[ { "path": ".gitignore", "content": "output\n_build\n*.pyc\n*~\npapers/maxwell_margenot/.ipynb_checkpoints\n" }, { "path": "LICENSE.txt", "content": "Copyright (c) 2016 PyData Cookbook Authors.\nAll rights reserved.\n\nThe majority of files are adapted from the scipy-proceedings project and are\n\nCopyright (c) 2010-2011 SciPy Developers.\nAll rights reserved.\n\nThe files code_block.py, rstmath.py and sphinx_highlight.py are\nadapted from the Sphinx project and are\n\nCopyright (c) 2007-2010 by the Sphinx team (see http://sphinx.pocoo.org).\n\nModifications to rstmath.py are\n\nCopyright (c) 2010 Marcin Cieslik \n\n\nThe following BSD license holds for all the above code:\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright notice,\n this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the\n distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\nARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR\nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\nLIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\nOUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\nDAMAGE.\n" }, { "path": "README.md", "content": "# PyData Cookbook\n\n We are starting a project to build a cookbook of advanced material for the PyData community. The cookbook will be published by Addison-Wesley. We have invited a number of contributors to see if such a project would have some interest and received overwhelmingly positive feedback. \n\nThe book will cover several major topics, organized as such, with some sample packages:\n\n- IDE: IPython/Jupyter\n- Data Structures / Numerics: NumPy, Pandas, Xray, PyTables\n- Viz: Matplotlib, PyViz (HoloViews, Bokeh, Datashader), Seaborn, yt\n- Algorithms / Science: SciPy, Scikit-learn, Scikit-image, statsmodels, sympy, gensim, XGBoost\n- Performance / Scale: Cython, Numexpr, Numba, Dask, pyspark\n\n\nWe expect each submission to be about 15 - 20 pages describing an example of the power of each library. While we have reached out to the projects about putting each submission together we are happy to accept chapters for libraries we did not initially identify.\n\n## FAQ\n\nQ: There are normally royalties for book authors. What happens to those?\n\nA: All royalties go to NumFOCUS and will be used to support the further development of projects in the PyData stack.\n\nQ: Is all content we contribute reusable for documentation of projects?\n\nA: We can use chapters for documentation of projects but not a collection of all chapters in one volume. Essentially, there needs to be some value to buying the book to be published.\n\n\n## Instructions for Reviewers\n\n- Click on the Pull Requests Tab and browse to find the chapters assigned to you\n- After reading the paper, you can start the review conversation by simply commenting\n on the chapter, taking into consideration [the suggested review criteria](review_criteria.md).\n- Authors will then respond to the comments and/or modify the paper to address the comments. \n- This will begin an iterative review process where authors and reviewers can discuss the\n evolving submission.\n- Reviewers may also apply one of the labels 'needs-more-review', 'pending-comment', or \n 'unready' to flag the current state of the review process.\n- Only once a reviewer is satisfied that the review process is complete and the submission should\n be accepted to the proceedings, should they affix the 'ready' label. \n- Reviewers should come to a final 'ready', 'unready' decision before **July 10th** at 18:00 PST.\n\n## Instructions for Authors\n\nSubmissions must be received by **March 31st, 2018** at 23:59 PST, but modifications are\nallowed during the open review period which ends Dec 15th at 18:00 PST. Submissions are\nconsidered received once a Pull Request has been opened following the procedure\noutlined below.\n\nPapers are formatted using reStructuredText and the compiled version should be\nno longer than 25 pages, including figures. Here are the steps to produce a\npaper:\n\n- Fork the\n [pydata-cookbook](https://github.com/pydata/pydata-cookbook)\n repository on GitHub.\n\n- An example paper is provided in ``examples/00_vanderwalt``. Create a new\n directory ``papers/firstname_surname``, copy the example paper into it, and\n modify to your liking.\n\n- Run ``./make_paper.sh papers/firstname_surname`` to compile your paper to\n PDF (requires LaTeX, docutils, Python--see below). The output appears in\n ``output/firstname_surname/paper.pdf``.\n\n- Once you are ready to submit your paper, file a pull request on GitHub.\n\n- Please do not modify any files outside of your paper directory.\n\n## Schedule Summary\n\nAuthors may make changes to their submissions throughout the review process.\n\nThere are many different styles of review (some do paragraph comments, others\ndo 'code review' style line edits) and the process is open.\n\nWe encourage authors and reviewers to work together iteratively to make each \nothers papers the best they can be.\nCombine the best principles of open source development and academic publication.\n\nThese dates are the \n\n- Sept 1st - Initial PR with title, abstract, and authors\n- Nov 15th - Initial submissions\n- Nov 22th - Reviewers assigned\n- Dec 30th - Reviews due\n- Dec 30th- Jan 31st: Authors revised papers based on reviews\n- Feb 1th - Submission for publication\n\n## General Guidelines\n\n- All figures and tables should have captions.\n- License conditions on images and figures must be respected (Creative Commons,\n etc.).\n- Code snippets should be formatted to fit inside a single column without\n overflow.\n- Avoid custom LaTeX markup where possible.\n\n## Review Criteria\n\nPlease follow the included [review criteria](review_criteria.md).\nSuggestions and amendments to these review criteria are enthusiastically\nwelcomed via discussion or pull request. \n\n## Other markup\n\nPlease refer to the example paper in ``papers/00_vanderwalt`` for\nexamples of how to:\n\n - Label figures, equations and tables\n - Use math markup\n - Include code snippets\n\n## Requirements\n\n - IEEETran (often packaged as ``texlive-publishers``, or download from\n [CTAN](http://www.ctan.org/tex-archive/macros/latex/contrib/IEEEtran/)) LaTeX\n class\n - AMSmath LaTeX classes (included in most LaTeX distributions)\n - alphaurl (often packaged as ``texlive-bibtex-extra``, or download from\n [CTAN](https://www.ctan.org/pkg/urlbst)) urlbst BibTeX style\n - `docutils` 0.8 or later (``easy_install docutils``)\n - `pygments` for code highlighting (``easy_install pygments``)\n - Due to a bug in the Debian packaging of ``pdfannotextractor``, you may have\n to execute ``pdfannotextractor --install`` to fetch the PDFBox library.\n\nOn Debian-like distributions:\n\n```\nsudo apt-get install python-docutils texlive-latex-base texlive-publishers \\\n texlive-latex-extra texlive-fonts-recommended \\\n texlive-bibtex-extra\n```\n\nNote you will still need to install `docutils` with `easy-install` or `pip` even on a Debian system.\n\nOn Fedora, the package names are slightly different:\n\n```\nsu -c `dnf install python-docutils texlive-collection-basic texlive-collection-fontsrecommended texlive-collection-latex texlive-collection-latexrecommended texlive-collection-latexextra texlive-collection-publishers texlive-collection-bibtexextra`\n```\n\n## Build Server\n\n**To be added**\n\n## For organizers\n\nTo build the whole proceedings, see the Makefile in the publisher directory.\n\n\n## Credit\n\nThis repo was lovingly copied from the excellent [scipy-proceedings](https://github.com/scipy-conference/scipy_proceedings).\nIt retains the BSD license from that project and uses the same license for all contributions.\n" }, { "path": "abstracts/00_pymc/pymc3.rst", "content": ":author: Christopher Fonnesbeck\n:email: chris.fonnesbeck@vanderbilt.edu\n:institution: Vanderbilt University Medical Center\n\n:author: Peadar Coyle\n:email: peadarcoyle@googlemail.com\n\n:author: Thomas Wiecki\n:email: thomas.wiecki@gmail.com\n\n---------------------------------------\nBayesian Regression Analysis with PyMC3\n---------------------------------------\n\n.. class:: abstract\n\nRegression analysis is a fundamental statistical method for relating two sets of variables to one another in order to provide inference or make predictions. It provides estimates of how outcomes of interest vary as the value of specific predictor variables change, along with estimates of uncertainty about this relationship. Adopting the Bayesian approach recasts the regression problem in probabilistic terms, with all model inputs and outputs expressed as probability distributions. This confers a great deal of flexibility to the modeling task, and makes model outputs easier to interpret. However, there has historically been a great deal of computational complexity associated with building and fitting Bayesian models. PyMC3 is a Python package for Bayesian statistical analysis that makes regression analysis easy by providing a high-level language for specifying models and a suite of powerful fitting algorithms for generating output. We present an example-driven guide to Bayesian regression modeling in PyMC3, starting with simple one-line models for linear regression, then expand the simple case to address realistic problems encountered by scientists, including the estimation of non-linear relationships, discrete and binary outcomes, and hierarchical variable relationships. We'll also introduce some of the methods for checking models, since we believe in the old saying that 'all models are wrong, some are useful' it is important to evaluate models before putting them to use.\n" }, { "path": "abstracts/00_rudiger/00_rudiger.rst", "content": ":author: Philipp J. F. Rudiger\n:email: philippjfr@continuum.io\n:institution: Continuum Analytics, Inc.\n:equal-contributor:\n\n:author: Jean-Luc R. Stevens\n:email: jstevens@continuum.io \n:institution: Continuum Analytics, Inc.\n:institution: University of Edinburgh\n:equal-contributor:\n\n:author: James A. Bednar\n:email: jbednar@continuum.io\n:institution: Continuum Analytics, Inc.\n:institution: University of Edinburgh\n:corresponding:\n\n:video: https://www.youtube.com/watch?v=0jhUivliNSo\n\n-------------------------------------------------\nHoloViews: Visualization\n-------------------------------------------------\n\n.. class:: abstract\n\n HoloViews_ provides a high-level declarative Python API to\n encapsulate multidimensional datasets in a form that can be\n instantly visualized and analyzed. HoloViews allows you to specify\n even complex animated, multi-figure layouts using just a couple of\n lines of Python code, producing publication-quality plots rendered\n using Matplotlib. You can just as easily build highly interactive\n web applications with sliders, selections, and streaming data,\n rendered using Bokeh_. You can now fill your Jupyter notebooks with\n informative, interactive visualizations specified clearly and\n succinctly, rather clogging it with pages and pages of\n domain-specific plotting code that is impossible to read or\n maintain. HoloViews_ (and its companion geographic library\n GeoViews_) makes it simple to designate how your data should\n appear, and then let you index, select, sample, slice, reduce, and\n aggregate it to show just the data you are interested in. Every\n HoloViews object preserves the original data from which it was\n constructed, allowing you to work as naturally and flexibly with a\n visualization in Python as you can with the original dataset,\n always being able to continue visualizing and analyzing at any\n point without having to replay your steps. These features all\n derive from the way that HoloViews cleanly separates the plotting\n details from the semantic aspects of your data (*how* your plot\n should appear vs. *what* it shows), making it much simpler to work\n with both your data and your visualizations over time. In this\n chapter we show how to create HoloViews objects from source data in\n NumPy, Pandas, and Xarray objects, how to manipulate them\n to show different aspects of the data, how to flexibly customize\n how the plots appear, and how to build dynamic, interactive\n visualizations using minimal code for maximal effect.\n \n.. _HoloViews: http://holoviews.org\n.. _GeoViews: http://geo.holoviews.org\n.. _Matplotlib: http://matplotlib.org\n.. _Bokeh: http://bokeh.pydata.org\n\n.. class:: keywords\n\n Visualization, Plotting, Python, Declarative APIs, Bokeh, Matplotlib\n" }, { "path": "abstracts/aaron_meurer/sympy.rst", "content": ":author: Aaron Meurer\n:email: asmeurer@gmail.com\n:institution: University of South Carolina\n:corresponding:\n\n-----\nSymPy\n-----\n\n.. class:: abstract\n\n SymPy is a symbolic mathematics library written in pure Python. It is a\n full-featured computer algebra system, including functionality for symbolic\n calculus, equation solving, symbolic matrices, code generation, and much\n more, as well as several domain-specific modules including statistics,\n quantum mechanics, and classical mechanics. In this chapter, we give an\n introduction to symbolic computing and the SymPy library, and show through\n an example how it can be used as part of a larger scientific workflow.\n\n.. class:: keywords\n\n symbolic mathematics, sympy\n" }, { "path": "abstracts/dask/dask.rst", "content": ":author: James Crist\n:email: jcrist@continuum.io\n:institution: Continuum Analytics\n\n:author: Matthew Rocklin\n:email: mrocklin@gmail.com\n:institution: Continuum Analytics\n\n----\nDask\n----\n\n.. class:: abstract\n\nDask is a general purpose library for parallel computing that is designed to\ncomplement other PyData libraries. At its core, Dask is a task scheduling\nsystem, similar to libraries like Luigi or Airflow, but designed for the\ncomputational and interactive workloads found in data science and analytic\nproblems.\n\nOn top of this core, Dask provides parallel and larger-than-memory versions of\nNumPy, Pandas, and lists by coordinating operations on many of these objects\nwith the task schedulers. For example one logical dask.array is comprised of a\ngrid of NumPy arrays and one logical dask.dataframe is a sequence of Pandas\ndataframes. Dask includes the parallel algorithms necessary to faithfully\nimplement a broad and commonly used subset of the functionality of those\nlibraries.\n\nThis chapter will cover both the use of the Dask collections like Dask.array\nand Dask.dataframe, and also the use of the core task scheduler, for parallel\nproblems that do not fit into either of these molds. This chapter discusses\nboth scaling up on a single machine, expanding workable data sizes from \"fits\nin memory\" to \"fits on disk\" as well as the use of Dask on a cluster.\n" }, { "path": "abstracts/mpi4py/mpi4py.rst", "content": ":author: Lisandro Dalcin\n:email: dalcinl@gmail.com\n:institution: KAUST\n:institution: CONICET\n\n\n:author: Thomas Spura\n:email: thomas.spura@gmail.com\n\n\n:author: Andy R. Terrel\n:email: andy.terrel@gmail.com\n\n\n------\nmpi4py\n------\n\n.. class:: abstract\n\nMPI for Python (mpi4py) is a package that provides Python bindings for the\nMessage Passing Standard (MPI) and can be used to employ parallel and high\nperformance computing with core packages of the PyData stack. It includes\nconvenient communication methods for any pickable Python object as well as\nefficient communication methods for buffer-like Python objects via the the\nstandard Python buffer interface (such as NumPy arrays, PyCUDA GPU arrays, or\nselected built-in types). In this paper, we show easy to follow examples and\napplications that use mpi4py to speed up the calculations of core packages of\nthe PyData stack.\n\n.. _mpi4py: https://bitbucket.org/mpi4py/mpi4py/\n.. _MPI: http://www.mpi-forum.org/\n\n.. class:: keywords\n\nparallel computing, parallel processing, high performance computing, message\npassing interface, dynamic process management, performance\n\n" }, { "path": "abstracts/ryan_abernathey/xarray.rst", "content": ":author: Ryan Abernathey\n:email: rpa@ldeo.columbia.edu\n:institution: Columbia University, New York, NY, USA\n:institution: Lamont Doherty Earth Observatory, Palisades, NY, USA\n:corresponding:\n\n:author: Joe Hamman\n:email: jhamman1@uw.edu\n:institution: Department of Civil & Environmental Engineering,\n University of Washington, Seattle, WA\n\n:author: Stephan Hoyer\n:email: shoyer@gmail.com\n:institution: Google Research, Mountain View, CA, USA\n\n-------------------------------------------------\nxarray: N-D labeled arrays and datasets in Python\n-------------------------------------------------\n\n.. class:: abstract\n\n xarray is an open source project and Python package that provides a toolkit\n and data structures for N-dimensional labeled arrays. The Common Data Model,\n an abstract data model for scientific datasets, is the foundation for\n xarray data stuctures. By combining the array-manipuation capabilites of\n NumPy, the out-of-core computations of dask, and the indexing and grouping\n functionality of Pandas, xarray streamlines and accelerates a wide range of\n data-science workflows, particularly when dealing with gridded datasets\n common in geosciences. Serialization to and from common storage formats\n (e.g. netCDF, HDF, etc.) is also supported. This paper reviews the basic\n xarray api and then dives into some examples from climate science.\n\n.. class:: keywords\n\n Data Analysis, Python, Pandas, dask, netCDF\n\nIntroduction\n------------\n\nMany categories of data can be represented as multidimensional (i.e.\nN-dimensional) numeric arrays. One-dimensional data are produced by continuous\npoint measurements, such as timeseries of temperature from a weather station.\nTwo dimensional data is associated with images and is commonly produced via\nremote sensing (aircraft and satellites). Such data can be actual visible\nimagery or other fields such as surface temperature, soil moisture, elevation,\netc. When two-dimensional image data is gathered over time, the resulting\ndataset becomes three-dimensional. Four dimensions are realized by earth system\nmodels, which simulate how a three-dimensional system evolves in time. Finally,\narbitrary numbers of dimensions result when ensembles of data products are\nconcatenated, compared, and synthesized in one analysis. (Our examples\nare mostly drawn from environmental sciences, where xarray was adopted early on,\nbut many other fields will recognize parallels, including physics, astronomy,\nfinance, and biomedical imaging.)\n\nThe day-to-day work of scientists and researchers in many fields consists of\norganizing, analyzing, and visualizing N-dimensional array data. Within the\npython ecosystem, ``numpy`` and ``matplotlib`` have been the backbone of these\nworkflows for more than a decade. ``xarray`` provides a layer on top of these\ntools which greatly simplifies and accelerates workflows, resulting in cleaner,\nmore-readable code; more intuitive, data-aware syntax; and intelligent\nvisualization. Furthermore, by integrating with ``dask`` (link to dask\nchapter?), ``xarray`` facilitates the analysis of very large datasets without\nforcing the user to learn specialized \"big data\" tools.\n\nA central concept behind ``xarray`` is the notion that *most data is labelled*.\nFor example, a weather station might gather the variables ``temperature`` and\n``humidity`` over the dimension ``time``. These labels are part of the data's\n\"metadata.\" It is unfortunately common practice in scientific data analysis to\ndiscard metadata during the data-processing phase. This can lead to bugs and\nmisinterpretations and creates a barrier to reproducibility. ``xarray`` keeps\nthe data's labels (and possibly other metadata) together with the raw data\nitself for the duration of the workflow, from ingestion through processing to\nvisualization.\n\nThis chapter first introduces basic ``xarray`` usage, including the data model\nindexing operators, computation, and grouping operators.We then discuss how to\nload common data formats, with a focus on common problems and pitfalls. Finally,\nwe have an in-depth example which demonstrates advanced usage.\n\nBasic Usage\n-----------\n\nThe Data Model\n^^^^^^^^^^^^^^\n\nDo we just copy the xarray docs here? Is that even allowed by copyright?\n\nIndexing\n^^^^^^^^\n\nGroupBy: split-apply-combine\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n\nReading and Writing Data\n------------------------\n\nBackends and Engines\n^^^^^^^^^^^^^^^^^^^^\n\nnetCDF, HDF, RasterIO, OpenDAP\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWriting\n^^^^^^^\n\nWe need to address compression, chunking, out-of-core!\n\n\nAdvanced Example\n----------------\n\nLoad a global surface temperature dataset. Remove seasonal cycle. Detrend.\nApply EOF analysis. (Can we use ``apply``?)\n" }, { "path": "environment.yml", "content": "name: pydata-cookbook\nchannels:\n - defaults\n - conda-forge\ndependencies:\n - pip:\n - docutils==0.13.1\n - pygments==2.2.0\n" }, { "path": "examples/00_bibderwalt/00_bibderwalt.rst", "content": ":author: Gaius Caesar\n:email: jj@rome.it\n:institution: Senate House, S.P.Q.R.\n:institution: Egyptian Embassy, S.P.Q.R.\n\n:author: Mark Anthony\n:email: mark37@rome.it\n:institution: Egyptian Embassy, S.P.Q.R.\n\n:author: Jarrod Millman\n:email: millman@rome.it\n:institution: Egyptian Embassy, S.P.Q.R.\n:institution: Yet another place, S.P.Q.R.\n\n:author: Brutus\n:email: brutus@rome.it\n:institution: Unaffiliated\n:bibliography: mybib\n\n\n:video: http://www.youtube.com/watch?v=dhRUe-gz690\n\n------------------------------------------------\nA Numerical Perspective to Terraforming a Desert\n------------------------------------------------\n\n.. class:: abstract\n\n A short version of the long version that is way too long to be written as a\n short version anyway. Still, when considering the facts from first\n principles, we find that the outcomes of this introspective approach is\n compatible with the guidelines previously established.\n\n In such an experiment it is then clear that the potential for further\n development not only depends on previous relationships found but also on\n connections made during exploitation of this novel new experimental\n protocol.\n\n.. class:: keywords\n\n terraforming, desert, numerical perspective\n\nIntroduction\n------------\n\nTwelve hundred years ago |---| in a galaxy just across the hill...\n\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum sapien\ntortor, bibendum et pretium molestie, dapibus ac ante. Nam odio orci, interdum\nsit amet placerat non, molestie sed dui. Pellentesque eu quam ac mauris\ntristique sodales. Fusce sodales laoreet nulla, id pellentesque risus convallis\neget. Nam id ante gravida justo eleifend semper vel ut nisi. Phasellus\nadipiscing risus quis dui facilisis fermentum. Duis quis sodales neque. Aliquam\nut tellus dolor. Etiam ac elit nec risus lobortis tempus id nec erat. Morbi eu\npurus enim. Integer et velit vitae arcu interdum aliquet at eget purus. Integer\nquis nisi neque. Morbi ac odio et leo dignissim sodales. Pellentesque nec nibh\nnulla. Donec faucibus purus leo. Nullam vel lorem eget enim blandit ultrices.\nUt urna lacus, scelerisque nec pellentesque quis, laoreet eu magna. Quisque ac\njusto vitae odio tincidunt tempus at vitae tortor.\n\n\nBibliographies, citations and block quotes\n------------------------------------------\n\nIf you want to include a ``.bib`` file, do so above by placing :code:`:bibliography: yourFilenameWithoutExtension` as above (replacing ``mybib``) for a file named :code:`yourFilenameWithoutExtension.bib` after removing the ``.bib`` extension. \n\n**Do not include any special characters that need to be escaped or any spaces in the bib-file's name**. Doing so makes bibTeX cranky, & the rst to LaTeX+bibTeX transform won't work. \n\nTo reference citations contained in that bibliography use the :code:`:cite:`citation-key`` role, as in :cite:`hume48` (which literally is :code:`:cite:`hume48`` in accordance with the ``hume48`` cite-key in the associated ``mybib.bib`` file).\n\nHowever, if you use a bibtex file, this will overwrite any manually written references. \n\nSo what would previously have registered as a in text reference ``[Atr03]_`` for \n\n:: \n\n [Atr03] P. Atreides. *How to catch a sandworm*,\n Transactions on Terraforming, 21(3):261-300, August 2003.\n\nwhat you actually see will be an empty reference rendered as **[?]**.\n\nE.g., [Atr03]_.\n\n\nIf you wish to have a block quote, you can just indent the text, as in \n\n When it is asked, What is the nature of all our reasonings concerning matter of fact? the proper answer seems to be, that they are founded on the relation of cause and effect. When again it is asked, What is the foundation of all our reasonings and conclusions concerning that relation? it may be replied in one word, experience. But if we still carry on our sifting humor, and ask, What is the foundation of all conclusions from experience? this implies a new question, which may be of more difficult solution and explication. :cite:`hume48`\n\n\nSource code examples\n--------------------\n\nOf course, no paper would be complete without some source code. Without\nhighlighting, it would look like this::\n\n def sum(a, b):\n \"\"\"Sum two numbers.\"\"\"\n\n return a + b\n\nWith code-highlighting:\n\n.. code-block:: python\n\n def sum(a, b):\n \"\"\"Sum two numbers.\"\"\"\n\n return a + b\n\nMaybe also in another language, and with line numbers:\n\n.. code-block:: c\n :linenos:\n\n int main() {\n for (int i = 0; i < 10; i++) {\n /* do something */\n }\n return 0;\n }\n\nOr a snippet from the above code, starting at the correct line number:\n\n.. code-block:: c\n :linenos:\n :linenostart: 2\n\n for (int i = 0; i < 10; i++) {\n /* do something */\n }\n \nImportant Part\n--------------\n\nIt is well known [Atr03]_ that Spice grows on the planet Dune. Test\nsome maths, for example :math:`e^{\\pi i} + 3 \\delta`. Or maybe an\nequation on a separate line:\n\n.. math::\n\n g(x) = \\int_0^\\infty f(x) dx\n\nor on multiple, aligned lines:\n\n.. math::\n :type: eqnarray\n\n g(x) &=& \\int_0^\\infty f(x) dx \\\\\n &=& \\ldots\n\nThe area of a circle and volume of a sphere are given as\n\n.. math::\n :label: circarea\n\n A(r) = \\pi r^2.\n\n.. math::\n :label: spherevol\n\n V(r) = \\frac{4}{3} \\pi r^3\n\nWe can then refer back to Equation (:ref:`circarea`) or\n(:ref:`spherevol`) later.\n\nMauris purus enim, volutpat non dapibus et, gravida sit amet sapien. In at\nconsectetur lacus. Praesent orci nulla, blandit eu egestas nec, facilisis vel\nlacus. Fusce non ante vitae justo faucibus facilisis. Nam venenatis lacinia\nturpis. Donec eu ultrices mauris. Ut pulvinar viverra rhoncus. Vivamus\nadipiscing faucibus ligula, in porta orci vehicula in. Suspendisse quis augue\narcu, sit amet accumsan diam. Vestibulum lacinia luctus dui. Aliquam odio arcu,\nfaucibus non laoreet ac, condimentum eu quam. Quisque et nunc non diam\nconsequat iaculis ut quis leo. Integer suscipit accumsan ligula. Sed nec eros a\norci aliquam dictum sed ac felis. Suspendisse sit amet dui ut ligula iaculis\nsollicitudin vel id velit. Pellentesque hendrerit sapien ac ante facilisis\nlacinia. Nunc sit amet sem sem. In tellus metus, elementum vitae tincidunt ac,\nvolutpat sit amet mauris. Maecenas [#]_ diam turpis, placerat [#]_ at adipiscing ac,\npulvinar id metus.\n\n.. [#] On the one hand, a footnote.\n.. [#] On the other hand, another footnote.\n\n.. figure:: figure1.png\n\n This is the caption. :label:`egfig`\n\n.. figure:: figure1.png\n :align: center\n :figclass: w\n\n This is a wide figure, specified by adding \"w\" to the figclass. It is also\n center aligned, by setting the align keyword (can be left, right or center).\n\n.. figure:: figure1.png\n :scale: 20%\n :figclass: bht\n\n This is the caption on a smaller figure that will be placed by default at the\n bottom of the page, and failing that it will be placed inline or at the top.\n Note that for now, scale is relative to a completely arbitrary original\n reference size which might be the original size of your image - you probably\n have to play with it. :label:`egfig2`\n\nAs you can see in Figures :ref:`egfig` and :ref:`egfig2`, this is how you reference auto-numbered\nfigures.\n\n.. table:: This is the caption for the materials table. :label:`mtable`\n\n +------------+----------------+\n | Material | Units |\n +============+================+\n | Stone | 3 |\n +------------+----------------+\n | Water | 12 |\n +------------+----------------+\n | Cement | :math:`\\alpha` |\n +------------+----------------+\n\n\nWe show the different quantities of materials required in Table\n:ref:`mtable`.\n\n\n.. The statement below shows how to adjust the width of a table.\n\n.. raw:: latex\n\n \\setlength{\\tablewidth}{0.8\\linewidth}\n\n\n.. table:: This is the caption for the wide table.\n :class: w\n\n +--------+----+------+------+------+------+--------+\n | This | is | a | very | very | wide | table |\n +--------+----+------+------+------+------+--------+\n\nUnfortunately, restructuredtext can be picky about tables, so if it simply\nwon't work try raw LaTeX:\n\n\n.. raw:: latex\n\n \\begin{table*}\n\n \\begin{longtable*}{|l|r|r|r|}\n \\hline\n \\multirow{2}{*}{Projection} & \\multicolumn{3}{c|}{Area in square miles}\\tabularnewline\n \\cline{2-4}\n & Large Horizontal Area & Large Vertical Area & Smaller Square Area\\tabularnewline\n \\hline\n Albers Equal Area & 7,498.7 & 10,847.3 & 35.8\\tabularnewline\n \\hline\n Web Mercator & 13,410.0 & 18,271.4 & 63.0\\tabularnewline\n \\hline\n Difference & 5,911.3 & 7,424.1 & 27.2\\tabularnewline\n \\hline\n Percent Difference & 44\\% & 41\\% & 43\\%\\tabularnewline\n \\hline\n \\end{longtable*}\n\n \\caption{Area Comparisons \\DUrole{label}{quanitities-table}}\n\n \\end{table*}\n\nPerhaps we want to end off with a quote by Lao Tse [#]_:\n\n *Muddy water, let stand, becomes clear.*\n\n.. [#] :math:`\\mathrm{e^{-i\\pi}}`\n\n.. Customised LaTeX packages\n.. -------------------------\n\n.. Please avoid using this feature, unless agreed upon with the\n.. proceedings editors.\n\n.. ::\n\n.. .. latex::\n.. :usepackage: somepackage\n\n.. Some custom LaTeX source here.\n\nReferences\n----------\n.. [Atr03] P. Atreides. *How to catch a sandworm*,\n Transactions on Terraforming, 21(3):261-300, August 2003.\n\n\n" }, { "path": "examples/00_bibderwalt/mybib.bib", "content": "@Book{hume48,\n author = \"David Hume\",\n year = \"1748\",\n title = \"An enquiry concerning human understanding\",\n address = \"Indianapolis, IN\",\n publisher = \"Hackett\",\n}\n" }, { "path": "examples/00_vanderwalt/00_vanderwalt.rst", "content": ":author: Gaius Caesar\n:email: jj@rome.it\n:institution: Senate House, S.P.Q.R.\n:institution: Egyptian Embassy, S.P.Q.R.\n:corresponding:\n\n:author: Mark Anthony\n:email: mark37@rome.it\n:institution: Egyptian Embassy, S.P.Q.R.\n\n:author: Jarrod Millman\n:email: millman@rome.it\n:institution: Egyptian Embassy, S.P.Q.R.\n:institution: Yet another place, S.P.Q.R.\n:equal-contributor:\n\n:author: Brutus\n:email: brutus@rome.it\n:institution: Unaffiliated\n:equal-contributor:\n\n:video: http://www.youtube.com/watch?v=dhRUe-gz690\n\n------------------------------------------------\nA Numerical Perspective to Terraforming a Desert\n------------------------------------------------\n\n.. class:: abstract\n\n A short version of the long version that is way too long to be written as a\n short version anyway. Still, when considering the facts from first\n principles, we find that the outcomes of this introspective approach is\n compatible with the guidelines previously established.\n\n In such an experiment it is then clear that the potential for further\n development not only depends on previous relationships found but also on\n connections made during exploitation of this novel new experimental\n protocol.\n\n.. class:: keywords\n\n terraforming, desert, numerical perspective\n\nIntroduction\n------------\n\nTwelve hundred years ago |---| in a galaxy just across the hill...\n\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum sapien\ntortor, bibendum et pretium molestie, dapibus ac ante. Nam odio orci, interdum\nsit amet placerat non, molestie sed dui. Pellentesque eu quam ac mauris\ntristique sodales. Fusce sodales laoreet nulla, id pellentesque risus convallis\neget. Nam id ante gravida justo eleifend semper vel ut nisi. Phasellus\nadipiscing risus quis dui facilisis fermentum. Duis quis sodales neque. Aliquam\nut tellus dolor. Etiam ac elit nec risus lobortis tempus id nec erat. Morbi eu\npurus enim. Integer et velit vitae arcu interdum aliquet at eget purus. Integer\nquis nisi neque. Morbi ac odio et leo dignissim sodales. Pellentesque nec nibh\nnulla. Donec faucibus purus leo. Nullam vel lorem eget enim blandit ultrices.\nUt urna lacus, scelerisque nec pellentesque quis, laoreet eu magna. Quisque ac\njusto vitae odio tincidunt tempus at vitae tortor.\n\nOf course, no paper would be complete without some source code. Without\nhighlighting, it would look like this::\n\n def sum(a, b):\n \"\"\"Sum two numbers.\"\"\"\n\n return a + b\n\nWith code-highlighting:\n\n.. code-block:: python\n\n def sum(a, b):\n \"\"\"Sum two numbers.\"\"\"\n\n return a + b\n\nMaybe also in another language, and with line numbers:\n\n.. code-block:: c\n :linenos:\n\n int main() {\n for (int i = 0; i < 10; i++) {\n /* do something */\n }\n return 0;\n }\n\nOr a snippet from the above code, starting at the correct line number:\n\n.. code-block:: c\n :linenos:\n :linenostart: 2\n\n for (int i = 0; i < 10; i++) {\n /* do something */\n }\n\nImportant Part\n--------------\n\nIt is well known [Atr03]_ that Spice grows on the planet Dune. Test\nsome maths, for example :math:`e^{\\pi i} + 3 \\delta`. Or maybe an\nequation on a separate line:\n\n.. math::\n\n g(x) = \\int_0^\\infty f(x) dx\n\nor on multiple, aligned lines:\n\n.. math::\n :type: eqnarray\n\n g(x) &=& \\int_0^\\infty f(x) dx \\\\\n &=& \\ldots\n\nThe area of a circle and volume of a sphere are given as\n\n.. math::\n :label: circarea\n\n A(r) = \\pi r^2.\n\n.. math::\n :label: spherevol\n\n V(r) = \\frac{4}{3} \\pi r^3\n\nWe can then refer back to Equation (:ref:`circarea`) or\n(:ref:`spherevol`) later.\n\nMauris purus enim, volutpat non dapibus et, gravida sit amet sapien. In at\nconsectetur lacus. Praesent orci nulla, blandit eu egestas nec, facilisis vel\nlacus. Fusce non ante vitae justo faucibus facilisis. Nam venenatis lacinia\nturpis. Donec eu ultrices mauris. Ut pulvinar viverra rhoncus. Vivamus\nadipiscing faucibus ligula, in porta orci vehicula in. Suspendisse quis augue\narcu, sit amet accumsan diam. Vestibulum lacinia luctus dui. Aliquam odio arcu,\nfaucibus non laoreet ac, condimentum eu quam. Quisque et nunc non diam\nconsequat iaculis ut quis leo. Integer suscipit accumsan ligula. Sed nec eros a\norci aliquam dictum sed ac felis. Suspendisse sit amet dui ut ligula iaculis\nsollicitudin vel id velit. Pellentesque hendrerit sapien ac ante facilisis\nlacinia. Nunc sit amet sem sem. In tellus metus, elementum vitae tincidunt ac,\nvolutpat sit amet mauris. Maecenas [#]_ diam turpis, placerat [#]_ at adipiscing ac,\npulvinar id metus.\n\n.. [#] On the one hand, a footnote.\n.. [#] On the other hand, another footnote.\n\n.. figure:: figure1.png\n\n This is the caption. :label:`egfig`\n\n.. figure:: figure1.png\n :align: center\n :figclass: w\n\n This is a wide figure, specified by adding \"w\" to the figclass. It is also\n center aligned, by setting the align keyword (can be left, right or center).\n\n.. figure:: figure1.png\n :scale: 20%\n :figclass: bht\n\n This is the caption on a smaller figure that will be placed by default at the\n bottom of the page, and failing that it will be placed inline or at the top.\n Note that for now, scale is relative to a completely arbitrary original\n reference size which might be the original size of your image - you probably\n have to play with it. :label:`egfig2`\n\nAs you can see in Figures :ref:`egfig` and :ref:`egfig2`, this is how you reference auto-numbered\nfigures.\n\n.. table:: This is the caption for the materials table. :label:`mtable`\n\n +------------+----------------+\n | Material | Units |\n +============+================+\n | Stone | 3 |\n +------------+----------------+\n | Water | 12 |\n +------------+----------------+\n | Cement | :math:`\\alpha` |\n +------------+----------------+\n\n\nWe show the different quantities of materials required in Table\n:ref:`mtable`.\n\n\n.. The statement below shows how to adjust the width of a table.\n\n.. raw:: latex\n\n \\setlength{\\tablewidth}{0.8\\linewidth}\n\n\n.. table:: This is the caption for the wide table.\n :class: w\n\n +--------+----+------+------+------+------+--------+\n | This | is | a | very | very | wide | table |\n +--------+----+------+------+------+------+--------+\n\nUnfortunately, restructuredtext can be picky about tables, so if it simply\nwon't work try raw LaTeX:\n\n\n.. raw:: latex\n\n \\begin{table*}\n\n \\begin{longtable*}{|l|r|r|r|}\n \\hline\n \\multirow{2}{*}{Projection} & \\multicolumn{3}{c|}{Area in square miles}\\tabularnewline\n \\cline{2-4}\n & Large Horizontal Area & Large Vertical Area & Smaller Square Area\\tabularnewline\n \\hline\n Albers Equal Area & 7,498.7 & 10,847.3 & 35.8\\tabularnewline\n \\hline\n Web Mercator & 13,410.0 & 18,271.4 & 63.0\\tabularnewline\n \\hline\n Difference & 5,911.3 & 7,424.1 & 27.2\\tabularnewline\n \\hline\n Percent Difference & 44\\% & 41\\% & 43\\%\\tabularnewline\n \\hline\n \\end{longtable*}\n\n \\caption{Area Comparisons \\DUrole{label}{quanitities-table}}\n\n \\end{table*}\n\nPerhaps we want to end off with a quote by Lao Tse [#]_:\n\n *Muddy water, let stand, becomes clear.*\n\n.. [#] :math:`\\mathrm{e^{-i\\pi}}`\n\n.. Customised LaTeX packages\n.. -------------------------\n\n.. Please avoid using this feature, unless agreed upon with the\n.. proceedings editors.\n\n.. ::\n\n.. .. latex::\n.. :usepackage: somepackage\n\n.. Some custom LaTeX source here.\n\nReferences\n----------\n.. [Atr03] P. Atreides. *How to catch a sandworm*,\n Transactions on Terraforming, 21(3):261-300, August 2003.\n\n\n" }, { "path": "make_paper.sh", "content": "#!/bin/bash\n\nDIR=$1\n\nif [[ ! -d $DIR ]]; then\n echo \"Usage: make_paper.sh source_dir\"\n exit -1\nfi\n\npython publisher/build_paper.py $DIR\nif [ \"$?\" -ne \"0\" ]; then\n echo \"Error building paper $DIR. Aborting.\"\n exit 1\nfi\n\n#cd $OUTDIR\n#$TEX2PDF > /dev/null && $TEX2PDF | (python $WD/publisher/page_count.py)\n" }, { "path": "papers/joshua_warner/00_scikitimage.rst", "content": ":author: Joshua D. Warner\n:email: joshua.dale.warner@gmail.com\n:institution: Mayo Clinic, Rochester, USA\n:first-author:\n\n:author: Alexandre F. de Siqueira\n:email: afdesiqueira@protonmail.com\n:institution: University of Campinas, Campinas, Brazil\n:institution: TU Bergakademie Freiberg, Freiberg, Germany\n:equal-contributor:\n\n:author: Emmanuelle Gouillart\n:email: emmanuelle.gouillart@nsup.org\n:institution: Joint Unit CNRS/Saint-Gobain Surface of Glass and Interfaces, Aubervilliers, France\n:equal-contributor:\n\n:author: Stéfan van der Walt\n:email: stefanv@berkeley.edu\n:institution: Berkeley Institute for Data Science, University of California at Berkeley, USA\n:corresponding:\n\n\n------------\nscikit-image\n------------\n\n3.. class:: abstract\n\n ``scikit-image`` is an image processing library that implements\n algorithms and utilities for use in research, education and\n industry applications. It is released under the liberal Modified\n BSD open source license, provides a well-documented API in the\n Python programming language, and is developed by an active,\n international team of collaborators. In this chapter we highlight\n the advantages of open source to achieve the goals of the\n ``scikit-image library``, and we showcase a few real-world image\n processing applications that uses ``scikit-image`` extensively. More\n information can be found on the project homepage,\n http://scikit-image.org.\n\n.. class:: keywords\n\n image processing, computer vision, python\n\nIntroduction\n------------\n\n``scikit-image`` is an image processing library designed to complement and extend the capabilities of the core scientific Python libraries NumPy and SciPy for image processing applications [Van14]_. Like other ``scikits``, it is domain-specific in scope and moves faster than the core libraries to meet users' evolving needs.\n\nImages are NumPy arrays\n***********************\n\nImages are simply a collection of data on a regular grid, represented by a NumPy array (see Figure :ref:`penguin`). Thus, ``scikit-image`` shares the foundational data representation of the scientific Python ecosystem, allowing maximum compatibliity [Van11]_.\n\nEssentially all operations in ``scikit-image`` are defined for at\nleast two-dimensional images and, where possible, are generalized to\nwork with images of arbitrary dimensionality. Some images, like color\nimages, may have more than one channel, e.g., red, green, and blue\n(RGB). Such an image is represented as an `(M, N, c)` array, with\ncolor information carried in the third dimension. To disambiguate\ncolor images from, e.g., 3-D volume data (a 3-D array with shape `(plane,\nrows, cols)`), certain operations support a `multichannel` flag.\n\n.. figure:: penguin.png\n :align: center\n\n This enlarged public domain image of Tux, the Linux kernel mascot, shows individual pixels. The inset illustrates the luminance gray value at each underlying point. :label:`penguin`\n\nA note on image coordinates\n***************************\n\nIt bears repeating that ``scikit-image`` shares NumPy conventions for\narray indexing. When specifying points, one must use ``(row,\ncolumn)`` indexing, not ``(x, y)`` coordinates. For a two-dimensional\nimage, the origin is in the upper left corner. Figure :ref:`row-col`\nillustrates how indexing works in NumPy and ``scikit-image``. Please refer to the `scikit-image user guide\n`_\nfor more detail.\n\n\n.. figure:: row-col.png\n :align: center\n\n Illustration of NumPy ``(row, column)`` indexing for a two-dimensional array or image. Note the origin is in the upper left. :label:`row-col`\n\n\nParallel & distributed processing via dask\n******************************************\n\n``scikit-image`` has very basic parallel processing support through\n`skimage.util.apply_parallel(function, image)`. The aim of\n`apply_parallel` is to make use of all processors on a single machine,\nby dividing the image into blocks and applying the specified function\nto each. To handle edge effects, the blocks can have some overlap,\nwith the central part of each processed block being used to construct\nthe output.\n\nThis approach addresses the most basic use-case, but for anything more\nchallenging we recommend the `dask` and `distributed` libraries. We\nnow follow along with a parallel processing image processing example\nby the author of the above-mentioned libraries, Matthew Rocklin.\n\n.. From: https://gist.github.com/mrocklin/611c64e4fb62486269b507a872984cc5\n\n.. Matthew writes:\n\n.. Email from last night from colleague at the NIH:\n\n.. Electron microscopy is probably generating the biggest ndarray\n.. datasets in the field - terabytes regularly. Neuroscience need EM to\n.. see connections between neurons because the critical features of\n.. neural synapses (connections) are below the diffraction limit of light\n.. microscopes. The hard part is machine vision on the data to follow\n.. small neuron parts from one slice to the next. This type of research\n.. has been called \"connectomics\".\n\n.. This data is from drosophila: http://emdata.janelia.org/\n\n.. Here is an example 2d slice of the data. If you take the below URL and change the last number you can get about 5000 different images. http://emdata.janelia.org/api/node/bf1/grayscale/raw/xy/2000_2000/1800_2300_5000\n\n.. The data is a bit sparse (many black pixels) but it's around a 25GB dataset if you pull down all the slices. And it's a 3d ndarray.*\n\n.. note:: TODO: Reduce the notebook at the above mentioned URL into a concise example.\n\nPackage roadmap\n---------------\n\nMost of the functionality in ``scikit-image`` is located in *subpackages*, which group similar tools. This is similar to how SciPy is designed [Oli07]_ [Jar11]_. There is far more functionality in ``scikit-image`` than can be conveyed in a single chapter, so a brief overview of the subpackages included as of version 0.12 is included below.\n\n``color``\n Color conversion routines, including grayscale to RGB (``rgb2gray``) and vice versa (``gray2rgb``) as well as many additional color spaces.\n``data``\n Test images shipped with the package, including ``astronaut`` (see crop in Figure :ref:`noise-types`).\n``draw``\n Routines to draw primitives including lines, shapes, and text.\n``exposure``\n Intensity and contrast adjustments.\n``feature``\n Feature detection, extraction, and matching. This subpackage includes ``ORB``, which is used in the panorama example to follow, as well as blob-finding and feature matching algorithms.\n``filters``\n Whole-image changes like sharpening. See also the rank filters exposed in ``skimage.filters.rank``.\n``future``\n Similar to Python's ability to import from the ``__future__``, this is a glimpse into the future of ``scikit-image``. Contains stable functions which are ready for use, but with API that may not be finalized.\n``graph``\n Graph theory, including path finding which is used in the panorama example to follow.\n``io``\n Reading and writing images; multiple plugins supported.\n``measure``\n Tools to quantify image properties such as length or shape. Also includes ``marching_cubes``, ``marching_squares``, and Hough transforms to find lines, circles, or ellipses.\n``morphology``\n Morphological operations, e.g., dilation and erosion. Binary and grayscale morphology supported.\n``novice``\n Simplified teaching interface.\n``restoration``\n Reduce noise or deconvolve images.\n``segmentation``\n Partition an image into two or more regions. Includes both unsupervised (``felzenszwalb``, ``slic``, ``quickshift``) and supervised (``random_walker``) methods.\n``transform``\n Warp or rotate images.\n``util``\n Common utility functions.\n``viewer``\n QT-based interactive GUI.\n\n\nReducing noise\n--------------\n\nThere are many types of noise which can affect images, and the first step to reducing unwanted noise is to understand what kind of noise is present. In scikit-image, there is a noise generation utility named ``random_noise`` located in ``skimage.util`` which can generate most commonly encountered types of noise. In Figure :ref:`noise-types` we show a comparison of several common noise types applied to a crop of the ``astronaut`` image available in ``skimage.data`` [#]_. This crop has both fine detail in the NASA patch and flat fields, so it is a good example to evaluate denoising algorithms.\n\n.. [#] Press photograph of NASA astronaut Eileen Collins, in the public domain.\n\n.. figure:: noise_types.png\n :align: center\n :scale: 90%\n\n Original, clean image and four different types of noise applied to it with ``skimage.util.random_noise``. Poisson noise is subtle, but difficult to remove, whereas gaussian as well as salt & pepper are not subtle but also challenging. :label:`noise-types`\n\nIt should come as no surprise that a particular denoising algorithm may be stronger or weaker at removing a particular kind of noise. In this example the noise type is speckle noise, which is a kind of multiplicative noise often encountered in ultrasound medical imaging. Three different denoising algorithms implemented in scikit-image will be applied: total variation, bilateral, and wavelet denoising.\n\nThe act of denoising is always a balance. It is almost never possible to entirely remove noise; doing so would eliminate the fine features and texture one desires to keep. When used to excess, or with parameters set too high, denoising algorithms typically produce “posterized” images with flat domains separated by sharp edges. Denoising is thus typically an iterative approach to control the tradeoff between smoothing and faithfulness to the original image by tuning function parameters.\n\n.. code-block:: python\n\n from skimage import data, img_as_float\n from skimage.util import random_noise\n\n astronaut = img_as_float(data.astronaut())\n astro = astronaut[300:450, 100:320]\n\n sigma = 0.3\n noisy = random_noise(img_astro, var=sigma**2)\n\nThe ``noisy`` image generated here and seen in Figure :ref:`denoise` is what our approaches below will attempt to fix. Denoising algorithms are located in ``skimage.restoration``, prefixed with ``denoise_``.\n\n.. figure:: denoise.png\n :align: center\n\n Top row: original image and with speckle noise applied. Subsequent rows show total variation, bilateral, and wavelet denoising respectively with pertinent settings in the titles. :label:`denoise`\n\nTotal variation minimization\n****************************\n\nDenoising by minimizing the total variation attempts to change the image in such a way as to reduce the total variation present. Thus, if applied too strongly it will eliminate fine features of the original image along with noise. The total variation norm being minimized is the L1 norm of the image gradient. This is an excellent method to reduce salt-and-pepper noise. As the norm being minimized is that of the gradient, when applied too strongly this algorithm results in very smooth results with no hard edges.\n\nThere are two approaches to total variation denoising implemented in scikit-image: split-Bregman [Get12]_ and Chambolle [Cha04]_. In this example the latter is used.\n\n.. code-block:: python\n\n from skimage.restoration import denoise_tv_chambolle\n\n tv_cham_low = denoise_tv_chambolle(\n img_noisy, weight=0.05, multichannel=True)\n tv_cham_high = denoise_tv_chambolle(\n img_noisy, weight=0.1, multichannel=True)\n\nThe function ``denoise_tv_chambolle`` accepts several parameters, of which the most pertinent are ``weight`` and ``multichannel``\n\n* ``weight`` represents the denoising strength: the greater the weight, the more noise is removed (at the expense of fidelity to the input image).\n* ``multichannel`` enables the option to apply total-variation denoising separately for each color channel. This parameter defaults to ``False`` but should be set ``True`` for color images; if not, the result will have color fringing artifacts.\n\nThe results of total variation denoising via the Chambolle method are shown in the second row of Figure :ref:`denoise`.\n\n\nBilateral filter\n****************\n\nA bilateral filter [Tom98]_ reduces noise while preserving edges. It assigns new values based on a local, weighted mean with two main features: proximity and similar value. The bilateral filter is implemented by the function `denoise_bilateral`, contained in the module `restoration`. This filter tends to produce piecewise-constant or cartoon-like images if applied to excess.\n\n.. code-block:: python\n\n from skimage.restoration import denoise_bilateral\n\n bilat_low = denoise_bilateral(\n img_noisy, sigma_color=0.05, sigma_spatial=25)\n bilat_high = denoise_bilateral(\n img_noisy, sigma_color=0.1, sigma_spatial=20)\n\n``denoise_bilateral`` allows the user to control the weight given to closeness in color and spatial proximity separately with the keyword arguments ``sigma_color`` and ``sigma_spatial``:\n\n* ``sigma_color`` represents the radiometric similarity, i.e., the standard deviation for color/value distance. The expected value is on the range [0, 1]. In the default case, `None`, the standard deviation of the input image is used.\n* ``sigma_spatial`` is the standard deviation for range distance. A larger value allows more distant pixels to more strongly influence the result.\n\nThe results of bilateral filter denoising are shown in the third row of Figure :ref:`denoise`.\n\nWavelet denoising\n*****************\n\nWavelets [#]_ are a fascinating mathematical construct that can be thought of as a way to combine the best of frequency and time domain analysis. They are applied at multiple scales. For brevity, the most important feature of wavelets for denoising purposes is that of *sparsity*.\n\n.. [#] At time of writing, wavelet algorithms are only available in the devevelopment version of scikit-image. They will be available in stable version of scikit-image 0.13 and above.\n\nWavelets, when applied to 2-dimensional images, decompose the image into a representation made up of many individual wavelets. This representation is sparse, i.e., there are relatively few wavelet coefficients with high values and many that are quite low. Denoising simply sets a threshold below which small coefficients are discarded, then inverts the result yielding an image with less noise. Sparse representations are similarly useful for image compression.\n\n.. code-block:: python\n\n from skimage.restoration import (denoise_wavelet,\n estimate_sigma)\n # Need to estimate noise present\n sigma_est = estimate_sigma(\n noisy, multichannel=True, average_sigmas=True)\n\n wave_low = denoise_wavelet(noisy, sigma=sigma_est,\n multichannel=True)\n wave_high = denoise_wavelet(noisy,\n sigma=1.4*sigma_est,\n multichannel=True)\n\nThe primary control over denoising strength is ``sigma=``, and there is also an algorithm to estimate the noise present ``estimage_sigma``. Generally this is an underestimate due to clipping, as true Gaussian noise has no limit to its range but the image data does.\n\nThe results of wavelet denoising are shown in the fourth row of Figure :ref:`denoise`.\n\nCorner detection\n----------------\n\nCorner detection is used to extract sharp features from an image. There are several corner detectors implemented on scikit-image. This example shows the Harris corner detector [Har88]_, which finds corner points and determine their position with sub-pixel precision.\n\nThe input image will be based on an image of a checkerboard, given by the function ``data.checkerboard()``, but a rectangular checkerboard is too easy. Using the functions ``warp`` and ``AffineTransform`` contained in in ``skimage.transform``, the checkerboard can be stretched and warped out of shape (see Figure :ref:`corners`)\n\n.. code-block:: python\n\n from skimage import data\n from skimage.transform import warp, AffineTransform\n\n affine = AffineTransform(\n scale=(0.8, 1.1), rotation=1, shear=0.7,\n translation=(220, 50))\n image = warp(data.checkerboard(), affine.inverse,\n output_shape=(200, 310))\n\nThen we use three functions from ``skimage.feature``:\n\n* ``corner_harris`` computes the Harris corner measure response image.\n* ``corner_peaks`` identifies corners in a corner measure response image, like the one returned by ``corner_harris``.\n* ``corner_subpix`` determines the sub-pixel position of corners.\n\n.. code-block:: python\n\n from skimage.feature import (corner_harris,\n corner_subpix,\n corner_peaks)\n\n harris_coords = corner_peaks(corner_harris(image))\n harris_subpix = corner_subpix(image, harris_coords)\n\nThe detected corners are shown in Figure :ref:`corners`.\n\n.. figure:: harris_corners.png\n :align: center\n\n On left, the warped checkerboard. On right, corners detected with the Harris corner detector are marked in red. These corners are defined with sub-pixel precision, but the markers are larger for legibility. :label:`corners`\n\nPanorama stitching\n------------------\n\nThis example stitches three images into a seamless panorama using several tools in scikit-image, including feature detection [Rub11]_, RANdom SAmple Consensus (RANSAC) [Fis81]_, graph theory, and affine transformations. The images used in this example are available at https://github.com/scikit-image/skimage-tutorials/tree/master/images/pano named ``JDW_9*.jpg``, released under the CC-BY 4.0 by the author.\n\nLoad images\n***********\n\nThe ``io`` module in scikit-image allows images to be loaded and saved. In this case the color panorama images will be loaded into an iterable `ImageCollection`, though one could also load them individually.\n\n.. code-block:: python\n\n from skimage import io\n pano_images = io.ImageCollection(\n '/path/to/images/JDW_9*')\n\n.. figure:: pano0_originals.png\n :align: center\n :figclass: w\n :scale: 60%\n\n Panorama source images, taken on the trail to Delicate Arch in Arches National Park, USA. Released under CC-BY 4.0 by Joshua D. Warner. :label:`fig-pano0`\n\nFeature detection and matching\n******************************\n\nTo correctly align the images, a *projective* transformation relating them is required.\n\n1. Define one image as a *target* or *destination* image, which will remain anchored while the others are warped.\n2. Detect features in all three images.\n3. Match features from left and right images against the features in the center, anchored image.\n\nIn this series, the middle image is the logical anchor point. Numerous feature detection algorithms are available; this example will use Oriented FAST and rotated BRIEF (ORB) features available as ``skimage.feature.ORB`` [Rub11]_.\n\n.. code-block:: python\n\n import matplotlib.pyplot as plt\n from skimage.color import rgb2gray\n from skimage.feature import (ORB, match_descriptors,\n plot_matches)\n\n # Initialize ORB\n orb = ORB(n_keypoints=800, fast_threshold=0.05)\n keypoints = []\n descriptors = []\n\n # Detect features\n for image in pano_images:\n orb.detect_and_extract(rgb2gray(image))\n keypoints.append(orb.keypoints)\n descriptors.append(orb.descriptors)\n\n # Match features from images 0 -> 1 and 2 -> 1\n matches01 = match_descriptors(descriptors[0],\n descriptors[1],\n cross_check=True)\n matches12 = match_descriptors(descriptors[1],\n descriptors[2],\n cross_check=True)\n\n # Show raw matched features from left to center\n fig, ax = plt.subplots()\n plot_matches(ax, pano_images[0], pano_images[1],\n keypoints[0], keypoints[1], matches01)\n ax.axis('off');\n\n.. figure:: pano1_ORB-raw.png\n :align: center\n\n Matched ORB keypoints from left and center images from :ref:`fig-pano0`. Most features line up similarly, but there are a number of obvious outliers or false matches. :label:`fig-pano1`\n\nTransform estimation\n********************\n\nTo filter out the false matches observed in Figure :ref:`fig-pano1`, RANdom SAmple Consensus (RANSAC) is used [Fis81]_. RANSAC is a powerful method of rejecting outliers available in ``skimage.transform.ransac``. The transformation is estimated using an iterative process based on randomly chosen subsets, finally selecting the model which corresponds best with the majority of matches.\n\nIt is important to note the randomness inherent to RANSAC. The results are robust, but will vary slightly every time. Thus, it is expected that readers' results will deviate slightly from the published figures after this point.\n\n.. code-block:: python\n\n from skimage.measure import ransac\n from skimage.transform import ProjectiveTransform\n\n # Keypoints from left (src) to middle (dst) images\n src = keypoints[0][matches01[:, 0]][:, ::-1]\n dst = keypoints[1][matches01[:, 1]][:, ::-1]\n\n model_ransac01, inliers01 = ransac(\n (src, dst), ProjectiveTransform, min_samples=4,\n residual_threshold=1, max_trials=300)\n\n # Keypoints from right (src) to middle (dst) images\n src = keypoints[2][matches12[:, 1]][:, ::-1]\n dst = keypoints[1][matches12[:, 0]][:, ::-1]\n\n model_ransac12, inliers12 = ransac(\n (src, dst), ProjectiveTransform, min_samples=4,\n residual_threshold=1, max_trials=300)\n\n # Show robust, RANSAC-matched features\n fig, ax = plt.subplots()\n plot_matches(ax, pano_images[0], pano_images[1],\n keypoints[0], keypoints[1],\n matches01[inliers01])\n ax.axis('off');\n\nThe results of robust transform estimation with RANSAC are shown in Figure :ref:`fig-pano2`.\n\n.. figure:: pano2_ORB-RANSAC.png\n :align: center\n\n The best RANSAC transform estimation uses only these keypoints. The outliers are now excluded (compare with Figure :ref:`fig-pano1`). :label:`fig-pano2`\n\nWarp images into place\n**********************\n\nBefore producing the panorama, the correct size for a new canvas to hold all three warped images is needed. The entire size, or extent, of this image is carefully found.\n\n.. code-block:: python\n\n # All three images have the same size\n r, c = pano_images[1].shape[:2]\n\n # Note that transformations take coordinates in\n # (x, y) format, not (row, column), for literature\n # consistency\n corners = np.array([[0, 0],\n [0, r],\n [c, 0],\n [c, r]])\n\n # Warp image corners to their new positions\n warped_corners01 = model_ransac01(corners)\n warped_corners12 = model_ransac12(corners)\n\n # Extents of both target and warped images\n all_corners = np.vstack((warped_corners01,\n warped_corners12,\n corners))\n\n # Overall output shape is max - min\n corner_min = np.min(all_corners, axis=0)\n corner_max = np.max(all_corners, axis=0)\n output_shape = (corner_max - corner_min)\n\n # Ensure integer shape\n output_shape = np.ceil(\n output_shape[::-1]).astype(int)\n\n\nNext, each image is warped and placed into a new canvas of shape ``output_shape``.\n\nTranslate middle target image\n*****************************\n\nThe middle image is stationary, but still needs to be shifted into the center of the larger canvas. This is done with simple translation using a ``SimilarityTransform``.\n\n.. code-block:: python\n\n from skimage.transform import warp\n from skimage.transform import SimilarityTransform\n\n offset1 = SimilarityTransform(\n translation= -corner_min)\n\n # Translate pano1 into place\n pano1_warped = warp(\n pano_images[1], offset1.inverse, order=3,\n output_shape=output_shape, cval=-1)\n\n # Acquire the image mask for later use\n # Mask == 1 inside image, then return backgroun to 0\n pano1_mask = (pano1_warped != -1)[..., 0]\n pano1_warped[~pano1_mask] = 0\n\n\nApply RANSAC-estimated transforms\n*********************************\n\nThe other two images are warped by ``ProjectiveTransform`` into place.\n\n.. code-block:: python\n\n # Warp left image\n transform01 = (model_ransac01 + offset1).inverse\n pano0_warped = warp(\n pano_images[0], transform01, order=3,\n output_shape=output_shape, cval=-1)\n\n # Mask == 1 inside image, then return backgroun to 0\n pano0_mask = (pano0_warped != -1)[..., 0]\n pano0_warped[~pano0_mask] = 0\n\n # Warp right image\n transform12 = (model_ransac12 + offset1).inverse\n pano2_warped = warp(\n pano_images[2], transform12, order=3,\n output_shape=output_shape, cval=-1)\n\n # Mask == 1 inside image, then return backgroun to 0\n pano2_mask = (pano2_warped != -1)[..., 0]\n pano1_warped[~pano1_mask] = 0\n\nSee the warped images in :ref:`fig-pano3`.\n\n.. figure:: pano3_warped.png\n :align: center\n\n Each image is now correctly warped into a new frame with room for the others, ready to be composited/stitched together. :label:`fig-pano3`\n\n\nImage stitching using minimum-cost path\n***************************************\n\nBecause of optical non-linearities, simply averaging these images together will not work. The overlapping areas become significantly blurred. Instead, a minimum-cost path can be found with the assistance of ``skimage.graph.route_through_array``. This function allows one to\n\n* start at any point on an array\n* find a particular path to any other point in the array\n* the path found *minimizes* the sum of values on the path.\n\nThe array in this instance is a *cost array* which is carefully defined so the path found will be desired one, while the path itself is the *minimum-cost path*, or MCP. To use this technique we need starting and ending points, as well as a cost array.\n\nDefine seed points\n******************\n\n.. code-block:: python\n\n ymax = output_shape[1] - 1\n xmax = output_shape[0] - 1\n\n # Start anywhere along the top and bottom\n mask_pts01 = [[0, ymax // 3],\n [xmax, ymax // 3]]\n\n # Start anywhere along the top and bottom\n mask_pts12 = [[0, 2*ymax // 3],\n [xmax, 2*ymax // 3]]\n\n\nConstruct cost array\n********************\n:label:`construct-costs`\n\nFor optimal results, great care goes into the creation of the cost array. The function below is designed to construct the best possible cost array. Its tasks are:\n\n1. Start with a high-cost image filled with ones.\n2. Use the mask - which defines where the overlapping region will be - to find the distance from the top/bottom edges to the masked area.\n3. Reject mostly vertical areas.\n4. Give a cost break to areas slightly further away, if the warped overlap is not parallel with the image edges, to ensure fair competition\n5. Put the absolute value of the *difference* of the overlapping images in place\n\nA convenience function ``generate_costs`` is provided in the Appendix which accomplishes the above.\n\n.. code-block:: python\n\n # Use the generate_costs function\n costs01 = generate_costs(pano0_warped - pano1_warped,\n pano0_mask & pano1_mask)\n costs12 = generate_costs(pano1_warped - pano2_warped,\n pano1_mask & pano2_mask)\n\n\nFind minimum-cost path and masks\n********************************\n\nOnce the cost function is generated, the minimum cost path can be found simply and efficiently.\n\n.. code-block:: python\n\n from skimage.graph import route_through_array\n\n # Find the MCP\n pts01, _ = route_through_array(\n costs01, mask_pts01[0], mask_pts01[1],\n fully_connected=True)\n\n pts01 = np.array(pts01)\n\n # Create final mask for the left image\n mask0 = np.zeros_like(pano0_warped[..., 0],\n dtype=np.uint8)\n mask0[pts01[:, 0], pts01[:, 1]] = 1\n\n # Fill left side with flood_fill (in appendix)\n flood_fill(mask0, (0, 0), 1)\n\n.. figure:: pano4_mcp.png\n :align: center\n :figclass: w\n :scale: 98%\n\n The minimum cost path in blue is the ideal stitching boundary. It stays as close to zero (mid-gray) as possible throughout its path. The background is the cost array, with zero set to mid-gray for better visibility. Note the subtle shading effect of cost reduction below the difference region. Readers' paths may differ in appearance, but are optimal for their RANSAC-chosen transforms.\n\nBecause ``mask0`` is a *final* mask for the left image, it needs to constrain the solution for the right image. This step is essential if there is large overlap such that the left and right images could theoretically occupy the same space. It ensures the MCPs will not cross.\n\n.. code-block:: python\n\n # New constraint modifying cost array\n costs12[mask0 > 0] = 1\n\n pts12, _ = route_through_array(\n costs12, mask_pts12[0], mask_pts12[1],\n fully_connected=True)\n\n pts12 = np.array(pts12)\n\n # Final mask for right image\n mask2 = np.zeros_like(mask0, dtype=np.uint8)\n mask2[pts12[:, 0], pts12[:, 1]] = 1\n\n # Fill right side of image\n flood_fill(mask2, (mask2.shape[0] - 1,\n mask2.shape[1] - 1), 1)\n\n # Mask for middle image is one of exclusion\n mask1 = ~(mask0 | mask2).astype(bool)\n\n\nBlend images together with alpha channels\n*****************************************\n\nMost image formats can support an alpha channel as an optional fourth channel, which defines the transparency at each pixel. We now have three warped images and three corresponding masks. These masks can be incorporated as alpha channels to seamlessly blend them together.\n\n.. code-block:: python\n\n # Convenience function for alpha blending\n def add_alpha(img, mask=None):\n \"\"\"\n Adds a masked alpha channel to an image.\n\n Parameters\n ----------\n img : (M, N[, 3]) ndarray\n Image data, should be rank-2 or rank-3\n with RGB channels\n mask : (M, N[, 3]) ndarray, optional\n Mask to be applied. If None, the alpha channel\n is added with full opacity assumed (1) for all\n locations.\n \"\"\"\n from skimage.color import gray2rgb\n if mask is None:\n mask = np.ones_like(img)\n\n if img.ndim == 2:\n img = gray2rgb(img)\n\n return np.dstack((img, mask))\n\n # Applying this function\n left_final = add_alpha(pano0_warped, mask0)\n middle_final = add_alpha(pano1_warped, mask1)\n right_final = add_alpha(pano2_warped, mask2)\n\n\nMatplotlib's ``imshow`` supports alpha blending, but the default interpolation mode causes edge effects [Hunt07]_. So as we create our final composite image, interpolation is disabled.\n\n.. code-block:: python\n\n fig, ax = plt.subplots()\n\n # Turn off matplotlib's interpolation\n ax.imshow(left_final, interpolation='none')\n ax.imshow(middle_final, interpolation='none')\n ax.imshow(right_final, interpolation='none')\n\n ax.axis('off')\n fig.tight_layout()\n fig.show()\n\n.. figure:: pano5_final.png\n :align: center\n :figclass: w\n :scale: 31%\n\n The final, seamlessly stitched panorama.\n\n\nFinal thoughts\n--------------\nPlease cite the scikit-image paper [Van14]_ if you find ``scikit-image`` useful! Citations allow developers to justify time invested in the package.\n\nThe authors would like to acknowledge and thank every contributor to ``scikit-image``.\n\n\nReferences\n----------\n.. [Van14] van der Walt, S.; Schönberger, J. L.; Nunez-Iglesias, J;\n Boulogne, F; Warner, J. D.; Yager, N; Gouillart, E; Yu, T;\n the scikit-image contributors. *scikit-image: image\n processing in Python*, PeerJ, 2:e453, 2014.\n DOI:10.7717/peerj.453\n\n.. [Oli07] Travis E. Oliphant. *Python for Scientific Computing.*\n Computing in Science & Engineering, 9:10-20, 2007. DOI:10.1109/MCSE.2007.58\n\n.. [Jar11] Millman, K. J.; Aivazis, M. *Python for Scientists and\n Engineers.* Computing in Science & Engineering, 13:9-12,\n 2011. DOI:10.1109/MCSE.2011.36\n\n.. [Van11] van der Walt, S.; Colbert, S. C.; Varoquaux, G. *The\n NumPy Array: A Structure for Efficient Numerical\n Computation.* Computing in Science & Engineering, 13:22-30,\n 2011. DOI:10.1109/MCSE.2011.37\n\n.. [Hunt07] Hunter, J. D. *Matplotlib: A 2D graphics environment*,\n Computing In Science & Engineering, 9(3):90-95, 2007.\n DOI:10.5281/zenodo.61948\n\n.. [Get12] Getreuer, P. *Rudin-Osher-Fatemi total variation\n denoising using split Bregman.* Image Processing On Line,\n 2:74-95, 2012. DOI:10.5201/ipol.2012.g-tvd\n\n.. [Cha04] Chambolle, A. *An algorithm for total variation\n minimization and applications.* Journal of Mathematical\n imaging and vision, 20(1-2):89-97, 2004.\n DOI: 10.1023/B:JMIV.0000011325.36760.1e\n\n.. [Har88] Harris, C.; Stephens, M. *A combined corner and edge\n detector.* In Alvey vision conference 15:50, 1988.\n\n.. [Tom98] Tomasi, C.; Manduchi, R. *Bilateral filtering for gray\n and color images.* IEEE Computer Vision, 1998. Sixth International Conference on, 839-846. 1998.\n\n.. [Rub11] Rublee, E.; Rabaud, V.; Konolige, K.; Bradski, G.\n *ORB: an efficient alternative to SIFT or SURF*,\n IEEE International Conference on Computer Vision (ICCV),\n 2564-2571, 2011. DOI:10.1109/ICCV.2011.6126544\n\n.. [Fis81] Fischler, M. A.; Robert C. B. *Random sample consensus:\n a paradigm for model fitting with applications to image\n analysis and automated cartography.* Communications of\n the ACM, 24(6):381-395, 1981.\n\n\nAppendix\n--------\n\nThis supplemental appendix includes convenience functions which were deemed obstructive for the flow of the main chapter text. They are referenced where appropriate above. Including them resulted in more elegant and intuitive examples.\n\nMinimum-cost-path cost array creation\n*************************************\n:label:`cost-arr-func`\n\nThis function generates an ideal cost array for panorama stitching, using the principles set forth in :ref:`construct-costs`.\n\n.. code-block:: python\n\n def generate_costs(diff_image, mask,\n vertical=True,\n gradient_cutoff=2.,\n zero_edges=True):\n \"\"\"\n Ensure equal-cost paths from edges to\n region of interest.\n\n Parameters\n ----------\n diff_image : (M, N) ndarray of floats\n Difference of two overlapping images.\n mask : (M, N) ndarray of bools\n Mask representing the region of interest in\n ``diff_image``.\n vertical : bool\n Control if stitching line is vertical or\n horizontal.\n gradient_cutoff : float\n Controls how far out of parallel lines can\n be to edges before correction is terminated.\n The default (2.) is good for most cases.\n zero_edges : bool\n If True, the edges are set to zero so the\n seed is not bound to any specific horizontal\n location.\n\n Returns\n -------\n costs_arr : (M, N) ndarray of floats\n Adjusted costs array, ready for use.\n \"\"\"\n if vertical is not True: # run transposed\n return generate_costs(\n diff_image.T, mask.T, vertical=True,\n gradient_cutoff=gradient_cutoff).T\n\n # Start with a high-cost array of 1's\n diff_image = rgb2gray(diff_image)\n costs_arr = np.ones_like(diff_image)\n\n # Obtain extent of overlap\n row, col = mask.nonzero()\n cmin = col.min()\n cmax = col.max()\n\n # Label discrete regions\n cslice = slice(cmin, cmax + 1)\n labels = mask[:, cslice].astype(np.uint8).copy()\n\n # Fill top and bottom with unique labels\n masked_pts = np.where(labels)\n flood_fill(labels, (masked_pts[0][0],\n masked_pts[1][0]), 2)\n flood_fill(labels, (0, labels.shape[0] // 2), 1)\n flood_fill(labels, (labels.shape[0] - 1,\n labels.shape[1] // 2), 3)\n\n # Find distance from edge to region\n upper = (labels == 1).sum(axis=0)\n lower = (labels == 3).sum(axis=0)\n\n # Reject areas of high change\n ugood = np.abs(\n np.gradient(upper)) < gradient_cutoff\n lgood = np.abs(\n np.gradient(lower)) < gradient_cutoff\n\n # Cost break to areas slightly farther from edge\n costs_upper = np.ones_like(upper,\n dtype=np.float64)\n costs_lower = np.ones_like(lower,\n dtype=np.float64)\n costs_upper[ugood] = (\n upper.min() / np.maximum(upper[ugood], 1))\n costs_lower[lgood] = (\n lower.min() / np.maximum(lower[lgood], 1))\n\n # Expand from 1d back to 2d\n vdis = mask.shape[0]\n costs_upper = (\n costs_upper[np.newaxis, :].repeat(\n vdis, axis=0))\n costs_lower = (\n costs_lower[np.newaxis, :].repeat(\n vdis, axis=0))\n\n # Place these in output array\n costs_arr[:, cslice] = costs_upper * (labels==1)\n costs_arr[:, cslice] += costs_lower * (labels==3)\n\n # Finally, place the difference image\n costs_arr[mask] = np.abs(diff_image[mask])\n\n if zero_edges is True: # top & bottom rows = 0\n costs_arr[0, :] = 0\n costs_arr[-1, :] = 0\n\n return costs_arr\n\n\nFlood fill\n**********\n:label:`flood-fill`\n\nThis Cython function is a basic flood fill algorithm which accepts an array and modifies it in place. The flood starts at a defined point, which is changed to a new value, then iteratively fills outward by doing the same at all connected points which carry the original value.\n\nThe conceptual analogy of this algorithm is the \"bucket\" tool in many photo editing programs.\n\n.. code-block:: cython\n\n import cython\n import numpy as np\n cimport numpy as cnp\n\n\n # Compiler directives\n @cython.cdivision(True)\n @cython.boundscheck(False)\n @cython.nonecheck(False)\n @cython.wraparound(False)\n def flood_fill(unsigned char[:, ::1] image,\n tuple start_coords,\n Py_ssize_t fill_value):\n \"\"\"\n Flood fill algorithm\n\n Parameters\n ----------\n image : (M, N) ndarray of uint8 type\n Image with flood to be filled. Modified\n inplace.\n start_coords : tuple\n Length-2 tuple of ints defining (row, col)\n start coordinates.\n fill_value : int\n Value to fill flooded area with.\n\n Returns\n -------\n None. ``image`` is modified inplace.\n \"\"\"\n cdef:\n Py_ssize_t x, y, xsize, ysize, orig_value\n set stack\n\n xsize = image.shape[0]\n ysize = image.shape[1]\n orig_value = image[start_coords[0],\n start_coords[1]]\n\n if fill_value == orig_value:\n raise ValueError(\n \"Filling region with same value \"\n \"already present is unsupported. \"\n \"Did you already fill this region?\")\n\n stack = set(((start_coords[0],\n start_coords[1]), ))\n\n while stack:\n x, y = stack.pop()\n\n if image[x, y] == orig_value:\n image[x, y] = fill_value\n\n if x > 0:\n stack.add((x - 1, y))\n if x < (xsize - 1):\n stack.add((x + 1, y))\n if y > 0:\n stack.add((x, y - 1))\n if y < (ysize - 1):\n stack.add((x, y + 1))\n" }, { "path": "papers/joshua_warner/reproduction_corner.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import division, print_function\\n\",\n \"%matplotlib inline\\n\",\n \"%load_ext Cython\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Corner detection reproduction\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage import data\\n\",\n \"from skimage.transform import warp, AffineTransform\\n\",\n \"\\n\",\n \"affine = AffineTransform(\\n\",\n \" scale=(0.8, 1.1), rotation=1, shear=0.7, \\n\",\n \" translation=(220, 50))\\n\",\n \"image = warp(data.checkerboard(), affine.inverse, \\n\",\n \" output_shape=(200, 310))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"ax.imshow(image, cmap='gray', interpolation='none')\\n\",\n \"ax.axis('off');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage.feature import (corner_harris,\\n\",\n \" corner_subpix, \\n\",\n \" corner_peaks)\\n\",\n \"\\n\",\n \"harris_coords = corner_peaks(corner_harris(image))\\n\",\n \"harris_subpix = corner_subpix(image, harris_coords)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots(1, 2, figsize=(8, 4))\\n\",\n \"ax[0].imshow(image, cmap='gray', interpolation='none')\\n\",\n \"ax[0].set_title('Warped checkerboard', fontsize=20)\\n\",\n \"ax[0].axis((0, 299, 199, 0))\\n\",\n \"ax[0].axis('off')\\n\",\n \"\\n\",\n \"ax[1].imshow(image, cmap='gray', interpolation='none')\\n\",\n \"ax[1].plot(harris_coords[:, 1], harris_coords[:, 0], '.b', markersize=10)\\n\",\n \"ax[1].plot(harris_subpix[:, 1], harris_subpix[:, 0], '*r', markersize=10)\\n\",\n \"ax[1].set_title('Sub-pixel corners', fontsize=20)\\n\",\n \"ax[1].axis((0, 299, 199, 0))\\n\",\n \"ax[1].axis('off')\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"fig.savefig('./harris_corners.png', dpi=300, bbox_inches=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/joshua_warner/reproduction_denoise.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import division, print_function\\n\",\n \"%matplotlib inline\\n\",\n \"%load_ext Cython\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Denoise reproduction\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Types of image noise\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"noise_types = ['gaussian',\\n\",\n \" 'poisson',\\n\",\n \" 's&p',\\n\",\n \" 'speckle']\\n\",\n \"\\n\",\n \"astro = img_as_float(data.astronaut())\\n\",\n \"\\n\",\n \"# Detail patch\\n\",\n \"astro0 = astro[300:450, 100:320]\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(nrows=5, figsize=(3, 10))\\n\",\n \"\\n\",\n \"pretty_subplot(ax[0], astro0, 'original')\\n\",\n \"\\n\",\n \"gauss = random_noise(astro0, mode='gaussian', seed=42,\\n\",\n \" var=0.15)\\n\",\n \"poisson = random_noise(astro0, mode='poisson', seed=42)\\n\",\n \"saltpepper = random_noise(astro0, mode='s&p', amount=0.2)\\n\",\n \"speckle = random_noise(astro0, mode='speckle', var=0.15)\\n\",\n \"\\n\",\n \"pretty_subplot(ax[1], gauss, 'gaussian')\\n\",\n \"pretty_subplot(ax[2], poisson, 'poisson')\\n\",\n \"pretty_subplot(ax[3], saltpepper, 'salt & pepper')\\n\",\n \"pretty_subplot(ax[4], speckle, 'speckle')\\n\",\n \"\\n\",\n \"fig.tight_layout()\\n\",\n \"plt.show()\\n\",\n \"fig.savefig('./noise_types.png', dpi=300, bbox_inches=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"random_noise?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from skimage import data, img_as_float\\n\",\n \"from skimage.restoration import (denoise_tv_chambolle, denoise_bilateral,\\n\",\n \" denoise_wavelet, estimate_sigma)\\n\",\n \"from skimage.util import random_noise\\n\",\n \"\\n\",\n \"\\n\",\n \"# astro = img_as_float(data.astronaut())\\n\",\n \"# astro = astro[220:300, 220:320]\\n\",\n \"astro=astro0\\n\",\n \"\\n\",\n \"sigma = 0.3\\n\",\n \"noisy = random_noise(astro, 'speckle', var=sigma**2, seed=42)\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(nrows=4, ncols=2, figsize=(5, 7.3))\\n\",\n \"\\n\",\n \"def pretty_subplot(ax, image, title=None, ylabel=None):\\n\",\n \" ax.imshow(image)\\n\",\n \" ax.set_xticks([]) \\n\",\n \" ax.set_yticks([])\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['bottom'].set_visible(False)\\n\",\n \" ax.spines['left'].set_visible(False)\\n\",\n \" if title is not None:\\n\",\n \" ax.set_title(title)\\n\",\n \" if ylabel is not None:\\n\",\n \" ax.set_ylabel(ylabel)\\n\",\n \"\\n\",\n \"pretty_subplot(ax[0, 0], astro, 'original')\\n\",\n \"pretty_subplot(ax[0, 1], noisy, 'noisy')\\n\",\n \"\\n\",\n \"pretty_subplot(ax[1, 0], \\n\",\n \" denoise_tv_chambolle(noisy, weight=0.05, \\n\",\n \" multichannel=True),\\n\",\n \" 'weight: 0.05',\\n\",\n \" 'TV-Chambolle')\\n\",\n \"pretty_subplot(ax[1, 1], \\n\",\n \" denoise_tv_chambolle(noisy, weight=0.1, \\n\",\n \" multichannel=True),\\n\",\n \" 'weight: 0.1')\\n\",\n \"\\n\",\n \"pretty_subplot(ax[2, 0],\\n\",\n \" denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15),\\n\",\n \" r'$\\\\sigma_{color}$:0.05, $\\\\sigma_{spatial}$:25',\\n\",\n \" 'Bilateral')\\n\",\n \"pretty_subplot(ax[2, 1],\\n\",\n \" denoise_bilateral(noisy, sigma_color=0.1, sigma_spatial=20),\\n\",\n \" r'$\\\\sigma_{color}$:0.1, $\\\\sigma_{spatial}$:20')\\n\",\n \"\\n\",\n \"# Estimate sigma for wavelet\\n\",\n \"sigma_est = estimate_sigma(noisy, multichannel=True, average_sigmas=True)\\n\",\n \"pretty_subplot(ax[3, 0],\\n\",\n \" denoise_wavelet(noisy, sigma=sigma_est,\\n\",\n \" multichannel=True),\\n\",\n \" r'$\\\\sigma$: estimated',\\n\",\n \" 'Wavelet')\\n\",\n \"pretty_subplot(ax[3, 1],\\n\",\n \" denoise_wavelet(noisy, sigma=1.4*sigma_est,\\n\",\n \" multichannel=True),\\n\",\n \" r'$\\\\sigma$: 1.4*estimated')\\n\",\n \"\\n\",\n \"fig.tight_layout(h_pad=0.7, w_pad=0.3)\\n\",\n \"\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"fig.savefig('./denoise.png', dpi=300, bbox_inches=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/joshua_warner/reproduction_pano.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import division, print_function\\n\",\n \"%matplotlib inline\\n\",\n \"%load_ext Cython\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Reproduction notebook for Panorama stitching\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage import io\\n\",\n \"pano_images = io.ImageCollection(\\n\",\n \" './images/JDW_9*')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def compare(*images, **kwargs):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Utility function to display images side by side.\\n\",\n \" \\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" image0, image1, image2, ... : ndarrray\\n\",\n \" Images to display.\\n\",\n \" labels : list\\n\",\n \" Labels for the different images.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if 'vertical' in kwargs:\\n\",\n \" vertical = kwargs.pop('vertical')\\n\",\n \" else:\\n\",\n \" vertical = False\\n\",\n \" \\n\",\n \" if vertical is not True:\\n\",\n \" f, axes = plt.subplots(1, len(images), **kwargs)\\n\",\n \" else:\\n\",\n \" f, axes = plt.subplots(len(images), 1, **kwargs)\\n\",\n \"\\n\",\n \" axes = np.array(axes, ndmin=1)\\n\",\n \" \\n\",\n \" labels = kwargs.pop('labels', None)\\n\",\n \" if labels is None:\\n\",\n \" labels = [''] * len(images)\\n\",\n \" \\n\",\n \" for n, (image, label) in enumerate(zip(images, labels)):\\n\",\n \" axes[n].imshow(image, interpolation='nearest', cmap='gray')\\n\",\n \" axes[n].set_title(label)\\n\",\n \" axes[n].axis('off')\\n\",\n \" \\n\",\n \" f.subplots_adjust(left=0, right=1, top=1, bottom=0, hspace=0.01, wspace=0.01)\\n\",\n \" return f, axes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"f, axes = compare(*pano_images, figsize=(12, 10));\\n\",\n \"# f.savefig('./pano0-originals.png', dpi=300, pad_inches=0, bbox_inches='tight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"from skimage.color import rgb2gray\\n\",\n \"from skimage.feature import (ORB, match_descriptors,\\n\",\n \" plot_matches)\\n\",\n \"\\n\",\n \"# Initialize ORB\\n\",\n \"orb = ORB(n_keypoints=800, fast_threshold=0.05)\\n\",\n \"keypoints = []\\n\",\n \"descriptors = []\\n\",\n \"\\n\",\n \"# Detect features\\n\",\n \"for image in pano_images:\\n\",\n \" orb.detect_and_extract(rgb2gray(image))\\n\",\n \" keypoints.append(orb.keypoints)\\n\",\n \" descriptors.append(orb.descriptors)\\n\",\n \"\\n\",\n \"# Match features from images 0 -> 1 and 2 -> 1\\n\",\n \"matches01 = match_descriptors(descriptors[0],\\n\",\n \" descriptors[1],\\n\",\n \" cross_check=True)\\n\",\n \"matches12 = match_descriptors(descriptors[1],\\n\",\n \" descriptors[2],\\n\",\n \" cross_check=True)\\n\",\n \"\\n\",\n \"# Show raw matched features from left to center\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"plot_matches(ax, pano_images[0], pano_images[1],\\n\",\n \" keypoints[0], keypoints[1], matches01)\\n\",\n \"ax.axis('off');\\n\",\n \"# fig.savefig('./pano1_ORB-raw.png', dpi=500, pad_inches=0, bbox_inches='tight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage.measure import ransac\\n\",\n \"from skimage.transform import ProjectiveTransform\\n\",\n \"\\n\",\n \"# Keypoints from left (src) to middle (dst) images\\n\",\n \"src = keypoints[0][matches01[:, 0]][:, ::-1]\\n\",\n \"dst = keypoints[1][matches01[:, 1]][:, ::-1]\\n\",\n \"\\n\",\n \"model_ransac01, inliers01 = ransac(\\n\",\n \" (src, dst), ProjectiveTransform, min_samples=4,\\n\",\n \" residual_threshold=1, max_trials=300)\\n\",\n \"\\n\",\n \"# Keypoints from right (src) to middle (dst) images\\n\",\n \"src = keypoints[2][matches12[:, 1]][:, ::-1]\\n\",\n \"dst = keypoints[1][matches12[:, 0]][:, ::-1]\\n\",\n \"\\n\",\n \"model_ransac12, inliers12 = ransac(\\n\",\n \" (src, dst), ProjectiveTransform, min_samples=4,\\n\",\n \" residual_threshold=1, max_trials=300)\\n\",\n \"\\n\",\n \"# Show robust, RANSAC-matched features\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"plot_matches(ax, pano_images[0], pano_images[1],\\n\",\n \" keypoints[0], keypoints[1],\\n\",\n \" matches01[inliers01])\\n\",\n \"ax.axis('off');\\n\",\n \"# fig.savefig('./pano2_ORB-RANSAC.png', dpi=500, pad_inches=0, bbox_inches='tight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# All three images have the same size\\n\",\n \"r, c = pano_images[1].shape[:2]\\n\",\n \"\\n\",\n \"# Note that transformations take coordinates in\\n\",\n \"# (x, y) format, not (row, column), for literature\\n\",\n \"# consistency\\n\",\n \"corners = np.array([[0, 0],\\n\",\n \" [0, r],\\n\",\n \" [c, 0],\\n\",\n \" [c, r]])\\n\",\n \"\\n\",\n \"# Warp image corners to their new positions\\n\",\n \"warped_corners01 = model_ransac01(corners)\\n\",\n \"warped_corners12 = model_ransac12(corners)\\n\",\n \"\\n\",\n \"# Extents of both target and warped images\\n\",\n \"all_corners = np.vstack((warped_corners01,\\n\",\n \" warped_corners12,\\n\",\n \" corners))\\n\",\n \"\\n\",\n \"# Overall output shape is max - min\\n\",\n \"corner_min = np.min(all_corners, axis=0)\\n\",\n \"corner_max = np.max(all_corners, axis=0)\\n\",\n \"output_shape = (corner_max - corner_min)\\n\",\n \"\\n\",\n \"# Ensure integer shape\\n\",\n \"output_shape = np.ceil(\\n\",\n \" output_shape[::-1]).astype(int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage.transform import warp, SimilarityTransform\\n\",\n \"\\n\",\n \"offset1 = SimilarityTransform(translation= -corner_min)\\n\",\n \"\\n\",\n \"# Translate pano1 into place\\n\",\n \"pano1_warped = warp(\\n\",\n \" pano_images[1], offset1.inverse, order=3,\\n\",\n \" output_shape=output_shape, cval=-1)\\n\",\n \"\\n\",\n \"# Acquire the image mask for later use\\n\",\n \"# Mask == 1 inside image, then return backgroun to 0\\n\",\n \"pano1_mask = (pano1_warped != -1)[..., 0]\\n\",\n \"pano1_warped[~pano1_mask] = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage.transform import warp, SimilarityTransform\\n\",\n \"\\n\",\n \"offset1 = SimilarityTransform(translation= -corner_min)\\n\",\n \"\\n\",\n \"# Translate pano1 into place\\n\",\n \"pano1_warped = warp(\\n\",\n \" pano_images[1], offset1.inverse, order=3,\\n\",\n \" output_shape=output_shape, cval=-1)\\n\",\n \"\\n\",\n \"# Acquire the image mask for later use\\n\",\n \"# Mask == 1 inside image, then return backgroun to 0\\n\",\n \"pano1_mask = (pano1_warped != -1)[..., 0]\\n\",\n \"pano1_warped[~pano1_mask] = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Warp left image\\n\",\n \"transform01 = (model_ransac01 + offset1).inverse\\n\",\n \"pano0_warped = warp(\\n\",\n \" pano_images[0], transform01, order=3,\\n\",\n \" output_shape=output_shape, cval=-1)\\n\",\n \"\\n\",\n \"# Mask == 1 inside image, then return backgroun to 0\\n\",\n \"pano0_mask = (pano0_warped != -1)[..., 0]\\n\",\n \"pano0_warped[~pano0_mask] = 0\\n\",\n \"\\n\",\n \"# Warp right image\\n\",\n \"transform12 = (model_ransac12 + offset1).inverse\\n\",\n \"pano2_warped = warp(\\n\",\n \" pano_images[2], transform12, order=3,\\n\",\n \" output_shape=output_shape, cval=-1)\\n\",\n \"\\n\",\n \"# Mask == 1 inside image, then return backgroun to 0\\n\",\n \"pano2_mask = (pano2_warped != -1)[..., 0]\\n\",\n \"pano1_warped[~pano1_mask] = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"f, ax = compare(pano0_warped, pano1_warped, pano2_warped, vertical=True)\\n\",\n \"# f.savefig('./pano3_warped.png', dpi=500, pad_inches=0, bbox_inches='tight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ymax = output_shape[1] - 1\\n\",\n \"xmax = output_shape[0] - 1\\n\",\n \"\\n\",\n \"# Start anywhere along the top and bottom\\n\",\n \"mask_pts01 = [[0, ymax // 3],\\n\",\n \" [xmax, ymax // 3]]\\n\",\n \"\\n\",\n \"# Start anywhere along the top and bottom\\n\",\n \"mask_pts12 = [[0, 2*ymax // 3],\\n\",\n \" [xmax, 2*ymax // 3]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Cost array and flood fill functions from appendix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%%cython\\n\",\n \"import cython\\n\",\n \"import numpy as np\\n\",\n \"cimport numpy as cnp\\n\",\n \"\\n\",\n \"\\n\",\n \"# Compiler directives\\n\",\n \"@cython.cdivision(True)\\n\",\n \"@cython.boundscheck(False)\\n\",\n \"@cython.nonecheck(False)\\n\",\n \"@cython.wraparound(False)\\n\",\n \"def flood_fill(unsigned char[:, ::1] data, tuple start_coords,\\n\",\n \" Py_ssize_t fill_value):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Flood fill algorithm\\n\",\n \" \\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" data : (M, N) ndarray of uint8 type\\n\",\n \" Image with flood to be filled. Modified inplace.\\n\",\n \" start_coords : tuple\\n\",\n \" Length-2 tuple of ints defining (row, col) start coordinates.\\n\",\n \" fill_value : int\\n\",\n \" Value the flooded area will take after the fill.\\n\",\n \" \\n\",\n \" Returns\\n\",\n \" -------\\n\",\n \" None, ``data`` is modified inplace.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" cdef:\\n\",\n \" Py_ssize_t x, y, xsize, ysize, orig_value, ystart, xstart\\n\",\n \" set stack\\n\",\n \" \\n\",\n \" xsize = data.shape[0]\\n\",\n \" ysize = data.shape[1]\\n\",\n \" xstart = start_coords[0]\\n\",\n \" ystart = start_coords[1]\\n\",\n \" orig_value = data[start_coords[0], start_coords[1]]\\n\",\n \" \\n\",\n \" if fill_value == orig_value:\\n\",\n \" raise ValueError(\\\"Filling region with same value \\\"\\n\",\n \" \\\"already present is unsupported. \\\"\\n\",\n \" \\\"Did you already fill this region?\\\")\\n\",\n \" \\n\",\n \" stack = set(((start_coords[0], start_coords[1]),))\\n\",\n \"\\n\",\n \" while stack:\\n\",\n \" x, y = stack.pop()\\n\",\n \"\\n\",\n \" if data[x, y] == orig_value:\\n\",\n \" data[x, y] = fill_value\\n\",\n \" if x > 0:\\n\",\n \" stack.add((x - 1, y))\\n\",\n \" if x < (xsize - 1):\\n\",\n \" stack.add((x + 1, y))\\n\",\n \" if y > 0:\\n\",\n \" stack.add((x, y - 1))\\n\",\n \" if y < (ysize - 1):\\n\",\n \" stack.add((x, y + 1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def generate_costs(diff_image, mask, vertical=True,\\n\",\n \" gradient_cutoff=2.,\\n\",\n \" zero_edges=True):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Ensure equal-cost paths from edges to\\n\",\n \" region of interest.\\n\",\n \"\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" diff_image : (M, N) ndarray of floats\\n\",\n \" Difference of two overlapping images.\\n\",\n \" mask : (M, N) ndarray of bools\\n\",\n \" Mask representing the region of interest in\\n\",\n \" ``diff_image``.\\n\",\n \" vertical : bool\\n\",\n \" Control if stitching line is vertical or\\n\",\n \" horizontal.\\n\",\n \" gradient_cutoff : float\\n\",\n \" Controls how far out of parallel lines can\\n\",\n \" be to edges before correction is terminated.\\n\",\n \" The default (2.) is good for most cases.\\n\",\n \" zero_edges : bool\\n\",\n \" If True, the edges are set to zero so the\\n\",\n \" seed is not bound to any specific horizontal\\n\",\n \" location.\\n\",\n \"\\n\",\n \" Returns\\n\",\n \" -------\\n\",\n \" costs_arr : (M, N) ndarray of floats\\n\",\n \" Adjusted costs array, ready for use.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" if vertical is not True: # run transposed\\n\",\n \" return generate_costs(\\n\",\n \" diff_image.T, mask.T, vertical=True,\\n\",\n \" gradient_cutoff=gradient_cutoff).T\\n\",\n \"\\n\",\n \" # Start with a high-cost array of 1's\\n\",\n \" diff_image = rgb2gray(diff_image)\\n\",\n \" costs_arr = np.ones_like(diff_image)\\n\",\n \"\\n\",\n \" # Obtain extent of overlap\\n\",\n \" row, col = mask.nonzero()\\n\",\n \" cmin = col.min()\\n\",\n \" cmax = col.max()\\n\",\n \"\\n\",\n \" # Label discrete regions\\n\",\n \" cslice = slice(cmin, cmax + 1)\\n\",\n \" labels = mask[:, cslice].astype(np.uint8).copy()\\n\",\n \"\\n\",\n \" # Fill top and bottom with unique labels\\n\",\n \" masked_pts = np.where(labels)\\n\",\n \" flood_fill(labels, (masked_pts[0][0], \\n\",\n \" masked_pts[1][0]), 2)\\n\",\n \" flood_fill(labels, (0, labels.shape[0] // 2), 1)\\n\",\n \" flood_fill(labels, (labels.shape[0] - 1, \\n\",\n \" labels.shape[1] // 2), 3)\\n\",\n \"\\n\",\n \" # Find distance from edge to region\\n\",\n \" upper = (labels == 1).sum(axis=0)\\n\",\n \" lower = (labels == 3).sum(axis=0)\\n\",\n \"\\n\",\n \" # Reject areas of high change\\n\",\n \" ugood = np.abs(\\n\",\n \" np.gradient(upper)) < gradient_cutoff\\n\",\n \" lgood = np.abs(\\n\",\n \" np.gradient(lower)) < gradient_cutoff\\n\",\n \"\\n\",\n \" # Cost break to areas slightly farther from edge\\n\",\n \" costs_upper = np.ones_like(upper,\\n\",\n \" dtype=np.float64)\\n\",\n \" costs_lower = np.ones_like(lower,\\n\",\n \" dtype=np.float64)\\n\",\n \" costs_upper[ugood] = (\\n\",\n \" upper.min() / np.maximum(upper[ugood], 1))\\n\",\n \" costs_lower[lgood] = (\\n\",\n \" lower.min() / np.maximum(lower[lgood], 1))\\n\",\n \"\\n\",\n \" # Expand from 1d back to 2d\\n\",\n \" vdis = mask.shape[0]\\n\",\n \" costs_upper = (\\n\",\n \" costs_upper[np.newaxis, :].repeat(vdis, axis=0))\\n\",\n \" costs_lower = (\\n\",\n \" costs_lower[np.newaxis, :].repeat(vdis, axis=0))\\n\",\n \"\\n\",\n \" # Place these in output array\\n\",\n \" costs_arr[:, cslice] = costs_upper * (labels==1)\\n\",\n \" costs_arr[:, cslice] += costs_lower * (labels==3)\\n\",\n \"\\n\",\n \" # Finally, place the difference image\\n\",\n \" costs_arr[mask] = np.abs(diff_image[mask])\\n\",\n \"\\n\",\n \" if zero_edges is True: # top & bottom rows = zero\\n\",\n \" costs_arr[0, :] = 0\\n\",\n \" costs_arr[-1, :] = 0\\n\",\n \"\\n\",\n \" return costs_arr\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Use the generate_costs function\\n\",\n \"costs01 = generate_costs(pano0_warped - pano1_warped,\\n\",\n \" pano0_mask & pano1_mask)\\n\",\n \"costs12 = generate_costs(pano1_warped - pano2_warped,\\n\",\n \" pano1_mask & pano2_mask)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage.graph import route_through_array\\n\",\n \"\\n\",\n \"# Find the MCP\\n\",\n \"pts01, _ = route_through_array(\\n\",\n \" costs01, mask_pts01[0], mask_pts01[1],\\n\",\n \" fully_connected=True)\\n\",\n \"\\n\",\n \"pts01 = np.array(pts01)\\n\",\n \"\\n\",\n \"# Create final mask for the left image\\n\",\n \"mask0 = np.zeros_like(pano0_warped[..., 0],\\n\",\n \" dtype=np.uint8)\\n\",\n \"mask0[pts01[:, 0], pts01[:, 1]] = 1\\n\",\n \"flood_fill(mask0, (0, 0), 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(12, 12))\\n\",\n \"import skimage.morphology as morph\\n\",\n \"\\n\",\n \"# Plot the difference image\\n\",\n \"ax.imshow(costs01, cmap='gray', vmin=-1 * costs01.max(), vmax=costs01.max())\\n\",\n \"\\n\",\n \"# Overlay the minimum-cost path\\n\",\n \"ax.plot(pts01[:, 1], pts01[:, 0]) \\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"ax.axis('off');\\n\",\n \"# fig.savefig('./pano4_mcp.png', dpi=600, bbox_inches='tight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# New constraint modifying cost array\\n\",\n \"costs12[mask0 > 0] = 1\\n\",\n \"\\n\",\n \"pts12, _ = route_through_array(\\n\",\n \" costs12, mask_pts12[0], mask_pts12[1],\\n\",\n \" fully_connected=True)\\n\",\n \"\\n\",\n \"pts12 = np.array(pts12)\\n\",\n \"\\n\",\n \"# Final mask for right image\\n\",\n \"mask2 = np.zeros_like(mask0, dtype=np.uint8)\\n\",\n \"mask2[pts12[:, 0], pts12[:, 1]] = 1\\n\",\n \"flood_fill(mask2, (mask2.shape[0] - 1,\\n\",\n \" mask2.shape[1] - 1), 1)\\n\",\n \"\\n\",\n \"# Mask for middle image is one of exclusion\\n\",\n \"mask1 = ~(mask0 | mask2).astype(bool)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(12, 12))\\n\",\n \"import skimage.morphology as morph\\n\",\n \"\\n\",\n \"# Plot the difference image\\n\",\n \"ax.imshow(costs12, cmap='gray', vmin=-1 * costs12.max(), vmax=costs12.max())\\n\",\n \"\\n\",\n \"# Overlay the minimum-cost path\\n\",\n \"ax.plot(pts12[:, 1], pts12[:, 0]) \\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"ax.axis('off');\\n\",\n \"# fig.savefig('./pano4_mcp.png', dpi=600, bbox_inches='tight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Convenience function for alpha blending\\n\",\n \"def add_alpha(img, mask=None):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Adds a masked alpha channel to an image.\\n\",\n \"\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" img : (M, N[, 3]) ndarray\\n\",\n \" Image data, should be rank-2 or rank-3\\n\",\n \" with RGB channels\\n\",\n \" mask : (M, N[, 3]) ndarray, optional\\n\",\n \" Mask to be applied. If None, the alpha channel\\n\",\n \" is added with full opacity assumed (1) for all\\n\",\n \" locations.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" from skimage.color import gray2rgb\\n\",\n \" if mask is None:\\n\",\n \" mask = np.ones_like(img)\\n\",\n \"\\n\",\n \" if img.ndim == 2:\\n\",\n \" img = gray2rgb(img)\\n\",\n \"\\n\",\n \" return np.dstack((img, mask))\\n\",\n \"\\n\",\n \"# Applying this function\\n\",\n \"left_final = add_alpha(pano0_warped, mask0)\\n\",\n \"middle_final = add_alpha(pano1_warped, mask1)\\n\",\n \"right_final = add_alpha(pano2_warped, mask2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"\\n\",\n \"# Turn off matplotlib's interpolation\\n\",\n \"ax.imshow(left_final, interpolation='none')\\n\",\n \"ax.imshow(middle_final, interpolation='none')\\n\",\n \"ax.imshow(right_final, interpolation='none')\\n\",\n \"\\n\",\n \"ax.axis('off')\\n\",\n \"fig.tight_layout()\\n\",\n \"fig.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from skimage.color import gray2rgb\\n\",\n \"\\n\",\n \"# Start with empty image\\n\",\n \"pano_combined = np.zeros_like(pano0_warped)\\n\",\n \"\\n\",\n \"# Place the masked portion of each image into the array\\n\",\n \"# masks are 2d, they need to be (M, N, 3) to match the color images\\n\",\n \"pano_combined += pano0_warped * gray2rgb(mask0)\\n\",\n \"pano_combined += pano1_warped * gray2rgb(mask1)\\n\",\n \"pano_combined += pano2_warped * gray2rgb(mask2)\\n\",\n \"\\n\",\n \"# Save the output - precision loss warning is expected\\n\",\n \"# moving from floating point -> uint8\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.imshow(pano_combined)\\n\",\n \"ax.axis('off');\\n\",\n \"plt.show()\\n\",\n \"# io.imsave('./pano5_final.png', pano_combined)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/maxwell_margenot/bayesian_linear_regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Bayesian Generalized Linear Models in PyMC3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Introduction\\n\",\n \"\\n\",\n \"There are a number of packages in the Python ecosystem that are used heavily in statistical modeling. We will make use of several of them (`NumPy`, `pandas`) throughout the course of this chapter, but our main focus here is Bayesian modeling. Specifically we will be focusing on modifying several of our standard statistical methodologies to function within a Bayesian framework. We will discuss why a Bayesian approach is a reasonable one to take and show examples of Generalized Linear Model (GLM) implementation in PyMC3, a probabilistic programming package with intuitive syntax for defining flexible statistical models. We start with a basic Bayesian linear regression model, explaining the theory, and build up into a hierarchical linear regression model.\\n\",\n \"\\n\",\n \"## Generalized Linear Models (GLM)\\n\",\n \"\\n\",\n \"A Generalized Linear Model (GLM) is a blanket term for many of our standard linear models in statistics. This generalization allows for the assignment of arbitrary distributions to response variables, encompassing both linear and logistic regression, and allows us to create Bayesian implementations of these classical ideas. We can formulate parameters and outcomes of these models as random variables, quantifying the uncertainty in terms of probabilities.\\n\",\n \"\\n\",\n \"## Probabilistic Programming and Bayes' Rule\\n\",\n \"\\n\",\n \"In probabilistic programming, we define the components of our model as random variables. We apply Bayes' rule to adapt our initial model, incorporating observed data $\\\\mathbf{X}$ to infer unknown causes $\\\\theta$. Bayes' rule is defined as follows:\\n\",\n \"\\n\",\n \"$$ P(\\\\theta\\\\ |\\\\ \\\\mathbf{X}) = \\\\frac{P(\\\\mathbf{X}\\\\ |\\\\ \\\\theta)\\\\ P(\\\\theta)}{P(\\\\mathbf{X})} \\\\propto P(\\\\mathbf{X}\\\\ |\\\\ \\\\theta)\\\\ P(\\\\theta) $$\\n\",\n \"\\n\",\n \"Bayes' rule allows us to derive a posterior distribution $P(\\\\theta\\\\ |\\\\ \\\\mathbf{X})$ based on our likelihood $P(\\\\mathbf{X}\\\\ |\\\\ \\\\theta)$ and prior distribution $P(\\\\theta)$. \\n\",\n \"\\n\",\n \"Calculating the posterior distribution analytically can be complicated, especially with more complex models. Fortunately, we do not have to deal with that. With probabilistic programming, we define our priors and our likelihood and let sampling algorithms approximate the posterior! Let's modify our definition of linear regression to incorporate these principles.\\n\",\n \"\\n\",\n \"## Linear Regression\\n\",\n \"\\n\",\n \"The first GLM that we will cover is linear regression. Linear regression is a standard quantitative tool in every statistician's toolbox. It provides a simple, easy-to-understand framework for expressing a linear relationship between dependent and independent variables and can be readily applied in many situations. A standard linear regression takes the form of:\\n\",\n \"\\n\",\n \"$$ Y = X\\\\beta + \\\\epsilon $$\\n\",\n \"\\n\",\n \"Where $Y$ is the dependent variable, $X$ is our independent variable, $\\\\beta$ are the coefficients for each feature in $X$, and $\\\\epsilon$ is our error, assumed to be normally-distributed.\\n\",\n \"\\n\",\n \"There are several ways to fit the coefficients. We typically use either Ordinary Least Squares (OLS) to find the Maximum Likelihood Estimate (MLE).\\n\",\n \"\\n\",\n \"## Probabilistic Regression\\n\",\n \"\\n\",\n \"To reformulate this linear regression with a Bayesian methodology, we say that:\\n\",\n \"\\n\",\n \"$$ Y \\\\sim \\\\mathcal{N}(X\\\\beta, \\\\sigma^2) $$\\n\",\n \"\\n\",\n \"In other words, we assume that our response variable will follow a normal distribution with mean $X\\\\beta$ and variance $\\\\sigma^2$. With Bayesian inference, the key component is that we define the pieces of our model as probability distributions. The above is the basic formulation of the model, but *everything* is a probability distribution. All of the parameters of our model will have prior distributions associated with them so we need a way to easily define random variables. This is where we apply PyMC3 and the principles of probabilistic programming.\\n\",\n \"\\n\",\n \"## PyMC3\\n\",\n \"\\n\",\n \"All of our examples in this chapter will showcase the use of PyMC3 to formulate models in the aforementioned way. PyMC3 is an open source probabilistic programming framework that allows us to define entire Bayesian models using Python code. It uses Marokov Chain Monte Carlo and variational inference methods to approximate the posterior. For speed, PyMC3 uses `Theano` as a backend.\\n\",\n \"\\n\",\n \"## Advantages of the Bayesian Approach\\n\",\n \"\\n\",\n \"At their cores, both the frequentist and Bayesian approaches try to get at the same solution. They only differ in how they go about it. By incorporating Bayesian statistics into our methodology, we allow for the inclusion of prior distributions. Priors allow us to include our own views about our parameters into the model which act as regularizers. \\n\",\n \"\\n\",\n \"In addition, the $\\\\beta$ \\\"values\\\" that we calculate through this Bayesian approach are themselves complete posterior distributions instead of point estimates (hence the scare-quotes). A point estimate will have a confidence interval, but a posterior distribution provides us with a much more robust understanding of the uncertainty. Quantifying uncertainty as probability is the cornerstone of Bayesian inference.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Defining a Toy Example\\n\",\n \"\\n\",\n \"Let's generate some simple data for use with our new approach. Toy examples are always a little boring, but it is important to understand the basic approach before we get into a real world problem.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"import pymc3 as pm\\n\",\n \"import theano.tensor as tt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sns.set_style('whitegrid')\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"N = 100\\n\",\n \"intercept = 1\\n\",\n \"slope = 2\\n\",\n \"noise = 0.5\\n\",\n \"\\n\",\n \"x = np.random.normal(0, 1, N)\\n\",\n \"true_line = intercept + slope*x\\n\",\n \"y = true_line + np.random.normal(0, noise, size=N)\\n\",\n \"\\n\",\n \"data = dict(x=x, y=y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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2HZX9/rHH9/SdKpjmHIkvdbazh/vR6TPXx3uOxbbBwvl1tg1FDDe7K7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\\n/wG8b0Uh+S+IGQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(11, 9))\\n\",\n \"ax = fig.add_subplot(111, xlabel='x', ylabel='y', title='Generated data and underlying model')\\n\",\n \"ax.plot(x, y, '.', label='sampled data')\\n\",\n \"ax.plot(x, true_line, label='true regression line', lw=2.)\\n\",\n \"plt.legend(loc=0);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The data points in this scatter plot are our observed information about the true relationship between $x$ and $y$. This observed information contorts our initial impressions of how our parameters are distributed into our posterior distributions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Creating Our Model\\n\",\n \"\\n\",\n \"Let's define our priors for the Bayesian model. With PyMC3, it is remarkably easy to lay out which components of the model will follow which distributions, however, this may be confusing if you are unfamiliar with the Bayesian framework. The typical model definition is laid out here. In PyMC3, we define all of our variables using the `with` context.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = -97.235: 100%|██████████| 200000/200000 [00:15<00:00, 12711.45it/s]\\n\",\n \"Finished [100%]: Average ELBO = -97.231\\n\",\n \"100%|██████████| 1000/1000 [00:01<00:00, 566.58it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with pm.Model() as toy_model:\\n\",\n \" # Defining priors\\n\",\n \" sigma = pm.HalfCauchy('sigma', beta=10, testval=1.0)\\n\",\n \" intercept = pm.Normal('Intercept', mu=0, sd=20)\\n\",\n \" x_beta = pm.Normal('x', mu=0, sd=20)\\n\",\n \" \\n\",\n \" # Defining likelihood\\n\",\n \" likelihood = pm.Normal('y', mu=(intercept + x_beta * x), sd=sigma, observed=y)\\n\",\n \" \\n\",\n \" # Running Inference\\n\",\n \" toy_trace = pm.sample(1000, tune=100) # Draw posterior samples\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are less familiar with probabilistic programming, this may look like a lot of nonsense. The syntax for defining probability distributions is intuitive, but this is a lot of extra buzz around the implementation of a basic linear regression. \\n\",\n \"\\n\",\n \"For this reason, the PyMC3 developers introduced a `glm` module to simplify the definition of Generalized Linear Models (GLM), including linear regressions such as ours.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = -118.85: 100%|██████████| 200000/200000 [00:19<00:00, 10507.78it/s]\\n\",\n \"Finished [100%]: Average ELBO = -118.85\\n\",\n \"100%|██████████| 1000/1000 [00:01<00:00, 648.82it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with pm.Model() as toy_model:\\n\",\n \" pm.glm.glm('y ~ x', data)\\n\",\n \" toy_trace = pm.sample(1000, tune=100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For a more comprehensive GLM framework that allows model specification in a similar way, see [Bambi](https://github.com/bambinos/bambi) which uses PyMC3 under the hood.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Basic Diagnostics\\n\",\n \"\\n\",\n \"We have a few things to examine after fitting a Bayesian model. Here are the plots that show our posterior distributions as well as our traceplots: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"image/png\": 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prNGcSTGeLJDOHlJCOTEQ4fNUPNOB1w5Z4hnv6Ey3jW9bvYMhhYqcsQ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\\n7JSSJEmSJEnSyNkpJUmSJEmSpJH7H2R6uDT3lZElAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11, 9))\\n\",\n \"pm.traceplot(toy_trace)\\n\",\n \"plt.tight_layout();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The posterior distributions on the left show how the various components of our regression are distributed after incorporating the observed data.\\n\",\n \"\\n\",\n \"The traceplots on the right show us whether our traces have converged. The trace is the set of samples from the posterior distribution generated by the sampler. The risk with our sampling methods is that we may end up with a bunch of junk values before we have proper samples of the posterior distribution. This is why PyMC3 silently discarded the first 500 samples for us. This is called a **burn-in period**. A non-stationary trace indicates that we need a longer burn-in period.\\n\",\n \"\\n\",\n \"What's nice here is that the means of our posterior distributions are fairly close to the values that we used to generate the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-------Means-------\\n\",\n \"Intercept: 0.990663287482\\n\",\n \"x_beta: 1.999168793\\n\",\n \"sd: 0.551319937496\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('-------Means-------')\\n\",\n \"print('Intercept: ', toy_trace['Intercept'].mean())\\n\",\n \"print('x_beta: ', toy_trace['x'].mean())\\n\",\n \"print('sd: ', toy_trace['sd'].mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In addition to looking at the mean posterior of our parameters, we can also examine the highest posterior density (HPD) interval of each parameter. The HPD is a type of **credible interval**, a Bayesian interval estimate. Credible intervals are not unique, however. As long as a parameter has a probability of $0.95$ to lie within a particular interval, that interval is a $95\\\\%$ credible interval for that parameter. Here we specifically want to look at the $95\\\\%$ HPD interval. This is the narrowest credible interval and it tends to contain the values that have the highest probability density. There are other informative credible intervals, such as the **equal-tailed interval**, where the probability of being below the interval is equal to the probability of being above it, but we only look at the HPD here.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x HPD: (1.899, 2.104)\\n\",\n \"sd_log_ HPD: (-0.718, -0.459)\\n\",\n \"Intercept HPD: (0.884, 1.103)\\n\",\n \"sd HPD: (0.488, 0.632)\\n\"\n ]\n }\n ],\n \"source\": [\n \"HPD = pm.stats.hpd(toy_trace)\\n\",\n \"for key, value in HPD.items():\\n\",\n \" print(key, \\\" HPD: \\\", \\\"({:.3f}, {:.3f})\\\".format(value[0], value[1]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, let's have a look at our response variable. Instead of drawing a single line as with the frequentist approach, here we have a distribution of regression lines. We plot our possibilities by examining the posterior predictive distribution. This entails sampling from our calculated posteriors and drawing regression lines using this new set of samples.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FGU3WgHg0ugKYQQL67nf8rNi+FLtGnJEwBHtF1ddwLjVVVNfZYVE0II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\\nERERERGxq/8P36yTTHnF1p4AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11, 9))\\n\",\n \"plt.plot(x, y, '.', label='data')\\n\",\n \"pm.glm.plot_posterior_predictive(\\n\",\n \" toy_trace,\\n\",\n \" samples=100,\\n\",\n \" eval=np.linspace(-3, 3, 100),\\n\",\n \" label='posterior predictive regression lines'\\n\",\n \")\\n\",\n \"plt.plot(x, true_line, label='true regression line', lw=3., c='y')\\n\",\n \"\\n\",\n \"plt.title('Posterior predictive regression lines')\\n\",\n \"plt.legend(loc=0)\\n\",\n \"plt.xlabel('x')\\n\",\n \"plt.ylabel('y');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The variation of the lines here (our distribution) give us a measure of uncertainty. By sampling from the posterior distribution, we can define our uncertainty in terms of a probability distribution (or the samples of a probabilitity distribution), the key idea behind Bayesian inference!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Logistic Regression\\n\",\n \"\\n\",\n \"The next GLM we will cover is logistic regression. Extending logistic regression to work within a Bayesian framework is again simple in PyMC3. Logistic regression is a fan favorite method for classification and is applied in many different situations. It is easy to understand, like linear regression, which is likely a large part of its longetivity in the face of so many powerful machine learning classifiers. Our typical logistic regression looks like this:\\n\",\n \"\\n\",\n \"$$ F(x) = \\\\frac{1}{1 + e^{X\\\\beta + \\\\epsilon}} $$\\n\",\n \"\\n\",\n \"Essentially, we take a linear regression and squash it into a logistic function. When we make it Bayesian, we say that:\\n\",\n \"\\n\",\n \"$$ F \\\\sim \\\\text{Bernoulli}(p),\\\\ \\\\ p = \\\\frac{1}{1 + e^{Y}},\\\\ \\\\ Y \\\\sim \\\\mathcal{N}(X\\\\beta, \\\\sigma^2)$$\\n\",\n \"\\n\",\n \"We model the outcome as a Bernoulli random variable because this is a classification problem. Bernoulli random variables are binary, taking on a value of $1$ with probability $p$ and taking on a value of $0$ with probability $(1-p)$. The logistic function will output the probability of our model taking on a value of $1$ and we truncate this to either $1$ or $0$ for classification. As with the above linear regression, we can create a Bayesian logistic regression in PyMC3 using the `glm` module.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Predicting Credit Card Defaults\\n\",\n \"\\n\",\n \"A common problem to attempt to predict is the probability of credit card default. The binary outcome of \\\"default or not\\\" lends itself intuitively to a binary classification problem. \\n\",\n \"\\n\",\n \"We will use the \\\"Default of Credit Card Clients\\\" data set from the UC Irvine Machine Learning Repository [1] here to develop a logistic model. We pull in the data with `pandas`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data = pd.read_excel(\\n\",\n \" \\\"http://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls\\\",\\n\",\n \" header=1,\\n\",\n \" index_col=0\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\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 \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5...BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
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1200002212422-1-1-2...000068900001
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3900002223400000...1433114948155491518150010001000100050000
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55000012157-10-100...2094019146191312000366811000090006896790
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5 rows × 24 columns

