX+bRoHnTo+e+GXjlrJJiKVpABqGoqT6m+bF7777TtrIddhVB3y9KldE1HPzRBWp2uYE8jQx88aTmGwDRwgMnPAo9O5tSSKhaVqjBWAUiCCQkfsfPY5K4JRpjYi4dTAq/AmjeJEWQt30+tOJvZOTVLd8yvAiESkGDGMtl8DMekIT0dVV9pi5cv7M1dOHAVw+d7pkJrzguIzaDXwbt2wLmkHZmtA6C4LwTtNaA9o1XjRjitFoCLl1/dG9EAAvYqPVF0osvQBEUVw0c6qaWUfNhfZvwLvvvqtsN2yYe57IFy9eTJo0Sdr+dcn88KePS6Jmbw3eXoHjZTYPQCD/QBrr18HDe8HyFk0T9eBFMQDatrV2KSgB3Lh0Xr378cCeC7+aLAi81cAUcOyQlLxeQpd3B3foZ5k2zzRkX7x4sQRo5uXc6xQNRpP/UkyarafOtk7yEh0ZkTOqmS+e9hQVFTVp0qT3339/+3Y573ndunVr1qwJmNfWFGvTte8gr/IVPx3/6ZnMMqBZRR7a99fG5MSEuNhYsHLExOGdW4MfPknPlGOIbBgKgIamGCXEAABw8uTJ+Pj4vGr10UcfKdsTJ058pTfya21WxVevZ86QwoskNT3NKIhSHTiRnD1l8pA1eTwQQgRCMgxisi7D/AkIAVCnYWNGYTfP8dDimLwbNW0p1y1vEALaRsvYWGsL1HCw17IsAyBDl/7dpHGxTx8D2Lt1Y/iTkpvz+n8wlmY1oOk2XXpcPS9nOws4fnjKB4O+GD30s/feXfLtNKOlxaFqTTnE+l7QzYBjh6x+h3/QqpLzWy9cuHDo0KFdu3adOnXq6NG5O4SevROm3n0RHZ1rsf+haHhdH47ExMR/CYtcgZDanxRj9ixFP+t0uHRcIP+AxFGYgY/nuI2rl0U8e1Law3Ps5OnOrm4AaptI2a06dgWf6j+fvZFXTEGxIvSOdTLxsyf+btO5e4MmzXKGeEiY9v2PlG/vDdcsmTAoswq6BNg4xo0bt3//filLlnolXLNuw/7vj/zh6y+UI3/9vnrCjNnZhB3+yZQdv6/MtcKAHJbJ8UJq8ku8aUP79OnTpXSXJ0+ezLZ182vVFkDr7n0ePXqkCGoE6D30gyk9GsOzmqTtUOSGP1Yu+ePvALfunyhEFzcDL9x0b43yss5JIoeo5KIFEJNuVL9aPlaVjh07hoWFhYeH16hRw97e/pXeqPu7g2iavnXlok+N2om1moc9TFFOGQXRrDqizV4FoGjR5HEpz2RmUnMCUfSpUdu9jCcTISeLKZkANHsHh0HrLvg/TkzLdyAxCiKlsc3Hx8deQ0tiX0JcLADZt4NmIp8/RdvGSrHic+O4cfn8vlOXSJU+tnYOA0d9cmm3eXkQ9kBmWjt1eN+pw/ukbRe3UhO++i42KmLd8sUAqV2/kZSRTn3PEg6eS0hImD9/fnhcQrVadT/8dIrVWU9PzzVrciEhVMOnRi31bp2Gr0Wh+z9Y4XUFDj8/vyZNmowaNapnz57/LP/uK8EoED0nDw8nnqRwIlb1+rewZgWeO33y4B4nF1dnF5fdm+U+z3Pc9PlLAbTu1G3i19/fvHxeKF/nhuqqMp7l3NyLnf4hV0gpwq2gS08bPHKcnb3dqcP7M3UZUeFP1WcZVqMXxGzTJ6jjYf8oMcuhVOmardvTNP31l1NKwLO9adOm3y37Zf6ec2Dk8MXBIz8qW8G70zvvOjo529jYKnTg+7dtGvThuF8f8i27j+wY8fTsiSPyLay06AIncQ8EXQ9sX9eC5OM1YTQa1cm17wffkgSOHu8O3rhqmVo4eP7sKSCxWdCgmPp+Le+eMEl1Pb802DAQWdg5o9mgnuXp47BRMs/RRADwbYeKyy5FxaQblVf75ptv8ueBcHV1zcerNFco0nbXPgMkqoO11yzyyYHRqNOjuFTzTUt4hqi76h9cEEHRRGGwgMAD4rPHDwBUcbNzsmF0RqHEQjzissQ0sYDRz2DkRDq/8bZxOafxTb0AeFf2AQAiAABF123UOJ+rXhOK+PLkYejXn46Cd31U6WvkBVutfaeefQOOH87n2rSU5MrVan4y+B1p92ZomM34jUbLMrevBTat0L24ap8Ds2bNOnjwIICr5wPc3Ev7fj5BOVVIQc1Wq93hf+Xwrq0Cz/ca9J6z6z+whPsP43VLQ+YRAAAgAElEQVQFjmfPnp0+fXrr1q1Tpkzp37//yJEj87KNSdDpdEuXLjUYDDVr1hwzxjqNRYlBFIniSp2YySVlGvMvX2J48jB09mRZTa1mcDp5aK8kcADoN/zD4WM+ufQ08cap50qBf0R1GRKfGZtu7Nl/SGJ83NbfVqlPterQRZee3rJDt75DPzx74u+FX5mpGipVrX76yIGku/HG8q0BUCKfuH851fZjWzt7KWtaERZwoijOmjVrw4YNAA4dOtSqVasCLwmOy6heuy6oC+bFNCHOLm4ODo4AgiwDanie40Ri4MnMH1d16tXvQUjQ9vW/mJVMBKAAgYNGC4oqpQrSeX0YDAZbW9uOHTuePXtWOlKjjmyA+HvPNsCCrHbFtoN1+n18Py5NUgy0fW/C3fPHkJ0OABQt0iwoFm5l2ab929XljgdEKe/uqNVMb1fehqGM+mzAbFIpXfrNS7FR4c/uB9/meY6IxLd56/IVK1k7vjAadSiHWMm3Wh2/9Gwu0UgpzVwkRBBh/nYCJ/nrBl0P7OzX/Gq04/nnaSW2vC5M5xMLSv1KAw4aBoDGxmbrsfNL950LAT6dMce9oOb0RnQeIbeug2GhdQRAKPry+TMzFv3UsmOXtJSUVh06v9e9Ta5XZZjCagDAxo6yd7GyY3075ZPWNY+UGPeXJG1IeBx6N/9fJq+zZTy9xk6eXiz1e+vxugIHwzDdu3dv0aLFtm3bZs6cuWnTJh8fn9WrV+c14h85cqRt27Zdu3ZdsmRJRESEFdtjiUEEFL4/Qv4xb+qcuBd0S9lOTX6pbOdkc7LydS12H8vc8Cgx63pMxv3EzC8mfN68XadNq3+6deVi/SbNvl2y6ui+nVvW/gyga58BH463IIaKeBoW8TQMohfKtQZA9BmJ14+h2Yf613AoOnjwoCRtAHj33XcTExMLc1U570otOnS9atRAFNH6/T07/gK34cHdYM9avn/v3qYU6/TOu2UrVBQfRhx5/PJZavaU9p09vMqpBQ6WJjyh5JTihfNVLAyuXLnSt29fAL17954zZ46rq2tGRkanTp2atu8EgDMaZSFPbVxr+M6D7HSUsQHNgqK23k3u9vmSU4vGA6BZlhMIaIDR8Dx35PyVspXaxxHG9ApM8wpOAOwd7ACzSaV+/foABEFYs3jOwR1/durUacaMGUpStyLg3LlzowcPVh/puPjglbgssKqhn7GklgeVLdAGiiXEopkLBGZaMIGTpv3p40a0bN/ZddgcqD5EcftdFm4AKSDeJPjGlYPhmb2HjGBZtmyFih279w65GuNVwRzZ+8bfQn3DKtVrom4XtJTHmW0bfu3qV7djjz7SbutO3S4HnLK6/Lulayy0mxQtgIGV7EjRly5dKjGBY/DgwXv27JG26/r6FVj+H+QuejvxugLH9u3bd+3aFRQU1Lt37yNHjrRp0yY4OHjw4MFPnuSeZ/zFixfNmjUDUL169bCwMEngSExMfP78OYCyZcs6Ozu/ZpVyhZIdg6ZpABRFq/2nQ0OCJtzYuuCbaZ6eb3JtWpj6WKFmXQvzxJjJ0+7fue1dpeqoz75QX3LkYdKWW7HqkqIIlmVL0qrFsixoOiGTi04zsCxbr1GTn/6Q/RmTkxIlaQOA/9/7u787aOWfew9u36K1s2vcovXCGVMA1TQpfQhR1BuN0jsW4S2iLX27BEHI3xBwJ1YnPat63fpXg+JB0/DtjdAzSI3bd9zfvlQXvPcTDs5DVhqA75aupiiKUNAZxCyOsCwbePYUfPugnGzupYkIMLQpJHb6Jx+2qXO+du3aeT6+cJCkDQBHjhxp2rTppk2blMoDIEoArlrgYDSEYsBopKW/nhO0LnLOepGY9BaMBqJwzauzg60j9LKQlJKUwBkr2tk7aBgWgG/LNpq0uNEjhkrUGnv37j24408AAQEBAQEBqRLzRJGwY8cOqyNXQ0L11hnYLeZmTiCcCC53mVouSb+MFMNkuqor58907DkRYCiGkdqSuu+oW5fUDIrQ3qz6rzKU0BRV5Ki36OdP1vzxe3payphJ0wBoWHmkYlk2NFGvfqhVhYvc8dXdrWmrdr43I4M0JrdWmnke9rBy1eoAgmJ1X8z98enD+4ob+Ngp0z/4RF5FbD16bs+WDYLANx04du7NbOtn0IzWy6fExqUlS5bwNg5x0VG+zVr1GfweRVHKo3P+SjlHmzuxOoZhcg7O/4+8BRiGoWn6X1vh1xU4AgICJk6c2KlTJ4WO2tfXd9Eia39+BT4+PgEBAY6OjpcvX1ZCKh48eLB+/XoAH330UWGU4UWAnZ08sEqBfKyli0B8XNyev9fr4qNKJkO9uj5WaNa63YwFy86fOmar1Y6Z+GWt+rnbpzKMKalWHAMUZWdn5/CKmVmKjNvRaXZ2dgzDcgIRATvLYH0iWtSN54xtO3dr0bYDAF16mrXAIRUmAs3YSfcpwlv07dt3/vz50vaAAQPc3QtIK6P8/skvJTUSpXhT1m/a6rnGFqUqwNYRWWkduveyt7efdPB+ql7gRBEUY2dn5+FVDpUc4SHHAxuzM2DnIhoNkCpO0deuXfPzK3iBVXi8fPnS39//7NmzLhWq9R48nGU1dnZ2YydP/2PVUgtXEskYwdhIdhaRZu4rYU0Sr6VURuAYjW22MoeLQlpK0s/ffzN/1TobDQug1+ARl3b8evz48apVq3bs2NEqaMjW1jafNN/5w8nJyeqIVRaSnOBFwomEy1eNIKbEIuiIsutkywKE1dg8SuFh2T7VrUtqBq/a3qTGb3HIrOuSScheGYRIXqJPHoZKN9fa2gCgNRrrZ+WosJ0dX7SOr359Ozu+VcfuQVdNFDgdxnHlG0qP/j7g3rpB9X7etHPVojlXzp/p2W/wh59MFhgbnYH3crKtUaferB9XAAiNz8DNEKtHfDjh83ZdupfYuOTg4DBzwTKrI9JGzl8p59en03QajSZn2y6x+r8+pAnu38PuaoXXFTgUPbYaQ4YMyat8r1699u3bt3Pnzpo1aypDT7t27dq1awdAp9O9zuIpH+h0svJQq9WmpST//vNKVO5tPk0zAE6cOBEVFZVzQCzW+uRE174Du/YdaCqmy7UMLwhGy/BLo8DrdLrzD3QloyHU6TJSXibdvHpTBy+NjY1VPZ3d3NU62Mjnz2YsXtHat15UyNXH90N6DRqWlZkV4VnbxL1AANAAxWqk+6Sm5je1xMTE3Llzp06dOlWqmGnEKleufOzYsQMHDnh5eY0ePbrAViT9/uFPHx/dtxtNBwBmpqYufQeufSyApqWJvEf/oTqd7t6LdK2G0XPi07BHP13fXKteQ5rOEhXNP88BkhuBAJoBTXt7e79+Sx47duwff/whbWu12sEmS8TTxw8//uJrAMPHTXjm3SEoiU/lTNfQDFizA4RI0bTCnK2SS1hbLWjGyJuakMBBFK9dOqfT6USeB7Bg6c8IDQDI4cOHd5256lnLbEN59913MzKKotuPior6/fffc7m2EAmTjbzI5U+pZqlX8PTwRNSLjKxMnc765urWJTWD8w90eBXPoZydl+Nk+ZWGWKDpJHeYTELZmVlSL5CiTzOzsnMOAlYdRKfLyL/L5AXpRaRrdboMThAUyUnjUSWTtpUezYtiekZm5bLl56+WR3uO5w/fiw9NyPq6nZyF5GbghZnzfsCwH60e0X3AcJ1OV7TqFQ1WX0d5tE6XYTU8ql9fAs8TjuOMRmuXvpKs/2tC0tAUN1lqkV27Spra/PHjxw0aNBg6dOiKFSteX+dcNKz/+ceQ2/csBA5TN3N0LInZWjKdPk/Rv8zi/coX5YkiUaWAAlBSEYAKdGmpgzs2RcvhaNLPyPNEFCnVfGY0GoaOHt+iXSeGZe9cv7J2yTz0+/bcL3/gnr9UYOAHYx8ZTJOkKAIQeY4vBNmX4tYAYMOGDWomn6ZNmxaGWlttt74VeFE1DVOg6K5DRms8fQz3HoG1leSP21cv+rVqa+T5LF0aNHbh4U/DD28AETFgrjkLhjEL++eCEE3r4ZxXrQ/Gf+5SvbHVQ4sgCC5cuNDPz+/58+fdu3dXh/Pt3rxOEjgAGFiHND7d4jJGo1SMEDRs3ubxMW90/Uw9EQqCSNGqBiNwICQtJRkARREA6D4F0aFIewHgycP7zdt2XLLur0tHdpcqVWrWrFmv+iIADAaD2vOjZr0GEosUUACDhYQCpA0AlsHtDE0BKA72uFwdKYy83Jj1GTqJ7PWVIXDSKwRdD4yJDC9fsbLEOloCfq/KG6mNQTwoQYTOKOwNTRQIOfggqXt1t9ql5cjnGJ0xVc9zpjWPKAozx49EhVzyzPH/jqSSBbq/SAX+H2ky/p+ipIm/KleufOjQocWLF1eoUKFs2bIl/HQJD7Nt4dff4hBFN2/X6dSpU1QhFltvCjdjdGeepRRcLjfkjEkpSXkjOC4j6PoVQCJishEEMToyXDmb+CKuV9Pakz8Y8NPcmaxGI0ftU7Sa6TnoSVRsosklVnoXIgqEKtD8rfgxANi1a9drvohXeW+L2c7R3f95+pY78ZKWQnKcZFnN3dvXeV4AawOGRakK+FBx1ZRbi4Zlm5Z3eqdJjca+jQC8O2LUa1ZMAsMwgwYNmj59eoMGDdTu1W269FC2BZIjRILRqGWLM7FctS//cKpsMRkQirFIL5ydDkMGgOSkxFiFqdr0varWrA3gcsCpw4cPb968ecKECTmXgAUiLMyCT8ksbagelA+4guYt99JmNqBvflghiZElxvqampIMAAIPgSuobB7gjYrT8ZOH92FSSBUyF+Ab8SdV/1wStVpiJnfscQrPi5eevgxLzFLO/nEz7uzzNGXMyZR0MLkJjv9feJx5jrt6PuDG5QuiKN4Lujlz/Idd2rVauOr3fxdV6v9/lLSGw97efubMmSX8UCuUqu6L7FLqI/X9Wi7sM6qE3ZVFFJ3kNOcoVMIdWw7ZlcQImpGi1UVROPj3sb8emRMtLpLcNdzKg6Lh7IEylZEYDiC9Xh9oTJ9ACj0QBQJk86K9Jj8hWG1eLbJ0SAi5F3ST54y37es4NHQxV7frZ9BnGHmR0dgIoFGhriPDb9+wdvuGtZh6ABKJgp0TeA5dPpMCCKXbeZUrt3jKZgALzkcC+uIIUZ48eXJ4ePjBgwebt+s0bspX6nexLmoZ4pGclpGt0xF7F4vwAYqymMGj7uH0LwAIEQ2ZOtCugDzjzV+9obSHV1LCi0M7/5TK+vv7nzlzpmfPnq9U//Lly+d1qnL1WuEFKYDz/0VpimrfpefIyYP3bFmflZnh//eBG5n+6PRJakoKypeEhVQO6hF5CLxsVisAxNrycmMfHpyTNiXPcQavpuF4VS1aThnlpWVsV+iLjJeZRl1GBg82NTPz6P6Lfb7+VFJkCgR6XhQI4UXC0pSTi6tL7VZplVvkfEpEqt7bxTrf0L8NBoPh20ljbwZeBNCxR2+ZaKdRr7NxQhv/4+27vfMP1+8/hLeI2lzy6r9z/UpMdCSlsfCpYTUl52JjVmCKJFZn+O5MeBFuklO8KGHVZYMmzfqPGCXNSTRrczGecAL5demCtWtWpdt7oXpLZcxdtXUfhi+Fa1lb3x4VBn4BoM+Q9ytXr81oZfWsVmv36fRvy1XwBqAzFKAEV/Nnf/LJJ0WoOSFk8cypn48aMv2j9y9cvaVnVfyYrC00tilp6YI0GTQfnOFeQz5F07KrgeRbWqstHGSBiaGInVZ265PV4MVAXevk5LR+/frExMSFa/6ooMqBl9si3nImYzXZBoMhjxgP822AXoOGu5fxHN2yKhJk9rD23d5p2b4zAGLZukTVG2ZnZ9+7dy8tLS3/+ru5uSnaqcmz5i3ftKtZmw4Axkye5uwm/5JFVi9qGOpFTOSYfl3+Wrd6/7ZNNy6fl8wTpw5bZ/pF8YTIOrq4AoDAyX8FgRIFWDpWIztdoktxcSslSfOyhqOkFhIPQoKCb6qpBHHlyYsDN8J4QQRNg9U8DXv89PED6ZRICCeQyBR9/w2BXRpUmTVxbJrWHT4qgyaRNW9zz0bGpP9bWI4ABMdlLF68uEyZMr0HDLkffFs6eOnSJUnaAGBB60ezMZHPc73P/1A0vBXp6W/fvt29e3cAbTp3v3TmJJoPQbmW6gJRkeFZGWWAEtVwCAQvs7iXWUXRweZM2FHyGWE+mzGHuhK1/1EqofDrjbjG5RwObNuMsjVAM+g+BVunIu3FyE+n1mnY2O5eqL2bm0BIjbIVNoc8B/C1fzihWGlI8ipbbmC/9scPhgF8hkHwdMwvoKtOnTrh4eEPHjyoWrVq0Xjco8OfKfyJKSnJcPUxn2M14DVmkwRjI4tNNGMOv9TY2tlpM7P1ip8ESxFlppQSkRRykngjHAAFf3dGA1ZD8p3Mq9ao/fmYg7XqNQRQr3bNeg+N99IwZMynIwfJ7jLuHh7lKlSMjY4EUKlSpc6dO0v1d8pOaN68uVRm586d0vG80Lt379Mh5uG7QRM5dd/1YzILLUtT+Yei5AWWQmDASSQmmA8REUBmVlae1+RAYT5HnsIKRQHoXMX10c270fYuCn+rdCaX5iBZT9SKEJP8kZaSfGD7luFjP5WE1wK/7xuRnwLP+s+e8jFajYBHE+UgJ4oQRNgwUr4h0IzGtDATCYyiqM/Q63kC4NqFAMcOH2SoExlKUhdrA4AvmQTKhUPgWf/ly5cDuHbx7LWLZ/2DnwXHZURlWAh/TVq2vXXlIigGNOPXqmUed/ofioK3QsPx44+y7/SlMycBC+u7hKSkxA0rl5RwrURCjAIRVDUhorh60RwpM/Ld2+bVxqWItGjTKiE5MWHWxLF/rV+rvhVFUTqjsPxysecZUud93nUv8cFLIwBCYOBFQUS95u3gVEYysrh7lgWw5dcV65YvFkUYRZETiDITi4QoShpamtxpADh7/tyx/bvS09OtH6yCg4ODn59f0aSN4LgMmlGN8tYpxTUKg4W8SzNwK4eRZodNEVRmVhbFmPOiaW00SlZx2VexBJVNBZvSpNkiX9Rt4CtJGxJKl3IB0KFnX1uT5ubO9auStAEgIiLipRxLbMq4CwCQItsLg4f3gpfN/mrVwtmxUREwpVijKKpgV448wJIczhOiCKB8paq5X/CmISmA/KqVWzC2X+XYy6ozhBJz0S0RlceG6ZD5O6anphBRfHwvGEBabrFOb1xJc+boQSC37sCaOOYZjYt7GXtH2T4liMTIE06kFJ/rjIwMsw81AIG31ZhCpf5NXhBRiosSACA7KwtAQ7/mHXrIMQSDPhj77dLVzSYt82zWvXajJrnmbfgfioy3QuAICLBMIZYzkTTFxMcW+2xtBYEQTiRqp7CA44cVS/nno8yhxSeepNyLl4eYrb+vuubQCDVay+d4IwBa5A28eCO2eOkUrYa5e/GZz1LMDAq/LV98j3NBm5GgGYpmlDlp9+Z1gkiMAjGKoiRwhNy6/vjBfdN1JPFFzNXzAQxFAWTn6SvL584cOXJkZqbZs+LNDq/lK1ZWaFvd3D0szlE0WBuzZp9hHZxdy/nUso47oBki8XZIN8mKT/57pSQmRoQ9gmXEkFpEe7PIzsq6diEgK7MQi/h8BQ6KAstYzPX2rGSnN7+FXm9B6KTX6602Co/EF3ET3+t34uCew7u2ftirA2c0Sq4DhDPwhldQSKgxrK47ru9VdmvXb9TAtwmAFp265nPVm/oux8JS9LwIICUpoZxH6cUzpprP8ZzI5WZQsHIvFQV1lE3vwe+tWjR72+8rAWxd/0t0RLFr9V3c3IEcA6OU0cbUHdLSdauXLJDrK9lwaVZR0tRv0txiFSdwNhr5VEkqOBISEtYtX7T0u+l3rl/JtYBvczPPU/N2newdHADQNPPtktUb9p/cE3Bt8qx5Ts4uNtWaiu6VjDYOiy8Ue9rqtwpvhcAxbtw4ZfvrxT/71MwRvkXTnuUqlGidAJFQRoGoe2NcTDTcvZVdKRDf/2mqUSDKFJaclAhbR9ibEngKPACBM6BkOzZktap5Trp2/QYYG2hsJYFDPXiJIJwgcoIYfPf+Nxv2fzFviU4hYxAEXWrKt5PGtuAeIjoU3vUBXLp0KTAwEMWGSd/M27D/5Nqdh7UO1ip0WmPh4JZZ3je2cgdJM2wGxagTsYaH3owPlPniHoTcRh4mFavprWiznXSVLj1t46qlfVrUnTVxbNij+wVeZeVGagXCc9cvBcTHxihH7G0YWBK0N27eqkZdOYFLxYoVvb3lVjpq1CiljBKunD/uhwSpd08c2B3/Ig4ABJ7X5+CpLBzKuDkvX7elbZee7bv3Wvnn3tXbDrwzYCgAUiLj2/obcTHpBgDrli34+tNRWRkq/ZzIQ7TQZJhy3XIQebOQYSLhkOBZrsLfu7ch4RkSn4Fmzp88WphqREc8P3v2bP7awbzw3kcTXNxKWWs4aIZSt/x6XQOpqgBG7nsUrzMCEEBpbO36Dv1gyrfzaza0ZLsXOGOW3MJfKYWeIAgLFiwYPnz4p59+GhsbW/AFlhj18YTdm9efPLR32rj3nj56gBwSf4069ffu3Tts2LBhY8Z/ZcpOJaFytRqKd5RIiFEkSRncrWJexRUfipvOv2h4K3w45s+f71qxRkxkePO2Hf1atom+HvPstkVTdnYrNW7EjBLg1VcaQWhCZlKmkReJenIq27g99LWxYRyAhn4tQm5db9Ss5eqrMa5ajRLE2KZzj8u30i1yVgEahuZKMAhQfrJILEIkuk8FbwDDgmJEiqpaq97TxHAA1evUeyIrjCkd63g9W4te02E0rYxNQ23Ws2AYy8JDzkrv4uISFRXl7OwcnlVkRXsuCLoeOH3cCACdevadNm9J3P79qOCqLiBaeVy6eILTW2sIGMZiMacW9IiINyf5GY3GlStX3rlzp0mTJhMnTpQMWAAWz5x6/dI5uVAhSCwKoKISuOjnTycMXz7qsy/6DBkBwF7DABCJ+Sqtnf3jUDmQNTIycseOHSNHjgTAlqu541Rg6J1b3Vs2qlWrVm53t0bFKhZmjpULv8P7K6RqWM3NeSJnGAghtes3imvfKSsjw6t8BZhjSkuiR4jEZDUg5NaVSzM+HoH+SyEKoGkInIkMRA5LoYkgUIzTi3u0p08WU1rKl6y10ehNaWK69ukvh2JlpiD2EWq0yXTM081LGU8O79q6auFsafvGjRuVK1d+pVdwdXOnaTqXtsQw5kFF6wgbu8QXcekGgaLltsHxQoBzK49k47PHtyx4OLJSWZYYWAe8okll8+bNK1eulLYNBsPGjRsLf21KSsqNy+eV3TvXA51dXNPTUg7t3Hps3862XXp+Mu0br3IV2rdv3759+/znY8krlgDsW7EkLzm8FQIHy7I9+pnzReXMrcXxgn1BlC8hISF79uwpVarU6NGjXV1d8y9cIHbeTbyXkAVLe3/5ytWcHpEuI0bdvXUj+ObV4JtX/Vq1E9pM5wQhJD7TyZbpWMW1a5/++7ODwpRpWOAAODnYJ+vFN5UzrJCwnlVt7UDTMlkFwVPRFa5eSE8MSzaaxyzGBoRQNENYlcBERAB1GzW+FqmNotzAsKM+eH/NmjXHjx8HMOXbBdIs+EYgSRsAAo4frte4CaiCopNYDUQhx0BsOX+rpS5RwJuLLFi6dOmKFSsAnDp1ymg09hw5EUBq8kuztIFCChz5gudAxLSU5JULvvXwKte8XUcNTQBsXPMTHXu/Ws06w8dNcHGzCCNXM52X8SrboUfvWoWQ1KUhvkr1ml/MWbz8+6+tXyH8NngD6nUtOKZU4EDR6pgWGxt2/vSJgWf9Afzy4/e7A64zsEHOJlqIGhYlU7HSvokIICkuVq4kYcBzss+owEtiq2A0wNZe578erd6Dtgy0DgAlcoYO3Xq6pNYqW95bau3fLV0zf/pEiAI8fDwaeOTxZDMUaQPAn3/+OXv27HwK58SdG1dSXibltDVbNGRGA5o+73/MIDQxu8HRTIbGJSMrFaVMGmKBB8Ni3xzvj354CCCHPS5/3L17V9l+1UQTVsPyo3shvy5doOxePH2cojD7J7P3283Ai49Cg+s3bqb4LysgBEaBEEJo9k0ueP6Ht05+23I7LviFQhUs96dsvSE5Kb/8ok+fPu3cufNvv/22aNGi8ePHF+3RaplaJDDyBIAgkqOPk3mRZHNicjYHik1oOPTJM9lqezPwgiCIRoGExGddjUg7e+Jv/7/32zmqqAU4vQOfqdVoUOLOWdaPYzS0xtasDGjYExXqw70S+n6jKsOCZmiGNRspMl4iPeGD8ZPbdO5RqUpVUNSfp656VKsnSRsAVi74ludzWfi+qsIwJSVFnboaQFJ8QlnvKnmVV17KwhUuV5gU45Wr1Xin3xAAP12J1hmF1/feuHfvnrIdHCynRHFycbEoRFEAGAosna8aIx8EH0PISWnz/KmjAK4EnAJwN+hW8I2r+/7auPz7rymK6trHzJUnxXwVGe8MHHbitokHrPkQ2NoDwPObeH6zUMRZlg4QFIWUpCRJ2pBw/eJZWcPx6u67+XyyXE9lZugMBpOXhkxhJwBSiCwPgbdhaQCKB6VccyJCFGTeDsAYee/c3q1Va9YZ9OE4yVG3ffdeR2886DtoCAD6FdPWFImfhkLZWnAqk18RmoGt429nQowcZ86rQFGQurOp4zMUAdBvxMiHIXLi6x0bC+tNDKBFCzOZRz4pMnIFRVErtuxp3rYjgOFjJyjBaAou+B9Xtg/v2jpz/IebVv/0xeihp48ctCopiIQXiUBKLiz5LcFboeFQI/RFxrOXJonbtOwAzbiWKpXPVefPmzV1Z86cSUxMLFMm386ZA1ajlSASyQFCIPjtRlzjso5XotMP3X9pFMnNRA72zjDKDnQiKKNIGCLeuXXjzJbpABzHroFLRQAgInjDIP76WaYHSiRkP/TOrbPHD7u4ufd7b6RVfASlsRVF0bzglkI8JJ2HAlZDE1EgKjfG2Ie1E6/2+2oDRfBUBc8AACAASURBVFFSoIqDk2uWymMUAJeD1/JVJ/K4uLgGDRpYHWzTuXvQc/u4pHwdFRkWYr4Ch2C2xIc/edxYAwBPX+rT9YKTzeuujdSK8apVZUsEY+WQQdMAWIamisDFwhsgCEiJlSjMAZT1rgjg/p2b6NBI+ZSBZ/0FgZ86e1H1OvXjoiPHDBsoUcjfvnr5wLZNFEUNeH9Mw3fzc8+0AhHF+NiY5m07Xrt4Fn79YWMPAIRAFCBwSkwpS0EQSS4BvZKGw9SuCM/9/L0Fl6BLKXdRiiktzh7Bcxyr0RzcsUWkTR7cUkuQLGoCD0IQFWJ4eB6DF9IQAQYAOD2S0yFwSHyOeG+UrwNGg1OrkZUWdPWy5HoiwdZW6+rkBGQV5qtO+/7HZXNmSNtqx5pckbP7NGrW0rbNcEOFRvlfqPGoxJWqYMUaDEaj0TpwtNws7bS2GUaR1+uVQN9b1y4TQgopBg0dOjQjI+PcuXM+Pj5ffPFFYS5Ro0ad+gM/GDtm8nRnF9cdf6y1Ott36AcAguMy7gXd3LHBHGB1wf9Yl9791CUV3dj/BI43i7dO4BBAlBQAlMRnRETQzJMH92vUzTMCSk0sjRy6uyJANHFEEkJ4EQIBL8AgEE4gLE116TPw9NZfALTu1C0Q4AQiUGLGyyQAKF0pIzMT0ipX4BwcHPuPGHUhIB7FH435POzRlA8HSduPQoPFnt9YnJbShilgNKAZl1Kl0yw0BJQIaY40jT5EfBAS9PWno9buPCxRKwqi2LZL9zWL50jnew4Yamev4uYqEg4cOKDefW/cZ+269axWq67w/ElBl1IFxJSaTEISPMIvVnFt9TzVWGC0KiEkLjqykl35fNrS119/bTAYtm7dOmrUqBkzZjwzTROlPb2S4l+YKsgAsKEpAK8cMSIFZ5rqX7FK1SEjPwJQsXKVSJhjFvxatWUYlmHYASNGAyjLZE2dOjUqKurChQtSgcBzp3u1fpBXPieDwfDHH388fPjQx7dlxx59sjIy5k+fKNnam7XpcINmZJGCiIi6ixMr0e9bU+30ANScFrL3hshDVKlmBR5ErFilauTzpwDcPTyvnT/jbnAAHN6gV5N6kr5y/sx3k8bB1ath7w9euNYyWyKkX5I30EF/i5WbQGMLYxaiQ/HwQna5WnD2AACBw+bPAODBOQg8vGoAsmqkSo2aVg/V5ggXygs9+g9p3KLNi5iogZ1b2r96f2FZ1qtCxYiCZALaxhbEaG3zohmtkzNnkNWQGcmJcCx1ZNdWuHujdGVUaQKKLrzShaKocePGqd38C4/09PRZn40Juh4IYPDIjzq9827AsUMAKvlUq1K9VrVadfq/PxpAVPizqSMHqy90dLIIQ1seGJ2aLb9OCdup//N4iwQOzmiMjYrgOVbJy0A4A1hbCDwo+va1y/kIHJ07d540adLq1asB7NixQ6MpSM1eENTNmBdJZJpeZxSMIhEJ4URq2EcTRwzqr9dn+9Ss0/3PUJ4QmgIYDcr4YMgicPLM4mhn5+3l5ujkTNPxyIOS4Q16wt4MvKBsXz0fULPbDPVZQizDL2mmfI8xzk6OaUmW8yDNWvhAEBHA4/t3nz68T9NOAARQZcp4Pnz48NSpUzraoVnbjq9fc1tbi9iTURM/p2kGQGZmVsG5PfN3KbCMLzi+d0dEUzuUrxsTHeXtUi2viwwGw5wpHweeOw1g4tffz/liQq7FnJ2dly9fLvEUAYAptGfKrPnfTf5I2rbR2hsBlim8Gl1Fqi3w6liJRWs3S/r85m3aR0ab6LqB8Kdhl06fUBK4zJgx48iRI1Y3PXo5aGQeSo65c+fKOaV37BAFITUlWfHsu37pHNN0irwQJgS8EYmqEFDJ+mAhcPCgGQg8QJlfRDCCEF26THX6MiH+yN4duPUY/ecUR0ZDQsh3k8YBQJWmwWxlOxs32TAt8L5Nm3+94RdRFFxcS409+DjLYEyVks4fW4bRv4ICRQjDstJU5lmuQooNazRZWAZ9MGbQBxazbExkeMzTaMClkJx+HmXLeZQtF5YmIq0oXd6rQsWImEzpDfPifKVttIxACTnchjhB1ag4A7ZPB29A/BNE3UWVJp99Pe9VK1M0HDlyJOh6IOxd0WHsni3LD10O7tZ3YFZmRvO2HRRGGQBqiiMA9Xz9PpwwVX3kUqQ5PW/J5sT87+Nt8eGIiYzo6VdzbP9ut66p4i0l7h2BA81sWPHjh706ZFoq89WYPXt2YmJiYmJily5dXr8+asGZEKy9FnfheaqkeiGECCK8q1QtU6XWupvxcnkCbcW6ePcbMAxMoZs2LE3TFIAuVVxL2WushPE3ywARHJdRvmJl9ZG0lByZ5yxHomzWPjGLsx68GOv4Aul//ItYE7WiCMDd3X348OHN23WS1kav+SJDhw71a9VO2p48a54kbezevD5GlXOuaNDG3sXzm8puxLMwKQDn4K6t+Vx14MABSdoAsGbxnFfNhdayQ5dfdx/p0X9wqc93aNw8AQj6TEFfOBILFd8USxE1A8SLGNkVtLx3JcCs4UiKfzH3i08fh8refDmlDQBVqluvzhXI0gYA4Pql83pL9k8zD5vk+iBVRjav8NbsWFJS1nv+SHxuTgVr1IM3pkj6PwUxD8rwyfsfvAzKreUUpmvkVcBo0ANAhXpw8QSjESDXv2fprDnTp5QqXaa0h5fGxsZOa+vt5ogMU62ICJ4jvJE3GgDMWPhTfGy0MTtLcemo26jJ1QsBWSaZMuD44ZG9Ox7dsQlStHyRXiF/6NLTNqz4cdGMKedPHQNgNl3l7UbDi2BttTnFEc5CJiJ48Vjaatq6DYByKiZ+FGe4pqxHsXdBpUYY81uygfi1atuua0+1tIEccVLLNmz3suREEEVi5M1v9I9YVYqPv+efxdsicOza9Ju8ZRpGKcrUtQQeGi3qdo6Nivjqq6/yuMEbhpU2wiiIBsHcyqUmnpDBnX5qntQFhgXNqIkj63o4DK1XGsDgemUqONsUHw2H1PRbdugyZNTH0pF5K9a9iCsgSj49XZf0MjnHYXVAqXm2q1G3fiG5nIsAR0fHRWs3bjhwcvPhM08ehH4zYfTaH+etW75IlpBI0R+pf3QF0aEWhzg9AL0+l3Rk0s8oiqIVWUJhBA6r0adazTrd+g7KYuwzORFA+onf0k7+lselllBN4bw+E6KgyHw+NeS4VkmKtRIfQ+/IPoC9evXKeVerMBY1evfurWx7V/bp9I6ZrqNpmw4KSX+nnn2HjP5ENvwLHASOAZF6qLnFSB329t9IT6CIqfEcWoCokOq161k81ZhV1h6JmVyszvzbXohIj0wtKE1cQbDV2rXv3guN+8CnKRgbmHwXajqJYpa8Ms7OyjLqs/tUc9o0Z0qvwcN7Dx7h6VUOAicFBEH5MUVRfiNR/P7Lz+Z9OaFvq/ppKckAzkhujFw2gNC7ZuaS+wlZSUVKhpATK+Z9s3PjbwHHD8+f9tmNy+czdAo1Tp7BybzJ88wKFmoAVVD4jQtnkZszezFNpX369GnSsg0oBhTDOpU6dzZAkaHVqOfrN37aLADN2nRYtmE7m0NdLRKiFqH+QWb2/57M8VYIHPcTs17QJiZsZRgVRRgycX0vBCNs7NByOIDw8PCSqZJVG+YEwgmi0m/3hiY9TsoWYNG9CcWAZtUCh7s926qibH20oSlCcssdmgNFbsQURX38xdenQ56fDnnuXcWnwIBMnhTAcal2gDDq9VIikmJKe0vTTOWqNXZvWX9s/67rl87t37apnHclE10DjyIb+3PWlssGUK9x01wKA7t27fL09Jw1a5ZypOeAoY6Or6YAl/LPfTF6qN5gmj4NWTBkqfOB0XnZWEReLkYIHl+GKFSoWKlDj95rth+UUv5CGRQsbUnV68gz+uLFi4cNG1alupl1QwpgyWtNNnfuXCm1bJfe/b0r+8THxmw7eanbuwMbt2hz49I5IokTotimU7fqtes28msGyHEons7axjUqA6AUfg5jNu6ehGCEKGho09SYmQpCfFu0tpI5BKMBllQcZ56m3E3IU4VZeMxY8JNPzXr2Lm6Obu6c6fYr5n0zoF3jZ48fPg971KdF3aiHdxd9/tH+bZuO7tlxZM+2tJeJcl43IgI4smc7ACQ+x+0jeHJV4guWcPH0iYZlHZ21LABJW0arPsTu0KRr0WaFfxGgfCNJsSHh9tXL5t9JzFOgKXCEoQBndRQVEQFwHFcyE6ejo+PCNRu/nL+UtdHygrDlt1Xv92yn8MeoMejDcadDni9au6lRM3OqFJ7nN65eNnP8SANnkdLn/6/f6L9QXnkrfDguRaTR9bthz68oX0dZkRDOAJHHpT9Rsw0gCyLt27cvgfoQQmKjImFrdrIzioRRTRA3onWNyzp4u9iqGz5hNKBptcelek5RsniUAH0ZgPKVKts5RBQQX89oChBKVA4QZct709EvUMzd+2VCvLKtr9lJ9g8QOFAFOYfmhRzaEc/S7vFA6y65ZG/XZ2eps90OHvmRb/NWTVsXtsmlpabcuHS+tIenexkPOeSPUrkrEtk4KB2QU3iof39RAM3YsoxBKpaZjMDtqNqsa59+IzpbJBaX2tLk7xYJd09fv3SOoqjWnbrV8/W7c/3Kk4eh/bq0Xb169Z1Y3d4/NwTfvOpV3nvkpxYmcCtUqlTpzz//vPIkfs7UT+ZPnwignHfF2KhIaLRo0FNuxOG35m35DCkxsHWA30QIHMPQdTwcykWfuc20FDkjbFkAdlpttv8v0rvY2doaMzLB2kifoFa9hrs3/a5+bujNK/AboF5ep6Qk3465V5epXrlqDeSNAodpG1vbMmXLv0zKNAqEKA2AiAAO7tjCcxwA7J8DUfh7t6z90mdlwJ4FES3WyxkvEXIcIcetbg7go48+8vf3hzEbQO1GfspZUSRvis2sWZsOCqGLayl3jufl6UCKrylS6l4NQ6UnvjTviyIAwytaDF8HrEZT0acGf/8RKJnHbN60zzYfDsipxsiJXRt/27TmJwBo+Kna8isQUjKDaoEoZDXuxOpe1UpbYvjvCxyiKD68fz8tXb98086Z97RalkmXViXK8loammkGwNixY4ujDlZD2J3rgampqfA0CxycQARVBzeKJN0gJmby6kBHQSQ2do5GFf+jKKhXtAAgEMIW6AX5JkDTTJmy5SLT89XuSgagfJAUgeRoABO+mk3RNCOzQwLFJpur3VCSa3WHjR0ACDzNsEXUmxLRq7x3GU8vxRMtPjwMnr65uivqLHO4V6paXUrRXhgkvogb3k1OA9G1t4kSwyxwEEBkVCoOwZgNhgWr8pYVONCM1sbGkJUNjVYS9VxdXOuo0rZJkNqSnYNjJd8mHmXLNWjSzMnF9e/df61c8B2A35Zh48aNFf06Dh750eCRHxWy/jcunQu6Jmc1i42KRPk6qN4atWTHGlzYjJQYAIpJReDFO9dCrp//Ex+0VBwdynh4tBozPjkpsUaXblEal5OPDXrIM73GxqaRX4s7N6+aH8kZoJJfR266GJOQhPtnzy+duHTDNt9m5pwahUfks7CZ40cmvIizGbaY9q7LqzqjVI1j+3bWbuBrfhEFD8+jYkNo7JAal8/9m7ft2L57LwAdO3bcf+H2lYcRSx/C2dVdKSAQ8G9IIh8/bRar0QSe9a/dwHf9zz9gyCJUqEeDMAzNCZw1nX/hoKFpjYOjWYlEBAAaW7u8r3iTkAaNgBNHQNcCoaQh/UVM9N3b132bty7oajxQTFeWo9a/Kdnt/3v8900qM2bM2Lplc9jDB19MngiKMbtYG7Mh5VWSxA6KXvzrZnd3uW8Xq89Ohi7ditRPJEQtWxgFcvTRyw23XlhdaDXAHdq+edZnY6QsGJIKvZB94428WnKy2T8jz+VQ/uukkBMIPbPDP3DA+6MB0Ch27oQxk6cpOUHMbIkCp2EK1xGs60Zatu301/ELP2/e3aqDyZWY0wOIiYrMebWDk7OLSuHcuIU8CBbmc5w7aXbV9D9yoGwFb8DkkCSFQkTdqx4rhxHRFJXT45KVPoXIyceJCGCcb+mLf67489eVujRzVlJJ4LgY4D9hWN85Uz/p39b3RWz01QtnlQL79u0rsMJqBMdlaGxUE1gZH1RugiqNzXHUip6ANyDob3DZyHiZ9PxRemIcDi+SrFQ0ETQadtzUGV8tWNazRcOQv7fopawlhAD4btI4o1WaNE4PlUtQjNEG9i7SJOR/eH8hq231aeZNm5jwIg6AkeMMnGBBfGJqGw8sk8XIuHUIqXGIf4JTq9SHXVzNvi/f/LhqweoNtrZyVI6zq1vVGjWh6hEBz1INgvimQm8q+lSbt3LdF3N/kCtM0QBoiF5eXrZFjcITjNlarbZR05YAqtas3WvAUBSbkTQvREU+lxJWK2Mskzd336N7If5/H0h8EQegao068lHLwfn/r0nlX4j/uIZDEITNmzejXld4VsXghbxaHRl+C5e2AqZZmqb9WllLwcWkSfNt3hpB5/MpQAgxCCRnQ7cQJwQORLx28exvyxY2aNIsAdUB5xJLpxIan5mRkQFXJ4Dg/rnSlaolOngXfJkVRBFA6TKe0p5EInw5Mq1qKW0+YZ5FkJaUS25evmC26SqipyHTIHBwLVvgfViK8GoFEs81bt4yOSlx4VeTg5W1tSgCuHHlUtta3vaWzhm//7QwTaXkSE9N8fAqV8j6a+0slolx0VHmVxCMICI4fQVb4SFAUdDQlNbZKS3dwtjv7OyUnMV5GuLTUjJh7yLVc9lY2aPzUWjIwjV/SNuS8KrE0QA4dWifWmIQNPYAYiLD//x1ZXZWZpfe/dt1zcWEpEazth1bd+p2OeAUgDL9pyYaWTAaSmNrilAXK1etHv40DITg7Hp8sArX9+HZdQB4chVtR0HgRc6QkpS9fcOp8Cdh7mU8nj++D89m0rXSPRirAChOD0A0yeCMRiMINtJc4uRsydaaL9TtzayjomhiFS+tEvbLVqgYF51D4hSFarXqNKo8Tp+VdT/49rPHDwGkpZql9r93bf18zHD1FfIqwjQOrL4Wq2VpoVzRR6ScfefymZOmh9EA+CydRxmtt7dLYGRRksDpn97W3/Nv2rDKsbWbbGxt/Z+mHr0YJQlIJWaV8Kle+7YBCjtZxx596vvKNqkHIUF//b762sWz46Z8NWzsp/u2/qFwn6/defi9jz/Tc8LNLPsISyVxyQscrzTE/UvMPYXEf1zDIY9BogCNFoyGUJRZISkKMGTKG4Ct1qFIlMBFgaOTc8Wq1fMvwwkil/9SRuClMe7i6eO//Ph92L1gADwnohgCYnMe/OZ0BFy85GoYsxKjw4t0b1Kles2kxARpZ2Dd0mXsNduCExMy3owfvho8z98MvHDbpNUHVAJH4HZc3ZX/5VLTYBmLFmKrYct5e69aNDtYrcknIoBzJ49+/+WnalJ2IopH9+5QX75u+eLZkz+SeIoK/F5deg9QvD2atGyrrn9nbYwUKXP68F4ALE1pGMrBzq5MaXf1HRxYalj9MuP9yuJlpJrvS8K1CwEKlYX8lqoJVaPRvP/JFGnbt3nr98dPJoSsmDfrzNGDgWf953054dG9XFzz1GBZdu7Pv63be/yv4xeq1a7vWbFKKc9yRGUSqlqzjrk0sUjXDiJIHpfJiQkbVy0LOHZoz5b1EAXweiQ+kwwuPfoP6dZnoMUjJYHD9MVoVgNWA5pp3KL10NGfmB6LxEzux4uFTUEue872mm5O16yqpbKVi7QBQOCdHB1r1/dt0KS5JG1Y4e7tGxxnbvkNyzrKdlLTLyGKMArim53/aPNqngaAa7u9gndPbFYoOTgXZKfj2Y0TB3ZL0c6m+hevTcKq7zRv31naqNOp/+JN+2YtWUXRNABCyKT3B1y7eBbAhpVLbgZeUGdaOb5/l62ttv3QMdFeza3uzwv4H94U/uMCB4DNmzeDiGBsNHb2RK0UV7bOrbchRlpTKGXPm5rISUEhHpxIChA4jFlq53ZpgI7OdaQrBgiKW5nAu7mVsrZYFxKi+Dzs0W9L50t7d8+fTIwIA/Dzgu+MhtcNX1SDMxpnT/5o5viRh3f9ZT6qDLUiZ809kCMyUNK40ERUW1UEo/78iSOXTp+wKEpkl6BbVy49DzPPK2IO99LbVy8Hnjs9fdwIKRIy/6ZlZ2+/6JeNmw6d/nnTLgdHJzAa+MiBMNd3/Q7eAJi8GRhKw1BV3Gw7+LgpBwEkxsUMq2FvZ6+VGcRz1MfRlKNHWlh37CV7ilSqWt2xdos98Q6zDgXtPXdz6fq/vMpVSEtJlkQlCfeDb+dTeQkURfnUqOVV3lsk4ASi5wQ14eyZY4fMRZMikKkKqBZFiDwETq1FsBOzkRiOrVMbNmn63bI1TVu3+8mS4FzyuDS7OVEUq3Vo0aFbv+EjXUx5DI4/SZl7NuJWXGFDV75f8VuPfkPoKk00pbysz6m6QOde/dbuPNypZ9/WnbqZC9zcH/TLtPnTPls0c0quN3ct5W7FKCi1OkVtKYJwIrl7//Hs77796/fV2VkFMK8UZrAaOmY8APSZCQdXADBmh9299eM3+XkB5wdTo7p6PgAmgUPRKut0eTozvsEFkpLwOdyrRbxjZWn7hc6wZvFcdbHwJ48Vbh4AElfH0b27hBxzovAaYfOvj/xXjzlP3Yl9rSCm4sZ/X+Do1avXqhU/M7ZaSu1AB2IecKND3R3tCi+Fv37fiEozFMiYaxRyD3k3I3A7buzr/94oeVcUAdhbcvQWH8x2WYFLSUooosBBRJjC8wghC76aJK1Kb1+7cupwwV4ChVTkBMdl3L19wyLDqgRJYJKIKKRkWgpE60BZG4YCoLXRqEUT/uahUxuXK/QVpmtllyAArm5mHQPDsCM+nojcEP7kcYFvAYCiaXtHx89HD73gfwylvNF9snRcl2YiaxEFAMbMDH1G2rNrAaG/zQBM3BWioM/K+HbS2IpVquYqcCzdsI0ySWCSwMGUrzV5wWoAEU/DVt41hEQmZhpF11LyG1kRbyhBsxLy/y6CSIyCpTxtNaafWIGEZ6qzAngOsQ+rvLyjHMsOv4cTKwB8NnNOtz4DzuTIv9WkSROoZ2uR8CK5ejt49vyF86dNlMI7szlBZxALvwRnGHbavB+1jo5sDkfI6rXr+bVs29CvxZjJ06d+t7BGnfrf/LiytIdKLtFnQG/9s5T28FS2u/YeYH5jQjZv3rz2x7kAOCNnegUYjMLNNDbQ6Ln5l+Vrl7wBBs96vn5HroXCuz4cZPH0cWhIsEqUlGpT2NuZSs79/JPQO7dSkhIAhATdMhoMKxd86+PjU758+QPbNr9+tfPC1ecvF+6VrdVZBsPdO7cB8Bw3ZuedQydPq0s2atayw5AxKF8XgG+zVgM/GAsgKzs7Z7Taof9j76rjo7ja7pmZ3Y0LEZIQHBLcXYIGh1KguAb3AsUpFIoVdyla3N0TnOAWNDiBEHddG/n+mNnZmdlNCJT27dvvPb/+ymZmdmxn7n3uc89zTniynv5Pxhz/JvzLORw8/tiymfFtxtA0RK9hWtbg0llpDOlk/ct/AUadeqs1fqaH/rwRl1EPxujk4tK+W59je7fzl+Pg9GdNXnLB43u3x/XvBqBZu45sqSBhKZ+cZ3MUC8oRfPUd0LJDFwAMP/vAq7aTpJTDyEOcqjyxf9ftaxdd87n3HT7W0/vzxAsAKku/TYIQhtdGPRgaFGuuKeU4iuAYhoHEJo2jjQBl0Gbbamx14oPz+CyyUhwci8v2bOIgDx43RXF6QSN/qt+0RWJ8XOFiJfu2bSQuz0Wmk79whqEP7dh668p5kqKgtkWxaijb2Nwyiv0BxwKgddl0VrL29dOYJ/dQH2BoqGz4CuSnD+7RNN2qfcfLEWlGo0r8zeo0bCqt2nBm0m31aeffIvSayYKVpNKztOHPHj/bfrxIcb8OPfvZ2NptOHhmx/qVBoM+oGmL8qZpcj7UiHjzasboRTcunx85cuSIESNm/Dj6xqWQ6nUbjJo6y7dwURYwMCwtfcDlXVr1ug2kOvqCQFZ2auMK3tlPC8ZFy5Q3B3dqdfzGY8u6xy49+9y/HCfGEsK/pRtApQk9s/RTxLtCxUowLAwM+6Upf5ZVaGsCwPAJP1co4x8d+cHO3kF0/zm2d3tOO/ErW75Ow8AOPfqGPwl7dO92pao1akhKlnbt2jVhwgS4eMO/x5OwB6hfBADDcQwIqDT8k3nm8L6fZv4GgOW4l4naMp5faTlEEAQISnja+R9CVGohCHAc8lq0wkkb1YXTx0epffDdlPOnjycEb3l0V5h2XLNgVmDb7/GNaAeK0Pbw2fPp9iZLAUp14cxx3+RnRoPeYBOAii3x4iri3gAIbNuhuH/pp1xqwPiVfYvShYoWpyjVq2dP7J2cLXnuh54ntvHPzdrzf8g7/v0BR2Ji4oN7d+HbXBa6mggQPPTpKayLE/4uAg5vfPxnIUQYzn2GjRnw48Q1D5LOvU2zOr8rvaivS8/QRuP9m9emjRRqhkNOHCb8+gnreDMOOg+sC0VxP2MEx9ao13D4hOkAVGp1q45dz/ABB0E2aG5FzhLAuXPnVswRzL1SU5LmrNqcl/OvULVGo5ZtL5+VaHKLU1on5lcv5HovRmv2KWWMjF4HNaQBhzYjDY5umSmJsHOCnWn+nmUBZGbIc5gcC+DnxWsalbRiZuZXpjyvT7V0y95DO7cQJPlD7wGi4lZO2Lf19y0rFwt/tJmAfAWgcbAScPD9BEvj1n68uyNoVNAG2DiIKY32dSvC3hX+9VwbdhNjuh/6DATAPySPYjJPblqmy/ZGxRY60XidJBmGPHfsMB6eABATFTl2xjy1Wt2weevSFSsrlKEBrF88l48YVq9eHRYWdiM0FMC9G1cXTB3XcfbWhKQkA62RPgzN2nWoX6vGL2OGAKher8G96+Zoo1ZA49s396BUADh2y6rFsIZbVy50Hzhcaj4OYNKgnui5lH8jMJVjtgAAIABJREFU7t0M5TgXAFBp+LDS1s4eJk3JL2VFsJyV97dQkaKzJ4y8cu4UgD7DxvQZ9iOA6nUD7t24ZnUnIyfPLFe5GoCa9RvVCmjs4uKSmmoOskNDQwHhWfrwKSpNz7jYUMIYhFLxNjfifM2HVP3P517XfrnT3dOrU58BLq75rL7mioVpKck79x9O1nOdWzYi1WphJoKVCMzzcQZjBENbCTgkagIC5MSgqA8RKJEfAAhSjDZ4pKelAcpnhj+9P9n8Rkd+hF8Z018ECGr7uuUAMLIhStbmC4Xy+xQ4f/LI+ZNH6o1fSRaqVLREcQAhJw4vmPYTfEqhex3L3f5z8hv/XRRRS/z7p1ScnJyEaXWL3k78y9ZGzXJ5Uen8NhDnI3KUg8wLOK52wyatO3YDYO/goKIo/DUvhtFg+HnUQDHaEA4u/ksbwNJI/YzMOWDh0cAYwbF3r1958fRRJR/HSj6O42bM403k5675w7dwEav7uHfPbFxy68pFlmX4iZXcAymCJKctWPn7gdObDp0NaNbKxsbWxt6BXzV50qR5azavXbqwCsszBzkVr3mvyNkYsvHyGnSZ0GcDHC+NVaNO/b7Dx/r4yptOlgVA5FyJx6Ni9Vqzlv8+c+k6MTeQC+7fDDX/obaBxgG8zj0P8Uk26HBpAwxa4UGgDQUvzNdQEEpkxc2yU5GZnJqYAKBT7wFn77+sVKO22IpV8nFMS00RmSjiHZQe8dTBPcEnDgV1aDFn2oReLQNWzptxJfg0a5qT4jhOmp94+jxc/Pz88cPlN6M+pWgV48iE6Mia9RvxIrYKJbS4mCi8u4vsVOkL61dW5rPo6OziV6Z8ZGRkaGjovn0m/i/HAtDrDbTROHmkSS+EIEFSPQaO4JNPLDgDy32pmr7V5GPY7et8tAFg+7rlPC+n15DROe3k3avwnFYBKF26tHgJKN1g/Z1omNsNAiRZv2mLIT9NBRCTYQgJe52WnnHu2MHdm9Ysmj5eup+c3guWZeZOGn3kxqMrOs+RZz+KM7y+BQvPWLK2YpVqgGg1RVvXHmUsmU9G5eQLf/7ycp5aDZoUKFT424oOiC2Ao0s+2aMle4DVIKlylarG854MHkWu376fla3N0DM71q9cMO0nYTNr+FZia38p/ivsV/79AYeNjU3RwoWVS5+G4IUw1Ve/aYtCRYoC4P6umlJxuoT8ExHChF9/m7Nqs5i8FYxI/gJzw0f3bsvy2zC/lioCuHcYD08h7q15pWUQJY68pYh4gLQ4AGLlCEGSRQoXBODq4ZnTyVSuXFn8XCugMZm7lav0lAmiRKky+Tw8r4Wc0bPQ+wm92m9TxjSvXPLG6UM1vG0AgDbSeq0wqpOCoXFqEQ7NQORjimPVJAFg1rK1vYeO/q5bH9mWHAu59sCZV8kXzpxaNe+X04f3MQwNgOO4kwd2zxw7dMvKxaJfVy7w8ZWUHBOkysZOZecgLljw+7bf1m+v06gpwOHhSbAMWNbNw7NLv0ErV6/z9PSyUVHIToNWUujICRYqh3ZsFotTRFSuUVsgW4rtL0lBpZHqEyybNRUVmqPxYADH9+6YPX7E4hmTxFvdsHlrccuCRWRTTgzLcBbT5GF3bvJVuJkZ6U5yHpJAcMlIRIZZwtK/bPkRk37hPzdt8329xs0A2NralipVyiwPw7EAtFpteGQc+q0Vv1vUr3T/0UKvzLIw0F+c4VC8ZPyrl5Ysc48LfxwGoHyV6ttPXba6E75cQgrpyHXYsGFBQUEmXhGRrmdOvDRffq2GzWYuW1+gUBEAtyIzzn7QiT/NrSsXP0smBRDz6dOD569h4wiNPdQ24g8dFRlRqlyFzLQUsAxoPR9taCgKAKFoHi2t9RgjONbNM795iaA4QM1ctv7ApbsjJ88c/+vCGUvW/HX1gGKJigD/egjoCwAEySe3Sor697W7wb9eZLq+/8Fn29YuExbKFTjE0/ybpUQs8Vnq6D8/1ODx759SAdCsaZONcmfTgT1+eH0siWHKdezRr2L1WtMvRAA6hv2MFfk3QUJsDMsKkwu0Tgubr5p5ZZlHd2+1KG8OpASZzr/gxdBoZNnU1p26N+/QZcwDACDBISMJ2alwNocIKoIwyk+DYGmOpJTjoatbkZUCoIp/UXGZnYoEcPLQ3uCkiCatv+NzzlK0adNm2MTpD25dd3RyDho57kuvRSisyFcA9XoKizgWwM4Nq+DojsFbhWqIhAhkJKDa98I2YnrAqAPLUiR0mWmwdZo2csDPcxdWrxuwZs+xEd3bS3coHTSvuvnRsGMpEiLAsXHRn4JG/hRy4vDy2bydyrnkpITxsxZYnqo0dzp80i/3b4YmxMUAAEHSINSU+UcpXsKf0KbdvHxB+PvuIcS99atRbfC4qQAoKoEigU9PcWmDee/adGQLCfy0lOR87rIIr02n7r/fWgBIB4gEA0L6ehgNBqhsRONiAMHHDzk4OjVo3rpC1RpjZszzKVQkPia6QWDLIiX9g74TuoHvuvQKhjUVeY6ljYanD++N6dvZ8lYAwIPj4se6jQL7j57g4pqvxfed9Tqtm4cnSVGVPAW9LLM7AcsA+KRX3UtTkQ4u4g/i6mHuEVkODAcC3Oln0c3L+KjIz3eEln4iGorQ0ZzCg5Q2qZAVKFRk7potq+bNiJWbvuauBWJjY9OuXbuPmQz/o35K1295YA4jpCxXFhwoNUjzKd29fiX0wjmvAr5d+g12cnaxmoTP5+6BBkHwLApKDYKSVGyx92+Gvgt/gsgB6DIfalvQRoNBC42jiuCMnDxJrEBUuIsuoWnbDgdEjfmYF/j0tPuwn+pV9SII4vsefXO55G8COwdHQNL1ungJ1fskRWpsWYIys2pIkrJxMHBkZpZpe4d88Cop3RtlklH4b0hw/Hfg/0XA0bZ1q427ZJaeR3ZtLW1jHDhmUqGixSET2MmtxfnqKNJoMIi6STFRn0AIjWMuPkmfAWMMPnkgsIyvqFZJfYnSaN7xKCazQtUaTVp9x5t3FPcvnRQfe+PKRTg1A9CnnMum1S8BWU0gS+tBymIUjjZAZQOG5u08TNsJ5xoebs4t26spAGeOHkTU82N7t284eEZaA8Lf/069+nfq1f8rriU9NWXOhFEgSKhtIXbYcsYlGCN0mUh4jyfBqPa9cMLSCTiWMWRn8gmAh7dCj+/d0Xvo6FLlKubLly8lJUW8LumQiGFYtByLu4cQfvlN+DMA0ints0f2Ww04eKSlpa1bND/q4/tqdQPOHtkPggBJgVIbWc7kiw6SIIxSkc1X1wHk9xGkFCiCsFWrsiXzEZ37DDywfROOzgZQs36jEqXKMPJJBY2NBiYyDSAQbDmApw6YYdJWEnFk9x9Hdv+xfNuB8lWqD/zRbLy862zorasXPL0LRH/8YDDQhEqjaMDLVKhUp2HgQvl0QE5o/UN3hqbnTBwVdvdWalJi8/adJs9ZAghBjKenZ1hY2IEDByJop11GPI3PTtHSBKUWx+dSpe0Xz54A+TkOS69/ckdGrXK5sXcB0Cz3wKIRsFWR/u52ZSsUrlS9tqjIUqFaTXGDmE+Rimijet0GfUeMS0tJ3r5uRUJsdPV6DfsMkVUwhYWFdezYEbZOGP4DAEVRj/TXYlgQag3FEAxQK6BxtToBv/40nF8V/TFi+uI1Vi/E3sGhVMWqLxOyoFKDUokZjj5DR727fxYAMpPBMqZqZBqAiiKNkowGRYBRuK68vpEWfvnAbclh9NlFnNV7niQUdrVtViKvfHaO4z59+uTk5OTq+sUUeOWIi1I75XPz8Cv9niRZjpAlMAiKAdKzsjkxmvCri5rmeJcioKYIPhv9j5pSESPI/5ashhT/LwIONaUMI5LiYq6/uRX98cPGw2chzkdYe6j+JEknMTFx9OjRISEhVWrVGz5xejG/UsVLlcUTvuSPE4plPqfJYQWMERx75dwpMeDwddaoSOJThr6Eu63l5pZXIT6sn706giSnLljRY9CI3RvXXDxz/N2rF7dv3cSoZgAal/ZtdPTshVPHQh+9eC2eml5H2NvIRoHiZLDEXUzswuPj400L2NfPHwNe4g25d+Oasuj0TyDs7i1UboP8JeBV0kwIlUQSAEAbcXM33t6GrRNgskOTBxygjSK3TpudBaCSj+OKFSv69Okj7vDssUPNfxooHIEgobHjkwH2Do6Ql6VINToteXNBQUHXrklYhwM2ClQ+SU+vy876sae5opKHOFNDEYRGRTVv18HWS69Wq7/v0denYOEeg0ZcPHNcrda06dRVpVIzjEz15PThfbwRidBASydWpCCVAQePW1cuKIgpXgV823frMy6o2+P7tzHmiHxzDiDadupm7+iYx3KRn0cOkBJLg48dKlWuYoWxw8UNfH19x4wZc/pl8q7DLwEY5RxPsU+6F5155+lLlMoPAJT64plTnw04ItP0868qVcLUFLG0VXEA0xevPnlgt0Gvb9Wxi7RsePX8XxRfuXfj6toFvzI0zU+s3Lh83tunwKCu34kbXLlyBTA/nEaaNUiuQdr/sRxnZDiCpM4/fg9gw9L55p0En/6Z46zOX1y/GJwQp1PZedEwWzoTBOFXpryeTTq6ZztgetQ/PUX4ZXSeY8zOgsZRWE5STOJHqG1h52Lmk1rTq9BQJABdnsWzDAbD4MGDT506BWDevHmDBuXVrIeH8gmi1JRa8/7NS4AAQcDeFa4+gp0NQUKl4UCYaQUkKeXGkuDUFKk1svgnkUb/2/Hv53AAsJIp5VgA79+8zMrMgKld/SvmI5YvXx4SEgLg4e3rf6xZCuBSsMAsA8MPIPJYUCo/t+QoZKWWkzTr35V293ZUz7788a+Q6QRQtKS/4FAKcw9EEvD2LeRbuGjMx/fmTRkjoUi3GnUw6kAbNKLwK8zNU+vWwnz/H2uXXecLDUwDkYJFin7DSyBJEkWrwLeMbBpLEXAwNFgaHCe6iAGcjHGpTdMwOhiycGM3aGPz9oK6ZatWrbp16ybukNceEI7Azx8TFICSpcsC+L5HPz5J06TVd8MmTM/phIODg2XRBgBbR9g4Krr5O1cvJMYrbXcKFim6eeWiqcODUhNjCKMu+NiB4/t20DTNV5Q4ubi279andadudvYOsEDEm9e8fQkICnYuPFEDAFx94CPpkk0Zju+7yVLlXhZFKwBomn58/7bwLSkYGkCxEv4A2nbuIV3j7JrPt3BRwQtNDmkZC4DoyA+W24j2OAoBPbGzPvEiiSpRw7S1iqA+P/piOM5gMS7JTEs1GgwAXN3cew0Z1X/0eJ+CFqQxC9y4FCKlcYTdvSldW6JECUAScLDynJk04ABnYDiG425EptMsV0QiYVyjXkOr0cbbl+G/jBmSnJwkRBumx4nTZT26cblRy7aOPI2GY8EYoc9G5BOXo9PpbFM1Fv92n1yA6Bfm5ovjrMp1JEZ9gHyG0RLSkfqJEyf4aAPA1KlT9bkKAFoO8ZU8PJJKTUk1t1flmqCuaS6VJEFpePFZfoGXbxHpZB9r0Iodx2dlk/6BOPcm5VXiZ/y8/378vwg41HIqkCD0BKjU6uSEeAAEx0EisPMNERdn9kPnfat5y00AAjPRcipUAbEjlOLEfE161N3rlyMjzPpIFEkCMObwbvx5YtF3XXsDgHN+dBIUhwx6bdTHiDkTR2XyxEOOA20AQ7NKGy0t1vdxcXZydLADOJP5J1ekhN+8tVu7du3Kb/Us7L4oYgGga9CQOo0Cn8Rlf6vyoZKlywpkdWmNH8d5evkAEGZ8GIMwSmKMMOqcHR1UBNyNSe4R1/kxa8n08CH1SxYqXHRCm2pHrj2QGp2np6cDpkGWj/+byBjTEQBKyEm8fPYkJSlRpVKV7zq6z/bbUxes4MslMtLTwp+E8XQ/8Zfad1yuYcrfGYv6lzNHrOiynz95dM+mtXdCLyeuHxFz4zh/Vsf2br9w+rjlxgpUqVWHl+kEScLJHaUFVgRZOsC9WVDdxs38SpcTToakANAsvXKHINTWskOXVh27Wu7TZHRCKEnFRp0bqSvh7wfgolRsFEhPTYn6GGHVC62y3Os1oGlLy21I0/SoIuBIN5FkWY4jJByUUhauuZZgWVgGHNqsjCO7/8jlW4PHTRE/O7uYpwnyuZsLp1s2lF1RmzZtKleuLA7YjfKRu/QPlgXNchyHBVc/xWYam7fr2KXfIAB1GzcbOp7nCZmfqKcP721asXD66IEAX7ihkdZ+48isg3N/fPsyPDODf5IZMEYVRdVu2LREfmfztKkgJceCY8yTwpIsIJ/G45ES9wkS+bXP4kVUkvRPnU6Xxy8KZ2HZVvBGbgAAla1DnSYt+o0YBxAgSEGMh6RQpzs0dllZmdJQngSnNvUbzN9VT/ANcTsyPTzx8/Thvxn/LwIOpRWoqXyLNhqD2gfeuBRy+fQxAF2a1X374vm3PXT79u3Fz6UrVAYkI7zMJEQ+likq8iI8cggmn4qAg2MNev2lMydWzp0hLrNVyaye8og8BiI0TTs4OXr7FnQv4kf6CNSqvm0azRo3HDC7isOoVdbL8ZInRl270u4l3exUJEGBBVCzbsDGg2ek/uzxsdHCfggSQL8R4wiC+Pl8RFzWtwkEvX0LueX3BqWWkRY5LiEupmqteqD12DQQSZHITgMA2oB1vUmO0aipBvVqL5g4nC90fPPi2c1LwY4Oji3a/+DpJdO3HjZsGGCSNi8XGME4gY82CAIqNZ+2uRpyunPjGn3aNHoZGfcpXRi9Pbxzo0P9yqN6dmhXu5z0CSxQyGKgzAdMcrx6+sjyYqM+RgifdBl4fA7PBEqp1WSAYlptZO/OXXr0BgCSlIY4LIgKNeqM+2X+6xfPTGt5B/DIAoWK8EWt42ctsCKzBhAE0WfYGEUVAAAHO9sdXStQBJGSlHjeQi2Uh2+RYtI/V2w/OHLKL9XqBPDpmTIVq/BMLBFarXb37t0XTgmWsIqA492b129ePAPAcgQjYUGuWTTXmIPwtggG1qrnOe7j+7fWNhegVpsDXF5Cm0fZStWatG5fq0GTvsPH9O0ryxKFhYWFhYWBNiD8MgCFTKBiSoX/YGRZhmUJkhw8bur5x+9/XbGhiNyw6d6Na2P6dt67eZ1QF0pSoCgZa02XCXDmRCZD40NYUVtDlZp1WJYB/x9MbRHHIvIJPoTxcYa9rW2vQSOWbNl76OqDQWMlGvNGPYBLZ0/zr48lFI2PVAy+W7duUnflvIDlLDI6JCU2uTQIUqXOSE9D0Do4m7jDJIkqbVGyTiYne7OcHR00pp6DyeuM0D8I7D/S5/bfH3AYDIaD+/bKFslNGf5Ys1QcWO/auNpyDy9evJg1a9by5cst5S8/i7Zt2x45cqTXkFG/LF3ba8goAEGjJwjrDs3AhfUIl1XHqS1+EFqfLZyzFKbzf3j7ujAcAWxVJP6yCq5dG1bt2bQuNupTUkKC6FYAlhXkBMSAQ5+NtFhezo8HBc7Nw3P9/pP9Asp6Oqg1FOnq7AJg2ZbdCnVIe3tHaeE+328x3Oc8ZfKMs69TfIuXsnNyJTUSmgvHAChVoRIAZCbh5ALEmzoPWq9Rqxm99umDu1LftXvBRwu7ylixBoNh6NCh7dq1A1DQR1AX5aRGGCaxJh7RkR/OHN7/9vWrkKs3ABzesUVcte8Pcy1J+259eBlWs7EZScmGpPzOOXbE5JkFclAuAYCUKKRE8R9rBTR+/ujB5KF9AysWW7dwdk60iXr16wMQ6hckR0xJThYUCwpXhIs3KArAvetXh3RuLQ1lOI67Enx60/IFt69eFBcG9hrq8/MJxYGy0tNWzP4ZgFqTo5bliEnTf1m6rm3nHv1Hjd97/ma5ytWKlvB38/DgCTThjx9uWrFQ3JhhmH79+v3444+blwtUXD0tD9YJ6uLNuwBYRexAkLFyDVNLWE+tc6xCO0REcmLCpCF91iyYJS4RSo0AABobm6m/LZ+7enOfYWNIeShmchXm8OYmLGYKGBYpWuGixDMyfk5O8GrIafMf1dpDY6fkyHMsgH1b1gt/nlpoc3vXm9N/rFs0J+zOTWGGBRwMWjw8CVqP9/fx9g4ff7BGfaEiRStVr+Ximq9dl55mHrRRByD86aN5k3/MS6oyn7vHy5cvV61atXv37hUrVnx2eynSUlMuScX9ePBBMwCA5fDo/t1DOzbDzgl2orQ0AUqNWp1RUib59VODolVNgfjf5sKNb0cFZdl/ou3cvz/g+O2331YuXyZbpM8SSPgAAJKixH7OYFBOGcbHRAcEBKxevXru3LlzJ43+ivR+/fr1+40YFxDYis9eVK4p0DyFg0pbfIY26iySYALjUgw4OMn/UaVWPUeTboGtisI3reASH/3Du7buWL8Sto5wzo/8xaSeWwAKFinGd9sqgutY0t797g6ErBZPUqMivLx9SpYuB4AiCTVJ2KoJirBIOwHF/EuLRiTF/Uvz1h4sx31RnJ7L67ruTkxspoEhCFb62HMcAH7Iy8O/TPneQ0f3HvrjHycuxkdF6jMzXj97LDOAiHszob5EGAPYvXv3oUPCnMKn0GOliEQADMtpjeypl/zAjujYW6aclpae9iGDWRCmPX/yqLQ7YSSpLLVGM37WgvOP3y/aaPKcI0j5kDQLiR/qNWzyffc+Jfwlbqs5YPm2A6XKVxzduxMvrHJo55ackgoqkgRgX6s9mgyVHvHR/bt3r18BgCrtUKyaKQVNJCXE92nTSK8T5oz3bf199vgRe7esnzZywNmjB/iFWQYmKdMihcCxwccPcRzn6ORcsZrSqNPJ2eWnmb/VrN84ILDlmOlzewwaIbqTpKeZ5UNSJaPn169fX7x4EZC8WQrWiGexc+rKsCw9ICmZ3ok1WBlAg/Pw9JTqjkixZdXi+zetK40CiHz/JqdVtWrVCggIAIQMgQIRqbqRp94A+P1e7M1IYbzBcZ8pppAZu9TqAhcvxQZNWrWT/Z2RqM+W2NrxFjy0ERyDSxvMIuiMEYAuM23+5DGxUQKjNrBdR4HPzs/NEeT9m6EJsTHIA9zc3Lp169asWTPSIh+WOw5t3xwbHaVcSlLSvFomS6HpMBCULFNIqUGp1SY+EwkOQCl3ey9HjZMNhb+g+u9vAMP95+VDLPHvDzjCw8OV1mL7pyJaKMWs07Bp9wHDTQI1ZKDJIVOE1ND83o1rM8cOzeNrYxVZRmb9BVMCnD+o1ICUMXK0RYvMhxoMTQpeoCSAwDbtHRydPL18+PlaHrZqvtbmGz9kWZkZaxf8CgDNRuK7Kajxg2QlB+DTh/f8tdB6bVp0xIgx44sWL8mfMwB3B1tnG2GITBJQU6SditrVubTKojXp0neQKHDZZ6jgqMlyWHMr5l70n436Y6Mi9UZDSlqagZYrrLNs576DHB3NYlNFSvr1HT627/Ax8THR4FiRMVqhqkAwrFCt5rQR/Y/v22HeeayMs/ny0T0AWp0uKkO/9YFA4nHK5+ZfrqJ5I6E6V33t/JkW7c23tD1PlJHD2TXf4asPxkyfqyRAMEZsH3X9wtnM9DTa+JnpgO4DhpevUp02ylJlm1cumjl26Lt37xQb82w5LdSexeRVQkrpRhUADNjAW3+9e/2SX/nk/h3xGzcuCYYsV8+fM1iyBzkWAEEQb148E1ilAIBu/YfOWLL2SGiYyAihjUZpWr6GxOqzTsMm4mdzLWVOJp+UilTbAEhOkkl1BU2ZzxBWim5EBL9JWXrDIgXCMFkZ6Xq9MIAx6PXJiQniyqT4OOX2Erx58VynzbY612BnZ7dt27ahP037rkMny7U0K3BXo9P1MZIYLvRiCJMzCf2H3gPMExaKHhcA0G94riaxLCOIfZm6XzsHRzHgwIeHSIu5GiKwjlQq1cQ5iz3ye5vqqykAovPfX4HU5KQLp49aqfgjVWgmKTl28oBfXZDyqUmScsznobYVAg6K4ACQBLqU9+hTyQv/AOGvrwDLcZ835Prb8e8POPLly6eYj2jesi3/oX23PrNXbWrYvHVA0+YA+o+eVKFazaFDh3p6ei6ZOZln8HkV8JV+9/rF4DV/wqQxKt3w0sHE8+c4tUZjfmMBoWhFZlvKQJ+FByec7O1sNCoAvMbl+eOHsjIzEuJipgzrJxZA2qn+kmDcLIVJaaC2kzEuWbYRfzNNNaXPHt77dfyIiNfhgMAjc0yNmBsoZPspglBTBEnAw96K8ncxv1IjJ88AMGbG/PqBAg2Q4fAhTRcvp3Gk6ZiojBz7V2mS4+br2JnjhgVWLNarVQOaZmhWKh/I32eucat2/Ub+VKVWPQDV6tTvO3wsv97btyBYBqwwATf1txVHrj2sXjfgyf07t69dWjl3xq2rFwFU8nFs00bu/MJLTkV+ZFmIlr8sCFmJr9Bhk65u7gGBrbafujxz2fo9ITcrVleO8nk4u+bzt2Q1mh6V6xdDAtspK2MB/Lpy456Qm5PnLVu378SAHycAUKnVAYHmQtzIiHcXTh+fMmWK4ot8oTjHQWlZLDboBMGfPwDYOUNjD4kiqp2DufjF3SQ9GXoxBBaVIBq1etXOwwBS5N1/QGAracHwleDTLav5d2pY7ZcxQ/je/fsefWcuW9+l3+BfV2wweyYD3t7eM2bMAExkGmtgOLxI1BrkUy37I6lL73ObM80wMGl6xT45MEZtZsbV4NMArpw71bpG6S5Nas4YPYhP9vAPVS5oW6tcp4bV5k8Zy1jQBDZt2rR+ydzj23+3+kV+9oTlOIPEBG/XpnWbJbNLCtg7Os5a/vvZ+6+Wbt2nsrGRu2cDAEkSP/QdKF3yfXeBWeJftgIu/o6Pj6T65fb29qZ5FuDqH0iKdHI2B+6REe8S42NF9vHwSTM0NrIjflt9zF0bV8dFR1mShGDjAD8JIZd/aC0kZDiCMpgsYY3aLACbls2HqWDunxZw5OXW/S/g+M9gxowZ3wU2hN48VRF8/ECFqjWWbNk7auosANmZmS8ePwSwZc3Ssf268LnxM4f38XyOKjXrdh/RpZsDAAAgAElEQVQ4XLrD0PNnRf+OL31hWA6c+EpwrNFgEMbQMFW9y+tWVCQ8PTznd63nmT+/miIJwqQpIhm9Xb8UnJaa8seapa8f3YGcUD3z0odMw5+dx/P09qnbuBlgqlOX9hkce/nsSXdPL6Fxp/WxH9+ZT4+hwRizM82K2tV9HasXcMpFztHezh6AvWmSiOM4juMMDCdKtrMsc+7YwTlbDq48/0TxXas/x/5tG0LPm2o9eFq+CIMWMS/AGPO5efgWLrJww46eg0clJST0atUgsGKx8yePFihUpH4hBzw4iY+PJsxe5OntY2tnL/XiunBKqKqoUKHCpUuXhg4dKqww6gDY2DkwMFdRMgzOHtkvbOBTmnD2hEpNqtS8y1eBQkXqN22hYKFKkaZjFl16bbFY2PnlcycaNm+tKCsFULVWXU8v78C23/uJis7A+F8X9Bsxzq9MOXHJxYsXaXnvS5l+JGXRkzTDQVJChoMkQZLTF68Rh7D9R43n6cC1GzbpPfRHAFdDzkR+eA8LUBTJF76Wq2RWla1au16JUmWkm80eP4L/cP1icPCxg/zn+k1bDB43RUoz5DFq1Kh9+/YNGjMROYCmmSkh77PVMg11PcPlbuHMspzSJJZh+A74+sXgXRtXz54gjKRvXD7PTyR17jNgyE9TNTZWpHGkOH/yyLFjsgqdjIyMefPmAYA+y+pXaIYXtJV3KiS1/4+NuR9LpVZXrFaTotSchYYKRRCNWrSVLunQs9+5h6+PhIat3Xu83w/fwZAtrUZJSogHQ0NIynL5fQpUrV2fX8UwtI3GBjAlaElSmpHKHbm3q2lpaVevXj13/4Vim+SEeDTsL/AwpPGBg9wZkU/LWSRCsnR6c+TG0ABOHdyt1+v4WifLxNzfILo18uSbxOyv5MvfjcowMOxfYHTxZ/HvDzi8vb2nTJnsaytpSzj2yYO7x00at7euXkiIiQIAkpIS3z6+E2ZYB4yeMHHOEnF5fu8CLPuVvTgrIarZ2tryi4TSMr64Q+7iQeu0CbFRrm7uFDhdeipHG9MS4wHZG3Xu2MF5k0bv/H3V2ycPAMRFm33U7kVlpuu/3DjeAjMWr/GbvNereBkXDy8beyfzCo4FkJQQB4POLeJaUIF0fHoKALQB2nTQBtBGqSZBDV+nGgUdc3FSEBXY9Hrd8+dPL76MAWBgWJG0tWn5gkXTJzy8d+f+7VunDu3JaT8iEuMkkx1y/iMYI/ZMBMdRKhWAK+dO7dqwKuKNMCnw29SxCXGxM7sHnt+59vzJo/ysh1qjkdIDzxzZf87U+ZUvX3727Nlz5swBAIMOgIeXDyuxBPyYrnOr3Ej4Zs1OXPEaoDQsh9fPZRq4OSEh2/BRY6FvYUpn8WGQZcBh1WvGwdGp15BRQaPMsp4tWrRQlJaI7YKSsSsXAXP18Gr20xJKbbPhcLCUx1CgUJF5a7cGh72Zs2qzm4cngPDHD5VCpQAArSl/Zu/oePDyvSE/TR0x6ZcZS9ZKCcWKYCg9VZmHqOIrCx3Gjx/ftWvXjYtmQ5cBa8jWarU6XZZWRo+g2c+whVjF3eBzk4wRHBt64dzWVUukG2ekpQEgSDIzI92gNzPGivuXXrP7WLf+Q/uL5HEAwJ49sofZINbL5HQJ2dmpKUkvnz+VLSVJLx9fkUWeC6xe6Y51K54+uNvmh+78n936D/UtXJSiVLwKu7dvIWFWRTpX9ekJTi8BAJaNj4n+Y80SAFtWLm5RxW90nx+cnF34R9S/fJVCcun3r8Pbt29LlizZqVOnXi0DxFePR8MWbeHsBScPQE6xV8ujPV53hLJ4FKUzLEY9Ti6AQUtRqr/OMiIXpOmY365FRmcav3q4+Nu1yMg0A/PP4578+wOO27dv16lTJ+rdK8DUT3McAJ1WILhxHBD3GrQBbjLKWOWaZtJyMT+z3EJ8bDTvzJRHSGNho6Tp1GVnAkBaHKJMExAvruLlNUhpHIwRHBd8/FD1pFDD69tgjNBn4f4xZydzrx9256bgJkrrAYQ/eyyuYrkvc8LMKW2jUqujOEfWzplmIYwD+EZHoguefHjR1gXThSW0Aet6gzHa29oULiqrafRzt29Xyg3WUMnHkc/+RH6MaFOjzOjRY+ZfegeBfi8cSBjAkSRIatmsqRFvX+V+RfXNCg2Eclhjajd3rl8J4N2rF4rvxlirIP1x+hwPSR7CnD4BAAwZMuTsg9eDR48FwLAyjuH1jxmuDU0BAZ9rIQiQ1NOHd3O/BINev2vD6nWL57GW89Om21IroDGAkqXLrdhxUIzwylWu9ur5EwDvX7+0vFE16zca/+vCgKYt+vXrt2jRIsVa0pThUGZlpRkOIIMmQjg/hsMra2GTNNwpWbYibC1FxjhnF3Os4Orm3rnvoA49+znK/dtUKlXrTt3EPxvkwNAUTpimt23bBgAsI/SFliBImmYsFWteJmaHfkhL01lp5fc+SQiLyZLdDcYIxminUSkpYgCARqZ5210bZIVvFavVLFW+4sAxk3oMHO4mMSlMS5NZ6Lm7uwv6NLQBlrwuAAT58/7raVr5Kod8cfHxf6zO4aolsJprP31o9/rFczMz0vdfuH3oyv2BYyaJqziW9fDyAssgMxEpEmtohkZihCCCDoScOPLi6aPdmwQ99Yz0ND73+er50+mjBvIl37ciMyLTcpPzygVbt24VPy+aLovYXPO5SRxhch5l8eYAlhYW0lEQx+LV9Uo1aqtUKnEI9HfqiKfo6LufMhjmi32MRbAcDAz7vyqV/wCEoQMnCkcKPY0YT8RFf0L4ZWQmo80EOHkMGD2hQbNWo6bO6mCav4SFPMaX0qcB3IvOPP0q+cAjybvK9xYJ7wRXLdqI6HDcOYCzS83bxL7G47M2tnaGxChhtoWlcWWzl8kpQwaWBeDm6WNewFnXa/8sLMMOhoOBYQ2sKU1n0OLTM8sXm1fS5C8PHGujVhajeNqrmpfMhxxw58oFALufpqBIZUEKEOA4vHr+nB+3CfRAggRJQmN/2bIKTn4VtRs2WbRxZ8eeQVZMbE0BR1xsNABL8gRfWaOAd4GCFarUEP90dHZWbMCxbHZKAoCL71M3PTDnVziOK1SiVPW6AcL58+IWBKVwWrfE1tVLtq5e8uj+Hc6CAKGiCACeXt7dBw7jl5SrVO2PExd46sCzsPtj+naeOjxoUKeWAzu0WPjzeEWNVcvvOy/4ffvq1at9fHwUeyYJznTaihUU+Ll/ggTAmKaoFs+aOqxr2yUzJ8d8+mh5CUaDYf/9CDQZqlhOgvMwMTwq+TjmorI/dvrcGUvWDp80Y+eZqwrVDQUo6eA1h/kIQZvBgjX5MCZrx+P4ESetFI+EJ2S/T5GJUNnZaPK5utYr6YWzy8WF8+fPnz9//q6zoQVN2iHCLw4A8PYt1GeYmZhZM6Cx+Llo0aKKI65atWr//v0/L1xlZ2ttRoak3mkKwdFDtjBwOEo3jPmkFF+3BGuVUcuxAK6cO+Xsmu/Zowfzp4zdsHR+WmpKdlbWtJEDxg/oAZZF7CucXiz7FkNjQxAfFXXo2U/JkxUKAMmbVy4M6dIGwNk3KWExOfwufwL7tv5uHlFIvQgs8VmLTo4F8Ojurcj3b/m5xb/ZS4VhOYYDiy8r0JOCV6j7B+pw/Pu9VJz4ZIAoFKHSgGM79R7Qua9Q35HOq2vwEQmlUjA2eJQoVbZ+YEt+OOvmmd/ZJccu0xJpKcnH9m4PocrD1VvHyONoHuK58XwOfgDBMiBJpMfh+UXvTo1PHtgD3wY8RbyYX+nX4U8tjiPssHBxs+Ehw30zw3qG5YysZLxLG7BfyTQEMGb63JE9TZU+LGPUZZO5OnBK+5j09PTzJw6j3WQ4ecA5PxI/iATV86ePn5/Vd//FO0GjJtwMew4be3gWQ88lzq6frxiqUquep2/Rw/ctpJlMb2OFKtUBVK8bMHXBijOH96WlJLt5eFaoVjM7O9Pe0UoX2G/kuNSU5Ie3r1ev20BkmPKIiIhoVaMG/Ouj7cTI5Axne2VXIVBAxMkdkvIqYC12lECY2lMUxAIwadcmxMVeCT5d3hQGcSz3UFJadSf0Mv8h+Pihjr2CrEZRlqBy8vdxL4TeK3BohpKdR5Kvw5+9Dn925vC+Bb/vqFanvrhGr9dNGNjjraowaim9f9UkQebNSIggSSmHNLctCaJQoUKRkZEAr2RlgtQ4kBRN6WQwMqzOSOgZ9mlcVrn89tJhBsspCRx2NhoNRbo6ORZysYk0mVH7+fmJdrV8yO7oZJauKlrCz9k1H0yP/cAfJ2qzMq8En27fvr0wGSe/kMaNG7vFZK7YrVAj5AACJEmpKJWdvSzkV9tCpXZ1d13483g7e4fOfQd6WxT6JmTTv139aN2l0pSBf/744YzRQvO4/48NAc1aCU+RXDSox8ARUR/ff3j7OikhnnNy9i9XIWjUT4yRtrJPUXrLaPzSQncp+vfv//vvAot24hxZ3MNJbeT4ahpVjsoun4Hp9JIS4kkPd1iQRv+6bAe/Z57vyXFfP5XDgqPZ/5FG/xMYMWJErVq1zHrVADhW2iA2bNEGAB+hB373g7V9gCAIN5MOcXJC/NY1n09a8mBZdu6k0dvXrYiJ+hQTE5OSZpIAB5q2/k6QkhTOzSBEHsL/adACIfzM4f1vXjwX5ow5NilBaZwhgGMB3L5+DQDLcW+StRzHPU/IVqSIYzMNXyF5y3Iw0JIRsnwAYWdvX71ug/GzFpSuUNnMcmAZRDzwZqwrDFoiKSnJvFuCkjHJSRLAtrXLPL283TtPQakAaOwJjV1M5AepuLsIRYZGZ+9GBg6zuCTW3dNr2MTpXYIEr5Amrb5btHFXucrV7t24tnXVkm6BdayO130LF120ceeJW8+W/7FP0aCvWbMGAAzZAFiCytbJUsfm5kOMHghi5Kr9799H5HJb8vGJd6sVm6bbFf3RPPujkFOTQkGGqOTjqGA/iMgxTLSxh8YOPZcKrt8ifEqL7Lx1i2anp6aIaxZMHff80UPeK0t+8pyt2rI4+htAiDYAaCVsBtbkIAgIgtYWMLKcgWYZDtMvfojOkPH1WIuCHTVFqClCm5UpfQIfPpQJsVfycXTSmO+kIsPk6uY+ffGa84/fb968OX/+/MgRpm+xDABC+NEJmuPUtvayDQkSJHXu6MHg44eO7d2+5JfJllWy6Tr6bbKlXrhZ3WfZH/vD7tyQrrvGOxwBSPyAROFJc/Pw7Dl4xPTFa/zLVcxIT8vMSH9w6/qZQ/ucXFz3nr8ZFBRUvnz5+k1b9B3+IyAkFeo1aa5Sq400fe9m6Io5P/NiMF+E4sWLv337dtHGnTvPXmv+naxguHq9BpIMRx4sI3KChKRy6tAe/j346qmNrwPLgeE4huO+erjIdyn/Czj+A/D29j527FjNeg0JAAYdstPadOwipUyXqVB5w8Ez+T3cAXQbYE5vjDn9NkNSBZeUEC9+1mblNSUYGRn54NZ19P8djm5mUW2DFkkfJ89e2KB5G0BIrniE7WtS2Q8QHxaj+Oi/fPYEAGgDDDpwnCVpTgDLwjRB8C5FN/18BIA/wmJvfZKRyK5FpB0PT1J89bMVNxzk8bI84NBmZ09bsIKXxRz440TBnUSbnnn7+N09K3PZrRTv3783F/WSZCvJtD3f3Z46uKdDQJWkxCSobKDScAR5ZPe2oO+a5qSaLIJlwVp02D6+vvsu3OrUq7+UakDT9In9u8Q/c5mysbO3t1yo5YlBJrEjhpO9XwzLNWrZjl9lXlqj07VbUktvJQaNndysXUcr9X4wD8UUPiDLtwlaW+269KrTqCn/uV6T5v5lyyNvoHKUViQEDQMFF692NxQXUiwRb17NnTSaY1kARoPhKt9dyacwCECjIjUW8cafcWYW0aJFC+GTLlMkWJCCzl5uHGoDwxlYjmE4I00vnzN9xuhBvL/awWeJWiMrMkb5QjEXG1VFL4c3105J91CjRg3FPjt0MEv78J71X3SNd0IvZ6SaHm/GCIAzCa7QLCdWcpoheZgf3rkRHamMmFmr0r20MBKrUb9hhao1ihT3U25ggeTEhKWzptI0HXz8kLgw/EkYAI/83gsXLrx06dLMZeubtWkPwLdIia5BQ8bN/A3Au1evboVePbF/1+ShfcPu3Mxp/znB2dm5Sq163hYGgR17BvlXqAIABKDLgNY62fbzkEzHpCQlmTgcf2vPzXIczfLKXV+5B/6Ec/LV+g/inzWlolKprBox/Hnky+eqSUn2LVa0j8ObW1cN29ev6DlwhJOLCz/Cs7OzK/LxZfynNI2NrZ2dYHbwJkVnJFV2dkLD2rB56+sXg/nPlWvUFjdzcLDitylCmJp1yAddhjngoPXYNnKLalj/kePioj5dCjmDT88SH127GMb1Hz3+xNGjCQAYIwjSzd3j152HR/bqCAC39iHqGSoLZLTOfQa+e/bQxsZm/PjxBw4c2LNnD/+e+JeraGdnR6lZPcMBMDAgKZV4tgAotZogjdIlnwWrVIG2nNtHdOSHyt4+AMpXqVa2UtUrwadwcDoApnShnI5la2srvXvu7u6Ie434d8hfHCRVs0GTMzdNA2XpeFSozlWLg5hXzx7nkm/XarPPnTwElZIqoVGrLU/Mcgyay41SqVQEQUgvoV69evv27RM7OSXNkyRL+Je6fPaELHpQqb0KFrZ6lDcvnt2/db1kqbIj5ywv/Tp51Q1Z50ESRD4PjzJNW1SoWqNb/yEqialbjboB118LaTCWYe7fvk6AqFq7noJ75ODgoFarKYqy5CTZ2+dsmsXrnSsCOJVauuT+zdDIiHelylUQXueKrVCgtHQKgwKrIlWlPB1rF3bmr128jXZ2n6G68e/swyghjCYIQqPRSC9h7ty5586dAyA4BZIUAJIPZhmjMlSSgGb5hp7V6Q0P791G4ocbl88fvnJ/5+MEBw0pBtxqimRodtX3ZThtVsuhZvltNze35s3NNbp2doyDg0O3bt0ePnx4+vRpFw/vlu071S1p1vesW9JBvATk0JKcOrgHJYWyETA01OaYieNg5U7xXmWmJ7Bw0eK28kdLrWGt9EP8BATL0gaDnZ1di/ad3r0K37Vxjbh+3/kbj+7difoQsW2dmbBy4dTRxPjYVh27njks2AdWrFZTcYFnXyQCqFYvYHTDPvzCpKRE2DvDxRtpsQ9uhUpF26Tg78bDqHTLJJzlExJ291bIiSMZ3oEgXFQE6E/P8OAYBm3BV4A1Zzi6BQ1hbWwAkCqVnZ2d4hFV/F5WTzWPEL/L71mlFpLJOx4n9FGpaxb6AkMZgiAIghAugCBz757+fvyzAg6apnP3I/56sIyKJJLjY2euEnIYH96+nrFkbVYWBUCr1WpIDoBWq9dqhWaRYbmsbJ1WLbycTVq3t7V3eHT3VqlyFRq3bKc1Fbnwe5CCTxWI45gJsxctSiBBqQm1jVD7zjIA9mxe12PQSFs7ezC0yIcoWKREQvRHpESBUoMgk+NjR/bqmN/bJz42BkYd3t3D+/sAPL28D2zfxH+lY8eOo0aNGjNmzKkY1WvAr1wlrVar1er4gMPIsDq9UTzbjLTUR/fvJpOO6WkeubhXSMFxiMm0+FEsOFleBQqKR2nYos2VYGHkV7VWfXG5FDY2NjqdLivL3E9UrFixcf16l2JfI39xOLidPH8VDqYoQRpwCJJZlNjDuef3ToiPWzpz8tWQMzXrNxo2cbqUV7h4xqSzd56gmxVBJKsnNnnest+mjgVQp2HTJq2/s7oND1tbW51OJ30AhGS+6eYo2nUjzdRr0mLzysXyehnCv3wVy6Pcu3Ft8lChja47bsVzW+WgU0MRbi4eM5etB2A00kZjjmN3nt5h+WZlZVEEQTAMY3l0Qy4unQLjUt50UCoUKA19JuLf8iSk/t8369x3UNlKVXoNHrUz1gleJaWb03odnW10LKBpWVx4bMTbmMsNF09bulnlAk4Gg0H6rZcvX5q3NuXMSIaGGjZqleWjLCU06PnJc4KEsydSY0Hrnz8OY1kvrV5mQQCQy2ZOPXtgu3RHycnJ79698/ISelx/VyorKys4OHjVqlUA8PbtwukTGx45YHlFBEHY2tpmyfOmOp1u48aNz8Luo4TJfdckOpzLzfFuOdCjcbunC4Nq1m/0Q5+BnMX9TExIsCJjZdQhIwGM8eGdm/z2QaPGt+3ck8/2te3cwz2/T5PW7aM+fpAGHAAe3b01YPQElUr97lW4m7tHucrVsrKyoqKiLly44OXl9ejRo5D74ag8ODMrS6vV6nXawac+wNkTnkXh6I7TS6SNhgLir6xoYC0TsW9ePBvR43sA6FIaBV00KrKIf+m3dw/h65AW585mlAxo3LFXULU6ATci0wHo9QbpmSieWHGhZV+QR4jf5fesNb2qLxKy3iVmVPD4AjIKSZI0SN70R2eks/KcjP8ifNF4VYp/VsDx14EiYNRmZSWb1Qyvhpyp4GXOittSBIBVC3/11Rj7jx7v4p6ftajLr9sosG6jwJwOkdOUxK0rF1F+MCgNR4p3W9jtpMG9nsvdt5/cvw3agK3DELQOBMUnEuJjYxq3bJcQF/P04T1+iZtH/gSJwsSqVauGDx/eplOP5bdizRKEDAe5ukBmRnqH2VvhUQSZ72dd3zR7xUYiD1Po71N1k0MilEslFd6Fi5ccPnG6VLe4QbNWK7YffHzvtn+5ilK6TO4gCCIuNhruvgBQrsl9yc0vVr/N+5gXeHsHgKOzSyYff5AkgGETpxf3L71x2W986v5O6GWNjQ3fDfM4e/QAfK0wJYkcZg0C235fr0nzzPQ03jj+iyB06jmUvzMMChcv2XTswgvxsvfO6ubnTx4WP0d+iNAVL6bYgNds/Yug4HCQBCH2UpTGxhppn0Ch8shfHDb22Ci4xhzYthFAnUaBcK2jLAlhjGCMMR8jgM9n76XIy3xEpUqSCSaT3qghK63w86NZNXsohWkYRho80SxHEAQIEk2H4eYePD1fskxZJjLJqDfA9P4atZmwdT57RG4JCQBwtqhaevDggfj5Tuhlo9GozplkI8XEiROFCjvx+aD1yEjIPeBITM/M1mor/Lx3XhcrkrVhHxImr9iMKu2UK1ga20dBbtbq6e3Tf/R46Va+hYuMn7Vg8S+TpAtvXAp59vDeu9cvAFy7cC5j8eLx4yXfcsiHyoPv3bi+933IpuULMHy32tmNBcWQqnZderbs0PkzdyEPEEQBILCAmxa07dT2u6g6xaZZI9Z/Fg2rlJ3evBQgxPr89BnfqD6KyfwmU36fhegRaGS4L5XSiMkwjDsjEOTpf57y17+fw8Gy7Pjx408f2m3IzpSOy6vXbSBNwyZ8egfgadj9c8cODh4ypMvWm+w34gpdO39GGBGKw3SOAzByyi+KaGPcL7+Z3TtZFs8u4JWga3np7AmpOEG7rj0VR1m7du3tKxdgmr1jJbMD2aYxxMPb12HvCkc3kNStKxejrOlMWIJmOJ2Vgm7zozx2+rxbVy/+PGrAukVzRPuucpWrdR84PO/RBo/njx7waiJQaSCRXo4w2jUYPmf5tgOn7jwvYRLNVNvYnn/8vlOv/gDiYsymTaEXzkn3WbN+I6sECCrn7trO3j6P0YaiAVq5ciWQo6i2kWXfJmVfMBRWMC4Za6GPk4ur+Dny4wedQdnNqCmStFprkDfk3nSS8goOteReESRFWOomAaDUUNmAVI5hbl4+r7aRKuJzgBBwaDS5db2WhbJ5bO69vLyWLjXVlosvFMd9vHwoKV5JuCZYWjE/yHEcSEpt51SibMX5G3Y/zLBlOY6RJKUYbSYOTLOUx1i6dKnlyK9aNXNtTq2AxnmMNiCVAhN1PhgjNg6wLsthAs0inVY9SdBHvLEiURNy/zn8rb2SprukkCOzRMsOXXiqFg8v34J7t6znow0eGzfKpU5ZFkC6zrhpk6CgY2QAlaZeYMthE6ZTFpXeIqyyyqwO6nwLFQGAOt1h4wDgxO8LN/w8okRJf0CSiM2lSlYOJ3nI+B/icAgfpMPFCefeX4lIi8+UkWHjM43hCbIKAC3NiNTD2CyD4R8Wc/z7A47jx49v27YNLCtlAxXzKzV0/DTpZplJcYDA5kvL1KbraKtVSdlGNln7Bdqdj2Iy6zQMBEFAMsVOEeTGw2fbd+ur2HjprMkvnojWbgziXiMhQlx76+rFcTPnj/914baTlzt07zt84gzF1+OjPgHYcj/uYWzmzStmW/Atq5asXzwXgKOTs0CAcPZC20kKeaWcwEDm12A6PeFO1m0U+PxGyNHd225duXhox+bt677MUVqB6nUbCIxLSi0dd3IcVBrb8lWq29jaiVMD0mG2dGSmKGyW0ddNIAgQf6K3tgqzOmQOo5K3KbqfL3wQfgIJrDZn3foPFfw2wc8iyfp4ikAxV5v6RYVfMHcRi6+AhiLcJH43KpKAqYyU4XKwTOZ/MpJC94Wwl8w6V+/gXaykOcPBm4YwtJ2tpmgJP/H8v+HJAzh61OSCK1Z+8VGgxU/D8WqhFlDb2ddr2yXbu+yme7Esy3HSmJVjESlT1p84e1FUVFTv3laM95o1a7Zo0aKmTZs2/67TqGlfYMMkltdKek1etzBXjgv/K1CqObdTLBXM1LZ2lnY24iGq1QkoUryklbVyjPtl3qCxkyvXrNOxZ5ClYLky5OJPvkRNBPQDeAUaDcPh+qULrWuUXvzLJF61OU3HLL1uYYyXN9Rr2iKgaUtUaSv433LsjcvnV8+bAUgkR3NMCykfZcUwpGg+u6KuNsGvU57EfXFl31dD+oyKjcPbFN22sLjbUbIKgJuf0g8+l5kQsSwhWiTqaFZD/WVZ0K/Cvz/giOalvlkWtFB36ubhuXbP8aIl/aWbFeBnXnlagIlRL+0JjAbDmcP7Jq/ZNefoHctis1xKPIZO4iMD8w/P0IZBHVveCb0iVQHi8fLZo4bNWw/5aap7PudW7dr3HjpaunbpzCmVa45sqqgAACAASURBVNT2LVwEAGcRs6ckxQOIzTSEvXh3YPtm8wqSOrh909sXzyvXrOvi5gFKDUd3B7/qeTRvZK3Wc5uO/l23Pu/fmz0y+HEVTdMJsTG5GFfysOxmqtauJ9hLUippiAbTb7Fv6+8vnj01LUFYRBz/EzVu2W7Oqs0dewaN/3VhvxEybYxs1yKooLTbUJN5mU36Mmg0mrZt2wI5Dqf0NMeAIEilUee+JwnP4pVTre6eXgs37Nx/4XbVflPhWUz6/KhIQk2RhVxsupb3xJcg73GJiiTmBxYV/+QdfCiTo5t1UGqoNCBVcC8MG/NRqLo99Bonc3qP7wNehU5sXr6Vv4e1HX2D+OnqVVPJJWeqh8+pt6aNVnsjA8MFv0ndFhZHcyyruGrJH01at98TfKN5+x80OTOi+vXrt3fv3l2b11vWVuSC7t1NXFHRmYiPlnJPspMUVBoQVCxjp7A10BpZ70JFKY3F7DvL8Ifo1l+pzGb9CCTVNWjI4k27h0+aIVooi5g2bZqUOSvecAcXNwDmWneSBHD2yP5r588BSNIab36yXleSe/VcVLphx+lL165fN5dBcRyAl0/DAJhtqqxXyXKWP70it+dpr6ru6xSVYYhI/Qy16KuhuEAdzUq9KsU+iGU5A63UHmVZMCykCuispJzwx9oy59F/Av79AUdgYCAAvLmJSxsQsgZAcmJC8PFDihbNv0xZAF4FCwEQx6AnXiS/M8kLzp00esmWPc/ff3ocdn94t/YG0zj7swWl+X0sWhmOBTBtRNCbF8+2n7qkWMmwbOe+g/7oG/DToD7dBwxXGE4u/mWS0WCIj41Zt0ipFJScIGj8xcTGw1ZydSQFICM97VnY/bTUVL5j0Bs+42YOIDYqctqI/j/27WplnalPLVWugp+feRq+ZOly7169aFnVr3vzui2q+Il+NHmEPinaZLNHKLSJoqM+LZw+YeOy36TpikmXouOyhAup3bDJ8EkzWn7fWZGnzbL3QpHKigOpv0IsNg8QbkUOY1CW4xiWBUEqJKcexGR+SLXOlXbzzM/61YVvWelCXgHCUjo19076S7vwfHbm26gmhePm9gW+CMvCipORO5oKTf/t/R5Znwq7Kg1L845cLofjuAKinJqoqmfureURE0sLZkZy0CxnYDkDzdGMRYwlCSgvnj62/4/fU5M/U5i9efNmT0/PwIrFNm/enPuWIqpUqWK+BI4DY+SHvuVKl3JUy38IlpFdFKUGSep0ulW//aqX2LgsuxkV/DaVU5BpeFVyjgVwaOeWL6UMqNXKMGvnzp27du169EjM1Ao79PAuMP7XhaSY2zM9IckJ8eBfjZxz/1Yb2MyM9NW/zfxl+fq9rw3ovcL81HEsgIRPH3D/KK+IYzaoUowELOU6WOb21Ysn9u86sX/XmxeCWr+NyuzfJm3t/yL5r5A3qevumAWpY9MND2MyT71KZjnOYEErZMF9TNMNPm62dWQlg0Orptz/Wfz7Aw5/f/+QkBD7lAhEPkGykLW7dOa4dJvkhPiw26EA2vzQEzBnOK5GpL5K1ALIzswMvXAOVdujaFWQ1NuXz+/fCkXeYIUTblpy41LIzg1rpHbhABq3bAfATk0C0NjYzFiyRro27M7NbeuWK7y8TUcSXqdw0huNBpmXk1SVWvXKVqp6/UIw70ICSk2z7Munj6zsBNDrdb9NGRtYsdjYoK63r13KXQHiTujlKVOmjBo1qmb9Rj0Gjug5eOTO383CG186w1KhQgWhjbDA6xfhwccOoscSOJjJDQxISyUCBSi1xlJzUEMRIi/yG85HPHnyBMhtDMqysLyfegN9Nyrz+Iskqx4TtvYO9nJlWzVF/hkCRx4vNp+tSmW6RRoVSRDQWLBeZMNBwbCeUgQcJKWSCT/wiUaW9rEQwczpbL/014mOjo4WLQz5PiY9AcmRAPDpGVKlNA4O0eGIVtro8DAyrJHhjIpf05QPEHFk97YfGlVr3Ljxhw/WSVERERGTJ0/mP0+ePDkiIiIvVxEWZjJs4i3gGSNY9oc+A1e0L1u5gHwylDFC6m7P/wok+eDOrWCJw5meZrMNrKw54vdMG/jX+daVC+9eSwp88oBfTS6+Injb2wIFCkybNk04BADgw/t3Br3efGxTiVmdRk21RjY+0/ilIlfrF805untbxLs3BpBQacyvlXCBXKXscEFyVJw1U4QX/FrZEmNMZMSKOT+vmPPz0C5tL509AcBORQFgc1SmMeMr/MMtv8twnE4SoIdGZqy5HbP1QRzDwciw0gxHVIYhTctkG1jp9tJkdC40tf8U/v0BB4Do6OhifqWkS9w8zLp+er1+xYxxD25cA7BlzTIAIIUxqIEW3k8bXo2DIKDS8CNs0nKAmQOsvEeSBiv42MEeg4aPnvZr0zbfd+wZtHzbgYbNW3Mcl5wQz7KMNjvbUnvq/asXJUqVFlRKre2WBmXrbO6i6jRq9uuKDRobG3snJyE9QBAgqGhrMpoAFkz76fypowASYmMAWBIgeJ8U/pOdg6Odnd2MGTPmrd3af/R4jY2NwWDuNS+fO6mwN8sdvXv3lslRS8E3KO6FYCdpbQnys46Ijx/es3TNUCe+7V4hF23Hr4SQV5d3SNLX3sCwnEWgwIB4mZB1/EXS5JD3ilUvnj56Ff48WydLR6lIQkMSSpeabw2CEKgbJEHYqsn1bf34oZ4UakWLxnM4SApV2sHbDwSJ/2PvOuOjqL72MzO7m7LpBRKSEEILNfTeAqE36SCCICiIAiI2BBRFRUWlijQBpYmAdBSpUkIPEHqHUAPpfXenvR9mdnZmdjbZQBLy/vX5kF/2Ttk7OzP3nnvOc55TtjJIKk/uTqNNWDrik2/nOoroPb/xFxgoizRxrEiHEiqeHFwm50WBZXDtMK5qS15aWN7C2alWOKjTcfHixc8//1zzPA8fPpR/fPDAKbKCjD/OCmaBq6vr6Pcm79m++bKq4J9QYkmCcBcICuWqJabbQnUcz1tUHnmpPLXVJ/f8MkiS0NmpU6fEr0i6AwCe/vPis6SfMjSi0mvj3vt1xz9BIWF7bqUtOpXIFJKYuWvLBpStDI8Am5krwHp3qlSvaa3oazUslOaFi55y0esUziHJEwYA2L9zKwBXHYmCAllFCJZXkKRolrOwPMvxQuKhfLhbfiZx/510muXkXg25C8RRIt4LxP++wXH//v1hw4ZdOhcnb3xl1Fjp/6tXr8bGxoqPqTgfiz8LB2LfnzuSEh9TlG7kOx+CJKHTg6QCg4Ib2LGlHEHLw6F4eDetXt5z4NCPv5791kef1qrX8MaVi++PfHlATJOOdSv3aFpz7pdTVUdfv3zhyw/GN4/uYPdN1gxAjmdkd7ZCZA1BFnPA8Dd0BqsTmySjGjTW7PDFM7LhjCBhH/RlxDG3WZuYpq3bqjZGd1Ik3X020U5T3DFIkmzZOtrBNgW9RgKbbypR0pPEy+fP2dlM/NNbl5qEaEiFPicmTZrUunVrVflQ1myL/lq01TIIC8tbWNDWSzHl5V48e/rSubixP/ySmpFpu2TGAsBAEjrSRnrVnKGLxG0jkDb0JEGBqOTvStn5ZjSCLEJdvYgGCIxAQHn0nMzxvEIAjedGjBjZJt+Kr88Jg8EQEWHNIhYW8YplvezuCJsccG5olqdZO4as6mwyPHnyRLNdkaYL1K2rDvBpon379mFhYQBw4xiS7+L+hUFh3OX4M99OmZiqyrWxDw0ITqamA11rRQsNx+9n0RxvVvkDBf6K9Yq69Xs5vFLhspRff+dDVctXX30l/CMaTDyPE+sBwOiH8rbfwTugzCtvjBXoaAzPm1iOZbnzFy44ICRroEOPPmjUF1WaCWOy7R3nuMYto6fPXXL/7m0xYGTzcCjePleD3pyTJaqsCqDN8pfX4OICq8FRYokqnNLApVmeZlmW5zmOp1n+Zpop9p7IG2U5mFlOHmehWV7OFHZ+VVxi+N83OG7cuKFq+X3vMfl7JZbKtFY1BBRr+vgzJ7/79AMAL48cE1m7LmVwLxsavnL7AeeXApwG41LRkp2VJTFC5n316ZiBPeJP56d1nZaSvGfH5o2r7ILBeRnCG0WzCprn0XuZf1xOBuDi4lqhSjWh0eDm7h9YVn0GAIBQX0qAsXl/Q+fx6j1YMZNw2uxFcl1wAe279+rW92V5C6dVv1sT9+7du3P9svY2rzLo/pHKXQ8gOzc/9vjqPcfhp+TQ8LxQpMa+58+P6tWr//HHH3ahYtuIxjv4UhawsBzLcUcSMu8lJHRvUnPCsP7vvNoPzQbBq4yMcckA0FNEeW/Xcl7PWpvKaVAUIXydMHDZWxc6UlMLhIDOAJ9gBEeC0qnLzvF8mXJFw2XLx6iy1TG3ViCybZOvVWmzjYxpB/uCbQCQk45sbcaGXMVcDg8Pj7Nnz06YMOHdd989d+6ch1ZFQHssWbJE1JE79QdSH4I2Na9V6dql84B6xeJq0Ovt454E4eHj5+0n0nK/PXL/QYZdtpk0GXPsB1/MfPfTGcJP6rypOmjkmK8X/Sp9/PTTTyX3Ut++1lonws8r6SwLbTLHL8chKzuH4fiJB1MG9+vlZFjnzQ+mlg0N17kaBY1EWaSSn/HTiuZtO7gYXMBzYGikP8bdOMSuVlUP5s05sOQqnEOn/sBJmzLbK6PGwRrd1hjGiwcqD6jAJWI4ngM4nj/zKHv/HbG0BcfzFoanWV7Sxdl9K23uMZv/LP/CmS8E//sGR+3aak3rO8oHukyZMmPHjhXfinLV4ebd4SVZCTeSOnM8NjU5iWWZJ48fswT5JCXti2Xrne9A/iEVAMf+2du1UbWZU99/Z1i/bb+vcv7Majy6ikv7AKhGyYR0082UPADL455YrHc8Hw/ha2Pfk/7PMVss9g+JKRPmnA+//MGR1dVnyGvS/z0HDnV+ap8xY8ZDrWJsAODhb6hQx77CZyqjz6UdXsw1MghhUYomlgbLtGqnzlt5BjhSMg4KVk6oBWXrQFzHcCyH7448+G2D7OlSenR0JACMrB/0YavQblX97M+jmi2e2c8hHCXceKOBdNGR0Bq/SJ7TmOoAUHpUbYEGvdXBLJ4HzzaWyvsVvktOombNmh9++CEAsQKz/HGXm7/bvsa9ePAsHNjEGi/vo8vYquZrA3j55Zdff/11R/0JDQ2dMmXK5MmTQ0KcNbZOnjxp+8CzLi6GYwf3hgoSusre9qgX0aWmxmlpDgl37zx59HD6e29lZWalpKWpFdsEa+P+BRxY0rxt4d4IhmFiD+xZOvubOzeu/bz57x+W/bZud+y4ceOkHfr16xcbGwtAVNahdPKH4enTJ1JN44yMDJoDCAIGt6TUtFF9O+c6oY/p7eNbMbI6dHqrKWN9ODnuj1XLAHj6+IrmZvI9HFyOE+ul302IFfYz3kfCWYVzyJQtGSU/LF9XsWo1SAYHX0KTt71HnOZ4mhNXqTRrS1TheN7CcQzHsxy/+2aaheVpls+TaSaVwtn9f19pNDAwcOPGjf362WyIyJpRqn2aNWv246GbANCoL1IfRNaqu+eKdZIgSAA+fn6/r1iSnu4LYzkEVDhqzk5NeuoX6BQJQM02krlwjR4eOdkiZUFeA8lJtGnT5uDBg8ovYwDYq2YIz+jmq8kSC53h+M3rVvYe9Kr9aVu067h+/8kHCXduXLqw8OAVUHYr6XvxI6q7d+yp1hGREF6pyqo/Dx4/uL9sSGizNjHOX1FmZqZDiR5KD16Dcbng5CMzG9Spsq/mQX4BZXV5yfIJ3+jmpqNQJkjcv1DTmLBz/ryw3NzcxAdy8qCQXwCC53jHpdhpliMIEkKBDNlsQpAUL/v9PdxcszmiaZin7vnWLk5etRBSqV/O861GwdJHGzg29UmizssfpB0ZXsxY4VQGB8mzIRUqyl1oRdthOXx8fACAZcGYoWJKSshOBs+BY8FYNEKHmtByrf+0blv/mGaF7WH+yMyUl7rlzHm5K+Yvrd+0xaARY3ZkeEiPIEngzKafOQBVX1KdwcywW3f+tfW7f2DORrXXaKEUi3QVBIHsNB3BlS8fOvmt+Z5eBRTseHgvYdu6lQB6DhoaFBI6bcLoE4dEsZ8lP8xYtnl3WEQl1SFVq1Zt3aHroUt3ACuV1YqUlKQP3v3io48nd+jRh2Gtz4l1n9vXr9Sq17DAn4jjCY6gQOqULjRu4Xdfduv3ssHFYJVfEt8oF4OBBs+B0JEEw/Fdur+07Z9jyQ+voHJT4cDIGrWaxdR/mvi4TcdudRqKUq3uegqFCakUSpDUfjCx/yJaJn1jYXnJ18JxYj1YlsfiU48jA9xUGWGlL6JSGm2gokebNm0WrN3apmPXFu06zlu5sUxwOdUOP/30k20YIqkfv7Xp84RVqjp/9aYpb49cNnemyKymdCApeb6ZJiTWsdo0tjp4ff39JWvjGTB8+PBly5Z169ZN3lgnKgpaQ6IoP8rBTFtnM4JYklXVkYiZX0BgVIPGBEWCtKuaAYDnuvUVc2UdvVrBoeV7vzK8eXR7jfRNx4iOjrYzOKwXQ1IsD9gxLvMszN5b6SvOJmpeC08QUglvgUKlp7QZlwWOEU4OIgaDQXEJVsIaYV+nVLZOZXlYGI5hOY7n23TpKbZ6BhCUjpDpkfh4uK0bUK1Aa6Ookm4E644ixEWe2thjaWQ+ZVIeahwp0KspvUpMxd3Vxf1ZqzAUFi+//HKZ4HLYuwD34oU7UqFyZKuYTgr3gPD//YvY86OTpw0I1NA+OXMi1nnygZOwpcVCoL5yAM4cj123fGF2hq1eNEebb125eMeaw6kAQaJqS3R+BwO/AUEq7oWwrN/8GbPxs8auKSpRInvkZmcP6x79x+rlf6xePqx72+MH90vWhoBDUgl7JTw8PUHnAVBFQuEfjpe/O7x3FwAPL29xbqR0whj7+/JFqjisZgIIx/O8ytrgxCTmuzevd+83GGe34+EliaURWj5cT5EAONoEwGDQJcduwbXDwhtK8uyro8eOGPf+xGlfyyWSBe0sDSqesnv5bC0U7H3P8vg4x4uMjdRcmpPVmqQ5Pt3EmllOvrN6hVAK8K8wOABE1or65PsFn89ZLKg3qkZkT09P230mKDmHo33PvuUrVj4VexCwcjt0eoOLW3CoXZKIA7Cs0lvLWFwMLt37v5KWoq4RL6BarToNHTNSg0PCfttzLPZG4ogRI9q0abNzp6I6tmKQkuHgnl3toyJohpFzCBidW57jYASA5CeJWikqAMd5+zolGlZYjBo1atQ7ikoN8ngEq5UIarLQiVnmY/eyJvypEYvJzsqSbCyeoQFwFpOeooqPTvXo0SPQJpxYb9PwtuTi2G+c2Y5roiT6sTxohmVY/kFqDnQuCAjHwG84HrzM4GMZxse15LySFEHoSEJip1IEoTAfWRpxWxC3xcHBelAGlYHoQpF2FemLCxcuXHj6+BGS7sCSB55bsGbLz5t21W7QRGEOCgM2Y8YTNdNLEyQBTfGun2d/+8EHBYiCFxbDhsk8iCqlDTnvVWBwc5y2a1BngN5VZDnIXZXCs8dxsOQ+vVPwtd+4oihMMm3CaNUO3r7aXis9axK1g1UgKVB6wdfF2KIAhGDVHju47+LZOI2jlGB5Xp3zdTcO6Y8B0BY6vFKVTUtmD4rQRbF323TqtnjDnxQJxpQH8JbcHADpKSld+w6SZMF0qsfbCgoEHMSgi0OKg7dLLVGZOizPZ5qYMdtv3ZNl0VtYfs6xh39fT5PvWfrsjX+NwZE/pk+fbpuBBOUiKzgOOVlZAFC7o1XOgbAwzJUL5+xOowGWZWZNV6aZ3Is3717w9LHWuhBo1/Wlz+cuJh2nPDZv11EIpbdu3VqVbteu60uxf2/VPowUUskV4Akyf7P9ptkdPmpvEDi2TgONulBFhRbtlCEYOQHCLrsVACidiWbyGE7NwAc2r/nlysXznDQksTQA2pTrZiDyV/x95ioeEFIVeB6xq8V1FUuDY3HsNw2WgD23g6Q48CuvmxDZUoxKEHIBNL4ISWCLFy8eOHBg7969jx075mgfioCesnmD+tcMqBYg80/cPI60hw2aNndwsN7eN9YwxOPj1oVQ23wepKVZB19TNsw5ru7uAJKfJlpvhCA8Kitd5AQ42nwx7qTmpl9//dWUT4ndwqNGjRpxcdZJN+UeUmRJ7PLe5qTBlAWO0a6xQulB6V3cPaRUfxGCwcFzAIx2Bedg9woE5Sua0jKmU6de2mXY+vTpo6JqStAZDMPGTNjy28pVS+bbWq36HBxbANM8NU9DABmHVwqCK48f3gPg5eP7+tgJs5as/OS7HyOqVH1y9wabmw6WBW3CjaPXz8eNmvhxo2YtdRQBwOjmoilcIVjIjnQ4nkd7QxMFxm4S0s2jt92wcAr5DYbjTTSbp+QclcLZvRR26QVg5syZnLTcJCmjLJyZnJz07vABABD9OrwCpX1WL5oHJ3D+9IkzJ48qmszZeHz95JF/NPfPzszYuXEd7UAG1MPT649VywbGNJ03Y5r91v1/br1x1kF6i1cAOo63t3jz19rJrdRCrdHJsWDpypFiqsvzuO4bhPlotqufSHkkQpsDQeSYzDSr1uADsODbzxVhAJYGYCAxvG7QwNoORcGdjK042i0qKsrb1w+wuusfXxN9AKoFqKgdqXE5ZpZHWG3UaKvSKyN5Lh/9bGf6LOH48eNTp07dv3//rl27OnTowDDakTWSJPQyZ1DbCB9/mXbh1N7NVqxc3UXOsFYcTKniKQD0FBHkUezJNQJatrR6xU+sb6x/GhZREUDzth1EGqkoPWl9ZpxMpGK0RTgEOF+YzUmUL1/+pUFDAeDSPlzca9sgd3hs+gyPruLBJUFGWQVCZ/AOKEvq9ep3x2LCid8FOuf231cXGA8qWy7k/ekzhf/bdu4u37TpYNxnsxe5uLhqcqi7du369ZyfNIkvBnejm9H448r1Yg0UASQFoFmbmFp2oukqvLn95gN7oTzrFz26lzBj0oRNa1YIBRZysrMmDBuQsWw8EuJF2ZLt34SGhXl4er00cIi3hycEdTstq4KUFYwtARTITjXRnJkDx8GiXGJZOFtSleCqKYVZKv/7pNECYTabN27ciMhW4meSysmx0bUOHDiQm5kLCPkCpLSPKIpVEBIfPlDTDvJ9t08e+ceRLQIgO0vs2O8rFrds2fLIETu1U0eqWZ6BcPOxJ0Bo1Rm3wdsvQJf5VDEXsbThmcIRztAtBSjOzrHOpHhYGM5EMwRJvv/3nU/bhHlZgw6NWrQ5JR9nGQuArzpXrRLi8ZzRzXyMEhcXl14vD/v1p9niHJadiuux4rXIIYx6PKdhRVF6lKshCnfKQHBMUQ0gV68qtDWTkpLE5HBVRwjoCUIuaSp3vUXVa+jnrr+bkOH4e/JVJgVQDGXbJHh5eW06dObg7p0kSeXl5iyf932r9l2CQkJdDHqzoDxB6YXYRJuOXU6ciXfKO6HMsG3Usg1FUccP7gcwe/ZsSrOI7vNh3OTpdRu3+GfXtuOH9pslD4qQ7amTqcwxZiRqlIflSYrmeNp+tuRZxK6RPrEsq9PpHN0L0cIe81rnXv0BZGVmpKelCTkm4yZ/7uWrkS0lR6PmrairZ1hCbY1xHLIzM1C/hzyP7O3Jn0e4WKIaNi4wtY1mObPZAih34zkAMd1eWr1kPoD9f27Nycoa+ub4v7duvBx/RrxwlgbP167faMX8Hzy9fUJa987JIGHwJlIerP9pw1EXZsDw0XKen/DQllhabIFfZGF5HQ8O6pxtmrMJ0xtIwszypdCd8J/BYacOSShCKrmVWsJMY/c8RcVOgmzQvBWcQEryUzXXzm6FZPTwzMnWLlyUDyZOnBgZGXn//v2IiIjTp0+L3lcH3ktCZ3BxdbMfUmceuf9q3bJNQrWzHliO56Q5j+dAkCTPGV3dCzvtSQNZnWCPAm0OhVXO2SkaaYLS0xwPlr6cyCRlZHu5ir6TPkNeO3XoqbRXRHj5ic1DqgU61PvSDKM8g7+0Rp16gPVG29bQSvlRAm5ubtksYy+7buVaqg0OnqWfjXpiP5E0a2ZLqWjbtm1QUJDmgRRJ6GUa8LDGswWQVh1SkiDyj83ZDinZFZeXj2+PAUO++nC8oFG9bvmiljGdzJZgWS03HoBO72LKyQYAjlVzG1UwZ8sZCfWatOj/6uu+dKqPj4+3dwFZHs8MiiIP7v5T0ZSVhMwn8AsD5FEhbScNzXIa8v+yUah2/UZOqgpdvRj/64LZp2IP9h48/JvpnwYHBz+FpzMHElqPB8vzZcuF+gSUSZe9Ap4+fnVrR9jvbA+Og4VhoVPcLy8vr5lLl6yY94OtzxfOAZCEjgQfrV6nuyDJG/79J4Ij0f+rxGUTE01Zp4F7d27NXLJaev1LuEJ9gbE9C8cTBHhe7XSRZ8zqKcLMQlMk58WiFNpAJQ2CIKZOnWqbD0hKYSKQlBiKJmX6RSTVe7DDpFA5/APKqJewPA/A6CG+qMEhYeXKVyhsn7t3796iRYtvvvlmzZo1X375Zf/+1gCqumyY+EjypI7Vikc8TMtZHf90gaxWkIQ9t9LNLMcTkuQUDYCjzaaczGIVsFOQKxhaHmxWQD68kpSF4cwW2kLTc7761JQn0jMbtWhTpYZN3DA7M626Y2ujqJCdnb1p9Qqxh3IVS+WtYc15ppwsbXNKUCzQ6VUhMM/sxD41ioarGxkZuXnz5r59+44ZM2bhwoWOMokogtBTiudG7uEQjhL2cepbObbkWWwMwwjWhoCE2zdxYRfi/7SxJoF9O7fYisrmA57DsXWibiYAwN3dfdWiuXPmzDl79mxxdJ5l2QXTJn76zij1hrM7cHqL+ArYHjAHeqmazTLz97PZixx1QMXkGDu4l0Cf37z2l1u3bml6xTSh0yKlsRwIgtAbDHLDetHuuEuJTpn4HHj7mF1memrCjetlZUI4gUHBAGK6WnOGOU5PEbQ8x5CxIPEGYHtDzxyPlRerUSN1RAAAIABJREFUEvpeKAfH87A6BA9HPploPM+zvEYEmeF4ye4Xyzs/a7ml4sO/xeC4ceXi1HEj20dFLJ//g/3WEydO2CYwklKZCJ6+/uXCwhWNJPnG9tsZ5oLjvh169Klep4HtszW9TXJpPH54/8blC85fSLPomOlzl6xdu1Yqd2qxWEaMGDFmzBjAjvvG2i6K1QoNmix0toW9kZz31nZ1Wdf5xx9ef/DURgIXixHQBGPOn3H5nFAEO2iB4q5Fd1C28AQppBdduXg+/tRx2wbpveXYpMePLp49/QxdKpTbf8aMGScOHxC+UeGBV92aY2uZ3T8KQm0aUMoyCggP8m8boU18eQa0bNly0aJFs2bNCg11yOKMCjI2D/Oq4OsqtQi2pvUvAIT5uDQPde73YelnLjj3zFCt3avVrov0RKQ+hCUP/yy1ES2lorL5lJ+QomBW7Nj428qFc1evXt2/f3+xdEiRIjY2dvPmzdrbrLkVdRo0bt+9d70mzes2LID0YINVKRhA2XKh3gXFRASoiD6LFy/OysqKCjIe2LX9p2+nH9i13RERZOnsb0y5Gp5Xjuc/3nM3Fy5ywzrdt3LcLW1CvfpwDvYxO/D8mqULXhs7sUOP3gDad+81Ytz7AAKDgjcdOjNl5rx2XbrpCDvjjGMAhQCOj+w3ER5aefQ5MduSaymW2iqx9zKF+uR6ishnlGU5DQNI/vO7UOTQumXc9P8ZHC8IYwb2EEKty+d/b58yzvM87p3D7VOAOksFQFZm5qOnquqsRA5l3L5lk+Z3ZaanHdi1XZjbdHp9/Q6SIA/vqPKTACnVVnhJNPH5nMXN23YQhtHs7Oxhw4aFhISUKVNm4cKFgGJWIwiQhCyBW3Osp/TZOblJmbkpuZZdm9e3j4poHxWxbvnCK+fPmsxmm2gHrBM8y7Tzy+1fMz/GpTPqlvnsoLDs78Zh02dY/a6aga/J7aAokBSiX78PH8n8N1usdglLg+OuXtAukFuEsBXmVlXxUDmfbh7H/QuKQmJyaBkc508d+37aR6phvfg4EADebBT8RsOgDpVsVo4wCArrJ+FOhXga6jJ3nTmbQZdP9lUxYvT7k6X/q1SrAVgrup3ZbjMvWAaMRVQ6d+Q8Z2jwPCVLvbl5xaZ+sX//fq1jngt5eVoJpQKEpwuIP3Vs747NZ08cPXf8qMOdVZCNQlVrqoWYHUGn07XuYKtrHR8f//XXXy9ZsuSrD8dvWrPiqw/H//ijWsuEZdnp06f/vmKxo0Hv0tMc3l0ZiiIpd09tAV/1yTVvE8/du33zQcKdj76atff8nUkzZksqc14+vm0796hdqbx/zn2pXF/Z4JCa9RqAZfDrWDDmilWrtWjb4ce1Wwjrcq5OsIfwrzx+sTQu8cDddOSLAp0cmukt+26lnUvMBqAjCU23kACO5/MPYpIEMaxu2UJpIJUM/hUGhypdLS9RXZMzOjoajAVpDwFBh0N5nwIjMGS2+qSU7pef5tpL8CYlPu7Tuv5XH46fMKz/ou+/ArAt1ppAy9qVdZChbuNmjx/cA9Cqfef01JQho8Zp7kbI7IYlS5b8+acyuCub1Uie5ywFMeFIKisnNyU1LSU5+ftpogDGz3NmjhvSh+OVaagsAyCkjH/HttGCDJSTKOyMSFrL2QIAxyEvE09vazMu1SBAEC4hVQ9keP14XKT0WqR8H5YGz3koMwCLY7a2PWyiyqHMw6GQnBIYHg6cZELBTxV4ftfm9bevXSnK7hYSJEkQBCHUqZfcFXq9U2RJDzd34kX4eM+dtHm8juzbDQg/u129m0WvgjHbCn3Zg6XBcZEOZuhKldQ6m88PW2avPThWzLvmlCEVZ6gGLC3kfzZv22HCVA2Zdkd47/NvQ2Tx3zt37siVjg8cOKDaf+3atfPnzxd7KwNhfSksLG9wU7+DcSeOJdzKTxokLzf37IlY7RmXV/iP7dEj0n9K17qSeMy0WQsvCYIfKfcB1G3U9OuFv1SrpSi2J5JGZV/HavJwiwKc9QbqKVJdirkwkC81SxX+FQaHq6tr0zbtpI/R0dGqHUaPHj1rxe9NGjcBUDY0XM26cHGHwT72T4Ak9+3YLGWOCJAHjDeu/JlhGIuLp/gQiZWgxZetQYMG8gPPnRTlEA7v3bVpzYrV8tx0K1q26zTny6mT3nx16dKlABITE9V7yKwZ1mJyJsVDpAuQFIKr2lyUrh4glBqj5pxyiXHLBzeoUUabBuF82Y78d3OhyFpl3A2Cde8oHuF4VtC7eWRZuByrbyZPmv6zkpH2sHZBuXbPj7ZtreVzeaV9eWgFHsjkIIV254teW+kgd27ZkhGK1b2hCYogDCQh93AA8HdQaF6F1hHezco7tXgtYsimigcJtwFbZFOA0cNTrzfAkivaiPZvjcTw4Lng0HCpuV6T5o1bRgMYM2ZMnz59irzjCQkJDrfxHFiLrW+QR4UKgimbzctesW3f9LlLnIynCDB6eEZ3suXE1qpVKyAgQPoYaKfBevmytRCjcpXFWx2WDMfnmtQSAKeOHRnZu+O924og78Wzpw/s2p6Rnpb8NLFHu+YfbDqjTarg+XpNmguv+bmTx+Z99emapT/mKgWdq1SvtfPk5aV/7Fq185/jh/ZHWOtZAmjQtKX6hNYgrzykwnFgeV5LBsRZOPJ/SE+lntQqxew0SqFvQ8C/JUvlvc++XbdsYWpyUude/Zo2bZpj55mIatD4pnvyiROPy1eq+uSaUtRLWQXAhhZD5/62be7c2Ru2/+3rL754BqH+e8VGuBMHnqNI0j2ogpmloTOI06R1+Fu+fHlCQsJHH3105UrBa9ZKlSq169n/VOzBI/v/BnD66OG8vLyePXtKVTH79OmzadMmxQSWz1pNDp0BLAGORd/pWDkemU8REI4eH4OUFWDkeXBM1ZTTFDG84BPmiwLnSB1JzOlaqffayxaAd8C4JMFzguvb7r2ysJxQzRnAlt3/ZGRz8DQCwONrdTPPhpR/x/meFGo3CW+99daWLVsSEhLECh3SHbl/AXUFHXoeIKwGh7N1dIXlNYDMdMer3uKHQBF115NtIvwlKk/c0YNwaaF5OyQQ4KOCPGo5sFaLFW06dRNZNUBqchIAlS6nbUEshepU6SpCMtGVf5ByLzPD2KhFm7s3r4dVrDTpyx/8AssUn9kXHR39/fffa2/jWNBmXD1kizZKVpF93pMK9y/gwOLXNjArtu61L4CSP4aMHlfOx+38+fNRUVETJkxITU29++jpsYP7OnXqNG2aWhyoRYsWP//8MyCbSIUfVlbVhbETCxXWez/N/OLDL74T6lX9POfbdctFZutLg4bC3QcRDaCFylWrT5+71MXV7cqFc++/PlhovHnl8rRZP8l3c3F1Cw4N696kptTSpHW7VjGd2nXtSdPqMdOqwyHzcPD483ra3XTze82Lpu6xBElerLKfWxV/t1/OPnmGk5AkUQpFzQX8WwwOX/+AMR9+AsDV1dXRPl4GHQDSLjvAocERHAmfYCTeOLTnz5esVdA69ux7/NC+0/XHYtX4CePH79j4W5q5HAwMdAawDLKSkZNWp2GTabMWlitXzmg0OmNt1KtXb9euXSRJBgZ+IzUuXbr0+PHju3bt2rt3b+XKlbt27Xrw4MEUuTyfJQ+0g5CKfG6QLo0gRJeGzgCDm4KQxdLguH/+3jlu8nQnl0TPnFMqdoqEniJ8ygSJWa1KTwB3YgPKVoGHv/3YamE4MyUuR1beZOBp1RTiuYxU7ariRQs/P78pU6aMGjUKHIez20VikNhvYQ3KgNKD5/0CyqTybP7ztA1W31ilqtWLqefOQLgvFEm+3dgmVODt7Q1TAVMdwbMvypvasWffKtVr3bp2pUbd+q92bQMADy/j4AqNXa0iDdDRSoODhs6A05thzok/lWGxmAEkPXl84+qlJs5VcHw2NGnSZN26dcvWrH/88P7FM0pSKseCY/CnaI4MHTO+QuVqO3LYs/fz4GIs6MScYFf98/fOoW+OL1SX9AaDWIYXABAUFPTF/J8B1An2MBqNqoVc9+7dZ8+e/e6779oWDCwNkpLHQzmSUvN0y1VDtTanjx4cENNk+/FLLq4ukrUB4PC+v0F4aIgO8zwIokKlym7u7gBOHbHFeg7v/YuhaZ1Slu36ZYVYe79XR9ZrrK2ZKyqNKguaZJmZXIvTqwWnIY1zgUZ9j0j/ZzM4DGSpdXD8O0IqcG6d6uOmA2DwKxteqwEgS2h0ZHBQeuhdQOkIWQjG3cNjxk8rDG5u89duaxYdM3fGNBh9rYxLGic34Oz2+NMnkp8+AZCVpYg1tu7QpdfLivKtzaI77Nq1a/fu3SRJJicriKsCaTwiIqJt27bt27efOnVqSkqKYmK+ewZbvgCgJt5b+e02kBRICgQl6AoPHTPB4K78uayOmUvnCi5wUCSgSFJPkuUrWpdfKk/A5QPIShbLXqtAELkm2mSxAGAM7rgnUTh53wCHXNeiRa9evYa/PdHd6BYe6ItsmZUjy70cNXHSt0tWij58J6AnieCQsDEfTK3TqGmxdNo5BLjryxjVweWYbi8BAsvHoZNZX+IiHJC99RFVItt371UutPyET74EAEsuHl4CIGckAABLgzZrqL/IcrMFa0PAKccafUWFmJiYN4YOUlsbsBUFBBASXmHVwnn3b9/8rlfdwMDAAn5mWTjJ07twSU+aYdP8h9YhQ4bMX73JxaqQSwnUGXnQyv6RCamBSo2Ff2d9PolQBrhTk56CJDVMW9YCoP+rI8RzlA+Xb9TZicAGllVoz5QJshnQqssUuErnn+Suu5AkfhXPW7SkjTVRqEWX4OEgCJAgnrnokI569mOLG6W1X8UGTf1dAHm5ubvW/wrgcEJWQkA9MLJQriNzUSfU4KZaxXSWN5MkxYNw9/RKTU6C0RckZTM4pOrwDA0gNDS0cuXK0oHuFO+lfC+iO3WVqB4qq7VTp06xsbGRkZHdunWrXLmyyNiSx0o5FqYsAGDtGJdaVTxAkqjdATXaXczWEXqD+hCehSyPBsVMIKDAG3REcqJVIMReF/zkBpz8Q/NYhsfpo7Gnjx4iXL2886zCXxw38DV1xaliAkEQQ0aP6988qnfXjooNsih709bREZUj69Spq13ayg7hZf169ujed+jIou9uYdCvZsCAmoGq4czdxRWAt5en3vHSyt3NtVjlW5xEt74vS9FPAL0HD1/6x67mbTsAiKxZBztm4tYJmHOVM6I1K9tuggkNr1jcNBqWZQcMGKCx4f55/Clm+D9MuAvglwWzTh895OpiIB0xkcUziouHWvUadumtXQClaKHT681WaRxWMNfyN7IpPXyCMWQOgAN/bQfw3meiZ7d2/YaAWptRBEMDCC4n1nxp16Vn71eGA2jYvPW8VRoDRXBo+bGTPhP+H/PhJ2rTUwbhaU83MTdS8wDsv5NuZnkLy+Uv0/xsEJS7dCRBEKKKxjMQOVwpUlcK3jVN/OsMDkfYvnzuP39ts33mmIIZl5RocGRkpF48e3rahNHTJoyOP3U8y8xyHM9wiKgcWbVJOwCgTWDMeHABWUkAGrZo3betWP/s5k0bN2rXrl0rV66Uf0N6mrg+Tk1N3bBhQ52G4uq2cePGn3zyyeLFi6U9xeQIuSeA5ymOxu+T1DmlLG3Q2TlshHc4shUa9b1Ge5tVakH34nFwecv2nStUqlLAb1JEoEhSTxEWk3UyVno45q/aEODtKZpTGgfrQOmWL5idw3AVQ8oCIMF3eqmvvOR0CeCVKH8yS+kR5TmAhznP/e7x0HIhprzcIDYVmz935my6s1sauxWQiVcyoEio5DR0FEEShIue1Os0qPXCZ4OuVKy6CJKcv2pThUpiNfYfv/nswF/bp89dsivu+rVL8chKxrHfELsatDAvitozsJhw4W+VR61bv5e79x9c3B3OyHCgHM+xyFBzxs+dOp6dkSZ3fmjASmQ2enrZJ9k9G/K3uk7FHrKFVGgTds12JIgsgtLDzQse/ohsBUpHEESnXv1+XLt1/urN0Z17ILIV2o/ROIqx6AjorPMzQZJvfzRt/upNDEOPH9q3fVTE7OmTWeXP0mvwsL3n7+w9f6fvkBEP7yUcO7hPrvclQU8SHgYKVmtgwfFHDzJMNMcrhcULgXx8HkIuTIC73tdNzIrVW5NjnbfX64d4TW3jbDHzEkYpGANKEPm8GLdv3y4045IgBYPj1wVzJgzrH7t/d+z+3e99NOmtbdc4gOV5nV7ffeQ7AECbsHAo9i5E6gMAH07/jnRu9H1jUG8AOTk5I0eO/OSTT+JPH2/QoMGWLVv27t3r6uoql2QQPSVy2UGeYxkGDy+rcy9Zx3rhOgN0epYn1E4dcw4eXzuyd9e29auFhuJe2FEkWpT3ql/WWptUmKohWh7hFatUiqyej6IJKN31Rm+B487uWAMeBh3l7V2i+REsy3z27phZn32saBU4iRwztoEfSZJLZn3z99YNDsvfyMFzV8+fWfjdF8XU20KBJNWKyQaKWN030lVH6klCZ7ciE1ISosPcK3g7pE+VJIJCwwT/ooC1Py8A8PjhffEzx+JuHLZ+CUh6UDQ4BnsWSB6OiCrVdp68/O6nM+wd9UUOPz+/OnXqFLwfAGDdsoVpSU9Yi0kraVwEyYuE2ROH9q/RSoUrWmRmZi6f950id/fyAXWKuLqLlM7FDSSJmDff/GJ+VmbG5LdeGzv4pXFDej+6nwB3Hxg1aGSBgf7rB1aXaxJmpqeNG9JHyv7bufG3Pdu0lZP27tgyrHv0J+Ne79q4xrVL59XdIYgJzUJgtQY4HjTL87y6fmxKHj1l7115S2ELyf544lGGiQEwoFbAoNqBFEkYKMJFZ00+d+DrsDdE9CQR7FlCJRILi3+XwZEPIiIibG+FOdcmqp0/dAYQVFpKEjqOg8ENAHQumXkWnse29WtTk57m8HpXHQGOk/ibUQ2btI2qKJ2gXr16js7dpc+AxYsXjxo1atGiRVKdtri4OIPBIAh/DRtmk1fnOFmaZf4al8kJlhQHWn6UHpTevs67dAaFiKcTeGa7hCSIesEe7wwf+OGX39dp2NSmOsDSACgC1WrXzW/McveBTxAYCx5dgSWHZyzcM69HnglXL8QfPbBH3lK3UTOBseFudG/X5SUA235fBTiXqMLS4PnTRw+zzuQ5FzOMesrdzkNWxkMvZsyShDqswtIAPG4cKONR7NOzk1CJp2VlZgSULSvfjOwUAGBpMbSqNG0//PJ7F1c35/PAnwePHz+2Sck5Rr2mLTr3HgBY330HkqkEz1G8TTQvJSmpSDurgaNHjwIAb7UwhF8ydhUeXMznKEJvcDV6uXt6N2zZdufG304fPSy0/7FqebU69TUZo5mpKQZe4cq9c+Oaaq/ERw9ULaa83A2/Ll3xo01+evOaX+z7I3gZhCGEBW9hedhFqjNN7NXkXM3LkZsd9iZIholNSDfdTTcduJ2enMvAKrakI4kNg6q76SgABEHoraWLVIfb+xRLgyvREUpx10oWU6ZMqVXXOvebMvHgImJX2e+mtjJJChQ17O2JiGwFN28AIMicPBOAXds2ff7+20+yzEEeLgFlbVT2V94YKz9BWflIp8T5uJOLFy/evHnzN998I2+X8t3bt29/9uzZ1atXnzp16sSJE4A1fVQYKG0al4rhMjh+HZ7e0ub3CUEiTQY4AKBSZA1HvS0qCIM4RYAiQJBkx559p836CfsWCSOUQUfCSpIoF+IwJ83FLwiQtC5Yc3ZG3BG1KlGxgrQrHHru1LHa9Rt6eRjLBpUT7EVBfVm8ZfmbHQwNjmvetoNc5vJFoU6Q8euOFezbSRI6itSTpFoWjqUBsHbZhi8Qr455R/7xmykTP5vwpmIP4XYwtEggVRooQnmOkoE85OoIjVtFf7dkdfXadQGA48BYwDKE1hPFMxb66G84LcqlR3fqVqSd1YCfnx8APLyC9EdidSEA9+KRl6nckZe/AjQHC8MwLJuXZ8qTy6JXbBzRpL3BqPRWcixY2pyXO+nNYeuWL2JoWrAF7TN+m7aJUbX8MG3S4h9mPJEZImmpKfZXQcm0vzgOggKHijTK8nw+qjr5uDoO3k2fcej+h7vvcjwsLAcZvdqopygCBGGT5VCZFwRB2FddKaX0DQD/GRwSjEbjK6+/LX4wZSMvEwnn7Hcj7LgdpM6wY8MancGlXtNWTWK6eoVWEidskrp09nTC0/Qq/q5kjk07QeXGHD58uPxjeLiNXC1wwQS0aNFC+GfKlCkVKlSQ2kNDQzt16mRrsZhwbidos9LDoRh6fPz8HJZ9JykNg8MqOVWhctX+w97QOKoYQFG2hbKXj+/ObVtq16oFwM/To11FHz0JhqYf3bvrSFfRzFOAzeBA3NY7f8zhnBe9eG5UqxnVTqoXZUVaUiLJ89L4MGri5IbNW4neI7tZTQ4DRTaPbvfupzOKq7uFhGZlKR1BGChST6miKiLjsm1njbmt5IXLBMR069Vj4BDp44mD++OOHVHswdJ4dAW0CSwNU6acc1Clei2fwohlPSdq1aqVz9bOvQeMnfTZp98vANCxZ9/ozt1xYDHun/f18dZpak2yNHIzYcrq1u/l735e06b4DY7GjRv3GjwMB5fh6W2SZxWCHIqOKeO8lJ7jYbHQ3/+4MKC8jVaPmjFnHmcry7LzVkFF/uLZ0z/P+XbV4nnCBr+AwG8W/dq4ZZvyFSs3b9thzq8bVBKiHMfKdRoF5OVoWAauOhJWC0OSHFVpjQpqYPn+GNpgOZgYjuF4FjzN8VDmc1EUoSMJPUkKL53q1RNU+OReD4IgqNJXJFbCfwaHDcHWFbOvu8HmA1SCMeep4qMcj4N/72RY3lSu5gXXqpm1XxJrGJIUgOQ8xnxuV+66T6T9z8edlB8eExNz7ty5mTNnTpgwoUGDBpK2YGRkpHy3N9544+HDhw8fPpwwYYJm53fu3AkAPNczIEdHEnh4GYlWSUplPcmcjHQx/U8TlJ0MidV2uXvzulArsgTgqqOEl1yAi6tbYGAZAHqKmNw6jCCIlKQnBeaUiporHIusJGSn3Lx6OZ+dixYESU7+Zs7i9TsFJUoBD27dSH/66OmjB4JL//cVi08fPSw8ZpQ4MWtXZPL19e7Rf7A8vaIEUFhr4O3G5dpF+AQZDfJFmJ4gAv18m4e4lwsKyufY4oOjq6hVV1s5SgTHYt1Hoqfw/kX8IUpala9YecZPWgIexQZfX999+/ZVqSHqqasisD36D67buNlP306f/t5bu7dtfO+zb3du2zpo8BAPN1c7D4cgdswIHrWHCXfqNiyh/Oqxkz7bdvRCTOfuHu5urm5WHo+KgKVKnSNIkCRIKiGoycp9spRgksw1WZSOBWsIyXrCJ7evShsbNm8946dflm/ZM33uklr1Gqo6RmqJHbi6a2jTCcUc8izc3zfTpNwUTpmlwoJnOSw9nZhDF25hw4G3sDzL8hwnLjrkFgQJ6ClCTxECPUXF5NCRhJ5UWCEGknghBQScxH8Ghw3CrZzRoUKdapVlQUflHGDPuBSeWpK4oiufG1gVehfRp0VSAB6l5xzavEYlf84pXW8hISGvvfba+++/HxdnU7m4ds0WgBw+fHi3bt0MBoPB4JAK1KhRo+XLlw99c3x0lx6enh64dgg3rXwLaehhaQD3bt3AiQ04pZ1TCnunvSyGffv6VfXW4sG3HSpElVXoF5EkCIKQQphlg0Pc3d3yzyQSk/GswgMP7t4uru46QKVqNWK697J9jtuCuM1Z6Wk3r15iGGbjyp8BMaQi6NBruMFZGkD/moGV/dzUm0oZapRxf6VOYL9a/gZZsNmgo7y8PKZ3qPTCpYhUZQXbdek5YPgbAJq2aVelek3tYwRHIG+TJV30+44SNvsAREVFLVy3bfVfh3bs2LF+/Xr5pkvxZ17v0+nPTb8f2vPX7OlTejSt+ehewpO/lqbvXWHJU2aCSBk3PA/g3KnjgmaxgOLmo7h7eKQmPclMeWqS/AfqYgUMoSa6EiBJ6F3S0mTJWQRJs1qWiszgKF++EAkac37doGoZ0l9Dot5NTwG4n2n+OS5RcmzI/RmnHmatiX/KcNxfN9NScgpBtLqdZkrJpS0sx8gqzsuJGhRF6ElCTxF6knChSPkyDKItQsmtED2l5nSXKvxncNgg3OZq/m6eHh7gedAmxP8pFiyQwNJqYTyxuixhJUBYJ2ySAqVjXTyFVFgJbTp100xRcXFxcdSxoUOHFtj577//fsSIEasWzZv42sC0pKeK91laTDBW/SJzDsxO06dl3v4SU51SvVcQRLVJENYfnyDJFjUr4czWfE7Cs1Z6BM8CCK0QUSx9zRcXz5y2fchNR3YqeJbneEp6BoThkjGDo3nGzmHD0gDahLn5ur149oYzqBFoHF4/COLAR+gpgiqV6y2CJEdNnLz3/J0v5y8LDa+ovZOSfB1SPtxgfUlLPhgUFBLWpEkT1SiR/ESdGfv1lIkHNqzIit+PS3sB9WID9y8gQ0zVfvxQzaAsVpw9fhiZSXhqNfrVqXM0r+GtJEDpy1tzmNF5Aty8Gdj5X2Uejiat2k6ePNnuPA5Rq15DITN25c5/Pvzy+5Xb97/66qv2u7nqCAA0xzMsLzGOLSx/1srMeJhpuZ6cxwGs04JgAtbEPz14N4PmeJbjJZeJIqRCEHqSNJCEi47cMKi6q05x+XqK0FHQE/8ZHP8PQQorM5LQUURIWDg4FvsWqQMr8X9W9FNm95E6kRYsGBySBB6pg4c/wZhgFqnLVWvUHvbWuxOnfe2oAxs3brRv7NWrV/5xXAFnzpyxfeBZhWNGugRLbjCbGibQRJwnNNw6jrtnWrTtMG3WT1ENGjt7VFGDIqAnSXl4ctSoUbicLxVU4nDwPACjxwuoHKa2ch5crJd3tUr1mgRJTvjkKwBgLFj0KhiLdmIUywDYvGp5yfT2+eHpQjUL9YQ1uqwntWXzSibFw0kHqYo8AAAgAElEQVS8OuadqjW0asCe3Y4T63EnDkBwaPmF63YIzS+w525ubu+9957wf4NmLTv07KvaQSwmnJWEuK2ATGJL+Oefn5FyT2jwDyhGUXYVWJYBxyHpNvb8CKBcWLg6pHInDrdPaxxJ6UmdvmVMJ1//ALJyE3j4MZzK4GDA0rDkCjHiytVrent7a5ynIPgHlunYs2+VGhojbZ1gD6OeAkCzvEUWRknIMH19SMymFrRHeR40xxXK4GB5WFieZnmWt9Uvlr8yFAg9RdQN9ni/Rai7nqQoQh5UGVy7TKvy3mU9bJ7vukEedYM9ne9ACeP/x7KpSFDgMCHcSJIgKvi4hnSOWfnn/Mz0NGliJgAeWDjpze1JbjeuywpokRSqtQEASgdexrikdPAMcGNzW/fqn5aSHF6p8ohxH+SfuN+mTZsLFy5MmjRp586d3bt3HzhwoL+/f8OGDZ1xR+vlZ+ZYpeQoZ62ZRIefXXtcKMPofJ3ShHN4cHHaji2aIc8SAynQo2TrG1//AFuunb3yIOQGB9cyplO5sHCNfYoZkTWjpP+Hv/1u3cbNa9ZtINzQ7v0Ht2rf+dGDe+Ne6S167wlSWTZMZFwm3FIn+JU8nC+OQ8mCzV6uOqPhRT42ziAsotLMpat7tRAZhR6e3tlZGQBw5R9pn7c++tTdo1RYSJMmTerXr9+6P/flZGV99eE41dZW7bsc3vsXABsZWe9qyyqXDQvVajsr7/H8oCidfIVjMZvU40/CObAW1OmszrEgqbtUmQYDp342O7Tz8jMcy6gNWMYsEvzjtgAIDS/Ai5mVkb7h15+TnjyK7tS9Set2ABIf3p/1+cdnjsfWb9pi8tezawZWsz/K3UASBDiel9MzeN5WQpbjeCHBpLCCYBzPWxh1sXu5U7BdRZ/4JzmuOjLC1xWi/UGyDKcjCYbjW1Xw8nHVVfRz/ergfY7nCQJNy3u1rOBjsahr8JYS/IsMjgIh5D5RBLpV9TuwK1Ysy2l9RXnGAp2hfMVKVLLSjWlwQ6d3AECnV8x8pA6egaH+3u+/OdP5PgQFBf3yyy/P0PlJkyZlZ2cfPnwYsDc4GDAWGNzAc8cP7Rcb81HNUoHnuvYd9GKtDVjrlKqCUTMX//rhaQYsDcqgHolMWeC5hs3b3A0I6Dp6/Cudmr8QGoFI1AAA3LlxbchoRa0sb18/b1+/LbHxozZfyTFZck0WnpWVDWMZWPLw4FK9ho1Kss/2KNSaXniPhFyVukEeoxu9GLqokxAu7af9u6WW7KyMCpWr+gUEnjkeKzWuWjj35pVLfYeOdDcWWBqt2LF27dr589WaXbN/WV+tVp3cnGye547s+9tWKZBlbMm91ll/zAdT8xHzLg6EV4hIuHNL+J+haaXBwYPnwHFiXUMVvMpeeJL71o6bHKDcygMEdn6H5ARwDHj+pUFD23ezUaY0TeQfPv/4yN5dAPZs3zxrxe9RDRr/smCWcKPPHI9dMvvbjqs1SMEUQRgo0sxwnDoVVvyHA4QEE7kVooKmsc7xPG239pMHRXrX8E/JoxnrOSmC0JOECdBTBMPxpNgIPUmYWZ4iiOeoaV8S+M/gsEGYzEiC4Dnurz9+F1ulp4FloDOQIGhzLsFzvGRYGCQ2H6F4Hyhdw5juAcEBe7ZvSkl62jKmU4HW9/OgRo0amzZt+nXbnp0b150i2YDQctDl3L15DQD+nosWQ1GpseIl55yrU8qxrWI6TXhzSAG7FT8EXSmVHyOiUhWcvkKBZ+1LleZlAsTpowdx90nYBxNflIIFy9gYZAyjZpPxHPf31o2X4uPYOyk5cENYbYTWgl6M2ekpwuDm+lpw0ksDNEg8pSckoYKrjpzSJmxNfFIeWILQkCoqhT1XlTG7e/P60DffMbi4HD8oGujXL1+4fvnCo/t3P/pq1ovooA0sy9pbGwBq1KlHUTpvg5/ISWfM2LcI9boTLu68kJW2fJRQ5eDnzbtLrEaBhDqNGic9TRSiy+lpqQoOB8uIYmtPbyHYzsGg06fmmBhSDxCgDIqjKD0ynoClF2/YWbFq9QJXFBzHCtaGgLijh6MaNM6RVdC8cv5sWlqau1aiio4g7PP6JPuD521B7EIpDHKcaKnIoRrl2lX0kb6odpA7gKspuXqKzKM5wkoD0FOEmYWBIuRlB0rhi/Yfh8MGA0X4uet1JFYumnvmhHVxI3kC6Tw9aybBHtnzFy+vTqKXpQ/I3QAkVa5Gg+vH93875b2f53w7vEe7+1YDv5hw+PDh90cOPvDXtuxfJ9YPD5y36o8W7ToCQHICGLNNdUcA71SdUleDrmOPPi/cvQFAR8FVT1JKF4dozgviKCpWiilL9OI8urprvaJCDUrwVRT1HwEAXfsOUm39/Zcl30/76K9N61PO7sPZHbi0T8q7IQi4u7qElC/fa/Aw+zIkJdP/Z/6WthE+FAkdocFfe+GDoGYHatdv1LmXokZaemryx1/PeX3CR/JI3J7tm4u9fwVBk3I+aOQYyaS22bjxfxI8R/LWXBsrXdQ/sOTYGxIql/EJcpM9DXJ1c6E2ZNId/DVb40hKz5M6luN5EJDp6YlZLTw7acbsSpE1nPFfqsax8EqVAQil+wTcv3t77Nix6sPEYzUapeiJnLdRqKJunFr2VsjFU7RU9HWVktRGNQyu5O+qIwmBKGot8EZIsmClmjL6n8EhhwtFrh9QjSCIVYvmSY02liJtoucO2PnHuuwjv+O8LaNMI4/U2v40y3w7ziYodGTf39p7FhG2b7eJ2Gxas6Jns1qpSU8/n7P4j4NxarFzWFP+CoKeIkvJA9yvZuBnbcPHNlGIPAp0Adacp659w7FIvCEZWGpZpxJEi3Ydf9m+f/I3c1bu/KdJq7aqrRdUZcczn+LRFQAkx2h6dP4fgSKIMh4Gf/fSImeePwiCeH/6t81kSpTN2rQ3engOGvGmq5ttvesXEPgieqfg2BIEMWuWwssSXrHy6+98KH2UFynkOYY1m+Tv/uDX3/b0ehZa5TND6HnXqn4D6ttMN6/s+0g4K/xPSCR3LSY7odPnWSx5JhNIUs7wcDXoAYz/+HNKR9EFURakpOjF63c2bdMOwMDXRrft0hNAlz4DO/SwpcJu375dkwChOQxKdoaiDFdhDA57woeBJPKv00YShJ4kXHRkhK+rQSeqgblQJAA9RVIvOv88f/wXUtFAi3YdY60xXVdXVzGfnecA/pcfZyE9DWmPHB8tIqhZ9+Q8Vp4TW9wZ/MHBasXlKxfOTZswOrBssEvTV83WQSekfAW9weVuThrSH6FsZa0z2UCBLyUGh59WXqgLRfrSaWm02Zq1b00gOrAEDI0wkbAZUpzBrAIRGh7hKJrm6+ev+Jx0F/sWokpzsIzBxfD/gnHpCK0reEdHeAd5lIoiUk66VT794ad9f27NSEtt06lbmeByQmNwaNjt61eE/yOqRDo+uuQwdOjQgMj630yeeDn+DICxH38mbdr/59afZsqK/AnieHmZsIi5cpWqFXt1AnswDHNo987szIzpc5c+fpBwaM9fl87uA6tDREMA/LHfRZVCLWIZzyPXbAapU835BGPxS7k8b/an4Lkmrdt9OW+pM/WIK1Wr8eX8ZcL/EsmjcavoPdttdd005Y4okgTU9pDA4cgys2aZ4XAn3VQlwM3TiTf328P30/LUbuYRDcrWLKMR05H1BHqS0FFY+pIYF4sqa/ywVejEv27rKYLQrFlRavCfwaGBIaPGPbh7O+H2zTqNmib7+ORk0YBoxFqZpAXnlNLGgKQcy7hx4+d/MBJAuy4923fvXazdHjVq1Pnz53fs2KFqT3ryGLk5YESDI7Jm1P6/tgHAjpkYucQhk4PnQRAeLjpX+1r2pQY6khju+3B2uhGkDiRluxbBSWsdvx4m3ElJeuIf6LBszYvCiHHvp6WmnJCYvBAfMxe9Tk8SdYM83tRiXL7wqIQzGFT7xTgDngcsy5Ak6RcQKBcv7/fq69LyQ8X5fYEoFxY+b9UfWRnpRk/PeiHeAOIfZzMMM2OSUomYNoGk8PAyDoqzbPyp4206dhX+dz7z6DnxztC+9lVYbf6MG0eRm6FoUYEywC6qa87Nzl09Vfj/xKH9CXduVpAUO5Qo8H2J7tT9xuWL639Z0qVLl0mTJml3QWsi53me5/n5xx+dS7T9hsvjnrjqyG5VC9a/P/kgy94b0rCch6dLfkMuRRB6ipRzNfQUIcRcKvm5BhhLtU+xpA2OjRs3CmXGsrOzGzdu/Nprr5VwB5zBhl+XJty+CSD+1PFynViRIqpQtiiYGJSSkU146DvFRPc4d9NiNssds8UEo9H40UcfRUQ1nj/jU/U2gbHB8wA69OxTplzIumULrTx2WkPOXGjXGUaH59UuW+w9fx40bNEat06CtYAkIVFHhQixbMHk7evv8BTFACeHcr/AMl/9uMxiNn/xwdhj/+wF4OXtlQl4GN14HqR95dUXYW3k840lNmMVE+SXRlssn74zSkhY2Ltj85fzl+kNBgC16zf649CZhFs3KlSq4uXj+8L6qgUV1zUl6Yn8o7vRI3fzdDQbJI9EVIqsrjpJcT9Rly9f1rA2AHCcmNanrL2gAYqyL0nGKlXynkdlhyCIURM/HjXx41aR5cxms9msUfbBUWj54N3MXIbNk1XYtnAc41xUheNhsWeMFhQTIQkNdS+hnu37LUK9XP8zOGTo169fv379ACxYsKBLly4l/O3OwGI2iw4AAEDi/QTKL5jl4WZ07/XG22uXLgCc8nDA6ONKcIJiZglYGwC2bt36+uuvC//Xb9rixpVLWRlWVWCORW66UKExrELF+k1bhISFb9mx/RasikA6Oy8iy0BnsGSlqdtL2QrbP7As9v6EgAqoUA8VGlgvhJcUzQHMXLJaKND6opD/xGxwcfli3tLERw8IEP5B5YZsvOZCESwPe+9oqfrl/8dw7dJ5KQ827tiRa5fOS9U3vH18Jcm70nwLEh/cl38c/f6U2Z9/jOtHpcl60Ig3u/QZoHFkcSIlRaP+KmBdBelchIVQZK061246Kj6gNQfLVn1RDRoHltXOvi6q++XIDvgu9kGgu04upMHzzlZxkyrdK76ooBi2q45015Oqgik6kjBQhH2p+tKGFzMK37t3z83NLchazykjI+PRo0cAfH19xYJbxQaSJAmC0Jx+KIoC4OqmKFrBWfK4vGzK1SO0fPigtk1Eg0NYPRdA6SPKeOgpuzLlAJ5/8iNJ0v4kGzbY6gK4Gz02Hz6zZe2vW39fff/OLXAc7l/AkZUADu356+WRYzr36r9n337AWmSB0qkvh6UB1GvYxP4Simry1ul0nJavSPNHcwSKomZMmzL5rddgzkF5gbTBWz0c/NSZ82Jkqfnyr37GTsugeRdUfQNQt5yn/KMjhFizITa8XHPkpms8eBc9pTqkyM0mkiR5ns/ntBRFybc2CPM598iWRihsKtT9KvJLIAgi/xvRIMxH1SJ1WDqKoigvpbfA28e3mF5eewh+rEKdWd436S6EhleQ79O0VXSNOvUFngeA1X8dkrQ3pGeyCOHoLjRqpKEi0zKm05EHuWBo6FwE06FB0xbXrt8AoNS+cwCOdTEYJEfE+biTAC/k6RTqlYTykSYIQvXAS0epyJgEIbq8aY6nOV7l0uB5wpmXgrPPUQH0lPqtV+GVusHVynisOvtEvhsF/DG4lruBIkmSJElh04tdZWnixXRo48aNb7xhK3R++vTpefPmARg/fnx0dHSxfrWQVKZ5J4xGcfKbtWztxJGDxdbNn6PdaLZCPR3lExRcrs/g4ZvW/iJq1Nh7BZQI9nYz2skE1Q8tAoq45rvtJjOV3Nzcvby8X31z/Ktvjuc4rmn/N6QkDr+AAKPRuHbZwvOnjqHRWLFdWGrYwBvdXHOAMmXLGuykZDw9i2a0sv9xrO2FSmVH+649299NeffLH2KFdGUhCdCU7edt7DlAvZ4rkt9fAEmS+nylY41GTv51zl+XQU8ObRDaPNxXRRotql9egvAg5VMU0GjkVF8qXYV0aYW6X0V+CQBIksznEuwhdVjqTKtIT6PROGTU2NVLfgQwdPS4mnXqaR5bHP1/hjPLf3PhQKORi6hc9fPZC6e9OwbA57MXenn7SNYGAD//AOmNK6a7oDmuenp6bo09t+HXZQxNh4SHX4g7FVohYsiosZtO3fjxxGMA4LlPvpvfqWdfysV9FaAjUHD6HEtzymJvHh6ewtie/6W1ivQ88yDD0SOt0+koirIva2U0cjpKWTWNJAVpUY7jGbvHn9LpHQ1ucnBajhCju5vRmN+q2wi08zDWCfM3GhXPvPwLhdeh+J7VZ8YLMDhycnLy8vLkv0VMTExMTAyArKystDQNH34Rwt3dnSCInJwc+02ZmaLHO6pRs3c/nTF7+mQAMGUJGaQ8x2VmZr41aVrrTt0+W7gy3V5pSgEeIJJvXsrMrKDakJZWuNlUE0aj0f4SxowZs2XLFuH/fsPeyMy0laj9qE+rb6e8B6BZm5hW7btkZmbevHrZxuEAwOoUBgfL5GQkeZYNy83ONNn5EovkEgICAjIyMjQ9HNKNKBSmvv3636cvzrkCMQnwwcXmqUfOn6lYobKCSlYknRfg7u6em5ubzw6Zmdnyr3P+ulqX9wo3gjXlZJoU7UXYeQEeHh4sy+bl5TnaQXUJkF2F1F6o+1Xkl0AQhKuraz6XYA/7SxAah499r+/QkQA8vX3kr48cRd5/AARB+Pv7F2roky6hTrCHcGCEEfGPs1vEdN57/o6w6dzJY/JDPnhjSI26DQa//pant09xXIXmoCSgedUQ49iJwv9d+gwCwPEILFO2TBnmaQ6zbNPf4aHBeSYTSYJiLSQ4FOgeYGlaxrQYN/nz7OxsyH6NfGD/SAs/XZ1gD5qmNTkcmZnZKn6JjoRFKlFlZ3HkmEyOnh85NJkeOdk5mShYmNwFyFSNDgCsZp+Q2Vscd1lAQMAzZly+AIMjLi4uKiqq4P1eKLr1ezkoJGzZ3JnXL18Az/r7+kihtVr1Gg593bjgdDIHOEzxYGjoDFyGupZjsaJ+/fr37t3bHXelXKitrKWADj36tGjXKScrMzBITJ2NqFINljzsmo0GvQGAVT4JLA3aNEIfTxIF140rJdBTRKs6NeZcuawjeIbnAOxYv2bH+jU/b/pbsjlKcwBejgG1SroGesmg9P/+Khrm/2tEVFWodl6KP3Mp/kzyk8QpM+c5OqQEcPzg/k1rVqQ8TSzT4iWqQjsAgWXLAtiy9pdf538PkmJHLIZXAYF1kmM5qwVQr2mLlwZplHgtWlBKeoSOIkGLHbAnfibnMHfSTEL1E01kmNjFpx/b8TcABwpj/zN4ARd36tSpunXrlvz3FhYNmrX8ad22vefvdO3dv1G4/8h6tqTKbg2rfd+3PgS1O01mNccAqFXVQdnrYoObm1uFSlUNWpXu3Y1GydoAcOHMSYDH5QNgLaDNaqVOlgHPBWjxsEpyzihsTVFhTGBMeXJCWeyB3Q4P+A//SjzzM1x6DCZneuLt4zt/9aaW7TvJk2sO7NqezyHFjUf3E6aOG3nm+JGE2zdPHT7w+M6NKn4uBoo4fnD/gm+nAwDHOlPIuqIncHSN8H9LQU+5mCGRRoUIs7wiPG1nOPxzN33t+SQ4RpqJOf4gy57Agf91Lc4X4OGQyiv/fwFFkJ4GMirIFiLTkQST/AAAz9CgeK0UDxpAoFvpzVDKk2IB22agfF1UaoIK9axcLR4s7esX0LR16xfXwWeBSOxiFVZgQBnRbCo9s8V/KIX4/57iq4nqUfVMubmiehAApZJ3yePWtcu2D4nXEbtqdPR7OrLq9g1rbO0cqyhWpQXCnN2onNGz+kttu/SQ68MWHyQHh44iWYbTy5ht9lQMC8szHJ+ax2jKFQJgOZ52UFW2tEg7Fw/+t82pwsHRhESRGmGTB3du4P/au/P4Juq8D+Df30wmSZP0sgdQagtSKqWVWu77WhAtiyDLtaLI8hJeHIqgu+z6ALsgy6J4rKDILqyu4sGz6CIoKrJIRYWHcxGWS8uxgoLSlkJp0yPJzPPHtCFNQo+QyUwmn/dfk2bafKe/Ob75nUTeM2rXkuTFoK98dyLIIQZPz/6Da7fKL9PxHfTNFybBQESMkZHn2qSmtGubpsryquRTEE2v54gSuM7XDpGjyj1pyqB7RvgdpaIFza2/USVhasqHhmkmF6ZhN4vL5Tyw+0vPn9w7Ts2FGDM6ZF9/UVVOZw96VrvKBJ43CTw1MGOmy1l47Mj+XV/s+HhzANO0+y33Rk8Gd5OKkZNnE29owgyHS/rvlapZW07daAdR8l6SnogMHKO6IUs3qbn3lpBBwtE4I88En5EarVPTiG6QcLic5KymmsoeedrtqjJi3MSJUz2WKRJdUUaBiHhGksNhvHj8kQ7e/bY0exK7MUY/75DkOcfo7PlPyT3ntRa51uKJNBHy//ddJPmTTRtUiUTWKjVt/vKVnnNmbHh9zeXiS8NGjnH/JLlVK6OBFxp47tbNYUhELyz63eWiS4rFe92k3BY5yRYiEgwcY2QR+CjDDZ+eoiRVOcUapzjzw1MlPpOXX650FpZU+k4OJo8H1HUFBxKOJpiQk/SLjt6d+I7+ey8RkctZu7ayJ5eTaipz9q7o3K1HqGIMxC8enOLezmnbunPrGCJyVtkdVRXfHjsyefiASo8hGCG+QQfwcS6X89PN753+5nhW5Sn6qfa7xX19884WfhPs6ADCxr3j61Vp1FT5GdoQMpcuXlg6b3bRT9d703/03vpxg3s89cTMxxb88U+v/H3u7/90peinymtXea6BGg6Hu5PWubOnVy77g9JhE1HX1ra4KIGITDz7y4j2y+5q2y21oXuUwyW5JLpYXnOt2rtLylffXX3riJ8kSYiAhENzE4NoUIzZz3+JOaqp5CQZo4gkclZfXzaMaodlpqamhi7EgMTExf/jsz2ffbQ5ymIZ8vPRlcy44+zV2jobSSSi098cd0+2qCl+m9tf+tMftrz7jryd1SnvxJHatSj/+earv35qeYgj9Cu3lU0fXQQCyz4jpFJBa2bPX2KOsm54/a/yy8HDR6pYEDs+3nyjt1b8ccFrm7f/z8xf0S+fIytzOJ28JdbfOA6JSS7Jo5PWV9u3KhKrD3kmDo5Ru1vMROQ7rSdjzN0PtEYUJYk4ifl21XCJVOP0PjC5LVvgRblhRZc9iggJR8Duvnf0umF9aNwy4gUyRpHBVNfRQ5If2N36DPD9La3dcxOSWoybPE3erpEzcafD/QWiRYrWcyZP7myDiEpLit3bfqf6AGgurV28vvHc6Ck17fHfDbx7+PHDhzrm5mV2vCNE8fkj+BtA53bm25NERAc3UUZPat3RwJHLd8CKyxll4O0eDdl9h9wd9Di9yF8VeMYYu951w7cPh+AxOYfDJTEiidHFazUp0cYo4XpjgkjeHTgMHGOMjBzbMC7LxOu52QEJR4CSW6Vs2nX48P69u6+Ydp0vKzda5LEqnERmgUtOSx9wV3e1Y2xemix3U4mNjbm6cz2d3vfbpc/faHkC7cvJ6/rjD7XrSoy6/yG1wtDaIwoapZtaKC+ZHe9QN9WQ3T1q7L4vCw7+31e+b+X/YkLHTnlERN9+RS3bk+gUeK7aT/2AY3RmTNc+4zdZL18uKUpJTZtcN6WY0nhWmxm4X3pijAw8c6+N4hQlRszASav3X5jYKTm/bvHYVfsufFNc6aj/LUjgGWPEcazhdWJ1AAlH4GzRMX0GD+1D1O3s1T/v/qHC4WKMmQUuP6f9rzp7r4Su4rOnifdQE8/dFm8WJdPvnvpDt9aamxO3US++8e6ch8YS0YC78uf+funkRx7/7tS3HTrlxWpshc/AIHcBHbDaopetfv2706dcLtdba176avvWPoPv6tgpLyUtve/gYft27azdz1FFNfby0hKyel+8PJMSYiw52e1ynn0pxMHzHBP466N1vcavCly9tdMkiSSSXCJVOUXPFdqKKpxFFQ6HR49RxpiBY1wT1onVASQcQcBzJPCMHCRwJHCMY6SFarHmPqIEnq0Z2f6RLacMPkNytPm080qkcvK6bj9y1ulwGASBiFqmpLbUapOQ5/9Tr1+pvWjzFGq6cI9fI+R/Y17rLocvls9/esX6V1/59vh/qqurevb/GeO4k//5una/fe/RT4U0cBoRcSSJxIiIkSQRc1VXrVjyP9X3DR0z6eEQB88x+fbungHMK+HgBI4jqtcI5JLIIUqei8fKA2I9hxkIHAk8x+m9u6hM/eeiNjVrCKic+ZJ8zvGcb6IaRnerRYPTc1s0vuyQZhkaXE0Nbl4YncyB8b32dX/Iqnjrry+tW71iz84d61avWPeXFUTU4Y66GahdDjr7b9r8R6LaWZuJSJKXZnQ6SBL/8txSsQkTkgYXzzijR8LhNQe5gSff2RNIHq5S135SVuUUJanGqz2F4wSOBJ7pe1JzWQQcovLyWtoe7ZlCRALPBJ7x4VwzlmgRwrdmr7SkeM0Ly5Yv+PWhvbuo7smhtaeF1uIJCg3+n0HjTp085t4+880JIurRb9BjC5Z07zswNb0tSSJdKyIisaZuKTWng4jo87X0/TEi74XdQ3AG8hwZec5dec1RvftknFmINfEGn2oKpyidulz51XdlRDR186nvr1Z79xjlSahtrAnXG2/ToUklCKIELivRQkQCR3dn3NI7TdMdIMJlwFUAt49nF/5m31efE9G2D/65+h8f5rbqGfywIDL4XiaSJD399NNff/211WqdP39+u3bt1IuuIWHRSJfbypaa3nbvlwXyy5S0dHljxLgHRox7QBLFmb8cWVj4LZG8TIREUl1Vxw/HqaZy0ow5vnOaKY1nLLeVdVRW7ZxMXs3m93dKijHxiwvOec3oJUl06GK5S5L6psfUiCI5OK8djBxLjTW1iTMnWmprZ923vsDu1XemRPsueKsRSDiCozNTAQ0AABM/SURBVHZFH57PTDQ3sEgg3LwbJSIV5dfkbEN2aO/uMYORcISU30dduNd8uON/9913X3jhBXm7srJy/fr16gUVOO0Ux0OzHq+pqflww1sjxj3wq1n1FthiHNeld7/CE8do7z8osy+ZrES1C1Rl3J712Lz5WZ1UWP6T5yjKwN9Wd3vnOc7AMXf2wDFqG28e0SHh3aPey7Y5XFJxhfP3O75zSZI8P4enezJvGZZxSwtbRLQFo0klOASeY4wZecZ8qsVCP0endu4pQSQfVAOHZrHWeyutbYbX74Kb0+ncsGHD8uXLDx06pHYsYeP48esLj23fvt3vUp8acaMTXt2xcl4/sVitjy1Ysv3I2ccWLLHYvN99cPrs3G49aNfb5HKS6CSXgxxVdO7wqaOHDu3bFaqo6+EY8xyZMjwzflRWwvV3iSVahJEdbvH9RYdLKrE7jl2qEEU/S8ve2yEhQrINQsLRsGYtG/b3+zK7p9paRfusHAshwRhbse69Hv0GEdGkGY/1HDC40V+JWPPnz581a9azzz7bu3fvPXv2qB2Odnle/n379nVvjxgxQq2lDSOEyWR+ctmfiYgkV+30xy4XvbeQRFdoFk/xNbRd3F3t4twvW9qMqTG185jxrLYPKe+v52eNKDlckijKQ1Su9xiVO3xE1DMYTSpBkxpjnNbVe+VDrdHZd32vOvzsO7ssXfWaivE0SiOTgr/22vX/0vvvv5+bmxusvxwWHQgCM2TIkNWrV2/ZsiUtLW3OnDlqh6N/icktN+8+MvPDwgpRqKm228trH9UD7x7htWdobmvpcd5t5e4KD4Hn5Lpt3t8it5IkOSTJJUmiRJ4VHALPnKLEImE4bB0kHN6Ce+7q4AGvg0PQOHW78bZqFeQsWcc5x5gxY8aMGdP4fhqgg1KoKL+24+PN1660chmt0dHRMcbWd82Y03PAYC1MmSpzD4N1D2r1W8NBRDVOUZQkr+YUgecqHSIfAYNT3JBw6JPfLAGpQzhSotQ++OCDe++9l4hGjRo1derUoP/9cBkJBZp18ftzD+YPICJKyaLswRUm28S7+1vPRZcWF0mSpJH2LPcMAjZj7Wr1N6qtcLhEiZhXvx8Tz26NNRkNTToWfVxTSDiaRx+lTrr4AhQJFMoRe/XqVVRUVFVVlZiY6HK5KisrlfgUAE8NnMy+99WP/vm/tVsXTlC7HlRT+fYjw+UfjJs8bdrjTyoYaJPxHHGMiZK0bGjb1Bgj1U0/yhh5dSl2SUQ+rS0Cx/39vsymfJDnQFnP/1LYPY8iqsPKzUINASgn9GeX2Yzx26BR9eowTu+hU9e7Nm94fY17O2SD8vx+Cs9q55g2G+pNP+o7/ZdfzF+HjyZ+dLN20A4kHBEhjM7ISKa/YtLfEUGw+J4bnj8ZMXbi9TcunDSVnAlNVM3CMzLKI03q0qO6heab9GDlFOguemeKpqedRMIB4U2v844A3CTPSyPsLpPkVilb9h57auVa+WV1VZX7rY65nVUKyhtXt4qWO3MwcOzl4RlWU+MPVo6xwBaRCOv+eUg46mm0lbHR3UA5kfBv19Mx6ulYwlqYFoQ5ymK1+on8wemzQx+MX+4mFc/n6O2JUU1ZFUXgI2o8bC10GgXQqDB9TgAES3q79p4vf7Pk2Tu79WqR0pq0cXUYOGbkOfJpHPG3aqw3I+c/4dDCcSkHCUek8NtiGkbdm5tF3xctQNNp/1poIMK4WxKeXfvW+2+/TkSj7p/cuWefkEXVFJ1aWJ/sd+vMLae8qjR4njHmPQjWi8AH2KQS1pBwKEj7lzoAgJbl9eiT10NbeYabwLP2CeaBbWNNglcNBzNyrNpn2RRPKdHG2xMtQQwmLL5Aog9H4yI5bwiXY5fjdLmce78s2LNzh8PhUDuiwIVd/z6AiMUYWzAgja9fV8FxjVRgMEZt4s0zugc+ya/fu4T27xuo4YhoYTdvTANE0bV47ozdn28nooJNP1u3bp3RiIX0ACDU5M6kgsiqnf4rOQTOz7riTaf9xOJGUMMBOsEV/1fONojos88+O3DggLrx3KTwvacARDieMQPHBO6GlRwCx/ER+eyNyIOGpgmvZ55XfQaqNwBAFWOyEzsmWwS+dtCsL4HXyGowoYaEo0nCsbUs0mRlZT300EPy9oQJE7p06aJuPADQXProwDSgTWySRRB4Jvgb+coxJnDUlLk69Ad9OMB/9+ZwvOyfe+65WbNmuVyujIwMtWMBgEYEfJPR/t2JZ0zg/NdwCDyLNhksxkj8to+EA4jCZEhVU7Rt21btEAAg0rWONaXFmk5driKqXVHW/ZaRYxM7JfdrE6NedKqJxCQLAABAOfnt46d0binPtOHVsCLwjL9xf1J9Q8KhCH20RIYpl8u1ZcuWN998s6SkRO1YACBCtY03z+ubSkTuhhV52XoDz5q2mqwOoUklcHqaxILqt6qEdbY0c+bMjRs3EtHjjz9+7Nix5ORktSMCgLAn3yE9742N3vzlsa/uhEPgmVOUWttM8eYIffJGaqIF/uigYqa0tFTONmRbt25VMRgACEC434XcjBwb0CbWInA8I8ZI4Dki+v2gtKwkP5Oa6+D22ygkHKArUVFRni9jY2PVigQAIhxjbOHANIFjBp4zcExgjIgitj2FkHCAr7DOss1m8zPPPCNvjxw5Mj8/X914ACDC8Twzcsw9D9jNTGoe7iK0JSmIdNaTQwemTJkyduxYu93eokULtWMBgEA0MFA/7L4RyUurcBKZDey3/W61CJH7PR8JR/CF3fWgP9HR0dHR0WpHAQBAPDGBZ7xIPMeGtotTOxw1RW6qBQAAoLQRHW65PSHKwHOR3JgiQ8IBAACaE15VxQ2MMbkrI75ltNEicEZDpCcc2mpSMRgMBoOyIQmCQERWqzW4f7Z3hvXQD2XydtD/uC9BEELwKUqzWCySx4y/YUcQhHBf81EQBJ7nuXDuN88Y08EhUEjuG4pS4qbkeV+V5bVWcEZwnudNJlMDz6CoKJfnMUZFudzb8s99A5aZBKHbrXGTu7SOMvF+/3KwjosxxnGc0o/RgGkrLKfTWV1drehHWCwWxlhFRUXQ/3JlZSUR5bayKfHHvVit1hB8iqKioqLsdrsoimoHEjiLxWK329WO4qYwxlwul3zqhinGmNls1sEhhPsVrdBNKTOO9+w9WlHh/4EdFDzPV1dXN/AMyozjPY/R86xzB+b3VByQbhMlMog1fs/TID41eJ43GAxKP0a9Zh9oOm0lHAAAADrTJs58o7fCq+XoJiHhCJqIOm8AAEJJgzdY3SyyHTJh3PAJAAAA4QIJBwAAACgOTSoAAKBdGmxMgcAg4QAAAAi1CEyk0KQCAAAQuAhMHQKDhAMAAAAUh4QDAAAgOFDb0QAkHAAAAEGDnONGkHAAAAAEArlFsyDhAAAACKnIzFSQcAAAAAQoMlOHwCDhAAAAAMUh4QAAAADFIeEAAAAAxSHhAAAACCZ07PALCQcAAAAoDgkHAABAkOW2sqGewwsSDgAAgNCJ2EQECQcAAAAoDgkHAAAAKA4JB+hEcXHxlClTkpKSpk2bdvnyZbXDAQBAT456kHCATixZsuTDDz8kovfff//pp59WOxwAAKgHCQfoxKVLl9zb33//vYqRAACALyQcoBPdu3d3b/fp00fFSAAAwJdB7QAAgmP27Nnx8fEHDx7s0aPH/fffr3Y4AAC1clvZDl8sR2cOJBygEzzPT548OT8/Pz4+nuNQdQcAoC24L4NOlJaWjhs3Ljs7OyUlZefOnWqHAwAA9SDhAJ1Ys2ZNQUGBvL1q1Sp1gwEA8IT2FELCAbpx5coV97Y78wAAAI1AwgE6MXz4cPd2ly5dVIwEAAB8IeEAnbhw4YJ7++DBg2VlZSoGAwAAXpBwgE5IktTASwAAUBcSDtCJ4cOHDxgwQN6ePn16bGysuvEAAIAnzMMBOmGz2d555509e/YkJCRkZ2erHQ4AANSDhAP0w2g09u/fX+0oAADADzSpAAAAgOKQcAAAAIDikHAAAACA4pBwAAAAgOKQcAAAAIDiQj1Kpbi4+IknnkhOTiaiuXPnpqSkhDgAAAAACL1QJxxFRUX5+fnjx48P8ecCAACAikKdcPz0008XLlx4+eWXs7OzBw0aJP/QbreXlpYSkdFoNBiUDYnjOCLieV7RT1EaYyzcD4GIeJ5njKkdReB0UAqMMY7jwvooGGPhXhDyVRDWh0C4HLSB4zgtHwIL8ZIT+/btu3r1al5e3ooVK8aOHdupUyci2rp16/Lly4lo3rx5w4YNUzQA+drGQhuqYyzU5x74QiloBApCC1AKTRTwF0XV/r8FBQUlJSVjxozx/OG1a9eqq6sV/VyLxcIYq6ioUPRTlGa1WsP9EBITEy9fviyKotqBBM5isdjtdrWjuCk2m83lclVWVqodSOAYY2azOdwPISEhobi4WO1AbooObkoxMTHV1dVKP4MUxfO8wWBQ+hASExMD+8VQj1JZv3794cOHiejcuXMtW7YM8acDAACAKkLdh2PIkCF/+9vfNm7cGB8f37t37xB/OgAAAKgi1AlHUlLSk08+GeIPBQAAAHVpa7XYEHSvLS8vlyTJaDQq+ikhoNl+yE10/vx5q9Ua1kehg275V65c4ThOEAS1AwmcDkapSJJ0/vx5m82mdiA3K6xLgYhKSkoMBoPSIyUVxXGcli+HiOuU+8orr9TU1MyZM0ftQCJdr169tmzZkpCQoHYgEW3JkiXp6emTJk1SO5CIVlZWNnjw4AMHDqgdSKSbO3fukCFDhg8frnYguoWpzQEAAEBxYVx3FJjs7Gyn06l2FECjRo0ym81qRxHpOnfujEom1QmCMHr0aLWjAOrVq1daWpraUehZxDWpAAAAQOihSQUAAAAUp88mFbvdvmzZMqfTabVa582bx3HcypUry8vL09LSJk+e7HQ6PV/a7fbVq1fb7faWLVs+/PDDYb26h9Y0XBDyPs8///ysWbPMZrNXuagZt740qxS8dtbBeC6NaFYpyC8PHz68Y8eOuXPnqha0HjWrICRJWrt27aVLl2JiYh599FE8HW4Sv2jRIrVjCL5t27YlJiY++uijP/zwQ3Fx8Y8//sjz/IwZM/71r3+lp6cfPXrU8+WBAwesVuv06dO/+OKLhIQENGkHUcMFwfP8kiVL9u3bN3bsWIPBsHv3bs93Y2Ji1A5fJ5pVCl4733bbbWqHrxPNKgUistvtq1atslqtvXr1Ujt2XWlWQRw8eLC6unr69Ok1NTUmk0kHQ5fVpc8mlfbt2w8cOJCIbDabIAiFhYU5OTlElJOTU1hY6PUyKSnp3LlzpaWlxcXFcXFx6kauMw0XhNVqXbx48R133CHv7PWuelHrTbNKwWtn1YLWnWaVAhG98cYbXktNQVA0qyCOHTtGRCtXrqysrMRaHDdPnwlHZmZmQkLC/v37d+/e3a1bN7vdbrFYiCgqKqq8vNzrZUZGxpkzZ5YvX24wGOLj49WOXVcaLgiqm6ZG3tn3XQiKZpWC185qxq0vzSqF/fv3t2jRAiMmlNCsgigvLz9z5syECRP27t176NAhNePWBX324SCitWvXlpeXL1iwwGKxWCwWeTHJyspKm83m9XLDhg2TJk3q2rXrxo0bP//886FDh6odu640UBBeezb8LtyMppeC184hj1TPml4KmzZtMpvNhw8fPn/+/Keffjps2DA14tWtpheE3KSVnJzcr1+/06dP5+XlqRGvfuizhmPXrl0Gg2Hu3LlWq5WI2rdvf+LECSI6efJkRkaG10uHwyGPDRZFEVN0BFfDBeG1c8PvQsCaVQpeO0OwNKsUli5dunDhwlmzZuXk5CDbCK5mFURGRsapU6eI6OzZsy1atAh9tDqjz06jH3/88dGjR3fu3Ll9+3aTydSrV69t27Z9+eWXiYmJ/fv3T0lJ8XyZnp7+xhtv7Ny58+rVqxMnTgzrifS1puGCkPcpKCjo37+/wWDwKhd1I9eTZpWC187p6enqBq8bzSoF+WVFRcWRI0fQaTS4mntT+uSTTz766CNRFMePH89x+vyKHjKY+AsAAAAUh3wNAAAAFIeEAwAAABSHhAMAAAAUh4QDAAAAFIeEAwAAABSHhAMAFPHMM8+sWLFC7SgAQCuQcAAAAIDikHAAQNA4HI4ZM2a0adOmR48eR44cIaKysrLJkydnZWX179+/oKBA7QABQDWYVRMAgubVV189efJkYWFhaWlpp06dunfv/vbbb4uieOLEie3bt2/evHnQoEFqxwgA6kANBwAEzc6dOx988EFBEJKTk/Pz84mod+/eO3bsWLhwoc1me/HFF9UOEABUg4QDAIKG4zj3ehPymiC5ubl79uxJSUlZtGjR6NGjVY0OANSEtVQAIGjWrFmzYcOGrVu3Xrt2LScnZ968eWVlZU6nc/HixRcvXszMzCwrK2OMqR0mAKgACQcABI3D4Zg9e/a2bduSkpLuueeetm3b9uvXb8KECUVFRbfeeuvUqVMfeOABtWMEAHUg4QAAAADFoQ8HAAAAKA4JBwAAACgOCQcAAAAoDgkHAAAAKA4JBwAAACgOCQcAAAAoDgkHAAAAKO7/AWOJ18pRN8ybAAAAAElFTkSuQmCC\\n\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%R -w 10 -h 6 -u in\\n\",\n \"m <- prophet(df, changepoints = c('2014-01-01'))\\n\",\n \"forecast <- predict(m, future)\\n\",\n \"plot(m, forecast)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"m = Prophet(changepoints=['2014-01-01'])\\n\",\n \"forecast = m.fit(df).predict(future)\\n\",\n \"fig = m.plot(forecast)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/uncertainty_intervals.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The rpy2.ipython extension is already loaded. To reload it, use:\\n\",\n \" %reload_ext rpy2.ipython\\n\"\n ]\n }\n ],\n \"source\": [\n \"%load_ext rpy2.ipython\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"from prophet import Prophet\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import logging\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"logging.getLogger('prophet').setLevel(logging.ERROR)\\n\",\n \"logging.getLogger('cmdstanpy').setLevel(logging.ERROR)\\n\",\n \"warnings.filterwarnings('ignore')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"df = df.loc[:180,] # Limit to first six months\\n\",\n \"m = Prophet()\\n\",\n \"m.fit(df)\\n\",\n \"future = m.make_future_dataframe(periods=60)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"block_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Loading required package: Rcpp\\n\",\n \"\\n\",\n \"R[write to console]: Loading required package: rlang\\n\",\n \"\\n\",\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"library(prophet)\\n\",\n \"df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')\\n\",\n \"df <- df[1:180,]\\n\",\n \"m <- prophet(df)\\n\",\n \"future <- make_future_dataframe(m, periods=60)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default Prophet will return uncertainty intervals for the forecast `yhat`. There are several important assumptions behind these uncertainty intervals.\\n\",\n \"\\n\",\n \"There are three sources of uncertainty in the forecast: uncertainty in the trend, uncertainty in the seasonality estimates, and additional observation noise.\\n\",\n \"\\n\",\n \"### Uncertainty in the trend\\n\",\n \"The biggest source of uncertainty in the forecast is the potential for future trend changes. The time series we have seen already in this documentation show clear trend changes in the history. Prophet is able to detect and fit these, but what trend changes should we expect moving forward? It's impossible to know for sure, so we do the most reasonable thing we can, and we assume that the *future will see similar trend changes as the history*. In particular, we assume that the average frequency and magnitude of trend changes in the future will be the same as that which we observe in the history. We project these trend changes forward and by computing their distribution we obtain uncertainty intervals.\\n\",\n \"\\n\",\n \"One property of this way of measuring uncertainty is that allowing higher flexibility in the rate, by increasing `changepoint_prior_scale`, will increase the forecast uncertainty. This is because if we model more rate changes in the history then we will expect more in the future, and makes the uncertainty intervals a useful indicator of overfitting.\\n\",\n \"\\n\",\n \"The width of the uncertainty intervals (by default 80%) can be set using the parameter `interval_width`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df, interval.width = 0.95)\\n\",\n \"forecast <- predict(m, future)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"forecast = Prophet(interval_width=0.95).fit(df).predict(future)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Again, these intervals assume that the future will see the same frequency and magnitude of rate changes as the past. This assumption is probably not true, so you should not expect to get accurate coverage on these uncertainty intervals.\\n\",\n \"\\n\",\n \"### Uncertainty in seasonality\\n\",\n \"By default Prophet will only return uncertainty in the trend and observation noise. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do this here for the first six months of the Peyton Manning data from the Quickstart:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R[write to console]: Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.\\n\",\n \"\\n\",\n \"R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 1).\\n\",\n \"Chain 1: \\n\",\n \"Chain 1: Gradient evaluation took 8.6e-05 seconds\\n\",\n \"Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.\\n\",\n \"Chain 1: Adjust your expectations accordingly!\\n\",\n \"Chain 1: \\n\",\n \"Chain 1: \\n\",\n \"Chain 1: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 1: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 1: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 1: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 1: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 1: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 1: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 1: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 1: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 1: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 1: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 1: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 1: \\n\",\n \"Chain 1: Elapsed Time: 2.10541 seconds (Warm-up)\\n\",\n \"Chain 1: 2.37589 seconds (Sampling)\\n\",\n \"Chain 1: 4.4813 seconds (Total)\\n\",\n \"Chain 1: \\n\",\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 2).\\n\",\n \"Chain 2: \\n\",\n \"Chain 2: Gradient evaluation took 7.4e-05 seconds\\n\",\n \"Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.\\n\",\n \"Chain 2: Adjust your expectations accordingly!\\n\",\n \"Chain 2: \\n\",\n \"Chain 2: \\n\",\n \"Chain 2: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 2: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 2: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 2: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 2: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 2: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 2: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 2: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 2: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 2: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 2: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 2: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 2: \\n\",\n \"Chain 2: Elapsed Time: 2.04288 seconds (Warm-up)\\n\",\n \"Chain 2: 2.12795 seconds (Sampling)\\n\",\n \"Chain 2: 4.17083 seconds (Total)\\n\",\n \"Chain 2: \\n\",\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 3).\\n\",\n \"Chain 3: \\n\",\n \"Chain 3: Gradient evaluation took 7.1e-05 seconds\\n\",\n \"Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.\\n\",\n \"Chain 3: Adjust your expectations accordingly!\\n\",\n \"Chain 3: \\n\",\n \"Chain 3: \\n\",\n \"Chain 3: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 3: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 3: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 3: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 3: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 3: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 3: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 3: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 3: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 3: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 3: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 3: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 3: \\n\",\n \"Chain 3: Elapsed Time: 2.24488 seconds (Warm-up)\\n\",\n \"Chain 3: 2.5196 seconds (Sampling)\\n\",\n \"Chain 3: 4.76448 seconds (Total)\\n\",\n \"Chain 3: \\n\",\n \"\\n\",\n \"SAMPLING FOR MODEL 'prophet' NOW (CHAIN 4).\\n\",\n \"Chain 4: \\n\",\n \"Chain 4: Gradient evaluation took 6.5e-05 seconds\\n\",\n \"Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.65 seconds.\\n\",\n \"Chain 4: Adjust your expectations accordingly!