[ { "path": "LICENSE", "content": "===========\nMIT License\n===========\n\nCopyright (c) 2018, Mat Leonard\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." }, { "path": "README.md", "content": "# Sampyl\n\nMay 29, 2018: version 0.3\n\nSampyl is a package for sampling from probability distributions using MCMC methods. Similar to PyMC3 using theano to compute gradients, Sampyl uses [autograd](https://github.com/HIPS/autograd) to compute gradients. However, you are free to write your own gradient functions, autograd is not necessary. This project was started as a way to use MCMC samplers by defining models purely with Python and numpy.\n\nSampyl includes these samplers currently:\n\n* Metropolis-Hastings\n* Hamiltonian\n* NUTS\n* Slice\n\nFor each sampler, you pass in a function that calculates the log probability of the distribution you wish to sample from. For the Hamiltonian and NUTS samplers, gradient log probability functions are also required. If autograd is installed, the gradients are calculated automatically. Otherwise, the samplers accept gradient log-p functions which can be used whether autograd is installed or not.\n\nIt is still under active development with more features coming soon!\n\n### Dependencies\n\nWorks for Python 2 or 3.\n\nCurrently, [numpy](http://www.numpy.org/) and [scipy](http://www.scipy.org/) are the only dependencies. To use the automatic gradient log-P capabilities, you will need to install [autograd](https://github.com/HIPS/autograd).\n\n\n### Installation\n\nUnfortunately, there was a name collision, so use this to install from PyPI:\n\n`pip install sampyl-mcmc`\n\n\n### Documentation\n\nYou can find the documentation at http://mcleonard.github.io/sampyl/. It is a work in progress, of course, but we'll cover all the important bits soon enough.\n\n\n### Tests\n\nTests are written for use with pytest, in the tests folder.\n\n\n### License\n\n[MIT](https://github.com/mcleonard/sampyl/blob/master/LICENSE)" }, { "path": "docs/.buildinfo", "content": "# Sphinx build info version 1\n# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.\nconfig: f4e2f148c63ddca2b262009b56823e6f\ntags: 645f666f9bcd5a90fca523b33c5a78b7\n" }, { "path": "docs/.nojekyll", "content": "" }, { "path": "docs/_sources/distributions.rst.txt", "content": "Distributions\n=============\n\n.. automodule:: distributions\n\t:members:" }, { "path": "docs/_sources/examples/german_tank_problem.rst.txt", "content": "German Tank Problem\n-------------------\n\nHere we will cover a `classic problem`_ in statistics, estimating the total number of tanks from a small sample. Suppose four tanks are captured with the serial numbers 10, 256, 202, and 97. Assuming that each tank is numbered in sequence as they are built, how many tanks are there in total?\n\n.. _classic problem: https://en.wikipedia.org/wiki/German_tank_problem\n\nSince we are Bayesianists, we don't want a singular estimate, we want a probability distribution for the total number of tanks. Therefore, we need to calculate the distribution of total tanks :math:`N`, given the serial numbers :math:`D`:\n\n.. math ::\n\n P(N \\mid D) \\propto P(D \\mid N) \\, P(N)\n\n\nTo build the model, first let's think about the likelihood, :math:`P(D \\mid N)`. For those not familiar with statistical notation, this is the probability that we would see these serial numbers, given the total number of tanks. To decide how to model the likelihood, we can think about how we would create our data. Simply, we just have some number of tanks, with serial numbers :math:`1, 2, 3, ..., N`, and we uniformly draw four tanks from the group. Therefore, we should use a discrete uniform distribution. \n\nNext, we want to consider our prior information about :math:`N`, :math:`P(N)`. We know that it has to be at least equal to the largest serial number, :math:`m`. As for an upper bound, we can guess that it isn't into the millions, since every serial number we saw is less than 300. We also know that :math:`N` must be an integer and any value above 256 is equally likely, *a priori*, that is, before we saw the serial numbers. So a good choice here is the discrete uniform distribution again. I'll set an upper bound at 10000, just to have it high enough for it not to affect our results. In statistical notation, we would write\n\n.. math ::\n P(N \\mid D) &\\propto P(D \\mid N) \\, P(N) \\\\\n P(D \\mid N) &\\sim \\mathrm{DiscreteUniform}(D, min=0, max=N) \\\\\n P(N) &\\sim \\mathrm{DiscreteUniform}(N, min=m, max=10000) \\\\\n\nNow we can build the model with Sampyl and sample from the posterior. ::\n\n import sampyl as smp\n from sampyl import np\n\n # Data\n serials = np.array([10, 256, 202, 97])\n m = np.max(serials)\n \n # log P(N | D)\n def logp(N):\n # Samplers will pass in floats, we need to make them integers\n N = np.floor(N).astype(int)\n \n # Log-likelihood\n llh = smp.discrete_uniform(serials, lower=1, upper=N)\n \n prior = smp.discrete_uniform(N, lower=m, upper=10000)\n \n return llh + prior\n\n # Slice sampler for drawing from the posterior\n sampler = smp.Slice(logp, {'N':300})\n chain = sampler.sample(20000, burn=4000, thin=4)\n\n posterior = np.floor(chain.N)\n plt.hist(posterior, range=(0, 1000), bins=100, \n histtype='stepfilled', normed=True)\n plt.xlabel(\"Total number of tanks\")\n plt.ylabel(\"Posterior probability mass\")\n\n.. image:: _static/German_tanks.png\n :align: center\n\nAbove I've plotted the posterior distribution of :math:`N`. We can see that most of the probability is concentrated near the largest serial number in our data. In fact, the first 50% of the distribution is {256, 321}, and the first 95% is {256, 717}. This means there is a 50% probability that :math:`N` lies below 321, and 95% probability it is below 717. As far as an estimate, I think these intervals are much more meaningful than the mean since the posterior is so skewed. However, I'll report some of those statistics. The mean, median, and mode are 381.6, 321.0, 269, respectively." }, { "path": "docs/_sources/examples.rst.txt", "content": ".. _examples:\n\nExamples\n========\n\nHere are various models built with Sampyl.\n\n.. toctree::\n\texamples/german_tank_problem" }, { "path": "docs/_sources/index.rst.txt", "content": ".. Sampyl documentation master file, created by\n sphinx-quickstart on Thu Aug 6 23:09:13 2015.\n\n\nSampyl: MCMC samplers in Python \n===============================\n\nRelease v\\ |version|\n\nSampyl is a Python library implementing Markov Chain Monte Carlo (MCMC) samplers\nin Python. It's designed for use in Bayesian parameter estimation and provides a collection of distribution log-likelihoods for use in constructing models.\n\nOur goal with Sampyl is allow users to define models completely with Python and\ncommon packages like Numpy. Other MCMC packages require learning new syntax and\nsemantics while all that is really needed is a function that calculates :math:`\\log{P(X)}`\nfor the sampling distribution.\n\nSampyl allows the user to define a model any way they want, all that is required\nis a function that calculates log P(X). This function can be written completely \nin Python, written in C/C++ and wrapped with Python, or anything else a user can\nthink of. For samplers that require the gradient of P(X), such as :ref:`NUTS `, \nSampyl can calculate the gradients automatically with autograd_. \n\n.. _autograd: https://github.com/HIPS/autograd/\n\nTo show you how simple this can be, let's sample from a 2D correlated normal distribution. ::\n \n # To use automatic gradient calculations, use numpy (np) provided \n # by autograd through Sampyl\n import sampyl as smp\n from sampyl import np\n import seaborn\n \n icov = np.linalg.inv(np.array([[1., .8], [.8, 1.]]))\n def logp(x, y):\n d = np.array([x, y])\n return -.5 * np.dot(np.dot(d, icov), d)\n\n start = {'x': 1., 'y': 1.}\n nuts = smp.NUTS(logp, start)\n chain = nuts.sample(1000)\n\n seaborn.jointplot(chain.x, chain.y, stat_func=None)\n\n.. image:: _static/normal_example.png\n\t:align: center\n\n\n\nStart here\n----------\n.. toctree::\n :maxdepth: 2\n\n introduction\n tutorial\n\n\nExamples\n--------\n\n.. toctree::\n :maxdepth: 2\n \n examples\n\nAPI\n---\n.. toctree::\n :maxdepth: 2\n\n distributions\n model\n samplers\n state\n\n\nIndices and tables\n------------------\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n\n" }, { "path": "docs/_sources/introduction.rst.txt", "content": "Introduction\n============\n\nWhat are we doing here?\n-----------------------\n\nSampyl provides `Markov Chain Monte Carlo `_ (MCMC) samplers for drawing from probability distributions. Typically, this is used to sample from the posterior distribution of a Bayesian model. Other MCMC packages such as `PyMC `_ and `PyStan `_, while great and you should check them out, require you to create models using non-Pythonic syntax and semantics. Sampyl allows you to create models completely with Python and Numpy. All that is required is a function that calculates :math:`\\log{P(X)}` for the sampling distribution. You can create this function however you want.\n\n\nInstallation\n------------\n\nYou can install Sampyl from PyPI with ::\n\n\tpip install sampyl-mcmc\n\nSampyl depends on Numpy, Scipy, and `autograd`_. You'll also need matplotlib for the examples notebooks.\n\n.. _autograd: https://github.com/HIPS/autograd/" }, { "path": "docs/_sources/model.rst.txt", "content": ".. _model:\n\nModel\n=====\n\nThe model is a class to make accessing log P(X) and grad log P(X) functions easier. Models contain caches for both log P(X) and the gradient. This is intended to be used when building new samplers, users won't typically need this.\n\nThere are two models currently. :ref:`Model ` expects separate\nlog P(X) and gradient functions. :ref:`SingleModel `\nexpects one function that returns both log P(x) and the gradient.\n\nExample usage::\n \n def logp(X):\n ...\n \n model = init_model(logp)\n x = some_state\n logp_val = model.logp(x)\n grad_val = model.grad(x)\n logp_val, grad_val = model(x)\n\n\n.. _model_class:\n\n.. module:: model\n\n.. autofunction:: init_model\n\n.. autoclass:: Model\n :members:\n :inherited-members:\n\n .. method:: __call__(state)\n\n Return log P(X) and grad log P(X) given a :ref:`state ` X \n\n\n.. _single_model_class:\n\n.. autoclass:: SingleModel\n :members:\n :inherited-members:\n\n .. method:: __call__(state)\n\n Return log P(X) and grad log P(X) given a :ref:`state ` X" }, { "path": "docs/_sources/samplers/custom.rst.txt", "content": ".. _custom:\n\nCustom Samplers\n===============\n\nYou can build your own sampler by subclassing Sampler. A :ref:`model `\nis automatically generated from `logp`. The sampler is also initialized with\na :ref:`state ` generated from `start` and the arguments of `logp`. With these,\nyou define the `step` method, which should generate one sample and return a \n:ref:`state `.\n\nAs an example, here's snippet from the :ref:`Metropolis ` sampler. ::\n\n from sampyl import Sampler\n from sampyl import np\n \n class Metropolis(Sampler):\n\n def __init__(self, logp, start, **kwargs):\n # No gradient is needed, so set it to None, and the flag to False\n super(Metropolis, self).__init__(logp, start, None, grad_logp_flag=False, **kwargs)\n\n def step(self):\n \"\"\" Perform a Metropolis-Hastings step. \"\"\"\n x = self.state\n y = proposal(x, self.scale)\n if accept(x, y, self.model.logp):\n self.state = y\n\n return self.state\n\n def proposal(state, scale):\n proposed = State.fromkeys(state.keys())\n for i, var in enumerate(state):\n proposed.update({var: np.random.normal(state[var], scale[var])})\n return proposed\n\n def accept(x, y, logp):\n delp = logp(y) - logp(x)\n if np.isfinite(delp) and np.log(np.random.uniform()) < delp:\n return True\n else:\n return False\n\n\n.. module:: sampyl\n\n.. autoclass:: Sampler\n :members:\n\n **Attributes**\n\n .. attribute:: model\n \n :ref:`Model ` with logp and grad functions.\n\n .. attribute:: state\n\n The current :ref:`state ` of the model.\n\n .. attribute:: sampler\n\n Calling the sample method creates an infinite generator which returns \n samples as :ref:`states `.\n\n **Methods**\n" }, { "path": "docs/_sources/samplers/hamiltonian.rst.txt", "content": ".. _hamiltonian:\n\nHamiltonian MCMC Sampler\n========================\n\n.. module:: sampyl\n\n.. autoclass:: Hamiltonian\n :members:\n :inherited-members:\n" }, { "path": "docs/_sources/samplers/metropolis.rst.txt", "content": ".. _metropolis:\n\nMetropolis-Hastings Sampler\n===========================\n\n.. module:: sampyl\n\n.. autoclass:: Metropolis\n :members:\n :inherited-members:" }, { "path": "docs/_sources/samplers/nuts.rst.txt", "content": ".. _nuts:\n\nNo-U-Turn Sampler (NUTS)\n========================\n\n.. module:: sampyl\n\n.. autoclass:: NUTS\n\t:members:\n\t:inherited-members:" }, { "path": "docs/_sources/samplers/slice.rst.txt", "content": "Slice Sampler\n=============\n\n.. module:: sampyl\n\n.. autoclass:: Slice\n :members:\n :inherited-members:\n" }, { "path": "docs/_sources/samplers.rst.txt", "content": ".. _samplers:\n\nSamplers\n========\n\nEach sampler has the same API. First you create a function calculating log P(X),\nthen pass it to a sampler. To generate a chain, call the sample method.\n\nExample::\n \n import sampyl as smp\n def logp(x, y):\n ...\n\n start = {'x': x_start, 'y': y_start}\n nuts = smp.NUTS(logp, start)\n chain = nuts.sample(1000)\n\nCreating your own :ref:`custom samplers ` is possible and straightfoward.\n\n.. toctree::\n\n samplers/nuts\n samplers/metropolis\n samplers/slice\n samplers/hamiltonian\n samplers/custom" }, { "path": "docs/_sources/state.rst.txt", "content": ".. _state:\n\nState\n=====\n\nState objects are used to store the current state of the Markov chain. States are subclassed from collections.OrderedDict so that the elements are in a known order.\n\n.. module:: sampyl\n.. module:: state\n\n.. autoclass:: State\n :members:" }, { "path": "docs/_sources/tutorial.rst.txt", "content": "Tutorial\n========\n\nDefining a model\n----------------\n\nHere I'll demonstrate how to use Sampyl with a simple linear model. First, I will create some fake data. Then, I will build a model and sample from the posterior distribution to estimate the coefficients from the synthetic data. \n\nWith a linear model, we assume the data is drawn from a normal distribution\n\n.. math ::\n Y &\\sim N(\\mu, \\sigma^2) \\\\\n \\mu &= \\beta_0 + \\beta_1 x_1 + ... + \\beta_n x_n\n\nwhere :math:`\\beta_n` are coefficients we want to estimate and :math:`x_n` are the predictor variables in our data. \n\nLet's start by making some fake data. ::\n\n # Number of data points\n N = 200\n\n # True parameters\n sigma = 1\n true_b = np.array([2, 1, 4])\n\n # Features, including a constant\n X = np.ones((N, len(true_b)))\n X[:,1:] = np.random.rand(N, len(true_b)-1)\n\n # Outcomes\n y = np.dot(X, true_b) + np.random.randn(N)*sigma\n\n.. image:: _static/linear_model_data.png\n :align: center\n\nAbove I've plotted the data we generated. We have the outcomes :code:`y` and our features :code:`X`, now we want to build a model to estimate the coefficients from this data. We can start with Bayes theorem\n\n.. math ::\n\n P(\\beta, \\sigma \\mid D) \\propto P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma)\n\nThe left hand side :math:`P(\\beta, \\sigma \\mid D)` is the posterior distribution. This is a probability distribution of likely values for :math:`\\beta` and :math:`\\sigma`, given our data :math:`D`. On the right side is the data likelihood :math:`P(D \\mid \\beta, \\sigma)` which gives the probability that we would see our data, given the parameters :math:`\\beta` and :math:`\\sigma`. Finally, we have our priors, :math:`P(\\beta)` and :math:`P(\\sigma)`. These distributions represent our prior information about the parameters.\n\nBayes theorem is quite intuitive because it is very similar to how humans think naturally. Suppose you are on a road trip, and you know its roughly an hour until you arrive, but its been a while since you saw the last sign with the distance so you aren't too sure. Then, you do see a sign and it says you still have 65 miles (104 km). Now you have a pretty good idea that it'll be another hour, but you aren't completely certain because traffic might be slow, or it could take a bit once you get into the city. This is how Bayes theorem works too. You have some prior information about your arrival time, but you're fairly uncertain. Then, you see some data, the distance remaining, and you update your information about the arrival time.\n\nContinuing on with the model, we want the likelihood, :math:`P(D \\mid \\beta, \\sigma)`, to model our data. We are assuming we can predict the data with a linear sum of coefficients :math:`\\beta_n` with our predictors :math:`x_n`, with some normally distributed noise on the scale of :math:`\\sigma`. So our likelihood would be well modeled with a normal distribution.\n\nWe also want to assign probability distributions to :math:`\\beta` and :math:`\\sigma` to account for our uncertainty in those parameters and our prior information about them. This is done through the priors :math:`P(\\beta)\\, P(\\sigma)`. For :math:`\\sigma`, we must choose a distribution that is defined only above 0. An exponential is good for this and we can control the *diffuseness* of the prior by changing the rate parameter. High rates pull the prior closer to 0, while lower rates push it out. We are using fairly wide normal priors for the coefficients :math:`\\beta`, since we aren't sure *a priori* how large the coefficients will be. Putting this all together, we get our model\n\n.. math ::\n P(\\beta, \\sigma \\mid D) &\\propto P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma) \\\\\n P(D \\mid \\beta, \\sigma) &\\sim \\mathrm{Normal}(\\mu, \\sigma^2) \\\\\n \\mu &= \\sum \\beta_i x_i \\\\\n \\beta &\\sim \\mathrm{Normal}(0, 100) \\\\\n \\sigma &\\sim \\mathrm{Exponential}(1) \n\n\nEvery sampler implemented in Sampyl takes a function that calculates :math:`\\log{P(\\theta)}` of the sampling distribution. We want to sample from the model posterior, so we need to write a Python function that calculates :math:`\\log{P(\\beta, \\sigma \\mid D)}`. I'll build the full model then go through it with explanations. ::\n \n import sampyl as smp\n from sampyl import np\n\n # Here, b is a length 3 array of coefficients\n def logp(b, sig):\n \n model = smp.Model()\n \n # Predicted value\n y_hat = np.dot(X, b)\n \n # Log-likelihood\n model.add(smp.normal(y, mu=y_hat, sig=sig))\n \n # log-priors\n model.add(smp.exponential(sig),\n smp.normal(b, mu=0, sig=100))\n \n return model()\n\nFirst, look at the imports, particularly ``from sampyl import np``. If you have worked with Numpy, you know it is usually abbreviated as ``np``, but why have we imported it from Sampyl here? Some of the samplers we have implemented require the gradient of :math:`\\log{P(\\theta)}`. To make things simpler, we use a wonderful package `autograd`_ to automatically calculate gradients. To use autograd, our :math:`\\log{P(\\theta)}` function needs to be written with Numpy provided by autograd. So, Sampyl imports Numpy from autograd, which you then import from Sampyl to build the model.\n\n.. _autograd: https://github.com/HIPS/autograd\n\nNext, check out the function definition. We named it ``logp`` but this can be anything. The arguments ``b`` and ``sig`` are the parameters of the model. They can either be Numpy arrays or scalars such as integers or floats.\n\nThen, we create a model with ``model = smp.Model()``. This is a convenience class that makes defining a model more concise. You add log-probabilities of the priors and likelihood with ``model.add()``. Then, you can call ``model()`` to sum up all the log-probabilities and get the log-posterior (well, something proportional to the posterior).\n\nOnwards: ::\n\n y_hat = np.dot(X, b)\n\nHere our function has been passed ``b`` which is an length 3 numpy array, a vector. We want to make a prediction of the outcome, ``y_hat``, then use this as the mean of our likelihood. ::\n\n model.add(smp.normal(y, mu=y_hat, sig=sig))\n\nThis is the log-likelihood of our data, given our parameters, :math:`P(D \\mid \\beta, \\sigma)`. It is important to note that ``smp.normal(y, mu=y_hat, sig=sig)`` returns a number, the log-likelihood of a normal distribution with the passed parameters. There is nothing going on behind the scene, just a function that returns a number. Then, ::\n\n model.add(smp.exponential(sig, rate=1),\n smp.normal(b, mu=0, sig=10)\n\nHere we have defined our priors. Again, ``smp.exponential`` and ``smp.normal`` return a scalar value of the distribution log-likelihood.\n\nFinally ::\n\n return model()\n\nAs I noted earlier, the distribution functions return log-likelihoods, just numbers. Since we are calculating :math:`\\log{P(\\beta, \\sigma \\mid D)}`,\n\n.. math ::\n \n \\log{P(\\beta, \\sigma \\mid D)} &\\propto \\log{\\left[P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma) \\right]} \\\\\n \\log{P(\\beta, \\sigma \\mid D)} &\\propto \\log{P(D \\mid \\beta, \\sigma)} + \\log{P(\\beta)} + \\log{P(\\sigma)}\n\nwe just add up the log-likelihoods! The ``model`` object does this for us. Again, nothing fancy going on here, just beautiful math with logarithms. Now that we have defined :math:`\\log{P(\\beta, \\sigma \\mid D)}`, we can draw samples from it using one of our samplers.\n\n\nSampling from the posterior\n---------------------------\n\nEach sampler provided by Sampyl requires a :math:`\\log{P(X)}` function, ``logp`` here, and a starting state. A good place to start is the maximum of the posterior, typically called the *maximum a posteriori* or MAP. You can also define ``start`` yourself, it just needs to be a dictionary where the keys are the arguments of ``logp``. Here we'll use the No-U-Turn Sampler (NUTS) ::\n\n start = smp.find_MAP(logp, {'b': np.ones(3), 'sig': 1.})\n nuts = smp.NUTS(logp, start)\n chain = nuts.sample(2100, burn=100)\n\nSo first, we find the MAP and pass it as a start value to the NUTS sampler. This returns the sampler object itself ``nuts``. It is important to provide the correct size arguments as starting values. Our function ``logp`` expects ``b`` to be a length 3 Numpy array, so that is what we need to provide to ``find_MAP`` or ``NUTS``.\n\nCalling ``nuts.sample(2100, burn=100)`` returns a chain of 2000 samples from the posterior distribution, where we have discarded the first 100 as a burn-in period. The chain we get is a `Numpy record array `_ so that we can access the posterior distribution for each parameter by name. For instance, to get the posterior samples for ``b`` with ``chain.b`` ::\n\n import matplotlib.pyplot as plt\n plt.plot(chain.b)\n\n.. image:: _static/linear_model_coefficients.png\n :align: center\n\nBelow is a plot of the posterior samples for each parameter.\n\n.. image:: _static/linear_model_posterior.png\n :align: center\n\nI've also included dashed lines indicating the true parameters. In the future, we will be providing functionality to return various statistics and intervals for the posterior. For more guidance, please look through the other examples provided in the documentation." }, { "path": "docs/_static/alabaster.css", "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n@import url(\"basic.css\");\n\n/* -- page layout ----------------------------------------------------------- */\n\nbody {\n font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro', serif;\n font-size: 17px;\n background-color: #fff;\n color: #000;\n margin: 0;\n padding: 0;\n}\n\n\ndiv.document {\n width: 940px;\n margin: 30px auto 0 auto;\n}\n\ndiv.documentwrapper {\n float: left;\n width: 100%;\n}\n\ndiv.bodywrapper {\n margin: 0 0 0 220px;\n}\n\ndiv.sphinxsidebar {\n width: 220px;\n font-size: 14px;\n line-height: 1.5;\n}\n\nhr {\n border: 1px solid #B1B4B6;\n}\n\ndiv.body {\n background-color: #fff;\n color: #3E4349;\n padding: 0 30px 0 30px;\n}\n\ndiv.body > .section {\n text-align: left;\n}\n\ndiv.footer {\n width: 940px;\n margin: 20px auto 30px auto;\n font-size: 14px;\n color: #888;\n text-align: right;\n}\n\ndiv.footer a {\n color: #888;\n}\n\np.caption {\n font-family: inherit;\n font-size: inherit;\n}\n\n\ndiv.relations {\n display: none;\n}\n\n\ndiv.sphinxsidebar a {\n color: #444;\n text-decoration: none;\n border-bottom: 1px dotted #999;\n}\n\ndiv.sphinxsidebar a:hover {\n border-bottom: 1px solid #999;\n}\n\ndiv.sphinxsidebarwrapper {\n padding: 18px 10px;\n}\n\ndiv.sphinxsidebarwrapper p.logo {\n padding: 0;\n margin: -10px 0 0 0px;\n text-align: center;\n}\n\ndiv.sphinxsidebarwrapper h1.logo {\n margin-top: -10px;\n text-align: center;\n margin-bottom: 5px;\n text-align: left;\n}\n\ndiv.sphinxsidebarwrapper h1.logo-name {\n margin-top: 0px;\n}\n\ndiv.sphinxsidebarwrapper p.blurb {\n margin-top: 0;\n font-style: normal;\n}\n\ndiv.sphinxsidebar h3,\ndiv.sphinxsidebar h4 {\n font-family: 'Garamond', 'Georgia', serif;\n color: #444;\n font-size: 24px;\n font-weight: normal;\n margin: 0 0 5px 0;\n padding: 0;\n}\n\ndiv.sphinxsidebar h4 {\n font-size: 20px;\n}\n\ndiv.sphinxsidebar h3 a {\n color: #444;\n}\n\ndiv.sphinxsidebar p.logo a,\ndiv.sphinxsidebar h3 a,\ndiv.sphinxsidebar p.logo a:hover,\ndiv.sphinxsidebar h3 a:hover {\n border: none;\n}\n\ndiv.sphinxsidebar p {\n color: #555;\n margin: 10px 0;\n}\n\ndiv.sphinxsidebar ul {\n margin: 10px 0;\n padding: 0;\n color: #000;\n}\n\ndiv.sphinxsidebar ul li.toctree-l1 > a {\n font-size: 120%;\n}\n\ndiv.sphinxsidebar ul li.toctree-l2 > a {\n font-size: 110%;\n}\n\ndiv.sphinxsidebar input {\n border: 1px solid #CCC;\n font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro', serif;\n font-size: 1em;\n}\n\ndiv.sphinxsidebar hr {\n border: none;\n height: 1px;\n color: #AAA;\n background: #AAA;\n\n text-align: left;\n margin-left: 0;\n width: 50%;\n}\n\n/* -- body styles ----------------------------------------------------------- */\n\na {\n color: #004B6B;\n text-decoration: underline;\n}\n\na:hover {\n color: #6D4100;\n text-decoration: underline;\n}\n\ndiv.body h1,\ndiv.body h2,\ndiv.body h3,\ndiv.body h4,\ndiv.body h5,\ndiv.body h6 {\n font-family: 'Garamond', 'Georgia', serif;\n font-weight: normal;\n margin: 30px 0px 10px 0px;\n padding: 0;\n}\n\ndiv.body h1 { margin-top: 0; 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*/\n}\n\ntt.xref, code.xref, a tt {\n background-color: #FBFBFB;\n border-bottom: 1px solid #fff;\n}\n\na.reference {\n text-decoration: none;\n border-bottom: 1px dotted #004B6B;\n}\n\n/* Don't put an underline on images */\na.image-reference, a.image-reference:hover {\n border-bottom: none;\n}\n\na.reference:hover {\n border-bottom: 1px solid #6D4100;\n}\n\na.footnote-reference {\n text-decoration: none;\n font-size: 0.7em;\n vertical-align: top;\n border-bottom: 1px dotted #004B6B;\n}\n\na.footnote-reference:hover {\n border-bottom: 1px solid #6D4100;\n}\n\na:hover tt, a:hover code {\n background: #EEE;\n}\n\n\n@media screen and (max-width: 870px) {\n\n div.sphinxsidebar {\n \tdisplay: none;\n }\n\n div.document {\n width: 100%;\n\n }\n\n div.documentwrapper {\n \tmargin-left: 0;\n \tmargin-top: 0;\n \tmargin-right: 0;\n \tmargin-bottom: 0;\n }\n\n div.bodywrapper {\n \tmargin-top: 0;\n \tmargin-right: 0;\n \tmargin-bottom: 0;\n \tmargin-left: 0;\n }\n\n ul {\n \tmargin-left: 0;\n }\n\n\tli > ul {\n /* Matches the 30px from the \"ul, ol\" selector above */\n\t\tmargin-left: 30px;\n\t}\n\n .document {\n \twidth: auto;\n }\n\n .footer {\n \twidth: auto;\n }\n\n .bodywrapper {\n \tmargin: 0;\n }\n\n .footer {\n \twidth: auto;\n }\n\n .github {\n display: none;\n }\n\n\n\n}\n\n\n\n@media screen and (max-width: 875px) {\n\n body {\n margin: 0;\n padding: 20px 30px;\n }\n\n div.documentwrapper {\n float: none;\n background: #fff;\n }\n\n div.sphinxsidebar {\n display: block;\n float: none;\n width: 102.5%;\n margin: 50px -30px -20px -30px;\n padding: 10px 20px;\n background: #333;\n color: #FFF;\n }\n\n div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p,\n div.sphinxsidebar h3 a {\n color: #fff;\n }\n\n div.sphinxsidebar a {\n color: #AAA;\n }\n\n div.sphinxsidebar p.logo {\n display: none;\n }\n\n div.document {\n width: 100%;\n margin: 0;\n }\n\n div.footer {\n display: none;\n }\n\n div.bodywrapper {\n margin: 0;\n }\n\n div.body {\n min-height: 0;\n padding: 0;\n }\n\n .rtd_doc_footer {\n display: none;\n }\n\n .document {\n width: auto;\n }\n\n .footer {\n width: auto;\n }\n\n .footer {\n width: auto;\n }\n\n .github {\n display: none;\n }\n}\n\n\n/* misc. */\n\n.revsys-inline {\n display: none!important;\n}\n\n/* Make nested-list/multi-paragraph items look better in Releases changelog\n * pages. Without this, docutils' magical list fuckery causes inconsistent\n * formatting between different release sub-lists.\n */\ndiv#changelog > div.section > ul > li > p:only-child {\n margin-bottom: 0;\n}\n\n/* Hide fugly table cell borders in ..bibliography:: directive output */\ntable.docutils.citation, table.docutils.citation td, table.docutils.citation th {\n border: none;\n /* Below needed in some edge cases; if not applied, bottom shadows appear */\n -moz-box-shadow: none;\n -webkit-box-shadow: none;\n box-shadow: none;\n}" }, { "path": "docs/_static/basic.css", "content": "/*\n * basic.css\n * ~~~~~~~~~\n *\n * Sphinx stylesheet -- basic theme.\n *\n * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.\n * :license: BSD, see LICENSE for details.\n *\n */\n\n/* -- main layout ----------------------------------------------------------- */\n\ndiv.clearer {\n clear: both;\n}\n\n/* -- relbar ---------------------------------------------------------------- */\n\ndiv.related {\n width: 100%;\n font-size: 90%;\n}\n\ndiv.related h3 {\n display: none;\n}\n\ndiv.related ul {\n margin: 0;\n padding: 0 0 0 10px;\n list-style: none;\n}\n\ndiv.related li {\n display: inline;\n}\n\ndiv.related li.right {\n float: right;\n margin-right: 5px;\n}\n\n/* -- sidebar --------------------------------------------------------------- */\n\ndiv.sphinxsidebarwrapper {\n padding: 10px 5px 0 10px;\n}\n\ndiv.sphinxsidebar {\n float: left;\n width: 230px;\n margin-left: -100%;\n font-size: 90%;\n word-wrap: break-word;\n overflow-wrap : break-word;\n}\n\ndiv.sphinxsidebar ul {\n list-style: none;\n}\n\ndiv.sphinxsidebar ul ul,\ndiv.sphinxsidebar ul.want-points {\n margin-left: 20px;\n list-style: square;\n}\n\ndiv.sphinxsidebar ul ul {\n margin-top: 0;\n margin-bottom: 0;\n}\n\ndiv.sphinxsidebar form {\n margin-top: 10px;\n}\n\ndiv.sphinxsidebar input {\n border: 1px solid #98dbcc;\n font-family: sans-serif;\n font-size: 1em;\n}\n\ndiv.sphinxsidebar #searchbox input[type=\"text\"] {\n float: left;\n width: 80%;\n padding: 0.25em;\n box-sizing: border-box;\n}\n\ndiv.sphinxsidebar #searchbox input[type=\"submit\"] {\n float: left;\n width: 20%;\n border-left: none;\n padding: 0.25em;\n box-sizing: border-box;\n}\n\n\nimg {\n border: 0;\n max-width: 100%;\n}\n\n/* -- search page ----------------------------------------------------------- */\n\nul.search {\n margin: 10px 0 0 20px;\n padding: 0;\n}\n\nul.search li {\n padding: 5px 0 5px 20px;\n background-image: url(file.png);\n background-repeat: no-repeat;\n background-position: 0 7px;\n}\n\nul.search li a {\n font-weight: bold;\n}\n\nul.search li div.context {\n color: #888;\n margin: 2px 0 0 30px;\n text-align: left;\n}\n\nul.keywordmatches li.goodmatch a {\n font-weight: bold;\n}\n\n/* -- index page ------------------------------------------------------------ */\n\ntable.contentstable {\n width: 90%;\n margin-left: auto;\n margin-right: auto;\n}\n\ntable.contentstable p.biglink {\n line-height: 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--------------------------------------------------- */\n\ntable.modindextable td {\n padding: 2px;\n border-collapse: collapse;\n}\n\n/* -- general body styles --------------------------------------------------- */\n\ndiv.body {\n min-width: 450px;\n max-width: 800px;\n}\n\ndiv.body p, div.body dd, div.body li, div.body blockquote {\n -moz-hyphens: auto;\n -ms-hyphens: auto;\n -webkit-hyphens: auto;\n hyphens: auto;\n}\n\na.headerlink {\n visibility: hidden;\n}\n\nh1:hover > a.headerlink,\nh2:hover > a.headerlink,\nh3:hover > a.headerlink,\nh4:hover > a.headerlink,\nh5:hover > a.headerlink,\nh6:hover > a.headerlink,\ndt:hover > a.headerlink,\ncaption:hover > a.headerlink,\np.caption:hover > a.headerlink,\ndiv.code-block-caption:hover > a.headerlink {\n visibility: visible;\n}\n\ndiv.body p.caption {\n text-align: inherit;\n}\n\ndiv.body td {\n text-align: left;\n}\n\n.first {\n margin-top: 0 !important;\n}\n\np.rubric {\n margin-top: 30px;\n font-weight: bold;\n}\n\nimg.align-left, .figure.align-left, object.align-left {\n clear: left;\n float: left;\n margin-right: 1em;\n}\n\nimg.align-right, .figure.align-right, object.align-right {\n clear: right;\n float: right;\n margin-left: 1em;\n}\n\nimg.align-center, .figure.align-center, object.align-center {\n display: block;\n margin-left: auto;\n margin-right: auto;\n}\n\n.align-left {\n text-align: left;\n}\n\n.align-center {\n text-align: center;\n}\n\n.align-right {\n text-align: right;\n}\n\n/* -- sidebars -------------------------------------------------------------- */\n\ndiv.sidebar {\n margin: 0 0 0.5em 1em;\n border: 1px solid #ddb;\n padding: 7px 7px 0 7px;\n background-color: #ffe;\n width: 40%;\n float: right;\n}\n\np.sidebar-title {\n font-weight: bold;\n}\n\n/* -- topics ---------------------------------------------------------------- */\n\ndiv.topic {\n border: 1px solid #ccc;\n padding: 7px 7px 0 7px;\n margin: 10px 0 10px 0;\n}\n\np.topic-title {\n font-size: 1.1em;\n font-weight: bold;\n margin-top: 10px;\n}\n\n/* -- admonitions ----------------------------------------------------------- */\n\ndiv.admonition {\n margin-top: 10px;\n margin-bottom: 10px;\n padding: 7px;\n}\n\ndiv.admonition dt {\n font-weight: bold;\n}\n\ndiv.admonition dl {\n margin-bottom: 0;\n}\n\np.admonition-title {\n margin: 0px 10px 5px 0px;\n font-weight: bold;\n}\n\ndiv.body p.centered {\n text-align: center;\n margin-top: 25px;\n}\n\n/* -- tables ---------------------------------------------------------------- */\n\ntable.docutils {\n border: 0;\n border-collapse: collapse;\n}\n\ntable.align-center {\n margin-left: auto;\n margin-right: auto;\n}\n\ntable caption span.caption-number {\n font-style: italic;\n}\n\ntable caption span.caption-text {\n}\n\ntable.docutils td, table.docutils th {\n padding: 1px 8px 1px 5px;\n border-top: 0;\n border-left: 0;\n border-right: 0;\n border-bottom: 1px solid #aaa;\n}\n\ntable.footnote td, table.footnote th {\n border: 0 !important;\n}\n\nth {\n text-align: left;\n padding-right: 5px;\n}\n\ntable.citation {\n border-left: solid 1px gray;\n margin-left: 1px;\n}\n\ntable.citation td {\n border-bottom: none;\n}\n\n/* -- figures --------------------------------------------------------------- */\n\ndiv.figure {\n margin: 0.5em;\n padding: 0.5em;\n}\n\ndiv.figure p.caption {\n padding: 0.3em;\n}\n\ndiv.figure p.caption span.caption-number {\n font-style: italic;\n}\n\ndiv.figure p.caption span.caption-text {\n}\n\n/* -- field list styles ----------------------------------------------------- */\n\ntable.field-list td, table.field-list th {\n border: 0 !important;\n}\n\n.field-list ul {\n margin: 0;\n padding-left: 1em;\n}\n\n.field-list p {\n margin: 0;\n}\n\n.field-name {\n -moz-hyphens: manual;\n -ms-hyphens: manual;\n -webkit-hyphens: manual;\n hyphens: manual;\n}\n\n/* -- other body styles ----------------------------------------------------- */\n\nol.arabic {\n list-style: decimal;\n}\n\nol.loweralpha {\n list-style: lower-alpha;\n}\n\nol.upperalpha {\n list-style: upper-alpha;\n}\n\nol.lowerroman {\n list-style: lower-roman;\n}\n\nol.upperroman {\n list-style: upper-roman;\n}\n\ndl {\n margin-bottom: 15px;\n}\n\ndd p {\n margin-top: 0px;\n}\n\ndd ul, dd table {\n margin-bottom: 10px;\n}\n\ndd {\n margin-top: 3px;\n margin-bottom: 10px;\n margin-left: 30px;\n}\n\ndt:target, span.highlighted {\n background-color: #fbe54e;\n}\n\nrect.highlighted {\n fill: #fbe54e;\n}\n\ndl.glossary dt {\n font-weight: bold;\n font-size: 1.1em;\n}\n\n.optional {\n font-size: 1.3em;\n}\n\n.sig-paren {\n font-size: larger;\n}\n\n.versionmodified {\n font-style: italic;\n}\n\n.system-message {\n background-color: #fda;\n padding: 5px;\n border: 3px solid red;\n}\n\n.footnote:target {\n background-color: #ffa;\n}\n\n.line-block {\n display: block;\n margin-top: 1em;\n margin-bottom: 1em;\n}\n\n.line-block .line-block {\n margin-top: 0;\n margin-bottom: 0;\n margin-left: 1.5em;\n}\n\n.guilabel, .menuselection {\n font-family: sans-serif;\n}\n\n.accelerator {\n text-decoration: underline;\n}\n\n.classifier {\n font-style: oblique;\n}\n\nabbr, acronym {\n border-bottom: dotted 1px;\n cursor: help;\n}\n\n/* -- code displays --------------------------------------------------------- */\n\npre {\n overflow: auto;\n overflow-y: hidden; /* fixes display issues on Chrome browsers */\n}\n\nspan.pre {\n -moz-hyphens: none;\n -ms-hyphens: none;\n -webkit-hyphens: none;\n hyphens: none;\n}\n\ntd.linenos pre {\n padding: 5px 0px;\n border: 0;\n background-color: transparent;\n color: #aaa;\n}\n\ntable.highlighttable {\n margin-left: 0.5em;\n}\n\ntable.highlighttable td {\n padding: 0 0.5em 0 0.5em;\n}\n\ndiv.code-block-caption {\n padding: 2px 5px;\n font-size: small;\n}\n\ndiv.code-block-caption code {\n background-color: transparent;\n}\n\ndiv.code-block-caption + div > div.highlight > pre {\n margin-top: 0;\n}\n\ndiv.code-block-caption span.caption-number {\n padding: 0.1em 0.3em;\n font-style: italic;\n}\n\ndiv.code-block-caption span.caption-text {\n}\n\ndiv.literal-block-wrapper {\n padding: 1em 1em 0;\n}\n\ndiv.literal-block-wrapper div.highlight {\n margin: 0;\n}\n\ncode.descname {\n background-color: transparent;\n font-weight: bold;\n font-size: 1.2em;\n}\n\ncode.descclassname {\n background-color: transparent;\n}\n\ncode.xref, a code {\n background-color: transparent;\n font-weight: bold;\n}\n\nh1 code, h2 code, h3 code, h4 code, h5 code, h6 code {\n background-color: transparent;\n}\n\n.viewcode-link {\n float: right;\n}\n\n.viewcode-back {\n float: right;\n font-family: sans-serif;\n}\n\ndiv.viewcode-block:target {\n margin: -1px -10px;\n padding: 0 10px;\n}\n\n/* -- math display ---------------------------------------------------------- */\n\nimg.math {\n vertical-align: middle;\n}\n\ndiv.body div.math p {\n text-align: center;\n}\n\nspan.eqno {\n float: right;\n}\n\nspan.eqno a.headerlink {\n position: relative;\n left: 0px;\n z-index: 1;\n}\n\ndiv.math:hover a.headerlink {\n visibility: visible;\n}\n\n/* -- printout stylesheet --------------------------------------------------- */\n\n@media print {\n div.document,\n div.documentwrapper,\n div.bodywrapper {\n margin: 0 !important;\n width: 100%;\n }\n\n div.sphinxsidebar,\n div.related,\n div.footer,\n #top-link {\n display: none;\n }\n}" }, { "path": "docs/_static/custom.css", "content": "/* This file intentionally left blank. */\n" }, { "path": "docs/_static/doctools.js", "content": "/*\n * doctools.js\n * ~~~~~~~~~~~\n *\n * Sphinx JavaScript utilities for all documentation.\n *\n * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.\n * :license: BSD, see LICENSE for details.\n *\n */\n\n/**\n * select a different prefix for underscore\n */\n$u = _.noConflict();\n\n/**\n * make the code below compatible with browsers without\n * an installed firebug like debugger\nif (!window.console || !console.firebug) {\n var names = [\"log\", \"debug\", \"info\", \"warn\", \"error\", \"assert\", \"dir\",\n \"dirxml\", \"group\", \"groupEnd\", \"time\", \"timeEnd\", \"count\", \"trace\",\n \"profile\", \"profileEnd\"];\n window.console = {};\n for (var i = 0; i < names.length; ++i)\n window.console[names[i]] = function() {};\n}\n */\n\n/**\n * small helper function to urldecode strings\n */\njQuery.urldecode = function(x) {\n return decodeURIComponent(x).replace(/\\+/g, ' ');\n};\n\n/**\n * small helper function to urlencode strings\n */\njQuery.urlencode = encodeURIComponent;\n\n/**\n * This function returns the parsed url parameters of the\n * current request. Multiple values per key are supported,\n * it will always return arrays of strings for the value parts.\n */\njQuery.getQueryParameters = function(s) {\n if (typeof s === 'undefined')\n s = document.location.search;\n var parts = s.substr(s.indexOf('?') + 1).split('&');\n var result = {};\n for (var i = 0; i < parts.length; i++) {\n var tmp = parts[i].split('=', 2);\n var key = jQuery.urldecode(tmp[0]);\n var value = jQuery.urldecode(tmp[1]);\n if (key in result)\n result[key].push(value);\n else\n result[key] = [value];\n }\n return result;\n};\n\n/**\n * highlight a given string on a jquery object by wrapping it in\n * span elements with the given class name.\n */\njQuery.fn.highlightText = function(text, className) {\n function highlight(node, addItems) {\n if (node.nodeType === 3) {\n var val = node.nodeValue;\n var pos = val.toLowerCase().indexOf(text);\n if (pos >= 0 &&\n !jQuery(node.parentNode).hasClass(className) &&\n !jQuery(node.parentNode).hasClass(\"nohighlight\")) {\n var span;\n var isInSVG = jQuery(node).closest(\"body, svg, foreignObject\").is(\"svg\");\n if (isInSVG) {\n span = document.createElementNS(\"http://www.w3.org/2000/svg\", \"tspan\");\n } else {\n span = document.createElement(\"span\");\n span.className = className;\n }\n span.appendChild(document.createTextNode(val.substr(pos, text.length)));\n node.parentNode.insertBefore(span, node.parentNode.insertBefore(\n document.createTextNode(val.substr(pos + text.length)),\n node.nextSibling));\n node.nodeValue = val.substr(0, pos);\n if (isInSVG) {\n var bbox = span.getBBox();\n var rect = document.createElementNS(\"http://www.w3.org/2000/svg\", \"rect\");\n \t rect.x.baseVal.value = bbox.x;\n rect.y.baseVal.value = bbox.y;\n rect.width.baseVal.value = bbox.width;\n rect.height.baseVal.value = bbox.height;\n rect.setAttribute('class', className);\n var parentOfText = node.parentNode.parentNode;\n addItems.push({\n \"parent\": node.parentNode,\n \"target\": rect});\n }\n }\n }\n else if (!jQuery(node).is(\"button, select, textarea\")) {\n jQuery.each(node.childNodes, function() {\n highlight(this, addItems);\n });\n }\n }\n var addItems = [];\n var result = this.each(function() {\n highlight(this, addItems);\n });\n for (var i = 0; i < addItems.length; ++i) {\n jQuery(addItems[i].parent).before(addItems[i].target);\n }\n return result;\n};\n\n/*\n * backward compatibility for jQuery.browser\n * This will be supported until firefox bug is fixed.\n */\nif (!jQuery.browser) {\n jQuery.uaMatch = function(ua) {\n ua = ua.toLowerCase();\n\n var match = /(chrome)[ \\/]([\\w.]+)/.exec(ua) ||\n /(webkit)[ \\/]([\\w.]+)/.exec(ua) ||\n /(opera)(?:.*version|)[ \\/]([\\w.]+)/.exec(ua) ||\n /(msie) ([\\w.]+)/.exec(ua) ||\n ua.indexOf(\"compatible\") < 0 && /(mozilla)(?:.*? rv:([\\w.]+)|)/.exec(ua) ||\n [];\n\n return {\n browser: match[ 1 ] || \"\",\n version: match[ 2 ] || \"0\"\n };\n };\n jQuery.browser = {};\n jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;\n}\n\n/**\n * Small JavaScript module for the documentation.\n */\nvar Documentation = {\n\n init : function() {\n this.fixFirefoxAnchorBug();\n this.highlightSearchWords();\n this.initIndexTable();\n \n },\n\n /**\n * i18n support\n */\n TRANSLATIONS : {},\n PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },\n LOCALE : 'unknown',\n\n // gettext and ngettext don't access this so that the functions\n // can safely bound to a different name (_ = Documentation.gettext)\n gettext : function(string) {\n var translated = Documentation.TRANSLATIONS[string];\n if (typeof translated === 'undefined')\n return string;\n return (typeof translated === 'string') ? translated : translated[0];\n },\n\n ngettext : function(singular, plural, n) {\n var translated = Documentation.TRANSLATIONS[singular];\n if (typeof translated === 'undefined')\n return (n == 1) ? singular : plural;\n return translated[Documentation.PLURALEXPR(n)];\n },\n\n addTranslations : function(catalog) {\n for (var key in catalog.messages)\n this.TRANSLATIONS[key] = catalog.messages[key];\n this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');\n this.LOCALE = catalog.locale;\n },\n\n /**\n * add context elements like header anchor links\n */\n addContextElements : function() {\n $('div[id] > :header:first').each(function() {\n $('\\u00B6').\n attr('href', '#' + this.id).\n attr('title', _('Permalink to this headline')).\n appendTo(this);\n });\n $('dt[id]').each(function() {\n $('\\u00B6').\n attr('href', '#' + this.id).\n attr('title', _('Permalink to this definition')).\n appendTo(this);\n });\n },\n\n /**\n * workaround a firefox stupidity\n * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075\n */\n fixFirefoxAnchorBug : function() {\n if (document.location.hash && $.browser.mozilla)\n window.setTimeout(function() {\n document.location.href += '';\n }, 10);\n },\n\n /**\n * highlight the search words provided in the url in the text\n */\n highlightSearchWords : function() {\n var params = $.getQueryParameters();\n var terms = (params.highlight) ? params.highlight[0].split(/\\s+/) : [];\n if (terms.length) {\n var body = $('div.body');\n if (!body.length) {\n body = $('body');\n }\n window.setTimeout(function() {\n $.each(terms, function() {\n body.highlightText(this.toLowerCase(), 'highlighted');\n });\n }, 10);\n $('

' + _('Hide Search Matches') + '