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\"\n ],\n \"text/plain\": [\n \" LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 \\\\\\n\",\n \"ID \\n\",\n \"1 20000 2 2 1 24 2 2 -1 -1 \\n\",\n \"2 120000 2 2 2 26 -1 2 0 0 \\n\",\n \"3 90000 2 2 2 34 0 0 0 0 \\n\",\n \"4 50000 2 2 1 37 0 0 0 0 \\n\",\n \"5 50000 1 2 1 57 -1 0 -1 0 \\n\",\n \"\\n\",\n \" PAY_5 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 \\\\\\n\",\n \"ID ... \\n\",\n \"1 -2 ... 0 0 0 \\n\",\n \"2 0 ... 3272 3455 3261 \\n\",\n \"3 0 ... 14331 14948 15549 \\n\",\n \"4 0 ... 28314 28959 29547 \\n\",\n \"5 0 ... 20940 19146 19131 \\n\",\n \"\\n\",\n \" PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 \\\\\\n\",\n \"ID \\n\",\n \"1 0 689 0 0 0 0 \\n\",\n \"2 0 1000 1000 1000 0 2000 \\n\",\n \"3 1518 1500 1000 1000 1000 5000 \\n\",\n \"4 2000 2019 1200 1100 1069 1000 \\n\",\n \"5 2000 36681 10000 9000 689 679 \\n\",\n \"\\n\",\n \" default payment next month \\n\",\n \"ID \\n\",\n \"1 1 \\n\",\n \"2 1 \\n\",\n \"3 0 \\n\",\n \"4 0 \\n\",\n \"5 0 \\n\",\n \"\\n\",\n \"[5 rows x 24 columns]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the sake of simplicity, we will drop most of the attributes here so that we only have some basic features to include in our model. The attribute `BILL_AMT1` contains the credit card bill amount for each individual client in September 2005 and the `PAY_AMT1` variable contains the amount that was paid in September 2005. `EDUCATION` and `MARRIAGE` are each categorical variables that indicate the education level and marital status of each client with an integer code, detailed here:\\n\",\n \"\\n\",\n \"* EDUCATION: (1 = graduate school; 2 = university; 3 = high school; 4 = others)\\n\",\n \"* MARRIAGE: (1 = married; 2 = single; 3 = others)\\n\",\n \"\\n\",\n \"We store whether the client defaulted on a payment as well for our response variable.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"default = data['default payment next month']\\n\",\n \"data = data[['EDUCATION', 'MARRIAGE', 'BILL_AMT1', 'PAY_AMT1']]\\n\",\n \"data['default'] = default\\n\",\n \"data = data.dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\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 \"
EDUCATIONMARRIAGEBILL_AMT1PAY_AMT1default
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121391301
222268201
3222923915180
4214699020000
521861720000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" EDUCATION MARRIAGE BILL_AMT1 PAY_AMT1 default\\n\",\n \"ID \\n\",\n \"1 2 1 3913 0 1\\n\",\n \"2 2 2 2682 0 1\\n\",\n \"3 2 2 29239 1518 0\\n\",\n \"4 2 1 46990 2000 0\\n\",\n \"5 2 1 8617 2000 0\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we define the model and sample:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = -99,030: 100%|██████████| 200000/200000 [09:16<00:00, 359.36it/s] \\n\",\n \"Finished [100%]: Average ELBO = -98,858\\n\",\n \"100%|██████████| 1000/1000 [16:12<00:00, 1.12it/s] \\n\"\n ]\n }\n ],\n \"source\": [\n \"with pm.Model() as logistic_model:\\n\",\n \" pm.glm.glm(\\n\",\n \" 'default ~ EDUCATION + MARRIAGE + BILL_AMT1 + PAY_AMT1', \\n\",\n \" data, \\n\",\n \" family=pm.glm.families.Binomial()\\n\",\n \" )\\n\",\n \" logistic_trace = pm.sample(1000, tune=500)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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n2GQsEAK03fExERKV4aKSV5l5q6V14W4ozVC07pWCuXzmHBnCogSEqJ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\\nSomIiIiIiIiISOz+H9eLzlc2QHFXAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pm.traceplot(logistic_trace);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To generate predictions using our model, we will sample from the posterior distribution. We define some particular instances of our model using lambda functions. We vary the September bill amount of our theoretical clients to see what the default curve would look like for someone who is single, paid $1000 on their September bill, and is of a given education level (which we change for each instance to compare). We want to look at how education and bill amount relate to the probability of default.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"lm = lambda x, samples: 1/(\\n\",\n \" 1 + np.exp(\\n\",\n \" -(samples['Intercept']+samples['EDUCATION']*1+samples['MARRIAGE']*2+\\n\",\n \" samples['BILL_AMT1']*x+samples['PAY_AMT1']*1000\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\\n\",\n \"lm2 = lambda x, samples: 1/(\\n\",\n \" 1 + np.exp(\\n\",\n \" -(samples['Intercept']+samples['EDUCATION']*2+samples['MARRIAGE']*2+\\n\",\n \" samples['BILL_AMT1']*x+samples['PAY_AMT1']*1000\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\\n\",\n \"lm3 = lambda x, samples: 1/(\\n\",\n \" 1 + np.exp(\\n\",\n \" -(samples['Intercept']+samples['EDUCATION']*3+samples['MARRIAGE']*2+\\n\",\n \" samples['BILL_AMT1']*x+samples['PAY_AMT1']*1000\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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eTn5zN16lQuv/xy9txzT+LxOGeddRaFhYU88cQTLFiwgEsvvZR1110X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\\nka2hECsiIiIiIiJb4/8HgqnWlR2XYWQAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11, 9))\\n\",\n \"pm.glm.plot_posterior_predictive(\\n\",\n \" logistic_trace,\\n\",\n \" eval=np.linspace(0, 500000, 10000),\\n\",\n \" lm=lm,\\n\",\n \" samples=100,\\n\",\n \" color='b',\\n\",\n \" alpha=0.15\\n\",\n \")\\n\",\n \"pm.glm.plot_posterior_predictive(\\n\",\n \" logistic_trace,\\n\",\n \" eval=np.linspace(0, 500000, 10000),\\n\",\n \" lm=lm2, samples=100,\\n\",\n \" color='g',\\n\",\n \" alpha=0.15\\n\",\n \")\\n\",\n \"pm.glm.plot_posterior_predictive(\\n\",\n \" logistic_trace,\\n\",\n \" eval=np.linspace(0, 500000, 10000),\\n\",\n \" lm=lm3,\\n\",\n \" samples=100,\\n\",\n \" color='r',\\n\",\n \" alpha=0.15\\n\",\n \")\\n\",\n \"import matplotlib.lines as lines\\n\",\n \"blue_line = lines.Line2D(['lm'], [], color='b', label='Graduate School')\\n\",\n \"green_line = lines.Line2D(['lm2'], [], color='g', label='Undergraduate Degree')\\n\",\n \"red_line = lines.Line2D(['lm3'], [], color='r', label='High School')\\n\",\n \"\\n\",\n \"plt.legend(handles=[blue_line, green_line, red_line], loc='upper right')\\n\",\n \"plt.ylabel(\\\"P(Default)\\\")\\n\",\n \"plt.xlabel(\\\"Last Bill Amount\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each color here corresponds to the predicted probability of default for a given education level. This seems to imply that those with lower education levels will be more likely to default than those with higher education levels, but there is significantly more uncertainty for all education levels as the bill amount gets higher, resulting in a lot of overlap.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A fun part of logistic regression is that if we exponentiate the coefficient of a feature, we get its odds ratio. The odds ratio indicates whether there is an association between an increase in the given feature and an increase in the outcome probability. We look at the odds ratio of the bill amount to see how increases in the bill amount affect the probability of default.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gPmAu1h0+/FAuCVcef00LPoKLutta8k+Z0kv1N98J9fTfKkqY7//0o+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\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"bill_odds = np.exp(logistic_trace['BILL_AMT1'])\\n\",\n \"plt.figure(figsize=(11, 9))\\n\",\n \"plt.hist(bill_odds, range=[0.9, 1.1], bins=10)\\n\",\n \"plt.title('Posterior Distribution of the Odds Ratio of BILL_AMT1')\\n\",\n \"plt.xlabel('Odds Ratio')\\n\",\n \"plt.ylabel('Density');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Probability that the Odds ratio is greater than 1: 0.962\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('Probability that the Odds ratio is greater than 1:', (bill_odds > 1).mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In our case, we exponentiate the posterior distribution of the coefficient, giving us a posterior distribution of the odds ratio. We can see here that the majority of the mass is greater than $1$, so there we can reliably say that, given our model, an increase in the bill amount increases the risk of default.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hierarchical Regression\\n\",\n \"\\n\",\n \"Bayesian modeling lends itself easily to hierarchical structure. We can relate lower-level aspects of the model by establishing relationships between prior distributions. If we hypothesize that several parameters are related, we can connect them by relating their distributions and linking their hyperparameters.\\n\",\n \"\\n\",\n \"Let's explore how we could handle a dataset with multiple related components using a practical example. In the stock market, everything exists under the same umbrella, that of the market. A classic risk factor to compute for a given portfolio is directly related to this pervasive influence. In specific, this risk factor is defined as the exposure of the returns of our portfolio to the returns of the market. The market can be a powerful positive force in any strategy, but it can also be a very powerful negative force. We want to be able to accurately account for this exposure to make sure that we are appropriately managing the risk exposure of our portfolio. The classical formulation of the model is called the Capital Assets Pricing Model, the CAPM.If we can come up with a reasonable estimate of the influence of the market on a given portfolio, we can take steps to counteract it.\\n\",\n \"\\n\",\n \"An easy way to compute this exposure is to use a linear regression:\\n\",\n \"\\n\",\n \"$$ R_P = \\\\alpha + \\\\beta_M \\\\cdot R_M + \\\\epsilon $$\\n\",\n \"\\n\",\n \"Where $R_P$ is the return of your portfolio, $R_M$ is the returns of the market, $\\\\alpha$ is the intercept term, and $\\\\epsilon$ is a standard normal error. We want to come up with an estimate, $\\\\hat{\\\\beta}_M$, of the influence of the market on the returns of our chosen security or portfolio of securities. To represent the returns of the market, we will use the returns of SPY, an exchange-traded fund that tracks the S&P 500 Index. By expressing the returns of our portfolio like this, we can calculate how much of our portfolio return is actually the market. \\n\",\n \"\\n\",\n \"Here we pull the pricing data of a few technology stocks and SPY and put it into the format we want for PyMC3:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"import pandas_datareader.data as web\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import pymc3 as pm\\n\",\n \"\\n\",\n \"# Name the stocks we want\\n\",\n \"stocks = ['GOOG', 'TSLA', 'AAPL', 'AMZN', 'FB']\\n\",\n \"n_securities = len(stocks)\\n\",\n \"# Define start and end dates\\n\",\n \"start_date = \\\"2014-01-01\\\"\\n\",\n \"end_date = \\\"2015-01-01\\\"\\n\",\n \"# Name our benchmark and get its returns\\n\",\n \"spy = web.DataReader('SPY', 'google', start_date, end_date)['Close']\\n\",\n \"spy = spy.pct_change()[1:]\\n\",\n \"spy.name = 'SPY'\\n\",\n \"\\n\",\n \"# Acquire data\\n\",\n \"returns = pd.DataFrame()\\n\",\n \"i = 0\\n\",\n \"for elt in stocks:\\n\",\n \" x = web.DataReader(elt, 'google', start_date, end_date)['Close']\\n\",\n \" x = x.pct_change()[1:]\\n\",\n \" x.name = \\\"Returns\\\"\\n\",\n \" symbol = pd.Series(index=x.index, data=elt)\\n\",\n \" symbol.name = \\\"Symbol\\\"\\n\",\n \" symbol_code = pd.Series(index=x.index, data=i)\\n\",\n \" symbol_code.name = \\\"Symbol_Code\\\"\\n\",\n \" i+=1\\n\",\n \" current_returns = pd.concat([symbol, x, spy, symbol_code], axis=1)\\n\",\n \" returns = pd.concat([returns, current_returns])\\n\",\n \"\\n\",\n \"# Create a dummy variable for each stock symbol\\n\",\n \"stock_idx = returns.Symbol_Code.values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And this is what our returns data looks like:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\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 \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SymbolReturnsSPYSymbol_Code
Date
2014-01-03GOOG-0.007284-0.0002190
2014-01-06GOOG0.011142-0.0028430
2014-01-03TSLA-0.003598-0.0002191
2014-01-06TSLA-0.017117-0.0028431
2014-01-03AAPL-0.022020-0.0002192
2014-01-06AAPL0.005435-0.0028432
2014-01-03AMZN-0.003845-0.0002193
2014-01-06AMZN-0.007088-0.0028433
2014-01-03FB-0.002742-0.0002194
2014-01-06FB0.048387-0.0028434
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Symbol Returns SPY Symbol_Code\\n\",\n \"Date \\n\",\n \"2014-01-03 GOOG -0.007284 -0.000219 0\\n\",\n \"2014-01-06 GOOG 0.011142 -0.002843 0\\n\",\n \"2014-01-03 TSLA -0.003598 -0.000219 1\\n\",\n \"2014-01-06 TSLA -0.017117 -0.002843 1\\n\",\n \"2014-01-03 AAPL -0.022020 -0.000219 2\\n\",\n \"2014-01-06 AAPL 0.005435 -0.002843 2\\n\",\n \"2014-01-03 AMZN -0.003845 -0.000219 3\\n\",\n \"2014-01-06 AMZN -0.007088 -0.002843 3\\n\",\n \"2014-01-03 FB -0.002742 -0.000219 4\\n\",\n \"2014-01-06 FB 0.048387 -0.002843 4\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"returns.groupby('Symbol').head(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We want to calculate the exposure to the market for each of these individual stocks. Now that we have our data, we have a few different options for constructing our model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## No Hierarchical Structure\\n\",\n \"\\n\",\n \"We can treat each security in our list as an entity totally independent of the others. With this approach, we will calculate a separate $\\\\hat{\\\\beta}_{M,i}$ for each security in our universe. Essentially, with $N$ securities we have:\\n\",\n \"\\n\",\n \"\\\\begin{eqnarray*}\\n\",\n \"R_{S_1} &=& \\\\alpha_1 + \\\\beta_{M,1} \\\\cdot R_M + \\\\epsilon_1\\\\\\\\ \\n\",\n \"R_{S_2} &=& \\\\alpha_2 + \\\\beta_{M,2} \\\\cdot R_M + \\\\epsilon_2\\\\\\\\\\n\",\n \" &\\\\vdots& \\\\\\\\\\n\",\n \"R_{S_N} &=& \\\\alpha_N + \\\\beta_{M,N} \\\\cdot R_M + \\\\epsilon_N \\n\",\n \"\\\\end{eqnarray*}\\n\",\n \"\\n\",\n \"This is a common approach and is akin to simply repeating our Bayesian linear regression from above $N$ times. Because we are making several separate models, we will look at the Gelman-Rubin diagnostic to check for convergence instead of checking visually. We construct several discrete sampling chains at the same time (by setting `njobs=4` in the sampler) and the Gelman-Rubin diagnostic tells us whether our values have converged. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = 767.33: 100%|██████████| 200000/200000 [00:14<00:00, 13909.82it/s]\\n\",\n \"Finished [100%]: Average ELBO = 767.31\\n\",\n \"100%|██████████| 1000/1000 [00:05<00:00, 187.55it/s]\\n\",\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = 515.02: 100%|██████████| 200000/200000 [00:16<00:00, 12056.81it/s]\\n\",\n \"Finished [100%]: Average ELBO = 515.02\\n\",\n \"100%|██████████| 1000/1000 [00:09<00:00, 110.17it/s]\\n\",\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = 712.33: 100%|██████████| 200000/200000 [00:18<00:00, 11023.81it/s]\\n\",\n \"Finished [100%]: Average ELBO = 712.33\\n\",\n \"100%|██████████| 1000/1000 [00:05<00:00, 193.41it/s]\\n\",\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = 624.46: 100%|██████████| 200000/200000 [00:14<00:00, 13943.23it/s]\\n\",\n \"Finished [100%]: Average ELBO = 624.46\\n\",\n \"100%|██████████| 1000/1000 [00:04<00:00, 203.12it/s]\\n\",\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = 604.74: 100%|██████████| 200000/200000 [00:14<00:00, 14062.70it/s]\\n\",\n \"Finished [100%]: Average ELBO = 604.75\\n\",\n \"100%|██████████| 1000/1000 [00:05<00:00, 182.03it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"indiv_traces = {}\\n\",\n \"for symbol in stocks:\\n\",\n \" # Select subset of data belonging to county\\n\",\n \" s_data = returns.ix[returns.Symbol == symbol]\\n\",\n \" s_spy = s_data.SPY\\n\",\n \" \\n\",\n \" with pm.Model() as individual:\\n\",\n \" # Intercept prior (variance == sd**2)\\n\",\n \" a = pm.Normal('alpha', mu=0, sd=100**2)\\n\",\n \" # Slope prior\\n\",\n \" b = pm.Normal('beta', mu=0, sd=100**2)\\n\",\n \" \\n\",\n \" # Model error prior\\n\",\n \" e = pm.Uniform('epsilon', lower=0, upper=10)\\n\",\n \" \\n\",\n \" # Data likelihood\\n\",\n \" exposure_likelihood = pm.Normal(\\n\",\n \" 'exposure_likelihood',\\n\",\n \" mu=(a + b * s_spy),\\n\",\n \" sd=e,\\n\",\n \" observed=s_data.Returns.values\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Inference button (TM)!\\n\",\n \" unpooled_trace = pm.sample(1000, njobs=4, tune=500)\\n\",\n \" \\n\",\n \" # keep trace for later analysis\\n\",\n \" indiv_traces[symbol] = unpooled_trace\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we examine our Gelman-Rubin diagnostics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Diagnostics for GOOG {'alpha': 0.9999641835591554, 'epsilon_interval_': 0.99975253192258873, 'beta': 0.99971810337989564, 'epsilon': 0.99977077217826482}\\n\",\n \"Diagnostics for AMZN {'alpha': 0.9996472985051702, 'epsilon_interval_': 1.0000156060382475, 'beta': 1.0005179149418404, 'epsilon': 0.99996154680828842}\\n\",\n \"Diagnostics for AAPL {'alpha': 0.9998090139051018, 'epsilon_interval_': 1.0004589715357, 'beta': 1.0000610819959541, 'epsilon': 1.00047788509586}\\n\",\n \"Diagnostics for FB {'alpha': 0.99970133258419347, 'epsilon_interval_': 1.0009541065389143, 'beta': 0.99994888687558836, 'epsilon': 1.0009074336358321}\\n\",\n \"Diagnostics for TSLA {'alpha': 1.0000347402266254, 'epsilon_interval_': 1.000585189306503, 'beta': 0.99964555493931939, 'epsilon': 1.0006332240560112}\\n\"\n ]\n }\n ],\n \"source\": [\n \"for symbol in indiv_traces:\\n\",\n \" print('Diagnostics for ', symbol, ' ', pm.diagnostics.gelman_rubin(indiv_traces[symbol]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The values of our diagnostic for all of our parameters are close to $1$ so it is safe to say that our values have converged. We can therefore look at our trace to see the posterior distributions of our market betas.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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//93//lO9/5DitXrqSxsZEpU6Zw8cUXM3369A7LXLJkCffffz9vvvkm\\nI0eO5PDDD2fOnDlMmTKlQ9+f/vSn/PSnP+Wtt96iqamJgw46iFmzZnHiiScO6Hb3lAFVkiRJ0pDW\\n0NBARUXFoK6zpKSE7OzsXs2zdetWZs6cybZt27juuusIIVBdXc0DDzzAeeedx6JFiygrK+PZZ5/l\\nc5/7HOeeey5XXXUVeXl5PPXUU1x99dXMmTOH2bNntyzzzjvv5Ac/+AFXXXUV06ZN49133+Whhx5i\\n5syZ3HPPPXz0ox9t6Xv99dezfPlyrr76ao477jgaGhp47LHH+PznP8/tt9/Oqaee2m/7p68MqJIk\\nSZKGtIqKCu5f9CIFhcWDsr4ttVV8ZsaRlJWV9Wq+u+66i+rqapYuXcqoUaMAKC0tZe7cuWzYsIEf\\n/OAHXHPNNVx33XVceumlXHnllS3zXnzxxey99978y7/8C9OmTSOEwGuvvcY999zDfffd1yaI3nLL\\nLQwfPpzrrruOFStWMHz4cJ555hkWLFjAI488wsEHH9zSd/bs2TQ0NHDXXXcZUCVJkiSpPxQUFlM8\\nbkKmy+hSU1MTCxcuZPbs2S3htLXbbruNgoICnnzySWpqapg1a1aHPqeccgr77bcf8+fP57rrruNn\\nP/sZH/rQh9qE02Zz5szhoYce4tlnn+Xkk09m3rx5HH/88W3CabPPfOYznHfeef2zobvIQZIkSZIk\\naYD9+c9/pqqqiqlTp3baPm7cOIYPH85rr73GBz7wgU5DLMBhhx3Gb3/7WwBee+01PvzhD3far7i4\\nmEmTJrX0/c1vfsPhhx/ead+RI0cyZsyY3m7SgPAKqiRJkiQNsOrqarKysigqKmqZ9vzzzzNnzpyW\\nz2VlZRx66KEUFBR0uZzCwkJqamoAqK2t7bZvQUFBS9/q6moKCwtb2t577z2OOuoosrKyaGpqAmDZ\\nsmWUlJT0bQP7iQFVkiRJkgZYQUEBTU1NbNmypWXa1KlT+fnPfw7AihUrePjhhykqKmLTpk1dLqey\\nsrIl5BYWFu6071FHHdXS95133mlpGz58eMu6KyoquPDCC2lsbOz7BvYTb/GVJEmSpAG27777UlRU\\nxCuvvNIyLS8vj3322Yd99tmHsWPHAnDIIYewbt06amtrO13O7373u5bnSA8++GBee+21Tvtt3LiR\\nDRs2tOnbet1Ay7onTpy4y9vXXwyokiRJkjTAsrOzOfvss7n//vt59913O7Q3vybn+OOPZ/z48Xzn\\nO9/p0Gf58uX86U9/4qyzzgLgnHPO4c033+TJJ5/s0Peee+5h/PjxfOxjHwPg3HPP5emnn+b111/v\\nct1J4C2+kiRJkjQIrrjiClauXMl5553H5ZdfzkEHHURVVRU//elPmT9/PqeddhrDhw9n7ty5XHbZ\\nZUAqhObn5/P000/zzW9+kyuvvJIQAgBTpkzhyiuv5JprruELX/gC06ZNo66ujp/85CcsWLCAe+65\\nh+HDhwMwbdo0Zs6cyUUXXcQVV1zBscceS2NjI0888QT33nsvBxxwQJtnVDPFgCpJkiRpyNtSW5X4\\ndY0YMYIHH3yQ+++/n3vuuYc1a9YwfPhwDj74YO68805OOukkAD760Y/y8MMPc/fdd3PRRRexfft2\\nDjzwQL7+9a+39Gk2e/Zs9ttvP+677z6+9a1vMXz4cI444gh+/OMf88EPfrBN3+uvv57DDz+cH/3o\\nR9x555289957TJ48mauuuop/+Id/aAmzmWRAlSRJkjSklZSU8JkZRw76OvsiJyeHSy65hEsuuaTb\\nflOmTOHb3/52j5Y5ffp0pk+f3qO+H//4x/n4xz/eo76ZYECVJEmSNKRlZ2dTVlaW6TLUDxwkSZIk\\nSZKUCAZUSZIkSVIiGFAlSZIkSYlgQJUkSZIkJYIBVZIkSZKUCAZUSZIkSVIiGFAlSZIkSYlgQJUk\\nSZIkJYIBVZIkSZIG0fz585kyZQrz5s1rM/3Tn/40U6ZMYdGiRR3m+eMf/8iUKVO48MILAViwYAFT\\npkzhwAMPZMqUKW3+NPf59Kc/zSmnnMJ7773XZlnr1q1jypQprF+/foC2sO9yMl2AJEmSJO2KhoYG\\nKioqBnWdJSUlZGdn92neJUuWsO+++7Jw4ULOPvvsNm25ubk89dRTzJgxo830J554gqysrJbPf/d3\\nf8fxxx/fps+bb77JZz/7WU444YSWaW+//Tbf/e53ueKKK9r0bb2sJDGgSpIkSRrSKioq+NHz8ygY\\nUzgo69tSXcv5R59NWVlZr+etqqrihRdeYO7cuVx77bWsW7euzXKOOOIInnvuOXbs2EFOzl/j2hNP\\nPMFHPvKRls/Dhw9n7NixLZ+3bdvGzTffzKGHHsqsWbNappeVlfG9732PGTNm8P73v7/X9Q42A6ok\\nSZKkIa9gTCHFE8buvGOGLVu2jIKCAk4//XS+/vWvs3DhQj73uc+1tH/kIx8hxsgLL7zAcccdB0Bl\\nZSVr1qxh5syZvPzyy50ud+7cuWzatIkf/OAHbabPmDGDX/ziF9x88818//vfH7gN6yc+gypJkiRJ\\ng2Tp0qUtt+CedNJJHZ43HTZsGCeccAJPPfVUy7QnnniC448/vs0V1daeeeYZfvzjH3PDDTdQWlra\\npi0rK4ubbrqJ559/nmXLlvXvxgwAA6okSZIkDYKKigpefvllpk+fDsCpp57K2rVrWblyZZt+J510\\nUpuA+uSTT3LKKad0usyamhquv/56Tj31VM4444xO+xx00EGcd955fO1rX2Pr1q39tDUDw4AqSZIk\\nSYNg8eLFjBgxouXW3SOOOIKCggIWLlzYpt+xxx5LTU0Nr7/+Ou+88w6vvvoqH/vYxzpd5pe//GWy\\nsrK45ZZbul33F77wBRoaGrjjjjsAaGpq6oct6n8GVEmSJEkaBEuXLmXbtm1MnTqVgw46iEMOOYQt\\nW7awfPlytm/f3tJvxIgRHHPMMTz55JM888wzHHnkkeTn53dY3oIFC3j88ce59dZbKSoq6nbdo0eP\\n5pprruFHP/oRr7/+uqP4SpIkSdKeavXq1fz+97/nxhtv5KijjmqZ/uabb/LFL36Rxx9/vE3/k08+\\nmYceeoj3v//9nd7eu27dOv793/+d8847r8urq+2dfvrpzJ8/n7lz5+7axgwgr6BKkiRJ0gBbvHgx\\nRUVFfOpTn+KAAw5o+fPJT36S/fffnwULFrTpf+KJJxJj5LnnnuPEE0/ssLwvfelLFBYWMmvWLDZt\\n2tTmT1VVVZd13HjjjWzYsKHft6+/eAVVkiRJ0pC3pbo20etaunQpM2bMIDc3t0PbzJkzufXWW9l7\\n771bphUXF3PIIYeQk5PT5vbd5ltzX3rpJbKysjj11FM7LG/ixIk8+eSTndax//77c8kll3Dvvff2\\nehsGQ1ZSH44dDCtXrpwKrJw0aVKbl9xK3dm6dSuvv/46Bx54ICNHjsx0ORoiPG/UF5436gvPG/XF\\nUD9vGhoaqKioGNR1lpSUkJ2dPajrTJrNmzezevVqgMMOO+ywzl/Q2kteQZUkSZI0pGVnZ1NWVpbp\\nMtQPfAZVkiRJkpQIBlRJkiRJUiIYUCVJkiRJiWBAlSRJkiQlgoMkSRoSejI6n6PpSZIkDW0GVElD\\nQkVFBb/94YOMLSjotH3zli1w4QWO4CdJkjSEGVAlDRljCwooKS7OdBmSJEkaID6DKkmSJElKBAOq\\nJEmSJA2i+fPnM2XKFObNm9dp+9tvv82UKVO49tprO7QtWLCAKVOmcOCBB7Z8Pfzww7nyyiv54x//\\nCMC6deuYMmUK69evH9DtGAje4itJkiRpSOvJYIr9bVcGZ1yyZAn77rsvCxcu5Oyzz+7QvnTpUvbd\\nd18ef/xxbrrpJvLz89u0l5aWMm/ePJqammhqaqKmpoZbbrmFyy67jBUrVgCQlZXVp9oyzYAqSZIk\\naUjb2WCK/W1XBmesqqrihRdeYO7cuVx77bWsW7euw3IWL17MBRdcwF133cWKFSs444wz2rQPGzaM\\n4lbjcowbN44vfvGLnHfeebzxxhuMHj2apqamvm1chhlQJUmSJA15Q2UwxWXLllFQUMDpp5/O17/+\\ndRYuXMjnPve5lvY//OEPrFq1iqOOOopXX32VBQsWdAionRk2LPX0Zm5uLjB0r6D6DKokSZIkDZKl\\nS5dywgknAHDSSSexaNGiNu2LFy9m4sSJfPCDH+Tkk0/mpZdeory8vNtlbtiwgTvuuIP999+f/fbb\\nb6BKHxQGVEmSJEkaBBUVFbz88stMnz4dgFNPPZW1a9eycuXKlj7Lli1raZ82bRq5ubksXLiwzXLW\\nr1/P1KlTOfTQQznkkEM44YQTqKqq4vbbbx+yV06beYuvJEmSJA2CxYsXM2LECI477jgAjjjiCAoK\\nCli4cCGHHXYYv/3tb1mzZg0nn3wyACNHjuSYY45h4cKFXHbZZS3LmTBhAg888ACQupW3qKiIUaNG\\nDf4GDQADqiRJkiQNgqVLl7Jt2zamTp3aMq2xsZHly5dz4403smTJEgBmzZrVMshR80i9r7zyCoce\\neigA2dnZ7LPPPoO/AYPAgCpJkiRJA2z16tX8/ve/58Ybb+Soo45qmf7mm2/yxS9+kccee4zly5dz\\nxhlncOmll7a079ixgwsuuIAFCxa0BNTdmQFVkiRJkgbY4sWLKSoq4lOf+lTLSLsABxxwAHfffTc/\\n+clPqKys5NOf/jQHHHBAm3lPP/10Fi9ezA033NDj9TU1NfHiiy8yduzYNtM/9rGP7dqGDDADqiRJ\\nkqQhb/OWLYO6rt6+AXXp0qXMmDGjTThtNnPmTG699Vb23XdfDjrooE7bH374YZ544okery8rK4vr\\nrruuw/TXXnut5ZU0SWRAlSRJkjSklZSUwIUXDNr6yprX2QtLly7tsu3888/n/PPP77J98uTJvP76\\n6y2fzzzzzO7rKytr038oMaBKkiRJGtKys7MpK+vtNU0lUXKv7UqSJEmS9igGVEmSJElSIhhQJUmS\\nJEmJkKhnUEMIS4ANMcZZ6c+TgP8HHA2sBr4QY3y8Vf/pwDeB/YDngc/GGP80yGVLkiRJkvpBYq6g\\nhhDOAz7RbvJCYD1wGPAgsCCEsHe6/z7AAuD7wOHApnR/SZIkSdIQlIiAGkIYA9wGvNhq2kmkroz+\\nU0z5GqmrpLPSXT4LvBRj/FaM8XXgYmBSCOH4wa1ekiRJktQfEhFQgduBHwKtX9ZzFPByjHFbq2m/\\nJHW7b3P7L5obYox1wMut2iVJkiRJQ0jGA2r6SunHgK+2ayoldXtvaxuAvXvYLkmSJEkaQjI6SFII\\nIQ/4v8CcGOP2EELr5pHA9nazbAfyetguSZIkaQ/Q0NBARUXFoK6zpKSE7OzsHvc/6aSTWL++7fW1\\nrKwspk6dyvvf/34WLFhAVlYWTU1NjBgxggMPPJB/+7d/48Mf/nB/l55omR7F9yZSz5E+0UnbNqC4\\n3bQ8YGur9vZhNA+o7m0R27dvZ+vWrTvvKAF1dXVtvmpw1NXVUV9fT319faft9fX11NXVJfbvsudN\\n7zQ0NLBhw4ZO2yZMmNCrHwiGMs8b9YXnjfpiqJ8369ev54mlv6GwcMygrK+2tprpn/wIEydO7PE8\\nTU1NXHPNNZx66qltpufm5nL77bdz6qmncs011wDwzjvv8LOf/YzZs2ezePFi8vPz+7X+/rJ9e/vr\\nhbsu0wH1XGBCCOGd9Oc8gBDCOcCtwN+0618ClKe/X5f+3L79ld4WUV5eTnl5+c47Sq2sXr060yXs\\nUSorK6mv2kx2Q0On7Ztra9iyahW1tbWDXFnveN70TGVlJY+9uJZRowvbTP/LO7WceuQ+7LXXXhmq\\nLDM8b9QXnjfqi6F63lRWVtKwoxEaB+cJxoYdjazq5c8d9fX11NbWdvoL2JqaGrKystq0/e3f/i3z\\n5s1j/vz5TJ06tV/qHgoyHVCnAbmtPt8GNAHXAJOAL4UQ8mKMzdH8OODZ9PcvpD8DEEIYCRwKfKW3\\nRZSWllJUVNTr4rVnqqurY/Xq1UyaNCmxv83aHRUWFrIhrmJ8cfsbK1IasrOZMHlyr36TOZg8b3qn\\nsLCQWJnLmLET2kyv3ryByZMnJfY49zfPG/WF5436YqifN4WFhdRUrmHc2PGDs8JhjUyevG+v/j/K\\nzc1l4sSJHHjggR3aioqKyMrK6tCWl5fHPvvs0+k8SVBTU9PvF/oyGlBjjGtbf05fSW2KMf4phLAG\\nWAv8VwiRCHo3AAAgAElEQVThq8DpwBHARenu9wFXhxCuARaTCqZvxRif6W0deXl5jBw5su8boj1S\\nfn6+580gys/PJzc3l9zc3E7bc3Nzh8QxGQo1JkF+fj45OR2Pd07O0DjO/W1P3GbtOs8b9cVQPW+6\\n+n9joPTl/6OsrKwuc0dOTiqWNbc1NDTwyCOPkJeXx7Rp0xL7S4OBuCU801dQuxRjbAwhzAC+D/wa\\n+ANwRozx7XT7mhDCWcAdwJeB54AzM1WvJEmSJHXnK1/5CjfffHPL56ysLH71q18B8Oijj7J8+XIA\\n3nvvPRobG/nSl76U2HA6UBIVUGOMF7f7/EfgxG76rwCmDHRdkiRJkrSrPv/5z3PKKae0mTZixAgg\\nNcrvv/7rvwKpwYdWrlzJrbfeSmFhIWecccag15opiQqokiRJkrS7Ki4uZp999um07X3ve1+btgMO\\nOIDXXnuNBx98cI8KqIMzzJUkSZIkqdcaGxszXcKg8gqqJEmSJGXY9u3b2bRpE5AaJGnlypU8+uij\\nzJkzJ8OVDS4DqiRJkqQhr7a2apDXtX+v5snKyuq2fdmyZSxbtgyA7OxsSktLueyyy7j00kv7WuaQ\\nZECVJEmSNKSVlJTwiRlHDOIa96ekpKRXczz55JNdts2dO5e5c+fualG7BQOqJEmSpCEtOzubsrKy\\nTJehfuAgSZIkSZKkRDCgSpIkSZISwYAqSZIkSUoEA6okSZIkKREMqJIkSZKkRDCgSpIkSZISwYAq\\nSZIkSUoE34MqSZISo6GhgYqKih71LSkpITs7e4ArkiQNJgOqJElKjIqKCpYteonCwuJu+9XWVvGJ\\nGUdQVlY2SJVJSrLe/HKrv/Tml2TXXXcdCxYsICsri6ampjZtWVlZ/PCHP2TkyJF84xvf4JVXXqGp\\nqYkPfehDXHbZZRxzzDEAvPjii1x44YW88cYb3a6rrq6Oo48+mg996EM8+OCDfdu4DDKgSpKkRCks\\nLGb8uAmZLkPSEFJRUcFrK3/O2OLCQVnf5qpaOOz0Hv+S7Prrr+fqq68GYMmSJfzgBz9g3rx5LWH1\\nvffe47TTTuOSSy7hhhtuICsri8WLFzN79mweeughDj74YCAVZnfmqaeeYq+99uLll1/m7bffZu+9\\n9+7jVmaGAVWSJEnSkDe2uJDSknGZLqNTo0aNYtSoUQCMHj2aYcOGUVz81ztFHnjgAfbZZx8uu+yy\\nlmmXX345r7zyCvPnz28JqD2xePFipk+fzq9+9SsWLlzI5Zdf3n8bMggcJEmSJEmSMmjYsGGsW7eO\\nP//5z22mz507lyuvvLLHy9myZQu//OUvOeKII5g2bRqLFi3q71IHnAFVkiRJkjLoE5/4BMOHD+eT\\nn/wkl1xyCd///vdZtWoVe+21V5srrTuzYsUKcnJyOOaYYzj55JNZu3Ytv/71rwew8v5nQJUkSZKk\\nDCouLmbevHmcc845vPHGG9x+++2cdtppXHTRRVRVVfV4OUuXLuXYY48lLy+Pgw8+mJKSEhYuXDiA\\nlfc/A6okSZIkZdiECRO46aabeO655/jZz37G7NmzefXVV7nxxht7NP+mTZt48cUXOfnkk1umTZ8+\\nneXLl7N9+/aBKrvfOUiSJEmSJGXQvffey4c//GGOPvpoAA466CAOOuggJk6cyH/8x3/0aBnLli2j\\noaGBG2+8kRtuuKFlemNjI48//jh///d/PyC19zcDqiRJkiRl0CuvvMKrr77aElCbjR49usfPoC5Z\\nsoRjjjmG66+/vs27VufMmcOCBQsMqJIkSZKknZs9ezYXXnghN9xwAzNnzmT06NH87ne/4/bbb+fS\\nSy9t6dfU1MSzzz7bZt68vDzKysr4zW9+w5133sn+++/fpv3cc8/lG9/4BpWVley1116Dsj27woAq\\nSZIkacjbXFU7qOsq+UD/Le/QQw/l/vvv5+6772bWrFls27aNSZMmcfnll3P22We39MvKymL27Nlt\\n5p0wYQL/+I//SHFxMSeeeGKHZZ911ll8+9vfZtGiRXz2s5/tv6IHiAFVkiRJ0pBWUlICh50+eOv7\\nQHqdfXDmmWdy5plndpg+depUvve973U535FHHsnrr7/eZXv74NpszJgxvPrqq70vNEMMqJIkSZKG\\ntOzsbMrKyjJdhvqBAVXSbqGhsZHy8vIu20tKSsjOzh7EiiRJktRbBlRJu4XNW7awbeHPaSwt7bSN\\nCy/wN6uSJEkJZ0CVtNsYM2oUJT0cil2SJEnJMyzTBUiSJEmSBAZUSZIkSVJCGFAlSZIkSYlgQJUk\\nSZIkJYIBVZIkSZKUCAZUSZIkSVIiGFAlSZIkSYlgQJUkSZIkJUJOpguQJEkaahoaGqioqNilZZSU\\nlJCdnd1PFUnS7sGAKkmS1EsVFRW8tvLnjC0u7NP8m6tq4bDTKSsr6+fKJGloM6BKkiT1wdjiQkpL\\nxmW6DEnarfgMqiRJkiQpEQyokiRJkqREMKBKkiRJkhLBZ1AlSUNGY2MD5eXlnbY5IqokSUOfAVVS\\nIuzslQ3l5eU0NjYOYkVKoi01VSx4ej2lE99tO722is/MONIRUSVJGuIMqJISoaKigt/+8EHGFhR0\\n2h7XrmViURGMc8TMPd3ogiKKx03IdBmSJGkAGFAlJcbYggJKios7bausqRnkaiRJkjTYHCRJkiRJ\\nkpQIBlRJkiRJUiIYUCVJkiRJiWBAlSRJkiQlgoMkSZKGPN+PKknS7sGAKkka8nw/qiRJuwcDqiRp\\nt+D7USVJGvp8BlWSJEmSlAgGVEmSJElSIhhQJUmSJEmJYECVJEmSJCWCgyRJkjKmoaGBioqKDtPL\\ny8tpbGzMQEV919W2tOdrbyRJ6poBVZKUMRUVFdy/6EUKCovbTF+7ZhVjxpZkqKq+qaio4EfPz6Ng\\nTGGXfbZU13L+0Wf72htJkrpgQJUkZVRBYXGH18PUVG3MUDW7pmBMIcUTxma6jETq6RXm8vJymobY\\n1XNJUv8xoEqSpAFXUVHBskUvUdjuanl7q9esYvwQu3ouSeo/BlRJkjQoCguLGd/uanl7VUP06rkk\\nqX84iq8kSZIkKREMqJIkSZKkREjELb4hhP2B7wDHApuBu2KMt6fb7gCuAJqArPTXK2KMd6fbpwPf\\nBPYDngc+G2P806BvhCRpt9WTAX4c3EeSpF2X8YAaQsgClgD/A3wEmAw8EkJ4O8b4CHAgcC1wf6vZ\\ntqTn3QdYANwIrAC+AiwEDhm0DZAk7fZ68gqZtX9Yw5iSYsYyfhArkyRp95LxgApMAF4B5sQY3wXe\\nCiE8CRwHNAfU22KMlZ3MeynwUozxWwAhhIuBihDC8THGXwxO+ZKkPcHOXiFTs6l6EKuRJGn3lPGA\\nGmOsAGY2fw4hHAscD/xzCGE0UAa82cXsHwVagmiMsS6E8DJwdOvpkiRJkqTkS9QgSSGE1aSC5a+A\\n+cDfkHrm9IYQwtoQwm9CCBe2mqUUWN9uMRuAvQe+WkmSJElSf0pUQAXOAk4DDgW+BQSgEfg98Ang\\ne8C9IYQZ6f4jge3tlrEdyBuUaiVJkiRJ/Sbjt/i2FmN8GSCE8AXgQaAA+HmMsSbd5XchhA8ClwGL\\ngG10DKN5QK8eBNq+fTtbt27dldK1B6mrq2vzVf2jrq6O+vp66uvrO23fUV9PA/Spvb6+nrq6uoz+\\nPfe86VxdXR07dnQ87jsadsCOhl2fvqN/jn1dXR31ndTZWv2OetjR9Tna3Kc39exO501Xx7q9HQ07\\nGNbJsezQr5+ObV/1dHu6MpD1707njQaP5436Yvv29tcKd13GA2oIYS/g6BjjolaTfw8MB0bHGKva\\nzfI6cGL6+3VASbv2ElKDLvVYeXk55eXlvZlFYvXq1ZkuYbdSWVlJfdVmshsaOm2vrqlme04uG0eM\\n6HX75toatqxaRW1tbb/W3BeeN21VVlayefMWdjS2vaGnurqanNxtjBi5cZem11ZvZtWq+l0+9pWV\\nlVRVb6Yxq+vXyNRUV5MzIpf8jSO77rO5mlWNvT8Xd4fzprKykqrN70Bj9zdv1VRXMzx3G5vaHcv2\\nqqo3s2rVexn7e11ZWQl1VeRkN/Vp/qqqaqrqBvbfpd3hvNHg87xRpmU8oAIfAOaHEPaOMTanxMOB\\njcDnQwjHxBhPadX/UOCN9PcvkBrtF4AQwsh0+1d6U0BpaSlFRUV9rV97mLq6OlavXs2kSZPIz8/P\\ndDm7jcLCQjbEVYwvLu60veKddxiZm8v48Z2/wqO79obsbCZMnszEiRP7tebe8LzpXGFhIbFyNWPG\\ntj1uf6mtJCc3v8Px7O30nGGNTJ48aZePfWFhIX94ax3F47sexXfLxlpyR+QyrotzFGBY0zAm79/z\\nc3F3Om8KCwupqVzDuLHdv4anqraS4bn53e5HAIY1Mnnyvhn7e11YWMjmtysZN25cn+bf0ZDF2L0H\\n5t+l3em80eDxvFFf1NTU9PuFviQE1JeAXwP3hRCuIhVYbwP+D6kA+qX09IXAx4ELgBPS894HXB1C\\nuAZYTCqYvhVjfKY3BeTl5TFyZNe/8ZY6k5+f73nTj/Lz88nNzSU3N7fT9pzcXLKzs/vUnpubm5jj\\nlZQ6kiI/P5+cnI7HPSc7h5ycjsez19Nz+ufY5+fnk9tJna3l5uSSk5Oz0z59qWd3OG+6Otbt5WTn\\nkNvJsezQr5+ObV/1dHu6Mhj17w7njQaf5416YyBuCc94QI0xNqYHPbqL1Oi97wLfijHeBRBCOAf4\\navrPamBmjPHF9LxrQghnAXcAXwaeA84c9I2QJKkHGhsae/Sb5pKSErKzswehIkmSkiXjARVa3oV6\\nThdtjwKPdjPvCmDKAJUmSVK/2VJdy6PrH6O0tqzbPucffTZlZV33kSRpd5WIgCpJ0p5i9JgCiid0\\n/SyrJEl7sqS9B1WSJEmStIcyoEqSJEmSEsGAKkmSJElKBAOqJEmSJCkRDKiSJEmSpEQwoEqSJEmS\\nEsGAKkmSJElKBAOqJEmSJCkRcjJdgCRJkgZPQ0MDFRUVfZ6/pKSE7OzsfqxIkv7KgCpJkrQHqaio\\n4LWVP2dscWGv591cVQuHnU5ZWdkAVCZJBlRJkqQ9ztjiQkpLxmW6DEnqwGdQJUmSJEmJYECVJEmS\\nJCWCAVWSJEmSlAgGVEmSJElSIhhQJUmSJEmJ4Ci+kgZNd+/eKy8vp7GxcZArkiRJUpIYUCUNmoqK\\nCn77wwcZW1DQoS2uXcvEoiIY52sPJEmS9lQGVEmDamxBASXFxR2mV9bUZKAaScqMhoZGysvL+zx/\\nSUkJ2dnZ/ViRJCWDAVWSJGmQba6qYVv502RtK+vDvLVw2OmUlfV+XklKOgOqJElSBhSPKaC0xMca\\nJKk1R/GVJEmSJCWCAVWSJEmSlAgGVEmSJElSIhhQJUmSJEmJYECVJEmSJCWCAVWSJEmSlAi+ZkaS\\nJGkIaWhopLy8vMv2uro6KisrKSwsJD8/v0N7eXk5TU2NA1miJPWZAVWSJGkI2VxVw7byp8naVtZp\\n+44d9VBXxea3K8nJye3Q/uaqNZRNLB7oMiWpTwyokiRJQ0zxmAJKS8Z12lZfX09OdhPjxo0jN7dj\\nQK3cWDXQ5UlSn/kMqiRJkiQpEQyokiRJkqRE8BZfSZISpLHVADg7G+ympKSE7OzswS5RkqQBY0CV\\nJClBtlTX8uj6xyitLaN+Rz1V1Zv5w1vryG032M2W6lrOP/psyso6HyhHkqShyIAqSVLCjB5TQPGE\\nsdTX19OY1Ujx+LGdDnYjSdLuxmdQJUmSJEmJYECVJEmSJCWCAVWSJEmSlAgGVEmSJElSIjhIkqTd\\nXkPjX1/b0RVf17F7amxs6PLYe8wlSUoeA6qk3d7mLVvYtvDnNJaWdtnOhRf4uo7d0JaaKhY8vZ7S\\nie+2nV5bxWdmHOkxlyQpYQyokvYIY0aNoqS4ONNlKANGFxRRPG5CpsuQJEk94DOokiRJkqREMKBK\\nkiRJkhLBW3wlSVIbDQ0NVFRU9Kivg01JkvqTAVWSJLVRUVHBskUvUVjY/XPbtbVVfGLGEQ42JUnq\\nNwZUSZLUQWFhMeMdXEqSNMgMqJIkqU+6e89se+Xl5TQ1Ng5wRZKkoc6AKkmS+qSmpornnl7PxIlb\\nd9p39ZpVjB9bMghVSZKGMgOqpD1eQ2Njt1eBHARG6lphQVGPbgWuqto4CNVIkoY6A6qkPd7mLVvY\\ntvDnNJaWdtrGhRc4CIw0hDkqsSQNHQZUSQLGjBpFSXH3I5ZKGpoclViShg4DqqR+s7OrFOXl5TQ6\\nSIqkDHBUYkkaGgyokvpNRUUFv/3hg4wtKOi0Pa5dy8SiIhg3bpArkyRJ0lBgQJXUr8YWFHR5q2xl\\nTc0gVyNJkqShZFimC5AkSZIkCQyokiRJkqSEMKBKkiRJkhLBgCpJkiRJSgQDqiRJkiQpEQyokiRJ\\nkqREMKBKkiRJkhLBgCpJkiRJSgQDqiRJkiQpEXIyXQBACGF/4DvAscBm4K4Y4+3ptknA/wOOBlYD\\nX4gxPt5q3unAN4H9gOeBz8YY/zSY9UuSutfQ0EBFRUWH6eXl5TQ2NmagIkmSlEQZD6ghhCxgCfA/\\nwEeAycAjIYS3Y4yPAIuA3wCHAWcCC0IIU2KMb4cQ9gEWADcCK4CvAAuBQwZ/SyRJXamoqOD+RS9S\\nUFjcZvraNasYM7YkQ1VJkqSkyXhABSYArwBzYozvAm+FEJ4EjgshbAA+ABwVY9wGfC2EcDIwC7gF\\n+CzwUozxWwAhhIuBihDC8THGX2RiYyRJnSsoLKZ43IQ202qqNmaoGkmSlEQZD6gxxgpgZvPnEMKx\\nwMeAOcBHgZfT4bTZL0nd7gtwFNASRGOMdSGEl9PtBlRJkiRJGkISNUhSCGE1qWD5PDAfKAXWt+u2\\nAdg7/f3O2iVJkiRJQ0SiAipwFnAaqWdRvwmMBLa367MdyEt/v7N2SZIkSdIQkfFbfFuLMb4MEEK4\\nCvgR8H1gTLtuecDW9Pfb6BhG84Dq3qx3+/btbN26decdJaCurq7NV/1VXV0d9fX11NfXd9q+o76e\\nBui0vbu2XW3flXnr6+upq6vb5X8j9vTzpq6ujh07Op4bOxp2wI6GwZ++o3fHta6ujvpO6m+tfkc9\\n7Oj6POtLnx3pfjs6Ozd7uQ290dXxam9Hww6GdbJ/d6Vvj/v1Yvt7vD0DsMyu17WDHfXD+jT/zuat\\n31Hf5mv/rnvgzjtl1p7+/5T6Zvv29tcKd13GA2oIYS/g6BjjolaTfw8MB8qBA9vNUpKeDrAu/bl9\\n+yu9qaG8vJzy8vKdd5RaWb16daZLSJzKykrqqzaT3dDQaXt1TTXbc3LZOGJEr9p2tX1X5t1cW8OW\\nVauora3tdN7e2lPPm8rKSjZv3sKOxrY37lRXV5OTu40RIzcO6vTa6s2sWlXf4+NaWVlJVfVmGrO6\\nfiVOTXU1OSNyyd84st/71NTUdOy3uZpVjf13brZWWVlJ1eZ3oLH7G61qqqsZnruNTSN3PthVT/v2\\ntF9V9WZWrXqvR9vf0+3p7TKpqyInu2mnfTtTU1PNe9uHs2lT1+fCrs5b28l5s6vrrqqqpqpuYM47\\nJcOe+v+UkiPjAZXUKL3zQwh7xxibU+LhQCWpAZH+NYSQF2NsjufHAc+mv38h/RmAEMJI4FBSr5vp\\nsdLSUoqKinZhE7QnqaurY/Xq1UyaNIn8/PxMl5MohYWFbIirGF9c3Gl7xTvvMDI3l/Hjx/eqbVfb\\nd2XehuxsJkyezMSJEzudt6f29POmsLCQWLmaMWPb7uO/1FaSk5vfYd8P9PScYY1Mnjypx8e1sLCQ\\nP7y1juLxY7vss2VjLbkjchnXxXnWlz476uupqamhqKiInNzcNv2GNQ1j8v67fm52prCwkJrKNYwb\\n23WdAFW1lQzPze92e3rbt8fLHNbI5Mn79mj7e7o9vV3m5rcrGTdu3E77dqaoqIaR+cMZN27n+663\\n89bvqKe2pobCoiJyc3I7tO/Kunc0ZDF274E575RZe/r/U+qbmpqafr/Ql4SA+hLwa+C+9K29HwBu\\nA/4PqQGT1gL/FUL4KnA6cARwUXre+4CrQwjXAItJBdO3YozP9KaAvLw8Ro7s/W8RtWfLz8/3vGkn\\nPz+f3NxccnM7/kAEkJObS3Z2dqft3bXtavuuzJubm9uvx3pPPW/y8/PJyel4buRk55CT03HfD/j0\\nnN4d1/z8fHI7qb+13JxccnJyBqRPTid/r3J7uQ290dXxai8nO4fcTvbvrvTtcb9ebH+Pt2cAltn1\\nunLIye3ZvuvrvF2ds7u27oE775QMHl/1xkDcEp7xQZJijI3ADOBd4FfAvcC3Yox3pdtOJ3Xb7q+B\\nfwTOiDG+nZ53DamBlWYBLwJFwJmDvhGSJEmSpF2WhCuoze9CPaeLtj8CJ3Yz7wpgygCVJklSIjU2\\nNPbotqqSkhKys7MHoSLtCRp6eN51x3NSUncSEVAlSVLvbKmu5dH1j1FaW9Ztn/OPPpuysq77SL2x\\nuaqGbeVPk7Wtb+fU5qpaOOx0z0lJXTKgSpI0RI0eU0DxhK4HbpIGQvGYAkpL+jY4lCTtTMafQZUk\\nSZIkCQyokiRJkqSE8BZfSZI05DQ2NvR4sJ7y8nKaGhsHuCJJUn8woEqSpCGnpqaK555ez8SJW3fa\\nd/WaVYwfWzIIVUmSdpUBVZIkDUmFBUWMHzdhp/2qqjYOQjWSpP7gM6iSJEmSpEQwoEqSJEmSEsGA\\nKkmSJElKBAOqJEmSJCkRDKiSJEmSpEQwoEqSJEmSEsHXzEiSpCGpsbGBjZs27LRfdc1m8nLzeN+o\\n0R3aiseMIzs7eyDKkyT1gQFVkiQNSbW11Qxr+h1jCgu67VcyZhPDsnMYzjttplfXbgGO69G7VCVJ\\ng8OAKkmShqwxhQWMH1fcbZ9hWTvIzs6hqKhjv/cGqjBJUp/4DKokSZIkKREMqJIkSZKkRDCgSpIk\\nSZISwYAqSZIkSUoEA6okSZIkKREMqJIkSZKkRDCgSpIkSZISwYAqSZIkSUoEA6okSZIkKREMqJIk\\nSZKkRDCgSpIkSZISwYAqSZIkSUqEnEwXIEmSlAkNjY1U1Wxs+VxVtZHy8pE9mre8vJympsaBKk2S\\n9lgGVEmStEeqqdlCU/0mhrMXAIX5W3l38yYqto3e6bxvrlpD2cTigS5RkvY4BlRJkrTHKioYxfhx\\nqaC5dWseE/YaQ8Hogp3OV7mxaqBLk6Q9ks+gSpIkSZISwYAqSZIkSUoEA6okSZIkKREMqJIkSZKk\\nRDCgSpIkSZISwYAqSZIkSUoEA6okSZIkKREMqJIkSZKkRDCgSpIkSZISwYAqSZIkSUoEA6okSZIk\\nKREMqJIkSZKkRDCgSpIkSZISwYAqSZIkSUoEA6okSZIkKREMqJIkSZKkRDCgSpIkSZISwYAqSZIk\\nSUoEA6okSZIkKREMqJIkSZKkRDCgSpIkSZISISfTBUiSJCVBU1Mjf3nnLz3q++7Wv5A/YswAVyRJ\\nex4DqiRJErBtWx1rV7/D6NH1O+379ppNjByRNwhVSdKexYAqSZKUlpc3gpEj37fTfsNzDaeSNBB8\\nBlWSJEmSlAgGVEmSJElSIhhQJUmSJEmJ4DOokiTtphobGikvL99pv5KSErKzswehIkmSumdAlSRp\\nN7WlupZH1z9GaW1Zt33OP/psysq67iNJ0mAxoEqStBsbPaaA4gljM12GJEk9YkCVJGkP0NjQQM2m\\nmg7TazZVd7gNuLy8nKbGxsEqTZKkFgZUSZL2ADWbaljzfA2jRhe2mb5t6wh+V7OBP43a2jJt9ZpV\\njB9bMtglSpJkQJUkaU8xanQhRcVtb/etG17H2OK9GF1Q0DKtqmrjYJcmSRJgQJUk/X/27j1Iku0+\\n6Py3qjLrNf2eGd2eO1fmorVIXW3EgkM2xlgYZGzAsMiPAPMSDtuBcBDGbABeG9ZaC6NYAtmEH5gl\\nWB5ey+s1LBu2HvYuOLQOI2FsITvkMBhJqYt85/pKt/v29KO6e7qzqisra/+o7p6qrqru6WdlT38/\\nER3Tdc7vnPOr7Jzq+nVmZUrSFek84ZWlx/GK09LTzwJVkiRJV2JtvUFz6RcpNE9/1ei19U14y9u9\\n4rT0lJt4gRpF0bPAPwTeBuwC/xr423Ec70VR9CPAdwBdoLD/73fEcfyP98d+FfBDwBuAXwHeGcfx\\nS1f/LCRJkvQkFuZnuLd4Z9JpSMqp4qQTAH4aqAJfDvxZ4E8C79nvewH4buAesLj/748BRFH0euD9\\nwL8AvhhYBT5wlYlLkiRJki7ORI+gRlEUAb8XeCaO49X9tu8FfoBeYfoC8P1xHK+MGP6XgF+N4/iH\\n98d9C7AcRdFXxHH80St5ApIkSZKkCzPpI6jLwB87KE73FYDZKIqmgfvAZ8aM/X3AYSEax3ECfAL4\\nskvKVZIkSZJ0iSZ6BDWO403gwwePoygqAH8V+P/oHT3tAu+KouhrgDXgB+M4/on98HvAq0emfA14\\n7rLzliRJkiRdvEkfQT3qB4DfA7wLeBOQAZ8Evgb458A/jaLoa/dj60DryPgWULmaVCVJkiRJF2ni\\nV/E9EEXRe4G/BnxjHMefBD4ZRdGH4jhu7If8ZhRFvwv4K8AHgSbDxWgF2Djt2q1Wi93d3bMnrxsl\\nSZKBf/VYkiS0223a7fbI/rTdpgMj+4/rO2//eca2222SJDn3a8RN32+SJCFNh/eNtJNC2rny9r29\\nJi+99NLIn8czzzwzdJ/FJEloj8i/XzttQzp+PztLTLofl47aN085Vzttk2VFsk42EJNlHdppOjBP\\n2kkpjthuRz1p3GXMmXZS2p2MLOt9HSfrZhSy7lDcwdiD9nFxI+fMMtIj2+000jQlbRfPNP6kse20\\nPfDvVa59mePT9GJejzXaTf89pbNptY4eLzy/XBSoURT9KPBtwF+I4/jwSrx9xemBT9G7HQ3A5+ld\\n2bffIvDrp11/aWnpXDeN1s304MGDSaeQOysrK7TX1yh1OiP7NxobtIKQh9XqqfrO23+esWubDbZe\\nfG/OwFcAACAASURBVJHNzc2RY0/rpu43KysrrK1tkWaDJ+5sbGwQhE2q9YdX2v7Kg9/iM/Eed183\\n+Gvk0fYmf+T3vp7Xve51Q/mvb6yRFcYXLo2NDYJqSO1h/cJjGo2jvw5PP1djbYNWcotyZXA/byZN\\n1tfWaDWbA+PKYZPVI9tt1PxPEncZczY2NkiSJsluk92d40+eaiZNSqWU3Z3ykfaEYqHD7s7OsXGj\\ntFpNtrY2WV09+bmPzL+xwV6rzOrq+J/fecdujthvrmrtyxi/vr7BenJxr8ca7ab+nlJ+TLxAjaLo\\n3cBfBv5MHMfv72v/PuD3x3H81X3hXwR8ev/7jwFv7Yuv7/e/+7Q53Lt3j7m5uTNkr5soSRIePHjA\\n888/T61Wm3Q6uTI7O8tr8YvcXVgY2b+8vU09DLl79+6p+s7bf56xnVKJZ974Rp599tmRY5/UTd9v\\nZmdniVceMH97cBs/2lwhCGtD2/6q2l//O94w0L6x9hpvfOPzQz/v2dlZ/utnP8/C3dtjn+PWw03C\\nasidMfvZWWLSdptGo8Hc3BxBGJ5rrmK3SOO1IvX6rYGYQrfAwsJtpqenD9vWN1coh7Vj5z5N3GXM\\nub65QljepVYvUr9169jYpLVDqRgOxVVrNarV8mH7uLhRKpUqMzOz3Llz8nMfZW6uQb1WPtP4k8a2\\n0zabjQazc3OEQTjUf5lrX+b4tFPg9nPnfz3WaDf995TOptFoXPiBvknfZuYFep83/XvAL0dR9Exf\\n988CfyuKor9B7/6mfxR4B/CH9vt/DPjOKIq+C/g5eoXpZ+M4/shp86hUKtTrZ/tLoG6uWq3mfnNE\\nrVYjDEPCcPgNEUAQhpRKpZH9x/Wdt/88Y8MwvNCf9U3db2q1GkEwvG8EpYAgGN72F9GedTrsbm9S\\nCltsb64PxPe3z83fobh/Sm8QjP5512o1whH59wuDkCAILiUmGPH/6rRzhUFIsdilWBo8il0slgiP\\nrlcKCEds56OeNO4y5gxKAWGpSLHY+zpOsVCkWCwMxR2MPWgfFzdyzmLxxO1/bP5BQBA+2bY769hx\\n++xVrH0Z48f9/9TFchvrNC7jlPBJH0F9O70LNb1r/wt6t5npxnFciqLoTwHv2f96APy5OI4/DhDH\\n8ctRFH0D8CPA9wL/Afj6q01fkpRXjY1VXu58ilvTs+w2BwvUzfoqxVLA6srL/G7+IAt3nhkziyRJ\\nukqTvs3Me4H3HtP/s/SOpI7r/3l6V/uVJGlIbfoWU/OzzMzOD7R3Cm2KpYBCWphQZpIkaZS83WZG\\nkiRJknRDWaBKkiRJknLBAlWSJEmSlAsWqJIkSZKkXLBAlSRJkiTlggWqJEmSJCkXLFAlSZIkSblg\\ngSpJkiRJyoVg0glIUp51soylpaVjYxYXFymVSleUkSRJ0tPLAlWSjrG2tUXzAx8iu3dvbD/f9A7u\\n379/xZnlU6fTYXl5eah9aWmJLMsmkJEkSbpOLFAl6QTzU1MsLixMOo1rYXl5mfd98OPMzA5ur1de\\nfpH524sTykqSJF0XZypQoyh6L/BjcRzHF5yPJOmam5ldYOHOMwNtjfWHE8pGkiRdJ2e9SNJXAJ+M\\nouhjURT95SiKZi4yKUmSJEnSzXOmAjWO4y8DXgB+AfifgOUoin4qiqI/EkVR4SITlCRJkiTdDGe+\\nzUwcx5+J4/h74jh+HvgaYB34GeDlKIq+L4oirxgiSZIkSXpi574PahRFXwJ8A/D2/aaP0DsF+MUo\\niv7CeeeXJEmSJN0MZ71I0uuBv7j/FQH/EXgP8K/iON7ej/k7wA8D/+eFZCpJkiRJeqqd9TYzD4CH\\nwP8BfEMcx58aEfMJ4DNnnF+SJEmSdMOctUD9euD/ieO4c7QjiqLFOI6X4zj+EPChc2UnSZIkSbox\\nzvoZ1PcDQ3etj6LoeeC/nichSZIkSdLN9MRHUKMo+lbgHfsPC8D7oyjaOxL2LLBxQblJyqFOp8Py\\n8vLIvqWlJbIsu+KMJEmS9LQ4zSm+HwDeSq84BfgckPT1d4HfBN53MalJyqPl5WX+00/8JLdnZob6\\n4lde4dm5ObhzZwKZSZIk6bp74gI1juN14FsBoigC+GsHV+yVdLPcnplhcWHoLH9WGo0JZKNJGHck\\n3aPokiTpPE5ziu8XAK/EcdwF3g3MR1E0Pyo2juPfvqD8JEk5tLy8zPs++HFmZgf/UPHKyy8yf3tx\\nQllJkqTr7jSn+L4E3ANW6N1mpjsiprDfXjp3ZpKkXJuZXWDhzjMDbY31hxPKRpIkPQ1OU6B+JbC+\\n//3bLiEXSZJ0w3Q6HdY3Vg8fbzTWqIQVbk1NHztuo7FGsrtL9zn/Ji5JT5PTfAb1I6O+PxBF0Z04\\njlePtkuSJI2zvrHKzuYvMT/bu/Da4vwqxVJAmeMvc7E4v8oryUOazd9xFWlKkq7IaY6gHoqiaA74\\nfuBHgU8C/xb4yiiKPgP88TiOX7q4FCVJ0tNsfnaGu3d6n2cuFlJKpYC5ueELsfUrFlIa27tXkZ4k\\n6QoVzzjuh+id8psCXw/8AeAvAp8B/sHFpCZJkiRJuknOWqD+ceAvxnH8KeC/Bz4cx/FPAd9Dr3CV\\nJEmSJOlUzlqgTgGv7H//1cCH979P8Aq+kiRJkqQzONNnUOl97vRPRFH0Cr1bz/yb/fZ3Ap+6iMQk\\nSbpsWZYN3Bqnsf6QpaVbQ3FLS0t0s+wqU5Mk6UY6a4H6vcDPAGXgp+I4fjGKoh8Evp3eZ1IlScq9\\n3c0tPtP+Nebrvfu5Nos7/LvPLzO1OXiLk1f+68vMLy5wm7uTSFOSpBvjTAVqHMf/Joqi54Dn4jj+\\njf3mfwX80ziOP31h2UmSdMlqM1PM3J4HIExC5u8uMD0zMxDTWN2YRGqSJN04Zz2CShzHa8Ba3+OP\\nX0hGkiRJkqQb6az3QX0T8I+AL6d3mu+AOI69UJIkSZIk6VTOegT1nwCvA74b2Ly4dCRJkiRJN9VZ\\nC9QvBb48juNPXGQykiRJkqSb66z3QV0F9i4yEUmSJEnSzXbWI6g/Cvy9KIreEcfx1kUmJEnSpHS7\\nXbYfPRpq39l5xHRldgIZKa+63YzdZJet7ZPfBk1NTVEsnPWYgCTdLGctUL8a+APAehRFrwGt/s44\\njt9w3sQkSbpqrWbCpx9sMzU9eJLQb7+8wpur4YSyOl7W6dBYbYzs21xvEFQC1qfWaKxu0O1aZF+U\\nvb0Wr72aMnfr+FsQNVsJXxg9y8z0zLFxkqSesxaov7T/JUnSU6VcqVKrTQ22lasTyuZkjdUGL/9K\\ng6np4eKzszZHViqwtt1l6fObzM/n93lcR+VymXr91qTTkKSnypkK1DiOv++iE5EkSWczNT3L3MLt\\nofZuWqAYFJldmGOrcfyRPinvOp2MpaWlc82xuLhIqeTdEKU8O+sRVKIo+t3A/wC8CfjTwNcC/yWO\\n449cUG6SJEkSAGvrDZpLv0ihef+M4zfhLW/n/v2zjZd0Nc5UoEZR9BbgPwAfA94CVIAvAn44iqKv\\ni+P4/724FCVJkiRYmJ/h3uKdSach6RKd9ZJy7wX+QRzHf4j9283EcfxO4B8Bf+dCMpMkSZIk3Shn\\nLVC/GPiJEe3/K/Dms6cjSZIkSbqpzlqg7gGjrpf+emDn7OlIkiRJkm6qsxaoHwD+lyiK5vYfd6Mo\\nehPwI8DPXUhmkiRJkqQb5awF6ncCU8AqcAv4BPBfgA7wP15MapIkSZKkm+Ss90HdiqLojwJvB95A\\n75Tf3wT+bRzH2QXmJ0mSJEm6IU5VoEZRNE3vCOmfo1eYHngR+Eng3wG7F5WcJEmSJOnmeOICNYqi\\n28BH6V0I6f3A/wY0gFl690L928A3RlH0B+I43ryEXCVJkiRJT7HTHEF9D73PrP63cRy/crQziqLn\\ngH8D/E3gey8mPUmSdJm63S7bjx4NtO3uPKLd7rC9tXXYNjU1RaF41ktXSJL0ZE5ToP4J4NtHFacA\\ncRx/LoqidwE/gAWqJEnXQitp8pntzzLVfHz3uFcaS1TKVZq1/Zhmk//uuReYnhl1hzlJki7OaQrU\\nZ4D/fELMbwBfcPZ0JEnSVStXK9SmaoePq7UK5XJ1oE2SpKtwmnN1ykByQkwChGdPR5IkSZJ0U/lh\\nEkmSJElSLpz2Pqh/M4qinWP6p86TjCRJkiTp5jpNgfrbwDc+YZwkSZIkSafyxAVqHMfPX2IekiRJ\\nkqQbzs+gSpIkSZJy4bSfQZUkSTqUZR0err52YtxGY41KWOHW1PRA+/r6Q56Z715WepKka8YCVZIk\\nndnm5gbF7m8yPztzbNzi/CrFUkCZ7YH23e1XaNZuX2aKkqRrxAJVkiSdy/zsDHfvLBwbUyyklEoB\\nc3ODcWvrjctMTZJ0zUy8QI2i6FngHwJvA3aBfw387TiO96Ioeh74Z8CXAQ+Avx7H8Yf7xn4V8EPA\\nG4BfAd4Zx/FLV/oEJEmSJEkXIg8XSfppoAp8OfBngT8JvGe/74PAq8BbgJ8E3h9F0XMAURS9Hng/\\n8C+ALwZWgQ9caeaSJEmSpAsz0QI1iqII+L3AN8dx/Ok4jv8D8L3An4+i6G3A7wS+Le75+/SOkn7r\\n/vB3Ar8ax/EPx3H8KeBbgOejKPqKq38mkiRJkqTzmvQR1GXgj8VxvHqkfRb4fcAn4jhu9rX/Er3T\\nfQG+FPjoQUccxwnwib5+SZIkSdI1MtHPoMZxvAn0f6a0APxV4BeAe/RO7+33GvDc/vcn9UuSJEmS\\nrpFJH0E96geALwK+B6gDrSP9LaCy//1J/ZIkSZKka2TiV/E9EEXRe4G/BnxjHMefjKKoCRy9Zn2F\\n3pV+AZoMF6MVYOO0a7daLXZ3d08OlIAkSQb+vWmSJKHdbtNut4f60nabDozsO6n/PGMvc+6Txrbb\\nbZIkOfE15Gnbb5IkIU2H94O0k0LayUV7mrbJsmz/qzMQn2UZFDK6We/roP+g/Wh8N8vojFi3Xztt\\nQzp+XzlLTLofl46IP4xL22RZkayTDcVkWQYZZJ3H2+FoXH9Mf1t/bJZ1aKfpyO3ePpx7eP2BdboZ\\nhaw7FPf4Z5QdGzdqPvbjrnrtXmyX7EnyzDqk7RHbLk1J28Vj94VxThrbTtsD/552/HnWvszx51/7\\nyV6vb6qn7feUrkardfR44fnlokCNouhHgW8D/kIcxwdX4v088OYjoYvAUl//4oj+Xz/t+ktLSywt\\nLZ0cKPV58ODBpFOYiJWVFdrra5Q6naG+jcYGrSDkYbU6cuxx/ecZe5lznzR2bbPB1osvsrm5ObL/\\nqKdlv1lZWWFtbYs0GzwRZ2NjgyBsUq0/nHj75sYarVaTZtIkCAffkDabCcViSNJsEpCys/+G9aB9\\nOL7J5tYmqw8H1+3X2NggqIbUHtYvPKbRGL5X6EFcq9WildyiXBneR5vNhGJQItzdodlMyDLY3d0Z\\nG9Pf1h/bTJqsr63RajYHxjY2NkiSJsluk92d409gaiZNSqWU3Z3ykfaEYqHD7s7OsXGj5mvutWg2\\nk8OxV7U2wF6rRSssnbh20kxYX+/Qah3Zdo0N9lplVlfH7wvjPOnYzRH7zVWtfRnjz7v2+voG68mT\\nv17fVE/L7yldXxMvUKMoejfwl4E/E8fx+/u6PgZ8dxRFlTiOD0rztwL/vq//rX3z1OmdHvzu0+Zw\\n79495ubmzpK+bqAkSXjw4AHPP/88tVpt0ulcudnZWV6LX+TuwtETHGB5e5t6GHL37t2RY4/rP8/Y\\ny5z7pLGdUoln3vhGnn322ZH9B562/WZ2dpZ45QHztwe3y6PNFYKwNrS9JtEeFDMqnSrVWpVb9cE3\\ntHvNGsVSQFZtEdTCw/6D9qPx1WqV2ZlZ7ozZDwC2Hm4SVsMLjUnbbRqNBnNzcwRhODLu1tQUjdeK\\n1Ou3huZq7bQoBr2+arVGuVwdiuuPefx8B2ML3QILC7eZnp4eGLu+uUJY3qVWL1K/Nbx+v6S1Q6kY\\nDsVVazWq1fJh+7i4UfNVyxWq1dqVrw1QrlSolMsnxxa6LCzMDW27ubkG9VqZO3fG7wvjnDS2nbbZ\\nbDSYnZsjDMKh/stc+zLHn3fttFPg9nMnv17fVE/b7yldjUajceEH+iZaoEZR9ALwLuDvAb8cRdEz\\nfd0fAV4BfjyKovcAbwe+BPjm/f4fA74ziqLvAn6OXmH62TiOP3LaPCqVCvX62f4ap5urVqvdyP2m\\nVqsRhiFhOPymJwhDSqXSyL6T+s8z9jLnPmlsGIan2heelv2mVqsRBMP7QVAKCILh7TWJ9iAIKRaL\\n+1+lgfiD9sL+10H/uPhCsUhpxLr9wiAkCIJLiQlG/J87jAtCisUuxdLwZSUOn0/p8fM6GtcfM9j2\\nOLZYLBGOyDsoBYSHcx9/WYtioUixWBiKe7zNi8fGjZqP/birXrsXW3iyPIslgnDEtgsCgvD4fWqc\\nJx0bjvg/elVrX8b48699utfrm8ptpNO4jFPCJ32RpLfv5/AuelfkfZXeKbyvxnGcAV9H77TdXwP+\\nPPB1cRx/DiCO45eBb6B3X9SPA3PA11/1E5AkSZIkXYxJ32bmvcB7j+n/LPC2Y/p/HnjTJaQmSZIk\\nSbpikz6CKkmSJEkSkIOLJEmSdFpZp0NjY/Xw8VZjjVJYZX3q8YVoGusP6XZPvl2IJEnKDwtUSdK1\\n09hY5TdWPkJ9pleQbtZXKZYCdpvrhzEPVz8PFQtUSZKuEwtUSdK1VJ+ZZub2PACdQptiKWBmdv6w\\n/9FGg2Rve1LpSZKkM/AzqJIkSZKkXLBAlSRJkiTlggWqJEmSJCkXLFAlSZIkSblggSpJkiRJygUL\\nVEmSJElSLligSpIkSZJywQJVkiRJkpQLFqiSJEmSpFywQJUkSZIk5YIFqiRJkiQpFyxQJUmSJEm5\\nYIEqSZIkScoFC1RJkiRJUi5YoEqSJEmScsECVZIkSZKUCxaokiRJkqRcsECVJEmSJOVCMOkEJOk6\\n62QZS0tLY/sXFxcplUpXmJEkSdL1ZYEqSeewtrVF8wMfIrt3b2Qf3/QO7t+/P4HMJEmSrh8LVEk6\\np/mpKRYXFiadhiRJ0rVngSpJknRJut2MR9uPhtp3dh/RzSpsbW8dtk1NTVEseHkQSTebBaokSdIl\\naTYTXnmwzfR0e6B95dVt6rUm5eJUL66V8IXRs8xMz0wiTUnKDQtUSZKkS1SpVKnXbw20Vas1qtXy\\nULsk3XSeRyJJkiRJygULVEmSJElSLligSpIkSZJywQJVkiRJkpQLXiRJ0oBOp8Py8vLY/qWlJbIs\\nu8KMJEmSdFNYoEoasLy8zH/6iZ/k9szoWx3Er7zCs3NzcOfOFWcmSZKkp50FqqQht2dmWFxYGNm3\\n0mhccTaSJEm6KfwMqiRJkiQpFyxQJUmSJEm5YIEqSZIkScoFP4MqScqNrNOhsbHKVmONUlhlfWp6\\noP+gPd1r0q15NWlJkp42FqiSpNxobKzyGysfoV1vUSwF7DbXB/o366sUSwHN1R2mXzc3oSwlSdJl\\nsUCVJOVKfWaaTqFKsRQwMzs/0NcptCmWAgrphJKTJEmXygJVkqQTdLtdkt1dtre2hvqmpqYoFL2k\\ngyRJF8ECVZKkE+zttXjp1T06twZPOW41E35PdJ/pmZkJZSZJ0tPFAlWSpCdQLleo1aYmnYYkSU81\\nz0mSJEmSJOWCBaokSZIkKRcsUCVJkiRJueBnUCVJY3U6HZaXl4fal5aWyLJsAhlJkqSnmQWqJGms\\n5eVl3vfBjzMzuzDQ/srLLzJ/e3FCWUmSpKeVBaok6Vgzswss3HlmoK2x/nBC2UiSpKeZn0GVJEmS\\nJOWCBaokSZIkKRcsUCVJkiRJuWCBKkmSJEnKBQtUSZIkSVIuWKBKkiRJknLB28xIkqRjdbtdth89\\nGmrf3XnETpKwt7fHXqsFQLlchkLhqlOUJD0lLFAlSdKxWkmTz2x/lqnmzED7K40l9ppN7iZdCrsF\\nOmnKMzN3KFcqE8pUknTdWaBKkqQTlasValO1gbZqrVeIhmGbsOxbCknS+fkZVEmSJElSLligSpIk\\nSZJywQJVkiRJkpQLFqiSJEmSpFywQJUkSZIk5YIFqiRJkiQpFyxQJUmSJEm5kKublkVRVAF+Dfj2\\nOI4/ut/2I8B3AF2gsP/vd8Rx/I/3+78K+CHgDcCvAO+M4/ilCaQvSZIkSTqH3BSo+8XpvwTefKTr\\nBeC7gff1tW3tj3k98H7gfwZ+Hng38AHgd192vpIkSbo+Op2MpaWlM49fXFykVCpdYEaSRslFgRpF\\n0QvAT43pfgH4/jiOV0b0/SXgV+M4/uH9eb4FWI6i6CsOjsBKkiRJa+sNmku/SKF5/wxjN+Etb+f+\\n/dOPlXQ6uShQgT8I/ALwLmD3oDGKomngPvCZMeN+H3BYiMZxnERR9Angy/rbJUmSpIX5Ge4t3pl0\\nGpKOkYsCNY7jf3LwfRRF/V0v0PvM6buiKPoaYA34wTiOf2K//x7w6pHpXgOeu7xsJUmSJEmXIe9X\\n8X0TkAGfBL4G+OfAP42i6Gv3++tA68iYFlC5sgwlSZIkSRciF0dQx4nj+CeiKPpQHMeN/abfjKLo\\ndwF/Bfgg0GS4GK0AG6dZp9Vqsbu7e3KgBCRJMvDvddTpdHjttddG9i0vL9Pda9Fut0f2p+02HRjZ\\nf1zfZY7Na17tdpskSdjd3b22+02SJKRpe+j5pZ0U0s6Ft6dpm27WIStkUMjIss5AfJb12rtZ7+ug\\n/6C9P77X3yXLzjfP4Vzd7sh52mlKu92mnbYhHb+vAKeOSQ+2y6j96yAubZNlRbJONhSTZRlkkHWy\\nw+1wNK4/pr+tP3ZUzEF7t9uh2+3uf2V0st5aQ7l0Mwr7P4+jc2R9Y8bFjZqPw5/vybEXuXYvtnu4\\nf13I2lmHtJ0eu28cSNOUtF0cG9tO2wP/nnb8eda+zPGTXfvx6/nT6rr+ntJktVpHjxWeX64LVIC+\\n4vTAp4C37X//eWDxSP8i8OunWWNpaelcV3XTzfTgwYNJp3BmKysrPPzwLzA3dWuo77eWl7k3M0uY\\ndUeO3Whs0ApCHlarp+q7zLF5zWtts8HWiy+yubl52Hbd9puVlRXW1rZIs8ETbjY2NgjCJtX6wwtt\\n39xYYzdIyIopxWJIEA6+GWw2E4rFkKTZJCBlZ//N4kF7f3zSbLKXtmgmzXPNA9Daa9EtFQ/jDtuT\\nXdbXOrSaTRobGwTVkNrD+tjtedaYRuPor8LHca1Wi1Zyi3JleB9sNhOKQYlwd4dmMyHLYHd3Z2xM\\nf1t/7KiYg/ZWc4/0oKBOU5IkoZOmw7kkTUqllN2d8pH2hGKhw+7OzrFxo+Zr7rVoNpPDscfFXuTa\\nAHutFq2wdGFrJ82E9fUOrVbzxLUbjQ32WmVWV8fvRwCbI/ab04y/6LHXee319Q3Wk8HX86fVdfs9\\npadPrgvUKIq+D/j9cRx/dV/zFwGf3v/+Y8Bb++Lr+/3vPs069+7dY25u7pzZ6qZIkoQHDx7w/PPP\\nU6vVJp3OmczOzjITv8jiwsJQX1YsUQ9D7t69O3Ls8vb22P7j+i5zbF7z6pRKPPPGN/Lss89e2/1m\\ndnaWeOUB87cHn9+jzRWCsDb0vM/bHhQzNvdqpIWUYingVn3wjeRes0axFJBVWwS18LD/oL0/vlat\\n0t1Lqdaq55oHoFKuUK5WhtqLhS4Lt+eYnp5m6+EmYTXkzph9BTh1TNpu02g0mJubIwjDkXG3pqZo\\nvFakXh/+g1Nrp0Ux6PVVqzXK5epQXH/MgaOxo2IO4goUCIIOYRhCF2q1GpXK8CdtktYOpWJI/daR\\nOWo1qtXyYfu4uFHzVcsVqtXaE8Ve5NoA5UqFSrl8YWtT6LKw0NuXTjI316BeK3Pnzuj9qJ222Ww0\\nmJ2bIwzCof6Txp9n7cscP8m1006B28/1Xs+fVtf195Qmq9FoXPiBvlwXqMDPAn8riqK/Qe/+pn8U\\neAfwh/b7fwz4ziiKvgv4OXqF6WfjOP7IaRapVCrU62f7a5xurlqtdm33m1qtRhiGvTeURwRhSKlU\\nGtl3Uv+kxuY1rzAMh/aT67bf1Go1gmB4XwlKAUEw/LzP2x4EIYW0RLGQUSwWKRYH7znYaytS2P86\\n6D9o74/v9RfOPc/hXIXCyHnCIOj9fwpCgv3vxzlrTDDi/+thXBBSLHYploYvK3H4fEqPn9fRuP6Y\\nwbbHsaNiDtoLhRKFQkahUKBQKFLajx3KpfD45zFy/f32cXGj5uPw53ty7EWu3YstPHGeT7R2sUQQ\\nHr9vHAiCgCAc/7p0IBzxf/c048+z9mWMn+zaw6/nT6ub8jx1MS7jlPA8XiTp8LzCOI5/DfhTwDcB\\n/xn4q8Cfi+P44/v9LwPfAHwr8HFgDvj6q05YkiRJknR+uTuCGsdx6cjjn6V3JHVc/M/Tu9qvJEmS\\nJOkay12BKkmSrlaWZTTW1w4fb281CMMW1Xrvwktbmw0KpQJdBq9ivL3VoNXco9v182qSpIthgSpJ\\n0g23vdVgcepVZmemALh17xGlUpNb9d7tOOZpUigWqFS3BsbduveIpeU19lr3rzxnSdLTyQJVkiQx\\nOzPF7du9K9oHxYxSKWRqpndF2aSaUCgWqNYGb2UTFDO2t71noiTp4uTxIkmSJEmSpBvIAlWSJEmS\\nlAsWqJIkSZKkXLBAlSRJkiTlggWqJEmSJCkXLFAlSZIkSblggSpJkiRJygULVEmSJElSLgSTTkCS\\nJA3LOh3WV9ZorG1Q7BYJg3Cgf3O9QVAJSJsp3e7shLKUJOliWaBKkpRDjdUGn/uP25SCWzReK1Is\\ndgf6O2tzZKUCGzsN5uerE8pSkqSLZYEqSVJOTU3PUq5UqddvUSwNfiqnmxYoBkUgm0xykiRdAj+D\\nKkmSJEnKBQtUSZIkSVIuWKBKkiRJknLBAlWSJEmSlAsWqJIkSZKkXPAqvpKkK5FlHdZXXxto22qs\\nUQqrrE9NA9BYf0i3lkFhEhlKkqRJs0CVpEvSyTKWlpYASJKElZUVZmdnqdVqhzGLi4uUSqVJ3OGA\\nwQAAIABJREFUpXiltjcbvNT8Deoz04dtm/VViqWA3eY6AA9XP8/06+YIa+VJpSlJkibIAlWSLsna\\n1hbND3yI7N492u027fU1XotfJAzDw36+6R3cv39/wplenfrMNDO35w8fdwptiqWAmdle26ONxqRS\\nkyRJOWCBKkmXaH5qisWFBdrtNqVOh7sLC4cFqiRJkgZZoEqSJE1Yt5vxaPvRE8Xu7D6iVp0/OVCS\\nriELVEmSpAlrNhNeebDN9HT7xNjPvbxKvVq5gqwk6epZoEqSpAvSpbW3N7Kn3d6jk3XZa7Uol8tQ\\n8FLNR1UqVer1WyfGlUOLU0lPLwtUSZJ0IdJ2ylprg3I6XEBtNR9RKgW0SHlm5g7likWWJGmYBaok\\nSbowpaBEWB5+exEEJUqlgFLgWw9J0njFSScgSZIkSRJYoEqSJEmScsICVZIkSZKUCxaokiRJkqRc\\nsECVJEmSJOWCBaokSZIkKRcsUCVJkiRJuWCBKkmSJEnKBQtUSZIkSVIuWKBKkiRJknLBAlWSJEmS\\nlAvBpBOQJEmS8qzTyVhaWjrXHIuLi5RKpQvKSHp6WaBKkiRJx1hbb9Bc+kUKzftnHL8Jb3k79++f\\nbbx0k1igSpIkSSdYmJ/h3uKdSachPfX8DKokSZIkKRcsUCVJkiRJuWCBKkmSJEnKBQtUSZIkSVIu\\nWKBKkiRJknLBAlWSJEmSlAsWqJIkSZKkXLBAlSRJkiTlggWqJEmSJCkXLFAlSZIkSblggSpJkiRJ\\nygULVEmSJElSLligSpIkSZJywQJVkiRJkpQLFqiSJEmSpFwIJp2AJEk3RZZlbK41WJ9aGxuzud4g\\nqASkzZRud+oKs5MkafIsUCVJuiLbjS22fyugut0dG9NZmyMrFdjYaTA7G1Kp1q8wQ0mSJssCVZKk\\nK3Rrapq5hdtj+7tpgWJQBLKrS0qSpJywQJUk0el0WF5eHmpfWloiyyyUdJG6tPb2Dh+12232Athr\\ntfYf79HJutDtQqEwqSQlSRNigSpJYnl5mfd98OPMzC4MtL/y8ovM316cUFZ6GqXtlLXWBuW0AsBG\\nc4u9QpnV3QYAW81H0IW5mTnKlcokU5UkTYAFqiQJgJnZBRbuPDPQ1lh/OKFs9DQrBSXCcu8tSBgE\\nBEFw+DgISnS7HjmVpJvK28xIkiRJknLBAlWSJEmSlAu5OsU3iqIK8GvAt8dx/NH9tueBfwZ8GfAA\\n+OtxHH+4b8xXAT8EvAH4FeCdcRy/dLWZS5IkSZLOKzdHUPeL038JvPlI1weAV4G3AD8JvD+Kouf2\\nx7weeD/wL4AvBlb34yVJkiRJ10wuCtQoil4APgb8ziPtX0nvyOi3xT1/n95R0m/dD3kn8KtxHP9w\\nHMefAr4FeD6Koq+4uuwlSZIkSRchL6f4/kHgF4B3Abt97V8KfCKO42Zf2y/RO933oP+jBx1xHCdR\\nFH1iv/+jSJIGeL9TSZKUZ7koUOM4/icH30dR1N91j97pvf1eA557wn5JUh/vdypJkvIsFwXqMepA\\n60hbC6g8Yb90I407SnbAo2U3m/c7lSRJeZX3ArUJLBxpq/D4NOAmw8VoBdg4zSKtVovd3d2TAyUg\\nSZKBf/Po1Vdf5VP/8v9iYXp6ZP+Ln/88z87NcXd2dqgvbbfpAO12e+TY4/onNTavefX3HfT3x7Xb\\nbZIkudLXnyRJSNP2UL5pJ4W0c6ntnU6HbtYhyzqH7VmWQSE7bOtmGd0sG2o/Gv84rjNynoO5sqxL\\nlp1vnsO5ut2R87TTtPczTtuQjt9XANI0pZB1yTrj/0CUZRlk7Oed7bd1TojrjJzzMKaTjY3Lsoxu\\n1qXb7X0B+99nA4/pcvj4cLt0u3R5PHZc3NCcfTEDYw8fQ6fv+Y/cTt0MDn++x//BLetmve2eDT/3\\nge08Jm70nN3D/evK184y0v39bpR22h7496g0TUnbxWP31XHOM/Zmr331r/endR3e3yh/Wq2jxwrP\\nL+8F6ucZvqrvIrDU13/0nLRF4NdPs8jS0hJLS0snB0p9Hjx4MOkUxlpZWaHb3qPUGX5TC9Bt77Gx\\nscHDanWob6OxQSsIR/ad1D+psXnNa1Rfo9E4/H5ts8HWiy+yubk5cu7LsLKywtraFmk2eI28jY0N\\ngrBJtf7w0tqTpEk2nVDcffx3xWYzoVgMCcLem7ak2SQgJSx0BtqPxh/E7ey/2Ts6z8Fce2mLZtI8\\n1zwArb0W3VLxMO6wPdllfa1Dq9mksbFBUA2pPawzzubmJtXWFLu7O2Njms2EYlCi2Uwolw/amsfG\\nZRkj5zyICXd3xsY1mwnNVnOg4EnTlG6XgceFYmH4Dw9pSift0O70xo6L65+z2y0MxKSdlHanOLAW\\n3QJJktBJ0/HbKWnS3GvRbCbs7ozfngexpVLK7k75SHtCsdA5HD8ubpS9VotWWJrI2q1Wk62tTVZX\\njz/zYbPv9aZfo7HBXqvM6ur4fXWc84y9yWuvr2+wnlzt6/1Z5fn9jW6GvBeoHwO+O4qiShzHB+X5\\nW4F/39f/1oPgKIrqwBcB7z7NIvfu3WNubu4C0tVNkCQJDx484Pnnn6dWq006nZFmZ2d5LX6RuwtH\\nT0DoWd7eph6G3L1791R9eR2b17z6+9rtNo1Gg7m5OcIwBKBTKvHMG9/Is88+O3LuyzA7O0u88oD5\\n24P5PtpcIQhrQ8/jSduzTofNxiqlQpdSISMoDh4FOmivVKrcqj9+g7fXrFEsBYdttWqVoBYSVMsD\\n7Ufjs2qLoBYe9h+d52Cu7l5KtVY91zwAlXKFcrUy1F4sdFm4Pcf09DRbDzcJqyF3xuwr0Nv+hXaV\\nev3W2JjWTotiUKRVrREEvWKlWq1SLJbGxpXLo+c8iKnXb1EdE9faaZGmewTB3uG+GQQBpVJw+DgN\\neoXnweMDQRBQCkqE+7Hj4vrnLJWCgZigFByOP4jrdgvUajUqlfGf2ElaO1TLFarVGvVb47fnQWyp\\nGA7FVWs1qtXyYfu4uFHKlQqVcnkia1cqVWZmZrlzZ/S+1k7bbDYazM7NEQbDP4u5uQb1Wnns+OOc\\nZ+xNXjvtFLj93NW+3p/WdXh/o/xpNBoXfqAv7wXqR4BXgB+Poug9wNuBLwG+eb//x4DvjKLou4Cf\\no1eYfjaO44+cZpFKpUK9fra/iOnmqtVqud1varUaYRiOfJMIEIQhpVJp9JvIY/ryOjaveY3q6/+5\\nhGF45ftRrVYjCIb3jaAUEATDz+NJ29c31/kv679Me7pFsRTQSgePEmxOr7Lx6CGL6RcMFFrFYnH/\\nq9dWKBYpHLYVh4qyg/bHcaWR8xzMVSwWzj3P4VyFwsh5wqBXXIVBSBAEY/cV6BVfWbFAsTT+Lm/F\\ngedf3G8rDY0pHtlOo+Y87C+Nj+tthwKFQu8L2P++eOTx4/7D7VIoUKAw0D8q7uicw2sNPoYCpb7n\\nP3I7FYpw+PM9/q55xcLjfWHcNjwubvSchSeKvZS1i8UT9zWAcMT/dejth0E4/nXtOOcZe7PXvvrX\\n+7O6LnkqHy7jlPBc3Af1iMMPrsRxnAFfS++03V8D/jzwdXEcf26//2XgG+jdF/XjwBzw9VedsCQJ\\n6jPTTM3PMjU/y8zt+YGvqflZqlMnHxmSJEk3W+6OoMZxXDry+LeAtx0T//PAmy47L0mSpDzodjN2\\nk122trdG9qftlJ2dHSqVKnPzc72jzZJ0TeSuQJUkSdJ4e3stXns1Ze7W6JsWZFmHJNmjsb7M73oh\\nYGZ65oozlKSzs0CVJEm6Zsrl8tiLbWVZBt0CFIZv9yNJeWeBKkkT0smyE698t7i4SKlUOjZGkiTp\\naWGBKkkTsra1RfMDHyK7d29sP9/0Du7fv3/FmUmSJE2GBaokTdD81BSLY+5Xq/zrdrtsP3oEwM7O\\nI0qdkO2t3oVrpqamKDzBLUMkSdJjFqiSJJ1Rq5nw6QfbTE3vsby0RVANeVRYp9VM+D3RfaZnvDiN\\nJEmnYYEqSdI5lCtVarUpKpUaQTWkVpuadEqSJF1bnnskSZIkScoFj6BKknTNZVlGY31tqH1rs0Gh\\nVKBLh+2tBmHYolqvDsU82tmke69wVelKkjSWBaokSdfco+0t7lRfZnZm8PTieZoUigUq1S1u3XtE\\nqdTkVr09FPO51gp7rdddZcqSJI1kgSpJ0lNgdmaK27fnBtqSakKhWKBaqxIUM0qlkKmZ6aGYza3t\\nq0xVkqSx/AyqJEmSJCkXLFAlSZIkSbngKb6SJJ1T1snY2dwiaIVsVTdoNnfYqEM7Gfy853Zji1vd\\nuTGzSJIkC1RJks7pUWOTYOUWt2ZmKKZVKmmR7d2QVrk7ELf9UpHw9t6EspQkKf8sUCVJugD1+hTT\\nM/Pcmpkhbe8xN1ejXKkMxUiSpPH8DKokSZIkKRcsUCVJkiRJuWCBKkmSJEnKBT+DKkmS8qXbpbV3\\n/MWk2m0vNiVJTyMLVEmSlCudToe1nQ3KaWVszMajBmm7PbZfknQ9WaBKkqTcKQUlwvL4tymlwLcw\\nkvQ08jOokiRJkqRcsECVJEmSJOWCBaokSZIkKRcsUCVJkiRJuWCBKkmSJEnKBQtUSZIkSVIuWKBK\\nkiRJknLBAlWSJEmSlAsWqJIkSZKkXLBAlSRJkiTlggWqJEmSJCkXgkknIEnS06YLtPb2htrTtE23\\ne/X56Gbqdrs82n401L6z+4huVmFre2ugfWpqimLBYxeSJssCVZKkC9ZJ26w1WpTLnYH2xnZCtXZr\\nQlnppmm2El55sMP0dHugfeXVbeq1JuXi1EDsF0bPMjM9c9VpStIAC1RJki5BqRQQhOWhNukqVSpV\\n6vXBP4pUqzWq1fJQuyTlgedxSJIkSZJywQJVkiRJkpQLFqiSJEmSpFywQJUkSZIk5YIFqiRJkiQp\\nFyxQJUmSJEm5YIEqSZIkScoFC1RJkiRJUi5YoEqSJEmScsECVZIkSZKUCxaokiRJkqRcsECVJEmS\\nJOWCBaokSZIkKRcsUCVJkiRJuRBMOgFJkgRZ1mGr0WBrs0GhVGB7q0EQlOkWuuy1mhSLg39T7o9r\\nNffodmsTylySpItjgSpJUg5sNRrU008xfzugUCzQupVQLJaoVFsEQUChUBiIn6d5GLe0vMZe6/6E\\nMpck6eJYoEqSlBOzM1PUq5Ve4dkMewVqrUYYhkMFalJNDuO2t5MJZSxJ0sXyM6iSJEmSpFywQJUk\\nSZIk5YKn+EqSJEmXqNPJWFpaOvP4xcVFSqXSBWYk5ZcFqiTpWFnWYX31tYG2rcYapbDK+tQ0AI31\\nh3RrGRRGzSBJN9vaeoPm0i9SaJ7+YmZr65vwlrdz/74XQtPNYIEqSTrW9maDl5q/QX1m+rBts75K\\nsRSw21wH4OHq55l+3RxhrTypNCUp1xbmZ7i3eGfSaUi5Z4EqSTpRfWaamdvzh487hTbFUsDMbK/t\\n0UZjUqlJkqSniBdJkiRJkiTlggWqJEmSJCkXLFAlSZIkSblggSpJkiRJygUvkiRJT6FOp8Py8vJQ\\n+9LSElmWTSAjSdJZnPcequB9VHW9WKBK0lNoeXmZ933w48zMLgy0v/Lyi8zfXpxQVpKk0zrPPVR7\\n472Pqq4XC1RJekrNzC6wcOeZgbbG+sMJZSNJOivvoaqbJPcFahRFXwf8DNAFCvv//nQcx98YRdHz\\nwD8Dvgx4APz1OI4/PKFUJUmSJEnncB0ukvRm4EPA4v7XPeAv7fd9EHgVeAvwk8D7oyh6bhJJSpIk\\nSZLOJ/dHUIEXgN+M43jgvLQoir4S+J3Al8Zx3AT+fhRFfxj4VuDvXn2akiRJkqTzuA4F6puBUaft\\nfinwif3i9MAv0TvdV5Kkc8k6GY8amwAkW4/otNtsrW0MxDxqbFIsBRRSKHZrk0hTkqSnynUoUCPg\\nj0VR9D1ACfi/ge+ld6rvq0diXwM8xVfSU6GTHX9rAW8bcLkeNTZpfnqP+tQMt9tfQNANKH6uOhBT\\nby5QKBTZXH9IWPRnIUnSeeW6QI2i6AuAGpAAf5reKb3/cL+tDrSODGkBlavMUZIuy9rWFs0PfIjs\\n3r2RfXzTO7xtwCWrT80wM7fAXtIiCANm5gZv29NMKhQKRfZaTdKkPaEsJUl6euS6QI3j+LejKLod\\nx3Fjv+k/RVFUondBpP8dmD8ypALsnnadVqvF7u6ph+mGSpJk4N88SpKEdrtNuz36DXPabtOBkf3H\\n9eV1bF7z6u876O+Pe5K5Z2o1bk9PD/W1222SJBn72pUkCWk6vA+knRTSzqnaO50O3axDlnUO27Ms\\ng0J22NbNMrpZNtTeH591D2LONw+FrC+uM3Keg7myrNtb+wzzdLMO3W6XbrcL3S69f7oD8/Qed4/E\\ndQfaj8ZnWZesk3FUlmV989A3z/C6h20Hcf05jIvpdul2s5Ex3RHr9cf2zzM0tm/tcXFDc/bFDIw9\\n8njUPI/n6//5Dm/PgW3bzSjsxw607489aB8XN3rO7uH6eVs76+zvz52MrHjy2r22Dmk7Hft6dCBN\\nU9J28cS4yxjv2mdd+/jfFweuw/sb5U+rdfR44fnlukAF6CtOD3wKqALL9C6g1G8RGH8+3BhLS0vH\\nnkYnjfLgwYNJpzDWysoK7fU1Sp3OyP6NxgatIORhtXqqvryOzWteo/oajcYTjT2pf22zwdaLL7K5\\nuTly7MrKCmtrW6TZ4MXaNzY2CMIm1frDJ25PkibZdEJx9/EJKs1mQrEYEoS9NzxJs0lASljoDLT3\\nx+8190iShJ2+N0lnmadYDA/jDuY6Os/BXHtpi2bSPNM8u0lCNQ1pt9uknQ7dQmG4gE9TCoUiadqm\\n0+nQTntv8A/ai0fiO2lKu73H7u4ORyXJLmklJU1LFIoF0jTl4AzuNE2H4tM0PYzrpB3aneHi4iDm\\nIKdud/gPImmaknbSgfFHY/vnOTq2f+1xcf1zdruFgZje2sWBtTqdjDQ9vlhK0w7tvT2azYTdneHt\\n2a+ZNCmVUnZ3ykfaE4qFzuH4cXGj7LVatMJSrtfe29uj08lOXBsgaSasr3dotZpHpxnQaGyw1yqz\\nulo/Mc+LHu/aZ1t7fX2D9WT874uj8vz+RjdDrgvUKIr+CPBTwHN9F0P6ImAV+PfAd0ZRVInj+KB0\\nf+t++6ncu3ePubm5i0hZN0CSJDx48IDnn3+eWi2fF0WZnZ3ltfhF7i4sjOxf3t6mHobcvXv3VH15\\nHZvXvPr72u02jUaDubk5wjA899ydUoln3vhGnn322ZFjZ2dniVceMH97cOyjzRWCsDY053HtlfIu\\nzVqHW/XHb472mjWKpeCwrVatEtRCgmp5oL0/vlwtU6vVzj1PsRSQVVsEtfCw/+g8B3N191KqteqZ\\n5smSFsUgJAxDglKJUhAc/uwOdNKAQqFIEIRQgnA/5qD9aHxvjjL1+i2O2ms1CYKAIAgoFAt0goDi\\n/udagyCgUCgMxKdBehhXCkqEpeH8DmLCMCQIAkpjYoJSMDD+aGz/PP2CI2uPi+ufs1QKBmJGrU23\\nQzBiew/OVyIsl6lWa9RvDW/Pfklrh1IxHIqr1mpUq+XD9nFxo5QrFSrlci7Xzjodms0m5XKZMKyc\\nuDYAhS4LC3NMjzhjo9/cXIN6rcydO6Nft05ynvGufba1006B28+N/31x4Dq8v1H+NBqNCz/Ql+sC\\nFfhleqfs/vMoiv4u8N8A3w+8F/go8Arw41EUvQd4O/AlwDefdpFKpUK9fra/SunmqtVqud1varUa\\nYRiOfXMXhCGlUmn0m8hj+vI6Nq95jerr/7mcZ+4wDI/dB2u1GkEwvA8EpaD3xv4U7aVSiUKx9P+3\\nd+9RkuZ1fcffz6WuXV0z0zPszM6IF9iTn6iIsEZRVDDJySZGETAKkZMDwRNONByNJOeYGIzRGI0K\\nyPECHC8x2RCVSwgSL2BUwAsY2UUXDPDDxew6u9u9M93TXZeueu5P/niqL1VdfZvprqru/rz29Omp\\n3/N7nudbfZ791fOt3+/5/TaTJQDXdQc/RZnjujibZe5Q3c36zkadOzzOoM72Y42rX2x3bvs4juvh\\nOE6RGDoOxa/hJHFj+3C9kbKR+q7r4Ho7lyF3XXfHvhv773aszR92P9/wj7tLHcacz91x/rH7sjPm\\n0Xqjxxz33sa9HnecbUckyzOSNCHZZ+ijg7N5LWznbrvWAFzHHVtvHNcZf8yd9cYfcyLn9g527qLM\\nwy/t/aUAFF8g+KXd26393Mn+Ovftnnvvz4tRs3x/I7PnOIaEz3SCaq3tGmPuA94EfBToAG+11r4B\\nwBjzQuCXgAeAh4EXWWsfm1a8IiIiMhlZmtINurSCGsu90aeBtqRJQinNi15uERGZeTOdoAJYaz8F\\n3LfLtr8Cvn6yEYmIiMgs8FyvGApc3ud2JtUMyyIiJ8X+40hEREREREREJkAJqoiIiIiIiMwEJagi\\nIiIiIiIyE2b+GVQRGS9NU5aWlsZuW1xcPNBi7yIiIiIis0QJqsgJtbS0xMfvfxsXm80d2+z161w9\\nfx4uXZpCZCIiIiIit0cJqsgJdrHZ5MrCwo7yG2u7L7kgIiIiIjKrlKCKiIiInHF5ntHtdPett97r\\nUqtemEBEInJWKUEVEREROeOCoM/1RzrMz++9Zuxjjy5Tr1YmFJWInEVKUEVERESESqVKvT63Z51y\\nScmpiBwvLTMjIiIiIiIiM0EJqoiIiIiIiMwEDfEVmVF7rXMKWuv0rEuzjMXFxV23F9dHOsGI5CBy\\nIIkTojAcszGfeDwiIiKzRgmqyIzaa51T0FqnZ91Ku03wnveS3X332O1/vbhI+/KXcumuqxOOTPaS\\npQmdXsTyWn+oPEkSql40pahERERmhxJUkRm22zqnoLVOBS40GrteH51OZ8LRyEG5rodfKu/ckCtB\\nFRERUYIqInJGZWnK2uoyAO21FbxSlVuN+aE67bUVer0+7qXTM5w8SzO6qy1cz6fXauOHJdrVVQC6\\na0W5kzh0V9eYzy4f/LhZRqe9SpolhEEPx3EJg+Ge0n6vS57ltNduDZWnSYSX98nPaZiviIicbUpQ\\nRUTOqLXVZR668SHqzXla9eUiYQuGE6dWfZnV1k2uhJ87pSiPXnetBY+WqDXO4fdrOIGLm1QBqAcL\\nOI6L26oSLsZUzx+8V7Pf6/L0KyssLCyQpDHg4HvDH7PnvxB8P+bcwvDz5VmWsnxzmSi8yFytesfv\\nUURE5KRSgioicobVm/M0L14gdWJcz6d57sLQ9tSJ6Xd6U4ru+NTn5pk/d4GoFOL6LnODZ72DfgXH\\ncalUa3Tbhx9GPz8/x8LCOZJkkKD6wx+zadjFL5W5sHBuqDzLUoL+6fs7z4acKE5wvXjH5FRxHBP5\\nEIUh5fKYYdciIjJxSlBFRE6wLMvodrs7ytd762S10zMsV+R2JXFCq98mJCEZuetZDdpETpkn28tc\\nbmrCORGRWaAEVUTkBOt2uzxkH6dSqQ2VP/r4Tfr1/i57iZwtrufi+z6l8vBtT8n38X0fz9ftkIjI\\nrFCLLCJywlUqNWr1xlBZqVyZUjQiIiIit8+ddgAiIiIiIiIioARVREREREREZoSG+IqIiIiInFJp\\nmrG4uLhvvX6/z40bNzh37hy12ta8BleuXMHzvOMMUWSIElQRERERkVNq5dYaweIHcIJre9ZLkhj6\\nt1h57Aa+Xxrs24J7X8i1a3vvK3KUlKCKiIiIiJxiCxea3H1l76WU4jjG93IuXbpEqVSaUGQiO+kZ\\nVBEREREREZkJ6kEVERERkQPJ84xev0e70963bqPRwHXUFyIih6MEVUREREQOJIpCnnwi4fzc6p71\\ngrDPPeYqzfnmhCITkdNCCaqIiIiIHFi5XKZen5t2GCJySmnchYiIiIiIiMwEJagiIiIiIiIyE5Sg\\nioiIiIiIyExQgioiIiIiIiIzQQmqiIiIiIiIzATN4isiIjJlOZBlGWmakGUpDi5plpLnDnk+7ehE\\nREQmRwmqiIjIlOVZRhAm+L5PEKY4bvHacXIqtRQoTTtEERGRiVCCKiJyCuV5Rqe1yq3lJ4fK22sr\\neKUqtxrzrN26SV7LphThbMqyjF63RRj0We+0iEoh5VqFbmcN3/dxfW+ofhj0cByX9U6Lfq9Dnp+/\\n/ZM7Lq7r4bgujusU/3acO3xHIiIiJ4sSVBGRUyiOQq7Hn8QLhseHturLuJ5PL7jFzeXHmb/rDhKq\\nU2i90+Lu2nUuXDjH5VIfz02ozeVcrazjui6VWjxUP0ljwCFo9HncaxPH9ekELiIickooQRUROaWq\\njTmaFy8MlaVOjOv5NM9doLu6NqXIZtt8c46FhXOUvAzPK1FvNOiVPVzXpVofTkCTpEhQ+2WXtVud\\n6QQsIiJyiihBFRGRUyHLMtorq5uv11ttgmidbr2Fk2wNle2urlHO56cRooiIiOxDCarIlKRpytLS\\n0q7bFxcXyTI9HyiFLMvodrubr9e7XbKST7dUItc0rwD0Wh1Kj9epN5oANLuXqacRtRvzuK3qZr1w\\nMcYdGaorIiI7pWnG4uLibe9/5coVPM/bv6LINkpQRaZkaWmJj9//Ni42m2O32+vXuXr+PFy6NOHI\\nZBZ1u10eso9TqdQA+H+Lbeq+z/JyQLXWmHJ0s6PeaNI8vwCAk7kkaUh9vkmlWtus022vgb77ERHZ\\n18qtNYLFD+AE125j3xbc+0KuXTv8vnK2KUEVmaKLzSZXFhbGbruxpucDZVilUqNWL5LRarVOxfco\\n+1p+REREjs/ChSZ3X9GX5TI5SlBFRETkjMsJo4g4jkiznCgMx9Yql8ugpX9ERI6VElQRERE505I4\\nYSVcJQh6eJ5PMubuKE0SLjcvUa5UJh+giMgZogRVREREzjzP9/B9D8/zKZV1e3Sn8jyj2+nuKF/v\\ndcmzCu1Oe6i80WjgOu6kwhORGaYWWERERESOVBD0uf5Ih/n54RmzbzzRoV4LKLtbk7sFYZ97zFWa\\n8+MnDRSRs0UJqojIDNm+nMzGUjLtdptut0vQ721VzDNAU/efdjnFNZGkKa6efZQTplLKp6O4AAAX\\n0ElEQVSpUq/PDZVVqzWq1fKOchGRDUpQRY7RXmudap1TGafb7fKJxz5FpVrh0dYidb9EUomIooi2\\nE9BLSsRhRBB61Eqawfe0y7OcIEpJ82Robp4sy/DJ8Fx9jE9OMZHShjhJiONkx4RKmkhJROTO6JNN\\n5Bjttdap1jmV3VSqFWqNOpValYrvU2vU8UKPcgylUnlQK51qjDI5ruvieh47Uh59wTVRGxMplZNi\\nkqRu2INSznJva0kwTaQkInLnlKCKHLPd1jrVOqciIieL53ubEyi5vovneZpQSUTkiKlVFRGZIXme\\nE0URXuiRxBFxlhGF4WBoYb5VJwwIfR/fj3E9n36vPHScOAzJc/XiiIiIyMmiBFVEZIasr69zc7VL\\nLYbVTkjop5TX+oRBH79UplQqks9WfBM3DaBWw3Fd4qQ/dJxOvkYSVaf0LkRERERujxJUEZEZ43k+\\npVIZ3y9t/jtJhpdq8MslSpUK5VoVx3GpVGsj289m855lGe21WwCst1skaUSapYTBVgK/3mlBDuVW\\nlbgf4vguaZYA0O204K58KrGLiIiIElQREZlhWZrRXWttvu6utXA9n16rjR+WaFdXN8v7nR5uu8LT\\nnrJCc75BVA/I8gy/HOJ7Wx93l0t9yKE6t0wWJziuQ6m8DoDfeZw4Oj/ZNykiIiKblKCKiMjM6q61\\nCD4dUW8UM2HXgwUcx8Xv13ACFzepbpb3boTE1YjmfIOFhXOE/QpZnlIqV/D9rY+7kpdBDrXGPOkg\\nQd2YdXV55dbk36SIiIhsUoIqIiIzrd5o0jxfzIQd9Cs4jktUCnF9l7nBEk5Bv8J6tw1aeUVERORE\\nU4IqInIKZVlOv9OlvbI6VN5da4HjQpizvm2Y7MbQWScpVttsnD+H67nTCJ0sy+iubsQDbntrsqcw\\n6OE47o5nR8OgR6/bhswhzxtTiVsE8sGM21viOCLNcqIwHKmqZ51FRMZRgioicgp1g4AvfKzP55U+\\nO1QeBD2eWF7lXL3OXa6L67vUl3sEQQ8cl2plhXavx8pXPJPmxQtTib3f7vK0covmfAMnB4eAciUA\\nIEljwNnx7GiSxnxOPebGzRZxVJpK3CJJnLASrlJOtpZ4agddPM8n2XbHlSYJcZIAWgpKRGSUElQR\\nkVNqrlrlQnN+qKxfcmmt92nUasyVfFzfY645T7/k4jgu1cFswCvTCHib+WadCxebOBk4vr85S3Ex\\nm7Gz49nRJIkpezm9XrzHUU+PHEjTDMfNSLO0+EkT0jTF87xph3emeb5Hadss2r7vFbNxn9GZtY9S\\nlmesd3u0O+0D1W80GrjOdEaCiMjtU2spIiITtzE77+jQ4vVWmygKiBsOURDgZFDWkN0d8iwjjBL8\\nDIIwIYozgiil2wtp1CtKUmdeThRFRHFpc+jvuKHA5XIZHGdaQc6cfr/Hyo0WZXf/NiEI+9xjrtKc\\nb04gMhknTTMWFxfv6BhXrly5rfYsTVOWlpamcm65c0pQRURk4jZn5/WLWXndVvGcabN7mV6/g3+h\\nT2m9StDv4VfTKUc7mxzHxXFdXNfDdQa/XfUWnQRJnNAKOlDJWO6tATuHAqdJwuXmpc1RAlKolCrU\\n63PTDkMOYOXWGsHiB3CCa7e5fwvufSHXrh1+/6WlJf7vg+/l4sK5iZ9b7pwSVJE7sN83dIuLi2SZ\\nphU9C9bX18lHJj3p9Xrg+3S7XeI4JolPxvDTYpKi4qa5u9ai1+5SKm2tOdo4f3sf+KPqjSblSjEr\\n78YQXidzcXyXcjWnXKmRJsmRnOusyIEk3UrosyzFwSVNleTPGs9z8f2tob8aCiyn0cKFJndfuXRb\\n+95JD+zi4iIXLszf9rllutQKityBpaUlPn7/27jYHD+EyF6/ztXz5+GSGsjTLMsz/vzhj1Np1IbK\\nH7m1SN33iUoBWZYSr4XcffXqlKI8uHavx9VPPMzFhQsEQY9+p0ulE1Ff7m1OoIRGHc6kPMvoByme\\nV3wxFoUpjpsRZ5BmKZ6nCaRE5GS4kx7Yz/zlo1y7unAMUckkKEEVuUMXm02uLIxvBG+srU04GpmW\\ncqVCbb4+VFapV6l4Hl7Zg8whjrt0Oh18v2h6syyDGX2+pVGrcWEweVI5y6nW68wNJlzabwKlLMvG\\nLm+zvSe2u7qG265SKpdxHJcw6AOw3m7R63e0BMcdcAbDfQEc18VxHVzXRX2oInLS3G4P7I2bt44h\\nGpmUE5+gGmMqwJuBlwA94A3W2jdONyoRkUIcxywtt3Fcj9bKOh//zBO4jksY9oiyLgvz88yVtnq1\\nbt68uWOo8CyIkgQ/jonimDiMSJ0Y18uIBxO6JFEMXo5bcgl7Ifd4LeabW8+JBY0e/asOlVpA3V8m\\nmg9wCHB9D3DwveLjKKoHPLF4gyiczhI3IjJ5eZ7R7XT3rddb7+u7K5Ez4MQnqMDrgecALwA+H7jf\\nGPOItfbd0wxKRGSD55dIo4S0FtEvtXEchyDt8ejSk8z36qyk65t1W8vL1EaWhpm2IIp59KbDwoJP\\nEM4RPBySORmOk0GlWO4hXXXISzlr+SLnnrbAfHOOhUtbz6r2+z4V36VarzHXbBL2yji+j+f7gLPZ\\nqxz2K7QOcKMqIqdHEPS5/kiH+fm9n9N/4vEVLl44mmfgRWR2negE1RhTB74DuM9a+xDwkDHmJ4DX\\nAEpQRWRi0jRhZWV1aEmIVrtDOc+5WC16Ev1yiUq9iuO4ZE5GuVanVp9jbtszzP319R3Hnr6cc/MX\\nuXLpc+lHIZWnLJA76dDkRjW/ieu7PN7qTzlWETmJKpXqvrPzVkrlCUUjItN0ohNU4FkU7+Ej28r+\\nCPj+6YQjImdVnCSEno/vb91AZZSI0pgsOz1j0vI8JwoCcjIc14WsSMjjIAQX1p5cJpmLiWo+YW/r\\nbxEFAXEY4bouvt8nCoId65vmOWRJSp5kZGlKmiSkSQq5QxIXs/nm5DhaF1JE9jFu2PB6r0ueVWh3\\n2kPljUYD19ESTSKz4qQnqHcDy9ba7esQPAlUjTEXrbX7zeUhsq8/fN/76D782c3XSRRzc2WZJy7+\\nIWu9deY73V0nSRKZphv9W5zrPT5UliQReaVH341IcHEzh/VehySJAAc/KxH7XdY6ARcWzu84ZhTH\\nzN+4Rak0eHbU7wFQD0I6vTYLrQYs1nHmMxx/6yOmlNQg9fEDHyfzSbpZsb5paatOmiRU+wHVOKYa\\nOdT6IWkQ4nspVccjTTOCWhmvpBtJEdnbuGHDN57oUK8FlN2tL8eCsM895irN+fGz8YvI5J30BLUO\\nhCNlG6+1srUciTyO+eKLWzPIxXHMzTznKZcusdKp8Phaa4rRieyuOV/lS8yVobI4iWl1e1RLJUqu\\ni+O6lCtl4iTGGTwLutrp8thjK9xqtyFNaK336OQd1jprtNe7+I5Llvk4OKRpMcFTFAWEcR+XnCCK\\ncd3y8JImOeReMdtxuVIhCoOxMbueh+u6eI5HyfPwXRfPcyl5PqA1UY9DlqVkWU6aJpvrpRZL0pz0\\nWwQ560aHDVerNarV8r5DiUVkuk76p0/AzkR043XvAPtXAbpdTcghu0ty6G9bCiTJc2g0CHyfpFym\\nVy7x2C7XUNeBxGHs9r223en2s7bvtOMKgb5XIfZ9sm3XSqnRwEkzIs8nJyP2qrTDGMdxiBNwqzVS\\nz6cdRpv7RH4Zz/VohxG5XyZ1XdphRJyA4+ZEYUTguHi1Oqnn09tWvl1pvkl93iXMh5/ZSnMfSi6Z\\n55E4LrgOee4V5Q6kuUvuN0jcPm4zo1auUFq4yCWvTLXcppmlOIDjRjiD/6BYC3Yhdbgr8uh0bxG1\\n5umlW0Nx8zwnSzN6vRi/FBKFEY6T4vcSwMFxHfIsx48SErdMkHvc6oYEcY4bZwRJnyzPi3U9vZg8\\nzcEBzy96R7IsIwpzHKdEFGWsrXZJ45ScDNdLcd2tXtewn4IDcdrbcZw8c0lih7XVLmE/xfUyohji\\nKMJ1XIIwG/p7ZlnxOg5TXK9CmhT7bpRvPy9AFDukWc7aanfHcfLMIYoyup0QHEjiQZxJgOvG2x9v\\nJssysiTFcROSON183+3BM8Ab500Hx3A9lzRO8LyAXn9ryHmeZ/SDkCiCznrK4tLa4G8UUqlAEBVx\\nxvHgC4z14S8Jgn5Mjk8/SFle7u5ab6Ou62Y4bjJUJ4pyHKfYf6NelubEaTD2OBt6/Rzcyua5dxPH\\nMVEASRoSRMPDwzfO7bQDHBySpIhxtN74v0GZJPWGzj3u/W+87+3HjOOYJPWHYh+tl6Upad/dnEBs\\nuyT3CGKHxRurO7YBRXsTxYRRiOen9KPher0wIYWh/dd7fRzX31H3sOfe73iTOPduxxx37jiJ8J8s\\n0ersvRBTZz0lTh2uP7H/uY96/0mdO0lTeusxQdLCH3yWnYS4j+PcrXZAo9tlZUWDMfezLY+qHtUx\\nnVlczuCgjDFfBXwIqFprs0HZC4DfsNY29toX4MEHH/x24L8fa5AiIiIiIiKn28vvvffeXzmKA530\\nHtQ/B2LgucCHB2VfC3z0gPu/H3g58AhFb6yIiIiIiIgcTJViqc/3H9UBT3QPKoAx5i3A84BXAZ8D\\n/BfgFdbaX59mXCIiIiIiInI4J70HFeC1wJuB3wdawA8oORURERERETl5TnwPqoiIiIiIiJwOWkxO\\nREREREREZoISVBEREREREZkJSlBFRERERERkJihBFRERERERkZmgBFVERERERERmwmlYZmZPxpgK\\nxTI0LwF6wBustW/cpe6zgbcAzwT+AvhOa+3HJhWrzI5DXje/DnwTkAPO4Pc3WWt/a0LhyowZXD8P\\nAP/cWvsHu9RReyNDDnjdqL0RAIwxV4GfBr6e4nPqHcC/sdZGY+qqvRHg0NeN2hsBwBjzdODngOcB\\nK8DPWmtfv0vdO25vzkIP6uuB5wAvAL4L+EFjzEtGKxlj6sBvAh8a1P8I8JvGmNrkQpUZcqDrZuAZ\\nwLcDdwNXBr//9wRilBk0SDJ+FfiiPeqovZEhB7luBtTeyIb/AVQpbhhfRpFI/IfRSmpvZMSBrpsB\\ntTeCMcahaEOeBL4M+GfA64wxLxtT90jam1Pdgzr4I30HcJ+19iHgIWPMTwCvAd49Uv1lQM9a+32D\\n1//CGPMNwLcC908qZpm+w1w3xpgy8AXAA9baGxMPVmaKMeYZwK8coKraG9l00OtG7Y1sMMYY4CuA\\ny9ba5UHZvwN+Evi+kepqbwQ43HWj9ka2uQz8GfBd1tp14LPGmN8Dvgb4tZG6R9LenPYe1GdRJOEf\\n2Vb2R8BXjqn7lYNt2/0x8FXHE5rMsMNcNwbIgL+aQFwy+54P/B5Fu+HsUU/tjWx30OtG7Y1sWAL+\\n3kaSMeAA58bUVXsjGw5z3ai9EQCstUvW2n80SE4xxjwP+DrgA2OqH0l7c6p7UCmGIixba5NtZU8C\\nVWPMRWvtykjdvxjZ/0ngi485Rpk9h7lungG0gbcZY14AXAd+0Fr7volFKzPDWvvWjX8XX1TvSu2N\\nbDrEdaP2RgCw1rbYNtRyMATvNcDvjqmu9kaAQ183am9kB2PMI8BTgd9g52hUOKL25rT3oNaBcKRs\\n43XlgHVH68npd5jr5guBGvDbwH3AbwH/yxjznGONUE46tTdyO9TeyG5+kuLZsH87ZpvaG9nNXteN\\n2hsZ5yUUzy0/G3jTmO1H0t6c9gQ1YOcfZON174B1R+vJ6Xfg68Za+8PANWvtf7PWfsJa+0MUjfmr\\njz9MOcHU3sihqb2RcYwxPw58N/Bya+2nxlRReyM77HfdqL2Rcay1HxvM4vy9wKuNMaOjcY+kvTnt\\nCerjwCVjzPb3eQXoW2vXxtS9MlJ2BVg8xvhkNh3mutkYMrPdp4BrxxifnHxqb+S2qL2R7YwxP0Nx\\no/hya+17dqmm9kaGHPC6UXsjABhj7jLGfPNI8SeBMtAcKT+S9ua0J6h/DsTAc7eVfS3w0TF1/wT4\\n6pGy5w3K5Ww58HVjjPllY8wvjRR/GfDp4wtPTgG1N3Joam9kO2PMD1L0Zr3UWvvOPaqqvZFNB71u\\n1N7INl8AvNsYc/e2si8Hblprb43UPZL25lRPkmSt7Rtj7gfeaox5FfA5wL8EXgFgjLkMtKy1AfAu\\n4MeMMT8F/DzFGj91igWM5Qw55HXzXuBXjTEfBD4MvJzif8R/Oo3YZXapvZHbofZGxhksTfQ64EeB\\nDw+uEwCstU+qvZFxDnndqL2RDR8FHgD+szHmtRQJ608APwLHc39z2ntQAV4LPAj8PvAzwA9Ya399\\nsG0R+DYAa20H+EaKaZMfoFgn6u9ba/sTj1hmwUGvm/8JfBdFg/8JigfH77PW/vXEI5ZZk4+8Vnsj\\nB7HXdaP2Rja8kOIe7nXAE4OfxcFvUHsj4x3mulF7IwBYazPgm4F1ii8rfh54k7X2ZwdVjry9cfJ8\\n9LNQREREREREZPLOQg+qiIiIiIiInABKUEVERERERGQmKEEVERERERGRmaAEVURERERERGaCElQR\\nERERERGZCUpQRUREREREZCYoQRUREREREZGZoARVREREREREZoISVBEREREREZkJSlBFRERERERk\\nJihBFRERERERkZngTzsAERGRk8IY8wjwucBrrbVvGrP9rcCrgX9vrf3hOzhPBrzSWnv/be7/VOCr\\nrbVvP0DdBeAfAxFwF/CQtfY9t3NeERGRO6UeVBERkYPLKRK5fzi6wRjjAS8BskkHNcZ/Be7br5Ix\\nZg54FfDT1tq3AD8O/OIxxyYiIrIrJagiIiKH87vAc40xV0fK/xawDlyffEg7OAes90rgp6y1+eD1\\n3wCWjyUiERGRA9AQXxERkcP5U+AZFL2oP72t/KXArwEv2ygwxnwJ8GPA84A54DHg56y1b9xWJwN+\\nmCJZLAHP334yY8wV4IPAo8ALgQrweuBFQBl4APg+a+2Dg/ofGBzj+caYF1hrnzbuTQyO+6i1Nt1W\\n/L3ADxz4LyEiInLE1IMqIiJyeO8AvnXjhTGmBLyYIkHdKKsBvwPcBJ4LfNFgv9cbY7505HjfSTE8\\n+MXW2oe3HeMSRY/tZ4FvtNaGwG8Dnwd8A/AVwJ8Af2yMedZgtxcDHwHeDnz5Hu/hHwC/Y4x5hjHm\\n/caYHwWa1tp3HuYPISIicpSUoIqIiBzeO4GvNsbcPXh9H/CktfahbXXmgJ8CXmOt/Yy19rPADw22\\nPXPkePdbaz9mrf3TbWUbyekjwIustbEx5m8DXwm81Fr7wOC4r6NIUr8HwFq7RvGcbN9ae2uP91Cy\\n1kbW2k9Za++z1n4/8HnGmHsP96cQERE5OhriKyIickjW2o8ZY/4K+BbgZ4FvA351pM6yMeYtwMuN\\nMc8G7gGeRTHRkjdyyIfZ6T9SDPn9qLU2HpQ9m+LL5evGmO11y4Ofw4jHlNWALwMePOSxREREjoQS\\nVBERkdvzDuBbjTG/AHwzI8NpjTGXgf8DLAHvBd4PfJTiOdRR/TFlvwP8MvBuY8zbrbW/S5GctoDn\\nsHMipPCggRtjngF8eqRsDnga8NcHPY6IiMhRU4IqIiJye94B/GvgnwCftdb+5cj2bwfOA0+z1mYA\\nxpiNob0HmWX3Xdba9xhj3g78wmDCpb8AmkDFWruZYA6S5D8D3jwoynccbdjzKSZe2u5bKJLpDxwg\\nNhERkWOhZ1BFRERuw+B5078E/hPbJkfa5jrFc6gvNcY81RjzdymGAecUM/HuZyOJ/R6KpPT1wPuA\\nh4C3G2NeYIx5ujHmjcArgE9u27cLfL4x5toux64CX7fxwhhzDvhXwCuttckBYhMRETkWSlBFREQO\\nbrRn8h3APMMJag7k1tp3AT8JvAH4FPBG4BeBPwD+5h7HHCqz1t6gSB5fTdHz+XcolpZ5O0Wy+jUU\\nkyh9cNv+b6WYiOkhY8y43toO8EljzGuNMd8N/AjwKmvth3Z95yIiIhPg5Pl+o4BERETktDDG3AM8\\n1VqrobwiIjJz1IMqIiJytjyPYlkaERGRmaMEVURE5GypW2vHzRosIiIydRriKyIiIiIiIjNBPagi\\nIiIiIiIyE5SgioiIiIiIyExQgioiIiIiIiIzQQmqiIiIiIiIzAQlqCIiIiIiIjITlKCKiIiIiIjI\\nTFCCKiIiIiIiIjNBCaqIiIiIiIjMBCWoIiIiIiIiMhOUoIqIiIiIiMhMUIIqIiIiIiIiM+H/A2Sf\\nUPdbZmr4AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11,9))\\n\",\n \"for symbol in indiv_traces:\\n\",\n \" plt.hist(indiv_traces[symbol]['beta'], bins=30, alpha = 0.50, label=symbol)\\n\",\n \"plt.title(r'Posterior Distribution of Market $\\\\beta$')\\n\",\n \"plt.xlabel(r'Market $\\\\beta$')\\n\",\n \"plt.ylabel('Density')\\n\",\n \"plt.legend()\\n\",\n \"plt.show();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These are all separate estimates of market beta. They are perfectly valid estimates, but they are not linked upstream ignore this intimate economic link between everything in the market that we have been discussing.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Making Regression Hierarchical\\n\",\n \"\\n\",\n \"We have only fetched returns data for technology stocks in this example. Because these all live within the same industry, it is reasonable to think that there is some fundamental relationship that ties all of them together. As such, we should be able to express this with our model. This is just one example of a potential hierarchical relationship when handling data. Many situations may be advantaged by adding structure like this.\\n\",\n \"\\n\",\n \"In the previous example, we modeled each individual stock with a different beta. We could instead model them all with the same beta, but this does not necessarily make sense. The beta values we already have indicate that there are some differences in how each stock is affected by the market and that to paint everything with the same brush would be ridiculous. To add hierarchical structure, we will instead connect the priors of those market betas by having them be drawn from the same distribution.