\\n\",\n \"Chain 4: \\n\",\n \"Chain 4: \\n\",\n \"Chain 4: Iteration: 1 / 300 [ 0%] (Warmup)\\n\",\n \"Chain 4: Iteration: 30 / 300 [ 10%] (Warmup)\\n\",\n \"Chain 4: Iteration: 60 / 300 [ 20%] (Warmup)\\n\",\n \"Chain 4: Iteration: 90 / 300 [ 30%] (Warmup)\\n\",\n \"Chain 4: Iteration: 120 / 300 [ 40%] (Warmup)\\n\",\n \"Chain 4: Iteration: 150 / 300 [ 50%] (Warmup)\\n\",\n \"Chain 4: Iteration: 151 / 300 [ 50%] (Sampling)\\n\",\n \"Chain 4: Iteration: 180 / 300 [ 60%] (Sampling)\\n\",\n \"Chain 4: Iteration: 210 / 300 [ 70%] (Sampling)\\n\",\n \"Chain 4: Iteration: 240 / 300 [ 80%] (Sampling)\\n\",\n \"Chain 4: Iteration: 270 / 300 [ 90%] (Sampling)\\n\",\n \"Chain 4: Iteration: 300 / 300 [100%] (Sampling)\\n\",\n \"Chain 4: \\n\",\n \"Chain 4: Elapsed Time: 2.2803 seconds (Warm-up)\\n\",\n \"Chain 4: 2.73488 seconds (Sampling)\\n\",\n \"Chain 4: 5.01518 seconds (Total)\\n\",\n \"Chain 4: \\n\"\n ]\n }\n ],\n \"source\": [\n \"%%R\\n\",\n \"m <- prophet(df, mcmc.samples = 300)\\n\",\n \"forecast <- predict(m, future)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"m = Prophet(mcmc_samples=300)\\n\",\n \"forecast = m.fit(df, show_progress=False).predict(future)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This replaces the typical MAP estimation with MCMC sampling, and can take much longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the uncertainty in seasonal components when you plot them:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"output_hidden\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n 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omvzM5YNs2sZTFUDxFbyRJKG6AAh5dxa1r2aLHoUj6o2nZbOuI8PD+Yba0RkhZNgtLXXzyjAouaCgm4NKAFOH+bgDCg/3ZeyUNi0jKRCGd1Vo7sp/PSpEcDtMzPMWdn0In8vXgB5b4LNqGEhxsieF1qNlTQILAt/+qmttf7uM3Ozr58+F+vviWOTSVpmf93Da8tr+Xw606i8p90+b0kJny/2AqFPpYFHr/RxT6OBR6/8cqlLFQ7JFjFqbYb37zGx566CF+9rOfAfCLX/yC559/nh/+8IcTXm+aJqWlpQwNDR33fdetW8e2bduO+Xp7e/u0XGo2TItIpmZdTzhJbyRJ0rJQUfA7tWMGDgf7o/xhVxf3v9ZNbyRJ0KPzjiWVbFg2h4UVvgk/B6C3uxPVX4phpU/0aCzzUp7Zx1ZI3uzXw1Asxc6OYYYTJqXe8fsDtx4e4EsP72EonuKTb5nPVatrsvswh+MpUqbNqtpiKovyvyQ8Xf8f5EOhj0Wh939EoY9Dofd/rNk2FseKi/I2Azh37lyef/55otEoHo+Hxx57jHXr1o27pqOjg+rqagDuvfdeli5dmo+mTrr07J5JKGHSG0nQH00RSZjYpOvRuXWVogmWdkeEEwaP7unhD7u6+EtHCE2Bc+aXsmFZFefOLz1mEGfbNqFEOoM3mTJZXual0u+i2C3HmZ2ogMfB2Q2ltAzGeK07hEtTs+O3fm4Jd1+zhq8+uodvP3WA5w8N8KWLF1Huc1LsdpA0LP7cMsSCMoOFFT5ZEhZCCJE3eQsA169fz+WXX86aNWvQdZ3Vq1ezceNGbr75ZtatW8eGDRv47ne/y7333ouu65SWlnLHHXfkq7knLWlYRJIG0aRJKGkwHDMYiqcwLBtFSZ9b69Y1KvzO476PZdtsaxnkD7u6eHxfHwnDorHMy6fPm8+lSyop9x3785OGxXDCwLJtaord1Jd4iA0Y1Jb7J7u7BUFVFeaVein3OflLxzDdkQTlXidqprTMf757Gb/d0cm3nzzAVXdu50sXL+Lc+aU4dZVKv5ODAxH6oklOryk+bva1EEIIkSt5WwLOlXwtAVuWTTRlkjQtkobFQCy9dy+SNMBOn2vr0BScmopLV49ba2+slsEY9+3q4oHXuukIJShy6VyyuIJ3L6tiWZX/uHX34obJcNzArWssKPNSVezCpaeXkmfbFPfJOtVxMC2bA30R9vZE8Ls0fGPKvhzoi/D/HtzNnt4IV6yq4aZz52fL5oTiBgnTZMWcYmoC7imvnyj//qMKfSwKvf8jCn0cCr3/Y822sZh2S8CzQSRhMBhL0RVK0BtNYtk22AAKDk3B69Co8Lne6G2OEoobPLq3h/t2dfOXjmFUJb28+MlzG3jbgvIJz7AdK2Gkk0e8Do3VtQEq/C5ZbswRTVVYWOGn0u9iR8cwPZEEZZnZwMYyH3dcuYrvPdPMXS+380r7MLdctoT6oIcit47HVHmlfZjeaJKllUWzoqaiEEKImUECwJOUNCyeO9iPaYHHMXrE2skyLJvnDw1w/2tdPLm/j6Rp01jq5f87t4HLllRS4T9+IGnbNuGEScww8Tg0VtUUU1XkntU1+6aTkb2B+zOzgUGPjlvXcOoq//C2BayrD/KVR/Zwza9e4p8vWsjFiyvQtfSScHcoSX+0n9W1AYIe2Y8phBAi9yQAPEk2NpbNG+7dO+572DavdYd5eHcPD73eTV80RcCt894Vc3jXsiqWVh5/iRfSy7yhhIGCQqXfycrSYko8jhlzJNtsoqkKiyr8lHmdvNQ2RDxlZQO6ty4o41dXr+aLD77OFx98nT+3DPIPb2vErWuUeh3EUibPHexnYbmPxjJJEBFCCJFbEgDmQXN/lId3d/Pw7h5aBuPoqsI5DSW8+w2yeMcKJ9KJJT6XzsrqYsp9LllCnCbKfE7eMr+Uv7QPjUsQmVPsZtPlK/nRlkP8fFsrOzqHufWypTSUevE4NFy6yv6+CL2RpBwjJ4QQIqfkJ8wU6RyO8/CeHh7e3cOenggKsK4+wEfX1XNhU9kJl2IJJwyiKZMyn5PTamS2b7ryODTW1ZcctSSsayr/37nzWVsX4OaHd3PNr17iHy9s4l3LqlAzx8gNxVM82zzA6tpiyt9g6V8IIYQ4GRIA5tBANMkf9/by8O4eXm5PH6exvKqIz5zfyNsXlb/hvr4RY/f3lfmcrKwppsR78kvPYmqMXRJ+uW2IaNLMBuznNJRy19Vr+OcHd/PlR/awrWWQL1zYhMehEXA7iBsmL7QM0lTuY4EsCQshhJhkEgBOsnDC4E/7+3hkdw9bDw9g2tBY6uXGs+dxyeIK6oOeE34v27YZjKVIWTaVfhenl0ngNxOV+Zyc21jK691hWgfjlHocOHWVCr+LH37gNH629TA/3XqYnZ0hbn3nUprKfekEEp/Kgb4IPWGpGSiEEGJyyU+USZAwLJ492M/Du3t45kA/CdOiusjFNWvruHRxJU3l3je1TGtadvroMMumPuihodQrP/xnOJeucXpNgKpMuRjVUAi600fJ/e3Z81hTF+D/Pfg6H73rZT77tkbeu2JOdkk4FDd45kAfK+YUUxuc+pqBQgghZh+JKk6SYVq82DrI84cGeXxfL5HM8t57VlRxyeJKVlYXvekf1HHDZDhhoCkK9UEP9UGPBH6zzJxiN0GPg52dIbrD6ZqBmqpwRn2QX129hn95eDffeGwff24Z4osXNeF36dmagX/pHKY3mmBZVbEk/AghhDglEl2cBMO0WPYfT3J4IIbPqXFhUzmXLK5gXX0Q/ST2aqXPBTbwOXVOry6mwu86oUxgMTO5HRpragMc7I/yWneIoMeBW9co8zn5/vtWcMefW/jxlkO82hnia5cuZmVNMbqmUuV30RNO8ky0n9Nriik7zvF/QgghxPFIAHgSdE3lk29pYDhu8I4llW94MsexjBzVVuTSOaM+SJnPKct7BUJVFRrLfQS9Dra3jtYMVBWF686cy9q6IP/y0Ov8zW9e4fr1c7nuzLnoqkKp10k8ZbL10ABzSzwsqvDLbKAQQog3TX5ynKRPnTefc+eXnlTwF0+ZdIUTpEyb1bUBzmkopdzvkuCvAJV6nZw7v5Qil053OIlppY/mPr2mmF9dvYaLF1ey6fnDbPzNK7QNxYH0DGKl30n7cJynDvTRNRxnlh3pLYQQIsckAJwiViajtzucxAJW1RRzXmMZc4rluLZC53ZorKsPsqjCS280STxlAuB36Xzt0sV8/dLF7O+L8uFfbueB17qxbRtFUSjzOvE6NLa1DvFK+xAJw8xzT4QQQswUsgScY4ZpMRg3AKgLuKkNegi4dZntE+OoqsKCcj+lXicvtw0TSSYp9aZrBl66pJKV1cXc/PBubn54N88e7OcfL2iiyK3j0lXmFLnojSR5+kA/K+YUUVUks8lCCCGOT2YAcyRhWPREEgwnTBZX+rmgqZzl1cUE5eQOcRwl3vQxctXFLrrDSZKGBUBNwM1PLl/JjWfP4497erjql9t5qW1o9PM8TnwOje1tQ7xweIDheCpfXRBCCDED5DUA/Pa3v83y5ctZsWIFV111FfF4fNzriUSCK664gqamJtavX8/Bgwfz09A3IZI06A4nSJoWy6uKeOuCMhpKvbJRX5wwp65yWk2ANXUBQgmDoUwwp6kKN6yfy88+dDq6qrDxN3/h35/YRyRpZD+vyu8ilrR4trmf17vC2QBSCCGEGCtvUUlbWxvf/e532bZtGzt37sQ0Te6+++5x1/zsZz+jpKSEffv28fd///d84QtfyFNr31gkadAVSqBrKmfUB3nrgjLqSyTwEydvTrGbcxtL8Tk1usKJbILIiupifnn1aj60qobfvNLBh37xIk8d6Mt+XpFbp9zn5PBAlKcP9NEpSSJCCCGOkNfoxDAMYrEYhmEQjUapqakZ9/rmzZv56Ec/CsDll1/OY489Nu1+kEWTZjbwWz+vhLPnlUhGr5g0XqfOGfUlLKnw0xtJEsskiPicOp972wL++4rT8bl0PnPvLv7x/tfojSQBUBWFMp8Tj0PjxdYhXmwZJJIw8tkVIYQQ00jekkBqa2v57Gc/y9y5c/F4PFx88cVcfPHF465pa2ujvr4eAF3XCQQC9PX1UV5ePu66TZs2sWnTJgA6Oztpb28/5n17enompf0p0yLcP4Tt0pgf9FDsMEgMxekYeuPPnQ4maxxmupkyDm5ggcdgd3cfhmUTcKf/69Y74AcX1/LrXf38ckcfzx/qZ+PqCt7RVJz9JcQNtIUMDrbYzCvxUB1w09fbm7/OTDMz5WsgVwq9/yMKfRwKvf9jFcpY5C0AHBgYYPPmzTQ3NxMMBvngBz/InXfeyTXXXPOm32vjxo1s3LgRgHXr1h01k3ikN3r9RAXLqyiewRm9kzUOM91MGof59Ra7uoZpH0pQ5nNmT575ZNUc3nV6lH99bB/f2trFn1rjfPGiJhpKvQAESZ8x3RdLEo1pVPqDM6rfuVboY1Ho/R9R6ONQ6P0fqxDGIm9LwH/84x+ZP38+FRUVOBwO3v/+9/Pcc8+Nu6a2tpaWlhYgvVw8NDREWVlZPpo7oYBk9Iop5tRVTq8JsKK6iP5oivCYZd2GUi8/vvw0/t9fLWRvb4QP/3I7P916mJSZTgTRVIUKnwtNVdjROcwr7UPZJWUhhBCFJW8B4Ny5c3n++eeJRqPYts1jjz3G0qVLx12zYcMGfv7znwNwzz33cOGFF0rAJQqeoijMLfHylvkl6KpCd2Q0QURVFN67Yg6/uXYt5zeW8eMth7jmVy+xo2M4+/keh0aZx0lvOMFT+/s42B/NBolCCCEKQ94CwPXr13P55ZezZs0aTjvtNCzLYuPGjdx8883ce++9AFx//fX09fXR1NTEt771LW699dZ8NVeIaafY7eCshlIWl/vpjSYJxUdnA8t9Tm5951K+tWEZ4YTBdb9+ZVzJGBQIepwEPQ52d4f50/4+DvVHMSQQFEKIgpDXk0C+8pWv8JWvfGXcc1/96lezf3e73fzmN7+Z6mYJMWNoqkJjuY8Kv4udncN0hROUeUf3Bp7fWMaa2gA/fO4gv3mlgyf39/GFC5tYWZz+fF1VKPc5SZkWr3WH2N8XZVG5j+qAG02OKBRCiFlLitQJMQsUuXXWzy1heVURA9HkuJNA/C6dz1/QxM8+NFoy5qtPtWdLxgA4NJUKnwuPrrKjM8RTB/roGIphWdOr7JIQQojJIQGgELOEqirMK/VybmMZPqeePpFmzEkgK2uK+eWHV3Pj2fPY0hrhg794kd/v6MjuH4R0kkml34lLU3m5fZgtB/sZiCYnup0QQogZTAJAIWYZv0vnjLlBVtUUE06aDMSS2QLqDk3lhvVz2fTOeSws9/KNx/Zx9a+280xz/7gi6y5dpdLvwrRtthwcYHvL4LiMYyGEEDObBIBCzEKKolAd8HD+glIq/C66w0kSY2YD6wNOfnz5Sr7xjsUkDItPb36Vv/nNX3i5fXwlc59Tp6rIxUAsxdMH+nm1Y5hoUgJBIYSY6fKaBCKEyC2XrnF6TYA5fhc7OkNEkgYlHgeQLhlzyeJKLmoq5/9e7eK25w9xw//+hfMaS/nEOQ00lfuy7xP0OLBsm45QnMODMeYGPcwr9eJ3ybcQIYSYieS7txAFoKrYTdCbLvnSMhhDHVPuRddULl9ZzTuXVnL3S+38fFsLV925ncuWVvK3Z82jJuAG0gFjiceZDgSH04FgbcDNgjIfPgkEhRBiRpHv2kIUCJeusbImQE2xm+de7ac3kqTU60DNFFf3ODQ+dmY97zttDj/f1sr/vtzOI3t6uPy0aj52Zj2lXieQCQS9TmzbpiecpG0oTm3AzdwSL4EZfDSiEEIUEgkAhSgw5X4Xq2oDRB1eDvRF8Dm1cUu5QY+Dm86bz5Wrarht62F+/Uo7m1/t4uo1tVyzthafM32toigEPQ5s26Y3kg4Ei1w6jWVeKvwuHJpsMRZCiOlKvkMLUYB0VWFJlZ9z5pfi0lW6QgmiyfHnAlcVufh/f7WQ//3IWs6aF+S2rYd5z+1/5q6X2saVl1EUhYDbQaXfhQK80jHME/t62dU5zFAshRBCiOlHAkAhCljQ42D9vBLWzytBVaArnCCeGh8INpR6+fd3LeOOK1exsNzHfz55gA/8fBv37eoaV0MQwO3QqPS5CLoddAwneO5gP88f7Kc3nBhXZkYIIUR+SQAoRIFTFIUyn5Nz5pdyRn0Q04bucGJc2RiAFXOK+OH7T+P771tBwOPgy4/s4cO/3M6T+/uOCu40Nb08XOl3kTQt/twyyNMH+ukcjh8VNAohhJh6sgdQCAGkA8EKv4syr5PucILXusMMxQ1KPHp2P5+iKJw1r4Qz5wZ5bG8vP3zuIP/wh13MDXp4/2lzeNeyKoKZMjMjfE4dn1MnnjJ5qW0It66xsNxHud+J26Hlo6tCCFHwJAAUQoyjqgpzit1U+F10DMV5vSeMaaXrB2pqOsNXVRTevqiCCxaU8cieXn67o4P/erqZHz53kIsWVvCB0+Zwek3xuIxgt0PD7dBIGhY7u4ahS8Hn1KjyuyjzOfG7NFy6BIRCCDEVJAAUQkxIUxXqSjxUFbs42B9lX28Ep6aOm+HTNZXLllZy2dJK9vVG+N2ODu5/rZsHX++msczLB06r5rIllRS5R7/VOHWVCt0FQMKwODwQZX9fBEgHiWVeB2VeJz6Xjs+pSTaxEELkQN4CwN27d3PFFVdkHx84cICvfvWrfPrTn84+96c//Yn3vOc9zJ8/H4D3v//93HzzzVPdVCEKmkNTWVjhp6bYze7uMJ2hBMVuHc8Ry7dN5T4+f0ET/9+583lkTw+//UsH//Gn/Xz3mWYuWVzBB06rZlmVf9ysoEtXcenO7GPDtOiPpOgYjmPZoCjg0TVKvQ5KvU48Tg2XpuLSVfRZEBjato1lg2nZWPbIn9HHmqpkA2DLGn2sAJZtY9p25tp0wK4pCrqqoKpSi1GI6ci2baJJk3DSpMLnzOv/1bwFgIsXL+bll18GwDRNamtred/73nfUdeeddx733XffFLdOCHEkn0tnTX2Q3nCCXV1husIJyjyOowIxj0PjPcvn8J7lc3itK8TvdnTy0O5u7n21i8UVPj6wsppLF1fidR693KtrKkWaStGYb00p06IvkqJ9OI5N5pulDU5NSS8r6ypuh4bfqWWWmdMBoqYoqIqCojBpxalNyyZlWpiWjQ3YNqijTRoXwKVMi2jSJJI0iKQsrEzyi2XbGFbmj2mjKOkfCumuKShkHqNknsv8PXMXBYX03Tn6+cz7OFQVj0PD59RwOVTcuobHoeHUFJyaiqamP0qgKMTUSBoWXaE4+/uixFImtg0XLCzHo+Zv28u0WAJ+7LHHWLBgAfPmzct3U4QQb6Dc7+ItXiftQzFe7w6jKBB0OyYMspZWFfHPVUXcdN58Hnq9m9/u6ORfH9vHf/7pAEur/JxWXcxp1UWsrC6m3Oec4G7pGUjHEUEhkAmiLCJJk6F4ihbTzgRGo0FROk5SUFXQlfTM2MirFulAbbhvgKJQ+gQTFVBUJTsDN9bIQ3vsLUbuM+aJkQBOURTUTDDm0JTs+GiKgkNPP9ZyFICZlk3KshiMpTAi9riAFcUGO922Eq8DJRLHWZzA49BmzcyqEPlk2zbhhMlwPEV3JEk4YRBJmKDYFLscFLl0eiLJfDfz1ALAd7/73cf9zfree+89ofe5++67ueqqqyZ8bcuWLZx++unU1NTwzW9+k+XLl59UW4UQk0dTFepL0id+7OkJ0zYUR1cVit0O9AmCGr9L5/LTa/jAymp2doZ4ZE8POzpC3PVSG//zYjqymlPkygaDp80pYnGl/7j7/3RVQc/+9nz836JHllrHlatRMsGgSyfgcWCPxIukgzglMwE3NrybKcfcaaqCpmrH/Q5v2TbxlEXPQIxeZSjzrI2uqngdGl6nhkNLB6lONT2r6nFqFLl02ZcpxBHiKZNwwqAnkqR9KE7StFAVBbee/r9T7pv4l+R8OqUA8LOf/SwAv/vd7+js7OSaa64B4K677qKqquqE3iOZTHLvvfdyyy23HPXamjVrOHToEH6/nwceeID3vve97N2796jrNm3axKZNmwDo7Oykvb39mPfr6ek5oXbNdjIOaYU6DpPZ73LA5zXpDSfp6IhjmDa6quBz6qgTxAn1Drh+uR+W+0maFvv6E+zqjbOrJ8bLrYM8uqcXAIeqsLDURWOJizKPTrlXp8yjU+bVKfNoFLu07DnGpyIy1M80+748pbTEMI7Y6I8C24Zhy6bftNLL2tjYFph2+jUF8Ls1yrxOilwaHqc+YdA/0xTq94IRhd7/sU50LFKmRX80SftQgnjKwsbGoSnp7RaZ/xNG5s+RIjGDTm8Sl56/X6YUexLK869bt45t27a94XMT2bx5Mz/4wQ945JFH3vDahoYGtm3bRnl5+Ztqy1jt7e3U1NS84b1mOxmHtEIdh1z127JshhMGHcNxWgZjmLZNsUvH/SbKu3SHE+zoCLGjY5i/dIQ4PBhlMHb0t1BdVSj3OanwOSn3pz9W+FxU+J14nRpaZolVV8d8zDw39vnoUB8lpRXZ50f27yVMi5RpkzQskmbmj2GRNO3Rx6ZF0hh9nDItEplrEsbo5ySyn5t+PWWml2VN28a2bUw7k9SRSejIJoRkanE7NCWzFJ75mNnDp2f29Dk0BYeqUuTWKfc5R8dlzB+fU5twBmKwt4tg+Yn9wg7pWdSEkV56N+30HkafM33fMm+6nI/HMfG9prNC/V4wotD7P9axxsKybOKGSSRp0jIYoyuUQFUU/C7tTX2PA+iJJHnrgrKjkuly4Vhx0aTsAYxEIhw4cIDGxkYAmpubiUQiJ/S5d9111zGXfzs7O6mqqkJRFF544QUsy6KsrGwymiyEyAE1cwJI0OOgqdxHTzjBvt4o3eEELl2lyKW/4axdpd/FRQtdXLRw9Be9dCJIkp6RP+EkvZEkPeEEPZEkhwZivNgyxHBiot+1T8TBk/y8UQojWc0qTk3Fqau4MsHZyHNepyPznJrJ2iW7F1BV0vsD1czf1UwSC6STRVKWnQky0wFqykzv8xv5eySZ4vBgjJ5I8qhTXMi0baLgMKAkWGJ6qQt4xpXrOWY/FSVb03FEwrBoH4pzaCCGbds4NZVSn5MKn4MilwOfU5O9hWLGSGfqGsQNi0jCYDCWYjizj88iPRPudahU+Jwz7hedsSYlAPz2t7/N2972NhobG7Ftm0OHDvGTn/zkDT8vEonw6KOPjrv2xz/+MQA33ngj99xzDz/60Y/QdR2Px8Pdd989owdbiELi0FRqAh6qi90MxlK0DcVpHYyDYhN0O97UPjKHpjKn2M2cYvdxr4sbJr2RJPGUlc20Na3R2TbDzHwc8/zw0CAuX3HmdStbasWlpwO1kSBuJKhzakrm49g/6SBuOnx/sm2bSDI9DiN/esb+PZxkT2+ELYcGiCRHzn3uACDo0akPeKgLeqgLuKkPeqgPpj8G3Pox+zcS+I4wLJvhWIruUDy9bJyZJQl6HATdDorc6dNhcpUEI8SbYZgWwwmD/miS7lCSjo4BfKF0vVMFcGX+75d6p98+vlMxKUvAAIlEgtdffx2AJUuW4HK5JuNt3zRZAj4xMg5phToO+ep30rDoGE6XQkiaJpCeBUsHEFpe9pK92SXQ2SSaNHn9UBtDipeWwfSyfetQjJbBOF2hBGN/OPidGvVBD3VBN/NLvTSWeplf5mVu0POGwbxt25llcZOkmSliY9sUuXVKPc50XUlnpqSPruWlPE2hfi8YUSj9N8z09oVwwmA4bhBKGAzEUlg26Cp4HRrxwV6CFbn9njBrloABXnzxRQ4ePIhhGLzyyisAXHvttZP19kKIWcCpq8wr9VIf9BBNmemCqJklloFYiqRpAQoOFVx6uiyJzBLljtep0VjiIjjBvuqEYdGe2cvZOhinZSj9cVdnmD/u6c0Gh5oC9UEP88u8o4FhqZd5pZ7svihFUXDpyrhZQtu2SZgWHaE4hwetbI1HBRuXruF3afhdOkVOHXemRM3IfsfZNAsjJp+Vmf0f2Zeb3iJh0hVOMBRLZco0pVcWnJpK2REze/EC+fKalADwIx/5CPv372fVqlVo2uh/eAkAhRATUVUFv0vH79KpLEqvFti2TSxlZuv6DURTDMUNDCsdFI6UZMlcnK7TkimArI/soVPTyRC6pmTLuozssztZI/UA06dvjJ7AYR+RuJEtNQOj9QjH1N1DsTM1AmE07Tjz3oqSrR2IbY8WvIbx/R65ntF9gkcmu0xWwOzSVeZngrkjxQ2TQ/0xmvujHOiP0twX5UBflKf292Hao+2uDbiZXzYaFDaWeWko8eLNJKS49Yk3zxumRTxlMRSLY1jWaO1FO12n0a2p2SLgPpeGz6lng0PnmD2WYvZJzyZbxFLp/XmhpEE4YWSTr1Kmlf4/m/2aUVCU9PcJr1M7KtgrZJMSAG7bto1du3bJoAohTpqiKHidOl6nToV/dAtJyrQywdzoKRsjhZpTpk3SNIkmLVKWRSJlEUmZhGIGWqbOX8pOlzFRlExglQnK0idqpMsxpCIJshHGmGADFDSVbBFlTVWyp4w4dQU9s//PoaqZuoTjs4yPTHwYqS+Y/ruSrTU4YmyAOXJyyIiRNqSPf8tkH1sWqUxWccwwiSTM0dNFjrivnXmTkUA53T4lm2n8Zrh1jcWVfhZX+sc9nzQsDg9mAsO+KM396T9bDg5gjOlPdZFrNDAs8zK/JB0gjiSh6JqKrjHh8pg9cpKKaTGYMukJJ9IB+RGnojgyv2R4nSpeR3qJ2aGm92+6dU1mEmcAK1PQPJay6Isk6AwlCCfNTFH29L94Ois+/f/P61DRjpHtLo42KQHgihUr6OzspLq6ejLeTgghsiaj6PBIwDgSSI5MENi2TacnQdWcdHWBdDZuenZNGfNxJhl7vrBhWZgW2QQXw0qXtYkZJrGUlZ5xTRmY4QSZM+kgk42cDmhHS8+cCKeu0lTuo6ncN+55w7RoHYqPnzHsj/JiyxAJczQCrfA5j5oxnF/qJehxZK9RFCXTpuO3ZWQJMBo2Mawjg8R0dx2Kgsuh4tE14kNRDHcUV+bIPMeYGcWZ9jUwXY2UEBo5MnHk/+NIZvvYoxOjCYNkZvYf20ZVFHwujVKPY1Lqf4pJCgB7e3tZtmwZZ5555rjkjxM9CUQIIXJJUxU0Jv6h4XZo+FzT4lTMSaEo6cQaTVVw8saBW7szTkVVBfHUaG3DWNLMBInpWcXBWCq9ip2ZS3Soo+VuTmSpVddUGkq9NJR6uWDM86Zl0zEc58ARM4abX+0klhoNDEu9jnGJJyMB4vGyMrMnxTgmfDlbf9G0bMJJg4FIglB3eMxxgumPCunA1ufQ8DjSyUpuPZ0VOlrOZ3Q2d2xzRnYqqCP1JzNlfmYb00qH1mP7NrJUG09ZRFMmfZEk3aEESXPk3Ov0lgfbGt0GoWX25emqgs+lE5iFYzWdTMp3vS9/+cuT8TZCCCHyYOS85WMxrfT+zJHi1qF4OnEnlDBImfbIFkdQbHRFOeEEHk1V0iVngh7Obxyt8WrZNt2hRDowzMwYNvdHefD1bsLZ0jVQ7NInnDGs9L9xfTZFUdCVdKDoQiXl1AlOcB61ndnnmczMUJl2EsOyR7ehMua4wNFJVEa2etrpTaGZLQikZ1VVBVcmscWlqensZ4eWXZZXGDMDjZIt+D0V+xpHltiThpXdRmDbkLKs7H7XlGkSipuZ2nhGJnkrPQCqmp5BNqzRbG9IJwFJUDe9TEoA+Na3vpVDhw6xd+9e/uqv/opoNIppmm/8iUIIIaY9LZu0k35cXTz6mpE9FSW9vBdKpBiMpRiMGRimnYkMQWX01BI9M8tzLKqiZOs+ntNQmn3etm16I8lxy8jN/VEe39fLUHy0CLjPqWUTWBrHZCfPKXa96eVDRUkHYLrGMWcT34yRxCHTtAgZJoOZPa2GZY8GjGQi6mzCUPrvzjHJLw5NwaGrqIzuOx0J4VUVkoZNJJkO0A3LxgLUzGsqCoqann0bqZfZ3z3IrrAD00qfTjPR+YgjSUzpDFoFl6YeFdSNbLWYidsnCs2kBIC33XYbmzZtor+/n/3799PW1saNN97IY489NhlvL4QQYppKJ2yojOQKV43J6k4YVvZPNGUSiqfS+7tSJknDxCad/awqSnZ59XiBoaIoVPhdVPhdrJ9bkn3etm0GYqlxy8jN/VGeO9jPH3Z1Za9zZzKbG45YTq4NHL/A+GRSFQX1BPYwTmQkUIwkzXHZ6LZtZ5OExpbn0VU1U6A8HbyZgG2ODdKU7BK1Q1cpdp16cW71yMwmMW1NSgD4gx/8gBdeeIH169cDsHDhQrq7uyfjrYUQQsxAEx0ZN9bIuaqxlEUoYdAfSTIYS5EwrWzA4tIy9f/04+9lVBSFUq+TUq+TdfXBca8NxVPpgLBvdDl5e+sgD74++jPKqSnUFTloqhwYnTEs81IfcE+rI+wms8zPkWI5fG8xPU1KAOhyuXA6R/dOGIYhU79CCCGOSVVHyv5Amc9JQ6beYDIzWxjJHM01GDcYjiSzWdvOzDF9Ll09oeXcgNvBqpoAq2oC454PJwwO9kdp7o9xoD/Kns4BXu0M8cienuw1mqowL+ihodQzZinZx9wSz7ii1kLMRJO2B/Bf//VficViPProo/zwhz/k3e9+92S8tRBCiALizMz4BT0OaoMeIL30GU2ms5LT+wvTJ8eYmX1zkN4f59G1N5wtHOF36ayoLmZFZkPjYK+XYHkVsZTJoYFYdjn5QF+Ufb1R/rS/j5FShqoCdQHP+D2GZV4aSjzHnPEUYrqZlADw3/7t3/jpT3/Kaaedxk9+8hMuu+wybrjhhsl4ayGEEAVOUxWK3DpF7vEnx4zsL4wbFv2RJN3hRDoZREnPFLoz2chvhsehsaTSz5IjilwnDIvDAzEO9EeygeHB/hjPHOzHHCllAtQUu7PH4s0v9dCY2XPon0WlhsTscMpfkaZpsnz5cl5//XX+5m/+ZjLaJIQQQhzX2D2GAdLJJ0spIpYyGY6n9xT2RZP0hJPY2KhK+gQTj0M7qb1uLl1lYYWPhRVHF7luGYqPmzFs7o+y9fAAqZFz8YAqvzMdFJaNT0Apdk9CarEQJ+GUA0BN01i8eDGHDx9m7ty5k9EmIYQQ4qR4HOkgbyQb2TAtIkmTUNygO5ygL5rEyJSt01XwOic+j/hE6drEZyYblk37UPyokjW/29FJwhgtcl3mdYwrVTMSGJZ4j65JKMRkmpQ56YGBAZYvX86ZZ56Jzzf625GcBCKEECKfdE0l4FEJeBzUlXiyJ1REkib90SRdoQTd4QSRaApiKbyOE99HeNz7qgpzSzzMLfHwtgXji1x3DieyAeHIjOH9r3UTGVPkOuDWJwwMy31vXORaiBMxKQFgPB7nvvvuyz62bZsvfOELx/2c3bt3c8UVV2QfHzhwgK9+9at8+tOfHvc+N910Ew888ABer5c77riDNWvWTEaThRBCFCAle1KJRqnXSVO5n6Rhsf9QHLXIQ1cowVAkCYBDVSYtIByhKgo1ATc1ATfnzh9f5Lo7nBwXFB7oj/LHPb0MJ0aLXPudWjYwHAkKG0u9VBW5JDAUb8qkBICGYfDWt7513HOxWOy4n7N48WJefvllIL2PsLa2lve9733jrnnwwQfZu3cve/fuZevWrXz84x9n69atk9FkIYQQAkhnHgc8Dmoq/Syu9BNPmYQSBr2Z82sH4ykgPavnzRzhNtnBlqIoVBW5qCpycda88UWu+6OpbEA4spz8dHM/m18dLXLtdWg0lHrGnYDSWOqlutgt9f3EhE4pAPzRj37ED3/4Qw4cOMDKlSuzz4dCId7ylrec8Ps89thjLFiwgHnz5o17fvPmzVx77bUoisJZZ53F4OAgHR0dVFdXn0qzhRBCiGMaSS6p8LtYWlVEwjAJJ0yGYim6I0l6IylQ0jOEPqd23HOUT5WiKJT5nJT5ji5yPRhLHTVj+MLhQe5/bbTItUtTmZfJRh57bnJd0HPcU1fE7HdKAeCHP/xh3vGOd/BP//RP3Hrrrdnni4qKKC0tPc5njnf33Xdz1VVXHfV8W1sb9fX12cd1dXW0tbVJACiEEGLKjCwZl/mcNJb7SJkWw5mkks5QgoFYeobQo2t4nSeXZXwygh4Hq2sDrK4dX+Q6FDfSp58MjM4YvtI+zEO7R4tc66rCvJLRGcMqZ4oV+Jkb9EzqkreYvk4pAAwEAgQCAe66666Tfo9kMsm9997LLbfcctLvsWnTJjZt2gRAZ2cn7e3tx7y2p6fnmK8VEhmHtEIdh0Lt90QKfSwKvf8jTmYcAkDAC/GUSTRl0RdJ0NqbwrJsFEXB63jjY+xyZa4T5lYpvLXKB6STM2Mpi8PDSQ4NJTk0lODwYJJdHUM8trc3fYbw0x2oCtQWOZgXcDE34GRe5k99sbNgTj8JD/bn/B6RmEGnN5nXMc17ZcoHH3yQNWvWUFVVddRrtbW1tLS0ZB+3trZSW1t71HUbN25k48aNAKxbt46amprj3vONXi8UMg5phToOhdrviRT6WBR6/0dMxjhYls1wwmAgmqIzFGcongIbHJo66Qklb1YQqK6G9Uc8HzdMdh5opdfyZLOTm/uibGntZ6SUoQLUBtzjlpFH/nids+/0k2D50THJZEpFksypLsOTx5Nj8h4A3nXXXRMu/wJs2LCB73//+1x55ZVs3bqVQCAgy79CCCGmLVVVCHocBD0O5pd5SRoWw/EUfdEUXaEEg+EEkD66Lt8B4Qi3rtFU6mZdeeW451OmxeHB2Ggdw8zHLQcHMKzRItdzilzZPYbZsjWlXorceQ8xxHHk9V8nEonw6KOP8pOf/CT73I9//GMAbrzxRi677DIeeOABmpqa8Hq93H777flqqhBCCPGmOXWVcr+Lcr+LxZV+Eka6KHVvNEl3KJkOCBXwaBo+l4Y6jUq5ODSVBWU+FpQdcfqJZdM6GBuXmdzcH+XF1iES5miR6wqfc9yM4Uj5mqBHTj+ZDvIaAPp8Pvr6+sY9d+ONN2b/rigKP/jBD6a6WUIIIUROuHQNl1+j3O9iSWV6/+BQ3KBjOE53OIFppU8o8Tv1aTE7OBFdVWjInHF8wZjnTcumIxQ/asZw86udxFKjgWGJxzEuIBz5WOZ1SC3DKSTzs0IIIUSejJScqSpyYVl2pv5ggpbBOIMJA42R4+0mv/bgZNNUhbqAh7qAh/Max59+0h1Kn34ydsbwode7CY85/aTYpU84Y1jpl9NPckECQCGEEGIaUFWFgMdBwOOgscxHJGkyEE3SMZygL5rCsm0cqoLfpee09uBkUxWFOcVu5hS7Oadh/OknfdEUB/oi4wLDx/f1MhQfPf3E59RoKDl6xrC62DWtlsxnGgkAhRBCiGlGUdKBnt+lU1/ixbRshuMpeiNJ2obiDMRSKMpo7cGZGAgpikK5z0m5z8mZc0vGvTYQTY5bRm7uj/LcwX7+sGv09BOXro6efDKm0HVNwC1Frk+ABIBCCCHENKepCiVeJyVeJwsr/ESTBoOxFJ3DCXqjSUzLRlUU/M7cHFU31Uq8TtZ6naytC457fjieork/Nu4ElO1tQzz4+ujpJ05NYW6myPXYwLA+6JlRM6e5JgGgEEIIMcN4nTpep05NwINhWoSTJoOxFB3DcXojKWzAoTLjlovfSLHbwek1Dk6vKR73fDhhcHAgNm7GcFdnmEf39Gav0VSFuUH3+ASUUh9zSzwFU+R6LAkAhRBCiBlM11SCHpWgx0FDqTd7VF1vJEn7cJyBeAqHqlDsckzZMXVTze/SWTGniBVzisY9H0uZHBqIZWcLm/uj7OuL8qf9fYyUMlQVqAuMHIvnocppsMJMZznns1BzrkkAKIQQQswiDk2lzOekzOdkUYWPUMKgfSjBoYEotg1Fbg23PnsDm7E8Do0llX6WVPrHPZ8wLFoGRwPDkf2Gzxzsx7RseK4TgJpi1xEzhunA0O+a+eHTzO+BEEIIISakKArFbgfFbgfzyzx0hxIcGojRHUmiK+kl1UJMmHDpKk3lPprKjyhybVq8erCVXsubnTFs7ovy55ZBkubo6SdVfmd6xrDMO+4UlIB75hS5lgBQCCGEKAAuXaO+xEt9iZdQ3KAzFOfQQIyUaZFKGhTb9ozMJp5MuqYyL+Di9PLycc+blk37cPyoGcPf7+gkbowWuS7zOsYHhpnZwxLP9CtyLQGgEEIIUWCK3DpFbj+NZT4GYyl27huiP5rCwsbn0PA6tGkXsOSTpirUBz3UBz28dcH4Itedw4ls4slIgPjAa91ExhS5Drj1ccvIpT4n580vnehWU0YCQCGEEKJAaapCmS9dWqaiqpy+SJLWwTg9kQSQPp3DPYsTIU6VqijUBNzUBNycO398keueSHJ0xjDz8Y97ehlOGPidGjedNz+PLZcAUAghhBCkk0dGTuxIGCa94STN/VG6w4l0FrF79mYRTzZFUaj0u6j0uzhr3miRa9u26Y+m2N0TyfsMqwSAQgghhBjHpWvUBj3UBNyEEgZtQ3FaBuOYlo3fmT59RLx5ipKZcc13Q5AAUAghhBDHMDaLeGG5j95I+oi27nACp6ZS7NYLPnFkppIAUAghhBBvSB+zRDwcT9EyEKNlKIaCQtCto8+iE0cKQV7/tQYHB7n88stZsmQJS5cuZcuWLeNe/9Of/kQgEGDVqlWsWrWKr371q3lqqRBCCCFGFLsdLK8u5oKmchZV+AklTXoiSeIp840/WUwLeZ0BvOmmm7j00ku55557SCaTRKPRo64577zzuO+++/LQOiGEEEIcj0vXmF/mpT7opjucSCeNRJI4VAi4HbI8PI3lLQAcGhriqaee4o477gDA6XTidDrz1RwhhBBCnCRdU6kJeKgJeAgnDA4PRjnUH0NVFEo8kj08HeUtAGxubqaiooKPfexjvPLKK6xdu5bvfOc7+Hzjj2XZsmULp59+OjU1NXzzm99k+fLlR73Xpk2b2LRpEwCdnZ20t7cf8749PT2T25EZSsYhrVDHoVD7PZFCH4tC7/+IQh+Hye5/EPD6LLpCCVrb4tiA36XNiGPnwoP9Ob9HJGbQ6U3i0vO3Ey9vAaBhGGzfvp3vfe97rF+/nptuuolbb72Vr33ta9lr1qxZw6FDh/D7/TzwwAO8973vZe/evUe918aNG9m4cSMA69ato6am5rj3fqPXC4WMQ1qhjkOh9nsihT4Whd7/EYU+DrnofwOQMEw6hhPs742QNC18Tg2fc3rnoAbLq3L6/qlIkjnVZXjyWGQ7b6FnXV0ddXV1rF+/HoDLL7+c7du3j7umuLgYv98PwGWXXUYqlaK3t3fK2yqEEEKIk+PSNRpKvbytqZy1dUGcmkp3OEFfNElyzDm6YmrlLQCcM2cO9fX17N69G4DHHnuMZcuWjbums7MT27YBeOGFF7Asi7KysqPeSwghhBDTm6YqVBa5OKuhlPMay2gq8xE3LXoiCazMz3oxdfI6B/u9732Pq6++mmQySWNjI7fffjs//vGPAbjxxhu55557+NGPfoSu63g8Hu6+++68H50ihBBCiFPjd+n4XTrzSr3s74uwryeCz6nhd03vpeHZJK8jvWrVKrZt2zbuuRtvvDH7909+8pN88pOfnOpmCSGEEGIKaKrCogo/lX4Xe3vCdIcTuHWVIpcuEz45JqG2EEIIIfIq6HFwxtwShmIp9vdG6MocNRdwSyCYKxIACiGEEGJaCHgcrKkPMhxP0dwXpW04jkNN1xKUQHBySQAohBBCiGml2O3g9NoACyt8HOyPcmgghktXCbgd+W7arCEBoBBCCCGmJa9TZ9mcYuqDXvb2hOkMJXA7VIplj+ApkwBQCCGEENNakVsftzTcMRxHURSCbh1dy99pGjOZBIBCCCGEmBFGloYXV/rpGE6wtzeCbRsEPY4ZcczcdCIBoBBCCCFmFLdDY36Zl9qAm9bBGPt6I6BAiceBKkvDJ0QCQCGEEELMSE5dpbHcR03AzYH+CIf6Y7h1lWJJFnlDsnAuhBBCiBnN7dBYVlXMufPLKHI76ArFCSeMfDdrWpMAUAghhBCzQpFbZ119kLMaSnHpKl2hhASCxyABoBBCCCFmlVKvk/XzSjiroQSXrtIZSpAyrXw3a1qRAFAIIYQQs46iKNlAcE1tMYOxlMwGjiEBoBBCCCFmLUVRqA54eMv8MnRVoTucIGnIbKAEgEIIIYSY9YrcOmc3lLKqppiYYdEbSWJadr6blTd5DQAHBwe5/PLLWbJkCUuXLmXLli3jXrdtm0996lM0NTWxcuVKtm/fnqeWCiGEEGKmU9X0bOB5jaU0lXvpjyYZjKXy3ay8yGsdwJtuuolLL72Ue+65h2QySTQaHff6gw8+yN69e9m7dy9bt27l4x//OFu3bs1Ta4UQQggxGzg0lQXlfqqL3ezqDNEVTlDiduDUC2dhNG89HRoa4qmnnuL6668HwOl0EgwGx12zefNmrr32WhRF4ayzzmJwcJCOjo48tFYIIYQQs43XqbO2Psiq6vSycE8kUTDLwnkLAJubm6moqOBjH/sYq1ev5oYbbiASiYy7pq2tjfr6+uzjuro62traprqpQgghhJilFEWhJujh/MZSllQWEUma9EaSWPbsDgTztgRsGAbbt2/ne9/7HuvXr+emm27i1ltv5Wtf+9qbfq9NmzaxadMmADo7O2lvbz/mtT09PSfd5tlExiGtUMehUPs9kUIfi0Lv/4hCH4dC7/8IJzDXnSJhhDjUEsOpK/ickx8qRWIGnd4krjwuOectAKyrq6Ouro7169cDcPnll3PrrbeOu6a2tpaWlpbs49bWVmpra496r40bN7Jx40YA1q1bR01NzXHv/UavFwoZh7RCHYdC7fdECn0sCr3/Iwp9HAq9/2PV1NQQihu83h2iJ5KkzONA1yYvWEtFksypLsPj0CbtPd+svIWec+bMob6+nt27dwPw2GOPsWzZsnHXbNiwgV/84hfYts3zzz9PIBCguro6H80VQgghRAEZOVbu9OpiBmIpQvHZVUQ6r1nA3/ve97j66qtJJpM0NjZy++238+Mf/xiAG2+8kcsuu4wHHniApqYmvF4vt99+ez6bK4QQQogCoigKtUEPAY+DVzuH6QolKHbreZ25myx5DQBXrVrFtm3bxj134403Zv+uKAo/+MEPprpZQgghhBBZfpfOmXNL6Isk2dUVpieSoMzrRFWUfDftpBVOwRshhBBCiJOkKArlfhdvmV9KY5mPnkiSSHLmLgtLACiEEEIIcYI0VWFRhZ9zGkrRlPTZwilz5p0tLAGgEEIIIcSbFPQ4OLuhlNPmFBNKGPRFk9gzqHZgXvcACiGEEELMVKqqUFfioaLIyb6eCAcHYgRmSJKIzAAKIYQQQpwCl66xvLqYcxpKsGzoiSQwpvmRchIACiGEEEJMghKvk3MaSlhU7mcglmRgGi8LyxKwEEIIIcQk0TWVxnIf1QE3e3vCtA7G8Tk1/K7pFXLJDKAQQgghxCTzODRW1gQ4Z34pDl2lJ5LAmkazgRIACiGEEELkSNDjYP3cEhpK0rUD44aZ7yYBEgAKIYQQQuSUpiosqfJzZn2QWMrCsPJfN3B6LUgLIYQQQsxS5X4X584v5WB/FF3N7zFyEgAKIYQQQkwRt0NjSVVRvpshS8BCCCGEEIVGAkAhhBBCiAIjAaAQQgghRIGRAFAIIYQQosBIACiEEEIIUWAkABRCCCGEKDCKPV1PKT5J5eXlNDQ0HPP1np4eKioqpq5B05SMQ1qhjkOh9nsihT4Whd7/EYU+DoXe/7Fm21gcPHiQ3t7eo56fdQHgG1m3bh3btm3LdzPyTsYhrVDHoVD7PZFCH4tC7/+IQh+HQu//WIUyFrIELIQQQghRYCQAFEIIIYQoMAUXAG7cuDHfTZgWZBzSCnUcCrXfEyn0sSj0/o8o9HEo9P6PVShjUXB7AIUQQgghCl3BzQAKIYQQQhS6aR8AtrS0cMEFF7Bs2TKWL1/Od77zHQD6+/t5+9vfzsKFC3n729/OwMAAALZt86lPfYqmpiZWrlzJ9u3bAXjiiSdYtWpV9o/b7eb//u//JrznpZdeSjAY5F3vete456+++moWL17MihUruO6660ilUrnr+BEmaxwAPv/5z7N8+XKWLl3Kpz71KY41CXzLLbfQ1NTE4sWLefjhh7PPX3fddVRWVrJixYoc9nhi02UcjtWO2d7veDzOmWeeyemnn87y5cv50pe+lNN+T2S6jMUI0zRZvXr1Ud8vcmU69b+hoYHTTjuNVatWsW7duhz2+mjTaRwGBwe5/PLLWbJkCUuXLmXLli057HnadOn/7t27x/1sLS4u5r/+679y2/kjTJexAPj2t7/N8uXLWbFiBVdddRXxeDyHPT9F9jTX3t5uv/jii7Zt2/bw8LC9cOFC+9VXX7U/97nP2bfccott27Z9yy232J///Odt27bt+++/37700ktty7LsLVu22GeeeeZR79nX12eXlJTYkUhkwnv+8Y9/tO+99177ne9857jn77//ftuyLNuyLPvKK6+0f/jDH05mV49rssbh2Weftc855xzbMAzbMAz7rLPOsp944omj7vfqq6/aK1eutOPxuH3gwAG7sbHRNgzDtm3bfvLJJ+0XX3zRXr58+RT0fLzpMg7Hasds77dlWXYoFLJt27aTyaR95pln2lu2bMlZvycyXcZixH/+53/aV1111VHfL3JlOvV/3rx5dk9PzxT0+mjTaRyuvfZa+7bbbrNt27YTiYQ9MDCQ495Pr/6PMAzDrqqqsg8ePJjDnh9tuoxFa2ur3dDQYEejUdu2bfuDH/ygffvtt+d+AE7StJ8BrK6uZs2aNQAUFRWxdOlS2tra2Lx5Mx/96EcB+OhHP5qdzdu8eTPXXnstiqJw1llnMTg4SEdHx7j3vOeee3jHO96B1+ud8J4XXXQRRUVFRz1/2WWXoSgKiqJw5pln0traOok9Pb7JGgdFUYjH4ySTSRKJBKlUiqqqqqPut3nzZq688kpcLhfz58+nqamJF154AYDzzz+f0tLSqen4EabLOByrHbO934qi4Pf7AUilUqRSKRRFyVm/JzJdxgKgtbWV+++/nxtuuGFqOs/06n8+TZdxGBoa4qmnnuL6668HwOl0EgwGC6b/Yz322GMsWLCAefPm5bbzR5hOY2EYBrFYDMMwiEaj1NTUTM0gnIRpHwCOdfDgQV566SXWr19PV1cX1dXVAMyZM4euri4A2traqK+vz35OXV3dUT+Y7777bq666qqTbkcqleJ//ud/uPTSS0/6PU7FqYzD2WefzQUXXEB1dTXV1dVccsklLF269Kh7nMg45tt0GYex7ZgK+e63aZqsWrWKyspK3v72t09ZvyeS77H49Kc/zb//+7+jqvn5Vprv/iuKwsUXX8zatWvZtGlTLrt6XPkch+bmZioqKvjYxz7G6tWrueGGG4hEIjnu8Xj5/joYcao/WydDPseitraWz372s8ydO5fq6moCgQAXX3xxjnt88mZMABgOh/nABz7Af/3Xf1FcXDzutZFZuRPR0dHBjh07uOSSS066LX/3d3/H+eefz3nnnXfS73GyTnUc9u3bx2uvvUZrayttbW08/vjjPP3007lsck5Ml3E4XjtyYTr0W9M0Xn75ZVpbW3nhhRfYuXPnm+7HZMj3WNx3331UVlaydu3ak2r/qcp3/wGeeeYZtm/fzoMPPsgPfvADnnrqqTfdj1OV73EwDIPt27fz8Y9/nJdeegmfz8ett956Un05Gfnu/4hkMsm9997LBz/4wTf9uZMl32MxMDDA5s2baW5upr29nUgkwp133nlSfZkKMyIATKVSfOADH+Dqq6/m/e9/PwBVVVXZpd2Ojg4qKysBqK2tpaWlJfu5ra2t1NbWZh//7//+L+973/twOBwAbN26Nbt59d57733DtnzlK1+hp6eHb33rW5PWvxM1GePw+9//nrPOOgu/34/f7+cd73gHW7Zs4fe//312HLZt2/aG45hP02UcJmpHIfR7RDAY5IILLuChhx7KddePMh3G4tlnn+Xee++loaGBK6+8kscff5xrrrmmYPo/8t4AlZWVvO9975vypeHpMA51dXXU1dVlZ8Ivv/zycUkFs73/Ix588EHWrFkz4ZLpVJgOY/HHP/6R+fPnU1FRgcPh4P3vfz/PPffcFI7Cm5TvTYhvxLIs+yMf+Yh90003jXv+s5/97LjNnZ/73Ods27bt++67b9zmzjPOOGPc561fv95+/PHH3/C+TzzxxFGbum+77Tb77LPPzm7wnEqTNQ533323fdFFF9mpVMpOJpP2hRdeaN97771H3W/nzp3jNrnOnz9/3Ibf5ubmvCSBTJdxOFY7cmW69Lu7uzu7wT0ajdrnnnuu/Yc//CF3HZ/AdBmLsSb6fpEr06X/4XDYHh4etm3btsPhsH322WfbDz74YA57Pt50GQfbtu1zzz3Xfv31123btu0vfelL9mc/+9lcdTtrOvXftm37iiuusP/7v/87R709vukyFs8//7y9bNkyOxKJ2JZl2ddee6393e9+N7edPwXTPgB8+umnbcA+7bTT7NNPP90+/fTT7fvvv9/u7e21L7zwQrupqcm+6KKL7L6+Ptu2018If/d3f2c3NjbaK1assP/85z9n36u5udmuqamxTdM87j3PPfdcu7y83Ha73XZtba390EMP2bZt25qm2Y2Njdl2fOUrX8ldx48wWeNgGIa9ceNGe8mSJfbSpUvtv//7vz/mPb/+9a/bjY2N9qJFi+wHHngg+/yVV15pz5kzx9Z13a6trbV/+tOf5rbzY0yXcThWO2Z7v1955RV71apV9mmnnWYvX758Sv8PjJguYzHWVAaA06X/+/fvt1euXGmvXLnSXrZsmf31r389950fY7qMg23b9ksvvWSvXbvWPu200+z3vOc9dn9/f247b0+v/ofDYbu0tNQeHBzMbaePYTqNxc0332wvXrzYXr58uX3NNdfY8Xg8t50/BXISiBBCCCFEgZkRewCFEEIIIcTkkQBQCCGEEKLASAAohBBCCFFgJAAUQgghhCgwEgAKIYQQQhQYCQCFEGKSffnLX+ab3/xmvpshhBDHJAGgEEIIIUSBkQBQCCEmwTe+8Q0WLVrEueeey+7duwH47ne/y7Jly1i5ciVXXnllnlsohBCj9Hw3QAghZroXX3yRu+++m5dffhnDMFizZg1r167l1ltvpbm5GZfLxeDgYL6bKYQQWTIDKIQQp+jpp5/mfe97H16vl+LiYjZs2ADAypUrufrqq7nzzjvRdfl9WwgxfUgAKIQQOXL//ffziU98gu3bt3PGGWdgGEa+mySEEIAEgEIIccrOP/98/u///o9YLEYoFOIPf/gDlmXR0tLCBRdcwL/9278xNDREOBzOd1OFEAKQPYBCCHHK1qxZwxVXXMHpp59OZWUlZ5xxBoqicM011zA0NIRt23zqU58iGAzmu6lCCAGAYtu2ne9GCCGEEEKIqSNLwEIIIYQQBUYCQCGEEEKIAiMBoBBCCCFEgZEAUAghhBCiwEgAKIQQQghRYCQAFEIIIYQoMBIACiGEEEIUGAkAhRBCCCEKjASAQgghhBAFRgJAIYQQQogCIwGgEEIIIUSB0fPdgMlWXl5OQ0PDlNwrlUrhcDim5F6FSMY3t2R8c0vGN7dkfHNLxje3pnJ8Dx48SG9v71HPz7oAsKGhgW3btk3Jvdrb26mpqZmSexUiGd/ckvHNLRnf3JLxzS0Z39yayvFdt27dhM/LErAQQgghRIGRAFAIIYQQosBIACiEEEIIUWAkABRCCCGEKDASAAohhBBCFBgJAIUQQgghCowEgGLasSybwwNRtrcO0jkcx7btfDdJCCGEmFVmXR1AMbOFEwY7OoYZjKVQFYXtrUNUFblYWuXH65QvVyGEEGIyyAygmBZMy+ZAb4SnD/SRSFlU+l24dJWqIheDsRRPH+jn8EAUy5LZQCGEEOJUyZSKyLvheIod7cMMJw3KvE40VRn3etDjwDAtXu0M0TYUZ/mcIordckSREEIIcbJkBlDkjWFa7O0J88yBfgzLptLnOir4G6FrKpV+F0nD4tnmfvb2hDFMa4pbLIQQQswOMgMo8mIgmmRHR4hoyqDC70RVJg78juR36XidGvv7IrQPxTmtpphSrzPHrRVCCCFmF5kBFFMqZVq83hVmy8EBFKDC5zrh4G+EqihUZGYLtxwcYGfHMElDZgOFEEKIEyUzgGLK9EWS/CUTrL2ZWb9j8Tg03LpK+3CczlCCFXOKqCpyoZzi+wohhBCznQSAIueShsWenjCHBmIE3TrFvslbslUUhTKvk6RhSckYIYQQ4gTJT0mRU92hODs6QpiWTZXfmbPZOeeYkjFPHehjaWUR9UEP6jGSSoQQQohCJgGgyImEYfJ6d5jWwRglHicufWq2mwY9DgzLZldXiLbhOCukZIwQQghxFAkAxaSybZuuUIIdHSEUxabKP/V78nRVodLvIpwweLa5n6ZyH/NLveia5DwJIYQQIAGgmESxlMlrnSE6QwlKPA6cUzTrdyxSMkYIIYSYmASA4pTZtk3bYJxdXSE0VaGqyJXvJmWNlIyJpUy2HBxgXomHhRU+XLqW76YJIYQQeSMBoDglkYTBrq4QPeEkpV4Hjmm6zColY4QQQohREgCKk2JZNi2DMV7rCmUzcKe7I0vGVBa5WCYlY4QQQhSg6TldI6a1UNxg6+EBdnWFKPE6CeQgy3Zfb4TOcGrS3xdGS8YMZUrGHOqPYll2Tu4lhBBCTEcy9SFOmGnZHOqP8npPGK+uUemf/Fm/hGHx4y2H+OX2VjRF4YazLK5dW5eTpeWRkjGvSskYIYQQBUYCQHFChmIpdnYMM5w0KPc60XJQYPnVzhBffmQ3zf0x3rO8ioFQhB89d4iHXu/mixctZHVtYNLvqasKVZmSMc8c6KepwkejlIwRQggxy+X1p9xDDz3E4sWLaWpq4tZbbz3mdb/97W9RFIVt27ZNYesEgGFa7O0J89zBfkzbptLnmvTgL2lY/ODZg1z365eJJE2+994V/MvbF3Hz+TX813uWE09Z/M1v/sLX/7iXoXhuloX9Lp0Kv5PmvijPNPfTF0nm5D5CCCHEdJC3GUDTNPnEJz7Bo48+Sl1dHWeccQYbNmxg2bJl464LhUJ85zvfYf369XlqaeEaiCbZ0REimjIo9zlRc5Ax+3p3mC89vJv9fVHevayKz5zfSJF79Mvy3PmlrL12LZueP8yvtrfy5P4+PvPWRi5dXDHpGbyqolDucxJPmWw9NMBcKRkjhBBilsrbDOALL7xAU1MTjY2NOJ1OrrzySjZv3nzUdf/yL//CF77wBdxudx5aWZhSpsVrXSG2HBxAASp8rkkP/lKmxU+2HOKjd73EUNzg2xuW8aWLF40L/kZ4HBo3nTef//nwamoDbv7lod184vc7aRmMTWqbRrgdGpV+J+3DcZ4+0E/ncBzbliQRIYQQs0feZgDb2tqor6/PPq6rq2Pr1q3jrtm+fTstLS28853v5D/+4z+O+V6bNm1i06ZNAHR2dtLe3p6bRh+hp6dnSu4zlYZiKfb2RkiZNgG3TjIBk70Yun8gwb8/18n+gQR/Nb+Iv1tXSbHLYLC3a9x14cH+cY8rFfjPC+dw314PP3u5lw/94kWuPq2UK5aV4tAmf3ZSAwzT5qmeLsq8DuaXenE7Zs9s4Gz8+p1OZHxzS8Y3t2R8c2s6jO+0TQKxLIvPfOYz3HHHHW947caNG9m4cSMA69ato6amJsetGzWV98qlpGGxpyfM4USMsgp/TgIdw7S4Y1srP916mGK3zjfftZS3NZUf93OC5VVHPffRyjlcdnoD//nkAe54pZcnW6I5SxIBKAcGYyn2xCyWFhVRH/Sg5iAJJh9my9fvdCXjm1syvrkl45tb+R7fvAWAtbW1tLS0ZB+3trZSW1ubfRwKhdi5cydve9vbgPTM3oYNG7j33ntZt27dVDd3VusOxdnREcKyodLvzMnpGPv7Inz54T281h3m4kUVfP6CBQQ9J19ypcLv4tZ3LuWZ5n7+7fF9/M1v/sJ7llfxqfPm56QuoZSMEUIIMZvkLQA844wz2Lt3L83NzdTW1nL33Xfzq1/9Kvt6IBCgt7c3+/htb3sb3/zmNyX4m0TxlMnunjBtg3GCHgcuffK3hBqWzZ0vtvKT5w/hc2rcetkS/mpRxaS9/5FJIk8d6Ofvz2/kHUsmP0lESsYIIYSYLfIWAOq6zve//30uueQSTNPkuuuuY/ny5dx8882sW7eODRs25Ktps55t23QOx9nZGUZR7JzN+h3sj/LlR/awszPEBU1l/NOFTZR6nZN+n5EkkXcsqeBfH9vHzQ/v5r5dXfzjhU3MLfFM+v38Lh2vU6O5L0rbUJyV1cWU+Sa/X0IIIUSu5HUP4GWXXcZll1027rmvfvWrE177pz/9aQpaNPvFUia7OkN0hhKUehw4czDrZ1o2d73Uxg+fO4hb1/j6pYu5JAdlW460qMLPzz50Or/f0cH3nj3IlXe+yHVnzuXatXWT3k8pGSOEEGImm7ZJIGJy2bZN22CcV7tC6KrCnKLJP8YN4PBAjK88uodX2oc5r7GUf75oIeVTODumqQqXn17D25rK+c8/7efHWw7x8O5u/unChaypm/wkEbdDw6WrdAzH6RhOsGKOnznF7pwHu0IIIcSpkACwAEQSBru6QvRGkpR6HDnZs2bZNv/7cjvfe/YgTk3lK5cs4rIllXkLhMp9Tm5551LemUkS2XjPX9iwvIpPnTv/lJJPJqIoCqVeJ0nD4qX2YSqHEyyr8uN1yn8vIYQQ05P8hJrFLMumZTDGa10hnLpKpT83s36tQzG++sgetrcNc05DCf/vrxbm7F5v1kiSyG3PH+aX21t56kAff39+Y06CU6euUuV3MRhL8fSBPpZUzq6SMUIIIWYPCQBnqVDcYGfnMAOxFGVeJ3oOghDLtvndXzr4zjPNqIrCv7x9IRuWVU275U+PQ+NT583nHUsq+cZje/nSw3u4f1d3zpJExpaMaR2Kc1q1lIwRQggxvUj9ilnGtGwO9EZ4prmfpGFR5XflJPjrGI7zyd/t5NYn9rOyuphfX7OG9yyfM+2Cv7EWVvj47ytO5x8vbGJXV4gr73yRn249TNKwJv1eIyVjDNPi2eZ+9vSEMczJv48QQghxMmQGcBYZiqXY0TFMKGlQ5nWi5SDws22b/9vZybefagbgixc18b4V0zvwG0tVFC5fWc3bFpTxn0+mk0Qeer2bL16UmyQRKRkjhBBiOpIZwFnAMNPHuD13sB/Ltqn0uXIS/HWGEnzq/17lG4/tY1mVn7uvWcP7T6vOSfCXNCwGY0bOZs3KfU5uuWwp33nPcpKmxcZ7/sJXH93DYCw16fcaKRnjVBW2HhpgR/sQCcOc9PsIIYQQJ0pmAGe4gWiSv7QPEzNMyn1O1BwEY7Ztc9+ubr755H5My+bzFyzg8pXVObkXwGAsiWUr1AbcDMYNdFWZ9MzdEW+ZX8r/1q3ltq2HufPF3CaJjJSM6Qwl6AwlpWSMEEKIvJEAcIZKmRb7eiM090UpdutU+HKTddsTTvCNx/bxTHM/q2uL+dLbF1EXnPzECUjPZPbFUlT5XSyfU0R/T4qSijJeyxSuztVxdW6Hxv937nwuXVzJv2aSREZOEplX4p3Uex1VMmYozrI5RVIyRgghxJSSnzozUF8kPeuXMq2cHeNm2zYPvt7Df/xpP0nD4jPnN3Ll6pqczfoNxVOkTJvTq4upCYzOinkcGqvrAvSEE+zsDBNOGJR4HTlpx8IKHz+74nR+t6OT7z/TzFV3bs/ZSSIjJWOG4ime2t/H0iopGSOEEGLqSAA4gySN9F6/wwMxAm6dYndukgn6IklueXwff9rfx8rqIr508aJJnwkbYVg2/dEkpV4Hp1UXTzgTpigKlUVuzvM62dcb4WB/FJ9Tw5eDWbOJkkQefL2bL17UxNq64KTfL+BOl4zZ1RWmdTDGiupiAjla7hZCCCFGSAA4Q3QNx9nZGcK07ZzO+j26p5d/e2IfsZTJTefN58Ora3OSUAIQThhEUybLTnD2y6GpLK0qorrYzasdw3SH04FjLsrcjCSJvGtpP//2xD7+9p4dvHtZFTedN/knieiqQqXfSThh8NzBfhaU+2gs9ebkxBYhhBACJACc9uIpk9e7Q7QP5W4PHKSTSW59Yj+P7e1lWZWfr1yymPmluZn1My2bvliSYpeDc+uCFLnf3Jdh0OPgrIZSWgZj7O4Ooavq1CSJbG/j6eY+Pn1eI+9cOvlJIlIyRgghxFSRAHCasm2bzsysn6JAVVHujlZ7bG8vtz6+j3DS4JNvaeCatXU5mVUDiCZNwkmDRRU+Gkp9Jz27qKkKDaVeKv1OdnWF6QwlKPU4Jn2vHowmibxjSTpJ5MuP7OH+13KTJDJSMiaeMtl6aIC6oJvFlX5cujap9xFCCFHYJACchmIpk12dIbrC6aDGkaOlwMFYiv/4034e3t3Dkko/P7r4NJrKfTm5l2Xb9EdTeBwa5zSUTto+N69TZ21dgK5Qgp2dIUIJg1KvIydL5E3lPn76odP5/Y5OvvdMM1feuZ3rzqjno+vqJz3wHCkZ0xVK0CUlY4QQQkwyCQCnEdu2aRuM82pXKHuUWK48ub+Pf31sL4NxgxvPnsdfr6vL2Z6zeMpkKGHQWOqjqXzy97YpisKcYjelXif7esMcHIhR5Ewvp042VVH4wMpq3rqgjG89eYCfPH+Yh3b35CRJZKKSMUurivC55L+tEEKIUyO7zKeJSMLgz4cH2dE5TNCt52xP23A8xc0P7+Yf/rCLUq+TX1y5ihvWz81J8GfbNn2RJEnT5qx5JSyp8uc0scGpqyybU8zZ80qwgZ5IAtOyc3Kvcp+Tf71sCd9973IM0+Zv79nBVx7JzUkiIyVjhhMGTx/o42B/FCtH/RJCCFEYZCohzyzLpmUwxmtdIVy6SmUOZ/2eae7nG3/cS380yQ3r67n+zLk5W15OGBaDsSRzS7wsqvDnZG/esZR4nbxlfimH+qPs7gnj1lWK3bkJqM9pKOXXHwnw062H+Z/tbdmTRHKRJDJSMua1rjBtUjJGCCHEKZAAMI9CcYOdncMMxFKUeZ05S7wIJwy+9dQB7n21i8YyL9/asIylVUU5uRfAQCwJKKyrD1JZ5M7ZfY5HUxUay31UFrl4rStEVyhBqTc3+yndDo1PnjufS8ckidy3q4t/umjyk0SOLBnTWOajscybs0BeCCHE7CQBYB6Yls3B/gh7eqJ4HWpO9/o9f2iArz26h55Ikr9eV8fGs+blbDYuZVr0R1NUF7tYNqdoWmSu+l066+qDmYzqMGBQ4pm6JJGPnVHPX+cgSWSkZMzB/ijtQ3FW1kjJGCGEECdOAsApNhRLsaNjmHDSoMzrzFmR5UjS4DtPN/O7HZ00lHj47w+dzorq4pzcC9IZxaZls7q2eNplqyqKQnXAQ6nPyd6eCIcHYhS7dTyO3CaJfPupA2x6/jAP7+7hny5sYl19cNLvNVIy5vlD/dQHPVIyRgghxAnJ67rRQw89xOLFi2lqauLWW2896vVvfetbLFu2jJUrV3LRRRdx6NChPLRychhm+hi3Zw/2Y9k2FT5XzoK/P7cMcuX/bOf3Ozr5yNpa7rx6dc6CP8Oy6Q4nCLh1zltQRnXAM62Cv7FcusaK6mLOaijBtOycJ4l84x1L+N57V2CYNjf+NndJIm6HRpXfRVcowVP7++kYimHbkiQihBDi2PIWAJqmySc+8QkefPBBdu3axV133cWuXbvGXbN69Wq2bdvGX/7yFy6//HI+//nP56m1p2YgmuTZ5n6a+6JU+Jw5OcMW0kWW/+2JfXz8tztwaCq3fWglN53XiDtHM0KhuMFANMmKOUWsrQ/mZEYtF0ozSSILynz0RVOE4kbO7nV2Qwm//sgaPnZGPQ+83s0Hfr6N+3Z1TXqANlIyxu/UeKl9mBdbBokkctcvIYQQM1veAsAXXniBpqYmGhsbcTqdXHnllWzevHncNRdccAFeb3oT/VlnnUVra2s+mnrSUqbFrq5hnjs4gKaml+vUHM2ObW8d4qpfbuc3r3Rw1aoafnX1albVBHJyL9Oy6Q4ncTlUzm0so77EO21n/Y5F11QWVvg5d34pHqdKVziBYVo5uZfbofGJtzTwyw+vZl6Jly8/socbf7uDg/3RSb/XRCVjcjXLKYQQYubK2x7AtrY26uvrs4/r6urYunXrMa//2c9+xjve8Y6paNqk6A0n2NERImVaVPmdOQuQ4imTHzx7kLtfbqcm4OYnl5826QWJxwonDKIpk8UVfhpKvag5WsaeKkVunTPnltA2GOe17vSxe0F3LpNEVvJ/Ozv53jMHueqXuUsSkZIxQgghjmdGJIHceeedbNu2jSeffHLC1zdt2sSmTZsA6OzspL29fUra1dPTc9RzKdPm8ECUjlCcIpeOU1MZiuXm/q/2xPj35zppC6XYsCjA36yuwONIMNjbNen3sm0YiqePcltY4cOdNOnsHJr0+4w10fjmigos9FgcGojS3JPAn/m3y4ULq1VWvWsuP3qxh03PH+aBVzv49PoqVs2Z3JIxAE5gYNjkkY4OagMe6oLubLmhqRzfQiTjm1syvrkl45tb02F88xYA1tbW0tLSkn3c2tpKbW3tUdf98Y9/5Bvf+AZPPvkkLtfE5VI2btzIxo0bAVi3bh01NTW5afQExt6razjOrs4QlstBY7A8Z7N+CcPix1sOcueLbVQVufjh+1dw5tySnNwL0mcTD8cNTqv3saDMl7PklYlM5b8lQMPc0dnbpGlR6nXkZNk+CPxHfS3PHxrglsf38dk/tvKupZV8+vzGST8FJkj6LOaBaIr9MZXTqosoz5QemurxLTQyvrkl45tbMr65le/xzVsAeMYZZ7B3716am5upra3l7rvv5le/+tW4a1566SX+9m//loceeojKyso8tfSNxVMmr3eHaB9KEPQ4cOXw1IudnSG+/PBuDg7EeN+KOdx03nz8OTob1rZt+qMpnLrK2Q0llHgLo85cud/FeY0ODvRH2d8bwevQcjbGZ81LJ4n8bGsLv3ixlaeb+/n0eY28a9nkniSiKgplmZIxWw8PUB/0UGzkZs+jEEKI6S9vAaCu63z/+9/nkksuwTRNrrvuOpYvX87NN9/MunXr2LBhA5/73OcIh8N88IMfBGDu3Lnce++9+WryUWzbpmMoxs7OEKoCVUW5K+icNCxu23qYn29rocLn5PvvW8FZ83I36xc3TIZiBg2lHhZW+AvupAldU1lU4WdOkYudnSG6wwlKPY6cnGXs1tNJIpcuqeAbf9zHVx7dw32vdfFPFzbRUDq5y8Juh0aVrtIdTrCnZ5BDSTfFHgdFLg2/U8ft0HDpKi5NnfH7O4UQQhybYs+ygmHr1q1j27ZtOb+PZdk88coeEu4gpZ7cHDE24rWuEF9+ZA/7+6JsWF7FZ85vzOms30AshaoonF5TnF0qzIf29va8T5FD+t+6dSjGa11hdFWZ9CXacfeybTbv7OS7zxwkbpj89bp6/vqM+pzMKg/2duErqSBhWKRMC8MClPS3AwUFrzM98xlw6fhcOm5dTQeHujrjsr7zYbp8/c5WMr65JeObW1M5vseKi2ZEEsh0lLIsBmMGDeW5C5BSpsXPXjjM7S+0UOJ18l/vWc6580tzdr+kYdEfS1EfdMuJEmOoqsLcEi8Vfhevd4XoGM7dUr+qKLzvtGrOb0yfJHLb1sM8vKeHL+bgJBEAh6ZO+MuLbdukTJvhWIrecALTtrHtdGCoKODPzBgWux34nOlZQ7dDxalJcCiEEDOBBIDT1J6eMF9+eA97eiNctrSSz761kWJ37maeBuMpLMtmbV2AOcXunN1nJvM4NFbVBqgNJNjZGSacMCjJUZJImc/J19+xhHctq+KWx/dx42935CxJZCKKouDUlQnL09i2TdK0GYyl6M4Eh9iAkgkOnRrFLp2iMcGhS5fgUAghphMJAKcZw7S4Y1srt209TNCt8813L+NtC8pyer++aIqqIhfL5xThniGneeSLoihUFrk5z+tkf2+UA/0R/E4tZ6e7TFWSyJuhKAouXZlwBtTKzBz2RVJ0DCcwsVFIt1NVwO/UKXJpBNwOPE4Nt54OECe7DqIQQojjO6GfWt/73ve45pprKCnJXdKBgH29Eb7yyB5e6w5z8aIKPn/BgpzO9gzHUyRMi5XVxdQG3TI78yY4NJUlVX6qi13s7BimO5yk1OvI1tibTGOTRP71sXSSyB92dfHFiyY/SeRUqW8QHCZNi75IivbhBNnNx7aNpioUudPJKMUuHY9Dw6VruB0TL1ELIYQ4NScUAHZ1dXHGGWewZs0arrvuOi655BIJFiaRYdnc+WIrP3n+ED6nxq3vXMJfLazI6f36o+mAZX11Cd4czV4VgoDHwdkNpRwejLG7O4SuqjkL2heU+bjtgyuzSSJX/XJ7TpNEJpuqKLh1DbcORUe8Zlo2KdOiO2TQOhgfDQ6x0VU1vaTs0ilya3gcenrPoa7mJCtbCCEKwQn95P/617/O1772NR555BFuv/12PvnJT/KhD32I66+/ngULFuS6jbNac3+ULz+8h1e7QlzYVMY/XthEaQ7r7YUTBpGUyfKqIuqDHin1MQlUVaGh1Eul38murjBdoQQlHkdOljXHJon819OZJJHdPfzTRU2ckYMkkamiqQqaqk24BWEkOOwIxTk8aGOns1GwbXBpKn6XTrFHpyhTxmYkW1mCQyGEOLYTnvpRFIU5c+YwZ84cdF1nYGCAyy+/nLe//e38+7//ey7bOCuZls2vXmrjR88dxOPQ+MY7FnPxooqczaxatk1vNEmxy8F5dUGK3DLrN9m8Tp21dQG6Qgle7QwRShiUenNzrnCZz8nXLl3CO5dWcevj+/j4b3fwzqWVfPq8+bOuYPfxgkPDSi8rtw3GMSwLO5OMgg0uh5qub+jSCbgd2VlDl65N6Wk2QggxHZ1QFPCd73yHX/ziF5SXl3PDDTfwH//xHzgcDizLYuHChRIAvkmHBqJ85ZG9/KVjmPMbS/niRQsp9+Xuh3Y0aRJOGiyq8NFQOrVHuRUaRVGYU+ym1OtkX2+Y5v4YxS4drzM3yTVnzSvh7o+s4b9faOEX21p5prmfm86bz7uXVRXENg1dVdBVDc8xgsNY0mIoFuegFQWUdLKybeNyaARcenr20K1nM5Xduiaz4kKIgnBCAWB/fz+/+93vmDdv3rjnVVXlvvvuy0nDZiPLtvn1y+18/9mDODWVr16ymHcsye2sX380hcehcU5DKYEpKB8i0py6yrI5xVQXu9nREaI7kqDM48xJ8O3WNf7unAYuWVzBLY/t46uP7uW+Xd3TMklkKumqgu7U8DJBcGhahJMGA7EUhmVnZg7TIaJb1yjKLCvL6ShCiNnquAFgf38/ADfddNO4xyNKS0tZunRpjpo2u7QOxvjqo3vY3jbMWxpK+H9/tZCKHJ6yEU+ZDCUM5pd6WVjuk/1QeVLidfKW+aUc6o+yuyeMW1dzVs9xQZmPTR9cyb2vdvGdp5u58s7tfOyMmZMkMpV07dh7BFOZ4LA/msSwwMZm5Hc0nzMza5iZPRxdVpYah0KImeW4AeDatWtRFIWJTotTFIUDBw7krGGzhWXb/PYvHXzn6WY0VeHmty/M6fKcnZn1c2gqZ80ryWlCiTgxmqrQWO6jqsjFrq70ucIlOTo+UFUU3rtiDufNL51VSSJTaeR0lCN3ZYw9HaUvnMAY+b5opwtge0cKYGeCw1DCYDieyp6eMvI/XlHI/v9XRh5nXh1/nZJ9feSxEEJMluMGgM3Nzcd8bZYdIZwT7UNxvvbHPfy5ZYiz5gb5f29fxJyi3M36JQyLwViSuSVeFlX4pbjuNONz6ayrD9I5HGdnZxgwKPFMXZLIZUsr+ftZmCQyVd7odJTUmNNRDMsmOjDMwYR7XEmb9C/Uo4/TH5TM8jPZDOeRoHLksUL68xQ1/bI6EhwCiqpknku3UVVGj+xTFAWVkaATVNJ/URXGfM7IdUr2/RUUVDXzcaL3UdQJg1UYDWKzQe6RjxkT1J5EcDzyuvwMEuLUnNAewJtvvpmvfvWr2ceWZfGRj3yEX/7ylzlr2Exm2za/39nJfz2VDqD/+aIm3rtiTk5/gx+IJQGFdfVBKovkKLfpSlEUqgMeSn1O9vZEODQQI+DWJ0ximAwjSSK3v9DCz7e18mxzP586bz4bllXl5H6FaqLgcDDuIDjJyV0jQY9NugzO2GLaI8+NvG7ZNtg2ps0Rr9mMjZ3S7zP+fcfezx533RFtyQSrACjpV5VMss3IJylK5rE9es3YowPt0RfTbzdyQfZ9R94n85qdvioyMEBF3E3ArVPiceB1ppOB3A7J8hbiRJxQANjS0sItt9zCP/3TP5FIJPjQhz7E6tWrc922GakzlOBrj+5h6+FBzqgP8C9/tYiaQO4CslTmKLeaYhfL5hTh0uUot5nApWusqC6mJuBmR/swPZEEpTlMEvl4JknkXx/bx9ce3cv9u7r40OIiVjjjVBa5cnKesZh8Y2fHGPdPVnj/fo64A4eqMBhL0Rkaf7KMz6UTcOsE3Q58Lh23ruJxSIa3EGOdUAD43//931x99dXccsstPPHEE1x22WV8+tOfznHTZhbbtvnDri7+88kDmJbNFy5YwAdWVuf0B+tgLIVp2ayuKaY6IEe5zUSlmSSRgwNR9vZE8ehqzmo0No5JEvnu0838Y9sw0IZLU6kNupkb9FAf9DA36E5/LPFQ4XPK15WYtpyZc6TH5tONLMf3Z86jTs9UpuclvU6doFsn6HHgdep4HFL6RxSu4/6k2b59e/bvN910E3/7t3/LW97yFs4//3y2b9/OmjVrct7AmaAnnODrf9zLswcHWFNbzM0XL6Iu4MnZ/QzLpi+apNLnZHl1cc6WD8XU0DWVpnI/VX43u7qGpyRJ5KKmcrbta6HfctMyGOPwYIxDA1GePdhPyhxd6HPrKvVBD/UjAWKJJxsoluWoyLUQp2LscvzYIwdHAsPsWdRjAsORzO6gZ3TGUAJDMdsdNwD8h3/4h3GPS0pK2LVrF//wD/+Aoig8/vjjOW3cdGfbNve/1sU3/3SApGnx2bc28qFVNTmd9QvFDeKGyYo56aPc5Afw7FHk1jlzbgntQ3F2dYVQFAi6cxNkFbl1Vs/xEiwfvxfQtGy6QgkOD8aygWHLYJx9fVGePNCPaY0Ghz6nRl3g6MBwbtBNMEfJLUKcrOMFhskxgaGV2beoAj6XRtDjIODWMzOGmtSDFLPGcQPAJ554YqraMeN0hhL825YutnVEWVldzJcuXsi8ktwV3TUtm75oiqBHZ93cIH6XHOU2GymKQm3QQ5nPyZ6eMK2DMQJux4THoOWCpirUBNzUBNycNa9k3GuGZdM5HOfwmMCwZSDGa91hHt/Xy5iJQ/xOjbklIwHhmKXlEg+BHNVBFOJkKIqCS1dwHSMw7A4laB2MYWdSVBQUilw6AU96j6FnJPlEakGKGeaEooiuri6++MUv0t7ezoMPPsiuXbvYsmUL119/fa7bNy3FUibnfPcZesJJPn3efK5aXZvTrLNI0iCSNFlc4aeh1Cu/fRYAt0NjZU2AmmI3OzpDhCNJSr2OvCZr6KpCXdBDXdDDOUe8ZpgWbcNxWgbjHB4YnT3c0THMI7t7xmWPBtx6Zll5/H7DuUGP/GIjpo2xgeFYtm2TMK1sYGjZCopioyoKfmc6I7nYnT7+0S2BoZjGTui77V//9V/zsY99jG984xsALFq0iCuuuKJgA0CPQ+Mbly1hqK+Pc5fX5ew+lp3e6+d36rxlfmnOTpAQ01e538V58x0c6I+yryeCz6lNyyBJ11TmlXjTs+Dzx7+WNNLB4djAsGUwxva2IR58vXvctSUex7jZwtHZQ0/OzlMW4s1QFAW3ruE+ouLCSGDYEYpzeNDCthVsbDRVodjtIJCZNfQ40jOGcnqMyLcT+knS29vLhz70IW655Zb0J+k6mlbY34yvWl3LfS+Ecvb+sZTJcNygqcLHgjKf1LUqYLqmsqjCz5wiFzs70yeJlHocM+Z4P6euMr/Uy/wJziWOGyatg/Fx+w0PD8TYeniQ+14bHxyWeR0TLCunE1SmaolciGM5VmBo2TZJIx0YHhq0svUNVRWK3Q6CHp2A25FdRpbAUEyVEwoAfT4ffX192S/K559/nkAgcMo3f+ihh7jpppswTZMbbriBf/zHfxz3eiKR4Nprr+XFF1+krKyMX//61zQ0NJzyfaez7FFuusrZDSVyaoPIKnY7OGtuCW1DMXZ1hdFVhaBnZs8Ku3WNpnIfTeW+o16LpczRwHAgnp05fLa5n3ujqXHXVvqdE+43rAt45BxkkVeqoqSXgh0TB4Ztg3EOWbF0MW5bQVOhKBMYBjP7fz0OFacmgaGYXCcUAH7rW99iw4YN7N+/n7e85S309PRwzz33nNKNTdPkE5/4BI8++ih1dXWcccYZbNiwgWXLlmWv+dnPfkZJSQn79u3j7rvv5gtf+AK//vWvT+m+01ncMBmKGTSUelhY4c9JGRAxs6mqQn2Jl3K/i9e7QnQMJwh6HLMyyPE4NBZV+FlU4T/qtXDCoHUoPVs4Ehi2DMZ4Yn8vgzEje50CVBW5sjOFI8vKc4MeagNu+T8m8uZEAsODZixzeoqCpkDA7SCQyUpOn3qiSvF/cdJOKABcs2YNTz75JLt378a2bRYvXozDcWozDy+88AJNTU00NjYCcOWVV7J58+ZxAeDmzZv58pe/DMDll1/OJz/5SexMiv5sYts2A7EUqqJw5twg5f7cnRcsZgePQ2N1XZDaUPpc4XDCoCTPSSJTye/SWVLpZ0nl0cFhKG4cUcYmxuGBOI/u6WU4MRocqgpUF7nHBYYjs4g1xa4Zs8QuZpdjBYamZZM0LVoHYzSbNpCuY6irSraGYbF7dI+hnAUv3sgJBYDRaJRvfetbHDp0iNtuu429e/eye/du3vWud530jdva2qivr88+rqurY+vWrce8Rtd1AoEAfX19lJeXn/R9p5ukYdEfS1EXdLOk0i+/zYk3pbLIzXleJ/t7oxzoj+B3avic0y9JZCoVuXWWzyli+Zyio14bjKWOCgxbBmPseK2bSNLMXqepCjXFrqP3G5a4qS5yy55cMeU0VcGjakcV/h8JDFsGY6QsK3tgs66pBD06xa70rOHIcXgSGIoRJ/ST4mMf+xhr165ly5YtANTW1vLBD37wlALAybRp0yY2bdoEQGdnJ+3t7Tm/Z8q0iA0PMNh78j9sw0kDy4Kmch9lmPR1RyaxhTNfT09PvpswYxQDTW6D/b0RupMWxW6dN5rACg/2T0nbppt6B9RXABUewAOUYNs2gwmTtuEUbaEkbaEUrcNJ2oaibG8dJG6MFrLRVaj2O6gtclJb5KC2OP2xrshJhU/PzsIW6vhOFRnfo41dl7Ms6Bq0aLMsDMvGttNbInRNwe/S8bt0fE49U+pGQz/ilxr5/ptb02F8Tyh62b9/P7/+9a+56667APB6veljdE5BbW0tLS0t2cetra3U1tZOeE1dXR2GYTA0NERZWdlR77Vx40Y2btwIwLp166ipqTmltp2IhGHiaRk66iSFE2GYFn3RFHXlLpbNKZKj3I5jKv4tZ5OmeTYtgzFe7w6Bqr5hksjJfP3OViUcVcEGSG/R6Iumxu03HPn4UtcwCcPKXuvUFOoC6RnDIs3A7wvj0BR0VcWpKTg0FV1TcKgqjrEftfGPR65xauMfZ6/R0x8LfSZSvn7fPMNK7zEMGSYDhg2Ggm3buLT094vizKyhL2lSXV0967ZcTSf5/vl2QgGg0+kkFotlvxD279+Py3Vq+9TOOOMM9u7dS3NzM7W1tdx999386le/GnfNhg0b+PnPf87ZZ5/NPffcw4UXXjjjvxiH4ykSpsXK6mJqg+4Z3x8xvaiqwrxSLxV+J7u6wnSF0ucKy7LPyVMUhXKfk3KfkzV146sfWLZNTziZDQpHgsTDg1EGIkkMO0zKsjFMa9xJKZNFVcChZoJKTUkHjGMejwsw1QkCUO3IQDQdrI6+l4J+1DVHP9bVMfcf83jkGvk+N33oqoLu1I6qq2lYNuGkQX80iWHbhPuHaDM8zC/1UlnkklJLs9AJBYBf+cpXuPTSS2lpaeHqq6/m2Wef5Y477ji1G+s63//+97nkkkswTZPrrruO5cuXc/PNN7Nu3To2bNjA9ddfz0c+8hGampooLS3l7rvvPqV75pNh2fRH06c5nDmnBN80LOYrZg+vU2dtXYDuUIKdnSFCCYNSr5zPO9lURaGqyEVVkYt19cFxrw32do2boTItG8OySZlW+o9lkzLTj43MPq6xj0evGX3+WJ9jjL3WskkZ4x8bpkUkaZE0U5nH499/7HvlwpEBoZ4JLp1HBaRKNoDNzn5OEMQ6NAWXGef0hJumct+0LI4+06QDQx1fpvKYI+bAoans6grzaleIOUUu5pV6CbodchrVLKHYJ7CWe80117By5Uo8Hg+NjY2sX79+2iZirFu3jm3btuX8PgnD5L4XXqehvvYNrw0nDCIpk2VVRcwNeuQ/zwlqb2/P+xT5bJA0LPb1hjnYH6PIpWd/8z8yQBGTayaOr23bmJZ9jMAz8zgTUCaPeDxyTfLIgNQcDXyTRzw+1n0M0yZlHfleo9eY1vgfW7XFbhZW+FhY7mNRhY+FFT5qit0FkxWfC2O/fm3bJpQwiBsWbl1jfpmXqiKXbF86BVP58+1YcdEJ/dp0/fXX8/TTT/Poo4+yf/9+Vq9ezfnnn89NN9006Q2dTSzbpjeapNipc978Morc8luqmHpOXWXZnGKqi93pk0QiCco8UmBcHE1R0vsNdY1p/cPdtGwOtLTRZXnZ0xNmb0+EPb0Rntzflz132ufUWFCWCQgzgWFTuW9a92u6UpT0cXbFpBMg93SHea0rTKXfybwSDyVeZ8HvR52JTigiueCCCzj//PP585//zBNPPMGPf/xjXn31VQkAjyOaNAklDRaW+2iUo9zENFDidXJOQymH+qPs7gmTTBh4DUv2aIkZR1MVKnwOFpaXcu780uzz8ZTJvr4oe3vC7OmNsK83woOvd3NPpsSPAtQHPdnZwpHAcE6RS/4PnCCHplLmc2LbNuGEwZ9bBnFoKg2lHqqL3LK9aQY5oX+piy66iEgkwtlnn815553Hn//8ZyorK3PdthnJyhzl5nFonNNQOuOP6hKzi6YqNJb7qCpy8eLuYQzbZjhqkN4Iki4sq5DOZh3Z0C+/vIiZwu3QWDGniBVjakDatk3HcII9vZFsYLi7O8xje3uz1xS5dBaWe1lY4c8GhY1l3qPO9RWjFGW0nIxhWhzoi7K3J0Kp18n8Ui+l3plzXnmhOqEAcOXKlbz44ovs3LmTQCBAMBjk7LPPxuPx5Lp9M0o8ZTKUMJhf6mVhuU+++MW05XPpNJX7qKkpw7bTCQAJI/0nlpm9DiVMhhMGhpUpc5KODzMlTUY35gsxnSmKQk3ATU3AzdsWjJYRiyQN9vdGs4Hh3t4I977aSSyV/npXFZibmS1cVO5Pf6zwUeFzymzhEXRNpSxzbn0kafBi6yCaqjKvxENNsVu2P01TJ/Sv8u1vfxuAUCjEHXfcwcc+9jE6OztJJBI5bdxMYWdm/XRN5ax5JZR6ZX+VmDkUJV0I9lin0KTGBIcJwyScMAknDEIJg6F4irHb8TVldObQoSmyCV9MWz6nzsqaYlbWFGefs2ybtqE4e3oi7O0Ns7cnyqudIR7dMzpbGHDr6eXjMYFhY6lXSi1l+JzpAtOGZXN4IMqBvgjFbp3G0vQZ5vJL4/RxQgHg97//fZ5++mlefPFFGhoauO666zjvvPNy3bYZIWFYDMSSzCvxsqjCL98ExKzjyAR0Ex1RbVk2iTEBYiQTGIaTBv0xA9vKTBuS3n91ZK04IaYTVVGozxz9d9HC0UoX4YTB3t5IJjCMsKcnzO92dGaLgGuqQkOJJ5Nw4s8GiOW+wp0M0FUlOxkSS5m83DGMikJ90E1twEOxW5eZ1Dw7oQAwHo/zmc98hrVr16LrMpU7wrRtoimTM+qDVBa5890cIaaceuT5pEWjUaJtp0t3jMwcxg2TUNxMB4gJg6SZOZsKwE4fsebQRpeX5YeDmC78Lp3V/397dx7fVJkvfvxzkjRtk+47eyk7tLQUEAq0VJHlAoKOgwwqiIrMOIOOOjIXr3rBe2V+zMVtHGfUOiMFd2REFDdArYAwQkHc2KpQBAptum9pm+X5/ZE0UAoISNq0/b5fL182yck5J08PJ98+3+d5vl1CGdLl1ELgDqer6s7pgeHuYxV8sP9Uia8Ik9+ppWmigugbbSY+PLDDDQ8K9HPdIxxO13jMI2VWgvwN9Ax3LTItHSet44Kiufvvv9/b59HmGNzjG5J7RsgK6UKchaZpGA0aRoOO4LPcahrXkmvsPax2jzusrrefMTEFdLh7DqUEmvARep1GfISJ+AgT4/tGe54vt9r4/rSgMK+4hjf2FLj+4MHVM5YQYXLNRI420zfKTN/ooA4xYVCv0zyfs87u4NvCSrRC1xjNrqEBhAXKYvUtSbrzLpFe50oVSPAnxKUx6HUY9DrONmRWKXXauEMntTYHNRcwMcXorjIhRGsJC/RjWLewJpVh7A4nR8qtrvUK3YHhv4+U8d6+Is820WZjs+Vpuoeb2u1QiQCDngCD3l1OsZ5j5VYC/fT0jHAtMi3frd4nAaAQwudomkaAn/6cXwKnT0yps7lmLbsmpjiot9pOpZZpOjHFKKll0QoMeh29Is30ijQzqf+p50trG1y9hO6FrPMsNez4sRy7u9KJv15HQqTJ01PYGCCGBLSf3kKdphHq/jwNdif7i6rZV1hFbLA/3cNNhAdK6TlvkQBQCNHmnG9iisPZdFmb0yemlFgbU8suMjFFtKYIk5ER3Y2M6B7uec7mcHK4tLZJCnnLoVLe+a7Qs01ssL8nIGz8f9fQwDY/NMJo0BFlcC0yXWG1s6OqjACDnh7hgcSF+GMySshyOUlrCiHaFf1PTExpcDhpsCvq7Q6sNteyNmebmKLhGq/VGCAadNJ7KLzPT6+jb3QQfaODPM8ppSiptXHQUn0qMLTUsC2/FPfQQgIMOnq708eNgWHvKDNBbbAyh6ZpBAcYCMaAzeEkr7iGA5Yaos1GekQEEiGl5y6LtndlCCHEJTq15iHnnJhSb3d6lrapqrNT3eAaf1hRd+6JKUa9TtY8FF6jaRpRZiNR5ghGxZ8qfVdvb+wtPFUP+eO8YtZ+e9KzTZeQAE/quLEucufQgDZzvfrpdUS5S8/VNNjJdZeea1xkWkrPXTppOSGEcGucmGJ2P+50ao1gnGeklmttDqrqbFTVOyi32lzjtjTQ3BNT6urt6Ops6DQNTQMNDZ3m+jLXaa4eRs/PbeTLWPgWf4OO/jFB9I9p2ltYWN3gKXuX5+4x/OyHEs+i7Wajnl6RpwLCvtGu3sJAH554cWbpucOltXxf7Co9Fx8eSKTZKBPALpIEgEIIcQF0Oo0A3YVPTDnqqCY42B+HU+FQ4HA6sTtdgaTNqbA7lfs1hXJCY88iaKBw/6zQNM21oDbaqccoUKcea+AJJDV376SmcSr4dD+nc0Wd6NyvifZH0zTigv2JC/YnPeFU6bs6m4PvS2qbBIYf7C9iTYPD9T6gW1ggvaNcRQ0STA7GRiifTLWeWXpu9/EKKT13CaSVhBDiMjhzYopWG0jnuJDzv8lNKYVTnfq/UymcSqEUnscK9/NO97bux42BpaMxoHQq7E4nDuUKNu2NwaZSNDhOBZ2ufTQNJNWpyNMdbp7tsas3E/AEkpq7R7PxZ507KD29h/P0IFW0vAA/PYlxwSTGBXueU0pRUFnvHlPoCgwPWmr45PsSAGK/KGLygFimDoyhR7iptU79vBpLzzmk9NxFkwBQCCFamaZp6Bt7/1qQ03laYHnWIPTcj+0OpyewdKjTg09wqjN7O52ewBP3Php7O12BJXi6PdWp3k5XGcGz93bW1Nvxtzkw6mVh8EulaRpdQgPoEhpAZq9TvYU1DXY2fZPPJ0frWJl7lBU7jzK4UzBTB8Yyvk+0T/aw6aX03EXzvd+iEEKIFtG4vpq+BQPPy9XbWWivxKDXUVFnw+4ETXOFkUa9Dn+DDn+9Tr7wL5HZaGBsj2CmD+1NcU0D7+8rYv3eQv708fc8nnOIzN6RTB0QyxXdw3wy+G4sPedUp0rPmY2uXsHoYCP+Bt8d69iSJAAUQgjRYi5Xb2dgg4nOncM9S/tYbU5qG+xUWO2UWW2U1tpwqFO9hv7uwFDqTF+cKLOROcO6MntoF/YWVrN+byEfHbDw0QELMUFGJvePYerAWOIjfC9FrNOal56jEDqHBNAtLLDDl56TAFAIIUSbdWppHz1hgX50DnU9r5Sizu7EanNQU2+n3GqjvM5ORY3NnVoGHa6ZtAEGncwg/QmapjEoLphBccHcm5HA5sMlrN9bxEu7jpGde4ykOFeKeEJf30wRn156rrimgeMVdQT46UnowKXnfO+3JIQQQvxMmqZ5UoERJiPd3MU2HE5Fnc29CHiDgzKrjXKrjTqrzd0bpDB4gkoZX3g2RoOOq/tEc3WfaIprGvhgfxHv7i3k/33yPY9/9gNje0VyzcBYRnQP97n2O1vpub3u0nM9OljpuVYJAEtLS5k5cyb5+fnEx8ezevVqwsPDm2yzZ88e7rzzTiorK9Hr9Tz44IPMnDmzNU5XCCFEO6HXaZj9DZj9DUQB8e7n7e40stXmoLLeFRRWWO3YHE73Fq6qMAEGHUaDLPzdKMpsZPbQrtyc2oX9RdW8604RbzxYTLTZyOQBrhRxTx9MEZ9eeq7SXXrOX68jPsLUIUrPtcqnW7ZsGePGjWPRokUsW7aMZcuW8ec//7nJNiaTiVWrVtGnTx8KCgoYOnQoEydOJCwsrDVOWQghRDtm0OsI1usIDjAQc1r5wAZ3Gtlqc1BeZ6PcPcbQ6Y4LNU25J57oMXbg8YWapjEgNpgBscHck57AlsOlrN9XyMu7jrEy9xiJnhRxFCHuHjhf0VFLz7VKALhu3TpycnIAuOWWW8jMzGwWAPbt29fzc+fOnYmJicFisUgAKIQQosUY3T1+oYF+xIUEAK7xhfXuwLC2oTEwtFFaa8e1prdr8kmAoXHiSccaX2g06BjXJ4pxfaIoqWnggwNFvPtdIcs++Z4nPvuBsQmRTB0Yy4ge4Rh8LLA6X+m5TiEBbbK28rm0yicpLCykU6dOAMTFxVFYWHje7Xfs2EFDQwO9evVqidMTQgghzknTNAL8XFVhwk3QhUDAte5hnd2B1eakpt7uGV9YUWf3LKqt11wTT/wNep8Lfrwh0mzk5tSu3DSkCwcsNby7t5AP9xexMa+YKHPjLOIYEiLNP72zFnRm6bl8d+m58EA/4iNMRLWD0nNeCwCvvvpqTp482ez5pUuXNnnsKl107n8EJ06cYPbs2axcuRKd7uyNnZWVRVZWFgAnT56koKDgZ5z5hbNYLC1ynI5K2te7pH29S9rXu3y9fQ1AtAbRJtcahvV2J3V2J7X1dqpq7ZQ12LE7Ti16bdBprmoyOh3n+KprUdXlpZd9n3E6uCMxiFsGmPjieA0bDlXy8u5jrNp1jH6R/kxICOXK+GBC/H1vRq4OMAIlVU6OHneg12nEBQcQHeSH+RLGCvrC9eu1AHDTpk3nfC02NpYTJ07QqVMnTpw4QUxMzFm3q6ysZMqUKSxdupSRI0eec3/z589n/vz5AAwbNozOnTv/vJO/CC15rI5I2te7pH29S9rXu9p6+9ocjeMLnVS4ewsr6+3YnApQaO6JJ621sHVYVKzX9n1NLFyTCiU1DXx4wMK7e0/y151FPLfb4kkRj/TBFHEjh1NRUWejwqoIUQZ6RpiIvsjSc619/bZKCnjatGmsXLmSRYsWsXLlSqZPn95sm4aGBq677jrmzJnDL3/5y1Y4SyGEEMJ7GutHhwRA7GkTT+rdaeTGha0r6u2UWm043WXy2tPC1pFmIzelduHGIZ05YKnhvb2FfHCgiE15xUSa/Dy1iHv5WIr4zNJzX7lLz3UNDaBrWNsoPdcqAeCiRYu44YYb+Oc//0mPHj1YvXo1ALm5uTz33HP84x//YPXq1WzevJmSkhKys7MByM7OJiUlpTVOWQghhGgRP7WwdW2DnbJaV29hSa0NRdOFrdvixBNN0+gfE0T/mCDuTu/J54dLeXdvEa9+eZyXdh1jYGwQUwfGMrFftGcdP19xeum5k1X1/FjeNkrPaUop1doncTkNGzaM3NzcFjlWQUFBq3fhtmfSvt4l7etd0r7eJe3r4nQqzzI11Q0O9/qFNqw2p7va3qmFrY0G3QWnVMuLC72aAr5QpbUNfLjfwvp9hRy01OCn18hIcC007csp4jq7g6p6O3D20nMtef2eKy5qP/OZhRBCiA5Gd8bC1o3OXNi60uoqh9dwxsLWjT2GvrqwdYTJyI2pXbgxtQsHiqpZv6+QD/YX8bE7Rfwf7lrEvaN8K0X8U6XnfIEEgEIIIUQ7c6ELWzcGho4zFra2OXwvOdgvJoh+MUHcPaYnn+eXsX5vIa/tKeDl3ccZEHMqRRwW6Dsp4nOVngtsqCI2TrXqAtMSAAohhBAdxIUsbF1ZZ+dwuaKouh69TiPU3+BTa9756XVk9ooks1ckZbUNfHTAwrt7C1me8wNPbj5ERkIEUwfGMqpHuE+d9+ml5/JLXb2xgbrWGx8oAaAQQgjRgTVf2BpCnaGEREZSWFVHfqmVBoeNAIOOIH+DT6WLw01GfjWkC78a0oWDlmrW7y3ig/1FfPJ9CRHuFPE1PpYi/qn1j1uKBIBCCCGEaMZVCSOInhFmyutsHC2zcqKqDqUgxN9AgJ9vzW7tGx3EfWODuHtMPNuOlPHu3kLe2FPAK7uP0z8miKkDY5jUL8anUsStSQJAIYQQQpyTzr3mXYTJyAB7MMU19RwuraWopgGDBiEBfj41G9eg15GREElGQiTlVhsfHihi/d4iHss5xFObD5Pe05UiHh3vWyniliYBoBBCCCEuiNGgo3NoIJ1DA6mqs3Oyqo4jZVZsDkWgQUeQv94n0puNwgL9+FVKF36V0oXvi2tYv7eQ9/cX8ekPJYQHnkoR94n2nRRxS5EAUAghhBAXLTjAQHBAEAmRZspqGzhabuVkVT06TSPI37UMii/pHWXmnowEFoyOZ7s7Rbz6qwJe/fI4/aLNTB0Yy6R+0YS7K3y0dxIACiGEEOKS6XUaUUH+RAX5U293YKlqIL+slqLqegw6zSdTxOkJkaS7U8QfHbCwfm8hj392iKe2nEoRj2nnKWIJAIUQQghxWfgb9HQND6RLWABV9XZOVLpKo9kdTkx+esxG30sRz0zpzMyUznxfXMN7+wp5f18ROe4U8aT+0VwzMJa+0UGtfaqXnQSAQgghhLisNM3V8xcS4EfvKDOltQ38WGalqNqVIg72N+Bv8K3etd5RZn6fnsDvRvfk3+4U8ZqvT/DalwX0jXKniPtHE9FOUsQSAAohhBDCa/Q6jeggf6KD/KmzOSiqqie/zEpFdT1GvY5gf0OrVsQ4k0GnMaZnBGN6RlBRZ2ODe6HpJzYf4i9bDzMmPoJrBsYwumcEfm04RSwBoBBCCCFaRICfnu4RJrqFB1JZZ+d4pZVj5fU4nK4UcZC/b4UloQF+zEjuzIzkzvxQUsP6vUW8v6+Qzw6VEBZo4D/6uWoR94tpeyli32ppIYQQQrR7mqYRGuhHaKAffaOclNbaOFJWS1F1A3oNgv0NGH0sRdwr0szv03vyu9HxfHHEVYt4zTcneG1PAX3cKeL/aEMpYgkAhRBCCNFqDHodMcH+xAT7U9tgp6i6gcOltZRbbRgNOkICfKv8nEGnMbpnBKPdKeKNByy8u7eIJzcf4umthxkdH841A2MZ4+MpYgkAhRBCCOETTEYD8REGeoQHUlFn53i5lWMVdTgVBBn1mIy+tbZgaIAfv0zuzC+TO3OoMUW8v4jNh0oJDTAwyb3QdL9os0/NfgYJAIUQQgjhYzRNIyzQj7BAP/rGBFFS00B+aa1nFnFogMHnetcSIs3cnd6T346OZ8ePrlnEa785wRt7CugdZXKliPvFEGn2jRSxBIBCCCGE8Fl+eh1xIQHEhQRQU2+nsKqeI2VWyqw2/A2uWcS+liIeFR/BqPgIKutsbDhYzHt7C3lq82H+uuUwo3pGMCLGwIRUJ/i1Xo+mBIBCCCGEaBPM/gYS/A3ER5gor7NxvKKO4+V1oCmCjAYCWzGgOpuQAD9+ObgTvxzcifzSWtbvLeS9fUV8ddzOw9e07rlJACiEEEKINkWn04gwGYkwGekXHURJTT2H3QtN63Uaof4GnyvjFh9hYsGYntw5Kp4v9ue3egpbAkAhhBBCtFlGg45OoYF0Cg2kut7Oyao6jpRaaXDYCHCniH1pAoZep9E5uPXHAbZK+FlaWsr48ePp06cP48ePp6ys7JzbVlZW0rVrVxYsWNCCZyiEEEKItibI30DvqCCu7B3FFd3DCTcZKa5twFJTT53N0dqn51NaJQBctmwZ48aNIy8vj3HjxrFs2bJzbvvwww+TkZHRgmcnhBBCiLZMp9OINBtJ6RLKlb2jSIoLAaCoup7S2gbsTtXKZ9j6WiUAXLduHbfccgsAt9xyC2+//fZZt9u1axeFhYVMmDChBc9OCCGEEO2Fv0FPl7BARidEMqZnJN3DTVTV2ymqrqeqzo5SHTMYbJUAsLCwkE6dOgEQFxdHYWFhs22cTid/+MMfeOyxx1r69IQQQgjRDgUHGOgX40oRD+8WRkiggeKaBoprGqizd6wUsdcmgVx99dWcPHmy2fNLly5t8ljTtLMOzvz73//O5MmT6dq1608eKysri6ysLABOnjxJQUHBJZ71xbFYLC1ynI5K2te7pH29S9rXu6R9vaujtG+cDiLMTsqsDRRY6rHaHBh0GmajAZ0Xu8islWWcPHEC/1asd+y1AHDTpk3nfC02NpYTJ07QqVMnTpw4QUxMTLNttm/fzpYtW/j73/9OdXU1DQ0NBAUFnXW84Pz585k/fz4Aw4YNo3Pnzpfvg/yEljxWRyTt613Svt4l7etd0r7e1ZHaNx5IUYqqejvHK+o4Wl5Hg9OJyU+P2ai/7LOIy6124jp1atV1C1tlGZhp06axcuVKFi1axMqVK5k+fXqzbV555RXPz9nZ2eTm5p53sogQQgghxKXSNI2QAD9CAvzoE2WmzGrjSKkVS42r/Fywv6FVe+wut1b5JIsWLWLjxo306dOHTZs2sWjRIgByc3OZN29ea5ySEEIIIQQABr2O6CB/hnUPI7N3FP1jgrA5nBRVN1ButeFoB7OIW6UHMDIyko8//rjZ88OGDeMf//hHs+fnzp3L3LlzW+DMhBBCCCFOCfTT0yPCRPfwQCrq7BRUWDlWUYdTgclPh9nYNmtqtM2zFkIIIYRoQZqmERboR1igH32jgyittZFfVktRdT06DUL8/TC2oRSxBIBCCCGEEBfBoNcRE+xPTLA/tQ12iqobOFxaS3mdDaNeR0iAAZ0PlZ87GwkAhRBCCCEukcloID7CQI/wQMqtNo5X1HG8og4FmP30mIytN9P3fCQAFEIIIYT4mTRNI9xkJNxkpF9MEMXV9Rwps1JUXY9epxHib8BP7zspYgkAhRBCCCEuIz+9jk6hgXQKDaSm3k5hVT35pbXUO2z4G3Q+UX5OAkAhhBBCCC8x+xtI8DcQH2GivM7GsXIrVQaN1h4hKAGgEEIIIYSX6XQaESaj6z9VTUArVgGBVloIWgghhBCio/KFGcISAAohhBBCdDASAAohhBBCdDASAAohhBBCdDASAAohhBBCdDASAAohhBBCdDCa8oXVCC+jqKgo4uPjW+RYFouF6OjoFjlWRyTt613Svt4l7etd0r7eJe3rXS3Zvvn5+RQXFzd7vt0FgC1p2LBh5ObmtvZptFvSvt4l7etd0r7eJe3rXdK+3uUL7SspYCGEEEKIDkYCQCGEEEKIDkYCwJ9h/vz5rX0K7Zq0r3dJ+3qXtK93Sft6l7Svd/lC+8oYQCGEEEKIDkZ6AIUQQgghOpgOGwAuXbqUQYMGMXjwYFJSUvjiiy9+9j6XLFnCY489dhnOru3SNI2bb77Z89hutxMdHc3UqVMvy/47chuXlJSQkpJCSkoKcXFxdOnSxfO4oaHhsh0nJyfnsv2+fMW9997LU0895Xk8ceJE5s2b53n8hz/8gSeeeOIn95Ofn09iYqI3TtEjKCjIq/tvSee6ZsPCwhg4cKDXj5+dnc2CBQu8fhxfptfrPb+DlJQU8vPzm20zefJkysvLmz3fke+3p7uYeCE7O5uCgoKffcz4+PizLt1yORm8uncftX37dtavX8/u3bvx9/enuLj4sn6BdmRms5lvv/0Wq9VKYGAgGzdupEuXLq19Wu1CZGQke/bsAVw35qCgIO6///7WPak2YvTo0axevZp77rkHp9NJcXExlZWVnte3bdvGk08+2Ypn2D6d65rNz8//WX9k2O12DIYO+fV10QIDAz2/gzMppVBK8f7777fsSbUhFxsvZGdnk5iYSOfOnS/4GK11PXfIHsATJ04QFRWFv78/4Fo8unPnzk0i7tzcXDIzMwHXjeu2224jMzOThIQEnn76ac++li5dSt++fRkzZgwHDhzwPP/CCy8wfPhwkpOTuf7666mtraWqqoqePXtis9kAqKysbPK4vZg8eTLvvfceAK+99hqzZs3yvFZaWsq1117L4MGDGTlyJF9//TUgbXyp5s6dy5o1azyPT+89Wr58OcOHD2fw4MEsXrwYgJqaGqZMmUJycjKJiYm88cYbAHz44Yf079+f1NRU3nrrLc8+duzYQVpaGkOGDGHUqFGe9s/IyGjypTJmzBi++uorb37Un2XUqFFs374dgO+++47ExESCg4MpKyujvr6effv2oWkaY8eOZejQoUycOJETJ04AsGvXLpKTk0lOTuZvf/ubZ5/Z2dn84he/YNKkSfTp04c//vGPntc2bNhAWloaqampzJgxg+rqagAWLVrEwIEDGTx4sCd4P3z4MGlpaSQlJfHQQw959lFdXc24ceNITU0lKSmJdevWAfDf//3fTXozH3zwQf7yl794p+G8yOFwcMcddzBo0CAmTJiA1WoFIDMz07M+WnFxsWdh/+zsbKZNm8ZVV13FuHHjOHHiBBkZGaSkpJCYmMiWLVsAWLFiBX379uWKK67g888/9xzv3XffZcSIEQwZMoSrr76awsJCnE4nffr0wWKxAOB0Oundu7fncXuUn59Pv379mDNnDomJiRw9erTJd5/cb5s6V7zwP//zPwwfPpzExETmz5+PUoo1a9aQm5vLTTfdREpKClar9bxxxezZsxk9ejSzZ8+mpKSECRMmMGjQIObNm8fp0zOuvfZahg4dyqBBg8jKygLgxRdf5J577vFs88ILL3Dvvfde3IdTHVBVVZVKTk5Wffr0UXfeeafKyclRSinVo0cPZbFYlFJK7dy5U40dO1YppdTixYtVWlqaqqurUxaLRUVERKiGhgaVm5urEhMTVU1NjaqoqFC9evVSy5cvV0opVVxc7Dnegw8+qJ5++mmllFJz585Va9euVUop9fzzz6v77ruvhT51yzCbzeqrr75S119/vbJarSo5OVl9+umnasqUKUoppRYsWKCWLFmilFLq448/VsnJyUopaeOLtXjxYrV8+XJ1yy23qDfffNPzvNlsVkop9dFHH6k77rhDOZ1O5XA41JQpU9Rnn32m1qxZo+bNm+fZvry8XFmtVtW1a1d18OBB5XQ61YwZMzy/r4qKCmWz2ZRSSm3cuFH94he/UEoplZ2drX7/+98rpZQ6cOCAGjp0aEt87J8lPj5eHTlyRD333HPq2WefVQ899JB677331NatW9XIkSNVWlqaKioqUkop9frrr6tbb71VKaVUUlKS+uyzz5RSSt1///1q0KBBSimlVqxYoXr27Olpw+7du6sff/xRWSwWlZ6erqqrq5VSSi1btkw98sgjqri4WPXt21c5nU6llFJlZWVKKaWuueYatXLlSqWUUs8884znd2iz2VRFRYVSSimLxaJ69eqlnE6nOnz4sBoyZIhSSimHw6ESEhKa/FvwVY3XrFJKHT58WOn1evXll18qpZSaMWOGeumll5RSSo0dO1bt3LlTKeX63D169FBKudq7S5cuqqSkRCml1GOPPaYeffRRpZRSdrtdVVZWqoKCAtWtWzdVVFSk6uvr1ahRo9Tvfvc7pZRSpaWlnrZ/4YUXPPeFJUuWqCeffFIp5fp303iNtxc6nU4lJyer5ORkde2116rDhw8rTdPU9u3bPds0fvfJ/ba5c8ULjdehUkrdfPPN6p133lFKNb1+lTp/XJGamqpqa2uVUkrddddd6pFHHlFKKbV+/XoFeN7XeKza2lo1aNAgVVxcrKqqqlRCQoJqaGhQSimVlpamvv7664v6bB2yBzAoKIhdu3aRlZVFdHQ0M2fOJDs7+7zvmTJlCv7+/kRFRRETE0NhYSFbtmzhuuuuw2QyERISwrRp0zzbf/vtt6Snp5OUlMQrr7zCd999B8C8efNYsWIF4PpL9dZbb/Xa52wtgwcPJj8/n9dee43Jkyc3eW3r1q3Mnj0bgKuuuoqSkhJPKk7a+PLZsGEDGzZsYMiQIaSmprJ//37y8vJISkpi48aN/Od//idbtmwhNDSU/fv307NnT/r06dNsDGdFRQUzZswgMTGRe++919PGM2bMYP369dhsNl588UXmzp3bSp/0wo0aNYpt27axbds20tLSSEtL8zzu0qUL3377LePHjyclJYVHH32UY8eOUV5eTnl5ORkZGQCea7fRuHHjCA0NJSAggIEDB3LkyBH+/e9/s3fvXkaPHk1KSgorV67kyJEjnu1uv/123nrrLUwmEwCff/65p5f89P0rpfiv//ovBg8ezNVXX83x48cpLCwkPj6eyMhIvvzyS8/vODIysoVa8fLp2bMnKSkpAAwdOvSsY9PONH78eCIiIgAYPnw4K1asYMmSJXzzzTcEBwfzxRdfkJmZSXR0NEajkZkzZ3ree+zYMSZOnEhSUhLLly/3XMu33XYbq1atAly9Ku3tftGYAt6zZw9r164FoEePHowcObLZtnK/be5c8cKnn37KiBEjSEpK4pNPPvG0x8WYNm0agYGBAGzevNlz750yZQrh4eGe7Z5++mmSk5MZOXIkR48eJS8vj6CgIK666irWr1/P/v37sdlsJCUlXdTxO+wgCr1eT2ZmJpmZmSQlJbFy5UoMBgNOpxOAurq6Jts3dv82vtdut593/3PnzuXtt98mOTmZ7OxscnJyANdYpPz8fHJycnA4HF4fUN5apk2bxv33309OTg4lJSUX9B5p44t3+jXrdDo9Y1OUUjzwwAP8+te/bvae3bt38/777/PQQw8xbty4Jjf5Mz388MNceeWVrF27lvz8fE/6wmQyMX78eNatW8fq1avZtWvX5f9wl9no0aPZtm0b33zzDYmJiXTr1o3HH3+ckJAQMjMzOX78uCdN3OhsA+NPd7ZrVinF+PHjee2115ptv2PHDj7++GPWrFnDM888wyeffAK4Jk+d6ZVXXsFisbBr1y78/PyIj4/33JfmzZtHdnY2J0+e5LbbbrvYpvAJZ7ZdYwr4fPdhs9ns+TkjI4PNmzfz3nvvMXfuXO677z5CQkLOeby77rqL++67j2nTppGTk8OSJUsA6NatG7GxsXzyySfs2LGDV1555XJ9RJ91ejteqI58vz0zXnj++ef5+uuvyc3NpVu3bixZsqTZtdroQq/nc8nJyWHTpk1s374dk8lEZmZmk/vAn/70J/r3739JgXeH7AE8cOAAeXl5nsd79uyhR48exMfHe77I/vWvf/3kfjIyMnj77bexWq1UVVXx7rvvel6rqqqiU6dO2Gy2ZjeUOXPmcOONN7a7v5ROd9ttt7F48eJmf5Gkp6d72iMnJ4eoqKjz3rSljc/v9Gv2nXfe8Yy9mThxIi+++KJn7Nnx48cpKiqioKAAk8nEzTffzMKFC9m9ezf9+/cnPz+fH374AaBJ4FJRUeGZxHNmL/m8efO4++67GT58eJO/Vn3VqFGjWL9+PREREej1eiIiIigvL2f79u3MmjULi8XiCQBtNhvfffcdYWFhhIWFsXXrVoALCg5GjhzJ559/zvfffw+4xl0ePHiQ6upqKioqmDx5Mk8++aRnzOTo0aN5/fXXm+2/oqKCmJgY/Pz8+PTTTzly5Ijnteuuu44PP/yQnTt3MnHixMvTQD7i9Gv69PGtZzpy5AixsbHccccdzJs3j927dzNixAg+++wzSkpKsNlsvPnmm57tT7+WV65c2WRf8+bN4+abb2bGjBno9XovfKq2Qe63zZ0tXujXrx/gGg9YXV3d5DoNDg6mqqrK8/hC44qMjAxeffVVAD744APKysoA13UbHh6OyWRi//79/Pvf//a8Z8SIERw9epRXX321yVj7C9UhewCrq6u56667KC8vx2Aw0Lt3b7Kysti3bx+33347Dz/8sKen43xSU1OZOXMmycnJxMTEMHz4cM9r//u//8uIESOIjo5mxIgRTS6Im266iYceeuiSfmFtRdeuXbn77rubPd842WPw4MGYTKZmN+IzSRuf3x133MH06dNJTk5m0qRJnr8oJ0yYwL59+0hLSwNcaYyXX36Z77//noULF6LT6fDz8+PZZ58lICCArKwspkyZgslkIj093dOWf/zjH7nlllt49NFHmTJlSpNjDx06lJCQkDZz009KSqK4uJgbb7yxyXPV1dXExMSwZs0a7r77bioqKrDb7dxzzz0MGjSIFStWcNttt6FpGhMmTPjJ40RHR5Odnc2sWbOor68H4NFHHyU4OJjp06dTV1eHUsqz7Mxf/vIXbrzxRv785z8zffp0z35uuukmrrnmGpKSkhg2bBj9+/f3vGY0GrnyyisJCwtrdwHL/fffzw033OC5Js8lJyeH5cuX4+fnR1BQEKtWraJTp04sWbKEtLQ0wsLCPClmcN17ZsyYQXh4OFdddRWHDx/2vDZt2jRuvfXWNnMte4vcb5s7V7wQFhZGYmIicXFxTdpp7ty5/OY3vyEwMJDt27ezePHiC4orFi9ezKxZsxg0aBCjRo2ie/fuAEyaNInnnnuOAQMG0K9fv2ap+xtuuIE9e/Zc0h/hUgmkFaxZs4Z169bx0ksvtfaptFvSxt5XUFBAZmYm+/fvR6frkMmEVuN0OklNTeXNN9+kT58+rX06bV5ubi733nuvZyaxuDhyv209U6dO5d5772XcuHEX/d4O2QPYmu666y4++OADWXfJi6SNvW/VqlU8+OCDPPHEExL8tbC9e/cydepUrrvuOgn+LoNly5bx7LPPdoixf94g99vWUV5ezhVXXEFycvIlBX8gPYBCCCGEEB2O/OkuhBBCCNHBSAAohBBCCNHBSAAohBBCCNHBSAAohGjX9Ho9KSkpDBo0iOTkZB5//HHPwqzesnDhQgYNGsTChQu9epz8/Px2ufCuEML7ZBawEKJdayyFBVBUVMSNN95IZWUljzzyiNeOmZWVRWlpabtbo08I0X5ID6AQosOIiYkhKyuLZ555BqUU+fn5pKenk5qaSmpqKtu2bQNclQ3efvttz/tuuukm1q1b12RfSikWLlxIYmIiSUlJvPHGG4BrUeHq6mqGDh3qea5RUlIS5eXlKKWIjIz01KCdM2cOGzduxOFwsHDhQoYPH87gwYN5/vnnPe9dvny55/nFixc3+2yHDh1iyJAh7Ny587K0lRCifZMeQCFEh5KQkIDD4aCoqIiYmBg2btxIQEAAeXl5zJo1i9zcXG6//XaefPJJrr32WioqKti2bVuzqjVvvfUWe/bs4auvvqK4uJjhw4eTkZHBO++8Q1BQkKfX8XSjR4/m888/p0ePHiQkJLBlyxbmzJnD9u3befbZZ/nnP/9JaGgoO3fupL6+ntGjRzNhwgTy8vLIy8tjx44dKKWYNm0amzdv9lQLOHDgAL/61a/Izs4mOTm5JZpRCNHGSQAohOiwbDYbCxYsYM+ePej1eg4ePAjA2LFj+e1vf4vFYuFf//oX119/PQZD09vl1q1bmTVrFnq9ntjYWMaOHcvOnTuZNm3aOY+Xnp7O5s2b6dGjB3feeSdZWVkcP36c8PBwzGYzGzZs4Ouvv/bUFq2oqCAvL48NGzawYcMGhgwZArjKU+Xl5dG9e3csFgvTp0/nrbfeYuDAgV5qKSFEeyMBoBCiQzl06BB6vZ6YmBgeeeQRYmNj+eqrr3A6nQQEBHi2mzNnDi+//DKvv/46K1asuCzHzsjI4G9/+xs//vgjS5cuZe3ataxZs4b09HTAlVb+61//ysSJE5u876OPPuKBBx7g17/+dZPn8/PzCQ0NpXv37mzdulUCQCHEBZMxgEKIDsNisfCb3/yGBQsWoGkaFRUVdOrUCZ1Ox0svvYTD4fBsO3fuXJ566imAswZW6enpvPHGGzgcDiwWC5s3b+aKK6447/G7detGcXExeXl5JCQkMGbMGB577DEyMjIAmDhxIs8++yw2mw2AgwcPUlNTw8SJE3nxxReprq4G4Pjx4xQVFQFgNBpZu3Ytq1at4tVXX/3ZbSSE6BikB1AI0a5ZrVZSUlKw2WwYDAZmz57NfffdB8Bvf/tbrr/+elatWsWkSZMwm82e98XGxjJgwACuvfbas+73uuuuY/v27SQnJ6NpGv/3f/9HXFzcT57PiBEjPIFmeno6DzzwAGPGjAFg3rx55Ofnk5qailKK6Oho3n77bSZMmMC+fftIS0sDICgoiJdfftkzy9hsNrN+/XrGjx9PUFDQedPQQggBUgtYCCHOqra2lqSkJHbv3k1oaGhrn44QQlxWkgIWQogzbNq0iQEDBnDXXXdJ8CeEaJekB1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\"text/plain\": [\n \"