')\n .appendTo($('#searchbox'));\n }\n },\n\n /**\n * init the domain index toggle buttons\n */\n initIndexTable : function() {\n var togglers = $('img.toggler').click(function() {\n var src = $(this).attr('src');\n var idnum = $(this).attr('id').substr(7);\n $('tr.cg-' + idnum).toggle();\n if (src.substr(-9) === 'minus.png')\n $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');\n else\n $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');\n }).css('display', '');\n if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {\n togglers.click();\n }\n },\n\n /**\n * helper function to hide the search marks again\n */\n hideSearchWords : function() {\n $('#searchbox .highlight-link').fadeOut(300);\n $('span.highlighted').removeClass('highlighted');\n },\n\n /**\n * make the url absolute\n */\n makeURL : function(relativeURL) {\n return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;\n },\n\n /**\n * get the current relative url\n */\n getCurrentURL : function() {\n var path = document.location.pathname;\n var parts = path.split(/\\//);\n $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\\//), function() {\n if (this === '..')\n parts.pop();\n });\n var url = parts.join('/');\n return path.substring(url.lastIndexOf('/') + 1, path.length - 1);\n },\n\n initOnKeyListeners: function() {\n $(document).keyup(function(event) {\n var activeElementType = document.activeElement.tagName;\n // don't navigate when in search box or textarea\n if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {\n switch (event.keyCode) {\n case 37: // left\n var prevHref = $('link[rel=\"prev\"]').prop('href');\n if (prevHref) {\n window.location.href = prevHref;\n return false;\n }\n case 39: // right\n var nextHref = $('link[rel=\"next\"]').prop('href');\n if (nextHref) {\n window.location.href = nextHref;\n return false;\n }\n }\n }\n });\n }\n};\n\n// quick alias for translations\n_ = Documentation.gettext;\n\n$(document).ready(function() {\n Documentation.init();\n});" }, { "path": "docs/_static/documentation_options.js", "content": "var DOCUMENTATION_OPTIONS = {\n URL_ROOT: '',\n VERSION: '0.3',\n LANGUAGE: 'None',\n COLLAPSE_INDEX: false,\n FILE_SUFFIX: '.html',\n HAS_SOURCE: true,\n SOURCELINK_SUFFIX: '.txt'\n};" }, { "path": "docs/_static/jquery-3.2.1.js", "content": "/*!\n * jQuery JavaScript Library v3.2.1\n * https://jquery.com/\n *\n * Includes Sizzle.js\n * https://sizzlejs.com/\n *\n * Copyright JS Foundation and other contributors\n * Released under the MIT license\n * https://jquery.org/license\n *\n * Date: 2017-03-20T18:59Z\n */\n( function( global, factory ) {\n\n\t\"use strict\";\n\n\tif ( typeof module === \"object\" && typeof module.exports === \"object\" ) {\n\n\t\t// For CommonJS and CommonJS-like environments where a proper `window`\n\t\t// is present, execute the factory and get jQuery.\n\t\t// For environments that do not have a `window` with a `document`\n\t\t// (such as Node.js), expose a factory as module.exports.\n\t\t// This accentuates the need for the creation of a real `window`.\n\t\t// e.g. var jQuery = require(\"jquery\")(window);\n\t\t// See ticket #14549 for more info.\n\t\tmodule.exports = global.document ?\n\t\t\tfactory( global, true ) :\n\t\t\tfunction( w ) {\n\t\t\t\tif ( !w.document ) {\n\t\t\t\t\tthrow new Error( \"jQuery requires a window with a document\" );\n\t\t\t\t}\n\t\t\t\treturn factory( w );\n\t\t\t};\n\t} else {\n\t\tfactory( global );\n\t}\n\n// Pass this if window is not defined yet\n} )( typeof window !== \"undefined\" ? window : this, function( window, noGlobal ) {\n\n// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1\n// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode\n// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common\n// enough that all such attempts are guarded in a try block.\n\"use strict\";\n\nvar arr = [];\n\nvar document = window.document;\n\nvar getProto = Object.getPrototypeOf;\n\nvar slice = arr.slice;\n\nvar concat = arr.concat;\n\nvar push = arr.push;\n\nvar indexOf = arr.indexOf;\n\nvar class2type = {};\n\nvar toString = class2type.toString;\n\nvar hasOwn = class2type.hasOwnProperty;\n\nvar fnToString = hasOwn.toString;\n\nvar ObjectFunctionString = fnToString.call( Object );\n\nvar support = {};\n\n\n\n\tfunction DOMEval( code, doc ) {\n\t\tdoc = doc || document;\n\n\t\tvar script = doc.createElement( \"script\" );\n\n\t\tscript.text = code;\n\t\tdoc.head.appendChild( script ).parentNode.removeChild( script );\n\t}\n/* global Symbol */\n// Defining this global in .eslintrc.json would create a danger of using the global\n// unguarded in another place, it seems safer to define global only for this module\n\n\n\nvar\n\tversion = \"3.2.1\",\n\n\t// Define a local copy of jQuery\n\tjQuery = function( selector, context ) {\n\n\t\t// The jQuery object is actually just the init constructor 'enhanced'\n\t\t// Need init if jQuery is called (just allow error to be thrown if not included)\n\t\treturn new jQuery.fn.init( selector, context );\n\t},\n\n\t// Support: Android <=4.0 only\n\t// Make sure we trim BOM and NBSP\n\trtrim = /^[\\s\\uFEFF\\xA0]+|[\\s\\uFEFF\\xA0]+$/g,\n\n\t// Matches dashed string for camelizing\n\trmsPrefix = /^-ms-/,\n\trdashAlpha = /-([a-z])/g,\n\n\t// Used by jQuery.camelCase as callback to replace()\n\tfcamelCase = function( all, letter ) {\n\t\treturn letter.toUpperCase();\n\t};\n\njQuery.fn = jQuery.prototype = {\n\n\t// The current version of jQuery being used\n\tjquery: version,\n\n\tconstructor: jQuery,\n\n\t// The default length of a jQuery object is 0\n\tlength: 0,\n\n\ttoArray: function() {\n\t\treturn slice.call( this );\n\t},\n\n\t// Get the Nth element in the matched element set OR\n\t// Get the whole matched element set as a clean array\n\tget: function( num ) {\n\n\t\t// Return all the elements in a clean array\n\t\tif ( num == null ) {\n\t\t\treturn slice.call( this );\n\t\t}\n\n\t\t// Return just the one element from the set\n\t\treturn num < 0 ? this[ num + this.length ] : this[ num ];\n\t},\n\n\t// Take an array of elements and push it onto the stack\n\t// (returning the new matched element set)\n\tpushStack: function( elems ) {\n\n\t\t// Build a new jQuery matched element set\n\t\tvar ret = jQuery.merge( this.constructor(), elems );\n\n\t\t// Add the old object onto the stack (as a reference)\n\t\tret.prevObject = this;\n\n\t\t// Return the newly-formed element set\n\t\treturn ret;\n\t},\n\n\t// Execute a callback for every element in the matched set.\n\teach: function( callback ) {\n\t\treturn jQuery.each( this, callback );\n\t},\n\n\tmap: function( callback ) {\n\t\treturn this.pushStack( jQuery.map( this, function( elem, i ) {\n\t\t\treturn callback.call( elem, i, elem );\n\t\t} ) );\n\t},\n\n\tslice: function() {\n\t\treturn this.pushStack( slice.apply( this, arguments ) );\n\t},\n\n\tfirst: function() {\n\t\treturn this.eq( 0 );\n\t},\n\n\tlast: function() {\n\t\treturn this.eq( -1 );\n\t},\n\n\teq: function( i ) {\n\t\tvar len = this.length,\n\t\t\tj = +i + ( i < 0 ? len : 0 );\n\t\treturn this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );\n\t},\n\n\tend: function() {\n\t\treturn this.prevObject || this.constructor();\n\t},\n\n\t// For internal use only.\n\t// Behaves like an Array's method, not like a jQuery method.\n\tpush: push,\n\tsort: arr.sort,\n\tsplice: arr.splice\n};\n\njQuery.extend = jQuery.fn.extend = function() {\n\tvar options, name, src, copy, copyIsArray, clone,\n\t\ttarget = arguments[ 0 ] || {},\n\t\ti = 1,\n\t\tlength = arguments.length,\n\t\tdeep = false;\n\n\t// Handle a deep copy situation\n\tif ( typeof target === \"boolean\" ) {\n\t\tdeep = target;\n\n\t\t// Skip the boolean and the target\n\t\ttarget = arguments[ i ] || {};\n\t\ti++;\n\t}\n\n\t// Handle case when target is a string or something (possible in deep copy)\n\tif ( typeof target !== \"object\" && !jQuery.isFunction( target ) ) {\n\t\ttarget = {};\n\t}\n\n\t// Extend jQuery itself if only one argument is passed\n\tif ( i === length ) {\n\t\ttarget = this;\n\t\ti--;\n\t}\n\n\tfor ( ; i < length; i++ ) {\n\n\t\t// Only deal with non-null/undefined values\n\t\tif ( ( options = arguments[ i ] ) != null ) {\n\n\t\t\t// Extend the base object\n\t\t\tfor ( name in options ) {\n\t\t\t\tsrc = target[ name ];\n\t\t\t\tcopy = options[ name ];\n\n\t\t\t\t// Prevent never-ending loop\n\t\t\t\tif ( target === copy ) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\n\t\t\t\t// Recurse if we're merging plain objects or arrays\n\t\t\t\tif ( deep && copy && ( jQuery.isPlainObject( copy ) ||\n\t\t\t\t\t( copyIsArray = Array.isArray( copy ) ) ) ) {\n\n\t\t\t\t\tif ( copyIsArray ) {\n\t\t\t\t\t\tcopyIsArray = false;\n\t\t\t\t\t\tclone = src && Array.isArray( src ) ? src : [];\n\n\t\t\t\t\t} else {\n\t\t\t\t\t\tclone = src && jQuery.isPlainObject( src ) ? src : {};\n\t\t\t\t\t}\n\n\t\t\t\t\t// Never move original objects, clone them\n\t\t\t\t\ttarget[ name ] = jQuery.extend( deep, clone, copy );\n\n\t\t\t\t// Don't bring in undefined values\n\t\t\t\t} else if ( copy !== undefined ) {\n\t\t\t\t\ttarget[ name ] = copy;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Return the modified object\n\treturn target;\n};\n\njQuery.extend( {\n\n\t// Unique for each copy of jQuery on the page\n\texpando: \"jQuery\" + ( version + Math.random() ).replace( /\\D/g, \"\" ),\n\n\t// Assume jQuery is ready without the ready module\n\tisReady: true,\n\n\terror: function( msg ) {\n\t\tthrow new Error( msg );\n\t},\n\n\tnoop: function() {},\n\n\tisFunction: function( obj ) {\n\t\treturn jQuery.type( obj ) === \"function\";\n\t},\n\n\tisWindow: function( obj ) {\n\t\treturn obj != null && obj === obj.window;\n\t},\n\n\tisNumeric: function( obj ) {\n\n\t\t// As of jQuery 3.0, isNumeric is limited to\n\t\t// strings and numbers (primitives or objects)\n\t\t// that can be coerced to finite numbers (gh-2662)\n\t\tvar type = jQuery.type( obj );\n\t\treturn ( type === \"number\" || type === \"string\" ) &&\n\n\t\t\t// parseFloat NaNs numeric-cast false positives (\"\")\n\t\t\t// ...but misinterprets leading-number strings, particularly hex literals (\"0x...\")\n\t\t\t// subtraction forces infinities to NaN\n\t\t\t!isNaN( obj - parseFloat( obj ) );\n\t},\n\n\tisPlainObject: function( obj ) {\n\t\tvar proto, Ctor;\n\n\t\t// Detect obvious negatives\n\t\t// Use toString instead of jQuery.type to catch host objects\n\t\tif ( !obj || toString.call( obj ) !== \"[object Object]\" ) {\n\t\t\treturn false;\n\t\t}\n\n\t\tproto = getProto( obj );\n\n\t\t// Objects with no prototype (e.g., `Object.create( null )`) are plain\n\t\tif ( !proto ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// Objects with prototype are plain iff they were constructed by a global Object function\n\t\tCtor = hasOwn.call( proto, \"constructor\" ) && proto.constructor;\n\t\treturn typeof Ctor === \"function\" && fnToString.call( Ctor ) === ObjectFunctionString;\n\t},\n\n\tisEmptyObject: function( obj ) {\n\n\t\t/* eslint-disable no-unused-vars */\n\t\t// See https://github.com/eslint/eslint/issues/6125\n\t\tvar name;\n\n\t\tfor ( name in obj ) {\n\t\t\treturn false;\n\t\t}\n\t\treturn true;\n\t},\n\n\ttype: function( obj ) {\n\t\tif ( obj == null ) {\n\t\t\treturn obj + \"\";\n\t\t}\n\n\t\t// Support: Android <=2.3 only (functionish RegExp)\n\t\treturn typeof obj === \"object\" || typeof obj === \"function\" ?\n\t\t\tclass2type[ toString.call( obj ) ] || \"object\" :\n\t\t\ttypeof obj;\n\t},\n\n\t// Evaluates a script in a global context\n\tglobalEval: function( code ) {\n\t\tDOMEval( code );\n\t},\n\n\t// Convert dashed to camelCase; used by the css and data modules\n\t// Support: IE <=9 - 11, Edge 12 - 13\n\t// Microsoft forgot to hump their vendor prefix (#9572)\n\tcamelCase: function( string ) {\n\t\treturn string.replace( rmsPrefix, \"ms-\" ).replace( rdashAlpha, fcamelCase );\n\t},\n\n\teach: function( obj, callback ) {\n\t\tvar length, i = 0;\n\n\t\tif ( isArrayLike( obj ) ) {\n\t\t\tlength = obj.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tfor ( i in obj ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn obj;\n\t},\n\n\t// Support: Android <=4.0 only\n\ttrim: function( text ) {\n\t\treturn text == null ?\n\t\t\t\"\" :\n\t\t\t( text + \"\" ).replace( rtrim, \"\" );\n\t},\n\n\t// results is for internal usage only\n\tmakeArray: function( arr, results ) {\n\t\tvar ret = results || [];\n\n\t\tif ( arr != null ) {\n\t\t\tif ( isArrayLike( Object( arr ) ) ) {\n\t\t\t\tjQuery.merge( ret,\n\t\t\t\t\ttypeof arr === \"string\" ?\n\t\t\t\t\t[ arr ] : arr\n\t\t\t\t);\n\t\t\t} else {\n\t\t\t\tpush.call( ret, arr );\n\t\t\t}\n\t\t}\n\n\t\treturn ret;\n\t},\n\n\tinArray: function( elem, arr, i ) {\n\t\treturn arr == null ? -1 : indexOf.call( arr, elem, i );\n\t},\n\n\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t// push.apply(_, arraylike) throws on ancient WebKit\n\tmerge: function( first, second ) {\n\t\tvar len = +second.length,\n\t\t\tj = 0,\n\t\t\ti = first.length;\n\n\t\tfor ( ; j < len; j++ ) {\n\t\t\tfirst[ i++ ] = second[ j ];\n\t\t}\n\n\t\tfirst.length = i;\n\n\t\treturn first;\n\t},\n\n\tgrep: function( elems, callback, invert ) {\n\t\tvar callbackInverse,\n\t\t\tmatches = [],\n\t\t\ti = 0,\n\t\t\tlength = elems.length,\n\t\t\tcallbackExpect = !invert;\n\n\t\t// Go through the array, only saving the items\n\t\t// that pass the validator function\n\t\tfor ( ; i < length; i++ ) {\n\t\t\tcallbackInverse = !callback( elems[ i ], i );\n\t\t\tif ( callbackInverse !== callbackExpect ) {\n\t\t\t\tmatches.push( elems[ i ] );\n\t\t\t}\n\t\t}\n\n\t\treturn matches;\n\t},\n\n\t// arg is for internal usage only\n\tmap: function( elems, callback, arg ) {\n\t\tvar length, value,\n\t\t\ti = 0,\n\t\t\tret = [];\n\n\t\t// Go through the array, translating each of the items to their new values\n\t\tif ( isArrayLike( elems ) ) {\n\t\t\tlength = elems.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Go through every key on the object,\n\t\t} else {\n\t\t\tfor ( i in elems ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Flatten any nested arrays\n\t\treturn concat.apply( [], ret );\n\t},\n\n\t// A global GUID counter for objects\n\tguid: 1,\n\n\t// Bind a function to a context, optionally partially applying any\n\t// arguments.\n\tproxy: function( fn, context ) {\n\t\tvar tmp, args, proxy;\n\n\t\tif ( typeof context === \"string\" ) {\n\t\t\ttmp = fn[ context ];\n\t\t\tcontext = fn;\n\t\t\tfn = tmp;\n\t\t}\n\n\t\t// Quick check to determine if target is callable, in the spec\n\t\t// this throws a TypeError, but we will just return undefined.\n\t\tif ( !jQuery.isFunction( fn ) ) {\n\t\t\treturn undefined;\n\t\t}\n\n\t\t// Simulated bind\n\t\targs = slice.call( arguments, 2 );\n\t\tproxy = function() {\n\t\t\treturn fn.apply( context || this, args.concat( slice.call( arguments ) ) );\n\t\t};\n\n\t\t// Set the guid of unique handler to the same of original handler, so it can be removed\n\t\tproxy.guid = fn.guid = fn.guid || jQuery.guid++;\n\n\t\treturn proxy;\n\t},\n\n\tnow: Date.now,\n\n\t// jQuery.support is not used in Core but other projects attach their\n\t// properties to it so it needs to exist.\n\tsupport: support\n} );\n\nif ( typeof Symbol === \"function\" ) {\n\tjQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];\n}\n\n// Populate the class2type map\njQuery.each( \"Boolean Number String Function Array Date RegExp Object Error Symbol\".split( \" \" ),\nfunction( i, name ) {\n\tclass2type[ \"[object \" + name + \"]\" ] = name.toLowerCase();\n} );\n\nfunction isArrayLike( obj ) {\n\n\t// Support: real iOS 8.2 only (not reproducible in simulator)\n\t// `in` check used to prevent JIT error (gh-2145)\n\t// hasOwn isn't used here due to false negatives\n\t// regarding Nodelist length in IE\n\tvar length = !!obj && \"length\" in obj && obj.length,\n\t\ttype = jQuery.type( obj );\n\n\tif ( type === \"function\" || jQuery.isWindow( obj ) ) {\n\t\treturn false;\n\t}\n\n\treturn type === \"array\" || length === 0 ||\n\t\ttypeof length === \"number\" && length > 0 && ( length - 1 ) in obj;\n}\nvar Sizzle =\n/*!\n * Sizzle CSS Selector Engine v2.3.3\n * https://sizzlejs.com/\n *\n * Copyright jQuery Foundation and other contributors\n * Released under the MIT license\n * http://jquery.org/license\n *\n * Date: 2016-08-08\n */\n(function( window ) {\n\nvar i,\n\tsupport,\n\tExpr,\n\tgetText,\n\tisXML,\n\ttokenize,\n\tcompile,\n\tselect,\n\toutermostContext,\n\tsortInput,\n\thasDuplicate,\n\n\t// Local document vars\n\tsetDocument,\n\tdocument,\n\tdocElem,\n\tdocumentIsHTML,\n\trbuggyQSA,\n\trbuggyMatches,\n\tmatches,\n\tcontains,\n\n\t// Instance-specific data\n\texpando = \"sizzle\" + 1 * new Date(),\n\tpreferredDoc = window.document,\n\tdirruns = 0,\n\tdone = 0,\n\tclassCache = createCache(),\n\ttokenCache = createCache(),\n\tcompilerCache = createCache(),\n\tsortOrder = function( a, b ) {\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t}\n\t\treturn 0;\n\t},\n\n\t// Instance methods\n\thasOwn = ({}).hasOwnProperty,\n\tarr = [],\n\tpop = arr.pop,\n\tpush_native = arr.push,\n\tpush = arr.push,\n\tslice = arr.slice,\n\t// Use a stripped-down indexOf as it's faster than native\n\t// https://jsperf.com/thor-indexof-vs-for/5\n\tindexOf = function( list, elem ) {\n\t\tvar i = 0,\n\t\t\tlen = list.length;\n\t\tfor ( ; i < len; i++ ) {\n\t\t\tif ( list[i] === elem ) {\n\t\t\t\treturn i;\n\t\t\t}\n\t\t}\n\t\treturn -1;\n\t},\n\n\tbooleans = \"checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped\",\n\n\t// Regular expressions\n\n\t// http://www.w3.org/TR/css3-selectors/#whitespace\n\twhitespace = \"[\\\\x20\\\\t\\\\r\\\\n\\\\f]\",\n\n\t// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier\n\tidentifier = \"(?:\\\\\\\\.|[\\\\w-]|[^\\0-\\\\xa0])+\",\n\n\t// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors\n\tattributes = \"\\\\[\" + whitespace + \"*(\" + identifier + \")(?:\" + whitespace +\n\t\t// Operator (capture 2)\n\t\t\"*([*^$|!~]?=)\" + whitespace +\n\t\t// \"Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]\"\n\t\t\"*(?:'((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\"|(\" + identifier + \"))|)\" + whitespace +\n\t\t\"*\\\\]\",\n\n\tpseudos = \":(\" + identifier + \")(?:\\\\((\" +\n\t\t// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:\n\t\t// 1. quoted (capture 3; capture 4 or capture 5)\n\t\t\"('((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\")|\" +\n\t\t// 2. simple (capture 6)\n\t\t\"((?:\\\\\\\\.|[^\\\\\\\\()[\\\\]]|\" + attributes + \")*)|\" +\n\t\t// 3. anything else (capture 2)\n\t\t\".*\" +\n\t\t\")\\\\)|)\",\n\n\t// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter\n\trwhitespace = new RegExp( whitespace + \"+\", \"g\" ),\n\trtrim = new RegExp( \"^\" + whitespace + \"+|((?:^|[^\\\\\\\\])(?:\\\\\\\\.)*)\" + whitespace + \"+$\", \"g\" ),\n\n\trcomma = new RegExp( \"^\" + whitespace + \"*,\" + whitespace + \"*\" ),\n\trcombinators = new RegExp( \"^\" + whitespace + \"*([>+~]|\" + whitespace + \")\" + whitespace + \"*\" ),\n\n\trattributeQuotes = new RegExp( \"=\" + whitespace + \"*([^\\\\]'\\\"]*?)\" + whitespace + \"*\\\\]\", \"g\" ),\n\n\trpseudo = new RegExp( pseudos ),\n\tridentifier = new RegExp( \"^\" + identifier + \"$\" ),\n\n\tmatchExpr = {\n\t\t\"ID\": new RegExp( \"^#(\" + identifier + \")\" ),\n\t\t\"CLASS\": new RegExp( \"^\\\\.(\" + identifier + \")\" ),\n\t\t\"TAG\": new RegExp( \"^(\" + identifier + \"|[*])\" ),\n\t\t\"ATTR\": new RegExp( \"^\" + attributes ),\n\t\t\"PSEUDO\": new RegExp( \"^\" + pseudos ),\n\t\t\"CHILD\": new RegExp( \"^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\\\(\" + whitespace +\n\t\t\t\"*(even|odd|(([+-]|)(\\\\d*)n|)\" + whitespace + \"*(?:([+-]|)\" + whitespace +\n\t\t\t\"*(\\\\d+)|))\" + whitespace + \"*\\\\)|)\", \"i\" ),\n\t\t\"bool\": new RegExp( \"^(?:\" + booleans + \")$\", \"i\" ),\n\t\t// For use in libraries implementing .is()\n\t\t// We use this for POS matching in `select`\n\t\t\"needsContext\": new RegExp( \"^\" + whitespace + \"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\\\(\" +\n\t\t\twhitespace + \"*((?:-\\\\d)?\\\\d*)\" + whitespace + \"*\\\\)|)(?=[^-]|$)\", \"i\" )\n\t},\n\n\trinputs = /^(?:input|select|textarea|button)$/i,\n\trheader = /^h\\d$/i,\n\n\trnative = /^[^{]+\\{\\s*\\[native \\w/,\n\n\t// Easily-parseable/retrievable ID or TAG or CLASS selectors\n\trquickExpr = /^(?:#([\\w-]+)|(\\w+)|\\.([\\w-]+))$/,\n\n\trsibling = /[+~]/,\n\n\t// CSS escapes\n\t// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters\n\trunescape = new RegExp( \"\\\\\\\\([\\\\da-f]{1,6}\" + whitespace + \"?|(\" + whitespace + \")|.)\", \"ig\" ),\n\tfunescape = function( _, escaped, escapedWhitespace ) {\n\t\tvar high = \"0x\" + escaped - 0x10000;\n\t\t// NaN means non-codepoint\n\t\t// Support: Firefox<24\n\t\t// Workaround erroneous numeric interpretation of +\"0x\"\n\t\treturn high !== high || escapedWhitespace ?\n\t\t\tescaped :\n\t\t\thigh < 0 ?\n\t\t\t\t// BMP codepoint\n\t\t\t\tString.fromCharCode( high + 0x10000 ) :\n\t\t\t\t// Supplemental Plane codepoint (surrogate pair)\n\t\t\t\tString.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );\n\t},\n\n\t// CSS string/identifier serialization\n\t// https://drafts.csswg.org/cssom/#common-serializing-idioms\n\trcssescape = /([\\0-\\x1f\\x7f]|^-?\\d)|^-$|[^\\0-\\x1f\\x7f-\\uFFFF\\w-]/g,\n\tfcssescape = function( ch, asCodePoint ) {\n\t\tif ( asCodePoint ) {\n\n\t\t\t// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER\n\t\t\tif ( ch === \"\\0\" ) {\n\t\t\t\treturn \"\\uFFFD\";\n\t\t\t}\n\n\t\t\t// Control characters and (dependent upon position) numbers get escaped as code points\n\t\t\treturn ch.slice( 0, -1 ) + \"\\\\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + \" \";\n\t\t}\n\n\t\t// Other potentially-special ASCII characters get backslash-escaped\n\t\treturn \"\\\\\" + ch;\n\t},\n\n\t// Used for iframes\n\t// See setDocument()\n\t// Removing the function wrapper causes a \"Permission Denied\"\n\t// error in IE\n\tunloadHandler = function() {\n\t\tsetDocument();\n\t},\n\n\tdisabledAncestor = addCombinator(\n\t\tfunction( elem ) {\n\t\t\treturn elem.disabled === true && (\"form\" in elem || \"label\" in elem);\n\t\t},\n\t\t{ dir: \"parentNode\", next: \"legend\" }\n\t);\n\n// Optimize for push.apply( _, NodeList )\ntry {\n\tpush.apply(\n\t\t(arr = slice.call( preferredDoc.childNodes )),\n\t\tpreferredDoc.childNodes\n\t);\n\t// Support: Android<4.0\n\t// Detect silently failing push.apply\n\tarr[ preferredDoc.childNodes.length ].nodeType;\n} catch ( e ) {\n\tpush = { apply: arr.length ?\n\n\t\t// Leverage slice if possible\n\t\tfunction( target, els ) {\n\t\t\tpush_native.apply( target, slice.call(els) );\n\t\t} :\n\n\t\t// Support: IE<9\n\t\t// Otherwise append directly\n\t\tfunction( target, els ) {\n\t\t\tvar j = target.length,\n\t\t\t\ti = 0;\n\t\t\t// Can't trust NodeList.length\n\t\t\twhile ( (target[j++] = els[i++]) ) {}\n\t\t\ttarget.length = j - 1;\n\t\t}\n\t};\n}\n\nfunction Sizzle( selector, context, results, seed ) {\n\tvar m, i, elem, nid, match, groups, newSelector,\n\t\tnewContext = context && context.ownerDocument,\n\n\t\t// nodeType defaults to 9, since context defaults to document\n\t\tnodeType = context ? context.nodeType : 9;\n\n\tresults = results || [];\n\n\t// Return early from calls with invalid selector or context\n\tif ( typeof selector !== \"string\" || !selector ||\n\t\tnodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {\n\n\t\treturn results;\n\t}\n\n\t// Try to shortcut find operations (as opposed to filters) in HTML documents\n\tif ( !seed ) {\n\n\t\tif ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {\n\t\t\tsetDocument( context );\n\t\t}\n\t\tcontext = context || document;\n\n\t\tif ( documentIsHTML ) {\n\n\t\t\t// If the selector is sufficiently simple, try using a \"get*By*\" DOM method\n\t\t\t// (excepting DocumentFragment context, where the methods don't exist)\n\t\t\tif ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {\n\n\t\t\t\t// ID selector\n\t\t\t\tif ( (m = match[1]) ) {\n\n\t\t\t\t\t// Document context\n\t\t\t\t\tif ( nodeType === 9 ) {\n\t\t\t\t\t\tif ( (elem = context.getElementById( m )) ) {\n\n\t\t\t\t\t\t\t// Support: IE, Opera, Webkit\n\t\t\t\t\t\t\t// TODO: identify versions\n\t\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\t\tif ( elem.id === m ) {\n\t\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t// Element context\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\t// Support: IE, Opera, Webkit\n\t\t\t\t\t\t// TODO: identify versions\n\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\tif ( newContext && (elem = newContext.getElementById( m )) &&\n\t\t\t\t\t\t\tcontains( context, elem ) &&\n\t\t\t\t\t\t\telem.id === m ) {\n\n\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t// Type selector\n\t\t\t\t} else if ( match[2] ) {\n\t\t\t\t\tpush.apply( results, context.getElementsByTagName( selector ) );\n\t\t\t\t\treturn results;\n\n\t\t\t\t// Class selector\n\t\t\t\t} else if ( (m = match[3]) && support.getElementsByClassName &&\n\t\t\t\t\tcontext.getElementsByClassName ) {\n\n\t\t\t\t\tpush.apply( results, context.getElementsByClassName( m ) );\n\t\t\t\t\treturn results;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Take advantage of querySelectorAll\n\t\t\tif ( support.qsa &&\n\t\t\t\t!compilerCache[ selector + \" \" ] &&\n\t\t\t\t(!rbuggyQSA || !rbuggyQSA.test( selector )) ) {\n\n\t\t\t\tif ( nodeType !== 1 ) {\n\t\t\t\t\tnewContext = context;\n\t\t\t\t\tnewSelector = selector;\n\n\t\t\t\t// qSA looks outside Element context, which is not what we want\n\t\t\t\t// Thanks to Andrew Dupont for this workaround technique\n\t\t\t\t// Support: IE <=8\n\t\t\t\t// Exclude object elements\n\t\t\t\t} else if ( context.nodeName.toLowerCase() !== \"object\" ) {\n\n\t\t\t\t\t// Capture the context ID, setting it first if necessary\n\t\t\t\t\tif ( (nid = context.getAttribute( \"id\" )) ) {\n\t\t\t\t\t\tnid = nid.replace( rcssescape, fcssescape );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tcontext.setAttribute( \"id\", (nid = expando) );\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prefix every selector in the list\n\t\t\t\t\tgroups = tokenize( selector );\n\t\t\t\t\ti = groups.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tgroups[i] = \"#\" + nid + \" \" + toSelector( groups[i] );\n\t\t\t\t\t}\n\t\t\t\t\tnewSelector = groups.join( \",\" );\n\n\t\t\t\t\t// Expand context for sibling selectors\n\t\t\t\t\tnewContext = rsibling.test( selector ) && testContext( context.parentNode ) ||\n\t\t\t\t\t\tcontext;\n\t\t\t\t}\n\n\t\t\t\tif ( newSelector ) {\n\t\t\t\t\ttry {\n\t\t\t\t\t\tpush.apply( results,\n\t\t\t\t\t\t\tnewContext.querySelectorAll( newSelector )\n\t\t\t\t\t\t);\n\t\t\t\t\t\treturn results;\n\t\t\t\t\t} catch ( qsaError ) {\n\t\t\t\t\t} finally {\n\t\t\t\t\t\tif ( nid === expando ) {\n\t\t\t\t\t\t\tcontext.removeAttribute( \"id\" );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// All others\n\treturn select( selector.replace( rtrim, \"$1\" ), context, results, seed );\n}\n\n/**\n * Create key-value caches of limited size\n * @returns {function(string, object)} Returns the Object data after storing it on itself with\n *\tproperty name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)\n *\tdeleting the oldest entry\n */\nfunction createCache() {\n\tvar keys = [];\n\n\tfunction cache( key, value ) {\n\t\t// Use (key + \" \") to avoid collision with native prototype properties (see Issue #157)\n\t\tif ( keys.push( key + \" \" ) > Expr.cacheLength ) {\n\t\t\t// Only keep the most recent entries\n\t\t\tdelete cache[ keys.shift() ];\n\t\t}\n\t\treturn (cache[ key + \" \" ] = value);\n\t}\n\treturn cache;\n}\n\n/**\n * Mark a function for special use by Sizzle\n * @param {Function} fn The function to mark\n */\nfunction markFunction( fn ) {\n\tfn[ expando ] = true;\n\treturn fn;\n}\n\n/**\n * Support testing using an element\n * @param {Function} fn Passed the created element and returns a boolean result\n */\nfunction assert( fn ) {\n\tvar el = document.createElement(\"fieldset\");\n\n\ttry {\n\t\treturn !!fn( el );\n\t} catch (e) {\n\t\treturn false;\n\t} finally {\n\t\t// Remove from its parent by default\n\t\tif ( el.parentNode ) {\n\t\t\tel.parentNode.removeChild( el );\n\t\t}\n\t\t// release memory in IE\n\t\tel = null;\n\t}\n}\n\n/**\n * Adds the same handler for all of the specified attrs\n * @param {String} attrs Pipe-separated list of attributes\n * @param {Function} handler The method that will be applied\n */\nfunction addHandle( attrs, handler ) {\n\tvar arr = attrs.split(\"|\"),\n\t\ti = arr.length;\n\n\twhile ( i-- ) {\n\t\tExpr.attrHandle[ arr[i] ] = handler;\n\t}\n}\n\n/**\n * Checks document order of two siblings\n * @param {Element} a\n * @param {Element} b\n * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b\n */\nfunction siblingCheck( a, b ) {\n\tvar cur = b && a,\n\t\tdiff = cur && a.nodeType === 1 && b.nodeType === 1 &&\n\t\t\ta.sourceIndex - b.sourceIndex;\n\n\t// Use IE sourceIndex if available on both nodes\n\tif ( diff ) {\n\t\treturn diff;\n\t}\n\n\t// Check if b follows a\n\tif ( cur ) {\n\t\twhile ( (cur = cur.nextSibling) ) {\n\t\t\tif ( cur === b ) {\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn a ? 1 : -1;\n}\n\n/**\n * Returns a function to use in pseudos for input types\n * @param {String} type\n */\nfunction createInputPseudo( type ) {\n\treturn function( elem ) {\n\t\tvar name = elem.nodeName.toLowerCase();\n\t\treturn name === \"input\" && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for buttons\n * @param {String} type\n */\nfunction createButtonPseudo( type ) {\n\treturn function( elem ) {\n\t\tvar name = elem.nodeName.toLowerCase();\n\t\treturn (name === \"input\" || name === \"button\") && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for :enabled/:disabled\n * @param {Boolean} disabled true for :disabled; false for :enabled\n */\nfunction createDisabledPseudo( disabled ) {\n\n\t// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable\n\treturn function( elem ) {\n\n\t\t// Only certain elements can match :enabled or :disabled\n\t\t// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled\n\t\t// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled\n\t\tif ( \"form\" in elem ) {\n\n\t\t\t// Check for inherited disabledness on relevant non-disabled elements:\n\t\t\t// * listed form-associated elements in a disabled fieldset\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#category-listed\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled\n\t\t\t// * option elements in a disabled optgroup\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled\n\t\t\t// All such elements have a \"form\" property.\n\t\t\tif ( elem.parentNode && elem.disabled === false ) {\n\n\t\t\t\t// Option elements defer to a parent optgroup if present\n\t\t\t\tif ( \"label\" in elem ) {\n\t\t\t\t\tif ( \"label\" in elem.parentNode ) {\n\t\t\t\t\t\treturn elem.parentNode.disabled === disabled;\n\t\t\t\t\t} else {\n\t\t\t\t\t\treturn elem.disabled === disabled;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Support: IE 6 - 11\n\t\t\t\t// Use the isDisabled shortcut property to check for disabled fieldset ancestors\n\t\t\t\treturn elem.isDisabled === disabled ||\n\n\t\t\t\t\t// Where there is no isDisabled, check manually\n\t\t\t\t\t/* jshint -W018 */\n\t\t\t\t\telem.isDisabled !== !disabled &&\n\t\t\t\t\t\tdisabledAncestor( elem ) === disabled;\n\t\t\t}\n\n\t\t\treturn elem.disabled === disabled;\n\n\t\t// Try to winnow out elements that can't be disabled before trusting the disabled property.\n\t\t// Some victims get caught in our net (label, legend, menu, track), but it shouldn't\n\t\t// even exist on them, let alone have a boolean value.\n\t\t} else if ( \"label\" in elem ) {\n\t\t\treturn elem.disabled === disabled;\n\t\t}\n\n\t\t// Remaining elements are neither :enabled nor :disabled\n\t\treturn false;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for positionals\n * @param {Function} fn\n */\nfunction createPositionalPseudo( fn ) {\n\treturn markFunction(function( argument ) {\n\t\targument = +argument;\n\t\treturn markFunction(function( seed, matches ) {\n\t\t\tvar j,\n\t\t\t\tmatchIndexes = fn( [], seed.length, argument ),\n\t\t\t\ti = matchIndexes.length;\n\n\t\t\t// Match elements found at the specified indexes\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( seed[ (j = matchIndexes[i]) ] ) {\n\t\t\t\t\tseed[j] = !(matches[j] = seed[j]);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t});\n}\n\n/**\n * Checks a node for validity as a Sizzle context\n * @param {Element|Object=} context\n * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value\n */\nfunction testContext( context ) {\n\treturn context && typeof context.getElementsByTagName !== \"undefined\" && context;\n}\n\n// Expose support vars for convenience\nsupport = Sizzle.support = {};\n\n/**\n * Detects XML nodes\n * @param {Element|Object} elem An element or a document\n * @returns {Boolean} True iff elem is a non-HTML XML node\n */\nisXML = Sizzle.isXML = function( elem ) {\n\t// documentElement is verified for cases where it doesn't yet exist\n\t// (such as loading iframes in IE - #4833)\n\tvar documentElement = elem && (elem.ownerDocument || elem).documentElement;\n\treturn documentElement ? documentElement.nodeName !== \"HTML\" : false;\n};\n\n/**\n * Sets document-related variables once based on the current document\n * @param {Element|Object} [doc] An element or document object to use to set the document\n * @returns {Object} Returns the current document\n */\nsetDocument = Sizzle.setDocument = function( node ) {\n\tvar hasCompare, subWindow,\n\t\tdoc = node ? node.ownerDocument || node : preferredDoc;\n\n\t// Return early if doc is invalid or already selected\n\tif ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {\n\t\treturn document;\n\t}\n\n\t// Update global variables\n\tdocument = doc;\n\tdocElem = document.documentElement;\n\tdocumentIsHTML = !isXML( document );\n\n\t// Support: IE 9-11, Edge\n\t// Accessing iframe documents after unload throws \"permission denied\" errors (jQuery #13936)\n\tif ( preferredDoc !== document &&\n\t\t(subWindow = document.defaultView) && subWindow.top !== subWindow ) {\n\n\t\t// Support: IE 11, Edge\n\t\tif ( subWindow.addEventListener ) {\n\t\t\tsubWindow.addEventListener( \"unload\", unloadHandler, false );\n\n\t\t// Support: IE 9 - 10 only\n\t\t} else if ( subWindow.attachEvent ) {\n\t\t\tsubWindow.attachEvent( \"onunload\", unloadHandler );\n\t\t}\n\t}\n\n\t/* Attributes\n\t---------------------------------------------------------------------- */\n\n\t// Support: IE<8\n\t// Verify that getAttribute really returns attributes and not properties\n\t// (excepting IE8 booleans)\n\tsupport.attributes = assert(function( el ) {\n\t\tel.className = \"i\";\n\t\treturn !el.getAttribute(\"className\");\n\t});\n\n\t/* getElement(s)By*\n\t---------------------------------------------------------------------- */\n\n\t// Check if getElementsByTagName(\"*\") returns only elements\n\tsupport.getElementsByTagName = assert(function( el ) {\n\t\tel.appendChild( document.createComment(\"\") );\n\t\treturn !el.getElementsByTagName(\"*\").length;\n\t});\n\n\t// Support: IE<9\n\tsupport.getElementsByClassName = rnative.test( document.getElementsByClassName );\n\n\t// Support: IE<10\n\t// Check if getElementById returns elements by name\n\t// The broken getElementById methods don't pick up programmatically-set names,\n\t// so use a roundabout getElementsByName test\n\tsupport.getById = assert(function( el ) {\n\t\tdocElem.appendChild( el ).id = expando;\n\t\treturn !document.getElementsByName || !document.getElementsByName( expando ).length;\n\t});\n\n\t// ID filter and find\n\tif ( support.getById ) {\n\t\tExpr.filter[\"ID\"] = function( id ) {\n\t\t\tvar attrId = id.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\treturn elem.getAttribute(\"id\") === attrId;\n\t\t\t};\n\t\t};\n\t\tExpr.find[\"ID\"] = function( id, context ) {\n\t\t\tif ( typeof context.getElementById !== \"undefined\" && documentIsHTML ) {\n\t\t\t\tvar elem = context.getElementById( id );\n\t\t\t\treturn elem ? [ elem ] : [];\n\t\t\t}\n\t\t};\n\t} else {\n\t\tExpr.filter[\"ID\"] = function( id ) {\n\t\t\tvar attrId = id.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\tvar node = typeof elem.getAttributeNode !== \"undefined\" &&\n\t\t\t\t\telem.getAttributeNode(\"id\");\n\t\t\t\treturn node && node.value === attrId;\n\t\t\t};\n\t\t};\n\n\t\t// Support: IE 6 - 7 only\n\t\t// getElementById is not reliable as a find shortcut\n\t\tExpr.find[\"ID\"] = function( id, context ) {\n\t\t\tif ( typeof context.getElementById !== \"undefined\" && documentIsHTML ) {\n\t\t\t\tvar node, i, elems,\n\t\t\t\t\telem = context.getElementById( id );\n\n\t\t\t\tif ( elem ) {\n\n\t\t\t\t\t// Verify the id attribute\n\t\t\t\t\tnode = elem.getAttributeNode(\"id\");\n\t\t\t\t\tif ( node && node.value === id ) {\n\t\t\t\t\t\treturn [ elem ];\n\t\t\t\t\t}\n\n\t\t\t\t\t// Fall back on getElementsByName\n\t\t\t\t\telems = context.getElementsByName( id );\n\t\t\t\t\ti = 0;\n\t\t\t\t\twhile ( (elem = elems[i++]) ) {\n\t\t\t\t\t\tnode = elem.getAttributeNode(\"id\");\n\t\t\t\t\t\tif ( node && node.value === id ) {\n\t\t\t\t\t\t\treturn [ elem ];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn [];\n\t\t\t}\n\t\t};\n\t}\n\n\t// Tag\n\tExpr.find[\"TAG\"] = support.getElementsByTagName ?\n\t\tfunction( tag, context ) {\n\t\t\tif ( typeof context.getElementsByTagName !== \"undefined\" ) {\n\t\t\t\treturn context.getElementsByTagName( tag );\n\n\t\t\t// DocumentFragment nodes don't have gEBTN\n\t\t\t} else if ( support.qsa ) {\n\t\t\t\treturn context.querySelectorAll( tag );\n\t\t\t}\n\t\t} :\n\n\t\tfunction( tag, context ) {\n\t\t\tvar elem,\n\t\t\t\ttmp = [],\n\t\t\t\ti = 0,\n\t\t\t\t// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too\n\t\t\t\tresults = context.getElementsByTagName( tag );\n\n\t\t\t// Filter out possible comments\n\t\t\tif ( tag === \"*\" ) {\n\t\t\t\twhile ( (elem = results[i++]) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 ) {\n\t\t\t\t\t\ttmp.push( elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn tmp;\n\t\t\t}\n\t\t\treturn results;\n\t\t};\n\n\t// Class\n\tExpr.find[\"CLASS\"] = support.getElementsByClassName && function( className, context ) {\n\t\tif ( typeof context.getElementsByClassName !== \"undefined\" && documentIsHTML ) {\n\t\t\treturn context.getElementsByClassName( className );\n\t\t}\n\t};\n\n\t/* QSA/matchesSelector\n\t---------------------------------------------------------------------- */\n\n\t// QSA and matchesSelector support\n\n\t// matchesSelector(:active) reports false when true (IE9/Opera 11.5)\n\trbuggyMatches = [];\n\n\t// qSa(:focus) reports false when true (Chrome 21)\n\t// We allow this because of a bug in IE8/9 that throws an error\n\t// whenever `document.activeElement` is accessed on an iframe\n\t// So, we allow :focus to pass through QSA all the time to avoid the IE error\n\t// See https://bugs.jquery.com/ticket/13378\n\trbuggyQSA = [];\n\n\tif ( (support.qsa = rnative.test( document.querySelectorAll )) ) {\n\t\t// Build QSA regex\n\t\t// Regex strategy adopted from Diego Perini\n\t\tassert(function( el ) {\n\t\t\t// Select is set to empty string on purpose\n\t\t\t// This is to test IE's treatment of not explicitly\n\t\t\t// setting a boolean content attribute,\n\t\t\t// since its presence should be enough\n\t\t\t// https://bugs.jquery.com/ticket/12359\n\t\t\tdocElem.appendChild( el ).innerHTML = \"\" +\n\t\t\t\t\"\";\n\n\t\t\t// Support: IE8, Opera 11-12.16\n\t\t\t// Nothing should be selected when empty strings follow ^= or $= or *=\n\t\t\t// The test attribute must be unknown in Opera but \"safe\" for WinRT\n\t\t\t// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section\n\t\t\tif ( el.querySelectorAll(\"[msallowcapture^='']\").length ) {\n\t\t\t\trbuggyQSA.push( \"[*^$]=\" + whitespace + \"*(?:''|\\\"\\\")\" );\n\t\t\t}\n\n\t\t\t// Support: IE8\n\t\t\t// Boolean attributes and \"value\" are not treated correctly\n\t\t\tif ( !el.querySelectorAll(\"[selected]\").length ) {\n\t\t\t\trbuggyQSA.push( \"\\\\[\" + whitespace + \"*(?:value|\" + booleans + \")\" );\n\t\t\t}\n\n\t\t\t// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+\n\t\t\tif ( !el.querySelectorAll( \"[id~=\" + expando + \"-]\" ).length ) {\n\t\t\t\trbuggyQSA.push(\"~=\");\n\t\t\t}\n\n\t\t\t// Webkit/Opera - :checked should return selected option elements\n\t\t\t// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked\n\t\t\t// IE8 throws error here and will not see later tests\n\t\t\tif ( !el.querySelectorAll(\":checked\").length ) {\n\t\t\t\trbuggyQSA.push(\":checked\");\n\t\t\t}\n\n\t\t\t// Support: Safari 8+, iOS 8+\n\t\t\t// https://bugs.webkit.org/show_bug.cgi?id=136851\n\t\t\t// In-page `selector#id sibling-combinator selector` fails\n\t\t\tif ( !el.querySelectorAll( \"a#\" + expando + \"+*\" ).length ) {\n\t\t\t\trbuggyQSA.push(\".#.+[+~]\");\n\t\t\t}\n\t\t});\n\n\t\tassert(function( el ) {\n\t\t\tel.innerHTML = \"\" +\n\t\t\t\t\"\";\n\n\t\t\t// Support: Windows 8 Native Apps\n\t\t\t// The type and name attributes are restricted during .innerHTML assignment\n\t\t\tvar input = document.createElement(\"input\");\n\t\t\tinput.setAttribute( \"type\", \"hidden\" );\n\t\t\tel.appendChild( input ).setAttribute( \"name\", \"D\" );\n\n\t\t\t// Support: IE8\n\t\t\t// Enforce case-sensitivity of name attribute\n\t\t\tif ( el.querySelectorAll(\"[name=d]\").length ) {\n\t\t\t\trbuggyQSA.push( \"name\" + whitespace + \"*[*^$|!~]?=\" );\n\t\t\t}\n\n\t\t\t// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)\n\t\t\t// IE8 throws error here and will not see later tests\n\t\t\tif ( el.querySelectorAll(\":enabled\").length !== 2 ) {\n\t\t\t\trbuggyQSA.push( \":enabled\", \":disabled\" );\n\t\t\t}\n\n\t\t\t// Support: IE9-11+\n\t\t\t// IE's :disabled selector does not pick up the children of disabled fieldsets\n\t\t\tdocElem.appendChild( el ).disabled = true;\n\t\t\tif ( el.querySelectorAll(\":disabled\").length !== 2 ) {\n\t\t\t\trbuggyQSA.push( \":enabled\", \":disabled\" );\n\t\t\t}\n\n\t\t\t// Opera 10-11 does not throw on post-comma invalid pseudos\n\t\t\tel.querySelectorAll(\"*,:x\");\n\t\t\trbuggyQSA.push(\",.*:\");\n\t\t});\n\t}\n\n\tif ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||\n\t\tdocElem.webkitMatchesSelector ||\n\t\tdocElem.mozMatchesSelector ||\n\t\tdocElem.oMatchesSelector ||\n\t\tdocElem.msMatchesSelector) )) ) {\n\n\t\tassert(function( el ) {\n\t\t\t// Check to see if it's possible to do matchesSelector\n\t\t\t// on a disconnected node (IE 9)\n\t\t\tsupport.disconnectedMatch = matches.call( el, \"*\" );\n\n\t\t\t// This should fail with an exception\n\t\t\t// Gecko does not error, returns false instead\n\t\t\tmatches.call( el, \"[s!='']:x\" );\n\t\t\trbuggyMatches.push( \"!=\", pseudos );\n\t\t});\n\t}\n\n\trbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join(\"|\") );\n\trbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join(\"|\") );\n\n\t/* Contains\n\t---------------------------------------------------------------------- */\n\thasCompare = rnative.test( docElem.compareDocumentPosition );\n\n\t// Element contains another\n\t// Purposefully self-exclusive\n\t// As in, an element does not contain itself\n\tcontains = hasCompare || rnative.test( docElem.contains ) ?\n\t\tfunction( a, b ) {\n\t\t\tvar adown = a.nodeType === 9 ? a.documentElement : a,\n\t\t\t\tbup = b && b.parentNode;\n\t\t\treturn a === bup || !!( bup && bup.nodeType === 1 && (\n\t\t\t\tadown.contains ?\n\t\t\t\t\tadown.contains( bup ) :\n\t\t\t\t\ta.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16\n\t\t\t));\n\t\t} :\n\t\tfunction( a, b ) {\n\t\t\tif ( b ) {\n\t\t\t\twhile ( (b = b.parentNode) ) {\n\t\t\t\t\tif ( b === a ) {\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t};\n\n\t/* Sorting\n\t---------------------------------------------------------------------- */\n\n\t// Document order sorting\n\tsortOrder = hasCompare ?\n\tfunction( a, b ) {\n\n\t\t// Flag for duplicate removal\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\t// Sort on method existence if only one input has compareDocumentPosition\n\t\tvar compare = !a.compareDocumentPosition - !b.compareDocumentPosition;\n\t\tif ( compare ) {\n\t\t\treturn compare;\n\t\t}\n\n\t\t// Calculate position if both inputs belong to the same document\n\t\tcompare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?\n\t\t\ta.compareDocumentPosition( b ) :\n\n\t\t\t// Otherwise we know they are disconnected\n\t\t\t1;\n\n\t\t// Disconnected nodes\n\t\tif ( compare & 1 ||\n\t\t\t(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {\n\n\t\t\t// Choose the first element that is related to our preferred document\n\t\t\tif ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t\tif ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {\n\t\t\t\treturn 1;\n\t\t\t}\n\n\t\t\t// Maintain original order\n\t\t\treturn sortInput ?\n\t\t\t\t( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :\n\t\t\t\t0;\n\t\t}\n\n\t\treturn compare & 4 ? -1 : 1;\n\t} :\n\tfunction( a, b ) {\n\t\t// Exit early if the nodes are identical\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\tvar cur,\n\t\t\ti = 0,\n\t\t\taup = a.parentNode,\n\t\t\tbup = b.parentNode,\n\t\t\tap = [ a ],\n\t\t\tbp = [ b ];\n\n\t\t// Parentless nodes are either documents or disconnected\n\t\tif ( !aup || !bup ) {\n\t\t\treturn a === document ? -1 :\n\t\t\t\tb === document ? 1 :\n\t\t\t\taup ? -1 :\n\t\t\t\tbup ? 1 :\n\t\t\t\tsortInput ?\n\t\t\t\t( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :\n\t\t\t\t0;\n\n\t\t// If the nodes are siblings, we can do a quick check\n\t\t} else if ( aup === bup ) {\n\t\t\treturn siblingCheck( a, b );\n\t\t}\n\n\t\t// Otherwise we need full lists of their ancestors for comparison\n\t\tcur = a;\n\t\twhile ( (cur = cur.parentNode) ) {\n\t\t\tap.unshift( cur );\n\t\t}\n\t\tcur = b;\n\t\twhile ( (cur = cur.parentNode) ) {\n\t\t\tbp.unshift( cur );\n\t\t}\n\n\t\t// Walk down the tree looking for a discrepancy\n\t\twhile ( ap[i] === bp[i] ) {\n\t\t\ti++;\n\t\t}\n\n\t\treturn i ?\n\t\t\t// Do a sibling check if the nodes have a common ancestor\n\t\t\tsiblingCheck( ap[i], bp[i] ) :\n\n\t\t\t// Otherwise nodes in our document sort first\n\t\t\tap[i] === preferredDoc ? -1 :\n\t\t\tbp[i] === preferredDoc ? 1 :\n\t\t\t0;\n\t};\n\n\treturn document;\n};\n\nSizzle.matches = function( expr, elements ) {\n\treturn Sizzle( expr, null, null, elements );\n};\n\nSizzle.matchesSelector = function( elem, expr ) {\n\t// Set document vars if needed\n\tif ( ( elem.ownerDocument || elem ) !== document ) {\n\t\tsetDocument( elem );\n\t}\n\n\t// Make sure that attribute selectors are quoted\n\texpr = expr.replace( rattributeQuotes, \"='$1']\" );\n\n\tif ( support.matchesSelector && documentIsHTML &&\n\t\t!compilerCache[ expr + \" \" ] &&\n\t\t( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&\n\t\t( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {\n\n\t\ttry {\n\t\t\tvar ret = matches.call( elem, expr );\n\n\t\t\t// IE 9's matchesSelector returns false on disconnected nodes\n\t\t\tif ( ret || support.disconnectedMatch ||\n\t\t\t\t\t// As well, disconnected nodes are said to be in a document\n\t\t\t\t\t// fragment in IE 9\n\t\t\t\t\telem.document && elem.document.nodeType !== 11 ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\t\t} catch (e) {}\n\t}\n\n\treturn Sizzle( expr, document, null, [ elem ] ).length > 0;\n};\n\nSizzle.contains = function( context, elem ) {\n\t// Set document vars if needed\n\tif ( ( context.ownerDocument || context ) !== document ) {\n\t\tsetDocument( context );\n\t}\n\treturn contains( context, elem );\n};\n\nSizzle.attr = function( elem, name ) {\n\t// Set document vars if needed\n\tif ( ( elem.ownerDocument || elem ) !== document ) {\n\t\tsetDocument( elem );\n\t}\n\n\tvar fn = Expr.attrHandle[ name.toLowerCase() ],\n\t\t// Don't get fooled by Object.prototype properties (jQuery #13807)\n\t\tval = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?\n\t\t\tfn( elem, name, !documentIsHTML ) :\n\t\t\tundefined;\n\n\treturn val !== undefined ?\n\t\tval :\n\t\tsupport.attributes || !documentIsHTML ?\n\t\t\telem.getAttribute( name ) :\n\t\t\t(val = elem.getAttributeNode(name)) && val.specified ?\n\t\t\t\tval.value :\n\t\t\t\tnull;\n};\n\nSizzle.escape = function( sel ) {\n\treturn (sel + \"\").replace( rcssescape, fcssescape );\n};\n\nSizzle.error = function( msg ) {\n\tthrow new Error( \"Syntax error, unrecognized expression: \" + msg );\n};\n\n/**\n * Document sorting and removing duplicates\n * @param {ArrayLike} results\n */\nSizzle.uniqueSort = function( results ) {\n\tvar elem,\n\t\tduplicates = [],\n\t\tj = 0,\n\t\ti = 0;\n\n\t// Unless we *know* we can detect duplicates, assume their presence\n\thasDuplicate = !support.detectDuplicates;\n\tsortInput = !support.sortStable && results.slice( 0 );\n\tresults.sort( sortOrder );\n\n\tif ( hasDuplicate ) {\n\t\twhile ( (elem = results[i++]) ) {\n\t\t\tif ( elem === results[ i ] ) {\n\t\t\t\tj = duplicates.push( i );\n\t\t\t}\n\t\t}\n\t\twhile ( j-- ) {\n\t\t\tresults.splice( duplicates[ j ], 1 );\n\t\t}\n\t}\n\n\t// Clear input after sorting to release objects\n\t// See https://github.com/jquery/sizzle/pull/225\n\tsortInput = null;\n\n\treturn results;\n};\n\n/**\n * Utility function for retrieving the text value of an array of DOM nodes\n * @param {Array|Element} elem\n */\ngetText = Sizzle.getText = function( elem ) {\n\tvar node,\n\t\tret = \"\",\n\t\ti = 0,\n\t\tnodeType = elem.nodeType;\n\n\tif ( !nodeType ) {\n\t\t// If no nodeType, this is expected to be an array\n\t\twhile ( (node = elem[i++]) ) {\n\t\t\t// Do not traverse comment nodes\n\t\t\tret += getText( node );\n\t\t}\n\t} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {\n\t\t// Use textContent for elements\n\t\t// innerText usage removed for consistency of new lines (jQuery #11153)\n\t\tif ( typeof elem.textContent === \"string\" ) {\n\t\t\treturn elem.textContent;\n\t\t} else {\n\t\t\t// Traverse its children\n\t\t\tfor ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {\n\t\t\t\tret += getText( elem );\n\t\t\t}\n\t\t}\n\t} else if ( nodeType === 3 || nodeType === 4 ) {\n\t\treturn elem.nodeValue;\n\t}\n\t// Do not include comment or processing instruction nodes\n\n\treturn ret;\n};\n\nExpr = Sizzle.selectors = {\n\n\t// Can be adjusted by the user\n\tcacheLength: 50,\n\n\tcreatePseudo: markFunction,\n\n\tmatch: matchExpr,\n\n\tattrHandle: {},\n\n\tfind: {},\n\n\trelative: {\n\t\t\">\": { dir: \"parentNode\", first: true },\n\t\t\" \": { dir: \"parentNode\" },\n\t\t\"+\": { dir: \"previousSibling\", first: true },\n\t\t\"~\": { dir: \"previousSibling\" }\n\t},\n\n\tpreFilter: {\n\t\t\"ATTR\": function( match ) {\n\t\t\tmatch[1] = match[1].replace( runescape, funescape );\n\n\t\t\t// Move the given value to match[3] whether quoted or unquoted\n\t\t\tmatch[3] = ( match[3] || match[4] || match[5] || \"\" ).replace( runescape, funescape );\n\n\t\t\tif ( match[2] === \"~=\" ) {\n\t\t\t\tmatch[3] = \" \" + match[3] + \" \";\n\t\t\t}\n\n\t\t\treturn match.slice( 0, 4 );\n\t\t},\n\n\t\t\"CHILD\": function( match ) {\n\t\t\t/* matches from matchExpr[\"CHILD\"]\n\t\t\t\t1 type (only|nth|...)\n\t\t\t\t2 what (child|of-type)\n\t\t\t\t3 argument (even|odd|\\d*|\\d*n([+-]\\d+)?|...)\n\t\t\t\t4 xn-component of xn+y argument ([+-]?\\d*n|)\n\t\t\t\t5 sign of xn-component\n\t\t\t\t6 x of xn-component\n\t\t\t\t7 sign of y-component\n\t\t\t\t8 y of y-component\n\t\t\t*/\n\t\t\tmatch[1] = match[1].toLowerCase();\n\n\t\t\tif ( match[1].slice( 0, 3 ) === \"nth\" ) {\n\t\t\t\t// nth-* requires argument\n\t\t\t\tif ( !match[3] ) {\n\t\t\t\t\tSizzle.error( match[0] );\n\t\t\t\t}\n\n\t\t\t\t// numeric x and y parameters for Expr.filter.CHILD\n\t\t\t\t// remember that false/true cast respectively to 0/1\n\t\t\t\tmatch[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === \"even\" || match[3] === \"odd\" ) );\n\t\t\t\tmatch[5] = +( ( match[7] + match[8] ) || match[3] === \"odd\" );\n\n\t\t\t// other types prohibit arguments\n\t\t\t} else if ( match[3] ) {\n\t\t\t\tSizzle.error( match[0] );\n\t\t\t}\n\n\t\t\treturn match;\n\t\t},\n\n\t\t\"PSEUDO\": function( match ) {\n\t\t\tvar excess,\n\t\t\t\tunquoted = !match[6] && match[2];\n\n\t\t\tif ( matchExpr[\"CHILD\"].test( match[0] ) ) {\n\t\t\t\treturn null;\n\t\t\t}\n\n\t\t\t// Accept quoted arguments as-is\n\t\t\tif ( match[3] ) {\n\t\t\t\tmatch[2] = match[4] || match[5] || \"\";\n\n\t\t\t// Strip excess characters from unquoted arguments\n\t\t\t} else if ( unquoted && rpseudo.test( unquoted ) &&\n\t\t\t\t// Get excess from tokenize (recursively)\n\t\t\t\t(excess = tokenize( unquoted, true )) &&\n\t\t\t\t// advance to the next closing parenthesis\n\t\t\t\t(excess = unquoted.indexOf( \")\", unquoted.length - excess ) - unquoted.length) ) {\n\n\t\t\t\t// excess is a negative index\n\t\t\t\tmatch[0] = match[0].slice( 0, excess );\n\t\t\t\tmatch[2] = unquoted.slice( 0, excess );\n\t\t\t}\n\n\t\t\t// Return only captures needed by the pseudo filter method (type and argument)\n\t\t\treturn match.slice( 0, 3 );\n\t\t}\n\t},\n\n\tfilter: {\n\n\t\t\"TAG\": function( nodeNameSelector ) {\n\t\t\tvar nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();\n\t\t\treturn nodeNameSelector === \"*\" ?\n\t\t\t\tfunction() { return true; } :\n\t\t\t\tfunction( elem ) {\n\t\t\t\t\treturn elem.nodeName && elem.nodeName.toLowerCase() === nodeName;\n\t\t\t\t};\n\t\t},\n\n\t\t\"CLASS\": function( className ) {\n\t\t\tvar pattern = classCache[ className + \" \" ];\n\n\t\t\treturn pattern ||\n\t\t\t\t(pattern = new RegExp( \"(^|\" + whitespace + \")\" + className + \"(\" + whitespace + \"|$)\" )) &&\n\t\t\t\tclassCache( className, function( elem ) {\n\t\t\t\t\treturn pattern.test( typeof elem.className === \"string\" && elem.className || typeof elem.getAttribute !== \"undefined\" && elem.getAttribute(\"class\") || \"\" );\n\t\t\t\t});\n\t\t},\n\n\t\t\"ATTR\": function( name, operator, check ) {\n\t\t\treturn function( elem ) {\n\t\t\t\tvar result = Sizzle.attr( elem, name );\n\n\t\t\t\tif ( result == null ) {\n\t\t\t\t\treturn operator === \"!=\";\n\t\t\t\t}\n\t\t\t\tif ( !operator ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\n\t\t\t\tresult += \"\";\n\n\t\t\t\treturn operator === \"=\" ? result === check :\n\t\t\t\t\toperator === \"!=\" ? result !== check :\n\t\t\t\t\toperator === \"^=\" ? check && result.indexOf( check ) === 0 :\n\t\t\t\t\toperator === \"*=\" ? check && result.indexOf( check ) > -1 :\n\t\t\t\t\toperator === \"$=\" ? check && result.slice( -check.length ) === check :\n\t\t\t\t\toperator === \"~=\" ? ( \" \" + result.replace( rwhitespace, \" \" ) + \" \" ).indexOf( check ) > -1 :\n\t\t\t\t\toperator === \"|=\" ? result === check || result.slice( 0, check.length + 1 ) === check + \"-\" :\n\t\t\t\t\tfalse;\n\t\t\t};\n\t\t},\n\n\t\t\"CHILD\": function( type, what, argument, first, last ) {\n\t\t\tvar simple = type.slice( 0, 3 ) !== \"nth\",\n\t\t\t\tforward = type.slice( -4 ) !== \"last\",\n\t\t\t\tofType = what === \"of-type\";\n\n\t\t\treturn first === 1 && last === 0 ?\n\n\t\t\t\t// Shortcut for :nth-*(n)\n\t\t\t\tfunction( elem ) {\n\t\t\t\t\treturn !!elem.parentNode;\n\t\t\t\t} :\n\n\t\t\t\tfunction( elem, context, xml ) {\n\t\t\t\t\tvar cache, uniqueCache, outerCache, node, nodeIndex, start,\n\t\t\t\t\t\tdir = simple !== forward ? \"nextSibling\" : \"previousSibling\",\n\t\t\t\t\t\tparent = elem.parentNode,\n\t\t\t\t\t\tname = ofType && elem.nodeName.toLowerCase(),\n\t\t\t\t\t\tuseCache = !xml && !ofType,\n\t\t\t\t\t\tdiff = false;\n\n\t\t\t\t\tif ( parent ) {\n\n\t\t\t\t\t\t// :(first|last|only)-(child|of-type)\n\t\t\t\t\t\tif ( simple ) {\n\t\t\t\t\t\t\twhile ( dir ) {\n\t\t\t\t\t\t\t\tnode = elem;\n\t\t\t\t\t\t\t\twhile ( (node = node[ dir ]) ) {\n\t\t\t\t\t\t\t\t\tif ( ofType ?\n\t\t\t\t\t\t\t\t\t\tnode.nodeName.toLowerCase() === name :\n\t\t\t\t\t\t\t\t\t\tnode.nodeType === 1 ) {\n\n\t\t\t\t\t\t\t\t\t\treturn false;\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t// Reverse direction for :only-* (if we haven't yet done so)\n\t\t\t\t\t\t\t\tstart = dir = type === \"only\" && !start && \"nextSibling\";\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tstart = [ forward ? parent.firstChild : parent.lastChild ];\n\n\t\t\t\t\t\t// non-xml :nth-child(...) stores cache data on `parent`\n\t\t\t\t\t\tif ( forward && useCache ) {\n\n\t\t\t\t\t\t\t// Seek `elem` from a previously-cached index\n\n\t\t\t\t\t\t\t// ...in a gzip-friendly way\n\t\t\t\t\t\t\tnode = parent;\n\t\t\t\t\t\t\touterCache = node[ expando ] || (node[ expando ] = {});\n\n\t\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\t\tuniqueCache = outerCache[ node.uniqueID ] ||\n\t\t\t\t\t\t\t\t(outerCache[ node.uniqueID ] = {});\n\n\t\t\t\t\t\t\tcache = uniqueCache[ type ] || [];\n\t\t\t\t\t\t\tnodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];\n\t\t\t\t\t\t\tdiff = nodeIndex && cache[ 2 ];\n\t\t\t\t\t\t\tnode = nodeIndex && parent.childNodes[ nodeIndex ];\n\n\t\t\t\t\t\t\twhile ( (node = ++nodeIndex && node && node[ dir ] ||\n\n\t\t\t\t\t\t\t\t// Fallback to seeking `elem` from the start\n\t\t\t\t\t\t\t\t(diff = nodeIndex = 0) || start.pop()) ) {\n\n\t\t\t\t\t\t\t\t// When found, cache indexes on `parent` and break\n\t\t\t\t\t\t\t\tif ( node.nodeType === 1 && ++diff && node === elem ) {\n\t\t\t\t\t\t\t\t\tuniqueCache[ type ] = [ dirruns, nodeIndex, diff ];\n\t\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t// Use previously-cached element index if available\n\t\t\t\t\t\t\tif ( useCache ) {\n\t\t\t\t\t\t\t\t// ...in a gzip-friendly way\n\t\t\t\t\t\t\t\tnode = elem;\n\t\t\t\t\t\t\t\touterCache = node[ expando ] || (node[ expando ] = {});\n\n\t\t\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\t\t\tuniqueCache = outerCache[ node.uniqueID ] ||\n\t\t\t\t\t\t\t\t\t(outerCache[ node.uniqueID ] = {});\n\n\t\t\t\t\t\t\t\tcache = uniqueCache[ type ] || [];\n\t\t\t\t\t\t\t\tnodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];\n\t\t\t\t\t\t\t\tdiff = nodeIndex;\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t// xml :nth-child(...)\n\t\t\t\t\t\t\t// or :nth-last-child(...) or :nth(-last)?-of-type(...)\n\t\t\t\t\t\t\tif ( diff === false ) {\n\t\t\t\t\t\t\t\t// Use the same loop as above to seek `elem` from the start\n\t\t\t\t\t\t\t\twhile ( (node = ++nodeIndex && node && node[ dir ] ||\n\t\t\t\t\t\t\t\t\t(diff = nodeIndex = 0) || start.pop()) ) {\n\n\t\t\t\t\t\t\t\t\tif ( ( ofType ?\n\t\t\t\t\t\t\t\t\t\tnode.nodeName.toLowerCase() === name :\n\t\t\t\t\t\t\t\t\t\tnode.nodeType === 1 ) &&\n\t\t\t\t\t\t\t\t\t\t++diff ) {\n\n\t\t\t\t\t\t\t\t\t\t// Cache the index of each encountered element\n\t\t\t\t\t\t\t\t\t\tif ( useCache ) {\n\t\t\t\t\t\t\t\t\t\t\touterCache = node[ expando ] || (node[ expando ] = {});\n\n\t\t\t\t\t\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\t\t\t\t\t\tuniqueCache = outerCache[ node.uniqueID ] ||\n\t\t\t\t\t\t\t\t\t\t\t\t(outerCache[ node.uniqueID ] = {});\n\n\t\t\t\t\t\t\t\t\t\t\tuniqueCache[ type ] = [ dirruns, diff ];\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\tif ( node === elem ) {\n\t\t\t\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Incorporate the offset, then check against cycle size\n\t\t\t\t\t\tdiff -= last;\n\t\t\t\t\t\treturn diff === first || ( diff % first === 0 && diff / first >= 0 );\n\t\t\t\t\t}\n\t\t\t\t};\n\t\t},\n\n\t\t\"PSEUDO\": function( pseudo, argument ) {\n\t\t\t// pseudo-class names are case-insensitive\n\t\t\t// http://www.w3.org/TR/selectors/#pseudo-classes\n\t\t\t// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters\n\t\t\t// Remember that setFilters inherits from pseudos\n\t\t\tvar args,\n\t\t\t\tfn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||\n\t\t\t\t\tSizzle.error( \"unsupported pseudo: \" + pseudo );\n\n\t\t\t// The user may use createPseudo to indicate that\n\t\t\t// arguments are needed to create the filter function\n\t\t\t// just as Sizzle does\n\t\t\tif ( fn[ expando ] ) {\n\t\t\t\treturn fn( argument );\n\t\t\t}\n\n\t\t\t// But maintain support for old signatures\n\t\t\tif ( fn.length > 1 ) {\n\t\t\t\targs = [ pseudo, pseudo, \"\", argument ];\n\t\t\t\treturn Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?\n\t\t\t\t\tmarkFunction(function( seed, matches ) {\n\t\t\t\t\t\tvar idx,\n\t\t\t\t\t\t\tmatched = fn( seed, argument ),\n\t\t\t\t\t\t\ti = matched.length;\n\t\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\t\tidx = indexOf( seed, matched[i] );\n\t\t\t\t\t\t\tseed[ idx ] = !( matches[ idx ] = matched[i] );\n\t\t\t\t\t\t}\n\t\t\t\t\t}) :\n\t\t\t\t\tfunction( elem ) {\n\t\t\t\t\t\treturn fn( elem, 0, args );\n\t\t\t\t\t};\n\t\t\t}\n\n\t\t\treturn fn;\n\t\t}\n\t},\n\n\tpseudos: {\n\t\t// Potentially complex pseudos\n\t\t\"not\": markFunction(function( selector ) {\n\t\t\t// Trim the selector passed to compile\n\t\t\t// to avoid treating leading and trailing\n\t\t\t// spaces as combinators\n\t\t\tvar input = [],\n\t\t\t\tresults = [],\n\t\t\t\tmatcher = compile( selector.replace( rtrim, \"$1\" ) );\n\n\t\t\treturn matcher[ expando ] ?\n\t\t\t\tmarkFunction(function( seed, matches, context, xml ) {\n\t\t\t\t\tvar elem,\n\t\t\t\t\t\tunmatched = matcher( seed, null, xml, [] ),\n\t\t\t\t\t\ti = seed.length;\n\n\t\t\t\t\t// Match elements unmatched by `matcher`\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tif ( (elem = unmatched[i]) ) {\n\t\t\t\t\t\t\tseed[i] = !(matches[i] = elem);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}) :\n\t\t\t\tfunction( elem, context, xml ) {\n\t\t\t\t\tinput[0] = elem;\n\t\t\t\t\tmatcher( input, null, xml, results );\n\t\t\t\t\t// Don't keep the element (issue #299)\n\t\t\t\t\tinput[0] = null;\n\t\t\t\t\treturn !results.pop();\n\t\t\t\t};\n\t\t}),\n\n\t\t\"has\": markFunction(function( selector ) {\n\t\t\treturn function( elem ) {\n\t\t\t\treturn Sizzle( selector, elem ).length > 0;\n\t\t\t};\n\t\t}),\n\n\t\t\"contains\": markFunction(function( text ) {\n\t\t\ttext = text.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\treturn ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1;\n\t\t\t};\n\t\t}),\n\n\t\t// \"Whether an element is represented by a :lang() selector\n\t\t// is based solely on the element's language value\n\t\t// being equal to the identifier C,\n\t\t// or beginning with the identifier C immediately followed by \"-\".\n\t\t// The matching of C against the element's language value is performed case-insensitively.\n\t\t// The identifier C does not have to be a valid language name.\"\n\t\t// http://www.w3.org/TR/selectors/#lang-pseudo\n\t\t\"lang\": markFunction( function( lang ) {\n\t\t\t// lang value must be a valid identifier\n\t\t\tif ( !ridentifier.test(lang || \"\") ) {\n\t\t\t\tSizzle.error( \"unsupported lang: \" + lang );\n\t\t\t}\n\t\t\tlang = lang.replace( runescape, funescape ).toLowerCase();\n\t\t\treturn function( elem ) {\n\t\t\t\tvar elemLang;\n\t\t\t\tdo {\n\t\t\t\t\tif ( (elemLang = documentIsHTML ?\n\t\t\t\t\t\telem.lang :\n\t\t\t\t\t\telem.getAttribute(\"xml:lang\") || elem.getAttribute(\"lang\")) ) {\n\n\t\t\t\t\t\telemLang = elemLang.toLowerCase();\n\t\t\t\t\t\treturn elemLang === lang || elemLang.indexOf( lang + \"-\" ) === 0;\n\t\t\t\t\t}\n\t\t\t\t} while ( (elem = elem.parentNode) && elem.nodeType === 1 );\n\t\t\t\treturn false;\n\t\t\t};\n\t\t}),\n\n\t\t// Miscellaneous\n\t\t\"target\": function( elem ) {\n\t\t\tvar hash = window.location && window.location.hash;\n\t\t\treturn hash && hash.slice( 1 ) === elem.id;\n\t\t},\n\n\t\t\"root\": function( elem ) {\n\t\t\treturn elem === docElem;\n\t\t},\n\n\t\t\"focus\": function( elem ) {\n\t\t\treturn elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);\n\t\t},\n\n\t\t// Boolean properties\n\t\t\"enabled\": createDisabledPseudo( false ),\n\t\t\"disabled\": createDisabledPseudo( true ),\n\n\t\t\"checked\": function( elem ) {\n\t\t\t// In CSS3, :checked should return both checked and selected elements\n\t\t\t// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked\n\t\t\tvar nodeName = elem.nodeName.toLowerCase();\n\t\t\treturn (nodeName === \"input\" && !!elem.checked) || (nodeName === \"option\" && !!elem.selected);\n\t\t},\n\n\t\t\"selected\": function( elem ) {\n\t\t\t// Accessing this property makes selected-by-default\n\t\t\t// options in Safari work properly\n\t\t\tif ( elem.parentNode ) {\n\t\t\t\telem.parentNode.selectedIndex;\n\t\t\t}\n\n\t\t\treturn elem.selected === true;\n\t\t},\n\n\t\t// Contents\n\t\t\"empty\": function( elem ) {\n\t\t\t// http://www.w3.org/TR/selectors/#empty-pseudo\n\t\t\t// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),\n\t\t\t// but not by others (comment: 8; processing instruction: 7; etc.)\n\t\t\t// nodeType < 6 works because attributes (2) do not appear as children\n\t\t\tfor ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {\n\t\t\t\tif ( elem.nodeType < 6 ) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t},\n\n\t\t\"parent\": function( elem ) {\n\t\t\treturn !Expr.pseudos[\"empty\"]( elem );\n\t\t},\n\n\t\t// Element/input types\n\t\t\"header\": function( elem ) {\n\t\t\treturn rheader.test( elem.nodeName );\n\t\t},\n\n\t\t\"input\": function( elem ) {\n\t\t\treturn rinputs.test( elem.nodeName );\n\t\t},\n\n\t\t\"button\": function( elem ) {\n\t\t\tvar name = elem.nodeName.toLowerCase();\n\t\t\treturn name === \"input\" && elem.type === \"button\" || name === \"button\";\n\t\t},\n\n\t\t\"text\": function( elem ) {\n\t\t\tvar attr;\n\t\t\treturn elem.nodeName.toLowerCase() === \"input\" &&\n\t\t\t\telem.type === \"text\" &&\n\n\t\t\t\t// Support: IE<8\n\t\t\t\t// New HTML5 attribute values (e.g., \"search\") appear with elem.type === \"text\"\n\t\t\t\t( (attr = elem.getAttribute(\"type\")) == null || attr.toLowerCase() === \"text\" );\n\t\t},\n\n\t\t// Position-in-collection\n\t\t\"first\": createPositionalPseudo(function() {\n\t\t\treturn [ 0 ];\n\t\t}),\n\n\t\t\"last\": createPositionalPseudo(function( matchIndexes, length ) {\n\t\t\treturn [ length - 1 ];\n\t\t}),\n\n\t\t\"eq\": createPositionalPseudo(function( matchIndexes, length, argument ) {\n\t\t\treturn [ argument < 0 ? argument + length : argument ];\n\t\t}),\n\n\t\t\"even\": createPositionalPseudo(function( matchIndexes, length ) {\n\t\t\tvar i = 0;\n\t\t\tfor ( ; i < length; i += 2 ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t}),\n\n\t\t\"odd\": createPositionalPseudo(function( matchIndexes, length ) {\n\t\t\tvar i = 1;\n\t\t\tfor ( ; i < length; i += 2 ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t}),\n\n\t\t\"lt\": createPositionalPseudo(function( matchIndexes, length, argument ) {\n\t\t\tvar i = argument < 0 ? argument + length : argument;\n\t\t\tfor ( ; --i >= 0; ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t}),\n\n\t\t\"gt\": createPositionalPseudo(function( matchIndexes, length, argument ) {\n\t\t\tvar i = argument < 0 ? argument + length : argument;\n\t\t\tfor ( ; ++i < length; ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t})\n\t}\n};\n\nExpr.pseudos[\"nth\"] = Expr.pseudos[\"eq\"];\n\n// Add button/input type pseudos\nfor ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {\n\tExpr.pseudos[ i ] = createInputPseudo( i );\n}\nfor ( i in { submit: true, reset: true } ) {\n\tExpr.pseudos[ i ] = createButtonPseudo( i );\n}\n\n// Easy API for creating new setFilters\nfunction setFilters() {}\nsetFilters.prototype = Expr.filters = Expr.pseudos;\nExpr.setFilters = new setFilters();\n\ntokenize = Sizzle.tokenize = function( selector, parseOnly ) {\n\tvar matched, match, tokens, type,\n\t\tsoFar, groups, preFilters,\n\t\tcached = tokenCache[ selector + \" \" ];\n\n\tif ( cached ) {\n\t\treturn parseOnly ? 0 : cached.slice( 0 );\n\t}\n\n\tsoFar = selector;\n\tgroups = [];\n\tpreFilters = Expr.preFilter;\n\n\twhile ( soFar ) {\n\n\t\t// Comma and first run\n\t\tif ( !matched || (match = rcomma.exec( soFar )) ) {\n\t\t\tif ( match ) {\n\t\t\t\t// Don't consume trailing commas as valid\n\t\t\t\tsoFar = soFar.slice( match[0].length ) || soFar;\n\t\t\t}\n\t\t\tgroups.push( (tokens = []) );\n\t\t}\n\n\t\tmatched = false;\n\n\t\t// Combinators\n\t\tif ( (match = rcombinators.exec( soFar )) ) {\n\t\t\tmatched = match.shift();\n\t\t\ttokens.push({\n\t\t\t\tvalue: matched,\n\t\t\t\t// Cast descendant combinators to space\n\t\t\t\ttype: match[0].replace( rtrim, \" \" )\n\t\t\t});\n\t\t\tsoFar = soFar.slice( matched.length );\n\t\t}\n\n\t\t// Filters\n\t\tfor ( type in Expr.filter ) {\n\t\t\tif ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||\n\t\t\t\t(match = preFilters[ type ]( match ))) ) {\n\t\t\t\tmatched = match.shift();\n\t\t\t\ttokens.push({\n\t\t\t\t\tvalue: matched,\n\t\t\t\t\ttype: type,\n\t\t\t\t\tmatches: match\n\t\t\t\t});\n\t\t\t\tsoFar = soFar.slice( matched.length );\n\t\t\t}\n\t\t}\n\n\t\tif ( !matched ) {\n\t\t\tbreak;\n\t\t}\n\t}\n\n\t// Return the length of the invalid excess\n\t// if we're just parsing\n\t// Otherwise, throw an error or return tokens\n\treturn parseOnly ?\n\t\tsoFar.length :\n\t\tsoFar ?\n\t\t\tSizzle.error( selector ) :\n\t\t\t// Cache the tokens\n\t\t\ttokenCache( selector, groups ).slice( 0 );\n};\n\nfunction toSelector( tokens ) {\n\tvar i = 0,\n\t\tlen = tokens.length,\n\t\tselector = \"\";\n\tfor ( ; i < len; i++ ) {\n\t\tselector += tokens[i].value;\n\t}\n\treturn selector;\n}\n\nfunction addCombinator( matcher, combinator, base ) {\n\tvar dir = combinator.dir,\n\t\tskip = combinator.next,\n\t\tkey = skip || dir,\n\t\tcheckNonElements = base && key === \"parentNode\",\n\t\tdoneName = done++;\n\n\treturn combinator.first ?\n\t\t// Check against closest ancestor/preceding element\n\t\tfunction( elem, context, xml ) {\n\t\t\twhile ( (elem = elem[ dir ]) ) {\n\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\treturn matcher( elem, context, xml );\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t} :\n\n\t\t// Check against all ancestor/preceding elements\n\t\tfunction( elem, context, xml ) {\n\t\t\tvar oldCache, uniqueCache, outerCache,\n\t\t\t\tnewCache = [ dirruns, doneName ];\n\n\t\t\t// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching\n\t\t\tif ( xml ) {\n\t\t\t\twhile ( (elem = elem[ dir ]) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\t\tif ( matcher( elem, context, xml ) ) {\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\twhile ( (elem = elem[ dir ]) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\t\touterCache = elem[ expando ] || (elem[ expando ] = {});\n\n\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\tuniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});\n\n\t\t\t\t\t\tif ( skip && skip === elem.nodeName.toLowerCase() ) {\n\t\t\t\t\t\t\telem = elem[ dir ] || elem;\n\t\t\t\t\t\t} else if ( (oldCache = uniqueCache[ key ]) &&\n\t\t\t\t\t\t\toldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {\n\n\t\t\t\t\t\t\t// Assign to newCache so results back-propagate to previous elements\n\t\t\t\t\t\t\treturn (newCache[ 2 ] = oldCache[ 2 ]);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t// Reuse newcache so results back-propagate to previous elements\n\t\t\t\t\t\t\tuniqueCache[ key ] = newCache;\n\n\t\t\t\t\t\t\t// A match means we're done; a fail means we have to keep checking\n\t\t\t\t\t\t\tif ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {\n\t\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t};\n}\n\nfunction elementMatcher( matchers ) {\n\treturn matchers.length > 1 ?\n\t\tfunction( elem, context, xml ) {\n\t\t\tvar i = matchers.length;\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( !matchers[i]( elem, context, xml ) ) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t} :\n\t\tmatchers[0];\n}\n\nfunction multipleContexts( selector, contexts, results ) {\n\tvar i = 0,\n\t\tlen = contexts.length;\n\tfor ( ; i < len; i++ ) {\n\t\tSizzle( selector, contexts[i], results );\n\t}\n\treturn results;\n}\n\nfunction condense( unmatched, map, filter, context, xml ) {\n\tvar elem,\n\t\tnewUnmatched = [],\n\t\ti = 0,\n\t\tlen = unmatched.length,\n\t\tmapped = map != null;\n\n\tfor ( ; i < len; i++ ) {\n\t\tif ( (elem = unmatched[i]) ) {\n\t\t\tif ( !filter || filter( elem, context, xml ) ) {\n\t\t\t\tnewUnmatched.push( elem );\n\t\t\t\tif ( mapped ) {\n\t\t\t\t\tmap.push( i );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn newUnmatched;\n}\n\nfunction setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {\n\tif ( postFilter && !postFilter[ expando ] ) {\n\t\tpostFilter = setMatcher( postFilter );\n\t}\n\tif ( postFinder && !postFinder[ expando ] ) {\n\t\tpostFinder = setMatcher( postFinder, postSelector );\n\t}\n\treturn markFunction(function( seed, results, context, xml ) {\n\t\tvar temp, i, elem,\n\t\t\tpreMap = [],\n\t\t\tpostMap = [],\n\t\t\tpreexisting = results.length,\n\n\t\t\t// Get initial elements from seed or context\n\t\t\telems = seed || multipleContexts( selector || \"*\", context.nodeType ? [ context ] : context, [] ),\n\n\t\t\t// Prefilter to get matcher input, preserving a map for seed-results synchronization\n\t\t\tmatcherIn = preFilter && ( seed || !selector ) ?\n\t\t\t\tcondense( elems, preMap, preFilter, context, xml ) :\n\t\t\t\telems,\n\n\t\t\tmatcherOut = matcher ?\n\t\t\t\t// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,\n\t\t\t\tpostFinder || ( seed ? preFilter : preexisting || postFilter ) ?\n\n\t\t\t\t\t// ...intermediate processing is necessary\n\t\t\t\t\t[] :\n\n\t\t\t\t\t// ...otherwise use results directly\n\t\t\t\t\tresults :\n\t\t\t\tmatcherIn;\n\n\t\t// Find primary matches\n\t\tif ( matcher ) {\n\t\t\tmatcher( matcherIn, matcherOut, context, xml );\n\t\t}\n\n\t\t// Apply postFilter\n\t\tif ( postFilter ) {\n\t\t\ttemp = condense( matcherOut, postMap );\n\t\t\tpostFilter( temp, [], context, xml );\n\n\t\t\t// Un-match failing elements by moving them back to matcherIn\n\t\t\ti = temp.length;\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( (elem = temp[i]) ) {\n\t\t\t\t\tmatcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif ( seed ) {\n\t\t\tif ( postFinder || preFilter ) {\n\t\t\t\tif ( postFinder ) {\n\t\t\t\t\t// Get the final matcherOut by condensing this intermediate into postFinder contexts\n\t\t\t\t\ttemp = [];\n\t\t\t\t\ti = matcherOut.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tif ( (elem = matcherOut[i]) ) {\n\t\t\t\t\t\t\t// Restore matcherIn since elem is not yet a final match\n\t\t\t\t\t\t\ttemp.push( (matcherIn[i] = elem) );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tpostFinder( null, (matcherOut = []), temp, xml );\n\t\t\t\t}\n\n\t\t\t\t// Move matched elements from seed to results to keep them synchronized\n\t\t\t\ti = matcherOut.length;\n\t\t\t\twhile ( i-- ) {\n\t\t\t\t\tif ( (elem = matcherOut[i]) &&\n\t\t\t\t\t\t(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {\n\n\t\t\t\t\t\tseed[temp] = !(results[temp] = elem);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Add elements to results, through postFinder if defined\n\t\t} else {\n\t\t\tmatcherOut = condense(\n\t\t\t\tmatcherOut === results ?\n\t\t\t\t\tmatcherOut.splice( preexisting, matcherOut.length ) :\n\t\t\t\t\tmatcherOut\n\t\t\t);\n\t\t\tif ( postFinder ) {\n\t\t\t\tpostFinder( null, results, matcherOut, xml );\n\t\t\t} else {\n\t\t\t\tpush.apply( results, matcherOut );\n\t\t\t}\n\t\t}\n\t});\n}\n\nfunction matcherFromTokens( tokens ) {\n\tvar checkContext, matcher, j,\n\t\tlen = tokens.length,\n\t\tleadingRelative = Expr.relative[ tokens[0].type ],\n\t\timplicitRelative = leadingRelative || Expr.relative[\" \"],\n\t\ti = leadingRelative ? 1 : 0,\n\n\t\t// The foundational matcher ensures that elements are reachable from top-level context(s)\n\t\tmatchContext = addCombinator( function( elem ) {\n\t\t\treturn elem === checkContext;\n\t\t}, implicitRelative, true ),\n\t\tmatchAnyContext = addCombinator( function( elem ) {\n\t\t\treturn indexOf( checkContext, elem ) > -1;\n\t\t}, implicitRelative, true ),\n\t\tmatchers = [ function( elem, context, xml ) {\n\t\t\tvar ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (\n\t\t\t\t(checkContext = context).nodeType ?\n\t\t\t\t\tmatchContext( elem, context, xml ) :\n\t\t\t\t\tmatchAnyContext( elem, context, xml ) );\n\t\t\t// Avoid hanging onto element (issue #299)\n\t\t\tcheckContext = null;\n\t\t\treturn ret;\n\t\t} ];\n\n\tfor ( ; i < len; i++ ) {\n\t\tif ( (matcher = Expr.relative[ tokens[i].type ]) ) {\n\t\t\tmatchers = [ addCombinator(elementMatcher( matchers ), matcher) ];\n\t\t} else {\n\t\t\tmatcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );\n\n\t\t\t// Return special upon seeing a positional matcher\n\t\t\tif ( matcher[ expando ] ) {\n\t\t\t\t// Find the next relative operator (if any) for proper handling\n\t\t\t\tj = ++i;\n\t\t\t\tfor ( ; j < len; j++ ) {\n\t\t\t\t\tif ( Expr.relative[ tokens[j].type ] ) {\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn setMatcher(\n\t\t\t\t\ti > 1 && elementMatcher( matchers ),\n\t\t\t\t\ti > 1 && toSelector(\n\t\t\t\t\t\t// If the preceding token was a descendant combinator, insert an implicit any-element `*`\n\t\t\t\t\t\ttokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === \" \" ? \"*\" : \"\" })\n\t\t\t\t\t).replace( rtrim, \"$1\" ),\n\t\t\t\t\tmatcher,\n\t\t\t\t\ti < j && matcherFromTokens( tokens.slice( i, j ) ),\n\t\t\t\t\tj < len && matcherFromTokens( (tokens = tokens.slice( j )) ),\n\t\t\t\t\tj < len && toSelector( tokens )\n\t\t\t\t);\n\t\t\t}\n\t\t\tmatchers.push( matcher );\n\t\t}\n\t}\n\n\treturn elementMatcher( matchers );\n}\n\nfunction matcherFromGroupMatchers( elementMatchers, setMatchers ) {\n\tvar bySet = setMatchers.length > 0,\n\t\tbyElement = elementMatchers.length > 0,\n\t\tsuperMatcher = function( seed, context, xml, results, outermost ) {\n\t\t\tvar elem, j, matcher,\n\t\t\t\tmatchedCount = 0,\n\t\t\t\ti = \"0\",\n\t\t\t\tunmatched = seed && [],\n\t\t\t\tsetMatched = [],\n\t\t\t\tcontextBackup = outermostContext,\n\t\t\t\t// We must always have either seed elements or outermost context\n\t\t\t\telems = seed || byElement && Expr.find[\"TAG\"]( \"*\", outermost ),\n\t\t\t\t// Use integer dirruns iff this is the outermost matcher\n\t\t\t\tdirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),\n\t\t\t\tlen = elems.length;\n\n\t\t\tif ( outermost ) {\n\t\t\t\toutermostContext = context === document || context || outermost;\n\t\t\t}\n\n\t\t\t// Add elements passing elementMatchers directly to results\n\t\t\t// Support: IE<9, Safari\n\t\t\t// Tolerate NodeList properties (IE: \"length\"; Safari: ) matching elements by id\n\t\t\tfor ( ; i !== len && (elem = elems[i]) != null; i++ ) {\n\t\t\t\tif ( byElement && elem ) {\n\t\t\t\t\tj = 0;\n\t\t\t\t\tif ( !context && elem.ownerDocument !== document ) {\n\t\t\t\t\t\tsetDocument( elem );\n\t\t\t\t\t\txml = !documentIsHTML;\n\t\t\t\t\t}\n\t\t\t\t\twhile ( (matcher = elementMatchers[j++]) ) {\n\t\t\t\t\t\tif ( matcher( elem, context || document, xml) ) {\n\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif ( outermost ) {\n\t\t\t\t\t\tdirruns = dirrunsUnique;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Track unmatched elements for set filters\n\t\t\t\tif ( bySet ) {\n\t\t\t\t\t// They will have gone through all possible matchers\n\t\t\t\t\tif ( (elem = !matcher && elem) ) {\n\t\t\t\t\t\tmatchedCount--;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Lengthen the array for every element, matched or not\n\t\t\t\t\tif ( seed ) {\n\t\t\t\t\t\tunmatched.push( elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// `i` is now the count of elements visited above, and adding it to `matchedCount`\n\t\t\t// makes the latter nonnegative.\n\t\t\tmatchedCount += i;\n\n\t\t\t// Apply set filters to unmatched elements\n\t\t\t// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`\n\t\t\t// equals `i`), unless we didn't visit _any_ elements in the above loop because we have\n\t\t\t// no element matchers and no seed.\n\t\t\t// Incrementing an initially-string \"0\" `i` allows `i` to remain a string only in that\n\t\t\t// case, which will result in a \"00\" `matchedCount` that differs from `i` but is also\n\t\t\t// numerically zero.\n\t\t\tif ( bySet && i !== matchedCount ) {\n\t\t\t\tj = 0;\n\t\t\t\twhile ( (matcher = setMatchers[j++]) ) {\n\t\t\t\t\tmatcher( unmatched, setMatched, context, xml );\n\t\t\t\t}\n\n\t\t\t\tif ( seed ) {\n\t\t\t\t\t// Reintegrate element matches to eliminate the need for sorting\n\t\t\t\t\tif ( matchedCount > 0 ) {\n\t\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\t\tif ( !(unmatched[i] || setMatched[i]) ) {\n\t\t\t\t\t\t\t\tsetMatched[i] = pop.call( results );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Discard index placeholder values to get only actual matches\n\t\t\t\t\tsetMatched = condense( setMatched );\n\t\t\t\t}\n\n\t\t\t\t// Add matches to results\n\t\t\t\tpush.apply( results, setMatched );\n\n\t\t\t\t// Seedless set matches succeeding multiple successful matchers stipulate sorting\n\t\t\t\tif ( outermost && !seed && setMatched.length > 0 &&\n\t\t\t\t\t( matchedCount + setMatchers.length ) > 1 ) {\n\n\t\t\t\t\tSizzle.uniqueSort( results );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Override manipulation of globals by nested matchers\n\t\t\tif ( outermost ) {\n\t\t\t\tdirruns = dirrunsUnique;\n\t\t\t\toutermostContext = contextBackup;\n\t\t\t}\n\n\t\t\treturn unmatched;\n\t\t};\n\n\treturn bySet ?\n\t\tmarkFunction( superMatcher ) :\n\t\tsuperMatcher;\n}\n\ncompile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {\n\tvar i,\n\t\tsetMatchers = [],\n\t\telementMatchers = [],\n\t\tcached = compilerCache[ selector + \" \" ];\n\n\tif ( !cached ) {\n\t\t// Generate a function of recursive functions that can be used to check each element\n\t\tif ( !match ) {\n\t\t\tmatch = tokenize( selector );\n\t\t}\n\t\ti = match.length;\n\t\twhile ( i-- ) {\n\t\t\tcached = matcherFromTokens( match[i] );\n\t\t\tif ( cached[ expando ] ) {\n\t\t\t\tsetMatchers.push( cached );\n\t\t\t} else {\n\t\t\t\telementMatchers.push( cached );\n\t\t\t}\n\t\t}\n\n\t\t// Cache the compiled function\n\t\tcached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );\n\n\t\t// Save selector and tokenization\n\t\tcached.selector = selector;\n\t}\n\treturn cached;\n};\n\n/**\n * A low-level selection function that works with Sizzle's compiled\n * selector functions\n * @param {String|Function} selector A selector or a pre-compiled\n * selector function built with Sizzle.compile\n * @param {Element} context\n * @param {Array} [results]\n * @param {Array} [seed] A set of elements to match against\n */\nselect = Sizzle.select = function( selector, context, results, seed ) {\n\tvar i, tokens, token, type, find,\n\t\tcompiled = typeof selector === \"function\" && selector,\n\t\tmatch = !seed && tokenize( (selector = compiled.selector || selector) );\n\n\tresults = results || [];\n\n\t// Try to minimize operations if there is only one selector in the list and no seed\n\t// (the latter of which guarantees us context)\n\tif ( match.length === 1 ) {\n\n\t\t// Reduce context if the leading compound selector is an ID\n\t\ttokens = match[0] = match[0].slice( 0 );\n\t\tif ( tokens.length > 2 && (token = tokens[0]).type === \"ID\" &&\n\t\t\t\tcontext.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) {\n\n\t\t\tcontext = ( Expr.find[\"ID\"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];\n\t\t\tif ( !context ) {\n\t\t\t\treturn results;\n\n\t\t\t// Precompiled matchers will still verify ancestry, so step up a level\n\t\t\t} else if ( compiled ) {\n\t\t\t\tcontext = context.parentNode;\n\t\t\t}\n\n\t\t\tselector = selector.slice( tokens.shift().value.length );\n\t\t}\n\n\t\t// Fetch a seed set for right-to-left matching\n\t\ti = matchExpr[\"needsContext\"].test( selector ) ? 0 : tokens.length;\n\t\twhile ( i-- ) {\n\t\t\ttoken = tokens[i];\n\n\t\t\t// Abort if we hit a combinator\n\t\t\tif ( Expr.relative[ (type = token.type) ] ) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif ( (find = Expr.find[ type ]) ) {\n\t\t\t\t// Search, expanding context for leading sibling combinators\n\t\t\t\tif ( (seed = find(\n\t\t\t\t\ttoken.matches[0].replace( runescape, funescape ),\n\t\t\t\t\trsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context\n\t\t\t\t)) ) {\n\n\t\t\t\t\t// If seed is empty or no tokens remain, we can return early\n\t\t\t\t\ttokens.splice( i, 1 );\n\t\t\t\t\tselector = seed.length && toSelector( tokens );\n\t\t\t\t\tif ( !selector ) {\n\t\t\t\t\t\tpush.apply( results, seed );\n\t\t\t\t\t\treturn results;\n\t\t\t\t\t}\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Compile and execute a filtering function if one is not provided\n\t// Provide `match` to avoid retokenization if we modified the selector above\n\t( compiled || compile( selector, match ) )(\n\t\tseed,\n\t\tcontext,\n\t\t!documentIsHTML,\n\t\tresults,\n\t\t!context || rsibling.test( selector ) && testContext( context.parentNode ) || context\n\t);\n\treturn results;\n};\n\n// One-time assignments\n\n// Sort stability\nsupport.sortStable = expando.split(\"\").sort( sortOrder ).join(\"\") === expando;\n\n// Support: Chrome 14-35+\n// Always assume duplicates if they aren't passed to the comparison function\nsupport.detectDuplicates = !!hasDuplicate;\n\n// Initialize against the default document\nsetDocument();\n\n// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)\n// Detached nodes confoundingly follow *each other*\nsupport.sortDetached = assert(function( el ) {\n\t// Should return 1, but returns 4 (following)\n\treturn el.compareDocumentPosition( document.createElement(\"fieldset\") ) & 1;\n});\n\n// Support: IE<8\n// Prevent attribute/property \"interpolation\"\n// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx\nif ( !assert(function( el ) {\n\tel.innerHTML = \"\";\n\treturn el.firstChild.getAttribute(\"href\") === \"#\" ;\n}) ) {\n\taddHandle( \"type|href|height|width\", function( elem, name, isXML ) {\n\t\tif ( !isXML ) {\n\t\t\treturn elem.getAttribute( name, name.toLowerCase() === \"type\" ? 1 : 2 );\n\t\t}\n\t});\n}\n\n// Support: IE<9\n// Use defaultValue in place of getAttribute(\"value\")\nif ( !support.attributes || !assert(function( el ) {\n\tel.innerHTML = \"\";\n\tel.firstChild.setAttribute( \"value\", \"\" );\n\treturn el.firstChild.getAttribute( \"value\" ) === \"\";\n}) ) {\n\taddHandle( \"value\", function( elem, name, isXML ) {\n\t\tif ( !isXML && elem.nodeName.toLowerCase() === \"input\" ) {\n\t\t\treturn elem.defaultValue;\n\t\t}\n\t});\n}\n\n// Support: IE<9\n// Use getAttributeNode to fetch booleans when getAttribute lies\nif ( !assert(function( el ) {\n\treturn el.getAttribute(\"disabled\") == null;\n}) ) {\n\taddHandle( booleans, function( elem, name, isXML ) {\n\t\tvar val;\n\t\tif ( !isXML ) {\n\t\t\treturn elem[ name ] === true ? name.toLowerCase() :\n\t\t\t\t\t(val = elem.getAttributeNode( name )) && val.specified ?\n\t\t\t\t\tval.value :\n\t\t\t\tnull;\n\t\t}\n\t});\n}\n\nreturn Sizzle;\n\n})( window );\n\n\n\njQuery.find = Sizzle;\njQuery.expr = Sizzle.selectors;\n\n// Deprecated\njQuery.expr[ \":\" ] = jQuery.expr.pseudos;\njQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;\njQuery.text = Sizzle.getText;\njQuery.isXMLDoc = Sizzle.isXML;\njQuery.contains = Sizzle.contains;\njQuery.escapeSelector = Sizzle.escape;\n\n\n\n\nvar dir = function( elem, dir, until ) {\n\tvar matched = [],\n\t\ttruncate = until !== undefined;\n\n\twhile ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {\n\t\tif ( elem.nodeType === 1 ) {\n\t\t\tif ( truncate && jQuery( elem ).is( until ) ) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tmatched.push( elem );\n\t\t}\n\t}\n\treturn matched;\n};\n\n\nvar siblings = function( n, elem ) {\n\tvar matched = [];\n\n\tfor ( ; n; n = n.nextSibling ) {\n\t\tif ( n.nodeType === 1 && n !== elem ) {\n\t\t\tmatched.push( n );\n\t\t}\n\t}\n\n\treturn matched;\n};\n\n\nvar rneedsContext = jQuery.expr.match.needsContext;\n\n\n\nfunction nodeName( elem, name ) {\n\n return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();\n\n};\nvar rsingleTag = ( /^<([a-z][^\\/\\0>:\\x20\\t\\r\\n\\f]*)[\\x20\\t\\r\\n\\f]*\\/?>(?:<\\/\\1>|)$/i );\n\n\n\nvar risSimple = /^.[^:#\\[\\.,]*$/;\n\n// Implement the identical functionality for filter and not\nfunction winnow( elements, qualifier, not ) {\n\tif ( jQuery.isFunction( qualifier ) ) {\n\t\treturn jQuery.grep( elements, function( elem, i ) {\n\t\t\treturn !!qualifier.call( elem, i, elem ) !== not;\n\t\t} );\n\t}\n\n\t// Single element\n\tif ( qualifier.nodeType ) {\n\t\treturn jQuery.grep( elements, function( elem ) {\n\t\t\treturn ( elem === qualifier ) !== not;\n\t\t} );\n\t}\n\n\t// Arraylike of elements (jQuery, arguments, Array)\n\tif ( typeof qualifier !== \"string\" ) {\n\t\treturn jQuery.grep( elements, function( elem ) {\n\t\t\treturn ( indexOf.call( qualifier, elem ) > -1 ) !== not;\n\t\t} );\n\t}\n\n\t// Simple selector that can be filtered directly, removing non-Elements\n\tif ( risSimple.test( qualifier ) ) {\n\t\treturn jQuery.filter( qualifier, elements, not );\n\t}\n\n\t// Complex selector, compare the two sets, removing non-Elements\n\tqualifier = jQuery.filter( qualifier, elements );\n\treturn jQuery.grep( elements, function( elem ) {\n\t\treturn ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1;\n\t} );\n}\n\njQuery.filter = function( expr, elems, not ) {\n\tvar elem = elems[ 0 ];\n\n\tif ( not ) {\n\t\texpr = \":not(\" + expr + \")\";\n\t}\n\n\tif ( elems.length === 1 && elem.nodeType === 1 ) {\n\t\treturn jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];\n\t}\n\n\treturn jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {\n\t\treturn elem.nodeType === 1;\n\t} ) );\n};\n\njQuery.fn.extend( {\n\tfind: function( selector ) {\n\t\tvar i, ret,\n\t\t\tlen = this.length,\n\t\t\tself = this;\n\n\t\tif ( typeof selector !== \"string\" ) {\n\t\t\treturn this.pushStack( jQuery( selector ).filter( function() {\n\t\t\t\tfor ( i = 0; i < len; i++ ) {\n\t\t\t\t\tif ( jQuery.contains( self[ i ], this ) ) {\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} ) );\n\t\t}\n\n\t\tret = this.pushStack( [] );\n\n\t\tfor ( i = 0; i < len; i++ ) {\n\t\t\tjQuery.find( selector, self[ i ], ret );\n\t\t}\n\n\t\treturn len > 1 ? jQuery.uniqueSort( ret ) : ret;\n\t},\n\tfilter: function( selector ) {\n\t\treturn this.pushStack( winnow( this, selector || [], false ) );\n\t},\n\tnot: function( selector ) {\n\t\treturn this.pushStack( winnow( this, selector || [], true ) );\n\t},\n\tis: function( selector ) {\n\t\treturn !!winnow(\n\t\t\tthis,\n\n\t\t\t// If this is a positional/relative selector, check membership in the returned set\n\t\t\t// so $(\"p:first\").is(\"p:last\") won't return true for a doc with two \"p\".\n\t\t\ttypeof selector === \"string\" && rneedsContext.test( selector ) ?\n\t\t\t\tjQuery( selector ) :\n\t\t\t\tselector || [],\n\t\t\tfalse\n\t\t).length;\n\t}\n} );\n\n\n// Initialize a jQuery object\n\n\n// A central reference to the root jQuery(document)\nvar rootjQuery,\n\n\t// A simple way to check for HTML strings\n\t// Prioritize #id over to avoid XSS via location.hash (#9521)\n\t// Strict HTML recognition (#11290: must start with <)\n\t// Shortcut simple #id case for speed\n\trquickExpr = /^(?:\\s*(<[\\w\\W]+>)[^>]*|#([\\w-]+))$/,\n\n\tinit = jQuery.fn.init = function( selector, context, root ) {\n\t\tvar match, elem;\n\n\t\t// HANDLE: $(\"\"), $(null), $(undefined), $(false)\n\t\tif ( !selector ) {\n\t\t\treturn this;\n\t\t}\n\n\t\t// Method init() accepts an alternate rootjQuery\n\t\t// so migrate can support jQuery.sub (gh-2101)\n\t\troot = root || rootjQuery;\n\n\t\t// Handle HTML strings\n\t\tif ( typeof selector === \"string\" ) {\n\t\t\tif ( selector[ 0 ] === \"<\" &&\n\t\t\t\tselector[ selector.length - 1 ] === \">\" &&\n\t\t\t\tselector.length >= 3 ) {\n\n\t\t\t\t// Assume that strings that start and end with <> are HTML and skip the regex check\n\t\t\t\tmatch = [ null, selector, null ];\n\n\t\t\t} else {\n\t\t\t\tmatch = rquickExpr.exec( selector );\n\t\t\t}\n\n\t\t\t// Match html or make sure no context is specified for #id\n\t\t\tif ( match && ( match[ 1 ] || !context ) ) {\n\n\t\t\t\t// HANDLE: $(html) -> $(array)\n\t\t\t\tif ( match[ 1 ] ) {\n\t\t\t\t\tcontext = context instanceof jQuery ? context[ 0 ] : context;\n\n\t\t\t\t\t// Option to run scripts is true for back-compat\n\t\t\t\t\t// Intentionally let the error be thrown if parseHTML is not present\n\t\t\t\t\tjQuery.merge( this, jQuery.parseHTML(\n\t\t\t\t\t\tmatch[ 1 ],\n\t\t\t\t\t\tcontext && context.nodeType ? context.ownerDocument || context : document,\n\t\t\t\t\t\ttrue\n\t\t\t\t\t) );\n\n\t\t\t\t\t// HANDLE: $(html, props)\n\t\t\t\t\tif ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {\n\t\t\t\t\t\tfor ( match in context ) {\n\n\t\t\t\t\t\t\t// Properties of context are called as methods if possible\n\t\t\t\t\t\t\tif ( jQuery.isFunction( this[ match ] ) ) {\n\t\t\t\t\t\t\t\tthis[ match ]( context[ match ] );\n\n\t\t\t\t\t\t\t// ...and otherwise set as attributes\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tthis.attr( match, context[ match ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\treturn this;\n\n\t\t\t\t// HANDLE: $(#id)\n\t\t\t\t} else {\n\t\t\t\t\telem = document.getElementById( match[ 2 ] );\n\n\t\t\t\t\tif ( elem ) {\n\n\t\t\t\t\t\t// Inject the element directly into the jQuery object\n\t\t\t\t\t\tthis[ 0 ] = elem;\n\t\t\t\t\t\tthis.length = 1;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\n\t\t\t// HANDLE: $(expr, $(...))