\\n\",\n \"\\n\",\n \"We construct a core beta and a core alpha parameter, as before, but we allow their parameters to vary, borrowing the parametrization from [3]. We then give each individual stock an offset from these core parameters, linking them at their hearts and allowing them to vary around the centers.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We express this in PyMC3 code like so:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with pm.Model() as hierarchical:\\n\",\n \" # Hyperpriors - These connect the core beta and alpha values of each security\\n\",\n \" mu_a = pm.Normal('mu_alpha', mu=0, sd=10)\\n\",\n \" sigma_a = pm.HalfCauchy('sigma_alpha', beta=10)\\n\",\n \" mu_b = pm.Normal('mu_beta', mu=1, sd=10)\\n\",\n \" sigma_b = pm.HalfCauchy('sigma_beta', beta=10)\\n\",\n \" \\n\",\n \" # Individual priors for each security, all linked by the hyperpriors\\n\",\n \" a_offset = pm.Normal('a_offset', mu=0, sd=1, shape=n_securities)\\n\",\n \" alpha = pm.Deterministic(\\\"alpha\\\", mu_a + a_offset*sigma_a)\\n\",\n \" b_offset = pm.Normal('b_offset', mu=0, sd=1, shape=n_securities)\\n\",\n \" beta = pm.Deterministic('beta', mu_b + b_offset*sigma_b)\\n\",\n \" \\n\",\n \" # Error\\n\",\n \" e = pm.HalfCauchy('e', 5)\\n\",\n \" \\n\",\n \" # Data likelihood\\n\",\n \" exposure_likelihood = pm.Normal(\\n\",\n \" 'exposure_likelihood',\\n\",\n \" mu=alpha[stock_idx] + beta[stock_idx]*returns.SPY.values,\\n\",\n \" sd=e,\\n\",\n \" observed=returns.Returns.values\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more explanations and details on this model, see [here](http://twiecki.github.io/blog/2017/02/08/bayesian-hierchical-non-centered/). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Auto-assigning NUTS sampler...\\n\",\n \"Initializing NUTS using advi...\\n\",\n \"Average ELBO = 3,205.1: 100%|██████████| 200000/200000 [00:42<00:00, 4735.56it/s]\\n\",\n \"Finished [100%]: Average ELBO = 3,205.1\\n\",\n \"100%|██████████| 1000/1000 [00:57<00:00, 68.63it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"with hierarchical:\\n\",\n \" hierarchical_trace = pm.sample(1000, njobs=4, tune=500)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And now our Gelman-Rubin diagnostics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Diagnostics for our hierarchical model:\\n\",\n \" {'a_offset': array([ 1.00130725, 1.00022803, 0.99958049, 1.00070658, 0.99976498]), 'b_offset': array([ 1.00249857, 1.00875868, 1.00035379, 1.00348917, 1.0061286 ]), 'e': 1.0001656620867612, 'alpha': array([ 0.99999778, 1.00003256, 1.00314967, 1.00026183, 1.00111545]), 'sigma_beta_log_': 1.0056771987952779, 'sigma_alpha': 1.0037922481368866, 'mu_beta': 1.0172544880984475, 'sigma_beta': 1.0035763094189027, 'sigma_alpha_log_': 1.0028403717511034, 'e_log_': 1.0001711562893625, 'mu_alpha': 1.0020375419586338, 'beta': array([ 0.99967634, 1.00005686, 1.00006419, 0.99965113, 1.00024636])}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('Diagnostics for our hierarchical model:\\\\n', pm.diagnostics.gelman_rubin(hierarchical_trace))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From observing our Gelman-Rubin diagnostics, we can see that all of our parameters again have converged. We can also look at the traceplot below to see this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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RTZDMClV23g9e99EVd//MOo978PXziMx3Z40UNJ6p12ejYfBWAgmuaH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\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Cng3lar9fVihV2IC/H3Kv7k6Y/y2VNfAOAlWwmv/uwGn9l7K3+953rs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\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TfQru3nOQfoGNHWoq1Noc0CUnUwQgqtVQhKCWIso8IiZUsbxugMVla+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\\nhBBCCCHSToJSQgghhBBCCCGEECLt/j+Zeqh5kTE0SAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pm.traceplot(hierarchical_trace)\\n\",\n \"plt.show();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The intercept terms for our linear regressions, $\\\\lbrace\\\\alpha_i\\\\rbrace_{i=0}^{n=4}$, are all given a normal distribution whose parameters are shown above on the graphs for `mu_a` and `sigma_a`. Similarly, our set of betas are all normally distributed with their parameters given by `mu_b` and `sigma_b`. We do not have any strange patterns in the traces of any of our parameters, so it looks like everything is all set.\\n\",\n \"\\n\",\n \"So now let's have a look at our posterior distributions for our market betas!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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EhnZyd77rkn5513Hsccc8wG+5w3bx533nknr7/+OmVlZRxwwAFcfPHF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\\niIiIiMyE/w/uu3GL1EVLfQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(11,9))\\n\",\n \"for idx, symbol in enumerate(stocks):\\n\",\n \" plt.hist(hierarchical_trace['beta'][:,idx], bins=30, alpha=0.50, label=symbol)\\n\",\n \"plt.title(r'Posterior Distribution of Market $\\\\beta$')\\n\",\n \"plt.xlabel(r'Market $\\\\beta$')\\n\",\n \"plt.ylabel('Density')\\n\",\n \"plt.legend()\\n\",\n \"plt.show();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It looks like connecting our betas at the top brought their posterior distributions much closer together, an effect known as shrinkage. The less information we have about each individual beta, the more shrinkage we will see towards the group mean. Thus, hierarchical models have automatic regularization built in which makes them robust to overfitting.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summary\\n\",\n \"\\n\",\n \"Adapting our traditional statistical models to Bayesian frameworks can result in a good intuitive understanding of our results. By quantifying our uncertainty as probabilities, Bayesian methods can more clearly reflect how we look at models and the assumptions that underlie them. PyMC3 allows us to define probabilistic models simply, providing easy definitions of classic GLMs.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Citations TODO\\n\",\n \"\\n\",\n \"[1] Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.\\n\",\n \"\\n\",\n \"[2] https://pymc-devs.github.io/pymc3/notebooks/GLM-logistic.html\\n\",\n \"\\n\",\n \"[3] http://twiecki.github.io/blog/2017/02/08/bayesian-hierchical-non-centered/\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:bayes]\",\n \"language\": \"python\",\n \"name\": \"conda-env-bayes-py\"\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "papers/maxwell_margenot/maxwell_margenot.rst", "content": ":author: Maxwell Margenot\n:email: maxwell.margenot@gmail.com\n:email: max@quantopian.com\n:institution: Quantopian\n\n:author: Mark Anthony\n:email: mark37@rome.it\n:institution: Egyptian Embassy, S.P.Q.R.\n\n:author: Jarrod Millman\n:email: millman@rome.it\n:institution: Egyptian Embassy, S.P.Q.R.\n:institution: Yet another place, S.P.Q.R.\n\n:author: Brutus\n:email: brutus@rome.it\n:institution: Unaffiliated\n:bibliography: mybib\n\n\n:video: http://www.youtube.com/watch?v=dhRUe-gz690\n\n------------------------------------------------\nA Numerical Perspective to Terraforming a Desert\n------------------------------------------------\n\n.. class:: abstract\n\n A short version of the long version that is way too long to be written as a\n short version anyway. Still, when considering the facts from first\n principles, we find that the outcomes of this introspective approach is\n compatible with the guidelines previously established.\n\n In such an experiment it is then clear that the potential for further\n development not only depends on previous relationships found but also on\n connections made during exploitation of this novel new experimental\n protocol.\n\n.. class:: keywords\n\n terraforming, desert, numerical perspective\n\nIntroduction\n------------\n\nTwelve hundred years ago |---| in a galaxy just across the hill...\n\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum sapien\ntortor, bibendum et pretium molestie, dapibus ac ante. Nam odio orci, interdum\nsit amet placerat non, molestie sed dui. Pellentesque eu quam ac mauris\ntristique sodales. Fusce sodales laoreet nulla, id pellentesque risus convallis\neget. Nam id ante gravida justo eleifend semper vel ut nisi. Phasellus\nadipiscing risus quis dui facilisis fermentum. Duis quis sodales neque. Aliquam\nut tellus dolor. Etiam ac elit nec risus lobortis tempus id nec erat. Morbi eu\npurus enim. Integer et velit vitae arcu interdum aliquet at eget purus. Integer\nquis nisi neque. Morbi ac odio et leo dignissim sodales. Pellentesque nec nibh\nnulla. Donec faucibus purus leo. Nullam vel lorem eget enim blandit ultrices.\nUt urna lacus, scelerisque nec pellentesque quis, laoreet eu magna. Quisque ac\njusto vitae odio tincidunt tempus at vitae tortor.\n\n\nBibliographies, citations and block quotes\n------------------------------------------\n\nIf you want to include a ``.bib`` file, do so above by placing :code:`:bibliography: yourFilenameWithoutExtension` as above (replacing ``mybib``) for a file named :code:`yourFilenameWithoutExtension.bib` after removing the ``.bib`` extension. \n\n**Do not include any special characters that need to be escaped or any spaces in the bib-file's name**. Doing so makes bibTeX cranky, & the rst to LaTeX+bibTeX transform won't work. \n\nTo reference citations contained in that bibliography use the :code:`:cite:`citation-key`` role, as in :cite:`hume48` (which literally is :code:`:cite:`hume48`` in accordance with the ``hume48`` cite-key in the associated ``mybib.bib`` file).\n\nHowever, if you use a bibtex file, this will overwrite any manually written references. \n\nSo what would previously have registered as a in text reference ``[Atr03]_`` for \n\n:: \n\n [Atr03] P. Atreides. *How to catch a sandworm*,\n Transactions on Terraforming, 21(3):261-300, August 2003.\n\nwhat you actually see will be an empty reference rendered as **[?]**.\n\nE.g., [Atr03]_.\n\n\nIf you wish to have a block quote, you can just indent the text, as in \n\n When it is asked, What is the nature of all our reasonings concerning matter of fact? the proper answer seems to be, that they are founded on the relation of cause and effect. When again it is asked, What is the foundation of all our reasonings and conclusions concerning that relation? it may be replied in one word, experience. But if we still carry on our sifting humor, and ask, What is the foundation of all conclusions from experience? this implies a new question, which may be of more difficult solution and explication. :cite:`hume48`\n\n\nSource code examples\n--------------------\n\nOf course, no paper would be complete without some source code. Without\nhighlighting, it would look like this::\n\n def sum(a, b):\n \"\"\"Sum two numbers.\"\"\"\n\n return a + b\n\nWith code-highlighting:\n\n.. code-block:: python\n\n def sum(a, b):\n \"\"\"Sum two numbers.\"\"\"\n\n return a + b\n\nMaybe also in another language, and with line numbers:\n\n.. code-block:: c\n :linenos:\n\n int main() {\n for (int i = 0; i < 10; i++) {\n /* do something */\n }\n return 0;\n }\n\nOr a snippet from the above code, starting at the correct line number:\n\n.. code-block:: c\n :linenos:\n :linenostart: 2\n\n for (int i = 0; i < 10; i++) {\n /* do something */\n }\n \nImportant Part\n--------------\n\nIt is well known [Atr03]_ that Spice grows on the planet Dune. Test\nsome maths, for example :math:`e^{\\pi i} + 3 \\delta`. Or maybe an\nequation on a separate line:\n\n.. math::\n\n g(x) = \\int_0^\\infty f(x) dx\n\nor on multiple, aligned lines:\n\n.. math::\n :type: eqnarray\n\n g(x) &=& \\int_0^\\infty f(x) dx \\\\\n &=& \\ldots\n\nThe area of a circle and volume of a sphere are given as\n\n.. math::\n :label: circarea\n\n A(r) = \\pi r^2.\n\n.. math::\n :label: spherevol\n\n V(r) = \\frac{4}{3} \\pi r^3\n\nWe can then refer back to Equation (:ref:`circarea`) or\n(:ref:`spherevol`) later.\n\nMauris purus enim, volutpat non dapibus et, gravida sit amet sapien. In at\nconsectetur lacus. Praesent orci nulla, blandit eu egestas nec, facilisis vel\nlacus. Fusce non ante vitae justo faucibus facilisis. Nam venenatis lacinia\nturpis. Donec eu ultrices mauris. Ut pulvinar viverra rhoncus. Vivamus\nadipiscing faucibus ligula, in porta orci vehicula in. Suspendisse quis augue\narcu, sit amet accumsan diam. Vestibulum lacinia luctus dui. Aliquam odio arcu,\nfaucibus non laoreet ac, condimentum eu quam. Quisque et nunc non diam\nconsequat iaculis ut quis leo. Integer suscipit accumsan ligula. Sed nec eros a\norci aliquam dictum sed ac felis. Suspendisse sit amet dui ut ligula iaculis\nsollicitudin vel id velit. Pellentesque hendrerit sapien ac ante facilisis\nlacinia. Nunc sit amet sem sem. In tellus metus, elementum vitae tincidunt ac,\nvolutpat sit amet mauris. Maecenas [#]_ diam turpis, placerat [#]_ at adipiscing ac,\npulvinar id metus.\n\n.. [#] On the one hand, a footnote.\n.. [#] On the other hand, another footnote.\n\n.. figure:: figure1.png\n\n This is the caption. :label:`egfig`\n\n.. figure:: figure1.png\n :align: center\n :figclass: w\n\n This is a wide figure, specified by adding \"w\" to the figclass. It is also\n center aligned, by setting the align keyword (can be left, right or center).\n\n.. figure:: figure1.png\n :scale: 20%\n :figclass: bht\n\n This is the caption on a smaller figure that will be placed by default at the\n bottom of the page, and failing that it will be placed inline or at the top.\n Note that for now, scale is relative to a completely arbitrary original\n reference size which might be the original size of your image - you probably\n have to play with it. :label:`egfig2`\n\nAs you can see in Figures :ref:`egfig` and :ref:`egfig2`, this is how you reference auto-numbered\nfigures.\n\n.. table:: This is the caption for the materials table. :label:`mtable`\n\n +------------+----------------+\n | Material | Units |\n +============+================+\n | Stone | 3 |\n +------------+----------------+\n | Water | 12 |\n +------------+----------------+\n | Cement | :math:`\\alpha` |\n +------------+----------------+\n\n\nWe show the different quantities of materials required in Table\n:ref:`mtable`.\n\n\n.. The statement below shows how to adjust the width of a table.\n\n.. raw:: latex\n\n \\setlength{\\tablewidth}{0.8\\linewidth}\n\n\n.. table:: This is the caption for the wide table.\n :class: w\n\n +--------+----+------+------+------+------+--------+\n | This | is | a | very | very | wide | table |\n +--------+----+------+------+------+------+--------+\n\nUnfortunately, restructuredtext can be picky about tables, so if it simply\nwon't work try raw LaTeX:\n\n\n.. raw:: latex\n\n \\begin{table*}\n\n \\begin{longtable*}{|l|r|r|r|}\n \\hline\n \\multirow{2}{*}{Projection} & \\multicolumn{3}{c|}{Area in square miles}\\tabularnewline\n \\cline{2-4}\n & Large Horizontal Area & Large Vertical Area & Smaller Square Area\\tabularnewline\n \\hline\n Albers Equal Area & 7,498.7 & 10,847.3 & 35.8\\tabularnewline\n \\hline\n Web Mercator & 13,410.0 & 18,271.4 & 63.0\\tabularnewline\n \\hline\n Difference & 5,911.3 & 7,424.1 & 27.2\\tabularnewline\n \\hline\n Percent Difference & 44\\% & 41\\% & 43\\%\\tabularnewline\n \\hline\n \\end{longtable*}\n\n \\caption{Area Comparisons \\DUrole{label}{quanitities-table}}\n\n \\end{table*}\n\nPerhaps we want to end off with a quote by Lao Tse [#]_:\n\n *Muddy water, let stand, becomes clear.*\n\n.. [#] :math:`\\mathrm{e^{-i\\pi}}`\n\n.. Customised LaTeX packages\n.. -------------------------\n\n.. Please avoid using this feature, unless agreed upon with the\n.. proceedings editors.\n\n.. ::\n\n.. .. latex::\n.. :usepackage: somepackage\n\n.. Some custom LaTeX source here.\n\nReferences\n----------\n.. [Atr03] P. Atreides. *How to catch a sandworm*,\n Transactions on Terraforming, 21(3):261-300, August 2003.\n\n\n" }, { "path": "papers/maxwell_margenot/mybib.bib", "content": "@Book{hume48,\n author = \"David Hume\",\n year = \"1748\",\n title = \"An enquiry concerning human understanding\",\n address = \"Indianapolis, IN\",\n publisher = \"Hackett\",\n}\n" }, { "path": "papers/numba/00_numba.rst", "content": ":author: Siu Kwan Lam\n:email: siu@continuum.io\n:institution: Continuum Analytics\n\n:author: Antoine Pitrou\n:email: antoine.pitrou@continuum.io\n:institution: Continuum Analytics\n\n:author: Stan Seibert\n:email: stan.seibert@continuum.io\n:institution: Continuum Analytics\n\n------------------------------------------------\nNumba: A Compiler for Python and NumPy\n------------------------------------------------\n\n.. class:: abstract\n\n Numba is a just-in-time compiler for Python functions aimed primarily at numerical applications. Numba performs automatic type inference and generates optimized machine code using the LLVM compiler toolkit. Numba includes built-in support for NumPy arrays and many standard NumPy functions, but can be extended to support additional data types and custom operations. The Numba compiler can produce several different kinds of executable objects such as regular functions, NumPy universal functions (ufuncs), and GPU compute kernels. In addition, Numba can automatically parallelize ufuncs for execution on multicore CPUs and GPUs.\n\n.. class:: keywords\n\n compiler, hpc, numpy\n\n.. COMMENT .. include:: output/numba/intro/intro.rst\n\n.. include:: output/numba/array_vs_list/array_vs_list.rst\n\n.. include:: output/numba/piecewise/piecewise.rst\n\n.. include:: output/numba/gufunc/gufunc.rst\n\n.. include:: output/numba/looplifting/looplifting.rst\n\n.. include:: output/numba/nogilthreads/nogilthreads.rst\n" }, { "path": "papers/numba/array_vs_list/array_vs_list.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy\\n\",\n \"import numba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1.0, 2.0, 3.0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def make_list():\\n\",\n \" return [1, 2, 3.0]\\n\",\n \"\\n\",\n \"print(make_list())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def return_first(a_list):\\n\",\n \" return a_list[0]\\n\",\n \"\\n\",\n \"def return_first_py(a_list):\\n\",\n \" return a_list[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 4125.77 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"10000 loops, best of 3: 18.8 µs per loop\\n\",\n \"The slowest run took 6.85 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"10000000 loops, best of 3: 158 ns per loop\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"119.12829532275568\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"long_list = [0] * 1000\\n\",\n \"nb_result = %timeit -o return_first(long_list)\\n\",\n \"py_result = %timeit -o return_first_py(long_list)\\n\",\n \"\\n\",\n \"nb_result.best / py_result.best\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 81451.22 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"1000000 loops, best of 3: 357 ns per loop\\n\",\n \"The slowest run took 135.03 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"1000000 loops, best of 3: 223 ns per loop\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"1.6050316349021772\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"long_array = numpy.array(long_list)\\n\",\n \"nb_result = %timeit -o return_first(long_array)\\n\",\n \"py_result = %timeit -o return_first_py(long_array)\\n\",\n \"\\n\",\n \"nb_result.best / py_result.best\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def filter_bad_try1(values, lower, upper):\\n\",\n \" good = []\\n\",\n \" for v in values:\\n\",\n \" if lower < v < upper:\\n\",\n \" good.append(v)\\n\",\n \" return numpy.array(good)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"a = numpy.random.uniform(-2000, 2000, int(1e7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 loop, best of 3: 123 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit filter_bad_try1(a, -1000, 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def filter_bad_try2(values, lower, upper):\\n\",\n \" good = numpy.empty_like(values)\\n\",\n \" next_index = 0\\n\",\n \" for v in values:\\n\",\n \" if lower < v < upper:\\n\",\n \" good[next_index] = v\\n\",\n \" next_index += 1\\n\",\n \" return good[:next_index]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10 loops, best of 3: 60.4 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit filter_bad_try2(a, -1000, 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 997.9944078 , 687.29342808, -105.7498187 , ..., 905.98062605,\\n\",\n \" -1.43046327, -55.9636759 ])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"filter_bad_try2(a, -1000, 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def filter_bad_try3(values, lower, upper):\\n\",\n \" good = numpy.empty_like(values)\\n\",\n \" next_index = 0\\n\",\n \" for v in values:\\n\",\n \" if lower < v < upper:\\n\",\n \" good[next_index] = v\\n\",\n \" next_index += 1\\n\",\n \" return good[:next_index].copy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10 loops, best of 3: 73.6 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit filter_bad_try3(a, -1000, 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def filter_bad_try4(values, lower, upper):\\n\",\n \" return values[(lower < values) & (values < upper)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10 loops, best of 3: 95.9 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit filter_bad_try4(a, -1000, 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def filter_bad_try5(values, lower, upper):\\n\",\n \" return values[(lower < values) & (values < upper)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 loop, best of 3: 59.7 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit filter_bad_try5(a, -1000, 1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:numba_pydata]\",\n \"language\": \"python\",\n \"name\": \"conda-env-numba_pydata-py\"\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/numba/array_vs_list/array_vs_list.rst", "content": "Arrays vs. Lists\n----------------\n\nLists are a very common data structure in Python, providing a way to represent\nan ordered sequence of values. They are incredibly convenient, allowing\nelements to be read and replaced quickly. Elements can be inserted into and\nappended to lists as well, though much more slowly. In fact, the convience of\nlists can hide some serious performance drawbacks for numerical code.\n\nThe first problem is that lists are more general than is needed for most\nnumerical algorithms. Lists contain sequences of Python objects, which are\nlarger in memory than bare numerical values like ``float64`` (8 bytes) or\n``int32`` (4 bytes). In addition, the Python objects corresponding to\nelements of a list are not guaranteed to be next to each other in memory, so\nloop that process the elements in order may result in non-consecutive memory\naccesses, which limit memory bandwidth and frequently result in slower code.\n\nNumba has support for Python lists in nopython mode, with some limitations\nthat improve performance. First, all elements of a list in nopython mode must\nhave the same basic numerical type: integer, floating point, or complex. Numba\nwill apply its standard casting rules for math expressions to determine this\ncommon type. Roughly, types of different sizes will be cast to the largest\nsize, integers and floats cast up to floats, and floats and\ncomplex cast up to complex. For example:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def make_list():\n return [1, 2, 3.0]\n\n print(make_list())\n\nPrints a list of three floats:\n\n.. code-block: python\n\n [1.0, 2.0, 3.0]\n\nThere are some drawbacks to lists in Numba, however. Because nopython mode\nrequires all arguments to be translated into machine-native forms (hence the\n\"nopython\" name), any Python list passed into a Numba-compiled function as an\nargument must go through an \"unwrapping\" step which can be quite time\nconsuming for large lists. This trivial function:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def return_first(a_list):\n return a_list[0]\n\nis more than 100x slower in Numba than in pure Python when ``a_list`` is a\n1000 element list.\n\nAs an alternative to lists, NumPy includes a typed multidimensional array\nobject (which we'll call a \"NumPy array\" for short). NumPy arrays can be used\nin much the same way as lists in numerical code, but have several major\nbenefits:\n\n * They store their data in a native, packed format that is much more space efficient, and memory bandwidth efficient.\n * Numba can \"unwrap\" a NumPy array very quickly.\n * Multi-dimensional arrays work in Numba, but nested lists are not supported.\n\nIn comparison to above, the ``return_first`` function is only 1.9x slower in\nNumba than in Python. Numba continues to be slower in this case because the\nfunction body itself is so simple, the fixed overhead of function dispatch is\ndominating the runtime.\n\nLet's consider a situation where we might be tempted to use a list, and see\nhow a NumPy array might be better. A common operation when dealing with large\namounts of data is *filtering*. Suppose we have a million values from a\nfaulty sensor, where incorrect readings appear with values less than -1000.\nBefore we do any further statistics on this data, we need to remove the\nbad values.\n\nFollowing the advice given above, we would rightly decide to store the data in\na NumPy array. Passing the data in as a Python list would be tremendously\nslow due to the unwrapping overhead. However, inside the function we don't\nknow ahead of time how many values we need to emit, so we might decide to\ncreate an empty list and append to it:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def filter_bad_try1(values, lower, upper):\n good = []\n for v in values:\n if lower < v < upper:\n good.append(v)\n return numpy.array(good)\n\nFor consistency with the rest of our application, we cast the list back into a\nNumPy array at the end of the function. On our development computer, this\nfunction takes 123 ms for ten million values when 50% of them are bad.\n\nWe can do better than this, though. Appending to an array is slow because it\nrequires expanding the array storage several times to accomodate new values.\nIt would be much quicker to allocate a temporary NumPy array of the maximum\nsize required, and write elements into it. Then we can slice the array to its\nfinal length at the end of the function and return it:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def filter_bad_try2(values, lower, upper):\n good = numpy.empty_like(values)\n next_index = 0\n for v in values:\n if lower < v < upper:\n good[next_index] = v\n next_index += 1\n return good[:next_index]\n\nThis version of the function takes 60 ms, which is a significant improvement.\nHowever, it does have a small drawback. When Numba returns a slice of a NumPy\narray, it returns a view which references the block of memory associated with\nthe original array. Normally that's fine, but in the previous example, this\nresults in wasted memory as the end of the ``good`` array (everything from\n``next_index`` to the end) is unused, but not freed until the returned slice\nis also freed. We can fix this by returning a copy of the slice, which will\nhave no wasted space, and allowing the ``good`` array to go out of scope and\nbe freed:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def filter_bad_try3(values, lower, upper):\n good = numpy.empty_like(values)\n next_index = 0\n for v in values:\n if lower < v < upper:\n good[next_index] = v\n next_index += 1\n return good[:next_index].copy()\n\nThis does slow down the function slightly to 74 ms, so the `copy()` could be\nomitted if speed is more important than memory consumption.\n\nExperienced NumPy users would rightly point that the above algorithm could be\nmuch more compactly expressed using boolean indexing of a NumPy array:\n\n.. code-block:: python\n\n def filter_bad_try4(values, lower, upper):\n return values[(lower < values) & (values < upper)]\n\nUncompiled, this function needs to make several temporary arrays to evaluate\nthe boolean index array, so it requires 96 ms to run on our test dataset. This\nis better than the version 1 of the filter function, but not as good as the\nsecond. Numba can optimize the generated machine code for some array\nexpressions as well, so this final version of the function:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def filter_bad_try5(values, lower, upper):\n return values[(lower < values) & (values < upper)]\n\nfilters the test set in 60 ms, equivalent to version 3, but with far less\ncode.\n" }, { "path": "papers/numba/gufunc/gufunc.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy\\n\",\n \"import numba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 4.21986594e-06, 6.04823582e-06, 8.60534661e-06,\\n\",\n \" 1.21539546e-05, 1.70402689e-05, 2.37161871e-05,\\n\",\n \" 3.27659589e-05, 4.49376634e-05, 6.11797478e-05,\\n\",\n \" 8.26826654e-05])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = numpy.random.uniform(-2, 2, size=1000000).reshape(10000,100)\\n\",\n \"weights = numpy.exp(-numpy.linspace(-3, 3, 100)**2)\\n\",\n \"weights /= weights.sum()\\n\",\n \"weights[:10]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def np_weighted_avg(x, weights):\\n\",\n \" return (x * weights).sum(axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100 loops, best of 3: 4.03 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"np_time = %timeit -o np_weighted_avg(a, weights)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit\\n\",\n \"def np_compiled_weighted_avg(x, weights):\\n\",\n \" return (x * weights).sum(axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 20.54 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"100 loops, best of 3: 3.68 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit nb_weighted_avg(a, weights)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nopython=True)\\n\",\n \"def nb_weighted_avg_loops(x, weights):\\n\",\n \" result = numpy.empty(x.shape[0], x.dtype)\\n\",\n \" for i in range(x.shape[0]):\\n\",\n \"\\n\",\n \" row_sum = 0.0\\n\",\n \" for v, w in zip(x[i], weights):\\n\",\n \" row_sum += v*w\\n\",\n \" \\n\",\n \" result[i] = row_sum\\n\",\n \" \\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"numpy.testing.assert_allclose(np_weighted_avg(a, weights), nb_weighted_avg_loops(a, weights))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000 loops, best of 3: 1.53 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"nb_loops_time = %timeit -o nb_weighted_avg_loops(a, weights)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2.6322916063187707\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np_time.best / nb_loops_time.best\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.guvectorize(['(float64[:], float64[:], float64[:])', '(float32[:], float32[:], float32[:])'],\\n\",\n \" '(i),(i)->()')\\n\",\n \"def gufunc_weighted_avg(row, weights, result):\\n\",\n \" row_sum = 0.0\\n\",\n \" for v, w in zip(row, weights):\\n\",\n \" row_sum += v*w\\n\",\n \" result[0] = row_sum\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 7.88 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"1000 loops, best of 3: 985 µs per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"gufunc_time = %timeit -o gufunc_weighted_avg(a, weights)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"numpy.testing.assert_allclose(np_weighted_avg(a, weights), gufunc_weighted_avg(a, weights))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4.088664738999888\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np_time.best / gufunc_time.best\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:numba_pydata]\",\n \"language\": \"python\",\n \"name\": \"conda-env-numba_pydata-py\"\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/numba/gufunc/gufunc.rst", "content": "Generalized Ufuncs\n------------------\n\nIn a previous section, we saw the power of ufuncs for expressing functions on\narrays as functions on scalars combined with broadcasting. This covers a\nlarge number of situations, but the elements of the output array can only\ndepend on one element of each input at time. If multiple elements from a\nsingle input array need to be combined, some other approach is required.\n\nFor concreteness, let's suppose we want to compute a weighted average of the\nelements in each row of a 2D array, producing a 1D array as output. A pure\nNumPy approach to this problem would look like:\n\n.. code-block:: python\n\n def np_weighted_avg(x, weights):\n return (x * weights).sum(axis=1)\n\nWe assume that ``weights`` is a 1D array and has already been normalized so\nthat ``weights.sum() == 1``. For a 10000x100 input array (producing a 10000\nelement output array), the above function takes 3.6 ms on our test system.\n\nIf we thinking about how NumPy works in the Python interpreter, we realize\nthat the above implementation creates a temporary array with the weighted\nvalues of ``x``, only to immediately throw it away after summing along axis 1.\nNumba allows us to express the operation without temporaries:\n\n.. code-block:: python\n\n @numba.jit(nopython=True)\n def nb_weighted_avg_loops(x, weights):\n result = numpy.empty(x.shape[0], x.dtype)\n for i in range(x.shape[0]):\n\n row_sum = 0.0\n for v, w in zip(x[i], weights):\n row_sum += v*w\n \n result[i] = row_sum\n \n return result\n\nThis is much how one would write this function in C or FORTRAN, although we\ncan use some Python conviences functions like ``zip`` to more naturally loop\nthrough each row and the ``weights`` array at the same time. This version of\nthe function runs in 1.5 ms, or an improvement of 2.6x over NumPy.\n\nNow, we might be content to stop here, but this form of the function has some\ndisadvantages. First, the explicit looping limits this implementation to only\nwork on 2D arrays. If we had a 3D array (say 1000x1000x100), we would have to\nwrite another version of the above function to operate on 3D arrays. Second,\nit would be nice to make this more like a ufunc so we do not have to manage\nallocating the output array, and so we can focus on core piece of the\nalgorithm: the weighted sum itself.\n\nNumPy has a generalization of the ufunc, called a *gufunc*, that Numba can\nalso create. A gufunc is distinguished from a ufunc by having a layout\nsignature that describes the dimensionality of the inputs that should be\npassed to the core function. The signature is a mini-language described more\nfully in the NumPy documentation, though we will give some examples below. A\nregular ufuncs with two input arguments implicitly have a signature of\n``(),()->()``, indicating that the core function takes two scalar inputs and\nproduces a scalar output, for example.\n\nNumba creates gufuncs using the ``@guvectorize`` decorator. Unlike the\n``@vectorize`` decorator for making ufuncs, ``@guvectorize`` always requires a\ntype signature (along with the gufunc signature) for the function inputs. If\nwe wish to compile our weighted average gufunc for float32 and float64 inputs,\nwe would write the following (explained further below):\n\n.. code-block:: python\n\n @numba.guvectorize(['(float64[:], float64[:], float64[:])', \n '(float32[:], float32[:], float32[:])'],\n '(i),(i)->()')\n def gufunc_weighted_avg(row, weights, result):\n row_sum = 0.0\n for v, w in zip(row, weights):\n row_sum += v*w\n result[0] = row_sum\n\nOur core function takes three 1D arrays as input: the row, the weights, and\nthe output. All gufuncs pass in the output array as the last argument. This\nallows NumPy to allocate the full output array up front, and then pass views\nonto slices of the output array into the core function. Following the\nbehavior of the underlying C implementation of gufuncs, a Numba gufunc which\nreads or writes a scalar must access it via the first element of a 1D array\ninstead.\n\nWhen we call this gufunc from our application, we do not have to pass the\nresult argument present in the core function:\n\n.. code-block:: python\n\n result = gufunc_weighted_avg(x, w)\n\n\nThe layout signature for this gufunc, ``(i),(i)->()``, indicates that two 1D\narrays of identical length should be passed to the core function, and a scalar\nis returned. The looping over additional dimensions is implicitly handled by\nNumPy. A 3D input array and a 2D weight array could be processed by this\ngufunc (producing a 2D output), as long as the length of the last dimension of\neach input array was identical.\n\nThe type signatures for functions in Numba use a special mini-language which\nhas the following rules:\n\n 1. Scalar types have the NumPy dtype names: ``float32``, ``int8``, ``complex128``, etc.\n\n 2. Array types use a scalar type and colons in brackets to indicate dimensions:\n\n * ``int32[:]`` = 1D\n\n * ``int32[:,:]`` = 2D\n\n * ``int32[:,:,:]`` == 3D\n\n 3. The input arguments to a function are represented by a tuple of types.\n\nFor our test case of a 10000x100 input array, the gufunc version of the\nweighted average executes in 0.96 ms, or 4x faster than the original NumPy.\nThat's already a nice improvement, but we can even go even further with\nauto-parallelization, as shown in the next section.\n" }, { "path": "papers/numba/intro/intro.rst", "content": "Introduction\n------------\n\n" }, { "path": "papers/numba/looplifting/LoopLiftingCode.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy\\n\",\n \"import numba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import collections\\n\",\n \"import pprint\\n\",\n \"\\n\",\n \"\\n\",\n \"@numba.jit\\n\",\n \"def assign_bin(val):\\n\",\n \" limit = 32\\n\",\n \" while limit < 2**12:\\n\",\n \" if val < limit:\\n\",\n \" return limit\\n\",\n \" limit *= 2\\n\",\n \" return limit\\n\",\n \" \\n\",\n \"\\n\",\n \"@numba.jit\\n\",\n \"def binning(ary, debug=False):\\n\",\n \" bins = numpy.zeros(ary.size, dtype=numpy.int32)\\n\",\n \" for i, v in enumerate(ary):\\n\",\n \" bins[i] = assign_bin(v)\\n\",\n \" if debug:\\n\",\n \" ctr = collections.defaultdict(int)\\n\",\n \" for k in bins:\\n\",\n \" ctr[k] += 1\\n\",\n \" pprint.pprint(ctr)\\n\",\n \" return bins\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000\\n\"\n ]\n }\n ],\n \"source\": [\n \"ary = numpy.random.randint(0, 2**14, 10000)\\n\",\n \"bins = binning(ary)\\n\",\n \"print(len(bins))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(,\\n\",\n \" {32: 21,\\n\",\n \" 64: 23,\\n\",\n \" 128: 43,\\n\",\n \" 256: 70,\\n\",\n \" 512: 138,\\n\",\n \" 1024: 309,\\n\",\n \" 2048: 631,\\n\",\n \" 4096: 8765})\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([4096, 4096, 4096, ..., 4096, 4096, 4096], dtype=int32)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"binning(ary, debug=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"binning_no_lift = numba.jit(forceobj=True, looplift=False)(binning.py_func)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000 loops, best of 3: 36.6 µs per loop\\n\",\n \"100 loops, best of 3: 6.04 ms per loop\\n\",\n \"The slowest run took 29.10 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"100 loops, best of 3: 5.85 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit binning(ary)\\n\",\n \"%timeit binning.py_func(ary)\\n\",\n \"%timeit binning_no_lift(ary)\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/numba/looplifting/inspect_types_ex.py", "content": "import numba\nimport numpy\nimport collections\nimport pprint\n\n\n@numba.jit\ndef assign_bin(val):\n limit = 32\n while limit < 2**12:\n if val < limit:\n return limit\n limit *= 2\n return limit\n\n\n@numba.jit\ndef binning(ary, debug=False):\n bins = numpy.zeros(ary.size, dtype=numpy.int32)\n ctr = collections.defaultdict(int)\n for i, v in enumerate(ary):\n b = assign_bin(v)\n ctr[b] += 1 # uses a defaultdict\n bins[i] = b\n pprint.pprint(ctr)\n return bins\n\n\narr = numpy.random.random(10)\nbinning(arr)\n\nbinning.inspect_types()\n" }, { "path": "papers/numba/looplifting/looplifting.rst", "content": "Loop Lifting\n------------\n\nIn a real application, functions may depend on features that Numba has no\noptimization strategy. Such functions will be compiled by Numba using the\n*object mode* [#]_, which entirely relies on the *object protocol* in the Python\nC-API. In effect, this is equivalent to unrolling and specializing the\ninterpreter loop to the bytecode of the function. The performance advantage\nis minimal.\n\n.. [#] https://docs.python.org/3.5/c-api/object.html\n\nFortunately, the loops in numerical code are usually the hotspots.\nThey are more likely to contain Python and Numpy features that Numba can\noptimize. Leveraging this simple insight, Numba can optimize functions that\ncannot be fully compiled into fast machine code. We call this *Loop-lifting*,\nwhich extracts (\"lifts\") loops in functions and transforms them into new\nfunctions for deferred compilation. When the extracted loops are executed,\nthe compiler can use the new information available at runtime to further\noptimize the loop into efficient machine code.\n\nSuppose we have a simple binning operation we applied to an array. The binning\nfunction looks like:\n\n.. code-block:: python\n\n @numba.jit\n def assign_bin(val):\n limit = 32\n while limit < 2**12:\n if val < limit:\n return limit\n limit *= 2\n return limit\n\n @numba.jit\n def binning(ary):\n bins = numpy.zeros(ary.size, dtype=numpy.int32)\n for i, v in enumerate(ary):\n bins[i] = assign_bin(v)\n return bins\n\n\nThe ``binning`` function returns a new array of the bin assignment for each\nvalue in the input array. The ``assign_bin`` function assigns values to bins\nof power-of-twos between 32 and 4096. Both functions are marked for compilation\nwith ``@numba.jit``.\n\nLet's suppose ``binning`` is used in a larger application and we want to insert\nsome debugging code to investigate a problem. So, we modify it to the\nfollowing:\n\n.. code-block:: python\n\n @numba.jit\n def binning(ary, debug=False):\n bins = numpy.zeros(ary.size, dtype=numpy.int32)\n for i, v in enumerate(ary):\n bins[i] = assign_bin(v)\n # New debug code\n if debug:\n ctr = collections.defaultdict(int)\n for k in bins:\n ctr[k] += 1\n pprint.pprint(ctr)\n return bins\n\n\nThe newly added if-branch would allow us to inspect the number of items in each\nbin. However, Numba has no strategy in generating machine code for the usage\nof ``collections.defaultdict`` and ``pprint.pprint``. It will fallback to\n*object mode*. Fortunately, loop-lifting will \"lift\" the loop that calls\n``assign_bin`` and generate fast code for it. Without loop-lifting, the entire\nfunction will execute in *object mode*.\n\n.. table:: Timings for ``binning`` function execution on a 10,000 element input\n with different optimization settings.\n :label:`looplifting-binning-times`\n\n +----------------------+---------------------------+\n | Optimization Setting | Time |\n +======================+===========================+\n | interpreted | :math:`6.04\\text{ms}` |\n +----------------------+---------------------------+\n | no looplift | :math:`5.85\\text{ms}` |\n +----------------------+---------------------------+\n | looplift | :math:`0.04\\text{ms}` |\n +----------------------+---------------------------+\n\nTable :ref:`looplifting-binning-times` shows the execution timings for the\n``binning`` function under different optimization setting for a 10,000 element\ninput array. With the *interpreted* setting, the function is executed by the\nCPython interpreter and no optimization are applied. With the *no looplift*\nsetting, the entire function is compiled using the *object mode* and the loop\nis not lifted. Its performance is similar to the *interpreted* setting.\nThe *looplift* setting represents the optimal case where the loop is lifted\nand compiled into fast machine code. It runs at 100 times faster than the\nother two settings.\n\nEven though *loop-lifting* is convenient for quickly optimizing numerical loops\nin arbitrary functions with no code modification, it does not provide immediate\nfeedback when the loops fail to be optimized. For *loop-lifting* to work, the\nloop must not contain any code that would trigger *object mode*. For example,\nthe following variation will run slow:\n\n.. code-block:: python\n\n @numba.jit\n def binning(ary, debug=False):\n bins = numpy.zeros(ary.size, dtype=numpy.int32)\n ctr = collections.defaultdict(int)\n for i, v in enumerate(ary):\n b = assign_bin(v)\n ctr[b] += 1 # uses a defaultdict\n bins[i] = b\n pprint.pprint(ctr)\n return bins\n\nThe reference to ``ctr``, which is a ``defaultdict``, in the loop forces the\nloop to run in *object mode*. User can inspect the types for each statement\nby calling ``binning.inspect_types()`` to get source code with type annotation\nand manually checks for the abscence of Python object inside the loop. Shown\nbelow is an example output for loop in the previous function:\n\n.. code-block:: python\n\n for i, v in enumerate(ary):\n\n # --- LINE 22 ---\n # $58.5 = global(assign_bin: ...) :: pyobject\n # $58.7 = call $58.5(v) :: pyobject\n # b = $58.7 :: pyobject\n\n b = assign_bin(v)\n\n # --- LINE 23 ---\n # $58.12 = getitem(...) :: pyobject\n # $const58.13 = const(int, 1) :: pyobject\n # $58.14 = inplace_binop(...) :: pyobject\n # ctr[b] = $58.14 :: pyobject\n\n ctr[b] += 1 # uses a defaultdict\n\n\nEach line in the Python source code is preceded with the corresponding internal\nrepresentation encoded as comments. The right-hand-side of \"::\"\nindicates the output type of the operation. A \":: pyobject\" indicates the use\nof `PyObject` and the use of *object mode*.\n\nIf performance is critical, users are advised to manually extract any loop into\na separate function and decorate the function with ``@numba.jit(nopython=True)``.\nThe ``nopython`` flag will cause an exception to be raised if the function\nfallbacks to *object mode*.\n\n\n.. comment: maybe add a discussion the numba html annotate feature.\n\n" }, { "path": "papers/numba/nogilthreads/BadThreadsExample.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numba\\n\",\n \"import numpy\\n\",\n \"import concurrent.futures\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Low rank approx\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nogil=True)\\n\",\n \"def low_rank_approx(x, k=10):\\n\",\n \" u, s, v = numpy.linalg.svd(x)\\n\",\n \" return numpy.dot(u[:, :k], numpy.dot(numpy.diag(s[:k]), v[:k, :]))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"xs = numpy.random.random((30, 800, 600))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(800, 600) (800, 600)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(349.1584646167392, 399.81054759950013)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print(low_rank_approx(xs[0]).shape, xs[0].shape)\\n\",\n \"numpy.linalg.norm(low_rank_approx(xs[0])), numpy.linalg.norm(xs[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 loop, best of 3: 3.7 s per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"zs = [low_rank_approx(x) for x in xs]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 loop, best of 3: 3.58 s per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"with concurrent.futures.ThreadPoolExecutor(max_workers=2) as exe:\\n\",\n \" futs = exe.map(low_rank_approx, xs)\\n\",\n \" zs = list(futs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 loop, best of 3: 4.09 s per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"with concurrent.futures.ThreadPoolExecutor(max_workers=4) as exe:\\n\",\n \" futs = exe.map(low_rank_approx, xs)\\n\",\n \" zs = list(futs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10 loops, best of 3: 133 ms per loop\\n\",\n \"10 loops, best of 3: 134 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit low_rank_approx(xs[0])\\n\",\n \"%timeit low_rank_approx.py_func(xs[0])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "papers/numba/nogilthreads/Mandel.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy\\n\",\n \"import numba\\n\",\n \"import concurrent.futures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit\\n\",\n \"def mandel(x, y, max_iters=20):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Given the real and imaginary parts of a complex number,\\n\",\n \" determine if it is a candidate for membership in the Mandelbrot\\n\",\n \" set given a fixed number of iterations.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" i = 0\\n\",\n \" c = complex(x, y)\\n\",\n \" z = 0.0j\\n\",\n \" for i in range(max_iters):\\n\",\n \" z = z ** 2 + c\\n\",\n \" if (z.real ** 2 + z.imag ** 2) >= 4:\\n\",\n \" return i\\n\",\n \"\\n\",\n \" return max_iters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"width = 2048\\n\",\n \"height = 2048\\n\",\n \"xs = numpy.linspace(-2.0, 1.0, width)\\n\",\n \"ys = numpy.linspace(-1.0, 1.0, height)\\n\",\n \"img = numpy.zeros((width, height), dtype=numpy.int8)\\n\",\n \"img1 = img.copy()\\n\",\n \"img2 = img.copy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit(nogil=True)\\n\",\n \"def mandel_tile(xs, ys, out):\\n\",\n \" for i in range(xs.size):\\n\",\n \" for j in range(ys.size):\\n\",\n \" out[i, j] = mandel(xs[i], ys[j])\\n\",\n \" return out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Warm up\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" [0, 0, 0, ..., 0, 0, 0],\\n\",\n \" ..., \\n\",\n \" [1, 1, 1, ..., 1, 1, 1],\\n\",\n \" [1, 1, 1, ..., 1, 1, 1],\\n\",\n \" [1, 1, 1, ..., 1, 1, 1]], dtype=int8)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mandel_tile(xs, ys, img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10 loops, best of 3: 133 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"\\n\",\n \"mandel_tile(xs, ys, img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"23559.225921069647\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"numpy.linalg.norm(img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 4.23 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"10 loops, best of 3: 43.5 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"\\n\",\n \"npar = 2\\n\",\n \"\\n\",\n \"x_step = img.shape[0] // npar\\n\",\n \"y_step = img.shape[1] // npar\\n\",\n \"with concurrent.futures.ThreadPoolExecutor(4) as exe:\\n\",\n \" futs = []\\n\",\n \" for pos_x in range(0, img.shape[0], x_step):\\n\",\n \" for pos_y in range(0, img.shape[1], y_step):\\n\",\n \" futs.append(exe.submit(mandel_tile, xs[pos_x : pos_x + x_step], \\n\",\n \" ys[pos_y : pos_y + y_step], img1[pos_x:, pos_y:]))\\n\",\n \" for f in futs:\\n\",\n \" f.result()\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"23559.225921069647\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"numpy.linalg.norm(img1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10 loops, best of 3: 24.8 ms per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"\\n\",\n \"npar = 8\\n\",\n \"\\n\",\n \"x_step = img.shape[0] // npar\\n\",\n \"y_step = img.shape[1] // npar\\n\",\n \"with concurrent.futures.ThreadPoolExecutor(8) as exe:\\n\",\n \" futs = []\\n\",\n \" for pos_x in range(0, img.shape[0], x_step):\\n\",\n \" for pos_y in range(0, img.shape[1], y_step):\\n\",\n \" futs.append(exe.submit(mandel_tile, xs[pos_x : pos_x + x_step], \\n\",\n \" ys[pos_y : pos_y + y_step], img2[pos_x:, pos_y:]))\\n\",\n \" for f in futs:\\n\",\n \" f.result()\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"23559.225921069647\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"numpy.linalg.norm(img2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"numpy.all(img1 == img2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"numpy.all(img == img2)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "papers/numba/nogilthreads/nogilthreads.rst", "content": "Release the GIL for Parallel Execution\n--------------------------------------\n\nSome algorithms can be trivially parallelized to take advantage of the multicore\nprocessors. To identify subprograms that are potential candidates for\nparallelism, look for *pure* functions and *data-independent* loops.\nA function is *pure* if its outputs solely depends on its input arguments and\nit does not have any side-effects that may mutate any global states.\nFor a loop to be *data independent*, each iteration has no dependency on each\nother. Therefore, the iterations can be completed in arbitrary order.\n\nLet's consider the following function:\n\n.. code-block:: python\n\n @numba.jit\n def mandel(x, y, max_iters=20):\n i = 0\n c = complex(x, y)\n z = 0.0j\n for i in range(max_iters):\n z = z ** 2 + c\n if (z.real ** 2 + z.imag ** 2) >= 4:\n return i\n return max_iters\n\nThe ``mandel`` function determines if a given real and imaginary parts\n(as ``x`` and ``y`` arguments) of a complex number is a candidate for membership\nin the Mandelbrot set given a fix number of iterations (``max_iters``).\nThe function returns the depth of the iteration for the complex number to escape.\nThe function is *pure* given that it calculates its return value from the inputs\nand it does not depend on any global variables.\n\nTo visualize the Mandelbrot set, the ``mandel`` function is invoked on a set\nof coordinates as shown below:\n\n.. code-block:: python\n\n width = 2048\n height = 2048\n xs = numpy.linspace(-2.0, 1.0, width)\n ys = numpy.linspace(-1.0, 1.0, height)\n img = numpy.zeros((width, height), dtype=numpy.int8)\n for i in range(xs.size):\n for j in range(ys.size):\n img[i, j] = mandel(xs[i], ys[j])\n\nThe loop nest calls ``mandel`` on each iteration and storing the depth as pixel\nvalue for the image.\nSince ``mandel`` is pure and each assignment to ``img[i, j]`` is independent,\nthe entire loop nest is data-independent. The loop can be executed concurrently\nby multithreads with arbitrary order.\n\nHowever, the CPython interpreter has a *global interpreter lock* (GIL) that\nprevents the execution of Python instructions in parallel. It guards against\nrace conditions from happening internally but serializes the execution of\ninstructions. To take advantage of multicore processors, it is common to\nrewrite the performance critical parts of a program in C so that the GIL can\nbe released for parallel execution. However, writing complex algorithms\nin C can be error-prone and time consuming.\n\nSince Numba compiles Python functions into native machine code without any\ndependency on the CPython API, the GIL can be released during the execution\nof compiled functions. The ``numba.jit`` decorator provides the ``nogil``\noption to release of the GIL when the decorated function is called.\nFor example, the above loop-nest can be generalized into the following\nfucntion:\n\n.. code-block:: python\n\n @numba.jit(nogil=True)\n def mandel_tile(xs, ys, out):\n for i in range(xs.size):\n for j in range(ys.size):\n out[i, j] = mandel(xs[i], ys[j])\n return out\n\nThe ``mandel_tile`` will draw the Mandelbrot to ``out`` for the coordinates\ngiven in ``xs`` and ``ys``. The ``nogil`` option ensures the release of the\nGIL when the function is called so that multiple threads can execute this\nfunction in parallel.\n\nTo speedup the generation of the image, we can use the thread pool in\n``concurrent.futures`` to execute above function for non-overlapping image\ntiles. For example:\n\n.. code-block:: python\n\n npar = 2\n x_step = img.shape[0] // npar\n y_step = img.shape[1] // npar\n with concurrent.futures.ThreadPoolExecutor(4) as exe:\n futs = []\n # Submit job for each tile\n for pos_x in range(0, img.shape[0], x_step):\n for pos_y in range(0, img.shape[1], y_step):\n futs.append(exe.submit(mandel_tile, xs[pos_x : pos_x + x_step],\n ys[pos_y : pos_y + y_step], img1[pos_x:, pos_y:]))\n # Wait for all futures to complete\n for f in futs:\n f.result()\n\nThe output image is splitted into tiles.\nIn the first loop, tasks are submitted to the thread pool to compute on each\ntile. The call to ``exe.submit()`` enqueue the task on the thread pool and\nreturns before the task is completed. It's return value is a future that is\nused to track the progress of the computation. The seecond loop waits for\nall task to complete. Since we use output arguments, the returned value by\neach task is unnecessary and discarded. Otherwise, the returned value can be\ncollected from ``f.result()``.\n\nUsing 4 threads and 4 tiles on a quardcore machine,\na 2048x2048 image can be generated in 43.5ms.\nIt is 4x faster than the serial execution, which takes 133ms.\n\nSpeedup may not always be linear to the amount of parallelism.\nLinear speedup is possible in the previous example due to the compute-bound\nnature of the algorithm. For each call to ``mandel``, only two doubles are\nconsumed but it can iterate up to 20 times and calling 6 floating-point\noperations each time. The program will run faster given more execution units.\nOn the other hand, a memory-bound program will be less likely to gain\nlinear speedup with multithreads. In a memory-bound program, instruction\nexecution is stalled by pending memory requests. More execution units will not\nspeedup the completion of memory requests.\n\nScientific applications that depends on linear algebra routines may not\nsee any speedup by using multiple threads due to the parallel implementation of\nmany underlying BLAS routines. Nesting parallel code will just oversubscribe\nthe processor and increase the frequency of context-switching. In the worst\ncase, the application performance can degrade.\n\nLet's consider the following function:\n\n.. code-block:: python\n\n @numba.jit(nogil=True)\n def low_rank_approx(x, k=10):\n u, s, v = numpy.linalg.svd(x)\n return numpy.dot(u[:, :k], numpy.dot(numpy.diag(s[:k]), v[:k, :]))\n\n\nThe above ``low_rank_approx`` function computes the low-rank approximation\nof any matrix ``x`` using the singular-value decomposition (SVD) and matrix\nmultiplication (via ``dot``) routines from Numpy. The majority of the\ncomputation time will be in these two routines. In fact, simply applying\n``jit`` on the function provides little speedup. For a :math:`800\\times600`\ninput matrix, there is less than 10% speedup for the Numba compiled version.\nThe reason is that Numba uses the same underlying BLAS routines for the linear\nalgebra operations as NumPy.\n\nIf we compare the following single-threaded version:\n\n.. code-block:: python\n\n xs = numpy.random.random((30, 800, 600))\n zs = [low_rank_approx(x) for x in xs]\n\nand the 4-threaded version using ``concurrent.futures``:\n\n.. code-block:: python\n\n xs = numpy.random.random((30, 800, 600))\n with concurrent.futures.ThreadPoolExecutor(max_workers=4) as exe:\n futs = exe.map(low_rank_approx, xs)\n zs = list(futs)\n\nThe single-threaded version took 3.7s verus 4.1s for the 4-threaded\nversion. Executing in multiple threads is harmful to the performance.\n" }, { "path": "papers/numba/piecewise/piecewise.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy\\n\",\n \"import numba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"a = numpy.random.uniform(-2, 2, size=10000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def np_manual_piecewise(x):\\n\",\n \" output = numpy.empty_like(x)\\n\",\n \" \\n\",\n \" selector = x < 0\\n\",\n \" output[selector] = x[selector]**2\\n\",\n \"\\n\",\n \" selector = (0 <= x) & (x <= 1)\\n\",\n \" output[selector] = 0\\n\",\n \" \\n\",\n \" selector = x > 1\\n\",\n \" output[selector] = (x[selector] - 1)**2\\n\",\n \" \\n\",\n \" return output\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000 loops, best of 3: 334 µs per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit np_manual_piecewise(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def np_piecewise(x):\\n\",\n \" return numpy.piecewise(x,\\n\",\n \" [x < 0, \\n\",\n \" (0 <= x) & (x <= 1), \\n\",\n \" x > 1],\\n\",\n \" [lambda v: v**2,\\n\",\n \" lambda v: 0, \\n\",\n \" lambda v: (v - 1)**2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000 loops, best of 3: 431 µs per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit np_piecewise(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.vectorize\\n\",\n \"def nb_piecewise(x):\\n\",\n \" if x < 0:\\n\",\n \" return x**2\\n\",\n \" elif x <= 1:\\n\",\n \" return 0\\n\",\n \" else:\\n\",\n \" return (x - 1)**2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The slowest run took 18.63 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n \"100000 loops, best of 3: 14.2 µs per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit nb_piecewise(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"numpy.testing.assert_equal(nb_piecewise(a), np_piecewise(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"numpy.testing.assert_almost_equal(nb_piecewise(a), np_manual_piecewise(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"@numba.jit\\n\",\n \"def nb_manual_piecewise(x):\\n\",\n \" output = numpy.empty_like(x)\\n\",\n \" \\n\",\n \" selector = x < 0\\n\",\n \" output[selector] = x[selector]**2\\n\",\n \"\\n\",\n \" selector = (0 <= x) & (x <= 1)\\n\",\n \" output[selector] = 0\\n\",\n \" \\n\",\n \" selector = x > 1\\n\",\n \" output[selector] = (x[selector] - 1)**2\\n\",\n \" \\n\",\n \" return output\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000 loops, best of 3: 178 µs per loop\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit nb_manual_piecewise(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:numba_pydata]\",\n \"language\": \"python\",\n \"name\": \"conda-env-numba_pydata-py\"\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\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "papers/numba/piecewise/piecewise.rst", "content": "Piecewise Functions\n-------------------\n\nLet's suppose we are creating some kind of scoring function where we need to\ncompute a piecewise penalty function on every element of an input array. The\nfunction looks like:\n\n.. math::\n\n f(x) = \\begin{cases}\n x^2 & \\text{if $x < 0$}\\\\\n 0 & \\text{if $0 \\le x \\le 1$}\\\\\n (x-1)^2 & \\text{if $x > 1$}\n \\end{cases}\n\nA first attempt to efficiently implement this function for a NumPy array\nwithout Numba might look like:\n\n.. code-block:: python\n\n def np_manual_piecewise(x):\n output = numpy.empty_like(x)\n \n selector = x < 0\n output[selector] = x[selector]**2\n\n selector = (0 <= x) & (x <= 1)\n output[selector] = 0\n \n selector = x > 1\n output[selector] = (x[selector] - 1)**2\n \n return output\n\nIn the above, we compute a boolean array ``selector`` that is used to\nselectively apply the right expressions to different parts of the array.\n\nThis code pattern is common enough that NumPy offers a standard function, \n``numpy.piecewise``, that allows us to rewrite the above as:\n\n.. code-block:: python\n\n def np_piecewise(x):\n return numpy.piecewise(x,\n [x < 0, \n (0 <= x) & (x <= 1), \n x > 1],\n [lambda v: v**2,\n lambda v: 0, \n lambda v: (v - 1)**2])\n\nUsing ``numpy.piecewise`` makes the code much more compact, but requires\nlisting the boolean selector arrays separate from the expressions used for\neach component. It is also 30% slower than the previous implementation.\n\nWe can also use Numba to implement our piecewise function. In fact, the first\nversion can be compiled directly by Numba, just by adding a ``@jit`` \ndecorator:\n\n.. code-block:: python\n\n @numba.jit\n def nb_manual_piecewise(x):\n output = numpy.empty_like(x)\n \n selector = x < 0\n output[selector] = x[selector]**2\n\n selector = (0 <= x) & (x <= 1)\n output[selector] = 0\n \n selector = x > 1\n output[selector] = (x[selector] - 1)**2\n \n return output\n\nThis speeds up the function by a factor of 2, thanks to Numba's automatic\ncompilation of array expressions. However, we can improve the speed and\nreadability of this function by changing it into a NumPy \"universal function\",\nalso known as a \"ufunc\".\n\nA ufunc is a function with scalar inputs and outputs that can be automatically\n*broadcast* over one or more input arrays. The scalar function is computed\nfor each element in the input arrays, creating an output array. Most of the\nbuilt-in math functions in NumPy are actually ufuncs.\n\nNumba has the ability to create new ufuncs by applying the ``vectorize``\ndecorator to a scalar function. For example, we can implement our piecewise\nfunction like this:\n\n.. code-block:: python\n\n @numba.vectorize\n def nb_piecewise(x):\n if x < 0:\n return x**2\n elif x <= 1:\n return 0\n else:\n return (x - 1)**2\n\nLike ``@jit``, we do not need to specify the data types [#]_ of the input.\nThis implementation is much more readable, and for our test case of 50,000\ninput elements, it is 24x faster than the original!\n\n.. [#] Types are needed for ``@vectorize`` when using compilation targets like ``\"parallel\"`` or ``\"cuda\"``.\n\n\nThis miraculous-seeming result is a result of rewriting the function so that\nfewer temporary arrays need to be allocated. Memory allocation can be quite\nslow, and the first implementation of this piecewise function needed to\nallocate 8 temporary arrays. The ``@vectorize``-based implementation only\nallocates one array: the output array.\n\n.. table:: Timings for piecewise function execution on a 50,000 element input. :label:`piecewise-times`\n\n +----------------------+---------------------------+\n | Function | Time |\n +======================+===========================+\n | np_manual_piecewise | :math:`334\\mu\\text{s}` |\n +----------------------+---------------------------+\n | np_piecewise | :math:`431\\mu\\text{s}` |\n +----------------------+---------------------------+\n | nb_manual_piecewise | :math:`178\\mu\\text{s}` |\n +----------------------+---------------------------+\n | nb_piecewise | :math:`14\\mu\\text{s}` |\n +----------------------+---------------------------+\n" }, { "path": "papers/prabhu_ramachandran/code/viewer0.py", "content": "from traits.api import HasTraits, Instance\nfrom traitsui.api import View, Item, Group\nfrom mayavi.core.ui.api import MlabSceneModel, SceneEditor, MayaviScene\nfrom mayavi.core.api import PipelineBase\n\n\nclass PySPHViewer(HasTraits):\n\n scene = Instance(MlabSceneModel, ())\n\n plot = Instance(PipelineBase)\n\n view = View(\n Group(Item(\n 'scene',\n editor=SceneEditor(scene_class=MayaviScene),\n height=400,\n width=400,\n show_label=False\n )),\n resizable=True,\n title='PySPH viewer'\n )\n\nviewer = PySPHViewer()\nviewer.configure_traits()\n" }, { "path": "papers/prabhu_ramachandran/code/viewer1.py", "content": "import sys\nimport os\n\nfrom traits.api import (HasTraits, Instance, Str)\nfrom traitsui.api import View, Item, Group\nfrom mayavi.core.ui.api import MlabSceneModel, SceneEditor, MayaviScene\nfrom mayavi.core.api import PipelineBase\n\nfrom pysph.base.particle_array import ParticleArray\nfrom pysph.solver.utils import load\n\n\nclass PySPHViewer(HasTraits):\n\n scene = Instance(MlabSceneModel, ())\n\n scalar = Str(\"rho\")\n\n particle_array = Instance(ParticleArray)\n\n plot = Instance(PipelineBase)\n\n file_name = Str\n\n view = View(\n Group(Item(\n 'scene',\n editor=SceneEditor(scene_class=MayaviScene),\n height=400,\n width=400,\n show_label=False\n )),\n Item('file_name'),\n resizable=True,\n title='PySPH viewer'\n )\n\n def update_plot(self):\n mlab = self.scene.mlab\n pa = self.particle_array\n if self.plot is None:\n self.plot = mlab.points3d(\n pa.x, pa.y, pa.z,\n getattr(pa, self.scalar),\n mode='point'\n )\n self.plot.actor.property.point_size = 3\n else:\n self.plot.mlab_source.reset(\n x=pa.x, y=pa.y, z=pa.z,\n scalars=getattr(pa, self.scalar)\n )\n\n def _particle_array_changed(self, pa):\n self.update_plot()\n\n def _file_name_changed(self, fname):\n if os.path.exists(fname):\n data = load(fname)\n self.particle_array = data['arrays']['fluid']\n\n\nviewer = PySPHViewer(file_name=sys.argv[1])\nviewer.configure_traits()\n" }, { "path": "papers/prabhu_ramachandran/code/viewer2.py", "content": "import sys\nimport os\n\nfrom traits.api import (HasTraits, Instance, Str, List)\nfrom traitsui.api import View, Item, Group, EnumEditor\nfrom mayavi.core.ui.api import MlabSceneModel, SceneEditor, MayaviScene\nfrom mayavi.core.api import PipelineBase\n\nfrom pysph.base.particle_array import ParticleArray\nfrom pysph.solver.utils import load\n\n\nclass PySPHViewer(HasTraits):\n\n scene = Instance(MlabSceneModel, ())\n\n scalar = Str(\"rho\")\n\n scalar_list = List(Str)\n\n particle_array = Instance(ParticleArray)\n\n plot = Instance(PipelineBase)\n\n file_name = Str\n\n view = View(\n Group(Item(\n 'scene',\n editor=SceneEditor(scene_class=MayaviScene),\n height=400,\n width=400,\n show_label=False\n )),\n Item('file_name'),\n Item('scalar', editor=EnumEditor(name='scalar_list')),\n resizable=True,\n title='PySPH viewer'\n )\n\n def update_plot(self):\n mlab = self.scene.mlab\n pa = self.particle_array\n if self.plot is None:\n self.plot = mlab.points3d(\n pa.x, pa.y, pa.z,\n getattr(pa, self.scalar),\n mode='point'\n )\n self.plot.actor.property.point_size = 3\n else:\n self.plot.mlab_source.reset(\n x=pa.x, y=pa.y, z=pa.z,\n scalars=getattr(pa, self.scalar)\n )\n\n def _particle_array_changed(self, pa):\n self.scalar_list = list(pa.properties.keys())\n self.update_plot()\n\n def _file_name_changed(self, fname):\n if os.path.exists(fname):\n data = load(fname)\n self.particle_array = data['arrays']['fluid']\n\n def _scalar_changed(self, scalar):\n pa = self.particle_array\n if pa is not None and scalar in pa.properties:\n self.update_plot()\n\n\nviewer = PySPHViewer(file_name=sys.argv[1])\nviewer.configure_traits()\n" }, { "path": "papers/prabhu_ramachandran/mayavi_chapter.rst", "content": ":author: Prabhu Ramachandran\n:email: prabhu@aero.iitb.ac.in\n:institution: Department of Aerospace Engineering,\n Indian Institute of Technology Bombay, Powai, Mumbai, India\n:corresponding:\n\n\n-------------------------------------------------\nMayavi: 3D Visualization and Plotting with Python\n-------------------------------------------------\n\n.. class:: abstract\n\n Mayavi_ is an open source, Python package for general-purpose 3D\n visualization. It uses the powerful VTK_ library and provides a Pythonic API\n to it. Mayavi provides a standalone application for visualizing data but more\n importantly provides a convenient high-level library for 3D visualization.