    \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = m.plot_components(forecast)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can access the raw posterior predictive samples in Python using the method `m.predictive_samples(future)`, or in R using the function `predictive_samples(m, future)`.\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Edit Metadata\",\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/LICENSE", "content": "MIT License\n\nCopyright (c) Facebook, Inc. and its affiliates.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "python/MANIFEST.in", "content": "include stan/*.stan\ninclude LICENSE\ninclude pyproject.toml\n\n# Ensure in-place built models do not get included in the source dist.\nprune prophet/stan_model\n\n# Necessary for tests to run\ninclude prophet/tests/*.csv\ninclude prophet/tests/*.json\n\n# PEP 561\ninclude prophet/py.typed\n" }, { "path": "python/README.md", "content": "# Prophet: Automatic Forecasting Procedure\n\nProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.\n\nProphet is [open source software](https://code.facebook.com/projects/>) released by [Facebook's Core Data Science team ](https://research.fb.com/category/data-science/).\n\nFull documentation and examples available at the homepage: https://facebook.github.io/prophet/\n\n## Important links\n\n- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html\n- Issue tracker: https://github.com/facebook/prophet/issues\n- Source code repository: https://github.com/facebook/prophet\n- Implementation of Prophet in R: https://cran.r-project.org/package=prophet\n\n## Other forecasting packages\n\n- Rob Hyndman's [forecast package](http://robjhyndman.com/software/forecast/)\n- [Statsmodels](http://statsmodels.sourceforge.net/)\n\n## Installation - PyPI release\n\nSee [Installation in Python - PyPI release](https://github.com/facebook/prophet#installation-in-python---pypi-release)\n\n## Installation - Development version\n\nSee [Installation in Python - Development version](https://github.com/facebook/prophet#installation-in-python---development-version)\n\n### Installation using Docker and docker-compose (via Makefile)\n\nSimply type `make build` and if everything is fine you should be able to `make shell` or alternative jump directly to `make py-shell`.\n\nTo run the tests, inside the container `cd python/prophet` and then `python -m pytest prophet/tests/`\n\n### Example usage\n\n```python\n >>> from prophet import Prophet\n >>> m = Prophet()\n >>> m.fit(df) # df is a pandas.DataFrame with 'y' and 'ds' columns\n >>> future = m.make_future_dataframe(periods=365)\n >>> m.predict(future)\n```\n" }, { "path": "python/prophet/__init__.py", "content": "# Copyright (c) 2017-present, Facebook, Inc.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree. An additional grant\n# of patent rights can be found in the PATENTS file in the same directory.\n\nfrom prophet.__version__ import __version__\nfrom prophet.forecaster import Prophet\n\n__all__ = [\"Prophet\", \"__version__\"]\n" }, { "path": "python/prophet/__version__.py", "content": "__version__ = \"1.3.0\"\n" }, { "path": "python/prophet/diagnostics.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nimport logging\nfrom tqdm.auto import tqdm\nfrom copy import deepcopy\nimport concurrent.futures\nimport multiprocessing\nimport sys\nfrom typing import TYPE_CHECKING, Final, Literal, cast\n\nimport numpy as np\nimport pandas as pd\n\nif TYPE_CHECKING:\n from typing import (\n Any,\n Callable,\n Iterable,\n Protocol,\n TypeAlias,\n TypeVar,\n type_check_only,\n )\n\n import dask.distributed as dd\n\n from .forecaster import Prophet\n\n _ModelT = TypeVar('_ModelT', bound=Prophet)\n\n _PerformanceMetric: TypeAlias = Callable[[pd.DataFrame, int], pd.DataFrame]\n _PerformanceMetricT = TypeVar(\"_PerformanceMetricT\", bound=_PerformanceMetric)\n\n @type_check_only\n class _SupportsMap(Protocol):\n def map(self, __f: Callable[..., object], *its: Iterable[object]) -> Any: ...\n\n\nlogger: logging.Logger = logging.getLogger('prophet')\n\n\ndef generate_cutoffs(\n df: pd.DataFrame,\n horizon: pd.Timedelta,\n initial: pd.Timedelta,\n period: pd.Timedelta,\n) -> list[pd.Timestamp]:\n \"\"\"Generate cutoff dates\n\n Parameters\n ----------\n df: pd.DataFrame with historical data.\n horizon: pd.Timedelta forecast horizon.\n initial: pd.Timedelta window of the initial forecast period.\n period: pd.Timedelta simulated forecasts are done with this period.\n\n Returns\n -------\n list of pd.Timestamp\n \"\"\"\n # Last cutoff is 'latest date in data - horizon' date\n cutoff = df['ds'].max() - horizon\n if cutoff < df['ds'].min():\n raise ValueError('Less data than horizon.')\n result = [cutoff]\n while result[-1] >= min(df['ds']) + initial:\n cutoff -= period\n # If data does not exist in data range (cutoff, cutoff + horizon]\n if not (((df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)).any()):\n # Next cutoff point is 'last date before cutoff in data - horizon'\n if cutoff > df['ds'].min():\n closest_date = df[df['ds'] <= cutoff].max()['ds']\n cutoff = closest_date - horizon\n # else no data left, leave cutoff as is, it will be dropped.\n result.append(cutoff)\n result = result[:-1]\n if len(result) == 0:\n raise ValueError(\n 'Less data than horizon after initial window. '\n 'Make horizon or initial shorter.'\n )\n logger.info('Making {} forecasts with cutoffs between {} and {}'.format(\n len(result), result[-1], result[0]\n ))\n return list(reversed(result))\n\n\ndef cross_validation(\n model: Prophet,\n horizon: str | pd.Timedelta,\n period: str | pd.Timedelta | None = None,\n initial: str | pd.Timedelta | None = None,\n parallel: Literal[\"processes\", \"threads\", \"dask\"] | _SupportsMap | None = None,\n cutoffs: list[pd.Timestamp] | None = None,\n disable_tqdm: bool = False,\n extra_output_columns: str | list[str] | None = None,\n) -> pd.DataFrame:\n \"\"\"Cross-Validation for time series.\n\n Computes forecasts from historical cutoff points, which user can input.\n If not provided, begins from (end - horizon) and works backwards, making\n cutoffs with a spacing of period until initial is reached.\n\n When period is equal to the time interval of the data, this is the\n technique described in https://robjhyndman.com/hyndsight/tscv/ .\n\n Parameters\n ----------\n model: Prophet class object. Fitted Prophet model.\n horizon: string with pd.Timedelta compatible style, e.g., '5 days',\n '3 hours', '10 seconds'.\n period: string with pd.Timedelta compatible style. Simulated forecast will\n be done at every this period. If not provided, 0.5 * horizon is used.\n initial: string with pd.Timedelta compatible style. The first training\n period will include at least this much data. If not provided,\n 3 * horizon is used.\n cutoffs: list of pd.Timestamp specifying cutoffs to be used during\n cross validation. If not provided, they are generated as described\n above.\n parallel: {None, 'processes', 'threads', 'dask', object}\n How to parallelize the forecast computation. By default no parallelism\n is used.\n\n * None: No parallelism.\n * 'processes': Parallelize with concurrent.futures.ProcessPoolExecutor.\n * 'threads': Parallelize with concurrent.futures.ThreadPoolExecutor.\n Note that some operations currently hold Python's Global Interpreter\n Lock, so parallelizing with threads may be slower than training\n sequentially.\n * 'dask': Parallelize with Dask.\n This requires that a dask.distributed Client be created.\n * object: Any instance with a `.map` method. This method will\n be called with :func:`single_cutoff_forecast` and a sequence of\n iterables where each element is the tuple of arguments to pass to\n :func:`single_cutoff_forecast`\n\n .. code-block::\n\n class MyBackend:\n def map(self, func, *iterables):\n results = [\n func(*args)\n for args in zip(*iterables)\n ]\n return results\n\n disable_tqdm: if True it disables the progress bar that would otherwise show up when parallel=None\n extra_output_columns: A String or List of Strings e.g. 'trend' or ['trend'].\n Additional columns to 'yhat' and 'ds' to be returned in output.\n\n Returns\n -------\n A pd.DataFrame with the forecast, actual value and cutoff.\n \"\"\"\n\n if model.history is None:\n raise Exception('Model has not been fit. Fitting the model provides contextual parameters for cross validation.')\n\n df = model.history.copy().reset_index(drop=True)\n horizon = pd.Timedelta(horizon)\n predict_columns = ['ds', 'yhat']\n\n if model.uncertainty_samples:\n predict_columns.extend(['yhat_lower', 'yhat_upper'])\n\n if extra_output_columns is not None:\n if isinstance(extra_output_columns, str):\n extra_output_columns = [extra_output_columns]\n predict_columns.extend([c for c in extra_output_columns if c not in predict_columns])\n\n # Identify the largest seasonality period\n period_max = 0.\n for s in model.seasonalities.values():\n period_max = max(period_max, s['period'])\n seasonality_dt = pd.Timedelta(str(period_max) + ' days')\n\n if cutoffs is None:\n # Set period\n period = 0.5 * horizon if period is None else pd.Timedelta(period)\n\n # Set initial\n initial = (\n max(3 * horizon, seasonality_dt) if initial is None\n else pd.Timedelta(initial)\n )\n\n # Compute Cutoffs\n cutoffs = generate_cutoffs(df, horizon, initial, period)\n else:\n # add validation of the cutoff to make sure that the min cutoff is strictly greater than the min date in the history\n if min(cutoffs) <= df['ds'].min():\n raise ValueError(\"Minimum cutoff value is not strictly greater than min date in history\")\n # max value of cutoffs is <= (end date minus horizon)\n end_date_minus_horizon = df['ds'].max() - horizon\n if max(cutoffs) > end_date_minus_horizon:\n raise ValueError(\"Maximum cutoff value is greater than end date minus horizon, no value for cross-validation remaining\")\n initial = cutoffs[0] - df['ds'].min()\n\n # Check if the initial window\n # (that is, the amount of time between the start of the history and the first cutoff)\n # is less than the maximum seasonality period\n if initial < seasonality_dt:\n msg = 'Seasonality has period of {} days '.format(period_max)\n msg += 'which is larger than initial window. '\n msg += 'Consider increasing initial.'\n logger.warning(msg)\n\n if parallel:\n valid = {\"threads\", \"processes\", \"dask\"}\n\n if parallel == \"threads\":\n pool = concurrent.futures.ThreadPoolExecutor()\n elif parallel == \"processes\":\n if sys.platform.startswith(\"win\") or sys.platform == \"darwin\":\n ctx = multiprocessing.get_context(\"spawn\")\n else:\n ctx = multiprocessing.get_context(\"forkserver\")\n pool = concurrent.futures.ProcessPoolExecutor(mp_context=ctx)\n elif parallel == \"dask\":\n try:\n from dask.distributed import get_client\n except ImportError as e:\n raise ImportError(\"parallel='dask' requires the optional \"\n \"dependency dask.\") from e\n pool = get_client()\n # delay df and model to avoid large objects in task graph.\n df, model = pool.scatter([df, model])\n elif hasattr(parallel, \"map\"):\n pool = parallel\n else:\n msg = (\"'parallel' should be one of {} for an instance with a \"\n \"'map' method\".format(', '.join(valid)))\n raise ValueError(msg)\n\n iterables = ((df, model, cutoff, horizon, predict_columns)\n for cutoff in cutoffs)\n iterables = zip(*iterables)\n\n logger.info(\"Applying in parallel with %s\", pool)\n predicts = pool.map(single_cutoff_forecast, *iterables)\n if parallel == \"dask\":\n # convert Futures to DataFrames\n predicts = cast(\"dd.Client\", pool).gather(predicts)\n\n else:\n predicts = [\n single_cutoff_forecast(df, model, cutoff, horizon, predict_columns)\n for cutoff in (tqdm(cutoffs) if not disable_tqdm else cutoffs)\n ]\n\n # Combine all predicted pd.DataFrame into one pd.DataFrame\n return pd.concat(cast(\"Iterable[Any]\", predicts), axis=0).reset_index(drop=True)\n\n\ndef single_cutoff_forecast(\n df: pd.DataFrame,\n model: Prophet,\n cutoff: pd.Timestamp,\n horizon: pd.Timedelta,\n predict_columns: list[str],\n) -> pd.DataFrame:\n \"\"\"Forecast for single cutoff. Used in cross validation function\n when evaluating for multiple cutoffs either sequentially or in parallel.\n\n Parameters\n ----------\n df: pd.DataFrame.\n DataFrame with history to be used for single\n cutoff forecast.\n model: Prophet model object.\n cutoff: pd.Timestamp cutoff date.\n Simulated Forecast will start from this date.\n horizon: pd.Timedelta forecast horizon.\n predict_columns: List of strings e.g. ['ds', 'yhat'].\n Columns with date and forecast to be returned in output.\n\n Returns\n -------\n A pd.DataFrame with the forecast, actual value and cutoff.\n\n \"\"\"\n\n # Generate new object with copying fitting options\n m = prophet_copy(model, cutoff)\n # Train model\n history_c = df[df['ds'] <= cutoff]\n if history_c.shape[0] < 2:\n raise Exception(\n 'Less than two datapoints before cutoff. '\n 'Increase initial window.'\n )\n m.fit(history_c, **model.fit_kwargs)\n # Calculate yhat\n index_predicted = (df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)\n # Get the columns for the future dataframe\n columns = ['ds']\n if m.growth == 'logistic':\n columns.append('cap')\n if m.logistic_floor:\n columns.append('floor')\n columns.extend(m.extra_regressors.keys())\n columns.extend([\n props['condition_name']\n for props in m.seasonalities.values()\n if props['condition_name'] is not None])\n yhat = m.predict(df[index_predicted][columns])\n # Merge yhat(predicts), y(df, original data) and cutoff\n\n assert m.stan_backend\n m.stan_backend.cleanup()\n\n return pd.concat([\n yhat[predict_columns],\n df[index_predicted][['y']].reset_index(drop=True),\n pd.DataFrame({'cutoff': [cutoff] * len(yhat)})\n ], axis=1)\n\n\ndef prophet_copy(m: _ModelT, cutoff: pd.Timestamp | None = None) -> _ModelT:\n \"\"\"Copy Prophet object\n\n Parameters\n ----------\n m: Prophet model.\n cutoff: pd.Timestamp or None, default None.\n cutoff Timestamp for changepoints member variable.\n changepoints are only retained if 'changepoints <= cutoff'\n\n Returns\n -------\n Prophet class object with the same parameter with model variable\n \"\"\"\n if m.history is None:\n raise Exception('This is for copying a fitted Prophet object.')\n\n if m.specified_changepoints:\n changepoints = m.changepoints\n if cutoff is not None:\n # Filter change points '< cutoff'\n last_history_date = max(m.history['ds'][m.history['ds'] <= cutoff])\n assert changepoints is not None\n changepoints = changepoints[changepoints < last_history_date]\n else:\n changepoints = None\n\n # Auto seasonalities are set to False because they are already set in\n # m.seasonalities.\n m2 = m.__class__(\n growth=m.growth,\n n_changepoints=m.n_changepoints,\n changepoint_range=m.changepoint_range,\n changepoints=changepoints,\n yearly_seasonality=False,\n weekly_seasonality=False,\n daily_seasonality=False,\n holidays=m.holidays,\n holidays_mode=m.holidays_mode,\n seasonality_mode=m.seasonality_mode,\n seasonality_prior_scale=m.seasonality_prior_scale,\n changepoint_prior_scale=m.changepoint_prior_scale,\n holidays_prior_scale=m.holidays_prior_scale,\n mcmc_samples=m.mcmc_samples,\n interval_width=m.interval_width,\n uncertainty_samples=m.uncertainty_samples,\n stan_backend=(\n m.stan_backend.get_type() if m.stan_backend is not None\n else None\n ),\n )\n m2.extra_regressors = deepcopy(m.extra_regressors)\n m2.seasonalities = deepcopy(m.seasonalities)\n m2.country_holidays = deepcopy(m.country_holidays)\n return m2\n\n\nPERFORMANCE_METRICS: Final[dict[str, Callable[..., Any]]] = {}\ndef register_performance_metric(func: _PerformanceMetricT) -> _PerformanceMetricT:\n \"\"\"Register custom performance metric\n\n Parameters that your metric should contain\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Registered metric should return following\n -------\n Dataframe with columns horizon and metric.\n \"\"\"\n PERFORMANCE_METRICS[func.__name__] = func\n return func\n\n\ndef performance_metrics(\n df: pd.DataFrame,\n metrics: list[str] | None = None,\n rolling_window: float = 0.1,\n monthly: bool = False,\n) -> pd.DataFrame | None:\n \"\"\"Compute performance metrics from cross-validation results.\n\n Computes a suite of performance metrics on the output of cross-validation.\n By default, the following metrics are included:\n 'mse': mean squared error\n 'rmse': root mean squared error\n 'mae': mean absolute error\n 'mape': mean absolute percent error\n 'mdape': median absolute percent error\n 'smape': symmetric mean absolute percentage error\n 'coverage': coverage of the upper and lower intervals\n\n A subset of these can be specified by passing a list of names as the\n `metrics` argument.\n\n Metrics are calculated over a rolling window of cross validation\n predictions, after sorting by horizon. Averaging is first done within each\n value of horizon, and then across horizons as needed to reach the window\n size. The size of that window (number of simulated forecast points) is\n determined by the rolling_window argument, which specifies a proportion of\n simulated forecast points to include in each window. rolling_window=0 will\n compute it separately for each horizon. The default of rolling_window=0.1\n will use 10% of the rows in df in each window. rolling_window=1 will\n compute the metric across all simulated forecast points. The results are\n set to the right edge of the window.\n\n If rolling_window < 0, then metrics are computed at each datapoint with no\n averaging (i.e., 'mse' will actually be squared error with no mean).\n\n The output is a dataframe containing column 'horizon' along with columns\n for each of the metrics computed.\n\n Parameters\n ----------\n df: The dataframe returned by cross_validation.\n metrics: A list of performance metrics to compute. If not provided, will\n use ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'].\n rolling_window: Proportion of data to use in each rolling window for\n computing the metrics. Should be in [0, 1] to average.\n monthly: monthly=True will compute horizons as numbers of calendar months\n from the cutoff date, starting from 0 for the cutoff month.\n\n Returns\n -------\n Dataframe with a column for each metric, and column 'horizon'\n \"\"\"\n valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage']\n if metrics is None:\n metrics = valid_metrics\n if ('yhat_lower' not in df or 'yhat_upper' not in df) and ('coverage' in metrics):\n metrics.remove('coverage')\n if len(set(metrics)) != len(metrics):\n raise ValueError('Input metrics must be a list of unique values')\n if not set(metrics).issubset(set(PERFORMANCE_METRICS)):\n raise ValueError(\n 'Valid values for metrics are: {}'.format(valid_metrics)\n )\n df_m = df.copy()\n if monthly:\n df_m['horizon'] = df_m['ds'].dt.to_period('M').astype(int) - df_m['cutoff'].dt.to_period('M').astype(int)\n else:\n df_m['horizon'] = df_m['ds'] - df_m['cutoff']\n df_m.sort_values('horizon', inplace=True)\n if 'mape' in metrics and df_m['y'].abs().min() < 1e-8:\n logger.info('Skipping MAPE because y close to 0')\n metrics.remove('mape')\n if len(metrics) == 0:\n return None\n w = int(rolling_window * df_m.shape[0])\n if w >= 0:\n w = max(w, 1)\n w = min(w, df_m.shape[0])\n # Compute all metrics\n dfs = {}\n for metric in metrics:\n dfs[metric] = PERFORMANCE_METRICS[metric](df_m, w)\n res = dfs[metrics[0]]\n for i in range(1, len(metrics)):\n res_m = dfs[metrics[i]]\n assert np.array_equal(res['horizon'].values, res_m['horizon'].values)\n res[metrics[i]] = res_m[metrics[i]]\n return res\n\n\ndef rolling_mean_by_h(x: np.ndarray, h: np.ndarray, w: int, name: str) -> pd.DataFrame:\n \"\"\"Compute a rolling mean of x, after first aggregating by h.\n\n Right-aligned. Computes a single mean for each unique value of h. Each\n mean is over at least w samples.\n\n Parameters\n ----------\n x: Array.\n h: Array of horizon for each value in x.\n w: Integer window size (number of elements).\n name: Name for metric in result dataframe\n\n Returns\n -------\n Dataframe with columns horizon and name, the rolling mean of x.\n \"\"\"\n # Aggregate over h\n df = pd.DataFrame({'x': x, 'h': h})\n df2 = (\n df.groupby('h').agg(['sum', 'count']).reset_index().sort_values('h')\n )\n xs = df2['x']['sum'].values\n ns = df2['x']['count'].values\n hs = df2.h.values\n\n trailing_i = len(df2) - 1\n x_sum = 0\n n_sum = 0\n # We don't know output size but it is bounded by len(df2)\n res_x = np.empty(len(df2))\n\n # Start from the right and work backwards\n for i in range(len(df2) - 1, -1, -1):\n x_sum += xs[i]\n n_sum += ns[i]\n while n_sum >= w:\n # Include points from the previous horizon. All of them if still\n # less than w, otherwise weight the mean by the difference\n excess_n = n_sum - w\n excess_x = excess_n * xs[i] / ns[i]\n res_x[trailing_i] = (x_sum - excess_x)/ w\n x_sum -= xs[trailing_i]\n n_sum -= ns[trailing_i]\n trailing_i -= 1\n\n res_h = hs[(trailing_i + 1):]\n res_x = res_x[(trailing_i + 1):]\n\n return pd.DataFrame({'horizon': res_h, name: res_x})\n\n\n\ndef rolling_median_by_h(x: np.ndarray, h: np.ndarray, w: int, name: str) -> pd.DataFrame:\n \"\"\"Compute a rolling median of x, after first aggregating by h.\n\n Right-aligned. Computes a single median for each unique value of h. Each\n median is over at least w samples.\n\n For each h where there are fewer than w samples, we take samples from the previous h,\n moving backwards. (In other words, we ~ assume that the x's are shuffled within each h.)\n\n Parameters\n ----------\n x: Array.\n h: Array of horizon for each value in x.\n w: Integer window size (number of elements).\n name: Name for metric in result dataframe\n\n Returns\n -------\n Dataframe with columns horizon and name, the rolling median of x.\n \"\"\"\n # Aggregate over h\n df = pd.DataFrame({'x': x, 'h': h})\n grouped = df.groupby('h')\n df2 = grouped.size().reset_index().sort_values('h')\n hs = df2['h']\n\n res_h = []\n res_x = []\n # Start from the right and work backwards\n i = len(hs) - 1\n while i >= 0:\n h_i = hs[i]\n xs = grouped.get_group(h_i).x.tolist()\n\n # wrap in array so this works if h is pandas Series with custom index or numpy array\n next_idx_to_add = np.array(h == h_i).argmax() - 1\n while (len(xs) < w) and (next_idx_to_add >= 0):\n # Include points from the previous horizon. All of them if still\n # less than w, otherwise just enough to get to w.\n xs.append(x[next_idx_to_add])\n next_idx_to_add -= 1\n if len(xs) < w:\n # Ran out of points before getting enough.\n break\n res_h.append(hs[i])\n res_x.append(np.median(xs))\n i -= 1\n res_h.reverse()\n res_x.reverse()\n return pd.DataFrame({'horizon': res_h, name: res_x})\n\n\n# The functions below specify performance metrics for cross-validation results.\n# Each takes as input the output of cross_validation, and returns the statistic\n# as a dataframe, given a window size for rolling aggregation.\n\n\n@register_performance_metric\ndef mse(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Mean squared error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and mse.\n \"\"\"\n se = (df['y'] - df['yhat']) ** 2\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'mse': se})\n return rolling_mean_by_h(\n x=cast(np.ndarray, se.values),\n h=cast(np.ndarray, df['horizon'].values),\n w=w,\n name='mse',\n )\n\n\n@register_performance_metric\ndef rmse(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Root mean squared error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and rmse.\n \"\"\"\n res = mse(df, w)\n res['mse'] = np.sqrt(res['mse'])\n res.rename({'mse': 'rmse'}, axis='columns', inplace=True)\n return res\n\n\n@register_performance_metric\ndef mae(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Mean absolute error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and mae.\n \"\"\"\n ae = (df['y'] - df['yhat']).abs()\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'mae': ae})\n return rolling_mean_by_h(\n x=cast(np.ndarray, ae.values),\n h=cast(np.ndarray, df['horizon'].values),\n w=w,\n name='mae',\n )\n\n\n@register_performance_metric\ndef mape(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Mean absolute percent error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and mape.\n \"\"\"\n ape = ((df['y'] - df['yhat']) / df['y']).abs()\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'mape': ape})\n return rolling_mean_by_h(\n x=cast(np.ndarray, ape.values),\n h=cast(np.ndarray, df['horizon'].values),\n w=w,\n name='mape',\n )\n\n\n@register_performance_metric\ndef mdape(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Median absolute percent error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and mdape.\n \"\"\"\n ape = ((df['y'] - df['yhat']) / df['y']).abs()\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'mdape': ape})\n return rolling_median_by_h(\n x=cast(np.ndarray, ape.values),\n h=cast(np.ndarray, df['horizon'].values),\n w=w,\n name='mdape',\n )\n\n\n@register_performance_metric\ndef smape(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Symmetric mean absolute percentage error\n based on Chen and Yang (2004) formula\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and smape.\n \"\"\"\n sape = (df['y'] - df['yhat']).abs() / ((np.abs(df['y']) + np.abs(df['yhat'])) / 2)\n sape = sape.fillna(0)\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'smape': sape})\n return rolling_mean_by_h(\n x=cast(np.ndarray, sape.values),\n h=cast(np.ndarray, df['horizon'].values),\n w=w,\n name='smape',\n )\n\n\n@register_performance_metric\ndef coverage(df: pd.DataFrame, w: int) -> pd.DataFrame:\n \"\"\"Coverage\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and coverage.\n \"\"\"\n is_covered = (df['y'] >= df['yhat_lower']) & (df['y'] <= df['yhat_upper'])\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'coverage': is_covered})\n return rolling_mean_by_h(\n x=cast(np.ndarray, is_covered.values),\n h=cast(np.ndarray, df['horizon'].values),\n w=w,\n name='coverage',\n )\n" }, { "path": "python/prophet/forecaster.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nimport dataclasses\nimport logging\nfrom collections import OrderedDict, defaultdict\nfrom copy import deepcopy\nfrom datetime import timedelta\nfrom typing import TYPE_CHECKING, Any, Literal, SupportsFloat, cast\n\nif TYPE_CHECKING:\n from typing import TypeAlias\n from typing_extensions import Self\n\n import matplotlib.pyplot as plt\n\n _Mode: TypeAlias = Literal['additive', 'multiplicative']\n\nimport numpy as np\nimport numpy.typing as npt\nimport pandas as pd\n\nfrom prophet.make_holidays import get_holiday_names, make_holidays_df\nfrom prophet.models import StanBackendEnum, ModelInputData, ModelParams, TrendIndicator, IStanBackend\nfrom prophet.plot import plot, plot_components\n\nlogger: logging.Logger = logging.getLogger('prophet')\nlogger.setLevel(logging.INFO)\nNANOSECONDS_TO_SECONDS = 1000 * 1000 * 1000\n\nclass Prophet:\n \"\"\"Prophet forecaster.\n\n Parameters\n ----------\n growth: String 'linear', 'logistic' or 'flat' to specify a linear, logistic or\n flat trend.\n changepoints: List of dates at which to include potential changepoints. If\n not specified, potential changepoints are selected automatically.\n n_changepoints: Number of potential changepoints to include. Not used\n if input `changepoints` is supplied. If `changepoints` is not supplied,\n then n_changepoints potential changepoints are selected uniformly from\n the first `changepoint_range` proportion of the history.\n changepoint_range: Proportion of history in which trend changepoints will\n be estimated. Defaults to 0.8 for the first 80%. Not used if\n `changepoints` is specified.\n yearly_seasonality: Fit yearly seasonality.\n Can be 'auto', True, False, or a number of Fourier terms to generate.\n weekly_seasonality: Fit weekly seasonality.\n Can be 'auto', True, False, or a number of Fourier terms to generate.\n daily_seasonality: Fit daily seasonality.\n Can be 'auto', True, False, or a number of Fourier terms to generate.\n holidays: pd.DataFrame with columns holiday (string) and ds (date type)\n and optionally columns lower_window and upper_window which specify a\n range of days around the date to be included as holidays.\n lower_window=-2 will include 2 days prior to the date as holidays. Also\n optionally can have a column prior_scale specifying the prior scale for\n that holiday.\n seasonality_mode: 'additive' (default) or 'multiplicative'.\n seasonality_prior_scale: Parameter modulating the strength of the\n seasonality model. Larger values allow the model to fit larger seasonal\n fluctuations, smaller values dampen the seasonality. Can be specified\n for individual seasonalities using add_seasonality.\n holidays_prior_scale: Parameter modulating the strength of the holiday\n components model, unless overridden in the holidays input.\n changepoint_prior_scale: Parameter modulating the flexibility of the\n automatic changepoint selection. Large values will allow many\n changepoints, small values will allow few changepoints.\n mcmc_samples: Integer, if greater than 0, will do full Bayesian inference\n with the specified number of MCMC samples. If 0, will do MAP\n estimation.\n interval_width: Float, width of the uncertainty intervals provided\n for the forecast. If mcmc_samples=0, this will be only the uncertainty\n in the trend using the MAP estimate of the extrapolated generative\n model. If mcmc.samples>0, this will be integrated over all model\n parameters, which will include uncertainty in seasonality.\n uncertainty_samples: Number of simulated draws used to estimate\n uncertainty intervals. Settings this value to 0 or False will disable\n uncertainty estimation and speed up the calculation.\n stan_backend: str as defined in StanBackendEnum default: None - will try to\n iterate over all available backends and find the working one.\n scaling: 'absmax' (default) or 'minmax'.\n holidays_mode: 'additive' or 'multiplicative'. Defaults to seasonality_mode.\n \"\"\"\n\n growth: Literal[\"linear\", \"logistic\", \"flat\"]\n changepoints: pd.Series[pd.Timestamp] | None\n n_changepoints: int\n specified_changepoints: bool\n changepoint_range: float\n yearly_seasonality: Literal[\"auto\"] | int\n weekly_seasonality: Literal[\"auto\"] | int\n daily_seasonality: Literal[\"auto\"] | int\n holidays: pd.DataFrame | None\n seasonality_mode: _Mode\n holidays_mode: _Mode\n seasonality_prior_scale: float\n holidays_prior_scale: float\n changepoint_prior_scale: float\n mcmc_samples: int\n interval_width: float\n uncertainty_samples: int\n scaling: Literal[\"absmax\", \"minmax\"]\n\n start: pd.Timestamp | None\n y_min: float | None\n y_scale: float | None\n logistic_floor: bool\n t_scale: pd.Timedelta | None\n changepoints_t: npt.NDArray[np.float64] | None\n seasonalities: OrderedDict[str, dict[str, Any]]\n extra_regressors: OrderedDict[str, dict[str, Any]]\n country_holidays: str | None\n stan_fit: Any | None\n params: dict[str, Any]\n history: pd.DataFrame | None\n history_dates: pd.Series[pd.Timestamp] | None\n train_component_cols: pd.DataFrame | None\n component_modes: dict[_Mode, list[str]] | None\n train_holiday_names: pd.Series | None\n fit_kwargs: dict[str, Any]\n stan_backend: IStanBackend | None\n\n def __init__(\n self,\n growth: Literal[\"linear\", \"logistic\", \"flat\"] = \"linear\",\n changepoints: pd.Series | list[pd.Timestamp] | None = None,\n n_changepoints: int = 25,\n changepoint_range: float = 0.8,\n yearly_seasonality: Literal[\"auto\"] | int = \"auto\",\n weekly_seasonality: Literal[\"auto\"] | int = \"auto\",\n daily_seasonality: Literal[\"auto\"] | int = \"auto\",\n holidays: pd.DataFrame | None = None,\n seasonality_mode: _Mode = \"additive\",\n seasonality_prior_scale: SupportsFloat = 10.0,\n holidays_prior_scale: SupportsFloat = 10.0,\n changepoint_prior_scale: SupportsFloat = 0.05,\n mcmc_samples: int = 0,\n interval_width: float = 0.80,\n uncertainty_samples: int = 1000,\n stan_backend: str | None = None,\n scaling: Literal[\"absmax\", \"minmax\"] = \"absmax\",\n holidays_mode: _Mode | None = None,\n ) -> None:\n self.growth = growth\n\n if changepoints is not None:\n self.changepoints = pd.Series(pd.to_datetime(changepoints), name='ds')\n self.n_changepoints = len(self.changepoints)\n self.specified_changepoints = True\n else:\n self.changepoints = changepoints\n self.n_changepoints = n_changepoints\n self.specified_changepoints = False\n\n self.changepoint_range = changepoint_range\n self.yearly_seasonality = yearly_seasonality\n self.weekly_seasonality = weekly_seasonality\n self.daily_seasonality = daily_seasonality\n self.holidays = holidays\n\n self.seasonality_mode = seasonality_mode\n self.holidays_mode = holidays_mode or self.seasonality_mode\n\n self.seasonality_prior_scale = float(seasonality_prior_scale)\n self.changepoint_prior_scale = float(changepoint_prior_scale)\n self.holidays_prior_scale = float(holidays_prior_scale)\n\n self.mcmc_samples = mcmc_samples\n self.interval_width = interval_width\n self.uncertainty_samples = uncertainty_samples\n if scaling not in (\"absmax\", \"minmax\"):\n raise ValueError(\"scaling must be one of 'absmax' or 'minmax'\")\n self.scaling = scaling\n\n # Set during fitting or by other methods\n self.start = None\n self.y_min = None\n self.y_scale = None\n self.logistic_floor = False\n self.t_scale = None\n self.changepoints_t = None\n self.seasonalities = OrderedDict({})\n self.extra_regressors = OrderedDict({})\n self.country_holidays = None\n self.stan_fit = None\n self.params = {}\n self.history = None\n self.history_dates = None\n self.train_component_cols = None\n self.component_modes = None\n self.train_holiday_names = None\n self.fit_kwargs = {}\n self.validate_inputs()\n self._load_stan_backend(stan_backend)\n\n def _load_stan_backend(self, stan_backend: str | None) -> None:\n if stan_backend is None:\n for i in StanBackendEnum:\n try:\n logger.debug(\"Trying to load backend: %s\", i.name)\n return self._load_stan_backend(i.name)\n except Exception as e:\n logger.debug(\"Unable to load backend %s (%s), trying the next one\", i.name, e)\n else:\n # pyrefly:ignore[not-callable]\n self.stan_backend = StanBackendEnum.get_backend_class(stan_backend)()\n\n assert self.stan_backend\n logger.debug(\"Loaded stan backend: %s\", self.stan_backend.get_type())\n\n def validate_inputs(self) -> None:\n \"\"\"Validates the inputs to Prophet.\"\"\"\n if self.growth not in ('linear', 'logistic', 'flat'):\n raise ValueError(\n 'Parameter \"growth\" should be \"linear\", \"logistic\" or \"flat\".')\n if not isinstance(self.changepoint_range, (int, float)):\n raise ValueError(\"changepoint_range must be a number in [0, 1]'\")\n if ((self.changepoint_range < 0) or (self.changepoint_range > 1)):\n raise ValueError('Parameter \"changepoint_range\" must be in [0, 1]')\n if self.holidays is not None:\n if not (\n isinstance(self.holidays, pd.DataFrame)\n and 'ds' in self.holidays # noqa W503\n and 'holiday' in self.holidays # noqa W503\n ):\n raise ValueError('holidays must be a DataFrame with \"ds\" and '\n '\"holiday\" columns.')\n self.holidays['ds'] = pd.to_datetime(self.holidays['ds'])\n if (\n self.holidays['ds'].isnull().any()\n or self.holidays['holiday'].isnull().any()\n ):\n raise ValueError('Found a NaN in holidays dataframe.')\n has_lower = 'lower_window' in self.holidays\n has_upper = 'upper_window' in self.holidays\n if has_lower + has_upper == 1:\n raise ValueError('Holidays must have both lower_window and ' +\n 'upper_window, or neither')\n if has_lower:\n if self.holidays['lower_window'].max() > 0:\n raise ValueError('Holiday lower_window should be <= 0')\n if self.holidays['upper_window'].min() < 0:\n raise ValueError('Holiday upper_window should be >= 0')\n for h in self.holidays['holiday'].unique():\n self.validate_column_name(h, check_holidays=False)\n if self.seasonality_mode not in ['additive', 'multiplicative']:\n raise ValueError(\n 'seasonality_mode must be \"additive\" or \"multiplicative\"'\n )\n if self.holidays_mode not in ['additive', 'multiplicative']:\n raise ValueError(\n 'holidays_mode must be \"additive\" or \"multiplicative\"'\n )\n\n def validate_column_name(\n self,\n name: str,\n check_holidays: bool = True,\n check_seasonalities: bool = True,\n check_regressors: bool = True,\n ) -> None:\n \"\"\"Validates the name of a seasonality, holiday, or regressor.\n\n Parameters\n ----------\n name: string\n check_holidays: bool check if name already used for holiday\n check_seasonalities: bool check if name already used for seasonality\n check_regressors: bool check if name already used for regressor\n \"\"\"\n if '_delim_' in name:\n raise ValueError('Name cannot contain \"_delim_\"')\n reserved_names = [\n 'trend', 'additive_terms', 'daily', 'weekly', 'yearly',\n 'holidays', 'zeros', 'extra_regressors_additive', 'yhat',\n 'extra_regressors_multiplicative', 'multiplicative_terms',\n ]\n rn_l = [n + '_lower' for n in reserved_names]\n rn_u = [n + '_upper' for n in reserved_names]\n reserved_names.extend(rn_l)\n reserved_names.extend(rn_u)\n reserved_names.extend([\n 'ds', 'y', 'cap', 'floor', 'y_scaled', 'cap_scaled'])\n if name in reserved_names:\n raise ValueError(\n 'Name {name!r} is reserved.'.format(name=name)\n )\n if (check_holidays and self.holidays is not None and\n name in self.holidays['holiday'].unique()):\n raise ValueError(\n 'Name {name!r} already used for a holiday.'.format(name=name)\n )\n if (check_holidays and self.country_holidays is not None and\n name in get_holiday_names(self.country_holidays)):\n raise ValueError(\n 'Name {name!r} is a holiday name in {country_holidays}.'\n .format(name=name, country_holidays=self.country_holidays)\n )\n if check_seasonalities and name in self.seasonalities:\n raise ValueError(\n 'Name {name!r} already used for a seasonality.'\n .format(name=name)\n )\n if check_regressors and name in self.extra_regressors:\n raise ValueError(\n 'Name {name!r} already used for an added regressor.'\n .format(name=name)\n )\n\n def setup_dataframe(self, df: pd.DataFrame, initialize_scales: bool = False) -> pd.DataFrame:\n \"\"\"Prepare dataframe for fitting or predicting.\n\n Adds a time index and scales y. Creates auxiliary columns 't', 't_ix',\n 'y_scaled', and 'cap_scaled'. These columns are used during both\n fitting and predicting.\n\n Parameters\n ----------\n df: pd.DataFrame with columns ds, y, and cap if logistic growth. Any\n specified additional regressors must also be present.\n initialize_scales: Boolean set scaling factors in self from df.\n\n Returns\n -------\n pd.DataFrame prepared for fitting or predicting.\n \"\"\"\n if 'y' in df: # 'y' will be in training data\n df['y'] = pd.to_numeric(df['y'])\n if np.isinf(df['y'].values).any():\n raise ValueError('Found infinity in column y.')\n if df['ds'].dtype == np.int64:\n df['ds'] = df['ds'].astype(str)\n df['ds'] = pd.to_datetime(df['ds'])\n if df['ds'].dt.tz is not None:\n raise ValueError(\n 'Column ds has timezone specified, which is not supported. '\n 'Remove timezone.'\n )\n if df['ds'].isnull().any():\n raise ValueError('Found NaN in column ds.')\n for name in self.extra_regressors:\n if name not in df:\n raise ValueError(\n 'Regressor {name!r} missing from dataframe'\n .format(name=name)\n )\n df[name] = pd.to_numeric(df[name])\n if df[name].isnull().any():\n raise ValueError(\n 'Found NaN in column {name!r}'.format(name=name)\n )\n for props in self.seasonalities.values():\n condition_name = props['condition_name']\n if condition_name is not None:\n if condition_name not in df:\n raise ValueError(\n 'Condition {condition_name!r} missing from dataframe'\n .format(condition_name=condition_name)\n )\n if not df[condition_name].isin([True, False]).all():\n raise ValueError(\n 'Found non-boolean in column {condition_name!r}'\n .format(condition_name=condition_name)\n )\n df[condition_name] = df[condition_name].astype('bool')\n\n if df.index.name == 'ds':\n df.index.name = None\n df = df.sort_values('ds', kind='mergesort')\n df = df.reset_index(drop=True)\n\n self.initialize_scales(initialize_scales, df)\n\n if self.logistic_floor:\n if 'floor' not in df:\n raise ValueError('Expected column \"floor\".')\n else:\n if self.scaling == \"absmax\":\n df['floor'] = 0.\n elif self.scaling == \"minmax\":\n df['floor'] = self.y_min\n if self.growth == 'logistic':\n if 'cap' not in df:\n raise ValueError(\n 'Capacities must be supplied for logistic growth in '\n 'column \"cap\"'\n )\n if (df['cap'] <= df['floor']).any():\n raise ValueError(\n 'cap must be greater than floor (which defaults to 0).'\n )\n df['cap_scaled'] = (df['cap'] - df['floor']) / self.y_scale # pyrefly:ignore[unsupported-operation]\n\n df['t'] = (df['ds'] - self.start) / self.t_scale # pyrefly:ignore[unsupported-operation]\n if 'y' in df:\n df['y_scaled'] = (df['y'] - df['floor']) / self.y_scale # pyrefly:ignore[unsupported-operation]\n\n for name, props in self.extra_regressors.items():\n df[name] = ((df[name] - props['mu']) / props['std'])\n return df\n\n def initialize_scales(self, initialize_scales: bool, df: pd.DataFrame) -> None:\n \"\"\"Initialize model scales.\n\n Sets model scaling factors using df.\n\n Parameters\n ----------\n initialize_scales: Boolean set the scales or not.\n df: pd.DataFrame for setting scales.\n \"\"\"\n if not initialize_scales:\n return\n\n if self.growth == 'logistic' and 'floor' in df:\n self.logistic_floor = True\n if self.scaling == \"absmax\":\n self.y_min = float((df['y'] - df['floor']).abs().min())\n self.y_scale = float((df['y'] - df['floor']).abs().max())\n elif self.scaling == \"minmax\":\n self.y_min = df['floor'].min()\n self.y_scale = float(df['cap'].max() - self.y_min)\n else:\n if self.scaling == \"absmax\":\n self.y_min = 0.\n self.y_scale = float((df['y']).abs().max())\n elif self.scaling == \"minmax\":\n self.y_min = df['y'].min()\n self.y_scale = float(df['y'].max() - self.y_min)\n if self.y_scale == 0:\n self.y_scale = 1.0\n\n self.start = df['ds'].min()\n self.t_scale = df['ds'].max() - self.start\n for name, props in self.extra_regressors.items():\n standardize = props['standardize']\n n_vals = len(df[name].unique())\n if n_vals < 2:\n standardize = False\n if standardize == 'auto':\n if set(df[name].unique()) == {1, 0}:\n standardize = False # Don't standardize binary variables.\n else:\n standardize = True\n if standardize:\n mu = float(df[name].mean())\n std = float(df[name].std())\n self.extra_regressors[name]['mu'] = mu\n self.extra_regressors[name]['std'] = std\n\n def set_changepoints(self) -> None:\n \"\"\"Set changepoints\n\n Sets m$changepoints to the dates of changepoints. Either:\n 1) The changepoints were passed in explicitly.\n A) They are empty.\n B) They are not empty, and need validation.\n 2) We are generating a grid of them.\n 3) The user prefers no changepoints be used.\n \"\"\"\n if self.changepoints is not None:\n if len(self.changepoints) == 0:\n pass\n else:\n history = cast(pd.DataFrame, self.history)\n too_low = min(self.changepoints) < history['ds'].min()\n too_high = max(self.changepoints) > history['ds'].max()\n if too_low or too_high:\n raise ValueError('Changepoints must fall within training data.')\n else:\n # Place potential changepoints evenly through first\n # `changepoint_range` proportion of the history\n history = cast(pd.DataFrame, self.history)\n hist_size = int(np.floor(history.shape[0] * self.changepoint_range))\n if self.n_changepoints + 1 > hist_size:\n self.n_changepoints = hist_size - 1\n logger.info(\n 'n_changepoints greater than number of observations. '\n 'Using {n_changepoints}.'\n .format(n_changepoints=self.n_changepoints)\n )\n if self.n_changepoints > 0:\n cp_indexes = (\n np.linspace(0, hist_size - 1, self.n_changepoints + 1)\n .round()\n .astype(int)\n )\n self.changepoints = history.iloc[cp_indexes]['ds'].tail(-1)\n else:\n # set empty changepoints\n self.changepoints = pd.Series(pd.to_datetime([]), name='ds')\n if len(self.changepoints) > 0:\n self.changepoints_t = np.sort(np.array(\n (self.changepoints - self.start) / self.t_scale)) # pyrefly:ignore[unsupported-operation]\n else:\n self.changepoints_t = np.array([0]) # dummy changepoint\n\n @staticmethod\n def fourier_series(\n dates: pd.Series,\n period: float,\n series_order: int,\n ) -> np.ndarray[tuple[int, int], np.dtype[np.float64]]:\n \"\"\"Provides Fourier series components with the specified frequency\n and order.\n\n Parameters\n ----------\n dates: pd.Series containing timestamps.\n period: Number of days of the period.\n series_order: Number of components.\n\n Returns\n -------\n Matrix with seasonality features.\n \"\"\"\n if not (series_order >= 1):\n raise ValueError(\"series_order must be >= 1\")\n\n epoch = pd.Timestamp(\"1970-01-01\", tz=dates.dt.tz)\n t = (dates - epoch).dt.total_seconds() / (24 * 60 * 60)\n\n x_T = np.pi * 2 * t\n fourier_components = np.empty((dates.shape[0], 2 * series_order))\n for i in range(series_order):\n c = (i + 1) / period * x_T\n fourier_components[:, 2 * i] = np.sin(c)\n fourier_components[:, (2 * i) + 1] = np.cos(c)\n return fourier_components\n\n @classmethod\n def make_seasonality_features(\n cls,\n dates: pd.Series[pd.Timestamp],\n period: float,\n series_order: int,\n prefix: str,\n ) -> pd.DataFrame:\n \"\"\"Data frame with seasonality features.\n\n Parameters\n ----------\n cls: Prophet class.\n dates: pd.Series containing timestamps.\n period: Number of days of the period.\n series_order: Number of components.\n prefix: Column name prefix.\n\n Returns\n -------\n pd.DataFrame with seasonality features.\n \"\"\"\n features = cls.fourier_series(dates, period, series_order)\n columns = [\n '{}_delim_{}'.format(prefix, i + 1)\n for i in range(features.shape[1])\n ]\n return pd.DataFrame(features, columns=columns)\n\n def construct_holiday_dataframe(self, dates: pd.Series[pd.Timestamp]) -> pd.DataFrame:\n \"\"\"Construct a dataframe of holiday dates.\n\n Will combine self.holidays with the built-in country holidays\n corresponding to input dates, if self.country_holidays is set.\n\n Parameters\n ----------\n dates: pd.Series containing timestamps used for computing seasonality.\n\n Returns\n -------\n dataframe of holiday dates, in holiday dataframe format used in\n initialization.\n \"\"\"\n all_holidays = pd.DataFrame()\n if self.holidays is not None:\n all_holidays = self.holidays.copy()\n if self.country_holidays is not None:\n year_list = list({x.year for x in dates})\n country_holidays_df = make_holidays_df(\n year_list=year_list, country=self.country_holidays\n )\n all_holidays = pd.concat((all_holidays, country_holidays_df),\n sort=False)\n all_holidays.reset_index(drop=True, inplace=True)\n # Drop future holidays not previously seen in training data\n if self.train_holiday_names is not None:\n # Remove holiday names didn't show up in fit\n index_to_drop = all_holidays.index[\n np.logical_not(\n all_holidays.holiday.isin(self.train_holiday_names)\n )\n ]\n all_holidays = all_holidays.drop(index_to_drop)\n # Add holiday names in fit but not in predict with ds as NA\n holidays_to_add = pd.DataFrame({\n 'holiday': self.train_holiday_names[\n np.logical_not(self.train_holiday_names\n .isin(all_holidays.holiday))\n ]\n })\n all_holidays = pd.concat((all_holidays, holidays_to_add),\n sort=False)\n all_holidays.reset_index(drop=True, inplace=True)\n return all_holidays\n\n def make_holiday_features(\n self,\n dates: pd.Series[pd.Timestamp],\n holidays: pd.DataFrame,\n ) -> tuple[pd.DataFrame, list[float], list[str]]:\n \"\"\"Construct a dataframe of holiday features.\n\n Parameters\n ----------\n dates: pd.Series containing timestamps used for computing seasonality.\n holidays: pd.Dataframe containing holidays, as returned by\n construct_holiday_dataframe.\n\n Returns\n -------\n holiday_features: pd.DataFrame with a column for each holiday.\n prior_scale_list: List of prior scales for each holiday column.\n holiday_names: List of names of holidays\n \"\"\"\n # Holds columns of our future matrix.\n expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0]))\n prior_scales = {}\n # Makes an index so we can perform `get_loc` below.\n # Strip to just dates.\n row_index = pd.DatetimeIndex(dates.dt.date)\n\n for row in holidays.itertuples():\n dt = cast(pd.Timestamp, row.ds).date()\n try:\n lw = int(getattr(row, 'lower_window', 0))\n uw = int(getattr(row, 'upper_window', 0))\n except ValueError:\n lw = 0\n uw = 0\n ps = float(getattr(row, 'prior_scale', self.holidays_prior_scale))\n if np.isnan(ps):\n ps = float(self.holidays_prior_scale)\n if row.holiday in prior_scales and prior_scales[row.holiday] != ps:\n raise ValueError(\n 'Holiday {holiday!r} does not have consistent prior '\n 'scale specification.'.format(holiday=row.holiday)\n )\n if ps <= 0:\n raise ValueError('Prior scale must be > 0')\n prior_scales[row.holiday] = ps\n\n for offset in range(lw, uw + 1):\n occurrence = pd.to_datetime(dt + timedelta(days=offset))\n try:\n loc = row_index.get_loc(occurrence)\n except KeyError:\n loc = None\n key = '{}_delim_{}{}'.format(\n row.holiday,\n '+' if offset >= 0 else '-',\n abs(offset)\n )\n if loc is not None:\n expanded_holidays[key][loc] = 1.