\n\t\t\t} else if ( !context || context.jquery ) {\n\t\t\t\treturn ( context || root ).find( selector );\n\n\t\t\t// HANDLE: $(expr, context)\n\t\t\t// (which is just equivalent to: $(context).find(expr)\n\t\t\t} else {\n\t\t\t\treturn this.constructor( context ).find( selector );\n\t\t\t}\n\n\t\t// HANDLE: $(DOMElement)\n\t\t} else if ( selector.nodeType ) {\n\t\t\tthis[ 0 ] = selector;\n\t\t\tthis.length = 1;\n\t\t\treturn this;\n\n\t\t// HANDLE: $(function)\n\t\t// Shortcut for document ready\n\t\t} else if ( jQuery.isFunction( selector ) ) {\n\t\t\treturn root.ready !== undefined ?\n\t\t\t\troot.ready( selector ) :\n\n\t\t\t\t// Execute immediately if ready is not present\n\t\t\t\tselector( jQuery );\n\t\t}\n\n\t\treturn jQuery.makeArray( selector, this );\n\t};\n\n// Give the init function the jQuery prototype for later instantiation\ninit.prototype = jQuery.fn;\n\n// Initialize central reference\nrootjQuery = jQuery( document );\n\n\nvar rparentsprev = /^(?:parents|prev(?:Until|All))/,\n\n\t// Methods guaranteed to produce a unique set when starting from a unique set\n\tguaranteedUnique = {\n\t\tchildren: true,\n\t\tcontents: true,\n\t\tnext: true,\n\t\tprev: true\n\t};\n\njQuery.fn.extend( {\n\thas: function( target ) {\n\t\tvar targets = jQuery( target, this ),\n\t\t\tl = targets.length;\n\n\t\treturn this.filter( function() {\n\t\t\tvar i = 0;\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tif ( jQuery.contains( this, targets[ i ] ) ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t},\n\n\tclosest: function( selectors, context ) {\n\t\tvar cur,\n\t\t\ti = 0,\n\t\t\tl = this.length,\n\t\t\tmatched = [],\n\t\t\ttargets = typeof selectors !== \"string\" && jQuery( selectors );\n\n\t\t// Positional selectors never match, since there's no _selection_ context\n\t\tif ( !rneedsContext.test( selectors ) ) {\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tfor ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {\n\n\t\t\t\t\t// Always skip document fragments\n\t\t\t\t\tif ( cur.nodeType < 11 && ( targets ?\n\t\t\t\t\t\ttargets.index( cur ) > -1 :\n\n\t\t\t\t\t\t// Don't pass non-elements to Sizzle\n\t\t\t\t\t\tcur.nodeType === 1 &&\n\t\t\t\t\t\t\tjQuery.find.matchesSelector( cur, selectors ) ) ) {\n\n\t\t\t\t\t\tmatched.push( cur );\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );\n\t},\n\n\t// Determine the position of an element within the set\n\tindex: function( elem ) {\n\n\t\t// No argument, return index in parent\n\t\tif ( !elem ) {\n\t\t\treturn ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;\n\t\t}\n\n\t\t// Index in selector\n\t\tif ( typeof elem === \"string\" ) {\n\t\t\treturn indexOf.call( jQuery( elem ), this[ 0 ] );\n\t\t}\n\n\t\t// Locate the position of the desired element\n\t\treturn indexOf.call( this,\n\n\t\t\t// If it receives a jQuery object, the first element is used\n\t\t\telem.jquery ? elem[ 0 ] : elem\n\t\t);\n\t},\n\n\tadd: function( selector, context ) {\n\t\treturn this.pushStack(\n\t\t\tjQuery.uniqueSort(\n\t\t\t\tjQuery.merge( this.get(), jQuery( selector, context ) )\n\t\t\t)\n\t\t);\n\t},\n\n\taddBack: function( selector ) {\n\t\treturn this.add( selector == null ?\n\t\t\tthis.prevObject : this.prevObject.filter( selector )\n\t\t);\n\t}\n} );\n\nfunction sibling( cur, dir ) {\n\twhile ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}\n\treturn cur;\n}\n\njQuery.each( {\n\tparent: function( elem ) {\n\t\tvar parent = elem.parentNode;\n\t\treturn parent && parent.nodeType !== 11 ? parent : null;\n\t},\n\tparents: function( elem ) {\n\t\treturn dir( elem, \"parentNode\" );\n\t},\n\tparentsUntil: function( elem, i, until ) {\n\t\treturn dir( elem, \"parentNode\", until );\n\t},\n\tnext: function( elem ) {\n\t\treturn sibling( elem, \"nextSibling\" );\n\t},\n\tprev: function( elem ) {\n\t\treturn sibling( elem, \"previousSibling\" );\n\t},\n\tnextAll: function( elem ) {\n\t\treturn dir( elem, \"nextSibling\" );\n\t},\n\tprevAll: function( elem ) {\n\t\treturn dir( elem, \"previousSibling\" );\n\t},\n\tnextUntil: function( elem, i, until ) {\n\t\treturn dir( elem, \"nextSibling\", until );\n\t},\n\tprevUntil: function( elem, i, until ) {\n\t\treturn dir( elem, \"previousSibling\", until );\n\t},\n\tsiblings: function( elem ) {\n\t\treturn siblings( ( elem.parentNode || {} ).firstChild, elem );\n\t},\n\tchildren: function( elem ) {\n\t\treturn siblings( elem.firstChild );\n\t},\n\tcontents: function( elem ) {\n if ( nodeName( elem, \"iframe\" ) ) {\n return elem.contentDocument;\n }\n\n // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only\n // Treat the template element as a regular one in browsers that\n // don't support it.\n if ( nodeName( elem, \"template\" ) ) {\n elem = elem.content || elem;\n }\n\n return jQuery.merge( [], elem.childNodes );\n\t}\n}, function( name, fn ) {\n\tjQuery.fn[ name ] = function( until, selector ) {\n\t\tvar matched = jQuery.map( this, fn, until );\n\n\t\tif ( name.slice( -5 ) !== \"Until\" ) {\n\t\t\tselector = until;\n\t\t}\n\n\t\tif ( selector && typeof selector === \"string\" ) {\n\t\t\tmatched = jQuery.filter( selector, matched );\n\t\t}\n\n\t\tif ( this.length > 1 ) {\n\n\t\t\t// Remove duplicates\n\t\t\tif ( !guaranteedUnique[ name ] ) {\n\t\t\t\tjQuery.uniqueSort( matched );\n\t\t\t}\n\n\t\t\t// Reverse order for parents* and prev-derivatives\n\t\t\tif ( rparentsprev.test( name ) ) {\n\t\t\t\tmatched.reverse();\n\t\t\t}\n\t\t}\n\n\t\treturn this.pushStack( matched );\n\t};\n} );\nvar rnothtmlwhite = ( /[^\\x20\\t\\r\\n\\f]+/g );\n\n\n\n// Convert String-formatted options into Object-formatted ones\nfunction createOptions( options ) {\n\tvar object = {};\n\tjQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {\n\t\tobject[ flag ] = true;\n\t} );\n\treturn object;\n}\n\n/*\n * Create a callback list using the following parameters:\n *\n *\toptions: an optional list of space-separated options that will change how\n *\t\t\tthe callback list behaves or a more traditional option object\n *\n * By default a callback list will act like an event callback list and can be\n * \"fired\" multiple times.\n *\n * Possible options:\n *\n *\tonce:\t\t\twill ensure the callback list can only be fired once (like a Deferred)\n *\n *\tmemory:\t\t\twill keep track of previous values and will call any callback added\n *\t\t\t\t\tafter the list has been fired right away with the latest \"memorized\"\n *\t\t\t\t\tvalues (like a Deferred)\n *\n *\tunique:\t\t\twill ensure a callback can only be added once (no duplicate in the list)\n *\n *\tstopOnFalse:\tinterrupt callings when a callback returns false\n *\n */\njQuery.Callbacks = function( options ) {\n\n\t// Convert options from String-formatted to Object-formatted if needed\n\t// (we check in cache first)\n\toptions = typeof options === \"string\" ?\n\t\tcreateOptions( options ) :\n\t\tjQuery.extend( {}, options );\n\n\tvar // Flag to know if list is currently firing\n\t\tfiring,\n\n\t\t// Last fire value for non-forgettable lists\n\t\tmemory,\n\n\t\t// Flag to know if list was already fired\n\t\tfired,\n\n\t\t// Flag to prevent firing\n\t\tlocked,\n\n\t\t// Actual callback list\n\t\tlist = [],\n\n\t\t// Queue of execution data for repeatable lists\n\t\tqueue = [],\n\n\t\t// Index of currently firing callback (modified by add/remove as needed)\n\t\tfiringIndex = -1,\n\n\t\t// Fire callbacks\n\t\tfire = function() {\n\n\t\t\t// Enforce single-firing\n\t\t\tlocked = locked || options.once;\n\n\t\t\t// Execute callbacks for all pending executions,\n\t\t\t// respecting firingIndex overrides and runtime changes\n\t\t\tfired = firing = true;\n\t\t\tfor ( ; queue.length; firingIndex = -1 ) {\n\t\t\t\tmemory = queue.shift();\n\t\t\t\twhile ( ++firingIndex < list.length ) {\n\n\t\t\t\t\t// Run callback and check for early termination\n\t\t\t\t\tif ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&\n\t\t\t\t\t\toptions.stopOnFalse ) {\n\n\t\t\t\t\t\t// Jump to end and forget the data so .add doesn't re-fire\n\t\t\t\t\t\tfiringIndex = list.length;\n\t\t\t\t\t\tmemory = false;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Forget the data if we're done with it\n\t\t\tif ( !options.memory ) {\n\t\t\t\tmemory = false;\n\t\t\t}\n\n\t\t\tfiring = false;\n\n\t\t\t// Clean up if we're done firing for good\n\t\t\tif ( locked ) {\n\n\t\t\t\t// Keep an empty list if we have data for future add calls\n\t\t\t\tif ( memory ) {\n\t\t\t\t\tlist = [];\n\n\t\t\t\t// Otherwise, this object is spent\n\t\t\t\t} else {\n\t\t\t\t\tlist = \"\";\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t// Actual Callbacks object\n\t\tself = {\n\n\t\t\t// Add a callback or a collection of callbacks to the list\n\t\t\tadd: function() {\n\t\t\t\tif ( list ) {\n\n\t\t\t\t\t// If we have memory from a past run, we should fire after adding\n\t\t\t\t\tif ( memory && !firing ) {\n\t\t\t\t\t\tfiringIndex = list.length - 1;\n\t\t\t\t\t\tqueue.push( memory );\n\t\t\t\t\t}\n\n\t\t\t\t\t( function add( args ) {\n\t\t\t\t\t\tjQuery.each( args, function( _, arg ) {\n\t\t\t\t\t\t\tif ( jQuery.isFunction( arg ) ) {\n\t\t\t\t\t\t\t\tif ( !options.unique || !self.has( arg ) ) {\n\t\t\t\t\t\t\t\t\tlist.push( arg );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else if ( arg && arg.length && jQuery.type( arg ) !== \"string\" ) {\n\n\t\t\t\t\t\t\t\t// Inspect recursively\n\t\t\t\t\t\t\t\tadd( arg );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} );\n\t\t\t\t\t} )( arguments );\n\n\t\t\t\t\tif ( memory && !firing ) {\n\t\t\t\t\t\tfire();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Remove a callback from the list\n\t\t\tremove: function() {\n\t\t\t\tjQuery.each( arguments, function( _, arg ) {\n\t\t\t\t\tvar index;\n\t\t\t\t\twhile ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {\n\t\t\t\t\t\tlist.splice( index, 1 );\n\n\t\t\t\t\t\t// Handle firing indexes\n\t\t\t\t\t\tif ( index <= firingIndex ) {\n\t\t\t\t\t\t\tfiringIndex--;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Check if a given callback is in the list.\n\t\t\t// If no argument is given, return whether or not list has callbacks attached.\n\t\t\thas: function( fn ) {\n\t\t\t\treturn fn ?\n\t\t\t\t\tjQuery.inArray( fn, list ) > -1 :\n\t\t\t\t\tlist.length > 0;\n\t\t\t},\n\n\t\t\t// Remove all callbacks from the list\n\t\t\tempty: function() {\n\t\t\t\tif ( list ) {\n\t\t\t\t\tlist = [];\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Disable .fire and .add\n\t\t\t// Abort any current/pending executions\n\t\t\t// Clear all callbacks and values\n\t\t\tdisable: function() {\n\t\t\t\tlocked = queue = [];\n\t\t\t\tlist = memory = \"\";\n\t\t\t\treturn this;\n\t\t\t},\n\t\t\tdisabled: function() {\n\t\t\t\treturn !list;\n\t\t\t},\n\n\t\t\t// Disable .fire\n\t\t\t// Also disable .add unless we have memory (since it would have no effect)\n\t\t\t// Abort any pending executions\n\t\t\tlock: function() {\n\t\t\t\tlocked = queue = [];\n\t\t\t\tif ( !memory && !firing ) {\n\t\t\t\t\tlist = memory = \"\";\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\t\t\tlocked: function() {\n\t\t\t\treturn !!locked;\n\t\t\t},\n\n\t\t\t// Call all callbacks with the given context and arguments\n\t\t\tfireWith: function( context, args ) {\n\t\t\t\tif ( !locked ) {\n\t\t\t\t\targs = args || [];\n\t\t\t\t\targs = [ context, args.slice ? args.slice() : args ];\n\t\t\t\t\tqueue.push( args );\n\t\t\t\t\tif ( !firing ) {\n\t\t\t\t\t\tfire();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Call all the callbacks with the given arguments\n\t\t\tfire: function() {\n\t\t\t\tself.fireWith( this, arguments );\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// To know if the callbacks have already been called at least once\n\t\t\tfired: function() {\n\t\t\t\treturn !!fired;\n\t\t\t}\n\t\t};\n\n\treturn self;\n};\n\n\nfunction Identity( v ) {\n\treturn v;\n}\nfunction Thrower( ex ) {\n\tthrow ex;\n}\n\nfunction adoptValue( value, resolve, reject, noValue ) {\n\tvar method;\n\n\ttry {\n\n\t\t// Check for promise aspect first to privilege synchronous behavior\n\t\tif ( value && jQuery.isFunction( ( method = value.promise ) ) ) {\n\t\t\tmethod.call( value ).done( resolve ).fail( reject );\n\n\t\t// Other thenables\n\t\t} else if ( value && jQuery.isFunction( ( method = value.then ) ) ) {\n\t\t\tmethod.call( value, resolve, reject );\n\n\t\t// Other non-thenables\n\t\t} else {\n\n\t\t\t// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:\n\t\t\t// * false: [ value ].slice( 0 ) => resolve( value )\n\t\t\t// * true: [ value ].slice( 1 ) => resolve()\n\t\t\tresolve.apply( undefined, [ value ].slice( noValue ) );\n\t\t}\n\n\t// For Promises/A+, convert exceptions into rejections\n\t// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in\n\t// Deferred#then to conditionally suppress rejection.\n\t} catch ( value ) {\n\n\t\t// Support: Android 4.0 only\n\t\t// Strict mode functions invoked without .call/.apply get global-object context\n\t\treject.apply( undefined, [ value ] );\n\t}\n}\n\njQuery.extend( {\n\n\tDeferred: function( func ) {\n\t\tvar tuples = [\n\n\t\t\t\t// action, add listener, callbacks,\n\t\t\t\t// ... .then handlers, argument index, [final state]\n\t\t\t\t[ \"notify\", \"progress\", jQuery.Callbacks( \"memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"memory\" ), 2 ],\n\t\t\t\t[ \"resolve\", \"done\", jQuery.Callbacks( \"once memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"once memory\" ), 0, \"resolved\" ],\n\t\t\t\t[ \"reject\", \"fail\", jQuery.Callbacks( \"once memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"once memory\" ), 1, \"rejected\" ]\n\t\t\t],\n\t\t\tstate = \"pending\",\n\t\t\tpromise = {\n\t\t\t\tstate: function() {\n\t\t\t\t\treturn state;\n\t\t\t\t},\n\t\t\t\talways: function() {\n\t\t\t\t\tdeferred.done( arguments ).fail( arguments );\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\t\t\t\t\"catch\": function( fn ) {\n\t\t\t\t\treturn promise.then( null, fn );\n\t\t\t\t},\n\n\t\t\t\t// Keep pipe for back-compat\n\t\t\t\tpipe: function( /* fnDone, fnFail, fnProgress */ ) {\n\t\t\t\t\tvar fns = arguments;\n\n\t\t\t\t\treturn jQuery.Deferred( function( newDefer ) {\n\t\t\t\t\t\tjQuery.each( tuples, function( i, tuple ) {\n\n\t\t\t\t\t\t\t// Map tuples (progress, done, fail) to arguments (done, fail, progress)\n\t\t\t\t\t\t\tvar fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];\n\n\t\t\t\t\t\t\t// deferred.progress(function() { bind to newDefer or newDefer.notify })\n\t\t\t\t\t\t\t// deferred.done(function() { bind to newDefer or newDefer.resolve })\n\t\t\t\t\t\t\t// deferred.fail(function() { bind to newDefer or newDefer.reject })\n\t\t\t\t\t\t\tdeferred[ tuple[ 1 ] ]( function() {\n\t\t\t\t\t\t\t\tvar returned = fn && fn.apply( this, arguments );\n\t\t\t\t\t\t\t\tif ( returned && jQuery.isFunction( returned.promise ) ) {\n\t\t\t\t\t\t\t\t\treturned.promise()\n\t\t\t\t\t\t\t\t\t\t.progress( newDefer.notify )\n\t\t\t\t\t\t\t\t\t\t.done( newDefer.resolve )\n\t\t\t\t\t\t\t\t\t\t.fail( newDefer.reject );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tnewDefer[ tuple[ 0 ] + \"With\" ](\n\t\t\t\t\t\t\t\t\t\tthis,\n\t\t\t\t\t\t\t\t\t\tfn ? [ returned ] : arguments\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t} );\n\t\t\t\t\t\tfns = null;\n\t\t\t\t\t} ).promise();\n\t\t\t\t},\n\t\t\t\tthen: function( onFulfilled, onRejected, onProgress ) {\n\t\t\t\t\tvar maxDepth = 0;\n\t\t\t\t\tfunction resolve( depth, deferred, handler, special ) {\n\t\t\t\t\t\treturn function() {\n\t\t\t\t\t\t\tvar that = this,\n\t\t\t\t\t\t\t\targs = arguments,\n\t\t\t\t\t\t\t\tmightThrow = function() {\n\t\t\t\t\t\t\t\t\tvar returned, then;\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.3\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-59\n\t\t\t\t\t\t\t\t\t// Ignore double-resolution attempts\n\t\t\t\t\t\t\t\t\tif ( depth < maxDepth ) {\n\t\t\t\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\treturned = handler.apply( that, args );\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.1\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-48\n\t\t\t\t\t\t\t\t\tif ( returned === deferred.promise() ) {\n\t\t\t\t\t\t\t\t\t\tthrow new TypeError( \"Thenable self-resolution\" );\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ sections 2.3.3.1, 3.5\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-54\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-75\n\t\t\t\t\t\t\t\t\t// Retrieve `then` only once\n\t\t\t\t\t\t\t\t\tthen = returned &&\n\n\t\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.4\n\t\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-64\n\t\t\t\t\t\t\t\t\t\t// Only check objects and functions for thenability\n\t\t\t\t\t\t\t\t\t\t( typeof returned === \"object\" ||\n\t\t\t\t\t\t\t\t\t\t\ttypeof returned === \"function\" ) &&\n\t\t\t\t\t\t\t\t\t\treturned.then;\n\n\t\t\t\t\t\t\t\t\t// Handle a returned thenable\n\t\t\t\t\t\t\t\t\tif ( jQuery.isFunction( then ) ) {\n\n\t\t\t\t\t\t\t\t\t\t// Special processors (notify) just wait for resolution\n\t\t\t\t\t\t\t\t\t\tif ( special ) {\n\t\t\t\t\t\t\t\t\t\t\tthen.call(\n\t\t\t\t\t\t\t\t\t\t\t\treturned,\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Thrower, special )\n\t\t\t\t\t\t\t\t\t\t\t);\n\n\t\t\t\t\t\t\t\t\t\t// Normal processors (resolve) also hook into progress\n\t\t\t\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t\t\t\t// ...and disregard older resolution values\n\t\t\t\t\t\t\t\t\t\t\tmaxDepth++;\n\n\t\t\t\t\t\t\t\t\t\t\tthen.call(\n\t\t\t\t\t\t\t\t\t\t\t\treturned,\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Thrower, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity,\n\t\t\t\t\t\t\t\t\t\t\t\t\tdeferred.notifyWith )\n\t\t\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// Handle all other returned values\n\t\t\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t\t\t// Only substitute handlers pass on context\n\t\t\t\t\t\t\t\t\t\t// and multiple values (non-spec behavior)\n\t\t\t\t\t\t\t\t\t\tif ( handler !== Identity ) {\n\t\t\t\t\t\t\t\t\t\t\tthat = undefined;\n\t\t\t\t\t\t\t\t\t\t\targs = [ returned ];\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t// Process the value(s)\n\t\t\t\t\t\t\t\t\t\t// Default process is resolve\n\t\t\t\t\t\t\t\t\t\t( special || deferred.resolveWith )( that, args );\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t},\n\n\t\t\t\t\t\t\t\t// Only normal processors (resolve) catch and reject exceptions\n\t\t\t\t\t\t\t\tprocess = special ?\n\t\t\t\t\t\t\t\t\tmightThrow :\n\t\t\t\t\t\t\t\t\tfunction() {\n\t\t\t\t\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\t\t\t\t\tmightThrow();\n\t\t\t\t\t\t\t\t\t\t} catch ( e ) {\n\n\t\t\t\t\t\t\t\t\t\t\tif ( jQuery.Deferred.exceptionHook ) {\n\t\t\t\t\t\t\t\t\t\t\t\tjQuery.Deferred.exceptionHook( e,\n\t\t\t\t\t\t\t\t\t\t\t\t\tprocess.stackTrace );\n\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.4.1\n\t\t\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-61\n\t\t\t\t\t\t\t\t\t\t\t// Ignore post-resolution exceptions\n\t\t\t\t\t\t\t\t\t\t\tif ( depth + 1 >= maxDepth ) {\n\n\t\t\t\t\t\t\t\t\t\t\t\t// Only substitute handlers pass on context\n\t\t\t\t\t\t\t\t\t\t\t\t// and multiple values (non-spec behavior)\n\t\t\t\t\t\t\t\t\t\t\t\tif ( handler !== Thrower ) {\n\t\t\t\t\t\t\t\t\t\t\t\t\tthat = undefined;\n\t\t\t\t\t\t\t\t\t\t\t\t\targs = [ e ];\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t\tdeferred.rejectWith( that, args );\n\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t};\n\n\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.1\n\t\t\t\t\t\t\t// https://promisesaplus.com/#point-57\n\t\t\t\t\t\t\t// Re-resolve promises immediately to dodge false rejection from\n\t\t\t\t\t\t\t// subsequent errors\n\t\t\t\t\t\t\tif ( depth ) {\n\t\t\t\t\t\t\t\tprocess();\n\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t// Call an optional hook to record the stack, in case of exception\n\t\t\t\t\t\t\t\t// since it's otherwise lost when execution goes async\n\t\t\t\t\t\t\t\tif ( jQuery.Deferred.getStackHook ) {\n\t\t\t\t\t\t\t\t\tprocess.stackTrace = jQuery.Deferred.getStackHook();\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\twindow.setTimeout( process );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t};\n\t\t\t\t\t}\n\n\t\t\t\t\treturn jQuery.Deferred( function( newDefer ) {\n\n\t\t\t\t\t\t// progress_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 0 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tjQuery.isFunction( onProgress ) ?\n\t\t\t\t\t\t\t\t\tonProgress :\n\t\t\t\t\t\t\t\t\tIdentity,\n\t\t\t\t\t\t\t\tnewDefer.notifyWith\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\n\t\t\t\t\t\t// fulfilled_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 1 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tjQuery.isFunction( onFulfilled ) ?\n\t\t\t\t\t\t\t\t\tonFulfilled :\n\t\t\t\t\t\t\t\t\tIdentity\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\n\t\t\t\t\t\t// rejected_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 2 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tjQuery.isFunction( onRejected ) ?\n\t\t\t\t\t\t\t\t\tonRejected :\n\t\t\t\t\t\t\t\t\tThrower\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\t\t\t\t\t} ).promise();\n\t\t\t\t},\n\n\t\t\t\t// Get a promise for this deferred\n\t\t\t\t// If obj is provided, the promise aspect is added to the object\n\t\t\t\tpromise: function( obj ) {\n\t\t\t\t\treturn obj != null ? jQuery.extend( obj, promise ) : promise;\n\t\t\t\t}\n\t\t\t},\n\t\t\tdeferred = {};\n\n\t\t// Add list-specific methods\n\t\tjQuery.each( tuples, function( i, tuple ) {\n\t\t\tvar list = tuple[ 2 ],\n\t\t\t\tstateString = tuple[ 5 ];\n\n\t\t\t// promise.progress = list.add\n\t\t\t// promise.done = list.add\n\t\t\t// promise.fail = list.add\n\t\t\tpromise[ tuple[ 1 ] ] = list.add;\n\n\t\t\t// Handle state\n\t\t\tif ( stateString ) {\n\t\t\t\tlist.add(\n\t\t\t\t\tfunction() {\n\n\t\t\t\t\t\t// state = \"resolved\" (i.e., fulfilled)\n\t\t\t\t\t\t// state = \"rejected\"\n\t\t\t\t\t\tstate = stateString;\n\t\t\t\t\t},\n\n\t\t\t\t\t// rejected_callbacks.disable\n\t\t\t\t\t// fulfilled_callbacks.disable\n\t\t\t\t\ttuples[ 3 - i ][ 2 ].disable,\n\n\t\t\t\t\t// progress_callbacks.lock\n\t\t\t\t\ttuples[ 0 ][ 2 ].lock\n\t\t\t\t);\n\t\t\t}\n\n\t\t\t// progress_handlers.fire\n\t\t\t// fulfilled_handlers.fire\n\t\t\t// rejected_handlers.fire\n\t\t\tlist.add( tuple[ 3 ].fire );\n\n\t\t\t// deferred.notify = function() { deferred.notifyWith(...) }\n\t\t\t// deferred.resolve = function() { deferred.resolveWith(...) }\n\t\t\t// deferred.reject = function() { deferred.rejectWith(...) }\n\t\t\tdeferred[ tuple[ 0 ] ] = function() {\n\t\t\t\tdeferred[ tuple[ 0 ] + \"With\" ]( this === deferred ? undefined : this, arguments );\n\t\t\t\treturn this;\n\t\t\t};\n\n\t\t\t// deferred.notifyWith = list.fireWith\n\t\t\t// deferred.resolveWith = list.fireWith\n\t\t\t// deferred.rejectWith = list.fireWith\n\t\t\tdeferred[ tuple[ 0 ] + \"With\" ] = list.fireWith;\n\t\t} );\n\n\t\t// Make the deferred a promise\n\t\tpromise.promise( deferred );\n\n\t\t// Call given func if any\n\t\tif ( func ) {\n\t\t\tfunc.call( deferred, deferred );\n\t\t}\n\n\t\t// All done!\n\t\treturn deferred;\n\t},\n\n\t// Deferred helper\n\twhen: function( singleValue ) {\n\t\tvar\n\n\t\t\t// count of uncompleted subordinates\n\t\t\tremaining = arguments.length,\n\n\t\t\t// count of unprocessed arguments\n\t\t\ti = remaining,\n\n\t\t\t// subordinate fulfillment data\n\t\t\tresolveContexts = Array( i ),\n\t\t\tresolveValues = slice.call( arguments ),\n\n\t\t\t// the master Deferred\n\t\t\tmaster = jQuery.Deferred(),\n\n\t\t\t// subordinate callback factory\n\t\t\tupdateFunc = function( i ) {\n\t\t\t\treturn function( value ) {\n\t\t\t\t\tresolveContexts[ i ] = this;\n\t\t\t\t\tresolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;\n\t\t\t\t\tif ( !( --remaining ) ) {\n\t\t\t\t\t\tmaster.resolveWith( resolveContexts, resolveValues );\n\t\t\t\t\t}\n\t\t\t\t};\n\t\t\t};\n\n\t\t// Single- and empty arguments are adopted like Promise.resolve\n\t\tif ( remaining <= 1 ) {\n\t\t\tadoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,\n\t\t\t\t!remaining );\n\n\t\t\t// Use .then() to unwrap secondary thenables (cf. gh-3000)\n\t\t\tif ( master.state() === \"pending\" ||\n\t\t\t\tjQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {\n\n\t\t\t\treturn master.then();\n\t\t\t}\n\t\t}\n\n\t\t// Multiple arguments are aggregated like Promise.all array elements\n\t\twhile ( i-- ) {\n\t\t\tadoptValue( resolveValues[ i ], updateFunc( i ), master.reject );\n\t\t}\n\n\t\treturn master.promise();\n\t}\n} );\n\n\n// These usually indicate a programmer mistake during development,\n// warn about them ASAP rather than swallowing them by default.\nvar rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;\n\njQuery.Deferred.exceptionHook = function( error, stack ) {\n\n\t// Support: IE 8 - 9 only\n\t// Console exists when dev tools are open, which can happen at any time\n\tif ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {\n\t\twindow.console.warn( \"jQuery.Deferred exception: \" + error.message, error.stack, stack );\n\t}\n};\n\n\n\n\njQuery.readyException = function( error ) {\n\twindow.setTimeout( function() {\n\t\tthrow error;\n\t} );\n};\n\n\n\n\n// The deferred used on DOM ready\nvar readyList = jQuery.Deferred();\n\njQuery.fn.ready = function( fn ) {\n\n\treadyList\n\t\t.then( fn )\n\n\t\t// Wrap jQuery.readyException in a function so that the lookup\n\t\t// happens at the time of error handling instead of callback\n\t\t// registration.\n\t\t.catch( function( error ) {\n\t\t\tjQuery.readyException( error );\n\t\t} );\n\n\treturn this;\n};\n\njQuery.extend( {\n\n\t// Is the DOM ready to be used? Set to true once it occurs.\n\tisReady: false,\n\n\t// A counter to track how many items to wait for before\n\t// the ready event fires. See #6781\n\treadyWait: 1,\n\n\t// Handle when the DOM is ready\n\tready: function( wait ) {\n\n\t\t// Abort if there are pending holds or we're already ready\n\t\tif ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Remember that the DOM is ready\n\t\tjQuery.isReady = true;\n\n\t\t// If a normal DOM Ready event fired, decrement, and wait if need be\n\t\tif ( wait !== true && --jQuery.readyWait > 0 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// If there are functions bound, to execute\n\t\treadyList.resolveWith( document, [ jQuery ] );\n\t}\n} );\n\njQuery.ready.then = readyList.then;\n\n// The ready event handler and self cleanup method\nfunction completed() {\n\tdocument.removeEventListener( \"DOMContentLoaded\", completed );\n\twindow.removeEventListener( \"load\", completed );\n\tjQuery.ready();\n}\n\n// Catch cases where $(document).ready() is called\n// after the browser event has already occurred.\n// Support: IE <=9 - 10 only\n// Older IE sometimes signals \"interactive\" too soon\nif ( document.readyState === \"complete\" ||\n\t( document.readyState !== \"loading\" && !document.documentElement.doScroll ) ) {\n\n\t// Handle it asynchronously to allow scripts the opportunity to delay ready\n\twindow.setTimeout( jQuery.ready );\n\n} else {\n\n\t// Use the handy event callback\n\tdocument.addEventListener( \"DOMContentLoaded\", completed );\n\n\t// A fallback to window.onload, that will always work\n\twindow.addEventListener( \"load\", completed );\n}\n\n\n\n\n// Multifunctional method to get and set values of a collection\n// The value/s can optionally be executed if it's a function\nvar access = function( elems, fn, key, value, chainable, emptyGet, raw ) {\n\tvar i = 0,\n\t\tlen = elems.length,\n\t\tbulk = key == null;\n\n\t// Sets many values\n\tif ( jQuery.type( key ) === \"object\" ) {\n\t\tchainable = true;\n\t\tfor ( i in key ) {\n\t\t\taccess( elems, fn, i, key[ i ], true, emptyGet, raw );\n\t\t}\n\n\t// Sets one value\n\t} else if ( value !== undefined ) {\n\t\tchainable = true;\n\n\t\tif ( !jQuery.isFunction( value ) ) {\n\t\t\traw = true;\n\t\t}\n\n\t\tif ( bulk ) {\n\n\t\t\t// Bulk operations run against the entire set\n\t\t\tif ( raw ) {\n\t\t\t\tfn.call( elems, value );\n\t\t\t\tfn = null;\n\n\t\t\t// ...except when executing function values\n\t\t\t} else {\n\t\t\t\tbulk = fn;\n\t\t\t\tfn = function( elem, key, value ) {\n\t\t\t\t\treturn bulk.call( jQuery( elem ), value );\n\t\t\t\t};\n\t\t\t}\n\t\t}\n\n\t\tif ( fn ) {\n\t\t\tfor ( ; i < len; i++ ) {\n\t\t\t\tfn(\n\t\t\t\t\telems[ i ], key, raw ?\n\t\t\t\t\tvalue :\n\t\t\t\t\tvalue.call( elems[ i ], i, fn( elems[ i ], key ) )\n\t\t\t\t);\n\t\t\t}\n\t\t}\n\t}\n\n\tif ( chainable ) {\n\t\treturn elems;\n\t}\n\n\t// Gets\n\tif ( bulk ) {\n\t\treturn fn.call( elems );\n\t}\n\n\treturn len ? fn( elems[ 0 ], key ) : emptyGet;\n};\nvar acceptData = function( owner ) {\n\n\t// Accepts only:\n\t// - Node\n\t// - Node.ELEMENT_NODE\n\t// - Node.DOCUMENT_NODE\n\t// - Object\n\t// - Any\n\treturn owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );\n};\n\n\n\n\nfunction Data() {\n\tthis.expando = jQuery.expando + Data.uid++;\n}\n\nData.uid = 1;\n\nData.prototype = {\n\n\tcache: function( owner ) {\n\n\t\t// Check if the owner object already has a cache\n\t\tvar value = owner[ this.expando ];\n\n\t\t// If not, create one\n\t\tif ( !value ) {\n\t\t\tvalue = {};\n\n\t\t\t// We can accept data for non-element nodes in modern browsers,\n\t\t\t// but we should not, see #8335.\n\t\t\t// Always return an empty object.\n\t\t\tif ( acceptData( owner ) ) {\n\n\t\t\t\t// If it is a node unlikely to be stringify-ed or looped over\n\t\t\t\t// use plain assignment\n\t\t\t\tif ( owner.nodeType ) {\n\t\t\t\t\towner[ this.expando ] = value;\n\n\t\t\t\t// Otherwise secure it in a non-enumerable property\n\t\t\t\t// configurable must be true to allow the property to be\n\t\t\t\t// deleted when data is removed\n\t\t\t\t} else {\n\t\t\t\t\tObject.defineProperty( owner, this.expando, {\n\t\t\t\t\t\tvalue: value,\n\t\t\t\t\t\tconfigurable: true\n\t\t\t\t\t} );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn value;\n\t},\n\tset: function( owner, data, value ) {\n\t\tvar prop,\n\t\t\tcache = this.cache( owner );\n\n\t\t// Handle: [ owner, key, value ] args\n\t\t// Always use camelCase key (gh-2257)\n\t\tif ( typeof data === \"string\" ) {\n\t\t\tcache[ jQuery.camelCase( data ) ] = value;\n\n\t\t// Handle: [ owner, { properties } ] args\n\t\t} else {\n\n\t\t\t// Copy the properties one-by-one to the cache object\n\t\t\tfor ( prop in data ) {\n\t\t\t\tcache[ jQuery.camelCase( prop ) ] = data[ prop ];\n\t\t\t}\n\t\t}\n\t\treturn cache;\n\t},\n\tget: function( owner, key ) {\n\t\treturn key === undefined ?\n\t\t\tthis.cache( owner ) :\n\n\t\t\t// Always use camelCase key (gh-2257)\n\t\t\towner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ];\n\t},\n\taccess: function( owner, key, value ) {\n\n\t\t// In cases where either:\n\t\t//\n\t\t// 1. No key was specified\n\t\t// 2. A string key was specified, but no value provided\n\t\t//\n\t\t// Take the \"read\" path and allow the get method to determine\n\t\t// which value to return, respectively either:\n\t\t//\n\t\t// 1. The entire cache object\n\t\t// 2. The data stored at the key\n\t\t//\n\t\tif ( key === undefined ||\n\t\t\t\t( ( key && typeof key === \"string\" ) && value === undefined ) ) {\n\n\t\t\treturn this.get( owner, key );\n\t\t}\n\n\t\t// When the key is not a string, or both a key and value\n\t\t// are specified, set or extend (existing objects) with either:\n\t\t//\n\t\t// 1. An object of properties\n\t\t// 2. A key and value\n\t\t//\n\t\tthis.set( owner, key, value );\n\n\t\t// Since the \"set\" path can have two possible entry points\n\t\t// return the expected data based on which path was taken[*]\n\t\treturn value !== undefined ? value : key;\n\t},\n\tremove: function( owner, key ) {\n\t\tvar i,\n\t\t\tcache = owner[ this.expando ];\n\n\t\tif ( cache === undefined ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( key !== undefined ) {\n\n\t\t\t// Support array or space separated string of keys\n\t\t\tif ( Array.isArray( key ) ) {\n\n\t\t\t\t// If key is an array of keys...\n\t\t\t\t// We always set camelCase keys, so remove that.\n\t\t\t\tkey = key.map( jQuery.camelCase );\n\t\t\t} else {\n\t\t\t\tkey = jQuery.camelCase( key );\n\n\t\t\t\t// If a key with the spaces exists, use it.\n\t\t\t\t// Otherwise, create an array by matching non-whitespace\n\t\t\t\tkey = key in cache ?\n\t\t\t\t\t[ key ] :\n\t\t\t\t\t( key.match( rnothtmlwhite ) || [] );\n\t\t\t}\n\n\t\t\ti = key.length;\n\n\t\t\twhile ( i-- ) {\n\t\t\t\tdelete cache[ key[ i ] ];\n\t\t\t}\n\t\t}\n\n\t\t// Remove the expando if there's no more data\n\t\tif ( key === undefined || jQuery.isEmptyObject( cache ) ) {\n\n\t\t\t// Support: Chrome <=35 - 45\n\t\t\t// Webkit & Blink performance suffers when deleting properties\n\t\t\t// from DOM nodes, so set to undefined instead\n\t\t\t// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)\n\t\t\tif ( owner.nodeType ) {\n\t\t\t\towner[ this.expando ] = undefined;\n\t\t\t} else {\n\t\t\t\tdelete owner[ this.expando ];\n\t\t\t}\n\t\t}\n\t},\n\thasData: function( owner ) {\n\t\tvar cache = owner[ this.expando ];\n\t\treturn cache !== undefined && !jQuery.isEmptyObject( cache );\n\t}\n};\nvar dataPriv = new Data();\n\nvar dataUser = new Data();\n\n\n\n//\tImplementation Summary\n//\n//\t1. Enforce API surface and semantic compatibility with 1.9.x branch\n//\t2. Improve the module's maintainability by reducing the storage\n//\t\tpaths to a single mechanism.\n//\t3. Use the same single mechanism to support \"private\" and \"user\" data.\n//\t4. _Never_ expose \"private\" data to user code (TODO: Drop _data, _removeData)\n//\t5. Avoid exposing implementation details on user objects (eg. expando properties)\n//\t6. Provide a clear path for implementation upgrade to WeakMap in 2014\n\nvar rbrace = /^(?:\\{[\\w\\W]*\\}|\\[[\\w\\W]*\\])$/,\n\trmultiDash = /[A-Z]/g;\n\nfunction getData( data ) {\n\tif ( data === \"true\" ) {\n\t\treturn true;\n\t}\n\n\tif ( data === \"false\" ) {\n\t\treturn false;\n\t}\n\n\tif ( data === \"null\" ) {\n\t\treturn null;\n\t}\n\n\t// Only convert to a number if it doesn't change the string\n\tif ( data === +data + \"\" ) {\n\t\treturn +data;\n\t}\n\n\tif ( rbrace.test( data ) ) {\n\t\treturn JSON.parse( data );\n\t}\n\n\treturn data;\n}\n\nfunction dataAttr( elem, key, data ) {\n\tvar name;\n\n\t// If nothing was found internally, try to fetch any\n\t// data from the HTML5 data-* attribute\n\tif ( data === undefined && elem.nodeType === 1 ) {\n\t\tname = \"data-\" + key.replace( rmultiDash, \"-$&\" ).toLowerCase();\n\t\tdata = elem.getAttribute( name );\n\n\t\tif ( typeof data === \"string\" ) {\n\t\t\ttry {\n\t\t\t\tdata = getData( data );\n\t\t\t} catch ( e ) {}\n\n\t\t\t// Make sure we set the data so it isn't changed later\n\t\t\tdataUser.set( elem, key, data );\n\t\t} else {\n\t\t\tdata = undefined;\n\t\t}\n\t}\n\treturn data;\n}\n\njQuery.extend( {\n\thasData: function( elem ) {\n\t\treturn dataUser.hasData( elem ) || dataPriv.hasData( elem );\n\t},\n\n\tdata: function( elem, name, data ) {\n\t\treturn dataUser.access( elem, name, data );\n\t},\n\n\tremoveData: function( elem, name ) {\n\t\tdataUser.remove( elem, name );\n\t},\n\n\t// TODO: Now that all calls to _data and _removeData have been replaced\n\t// with direct calls to dataPriv methods, these can be deprecated.\n\t_data: function( elem, name, data ) {\n\t\treturn dataPriv.access( elem, name, data );\n\t},\n\n\t_removeData: function( elem, name ) {\n\t\tdataPriv.remove( elem, name );\n\t}\n} );\n\njQuery.fn.extend( {\n\tdata: function( key, value ) {\n\t\tvar i, name, data,\n\t\t\telem = this[ 0 ],\n\t\t\tattrs = elem && elem.attributes;\n\n\t\t// Gets all values\n\t\tif ( key === undefined ) {\n\t\t\tif ( this.length ) {\n\t\t\t\tdata = dataUser.get( elem );\n\n\t\t\t\tif ( elem.nodeType === 1 && !dataPriv.get( elem, \"hasDataAttrs\" ) ) {\n\t\t\t\t\ti = attrs.length;\n\t\t\t\t\twhile ( i-- ) {\n\n\t\t\t\t\t\t// Support: IE 11 only\n\t\t\t\t\t\t// The attrs elements can be null (#14894)\n\t\t\t\t\t\tif ( attrs[ i ] ) {\n\t\t\t\t\t\t\tname = attrs[ i ].name;\n\t\t\t\t\t\t\tif ( name.indexOf( \"data-\" ) === 0 ) {\n\t\t\t\t\t\t\t\tname = jQuery.camelCase( name.slice( 5 ) );\n\t\t\t\t\t\t\t\tdataAttr( elem, name, data[ name ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tdataPriv.set( elem, \"hasDataAttrs\", true );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn data;\n\t\t}\n\n\t\t// Sets multiple values\n\t\tif ( typeof key === \"object\" ) {\n\t\t\treturn this.each( function() {\n\t\t\t\tdataUser.set( this, key );\n\t\t\t} );\n\t\t}\n\n\t\treturn access( this, function( value ) {\n\t\t\tvar data;\n\n\t\t\t// The calling jQuery object (element matches) is not empty\n\t\t\t// (and therefore has an element appears at this[ 0 ]) and the\n\t\t\t// `value` parameter was not undefined. An empty jQuery object\n\t\t\t// will result in `undefined` for elem = this[ 0 ] which will\n\t\t\t// throw an exception if an attempt to read a data cache is made.\n\t\t\tif ( elem && value === undefined ) {\n\n\t\t\t\t// Attempt to get data from the cache\n\t\t\t\t// The key will always be camelCased in Data\n\t\t\t\tdata = dataUser.get( elem, key );\n\t\t\t\tif ( data !== undefined ) {\n\t\t\t\t\treturn data;\n\t\t\t\t}\n\n\t\t\t\t// Attempt to \"discover\" the data in\n\t\t\t\t// HTML5 custom data-* attrs\n\t\t\t\tdata = dataAttr( elem, key );\n\t\t\t\tif ( data !== undefined ) {\n\t\t\t\t\treturn data;\n\t\t\t\t}\n\n\t\t\t\t// We tried really hard, but the data doesn't exist.\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Set the data...\n\t\t\tthis.each( function() {\n\n\t\t\t\t// We always store the camelCased key\n\t\t\t\tdataUser.set( this, key, value );\n\t\t\t} );\n\t\t}, null, value, arguments.length > 1, null, true );\n\t},\n\n\tremoveData: function( key ) {\n\t\treturn this.each( function() {\n\t\t\tdataUser.remove( this, key );\n\t\t} );\n\t}\n} );\n\n\njQuery.extend( {\n\tqueue: function( elem, type, data ) {\n\t\tvar queue;\n\n\t\tif ( elem ) {\n\t\t\ttype = ( type || \"fx\" ) + \"queue\";\n\t\t\tqueue = dataPriv.get( elem, type );\n\n\t\t\t// Speed up dequeue by getting out quickly if this is just a lookup\n\t\t\tif ( data ) {\n\t\t\t\tif ( !queue || Array.isArray( data ) ) {\n\t\t\t\t\tqueue = dataPriv.access( elem, type, jQuery.makeArray( data ) );\n\t\t\t\t} else {\n\t\t\t\t\tqueue.push( data );\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn queue || [];\n\t\t}\n\t},\n\n\tdequeue: function( elem, type ) {\n\t\ttype = type || \"fx\";\n\n\t\tvar queue = jQuery.queue( elem, type ),\n\t\t\tstartLength = queue.length,\n\t\t\tfn = queue.shift(),\n\t\t\thooks = jQuery._queueHooks( elem, type ),\n\t\t\tnext = function() {\n\t\t\t\tjQuery.dequeue( elem, type );\n\t\t\t};\n\n\t\t// If the fx queue is dequeued, always remove the progress sentinel\n\t\tif ( fn === \"inprogress\" ) {\n\t\t\tfn = queue.shift();\n\t\t\tstartLength--;\n\t\t}\n\n\t\tif ( fn ) {\n\n\t\t\t// Add a progress sentinel to prevent the fx queue from being\n\t\t\t// automatically dequeued\n\t\t\tif ( type === \"fx\" ) {\n\t\t\t\tqueue.unshift( \"inprogress\" );\n\t\t\t}\n\n\t\t\t// Clear up the last queue stop function\n\t\t\tdelete hooks.stop;\n\t\t\tfn.call( elem, next, hooks );\n\t\t}\n\n\t\tif ( !startLength && hooks ) {\n\t\t\thooks.empty.fire();\n\t\t}\n\t},\n\n\t// Not public - generate a queueHooks object, or return the current one\n\t_queueHooks: function( elem, type ) {\n\t\tvar key = type + \"queueHooks\";\n\t\treturn dataPriv.get( elem, key ) || dataPriv.access( elem, key, {\n\t\t\tempty: jQuery.Callbacks( \"once memory\" ).add( function() {\n\t\t\t\tdataPriv.remove( elem, [ type + \"queue\", key ] );\n\t\t\t} )\n\t\t} );\n\t}\n} );\n\njQuery.fn.extend( {\n\tqueue: function( type, data ) {\n\t\tvar setter = 2;\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tdata = type;\n\t\t\ttype = \"fx\";\n\t\t\tsetter--;\n\t\t}\n\n\t\tif ( arguments.length < setter ) {\n\t\t\treturn jQuery.queue( this[ 0 ], type );\n\t\t}\n\n\t\treturn data === undefined ?\n\t\t\tthis :\n\t\t\tthis.each( function() {\n\t\t\t\tvar queue = jQuery.queue( this, type, data );\n\n\t\t\t\t// Ensure a hooks for this queue\n\t\t\t\tjQuery._queueHooks( this, type );\n\n\t\t\t\tif ( type === \"fx\" && queue[ 0 ] !== \"inprogress\" ) {\n\t\t\t\t\tjQuery.dequeue( this, type );\n\t\t\t\t}\n\t\t\t} );\n\t},\n\tdequeue: function( type ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.dequeue( this, type );\n\t\t} );\n\t},\n\tclearQueue: function( type ) {\n\t\treturn this.queue( type || \"fx\", [] );\n\t},\n\n\t// Get a promise resolved when queues of a certain type\n\t// are emptied (fx is the type by default)\n\tpromise: function( type, obj ) {\n\t\tvar tmp,\n\t\t\tcount = 1,\n\t\t\tdefer = jQuery.Deferred(),\n\t\t\telements = this,\n\t\t\ti = this.length,\n\t\t\tresolve = function() {\n\t\t\t\tif ( !( --count ) ) {\n\t\t\t\t\tdefer.resolveWith( elements, [ elements ] );\n\t\t\t\t}\n\t\t\t};\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tobj = type;\n\t\t\ttype = undefined;\n\t\t}\n\t\ttype = type || \"fx\";\n\n\t\twhile ( i-- ) {\n\t\t\ttmp = dataPriv.get( elements[ i ], type + \"queueHooks\" );\n\t\t\tif ( tmp && tmp.empty ) {\n\t\t\t\tcount++;\n\t\t\t\ttmp.empty.add( resolve );\n\t\t\t}\n\t\t}\n\t\tresolve();\n\t\treturn defer.promise( obj );\n\t}\n} );\nvar pnum = ( /[+-]?(?:\\d*\\.|)\\d+(?:[eE][+-]?\\d+|)/ ).source;\n\nvar rcssNum = new RegExp( \"^(?:([+-])=|)(\" + pnum + \")([a-z%]*)$\", \"i\" );\n\n\nvar cssExpand = [ \"Top\", \"Right\", \"Bottom\", \"Left\" ];\n\nvar isHiddenWithinTree = function( elem, el ) {\n\n\t\t// isHiddenWithinTree might be called from jQuery#filter function;\n\t\t// in that case, element will be second argument\n\t\telem = el || elem;\n\n\t\t// Inline style trumps all\n\t\treturn elem.style.display === \"none\" ||\n\t\t\telem.style.display === \"\" &&\n\n\t\t\t// Otherwise, check computed style\n\t\t\t// Support: Firefox <=43 - 45\n\t\t\t// Disconnected elements can have computed display: none, so first confirm that elem is\n\t\t\t// in the document.\n\t\t\tjQuery.contains( elem.ownerDocument, elem ) &&\n\n\t\t\tjQuery.css( elem, \"display\" ) === \"none\";\n\t};\n\nvar swap = function( elem, options, callback, args ) {\n\tvar ret, name,\n\t\told = {};\n\n\t// Remember the old values, and insert the new ones\n\tfor ( name in options ) {\n\t\told[ name ] = elem.style[ name ];\n\t\telem.style[ name ] = options[ name ];\n\t}\n\n\tret = callback.apply( elem, args || [] );\n\n\t// Revert the old values\n\tfor ( name in options ) {\n\t\telem.style[ name ] = old[ name ];\n\t}\n\n\treturn ret;\n};\n\n\n\n\nfunction adjustCSS( elem, prop, valueParts, tween ) {\n\tvar adjusted,\n\t\tscale = 1,\n\t\tmaxIterations = 20,\n\t\tcurrentValue = tween ?\n\t\t\tfunction() {\n\t\t\t\treturn tween.cur();\n\t\t\t} :\n\t\t\tfunction() {\n\t\t\t\treturn jQuery.css( elem, prop, \"\" );\n\t\t\t},\n\t\tinitial = currentValue(),\n\t\tunit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? \"\" : \"px\" ),\n\n\t\t// Starting value computation is required for potential unit mismatches\n\t\tinitialInUnit = ( jQuery.cssNumber[ prop ] || unit !== \"px\" && +initial ) &&\n\t\t\trcssNum.exec( jQuery.css( elem, prop ) );\n\n\tif ( initialInUnit && initialInUnit[ 3 ] !== unit ) {\n\n\t\t// Trust units reported by jQuery.css\n\t\tunit = unit || initialInUnit[ 3 ];\n\n\t\t// Make sure we update the tween properties later on\n\t\tvalueParts = valueParts || [];\n\n\t\t// Iteratively approximate from a nonzero starting point\n\t\tinitialInUnit = +initial || 1;\n\n\t\tdo {\n\n\t\t\t// If previous iteration zeroed out, double until we get *something*.\n\t\t\t// Use string for doubling so we don't accidentally see scale as unchanged below\n\t\t\tscale = scale || \".5\";\n\n\t\t\t// Adjust and apply\n\t\t\tinitialInUnit = initialInUnit / scale;\n\t\t\tjQuery.style( elem, prop, initialInUnit + unit );\n\n\t\t// Update scale, tolerating zero or NaN from tween.cur()\n\t\t// Break the loop if scale is unchanged or perfect, or if we've just had enough.\n\t\t} while (\n\t\t\tscale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations\n\t\t);\n\t}\n\n\tif ( valueParts ) {\n\t\tinitialInUnit = +initialInUnit || +initial || 0;\n\n\t\t// Apply relative offset (+=/-=) if specified\n\t\tadjusted = valueParts[ 1 ] ?\n\t\t\tinitialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :\n\t\t\t+valueParts[ 2 ];\n\t\tif ( tween ) {\n\t\t\ttween.unit = unit;\n\t\t\ttween.start = initialInUnit;\n\t\t\ttween.end = adjusted;\n\t\t}\n\t}\n\treturn adjusted;\n}\n\n\nvar defaultDisplayMap = {};\n\nfunction getDefaultDisplay( elem ) {\n\tvar temp,\n\t\tdoc = elem.ownerDocument,\n\t\tnodeName = elem.nodeName,\n\t\tdisplay = defaultDisplayMap[ nodeName ];\n\n\tif ( display ) {\n\t\treturn display;\n\t}\n\n\ttemp = doc.body.appendChild( doc.createElement( nodeName ) );\n\tdisplay = jQuery.css( temp, \"display\" );\n\n\ttemp.parentNode.removeChild( temp );\n\n\tif ( display === \"none\" ) {\n\t\tdisplay = \"block\";\n\t}\n\tdefaultDisplayMap[ nodeName ] = display;\n\n\treturn display;\n}\n\nfunction showHide( elements, show ) {\n\tvar display, elem,\n\t\tvalues = [],\n\t\tindex = 0,\n\t\tlength = elements.length;\n\n\t// Determine new display value for elements that need to change\n\tfor ( ; index < length; index++ ) {\n\t\telem = elements[ index ];\n\t\tif ( !elem.style ) {\n\t\t\tcontinue;\n\t\t}\n\n\t\tdisplay = elem.style.display;\n\t\tif ( show ) {\n\n\t\t\t// Since we force visibility upon cascade-hidden elements, an immediate (and slow)\n\t\t\t// check is required in this first loop unless we have a nonempty display value (either\n\t\t\t// inline or about-to-be-restored)\n\t\t\tif ( display === \"none\" ) {\n\t\t\t\tvalues[ index ] = dataPriv.get( elem, \"display\" ) || null;\n\t\t\t\tif ( !values[ index ] ) {\n\t\t\t\t\telem.style.display = \"\";\n\t\t\t\t}\n\t\t\t}\n\t\t\tif ( elem.style.display === \"\" && isHiddenWithinTree( elem ) ) {\n\t\t\t\tvalues[ index ] = getDefaultDisplay( elem );\n\t\t\t}\n\t\t} else {\n\t\t\tif ( display !== \"none\" ) {\n\t\t\t\tvalues[ index ] = \"none\";\n\n\t\t\t\t// Remember what we're overwriting\n\t\t\t\tdataPriv.set( elem, \"display\", display );\n\t\t\t}\n\t\t}\n\t}\n\n\t// Set the display of the elements in a second loop to avoid constant reflow\n\tfor ( index = 0; index < length; index++ ) {\n\t\tif ( values[ index ] != null ) {\n\t\t\telements[ index ].style.display = values[ index ];\n\t\t}\n\t}\n\n\treturn elements;\n}\n\njQuery.fn.extend( {\n\tshow: function() {\n\t\treturn showHide( this, true );\n\t},\n\thide: function() {\n\t\treturn showHide( this );\n\t},\n\ttoggle: function( state ) {\n\t\tif ( typeof state === \"boolean\" ) {\n\t\t\treturn state ? this.show() : this.hide();\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tif ( isHiddenWithinTree( this ) ) {\n\t\t\t\tjQuery( this ).show();\n\t\t\t} else {\n\t\t\t\tjQuery( this ).hide();\n\t\t\t}\n\t\t} );\n\t}\n} );\nvar rcheckableType = ( /^(?:checkbox|radio)$/i );\n\nvar rtagName = ( /<([a-z][^\\/\\0>\\x20\\t\\r\\n\\f]+)/i );\n\nvar rscriptType = ( /^$|\\/(?:java|ecma)script/i );\n\n\n\n// We have to close these tags to support XHTML (#13200)\nvar wrapMap = {\n\n\t// Support: IE <=9 only\n\toption: [ 1, \"\" ],\n\n\t// XHTML parsers do not magically insert elements in the\n\t// same way that tag soup parsers do. So we cannot shorten\n\t// this by omitting or other required elements.\n\tthead: [ 1, \"\", \"
\" ],\n\tcol: [ 2, \"\", \"
\" ],\n\ttr: [ 2, \"\", \"
\" ],\n\ttd: [ 3, \"\", \"
\" ],\n\n\t_default: [ 0, \"\", \"\" ]\n};\n\n// Support: IE <=9 only\nwrapMap.optgroup = wrapMap.option;\n\nwrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;\nwrapMap.th = wrapMap.td;\n\n\nfunction getAll( context, tag ) {\n\n\t// Support: IE <=9 - 11 only\n\t// Use typeof to avoid zero-argument method invocation on host objects (#15151)\n\tvar ret;\n\n\tif ( typeof context.getElementsByTagName !== \"undefined\" ) {\n\t\tret = context.getElementsByTagName( tag || \"*\" );\n\n\t} else if ( typeof context.querySelectorAll !== \"undefined\" ) {\n\t\tret = context.querySelectorAll( tag || \"*\" );\n\n\t} else {\n\t\tret = [];\n\t}\n\n\tif ( tag === undefined || tag && nodeName( context, tag ) ) {\n\t\treturn jQuery.merge( [ context ], ret );\n\t}\n\n\treturn ret;\n}\n\n\n// Mark scripts as having already been evaluated\nfunction setGlobalEval( elems, refElements ) {\n\tvar i = 0,\n\t\tl = elems.length;\n\n\tfor ( ; i < l; i++ ) {\n\t\tdataPriv.set(\n\t\t\telems[ i ],\n\t\t\t\"globalEval\",\n\t\t\t!refElements || dataPriv.get( refElements[ i ], \"globalEval\" )\n\t\t);\n\t}\n}\n\n\nvar rhtml = /<|&#?\\w+;/;\n\nfunction buildFragment( elems, context, scripts, selection, ignored ) {\n\tvar elem, tmp, tag, wrap, contains, j,\n\t\tfragment = context.createDocumentFragment(),\n\t\tnodes = [],\n\t\ti = 0,\n\t\tl = elems.length;\n\n\tfor ( ; i < l; i++ ) {\n\t\telem = elems[ i ];\n\n\t\tif ( elem || elem === 0 ) {\n\n\t\t\t// Add nodes directly\n\t\t\tif ( jQuery.type( elem ) === \"object\" ) {\n\n\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\tjQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );\n\n\t\t\t// Convert non-html into a text node\n\t\t\t} else if ( !rhtml.test( elem ) ) {\n\t\t\t\tnodes.push( context.createTextNode( elem ) );\n\n\t\t\t// Convert html into DOM nodes\n\t\t\t} else {\n\t\t\t\ttmp = tmp || fragment.appendChild( context.createElement( \"div\" ) );\n\n\t\t\t\t// Deserialize a standard representation\n\t\t\t\ttag = ( rtagName.exec( elem ) || [ \"\", \"\" ] )[ 1 ].toLowerCase();\n\t\t\t\twrap = wrapMap[ tag ] || wrapMap._default;\n\t\t\t\ttmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];\n\n\t\t\t\t// Descend through wrappers to the right content\n\t\t\t\tj = wrap[ 0 ];\n\t\t\t\twhile ( j-- ) {\n\t\t\t\t\ttmp = tmp.lastChild;\n\t\t\t\t}\n\n\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\tjQuery.merge( nodes, tmp.childNodes );\n\n\t\t\t\t// Remember the top-level container\n\t\t\t\ttmp = fragment.firstChild;\n\n\t\t\t\t// Ensure the created nodes are orphaned (#12392)\n\t\t\t\ttmp.textContent = \"\";\n\t\t\t}\n\t\t}\n\t}\n\n\t// Remove wrapper from fragment\n\tfragment.textContent = \"\";\n\n\ti = 0;\n\twhile ( ( elem = nodes[ i++ ] ) ) {\n\n\t\t// Skip elements already in the context collection (trac-4087)\n\t\tif ( selection && jQuery.inArray( elem, selection ) > -1 ) {\n\t\t\tif ( ignored ) {\n\t\t\t\tignored.push( elem );\n\t\t\t}\n\t\t\tcontinue;\n\t\t}\n\n\t\tcontains = jQuery.contains( elem.ownerDocument, elem );\n\n\t\t// Append to fragment\n\t\ttmp = getAll( fragment.appendChild( elem ), \"script\" );\n\n\t\t// Preserve script evaluation history\n\t\tif ( contains ) {\n\t\t\tsetGlobalEval( tmp );\n\t\t}\n\n\t\t// Capture executables\n\t\tif ( scripts ) {\n\t\t\tj = 0;\n\t\t\twhile ( ( elem = tmp[ j++ ] ) ) {\n\t\t\t\tif ( rscriptType.test( elem.type || \"\" ) ) {\n\t\t\t\t\tscripts.push( elem );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn fragment;\n}\n\n\n( function() {\n\tvar fragment = document.createDocumentFragment(),\n\t\tdiv = fragment.appendChild( document.createElement( \"div\" ) ),\n\t\tinput = document.createElement( \"input\" );\n\n\t// Support: Android 4.0 - 4.3 only\n\t// Check state lost if the name is set (#11217)\n\t// Support: Windows Web Apps (WWA)\n\t// `name` and `type` must use .setAttribute for WWA (#14901)\n\tinput.setAttribute( \"type\", \"radio\" );\n\tinput.setAttribute( \"checked\", \"checked\" );\n\tinput.setAttribute( \"name\", \"t\" );\n\n\tdiv.appendChild( input );\n\n\t// Support: Android <=4.1 only\n\t// Older WebKit doesn't clone checked state correctly in fragments\n\tsupport.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;\n\n\t// Support: IE <=11 only\n\t// Make sure textarea (and checkbox) defaultValue is properly cloned\n\tdiv.innerHTML = \"\";\n\tsupport.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;\n} )();\nvar documentElement = document.documentElement;\n\n\n\nvar\n\trkeyEvent = /^key/,\n\trmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,\n\trtypenamespace = /^([^.]*)(?:\\.(.+)|)/;\n\nfunction returnTrue() {\n\treturn true;\n}\n\nfunction returnFalse() {\n\treturn false;\n}\n\n// Support: IE <=9 only\n// See #13393 for more info\nfunction safeActiveElement() {\n\ttry {\n\t\treturn document.activeElement;\n\t} catch ( err ) { }\n}\n\nfunction on( elem, types, selector, data, fn, one ) {\n\tvar origFn, type;\n\n\t// Types can be a map of types/handlers\n\tif ( typeof types === \"object\" ) {\n\n\t\t// ( types-Object, selector, data )\n\t\tif ( typeof selector !== \"string\" ) {\n\n\t\t\t// ( types-Object, data )\n\t\t\tdata = data || selector;\n\t\t\tselector = undefined;\n\t\t}\n\t\tfor ( type in types ) {\n\t\t\ton( elem, type, selector, data, types[ type ], one );\n\t\t}\n\t\treturn elem;\n\t}\n\n\tif ( data == null && fn == null ) {\n\n\t\t// ( types, fn )\n\t\tfn = selector;\n\t\tdata = selector = undefined;\n\t} else if ( fn == null ) {\n\t\tif ( typeof selector === \"string\" ) {\n\n\t\t\t// ( types, selector, fn )\n\t\t\tfn = data;\n\t\t\tdata = undefined;\n\t\t} else {\n\n\t\t\t// ( types, data, fn )\n\t\t\tfn = data;\n\t\t\tdata = selector;\n\t\t\tselector = undefined;\n\t\t}\n\t}\n\tif ( fn === false ) {\n\t\tfn = returnFalse;\n\t} else if ( !fn ) {\n\t\treturn elem;\n\t}\n\n\tif ( one === 1 ) {\n\t\torigFn = fn;\n\t\tfn = function( event ) {\n\n\t\t\t// Can use an empty set, since event contains the info\n\t\t\tjQuery().off( event );\n\t\t\treturn origFn.apply( this, arguments );\n\t\t};\n\n\t\t// Use same guid so caller can remove using origFn\n\t\tfn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );\n\t}\n\treturn elem.each( function() {\n\t\tjQuery.event.add( this, types, fn, data, selector );\n\t} );\n}\n\n/*\n * Helper functions for managing events -- not part of the public interface.\n * Props to Dean Edwards' addEvent library for many of the ideas.\n */\njQuery.event = {\n\n\tglobal: {},\n\n\tadd: function( elem, types, handler, data, selector ) {\n\n\t\tvar handleObjIn, eventHandle, tmp,\n\t\t\tevents, t, handleObj,\n\t\t\tspecial, handlers, type, namespaces, origType,\n\t\t\telemData = dataPriv.get( elem );\n\n\t\t// Don't attach events to noData or text/comment nodes (but allow plain objects)\n\t\tif ( !elemData ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Caller can pass in an object of custom data in lieu of the handler\n\t\tif ( handler.handler ) {\n\t\t\thandleObjIn = handler;\n\t\t\thandler = handleObjIn.handler;\n\t\t\tselector = handleObjIn.selector;\n\t\t}\n\n\t\t// Ensure that invalid selectors throw exceptions at attach time\n\t\t// Evaluate against documentElement in case elem is a non-element node (e.g., document)\n\t\tif ( selector ) {\n\t\t\tjQuery.find.matchesSelector( documentElement, selector );\n\t\t}\n\n\t\t// Make sure that the handler has a unique ID, used to find/remove it later\n\t\tif ( !handler.guid ) {\n\t\t\thandler.guid = jQuery.guid++;\n\t\t}\n\n\t\t// Init the element's event structure and main handler, if this is the first\n\t\tif ( !( events = elemData.events ) ) {\n\t\t\tevents = elemData.events = {};\n\t\t}\n\t\tif ( !( eventHandle = elemData.handle ) ) {\n\t\t\teventHandle = elemData.handle = function( e ) {\n\n\t\t\t\t// Discard the second event of a jQuery.event.trigger() and\n\t\t\t\t// when an event is called after a page has unloaded\n\t\t\t\treturn typeof jQuery !== \"undefined\" && jQuery.event.triggered !== e.type ?\n\t\t\t\t\tjQuery.event.dispatch.apply( elem, arguments ) : undefined;\n\t\t\t};\n\t\t}\n\n\t\t// Handle multiple events separated by a space\n\t\ttypes = ( types || \"\" ).match( rnothtmlwhite ) || [ \"\" ];\n\t\tt = types.length;\n\t\twhile ( t-- ) {\n\t\t\ttmp = rtypenamespace.exec( types[ t ] ) || [];\n\t\t\ttype = origType = tmp[ 1 ];\n\t\t\tnamespaces = ( tmp[ 2 ] || \"\" ).split( \".\" ).sort();\n\n\t\t\t// There *must* be a type, no attaching namespace-only handlers\n\t\t\tif ( !type ) {\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// If event changes its type, use the special event handlers for the changed type\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\n\t\t\t// If selector defined, determine special event api type, otherwise given type\n\t\t\ttype = ( selector ? special.delegateType : special.bindType ) || type;\n\n\t\t\t// Update special based on newly reset type\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\n\t\t\t// handleObj is passed to all event handlers\n\t\t\thandleObj = jQuery.extend( {\n\t\t\t\ttype: type,\n\t\t\t\torigType: origType,\n\t\t\t\tdata: data,\n\t\t\t\thandler: handler,\n\t\t\t\tguid: handler.guid,\n\t\t\t\tselector: selector,\n\t\t\t\tneedsContext: selector && jQuery.expr.match.needsContext.test( selector ),\n\t\t\t\tnamespace: namespaces.join( \".\" )\n\t\t\t}, handleObjIn );\n\n\t\t\t// Init the event handler queue if we're the first\n\t\t\tif ( !( handlers = events[ type ] ) ) {\n\t\t\t\thandlers = events[ type ] = [];\n\t\t\t\thandlers.delegateCount = 0;\n\n\t\t\t\t// Only use addEventListener if the special events handler returns false\n\t\t\t\tif ( !special.setup ||\n\t\t\t\t\tspecial.setup.call( elem, data, namespaces, eventHandle ) === false ) {\n\n\t\t\t\t\tif ( elem.addEventListener ) {\n\t\t\t\t\t\telem.addEventListener( type, eventHandle );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif ( special.add ) {\n\t\t\t\tspecial.add.call( elem, handleObj );\n\n\t\t\t\tif ( !handleObj.handler.guid ) {\n\t\t\t\t\thandleObj.handler.guid = handler.guid;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Add to the element's handler list, delegates in front\n\t\t\tif ( selector ) {\n\t\t\t\thandlers.splice( handlers.delegateCount++, 0, handleObj );\n\t\t\t} else {\n\t\t\t\thandlers.push( handleObj );\n\t\t\t}\n\n\t\t\t// Keep track of which events have ever been used, for event optimization\n\t\t\tjQuery.event.global[ type ] = true;\n\t\t}\n\n\t},\n\n\t// Detach an event or set of events from an element\n\tremove: function( elem, types, handler, selector, mappedTypes ) {\n\n\t\tvar j, origCount, tmp,\n\t\t\tevents, t, handleObj,\n\t\t\tspecial, handlers, type, namespaces, origType,\n\t\t\telemData = dataPriv.hasData( elem ) && dataPriv.get( elem );\n\n\t\tif ( !elemData || !( events = elemData.events ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Once for each type.namespace in types; type may be omitted\n\t\ttypes = ( types || \"\" ).match( rnothtmlwhite ) || [ \"\" ];\n\t\tt = types.length;\n\t\twhile ( t-- ) {\n\t\t\ttmp = rtypenamespace.exec( types[ t ] ) || [];\n\t\t\ttype = origType = tmp[ 1 ];\n\t\t\tnamespaces = ( tmp[ 2 ] || \"\" ).split( \".\" ).sort();\n\n\t\t\t// Unbind all events (on this namespace, if provided) for the element\n\t\t\tif ( !type ) {\n\t\t\t\tfor ( type in events ) {\n\t\t\t\t\tjQuery.event.remove( elem, type + types[ t ], handler, selector, true );\n\t\t\t\t}\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\t\t\ttype = ( selector ? special.delegateType : special.bindType ) || type;\n\t\t\thandlers = events[ type ] || [];\n\t\t\ttmp = tmp[ 2 ] &&\n\t\t\t\tnew RegExp( \"(^|\\\\.)\" + namespaces.join( \"\\\\.(?:.*\\\\.|)\" ) + \"(\\\\.|$)\" );\n\n\t\t\t// Remove matching events\n\t\t\torigCount = j = handlers.length;\n\t\t\twhile ( j-- ) {\n\t\t\t\thandleObj = handlers[ j ];\n\n\t\t\t\tif ( ( mappedTypes || origType === handleObj.origType ) &&\n\t\t\t\t\t( !handler || handler.guid === handleObj.guid ) &&\n\t\t\t\t\t( !tmp || tmp.test( handleObj.namespace ) ) &&\n\t\t\t\t\t( !selector || selector === handleObj.selector ||\n\t\t\t\t\t\tselector === \"**\" && handleObj.selector ) ) {\n\t\t\t\t\thandlers.splice( j, 1 );\n\n\t\t\t\t\tif ( handleObj.selector ) {\n\t\t\t\t\t\thandlers.delegateCount--;\n\t\t\t\t\t}\n\t\t\t\t\tif ( special.remove ) {\n\t\t\t\t\t\tspecial.remove.call( elem, handleObj );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Remove generic event handler if we removed something and no more handlers exist\n\t\t\t// (avoids potential for endless recursion during removal of special event handlers)\n\t\t\tif ( origCount && !handlers.length ) {\n\t\t\t\tif ( !special.teardown ||\n\t\t\t\t\tspecial.teardown.call( elem, namespaces, elemData.handle ) === false ) {\n\n\t\t\t\t\tjQuery.removeEvent( elem, type, elemData.handle );\n\t\t\t\t}\n\n\t\t\t\tdelete events[ type ];\n\t\t\t}\n\t\t}\n\n\t\t// Remove data and the expando if it's no longer used\n\t\tif ( jQuery.isEmptyObject( events ) ) {\n\t\t\tdataPriv.remove( elem, \"handle events\" );\n\t\t}\n\t},\n\n\tdispatch: function( nativeEvent ) {\n\n\t\t// Make a writable jQuery.Event from the native event object\n\t\tvar event = jQuery.event.fix( nativeEvent );\n\n\t\tvar i, j, ret, matched, handleObj, handlerQueue,\n\t\t\targs = new Array( arguments.length ),\n\t\t\thandlers = ( dataPriv.get( this, \"events\" ) || {} )[ event.type ] || [],\n\t\t\tspecial = jQuery.event.special[ event.type ] || {};\n\n\t\t// Use the fix-ed jQuery.Event rather than the (read-only) native event\n\t\targs[ 0 ] = event;\n\n\t\tfor ( i = 1; i < arguments.length; i++ ) {\n\t\t\targs[ i ] = arguments[ i ];\n\t\t}\n\n\t\tevent.delegateTarget = this;\n\n\t\t// Call the preDispatch hook for the mapped type, and let it bail if desired\n\t\tif ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Determine handlers\n\t\thandlerQueue = jQuery.event.handlers.call( this, event, handlers );\n\n\t\t// Run delegates first; they may want to stop propagation beneath us\n\t\ti = 0;\n\t\twhile ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {\n\t\t\tevent.currentTarget = matched.elem;\n\n\t\t\tj = 0;\n\t\t\twhile ( ( handleObj = matched.handlers[ j++ ] ) &&\n\t\t\t\t!event.isImmediatePropagationStopped() ) {\n\n\t\t\t\t// Triggered event must either 1) have no namespace, or 2) have namespace(s)\n\t\t\t\t// a subset or equal to those in the bound event (both can have no namespace).\n\t\t\t\tif ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) {\n\n\t\t\t\t\tevent.handleObj = handleObj;\n\t\t\t\t\tevent.data = handleObj.data;\n\n\t\t\t\t\tret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||\n\t\t\t\t\t\thandleObj.handler ).apply( matched.elem, args );\n\n\t\t\t\t\tif ( ret !== undefined ) {\n\t\t\t\t\t\tif ( ( event.result = ret ) === false ) {\n\t\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Call the postDispatch hook for the mapped type\n\t\tif ( special.postDispatch ) {\n\t\t\tspecial.postDispatch.call( this, event );\n\t\t}\n\n\t\treturn event.result;\n\t},\n\n\thandlers: function( event, handlers ) {\n\t\tvar i, handleObj, sel, matchedHandlers, matchedSelectors,\n\t\t\thandlerQueue = [],\n\t\t\tdelegateCount = handlers.delegateCount,\n\t\t\tcur = event.target;\n\n\t\t// Find delegate handlers\n\t\tif ( delegateCount &&\n\n\t\t\t// Support: IE <=9\n\t\t\t// Black-hole SVG instance trees (trac-13180)\n\t\t\tcur.nodeType &&\n\n\t\t\t// Support: Firefox <=42\n\t\t\t// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)\n\t\t\t// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click\n\t\t\t// Support: IE 11 only\n\t\t\t// ...but not arrow key \"clicks\" of radio inputs, which can have `button` -1 (gh-2343)\n\t\t\t!( event.type === \"click\" && event.button >= 1 ) ) {\n\n\t\t\tfor ( ; cur !== this; cur = cur.parentNode || this ) {\n\n\t\t\t\t// Don't check non-elements (#13208)\n\t\t\t\t// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)\n\t\t\t\tif ( cur.nodeType === 1 && !( event.type === \"click\" && cur.disabled === true ) ) {\n\t\t\t\t\tmatchedHandlers = [];\n\t\t\t\t\tmatchedSelectors = {};\n\t\t\t\t\tfor ( i = 0; i < delegateCount; i++ ) {\n\t\t\t\t\t\thandleObj = handlers[ i ];\n\n\t\t\t\t\t\t// Don't conflict with Object.prototype properties (#13203)\n\t\t\t\t\t\tsel = handleObj.selector + \" \";\n\n\t\t\t\t\t\tif ( matchedSelectors[ sel ] === undefined ) {\n\t\t\t\t\t\t\tmatchedSelectors[ sel ] = handleObj.needsContext ?\n\t\t\t\t\t\t\t\tjQuery( sel, this ).index( cur ) > -1 :\n\t\t\t\t\t\t\t\tjQuery.find( sel, this, null, [ cur ] ).length;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif ( matchedSelectors[ sel ] ) {\n\t\t\t\t\t\t\tmatchedHandlers.push( handleObj );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif ( matchedHandlers.length ) {\n\t\t\t\t\t\thandlerQueue.push( { elem: cur, handlers: matchedHandlers } );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Add the remaining (directly-bound) handlers\n\t\tcur = this;\n\t\tif ( delegateCount < handlers.length ) {\n\t\t\thandlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );\n\t\t}\n\n\t\treturn handlerQueue;\n\t},\n\n\taddProp: function( name, hook ) {\n\t\tObject.defineProperty( jQuery.Event.prototype, name, {\n\t\t\tenumerable: true,\n\t\t\tconfigurable: true,\n\n\t\t\tget: jQuery.isFunction( hook ) ?\n\t\t\t\tfunction() {\n\t\t\t\t\tif ( this.originalEvent ) {\n\t\t\t\t\t\t\treturn hook( this.originalEvent );\n\t\t\t\t\t}\n\t\t\t\t} :\n\t\t\t\tfunction() {\n\t\t\t\t\tif ( this.originalEvent ) {\n\t\t\t\t\t\t\treturn this.originalEvent[ name ];\n\t\t\t\t\t}\n\t\t\t\t},\n\n\t\t\tset: function( value ) {\n\t\t\t\tObject.defineProperty( this, name, {\n\t\t\t\t\tenumerable: true,\n\t\t\t\t\tconfigurable: true,\n\t\t\t\t\twritable: true,\n\t\t\t\t\tvalue: value\n\t\t\t\t} );\n\t\t\t}\n\t\t} );\n\t},\n\n\tfix: function( originalEvent ) {\n\t\treturn originalEvent[ jQuery.expando ] ?\n\t\t\toriginalEvent :\n\t\t\tnew jQuery.Event( originalEvent );\n\t},\n\n\tspecial: {\n\t\tload: {\n\n\t\t\t// Prevent triggered image.load events from bubbling to window.load\n\t\t\tnoBubble: true\n\t\t},\n\t\tfocus: {\n\n\t\t\t// Fire native event if possible so blur/focus sequence is correct\n\t\t\ttrigger: function() {\n\t\t\t\tif ( this !== safeActiveElement() && this.focus ) {\n\t\t\t\t\tthis.focus();\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t},\n\t\t\tdelegateType: \"focusin\"\n\t\t},\n\t\tblur: {\n\t\t\ttrigger: function() {\n\t\t\t\tif ( this === safeActiveElement() && this.blur ) {\n\t\t\t\t\tthis.blur();\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t},\n\t\t\tdelegateType: \"focusout\"\n\t\t},\n\t\tclick: {\n\n\t\t\t// For checkbox, fire native event so checked state will be right\n\t\t\ttrigger: function() {\n\t\t\t\tif ( this.type === \"checkbox\" && this.click && nodeName( this, \"input\" ) ) {\n\t\t\t\t\tthis.click();\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t},\n\n\t\t\t// For cross-browser consistency, don't fire native .click() on links\n\t\t\t_default: function( event ) {\n\t\t\t\treturn nodeName( event.target, \"a\" );\n\t\t\t}\n\t\t},\n\n\t\tbeforeunload: {\n\t\t\tpostDispatch: function( event ) {\n\n\t\t\t\t// Support: Firefox 20+\n\t\t\t\t// Firefox doesn't alert if the returnValue field is not set.\n\t\t\t\tif ( event.result !== undefined && event.originalEvent ) {\n\t\t\t\t\tevent.originalEvent.returnValue = event.result;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n};\n\njQuery.removeEvent = function( elem, type, handle ) {\n\n\t// This \"if\" is needed for plain objects\n\tif ( elem.removeEventListener ) {\n\t\telem.removeEventListener( type, handle );\n\t}\n};\n\njQuery.Event = function( src, props ) {\n\n\t// Allow instantiation without the 'new' keyword\n\tif ( !( this instanceof jQuery.Event ) ) {\n\t\treturn new jQuery.Event( src, props );\n\t}\n\n\t// Event object\n\tif ( src && src.type ) {\n\t\tthis.originalEvent = src;\n\t\tthis.type = src.type;\n\n\t\t// Events bubbling up the document may have been marked as prevented\n\t\t// by a handler lower down the tree; reflect the correct value.\n\t\tthis.isDefaultPrevented = src.defaultPrevented ||\n\t\t\t\tsrc.defaultPrevented === undefined &&\n\n\t\t\t\t// Support: Android <=2.3 only\n\t\t\t\tsrc.returnValue === false ?\n\t\t\treturnTrue :\n\t\t\treturnFalse;\n\n\t\t// Create target properties\n\t\t// Support: Safari <=6 - 7 only\n\t\t// Target should not be a text node (#504, #13143)\n\t\tthis.target = ( src.target && src.target.nodeType === 3 ) ?\n\t\t\tsrc.target.parentNode :\n\t\t\tsrc.target;\n\n\t\tthis.currentTarget = src.currentTarget;\n\t\tthis.relatedTarget = src.relatedTarget;\n\n\t// Event type\n\t} else {\n\t\tthis.type = src;\n\t}\n\n\t// Put explicitly provided properties onto the event object\n\tif ( props ) {\n\t\tjQuery.extend( this, props );\n\t}\n\n\t// Create a timestamp if incoming event doesn't have one\n\tthis.timeStamp = src && src.timeStamp || jQuery.now();\n\n\t// Mark it as fixed\n\tthis[ jQuery.expando ] = true;\n};\n\n// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding\n// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html\njQuery.Event.prototype = {\n\tconstructor: jQuery.Event,\n\tisDefaultPrevented: returnFalse,\n\tisPropagationStopped: returnFalse,\n\tisImmediatePropagationStopped: returnFalse,\n\tisSimulated: false,\n\n\tpreventDefault: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isDefaultPrevented = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.preventDefault();\n\t\t}\n\t},\n\tstopPropagation: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isPropagationStopped = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.stopPropagation();\n\t\t}\n\t},\n\tstopImmediatePropagation: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isImmediatePropagationStopped = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.stopImmediatePropagation();\n\t\t}\n\n\t\tthis.stopPropagation();\n\t}\n};\n\n// Includes all common event props including KeyEvent and MouseEvent specific props\njQuery.each( {\n\taltKey: true,\n\tbubbles: true,\n\tcancelable: true,\n\tchangedTouches: true,\n\tctrlKey: true,\n\tdetail: true,\n\teventPhase: true,\n\tmetaKey: true,\n\tpageX: true,\n\tpageY: true,\n\tshiftKey: true,\n\tview: true,\n\t\"char\": true,\n\tcharCode: true,\n\tkey: true,\n\tkeyCode: true,\n\tbutton: true,\n\tbuttons: true,\n\tclientX: true,\n\tclientY: true,\n\toffsetX: true,\n\toffsetY: true,\n\tpointerId: true,\n\tpointerType: true,\n\tscreenX: true,\n\tscreenY: true,\n\ttargetTouches: true,\n\ttoElement: true,\n\ttouches: true,\n\n\twhich: function( event ) {\n\t\tvar button = event.button;\n\n\t\t// Add which for key events\n\t\tif ( event.which == null && rkeyEvent.test( event.type ) ) {\n\t\t\treturn event.charCode != null ? event.charCode : event.keyCode;\n\t\t}\n\n\t\t// Add which for click: 1 === left; 2 === middle; 3 === right\n\t\tif ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {\n\t\t\tif ( button & 1 ) {\n\t\t\t\treturn 1;\n\t\t\t}\n\n\t\t\tif ( button & 2 ) {\n\t\t\t\treturn 3;\n\t\t\t}\n\n\t\t\tif ( button & 4 ) {\n\t\t\t\treturn 2;\n\t\t\t}\n\n\t\t\treturn 0;\n\t\t}\n\n\t\treturn event.which;\n\t}\n}, jQuery.event.addProp );\n\n// Create mouseenter/leave events using mouseover/out and event-time checks\n// so that event delegation works in jQuery.\n// Do the same for pointerenter/pointerleave and pointerover/pointerout\n//\n// Support: Safari 7 only\n// Safari sends mouseenter too often; see:\n// https://bugs.chromium.org/p/chromium/issues/detail?id=470258\n// for the description of the bug (it existed in older Chrome versions as well).\njQuery.each( {\n\tmouseenter: \"mouseover\",\n\tmouseleave: \"mouseout\",\n\tpointerenter: \"pointerover\",\n\tpointerleave: \"pointerout\"\n}, function( orig, fix ) {\n\tjQuery.event.special[ orig ] = {\n\t\tdelegateType: fix,\n\t\tbindType: fix,\n\n\t\thandle: function( event ) {\n\t\t\tvar ret,\n\t\t\t\ttarget = this,\n\t\t\t\trelated = event.relatedTarget,\n\t\t\t\thandleObj = event.handleObj;\n\n\t\t\t// For mouseenter/leave call the handler if related is outside the target.\n\t\t\t// NB: No relatedTarget if the mouse left/entered the browser window\n\t\t\tif ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {\n\t\t\t\tevent.type = handleObj.origType;\n\t\t\t\tret = handleObj.handler.apply( this, arguments );\n\t\t\t\tevent.type = fix;\n\t\t\t}\n\t\t\treturn ret;\n\t\t}\n\t};\n} );\n\njQuery.fn.extend( {\n\n\ton: function( types, selector, data, fn ) {\n\t\treturn on( this, types, selector, data, fn );\n\t},\n\tone: function( types, selector, data, fn ) {\n\t\treturn on( this, types, selector, data, fn, 1 );\n\t},\n\toff: function( types, selector, fn ) {\n\t\tvar handleObj, type;\n\t\tif ( types && types.preventDefault && types.handleObj ) {\n\n\t\t\t// ( event ) dispatched jQuery.Event\n\t\t\thandleObj = types.handleObj;\n\t\t\tjQuery( types.delegateTarget ).off(\n\t\t\t\thandleObj.namespace ?\n\t\t\t\t\thandleObj.origType + \".\" + handleObj.namespace :\n\t\t\t\t\thandleObj.origType,\n\t\t\t\thandleObj.selector,\n\t\t\t\thandleObj.handler\n\t\t\t);\n\t\t\treturn this;\n\t\t}\n\t\tif ( typeof types === \"object\" ) {\n\n\t\t\t// ( types-object [, selector] )\n\t\t\tfor ( type in types ) {\n\t\t\t\tthis.off( type, selector, types[ type ] );\n\t\t\t}\n\t\t\treturn this;\n\t\t}\n\t\tif ( selector === false || typeof selector === \"function\" ) {\n\n\t\t\t// ( types [, fn] )\n\t\t\tfn = selector;\n\t\t\tselector = undefined;\n\t\t}\n\t\tif ( fn === false ) {\n\t\t\tfn = returnFalse;\n\t\t}\n\t\treturn this.each( function() {\n\t\t\tjQuery.event.remove( this, types, fn, selector );\n\t\t} );\n\t}\n} );\n\n\nvar\n\n\t/* eslint-disable max-len */\n\n\t// See https://github.com/eslint/eslint/issues/3229\n\trxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\\/\\0>\\x20\\t\\r\\n\\f]*)[^>]*)\\/>/gi,\n\n\t/* eslint-enable */\n\n\t// Support: IE <=10 - 11, Edge 12 - 13\n\t// In IE/Edge using regex groups here causes severe slowdowns.\n\t// See https://connect.microsoft.com/IE/feedback/details/1736512/\n\trnoInnerhtml = /\\s*$/g;\n\n// Prefer a tbody over its parent table for containing new rows\nfunction manipulationTarget( elem, content ) {\n\tif ( nodeName( elem, \"table\" ) &&\n\t\tnodeName( content.nodeType !== 11 ? content : content.firstChild, \"tr\" ) ) {\n\n\t\treturn jQuery( \">tbody\", elem )[ 0 ] || elem;\n\t}\n\n\treturn elem;\n}\n\n// Replace/restore the type attribute of script elements for safe DOM manipulation\nfunction disableScript( elem ) {\n\telem.type = ( elem.getAttribute( \"type\" ) !== null ) + \"/\" + elem.type;\n\treturn elem;\n}\nfunction restoreScript( elem ) {\n\tvar match = rscriptTypeMasked.exec( elem.type );\n\n\tif ( match ) {\n\t\telem.type = match[ 1 ];\n\t} else {\n\t\telem.removeAttribute( \"type\" );\n\t}\n\n\treturn elem;\n}\n\nfunction cloneCopyEvent( src, dest ) {\n\tvar i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;\n\n\tif ( dest.nodeType !== 1 ) {\n\t\treturn;\n\t}\n\n\t// 1. Copy private data: events, handlers, etc.\n\tif ( dataPriv.hasData( src ) ) {\n\t\tpdataOld = dataPriv.access( src );\n\t\tpdataCur = dataPriv.set( dest, pdataOld );\n\t\tevents = pdataOld.events;\n\n\t\tif ( events ) {\n\t\t\tdelete pdataCur.handle;\n\t\t\tpdataCur.events = {};\n\n\t\t\tfor ( type in events ) {\n\t\t\t\tfor ( i = 0, l = events[ type ].length; i < l; i++ ) {\n\t\t\t\t\tjQuery.event.add( dest, type, events[ type ][ i ] );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// 2. Copy user data\n\tif ( dataUser.hasData( src ) ) {\n\t\tudataOld = dataUser.access( src );\n\t\tudataCur = jQuery.extend( {}, udataOld );\n\n\t\tdataUser.set( dest, udataCur );\n\t}\n}\n\n// Fix IE bugs, see support tests\nfunction fixInput( src, dest ) {\n\tvar nodeName = dest.nodeName.toLowerCase();\n\n\t// Fails to persist the checked state of a cloned checkbox or radio button.\n\tif ( nodeName === \"input\" && rcheckableType.test( src.type ) ) {\n\t\tdest.checked = src.checked;\n\n\t// Fails to return the selected option to the default selected state when cloning options\n\t} else if ( nodeName === \"input\" || nodeName === \"textarea\" ) {\n\t\tdest.defaultValue = src.defaultValue;\n\t}\n}\n\nfunction domManip( collection, args, callback, ignored ) {\n\n\t// Flatten any nested arrays\n\targs = concat.apply( [], args );\n\n\tvar fragment, first, scripts, hasScripts, node, doc,\n\t\ti = 0,\n\t\tl = collection.length,\n\t\tiNoClone = l - 1,\n\t\tvalue = args[ 0 ],\n\t\tisFunction = jQuery.isFunction( value );\n\n\t// We can't cloneNode fragments that contain checked, in WebKit\n\tif ( isFunction ||\n\t\t\t( l > 1 && typeof value === \"string\" &&\n\t\t\t\t!support.checkClone && rchecked.test( value ) ) ) {\n\t\treturn collection.each( function( index ) {\n\t\t\tvar self = collection.eq( index );\n\t\t\tif ( isFunction ) {\n\t\t\t\targs[ 0 ] = value.call( this, index, self.html() );\n\t\t\t}\n\t\t\tdomManip( self, args, callback, ignored );\n\t\t} );\n\t}\n\n\tif ( l ) {\n\t\tfragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );\n\t\tfirst = fragment.firstChild;\n\n\t\tif ( fragment.childNodes.length === 1 ) {\n\t\t\tfragment = first;\n\t\t}\n\n\t\t// Require either new content or an interest in ignored elements to invoke the callback\n\t\tif ( first || ignored ) {\n\t\t\tscripts = jQuery.map( getAll( fragment, \"script\" ), disableScript );\n\t\t\thasScripts = scripts.length;\n\n\t\t\t// Use the original fragment for the last item\n\t\t\t// instead of the first because it can end up\n\t\t\t// being emptied incorrectly in certain situations (#8070).\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tnode = fragment;\n\n\t\t\t\tif ( i !== iNoClone ) {\n\t\t\t\t\tnode = jQuery.clone( node, true, true );\n\n\t\t\t\t\t// Keep references to cloned scripts for later restoration\n\t\t\t\t\tif ( hasScripts ) {\n\n\t\t\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\t\t\tjQuery.merge( scripts, getAll( node, \"script\" ) );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tcallback.call( collection[ i ], node, i );\n\t\t\t}\n\n\t\t\tif ( hasScripts ) {\n\t\t\t\tdoc = scripts[ scripts.length - 1 ].ownerDocument;\n\n\t\t\t\t// Reenable scripts\n\t\t\t\tjQuery.map( scripts, restoreScript );\n\n\t\t\t\t// Evaluate executable scripts on first document insertion\n\t\t\t\tfor ( i = 0; i < hasScripts; i++ ) {\n\t\t\t\t\tnode = scripts[ i ];\n\t\t\t\t\tif ( rscriptType.test( node.type || \"\" ) &&\n\t\t\t\t\t\t!dataPriv.access( node, \"globalEval\" ) &&\n\t\t\t\t\t\tjQuery.contains( doc, node ) ) {\n\n\t\t\t\t\t\tif ( node.src ) {\n\n\t\t\t\t\t\t\t// Optional AJAX dependency, but won't run scripts if not present\n\t\t\t\t\t\t\tif ( jQuery._evalUrl ) {\n\t\t\t\t\t\t\t\tjQuery._evalUrl( node.src );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tDOMEval( node.textContent.replace( rcleanScript, \"\" ), doc );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn collection;\n}\n\nfunction remove( elem, selector, keepData ) {\n\tvar node,\n\t\tnodes = selector ? jQuery.filter( selector, elem ) : elem,\n\t\ti = 0;\n\n\tfor ( ; ( node = nodes[ i ] ) != null; i++ ) {\n\t\tif ( !keepData && node.nodeType === 1 ) {\n\t\t\tjQuery.cleanData( getAll( node ) );\n\t\t}\n\n\t\tif ( node.parentNode ) {\n\t\t\tif ( keepData && jQuery.contains( node.ownerDocument, node ) ) {\n\t\t\t\tsetGlobalEval( getAll( node, \"script\" ) );\n\t\t\t}\n\t\t\tnode.parentNode.removeChild( node );\n\t\t}\n\t}\n\n\treturn elem;\n}\n\njQuery.extend( {\n\thtmlPrefilter: function( html ) {\n\t\treturn html.replace( rxhtmlTag, \"<$1>\" );\n\t},\n\n\tclone: function( elem, dataAndEvents, deepDataAndEvents ) {\n\t\tvar i, l, srcElements, destElements,\n\t\t\tclone = elem.cloneNode( true ),\n\t\t\tinPage = jQuery.contains( elem.ownerDocument, elem );\n\n\t\t// Fix IE cloning issues\n\t\tif ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&\n\t\t\t\t!jQuery.isXMLDoc( elem ) ) {\n\n\t\t\t// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2\n\t\t\tdestElements = getAll( clone );\n\t\t\tsrcElements = getAll( elem );\n\n\t\t\tfor ( i = 0, l = srcElements.length; i < l; i++ ) {\n\t\t\t\tfixInput( srcElements[ i ], destElements[ i ] );\n\t\t\t}\n\t\t}\n\n\t\t// Copy the events from the original to the clone\n\t\tif ( dataAndEvents ) {\n\t\t\tif ( deepDataAndEvents ) {\n\t\t\t\tsrcElements = srcElements || getAll( elem );\n\t\t\t\tdestElements = destElements || getAll( clone );\n\n\t\t\t\tfor ( i = 0, l = srcElements.length; i < l; i++ ) {\n\t\t\t\t\tcloneCopyEvent( srcElements[ i ], destElements[ i ] );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tcloneCopyEvent( elem, clone );\n\t\t\t}\n\t\t}\n\n\t\t// Preserve script evaluation history\n\t\tdestElements = getAll( clone, \"script\" );\n\t\tif ( destElements.length > 0 ) {\n\t\t\tsetGlobalEval( destElements, !inPage && getAll( elem, \"script\" ) );\n\t\t}\n\n\t\t// Return the cloned set\n\t\treturn clone;\n\t},\n\n\tcleanData: function( elems ) {\n\t\tvar data, elem, type,\n\t\t\tspecial = jQuery.event.special,\n\t\t\ti = 0;\n\n\t\tfor ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {\n\t\t\tif ( acceptData( elem ) ) {\n\t\t\t\tif ( ( data = elem[ dataPriv.expando ] ) ) {\n\t\t\t\t\tif ( data.events ) {\n\t\t\t\t\t\tfor ( type in data.events ) {\n\t\t\t\t\t\t\tif ( special[ type ] ) {\n\t\t\t\t\t\t\t\tjQuery.event.remove( elem, type );\n\n\t\t\t\t\t\t\t// This is a shortcut to avoid jQuery.event.remove's overhead\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tjQuery.removeEvent( elem, type, data.handle );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Support: Chrome <=35 - 45+\n\t\t\t\t\t// Assign undefined instead of using delete, see Data#remove\n\t\t\t\t\telem[ dataPriv.expando ] = undefined;\n\t\t\t\t}\n\t\t\t\tif ( elem[ dataUser.expando ] ) {\n\n\t\t\t\t\t// Support: Chrome <=35 - 45+\n\t\t\t\t\t// Assign undefined instead of using delete, see Data#remove\n\t\t\t\t\telem[ dataUser.expando ] = undefined;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n} );\n\njQuery.fn.extend( {\n\tdetach: function( selector ) {\n\t\treturn remove( this, selector, true );\n\t},\n\n\tremove: function( selector ) {\n\t\treturn remove( this, selector );\n\t},\n\n\ttext: function( value ) {\n\t\treturn access( this, function( value ) {\n\t\t\treturn value === undefined ?\n\t\t\t\tjQuery.text( this ) :\n\t\t\t\tthis.empty().each( function() {\n\t\t\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\t\t\tthis.textContent = value;\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t}, null, value, arguments.length );\n\t},\n\n\tappend: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\tvar target = manipulationTarget( this, elem );\n\t\t\t\ttarget.appendChild( elem );\n\t\t\t}\n\t\t} );\n\t},\n\n\tprepend: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\tvar target = manipulationTarget( this, elem );\n\t\t\t\ttarget.insertBefore( elem, target.firstChild );\n\t\t\t}\n\t\t} );\n\t},\n\n\tbefore: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.parentNode ) {\n\t\t\t\tthis.parentNode.insertBefore( elem, this );\n\t\t\t}\n\t\t} );\n\t},\n\n\tafter: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.parentNode ) {\n\t\t\t\tthis.parentNode.insertBefore( elem, this.nextSibling );\n\t\t\t}\n\t\t} );\n\t},\n\n\tempty: function() {\n\t\tvar elem,\n\t\t\ti = 0;\n\n\t\tfor ( ; ( elem = this[ i ] ) != null; i++ ) {\n\t\t\tif ( elem.nodeType === 1 ) {\n\n\t\t\t\t// Prevent memory leaks\n\t\t\t\tjQuery.cleanData( getAll( elem, false ) );\n\n\t\t\t\t// Remove any remaining nodes\n\t\t\t\telem.textContent = \"\";\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\tclone: function( dataAndEvents, deepDataAndEvents ) {\n\t\tdataAndEvents = dataAndEvents == null ? false : dataAndEvents;\n\t\tdeepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;\n\n\t\treturn this.map( function() {\n\t\t\treturn jQuery.clone( this, dataAndEvents, deepDataAndEvents );\n\t\t} );\n\t},\n\n\thtml: function( value ) {\n\t\treturn access( this, function( value ) {\n\t\t\tvar elem = this[ 0 ] || {},\n\t\t\t\ti = 0,\n\t\t\t\tl = this.length;\n\n\t\t\tif ( value === undefined && elem.nodeType === 1 ) {\n\t\t\t\treturn elem.innerHTML;\n\t\t\t}\n\n\t\t\t// See if we can take a shortcut and just use innerHTML\n\t\t\tif ( typeof value === \"string\" && !rnoInnerhtml.test( value ) &&\n\t\t\t\t!wrapMap[ ( rtagName.exec( value ) || [ \"\", \"\" ] )[ 1 ].toLowerCase() ] ) {\n\n\t\t\t\tvalue = jQuery.htmlPrefilter( value );\n\n\t\t\t\ttry {\n\t\t\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\t\t\telem = this[ i ] || {};\n\n\t\t\t\t\t\t// Remove element nodes and prevent memory leaks\n\t\t\t\t\t\tif ( elem.nodeType === 1 ) {\n\t\t\t\t\t\t\tjQuery.cleanData( getAll( elem, false ) );\n\t\t\t\t\t\t\telem.innerHTML = value;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\telem = 0;\n\n\t\t\t\t// If using innerHTML throws an exception, use the fallback method\n\t\t\t\t} catch ( e ) {}\n\t\t\t}\n\n\t\t\tif ( elem ) {\n\t\t\t\tthis.empty().append( value );\n\t\t\t}\n\t\t}, null, value, arguments.length );\n\t},\n\n\treplaceWith: function() {\n\t\tvar ignored = [];\n\n\t\t// Make the changes, replacing each non-ignored context element with the new content\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tvar parent = this.parentNode;\n\n\t\t\tif ( jQuery.inArray( this, ignored ) < 0 ) {\n\t\t\t\tjQuery.cleanData( getAll( this ) );\n\t\t\t\tif ( parent ) {\n\t\t\t\t\tparent.replaceChild( elem, this );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Force callback invocation\n\t\t}, ignored );\n\t}\n} );\n\njQuery.each( {\n\tappendTo: \"append\",\n\tprependTo: \"prepend\",\n\tinsertBefore: \"before\",\n\tinsertAfter: \"after\",\n\treplaceAll: \"replaceWith\"\n}, function( name, original ) {\n\tjQuery.fn[ name ] = function( selector ) {\n\t\tvar elems,\n\t\t\tret = [],\n\t\t\tinsert = jQuery( selector ),\n\t\t\tlast = insert.length - 1,\n\t\t\ti = 0;\n\n\t\tfor ( ; i <= last; i++ ) {\n\t\t\telems = i === last ? this : this.clone( true );\n\t\t\tjQuery( insert[ i ] )[ original ]( elems );\n\n\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t// .get() because push.apply(_, arraylike) throws on ancient WebKit\n\t\t\tpush.apply( ret, elems.get() );\n\t\t}\n\n\t\treturn this.pushStack( ret );\n\t};\n} );\nvar rmargin = ( /^margin/ );\n\nvar rnumnonpx = new RegExp( \"^(\" + pnum + \")(?!px)[a-z%]+$\", \"i\" );\n\nvar getStyles = function( elem ) {\n\n\t\t// Support: IE <=11 only, Firefox <=30 (#15098, #14150)\n\t\t// IE throws on elements created in popups\n\t\t// FF meanwhile throws on frame elements through \"defaultView.getComputedStyle\"\n\t\tvar view = elem.ownerDocument.defaultView;\n\n\t\tif ( !view || !view.opener ) {\n\t\t\tview = window;\n\t\t}\n\n\t\treturn view.getComputedStyle( elem );\n\t};\n\n\n\n( function() {\n\n\t// Executing both pixelPosition & boxSizingReliable tests require only one layout\n\t// so they're executed at the same time to save the second computation.\n\tfunction computeStyleTests() {\n\n\t\t// This is a singleton, we need to execute it only once\n\t\tif ( !div ) {\n\t\t\treturn;\n\t\t}\n\n\t\tdiv.style.cssText =\n\t\t\t\"box-sizing:border-box;\" +\n\t\t\t\"position:relative;display:block;\" +\n\t\t\t\"margin:auto;border:1px;padding:1px;\" +\n\t\t\t\"top:1%;width:50%\";\n\t\tdiv.innerHTML = \"\";\n\t\tdocumentElement.appendChild( container );\n\n\t\tvar divStyle = window.getComputedStyle( div );\n\t\tpixelPositionVal = divStyle.top !== \"1%\";\n\n\t\t// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44\n\t\treliableMarginLeftVal = divStyle.marginLeft === \"2px\";\n\t\tboxSizingReliableVal = divStyle.width === \"4px\";\n\n\t\t// Support: Android 4.0 - 4.3 only\n\t\t// Some styles come back with percentage values, even though they shouldn't\n\t\tdiv.style.marginRight = \"50%\";\n\t\tpixelMarginRightVal = divStyle.marginRight === \"4px\";\n\n\t\tdocumentElement.removeChild( container );\n\n\t\t// Nullify the div so it wouldn't be stored in the memory and\n\t\t// it will also be a sign that checks already performed\n\t\tdiv = null;\n\t}\n\n\tvar pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal,\n\t\tcontainer = document.createElement( \"div\" ),\n\t\tdiv = document.createElement( \"div\" );\n\n\t// Finish early in limited (non-browser) environments\n\tif ( !div.style ) {\n\t\treturn;\n\t}\n\n\t// Support: IE <=9 - 11 only\n\t// Style of cloned element affects source element cloned (#8908)\n\tdiv.style.backgroundClip = \"content-box\";\n\tdiv.cloneNode( true ).style.backgroundClip = \"\";\n\tsupport.clearCloneStyle = div.style.backgroundClip === \"content-box\";\n\n\tcontainer.style.cssText = \"border:0;width:8px;height:0;top:0;left:-9999px;\" +\n\t\t\"padding:0;margin-top:1px;position:absolute\";\n\tcontainer.appendChild( div );\n\n\tjQuery.extend( support, {\n\t\tpixelPosition: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn pixelPositionVal;\n\t\t},\n\t\tboxSizingReliable: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn boxSizingReliableVal;\n\t\t},\n\t\tpixelMarginRight: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn pixelMarginRightVal;\n\t\t},\n\t\treliableMarginLeft: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn reliableMarginLeftVal;\n\t\t}\n\t} );\n} )();\n\n\nfunction curCSS( elem, name, computed ) {\n\tvar width, minWidth, maxWidth, ret,\n\n\t\t// Support: Firefox 51+\n\t\t// Retrieving style before computed somehow\n\t\t// fixes an issue with getting wrong values\n\t\t// on detached elements\n\t\tstyle = elem.style;\n\n\tcomputed = computed || getStyles( elem );\n\n\t// getPropertyValue is needed for:\n\t// .css('filter') (IE 9 only, #12537)\n\t// .css('--customProperty) (#3144)\n\tif ( computed ) {\n\t\tret = computed.getPropertyValue( name ) || computed[ name ];\n\n\t\tif ( ret === \"\" && !jQuery.contains( elem.ownerDocument, elem ) ) {\n\t\t\tret = jQuery.style( elem, name );\n\t\t}\n\n\t\t// A tribute to the \"awesome hack by Dean Edwards\"\n\t\t// Android Browser returns percentage for some values,\n\t\t// but width seems to be reliably pixels.\n\t\t// This is against the CSSOM draft spec:\n\t\t// https://drafts.csswg.org/cssom/#resolved-values\n\t\tif ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) {\n\n\t\t\t// Remember the original values\n\t\t\twidth = style.width;\n\t\t\tminWidth = style.minWidth;\n\t\t\tmaxWidth = style.maxWidth;\n\n\t\t\t// Put in the new values to get a computed value out\n\t\t\tstyle.minWidth = style.maxWidth = style.width = ret;\n\t\t\tret = computed.width;\n\n\t\t\t// Revert the changed values\n\t\t\tstyle.width = width;\n\t\t\tstyle.minWidth = minWidth;\n\t\t\tstyle.maxWidth = maxWidth;\n\t\t}\n\t}\n\n\treturn ret !== undefined ?\n\n\t\t// Support: IE <=9 - 11 only\n\t\t// IE returns zIndex value as an integer.\n\t\tret + \"\" :\n\t\tret;\n}\n\n\nfunction addGetHookIf( conditionFn, hookFn ) {\n\n\t// Define the hook, we'll check on the first run if it's really needed.\n\treturn {\n\t\tget: function() {\n\t\t\tif ( conditionFn() ) {\n\n\t\t\t\t// Hook not needed (or it's not possible to use it due\n\t\t\t\t// to missing dependency), remove it.\n\t\t\t\tdelete this.get;\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Hook needed; redefine it so that the support test is not executed again.\n\t\t\treturn ( this.get = hookFn ).apply( this, arguments );\n\t\t}\n\t};\n}\n\n\nvar\n\n\t// Swappable if display is none or starts with table\n\t// except \"table\", \"table-cell\", or \"table-caption\"\n\t// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display\n\trdisplayswap = /^(none|table(?!-c[ea]).+)/,\n\trcustomProp = /^--/,\n\tcssShow = { position: \"absolute\", visibility: \"hidden\", display: \"block\" },\n\tcssNormalTransform = {\n\t\tletterSpacing: \"0\",\n\t\tfontWeight: \"400\"\n\t},\n\n\tcssPrefixes = [ \"Webkit\", \"Moz\", \"ms\" ],\n\temptyStyle = document.createElement( \"div\" ).style;\n\n// Return a css property mapped to a potentially vendor prefixed property\nfunction vendorPropName( name ) {\n\n\t// Shortcut for names that are not vendor prefixed\n\tif ( name in emptyStyle ) {\n\t\treturn name;\n\t}\n\n\t// Check for vendor prefixed names\n\tvar capName = name[ 0 ].toUpperCase() + name.slice( 1 ),\n\t\ti = cssPrefixes.length;\n\n\twhile ( i-- ) {\n\t\tname = cssPrefixes[ i ] + capName;\n\t\tif ( name in emptyStyle ) {\n\t\t\treturn name;\n\t\t}\n\t}\n}\n\n// Return a property mapped along what jQuery.cssProps suggests or to\n// a vendor prefixed property.\nfunction finalPropName( name ) {\n\tvar ret = jQuery.cssProps[ name ];\n\tif ( !ret ) {\n\t\tret = jQuery.cssProps[ name ] = vendorPropName( name ) || name;\n\t}\n\treturn ret;\n}\n\nfunction setPositiveNumber( elem, value, subtract ) {\n\n\t// Any relative (+/-) values have already been\n\t// normalized at this point\n\tvar matches = rcssNum.exec( value );\n\treturn matches ?\n\n\t\t// Guard against undefined \"subtract\", e.g., when used as in cssHooks\n\t\tMath.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || \"px\" ) :\n\t\tvalue;\n}\n\nfunction augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) {\n\tvar i,\n\t\tval = 0;\n\n\t// If we already have the right measurement, avoid augmentation\n\tif ( extra === ( isBorderBox ? \"border\" : \"content\" ) ) {\n\t\ti = 4;\n\n\t// Otherwise initialize for horizontal or vertical properties\n\t} else {\n\t\ti = name === \"width\" ? 1 : 0;\n\t}\n\n\tfor ( ; i < 4; i += 2 ) {\n\n\t\t// Both box models exclude margin, so add it if we want it\n\t\tif ( extra === \"margin\" ) {\n\t\t\tval += jQuery.css( elem, extra + cssExpand[ i ], true, styles );\n\t\t}\n\n\t\tif ( isBorderBox ) {\n\n\t\t\t// border-box includes padding, so remove it if we want content\n\t\t\tif ( extra === \"content\" ) {\n\t\t\t\tval -= jQuery.css( elem, \"padding\" + cssExpand[ i ], true, styles );\n\t\t\t}\n\n\t\t\t// At this point, extra isn't border nor margin, so remove border\n\t\t\tif ( extra !== \"margin\" ) {\n\t\t\t\tval -= jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\t\t\t}\n\t\t} else {\n\n\t\t\t// At this point, extra isn't content, so add padding\n\t\t\tval += jQuery.css( elem, \"padding\" + cssExpand[ i ], true, styles );\n\n\t\t\t// At this point, extra isn't content nor padding, so add border\n\t\t\tif ( extra !