\n The library integrates well with the Python stack and interfaces seamlessly\n with NumPy arrays. Mayavi provides a simple yet powerful entry-point for\n scientists via its ``mlab`` module which is similar in spirit to matplotlib's\n ``pylab`` module. Due to its use of VTK, Mayavi supports image data,\n structured grids, polygonal data, and unstructured grid datasets. A large\n number of visualization algorithms can be applied to data. Custom user\n interfaces that embed Mayavi plots can be easily made and these can be\n embedded into larger GUI applications that are written in either wxPython or\n Qt. Mayavi provides basic support for embedding interactive 3D plots inside\n Jupyter notebooks. In this chapter we introduce the reader to the Mayavi\n library by considering a simple application. We demonstrate how one can use\n the library to start with exploratory analysis, polish the visualization,\n animate it, and finally build a simple customized GUI application using the\n libary.\n\n\n.. _Mayavi: http://code.enthought.com/projects/mayavi\n.. _VTK: http://www.vtk.org\n\n.. class:: keywords\n\n Visualization, Plotting, Python\n\n\nIntroduction\n------------\n\nMayavi_ is a powerful general-purpose 3D visualization library. It provides\nboth a full-featured GUI in addition to a completely scriptable API. This\nmakes it easy for a casual user to start with a simple plot, refine it, and\nthen automate it. Mayavi uses the VTK_ library under the hood. This makes\nMayavi full-featured. VTK is a mature, respected, open source package for 3D\nvisualization that has been developed for over 2 decades. VTK is a very\npowerful library provding functionality for visualization, imaging, and\ngraphics. It is implemented in C++ and provides over 2000 classes. While using\nVTK does add a significant and complex dependency, the benefits are that the\nuser can use all of the features of VTK and any improvements to VTK are\navailable to Mayavi users. For example, with VTK 7.x and above, the\nprogrammable OpenGL interface is used for visualization, providing an order of\nmagnitude (and sometimes even two orders of magnitude) speed improvements.\nMayavi hides many of the VTK details from the user and when it cannot hide\nthem it makes it easier to deal with. It also provides a convenient GUI so new\nusers can easily create and modify their visualizations. Furthermore, Mayavi\nprovides an automatic script recorder which can record all the UI actions and\nprovide executable Python code corresponding to those actions. This allows for\na user to explore and learn the scripting API as well as rapidly automate\ntheir visualizations. Mayavi presents a Pythonic API and interfaces cleanly\nwith numpy_ arrays, making it a breeze to start with typical numerical data\nand make visualizations.\n\n.. _numpy: http://numpy.org\n\nMayavi uses the Enthought Tool Suite which provides a suite of tools to make\nit easy for a typical scientist/engineer to create a user interface without\nknowing the specifics of a particular GUI toolkit like Qt_ or wxPython_. This\nmakes it very easy to create fairly complex GUI applications which use the\nfull power of Mayavi and combine it with a custom UI, relatively easily.\n\n.. _Qt: http://www.qt.io\n.. _wxPython: http://www.wxpython.org\n\nIn the next section we explore the basic functionality that Mayavi provides to\nrapidly visualize simple data. Thereafter we look at more complex datasets and\nhow they can be visualized, we then show simple examples of how one can build\na custom user interface embedding Mayavi.\n\n\nExploratory visualization\n-------------------------\n\nMayavi's ``mlab`` module provides convenient functionality for exploratory\nvisualization of data. This module makes it easy to quickly visualize data and\nis inspired by matplotlib's pylab. However, this module also provides complete\naccess to all of Mayavi's functionality. We now explore visualizing simple\ndata using this interface. To get started one may use the IPython_ console or\nan IPython notebook as follows::\n\n $ ipython # or\n $ jupyter console\n\n In [1]: %gui qt\n\nIt is important to set the ``%gui``, Mayavi works with both Qt and wxPython so\neither option will work.\n\n.. _IPython: http://ipython.org\n\nThe first simple example we consider is to show a collection of points in 3D.\nLet us construct some simple data::\n\n import numpy as np\n t = np.linspace(0, 2*np.pi)\n x, y, z = np.sin(t), np.cos(t), np.zeros_like(t)\n\nThe arrays ``x, y, z`` represent points on the circumference of a circle. We\ncan easily plot these as follows::\n\n from mayavi import mlab\n mlab.points3d(x, y, z)\n\nThis produces the dialog shown in :ref:`fig:points3d`. One can interact with\nthe produced plot. One may interact using the mouse, keyboard, and the\ntoolbar. Using the mouse one can rotate the camera using left-click and drag,\npan the camera using Shift-Left-Click (or middle-click) and drag, zoom using\nthe wheel or by right clicking. The arrow keys can also be used to rotate the\ncamera, the +/- keys for zooming in and out and \"Shift+Arrow\" keys to pan. The\ntoolbar features several icons, the group of icons with X, Y, and Z can be\nused to view the plot along the x, y, and z axes respectively and the last of\nthese provides an isometric view. The file icon on the right end is used to\nsave the scene to a variety of file formats (various images, VRML, OBJ, RIB,\nX3D, etc.). The right-most icon can be used to configure the background.\n\n.. figure:: images/points3d_simple.png\n :align: center\n\n Dialog produced by ``mlab.points3d``. :label:`fig:points3d`\n\nThe icon on the left is the logo of Mayavi and clicking on this brings up the\nMayavi Pipeline Editor. Figure :ref:`fig:pipeline` shows the dialog produced.\nOn the left panel is a tree-view of the Mayavi pipeline which we discuss\nlater. On the right are widgets that are used to configure every object in the\nMayavi pipeline. The toolbar provides various conveniences. We look at the\npipeline in greater detail later on. For now, it is useful to understand that\nat the root of the tree is a Mayavi Scene representing the area in which the\n3D visualization is made. Below this is a \"data source\" node, in this case a\n\"ScalarScatter\" which as its name describes represents the points we just\nplotted. Below this is the \"Colors and legends\" node which allow us to\nconfigure how the data is represented as colors. Below this is a \"Glyph\" node\nwhich essentially plots some kind of shape at each of the points. As can be\nseen from the plot, it appears as if a sphere has been placed at each point we\nsupplied to ``mlab.points3d``. One can click on any of these tree nodes to\nconfigure the objects entirely graphically. This is a very powerful and useful\nfeature of Mayavi in that one does not need to learn an API to configure a\nplot.\n\n.. figure:: images/pipeline.png\n :align: center\n\n The Mayavi Pipeline Editor. :label:`fig:pipeline`\n\nJust like matplotlib's ``pylab`` module, Mayavi also provides a ``clf``\nfunction to clear the scene::\n\n mlab.clf()\n\nThis will clear out the scene and if one looks at the resulting \"pipeline\" the\nscalar scatter node and everything under it has been removed. The ``points3d``\nfunction also takes a variety of keyword arguments that are documented. These\nmay be perused using the IPython console/notebook. One extra argument that the\nfunction takes is a ``scalars`` argument. Notice that the original plot did\nnot have any coloring, this was because we just plotted the points and there\nwere no scalar values associated with each point. Had we done this::\n\n mlab.points3d(x, y, z, t)\n\nThen we associate the value of t with each point and the resulting points\nwould be colored and scaled as per the value of the scalars. If we do not want\nthe scaling, we can try::\n\n mlab.clf()\n mlab.points3d(x, y, z, t, scale_mode='none')\n\nThis a typical workflow for a quick visualization and is very similar to what\nmany other two dimensional plotting utilities provide. Note that plots are by\ndefault overlaid on top of each other which often necessitates a call to\n``mlab.clf()``. Mayavi also provides an ``mlab.figure`` function analogous to\nthat provided by pylab to create multiple plots.\n\nMayavi provides several other options to visualize simple data and we take a\nquick look at a few of these. If one wished to plot a line joining the points\nwe just created we could do::\n\n mlab.plot3d(x, y, z, t)\n\nAnd this would produce a tube colored as per the local scalar value. One can\nlook at the legend with::\n\n mlab.scalarbar()\n\nNote that most of the standard colormaps are provided, and one could do::\n\n mlab.plot3d(x, y, z, t, colormap='viridis')\n\nto use the new ``viridis`` colormap resulting in Figure :ref:`fig:plot3d`.\n\n.. figure:: images/plot3d_viridis.png\n :align: center\n\n Result of ``mlab.plot3d`` with the viridis colormap. :label:`fig:plot3d`\n\nFor two dimensional data with points that are rectilinear one can use\n``mlab.surf``::\n\n x, y = np.mgrid[-3:3:100j,-3:3:100j]\n z = sin(x*x + y*y)\n mlab.surf(x, y, z)\n\nThis produces a carpet plot. Notice that the ``x, y`` are rectilinear.\nVariants of this function are ``mlab.contour_surf`` which plots contours. For\npoints that are not rectilinear but are mappable to a rectilinear set of\npoints one can use ``mlab.mesh``. For example::\n\n phi, theta = np.mgrid[0:pi:20j, 0:2*pi:20j]\n x = np.sin(phi)*np.cos(theta)\n y = np.sin(phi)*np.sin(theta)\n x = np.cos(phi)\n mlab.mesh(x, y, z, representation='wireframe')\n\nplots the surface of a unit sphere using a wireframe.\n\nFor data with explicit topology like a set of triangles representing a\npolygonal surface one can use ``mlab.triangular_mesh``. For example::\n\n x, y, z = [[0., 1., 1], [0., 0, 1], [0., 0, 0]]\n t = [[0, 1, 2]]\n mlab.triangular_mesh(x, y, z, t)\n\nHere, the triangles are explicitly specified by referring to indices in the\npoint arrays. Images can also be rendered using ``mlab.imshow``. For example::\n\n s = np.random.random((2<<12, 2<<12))\n mlab.imshow(s)\n\n\nFor three-dimensional volumetric data that is rectilinear one could do::\n\n x, y, z = ogrid[-5:5:64j,-5:5:64j,-5:5:64j]\n mlab.contour3d(x*x*0.5 + y*y + z*z*2)\n\nThus far all the functions we have looked at dealt with scalar fields,\n``mlab`` provides support for a few simple vector visualizations as well. For\nexample::\n\n x, y, z, u, v, w = np.random.random((6, 50))\n mlab.quiver3d(x, y, z, u, v, w)\n\n\nWill plot arrows and works for any collection of points. For more structured\nvolumetric vector fields one can use ``mlab.flow`` which plots streamlines as\nthe following example demonstrates::\n\n x, y, z = mgrid[-2:3, -2:3, -2:3]\n r = sqrt(x**2 + y**2 + z**4)\n u = y*sin(r)/(r+0.001)\n v = -x*sin(r)/(r+0.001)\n w = zeros_like(z)\n obj = mlab.flow(x, y, z, u, v, w, seedtype='plane')\n\nThese basic functions are only a small subset of what Mayavi itself offers.\nThe simpler ``mlab`` functions only support a few limited options. Since\nMayavi is built on top of VTK, it supports structured grids, unstructured\ngrids, volume rendering, and simple tensor field visualization.\n\nFor example, building on the ``mlab.flow`` example above, one could do::\n\n vcp = mlab.pipeline.vector_cut_plane(obj)\n\nand this would generate a cut plane through the vector field and show arrows\nsuitably oriented. Furthermore, one can use the UI to configure a variety of\nparameters very easily. As a slightly more complex example, we can remove the\nvector cut plane, extract the vector norm from the vector field, and show a\nscalar cut plane using just a few lines of code::\n\n vcp.remove()\n scp = mlab.pipeline.scalar_cut_plane(\n mlab.pipeline.extract_vector_norm(obj)\n )\n\nOne could also have done this on the pipeline editor UI by right clicking on\nan appropriate node and choosing one of the Mayavi filters or modules.\n\nIn addition to these functions, there are also several other utility functions\nthat are similar in usage to those available in ``pylab``:\n\n- ``mlab.gcf``\n- ``mlab.savefig``\n- ``mlab.figure``\n- ``mlab.axes``, ``mlab.outline``\n- ``mlab.title``, ``mlab.xlabel``, ``mlab.ylabel``, ``mlab.zlabel``\n- ``mlab.colorbar``, ``mlab.scalarbar``, ``mlab.vectorbar``\n- ``mlab.show``\n- ``mlab.text3d, mlab.orientation_axes``\n- ``mlab.show_pipeline``\n- ``mlab.view, mlab.roll, mlab.yaw, mlab.move``\n\nMore information is available on these in the user guide. There are also\nseveral functions provided in ``mlab`` module (``mlab.test_*``) that provide\nconvenient examples to get started with mlab. Users can look at the source of\nthese functions and execute them to quickly look at useful examples.\n\nSimple Animations\n~~~~~~~~~~~~~~~~~~\n\n``mlab`` makes it easy to perform simple animations, for example::\n\n x, y = np.mgrid[0:3:1, 0:3:1]\n s = mlab.surf(x, y, x*0.1)\n for i in range(10):\n s.mlab_source.scalars = x*0.1*(i + 1)\n\nThe first few lines show a plane with a slight elevation. The ``for`` loop\nautomatically updates the visualization to rotate the plane about the y axis.\nThe ``mlab_source`` attribute is a special attribute that is specific to the\nsimple visualizations produced using ``mlab``. One could also change ``x, y``,\nand ``scalars`` together using the ``s.mlab_source.set`` method.\n\nSometimes, one may wish to change the shape of the data, for example if the\nmesh defining the points itself changes, just calling ``mlab_source.set`` is\nnot enough and one should call ``mlab_source.reset``. Unfortunately, if one\nwere to run this, the visualization would not produce a smooth animation due\nto the GUI toolkit mainloop, ``mlab`` provides a convenient decorator for\nthis, and converting the above into a simple generator facilitates this::\n\n @mlab.animate\n def anim():\n x, y = np.mgrid[0:3:1, 0:3:1]\n s = mlab.surf(x, y, x*0.1)\n for i in range(10):\n s.mlab_source.scalars = x*0.1*(i + 1)\n yield\n\n anim()\n\nThis will interact with the GUI toolkits mainloop smoothly. Automatically\nsaving a screenshot of the animation is also possible in this case, for\nexample::\n\n f = mlab.figure()\n f.scene.movie_maker.record = True\n anim()\n\nWill automatically save each iteration of the for loop into an image located\nin ``~/Documents/mayavi_movies``. This can be configured.\n\nLoading file data\n~~~~~~~~~~~~~~~~~\n\nThese are not the only things one can do with Mayavi, VTK supports reading a\nvariety of different file formats. Mayavi supports a subset of these and any\nsupported file can be opened by simply doing::\n\n src = mlab.pipeline.open('filename.ext')\n\nOnce the data is loaded one may apply a variety of filters and visualization\nmodules to this data with Mayavi. Currently, Mayavi supports over 40 different\nfile formats, these include a variety of image file formats, a variety of\npolygonal files like 3D Studio files, VRML, OBJ, STL, and the various VTK\nfiles.\n\n\nDoing more with automation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nMayavi can be fairly complex given the vast number of options that each\nvisualization can often require. While it is easy to quickly whip up a pretty\nvisualization using the UI, this does not lend itself for automated plots\nwhich are extremely important for scripting and reproducibility. Mayavi\nprovides a very powerful feature called automatic script recording. On the\npipeline dialog, one can click on the red record button shown in the figure\nbelow.\n\n.. figure:: images/record_button.png\n :align: center\n\n Record button for automatic script recording. :label:`fig:record`\n\nWhen one does this, a text window is shown where one can see the Python code\nfor each action performed on the UI. This code is typically executable and can\nbe cut/paste to generate a Python script for the visualization. This is\nconvenient both for automation and also to learn the Mayavi API. One could\nalso turn on the script recording by doing::\n\n mlab.start_recording()\n\nand turn it off using `mlab.stop_recording()`. This also takes a parameter\nthat allows one to save the resulting script to a file.\n\nThese features make Mayavi a powerful visualization tool in the hands of\nnovice and advanced users.\n\n\nPreparing data for visualization: making datasets\n--------------------------------------------------\n\nThe various ``mlab`` commands internally create VTK datasets in order to\nvisualize them. It is important to understand why the notion of \"datasets\" is\nso important in three dimensional data visualization.\n\nAs a simple example, consider the set of points generated by the following code::\n\n phi, theta = np.mgrid[0:pi:20j, 0:2*pi:20j]\n x = np.sin(phi)*np.cos(theta)\n y = np.sin(phi)*np.sin(theta)\n x = np.cos(phi)\n\nOne could think of visualizing these as:\n\n- a collection of points on the surface of a sphere.\n- a set of lines connecting these points that lie on the surface of a sphere.\n- a set of triangles representing the surface of the sphere.\n- a set of tetrahedron that represent the interior of the sphere.\n\nClearly, the points alone do not provide enough information. We need to know\nhow these points are connected and what they form when they are connected.\nThis topological information is what often makes specification of the data for\n3D visualization a bit challenging.\n\nWhen the points are uniformly placed or are mappable to a regular mesh of\npoints it is easy to define the topology implicitly but when the points are\ndisorganized as in the case of the following points::\n\n x, y, z = np.random.random((3, 100))\n\nit is not easy to automatically determine the connectivity and the topology\nmust be explicitly specified.\n\n``mlab`` provides a fair amount of functionality to deal with uniformly spaced\npoints with an implicit topology or for completely disorganized points. For\nsimple cases like a set of lines, and for triangles, ``mlab`` provides\nconvenient functions. For more complex datasets one needs to use the\nlower-level VTK data structures. Mayavi provides a powerful wrapper to the\nunderlying VTK data structures through the ``tvtk`` package. We first go over\nthe basic functions that ``mlab`` provides and then show a few high-level\nexamples of more complex datasets that can be created with ``tvtk``.\n\nGiven a set of points, one can categorize the connectivity between them in the\nfollowing ways as seen before:\n\n- unconnected points.\n- implicit connectivity of points.\n- explicit connectivity of points.\n\n``mlab.pipeline`` provides for each of these using the following functions\nwhich can be used to create datasets of\n\n- unconnected: ``scalar_scatter``, ``vector_scatter``.\n- implicitly connected: ``scalar_field``, ``vector_field, array2d_source``,\n- explicitly connected: ``line_source``, ``triangular_mesh_source``\n\nFor example, if we wished to visualize the data of a set of arbitrary points::\n\n x, y, z, temp = np.random.random((4, 100))\n src = mlab.pipeline.scalar_scatter(x, y, z, temp)\n g = mlab.pipeline.glyph(\n src, scale_mode='none', scale_factor=0.1\n )\n\nThis will plot spheres at each of the points, colored by the temperature. The\n``scalar_scatter`` creates a suitable dataset. When one uses\n``mlab.points3d``, it uses ``scalar_scatter`` internally. Similarly, the other\n``mlab`` functions use different functions to create suitable data sources.\n\nIn addition to these simple datasets, VTK also provides a structured grid and\nan unstructured grid dataset. These cannot be created with ``mlab`` directly\nbut may be created using TVTK. A structured grid is one with an implicit\nordering of points, i.e. the points map to a set of indices ``i, j, k`` and\nthe connectivity is therefore implicit. In an unstructured grid, one must\nexplicitly specify the connectivity. This is similar to the\n``mlab.triangular_mesh`` example we looked at earlier which we recall here::\n\n x, y, z = [[0., 1., 1], [0., 0, 1], [0., 0, 0]]\n t = [[0, 1, 2]]\n mlab.triangular_mesh(x, y, z, t)\n\nNotice that here the triangle is explicitly specified. In a similar fashion\none could create an unstructured grid. Consider the following code::\n\n from tvtk.api import tvtk\n points = array([[0.,0,0], [1,0,0], [0,1,0], [0,0,1]])\n tets = array([[0, 1, 2, 3]])\n tet_type = tvtk.Tetra().cell_type # VTK_TETRA == 10\n\n ug = tvtk.UnstructuredGrid(points=points)\n ug.set_cells(tet_type, tets)\n # Attribute data.\n temperature = array([10, 20 ,20, 30], 'f')\n ug.point_data.scalars = temperature\n ug.point_data.scalars.name = 'temperature'\n\nHere the first four lines define the points and the ``tets`` attribute\nspecifies which points constitute the tetrahedron, and the ``tet_type`` is\nwhat tells VTK what \"cell type\" this data is. The next line constructs the\nunderlying VTK unstructured grid object using a very Python-friendly syntax.\nThis can be extended to specify a large number of tetrahedron or other cell\ntype. This allows a user to create fairly complex datasets and visualize them.\n\nWe can also add vector data to the points using the following::\n\n velocity = array([[0.,0,0], [1,0,0],[0,1,0],[0,0,1]])\n ug.point_data.vectors = velocity\n ug.point_data.vectors.name = 'velocity'\n\nWe can easily visualize this with Mayavi using the following::\n\n src = mlab.pipeline.add_dataset(ug)\n surf = mlab.pipeline.surface(src)\n vec = mlab.pipeline.vectors(src)\n\nProducing the following image.\n\n.. figure:: images/ug.png\n :align: center\n\n Visualization of a single tetrahedron that is part of an unstructured grid.\n The dataset also has scalar and vector attributes associated with the\n points. :label:`fig:ug`\n\n\nIt should be noted that if one only has a set of disorganized points with a\nset of scalars or vectors but without any connectivity information, it is\npossible to build a mesh out of these points. VTK provides a simple 2D and 3D\nDelaunay triangulation algorithm for this. For example let us say we have the\nfollowing points and scalars::\n\n x, y, z = np.random.random((3, 1000))\n s = x*x + y*y + z*z/2\n\nWe could load this data up as a set of scattered points with::\n\n src = mlab.pipeline.scalar_scatter(x, y, z, s)\n\nUnfortunately, since these just represent a set of points, one cannot\nvisualize say, iso-contours of the scalar field. This requires data to be in\nthe form of an unstructured mesh. To do this, one could run a Delaunay 3D\nfilter on the data and visualize iso-contours of the resulting scalar field::\n\n ug = mlab.pipeline.delaunay3d(src)\n iso = mlab.pipeline.iso_surface(ug, contours=8)\n\nThus, one could either setup the datasets manually or use some approach like\nthis to generate a suitable dataset and then visualize it very easily with Mayavi.\n\n\nCustom UIs with Mayavi\n-----------------------\n\nThus far we've explored Mayavi as a library to perform interactive and\nexploratory visualizations. Mayavi can also be used to create custom\napplications for three dimensional visualization. In this section we look at a\ncustom viewer that has been implemented in another open source project.\n\nPySPH_ is an open source framework for Smoothed Particle Hydrodynamics\nsimulations. The SPH method simulates fluid flow and other problems using\nmoving particles. As a result the simulation results are in the form of an\nunstructured collection of particles along with various properties. PySPH\ndumps the particle data in the form of HDF files or as compressed NumPy array\nfiles (``*.npz``). These need to be quickly visualized. The nature of the\nsimulation and data files makes it hard to directly fit into any standard\nvisualization file format. Users typically want to do the following:\n\n- load the saved data and visualize the positions of the particles and the\n various scalar properties.\n- view the evolution of the particles over time.\n- visualize the velocity vectors.\n\nWhile these can be done using the generic Mayavi viewer or using custom\nscripts, it is much more convenient to have a specialized UI for the common\ntasks. PySPH therefore provides a convenient viewer for such data. The UI\nlooks like the following figure.\n\n.. figure:: images/pysph_viewer.png\n :align: center\n\n The customized PySPH viewer. :label:`fig:pysphviewer`\n\nThe entire customized viewer is about 1150 lines of code and supports a\nvariety of features. It is relatively simple to implement a simple viewer for\nsuch data. This is because Mayavi is implemented using the high-level traits_\nand traitsui_ packages. In the following we explore writing a very simple UI\nsimilar to the one provided in PySPH to load the particle data and visualize\nit using Mayavi.\n\n\nLet us first see how to load the data in pysph::\n\n from pysph.solver.utils import load\n data = load('some_output_file.npz')\n fluid = data['arrays']['fluid']\n\nWe can easily visualize the fluid particles with the following code::\n\n mlab.points3d(fluid.x, fluid.y, fluid.z, fluid.p)\n\nThe user may desire to change the scalar to something else. In the above code\n``fluid`` is a particle array.\n\nLet us build a simple UI to just view a single particle array called \"fluid\"\ngiven a file name. We first consider a typical template for creating a GUI\nusing Mayavi and the traitsui_ package.\n\n.. code-block:: python\n\n from traits.api import HasTraits, Instance\n from traitsui.api import View, Item, Group\n from mayavi.core.ui.api import (MlabSceneModel,\n SceneEditor, MayaviScene)\n from mayavi.core.api import PipelineBase\n\n\n class PySPHViewer(HasTraits):\n scene = Instance(MlabSceneModel, ())\n plot = Instance(PipelineBase)\n\n view = View(\n Group(Item(\n 'scene',\n editor=SceneEditor(scene_class=MayaviScene),\n height=400,\n width=400,\n show_label=False\n )),\n resizable=True,\n title='PySPH viewer'\n )\n\n\n viewer = PySPHViewer()\n viewer.configure_traits()\n\nSimply running this, will produce a Mayavi plot window that is similar to one\nproduced using ``mlab.figure()``. However, this placeholder script allows us\nto do a lot more and incrementally add other UI elements.\n\nLet us look at this boilerplate code a little closer. The imports are fairly\nstraightforward and basically import some functionality from traits_,\ntraitsui_ and Mayavi. We have created a class called ``PySPHViewer`` that\nsubclasses ``HasTraits``. We then define a few instance attributes (which are\ncalled *traits*):\n\n- The ``scene`` is an instance of ``MlabSceneModel``, this is a special\n instance that allows us to embed the Mayavi scene in a traits object.\n- ``plot`` is the actual Mayavi object produced by the visualization. This is\n not used in the present code but if we say created a small plot with\n ``mlab.points3d`` say, we could store a reference to the resulting plot in\n this attribute.\n- ``view`` this is a special attribute that declaratively describes how the UI\n should look. The code for the scene is boilerplate and all examples use\n something like the code shown above. A complete explanation is out of scope\n of this article but the ``Item`` specifies that the ``scene`` trait should\n be shown on the UI. The ``editor`` keyword argument specifies the UI element\n to be used and the height and width specify the size of the Mayavi widget,\n the ``show_label`` keyword argument specifies that no label should be shown\n for this item.\n\nThereafter we simply instantiate the ``PySPHViewer`` class and call its\n``configure_traits()`` method.\n\nLet us now add the code to this to load the PySPH data and make a plot. In the\nfollowing code sample, we ignore most of the previous boilerplate for clarity\nand only show the major changes.\n\n.. code-block:: python\n\n # Boilerplate imports ...\n import sys\n import os\n from traits.api import Str\n from pysph.base.particle_array import ParticleArray\n from pysph.solver.utils import load\n\n\n class PySPHViewer(HasTraits):\n # scene, plot traits...\n\n scalar = Str(\"rho\")\n particle_array = Instance(ParticleArray)\n file_name = Str\n\n view = View(\n Group(Item(\n 'scene',\n # ...\n )),\n Item('file_name'),\n resizable=True,\n title='PySPH viewer'\n )\n\n def update_plot(self):\n mlab = self.scene.mlab\n pa = self.particle_array\n if self.plot is None:\n self.plot = mlab.points3d(\n pa.x, pa.y, pa.z,\n getattr(pa, self.scalar),\n mode='point'\n )\n self.plot.actor.property.point_size = 3\n else:\n self.plot.mlab_source.reset(\n x=pa.x, y=pa.y, z=pa.z,\n scalars=getattr(pa, self.scalar)\n )\n\n def _particle_array_changed(self, pa):\n self.update_plot()\n\n def _file_name_changed(self, fname):\n if os.path.exists(fname):\n data = load(fname)\n pa = data['arrays']['fluid']\n self.particle_array = pa\n\n\n viewer = PySPHViewer(file_name=sys.argv[1])\n viewer.configure_traits()\n\nLet us say this script is called ``viewer.py``. When this script is invoked as::\n\n $ python viewer.py elliptical_drop_100.hdf5\n\nIt will produce a UI that shows the data as points. Below the view it also\nshows a simple text box for entering the file name. When this is changed to a\nvalid file, the view automatically updates. A screenshot of this is shown\nbelow:\n\n.. figure:: images/viewer1.png\n :align: center\n\n Simple PySPH viewer. :label:`fig:viewer1`\n\nNote that the top left toolbar icon is the Mayavi logo and clicking that will\nopen up the Mayavi pipeline editor which can still be used to configure the\nvisualization entirely. This is a very powerful and handy feature.\n\nLet us explore the code a little bit to understand it a little better. The\nadditions to the previous code are:\n\n- An import for the ``Str`` from ``traits.api``, additional imports to load\n the pysph file.\n- ``scalar`` is a string that is the particular scalar that we wish to\n visualize. It defaults to \"rho\" which is the density.\n- ``particle_array`` is the PySPH particle array instance we wish to view.\n- ``file_name`` is a string with the name of the file.\n- The view simply has an additional item to view the ``file_name`` trait.\n\n- The visualization code is in ``update_plot`` which is rather straightforward\n code using ``mlab``. The only special thing is that we use\n ``self.scene.mlab`` instead of a raw ``mlab`` in order to ensure that the\n scene is first setup before any mlab commands are drawn. If the\n ``self.plot`` is None, we create a new visualization using ``points3d``,\n otherwise we simply reset the data.\n\nFinally, whenever the ``file_name`` trait is changed, the\n``_file_name_changed`` method is automatically called and if the filename is\nvalid, we try to open it and set the particle_array and each time the\n``particle_array`` trait is changed it calls ``self.update_plot`` and thus\nrefreshes the plot.\n\nThis shows that once one crosses the hurdle of the initial boiler plate code,\nit is relatively easy to quickly build a simple user interface declaratively\nwith the traits_ and traitsui_ package.\n\nIf we wish to add a simple text entry to select an appropriate scalar we can\nmodify the code as follows::\n\n # ...\n\n class PySPHViewer(HasTraits):\n\n view = View(\n # ...\n Item('file_name'),\n Item('scalar'), # Added!\n # ...\n )\n\n def _scalar_changed(self, scalar):\n pa = self.particle_array\n if pa is not None and scalar in pa.properties:\n self.update_plot()\n\n\nThis simply checks if the specified scalar is a valid one and sets it. A nicer\nway to do this would be to only allow certain scalars. This can be done with a\nbit more code as follows::\n\n from traits.api import List\n from traitsui.api import EnumEditor\n\n class PySPHViewer(HasTraits):\n\n scalar_list = List(Str)\n\n view = View(\n # ...\n Item('file_name'),\n Item('scalar',\n editor=EnumEditor(name='scalar_list')),\n # ...\n )\n\n def _particle_array_changed(self, pa):\n self.scalar_list = list(pa.properties.keys())\n self.update_plot()\n\n\nThe resulting UI will show a drop down which automatically has a list of\npossible values to choose from. This shows how it is easy to build a fairly\ncomplex UI with a very small amount of code.\n\nIn similar fashion it is possible to modify the UI to support multiple files\nalong with a slider to switch between these files. This is a very useful\nfeature. Once again, doing this is not particularly difficult and only\nrequires a few more lines of code.\n\nSince traitsui_ supports both Qt and wxPython, the UIs built can actually be\nembedded into any GUI application using these toolkits. Thus, one could embed\nsuch a UI in a more complex application if needed.\n\nWe see that we are able to quickly build customized user interfaces with the\ncomplete power of Mayavi with a minimum amount of code while retaining a very\nPythonic implementation.\n\n\n\n.. _PySPH: https://github.com/pypr/pysph\n\n.. _traits: https://github.com/enthought/traits\n.. _traitsui: https://github.com/enthought/traits\n\n\n\n\nFuture\n------\n\nAs seen in the previous sections, Mayavi can be used in a variety of ways. One\ncan use it to make simple plots, make more sophisticated plots, and also build\ncustomized user interfaces.\n\nMayavi supports very limited support for Jupyter notebooks. One can embed\nMayavi plots as X3D data in a notebook. This allows for the plots to be\nrendered by the browser. This is limited as it does not support all the\ndifferent interactive visualization that Mayavi provides but it does allow a\nuser to view (in 3D) the rendered visualization on a browser. For example, one\ncould run the following code in a notebook cell::\n\n from mayavi import mlab\n mlab.init_notebook()\n\n mlab.test_plot3d()\n\nand this will produce a 3D view of the plot.\n\nWhile mayavi is already very useful. Installation can be difficult since VTK\nis typically fairly hard to install. Traitsui_ only has recently added support\nfor Qt5 making installation with Qt5 a bit difficult.\n\nThese will be improved over the next year. Mayavi does not yet support\nmulti-block datasets, large datasets or parallel rendering. We are looking at\nadding some new features to allow some of these in the future.\n\nConclusions\n-----------\n\nWe have presented the Mayavi package and shown how easy it is to use and\nquickly visualize data. Mayavi provides a powerful, scriptable API, and also\nprovides a full-fledged user interface. In addition, it provides a unique\nfeature of being able to record all UI actions into a readable Python script.\nThis is a feature commonly found only in expensive commercial packages. Mayavi\nalso builds on top of Enthought's ``traits`` and ``traitsui`` packages which\nmake it very easy to build customized user interfaces with a small amount of\ncode. These features make Mayavi a very useful tool for a scientist.\n" }, { "path": "papers/scipy/code/signal/LICENSE.txt", "content": "The data in \"pressure.dat\" was provided by stackoverflow user Nimal Naser at:\n\n https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy\n\nAccording to the stackoverflow website, user contributions are licensed\nunder the CC-BY-SA license:\n\n https://creativecommons.org/licenses/by-sa/3.0/\n\nAs stipulated under that license, this data is provided here under the same\nlicense.\n" }, { "path": "papers/scipy/code/signal/bandpass_batch_example.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import sosfilt\nimport matplotlib.pyplot as plt\nfrom butter import butter_bandpass\nfrom make_data import make_data\n\n\n# Sample rate and desired cutoff frequencies (in Hz).\nfs = 4800.0\nlowcut = 400.0\nhighcut = 1200.0\n\nT = 0.06\nt, x = make_data(T, fs)\n\nsos = butter_bandpass(lowcut, highcut, fs, 12)\n\nbatch_size = 72\n\n# Array of initial conditions for the SOS filter.\nz = np.zeros((sos.shape[0], 2))\n\n# Preallocate space for the filtered signal.\ny = np.empty_like(x)\n\nstart = 0\nwhile start < len(x):\n stop = min(start + batch_size, len(x))\n y[start:stop], z = sosfilt(sos, x[start:stop], zi=z)\n start = stop\n\n\nplt.figure(figsize=(4.0, 3.2))\n\nplt.plot(t, x, 'k', alpha=0.4, linewidth=1, label='Noisy signal')\n\nstart = 0\nalpha = 0.5\nwhile start < len(x):\n stop = min(start + batch_size, len(x))\n if start == batch_size:\n label = 'Filtered signal'\n else:\n label = None\n plt.plot(t[start:stop+1], y[start:stop+1], 'C0', alpha=alpha, label=label)\n alpha = 1.5 - alpha\n start = stop\n\nplt.xlabel('Time (seconds)')\nplt.grid(alpha=0.5)\nplt.axis('tight')\nplt.xlim(0, T)\nplt.legend(framealpha=1, shadow=True, loc='upper left')\nplt.tight_layout()\nplt.savefig(\"bandpass_batch_example.pdf\")\n" }, { "path": "papers/scipy/code/signal/bandpass_example.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import sosfreqz\nimport matplotlib.pyplot as plt\nfrom butter import butter_bandpass, butter_bandpass_filt\nfrom make_data import make_data\n\n\n# Sample rate and desired cutoff frequencies (in Hz).\nfs = 4800.0\nlowcut = 400.0\nhighcut = 1200.0\n\n# Plot the frequency response for a few different orders.\nplt.figure(figsize=(4.0, 4.0))\n# First plot the desired ideal response as a green(ish) rectangle.\nrect = plt.Rectangle((lowcut, 0), highcut - lowcut, 1.0,\n facecolor=\"#60ff60\",\n edgecolor='k',\n alpha=0.15)\nplt.gca().add_patch(rect)\nfor order in [3, 6, 12]:\n sos = butter_bandpass(lowcut, highcut, fs, order)\n w, h = sosfreqz(sos, worN=2000)\n plt.plot((fs*0.5/np.pi)*w, abs(h), 'k',\n alpha=(order+1)/13, label=\"order = %d\" % order)\n\nplt.plot([0, 0.5 * fs], [np.sqrt(0.5), np.sqrt(0.5)],\n 'k--', alpha=0.6, linewidth=1, label=r'$\\sqrt{2}/2$')\nplt.xlim(0, 0.5*fs)\nplt.xlabel('Frequency (Hz)')\nplt.ylabel('Gain')\nplt.grid(alpha=0.5)\nplt.legend(framealpha=1, shadow=True, loc='best')\nplt.title(\"Amplitude response for\\nButterworth bandpass filters\", fontsize=10)\nplt.text(430, 0.07, \"lowcut: %4g Hz\\nhighcut: %4g Hz\" % (lowcut, highcut),\n fontsize=8)\nplt.tight_layout()\nplt.savefig(\"bandpass_example_response.pdf\")\n\nT = 0.03\nt, x = make_data(T, fs)\n\ny = butter_bandpass_filt(x, lowcut, highcut, fs, order=12)\n\nplt.figure(figsize=(4.0, 2.8))\nplt.plot(t, x, 'k', alpha=0.4, linewidth=1, label='Noisy signal')\n\nplt.plot(t, y, 'C0', label='Filtered signal')\nplt.xlabel('Time (seconds)')\nplt.grid(alpha=0.5)\nplt.axis('tight')\nplt.xlim(0, T)\nplt.legend(framealpha=1, shadow=True, loc='upper left')\nplt.tight_layout()\nplt.savefig(\"bandpass_example_signals.pdf\")\n" }, { "path": "papers/scipy/code/signal/butter.py", "content": "from __future__ import division, print_function\n\nfrom scipy.signal import butter, sosfilt, sosfiltfilt\n\n\ndef butter_lowpass(cutoff, fs, order):\n normal_cutoff = cutoff / (0.5*fs)\n sos = butter(order, normal_cutoff,\n btype='low', output='sos')\n return sos\n\n\ndef butter_lowpass_filtfilt(data, cutoff, fs,\n order):\n sos = butter_lowpass(cutoff, fs, order)\n y = sosfiltfilt(sos, data)\n return y\n\n\ndef butter_bandpass(lowcut, highcut, fs, order):\n nyq = 0.5 * fs\n low = lowcut / nyq\n high = highcut / nyq\n sos = butter(order, [low, high], btype='band',\n output='sos')\n return sos\n\n\ndef butter_bandpass_filt(data, lowcut, highcut, fs,\n order):\n sos = butter_bandpass(lowcut, highcut, fs, order)\n y = sosfilt(sos, data)\n return y\n" }, { "path": "papers/scipy/code/signal/firlp_lowpass_example.py", "content": "from __future__ import division, print_function\n\nimport warnings\nimport numpy as np\nfrom scipy.optimize import linprog\nfrom scipy.signal import remez, freqz\nimport matplotlib.pyplot as plt\n\n\ndef solve_linprog(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, tol=None):\n \"\"\"\n Solve the linear programming problem:\n\n minimize\n c.dot(x)\n subject\n A_ub.dot(x) <= b_ub\n A_eq.dot(x) == b_eq\n\n and convert the solution `x` to the FIR filter coefficients.\n\n Warnings generated by `scipy.optimize.linprog` are converted to errors.\n \"\"\"\n if tol is None:\n tol = 1e-6\n\n options_ip = dict(maxiter=5000, tol=tol)\n\n with warnings.catch_warnings():\n warnings.simplefilter('error')\n linprog_warning = None\n try:\n # Note: the interior point method was added in scipy 1.0.0.\n result = linprog(c, A_ub, b_ub, A_eq=A_eq, b_eq=b_eq,\n bounds=(None, None),\n method='interior-point',\n options=options_ip)\n except RuntimeWarning as wrn:\n linprog_warning = wrn\n\n if linprog_warning is not None:\n raise RuntimeWarning('linprog warning converted to error: %s' %\n (linprog_warning.args,))\n\n if not result.success:\n raise RuntimeError('linprog failed: %s' % (result.message,))\n\n # Convert the solution to the linear programming problem to\n # the FIR filter coefficients. If p is, for example, [a, b, c, d],\n # then taps = 0.5*[d, c, b, 2*a, b, c, d].\n p = result.x[:-1]\n taps = 0.5*np.concatenate((p[:0:-1], [2*p[0]], p[1:]))\n return taps\n\n\ndef firlp_lowpass1(numtaps, deltap, deltas, cutoff, width, fs, tol=None):\n # Edges of the transition band, expressed as radians per sample.\n wp = np.pi*(cutoff - 0.5*width)/(0.5*fs)\n ws = np.pi*(cutoff + 0.5*width)/(0.5*fs)\n # Grid density.\n density = 16*numtaps/np.pi\n # Number of grid points in the pass band.\n numfreqs_pass = int(np.ceil(wp*density))\n # Number of grid points in the stop band.\n numfreqs_stop = int(np.ceil((np.pi - ws)*density))\n\n # Grid of frequencies in the pass band.\n wpgrid = np.linspace(0, wp, numfreqs_pass)\n # Remove the first; the inequality associated with this frequency\n # will be replaced by an equality constraint.\n wpgrid = wpgrid[1:]\n # Grid of frequencies in the pass band.\n wsgrid = np.linspace(ws, np.pi, numfreqs_stop)\n\n # wgrid is the combined array of frequencies.\n wgrid = np.concatenate((wpgrid, wsgrid))\n\n # The array of weights in the linear programming problem.\n weights = np.concatenate((np.full_like(wpgrid, fill_value=1/deltap),\n np.full_like(wsgrid, fill_value=1/deltas)))\n # The array of desired frequency responses.\n desired = np.concatenate((np.ones_like(wpgrid),\n np.zeros_like(wsgrid)))\n\n R = (numtaps - 1)//2\n C = np.cos(wgrid[:, np.newaxis] * np.arange(R+1))\n V = 1/weights[:, np.newaxis]\n\n A = np.block([[C, -V], [-C, -V]])\n b = np.block([[desired, -desired]]).T\n c = np.zeros(R+2)\n c[-1] = 1\n\n # The equality constraint corresponding to H(0) = 1.\n A_eq = np.ones((1, R+2))\n A_eq[:, -1] = 0\n b_eq = np.array([1])\n\n print(\"numfreqs_pass =\", numfreqs_pass, \" numfreqs_stop =\", numfreqs_stop)\n\n print(\"R =\", R)\n print(\"c.shape =\", c.shape)\n print(\"A.shape =\", A.shape)\n print(\"b.shape =\", b.shape)\n print(\"A_eq.shape =\", A_eq.shape)\n print(\"b_eq.shape =\", b_eq.shape)\n\n taps_lp = solve_linprog(c, A, b, A_eq=A_eq, b_eq=b_eq, tol=tol)\n return taps_lp\n\n\nfs = 2*np.pi\ncutoff = 0.2*np.pi\nwidth = 0.08*np.pi\n\ndeltap = 0.001\ndeltas = 0.002\n\nnumtaps = 81\nprint(\"numtaps =\", numtaps)\n\ntaps_lp1 = firlp_lowpass1(numtaps, deltap, deltas, cutoff, width, fs, tol=1e-6)\n\n#----------------------------------------------------------------------\n\nplt.figure(figsize=(4.0, 2.5))\n\nnfreqs = max(4000, 96*numtaps)\n\nwlp1, hlp1 = freqz(taps_lp1, worN=nfreqs)\nwlp1 *= 0.5*fs/np.pi\nlabel = 'linear programming, H(0)=1'\nplt.plot(wlp1, np.abs(hlp1), label=label)\n\nbands = np.array([0, cutoff - 0.5*width,\n cutoff + 0.5*width, 0.5*fs])\ndesired = np.array([1, 0])\nweight = np.array([1/deltap, 1/deltas])\ntaps_remez = remez(numtaps, bands, desired, weight=weight, fs=fs, maxiter=1000)\n\nw, h = freqz(taps_remez, worN=nfreqs)\nw *= 0.5*fs/np.pi\nplt.plot(w, np.abs(h), label='remez', ls=(0, (4, 1)))\n\nplt.axvline(cutoff - 0.5*width, color='k', alpha=0.2)\nplt.axvline(cutoff + 0.5*width, color='k', alpha=0.2)\nplt.legend(framealpha=1, shadow=True)\nplt.grid(alpha=0.25)\n\nax = plt.gca()\nax.set_xticks([0, 0.16*np.pi])\nax.set_xticklabels(['0', '0.16$\\pi$'])\nplt.xlim(0, 1.05*(cutoff - 0.5*width))\nplt.ylim(0.9985, 1.0025)\nplt.xlabel('Frequency (radians/sample)')\nplt.ylabel('Gain')\nplt.title('Pass band detail', fontsize=10)\nplt.tight_layout()\n\nplt.savefig('firlp_lowpass_example.pdf')\n" }, { "path": "papers/scipy/code/signal/firls_example.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import firls, freqz\nimport matplotlib.pyplot as plt\n\n\nnumtaps = 43\n\nfs = 200\nf1 = 15\nf2 = 30\n\nbands = np.array([0, f1, f1, f2, f2, 0.5*fs])\ndesired = np.array([1, 1, 1, 0, 0, 0])\ntaps1 = firls(numtaps, bands, desired, fs=fs)\n\nw1, h1 = freqz(taps1, worN=8000)\nw1 *= 0.5*fs/np.pi\n\nwts = [100, .01, 1]\ntaps2 = firls(numtaps, bands, desired, weight=wts, fs=fs)\n\nw2, h2 = freqz(taps2, worN=8000)\nw2 *= 0.5*fs/np.pi\n\n\nplt.figure(figsize=(4.0, 4.5))\n\nplt.subplot(3, 1, 1)\n\nfor band, des in zip(bands.reshape(-1, 2), desired.reshape(-1, 2)):\n plt.plot(band, des, 'k', alpha=0.1, linewidth=4)\nplt.plot(w1, np.abs(h1), alpha=0.9, label='uniform weight')\nplt.plot(w2, np.abs(h2), alpha=0.9, label='weight:%s' % (wts,))\n\nplt.ylim(-0.1, 1.1)\nplt.ylabel('Gain')\nax = plt.gca()\nxticks = np.r_[bands[::2], bands[-1]]\nax.set_xticks(xticks)\nxticklabels = ['%g' % f for f in xticks]\nax.set_xticklabels(xticklabels)\nplt.grid(alpha=0.25)\nplt.legend(framealpha=1, shadow=True)\nplt.title('Least Squares Filter Design', fontsize=10)\n\nplt.subplot(3, 1, 2)\n\nfor band, des in zip(bands.reshape(-1, 2), desired.reshape(-1, 2)):\n plt.plot(band, des, 'k', alpha=0.1, linewidth=4)\nplt.plot(w1, np.abs(h1), alpha=0.9)\nplt.plot(w2, np.abs(h2), alpha=0.9)\n\nax = plt.gca()\nax.set_xticks(xticks)\nax.set_xticklabels(xticklabels)\n\nplt.ylim(0.985, 1.015)\nplt.ylabel('Gain')\nplt.xlim(0, 1.1*f1)\n\nplt.grid(alpha=0.25)\n\nplt.subplot(3, 1, 3)\n\nfor band, des in zip(bands.reshape(-1, 2), desired.reshape(-1, 2)):\n plt.plot(band, des, 'k', alpha=0.1, linewidth=4)\nplt.plot(w1, np.abs(h1), alpha=0.9)\nplt.plot(w2, np.abs(h2), alpha=0.9)\n\nax = plt.gca()\nax.set_xticks(xticks)\nax.set_xticklabels(xticklabels)\n\nplt.ylim(-0.002, 0.02)\nplt.xlim(0.87*f2, 0.5*fs)\nplt.grid(alpha=0.25)\nplt.ylabel('Gain')\n\nplt.xlabel('Frequency (Hz)')\n\nplt.tight_layout()\nplt.savefig('firls_example.pdf')\n" }, { "path": "papers/scipy/code/signal/firwin2_examples.py", "content": "# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import firwin2, freqz, boxcar, hamming, kaiser\nimport matplotlib.pyplot as plt\n\n\nfs = 2000\nfreqs = [0, 48, 60, 72, 150, 175, 1000]\ngains = [1, 1, 0.1, 1, 1, 0, 0]\n\nnumtaps = 185\n\ntaps_none = firwin2(numtaps, freqs, gains, fs=fs, window=None)\ntaps_h = firwin2(numtaps, freqs, gains, fs=fs)\n\nbeta = 2.70\ntaps_k = firwin2(numtaps, freqs, gains, fs=fs, window=('kaiser', beta))\n\nw_none, h_none = freqz(taps_none, 1, worN=2000)\nw_h, h_h = freqz(taps_h, 1, worN=2000)\nw_k, h_k = freqz(taps_k, 1, worN=2000)\n\nplt.figure(figsize=(4.0, 2.8))\n\nwin_boxcar = boxcar(numtaps)\nwin_hamming = hamming(numtaps)\nwin_kaiser = kaiser(numtaps, beta)\n\nplt.plot(win_hamming, label='Hamming')\nplt.plot(win_kaiser, label='Kaiser, $\\\\beta$=%.2f' % beta)\nplt.plot(win_boxcar, label='rectangular')\nplt.xticks([0, (numtaps - 1)//2, numtaps - 1])\nplt.xlabel('Sample number')\nplt.ylim(0, 1.05)\nplt.grid(alpha=0.25)\nplt.title(\"Window functions\", fontsize=10)\nplt.legend(framealpha=1, shadow=True)\nplt.tight_layout()\n\nplt.savefig(\"firwin2_examples_windows.pdf\")\n\nplt.figure(figsize=(4.0, 3.5))\nplt.plot(freqs, gains, 'k--', alpha=0.5, linewidth=1, label='ideal')\n\nplt.plot(0.5*fs*w_h/np.pi, np.abs(h_h), label='Hamming')\nplt.plot(0.5*fs*w_k/np.pi, np.abs(h_k), label='Kaiser, $\\\\beta$=%.2f' % beta)\nplt.plot(0.5*fs*w_none/np.pi, np.abs(h_none), label='rectangular')\nplt.xlim(0, 210)\nplt.xlabel('Frequency (Hz)')\nplt.ylabel('Gain')\nplt.legend(framealpha=1, shadow=True, loc='center right')\nplt.grid(alpha=0.25)\n\nplt.text(9, .08, \"%d taps\" % numtaps, fontsize=9)\n\nplt.title('Filters designed with the window method', fontsize=10)\nplt.tight_layout()\n\nplt.savefig(\"firwin2_examples.pdf\")\n" }, { "path": "papers/scipy/code/signal/initial_conditions.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import butter, sosfilt, sosfilt_zi\nimport matplotlib.pyplot as plt\n\n\nn = 101\nt = np.linspace(0, 1, n)\nnp.random.seed(123)\nx = 0.45 + 0.1*np.random.randn(n)\n\nsos = butter(8, 0.125, output='sos')\n\n# Filter using the default initial conditions.\ny = sosfilt(sos, x)\n\n# Filter using the state for which the output\n# is the constant x[:4].mean() as the initial\n# condition.\nzi = x[:4].mean() * sosfilt_zi(sos)\ny2, zo = sosfilt(sos, x, zi=zi)\n\n# Plot everything.\nplt.figure(figsize=(4.0, 2.8))\nplt.plot(t, x, alpha=0.75, linewidth=1, label='x')\nplt.plot(t, y, label='y (zero ICs)')\nplt.plot(t, y2, label='y2 (mean(x[:4]) ICs)')\n\nplt.legend(framealpha=1, shadow=True)\nplt.grid(alpha=0.25)\nplt.xlabel('t')\nplt.title('Filter with different '\n 'initial conditions',\n fontsize=10)\nplt.tight_layout()\nplt.savefig(\"initial_conditions.pdf\")\n" }, { "path": "papers/scipy/code/signal/kaiser_lowpass_filter_design.py", "content": "# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import kaiserord, firwin, freqz\nimport matplotlib.pyplot as plt\n\n\ndef kaiser_lowpass(delta_db, cutoff, width, fs):\n \"\"\"\n Design a lowpass filter using the Kaiser window method.\n \"\"\"\n # Convert to normalized frequencies\n nyq = 0.5*fs\n cutoff = cutoff / nyq\n width = width / nyq\n\n # Design the parameters for the Kaiser window FIR filter.\n numtaps, beta = kaiserord(delta_db, width)\n numtaps |= 1 # Ensure a Type I FIR filter.\n\n taps = firwin(numtaps, cutoff, window=('kaiser', beta), scale=False)\n\n return taps, beta\n\n\n# User inputs...\n# Values in Hz\nfs = 1000.0\ncutoff = 180.0\nwidth = 30.0\ndeltap = 0.005\ndeltas = 0.002\ndelta = min(deltap, deltas)\nstop_db = -20*np.log10(delta)\n\n# Filter design...\ntaps, beta = kaiser_lowpass(stop_db, cutoff, width, fs)\n\nprint(\"Inputs\")\nprint(\"------\")\nprint(\"fs:\", fs)\nprint(\"cutoff:\", cutoff)\nprint(\"transition band width:\", width)\nprint(\"delta:\", delta, \" (%.3f dB)\" % stop_db)\nprint()\nprint(\"Kaiser design\")\nprint(\"-------------\")\nprint(\"numtaps:\", len(taps))\nprint(\"beta: %.3f\" % beta)\n\n# Compute and plot the frequency response...\nw, h = freqz(taps, worN=8000)\nw *= 0.5*fs/np.pi\n\nplt.figure(figsize=(4.0, 4.6))\n\nplt.subplot(3, 1, 1)\nplt.plot(w, 20*np.log10(np.abs(h)))\nupper_ripple = 20*np.log10(1 + delta)\nlower_ripple = 20*np.log10(1 - delta)\nlower_trans = cutoff - 0.5*width\nupper_trans = cutoff + 0.5*width\n\nplt.plot([0, lower_trans], [upper_ripple, upper_ripple], 'r',\n linewidth=1, alpha=0.4)\nplt.plot([0, lower_trans], [lower_ripple, lower_ripple], 'r',\n linewidth=1, alpha=0.4)\nplt.plot([upper_trans, 0.5*fs], [-stop_db, -stop_db], 'r',\n linewidth=1, alpha=0.4)\nplt.plot([lower_trans, lower_trans], [-stop_db, upper_ripple], color='r',\n linewidth=1, alpha=0.4)\nplt.plot([upper_trans, upper_trans], [-stop_db, upper_ripple], color='r',\n linewidth=1, alpha=0.4)\n\nplt.ylim(-1.8*stop_db, 10)\nplt.ylabel('Gain (dB)')\nplt.title('Kaiser Window Filter Design', fontsize=10)\n\nplt.grid(alpha=0.25)\n\nplt.subplot(3, 1, 2)\nplt.plot(w, np.abs(h))\nplt.plot([0, lower_trans], [1 + delta, 1 + delta], 'r',\n linewidth=1, alpha=0.4)\nplt.plot([0, lower_trans], [1 - delta, 1 - delta], 'r',\n linewidth=1, alpha=0.4)\nplt.plot([upper_trans, 1], [delta, delta], 'r', linewidth=1, alpha=0.4)\nplt.plot([lower_trans, lower_trans], [delta, 1 + delta], color='r',\n linewidth=1, alpha=0.4)\nplt.plot([upper_trans, upper_trans], [delta, 1 + delta], color='r',\n linewidth=1, alpha=0.4)\n\nplt.ylim(1 - 1.5*delta, 1 + 1.5*delta)\nplt.ylabel('Gain')\nplt.xlim(0, cutoff)\nplt.grid(alpha=0.25)\n\nplt.subplot(3, 1, 3)\ndesired = w < cutoff\ndeviation = np.abs(np.abs(h) - desired)\ndeviation[(w >= cutoff-0.5*width) & (w <= cutoff + 0.5*width)] = np.nan\nplt.plot(w, deviation)\nplt.plot([0, 0.5*fs], [deltas, deltas], 'r', linewidth=1, alpha=0.4)\nplt.ylabel(u'|A(ω) - D(ω)|')\nplt.grid(alpha=0.25)\n\nplt.xlabel('Frequency (Hz)')\nplt.tight_layout()\n\nplt.savefig('kaiser_lowpass_filter_design.pdf')\n" }, { "path": "papers/scipy/code/signal/lowpass_design_specs.py", "content": "from __future__ import division, print_function\n\nimport matplotlib.pyplot as plt\n\n\ndeltap = 0.13\ndeltas = 0.15\n\nomegac = 0.4\ndelta_omega = 0.1\n\nplt.figure(figsize=(4.0, 2.5))\n\nplt.plot([0, omegac - 0.5*delta_omega], [1 + deltap, 1 + deltap], 'k',\n linewidth=1)\nplt.plot([0, omegac - 0.5*delta_omega], [1 - deltap, 1 - deltap], 'k',\n linewidth=1)\nplt.plot([0, omegac - 0.5*delta_omega], [1, 1], 'k--', alpha=0.5,\n linewidth=1)\nplt.plot([omegac + 0.5*delta_omega, 1], [deltas, deltas], 'k',\n linewidth=1)\n\n\nplt.xlim(0, 1)\nax = plt.gca()\nax.set_xticks([0, omegac, 1])\nax.set_xticklabels(['0', r'$\\omega_c$', r'$\\pi$'])\nax.set_yticks([0, deltas, 1-deltap, 1, 1+deltap])\nax.set_yticklabels(['0', r'$\\delta_s$', r'$1 - \\delta_p$', '1',\n r'$1 + \\delta_p$'])\nplt.ylim(0, 1.2)\n\nplt.axvline(omegac - 0.5*delta_omega, color='k', linewidth=1, alpha=0.15)\nplt.axvline(omegac + 0.5*delta_omega, color='k', linewidth=1, alpha=0.15)\n\nrect = plt.Rectangle((0, 1 + deltap),\n omegac - 0.5*delta_omega, 0.1,\n facecolor='k',\n edgecolor='k',\n alpha=0.15)\nax.add_patch(rect)\nrect = plt.Rectangle((0, 1 - deltap),\n omegac - 0.5*delta_omega, -1,\n facecolor='k',\n edgecolor='k',\n alpha=0.15)\nax.add_patch(rect)\nrect = plt.Rectangle((omegac + 0.5*delta_omega, deltas),\n 1 - omegac - 0.5*delta_omega, 1.2,\n facecolor='k',\n edgecolor='k',\n alpha=0.15)\nax.add_patch(rect)\n\nplt.annotate(r'$\\Delta\\omega$', (omegac+0.5*delta_omega, 0.5),\n xytext=(15, 0),\n textcoords='offset points',\n va='center', ha='left',\n arrowprops=dict(arrowstyle='->'))\nplt.annotate('', (omegac-0.5*delta_omega, 0.5),\n xytext=(-15, 0),\n textcoords='offset points',\n va='center', ha='right',\n arrowprops=dict(arrowstyle='->'))\n\nplt.xlabel('Frequency (radians per sample)')\nplt.ylabel(r'$|H(e^{j\\omega})|$')\nplt.title('Lowpass Filter Design Specifications', fontsize=10)\nplt.tight_layout()\nplt.savefig(\"lowpass_design_specs.pdf\")\n" }, { "path": "papers/scipy/code/signal/make_data.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\n\n\ndef make_data(T, fs):\n nsamples = int(T * fs)\n t = np.linspace(0, T, nsamples, endpoint=False)\n a = 0.025\n f0 = 550.0\n x = 0.07 * np.sin(2 * np.pi * 1.2 * np.sqrt(t))\n x += 0.01 * np.cos(2 * np.pi * 312 * t + 0.1)\n x += a * np.cos(2 * np.pi * f0 * t + .11)\n x += 0.03 * np.cos(2 * np.pi * 2000 * t)\n return t, x\n" }, { "path": "papers/scipy/code/signal/moving_avg_freq_response.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import freqz\nimport matplotlib.pyplot as plt\n\n\nplt.figure(figsize=(4.0, 3.0))\n\nfor n in [3, 7, 21]:\n taps = np.full(n, fill_value=1.0/n)\n w, h = freqz(taps, worN=2000)\n plt.plot(w, abs(h), label=\"n = %d\" % n)\n\nplt.xlim(0, np.pi)\nplt.xlabel('Frequency (radians/sample)')\nplt.ylabel('Gain')\nxaxis = plt.gca().xaxis\nxaxis.set_ticks([0, 0.25*np.pi, 0.5*np.pi, 0.75*np.pi, np.pi])\nxaxis.set_ticklabels(['0', r'$\\frac{\\pi}{4}$', r'$\\frac{\\pi}{2}$',\n r'$\\frac{3\\pi}{4}$', r'$\\pi$'])\nplt.grid(alpha=0.5)\nplt.legend(framealpha=1, shadow=True, loc='best')\nplt.title(\"Amplitude Response for\\nMoving Average Filter\", fontsize=10)\nplt.tight_layout()\nplt.savefig(\"moving_avg_freq_response.pdf\")\n" }, { "path": "papers/scipy/code/signal/opt_lowpass.py", "content": "# -*- coding: utf-8 -*-\n\nfrom __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import remez, freqz\nimport matplotlib.pyplot as plt\n\n\ndef bellanger_estimate(deltap, deltas, width, fs):\n \"\"\"\n Estimate the number of taps required for the given filter specifications.\n \"\"\"\n n = (-2/3)*np.log10(10*deltap*deltas)*fs/width\n n = int(np.ceil(n))\n return n\n\n\ndef remez_lowpass(deltap, deltas, cutoff, width, fs):\n\n numtaps = bellanger_estimate(deltap, deltas, width, fs)\n numtaps |= 1 # Bitwise OR with 1 to ensure an odd number of taps.\n trans_lo = cutoff - 0.5*width\n trans_hi = cutoff + 0.5*width\n taps = remez(numtaps,\n bands=[0, trans_lo, trans_hi, 0.5*fs],\n desired=[1, 0],\n weight=[1/deltap, 1/deltas],\n fs=fs)\n return taps\n\n\n#---------------------------------------\n# User inputs...\n\n# Frequency values in Hz\nfs = 1000.0\ncutoff = 180.0\nwidth = 30.0\n# Desired pass band ripple and stop band attenuation\ndeltap = 0.005\ndeltas = 0.002\n\nprint(u\"Pass band: 1 ± %g ([%.3g, %.3g] dB)\" %\n (deltap, 20*np.log10(1 - deltap), 20*np.log10(1 + deltap)))\nprint(\"Stop band rejection: %g (%.3g dB)\" % (deltas, -20*np.log10(deltas),))\n\n#---------------------------------------\n# Design the filter...\n\ntaps = remez_lowpass(deltap, deltas, cutoff, width, fs)\n\n#----------------------------------------\n# Plot the frequency response...\n\nupper_ripple_db = 20*np.log10(1 + deltap)\nlower_ripple_db = 20*np.log10(1 - deltap)\nstop_db = -20*np.log10(deltas)\n\nprint(\"Inputs\")\nprint(\"------\")\nprint(\"fs:\", fs)\nprint(\"cutoff:\", cutoff)\nprint(\"transition band width:\", width)\nprint(\"deltap:\", deltap, \" (%.3f dB)\" % (-20*np.log10(deltap),))\nprint(\"deltas:\", deltas, \" (%.3f dB)\" % (-20*np.log10(deltas),))\nprint()\nprint(\"Design\")\nprint(\"------\")\nprint(\"numtaps:\", len(taps))\n\nw, h = freqz(taps, worN=8000)\nw *= 0.5*fs/np.pi\n\ncutoff_lower_trans = cutoff - 0.5*width\ncutoff_upper_trans = cutoff + 0.5*width\n\n\nplt.figure(figsize=(4.0, 4.6))\nplt.subplot(3, 1, 1)\n\nplt.plot(w, 20*np.log10(np.abs(h)))\n\nplt.plot([0, cutoff_lower_trans], [upper_ripple_db, upper_ripple_db], 'r',\n alpha=0.4)\nplt.plot([0, cutoff_lower_trans], [lower_ripple_db, lower_ripple_db], 'r',\n alpha=0.4)\n\nplt.plot([cutoff_upper_trans, 0.5*fs], [-stop_db, -stop_db], 'r', alpha=0.4)\n\nplt.axvline(cutoff_lower_trans, color='k', alpha=0.4, linewidth=1)\nplt.axvline(cutoff_upper_trans, color='k', alpha=0.4, linewidth=1)\n\nwidthstr = '%g Hz' % width\nif cutoff < 0.25*fs:\n lefttext = ''\n righttext = widthstr\nelse:\n lefttext = widthstr\n righttext = ''\nplt.annotate(righttext, (cutoff_upper_trans, -0.5*stop_db),\n xytext=(18, 0),\n textcoords='offset points',\n va='center', ha='left',\n arrowprops=dict(arrowstyle='->'))\nplt.annotate(lefttext, (cutoff_lower_trans, -0.5*stop_db),\n xytext=(-18, 0),\n textcoords='offset points',\n va='center', ha='right',\n arrowprops=dict(arrowstyle='->'))\n\n\nplt.ylim(-1.25*stop_db, 10)\nplt.grid(alpha=0.25)\nplt.ylabel('Gain (dB)')\nplt.title(\"Lowpass Filter\\nOptimal Remez Design\",\n fontsize=10)\n\n\nplt.subplot(3, 1, 2)\n\nplt.plot(w, np.abs(h))\n\nplt.plot([0, cutoff_lower_trans], [1 + deltap, 1 + deltap], 'r', alpha=0.4)\nplt.plot([0, cutoff_lower_trans], [1 - deltap, 1 - deltap], 'r', alpha=0.4)\n\nplt.plot([cutoff_upper_trans, 0.5*fs], [deltas, deltas], 'r', alpha=0.4)\n\nplt.axvline(cutoff_lower_trans, color='k', alpha=0.4, linewidth=1)\nplt.axvline(cutoff_upper_trans, color='k', alpha=0.4, linewidth=1)\n\nplt.xlim(0, (cutoff + 0.6*width))\nplt.ylim(1 - 1.6*deltap, 1 + 1.6*deltap)\n\nplt.ylabel('Gain')\n\nplt.grid(alpha=0.25)\n\nplt.subplot(3, 1, 3)\ndesired = w < cutoff\ndeviation = np.abs(np.abs(h) - desired)\ndeviation[(w >= cutoff-0.5*width) & (w <= cutoff + 0.5*width)] = np.nan\nplt.plot(w, deviation)\nplt.plot([0, cutoff - 0.5*width], [deltap, deltap], 'r',\n linewidth=1, alpha=0.4)\nplt.plot([cutoff + 0.5*width, 0.5*fs], [deltas, deltas], 'r',\n linewidth=1, alpha=0.4)\nplt.ylabel(u'|A(ω) - D(ω)|')\nplt.grid(alpha=0.25)\n\nplt.xlabel('Frequency (Hz)')\n\nplt.tight_layout()\nplt.savefig(\"opt_lowpass.pdf\")\n" }, { "path": "papers/scipy/code/signal/pressure_example.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.signal import spectrogram\nfrom butter import butter_lowpass_filtfilt\n\n\ntime, pressure = np.loadtxt('pressure.dat', skiprows=2,\n delimiter=',', unpack=True)\n\nt0 = time.min()\nt1 = time.max()\n\nfs = 50000\n\nnperseg = 80\nnoverlap = nperseg - 4\n\nprint(\"Spectrogram window length: %g ms (%d samples)\" %\n (1000*nperseg/fs, nperseg))\n\nf, t, spec = spectrogram(pressure, fs=fs, nperseg=nperseg, noverlap=noverlap,\n window='hann')\nt += t0/1000\n\ncutoff = 1250\npressure_filtered = butter_lowpass_filtfilt(pressure, cutoff, fs, order=8)\n\nf, t, filteredspec = spectrogram(pressure_filtered, fs=fs, nperseg=nperseg,\n noverlap=noverlap, window='hann')\nt += t0/1000\n\ntlo = t[0]*1000\nthi = t[-1]*1000\n\nspec_db = 10*np.log10(spec)\nfilteredspec_db = 10*np.log10(filteredspec)\n\nvmax = max(spec_db.max(), filteredspec_db.max())\nvmin = vmax - 80.0 # Clip display of values below 80 dB\n\nlinecolor = 'k'\ncmap = plt.cm.coolwarm\n\nplt.figure(figsize=(4.0, 4.5))\nplt.subplot(211)\nplt.plot(time, pressure, color=linecolor, alpha=0.8)\nplt.xlim(tlo, thi)\nplt.ylim(0, 10)\nplt.ylabel(\"Pressure (MPa)\")\nplt.grid(alpha=0.25)\n\nplt.subplot(212)\nplt.pcolormesh(1000*t, f/1000, spec_db, vmin=vmin, vmax=vmax,\n cmap=cmap, shading='gouraud')\nplt.xlim(tlo, thi)\nplt.ylabel('Frequency (kHz)')\nplt.xlabel('Time (ms)')\nplt.tight_layout()\nplt.savefig(\"pressure_example_input.pdf\")\n\nplt.figure(2, figsize=(4.0, 4.5))\nplt.subplot(211)\nplt.plot(time, pressure, color=linecolor, alpha=0.15)\nplt.plot(time, pressure_filtered, color=linecolor, alpha=0.8)\nplt.xlim(tlo, thi)\nplt.ylim(0, 10)\nplt.ylabel(\"Pressure (MPa)\")\nplt.grid(alpha=0.25)\n\nplt.subplot(212)\nplt.pcolormesh(1000*t, f/1000, filteredspec_db, vmin=vmin, vmax=vmax,\n cmap=cmap, shading='gouraud')\nplt.axhline(cutoff/1000, color='k', linestyle='--', linewidth=1, alpha=0.5)\nplt.xlim(tlo, thi)\nplt.ylabel('Frequency (kHz)')\nplt.xlabel('Time (ms)')\nplt.tight_layout()\nplt.savefig(\"pressure_example_filtered.pdf\")\n" }, { "path": "papers/scipy/code/signal/remez_example.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import remez, freqz\nimport matplotlib.pyplot as plt\n\n\nfs = 2000\n\nbands = [0, 250, 350, 550, 700, 0.5*fs]\ndesired = [0, 1, 0]\nweights = [1, 1, 1]\n\nfor numtaps in [31, 47]:\n\n taps = remez(numtaps, bands, desired, fs=fs)\n\n w, h = freqz(taps, worN=8000)\n w *= 0.5*fs/np.pi\n\n weights = [1, 25, 1]\n taps2 = remez(numtaps, bands, desired, weights, fs=fs)\n\n w2, h2 = freqz(taps2, worN=8000)\n w2 *= 0.5*fs/np.pi\n\n plt.figure(figsize=(4.0, 3.0))\n\n plt.plot(w, np.abs(h), linewidth=1, label='(a)')\n plt.plot(w2, np.abs(h2), linewidth=1, label='(b)')\n\n rect = plt.Rectangle((bands[1], 0), bands[2]-bands[1], 1.0,\n facecolor='k',\n edgecolor='k',\n alpha=0.075)\n plt.gca().add_patch(rect)\n rect = plt.Rectangle((bands[3], 0), bands[4]-bands[3], 1.0,\n facecolor='k',\n edgecolor='k',\n alpha=0.075)\n plt.gca().add_patch(rect)\n\n plt.text(10, .9, \"%d taps\" % numtaps, fontsize=9)\n\n plt.grid(alpha=0.25)\n plt.legend(loc=\"center right\", framealpha=1, shadow=True)\n plt.xlabel('Frequency (Hz)')\n plt.ylabel('Gain')\n plt.title(\"Bandpass filters designed with remez\", fontsize=10)\n plt.tight_layout()\n\n plt.savefig(\"remez_example_%dtaps.pdf\" % numtaps)\n" }, { "path": "papers/scipy/code/signal/sos_bandpass_response.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import butter, sosfreqz, sosfilt\nimport matplotlib.pyplot as plt\n\n\nsos = butter(10, [0.04, 0.16],\n btype=\"bandpass\", output=\"sos\")\nw, h = sosfreqz(sos, worN=8000)\n\n# Plot the magnitude and phase of the frequency response.\nplt.figure(figsize=(4.0, 4.0))\nplt.subplot(211)\nplt.plot(w/np.pi, np.abs(h))\nplt.grid(alpha=0.25)\nplt.ylabel('Gain')\nplt.subplot(212)\nplt.plot(w/np.pi, np.angle(h))\nyaxis = plt.gca().yaxis\nyaxis.set_ticks([-np.pi, -0.5*np.pi, 0, 0.5*np.pi, np.pi])\nyaxis.set_ticklabels([r'$-\\pi$', r'$-\\pi/2$', '0', r'$\\pi/2$', r'$\\pi$'])\nplt.xlabel('Normalized frequency')\nplt.grid(alpha=0.25)\nplt.ylabel('Phase')\nplt.tight_layout()\nplt.savefig(\"sos_bandpass_response_freq.pdf\")\n\n# Plot the step response.\nx = np.ones(200)\ny = sosfilt(sos, x)\n\nplt.figure(figsize=(4.0, 2.0))\nplt.plot(y)\nplt.grid(alpha=0.25)\nplt.xlabel('Sample number')\nplt.tight_layout()\nplt.savefig(\"sos_bandpass_response_step.pdf\")\n" }, { "path": "papers/scipy/code/signal/unstable_butterworth.py", "content": "from __future__ import division, print_function\n\nimport numpy as np\nfrom scipy.signal import butter, lfilter\nimport matplotlib.pyplot as plt\n\n\nb, a = butter(10, [0.04, 0.16], btype=\"bandpass\")\nx = np.ones(125)\ny = lfilter(b, a, x)\n\nplt.figure(figsize=(4.0, 2.0))\nplt.plot(y)\nplt.xlabel('Sample number')\nplt.grid(alpha=0.25)\nplt.tight_layout()\nplt.savefig('unstable_butterworth.pdf')\n" }, { "path": "papers/scipy/scipy.rst", "content": ":author: Warren Weckesser\n:email: warren.weckesser@gmail.com\n\n..\n Typography question: \"lowpass\", \"low-pass\" or \"low pass\"?\n I (WW) will follow the convention used in the two books that I happen\n to have handy (Oppenheim and Schafer, \"Discrete-Time Signal Processing\",\n and Richard G. Lyons, \"Understanding Digital Signal Processing\"), and will\n use \"lowpass\", \"highpass\" and \"bandpass\" when discussing filters. I don't\n really have a strong preference, but it will save some copy-editing later\n if we agree on the convention now.\n\n..\n Some LaTeX typography comments:\n I (WW) find LaTeX's default size for subscripts is too big. That why\n I write, for example, `a_{_N}` instead of just `a_N`. If you leave it\n as `a_N`, then in a formula such as `a_N z`, the N is practically the\n same size as and side-by-side with the z. Using `a_{_N}` makes it\n very clear that N is a subscript of a.\n\n--------------------------------------------\nSignal Processing with SciPy: Linear Filters\n--------------------------------------------\n\n.. class:: abstract\n\nThe SciPy_ library is one of the core packages of the PyData stack. It\nincludes modules for statistics, optimization, interpolation, integration,\nlinear algebra, Fourier transforms, signal and image processing, ODE solvers,\nspecial functions, sparse matrices, and more.\nIn this chapter, we demonstrate many of the tools provided by the ``signal``\nsubpackage of the SciPy library for the design and analysis of linear\nfilters for discrete-time signals, including filter representation,\nfrequency response computation and optimal FIR filter design.\n\n\n\n.. _SciPy: http://scipy.org/scipylib/index.html\n\n.. class:: keywords\n\nalgorithms, signal processing, IIR filter, FIR filter\n\n.. contents::\n\nIntroduction\n============\nThe SciPy_ library, created in 2001, is one of the core packages of the\nPyData stack. It includes modules for statistics, optimization,\ninterpolation, integration, linear algebra, Fourier transforms, signal\nand image processing, ODE solvers, special functions, sparse matrices,\nand more.