\n else:\n expanded_holidays[key] # Access key to generate value\n holiday_features = pd.DataFrame(expanded_holidays)\n # Make sure column order is consistent\n holiday_features = holiday_features[sorted(holiday_features.columns\n .tolist())]\n prior_scale_list = [\n prior_scales[h.split('_delim_')[0]]\n for h in holiday_features.columns\n ]\n holiday_names = list(prior_scales.keys())\n # Store holiday names used in fit\n if self.train_holiday_names is None:\n self.train_holiday_names = pd.Series(holiday_names)\n return holiday_features, prior_scale_list, holiday_names\n\n def add_regressor(\n self,\n name: str,\n prior_scale: float | None = None,\n standardize: Literal['auto'] | bool = 'auto',\n mode: _Mode | None = None,\n ) -> Self:\n \"\"\"Add an additional regressor to be used for fitting and predicting.\n\n The dataframe passed to `fit` and `predict` will have a column with the\n specified name to be used as a regressor. When standardize='auto', the\n regressor will be standardized unless it is binary. The regression\n coefficient is given a prior with the specified scale parameter.\n Decreasing the prior scale will add additional regularization. If no\n prior scale is provided, self.holidays_prior_scale will be used.\n Mode can be specified as either 'additive' or 'multiplicative'. If not\n specified, self.seasonality_mode will be used. 'additive' means the\n effect of the regressor will be added to the trend, 'multiplicative'\n means it will multiply the trend.\n\n Parameters\n ----------\n name: string name of the regressor.\n prior_scale: optional float scale for the normal prior. If not\n provided, self.holidays_prior_scale will be used.\n standardize: optional, specify whether this regressor will be\n standardized prior to fitting. Can be 'auto' (standardize if not\n binary), True, or False.\n mode: optional, 'additive' or 'multiplicative'. Defaults to\n self.seasonality_mode.\n\n Returns\n -------\n The prophet object.\n \"\"\"\n if self.history is not None:\n raise Exception(\n \"Regressors must be added prior to model fitting.\")\n self.validate_column_name(name, check_regressors=False)\n if prior_scale is None:\n prior_scale = float(self.holidays_prior_scale)\n if mode is None:\n mode = self.seasonality_mode\n if prior_scale <= 0:\n raise ValueError('Prior scale must be > 0')\n if mode not in ['additive', 'multiplicative']:\n raise ValueError(\"mode must be 'additive' or 'multiplicative'\")\n self.extra_regressors[name] = {\n 'prior_scale': prior_scale,\n 'standardize': standardize,\n 'mu': 0.,\n 'std': 1.,\n 'mode': mode,\n }\n return self\n\n def add_seasonality(\n self,\n name: str,\n period: float,\n fourier_order: int,\n prior_scale: float | None = None,\n mode: _Mode | None = None,\n condition_name: str | None = None,\n ) -> Self:\n \"\"\"Add a seasonal component with specified period, number of Fourier\n components, and prior scale.\n\n Increasing the number of Fourier components allows the seasonality to\n change more quickly (at risk of overfitting). Default values for yearly\n and weekly seasonalities are 10 and 3 respectively.\n\n Increasing prior scale will allow this seasonality component more\n flexibility, decreasing will dampen it. If not provided, will use the\n seasonality_prior_scale provided on Prophet initialization (defaults\n to 10).\n\n Mode can be specified as either 'additive' or 'multiplicative'. If not\n specified, self.seasonality_mode will be used (defaults to additive).\n Additive means the seasonality will be added to the trend,\n multiplicative means it will multiply the trend.\n\n If condition_name is provided, the dataframe passed to `fit` and\n `predict` should have a column with the specified condition_name\n containing booleans which decides when to apply seasonality.\n\n Parameters\n ----------\n name: string name of the seasonality component.\n period: float number of days in one period.\n fourier_order: int number of Fourier components to use.\n prior_scale: optional float prior scale for this component.\n mode: optional 'additive' or 'multiplicative'\n condition_name: string name of the seasonality condition.\n\n Returns\n -------\n The prophet object.\n \"\"\"\n if self.history is not None:\n raise Exception(\n 'Seasonality must be added prior to model fitting.')\n if name not in ['daily', 'weekly', 'yearly']:\n # Allow overwriting built-in seasonalities\n self.validate_column_name(name, check_seasonalities=False)\n if prior_scale is None:\n ps = self.seasonality_prior_scale\n else:\n ps = float(prior_scale)\n if ps <= 0:\n raise ValueError('Prior scale must be > 0')\n if fourier_order <= 0:\n raise ValueError('Fourier Order must be > 0')\n if mode is None:\n mode = self.seasonality_mode\n if mode not in ['additive', 'multiplicative']:\n raise ValueError('mode must be \"additive\" or \"multiplicative\"')\n if condition_name is not None:\n self.validate_column_name(condition_name)\n self.seasonalities[name] = {\n 'period': period,\n 'fourier_order': fourier_order,\n 'prior_scale': ps,\n 'mode': mode,\n 'condition_name': condition_name,\n }\n return self\n\n def add_country_holidays(self, country_name: str) -> Self:\n \"\"\"Add in built-in holidays for the specified country.\n\n These holidays will be included in addition to any specified on model\n initialization.\n\n Holidays will be calculated for arbitrary date ranges in the history\n and future. See the online documentation for the list of countries with\n built-in holidays.\n\n Built-in country holidays can only be set for a single country.\n\n Parameters\n ----------\n country_name: Name of the country, like 'UnitedStates' or 'US'\n\n Returns\n -------\n The prophet object.\n \"\"\"\n if self.history is not None:\n raise Exception(\n \"Country holidays must be added prior to model fitting.\"\n )\n # Validate names.\n for name in get_holiday_names(country_name):\n # Allow merging with existing holidays\n self.validate_column_name(name, check_holidays=False)\n # Set the holidays.\n if self.country_holidays is not None:\n logger.warning(\n 'Changing country holidays from {country_holidays!r} to '\n '{country_name!r}.'\n .format(\n country_holidays=self.country_holidays,\n country_name=country_name,\n )\n )\n self.country_holidays = country_name\n return self\n\n def make_all_seasonality_features(self, df: pd.DataFrame) -> tuple[\n pd.DataFrame,\n list[float],\n pd.DataFrame,\n dict[_Mode, list[str]],\n ]:\n \"\"\"Dataframe with seasonality features.\n\n Includes seasonality features, holiday features, and added regressors.\n\n Parameters\n ----------\n df: pd.DataFrame with dates for computing seasonality features and any\n added regressors.\n\n Returns\n -------\n pd.DataFrame with regression features.\n list of prior scales for each column of the features dataframe.\n Dataframe with indicators for which regression components correspond to\n which columns.\n Dictionary with keys 'additive' and 'multiplicative' listing the\n component names for each mode of seasonality.\n \"\"\"\n seasonal_features = []\n prior_scales = []\n modes: dict[_Mode, list[str]] = {'additive': [], 'multiplicative': []}\n\n # Seasonality features\n for name, props in self.seasonalities.items():\n features = self.make_seasonality_features(\n df['ds'],\n props['period'],\n props['fourier_order'],\n name,\n )\n if props['condition_name'] is not None:\n features[~df[props['condition_name']]] = 0\n seasonal_features.append(features)\n prior_scales.extend(\n [props['prior_scale']] * features.shape[1])\n modes[props['mode']].append(name)\n\n # Holiday features\n holidays = self.construct_holiday_dataframe(df['ds'])\n if len(holidays) > 0:\n features, holiday_priors, holiday_names = (\n self.make_holiday_features(df['ds'], holidays)\n )\n seasonal_features.append(features)\n prior_scales.extend(holiday_priors)\n modes[self.holidays_mode].extend(holiday_names)\n\n # Additional regressors\n for name, props in self.extra_regressors.items():\n seasonal_features.append(pd.DataFrame(df[name]))\n prior_scales.append(props['prior_scale'])\n modes[props['mode']].append(name)\n\n # Dummy to prevent empty X\n if len(seasonal_features) == 0:\n seasonal_features.append(\n pd.DataFrame({'zeros': np.zeros(df.shape[0])}))\n prior_scales.append(1.)\n\n seasonal_features = pd.concat(seasonal_features, axis=1)\n component_cols, modes = self.regressor_column_matrix(\n seasonal_features, modes\n )\n return seasonal_features, prior_scales, component_cols, modes\n\n def regressor_column_matrix(\n self,\n seasonal_features: pd.DataFrame,\n modes: dict[_Mode, list[str]],\n ) -> tuple[pd.DataFrame, dict[_Mode, list[str]]]:\n \"\"\"Dataframe indicating which columns of the feature matrix correspond\n to which seasonality/regressor components.\n\n Includes combination components, like 'additive_terms'. These\n combination components will be added to the 'modes' input.\n\n Parameters\n ----------\n seasonal_features: Constructed seasonal features dataframe\n modes: Dictionary with keys 'additive' and 'multiplicative' listing the\n component names for each mode of seasonality.\n\n Returns\n -------\n component_cols: A binary indicator dataframe with columns seasonal\n components and rows columns in seasonal_features. Entry is 1 if\n that columns is used in that component.\n modes: Updated input with combination components.\n \"\"\"\n components = pd.DataFrame({\n 'col': np.arange(seasonal_features.shape[1]),\n 'component': [\n x.split('_delim_')[0] for x in seasonal_features.columns\n ],\n })\n # Add total for holidays\n if self.train_holiday_names is not None:\n components = self.add_group_component(\n components, 'holidays', self.train_holiday_names.unique())\n # Add totals additive and multiplicative components, and regressors\n for mode in ('additive', 'multiplicative'):\n components = self.add_group_component(\n components, mode + '_terms', modes[mode]\n )\n regressors_by_mode = [\n r for r, props in self.extra_regressors.items()\n if props['mode'] == mode\n ]\n components = self.add_group_component(\n components, 'extra_regressors_' + mode, regressors_by_mode)\n # Add combination components to modes\n modes[mode].append(mode + '_terms')\n modes[mode].append('extra_regressors_' + mode)\n # After all of the additive/multiplicative groups have been added,\n modes[self.holidays_mode].append('holidays')\n # Convert to a binary matrix\n component_cols = pd.crosstab(\n components['col'], components['component'],\n ).sort_index(level='col')\n # Add columns for additive and multiplicative terms, if missing\n for name in ['additive_terms', 'multiplicative_terms']:\n if name not in component_cols:\n component_cols[name] = 0\n # Remove the placeholder\n component_cols.drop('zeros', axis=1, inplace=True, errors='ignore')\n # Validation\n if (max(component_cols['additive_terms']\n + component_cols['multiplicative_terms']) > 1):\n raise Exception('A bug occurred in seasonal components.')\n # Compare to the training, if set.\n if self.train_component_cols is not None:\n component_cols = component_cols[self.train_component_cols.columns]\n if not component_cols.equals(self.train_component_cols):\n raise Exception('A bug occurred in constructing regressors.')\n return component_cols, modes\n\n def add_group_component(\n self,\n components: pd.DataFrame,\n name: str,\n group: list[str] | np.ndarray,\n ) -> pd.DataFrame:\n \"\"\"Adds a component with given name that contains all of the components\n in group.\n\n Parameters\n ----------\n components: Dataframe with components.\n name: Name of new group component.\n group: List of components that form the group.\n\n Returns\n -------\n Dataframe with components.\n \"\"\"\n new_comp = components[components['component'].isin(set(group))].copy()\n group_cols = new_comp['col'].unique()\n if len(group_cols) > 0:\n new_comp = pd.DataFrame({'col': group_cols, 'component': name})\n components = pd.concat([components, new_comp], ignore_index=True)\n return components\n\n def parse_seasonality_args(\n self,\n name: str,\n arg: Literal['auto'] | int,\n auto_disable: bool,\n default_order: int,\n ) -> int:\n \"\"\"Get number of fourier components for built-in seasonalities.\n\n Parameters\n ----------\n name: string name of the seasonality component.\n arg: 'auto', True, False, or number of fourier components as provided.\n auto_disable: bool if seasonality should be disabled when 'auto'.\n default_order: int default fourier order\n\n Returns\n -------\n Number of fourier components, or 0 for disabled.\n \"\"\"\n if arg == 'auto':\n fourier_order = 0\n if name in self.seasonalities:\n logger.info(\n 'Found custom seasonality named {name!r}, disabling '\n 'built-in {name!r} seasonality.'.format(name=name)\n )\n elif auto_disable:\n logger.info(\n 'Disabling {name} seasonality. Run prophet with '\n '{name}_seasonality=True to override this.'\n .format(name=name)\n )\n else:\n fourier_order = default_order\n elif arg is True:\n fourier_order = default_order\n elif arg is False:\n fourier_order = 0\n else:\n fourier_order = int(arg)\n return fourier_order\n\n def set_auto_seasonalities(self) -> None:\n \"\"\"Set seasonalities that were left on auto.\n\n Turns on yearly seasonality if there is >=2 years of history.\n Turns on weekly seasonality if there is >=2 weeks of history, and the\n spacing between dates in the history is <7 days.\n Turns on daily seasonality if there is >=2 days of history, and the\n spacing between dates in the history is <1 day.\n \"\"\"\n history = cast(pd.DataFrame, self.history)\n first = history['ds'].min()\n last = history['ds'].max()\n dt = history['ds'].diff()\n min_dt = dt.iloc[cast(np.ndarray, dt.values).nonzero()[0]].min()\n\n # Yearly seasonality\n yearly_disable = last - first < pd.Timedelta(days=730)\n fourier_order = self.parse_seasonality_args(\n 'yearly', self.yearly_seasonality, yearly_disable, 10)\n if fourier_order > 0:\n self.seasonalities['yearly'] = {\n 'period': 365.25,\n 'fourier_order': fourier_order,\n 'prior_scale': self.seasonality_prior_scale,\n 'mode': self.seasonality_mode,\n 'condition_name': None\n }\n\n # Weekly seasonality\n weekly_disable = ((last - first < pd.Timedelta(weeks=2)) or\n (min_dt >= pd.Timedelta(weeks=1))) # pyrefly:ignore[unsupported-operation]\n fourier_order = self.parse_seasonality_args(\n 'weekly', self.weekly_seasonality, weekly_disable, 3)\n if fourier_order > 0:\n self.seasonalities['weekly'] = {\n 'period': 7,\n 'fourier_order': fourier_order,\n 'prior_scale': self.seasonality_prior_scale,\n 'mode': self.seasonality_mode,\n 'condition_name': None\n }\n\n # Daily seasonality\n daily_disable = ((last - first < pd.Timedelta(days=2)) or\n (min_dt >= pd.Timedelta(days=1))) # pyrefly:ignore[unsupported-operation]\n fourier_order = self.parse_seasonality_args(\n 'daily', self.daily_seasonality, daily_disable, 4)\n if fourier_order > 0:\n self.seasonalities['daily'] = {\n 'period': 1,\n 'fourier_order': fourier_order,\n 'prior_scale': self.seasonality_prior_scale,\n 'mode': self.seasonality_mode,\n 'condition_name': None\n }\n\n @staticmethod\n def linear_growth_init(df: pd.DataFrame) -> tuple[float, float]:\n \"\"\"Initialize linear growth.\n\n Provides a strong initialization for linear growth by calculating the\n growth and offset parameters that pass the function through the first\n and last points in the time series.\n\n Parameters\n ----------\n df: pd.DataFrame with columns ds (date), y_scaled (scaled time series),\n and t (scaled time).\n\n Returns\n -------\n A tuple (k, m) with the rate (k) and offset (m) of the linear growth\n function.\n \"\"\"\n i0, i1 = cast(int, df['ds'].idxmin()), cast(int, df['ds'].idxmax())\n T = df['t'].iloc[i1] - df['t'].iloc[i0]\n k = (df['y_scaled'].iloc[i1] - df['y_scaled'].iloc[i0]) / T\n m = df['y_scaled'].iloc[i0] - k * df['t'].iloc[i0]\n return (k, m)\n\n @staticmethod\n def logistic_growth_init(df: pd.DataFrame) -> tuple[float, float]:\n \"\"\"Initialize logistic growth.\n\n Provides a strong initialization for logistic growth by calculating the\n growth and offset parameters that pass the function through the first\n and last points in the time series.\n\n Parameters\n ----------\n df: pd.DataFrame with columns ds (date), cap_scaled (scaled capacity),\n y_scaled (scaled time series), and t (scaled time).\n\n Returns\n -------\n A tuple (k, m) with the rate (k) and offset (m) of the logistic growth\n function.\n \"\"\"\n i0, i1 = cast(int, df['ds'].idxmin()), cast(int, df['ds'].idxmax())\n T = df['t'].iloc[i1] - df['t'].iloc[i0]\n\n # Force valid values, in case y > cap or y < 0\n C0 = df['cap_scaled'].iloc[i0]\n C1 = df['cap_scaled'].iloc[i1]\n y0 = max(0.01 * C0, min(0.99 * C0, df['y_scaled'].iloc[i0]))\n y1 = max(0.01 * C1, min(0.99 * C1, df['y_scaled'].iloc[i1]))\n\n r0 = C0 / y0\n r1 = C1 / y1\n\n if abs(r0 - r1) <= 0.01:\n r0 = 1.05 * r0\n\n L0 = np.log(r0 - 1)\n L1 = np.log(r1 - 1)\n\n # Initialize the offset\n m = L0 * T / (L0 - L1)\n # And the rate\n k = (L0 - L1) / T\n return (k, m)\n\n @staticmethod\n def flat_growth_init(df: pd.DataFrame) -> tuple[float, float]:\n \"\"\"Initialize flat growth.\n\n Provides a strong initialization for flat growth. Sets the growth to 0\n and offset parameter as mean of history y_scaled values.\n\n Parameters\n ----------\n df: pd.DataFrame with columns ds (date), y_scaled (scaled time series),\n and t (scaled time).\n\n Returns\n -------\n A tuple (k, m) with the rate (k) and offset (m) of the linear growth\n function.\n \"\"\"\n k = 0\n m = df['y_scaled'].mean()\n return k, m\n\n def preprocess(self, df: pd.DataFrame, **kwargs: Any) -> ModelInputData:\n \"\"\"\n Reformats historical data, standardizes y and extra regressors, sets seasonalities and changepoints.\n\n Saves the preprocessed data to the instantiated object, and also returns the relevant components\n as a ModelInputData object.\n \"\"\"\n if ('ds' not in df) or ('y' not in df):\n raise ValueError(\n 'Dataframe must have columns \"ds\" and \"y\" with the dates and '\n 'values respectively.'\n )\n history = df[df['y'].notnull()].copy()\n if history.shape[0] < 2:\n raise ValueError('Dataframe has less than 2 non-NaN rows.')\n self.history_dates = pd.to_datetime(pd.Series(df['ds'].unique(), name='ds')).sort_values()\n\n self.history = self.setup_dataframe(history, initialize_scales=True)\n self.set_auto_seasonalities()\n seasonal_features, prior_scales, component_cols, modes = (\n self.make_all_seasonality_features(self.history))\n self.train_component_cols = component_cols\n self.component_modes = modes\n self.fit_kwargs = deepcopy(kwargs)\n\n self.set_changepoints()\n\n if self.growth in ['linear', 'flat']:\n cap = np.zeros(self.history.shape[0])\n else:\n cap = self.history['cap_scaled']\n\n return ModelInputData(\n T=self.history.shape[0],\n S=len(cast(npt.NDArray[np.float64], self.changepoints_t)),\n K=seasonal_features.shape[1],\n tau=self.changepoint_prior_scale,\n trend_indicator=TrendIndicator[self.growth.upper()].value,\n y=self.history['y_scaled'],\n t=self.history['t'],\n t_change=cast(npt.NDArray[np.float64], self.changepoints_t),\n X=seasonal_features,\n sigmas=prior_scales,\n s_a=component_cols['additive_terms'],\n s_m=component_cols['multiplicative_terms'],\n cap=cap,\n )\n\n def calculate_initial_params(self, num_total_regressors: int) -> ModelParams:\n \"\"\"\n Calculates initial parameters for the model based on the preprocessed history.\n\n Parameters\n ----------\n num_total_regressors: the count of seasonality fourier components plus holidays plus extra regressors.\n \"\"\"\n history = cast(pd.DataFrame, self.history)\n if self.growth == 'linear':\n k, m = self.linear_growth_init(history)\n elif self.growth == 'flat':\n k, m = self.flat_growth_init(history)\n else:\n assert self.growth == \"logistic\"\n k, m = self.logistic_growth_init(history)\n return ModelParams(\n k=k,\n m=m,\n delta=np.zeros_like(self.changepoints_t),\n beta=np.zeros(num_total_regressors),\n sigma_obs=1.0,\n )\n\n def fit(self, df: pd.DataFrame, **kwargs: Any) -> Self:\n \"\"\"Fit the Prophet model.\n\n This sets self.params to contain the fitted model parameters. It is a\n dictionary parameter names as keys and the following items:\n k (Mx1 array): M posterior samples of the initial slope.\n m (Mx1 array): The initial intercept.\n delta (MxN array): The slope change at each of N changepoints.\n beta (MxK matrix): Coefficients for K seasonality features.\n sigma_obs (Mx1 array): Noise level.\n Note that M=1 if MAP estimation.\n\n Parameters\n ----------\n df: pd.DataFrame containing the history. Must have columns ds (date\n type) and y, the time series. If self.growth is 'logistic', then\n df must also have a column cap that specifies the capacity at\n each ds.\n kwargs: Additional arguments passed to the optimizing or sampling\n functions in Stan.\n\n Returns\n -------\n The fitted Prophet object.\n \"\"\"\n if self.history is not None:\n raise Exception('Prophet object can only be fit once. '\n 'Instantiate a new object.')\n\n model_inputs = self.preprocess(df, **kwargs)\n initial_params = self.calculate_initial_params(model_inputs.K)\n\n dat = dataclasses.asdict(model_inputs)\n stan_init = dataclasses.asdict(initial_params)\n stan_backend = cast(IStanBackend, self.stan_backend)\n\n history = cast(pd.DataFrame, self.history)\n if history['y'].min() == history['y'].max() and \\\n (self.growth == 'linear' or self.growth == 'flat'):\n self.params = stan_init\n self.params['sigma_obs'] = 1e-9\n for par in self.params:\n self.params[par] = np.array([self.params[par]])\n elif self.mcmc_samples > 0:\n self.params = stan_backend.sampling(stan_init, dat, self.mcmc_samples, **kwargs)\n else:\n self.params = stan_backend.fit(stan_init, dat, **kwargs)\n\n self.stan_fit = stan_backend.stan_fit\n # If no changepoints were requested, replace delta with 0s\n if len(cast(pd.Series, self.changepoints)) == 0:\n # Fold delta into the base rate k\n self.params['k'] = (\n self.params['k'] + self.params['delta'].reshape(-1)\n )\n self.params['delta'] = (np.zeros(self.params['delta'].shape)\n .reshape((-1, 1)))\n\n return self\n\n def predict(self, df: pd.DataFrame | None = None, vectorized: bool = True) -> pd.DataFrame:\n \"\"\"Predict using the prophet model.\n\n Parameters\n ----------\n df: pd.DataFrame with dates for predictions (column ds), and capacity\n (column cap) if logistic growth. If not provided, predictions are\n made on the history.\n vectorized: Whether to use a vectorized method to compute uncertainty intervals. Suggest using\n True (the default) for much faster runtimes in most cases,\n except when (growth = 'logistic' and mcmc_samples > 0).\n\n Returns\n -------\n A pd.DataFrame with the forecast components.\n \"\"\"\n if self.history is None:\n raise Exception('Model has not been fit.')\n\n if df is None:\n df = self.history.copy()\n else:\n if df.shape[0] == 0:\n raise ValueError('Dataframe has no rows.')\n df = self.setup_dataframe(df.copy())\n\n df['trend'] = self.predict_trend(df)\n seasonal_components = self.predict_seasonal_components(df)\n if self.uncertainty_samples:\n intervals = self.predict_uncertainty(df, vectorized)\n else:\n intervals = None\n\n # Drop columns except ds, cap, floor, and trend\n cols = ['ds', 'trend']\n if 'cap' in df:\n cols.append('cap')\n if self.logistic_floor:\n cols.append('floor')\n # Add in forecast components\n df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)\n df2['yhat'] = (\n df2['trend'] * (1 + df2['multiplicative_terms'])\n + df2['additive_terms']\n )\n return df2\n\n @staticmethod\n def piecewise_linear(\n t: np.ndarray,\n deltas: np.ndarray,\n k: float,\n m: float,\n changepoint_ts: np.ndarray,\n ) -> np.ndarray[tuple[int], np.dtype[np.float64]]:\n \"\"\"Evaluate the piecewise linear function.\n\n Parameters\n ----------\n t: np.array of times on which the function is evaluated.\n deltas: np.array of rate changes at each changepoint.\n k: Float initial rate.\n m: Float initial offset.\n changepoint_ts: np.array of changepoint times.\n\n Returns\n -------\n Vector y(t).\n \"\"\"\n deltas_t = (changepoint_ts[None, :] <= t[..., None]) * deltas\n k_t = deltas_t.sum(axis=1) + k\n m_t = (deltas_t * -changepoint_ts).sum(axis=1) + m\n return k_t * t + m_t\n\n @staticmethod\n def piecewise_logistic(\n t: np.ndarray,\n cap: np.ndarray | pd.Series,\n deltas: np.ndarray,\n k: float,\n m: float,\n changepoint_ts: np.ndarray,\n ) -> np.ndarray[tuple[int], np.dtype[np.float64]]:\n \"\"\"Evaluate the piecewise logistic function.\n\n Parameters\n ----------\n t: np.array of times on which the function is evaluated.\n cap: np.array of capacities at each t.\n deltas: np.array of rate changes at each changepoint.\n k: Float initial rate.\n m: Float initial offset.\n changepoint_ts: np.array of changepoint times.\n\n Returns\n -------\n Vector y(t).\n \"\"\"\n # Compute offset changes\n # Ensure k and m are scalars for numpy 2.x compatibility\n k_scalar: float = np.asarray(k).item() if np.asarray(k).size == 1 else k\n m_scalar: float = np.asarray(m).item() if np.asarray(m).size == 1 else m\n k_cum = np.concatenate((np.atleast_1d(k_scalar), np.cumsum(deltas) + k_scalar))\n gammas = np.zeros(len(changepoint_ts))\n for i, t_s in enumerate(changepoint_ts):\n gammas[i] = (\n (t_s - m_scalar - np.sum(gammas))\n * (1 - k_cum[i] / k_cum[i + 1]) # noqa W503\n )\n # Get cumulative rate and offset at each t\n k_t = k_scalar * np.ones_like(t)\n m_t = m_scalar * np.ones_like(t)\n for s, t_s in enumerate(changepoint_ts):\n indx = t >= t_s\n k_t[indx] += deltas[s]\n m_t[indx] += gammas[s]\n return cap / (1 + np.exp(-k_t * (t - m_t)))\n\n @staticmethod\n def flat_trend(\n t: np.ndarray,\n m: float,\n ) -> np.ndarray[tuple[int], np.dtype[np.float64]]:\n \"\"\"Evaluate the flat trend function.\n\n Parameters\n ----------\n t: np.array of times on which the function is evaluated.\n m: Float initial offset.\n\n Returns\n -------\n Vector y(t).\n \"\"\"\n m_t = m * np.ones_like(t)\n return m_t\n\n def predict_trend(\n self,\n df: pd.DataFrame,\n ) -> np.ndarray[tuple[int], np.dtype[np.float64]]:\n \"\"\"Predict trend using the prophet model.\n\n Parameters\n ----------\n df: Prediction dataframe.\n\n Returns\n -------\n Vector with trend on prediction dates.\n \"\"\"\n k = np.nanmean(self.params['k'])\n m = np.nanmean(self.params['m'])\n deltas = np.nanmean(self.params['delta'], axis=0)\n\n t = np.array(df['t'])\n changepoints_t = cast(npt.NDArray[np.float64], self.changepoints_t)\n if self.growth == 'linear':\n trend = self.piecewise_linear(t, deltas, k, m, changepoints_t)\n elif self.growth == 'logistic':\n cap = df['cap_scaled']\n trend = self.piecewise_logistic(t, cap, deltas, k, m, changepoints_t)\n else:\n # constant trend\n assert self.growth == \"flat\"\n trend = self.flat_trend(t, m)\n\n return trend * cast(float, self.y_scale) + df['floor']\n\n def predict_seasonal_components(self, df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Predict seasonality components, holidays, and added regressors.\n\n Parameters\n ----------\n df: Prediction dataframe.\n\n Returns\n -------\n Dataframe with seasonal components.\n \"\"\"\n seasonal_features, _, component_cols, _ = (\n self.make_all_seasonality_features(df)\n )\n if self.uncertainty_samples:\n lower_p = 100 * (1.0 - self.interval_width) / 2\n upper_p = 100 * (1.0 + self.interval_width) / 2\n\n X = seasonal_features.values\n data = {}\n for component in component_cols.columns:\n beta_c = self.params['beta'] * component_cols[component].values\n\n comp = np.matmul(X, beta_c.transpose())\n assert self.component_modes is not None\n if component in self.component_modes['additive']:\n comp *= self.y_scale\n data[component] = np.nanmean(comp, axis=1)\n if self.uncertainty_samples:\n # pyrefly:ignore[unbound-name]\n data[component + '_lower'] = self.percentile(comp, lower_p, axis=1)\n # pyrefly:ignore[unbound-name]\n data[component + '_upper'] = self.percentile(comp, upper_p, axis=1)\n return pd.DataFrame(data)\n\n def predict_uncertainty(self, df: pd.DataFrame, vectorized: bool) -> pd.DataFrame:\n \"\"\"Prediction intervals for yhat and trend.\n\n Parameters\n ----------\n df: Prediction dataframe.\n vectorized: Whether to use a vectorized method for generating future draws.\n\n Returns\n -------\n Dataframe with uncertainty intervals.\n \"\"\"\n sim_values = self.sample_posterior_predictive(df, vectorized)\n\n lower_p = 100 * (1.0 - self.interval_width) / 2\n upper_p = 100 * (1.0 + self.interval_width) / 2\n\n series = {}\n for key in ['yhat', 'trend']:\n series['{}_lower'.format(key)] = self.percentile(\n sim_values[key], lower_p, axis=1)\n series['{}_upper'.format(key)] = self.percentile(\n sim_values[key], upper_p, axis=1)\n\n return pd.DataFrame(series)\n\n def sample_posterior_predictive(\n self,\n df: pd.DataFrame,\n vectorized: bool,\n ) -> dict[str, npt.NDArray[np.float64]]:\n \"\"\"Prophet posterior predictive samples.\n\n Parameters\n ----------\n df: Prediction dataframe.\n vectorized: Whether to use a vectorized method to generate future draws.\n\n Returns\n -------\n Dictionary with posterior predictive samples for the forecast yhat and\n for the trend component.\n \"\"\"\n n_iterations = self.params['k'].shape[0]\n samp_per_iter = max(1, int(np.ceil(\n self.uncertainty_samples / float(n_iterations)\n )))\n # Generate seasonality features once so we can re-use them.\n seasonal_features, _, component_cols, _ = (\n self.make_all_seasonality_features(df)\n )\n sim_values = {'yhat': [], 'trend': []}\n for i in range(n_iterations):\n if vectorized:\n sims = self.sample_model_vectorized(\n df=df,\n seasonal_features=seasonal_features,\n iteration=i,\n s_a=component_cols['additive_terms'],\n s_m=component_cols['multiplicative_terms'],\n n_samples=samp_per_iter\n )\n else:\n sims = [\n self.sample_model(\n df=df,\n seasonal_features=seasonal_features,\n iteration=i,\n s_a=component_cols['additive_terms'],\n s_m=component_cols['multiplicative_terms'],\n ) for _ in range(samp_per_iter)\n ]\n for key in sim_values:\n for sim in sims:\n sim_values[key].append(sim[key])\n\n return {k: np.column_stack(v) for k, v in sim_values.items()}\n\n def sample_model(\n self,\n df: pd.DataFrame,\n seasonal_features: pd.DataFrame,\n iteration: int,\n s_a: pd.Series,\n s_m: pd.Series,\n ) -> dict[str, npt.NDArray[np.float64]]:\n \"\"\"Simulate observations from the extrapolated generative model.\n\n Parameters\n ----------\n df: Prediction dataframe.\n seasonal_features: pd.DataFrame of seasonal features.\n iteration: Int sampling iteration to use parameters from.\n s_a: Indicator vector for additive components\n s_m: Indicator vector for multiplicative components\n\n Returns\n -------\n Dictionary with `yhat` and `trend`, each like df['t'].\n \"\"\"\n trend = self.sample_predictive_trend(df, iteration)\n\n y_scale = cast(float, self.y_scale)\n beta = self.params['beta'][iteration]\n Xb_a = np.matmul(seasonal_features.values,\n beta * s_a.values) * y_scale\n Xb_m = np.matmul(seasonal_features.values, beta * s_m.values)\n\n sigma = self.params['sigma_obs'][iteration]\n noise = np.random.normal(0, sigma, df.shape[0]) * y_scale\n\n return {\n 'yhat': trend * (1 + Xb_m) + Xb_a + noise,\n 'trend': trend\n }\n\n def sample_model_vectorized(\n self,\n df: pd.DataFrame,\n seasonal_features: pd.DataFrame,\n iteration: int,\n s_a: pd.Series,\n s_m: pd.Series,\n n_samples: int,\n ) -> list[dict[str, np.ndarray]]:\n \"\"\"Simulate observations from the extrapolated generative model. Vectorized version of sample_model().\n\n Parameters\n ----------\n df: Prediction dataframe.\n seasonal_features: pd.DataFrame of seasonal features.\n iteration: Int sampling iteration to use parameters from.\n s_a: Indicator vector for additive components.\n s_m: Indicator vector for multiplicative components.\n n_samples: Number of future paths of the trend to simulate.\n\n Returns\n -------\n List (length n_samples) of dictionaries with arrays for trend and yhat, each ordered like df['t'].\n \"\"\"\n # Get the seasonality and regressor components, which are deterministic per iteration\n beta = self.params['beta'][iteration]\n Xb_a = np.matmul(seasonal_features.values,\n beta * s_a.values) * self.y_scale\n Xb_m = np.matmul(seasonal_features.values, beta * s_m.values)\n # Get the future trend, which is stochastic per iteration\n trends = self.sample_predictive_trend_vectorized(df, n_samples, iteration) # already on the same scale as the actual data\n sigma = self.params['sigma_obs'][iteration]\n noise_terms = np.random.normal(0, sigma, trends.shape) * cast(float, self.y_scale)\n\n simulations = []\n for trend, noise in zip(trends, noise_terms):\n simulations.append({\n 'yhat': trend * (1 + Xb_m) + Xb_a + noise,\n 'trend': trend\n })\n return simulations\n\n def sample_predictive_trend(self, df: pd.DataFrame, iteration: int) -> np.ndarray:\n \"\"\"Simulate the trend using the extrapolated generative model.\n\n Parameters\n ----------\n df: Prediction dataframe.\n iteration: Int sampling iteration to use parameters from.\n\n Returns\n -------\n np.array of simulated trend over df['t'].\n \"\"\"\n k = self.params['k'][iteration]\n m = self.params['m'][iteration]\n deltas = self.params['delta'][iteration]\n\n t = np.array(df['t'])\n T = t.max()\n\n # New changepoints from a Poisson process with rate S on [1, T]\n changepoints_t = cast(np.ndarray, self.changepoints_t)\n if T > 1:\n S = len(changepoints_t)\n n_changes = np.random.poisson(S * (T - 1))\n else:\n n_changes = 0\n if n_changes > 0:\n changepoint_ts_new = 1 + np.random.rand(n_changes) * (T - 1)\n changepoint_ts_new.sort()\n else:\n changepoint_ts_new = []\n\n # Get the empirical scale of the deltas, plus epsilon to avoid NaNs.\n lambda_ = np.mean(np.abs(deltas)) + 1e-8\n\n # Sample deltas\n deltas_new = np.random.laplace(0, lambda_, n_changes)\n\n # Prepend the times and deltas from the history\n changepoint_ts = np.concatenate((changepoints_t,\n changepoint_ts_new))\n deltas = np.concatenate((deltas, deltas_new))\n\n if self.growth == 'linear':\n trend = self.piecewise_linear(t, deltas, k, m, changepoint_ts)\n elif self.growth == 'logistic':\n cap = df['cap_scaled']\n trend = self.piecewise_logistic(t, cap, deltas, k, m,\n changepoint_ts)\n else:\n assert self.growth == \"flat\"\n trend = self.flat_trend(t, m)\n\n return trend * cast(float, self.y_scale) + df['floor']\n\n def sample_predictive_trend_vectorized(\n self,\n df: pd.DataFrame,\n n_samples: int,\n iteration: int = 0,\n ) -> np.ndarray[tuple[int, int], np.dtype[np.float64]]:\n \"\"\"Sample draws of the future trend values. Vectorized version of sample_predictive_trend().\n\n Parameters\n ----------\n df: Prediction dataframe.\n iteration: Int sampling iteration to use parameters from.\n n_samples: Number of future paths of the trend to simulate.\n\n Returns\n -------\n Draws of the trend values with shape (n_samples, len(df)). Values are on the scale of the original data.\n \"\"\"\n deltas = self.params[\"delta\"][iteration]\n m = self.params[\"m\"][iteration]\n k = self.params[\"k\"][iteration]\n changepoints_t = cast(np.ndarray, self.changepoints_t)\n if self.growth == \"linear\":\n expected = self.piecewise_linear(\n cast(np.ndarray, df[\"t\"].values), deltas, k, m, changepoints_t\n )\n elif self.growth == \"logistic\":\n expected = self.piecewise_logistic(\n cast(np.ndarray, df[\"t\"].values),\n cast(np.ndarray, df[\"cap_scaled\"].values),\n deltas,\n k,\n m,\n changepoints_t,\n )\n elif self.growth == \"flat\":\n expected = self.flat_trend(cast(np.ndarray, df[\"t\"].values), m)\n else:\n raise NotImplementedError\n uncertainty = self._sample_uncertainty(df, n_samples, iteration)\n return (\n (np.tile(expected, (n_samples, 1)) + uncertainty) * cast(float, self.y_scale) +\n np.tile(cast(np.ndarray, df[\"floor\"].values), (n_samples, 1))\n )\n\n def _sample_uncertainty(\n self,\n df: pd.DataFrame,\n n_samples: int,\n iteration: int = 0,\n ) -> np.ndarray[tuple[int, int], np.dtype[np.float64]]:\n \"\"\"Sample draws of future trend changes, vectorizing as much as possible.\n\n Parameters\n ----------\n df: pd.DataFrame with columns `t` (time scaled to the model context), trend, and cap.\n n_samples: Number of future paths of the trend to simulate\n iteration: The iteration of the parameter set to use. Default 0, the first iteration.\n\n Returns\n -------\n Draws of the trend changes with shape (n_samples, len(df)). Values are standardized.\n \"\"\"\n # handle only historic data\n if df[\"t\"].max() <= 1:\n # there is no trend uncertainty in historic trends\n uncertainties = np.zeros((n_samples, len(df)))\n else:\n future_df = df.loc[df[\"t\"] > 1]\n n_length = len(future_df)\n # handle 1 length futures by using history\n if n_length > 1:\n single_diff = np.diff(future_df[\"t\"]).mean()\n else:\n single_diff = np.diff(cast(pd.DataFrame, self.history)[\"t\"]).mean()\n change_likelihood = len(cast(np.ndarray, self.changepoints_t)) * single_diff\n deltas = self.params[\"delta\"][iteration]\n m = self.params[\"m\"][iteration]\n k = self.params[\"k\"][iteration]\n mean_delta = np.mean(np.abs(deltas)) + 1e-8\n if self.growth == \"linear\":\n mat = self._make_trend_shift_matrix(mean_delta, change_likelihood, n_length, n_samples=n_samples)\n uncertainties = mat.cumsum(axis=1).cumsum(axis=1) # from slope changes to actual values\n uncertainties *= single_diff # scaled by the actual meaning of the slope\n elif self.growth == \"logistic\":\n mat = self._make_trend_shift_matrix(mean_delta, change_likelihood, n_length, n_samples=n_samples)\n uncertainties = self._logistic_uncertainty(\n mat=mat,\n deltas=deltas,\n k=k,\n m=m,\n cap=cast(np.ndarray, future_df[\"cap_scaled\"].values),\n t_time=cast(np.ndarray, future_df[\"t\"].values),\n n_length=n_length,\n single_diff=single_diff,\n )\n elif self.growth == \"flat\":\n # no trend uncertainty when there is no growth\n uncertainties = np.zeros((n_samples, n_length))\n else:\n raise NotImplementedError\n # handle past included in dataframe\n if df[\"t\"].min() <= 1:\n past_uncertainty = np.zeros((n_samples, np.sum(df[\"t\"] <= 1)))\n uncertainties = np.concatenate([past_uncertainty, uncertainties], axis=1)\n return uncertainties\n\n @staticmethod\n def _make_trend_shift_matrix(\n mean_delta: float, likelihood: float, future_length: int, n_samples: int\n ) -> np.ndarray[tuple[int, int], np.dtype[np.float64]]:\n \"\"\"\n Creates a matrix of random trend shifts based on historical likelihood and size of shifts.\n Can be used for either linear or logistic trend shifts.\n Each row represents a different sample of a possible future, and each column is a time step into the future.\n \"\"\"\n # create a bool matrix of where these trend shifts should go\n bool_slope_change = np.random.uniform(size=(n_samples, future_length)) < likelihood\n shift_values = np.random.laplace(0, mean_delta, size=bool_slope_change.shape)\n mat = shift_values * bool_slope_change\n n_mat = np.hstack([np.zeros((len(mat), 1)), mat])[:, :-1]\n mat = (n_mat + mat) / 2\n return mat\n\n @staticmethod\n def _make_historical_mat_time(\n deltas: npt.ArrayLike,\n changepoints_t: np.ndarray,\n t_time: npt.ArrayLike,\n n_row: int = 1,\n single_diff: float | None = None,\n ) -> tuple[\n np.ndarray[tuple[int], np.dtype[np.float64]],\n np.ndarray[tuple[int], np.dtype[np.float64]],\n ]:\n \"\"\"\n Creates a matrix of slope-deltas where these changes occured in training data according to the trained prophet obj\n \"\"\"\n if single_diff is None:\n single_diff = np.diff(t_time).mean()\n prev_time = np.arange(0, 1 + single_diff, single_diff)\n idxs = []\n for changepoint in changepoints_t:\n idxs.append(np.where(prev_time > changepoint)[0][0])\n prev_deltas = np.zeros(len(prev_time))\n prev_deltas[idxs] = deltas\n prev_deltas = np.repeat(prev_deltas.reshape(1, -1), n_row, axis=0)\n return prev_deltas, prev_time\n\n def _logistic_uncertainty(\n self,\n mat: np.ndarray,\n deltas: np.ndarray,\n k: float,\n m: float,\n cap: np.ndarray,\n t_time: np.ndarray,\n n_length: int,\n single_diff: float | None = None,\n ) -> np.ndarray:\n \"\"\"\n Vectorizes prophet's logistic uncertainty by creating a matrix of future possible trends.\n\n Parameters\n ----------\n mat: A trend shift matrix returned by _make_trend_shift_matrix()\n deltas: The size of the trend changes at each changepoint, estimated by the model\n k: Float initial rate.\n m: Float initial offset.\n cap: np.array of capacities at each t.\n t_time: The values of t in the model context (i.e. scaled so that anything > 1 represents the future)\n n_length: For each path, the number of future steps to simulate\n single_diff: The difference between each t step in the model context. Default None, inferred\n from t_time.\n\n Returns\n -------\n A numpy array with shape (n_samples, n_length), representing the width of the uncertainty interval\n (standardized, not on the same scale as the actual data values) around 0.\n \"\"\"\n\n def ffill(arr):\n mask = arr == 0\n idx = np.where(~mask, np.arange(mask.shape[1]), 0)\n np.maximum.accumulate(idx, axis=1, out=idx)\n return arr[np.arange(idx.shape[0])[:, None], idx]\n\n # for logistic growth we need to evaluate the trend all the way from the start of the train item\n historical_mat, historical_time = self._make_historical_mat_time(\n deltas, cast(np.ndarray, self.changepoints_t), t_time, len(mat), single_diff\n )\n mat = np.concatenate([historical_mat, mat], axis=1)\n full_t_time = np.concatenate([historical_time, t_time])\n\n # apply logistic growth logic on the slope changes\n k_cum = np.concatenate((np.ones((mat.shape[0], 1)) * k, np.where(mat, np.cumsum(mat, axis=1) + k, 0)), axis=1)\n k_cum_b = ffill(k_cum)\n gammas = np.zeros_like(mat)\n for i in range(mat.shape[1]):\n x = full_t_time[i] - m - np.sum(gammas[:, :i], axis=1)\n ks = 1 - k_cum_b[:, i] / k_cum_b[:, i + 1]\n gammas[:, i] = x * ks\n # the data before the -n_length is the historical values, which are not needed, so cut the last n_length\n k_t = (mat.cumsum(axis=1) + k)[:, -n_length:]\n m_t = (gammas.cumsum(axis=1) + m)[:, -n_length:]\n sample_trends = cap / (1 + np.exp(-k_t * (t_time - m_t)))\n # remove the mean because we only need width of the uncertainty centered around 0\n # we will add the width to the main forecast - yhat (which is the mean) - later\n return sample_trends - sample_trends.mean(axis=0)\n\n def predictive_samples(self, df: pd.DataFrame, vectorized: bool = True) -> dict[str, npt.NDArray[np.float64]]:\n \"\"\"Sample from the posterior predictive distribution. Returns samples\n for the main estimate yhat, and for the trend component. The shape of\n each output will be (nforecast x nsamples), where nforecast is the\n number of points being forecasted (the number of rows in the input\n dataframe) and nsamples is the number of posterior samples drawn.\n This is the argument `uncertainty_samples` in the Prophet constructor,\n which defaults to 1000.\n\n Parameters\n ----------\n df: Dataframe with dates for predictions (column ds), and capacity\n (column cap) if logistic growth.\n vectorized: Whether to use a vectorized method to compute possible draws. Suggest using\n True (the default) for much faster runtimes in most cases,\n except when (growth = 'logistic' and mcmc_samples > 0).\n\n Returns\n -------\n Dictionary with keys \"trend\" and \"yhat\" containing\n posterior predictive samples for that component.\n \"\"\"\n df = self.setup_dataframe(df.copy())\n return self.sample_posterior_predictive(df, vectorized)\n\n def percentile(self, a: npt.ArrayLike, *args: Any, **kwargs: Any) -> np.ndarray:\n \"\"\"\n We rely on np.nanpercentile in the rare instances where there\n are a small number of bad samples with MCMC that contain NaNs.\n However, since np.nanpercentile is far slower than np.percentile,\n we only fall back to it if the array contains NaNs. See\n https://github.com/facebook/prophet/issues/1310 for more details.\n \"\"\"\n fn = np.nanpercentile if np.isnan(a).any() else np.percentile\n return fn(a, *args, **kwargs)\n\n def make_future_dataframe(\n self, periods: int, freq: str | None = 'D', include_history: bool = True\n ) -> pd.DataFrame:\n \"\"\"Simulate the trend using the extrapolated generative model.\n\n Parameters\n ----------\n periods: Int number of periods to forecast forward.\n freq: Any valid frequency for pd.date_range, such as 'D' or 'M'.\n include_history: Boolean to include the historical dates in the data\n frame for predictions.\n\n Returns\n -------\n pd.Dataframe that extends forward from the end of self.history for the\n requested number of periods.\n \"\"\"\n if self.history_dates is None:\n raise Exception('Model has not been fit.')\n if freq is None:\n # taking the tail makes freq inference more reliable\n freq = pd.infer_freq(self.history_dates.tail(5))\n # returns None if inference failed\n if freq is None:\n raise Exception('Unable to infer `freq`')\n last_date = self.history_dates.max()\n dates = pd.date_range(\n start=last_date,\n periods=periods + 1, # An extra in case we include start\n freq=freq)\n dates = dates[dates > last_date] # Drop start if equals last_date\n dates = dates[:periods] # Return correct number of periods\n\n if include_history:\n dates = np.concatenate((np.array(self.history_dates), dates))\n\n return pd.DataFrame({'ds': dates})\n\n def plot(\n self,\n fcst: pd.DataFrame,\n ax: plt.Axes | None = None,\n uncertainty: bool = True,\n plot_cap: bool = True,\n xlabel: str = 'ds',\n ylabel: str = 'y',\n figsize: tuple[int, int] = (10, 6),\n include_legend: bool = False,\n ) -> plt.Figure:\n \"\"\"Plot the Prophet forecast.\n\n Parameters\n ----------\n fcst: pd.DataFrame output of self.predict.\n ax: Optional matplotlib axes on which to plot.\n uncertainty: Optional boolean to plot uncertainty intervals.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n xlabel: Optional label name on X-axis\n ylabel: Optional label name on Y-axis\n figsize: Optional tuple width, height in inches.\n include_legend: Optional boolean to add legend to the plot.\n\n Returns\n -------\n A matplotlib figure.\n \"\"\"\n return plot(\n m=self, fcst=fcst, ax=ax, uncertainty=uncertainty,\n plot_cap=plot_cap, xlabel=xlabel, ylabel=ylabel,\n figsize=figsize, include_legend=include_legend\n )\n\n def plot_components(\n self,\n fcst: pd.DataFrame,\n uncertainty: bool = True,\n plot_cap: bool = True,\n weekly_start: int = 0,\n yearly_start: int = 0,\n figsize: tuple[int, int] | None = None,\n ) -> plt.Figure:\n \"\"\"Plot the Prophet forecast components.\n\n Will plot whichever are available of: trend, holidays, weekly\n seasonality, and yearly seasonality.\n\n Parameters\n ----------\n fcst: pd.DataFrame output of self.predict.\n uncertainty: Optional boolean to plot uncertainty intervals.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n weekly_start: Optional int specifying the start day of the weekly\n seasonality plot. 0 (default) starts the week on Sunday. 1 shifts\n by 1 day to Monday, and so on.\n yearly_start: Optional int specifying the start day of the yearly\n seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts\n by 1 day to Jan 2, and so on.\n figsize: Optional tuple width, height in inches.\n\n Returns\n -------\n A matplotlib figure.\n \"\"\"\n return plot_components(\n m=self, fcst=fcst, uncertainty=uncertainty, plot_cap=plot_cap,\n weekly_start=weekly_start, yearly_start=yearly_start,\n figsize=figsize\n )\n " }, { "path": "python/prophet/make_holidays.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nimport numpy as np\nimport pandas as pd\n\nimport holidays\n\n\ndef get_country_holidays_class(country: str) -> type[holidays.HolidayBase]:\n \"\"\"Get class for a supported country.\n\n Parameters\n ----------\n country: country code\n\n Returns\n -------\n A valid country holidays class\n \"\"\"\n substitutions = {\n \"TU\": \"TR\", # For compatibility with Turkey as 'TU' cases.\n }\n\n country = substitutions.get(country, country)\n if not hasattr(holidays, country):\n raise AttributeError(f\"Holidays in {country} are not currently supported!\")\n\n return getattr(holidays, country)\n\n\ndef get_holiday_names(country: str) -> set[str]:\n \"\"\"Return all possible holiday names of given country\n\n Parameters\n ----------\n country: country name\n\n Returns\n -------\n A set of all possible holiday names of given country\n \"\"\"\n country_holidays = get_country_holidays_class(country)\n return set(country_holidays(language=\"en_US\", years=np.arange(1995, 2045)).values())\n\n\ndef make_holidays_df(\n year_list: list[int],\n country: str,\n province: str | None = None,\n state: str | None = None, # unused\n) -> pd.DataFrame:\n \"\"\"Make dataframe of holidays for given years and countries\n\n Parameters\n ----------\n year_list: a list of years\n country: country name\n province: province name\n\n Returns\n -------\n Dataframe with 'ds' and 'holiday', which can directly feed\n to 'holidays' params in Prophet\n \"\"\"\n country_holidays = get_country_holidays_class(country)\n holidays = country_holidays(expand=False, language=\"en_US\", subdiv=province, years=year_list)\n\n holidays_df = pd.DataFrame(\n [(date, holidays.get_list(date)) for date in holidays],\n columns=[\"ds\", \"holiday\"],\n )\n holidays_df = holidays_df.explode(\"holiday\")\n holidays_df.reset_index(inplace=True, drop=True)\n holidays_df[\"ds\"] = pd.to_datetime(holidays_df[\"ds\"])\n\n return holidays_df\n" }, { "path": "python/prophet/models.