== \"padding\" ) {\n\t\t\t\tval += jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\t\t\t}\n\t\t}\n\t}\n\n\treturn val;\n}\n\nfunction getWidthOrHeight( elem, name, extra ) {\n\n\t// Start with computed style\n\tvar valueIsBorderBox,\n\t\tstyles = getStyles( elem ),\n\t\tval = curCSS( elem, name, styles ),\n\t\tisBorderBox = jQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\";\n\n\t// Computed unit is not pixels. Stop here and return.\n\tif ( rnumnonpx.test( val ) ) {\n\t\treturn val;\n\t}\n\n\t// Check for style in case a browser which returns unreliable values\n\t// for getComputedStyle silently falls back to the reliable elem.style\n\tvalueIsBorderBox = isBorderBox &&\n\t\t( support.boxSizingReliable() || val === elem.style[ name ] );\n\n\t// Fall back to offsetWidth/Height when value is \"auto\"\n\t// This happens for inline elements with no explicit setting (gh-3571)\n\tif ( val === \"auto\" ) {\n\t\tval = elem[ \"offset\" + name[ 0 ].toUpperCase() + name.slice( 1 ) ];\n\t}\n\n\t// Normalize \"\", auto, and prepare for extra\n\tval = parseFloat( val ) || 0;\n\n\t// Use the active box-sizing model to add/subtract irrelevant styles\n\treturn ( val +\n\t\taugmentWidthOrHeight(\n\t\t\telem,\n\t\t\tname,\n\t\t\textra || ( isBorderBox ? \"border\" : \"content\" ),\n\t\t\tvalueIsBorderBox,\n\t\t\tstyles\n\t\t)\n\t) + \"px\";\n}\n\njQuery.extend( {\n\n\t// Add in style property hooks for overriding the default\n\t// behavior of getting and setting a style property\n\tcssHooks: {\n\t\topacity: {\n\t\t\tget: function( elem, computed ) {\n\t\t\t\tif ( computed ) {\n\n\t\t\t\t\t// We should always get a number back from opacity\n\t\t\t\t\tvar ret = curCSS( elem, \"opacity\" );\n\t\t\t\t\treturn ret === \"\" ? \"1\" : ret;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\t// Don't automatically add \"px\" to these possibly-unitless properties\n\tcssNumber: {\n\t\t\"animationIterationCount\": true,\n\t\t\"columnCount\": true,\n\t\t\"fillOpacity\": true,\n\t\t\"flexGrow\": true,\n\t\t\"flexShrink\": true,\n\t\t\"fontWeight\": true,\n\t\t\"lineHeight\": true,\n\t\t\"opacity\": true,\n\t\t\"order\": true,\n\t\t\"orphans\": true,\n\t\t\"widows\": true,\n\t\t\"zIndex\": true,\n\t\t\"zoom\": true\n\t},\n\n\t// Add in properties whose names you wish to fix before\n\t// setting or getting the value\n\tcssProps: {\n\t\t\"float\": \"cssFloat\"\n\t},\n\n\t// Get and set the style property on a DOM Node\n\tstyle: function( elem, name, value, extra ) {\n\n\t\t// Don't set styles on text and comment nodes\n\t\tif ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Make sure that we're working with the right name\n\t\tvar ret, type, hooks,\n\t\t\torigName = jQuery.camelCase( name ),\n\t\t\tisCustomProp = rcustomProp.test( name ),\n\t\t\tstyle = elem.style;\n\n\t\t// Make sure that we're working with the right name. We don't\n\t\t// want to query the value if it is a CSS custom property\n\t\t// since they are user-defined.\n\t\tif ( !isCustomProp ) {\n\t\t\tname = finalPropName( origName );\n\t\t}\n\n\t\t// Gets hook for the prefixed version, then unprefixed version\n\t\thooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];\n\n\t\t// Check if we're setting a value\n\t\tif ( value !== undefined ) {\n\t\t\ttype = typeof value;\n\n\t\t\t// Convert \"+=\" or \"-=\" to relative numbers (#7345)\n\t\t\tif ( type === \"string\" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {\n\t\t\t\tvalue = adjustCSS( elem, name, ret );\n\n\t\t\t\t// Fixes bug #9237\n\t\t\t\ttype = \"number\";\n\t\t\t}\n\n\t\t\t// Make sure that null and NaN values aren't set (#7116)\n\t\t\tif ( value == null || value !== value ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// If a number was passed in, add the unit (except for certain CSS properties)\n\t\t\tif ( type === \"number\" ) {\n\t\t\t\tvalue += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? \"\" : \"px\" );\n\t\t\t}\n\n\t\t\t// background-* props affect original clone's values\n\t\t\tif ( !support.clearCloneStyle && value === \"\" && name.indexOf( \"background\" ) === 0 ) {\n\t\t\t\tstyle[ name ] = \"inherit\";\n\t\t\t}\n\n\t\t\t// If a hook was provided, use that value, otherwise just set the specified value\n\t\t\tif ( !hooks || !( \"set\" in hooks ) ||\n\t\t\t\t( value = hooks.set( elem, value, extra ) ) !== undefined ) {\n\n\t\t\t\tif ( isCustomProp ) {\n\t\t\t\t\tstyle.setProperty( name, value );\n\t\t\t\t} else {\n\t\t\t\t\tstyle[ name ] = value;\n\t\t\t\t}\n\t\t\t}\n\n\t\t} else {\n\n\t\t\t// If a hook was provided get the non-computed value from there\n\t\t\tif ( hooks && \"get\" in hooks &&\n\t\t\t\t( ret = hooks.get( elem, false, extra ) ) !== undefined ) {\n\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\t// Otherwise just get the value from the style object\n\t\t\treturn style[ name ];\n\t\t}\n\t},\n\n\tcss: function( elem, name, extra, styles ) {\n\t\tvar val, num, hooks,\n\t\t\torigName = jQuery.camelCase( name ),\n\t\t\tisCustomProp = rcustomProp.test( name );\n\n\t\t// Make sure that we're working with the right name. We don't\n\t\t// want to modify the value if it is a CSS custom property\n\t\t// since they are user-defined.\n\t\tif ( !isCustomProp ) {\n\t\t\tname = finalPropName( origName );\n\t\t}\n\n\t\t// Try prefixed name followed by the unprefixed name\n\t\thooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];\n\n\t\t// If a hook was provided get the computed value from there\n\t\tif ( hooks && \"get\" in hooks ) {\n\t\t\tval = hooks.get( elem, true, extra );\n\t\t}\n\n\t\t// Otherwise, if a way to get the computed value exists, use that\n\t\tif ( val === undefined ) {\n\t\t\tval = curCSS( elem, name, styles );\n\t\t}\n\n\t\t// Convert \"normal\" to computed value\n\t\tif ( val === \"normal\" && name in cssNormalTransform ) {\n\t\t\tval = cssNormalTransform[ name ];\n\t\t}\n\n\t\t// Make numeric if forced or a qualifier was provided and val looks numeric\n\t\tif ( extra === \"\" || extra ) {\n\t\t\tnum = parseFloat( val );\n\t\t\treturn extra === true || isFinite( num ) ? num || 0 : val;\n\t\t}\n\n\t\treturn val;\n\t}\n} );\n\njQuery.each( [ \"height\", \"width\" ], function( i, name ) {\n\tjQuery.cssHooks[ name ] = {\n\t\tget: function( elem, computed, extra ) {\n\t\t\tif ( computed ) {\n\n\t\t\t\t// Certain elements can have dimension info if we invisibly show them\n\t\t\t\t// but it must have a current display style that would benefit\n\t\t\t\treturn rdisplayswap.test( jQuery.css( elem, \"display\" ) ) &&\n\n\t\t\t\t\t// Support: Safari 8+\n\t\t\t\t\t// Table columns in Safari have non-zero offsetWidth & zero\n\t\t\t\t\t// getBoundingClientRect().width unless display is changed.\n\t\t\t\t\t// Support: IE <=11 only\n\t\t\t\t\t// Running getBoundingClientRect on a disconnected node\n\t\t\t\t\t// in IE throws an error.\n\t\t\t\t\t( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?\n\t\t\t\t\t\tswap( elem, cssShow, function() {\n\t\t\t\t\t\t\treturn getWidthOrHeight( elem, name, extra );\n\t\t\t\t\t\t} ) :\n\t\t\t\t\t\tgetWidthOrHeight( elem, name, extra );\n\t\t\t}\n\t\t},\n\n\t\tset: function( elem, value, extra ) {\n\t\t\tvar matches,\n\t\t\t\tstyles = extra && getStyles( elem ),\n\t\t\t\tsubtract = extra && augmentWidthOrHeight(\n\t\t\t\t\telem,\n\t\t\t\t\tname,\n\t\t\t\t\textra,\n\t\t\t\t\tjQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\",\n\t\t\t\t\tstyles\n\t\t\t\t);\n\n\t\t\t// Convert to pixels if value adjustment is needed\n\t\t\tif ( subtract && ( matches = rcssNum.exec( value ) ) &&\n\t\t\t\t( matches[ 3 ] || \"px\" ) !== \"px\" ) {\n\n\t\t\t\telem.style[ name ] = value;\n\t\t\t\tvalue = jQuery.css( elem, name );\n\t\t\t}\n\n\t\t\treturn setPositiveNumber( elem, value, subtract );\n\t\t}\n\t};\n} );\n\njQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,\n\tfunction( elem, computed ) {\n\t\tif ( computed ) {\n\t\t\treturn ( parseFloat( curCSS( elem, \"marginLeft\" ) ) ||\n\t\t\t\telem.getBoundingClientRect().left -\n\t\t\t\t\tswap( elem, { marginLeft: 0 }, function() {\n\t\t\t\t\t\treturn elem.getBoundingClientRect().left;\n\t\t\t\t\t} )\n\t\t\t\t) + \"px\";\n\t\t}\n\t}\n);\n\n// These hooks are used by animate to expand properties\njQuery.each( {\n\tmargin: \"\",\n\tpadding: \"\",\n\tborder: \"Width\"\n}, function( prefix, suffix ) {\n\tjQuery.cssHooks[ prefix + suffix ] = {\n\t\texpand: function( value ) {\n\t\t\tvar i = 0,\n\t\t\t\texpanded = {},\n\n\t\t\t\t// Assumes a single number if not a string\n\t\t\t\tparts = typeof value === \"string\" ? value.split( \" \" ) : [ value ];\n\n\t\t\tfor ( ; i < 4; i++ ) {\n\t\t\t\texpanded[ prefix + cssExpand[ i ] + suffix ] =\n\t\t\t\t\tparts[ i ] || parts[ i - 2 ] || parts[ 0 ];\n\t\t\t}\n\n\t\t\treturn expanded;\n\t\t}\n\t};\n\n\tif ( !rmargin.test( prefix ) ) {\n\t\tjQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;\n\t}\n} );\n\njQuery.fn.extend( {\n\tcss: function( name, value ) {\n\t\treturn access( this, function( elem, name, value ) {\n\t\t\tvar styles, len,\n\t\t\t\tmap = {},\n\t\t\t\ti = 0;\n\n\t\t\tif ( Array.isArray( name ) ) {\n\t\t\t\tstyles = getStyles( elem );\n\t\t\t\tlen = name.length;\n\n\t\t\t\tfor ( ; i < len; i++ ) {\n\t\t\t\t\tmap[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );\n\t\t\t\t}\n\n\t\t\t\treturn map;\n\t\t\t}\n\n\t\t\treturn value !== undefined ?\n\t\t\t\tjQuery.style( elem, name, value ) :\n\t\t\t\tjQuery.css( elem, name );\n\t\t}, name, value, arguments.length > 1 );\n\t}\n} );\n\n\nfunction Tween( elem, options, prop, end, easing ) {\n\treturn new Tween.prototype.init( elem, options, prop, end, easing );\n}\njQuery.Tween = Tween;\n\nTween.prototype = {\n\tconstructor: Tween,\n\tinit: function( elem, options, prop, end, easing, unit ) {\n\t\tthis.elem = elem;\n\t\tthis.prop = prop;\n\t\tthis.easing = easing || jQuery.easing._default;\n\t\tthis.options = options;\n\t\tthis.start = this.now = this.cur();\n\t\tthis.end = end;\n\t\tthis.unit = unit || ( jQuery.cssNumber[ prop ] ? \"\" : \"px\" );\n\t},\n\tcur: function() {\n\t\tvar hooks = Tween.propHooks[ this.prop ];\n\n\t\treturn hooks && hooks.get ?\n\t\t\thooks.get( this ) :\n\t\t\tTween.propHooks._default.get( this );\n\t},\n\trun: function( percent ) {\n\t\tvar eased,\n\t\t\thooks = Tween.propHooks[ this.prop ];\n\n\t\tif ( this.options.duration ) {\n\t\t\tthis.pos = eased = jQuery.easing[ this.easing ](\n\t\t\t\tpercent, this.options.duration * percent, 0, 1, this.options.duration\n\t\t\t);\n\t\t} else {\n\t\t\tthis.pos = eased = percent;\n\t\t}\n\t\tthis.now = ( this.end - this.start ) * eased + this.start;\n\n\t\tif ( this.options.step ) {\n\t\t\tthis.options.step.call( this.elem, this.now, this );\n\t\t}\n\n\t\tif ( hooks && hooks.set ) {\n\t\t\thooks.set( this );\n\t\t} else {\n\t\t\tTween.propHooks._default.set( this );\n\t\t}\n\t\treturn this;\n\t}\n};\n\nTween.prototype.init.prototype = Tween.prototype;\n\nTween.propHooks = {\n\t_default: {\n\t\tget: function( tween ) {\n\t\t\tvar result;\n\n\t\t\t// Use a property on the element directly when it is not a DOM element,\n\t\t\t// or when there is no matching style property that exists.\n\t\t\tif ( tween.elem.nodeType !== 1 ||\n\t\t\t\ttween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {\n\t\t\t\treturn tween.elem[ tween.prop ];\n\t\t\t}\n\n\t\t\t// Passing an empty string as a 3rd parameter to .css will automatically\n\t\t\t// attempt a parseFloat and fallback to a string if the parse fails.\n\t\t\t// Simple values such as \"10px\" are parsed to Float;\n\t\t\t// complex values such as \"rotate(1rad)\" are returned as-is.\n\t\t\tresult = jQuery.css( tween.elem, tween.prop, \"\" );\n\n\t\t\t// Empty strings, null, undefined and \"auto\" are converted to 0.\n\t\t\treturn !result || result === \"auto\" ? 0 : result;\n\t\t},\n\t\tset: function( tween ) {\n\n\t\t\t// Use step hook for back compat.\n\t\t\t// Use cssHook if its there.\n\t\t\t// Use .style if available and use plain properties where available.\n\t\t\tif ( jQuery.fx.step[ tween.prop ] ) {\n\t\t\t\tjQuery.fx.step[ tween.prop ]( tween );\n\t\t\t} else if ( tween.elem.nodeType === 1 &&\n\t\t\t\t( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null ||\n\t\t\t\t\tjQuery.cssHooks[ tween.prop ] ) ) {\n\t\t\t\tjQuery.style( tween.elem, tween.prop, tween.now + tween.unit );\n\t\t\t} else {\n\t\t\t\ttween.elem[ tween.prop ] = tween.now;\n\t\t\t}\n\t\t}\n\t}\n};\n\n// Support: IE <=9 only\n// Panic based approach to setting things on disconnected nodes\nTween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {\n\tset: function( tween ) {\n\t\tif ( tween.elem.nodeType && tween.elem.parentNode ) {\n\t\t\ttween.elem[ tween.prop ] = tween.now;\n\t\t}\n\t}\n};\n\njQuery.easing = {\n\tlinear: function( p ) {\n\t\treturn p;\n\t},\n\tswing: function( p ) {\n\t\treturn 0.5 - Math.cos( p * Math.PI ) / 2;\n\t},\n\t_default: \"swing\"\n};\n\njQuery.fx = Tween.prototype.init;\n\n// Back compat <1.8 extension point\njQuery.fx.step = {};\n\n\n\n\nvar\n\tfxNow, inProgress,\n\trfxtypes = /^(?:toggle|show|hide)$/,\n\trrun = /queueHooks$/;\n\nfunction schedule() {\n\tif ( inProgress ) {\n\t\tif ( document.hidden === false && window.requestAnimationFrame ) {\n\t\t\twindow.requestAnimationFrame( schedule );\n\t\t} else {\n\t\t\twindow.setTimeout( schedule, jQuery.fx.interval );\n\t\t}\n\n\t\tjQuery.fx.tick();\n\t}\n}\n\n// Animations created synchronously will run synchronously\nfunction createFxNow() {\n\twindow.setTimeout( function() {\n\t\tfxNow = undefined;\n\t} );\n\treturn ( fxNow = jQuery.now() );\n}\n\n// Generate parameters to create a standard animation\nfunction genFx( type, includeWidth ) {\n\tvar which,\n\t\ti = 0,\n\t\tattrs = { height: type };\n\n\t// If we include width, step value is 1 to do all cssExpand values,\n\t// otherwise step value is 2 to skip over Left and Right\n\tincludeWidth = includeWidth ? 1 : 0;\n\tfor ( ; i < 4; i += 2 - includeWidth ) {\n\t\twhich = cssExpand[ i ];\n\t\tattrs[ \"margin\" + which ] = attrs[ \"padding\" + which ] = type;\n\t}\n\n\tif ( includeWidth ) {\n\t\tattrs.opacity = attrs.width = type;\n\t}\n\n\treturn attrs;\n}\n\nfunction createTween( value, prop, animation ) {\n\tvar tween,\n\t\tcollection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ \"*\" ] ),\n\t\tindex = 0,\n\t\tlength = collection.length;\n\tfor ( ; index < length; index++ ) {\n\t\tif ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {\n\n\t\t\t// We're done with this property\n\t\t\treturn tween;\n\t\t}\n\t}\n}\n\nfunction defaultPrefilter( elem, props, opts ) {\n\tvar prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,\n\t\tisBox = \"width\" in props || \"height\" in props,\n\t\tanim = this,\n\t\torig = {},\n\t\tstyle = elem.style,\n\t\thidden = elem.nodeType && isHiddenWithinTree( elem ),\n\t\tdataShow = dataPriv.get( elem, \"fxshow\" );\n\n\t// Queue-skipping animations hijack the fx hooks\n\tif ( !opts.queue ) {\n\t\thooks = jQuery._queueHooks( elem, \"fx\" );\n\t\tif ( hooks.unqueued == null ) {\n\t\t\thooks.unqueued = 0;\n\t\t\toldfire = hooks.empty.fire;\n\t\t\thooks.empty.fire = function() {\n\t\t\t\tif ( !hooks.unqueued ) {\n\t\t\t\t\toldfire();\n\t\t\t\t}\n\t\t\t};\n\t\t}\n\t\thooks.unqueued++;\n\n\t\tanim.always( function() {\n\n\t\t\t// Ensure the complete handler is called before this completes\n\t\t\tanim.always( function() {\n\t\t\t\thooks.unqueued--;\n\t\t\t\tif ( !jQuery.queue( elem, \"fx\" ).length ) {\n\t\t\t\t\thooks.empty.fire();\n\t\t\t\t}\n\t\t\t} );\n\t\t} );\n\t}\n\n\t// Detect show/hide animations\n\tfor ( prop in props ) {\n\t\tvalue = props[ prop ];\n\t\tif ( rfxtypes.test( value ) ) {\n\t\t\tdelete props[ prop ];\n\t\t\ttoggle = toggle || value === \"toggle\";\n\t\t\tif ( value === ( hidden ? \"hide\" : \"show\" ) ) {\n\n\t\t\t\t// Pretend to be hidden if this is a \"show\" and\n\t\t\t\t// there is still data from a stopped show/hide\n\t\t\t\tif ( value === \"show\" && dataShow && dataShow[ prop ] !== undefined ) {\n\t\t\t\t\thidden = true;\n\n\t\t\t\t// Ignore all other no-op show/hide data\n\t\t\t\t} else {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t}\n\t\t\torig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );\n\t\t}\n\t}\n\n\t// Bail out if this is a no-op like .hide().hide()\n\tpropTween = !jQuery.isEmptyObject( props );\n\tif ( !propTween && jQuery.isEmptyObject( orig ) ) {\n\t\treturn;\n\t}\n\n\t// Restrict \"overflow\" and \"display\" styles during box animations\n\tif ( isBox && elem.nodeType === 1 ) {\n\n\t\t// Support: IE <=9 - 11, Edge 12 - 13\n\t\t// Record all 3 overflow attributes because IE does not infer the shorthand\n\t\t// from identically-valued overflowX and overflowY\n\t\topts.overflow = [ style.overflow, style.overflowX, style.overflowY ];\n\n\t\t// Identify a display type, preferring old show/hide data over the CSS cascade\n\t\trestoreDisplay = dataShow && dataShow.display;\n\t\tif ( restoreDisplay == null ) {\n\t\t\trestoreDisplay = dataPriv.get( elem, \"display\" );\n\t\t}\n\t\tdisplay = jQuery.css( elem, \"display\" );\n\t\tif ( display === \"none\" ) {\n\t\t\tif ( restoreDisplay ) {\n\t\t\t\tdisplay = restoreDisplay;\n\t\t\t} else {\n\n\t\t\t\t// Get nonempty value(s) by temporarily forcing visibility\n\t\t\t\tshowHide( [ elem ], true );\n\t\t\t\trestoreDisplay = elem.style.display || restoreDisplay;\n\t\t\t\tdisplay = jQuery.css( elem, \"display\" );\n\t\t\t\tshowHide( [ elem ] );\n\t\t\t}\n\t\t}\n\n\t\t// Animate inline elements as inline-block\n\t\tif ( display === \"inline\" || display === \"inline-block\" && restoreDisplay != null ) {\n\t\t\tif ( jQuery.css( elem, \"float\" ) === \"none\" ) {\n\n\t\t\t\t// Restore the original display value at the end of pure show/hide animations\n\t\t\t\tif ( !propTween ) {\n\t\t\t\t\tanim.done( function() {\n\t\t\t\t\t\tstyle.display = restoreDisplay;\n\t\t\t\t\t} );\n\t\t\t\t\tif ( restoreDisplay == null ) {\n\t\t\t\t\t\tdisplay = style.display;\n\t\t\t\t\t\trestoreDisplay = display === \"none\" ? \"\" : display;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tstyle.display = \"inline-block\";\n\t\t\t}\n\t\t}\n\t}\n\n\tif ( opts.overflow ) {\n\t\tstyle.overflow = \"hidden\";\n\t\tanim.always( function() {\n\t\t\tstyle.overflow = opts.overflow[ 0 ];\n\t\t\tstyle.overflowX = opts.overflow[ 1 ];\n\t\t\tstyle.overflowY = opts.overflow[ 2 ];\n\t\t} );\n\t}\n\n\t// Implement show/hide animations\n\tpropTween = false;\n\tfor ( prop in orig ) {\n\n\t\t// General show/hide setup for this element animation\n\t\tif ( !propTween ) {\n\t\t\tif ( dataShow ) {\n\t\t\t\tif ( \"hidden\" in dataShow ) {\n\t\t\t\t\thidden = dataShow.hidden;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tdataShow = dataPriv.access( elem, \"fxshow\", { display: restoreDisplay } );\n\t\t\t}\n\n\t\t\t// Store hidden/visible for toggle so `.stop().toggle()` \"reverses\"\n\t\t\tif ( toggle ) {\n\t\t\t\tdataShow.hidden = !hidden;\n\t\t\t}\n\n\t\t\t// Show elements before animating them\n\t\t\tif ( hidden ) {\n\t\t\t\tshowHide( [ elem ], true );\n\t\t\t}\n\n\t\t\t/* eslint-disable no-loop-func */\n\n\t\t\tanim.done( function() {\n\n\t\t\t/* eslint-enable no-loop-func */\n\n\t\t\t\t// The final step of a \"hide\" animation is actually hiding the element\n\t\t\t\tif ( !hidden ) {\n\t\t\t\t\tshowHide( [ elem ] );\n\t\t\t\t}\n\t\t\t\tdataPriv.remove( elem, \"fxshow\" );\n\t\t\t\tfor ( prop in orig ) {\n\t\t\t\t\tjQuery.style( elem, prop, orig[ prop ] );\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\t// Per-property setup\n\t\tpropTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );\n\t\tif ( !( prop in dataShow ) ) {\n\t\t\tdataShow[ prop ] = propTween.start;\n\t\t\tif ( hidden ) {\n\t\t\t\tpropTween.end = propTween.start;\n\t\t\t\tpropTween.start = 0;\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunction propFilter( props, specialEasing ) {\n\tvar index, name, easing, value, hooks;\n\n\t// camelCase, specialEasing and expand cssHook pass\n\tfor ( index in props ) {\n\t\tname = jQuery.camelCase( index );\n\t\teasing = specialEasing[ name ];\n\t\tvalue = props[ index ];\n\t\tif ( Array.isArray( value ) ) {\n\t\t\teasing = value[ 1 ];\n\t\t\tvalue = props[ index ] = value[ 0 ];\n\t\t}\n\n\t\tif ( index !== name ) {\n\t\t\tprops[ name ] = value;\n\t\t\tdelete props[ index ];\n\t\t}\n\n\t\thooks = jQuery.cssHooks[ name ];\n\t\tif ( hooks && \"expand\" in hooks ) {\n\t\t\tvalue = hooks.expand( value );\n\t\t\tdelete props[ name ];\n\n\t\t\t// Not quite $.extend, this won't overwrite existing keys.\n\t\t\t// Reusing 'index' because we have the correct \"name\"\n\t\t\tfor ( index in value ) {\n\t\t\t\tif ( !( index in props ) ) {\n\t\t\t\t\tprops[ index ] = value[ index ];\n\t\t\t\t\tspecialEasing[ index ] = easing;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tspecialEasing[ name ] = easing;\n\t\t}\n\t}\n}\n\nfunction Animation( elem, properties, options ) {\n\tvar result,\n\t\tstopped,\n\t\tindex = 0,\n\t\tlength = Animation.prefilters.length,\n\t\tdeferred = jQuery.Deferred().always( function() {\n\n\t\t\t// Don't match elem in the :animated selector\n\t\t\tdelete tick.elem;\n\t\t} ),\n\t\ttick = function() {\n\t\t\tif ( stopped ) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\tvar currentTime = fxNow || createFxNow(),\n\t\t\t\tremaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),\n\n\t\t\t\t// Support: Android 2.3 only\n\t\t\t\t// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)\n\t\t\t\ttemp = remaining / animation.duration || 0,\n\t\t\t\tpercent = 1 - temp,\n\t\t\t\tindex = 0,\n\t\t\t\tlength = animation.tweens.length;\n\n\t\t\tfor ( ; index < length; index++ ) {\n\t\t\t\tanimation.tweens[ index ].run( percent );\n\t\t\t}\n\n\t\t\tdeferred.notifyWith( elem, [ animation, percent, remaining ] );\n\n\t\t\t// If there's more to do, yield\n\t\t\tif ( percent < 1 && length ) {\n\t\t\t\treturn remaining;\n\t\t\t}\n\n\t\t\t// If this was an empty animation, synthesize a final progress notification\n\t\t\tif ( !length ) {\n\t\t\t\tdeferred.notifyWith( elem, [ animation, 1, 0 ] );\n\t\t\t}\n\n\t\t\t// Resolve the animation and report its conclusion\n\t\t\tdeferred.resolveWith( elem, [ animation ] );\n\t\t\treturn false;\n\t\t},\n\t\tanimation = deferred.promise( {\n\t\t\telem: elem,\n\t\t\tprops: jQuery.extend( {}, properties ),\n\t\t\topts: jQuery.extend( true, {\n\t\t\t\tspecialEasing: {},\n\t\t\t\teasing: jQuery.easing._default\n\t\t\t}, options ),\n\t\t\toriginalProperties: properties,\n\t\t\toriginalOptions: options,\n\t\t\tstartTime: fxNow || createFxNow(),\n\t\t\tduration: options.duration,\n\t\t\ttweens: [],\n\t\t\tcreateTween: function( prop, end ) {\n\t\t\t\tvar tween = jQuery.Tween( elem, animation.opts, prop, end,\n\t\t\t\t\t\tanimation.opts.specialEasing[ prop ] || animation.opts.easing );\n\t\t\t\tanimation.tweens.push( tween );\n\t\t\t\treturn tween;\n\t\t\t},\n\t\t\tstop: function( gotoEnd ) {\n\t\t\t\tvar index = 0,\n\n\t\t\t\t\t// If we are going to the end, we want to run all the tweens\n\t\t\t\t\t// otherwise we skip this part\n\t\t\t\t\tlength = gotoEnd ? animation.tweens.length : 0;\n\t\t\t\tif ( stopped ) {\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\t\t\t\tstopped = true;\n\t\t\t\tfor ( ; index < length; index++ ) {\n\t\t\t\t\tanimation.tweens[ index ].run( 1 );\n\t\t\t\t}\n\n\t\t\t\t// Resolve when we played the last frame; otherwise, reject\n\t\t\t\tif ( gotoEnd ) {\n\t\t\t\t\tdeferred.notifyWith( elem, [ animation, 1, 0 ] );\n\t\t\t\t\tdeferred.resolveWith( elem, [ animation, gotoEnd ] );\n\t\t\t\t} else {\n\t\t\t\t\tdeferred.rejectWith( elem, [ animation, gotoEnd ] );\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t}\n\t\t} ),\n\t\tprops = animation.props;\n\n\tpropFilter( props, animation.opts.specialEasing );\n\n\tfor ( ; index < length; index++ ) {\n\t\tresult = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );\n\t\tif ( result ) {\n\t\t\tif ( jQuery.isFunction( result.stop ) ) {\n\t\t\t\tjQuery._queueHooks( animation.elem, animation.opts.queue ).stop =\n\t\t\t\t\tjQuery.proxy( result.stop, result );\n\t\t\t}\n\t\t\treturn result;\n\t\t}\n\t}\n\n\tjQuery.map( props, createTween, animation );\n\n\tif ( jQuery.isFunction( animation.opts.start ) ) {\n\t\tanimation.opts.start.call( elem, animation );\n\t}\n\n\t// Attach callbacks from options\n\tanimation\n\t\t.progress( animation.opts.progress )\n\t\t.done( animation.opts.done, animation.opts.complete )\n\t\t.fail( animation.opts.fail )\n\t\t.always( animation.opts.always );\n\n\tjQuery.fx.timer(\n\t\tjQuery.extend( tick, {\n\t\t\telem: elem,\n\t\t\tanim: animation,\n\t\t\tqueue: animation.opts.queue\n\t\t} )\n\t);\n\n\treturn animation;\n}\n\njQuery.Animation = jQuery.extend( Animation, {\n\n\ttweeners: {\n\t\t\"*\": [ function( prop, value ) {\n\t\t\tvar tween = this.createTween( prop, value );\n\t\t\tadjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );\n\t\t\treturn tween;\n\t\t} ]\n\t},\n\n\ttweener: function( props, callback ) {\n\t\tif ( jQuery.isFunction( props ) ) {\n\t\t\tcallback = props;\n\t\t\tprops = [ \"*\" ];\n\t\t} else {\n\t\t\tprops = props.match( rnothtmlwhite );\n\t\t}\n\n\t\tvar prop,\n\t\t\tindex = 0,\n\t\t\tlength = props.length;\n\n\t\tfor ( ; index < length; index++ ) {\n\t\t\tprop = props[ index ];\n\t\t\tAnimation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];\n\t\t\tAnimation.tweeners[ prop ].unshift( callback );\n\t\t}\n\t},\n\n\tprefilters: [ defaultPrefilter ],\n\n\tprefilter: function( callback, prepend ) {\n\t\tif ( prepend ) {\n\t\t\tAnimation.prefilters.unshift( callback );\n\t\t} else {\n\t\t\tAnimation.prefilters.push( callback );\n\t\t}\n\t}\n} );\n\njQuery.speed = function( speed, easing, fn ) {\n\tvar opt = speed && typeof speed === \"object\" ? jQuery.extend( {}, speed ) : {\n\t\tcomplete: fn || !fn && easing ||\n\t\t\tjQuery.isFunction( speed ) && speed,\n\t\tduration: speed,\n\t\teasing: fn && easing || easing && !jQuery.isFunction( easing ) && easing\n\t};\n\n\t// Go to the end state if fx are off\n\tif ( jQuery.fx.off ) {\n\t\topt.duration = 0;\n\n\t} else {\n\t\tif ( typeof opt.duration !== \"number\" ) {\n\t\t\tif ( opt.duration in jQuery.fx.speeds ) {\n\t\t\t\topt.duration = jQuery.fx.speeds[ opt.duration ];\n\n\t\t\t} else {\n\t\t\t\topt.duration = jQuery.fx.speeds._default;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Normalize opt.queue - true/undefined/null -> \"fx\"\n\tif ( opt.queue == null || opt.queue === true ) {\n\t\topt.queue = \"fx\";\n\t}\n\n\t// Queueing\n\topt.old = opt.complete;\n\n\topt.complete = function() {\n\t\tif ( jQuery.isFunction( opt.old ) ) {\n\t\t\topt.old.call( this );\n\t\t}\n\n\t\tif ( opt.queue ) {\n\t\t\tjQuery.dequeue( this, opt.queue );\n\t\t}\n\t};\n\n\treturn opt;\n};\n\njQuery.fn.extend( {\n\tfadeTo: function( speed, to, easing, callback ) {\n\n\t\t// Show any hidden elements after setting opacity to 0\n\t\treturn this.filter( isHiddenWithinTree ).css( \"opacity\", 0 ).show()\n\n\t\t\t// Animate to the value specified\n\t\t\t.end().animate( { opacity: to }, speed, easing, callback );\n\t},\n\tanimate: function( prop, speed, easing, callback ) {\n\t\tvar empty = jQuery.isEmptyObject( prop ),\n\t\t\toptall = jQuery.speed( speed, easing, callback ),\n\t\t\tdoAnimation = function() {\n\n\t\t\t\t// Operate on a copy of prop so per-property easing won't be lost\n\t\t\t\tvar anim = Animation( this, jQuery.extend( {}, prop ), optall );\n\n\t\t\t\t// Empty animations, or finishing resolves immediately\n\t\t\t\tif ( empty || dataPriv.get( this, \"finish\" ) ) {\n\t\t\t\t\tanim.stop( true );\n\t\t\t\t}\n\t\t\t};\n\t\t\tdoAnimation.finish = doAnimation;\n\n\t\treturn empty || optall.queue === false ?\n\t\t\tthis.each( doAnimation ) :\n\t\t\tthis.queue( optall.queue, doAnimation );\n\t},\n\tstop: function( type, clearQueue, gotoEnd ) {\n\t\tvar stopQueue = function( hooks ) {\n\t\t\tvar stop = hooks.stop;\n\t\t\tdelete hooks.stop;\n\t\t\tstop( gotoEnd );\n\t\t};\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tgotoEnd = clearQueue;\n\t\t\tclearQueue = type;\n\t\t\ttype = undefined;\n\t\t}\n\t\tif ( clearQueue && type !== false ) {\n\t\t\tthis.queue( type || \"fx\", [] );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar dequeue = true,\n\t\t\t\tindex = type != null && type + \"queueHooks\",\n\t\t\t\ttimers = jQuery.timers,\n\t\t\t\tdata = dataPriv.get( this );\n\n\t\t\tif ( index ) {\n\t\t\t\tif ( data[ index ] && data[ index ].stop ) {\n\t\t\t\t\tstopQueue( data[ index ] );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tfor ( index in data ) {\n\t\t\t\t\tif ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {\n\t\t\t\t\t\tstopQueue( data[ index ] );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tfor ( index = timers.length; index--; ) {\n\t\t\t\tif ( timers[ index ].elem === this &&\n\t\t\t\t\t( type == null || timers[ index ].queue === type ) ) {\n\n\t\t\t\t\ttimers[ index ].anim.stop( gotoEnd );\n\t\t\t\t\tdequeue = false;\n\t\t\t\t\ttimers.splice( index, 1 );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Start the next in the queue if the last step wasn't forced.\n\t\t\t// Timers currently will call their complete callbacks, which\n\t\t\t// will dequeue but only if they were gotoEnd.\n\t\t\tif ( dequeue || !gotoEnd ) {\n\t\t\t\tjQuery.dequeue( this, type );\n\t\t\t}\n\t\t} );\n\t},\n\tfinish: function( type ) {\n\t\tif ( type !== false ) {\n\t\t\ttype = type || \"fx\";\n\t\t}\n\t\treturn this.each( function() {\n\t\t\tvar index,\n\t\t\t\tdata = dataPriv.get( this ),\n\t\t\t\tqueue = data[ type + \"queue\" ],\n\t\t\t\thooks = data[ type + \"queueHooks\" ],\n\t\t\t\ttimers = jQuery.timers,\n\t\t\t\tlength = queue ? queue.length : 0;\n\n\t\t\t// Enable finishing flag on private data\n\t\t\tdata.finish = true;\n\n\t\t\t// Empty the queue first\n\t\t\tjQuery.queue( this, type, [] );\n\n\t\t\tif ( hooks && hooks.stop ) {\n\t\t\t\thooks.stop.call( this, true );\n\t\t\t}\n\n\t\t\t// Look for any active animations, and finish them\n\t\t\tfor ( index = timers.length; index--; ) {\n\t\t\t\tif ( timers[ index ].elem === this && timers[ index ].queue === type ) {\n\t\t\t\t\ttimers[ index ].anim.stop( true );\n\t\t\t\t\ttimers.splice( index, 1 );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Look for any animations in the old queue and finish them\n\t\t\tfor ( index = 0; index < length; index++ ) {\n\t\t\t\tif ( queue[ index ] && queue[ index ].finish ) {\n\t\t\t\t\tqueue[ index ].finish.call( this );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Turn off finishing flag\n\t\t\tdelete data.finish;\n\t\t} );\n\t}\n} );\n\njQuery.each( [ \"toggle\", \"show\", \"hide\" ], function( i, name ) {\n\tvar cssFn = jQuery.fn[ name ];\n\tjQuery.fn[ name ] = function( speed, easing, callback ) {\n\t\treturn speed == null || typeof speed === \"boolean\" ?\n\t\t\tcssFn.apply( this, arguments ) :\n\t\t\tthis.animate( genFx( name, true ), speed, easing, callback );\n\t};\n} );\n\n// Generate shortcuts for custom animations\njQuery.each( {\n\tslideDown: genFx( \"show\" ),\n\tslideUp: genFx( \"hide\" ),\n\tslideToggle: genFx( \"toggle\" ),\n\tfadeIn: { opacity: \"show\" },\n\tfadeOut: { opacity: \"hide\" },\n\tfadeToggle: { opacity: \"toggle\" }\n}, function( name, props ) {\n\tjQuery.fn[ name ] = function( speed, easing, callback ) {\n\t\treturn this.animate( props, speed, easing, callback );\n\t};\n} );\n\njQuery.timers = [];\njQuery.fx.tick = function() {\n\tvar timer,\n\t\ti = 0,\n\t\ttimers = jQuery.timers;\n\n\tfxNow = jQuery.now();\n\n\tfor ( ; i < timers.length; i++ ) {\n\t\ttimer = timers[ i ];\n\n\t\t// Run the timer and safely remove it when done (allowing for external removal)\n\t\tif ( !timer() && timers[ i ] === timer ) {\n\t\t\ttimers.splice( i--, 1 );\n\t\t}\n\t}\n\n\tif ( !timers.length ) {\n\t\tjQuery.fx.stop();\n\t}\n\tfxNow = undefined;\n};\n\njQuery.fx.timer = function( timer ) {\n\tjQuery.timers.push( timer );\n\tjQuery.fx.start();\n};\n\njQuery.fx.interval = 13;\njQuery.fx.start = function() {\n\tif ( inProgress ) {\n\t\treturn;\n\t}\n\n\tinProgress = true;\n\tschedule();\n};\n\njQuery.fx.stop = function() {\n\tinProgress = null;\n};\n\njQuery.fx.speeds = {\n\tslow: 600,\n\tfast: 200,\n\n\t// Default speed\n\t_default: 400\n};\n\n\n// Based off of the plugin by Clint Helfers, with permission.\n// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/\njQuery.fn.delay = function( time, type ) {\n\ttime = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;\n\ttype = type || \"fx\";\n\n\treturn this.queue( type, function( next, hooks ) {\n\t\tvar timeout = window.setTimeout( next, time );\n\t\thooks.stop = function() {\n\t\t\twindow.clearTimeout( timeout );\n\t\t};\n\t} );\n};\n\n\n( function() {\n\tvar input = document.createElement( \"input\" ),\n\t\tselect = document.createElement( \"select\" ),\n\t\topt = select.appendChild( document.createElement( \"option\" ) );\n\n\tinput.type = \"checkbox\";\n\n\t// Support: Android <=4.3 only\n\t// Default value for a checkbox should be \"on\"\n\tsupport.checkOn = input.value !== \"\";\n\n\t// Support: IE <=11 only\n\t// Must access selectedIndex to make default options select\n\tsupport.optSelected = opt.selected;\n\n\t// Support: IE <=11 only\n\t// An input loses its value after becoming a radio\n\tinput = document.createElement( \"input\" );\n\tinput.value = \"t\";\n\tinput.type = \"radio\";\n\tsupport.radioValue = input.value === \"t\";\n} )();\n\n\nvar boolHook,\n\tattrHandle = jQuery.expr.attrHandle;\n\njQuery.fn.extend( {\n\tattr: function( name, value ) {\n\t\treturn access( this, jQuery.attr, name, value, arguments.length > 1 );\n\t},\n\n\tremoveAttr: function( name ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.removeAttr( this, name );\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tattr: function( elem, name, value ) {\n\t\tvar ret, hooks,\n\t\t\tnType = elem.nodeType;\n\n\t\t// Don't get/set attributes on text, comment and attribute nodes\n\t\tif ( nType === 3 || nType === 8 || nType === 2 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Fallback to prop when attributes are not supported\n\t\tif ( typeof elem.getAttribute === \"undefined\" ) {\n\t\t\treturn jQuery.prop( elem, name, value );\n\t\t}\n\n\t\t// Attribute hooks are determined by the lowercase version\n\t\t// Grab necessary hook if one is defined\n\t\tif ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {\n\t\t\thooks = jQuery.attrHooks[ name.toLowerCase() ] ||\n\t\t\t\t( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );\n\t\t}\n\n\t\tif ( value !== undefined ) {\n\t\t\tif ( value === null ) {\n\t\t\t\tjQuery.removeAttr( elem, name );\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ( hooks && \"set\" in hooks &&\n\t\t\t\t( ret = hooks.set( elem, value, name ) ) !== undefined ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\telem.setAttribute( name, value + \"\" );\n\t\t\treturn value;\n\t\t}\n\n\t\tif ( hooks && \"get\" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {\n\t\t\treturn ret;\n\t\t}\n\n\t\tret = jQuery.find.attr( elem, name );\n\n\t\t// Non-existent attributes return null, we normalize to undefined\n\t\treturn ret == null ? undefined : ret;\n\t},\n\n\tattrHooks: {\n\t\ttype: {\n\t\t\tset: function( elem, value ) {\n\t\t\t\tif ( !support.radioValue && value === \"radio\" &&\n\t\t\t\t\tnodeName( elem, \"input\" ) ) {\n\t\t\t\t\tvar val = elem.value;\n\t\t\t\t\telem.setAttribute( \"type\", value );\n\t\t\t\t\tif ( val ) {\n\t\t\t\t\t\telem.value = val;\n\t\t\t\t\t}\n\t\t\t\t\treturn value;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\tremoveAttr: function( elem, value ) {\n\t\tvar name,\n\t\t\ti = 0,\n\n\t\t\t// Attribute names can contain non-HTML whitespace characters\n\t\t\t// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2\n\t\t\tattrNames = value && value.match( rnothtmlwhite );\n\n\t\tif ( attrNames && elem.nodeType === 1 ) {\n\t\t\twhile ( ( name = attrNames[ i++ ] ) ) {\n\t\t\t\telem.removeAttribute( name );\n\t\t\t}\n\t\t}\n\t}\n} );\n\n// Hooks for boolean attributes\nboolHook = {\n\tset: function( elem, value, name ) {\n\t\tif ( value === false ) {\n\n\t\t\t// Remove boolean attributes when set to false\n\t\t\tjQuery.removeAttr( elem, name );\n\t\t} else {\n\t\t\telem.setAttribute( name, name );\n\t\t}\n\t\treturn name;\n\t}\n};\n\njQuery.each( jQuery.expr.match.bool.source.match( /\\w+/g ), function( i, name ) {\n\tvar getter = attrHandle[ name ] || jQuery.find.attr;\n\n\tattrHandle[ name ] = function( elem, name, isXML ) {\n\t\tvar ret, handle,\n\t\t\tlowercaseName = name.toLowerCase();\n\n\t\tif ( !isXML ) {\n\n\t\t\t// Avoid an infinite loop by temporarily removing this function from the getter\n\t\t\thandle = attrHandle[ lowercaseName ];\n\t\t\tattrHandle[ lowercaseName ] = ret;\n\t\t\tret = getter( elem, name, isXML ) != null ?\n\t\t\t\tlowercaseName :\n\t\t\t\tnull;\n\t\t\tattrHandle[ lowercaseName ] = handle;\n\t\t}\n\t\treturn ret;\n\t};\n} );\n\n\n\n\nvar rfocusable = /^(?:input|select|textarea|button)$/i,\n\trclickable = /^(?:a|area)$/i;\n\njQuery.fn.extend( {\n\tprop: function( name, value ) {\n\t\treturn access( this, jQuery.prop, name, value, arguments.length > 1 );\n\t},\n\n\tremoveProp: function( name ) {\n\t\treturn this.each( function() {\n\t\t\tdelete this[ jQuery.propFix[ name ] || name ];\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tprop: function( elem, name, value ) {\n\t\tvar ret, hooks,\n\t\t\tnType = elem.nodeType;\n\n\t\t// Don't get/set properties on text, comment and attribute nodes\n\t\tif ( nType === 3 || nType === 8 || nType === 2 ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {\n\n\t\t\t// Fix name and attach hooks\n\t\t\tname = jQuery.propFix[ name ] || name;\n\t\t\thooks = jQuery.propHooks[ name ];\n\t\t}\n\n\t\tif ( value !== undefined ) {\n\t\t\tif ( hooks && \"set\" in hooks &&\n\t\t\t\t( ret = hooks.set( elem, value, name ) ) !== undefined ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\treturn ( elem[ name ] = value );\n\t\t}\n\n\t\tif ( hooks && \"get\" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {\n\t\t\treturn ret;\n\t\t}\n\n\t\treturn elem[ name ];\n\t},\n\n\tpropHooks: {\n\t\ttabIndex: {\n\t\t\tget: function( elem ) {\n\n\t\t\t\t// Support: IE <=9 - 11 only\n\t\t\t\t// elem.tabIndex doesn't always return the\n\t\t\t\t// correct value when it hasn't been explicitly set\n\t\t\t\t// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/\n\t\t\t\t// Use proper attribute retrieval(#12072)\n\t\t\t\tvar tabindex = jQuery.find.attr( elem, \"tabindex\" );\n\n\t\t\t\tif ( tabindex ) {\n\t\t\t\t\treturn parseInt( tabindex, 10 );\n\t\t\t\t}\n\n\t\t\t\tif (\n\t\t\t\t\trfocusable.test( elem.nodeName ) ||\n\t\t\t\t\trclickable.test( elem.nodeName ) &&\n\t\t\t\t\telem.href\n\t\t\t\t) {\n\t\t\t\t\treturn 0;\n\t\t\t\t}\n\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t}\n\t},\n\n\tpropFix: {\n\t\t\"for\": \"htmlFor\",\n\t\t\"class\": \"className\"\n\t}\n} );\n\n// Support: IE <=11 only\n// Accessing the selectedIndex property\n// forces the browser to respect setting selected\n// on the option\n// The getter ensures a default option is selected\n// when in an optgroup\n// eslint rule \"no-unused-expressions\" is disabled for this code\n// since it considers such accessions noop\nif ( !support.optSelected ) {\n\tjQuery.propHooks.selected = {\n\t\tget: function( elem ) {\n\n\t\t\t/* eslint no-unused-expressions: \"off\" */\n\n\t\t\tvar parent = elem.parentNode;\n\t\t\tif ( parent && parent.parentNode ) {\n\t\t\t\tparent.parentNode.selectedIndex;\n\t\t\t}\n\t\t\treturn null;\n\t\t},\n\t\tset: function( elem ) {\n\n\t\t\t/* eslint no-unused-expressions: \"off\" */\n\n\t\t\tvar parent = elem.parentNode;\n\t\t\tif ( parent ) {\n\t\t\t\tparent.selectedIndex;\n\n\t\t\t\tif ( parent.parentNode ) {\n\t\t\t\t\tparent.parentNode.selectedIndex;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n}\n\njQuery.each( [\n\t\"tabIndex\",\n\t\"readOnly\",\n\t\"maxLength\",\n\t\"cellSpacing\",\n\t\"cellPadding\",\n\t\"rowSpan\",\n\t\"colSpan\",\n\t\"useMap\",\n\t\"frameBorder\",\n\t\"contentEditable\"\n], function() {\n\tjQuery.propFix[ this.toLowerCase() ] = this;\n} );\n\n\n\n\n\t// Strip and collapse whitespace according to HTML spec\n\t// https://html.spec.whatwg.org/multipage/infrastructure.html#strip-and-collapse-whitespace\n\tfunction stripAndCollapse( value ) {\n\t\tvar tokens = value.match( rnothtmlwhite ) || [];\n\t\treturn tokens.join( \" \" );\n\t}\n\n\nfunction getClass( elem ) {\n\treturn elem.getAttribute && elem.getAttribute( \"class\" ) || \"\";\n}\n\njQuery.fn.extend( {\n\taddClass: function( value ) {\n\t\tvar classes, elem, cur, curValue, clazz, j, finalValue,\n\t\t\ti = 0;\n\n\t\tif ( jQuery.isFunction( value ) ) {\n\t\t\treturn this.each( function( j ) {\n\t\t\t\tjQuery( this ).addClass( value.call( this, j, getClass( this ) ) );\n\t\t\t} );\n\t\t}\n\n\t\tif ( typeof value === \"string\" && value ) {\n\t\t\tclasses = value.match( rnothtmlwhite ) || [];\n\n\t\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\t\tcurValue = getClass( elem );\n\t\t\t\tcur = elem.nodeType === 1 && ( \" \" + stripAndCollapse( curValue ) + \" \" );\n\n\t\t\t\tif ( cur ) {\n\t\t\t\t\tj = 0;\n\t\t\t\t\twhile ( ( clazz = classes[ j++ ] ) ) {\n\t\t\t\t\t\tif ( cur.indexOf( \" \" + clazz + \" \" ) < 0 ) {\n\t\t\t\t\t\t\tcur += clazz + \" \";\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Only assign if different to avoid unneeded rendering.\n\t\t\t\t\tfinalValue = stripAndCollapse( cur );\n\t\t\t\t\tif ( curValue !== finalValue ) {\n\t\t\t\t\t\telem.setAttribute( \"class\", finalValue );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\tremoveClass: function( value ) {\n\t\tvar classes, elem, cur, curValue, clazz, j, finalValue,\n\t\t\ti = 0;\n\n\t\tif ( jQuery.isFunction( value ) ) {\n\t\t\treturn this.each( function( j ) {\n\t\t\t\tjQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );\n\t\t\t} );\n\t\t}\n\n\t\tif ( !arguments.length ) {\n\t\t\treturn this.attr( \"class\", \"\" );\n\t\t}\n\n\t\tif ( typeof value === \"string\" && value ) {\n\t\t\tclasses = value.match( rnothtmlwhite ) || [];\n\n\t\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\t\tcurValue = getClass( elem );\n\n\t\t\t\t// This expression is here for better compressibility (see addClass)\n\t\t\t\tcur = elem.nodeType === 1 && ( \" \" + stripAndCollapse( curValue ) + \" \" );\n\n\t\t\t\tif ( cur ) {\n\t\t\t\t\tj = 0;\n\t\t\t\t\twhile ( ( clazz = classes[ j++ ] ) ) {\n\n\t\t\t\t\t\t// Remove *all* instances\n\t\t\t\t\t\twhile ( cur.indexOf( \" \" + clazz + \" \" ) > -1 ) {\n\t\t\t\t\t\t\tcur = cur.replace( \" \" + clazz + \" \", \" \" );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Only assign if different to avoid unneeded rendering.\n\t\t\t\t\tfinalValue = stripAndCollapse( cur );\n\t\t\t\t\tif ( curValue !== finalValue ) {\n\t\t\t\t\t\telem.setAttribute( \"class\", finalValue );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\ttoggleClass: function( value, stateVal ) {\n\t\tvar type = typeof value;\n\n\t\tif ( typeof stateVal === \"boolean\" && type === \"string\" ) {\n\t\t\treturn stateVal ? this.addClass( value ) : this.removeClass( value );\n\t\t}\n\n\t\tif ( jQuery.isFunction( value ) ) {\n\t\t\treturn this.each( function( i ) {\n\t\t\t\tjQuery( this ).toggleClass(\n\t\t\t\t\tvalue.call( this, i, getClass( this ), stateVal ),\n\t\t\t\t\tstateVal\n\t\t\t\t);\n\t\t\t} );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar className, i, self, classNames;\n\n\t\t\tif ( type === \"string\" ) {\n\n\t\t\t\t// Toggle individual class names\n\t\t\t\ti = 0;\n\t\t\t\tself = jQuery( this );\n\t\t\t\tclassNames = value.match( rnothtmlwhite ) || [];\n\n\t\t\t\twhile ( ( className = classNames[ i++ ] ) ) {\n\n\t\t\t\t\t// Check each className given, space separated list\n\t\t\t\t\tif ( self.hasClass( className ) ) {\n\t\t\t\t\t\tself.removeClass( className );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tself.addClass( className );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t// Toggle whole class name\n\t\t\t} else if ( value === undefined || type === \"boolean\" ) {\n\t\t\t\tclassName = getClass( this );\n\t\t\t\tif ( className ) {\n\n\t\t\t\t\t// Store className if set\n\t\t\t\t\tdataPriv.set( this, \"__className__\", className );\n\t\t\t\t}\n\n\t\t\t\t// If the element has a class name or if we're passed `false`,\n\t\t\t\t// then remove the whole classname (if there was one, the above saved it).\n\t\t\t\t// Otherwise bring back whatever was previously saved (if anything),\n\t\t\t\t// falling back to the empty string if nothing was stored.\n\t\t\t\tif ( this.setAttribute ) {\n\t\t\t\t\tthis.setAttribute( \"class\",\n\t\t\t\t\t\tclassName || value === false ?\n\t\t\t\t\t\t\"\" :\n\t\t\t\t\t\tdataPriv.get( this, \"__className__\" ) || \"\"\n\t\t\t\t\t);\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t},\n\n\thasClass: function( selector ) {\n\t\tvar className, elem,\n\t\t\ti = 0;\n\n\t\tclassName = \" \" + selector + \" \";\n\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\tif ( elem.nodeType === 1 &&\n\t\t\t\t( \" \" + stripAndCollapse( getClass( elem ) ) + \" \" ).indexOf( className ) > -1 ) {\n\t\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\n\t\treturn false;\n\t}\n} );\n\n\n\n\nvar rreturn = /\\r/g;\n\njQuery.fn.extend( {\n\tval: function( value ) {\n\t\tvar hooks, ret, isFunction,\n\t\t\telem = this[ 0 ];\n\n\t\tif ( !arguments.length ) {\n\t\t\tif ( elem ) {\n\t\t\t\thooks = jQuery.valHooks[ elem.type ] ||\n\t\t\t\t\tjQuery.valHooks[ elem.nodeName.toLowerCase() ];\n\n\t\t\t\tif ( hooks &&\n\t\t\t\t\t\"get\" in hooks &&\n\t\t\t\t\t( ret = hooks.get( elem, \"value\" ) ) !== undefined\n\t\t\t\t) {\n\t\t\t\t\treturn ret;\n\t\t\t\t}\n\n\t\t\t\tret = elem.value;\n\n\t\t\t\t// Handle most common string cases\n\t\t\t\tif ( typeof ret === \"string\" ) {\n\t\t\t\t\treturn ret.replace( rreturn, \"\" );\n\t\t\t\t}\n\n\t\t\t\t// Handle cases where value is null/undef or number\n\t\t\t\treturn ret == null ? \"\" : ret;\n\t\t\t}\n\n\t\t\treturn;\n\t\t}\n\n\t\tisFunction = jQuery.isFunction( value );\n\n\t\treturn this.each( function( i ) {\n\t\t\tvar val;\n\n\t\t\tif ( this.nodeType !== 1 ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ( isFunction ) {\n\t\t\t\tval = value.call( this, i, jQuery( this ).val() );\n\t\t\t} else {\n\t\t\t\tval = value;\n\t\t\t}\n\n\t\t\t// Treat null/undefined as \"\"; convert numbers to string\n\t\t\tif ( val == null ) {\n\t\t\t\tval = \"\";\n\n\t\t\t} else if ( typeof val === \"number\" ) {\n\t\t\t\tval += \"\";\n\n\t\t\t} else if ( Array.isArray( val ) ) {\n\t\t\t\tval = jQuery.map( val, function( value ) {\n\t\t\t\t\treturn value == null ? \"\" : value + \"\";\n\t\t\t\t} );\n\t\t\t}\n\n\t\t\thooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];\n\n\t\t\t// If set returns undefined, fall back to normal setting\n\t\t\tif ( !hooks || !( \"set\" in hooks ) || hooks.set( this, val, \"value\" ) === undefined ) {\n\t\t\t\tthis.value = val;\n\t\t\t}\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tvalHooks: {\n\t\toption: {\n\t\t\tget: function( elem ) {\n\n\t\t\t\tvar val = jQuery.find.attr( elem, \"value\" );\n\t\t\t\treturn val != null ?\n\t\t\t\t\tval :\n\n\t\t\t\t\t// Support: IE <=10 - 11 only\n\t\t\t\t\t// option.text throws exceptions (#14686, #14858)\n\t\t\t\t\t// Strip and collapse whitespace\n\t\t\t\t\t// https://html.spec.whatwg.org/#strip-and-collapse-whitespace\n\t\t\t\t\tstripAndCollapse( jQuery.text( elem ) );\n\t\t\t}\n\t\t},\n\t\tselect: {\n\t\t\tget: function( elem ) {\n\t\t\t\tvar value, option, i,\n\t\t\t\t\toptions = elem.options,\n\t\t\t\t\tindex = elem.selectedIndex,\n\t\t\t\t\tone = elem.type === \"select-one\",\n\t\t\t\t\tvalues = one ? null : [],\n\t\t\t\t\tmax = one ? index + 1 : options.length;\n\n\t\t\t\tif ( index < 0 ) {\n\t\t\t\t\ti = max;\n\n\t\t\t\t} else {\n\t\t\t\t\ti = one ? index : 0;\n\t\t\t\t}\n\n\t\t\t\t// Loop through all the selected options\n\t\t\t\tfor ( ; i < max; i++ ) {\n\t\t\t\t\toption = options[ i ];\n\n\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t// IE8-9 doesn't update selected after form reset (#2551)\n\t\t\t\t\tif ( ( option.selected || i === index ) &&\n\n\t\t\t\t\t\t\t// Don't return options that are disabled or in a disabled optgroup\n\t\t\t\t\t\t\t!option.disabled &&\n\t\t\t\t\t\t\t( !option.parentNode.disabled ||\n\t\t\t\t\t\t\t\t!nodeName( option.parentNode, \"optgroup\" ) ) ) {\n\n\t\t\t\t\t\t// Get the specific value for the option\n\t\t\t\t\t\tvalue = jQuery( option ).val();\n\n\t\t\t\t\t\t// We don't need an array for one selects\n\t\t\t\t\t\tif ( one ) {\n\t\t\t\t\t\t\treturn value;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Multi-Selects return an array\n\t\t\t\t\t\tvalues.push( value );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn values;\n\t\t\t},\n\n\t\t\tset: function( elem, value ) {\n\t\t\t\tvar optionSet, option,\n\t\t\t\t\toptions = elem.options,\n\t\t\t\t\tvalues = jQuery.makeArray( value ),\n\t\t\t\t\ti = options.length;\n\n\t\t\t\twhile ( i-- ) {\n\t\t\t\t\toption = options[ i ];\n\n\t\t\t\t\t/* eslint-disable no-cond-assign */\n\n\t\t\t\t\tif ( option.selected =\n\t\t\t\t\t\tjQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1\n\t\t\t\t\t) {\n\t\t\t\t\t\toptionSet = true;\n\t\t\t\t\t}\n\n\t\t\t\t\t/* eslint-enable no-cond-assign */\n\t\t\t\t}\n\n\t\t\t\t// Force browsers to behave consistently when non-matching value is set\n\t\t\t\tif ( !optionSet ) {\n\t\t\t\t\telem.selectedIndex = -1;\n\t\t\t\t}\n\t\t\t\treturn values;\n\t\t\t}\n\t\t}\n\t}\n} );\n\n// Radios and checkboxes getter/setter\njQuery.each( [ \"radio\", \"checkbox\" ], function() {\n\tjQuery.valHooks[ this ] = {\n\t\tset: function( elem, value ) {\n\t\t\tif ( Array.isArray( value ) ) {\n\t\t\t\treturn ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );\n\t\t\t}\n\t\t}\n\t};\n\tif ( !support.checkOn ) {\n\t\tjQuery.valHooks[ this ].get = function( elem ) {\n\t\t\treturn elem.getAttribute( \"value\" ) === null ? \"on\" : elem.value;\n\t\t};\n\t}\n} );\n\n\n\n\n// Return jQuery for attributes-only inclusion\n\n\nvar rfocusMorph = /^(?:focusinfocus|focusoutblur)$/;\n\njQuery.extend( jQuery.event, {\n\n\ttrigger: function( event, data, elem, onlyHandlers ) {\n\n\t\tvar i, cur, tmp, bubbleType, ontype, handle, special,\n\t\t\teventPath = [ elem || document ],\n\t\t\ttype = hasOwn.call( event, \"type\" ) ? event.type : event,\n\t\t\tnamespaces = hasOwn.call( event, \"namespace\" ) ? event.namespace.split( \".\" ) : [];\n\n\t\tcur = tmp = elem = elem || document;\n\n\t\t// Don't do events on text and comment nodes\n\t\tif ( elem.nodeType === 3 || elem.nodeType === 8 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// focus/blur morphs to focusin/out; ensure we're not firing them right now\n\t\tif ( rfocusMorph.test( type + jQuery.event.triggered ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( type.indexOf( \".\" ) > -1 ) {\n\n\t\t\t// Namespaced trigger; create a regexp to match event type in handle()\n\t\t\tnamespaces = type.split( \".\" );\n\t\t\ttype = namespaces.shift();\n\t\t\tnamespaces.sort();\n\t\t}\n\t\tontype = type.indexOf( \":\" ) < 0 && \"on\" + type;\n\n\t\t// Caller can pass in a jQuery.Event object, Object, or just an event type string\n\t\tevent = event[ jQuery.expando ] ?\n\t\t\tevent :\n\t\t\tnew jQuery.Event( type, typeof event === \"object\" && event );\n\n\t\t// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)\n\t\tevent.isTrigger = onlyHandlers ? 2 : 3;\n\t\tevent.namespace = namespaces.join( \".\" );\n\t\tevent.rnamespace = event.namespace ?\n\t\t\tnew RegExp( \"(^|\\\\.)\" + namespaces.join( \"\\\\.(?:.*\\\\.|)\" ) + \"(\\\\.|$)\" ) :\n\t\t\tnull;\n\n\t\t// Clean up the event in case it is being reused\n\t\tevent.result = undefined;\n\t\tif ( !event.target ) {\n\t\t\tevent.target = elem;\n\t\t}\n\n\t\t// Clone any incoming data and prepend the event, creating the handler arg list\n\t\tdata = data == null ?\n\t\t\t[ event ] :\n\t\t\tjQuery.makeArray( data, [ event ] );\n\n\t\t// Allow special events to draw outside the lines\n\t\tspecial = jQuery.event.special[ type ] || {};\n\t\tif ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Determine event propagation path in advance, per W3C events spec (#9951)\n\t\t// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)\n\t\tif ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) {\n\n\t\t\tbubbleType = special.delegateType || type;\n\t\t\tif ( !rfocusMorph.test( bubbleType + type ) ) {\n\t\t\t\tcur = cur.parentNode;\n\t\t\t}\n\t\t\tfor ( ; cur; cur = cur.parentNode ) {\n\t\t\t\teventPath.push( cur );\n\t\t\t\ttmp = cur;\n\t\t\t}\n\n\t\t\t// Only add window if we got to document (e.g., not plain obj or detached DOM)\n\t\t\tif ( tmp === ( elem.ownerDocument || document ) ) {\n\t\t\t\teventPath.push( tmp.defaultView || tmp.parentWindow || window );\n\t\t\t}\n\t\t}\n\n\t\t// Fire handlers on the event path\n\t\ti = 0;\n\t\twhile ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {\n\n\t\t\tevent.type = i > 1 ?\n\t\t\t\tbubbleType :\n\t\t\t\tspecial.bindType || type;\n\n\t\t\t// jQuery handler\n\t\t\thandle = ( dataPriv.get( cur, \"events\" ) || {} )[ event.type ] &&\n\t\t\t\tdataPriv.get( cur, \"handle\" );\n\t\t\tif ( handle ) {\n\t\t\t\thandle.apply( cur, data );\n\t\t\t}\n\n\t\t\t// Native handler\n\t\t\thandle = ontype && cur[ ontype ];\n\t\t\tif ( handle && handle.apply && acceptData( cur ) ) {\n\t\t\t\tevent.result = handle.apply( cur, data );\n\t\t\t\tif ( event.result === false ) {\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tevent.type = type;\n\n\t\t// If nobody prevented the default action, do it now\n\t\tif ( !onlyHandlers && !event.isDefaultPrevented() ) {\n\n\t\t\tif ( ( !special._default ||\n\t\t\t\tspecial._default.apply( eventPath.pop(), data ) === false ) &&\n\t\t\t\tacceptData( elem ) ) {\n\n\t\t\t\t// Call a native DOM method on the target with the same name as the event.\n\t\t\t\t// Don't do default actions on window, that's where global variables be (#6170)\n\t\t\t\tif ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) {\n\n\t\t\t\t\t// Don't re-trigger an onFOO event when we call its FOO() method\n\t\t\t\t\ttmp = elem[ ontype ];\n\n\t\t\t\t\tif ( tmp ) {\n\t\t\t\t\t\telem[ ontype ] = null;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prevent re-triggering of the same event, since we already bubbled it above\n\t\t\t\t\tjQuery.event.triggered = type;\n\t\t\t\t\telem[ type ]();\n\t\t\t\t\tjQuery.event.triggered = undefined;\n\n\t\t\t\t\tif ( tmp ) {\n\t\t\t\t\t\telem[ ontype ] = tmp;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn event.result;\n\t},\n\n\t// Piggyback on a donor event to simulate a different one\n\t// Used only for `focus(in | out)` events\n\tsimulate: function( type, elem, event ) {\n\t\tvar e = jQuery.extend(\n\t\t\tnew jQuery.Event(),\n\t\t\tevent,\n\t\t\t{\n\t\t\t\ttype: type,\n\t\t\t\tisSimulated: true\n\t\t\t}\n\t\t);\n\n\t\tjQuery.event.trigger( e, null, elem );\n\t}\n\n} );\n\njQuery.fn.extend( {\n\n\ttrigger: function( type, data ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.event.trigger( type, data, this );\n\t\t} );\n\t},\n\ttriggerHandler: function( type, data ) {\n\t\tvar elem = this[ 0 ];\n\t\tif ( elem ) {\n\t\t\treturn jQuery.event.trigger( type, data, elem, true );\n\t\t}\n\t}\n} );\n\n\njQuery.each( ( \"blur focus focusin focusout resize scroll click dblclick \" +\n\t\"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave \" +\n\t\"change select submit keydown keypress keyup contextmenu\" ).split( \" \" ),\n\tfunction( i, name ) {\n\n\t// Handle event binding\n\tjQuery.fn[ name ] = function( data, fn ) {\n\t\treturn arguments.length > 0 ?\n\t\t\tthis.on( name, null, data, fn ) :\n\t\t\tthis.trigger( name );\n\t};\n} );\n\njQuery.fn.extend( {\n\thover: function( fnOver, fnOut ) {\n\t\treturn this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );\n\t}\n} );\n\n\n\n\nsupport.focusin = \"onfocusin\" in window;\n\n\n// Support: Firefox <=44\n// Firefox doesn't have focus(in | out) events\n// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787\n//\n// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1\n// focus(in | out) events fire after focus & blur events,\n// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order\n// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857\nif ( !support.focusin ) {\n\tjQuery.each( { focus: \"focusin\", blur: \"focusout\" }, function( orig, fix ) {\n\n\t\t// Attach a single capturing handler on the document while someone wants focusin/focusout\n\t\tvar handler = function( event ) {\n\t\t\tjQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );\n\t\t};\n\n\t\tjQuery.event.special[ fix ] = {\n\t\t\tsetup: function() {\n\t\t\t\tvar doc = this.ownerDocument || this,\n\t\t\t\t\tattaches = dataPriv.access( doc, fix );\n\n\t\t\t\tif ( !attaches ) {\n\t\t\t\t\tdoc.addEventListener( orig, handler, true );\n\t\t\t\t}\n\t\t\t\tdataPriv.access( doc, fix, ( attaches || 0 ) + 1 );\n\t\t\t},\n\t\t\tteardown: function() {\n\t\t\t\tvar doc = this.ownerDocument || this,\n\t\t\t\t\tattaches = dataPriv.access( doc, fix ) - 1;\n\n\t\t\t\tif ( !attaches ) {\n\t\t\t\t\tdoc.removeEventListener( orig, handler, true );\n\t\t\t\t\tdataPriv.remove( doc, fix );\n\n\t\t\t\t} else {\n\t\t\t\t\tdataPriv.access( doc, fix, attaches );\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t} );\n}\nvar location = window.location;\n\nvar nonce = jQuery.now();\n\nvar rquery = ( /\\?/ );\n\n\n\n// Cross-browser xml parsing\njQuery.parseXML = function( data ) {\n\tvar xml;\n\tif ( !data || typeof data !== \"string\" ) {\n\t\treturn null;\n\t}\n\n\t// Support: IE 9 - 11 only\n\t// IE throws on parseFromString with invalid input.\n\ttry {\n\t\txml = ( new window.DOMParser() ).parseFromString( data, \"text/xml\" );\n\t} catch ( e ) {\n\t\txml = undefined;\n\t}\n\n\tif ( !xml || xml.getElementsByTagName( \"parsererror\" ).length ) {\n\t\tjQuery.error( \"Invalid XML: \" + data );\n\t}\n\treturn xml;\n};\n\n\nvar\n\trbracket = /\\[\\]$/,\n\trCRLF = /\\r?\\n/g,\n\trsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,\n\trsubmittable = /^(?:input|select|textarea|keygen)/i;\n\nfunction buildParams( prefix, obj, traditional, add ) {\n\tvar name;\n\n\tif ( Array.isArray( obj ) ) {\n\n\t\t// Serialize array item.\n\t\tjQuery.each( obj, function( i, v ) {\n\t\t\tif ( traditional || rbracket.test( prefix ) ) {\n\n\t\t\t\t// Treat each array item as a scalar.\n\t\t\t\tadd( prefix, v );\n\n\t\t\t} else {\n\n\t\t\t\t// Item is non-scalar (array or object), encode its numeric index.\n\t\t\t\tbuildParams(\n\t\t\t\t\tprefix + \"[\" + ( typeof v === \"object\" && v != null ? i : \"\" ) + \"]\",\n\t\t\t\t\tv,\n\t\t\t\t\ttraditional,\n\t\t\t\t\tadd\n\t\t\t\t);\n\t\t\t}\n\t\t} );\n\n\t} else if ( !traditional && jQuery.type( obj ) === \"object\" ) {\n\n\t\t// Serialize object item.\n\t\tfor ( name in obj ) {\n\t\t\tbuildParams( prefix + \"[\" + name + \"]\", obj[ name ], traditional, add );\n\t\t}\n\n\t} else {\n\n\t\t// Serialize scalar item.\n\t\tadd( prefix, obj );\n\t}\n}\n\n// Serialize an array of form elements or a set of\n// key/values into a query string\njQuery.param = function( a, traditional ) {\n\tvar prefix,\n\t\ts = [],\n\t\tadd = function( key, valueOrFunction ) {\n\n\t\t\t// If value is a function, invoke it and use its return value\n\t\t\tvar value = jQuery.isFunction( valueOrFunction ) ?\n\t\t\t\tvalueOrFunction() :\n\t\t\t\tvalueOrFunction;\n\n\t\t\ts[ s.length ] = encodeURIComponent( key ) + \"=\" +\n\t\t\t\tencodeURIComponent( value == null ? \"\" : value );\n\t\t};\n\n\t// If an array was passed in, assume that it is an array of form elements.\n\tif ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {\n\n\t\t// Serialize the form elements\n\t\tjQuery.each( a, function() {\n\t\t\tadd( this.name, this.value );\n\t\t} );\n\n\t} else {\n\n\t\t// If traditional, encode the \"old\" way (the way 1.3.2 or older\n\t\t// did it), otherwise encode params recursively.\n\t\tfor ( prefix in a ) {\n\t\t\tbuildParams( prefix, a[ prefix ], traditional, add );\n\t\t}\n\t}\n\n\t// Return the resulting serialization\n\treturn s.join( \"&\" );\n};\n\njQuery.fn.extend( {\n\tserialize: function() {\n\t\treturn jQuery.param( this.serializeArray() );\n\t},\n\tserializeArray: function() {\n\t\treturn this.map( function() {\n\n\t\t\t// Can add propHook for \"elements\" to filter or add form elements\n\t\t\tvar elements = jQuery.prop( this, \"elements\" );\n\t\t\treturn elements ? jQuery.makeArray( elements ) : this;\n\t\t} )\n\t\t.filter( function() {\n\t\t\tvar type = this.type;\n\n\t\t\t// Use .is( \":disabled\" ) so that fieldset[disabled] works\n\t\t\treturn this.name && !jQuery( this ).is( \":disabled\" ) &&\n\t\t\t\trsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&\n\t\t\t\t( this.checked || !rcheckableType.test( type ) );\n\t\t} )\n\t\t.map( function( i, elem ) {\n\t\t\tvar val = jQuery( this ).val();\n\n\t\t\tif ( val == null ) {\n\t\t\t\treturn null;\n\t\t\t}\n\n\t\t\tif ( Array.isArray( val ) ) {\n\t\t\t\treturn jQuery.map( val, function( val ) {\n\t\t\t\t\treturn { name: elem.name, value: val.replace( rCRLF, \"\\r\\n\" ) };\n\t\t\t\t} );\n\t\t\t}\n\n\t\t\treturn { name: elem.name, value: val.replace( rCRLF, \"\\r\\n\" ) };\n\t\t} ).get();\n\t}\n} );\n\n\nvar\n\tr20 = /%20/g,\n\trhash = /#.*$/,\n\trantiCache = /([?&])_=[^&]*/,\n\trheaders = /^(.*?):[ \\t]*([^\\r\\n]*)$/mg,\n\n\t// #7653, #8125, #8152: local protocol detection\n\trlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,\n\trnoContent = /^(?:GET|HEAD)$/,\n\trprotocol = /^\\/\\//,\n\n\t/* Prefilters\n\t * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)\n\t * 2) These are called:\n\t * - BEFORE asking for a transport\n\t * - AFTER param serialization (s.data is a string if s.processData is true)\n\t * 3) key is the dataType\n\t * 4) the catchall symbol \"*\" can be used\n\t * 5) execution will start with transport dataType and THEN continue down to \"*\" if needed\n\t */\n\tprefilters = {},\n\n\t/* Transports bindings\n\t * 1) key is the dataType\n\t * 2) the catchall symbol \"*\" can be used\n\t * 3) selection will start with transport dataType and THEN go to \"*\" if needed\n\t */\n\ttransports = {},\n\n\t// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression\n\tallTypes = \"*/\".concat( \"*\" ),\n\n\t// Anchor tag for parsing the document origin\n\toriginAnchor = document.createElement( \"a\" );\n\toriginAnchor.href = location.href;\n\n// Base \"constructor\" for jQuery.ajaxPrefilter and jQuery.ajaxTransport\nfunction addToPrefiltersOrTransports( structure ) {\n\n\t// dataTypeExpression is optional and defaults to \"*\"\n\treturn function( dataTypeExpression, func ) {\n\n\t\tif ( typeof dataTypeExpression !== \"string\" ) {\n\t\t\tfunc = dataTypeExpression;\n\t\t\tdataTypeExpression = \"*\";\n\t\t}\n\n\t\tvar dataType,\n\t\t\ti = 0,\n\t\t\tdataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];\n\n\t\tif ( jQuery.isFunction( func ) ) {\n\n\t\t\t// For each dataType in the dataTypeExpression\n\t\t\twhile ( ( dataType = dataTypes[ i++ ] ) ) {\n\n\t\t\t\t// Prepend if requested\n\t\t\t\tif ( dataType[ 0 ] === \"+\" ) {\n\t\t\t\t\tdataType = dataType.slice( 1 ) || \"*\";\n\t\t\t\t\t( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );\n\n\t\t\t\t// Otherwise append\n\t\t\t\t} else {\n\t\t\t\t\t( structure[ dataType ] = structure[ dataType ] || [] ).push( func );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n}\n\n// Base inspection function for prefilters and transports\nfunction inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {\n\n\tvar inspected = {},\n\t\tseekingTransport = ( structure === transports );\n\n\tfunction inspect( dataType ) {\n\t\tvar selected;\n\t\tinspected[ dataType ] = true;\n\t\tjQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {\n\t\t\tvar dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );\n\t\t\tif ( typeof dataTypeOrTransport === \"string\" &&\n\t\t\t\t!seekingTransport && !inspected[ dataTypeOrTransport ] ) {\n\n\t\t\t\toptions.dataTypes.unshift( dataTypeOrTransport );\n\t\t\t\tinspect( dataTypeOrTransport );\n\t\t\t\treturn false;\n\t\t\t} else if ( seekingTransport ) {\n\t\t\t\treturn !( selected = dataTypeOrTransport );\n\t\t\t}\n\t\t} );\n\t\treturn selected;\n\t}\n\n\treturn inspect( options.dataTypes[ 0 ] ) || !inspected[ \"*\" ] && inspect( \"*\" );\n}\n\n// A special extend for ajax options\n// that takes \"flat\" options (not to be deep extended)\n// Fixes #9887\nfunction ajaxExtend( target, src ) {\n\tvar key, deep,\n\t\tflatOptions = jQuery.ajaxSettings.flatOptions || {};\n\n\tfor ( key in src ) {\n\t\tif ( src[ key ] !== undefined ) {\n\t\t\t( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];\n\t\t}\n\t}\n\tif ( deep ) {\n\t\tjQuery.extend( true, target, deep );\n\t}\n\n\treturn target;\n}\n\n/* Handles responses to an ajax request:\n * - finds the right dataType (mediates between content-type and expected dataType)\n * - returns the corresponding response\n */\nfunction ajaxHandleResponses( s, jqXHR, responses ) {\n\n\tvar ct, type, finalDataType, firstDataType,\n\t\tcontents = s.contents,\n\t\tdataTypes = s.dataTypes;\n\n\t// Remove auto dataType and get content-type in the process\n\twhile ( dataTypes[ 0 ] === \"*\" ) {\n\t\tdataTypes.shift();\n\t\tif ( ct === undefined ) {\n\t\t\tct = s.mimeType || jqXHR.getResponseHeader( \"Content-Type\" );\n\t\t}\n\t}\n\n\t// Check if we're dealing with a known content-type\n\tif ( ct ) {\n\t\tfor ( type in contents ) {\n\t\t\tif ( contents[ type ] && contents[ type ].test( ct ) ) {\n\t\t\t\tdataTypes.unshift( type );\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Check to see if we have a response for the expected dataType\n\tif ( dataTypes[ 0 ] in responses ) {\n\t\tfinalDataType = dataTypes[ 0 ];\n\t} else {\n\n\t\t// Try convertible dataTypes\n\t\tfor ( type in responses ) {\n\t\t\tif ( !dataTypes[ 0 ] || s.converters[ type + \" \" + dataTypes[ 0 ] ] ) {\n\t\t\t\tfinalDataType = type;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif ( !firstDataType ) {\n\t\t\t\tfirstDataType = type;\n\t\t\t}\n\t\t}\n\n\t\t// Or just use first one\n\t\tfinalDataType = finalDataType || firstDataType;\n\t}\n\n\t// If we found a dataType\n\t// We add the dataType to the list if needed\n\t// and return the corresponding response\n\tif ( finalDataType ) {\n\t\tif ( finalDataType !== dataTypes[ 0 ] ) {\n\t\t\tdataTypes.unshift( finalDataType );\n\t\t}\n\t\treturn responses[ finalDataType ];\n\t}\n}\n\n/* Chain conversions given the request and the original response\n * Also sets the responseXXX fields on the jqXHR instance\n */\nfunction ajaxConvert( s, response, jqXHR, isSuccess ) {\n\tvar conv2, current, conv, tmp, prev,\n\t\tconverters = {},\n\n\t\t// Work with a copy of dataTypes in case we need to modify it for conversion\n\t\tdataTypes = s.dataTypes.slice();\n\n\t// Create converters map with lowercased keys\n\tif ( dataTypes[ 1 ] ) {\n\t\tfor ( conv in s.converters ) {\n\t\t\tconverters[ conv.toLowerCase() ] = s.converters[ conv ];\n\t\t}\n\t}\n\n\tcurrent = dataTypes.shift();\n\n\t// Convert to each sequential dataType\n\twhile ( current ) {\n\n\t\tif ( s.responseFields[ current ] ) {\n\t\t\tjqXHR[ s.responseFields[ current ] ] = response;\n\t\t}\n\n\t\t// Apply the dataFilter if provided\n\t\tif ( !prev && isSuccess && s.dataFilter ) {\n\t\t\tresponse = s.dataFilter( response, s.dataType );\n\t\t}\n\n\t\tprev = current;\n\t\tcurrent = dataTypes.shift();\n\n\t\tif ( current ) {\n\n\t\t\t// There's only work to do if current dataType is non-auto\n\t\t\tif ( current === \"*\" ) {\n\n\t\t\t\tcurrent = prev;\n\n\t\t\t// Convert response if prev dataType is non-auto and differs from current\n\t\t\t} else if ( prev !== \"*\" && prev !== current ) {\n\n\t\t\t\t// Seek a direct converter\n\t\t\t\tconv = converters[ prev + \" \" + current ] || converters[ \"* \" + current ];\n\n\t\t\t\t// If none found, seek a pair\n\t\t\t\tif ( !conv ) {\n\t\t\t\t\tfor ( conv2 in converters ) {\n\n\t\t\t\t\t\t// If conv2 outputs current\n\t\t\t\t\t\ttmp = conv2.split( \" \" );\n\t\t\t\t\t\tif ( tmp[ 1 ] === current ) {\n\n\t\t\t\t\t\t\t// If prev can be converted to accepted input\n\t\t\t\t\t\t\tconv = converters[ prev + \" \" + tmp[ 0 ] ] ||\n\t\t\t\t\t\t\t\tconverters[ \"* \" + tmp[ 0 ] ];\n\t\t\t\t\t\t\tif ( conv ) {\n\n\t\t\t\t\t\t\t\t// Condense equivalence converters\n\t\t\t\t\t\t\t\tif ( conv === true ) {\n\t\t\t\t\t\t\t\t\tconv = converters[ conv2 ];\n\n\t\t\t\t\t\t\t\t// Otherwise, insert the intermediate dataType\n\t\t\t\t\t\t\t\t} else if ( converters[ conv2 ] !== true ) {\n\t\t\t\t\t\t\t\t\tcurrent = tmp[ 0 ];\n\t\t\t\t\t\t\t\t\tdataTypes.unshift( tmp[ 1 ] );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Apply converter (if not an equivalence)\n\t\t\t\tif ( conv !== true ) {\n\n\t\t\t\t\t// Unless errors are allowed to bubble, catch and return them\n\t\t\t\t\tif ( conv && s.throws ) {\n\t\t\t\t\t\tresponse = conv( response );\n\t\t\t\t\t} else {\n\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\tresponse = conv( response );\n\t\t\t\t\t\t} catch ( e ) {\n\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\tstate: \"parsererror\",\n\t\t\t\t\t\t\t\terror: conv ? e : \"No conversion from \" + prev + \" to \" + current\n\t\t\t\t\t\t\t};\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn { state: \"success\", data: response };\n}\n\njQuery.extend( {\n\n\t// Counter for holding the number of active queries\n\tactive: 0,\n\n\t// Last-Modified header cache for next request\n\tlastModified: {},\n\tetag: {},\n\n\tajaxSettings: {\n\t\turl: location.href,\n\t\ttype: \"GET\",\n\t\tisLocal: rlocalProtocol.test( location.protocol ),\n\t\tglobal: true,\n\t\tprocessData: true,\n\t\tasync: true,\n\t\tcontentType: \"application/x-www-form-urlencoded; charset=UTF-8\",\n\n\t\t/*\n\t\ttimeout: 0,\n\t\tdata: null,\n\t\tdataType: null,\n\t\tusername: null,\n\t\tpassword: null,\n\t\tcache: null,\n\t\tthrows: false,\n\t\ttraditional: false,\n\t\theaders: {},\n\t\t*/\n\n\t\taccepts: {\n\t\t\t\"*\": allTypes,\n\t\t\ttext: \"text/plain\",\n\t\t\thtml: \"text/html\",\n\t\t\txml: \"application/xml, text/xml\",\n\t\t\tjson: \"application/json, text/javascript\"\n\t\t},\n\n\t\tcontents: {\n\t\t\txml: /\\bxml\\b/,\n\t\t\thtml: /\\bhtml/,\n\t\t\tjson: /\\bjson\\b/\n\t\t},\n\n\t\tresponseFields: {\n\t\t\txml: \"responseXML\",\n\t\t\ttext: \"responseText\",\n\t\t\tjson: \"responseJSON\"\n\t\t},\n\n\t\t// Data converters\n\t\t// Keys separate source (or catchall \"*\") and destination types with a single space\n\t\tconverters: {\n\n\t\t\t// Convert anything to text\n\t\t\t\"* text\": String,\n\n\t\t\t// Text to html (true = no transformation)\n\t\t\t\"text html\": true,\n\n\t\t\t// Evaluate text as a json expression\n\t\t\t\"text json\": JSON.parse,\n\n\t\t\t// Parse text as xml\n\t\t\t\"text xml\": jQuery.parseXML\n\t\t},\n\n\t\t// For options that shouldn't be deep extended:\n\t\t// you can add your own custom options here if\n\t\t// and when you create one that shouldn't be\n\t\t// deep extended (see ajaxExtend)\n\t\tflatOptions: {\n\t\t\turl: true,\n\t\t\tcontext: true\n\t\t}\n\t},\n\n\t// Creates a full fledged settings object into target\n\t// with both ajaxSettings and settings fields.\n\t// If target is omitted, writes into ajaxSettings.\n\tajaxSetup: function( target, settings ) {\n\t\treturn settings ?\n\n\t\t\t// Building a settings object\n\t\t\tajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :\n\n\t\t\t// Extending ajaxSettings\n\t\t\tajaxExtend( jQuery.ajaxSettings, target );\n\t},\n\n\tajaxPrefilter: addToPrefiltersOrTransports( prefilters ),\n\tajaxTransport: addToPrefiltersOrTransports( transports ),\n\n\t// Main method\n\tajax: function( url, options ) {\n\n\t\t// If url is an object, simulate pre-1.5 signature\n\t\tif ( typeof url === \"object\" ) {\n\t\t\toptions = url;\n\t\t\turl = undefined;\n\t\t}\n\n\t\t// Force options to be an object\n\t\toptions = options || {};\n\n\t\tvar transport,\n\n\t\t\t// URL without anti-cache param\n\t\t\tcacheURL,\n\n\t\t\t// Response headers\n\t\t\tresponseHeadersString,\n\t\t\tresponseHeaders,\n\n\t\t\t// timeout handle\n\t\t\ttimeoutTimer,\n\n\t\t\t// Url cleanup var\n\t\t\turlAnchor,\n\n\t\t\t// Request state (becomes false upon send and true upon completion)\n\t\t\tcompleted,\n\n\t\t\t// To know if global events are to be dispatched\n\t\t\tfireGlobals,\n\n\t\t\t// Loop variable\n\t\t\ti,\n\n\t\t\t// uncached part of the url\n\t\t\tuncached,\n\n\t\t\t// Create the final options object\n\t\t\ts = jQuery.ajaxSetup( {}, options ),\n\n\t\t\t// Callbacks context\n\t\t\tcallbackContext = s.context || s,\n\n\t\t\t// Context for global events is callbackContext if it is a DOM node or jQuery collection\n\t\t\tglobalEventContext = s.context &&\n\t\t\t\t( callbackContext.nodeType || callbackContext.jquery ) ?\n\t\t\t\t\tjQuery( callbackContext ) :\n\t\t\t\t\tjQuery.event,\n\n\t\t\t// Deferreds\n\t\t\tdeferred = jQuery.Deferred(),\n\t\t\tcompleteDeferred = jQuery.Callbacks( \"once memory\" ),\n\n\t\t\t// Status-dependent callbacks\n\t\t\tstatusCode = s.statusCode || {},\n\n\t\t\t// Headers (they are sent all at once)\n\t\t\trequestHeaders = {},\n\t\t\trequestHeadersNames = {},\n\n\t\t\t// Default abort message\n\t\t\tstrAbort = \"canceled\",\n\n\t\t\t// Fake xhr\n\t\t\tjqXHR = {\n\t\t\t\treadyState: 0,\n\n\t\t\t\t// Builds headers hashtable if needed\n\t\t\t\tgetResponseHeader: function( key ) {\n\t\t\t\t\tvar match;\n\t\t\t\t\tif ( completed ) {\n\t\t\t\t\t\tif ( !responseHeaders ) {\n\t\t\t\t\t\t\tresponseHeaders = {};\n\t\t\t\t\t\t\twhile ( ( match = rheaders.exec( responseHeadersString ) ) ) {\n\t\t\t\t\t\t\t\tresponseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ];\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\tmatch = responseHeaders[ key.toLowerCase() ];\n\t\t\t\t\t}\n\t\t\t\t\treturn match == null ? null : match;\n\t\t\t\t},\n\n\t\t\t\t// Raw string\n\t\t\t\tgetAllResponseHeaders: function() {\n\t\t\t\t\treturn completed ? responseHeadersString : null;\n\t\t\t\t},\n\n\t\t\t\t// Caches the header\n\t\t\t\tsetRequestHeader: function( name, value ) {\n\t\t\t\t\tif ( completed == null ) {\n\t\t\t\t\t\tname = requestHeadersNames[ name.toLowerCase() ] =\n\t\t\t\t\t\t\trequestHeadersNames[ name.toLowerCase() ] || name;\n\t\t\t\t\t\trequestHeaders[ name ] = value;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Overrides response content-type header\n\t\t\t\toverrideMimeType: function( type ) {\n\t\t\t\t\tif ( completed == null ) {\n\t\t\t\t\t\ts.mimeType = type;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Status-dependent callbacks\n\t\t\t\tstatusCode: function( map ) {\n\t\t\t\t\tvar code;\n\t\t\t\t\tif ( map ) {\n\t\t\t\t\t\tif ( completed ) {\n\n\t\t\t\t\t\t\t// Execute the appropriate callbacks\n\t\t\t\t\t\t\tjqXHR.always( map[ jqXHR.status ] );\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Lazy-add the new callbacks in a way that preserves old ones\n\t\t\t\t\t\t\tfor ( code in map ) {\n\t\t\t\t\t\t\t\tstatusCode[ code ] = [ statusCode[ code ], map[ code ] ];\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Cancel the request\n\t\t\t\tabort: function( statusText ) {\n\t\t\t\t\tvar finalText = statusText || strAbort;\n\t\t\t\t\tif ( transport ) {\n\t\t\t\t\t\ttransport.abort( finalText );\n\t\t\t\t\t}\n\t\t\t\t\tdone( 0, finalText );\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\t\t\t};\n\n\t\t// Attach deferreds\n\t\tdeferred.promise( jqXHR );\n\n\t\t// Add protocol if not provided (prefilters might expect it)\n\t\t// Handle falsy url in the settings object (#10093: consistency with old signature)\n\t\t// We also use the url parameter if available\n\t\ts.url = ( ( url || s.url || location.href ) + \"\" )\n\t\t\t.replace( rprotocol, location.protocol + \"//\" );\n\n\t\t// Alias method option to type as per ticket #12004\n\t\ts.type = options.method || options.type || s.method || s.type;\n\n\t\t// Extract dataTypes list\n\t\ts.dataTypes = ( s.dataType || \"*\" ).toLowerCase().match( rnothtmlwhite ) || [ \"\" ];\n\n\t\t// A cross-domain request is in order when the origin doesn't match the current origin.\n\t\tif ( s.crossDomain == null ) {\n\t\t\turlAnchor = document.createElement( \"a\" );\n\n\t\t\t// Support: IE <=8 - 11, Edge 12 - 13\n\t\t\t// IE throws exception on accessing the href property if url is malformed,\n\t\t\t// e.g. http://example.com:80x/\n\t\t\ttry {\n\t\t\t\turlAnchor.href = s.url;\n\n\t\t\t\t// Support: IE <=8 - 11 only\n\t\t\t\t// Anchor's host property isn't correctly set when s.url is relative\n\t\t\t\turlAnchor.href = urlAnchor.href;\n\t\t\t\ts.crossDomain = originAnchor.protocol + \"//\" + originAnchor.host !==\n\t\t\t\t\turlAnchor.protocol + \"//\" + urlAnchor.host;\n\t\t\t} catch ( e ) {\n\n\t\t\t\t// If there is an error parsing the URL, assume it is crossDomain,\n\t\t\t\t// it can be rejected by the transport if it is invalid\n\t\t\t\ts.crossDomain = true;\n\t\t\t}\n\t\t}\n\n\t\t// Convert data if not already a string\n\t\tif ( s.data && s.processData && typeof s.data !== \"string\" ) {\n\t\t\ts.data = jQuery.param( s.data, s.traditional );\n\t\t}\n\n\t\t// Apply prefilters\n\t\tinspectPrefiltersOrTransports( prefilters, s, options, jqXHR );\n\n\t\t// If request was aborted inside a prefilter, stop there\n\t\tif ( completed ) {\n\t\t\treturn jqXHR;\n\t\t}\n\n\t\t// We can fire global events as of now if asked to\n\t\t// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)\n\t\tfireGlobals = jQuery.event && s.global;\n\n\t\t// Watch for a new set of requests\n\t\tif ( fireGlobals && jQuery.active++ === 0 ) {\n\t\t\tjQuery.event.trigger( \"ajaxStart\" );\n\t\t}\n\n\t\t// Uppercase the type\n\t\ts.type = s.type.toUpperCase();\n\n\t\t// Determine if request has content\n\t\ts.hasContent = !rnoContent.test( s.type );\n\n\t\t// Save the URL in case we're toying with the If-Modified-Since\n\t\t// and/or If-None-Match header later on\n\t\t// Remove hash to simplify url manipulation\n\t\tcacheURL = s.url.replace( rhash, \"\" );\n\n\t\t// More options handling for requests with no content\n\t\tif ( !s.hasContent ) {\n\n\t\t\t// Remember the hash so we can put it back\n\t\t\tuncached = s.url.slice( cacheURL.length );\n\n\t\t\t// If data is available, append data to url\n\t\t\tif ( s.data ) {\n\t\t\t\tcacheURL += ( rquery.test( cacheURL ) ? \"&\" : \"?\" ) + s.data;\n\n\t\t\t\t// #9682: remove data so that it's not used in an eventual retry\n\t\t\t\tdelete s.data;\n\t\t\t}\n\n\t\t\t// Add or update anti-cache param if needed\n\t\t\tif ( s.cache === false ) {\n\t\t\t\tcacheURL = cacheURL.replace( rantiCache, \"$1\" );\n\t\t\t\tuncached = ( rquery.test( cacheURL ) ? \"&\" : \"?\" ) + \"_=\" + ( nonce++ ) + uncached;\n\t\t\t}\n\n\t\t\t// Put hash and anti-cache on the URL that will be requested (gh-1732)\n\t\t\ts.url = cacheURL + uncached;\n\n\t\t// Change '%20' to '+' if this is encoded form body content (gh-2658)\n\t\t} else if ( s.data && s.processData &&\n\t\t\t( s.contentType || \"\" ).indexOf( \"application/x-www-form-urlencoded\" ) === 0 ) {\n\t\t\ts.data = s.data.replace( r20, \"+\" );\n\t\t}\n\n\t\t// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.\n\t\tif ( s.ifModified ) {\n\t\t\tif ( jQuery.lastModified[ cacheURL ] ) {\n\t\t\t\tjqXHR.setRequestHeader( \"If-Modified-Since\", jQuery.lastModified[ cacheURL ] );\n\t\t\t}\n\t\t\tif ( jQuery.etag[ cacheURL ] ) {\n\t\t\t\tjqXHR.setRequestHeader( \"If-None-Match\", jQuery.etag[ cacheURL ] );\n\t\t\t}\n\t\t}\n\n\t\t// Set the correct header, if data is being sent\n\t\tif ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {\n\t\t\tjqXHR.setRequestHeader( \"Content-Type\", s.contentType );\n\t\t}\n\n\t\t// Set the Accepts header for the server, depending on the dataType\n\t\tjqXHR.setRequestHeader(\n\t\t\t\"Accept\",\n\t\t\ts.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?\n\t\t\t\ts.accepts[ s.dataTypes[ 0 ] ] +\n\t\t\t\t\t( s.dataTypes[ 0 ] !== \"*\" ? \", \" + allTypes + \"; q=0.01\" : \"\" ) :\n\t\t\t\ts.accepts[ \"*\" ]\n\t\t);\n\n\t\t// Check for headers option\n\t\tfor ( i in s.headers ) {\n\t\t\tjqXHR.setRequestHeader( i, s.headers[ i ] );\n\t\t}\n\n\t\t// Allow custom headers/mimetypes and early abort\n\t\tif ( s.beforeSend &&\n\t\t\t( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {\n\n\t\t\t// Abort if not done already and return\n\t\t\treturn jqXHR.abort();\n\t\t}\n\n\t\t// Aborting is no longer a cancellation\n\t\tstrAbort = \"abort\";\n\n\t\t// Install callbacks on deferreds\n\t\tcompleteDeferred.add( s.complete );\n\t\tjqXHR.done( s.success );\n\t\tjqXHR.fail( s.error );\n\n\t\t// Get transport\n\t\ttransport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );\n\n\t\t// If no transport, we auto-abort\n\t\tif ( !transport ) {\n\t\t\tdone( -1, \"No Transport\" );\n\t\t} else {\n\t\t\tjqXHR.readyState = 1;\n\n\t\t\t// Send global event\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( \"ajaxSend\", [ jqXHR, s ] );\n\t\t\t}\n\n\t\t\t// If request was aborted inside ajaxSend, stop there\n\t\t\tif ( completed ) {\n\t\t\t\treturn jqXHR;\n\t\t\t}\n\n\t\t\t// Timeout\n\t\t\tif ( s.async && s.timeout > 0 ) {\n\t\t\t\ttimeoutTimer = window.setTimeout( function() {\n\t\t\t\t\tjqXHR.abort( \"timeout\" );\n\t\t\t\t}, s.timeout );\n\t\t\t}\n\n\t\t\ttry {\n\t\t\t\tcompleted = false;\n\t\t\t\ttransport.send( requestHeaders, done );\n\t\t\t} catch ( e ) {\n\n\t\t\t\t// Rethrow post-completion exceptions\n\t\t\t\tif ( completed ) {\n\t\t\t\t\tthrow e;\n\t\t\t\t}\n\n\t\t\t\t// Propagate others as results\n\t\t\t\tdone( -1, e );\n\t\t\t}\n\t\t}\n\n\t\t// Callback for when everything is done\n\t\tfunction done( status, nativeStatusText, responses, headers ) {\n\t\t\tvar isSuccess, success, error, response, modified,\n\t\t\t\tstatusText = nativeStatusText;\n\n\t\t\t// Ignore repeat invocations\n\t\t\tif ( completed ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tcompleted = true;\n\n\t\t\t// Clear timeout if it exists\n\t\t\tif ( timeoutTimer ) {\n\t\t\t\twindow.clearTimeout( timeoutTimer );\n\t\t\t}\n\n\t\t\t// Dereference transport for early garbage collection\n\t\t\t// (no matter how long the jqXHR object will be used)\n\t\t\ttransport = undefined;\n\n\t\t\t// Cache response headers\n\t\t\tresponseHeadersString = headers || \"\";\n\n\t\t\t// Set readyState\n\t\t\tjqXHR.readyState = status > 0 ? 4 : 0;\n\n\t\t\t// Determine if successful\n\t\t\tisSuccess = status >= 200 && status < 300 || status === 304;\n\n\t\t\t// Get response data\n\t\t\tif ( responses ) {\n\t\t\t\tresponse = ajaxHandleResponses( s, jqXHR, responses );\n\t\t\t}\n\n\t\t\t// Convert no matter what (that way responseXXX fields are always set)\n\t\t\tresponse = ajaxConvert( s, response, jqXHR, isSuccess );\n\n\t\t\t// If successful, handle type chaining\n\t\t\tif ( isSuccess ) {\n\n\t\t\t\t// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.\n\t\t\t\tif ( s.ifModified ) {\n\t\t\t\t\tmodified = jqXHR.getResponseHeader( \"Last-Modified\" );\n\t\t\t\t\tif ( modified ) {\n\t\t\t\t\t\tjQuery.lastModified[ cacheURL ] = modified;\n\t\t\t\t\t}\n\t\t\t\t\tmodified = jqXHR.getResponseHeader( \"etag\" );\n\t\t\t\t\tif ( modified ) {\n\t\t\t\t\t\tjQuery.etag[ cacheURL ] = modified;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// if no content\n\t\t\t\tif ( status === 204 || s.type === \"HEAD\" ) {\n\t\t\t\t\tstatusText = \"nocontent\";\n\n\t\t\t\t// if not modified\n\t\t\t\t} else if ( status === 304 ) {\n\t\t\t\t\tstatusText = \"notmodified\";\n\n\t\t\t\t// If we have data, let's convert it\n\t\t\t\t} else {\n\t\t\t\t\tstatusText = response.state;\n\t\t\t\t\tsuccess = response.data;\n\t\t\t\t\terror = response.error;\n\t\t\t\t\tisSuccess = !error;\n\t\t\t\t}\n\t\t\t} else {\n\n\t\t\t\t// Extract error from statusText and normalize for non-aborts\n\t\t\t\terror = statusText;\n\t\t\t\tif ( status || !statusText ) {\n\t\t\t\t\tstatusText = \"error\";\n\t\t\t\t\tif ( status < 0 ) {\n\t\t\t\t\t\tstatus = 0;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Set data for the fake xhr object\n\t\t\tjqXHR.status = status;\n\t\t\tjqXHR.statusText = ( nativeStatusText || statusText ) + \"\";\n\n\t\t\t// Success/Error\n\t\t\tif ( isSuccess ) {\n\t\t\t\tdeferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );\n\t\t\t} else {\n\t\t\t\tdeferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );\n\t\t\t}\n\n\t\t\t// Status-dependent callbacks\n\t\t\tjqXHR.statusCode( statusCode );\n\t\t\tstatusCode = undefined;\n\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( isSuccess ? \"ajaxSuccess\" : \"ajaxError\",\n\t\t\t\t\t[ jqXHR, s, isSuccess ? success : error ] );\n\t\t\t}\n\n\t\t\t// Complete\n\t\t\tcompleteDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );\n\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( \"ajaxComplete\", [ jqXHR, s ] );\n\n\t\t\t\t// Handle the global AJAX counter\n\t\t\t\tif ( !( --jQuery.active ) ) {\n\t\t\t\t\tjQuery.event.trigger( \"ajaxStop\" );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn jqXHR;\n\t},\n\n\tgetJSON: function( url, data, callback ) {\n\t\treturn jQuery.get( url, data, callback, \"json\" );\n\t},\n\n\tgetScript: function( url, callback ) {\n\t\treturn jQuery.get( url, undefined, callback, \"script\" );\n\t}\n} );\n\njQuery.each( [ \"get\", \"post\" ], function( i, method ) {\n\tjQuery[ method ] = function( url, data, callback, type ) {\n\n\t\t// Shift arguments if data argument was omitted\n\t\tif ( jQuery.isFunction( data ) ) {\n\t\t\ttype = type || callback;\n\t\t\tcallback = data;\n\t\t\tdata = undefined;\n\t\t}\n\n\t\t// The url can be an options object (which then must have .url)\n\t\treturn jQuery.ajax( jQuery.extend( {\n\t\t\turl: url,\n\t\t\ttype: method,\n\t\t\tdataType: type,\n\t\t\tdata: data,\n\t\t\tsuccess: callback\n\t\t}, jQuery.isPlainObject( url ) && url ) );\n\t};\n} );\n\n\njQuery._evalUrl = function( url ) {\n\treturn jQuery.ajax( {\n\t\turl: url,\n\n\t\t// Make this explicit, since user can override this through ajaxSetup (#11264)\n\t\ttype: \"GET\",\n\t\tdataType: \"script\",\n\t\tcache: true,\n\t\tasync: false,\n\t\tglobal: false,\n\t\t\"throws\": true\n\t} );\n};\n\n\njQuery.fn.extend( {\n\twrapAll: function( html ) {\n\t\tvar wrap;\n\n\t\tif ( this[ 0 ] ) {\n\t\t\tif ( jQuery.isFunction( html ) ) {\n\t\t\t\thtml = html.call( this[ 0 ] );\n\t\t\t}\n\n\t\t\t// The elements to wrap the target around\n\t\t\twrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );\n\n\t\t\tif ( this[ 0 ].parentNode ) {\n\t\t\t\twrap.insertBefore( this[ 0 ] );\n\t\t\t}\n\n\t\t\twrap.map( function() {\n\t\t\t\tvar elem = this;\n\n\t\t\t\twhile ( elem.firstElementChild ) {\n\t\t\t\t\telem = elem.firstElementChild;\n\t\t\t\t}\n\n\t\t\t\treturn elem;\n\t\t\t} ).append( this );\n\t\t}\n\n\t\treturn this;\n\t},\n\n\twrapInner: function( html ) {\n\t\tif ( jQuery.isFunction( html ) ) {\n\t\t\treturn this.each( function( i ) {\n\t\t\t\tjQuery( this ).wrapInner( html.call( this, i ) );\n\t\t\t} );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar self = jQuery( this ),\n\t\t\t\tcontents = self.contents();\n\n\t\t\tif ( contents.length ) {\n\t\t\t\tcontents.wrapAll( html );\n\n\t\t\t} else {\n\t\t\t\tself.append( html );\n\t\t\t}\n\t\t} );\n\t},\n\n\twrap: function( html ) {\n\t\tvar isFunction = jQuery.isFunction( html );\n\n\t\treturn this.each( function( i ) {\n\t\t\tjQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html );\n\t\t} );\n\t},\n\n\tunwrap: function( selector ) {\n\t\tthis.parent( selector ).not( \"body\" ).each( function() {\n\t\t\tjQuery( this ).replaceWith( this.childNodes );\n\t\t} );\n\t\treturn this;\n\t}\n} );\n\n\njQuery.expr.pseudos.hidden = function( elem ) {\n\treturn !jQuery.expr.pseudos.visible( elem );\n};\njQuery.expr.pseudos.visible = function( elem ) {\n\treturn !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );\n};\n\n\n\n\njQuery.ajaxSettings.xhr = function() {\n\ttry {\n\t\treturn new window.XMLHttpRequest();\n\t} catch ( e ) {}\n};\n\nvar xhrSuccessStatus = {\n\n\t\t// File protocol always yields status code 0, assume 200\n\t\t0: 200,\n\n\t\t// Support: IE <=9 only\n\t\t// #1450: sometimes IE returns 1223 when it should be 204\n\t\t1223: 204\n\t},\n\txhrSupported = jQuery.ajaxSettings.xhr();\n\nsupport.cors = !!xhrSupported && ( \"withCredentials\" in xhrSupported );\nsupport.ajax = xhrSupported = !!xhrSupported;\n\njQuery.ajaxTransport( function( options ) {\n\tvar callback, errorCallback;\n\n\t// Cross domain only allowed if supported through XMLHttpRequest\n\tif ( support.cors || xhrSupported && !options.crossDomain ) {\n\t\treturn {\n\t\t\tsend: function( headers, complete ) {\n\t\t\t\tvar i,\n\t\t\t\t\txhr = options.xhr();\n\n\t\t\t\txhr.open(\n\t\t\t\t\toptions.type,\n\t\t\t\t\toptions.url,\n\t\t\t\t\toptions.async,\n\t\t\t\t\toptions.username,\n\t\t\t\t\toptions.password\n\t\t\t\t);\n\n\t\t\t\t// Apply custom fields if provided\n\t\t\t\tif ( options.xhrFields ) {\n\t\t\t\t\tfor ( i in options.xhrFields ) {\n\t\t\t\t\t\txhr[ i ] = options.xhrFields[ i ];\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Override mime type if needed\n\t\t\t\tif ( options.mimeType && xhr.overrideMimeType ) {\n\t\t\t\t\txhr.overrideMimeType( options.mimeType );\n\t\t\t\t}\n\n\t\t\t\t// X-Requested-With header\n\t\t\t\t// For cross-domain requests, seeing as conditions for a preflight are\n\t\t\t\t// akin to a jigsaw puzzle, we simply never set it to be sure.\n\t\t\t\t// (it can always be set on a per-request basis or even using ajaxSetup)\n\t\t\t\t// For same-domain requests, won't change header if already provided.\n\t\t\t\tif ( !options.crossDomain && !headers[ \"X-Requested-With\" ] ) {\n\t\t\t\t\theaders[ \"X-Requested-With\" ] = \"XMLHttpRequest\";\n\t\t\t\t}\n\n\t\t\t\t// Set headers\n\t\t\t\tfor ( i in headers ) {\n\t\t\t\t\txhr.setRequestHeader( i, headers[ i ] );\n\t\t\t\t}\n\n\t\t\t\t// Callback\n\t\t\t\tcallback = function( type ) {\n\t\t\t\t\treturn function() {\n\t\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\t\tcallback = errorCallback = xhr.onload =\n\t\t\t\t\t\t\t\txhr.onerror = xhr.onabort = xhr.onreadystatechange = null;\n\n\t\t\t\t\t\t\tif ( type === \"abort\" ) {\n\t\t\t\t\t\t\t\txhr.abort();\n\t\t\t\t\t\t\t} else if ( type === \"error\" ) {\n\n\t\t\t\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t\t\t\t// On a manual native abort, IE9 throws\n\t\t\t\t\t\t\t\t// errors on any property access that is not readyState\n\t\t\t\t\t\t\t\tif ( typeof xhr.status !== \"number\" ) {\n\t\t\t\t\t\t\t\t\tcomplete( 0, \"error\" );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tcomplete(\n\n\t\t\t\t\t\t\t\t\t\t// File: protocol always yields status 0; see #8605, #14207\n\t\t\t\t\t\t\t\t\t\txhr.status,\n\t\t\t\t\t\t\t\t\t\txhr.statusText\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tcomplete(\n\t\t\t\t\t\t\t\t\txhrSuccessStatus[ xhr.status ] || xhr.status,\n\t\t\t\t\t\t\t\t\txhr.statusText,\n\n\t\t\t\t\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t\t\t\t\t// IE9 has no XHR2 but throws on binary (trac-11426)\n\t\t\t\t\t\t\t\t\t// For XHR2 non-text, let the caller handle it (gh-2498)\n\t\t\t\t\t\t\t\t\t( xhr.responseType || \"text\" ) !== \"text\" ||\n\t\t\t\t\t\t\t\t\ttypeof xhr.responseText !== \"string\" ?\n\t\t\t\t\t\t\t\t\t\t{ binary: xhr.response } :\n\t\t\t\t\t\t\t\t\t\t{ text: xhr.responseText },\n\t\t\t\t\t\t\t\t\txhr.getAllResponseHeaders()\n\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t};\n\n\t\t\t\t// Listen to events\n\t\t\t\txhr.onload = callback();\n\t\t\t\terrorCallback = xhr.onerror = callback( \"error\" );\n\n\t\t\t\t// Support: IE 9 only\n\t\t\t\t// Use onreadystatechange to replace onabort\n\t\t\t\t// to handle uncaught aborts\n\t\t\t\tif ( xhr.onabort !== undefined ) {\n\t\t\t\t\txhr.onabort = errorCallback;\n\t\t\t\t} else {\n\t\t\t\t\txhr.onreadystatechange = function() {\n\n\t\t\t\t\t\t// Check readyState before timeout as it changes\n\t\t\t\t\t\tif ( xhr.readyState === 4 ) {\n\n\t\t\t\t\t\t\t// Allow onerror to be called first,\n\t\t\t\t\t\t\t// but that will not handle a native abort\n\t\t\t\t\t\t\t// Also, save errorCallback to a variable\n\t\t\t\t\t\t\t// as xhr.onerror cannot be accessed\n\t\t\t\t\t\t\twindow.setTimeout( function() {\n\t\t\t\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\t\t\t\terrorCallback();\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t}\n\n\t\t\t\t// Create the abort callback\n\t\t\t\tcallback = callback( \"abort\" );\n\n\t\t\t\ttry {\n\n\t\t\t\t\t// Do send the request (this may raise an exception)\n\t\t\t\t\txhr.send( options.hasContent && options.data || null );\n\t\t\t\t} catch ( e ) {\n\n\t\t\t\t\t// #14683: Only rethrow if this hasn't been notified as an error yet\n\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\tthrow e;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\n\t\t\tabort: function() {\n\t\t\t\tif ( callback ) {\n\t\t\t\t\tcallback();\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t}\n} );\n\n\n\n\n// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)\njQuery.ajaxPrefilter( function( s ) {\n\tif ( s.crossDomain ) {\n\t\ts.contents.script = false;\n\t}\n} );\n\n// Install script dataType\njQuery.ajaxSetup( {\n\taccepts: {\n\t\tscript: \"text/javascript, application/javascript, \" +\n\t\t\t\"application/ecmascript, application/x-ecmascript\"\n\t},\n\tcontents: {\n\t\tscript: /\\b(?:java|ecma)script\\b/\n\t},\n\tconverters: {\n\t\t\"text script\": function( text ) {\n\t\t\tjQuery.globalEval( text );\n\t\t\treturn text;\n\t\t}\n\t}\n} );\n\n// Handle cache's special case and crossDomain\njQuery.ajaxPrefilter( \"script\", function( s ) {\n\tif ( s.cache === undefined ) {\n\t\ts.cache = false;\n\t}\n\tif ( s.crossDomain ) {\n\t\ts.type = \"GET\";\n\t}\n} );\n\n// Bind script tag hack transport\njQuery.ajaxTransport( \"script\", function( s ) {\n\n\t// This transport only deals with cross domain requests\n\tif ( s.crossDomain ) {\n\t\tvar script, callback;\n\t\treturn {\n\t\t\tsend: function( _, complete ) {\n\t\t\t\tscript = jQuery( \"\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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Distributions¶