\n\nThe ``signal`` subpackage within the SciPy library includes tools\nfor several areas of computation, including signal processing, interpolation,\nlinear systems analysis and even some elementary image processing. In this\nCookbook chapter, we focus on a specific subset of the capabilities of\nof this subpackage: the design and analysis of linear filters for discrete-time\nsignals. These filters are commonly used in digital signal processing (DSP)\nfor audio signals and other time series data with a fixed sample rate.\n\nThe chapter is split into two main parts, covering\nthe two broad categories of linear filters: *infinite impulse\nresponse* (IIR) filters and *finite impulse response* (FIR) filters.\n\n*Note.* We will display some code samples as transcripts from the standard Python\ninteractive shell. In each interactive Python session, we will have executed\nthe following without showing it::\n\n >>> import numpy as np\n >>> import matplotlib.pyplot as plt\n >>> np.set_printoptions(precision=3, linewidth=50)\n\nAlso, when we show the creation of plots in the transcripts, we won't\nshow all the other plot commands (setting labels, grid lines, etc.)\nthat were actually used to create the corresponding figure in the paper.\nComplete scripts for all the figures in this paper are available\nin the ``papers/scipy`` directory at ``https://github.com/pydata/pydata-cookbook/``.\n\nIIR filters in ``scipy.signal``\n===============================\n\nAn IIR filter can be written as a linear recurrence relation, in which the\noutput :math:`y_{_n}` is a linear combination of :math:`x_{_n}`, the `M` previous\nvalues of :math:`x` and the `N` previous values of :math:`y`:\n\n.. math::\n :label: eq-filter-recurrence\n\n a_{_0} y_{_n} = \\sum_{i=0}^{M} b_{_i}x_{_{n-i}} -\n \\sum_{i=1}^{N} a_{_i} y_{_{n-N}} \n\n(See, for example, Oppenheim and Schafer [OS]_, Chapter 6.\nNote, however, that the sign convention for ``a[1], ..., a[N]``\nin Eqn. (:ref:`eq-filter-recurrence`) and used in SciPy is the opposite\nof that used in Oppenheim and Schafer.)\n\nBy taking the z-transform of Eqn. (:ref:`eq-filter-recurrence`),\nwe can express the filter as\n\n.. math::\n :label: eq-z-transform\n\n Y(z) = H(z)X(z)\n\nwhere\n\n.. math::\n :label: eq-transfer-function\n\n H(z) = \\frac{b_{_0} + b_{_1} z^{-1} + \\cdots + b_{_M} z^{-M}}\n {a_{_0} + a_{_1} z^{-1} + \\cdots + a_{_N} z^{-N}}\n\nis the *transfer function* associated with the filter.\nThe functions in SciPy that create filters generally set\n:math:`a_{_0} = 1`.\n\nEqn. (:ref:`eq-filter-recurrence`) is also known as an ARMA(N, M)\nprocess, where \"ARMA\" stands for *Auto-Regressive Moving Average*.\n:math:`b` holds the moving average coefficients, and :math:`a` holds the\nauto-regressive coefficients.\n\nWhen :math:`a_{_1} = a_{_2} = \\cdots = a_{_N} = 0`, the filter\nis a finite impulse response filter. We will discuss those later.\n\nIIR filter representation\n-------------------------\n\nIn this section, we discuss three representations of a linear filter:\n\n* transfer function\n* zeros, poles, gain (ZPK)\n* second order sections (SOS)\n\nSciPy also provides a state space representation,\nbut we won't discuss that format here.\n\n**Transfer function.**\nThe transfer function representation of\na filter in SciPy is the most direct representation of the data in\nEqn. (:ref:`eq-filter-recurrence`) or (:ref:`eq-transfer-function`).\nIt is two one-dimensional arrays, conventionally\ncalled ``b`` and ``a``, that hold the coefficients of the polynomials\nin the numerator and denominator, respectively, of the transfer function\n:math:`H(z)`.\n\nFor example, we can use the function ``scipy.signal.butter`` to\ncreate a Butterworth lowpass filter of order 6 with a normalized\ncutoff frequency of 1/8 the Nyquist frequency. The default representation\ncreated by ``butter`` is the transfer function, so we can use\n``butter(6, 0.125)``::\n\n >>> from scipy.signal import butter\n >>> b, a = butter(6, 0.125)\n >>> b\n array([ 2.883e-05, 1.730e-04, 4.324e-04,\n 5.765e-04, 4.324e-04, 1.730e-04,\n 2.883e-05])\n >>> a\n array([ 1. , -4.485, 8.529, -8.779, 5.148,\n -1.628, 0.217])\n\nThe representation of a filter as a transfer function with coefficients\n``(b, a)`` is convenient and of theoretical importance, but with finite\nprecision floating point, applying an IIR filter of even moderately\nlarge order using this format is susceptible to instability from numerical\nerrors. Problems can arise when designing a filter of high order, or a\nfilter with very narrow pass or stop bands.\n\n\n**ZPK.**\nThe ZPK representation consists of a tuple containing three\nitems, ``(z, p, k)``. The first two items, ``z`` and ``p``, are\none-dimensional arrays containing the zeros and poles, respectively,\nof the transfer function. The third item, ``k``, is a scalar that holds\nthe overall gain of the filter.\n\nWe can tell ``butter`` to create a filter using the ZPK representation\nby using the argument ``output=\"zpk\"``::\n\n >>> z, p, k = butter(6, 0.125, output='zpk')\n >>> z\n array([-1., -1., -1., -1., -1., -1.])\n >>> p\n array([ 0.841+0.336j, 0.727+0.213j,\n 0.675+0.072j, 0.675-0.072j,\n 0.727-0.213j, 0.841-0.336j])\n >>> k\n 2.8825891944002783e-05\n\nA limitation of the ZPK representation of a filter is that SciPy does\nnot provide functions that can directly apply the filter to a signal.\nThe ZPK representation must be converted to either the SOS format or the\ntransfer function format to actually filter a signal. If we are designing\na filter using ``butter`` or one of the other filter design functions,\nwe might as well create the filter in the transfer function or SOS format\nwhen the filter is created.\n\n**SOS.**\nIn the *second order sections (SOS)* representation, the filter is represented\nusing one or more cascaded second order filters (also known as \"biquads\").\nThe SOS representation is implemented as an array with shape (n, 6),\nwhere each row holds the coefficients of a second order transfer function.\nThe first three items in a row are the coefficients of the numerator of the\nbiquad's transfer function, and the second three items are the coefficients\nof the denominator.\n\nThe SOS format for an IIR filter is more numerically stable than the\ntransfer function format, so it should be preferred when using filters\nwith orders beyond, say, 7 or 8, or when the bandwidth of the passband\nof a filter is sufficiently small. (Unfortunately, we don't have a precise\nspecification for what \"sufficiently small\" is.)\n\nA disadvantage of the SOS format is that the function ``sosfilt`` (at\nleast at the time of this writing) applies an SOS filter by making\nmultiple passes over the data, once for each second order section.\nSome tests with an order 8 filter show that\n``sosfilt(sos, x)`` can require more than twice the time of\n``lfilter(b, a, x)``.\n\nHere we create a Butterworth filter using the SOS representation::\n\n >>> sos = butter(6, 0.125, output=\"sos\")\n >>> sos\n array([[ 2.883e-05, 5.765e-05, 2.883e-05,\n 1.000e+00, -1.349e+00, 4.602e-01],\n [ 1.000e+00, 2.000e+00, 1.000e+00,\n 1.000e+00, -1.454e+00, 5.741e-01],\n [ 1.000e+00, 2.000e+00, 1.000e+00,\n 1.000e+00, -1.681e+00, 8.198e-01]])\n\nThe array ``sos`` has shape (3, 6). Each row represents a biquad;\nfor example, the transfer function of the biquad stored in the last row is\n\n.. math::\n\n H(z) = \\frac{1 + 2z^{-1} + z^{-2}}{1 - 1.681 z^{-1} + 0.8198 z^{-2}}\n\n**Converting between representations.**\nThe ``signal`` module provides a collection of functions for\nconverting one representation to another::\n\n sos2tf, sos2zpk, ss2tf, ss2zpk,\n tf2sos, tf2zz, tf2zpk, zpk2sos, zpk2ss, zpk2tf \n\nFor example, ``zpk2sos``\nconverts from the ZPK representation to the SOS representation.\nIn the following, ``z``, ``p`` and ``k`` have the values defined earlier::\n\n >>> from scipy.signal import zpk2sos\n >>> zpk2sos(z, p, k) \n array([[ 2.883e-05, 5.765e-05, 2.883e-05,\n 1.000e+00, -1.349e+00, 4.602e-01],\n [ 1.000e+00, 2.000e+00, 1.000e+00,\n 1.000e+00, -1.454e+00, 5.741e-01],\n [ 1.000e+00, 2.000e+00, 1.000e+00,\n 1.000e+00, -1.681e+00, 8.198e-01]])\n\n\n**Limitations of the transfer function representation.**\nEarlier we said that the transfer function representation of\nmoderate to large order IIR filters can result in numerical problems.\nHere we show an example.\n\nWe consider the design of a Butterworth bandpass filter\nwith order 10 with normalized pass band cutoff frequencies of 0.04\nand 0.16.::\n\n >>> b, a = butter(10, [0.04, 0.16], btype=\"bandpass\")\n\nWe can compute the step response of this filter by applying it to\nan array of ones::\n\n >>> x = np.ones(125)\n >>> y = lfilter(b, a, x)\n >>> plt.plot(y)\n\nThe plot is shown in Figure :ref:`fig-unstable-butterworth`.\nClearly something is going wrong.\n\n.. figure:: figs/unstable_butterworth.pdf\n\n Incorrect step response of the Butterworth bandpass filter of order\n 10 created using the transfer function representation. Apparently the\n filter is unstable--something has gone wrong with this representation.\n :label:`fig-unstable-butterworth`\n\nWe can try to determine the problem by checking the poles\nof the filter::\n\n >>> z, p, k = tf2zpk(b, a)\n >>> np.abs(p)\n array([ 0.955, 0.955, 1.093, 1.093, 1.101,\n 1.052, 1.052, 0.879, 0.879, 0.969,\n 0.969, 0.836, 0.836, 0.788, 0.788,\n 0.744, 0.744, 0.725, 0.725, 0.723])\n\nThe filter should have all poles inside the unit circle in the complex\nplane, but in this case five of the poles have magnitude greater than 1.\nThis indicates a problem, which could be in the\nresult returned by ``butter``, or in the conversion done\nby ``tf2zpk``. The plot shown in Figure :ref:`fig-unstable-butterworth`\nmakes clear that *something* is wrong with the coefficients in\n``b`` and ``a``.\n\nLet's design the same 10th order Butterworth filter as above,\nbut in the SOS format::\n\n >>> sos = butter(10, [0.04, 0.16],\n ... btype=\"bandpass\", output=\"sos\")\n\nIn this case, all the poles are within the unit circle::\n\n >>> z, p, k = sos2zpk(sos)\n >>> np.abs(p)\n array([ 0.788, 0.788, 0.8 , 0.8 , 0.818,\n 0.818, 0.854, 0.854, 0.877, 0.877,\n 0.903, 0.903, 0.936, 0.936, 0.955,\n 0.955, 0.964, 0.964, 0.988, 0.988])\n\nWe can check the frequency response using ``scipy.signal.sosfreqz``::\n\n >>> w, h = sosfreqz(sos, worN=8000)\n >>> plt.plot(w/np.pi, np.abs(h))\n []\n\nThe plot is shown in Figure :ref:`fig-sos-bandpass-response-freq`.\n\n.. figure:: figs/sos_bandpass_response_freq.pdf\n\n Frequency response of the Butterworth bandpass filter with\n order 10 and normalized cutoff frequencies 0.04 and 0.16.\n :label:`fig-sos-bandpass-response-freq`\n\nAs above, we compute the step response by filtering an array of ones::\n\n >>> x = np.ones(200)\n >>> y = sosfilt(sos, x)\n >>> plt.plot(y)\n\nThe plot is shown in Figure :ref:`fig-sos-bandpass-response-step`.\nWith the SOS representation, the filter behaves as expected.\n\n.. figure:: figs/sos_bandpass_response_step.pdf\n\n Step response of the Butterworth bandpass filter with\n order 10 and normalized cutoff frequencies 0.04 and 0.16.\n :label:`fig-sos-bandpass-response-step`\n\n\n\n\nIn the remaining examples of IIR filtering, we will use only the\nSOS representation.\n\nLowpass filter\n--------------\n\nFigure :ref:`fig-pressure-example-input` shows a times series containing\npressure measurements [SO]_. At some point in the interval 20 < t < 22,\nan event occurs in which the pressure jumps and begins oscillating\naround a \"center\". The center of the oscillation decreases and\nappears to level off.\n\n.. figure:: figs/pressure_example_input.pdf\n\n *Top*: Pressure, for the interval 15 < t < 35 (milliseconds).\n *Bottom*: Spectrogram of the pressure time series (generated using a\n window size of 1.6 milliseconds).\n :label:`fig-pressure-example-input`\n\nWe are not interested in the oscillations, but we are interested in the mean\nvalue around which the signal is oscillating.\nTo preserve the slowly varying behavior while eliminating the high frequency\noscillations, we'll apply a low-pass filter. To apply the filter, we can\nuse either ``sosfilt`` or ``sosfiltfilt`` from ``scipy.signal``.\nThe function ``sosfiltfilt`` is a forward-backward filter--it applies the\nfilter twice, once forward and once backward. This effectively doubles the\norder of the filter, and results in zero phase shift.\nBecause we are interesting in the \"event\" that occurs in 20 < t < 22,\nit is important to preserve the phase characteristics of the signal, so\nwe use ``sosfiltfilt``.\n\nThe following code snippet defines two convenience functions. These\nfunctions allow us to specify the sampling frequency and the lowpass\ncutoff frequency in whatever units are convenient. They take care of\nscaling the values to the units expected by ``scipy.signal.butter``.\n\n\n.. code-block:: python\n\n from scipy.signal import butter, sosfiltfilt\n\n def butter_lowpass(cutoff, fs, order):\n normal_cutoff = cutoff / (0.5*fs)\n sos = butter(order, normal_cutoff,\n btype='low', output='sos')\n return sos\n\n def butter_lowpass_filtfilt(data, cutoff, fs,\n order):\n sos = butter_lowpass(cutoff, fs, order=order,\n output='sos')\n y = sosfiltfilt(sos, data)\n return y\n\nThe results of filtering the data using ``sosfiltfilt`` are shown in\nFigure :ref:`fig-pressure-example-filtered`.\n\n.. figure:: figs/pressure_example_filtered.pdf\n\n *Top*: Filtered pressure, for the interval 15 < t < 35 (milliseconds).\n The light gray curve is the unfiltered data.\n *Bottom*: Spectrogram of the filtered time series (generated using a\n window size of 1.6 milliseconds).\n The dashed line is at 1250 Hz.\n :label:`fig-pressure-example-filtered`\n\n**Comments on creating a spectrogram.**\nA spectrogram is a plot of the power spectrum of\na signal computed repeatedly over a sliding time window.\nThe spectrograms in Figures :ref:`fig-pressure-example-input`\nand :ref:`fig-pressure-example-filtered` were created using ``spectrogram``\nfrom ``scipy.signal`` and ``pcolormesh`` from ``matplotlib.pyplot``.\nThe function ``spectrogram`` has several options that control how\nthe spectrogram is computed. It is quite flexible, but obtaining a plot\nthat effectively illustrates the time-varying spectrum of a signal sometimes\nrequires experimentation with the parameters.\nIn keeping with the \"cookbook\" theme of this book, we include here the\ndetails of how those plots were generated.\n\nHere is the essential part of the code that computes the spectrograms.\n``pressure`` is the one-dimensional array of measured data.\n\n.. code-block:: python\n\n fs = 50000\n nperseg = 80\n noverlap = nperseg - 4\n f, t, spec = spectrogram(pressure, fs=fs,\n nperseg=nperseg,\n noverlap=noverlap,\n window='hann')\n\nThe spectrogram for the filtered signal is computed with\nthe same arguments:\n\n.. code-block:: python\n\n f, t, filteredspec = spectrogram(pressure_filtered,\n ...)\n\nNotes:\n\n* ``fs`` is the sample rate, in Hz.\n* ``spectrogram`` computes the spectrum over a sliding segment of the input signal.\n ``nperseg`` specifies the number of time samples to include in each segment.\n Here we use 80 time samples (1.6 milliseconds). This is smaller than the default\n of 256, but it provides sufficient resolution of the frequency axis for our plots.\n* ``noverlap`` is the length (in samples) of the overlap of the segments over which\n the spectrum is computed. We use ``noverlap = nperseq - 4``; in other words, the\n window segments slides only four time samples (0.08 milliseconds). This provides\n a fairly fine resolution of the time axis.\n* The spectrum of each segment of the input is computed after multiplying it by a\n window function. We use the Hann window.\n\nThe function ``spectrogram`` computes the data to be plotted.\nNext, we show the code that plots the spectrograms shown in\nFigures :ref:`fig-pressure-example-input` and :ref:`fig-pressure-example-filtered`.\nFirst we convert the data to decibels:\n\n.. code-block:: python\n\n spec_db = 10*np.log10(spec)\n filteredspec_db = 10*np.log10(filteredspec)\n\nNext we find the limits that we will use in the call to ``pcolormesh`` to ensure\nthat the two spectrograms use the same color scale. ``vmax`` is the overall max,\nand ``vmin`` is set to 80 dB less than ``vmax``. This will suppress the very low\namplitude noise in the plots.\n\n.. code-block:: python\n\n vmax = max(spec_db.max(), filteredspec_db.max())\n vmin = vmax - 80.0\n\nFinally, we plot the first spectrogram using ``pcolormesh()``:\n\n.. code-block:: python\n\n cmap = plt.cm.coolwarm\n plt.pcolormesh(1000*t, f/1000, spec_db,\n vmin=vmin, vmax=vmax,\n cmap=cmap, shading='gouraud')\n\nAn identical call of ``pcolormesh`` with ``filteredspec_db`` generates\nthe spectrogram in Figure :ref:`fig-pressure-example-filtered`.\n\n\nInitializing a lowpass filter\n-----------------------------\n\nBy default, the initial state of an IIR filter as implemented in\n``lfilter`` or ``sosfilt`` is all zero. If the input signal does not\nstart with values that are zero, there will be a transient during which\nthe filter's internal state \"catches up\" with the input signal.\n\nHere is an example. The script generates the plot shown in\nFigure :ref:`fig-initial-conditions`.\n\n.. code-block:: python\n\n import numpy as np\n from scipy.signal import butter, sosfilt, sosfilt_zi\n import matplotlib.pyplot as plt\n\n n = 101\n t = np.linspace(0, 1, n)\n np.random.seed(123)\n x = 0.45 + 0.1*np.random.randn(n)\n\n sos = butter(8, 0.125, output='sos')\n\n # Filter using the default initial conditions.\n y = sosfilt(sos, x)\n\n # Filter using the state for which the output\n # is the constant x[:4].mean() as the initial\n # condition.\n zi = x[:4].mean() * sosfilt_zi(sos)\n y2, zo = sosfilt(sos, x, zi=zi)\n\n # Plot everything.\n plt.plot(t, x, alpha=0.75, linewidth=1, label='x')\n plt.plot(t, y, label='y (zero ICs)')\n plt.plot(t, y2, label='y2 (mean(x[:4]) ICs)')\n\n plt.legend(framealpha=1, shadow=True)\n plt.grid(alpha=0.25)\n plt.xlabel('t')\n plt.title('Filter with different '\n 'initial conditions')\n plt.show()\n\nBy setting ``zi=x[:4].mean() * sosfilt_zi(sos)``, we are, in effect,\nmaking the filter start out as if it had been filtering the constant\n``x[:4].mean()`` for a long time. There is still a transient associated\nwith this assumption, but it is usually not as objectionable as the\ntransient associated with zero initial conditions.\n\n.. figure:: figs/initial_conditions.pdf\n \n A demonstration of two different sets of initial conditions for\n a lowpass filter. The orange curve is the output of the filter\n with zero initial conditions. The green curve is the output of\n the filter initialized with a state associated with the mean of\n the first four values of the input ``x``.\n :label:`fig-initial-conditions`\n\nThis initialization is usually not needed for a bandpass\nor highpass filter. Also, the forward-backward filters implemented\nin ``filtfilt`` and ``sosfiltfilt`` already have options for controlling\nthe initial conditions of the forward and backward passes.\n\nBandpass filter\n---------------\n\nIn this example, we will use synthetic data to demonstrate a\nbandpass filter. We have 0.03 seconds of data sampled at\n4800 Hz. We want to apply a bandpass filter to remove frequencies\nbelow 400 Hz or above 1200 Hz.\n\nJust like we did for the lowpass filter, we define two functions that\nallow us to create and apply a Butterworth bandpass filter with the\nfrequencies given in Hz (or any other units). The functions take care\nof scaling the values to the normalized range expected by\n``scipy.signal.butter``.\n\n.. code-block:: python\n\n from scipy.signal import butter, sosfilt\n\n def butter_bandpass(lowcut, highcut, fs, order):\n nyq = 0.5 * fs\n low = lowcut / nyq\n high = highcut / nyq\n sos = butter(order, [low, high], btype='band',\n output='sos')\n return sos\n\n def butter_bandpass_filt(data, lowcut, highcut,\n fs, order):\n sos = butter_bandpass(lowcut, highcut, fs,\n order)\n y = sosfilt(sos, data)\n return y\n\nFirst, we'll take a look at the frequency response of the Butterworth\nbandpass filter with order 3, 6, and 12. The code that generates\nFigure :ref:`fig-bandpass-example-response` demonstrates the use of\n``scipy.signal.sosfreqz``:\n\n.. code-block:: python\n\n for order in [3, 6, 12]:\n sos = butter_bandpass(lowcut, highcut, fs, order)\n w, h = sosfreqz(sos, worN=2000)\n plt.plot((fs*0.5/np.pi)*w, abs(h), 'k',\n alpha=(order+1)/13,\n label=\"order = %d\" % order)\n\n.. figure:: figs/bandpass_example_response.pdf\n\n Amplitude response for a Butterworth bandpass filter\n with several different orders.\n :label:`fig-bandpass-example-response`\n\nFigure :ref:`fig-bandpass-example-signals` shows the input signal and\nthe filtered signal. The order 12 bandpass Butterworth filter\nwas used. The plot shows the input signal `x`; the filtered signal\nwas generated with\n\n.. code-block:: python\n\n y = butter_bandpass_filt(x, lowcut, highcut, fs,\n order=12)\n\nwhere ``fs = 4800``, ``lowcut = 400`` and ``highcut = 1200``.\n\n.. figure:: figs/bandpass_example_signals.pdf\n\n Original noisy signal and the filtered signal.\n The order 12 Butterworth bandpass filter shown in\n Figure :ref:`fig-bandpass-example-response` was used.\n :label:`fig-bandpass-example-signals`\n\nFiltering a long signal in batches\n----------------------------------\n\nThe function ``lfilter`` applies a filter to an array that is\nstored in memory. Sometimes, however, the complete signal to\nbe filtered is not available all at once. It might not fit\nin memory, or it might be read from an instrument in small\nblocks and it is desired to output the filtered block before the\nnext block is available. Such a signal can be filtered in batches,\nbut the state of the filter at the end of one batch must be saved\nand then restored when ``lfilter`` is applied to the next batch.\nHere we show an example of how the ``zi`` argument of ``lfilter``\nallows the state to be saved and restored.\nWe will again use synthetic data generated by the same function\nused in the previous example, but for a longer time interval.\n\nA pattern that can be used to filter an input signal ``x`` in\nbatches is shown in the following code. The filtered signal\nis stored in ``y``. The array ``sos`` contains the filter\nin SOS format, and is presumed to have already been created.\n \n.. code-block:: python\n\n batch_size = N # Number of samples per batch\n\n # Array of initial conditions for the SOS filter.\n z = np.zeros((sos.shape[0], 2))\n\n # Preallocate space for the filtered signal.\n y = np.empty_like(x)\n\n start = 0\n while start < len(x):\n stop = min(start + batch_size, len(x))\n y[start:stop], z = sosfilt(sos, x[start:stop],\n zi=z)\n start = stop\n\nIn this code, the next batch of input is fetched\nby simply indexing ``x[start:stop]``, and the filtered\nbatch is saved by assigning it to ``y[start:stop]``.\nIn a more realistic batch processing system, the\ninput might be fetched from a file, or directly\nfrom an instrument, and the output might be written\nto another file, or handed off to another process\nas part of a batch processing pipeline.\n\n.. figure:: figs/bandpass_batch_example.pdf\n\n Original noisy signal and the filtered signal.\n The order 12 Butterworth bandpass filter shown in\n Figure :ref:`fig-bandpass-example-response` was used.\n The signal was filtered in batches of size 72 samples\n (0.015 seconds). The alternating light and dark blue\n colors of the filtered signal indicate batches that\n were processed in separate calls to ``sosfilt``.\n :label:`fig-bandpass-batch-example`\n\nSolving linear recurrence relations\n-----------------------------------\n\nVariations of the question::\n\n How do I speed up the following calculation?\n\n y[i+1] = alpha*y[i] + c*x[i]\n\noften arise on mailing lists and online forums. Sometimes more\nterms such as ``beta*y[i-1]`` or ``d*x[i-1]`` are included on the right.\nThese recurrence relations show up in, for example, GARCH models\nand other linear stochastic models.\nSuch a calculation can be written in the form of Eqn.\n(:ref:`eq-filter-recurrence`), so a solution can be computed\nusing ``lfilter``.\n\nHere's an example that is similar to several questions that\nhave appeared on the programming Q&A website ``stackoverflow.com``.\nThe one-dimensional array ``h`` is an input, and ``alpha``, ``beta`` and\n``gamma`` are constants::\n\n y = np.empty(len(h))\n y[0] = alpha\n for i in np.arange(1, len(h)):\n y[i] = alpha + beta*y[i-1] + gamma*h[i-1]\n\nTo use ``lfilter`` to solve the problem, we have to translate\nthe linear recurrence::\n\n y[i] = alpha + beta*y[i-1] + gamma*h[i-1]\n\ninto the form of Eqn. (:ref:`eq-filter-recurrence`), which will give us the\ncoefficients ``b`` and ``a`` of the transfer function. Define::\n\n x[i] = alpha + gamma*h[i]\n\nso the recurrence relation is::\n\n y[i] = x[i-1] + beta*y[i-1]\n\nCompare this to Eqn. (:ref:`eq-filter-recurrence`); \nwe see that :math:`a_{_0} = 1`, :math:`a_{_1} = -\\rm{beta}`,\n:math:`b_{_0} = 0` and :math:`b_{_1} = 1`.\nSo we have our transfer function coefficients::\n\n b = [0, 1]\n a = [1, -beta]\n\nWe also have to ensure that the initial condition is set correctly to\nreproduce the desired calculation.\nWe want the initial condition to be set as if we had values ``x[-1]``\nand ``y[-1]``, and ``y[0]`` is computed using the recurrence relation.\nGiven the above recurrence relation, the formula for ``y[0]`` is::\n\n y[0] = x[-1] + beta*y[-1]\n\nWe want ``y[0]`` to be ``alpha``, so we'll set ``y[-1] = 0`` and\n``x[-1] = alpha``. To create initial conditions for ``lfilter``\nthat will set up the filter to act like it had just operated on\nthose previous values, we use ``scipy.signal.lfiltic``::\n\n zi = lfiltic(b, a, y=[0], x=[alpha])\n\nThe ``y`` and ``x`` arguments are the \"previous\" values that will\nbe used to set the initial conditions. In general, one sets\n``y=[y[-1], y[-2], ..]`` and ``x=[x[-1], x[-2], ...]``, giving as\nmany values as needed to determine the initial condition for\n``lfilter``. In this example, we have just one previous value\nfor ``y`` and ``x``.\n\nPutting it all together, here is the code using ``lfilter`` that\nreplaces the for-loop shown above::\n\n b = [0, 1]\n a = [1, -beta]\n zi = lfiltic(b, a, y=[0], x=[alpha])\n y, zo = lfilter(b, a, alpha + gamma*h, zi=zi)\n\nFIR filters in ``scipy.signal``\n===============================\n\n..\n FIR filter notation:\n N length of the filter\n (XXX N is the order of the denominator of an IIR filter)\n M = N-1 order of the filter\n b_k filter coefficients, k = 0, 1, ..., M; OR -R <= k <= R\n R = (N - 1)//2 for a Type I filter\n L number of frequencies in the grid used in the\n linear programming method\n p_k Alternative representation of a Type I filter;\n p_0 = b_0\n p_k = 2*b_k, 1 <= k <= R\n\nA finite impulse response filter is basically a weighted moving\naverage. Given an input sequence :math:`{x_{_n}}` and the :math:`M+1`\nfilter coefficients :math:`\\{b_{_0}, \\ldots, b_{_M}\\}`, the filtered\noutput :math:`{y_{_n}}` is computed as the discrete convolution of\n:math:`x` and :math:`b`:\n\n.. math::\n :label: eq-fir-filter\n\n y_{_n} = \\sum_{i=0}^{M} b_{_i}x_{_{n-i}} = (b * x)_{_n}\n\nwhere :math:`*` is the convolution operator.\n:math:`M` is the *order* of the filter; a filter with order :math:`M`\nhas :math:`M + 1` coefficients. It is common to say that the filter has\n:math:`M + 1` *taps*.\n\nApply a FIR filter\n------------------\n\nTo apply a FIR filter to a signal, we can use ``scipy.signal.lfilter``\nwith the denominator set to the scalar 1, or we can use one of the\nconvolution functions available in NumPy or SciPy, such as\n``scipy.signal.convolve``.\nFor a signal :math:`\\{x_{_0}, x_{_1}, \\ldots, x_{_{S-1}}\\}` of finite length\n:math:`S`, Eq. (:ref:`eq-fir-filter`)\ndoesn't specify how to compute the result for :math:`n < M`.\nThe convolution functions in NumPy and SciPy have an option called\n``mode`` for specifying how to handle this. For example, ``mode='valid'``\nonly computes output values for which all the values of :math:`x_{_i}`\nin Eq. :ref:`eq-fir-filter` are defined, and ``mode='same'`` in effect\npads the input array :math:`x` with zeros so that the output is the\nsame length as the input. See the docstring of ``numpy.convolve``\nor ``scipy.signal.convolve`` for more details.\n\nFor example,\n\n.. code-block:: python\n\n from scipy.signal import convolve\n\n # Make a signal to be filtered.\n np.random.seed(123)\n x = np.random.randn(50)\n # taps is the array of FIR filter coefficients.\n taps = np.array([ 0.0625, 0.25 , 0.375 ,\n 0.25 , 0.0625])\n # Filtered signal. y has the same length as x.\n y = convolve(x, taps, mode='same')\n\nThere are also convolution functions in ``scipy.ndimage``.\nThe function ``scipy.ndimage.convolve1d`` provides an ``axis`` argument,\nwhich allows all the signals stored in one axis of a multidimensional array\nto be filtered with one call. For example,\n\n.. code-block:: python\n\n from scipy.ndimage import convolve1d\n\n # Make an 3-d array containing 1-d signals\n # to be filtered.\n x = np.random.randn(3, 5, 50)\n # Apply the filter along the last dimension.\n y = convolve1d(x, taps, axis=-1)\n\nNote that ``scipy.ndimage.convolve1d`` has a different set of options\nfor its ``mode`` argument. Consult the docstring for details.\n\nSpecialized functions that are FIR filters\n------------------------------------------\n\n..\n TODO: either expand or delete this section.\n\nThe uniform filter and the Gaussian filter implemented in ``scipy.ndimage``\nare FIR filters. In the case of one-dimensional time series, the specific\nfunctions are ``uniform_filter1d`` and ``gaussian_filter1d``.\n\nThe Savitzky-Golay filter [SavGol]_ is also a FIR filter. In the module\n``scipy.signal``, SciPy provides the function ``savgol_coeffs`` to create\nthe coefficients of a Savitzy-Golay filter. The function ``savgol_filter``\napplies the Savitzky-Golay filter to an input signal without returning the\nfilter coefficients.\n\nFIR filter frequency response\n-----------------------------\n\nThe function ``scipy.signal.freqz`` computes the frequency response of a\nlinear filter represented as a transfer function. This class of filters\nincludes FIR filters, where the representation of the numerator of the\ntransfer function is the array of taps and the denominator is the scalar\n:math:`a_{_0} = 1`.\n\nAs an example, we'll compute the frequency response of a uniformly\nweighted moving average. For a moving average of length :math:`n`,\nthe coefficients in the FIR filter are simply :math:`1/n`. Translated\nto NumPy code, we have ``taps = np.full(n, fill_value=1.0/n)``.\n\nThe response curves in Figure :ref:`fig-moving-avg-freq-response`\nwere generated with this code:\n\n.. code-block:: python\n\n for n in [3, 7, 21]:\n taps = np.full(n, fill_value=1.0/n)\n w, h = freqz(taps, worN=2000)\n plt.plot(w, abs(h), label=\"n = %d\" % n)\n\n.. figure:: figs/moving_avg_freq_response.pdf\n\n Frequency response of a simple moving average. ``n`` is the\n number of taps (i.e. the length of the sliding window).\n :label:`fig-moving-avg-freq-response`\n\nThe function ``freqz`` returns the frequencies\nin units of radians per sample, which is why the values on the abscissa\nin Figure :ref:`fig-moving-avg-freq-response` range from 0 to :math:`\\pi`.\nIn calculations where we have a given sampling frequency\n:math:`f_s`, we usually convert the frequencies returned by ``freqz``\nto dimensional units by multiplying by :math:`f_s/(2\\pi)`.\n\n\nFIR filter design\n-----------------\n\nWe'll demonstrate how SciPy can be used to design a FIR filter using\nthe following four methods.\n\n* *The window method.*\n The filter is designed by computing the impulse response of\n the desired ideal filter and then multiplying the coefficients\n by a window function.\n\n* *Least squares design.* The weighted integral of the squared\n frequency response error is minimized.\n\n* *Parks-McClellan equiripple design.* A \"minimax\" method, in which the\n maximum deviation from the desired response is minimized.\n\n* *Linear programming.* The \"minimax\" design problem can be formulated as\n a linear programming problem.\n\nIn the following sections, we discuss each design method.\nFor this discussion, we define the following functions,\nwhere :math:`\\omega` is the frequency in radians per sample:\n:math:`A(\\omega)`, the filter's (real, signed) frequency response;\n:math:`D(\\omega)`, the desired frequency response of the filter; and\n:math:`W(\\omega)`, the weight assigned to the response error at\n:math:`\\omega` (i.e. how \"important\" is the error\n:math:`A(\\omega) - D(\\omega)`).\n\n\nFIR filter design: the window method\n------------------------------------\n\nThe window method for designing a FIR filter is to compute the filter\ncoefficients as the impulse response of the desired ideal filter, and then\nmultiply the coefficents by a window function to both truncate the set of\ncoefficients (thus making a *finite* impulse response filter) and to shape\nthe actual filter response. Most textbooks on digital signal processing\ninclude a discussion of the method; see, for example, Section 7.5 of\nOppenheim and Schafer [OS]_.\n\nTwo functions in the module ``scipy.signal`` implement the window\nmethod, ``firwin`` and ``firwin2``.\nHere we'll show an example of ``firwin2``.\nWe'll use ``firwin`` when we discuss the Kaiser window method.\n\nWe'll design a filter with 185 taps for a signal that is sampled at 2000 Hz.\nThe filter is to be lowpass, with a *linear* transition from the pass\nband to the stop band over the range 150 Hz to 175 Hz. We also want\na notch in the pass band between 48 Hz and 72 Hz, with sloping sides,\ncentered at 60 Hz where the desired gain is 0.1. The dashed line in\nFigure :ref:`fig-firwin2-examples` shows the desired frequency response.\n\nTo use ``firwin2``, we specify the desired response at the endpoints\nof a piecewise linear profile defined over the frequency range [0, 1000]\n(1000 Hz is the Nyquist frequency).\n\n.. code:: python\n\n freqs = [0, 48, 60, 72, 150, 175, 1000]\n gains = [1, 1, 0.1, 1, 1, 0, 0]\n\nTo illustrate the affect of the window on the filter, we'll demonstrate\nthe design using three different windows: the Hamming window,\nthe Kaiser window with parameter :math:`\\beta` set to 2.70,\nand the rectangular or \"boxcar\" window (i.e. simple truncation without\ntapering).\n\n.. figure:: figs/firwin2_examples_windows.pdf\n\n Window functions used in the ``firwin2`` filter design example.\n :label:`fig-firwin2-examples-windows`\n\nThe code to generate the FIR filters is\n\n.. code-block:: python\n\n fs = 2000\n numtaps = 185\n\n # window=None is equivalent to using the\n # rectangular window.\n taps_none = firwin2(numtaps, freqs, gains,\n nyq=0.5*fs, window=None)\n # The default window is Hamming.\n taps_h = firwin2(numtaps, freqs, gains,\n nyq=0.5*fs)\n beta = 2.70\n taps_k = firwin2(numtaps, freqs, gains,\n nyq=0.5*fs, window=('kaiser', beta))\n\nFigure :ref:`fig-firwin2-examples` shows the frequency\nresponse of the three filters.\n\n.. figure:: figs/firwin2_examples.pdf\n\n Frequency response for a filter designed using ``firwin2`` with\n several windows.\n The ideal frequency response is a lowpass filter with a ramped\n transition starting at 150 Hz. There is also a notch with ramped\n transitions centered at 60 Hz.\n :label:`fig-firwin2-examples`\n\nFIR filter design: least squares\n--------------------------------\n\nThe weighted least squares method creates a filter for which the expression\n\n.. math::\n :label: eq-least-squares-functional\n\n \\int_{0}^{\\pi} W(\\omega) \\left(A(\\omega) - D(\\omega)\\right)^{2} \\, d\\omega\n\nis minimized.\nThe function ``scipy.signal.firls`` implements this method for piecewise\nlinear desired response :math:`D(\\omega)` and piecewise constant weight\nfunction :math:`W(\\omega)`. Three arguments (one optional) define the shape\nof the desired response: ``bands``, ``desired`` and (optionally) ``weights``.\n\nThe argument ``bands`` is sequence of frequency values with an even length.\nConsecutive pairs of values define the bands on which the desired response is\ndefined. The frequencies covered by ``bands`` does not have to include the\nentire spectrum from 0 to the Nyquist frequency. If there are gaps, the\nresponse in the gap is ignored (i.e. the gaps are \"don't care\" regions).\n\nThe ``desired`` input array defines the amplitude of the desired frequency\nresponse at each point in ``bands``.\n\nThe ``weight`` input, if given, must be an array with half the length of\n``bands``. The values in ``weight`` define the weight of each band in\nthe objective function. A weight of 0 means the band does not contribute\nto the result at all--it is equivalent to leaving a gap in ``bands``.\n\nAs an example, we'll design a filter for a signal sampled at 200 Hz.\nThe filter is a lowpass filter, with pass band [0, 15] and stop band\n[30, 100], and we want the gain to vary linearly from 1 down to 0 in the\ntransition band [15, 30]. We'll design a FIR filter with 43 taps.\n\nWe create the arrays ``bands`` and ``desired`` as described above:\n\n.. code-block:: python\n\n bands = np.array([0, 15, 15, 30, 30, 100])\n desired = np.array([1, 1, 1, 0, 0, 0])\n\nThen we call ``firls``:\n\n.. code-block:: python\n\n numtaps = 43\n taps1 = firls(numtaps, bands, desired, nyq=100)\n\nThe frequency response of this filter is the blue curve in\nFigure :ref:`fig-firls-example`.\n\nBy default, the ``firls`` function weights the bands uniformly\n(i.e. :math:`W(\\omega) \\equiv 1` in\nEqn. (:ref:`eq-least-squares-functional`)).\nThe ``weights`` argument can be used to control the weight\n:math:`W(\\omega)` on each band. The argument must be a sequence\nthat is half the length of ``bands``. That is, only piecewise\nconstant weights are allowed.\n\nHere we rerun ``firls``, giving the most weight to the pass band and the\nleast weight to the transition band:\n\n.. code-block:: python\n\n wts = [100, .01, 1]\n taps2 = firls(numtaps, bands, desired, nyq=100,\n weight=wts)\n\nThe frequency response of this filter is the orange curve in\nFigure :ref:`fig-firls-example`. As expected, the frequency response now\ndeviates more from the desired gain in the transition band, and the ripple\nin the pass band is significantly reduced. The rejection in\nthe stop band is also improved.\n\n\n.. figure:: figs/firls_example.pdf\n\n Result of a least squares FIR filter design. The desired frequency\n response comprises three bands. On [0, 15], the desired gain\n is 1 (a pass band). On [15, 30], the desired gain decreases\n linearly from 1 to 0. The band [30, 100] is a stop band, where the\n desired gain is 0. The filters have 43 taps. The middle and bottom\n plots are details from the top plot.\n :label:`fig-firls-example`\n\n\n**Equivalence of least squares and the window method.**\n\n..\n This subsection is just an observation; we could delete it.\n\nWhen uniform weights are used, and the desired result is specified\nfor the complete interval :math:`[0, \\pi]`, the least squares\nmethod is equivalent to the window method with no window function\n(i.e. the window is the \"boxcar\" function).\nTo verify this numerically, it is necessary to use a sufficiently\nhigh value for the ``nfreqs`` argument of ``firwin2``.\n\nHere's an example:\n\n.. code-block:: python\n\n >>> bands = np.array([0, 0.5, 0.5, 0.6, 0.6, 1])\n >>> desired = np.array([1, 1, 1, 0.5, 0.5, 0])\n >>> numtaps = 33\n >>> taps_ls = firls(numtaps, bands, desired)\n >>> freqs = bands[[0, 1, 3, 5]]\n >>> gains = desired[[0, 1, 3, 5]]\n >>> taps_win = firwin2(numtaps, freqs, gains,\n ... nfreqs=8193, window=None)\n >>> np.allclose(taps_ls, taps_win)\n True\n\nIn general, the window method cannot be used as a replacement for the\nleast squares method, because it does not provide an option for weighting\ndistinct bands differently; in particular, it does not allow for\n\"don't care\" frequency intervals (i.e. intervals with weight 0).\n\nFIR filter design: Parks-McClellan\n----------------------------------\n\nThe Parks-McClellan algorithm [PM]_ is based on the Remez exchange\nalgorithm [RemezAlg]_. This is a \"minimax\" optimization; that is,\nit miminizes the maximum value of :math:`|E(\\omega)|` over\n:math:`0 \\le \\omega \\le \\pi`, where\n:math:`E(\\omega)` is the (weighted) deviation of the actual frequency\nresponse from the desired frequency response:\n\n.. math::\n :label: eq-weighted-error-omega\n\n E(\\omega) = W(\\omega)(A(\\omega) - D(\\omega)), \\quad 0 \\le \\omega \\le \\pi,\n\nWe won't give a detailed description of the algorithm here; most\ntexts on digital signal processing explain the algorithm (e.g. Section\n7.7 of Oppenheim and Schafer [OS]_). The method is implemented in ``scipy.signal``\nby the function ``remez``.\n\nAs an example, we'll design a bandpass filter for a signal\nwith a sampling rate of 2000 Hz using ``remez``.\nFor this filter, we want the stop bands to be [0, 250] and [700, 1000],\nand the pass band to be [350, 550]. We'll leave the behavior outside\nthese bands unspecified, and see what ``remez`` gives us.\nWe'll use 31 taps.\n\n.. code-block:: python\n\n fs = 2000\n bands = [0, 250, 350, 550, 700, 0.5*fs]\n desired = [0, 1, 0]\n\n numtaps = 31\n\n taps = remez(numtaps, bands, desired, fs=fs)\n\nThe frequency response of this filter is the curve labeled ``(a)``\nin Fig. :ref:`fig-remez-example-31taps`.\n\n\nTo reduce the ripple in the pass band while using the same filter length,\nwe'll adjust the weights, as follows:\n\n.. code-block:: python\n\n weights = [1, 25, 1]\n taps2 = remez(numtaps, bands, desired, weights, fs=fs)\n\nThe frequency response of this filter is the curve labeled ``(b)``\nin Fig. :ref:`fig-remez-example-31taps`.\n\n.. figure:: figs/remez_example_31taps.pdf\n\n Frequency response of bandpass filters designed using\n ``scipy.signal.remez``. The stop bands are [0, 250] and [700, 1000],\n and the pass band is [350, 550]. The shaded regions are the \"don't care\"\n intervals where the desired behavior of the filter is unspecified.\n The curve labeled `(a)` uses the default weights--each band\n is given the same weight. For the curve labeled `(b)`,\n `weight = [1, 25, 1]` was used.\n\n :label:`fig-remez-example-31taps`\n\nIt is recommended to always check the frequency response of a filter\ndesigned with ``remez``. Figure :ref:`fig-remez-example-47taps` shows\nthe frequency response of the filters when the number of taps is\nincreased from 31 to 47. The ripple in the pass and stop bands is\ndecreased, as expected, but the behavior of the filter in the\ninterval [550, 700] might be unacceptable. This type of behavior\nis not unusual for filters designed with ``remez`` when there\nare intervals with unspecified desired behavior.\n\n.. figure:: figs/remez_example_47taps.pdf\n\n This plot shows the results of the same\n calculation that produced Figure :ref:`fig-remez-example-31taps`,\n but the number of taps has been increased from 31 to 47.\n Note the possibly undesirable behavior of the filter in the\n transition interval [550, 700].\n\n :label:`fig-remez-example-47taps`\n\nIn some cases, the exchange algorithm implemented in ``remez`` can fail\nto converge. Failure is more likely when the number of taps is large\n(i.e. greater than 1000). It can also happen that ``remez`` converges,\nbut the result does not have the expected equiripple behavior in\neach band. When a problem occurs, one can try increasing the ``maxiter``\nargument, to allow the algorithm more iterations before it gives up, and\none can try increasing ``grid_density`` to increase the resolution of the\ngrid on which the algorithm seeks the maximum of the response errors.\n\nFIR filter design: linear programming\n-------------------------------------\n\nThe design problem solved by the Parks-McClellan method can also\nbe formulated as a linear programming problem\n([Rabiner1972a]_ [Rabiner1972b]_).\n\nTo implement this method, we'll use the function ``linprog`` from\n``scipy.optimize``. In particular, we'll use the interior point\nmethod that was added in SciPy 1.0. In the following, we first\nreview the linear programming formulation, and then we discuss\nthe implementation.\n\n**Formulating the design problem as a linear program.**\nLike the Parks-McClellan method, this approach is a \"minimax\"\noptimization of Eq. (:ref:`eq-weighted-error-omega`).\nWe'll give the formulation for a Type I filter design (that is,\nan odd number of taps with even symmetry), but the same ideas\ncan be applied to other FIR filter types.\n\nFor convenience, we'll consider the FIR filter coefficients for\na filter of length :math:`2R + 1` using *centered* indexing:\n\n.. math::\n\n b_{_{-R}}, b_{_{-R+1}}, \\ldots, b_{_{-1}}, b_{_0}, b_{_1}, \\ldots, b_{_{M-1}}, b_{_R}\n\nConsider a sinusoidal signal with frequency :math:`\\omega` radians\nper sample. The frequency response can be written\n\n.. math::\n\n A(\\omega) = \\sum_{i=-R}^{R} b_{_i}\\cos(\\omega i)\n = b_{_0} + \\sum_{i=0}^{R} 2b_{_i} \\cos(\\omega i)\n = \\sum_{i=0}^{R} p_{_i} \\cos(\\omega i)\n\nwhere we define :math:`p_{_0} = b_{_0}` and,\nfor :math:`1 \\le i \\le R`, :math:`p_{_i} = 2b_{_i}`.\nWe've used the even symmetry of the cosine function and the of filter coefficients\nabout the middle coefficient (:math:`b_{_{-i}} = b_{_i}`).\n\nThe \"minimax\" problem is to minimize the maximum error. That is,\nchoose the filter coefficients such that\n\n.. math::\n\n |E(\\omega)| \\le \\epsilon \\quad \\textrm{for}\\quad 0 \\le \\omega \\le \\pi\n\nfor the smallest possible value of :math:`\\epsilon`. After substituting the\nexpression for :math:`E(\\omega)` from Eqn. (:ref:`eq-weighted-error-omega`),\nreplacing the absolute value with two inequalities, and doing a little\nalgebra, the problem can be written as\n\n.. math::\n\n \\begin{split}\n \\textrm{minimize} \\quad & \\epsilon \\\\\n \\textrm{over} \\quad & \\left\\{p_{_0},\\, p_{_1},\\, \\ldots,\\, p_{_R},\\, \\epsilon\\right\\} \\\\\n \\textrm{subject to} \\quad & A(\\omega) - \\frac{\\epsilon}{W(\\omega)} \\le D(\\omega) \\\\\n \\textrm{and} \\quad & -A(\\omega) - \\frac{\\epsilon}{W(\\omega)} \\le -D(\\omega)\n \\end{split}\n\n:math:`\\omega` is a continuous variable in the above formulation.\nTo implement this as a linear programming problem, we use a suitably dense\ngrid of :math:`L` frequencies\n:math:`{\\omega_{_0}, \\omega_{_1}, \\ldots, \\omega_{_{L-1}}}`\n(not necessarily uniformly spaced).\nWe define the\n:math:`L \\times (R+1)` matrix :math:`C` as\n\n.. math::\n :label: eq-freq-resp-coefficients\n\n C_{_{ij}} = \\cos(\\omega_{_{i-1}} (j-1)),\n \\quad 1 \\le i \\le L \\;\\textrm{and}\\; 1 \\le j \\le R+1\n\nThen the vector of frequency responses is the matrix product :math:`C\\textbf{p}`,\nwhere :math:`\\textbf{p} = [p_{_0}, p_{_1}, \\ldots, p_{_R}]^{\\textsf{T}}`.\n\nLet :math:`d_k = D(\\omega_k)`, and\n:math:`\\textbf{d} = [d_{_0}, d_{_1}, \\ldots, d_{_{L-1}}]^{\\textsf{T}}`.\nSimilarly, define\n:math:`\\textbf{v} = [v_{_0}, v_{_1}, \\ldots, v_{_{L-1}}]^{\\textsf{T}}`,\nwhere :math:`v_k = 1/W(\\omega_k)`.\nThe linear programming problem is\n\n.. math::\n\n \\begin{split}\n \\textrm{minimize} \\quad & \\epsilon \\\\\n \\textrm{over} \\quad & \\left\\{p_{_0},\\, p_{_1},\\, \\ldots,\\, p_{_R},\\, \\epsilon\\right\\} \\\\\n \\textrm{subject to} \\quad & \\left[\n \\begin{array}{rr}\n C & -\\textbf{v} \\\\\n -C & -\\textbf{v}\n \\end{array}\n \\right]\n \\left[\n \\begin{array}{c}\n \\textbf{p} \\\\\n \\epsilon\n \\end{array}\n \\right]\n \\le\n \\left[\n \\begin{array}{r}\n \\textbf{d} \\\\\n -\\textbf{d}\n \\end{array}\n \\right]\n \\end{split}\n\nThis is the formulation that can be used with, for example,\n``scipy.optimize.linprog``.\n\nThis formulation, however, provides no advantages over the solver provided\nby ``remez``, and in fact it is generally much slower and less robust than\n``remez``. When designing a filter beyond a hundred or so taps, there is\nmuch more likely to be a convergence error in the linear programming method\nthan in ``remez``.\n\nThe advantage of the linear programming method is its ability to\neasily handle additional constraints. Any constraint, either equality\nor inequality, that can be written as a linear constraint can be added\nto the problem.\n\nWe will demonstrate how to implement a lowpass filter design using linear\nprogramming with the constraint that the gain for a constant input is\nexactly 1. That is,\n\n.. math::\n\n A(0) = \\sum_{i=0}^R p_i = 1\n\nwhich may be written\n\n.. math::\n\n A_{\\textrm{eq}} \\left[\n \\begin{array}{c}\n \\textbf{p} \\\\\n \\epsilon\n \\end{array}\n \\right] = 1,\n\nwhere :math:`A_{\\textrm{eq}} = \\left[1, 1, \\ldots, 1, 0\\right]`.\n\n**Implementing the linear program.**\nLet's look at the code required to set up a call to ``linprog``\nto design a lowpass filter with a pass band of :math:`[0, \\omega_p]`\nand a stop band of :math:`[\\omega_s, \\pi]`, where the frequencies\n:math:`\\omega_p` and :math:`\\omega_s` are expressed in radians per\nsample, and :math:`0 < \\omega_p < \\omega_s < \\pi`. We'll also\nimpose the constraint that :math:`A(0) = 1`.\n\nA choice for the density of the frequency samples on :math:`[0, \\pi]`\nthat works well is :math:`16N`, where :math:`N` is the number of taps\n(``numtaps`` in the code). Then the number of samples in the pass band\nand the stop band can be computed as\n\n.. code-block:: python\n\n density = 16*numtaps/np.pi\n numfreqs_pass = int(np.ceil(wp*density))\n numfreqs_stop = int(np.ceil((np.pi - ws)*density))\n\nThe grids of frequencies on the pass and stop bands are then\n\n.. code-block:: python\n\n wpgrid = np.linspace(0, wp, numfreqs_pass)\n wsgrid = np.linspace(ws, np.pi, numfreqs_stop)\n\nWe will impose an equality constraint on :math:`A(0)`, so we can can\nremove that frequency from ``wpgrid``--there is no point in requiring\nboth the equality and inequality constraints at :math:`\\omega = 0`.\nThen ``wpgrid`` and ``wsgrid`` are concatenated to form ``wgrid``,\nthe grid of all the frequency samples.\n\n.. code-block:: python\n\n wpgrid = wpgrid[1:]\n wgrid = np.concatenate((wpgrid, wsgrid))\n\nLet ``wtpass`` and ``wtstop`` be the constant weights\nthat we will use in the pass and stop bands, respectively.\nWe create the array of weights on the grid with\n\n.. code-block:: python\n\n weights = np.concatenate(\n (np.full_like(wpgrid, fill_value=wtpass),\n np.full_like(wsgrid, fill_value=wtstop)))\n\nThe desired values of the frequency response are 1 in the pass band and 0\nin the stop band. Evaluated on the grid, we have\n\n.. code-block:: python\n\n desired = np.concatenate((np.ones_like(wpgrid),\n np.zeros_like(wsgrid)))\n\nNow we implement Eq. (:ref:`eq-freq-resp-coefficients`) and\ncreate the :math:`L \\times (R+1)` array of coefficients :math:`C` that are\nused to compute the frequency response, where :math:`R = M/2`:\n\n.. code-block:: python\n\n R = (numtaps - 1)//2\n C = np.cos(wgrid[:, np.newaxis]*np.arange(R+1))\n\nThe column vector of the reciprocals of the weights is\n\n.. code-block:: python\n\n V = 1/weights[:, np.newaxis]\n\nNext we assemble the pieces that define the inequality constraints\nthat are actually passed to ``linprog``:\n\n.. code-block:: python\n\n A = np.block([[ C, -V],\n [-C, -V]])\n b = np.block([[desired, -desired]]).T\n c = np.zeros(M+2)\n c[-1] = 1\n\nIn code, the arrays for the equality constraint needed to\ndefine :math:`A(0) = 1` are:\n\n.. code-block:: python\n\n A_eq = np.ones((1, R+2))\n A_eq[:, -1] = 0\n b_eq = np.array([1])\n\nFinally, we set up and call ``linprog``:\n\n.. code-block:: python\n\n options = dict(maxiter=5000, tol=1e-6)\n sol = linprog(c, A, b, A_eq=A_eq, b_eq=b_eq,\n bounds=(None, None),\n method='interior-point',\n options=options)\n if sol.success:\n p = sol.x[:-1]\n taps = 0.5*np.concatenate((p[:0:-1],\n [2*p[0]],\n p[1:]))\n\nNotes:\n\n* For different problems, the parameters defined in the\n dictionary ``options`` may have to be adjusted. See the documentation\n for ``linprog`` for more details.\n* By default, ``linprog`` assumes that all the variables must\n be nonnegative. We use the ``bounds`` argument to override that\n behavior.\n* We have had more success using the interior point method than the\n default simplex method.\n\n\nSee Figure :ref:`fig-firlp-lowpass-example` for a plot of the pass\nband response of the filter designed using ``linprog``. The number of taps\nwas :math:`N = 81`, and the transition boundary frequencies,\nexpressed in radians per sample, were :math:`\\omega_p = 0.16\\pi`\nand :math:`\\omega_s = 0.24\\pi`. For the weight in each band we\nused ``wtpass = 2`` and ``wtstop = 1``.\n\n.. figure:: figs/firlp_lowpass_example.pdf\n\n Result of solving a lowpass FIR filter design problem by linear\n programming with the constraint :math:`A(0) = 1`.\n The response without the extra constraint, solved using ``remez``,\n is also plotted.\n\n :label:`fig-firlp-lowpass-example`\n\n\nDetermining the order of a FIR filter\n-------------------------------------\n\nMost of the filter design tools in SciPy require the number of taps\nas an input. Typically, however, a designer has requirements on\nthe pass band ripple and the stop band rejection, and wants the FIR\nfilter with the minimum number of taps that satisfies these requirements.\nThe diagram shown in Figure :ref:`fig-lowpass-design-specs` illustrates\nthe design parameters for a lowpass filter. The graph of the magnitude\nof the frequency response of the filter must not enter the shaded area.\nThe parameter :math:`\\delta_p` defines the allowed pass band ripple,\nand :math:`\\delta_s` defines the required attenuation in the stop band.\nThe maximum width of the transition from the pass band to stop band is\n:math:`\\Delta \\omega`, and the cutoff frequency :math:`\\omega_c` is\ncentered in the transition band.\n\nIn the next two sections, we'll consider the following filter\ndesign problem. We need a lowpass filter for a signal that is\nsampled at 1000 Hz. The desired cutoff frequency is 180 Hz, and the\ntransition from the pass band to the stop band must not exceed\n30 Hz. In the pass band, the gain of the filter should deviate\nfrom 1 by no more than 0.005 (i.e. worst case ripple is 0.5%).\nIn the stop band, the gain must be less than 0.002 (about 54 dB attenuation).\nIn the next section, we'll tackle the design using the\nKaiser window method. After that, we'll obtain an optimal design\nby using the Parks-McClellan method.\n\nKaiser's window method\n----------------------\n\nThe Kaiser window is a window function with a parameter :math:`\\beta`\nthat controls the shape of the function. An example of a Kaiser window\nis plotted in Figure :ref:`fig-firwin2-examples-windows`.\nKaiser [Kaiser66]_ [Kaiser74]_ developed formulas that, for a given\ntransition width :math:`\\Delta \\omega` and error tolerance for\nthe frequency response,\ndetermine the order :math:`M` and the parameter :math:`\\beta` required\nto meet the requirements. Summaries of the method can be found in\nmany sources, including Sections 7.5.3 and 7.6 of the text by\nOppenheim and Schafer [OS]_.\n\nIn Kaiser's method, there is only one parameter that controls the passband\nripple and the stopband rejection. That is, Kaiser's method assumes\n:math:`\\delta_p = \\delta_s`. Let :math:`\\delta` be that common value.\nThe stop band rejection in dB is :math:`-20\\log_{10}(\\delta)`.\nThis value (in dB) is the first argument of the function ``kaiserord``.\nOne can interpret the argument ``ripple`` as the maximum deviation\n(expressed in dB) allowed in :math:`|A(\\omega) - D(\\omega)|`, where\n:math:`A(\\omega)` is the magnitude of the actual frequency response\nof the filter and :math:`D(\\omega)` is the desired frequency response.\n(That is, in the pass band, :math:`D(\\omega) = 1`, and in the stop band,\n:math:`D(\\omega) = 0`.) In Figure :ref:`fig-kaiser-lowpass-filter-design`,\nthe bottom plot shows :math:`|A(\\omega) - D(\\omega)|`.\n\nThe Kaiser window design method, then, is to determine the length of the\nfilter and the Kaiser window parameter :math:`\\beta` using Kaiser's formula\n(implemented in ``scipy.signal.kaiserord``), and then design the filter\nusing the window method with a Kaiser window (using, for example,\n``scipy.signal.firwin``)::\n\n numtaps, beta = kaiserord(ripple, width)\n taps = firwin(numtaps, cutoff,\n window=('kaiser', beta),\n [other args as needed])\n\nFor our lowpass filter design problem, we first define the input\nparameters:\n\n.. code-block:: python\n\n # Frequency values in Hz\n fs = 1000.0\n cutoff = 180.0\n width = 30.0\n # Desired pass band ripple and stop band attenuation\n deltap = 0.005\n deltas = 0.002\n\nAs already mentioned, the Kaiser method allows for only a single\nparameter to constrain the approximation error. To ensure we meet\nthe design criteria in the pass and stop bands, we take the minimum\nof :math:`\\delta_p` and :math:`\\delta_s`::\n\n delta = min(deltap, deltas)\n\nThe first argument of ``kaiserord`` must be expressed in dB, so we\nset::\n\n delta_db = -20*np.log10(delta)\n\nThen we call ``kaiserord`` to determine the number of taps and\nthe Kaiser window parameter :math:`\\beta`::\n\n numtaps, beta = kaiserord(delta_db, width/(0.5*fs))\n numtaps |= 1 # Must be odd for a Type I FIR filter.\n\nFor our lowpass filter design problem, we find ``numtaps`` is 109\nand :math:`\\beta` is 4.990.\n\nFinally, we use ``firwin`` to compute the filter coefficients::\n\n taps = firwin(numtaps, cutoff/(0.5*fs),\n window=('kaiser', beta), scale=False)\n\nThe results of the Kaiser method applied to our lowpass filter design\nproblem are plotted in Figure :ref:`fig-kaiser-lowpass-filter-design`.\nThe tip of the right-most ripple in the pass band violates the\n:math:`\\delta`-constraint by a very small amount; this is not unusual\nfor the Kaiser method.\nIn this case, it is not a problem, because the original requirement\nfor the pass band is :math:`\\delta_p = 0.005`, so the behavior in the\npass band is overly conservative.\n\n.. figure:: figs/lowpass_design_specs.pdf\n\n Lowpass filter design specifications. The magnitude of the\n frequency response of the filter should not enter the shaded\n regions.\n\n :label:`fig-lowpass-design-specs`\n\n.. figure:: figs/kaiser_lowpass_filter_design.pdf\n\n Result of the Kaiser window filter design of a lowpass filter.\n The number of taps is 109.\n *Top:* Magnitude (in dB) of the frequency response.\n *Middle:* Detail of the frequency response in the pass band.\n *Bottom:* The deviation of the actual magnitude of the\n frequency response from that of the ideal lowpass filter.\n\n :label:`fig-kaiser-lowpass-filter-design`\n\nOptimizing the FIR filter order\n-------------------------------\nThe Kaiser window method can be used to create *a* filter that meets\n(or at least is very close to meeting) the design requirements, but it\nwill not be optimal. That is, generally there will exist FIR filters with\nfewer taps that also satisfy the design requirements. At the time this\nchapter is being written, SciPy does not provide a tool that automatically\ndetermines the optimal number of taps given pass band ripple and stop band\nrejection requirements. It is not difficult, however, to use the existing\ntools to find an optimal filter in a few steps (at least if the filter\norder is not too large).\n\nHere we show a method that works well, at least for\nthe basic lowpass, highpass, bandpass and bandstop filters on which it has\nbeen tested.\nThe idea: given the design requirements, first estimate the length\nof the filter. Create a filter of that length using ``remez``, with\n:math:`1/\\delta_p` and :math:`1/\\delta_s` as the weights for the pass\nand stop bands, respectively.\nCheck the frequency response of the filter. If the initial estimate\nof the length was good, the filter should be close to satisfying\nthe design requirements. Based on the observed frequency response,\nadjust the number of taps, then create a new filter and reevaluate the\nfrequency response. Iterate until the shortest filter that meets the\ndesign requirements is found.\nFor moderate sized filters (up to 1000 or so taps), this simple iterative\nprocess can be automated. (For higher order filters, this method has\nat least two weaknesses: it might be difficult to get a reasonably\naccurate estimate of the filter length, and it is more likely that\n``remez`` will fail to converge.)\n\nA useful formula for estimating the length of a FIR filter was given\nby Bellanger [Bellanger]_:\n\n.. math::\n :label: eq-bellanger\n\n N \\approx -\\frac{2}{3} \\log_{10}\\left(10\\delta_p\\delta_s\\right)\\frac{f_s}{\\Delta f}\n\nwhich has a straightforward Python implementation:\n\n.. code-block:: python\n\n def bellanger_estimate(deltap, deltas, width, fs):\n n = (-2/3)*np.log10(10*deltap*deltas)*fs/width\n n = int(np.ceil(n))\n return n\n\n\nWe'll apply this method to the lowpass filter design problem\nthat was described in the previous section. As before, we define\nthe input parameters:\n\n.. code-block:: python\n\n # Frequency values in Hz\n fs = 1000.0\n cutoff = 180.0\n width = 30.0\n # Desired pass band ripple and stop band attenuation\n deltap = 0.005\n deltas = 0.002\n\nThen the code\n\n.. code-block:: python\n\n numtaps = bellanger_estimate(deltap, deltas,\n width, fs)\n numtaps |= 1\n\ngives ``numtaps = 89``. (Compare this to the result of the Kaiser\nmethod, where ``numtaps`` is 109.)\n\nNow we'll use ``remez`` to design the filter.\n\n.. code-block:: python\n\n trans_lo = cutoff - 0.5*width\n trans_hi = cutoff + 0.5*width\n taps = remez(numtaps,\n bands=[0, trans_lo,\n trans_hi, 0.5*fs],\n desired=[1, 0],\n weight=[1/deltap, 1/deltas],\n fs=fs)\n\nThe frequency response of the filter is shown in Figure :ref:`fig-opt-lowpass`.\nWe see that the filter meets the design specifications.\nIf we decrease the number of taps to 87 and check the response,\nwe find that the design specifications are no longer met, so we\naccept 89 taps as the optimum.\n\n.. figure:: figs/opt_lowpass.pdf\n\n Optimal lowpass filter frequency response. The number of taps is 89.\n\n :label:`fig-opt-lowpass`\n\n\nReferences\n==========\n.. [Bellanger]\n M. Bellanger, *Digital Processing of Signals: Theory and Practice* (3rd Edition),\n Wiley, Hoboken, NJ, 2000.\n.. [Kaiser66]\n J. F. Kaiser, Digital filters, in *System Analysis by Digital Computer*,\n Chapter 7, F. F. Kuo and J. F. Kaiser, eds., Wiley, New York, NY, 1966\n.. [Kaiser74]\n J. F. Kaiser, Nonrecursive digital filter design using the I0-sinh\n window function, *Proc. 1974 IEEE International Symp. on Circuits and\n Systems*, San Francisco, CA, 1974.\n.. [Lyons]\n Richard G. Lyons.\n *Understanding Digital Signal Processing* (2nd ed.),\n Pearson Higher Education, Inc., Upper Saddle River,\n New Jersey (2004)\n.. [OS]\n Alan V. Oppenheim, Ronald W. Schafer.\n *Discrete-Time Signal Processing* (3rd ed.),\n Pearson Higher Education, Inc., Upper Saddle River,\n New Jersey (2010)\n.. [PM]\n Parks-McClellan filter design algorithm. Wikipedia,\n https://en.wikipedia.org/wiki/Parks%E2%80%93McClellan_filter_design_algorithm\n.. [Rabiner1972a]\n L. R. Rabiner, The design of finite impulse response digital filters\n using linear programming techniques,\n *The Bell System Technical Journal*, Vol. 51, No. 6, July-August, 1972.\n.. [Rabiner1972b]\n L. R. Rabiner, Linear program design of finite impulse response (FIR)\n digital filters,\n *IEEE Trans. on Audio and Electroacoustics*, Vol. AU-20, No. 4, Oct. 1972.\n.. [RemezAlg]\n Remez algorithm. Wikipedia, ``https://en.wikipedia.org/wiki/Remez_algorithm``\n.. [SavGol]\n A. Savitzky, M. J. E. Golay. Smoothing and Differentiation of Data by\n Simplified Least Squares Procedures. *Analytical Chemistry*, 1964, 36 (8),\n pp 1627-1639.\n.. [SO]\n Nimal Naser, How to filter/smooth with SciPy/Numpy?, \n ``https://stackoverflow.com/questions/28536191``\n" }, { "path": "papers/yuan_tang_hongliang_liu/convert.sh", "content": "#convert MD to rst format and save to .txt for the main .rst include\npandoc --from=markdown --to=rst --output=${1}.txt ${1}.md\n" }, { "path": "papers/yuan_tang_hongliang_liu/distributed_and_gpus.md", "content": "# Title place holder (not to show in rst)\n\n## Distributed and GPU-accelerated XGBoost\n\nThough XGBoost has been proven to be highly efficient and scalable by industries, research labs,\nand data science competitions, it usually take hours or even days to train a model on\nextremely large dataset. Building highly accurate models using gradient boosting algorithm also requires\nextensive parameter tuning. For example, a classification task may involve running the algorithm\nmultiple times for different learning rates or maximum tree depths to explore the effect of different\nparameter combinations on classification metrics such as accuracy or AUC. In case of very\nlarge datasets that cannot fit into the memory of the machine, XGBoost can switch to out-of-core\nsetting to smoothly scale to billions of training samples with limited computing resources.\nIn addition, there's GPU implementation of XGBoost that could accelerate model training.\n\n\n### Distributed training\n\nXGBoost can train models in distributed settings without sacrificing its high performance.\nThe results in the [original XGBoost paper](https://dl.acm.org/ft_gateway.cfm?id=2939785&type=pdf)\nshows that XGBoost’s performance scales linearly as we add more machines. XGBoost is\nable to handle the entire 1.7 billion data with only four machines which greatly shows the\nsystem’s potential to handle even larger data.\n\nThe distributed XGBoost is currently available as a CLI program with a configuration file or a Python script.\nUsers should follow the detailed instruction to configure XGBoost for distributed training in\nthe [latest docs](https://xgboost.readthedocs.io/en/latest/).\n\nBelow is an example configuration file named \"dist.conf\" where we specify the parameters for defining\nboth the booster and the task:\n\n```\nbooster = gbtree\nobjective = binary:logistic\neta = 1.0\ngamma = 1.0\nmin_child_weight = 1\nmax_depth = 3\nnum_round = 2\nsave_period = 0\neval_train = 1\n```\n\nand then submit the distributed job to cluster, e.g.\n[Apache Hadoop YARN](https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html), using\n[dmlc-submit](https://github.com/dmlc/dmlc-core/tree/master/tracker) script:\n\n```\ndmlc-core/tracker/dmlc-submit --cluster=yarn --num-workers=2 --worker-cores=2 \\\n dist.conf nthread=2 \\\n data=s3://my-bucket/xgb-demo/train \\\n eval[test]=s3://my-bucket/xgb-demo/test \\\n model_dir=s3://my-bucket/xgb-demo/model\n```\n\nUsers can also submit a Python script to the cluster for distributed training.\nThe following is an example Python script named \"dist.py\" that performs training\nand testing of the model using XGBoost demo dataset:\n\n```python\nimport xgboost as xgb\n\nxgb.rabit.init()\n\ndtrain = xgb.DMatrix('demo/data/agaricus.txt.train')\ndtest = xgb.DMatrix('demo/data/agaricus.txt.test')\n\nparam = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}\n\nwatchlist = [(dtest,'eval'), (dtrain,'train')]\nnum_round = 20\n\nbst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)\n\nif xgb.rabit.get_rank() == 0:\n bst.save_model(\"test.model\")\n\nxgb.rabit.finalize()\n```\n\nFirst of all, call `xgb.rabit.init()` to initialize the distribute training and then load the\ndatasets. Note that the datasets will be automatically sharded in distributed mode.\n\nUsers can use `xgb.rabit.get_rank()` to get the current process. In the above example,\nwe explicilty tell process 0 to save the model after training.\n\nFinally, notify the tracker that the distributed training is successful using `xgb.rabit.finalize()`.\n\nWe can then submit the Python script using the same dmlc-submit script that we used earlier:\n\n```\ndmlc-core/tracker/dmlc-submit --cluster=local --num-workers=2 python dist.py\n```\n\n### GPU-accelerated training\n\nAdvancements in hardware accelerators, such as graphic processing units (GPUs),\noften bring significant performance boost to machine learning algorithms. Frameworks\nsuch as [TensorFlow Estimators](https://dl.acm.org/citation.cfm?id=3098171) and\n[H2O4GPU](https://github.com/h2oai/h2o4gpu) provide GPU implementations of many\nmachine learning algorithms to take advantage of hardware accelerations from\nmultiple GPUs.\n\nA [CUDA](https://en.wikipedia.org/wiki/CUDA)-based implementation of the decision\ntree constructions is also available within XGBoost. The tree construction\nalgorithm is executed exclusively on the graphics processing unit (GPU). It has shown\nshown high performance with a variety of datasets and settings, e.g. sparse input matrices.