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nfrom abc import abstractmethod, ABC\nfrom dataclasses import dataclass\nfrom collections import OrderedDict\nfrom enum import Enum\nimport importlib.resources\nimport pathlib\nimport platform\nfrom typing import TYPE_CHECKING, Any, Sequence, cast\n\nimport numpy as np\nimport pandas as pd\n\nimport logging\nlogger: logging.Logger = logging.getLogger('prophet.models')\n\nif TYPE_CHECKING:\n from cmdstanpy import CmdStanMCMC, CmdStanMLE, CmdStanModel\n from typing_extensions import TypedDict, Unpack, type_check_only, final\n\n @type_check_only\n @final\n class _IStanBackendOptions(TypedDict, total=False):\n newton_fallback: bool\n\nPLATFORM = \"win\" if platform.platform().startswith(\"Win\") else \"unix\"\n\nclass TrendIndicator(Enum):\n LINEAR = 0\n LOGISTIC = 1\n FLAT = 2\n\n@dataclass\nclass ModelInputData:\n T: int\n S: int\n K: int\n tau: float\n trend_indicator: int\n y: Sequence[float] | pd.Series # length T\n t: Sequence[float] | pd.Series # length T\n cap: Sequence[float] | np.ndarray | pd.Series # length T\n t_change: Sequence[float] | np.ndarray # length S\n s_a: Sequence[int] | pd.Series # length K\n s_m: Sequence[int] | pd.Series # length K\n X: Sequence[Sequence[float]] | pd.DataFrame # shape (T, K)\n sigmas: Sequence[float] # length K\n\n@dataclass\nclass ModelParams:\n k: float\n m: float\n delta: Sequence[float] | np.ndarray # length S\n beta: Sequence[float] | np.ndarray # length K\n sigma_obs: float\n\n\nclass IStanBackend(ABC):\n model: Any\n stan_fit: Any | None\n newton_fallback: bool\n \n def __init__(self) -> None:\n self.model = self.load_model()\n self.stan_fit = None\n self.newton_fallback = True\n\n def set_options(self, **kwargs: Unpack[_IStanBackendOptions]) -> None:\n \"\"\"\n Specify model options as kwargs.\n * newton_fallback [bool]: whether to fallback to Newton if L-BFGS fails\n \"\"\"\n for k, v in kwargs.items():\n if k == 'newton_fallback':\n self.newton_fallback = cast(bool, v)\n else:\n raise ValueError(f'Unknown option {k}')\n \n def cleanup(self) -> None:\n \"\"\"Clean up temporary files created during model fitting.\"\"\"\n pass\n\n\n @staticmethod\n @abstractmethod\n def get_type() -> str:\n pass\n\n @abstractmethod\n def load_model(self) -> Any:\n pass\n\n @abstractmethod\n def fit(\n self,\n stan_init: dict[str, float],\n stan_data: dict[str, Any],\n **kwargs: Any,\n ) -> dict[str, Any]:\n pass\n\n @abstractmethod\n def sampling(\n self,\n stan_init: dict[str, float],\n stan_data: dict[str, Any],\n samples: int,\n **kwargs: Any,\n ) -> dict[str, Any]:\n pass\n\n\nclass CmdStanPyBackend(IStanBackend):\n CMDSTAN_VERSION = \"2.37.0\"\n\n model: CmdStanModel\n stan_fit: CmdStanMLE | CmdStanMCMC | None\n \n def __init__(self) -> None:\n import cmdstanpy\n # this must be set before super.__init__() for load_model to work on Windows\n local_cmdstan = cast(\n pathlib.Path,\n importlib.resources.files(\"prophet\") / \"stan_model\" / f\"cmdstan-{self.CMDSTAN_VERSION}\",\n )\n if local_cmdstan.exists():\n cmdstanpy.set_cmdstan_path(str(local_cmdstan))\n super().__init__()\n\n @staticmethod\n def get_type() -> str:\n return StanBackendEnum.CMDSTANPY.name\n\n def load_model(self) -> CmdStanModel:\n import cmdstanpy\n model_file = importlib.resources.files(\"prophet\") / \"stan_model\" / \"prophet_model.bin\"\n return cmdstanpy.CmdStanModel(exe_file=str(model_file))\n\n def fit(\n self,\n stan_init: dict[str, float], \n stan_data: dict[str, Any],\n **kwargs: Any,\n ) -> OrderedDict[str, np.ndarray]:\n if 'inits' not in kwargs and 'init' in kwargs:\n stan_init = self.sanitize_custom_inits(stan_init, kwargs['init'])\n del kwargs['init']\n\n inits_list, data_list = self.prepare_data(stan_init, stan_data)\n args = dict(\n data=data_list,\n inits=inits_list,\n algorithm='Newton' if cast(float, data_list['T']) < 100 else 'LBFGS',\n iter=int(1e4),\n )\n args.update(kwargs)\n\n try:\n self.stan_fit = self.model.optimize(**args)\n except RuntimeError as e:\n # Fall back on Newton\n if not self.newton_fallback or args['algorithm'] == 'Newton':\n raise e\n logger.warning('Optimization terminated abnormally. Falling back to Newton.')\n args['algorithm'] = 'Newton'\n self.stan_fit = self.model.optimize(**args)\n params = self.stan_to_dict_numpy(\n self.stan_fit.column_names, self.stan_fit.optimized_params_np)\n for par in params:\n params[par] = params[par].reshape((1, -1))\n return params\n\n def sampling(\n self,\n stan_init: dict[str, float],\n stan_data: dict[str, Any],\n samples: int,\n **kwargs: Any,\n ) -> OrderedDict[str, np.ndarray]:\n if 'inits' not in kwargs and 'init' in kwargs:\n stan_init = self.sanitize_custom_inits(stan_init, kwargs['init'])\n del kwargs['init']\n\n inits_list, data_list = self.prepare_data(stan_init, stan_data)\n args = dict(\n data=data_list,\n inits=inits_list,\n )\n if 'chains' not in kwargs:\n kwargs['chains'] = 4\n iter_half = samples // 2\n kwargs['iter_sampling'] = iter_half\n if 'iter_warmup' not in kwargs:\n kwargs['iter_warmup'] = iter_half\n args.update(kwargs)\n\n self.stan_fit = self.model.sample(**args)\n res = self.stan_fit.draws()\n (samples, c, columns) = res.shape\n res = res.reshape((samples * c, columns))\n params = self.stan_to_dict_numpy(self.stan_fit.column_names, res)\n\n for par in params:\n s = params[par].shape\n if s[1] == 1:\n params[par] = params[par].reshape((s[0],))\n\n if par in ['delta', 'beta'] and len(s) < 2:\n params[par] = params[par].reshape((-1, 1))\n\n return params\n\n def cleanup(self) -> None:\n import cmdstanpy\n\n if hasattr(self, \"stan_fit\") and self.stan_fit is not None:\n fit_result: cmdstanpy.CmdStanMLE | cmdstanpy.CmdStanMCMC = self.stan_fit\n to_remove = (\n fit_result.runset.csv_files +\n fit_result.runset.diagnostic_files +\n fit_result.runset.stdout_files +\n fit_result.runset.profile_files\n )\n for fpath in to_remove:\n if pathlib.Path(fpath).is_file():\n pathlib.Path(fpath).unlink()\n\n @staticmethod\n def sanitize_custom_inits(\n default_inits: dict[str, Any],\n custom_inits: dict[str, Any],\n ) -> dict[str, float | Any]:\n \"\"\"Validate that custom inits have the correct type and shape, otherwise use defaults.\"\"\"\n sanitized = {}\n for param in ['k', 'm', 'sigma_obs']:\n try:\n sanitized[param] = float(custom_inits[param])\n except Exception:\n sanitized[param] = default_inits[param]\n for param in ['delta', 'beta']:\n if default_inits[param].shape == custom_inits[param].shape:\n sanitized[param] = custom_inits[param]\n else:\n sanitized[param] = default_inits[param]\n return sanitized\n\n @staticmethod\n def prepare_data(\n init: dict[str, Any],\n data: dict[str, Any],\n ) -> tuple[dict[str, float | list[float]], dict[str, float | list[float]]]:\n \"\"\"Converts np.ndarrays to lists that can be read by cmdstanpy.\"\"\"\n cmdstanpy_data = {\n 'T': data['T'],\n 'S': data['S'],\n 'K': data['K'],\n 'tau': data['tau'],\n 'trend_indicator': data['trend_indicator'],\n 'y': data['y'].tolist(),\n 't': data['t'].tolist(),\n 'cap': data['cap'].tolist(),\n 't_change': data['t_change'].tolist(),\n 's_a': data['s_a'].tolist(),\n 's_m': data['s_m'].tolist(),\n 'X': data['X'].to_numpy().tolist(),\n 'sigmas': data['sigmas']\n }\n\n cmdstanpy_init = {\n 'k': init['k'],\n 'm': init['m'],\n 'delta': init['delta'].tolist(),\n 'beta': init['beta'].tolist(),\n 'sigma_obs': init['sigma_obs']\n }\n return (cmdstanpy_init, cmdstanpy_data)\n\n @staticmethod\n def stan_to_dict_numpy(\n column_names: Sequence[str],\n data: np.ndarray,\n ) -> OrderedDict[str, np.ndarray]:\n output: OrderedDict[str, np.ndarray] = OrderedDict()\n\n prev: str | None = None\n\n start = 0\n end = 0\n two_dims = len(data.shape) > 1\n for cname in column_names:\n parsed = cname.split(\".\") if \".\" in cname else cname.split(\"[\")\n curr = parsed[0]\n if prev is None:\n prev = curr\n\n if curr != prev:\n if prev in output:\n raise RuntimeError(\n \"Found repeated column name\"\n )\n if two_dims:\n output[prev] = np.array(data[:, start:end])\n else:\n output[prev] = np.array(data[start:end])\n prev = curr\n start = end\n end += 1\n if prev in output:\n raise RuntimeError(\n \"Found repeated column name\"\n )\n assert prev is not None\n if two_dims:\n output[prev] = np.array(data[:, start:end])\n else:\n output[prev] = np.array(data[start:end])\n return output\n\n\n\n\nclass StanBackendEnum(Enum):\n CMDSTANPY = CmdStanPyBackend\n\n @staticmethod\n def get_backend_class(name: str) -> type[IStanBackend]:\n try:\n return StanBackendEnum[name].value\n except KeyError as e:\n raise ValueError(f\"Unknown stan backend: {name}\") from e\n" }, { "path": "python/prophet/plot.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import TYPE_CHECKING, cast\n\nimport numpy as np\nimport pandas as pd\n\n# TODO: separate performance_metrics into a different module. there is an implicit circular import between forecaster.py and diagnostics.py\nfrom prophet.diagnostics import performance_metrics\n\nif TYPE_CHECKING:\n from typing import Literal, Sequence, TypeVar, type_check_only\n from typing_extensions import TypedDict\n \n from prophet.forecaster import Prophet\n \n import matplotlib.pyplot as plt\n import plotly.graph_objs as go\n\n _AxT = TypeVar('_AxT', bound=plt.Axes)\n\n @type_check_only\n class _PlotlyProps(TypedDict):\n traces: list[go.Scatter]\n xaxis: go.layout.XAxis\n yaxis: go.layout.YAxis\n\n\nlogger: logging.Logger = logging.getLogger('prophet.plot')\n\n\ntry:\n from matplotlib import pyplot as plt\n from matplotlib.dates import (\n MonthLocator,\n num2date,\n AutoDateLocator,\n AutoDateFormatter,\n )\n from matplotlib.ticker import FuncFormatter\n\n from pandas.plotting import deregister_matplotlib_converters\n deregister_matplotlib_converters()\nexcept ImportError:\n logger.error('Importing matplotlib failed. Plotting will not work.')\n\ntry:\n import plotly.graph_objs as go\n from plotly.subplots import make_subplots\nexcept ImportError:\n logger.error('Importing plotly failed. Interactive plots will not work.')\n\n\ndef plot(\n m: Prophet,\n fcst: pd.DataFrame,\n ax: plt.Axes | None = None,\n uncertainty: bool = True,\n plot_cap: bool = True,\n xlabel: str = \"ds\",\n ylabel: str = \"y\",\n figsize: tuple[int, int] = (10, 6),\n include_legend: bool = False,\n) -> plt.Figure:\n \"\"\"Plot the Prophet forecast.\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n ax: Optional matplotlib axes on which to plot.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n xlabel: Optional label name on X-axis\n ylabel: Optional label name on Y-axis\n figsize: Optional tuple width, height in inches.\n include_legend: Optional boolean to add legend to the plot.\n\n Returns\n -------\n A matplotlib figure.\n \"\"\"\n user_provided_ax = False if ax is None else True\n if ax is None:\n fig = plt.figure(facecolor='w', figsize=figsize)\n ax = fig.add_subplot(111)\n else:\n fig = cast('plt.Figure', ax.get_figure())\n fcst_t = fcst['ds']\n history = cast('pd.DataFrame', m.history)\n ax.plot(history['ds'], history['y'], 'k.', label='Observed data points')\n ax.plot(fcst_t, fcst['yhat'], ls='-', c='#0072B2', label='Forecast')\n if 'cap' in fcst and plot_cap:\n ax.plot(fcst_t, fcst['cap'], ls='--', c='k', label='Maximum capacity')\n if m.logistic_floor and 'floor' in fcst and plot_cap:\n ax.plot(fcst_t, fcst['floor'], ls='--', c='k', label='Minimum capacity')\n if uncertainty and m.uncertainty_samples:\n ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'],\n color='#0072B2', alpha=0.2, label='Uncertainty interval')\n # Specify formatting to workaround matplotlib issue #12925\n locator = AutoDateLocator(interval_multiples=False)\n formatter = AutoDateFormatter(locator)\n ax.xaxis.set_major_locator(locator)\n ax.xaxis.set_major_formatter(formatter)\n ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n if include_legend:\n ax.legend()\n if not user_provided_ax:\n fig.tight_layout()\n return fig\n\n\ndef plot_components(\n m: Prophet,\n fcst: pd.DataFrame,\n uncertainty: bool = True,\n plot_cap: bool = True,\n weekly_start: int = 0,\n yearly_start: int = 0,\n figsize: tuple[int, int] | None = None,\n) -> plt.Figure:\n \"\"\"Plot the Prophet forecast components.\n\n Will plot whichever are available of: trend, holidays, weekly\n seasonality, yearly seasonality, and additive and multiplicative extra\n regressors.\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n weekly_start: Optional int specifying the start day of the weekly\n seasonality plot. 0 (default) starts the week on Sunday. 1 shifts\n by 1 day to Monday, and so on.\n yearly_start: Optional int specifying the start day of the yearly\n seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts\n by 1 day to Jan 2, and so on.\n figsize: Optional tuple width, height in inches.\n\n Returns\n -------\n A matplotlib figure.\n \"\"\"\n # Identify components to be plotted\n components = ['trend']\n if m.train_holiday_names is not None and 'holidays' in fcst:\n components.append('holidays')\n # Plot weekly seasonality, if present\n if 'weekly' in m.seasonalities and 'weekly' in fcst:\n components.append('weekly')\n # Yearly if present\n if 'yearly' in m.seasonalities and 'yearly' in fcst:\n components.append('yearly')\n # Other seasonalities\n components.extend([\n name for name in sorted(m.seasonalities)\n if name in fcst and name not in ['weekly', 'yearly']\n ])\n regressors = {'additive': False, 'multiplicative': False}\n for name, props in m.extra_regressors.items():\n regressors[props['mode']] = True\n for mode in ['additive', 'multiplicative']:\n if regressors[mode] and 'extra_regressors_{}'.format(mode) in fcst:\n components.append('extra_regressors_{}'.format(mode))\n npanel = len(components)\n\n figsize = figsize if figsize else (9, 3 * npanel)\n fig, axes = plt.subplots(npanel, 1, facecolor='w', figsize=figsize)\n\n if npanel == 1:\n axes = [axes]\n\n multiplicative_axes = []\n\n dt = cast('pd.DataFrame', m.history)['ds'].diff()\n min_dt = dt.iloc[cast('np.ndarray', dt.values).nonzero()[0]].min() \n\n for ax, plot_name in zip(axes, components):\n if plot_name == 'trend':\n plot_forecast_component(\n m=m, fcst=fcst, name='trend', ax=ax, uncertainty=uncertainty,\n plot_cap=plot_cap,\n )\n elif plot_name in m.seasonalities:\n if (\n (plot_name == 'weekly' or m.seasonalities[plot_name]['period'] == 7)\n and (min_dt == pd.Timedelta(days=1))\n ):\n plot_weekly(\n m=m, name=plot_name, ax=ax, uncertainty=uncertainty, weekly_start=weekly_start\n )\n elif plot_name == 'yearly' or m.seasonalities[plot_name]['period'] == 365.25:\n plot_yearly(\n m=m, name=plot_name, ax=ax, uncertainty=uncertainty, yearly_start=yearly_start\n )\n else:\n plot_seasonality(\n m=m, name=plot_name, ax=ax, uncertainty=uncertainty,\n )\n elif plot_name in [\n 'holidays',\n 'extra_regressors_additive',\n 'extra_regressors_multiplicative',\n ]:\n plot_forecast_component(\n m=m, fcst=fcst, name=plot_name, ax=ax, uncertainty=uncertainty,\n plot_cap=False,\n )\n assert m.component_modes is not None\n if plot_name in m.component_modes['multiplicative']:\n multiplicative_axes.append(ax)\n\n fig.tight_layout()\n # Reset multiplicative axes labels after tight_layout adjustment\n for ax in multiplicative_axes:\n ax = set_y_as_percent(ax)\n return fig\n\n\ndef plot_forecast_component(\n m: Prophet,\n fcst: pd.DataFrame,\n name: str,\n ax: plt.Axes | None = None,\n uncertainty: bool = True,\n plot_cap: bool = False,\n figsize: tuple[int, int] = (10, 6),\n) -> Sequence[plt.Artist]:\n \"\"\"Plot a particular component of the forecast.\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n name: Name of the component to plot.\n ax: Optional matplotlib Axes to plot on.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n figsize: Optional tuple width, height in inches.\n\n Returns\n -------\n a list of matplotlib artists\n \"\"\"\n artists = []\n if not ax:\n fig = plt.figure(facecolor='w', figsize=figsize)\n ax = fig.add_subplot(111)\n fcst_t = fcst['ds']\n artists += ax.plot(fcst_t, fcst[name], ls='-', c='#0072B2')\n if 'cap' in fcst and plot_cap:\n artists += ax.plot(fcst_t, fcst['cap'], ls='--', c='k')\n if m.logistic_floor and 'floor' in fcst and plot_cap:\n ax.plot(fcst_t, fcst['floor'], ls='--', c='k')\n if uncertainty and m.uncertainty_samples:\n artists += [ax.fill_between(\n fcst_t, fcst[name + '_lower'], fcst[name + '_upper'],\n color='#0072B2', alpha=0.2)]\n # Specify formatting to workaround matplotlib issue #12925\n locator = AutoDateLocator(interval_multiples=False)\n formatter = AutoDateFormatter(locator)\n ax.xaxis.set_major_locator(locator)\n ax.xaxis.set_major_formatter(formatter)\n ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)\n ax.set_xlabel('ds')\n ax.set_ylabel(name)\n assert m.component_modes\n if name in m.component_modes['multiplicative']:\n ax = set_y_as_percent(ax)\n return artists\n\n\ndef seasonality_plot_df(\n m: Prophet,\n ds: Sequence[pd.Timestamp] | pd.DatetimeIndex,\n) -> pd.DataFrame:\n \"\"\"Prepare dataframe for plotting seasonal components.\n\n Parameters\n ----------\n m: Prophet model.\n ds: List of dates for column ds.\n\n Returns\n -------\n A dataframe with seasonal components on ds.\n \"\"\"\n df_dict = {'ds': ds, 'cap': 1., 'floor': 0.}\n for name in m.extra_regressors:\n df_dict[name] = 0.\n # Activate all conditional seasonality columns\n for props in m.seasonalities.values():\n if props['condition_name'] is not None:\n df_dict[props['condition_name']] = True\n df = pd.DataFrame(df_dict)\n df = m.setup_dataframe(df)\n return df\n\n\ndef plot_weekly(\n m: Prophet,\n ax: plt.Axes | None = None,\n uncertainty: bool = True,\n weekly_start: int = 0,\n figsize: tuple[int, int] = (10, 6),\n name: str = 'weekly',\n) -> Sequence[plt.Artist]:\n \"\"\"Plot the weekly component of the forecast.\n\n Parameters\n ----------\n m: Prophet model.\n ax: Optional matplotlib Axes to plot on. One will be created if this\n is not provided.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n weekly_start: Optional int specifying the start day of the weekly\n seasonality plot. 0 (default) starts the week on Sunday. 1 shifts\n by 1 day to Monday, and so on.\n figsize: Optional tuple width, height in inches.\n name: Name of seasonality component if changed from default 'weekly'.\n\n Returns\n -------\n a list of matplotlib artists\n \"\"\"\n artists = []\n if not ax:\n fig = plt.figure(facecolor='w', figsize=figsize)\n ax = fig.add_subplot(111)\n # Compute weekly seasonality for a Sun-Sat sequence of dates.\n days = (pd.date_range(start='2017-01-01', periods=7) +\n pd.Timedelta(days=weekly_start))\n df_w = seasonality_plot_df(m, days)\n seas = m.predict_seasonal_components(df_w)\n days = days.day_name()\n artists += ax.plot(range(len(days)), seas[name], ls='-',\n c='#0072B2')\n if uncertainty and m.uncertainty_samples:\n artists += [ax.fill_between(range(len(days)),\n seas[name + '_lower'], seas[name + '_upper'],\n color='#0072B2', alpha=0.2)]\n ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)\n ax.set_xticks(range(len(days)))\n ax.set_xticklabels(days)\n ax.set_xlabel('Day of week')\n ax.set_ylabel(name)\n if m.seasonalities[name]['mode'] == 'multiplicative':\n ax = set_y_as_percent(ax)\n return artists\n\n\ndef plot_yearly(\n m: Prophet,\n ax: plt.Axes | None = None,\n uncertainty: bool = True,\n yearly_start: int = 0,\n figsize: tuple[int, int] = (10, 6),\n name: str = 'yearly',\n) -> Sequence[plt.Artist]:\n \"\"\"Plot the yearly component of the forecast.\n\n Parameters\n ----------\n m: Prophet model.\n ax: Optional matplotlib Axes to plot on. One will be created if\n this is not provided.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n yearly_start: Optional int specifying the start day of the yearly\n seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts\n by 1 day to Jan 2, and so on.\n figsize: Optional tuple width, height in inches.\n name: Name of seasonality component if previously changed from default 'yearly'.\n\n Returns\n -------\n a list of matplotlib artists\n \"\"\"\n artists = []\n if not ax:\n fig = plt.figure(facecolor='w', figsize=figsize)\n ax = fig.add_subplot(111)\n # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.\n days = (pd.date_range(start='2017-01-01', periods=365) +\n pd.Timedelta(days=yearly_start))\n df_y = seasonality_plot_df(m, days)\n seas = m.predict_seasonal_components(df_y)\n artists += ax.plot(\n df_y['ds'], seas[name], ls='-', c='#0072B2')\n if uncertainty and m.uncertainty_samples:\n artists += [ax.fill_between(\n df_y['ds'], seas[name + '_lower'],\n seas[name + '_upper'], color='#0072B2', alpha=0.2)]\n ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)\n months = MonthLocator(range(1, 13), bymonthday=1, interval=2)\n ax.xaxis.set_major_formatter(FuncFormatter(\n lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x))))\n ax.xaxis.set_major_locator(months)\n ax.set_xlabel('Day of year')\n ax.set_ylabel(name)\n if m.seasonalities[name]['mode'] == 'multiplicative':\n ax = set_y_as_percent(ax)\n return artists\n\n\ndef plot_seasonality(\n m: Prophet,\n name: str,\n ax: plt.Axes | None = None,\n uncertainty: bool = True,\n figsize: tuple[int, int] = (10, 6),\n) -> Sequence[plt.Artist]:\n \"\"\"Plot a custom seasonal component.\n\n Parameters\n ----------\n m: Prophet model.\n name: Seasonality name, like 'daily', 'weekly'.\n ax: Optional matplotlib Axes to plot on. One will be created if\n this is not provided.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n figsize: Optional tuple width, height in inches.\n\n Returns\n -------\n a list of matplotlib artists\n \"\"\"\n artists = []\n if not ax:\n fig = plt.figure(facecolor='w', figsize=figsize)\n ax = fig.add_subplot(111)\n # Compute seasonality from Jan 1 through a single period.\n start = pd.to_datetime('2017-01-01 0000')\n period = m.seasonalities[name]['period']\n end = start + pd.Timedelta(days=period)\n plot_points = 200\n # https://github.com/pandas-dev/pandas-stubs/issues/1645\n days = pd.to_datetime(np.linspace(start.value, end.value, plot_points)) # pyrefly:ignore[no-matching-overload]\n df_y = seasonality_plot_df(m, days)\n seas = m.predict_seasonal_components(df_y)\n artists += ax.plot(df_y['ds'], seas[name], ls='-',\n c='#0072B2')\n if uncertainty and m.uncertainty_samples:\n artists += [ax.fill_between(\n df_y['ds'], seas[name + '_lower'],\n seas[name + '_upper'], color='#0072B2', alpha=0.2)]\n ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)\n n_ticks = 8\n # https://github.com/pandas-dev/pandas-stubs/issues/1645\n xticks = pd.to_datetime(np.linspace(start.value, end.value, n_ticks) # pyrefly:ignore[no-matching-overload]\n ).to_pydatetime()\n ax.set_xticks(xticks)\n if name == 'yearly':\n fmt = FuncFormatter(\n lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x)))\n ax.set_xlabel('Day of year')\n elif name == 'weekly':\n fmt = FuncFormatter(\n lambda x, pos=None: '{dt:%A}'.format(dt=num2date(x)))\n ax.set_xlabel('Day of Week')\n elif name == 'daily':\n fmt = FuncFormatter(\n lambda x, pos=None: '{dt:%T}'.format(dt=num2date(x)))\n ax.set_xlabel('Hour of day')\n elif period <= 2:\n fmt = FuncFormatter(\n lambda x, pos=None: '{dt:%T}'.format(dt=num2date(x)))\n ax.set_xlabel('Hours')\n else:\n fmt = FuncFormatter(\n lambda x, pos=None: '{:.0f}'.format(pos * period / (n_ticks - 1)))\n ax.set_xlabel('Days')\n ax.xaxis.set_major_formatter(fmt)\n ax.set_ylabel(name)\n if m.seasonalities[name]['mode'] == 'multiplicative':\n ax = set_y_as_percent(ax)\n return artists\n\n\ndef set_y_as_percent(ax: _AxT) -> _AxT:\n yticks = 100 * ax.get_yticks()\n yticklabels = ['{0:.4g}%'.format(y) for y in yticks]\n ax.set_yticks(ax.get_yticks().tolist())\n ax.set_yticklabels(yticklabels)\n return ax\n\n\ndef add_changepoints_to_plot(\n ax: plt.Axes,\n m: Prophet,\n fcst: pd.DataFrame,\n threshold: float = 0.01,\n cp_color: str = 'r',\n cp_linestyle: str = '--',\n trend: bool = True,\n) -> list[plt.Line2D]:\n \"\"\"Add markers for significant changepoints to prophet forecast plot.\n\n Example:\n fig = m.plot(forecast)\n add_changepoints_to_plot(fig.gca(), m, forecast)\n\n Parameters\n ----------\n ax: axis on which to overlay changepoint markers.\n m: Prophet model.\n fcst: Forecast output from m.predict.\n threshold: Threshold on trend change magnitude for significance.\n cp_color: Color of changepoint markers.\n cp_linestyle: Linestyle for changepoint markers.\n trend: If True, will also overlay the trend.\n\n Returns\n -------\n a list of matplotlib artists\n \"\"\"\n artists = []\n if trend:\n artists.extend(ax.plot(fcst['ds'], fcst['trend'], c=cp_color))\n \n assert m.changepoints is not None\n signif_changepoints = m.changepoints[\n np.abs(np.nanmean(m.params['delta'], axis=0)) >= threshold\n ] if len(m.changepoints) > 0 else []\n for cp in signif_changepoints:\n artists.append(ax.axvline(x=cp, c=cp_color, ls=cp_linestyle))\n return artists\n\n\ndef plot_cross_validation_metric(\n df_cv: pd.DataFrame,\n metric: str,\n rolling_window: float = 0.1,\n ax: plt.Axes | None = None,\n figsize: tuple[int, int] = (10, 6),\n color: str = 'b',\n point_color: str = 'gray',\n) -> plt.Figure:\n \"\"\"Plot a performance metric vs. forecast horizon from cross validation.\n\n Cross validation produces a collection of out-of-sample model predictions\n that can be compared to actual values, at a range of different horizons\n (distance from the cutoff). This computes a specified performance metric\n for each prediction, and aggregated over a rolling window with horizon.\n\n This uses prophet.diagnostics.performance_metrics to compute the metrics.\n Valid values of metric are 'mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', and 'coverage'.\n\n rolling_window is the proportion of data included in the rolling window of\n aggregation. The default value of 0.1 means 10% of data are included in the\n aggregation for computing the metric.\n\n As a concrete example, if metric='mse', then this plot will show the\n squared error for each cross validation prediction, along with the MSE\n averaged over rolling windows of 10% of the data.\n\n Parameters\n ----------\n df_cv: The output from prophet.diagnostics.cross_validation.\n metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'].\n rolling_window: Proportion of data to use for rolling average of metric.\n In [0, 1]. Defaults to 0.1.\n ax: Optional matplotlib axis on which to plot. If not given, a new figure\n will be created.\n figsize: Optional tuple width, height in inches.\n color: Optional color for plot and error points, useful when plotting\n multiple model performances on one axis for comparison.\n\n Returns\n -------\n a matplotlib figure.\n \"\"\"\n if ax is None:\n fig = plt.figure(facecolor='w', figsize=figsize)\n ax = fig.add_subplot(111)\n else:\n fig = cast('plt.Figure', ax.get_figure())\n # Get the metric at the level of individual predictions, and with the rolling window.\n df_none = performance_metrics(df_cv, metrics=[metric], rolling_window=-1)\n df_h = performance_metrics(df_cv, metrics=[metric], rolling_window=rolling_window)\n \n assert df_none is not None\n assert df_h is not None\n\n # Some work because matplotlib does not handle timedelta\n # Target ~10 ticks.\n tick_w = max(df_none['horizon'].astype('timedelta64[ns]')) / 10.\n # Find the largest time resolution that has <1 unit per bin.\n dts: list[Literal[\"D\", \"h\", \"m\", \"s\", \"ms\", \"us\", \"ns\"]]\n dts = ['D', 'h', 'm', 's', 'ms', 'us', 'ns']\n dt_names = [\n 'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds',\n 'nanoseconds'\n ]\n dt_conversions = [\n 24 * 60 * 60 * 10 ** 9,\n 60 * 60 * 10 ** 9,\n 60 * 10 ** 9,\n 10 ** 9,\n 10 ** 6,\n 10 ** 3,\n 1.,\n ]\n for i, dt in enumerate(dts):\n if np.timedelta64(1, dt) < np.timedelta64(tick_w, 'ns'):\n break\n\n x_plt = np.asarray(df_none['horizon'].astype('timedelta64[ns]')).view(np.int64) / float(dt_conversions[i])\n x_plt_h = np.asarray(df_h['horizon'].astype('timedelta64[ns]')).view(np.int64) / float(dt_conversions[i])\n\n ax.plot(x_plt, df_none[metric], '.', alpha=0.1, c=point_color)\n ax.plot(x_plt_h, df_h[metric], '-', c=color)\n ax.grid(True)\n\n ax.set_xlabel('Horizon ({})'.format(dt_names[i]))\n ax.set_ylabel(metric)\n return fig\n\n\ndef plot_plotly(\n m: Prophet,\n fcst: pd.DataFrame,\n uncertainty: bool = True,\n plot_cap: bool = True,\n trend: bool = False,\n changepoints: bool = False,\n changepoints_threshold: float = 0.01,\n xlabel: str = 'ds',\n ylabel: str = 'y',\n figsize: tuple[int, int] = (900, 600)\n) -> go.Figure:\n \"\"\"Plot the Prophet forecast with Plotly offline.\n\n Plotting in Jupyter Notebook requires initializing plotly.offline.init_notebook_mode():\n >>> import plotly.offline as py\n >>> py.init_notebook_mode()\n Then the figure can be displayed using plotly.offline.iplot(...):\n >>> fig = plot_plotly(m, fcst)\n >>> py.iplot(fig)\n see https://plot.ly/python/offline/ for details\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n uncertainty: Optional boolean to plot uncertainty intervals.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n trend: Optional boolean to plot trend\n changepoints: Optional boolean to plot changepoints\n changepoints_threshold: Threshold on trend change magnitude for significance.\n xlabel: Optional label name on X-axis\n ylabel: Optional label name on Y-axis\n figsize: The plot's size (in px).\n\n Returns\n -------\n A Plotly Figure.\n \"\"\"\n prediction_color = '#0072B2'\n error_color = 'rgba(0, 114, 178, 0.2)' # '#0072B2' with 0.2 opacity\n actual_color = 'black'\n cap_color = 'black'\n trend_color = '#B23B00'\n line_width = 2\n marker_size = 4\n\n data = []\n # Add actual\n assert m.history\n data.append(go.Scatter(\n name='Actual',\n x=m.history['ds'],\n y=m.history['y'],\n marker=dict(color=actual_color, size=marker_size),\n mode='markers'\n ))\n # Add lower bound\n if uncertainty and m.uncertainty_samples:\n data.append(go.Scatter(\n x=fcst['ds'],\n y=fcst['yhat_lower'],\n mode='lines',\n line=dict(width=0),\n hoverinfo='skip'\n ))\n # Add prediction\n data.append(go.Scatter(\n name='Predicted',\n x=fcst['ds'],\n y=fcst['yhat'],\n mode='lines',\n line=dict(color=prediction_color, width=line_width),\n fillcolor=error_color,\n fill='tonexty' if uncertainty and m.uncertainty_samples else 'none'\n ))\n # Add upper bound\n if uncertainty and m.uncertainty_samples:\n data.append(go.Scatter(\n x=fcst['ds'],\n y=fcst['yhat_upper'],\n mode='lines',\n line=dict(width=0),\n fillcolor=error_color,\n fill='tonexty',\n hoverinfo='skip'\n ))\n # Add caps\n if 'cap' in fcst and plot_cap:\n data.append(go.Scatter(\n name='Cap',\n x=fcst['ds'],\n y=fcst['cap'],\n mode='lines',\n line=dict(color=cap_color, dash='dash', width=line_width),\n ))\n if m.logistic_floor and 'floor' in fcst and plot_cap:\n data.append(go.Scatter(\n name='Floor',\n x=fcst['ds'],\n y=fcst['floor'],\n mode='lines',\n line=dict(color=cap_color, dash='dash', width=line_width),\n ))\n # Add trend\n if trend:\n data.append(go.Scatter(\n name='Trend',\n x=fcst['ds'],\n y=fcst['trend'],\n mode='lines',\n line=dict(color=trend_color, width=line_width),\n ))\n # Add changepoints\n assert m.changepoints\n if changepoints and len(m.changepoints) > 0:\n signif_changepoints = m.changepoints[\n np.abs(np.nanmean(m.params['delta'], axis=0)) >= changepoints_threshold\n ]\n data.append(go.Scatter(\n x=signif_changepoints,\n y=fcst.loc[fcst['ds'].isin(signif_changepoints), 'trend'],\n marker=dict(size=50, symbol='line-ns-open', color=trend_color,\n line=dict(width=line_width)),\n mode='markers',\n hoverinfo='skip'\n ))\n\n layout = dict(\n showlegend=False,\n width=figsize[0],\n height=figsize[1],\n yaxis=dict(\n title=ylabel\n ),\n xaxis=dict(\n title=xlabel,\n type='date',\n rangeselector=dict(\n buttons=list([\n dict(count=7,\n label='1w',\n step='day',\n stepmode='backward'),\n dict(count=1,\n label='1m',\n step='month',\n stepmode='backward'),\n dict(count=6,\n label='6m',\n step='month',\n stepmode='backward'),\n dict(count=1,\n label='1y',\n step='year',\n stepmode='backward'),\n dict(step='all')\n ])\n ),\n rangeslider=dict(\n visible=True\n ),\n ),\n )\n fig = go.Figure(data=data, layout=layout)\n return fig\n\n\ndef plot_components_plotly(\n m: Prophet,\n fcst: pd.DataFrame,\n uncertainty: bool = True,\n plot_cap: bool = True,\n figsize: tuple[int, int] = (900, 200),\n) -> go.Figure:\n \"\"\"Plot the Prophet forecast components using Plotly.\n See plot_plotly() for Plotly setup instructions\n\n Will plot whichever are available of: trend, holidays, weekly\n seasonality, yearly seasonality, and additive and multiplicative extra\n regressors.\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n figsize: Set the size for the subplots (in px).\n\n Returns\n -------\n A Plotly Figure.\n \"\"\"\n\n # Identify components to plot and get their Plotly props\n components = {}\n components['trend'] = get_forecast_component_plotly_props(\n m, fcst, 'trend', uncertainty, plot_cap)\n if m.train_holiday_names is not None and 'holidays' in fcst:\n components['holidays'] = get_forecast_component_plotly_props(\n m, fcst, 'holidays', uncertainty)\n\n regressors = {'additive': False, 'multiplicative': False}\n for name, props in m.extra_regressors.items():\n regressors[props['mode']] = True\n for mode in ['additive', 'multiplicative']:\n if regressors[mode] and 'extra_regressors_{}'.format(mode) in fcst:\n components['extra_regressors_{}'.format(mode)] = get_forecast_component_plotly_props(\n m, fcst, 'extra_regressors_{}'.format(mode))\n for seasonality in m.seasonalities:\n components[seasonality] = get_seasonality_plotly_props(m, seasonality)\n\n # Create Plotly subplot figure and add the components to it\n fig = make_subplots(rows=len(components), cols=1, print_grid=False)\n fig['layout'].update(go.Layout(\n showlegend=False,\n width=figsize[0],\n height=figsize[1] * len(components)\n ))\n for i, name in enumerate(components):\n if i == 0:\n xaxis = fig['layout']['xaxis']\n yaxis = fig['layout']['yaxis']\n else:\n xaxis = fig['layout']['xaxis{}'.format(i + 1)]\n yaxis = fig['layout']['yaxis{}'.format(i + 1)]\n xaxis.update(components[name]['xaxis'])\n yaxis.update(components[name]['yaxis'])\n for trace in components[name]['traces']:\n fig.append_trace(trace, i + 1, 1)\n return fig\n\n\ndef plot_forecast_component_plotly(\n m: Prophet,\n fcst: pd.DataFrame,\n name: str,\n uncertainty: bool = True,\n plot_cap: bool = False,\n figsize: tuple[int, int] = (900, 300)\n) -> go.Figure:\n \"\"\"Plot a particular component of the forecast using Plotly.\n See plot_plotly() for Plotly setup instructions\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n name: Name of the component to plot.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n figsize: The plot's size (in px).\n\n Returns\n -------\n A Plotly Figure.\n \"\"\"\n props = get_forecast_component_plotly_props(m, fcst, name, uncertainty, plot_cap)\n layout = go.Layout(\n width=figsize[0],\n height=figsize[1],\n showlegend=False,\n xaxis=props['xaxis'],\n yaxis=props['yaxis']\n )\n fig = go.Figure(data=props['traces'], layout=layout)\n return fig\n\n\ndef plot_seasonality_plotly(\n m: Prophet,\n name: str,\n uncertainty: bool = True,\n figsize: tuple[int, int] = (900, 300)\n) -> go.Figure:\n \"\"\"Plot a custom seasonal component using Plotly.\n See plot_plotly() for Plotly setup instructions\n\n Parameters\n ----------\n m: Prophet model.\n name: Seasonality name, like 'daily', 'weekly'.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n figsize: Set the plot's size (in px).\n\n Returns\n -------\n A Plotly Figure.\n \"\"\"\n props = get_seasonality_plotly_props(m, name, uncertainty)\n layout = go.Layout(\n width=figsize[0],\n height=figsize[1],\n showlegend=False,\n xaxis=props['xaxis'],\n yaxis=props['yaxis']\n )\n fig = go.Figure(data=props['traces'], layout=layout)\n return fig\n\n\ndef get_forecast_component_plotly_props(\n m: Prophet,\n fcst: pd.DataFrame,\n name: str,\n uncertainty: bool = True,\n plot_cap: bool = False,\n) -> _PlotlyProps:\n \"\"\"Prepares a dictionary for plotting the selected forecast component with Plotly\n\n Parameters\n ----------\n m: Prophet model.\n fcst: pd.DataFrame output of m.predict.\n name: Name of the component to plot.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n plot_cap: Optional boolean indicating if the capacity should be shown\n in the figure, if available.\n\n Returns\n -------\n A dictionary with Plotly traces, xaxis and yaxis\n \"\"\"\n prediction_color = '#0072B2'\n error_color = 'rgba(0, 114, 178, 0.2)' # '#0072B2' with 0.2 opacity\n cap_color = 'black'\n zeroline_color = '#AAA'\n line_width = 2\n\n range_margin = (fcst['ds'].max() - fcst['ds'].min()) * 0.05\n range_x = [fcst['ds'].min() - range_margin, fcst['ds'].max() + range_margin]\n\n text = None\n mode = 'lines'\n if name == 'holidays':\n \n # Combine holidays into one hover text\n holidays = m.construct_holiday_dataframe(fcst['ds'])\n holiday_features, _, _ = m.make_holiday_features(fcst['ds'], holidays)\n holiday_features.columns = holiday_features.columns.str.replace('_delim_', '', regex=False)\n holiday_features.columns = holiday_features.columns.str.replace('+0', '', regex=False)\n text = pd.Series(data='', index=holiday_features.index)\n for holiday_feature, idxs in holiday_features.items():\n # https://github.com/facebook/pyrefly/issues/2248\n # pyrefly:ignore[unsupported-operation]\n text[idxs.astype(bool) & (text != '')] += '
    ' # Add newline if additional holiday\n text[idxs.astype(bool)] += holiday_feature # pyrefly:ignore[unsupported-operation]\n\n traces = []\n traces.append(go.Scatter(\n name=name,\n x=fcst['ds'],\n y=fcst[name],\n mode=mode,\n line=go.scatter.Line(color=prediction_color, width=line_width),\n text=text,\n ))\n if uncertainty and m.uncertainty_samples and (fcst[name + '_upper'] != fcst[name + '_lower']).any():\n if mode == 'markers':\n traces[0].update(\n error_y=dict(\n type='data',\n symmetric=False,\n array=fcst[name + '_upper'],\n arrayminus=fcst[name + '_lower'],\n width=0,\n color=error_color\n )\n )\n else:\n traces.append(go.Scatter(\n name=name + '_upper',\n x=fcst['ds'],\n y=fcst[name + '_upper'],\n mode=mode,\n line=go.scatter.Line(width=0, color=error_color)\n ))\n traces.append(go.Scatter(\n name=name + '_lower',\n x=fcst['ds'],\n y=fcst[name + '_lower'],\n mode=mode,\n line=go.scatter.Line(width=0, color=error_color),\n fillcolor=error_color,\n fill='tonexty'\n ))\n if 'cap' in fcst and plot_cap:\n traces.append(go.Scatter(\n name='Cap',\n x=fcst['ds'],\n y=fcst['cap'],\n mode='lines',\n line=go.scatter.Line(color=cap_color, dash='dash', width=line_width),\n ))\n if m.logistic_floor and 'floor' in fcst and plot_cap:\n traces.append(go.Scatter(\n name='Floor',\n x=fcst['ds'],\n y=fcst['floor'],\n mode='lines',\n line=go.scatter.Line(color=cap_color, dash='dash', width=line_width),\n ))\n\n xaxis = go.layout.XAxis(\n type='date',\n range=range_x)\n yaxis = go.layout.YAxis(rangemode='normal' if name == 'trend' else 'tozero',\n title=go.layout.yaxis.Title(text=name),\n zerolinecolor=zeroline_color)\n assert m.component_modes\n if name in m.component_modes['multiplicative']:\n yaxis.update(tickformat='%', hoverformat='.2%')\n return {'traces': traces, 'xaxis': xaxis, 'yaxis': yaxis}\n\n\ndef get_seasonality_plotly_props(\n m: Prophet,\n name: str,\n uncertainty: bool = True,\n) -> _PlotlyProps:\n \"\"\"Prepares a dictionary for plotting the selected seasonality with Plotly\n\n Parameters\n ----------\n m: Prophet model.\n name: Name of the component to plot.\n uncertainty: Optional boolean to plot uncertainty intervals, which will\n only be done if m.uncertainty_samples > 0.\n\n Returns\n -------\n A dictionary with Plotly traces, xaxis and yaxis\n \"\"\"\n prediction_color = '#0072B2'\n error_color = 'rgba(0, 114, 178, 0.2)' # '#0072B2' with 0.2 opacity\n line_width = 2\n zeroline_color = '#AAA'\n\n # Compute seasonality from Jan 1 through a single period.\n start = pd.to_datetime('2017-01-01 0000')\n period = m.seasonalities[name]['period']\n end = start + pd.Timedelta(days=period)\n assert m.history is not None\n if (m.history['ds'].dt.hour == 0).all(): # Day Precision\n plot_points = np.floor(period).astype(int)\n elif (m.history['ds'].dt.minute == 0).all(): # Hour Precision\n plot_points = np.floor(period * 24).astype(int)\n else: # Minute Precision\n plot_points = np.floor(period * 24 * 60).astype(int)\n days = pd.to_datetime(np.linspace(start.value, end.value, plot_points, endpoint=False))\n df_y = seasonality_plot_df(m, days)\n seas = m.predict_seasonal_components(df_y)\n\n traces = []\n traces.append(go.Scatter(\n name=name,\n x=df_y['ds'],\n y=seas[name],\n mode='lines',\n line=go.scatter.Line(color=prediction_color, width=line_width)\n ))\n if uncertainty and m.uncertainty_samples and (seas[name + '_upper'] != seas[name + '_lower']).any():\n traces.append(go.Scatter(\n name=name + '_upper',\n x=df_y['ds'],\n y=seas[name + '_upper'],\n mode='lines',\n line=go.scatter.Line(width=0, color=error_color)\n ))\n traces.append(go.Scatter(\n name=name + '_lower',\n x=df_y['ds'],\n y=seas[name + '_lower'],\n mode='lines',\n line=go.scatter.Line(width=0, color=error_color),\n fillcolor=error_color,\n fill='tonexty'\n ))\n\n # Set tick formats (examples are based on 2017-01-06 21:15)\n if period <= 2:\n tickformat = '%H:%M' # \"21:15\"\n elif period < 7:\n tickformat = '%A %H:%M' # \"Friday 21:15\"\n elif period < 14:\n tickformat = '%A' # \"Friday\"\n else:\n tickformat = '%B %e' # \"January 6\"\n\n range_margin = (df_y['ds'].max() - df_y['ds'].min()) * 0.05\n xaxis = go.layout.XAxis(\n tickformat=tickformat,\n type='date',\n range=[df_y['ds'].min() - range_margin, df_y['ds'].max() + range_margin]\n )\n\n yaxis = go.layout.YAxis(title=go.layout.yaxis.Title(text=name),\n zerolinecolor=zeroline_color)\n if m.seasonalities[name]['mode'] == 'multiplicative':\n yaxis.update(tickformat='%', hoverformat='.2%')\n\n return {'traces': traces, 'xaxis': xaxis, 'yaxis': yaxis}\n" }, { "path": "python/prophet/py.typed", "content": "" }, { "path": "python/prophet/serialize.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nfrom collections import OrderedDict\nfrom copy import deepcopy\nfrom io import StringIO\nimport json\nfrom typing import Any, Final\n\nimport numpy as np\nimport pandas as pd\n\nfrom prophet.__version__ import __version__\nfrom prophet.forecaster import Prophet\n\nSIMPLE_ATTRIBUTES: Final[list[str]] = [\n 'growth', 'n_changepoints', 'specified_changepoints', 'changepoint_range',\n 'yearly_seasonality', 'weekly_seasonality', 'daily_seasonality',\n 'seasonality_mode', 'seasonality_prior_scale', 'changepoint_prior_scale',\n 'holidays_prior_scale', 'mcmc_samples', 'interval_width', 'uncertainty_samples',\n 'y_scale', 'y_min', 'scaling', 'logistic_floor', 'country_holidays', 'component_modes',\n 'holidays_mode'\n]\n\nPD_SERIES: Final[list[str]] = [\"changepoints\", \"history_dates\", \"train_holiday_names\"]\n\nPD_TIMESTAMP: Final[list[str]] = [\"start\"]\n\nPD_TIMEDELTA: Final[list[str]] = [\"t_scale\"]\n\nPD_DATAFRAME: Final[list[str]] = [\"holidays\", \"history\", \"train_component_cols\"]\n\nNP_ARRAY: Final[list[str]] = [\"changepoints_t\"]\n\nORDEREDDICT: Final[list[str]] = [\"seasonalities\", \"extra_regressors\"]\n\n\ndef model_to_dict(model: Prophet) -> dict[str, Any]:\n \"\"\"Convert a Prophet model to a dictionary suitable for JSON serialization.\n\n Model must be fitted. Skips Stan objects that are not needed for predict.\n\n Can be reversed with model_from_dict.\n\n Parameters\n ----------\n model: Prophet model object.\n\n Returns\n -------\n dict that can be used to serialize a Prophet model as JSON or loaded back\n into a Prophet model.\n \"\"\"\n if model.history is None:\n raise ValueError(\n \"This can only be used to serialize models that have already been fit.\"\n )\n\n model_dict = {\n attribute: getattr(model, attribute) for attribute in SIMPLE_ATTRIBUTES\n }\n # Handle attributes of non-core types\n for attribute in PD_SERIES:\n if getattr(model, attribute) is None:\n model_dict[attribute] = None\n else:\n model_dict[attribute] = getattr(model, attribute).to_json(\n orient='split', date_format='iso'\n )\n for attribute in PD_TIMESTAMP:\n model_dict[attribute] = getattr(model, attribute).timestamp()\n for attribute in PD_TIMEDELTA:\n model_dict[attribute] = getattr(model, attribute).total_seconds()\n for attribute in PD_DATAFRAME:\n if getattr(model, attribute) is None:\n model_dict[attribute] = None\n else:\n model_dict[attribute] = getattr(model, attribute).to_json(orient='table', index=False)\n for attribute in NP_ARRAY:\n model_dict[attribute] = getattr(model, attribute).tolist()\n for attribute in ORDEREDDICT:\n model_dict[attribute] = [\n list(getattr(model, attribute).keys()),\n getattr(model, attribute),\n ]\n # Other attributes with special handling\n # fit_kwargs -> Transform any numpy types before serializing.\n # They do not need to be transformed back on deserializing.\n fit_kwargs = deepcopy(model.fit_kwargs)\n if 'init' in fit_kwargs:\n for k, v in fit_kwargs['init'].items():\n if isinstance(v, np.ndarray):\n fit_kwargs['init'][k] = v.tolist()\n elif isinstance(v, np.floating):\n fit_kwargs['init'][k] = float(v)\n model_dict['fit_kwargs'] = fit_kwargs\n\n # Params (Dict[str, np.ndarray])\n model_dict['params'] = {k: v.tolist() for k, v in model.params.items()}\n # Attributes that are skipped: stan_fit, stan_backend\n model_dict[\"__prophet_version\"] = __version__\n return model_dict\n\n\ndef model_to_json(model: Prophet) -> str:\n \"\"\"Serialize a Prophet model to json string.\n\n Model must be fitted. Skips Stan objects that are not needed for predict.\n\n Can be deserialized with model_from_json.\n\n Parameters\n ----------\n model: Prophet model object.\n\n Returns\n -------\n json string that can be deserialized into a Prophet model.\n \"\"\"\n model_json = model_to_dict(model)\n return json.dumps(model_json)\n\n\ndef _handle_simple_attributes_backwards_compat(model_dict: dict[str, Any]) -> None:\n \"\"\"Handle backwards compatibility for SIMPLE_ATTRIBUTES.\"\"\"\n # prophet<1.1.5: handle scaling parameters introduced in #2470\n if 'scaling' not in model_dict:\n model_dict['scaling'] = 'absmax'\n model_dict['y_min'] = 0.\n # prophet<1.1.5: handle holidays_mode parameter introduced in #2477\n if 'holidays_mode' not in model_dict:\n model_dict['holidays_mode'] = model_dict['seasonality_mode']\n\ndef model_from_dict(model_dict: dict[str, Any]) -> Prophet:\n \"\"\"Recreate a Prophet model from a dictionary.\n\n Recreates models that were converted with model_to_dict.\n\n Parameters\n ----------\n model_dict: Dictionary containing model, created with model_to_dict.\n\n Returns\n -------\n Prophet model.\n \"\"\"\n model = Prophet() # We will overwrite all attributes set in init anyway\n # Simple types\n _handle_simple_attributes_backwards_compat(model_dict)\n for attribute in SIMPLE_ATTRIBUTES:\n setattr(model, attribute, model_dict[attribute])\n for attribute in PD_SERIES:\n if model_dict[attribute] is None:\n setattr(model, attribute, None)\n else:\n s = pd.read_json(StringIO(model_dict[attribute]), typ='series', orient='split')\n if s.name == 'ds':\n if len(s) == 0:\n s = pd.to_datetime(s)\n s = s.dt.tz_localize(None)\n setattr(model, attribute, s)\n for attribute in PD_TIMESTAMP:\n pd_ts = pd.Timestamp.fromtimestamp(model_dict[attribute], tz=\"UTC\").tz_localize(None)\n setattr(model, attribute, pd_ts)\n for attribute in PD_TIMEDELTA:\n setattr(model, attribute, pd.Timedelta(seconds=model_dict[attribute]))\n for attribute in PD_DATAFRAME:\n if model_dict[attribute] is None:\n setattr(model, attribute, None)\n else:\n df = pd.read_json(StringIO(model_dict[attribute]), typ='frame', orient='table', convert_dates=['ds'])\n if attribute == 'train_component_cols':\n # Special handling because of named index column\n df.columns.name = 'component'\n df.index.name = 'col'\n setattr(model, attribute, df)\n for attribute in NP_ARRAY:\n setattr(model, attribute, np.array(model_dict[attribute]))\n for attribute in ORDEREDDICT:\n key_list, unordered_dict = model_dict[attribute]\n od = OrderedDict()\n for key in key_list:\n od[key] = unordered_dict[key]\n setattr(model, attribute, od)\n # Other attributes with special handling\n # fit_kwargs\n model.fit_kwargs = model_dict['fit_kwargs']\n # Params (Dict[str, np.ndarray])\n model.params = {k: np.array(v) for k, v in model_dict['params'].items()}\n # Skipped attributes\n model.stan_backend = None\n model.stan_fit = None\n return model\n\n\ndef model_from_json(model_json: str) -> Prophet:\n \"\"\"Deserialize a Prophet model from json string.\n\n Deserializes models that were serialized with model_to_json.\n\n Parameters\n ----------\n model_json: Serialized model string\n\n Returns\n -------\n Prophet model.\n \"\"\"\n model_dict = json.loads(model_json)\n return model_from_dict(model_dict)\n" }, { "path": "python/prophet/tests/__init__.py", "content": "" }, { "path": "python/prophet/tests/conftest.py", "content": "from pathlib import Path\n\nimport pandas as pd\nimport pytest\n\n\n@pytest.fixture(scope=\"package\")\ndef daily_univariate_ts() -> pd.DataFrame:\n \"\"\"Daily univariate time series with 2 years of data\"\"\"\n return pd.read_csv(Path(__file__).parent / \"data.csv\", parse_dates=[\"ds\"])\n\n\n@pytest.fixture(scope=\"package\")\ndef subdaily_univariate_ts() -> pd.DataFrame:\n \"\"\"Sub-daily univariate time series\"\"\"\n return pd.read_csv(Path(__file__).parent / \"data2.csv\", parse_dates=[\"ds\"])\n\n\n@pytest.fixture(scope=\"package\")\ndef large_numbers_ts() -> pd.DataFrame:\n \"\"\"Univariate time series with large values to test scaling\"\"\"\n return pd.read_csv(Path(__file__).parent / \"data3.csv\", parse_dates=[\"ds\"])\n\n\ndef pytest_configure(config):\n config.addinivalue_line(\"markers\", \"slow: mark tests as slow (include in run with --test-slow)\")\n\n\ndef pytest_addoption(parser):\n parser.addoption(\"--test-slow\", action=\"store_true\", default=False, help=\"Run slow tests\")\n parser.addoption(\n \"--backend\",\n nargs=\"+\",\n default=[\"CMDSTANPY\"],\n help=\"Probabilistic Programming Language backend to perform tests with.\",\n )\n\n\ndef pytest_collection_modifyitems(config, items):\n if config.getoption(\"--test-slow\"):\n return\n skip_slow = pytest.mark.skip(reason=\"Skipped due to the lack of '--test-slow' argument\")\n for item in items:\n if \"slow\" in item.keywords:\n item.add_marker(skip_slow)\n\n\ndef pytest_generate_tests(metafunc):\n \"\"\"\n For each test, if `backend` is used as a fixture, add a parametrization equal to the value of the\n --backend option.