\n\n\n\n\n\n\n\n\n\n
copyright:
    \n
  1. 2015 by Mat Leonard.
    2. \n
\n
license:

MIT, see LICENSE for more details.

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Distribution log likelihoods for building Bayesian models.

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These should all automatically sum the log likelihoods if x is a numpy array.

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\ndistributions.bernoulli(k, p)¶
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Bernoulli distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • k – int, np.array. Number of successes.
    • \n
  • p – int, float, np.array. Success probability.
    • \n
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Special case of binomial distribution, with n set to 1.

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\ndistributions.beta(x, alpha=1, beta=1)¶
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Beta distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – float, np.array. \\(0 < x < 1\\)
    • \n
  • alpha – (optional) int, float. Shape parameter, \\(\\alpha > 0\\)
    • \n
  • beta – (optional) int, float. Shape parameter, \\(\\beta > 0\\)
    • \n
\n
\n
\n\\[\\log{P(x; \\alpha, \\beta)} \\propto (\\alpha - 1)\\log{x} + (\\beta - 1) \\log{(1 - x)}\\]
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\ndistributions.binomial(k, n, p)¶
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Binomial distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • k – int, np.array. Number of successes. \\(k <= n\\)
    • \n
  • n – int, np.array. Number of trials. \\(n > 0\\)
    • \n
  • p – int, float, np.array. Success probability. \\(0<= p <= 1\\)
    • \n
\n
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\n\\[\\log{P(k; n, p)} \\propto k \\log{p} + (n-k)\\log{(1-p)}\\]
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\ndistributions.cauchy(x, alpha=0, beta=1)¶
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Cauchy distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array. \\(-\\infty < x < \\infty\\)
    • \n
  • alpha – int, float, nparray. Location parameter, \\(-\\infty < \\alpha < \\infty\\)
    • \n
  • beta – int, float. Scale parameter, \\(\\beta > 0\\)
    • \n
\n
\n
\n\\[\\log{P(x; \\alpha, \\beta)} \\propto -\\log{\\beta} - \\log{\\left[1 + \\left(\\frac{x - \\alpha}{\\beta}\\right)^2\\right]} \\]
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\ndistributions.discrete_uniform(x, lower=0, upper=1)¶
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Discrete Uniform distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, np.array[int].
    • \n
  • lower – (optional) int, float. Lower bound, default is 0.
    • \n
  • upper – (optional) int, float. Upper bound, default is 1.
    • \n
\n
\n
\n\\[\\log{P(x; a, b)} = -n\\log(b-a)\\]
\n
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\ndistributions.exponential(x, rate=1)¶
\n

Log likelihood of the exponential distribution.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array.
    • \n
  • rate – (optional) int, float, np.array. Rate parameter, \\(\\lambda > 0\\). Defaults to 1.
    • \n
\n
\n
\n\\[\\log{P(x; \\lambda)} \\propto \\log{\\lambda} - \\lambda x\\]
\n
\n\n
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\ndistributions.fails_constraints(*conditions)¶
\n

Utility function for catching out of bound parameters. Returns True if \nany of the conditions aren’t met. Typically you’ll use this at the\nbeginning of defining the log P(X) functions. Example

\n
def logp(x, y):\n    # Bound x and y to be greater than 0\n    if outofbounds(x > 0, y > 0):\n        return -np.inf\n
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\ndistributions.half_cauchy(x, alpha=0, beta=1)¶
\n

Half-Cauchy distribution log-likelihood (positive half).

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array. \\(-\\infty < x < \\infty\\)
    • \n
  • alpha – int, float, nparray. Location parameter, \\(-\\infty < \\alpha < \\infty\\)
    • \n
  • beta – int, float. Scale parameter, \\(\\beta > 0\\)
    • \n
\n
\n
\n\\[\\log{P(x; \\alpha, \\beta)} \\propto -\\log{\\beta} - \\log{\\left[1 + \\left(\\frac{x - \\alpha}{\\beta}\\right)^2\\right]} \\]
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\ndistributions.laplace(x, mu, tau)¶
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Laplace distribution log-likelihood

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array. \\(-\\infty < \\mu < \\infty\\)
    • \n
  • mu – int, float, np.array. Location parameter. \\(-\\infty < \\mu < \\infty\\)
    • \n
  • tau – int, float. Scale parameter, \\(\\tau > 0\\)
    • \n
\n
\n
\n\\[\\log{P(x; \\mu, \\tau)} \\propto \\log{\\tau/2} - \\tau \\left|x - \\mu \\right|\\]
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\ndistributions.normal(x, mu=0, sig=1)¶
\n

Normal distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array.
    • \n
  • mu – (optional) int, float, np.array. \nLocation parameter of the normal distribution. Defaults to 0.
    • \n
  • sig – (optional) int, float. \nStandard deviation of the normal distribution, \\(\\sigma > 0\\).\nDefaults to 1.
    • \n
\n
\n
\n\\[\\log{P(x; \\mu, \\sigma)} \\propto -\\log{\\sigma} - \\frac{(x - \\mu)^2}{2 \\sigma^2}\\]
\n
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\ndistributions.poisson(x, rate=1)¶
\n

Poisson distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array. Event count.
    • \n
  • rate – (optional) int, float, np.array. Rate parameter, \\(\\lambda > 0\\). Defaults to 1.
    • \n
\n
\n
\n\\[\\log{P(x; \\lambda)} \\propto x \\log{\\lambda} - \\lambda\\]
\n
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\ndistributions.student_t(x, nu=1)¶
\n

Student’s t log-likelihood

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array.
    • \n
  • nu – (optional) int. Degress of freedom.
    • \n
\n
\n
\n\\[\\log{P(x; \\nu)} \\propto \\log{\\Gamma \\left(\\frac{\\nu+1}{2} \\right)} - \\log{\\Gamma \\left( \\frac{\\nu}{2} \\right) } - \\frac{1}{2}\\log{\\nu} - \\frac{\\nu+1}{2}\\log{\\left(1 + \\frac{x^2}{\\nu} \\right)}\\]
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\ndistributions.uniform(x, lower=0, upper=1)¶
\n

Uniform distribution log-likelihood. Bounds are inclusive.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array.
    • \n
  • lower – (optional) int, float. Lower bound, default is 0.
    • \n
  • upper – (optional) int, float. Upper bound, default is 1.
    • \n
\n
\n
\n\\[\\log{P(x; a, b)} = -n\\log(b-a)\\]
\n
\n\n
\n
\ndistributions.weibull(x, l, k)¶
\n

Weibull distribution log-likelihood.

\n\n\n\n\n\n\n\n
Parameters:
    \n
  • x – int, float, np.array. \\(x > 0\\)
    • \n
  • l – float. Scale parameter. \\(\\lambda > 0\\)
    • \n
  • k – float. Shape parameter. \\(k > 0\\)
    • \n
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\n\n \n\n \n \n" }, { "path": "docs/examples/german_tank_problem.html", "content": "\n\n\n\n \n \n \n German Tank Problem — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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German Tank Problem¶

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Here we will cover a classic problem in statistics, estimating the total number of tanks from a small sample. Suppose four tanks are captured with the serial numbers 10, 256, 202, and 97. Assuming that each tank is numbered in sequence as they are built, how many tanks are there in total?

\n

Since we are Bayesianists, we don’t want a singular estimate, we want a probability distribution for the total number of tanks. Therefore, we need to calculate the distribution of total tanks \\(N\\), given the serial numbers \\(D\\):

\n
\n\\[P(N \\mid D) \\propto P(D \\mid N) \\, P(N)\\]
\n

To build the model, first let’s think about the likelihood, \\(P(D \\mid N)\\). For those not familiar with statistical notation, this is the probability that we would see these serial numbers, given the total number of tanks. To decide how to model the likelihood, we can think about how we would create our data. Simply, we just have some number of tanks, with serial numbers \\(1, 2, 3, ..., N\\), and we uniformly draw four tanks from the group. Therefore, we should use a discrete uniform distribution.

\n

Next, we want to consider our prior information about \\(N\\), \\(P(N)\\). We know that it has to be at least equal to the largest serial number, \\(m\\). As for an upper bound, we can guess that it isn’t into the millions, since every serial number we saw is less than 300. We also know that \\(N\\) must be an integer and any value above 256 is equally likely, a priori, that is, before we saw the serial numbers. So a good choice here is the discrete uniform distribution again. I’ll set an upper bound at 10000, just to have it high enough for it not to affect our results. In statistical notation, we would write

\n
\n\\[\\begin{split}P(N \\mid D) &\\propto P(D \\mid N) \\, P(N) \\\\\nP(D \\mid N) &\\sim \\mathrm{DiscreteUniform}(D, min=0, max=N) \\\\\nP(N) &\\sim \\mathrm{DiscreteUniform}(N, min=m, max=10000) \\\\\\end{split}\\]
\n

Now we can build the model with Sampyl and sample from the posterior.

\n
import sampyl as smp\nfrom sampyl import np\n\n# Data\nserials = np.array([10, 256, 202, 97])\nm = np.max(serials)\n\n# log P(N | D)\ndef logp(N):\n    # Samplers will pass in floats, we need to make them integers\n    N = np.floor(N).astype(int)\n\n    # Log-likelihood\n    llh = smp.discrete_uniform(serials, lower=1, upper=N)\n\n    prior = smp.discrete_uniform(N, lower=m, upper=10000)\n\n    return llh + prior\n\n# Slice sampler for drawing from the posterior\nsampler = smp.Slice(logp, {'N':300})\nchain = sampler.sample(20000, burn=4000, thin=4)\n\nposterior = np.floor(chain.N)\nplt.hist(posterior, range=(0, 1000), bins=100,\n         histtype='stepfilled', normed=True)\nplt.xlabel("Total number of tanks")\nplt.ylabel("Posterior probability mass")\n
\n
\n\"../_images/German_tanks.png\"\n

Above I’ve plotted the posterior distribution of \\(N\\). We can see that most of the probability is concentrated near the largest serial number in our data. In fact, the first 50% of the distribution is {256, 321}, and the first 95% is {256, 717}. This means there is a 50% probability that \\(N\\) lies below 321, and 95% probability it is below 717. As far as an estimate, I think these intervals are much more meaningful than the mean since the posterior is so skewed. However, I’ll report some of those statistics. The mean, median, and mode are 381.6, 321.0, 269, respectively.

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\n\n \n\n \n \n" }, { "path": "docs/examples.html", "content": "\n\n\n\n \n \n \n Examples — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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Examples¶

\n

Here are various models built with Sampyl.

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\n ©2015, Mat Leonard, Andrew Miller.\n \n |\n Powered by Sphinx 1.7.4\n & Alabaster 0.7.10\n \n |\n Page source\n
\n\n \n\n \n \n" }, { "path": "docs/genindex.html", "content": "\n\n\n\n\n \n \n \n Index — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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Index

\n\n
\n _\n | B\n | C\n | D\n | E\n | F\n | H\n | L\n | M\n | N\n | P\n | S\n | T\n | U\n | W\n \n
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_

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B

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C

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D

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E

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F

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H

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L

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M

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N

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P

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S

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T

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U

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W

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Sampyl

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\n\n \n\n \n \n" }, { "path": "docs/index.html", "content": "\n\n\n\n \n \n \n Sampyl: MCMC samplers in Python — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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Sampyl: MCMC samplers in Python¶

\n

Release v0.3

\n

Sampyl is a Python library implementing Markov Chain Monte Carlo (MCMC) samplers\nin Python. It’s designed for use in Bayesian parameter estimation and provides a collection of distribution log-likelihoods for use in constructing models.

\n

Our goal with Sampyl is allow users to define models completely with Python and\ncommon packages like Numpy. Other MCMC packages require learning new syntax and\nsemantics while all that is really needed is a function that calculates \\(\\log{P(X)}\\)\nfor the sampling distribution.

\n

Sampyl allows the user to define a model any way they want, all that is required\nis a function that calculates log P(X). This function can be written completely\nin Python, written in C/C++ and wrapped with Python, or anything else a user can\nthink of. For samplers that require the gradient of P(X), such as NUTS,\nSampyl can calculate the gradients automatically with autograd.

\n

To show you how simple this can be, let’s sample from a 2D correlated normal distribution.

\n
# To use automatic gradient calculations, use numpy (np) provided\n# by autograd through Sampyl\nimport sampyl as smp\nfrom sampyl import np\nimport seaborn\n\nicov = np.linalg.inv(np.array([[1., .8], [.8, 1.]]))\ndef logp(x, y):\n    d = np.array([x, y])\n    return -.5 * np.dot(np.dot(d, icov), d)\n\nstart = {'x': 1., 'y': 1.}\nnuts = smp.NUTS(logp, start)\nchain = nuts.sample(1000)\n\nseaborn.jointplot(chain.x, chain.y, stat_func=None)\n
\n
\n\"_images/normal_example.png\"\n\n
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Examples¶

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Indices and tables¶

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\n ©2015, Mat Leonard, Andrew Miller.\n \n |\n Powered by Sphinx 1.7.4\n & Alabaster 0.7.10\n \n |\n Page source\n
\n\n \n\n \n \n" }, { "path": "docs/introduction.html", "content": "\n\n\n\n \n \n \n Introduction — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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Introduction¶

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What are we doing here?¶

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Sampyl provides Markov Chain Monte Carlo (MCMC) samplers for drawing from probability distributions. Typically, this is used to sample from the posterior distribution of a Bayesian model. Other MCMC packages such as PyMC and PyStan, while great and you should check them out, require you to create models using non-Pythonic syntax and semantics. Sampyl allows you to create models completely with Python and Numpy. All that is required is a function that calculates \\(\\log{P(X)}\\) for the sampling distribution. You can create this function however you want.

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Installation¶

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You can install Sampyl from PyPI with

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pip install sampyl-mcmc\n
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Sampyl depends on Numpy, Scipy, and autograd. You’ll also need matplotlib for the examples notebooks.

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Model¶

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The model is a class to make accessing log P(X) and grad log P(X) functions easier. Models contain caches for both log P(X) and the gradient. This is intended to be used when building new samplers, users won’t typically need this.

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There are two models currently. Model expects separate\nlog P(X) and gradient functions. SingleModel\nexpects one function that returns both log P(x) and the gradient.

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Example usage:

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def logp(X):\n    ...\n\nmodel = init_model(logp)\nx = some_state\nlogp_val = model.logp(x)\ngrad_val = model.grad(x)\nlogp_val, grad_val = model(x)\n
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\nclass model.Model¶
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Convenience class for building models from log-priors and \nlog-likelihood.

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Example:

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# Linear regression model\ndef logp(b, sig):\n    model = Model()\n    \n    # Estimate from data and coefficients \n    y_hat = np.dot(X, b)\n    \n    # Add log-priors for coefficients and model error\n    model.add(smp.uniform(b, lower=-100, upper=100),\n              smp.half_normal(sig))\n\n    # Add log-likelihood\n    model.add(smp.normal(y, mu=y_hat, sig=sig))\n\n    return model()\n
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\n__call__(state)¶
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Return log P(X) and grad log P(X) given a state X

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Python Module Index

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\n state\n
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Custom Samplers¶

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You can build your own sampler by subclassing Sampler. A model\nis automatically generated from logp. The sampler is also initialized with\na state generated from start and the arguments of logp. With these,\nyou define the step method, which should generate one sample and return a\nstate.

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As an example, here’s snippet from the Metropolis sampler.

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from sampyl import Sampler\nfrom sampyl import np\n\nclass Metropolis(Sampler):\n\n    def __init__(self, logp, start, **kwargs):\n        # No gradient is needed, so set it to None, and the flag to False\n        super(Metropolis, self).__init__(logp, start, None, grad_logp_flag=False, **kwargs)\n\n    def step(self):\n        """ Perform a Metropolis-Hastings step. """\n        x = self.state\n        y = proposal(x, self.scale)\n        if accept(x, y, self.model.logp):\n            self.state = y\n\n        return self.state\n\ndef proposal(state, scale):\n    proposed = State.fromkeys(state.keys())\n    for i, var in enumerate(state):\n        proposed.update({var: np.random.normal(state[var], scale[var])})\n    return proposed\n\ndef accept(x, y, logp):\n    delp = logp(y) - logp(x)\n    if np.isfinite(delp) and np.log(np.random.uniform()) < delp:\n        return True\n    else:\n        return False\n
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\nclass sampyl.Sampler(logp, start, grad_logp=None, scale=None, condition=None, grad_logp_flag=True, random_seed=None)¶
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Attributes

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\nmodel¶
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Model with logp and grad functions.

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\nstate¶
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The current state of the model.

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\nsampler¶
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Calling the sample method creates an infinite generator which returns\nsamples as states.

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Methods

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\nsample(num, burn=0, thin=1, n_chains=1, progress_bar=True)¶
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Sample from \\(P(X)\\)

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Parameters:
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  • num – int. Number of samples to draw from \\(P(X)\\).
    • \n
  • burn – (optional) int.\nNumber of samples to discard from the beginning of the chain.
    • \n
  • thin – (optional) float.\nThin the samples by this factor.
    • \n
  • n_chains – (optional) int.\nNumber of chains to return. Each chain is given its own\nprocess and the OS decides how to distribute the processes.
    • \n
  • progress_bar – (optional) boolean.\nShow the progress bar, default = True.
    • \n
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Returns:

Record array with fields taken from arguments of \nlogp function.

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\nstep()¶
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This is what you define to create the sampler. Requires that a\nstate object is returned.

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Hamiltonian MCMC Sampler¶

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\nclass sampyl.Hamiltonian(logp, start, step_size=1, n_steps=5, **kwargs)¶
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\nsample(num, burn=0, thin=1, n_chains=1, progress_bar=True)¶
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Sample from \\(P(X)\\)

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Parameters:
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  • num – int. Number of samples to draw from \\(P(X)\\).
    • \n
  • burn – (optional) int.\nNumber of samples to discard from the beginning of the chain.
    • \n
  • thin – (optional) float.\nThin the samples by this factor.
    • \n
  • n_chains – (optional) int.\nNumber of chains to return. Each chain is given its own\nprocess and the OS decides how to distribute the processes.
    • \n
  • progress_bar – (optional) boolean.\nShow the progress bar, default = True.
    • \n
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Returns:

Record array with fields taken from arguments of \nlogp function.

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\nstep()¶
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This is what you define to create the sampler. Requires that a\nstate object is returned.

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Metropolis-Hastings Sampler¶

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\nclass sampyl.Metropolis(logp, start, tune_interval=100, **kwargs)¶
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Metropolis-Hastings sampler for drawing from a distribution\ndefined by a logp function.

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Has automatic scaling such that acceptance rate stays around 50%

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Parameters:
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  • logp – function\nlog P(X) function for sampling distribution.
    • \n
  • start – Dictionary of starting state for the sampler. Should have one\nelement for each argument of logp.
    • \n
  • scale – scalar or 1D array-like.\ninitial scaling factor for proposal distribution.
    • \n
  • tune_interval – int.
    • \n
  • scale – scalar or 1D array-like.\ninitial scaling factor for proposal distribution.
    • \n
  • tune_interval – int.\nnumber of samples between tunings of scale factor.
    • \n
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Example:

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def logp(x, y):\n    ...\n\nstart = {'x': x_start, 'y': y_start}\nmetro = sampyl.Metropolis(logp, start)\nchain = metro.sample(20000, burn=5000, thin=4)\n
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\nsample(num, burn=0, thin=1, n_chains=1, progress_bar=True)¶
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Sample from \\(P(X)\\)

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Parameters:
    \n
  • num – int. Number of samples to draw from \\(P(X)\\).
    • \n
  • burn – (optional) int.\nNumber of samples to discard from the beginning of the chain.
    • \n
  • thin – (optional) float.\nThin the samples by this factor.
    • \n
  • n_chains – (optional) int.\nNumber of chains to return. Each chain is given its own\nprocess and the OS decides how to distribute the processes.
    • \n
  • progress_bar – (optional) boolean.\nShow the progress bar, default = True.
    • \n
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Returns:

Record array with fields taken from arguments of \nlogp function.

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\nstep()¶
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Perform a Metropolis-Hastings step.

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No-U-Turn Sampler (NUTS)¶

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\nclass sampyl.NUTS(logp, start, step_size=0.25, adapt_steps=100, Emax=1000.0, target_accept=0.65, gamma=0.05, k=0.75, t0=10.0, **kwargs)¶
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No-U-Turn sampler (Hoffman & Gelman, 2014) for sampling from a\nprobability distribution defined by a log P(theta) function.

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For technical details, see the paper:\nhttp://www.stat.columbia.edu/~gelman/research/published/nuts.pdf

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Parameters:
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  • logp – log P(X) function for sampling distribution
    • \n
  • start – Dictionary of starting state for the sampler. Should have one\nelement for each argument of logp.
    • \n
  • grad_logp –

    (optional)\nFunction or list of functions that calculate grad log P(theta). \nPass functions here if you don’t want to use autograd for the \ngradients. If logp has multiple parameters, grad_logp must be \na list of gradient functions w.r.t. each parameter in logp.

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    If you wish to use a logp function that returns both the logp\nvalue and the gradient, set grad_logp = True.

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    • \n
  • scale – (optional) \nDictionary with same format as start. Scaling for initial \nmomentum in Hamiltonian step.
    • \n
  • step_size – (optional) float.\nInitial step size for the deterministic proposals.
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  • adapt_steps – (optional) int.\nInteger number of steps used for adapting the step size to \nachieve a target acceptance rate.
    • \n
  • Emax – (optional) float. Maximum energy.
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  • target_accept – (optional) float. Target acceptance rate.
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  • gamma – (optional) float.
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  • k – (optional) float. Scales the speed of step size \nadaptation.
    • \n
  • t0 – (optional) float. Slows initial step size adaptation.
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Example

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def logp(x, y):\n    ...\n\nstart = {'x': x_start, 'y': y_start}\nnuts = sampyl.NUTS(logp, start)\nchain = nuts.sample(1000)\n
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\nsample(num, burn=0, thin=1, n_chains=1, progress_bar=True)¶
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Sample from \\(P(X)\\)

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Parameters:
    \n
  • num – int. Number of samples to draw from \\(P(X)\\).
    • \n
  • burn – (optional) int.\nNumber of samples to discard from the beginning of the chain.
    • \n
  • thin – (optional) float.\nThin the samples by this factor.
    • \n
  • n_chains – (optional) int.\nNumber of chains to return. Each chain is given its own\nprocess and the OS decides how to distribute the processes.
    • \n
  • progress_bar – (optional) boolean.\nShow the progress bar, default = True.
    • \n
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Returns:

Record array with fields taken from arguments of \nlogp function.

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\nstep()¶
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Perform one NUTS step.

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Slice Sampler¶

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\nclass sampyl.Slice(logp, start, compwise=False, width=1.0, step_out=True, doubling_step=True, max_steps_out=10, verbose=False, **kwargs)¶
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Slice sampler (Neal, 2003) for creating a Markov chain that \nleaves the the distribution defined by logp invariant

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\n
For technical details, see Neal’s paper:
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http://projecteuclid.org/euclid.aos/1056562461
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\n

Andrew Miller (acm@seas.harvard.edu) 7-13-15

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Adapted from code written by Ryan Adams (rpa@seas.harvard.edu)

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Parameters:
    \n
  • logp – function. \\(\\log{P(X)}\\) function for sampling\ndistribution.
    • \n
  • start – scalar or 1D array-like. Starting state for sampler.
    • \n
  • compwise – (optional) boolean. Component-wise univariate \nslice sample\n(or random direction)
    • \n
  • width – (optional) int, float. (Initial) width of the slice
    • \n
  • step_out – (optional) boolean. Perform step-out procedure
    • \n
  • doubling_step – (optional) boolean. If stepping out, double\nslice width?
    • \n
  • max_steps_out – (optional) int. Max number of steps out to perform
    • \n
  • verbose – (optional) boolean. Print steps out
    • \n
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\ndirection_slice(direction, init_x)¶
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one dimensional directional slice sample along direction specified\nImplements the stepping out procedure from Neal

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\nsample(num, burn=0, thin=1, n_chains=1, progress_bar=True)¶
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Sample from \\(P(X)\\)

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Parameters:
    \n
  • num – int. Number of samples to draw from \\(P(X)\\).
    • \n
  • burn – (optional) int.\nNumber of samples to discard from the beginning of the chain.
    • \n
  • thin – (optional) float.\nThin the samples by this factor.
    • \n
  • n_chains – (optional) int.\nNumber of chains to return. Each chain is given its own\nprocess and the OS decides how to distribute the processes.
    • \n
  • progress_bar – (optional) boolean.\nShow the progress bar, default = True.
    • \n
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Returns:

Record array with fields taken from arguments of \nlogp function.

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\nstep()¶
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Perform a slice sample step

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Samplers¶

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Each sampler has the same API. First you create a function calculating log P(X),\nthen pass it to a sampler. To generate a chain, call the sample method.

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Example:

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import sampyl as smp\ndef logp(x, y):\n    ...\n\nstart = {'x': x_start, 'y': y_start}\nnuts = smp.NUTS(logp, start)\nchain = nuts.sample(1000)\n
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Creating your own custom samplers is possible and straightfoward.

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Tank Problem\",\"Sampyl: MCMC samplers in Python\",\"Introduction\",\"Model\",\"Samplers\",\"Custom Samplers\",\"Hamiltonian MCMC Sampler\",\"Metropolis-Hastings Sampler\",\"No-U-Turn Sampler (NUTS)\",\"Slice Sampler\",\"State\",\"Tutorial\"],titleterms:{api:3,custom:7,defin:13,distribut:0,doing:4,exampl:[1,3],from:13,german:2,hamiltonian:8,hast:9,here:[3,4],indic:3,instal:4,introduct:4,mcmc:[3,8],metropoli:9,model:[5,13],nut:10,posterior:13,problem:2,python:3,sampl:13,sampler:[3,6,7,8,9,10,11],sampyl:3,slice:11,start:3,state:12,tabl:3,tank:2,turn:10,tutori:13,what:4}})" }, { "path": "docs/state.html", "content": "\n\n\n\n \n \n \n State — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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State¶

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State objects are used to store the current state of the Markov chain. States are subclassed from collections.OrderedDict so that the elements are in a known order.

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\nclass state.State¶
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State object for storing parameter values.

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Inherits from OrderedDict.

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\nfreeze()¶
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Return a immutable tuple of the state values.

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\nstatic fromfunc(func)¶
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Initialize a State from the arguments of a function

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\nfromvector(vec)¶
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Update the state using a numpy array.

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Parameters:vec – np.array for updating the state.
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\ntovector()¶
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Return the parameter values as a flat vector.

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Sampyl

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\n ©2015, Mat Leonard, Andrew Miller.\n \n |\n Powered by Sphinx 1.7.4\n & Alabaster 0.7.10\n \n |\n Page source\n
\n\n \n\n \n \n" }, { "path": "docs/tutorial.html", "content": "\n\n\n\n \n \n \n Tutorial — Sampyl 0.3 documentation\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n\n
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Tutorial¶

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Defining a model¶

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Here I’ll demonstrate how to use Sampyl with a simple linear model. First, I will create some fake data. Then, I will build a model and sample from the posterior distribution to estimate the coefficients from the synthetic data.

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With a linear model, we assume the data is drawn from a normal distribution

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\n\\[\\begin{split}Y &\\sim N(\\mu, \\sigma^2) \\\\\n\\mu &= \\beta_0 + \\beta_1 x_1 + ... + \\beta_n x_n\\end{split}\\]
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where \\(\\beta_n\\) are coefficients we want to estimate and \\(x_n\\) are the predictor variables in our data.

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Let’s start by making some fake data.

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# Number of data points\nN = 200\n\n# True parameters\nsigma = 1\ntrue_b = np.array([2, 1, 4])\n\n# Features, including a constant\nX = np.ones((N, len(true_b)))\nX[:,1:] = np.random.rand(N, len(true_b)-1)\n\n# Outcomes\ny = np.dot(X, true_b) + np.random.randn(N)*sigma\n
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\n\"_images/linear_model_data.png\"\n

Above I’ve plotted the data we generated. We have the outcomes y and our features X, now we want to build a model to estimate the coefficients from this data. We can start with Bayes theorem

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\n\\[P(\\beta, \\sigma \\mid D) \\propto P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma)\\]
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The left hand side \\(P(\\beta, \\sigma \\mid D)\\) is the posterior distribution. This is a probability distribution of likely values for \\(\\beta\\) and \\(\\sigma\\), given our data \\(D\\). On the right side is the data likelihood \\(P(D \\mid \\beta, \\sigma)\\) which gives the probability that we would see our data, given the parameters \\(\\beta\\) and \\(\\sigma\\). Finally, we have our priors, \\(P(\\beta)\\) and \\(P(\\sigma)\\). These distributions represent our prior information about the parameters.

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Bayes theorem is quite intuitive because it is very similar to how humans think naturally. Suppose you are on a road trip, and you know its roughly an hour until you arrive, but its been a while since you saw the last sign with the distance so you aren’t too sure. Then, you do see a sign and it says you still have 65 miles (104 km). Now you have a pretty good idea that it’ll be another hour, but you aren’t completely certain because traffic might be slow, or it could take a bit once you get into the city. This is how Bayes theorem works too. You have some prior information about your arrival time, but you’re fairly uncertain. Then, you see some data, the distance remaining, and you update your information about the arrival time.

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Continuing on with the model, we want the likelihood, \\(P(D \\mid \\beta, \\sigma)\\), to model our data. We are assuming we can predict the data with a linear sum of coefficients \\(\\beta_n\\) with our predictors \\(x_n\\), with some normally distributed noise on the scale of \\(\\sigma\\). So our likelihood would be well modeled with a normal distribution.

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We also want to assign probability distributions to \\(\\beta\\) and \\(\\sigma\\) to account for our uncertainty in those parameters and our prior information about them. This is done through the priors \\(P(\\beta)\\, P(\\sigma)\\). For \\(\\sigma\\), we must choose a distribution that is defined only above 0. An exponential is good for this and we can control the diffuseness of the prior by changing the rate parameter. High rates pull the prior closer to 0, while lower rates push it out. We are using fairly wide normal priors for the coefficients \\(\\beta\\), since we aren’t sure a priori how large the coefficients will be. Putting this all together, we get our model

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\n\\[\\begin{split}P(\\beta, \\sigma \\mid D) &\\propto P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma) \\\\\nP(D \\mid \\beta, \\sigma) &\\sim \\mathrm{Normal}(\\mu, \\sigma^2) \\\\\n\\mu &= \\sum \\beta_i x_i \\\\\n\\beta &\\sim \\mathrm{Normal}(0, 100) \\\\\n\\sigma &\\sim \\mathrm{Exponential}(1)\\end{split}\\]
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Every sampler implemented in Sampyl takes a function that calculates \\(\\log{P(\\theta)}\\) of the sampling distribution. We want to sample from the model posterior, so we need to write a Python function that calculates \\(\\log{P(\\beta, \\sigma \\mid D)}\\). I’ll build the full model then go through it with explanations.

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import sampyl as smp\nfrom sampyl import np\n\n# Here, b is a length 3 array of coefficients\ndef logp(b, sig):\n\n    model = smp.Model()\n\n    # Predicted value\n    y_hat = np.dot(X, b)\n\n    # Log-likelihood\n    model.add(smp.normal(y, mu=y_hat, sig=sig))\n\n    # log-priors\n    model.add(smp.exponential(sig),\n              smp.normal(b, mu=0, sig=100))\n\n    return model()\n
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First, look at the imports, particularly from sampyl import np. If you have worked with Numpy, you know it is usually abbreviated as np, but why have we imported it from Sampyl here? Some of the samplers we have implemented require the gradient of \\(\\log{P(\\theta)}\\). To make things simpler, we use a wonderful package autograd to automatically calculate gradients. To use autograd, our \\(\\log{P(\\theta)}\\) function needs to be written with Numpy provided by autograd. So, Sampyl imports Numpy from autograd, which you then import from Sampyl to build the model.

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Next, check out the function definition. We named it logp but this can be anything. The arguments b and sig are the parameters of the model. They can either be Numpy arrays or scalars such as integers or floats.

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Then, we create a model with model = smp.Model(). This is a convenience class that makes defining a model more concise. You add log-probabilities of the priors and likelihood with model.add(). Then, you can call model() to sum up all the log-probabilities and get the log-posterior (well, something proportional to the posterior).

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Onwards:

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y_hat = np.dot(X, b)\n
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Here our function has been passed b which is an length 3 numpy array, a vector. We want to make a prediction of the outcome, y_hat, then use this as the mean of our likelihood.

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model.add(smp.normal(y, mu=y_hat, sig=sig))\n
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This is the log-likelihood of our data, given our parameters, \\(P(D \\mid \\beta, \\sigma)\\). It is important to note that smp.normal(y, mu=y_hat, sig=sig) returns a number, the log-likelihood of a normal distribution with the passed parameters. There is nothing going on behind the scene, just a function that returns a number. Then,

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model.add(smp.exponential(sig, rate=1),\n          smp.normal(b, mu=0, sig=10)\n
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Here we have defined our priors. Again, smp.exponential and smp.normal return a scalar value of the distribution log-likelihood.

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Finally

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return model()\n
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As I noted earlier, the distribution functions return log-likelihoods, just numbers. Since we are calculating \\(\\log{P(\\beta, \\sigma \\mid D)}\\),

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\n\\[\\begin{split}\\log{P(\\beta, \\sigma \\mid D)} &\\propto \\log{\\left[P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma) \\right]} \\\\\n\\log{P(\\beta, \\sigma \\mid D)} &\\propto \\log{P(D \\mid \\beta, \\sigma)} + \\log{P(\\beta)} + \\log{P(\\sigma)}\\end{split}\\]
\n

we just add up the log-likelihoods! The model object does this for us. Again, nothing fancy going on here, just beautiful math with logarithms. Now that we have defined \\(\\log{P(\\beta, \\sigma \\mid D)}\\), we can draw samples from it using one of our samplers.

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Sampling from the posterior¶

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Each sampler provided by Sampyl requires a \\(\\log{P(X)}\\) function, logp here, and a starting state. A good place to start is the maximum of the posterior, typically called the maximum a posteriori or MAP. You can also define start yourself, it just needs to be a dictionary where the keys are the arguments of logp. Here we’ll use the No-U-Turn Sampler (NUTS)

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start = smp.find_MAP(logp, {'b': np.ones(3), 'sig': 1.})\nnuts = smp.NUTS(logp, start)\nchain = nuts.sample(2100, burn=100)\n
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\n

So first, we find the MAP and pass it as a start value to the NUTS sampler. This returns the sampler object itself nuts. It is important to provide the correct size arguments as starting values. Our function logp expects b to be a length 3 Numpy array, so that is what we need to provide to find_MAP or NUTS.

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Calling nuts.sample(2100, burn=100) returns a chain of 2000 samples from the posterior distribution, where we have discarded the first 100 as a burn-in period. The chain we get is a Numpy record array so that we can access the posterior distribution for each parameter by name. For instance, to get the posterior samples for b with chain.b

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import matplotlib.pyplot as plt\nplt.plot(chain.b)\n
\n
\n\"_images/linear_model_coefficients.png\"\n

Below is a plot of the posterior samples for each parameter.

\n\"_images/linear_model_posterior.png\"\n

I’ve also included dashed lines indicating the true parameters. In the future, we will be providing functionality to return various statistics and intervals for the posterior. For more guidance, please look through the other examples provided in the documentation.