\n\nIndividual boosting iterations are executed in parallel within GPUs. An interleaved approach\nis used for shallow trees, switching to a more conventional radix sort-based approach for larger depths.\nThere's 3 to 6 times speedup using a Titan X compared to a 4 core i7 CPU, and 1.2 times speedup using\na Titan X compared to 2× Xeon CPUs (24 cores). The [original paper](https://peerj.com/articles/cs-127/)\nfor XGBoost GPU implementation shows that it is possible to process the\nHiggs dataset with 10 million samples and 28 variables entirely within GPU memory.\nThe implementation is exposed as a plug-in within XGBoost framework to fully support existing\nfunctionalities such as classification, regression, and rank learning tasks.\n\nTo use GPU-accelerated algorithms, users can simply change the value of \"tree_method\" in\nXGBoost training parameters to be \"gpu_hist\" instead of \"hist\". For example, the following\ncode would run XGBoost using only the CPU implementation:\n\n```python\nparams = {\n\t'objective': 'multi:softmax',\n\t'num_class': 2,\n\t'tree_method': 'hist'\n}\nxgb.train(params, training_data, num_round, ...)\n```\n\nwhile this next code snippet switches to use the GPU-accelerated algorithms:\n\n```python\nparams = {\n\t'objective': 'multi:softmax',\n\t'num_class': 2,\n\t'tree_method': 'gpu_hist'\n}\nxgb.train(params, training_data, num_round, ...)\n```\n\nThere are other parameters related to the GPU plug-in. For example, GPU-accelerated\nprediction is enabled by default if the above \"tree_method\" parameter is set to use GPU\nwith \"predictor\" parameter being set as \"gpu_predictor\" by default. However, users\ncan switch to only use CPU for making predictions by changing `predictor` parameter\nto `cpu_predictor` to help conserve GPU memory. There's also \"gpu_id\" parameter that\ncan be used to select the device ordinal.\n\n### External memory\n\nIf the data is too large to fit into the memory, e.g. large datasets from distributed file systems or\nlarge datasets saved in libsvm format locally, users can switch to use the external memory version\nof XGBoost by making one simple change to the dataset file path.\n\nThe native external memory version provided by XGBoost takes in the following filename format:\n`filename#cacheprefix`\nwhere the `filename` is the normal path to libsvm file to be loaded, `cacheprefix` is\na path to a cache file that XGBoost will use for external memory cache.\n\nFor example, instead of normally importing data like the following similar to other examples of this chapter:\n\n```python\ndata = xgb.DMatrix('data/agaricus.txt.train')\n```\n\nwe modify the code to the following to enable the external memory version:\n\n```python\ndata = xgb.DMatrix('data/agaricus.txt.train#dtrain.cache')\n```\n\nThe external memory mode natively works on distributed version, for example, you can specify\na path to your dataset stored in distributed file system:\n\n```python\ndata = xgb.DMatrix(\"hdfs://data/agaricus.txt.train#dtrain.cache\")\n```\n\nBesides the native external memory version provided by XGBoost, there's also\n[dask-xgboost](https://github.com/dask/dask-xgboost) integration that allows\ndistributed training of XGBoost model on larger-than-memory datasets using\n[Dask](https://github.com/dask/dask).\n" }, { "path": "papers/yuan_tang_hongliang_liu/distributed_and_gpus.txt", "content": "Title place holder (not to show in rst)\n=======================================\n\nDistributed and GPU-accelerated XGBoost\n---------------------------------------\n\nThough XGBoost has been proven to be highly efficient and scalable by\nindustries, research labs, and data science competitions, it usually\ntake hours or even days to train a model on extremely large dataset.\nBuilding highly accurate models using gradient boosting algorithm also\nrequires extensive parameter tuning. For example, a classification task\nmay involve running the algorithm multiple times for different learning\nrates or maximum tree depths to explore the effect of different\nparameter combinations on classification metrics such as accuracy or\nAUC. In case of very large datasets that cannot fit into the memory of\nthe machine, XGBoost can switch to out-of-core setting to smoothly scale\nto billions of training samples with limited computing resources. In\naddition, there's GPU implementation of XGBoost that could accelerate\nmodel training.\n\nDistributed training\n********************\n\nXGBoost can train models in distributed settings without sacrificing its\nhigh performance. The results in the `original XGBoost\npaper `__ shows\nthat XGBoost’s performance scales linearly as we add more machines.\nXGBoost is able to handle the entire 1.7 billion data with only four\nmachines which greatly shows the system’s potential to handle even\nlarger data.\n\nThe distributed XGBoost is currently available as a CLI program with a\nconfiguration file or a Python script. Users should follow the detailed\ninstruction to configure XGBoost for distributed training in the `latest\ndocs `__.\n\nBelow is an example configuration file named \"dist.conf\" where we\nspecify the parameters for defining both the booster and the task:\n\n::\n\n booster = gbtree\n objective = binary:logistic\n eta = 1.0\n gamma = 1.0\n min_child_weight = 1\n max_depth = 3\n num_round = 2\n save_period = 0\n eval_train = 1\n\nand then submit the distributed job to cluster, e.g. `Apache Hadoop\nYARN `__,\nusing\n`dmlc-submit `__\nscript:\n\n::\n\n dmlc-core/tracker/dmlc-submit --cluster=yarn --num-workers=2 --worker-cores=2 \\\n dist.conf nthread=2 \\\n data=s3://my-bucket/xgb-demo/train \\\n eval[test]=s3://my-bucket/xgb-demo/test \\\n model_dir=s3://my-bucket/xgb-demo/model\n\nUsers can also submit a Python script to the cluster for distributed\ntraining. The following is an example Python script named \"dist.py\" that\nperforms training and testing of the model using XGBoost demo dataset:\n\n.. code:: python\n\n import xgboost as xgb\n\n xgb.rabit.init()\n\n dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')\n dtest = xgb.DMatrix('demo/data/agaricus.txt.test')\n\n param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}\n\n watchlist = [(dtest,'eval'), (dtrain,'train')]\n num_round = 20\n\n bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)\n\n if xgb.rabit.get_rank() == 0:\n bst.save_model(\"test.model\")\n\n xgb.rabit.finalize()\n\nFirst of all, call ``xgb.rabit.init()`` to initialize the distribute\ntraining and then load the datasets. Note that the datasets will be\nautomatically sharded in distributed mode.\n\nUsers can use ``xgb.rabit.get_rank()`` to get the current process. In\nthe above example, we explicilty tell process 0 to save the model after\ntraining.\n\nFinally, notify the tracker that the distributed training is successful\nusing ``xgb.rabit.finalize()``.\n\nWe can then submit the Python script using the same dmlc-submit script\nthat we used earlier:\n\n::\n\n dmlc-core/tracker/dmlc-submit --cluster=local --num-workers=2 python dist.py\n\nGPU-accelerated training\n************************\n\nAdvancements in hardware accelerators, such as graphic processing units\n(GPUs), often bring significant performance boost to machine learning\nalgorithms. Frameworks such as `TensorFlow\nEstimators `__ and\n`H2O4GPU `__ provide GPU\nimplementations of many machine learning algorithms to take advantage of\nhardware accelerations from multiple GPUs.\n\nA `CUDA `__-based implementation of\nthe decision tree constructions is also available within XGBoost. The\ntree construction algorithm is executed exclusively on the graphics\nprocessing unit (GPU). It has shown shown high performance with a\nvariety of datasets and settings, e.g. sparse input matrices.\n\nIndividual boosting iterations are executed in parallel within GPUs. An\ninterleaved approach is used for shallow trees, switching to a more\nconventional radix sort-based approach for larger depths. There's 3 to 6\ntimes speedup using a Titan X compared to a 4 core i7 CPU, and 1.2 times\nspeedup using a Titan X compared to 2× Xeon CPUs (24 cores). The\n`original paper `__ for XGBoost GPU\nimplementation shows that it is possible to process the Higgs dataset\nwith 10 million samples and 28 variables entirely within GPU memory. The\nimplementation is exposed as a plug-in within XGBoost framework to fully\nsupport existing functionalities such as classification, regression, and\nrank learning tasks.\n\nTo use GPU-accelerated algorithms, users can simply change the value of\n\"tree\\_method\" in XGBoost training parameters to be \"gpu\\_hist\" instead\nof \"hist\". For example, the following code would run XGBoost using only\nthe CPU implementation:\n\n.. code:: python\n\n params = {\n 'objective': 'multi:softmax',\n 'num_class': 2,\n 'tree_method': 'hist'\n }\n xgb.train(params, training_data, num_round, ...)\n\nwhile this next code snippet switches to use the GPU-accelerated\nalgorithms:\n\n.. code:: python\n\n params = {\n 'objective': 'multi:softmax',\n 'num_class': 2,\n 'tree_method': 'gpu_hist'\n }\n xgb.train(params, training_data, num_round, ...)\n\nThere are other parameters related to the GPU plug-in. For example,\nGPU-accelerated prediction is enabled by default if the above\n\"tree\\_method\" parameter is set to use GPU with \"predictor\" parameter\nbeing set as \"gpu\\_predictor\" by default. However, users can switch to\nonly use CPU for making predictions by changing ``predictor`` parameter\nto ``cpu_predictor`` to help conserve GPU memory. There's also \"gpu\\_id\"\nparameter that can be used to select the device ordinal.\n\nExternal memory\n***************\n\nIf the data is too large to fit into the memory, e.g. large datasets\nfrom distributed file systems or large datasets saved in libsvm format\nlocally, users can switch to use the external memory version of XGBoost\nby making one simple change to the dataset file path.\n\nThe native external memory version provided by XGBoost takes in the\nfollowing filename format: ``filename#cacheprefix`` where the\n``filename`` is the normal path to libsvm file to be loaded,\n``cacheprefix`` is a path to a cache file that XGBoost will use for\nexternal memory cache.\n\nFor example, instead of normally importing data like the following\nsimilar to other examples of this chapter:\n\n.. code:: python\n\n data = xgb.DMatrix('data/agaricus.txt.train')\n\nwe modify the code to the following to enable the external memory\nversion:\n\n.. code:: python\n\n data = xgb.DMatrix('data/agaricus.txt.train#dtrain.cache')\n\nThe external memory mode natively works on distributed version, for\nexample, you can specify a path to your dataset stored in distributed\nfile system:\n\n.. code:: python\n\n data = xgb.DMatrix(\"hdfs://data/agaricus.txt.train#dtrain.cache\")\n\nBesides the native external memory version provided by XGBoost, there's\nalso `dask-xgboost `__ integration\nthat allows distributed training of XGBoost model on larger-than-memory\ndatasets using `Dask `__.\n" }, { "path": "papers/yuan_tang_hongliang_liu/intro.txt", "content": "XGBoost\n==================================\nIntroduction\n----------------------------------\n\nMachine learning approaches are becoming more and more important nowadays:\nrecommendation systems learn to generate recommendations of potential interest\nwith the right context according to user feedback and interactions; anomalous\nevent detection systems helps monitor assets to avoids downtime due to extreme\nconditions; fraud detection systems protect financial institutions from security\nattacks and malicious fraud behaviors. Among the popular machine learning methods\nused in industrial applications, gradient tree boosting is one method that stands\nout due to its state-of-the-art results on many classification benchmarks.\n\nXGBoost (Extreme Gradient Boosting) is an optimized distributed gradient boosting\nlibrary designed to be highly efficient, flexible and portable. XGBoost provides\na parallel tree boosting (also known as GBDT, GBM) that solve many data science\nproblems in a fast and accurate way. It is widely used by data scientists to\nachieve state-of-the-art results on many machine learning competitions. XGBoost\ncan run on various environments, including single machine as well as distributed\nenvironment such as Hadoop YARN, MPI, Apache Spark, Apache Flink, etc. It provides\nlanguage bindings such as Python, R, Java, Scala, C++, etc. The library includes\nefficient linear model solver and tree learning algorithms. It supports various\nobjective functions, including regression, classification and ranking and it's\nmade to be extensible so that users are also allowed to define their custom\nobjectives easily.\n\nXGBoost gives state-of-the-art results on a wide range of problems.\nSome winning solutions for Kaggle competitions that used XGBoost include:\npredicting if individual users will click a given context ad; predicting the\nprice a supplier will quote for a given tube assembly; predicting massive online\ncourse dropout rate; classifying high energy physics events. It's also being\ndeployed and incorporated into real-world production pipelines such as Tencent's\nclick-through rate prediction and Alibaba's ODPSCloud service.\n\nThe success of XGBoost lies in its scalability in many scenarios.\nFor example, it runs at least ten times faster than existing popular\nsolutions on a single machine and scales to billions of examples in distributed\nor memory-limited settings such as edge devices. A GPU implementation of the\ndecision tree constructions is also available within XGBoost to fully take\nadvantage of hardware accelerations. XGBoost has been through several\nimportant system-level and algorithmic optimizations to achieve such scalability.\nThe optimizations include: a novel tree learning algorithm for handling sparse\ndata; a theoretically justified weighted quantile sketch procedure enables\nhandling instance weights in approximate tree learning. Additionally, XGBoost\nemploys a lot of internal parallel and distributed computing processes that make\nmodel training faster to enable quicker model exploration. It exploits\nout-of-core computation and enables data scientists to process hundred millions\nof data records on devices with limited computational resources.\n" }, { "path": "papers/yuan_tang_hongliang_liu/main_functionalities.txt", "content": "Main Functionalities\n----------------------------------\n\n\nBasic Walk-through\n******************\n\nThe following is a basic example of using XGBoost to train an extreme gradient\nboosting model using `Mushroom Data Set `_\nfrom `UCI Machine Learning Repository `_.\nYou can find a binary buffer of the data set generated by XGBoost\n`here `_. XGBoost\nuses an internal data structure called `DMatrix`, which is optimized for\nboth memory efficiency and training speed. Various types of data sources can be\npassed in to construct `DMatrix` object, such as Numpy array, Scipy sparse matrix,\nPandas data frame, or a string that represents the path to a\n`LIBSVM format `_ or a binary file\nlike the following:\n\n.. code-block:: python\n\n\timport xgboost as xgb\n\timport numpy as np\n\n\tdtrain = xgb.DMatrix('agaricus.txt.train')\n\tdtest = xgb.DMatrix('agaricus.txt.test')\n\n\tparam = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic' }\n\twatchlist = [(dtest,'eval'), (dtrain,'train')]\n\tnum_round = 2\n\tbst = xgb.train(param, dtrain, num_round, watchlist)\n\tpreds = bst.predict(dtest)\n\tlabels = dtest.get_label()\n\terr = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))\n\n\nIn the above example, we first load the binary file into a `DMatrix` object that\ncan later be fed into XGBoost. We specify all the parameters used to train the\nbooster in a dictionary as well as the objective of the model, which in this case\nis logistic regression for binary classification problem. A more thorough guide\nto help you understand and tune your parameters is illustrated in the next section.\nWe then specify a list of tuples where each tuple represents a pair of (DMatrix,\nstring) so you can watch the results during training. For example, if you\nspecify `'eval_metric': 'logloss'` as an item in the above `param` dictionary and\ndefine the watchlist as in above example, you will see the provided metrics\nbeing evaluated during both training and evaluation period like the following:\n\n.. code-block:: python\n\n\t{'train': {'logloss': ['0.48253', '0.35953']},\n\t 'eval': {'logloss': ['0.480385', '0.357756']}}\n\nOnce a model is trained, we can easily save the model to disk and load it back\nagain later on. The foolowing example saves the model as well as the `DMatrix`\nbinary buffer from previous example so we can see whether the model loaded from\npreviously saved model gives us the same predictions on the same test data set.\n\n.. code-block:: python\n\n\tbst.save_model('xgb.model')\n\tbst2 = xgb.Booster(model_file='xgb.model')\n\tdtest2 = xgb.DMatrix('dtest.buffer')\n\tpreds2 = bst2.predict(dtest2)\n\tassert np.sum(np.abs(preds2-preds)) == 0\n\n\n\nCustom Objective and Evaluation Function\n****************************************\n\nIn XGBoost, we can provide custom objective function and evaluation function for\ntraining the model. They are often used for many applications so the model\nreflects more true characteristics of the real problems.\n\n.. code-block:: python\n\n\tdtrain = xgb.DMatrix('../data/agaricus.txt.train')\n\tdtest = xgb.DMatrix('../data/agaricus.txt.test')\n\n\tparam = {'max_depth': 2, 'eta': 1, 'silent': 1}\n\twatchlist = [(dtest, 'eval'), (dtrain, 'train')]\n\tnum_round = 2\n\n\tdef logregobj(preds, dtrain):\n\t labels = dtrain.get_label()\n\t preds = 1.0 / (1.0 + np.exp(-preds))\n\t grad = preds - labels\n\t hess = preds * (1.0-preds)\n\t return grad, hess\n\n\tdef evalerror(preds, dtrain):\n\t labels = dtrain.get_label()\n\t return 'error', float(sum(labels != (preds > 0.0))) / len(labels)\n\n\tbst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)\n\nWe first load the training and test set as usual and define necessary information\nsuch as parameters, watch list, and the number of rounds for training. Note that\nfor customized objective function, we leave objective in `params` as default.\nThen we define customized object function that accepts the prediction\nprobabilities `preds` and the training set `dtrain`, and then returns the\ngradient and second order gradient so in this case our objective is the\nlog-likelihood loss.\n\nSimilarly, our customized evaluation function accepts the same input but returns\na tuple with metric name as a string and the result of the metric. We then pass\nthe customized functions into `xgb.train` to train our model using them instead\nof built-in objectives and metrics. The customized evaluation function can also\nreturn a list of tuples, for example:\n\n.. code-block:: python\n\n\tdef evalerror_03(self, preds, dtrain):\n from sklearn.metrics import mean_squared_error\n labels = dtrain.get_label()\n return [('rmse', mean_squared_error(labels, preds)),\n ('error', float(sum(labels != (preds > 0.0))) / len(labels))]\n\n\nBoost from Existing Prediction\n****************************************\n\nUsers can set the base margin of booster to start training from. This can be\nused to specify a base margin for the prediction value of each data point in\nexisting model. For example, let's say we have a booster object trained like\nthe following:\n\n.. code-block:: python\n\n\tdtrain = xgb.DMatrix('data/agaricus.txt.train')\n\tdtest = xgb.DMatrix('data/agaricus.txt.test')\n\twatchlist = [(dtest, 'eval'), (dtrain, 'train')]\n\tparams = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}\n\tbst = xgb.train(params, dtrain, 1, watchlist)\n\nWe can then output the raw untransformed margin value during prediction,\nusing `output_margin=True` in `predict()`. We then use `set_base_margin()`\nto set the returned untransformed margin values to be the base margin of\nthe `DMatrix` objects that we use for training and testing, and then we\nresume boosting from the existing predictions.\n\n.. code-block:: python\n\n\tptrain = bst.predict(dtrain, output_margin=True)\n\tptest = bst.predict(dtest, output_margin=True)\n\tdtrain.set_base_margin(ptrain)\n\tdtest.set_base_margin(ptest)\n\tbst = xgb.train(params, dtrain, 1, watchlist)\n\n\nPredict Using First N Trees\n****************************************\n\nWe can also predict using only limited number of trees we specify. This is\navailable via the `ntree_limit` argument in `predict()`. For example, below\nwe use only the first 3 trees from the booster:\n\n.. code-block:: python\n\n\tdtrain = xgb.DMatrix('data/agaricus.txt.train')\n\tdtest = xgb.DMatrix('data/agaricus.txt.test')\n\tparam = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}\n\twatchlist = [(dtest, 'eval'), (dtrain, 'train')]\n\tnum_round = 3\n\tbst = xgb.train(param, dtrain, num_round, watchlist)\n\typred = bst.predict(dtest, ntree_limit=3)\n\n\nPredict Leaf Indices\n****************************************\n\nThere's also `pred_leaf` argument you can use in `predict()` to obtain the\npredicted leaf indices of each sample. If `pred_leaf` option is turned on,\na matrix will be returned with the shape of `(nsamples, ntrees)` where each\nrecord indicating the predicted indices of each sample in each tree. It's\nworth noting that the leaf indices of a tree is unique per tree so it's\npossible, for example, to obtain leaf 1 in both tree 1 and tree 0. This\nfeature is extremely useful when users want to understand the details of\nthe splitting. Users can even discover interesting insight when tree starts\nto split in an unusual direction. There are also users who use these predicted\nleaf indices as additional features fed into the model. The following is an\nexample on how to use this feature:\n\n.. code-block:: python\n\n\tdtrain = xgb.DMatrix('data/agaricus.txt.train')\n\tdtest = xgb.DMatrix('data/agaricus.txt.test')\n\tparam = {'max_depth':2, 'eta':1, 'silent':1, 'objective': 'binary:logistic'}\n\twatchlist = [(dtest, 'eval'), (dtrain, 'train')]\n\tnum_round = 3\n\tbst = xgb.train(param, dtrain, num_round, watchlist)\n\tleafindex = bst.predict(dtest, ntree_limit=2, pred_leaf=True)\n\n\nCross Validation\n****************************************\n\nXGBoost provides cross validation function that users can use to partition\nthe data into complementary subsets to be used for training and testing\nseparately. This is a very popular approach used in machine learning that\ncould greatly reduce the variability of a model. In other words, it's used\nvery often to access how generalizable a model is on independent data sets.\nFor example, we use `cv()` to do this like follows:\n\n.. code-block:: python\n\n\tdtrain = xgb.DMatrix('data/agaricus.txt.train')\n\tparam = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}\n\tnum_round = 2\n\txgb.cv(param, dtrain, num_round, nfold=5,\n\t metrics={'error'}, seed=0)\n\nCallbacks can be passed into `cv()` to monitor and control the process.\nFor example, here we use `print_evaluation()` from `callback` module to\nprint out standard deviation and use `early_stop()` to provide the necessary\nlogics to be used to stop the training process when certain conditions are met.\n\n.. code-block:: python\n\n\tres = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,\n\t metrics={'error'}, seed=0,\n\t callbacks=[xgb.callback.print_evaluation(show_stdv=True),\n\t xgb.callback.early_stop(3)])\n\nUsers can also define customized pre-processing function used to pre-process\ntraining and testing set, as well as parameters that might be changed dynamically\ninside this function. For example, we can define the pre-processing function as\nfollows to rescale the weights:\n\n.. code-block:: python\n\n\tdef fpreproc(dtrain, dtest, param):\n\t label = dtrain.get_label()\n\t ratio = float(np.sum(label == 0)) / np.sum(label == 1)\n\t param['scale_pos_weight'] = ratio\n\t return (dtrain, dtest, param)\n\nFor each fold, the training and testing set, as well as hyper-parameters will be\npassed into the defined function `fpreproc()` and the returned value of those\nwill be used to generate results of each particular fold. We then pass `fpreproc`\ninto `cv()` to use previously defined pre-processing function:\n\n.. code-block:: python\n\n\txgb.cv(param, dtrain, num_round, nfold=5, metrics={'auc'},\n\t\t seed=0, fpreproc=fpreproc)\n\nWe can also conduct cross-validation using customized objective and evaluation\nfunction. Very similar to what we've discussed in previous sections:\n\n.. code-block:: python\n\n\tdef logregobj(preds, dtrain):\n\t labels = dtrain.get_label()\n\t preds = 1.0 / (1.0 + np.exp(-preds))\n\t grad = preds - labels\n\t hess = preds * (1.0-preds)\n\t return grad, hess\n\n\tdef evalerror(preds, dtrain):\n\t labels = dtrain.get_label()\n\t return 'error', float(sum(labels != (preds > 0.0))) / len(labels)\n\n\tparam = {'max_depth':2, 'eta':1, 'silent':1}\n\txgb.cv(param, dtrain, num_round, nfold=5, seed=0,\n\t obj=logregobj, feval=evalerror)\n\n\nScikit-learn Wrapper\n*****************************************\n\nXGBoost provides a wrapper class to allow models to be treated like classifiers\nor regressors in the Scikit-learn framework. This allows users to use XGBoost\nalong with features of Scikit-learn easily. For example, the following is an\nexample of using `XGBClassifier` to conduct binary classification on digits\ndataset imported from `sklearn.datasets`. Here we use `KFold` from Scikit-learn\nto split the data into 2 folds of training and testing set, and then use\n`XGBClassifier.fit()` and `XGBClassifier.predict()` to train and test the\nmodel on every fold respectively.\n\n.. code-block:: python\n\n from sklearn.datasets import load_digits\n from sklearn.cross_validation import KFold\n\n digits = load_digits(2)\n y = digits['target']\n X = digits['data']\n kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)\n for train_index, test_index in kf:\n xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index])\n preds = xgb_model.predict(X[test_index])\n labels = y[test_index]\n err = sum(1 for i in range(len(preds))\n if int(preds[i] > 0.5) != labels[i]) / float(len(preds))\n\nSimilarly, we can train a regression model on boston housing data imported from\n`sklearn.datasets` using `XGBRegressor`. Similar to above example, we use\nfamiliar syntax from Scikit-learn to fit the model and generate predictions.\nAdditionally, the parameters in `xgboost.Booster.predict()` such as\n`output_margin` and `ntree_limit` introduced in previous sections are also\navailable, as shown in the below example:\n\n.. code-block:: python\n\n\tfrom sklearn.metrics import mean_squared_error\n\tfrom sklearn.datasets import load_boston\n\tfrom sklearn.cross_validation import KFold\n\n\tboston = load_boston()\n\ty = boston['target']\n\tX = boston['data']\n\tkf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)\n\tfor train_index, test_index in kf:\n\t xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index])\n\t preds1 = xgb_model.predict(X[test_index])\n\t preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)\n\t preds3 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=1)\n\nFurthermore, we can fully take advantage of other modules and helper functions,\nsuch as `grid_search.GridSearchCV`, to streamline model building and developing\nprocess. We pass a list of hyper-parameters to `grid_search.GridSearchCV` and\nthen call `fit()` to conduct the grid search on optimal hyper-parameters.\n\n.. code-block:: python\n\n\tfrom sklearn.grid_search import GridSearchCV\n\tfrom sklearn.datasets import load_boston\n\n\tboston = load_boston()\n\ty = boston['target']\n\tX = boston['data']\n\txgb_model = xgb.XGBRegressor()\n\tclf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],\n\t 'n_estimators': [50, 100, 200]}, verbose=1)\n\tclf.fit(X, y)\n" }, { "path": "papers/yuan_tang_hongliang_liu/model_tune.md", "content": "# Title place holder (not to show in rst)\n\n## Model tune up and parameter selection\n\nTODO!" }, { "path": "papers/yuan_tang_hongliang_liu/model_tune.txt", "content": "Title place holder (not to show in rst)\n=======================================\n\nModel tune up and parameter selection\n-------------------------------------\n\nTODO!\n" }, { "path": "papers/yuan_tang_hongliang_liu/parameter_explained.md", "content": "# Title place holder (not to show in rst)\n## Parameter explained: train `xgboost` for the best prediction\n\nThere are 3 types of hyper parameters in `xgboost`: \n\n* General Parameters for general boost function, commonly tree or linear model.\n* Booster Parameters for individual booster at each step which determines the model predictive power.\n* Learning Task Parameters for the optimizing prediction performance in the learning senarios.\n\nAll these parameters are documented in the `xgboost` [document](http://xgboost.readthedocs.io/en/latest/parameter.html).\n\n### General parameters\n\n`booster`, `silent` and `nthread` are important general parameters, while `xgboost` automatically defines `num_pbuffer` and `num_feature`.\n\n* `booster` [default=gbtree]: select boosters in `gbtree`, `gblinear` or `dart` (with `dropout` function in the recent development) where `gbtree` and `dart` use tree based model and `gblinear` uses linear function.\n* `silent` [default=0]: `0` means printing running messages while `1` means silent mode. For better understanding the model, one might want to set it to `0`.\n* `nthread` [default to maximum number of threads available in the system if not set]: number of parallel threads used to run xgboost. If ones wants to run on all cores/threads, this value can be left to default. If the system's CPU support hyper-threading, the default number is the number of hyper-threads instead of real cores.\n\n### Booster parameters\n\nBooster parameters determine the predictive power of the tree model, where understanding the meanings of these parameters can help provide the most optimal prediction. We here explain most about the tree model booster parameters in this section since the tree model is most commonly used, and it can outperform the linear booster for most cases. For `dart` and `tweetie regression` parameters, please refer to the `xgboost` [document](http://xgboost.readthedocs.io/en/latest/parameter.html).\n\n*Popular booster parameters*\n\n* `eta` [default=0.3]: the learning rate in GBM model. It is named as `learning_rate` if use the `scikit-learn` interface `XGBClassifier`.\n\t* the weights on each step is shrinking at each step and the model becomes more stable.\n\t* Recommened values: 0.01-0.2\n\t* The smaller `eta`, the slower shrinking, and the more boosting rounds are needed for reaching the optimal state, where the number of boosting rounds can be defined when initializing `xgboost` by the `n_rounds` parameter.\n\t* The larger `eta`, the faster shrinking, however, it may risk for not reaching the final optimal state.\n* `min_child_weight` [default=1]\n\t* Defines the minimum sum of weights of all observations required in a child tree.\n\t* This refers to min “sum of weights” of observations instead of `min_child_leaf ` in GBM.\n\t* This parameter controls the risk of over-fitting: the higher values prevent a model from learning relations which might come from some particular samples selected for a tree. However, it can lead to under-fitting if the value is set too high. This parameter should be tuned using cross-validation.\n\n* `max_depth` [default=6]\n\t* The maximum depth of a tree, same as in the GBDT.\n\t* Recommended values: 4-8\n\t* This parameter controls the risk of over-fitting: taller trees allows model to learn relations very specific to particular samples, while shallower trees don't have enough depth for learning enough from particular samples. This parameter should be tuned using cross-validation.\n\n* `max_leaf_nodes`: maximum number of terminal nodes or leaves in a tree.\n\t* It can be used for replacing `max_depth` while a depth of ‘n’ would produce a maximum of `2^n` leaves for binary tree. If this is defined, XGBoost will ignore `max_depth` parameter.\n\n* `gamma` [default=0]\n\t* It is the threshold of the minimum loss reduction required for splitting a node, where a node is split only when the resulting split provides a positive reduction in the loss function. \n\t* There are no default recommended values. The larger `gamma`, the more conservative of the algorithm.\n\n* `subsample` [default=1]\n\t* it defines the fraction of observations to be randomly samples for each tree. It can mitigate over-fitting for some cases since smaller values gives more conservative decision but it might under-fit if the value is too small.\n\t* Recommended values: 0.5-1, the smaller value, the more risk of underfitting.\n\n* `colsample_bytree` [default=1]\n\t* It defines the fraction of columns to be randomly samples for each tree. It may mitigate over-fitting for some cases, since it introduces randomness from the data for preventing the tree learning on too specific samples.\n\t* Recommended values: 0.5-1, the smaller value, the more risk of underfitting.\n\n* `scale_pos_weight` [default=1]\n\t* It defines the scale factor of positive vs negative samples in the training dataset. If the training dataset is highly imbalanced, it can helps faster learning and converging.\n\n* `alpha` [default=0]: it is also named as `reg_alpha` if use the `scikit-learn` interface `XGBClassifier`.\n\t* It defines L1 regularization term on weight (like that in `Lasso` regression). The larger value, the more conservative model.\n\t* If the features are in a very high dimension and has much sparsity, good alpha values can lead to faster algorithm.\n\n* `tree_method`: the method of generating trees. It can be chosen from `auto`, `exact`, `approx`, `hist`, `gpu_exact`, `gpu_hist`.\n\t* XGBoost needs to enumerate all possible split points of the features. If the full dataset is too large to load into memory, `exact` or 'approx' algorithm might be too slow or too biased, where `hist` provides a good histogram approximation for the split.\n\n*Rarely tuned booster parameters*\n\n`xgboost` also has some parameters which are set to default for most cases.\n\n* `max_delta_step` [default=0]\n\t* It sets the limit to each tree's weight estimation, if it is default as 0, there is no constraint, otherwise the algorithm becomes more consservative when update steps and has more risk of being underfitting. This parameter might be effective when train a highly imbalanced dataset. \n* `colsample_bylevel` [default=1]\n\t* Similar to `colsample_bytree`, it defines the subsample ratio of columns for each split in each level. However, `subsample` and `colsample_bytree` are usually used.\n* `lambda` [default=1]: it is also named as `reg_lambda` if use the `scikit-learn` interface `XGBClassifier`.\n\t* It defines `L2` regularization term on weights (like that in `Ridge` regression). The larger value, the more conservative model.\n\t* This used to handle the regularization part of XGBoost. It might mitigate overfitting for some cases.\n\t\n### Learning task parameters\n\nLearning task parameters define the optimization objective metric to be calculated at each step, a.k.a the loss function. For different use cases, different loss functions should be chosen.\n\n* `objective` [default=reg:linear]: the loss function to be minimized. \n\t* `binary:logistic` : logistic regression for binary classification, returns predicted probability\n\t* `multi:softmax` : multiclass classification using the softmax objective, returns predicted class (binary results, not probabilities). `num_class` parameter is needed for defining the number of classes\n\t* `multi:softprob` : same as softmax, but returns predicted probability of each data point belonging to each class.\n* `eval_metric` [ default according to objective ]: The evaluation metric for validation. The default values are `rmse` for regression and `error` for classification.\n* `seed` [default=0]: the random number seed\n\t* maintaining the seeds can generate reproducible results.\n\t* Using different seeds can introduce model variance. By keeping multiple seeds and their model output, one can ensemble multiple XGBoost models from multiple seeds and reduce the model variance.\n" }, { "path": "papers/yuan_tang_hongliang_liu/parameter_explained.txt", "content": "Title place holder (not to show in rst)\n=======================================\n\nParameter explained: train ``xgboost`` for the best prediction\n--------------------------------------------------------------\n\nThere are 3 types of hyper parameters in ``xgboost``:\n\n- General Parameters for general boost function, commonly tree or\n linear model.\n- Booster Parameters for individual booster at each step which\n determines the model predictive power.\n- Learning Task Parameters for the optimizing prediction performance in\n the learning senarios.\n\nAll these parameters are documented in the ``xgboost``\n`document `__.\n\nGeneral parameters\n******************\n\n``booster``, ``silent`` and ``nthread`` are important general\nparameters, while ``xgboost`` automatically defines ``num_pbuffer`` and\n``num_feature``.\n\n- ``booster`` [default=gbtree]: select boosters in ``gbtree``,\n ``gblinear`` or ``dart`` (with ``dropout`` function in the recent\n development) where ``gbtree`` and ``dart`` use tree based model and\n ``gblinear`` uses linear function.\n- ``silent`` [default=0]: ``0`` means printing running messages while\n ``1`` means silent mode. For better understanding the model, one\n might want to set it to ``0``.\n- ``nthread`` [default to maximum number of threads available in the\n system if not set]: number of parallel threads used to run xgboost.\n If ones wants to run on all cores/threads, this value can be left to\n default. If the system's CPU support hyper-threading, the default\n number is the number of hyper-threads instead of real cores.\n\nBooster parameters\n******************\n\nBooster parameters determine the predictive power of the tree model,\nwhere understanding the meanings of these parameters can help provide\nthe most optimal prediction. We here explain most about the tree model\nbooster parameters in this section since the tree model is most commonly\nused, and it can outperform the linear booster for most cases. For\n``dart`` and ``tweetie regression`` parameters, please refer to the\n``xgboost``\n`document `__.\n\n*Popular booster parameters*\n\n- ``eta`` [default=0.3]: the learning rate in GBM model. It is named as\n ``learning_rate`` if use the ``scikit-learn`` interface\n ``XGBClassifier``.\n\n - the weights on each step is shrinking at each step and the model\n becomes more stable.\n - Recommened values: 0.01-0.2\n - The smaller ``eta``, the slower shrinking, and the more boosting\n rounds are needed for reaching the optimal state, where the number\n of boosting rounds can be defined when initializing ``xgboost`` by\n the ``n_rounds`` parameter.\n - The larger ``eta``, the faster shrinking, however, it may risk for\n not reaching the final optimal state.\n\n- ``min_child_weight`` [default=1]\n\n - Defines the minimum sum of weights of all observations required in\n a child tree.\n - This refers to min “sum of weights” of observations instead of\n ``min_child_leaf`` in GBM.\n - This parameter controls the risk of over-fitting: the higher\n values prevent a model from learning relations which might come\n from some particular samples selected for a tree. However, it can\n lead to under-fitting if the value is set too high. This parameter\n should be tuned using cross-validation.\n\n- ``max_depth`` [default=6]\n\n - The maximum depth of a tree, same as in the GBDT.\n - Recommended values: 4-8\n - This parameter controls the risk of over-fitting: taller trees\n allows model to learn relations very specific to particular\n samples, while shallower trees don't have enough depth for\n learning enough from particular samples. This parameter should be\n tuned using cross-validation.\n\n- ``max_leaf_nodes``: maximum number of terminal nodes or leaves in a\n tree.\n\n - It can be used for replacing ``max_depth`` while a depth of ‘n’\n would produce a maximum of ``2^n`` leaves for binary tree. If this\n is defined, XGBoost will ignore ``max_depth`` parameter.\n\n- ``gamma`` [default=0]\n\n - It is the threshold of the minimum loss reduction required for\n splitting a node, where a node is split only when the resulting\n split provides a positive reduction in the loss function.\n - There are no default recommended values. The larger ``gamma``, the\n more conservative of the algorithm.\n\n- ``subsample`` [default=1]\n\n - it defines the fraction of observations to be randomly samples for\n each tree. It can mitigate over-fitting for some cases since\n smaller values gives more conservative decision but it might\n under-fit if the value is too small.\n - Recommended values: 0.5-1, the smaller value, the more risk of\n underfitting.\n\n- ``colsample_bytree`` [default=1]\n\n - It defines the fraction of columns to be randomly samples for each\n tree. It may mitigate over-fitting for some cases, since it\n introduces randomness from the data for preventing the tree\n learning on too specific samples.\n - Recommended values: 0.5-1, the smaller value, the more risk of\n underfitting.\n\n- ``scale_pos_weight`` [default=1]\n\n - It defines the scale factor of positive vs negative samples in the\n training dataset. If the training dataset is highly imbalanced, it\n can helps faster learning and converging.\n\n- ``alpha`` [default=0]: it is also named as ``reg_alpha`` if use the\n ``scikit-learn`` interface ``XGBClassifier``.\n\n - It defines L1 regularization term on weight (like that in\n ``Lasso`` regression). The larger value, the more conservative\n model.\n - If the features are in a very high dimension and has much\n sparsity, good alpha values can lead to faster algorithm.\n\n- ``tree_method``: the method of generating trees. It can be chosen\n from ``auto``, ``exact``, ``approx``, ``hist``, ``gpu_exact``,\n ``gpu_hist``.\n\n - XGBoost needs to enumerate all possible split points of the\n features. If the full dataset is too large to load into memory,\n ``exact`` or 'approx' algorithm might be too slow or too biased,\n where ``hist`` provides a good histogram approximation for the\n split.\n\n*Rarely tuned booster parameters*\n\n``xgboost`` also has some parameters which are set to default for most\ncases.\n\n- ``max_delta_step`` [default=0]\n\n - It sets the limit to each tree's weight estimation, if it is\n default as 0, there is no constraint, otherwise the algorithm\n becomes more consservative when update steps and has more risk of\n being underfitting. This parameter might be effective when train a\n highly imbalanced dataset.\n\n- ``colsample_bylevel`` [default=1]\n\n - Similar to ``colsample_bytree``, it defines the subsample ratio of\n columns for each split in each level. However, ``subsample`` and\n ``colsample_bytree`` are usually used.\n\n- ``lambda`` [default=1]: it is also named as ``reg_lambda`` if use the\n ``scikit-learn`` interface ``XGBClassifier``.\n\n - It defines ``L2`` regularization term on weights (like that in\n ``Ridge`` regression). The larger value, the more conservative\n model.\n - This used to handle the regularization part of XGBoost. It might\n mitigate overfitting for some cases.\n\nLearning task parameters\n************************\n\nLearning task parameters define the optimization objective metric to be\ncalculated at each step, a.k.a the loss function. For different use\ncases, different loss functions should be chosen.\n\n- ``objective`` [default=reg:linear]: the loss function to be\n minimized.\n\n - ``binary:logistic`` : logistic regression for binary\n classification, returns predicted probability\n - ``multi:softmax`` : multiclass classification using the softmax\n objective, returns predicted class (binary results, not\n probabilities). ``num_class`` parameter is needed for defining the\n number of classes\n - ``multi:softprob`` : same as softmax, but returns predicted\n probability of each data point belonging to each class.\n\n- ``eval_metric`` [ default according to objective ]: The evaluation\n metric for validation. The default values are ``rmse`` for regression\n and ``error`` for classification.\n- ``seed`` [default=0]: the random number seed\n\n - maintaining the seeds can generate reproducible results.\n - Using different seeds can introduce model variance. By keeping\n multiple seeds and their model output, one can ensemble multiple\n XGBoost models from multiple seeds and reduce the model variance.\n" }, { "path": "papers/yuan_tang_hongliang_liu/references.txt", "content": "References\n==========\n\n- `XGBoost: a scalable tree boosting\n system `__\n- `Latest documentation of\n XGBoost `__\n- `Accelerating the XGBoost algorithm using GPU\n computing `__\n- `Apache Hadoop\n YARN `__\n- `TensorFlow\n Estimators `__\n- `H2O4GPU `__\n- `dask-xgboost `__\n- `Dask `__\n- `LIBSVM format `__\n- `UCI Machine Learning\n Repository `__\n- `Mushroom data\n set `__\n" }, { "path": "papers/yuan_tang_hongliang_liu/vs_gbdt.md", "content": "# Title place holder (not to show in rst)\n## XGBoost vs. GBDT\n\nGradient Boosting Decision Tree (short as \"GBDT\") is a type of boosting algorithm, which produces a weak learner (a decision tree) after each iteration and each tree is trained from the residual from last iteration. Each weak decision tree must be simple, has low variance and high bias, and each iteration improves the classification power by reducing the variance. Usually GBDT uses Classification And Regression Tree (CART). Since each tree must be simple, the tree can't be deep, and the output decision tree is the weighted summation of each weak tree.\n\nCART as a binary tree at each iteration is generated by feature selection. For example, we can choose feature `j` from total `K` features as the first node to split the tree, and choose a value `v` as the threshold where two classes are determined by the split value `v` where one class is greater than `v` and the other less than `v`.\n\nTo find the optimal split value `v`, GBDT needs to sort all features values and this sorting is very time consuming. XGBoost sorts all features into blocks before training and keeps using these blocks during the training period for feature split. This pre-sorted blocks structure enables the key feature of XGBoost: parallelization where calculating the gain of each feature can be parallelized with these pre-sorted blocks.\n\nBeyond parallelization, XGBoost also considers both the first order and the second order derivative of the loss function. Meanwhile, it includes tree trimming and regularization during the training. All of these ensures its better prediction power and less chance of overfitting than GBDT. " }, { "path": "papers/yuan_tang_hongliang_liu/vs_gbdt.txt", "content": "Title place holder (not to show in rst)\n=======================================\n\nXGBoost vs. GBDT\n----------------\n\nGradient Boosting Decision Tree (short as \"GBDT\") is a type of boosting\nalgorithm, which produces a weak learner (a decision tree) after each\niteration and each tree is trained from the residual from last\niteration. Each weak decision tree must be simple, has low variance and\nhigh bias, and each iteration improves the classification power by\nreducing the variance. Usually GBDT uses Classification And Regression\nTree (CART). Since each tree must be simple, the tree can't be deep, and\nthe output decision tree is the weighted summation of each weak tree.\n\nCART as a binary tree at each iteration is generated by feature\nselection. For example, we can choose feature ``j`` from total ``K``\nfeatures as the first node to split the tree, and choose a value ``v``\nas the threshold where two classes are determined by the split value\n``v`` where one class is greater than ``v`` and the other less than\n``v``.\n\nTo find the optimal split value ``v``, GBDT needs to sort all features\nvalues and this sorting is very time consuming. XGBoost sorts all\nfeatures into blocks before training and keeps using these blocks during\nthe training period for feature split. This pre-sorted blocks structure\nenables the key feature of XGBoost: parallelization where calculating\nthe gain of each feature can be parallelized with these pre-sorted\nblocks.\n\nBeyond parallelization, XGBoost also considers both the first order and\nthe second order derivative of the loss function. Meanwhile, it includes\ntree trimming and regularization during the training. All of these\nensures its better prediction power and less chance of overfitting than\nGBDT.\n" }, { "path": "papers/yuan_tang_hongliang_liu/xgboost_chapter.rst", "content": ":author: Yuan Tang\n:email: terrytangyuan@gmail.com\n:institution:\n:corresponding:\n\n:author: Hongliang Liu\n:email: phunter.lau@gmail.com\n:institution:\n:equal-contributor:\n\n------------------------------------------------\n\nXGBoost: A Portable Distributed Gradient Boosting Library\n\n------------------------------------------------\n\n.. class:: abstract\n\n XGBoost is a distributed machine learning library under gradient boosting\n framework. It is designed and optimized for high efficiency,\n flexibility and portability. It provides a parallel tree boosting\n implementation (also known as GBDT or GBM) for solving many data science\n problems in a fast and accurate way. Beyond single machine parallelization,\n XGBoost also runs on distributed environments such as Hadoop YARN, Spark,\n and Flink, for distributed model training. It's also optimized on GPUs to\n accelerate the model training. More than half of the winning solutions in\n machine learning challenges hosted at Kaggle and a wide range\n of use cases across industries adopt XGBoost. In this chapter,\n we will briefly walk through main functionalities of XGBoost,\n explain the meanings of selected important parameters, give general guidance\n of parameter tuning, and introduce techniques for running large tasks in\n distributed environments and on GPUs.\n\n.. class:: keywords\n\n Gradient Boosting, Machine Learning, Predictive Analytics, Data Science\n\n\n.. include:: papers/yuan_tang_hongliang_liu/intro.txt\n\n.. include:: papers/yuan_tang_hongliang_liu/vs_gbdt.txt\n\n.. include:: papers/yuan_tang_hongliang_liu/main_functionalities.txt\n\n.. include:: papers/yuan_tang_hongliang_liu/parameter_explained.txt\n\n.. include:: papers/yuan_tang_hongliang_liu/model_tune.txt\n\n.. include:: papers/yuan_tang_hongliang_liu/distributed_and_gpus.txt\n\n.. include:: papers/yuan_tang_hongliang_liu/references.txt\n" }, { "path": "publisher/Makefile", "content": "BUILDDIR = _build\nTEXDIR = _build/tex\nHTMLDIR = _build/html\nPDFDIR = _build/pdfs\nPAPERDIR = ../output\nTEMPLATES = _templates\nSTATIC = _static\n\nBUILDTMPL = ./build_template.py\nTEX2PDF := cd $(TEXDIR) && pdflatex -interaction=batchmode\n\n.PHONY: front-pdf proceedings papers toc clean\n\nall: clean proceedings\n\nclean:\n\trm -rf $(PAPERDIR)/* $(BUILDDIR)/*\n\n$(TEXDIR):\n\tmkdir -p $@\n\n$(TEXDIR)/%.tex: $(TEXDIR)\n\t$(BUILDTMPL) $(@F)\n\n$(TEXDIR)/%.pdf: $(TEXDIR)/%.tex\n\t($(TEX2PDF) $(/dev/null)\n\ntitle-pdf: $(TEXDIR)/title.pdf\ncopyright-pdf: $(TEXDIR)/copyright.pdf\norganization-pdf: $(TEXDIR)/organization.pdf\nstudents-pdf: $(TEXDIR)/students.pdf\n\nfront-pdf: title-pdf copyright-pdf organization-pdf students-pdf\n\npapers:\n\t./build_papers.py\n\nproceedings: front-pdf papers $(TEXDIR)/proceedings.tex\n\t($(TEX2PDF) proceedings 1>/dev/null)\n\t($(TEX2PDF) proceedings 1>/dev/null)\n\tcp $(TEXDIR)/proceedings.pdf $(PDFDIR)/proceedings.pdf \n\nproceedings-html: proceedings\n\tpython build_html.py\n\t-convert $(STATIC)/logo.png -resize x100 $(HTMLDIR)/logo.png\n" }, { "path": "publisher/_static/common.css", "content": "/ common.css of MoinMoin theme \"sinorca4moin\" by David Linke.\n $Id: common.css 102 2006-08-30 16:12:16Z linke $\n*/\n\nhtml {\n\tbackground-color: white;\n\tcolor: black;\n\tfont-family: Verdana, Helvetica, Arial, sans-serif;\n\tfont-size: 0.75em;\n\tline-height: 1.25em;\n\tmargin: 0;\n\tpadding: 0;\n}\n\n/* Headings */\n\nh1, h2, h3, h4, h5 {\n\tmargin: 1em 0 0.5em 0;\n\tpadding: 0px 5px 2px;\n font-weight: bold;\n\tcolor: black;\n\tline-height: 1.2em;\n\tborder-bottom: 2px solid rgb(100,135,220);\n\tborder-left: 1px solid rgb(100,135,220);\n}\t\n \nh1 {font-size: 1.4em;}\nh2 {font-size: 1.25em;}\nh3 {font-size: 1.15em;}\nh4, h5 {font-size: 1em;}\n\n/* title of the current wiki-page should not be displayed as list */\n#pagelocation {\n margin: 0;\n font-size: 2em;\n line-height: 1.0em;\n}\n #pagelocation li {\n/* display: inline;*/\n list-style: none;\n}\n\nli p {\n\tmargin: .25em 0;\n}\n\nli.gap {\n\tmargin-top: 0.5em;\n}\n\na, img, img.drawing {\n\tborder: 0;\n}\n\ndt {\n\tfont-weight: bold;\n}\n\n/* fix problem with small font for inline code */\ntt {\n\tfont-size: 1.25em;\n}\n\npre {\n\tpadding: 5px;\n\tborder: 1px solid #c0c0c0;\n\tfont-family: courier, monospace;\n\twhite-space: pre;\n\t/* begin css 3 or browser specific rules - do not remove!\n\tsee: http://forums.techguy.org/archive/index.php/t-249849.html */\n \twhite-space: pre-wrap;\n\tword-wrap: break-word;\n\twhite-space: -moz-pre-wrap;\n\twhite-space: -pre-wrap;\n\twhite-space: -o-pre-wrap;\n\t/* end css 3 or browser specific rules */\n}\n\ntable {\n\tmargin: 0.5em 0;\n\tfont-size: 1em; 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');\n}\n" }, { "path": "publisher/_static/pydata-cookbook.css", "content": "@import \"common.css\";\n@import \"screen.css\";\n\nbody {\n font-family: Verdana, sans-serif;\n}\n\n#content {\n width: 40em;\n padding: 0.5em 1em 0.5em 1em;\n}\n\n.title {\n float: left;\n}\n\n.pagenr {\n float: right;\n}\n\n.authors {\n font-style: italic;\n padding-left: 1em;\n}\n\n.abstract {\n width: 80ex;\n text-align: justify\n padding-left: 1em;\n}\n" }, { "path": "publisher/_static/pydata.sty", "content": "% These preamble commands are from build_paper.py\n\n% DRAFT\n\\usepackage{status}\n\n% PDF Standard Fonts\n\\usepackage{mathptmx}\n\\usepackage[scaled=.90]{helvet}\n\\usepackage{courier}\n\n% Make verbatim environment smaller\n\\makeatletter\n\\g@addto@macro\\@verbatim\\footnotesize\n\\makeatother\n\n% Do not indent code sections\n\\renewcommand{\\quote}{}\n\n% Provide AMS mathematical commands such as \"align\"\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{bm}\n\n% Define colours for hyperref\n\\usepackage{color}\n\n\\definecolor{orange}{cmyk}{0,0.4,0.8,0.2}\n\\definecolor{darkorange}{rgb}{.71,0.21,0.01}\n\\definecolor{darkblue}{rgb}{.01,0.21,0.71}\n\\definecolor{darkgreen}{rgb}{.1,.52,.09}\n\n% allow line breaks on hyphens for long urls\n\\PassOptionsToPackage{hyphens}{url}\n\n\\usepackage{hyperref}\n\\hypersetup{pdftex, % needed for pdflatex\n breaklinks=true, % so long urls are correctly broken across lines\n colorlinks=true,\n urlcolor=blue,\n linkcolor=darkblue,\n citecolor=darkgreen,\n }\n\n% Include graphics for authors who raw-inlined figures\n% (then docutils won't automatically add the package)\n\\usepackage{graphicx}\n\n\\usepackage{longtable}\n\\ifthenelse{\\isundefined{\\longtable}}{}{\n \\renewenvironment{longtable}{\\begin{center}\\begin{tabular}}%\n {\\end{tabular}\\end{center}\\vspace{2mm}}\n\n% Another brittle table fix. Have a look at this if problems crop\n% up with tables again.\n \\renewcommand{\\endfirsthead}{}\n \\renewcommand{\\endlastfoot}{}\n \\renewcommand{\\endhead}{}\n \\renewcommand{\\endfoot}{}\n\n}\n\n\\usepackage{multirow}\n\\newlength{\\tablewidth}\n\\setlength{\\tablewidth}{\\linewidth}\n\n% Packages required for code highlighting\n\\usepackage{fancyvrb}\n" }, { "path": "publisher/_static/screen.css", "content": "/* screen.css of MoinMoin theme \"sinorca4moin\" by David Linke.\r\n $Id: screen.css 104 2006-08-30 20:41:40Z linke $\r\n*/\r\n\r\n/* ##### Common Styles ##### */\r\n\r\nbody {\r\n color: black;\r\n margin: 0;\r\n padding: 0;\r\n line-height: 1.0em;\r\n}\r\n\r\na {text-decoration: none;\r\n}\r\n\r\n\r\n/* ##### Header ##### */\r\n.superHeader {\r\n color: white;\r\n background-color: rgb(100,135,220);\r\n height: 2.4em;\r\n font-size: 0.8em;\r\n}\r\n\r\n.superHeader a {\r\n color: white;\r\n text-decoration: none;\r\n}\r\n\r\n.superHeader a:hover {\r\n text-decoration: underline;\r\n}\r\n\r\n.superHeader ul {\r\n display: inline;\r\n}\r\n\r\n.superHeader li {\r\n display: inline;\r\n padding: 0;\r\n margin: -10px;\r\n}\r\n\r\n.superHeader .left {\r\n text-align: left;\r\n float: left;\r\n margin: 0.5ex 0px 0.5ex;\r\n padding: 0 0 0 10px;\r\n display: inline; /* forces IE to calc correct margin */\r\n}\r\n\r\n.superHeader .right {\r\n text-align: right;\r\n float: right;\r\n margin: 0.5ex 10px 0.5ex 0px;\r\n padding 0;\r\n display: inline; /* forces IE to calc correct margin */\r\n}\r\n\r\n.midHeader {\r\n color: rgb(39,78,144);\r\n/* background-color: rgb(140,170,230); */ /* should be color of logo background */\r\n margin: 0;\r\n font-size: 3.2em;\r\n font-weight: normal;\r\n line-height: 1.4em;\r\n}\r\n\r\n.midHeader a {\r\n text-decoration: none; \r\n color: rgb(39,78,144);\r\n margin: 5px 0px 5px 10px;\r\n}\r\n\r\n#logo {\r\n /* contains image and/or text link */\r\n display: inline;\r\n}\r\n\r\n#logo img {\r\n /* logo image */\r\n vertical-align: bottom;\r\n margin: 5px 0px 5px 0px;\r\n}\r\n\r\n#username {\r\n white-space: nowrap;\r\n}\r\n\r\n#username ul {\r\n list-style-type: none;\r\n list-style-position: outside;\r\n margin: 0 0 5px 0;\r\n padding: 0;\r\n}\r\n\r\n#username li {\r\n padding: 0.1ex 15px;\r\n}\r\n\r\n.subHeader {\r\n color: white;\r\n background-color: rgb(0,51,153);\r\n font-size: 0.9em;\r\n margin: 0;\r\n padding: 1ex 1ex 1ex 1.5mm;\r\n}\r\n\r\n.subHeader a {\r\n color: white;\r\n background-color: transparent;\r\n text-decoration: none;\r\n font-weight: bold;\r\n margin: 0;\r\n padding: 0 0.75ex 0 0.5ex;\r\n} \r\n\r\n.subHeader a:hover {\r\n text-decoration: underline;\r\n}\r\n\r\n.superHeader .highlight, .subHeader .highlight {\r\n color: rgb(253,160,91);\r\n}\r\n\r\n\r\n/* ##### Side Bar ##### */\r\n\r\n#sidebar {\r\n/* margin: 5px, 0px;*/\r\n float: left;\r\n/* background-color: green; for debugging IE surprises*/\r\n width: 212px;\r\n padding: 0;\t\r\n overflow: hidden;\r\n display: inline; /* forces IE to calc correct margin */\r\n}\r\n\r\n#sidebar a {\r\n color: rgb(0,102,204);\r\n background-color: transparent;\r\n text-decoration: none;\r\n margin: 0;\r\n padding: 0.25em 0;\r\n display: block;\r\n}\r\n\r\n#sidebar .current {\r\n color: black;\r\n background-color: white;\r\n border: 1px solid rgb(253,160,91);\r\n}\r\n\r\n#sidebar a:hover {\r\n color: white;\r\n background-color: rgb(100,135,220);\r\n text-decoration: none;\r\n}\r\n\r\n.sidepanel {\r\n margin: 5px;\r\n float: left;\r\n width: 200px;\t\r\n border: 1px solid #9c9c9c;\r\n background: #e5e5e5;\r\n overflow: hidden; /* new 18:11 */\r\n display: inline; /* forces IE to calc correct margin */\r\n}\r\n\r\n.sidepanel h1 {\r\n margin: 0;\r\n padding: 0.4em 10px;\r\n border: none;\r\n font-size: 1em;\r\n color: black;\r\n}\r\n\r\n.sidepanel li {\r\n margin: 0;\r\n padding: 0.1ex 15px;\r\n}\r\n\r\n.sidepanel ul {\r\n list-style-type: none;\r\n list-style-position: outside;\r\n margin: 0 0 5px 0;\r\n padding: 0;\r\n}\r\n\r\n.actionsmenu {\r\n width: 180px;\r\n}\r\n\r\n\r\n/* ===== Iconbar ===== */\r\n#iconbar {\r\n float: right;\r\n margin: 10px 0px;\r\n padding: 0;\r\n white-space: nowrap;\r\n}\r\n\r\n*[dir=\"rtl\"] #iconbar {\r\n float: right;\r\n margin: 5px 10px 5px 0;\r\n}\r\n\r\n#iconbar li {\r\n display: inline;\r\n}\r\n\r\n#iconbar img {\r\n padding: 1px;\r\n}\r\n\r\n\r\n/* title of the current wiki-page */\r\n#pagelocation {\r\n padding: 10px 0;\r\n color: rgb(100,135,220);\r\n}\r\n\r\n\r\n/* ##### Main Copy ##### */\r\n\r\n#page {\r\n color: black;\r\n background-color: transparent;\r\n/* background-color: yellow; for debugging IE surprises*/\r\n/* text-align: justify;*/\r\n line-height: 1.4em;\r\n margin: 0 10px 10px 215px;\r\n padding: 0px 0px 10px 0px;\r\n}\r\n\r\n#page p {\r\n margin: 0 0 0.25em 0;\r\n padding: 0;\r\n}\r\n\r\n/* Use same margin for normal list item as for li p (see common.css) */\r\n#page li {\r\n padding: 0;\r\n margin: 0.25em 0;\r\n}\r\n\r\n#page a {\r\n color: rgb(0,102,204);\r\n background-color: transparent;\r\n}\r\n\r\n#page a.nonexistent, a.badinterwiki {\r\n color: #900000;\r\n border-bottom: dotted 1px;\r\n}\r\n\r\n#page a:hover {\r\n text-decoration: underline;\r\n}\r\n\r\ndl {\r\n margin: 1em 1ex 2em 1ex;\r\n padding: 0;\r\n}\r\n\r\ndt {\r\n font-weight: bold;\r\n margin: 0 0 0 0;\r\n padding: 0;\r\n}\r\n\r\ndd {\r\n margin: 0 0 0.5em 1em;\r\n padding: 0;\r\n}\r\n\r\npre {\r\n background-color: #FFF8ED;\r\n}\r\n\r\n/* ##### Footer ##### */\r\n\r\n#footer {\r\n color: white;\r\n background-color: rgb(100,135,220);\r\n font-size: 0.9em;\r\n margin: 0;\r\n padding: 1ex 2.5mm;\r\n clear: both;\r\n}\r\n\r\n#footer a {\r\n color: white;\r\n background-color: transparent;\r\n text-decoration: underline ;\r\n}\r\n\r\n#footer a:hover {\r\n text-decoration: none;\r\n}\r\n\r\n\r\n/* DL addition from rightsidebar/classic/modern screen.css*/\r\n\r\n#pagetrail {\r\n clear: left;\r\n margin: 0;\r\n padding: 0;\r\n font-size: 0.8em;\r\n height: 2em;\r\n border-bottom: 1px solid rgb(153,153,153);\r\n}\r\n\r\n*[dir=\"rtl\"] #pagetrail {\r\n clear: right;\r\n}\r\n\r\n/* for broken IE */ \r\n* html #pagetrail li {\r\n border-right: 1px solid #AAA;\r\n padding: 0 0.3em 0 0;\r\n}\r\n\r\n#pagetrail li {\r\n float: left;\r\n display: block;\r\n margin: 2px 0 3px 5px;\r\n padding: 0 2px;\r\n}\r\n\r\n*[dir=\"rtl\"] #pagetrail li {\r\n float: right;\r\n}\r\n\r\n#pagetrail a {\r\n text-decoration: none;\r\n color: rgb(0,102,204);\r\n}\r\n\r\n/* XXX Warning: non-ascii characters! */\r\n#pagetrail li:after {\r\n content: \" »\";\r\n}\r\n#pagetrail li:last-child:after {\r\n content: \"\";\r\n}\r\n\r\n#searchform {\r\n float: right;\r\n margin: 5px 10px 0px;\r\n padding: 0;\r\n white-space: nowrap;\r\n display: inline;\r\n}\r\n\r\n*[dir=\"rtl\"] #searchform {\r\n float: left;\r\n}\r\n\r\n#searchform form div {\r\n display: inline;\r\n}\r\n\r\n.editbar form, .editbar form div {\r\n display: inline;\r\n text-align: center;\r\n}\r\n\r\n#message {\r\n margin: 10px 10px 5px 215px;\r\n padding: 0.5ex;\r\n background-color: #FFFFA0;\r\n border: 2px solid red;\r\n line-height: 1.2em;\r\n}\r\n\r\n#message p{\r\n margin: 0;\r\n}\r\n\r\n#message a{\r\n margin: 0;\r\n color: rgb(0,102,204);\r\n}\r\n\r\n/* We use here dumb css1 ids because of IE suckiness */\r\n#editor-comment {width: 70%;}\r\n#editor-textarea {width: 99%;}\r\n\r\n#pagebottom {\r\n/* clear: both;*/\r\n}\r\n\r\n#preview {\r\n border: 2px solid #e5e5e5;\r\n padding: .5em;\r\n background: url(../img/draft.png);\r\n}\r\n\r\n.diff {\r\n width:99%;\r\n}\r\n\r\n.diff-title {\r\n background-color: #C0C0C0;\r\n}\r\n\r\n.diff-added {\r\n background-color: #E0FFE0;\r\n vertical-align: sub;\r\n}\r\n\r\n.diff-removed {\r\n background-color: #FFFFE0;\r\n vertical-align: sub;\r\n}\r\n\r\n.diff-added span {\r\n background-color: #80FF80;\r\n}\r\n\r\n.diff-removed span {\r\n background-color: #FFFF80;\r\n}\r\n\r\n.searchresult dd span {\r\n font-weight: bold;\r\n}\r\n\r\n#version{\r\n margin: 5px 5px;\r\n padding: 0px;\r\n text-align: center;\r\n font-size: 1em; \r\n color: #6C7680;\r\n}\r\n\r\n#credits{\r\n margin: 5px 5px;\r\n padding: 0px;\r\n text-align: center;\r\n font-size: 1em; \r\n}\r\n\r\n#timings{\r\n margin: 5px 0px;\r\n padding: 0;\r\n text-align: center;\r\n font-size: 0.8em; \r\n color: #6C7680;\r\n}\r\n#credits li {\r\n display: inline;\r\n padding: 0 10px;\r\n}\r\n\r\n#timings li {\r\n display: inline;\r\n padding: 0 5px;\r\n}\r\n\r\n#credits img {\r\n vertical-align: middle;\r\n}\r\n\r\n/* MonthCalendar css */\r\n\r\n/* days without and with pages linked to them */\r\na.cal-emptyday {\r\n color: #777777;\r\n text-align: center;\r\n}\r\na.cal-usedday {\r\n color: #000000;\r\n font-weight: bold;\r\n text-align: center;\r\n}\r\n/* general stuff: workdays, weekend, today */\r\ntd.cal-workday {\r\n background-color: #DDDDFF;\r\n text-align: center;\r\n}\r\ntd.cal-weekend {\r\n background-color: #FFDDDD;\r\n text-align: center;\r\n}\r\ntd.cal-today {\r\n background-color: #CCFFCC;\r\n border-style: solid;\r\n border-width: 2pt;\r\n text-align: center;\r\n}\r\n/* invalid places on the monthly calendar sheet */\r\ntd.cal-invalidday {\r\n background-color: #CCCCCC;\r\n}\r\n/* links to prev/next month/year */\r\na.cal-link {\r\n color: #000000;\r\n text-decoration: none;\r\n}\r\nth.cal-header {\r\n background-color: #DDBBFF;\r\n text-align: center;\r\n}\r\n\r\n/* for MonthCalendar mouseover info boxes */\r\nTABLE.tip {\r\n color: black;\r\n background-color: #FF8888;\r\n font-size: small;\r\n font-weight: normal;\r\n border-style: solid;\r\n border-width: 1px;\r\n}\r\n\r\nTH.tip {\r\n background-color: #FF4444;\r\n font-weight: bold;\r\n text-align: center;\r\n}\r\n\r\nTD.tip {\r\n text-align: left;\r\n}\r\n*[dir=\"rtl\"] TD.tip {\r\n text-align: right;\r\n}\r\n\r\n/* end MonthCalendar stuff */\r\n\r\n\r\n/* Spans for line-anchors - uses * html hack so that the rule only applies to\r\n * IE (where omitting the \"display: none\" triggers rendering bugs).\r\n */\r\n* html span.anchor { display: none; }\r\n\r\n/* IE6 has a bug with rendering of float elements. We workaround this bug by\r\n * assigning those elements a height attribute because we currently don't know\r\n * a better solution. This results in IE calculating the correct height of the\r\n * characters and displaying them correctly. We don't know any negative side\r\n * effects of this workaround:\r\n */\r\n/** html div#page { height: 0.001%; } this breaks sinorca4moin on IE6.0! */\r\n\r\n* html div#header, html form#editor { height: 0.001%; }\r\n\r\n/* Content Sidebar */\r\n\r\n#content .sidebar {float: right; width: 200px; margin: 0 0 20px 20px;\r\n padding: 0; font-size: 0.85em;}\r\n#content[dir=\"rtl\"] .sidebar {float: left; margin: 0 20px 20px 0;}\r\n#content .sidebar p {text-align: left;}\r\n#content[dir=\"rtl\"] .sidebar p {text-align: right;}\r\n/* All headings use darker blue and some white space above */\r\n#content .sidebar h3, #content .sidebar h4 {margin: 1.5em 0 0 0; \r\n padding: 2px 8px; background: #B8C5D9;}\r\n#content .sidebar *:first-child {margin-top: 0}\r\n/* all block elements use light blue background */\r\n#content .sidebar ul, #content .sidebar ol, #content .sidebar p, #content .sidebar table, #content .sidebar div {\r\n background: #F2F4F7;}\r\n#content .sidebar ul, #content .sidebar ol {margin: 0; padding: 0;}\r\n/* Paragraphs and list items separated with bottom border */\r\n#content .sidebar p, #content .sidebar li {margin: 0; padding: 4px 8px;\r\n border-bottom: 1pt solid #E6EAF0;}\r\n#content .sidebar li { display: block;}\r\n#content .sidebar li p {margin: 0; padding: 0; border: none;}\r\n\r\n/* Content Figures \r\n\r\nDefault figure float to the end of the page. Left of right classes float to the left or right :-)\r\n*/\r\n#content .figure {float: right; margin: 0 0 0 20px; padding: 0; \r\n font-size: 0.85em;}\r\n#content[dir=\"rtl\"] .figure {float: left; margin: 0 20px 0 0;}\r\n#content .figure.left {float: left; margin: 0 20px 0 0;}\r\n#content .figure.right {float: right; margin: 0 0 0 20px;}\r\n#content .figure p {margin: 0; text-align: center; font-weight: bold;}\r\n\r\n" }, { "path": "publisher/_static/status.sty", "content": "% DRAFT ??\n\\usepackage{draftwatermark}\n\\SetWatermarkLightness{0.9}\n" }, { "path": "publisher/_templates/article.bib.tmpl", "content": "{{py:\ndef bibtex_caps(words):\n from string import ascii_uppercase\n return ''.join(w if (w not in ascii_uppercase) else '{%s}' % w for w in words)\n}}\n\n@InProceedings{ {{'-'.join([article['paper_id'], proceedings['citation_key']])}},\n author = { {{bibtex_caps(' and '.join(article['author']))}} },\n title = { {{bibtex_caps(article['title'])}} },\n booktitle = { {{bibtex_caps(proceedings['title']['full'])}} },\n pages = { {{' - '.join([str(article['page']['start']), str(article['page']['stop'])])}} },\n year = { {{proceedings['year']}} },\n editor = { {{bibtex_caps(' and '.join(proceedings['editor']))}} }\n}\n" }, { "path": "publisher/_templates/article.html.tmpl", "content": "