\n\n This is used to re-run the test suite for different probabilistic programming language backends\n (e.g. cmdstanpy, numpyro).\n \"\"\"\n if \"backend\" in metafunc.fixturenames:\n metafunc.parametrize(\"backend\", metafunc.config.getoption(\"backend\"))\n" }, { "path": "python/prophet/tests/data.csv", "content": "ds,y\n2012-05-18,38.23\n2012-05-21,34.03\n2012-05-22,31.0\n2012-05-23,32.0\n2012-05-24,33.03\n2012-05-25,31.91\n2012-05-29,28.84\n2012-05-30,28.19\n2012-05-31,29.6\n2012-06-01,27.72\n2012-06-04,26.9\n2012-06-05,25.87\n2012-06-06,26.81\n2012-06-07,26.31\n2012-06-08,27.1\n2012-06-11,27.01\n2012-06-12,27.4\n2012-06-13,27.27\n2012-06-14,28.29\n2012-06-15,30.01\n2012-06-18,31.41\n2012-06-19,31.91\n2012-06-20,31.6\n2012-06-21,31.84\n2012-06-22,33.05\n2012-06-25,32.06\n2012-06-26,33.1\n2012-06-27,32.23\n2012-06-28,31.36\n2012-06-29,31.1\n2012-07-02,30.77\n2012-07-03,31.2\n2012-07-05,31.47\n2012-07-06,31.73\n2012-07-09,32.17\n2012-07-10,31.47\n2012-07-11,30.97\n2012-07-12,30.81\n2012-07-13,30.72\n2012-07-16,28.25\n2012-07-17,28.09\n2012-07-18,29.11\n2012-07-19,29.0\n2012-07-20,28.76\n2012-07-23,28.75\n2012-07-24,28.45\n2012-07-25,29.34\n2012-07-26,26.85\n2012-07-27,23.71\n2012-07-30,23.15\n2012-07-31,21.71\n2012-08-01,20.88\n2012-08-02,20.04\n2012-08-03,21.09\n2012-08-06,21.92\n2012-08-07,20.72\n2012-08-08,20.72\n2012-08-09,21.01\n2012-08-10,21.81\n2012-08-13,21.6\n2012-08-14,20.38\n2012-08-15,21.2\n2012-08-16,19.87\n2012-08-17,19.05\n2012-08-20,20.01\n2012-08-21,19.16\n2012-08-22,19.44\n2012-08-23,19.44\n2012-08-24,19.41\n2012-08-27,19.15\n2012-08-28,19.34\n2012-08-29,19.1\n2012-08-30,19.09\n2012-08-31,18.06\n2012-09-04,17.73\n2012-09-05,18.58\n2012-09-06,18.96\n2012-09-07,18.98\n2012-09-10,18.81\n2012-09-11,19.43\n2012-09-12,20.93\n2012-09-13,20.71\n2012-09-14,22.0\n2012-09-17,21.52\n2012-09-18,21.87\n2012-09-19,23.29\n2012-09-20,22.59\n2012-09-21,22.86\n2012-09-24,20.79\n2012-09-25,20.28\n2012-09-26,20.62\n2012-09-27,20.32\n2012-09-28,21.66\n2012-10-01,21.99\n2012-10-02,22.27\n2012-10-03,21.83\n2012-10-04,21.95\n2012-10-05,20.91\n2012-10-08,20.4\n2012-10-09,20.23\n2012-10-10,19.64\n2012-10-11,19.75\n2012-10-12,19.52\n2012-10-15,19.52\n2012-10-16,19.48\n2012-10-17,19.88\n2012-10-18,18.98\n2012-10-19,19.0\n2012-10-22,19.32\n2012-10-23,19.5\n2012-10-24,23.23\n2012-10-25,22.56\n2012-10-26,21.94\n2012-10-31,21.11\n2012-11-01,21.21\n2012-11-02,21.18\n2012-11-05,21.25\n2012-11-06,21.17\n2012-11-07,20.47\n2012-11-08,19.99\n2012-11-09,19.21\n2012-11-12,20.07\n2012-11-13,19.86\n2012-11-14,22.36\n2012-11-15,22.17\n2012-11-16,23.56\n2012-11-19,22.92\n2012-11-20,23.1\n2012-11-21,24.32\n2012-11-23,24.0\n2012-11-26,25.94\n2012-11-27,26.15\n2012-11-28,26.36\n2012-11-29,27.32\n2012-11-30,28.0\n2012-12-03,27.04\n2012-12-04,27.46\n2012-12-05,27.71\n2012-12-06,26.97\n2012-12-07,27.49\n2012-12-10,27.84\n2012-12-11,27.98\n2012-12-12,27.58\n2012-12-13,28.24\n2012-12-14,26.81\n2012-12-17,26.75\n2012-12-18,27.71\n2012-12-19,27.41\n2012-12-20,27.36\n2012-12-21,26.26\n2012-12-24,26.93\n2012-12-26,26.51\n2012-12-27,26.05\n2012-12-28,25.91\n2012-12-31,26.62\n2013-01-02,28.0\n2013-01-03,27.77\n2013-01-04,28.76\n2013-01-07,29.42\n2013-01-08,29.06\n2013-01-09,30.59\n2013-01-10,31.3\n2013-01-11,31.72\n2013-01-14,30.95\n2013-01-15,30.1\n2013-01-16,29.85\n2013-01-17,30.14\n2013-01-18,29.66\n2013-01-22,30.73\n2013-01-23,30.82\n2013-01-24,31.08\n2013-01-25,31.54\n2013-01-28,32.47\n2013-01-29,30.79\n2013-01-30,31.24\n2013-01-31,30.98\n2013-02-01,29.73\n2013-02-04,28.11\n2013-02-05,28.64\n2013-02-06,29.05\n2013-02-07,28.65\n2013-02-08,28.55\n2013-02-11,28.26\n2013-02-12,27.37\n2013-02-13,27.91\n2013-02-14,28.5\n2013-02-15,28.32\n2013-02-19,28.93\n2013-02-20,28.46\n2013-02-21,27.28\n2013-02-22,27.13\n2013-02-25,27.27\n2013-02-26,27.39\n2013-02-27,26.87\n2013-02-28,27.25\n2013-03-01,27.78\n2013-03-04,27.72\n2013-03-05,27.52\n2013-03-06,27.45\n2013-03-07,28.58\n2013-03-08,27.96\n2013-03-11,28.14\n2013-03-12,27.83\n2013-03-13,27.08\n2013-03-14,27.04\n2013-03-15,26.65\n2013-03-18,26.49\n2013-03-19,26.55\n2013-03-20,25.86\n2013-03-21,25.74\n2013-03-22,25.73\n2013-03-25,25.13\n2013-03-26,25.21\n2013-03-27,26.09\n2013-03-28,25.58\n2013-04-01,25.53\n2013-04-02,25.42\n2013-04-03,26.25\n2013-04-04,27.07\n2013-04-05,27.39\n2013-04-08,26.85\n2013-04-09,26.59\n2013-04-10,27.57\n2013-04-11,28.02\n2013-04-12,27.4\n2013-04-15,26.52\n2013-04-16,26.92\n2013-04-17,26.63\n2013-04-18,25.69\n2013-04-19,25.73\n2013-04-22,25.97\n2013-04-23,25.98\n2013-04-24,26.11\n2013-04-25,26.14\n2013-04-26,26.85\n2013-04-29,26.98\n2013-04-30,27.77\n2013-05-01,27.43\n2013-05-02,28.97\n2013-05-03,28.31\n2013-05-06,27.57\n2013-05-07,26.89\n2013-05-08,27.12\n2013-05-09,27.04\n2013-05-10,26.68\n2013-05-13,26.82\n2013-05-14,27.07\n2013-05-15,26.6\n2013-05-16,26.13\n2013-05-17,26.25\n2013-05-20,25.76\n2013-05-21,25.66\n2013-05-22,25.16\n2013-05-23,25.06\n2013-05-24,24.31\n2013-05-28,24.1\n2013-05-29,23.32\n2013-05-30,24.55\n2013-05-31,24.35\n2013-06-03,23.85\n2013-06-04,23.52\n2013-06-05,22.9\n2013-06-06,22.97\n2013-06-07,23.29\n2013-06-10,24.33\n2013-06-11,24.03\n2013-06-12,23.77\n2013-06-13,23.73\n2013-06-14,23.63\n2013-06-17,24.02\n2013-06-18,24.21\n2013-06-19,24.31\n2013-06-20,23.9\n2013-06-21,24.53\n2013-06-24,23.94\n2013-06-25,24.25\n2013-06-26,24.16\n2013-06-27,24.66\n2013-06-28,24.88\n2013-07-01,24.81\n2013-07-02,24.41\n2013-07-03,24.52\n2013-07-05,24.37\n2013-07-08,24.71\n2013-07-09,25.48\n2013-07-10,25.8\n2013-07-11,25.81\n2013-07-12,25.91\n2013-07-15,26.28\n2013-07-16,26.32\n2013-07-17,26.65\n2013-07-18,26.18\n2013-07-19,25.88\n2013-07-22,26.05\n2013-07-23,26.13\n2013-07-24,26.51\n2013-07-25,34.36\n2013-07-26,34.01\n2013-07-29,35.43\n2013-07-30,37.63\n2013-07-31,36.8\n2013-08-01,37.49\n2013-08-02,38.05\n2013-08-05,39.19\n2013-08-06,38.55\n2013-08-07,38.87\n2013-08-08,38.54\n2013-08-09,38.5\n2013-08-12,38.22\n2013-08-13,37.02\n2013-08-14,36.65\n2013-08-15,36.56\n2013-08-16,37.08\n2013-08-19,37.81\n2013-08-20,38.41\n2013-08-21,38.32\n2013-08-22,38.55\n2013-08-23,40.55\n2013-08-26,41.34\n2013-08-27,39.64\n2013-08-28,40.55\n2013-08-29,41.28\n2013-08-30,41.29\n2013-09-03,41.87\n2013-09-04,41.78\n2013-09-05,42.66\n2013-09-06,43.95\n2013-09-09,44.04\n2013-09-10,43.6\n2013-09-11,45.04\n2013-09-12,44.75\n2013-09-13,44.31\n2013-09-16,42.51\n2013-09-17,45.07\n2013-09-18,45.23\n2013-09-19,45.98\n2013-09-20,47.49\n2013-09-23,47.19\n2013-09-24,48.45\n2013-09-25,49.46\n2013-09-26,50.39\n2013-09-27,51.24\n2013-09-30,50.23\n2013-10-01,50.42\n2013-10-02,50.28\n2013-10-03,49.18\n2013-10-04,51.04\n2013-10-07,50.52\n2013-10-08,47.14\n2013-10-09,46.77\n2013-10-10,49.05\n2013-10-11,49.11\n2013-10-14,49.51\n2013-10-15,49.5\n2013-10-16,51.14\n2013-10-17,52.21\n2013-10-18,54.22\n2013-10-21,53.85\n2013-10-22,52.68\n2013-10-23,51.9\n2013-10-24,52.45\n2013-10-25,51.95\n2013-10-28,50.23\n2013-10-29,49.4\n2013-10-30,49.01\n2013-10-31,50.21\n2013-11-01,49.75\n2013-11-04,48.22\n2013-11-05,50.11\n2013-11-06,49.12\n2013-11-07,47.56\n2013-11-08,47.53\n2013-11-11,46.2\n2013-11-12,46.61\n2013-11-13,48.71\n2013-11-14,48.99\n2013-11-15,49.01\n2013-11-18,45.83\n2013-11-19,46.36\n2013-11-20,46.43\n2013-11-21,46.7\n2013-11-22,46.23\n2013-11-25,44.82\n2013-11-26,45.89\n2013-11-27,46.49\n2013-11-29,47.01\n2013-12-02,47.06\n2013-12-03,46.73\n2013-12-04,48.62\n2013-12-05,48.34\n2013-12-06,47.94\n2013-12-09,48.84\n2013-12-10,50.25\n2013-12-11,49.38\n2013-12-12,51.83\n2013-12-13,53.32\n2013-12-16,53.81\n2013-12-17,54.86\n2013-12-18,55.57\n2013-12-19,55.05\n2013-12-20,55.12\n2013-12-23,57.77\n2013-12-24,57.96\n2013-12-26,57.73\n2013-12-27,55.44\n2013-12-30,53.71\n2013-12-31,54.65\n2014-01-02,54.71\n2014-01-03,54.56\n2014-01-06,57.2\n2014-01-07,57.92\n2014-01-08,58.23\n2014-01-09,57.22\n2014-01-10,57.94\n2014-01-13,55.91\n2014-01-14,57.74\n2014-01-15,57.6\n2014-01-16,57.19\n2014-01-17,56.3\n2014-01-21,58.51\n2014-01-22,57.51\n2014-01-23,56.63\n2014-01-24,54.45\n2014-01-27,53.55\n2014-01-28,55.14\n2014-01-29,53.53\n2014-01-30,61.08\n2014-01-31,62.57\n2014-02-03,61.48\n2014-02-04,62.75\n2014-02-05,62.19\n2014-02-06,62.16\n2014-02-07,64.32\n2014-02-10,63.55\n2014-02-11,64.85\n2014-02-12,64.45\n2014-02-13,67.33\n2014-02-14,67.09\n2014-02-18,67.3\n2014-02-19,68.06\n2014-02-20,69.63\n2014-02-21,68.59\n2014-02-24,70.78\n2014-02-25,69.85\n2014-02-26,69.26\n2014-02-27,68.94\n2014-02-28,68.46\n2014-03-03,67.41\n2014-03-04,68.8\n2014-03-05,71.57\n2014-03-06,70.84\n2014-03-07,69.8\n2014-03-10,72.03\n2014-03-11,70.1\n2014-03-12,70.88\n2014-03-13,68.83\n2014-03-14,67.72\n2014-03-17,68.74\n2014-03-18,69.19\n2014-03-19,68.24\n2014-03-20,66.97\n2014-03-21,67.24\n2014-03-24,64.1\n2014-03-25,64.89\n2014-03-26,60.39\n2014-03-27,60.97\n2014-03-28,60.01\n2014-03-31,60.24\n2014-04-01,62.62\n2014-04-02,62.72\n2014-04-03,59.49\n2014-04-04,56.75\n2014-04-07,56.95\n2014-04-08,58.19\n2014-04-09,62.41\n2014-04-10,59.16\n2014-04-11,58.53\n2014-04-14,58.89\n2014-04-15,59.09\n2014-04-16,59.72\n2014-04-17,58.94\n2014-04-21,61.24\n2014-04-22,63.03\n2014-04-23,61.36\n2014-04-24,60.87\n2014-04-25,57.71\n2014-04-28,56.14\n2014-04-29,58.15\n2014-04-30,59.78\n2014-05-01,61.15\n2014-05-02,60.46\n2014-05-05,61.22\n2014-05-06,58.53\n2014-05-07,57.39\n2014-05-08,56.76\n2014-05-09,57.24\n2014-05-12,59.83\n2014-05-13,59.83\n2014-05-14,59.23\n2014-05-15,57.92\n2014-05-16,58.02\n2014-05-19,59.21\n2014-05-20,58.56\n2014-05-21,60.49\n2014-05-22,60.52\n2014-05-23,61.35\n2014-05-27,63.48\n2014-05-28,63.51\n2014-05-29,63.83\n2014-05-30,63.30\n" }, { "path": "python/prophet/tests/data2.csv", "content": "ds,y\n2017-01-01 00:05:00,0.0\n2017-01-01 00:10:00,0.0\n2017-01-01 00:15:00,0.0\n2017-01-01 00:20:00,0.0\n2017-01-01 00:25:00,-0.1\n2017-01-01 00:30:00,-0.1\n2017-01-01 00:35:00,-0.1\n2017-01-01 00:40:00,-0.1\n2017-01-01 00:45:00,-0.1\n2017-01-01 00:50:00,-0.1\n2017-01-01 00:55:00,-0.3\n2017-01-01 01:00:00,-0.2\n2017-01-01 01:05:00,-0.3\n2017-01-01 01:10:00,-0.4\n2017-01-01 01:15:00,-0.4\n2017-01-01 01:20:00,-0.3\n2017-01-01 01:25:00,-0.3\n2017-01-01 01:30:00,-0.2\n2017-01-01 01:35:00,-0.3\n2017-01-01 01:40:00,-0.3\n2017-01-01 01:45:00,-0.3\n2017-01-01 01:50:00,-0.3\n2017-01-01 01:55:00,-0.3\n2017-01-01 02:00:00,-0.3\n2017-01-01 02:05:00,-0.3\n2017-01-01 02:10:00,-0.3\n2017-01-01 02:15:00,-0.3\n2017-01-01 02:20:00,-0.3\n2017-01-01 02:25:00,-0.3\n2017-01-01 02:30:00,-0.3\n2017-01-01 02:35:00,-0.3\n2017-01-01 02:40:00,-0.3\n2017-01-01 02:45:00,-0.3\n2017-01-01 02:50:00,-0.3\n2017-01-01 02:55:00,-0.3\n2017-01-01 03:00:00,-0.3\n2017-01-01 03:05:00,-0.3\n2017-01-01 03:10:00,-0.3\n2017-01-01 03:15:00,-0.3\n2017-01-01 03:20:00,-0.3\n2017-01-01 03:25:00,-0.4\n2017-01-01 03:30:00,-0.6\n2017-01-01 03:35:00,-0.4\n2017-01-01 03:40:00,-0.3\n2017-01-01 03:45:00,-0.4\n2017-01-01 03:50:00,-0.7\n2017-01-01 03:55:00,-0.8\n2017-01-01 04:00:00,-0.4\n2017-01-01 04:05:00,-0.3\n2017-01-01 04:10:00,-0.4\n2017-01-01 04:15:00,-0.4\n2017-01-01 04:20:00,-0.4\n2017-01-01 04:25:00,-0.5\n2017-01-01 04:30:00,-0.5\n2017-01-01 04:35:00,-0.5\n2017-01-01 04:40:00,-0.4\n2017-01-01 04:45:00,-0.5\n2017-01-01 04:50:00,-0.5\n2017-01-01 04:55:00,-0.5\n2017-01-01 05:00:00,-0.6\n2017-01-01 05:05:00,-0.9\n2017-01-01 05:10:00,-0.9\n2017-01-01 05:15:00,-1.2\n2017-01-01 05:20:00,-1.4\n2017-01-01 05:25:00,-1.8\n2017-01-01 05:30:00,-2.0\n2017-01-01 05:35:00,-2.2\n2017-01-01 05:40:00,-1.6\n2017-01-01 05:45:00,-1.2\n2017-01-01 05:50:00,-1.2\n2017-01-01 05:55:00,-1.4\n2017-01-01 06:00:00,-1.2\n2017-01-01 06:05:00,-0.9\n2017-01-01 06:10:00,-0.9\n2017-01-01 06:15:00,-0.9\n2017-01-01 06:20:00,-0.9\n2017-01-01 06:25:00,-0.9\n2017-01-01 06:30:00,-1.2\n2017-01-01 06:35:00,-1.1\n2017-01-01 06:40:00,-1.2\n2017-01-01 06:45:00,-1.3\n2017-01-01 06:50:00,-1.4\n2017-01-01 06:55:00,-1.7\n2017-01-01 07:00:00,-1.7\n2017-01-01 07:05:00,-1.7\n2017-01-01 07:10:00,-1.8\n2017-01-01 07:15:00,-2.4\n2017-01-01 07:20:00,-2.9\n2017-01-01 07:25:00,-3.2\n2017-01-01 07:30:00,-3.4\n2017-01-01 07:35:00,-3.6\n2017-01-01 07:40:00,-3.6\n2017-01-01 07:45:00,-3.5\n2017-01-01 07:50:00,-3.5\n2017-01-01 07:55:00,-3.5\n2017-01-01 08:00:00,-3.6\n2017-01-01 08:05:00,-3.7\n2017-01-01 08:10:00,-3.6\n2017-01-01 08:15:00,-3.6\n2017-01-01 08:20:00,-3.8\n2017-01-01 08:25:00,-4.0\n2017-01-01 08:30:00,-3.9\n2017-01-01 08:35:00,-3.9\n2017-01-01 08:40:00,-4.1\n2017-01-01 08:45:00,-4.0\n2017-01-01 08:50:00,-4.1\n2017-01-01 08:55:00,-4.1\n2017-01-01 09:00:00,-4.2\n2017-01-01 09:05:00,-4.1\n2017-01-01 09:10:00,-4.2\n2017-01-01 09:15:00,-4.1\n2017-01-01 09:20:00,-4.0\n2017-01-01 09:25:00,-4.0\n2017-01-01 09:30:00,-4.0\n2017-01-01 09:35:00,-4.1\n2017-01-01 09:40:00,-4.1\n2017-01-01 09:45:00,-4.2\n2017-01-01 09:50:00,-4.3\n2017-01-01 09:55:00,-4.4\n2017-01-01 10:00:00,-4.5\n2017-01-01 10:05:00,-4.6\n2017-01-01 10:10:00,-4.7\n2017-01-01 10:15:00,-4.6\n2017-01-01 10:20:00,-4.6\n2017-01-01 10:25:00,-4.6\n2017-01-01 10:30:00,-4.5\n2017-01-01 10:35:00,-4.6\n2017-01-01 10:40:00,-4.6\n2017-01-01 10:45:00,-4.6\n2017-01-01 10:50:00,-4.6\n2017-01-01 10:55:00,-4.7\n2017-01-01 11:00:00,-4.7\n2017-01-01 11:05:00,-4.6\n2017-01-01 11:10:00,-4.5\n2017-01-01 11:15:00,-4.7\n2017-01-01 11:20:00,-4.7\n2017-01-01 11:25:00,-4.8\n2017-01-01 11:30:00,-4.8\n2017-01-01 11:35:00,-4.8\n2017-01-01 11:40:00,-4.8\n2017-01-01 11:45:00,-4.7\n2017-01-01 11:50:00,-4.6\n2017-01-01 11:55:00,-4.6\n2017-01-01 12:00:00,-4.8\n2017-01-01 12:05:00,-4.9\n2017-01-01 12:10:00,-4.9\n2017-01-01 12:15:00,-4.9\n2017-01-01 12:20:00,-5.0\n2017-01-01 12:25:00,-4.9\n2017-01-01 12:30:00,-4.9\n2017-01-01 12:35:00,-5.0\n2017-01-01 12:40:00,-5.1\n2017-01-01 12:45:00,-5.3\n2017-01-01 12:50:00,-5.5\n2017-01-01 12:55:00,-5.7\n2017-01-01 13:00:00,-5.8\n2017-01-01 13:05:00,-5.9\n2017-01-01 13:10:00,-5.9\n2017-01-01 13:15:00,-6.1\n2017-01-01 13:20:00,-6.1\n2017-01-01 13:25:00,-6.1\n2017-01-01 13:30:00,-6.2\n2017-01-01 13:35:00,-6.3\n2017-01-01 13:40:00,-6.4\n2017-01-01 13:45:00,-6.5\n2017-01-01 13:50:00,-6.6\n2017-01-01 13:55:00,-6.7\n2017-01-01 14:00:00,-6.7\n2017-01-01 14:05:00,-6.7\n2017-01-01 14:10:00,-6.6\n2017-01-01 14:15:00,-6.7\n2017-01-01 14:20:00,-6.7\n2017-01-01 14:25:00,-6.6\n2017-01-01 14:30:00,-6.7\n2017-01-01 14:35:00,-6.6\n2017-01-01 14:40:00,-6.6\n2017-01-01 14:45:00,-6.4\n2017-01-01 14:50:00,-6.5\n2017-01-01 14:55:00,-6.5\n2017-01-01 15:00:00,-6.4\n2017-01-01 15:05:00,-6.4\n2017-01-01 15:10:00,-6.3\n2017-01-01 15:15:00,-6.3\n2017-01-01 15:20:00,-6.4\n2017-01-01 15:25:00,-6.5\n2017-01-01 15:30:00,-6.6\n2017-01-01 15:35:00,-6.6\n2017-01-01 15:40:00,-6.6\n2017-01-01 15:45:00,-6.6\n2017-01-01 15:50:00,-6.5\n2017-01-01 15:55:00,-6.4\n2017-01-01 16:00:00,-6.3\n2017-01-01 16:05:00,-6.3\n2017-01-01 16:10:00,-6.2\n2017-01-01 16:15:00,-6.1\n2017-01-01 16:20:00,-6.0\n2017-01-01 16:25:00,-5.9\n2017-01-01 16:30:00,-5.8\n2017-01-01 16:35:00,-5.7\n2017-01-01 16:40:00,-5.4\n2017-01-01 16:45:00,-5.3\n2017-01-01 16:50:00,-5.1\n2017-01-01 16:55:00,-5.0\n2017-01-01 17:00:00,-4.8\n2017-01-01 17:05:00,-4.6\n2017-01-01 17:10:00,-4.3\n2017-01-01 17:15:00,-4.1\n2017-01-01 17:20:00,-3.9\n2017-01-01 17:25:00,-3.6\n2017-01-01 17:30:00,-3.3\n2017-01-01 17:35:00,-3.1\n2017-01-01 17:40:00,-2.8\n2017-01-01 17:45:00,-2.7\n2017-01-01 17:50:00,-2.4\n2017-01-01 17:55:00,-2.0\n2017-01-01 18:00:00,-1.6\n2017-01-01 18:05:00,-1.3\n2017-01-01 18:10:00,-1.1\n2017-01-01 18:15:00,-0.9\n2017-01-01 18:20:00,-0.7\n2017-01-01 18:25:00,-0.4\n2017-01-01 18:30:00,-0.4\n2017-01-01 18:35:00,-0.2\n2017-01-01 18:40:00,0.0\n2017-01-01 18:45:00,0.3\n2017-01-01 18:50:00,0.6\n2017-01-01 18:55:00,0.6\n2017-01-01 19:00:00,1.0\n2017-01-01 19:05:00,1.0\n2017-01-01 19:10:00,1.1\n2017-01-01 19:15:00,1.3\n2017-01-01 19:20:00,1.0\n2017-01-01 19:25:00,1.2\n2017-01-01 19:30:00,1.3\n2017-01-01 19:35:00,0.9\n2017-01-01 19:40:00,1.1\n2017-01-01 19:45:00,1.3\n2017-01-01 19:50:00,1.5\n2017-01-01 19:55:00,1.3\n2017-01-01 20:00:00,1.6\n2017-01-01 20:05:00,1.6\n2017-01-01 20:10:00,1.8\n2017-01-01 20:15:00,1.4\n2017-01-01 20:20:00,1.4\n2017-01-01 20:25:00,1.6\n2017-01-01 20:30:00,1.6\n2017-01-01 20:35:00,1.5\n2017-01-01 20:40:00,1.5\n2017-01-01 20:45:00,1.8\n2017-01-01 20:50:00,1.6\n2017-01-01 20:55:00,1.7\n2017-01-01 21:00:00,1.5\n2017-01-01 21:05:00,1.8\n2017-01-01 21:10:00,1.6\n2017-01-01 21:15:00,1.7\n2017-01-01 21:20:00,1.9\n2017-01-01 21:25:00,1.6\n2017-01-01 21:30:00,1.8\n2017-01-01 21:35:00,1.8\n2017-01-01 21:40:00,1.5\n2017-01-01 21:45:00,1.6\n2017-01-01 21:50:00,1.6\n2017-01-01 21:55:00,1.4\n2017-01-01 22:00:00,1.1\n2017-01-01 22:05:00,1.5\n2017-01-01 22:10:00,1.5\n2017-01-01 22:15:00,1.6\n2017-01-01 22:20:00,1.5\n2017-01-01 22:25:00,1.1\n2017-01-01 22:30:00,1.0\n2017-01-01 22:35:00,1.0\n2017-01-01 22:40:00,1.1\n2017-01-01 22:45:00,1.1\n2017-01-01 22:50:00,0.7\n2017-01-01 22:55:00,0.6\n2017-01-01 23:00:00,0.5\n2017-01-01 23:05:00,0.3\n2017-01-01 23:10:00,0.5\n2017-01-01 23:15:00,0.2\n2017-01-01 23:20:00,0.2\n2017-01-01 23:25:00,0.0\n2017-01-01 23:30:00,-0.2\n2017-01-01 23:35:00,-0.3\n2017-01-01 23:40:00,-0.5\n2017-01-01 23:45:00,-0.7\n2017-01-01 23:50:00,-1.1\n2017-01-01 23:55:00,-1.3\n2017-01-02 00:00:00,-1.4\n2017-01-02 00:05:00,-1.7\n2017-01-02 00:10:00,-2.1\n2017-01-02 00:15:00,-2.4\n2017-01-02 00:20:00,-2.6\n2017-01-02 00:25:00,-2.9\n2017-01-02 00:30:00,-3.2\n2017-01-02 00:35:00,-3.5\n2017-01-02 00:40:00,-3.9\n2017-01-02 00:45:00,-4.1\n2017-01-02 00:50:00,-4.2\n2017-01-02 00:55:00,-4.4\n2017-01-02 01:00:00,-4.6\n2017-01-02 01:05:00,-4.7\n2017-01-02 01:10:00,-5.0\n2017-01-02 01:15:00,-5.1\n2017-01-02 01:20:00,-4.8\n2017-01-02 01:25:00,-4.7\n2017-01-02 01:30:00,-4.5\n2017-01-02 01:35:00,-4.0\n2017-01-02 01:40:00,-3.6\n2017-01-02 01:45:00,-3.1\n2017-01-02 01:50:00,-3.0\n2017-01-02 01:55:00,-3.0\n2017-01-02 02:00:00,-3.0\n2017-01-02 02:05:00,-2.9\n2017-01-02 02:10:00,-3.0\n2017-01-02 02:15:00,-2.9\n2017-01-02 02:20:00,-3.0\n2017-01-02 02:25:00,-3.0\n2017-01-02 02:30:00,-3.0\n2017-01-02 02:35:00,-3.0\n2017-01-02 02:40:00,-3.2\n2017-01-02 02:45:00,-3.5\n2017-01-02 02:50:00,-3.7\n2017-01-02 02:55:00,-3.5\n2017-01-02 03:00:00,-3.5\n2017-01-02 03:05:00,-3.4\n2017-01-02 03:10:00,-3.3\n2017-01-02 03:15:00,-3.2\n2017-01-02 03:20:00,-3.2\n2017-01-02 03:25:00,-3.3\n2017-01-02 03:30:00,-3.3\n2017-01-02 03:35:00,-3.3\n2017-01-02 03:40:00,-3.4\n2017-01-02 03:45:00,-3.4\n2017-01-02 03:50:00,-3.4\n2017-01-02 03:55:00,-3.5\n2017-01-02 04:00:00,-3.5\n2017-01-02 04:05:00,-3.5\n2017-01-02 04:10:00,-3.5\n2017-01-02 04:15:00,-3.6\n2017-01-02 04:20:00,-3.6\n2017-01-02 04:25:00,-3.8\n2017-01-02 04:30:00,-3.8\n2017-01-02 04:35:00,-3.8\n2017-01-02 04:40:00,-3.9\n2017-01-02 04:45:00,-3.9\n2017-01-02 04:50:00,-3.9\n2017-01-02 04:55:00,-3.9\n2017-01-02 05:00:00,-3.9\n2017-01-02 05:05:00,-3.9\n2017-01-02 05:10:00,-3.9\n2017-01-02 05:15:00,-4.0\n2017-01-02 05:20:00,-3.9\n2017-01-02 05:25:00,-4.0\n2017-01-02 05:30:00,-4.2\n2017-01-02 05:35:00,-4.2\n2017-01-02 05:40:00,-4.4\n2017-01-02 05:45:00,-4.4\n2017-01-02 05:50:00,-4.4\n2017-01-02 05:55:00,-4.4\n2017-01-02 06:00:00,-4.4\n2017-01-02 06:05:00,-5.3\n2017-01-02 06:10:00,-5.2\n2017-01-02 06:15:00,-5.3\n2017-01-02 06:20:00,-5.2\n2017-01-02 06:25:00,-5.0\n2017-01-02 06:30:00,-4.9\n2017-01-02 06:35:00,-4.8\n2017-01-02 06:40:00,-4.8\n2017-01-02 06:45:00,-4.7\n2017-01-02 06:50:00,-4.7\n2017-01-02 06:55:00,-4.8\n2017-01-02 07:00:00,-4.7\n2017-01-02 07:05:00,-4.7\n2017-01-02 07:10:00,-4.7\n2017-01-02 07:15:00,-5.0\n2017-01-02 07:20:00,-5.0\n2017-01-02 07:25:00,-4.9\n2017-01-02 07:30:00,-4.8\n2017-01-02 07:35:00,-4.8\n2017-01-02 07:40:00,-4.7\n2017-01-02 07:45:00,-4.6\n2017-01-02 07:50:00,-4.6\n2017-01-02 07:55:00,-4.7\n2017-01-02 08:00:00,-4.6\n2017-01-02 08:05:00,-4.6\n2017-01-02 08:10:00,-4.5\n2017-01-02 08:15:00,-4.5\n2017-01-02 08:20:00,-4.5\n2017-01-02 08:25:00,-4.5\n2017-01-02 08:30:00,-4.5\n2017-01-02 08:35:00,-4.5\n2017-01-02 08:40:00,-4.6\n2017-01-02 08:45:00,-4.6\n2017-01-02 08:50:00,-4.6\n2017-01-02 08:55:00,-4.6\n2017-01-02 09:00:00,-4.6\n2017-01-02 09:05:00,-4.6\n2017-01-02 09:10:00,-4.5\n2017-01-02 09:15:00,-4.5\n2017-01-02 09:20:00,-4.5\n2017-01-02 09:25:00,-4.5\n2017-01-02 09:30:00,-4.5\n2017-01-02 09:35:00,-4.5\n2017-01-02 09:40:00,-4.5\n2017-01-02 09:45:00,-4.5\n2017-01-02 09:50:00,-4.4\n2017-01-02 09:55:00,-4.4\n2017-01-02 10:00:00,-4.4\n2017-01-02 10:05:00,-4.5\n2017-01-02 10:10:00,-4.5\n2017-01-02 10:15:00,-4.4\n2017-01-02 10:20:00,-4.5\n2017-01-02 10:25:00,-4.5\n2017-01-02 10:30:00,-4.5\n2017-01-02 10:35:00,-4.5\n2017-01-02 10:40:00,-4.5\n2017-01-02 10:45:00,-4.5\n2017-01-02 10:50:00,-4.5\n2017-01-02 10:55:00,-4.4\n2017-01-02 11:00:00,-4.4\n2017-01-02 11:05:00,-4.5\n2017-01-02 11:10:00,-4.5\n2017-01-02 11:15:00,-4.5\n2017-01-02 11:20:00,-4.5\n2017-01-02 11:25:00,-4.5\n2017-01-02 11:30:00,-4.5\n2017-01-02 11:35:00,-4.5\n2017-01-02 11:40:00,-4.5\n2017-01-02 11:45:00,-4.6\n2017-01-02 11:50:00,-4.6\n2017-01-02 11:55:00,-4.6\n2017-01-02 12:00:00,-4.6\n2017-01-02 12:05:00,-4.7\n2017-01-02 12:10:00,-4.8\n2017-01-02 12:15:00,-4.8\n2017-01-02 12:20:00,-4.9\n2017-01-02 12:25:00,-5.0\n2017-01-02 12:30:00,-5.3\n2017-01-02 12:35:00,-5.5\n2017-01-02 12:40:00,-5.5\n2017-01-02 12:45:00,-5.6\n2017-01-02 12:50:00,-5.9\n2017-01-02 12:55:00,-6.1\n2017-01-02 13:00:00,-6.0\n2017-01-02 13:05:00,-6.1\n2017-01-02 13:10:00,-6.1\n2017-01-02 13:15:00,-6.0\n2017-01-02 13:20:00,-5.7\n2017-01-02 13:25:00,-5.5\n2017-01-02 13:30:00,-5.3\n2017-01-02 13:35:00,-5.2\n2017-01-02 13:40:00,-5.1\n2017-01-02 13:45:00,-5.0\n2017-01-02 13:50:00,-5.0\n2017-01-02 13:55:00,-5.0\n2017-01-02 14:00:00,-4.9\n2017-01-02 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datetime\nimport itertools\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom prophet import Prophet, diagnostics\n\n\n@pytest.fixture(scope=\"module\")\ndef ts_short(daily_univariate_ts):\n return daily_univariate_ts.head(100)\n\n\nclass CustomParallelBackend:\n def map(self, func, *iterables):\n results = [func(*args) for args in zip(*iterables)]\n return results\n\n\nPARALLEL_METHODS = [None, \"processes\", \"threads\", CustomParallelBackend()]\ntry:\n from dask.distributed import Client\n\n client = Client(processes=False) # noqa\n PARALLEL_METHODS.append(\"dask\")\nexcept ImportError:\n pass\n\n@diagnostics.register_performance_metric\ndef mase(df, w):\n \"\"\"Mean absolute scale error\n\n Parameters\n ----------\n df: Cross-validation results dataframe.\n w: Aggregation window size.\n\n Returns\n -------\n Dataframe with columns horizon and mase.\n \"\"\"\n e = (df['y'] - df['yhat'])\n d = np.abs(np.diff(df['y'])).sum()/(df['y'].shape[0]-1)\n se = np.abs(e/d)\n if w < 0:\n return pd.DataFrame({'horizon': df['horizon'], 'mase': se})\n return diagnostics.rolling_mean_by_h(\n x=se.values, h=df['horizon'].values, w=w, name='mase'\n )\n\nclass TestCrossValidation:\n @pytest.mark.parametrize(\"parallel_method\", PARALLEL_METHODS)\n def test_cross_validation(self, ts_short, parallel_method, backend):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n # Calculate the number of cutoff points(k)\n horizon = pd.Timedelta(\"4 days\")\n period = pd.Timedelta(\"10 days\")\n initial = pd.Timedelta(\"115 days\")\n df_cv = diagnostics.cross_validation(\n m, horizon=\"4 days\", period=\"10 days\", initial=\"115 days\", parallel=parallel_method\n )\n assert len(np.unique(df_cv[\"cutoff\"])) == 3\n assert max(df_cv[\"ds\"] - df_cv[\"cutoff\"]) == horizon\n assert min(df_cv[\"cutoff\"]) >= min(ts_short[\"ds\"]) + initial\n dc = df_cv[\"cutoff\"].diff()\n dc = dc[dc > pd.Timedelta(0)].min()\n assert dc >= period\n assert (df_cv[\"cutoff\"] < df_cv[\"ds\"]).all()\n # Each y in df_cv and ts_short with same ds should be equal\n df_merged = pd.merge(df_cv, ts_short, \"left\", on=\"ds\")\n assert np.sum((df_merged[\"y_x\"] - df_merged[\"y_y\"]) ** 2) == pytest.approx(0.0)\n df_cv = diagnostics.cross_validation(\n m, horizon=\"4 days\", period=\"10 days\", initial=\"135 days\"\n )\n assert len(np.unique(df_cv[\"cutoff\"])) == 1\n with pytest.raises(ValueError):\n diagnostics.cross_validation(m, horizon=\"10 days\", period=\"10 days\", initial=\"140 days\")\n\n def test_bad_parallel_methods(self, ts_short, backend):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n # invalid alias\n with pytest.raises(ValueError, match=\"'parallel' should be one\"):\n diagnostics.cross_validation(m, horizon=\"4 days\", parallel=\"bad\")\n # no map method\n with pytest.raises(ValueError, match=\"'parallel' should be one\"):\n diagnostics.cross_validation(m, horizon=\"4 days\", parallel=object())\n\n def test_check_single_cutoff_forecast_func_calls(self, ts_short, monkeypatch, backend):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n\n def mock_predict(df, model, cutoff, horizon, predict_columns):\n nonlocal n_calls\n n_calls = n_calls + 1\n return pd.DataFrame(\n {\n \"ds\": pd.date_range(start=\"2012-09-17\", periods=3),\n \"yhat\": np.arange(16, 19),\n \"yhat_lower\": np.arange(15, 18),\n \"yhat_upper\": np.arange(17, 20),\n \"y\": np.arange(16.5, 19.5),\n \"cutoff\": [datetime.date(2012, 9, 15)] * 3,\n }\n )\n\n monkeypatch.setattr(diagnostics, \"single_cutoff_forecast\", mock_predict)\n # cross validation with 3 and 7 forecasts\n for args, forecasts in (\n ([\"4 days\", \"10 days\", \"115 days\"], 3),\n ([\"4 days\", \"4 days\", \"115 days\"], 7),\n ):\n n_calls = 0\n _ = diagnostics.cross_validation(m, *args)\n # check single forecast function called expected number of times\n assert n_calls == forecasts\n \n @pytest.mark.parametrize(\"extra_output_columns\", [\"trend\", [\"trend\"]])\n def test_check_extra_output_columns_cross_validation(self, ts_short, backend, extra_output_columns):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n df_cv = diagnostics.cross_validation(\n m,\n horizon=\"1 days\",\n period=\"1 days\",\n initial=\"140 days\",\n extra_output_columns=extra_output_columns\n )\n assert \"trend\" in df_cv.columns\n\n @pytest.mark.parametrize(\"growth\", [\"logistic\", \"flat\"])\n def test_cross_validation_logistic_or_flat_growth(self, growth, ts_short, backend):\n df = ts_short.copy()\n if growth == \"logistic\":\n df[\"cap\"] = 40\n m = Prophet(growth=growth, stan_backend=backend).fit(df)\n df_cv = diagnostics.cross_validation(\n m, horizon=\"1 days\", period=\"1 days\", initial=\"140 days\"\n )\n assert len(np.unique(df_cv[\"cutoff\"])) == 2\n assert (df_cv[\"cutoff\"] < df_cv[\"ds\"]).all()\n df_merged = pd.merge(df_cv, ts_short, \"left\", on=\"ds\")\n assert np.sum((df_merged[\"y_x\"] - df_merged[\"y_y\"]) ** 2) == pytest.approx(0.0)\n\n def test_cross_validation_extra_regressors(self, ts_short, backend):\n df = ts_short.copy()\n df[\"extra\"] = range(df.shape[0])\n df[\"is_conditional_week\"] = np.arange(df.shape[0]) // 7 % 2\n m = Prophet(stan_backend=backend)\n m.add_seasonality(name=\"monthly\", period=30.5, fourier_order=5)\n m.add_seasonality(\n name=\"conditional_weekly\",\n period=7,\n fourier_order=3,\n prior_scale=2.0,\n condition_name=\"is_conditional_week\",\n )\n m.add_regressor(\"extra\")\n m.fit(df)\n df_cv = diagnostics.cross_validation(\n m, horizon=\"4 days\", period=\"4 days\", initial=\"135 days\"\n )\n assert len(np.unique(df_cv[\"cutoff\"])) == 2\n period = pd.Timedelta(\"4 days\")\n dc = df_cv[\"cutoff\"].diff()\n dc = dc[dc > pd.Timedelta(0)].min()\n assert dc >= period\n assert (df_cv[\"cutoff\"] < df_cv[\"ds\"]).all()\n df_merged = pd.merge(df_cv, ts_short, \"left\", on=\"ds\")\n assert np.sum((df_merged[\"y_x\"] - df_merged[\"y_y\"]) ** 2) == pytest.approx(0.0)\n\n def test_cross_validation_default_value_check(self, ts_short, backend):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n # Default value of initial should be equal to 3 * horizon\n df_cv1 = diagnostics.cross_validation(m, horizon=\"32 days\", period=\"10 days\")\n df_cv2 = diagnostics.cross_validation(\n m, horizon=\"32 days\", period=\"10 days\", initial=\"96 days\"\n )\n assert ((df_cv1[\"y\"] - df_cv2[\"y\"]) ** 2).sum() == pytest.approx(0.0)\n assert ((df_cv1[\"yhat\"] - df_cv2[\"yhat\"]) ** 2).sum() == pytest.approx(0.0)\n\n def test_cross_validation_custom_cutoffs(self, ts_short, backend):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n # When specify a list of cutoffs\n # the cutoff dates in df_cv are those specified\n df_cv1 = diagnostics.cross_validation(\n m,\n horizon=\"32 days\",\n period=\"10 days\",\n cutoffs=[pd.Timestamp(\"2012-07-31\"), pd.Timestamp(\"2012-08-31\")],\n )\n assert len(df_cv1[\"cutoff\"].unique()) == 2\n\n def test_cross_validation_uncertainty_disabled(self, ts_short, backend):\n df = ts_short.copy()\n for uncertainty in [0, False]:\n m = Prophet(uncertainty_samples=uncertainty, stan_backend=backend)\n m.fit(df, algorithm=\"Newton\")\n df_cv = diagnostics.cross_validation(\n m, horizon=\"4 days\", period=\"4 days\", initial=\"115 days\"\n )\n expected_cols = [\"ds\", \"yhat\", \"y\", \"cutoff\"]\n assert all(col in expected_cols for col in df_cv.columns.tolist())\n df_p = diagnostics.performance_metrics(df_cv)\n assert \"coverage\" not in df_p.columns\n\n\nclass TestPerformanceMetrics:\n def test_performance_metrics(self, ts_short, backend):\n m = Prophet(stan_backend=backend)\n m.fit(ts_short)\n df_cv = diagnostics.cross_validation(\n m, horizon=\"4 days\", period=\"10 days\", initial=\"90 days\"\n )\n # Aggregation level none\n df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)\n assert set(df_none.columns) == {\n \"horizon\",\n \"coverage\",\n \"mae\",\n \"mape\",\n \"mdape\",\n \"mse\",\n \"rmse\",\n \"smape\",\n }\n assert df_none.shape[0] == 16\n # Aggregation level 0\n df_0 = diagnostics.performance_metrics(df_cv, rolling_window=0)\n assert len(df_0) == 4\n assert len(df_0[\"horizon\"].unique()) == 4\n # Aggregation level 0.2\n df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)\n assert len(df_horizon) == 4\n assert len(df_horizon[\"horizon\"].unique()) == 4\n # Aggregation level all\n df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)\n assert df_all.shape[0] == 1\n for metric in [\"mse\", \"mape\", \"mae\", \"coverage\"]:\n assert df_all[metric].values[0] == pytest.approx(df_none[metric].mean())\n assert df_all[\"mdape\"].values[0] == pytest.approx(df_none[\"mdape\"].median())\n # Custom list of metrics\n df_horizon = diagnostics.performance_metrics(\n df_cv,\n metrics=[\"coverage\", \"mse\", \"mase\"],\n )\n assert set(df_horizon.columns) == {\"coverage\", \"mse\", \"mase\",\"horizon\"}\n # Skip MAPE\n df_cv.loc[0, \"y\"] = 0.0\n df_horizon = diagnostics.performance_metrics(\n df_cv,\n metrics=[\"coverage\", \"mape\"],\n )\n assert set(df_horizon.columns) == {\"coverage\", \"horizon\"}\n # Handle zero y and yhat\n df_cv[\"y\"] = 0.0\n df_cv[\"yhat\"] = 0.0\n df_horizon = diagnostics.performance_metrics(\n df_cv,\n )\n assert set(df_horizon.columns) == {\"coverage\", \"horizon\", \"mae\", \"mdape\", \"mse\", \"rmse\", \"smape\"}\n df_horizon = diagnostics.performance_metrics(\n df_cv,\n metrics=[\"mape\"],\n )\n assert df_horizon is None\n # List of metrics containing non-valid metrics\n with pytest.raises(ValueError):\n diagnostics.performance_metrics(\n df_cv,\n metrics=[\"mse\", \"error_metric\"],\n )\n\n def test_rolling_mean(self):\n x = np.arange(10)\n h = np.arange(10)\n df = diagnostics.rolling_mean_by_h(x=x, h=h, w=1, name=\"x\")\n assert np.array_equal(x, df[\"x\"].values)\n assert np.array_equal(h, df[\"horizon\"].values)\n\n df = diagnostics.rolling_mean_by_h(x, h, w=4, name=\"x\")\n assert np.allclose(x[3:] - 1.5, df[\"x\"].values)\n assert np.array_equal(np.arange(3, 10), df[\"horizon\"].values)\n\n h = np.array([1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 7.0, 7.0])\n x_true = np.array([1.0, 5.0, 22.0 / 3])\n h_true = np.array([3.0, 4.0, 7.0])\n df = diagnostics.rolling_mean_by_h(x, h, w=3, name=\"x\")\n assert np.allclose(x_true, df[\"x\"].values)\n assert np.array_equal(h_true, df[\"horizon\"].values)\n\n df = diagnostics.rolling_mean_by_h(x, h, w=10, name=\"x\")\n assert np.allclose(np.array([7.0]), df[\"horizon\"].values)\n assert np.allclose(np.array([4.5]), df[\"x\"].values)\n\n def test_rolling_median(self):\n x = np.arange(10)\n h = np.arange(10)\n df = diagnostics.rolling_median_by_h(x=x, h=h, w=1, name=\"x\")\n assert np.array_equal(x, df[\"x\"].values)\n assert np.array_equal(h, df[\"horizon\"].values)\n\n df = diagnostics.rolling_median_by_h(x, h, w=4, name=\"x\")\n x_true = x[3:] - 1.5\n assert np.allclose(x_true, df[\"x\"].values)\n assert np.array_equal(np.arange(3, 10), df[\"horizon\"].values)\n\n h = np.array([1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 7.0, 7.0])\n x_true = np.array([1.0, 5.0, 8.0])\n h_true = np.array([3.0, 4.0, 7.0])\n df = diagnostics.rolling_median_by_h(x, h, w=3, name=\"x\")\n assert np.allclose(x_true, df[\"x\"].values)\n assert np.array_equal(h_true, df[\"horizon\"].values)\n\n df = diagnostics.rolling_median_by_h(x, h, w=10, name=\"x\")\n assert np.allclose(np.array([7.0]), df[\"horizon\"].values)\n assert np.allclose(np.array([4.5]), df[\"x\"].values)\n\n\nclass TestProphetCopy:\n @pytest.fixture(scope=\"class\")\n def data(self, daily_univariate_ts):\n df = daily_univariate_ts.copy()\n df[\"cap\"] = 200.0\n df[\"binary_feature\"] = [0] * 255 + [1] * 255\n return df\n\n def test_prophet_copy(self, data, backend):\n # These values are created except for its default values\n holiday = pd.DataFrame({\"ds\": pd.to_datetime([\"2016-12-25\"]), \"holiday\": [\"x\"]})\n products = itertools.product(\n [\"linear\", \"logistic\"], # growth\n [None, pd.to_datetime([\"2016-12-25\"])], # changepoints\n [3], # n_changepoints\n [0.9], # changepoint_range\n [True, False], # yearly_seasonality\n [True, False], # weekly_seasonality\n [True, False], # daily_seasonality\n [None, holiday], # holidays\n [\"additive\", \"multiplicative\"], # seasonality_mode\n [1.1], # seasonality_prior_scale\n [1.1], # holidays_prior_scale\n [0.1], # changepoint_prior_scale\n [100], # mcmc_samples\n [0.9], # interval_width\n [200], # uncertainty_samples\n )\n # Values should be copied correctly\n for product in products:\n m1 = Prophet(*product, stan_backend=backend)\n m1.country_holidays = \"US\"\n m1.history = m1.setup_dataframe(data.copy(), initialize_scales=True)\n m1.set_auto_seasonalities()\n m2 = diagnostics.prophet_copy(m1)\n assert m1.growth == m2.growth\n assert m1.n_changepoints == m2.n_changepoints\n assert m1.changepoint_range == m2.changepoint_range\n if m1.changepoints is None:\n assert m1.changepoints == m2.changepoints\n else:\n assert m1.changepoints.equals(m2.changepoints)\n assert False == m2.yearly_seasonality\n assert False == m2.weekly_seasonality\n assert False == m2.daily_seasonality\n assert m1.yearly_seasonality == (\"yearly\" in m2.seasonalities)\n assert m1.weekly_seasonality == (\"weekly\" in m2.seasonalities)\n assert m1.daily_seasonality == (\"daily\" in m2.seasonalities)\n if m1.holidays is None:\n assert m1.holidays == m2.holidays\n else:\n assert (m1.holidays == m2.holidays).values.all()\n assert m1.country_holidays == m2.country_holidays\n assert m1.holidays_mode == m2.holidays_mode\n assert m1.seasonality_mode == m2.seasonality_mode\n assert m1.seasonality_prior_scale == m2.seasonality_prior_scale\n assert m1.changepoint_prior_scale == m2.changepoint_prior_scale\n assert m1.holidays_prior_scale == m2.holidays_prior_scale\n assert m1.mcmc_samples == m2.mcmc_samples\n assert m1.interval_width == m2.interval_width\n assert m1.uncertainty_samples == m2.uncertainty_samples\n\n def test_prophet_copy_custom(self, data, backend):\n changepoints = pd.date_range(\"2012-06-15\", \"2012-09-15\")\n cutoff = pd.Timestamp(\"2012-07-25\")\n m1 = Prophet(changepoints=changepoints, stan_backend=backend)\n m1.add_seasonality(\"custom\", 10, 5)\n m1.add_regressor(\"binary_feature\")\n m1.fit(data)\n m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)\n changepoints = changepoints[changepoints < cutoff]\n assert (changepoints == m2.changepoints).all()\n assert \"custom\" in m2.seasonalities\n assert \"binary_feature\" in m2.extra_regressors\n" }, { "path": "python/prophet/tests/test_prophet.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom prophet import Prophet\nfrom prophet.utilities import warm_start_params\n\n\ndef train_test_split(ts_data: pd.DataFrame, n_test_rows: int) -> pd.DataFrame:\n train = ts_data.head(ts_data.shape[0] - n_test_rows)\n test = ts_data.tail(n_test_rows)\n return train.reset_index(), test.reset_index()\n\n\ndef rmse(predictions, targets) -> float:\n return np.sqrt(np.mean((predictions - targets) ** 2))\n\n\nclass TestProphetFitPredictDefault:\n @pytest.mark.parametrize(\n \"scaling,expected\",\n [(\"absmax\", 10.64), (\"minmax\", 11.13)],\n ids=[\"absmax\", \"minmax\"]\n )\n def test_fit_predict(self, daily_univariate_ts, backend, scaling, expected):\n test_days = 30\n train, test = train_test_split(daily_univariate_ts, test_days)\n forecaster = Prophet(stan_backend=backend, scaling=scaling)\n forecaster.fit(train, seed=1237861298)\n np.random.seed(876543987)\n future = forecaster.make_future_dataframe(test_days, include_history=False)\n future = forecaster.predict(future)\n res = rmse(future[\"yhat\"], test[\"y\"])\n # Higher threshold due to cmdstan 2.33.1 producing numerical differences for macOS Intel (ARM is fine).\n assert res == pytest.approx(expected, 0.1), \"backend: {}\".format(forecaster.stan_backend)\n\n @pytest.mark.parametrize(\n \"scaling,expected\",\n [(\"absmax\", 23.44), (\"minmax\", 11.29)],\n ids=[\"absmax\", \"minmax\"]\n )\n def test_fit_predict_newton(self, daily_univariate_ts, backend, scaling, expected):\n test_days = 30\n train, test = train_test_split(daily_univariate_ts, test_days)\n forecaster = Prophet(stan_backend=backend, scaling=scaling)\n forecaster.fit(train, algorithm=\"Newton\", seed=1237861298)\n np.random.seed(876543987)\n future = forecaster.make_future_dataframe(test_days, include_history=False)\n future = forecaster.predict(future)\n res = rmse(future[\"yhat\"], test[\"y\"])\n assert res == pytest.approx(expected, 0.01), \"backend: {}\".format(forecaster.stan_backend)\n\n @pytest.mark.parametrize(\n \"scaling,expected\",\n [(\"absmax\", 127.01), (\"minmax\", 93.45)],\n ids=[\"absmax\", \"minmax\"]\n )\n def test_fit_predict_large_numbers(self, large_numbers_ts, backend, scaling, expected):\n test_days = 30\n train, test = train_test_split(large_numbers_ts, test_days)\n forecaster = Prophet(stan_backend=backend, scaling=scaling)\n forecaster.fit(train, seed=1237861298, sig_figs=6)\n np.random.seed(876543987)\n future = forecaster.make_future_dataframe(test_days, include_history=False)\n future = forecaster.predict(future)\n res = rmse(future[\"yhat\"], test[\"y\"])\n assert res == pytest.approx(expected, 0.01), \"backend: {}\".format(forecaster.stan_backend)\n\n @pytest.mark.slow\n def test_fit_predict_sampling(self, daily_univariate_ts, backend):\n test_days = 30\n train, test = train_test_split(daily_univariate_ts, test_days)\n forecaster = Prophet(mcmc_samples=500, stan_backend=backend)\n # chains adjusted from 4 to 7 to satisfy test for cmdstanpy\n forecaster.fit(train, seed=1237861298, chains=7, show_progress=False)\n np.random.seed(876543987)\n future = forecaster.make_future_dataframe(test_days, include_history=False)\n future = forecaster.predict(future)\n # this gives ~ 215.77\n res = rmse(future[\"yhat\"], test[\"y\"])\n assert 236 < res < 193, \"backend: {}\".format(forecaster.stan_backend)\n\n def test_fit_predict_no_seasons(self, daily_univariate_ts, backend):\n test_days = 30\n train, _ = train_test_split(daily_univariate_ts, test_days)\n forecaster = Prophet(\n weekly_seasonality=False, yearly_seasonality=False, stan_backend=backend\n )\n forecaster.fit(train)\n future = forecaster.make_future_dataframe(test_days, include_history=False)\n result = forecaster.predict(future)\n assert (future.ds == result.ds).all()\n\n def test_fit_predict_no_changepoints(self, daily_univariate_ts, backend):\n test_days = daily_univariate_ts.shape[0] // 2\n train, future = train_test_split(daily_univariate_ts, test_days)\n forecaster = Prophet(n_changepoints=0, stan_backend=backend)\n forecaster.fit(train)\n forecaster.predict(future)\n assert forecaster.params is not None\n assert forecaster.n_changepoints == 0\n\n @pytest.mark.slow\n def test_fit_predict_no_changepoints_mcmc(self, daily_univariate_ts, backend):\n test_days = daily_univariate_ts.shape[0] // 2\n train, future = train_test_split(daily_univariate_ts, test_days)\n forecaster = Prophet(n_changepoints=0, mcmc_samples=100, stan_backend=backend)\n forecaster.fit(train, show_progress=False)\n forecaster.predict(future)\n assert forecaster.params is not None\n assert forecaster.n_changepoints == 0\n\n def test_fit_changepoint_not_in_history(self, daily_univariate_ts, backend):\n train = daily_univariate_ts[\n (daily_univariate_ts[\"ds\"] < \"2013-01-01\") | (daily_univariate_ts[\"ds\"] > \"2014-01-01\")\n ]\n future = pd.DataFrame({\"ds\": daily_univariate_ts[\"ds\"]})\n prophet = Prophet(changepoints=[\"2013-06-06\"], stan_backend=backend)\n forecaster = prophet\n forecaster.fit(train)\n forecaster.predict(future)\n assert forecaster.params is not None\n assert forecaster.n_changepoints == 1\n\n def test_fit_predict_duplicates(self, daily_univariate_ts, backend):\n \"\"\"\n The underlying model should still fit successfully when there are duplicate dates in the history.\n The model essentially sees this as multiple observations for the same time value, and fits the parameters\n accordingly.\n \"\"\"\n train, test = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n repeated_obs = train.copy()\n repeated_obs[\"y\"] += 10\n train = pd.concat([train, repeated_obs])\n forecaster = Prophet(stan_backend=backend)\n forecaster.fit(train)\n forecaster.predict(test)\n\n def test_fit_predict_constant_history(self, daily_univariate_ts, backend):\n \"\"\"\n When the training data history is constant, Prophet should predict the same value for all future dates.\n \"\"\"\n for constant in [0, 20]:\n train, test = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n train[\"y\"] = constant\n forecaster = Prophet(stan_backend=backend)\n forecaster.fit(train)\n result = forecaster.predict(test)\n assert result[\"yhat\"].values[-1] == constant\n\n def test_fit_predict_uncertainty_disabled(self, daily_univariate_ts, backend):\n test_days = daily_univariate_ts.shape[0] // 2\n train, future = train_test_split(daily_univariate_ts, test_days)\n for uncertainty in [0, False]:\n forecaster = Prophet(uncertainty_samples=uncertainty, stan_backend=backend)\n forecaster.fit(train)\n result = forecaster.predict(future)\n expected_cols = [\n \"ds\",\n \"trend\",\n \"additive_terms\",\n \"multiplicative_terms\",\n \"weekly\",\n \"yhat\",\n ]\n assert all(col in expected_cols for col in result.columns.tolist())\n\n\nclass TestProphetDataPrep:\n def test_setup_dataframe(self, daily_univariate_ts, backend):\n \"\"\"Test that the columns 't' and 'y_scaled' are added to the dataframe.\"\"\"\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n m = Prophet(stan_backend=backend)\n history = m.setup_dataframe(train, initialize_scales=True)\n\n assert \"t\" in history\n assert history[\"t\"].min() == 0.0\n assert history[\"t\"].max() == 1.0\n\n assert \"y_scaled\" in history\n assert history[\"y_scaled\"].max() == 1.0\n\n def test_setup_dataframe_ds_column(self, daily_univariate_ts, backend):\n \"\"\"Test case where 'ds' exists as an index name and column. Prophet should use the column.\"\"\"\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n train.index = pd.to_datetime([\"1970-01-01\" for _ in range(train.shape[0])])\n train.index.rename(\"ds\", inplace=True)\n m = Prophet(stan_backend=backend)\n m.fit(train)\n assert np.all(m.history[\"ds\"].values == train[\"ds\"].values)\n\n def test_logistic_floor(self, daily_univariate_ts, backend):\n \"\"\"Test the scaling of y with logistic growth and a floor/cap.\"\"\"\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n train[\"floor\"] = 10.0\n train[\"cap\"] = 80.0\n m = Prophet(growth=\"logistic\", stan_backend=backend)\n m.fit(train)\n assert m.logistic_floor\n assert \"floor\" in m.history\n assert m.history[\"y_scaled\"][0] == 1.0\n for col in [\"y\", \"floor\", \"cap\"]:\n train[col] += 10.0\n m2 = Prophet(growth=\"logistic\", stan_backend=backend)\n m2.fit(train)\n assert m2.history[\"y_scaled\"][0] == pytest.approx(1.0, 0.01)\n\n def test_logistic_floor_minmax(self, daily_univariate_ts, backend):\n \"\"\"Test the scaling of y with logistic growth and a floor/cap.\"\"\"\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n train[\"floor\"] = 10.0\n train[\"cap\"] = 80.0\n m = Prophet(growth=\"logistic\", stan_backend=backend, scaling=\"minmax\")\n m.fit(train)\n assert m.logistic_floor\n assert \"floor\" in m.history\n assert m.history[\"y_scaled\"].min() > 0.0\n assert m.history[\"y_scaled\"].max() < 1.0\n for col in [\"y\", \"floor\", \"cap\"]:\n train[col] += 10.0\n m2 = Prophet(growth=\"logistic\", stan_backend=backend, scaling=\"minmax\")\n m2.fit(train)\n assert m2.history[\"y_scaled\"].min() > 0.0\n assert m2.history[\"y_scaled\"].max() < 1.0\n # Check that the scaling is the same\n assert m2.history['y_scaled'].mean() == m.history['y_scaled'].mean()\n\n def test_make_future_dataframe(self, daily_univariate_ts, backend):\n train = daily_univariate_ts.head(468 // 2)\n forecaster = Prophet(stan_backend=backend)\n forecaster.fit(train)\n future = forecaster.make_future_dataframe(periods=3, freq=\"D\", include_history=False)\n correct = pd.DatetimeIndex([\"2013-04-26\", \"2013-04-27\", \"2013-04-28\"])\n assert len(future) == 3\n assert np.all(future[\"ds\"].values == correct.values)\n\n future = forecaster.make_future_dataframe(periods=3, freq=pd.tseries.offsets.MonthEnd(1), include_history=False)\n correct = pd.DatetimeIndex([\"2013-04-30\", \"2013-05-31\", \"2013-06-30\"])\n assert len(future) == 3\n assert np.all(future[\"ds\"].values == correct.values)\n\n def test_make_future_dataframe_include_history(self, daily_univariate_ts, backend):\n train = daily_univariate_ts.head(468 // 2).copy()\n #cover history with NAs\n train.loc[train.sample(10).index, \"y\"] = np.nan\n\n forecaster = Prophet(stan_backend=backend)\n forecaster.fit(train)\n future = forecaster.make_future_dataframe(periods=3, freq=\"D\", include_history=True)\n\n assert len(future) == train.shape[0] + 3\n\nclass TestProphetTrendComponent:\n def test_invalid_growth_input(self, backend):\n msg = 'Parameter \"growth\" should be \"linear\", ' '\"logistic\" or \"flat\".'\n with pytest.raises(ValueError, match=msg):\n Prophet(growth=\"constant\", stan_backend=backend)\n\n def test_growth_init(self, daily_univariate_ts, backend):\n model = Prophet(growth=\"logistic\", stan_backend=backend)\n train = daily_univariate_ts.iloc[:468].copy()\n train[\"cap\"] = train[\"y\"].max()\n\n history = model.setup_dataframe(train, initialize_scales=True)\n\n k, m = model.linear_growth_init(history)\n assert k == pytest.approx(0.3055671)\n assert m == pytest.approx(0.5307511)\n\n k, m = model.logistic_growth_init(history)\n assert k == pytest.approx(1.507925, abs=1e-4)\n assert m == pytest.approx(-0.08167497, abs=1e-4)\n\n k, m = model.flat_growth_init(history)\n assert k == 0\n assert m == pytest.approx(0.49335657, abs=1e-4)\n\n def test_growth_init_minmax(self, daily_univariate_ts, backend):\n model = Prophet(growth=\"logistic\", stan_backend=backend, scaling=\"minmax\")\n train = daily_univariate_ts.iloc[:468].copy()\n train[\"cap\"] = train[\"y\"].max()\n\n history = model.setup_dataframe(train, initialize_scales=True)\n\n k, m = model.linear_growth_init(history)\n assert k == pytest.approx(0.4053406)\n assert m == pytest.approx(0.3775322)\n\n k, m = model.logistic_growth_init(history)\n assert k == pytest.approx(1.782523, abs=1e-4)\n assert m == pytest.approx(0.280521, abs=1e-4)\n\n k, m = model.flat_growth_init(history)\n assert k == 0\n assert m == pytest.approx(0.32792770, abs=1e-4)\n\n @pytest.mark.parametrize(\"scaling\",[\"absmax\",\"minmax\"])\n def test_flat_growth(self, backend, scaling):\n m = Prophet(growth=\"flat\", stan_backend=backend, scaling=scaling)\n x = np.linspace(0, 2 * np.pi, 8 * 7)\n history = pd.DataFrame(\n {\n \"ds\": pd.date_range(start=\"2020-01-01\", periods=8 * 7, freq=\"D\"),\n \"y\": 30 + np.sin(x * 8.0),\n }\n )\n m.fit(history)\n future = m.make_future_dataframe(10, include_history=True)\n fcst = m.predict(future)\n m_ = m.params[\"m\"][0, 0]\n k = m.params[\"k\"][0, 0]\n assert k == pytest.approx(0.0)\n assert fcst[\"trend\"].unique()[0] == pytest.approx((m_ * m.y_scale) + m.y_min)\n assert np.round((m_ * m.y_scale) + m.y_min) == 30.0\n\n def test_piecewise_linear(self, backend):\n model = Prophet(stan_backend=backend)\n\n t = np.arange(11.0)\n m = 0\n k = 1.0\n deltas = np.array([0.5])\n changepoint_ts = np.array([5])\n\n y = model.piecewise_linear(t, deltas, k, m, changepoint_ts)\n y_true = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.5, 8.0, 9.5, 11.0, 12.5])\n assert (y - y_true).sum() == 0.0\n\n t = t[8:]\n y_true = y_true[8:]\n y = model.piecewise_linear(t, deltas, k, m, changepoint_ts)\n assert (y - y_true).sum() == 0.0\n\n def test_piecewise_logistic(self, backend):\n model = Prophet(stan_backend=backend)\n\n t = np.arange(11.0)\n cap = np.ones(11) * 10\n m = 0\n k = 1.0\n deltas = np.array([0.5])\n changepoint_ts = np.array([5])\n\n y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)\n y_true = np.array(\n [\n 5.000000,\n 7.310586,\n 8.807971,\n 9.525741,\n 9.820138,\n 9.933071,\n 9.984988,\n 9.996646,\n 9.999252,\n 9.999833,\n 9.999963,\n ]\n )\n\n t = t[8:]\n y_true = y_true[8:]\n cap = cap[8:]\n y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)\n assert (y - y_true).sum() == pytest.approx(0.0, abs=1e-5)\n\n def test_flat_trend(self, backend):\n model = Prophet(stan_backend=backend)\n t = np.arange(11)\n m = 0.5\n y = model.flat_trend(t, m)\n y_true = np.array([0.5] * 11)\n assert (y - y_true).sum() == 0.0\n t = t[8:]\n y_true = y_true[8:]\n y = model.flat_trend(t, m)\n assert (y - y_true).sum() == 0.0\n\n def test_get_changepoints(self, daily_univariate_ts, backend):\n \"\"\"\n By default, Prophet uses the first 80% of the history to detect changepoints.