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If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\nsys.path.insert(0, os.path.abspath('..'))\nsys.path.insert(0, os.path.abspath('../sampyl'))\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n\t'sphinx.ext.autodoc',\n 'sphinx.ext.mathjax',\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The encoding of source files.\n#source_encoding = 'utf-8-sig'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'Sampyl'\ncopyright = '2015, Mat Leonard, Andrew Miller'\nauthor = 'Mat Leonard, Andrew Miller'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = '0.3'\n# The full version, including alpha/beta/rc tags.\nrelease = '0.3'\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n#today = ''\n# Else, today_fmt is used as the format for a strftime call.\n#today_fmt = '%B %d, %Y'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = ['_build']\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\n#default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\n#add_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n#add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n#show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# A list of ignored prefixes for module index sorting.\n#modindex_common_prefix = []\n\n# If true, keep warnings as \"system message\" paragraphs in the built documents.\n#keep_warnings = False\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\nhtml_theme = 'alabaster'\n#html_theme = 'sphinx_rtd_theme'\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\nhtml_theme_options = {\n 'github_user': 'mcleonard',\n 'github_repo': 'sampyl',\n 'github_button': True,\n}\n\n# Add any paths that contain custom themes here, relative to this directory.\n#html_theme_path = []\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\n#html_title = None\n\n# A shorter title for the navigation bar. Default is the same as html_title.\n#html_short_title = None\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\n#html_logo = None\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\n#html_favicon = None\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Add any extra paths that contain custom files (such as robots.txt or\n# .htaccess) here, relative to this directory. These files are copied\n# directly to the root of the documentation.\n#html_extra_path = []\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\n#html_last_updated_fmt = '%b %d, %Y'\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\n#html_use_smartypants = True\n\n# Custom sidebar templates, maps document names to template names.\nhtml_sidebars = {\n '**': [\n 'about.html',\n 'navigation.html',\n 'relations.html',\n 'searchbox.html',\n ]\n}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n#html_additional_pages = {}\n\n# If false, no module index is generated.\n#html_domain_indices = True\n\n# If false, no index is generated.\n#html_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n#html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n#html_show_sourcelink = True\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\n#html_show_sphinx = True\n\n# If true, \"(C) Copyright ...\" is shown in the HTML footer. Default is True.\n#html_show_copyright = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a tag referring to it. The value of this option must be the\n# base URL from which the finished HTML is served.\n#html_use_opensearch = ''\n\n# This is the file name suffix for HTML files (e.g. \".xhtml\").\n#html_file_suffix = None\n\n# Language to be used for generating the HTML full-text search index.\n# Sphinx supports the following languages:\n# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'\n# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'\n#html_search_language = 'en'\n\n# A dictionary with options for the search language support, empty by default.\n# Now only 'ja' uses this config value\n#html_search_options = {'type': 'default'}\n\n# The name of a javascript file (relative to the configuration directory) that\n# implements a search results scorer. If empty, the default will be used.\n#html_search_scorer = 'scorer.js'\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'Sampyldoc'\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n# The paper size ('letterpaper' or 'a4paper').\n#'papersize': 'letterpaper',\n\n# The font size ('10pt', '11pt' or '12pt').\n#'pointsize': '10pt',\n\n# Additional stuff for the LaTeX preamble.\n#'preamble': '',\n\n# Latex figure (float) alignment\n#'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, 'Sampyl.tex', 'Sampyl Documentation',\n 'Mat Leonard, Andrew Miller', 'manual'),\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\n#latex_logo = None\n\n# For \"manual\" documents, if this is true, then toplevel headings are parts,\n# not chapters.\n#latex_use_parts = False\n\n# If true, show page references after internal links.\n#latex_show_pagerefs = False\n\n# If true, show URL addresses after external links.\n#latex_show_urls = False\n\n# Documents to append as an appendix to all manuals.\n#latex_appendices = []\n\n# If false, no module index is generated.\n#latex_domain_indices = True\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n (master_doc, 'sampyl', 'Sampyl Documentation',\n [author], 1)\n]\n\n# If true, show URL addresses after external links.\n#man_show_urls = False\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (master_doc, 'Sampyl', 'Sampyl Documentation',\n author, 'Sampyl', 'One line description of project.',\n 'Miscellaneous'),\n]\n\n# Documents to append as an appendix to all manuals.\n#texinfo_appendices = []\n\n# If false, no module index is generated.\n#texinfo_domain_indices = True\n\n# How to display URL addresses: 'footnote', 'no', or 'inline'.\n#texinfo_show_urls = 'footnote'\n\n# If true, do not generate a @detailmenu in the \"Top\" node's menu.\n#texinfo_no_detailmenu = False\n" }, { "path": "docs_source/distributions.rst", "content": "Distributions\n=============\n\n.. automodule:: distributions\n\t:members:" }, { "path": "docs_source/examples/german_tank_problem.rst", "content": "German Tank Problem\n-------------------\n\nHere we will cover a `classic problem`_ in statistics, estimating the total number of tanks from a small sample. Suppose four tanks are captured with the serial numbers 10, 256, 202, and 97. Assuming that each tank is numbered in sequence as they are built, how many tanks are there in total?\n\n.. _classic problem: https://en.wikipedia.org/wiki/German_tank_problem\n\nSince we are Bayesianists, we don't want a singular estimate, we want a probability distribution for the total number of tanks. Therefore, we need to calculate the distribution of total tanks :math:`N`, given the serial numbers :math:`D`:\n\n.. math ::\n\n P(N \\mid D) \\propto P(D \\mid N) \\, P(N)\n\n\nTo build the model, first let's think about the likelihood, :math:`P(D \\mid N)`. For those not familiar with statistical notation, this is the probability that we would see these serial numbers, given the total number of tanks. To decide how to model the likelihood, we can think about how we would create our data. Simply, we just have some number of tanks, with serial numbers :math:`1, 2, 3, ..., N`, and we uniformly draw four tanks from the group. Therefore, we should use a discrete uniform distribution. \n\nNext, we want to consider our prior information about :math:`N`, :math:`P(N)`. We know that it has to be at least equal to the largest serial number, :math:`m`. As for an upper bound, we can guess that it isn't into the millions, since every serial number we saw is less than 300. We also know that :math:`N` must be an integer and any value above 256 is equally likely, *a priori*, that is, before we saw the serial numbers. So a good choice here is the discrete uniform distribution again. I'll set an upper bound at 10000, just to have it high enough for it not to affect our results. In statistical notation, we would write\n\n.. math ::\n P(N \\mid D) &\\propto P(D \\mid N) \\, P(N) \\\\\n P(D \\mid N) &\\sim \\mathrm{DiscreteUniform}(D, min=0, max=N) \\\\\n P(N) &\\sim \\mathrm{DiscreteUniform}(N, min=m, max=10000) \\\\\n\nNow we can build the model with Sampyl and sample from the posterior. ::\n\n import sampyl as smp\n from sampyl import np\n\n # Data\n serials = np.array([10, 256, 202, 97])\n m = np.max(serials)\n \n # log P(N | D)\n def logp(N):\n # Samplers will pass in floats, we need to make them integers\n N = np.floor(N).astype(int)\n \n # Log-likelihood\n llh = smp.discrete_uniform(serials, lower=1, upper=N)\n \n prior = smp.discrete_uniform(N, lower=m, upper=10000)\n \n return llh + prior\n\n # Slice sampler for drawing from the posterior\n sampler = smp.Slice(logp, {'N':300})\n chain = sampler.sample(20000, burn=4000, thin=4)\n\n posterior = np.floor(chain.N)\n plt.hist(posterior, range=(0, 1000), bins=100, \n histtype='stepfilled', normed=True)\n plt.xlabel(\"Total number of tanks\")\n plt.ylabel(\"Posterior probability mass\")\n\n.. image:: _static/German_tanks.png\n :align: center\n\nAbove I've plotted the posterior distribution of :math:`N`. We can see that most of the probability is concentrated near the largest serial number in our data. In fact, the first 50% of the distribution is {256, 321}, and the first 95% is {256, 717}. This means there is a 50% probability that :math:`N` lies below 321, and 95% probability it is below 717. As far as an estimate, I think these intervals are much more meaningful than the mean since the posterior is so skewed. However, I'll report some of those statistics. The mean, median, and mode are 381.6, 321.0, 269, respectively." }, { "path": "docs_source/examples.rst", "content": ".. _examples:\n\nExamples\n========\n\nHere are various models built with Sampyl.\n\n.. toctree::\n\texamples/german_tank_problem" }, { "path": "docs_source/index.rst", "content": ".. Sampyl documentation master file, created by\n sphinx-quickstart on Thu Aug 6 23:09:13 2015.\n\n\nSampyl: MCMC samplers in Python \n===============================\n\nRelease v\\ |version|\n\nSampyl is a Python library implementing Markov Chain Monte Carlo (MCMC) samplers\nin Python. It's designed for use in Bayesian parameter estimation and provides a collection of distribution log-likelihoods for use in constructing models.\n\nOur goal with Sampyl is allow users to define models completely with Python and\ncommon packages like Numpy. Other MCMC packages require learning new syntax and\nsemantics while all that is really needed is a function that calculates :math:`\\log{P(X)}`\nfor the sampling distribution.\n\nSampyl allows the user to define a model any way they want, all that is required\nis a function that calculates log P(X). This function can be written completely \nin Python, written in C/C++ and wrapped with Python, or anything else a user can\nthink of. For samplers that require the gradient of P(X), such as :ref:`NUTS `, \nSampyl can calculate the gradients automatically with autograd_. \n\n.. _autograd: https://github.com/HIPS/autograd/\n\nTo show you how simple this can be, let's sample from a 2D correlated normal distribution. ::\n \n # To use automatic gradient calculations, use numpy (np) provided \n # by autograd through Sampyl\n import sampyl as smp\n from sampyl import np\n import seaborn\n \n icov = np.linalg.inv(np.array([[1., .8], [.8, 1.]]))\n def logp(x, y):\n d = np.array([x, y])\n return -.5 * np.dot(np.dot(d, icov), d)\n\n start = {'x': 1., 'y': 1.}\n nuts = smp.NUTS(logp, start)\n chain = nuts.sample(1000)\n\n seaborn.jointplot(chain.x, chain.y, stat_func=None)\n\n.. image:: _static/normal_example.png\n\t:align: center\n\n\n\nStart here\n----------\n.. toctree::\n :maxdepth: 2\n\n introduction\n tutorial\n\n\nExamples\n--------\n\n.. toctree::\n :maxdepth: 2\n \n examples\n\nAPI\n---\n.. toctree::\n :maxdepth: 2\n\n distributions\n model\n samplers\n state\n\n\nIndices and tables\n------------------\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n\n" }, { "path": "docs_source/introduction.rst", "content": "Introduction\n============\n\nWhat are we doing here?\n-----------------------\n\nSampyl provides `Markov Chain Monte Carlo `_ (MCMC) samplers for drawing from probability distributions. Typically, this is used to sample from the posterior distribution of a Bayesian model. Other MCMC packages such as `PyMC `_ and `PyStan `_, while great and you should check them out, require you to create models using non-Pythonic syntax and semantics. Sampyl allows you to create models completely with Python and Numpy. All that is required is a function that calculates :math:`\\log{P(X)}` for the sampling distribution. You can create this function however you want.\n\n\nInstallation\n------------\n\nYou can install Sampyl from PyPI with ::\n\n\tpip install sampyl-mcmc\n\nSampyl depends on Numpy, Scipy, and `autograd`_. You'll also need matplotlib for the examples notebooks.\n\n.. _autograd: https://github.com/HIPS/autograd/" }, { "path": "docs_source/model.rst", "content": ".. _model:\n\nModel\n=====\n\nThe model is a class to make accessing log P(X) and grad log P(X) functions easier. Models contain caches for both log P(X) and the gradient. This is intended to be used when building new samplers, users won't typically need this.\n\nThere are two models currently. :ref:`Model ` expects separate\nlog P(X) and gradient functions. :ref:`SingleModel `\nexpects one function that returns both log P(x) and the gradient.\n\nExample usage::\n \n def logp(X):\n ...\n \n model = init_model(logp)\n x = some_state\n logp_val = model.logp(x)\n grad_val = model.grad(x)\n logp_val, grad_val = model(x)\n\n\n.. _model_class:\n\n.. module:: model\n\n.. autofunction:: init_model\n\n.. autoclass:: Model\n :members:\n :inherited-members:\n\n .. method:: __call__(state)\n\n Return log P(X) and grad log P(X) given a :ref:`state ` X \n\n\n.. _single_model_class:\n\n.. autoclass:: SingleModel\n :members:\n :inherited-members:\n\n .. method:: __call__(state)\n\n Return log P(X) and grad log P(X) given a :ref:`state ` X" }, { "path": "docs_source/samplers/custom.rst", "content": ".. _custom:\n\nCustom Samplers\n===============\n\nYou can build your own sampler by subclassing Sampler. A :ref:`model `\nis automatically generated from `logp`. The sampler is also initialized with\na :ref:`state ` generated from `start` and the arguments of `logp`. With these,\nyou define the `step` method, which should generate one sample and return a \n:ref:`state `.\n\nAs an example, here's snippet from the :ref:`Metropolis ` sampler. ::\n\n from sampyl import Sampler\n from sampyl import np\n \n class Metropolis(Sampler):\n\n def __init__(self, logp, start, **kwargs):\n # No gradient is needed, so set it to None, and the flag to False\n super(Metropolis, self).__init__(logp, start, None, grad_logp_flag=False, **kwargs)\n\n def step(self):\n \"\"\" Perform a Metropolis-Hastings step. \"\"\"\n x = self.state\n y = proposal(x, self.scale)\n if accept(x, y, self.model.logp):\n self.state = y\n\n return self.state\n\n def proposal(state, scale):\n proposed = State.fromkeys(state.keys())\n for i, var in enumerate(state):\n proposed.update({var: np.random.normal(state[var], scale[var])})\n return proposed\n\n def accept(x, y, logp):\n delp = logp(y) - logp(x)\n if np.isfinite(delp) and np.log(np.random.uniform()) < delp:\n return True\n else:\n return False\n\n\n.. module:: sampyl\n\n.. autoclass:: Sampler\n :members:\n\n **Attributes**\n\n .. attribute:: model\n \n :ref:`Model ` with logp and grad functions.\n\n .. attribute:: state\n\n The current :ref:`state ` of the model.\n\n .. attribute:: sampler\n\n Calling the sample method creates an infinite generator which returns \n samples as :ref:`states `.\n\n **Methods**\n" }, { "path": "docs_source/samplers/hamiltonian.rst", "content": ".. _hamiltonian:\n\nHamiltonian MCMC Sampler\n========================\n\n.. module:: sampyl\n\n.. autoclass:: Hamiltonian\n :members:\n :inherited-members:\n" }, { "path": "docs_source/samplers/metropolis.rst", "content": ".. _metropolis:\n\nMetropolis-Hastings Sampler\n===========================\n\n.. module:: sampyl\n\n.. autoclass:: Metropolis\n :members:\n :inherited-members:" }, { "path": "docs_source/samplers/nuts.rst", "content": ".. _nuts:\n\nNo-U-Turn Sampler (NUTS)\n========================\n\n.. module:: sampyl\n\n.. autoclass:: NUTS\n\t:members:\n\t:inherited-members:" }, { "path": "docs_source/samplers/slice.rst", "content": "Slice Sampler\n=============\n\n.. module:: sampyl\n\n.. autoclass:: Slice\n :members:\n :inherited-members:\n" }, { "path": "docs_source/samplers.rst", "content": ".. _samplers:\n\nSamplers\n========\n\nEach sampler has the same API. First you create a function calculating log P(X),\nthen pass it to a sampler. To generate a chain, call the sample method.\n\nExample::\n \n import sampyl as smp\n def logp(x, y):\n ...\n\n start = {'x': x_start, 'y': y_start}\n nuts = smp.NUTS(logp, start)\n chain = nuts.sample(1000)\n\nCreating your own :ref:`custom samplers ` is possible and straightfoward.\n\n.. toctree::\n\n samplers/nuts\n samplers/metropolis\n samplers/slice\n samplers/hamiltonian\n samplers/custom" }, { "path": "docs_source/state.rst", "content": ".. _state:\n\nState\n=====\n\nState objects are used to store the current state of the Markov chain. States are subclassed from collections.OrderedDict so that the elements are in a known order.\n\n.. module:: sampyl\n.. module:: state\n\n.. autoclass:: State\n :members:" }, { "path": "docs_source/tutorial.rst", "content": "Tutorial\n========\n\nDefining a model\n----------------\n\nHere I'll demonstrate how to use Sampyl with a simple linear model. First, I will create some fake data. Then, I will build a model and sample from the posterior distribution to estimate the coefficients from the synthetic data. \n\nWith a linear model, we assume the data is drawn from a normal distribution\n\n.. math ::\n Y &\\sim N(\\mu, \\sigma^2) \\\\\n \\mu &= \\beta_0 + \\beta_1 x_1 + ... + \\beta_n x_n\n\nwhere :math:`\\beta_n` are coefficients we want to estimate and :math:`x_n` are the predictor variables in our data. \n\nLet's start by making some fake data. ::\n\n # Number of data points\n N = 200\n\n # True parameters\n sigma = 1\n true_b = np.array([2, 1, 4])\n\n # Features, including a constant\n X = np.ones((N, len(true_b)))\n X[:,1:] = np.random.rand(N, len(true_b)-1)\n\n # Outcomes\n y = np.dot(X, true_b) + np.random.randn(N)*sigma\n\n.. image:: _static/linear_model_data.png\n :align: center\n\nAbove I've plotted the data we generated. We have the outcomes :code:`y` and our features :code:`X`, now we want to build a model to estimate the coefficients from this data. We can start with Bayes theorem\n\n.. math ::\n\n P(\\beta, \\sigma \\mid D) \\propto P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma)\n\nThe left hand side :math:`P(\\beta, \\sigma \\mid D)` is the posterior distribution. This is a probability distribution of likely values for :math:`\\beta` and :math:`\\sigma`, given our data :math:`D`. On the right side is the data likelihood :math:`P(D \\mid \\beta, \\sigma)` which gives the probability that we would see our data, given the parameters :math:`\\beta` and :math:`\\sigma`. Finally, we have our priors, :math:`P(\\beta)` and :math:`P(\\sigma)`. These distributions represent our prior information about the parameters.\n\nBayes theorem is quite intuitive because it is very similar to how humans think naturally. Suppose you are on a road trip, and you know its roughly an hour until you arrive, but its been a while since you saw the last sign with the distance so you aren't too sure. Then, you do see a sign and it says you still have 65 miles (104 km). Now you have a pretty good idea that it'll be another hour, but you aren't completely certain because traffic might be slow, or it could take a bit once you get into the city. This is how Bayes theorem works too. You have some prior information about your arrival time, but you're fairly uncertain. Then, you see some data, the distance remaining, and you update your information about the arrival time.\n\nContinuing on with the model, we want the likelihood, :math:`P(D \\mid \\beta, \\sigma)`, to model our data. We are assuming we can predict the data with a linear sum of coefficients :math:`\\beta_n` with our predictors :math:`x_n`, with some normally distributed noise on the scale of :math:`\\sigma`. So our likelihood would be well modeled with a normal distribution.\n\nWe also want to assign probability distributions to :math:`\\beta` and :math:`\\sigma` to account for our uncertainty in those parameters and our prior information about them. This is done through the priors :math:`P(\\beta)\\, P(\\sigma)`. For :math:`\\sigma`, we must choose a distribution that is defined only above 0. An exponential is good for this and we can control the *diffuseness* of the prior by changing the rate parameter. High rates pull the prior closer to 0, while lower rates push it out. We are using fairly wide normal priors for the coefficients :math:`\\beta`, since we aren't sure *a priori* how large the coefficients will be. Putting this all together, we get our model\n\n.. math ::\n P(\\beta, \\sigma \\mid D) &\\propto P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma) \\\\\n P(D \\mid \\beta, \\sigma) &\\sim \\mathrm{Normal}(\\mu, \\sigma^2) \\\\\n \\mu &= \\sum \\beta_i x_i \\\\\n \\beta &\\sim \\mathrm{Normal}(0, 100) \\\\\n \\sigma &\\sim \\mathrm{Exponential}(1) \n\n\nEvery sampler implemented in Sampyl takes a function that calculates :math:`\\log{P(\\theta)}` of the sampling distribution. We want to sample from the model posterior, so we need to write a Python function that calculates :math:`\\log{P(\\beta, \\sigma \\mid D)}`. I'll build the full model then go through it with explanations. ::\n \n import sampyl as smp\n from sampyl import np\n\n # Here, b is a length 3 array of coefficients\n def logp(b, sig):\n \n model = smp.Model()\n \n # Predicted value\n y_hat = np.dot(X, b)\n \n # Log-likelihood\n model.add(smp.normal(y, mu=y_hat, sig=sig))\n \n # log-priors\n model.add(smp.exponential(sig),\n smp.normal(b, mu=0, sig=100))\n \n return model()\n\nFirst, look at the imports, particularly ``from sampyl import np``. If you have worked with Numpy, you know it is usually abbreviated as ``np``, but why have we imported it from Sampyl here? Some of the samplers we have implemented require the gradient of :math:`\\log{P(\\theta)}`. To make things simpler, we use a wonderful package `autograd`_ to automatically calculate gradients. To use autograd, our :math:`\\log{P(\\theta)}` function needs to be written with Numpy provided by autograd. So, Sampyl imports Numpy from autograd, which you then import from Sampyl to build the model.\n\n.. _autograd: https://github.com/HIPS/autograd\n\nNext, check out the function definition. We named it ``logp`` but this can be anything. The arguments ``b`` and ``sig`` are the parameters of the model. They can either be Numpy arrays or scalars such as integers or floats.\n\nThen, we create a model with ``model = smp.Model()``. This is a convenience class that makes defining a model more concise. You add log-probabilities of the priors and likelihood with ``model.add()``. Then, you can call ``model()`` to sum up all the log-probabilities and get the log-posterior (well, something proportional to the posterior).\n\nOnwards: ::\n\n y_hat = np.dot(X, b)\n\nHere our function has been passed ``b`` which is an length 3 numpy array, a vector. We want to make a prediction of the outcome, ``y_hat``, then use this as the mean of our likelihood. ::\n\n model.add(smp.normal(y, mu=y_hat, sig=sig))\n\nThis is the log-likelihood of our data, given our parameters, :math:`P(D \\mid \\beta, \\sigma)`. It is important to note that ``smp.normal(y, mu=y_hat, sig=sig)`` returns a number, the log-likelihood of a normal distribution with the passed parameters. There is nothing going on behind the scene, just a function that returns a number. Then, ::\n\n model.add(smp.exponential(sig, rate=1),\n smp.normal(b, mu=0, sig=10)\n\nHere we have defined our priors. Again, ``smp.exponential`` and ``smp.normal`` return a scalar value of the distribution log-likelihood.\n\nFinally ::\n\n return model()\n\nAs I noted earlier, the distribution functions return log-likelihoods, just numbers. Since we are calculating :math:`\\log{P(\\beta, \\sigma \\mid D)}`,\n\n.. math ::\n \n \\log{P(\\beta, \\sigma \\mid D)} &\\propto \\log{\\left[P(D \\mid \\beta, \\sigma)\\, P(\\beta)\\, P(\\sigma) \\right]} \\\\\n \\log{P(\\beta, \\sigma \\mid D)} &\\propto \\log{P(D \\mid \\beta, \\sigma)} + \\log{P(\\beta)} + \\log{P(\\sigma)}\n\nwe just add up the log-likelihoods! The ``model`` object does this for us. Again, nothing fancy going on here, just beautiful math with logarithms. Now that we have defined :math:`\\log{P(\\beta, \\sigma \\mid D)}`, we can draw samples from it using one of our samplers.\n\n\nSampling from the posterior\n---------------------------\n\nEach sampler provided by Sampyl requires a :math:`\\log{P(X)}` function, ``logp`` here, and a starting state. A good place to start is the maximum of the posterior, typically called the *maximum a posteriori* or MAP. You can also define ``start`` yourself, it just needs to be a dictionary where the keys are the arguments of ``logp``. Here we'll use the No-U-Turn Sampler (NUTS) ::\n\n start = smp.find_MAP(logp, {'b': np.ones(3), 'sig': 1.})\n nuts = smp.NUTS(logp, start)\n chain = nuts.sample(2100, burn=100)\n\nSo first, we find the MAP and pass it as a start value to the NUTS sampler. This returns the sampler object itself ``nuts``. It is important to provide the correct size arguments as starting values. Our function ``logp`` expects ``b`` to be a length 3 Numpy array, so that is what we need to provide to ``find_MAP`` or ``NUTS``.\n\nCalling ``nuts.sample(2100, burn=100)`` returns a chain of 2000 samples from the posterior distribution, where we have discarded the first 100 as a burn-in period. The chain we get is a `Numpy record array `_ so that we can access the posterior distribution for each parameter by name. For instance, to get the posterior samples for ``b`` with ``chain.b`` ::\n\n import matplotlib.pyplot as plt\n plt.plot(chain.b)\n\n.. image:: _static/linear_model_coefficients.png\n :align: center\n\nBelow is a plot of the posterior samples for each parameter.\n\n.. image:: _static/linear_model_posterior.png\n :align: center\n\nI've also included dashed lines indicating the true parameters. In the future, we will be providing functionality to return various statistics and intervals for the posterior. For more guidance, please look through the other examples provided in the documentation." }, { "path": "examples/Abalone Model.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hierarchical Model for Abalone Length\\n\",\n \"\\n\",\n \"Abalone were collected from various sites on the coast of California north of San Francisco. Here I'm going to develop a model to predict abalone lengths based on sites and harvest method - diving or rock-picking. I'm interested in how abalone lengths vary between sites and harvesting methods. This should be a hierarchical model as the abalone at the different sites are from the same population and should exhibit similar effects based on harvesting method. The hierarchical model will be beneficial since some of the sites are missing a harvesting method. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%config InlineBackend.figure_format = 'retina'\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import sampyl as smp\\n\",\n \"from sampyl import np\\n\",\n \"\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plt.style.use('seaborn')\\n\",\n \"plt.rcParams['font.size'] = 14.\\n\",\n \"plt.rcParams['legend.fontsize'] = 14.0\\n\",\n \"plt.rcParams['axes.titlesize'] = 16.0\\n\",\n \"plt.rcParams['axes.labelsize'] = 14.0\\n\",\n \"plt.rcParams['xtick.labelsize'] = 13.0\\n\",\n \"plt.rcParams['ytick.labelsize'] = 13.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load our data here. This is just data collected in 2017.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
data yearfull lengthsgroup_idsite_codeFull_IDMode
020171811.052017_06_24_005_30_01_01R
120171821.052017_06_24_005_30_01_01R
220171831.052017_06_24_005_30_01_01R
320171911.052017_06_24_005_30_01_01R
420171911.052017_06_24_005_30_01_01R
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" data year full lengths group_id site_code Full_ID Mode\\n\",\n \"0 2017 181 1.0 5 2017_06_24_005_30_01_01 R\\n\",\n \"1 2017 182 1.0 5 2017_06_24_005_30_01_01 R\\n\",\n \"2 2017 183 1.0 5 2017_06_24_005_30_01_01 R\\n\",\n \"3 2017 191 1.0 5 2017_06_24_005_30_01_01 R\\n\",\n \"4 2017 191 1.0 5 2017_06_24_005_30_01_01 R\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv('Clean2017length.csv')\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Important columns here are: \\n\",\n \"\\n\",\n \"* **full lengths:** length of abalone\\n\",\n \"* **mode:** Harvesting method, R: rock-picking, D: diving\\n\",\n \"* **site_code:** codes for 15 different sites\\n\",\n \"\\n\",\n \"First some data preprocessing to get it into the correct format for our model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Convert sites from codes into sequential integers starting at 0\\n\",\n \"unique_sites = data['site_code'].unique()\\n\",\n \"site_map = dict(zip(unique_sites, np.arange(len(unique_sites))))\\n\",\n \"data = data.assign(site=data['site_code'].map(site_map))\\n\",\n \"\\n\",\n \"# Convert modes into integers as well\\n\",\n \"# Filter out 'R/D' modes, bad data collection\\n\",\n \"data = data[(data['Mode'] != 'R/D')]\\n\",\n \"mode_map = {'R':0, 'D':1}\\n\",\n \"data = data.assign(mode=data['Mode'].map(mode_map))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## A Hierarchical Linear Model\\n\",\n \"\\n\",\n \"Here we'll define our model. We want to make a linear model for each site in the data where we predict the abalone length given the mode of catching and the site.\\n\",\n \"\\n\",\n \"$$ y_s = \\\\alpha_s + \\\\beta_s * x_s + \\\\epsilon $$\\n\",\n \"\\n\",\n \"where $y_s$ is the predicted abalone length, $x$ denotes the mode of harvesting, $\\\\alpha_s$ and $\\\\beta_s$ are coefficients for each site $s$, and $\\\\epsilon$ is the model error. We'll use this prediction for our likelihood with data $D_s$, using a normal distribution with mean $y_s$ and variance $ \\\\epsilon^2$ :\\n\",\n \"\\n\",\n \"$$ \\\\prod_s P(D_s \\\\mid \\\\alpha_s, \\\\beta_s, \\\\epsilon) = \\\\prod_s \\\\mathcal{N}\\\\left(D_s \\\\mid y_s, \\\\epsilon^2\\\\right) $$\\n\",\n \"\\n\",\n \"The abalone come from the same population just in different locations. We can take these similarities between sites into account by creating a hierarchical model where the coefficients are drawn from a higher-level distribution common to all sites.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{align} \\n\",\n \"\\\\alpha_s & \\\\sim \\\\mathcal{N}\\\\left(\\\\mu_{\\\\alpha}, \\\\sigma_{\\\\alpha}^2\\\\right) \\\\\\\\\\n\",\n \"\\\\beta_s & \\\\sim \\\\mathcal{N}\\\\left(\\\\mu_{\\\\beta}, \\\\sigma_{\\\\beta}^2\\\\right) \\\\\\\\\\n\",\n \"\\\\end{align}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class HLM(smp.Model):\\n\",\n \" \\n\",\n \" def __init__(self, data=None):\\n\",\n \" super().__init__()\\n\",\n \" self.data = data\\n\",\n \" \\n\",\n \" # Now define the model (log-probability proportional to the posterior)\\n\",\n \" def logp_(self, μ_α, μ_β, σ_α, σ_β, site_α, site_β, ϵ):\\n\",\n \"\\n\",\n \" # Population priors - normals for population means and half-Cauchy for population stds\\n\",\n \" self.add(smp.normal(μ_α, sig=500),\\n\",\n \" smp.normal(μ_β, sig=500),\\n\",\n \" smp.half_cauchy(σ_α, beta=5),\\n\",\n \" smp.half_cauchy(σ_β, beta=0.5))\\n\",\n \"\\n\",\n \" # Priors for site coefficients, sampled from population distributions\\n\",\n \" self.add(smp.normal(site_α, mu=μ_α, sig=σ_α),\\n\",\n \" smp.normal(site_β, mu=μ_β, sig=σ_β))\\n\",\n \"\\n\",\n \" # Prior for likelihood uncertainty\\n\",\n \" self.add(smp.half_normal(ϵ))\\n\",\n \"\\n\",\n \" # Our estimate for abalone length, α + βx\\n\",\n \" length_est = site_α[self.data['site'].values] + site_β[self.data['site'].values]*self.data['mode']\\n\",\n \"\\n\",\n \" # Add the log-likelihood\\n\",\n \" self.add(smp.normal(self.data['full lengths'], mu=length_est, sig=ϵ))\\n\",\n \"\\n\",\n \" return self()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sites = data['site'].values\\n\",\n \"modes = data['mode'].values\\n\",\n \"lengths = data['full lengths'].values\\n\",\n \"\\n\",\n \"# Now define the model (log-probability proportional to the posterior)\\n\",\n \"def logp(μ_α, μ_β, σ_α, σ_β, site_α, site_β, ϵ):\\n\",\n \" \\n\",\n \" model = smp.Model()\\n\",\n \" \\n\",\n \" # Population priors - normals for population means and half-Cauchy for population stds\\n\",\n \" model.add(smp.normal(μ_α, sig=500),\\n\",\n \" smp.normal(μ_β, sig=500),\\n\",\n \" smp.half_cauchy(σ_α, beta=5),\\n\",\n \" smp.half_cauchy(σ_β, beta=0.5))\\n\",\n \" \\n\",\n \" # Priors for site coefficients, sampled from population distributions\\n\",\n \" model.add(smp.normal(site_α, mu=μ_α, sig=σ_α),\\n\",\n \" smp.normal(site_β, mu=μ_β, sig=σ_β))\\n\",\n \" \\n\",\n \" # Prior for likelihood uncertainty\\n\",\n \" model.add(smp.half_normal(ϵ))\\n\",\n \" \\n\",\n \" # Our estimate for abalone length, α + βx\\n\",\n \" length_est = site_α[sites] + site_β[sites]*modes\\n\",\n \" \\n\",\n \" # Add the log-likelihood\\n\",\n \" model.add(smp.normal(lengths, mu=length_est, sig=ϵ))\\n\",\n \"\\n\",\n \" return model()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model = HLM(data=data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-509218.07501755428\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"start = {'μ_α': 201., 'μ_β': 5., 'σ_α': 1., 'σ_β': 1.,\\n\",\n \" 'site_α': np.ones(len(site_map))*201,\\n\",\n \" 'site_β': np.zeros(len(site_map)),\\n\",\n \" 'ϵ': 1.}\\n\",\n \"model.logp_(*start.values())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/mat/miniconda3/lib/python3.6/site-packages/autograd/tracer.py:14: UserWarning: Output seems independent of input.\\n\",\n \" warnings.warn(\\\"Output seems independent of input.\\\")\\n\",\n \"/Users/mat/miniconda3/lib/python3.6/site-packages/autograd/tracer.py:48: RuntimeWarning: overflow encountered in exp\\n\",\n \" return f_raw(*args, **kwargs)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 1100 of 1100 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = {'μ_α': 201., 'μ_β': 5., 'σ_α': 1., 'σ_β': 1.,\\n\",\n \" 'site_α': np.ones(len(site_map))*201,\\n\",\n \" 'site_β': np.zeros(len(site_map)),\\n\",\n \" 'ϵ': 1.}\\n\",\n \"\\n\",\n \"# Using NUTS is slower per sample, but more likely to give good samples (and converge)\\n\",\n \"sampler = smp.NUTS(logp, start)\\n\",\n \"chain = sampler(1100, burn=100, thin=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are some checks for convergence you can do, but they aren't implemented yet. Instead, we can visually inspect the chain. In general, the samples should be stable, the first half should vary around the same point as the second half.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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sfZ35+BGlqZatUdOOwk++OvnwCMGwo07ww8tARIa2HeJ8UvIS8YQ9wa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\\nJsbYvYyxtxljnDF2p8f+ScbYxYyxPzLGtjPG9jDGnmCM3cgYm6TY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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 503,\n \"width\": 907\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(16,9))\\n\",\n \"for each in chain.site_α.T:\\n\",\n \" ax.hist(each, range=(185, 210), bins=60, alpha=0.5)\\n\",\n \"ax.set_xticklabels('')\\n\",\n \"ax.set_yticklabels('');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fig.savefig('/Users/mat/Desktop/posteriors.png', dpi=300)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the posterior distribution, we can look at many different results. Here I'll make a function that plots the means and 95% credible regions (range that contains central 95% of the probability) for the coefficients $\\\\alpha_s$ and $\\\\beta_s$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def coeff_plot(coeff, ax=None):\\n\",\n \" if ax is None:\\n\",\n \" fig, ax = plt.subplots(figsize=(3,5))\\n\",\n \" CRs = np.percentile(coeff, [2.5, 97.5], axis=0)\\n\",\n \" means = coeff.mean(axis=0)\\n\",\n \" ax.errorbar(means, np.arange(len(means)), xerr=np.abs(means - CRs), fmt='o')\\n\",\n \" \\n\",\n \" ax.set_yticks(np.arange(len(site_map)))\\n\",\n \" ax.set_yticklabels(site_map.keys())\\n\",\n \" ax.set_ylabel('Site')\\n\",\n \" \\n\",\n \" ax.grid(True, axis='x', color=\\\"#CCCCCC\\\")\\n\",\n \" ax.tick_params(axis='both', length=0)\\n\",\n \" for each in ['top', 'right', 'left', 'bottom']:\\n\",\n \" ax.spines[each].set_visible(False)\\n\",\n \" \\n\",\n \" return ax\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can look at how abalone lengths vary between sites for the rock-picking method ($\\\\alpha_s$).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 313,\n \"width\": 218\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = coeff_plot(chain.site_α)\\n\",\n \"ax.set_xlim(175, 225)\\n\",\n \"ax.set_xlabel('Abalone Length (mm)');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here I'm plotting the mean and 95% credible regions (CR) of $\\\\alpha$ for each site. This coefficient measures the average length of rock-picked abalones. We can see that the average abalone length varies quite a bit between sites. The CRs give a measure of the uncertainty in $\\\\alpha$, wider CRs tend to result from less data at those sites. \\n\",\n \"\\n\",\n \"Now, let's see how the abalone lengths vary between harvesting methods (the difference for diving is given by $\\\\beta_s$).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 313,\n \"width\": 218\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = coeff_plot(chain.site_β)\\n\",\n \"#ax.set_xticks([-5, 0, 5, 10, 15])\\n\",\n \"ax.set_xlabel('Mode effect (mm)');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here I'm plotting the mean and 95% credible regions (CR) of $\\\\beta$ for each site. This coefficient measures the difference in length of dive picked abalones compared to rock picked abalones. Most of the $\\\\beta$ coefficients are above zero which indicates that abalones harvested via diving are larger than ones picked from the shore. For most of the sites, diving results in 5 mm longer abalone, while at site 72, the difference is around 12 mm. Again, wider CRs mean there is less data leading to greater uncertainty.\\n\",\n \"\\n\",\n \"Next, I'll overlay the model on top of the data and make sure it looks right. We'll also see that some sites don't have data for both harvesting modes but our model still works because it's hierarchical. That is, we can get a posterior distribution for the coefficient from the population distribution even though the actual data is missing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def model_plot(data, chain, site, ax=None, n_samples=20):\\n\",\n \" if ax is None:\\n\",\n \" fig, ax = plt.subplots(figsize=(4,6))\\n\",\n \" \\n\",\n \" site = site_map[site]\\n\",\n \" xs = np.linspace(-1, 3)\\n\",\n \"\\n\",\n \" for ii, (mode, m_data) in enumerate(data[data['site'] == site].groupby('mode')):\\n\",\n \" \\n\",\n \" a = chain.site_α[:, site]\\n\",\n \" b = chain.site_β[:, site]\\n\",\n \" \\n\",\n \" # now sample from the posterior...\\n\",\n \" idxs = np.random.choice(np.arange(len(a)), size=n_samples, replace=False)\\n\",\n \" \\n\",\n \" # Draw light lines sampled from the posterior\\n\",\n \" for idx in idxs:\\n\",\n \" ax.plot(xs, a[idx] + b[idx]*xs, color='#E74C3C', alpha=0.05)\\n\",\n \" \\n\",\n \" # Draw the line from the posterior means\\n\",\n \" ax.plot(xs, a.mean() + b.mean()*xs, color='#E74C3C')\\n\",\n \" \\n\",\n \" # Plot actual data points with a bit of noise for visibility\\n\",\n \" mode_label = {0: 'Rock-picking', 1: 'Diving'}\\n\",\n \" ax.scatter(ii + np.random.randn(len(m_data))*0.04,\\n\",\n \" m_data['full lengths'], edgecolors='none',\\n\",\n \" alpha=0.8, marker='.', label=mode_label[mode])\\n\",\n \"\\n\",\n \" ax.set_xlim(-0.5, 1.5)\\n\",\n \" ax.set_xticks([0, 1])\\n\",\n \" ax.set_xticklabels('')\\n\",\n \" ax.set_ylim(150, 250)\\n\",\n \" \\n\",\n \" ax.grid(True, axis='y', color=\\\"#CCCCCC\\\")\\n\",\n \" ax.tick_params(axis='both', length=0)\\n\",\n \" \\n\",\n \" for each in ['top', 'right', 'left', 'bottom']:\\n\",\n \" ax.spines[each].set_visible(False)\\n\",\n \" \\n\",\n \" return ax\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 302,\n \"width\": 609\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(10, 5), ncols=4, sharey=True)\\n\",\n \"\\n\",\n \"for ax, site in zip(axes, [5, 52, 72, 162]):\\n\",\n \" ax = model_plot(data, chain, site, ax=ax, n_samples=30)\\n\",\n \" ax.set_title(site)\\n\",\n \"\\n\",\n \"first_ax = axes[0]\\n\",\n \"first_ax.legend(framealpha=1, edgecolor='none')\\n\",\n \"first_ax.set_ylabel('Abalone length (mm)');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For site 5, there are few data points for the diving method so there is a lot of uncertainty in the prediction. The prediction is also pulled lower than the data by the population distribution. Similarly, for site 52 there is no diving data, but we still get a (very uncertain) prediction because it's using the population information.\\n\",\n \"\\n\",\n \"Finally, we can look at the harvesting mode effect for the population. Here I'm going to print out a few statistics for $\\\\mu_{\\\\beta}$. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Mean: 5.397\\n\",\n \"95% CR: [2.778, 8.408]\\n\",\n \"P(mu_b) > 0: 1.000\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 370\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"ax.hist(chain.μ_β, bins=30);\\n\",\n \"b_mean = chain.μ_β.mean()\\n\",\n \"b_CRs = np.percentile(chain.μ_β, [2.5, 97.5])\\n\",\n \"p_gt_0 = (chain.μ_β > 0).mean()\\n\",\n \"print(\\n\",\n \"\\\"\\\"\\\"Mean: {:.3f}\\n\",\n \"95% CR: [{:.3f}, {:.3f}]\\n\",\n \"P(mu_b) > 0: {:.3f}\\n\",\n \"\\\"\\\"\\\".format(b_mean, b_CRs[0], b_CRs[1], p_gt_0))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also look at the population distribution for $\\\\beta_s$ by sampling from a normal distribution with mean and variance sampled from $\\\\mu_\\\\beta$ and $\\\\sigma_\\\\beta$.\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\beta_s \\\\sim \\\\mathcal{N}\\\\left(\\\\mu_{\\\\beta}, \\\\sigma_{\\\\beta}^2\\\\right)\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import scipy.stats as stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5,0,'Dive harvesting effect (mm)')\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 263,\n \"width\": 370\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"samples = stats.norm.rvs(loc=chain.μ_β, scale=chain.σ_β)\\n\",\n \"plt.hist(samples, bins=30);\\n\",\n \"plt.xlabel('Dive harvesting effect (mm)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It's apparent that dive harvested abalone are roughly 5 mm longer than rock-picked abalone. Maybe this is a bias of the divers to pick larger abalone. Or, it's possible that abalone that stay in the water grow larger.\"\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\": \"3.6.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/Clean2017length.csv", "content": "data year,full 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}, { "path": "examples/Examples.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Sampyl Examples\\n\",\n \"\\n\",\n \"Here I will have some examples showing how to use Sampyl. This is for version 0.2.2. Let's import it and get started. Sampyl is a Python package used to sample from probability distributions using Markov Chain Monte Carlo (MCMC). This is most useful when sampling from the posterior distribution of a Bayesian model.\\n\",\n \"\\n\",\n \"Every sampler provided by Sampyl works the same way. Define $ \\\\log P(\\\\theta) $ as a function, then pass it to the sampler class. The class returns a sampler object, which you can then use to sample from $P(\\\\theta)$. For samplers which use the gradient, $\\\\nabla_{\\\\theta} \\\\log P(\\\\theta)$, Sampyl uses [autograd](https://github.com/HIPS/autograd) to automatically calculate the gradients. However, you can pass in your own $\\\\nabla_{\\\\theta} \\\\log P(\\\\theta)$ functions.\\n\",\n \"\\n\",\n \"Starting out simple, let's sample from a normal distribution.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%config InlineBackend.figure_format = 'retina'\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import sampyl as smp\\n\",\n \"from sampyl import np\\n\",\n \"\\n\",\n \"# Autograd throws some warnings that are useful, but this is\\n\",\n \"# a demonstration, so I'll squelch them.\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A normal distribution with mean $\\\\mu$ and variance $\\\\sigma^2$ is defined as:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"P(x,\\\\mu, \\\\sigma) = \\\\frac{1}{\\\\sigma \\\\sqrt{2 \\\\pi}} \\\\; \\\\mathrm{Exp}\\\\left( \\\\frac{-(x - \\\\mu)^2}{2\\\\sigma^2} \\\\right)\\n\",\n \"$$\\n\",\n \"\\n\",\n \"For numerical stability, it is typically better to deal with log probabilities, $\\\\log{P(\\\\theta)}$. Then for the normal distribution with known mean and variance,\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\log{P(x \\\\mid \\\\mu, \\\\sigma)} = -\\\\log{\\\\sigma} - \\\\frac{(x - \\\\mu)^2}{2\\\\sigma^2} \\n\",\n \"$$\\n\",\n \"\\n\",\n \"where we can drop constant terms since the MCMC samplers only require something proportional to $\\\\log{P(\\\\theta)}$. We can easily write this as a Python function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mu, sig = 3, 2\\n\",\n \"def logp(x):\\n\",\n \" return -np.log(sig) - (x - mu)**2/(2*sig**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First we'll use a Metropolis-Hastings sampler. Each sampler requires a $\\\\log{P(\\\\theta)}$ function and a starting state. We have included a function to calculate the *maximum a posteriori* (MAP) to find the peak of the distribution for use as the starting state. Then you call the sampler and a chain of samples is returned.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r\",\n \"Progress: [##############################] 10000 of 10000 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = smp.find_MAP(logp, {'x':1.})\\n\",\n \"metro = smp.Metropolis(logp, start)\\n\",\n \"chain = metro(10000, burn=2000, thin=4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"We can retrieve the chain by accessing the attributes defined by the parameter name(s) of `logp`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 372\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(chain.x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 377\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"_ = plt.hist(chain.x, bins=30)\\n\",\n \"_ = plt.vlines(mu, 0, 250, linestyles='--')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we have sampled from a normal distribution with a mean of 3, indicated with the dashed vertical line.\\n\",\n \"\\n\",\n \"There is also a No-U-Turn Sampler (NUTS), which avoids the random-walk nature of Metropolis samplers. NUTS uses the gradient of $\\\\log{P(\\\\theta)}$ to make intelligent state proposals. You'll notice here that we don't pass in any information about the gradient. Instead, it is calculated automatically with [autograd](https://github.com/HIPS/autograd).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 2100 of 2100 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"nuts = smp.NUTS(logp, start)\\n\",\n \"chain = nuts(2100, burn=100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 372\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(chain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 377\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"_ = plt.hist(chain.x, bins=30)\\n\",\n \"_ = plt.vlines(mu, 0, 250, linestyles='--')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Bayesian estimation of phone call rates\\n\",\n \"\\n\",\n \"Let's try something a little more complicated. Let's say you run a business and you put an advertisement in the paper. Then, to judge the effectiveness of the ad, you want to compare the number of incoming phone calls per hour before and after the placement of the add. Then we can build a Bayesian model using a Poisson likelihood with exponential priors for $\\\\lambda_1$ and $\\\\lambda_2$.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\begin{align}\\n\",\n \"P(\\\\lambda_1, \\\\lambda_2 \\\\mid D) &\\\\propto P( D \\\\mid \\\\lambda_1, \\\\lambda_2)\\\\, P(\\\\lambda_1)\\\\, P(\\\\lambda_2) \\\\\\\\\\n\",\n \"P( D \\\\mid \\\\lambda_1, \\\\lambda_2) &\\\\sim \\\\mathrm{Poisson}(D\\\\mid\\\\lambda_1)\\\\,\\\\mathrm{Poisson}(D\\\\mid\\\\lambda_2) \\\\\\\\\\n\",\n \"P(\\\\lambda_1) &\\\\sim \\\\mathrm{Exp}(1) \\\\\\\\\\n\",\n \"P(\\\\lambda_2) &\\\\sim \\\\mathrm{Exp}(1) \\n\",\n \"\\\\end{align}\\n\",\n \"\\n\",\n \"This analysis method is known as Bayesian inference or Bayesian estimation. We want to know likely values for $\\\\lambda_1$ and $\\\\lambda_2$. This information is contained in the posterior distribution $P(\\\\lambda_1, \\\\lambda_2 \\\\mid D)$. To infer values for $\\\\lambda_1$ and $\\\\lambda_2$, we can sample from the posterior using our MCMC samplers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Fake data for the day before and after placing the ad.\\n\",\n \"# We'll make the calls increase by 2 an hour. Record data for each\\n\",\n \"# hour over two work days.\\n\",\n \"before = np.random.poisson(7, size=16)\\n\",\n \"after = np.random.poisson(9, size=16)\\n\",\n \"\\n\",\n \"# Define the log-P function here\\n\",\n \"def logp(λ1, λ2):\\n\",\n \" model = smp.Model()\\n\",\n \" # Poisson log-likelihoods\\n\",\n \" model.add(smp.poisson(before, rate=λ1),\\n\",\n \" smp.poisson(after, rate=λ2))\\n\",\n \"\\n\",\n \" # Exponential log-priors for rate parameters\\n\",\n \" model.add(smp.exponential(λ1),\\n\",\n \" smp.exponential(λ2))\\n\",\n \" \\n\",\n \" return model()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 10000 of 10000 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = smp.find_MAP(logp, {'λ1':1., 'λ2':1.})\\n\",\n \"sampler = smp.Metropolis(logp, start)\\n\",\n \"chain = sampler(10000, burn=2000, thin=4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sampling returns a numpy record array which you can use to access samples by name. Variable names are taken directly from the argument list of `logp`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('λ1', 'λ2')\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sampler.var_names)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 370\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(chain.λ1)\\n\",\n \"plt.plot(chain.λ2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now to see if there is a significant difference between the two days. We can find the difference $\\\\delta = \\\\lambda_2 - \\\\lambda_1$, then find the probability that $\\\\delta > 0$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 377\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"delta = chain.λ2 - chain.λ1\\n\",\n \"_ = plt.hist(delta, bins=30)\\n\",\n \"_ = plt.vlines(2, 0, 250, linestyle='--')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.981 probability the rate of phone calls increased\\n\",\n \"delta = 1.916, 95% CR = {0.154 3.746}\\n\"\n ]\n }\n ],\n \"source\": [\n \"p = np.mean(delta > 0)\\n\",\n \"effect = np.mean(delta)\\n\",\n \"CR = np.percentile(delta, (2.5, 97.5))\\n\",\n \"print(\\\"{:.3f} probability the rate of phone calls increased\\\".format(p))\\n\",\n \"print(\\\"delta = {:.3f}, 95% CR = {{{:.3f} {:.3f}}}\\\".format(effect, *CR))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There true difference in rates is two per hour, marked with the dashed line. Our posterior is showing an effect, but our best estimate is that the rate increased by only one call per hour. The 95% credible region is {-0.735 2.743} which idicates that there is a 95% probability that the true effect lies with the region, as it indeed does.\\n\",\n \"\\n\",\n \"We can also use NUTS to sample from the posterior.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 2100 of 2100 samples\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 370\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"nuts = smp.NUTS(logp, start)\\n\",\n \"chain = nuts.sample(2100, burn=100)\\n\",\n \"_ = plt.plot(chain.λ1)\\n\",\n \"_ = plt.plot(chain.λ2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.982 probability the rate of phone calls increased\\n\",\n \"delta = 1.880, 95% CR = {0.073 3.624}\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 377\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"delta = chain.λ2 - chain.λ1\\n\",\n \"_ = plt.hist(delta, bins=30)\\n\",\n \"_ = plt.vlines(2, 0, 250, linestyle='--')\\n\",\n \"p = np.mean(delta > 0)\\n\",\n \"effect = np.mean(delta)\\n\",\n \"CR = np.percentile(delta, (2.5, 97.5))\\n\",\n \"print(\\\"{:.3f} probability the rate of phone calls increased\\\".format(p))\\n\",\n \"print(\\\"delta = {:.3f}, 95% CR = {{{:.3f} {:.3f}}}\\\".format(effect, *CR))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Linear models too\\n\",\n \"\\n\",\n \"When you build larger models, it would be cumbersome to have to include every parameter as an argument in the logp function. To avoid this, you can declare the size of variables when passing in the starting state.\\n\",\n \"\\n\",\n \"For instance, with a linear model it would be great to pass the coefficients as one parameter. First, we'll make some fake data, then infer the coefficients.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Number of data points\\n\",\n \"N = 200\\n\",\n \"# True parameters\\n\",\n \"sigma = 1\\n\",\n \"true_B = np.array([2, 1, 4])\\n\",\n \"\\n\",\n \"# Simulated features, including a constant\\n\",\n \"X = np.ones((N, len(true_B)))\\n\",\n \"X[:,1:] = np.random.rand(N, 2)*2\\n\",\n \"\\n\",\n \"# Simulated outcomes with normally distributed noise\\n\",\n \"y = np.dot(X, true_B) + np.random.randn(N)*sigma\\n\",\n \"\\n\",\n \"data = np.ones((N, len(true_B) + 1))\\n\",\n \"data[:, :-1] = X\\n\",\n \"data[:, -1] = y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5,0,'X2')\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 263,\n \"width\": 440\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(7,4),ncols=2)\\n\",\n \"for i, ax in enumerate(axes):\\n\",\n \" ax.scatter(X[:,i+1], y)\\n\",\n \"axes[0].set_ylabel('y')\\n\",\n \"axes[0].set_xlabel('X1')\\n\",\n \"axes[1].set_xlabel('X2')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fig.savefig('linear_model_data.png')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Here, β is a length 3 array of coefficients\\n\",\n \"def logp(β, sig):\\n\",\n \" \\n\",\n \" model = smp.Model()\\n\",\n \" \\n\",\n \" # Estimate from our data and coefficients\\n\",\n \" y_hat = np.dot(X, β)\\n\",\n \" \\n\",\n \" # Add log-likelihood\\n\",\n \" model.add(smp.normal(y, mu=y_hat, sig=sig))\\n\",\n \" \\n\",\n \" # Add prior for estimate error\\n\",\n \" model.add(smp.exponential(sig))\\n\",\n \" \\n\",\n \" # Uniform priors on coefficients\\n\",\n \" model.add(smp.uniform(β, lower=-100, upper=100))\\n\",\n \" \\n\",\n \" return model()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 20000 of 20000 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = smp.find_MAP(logp, {'β': np.ones(3), 'sig': 1.}, \\n\",\n \" bounds={'β':(-5, 10), 'sig':(0.01, None)})\\n\",\n \"sampler = smp.Metropolis(logp, start)\\n\",\n \"chain = sampler(20000, burn=5000, thin=4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 373\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"_ = plt.plot(chain.β)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And using NUTS too.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 2100 of 2100 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = smp.find_MAP(logp, {'β': np.ones(3), 'sig': 1.