{{html_quote(article['title']) | html}}

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\n\n{{for auth, inst in zip(article['author'], article['author_institution'])}}\n \n

\n {{html_quote(auth) | html}}
\n {{html_quote(inst) | html}}
\n

\n \n{{endfor}}\n\n{{if article['video'] }}\n\n

\n{{if 'youtube' in article['video'] }}\n\n{{else}}\nVideo: {{article['video']}}\n{{endif}}\n\n

\n\n\n\n{{endif}}\n\n\nAbstract\n

\n{{for p in article['abstract']}}\n

\n{{html_quote(p) | html}}\n

\n{{endfor}}\n

\n\n\n\nKeywords\n

{{html_quote(article['keywords']) | html}}\n

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\nBibtex entry\n

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\nFull text PDF\n

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\nCopyright © {{ proceedings['year']}} {{article['copyright_holder']}}\n{{proceedings['copyright']['article']}}\n

\n\n\n\n\n" }, { "path": "publisher/_templates/copyright.tex.tmpl", "content": "\\documentclass[letterpaper,compsoc,onecolumn,twoside]{IEEEtran}\n\\thispagestyle{empty}\n\\pagestyle{empty}\n\\usepackage[utf8]{inputenc}\n\\usepackage{parskip}\n\\usepackage{../../_static/status}\n\n\\begin{document}\n\n\\null\n\\vfill\n\n\\section*{%\n{{proceedings['title']['full']}}}\n\n%Edited by {{proceedings['editor'][0]}} and {{proceedings['editor'][1]}}.\n\nEdited by {{if len(proceedings['editor']) <=2:}}\n {{' and '.join(proceedings['editor'])}}%\n{{else}}\n {{', '.join(authlist[:-1] + ['and ' + authlist[-1],])}}%\n{{endif}}.\n\n\\bigskip\n\n{{proceedings['title']['acronym']}} {{proceedings['year']}}\\\\\n{{proceedings['location']}}\\\\\n{{proceedings['dates']}}, {{proceedings['year']}}\n\n\\bigskip\n\\bigskip\n\nCopyright \\copyright{ {{ proceedings['year']}}}. {{for line in proceedings['copyright']['proceedings']}}\n{{line}}\n\n{{endfor}}\n\n\\bigskip\n\\bigskip\n\\bigskip\n\\bigskip\n\n% ISBN-13: {{proceedings['isbn']}}\n\n\\end{document}\n" }, { "path": "publisher/_templates/header.html.tmpl", "content": "\n\n \n {{html_quote(proceedings['title']['short']) | html}}\n \n \n \n{{if locals().has_key('article')}}\n \n {{for auth in article['author']}}\n \n {{endfor}}\n \n \n \n \n \n{{endif}}\n\n\n\n
\n
\n
\n \n \"{{proceedings['title']['acronym']}}\n \n
\n
\n
\n
\n \n\tConference site\n\n\t»\n\tProceedings\n \n
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\n
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\n

Proceedings

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\n
\n
\n\n\t\n
\n
\n\t\n" }, { "path": "publisher/_templates/index.html.tmpl", "content": "

{{proceedings['title']['full']}} ({{proceedings['title']['acronym']}} {{proceedings['year']}})

\n\n
\n\n

{{', '.join(proceedings['editor'])}}

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{{proceedings['dates']}}

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{{proceedings['location']}}

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\n

Organizers | \nSponsored Students\n

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Articles

\n\n{{for line in toc}}\n \n

\n {{html_quote(line['title']) | html}}\n {{html_quote(line['page']['start']) | html}}
\n {{html_quote(line['authors']) | html}}\n

\n \n{{endfor}}\n\n
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\nCopyright © {{ proceedings['year']}}.\n{{for line in proceedings['copyright']['proceedings']}}\n{{line}}\n

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Organization

\n\n
\n\n{{for committee in organization}}\n\n

{{html_quote(committee['name']) | html}}

\n\n
    \n\n {{for member in committee['members'] }}\n
  • {{html_quote(member['name']) | html}} {{if 'org' in member}} ({{html_quote(member['org']) | html}}) {{endif}}
    • \n {{endfor}}\n
\n\n{{endfor}}\n\n
\n\n\n\n\n" }, { "path": "publisher/_templates/organization.tex.tmpl", "content": "\\documentclass[letterpaper,compsoc,onecolumn,twoside,english]{IEEEtran}\n\\thispagestyle{empty}\n\\pagestyle{empty}\n\\usepackage[utf8]{inputenc}\n\\usepackage{../../_static/status}\n\n\\begin{document}\n\n\\section*{Organization}\n\\vspace{4mm}\n\n{{for committee in organization}}\n\n \\subsection*{ {{committee['name']}} }\n\n \\begin{itemize}\n\n {{for member in committee['members'] }}\n \\item[] \\normalsize\\textsc{%\n{{member['name']}}}{{if 'org' in member}},\\small{ {{member['org']}} }\n {{endif}}\n {{endfor}}\n\n \\end{itemize}\n\n{{endfor}}\n\n\\end{document}\n" }, { "path": "publisher/_templates/proceedings.bib.tmpl", "content": "@Proceedings{ {{proceedings['citation_key']}},\n title = { {{proceedings['title']['full']}} },\n booktitle = { {{proceedings['title']['full']}} },\n year = { {{proceedings['year']}} },\n editor = { {{' and '.join(proceedings['editor'])}} },\n isbn = { {{proceedings['isbn']}} }\n}\n" }, { "path": "publisher/_templates/proceedings.tex.tmpl", "content": "\\documentclass[letterpaper,compsoc,onecolumn,twoside,english]{IEEEtran}\n\\usepackage[utf8]{inputenc}\n\\setlength{\\parindent}{0mm}\n\\thispagestyle{empty}\n\\pagestyle{empty}\n\\usepackage{pdfpages}\n\\usepackage{pax}\n% Define colours for hyperref\n\\usepackage{color}\n\n\\definecolor{orange}{cmyk}{0,0.4,0.8,0.2}\n\\definecolor{darkorange}{rgb}{.71,0.21,0.01}\n\\definecolor{darkblue}{rgb}{.01,0.21,0.71}\n\\definecolor{darkgreen}{rgb}{.1,.52,.09}\n\n\\usepackage{hyperref}\n\\hypersetup{pdftex, % needed for pdflatex\n breaklinks=true, % so long urls are correctly broken across lines\n colorlinks=true,\n urlcolor=blue,\n linkcolor=darkblue,\n citecolor=darkgreen,\n }\n\n\n\\begin{document}\n\n\\includepdf{title.pdf}\n\\includepdf[pages=-,openright]{copyright.pdf}\n\\includepdf[pages=-,openright]{organization.pdf}\n\\includepdf[pages=-]{students.pdf}\n\n\\section*{Contents}\n\\vspace{4mm}\n\n{{for line in toc}}\n\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n \\textsf{\\hyperlink{../pdfs/{{line['paper_id']}}.pdf.1}{%\n{{line['title']}}}%\n \\hfill%\n \\textbf{ {{line['page']['start']}} }%\n \\\\%\n\\small\\textit{%\n\\parbox{0.9\\textwidth}{\n{{line['authors']}} }%\n}%\n}%\n \\\\\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n{{endfor}}\n\n{{for line in toc}}\n\n\\includepdf[pages=-,link]{../pdfs/{{line['paper_id']}}.pdf}\n\n{{endfor}}\n\n\n\\end{document}\n" }, { "path": "publisher/_templates/students.html.tmpl", "content": "