\n \"\"\"\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n m = Prophet(stan_backend=backend)\n history = m.setup_dataframe(train, initialize_scales=True)\n m.history = history\n m.set_changepoints()\n cp = m.changepoints_t\n assert cp.shape[0] == m.n_changepoints\n assert len(cp.shape) == 1\n assert cp.min() > 0\n cp_indx = int(np.ceil(0.8 * history.shape[0]))\n assert cp.max() <= history[\"t\"].values[cp_indx]\n\n def test_set_changepoint_range(self, daily_univariate_ts, backend):\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n m = Prophet(changepoint_range=0.4, stan_backend=backend)\n history = m.setup_dataframe(train, initialize_scales=True)\n m.history = history\n m.set_changepoints()\n cp = m.changepoints_t\n assert cp.shape[0] == m.n_changepoints\n assert len(cp.shape) == 1\n assert cp.min() > 0\n cp_indx = int(np.ceil(0.4 * history.shape[0]))\n cp.max() <= history[\"t\"].values[cp_indx]\n for out_of_range in [-0.1, 2]:\n with pytest.raises(ValueError):\n m = Prophet(changepoint_range=out_of_range, stan_backend=backend)\n\n def test_get_zero_changepoints(self, daily_univariate_ts, backend):\n train, _ = train_test_split(daily_univariate_ts, daily_univariate_ts.shape[0] // 2)\n m = Prophet(n_changepoints=0, stan_backend=backend)\n history = m.setup_dataframe(train, initialize_scales=True)\n m.history = history\n m.set_changepoints()\n cp = m.changepoints_t\n assert cp.shape[0] == 1\n assert cp[0] == 0\n\n def test_override_n_changepoints(self, daily_univariate_ts, backend):\n train = daily_univariate_ts.head(20).copy()\n m = Prophet(n_changepoints=15, stan_backend=backend)\n history = m.setup_dataframe(train, initialize_scales=True)\n m.history = history\n m.set_changepoints()\n assert m.n_changepoints == 15\n cp = m.changepoints_t\n assert cp.shape[0] == 15\n\n\nclass TestProphetSeasonalComponent:\n def test_fourier_series_weekly(self, daily_univariate_ts):\n mat = Prophet.fourier_series(daily_univariate_ts[\"ds\"], 7, 3)\n # These are from the R forecast package directly.\n true_values = np.array([0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837, -0.9009689])\n assert np.sum((mat[0] - true_values) ** 2) == pytest.approx(0.0)\n\n def test_fourier_series_yearly(self, daily_univariate_ts):\n mat = Prophet.fourier_series(daily_univariate_ts[\"ds\"], 365.25, 3)\n # These are from the R forecast package directly.\n true_values = np.array(\n [0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249, 0.6874572]\n )\n assert np.sum((mat[0] - true_values) ** 2) == pytest.approx(0.0)\n\n def test_auto_weekly_seasonality(self, daily_univariate_ts, backend):\n # Should be enabled\n train = daily_univariate_ts.head(15)\n m = Prophet(stan_backend=backend)\n assert m.weekly_seasonality == \"auto\"\n m.fit(train)\n assert \"weekly\" in m.seasonalities\n assert m.seasonalities[\"weekly\"] == {\n \"period\": 7,\n \"fourier_order\": 3,\n \"prior_scale\": 10.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n # Should be disabled due to too short history\n train = daily_univariate_ts.head(9)\n m = Prophet(stan_backend=backend)\n m.fit(train)\n assert \"weekly\" not in m.seasonalities\n m = Prophet(weekly_seasonality=True, stan_backend=backend)\n m.fit(train)\n assert \"weekly\" in m.seasonalities\n # Should be False due to weekly spacing\n train = daily_univariate_ts.iloc[::7, :]\n m = Prophet(stan_backend=backend)\n m.fit(train)\n assert \"weekly\" not in m.seasonalities\n m = Prophet(weekly_seasonality=2, seasonality_prior_scale=3.0, stan_backend=backend)\n m.fit(daily_univariate_ts)\n assert m.seasonalities[\"weekly\"] == {\n \"period\": 7,\n \"fourier_order\": 2,\n \"prior_scale\": 3.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n\n def test_auto_yearly_seasonality(self, daily_univariate_ts, backend):\n # Should be enabled\n m = Prophet(stan_backend=backend)\n assert m.yearly_seasonality == \"auto\"\n m.fit(daily_univariate_ts)\n assert \"yearly\" in m.seasonalities\n assert m.seasonalities[\"yearly\"] == {\n \"period\": 365.25,\n \"fourier_order\": 10,\n \"prior_scale\": 10.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n # Should be disabled due to too short history\n train = daily_univariate_ts.head(240)\n m = Prophet(stan_backend=backend)\n m.fit(train)\n assert \"yearly\" not in m.seasonalities\n m = Prophet(yearly_seasonality=True, stan_backend=backend)\n m.fit(train)\n assert \"yearly\" in m.seasonalities\n m = Prophet(yearly_seasonality=7, seasonality_prior_scale=3.0, stan_backend=backend)\n m.fit(daily_univariate_ts)\n assert m.seasonalities[\"yearly\"] == {\n \"period\": 365.25,\n \"fourier_order\": 7,\n \"prior_scale\": 3.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n\n def test_auto_daily_seasonality(self, daily_univariate_ts, subdaily_univariate_ts, backend):\n # Should be enabled\n m = Prophet(stan_backend=backend)\n assert m.daily_seasonality == \"auto\"\n m.fit(subdaily_univariate_ts)\n assert \"daily\" in m.seasonalities\n assert m.seasonalities[\"daily\"] == {\n \"period\": 1,\n \"fourier_order\": 4,\n \"prior_scale\": 10.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n # Should be disabled due to too short history\n train = subdaily_univariate_ts.head(430)\n m = Prophet(stan_backend=backend)\n m.fit(train)\n assert \"daily\" not in m.seasonalities\n m = Prophet(daily_seasonality=True, stan_backend=backend)\n m.fit(train)\n assert \"daily\" in m.seasonalities\n m = Prophet(daily_seasonality=7, seasonality_prior_scale=3.0, stan_backend=backend)\n m.fit(subdaily_univariate_ts)\n assert m.seasonalities[\"daily\"] == {\n \"period\": 1,\n \"fourier_order\": 7,\n \"prior_scale\": 3.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n m = Prophet(stan_backend=backend)\n m.fit(daily_univariate_ts)\n assert \"daily\" not in m.seasonalities\n\n def test_set_seasonality_mode(self, backend):\n # Setting attribute\n m = Prophet(stan_backend=backend)\n assert m.seasonality_mode == \"additive\"\n m = Prophet(seasonality_mode=\"multiplicative\", stan_backend=backend)\n assert m.seasonality_mode == \"multiplicative\"\n with pytest.raises(ValueError):\n Prophet(seasonality_mode=\"batman\", stan_backend=backend)\n\n def test_set_holidays_mode(self, backend):\n # Setting attribute\n m = Prophet(stan_backend=backend)\n assert m.holidays_mode == \"additive\"\n m = Prophet(seasonality_mode=\"multiplicative\", stan_backend=backend)\n assert m.holidays_mode == \"multiplicative\"\n m = Prophet(holidays_mode=\"multiplicative\", stan_backend=backend)\n assert m.holidays_mode == \"multiplicative\"\n with pytest.raises(ValueError):\n Prophet(holidays_mode=\"batman\", stan_backend=backend)\n\n def test_seasonality_modes(self, daily_univariate_ts, backend):\n # Model with holidays, seasonalities, and extra regressors\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2016-12-25\"]),\n \"holiday\": [\"xmas\"],\n \"lower_window\": [-1],\n \"upper_window\": [0],\n }\n )\n m = Prophet(seasonality_mode=\"multiplicative\", holidays=holidays, stan_backend=backend)\n m.add_seasonality(\"monthly\", period=30, mode=\"additive\", fourier_order=3)\n m.add_regressor(\"binary_feature\", mode=\"additive\")\n m.add_regressor(\"numeric_feature\")\n # Construct seasonal features\n df = daily_univariate_ts.copy()\n df[\"binary_feature\"] = [0] * 255 + [1] * 255\n df[\"numeric_feature\"] = range(510)\n df = m.setup_dataframe(df, initialize_scales=True)\n m.history = df.copy()\n m.set_auto_seasonalities()\n seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(df)\n assert sum(component_cols[\"additive_terms\"]) == 7\n assert sum(component_cols[\"multiplicative_terms\"]) == 29\n assert set(modes[\"additive\"]) == {\n \"monthly\",\n \"binary_feature\",\n \"additive_terms\",\n \"extra_regressors_additive\",\n }\n assert set(modes[\"multiplicative\"]) == {\n \"weekly\",\n \"yearly\",\n \"xmas\",\n \"numeric_feature\",\n \"multiplicative_terms\",\n \"extra_regressors_multiplicative\",\n \"holidays\",\n }\n\n\nclass TestProphetCustomSeasonalComponent:\n def test_custom_monthly_seasonality(self, backend):\n m = Prophet(stan_backend=backend)\n m.add_seasonality(name=\"monthly\", period=30, fourier_order=5, prior_scale=2.0)\n assert m.seasonalities[\"monthly\"] == {\n \"period\": 30,\n \"fourier_order\": 5,\n \"prior_scale\": 2.0,\n \"mode\": \"additive\",\n \"condition_name\": None,\n }\n\n def test_duplicate_component_names(self, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2017-01-02\"]),\n \"holiday\": [\"special_day\"],\n \"prior_scale\": [4.0],\n }\n )\n m = Prophet(holidays=holidays, stan_backend=backend)\n\n with pytest.raises(ValueError):\n m.add_seasonality(name=\"special_day\", period=30, fourier_order=5)\n with pytest.raises(ValueError):\n m.add_seasonality(name=\"trend\", period=30, fourier_order=5)\n m.add_seasonality(name=\"weekly\", period=30, fourier_order=5)\n\n def test_custom_fourier_order(self, backend):\n \"\"\"Fourier order cannot be <= 0\"\"\"\n m = Prophet(stan_backend=backend)\n with pytest.raises(ValueError):\n m.add_seasonality(name=\"weekly\", period=7, fourier_order=0)\n with pytest.raises(ValueError):\n m.add_seasonality(name=\"weekly\", period=7, fourier_order=-1)\n\n def test_custom_priors(self, daily_univariate_ts, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2017-01-02\"]),\n \"holiday\": [\"special_day\"],\n \"prior_scale\": [4.0],\n }\n )\n m = Prophet(\n holidays=holidays,\n yearly_seasonality=False,\n seasonality_mode=\"multiplicative\",\n stan_backend=backend,\n )\n m.add_seasonality(\n name=\"monthly\", period=30, fourier_order=5, prior_scale=2.0, mode=\"additive\"\n )\n m.fit(daily_univariate_ts)\n assert m.seasonalities[\"monthly\"][\"mode\"] == \"additive\"\n assert m.seasonalities[\"weekly\"][\"mode\"] == \"multiplicative\"\n seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(\n m.history\n )\n assert sum(component_cols[\"monthly\"]) == 10\n assert sum(component_cols[\"special_day\"]) == 1\n assert sum(component_cols[\"weekly\"]) == 6\n assert sum(component_cols[\"additive_terms\"]) == 10\n assert sum(component_cols[\"multiplicative_terms\"]) == 7\n\n if seasonal_features.columns[0] == \"monthly_delim_1\":\n true = [2.0] * 10 + [10.0] * 6 + [4.0]\n assert sum(component_cols[\"monthly\"][:10]) == 10\n assert sum(component_cols[\"weekly\"][10:16]) == 6\n else:\n true = [10.0] * 6 + [2.0] * 10 + [4.0]\n assert sum(component_cols[\"weekly\"][:6]) == 6\n assert sum(component_cols[\"monthly\"][6:16]) == 10\n assert prior_scales == true\n\n def test_conditional_custom_seasonality(self, daily_univariate_ts, backend):\n m = Prophet(weekly_seasonality=False, yearly_seasonality=False, stan_backend=backend)\n m.add_seasonality(\n name=\"conditional_weekly\",\n period=7,\n fourier_order=3,\n prior_scale=2.0,\n condition_name=\"is_conditional_week\",\n )\n m.add_seasonality(name=\"normal_monthly\", period=30.5, fourier_order=5, prior_scale=2.0)\n df = daily_univariate_ts.copy()\n with pytest.raises(ValueError):\n # Require all conditions names in df\n m.fit(df)\n df[\"is_conditional_week\"] = [0] * 255 + [2] * 255\n with pytest.raises(ValueError):\n # Require boolean compatible values\n m.fit(df)\n df[\"is_conditional_week\"] = [0] * 255 + [1] * 255\n m.fit(df)\n assert m.seasonalities[\"conditional_weekly\"] == {\n \"period\": 7,\n \"fourier_order\": 3,\n \"prior_scale\": 2.0,\n \"mode\": \"additive\",\n \"condition_name\": \"is_conditional_week\",\n }\n assert m.seasonalities[\"normal_monthly\"][\"condition_name\"] is None\n seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(\n m.history\n )\n # Confirm that only values without is_conditional_week has non zero entries\n condition_cols = [\n c for c in seasonal_features.columns if c.startswith(\"conditional_weekly\")\n ]\n assert np.array_equal(\n (seasonal_features[condition_cols] != 0).any(axis=1).values,\n df[\"is_conditional_week\"].values,\n )\n\n\nclass TestProphetHolidays:\n def test_holidays_lower_window(self, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2016-12-25\"]),\n \"holiday\": [\"xmas\"],\n \"lower_window\": [-1],\n \"upper_window\": [0],\n }\n )\n model = Prophet(holidays=holidays, stan_backend=backend)\n df = pd.DataFrame({\"ds\": pd.date_range(\"2016-12-20\", \"2016-12-31\")})\n feats, priors, names = model.make_holiday_features(df[\"ds\"], model.holidays)\n assert feats.shape == (df.shape[0], 2)\n assert (feats.sum(axis=0) - np.array([1.0, 1.0])).sum() == 0.0\n assert priors == [10.0, 10.0] # Default prior\n assert names == [\"xmas\"]\n\n def test_holidays_upper_window(self, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2016-12-25\"]),\n \"holiday\": [\"xmas\"],\n \"lower_window\": [-1],\n \"upper_window\": [10],\n }\n )\n m = Prophet(holidays=holidays, stan_backend=backend)\n df = pd.DataFrame({\"ds\": pd.date_range(\"2016-12-20\", \"2016-12-31\")})\n feats, priors, names = m.make_holiday_features(df[\"ds\"], m.holidays)\n # 12 columns generated even though only 8 overlap\n assert feats.shape == (df.shape[0], 12)\n assert priors == [10.0 for _ in range(12)]\n assert names == [\"xmas\"]\n\n def test_holidays_priors(self, backend):\n # Check prior specifications\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2016-12-25\", \"2017-12-25\"]),\n \"holiday\": [\"xmas\", \"xmas\"],\n \"lower_window\": [-1, -1],\n \"upper_window\": [0, 0],\n \"prior_scale\": [5.0, 5.0],\n }\n )\n m = Prophet(holidays=holidays, stan_backend=backend)\n df = pd.DataFrame({\"ds\": pd.date_range(\"2016-12-20\", \"2016-12-31\")})\n feats, priors, names = m.make_holiday_features(df[\"ds\"], m.holidays)\n assert priors == [5.0, 5.0]\n assert names == [\"xmas\"]\n # 2 different priors\n holidays2 = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2012-06-06\", \"2013-06-06\"]),\n \"holiday\": [\"seans-bday\"] * 2,\n \"lower_window\": [0] * 2,\n \"upper_window\": [1] * 2,\n \"prior_scale\": [8] * 2,\n }\n )\n holidays2 = pd.concat((holidays, holidays2), sort=True)\n m = Prophet(holidays=holidays2, stan_backend=backend)\n feats, priors, names = m.make_holiday_features(df[\"ds\"], m.holidays)\n pn = zip(priors, [s.split(\"_delim_\")[0] for s in feats.columns])\n for t in pn:\n assert t in [(8.0, \"seans-bday\"), (5.0, \"xmas\")]\n holidays2 = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2012-06-06\", \"2013-06-06\"]),\n \"holiday\": [\"seans-bday\"] * 2,\n \"lower_window\": [0] * 2,\n \"upper_window\": [1] * 2,\n }\n )\n holidays2 = pd.concat((holidays, holidays2), sort=True)\n feats, priors, names = Prophet(\n holidays=holidays2, holidays_prior_scale=4, stan_backend=backend\n ).make_holiday_features(df[\"ds\"], holidays2)\n assert set(priors) == {4.0, 5.0}\n\n def test_holidays_bad_priors(self, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2016-12-25\", \"2016-12-27\"]),\n \"holiday\": [\"xmasish\", \"xmasish\"],\n \"lower_window\": [-1, -1],\n \"upper_window\": [0, 0],\n \"prior_scale\": [5.0, 6.0],\n }\n )\n df = pd.DataFrame({\"ds\": pd.date_range(\"2016-12-20\", \"2016-12-31\")})\n with pytest.raises(ValueError):\n Prophet(holidays=holidays, stan_backend=backend).make_holiday_features(\n df[\"ds\"], holidays\n )\n\n def test_fit_with_holidays(self, daily_univariate_ts, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2012-06-06\", \"2013-06-06\"]),\n \"holiday\": [\"seans-bday\"] * 2,\n \"lower_window\": [0] * 2,\n \"upper_window\": [1] * 2,\n }\n )\n model = Prophet(holidays=holidays, uncertainty_samples=0, stan_backend=backend)\n model.fit(daily_univariate_ts).predict()\n\n def test_fit_predict_with_country_holidays(self, daily_univariate_ts, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2012-06-06\", \"2013-06-06\"]),\n \"holiday\": [\"seans-bday\"] * 2,\n \"lower_window\": [0] * 2,\n \"upper_window\": [1] * 2,\n }\n )\n # Test with holidays and country_holidays\n model = Prophet(holidays=holidays, uncertainty_samples=0, stan_backend=backend)\n model.add_country_holidays(country_name=\"US\")\n model.fit(daily_univariate_ts).predict()\n # There are training holidays missing in the test set\n train = daily_univariate_ts.head(154)\n future = daily_univariate_ts.tail(355)\n model = Prophet(uncertainty_samples=0, stan_backend=backend)\n model.add_country_holidays(country_name=\"US\")\n model.fit(train).predict(future)\n # There are test holidays missing in the training set\n train = daily_univariate_ts.tail(355)\n model = Prophet(uncertainty_samples=0, stan_backend=backend)\n model.add_country_holidays(country_name=\"US\")\n model.fit(train)\n future = model.make_future_dataframe(periods=60, include_history=False)\n model.predict(future)\n\n def test_subdaily_holidays(self, subdaily_univariate_ts, backend):\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2017-01-02\"]),\n \"holiday\": [\"special_day\"],\n }\n )\n m = Prophet(holidays=holidays, stan_backend=backend)\n m.fit(subdaily_univariate_ts)\n fcst = m.predict()\n assert sum(fcst[\"special_day\"] == 0) == 575\n\n\n\nclass TestProphetRegressors:\n def test_added_regressors(self, daily_univariate_ts, backend):\n m = Prophet(stan_backend=backend)\n m.add_regressor(\"binary_feature\", prior_scale=0.2)\n m.add_regressor(\"numeric_feature\", prior_scale=0.5)\n m.add_regressor(\"numeric_feature2\", prior_scale=0.5, mode=\"multiplicative\")\n m.add_regressor(\"binary_feature2\", standardize=True)\n df = daily_univariate_ts.copy()\n df[\"binary_feature\"] = [\"0\"] * 255 + [\"1\"] * 255\n df[\"numeric_feature\"] = range(510)\n df[\"numeric_feature2\"] = range(510)\n with pytest.raises(ValueError):\n # Require all regressors in df\n m.fit(df)\n df[\"binary_feature2\"] = [1] * 100 + [0] * 410\n m.fit(df)\n # Check that standardizations are correctly set\n assert m.extra_regressors[\"binary_feature\"] == {\n \"prior_scale\": 0.2,\n \"mu\": 0,\n \"std\": 1,\n \"standardize\": \"auto\",\n \"mode\": \"additive\",\n }\n assert m.extra_regressors[\"numeric_feature\"][\"prior_scale\"] == 0.5\n assert m.extra_regressors[\"numeric_feature\"][\"mu\"] == 254.5\n assert m.extra_regressors[\"numeric_feature\"][\"std\"] == pytest.approx(147.368585, abs=1e-5)\n assert m.extra_regressors[\"numeric_feature2\"][\"mode\"] == \"multiplicative\"\n assert m.extra_regressors[\"binary_feature2\"][\"prior_scale\"] == 10.0\n assert m.extra_regressors[\"binary_feature2\"][\"mu\"] == pytest.approx(0.1960784, abs=1e-5)\n assert m.extra_regressors[\"binary_feature2\"][\"std\"] == pytest.approx(0.3974183, abs=1e-5)\n # Check that standardization is done correctly\n df2 = m.setup_dataframe(df.copy())\n assert df2[\"binary_feature\"][0] == 0\n assert df2[\"numeric_feature\"][0] == pytest.approx(-1.726962, abs=1e-4)\n assert df2[\"binary_feature2\"][0] == pytest.approx(2.022859, abs=1e-4)\n # Check that feature matrix and prior scales are correctly constructed\n seasonal_features, prior_scales, component_cols, modes = m.make_all_seasonality_features(\n df2\n )\n assert seasonal_features.shape[1] == 30\n names = [\"binary_feature\", \"numeric_feature\", \"binary_feature2\"]\n true_priors = [0.2, 0.5, 10.0]\n for i, name in enumerate(names):\n assert name in seasonal_features\n assert sum(component_cols[name]) == 1\n assert sum(np.array(prior_scales) * component_cols[name]) == true_priors[i]\n # Check that forecast components are reasonable\n future = pd.DataFrame(\n {\n \"ds\": [\"2014-06-01\"],\n \"binary_feature\": [0],\n \"numeric_feature\": [10],\n \"numeric_feature2\": [10],\n }\n )\n # future dataframe also requires regressor values\n with pytest.raises(ValueError):\n m.predict(future)\n future[\"binary_feature2\"] = 0\n fcst = m.predict(future)\n assert fcst.shape[1] == 37\n assert fcst[\"binary_feature\"][0] == 0\n assert fcst[\"extra_regressors_additive\"][0] == pytest.approx(\n fcst[\"numeric_feature\"][0] + fcst[\"binary_feature2\"][0]\n )\n assert fcst[\"extra_regressors_multiplicative\"][0] == pytest.approx(\n fcst[\"numeric_feature2\"][0]\n )\n assert fcst[\"additive_terms\"][0] == pytest.approx(\n fcst[\"yearly\"][0] + fcst[\"weekly\"][0] + fcst[\"extra_regressors_additive\"][0]\n )\n assert fcst[\"multiplicative_terms\"][0] == pytest.approx(\n fcst[\"extra_regressors_multiplicative\"][0]\n )\n assert fcst[\"yhat\"][0] == pytest.approx(\n fcst[\"trend\"][0] * (1 + fcst[\"multiplicative_terms\"][0]) + fcst[\"additive_terms\"][0]\n )\n\n def test_constant_regressor(self, daily_univariate_ts, backend):\n df = daily_univariate_ts.copy()\n df[\"constant_feature\"] = 0\n m = Prophet(stan_backend=backend)\n m.add_regressor(\"constant_feature\")\n m.fit(df)\n assert m.extra_regressors[\"constant_feature\"][\"std\"] == 1\n\n\nclass TestProphetWarmStart:\n def test_fit_warm_start(self, daily_univariate_ts, backend):\n m = Prophet(stan_backend=backend).fit(daily_univariate_ts.iloc[:500])\n m2 = Prophet(stan_backend=backend).fit(\n daily_univariate_ts.iloc[:510], init=warm_start_params(m)\n )\n assert len(m2.params[\"delta\"][0]) == 25\n\n def test_sampling_warm_start(self, daily_univariate_ts, backend):\n m = Prophet(mcmc_samples=100, stan_backend=backend).fit(\n daily_univariate_ts.iloc[:500], show_progress=False\n )\n m2 = Prophet(mcmc_samples=100, stan_backend=backend).fit(\n daily_univariate_ts.iloc[:510], init=warm_start_params(m), show_progress=False\n )\n assert m2.params[\"delta\"].shape == (200, 25)\n" }, { "path": "python/prophet/tests/test_serialize.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom prophet import Prophet\nfrom prophet.serialize import PD_DATAFRAME, PD_SERIES, model_from_json, model_to_json\n\n\nclass TestSerialize:\n def test_simple_serialize(self, daily_univariate_ts, backend):\n m = Prophet(stan_backend=backend)\n df = daily_univariate_ts.head(daily_univariate_ts.shape[0] - 30)\n m.fit(df)\n\n future = m.make_future_dataframe(2, include_history=False)\n fcst = m.predict(future)\n\n model_str = model_to_json(m)\n # Make sure json doesn't get too large in the future\n assert len(model_str) < 200000\n m2 = model_from_json(model_str)\n\n # Check that m and m2 are equal\n assert m.__dict__.keys() == m2.__dict__.keys()\n for k, v in m.__dict__.items():\n if k in [\"stan_fit\", \"stan_backend\"]:\n continue\n if k == \"params\":\n assert v.keys() == m2.params.keys()\n for kk, vv in v.items():\n assert np.array_equal(vv, m2.params[kk])\n elif k in PD_SERIES and v is not None:\n assert v.equals(m2.__dict__[k])\n elif k in PD_DATAFRAME and v is not None:\n # check_dtype=False since .fit() and .predict() will cooerce to the correct types\n pd.testing.assert_frame_equal(\n v, m2.__dict__[k], check_index_type=False, check_dtype=False\n )\n elif k == \"changepoints_t\":\n assert np.array_equal(v, m.__dict__[k])\n else:\n assert v == m2.__dict__[k]\n assert m2.stan_fit is None\n assert m2.stan_backend is None\n\n # Check that m2 makes the same forecast\n future2 = m2.make_future_dataframe(2, include_history=False)\n fcst2 = m2.predict(future2)\n\n assert np.array_equal(fcst[\"yhat\"].values, fcst2[\"yhat\"].values)\n\n def test_full_serialize(self, daily_univariate_ts, backend):\n # Construct a model with all attributes\n holidays = pd.DataFrame(\n {\n \"ds\": pd.to_datetime([\"2012-06-06\", \"2013-06-06\"]),\n \"holiday\": [\"seans-bday\"] * 2,\n \"lower_window\": [0] * 2,\n \"upper_window\": [1] * 2,\n }\n )\n # Test with holidays and country_holidays\n m = Prophet(\n holidays=holidays,\n seasonality_mode=\"multiplicative\",\n changepoints=[\"2012-07-01\", \"2012-10-01\", \"2013-01-01\"],\n stan_backend=backend,\n )\n m.add_country_holidays(country_name=\"US\")\n m.add_seasonality(\n name=\"conditional_weekly\",\n period=7,\n fourier_order=3,\n prior_scale=2.0,\n condition_name=\"is_conditional_week\",\n )\n m.add_seasonality(name=\"normal_monthly\", period=30.5, fourier_order=5, prior_scale=2.0)\n df = daily_univariate_ts.copy()\n df[\"is_conditional_week\"] = [0] * 255 + [1] * 255\n m.add_regressor(\"binary_feature\", prior_scale=0.2)\n m.add_regressor(\"numeric_feature\", prior_scale=0.5)\n m.add_regressor(\"numeric_feature2\", prior_scale=0.5, mode=\"multiplicative\")\n m.add_regressor(\"binary_feature2\", standardize=True)\n df[\"binary_feature\"] = [\"0\"] * 255 + [\"1\"] * 255\n df[\"numeric_feature\"] = range(510)\n df[\"numeric_feature2\"] = range(510)\n df[\"binary_feature2\"] = [1] * 100 + [0] * 410\n\n train = df.head(400)\n test = df.tail(100)\n\n m.fit(train)\n fcst = m.predict(test)\n # Serialize!\n m2 = model_from_json(model_to_json(m))\n\n # Check that m and m2 are equal\n assert m.__dict__.keys() == m2.__dict__.keys()\n for k, v in m.__dict__.items():\n if k in [\"stan_fit\", \"stan_backend\"]:\n continue\n if k == \"params\":\n assert v.keys() == m2.params.keys()\n for kk, vv in v.items():\n assert np.array_equal(vv, m2.params[kk])\n elif k in PD_SERIES and v is not None:\n assert v.equals(m2.__dict__[k])\n elif k in PD_DATAFRAME and v is not None:\n # check_dtype=False since .fit() and .predict() will cooerce to the correct types\n pd.testing.assert_frame_equal(\n v, m2.__dict__[k], check_index_type=False, check_dtype=False\n )\n elif k == \"changepoints_t\":\n assert np.array_equal(v, m.__dict__[k])\n else:\n assert v == m2.__dict__[k]\n assert m2.stan_fit is None\n assert m2.stan_backend is None\n\n # Check that m2 makes the same forecast\n fcst2 = m2.predict(test)\n assert np.array_equal(fcst[\"yhat\"].values, fcst2[\"yhat\"].values)\n\n def test_backwards_compatibility(self):\n old_versions = {\n \"0.6.1.dev0\": (29.3669923968994, \"fb\"),\n \"0.7.1\": (29.282810844704414, \"fb\"),\n \"1.0.1\": (29.282810844704414, \"\"),\n }\n for v, (pred_val, v_str) in old_versions.items():\n fname = Path(__file__).parent / f\"serialized_model_v{v}.json\"\n with open(fname, \"r\") as fin:\n model_str = json.load(fin)\n # Check that deserializes\n m = model_from_json(model_str)\n assert json.loads(model_str)[f\"__{v_str}prophet_version\"] == v\n # Predict\n future = m.make_future_dataframe(10)\n fcst = m.predict(future)\n assert fcst[\"yhat\"].values[-1] == pytest.approx(pred_val)\n" }, { "path": "python/prophet/tests/test_utilities.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\n\nfrom prophet import Prophet\nfrom prophet.utilities import regressor_coefficients\n\n\nclass TestUtilities:\n def test_regressor_coefficients(self, daily_univariate_ts, backend):\n m = Prophet(stan_backend=backend)\n df = daily_univariate_ts.copy()\n np.random.seed(123)\n df[\"regr1\"] = np.random.normal(size=df.shape[0])\n df[\"regr2\"] = np.random.normal(size=df.shape[0])\n m.add_regressor(\"regr1\", mode=\"additive\")\n m.add_regressor(\"regr2\", mode=\"multiplicative\")\n m.fit(df)\n\n coefs = regressor_coefficients(m)\n assert coefs.shape == (2, 6)\n # No MCMC sampling, so lower and upper should be the same as mean\n assert np.array_equal(coefs[\"coef_lower\"].values, coefs[\"coef\"].values)\n assert np.array_equal(coefs[\"coef_upper\"].values, coefs[\"coef\"].values)\n" }, { "path": "python/prophet/utilities.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom __future__ import annotations\n\nfrom typing import TYPE_CHECKING, cast\n\nimport numpy as np\nimport pandas as pd\n\nif TYPE_CHECKING:\n from prophet.forecaster import Prophet\n import numpy.typing as npt\n\n\ndef regressor_index(m: Prophet, name: str) -> np.intp:\n \"\"\"Given the name of a regressor, return its (column) index in the `beta` matrix.\n\n Parameters\n ----------\n m: Prophet model object, after fitting.\n name: Name of the regressor, as passed into the `add_regressor` function.\n\n Returns\n -------\n The column index of the regressor in the `beta` matrix.\n \"\"\"\n cols = cast(pd.DataFrame, m.train_component_cols)\n return np.extract(cols[name] == 1, cols.index)[0]\n\ndef regressor_coefficients(m: Prophet) -> pd.DataFrame:\n \"\"\"Summarise the coefficients of the extra regressors used in the model.\n\n For additive regressors, the coefficient represents the incremental impact\n on `y` of a unit increase in the regressor. For multiplicative regressors,\n the incremental impact is equal to `trend(t)` multiplied by the coefficient.\n\n Coefficients are measured on the original scale of the training data.\n\n Parameters\n ----------\n m: Prophet model object, after fitting.\n\n Returns\n -------\n pd.DataFrame containing:\n - `regressor`: Name of the regressor\n - `regressor_mode`: Whether the regressor has an additive or multiplicative\n effect on `y`.\n - `center`: The mean of the regressor if it was standardized. Otherwise 0.\n - `coef_lower`: Lower bound for the coefficient, estimated from the MCMC samples.\n Only different to `coef` if `mcmc_samples > 0`.\n - `coef`: Expected value of the coefficient.\n - `coef_upper`: Upper bound for the coefficient, estimated from MCMC samples.\n Only different to `coef` if `mcmc_samples > 0`.\n \"\"\"\n assert len(m.extra_regressors) > 0, 'No extra regressors found.'\n coefs = []\n for regressor, params in m.extra_regressors.items():\n beta = m.params['beta'][:, regressor_index(m, regressor)]\n if params['mode'] == 'additive':\n coef = beta * m.y_scale / params['std']\n else:\n coef = beta / params['std']\n percentiles = [\n (1 - m.interval_width) / 2,\n 1 - (1 - m.interval_width) / 2,\n ]\n coef_bounds = np.quantile(coef, q=percentiles)\n record = {\n 'regressor': regressor,\n 'regressor_mode': params['mode'],\n 'center': params['mu'],\n 'coef_lower': coef_bounds[0],\n 'coef': np.mean(coef),\n 'coef_upper': coef_bounds[1],\n }\n coefs.append(record)\n\n return pd.DataFrame(coefs)\n\ndef warm_start_params(m: Prophet) -> dict[str, npt.NDArray[np.float64] | np.float64]:\n \"\"\"\n Retrieve parameters from a trained model in the format used to initialize a new Stan model.\n Note that the new Stan model must have these same settings:\n n_changepoints, seasonality features, mcmc sampling\n for the retrieved parameters to be valid for the new model.\n\n Parameters\n ----------\n m: A trained model of the Prophet class.\n\n Returns\n -------\n A Dictionary containing retrieved parameters of m.\n \"\"\"\n res = {}\n for pname in ['k', 'm', 'sigma_obs']:\n if m.mcmc_samples == 0:\n res[pname] = m.params[pname][0][0]\n else:\n res[pname] = np.mean(m.params[pname])\n for pname in ['delta', 'beta']:\n if m.mcmc_samples == 0:\n res[pname] = m.params[pname][0]\n else:\n res[pname] = np.mean(m.params[pname], axis=0)\n return res\n" }, { "path": "python/pyproject.toml", "content": "[build-system]\nrequires = [\n \"setuptools>=64\",\n \"wheel\",\n \"cmdstanpy>=1.0.4\",\n]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"prophet\"\ndynamic = [\"version\"]\ndescription = \"Automatic Forecasting Procedure\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\ndependencies = [\n \"cmdstanpy>=1.0.4\",\n \"numpy>=1.21.6\",\n \"matplotlib>=3.5.3\",\n \"pandas>=1.3.5\",\n \"holidays>=0.25,<1\",\n \"tqdm>=4.63.2\",\n]\nauthors = [\n {name = \"Sean J. Taylor\", email = \"sjtz@pm.me\"},\n {name = \"Ben Letham\", email = \"bletham@fb.com\"},\n]\nmaintainers = [\n {name = \"Cuong Duong\", email = \"cuong.duong242@gmail.com\"},\n]\nlicense = {text = \"MIT\"}\nclassifiers = [\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Programming Language :: Python :: 3.13\",\n \"Programming Language :: Python :: 3.14\",\n \"Typing :: Typed\",\n]\n\n[project.optional-dependencies]\ndev = [\n \"setuptools>=64\",\n \"wheel\",\n \"pytest\",\n \"jupyterlab\",\n \"nbconvert\",\n \"plotly\",\n \"pyrefly==0.50.0\",\n]\nparallel = [\n \"dask[dataframe]\",\n \"distributed\",\n]\n\n[project.urls]\nHomepage = \"https://facebook.github.io/prophet/\"\nDocumentation = \"https://facebook.github.io/prophet/\"\nRepository = \"https://github.com/facebook/prophet\"\n\n\n[tool.pyrefly]\nproject-includes = [\"prophet\"]\nproject-excludes = [\"prophet/tests\"] # todo: 131 errors\npython-version = \"3.10\"\n" }, { "path": "python/scripts/generate_holidays_file.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport re\nimport unicodedata\n\nimport pandas as pd\nimport numpy as np\n\nfrom holidays import list_supported_countries\nfrom prophet.make_holidays import make_holidays_df\n\n\ndef utf8_to_ascii(text: str) -> str:\n \"\"\"Holidays often have utf-8 characters. These are not allowed in R package data (they generate a NOTE).\n TODO: revisit whether we want to do this lossy conversion.\n \"\"\"\n ascii_text = unicodedata.normalize(\"NFD\", text).encode(\"ascii\", \"ignore\").decode(\"ascii\")\n # Remove trailing empty brackets and spaces.\n ascii_text = re.sub(r\"\\(\\)$\", \"\", ascii_text).strip()\n\n # Check if anything converted\n if sum(1 for x in ascii_text if x not in [\" \", \"(\", \")\", \",\"]) == 0:\n return \"FAILED_TO_PARSE\"\n else:\n return ascii_text\n\n\ndef generate_holidays_df() -> pd.DataFrame:\n \"\"\"Generate csv file of all possible holiday names, ds, and countries, year combination.\"\"\"\n country_codes = set(list_supported_countries().keys())\n\n # For compatibility with Turkey as 'TU' cases.\n country_codes.add(\"TU\")\n\n all_holidays = []\n for country_code in country_codes:\n df = make_holidays_df(\n year_list=np.arange(1995, 2045, 1).tolist(),\n country=country_code,\n )\n df[\"country\"] = country_code\n all_holidays.append(df)\n\n generated_holidays = pd.concat(all_holidays, axis=0, ignore_index=True)\n generated_holidays[\"year\"] = generated_holidays.ds.dt.year\n generated_holidays.sort_values([\"country\", \"ds\", \"holiday\"], inplace=True)\n\n # Convert to ASCII, and drop holidays that fail to convert\n generated_holidays[\"holiday\"] = generated_holidays[\"holiday\"].apply(utf8_to_ascii)\n failed_countries = generated_holidays.loc[\n generated_holidays[\"holiday\"] == \"FAILED_TO_PARSE\", \"country\"\n ].unique()\n if len(failed_countries) > 0:\n print(\"Failed to convert UTF-8 holidays for:\")\n print(\"\\n\".join(failed_countries))\n assert \"FAILED_TO_PARSE\" not in generated_holidays[\"holiday\"].unique()\n return generated_holidays\n\n\nif __name__ == \"__main__\":\n import argparse\n import pathlib\n\n if not pathlib.Path.cwd().stem == \"python\":\n raise RuntimeError(\"Run script from prophet/python directory\")\n OUT_CSV_PATH = pathlib.Path(\".\") / \"..\" / \"R/data-raw/generated_holidays.csv\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-o\", \"--outfile\", default=OUT_CSV_PATH)\n args = parser.parse_args()\n df = generate_holidays_df()\n df.to_csv(args.outfile, index=False)\n" }, { "path": "python/setup.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport os\nimport platform\nfrom pathlib import Path\nfrom shutil import copy, copytree, rmtree\nfrom typing import List\nimport tempfile\n\nfrom setuptools import find_packages, setup, Extension\nfrom setuptools.command.build_ext import build_ext\nfrom setuptools.command.build_py import build_py\nfrom setuptools.command.editable_wheel import editable_wheel\nfrom wheel.bdist_wheel import bdist_wheel\n\n\nMODEL_DIR = \"stan\"\nMODEL_TARGET_DIR = os.path.join(\"prophet\", \"stan_model\")\n\nCMDSTAN_VERSION = \"2.37.0\"\nBINARIES_DIR = \"bin\"\nBINARIES = [\"diagnose\", \"print\", \"stanc\", \"stansummary\"]\nTBB_PARENT = \"stan/lib/stan_math/lib\"\nTBB_DIRS = [\"tbb\", \"tbb_2020.3\"]\n\n\nIS_WINDOWS = platform.platform().startswith(\"Win\")\n\ndef prune_cmdstan(cmdstan_dir: str) -> None:\n \"\"\"\n Keep only the cmdstan executables and tbb files (minimum required to run a cmdstanpy commands on a pre-compiled model).\n \"\"\"\n original_dir = Path(cmdstan_dir).resolve()\n parent_dir = original_dir.parent\n temp_dir = parent_dir / \"temp\"\n if temp_dir.is_dir():\n rmtree(temp_dir)\n temp_dir.mkdir()\n\n print(\"Copying \", original_dir, \" to \", temp_dir, \" for pruning\")\n copytree(original_dir / BINARIES_DIR, temp_dir / BINARIES_DIR)\n for f in (temp_dir / BINARIES_DIR).iterdir():\n if f.is_dir():\n rmtree(f)\n elif f.is_file() and f.stem not in BINARIES:\n os.remove(f)\n for tbb_dir in TBB_DIRS:\n copytree(original_dir / TBB_PARENT / tbb_dir, temp_dir / TBB_PARENT / tbb_dir)\n copy(original_dir / \"makefile\", temp_dir / \"makefile\")\n\n rmtree(original_dir)\n temp_dir.rename(original_dir)\n\n\ndef repackage_cmdstan():\n return os.environ.get(\"PROPHET_REPACKAGE_CMDSTAN\", \"\").lower() not in [\"false\", \"0\"]\n\n\ndef maybe_install_cmdstan_toolchain() -> bool:\n \"\"\"Install C++ compilers required to build stan models on Windows machines.\"\"\"\n import cmdstanpy\n\n try:\n cmdstanpy.utils.cxx_toolchain_path()\n return False\n except Exception:\n try:\n from cmdstanpy.install_cxx_toolchain import run_rtools_install\n except ImportError:\n # older versions\n from cmdstanpy.install_cxx_toolchain import main as run_rtools_install\n\n run_rtools_install({\"version\": None, \"dir\": None, \"verbose\": True})\n cmdstanpy.utils.cxx_toolchain_path()\n return True\n\ndef install_cmdstan_deps(cmdstan_dir: Path):\n import cmdstanpy\n from multiprocessing import cpu_count\n\n if repackage_cmdstan():\n if IS_WINDOWS:\n maybe_install_cmdstan_toolchain()\n print(\"Installing cmdstan to\", cmdstan_dir)\n if os.path.isdir(cmdstan_dir):\n rmtree(cmdstan_dir)\n\n if not cmdstanpy.install_cmdstan(\n version=CMDSTAN_VERSION,\n dir=cmdstan_dir.parent,\n overwrite=True,\n verbose=True,\n cores=cpu_count(),\n progress=True,\n ):\n raise RuntimeError(\"CmdStan failed to install in repackaged directory\")\n\n\ndef build_cmdstan_model(target_dir):\n \"\"\"\n Rebuild cmdstan in the build environment, then use this installation to compile the stan model.\n The stan model is copied to {target_dir}/prophet_model.bin\n The cmdstan files required to run cmdstanpy commands are copied to {target_dir}/cmdstan-{version}.\n\n Parameters\n ----------\n target_dir: Directory to copy the compiled model executable and core cmdstan files to.\n \"\"\"\n import cmdstanpy\n\n target_cmdstan_dir = (Path(target_dir) / f\"cmdstan-{CMDSTAN_VERSION}\").resolve()\n with tempfile.TemporaryDirectory() as tmp_dir:\n # long paths on windows can cause problems during build\n if IS_WINDOWS:\n cmdstan_dir = (Path(tmp_dir) / f\"cmdstan-{CMDSTAN_VERSION}\").resolve()\n else:\n cmdstan_dir = target_cmdstan_dir\n\n install_cmdstan_deps(cmdstan_dir)\n model_name = \"prophet.stan\"\n # note: ensure copy target is a directory not a file.\n temp_stan_file = copy(os.path.join(MODEL_DIR, model_name), cmdstan_dir.parent.resolve())\n sm = cmdstanpy.CmdStanModel(stan_file=temp_stan_file)\n target_name = \"prophet_model.bin\"\n copy(sm.exe_file, os.path.join(target_dir, target_name))\n\n if IS_WINDOWS and repackage_cmdstan():\n copytree(cmdstan_dir, target_cmdstan_dir)\n\n # Clean up\n for f in Path(MODEL_DIR).iterdir():\n if f.is_file() and f.name != model_name:\n os.remove(f)\n\n if repackage_cmdstan():\n prune_cmdstan(target_cmdstan_dir)\n\n\ndef get_backends_from_env() -> List[str]:\n return os.environ.get(\"STAN_BACKEND\", \"CMDSTANPY\").split(\",\")\n\n\ndef build_models(target_dir):\n print(\"Compiling cmdstanpy model\")\n build_cmdstan_model(target_dir)\n\n if \"PYSTAN\" in get_backends_from_env():\n raise ValueError(\"PyStan backend is not supported for Prophet >= 1.1\")\n\n\nclass BuildPyCommand(build_py):\n \"\"\"Custom build command to pre-compile Stan models.\"\"\"\n\n def run(self):\n if not self.dry_run:\n target_dir = os.path.join(self.build_lib, MODEL_TARGET_DIR)\n self.mkpath(target_dir)\n build_models(target_dir)\n\n build_py.run(self)\n\n\nclass BuildExtCommand(build_ext):\n \"\"\"Ensure built extensions are added to the correct path in the wheel.\"\"\"\n\n def run(self):\n pass\n\n\nclass EditableWheel(editable_wheel):\n \"\"\"Custom develop command to pre-compile Stan models in-place.\"\"\"\n\n def run(self):\n if not self.dry_run:\n target_dir = os.path.join(self.project_dir, MODEL_TARGET_DIR)\n self.mkpath(target_dir)\n build_models(target_dir)\n\n editable_wheel.run(self)\n\n\nclass BDistWheelABINone(bdist_wheel):\n def finalize_options(self):\n bdist_wheel.finalize_options(self)\n self.root_is_pure = False\n\n def get_tag(self):\n _, _, plat = bdist_wheel.get_tag(self)\n return \"py3\", \"none\", plat\n\n\nabout = {}\nhere = Path(__file__).parent.resolve()\nwith open(here / \"prophet\" / \"__version__.py\", \"r\") as f:\n exec(f.read(), about)\n\nsetup(\n version=about[\"__version__\"],\n packages=find_packages(),\n zip_safe=False,\n include_package_data=True,\n ext_modules=[Extension(\"prophet.stan_model\", [])],\n cmdclass={\n \"build_ext\": BuildExtCommand,\n \"build_py\": BuildPyCommand,\n \"editable_wheel\": EditableWheel,\n \"bdist_wheel\": BDistWheelABINone,\n },\n test_suite=\"prophet.tests\",\n)\n" }, { "path": "python/stan/prophet.stan", "content": "// Copyright (c) Facebook, Inc. and its affiliates.\n\n// This source code is licensed under the MIT license found in the\n// LICENSE file in the root directory of this source tree.\n\nfunctions {\n matrix get_changepoint_matrix(vector t, vector t_change, int T, int S) {\n // Assumes t and t_change are sorted.\n matrix[T, S] A;\n row_vector[S] a_row;\n int cp_idx;\n\n // Start with an empty matrix.\n A = rep_matrix(0, T, S);\n a_row = rep_row_vector(0, S);\n cp_idx = 1;\n\n // Fill in each row of A.\n for (i in 1:T) {\n while ((cp_idx <= S) && (t[i] >= t_change[cp_idx])) {\n a_row[cp_idx] = 1;\n cp_idx = cp_idx + 1;\n }\n A[i] = a_row;\n }\n return A;\n }\n\n // Logistic trend functions\n\n vector logistic_gamma(real k, real m, vector delta, vector t_change, int S) {\n vector[S] gamma; // adjusted offsets, for piecewise continuity\n vector[S + 1] k_s; // actual rate in each segment\n real m_pr;\n\n // Compute the rate in each segment\n k_s = append_row(k, k + cumulative_sum(delta));\n\n // Piecewise offsets\n m_pr = m; // The offset in the previous segment\n for (i in 1:S) {\n gamma[i] = (t_change[i] - m_pr) * (1 - k_s[i] / k_s[i + 1]);\n m_pr = m_pr + gamma[i]; // update for the next segment\n }\n return gamma;\n }\n\n vector logistic_trend(\n real k,\n real m,\n vector delta,\n vector t,\n vector cap,\n matrix A,\n vector t_change,\n int S\n ) {\n vector[S] gamma;\n\n gamma = logistic_gamma(k, m, delta, t_change, S);\n return cap .* inv_logit((k + A * delta) .* (t - (m + A * gamma)));\n }\n\n // Linear trend function\n\n vector linear_trend(\n real k,\n real m,\n vector delta,\n vector t,\n matrix A,\n vector t_change\n ) {\n return (k + A * delta) .* t + (m + A * (-t_change .* delta));\n }\n\n // Flat trend function\n\n vector flat_trend(\n real m,\n int T\n ) {\n return rep_vector(m, T);\n }\n}\n\ndata {\n int T; // Number of time periods\n int K; // Number of regressors\n vector[T] t; // Time\n vector[T] cap; // Capacities for logistic trend\n vector[T] y; // Time series\n int S; // Number of changepoints\n vector[S] t_change; // Times of trend changepoints\n matrix[T,K] X; // Regressors\n vector[K] sigmas; // Scale on seasonality prior\n real tau; // Scale on changepoints prior\n int trend_indicator; // 0 for linear, 1 for logistic, 2 for flat\n vector[K] s_a; // Indicator of additive features\n vector[K] s_m; // Indicator of multiplicative features\n}\n\ntransformed data {\n matrix[T, S] A = get_changepoint_matrix(t, t_change, T, S);\n matrix[T, K] X_sa = X .* rep_matrix(s_a', T);\n matrix[T, K] X_sm = X .* rep_matrix(s_m', T);\n}\n\nparameters {\n real k; // Base trend growth rate\n real m; // Trend offset\n vector[S] delta; // Trend rate adjustments\n real sigma_obs; // Observation noise\n vector[K] beta; // Regressor coefficients\n}\n\ntransformed parameters {\n vector[T] trend;\n if (trend_indicator == 0) {\n trend = linear_trend(k, m, delta, t, A, t_change);\n } else if (trend_indicator == 1) {\n trend = logistic_trend(k, m, delta, t, cap, A, t_change, S);\n } else if (trend_indicator == 2) {\n trend = flat_trend(m, T);\n }\n}\n\nmodel {\n //priors\n k ~ normal(0, 5);\n m ~ normal(0, 5);\n delta ~ double_exponential(0, tau);\n sigma_obs ~ normal(0, 0.5);\n beta ~ normal(0, sigmas);\n\n // Likelihood\n y ~ normal_id_glm(\n X_sa,\n trend .* (1 + X_sm * beta),\n beta,\n sigma_obs\n );\n}\n" }, { "path": "python_shim/LICENSE", "content": "MIT License\n\nCopyright (c) Facebook, Inc. and its affiliates.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "python_shim/MANIFEST.in", "content": "include LICENSE\ninclude requirements.txt\n\ninclude fbprophet/tests/DATA.csv\n" }, { "path": "python_shim/README.md", "content": "# Prophet: Automatic Forecasting Procedure\n\nAs of v1.0, Prophet has moved to use the name \"[prophet](https://pypi.org/project/prophet/)\" on PyPI and not the original name of \"fbprophet\". This package is now just a shim for using the prophet package. Please change references in your code to use \"prophet\" instead of \"fbprophet\".\n\nSee https://facebook.github.io/prophet/ for full documentation.\n" }, { "path": "python_shim/fbprophet/__init__.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\n\nfrom prophet.forecaster import Prophet\n\nlogger = logging.getLogger('fbprophet')\n\nlogger.warning(\n 'As of v1.0, the package name has changed from \"fbprophet\" to \"prophet\". '\n 'Please update references in your code accordingly.'\n)\n" }, { "path": "python_shim/fbprophet/diagnostics.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom prophet.diagnostics import *\n" }, { "path": "python_shim/fbprophet/forecaster.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom prophet.forecaster import *\n" }, { "path": "python_shim/fbprophet/make_holidays.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom prophet.make_holidays import *\n" }, { "path": "python_shim/fbprophet/models.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom prophet.models import *\n" }, { "path": "python_shim/fbprophet/plot.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom prophet.plot import *\n" }, { "path": "python_shim/fbprophet/serialize.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom prophet.serialize import *\n" }, { "path": "python_shim/fbprophet/tests/__init__.py", "content": "" }, { "path": "python_shim/fbprophet/tests/data.csv", "content": "ds,y\n2012-05-18,38.23\n2012-05-21,34.03\n2012-05-22,31.0\n2012-05-23,32.0\n2012-05-24,33.03\n2012-05-25,31.91\n2012-05-29,28.84\n2012-05-30,28.19\n2012-05-31,29.6\n2012-06-01,27.72\n2012-06-04,26.9\n2012-06-05,25.87\n2012-06-06,26.81\n2012-06-07,26.31\n2012-06-08,27.1\n2012-06-11,27.01\n2012-06-12,27.4\n2012-06-13,27.27\n2012-06-14,28.29\n2012-06-15,30.01\n2012-06-18,31.41\n2012-06-19,31.91\n2012-06-20,31.6\n2012-06-21,31.84\n2012-06-22,33.05\n2012-06-25,32.06\n2012-06-26,33.1\n2012-06-27,32.23\n2012-06-28,31.36\n2012-06-29,31.1\n2012-07-02,30.77\n2012-07-03,31.2\n2012-07-05,31.47\n2012-07-06,31.73\n2012-07-09,32.17\n2012-07-10,31.47\n2012-07-11,30.97\n2012-07-12,30.81\n2012-07-13,30.72\n2012-07-16,28.25\n2012-07-17,28.09\n2012-07-18,29.11\n2012-07-19,29.0\n2012-07-20,28.76\n2012-07-23,28.75\n2012-07-24,28.45\n2012-07-25,29.34\n2012-07-26,26.85\n2012-07-27,23.71\n2012-07-30,23.15\n2012-07-31,21.71\n2012-08-01,20.88\n2012-08-02,20.04\n2012-08-03,21.09\n2012-08-06,21.92\n2012-08-07,20.72\n2012-08-08,20.72\n2012-08-09,21.01\n2012-08-10,21.81\n2012-08-13,21.6\n2012-08-14,20.38\n2012-08-15,21.2\n2012-08-16,19.87\n2012-08-17,19.05\n2012-08-20,20.01\n2012-08-21,19.16\n2012-08-22,19.44\n2012-08-23,19.44\n2012-08-24,19.41\n2012-08-27,19.15\n2012-08-28,19.34\n2012-08-29,19.1\n2012-08-30,19.09\n2012-08-31,18.06\n2012-09-04,17.73\n2012-09-05,18.58\n2012-09-06,18.96\n2012-09-07,18.98\n2012-09-10,18.81\n2012-09-11,19.43\n2012-09-12,20.93\n2012-09-13,20.71\n2012-09-14,22.0\n2012-09-17,21.52\n2012-09-18,21.87\n2012-09-19,23.29\n2012-09-20,22.59\n2012-09-21,22.86\n2012-09-24,20.79\n2012-09-25,20.28\n2012-09-26,20.62\n2012-09-27,20.32\n2012-09-28,21.66\n2012-10-01,21.99\n2012-10-02,22.27\n2012-10-03,21.83\n2012-10-04,21.95\n2012-10-05,20.91\n2012-10-08,20.4\n2012-10-09,20.23\n2012-10-10,19.64\n" }, { "path": "python_shim/fbprophet/tests/test_package.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport os\nfrom unittest import TestCase\n\nimport pandas as pd\nfrom fbprophet import Prophet\nfrom fbprophet.diagnostics import cross_validation\nimport fbprophet.plot as plot\n\n\nDATA = pd.read_csv(\n os.path.join(os.path.dirname(__file__), 'data.csv'),\n parse_dates=['ds'],\n)\n\n\nclass TestFbprophet(TestCase):\n\n def test_shim(self):\n m = Prophet()\n m.fit(DATA)\n future = m.make_future_dataframe(10, include_history=False)\n fcst = m.predict(future)\n \n df_cv = cross_validation(\n model=m, horizon='4 days', period='10 days', initial='115 days',\n )\n\n fig = plot.plot_forecast_component(m=m, fcst=fcst, name='weekly')\n" }, { "path": "python_shim/requirements.txt", "content": "prophet\n" }, { "path": "python_shim/setup.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates.\n\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom setuptools import setup, find_packages\n\nwith open('README.md', 'r', encoding='utf-8') as f:\n long_description = f.read()\n\nwith open('requirements.txt', 'r') as f:\n install_requires = f.read().splitlines()\n\nsetup(\n name='fbprophet',\n version='1.0.1',\n description='Automatic Forecasting Procedure',\n url='https://facebook.github.io/prophet/',\n author='Sean J. Taylor , Ben Letham ',\n author_email='sjtz@pm.me',\n license='MIT',\n packages=find_packages(),\n setup_requires=[],\n install_requires=install_requires,\n python_requires='>=3',\n zip_safe=False,\n include_package_data=True,\n test_suite='fbprophet.tests',\n classifiers=[\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.7',\n ],\n long_description=long_description,\n long_description_content_type='text/markdown',\n)\n" } ]