})\\n\",\n \"nuts = smp.NUTS(logp, start)\\n\",\n \"chain = nuts.sample(2100, burn=100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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TGbRwHozTaJQrMVVnPrHsGl1MuQ2/U2w07+fHwjzb3lQmVnZd3Iu56nKXPkCfM5z988zCe2/Acpm6bimXHl1nWqw1jUlaIKBNeADB6/Wi7uAFTXx+zXZfV+zd9GvOpzX1lJYaroV65pXxNq/37nA3v0hv0+CzmM7y+9XWHSTNHUjNTseHcBozfNN5unbICTS0ua+5e7Dtq+i2lRGqm86SVNaM0YvKWyZjwzwTVfj4GowFbLm7BmaQzbh/TOvHyx6k/LFU+S4/n/HPxXPI5fPTvRzbL1JIzb+14S3V/5eMaFGBKKN3W38YvR3/BlotbLNtcTbmKwzcO2+3jKFEbdSoKPZb1wKd7P4WUEh/u/hC9lvWymbnRTK0Hm/Lz2RWjNFo+681i42ORmO68sb9SpiETCekJHu3z+4nfLVV3M/fMtCyff3A+Xtv8ms2wwIX/LcSUbVPw9LqnbT5DiIiIiCh3/KvpDHlO5ZovWZ9sqYp4d9e7eKTBI8g0Zlp+CV91chV+PPwjOlftjFci1BNSUaeiVHtqHLh+wGk4n8V8BsD0q/WopqPsLt6VF/u5ubh/dt2ztgskcOTmEeiEDg1KN8jxcXNSqaNGbYiGlNLul3ZHQ9ysH2tHj5N5+X83/3MYx9wDczGu5TjLfWUiYfT60Q73Ndt9ZTe+2PcFulbrarPcPLzuvZ3ZM759+O+HGNZoGAD74WcAMGf/HExrn13R4GgGsc9iPkPZkLI4eP0gRjUdhcphlW3Wb7qwCUMbDrXb71TiKZv7yotvnyeUFAm7IgFFbO4fuH4AnZd0xptt30Tf2n3t9v/rzF+WXlZpmWmYfb+piXemIROBukCnPbI6/dpJNdmg1rvK1TTsziqUdELndLitlBIvbHoB2y9txyMNHkGzss3QtVpXHE04igPXD2BAvQEoXbQ0AkSAzet/zZk1AIC91/aiVola6FK1C55u+jSEEPju0Hf4ct+XKBJQBH8O+BMVQis4jR/ITtYAtr2KcvMamLR5Eo4kHLFZppZQWnNmDWZ0mWG3XLmv+XH8ZO8nWH5iOQDgx94/okpYFTzw+wPQG/WY1m6azT6nk06rxvbx3o+RkJ6AHw7/gOCAYCw+uhiA6QeG3rV622yrllBKSE9AjfAaqsdW42higM5LOiNuRJzL/SUk7ujvoP/K/khIT8Cs+2ahe43uAOAyGalWSbrj8g58se8LAKb/B+f2mAvA9P8RYHqs39v1Hhb0WeAyNiJPTZw40fVGlK/wOSXKH/he1RYrlPI5taoc5Rfx6Tumo/Wi1mi2oBn2x+/HOzvfwcnEk/jh0A84lnAMN9Nu4tu4b21m/HLUoNWTC7Hjt467TM7kdPiR3qDHqSTbxMH2y9vxyB+PYHDUYI+mOVf++q9M8MzaMwt9lvexTAGfG2pTxjtKKAHA2aSzdkk4azmZctxVZYpaDM+sewYHrx+0XJiZmSuUHDXXVavEOZmYPeQu05iJqylXHZ576rapWHJsCXot72VX+aOsuJFS4kzSGbuLbWXzePNxfJVYcqfq7rb+NubHzVddt/H8RsvtTRc2AQDmHZiHdovbYeTakcgyZmHFiRVYf269TVLn4PWDDl8PasPJrCvJ1Dh777rq03Uq8RS2XNwCgzTgl6O/YMq2KRixdgRGrx+Nz2M/xyd7P3F6nMSMROyL34fZsbMt7+Uv930JwJS8cVSZqRQcEGy5rUyQvRL9So7e08qEEODZe0pZYTVzz0x8F/edJZkEmHoS/XH6D8v76t1d79rs89vx3+yOm5SRZFPls+BwdtJELQGllgxy9tpNzUzFz0d+xsZz2a9PZz3S3K1S+vHwj4hPi0eWzMKE6AmWije1Hx+sq4vUhuf9cSq7p9z2y9tVz+fs84YoN4oXL275QwUDn1Oi/IHvVW0xoZTPSSlxNeWqzRd7ZWWM9YXKk389aXPxf+H2BczaOwuzY2fj+Q3Pu2xY6+pC0tqkzZOcJkuAnPVfOZt0Fh1+6eB0my9iv3D7eLcybId5WF9IX0+9joX/LcTFOxcxcfPEXPcu2Re/z26Zs+FP4zeNxw+HfnC4Xm/Q4/AN+ySVI1JKjxMpYzeOdbjO/HpwlMhQq2iLjY+FURrx/aHv0X5xe7uLZUfu+fkeh+tupt1ElyVd8NDKh1weJy0rDXMOzEH7xe3xv+3qfa1yw91hnNaJNWvKiqZlx5fhq/1fQW/UIzY+Fq0WtcL/dvzPkhDJNGZiX/w+zPjXvhrGFUeJ15l7ZuLtnW/bLd8Xvw/fxn3rMpGZYbRPNBy/ddyy3+pTqzF+03i3Zvnaf91+mO3B6wdt7hulEb8e/dVuu2Cd44TS+nPrMXHzRLvPPG9VKDqi9hn7eeznNvczDc4TrYDt8M9DNw6h+2/dbdYrk/XKxLnaY++s6mzBfwvw0b8f4eXoly2JcUcVSgAQnxaPqylXnX7eZBmz7JJjb+8wve6se1KZDVw90PKDiVoFkzLBpTZs09UsmERERETkPiaU8jkJiZ7LeqLXsl4eV54ApmEg5uEwmcZM/H32b6/Fdjb5rMuEkqfTTAPA9J3TXc4e5sm03c76oSj7w1hf5N3W38bvJ37HsYRjWHpsKcZvHI8pW6cg7rrjoR5qM9E5rVCymtlJzeKji/Hon4863QbI7iXj6vlQ42yKcvMFqKNEm3W/L2ux12LxWcxnHj1Pjqw/tx6RSyORmOFeRUR6Vjq+3v81DNKAFSdX2PSNOZt01m42NE9kGbNy3YNLmVBSS+yYTdoyCa0Xtcbwv4bj4I2DDrdzZNcV+z5UiemJqk2Olx9fjuF/DberUlPjLElqFn0h2q0Y5x6Y67Kf0/pz6/H+bvtm0DYVSg6OsfPyTsvtP0//iY6/dMRLm17KVZN/Z5wlYcwkpGpCxdpjfz6GTGMmpJR4Y+sbLqs9Z+2dZdObSi0O8+eD3qDH6cTTNttbN5e3rhZzZNzGceixrAd6LOvhsD9SQnqC3TpXs/19sPsDh+uUCbHNF+0nrVAbijxrzyz0WtbLK1WoRERERIUJE0oFgITEzfSblkqW87fPu73vh/9+aHP/o38/wulE9f4cgOseSkqeNAXfF78P3x/63nIRcyPtBj6P+Rxrz2Y3lL2actWt4WwlipSwuZ+UkYQ9V/eoVh9subjF5r71hZZy+/lx8/HyPy8j9losPtn7Cd7a8RYGRw3Gu7veRfTFaESdjsLwtcMdxqX2q7p1BZmvvLblNSTrk3OUUHLG/PioVaw4q/RQVoXl1JaLW2wax7tDmXgyPyb/XvkX/Vb2Q+/lvTFn/xy3kiLWpJQY9fcoyzA1d6g9Rt5+jpyZe2Autl3aBsB0MT7/4HxMiJ6guu30ndMdHkd5Ie6NRKH1sZTJMmWlpHkInZL1DGqOHtfUzFSsOLECn8d8jte3vo7UrFRsurAJe69lzwh2NeUqNp7f6PEkAmqvIXeqspz1rzLLMGSg9aLWGPrHUJeJZwBY9N8iRC6NxI5LpqGiSRlJ9ueVEqcSTyHipwj0X9UfkUsjVRvDb7+0HR/s/sDp82xOvidlJDl8fnLCPHmAWqVR0cCiNvfHbxpv96OFOalvlEYkZSThWMIxLPxvIS6nXHaYACdyx+3bty1/qGDgc0qUP/C9qi025S5ArqddBwC8uvnVXB2n/6r+Dtep/eLrzLyD89zaLjE9EaPWjkKWzMKxhGOY0WUGvoj9AitOrgBgqgbqWaMneizr4dbxrKs8MgwZGLh6IOJT4zGs4TC80fYNy7pMY6ZdZdf8uPkY3mQ41p5dazfVujke6z43SlnGLNXm2wBUf6mfe2CuW/+m3NhwfgNKFS2FiW28e8FkvkhXJpR+OfoL+tXu53A/b/UvGrdxnOuNFM4k2/awyTJm4ULyBTy97mnLsq8PfI1iQcWQacxEsC4Yjzd6HAE6x8M97+jv4J8L/6gOaYyNj3W4X0pmil3y09dNw5Xe2fkOXm3zaq4upidunoj91fdbHqMjN+37DOWGskquWGAx0/LkC1h8dDGupFxR3c+6QsnRcK73dr+nuvxmuimxnWHIwJCoIUjMSESvmr08iltv1CNEF2K5f/zWca+/39V6OjkzZsMYLH9oueoPDwZpsHtPvbPzHXzX6zu7bX85+gtKFy3t1jlXn1qNIfWHIDXLvVn/3KkOU6s0sn6+zYZEDbG5rxM6GIwGPLHmCRy+eRhNyzZ1KyYiVz75JDtxOn36dO0CIa/hc0qUP/C9qi0mlAqYLGOWx9Mvay0pIwn3/3a/ZUYw8+xI5uQNYLqoKVWklNvHLBqQ/Uv1rsu7LDM8LT662JJQWnd2HaZtn2a3b2JGIrKMWZi0eVKO/j0AHA6bcjZ8zNd+O/6bxxfErpgrbJSVVx/s/sBuRjhrt/Xa/YKgnPFszZk1ltkJrX2892PL7ZTMFDzf8nm7ba6lXEN4kXD0X9XfZhYxd8WnxmueULqScsUrlRktF7XEzsd2Yl/8PszaO8sLkWVTJoOCAoIgpcSL/7zosBcVYEowZBozcSrxlMe9kdadXYdJmyehdNHSlqo2T4cEH75xGG0qtrHct5uZ0gG1Wfm8ac7+OSgWVMxuuZTS7rPLWS+ntWfWOlyn9ORfT7q9rTsVbmoJJXd6/AkhEBsfi0M3TUNT4264no2OiIiIiNRxyFsB8vuJ3zHsz2Fah+Gxbw5+49ZsZY6G4qgxD32QUtpd3JqHtE3cPNHhL+a5bcz74b8fql7w7L22F6P+HoWp26b6vPmvmmfWPePV45krlNT6tyj7T7m7Lq+pJZOUvj7wtd2yn4/8jO7LuuPen+/NUTIJAL6N+9bmfmpmqt3sdfnJzD0z8eb2N71+XOVQs0BdIDIMGU6TSYDp/d96UWsMiRrisieR0vpz6wGoVxW666m/n7Jpwu0vyf4N5ze4Pdzy/O3zDiuGlDNteour98DRhKOIOh1lsyw9K92t4aI66FSH+xERERGR55hQyocqhlZ0uM7T4Q/+YOF/C+2Wjfp7VK6OGZ8ajw6LO6D5wuZ2PUBGrh2J8ZvGO9y3bsm6lovJnNpycYtqH5SUzBTsuboHq0+tdjlte37gLCl2R3/H4TotK5RySlmpoWzmnhNrzqyxuT/0j6G5PqaWVpxc4ZOkiXVzaMDUG+ni7Ysu99t6aavXY/HUoNWDtA5BlVoVkKNhgcP/ctwXzhde+uclp+vf22U/TPHXo7+6laQ/lXTK6Y8TvmrGTkRERFQQMaGUD9UuUVvrEHxuz9U9udp/++XtuJ2pnrSIuxHndIapk4knMWXblFyd3x2+vth9tIHr2d9yy1lFgLkHjZr8mFDaH78fU7dNxeN/Pq46C1puJeuT3WquXBgpK8Qu3bmEAasHaBSNZxLSE3BHf8fvZhBT6wPnKEm6//p+X4fjEbXJIT6J+cQrDe21qBwlIiIiyq/YQykf4hfe/EFtyJu1qylX8dqW13x2/jol6/js2GbOXovOGjOvPLnSF+H41OStky0VHMpZx3KqRngNy+2zSWe9ckzyPy9Hv4zdV3ZrHYZLzpLA+cHJW86HQLoj3ZCOoIAg1xsSERERESuU8iMmlPxfuZByLrc5fus4/jrzl1fPW6JICbQq3wrT2k2za/bsC+YZ7dT4S78Yb3E0HCg3rJvHO5qpzNsG1x+cJ+ehbPkhmVQQHLt1LNfH8HVDdCIiIqKChAklBSHEk0IIefePdzsYe8njjR5H9+rdtQ6DnNAJHYRwXqHkC8MbD8fCPgvxSINHXFZIecO55HPQG/Wq6/J6trL8yPqxy6tGwSGBIa43Iiqk3t/9vtYhEBEREeUbHPJmRQhRDcCXAO4ACNM4HId61uyJCqEVsOH8Bq1DIQeupV7LdWPvnHii0RN5er4rKVeQmqk+U56j5ZQt05A9u+HX++1nkvOFQMGPfSIiIiIq2Nyd7GbJg57NBEy2WKF0lzCVk/wA4CaAuRqH41KgjheFlK1D5Q7YPWw3igUV89k5IqtF2i3LNGaqzhYFAKlZ2iaUvu/1vabnd0emMTuhlFf9a5x9dlQvXh3jWzmeATE/CBABWofgE2NbjNU6BKdCg0K1DsErKoVW4kxvRERERG5iQinbiwDuB/AUgBSNY3EpL6oMKhSr4PNzkHf8X7f/82kyCQDaVWpn97rTG/RojrlYAAAgAElEQVQ2SRFrKZnavo1alGuh6fndoTfoMXrdaPRb0S/Pzhmgc5xw0Qldnle5eduECMdTwudXOx7bgedbPp/r4xQPKu6FaNSNbTEWwxsPt1veqUonn53T23YP2411g9dpMlyZiIiIKD9imQsAIUQjAB8BmC2l3CKEuF/rmFwJ0nk2C42AgIR7v7r2qNEDY5qPQaWwSpi6dSqiL0bnIELf+SzyM5xNPovoC9HoXbM3ZuyZ4XT7iRET8UnMJ3kUne8EiACH02J7s2KtaZmm6FS1EzZf2IwjCdkztTUo1QAf3/cx9l7bi5+O/AQAMEgDMrLUK5QO3zxsc/+ViFfwacynXovTFU/fI3klJDDE0l/qVsYt7Lyy026bWiVq4UzSGZ+c31kyOkAEoFhQMXSr3k11Wvn8QCfy5neSl1q/hNmxs/PkXMWDvZMIKhNSBrczb+d4/7ol66JU0VLYc3WP3boAXYBqH7BKoZVyfL68VLdkXZ8n5algmz59utYhkJfxOSXKH/he1Vahr1ASQgQCWATgPIApOdg/Ru0PgIbejtWapwkEV9vP6DwD09tPx6Q2kzCt3TQ0KN0A4cHhqF2ydm7CRJsKbdzedlKbSW5tFxYchmeaPYOf+v6EdpXaudx+ZNORbsfgz+b2mOvZzG1OfmR31tT9lwd/wbiW4xAcEGyzvGX5luhWoxsm3zsZwbrsdSlZriuRnm76NLpV7+Y6Zi9SVhlUL14d3/f6Hgt6L0Cvmr28dp7iwcUxtqV7w5Eea/gYoh6OcrldvZL1chuWQ04rlHSm/xJKFS3ls/P7Wk4TSsv6LfNo+6ebPo35PedjbMuxmo29f7XNqx5tX75Y+Ryd56E6D2HL0C1Y0X8Fvu/1Pf555B+7bQJFIK6nXbdbPrDeQPSp2QcAUCWsSo7Onxc4jJyIiIjIc4U+oQTgfwBaARgppcw301J5O6HUsExDDKo/CMObDLe5mFTrm+OOABGAZ5s969EFz6MNH3VrO+tZqpQXj83LNXf7fP7GWUVNaFAo2lVqh4rFKtqty0nPmLfav4W4EXGIGxGHmuE1VbdR9kayfg1ZJ5vcGdrWoHQDlAkp43Z8EyMmur2tmo6VO9ote6zhY7in4j1oXaG1V2Y6q1OiDnY+thObH9mMZ5o+41aPm5blWqJ0SGmX2607ty7X8QFA+ZDyKFmkpM0yZ68zc/VS6aKuY1QKDw63uV8zvCZqlahlt91DdR6yuV+qiHeTV7XC7c/pjgalG7g9JOy9ju9BCIF2ldrh+RbPo3GZxpjXY57H51zQe4HH+5g1K9sMI5qMcDtJEx4cjh41euToXIPrD7b5f6FsSFm7pFKGIQPxqfE2y37q+xOalm2KGV1mYNewXZh0j3s/GmiBzeqJiIiIPFeoE0pCiHthqkr6REppP+7EDVLKCLU/AI56NViFIgFFnK6PrBppc99VQqloQFHV5a3Kt8KrbV5F31p93Y6tR40e2PvEXrzY+kU0KdsEH3T6AKOajsKvD/7qcJ/7q92P4IBgPFD7AZfHt66OUVbhvNz6ZdV9nFX2DK4/2OU53SGclQS5wVEvIgD4sfePAIAPOn9gt65L1S5uHb9xmcYAgDfbvomSRbOTDI6ScM5eY9YJpTHrx7g8d7AuGMUC3R9OMqDeAKevWUevk/c7vY+vu32Nz7p+ZrfOumLJ1fvHLG5EnOry73p+h58f+BlhwWEICghCUECQWz1udDqdVy5c3U0ivNXhLSzqs8hmmbPH1TykMicJJWWSrnWF1lj98Go83uhxm+WPN3ocB4cfxAedPsCrbV7FiCYjPD6XWYAIwLpB6zAxYiLKhpTF510/96yKT0GZ1HaUfFN7DDtU7oC4EXFoVb6V2+drXaG1zf1W5VvhpdYvubWvObamZZs63W5Zv2WY1GYSfn3gV7cqOtVYz0ZoVjakrM19vVFv89gH6YIsfcyEEAgNCrX97HbTkPpDbO5/0+Mbn1Q6sUKJiIiIyHOFNqFkNdTtOIBpGofjMWXVgbWnmz6Nh+s+bLPMWVVCqSKlUK5YOYfrRzQZgRldnPcpsmYwGmy+nPer0w8TIiagSZkmDmfe+rDzh06P2bSM6aJJOQxPp3gJFwsqhqebPm23/+eRnzs89lvt31IdwqH0YO0HMfmeyYh9Itbu3/HV/V9hx2M78ELLFxzu36+288bLzoZhNSxtGkFZv1R9rBm4BnVL1rWs61+3v+o+DUo1sLm/5MEliBsRh6ENbafQrFq8qur+U9tOtdye1WWWzTpP+xOFBodCCIHFfRc73MZ8IT6o3iCUKFIC5UKyX5PFg4tjVNNRAEyVBBNaT7CrdAFM1S+dq3ZWrUCyTvgZpdGj+K1NiJiAeyvdm6NZrXTQQQjhshKmdfnWTtc/1+I5t87XuUpnuyFu4cHheK/je6rbH791HIDrqqHIapF2z6XyMTcn7dKz0m2W6w16CCHQr04/jGgywuHjaH7NOzKl7RREDYhCpbBKGNl0JP555B90q94tVz2UlEN8lVVXZs6SD/vi97k8T4AIwGv3vAYAGNZwmGX5S61fcjvBZn4PqiV7rNUvVR/DmwxHtfBqqBSWs35GDcu4HsGdacjEpHsmWSom5/ecb7dNUIDt58bAegNdHrdnzZ4299tWamt57HJjSlvbEe5MKFFuXb582fKHCgY+p0T5A9+r2irM36DCANS/ezvdwawu84UQ82Fq1q1e+qIRVzM1FQm0rcBISE+wa05dqkgpPNbwMfSq1curDYydXawrK6EEBCbfO9nSDNVRlc/M+2biz9N/onPVzjbVJcqLx0ARiHEtxyHqVBTi0+IxtIEpeVKnZB3V44YFhQFwb9jY2JZjUa14NQDAPRXvwYqHVsAgDageXt1yMf1gnQfx1f6vVPf/oPMHaFSmEWbumWm37vV7X0f9UvXx99m/XcZRrXg1zOk+B4uPLEbF0IroWq2r6na1StTCKxGvYMvFLRjXcpzD441sMhKrTq7CtZRr+KjLR5bljco0wtIHl+JO5h27XljK/kqumJsKNyvXDBMjJuK3479haIOh+HLfl0g3pOPRBo9iarupuK2/bdn2k/s+wbA1povt2V1no0mZJqhevDrqlqqLCqEV8GqbV7H61GrLOVxV/li/Vhy9Rt/v9D5SMlOw7uw6jG4+GoCpImL0+tGWbawTXc4UCShiN2zQ/Dpz1Rj5rQ5vof9K9UQhYBo658qAugMghLB7b5coUgJdqnZB1eJVMXLtSNV9XQ3L61i5o6XizaxooO172/xeV/ZjSjfYJpgcJYDur3Y/jiY4LvS8p8I9lvejO8dzhzJRX6JICdxMv2m3nbPPiyphVXDpziWn59n26DaEBZs+e0Y3H41AXSBqlqiJiAoRAEy9jpTDxwBT5VhCegIAoEV5U/WPMkljrVnZZjmqzDMrF1IO73V6z2FizVqGIQNNyjTBmoFrYJRG1UR1aKBt8vCt9m/h9xO/qx6veFBxTLpnEsoWta2E0gmdy4Rw6/KtERsf63B9t+rd7CoztZ6VkvK/b775xnKbDWILBj6nRPkD36vaKswJpQwA3zlY1xqmvkrbABwDkKPhcL7Ws0ZP1V4rBmlQvXAY2XQketXshTe2vYEsYxY+ue8TVAit4PW4smSWw3XKRNeuYbvcmlmnWvFqqlUZykRgoC4QQQFB+O2h33Dk5hHcW+ley3I1n3c1VS65cxGqvNisW6qu3TauhmI4uhB9vNHjqjMnOVIxtCJeafOKy+2eavoUnmr6lNNtQgJD8MeAP3BHf8dmKBxgSiqp8bQ6xzqpMbLpSEuj9C5Vu+DYrWO4r+p9AGxns2pWrhnWDFiDLJll6cUzqP4gy3rlzFfOXneA7XPs6ML6wdoPQid0eKzhY5Zl5mE7ZsrEiSOhQaF2CSXzxX/jMo3x383/VPd7s+2bqF2iNt7u8DZWnVyFEU1G4KV/3BsGZXZf1fvwQitTtZzyNWdODjgbguiqQinDkGH3nlFWKJlnlXyq6VP4Nu5by/IaxWvYbKeWzO9YuSPaVGwDHHAcg7mBuJKnU74XDShqqZA0J5jNKoZWxOmk03b7OBue+nnXzzEkyjRMq2SRkkjMSLTbxpxMAkwzryl7CxmM6rM5fnX/V5gQPQEVQyvi2WbPAjAN2TUnopWJlIlt7HuRrey/Eg+vMlWwtq3UFqGBodh0YZPq+Vb0X+H2EELzY1I5rLLDbZSfG84+d7/u/jValm9pqZqz5uy9/uX9X6JNhTZo/0t7u3VtK7XFKxGvoEGpBnavE+sZLYmIiIjIPYU2oXS3AfczauuEENNhSigtkFJ+q7aNP5jeYToiq0WicZnGlgsEwDTExNEFc6WwSpZ+PN5k/ct5h8odHG5XItj24kSZTPK0D5Fye/PFc+mipdGxSnZjZmVCadZ9sxBRPsIy1M+dhJLyYtMRRxeRgPPKnpzOwOQNgbpAu2SSMycTTzpc17dWX6w5s8ZmWeVQ9YvMmiVqomaJmg6PVS3cvgLFLFAX6FY1iJn1BaSyugYwzTKo9jpQPmeO+o0pJaQnYGzLsfh6/9d2+w5vPByvb31ddb8GpU1DFQfWG2gZEjTrvlmYtNmUdChTtIwlWaPm1wd+RZOyTSz3la99dxIErhKjdzLv2F2QKxN87SubLujDg8Mxp/sczPh3BoY2GGo37Eo5bBUwVdcpe/Qoqe0HOP8MaVi6oV3V0+ahmy2fQ8pk4YgmI7Dj8g6746RlOZ6/oWHphogbEYe0rDT8fORnzI6d7XBbR5RVXGbNyjXDusHrbF6nbSu2xbc9v0V8ajyqhFXBiLXZQ+bKh9h/ptQpWQf7n9wPCYlAXSB6LHPcqNuTai+9Qe9yG3d+PDAzPxdS2r/WO1XupLpP+WLlHU4kESgC8Vb7t2yq2uqVqocTt064HRMRERER2Sq0PZQKguLBxdGvTj+74VwZhgynF5y+8MX9X6BFuRboWaOn09naKoRWQJ9apimkzb+w54bygsfR8A/lRbWyb5SrIW/Vi1d3u/JhQsQEh+s6VO5gc66u1briu56mQrka4TXwRKMnUC6knFtDTLSUZVSvEAgNCkWSPslm2SP1H7GpyPCm5Ixkt7e17lv0QO0H0KJcCwTpgvBw3Ycxu+tsDGs0THU/5WvHWcNqZVJFmdg1J6ceqP2Aw75dagmfnjV6YmC9gWherjnm9Zjn9ELfOpkE2A+PNR+/Yqj9jIFmYcFhdj1mrN3R37FbpnzNWiczOlXphKgBUXii8RN2+6n9W55u9jRqhtdEo9LqFXKO9gOyG4uraVuxrc39F1q+YJPkUA7Jal+pvWo/K2cJJbOQwBCH7xNXnB1f+e8WQqBtpbboV6efzTCzQBGI8qHqSeoAXYDldT2mueOm+q4+F60r+Z5s/KTTbQH3k/JA9ntHbXhbWHAYfu77MyZETMC6QevQsXJH1Ayvia/uzx5ubI5HQGBe93lYP2S93RDJKfdmv8ZHNhnpdmxEREREZFJoK5QKsnRDeq5nHPNUi3It8FPfn9zadmaXmZjadmquZmMyU15cKSugzJT9dZRJAVe/xE++d7LbMT1U5yEkZiTisxj7mcYqh1XGB50+wK4ruzCyyUi7JsCT752M1+55DbNjZ+O7Q45GZPqvIF2Q3VCqvrXdnyHQU656Eb3Q8gV8tf8rVCteDd1rdLcsD9QFYlGfRUjLSvOoagKA08bGIYEhuK3PjklZXWFdAVM2pCzmdZ+HMRtsL+jVkok6ocPbHd62OW7tErVVh2PZ7av43cCc9FL2NlJ6rOFj+GC3/ayCAGz+jWbK593dYZHK997jjR63VCd93f1rdF2q3iPMUYJX2QRcuc/4VuPx5b4v0aJcCzzb3DapXS28GnrX7I3159bjxdYvQgiBH3r/gCspV9B7eW/Ldu5+dl1LvebWdkrNyzbH/uv7AbjXL8usfLHymNZuGtafW49HGjziVs+kB2o/gLd3vq26ztXn4vhW41E1rCpql6yN6uHVXZ4rLDgMbSu1xe4ru+1mIlUyV/MZod4vqXm55pY+SHN7zLVbP7bFWFQNq4q6Jetahj4rtanYBjO7zMSZpDN2sxESERERkWusUFIhpZwupRT+PNzNmfSsdNUhPd7UsYrpF2EBgV8f+NXj/T1JJg2qN8jhOuUMR44uYoUQ+PL+L9G8bHM80egJm1nSAOe/xM/sMhNdqnZxO95AXaBlRjI1fWv3xTsd37FLJlnHOrLJSEtPmmnt/G8SQkcxBemC7JKZuZlRLbfGtBiDVf1XYUX/FXaVRkIIt5NJr0S8gmKBxfBUk6ecVigpL+APXLdtAqQcPtehSge72f/Ci7iuThNCYEHvBfgs0j5pqVQsqJgl5tJFS9v0s1r64FKbbd19rSkrsQD7flvuPrbKpIX1e7NsSFmHQ2gdJTucvZcFBEY3H41NQzZhUZ9FqseYdd8s7Hhsh+U9rBM6VAmrgnc6vAOd0KFuybroXbO33X5qlDMtuuvtDm+jVJFSKFWkFD7orJ7Uc+SRBo9gfs/56FHD8VA2ayGBIQ5nW3NVoVQ8uDiGNxmOTlXUh6CpmdNtDhb2WYhPu37qdDtzv71gnWcTAJiFBYdhWKNhDpNJZn1q9cHYlmO98gMHERERUWHDCqUCKD0r3SdTIA9vPBwL/1sIAHi59cuoV7IehBC5mlVJaVijYYg6HQXAVPEwpe0Um8oSJeUQN2fD0iKrRTrsr+Fs1jx3Lx6dUc6S5krJoiWxdtBaxKfG5/ii1JceafAI4m7EYcflHQjSBVn6GD3T7BnEXIux2Tanw368xVHizhNPNX0KI5qMcPlaVyaUlMk1tYqRM0lnbO67O+NiyaIl0b1GdzQo1QDHbh0DAFQNs59ZSyd0mNN9DtafW48Haj1gs0457M3dJMQzzUzt5+Z2n4ufjvyE/nX7o1eNXpZKl+rFq6smndQo37PK5t6OOOqh1LRsUzQp0wSHbx7GM82esWkIbn46rIe7qlFLhg2oNwCR1SIRHhzu9PPC2kN1HkLUqSgcvHEwO243Pi9rl6yN9UPWQwed01ncvOW55s9h1clVdsMFffH/SFBAEFqVb+VyO3OFUt2SddGsbDPE3YizzNpJRERERP6BCaUCyNx/o0SREkjKSHKxtfuea/EcyoaURY3wGmhYuqHXjmutadmmmNF5Bs4mn8WwhsNcNoquGFoRA+oOwJoza/By65dzfF61i7yiAUUxv+d8j2eNUuNOw1ql0kVLO62G0dq7Hd8FACTrk/HVvq9QLLAYhjQYYpdQala2mc9imNdjHsasNw0Zq17c9ZCb3HAnEWCXUFK8dtSSRcl69/tAqfmw84d4fM3jMEojPuuqXrHUuExj1apFZbzuJLMea/gYyoSUAWCqVLRufr9pyCZsvrgZnap0cjvRrEwMKZvTOxq+6+h9KYTAT31/wuU7l1E9vLpNQim3Q4FdDRNUKhZUDD/1/QlxN+Iw4q8RMEgDFvRe4Na+7gxX85ZKYZWw/KHl+Dz2c0RfiLYs98ZnX06Zh4cKIbCgzwKcTjyN+qXqaxYPEREREdljQqkAMk9VHhYU5tWEUvHg4i6noPcGT3vuvNPxHUxrP83tyg533Ff1PnzU+SOvNZPWGz1PKOUX4cHhNg2clQ3hfdWQGzA1OV/YZyHirsc5HLaTl8Y0H4MX/3kRgKnJ74lE2xmk1Gbyqx5eHedvnwfgvOG3I/VK1cOGIRsgpfR42I5yCJ6zapjm5ZqjXaV2GN54uMNtyhUrh8H1B3sUg7IKxt3ZDp0lrAJ1gao9fbxZTekuIQSal2uONQPXQAjhtBm6luqUrIOBdQfaJJTyQvmQ8ohPi7dZNjFios1zFaQLssx+SERERET+gz2UCgjrC1Hzr7jWTXxfv1d9ivKCwpvJJMB0oe3NREiZomW8dix/l9cX7a3Kt8LwJsN9mrhyV2S1SExtOxXPNnsWzzZ/FhlZGS73ee2e1xASGIIgXZDNLFWeCA8Oz1EPGGUyR9m83tqjDR7F+Fbjvd5rRln1Y5dQclAkk5PXWV5PVmCtUlglv00mmXWp2gUtyrVAoC4Q73R4J0/O+WW3L1E8qDhKFy2NNQPXYNOQTRjRZESenJuIiIiIcocVSgXEnO5zMGb9GIQGheKl1i8BAO6teC9mdpmJpIwkDKg3QOMI8xd3+7g4M7f7XDy34TkE6YKcTsFe0IxvNR7rzq6DhLS8FgsLIQQebfio5f69le7F3mt7AQA1w2uq7lOrRC1sHLIRRmnUpDHw9PbTsfC/hRjWcJjT3kC+GoJVsojtsFble89REignCSVnM/SRqZfcoj6LkJqV6vYsfbnVuExjbBiyAUG6oDzpF0XkK8WLu9c3jvIPPqdE+QPfq9piQqmAaFymMTYN2QSd0FkuCoUQ6FOrj8aR5R/WTceH1B+S6+N1rNIRfw38CyGBIZaeM4VBjfAa+LH3j7h05xJ61uypdTiaGtlkJPbH70dCegJmdJnhcDt3G1j7wqD6gzCovuOZFM18leyqW7IuWpVvhX3x+/BEoyfs1jtMKLlZYNutejdsPL8RxQKLoWeNwv16dIcQIs+SSWbuzghI5DPz7nNvuzGbHa6aOHGil4Ihf8HnlCh/4HtVW0woFSD8dTd3Xmr9ElqUa4FKoZXQrJx3GklXLW4/61Zh0LpCa7Su0FrrMDQXEhiCeT3maR2GVygribxFCIEfev2AS3cuoVrxanbrRzcfja2Xtqru5443272J5uWa454K93BqeCIiIiIiL2JCieiu4IDgQl9RQ2QtQARYppJXS/Z47Ty6ANUm2gDQsnxLfHzfxziZeBJzD8y1LHd3yFvZkLIY1XSUV+IkIiIiIqJsbMpNRESqvuv1HXrV7IXZXWdrOiypV81edkkhLWZsIyIiIiKibKxQIiIiVREVIhBRIULrMADY91LScsY2IiKlY8eOWW43aNBAw0jIW/icEuUPfK9qiwklIiLye8qKJFYoEZE/+eWXXyy3p0+frl0g5DV8TonyB75XtcVv5ERE5PeUFUkBIkCjSIiIiIiICGBCiYiI8gHlrG7uzvJGRERERES+wYQSERH5PWWFEoe8ERERERFpi9/IiYjI7ykTSGzKTURERESkLSaUiIjI7wkh0KtmLwBA75q9OeSNiIiIiEhjnOWNiIjyhVldZmFcy3GoGV5T61CIiIiIqAAY+sdQl9sseXBJHkSSPzGhRERE+YIQArVK1NI6DCIiKiQW7z4PAIj5cpvb+0SN7+SrcIiI/A6HvBERERERERERkUdYoUREREREBdO8+7SOgIiIqMBiQomIiIiIKBcqVaqkdQjkZZUqVULREilah0FELvDzV1tMKBERERER5cKYMWO0DoG8bMyYMfhD737vJCLSBj9/teXXCSUhRBCAbgAaAQiTUr57d3lRAOEAbkgpjRqGSERERERERERU6PhtU24hRG8AZwH8CeATANOtVrcEcAWA6zn+iIiIiIiIiIjIq/wyoSSEaANgJQAJYAKAxdbrpZS7AJwBMCDvoyMiIiIiIiIiKtz8dcjbNACpANpIKa8KId5S2WYPgNZ5GxYRERERka2YmBjL7YiICA0jIW+JiYlB4vljAICS1RtoHA0ROcLPX235a0KpI4CVUsqrTra5AOCBPIqHiIiIiEhVVFSU5TYvaAqGqKgoXDt0HgATSkT+jJ+/2vLLIW8AwgDccLFNMfhv/EREREREREREBZa/JmQuAWjiYpuWAE7nQSxERERERERERGTFXxNKfwHoJYTopLZSCNEHQAcAf+RpVERERERERERE5LcJpQ8BJAJYJ4SYAaAxAAghHrh7/zcAVwB8ql2IRERERERERESFk1825ZZSXhJC9ASwFMAkq1WrAQgApwAMlFK66rNERERERERERERe5pcJJQCQUsYKIRrANJNbewBlACQB2AVglZQyS8v4iIiIiIiIiIgKK79NKAGAlNIAU1XSaq1jISIiIiIiIiIiE3/toURERERERERERH7KLyuUhBDD3d1WSrnQl7EQEREREREREZEtv0woAfgRgHSxjbi7DRNKRERERKSZ+vXrax0CeVn9+vURdsbV5Yi9fl9u82j7qPGdPD4HEWXj56+2/DWh9JSD5SUB3APgUQDLAfyZZxEREREREakYNmyY1iGQlw0bNgy/3PQsOUREeY+fv9ryy4SSlHKBs/VCiB9gSiZ9kTcRERERERERERGRWb5syi2l3AhgLYB3tI6FiIiIiIiIiKiwyZcJpbuOA2ijdRBERERERERERIWNXw55c1NjuG7cTURERETkU9HR0ZbbkZGRmsVB3hMdHY0bJ+IAAGXrtdI4GiJyhJ+/2spXCSUhhA5ANQDPAugD4C9tIyIiIiKiwo4XNAVPdHQ0bp44D4AJJSJ/xs9fbfllQkkIYYTz6iMB4CaASXkTERERERERERFpaegfQ23uHzp+yHL7yB9H8jqcQs8vE0oAtkA9oWQEcAvAvwB+kFJez9OoiIiIiIiIiIjIPxNKUspIrWMgIiIiIiIiIiJ1+XmWNyIiIiIiIiIi0gATSkRERERERERE5BG/GPImhPg+h7tKKeXTuTjvDABtANQHUBZAGoBzAFYC+EpKeTOnxyYiIiIiIiIiKqj8IqEEYGQO95MAcpxQAjABQCyA9QDiAYQCaAdgOoDRQoh2UsoLuTg+EREREREREVGB4y8JpVoanTdcSpmuXCiEeB/AFABvABib51ERERER5ZAQIsbBqoZ5GggREREVaH6RUJJSntPovHbJpLuWwpRQqpeH4RARERERERER5Qt+kVDyQ/3u/n1Q0yiIiIiIPCSljFBbfrdyqXUeh1MoRESoPuSUj0VERGDN5RCtwyAiF0rXLa11CIWa3yeUhBABMDXMLqK2Xkp53gvneBVAGIASMDXp7gRTMukjN/ZlWTkRERFRIdavXz/XG1G+0q9fP3xztpTWYRCRC5Xvrax1CIWa3yaUhBEALEYAACAASURBVBDNYErodIWDZBJMTbm98W94FUAFq/trAYyUUl73wrGJiIiIiIiIiAoUv0woCSEaAthx9+56mIagHQBwDaZS7bIA/gGQ6+okAJBSVrx73goAOsCUyNonhHhQShnrYl+WlRMRERERERFRoaLTOgAHpgEIAtBBStn/7rIVUsreMM0I9wOAxgD+582TSimvSSlXAOgJoAyAhd48PhERERERERFRQeCXFUoAIgH8IaWMs1omAEBKmSKEGANTj6N3AYz09smllOeEEP8BaCmEKCulvOHtcxARERFRwRAVFWW5zX5KBUNUVBSuxf0HAKjQrKPG0RCRI5f/vWy5zX5Kec9fE0plAZywup8FoJj5jpQySwjxD4ABPozB/Go0+PAcRERERJTPxcRkz9HChFLBEBMTg8QLpu4aTCgR+a+EkwmW20wo5T1/HfKWANOsa2Y3AFRXbKOHaVa2HBFCNBRCVFRZrhNCvA+gPIAdUspbOT0HEREREREREVFB5K8VSqcA1LS6HwOghxCivJQyXggRCqA/gDO5OEdvALOEEFvunu8mTDO93QegNoCrAJ7NxfGJiIiIiIiIiAokf00orQPwmhAiVEqZAmAugAdgmnltB4AIADUATMzFOTYA+AZARwAtAJQEkALgOIBFAL6QUiY43p2IiIiIiIiIqHDy14TSfADHAIQASJFS/imEeBnAdACDAKQCmAHgi5yeQEp5CMC43IdKRERERERERFS4+GVCSUp5BcASxbIvhBD/B1PD7ngppdQkOCIiIiIiIiKiQs4vE0qOSCkNAK5pHQcRERERERERUWHml7O8CSF2CyGeF0KU0joWIiIiIiIiIiKy5ZcJJQBtAHwF4IoQ4jchxANCiACtgyIiIiIiIiIiIv9NKFUF8AaAUzA14V4N4JIQ4hMhRAtNIyMiIiIiIiIiKuT8sofS3abcMwHMFEJEABgJ4FEAEwC8LIQ4COBHAIullNe1ipOIiIiIKDIyUusQyMsiIyPxd0Kc1mEQkQvlm5XXOoRCzS8TStaklDEAYoQQEwD0AzACQB8AnwKYAaCohuERERERUWEw7z6HqyLNN8ZszotIKA9ERkaibJzfXyoRFXpMKGnLX4e82ZFSZkkpVwB4EsBbALIABGkbFRERERERERFR4ZMv0u5CCAGgJ0zVSf1hqkqSADZqGRcRERERERERUWHk1wklIURjmJJITwCoCEAAOAFgAYCFUsqLGoZHRERERERERFQo+WVCSQjxAkyJpNYwJZGSAHwLYIGUcoeWsRERERERWVv8b7zpRvHFGDZsmLbBkFcsXrwYl/YeAwBUadND42iIyJFz0ecst2tE1tAwksLJLxNKAL4AYASwHqZqpBVSynRtQyIiIiIisnf8WurdG8e1DYS85vjx47gTf0HrMIjIhduXb2sdQqHmrwmlKTANabusdSBERL4kpcT6/65BbzCid5OKCAzIN3MlEBERERFRIeaXCSUp5Udax0BElBe2nriB0YtiAABfPNYKD7WorHFERERERERErvllQomIqLB46dd9ltsv/rKPCSUiIiIf6fflNre3Pbb7vA8jISIqGDi2gjSTkpGFgxcTIaXUOhTKgR0nb2DV/ktI1Wch/jZbnBERERERERUmrFAiTWQajOj52RZcSkzDC13r4tVeDbQOiTwQdzEJw77dbbkvBDBjUHM80qaahlHlP3cysnArNVPrMDSVZTBiwtIDuJCQilmDm6NeheJah0RERERERG5ghRJpYt3ha7iUmAYA+OqfkxpHQ55698//bO5LCby27KBG0eRf32w5rXUImvvr0FVEHbiM/RcS0eOzLVqHQ0REREREbmJCiTSRnmnQOgSnDl1KwuA5OzB99WEOyVNx806G1iEUCLtP37RbZjAWrtdb7PlbNvczsuw/G1L1WbiWzGGVhdXX0SfR67MtWHvoitahEBEREZEVJpRIEzo/f+W988d/2HvuFn7ccRarD1zWOhy/k6pXTwgaC1kyJLfCQ4Lsls3dfEqDSLRTr7ztELeLt9Js7t9K0aP9h5vQ/sON+Pvw1bwMrdCQUuJyYprrDTVwLTkdM9cew7Frt/HcT7Fah0NEREREVvz8sh4QQoQKIVoJITprHQt5j04IrUNw6t8zCZbbv+29qGEk/slRQunMzZQ8jiR/KxoUYLds1t/HvH6emHO3MH31YRy6lOT1Y+dWaBHbxyBN8dqavfEEktIyYZTAmEUxOTrH+ZupuJWiz3GMBd0Li/ehw0eb8MbvcVqHYudqEivTiIiIiPyV3zblFkJUBTAbQD8AAQAk7sYrhOgE4BsAY6WU0VrFSDnn7wkla8eu3dY6BItDl5IwYcl+1CgTijlPtEZQgDY54eBA9fMmFvIG054KyIO3gcEoMWjODgDA4t3ncfz9Pr4/qQeUI0qVw2Gv387d8MpFu85h2spDKBKoQ/SkSFQqEZKr4xU0SamZ+DPONJTsl3/P48OBzVzuk5FlwLKYiyhVLBh9m1XyWizJ6Zk4eyMFTSqXQIDO9ObI6X8Vhy4lYerKQ2hUsTg+HNgMIh/9n0P5U7/mZe7e6KdtIOQ1FZp21DoEInJD5Xsrax1CoeaXCSUhRCUAuwFUALAaQHkA7a022X132VAA0XkdH+VefkooJab6T2XDY/N34XZ6Fk7E38Gq/ZcxOKKqJnEUC7avrAEAfZYRFxJS8dKv+xBaJBBzn4hAaBG//Jhxy58Hr2DFvksY1akmOtQp6/Xj77+Q6PVjKqVZJWj0BiOklH51cS1hm1FSVr8VCcpd0nTaykMAgIwsI/6Ku4pRnWrl6ngFTWKa559vP+86j3f+MDXm//rx1l5JKmVkGdD9k82Iv52BcV3rYFKvhgAAgZy9Vh//djeS0jJx4EIiOtcrhweaey/xRaQmosbd4bsREdoGQl5TsjpnICbKD0rXLa11CIWavw55ewumhFF3KeVAAOutV0opMwFsBcCfDvIpnRvXCNeS0/HP0XhkGoy+D8gJf0p+3U7PstzWcviSoz7lmQYjpq8+jNjzidh64gY+Xuf94Vt5JSUjC+MWx2LDkWsYNn+314+vzzLi7M1Urx9XSfnqzcjS9v2kpHwtKRNKasMCc0rrxt56P3vsASA5Lcvmvjt90MzJJAB4ecl+r8QRey4R8Xer0f7vn1OWyRBy+vGblJZdLbnn7P+zd97hUZRdG79n0wtJIEDoBELvHREFAZFmBRWxYMeC+r6KvXyiqNgQEQv4YqerIFV6771D6CWQEEJ6z+7O98dkd2ee6bub3YWc33Xtld2ddjItee455z6ZGnMSBEEQBEEQ7hKogtJgAAt1ytnOA6D8Nh9QEV3O9DIkikptGPj1Bjz+606MX3rM69tX4mBKDh74cSvGzN0v+T5QBCV2oJcQE+6nSAC7yjlRZrNj9bF05+cF+65dQ/N0D0ut9PCVCTJ7pJQ6LF7JK4HVT8Iteyo9O13qkxQe7D1Byc1kF9Ok5xZj+rZzSMkSBMPiMhuGfr8Z7T9YEXCdyq4WSM9zq46gxHbh85ZIFhosPTjFZd47H4ONPMEgCIIgCIIgTBOoglICgBM685QBiPJBLJWaDcevoOvHq/H4Lzu82sFL7//7RfsvIavcj+fnzWe8tl0t/m/hIWw7nYm/90hNuIMCZDBykREgqkXJO4T5CjWN8RyTcVNQYlWe8RpATTTzFrnF6n5T3hRx2d/jyKVcyec5O8+j+yercNvXG/ySQaP0m4r3DevXdeJyntv3InfLp8wyeuYevPvPIYz8Wbhvvj3/IPacz0ZRmQ0fLDqivwI3mLn9PD5cdMS0+Th7zVrt2ueAUoYbe065A3tIvXkuBso9nCAIgiAI4nojUAWlTAD1deZpBoB6SGtw7moBEt9cgsGTNrrtAzTy5x3IyC/B2uQrmL/3otdi08v6KSz1vRCx97yyn403xyI8z2P10cuYtyfF9KCYzSwps1Ws4KGFmuAhLoUBPCuvWn30MubvNb+fvEVFZOaJYUuNpNv23nbYdX3GdJF74++DsPPA6SsF+EfjGj+Qko3d5zK9vl+U1icWE1jf+f4TN+CFWdL28Rn5JZoCnQNfJRvuPJsFQNinm05mYN4e135NrYCuZYcv5eDt+Qfx8+YzpkvQrjIClF6GEq9wSQ/+ZiM2nrhiarssNma7JTbhfueNY0aCEuELpm64JLymTvV3KISXOLd5gfNFEETgcurfU84X4XsCVVDaDOBOjuNqKU3kOK4pgIEA1vo0qmuM3l+sAwAcSc3FN6tPerw+b3Y7Y//BP5shbTfvaw2BHcyIsXhxMLLjTCae/G0XXpm7H5NWy5PwLmQWYs/5LOVBNlOS5E9vqYo+PJtPZuDJ33bh5Tn7MXHVce1YKkj4qehzUEsAsan8TnY7j7XJ6Thu5lpkVlWkIdZeyVcu81uw7yLu/HYzhv2wFeMWH1WcJy2nGK/+uR+TV58wdUyU5hRfj0ri89KDac5tbDh+Bd0+XoWe49fIygiXHPB9eRl7XXpTiFfjsChDaP3xK5L9v/tcJl6Zsw8bjguCz6GLOfhj61mcu1qAP7aeRXKaNLvIqiNUq2XuPfbLTveCV1mvQ1T0RlaZmqCUnluMkT/vwDN/7LqmsymJwCA1p1R4pQZWWSvhPsU5V50vgiACl6KsIueL8D2BKih9ASAcwHqO4wYBiAQAjuOiyj8vAmAHMMF/IQYOV/NL8PfuFEl7bXZA5ygbK7PZFT1UjKA30DADO0b8D/NUvaLLjVi0BKUgL6Y1bD7l+qeEFZTOXS1Any/XYej3WzB31wXZsmxGkjePh1kq+viIfawmr1EXQ79akYx2Y1fgu7WeC6YsWueEGQ5dzMG0jaeRxmSmsF40Rrb929azePyXnRg0aSPOGzT0Zo/VXR3qqs4bEqR8rouFGbUS1HfmH8Rfu1MwYeVxzNh+3lBsABQVJT1BCXBdDyN/3gE7D+SVWPHDulOi6XaMnrlHcVkz7DqbiS+WH8OFTGP7+0CK1CyfLVWtCFiPoOxCl1j5ytz9mLf3Ikb+vAOnruTj/qlb8d6Cw+j9xTq8t+Awlh++LFlWr+RN7arw9HphN+vNkje1c+iDxUew4fgVLD98Gd+v8/49hCAIgiAI4nonIAUlnue3AxgFIBHAYgCvlk/KLf/cCMCTPM8f9kuAAcbTv+/CmD/344lfdzqFpLXJ6ZJ5qoQFIz2vGL0+X4uuH6/CgRTz7cqPpeVi/t4U7D6X5VG8yWl5WHFEOojZz7RP/33rOY+2YRYtgUTNQLyo1IYp60/hj23nDGdkaIl5E1Ycd5abvPH3Qdl0NvOhTGfgV5FUtN6XZqAbV6nVjm/WnEReiRVfLE/G4Uve7XrnDdGsoMSKET9uw0dLjmLo95slpZxaJYtq23b479jsPD5aYsyLh12TlkFxsEX5T4K4Y5YaYjP2d/85ZPia4BUkCiOCEpuxBwDZojjZbnGAeU/ui9lFeODHbfhu7Sm8MtdYKdk786XXri+66rEG1o5MM57nJR5JHy85qrhfxLiboeQpbFae4/qoyJI3sVD61+4UxXkIgiAIgiAIdQJSUAIAnud/AdAGwDcAdgA4BWAPgO8BtON5foYfwwsY7HYee8q9fw5ezEHy5TxYbXb8tkUqyDSsHokJy48jNacYecVWjPx5h+ltbTl1FS/P2Y/7pmzBuasF+gsokJZTjCHfbMRMnQyGhvGRks8V7WejtXrWw8XB3F0X8Om/x/DeP4ew9KAxO6+Y8GDJZ7E/0IUsaQZEr8/XYsupDOfnMmZg6t8MJdf75glVNOc14m0jxoh4obTe+6dsNbUdAEjJKsTmkxmK2RXeOOXOXi1AXnkpzaWcYuw442pfrnX8jGR7qJWnsbDXjnjVrLeamtak56ujRJ7BEiKl/Wxke+z1AADxUaHO90U6wokR/j2Y6oxl59ksPPnrTjR/91/NbKWLTLq1UpxaWG12bD991VT8rFDtOLfcKZMt0slgrShBSbXkjTkn7XYeHy85guem78bF7CJ8s/oEun+yCjO2qz+EMOKh5CvDdoIgCIIgiOuJgBWUAIDn+RM8z7/M83wPnueb8Tzflef5F3meT9ZfunLADhgGfr0Rw37YIhuQ5hZZJR5I4pIIs9h54LNlx9xa9qdNpw0NFnMZUeHQxVzc+8MWvPbn/goxadYaJF3OLVHMLHp/oStB7t1/5BlFSrDZTuJBd2SotD36+cxCPPi/7Xh/wSEA8mPtrzbvAq799XjPRM05351/SHtNzL6ftvG0oQhYA+kCkwLC1fwS9JuwHg9N246kt5fic+acZq8ho+fd5dxinEzPF2IqkcYkPo8uZquLEkaSz/KK1QUbcSYUG7Y4I+gsUzanlk2Tz2zrqkExywh6Hko2lZ2x+GCq7Jg4hAOe5xU938xmu7DZXKuPpaPEasfNn2vY9zHbUMqkStfIwHt57n4M/3Eb7pu6xbCQnskYazv2H5u5ZKSMzFFuq7btitL22WNZarMpxrDmWDr+t/EM/j2UhvunbMVXK4/jcm4J3tG4zxgSlEhPIgiCIAiCME1AC0qEPkoDwP0pObLyn/OZhdjHlJVtOZkBd1l6MA19v1yHET9uM+XJZNQkNyNfOkAa9ccu7DqXhT93p+CvPd4vTdB76t7ivWU4k6GelZVlUKD7gumwJc5CiI8KU1zmt63ncCAlW1YiVean7meAdFBZJTxEc96F+y+pTpuy/hS6fLRK4l9y6kq+oRg+WqJsDm2U6dvOS66f79edkvgSsV4yRjIzktPy0OfLdbj1q/VYciBVZvTrOGQzt5/Hd2vVO1GomXKLUctg+XDREbT6v+V4a94BAPKSMvGq2QylbIXsMCVx5utVckN5FpvBDDqlX9UhiBxNzcU3Kh5a7/1zCL9sOSv5ziEAjVt8FI+6kYXJEqyWnqhBr2Y1JJ+VRJxun6xW9f1aVH69HLqYixPpxq6Fb5l1Oc5d9t5spPzuxw2nMXHlcXQatxI/b5L7ZRkRlI6l5eLlOfswf6/yvTqrQN51lL2d/bL5LDp/tAqf/isVepcddmWDGvWn0irzdKDXeZQgCIIgCIKQExCCEsdxDdx9+Tt2f6P2xNmIwPHgtO34jRmQmeF0RgG2nr6Kb9ecNPwk/ZKBltmLD1ySDRTErbbXJ6u3p84tLsPHS47gm9UnYLXZceJynqHMEiPazP8tkD4BDwuWXj6OrI3CUitenrMPiW8uQeKbSzBrh1Ded0IhY+JqgbBMmc2O1Ucvy6Y7OH45X1au4s8MJbG4Eqxi5CxG6fyw2Xl8+u8xXC0oxefLkp0m1V0Tq0nmYzO3zFJYasXiA5ecpthnMgrw0eIjim3OU3Nc5x1bkmbkHBk9c4/To2bWjvPIlwlKwkrenq+d0eauwfGVvBKnafasHRcEQYHNUBIdC7a8MCNPnnmkdC9JNXAdGy2TU/JQcggij+t0DmNFmeAgDlkFparG4WYxIkSwVI2UCqxq5uusuKzEbRM3SMrreJ7HrrOZuobsjvOHFR3ZhwpqTFp9AlmFZfhwsdyny4iw+uSvuzB/70W8PGc/UphS3jf+OoCO41Zi7EKpBSJ7zi8+kIrMglKsY+73CTHKwjugXtJnRCyKCvPsPkMQBEEQBFEZCQhBCcBZAGfceBmrjbmOUSqnMMP7Cw/jsgEDZC2+XXsS/SaslwzGldASdurGRQAQBsQvzNyruR4tH5BJq07gfxvP4KuVx9HknX/Rf+IGPD9Dv9OTEUFs4wlpRlcVxg/JkcE0ec1JSavwt+YdRHJanqLR+Ojy2D5fdkyzZKuw1Co35faDh9LpK/n4pnyw6UCtM5gY1oQdALKY7JgLmcL5w2aFGO32lFQjSvbduuR0tPq/5Xhh5l7c/d1m7DybiT5frsO0TWewS8FcXlySyAoiRgbSJ0UZJZtOZsja2BvVicz61FzNL8HV/BKZEFtYapNtU/x5/FJp9gfryzRt42l0GrdSYYtGRFrXPKVWu0xcc65JYVWO5DA9c3a21CvYYlHMsnKw7XRmhfuxFZVKz9fLuZ6VB4pFnTk7L+DeKVvRd8I6idDE6l6Oc1fPD0mJ6tFSwUbuwaW//8Tn4a6zruussNSKOeUldb8yDzOMnvNxEaGq0xyiMSu2G1lzZGiw/kwEQRAEQRCEhED5D+p3GPufj2AocWPAwJKSVYiEmHCP1nE6owA9xq/Be7e3whM9ExU7oxVqxHoxuwjP/LELNaqoP312oCUo/aRQorHscBpKrDYcS81DvaoRiI+Wb8PoQP/81UK8Nf8AalYJR0xEiKw0DwBm75Abjv+56wL+2CYXlM5eLQTP8/jfRu2MioISGyJCpE/QHQOwC5mFWHYoDf1bJSCxulxU8RY8z+Pp33fh1BVp6Z9aZzAxG09cwYDWtSTfsWJAem4xmtSMlgmPVjsPu52HhRk1R4UGSUQ41i8GAB4TZbik5RbjPhPG3UYFpX8PpuLrVSdwd8e6smnjmXIdo2KGkQwlxyV25FIu7v5uM3jweGtQS8k8haVWmX+MOARWsGG9ktTKCpXCs3DS7x37L6+4DIMmbURKVhGmPNwJA9vUFsWilJ8kZCiJDcyNEmzhNPfd7nNZeGHmXsREhOCxGxPRvJa2obyR0kOWYpWMJHfZfvqq8/2b84TMNqudxweLDmPao10BAA3joyQluY59YFSMFdOjcbykTNVq5yWisdldIn7owXqKiTEqKGllRL4way8WjO4pE9u/WpGMJ29q5Py85thlTFwpLdsMDQ6U52sEQRAEQRDXDgEhKPE8/5i/Y7hW8TRDCQDCQ7yX6j9u8REs2n8J/VslYFSvxlh6MBW/bz2HkT0aokdSvOayyw9f1ixncOBOx6kp605j4qrjiAwNwtY3+yGWKUsxMpjpllgNr8zdp5jZArhMoZXWlK5QSuTAiK9JUakVdl4as2PQ+PTvu3AsLQ+zd57Hqld6K4p53uB0RoFMTAKMlbwduZQr+44tt3LsP6XjW2qzI9wiPU9vaBwvaVVvxstLDbHgw2Y5qAkVz5VnmRkxqjc6GBdv69DFHMzZeQFD2tVWnPeVufuc9wG2RKm4zCbLvHCc60qlWEaNzR37ad6eFPyw7hQe7N4AkaHBkiwkh4fS4gOpSCnvfPbs9D04M34wCkpteHjadlwtKEGvpjVk67fZeey7oHydaWGxcLqi3ZKDgo/brB3nsWZMbzSuEa06r5FbzdrkdKRmF2Nop7oIDwky3dVND7XMmRRRNzn23B//71F8eV97mQ+YEdjrstRqR4goa1Br93639qQz29SB+AGA1jVqtMxTqzvi/vKSPvbvInteP/HrLtmyRu7DBEEQFcEdkzeZXmbRizdVQCQEQRDmCQhBiXAfd55As7gx5tBk34Vs7LuQjajQIIxdJAxwd5/LwqpXeusua6Q8xEjra5aJq44DEEqAvl93Em8NlmZyGBGUasaEYbGGqbjDgFmxhEdj/UoZTSw5RWWozQzU7DyPUqsdx9IEb6ZTVwpQXGZHhIeeQ2pcVcjGAiAZbKqx53y2LMuIPY6O/afU1avUZpcJn4XMINHG8+B5HnvOZ6NJzWjERmibhevBDqyVxrtmvY7UzoM729eRZIWI53v05x24WlCqmOEGAOc0/HQKS22y/eYQXA5dlIt8rIm4Gi1rxwAAXpm7HwDwwSK5144ju4dd5/nMQszccd7p5zNju/z8t9l5BBnIfFPCzCHpO2E9zn46xPl51ZHLOJaWi4e6N0TVqFDNMl2e53HoYq7T5ymrsBSj+zRxS/BOyylGXGSIorivdj2LS0ZZMeTQxVw8+esufHFvO9OxsOd9idUOcb8ArXuZki+UWADSEpSMZigZeYii9HfxkZ+245N72qJ+tUjFZfZfyMbF7CKZIEYEOFP1/6/wFSO61hTePDDCv4EQXqNu51v9HQJBEAZo0KvS2yr7lYDP8eY4rj7HcXdyHPdI+c/6/o4pkFDr9GQGd8o6jDCWGWQ62lF7itYTaiPkKQyajewCvXnynYKSfEatZdn9pER2UZlswGWz8ziSKhcFKgo1scGocTE70GaPo8PoWWlAPmbufpkAxWbY2Ow8vlyRjGE/bEH/r9Z7nLHEluQpHVfWB0oPNa2hcY0oJMa7Bro2u2C+PGfneVxV6IgFCAN9nuc1PazOZBTgaKrUDN4RAmsqDwhij6Mzl5ZYZkQzcQiDUWHS5xZlNjt2n9XOPrLZebcMsW123rT/FCBkK93wyWo89fsufLniOD5eetS5Pq1tfV0uVAMuMcWs4L1g30X0+HQ1bvpsDXKL5f5PbKmrA/H1o1T6fDG7CJkmz09ALvpMXX8KOUVl+HDREXy96rjprCfx/mA9nXiex+XcYjzy03a8PGe/ofXp3f8PXcxRPAYbT2QomoyLefPvA4ZiIAglmteKFF7Nm/s7FMJLRCc0cL4IgghcYurFOF+E7wnYDCWO45oC+B5AX4VpawCM5nn+uGzBSobRdvVaaA2agi2cW0/clfhxg3c81N3JUBLjGG+WWu1Ym5yOlrViEBKsP3jVG6i+/tcBxKlkxbgzyBVTWGqTZUvY7EKJoRhlRxrvoGaqrJahFB0mLYFi9wE7MF2w7yKeuyVJsd38yiOXMWPbOZTa7Phl81k806uxzDPJbufx3dpTAIQSw39ExuhGmbbpDLo3FkozWUHpxw2n8frAFpLvMvLNGS6rlWMVldok2VunrxTg2em7Na+9K3kl6DhupWaJ439m75N95zgOatf9y3P241haHh7oqv4PdInVhj3ntUUhR+ys2GHn9bParHZe5v1kBDtvXlBKyynGW/OkXff+2p2CL+9rryjwOLDxvOI1YTZr1HGMMvJL8btC183oMOU/0+LfUq1cy2jGmRj2/jp1w2mUWO1OE22zDzHEfkbssjwPvDP/oKzhgRZ6gtZrfx3ADw91Upy28shlzZLI7W74dhEEQRAEQVRmAlJQ4jiu5QhiiAAAIABJREFUCYAtAOIBnAKwCUAagFoAbgLQD8AmjuNu5Hn+pOqKrnNmbj+v237cCFoDsCAvCkreQskI2x2+XJGMHzecRpWwYPz5XA/d+Y0MVEf9sVvW/Q0A/j2U5laMDgpLrQrdunjsZvycKvJQqWYoqWTI1Ksa4SzHA6RZWsVlNiSn5Uvmdxiyq51vny9Pdpa5jV10BI2Zrm6sT4pZsQcQBpwL9l3EskNpMt+r79edwmM3JqJmTDgOXczBnvNZiI/S9/wS49gHrIH1ba1rYdVRVye852fsMXTdZbshKDti0BqYT11/WtKdi6XUasdrf2pnlDgEK1bs+G3LWexP0W5fb3NbUDJnGl03LgL3fL9Zcdo/ey/i61UnFKcBwnaUjJw9uV8qZaOFBHOw2uyy7oeZBaVIySrEmmPpqttcI/IYM4pSSZm4I9tUkw8GHHqnzc4j+bI0W87O81h11FyMk9do/8k/mpqrmZml1R0zxI1zjiAIgiAIojITkIISgPEQxKT/APiO53nnf7gcx1kAvAhgIoBPANzvlwgDgK9Wyv0q3EErQ8lSQQbPnnAxuwj5JVbnk/vd5zKx6mg67u9irBpy8f5LGD+0rTNjKq/Eitk79MvxjI4TWW8fbyC0f5eXvMVGhEg8T9h5ists2Hr6Kjo3rIqY8BDkFZehSri6t9DOs5n4Y+s53N2xDvq2SHB+z/O8rM23A7Uub+zA3lFaWVxmw9Dvt8jK9UrKM47UhDt2v5YodHUTo1TaaASlrB4HqTnFCAsJwvCpWw0bWItx/G6hwRZJhlWnBnESw3NvmO2rxyD81CsdYsVKMTO2n9ctSXPcV/7cnSJbVo/0vGJJ1zKj8DxvytdK3N6e5b9z1M8DQP2+aTRDqWqk/DqsopCNtPnkVbQduwId6sfJpg2YuEHzPFx++LLqNDXScor1ZzIBB2FfJb29VDatovTvnxW6fQJAaJBF0YzeASvaEQRBEARBENoEqqDUD8BSnucnsxPKxaVJHMfdBqDSuuXlFJV5LVNHy3jWnSwBX7D0QCru71ofxWU2jPjfdpRa7Vh+2FgWkJLQsMxABpE3W76bpbDEJluvjedlnch4Ziz739n7sOxwGpJqROH+LvXx2bJj6NsiAdMe7aK4nfumbAUALNx/CcfGDXSaBO85ny3JNhKj5eEjxl5umv3WvIOK3k+OgZ7RDI+CUm3BKK/YPUFJi8d+2YF3hrRyS0wCXGJOkEio/eDO1hXWmU8Jx3nsaeah3vKO6SfT8zXnU+KNv93LvEzPLcHlXO8KImrYFMrrxi89ioMXc2TzrhnTG30nrJd8Z+flPmC5KudsUbkwzOLueaiFVgaPu3yw6LDi956WAquh1jyhVZ0YzW5uSr5iBGGUCSvLxfP8CRgzZox/gyG8wqk1s53vk/o+4MdICILQInm+K8mi+T3kY+drAvW/p1AA2o+HhemetXG6hlHzs3EHLVPuwJST4PQ8Op9Z6MwIOK3Q0l6NdGbQmWZgEOrPyr+iMpss48du51Eo86eRzrSsXGQ7daUA4/89BjsPrDp6GUdFgk5OYZmiCJYqylQ4ckk+SHag5ofD+jnZ7TzWHEvHfBVvI8dAz6ggp+cPk18BglJWYRle1Sn10sKxT8pEv+Pwrr7tM+DYckWXsmoJ1RXFnF0XMOqP3T7ZltLvp1QONuXhTorCvJ3nkc50tVTLAryWsdp5rDqinCm184y2D5c7aIlC4SEWTUGperS5ElaCEJNXbBVeecoPX4hrD2txofNFEETgUlZU5nwRvidQBaX9AJrozNMEQKVtycJmpgDAawOaw51Eh1G/78ZTv+2SmRADcGt9viA0KAhZBaWqHZD0eG7GHtPLeOtpesvaMXiiZyNTy5TZ7LLtF5YqiEyiL7QG9I4yuaUHU9H141W4VaErWkGJFXY7j3l7UjBToySQ7eLlgI2tuMyOJ3/bpboex0DPaBc/vUyKhfsvGVqPL7HzwnERGx/rGVR7m+xyfxmle4g3uXfKVrz3z6EK3YY/sdl5ZBVo/+Py+sDm6N+qlmLpMM/Lu55dj+w8m4kQFZFH3CXPW9SrGqE6LcjCaXZ/1CqHIwiCIAiCIOQEasnbJwDmcxw3iOf5f9mJHMcNAXAPgLt9HlmAoDSYDrZwqB4dhit55syIi8psWHX0Mr5ckYxP7mkrmWYJ0JK30TP3IDTIgrF3tnZreS1/GDW8lXBx7moBOjeU+6FokZpTjOOXpaVDSqbT4hi1SsIcR/X5cmHtTEYBpm2UZlfkl1ix/HAaXpmrnZETFaos6rFZdH/t1vapcnQDs5lsS34twfM88kutTrEtKjTI52Wlc3eloHZshOESTk/4Y9u5Ct+Gv8gqLFMs3RRzZ/s6CLJwisK8nedNd4S7FlmXfEV12i437sN6JNWIRkpWkWImUpmN1/ReYztHEgRBEARBENoEaoZSPIB/ASzmOG4Fx3Hvchz3dPnPlQAWAlgKoDrHcSPFL79G7UOUyoJCgiy4tWVNt9c5a4fcLDcoUFOUIBgXe6PLnVE2HFcfGJlBKbPICH/vkZobH74kH8yKRYKCEnNP27edlrbMLiix4vmZ+plcasbtD3aTtp0/e1U7ZTwttxgTVx7HIhX/k+sBu51HjqgzW0yEq2r3md6NfRbHpNUn8I1OtyxCm3XJ+t3JHNlnSh5ZVhuPJQd9f67XrxaBpS/d7PPtViRNa0Y739vsvKScc7ioWYPNzmtmIWmVwxEEQRAEQRByAjVD6VcIVh8cBONtJfPtOwHcIfrMlS/ze0UHFwiUKZSrhARxiAp1/5AqlY8FsJ50zVIrJrzCuhsZzlBSOLCbTmZIPqdkFRkSvtTOkUFta2PCSuMlLXZeEDquZ3gAucUuQSlWJCjd1KQ6pq4315Ldl3CcvIzRlzx9cyO0rx+HF2bu9V8QIoyYVzsEJaUktFKbHT+sO+XtsFSJDA3C2ldvQUJMOACgZ5N4bD4pN/q+FhGX3Zba7M4HLhwHDO9WH3N2CdmRu89l4Z7vt0iW/XRoW7w5T3gwUVIJShAJgiAIgiC8SaAKSo9X9AY4jouHUDY3BEBbAHUBlAI4COAXAL+Ud5QLSNQMddX8bIxQWGrDwv2XcFurBGd3L192n6osjL2zlSwbyFuIPZSUREcH90/diodvaKA6HQDeX6jcmYlF7RyJCA1C7dhwp7l3oPrFLPvvzRj49UafbGv10XRJRpfYBDiQswG7NKyK/97aDA//tN3n226WEI1ZT9+A+OiwcqEgMAQlI4fL0QFRLYvPl4zskegUk4DAiImlZpUwpJss2QaAqDDXwxBxqW1IkAUhFvVE7JubVsc9neq6BCXKUCIIgiAIgjBFQApKPM//5oPN3AfgBwCpANYCOA8gAcBQANMADOI47j7eF0YjbqBkqJtTVIY6ceEKcxvnpVl7MaxTPUy4v71H6yHUGdimNtrVi6uQjk5iQUmvW9r0bfISR7NojUktnHTQWlwBLc69QbQHIqxZNp3MkGSCNRGV6hj1UurfKgFJNaIxZb3vsltCgix+y1asFhWK+HLhzR2/qdcHNsfny5L1Z9Thwe4NsPxQGq6WNy8wYtLvKnnzePMe4xC3HATiw4KfH+uK2ydvMr2cODN37/ls5/vQIIvmORMWHIRQkSm+I7vJ175mBEEQBEEQ1yqB6qHkC45DKJurx/P8QzzPv8Xz/BMAWgC4AGAYBHEpIFHKUCostUkGqFp0S6ymOu3vPSnODmH+ktOSakT5ZDuj+yQhVKPNdEVRJy4Cvz7eVTKY8Qbi4+ULb2utLAcLx0GcHBCoGUrBGhkMFU1cpKvkzegg9taWNfHmoBZIiPFei/OeTeKx4bU+qtNDgi1+64AVH+X+79mrWQ08f4tew1Bj5BSWoUq4S7gos5opefO/QMGe5/6PSEq/FjUN//1iUcvMLSi1IjhIQ1AKsYDjOISJ/gZUBqN0giAIgiAIb1FpBSWe59fwPL+ILWvjeT4NwJTyj7f4PDCDKLVWLyy1ISxYueMWS22dTKYXZ+8tN3j2j6K06pXePtnOawNaoFaMZ1ld7nJL85o4/vEgr65TkqHkAzVQa1DKQVrGteVUYPq1aA04KxpxdpRRQSmoXBgIV/A8c5enbm6MBvGRqtNDgzjN7lgVSaxIdAOAqY90NrysNxNNcorKJMbypzPyNeYWcBzTQBBv2PM80JJwujaq5vY5rZYtxvNC91M1HEKSeLvFASp8EwRBEARBBCIBKyhxHNeN47i/OY47xXFcCcdxNoWXuuuwZzhccytq/R5TppB+Uq9qhOFsG73B65IDqWj01lJk5Je6FZ9RejWrofg9+9S4ImhfPw6Ae4LCawOa48HuDfDqbc0Upx//aBBub1fbo/jcQZy4plfy5g20Mi84jguIzAw9okKD8dHdbXTnq1HFexlBDrIKXdeXcUFJ+OnNgW9cRIjmdDvvXQHLDGwWX2K88exFb55/hYzJ/YJ9l/wSh7uw+zEQYhLz2I2JAICuiVVNL5uRr+67FKKRBZpbJPypF/+tIR8lgiAIgiAI4wSkhxLHcfcCmA1B8DoLYAd8JO5wHBcMYGT5x2UG5t+tMqmF14JSgM1QalozGg/f0BBp5QbIWli4wDEAjtUYyLIPne/uUAf/mBjE6XF/l3oAtJ9gq9G0ZjRG92mCnMIyfLlC3sksNNiiWC5YNy7C9LbMYOd52O08sovKfCIoOVIvPrmnLd6ef1AyycIBlkBLgyjnvdtbYf7eFNzbqR4iQoPwUPcGePefQ6rz/zO6J+rEhWPenotYcTgNe0Q+LZ6QXejq+GY2Q+lyrnnzYjUcQnSvZjWw4fgV2fQtpzLwv5FdvLY9M7DXp5kqUW+efm8Nbon7pmx1a9lAEG9Y4TwAQnLy8A0NnIKlOyWoNzWpodixrkfjeMSEq/+NKSgRRFnKUCK8waibyx8iPTLKv4EQXqNhzzv9HQJBEAZIGpDk7xAqNQEpKAEYC6AAwBCe5807dHrGpwDaAFjK8/xyH2/bEB8tPoJpm844P9/ctDp+e7wbLBYOIQpZPTc3rY6NJ1xGwBzHITLUP9kGLDHh6qcgW7KVEOud0rSODeJQr2okhnUSBCXOjYKUyHITWK3sJqUyjI4N4kxvywxzdl7AjxuE1vMRPsgocQzYh3etj6qRIXhuxh7RNE51QN+ydgyOpuZWeHxq3N6uNp68qZHzM8dxCA22qPqnNKkZjeiwYDzbOwnP9k7C9G3nVAWohvGROCcqjdLirg51ne+NCkoOgaVvi5pYcyzd0DJ6ODI0PhvWFj3Gr5FNLy6zI8jCYXSfJHy3VtsMfFSvxsjIK8GiA5dQZuMlnf7cgb2nmTGT9obxdNOa0Xh1QHN0aVgVD3VvgBnbzZvZcwGQCxwcZH4/Vo8O08z+8Rb3dKznfG8mY7RhfCSevKkRejWtgc+WHZNNb16risT3isUhpFKGEuEN6sSVZ7HWqePfQAivER5b3d8hEARhgIj4in1gT2gTAP/mKtIEwCxfi0kcx70EYAyAYwAeMbIMz/OdlV7l66gQDqTkSD7HRYY6M0GUTJ7H3SUt5+F5Hnd1rCubzwi9VUrUlBjdJwkT7tPuFhejkaEk3tZNTaprtn82w9xnemDyiI7Op9LujDkjygU5pcHPnFE3AFAWlCraANwhJgG+McEuLvfVCbJw6NOipmSaheNwVaFkslXtGL9nAShljKiJSYPa1JJ1gruzg3TAMPOp7hjUphZ+erSLKSFPXN5jNFPOITy92Nc7ZtMAnN5rtWMjsGB0T9X5XurXFN8/1Em1HPXToW3x9uCW+Gp4B2x5sx/G3dUaM57q7lFsIWyGkokL1huZmM/0TsKA1rXAcRx6NnFvcBEIyUDsfjRyusWEB6NH43i3tjd5REe8NqC55Ltbmiv//ejc0Px1AACfDWuHkT0SkVg9SrYtANifkq2ZJfnqbcIylKFEEARBEAThHoEqKKXB5WPkEziOGw1gEoAjAPrwPJ/py+2bgRUx2tSJcb5XEiwSq0dJsh/sPNCpgXmfCgBoLdqWHq8NaIFODbW3o1WOMO7uNmhUPQqNqkfh02FtvWaerOWpYZSoMGEAwopca8b0RvfyAZhSyVvrOrGmtmNGwNNjpocDez1YkYazwNliXcyR1FycySio0Fj0MJoNtOqV3vjuwU6y72PCQ/BCnyaICg3Cq7c1w41NquOHhzujX8sEw+dp4+pRkiwRo2VRjgF3h/rey3aLjw51vq8aGSqbPqJbfQCC8DS4bW20rK18H3igWwPn+xpVwvBIj0Q0ruFe5y4HbGaNmZbu3tCgW9Sq4nzvbtZfIJS8sfc9I5mZRWU2/O/RLpg2sgvqVTX39C/IwiGBaXjw2bB2usuxx1sLsRA0uk8T3Nu5nmR6ukZZaLOEaLStJ9yPxQIpCUoEQRAEQRDGCVRB6U8A/TmOk49sKgCO4/4L4FsAhyCISWm+2K67sAOqAa1rOd+rZQ5Eeqn8yWiGzaM9GgKQZwjUYcrWYiLUyxHqxkVg9Su9sWZMb9SrGuk3U2AlIkOEuLWefr8xSGqj1S2xGkaW7xcxaoNzAOjXsqbqNDP0blYDnd0wuzUDuysCYRCthtHMlSY1o1WP8asDmuPg2AF4oW9T6boNqhj3d60v+WzUO8bZOcyD/csKw44STkAuwlSPDsMr/aXZH0qizpc62Yjuwgp08dGhhoUdo/to+pPdER6ivP/b1HWJwBFulgqbPVTeFAsdyLq8GTjdispsiA4Lxq2tEmRZenpYOA53d6iDVrVjEBpkwaQHOiAhJhwJMVJz+w/vai35rPWQgSU+SvovAnsM1X7H4V3q47cnujk/H7zoyvr9a3eK4e0TBEEQBEFUdgJVUHofQDaAuRzHyUfgXoTjuDcATASwD4KY5B1TkgqELQkQizxq2TfuDoRY9ASlAa0T8P1DnfDmoJYA5AOpS4yXStOaVTBOo8OWxcI5B4XhFVQudiwtT3Va90bVFL9X25/ipKSkGtGYM+oGTLivPY5+OBBzn+2heHzeG9JSdfvigb4nBFk4Z1mTO3RLrIabdMp9ZBlKKvNNeVie8aPGkLb6nfK6qRwjB0qZFV6qnlQUm9jSIiX+fLYHnu0tNRA0GpNYzGlS073sH3F3xRsaS/cfKxZNebiTrMMdK8i9f0crWXaIt2CzACNDgzH1kc6GljUqat6YFI8Nr/fRnc8XvmQA8PNjXXXnCTGZscmWFxsR27SaJjhIqqHcdS/IwiE4yIIlL92E3e/d6vQLY3sFPCjKagOEDppGf7f61SIln8OZe5yScBwSxOGze9uhdqzrviD2TfqTBCXCTcYuOiu8xo71dyiEl0he+rPzRRBE4HJo5iHni/A9ASko8TxfCGAUgJsBnOY47irHcacVXtrusDpwHPceBBPu3QD68TyfobNIQMBmQIizktTKQZRank95uDOCLBzio0Kx8IWeePrmRrgxSdsvQ8/HqGeT6hjctrZTcFGKZ9IDHRAbEYJ7OtZFt0bVMLxLfVSL0k9G81WGksP3KSo0CI/3TFScx1HyxsKWuXVvHI9hnetpCno9NPY5r1Q35waOgXUXnRJENYZ1rovpT3XX9FNhxRW18ao4o06LdvVi8eFdrfH6wOaY+bR6ud7kER0111NFIeNB6bzUO/eNoleS1bZuLLomykWwOIVSM731j72jNcKCLQgPsZgy2u/UoCqmPNwZL/Rpgi/ulWYWsYNwpXOXvQ3ER8vvL+4QZOHw3C1SoY2H/BroZbAU1LGr3ru9lfZ8Fs5Q1pq7ZbdmyvQA6B7LR3s0xL//udnUOhtXlwo/ahE1EIk0X92vn3XWMF5ZUAoWZdKJr0G2+yRb4lYrNhxrxtyiu10lohkDbsd9T/wQpswmP59qiUrzfCUaEgRBEARx7TB88XBDr8pIQApKHMfdBGALgKoAbAAKIfz/y77cjp/juEcBfFi+/o0AXuI4bizzesyjX6SCYDOUwph/gN+/wzV4cjzJbxgvfZILAAPb1MK2t/ph85t90a5eHN4Z0gr9WyVoblvvyTH7RFtpIHVXh7rY+15/TBzeAYCQ9SRuSa5W7mE2y8oxKHuwewNnacRD3RtoLQIAGNa5Hla90hub3+yrahrOPgl3wJZgGIHjOCwY3ROP90yUGct6SU9ytlqfNKIj3h7cQntmBWzlD/AHtjEmBqkREx5suAzJauMRHx2G529pghuTlLOjIkODZD4tLEqlTEqZKx/c2Vr2nTvoeXSpiQtGS4oKS1weLzc1rY7979+GbW/1k2U8adG9cTUMbFMLrw5oLsvyYIVBJU8l9ndQK7U1y0t9m+KNgdLz0+ZB0y3HcX7sxkTdbC6lc2J4F2lZohlRW5yxFRYchDcHtZCVe4mpGydkzHRuWFV3O0k1o9GkZhX89kQ3hAVbUCc2HBt1MqxYwVLp9/3p0S7Y8HofrHqlF1a90hudG7qET/a65ThBcGpQTf63BQCqqtwLWUFJCfacVOIjhcxWVtgcU266rScEvi3KEnX4KhEEQRAEQRD6eKeexvt8BiAEwEgAM3mer4g+vo6e4UEA/qsyz3oAv1bAtj2C/eeY7ew2skciQoIsCAnicGtLQSBKUjHGZTOX7ulYFx8sOqK6bb0n7WyLZrWSE3bQ2rlhVbw9uAU2nbyKMf2bKS5jdtA6//meOJKag4Gta+Olvk2xPyXbsMm1Y/CpNPhpUzdGEv/3D3XC5DUnMaxTXdVBlB7t68ehff04rE2WVlzW1BiAAtBsdS/G4c9TNy4Co3oloUG1SDw7fY/h+PqWd3C7q0MdvL/wsKFllI69mRK+MgNKwlM3NdKdRynjQOk8bppQBb2a1cCG41eMBaiC3jWiJcre1ioBK45cdn6uExuO8JAgnC43MQ+2cOjQQCq4hocEITwkCDcmxeOrldL19W5WA+sVfh8tnxo2U0dJUGKPrZ4AUjs2HKlMuasSSrcLpW6JANC+Xiz2l3e87JpYFTvPZsnmcdwbgywcbmlWAyfT81W3rVS+eEd7aTc/tfuoEu8MlpayPts7Cc/2TkLncSsVzepnj7oBa5PTcVsrfdHWIe70blYDO9+9FZEhQbpm1mzWk9Jp6tj/TWpWkU3rUD8OR1NzAQBVI0Ow9D83o3ZsBC5lFyluT01cNyIoafHagOYItnAYzniQAUAc8wBgULkArpd91lQkNmYpHBuCIAiCIAhCmUAVlNoDmMXz/PSK2gDP82MBjK2o9Vck7JNidoAaZOHw8A1S66k6ccY69MRFhuL7hzrh+RkqYoPOP+ZseZGZSo9RvZIwqpd6lgWbiaVESBCHMhuPm5tWR/NaVdC8vENTRGgQasWaz65hs0beGtQCgxlfn8Fta8u+cxcbU46hJ4C90r8Zlh1Kw74L2ZrzsYPlAa1roVODOOw5r70cAKwe0xu1ys3U4yJD8c/onrj7u82K8z7QtT5m77yAO9rXURQZHB5cH9/TBu/M165zNiIoGTknlOJQa03uDRtxvSw+rQ53X9zXHgOOXEarOjG4nFuMzg2rokp4CLILS/HX7hS0qxeH6irlZaw4fPqTwTialqsoKGkRxMSvlBnI3oP0/M2mPdoFz/yxGzWqhOHXx7thzNx9WHVUblentOfUMre+Gt4Bd07eBAAYe2drDPlmk2yepJquciw9oU9pstKx/OnRLnjyt12a67q1ZYKquBwbGSITlMJDLKhfLRIjeyRqrlcpVqMm1rIubwr3cq3swTcHtsDOs5nILSrDz491dXoQqWVxqv3N8URQmj3qBtygUXbLbtNx39M79jWrhKF/qwRUiwxF/WrmutkRBEEQBEFUZgJVUMoHkOnvIAKVnKIyyWcjJUR6ZThilMrjHNyiI3CwBshmvUO06NE4HnViw2XG3g7a1o3F+KFtkZyWZ6o0KyEmDJdV2kt3qB+H7o2qYfuZTLw2oDmeMVFW5A42JhuD4zi8PrA5Pl+WLJuX44D+rRKw4rB+U0J2XMxxHP569kZczC5C7dhwfLf2FCauOq64bCLjkdK+XixGdGuA+XtT8Opt0hK9T4e1w0v9mqJ2uQD12I2J+HXLWed0xwD9wW4NDAhK+gNPPaNwQJ7Z9kKfJh51SNPj1BV1wQgAMvLVMyBiI0IwrLxUStz9Ly4yFE/d3FhzvQ3jozCiW30sO5SGd4e0gsXCoZVGB0E1YsJD8MgNDTF753m8xHSwc3DkUo7ks16GUus6sdj4eh/nfp/2aFckvrlENp/jsPynX1NMWn0CcZEhqmW4STWisevd/rDa7Yo+WYCQ+ehauXJsieX3O6XfQUn76NcyAX8/dyOG/bBFeYVwlZgqUSc2AqeZc2TuMz3UF1DAGx0UlVahtdbYyBCsfLkXbHZekg2lJmip3fvdFZRqx4ZrikmAcG9qXD0KpzMK0LOJa169v3/x0WGSsmuCIAiCIAjCGIEqKC0F0NvfQQQqJy6rdyVTw0xHIKX25YPb1sId7epoelu8d3srSeccAIgy2Wpai/CQIKx97RaczSjEifQ8vDBzr2T6V/e3R9OEKpI230Z46qbG+HjpUcVpHMdh9qgbkJFfqmhs7m3sCoMttuJn6Us3Y+nBVLSqE4OkGtGGBpcFpTbZdxYL5zyeabnKZSuAfGDIcRzGD22Lj+5uozhoFGcJDO9anxGULM513NqypmKWioMSA6V87cv9trRK/9jOhFrZenXitP2YjNA1sapmFlJFMn5oO3xyT1uncKMknH18j3pXRQfj7m6Dd29vabgzoBFvISMinmOel/s3w7BO9RAbGaLZaUzInlLfds0qruOpdp28NkDwbAoJsuC+zvUkXb7Utl1T516gJaSz6/x8WDu0q6fsG6eGEZ2+Vkw40nLVywyV9ofevYTjOFnJdUyE/B4/6+kbVNdhtbtXwW7ETy44yII/nuqObaeuOsu9Ae8+2CAIgiAIgiBcBKQpN4A3AcRwHPcdx3HKLWQqMcVlcnHQ/pZ2AAAgAElEQVRADyWRSA2lf77HD22HQRplXc0SovGEQke0kCCLXpWcKcKCg9C8VhVZ1gwgeOC4g57ZN8dxPhGTAHmGEiAXmVrVicGrA5o7y+yMCEorRb48ShQpCE56GBmksb4tYnFn8ohO+OnRLqgaqTxo79VUmn3EdqgTD+q7N3KZBzdLkPrcsAbqauVugCAuxEaEwMLBcGt6lraMoPnP6J6Sz1qd8ryBnnDDtmlXQ0tMYreh5nPkCQ3iIw21rddCnJ0WyYheZz8dgpMfD8KQdq772hf3tUf7clPmHo3j0aqOcoYXW0L6TC9p9pjWMWAFGXfEDrX1v9CnCQAhc2903ybO71kvKACKZtru3KvZDKXHeyZqdq50t+LN6DlWNy4CwzoLYqQDrWueIAiCIAiCcJ9AzVCaDSAPwLMARnIcdxxAjsJ8PM/z/XwaWQBgpBSIxVyGknzeKBXR5a1BLRAabMGtLRNUBzm9miobA3sC60/iTnc1B4H09FqpHETvaHtDsCt0Q1AyAivWiUtPIkKD0K9lAuIiQ5FVKC3jbFGrCt5kutF9dX8H9PpireJ2vri3PYZ8sxGlNjsm3NcBd3zr8tOpzWQdKZkvO6gWFYqtb/VFbpHV6RtlFnb9STWinCVS1aJC8emwtm6t11t4o9yvhBG19TrtGcXblYhiUaxXsxqYsFIo62xcQxCklYysF7xwEwpKrJrZlazJM+sjpGUCzYr77tx/2EYMDl4d0ByP9GiImlXCwPPCfbGgxKooKCl5BblTSsf+7uKsMD3M/OqeSJasLxhBEARBEAThHQJVULpF9D4KQEeV+bz/WPwawIhZMYsZDyUl1DoI1YoNx10d6mou64nYowbbzUepa5JRHMbdgYCSoKT3ZN7IIPCXx7tqTu/WqJqku5iDh7oby2ZRg+3qpiRssoPKAa0TMOXhzjLhowHj7SU+p2vFhmPb2/1QZrPLtsmaOmcWKPtliWM2042OhRVkLRyHzg2rIvmjgeDAyUrwfEm3xGr6MxmALUes5sY1/vmwdnj97wOS7zgPbNGf7Z2EKetPSb4LC3Ht6/b14zB+aFvsPJOJkTcmaq5Lr1SXTfhkr0EtkYi9BtQEzhlPdccjP22XZfRUiwrV9IhziHscB81mAUpZq3UNNm8Qw2YoeVperQbvQRZcmzqxuJAplPXqlSsSBEEQBEEQxglIQYnn+UAtxQsI3BGU2DILLZTKrtQwUobwxqAWWHwwFaVWOyYOb2943VqwpVSe0KlBVTx2YyI2nLiC/7u9ldfW6w7KgpL2Mu3qxWLr6avOzwkxYejXMgEzt58HAHz3YCf0aV5Tcx2P9GiIj5a4fKRG9WqMB7rWR6PqnlWcRoSw5WbyS5sdfO+/kKOaRfPdg50weqbQgfDrBzpIpoUEWQwJp2k52oKSp7DiguOzUT8ib/PDQ53w8tx9qBsXgRlPd/fKOkvduAex3N+1PnaezZR4FnmSofRSvyYyQYnN5BnRrQFGGCz504LNQGJFFNYIXgx7L1Yrx+rZpDq2vNkPESFBSMkuRF6xFdmFpWhXL86QZ5Ue8q5v2g0Z1IhTKVk1gpnsLA+aw+GDu1pjz/ksFJXayHybIAiCIAjCiwSkoERoI/7H2ugArGMDl/9MZ8aLxqNYDIwrE2LCsfmNvsjIL5F0rvIEVnAY2Np4Vzclxt7Z2qPlvYXYSLZfC0EE0iqfAYBBbWtj6obTzs/fPNAR3RvH470hrXT9oRyEBQfh9CeD8feeFOQWWzGyR0OPs9oA+YBRSTArKJGWT2VqZJsNalMLvzzeFZEhQTJPJTUsHIc+zWtgbbJQdnlXB3n5jzdhBYMKbChniEFta6NPi5oIC7Z4rbudWEf2JONqzTGpKbsn0UWGBuOO9nWwaP8l53da5Y2ewIqGrCikJfiwoqpWhqGj7DI20lyjASOwIlhESJBb5wf7u5rx0zLjbeRJhlLNKsLfIKud94oYRxBKjOkvdOjEE2P8GwjhNZL6PuDvEAiCMEDzu5vrz0RUGCQoXYM806uxU0B4zmAb++iwYMx7/kZsOH4F93Wprzlv4+pRaFozGifS8wEAk5hMEDFGBw81qoR53dj6l8e74uU5+9AwPgrfPdTJq+v2F1WjQvHXsz2w42wm7ussHKeRPRri+3UnUWK1Y3Qf+fFuxBiUtygX7YyKSQ4sFk733PAUpey6i9nSDnNa2S8WC6ebbSVbhgM+G9YOk9ecRFKNKGdnuIqCFQi8Icx5SkUOomPC3f8zwpbOeVrDnF3ofumrGVihii0JFpfasbAiir883NiYPcn6dHTHqxYVirs7apdAizEj+HmSoQQIv6+fkgSJSkIVx72wSuCU0ROeERxuPmuTIAjfE+JBtjThOQEvKHEcVw9AXQCKagTP8xt8G5H/Gd23CXKKyhBk4fCCqJOPHp0aVEWnBvpZHRzH4a/nbsSec1nokRSvORitgOZOhunTvCb2vtffa1kXgUKXxGroIvK6qRoViiUv3Yzjl/PQr6VcTImNDMGIbvUxe+cFPHVTI487Y1UkVgOjwg+8nC1WUGpDzZhwjLu7jVfXqwZ7PgaS6XtFUCXc/fNt0gMd8ORvu5yfz10t9CiW/ReyPVreKOwxZUvr2M6CYlghx18dyGQZSh4ISh/f0xb9WyWgZe0YU6bcZn73iugkSBAEQRAEQXhGwApKHMfdBmAigBY6s1a6Z44x4SH4dFi7Ct1GbEQI+rTQzwSpGeNfg9PrTUxSo0nNaDSpGa06ffzQdnjv9lYemUn7AiNDwuFdvZslVa+qeaNhT/CkNOda4YmejfDz5jMAgOduMZYlqURf5h7jaYaREcHSG7BlqGyZI2sEL8aoKXdFw2bORXlw7wgNtuA2N8qOTYmt1/9lRRAEQRAEcc3h/1oMBTiO6w5gMYA4AN9CsNbYAOB/AI6Vf14E4EN/xViZ+er+9ggPsaBXsxro3ayGv8Mhygl0MQnQF1ta1Y7xSnnWnFE3oHp0KLo0rKrZEasiqAyZFC/1a4LHeyZiTP9muLdTPbfXwwrCBaU2lTmNUWr13CzcCKy3fHCQBU/f3AgAUCUsGA90UxdFWQ+lEH+VvFm8l6HkLuZMua//64q4tskrtgqvvDx/h0J4CWtxofNFEETgUlZY5nwRvidQR6BvAygG0JXn+Uscx70IYC3P8x9ywghkLIAxAN7xY4yVlqGd6mFIu9p+61pFXLuU2eSDws+GtcUbfx8EAHwytK1XttO9cTx2vH0rOM73WWxeaIAW8MRFhuL9O7xvZF9YYvVoeV9lKMl8siwcXhvQAl0Tq6Fl7RjNMkA2mynGTyWqbIaSNztnGoXtAqmFjw4tQbjNhJXlHSuzJmDs2LF+jYXwDqfWzHa+bz74CT9GQhCEFsn/JDvft3nQNxYXhItAFZR6AFjI8/wl0XcWAOCFFIf3OY4bDOADAPf6Ib5KD4lJhDvYFNoCDutUD7ERoYiNCEEHLxpm+6uUiDIp3MfTDCUxnnSf04PN7uE4znDZF+u3FOcnI0lWFMvI842huZhoE4buPNW8mYLjuN0qk/RsBAiCIAiCIAwTkCVvAGIBnBd9LgUQxcyzGUAvn0VEEITH1I6V+xkFB1kwsE0t9EiK90NE3qcyeCh5E3HXPW+W0LLCjTdhs95KrMaFsIbx0q5BcZGhXonJLGwnuvS8Yt/HYEL0owwlgiAIgiCIwCNQBaV0AFWZz6zzawgA37rtEgRhmt+f6AZAMCMe6+UOboEIDXzN8e2IjmhfPw43N62O0X3cN/hmqcgMJZZCE5lVvZrVQOMawvORIW1raxp4VyRJNaQm/1k+8h2Ij3IJaB01uo7WjpV2i7uzfZ0Ki+l6hOf5zkovCD6UBEEQBEEQXiFQBaXjkApI2wD05ziuGQBwHFcLwDAAJ/wQG0EQJujVrAY2vt4HW9/qh0bV2UTD6w8bKUqmqF8tEgtG98QfT3bX9B4ywtfDOzjfTx7R0dPQDFNkQlAKDwnC8v/2wqpXeuPbB30XoxLDu7jMw4e0q+2Tbf7+ZDdEhQahRpUwjOzRUHW+qY90RvVoQXy6uWl1vDO4pU/iIwiCIAiCIIwTqB5KywB8xHFcNZ7nMwFMAjAUwF6O444AaAqgCoDX/RgjQRAGqV8tUn+m6wQqefMft7erjeAgDhEhQbixgksoR3RrgFk7ziM02IK7OpjLngkJsqBJzWj9GSuYd25viTMZBSgoteKNAb6x1mldJxb73r8NNjuv2dGxXb047Hq3v09iIgiCIAiCINwjUAWlqQA2ACgDAJ7nN3Mcdx+AcQDaADgL4HWe53/3W4QEQRAKtK4b6+8QKi3BQRbc3s43pVFvDmyB5gnRaFsvDvHRYT7ZpreJCQ/B3Gd7+Hy7IUEWmGjwRhAEQRAEQQQoASko8TyfC2A78918APP9ExFBEIQxOjWoiuduScKWkxl4Z0grf4dDVBCxkSF4rGcjf4dBEARBEARBEH4jIAUlgiCIa5k3BlJnboIgCIIgCIIgrm+uGUGJ47g7AfQFwAHYwPP8334OiSAIgiAIgiAIgiAIolISMF3eOI67g+O4DRzH9VaY9guEcreXALwIYC7HcSQoEQRBEARBEARBEARB+IFAylC6E0AnMN5JHMfdDuBRAAUAJgLIAzAKwN0cx43geX6WrwMlCIIgCIIgCAdj70gU3jwz1p9hEF6k+eAn/B0CQRAGaPNgG3+HUKkJJEGpG4CtPM8XM98/AYAH8DjP838BAMdxfwA4BeAhACQoEQRBEARBEARBEMQ1yvDFw/0dAuEGAVPyBqAWBJGIpReAbADOEjee59MALAHQ0TehEQRBEARBEARBEARBEA4CSVCqCiBT/AXHcQ0AVAOwied5npn/DIB4H8VGEARBEARBEARBEARBlBNIJW95AOox33Uu/7lXZRm2PI4gCIIgCIIgfMql7JLyN5dQp04d/wZDeIXinAzn+/DY6n6MhCAILYquFjnfR8RH+DGSykkgCUoHAQzhOC6a5/n88u/ugeCftElh/kYAUn0VHEEQBEEQBEEo8ePG8n9Ji37E2LFj/RoL4R3ObV7ofE8G3QQRuJxa7nLNIYNu3xNIJW8zIJS9rec47iWO476FYLqdBmCteEaO4zgANwE44vMoCYIgCIIgCIIgCIIgKjmBlKH0E4ChAAYA6ACAA1AG4D88z9uYeftBMPFe5dMICYIgCIIgCIIICO6YrFTEQBAEQfiKgBGUeJ63cxw3BMAIADcCuApgHs/z+xRmrw5gEoCFCtMIgiAIgiAIgiAIgiCICiRgBCVAEJUglL7N0JlvNoDZPgmKIAiCIAiCIIyw6xdg6lrteZ5Z75tYCIIgCKKCCSQPJYIgCIIgCIIgCIIgCOIagAQlgiAIgiAIgiAIgiAIwhQkKBEEQRAEQRAEQRAEQRCmIEGJIAiCIAiCIAiCIAiCMAUJSgRBEAQhhuf9HQFBEARBEARBBDwkKBHE9QINggnCc/ZOBz6IA3693d+REARxDVElPNj5Iq4PgsMjnS+CIAKXkIgQ54vwPfRXjyCudVIPAAueF94/+CcQU9u/8RDEtcyC0cLPsxuBjV8BN7/i33gIgrgmGNO/nr9DILxMUt8H/B0CQRAGaH5Pc3+HUKkhQYkgrnX+fBTIPC283zcD6PWqf+MhXFzaC2yeBDQbBLQf7u9oCD3YLL/VH5CgRBD+Ympvf0dAEAHLHZM3mZp/0Ys3VVAkBEFUdqjkjSCudRxiEgAcX+6/OAg50/oDh+cD80cBeWn+jobQozDT3xEQBEEQBEEQxDUDCUoEcT1RWuDvCCoeu/3a8Yuyl7nepx/1XxyEMfIZ0a92B//EQRAEQRAEQRDXAFTyRhCBDs8D/74OXNwNDPoCqNdZfV5rse/i8gcZJ4EZw4DgCODRRUB0DX9HZJwgPxoFluQDlmAgJNz4MjYrsPVboChLKKMMq1Jx8XmLrLNAdAIQEuHe8mwWWdWGHodEEETlIDmt0Pm+eS0ycb4eyL983vk+OqGBHyMhCEKL3JRc5/uYejF+jKRyQoISQQQ6J1YAO34U3v8yCHgvXX1ea4mxdfK8IBZkXwB6vXbtCDN/PS6IBoAgst33i1/DMYXFT7fb9KPATwMA3g48vQao0czYclu+ETyEACAsWjhPApk9vwMLXxQEpZf2AqFR5teRfkT62Wb1TmwEQVz3zNrp+ts89o5E/wVCeI2Lu1c53zcf/IQfIyEIQovzG1zib5sH2/gxksoJlbwRlQubFZj3DPDLEODqKX9HY4zz21zvbTqCkdEMpePLgRXvAjumAsvfcj82X8LzQNoB1+dzm/0XiztY/JShtOI9oCQHKM0D/jLxD7FDTAKAU+u8HhbSDgFLXgVOr/fO+ha+KPzMvwxsn+LeOjLPSD+LSxYJgiAIgiAIgpBAghKhDM8D+RqZMNcq+2cBB2YD5zYJ3dGuCUz4BRnNUNr1k+v9wT/NheNtji0BvmoNLPqv9nyn1+qvK/UAsPpDIP2Yd2LzJkF+ylC6tMf1/vJB99bhyArzJnMeBnb+D/j9TuDPx4F/3wTKvFSyWZTl3nLs9ZOy0/NYCIIgCIIgCOI6hQSlysTe6cDMB4Dz2/Xnnf0Q8GVTYNUH+vNeS5wQdUFLUxhc223AyveFLKZA6cplxoDaVlpxcXgTuw1YO17IUJn9IJCbAuz+RZqNxTL9Xuln3i5f5//6AhsnAL8O8X7MZrEz8XFBvtt2+lHg0N+CQBMUan754lzpZ3fKx/TIEmUDHZ4HbP8BSF7inXVfPa0/jxJsBmBRlrrIlZ8O7J1xfQrvgY67giFBEARBEAThVUhQqizkpgILRgPH/wV+Gag9b06Ka2C36auKj81TrCZElDAdo7aji4DNXwtZTI4SGr9jQlAyXKLDuRWJ19g/C1j/qZChIob1sBHD26SfC65IRZuSXNfvX5jhnTg9wV/lUgVXgam9hfK2DZ8rd/4rKxIEp0wV4YUVW9XMvEvyBSHPW5zbanxengdOrhLKN1nxLnkJsPR189tXyvBLP6y87Rn3AQueB2Y94Pruwk51sSPvMrBvJlAQAOfmtcy8UcBnicDyd/wdCUEQBEEQRKWnUgtKHMfdy3HcZI7jNnIcl8txHM9x3HR/x1UhiAdFjsyOy4eBbT8AhZnSeb05QKxIbFZgWn/giyQgeZmxZfQ6VR2Y63p/YoX7sXkTNhPHW/OKObkaOLPR+PxF2UDGCfe2BQBbv1P+vrRQ+Xs2Y8bBpb2u92YyuXwBmy3m7rExy/YfXJk2GycAZQr7dPk7guD0Yx9BFGIRe1UBQKjCdXNyFfBFE2ByJ6Akz/O4AXPG5adWA9OHATPvB44ukE/fMdX89o8ulH+Xf0X+nd0GpO4T3l/cLQhR6z8DfroVmNxFEOzE8LwQ6z/PCdmfW78D1n+ufr4DwOUjwN9PAXv+MP97AMJDhA1fGMtIFXNxN7BkjLnl8tOFWJe+Bti8JKQW5wC/3i6co9nlZptF2cCBOcL7rd8Cq8dJ7wHucnEP8G1XIYPXW/ETBEEQBEFUAiq1oATgXQAvAOgA4KKfY6lY2CyesiLg54HAsjeB3+6UDsYtTGnO/jnqAx+eF4xsS/LU57FZheyo3+/yrhH2nl+BlB1CZsqs4caWCWFa+bKDh8iqXgnNq+gJJVqDcLtNOUOFZfpQ4LfbjRkk56cDkzsD33ZxP0tA7XdixQ+bVRAutn6rPH/2Wdf6LuxwL5aiLODEKvk1UpInfK8mZilRkuf63WSZcz4SvGSlgAqdyhweWsXZQqYSC5uhFKrQAnv6MMBaJPgrbZnsVqgywk20ep37mOv9n48pz6N17VhLgMPzXX5batdJmcL37D4tyQfWjRfeF2ZIhWkAKM13+Vdd2AYsfxtY+7E0Q2/XL8DUXsDBv4TPi/8r+JstfME9T7CFLwJrPgL+uEf/HLbbBTFo+jChbHTnNGDGvfLML5bkZcDnSUJ59ME/hW6Ue37Tj23TREHA2fClekbXyveBsxsFDzDHfYYtQ974pVAK66n31qL/ABnHhQzebd97ti6CIAiCIIhKRGUXlF4G0AxADIDn/BxLxcJ2/8q5KAgxgDDQyTjumrb5G+m880epdwLb+h3wTQdgfD3hdUrBOHnPb4J/0+l1Qtt3JcqKzGdGXTmuPi3zjPA0m4XNGnH4n+RfEeKLrC6dbqaczhNyU41v68/HpaU5at3D8tKAia2BL5vLS4nEXlJiZj/ken/ob+FcYDNYTq1xlZSpCT1iCq4Kg9sV7wnvAaiKK6X5wja/7SoMytd+JAxy13+mPL+jK9fOacZERZtVep7Z7cIAekb5QNohQJxcJZzPM4YB024VBqypB7SzF/ZOBz5rBPw8QJg/gzk/fZWhZNarSckDqIDJyjmxQhj4F+cAW74V9o+YrHPmtqmGGb+nUgNZUeL7Xk6KVPReN14Qon68Rbj+1fzHWLGiKFsuPuUyzyMyGeFc7div/D/hZ2mhICCl7gf+flL47oIoQyh5qfLyalhLgZMrhfdlBcAVFUEq8wyw7lPh+trxo/S4luQKsRxRyP4ChOto1nB5eemSMdrXSUEGsGqscH2sGQdMaKEsBu+b6XrvyBzb/oN8vsIM4MpR4X1xjrZ4ln5MyPxiEWfkHf5HfXmCIAiCIAhCQqUWlHieX8vz/AmeD7RaGQMUZAilCUZDZwdLrMDkGHBc2KlcKrL7V+Wn1StEGSq8Dfjjbvk8x0XlaKn75dMv7BQGFd90kJffiXFkoVzYIbxn43QMjI8sENb1VSvpE+2zm+QCiGOQOaWnkEG1hRHTTq1Wj8db7J8NTGwlxOwY8IoFI/YYH54nHfQEqQhK/zwH5KUKA+8lY/SzDQDXIP3UWqEkauV7wLR+QvbEn48LZTAnVhr/3QozgS8aA3t+F/btqvIBtNoAe/MkYZsZx4F5TwuZDFo4uo8tfVU+LWU3sPhll9H3gbnAuHhgUgdXCc3F3S4focsHXQP8NR+71pORLGRjTb0Z+P1u9WtuwWjBN+nCduDjBLlXGc8bOwaewpn0x1r7kVzMZQUlANg3S8h4WfGOIPKJ8ZZpt7dFN4fwc+hvYGIb4Os2LqHZcW5Zi4SMLbVjYxWVr51YCXzWUDinxbD3jc2TpJ/17tOlCmWHYgqvak9nubhL+plT+VM//xlBWFv/qfL0w/OAuSOVRcczG9S3v3+W+rT8y9LP1mLh7wsLa5AOAMdUhLW8NOEBw6cNgE/ry/c/IIjq398A/NBDLoiKybmgPo0gCIIgCIKQUKkFpWuWoizgm45CRoWRDBFA7unBCkpzRwpij1rmCiD/B9+mUEqjiM4Ad/pQofQm+zzweSP1Tl+n1wI/9Rdep9bIp3/ZVBCI5o4UPpcVuAQCQBADWHJTBANuxyCHHdAW5wg/V48DxsYKr+063iwpuwTj2OMa+9KBzSoM6ni7kOWw7XshY+CTuoKAk7pfEDRYxBkHbMnbP88LmRfifZR+GPiktrA+vcGtzQrMf1a6rcX/FQaXP98GHPpL//dywJq67y23KPOWhqs10P6pP7DrZ0H8OLtZEKgAIOe8y3CdPae3fCP4tlzaI/3eMcg8t0kQ6dzhp/5C1pPSYNebqIkHWtPYa07JOJqzCFksSjgEJU/914wu7xAS9Uj+V7iG/3oCAC+cLwtGy8+/0nzl0kDAde/cP0coA1Pi4J/acWgJZRu+1L8eOAtwaJ5Q3nWSEbmLc+T7jTUkZ+/3gHCdXzDok/RlU8GraPevgi9XSb4rk0oJcUMDu036oEDJs0spm1QJJZEJEPbvmnGuzyv/T14et+snODMjWUFUjJKYShAEQRAEQShiwgGVUILjuN0qk1pU2EZ3TnOVq614F7ix/J/34lwhWyUkQr4M61OhNMCYPgzoojFIYAdF1iLl+cxSwpQo/DwAGJsjn2/hS67304cqr2tia+nnnBThJ88rd90qyhZK3dSwFgvLbvzS9d2/rwPdRqlngvzUX9hXB+YAzQYK5R8jZgHBYdL5/n0D2M34jeRecvnbHJ4nvJQQZyWxgtK+Geq/y9ReytPEjIvXn8coat4vVz0w9BajmdFWPsguzQd+HSyd5igBVBIwzuqYkx9fDnRRKd3Uwm4VXiv/D+j5H/PL65GbKmSNaA3O1QQlG5MRpzSoVuv0BgBVagki6tFFwMDxQOfHDIUsQ03UEZN/BZjU3tj6Fr4gZLyJObYY+CCO2a5dQ1Aqv1fOH2Vsm//P3n2GuVGdfx//He+64W6wsakGA8b03juhxTaEFgg1tEASSGhJ/klIMCGFFEhCSSghQCgGHmpMD8V0bLApNqYY44IL7r2s17vneXEkrzSakWakkUa7+n6uS9dKoylHmhmt5tZ97uMn3+t6+Vpp0DH5l29qbOku/MX/Wj4fJ70oPXS666p70esusLfgi5Z6TWl+n/febnqF3H5Iy/01K8JlTTU1Srce6DIOj7vFtS3zs3TdfD6BorqO2dOXz2sJ8Hs1N0lzJmRPu36QdPXils9pb5B0+Typax93v/vG0d8PwKN/jwhddtEqdOoR4/chAGXTuZfPtS8qhoBSa+R3ET19tOuy1WE96YKXXTeejXaVOqeKTHu//PsVMV0xL7cgd6Z0QGnlQmnKqy3FY8th2lvS5vtlT/O7KCokXcsj6ILupWuCL1Ik1/XMr/D00pmp7CcjDble6py6QLU2O/CW7u73+42lX2dc0CyfJ42+NXe96RGMCmlcKY25Q+qxaXCXtyR89LDLmtvzfGm3s3KzBCTXxTEuq/IElPJJBxeL6WL11KXu+O+5mXT41fnPmSDW+gckVy9xtaY23Vva8WQXLJj0gnTA5dI3rg5e35oV0g0hYthB50FmhkzjSv9zLV9QZPrbLpgkuQLH+QJKy+e5wQB829HkCvcbI/Xe0n+eh88MXrefoKLPmdLBPj+v/8UFzIq1eqnrbpuPdyQ3b/c7b+2g5iYXOEXEOxoAACAASURBVE5nAi2d4d7TORNzg0mSe0+3+kb2tM+eLdz2IK9fH26+CY+11Dd64qLg+bz/j5qblFNnzRswyrRgUkvX1UxfjZY228fd7+Kpj/eXraTTH5G2PkLq1LMloHRenu5wQB4XHrRR0k1AzDbf/7ikmwAghIHHDEy6CTWNgFKJrLW7+01PZS7tVpaN+mUZ3DPU1Ulau6rl1/v11pcuHe9+tfb++hpU0DZvQMm6291DXReq0O2NWNNFkh48TfrZ1OxpnXtF746werE0+vbgoEu+YJLkuq+Nfzh3+uMXtWSydN9I2uf7rs1BhbW92VFBv+4XqqWSFteoWnFLdyv77yXSTqf6v87X/hTf9vJlKOWTDiTZIrtoffy4+7vVN6QtQmR9eU1+2S039XWp307uYtdaVwNGcoXsp7zqgkmS6zq483ekPtv4r++JCGMKfOTTPeuNv0r9d3btWBvQrShf/ad0MCmtYZnUsZv/vK/8Lrjb5MePu7aYdtK5L0ib7pn9/Dv/dMGruNlm6enL/Z9bu9plOhWjaa3LClw0Jf983oBRUIHwtA8eyO5WJuUPRj/7U2nvC7OnfRGhFloxrA3fPTTzc69huXvPvO9Buu6Zn5d+4z89s16TX52v+09y2V6ZmVBRRhoEAACocdRQao1862H4XICsXCDNSGWDeL+Mf/6C/7rzjRBlm90X/3zBpO6b+K00eP4g6RGlMm26d/T1zP9cevYnLqukGH7BJCm7W9TYu92Q27PeDx9oKybbqrVpapDqfGLW+UaAimrVouLrMa1dU3qR7AWTC8/j5/ELXSbSvce7QsFrG3KDvt4AwS17ttQHS1u9RPr3McEjcfl57PzcaVNfb8kaCgpmRAm+Xb+ty5ZpbsoeWU2Sxt4VvFy6NpJtlt73ZO1YG5zZVKqls/IXai7WwsmFg0l+Glfmf76YAJc3M7Bz7+jriOLB06UX82TVZZr9gSu4ba2r0+UdJU+SRv4od1ohmd2B842imfmct2syAAAAAhFQao28wyLn+6I86wPp1T+50awyeesWpeW7ALDNhYMBS2e44NXYe9xIZM3N0udFdq14+bfZj+MaSSpuHbq6uiyFNDe37LugLJC2xNrc+k6SK64e2zaaCmeZBfnDxi31qorl9/rCWNvQkpWyYp6ry+QtnO9n4pPSlFQwc/LLLqNp+lvFtcErXVg6KKAUJZizZrn0/M+lGwa7EQz9iugXMu6e7C6TcY8Al+mzp8uz3oaATNBCCnWRK8bjngylYrPzwor6nj74Hfe6vZ/7pWiXkZmaL4ifmaFUR0AJAAAgLLq8tUbebkS/7RM8b1CAqJgsEWvDFc39244t93c7K3i+5ub8XTvG3C71Hui6i3Xo0jIEeLVZNivcfP/c12W07HNRcdlW1SpdD8ibKWSbg7s9xanYOkpNa1pqXBXrvxe7Lo9bHR5tOW9A15jwWWv3DJV+OcdlN5VDXBlkmcXuH79IOuOx6Fkmdxwu/fgDafIr/jVyql1Q4L6QqHWiwvBm/cSZKRiXxy+MOdCV8Zm0fK7/LO/dlV3IvtggMWre2GktAeTdN6/A/z6U3eLpLaPs9txsUIItAZDPwi9argV6b1XmDGzk4JtTaxRmdJ1CJoUY0t7LFggA+Rn3n9xpzc3uNdxxaOEg0XM/i7a9ajYvNeLZWzdJqtIaSMVobnJd27wjpn3wQPjhwEuxMkTR5TgM+qb02TO50+87wdUqK8Xo2wqPLpdp8bTStpdPOQINy+dIt+4ffbmlM1JdSq+MvUkV8eGDSbcgmN8Ih0kL84NFFE/8QPrpZGnkpdK0gKwvb3foYorsA5JGftTy3YyAUtswZ8Kb6+4TUAKq16wxLT/uE1CqvJoOKBljviXpW6mH6WF89jXG3J26P99aW31XMgdenltLpRJsc25x6WLcvIfUd7C05KvS14VoznlWuqvAEOVRpbsieQuKP//zeLcTZMxtldnOrmf6B5Sk7Ky8YkQJJknS1yUGsPKJGjQut//9OukWFOedf4YftbESOnqKTccdvIlD3EGulfPdjxb56nZ5EVACAAAIraYDSpJ2kXS2Z9qWqZskTZNUfQGl7RIaxrRhqXTvCaWvZ+Fk/6KrKL/2neNfp21yNbOKKZobh0pdtPfavDLbCeOr0eVb9+oKZJVFUahAdbUqVwHxsK5eLF3Ts+Vxw1JpxQKpfSdpwmPFZanGaeBhubW1Vs6PfztRC+fnG5gCAAAAWWq6KLe1dri11uS5DUi6jVXl2Z9KS/IM3Yzq174Mhc0nvZBcMKmSOnaTvu3ThTMJY24vz3oXTJbuGVaedaNyjv6jq8vlHWHzwdOkUX8obpS4uG2wjWQq8BVkyYxo85OhBAAAEFpNB5QQUTEjNaG6lCNDKYnul0kwdS47cMMdkm5J+dy0W9ItaD3OejLpFgRLn+cne7p6ffVOqoZbFeg7WOrSt/zbmfJq9uPN9s0/P0W5AQAAQiOg1FodelXSLUA1Or9A0K/9esHPbTs03ra0NenMhboOybajWhxbJYGJpPQZXJkMm2KkgyI9q6ibptcuZ0gDDij/dkbf2nL/oJ9Ih/4y//x0eQMAAAitSr8No6CDfyId8ZukW9F6eQvUthVdNsj/fIeAgFJ9Z2m3Ksg0OufZpFsQLH2hOeQvLdO2PCSJllSH+k5Jt6B4Aw7M//wuZxReR7v66g2qpQNKxeyjo/8Yb1v8bL6/Gxlyw+3Kv61M3foVzkBqx9ciAACAsPjm1JrtdWHSLWh9uvZzXVUuGZt0S+LhzSrKlzFx7E3+F5g/nyn9Ypa05aFS943jbV9Um+8n9RqQ3PYP+mlwkCidobTx7tJ5L0pnPOpGfotis/1KaV11sTbpFuRXn6d75/G3St+4RtrjvNzn+gyWNi/QLUpyx0OnHsW3r5zSx2p9x+jLrrd+vG3x0yFVy61DzEOrD/2rtMmewc8PPDx/jSSykwAAACIhoNSate8k7RtzcdVv/qV8XZ8GHlae9aatv3Xwc3UdpT3OlX7wtgsYdOlT3DY23z/cfEf8Rhr0zZDzXltcW9Zb3xWKzmSM/7yS1H8X93y79tnTO3Z1v8rXd5B+8E7ucpvuXVz7irXzaZXdXqa6DtJWR/g/lxms23RPaatvRM8AOehKac/zC893/kvSkb+V+mwbbf0VVeUBpXzneI9NpAMulbY5Ove5ZbOktQ2F19+uPr4AxNZHFp5n4OHh15fOwimmZlqPTaIvE1W6XR1iHiSg52bSef/L8/zm+fcZBbkBAAAiIaDU2nkDCnEoV42YfPV74rD+wODnTvq3+/V6vd7ucb7ASz4n3ikd9itp93OC5znlfmmfH4Rb374XS7uG6F7jp//OufvKtAvurrOuG0yerIVO3XOL1p73gsvsCtJvx8JtlaRhf/fvbtJnsPubDuQUW5em306506JmkBhJO50ibTDI5zmfdkU9V/rtKA25Xjrg8vzzde0r7XeJ9MPR0da/Ych9EYdYM5SKPB/zsU2F59ny4Nxpq5dITY2Fl21XH66Ac7eN/Kd36CZ16Cp17u0+mwpZsyJ32s7fCW6bVFyXt0oElOZPcn/jDiiZuvyf7e3aSbY5z/MU5AYAAIiCgFJrt+PJ8a5v8/3KVxcmzhHGDrxCOveF7GnNa6WzR0ob7Zo7v9/FWJBhf/efvsXBUvf+LsskX9bO4KFSXfvg5zPZ5uJ/Fa/r4BMcMtJRv5MGHytt9y3PU6ZluXwyM9T2vMD9zbfv1q4J1VxtO0z65RzpOw9mTz97pHTSXS7oJ0WrYTL0b9JuZ0s//kja4qDc51cvyb/8mY97Jhipax+XqbXL6dlP+e2n+oD3sr6zy4rz6poa1erQX+SvWeXNIiukYw8XCDz8V9GWy+egnxSYIcaAkt85W6rmtYXnqe8o/Wxqdk2lYX93I5AV0q7e1QEqJChAs9ne0hWfSpd/UjiI03c7aRef4NHxt0pXzfVvm1Rcl7eOXaMvE1U6aN2hiG3lCzinz1G/z4K0fIFGuryhBNtsuN66G9qGrn03XXcDUL26bdRt3Q2Vx89xrV3nXqWvo89gaeGX0hHXSBtu77rZTHtLWjBJmhlnraEYsxAO/r/ci/mmRnch8b1R0tJZ0g0ZF4WrFoVfd9C8mcNPx9U1ormp+IuYug4+GUpG6txTOuVe93h4RoZO+kKs0EXmbmdKCye7gMgh/+emNeUJGoUN1rVr5y7AvUOFd+0j7XBCbjvD2OMcSalssWKyzrwXtOl1tGuX2w6//RQUnOvcK/c4ysweqmsvDT5OGvcf/+WjZD5tdYR02kOuvVNeC79c2nbHSROfzJ1eKHskzgylcmRFhgkoSW5fnT1Sev9eqXGVy/qp6+CyEMfeFbxcu7r83WzTem8hzRiTO920y84w7T3QnXdeu5whHXCZqy323r+l2R+66Sel2lbf0QXEpr6e0bbUv3a/oGYh+WpPbbyHNPO9PAsbqc8gad6n+beRDlYGHWPfulV6+2ZpzgT3A8eXo1qeO/K30vO/CNh86hw94PLccyF9ruc7LtYsy99uII/T9upbeCa0KhvvEdAFHkBV2fyQ6hnV9pSnTgk130NDHypzSyqHDKXWLo7Ryo67WfrVXGmf77vH7eqkE26TLng53m5qfhdVYWy0m+velckvMyQzUNLd081kw+3Dby9fUde0uIYLt02Fg1N9A9ruG1DK0651AZECgZdOPVwXnGOuc8EpSVo6M3j+pTPyry9t3VDmm+WfL2yALZ09VYp8BYi976XffgoKhJx8V26Ay/u258tiy8x8KfQ6m9a4thkTPjMuba/vueCsbxsKBXkiBpTqOvjXCtrhxOjtDqM5RJe3NGNcxtjeF7rPEWOkYX+ThufJcDNG6rmpdNTvXcbiyff4zxcYwPUcEEdeK63nM0rjN/8sbbCVOyYufE26ZJx0wSvS9sdnrCrgWC2UQbXJni77KVN7Tze5rhu23B90TP71/WyKdPoj/s9tO9Rl3g253mV6SsEBpe2OdT8MXDzWdTHO5A1IZ0q/br+urqf/P/c3ynEBAACAvAgotXZ19f7Fn4OGxc68CEnzBmsyhSlOe/ztrrZQIYXqeWzjc7HyvVddgeIwFwHe4Nq5L7i6OnueLw04IP+yu5whbX6AtPdFwYW3dzq15X5Q4OYb1xRuZ6atjypct+OCl/2n13f07/IWJB3gKLZ+VKnSgaKufaRhN7pR5c4emTtf2Oyvnb4dbr6gOjN9t8utu5W5X73t8M1Q8gRC9rpQ+u7T0mb7KGdfeI+ZfEGUzC5vh13lirwHXahnZc5FSDrd4mAXrOgQEDQupZ7MuS+kPmtS70G3/tLR17lgwu7nuP3//belITe4aXEHlA79Zf5aOVH41dPKtO8PXZ2x7b/lBjXw2vsi/+W85+G2Q6QrJ+XO5+1uuv5AaePdspf37qvMxz3ydNU4dYS03KfL3Ml3S516um6zJ/7LZU/tcKKr65XvRwy/jNnuG7vtnHq/9Kt52UXpgwJK9Z3dMbHBVso5j9JBbj/pc7T3ltkB0TOfcN25g9oIAACAohBQagtOuS872CG57hA7nCitv1X2dG9tlnOfz38xF6aw7c6nSDueVLj7R74LvCN/J532oHT1Ypc1Ibli1Rvt4rofbZ2Rdtx/F/91eH+V3mxv6aLX3QVroSBK38HSOU9Lx/wxeN7MoJRf0KP3QDdyVD67nS1d/J4Lnh1wuXtd3ovBHpkZPHnaXdc+d9/le53d+rUsl4TM92z3s6WznvCvdRIUrNvt7OD1uQX9lxv6V3eOXPF59vQjf5s7b1bxcc/6/Go7ebN49jin5TjJ2Reex/kygDKf69xT2v/H2edApk32arkfpQhzOujsV69p4OGFh48ffGzLsO+7nO6O/7QNt3NBiavmuiyfKz6V9jzPZacN+5vb/xtu56Z17pX7Xlz+ieteVYyDf+YCH2G7vBWy/4/Cz7vXBdkBl832y5Md6XO8eo+xnU4JFwDOF1A641FXc87rG8NdcHejjM/TdIH97Y93taW+fY87R380ztU4q+8ofTugm2aa9/y9fKK0bepHD+9rCaqhlK+OWrc8AwQ0pX4A6dzTBXb3vdj9IDHw0JZ5+u/kjl0p9/8jAAAAIqGGUlvQrk7qs032tK593AXAjLHSvw5rmd7sGb1os33ia8fQG6R7hrU8ru8srV3V8rip0WUpPOfTxWarb7i/xrisiWP+lH3xsc8PpU+fkRpXul/M00xdS9Bri4CsrFBCdN/JzBTwy1Y549HC69hwB2mDrV3wLMj6W0pbf0Oa8Z7LeGjfKbeOiJSqj1IgC+b8l6Qxd7gaRelsgGLq1Qw8TJockCkVVtiMl6AubzueJI3L6FbUoVvu82/dmD3tpH+7/TZ4mHKkC3Zvvr807U13f2DGuRLmmPBeEPca0HK/cWX2czndkvK8H1FqdB13c8v9DbYJmOcWaeSl2ed/utuR9yJ/g21cNtTHj+Xfbqfu0jnPuK6sO5woLZsjjb3bBQ/StYGCipZ7rZif/bhdvQtcvPk3SUYac1u49exxnit4LgUHlMKOwLiuLREDsGc8Kj18ljsWznoieL6ggN0Jd0iPXeAC58eHfN35Akp9BkmH/1p6/frsefZOdXE++jrprm+6z9HMYFFQIKtPgYytHhtLm+4jffVO7g8dXmFGeeu3o3uvVi5wn4NBo+ZJUsPylvub7uVufk5J1ctq3zm7zhxQglGfLV53/5BBeTLpEGjYTW8k3YQs8ye9v+7+BluXYfAIALGYO74l27rvjtSzqzQCSm1F4yr/6d7uEmGGwy6WN9vEe0HSvNbVadp2iLT4K+nujK566/XOv2zXPi1DqGc+d+Id0sjL3PDfg4+L1t6tj5QmpUaKS/9indZrgLRoava0zF+zvcGBU+53xXcz7X6O9NkzLY833iNVRLqAFQukszxFkk9/xBUNfuqylml17YO7NqZtsoe7ZSomoDTkeunxi6SvIgxjv9Mp0kcZBefCBkmCshO8XVW8o1F5u27udKoLdGTK3K/pWllDrpfG3C5tc7TUbUNF0nNTaYeTpE+flo76bfb55s3I8x7T+fZD2G6Jx9+WfYHfvpN09B+l536WPd+uZ0jbnyD9vn/LtPT76Q2YnPNs+JH2+u/kbun1HXNduOW8vIWe29W7EfG++Wdp3ufBAaV29S6Ac9+JLrh30JUtz3Xs5oIQ6fkO+qkrunzQT6O1LV+XYD+b7iVdNrHwe9i9v//0nb4dvjtnmndbhc61U+5rqZW0wdbSZRPcZ1qY7MUw9ePOHukKagdlk6Z16OJG+Jv1fvA89R2k7z7jAto7nJj7vyKrbRG686bPVW9Bc6BIoz4noNTWLCCgBLQKBJSSRUCprQgbUIqrG0iQ7U9oyWzY8eTsjJJ0MKvnZtKCL7KX65znIiHN72JhhxOl7Y6PNtR82rAbpXfvcIGFXp7RAU65X3r+59LCqS6rY9sh2V1DvBds/XbIXf/WR7huJWPvcRdwfvP48etmWNfe1bzJVN8xt5tfmAuqrQ53F3uSf90qP723dHViovya32/H7IBSWEEXrJ08X9ALDTnud3F82sPSmze6bLaeqdoyfQe7bnHFOulON+KZX8Aoc3S8KDWUwtrJZySJfS6SdjlNui71+tKjdnlrJaU/G7r2ccO4T3/bjfjWJVUYOt8obntdWFq7vUy77ABc5r7O1+22Y3eXtXLlJPd+ZwYZT75Humeou3/Go26+YvTd1p3HLw5vmdZvp/zLeD+Pdj5N+vCB7Gn5um5FlS9DyU9XT+C00MiPmcIUza/v4Oo8FVyXkc76r/TsT6UPR7hpg4bkztd3W3dLW39rNwqp18DDC2/Ta+jfpJt3j74cALQiUbO/Rl5SoPYoAKRQQ6mtCAwoeS4iNwwZ1CjWMX903de2OUY6wlOgOjOYlVW7whQejSifYoJJkssQOPzX/iMX9dvB/cp+2XhXz2WIp7uINzjgV6jWGDfc948/KBxMOunfLfePDsjy8F701XX0qaEU4r04+GfuV/nOvaW9v1d4/kzpOkaDj3XrySczWBVU6NyP3wXrKffnFuMtFFDyK97bZ5D0rVuknQt0xZHyB1S8/AJ5OaN+FeieGFXvgcEBxE7dpVMfkHb8tqsl4ydzePiznnSFtE/8t/+8mfb7kXTYL6O3N5/MNm5xUPY57e06mKlT6rxbr3duxtoWB7qRwr43qvhgUtoBl7kR1jp0c7eT7462/JG/lQ69KntamNEkw8oJKPmcQ0NucEG3QUNK23aU7phhdOouHX+re39PHeGC74Uc/mv/6cX8H9lgK7e+jXYN120ZAAAA65Ch1FasXe0/3ZuhtMc5LjtlxnvSt/4ZbRvpgsXLv26Z5q2R0bVv8JfyzPotPTdzF1mTX4n/4jRufhft3kBOoeBGIdsdL32ni/tl369QtZSqmeRpQ5RR3tI6dJG++5R/Vk0hw/7uuhX12NQt+9Xo3NpOaRts5Y6xaW+5C/Kw/C5YBw917d10b7fNLQ4uHEj0ZjRV2rae0Re9ASS/oc0ll7ER5NCrpFdSxcRPvqvA9oe4W5DM4eHrO7oi9mEceW24+aLYfD8X1JryurT7d7Ofyyz27eXt0ui1UYxdFPrv7ApM17XP/VwtpMv60sE/cQW6X7za7ZeoXeny8QaU/GoT7Xmey1yL2navco0S2X/n8O/Jdse6QvVv/r1lWtTaWJkOvMK/cDkAAADyIqDUVgT9iu/NUOrSVzrtIam5OXpmT8dubgj7BZOk2R+5WjT7FRgBKbMI9zF/zn5uv0vcrTXyDiUetvhwkHbtpEFH55/HG8Sq75hbhydK1ksxF4bGuGBg2llPurpOY+92F7WZXbwkdwG7y2kRt+F5DeljzBgXrJz+TriMp6CCvGGFLSIelvf9Tncty3TRGy0jbfnZ94cuG6fn5qUHJLoW6nLlk6F1+NWlbTOfLQ/xzyTq3FP69r3Sw2dmT992aPR6SKXq5JOJGMW238wNNMbBm9XnLVifVmowSYr/vCjWQT/JDigFZS0BAIBEnfKUT4kGtBl0eWsrMoszZ17w19VLu6YuxHY7qyXwUUw3sboO7oJq491dptMR17hf3vPZ41zpqD+40coKZRO0Jt37u0BH+/Wkw35VmW3mdHnr4BNQKlP2QD5D/yr9fKarMxMH7+vc9+KW+x27udpU3npAaWc+7kYp2+t7wZleYcVRbyyza9G2Q3Of7+mp3ZUvmCS5173neW4UwGIM/avr6rj/j4OLQqdljnjXrb90+qOFA8jlst2xudNOvT87y6qWeT/Pvd3/4tSxW2nZQHG24wejXdfbH7wTT7AMAAAAkVTJT40o2W5nS1PfkBZPc0OEZzruZpdZ0LVPadsopohwfUdp3yq4+CiHI6+VvnFN8TWcovLr8pYzUlgCASXJBTq82XDF6ujpCuaXyRNk4GHSxe/G044em5S+jhPucBlc3fpLe/sUsh5woPTBNHe/1xa5z8dtj3Pd6INhAo9dNnDd76a+4bqh9di47M1Dkb4ak/04SpHtYhz9B9dlecR3pEnPu2lxduELq++2Ut9fVH67AAAAkERAqe2oq89fU6XYYJKpaxllacPti1tHW1apYJLftky76shQStv5VOnla91Q7ftfWvx6vDWU4i4CHNZe35PG3CEtnyOdcHtx6+i9hXTWE8HPH3mtNP0tafUSN1pcJUQ5RrY82N2qQbv22XXY0GLp7Mpvs12dy3i7e4jL5gtT0B0AAABtCgEl5HfczdIT33fZMXucm3RrkMlan4BWggGl9p2lH46R5nwsDShhuNnMLptJ6tRd+vGH0qpFhbuHFWu93tIl46SmxtLrcLV16/V2wT3kaliSzHZ7bOyOX9nkAr8AAABIDDWUkN8up0mXfSz9+ANpkz2Sbg0yde7l/u5/qctW2vOC5GvKdNnAZbSUcnG5/kBXrL37JtKJFcraCdK+U/mCSWnGEEwKo7kp6RZUr24btdxff6vKbrtdO4JJAAAANYoMJRQWRy0ZxGOLg6Upr0pdN5S2ShVmPuIa6eCf+g8V3lod+Vt3A9Jsc9ItqF4n3C7dM8wFJ0++J+nWAPndViXdaGO2++YBoyui1eq56aDCMwFIXO+teifdhJpGQAloTU64XRr/iDTw0OyRzt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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 371,\n \"width\": 586\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(8,5), nrows=2, ncols=2)\\n\",\n \"for i, (row, param) in enumerate(zip(axes, [chain.