Sponsored Students

\n\n
\n\n
    \n\n {{for student in sponsored_students}}\n
  • {{html_quote(student['name']) | html}},\n {{html_quote(student['org']) | html}}\n {{if student['location']}}, {{endif}}\n {{html_quote(student['location']) | html}}\n
    • \n {{endfor}}\n\n
\n\n
\n\n\n\n\n" }, { "path": "publisher/_templates/students.tex.tmpl", "content": "\\documentclass[letterpaper,compsoc,onecolumn,twoside,english]{IEEEtran}\n\\thispagestyle{empty}\n\\pagestyle{empty}\n\\usepackage[utf8]{inputenc}\n\\usepackage{../../_static/status}\n\n\\begin{document}\n\n\\section*{Sponsored Students}\n\\vspace{4mm}\n\n\\begin{itemize}\n\n{{for student in sponsored_students}}\n \\item[] \\normalsize\\textsc{%\n{{student['name']}}}, \\small{%\n{{student['org']}}{{if student['location']}}, {{endif}}{{student['location']}}}\n{{endfor}}\n\n\\end{itemize}\n\n\\end{document}\n" }, { "path": "publisher/_templates/title.tex.tmpl", "content": "\\documentclass[pdftex,letterpaper]{article}\n\\usepackage[pdftex]{graphicx}\n\\usepackage{palatino}\n\\usepackage[utf8]{inputenc}\n\\usepackage{../../_static/status}\n\\newcommand{\\gargantuan}{\\fontsize{34}{38}\\selectfont}\n\\addtolength{\\oddsidemargin}{-1.125in}\n\\addtolength{\\textwidth}{2.0in}\n\\addtolength{\\textheight}{0.75in}\n\n\\begin{document}\n\\begin{titlepage}\n\n\\begin{center}\n\n\\IfFileExists{../../_static/logo.png}{\\includegraphics{../../_static/logo.png} }{ \\null \\vspace{3 cm} }\n\\bigskip\n\\bigskip\n\\bigskip\n% Title\n{ \\gargantuan \\bfseries \\sffamily Proceedings of the {{proceedings['title']['ordinal']}} \\\\\n{{proceedings['title']['conference']}}}\n\n\\bigskip\n\\bigskip\n\\bigskip\n{ \\huge \\sffamily {{proceedings['dates']}}}\n\\hspace{2 mm}{ \\huge \\textbullet }\\hspace{2 mm}\n{ \\huge \\sffamily {{proceedings['location']}}}\\\\[1cm]\n\n\\end{center}\n\n\\vfill\n\n{{for editor in proceedings['editor']}}\n\n \\hfill { \\huge \\sffamily {{editor}}}\\\\\n{{endfor}}\n\n\\end{titlepage}\n\\end{document}\n" }, { "path": "publisher/_templates/toc.tex.tmpl", "content": "\\documentclass[letterpaper,compsoc,onecolumn,twoside,english]{IEEEtran}\n\\usepackage[utf8]{inputenc}\n\\setlength{\\parindent}{0mm}\n\\thispagestyle{empty}\n\\pagestyle{empty}\n\n\n\\begin{document}\n\n\\section*{Contents}\n\\vspace{4mm}\n\n{{for line in toc}}\n\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n \\textsf{%\n{{line['title']}}%\n \\hfill%\n \\textbf{ {{line['page']['start']}} }%\n \\\\%\n\\small\\textit{%\n\\parbox{0.9\\textwidth}{\n{{line['authors']}} }%\n}%\n}%\n \\\\\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n{{endfor}}\n\n\\end{document}\n" }, { "path": "publisher/build_html.py", "content": "#!/usr/bin/env python\n\nimport os\nimport glob\nimport shutil\n\nfrom conf import bib_dir, template_dir, html_dir, static_dir, pdf_dir\nfrom options import get_config, mkdir_p\nfrom build_template import bib_from_tmpl, html_from_tmpl, from_template\n\nconfig = get_config()\nmkdir_p(bib_dir)\nfor file in glob.glob(os.path.join(static_dir,'*.css')):\n shutil.copy(file, html_dir)\nhtml_pdfs = os.path.join(html_dir, 'pdfs')\nmkdir_p(html_pdfs)\nfor file in glob.glob(os.path.join(pdf_dir,'*.pdf')):\n shutil.copy(file, html_pdfs)\n\ncitation_key = config['proceedings']['citation_key'] # e.g. proc-scipy-2010\n\nbib_from_tmpl('proceedings', config, citation_key)\n\nproc_dict = dict(config.items() +\n {'pdf': 'pdfs/proceedings.pdf'}.items() +\n {'bibtex': 'bib/' + citation_key}.items())\n\nfor dest_fn in ['index', 'organization', 'students']:\n html_from_tmpl(dest_fn+'.html', proc_dict, dest_fn)\n\nfor article in config['toc']:\n art_dict = dict(config.items() +\n {'article': article}.items() +\n {'pdf': 'pdfs/'+article['paper_id']+'.pdf'}.items() +\n {'bibtex': 'bib/'+article['paper_id']+'.bib'}.items())\n bib_from_tmpl('article', art_dict, article['paper_id'])\n html_from_tmpl('article.html',art_dict, article['paper_id'])\n" }, { "path": "publisher/build_paper.py", "content": "#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport docutils.core as dc\nimport os.path\nimport sys\nimport re\nimport tempfile\nimport glob\nimport shutil\nfrom distutils import dir_util\n\nfrom writer import writer\nfrom conf import papers_dir, output_dir\n\nimport options\n\nheader = r'''\n.. role:: ref\n\n.. role:: label\n\n.. role:: cite(raw)\n :format: latex\n\n.. raw:: latex\n\n \\InputIfFileExists{page_numbers.tex}{}{}\n \\newcommand*{\\docutilsroleref}{\\ref}\n \\newcommand*{\\docutilsrolelabel}{\\label}\n \\providecommand*\\DUrolecite[1]{\\cite{#1}}\n\n.. |---| unicode:: U+2014 .. em dash, trimming surrounding whitespace\n :trim:\n\n.. |--| unicode:: U+2013 .. en dash\n :trim:\n\n\n'''\n\ndef rst2tex(in_path, out_path):\n\n dir_util.copy_tree(in_path, out_path)\n\n base_dir = os.path.dirname(__file__)\n pydata_status = os.path.join(base_dir, '_static/status.sty')\n shutil.copy(pydata_status, out_path)\n pydata_style = os.path.join(base_dir, '_static/pydata.sty')\n shutil.copy(pydata_style, out_path)\n preamble = r'''\\usepackage{pydata}'''\n\n # Add the LaTeX commands required by Pygments to do syntax highlighting\n\n pygments = None\n\n try:\n import pygments\n except ImportError:\n import warnings\n warnings.warn(RuntimeWarning('Could not import Pygments. '\n 'Syntax highlighting will fail.'))\n\n if pygments:\n from pygments.formatters import LatexFormatter\n from writer.sphinx_highlight import SphinxStyle\n\n preamble += LatexFormatter(style=SphinxStyle).get_style_defs()\n\n settings = {'documentclass': 'IEEEtran',\n 'use_verbatim_when_possible': True,\n 'use_latex_citations': True,\n 'latex_preamble': preamble,\n 'documentoptions': 'letterpaper,compsoc,twoside',\n 'halt_level': 3, # 2: warn; 3: error; 4: severe\n }\n\n try:\n rst, = glob.glob(os.path.join(in_path, '*.rst'))\n except ValueError:\n raise RuntimeError(\"Found more than one input .rst--not sure which \"\n \"one to use.\")\n\n\n content = header + open(rst, 'r').read()\n\n tex = dc.publish_string(source=content, writer=writer,\n settings_overrides=settings)\n\n\n stats_file = os.path.join(out_path, 'paper_stats.json')\n d = options.cfg2dict(stats_file)\n try:\n d.update(writer.document.stats)\n options.dict2cfg(d, stats_file)\n except AttributeError:\n print(\"Error: no paper configuration found\")\n\n tex_file = os.path.join(out_path, 'paper.tex')\n with open(tex_file, 'wb') as f:\n try:\n tex = tex.encode('utf-8')\n except (AttributeError, UnicodeDecodeError):\n pass\n f.write(tex)\n\n\ndef tex2pdf(out_path):\n\n # Sometimes Latex want us to rebuild because labels have changed.\n # We will try at most 5 times.\n for i in range(5):\n out, retry = tex2pdf_singlepass(out_path)\n if not retry:\n # Building succeeded or failed outright\n break\n return out\n\n\ndef tex2pdf_singlepass(out_path):\n \"\"\"\n Returns\n -------\n out : str\n LaTeX output.\n retry : bool\n Whether another round of building is needed.\n \"\"\"\n\n import subprocess\n command_line = 'pdflatex -halt-on-error paper.tex'\n\n # -- dummy tempfile is a hacky way to prevent pdflatex\n # from asking for any missing files via stdin prompts,\n # which mess up our build process.\n dummy = tempfile.TemporaryFile()\n\n run = subprocess.Popen(command_line, shell=True,\n stdin=dummy,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=out_path,\n )\n out, err = run.communicate()\n\n if b\"Fatal\" in out or run.returncode:\n print(\"PDFLaTeX error output:\")\n print(\"=\" * 80)\n print(out)\n print(\"=\" * 80)\n if err:\n print(err)\n print(\"=\" * 80)\n\n # Errors, exit early\n return out, False\n\n # Compile BiBTeX if available\n stats_file = os.path.join(out_path, 'paper_stats.json')\n d = options.cfg2dict(stats_file)\n bib_file = os.path.join(out_path, d[\"bibliography\"] + '.bib')\n\n if os.path.exists(bib_file):\n bibtex_cmd = 'bibtex paper && ' + command_line\n run = subprocess.Popen(bibtex_cmd, shell=True,\n stdin=dummy,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=out_path,\n )\n out_bib, err = run.communicate()\n if err or b'Error' in out_bib:\n print(\"Error compiling BiBTeX\")\n print(\"bibtex error output:\")\n print(\"=\" * 80)\n print(out_bib)\n print(\"=\" * 80)\n return out_bib, False\n\n if b\"Label(s) may have changed.\" in out:\n return out, True\n\n return out, False\n\n\ndef page_count(pdflatex_stdout, paper_dir):\n \"\"\"\n Parse pdflatex output for paper count, and store in a .ini file.\n \"\"\"\n if pdflatex_stdout is None:\n print(\"*** WARNING: PDFLaTeX failed to generate output.\")\n return\n\n regexp = re.compile(b'Output written on paper.pdf \\((\\d+) pages')\n cfgname = os.path.join(paper_dir, 'paper_stats.json')\n\n d = options.cfg2dict(cfgname)\n\n for line in pdflatex_stdout.splitlines():\n m = regexp.match(line)\n if m:\n pages = m.groups()[0]\n d.update({'pages': int(pages)})\n break\n\n options.dict2cfg(d, cfgname)\n\n\ndef build_paper(paper_id):\n out_path = os.path.join(output_dir, paper_id)\n in_path = os.path.join(papers_dir, paper_id)\n print(\"Building:\", paper_id)\n\n rst2tex(in_path, out_path)\n pdflatex_stdout = tex2pdf(out_path)\n page_count(pdflatex_stdout, out_path)\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 2:\n print(\"Usage: build_paper.py paper_directory\")\n sys.exit(-1)\n\n in_path = os.path.normpath(sys.argv[1])\n if not os.path.isdir(in_path):\n print(\"Cannot open directory: %s\" % in_path)\n sys.exit(-1)\n\n paper_id = os.path.basename(in_path)\n build_paper(paper_id)\n" }, { "path": "publisher/build_papers.py", "content": "#!/usr/bin/env python\n\nimport os\nimport sys\nimport shutil\nimport subprocess\n\nimport conf\nimport options\nfrom build_paper import build_paper\n\noutput_dir = conf.output_dir\nbuild_dir = conf.build_dir\nbib_dir = conf.bib_dir\npdf_dir = conf.pdf_dir\ntoc_conf = conf.toc_conf\nproc_conf = conf.proc_conf\ndirs = conf.dirs\n\n\ndef paper_stats(paper_id, start):\n stats = options.cfg2dict(os.path.join(output_dir, paper_id, 'paper_stats.json'))\n\n # Write page number snippet to be included in the LaTeX output\n if 'pages' in stats:\n pages = stats['pages']\n else:\n pages = 1\n\n stop = start + pages - 1\n\n print('\"%s\" from p. %s to %s' % (paper_id, start, stop))\n\n with open(os.path.join(output_dir, paper_id, 'page_numbers.tex'), 'w') as f:\n f.write('\\setcounter{page}{%s}' % start)\n\n # Build table of contents\n stats.update({'page': {'start': start,\n 'stop': stop}})\n stats.update({'paper_id': paper_id})\n\n return stats, stop\n\nif __name__ == \"__main__\":\n\n start = 0\n toc_entries = []\n\n options.mkdir_p(pdf_dir)\n for paper_id in dirs:\n build_paper(paper_id)\n\n stats, start = paper_stats(paper_id, start + 1)\n toc_entries.append(stats)\n\n build_paper(paper_id)\n\n src_pdf = os.path.join(output_dir, paper_id, 'paper.pdf')\n dest_pdf = os.path.join(pdf_dir, paper_id+'.pdf')\n shutil.copy(src_pdf, dest_pdf)\n\n command_line = 'cd '+pdf_dir+' ; pdfannotextractor '+paper_id+'.pdf'\n run = subprocess.Popen(command_line, shell=True, stdout=subprocess.PIPE)\n out, err = run.communicate()\n\n toc = {'toc': toc_entries}\n options.dict2cfg(toc, toc_conf)\n" }, { "path": "publisher/build_template.py", "content": "#!/usr/bin/env python\n\nimport os\nimport sys\nimport shlex, subprocess\n\nimport tempita\nfrom conf import bib_dir, build_dir, template_dir, html_dir\nfrom options import get_config\n\nclass TeXTemplate(tempita.Template):\n def _repr(self, value, pos):\n if sys.version_info[0] < 3 and isinstance(value, unicode):\n value = value.replace('&', '\\&')\n elif sys.version_info[0] >= 3 and isinstance(value, str):\n value = value.replace('&', '\\&')\n else:\n value = str(value)\n return value.encode('utf-8')\n\ndef _from_template(tmpl_basename, config, use_html=True):\n tmpl = os.path.join(template_dir, tmpl_basename + '.tmpl')\n if use_html:\n template = tempita.HTMLTemplate(open(tmpl, 'r').read())\n else:\n template = TeXTemplate(open(tmpl, 'r').read())\n return template.substitute(config)\n\ndef from_template(tmpl_basename, config, dest_fn):\n extension = os.path.splitext(dest_fn)[1][1:]\n\n use_html = False if 'tex' in extension else True\n outfile = _from_template(tmpl_basename, config, use_html=use_html)\n outname = os.path.join(build_dir, extension, dest_fn)\n\n with open(outname, mode='w') as f:\n f.write(outfile)\n\ndef bib_from_tmpl(bib_type, config, target):\n tmpl_basename = bib_type + '.bib'\n dest_path = os.path.join(bib_dir, target + '.bib')\n from_template(tmpl_basename, config, dest_path)\n command_line = 'recode -d u8..ltex ' + dest_path\n run = subprocess.Popen(command_line, shell=True, stdout=subprocess.PIPE)\n out, err = run.communicate()\n\ndef get_html_header(config):\n return _from_template('header.html', config)\n\ndef get_html_content(tmpl, config):\n return _from_template(tmpl, config)\n\ndef html_from_tmpl(src, config, target):\n\n header = get_html_header(config)\n content = _from_template(src, config)\n\n outfile = header+content\n dest_fn = os.path.join(html_dir, target + '.html')\n extension = os.path.splitext(dest_fn)[1][1:]\n outname = os.path.join(build_dir, extension, dest_fn)\n with open(outname, mode='w') as f:\n f.write(outfile)\n\nif __name__ == \"__main__\":\n\n if not len(sys.argv) == 2:\n print(\"Usage: build_template.py destination_name\")\n sys.exit(-1)\n\n dest_fn = sys.argv[1]\n template_fn = os.path.join(template_dir, dest_fn+'.tmpl')\n\n if not os.path.exists(template_fn):\n print(\"Cannot find template.\")\n sys.exit(-1)\n\n config = get_config()\n from_template(dest_fn, config, dest_fn)\n" }, { "path": "publisher/conf.py", "content": "import glob\nimport os\n\nexcludes = ['vanderwalt','bibderwalt']\n\nwork_dir = os.path.dirname(__file__)\npapers_dir = os.path.join(work_dir,'../papers')\noutput_dir = os.path.join(work_dir,'../output')\ntemplate_dir = os.path.join(work_dir,'_templates')\nstatic_dir = os.path.join(work_dir,'_static')\ncss_file = os.path.join(static_dir,'pydata-cookbook.css')\ntoc_list = os.path.join(static_dir,'toc.txt')\nbuild_dir = os.path.join(work_dir,'_build')\npdf_dir = os.path.join(build_dir, 'pdfs')\nhtml_dir = os.path.join(build_dir, 'html')\nbib_dir = os.path.join(html_dir, 'bib')\ntoc_conf = os.path.join(build_dir, 'toc.json')\nproc_conf = os.path.join(work_dir,'../pydata-cookbook.json')\n\nif os.path.isfile(toc_list):\n with open(toc_list) as f:\n dirs = f.read().splitlines()\nelse:\n dirs = sorted([os.path.basename(d)\n for d in glob.glob('%s/*' % papers_dir)\n if os.path.isdir(d) and not any(e in d for e in excludes)])\n" }, { "path": "publisher/mail/_mailer.py", "content": "import argparse\nimport smtplib\nimport os\nimport getpass\nfrom email.mime.text import MIMEText\n\nimport sys\nsys.path.insert(0, '..')\nfrom conf import work_dir\nfrom options import cfg2dict\nfrom build_template import _from_template\n\n\nargs = None\npassword = None\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Invite reviewers.\")\n parser.add_argument('--send', action='store_true')\n parser.add_argument('--template', default=None)\n\n global args\n args = parser.parse_args()\n args.dry_run = not args.send\n\n if args.dry_run:\n print('*** This is a dry run. Use --send to send emails.')\n\n return args\n\n\ndef load_config(conf_file):\n return cfg2dict(conf_file)\n\n\ndef get_password(sender):\n global password\n if not args.dry_run and not password:\n password = getpass.getpass(sender + \"'s password: \")\n\n\ndef email_addr_from(name_email):\n return '\"%s\" <%s>' % (name_email['name'], name_email['email'])\n\n\ndef send_template(sender, recipient, template, template_data,\n smtp_server='smtp.gmail.com', smtp_port=587):\n if args.dry_run:\n print('Dry run -> not sending mail to %s' % recipient)\n else:\n get_password(sender['login'])\n print('-> %s' % recipient)\n\n template_data['email'] = recipient\n message = _from_template('../mail/templates/' + template, template_data)\n\n if args.dry_run:\n print(\"=\" * 80)\n print(message)\n print(\"=\" * 80)\n\n return\n\n session = smtplib.SMTP(smtp_server, smtp_port)\n\n session.ehlo()\n session.starttls()\n session.ehlo\n session.login(sender['login'], password)\n\n session.sendmail(sender['name'], recipient, message)\n session.quit()\n" }, { "path": "publisher/mail/email.json", "content": "{\n\n \"conference\": \"SciPy 2011\",\n \"year\": \"2011\",\n \"due\": \"April 29, 2012\",\n \"download\": \"http://jarrodmillman.com/scipy2011\",\n \"editors\": \"Stefan van der Walt and Jarrod Millman\",\n \"sender\": {\n \"login\": \"jarrod.millman\",\n \"name\": \"Jarrod Millman \",\n \"password\": \"test\"\n },\n\n \"submit_email\": \"jarrod.millman+scipy2011@gmail.com\",\n \"review_form\": \"https://raw.github.com/scipy/scipy_proceedings/master/reviews/review-template.rst\",\n \"cced\": \"millman.ucb@gmail.com, sjvdwalt@gmail.com\",\n\n \"reviews_url\": \"http://jarrodmillman.com/scipy2011-reviews\",\n \"built_proceedings_url\": \"http://jarrodmillman.com/scipy2011\",\n\n \"reviewers\": [\n {\"name\": \"Stefan van der Walt\",\n \"email\": \"sjvdwalt@gmail.com\",\n \"papers\": [\"00_vanderwalt\", \"00_vanderwalt\"]},\n {\"name\": \"Jarrod Millman\",\n \"email\": \"jarrod.millman+2011@gmail.com\",\n \"papers\": [\"00_vanderwalt\"]}\n ],\n\n \"authors\": [\n {\n \"name\": \"Jarrod Millman\",\n \"email\": \"millman@berkeley.edu\"\n },\n {\n \"name\": \"Jarrod Millman\",\n \"email\": \"jarrod.millman+2011@gmail.com\"\n }\n ]\n\n}\n" }, { "path": "publisher/mail/mail_authors.py", "content": "#!/usr/bin/env python\n\nimport _mailer as mailer\n\nargs = mailer.parse_args()\nconfig = mailer.load_config('email.json')\n\nfor author in config['authors']:\n to = mailer.email_addr_from(author)\n mailer.send_template(config['sender'], to, args.template, config)\n\nprint(\"Mail for %d authors.\" % len(config['authors']))\n" }, { "path": "publisher/mail/mail_authors_example.txt", "content": "./mail_authors.py --template author-revision.txt\n" }, { "path": "publisher/mail/mail_reviewers.py", "content": "#!/usr/bin/env python\n\nimport _mailer as mailer\nimport os\nfrom conf import work_dir\n\nargs = mailer.parse_args()\nconfig = mailer.load_config('email.json')\n\n\nfor reviewer_info in config['reviewers']:\n for p in reviewer_info['papers']:\n if not os.path.isdir(os.path.join(work_dir, '../papers/', p)):\n raise RuntimeError(\"Paper %s not found..refusing to generate emails.\" % p)\n\n\nfor reviewer_info in config['reviewers']:\n reviewer_config = config.copy()\n reviewer_config.update(reviewer_info)\n reviewer = reviewer_info['email']\n\n to = mailer.email_addr_from(reviewer_info)\n mailer.send_template(config['sender'], to + ', ' + config['cced'],\n 'reviewer-invite.txt', reviewer_config)\n\n\n# Generate a summary of emails sent\n\npaper_reviewers = {}\nfor reviewer_info in config['reviewers']:\n for paper in reviewer_info['papers']:\n d = paper_reviewers.setdefault(paper, [])\n d.append(reviewer_info['name'])\n\nfor paper in paper_reviewers:\n print(\"%s:\" % paper)\n for reviewer in paper_reviewers[paper]:\n print(\"->\", reviewer)\n print\n\nprint(\"Papers:\", len(paper_reviewers))\nprint(\"Reviewers:\", len(config['reviewers']))\nprint\n" }, { "path": "publisher/mail/templates/author-revision.txt.tmpl", "content": "From: {{sender['name'] | html}}\nSubject: [{{conference}}] reviews submitted, revisions requested\nTo: {{email | html}}\nCc: {{cced | html}}\nMIME-Version: 1.0\nContent-Type: text/plain\n\nDear colleagues and friends,\n\nYour submitted articles have now all received at least one review. You can\nbrowse the reviews and PDFs for all papers here:\n\n {{reviews_url}}\n {{built_proceedings_url}}\n\nYou can also access the complete build system including the original\nrestructured text manuscripts:\n\n https://github.com/scipy/scipy_proceedings/tree/{{year}}\n\nIf you have any problems with the build system or the generated PDF, please let\nus know ASAP.\n\nPlease revise your articles based on the reviews. To make edits to your paper,\nmodify the restructured text documents on the {{year}} branch:\n\n https://github.com/scipy/scipy_proceedings/tree/{{year}}/papers\n\nUpdates can be submitted via a GitHub pull request. The deadline for changes\nis {{due}}.\n\nWith best regards,\n\n{{editors}}\n" }, { "path": "publisher/mail/templates/reviewer-invite.txt.tmpl", "content": "From: {{sender['name'] | html}}\nSubject: [{{conference}}] invitation to review proceedings\nTo: {{email | html}}\nCc: {{cced | html}}\nMIME-Version: 1.0\nContent-Type: text/plain\n\nDear colleague and friend,\n\nConsidering your expertise, we would like to invite you to review the\nfollowing papers for the {{conference}} conference proceedings:\n\n\n{{for p in papers}}\n {{download}}/{{p}}.html\n{{endfor}}\n\nWe would appreciate it if, within the next week, you could confirm\nwhether or not you'll be able to take part in this process. Reviews are due by\n{{due}} and should be submitted via email to {{submit_email}}.\n\nFor further instructions, please refer to the reviewer report:\n\n {{review_form}}\n\nOf course, you are more than welcome (and encouraged!) to review additional\npapers, if time and interest permits. You can browse all submitted papers\nhere:\n\n {{download}}\n\nPlease note that reviews are made public, including the reviewer's name.\n\nWith best regards,\n\n{{editors}}\n" }, { "path": "publisher/options.py", "content": "\"\"\"\nConfiguration utilities.\n\"\"\"\n\n__all__ = ['options']\n\nimport os.path\nimport json\nimport codecs\n\nimport conf\ntoc_conf = conf.toc_conf\nproc_conf = conf.proc_conf\n\ndef get_config():\n config = cfg2dict(proc_conf)\n config.update(cfg2dict(toc_conf))\n return config\n\ndef cfg2dict(filename):\n \"\"\"Return the content of a JSON config file as a dictionary.\n\n \"\"\"\n if not os.path.exists(filename):\n print('*** Warning: %s does not exist.' % filename)\n return {}\n\n return json.loads(codecs.open(filename, 'r', 'utf-8').read())\n\ndef dict2cfg(d, filename):\n \"\"\"Write dictionary out to config file.\n\n \"\"\"\n json.dump(d, codecs.open(filename, 'w', 'utf-8'), ensure_ascii=False)\n\ndef mkdir_p(dir):\n if os.path.isdir(dir):\n return\n os.makedirs(dir)\n\noptions = cfg2dict(proc_conf)\n" }, { "path": "publisher/tempita/__init__.py", "content": "\"\"\"\nA small templating language\n\nThis implements a small templating language. This language implements\nif/elif/else, for/continue/break, expressions, and blocks of Python\ncode. The syntax is::\n\n {{any expression (function calls etc)}}\n {{any expression | filter}}\n {{for x in y}}...{{endfor}}\n {{if x}}x{{elif y}}y{{else}}z{{endif}}\n {{py:x=1}}\n {{py:\n def foo(bar):\n return 'baz'\n }}\n {{default var = default_value}}\n {{# comment}}\n\nYou use this with the ``Template`` class or the ``sub`` shortcut.\nThe ``Template`` class takes the template string and the name of\nthe template (for errors) and a default namespace. Then (like\n``string.Template``) you can call the ``tmpl.substitute(**kw)``\nmethod to make a substitution (or ``tmpl.substitute(a_dict)``).\n\n``sub(content, **kw)`` substitutes the template immediately. You\ncan use ``__name='tmpl.html'`` to set the name of the template.\n\nIf there are syntax errors ``TemplateError`` will be raised.\n\"\"\"\n\nimport re\nimport sys\nimport cgi\nfrom urllib import quote as url_quote\nimport os\nimport tokenize\nfrom cStringIO import StringIO\nfrom tempita._looper import looper\nfrom tempita.compat3 import bytes, basestring_, next, is_unicode, coerce_text\n\n__all__ = ['TemplateError', 'Template', 'sub', 'HTMLTemplate',\n 'sub_html', 'html', 'bunch']\n\nin_re = re.compile(r'\\s+in\\s+')\nvar_re = re.compile(r'^[a-z_][a-z0-9_]*$', re.I)\n\n\nclass TemplateError(Exception):\n \"\"\"Exception raised while parsing a template\n \"\"\"\n\n def __init__(self, message, position, name=None):\n Exception.__init__(self, message)\n self.position = position\n self.name = name\n\n def __str__(self):\n msg = ' '.join(self.args)\n if self.position:\n msg = '%s at line %s column %s' % (\n msg, self.position[0], self.position[1])\n if self.name:\n msg += ' in %s' % self.name\n return msg\n\n\nclass _TemplateContinue(Exception):\n pass\n\n\nclass _TemplateBreak(Exception):\n pass\n\n\ndef get_file_template(name, from_template):\n path = os.path.join(os.path.dirname(from_template.name), name)\n return from_template.__class__.from_filename(\n path, namespace=from_template.namespace,\n get_template=from_template.get_template)\n\n\nclass Template(object):\n\n default_namespace = {\n 'start_braces': '{{',\n 'end_braces': '}}',\n 'looper': looper,\n }\n\n default_encoding = 'utf8'\n default_inherit = None\n\n def __init__(self, content, name=None, namespace=None, stacklevel=None,\n get_template=None, default_inherit=None, line_offset=0,\n delimeters=None):\n self.content = content\n\n # set delimeters\n if delimeters is None:\n delimeters = (self.default_namespace['start_braces'],\n self.default_namespace['end_braces'])\n else:\n assert len(delimeters) == 2 and all([isinstance(delimeter, basestring)\n for delimeter in delimeters])\n self.default_namespace = self.__class__.default_namespace.copy()\n self.default_namespace['start_braces'] = delimeters[0]\n self.default_namespace['end_braces'] = delimeters[1]\n self.delimeters = delimeters\n \n self._unicode = is_unicode(content)\n if name is None and stacklevel is not None:\n try:\n caller = sys._getframe(stacklevel)\n except ValueError:\n pass\n else:\n globals = caller.f_globals\n lineno = caller.f_lineno\n if '__file__' in globals:\n name = globals['__file__']\n if name.endswith('.pyc') or name.endswith('.pyo'):\n name = name[:-1]\n elif '__name__' in globals:\n name = globals['__name__']\n else:\n name = ''\n if lineno:\n name += ':%s' % lineno\n self.name = name\n self._parsed = parse(content, name=name, line_offset=line_offset, delimeters=self.delimeters)\n if namespace is None:\n namespace = {}\n self.namespace = namespace\n self.get_template = get_template\n if default_inherit is not None:\n self.default_inherit = default_inherit\n\n def from_filename(cls, filename, namespace=None, encoding=None,\n default_inherit=None, get_template=get_file_template):\n f = open(filename, 'rb')\n c = f.read()\n f.close()\n if encoding:\n c = c.decode(encoding)\n return cls(content=c, name=filename, namespace=namespace,\n default_inherit=default_inherit, get_template=get_template)\n\n from_filename = classmethod(from_filename)\n\n def __repr__(self):\n return '<%s %s name=%r>' % (\n self.__class__.__name__,\n hex(id(self))[2:], self.name)\n\n def substitute(self, *args, **kw):\n if args:\n if kw:\n raise TypeError(\n \"You can only give positional *or* keyword arguments\")\n if len(args) > 1:\n raise TypeError(\n \"You can only give one positional argument\")\n if not hasattr(args[0], 'items'):\n raise TypeError(\n \"If you pass in a single argument, you must pass in a dictionary-like object (with a .items() method); you gave %r\"\n % (args[0],))\n kw = args[0]\n ns = kw\n ns['__template_name__'] = self.name\n if self.namespace:\n ns.update(self.namespace)\n result, defs, inherit = self._interpret(ns)\n if not inherit:\n inherit = self.default_inherit\n if inherit:\n result = self._interpret_inherit(result, defs, inherit, ns)\n return result\n\n def _interpret(self, ns):\n __traceback_hide__ = True\n parts = []\n defs = {}\n self._interpret_codes(self._parsed, ns, out=parts, defs=defs)\n if '__inherit__' in defs:\n inherit = defs.pop('__inherit__')\n else:\n inherit = None\n return ''.join(parts), defs, inherit\n\n def _interpret_inherit(self, body, defs, inherit_template, ns):\n __traceback_hide__ = True\n if not self.get_template:\n raise TemplateError(\n 'You cannot use inheritance without passing in get_template',\n position=None, name=self.name)\n templ = self.get_template(inherit_template, self)\n self_ = TemplateObject(self.name)\n for name, value in defs.iteritems():\n setattr(self_, name, value)\n self_.body = body\n ns = ns.copy()\n ns['self'] = self_\n return templ.substitute(ns)\n\n def _interpret_codes(self, codes, ns, out, defs):\n __traceback_hide__ = True\n for item in codes:\n if isinstance(item, basestring_):\n out.append(item)\n else:\n self._interpret_code(item, ns, out, defs)\n\n def _interpret_code(self, code, ns, out, defs):\n __traceback_hide__ = True\n name, pos = code[0], code[1]\n if name == 'py':\n self._exec(code[2], ns, pos)\n elif name == 'continue':\n raise _TemplateContinue()\n elif name == 'break':\n raise _TemplateBreak()\n elif name == 'for':\n vars, expr, content = code[2], code[3], code[4]\n expr = self._eval(expr, ns, pos)\n self._interpret_for(vars, expr, content, ns, out, defs)\n elif name == 'cond':\n parts = code[2:]\n self._interpret_if(parts, ns, out, defs)\n elif name == 'expr':\n parts = code[2].split('|')\n base = self._eval(parts[0], ns, pos)\n for part in parts[1:]:\n func = self._eval(part, ns, pos)\n base = func(base)\n out.append(self._repr(base, pos))\n elif name == 'default':\n var, expr = code[2], code[3]\n if var not in ns:\n result = self._eval(expr, ns, pos)\n ns[var] = result\n elif name == 'inherit':\n expr = code[2]\n value = self._eval(expr, ns, pos)\n defs['__inherit__'] = value\n elif name == 'def':\n name = code[2]\n signature = code[3]\n parts = code[4]\n ns[name] = defs[name] = TemplateDef(self, name, signature, body=parts, ns=ns,\n pos=pos)\n elif name == 'comment':\n return\n else:\n assert 0, \"Unknown code: %r\" % name\n\n def _interpret_for(self, vars, expr, content, ns, out, defs):\n __traceback_hide__ = True\n for item in expr:\n if len(vars) == 1:\n ns[vars[0]] = item\n else:\n if len(vars) != len(item):\n raise ValueError(\n 'Need %i items to unpack (got %i items)'\n % (len(vars), len(item)))\n for name, value in zip(vars, item):\n ns[name] = value\n try:\n self._interpret_codes(content, ns, out, defs)\n except _TemplateContinue:\n continue\n except _TemplateBreak:\n break\n\n def _interpret_if(self, parts, ns, out, defs):\n __traceback_hide__ = True\n # @@: if/else/else gets through\n for part in parts:\n assert not isinstance(part, basestring_)\n name, pos = part[0], part[1]\n if name == 'else':\n result = True\n else:\n result = self._eval(part[2], ns, pos)\n if result:\n self._interpret_codes(part[3], ns, out, defs)\n break\n\n def _eval(self, code, ns, pos):\n __traceback_hide__ = True\n try:\n try:\n value = eval(code, self.default_namespace, ns)\n except SyntaxError, e:\n raise SyntaxError(\n 'invalid syntax in expression: %s' % code)\n return value\n except:\n exc_info = sys.exc_info()\n e = exc_info[1]\n if getattr(e, 'args', None):\n arg0 = e.args[0]\n else:\n arg0 = coerce_text(e)\n e.args = (self._add_line_info(arg0, pos),)\n raise exc_info[0], e, exc_info[2]\n\n def _exec(self, code, ns, pos):\n __traceback_hide__ = True\n try:\n exec code in self.default_namespace, ns\n except:\n exc_info = sys.exc_info()\n e = exc_info[1]\n if e.args:\n e.args = (self._add_line_info(e.args[0], pos),)\n else:\n e.args = (self._add_line_info(None, pos),)\n raise exc_info[0], e, exc_info[2]\n\n def _repr(self, value, pos):\n __traceback_hide__ = True\n try:\n if value is None:\n return ''\n if self._unicode:\n try:\n value = unicode(value)\n except UnicodeDecodeError:\n value = bytes(value)\n else:\n if not isinstance(value, basestring_):\n value = coerce_text(value)\n if (is_unicode(value)\n and self.default_encoding):\n value = value.encode(self.default_encoding)\n except:\n exc_info = sys.exc_info()\n e = exc_info[1]\n e.args = (self._add_line_info(e.args[0], pos),)\n raise exc_info[0], e, exc_info[2]\n else:\n if self._unicode and isinstance(value, bytes):\n if not self.default_encoding:\n raise UnicodeDecodeError(\n 'Cannot decode bytes value %r into unicode '\n '(no default_encoding provided)' % value)\n try:\n value = value.decode(self.default_encoding)\n except UnicodeDecodeError, e:\n raise UnicodeDecodeError(\n e.encoding,\n e.object,\n e.start,\n e.end,\n e.reason + ' in string %r' % value)\n elif not self._unicode and is_unicode(value):\n if not self.default_encoding:\n raise UnicodeEncodeError(\n 'Cannot encode unicode value %r into bytes '\n '(no default_encoding provided)' % value)\n value = value.encode(self.default_encoding)\n return value\n\n def _add_line_info(self, msg, pos):\n msg = \"%s at line %s column %s\" % (\n msg, pos[0], pos[1])\n if self.name:\n msg += \" in file %s\" % self.name\n return msg\n\n\ndef sub(content, delimeters=None, **kw):\n name = kw.get('__name')\n tmpl = Template(content, name=name, delimeters=delimeters)\n return tmpl.substitute(kw)\n\n\ndef paste_script_template_renderer(content, vars, filename=None):\n tmpl = Template(content, name=filename)\n return tmpl.substitute(vars)\n\n\nclass bunch(dict):\n\n def __init__(self, **kw):\n for name, value in kw.iteritems():\n setattr(self, name, value)\n\n def __setattr__(self, name, value):\n self[name] = value\n\n def __getattr__(self, name):\n try:\n return self[name]\n except KeyError:\n raise AttributeError(name)\n\n def __getitem__(self, key):\n if 'default' in self:\n try:\n return dict.__getitem__(self, key)\n except KeyError:\n return dict.__getitem__(self, 'default')\n else:\n return dict.__getitem__(self, key)\n\n def __repr__(self):\n items = [\n (k, v) for k, v in self.iteritems()]\n items.sort()\n return '<%s %s>' % (\n self.__class__.__name__,\n ' '.join(['%s=%r' % (k, v) for k, v in items]))\n\n############################################################\n## HTML Templating\n############################################################\n\n\nclass html(object):\n\n def __init__(self, value):\n self.value = value\n\n def __str__(self):\n return self.value\n\n def __html__(self):\n return self.value\n\n def __repr__(self):\n return '<%s %r>' % (\n self.__class__.__name__, self.value)\n\n\ndef html_quote(value, force=True):\n if not force and hasattr(value, '__html__'):\n return value.__html__()\n if value is None:\n return ''\n if not isinstance(value, basestring_):\n value = coerce_text(value)\n if sys.version >= \"3\" and isinstance(value, bytes):\n value = cgi.escape(value.decode('latin1'), 1)\n value = value.encode('latin1')\n else:\n value = cgi.escape(value, 1)\n if sys.version < \"3\":\n if is_unicode(value):\n value = value.encode('ascii', 'xmlcharrefreplace')\n return value\n\n\ndef url(v):\n v = coerce_text(v)\n if is_unicode(v):\n v = v.encode('utf8')\n return url_quote(v)\n\n\ndef attr(**kw):\n kw = list(kw.iteritems())\n kw.sort()\n parts = []\n for name, value in kw:\n if value is None:\n continue\n if name.endswith('_'):\n name = name[:-1]\n parts.append('%s=\"%s\"' % (html_quote(name), html_quote(value)))\n return html(' '.join(parts))\n\n\nclass HTMLTemplate(Template):\n\n default_namespace = Template.default_namespace.copy()\n default_namespace.update(dict(\n html=html,\n attr=attr,\n url=url,\n html_quote=html_quote,\n ))\n\n def _repr(self, value, pos):\n if hasattr(value, '__html__'):\n value = value.__html__()\n quote = False\n else:\n quote = True\n plain = Template._repr(self, value, pos)\n if quote:\n return html_quote(plain)\n else:\n return plain\n\n\ndef sub_html(content, **kw):\n name = kw.get('__name')\n tmpl = HTMLTemplate(content, name=name)\n return tmpl.substitute(kw)\n\n\nclass TemplateDef(object):\n def __init__(self, template, func_name, func_signature,\n body, ns, pos, bound_self=None):\n self._template = template\n self._func_name = func_name\n self._func_signature = func_signature\n self._body = body\n self._ns = ns\n self._pos = pos\n self._bound_self = bound_self\n\n def __repr__(self):\n return '' % (\n self._func_name, self._func_signature,\n self._template.name, self._pos)\n\n def __str__(self):\n return self()\n\n def __call__(self, *args, **kw):\n values = self._parse_signature(args, kw)\n ns = self._ns.copy()\n ns.update(values)\n if self._bound_self is not None:\n ns['self'] = self._bound_self\n out = []\n subdefs = {}\n self._template._interpret_codes(self._body, ns, out, subdefs)\n return ''.join(out)\n\n def __get__(self, obj, type=None):\n if obj is None:\n return self\n return self.__class__(\n self._template, self._func_name, self._func_signature,\n self._body, self._ns, self._pos, bound_self=obj)\n\n def _parse_signature(self, args, kw):\n values = {}\n sig_args, var_args, var_kw, defaults = self._func_signature\n extra_kw = {}\n for name, value in kw.iteritems():\n if not var_kw and name not in sig_args:\n raise TypeError(\n 'Unexpected argument %s' % name)\n if name in sig_args:\n values[sig_args] = value\n else:\n extra_kw[name] = value\n args = list(args)\n sig_args = list(sig_args)\n while args:\n while sig_args and sig_args[0] in values:\n sig_args.pop(0)\n if sig_args:\n name = sig_args.pop(0)\n values[name] = args.pop(0)\n elif var_args:\n values[var_args] = tuple(args)\n break\n else:\n raise TypeError(\n 'Extra position arguments: %s'\n % ', '.join(repr(v) for v in args))\n for name, value_expr in defaults.iteritems():\n if name not in values:\n values[name] = self._template._eval(\n value_expr, self._ns, self._pos)\n for name in sig_args:\n if name not in values:\n raise TypeError(\n 'Missing argument: %s' % name)\n if var_kw:\n values[var_kw] = extra_kw\n return values\n\n\nclass TemplateObject(object):\n\n def __init__(self, name):\n self.__name = name\n self.get = TemplateObjectGetter(self)\n\n def __repr__(self):\n return '<%s %s>' % (self.__class__.__name__, self.__name)\n\n\nclass TemplateObjectGetter(object):\n\n def __init__(self, template_obj):\n self.__template_obj = template_obj\n\n def __getattr__(self, attr):\n return getattr(self.__template_obj, attr, Empty)\n\n def __repr__(self):\n return '<%s around %r>' % (self.__class__.__name__, self.__template_obj)\n\n\nclass _Empty(object):\n def __call__(self, *args, **kw):\n return self\n\n def __str__(self):\n return ''\n\n def __repr__(self):\n return 'Empty'\n\n def __unicode__(self):\n return u''\n\n def __iter__(self):\n return iter(())\n\n def __bool__(self):\n return False\n\n if sys.version < \"3\":\n __nonzero__ = __bool__\n\nEmpty = _Empty()\ndel _Empty\n\n############################################################\n## Lexing and Parsing\n############################################################\n\n\ndef lex(s, name=None, trim_whitespace=True, line_offset=0, delimeters=None):\n \"\"\"\n Lex a string into chunks:\n\n >>> lex('hey')\n ['hey']\n >>> lex('hey {{you}}')\n ['hey ', ('you', (1, 7))]\n >>> lex('hey {{')\n Traceback (most recent call last):\n ...\n TemplateError: No }} to finish last expression at line 1 column 7\n >>> lex('hey }}')\n Traceback (most recent call last):\n ...\n TemplateError: }} outside expression at line 1 column 7\n >>> lex('hey {{ {{')\n Traceback (most recent call last):\n ...\n TemplateError: {{ inside expression at line 1 column 10\n\n \"\"\"\n if delimeters is None:\n delimeters = ( Template.default_namespace['start_braces'],\n Template.default_namespace['end_braces'] )\n in_expr = False\n chunks = []\n last = 0\n last_pos = (1, 1)\n token_re = re.compile(r'%s|%s' % (re.escape(delimeters[0]),\n re.escape(delimeters[1])))\n for match in token_re.finditer(s):\n expr = match.group(0)\n pos = find_position(s, match.end(), line_offset)\n if expr == delimeters[0] and in_expr:\n raise TemplateError('%s inside expression' % delimeters[0],\n position=pos,\n name=name)\n elif expr == delimeters[1] and not in_expr:\n raise TemplateError('%s outside expression' % delimeters[1],\n position=pos,\n name=name)\n if expr == delimeters[0]:\n part = s[last:match.start()]\n if part:\n chunks.append(part)\n in_expr = True\n else:\n chunks.append((s[last:match.start()], last_pos))\n in_expr = False\n last = match.end()\n last_pos = pos\n if in_expr:\n raise TemplateError('No %s to finish last expression' % delimeters[1],\n name=name, position=last_pos)\n part = s[last:]\n if part:\n chunks.append(part)\n if trim_whitespace:\n chunks = trim_lex(chunks)\n return chunks\n\nstatement_re = re.compile(r'^(?:if |elif |for |def |inherit |default |py:)')\nsingle_statements = ['else', 'endif', 'endfor', 'enddef', 'continue', 'break']\ntrail_whitespace_re = re.compile(r'\\n\\r?[\\t ]*$')\nlead_whitespace_re = re.compile(r'^[\\t ]*\\n')\n\n\ndef trim_lex(tokens):\n r\"\"\"\n Takes a lexed set of tokens, and removes whitespace when there is\n a directive on a line by itself:\n\n >>> tokens = lex('{{if x}}\\nx\\n{{endif}}\\ny', trim_whitespace=False)\n >>> tokens\n [('if x', (1, 3)), '\\nx\\n', ('endif', (3, 3)), '\\ny']\n >>> trim_lex(tokens)\n [('if x', (1, 3)), 'x\\n', ('endif', (3, 3)), 'y']\n \"\"\"\n last_trim = None\n for i in range(len(tokens)):\n current = tokens[i]\n if isinstance(tokens[i], basestring_):\n # we don't trim this\n continue\n item = current[0]\n if not statement_re.search(item) and item not in single_statements:\n continue\n if not i:\n prev = ''\n else:\n prev = tokens[i - 1]\n if i + 1 >= len(tokens):\n next_chunk = ''\n else:\n next_chunk = tokens[i + 1]\n if (not isinstance(next_chunk, basestring_)\n or not isinstance(prev, basestring_)):\n continue\n prev_ok = not prev or trail_whitespace_re.search(prev)\n if i == 1 and not prev.strip():\n prev_ok = True\n if last_trim is not None and last_trim + 2 == i and not prev.strip():\n prev_ok = 'last'\n if (prev_ok\n and (not next_chunk or lead_whitespace_re.search(next_chunk)\n or (i == len(tokens) - 2 and not next_chunk.strip()))):\n if prev:\n if ((i == 1 and not prev.strip())\n or prev_ok == 'last'):\n tokens[i - 1] = ''\n else:\n m = trail_whitespace_re.search(prev)\n # +1 to leave the leading \\n on:\n prev = prev[:m.start() + 1]\n tokens[i - 1] = prev\n if next_chunk:\n last_trim = i\n if i == len(tokens) - 2 and not next_chunk.strip():\n tokens[i + 1] = ''\n else:\n m = lead_whitespace_re.search(next_chunk)\n next_chunk = next_chunk[m.end():]\n tokens[i + 1] = next_chunk\n return tokens\n\n\ndef find_position(string, index, line_offset):\n \"\"\"Given a string and index, return (line, column)\"\"\"\n leading = string[:index].splitlines()\n return (len(leading) + line_offset, len(leading[-1]) + 1)\n\n\ndef parse(s, name=None, line_offset=0, delimeters=None):\n r\"\"\"\n Parses a string into a kind of AST\n\n >>> parse('{{x}}')\n [('expr', (1, 3), 'x')]\n >>> parse('foo')\n ['foo']\n >>> parse('{{if x}}test{{endif}}')\n [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))]\n >>> parse('series->{{for x in y}}x={{x}}{{endfor}}')\n ['series->', ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])]\n >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}')\n [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])]\n >>> parse('{{py:x=1}}')\n [('py', (1, 3), 'x=1')]\n >>> parse('{{if x}}a{{elif y}}b{{else}}c{{endif}}')\n [('cond', (1, 3), ('if', (1, 3), 'x', ['a']), ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))]\n\n Some exceptions::\n\n >>> parse('{{continue}}')\n Traceback (most recent call last):\n ...\n TemplateError: continue outside of for loop at line 1 column 3\n >>> parse('{{if x}}foo')\n Traceback (most recent call last):\n ...\n TemplateError: No {{endif}} at line 1 column 3\n >>> parse('{{else}}')\n Traceback (most recent call last):\n ...\n TemplateError: else outside of an if block at line 1 column 3\n >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}')\n Traceback (most recent call last):\n ...\n TemplateError: Unexpected endif at line 1 column 25\n >>> parse('{{if}}{{endif}}')\n Traceback (most recent call last):\n ...\n TemplateError: if with no expression at line 1 column 3\n >>> parse('{{for x y}}{{endfor}}')\n Traceback (most recent call last):\n ...\n TemplateError: Bad for (no \"in\") in 'x y' at line 1 column 3\n >>> parse('{{py:x=1\\ny=2}}')\n Traceback (most recent call last):\n ...\n TemplateError: Multi-line py blocks must start with a newline at line 1 column 3\n \"\"\"\n if delimeters is None:\n delimeters = ( Template.default_namespace['start_braces'],\n Template.default_namespace['end_braces'] )\n tokens = lex(s, name=name, line_offset=line_offset, delimeters=delimeters)\n result = []\n while tokens:\n next_chunk, tokens = parse_expr(tokens, name)\n result.append(next_chunk)\n return result\n\n\ndef parse_expr(tokens, name, context=()):\n if isinstance(tokens[0], basestring_):\n return tokens[0], tokens[1:]\n expr, pos = tokens[0]\n expr = expr.strip()\n if expr.startswith('py:'):\n expr = expr[3:].lstrip(' \\t')\n if expr.startswith('\\n') or expr.startswith('\\r'):\n expr = expr.lstrip('\\r\\n')\n if '\\r' in expr:\n expr = expr.replace('\\r\\n', '\\n')\n expr = expr.replace('\\r', '')\n expr += '\\n'\n else:\n if '\\n' in expr:\n raise TemplateError(\n 'Multi-line py blocks must start with a newline',\n position=pos, name=name)\n return ('py', pos, expr), tokens[1:]\n elif expr in ('continue', 'break'):\n if 'for' not in context:\n raise TemplateError(\n 'continue outside of for loop',\n position=pos, name=name)\n return (expr, pos), tokens[1:]\n elif expr.startswith('if '):\n return parse_cond(tokens, name, context)\n elif (expr.startswith('elif ')\n or expr == 'else'):\n raise TemplateError(\n '%s outside of an if block' % expr.split()[0],\n position=pos, name=name)\n elif expr in ('if', 'elif', 'for'):\n raise TemplateError(\n '%s with no expression' % expr,\n position=pos, name=name)\n elif expr in ('endif', 'endfor', 'enddef'):\n raise TemplateError(\n 'Unexpected %s' % expr,\n position=pos, name=name)\n elif expr.startswith('for '):\n return parse_for(tokens, name, context)\n elif expr.startswith('default '):\n return parse_default(tokens, name, context)\n elif expr.startswith('inherit '):\n return parse_inherit(tokens, name, context)\n elif expr.startswith('def '):\n return parse_def(tokens, name, context)\n elif expr.startswith('#'):\n return ('comment', pos, tokens[0][0]), tokens[1:]\n return ('expr', pos, tokens[0][0]), tokens[1:]\n\n\ndef parse_cond(tokens, name, context):\n start = tokens[0][1]\n pieces = []\n context = context + ('if',)\n while 1:\n if not tokens:\n raise TemplateError(\n 'Missing {{endif}}',\n position=start, name=name)\n if (isinstance(tokens[0], tuple)\n and tokens[0][0] == 'endif'):\n return ('cond', start) + tuple(pieces), tokens[1:]\n next_chunk, tokens = parse_one_cond(tokens, name, context)\n pieces.append(next_chunk)\n\n\ndef parse_one_cond(tokens, name, context):\n (first, pos), tokens = tokens[0], tokens[1:]\n content = []\n if first.endswith(':'):\n first = first[:-1]\n if first.startswith('if '):\n part = ('if', pos, first[3:].lstrip(), content)\n elif first.startswith('elif '):\n part = ('elif', pos, first[5:].lstrip(), content)\n elif first == 'else':\n part = ('else', pos, None, content)\n else:\n assert 0, \"Unexpected token %r at %s\" % (first, pos)\n while 1:\n if not tokens:\n raise TemplateError(\n 'No {{endif}}',\n position=pos, name=name)\n if (isinstance(tokens[0], tuple)\n and (tokens[0][0] == 'endif'\n or tokens[0][0].startswith('elif ')\n or tokens[0][0] == 'else')):\n return part, tokens\n next_chunk, tokens = parse_expr(tokens, name, context)\n content.append(next_chunk)\n\n\ndef parse_for(tokens, name, context):\n first, pos = tokens[0]\n tokens = tokens[1:]\n context = ('for',) + context\n content = []\n assert first.startswith('for ')\n if first.endswith(':'):\n first = first[:-1]\n first = first[3:].strip()\n match = in_re.search(first)\n if not match:\n raise TemplateError(\n 'Bad for (no \"in\") in %r' % first,\n position=pos, name=name)\n vars = first[:match.start()]\n if '(' in vars:\n raise TemplateError(\n 'You cannot have () in the variable section of a for loop (%r)'\n % vars, position=pos, name=name)\n vars = tuple([\n v.strip() for v in first[:match.start()].split(',')\n if v.strip()])\n expr = first[match.end():]\n while 1:\n if not tokens:\n raise TemplateError(\n 'No {{endfor}}',\n position=pos, name=name)\n if (isinstance(tokens[0], tuple)\n and tokens[0][0] == 'endfor'):\n return ('for', pos, vars, expr, content), tokens[1:]\n next_chunk, tokens = parse_expr(tokens, name, context)\n content.append(next_chunk)\n\n\ndef parse_default(tokens, name, context):\n first, pos = tokens[0]\n assert first.startswith('default ')\n first = first.split(None, 1)[1]\n parts = first.split('=', 1)\n if len(parts) == 1:\n raise TemplateError(\n \"Expression must be {{default var=value}}; no = found in %r\" % first,\n position=pos, name=name)\n var = parts[0].strip()\n if ',' in var:\n raise TemplateError(\n \"{{default x, y = ...}} is not supported\",\n position=pos, name=name)\n if not var_re.search(var):\n raise TemplateError(\n \"Not a valid variable name for {{default}}: %r\"\n % var, position=pos, name=name)\n expr = parts[1].strip()\n return ('default', pos, var, expr), tokens[1:]\n\n\ndef parse_inherit(tokens, name, context):\n first, pos = tokens[0]\n assert first.startswith('inherit ')\n expr = first.split(None, 1)[1]\n return ('inherit', pos, expr), tokens[1:]\n\n\ndef parse_def(tokens, name, context):\n first, start = tokens[0]\n tokens = tokens[1:]\n assert first.startswith('def ')\n first = first.split(None, 1)[1]\n if first.endswith(':'):\n first = first[:-1]\n if '(' not in first:\n func_name = first\n sig = ((), None, None, {})\n elif not first.endswith(')'):\n raise TemplateError(\"Function definition doesn't end with ): %s\" % first,\n position=start, name=name)\n else:\n first = first[:-1]\n func_name, sig_text = first.split('(', 1)\n sig = parse_signature(sig_text, name, start)\n context = context + ('def',)\n content = []\n while 1:\n if not tokens:\n raise TemplateError(\n 'Missing {{enddef}}',\n position=start, name=name)\n if (isinstance(tokens[0], tuple)\n and tokens[0][0] == 'enddef'):\n return ('def', start, func_name, sig, content), tokens[1:]\n next_chunk, tokens = parse_expr(tokens, name, context)\n content.append(next_chunk)\n\n\ndef parse_signature(sig_text, name, pos):\n tokens = tokenize.generate_tokens(StringIO(sig_text).readline)\n sig_args = []\n var_arg = None\n var_kw = None\n defaults = {}\n\n def get_token(pos=False):\n try:\n tok_type, tok_string, (srow, scol), (erow, ecol), line = next(tokens)\n except StopIteration:\n return tokenize.ENDMARKER, ''\n if pos:\n return tok_type, tok_string, (srow, scol), (erow, ecol)\n else:\n return tok_type, tok_string\n while 1:\n var_arg_type = None\n tok_type, tok_string = get_token()\n if tok_type == tokenize.ENDMARKER:\n break\n if tok_type == tokenize.OP and (tok_string == '*' or tok_string == '**'):\n var_arg_type = tok_string\n tok_type, tok_string = get_token()\n if tok_type != tokenize.NAME:\n raise TemplateError('Invalid signature: (%s)' % sig_text,\n position=pos, name=name)\n var_name = tok_string\n tok_type, tok_string = get_token()\n if tok_type == tokenize.ENDMARKER or (tok_type == tokenize.OP and tok_string == ','):\n if var_arg_type == '*':\n var_arg = var_name\n elif var_arg_type == '**':\n var_kw = var_name\n else:\n sig_args.append(var_name)\n if tok_type == tokenize.ENDMARKER:\n break\n continue\n if var_arg_type is not None:\n raise TemplateError('Invalid signature: (%s)' % sig_text,\n position=pos, name=name)\n if tok_type == tokenize.OP and tok_string == '=':\n nest_type = None\n unnest_type = None\n nest_count = 0\n start_pos = end_pos = None\n parts = []\n while 1:\n tok_type, tok_string, s, e = get_token(True)\n if start_pos is None:\n start_pos = s\n end_pos = e\n if tok_type == tokenize.ENDMARKER and nest_count:\n raise TemplateError('Invalid signature: (%s)' % sig_text,\n position=pos, name=name)\n if (not nest_count and\n (tok_type == tokenize.ENDMARKER or (tok_type == tokenize.OP and tok_string == ','))):\n default_expr = isolate_expression(sig_text, start_pos, end_pos)\n defaults[var_name] = default_expr\n sig_args.append(var_name)\n break\n parts.append((tok_type, tok_string))\n if nest_count and tok_type == tokenize.OP and tok_string == nest_type:\n nest_count += 1\n elif nest_count and tok_type == tokenize.OP and tok_string == unnest_type:\n nest_count -= 1\n if not nest_count:\n nest_type = unnest_type = None\n elif not nest_count and tok_type == tokenize.OP and tok_string in ('(', '[', '{'):\n nest_type = tok_string\n nest_count = 1\n unnest_type = {'(': ')', '[': ']', '{': '}'}[nest_type]\n return sig_args, var_arg, var_kw, defaults\n\n\ndef isolate_expression(string, start_pos, end_pos):\n srow, scol = start_pos\n srow -= 1\n erow, ecol = end_pos\n erow -= 1\n lines = string.splitlines(True)\n if srow == erow:\n return lines[srow][scol:ecol]\n parts = [lines[srow][scol:]]\n parts.extend(lines[srow+1:erow])\n if erow < len(lines):\n # It'll sometimes give (end_row_past_finish, 0)\n parts.append(lines[erow][:ecol])\n return ''.join(parts)\n\n_fill_command_usage = \"\"\"\\\n%prog [OPTIONS] TEMPLATE arg=value\n\nUse py:arg=value to set a Python value; otherwise all values are\nstrings.\n\"\"\"\n\n\ndef fill_command(args=None):\n import sys\n import optparse\n import pkg_resources\n import os\n if args is None:\n args = sys.argv[1:]\n dist = pkg_resources.get_distribution('Paste')\n parser = optparse.OptionParser(\n version=coerce_text(dist),\n usage=_fill_command_usage)\n parser.add_option(\n '-o', '--output',\n dest='output',\n metavar=\"FILENAME\",\n help=\"File to write output to (default stdout)\")\n parser.add_option(\n '--html',\n dest='use_html',\n action='store_true',\n help=\"Use HTML style filling (including automatic HTML quoting)\")\n parser.add_option(\n '--env',\n dest='use_env',\n action='store_true',\n help=\"Put the environment in as top-level variables\")\n options, args = parser.parse_args(args)\n if len(args) < 1:\n print('You must give a template filename')\n sys.exit(2)\n template_name = args[0]\n args = args[1:]\n vars = {}\n if options.use_env:\n vars.update(os.environ)\n for value in args:\n if '=' not in value:\n print('Bad argument: %r' % value)\n sys.exit(2)\n name, value = value.split('=', 1)\n if name.startswith('py:'):\n name = name[:3]\n value = eval(value)\n vars[name] = value\n if template_name == '-':\n template_content = sys.stdin.read()\n template_name = ''\n else:\n f = open(template_name, 'rb')\n template_content = f.read()\n f.close()\n if options.use_html:\n TemplateClass = HTMLTemplate\n else:\n TemplateClass = Template\n template = TemplateClass(template_content, name=template_name)\n result = template.substitute(vars)\n if options.output:\n f = open(options.output, 'wb')\n f.write(result)\n f.close()\n else:\n sys.stdout.write(result)\n\nif __name__ == '__main__':\n fill_command()\n" }, { "path": "publisher/tempita/_looper.py", "content": "\"\"\"\nHelper for looping over sequences, particular in templates.\n\nOften in a loop in a template it's handy to know what's next up,\npreviously up, if this is the first or last item in the sequence, etc.\nThese can be awkward to manage in a normal Python loop, but using the\nlooper you can get a better sense of the context. Use like::\n\n >>> for loop, item in looper(['a', 'b', 'c']):\n ... print loop.number, item\n ... if not loop.last:\n ... print '---'\n 1 a\n ---\n 2 b\n ---\n 3 c\n\n\"\"\"\n\nimport sys\nfrom tempita.compat3 import basestring_\n\n__all__ = ['looper']\n\n\nclass looper(object):\n \"\"\"\n Helper for looping (particularly in templates)\n\n Use this like::\n\n for loop, item in looper(seq):\n if loop.first:\n ...\n \"\"\"\n\n def __init__(self, seq):\n self.seq = seq\n\n def __iter__(self):\n return looper_iter(self.seq)\n\n def __repr__(self):\n return '<%s for %r>' % (\n self.__class__.__name__, self.seq)\n\n\nclass looper_iter(object):\n\n def __init__(self, seq):\n self.seq = list(seq)\n self.pos = 0\n\n def __iter__(self):\n return self\n\n def __next__(self):\n if self.pos >= len(self.seq):\n raise StopIteration\n result = loop_pos(self.seq, self.pos), self.seq[self.pos]\n self.pos += 1\n return result\n\n if sys.version < \"3\":\n next = __next__\n\n\nclass loop_pos(object):\n\n def __init__(self, seq, pos):\n self.seq = seq\n self.pos = pos\n\n def __repr__(self):\n return '' % (\n self.seq[self.pos], self.pos)\n\n def index(self):\n return self.pos\n index = property(index)\n\n def number(self):\n return self.pos + 1\n number = property(number)\n\n def item(self):\n return self.seq[self.pos]\n item = property(item)\n\n def __next__(self):\n try:\n return self.seq[self.pos + 1]\n except IndexError:\n return None\n __next__ = property(__next__)\n\n if sys.version < \"3\":\n next = __next__\n\n def previous(self):\n if self.pos == 0:\n return None\n return self.seq[self.pos - 1]\n previous = property(previous)\n\n def odd(self):\n return not self.pos % 2\n odd = property(odd)\n\n def even(self):\n return self.pos % 2\n even = property(even)\n\n def first(self):\n return self.pos == 0\n first = property(first)\n\n def last(self):\n return self.pos == len(self.seq) - 1\n last = property(last)\n\n def length(self):\n return len(self.seq)\n length = property(length)\n\n def first_group(self, getter=None):\n \"\"\"\n Returns true if this item is the start of a new group,\n where groups mean that some attribute has changed. The getter\n can be None (the item itself changes), an attribute name like\n ``'.attr'``, a function, or a dict key or list index.\n \"\"\"\n if self.first:\n return True\n return self._compare_group(self.item, self.previous, getter)\n\n def last_group(self, getter=None):\n \"\"\"\n Returns true if this item is the end of a new group,\n where groups mean that some attribute has changed. The getter\n can be None (the item itself changes), an attribute name like\n ``'.attr'``, a function, or a dict key or list index.\n \"\"\"\n if self.last:\n return True\n return self._compare_group(self.item, self.__next__, getter)\n\n def _compare_group(self, item, other, getter):\n if getter is None:\n return item != other\n elif (isinstance(getter, basestring_)\n and getter.startswith('.')):\n getter = getter[1:]\n if getter.endswith('()'):\n getter = getter[:-2]\n return getattr(item, getter)() != getattr(other, getter)()\n else:\n return getattr(item, getter) != getattr(other, getter)\n elif hasattr(getter, '__call__'):\n return getter(item) != getter(other)\n else:\n return item[getter] != other[getter]\n" }, { "path": "publisher/tempita/compat3.py", "content": "import sys\n\n__all__ = ['b', 'basestring_', 'bytes', 'next', 'is_unicode']\n\nif sys.version < \"3\":\n b = bytes = str\n basestring_ = basestring\nelse:\n\n def b(s):\n if isinstance(s, str):\n return s.encode('latin1')\n return bytes(s)\n basestring_ = (bytes, str)\n bytes = bytes\ntext = str\n\nif sys.version < \"3\":\n\n def next(obj):\n return obj.next()\nelse:\n next = next\n\nif sys.version < \"3\":\n\n def is_unicode(obj):\n return isinstance(obj, unicode)\nelse:\n\n def is_unicode(obj):\n return isinstance(obj, str)\n\n\ndef coerce_text(v):\n if not isinstance(v, basestring_):\n if sys.version < \"3\":\n attr = '__unicode__'\n else:\n attr = '__str__'\n if hasattr(v, attr):\n return unicode(v)\n else:\n return bytes(v)\n return v\n" }, { "path": "publisher/writer/__init__.py", "content": "__all__ = ['writer']\n\nimport docutils.core as dc\nimport docutils.writers\nfrom docutils import nodes\n\nfrom docutils.writers.latex2e import (Writer, LaTeXTranslator,\n PreambleCmds)\n\nfrom .rstmath import mathEnv\nfrom . import code_block\n\nfrom options import options\n\ntry:\n from collections import OrderedDict\nexcept ImportError:\n from ordereddict import OrderedDict\n\nPreambleCmds.float_settings = '''\n\\\\usepackage[font={small,it},labelfont=bf]{caption}\n\\\\usepackage{float}\n'''\n\nclass Translator(LaTeXTranslator):\n def __init__(self, *args, **kwargs):\n LaTeXTranslator.__init__(self, *args, **kwargs)\n\n # Handle author declarations\n\n self.current_field = ''\n\n self.copyright_holder = None\n self.author_names = []\n self.author_institutions = []\n self.author_institution_map = dict()\n self.author_emails = []\n self.corresponding = []\n self.equal_contributors = []\n self.paper_title = ''\n self.abstract_text = []\n self.keywords = ''\n self.table_caption = []\n self.video_url = ''\n self.bibliography = ''\n\n # This gets read by the underlying docutils implementation.\n # If present, it is a list with the first entry the style name\n # and the second entry the BiBTeX file (see `visit_field_body`)\n self.bibtex = None\n\n self.abstract_in_progress = False\n self.non_breaking_paragraph = False\n\n self.figure_type = 'figure'\n self.figure_alignment = 'left'\n self.table_type = 'table'\n\n self.active_table.set_table_style('booktabs', 'booktabs')\n self.active_table.set_table_style(['booktabs'], [])\n\n def visit_docinfo(self, node):\n pass\n\n def depart_docinfo(self, node):\n pass\n\n def visit_author(self, node):\n self.author_names.append(self.encode(node.astext()))\n self.author_institution_map[self.author_names[-1]] = []\n raise nodes.SkipNode\n\n def depart_author(self, node):\n pass\n\n def visit_author(self, node):\n self.author_names.append(self.encode(node.astext()))\n self.author_institution_map[self.author_names[-1]] = []\n raise nodes.SkipNode\n\n def depart_author(self, node):\n pass\n\n def visit_classifier(self, node):\n pass\n\n def depart_classifier(self, node):\n pass\n\n def visit_field_name(self, node):\n self.current_field = node.astext()\n raise nodes.SkipNode\n\n def visit_field_body(self, node):\n try:\n text = self.encode(node.astext())\n except TypeError:\n text = ''\n\n if self.current_field == 'email':\n self.author_emails.append(text)\n elif self.current_field == 'corresponding':\n self.corresponding.append(self.author_names[-1])\n elif self.current_field == 'equal-contributor':\n self.equal_contributors.append(self.author_names[-1])\n elif self.current_field == 'institution':\n self.author_institutions.append(text)\n self.author_institution_map[self.author_names[-1]].append(text)\n elif self.current_field == 'copyright_holder':\n self.copyright_holder = text\n elif self.current_field == 'video':\n self.video_url = text\n elif self.current_field == 'bibliography':\n self.bibtex = ['alphaurl', text]\n self._use_latex_citations = True\n self._bibitems = ['', '']\n self.bibliography = text\n\n self.current_field = ''\n\n raise nodes.SkipNode\n\n def depart_field_body(self, node):\n raise nodes.SkipNode\n\n def depart_document(self, node):\n LaTeXTranslator.depart_document(self, node)\n\n ## Generate footmarks\n\n # build map: institution -> (author1, author2)\n institution_authors = OrderedDict()\n for auth in self.author_institution_map:\n for inst in self.author_institution_map[auth]:\n institution_authors.setdefault(inst, []).append(auth)\n\n def footmark(n):\n \"\"\"Insert footmark #n. Footmark 1 is reserved for\n the corresponding author. Footmark 2 is reserved for\n the equal contributors.\\\n \"\"\"\n return ('\\\\setcounter{footnotecounter}{%d}' % n,\n '\\\\fnsymbol{footnotecounter}')\n\n # Build a footmark for the corresponding author\n corresponding_footmark = footmark(1)\n\n # Build a footmark for equal contributors\n equal_footmark = footmark(2)\n\n # Build one footmark for each institution\n institute_footmark = {}\n for i, inst in enumerate(institution_authors):\n institute_footmark[inst] = footmark(i + 3)\n\n footmark_template = r'\\thanks{%(footmark)s %(instutions)}'\n corresponding_auth_template = r'''%%\n %(footmark_counter)s\\thanks{%(footmark)s %%\n Corresponding author: \\protect\\href{mailto:%(email)s}{%(email)s}}'''\n\n equal_contrib_template = r'''%%\n %(footmark_counter)s\\thanks{%(footmark)s %%\n These authors contributed equally.}'''\n\n title = self.paper_title\n authors = []\n institutions_mentioned = set()\n equal_authors_mentioned = False\n corr_emails = []\n if len(self.corresponding) == 0:\n self.corresponding = [self.author_names[0]]\n for n, auth in enumerate(self.author_names):\n if auth in self.corresponding:\n corr_emails.append(self.author_emails[n])\n\n for n, auth in enumerate(self.author_names):\n # get footmarks\n footmarks = ''.join([''.join(institute_footmark[inst]) for inst in self.author_institution_map[auth]])\n if auth in self.equal_contributors:\n footmarks += ''.join(equal_footmark)\n if auth in self.corresponding:\n footmarks += ''.join(corresponding_footmark)\n authors += [r'%(author)s$^{%(footmark)s}$' %\n {'author': auth,\n 'footmark': footmarks}]\n\n if auth in self.equal_contributors and equal_authors_mentioned==False:\n fm_counter, fm = equal_footmark\n authors[-1] += equal_contrib_template % \\\n {'footmark_counter': fm_counter,\n 'footmark': fm}\n equal_authors_mentioned = True\n\n if auth in self.corresponding:\n fm_counter, fm = corresponding_footmark\n authors[-1] += corresponding_auth_template % \\\n {'footmark_counter': fm_counter,\n 'footmark': fm,\n 'email': ', '.join(corr_emails)}\n\n for inst in self.author_institution_map[auth]:\n if not inst in institutions_mentioned:\n fm_counter, fm = institute_footmark[inst]\n authors[-1] += r'%(footmark_counter)s\\thanks{%(footmark)s %(institution)s}' % \\\n {'footmark_counter': fm_counter,\n 'footmark': fm,\n 'institution': inst}\n\n institutions_mentioned.add(inst)\n\n ## Add copyright\n\n # If things went spectacularly wrong, we could not even parse author\n # info. Just fill in some dummy info so that we can see the error\n # messages in the resulting PDF.\n if len(self.author_names) == 0:\n self.author_names = ['John Doe']\n self.author_emails = ['john@doe.com']\n authors = ['']\n\n copyright_holder = self.copyright_holder or (self.author_names[0] + ('.' if len(self.author_names) == 1 else ' et al.'))\n author_notes = r'''%%\n\n \\noindent%%\n Copyright\\,\\copyright\\,%(year)s %(copyright_holder)s %(copyright)s%%\n ''' % \\\n {'email': self.author_emails[0],\n 'year': options['book']['year'],\n 'copyright_holder': copyright_holder,\n 'copyright': options['book']['copyright']['article']}\n\n authors[-1] += r'\\thanks{%s}' % author_notes\n\n\n ## Set up title and page headers\n\n if not self.video_url:\n video_template = ''\n else:\n video_template = r'\\\\\\vspace{5mm}\\tt\\url{%s}\\vspace{-5mm}' % self.video_url\n\n title_template = r'\\newcounter{footnotecounter}' \\\n r'\\title{%s}\\author{%s' \\\n r'%s}\\maketitle'\n title_template = title_template % (title, ', '.join(authors),\n video_template)\n\n marks = r'''\n \\renewcommand{\\leftmark}{%s}\n \\renewcommand{\\rightmark}{%s}\n ''' % (options['book']['title']['short'], title.upper())\n title_template += marks\n\n self.body_pre_docinfo = [title_template]\n\n # Save paper stats\n self.document.stats = {'title': title,\n 'authors': ', '.join(self.author_names),\n 'author': self.author_names,\n 'author_email': self.author_emails,\n 'author_institution': self.author_institutions,\n 'abstract': self.abstract_text,\n 'keywords': self.keywords,\n 'copyright_holder': copyright_holder,\n 'video': self.video_url,\n 'bibliography':self.bibliography}\n\n if hasattr(self, 'bibtex') and self.bibtex:\n self.document.stats.update({'bibliography': self.bibtex[1]})\n\n def end_open_abstract(self, node):\n if 'abstract' not in node['classes'] and self.abstract_in_progress:\n self.out.append('\\\\end{abstract}')\n self.abstract_in_progress = False\n elif self.abstract_in_progress:\n self.abstract_text.append(self.encode(node.astext()))\n\n\n def visit_title(self, node):\n self.end_open_abstract(node)\n\n if self.section_level == 1:\n if self.paper_title:\n import warnings\n warnings.warn(RuntimeWarning(\"Title set twice--ignored. \"\n \"Could be due to ReST\"\n \"error.)\"))\n else:\n self.paper_title = self.encode(node.astext())\n raise nodes.SkipNode\n\n elif node.astext() == 'References':\n raise nodes.SkipNode\n\n LaTeXTranslator.visit_title(self, node)\n\n def visit_paragraph(self, node):\n self.end_open_abstract(node)\n\n if 'abstract' in node['classes'] and not self.abstract_in_progress:\n self.out.append('\\\\begin{abstract}')\n self.abstract_text.append(self.encode(node.astext()))\n self.abstract_in_progress = True\n\n elif 'keywords' in node['classes']:\n self.out.append('\\\\begin{IEEEkeywords}')\n self.keywords = self.encode(node.astext())\n\n elif self.non_breaking_paragraph:\n self.non_breaking_paragraph = False\n\n else:\n if self.active_table.is_open():\n self.out.append('\\n')\n else:\n self.out.append('\\n\\n')\n\n def depart_paragraph(self, node):\n if 'keywords' in node['classes']:\n self.out.append('\\\\end{IEEEkeywords}')\n\n def visit_figure(self, node):\n self.requirements['float_settings'] = PreambleCmds.float_settings\n\n self.figure_type = 'figure'\n if 'classes' in node.attributes:\n placements = '[%s]' % ''.join(node.attributes['classes'])\n if 'w' in placements:\n placements = placements.replace('w', '')\n self.figure_type = 'figure*'\n\n self.out.append('\\\\begin{%s}%s' % (self.figure_type, placements))\n\n if node.get('ids'):\n self.out += ['\\n'] + self.ids_to_labels(node)\n\n self.figure_alignment = node.attributes.get('align', 'center')\n\n def depart_figure(self, node):\n self.out.append('\\\\end{%s}' % self.figure_type)\n\n def visit_image(self, node):\n align = self.figure_alignment or 'center'\n scale = node.attributes.get('scale', None)\n filename = node.attributes['uri']\n\n if self.figure_type == 'figure*':\n width = r'\\textwidth'\n else:\n width = r'\\columnwidth'\n\n figure_opts = []\n\n if scale is not None:\n figure_opts.append('scale=%.2f' % (scale / 100.))\n\n # Only add \\columnwidth if scale or width have not been specified.\n if 'scale' not in node.attributes and 'width' not in node.attributes:\n figure_opts.append(r'width=\\columnwidth')\n\n self.out.append(r'\\noindent\\makebox[%s][%s]' % (width, align[0]))\n self.out.append(r'{\\includegraphics[%s]{%s}}' % (','.join(figure_opts),\n filename))\n\n def visit_footnote(self, node):\n # Handle case where footnote consists only of math\n if len(node.astext().split()) < 2:\n node.append(nodes.label(text='_abcdefghijklmno_'))\n\n # Work-around for a bug in docutils where\n # \"%\" is prepended to footnote text\n LaTeXTranslator.visit_footnote(self, node)\n self.out[-1] = self.out[1].strip('%')\n\n self.non_breaking_paragraph = True\n\n def visit_table(self, node):\n classes = node.attributes.get('classes', [])\n if 'w' in classes:\n self.table_type = 'table*'\n else:\n self.table_type = 'table'\n\n self.out.append(r'\\begin{%s}' % self.table_type)\n LaTeXTranslator.visit_table(self, node)\n\n def depart_table(self, node):\n LaTeXTranslator.depart_table(self, node)\n\n self.out.append(r'\\caption{%s}' % ''.join(self.table_caption))\n self.table_caption = []\n\n self.out.append(r'\\end{%s}' % self.table_type)\n self.active_table.set('preamble written', 1)\n self.active_table.set_table_style('booktabs', 'booktabs')\n\n def visit_thead(self, node):\n # Store table caption locally and then remove it\n # from the table so that docutils doesn't render it\n # (in the wrong place)\n if self.active_table.caption:\n self.table_caption = self.active_table.caption\n self.active_table.caption = []\n\n opening = self.active_table.get_opening()\n opening = opening.replace('linewidth', 'tablewidth')\n self.active_table.get_opening = lambda: opening\n\n # For some reason, docutils want to process longtable headers twice. I\n # don't trust this fix entirely, but it does the trick for now.\n self.active_table.need_recurse = lambda: False\n\n LaTeXTranslator.visit_thead(self, node)\n\n def depart_thead(self, node):\n LaTeXTranslator.depart_thead(self, node)\n\n def visit_literal_block(self, node):\n self.non_breaking_paragraph = True\n\n if 'language' in node.attributes:\n # do highlighting\n from pygments import highlight\n from pygments.lexers import PythonLexer, get_lexer_by_name\n from pygments.formatters import LatexFormatter\n\n extra_opts = 'fontsize=\\\\footnotesize'\n\n linenos = node.attributes.get('linenos', False)\n linenostart = node.attributes.get('linenostart', 1)\n if linenos:\n extra_opts += ',xleftmargin=2.25mm,numbersep=3pt'\n\n lexer = get_lexer_by_name(node.attributes['language'])\n tex = highlight(node.astext(), lexer,\n LatexFormatter(linenos=linenos,\n linenostart=linenostart,\n verboptions=extra_opts))\n\n self.out.append(\"\\\\vspace{1mm}\\n\" + tex +\n \"\\\\vspace{1mm}\\n\")\n raise nodes.SkipNode\n else:\n LaTeXTranslator.visit_literal_block(self, node)\n\n def depart_literal_block(self, node):\n LaTeXTranslator.depart_literal_block(self, node)\n\n\n def visit_block_quote(self, node):\n self.out.append('\\\\begin{quotation}')\n LaTeXTranslator.visit_block_quote(self, node)\n\n def depart_block_quote(self, node):\n LaTeXTranslator.depart_block_quote(self, node)\n self.out.append('\\\\end{quotation}')\n\n\n # Math directives from rstex\n\n def visit_InlineMath(self, node):\n self.requirements['amsmath'] = r'\\usepackage{amsmath}'\n self.out.append('$' + node['latex'] + '$')\n raise nodes.SkipNode\n\n def visit_PartMath(self, node):\n self.requirements['amsmath'] = r'\\usepackage{amsmath}'\n self.out.append(mathEnv(node['latex'], node['label'], node['type']))\n self.non_breaking_paragraph = True\n raise nodes.SkipNode\n\n def visit_PartLaTeX(self, node):\n if node[\"usepackage\"]:\n for package in node[\"usepackage\"]:\n self.requirements[package] = r'\\usepackage{%s}' % package\n self.out.append(\"\\n\" + node['latex'] + \"\\n\")\n raise nodes.SkipNode\n\n\nwriter = Writer()\nwriter.translator_class = Translator\n" }, { "path": "publisher/writer/code_block.py", "content": "# --- Code-block directive from Sphinx ---\n\nfrom docutils import nodes\nfrom docutils.parsers.rst import Directive, directives\n\nclass CodeBlock(Directive):\n \"\"\"\n Directive for a code block with special highlighting or line numbering\n settings.\n \"\"\"\n\n has_content = True\n required_arguments = 1\n optional_arguments = 0\n final_argument_whitespace = False\n option_spec = {\n 'linenos': directives.flag,\n 'linenostart': directives.nonnegative_int,\n }\n\n def run(self):\n code = u'\\n'.join(self.content)\n literal = nodes.literal_block(code, code)\n literal['language'] = self.arguments[0]\n literal['linenos'] = 'linenos' in self.options\n literal['linenostart'] = self.options.get('linenostart', 1)\n return [literal]\n\ndirectives.register_directive('code-block', CodeBlock)\ndirectives.register_directive('code', CodeBlock)\n\n# --- End code-block directive from Sphinx ---\n" }, { "path": "publisher/writer/rstmath.py", "content": "# This code is from: http://pypi.python.org/pypi/rstex/\n\n#!/usr/bin/python2\nfrom docutils import utils, nodes\nfrom docutils.core import publish_cmdline\nfrom docutils.writers.latex2e import Writer, LaTeXTranslator\nfrom docutils.parsers.rst import roles, Directive, directives\n\n\nclass InlineMath(nodes.Inline, nodes.TextElement):\n pass\n\nclass PartMath(nodes.Part, nodes.Element):\n pass\n\nclass PartLaTeX(nodes.Part, nodes.Element):\n pass\n\ndef mathEnv(math, label, type):\n if label:\n eqn_star = ''\n else:\n eqn_star = '*'\n\n if type in (\"split\", \"gathered\"):\n begin = \"\\\\begin{equation%s}\\n\\\\begin{%s}\\n\" % (type, eqn_star)\n end = \"\\\\end{%s}\\n\\\\end{equation%s}\\n\" % (type, eqn_star)\n else:\n begin = \"\\\\begin{%s%s}\\n\" % (type, eqn_star)\n end = \"\\\\end{%s%s}\" % (type, eqn_star)\n if label:\n begin += \"\\\\label{%s}\\n\" % label\n return begin + math + '\\n' + end\n\ndef mathRole(role, rawtext, text, lineno, inliner, options={}, content=[]):\n latex = utils.unescape(text, restore_backslashes=True)\n return [InlineMath(latex=latex)], []\n\nclass MathDirective(Directive):\n has_content = True\n required_arguments = 0\n optional_arguments = 2\n final_argument_whitespace = True\n option_spec = {\n 'type': directives.unchanged,\n 'label': directives.unchanged,\n }\n def run(self):\n latex = '\\n'.join(self.content)\n if self.arguments and self.arguments[0]:\n latex = ' '.join(self.arguments) + '\\n\\n' + latex\n node = PartMath()\n node['latex'] = latex.strip()\n node['label'] = self.options.get('label', None)\n node['type'] = self.options.get('type', \"equation\")\n ret = [node]\n return ret\n\nclass LaTeXDirective(Directive):\n has_content = True\n required_arguments = 0\n optional_arguments = 1\n final_argument_whitespace = True\n option_spec = {\n 'usepackage': directives.unchanged\n }\n def run(self):\n latex = '\\n'.join(self.content)\n if self.arguments and self.arguments[0]:\n latex = self.arguments[0] + '\\n\\n' + latex\n node = PartLaTeX()\n node['latex'] = latex\n node['usepackage'] = self.options.get(\"usepackage\", \"\").split(\",\")\n ret = [node]\n return ret\n\n\nroles.register_local_role(\"math\", mathRole)\ndirectives.register_directive(\"math\", MathDirective)\ndirectives.register_directive(\"latex\", LaTeXDirective)\n\n" }, { "path": "publisher/writer/sphinx_highlight.py", "content": "from pygments.style import Style\nfrom pygments.styles.friendly import FriendlyStyle\nfrom pygments.token import Generic, Comment, Number\n\nclass SphinxStyle(Style):\n \"\"\"\n Like friendly, but a bit darker to enhance contrast on the green\n background.\n \"\"\"\n\n background_color = '#eeffcc'\n default_style = ''\n\n styles = FriendlyStyle.styles\n styles.update({\n Generic.Output: '#333',\n Comment: 'italic #408090',\n Number: '#208050',\n })\n" }, { "path": "pydata-cookbook.json", "content": "{\n \"book\": {\n \"citation_key\": \"pydata-cookbook-2018\",\n \"title\": {\n \"acronym\": \"PyData\",\n \"short\": \"PyData CookBook\",\n \"full\": \"PyData CookBook\"\n },\n \"year\": \"2018\",\n \"editor\": [\n \"Andy R. Terrel\",\n \"Jarrod Millman\"\n ],\n \"isbn\": \"value\",\n \"copyright\": {\n \"article\": \"\",\n \"license\": \"\"}\n }\n}\n\n\n" }, { "path": "review_criteria.md", "content": "# Suggested Review Criteria for PyData Cookbook\n\nThese criteria have been adapted from [the review criteria developed\nby a small subcommittee of the SciPy 2016 organizing \ncommittee](https://github.com/scipy-conference/scipy_proceedings/blob/master/review_criteria.md).\nThe goal is to help guide authors and reviewers alike. Suggestions and\namendments to these review criteria are enthusiastically welcomed via\ndiscussion or pull request.\n\n\n## Written Quality\n\n- Prose should be written in English.\n- Prose should clearly communicate the narrative.\n- The written style should convey information that furthers the knowledge or \n research of the reader.\n- Due to the interdisciplinary nature of the SciPy community, highly \n domain-specific jargon should be avoided or explained where possible.\n\n## Technical Content\n\n- The technical content should be scientifically sound.\n- Computational content should, likewise, be verified.\n- The work should describe the development or use of python software for \n approaching a problem of relevance to the SciPy community (as defined\n by, for instance, the domains covered by the \n [SciPy conference](http://conference.scipy.org).\n\n## Verifiability\n\n- Software descriptions should be accompanied by references to or examples of \n representative source code.\n- Source code essential to the conclusions of the paper should be made \n available to the reader.\n- Data sources should be identified (e.g., with citation to a persistent DOI).\n- Analysis should be accompanied by a workflow description sufficient\n to reproduce the results.\n\n## Other Requirements\n\n- All mentioned software should be formally cited wherever possible.\n- Acronyms should be spelled out upon first mention.\n- License conditions on images and figures must be respected (Creative Commons, \n etc.).\n- Mathematical and other symbols should be defined.\n- Definitions should include consistent units where appropriate.\n\n### Length\n\n- The compiled version should be no longer than 15 pages, including figures.\n\n### Figures\n\n- All figures and tables should have captions. \n- Figure text should be of a readable size.\n\n### Code Snippets\n\n- Code snippets should be formatted to fit inside a single column without\n overflow.\n- If not in Python, the language in the code snippet should be mentioned.\n- Code snippets should follow a common style guide. PEP8 is preferred.\n" }, { "path": "reviews/README", "content": "README\n======\n\nFor each SciPy review and update the substitutions in the two templates\nfor inviting reviewers to review the abstracts and papers::\n\n invite-abstract-reviews.rst\n invite-paper-reviews.rst\n\nGenerate the invite to review the abstracts::\n\n rst2html.py invite-abstract-reviews.rst > invite-abstract-reviews.rst\n\nGenerate the invite to review the papers as well as the review template::\n\n rst2html.py invite-paper-reviews.rst > invite-paper-reviews.rst\n rst2html.py review-template.rst > review-template.html\n" }, { "path": "reviews/invite-abstract-reviews.rst", "content": ".. note:: TODO-- improve abstract review criteria\n\n.. For each conference, update the following section,\n which will be inserted in the template below.\n\n.. |conference| replace:: SciPy\n.. |year| replace:: 2010\n.. |downloadurl| replace:: http://dip.sun.ac.za/~stefan/scipy2010/\n.. |submitted| replace:: 32\n.. |toreview| replace:: 10\n.. |duedate| replace:: Monday 19 April\n.. |email| replace::\n \"Stefan van der Walt\" and\n \"Jarrod Millman\" \n\nDear |conference| |year| Program Committee:\n\nI am happy to report that we have received several high quality abstracts for\nthe |conference| |year| conference. Now, it is our task to rate these abstracts\nand to choose the best ones for presentation.\n\nOf the |submitted| abstracts, you are each requested to review |toreview|, as\nset out in the table below. Of course, you are more than welcome (and\nencouraged!) to review more, if time permits. We have until the end of this\ncoming |duedate|; I realise this is not a lot of time, but I estimate the total\ncommitment for each reviewer to be no more than one hour in total.\n\n\n\nThe process works as follows:\n\n 1. Look up the papers you need to review on the table above.\n 2. Download the paper and score sheet from |downloadurl|.\n 3. For each reviewed abstract, fill in the appropriate marks on the provided\n score sheet.\n 4. Email the score sheet to |email|.\n\nThree marks are awarded per abstract:\n\n- Applicability (/10)\n- Novelty (/10)\n- General impression (/10)\n\nWe will use the average of the 3 marks to do the final evaluation. The \"General\nimpression\" field is necessarily vague: use it to impart your gut feel about\nthe abstract. A comment field is provided, please share any additional\nthoughts.\n\nThank you for giving your time to shape this year's conference!\n\nWith best regards,\n\nJarrod Millman, UC Berkeley, USA\n\nStéfan van der Walt, Stellenbosch University, South Africa\n" }, { "path": "reviews/invite-paper-reviews.rst", "content": ".. For each conference, update the following section,\n which will be inserted in the template below.\n\n.. |conference| replace:: SciPy \n.. |year| replace:: 2010\n.. |abstracts| replace:: 32\n.. |papers| replace:: 19\n.. |due| replace:: January 8, 2011\n.. |emails| replace::\n \"Stefan van der Walt\" and\n \"Jarrod Millman\" \n.. |download| replace:: http://cirl.berkeley.edu/scipy2010\n\nSubject: [|conference| |year|] invitation to review proceedings\n\nDear colleague and friend,\n\nConsidering your expertise, we would like to invite you to review the two\nattached articles for the |conference| |year| conference proceedings. Everyone who\npresented at this year's conference was invited to submit an article for\nconsideration in the peer-reviewed proceedings. The conference presentations\nwere selected by a program committee, based on submitted abstracts and the\nfollow criteria: applicability, novelty, and general impression. Each\nabstract was reviewed by at least four reviewers. Of the |abstracts| accepted\nabstracts, we have |papers| submitted papers to review.\n\nWe would appreciate it if, within the next three days, you could confirm\nwhether or not you'll be able to take part in this process. Reviews are due by\n|due| and should be submitted via email to |emails|.\n\nThe purpose of the conference proceedings is to increase the visibility of the\nconference and the presented work, as well as to provide academic recognition\nof the significant contributions made by the developers of software for science\nand engineering. To ensure the published proceedings are of sufficiently high\nquality, we have have instituted a formal review process in which each paper\nwill be reviewed by at least two reviewers according to explicit and uniform\ncriteria.\n\nThe primary goal of the review process is to provide independent feedback that\nguide authors in improving their papers. To review a paper, please complete the\nattached review form.\n\nPlease note that, while reviewers' recommendations regarding a paper's\nsuitability for publication are seriously considered, the final decision rests\nwith the proceeding editors.\n\nOf course, you are more than welcome (and encouraged!) to review additional\npapers, if time and interest permits. You can browse the submitted papers\nhere:\n\n |download|\n\nPlease note that all reviews are made public including the reviewer's name.\n\nWith best regards,\n\nJarrod Millman, UC Berkeley, USA\n\nStéfan van der Walt, Stellenbosch University, South Africa\n" }, { "path": "reviews/invite-reviewer.txt.in", "content": "Hello {{ reviewer }}!\n\nThe 2016 Scientific Computing with Python Conference\n(http://scipy2016.scipy.org) is fast approaching, and so too is the deadline\nfor proceedings submissions (May 16th) and consequently the start of the\nreview process. \n\nWe welcome you to be a member of the Proceedings Review Committee this year.\nThis should require a little of your time spread out over the review period\n(May 16th to July 4th) to evaluate three assigned submissions aligned with your\nareas of expertise.\n\nIn the spirit of open source software, the SciPy conference uses a fully open\nand collaborative review process to make proceedings acceptance decisions. \nTo learn more about this, you can read about it on the proceedings github page,\nhttps://github.com/scipy-conference/scipy_proceedings. \n\nPlease let us know if you wish to participate this year and we will include \nyou in subsequent communications. Also, please don't hesitate to connect us\nwith any other community members who you think we should consider inviting to\nreview. \n\nYours Truly, \nYour SciPy 2016 Proceedings co-chairs,\nScott Rostrup and Sebastian Benthall\n" }, { "path": "reviews/review-template.rst", "content": "Independent Review Report\n=========================\n\n.. note:: Please be aware that all reviews are made public including\n the reviewer's name.\n\nReviewer:\n\nDepartment/Center/Division:\n\nInstitution/University/Company:\n\nField of interest / expertise:\n\nCountry:\n\n\nArticle reviewed:\n\nINSTRUCTIONS\n------------\n\nPlease read the submitted article and fully complete this form. Since we don't\nhave a copy editor, we also request that you annotate the PDF [1]_ to highlight\ntypos, formatting issues, and grammatical mistakes.\n\nThe goal of the review process is two-fold. First, it guides authors in\nimproving their papers and, secondly, ensures that published works are of a\nprofessional academic standard.\n\nResearch in science and engineering increasingly relies on software for\ndata processing and management as well as theoretical exploration. However,\nthe effort necessary to develop this software is rarely recognized as having\nthe same academic worth as other aspects of the research. These proceedings\nare, at least in part, intended to address this shortcoming.\n\nAn article focused on software development necessarily differs from the\nstandard scientific article with respect to format. For instance, it is\nunlikely to have the same sections (i.e., introduction, methods, results,\nconclusion). You may therefore have to rely on other factors to decide whether\nthe paper sets a high enough standards as an academic publication.\n\nPlease note that, while reviewers' recommendations regarding a paper's\nsuitability for publication are seriously considered, the final decision rests\nwith the proceeding editors.\n\n.. [1] We recommend the free version of `PDF XChange Viewer\n `__ for\n Linux (Wine) and Windows. Under OSX, annotation is provided by Preview\n as well as `Skim `__.\n\nGENERAL EVALUATION\n------------------\n\nPlease rate the paper using the following criteria (please use the abbreviation\nto the right of the description)::\n\n below doesn't meet standards for academic publication\n meets meets or exceeds the standards for academic publication\n n/a not applicable\n\n- Quality of the approach:\n\n- Quality of the writing:\n\n- Quality of the figures/tables:\n\n\nSPECIFIC EVALUATION\n-------------------\n\nFor the following questions, please respond with 'yes' or 'no'. If you\nanswer 'no', please provide a brief, one- to two-sentence explanation.\n\n- Is the code made publicly available and does the article sufficiently\n describe how to access it? We aim not to publish papers that essentially\n advertise proprietary software. Therefore, if the code is not publicly\n available, please provide a one- to two- sentence response to each of the\n following questions:\n\n - Does the article focus on a topic other than the features\n of the software itself?\n - Can the majority of statements made be externally validated\n (i.e., without the use of the software)?\n - Is the information presented of interest to readers other than\n those at whom the software is aimed?\n - Is there any other aspect of the article that would\n justify including it despite the fact that the code\n isn't available?\n - Does the article discuss the reasons the software is closed?\n\n- Does the article present the problem in an appropriate context?\n Specifically, does it:\n\n - explain why the problem is important,\n - describe in which situations it arises,\n - outline relevant previous work,\n - provide background information for non-experts\n\n- Is the content of the paper accessible to a computational scientist\n with no specific knowledge in the given field?\n\n- Does the paper describe a well-formulated scientific or technical\n achievement?\n\n- Are the technical and scientific decisions well-motivated and\n clearly explained?\n\n- Are the code examples (if any) sound, clear, and well-written?\n\n- Is the paper factually correct?\n\n- Is the language and grammar of sufficient quality?\n\n- Are the conclusions justified?\n\n- Is prior work properly and fully cited?\n\n- Should any part of the article be shortened or expanded? Please explain.\n\n- In your view, is the paper fit for publication in the conference proceedings?\n Please suggest specific improvements and indicate whether you think the\n article needs a significant rewrite (rather than a minor revision).\n" }, { "path": "reviews/submission-template.rst", "content": ".. Abstract submission template for SciPy 2012: The 11th\n.. Python in Science Conference, to be held in Austin, TX,\n.. July 16 - 21 2012.\n..\n.. Program chairs:\n..\n.. Andy Terrel \n.. Matt McCormick \n..\n.. For more information, visit\n.. http://conference.scipy.org/scipy2012/\n\n\n========================\nTitle of my Presentation\n========================\n\n:Author: John Doe \n:Affiliation: Doe Laboratories\n\nHere, include a talk summary of no longer than 500 words. Aspects such as\nrelevance to Python in science, applicability, and novelty will be considered\nby the program committee.\n\n...............................................................\n\nPlease indicate with an X your preference::\n\n [ ] Only consider this presentation for a talk.\n\n [ ] Only consider this presentation for a poster.\n\n [ ] Consider this presentation for either a talk or a poster.\n\n...............................................................\n\nPlease indicate with an X whether you are willing to prepare an\naccompanying paper::\n\n [ ] Yes [ ] No\n\n...............................................................\n\nOptional: Indicate your preference for a specialized main track::\n\n [ ] High Performance Computing with Python\n [ ] Visualization\n\nOr for one of the smaller domain-specific sessions::\n\n [ ] Computational bioinformatics\n [ ] Meteorology and climatology\n [ ] Astronomy and astrophysics\n [ ] Geophysics\n\nPlease note that this selection is simply a guideline for the program\ncommittee, and that talks may be scheduled in a different session than\nindicated.\n\n...............................................................\n\nPlease email this form to 2012submissions@scipy.org\n" } ]