β, chain.sig])):\\n\",\n \" row[0].plot(param)\\n\",\n \" row[0].set_ylabel('Sample value')\\n\",\n \" #row[0].set_xlabel('Sample')\\n\",\n \" row[0].set_title(['β', 'sig'][i])\\n\",\n \" row[1].set_title(['β', 'sig'][i])\\n\",\n \" if len(param.shape) > 1:\\n\",\n \" for each in param.T:\\n\",\n \" row[1].hist(each, alpha=0.8, histtype='stepfilled')\\n\",\n \" row[1].set_yticklabels('')\\n\",\n \" row[1].vlines([2,1,4], 0, 600, linestyles='--', alpha=0.5)\\n\",\n \" \\n\",\n \" else:\\n\",\n \" row[1].hist(param, alpha=0.8, histtype='stepfilled')\\n\",\n \" row[1].set_yticklabels('')\\n\",\n \" row[1].vlines(1, 0, 600, linestyles='--', alpha=0.5)\\n\",\n \" #row[1].set_xlabel('Sample value')\\n\",\n \"fig.tight_layout(pad=0.1, h_pad=1.5, w_pad=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"fig.savefig('linear_model_posterior.png')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Using one logp function for both logp and gradient\\n\",\n \"\\n\",\n \"You can also use one `logp` function that returns both the logp value and the gradient. To let the samplers know about this, set `grad_logp = True`. I'm also using one argument `theta` as the parameter which contains the five $\\\\beta$ coefficients and $\\\\sigma$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from autograd import grad\\n\",\n \"grads = [grad(logp, 0), grad(logp, 1)]\\n\",\n \"def single_logp(theta):\\n\",\n \" b, sig = theta[:3], theta[-1]\\n\",\n \" logp_val = logp(b, sig)\\n\",\n \" grad_val = np.hstack([each(b, sig) for each in grads])\\n\",\n \" return logp_val, grad_val\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 2000 of 2000 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = {'theta': np.ones(4)}\\n\",\n \"nuts = smp.NUTS(single_logp, start, grad_logp=True)\\n\",\n \"chain = nuts.sample(2000, burn=1000, thin=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 250,\n \"width\": 373\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"_ = plt.plot(chain.theta[:, :4])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sampling in parallel\\n\",\n \"\\n\",\n \"We can make use of our multicore CPUs by running chains in parallel. To do this, simply request the number of chains you want when you call `sample`: `nuts.sample(1000, n_chains=4)`. Each chain is given its own process and the OS decides how to run the processes. Typically this means that each process will run on its own core. So, if you have four cores and four chains, they will all run in parallel. But, if you have two cores, only two will run at a time.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: [##############################] 1100 of 1100 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"start = smp.find_MAP(logp, {'β': np.ones(3), 'sig': 1.})\\n\",\n \"nuts = smp.NUTS(logp, start)\\n\",\n \"chains = nuts.sample(1100, burn=100, n_chains=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 195,\n \"width\": 587\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(figsize=(10,3), ncols=2)\\n\",\n \"for ax, chain in zip(axes, chains):\\n\",\n \" _ = ax.plot(chain.β)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The future\\n\",\n \"----------\\n\",\n \"\\n\",\n \"* Write a module that makes it easier to build logp functions from distributions\\n\",\n \"* Add various functions such as autocorrelation, HPD, etc.\\n\",\n \"* Plots!\"\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\": \"3.6.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "examples/effective_sample_size_example.py", "content": "import sys\nsys.path.append('..')\nimport sampyl as smp\nfrom sampyl import np\nfrom sampyl.diagnostics import diagnostics\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# correlated gaussian\ndef logp(x, y):\n icov = np.linalg.inv(np.array([[1., .8], [.8, 1.]]))\n d = np.array([x, y])\n return -.5 * np.dot(np.dot(d, icov), d)\n#logp_xy = lambda(th): logp(th[0], th[1])\n\nstart = {'x': 1., 'y': 1.}\n# compare the performance of NUTS and Metropolis by effective sample size\nnuts = smp.NUTS(logp, start)\nnuts_trace = nuts.sample(1000)\n\nmet = smp.Metropolis(logp, start)\nmet_trace = met.sample(1000)\n\n# compute effective sample size based on autocorrelation\nnuts_eff = diagnostics.compute_n_eff_acf(nuts_trace.x)\nmet_eff = diagnostics.compute_n_eff_acf(met_trace.x)\nprint(\"NUTS effective sample size: {:0.2f}\".format(nuts_eff))\nprint(\"MH effective sample size: {:0.2f}\".format(met_eff))\n\n# graphically compare samples\nfig, axarr = plt.subplots(1, 2)\naxarr[0].scatter(nuts_trace.x, nuts_trace.y)\naxarr[0].set_title(\"NUTS samples\")\naxarr[1].scatter(met_trace.x, met_trace.y)\naxarr[1].set_title(\"MH samples\")\nplt.show()\n\n" }, { "path": "examples/slice_sample.py", "content": "import sys\nsys.path.append('.')\nimport sampyl as smp\nfrom sampyl.state import State\nfrom sampyl import np\nfrom sampyl.diagnostics import diagnostics\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# correlated gaussian log likelihood\ndef logp(x, y):\n icov = np.linalg.inv(np.array([[1., .8], [.8, 1.]]))\n d = np.array([x, y])\n return -.5 * np.dot(np.dot(d, icov), d)\nlogp_xy = lambda(th): logp(th[0], th[1])\n\n# compare slice samplers, metropolis hastings, and the two variable \n# slice sampler\nssamp = smp.Slice(logp, start={'x': 4., 'y': 4.} )\nslice_trace = ssamp.sample(1000)\n\nmet = smp.Metropolis(logp, start={'x': 4., 'y': 4.})\nmet_trace = met.sample(1000)\n\nbslice = smp.Slice(logp_xy, start={'th': np.array([4., 4.])})\nbtrace = bslice.sample(1000)\n\n# compute effective sample size based on autocorrelation\nslice_eff = diagnostics.compute_n_eff_acf(slice_trace.x)\nmet_eff = diagnostics.compute_n_eff_acf(met_trace.x)\nb_eff = diagnostics.compute_n_eff_acf(btrace.th[:,0])\nprint \"Slice effective sample size: %2.2f\"%slice_eff\nprint \"MH effective sample size: %2.2f\"%met_eff\nprint \"two var slice effective sample size: %2.2f\"%b_eff\n\nprint \" ----- \"\nprint \"Slice sampler evals per sample: \", ssamp.evals_per_sample\n\n# graphically compare samples\nfig, axarr = plt.subplots(1, 3, figsize=(12,4))\naxarr[0].scatter(slice_trace.x, slice_trace.y)\naxarr[0].set_title(\"Slice samples\")\naxarr[1].scatter(met_trace.x, met_trace.y)\naxarr[1].set_title(\"MH samples\")\naxarr[2].scatter(btrace.th[:,0], btrace.th[:,1])\naxarr[2].set_title(\"Two var Slice samples\")\nfor ax in axarr:\n ax.set_xlim((-4, 4))\n ax.set_ylim((-4, 4))\nplt.show()\n\n" }, { "path": "requirements.txt", "content": "alabaster==0.7.6\nargh==0.26.1\nautograd==1.0.3\nBabel==2.0\ncertifi==14.5.14\ndocutils==0.12\nipython==3.2.1\nJinja2==2.7.3\njsonschema==2.4.0\nlivereload==2.4.0\nMarkupSafe==0.23\nmatplotlib==1.4.3\nmistune==0.7\nmock==1.3.0\nnumpy==1.9.2\npandas==0.16.2\npathtools==0.1.2\npbr==1.4.0\nptyprocess==0.4\npy==1.4.30\nPygments==2.0.2\npyparsing==2.0.3\npytest==2.7.2\npython-dateutil==2.4.2\npytz==2015.4\nPyYAML==3.11\npyzmq==14.7.0\nscipy==0.16.0\nseaborn==0.6.0\nsix==1.9.0\nsnowballstemmer==1.2.0\nSphinx==1.3.1\nsphinx-autobuild==0.5.2\nsphinx-rtd-theme==0.1.8\nterminado==0.5\ntornado==4.2.1\nwatchdog==0.8.3\n" }, { "path": "sampyl/__init__.py", "content": "\nname = \"sampyl-mcmc\"\n__version__ = \"0.3\"\n\nfrom .samplers import *\nfrom .core import np\nfrom .starting import find_MAP\nfrom . import exceptions\nfrom .distributions import *\nfrom .model import Model\n" }, { "path": "sampyl/core.py", "content": "\"\"\" Core file used for things a bunch of other files need. \"\"\"\n\nfrom .exceptions import AutogradError\n\n# Basically, all other files will import numpy from here. I did this way\n# so that if autograd is installed, the other files will use its numpy.\n# Otherwise, just use the normal numpy.\ntry:\n AUTOGRAD = True\n import autograd.numpy as np\n from autograd import grad\nexcept ImportError:\n AUTOGRAD = False\n import numpy as np\n\n\ndef auto_grad_logp(logp, names=None):\n \"\"\" Automatically builds gradient logps using autograd. Returns as list\n containing one grad logp with respect to each variable in logp.\n\n If logp has unknown number of arguments, you can set n to the desired\n number.\n \"\"\"\n if AUTOGRAD is False:\n raise AutogradError(\"Install autograd to use automatic \"\n \"gradient functionality.\")\n if names is None:\n n = logp.__code__.co_argcount\n names = logp.__code__.co_varnames[:n]\n return {var: grad(logp, i) for i, var in enumerate(names)}\n" }, { "path": "sampyl/diagnostics/__init__.py", "content": "" }, { "path": "sampyl/diagnostics/diagnostics.py", "content": "import numpy as np\nimport statsmodels.tsa.stattools as stattools\n\ndef compute_r_hat(theta_chains):\n \"\"\" given a Nchains x Nsamps array, compute the potential scale reduction \n factor, gelman-rubin convergence diagnostic.\n\n Input:\n theta_chains: m x n matrix of MCMC chains\n (m = number of chains, n = number of samples per chain)\n\n Output:\n R_hat: potential scale reduction factor (page 297 in BDA 2)\n \"\"\"\n m, n = theta_chains.shape\n chain_means = theta_chains.mean(axis=1) # Nchains mean values\n grand_mean = theta_chains.mean()\n\n # compute between chain variance\n B_over_n = 1. / (m - 1) * np.sum( (chain_means - grand_mean)**2 )\n\n # compute within sequence variance\n W = 1./(m*(n-1)) * np.sum( (theta_chains.T - chain_means)**2 )\n\n # compute pooled esitmate\n var_plus = (n-1.)/n * W + B_over_n\n\n # compute estimate scale reduction factor\n R_hat = (m+1.)/m * var_plus / W - (n-1.) / (m*n)\n return np.sqrt(R_hat)\n\ndef compute_n_eff(theta_chains):\n \"\"\" Compute n_effective from BDA \"\"\"\n m, n = theta_chains.shape\n chain_means = theta_chains.mean(axis=1) # Nchains mean values\n grand_mean = theta_chains.mean()\n\n # compute between chain variance\n B_over_n = 1. / (m - 1) * np.sum( (chain_means - grand_mean)**2 )\n # compute within sequence variance\n W = 1./(m*(n-1)) * np.sum( (theta_chains.T - chain_means)**2 )\n # compute pooled esitmate\n var_plus = (n-1.)/n * W + B_over_n\n n_eff = m * n * var_plus / (n*B_over_n)\n return n_eff\n\ndef compute_n_eff_acf(theta_chain):\n \"\"\" computes autocorrelation based effective sample size\"\"\"\n n = theta_chain.shape[0]\n return n / (1. + 2 * stattools.acf(theta_chain)[1:].sum())\n\n\n" }, { "path": "sampyl/distributions.py", "content": "\"\"\" \n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\nDistribution log likelihoods for building Bayesian models. \n\nThese should all automatically sum the log likelihoods if `x` is a numpy array.\n\n\"\"\"\n\nimport numbers\nfrom sampyl.core import np\nfrom scipy.special import gamma\n\n\ndef fails_constraints(*conditions):\n \"\"\" Utility function for catching out of bound parameters. Returns True if \n any of the conditions aren't met. Typically you'll use this at the\n beginning of defining the log P(X) functions. Example ::\n\n def logp(x, y):\n # Bound x and y to be greater than 0\n if outofbounds(x > 0, y > 0):\n return -np.inf\n \n \"\"\"\n\n for each in conditions:\n if not np.all(each):\n return True\n else:\n return False\n\n\ndef normal(x, mu=0, sig=1):\n \"\"\" Normal distribution log-likelihood.\n\n :param x: *int, float, np.array.*\n :param mu: (optional) *int, float, np.array.* \n Location parameter of the normal distribution. Defaults to 0.\n :param sig: (optional) *int, float.* \n Standard deviation of the normal distribution, :math:`\\sigma > 0`.\n Defaults to 1.\n\n .. math::\n \\log{P(x; \\mu, \\sigma)} \\propto -\\log{\\sigma} \\\n - \\\\frac{(x - \\mu)^2}{2 \\sigma^2}\n\n \"\"\"\n\n if np.size(mu) != 1 and len(x) != len(mu):\n raise ValueError('If mu is a vector, x must be the same size as mu.'\n ' We got x={}, mu={}'.format(x, mu))\n\n if fails_constraints(sig >= 0):\n return -np.inf\n\n return np.sum(-np.log(sig) - (x - mu)**2/(2*sig**2))\n\n\ndef half_normal(x, mu=0, sig=1):\n if fails_constraints(x >= 0):\n return -np.inf\n\n return normal(x, mu=mu, sig=sig)\n\n\ndef uniform(x, lower=0, upper=1):\n \"\"\" Uniform distribution log-likelihood. Bounds are inclusive.\n\n :param x: *int, float, np.array.*\n :param lower: (optional) *int, float.* Lower bound, default is 0.\n :param upper: (optional) *int, float.* Upper bound, default is 1.\n\n .. math ::\n\n \\log{P(x; a, b)} = -n\\log(b-a)\n\n \"\"\"\n\n if fails_constraints(x >= lower, x <= upper):\n return -np.inf\n \n return -np.size(x) * np.log(upper-lower)\n\n\ndef discrete_uniform(x, lower=0, upper=1):\n \"\"\" Discrete Uniform distribution log-likelihood.\n\n :param x: *int, np.array[int].* \n :param lower: (optional) *int, float.* Lower bound, default is 0.\n :param upper: (optional) *int, float.* Upper bound, default is 1.\n\n .. math ::\n\n \\log{P(x; a, b)} = -n\\log(b-a)\n \"\"\"\n\n if fails_constraints(x >= lower, x <= upper):\n return -np.inf\n\n if isinstance(x, np.ndarray):\n if x.dtype != np.int_:\n raise ValueError('x must be integers, function received {}'.format(x))\n else:\n return -np.size(x) * np.log(upper-lower)\n elif isinstance(x, numbers.Integral):\n return -np.log(upper-lower)\n else:\n return -np.inf\n\n\n\ndef exponential(x, rate=1):\n \"\"\" Log likelihood of the exponential distribution. \n\n :param x: *int, float, np.array.*\n :param rate: (optional) *int, float, np.array.* Rate parameter, :math:`\\lambda > 0`. Defaults to 1.\n\n .. math ::\n \n \\log{P(x; \\lambda)} \\propto \\log{\\lambda} - \\lambda x\n \"\"\"\n\n if fails_constraints(x > 0, rate > 0):\n return -np.inf\n\n if np.size(rate) != 1 and len(x) != len(rate):\n raise ValueError('If rate is a vector, x must be the same size as rate.'\n ' We got x={}, rate={}'.format(x, rate))\n return np.sum(np.log(rate) - rate*x)\n\n\ndef poisson(x, rate=1):\n \"\"\" Poisson distribution log-likelihood.\n\n :param x: *int, float, np.array.* Event count.\n :param rate: (optional) *int, float, np.array.* Rate parameter, :math:`\\lambda > 0`. Defaults to 1.\n \n\n .. math ::\n \\log{P(x; \\lambda)} \\propto x \\log{\\lambda} - \\lambda\n\n \"\"\"\n\n if fails_constraints(rate > 0):\n return -np.inf\n \n if np.size(rate) != 1 and len(x) != len(rate):\n raise ValueError('If rate is a vector, x must be the same size as rate.'\n ' We got x={}, rate={}'.format(x, rate))\n return np.sum(x*np.log(rate)) - np.size(x)*rate\n\n\ndef binomial(k, n, p):\n \"\"\" Binomial distribution log-likelihood.\n\n :param k: *int, np.array.* Number of successes. :math:`k <= n`\n :param n: *int, np.array.* Number of trials. :math:`n > 0`\n :param p: *int, float, np.array.* Success probability. :math:`0<= p <= 1`\n \n .. math::\n \\log{P(k; n, p)} \\propto k \\log{p} + (n-k)\\log{(1-p)}\n \"\"\"\n if k > n:\n raise ValueError(\"k must be less than or equal to n\")\n if fails_constraints(0 < p, p < 1):\n return -np.inf\n return np.sum(k*np.log(p) + (n-k)*np.log(1-p))\n\n\ndef bernoulli(k, p):\n \"\"\" Bernoulli distribution log-likelihood. \n\n :param k: *int, np.array.* Number of successes.\n :param p: *int, float, np.array.* Success probability.\n\n Special case of binomial distribution, with n set to 1.\n \"\"\"\n\n return binomial(k, 1, p)\n\n\ndef beta(x, alpha=1, beta=1):\n \"\"\" Beta distribution log-likelihood.\n\n :param x: *float, np.array.* :math:`0 < x < 1`\n :param alpha: (optional) *int, float.* Shape parameter, :math:`\\\\alpha > 0`\n :param beta: (optional) *int, float.* Shape parameter, :math:`\\\\beta > 0`\n\n .. math ::\n \\log{P(x; \\\\alpha, \\\\beta)} \\propto (\\\\alpha - 1)\\log{x} + \\\n (\\\\beta - 1) \\log{(1 - x)}\n \"\"\"\n\n if fails_constraints(0 < x, x < 1, alpha > 0, beta > 0):\n return -np.inf\n return np.sum((alpha - 1)*np.log(x) + (beta - 1)*np.log(1-x))\n\n\ndef student_t(x, nu=1):\n \"\"\" Student's t log-likelihood\n\n :param x: *int, float, np.array.*\n :param nu: (optional) *int.* Degress of freedom.\n \n .. math ::\n \\log{P(x; \\\\nu)} \\propto \\log{\\Gamma \\\\left(\\\\frac{\\\\nu+1}{2} \\\\right)} - \\\n \\log{\\Gamma \\left( \\\\frac{\\\\nu}{2} \\\\right) } - \\\n \\\\frac{1}{2}\\log{\\\\nu} - \\\n \\\\frac{\\\\nu+1}{2}\\log{\\left(1 + \\\\frac{x^2}{\\\\nu} \\\\right)}\n \"\"\"\n \n if fails_constraints(nu >= 1):\n return -np.inf\n\n return np.sum(np.log(gamma(0.5*(nu + 1))) - np.log(gamma(nu/2.)) - \\\n 0.5*np.log(nu) - (nu+1)/2*np.log(1+x**2/nu))\n\n\ndef laplace(x, mu, tau):\n \"\"\" Laplace distribution log-likelihood \n\n :param x: *int, float, np.array.* :math:`-\\infty < \\mu < \\infty`\n :param mu: *int, float, np.array.* Location parameter. :math:`-\\infty < \\mu < \\infty`\n :param tau: *int, float.* Scale parameter, :math:`\\\\tau > 0`\n\n .. math ::\n \\log{P(x; \\\\mu, \\\\tau)} \\propto \\log{\\\\tau/2} - \\\\tau \\\\left|x - \\mu \\\\right|\n\n \"\"\"\n if fails_constraints(tau > 0):\n return -np.inf\n \n return np.sum(np.log(tau) - tau*np.abs(x - mu))\n\n\ndef cauchy(x, alpha=0, beta=1):\n \"\"\" Cauchy distribution log-likelihood.\n\n :param x: *int, float, np.array.* :math:`-\\infty < x < \\infty`\n :param alpha: *int, float, nparray.* Location parameter, :math:`-\\infty < \\\\alpha < \\infty`\n :param beta: *int, float.* Scale parameter, :math:`\\\\beta > 0`\n\n .. math::\n \\log{P(x; \\\\alpha, \\\\beta)} \\propto -\\log{\\\\beta} - \\\n \\log{\\left[1 + \\left(\\\\frac{x - \\\\alpha}{\\\\beta}\\\\right)^2\\\\right]} \n\n\n \"\"\"\n if fails_constraints(beta > 0):\n return -np.inf\n\n return np.sum(-np.log(beta) - np.log(1 + ((x - alpha)/beta)**2))\n\n\ndef half_cauchy(x, alpha=0, beta=1):\n \"\"\" Half-Cauchy distribution log-likelihood (positive half).\n\n :param x: *int, float, np.array.* :math:`-\\infty < x < \\infty`\n :param alpha: *int, float, nparray.* Location parameter, :math:`-\\infty < \\\\alpha < \\infty`\n :param beta: *int, float.* Scale parameter, :math:`\\\\beta > 0`\n\n .. math::\n \\log{P(x; \\\\alpha, \\\\beta)} \\propto -\\log{\\\\beta} - \\\n \\log{\\left[1 + \\left(\\\\frac{x - \\\\alpha}{\\\\beta}\\\\right)^2\\\\right]} \n\n\n \"\"\"\n if fails_constraints(x > 0):\n return -np.inf\n\n return cauchy(x, alpha=alpha, beta=beta)\n\n\ndef weibull(x, l, k):\n \"\"\" Weibull distribution log-likelihood. \n\n :param x: *int, float, np.array.* :math:`x > 0`\n :param l: *float.* Scale parameter. :math:`\\\\lambda > 0`\n :param k: *float.* Shape parameter. :math:`k > 0`\n\n \"\"\"\n\n if fails_constraints(l > 0, k > 0, x > 0):\n return -np.inf\n\n return np.sum(np.log(k/l) + (k-1)*np.log(x/l) - (x/l)**k)\n\n\n\n\n\n\n\n\n" }, { "path": "sampyl/exceptions.py", "content": "class AutogradError(Exception):\n def __init__(self, message):\n self.message = message\n\n def __str__(self):\n return repr(self.message)\n" }, { "path": "sampyl/model.py", "content": "\"\"\"\nsampyl.model\n~~~~~~~~~~~~~~~~~~~~\n\nModel for building posterior distributions from\n\n:copyright: (c) 2018 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\n\nclass Model():\n \"\"\" Convenience class for building models from log-priors and \n log-likelihood.\n\n Example::\n \n # Linear regression model\n def logp(b, sig):\n model = Model()\n \n # Estimate from data and coefficients \n y_hat = np.dot(X, b)\n \n # Add log-priors for coefficients and model error\n model.add(smp.uniform(b, lower=-100, upper=100),\n smp.half_normal(sig))\n\n # Add log-likelihood\n model.add(smp.normal(y, mu=y_hat, sig=sig))\n\n return model()\n\n \"\"\"\n def __init__(self):\n\n self._logps = []\n\n def logp(self):\n\n return sum(self._logps)\n\n def add(self, *args):\n self._logps.extend(args)\n\n def __call__(self):\n return self.logp()\n" }, { "path": "sampyl/parallel.py", "content": "\"\"\"\nsampyl.parallel\n~~~~~~~~~~~~~~~~~~~~\n\nThis module implements generating multiple Markov chains in parallel.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\n\nfrom __future__ import division\n\nimport copy\nfrom functools import partial\nfrom multiprocessing import Pool\n\nfrom .core import np, AUTOGRAD\nfrom .samplers import *\nfrom .distributions import *\nfrom .state import State\nfrom .progressbar import update_progress\n\n\ndef f(n_samples, sampler):\n trace = sampler.sample(n_samples, progress_bar=False)\n return trace\n\n\ndef parallel(sampler, n_chains, samples, progress_bar=True, **kwargs):\n\n samplers = init_samplers(sampler, n_chains)\n chains = [copy.copy(samples) for _ in range(n_chains)]\n\n N_batches = 10\n n_samples = len(samples)\n batches = [n_samples//N_batches]*N_batches\n batches.append(n_samples % N_batches)\n\n pool = Pool(processes=n_chains)\n for i, N in enumerate(batches):\n func = partial(f, N)\n\n if i != 0:\n # Reinitialize samplers where the previous batch left off so that\n # the new batch starts at the correct state\n samplers = init_samplers(sampler, n_chains, chains=new_chains)\n new_chains = pool.map(func, samplers)\n\n for new, chain in zip(new_chains, chains):\n for j in range(N):\n chain[i*N + j] = new[j]\n\n if progress_bar:\n update_progress(N*(i+1), n_samples)\n\n if progress_bar:\n update_progress(n_samples, n_samples, end=True)\n\n burn, thin = kwargs.get('burn'), kwargs.get('thin')\n chains = [chain[burn::thin] for chain in chains]\n return chains\n\n\ndef init_samplers(sampler, n_chains, chains=None):\n\n samplers = [copy.deepcopy(sampler) for _ in range(n_chains)]\n for sampler in samplers:\n # Randomize start and seed\n sampler.state.update({var: val + np.random.randn(*np.shape(val))*val/5\n for var, val in sampler.state.items()})\n sampler.seed = np.random.randint(0, 2**16)\n\n # Can't pickle autograd grad functions, so clear it here, then\n # build them when sample method is called.\n if AUTOGRAD and hasattr(sampler.model, 'grad_func'):\n sampler.model.grad_func = None\n\n if chains is not None:\n # This way we can update the samplers' states to the last state\n # in each chain. This ensures that each batch starts off where\n # the previous one left off.\n for sampler, chain in zip(samplers, chains):\n sampler.state.update({var: val for var, val\n in zip(sampler.state, chain[-1])})\n\n return samplers\n" }, { "path": "sampyl/posterior.py", "content": "\"\"\"\nsampyl.posterior\n~~~~~~~~~~~~~~~~~~~~\n\nModels of a posterior distribution for access to logp and grad functions.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\nimport collections\n\nfrom sampyl.core import auto_grad_logp, np\nfrom sampyl.state import func_var_names, State\n\n\nclass BasePosterior(object):\n \"\"\" Base posterior model for subclassing. \"\"\"\n def __init__(self):\n self._logp_cache = {}\n self._grad_cache = {}\n\n def logp(self, state):\n \"\"\" Return log P(X) given a :ref:`state ` X\"\"\"\n pass\n\n def grad(self, state):\n pass\n\n def __call__(self, state):\n \"\"\" Return log P(X) and grad log P(X) given a :ref:`state ` X\"\"\"\n return self.logp(state), self.grad(state)\n\n def clear_cache(self):\n \"\"\" Clear caches. \"\"\"\n del self._logp_cache\n del self._grad_cache\n self._logp_cache = {}\n self._grad_cache = {}\n\n\nclass SinglePosterior(BasePosterior):\n \"\"\" A posterior model for a logp function that returns both the cost function\n and the gradient. Caches values to improve performance. \n\n :param logp_func: Function that returns log P(X) and its gradient.\n \"\"\"\n\n def __init__(self, logp_func):\n super(SinglePosterior, self).__init__()\n self.logp_func = logp_func\n\n def logp(self, state):\n \"\"\" Return log P(X) given a :ref:`state ` X\"\"\"\n frozen_state = state.freeze()\n if not isinstance(frozen_state, collections.Hashable):\n # uncacheable. a list, for instance.\n # better to not cache than blow up.\n logp_value, _ = self.logp_func(*state.values())\n return logp_value\n\n if frozen_state in self._logp_cache:\n logp_value = self._logp_cache[frozen_state]\n else:\n logp_value, grad_value = self.logp_func(*state.values())\n self._logp_cache[frozen_state] = logp_value\n self._grad_cache[frozen_state] = grad_value\n\n return logp_value\n\n def grad(self, state):\n \"\"\" Return grad log P(X) given a :ref:`state ` X \"\"\"\n # Freeze the state as a tuple so we can use it as a dictionary key\n frozen_state = state.freeze()\n if not isinstance(frozen_state, collections.Hashable):\n # uncacheable. a list, for instance.\n # better to not cache than blow up.\n _, grad_value = self.logp_func(*state.values())\n return grad_value\n\n if frozen_state in self._grad_cache:\n grad_value = self._grad_cache[frozen_state]\n else:\n logp_value, grad_value = self.logp_func(*state.values())\n self._logp_cache[frozen_state] = logp_value\n self._grad_cache[frozen_state] = grad_value\n\n return grad_value\n\n\nclass Posterior(BasePosterior):\n \"\"\" A posterior model for separate logp and grad_logp functions. \n\n :param logp: \n log P(X) function for sampling distribution.\n :param grad_logp: (optional) *function or list of functions.*\n Gradient log P(X) function. If left as None, then `grad_logp_flag`\n is checked. If the flag is `True`, then the gradient will be \n automatically calculated with autograd.\n :param grad_logp_flag: (optional) *boolean.*\n Flag indicating if the gradient is needed or not.\n \n\n \"\"\"\n def __init__(self, logp_func, grad_func=None, grad_logp_flag=False):\n super(Posterior, self).__init__()\n self.logp_func = check_logp(logp_func)\n self.grad_func = check_grad_logp(logp_func, grad_func, grad_logp_flag)\n\n def logp(self, state):\n \"\"\" Return log P(X) given a :ref:`state ` X\"\"\"\n # Freeze the state as a tuple so we can use it as a dictionary key\n frozen_state = state.freeze()\n if not isinstance(frozen_state, collections.Hashable):\n # uncacheable. a list, for instance.\n # better to not cache than blow up.\n logp_value = self.logp_func(*state.values())\n return logp_value\n\n if frozen_state in self._logp_cache:\n logp_value = self._logp_cache[frozen_state]\n else:\n logp_value = self.logp_func(*state.values())\n self._logp_cache[frozen_state] = logp_value\n\n return logp_value\n\n def grad(self, state):\n \"\"\" Return grad log P(X) given a :ref:`state ` X \"\"\"\n # Freeze the state as a tuple so we can use it as a dictionary key\n frozen_state = state.freeze()\n if not isinstance(frozen_state, collections.Hashable):\n # uncacheable. a list, for instance.\n # better to not cache than blow up.\n grad_value = grad_vec(self.grad_func, state)\n return grad_value\n\n if frozen_state in self._grad_cache:\n grad_value = self._grad_cache[frozen_state]\n else:\n grad_value = grad_vec(self.grad_func, state)\n self._grad_cache[frozen_state] = grad_value\n\n return grad_value\n\n\ndef init_posterior(logp, grad_logp=None, grad_logp_flag=False):\n \"\"\" Initialize a posterior model and return it.\n\n :param logp: \n log P(X) function for sampling distribution.\n :param grad_logp: (optional) *function, list of functions, or boolean.*\n Gradient log P(X) function. If left as None, then `grad_logp_flag`\n is checked. If the flag is `True`, then the gradient will be \n automatically calculated with autograd.\n\n If `grad_logp` is set to True, then a SingleModel is returned.\n :param grad_logp_flag: (optional) *boolean.*\n Flag indicating if the gradient is needed or not.\n \"\"\"\n\n if grad_logp is True:\n return SinglePosterior(logp)\n else:\n return Posterior(logp, grad_logp, grad_logp_flag)\n\n\ndef grad_vec(grad_logp, state):\n \"\"\" grad_logp should be a function, or a dictionary of gradient functions, \n respective to each parameter in logp\n \"\"\"\n if hasattr(grad_logp, '__call__'):\n # grad_logp is a single function\n return np.array([grad_logp(*state.values())])\n else:\n # got a dictionary instead\n\n grads = {each:grad_logp[each](*state.values()) for each in state}\n grads_state = state.copy()\n grads_state.update(grads)\n return grads_state\n\n\ndef check_logp(logp):\n if not hasattr(logp, '__call__'):\n raise TypeError(\"logp must be a function\")\n elif logp.__code__.co_argcount == 0:\n raise ValueError(\"logp must have arguments\")\n else:\n return logp\n\n\ndef check_grad_logp(logp, grad_logp, grad_logp_flag):\n var_names = func_var_names(logp)\n if grad_logp_flag and grad_logp is None:\n return auto_grad_logp(logp)\n elif grad_logp_flag and grad_logp != 'logp':\n # User defined grad_logp function\n if len(var_names) > 1 and len(grad_logp) != len(var_names):\n raise TypeError(\"grad_logp must be iterable with length equal\"\n \" to the number of parameters in logp.\")\n else:\n return grad_logp\n else:\n return grad_logp" }, { "path": "sampyl/progressbar.py", "content": "\"\"\"\nsampyl.progressbar\n~~~~~~~~~~~~~~~~~~~~\n\nProgress bar for samplers.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\nimport sys\n\ndef update_progress(current, total, width=30, end=False):\n bar_width = width\n block = int(round(bar_width * current/total))\n text = \"\\rProgress: [{0}] {1} of {2} samples\".\\\n format(\"#\"*block + \"-\"*(bar_width-block), current, total)\n if end:\n text = text +'\\n'\n sys.stdout.write(text)\n sys.stdout.flush()\n" }, { "path": "sampyl/samplers/NUTS.py", "content": "\"\"\"\nsampyl.samplers.NUTS\n~~~~~~~~~~~~~~~~~~~~\n\nThis module implements No-U-Turn Sampler (NUTS).\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\n\nfrom __future__ import division\n\nimport collections\n\nfrom ..core import np\nfrom .base import Sampler\nfrom .hamiltonian import energy, leapfrog, initial_momentum\n\n\nclass NUTS(Sampler):\n \"\"\" No-U-Turn sampler (Hoffman & Gelman, 2014) for sampling from a\n probability distribution defined by a log P(theta) function.\n\n For technical details, see the paper:\n http://www.stat.columbia.edu/~gelman/research/published/nuts.pdf\n\n :param logp: log P(X) function for sampling distribution\n :param start: \n Dictionary of starting state for the sampler. Should have one\n element for each argument of logp.\n :param grad_logp: (optional)\n Function or list of functions that calculate grad log P(theta). \n Pass functions here if you don't want to use autograd for the \n gradients. If logp has multiple parameters, grad_logp must be \n a list of gradient functions w.r.t. each parameter in logp.\n\n If you wish to use a logp function that returns both the logp\n value and the gradient, set grad_logp = True.\n :param scale: (optional) \n Dictionary with same format as start. Scaling for initial \n momentum in Hamiltonian step.\n :param step_size: (optional) *float.*\n Initial step size for the deterministic proposals.\n :param adapt_steps: (optional) *int.*\n Integer number of steps used for adapting the step size to \n achieve a target acceptance rate.\n :param Emax: (optional) *float.* Maximum energy.\n :param target_accept: (optional) *float.* Target acceptance rate.\n :param gamma: (optional) *float.*\n :param k: (optional) *float.* Scales the speed of step size \n adaptation.\n :param t0: (optional) *float.* Slows initial step size adaptation.\n\n Example ::\n\n def logp(x, y):\n ...\n\n start = {'x': x_start, 'y': y_start}\n nuts = sampyl.NUTS(logp, start)\n chain = nuts.sample(1000)\n\n \"\"\"\n\n def __init__(self, logp, start,\n step_size=0.25,\n adapt_steps=100,\n Emax=1000.,\n target_accept=0.65,\n gamma=0.05,\n k=0.75,\n t0=10.,\n **kwargs):\n\n super(NUTS, self).__init__(logp, start, **kwargs)\n\n self.step_size = step_size / len(self.state.tovector())**(1/4.)\n self.adapt_steps = adapt_steps\n self.Emax = Emax\n self.target_accept = target_accept\n self.gamma = gamma\n self.k = k\n self.t0 = t0\n\n self.Hbar = 0.\n self.ebar = 1.\n self.mu = np.log(self.step_size*10)\n\n def step(self):\n \"\"\" Perform one NUTS step.\"\"\"\n\n H = self.model.logp\n dH = self.model.grad\n x = self.state\n r0 = initial_momentum(x, self.scale)\n u = np.random.uniform()\n e = self.step_size\n xn, xp, rn, rp, y = x, x, r0, r0, x\n j, n, s = 0, 1, 1\n\n while s == 1:\n v = bern(0.5)*2 - 1\n if v == -1:\n xn, rn, _, _, x1, n1, s1, a, na = buildtree(xn, rn, u, v, j, e, x, r0,\n H, dH, self.Emax)\n else:\n _, _, xp, rp, x1, n1, s1, a, na = buildtree(xp, rp, u, v, j, e, x, r0,\n H, dH, self.Emax)\n\n if s1 == 1 and bern(np.min(np.array([1, n1/n]))):\n y = x1\n\n dx = (xp - xn).tovector()\n s = s1 * (np.dot(dx, rn.tovector()) >= 0) * \\\n (np.dot(dx, rp.tovector()) >= 0)\n n = n + n1\n j = j + 1\n\n if self._sampled >= self.adapt_steps:\n self.step_size = self.ebar\n else:\n # Adapt step size\n m = self._sampled + 1\n w = 1./(m + self.t0)\n self.Hbar = (1 - w)*self.Hbar + w*(self.target_accept - a/na)\n log_e = self.mu - (m**.5/self.gamma)*self.Hbar\n self.step_size = np.exp(log_e)\n z = m**(-self.k)\n self.ebar = np.exp(z*log_e + (1 - z)*np.log(self.ebar))\n\n self.state = y\n self._sampled += 1\n\n return y\n\n\ndef bern(p):\n return np.random.uniform() < p\n\n\ndef buildtree(x, r, u, v, j, e, x0, r0, H, dH, Emax):\n if j == 0:\n x1, r1 = leapfrog(x, r, v*e, dH)\n E = energy(H, x1, r1)\n E0 = energy(H, x0, r0)\n dE = E - E0\n\n n1 = (np.log(u) - dE <= 0)\n s1 = (np.log(u) - dE < Emax)\n return x1, r1, x1, r1, x1, n1, s1, np.min(np.array([1, np.exp(dE)])), 1\n else:\n xn, rn, xp, rp, x1, n1, s1, a1, na1 = \\\n buildtree(x, r, u, v, j-1, e, x0, r0, H, dH, Emax)\n if s1 == 1:\n if v == -1:\n xn, rn, _, _, x2, n2, s2, a2, na2 = \\\n buildtree(xn, rn, u, v, j-1, e, x0, r0, H, dH, Emax)\n else:\n _, _, xp, rp, x2, n2, s2, a2, na2 = \\\n buildtree(xp, rp, u, v, j-1, e, x0, r0, H, dH, Emax)\n if bern(n2/max(n1 + n2, 1.)):\n x1 = x2\n\n a1 = a1 + a2\n na1 = na1 + na2\n\n dx = (xp - xn).tovector()\n s1 = s2 * (np.dot(dx, rn.tovector()) >= 0) * \\\n (np.dot(dx, rp.tovector()) >= 0)\n n1 = n1 + n2\n return xn, rn, xp, rp, x1, n1, s1, a1, na1\n " }, { "path": "sampyl/samplers/__init__.py", "content": "from .metropolis import Metropolis\nfrom .hamiltonian import Hamiltonian\nfrom .NUTS import NUTS\nfrom .chain import Chain\nfrom .slice import Slice\nfrom .base import Sampler\n" }, { "path": "sampyl/samplers/base.py", "content": "from itertools import count\nimport time\nimport unicodedata\n\nfrom ..core import np, auto_grad_logp, AUTOGRAD\nfrom ..parallel import parallel\nfrom ..progressbar import update_progress\nfrom ..state import State, func_var_names\nfrom ..posterior import init_posterior\n\n\nclass Sampler(object):\n def __init__(self, logp, start,\n grad_logp=None,\n scale=None,\n condition=None,\n grad_logp_flag=True,\n random_seed=None):\n\n self.model = init_posterior(logp, grad_logp, grad_logp_flag)\n\n self._logp_func = logp\n self._grad_func = grad_logp\n self.var_names = func_var_names(logp)\n\n self.state = State.fromkeys(self.var_names)\n\n # Making sure we normalize here because if some parameters use unicode \n # symbols, they are normalized through the func_var_names function. Then, we\n # need to normalize them here as well or the keys in start won't match the\n # keys from var_names\n start = {unicodedata.normalize('NFKC', key): val for key, val in start.items()}\n\n self.state.update(start)\n\n self.scale = default_scale(scale, self.state)\n self.sampler = None\n self._sampled = 0\n self._accepted = 0\n self.conditional = condition\n self._grad_logp_flag = grad_logp_flag\n self.seed = random_seed\n\n if random_seed:\n np.random.seed(random_seed)\n\n if condition is not None:\n self._joint_logp = self._logp_func\n\n def _conditional_step(self):\n \"\"\" Build a conditional logp and sample from it. \"\"\"\n if self.conditional is None:\n return self.step()\n\n frozen_vars = self.conditional\n frozen_state = self.state\n free_vars = [var for var in self.state if var not in frozen_vars]\n\n def conditional_logp(*args):\n conditional_state = State([each for each in zip(free_vars, args)])\n # Insert conditional values here, then pass to full logp\n for i in frozen_vars:\n conditional_state.update({i: frozen_state[i]})\n return self._joint_logp(**conditional_state)\n\n self.state = State([(var, frozen_state[var]) for var in free_vars])\n self._logp_func = conditional_logp\n if self._grad_logp_flag and AUTOGRAD:\n self.model.grad_func = auto_grad_logp(conditional_logp, names=self.state.keys())\n self.model.logp_func = self._logp_func\n state = self.step()\n\n # Add the frozen variables back into the state\n new_state = State([(name, None) for name in self.var_names])\n for var in state:\n new_state.update({var: state[var]})\n for var in frozen_vars:\n new_state.update({var: frozen_state[var]})\n\n self.state = new_state\n\n return self.state\n\n def step(self):\n \"\"\" This is what you define to create the sampler. Requires that a\n :ref:`state ` object is returned.\"\"\"\n pass\n\n def sample(self, num, burn=0, thin=1, n_chains=1, progress_bar=True):\n \n \"\"\" \n Sample from :math:`P(X)`\n\n :param num: *int.* Number of samples to draw from :math:`P(X)`.\n :param burn: (optional) *int.*\n Number of samples to discard from the beginning of the chain.\n :param thin: (optional) *float.*\n Thin the samples by this factor.\n :param n_chains: (optional) *int.*\n Number of chains to return. Each chain is given its own\n process and the OS decides how to distribute the processes.\n :param progress_bar: (optional) *boolean.*\n Show the progress bar, default = True.\n :return: Record array with fields taken from arguments of \n logp function.\n\n \"\"\"\n if self.seed is not None:\n np.random.seed(self.seed)\n\n if AUTOGRAD and hasattr(self.model, 'grad_func') \\\n and self.model.grad_func is None:\n self.model.grad_func = auto_grad_logp(self._logp_func)\n\n # Constructing a recarray to store samples\n dtypes = [(var, 'f8', np.shape(self.state[var])) for var in self.state]\n samples = np.zeros(num, dtype=dtypes).view(np.recarray)\n\n if n_chains != 1:\n return parallel(self, n_chains, samples,\n burn=burn, thin=thin,\n progress_bar=progress_bar)\n\n if self.sampler is None:\n self.sampler = (self.step() for _ in count(start=0, step=1))\n\n start_time = time.time() # For progress bar\n \n # Start sampling, add each \n for i in range(num):\n samples[i] = tuple(next(self.sampler).values())\n\n if progress_bar and time.time() - start_time > 1:\n update_progress(i+1, num)\n start_time = time.time()\n\n if progress_bar:\n update_progress(i+1, num, end=True)\n\n # Clearing the cache after a run to save on memory.\n self.model.clear_cache()\n\n return samples[burn::thin]\n\n\n def __call__(self, num, burn=0, thin=1, n_chains=1, progress_bar=True):\n\n return self.sample(num, burn=burn, thin=thin, n_chains=n_chains, \n progress_bar=progress_bar)\n\n\ndef default_scale(scale, state):\n \"\"\" If scale is None, return a State object with arrays of ones matching\n the shape of values in state.\n \"\"\"\n\n if scale is None:\n new_scale = State.fromkeys(state.keys())\n for var in state:\n new_scale.update({var: np.ones(np.shape(state[var]))})\n return new_scale\n else:\n return scale\n" }, { "path": "sampyl/samplers/chain.py", "content": "from ..core import np\nfrom .base import Sampler\n\n\nclass Chain(Sampler):\n def __init__(self, steps, start, **kwargs):\n \"\"\" Sampler for chaining together multiple other samplers.\n\n Arguments\n ---------\n steps: list or tuple\n List of sampler objects conditioned on parameters. \n This sampler iterates through each sampler in steps, using the\n step method of each to update the state.\n start: dict\n Dictionary of starting state for the sampler. Should have one\n element for each argument of logp. So, if logp = f(x, y), then\n start = {'x': x_start, 'y': y_start}\n\n \"\"\"\n # Find the logp function with all the parameters\n logps = [each._logp_func for each in steps]\n logp_index = np.argmax([each.__code__.co_argcount for each in logps])\n\n super(Chain, self).__init__(logps[logp_index], start, **kwargs)\n\n self.steps = steps\n\n def step(self):\n\n for sampler in self.steps:\n sampler.state = self.state\n state = sampler._conditional_step()\n self.state = state\n\n self._sampled += 1\n return self.state\n" }, { "path": "sampyl/samplers/hamiltonian.py", "content": "\"\"\"\nsampyl.samplers.hamiltonian\n~~~~~~~~~~~~~~~~~~~~\n\nModule implementing Hamiltonian MCMC sampler.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\n\nfrom __future__ import division\n\nfrom ..core import np\nfrom ..state import State\nfrom .base import Sampler\nfrom ..model import Model\n\n\nclass Hamiltonian(Sampler):\n def __init__(self, logp, start, step_size=1, n_steps=5, **kwargs):\n\n \"\"\" Hamiltonian MCMC sampler. Uses the gradient of log P(theta) to\n make informed proposals.\n\n Arguments\n ----------\n logp: function\n log P(X) function for sampling distribution\n start: dict\n Dictionary of starting state for the sampler. Should have one\n element for each argument of logp. So, if logp = f(x, y), then\n start = {'x': x_start, 'y': y_start}\n\n Keyword Arguments\n -----------------\n grad_logp: function or list of functions\n Functions that calculate grad log P(theta). Pass functions\n here if you don't want to use autograd for the gradients. If\n logp has multiple parameters, grad_logp must be a list of\n gradient functions w.r.t. each parameter in logp.\n scale: dict\n Same format as start. Scaling for initial momentum in\n Hamiltonian step.\n step_size: float\n Step size for the deterministic proposals.\n n_steps: int \n Number of deterministic steps to take for each proposal.\n \"\"\"\n\n super(Hamiltonian, self).__init__(logp, start, **kwargs)\n\n self.step_size = step_size / (np.hstack(self.state.values()).size)**(1/4)\n self.n_steps = n_steps\n\n def step(self):\n\n x = self.state\n r0 = initial_momentum(x, self.scale)\n y, r = x, r0\n\n for i in range(self.n_steps):\n y, r = leapfrog(y, r, self.step_size, self.model.grad)\n\n if accept(x, y, r0, r, self.model.logp):\n x = y\n self._accepted += 1\n\n self.state = x\n self._sampled += 1\n return x\n\n @property\n def acceptance_rate(self):\n return self._accepted/self._sampled\n\n\ndef leapfrog(x, r, step_size, grad):\n\n r1 = r + step_size/2*grad(x)\n x1 = x + step_size*r1\n r2 = r1 + step_size/2*grad(x1)\n return x1, r2\n\n\ndef accept(x, y, r_0, r, logp):\n E_new = energy(logp, y, r)\n E = energy(logp, x, r_0)\n A = np.min(np.array([0, E_new - E]))\n return (np.log(np.random.rand()) < A)\n\n\ndef energy(logp, x, r):\n r1 = r.tovector()\n return logp(x) - 0.5*np.dot(r1, r1)\n\n\ndef initial_momentum(state, scale):\n new = State.fromkeys(state.keys())\n for var in state:\n mu = np.zeros(np.shape(state[var]))\n cov = np.diagflat(scale[var])\n try:\n new.update({var: np.random.multivariate_normal(mu, cov)})\n except ValueError:\n # If the var is a single float\n new.update({var: np.random.normal(0, scale[var])})\n\n return new" }, { "path": "sampyl/samplers/metropolis.py", "content": "\"\"\"\nsampyl.samplers.metropolis\n~~~~~~~~~~~~~~~~~~~~\n\nModule implementing Metropolis-Hastings MCMC sampler.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\nfrom __future__ import division\n\nfrom ..core import np\nfrom ..state import State\nfrom .base import Sampler\n\n\nclass Metropolis(Sampler):\n # TODO: Allow for sticking in different proposal distributions.\n \"\"\" Metropolis-Hastings sampler for drawing from a distribution\n defined by a logp function.\n\n Has automatic scaling such that acceptance rate stays around 50%\n\n :param logp: function\n log P(X) function for sampling distribution.\n :param start: \n Dictionary of starting state for the sampler. Should have one\n element for each argument of logp.\n :param scale: *scalar or 1D array-like.*\n initial scaling factor for proposal distribution.\n :param tune_interval: *int.*\n :param scale: **scalar or 1D array-like.**\n initial scaling factor for proposal distribution.\n :param tune_interval: *int.*\n number of samples between tunings of scale factor.\n\n Example::\n\n def logp(x, y):\n ...\n\n start = {'x': x_start, 'y': y_start}\n metro = sampyl.Metropolis(logp, start)\n chain = metro.sample(20000, burn=5000, thin=4)\n\n \"\"\"\n\n def __init__(self, logp, start, tune_interval=100, **kwargs):\n \n super(Metropolis, self).__init__(logp, start, None, grad_logp_flag=False,\n **kwargs)\n self.tune_interval = tune_interval\n self._steps_until_tune = tune_interval\n self._accepted = 0\n\n def step(self):\n \"\"\" Perform a Metropolis-Hastings step. \"\"\"\n x = self.state\n y = proposal(x, scale=self.scale)\n if accept(x, y, self.model.logp):\n self.state = y\n self._accepted += 1\n\n self._sampled += 1\n\n self._steps_until_tune -= 1\n if self._steps_until_tune == 0:\n self.scale = tune(self.scale, self.acceptance)\n self._steps_until_tune = self.tune_interval\n\n return self.state\n\n @property\n def acceptance(self):\n return self._accepted/self._sampled\n\n def __repr__(self):\n return 'Metropolis-Hastings sampler'\n\n\ndef proposal(state, scale):\n \"\"\" Sample a proposal x from a multivariate normal distribution. \"\"\"\n proposed = State.fromkeys(state.keys())\n for i, var in enumerate(state):\n proposed.update({var: np.random.normal(state[var], scale[var])})\n return proposed\n\n\ndef accept(x, y, logp):\n \"\"\" Return a boolean indicating if the proposed sample should be accepted,\n given the logp ratio logp(y)/logp(x).\n \"\"\"\n delp = logp(y) - logp(x)\n if np.isfinite(delp) and np.log(np.random.uniform()) < delp:\n return True\n else:\n return False\n\n\ndef tune(scale, acceptance):\n \"\"\" Borrowed from PyMC3 \"\"\"\n\n # Switch statement\n if acceptance < 0.001:\n # reduce by 90 percent\n scale *= 0.1\n elif acceptance < 0.05:\n # reduce by 50 percent\n scale *= 0.5\n elif acceptance < 0.2:\n # reduce by ten percent\n scale *= 0.9\n elif acceptance > 0.95:\n # increase by factor of ten\n scale *= 10.0\n elif acceptance > 0.75:\n # increase by double\n scale *= 2.0\n elif acceptance > 0.5:\n # increase by ten percent\n scale *= 1.1\n\n return scale\n" }, { "path": "sampyl/samplers/slice.py", "content": "\"\"\"\nsampyl.samplers.slice\n~~~~~~~~~~~~~~~~~~~~\n\nThis module implements the slice sampler.\n\n:copyright: (c) 2015 by Andrew Miller.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\n\nfrom __future__ import division\n\nfrom ..core import np\nfrom ..state import State\nfrom .base import Sampler\n\n\nclass Slice(Sampler):\n \"\"\" Slice sampler (Neal, 2003) for creating a Markov chain that \n leaves the the distribution defined by logp invariant\n\n For technical details, see Neal's paper:\n http://projecteuclid.org/euclid.aos/1056562461\n\n Andrew Miller (acm@seas.harvard.edu) 7-13-15\n\n Adapted from code written by Ryan Adams (rpa@seas.harvard.edu)\n\n :param logp: *function.* :math:`\\log{P(X)}` function for sampling\n distribution.\n :param start: *scalar or 1D array-like.* Starting state for sampler.\n :param compwise: (optional) *boolean.* Component-wise univariate \n slice sample\n (or random direction)\n :param width: (optional) *int, float.* (Initial) width of the slice\n :param step_out: (optional) *boolean.* Perform step-out procedure\n :param doubling_step: (optional) *boolean.* If stepping out, double\n slice width?\n :param max_steps_out: (optional) *int.* Max number of steps out to perform\n :param verbose: (optional) *boolean.* Print steps out\n \"\"\"\n\n def __init__(self, logp,\n start,\n compwise = False, \n width = 1.,\n step_out = True,\n doubling_step = True,\n max_steps_out = 10,\n verbose = False,\n **kwargs):\n \n \n super(Slice, self).__init__(logp, start, None, grad_logp_flag=False,\n **kwargs)\n self._num_evals = 0\n\n # sampler this is either a random direction or component-wise slice sampler\n self.compwise = compwise\n self.width = width\n self.step_out = step_out\n self.doubling_step = doubling_step\n self.max_steps_out = max_steps_out\n self.verbose = verbose\n\n def step(self):\n \"\"\" Perform a slice sample step \"\"\"\n dims = self.state.tovector().shape[0]\n if self.compwise:\n ordering = np.arange(dims)\n np.random.shuffle(ordering)\n new_x = self.state.tovector.copy()\n for d in ordering:\n direction = np.zeros((dims))\n direction[d] = 1.0\n new_x = self.direction_slice(direction, new_x)\n else:\n direction = np.random.randn(dims)\n direction = direction / np.sqrt(np.sum(direction**2))\n new_x = self.direction_slice(direction, self.state.tovector())\n\n self.state = self.state.fromvector(new_x)\n self._sampled += 1\n return self.state\n\n def direction_slice(self, direction, init_x):\n \"\"\" one dimensional directional slice sample along direction specified\n Implements the stepping out procedure from Neal\n \"\"\"\n def dir_logprob(z):\n self._num_evals += 1\n cstate = State.init_fromvector(direction*z + init_x, self.state)\n return self.model.logp(cstate)\n\n def acceptable(z, llh_s, L, U):\n while (U-L) > 1.1*self.width:\n middle = 0.5*(L+U)\n splits = (middle > 0 and z >= middle) or (middle <= 0 and z < middle)\n if z < middle:\n U = middle\n else:\n L = middle\n # Probably these could be cached from the stepping out.\n if splits and llh_s >= dir_logprob(U) and llh_s >= dir_logprob(L):\n return False\n return True\n\n upper = self.width*np.random.rand()\n lower = upper - self.width\n llh_s = np.log(np.random.rand()) + dir_logprob(0.0)\n\n l_steps_out = 0\n u_steps_out = 0\n if self.step_out:\n if self.doubling_step:\n while (dir_logprob(lower) > llh_s or\n dir_logprob(upper) > llh_s) and \\\n (l_steps_out + u_steps_out) < self.max_steps_out:\n if np.random.rand() < 0.5:\n l_steps_out += 1\n lower -= (upper-lower)\n else:\n u_steps_out += 1\n upper += (upper-lower)\n else:\n while dir_logprob(lower) > llh_s and \\\n l_steps_out < max_steps_out:\n l_steps_out += 1\n lower -= self.width\n while dir_logprob(upper) > llh_s and \\\n u_steps_out < max_steps_out:\n u_steps_out += 1\n upper += self.width\n\n start_upper = upper\n start_lower = lower\n\n steps_in = 0\n while True:\n steps_in += 1\n new_z = (upper - lower)*np.random.rand() + lower\n new_llh = dir_logprob(new_z)\n if np.isnan(new_llh):\n print(new_z, direction*new_z + init_x, new_llh,\n llh_s, init_x, dir_logprob(init_x))\n raise Exception(\"Slice sampler got a NaN\")\n if new_llh > llh_s and \\\n acceptable(new_z, llh_s, start_lower, start_upper):\n break\n elif new_z < 0:\n lower = new_z\n elif new_z > 0:\n upper = new_z\n else:\n raise Exception(\"Slice sampler shrank to zero!\")\n\n if self.verbose:\n print(\"Steps Out:\", l_steps_out, u_steps_out, \" Steps In:\", steps_in)\n\n return new_z*direction + init_x\n\n @property\n def evals_per_sample(self):\n return self._num_evals/float(self._sampled)\n\n def __repr__(self):\n return 'Slice sampler'\n\n\n" }, { "path": "sampyl/starting.py", "content": "\"\"\"\nsampyl.starting\n~~~~~~~~~~~~~~~~~~~~\n\nModule for calculating the maximum a posteriori for use in a starting\nvalue for the samplers.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\nfrom .core import np, AUTOGRAD, auto_grad_logp\nfrom scipy.optimize import minimize\nfrom .state import State\n\n\ndef find_MAP(logp, start, grad_logp=None,\n method=None, bounds=None, verbose=False, **kwargs):\n\n \"\"\" Find the maximum a posteriori of logp. Requires a starting state.\n Optimizing is done with scipy.optimize.minimize. Documentation can be\n found here:\n http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html\n\n Arguments\n ---------\n logp: function\n log P(X) function for sampling distribution\n start: dict\n Dictionary of starting state for the optimizer. Should have one\n element for each argument of logp. So, if logp = f(x, y), then\n start = {'x': x_start, 'y': y_start}\n\n Keyword Arguments\n -----------------\n grad_logp: function\n grad log P(X) function for calculating the gradient. Uses autograd\n automatically if it is installed. Otherwise, you can pass in a\n gradient function to the optimizer.\n method: string\n Optimizing method, one of these or a callable function:\n 'Nelder-Mead'\n 'Powell'\n 'CG'\n 'BFGS'\n 'Newton-CG'\n 'Anneal (deprecated as of scipy version 0.14.0)'\n 'L-BFGS-B'\n 'TNC'\n 'COBYLA'\n 'SLSQP'\n 'dogleg'\n 'trust-ncg'\n custom - a callable object (added in version 0.14.0)\n If not given, chosen to be one of BFGS, L-BFGS-B, SLSQP, depending \n if the problem has constraints or bounds.\n bounds: dict of tuples\n Tuples of bounding values for each parameter, example:\n bounds = {'x': (low, high), 'y': (low, high)}\n Use None if not bounded in a direction, ex: {'x': (0, None)}\n verbose: boolean\n Set to True to print out optimization information\n\n \"\"\"\n\n # Making sure to get the keys from logp function so that arguments are\n # ordered correctly, then update from starting state.\n state = State.fromfunc(logp)\n state.update(start)\n\n # We find the MAP by minimizing, we need to negate the logp function\n def neg_logp(x):\n # Because the minimize function passes a single array, we need to put\n # it back into a state form so we can pass each variable to logp if\n # there are multiple variables\n args = state.fromvector(x)\n return -1*logp(*args.values())\n\n if AUTOGRAD and grad_logp is None:\n jac = auto_grad_logp(neg_logp)['x']\n else:\n jac = grad_logp\n\n # Formatting bounds correctly to be passed to the minimize function\n # If a variable is an array, you would have to manually write out tuples\n # for each element in that array. Here, you can define one bound, and it\n # is expanded out to the size of the variable.\n if bounds is not None:\n bnds = []\n for var in bounds:\n bnds.extend([bounds[var]]*state.size()[var])\n else:\n bnds = None\n\n results = minimize(neg_logp, state.tovector(),\n jac=jac, method=method, bounds=bnds, **kwargs)\n\n if verbose:\n print(results)\n\n return state.fromvector(results.x)\n" }, { "path": "sampyl/state.py", "content": "\"\"\"\nsampyl.state\n~~~~~~~~~~~~~~~~~~~~\n\nModule for State object which stores sampler states in a dictionary.\n\n:copyright: (c) 2015 by Mat Leonard.\n:license: MIT, see LICENSE for more details.\n\n\"\"\"\n\nfrom __future__ import division\n\nimport sampyl\nfrom sampyl.core import np\nimport collections\n\n\nclass State(collections.OrderedDict):\n \"\"\" State object for storing parameter values.\n \n Inherits from OrderedDict.\n\n \"\"\"\n\n def tovector(self):\n \"\"\" Return the parameter values as a flat vector. \"\"\"\n return np.hstack(self.values())\n\n def fromvector(self, vec):\n \"\"\" Update the state using a numpy array. \n\n :param vec: np.array for updating the state.\n \"\"\"\n var_sizes = self.size()\n i = 0\n for var in self:\n self[var] = np.squeeze(vec[i:(i+var_sizes[var])])\n i += var_sizes[var]\n return self\n\n def freeze(self):\n \"\"\" Return a immutable tuple of the state values.\"\"\"\n return tuple(self.tovector())\n\n @staticmethod\n def init_fromvector(vec, state):\n \"\"\n vals = []\n var_sizes = state.size()\n i = 0\n for var in state:\n vals.append(np.squeeze(vec[i:(i+var_sizes[var])]))\n i += var_sizes[var]\n return State(zip(state.keys(), vals))\n\n @staticmethod\n def fromfunc(func):\n \"\"\" Initialize a State from the arguments of a function \"\"\"\n var_names = func_var_names(func)\n return State.fromkeys(var_names)\n\n def size(self):\n return State([(var, np.size(self[var])) for var in self])\n\n def __add__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n return handle_number(self, other, '__add__')\n elif isinstance(other, collections.Iterable):\n return handle_iterable(self, other, '__add__')\n else:\n raise TypeError(\"Addition not supported for State and {}\".format(other))\n\n def __sub__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n return handle_number(self, other, '__sub__')\n elif isinstance(other, collections.Iterable):\n return handle_iterable(self, other, '__sub__')\n else:\n raise TypeError(\"Subtraction not supported for State and {}\".format(other))\n\n def __mul__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n return handle_number(self, other, '__mul__')\n else:\n raise TypeError(\"Multiplication not supported for State and {}\".format(other))\n\n def __truediv__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n return handle_number(self, other, '__truediv__')\n else:\n raise TypeError(\"Division not supported for State and {}\".format(other))\n\n def __radd__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n # Commutative, so nothing changes\n return self + other\n else:\n raise TypeError(\"Can only broadcast from the left.\")\n\n def __rmul__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n # Commutative, so nothing changes\n return self * other\n else:\n raise TypeError(\"Can only broadcast from the left.\")\n\n def __rsub__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n return handle_number(self, other, '__rsub__')\n elif isinstance(other, collections.Iterable):\n return handle_iterable(self, other, '__rsub__')\n else:\n raise TypeError(\"Subtraction not supported for State and {}\".format(other))\n\n def __rtruediv__(self, other):\n if isinstance(other, int) or isinstance(other, float):\n return handle_number(self, other, '__truediv__')\n else:\n raise TypeError(\"Division not supported for State and {}\".format(other))\n\n\ndef handle_number(state, other, operator):\n vals = [getattr(state[var], operator)(other) for var in state]\n try:\n if NotImplemented in vals:\n vals = [getattr(other, operator)(state[var]) for var in state]\n except ValueError:\n pass\n return State([(var, val) for var, val in zip(state, vals)])\n\n\ndef handle_iterable(state, other, operator):\n if len(other) != len(state):\n # This might be the case:\n # State({'x': np.array(1, 2, 3)}) + np.array([2,3,4])\n # So check if both are numpy arrays, then add\n # But first, we can only do this is len(state) is 1.\n if len(state) != 1:\n raise ValueError(\"Can't broadcast with sizes state: {},\"\n \" other: {}\".format(len(state), len(other)))\n var = list(state.keys())[0]\n val = state[var]\n if type(val) == np.ndarray and type(other) == np.ndarray:\n return State([(var, getattr(val, operator)(other))])\n else:\n raise ValueError(\"Can only operate on numpy arrays.\")\n if isinstance(other, dict):\n vals = [getattr(state[var], operator)(other[var]) for var in state]\n else:\n # Otherwise, we have cases like\n # State({'x': foo, 'y': bar}) + [foo2, bar2]\n vals = [getattr(state[var], operator)(each) for var, each in zip(state, other)]\n return State([(var, val) for var, val in zip(state, vals)])\n\ndef special_math_func(state, other, operator):\n \"\"\" A function for special math functions used in the State class.\n So, we need to handle state + 1, state + np.array(),\n state1 + state2, etc. basically we want to do the same thing\n every time but with different operators.\n \"\"\"\n new = State([(var, vals) for var, each in zip(state, vals)])\n\n return new\n\n\ndef func_var_names(func):\n \"\"\" Returns a list of the argument names in func \"\"\"\n names = func.__code__.co_varnames[:func.__code__.co_argcount]\n return names" }, { "path": "sampyl/stats.py", "content": "\"\"\" Module for statistical \"\"\"\n\nfrom .core import np\n\n\ndef hpd(trace):\n pass\n\ndef percentile(trace):\n pass\n\ndef autocorrrelation(trace):\n pass" }, { "path": "sampyl/tests/__init__.py", "content": "from . import logps\n" }, { "path": "sampyl/tests/logps.py", "content": "from __future__ import division\n\nfrom ..core import np, auto_grad_logp\nimport sampyl as smp\n\n__all__ = ['normal_1D_logp', 'normal_1D_grad_logp', 'normal_logp',\n 'poisson_logp', 'poisson_with_grad', 'linear_model_logp']\n\nmu, sig = 3, 2\ndef normal_1D_logp(x):\n return -0.5*np.log(2*np.pi) - 0.5*np.log(sig**2) - \\\n np.sum((x - mu)**2)/(2*sig**2)\n\n\ndef normal_1D_grad_logp(x=0.):\n return -2*(x - mu)/(2*sig**2)\n\n\nmu, sig = 10, 3\ndata = (np.random.randn(20)*sig + mu)\nn = len(data)\ndef normal_logp(mu, sig):\n likelihood = -n*0.5*np.log(sig**2) - \\\n np.sum((data - mu)**2)/(2*sig**2)\n mu_prior = smp.uniform(mu, 5, 15)\n sig_prior = -np.log(np.abs(sig))\n return likelihood + mu_prior + sig_prior\n\n###### Poisson model ######\nbefore = np.random.poisson(7, size=12)\nafter = np.random.poisson(9, size=12)\ndef poisson_logp(lam1, lam2):\n # Rates for Poisson must be > 0\n if lam1 <= 0 or lam2 <=0:\n return -np.inf\n else:\n # logps for likelihoods\n llh1 = np.sum(before*np.log(lam1)) - before.size*lam1\n llh2 = np.sum(after*np.log(lam2)) - after.size*lam2\n\n # logps for priors\n lam1_prior = -lam1\n lam2_prior = -lam2\n return llh1 + llh2 + lam1_prior + lam2_prior\n\n\ndef poisson_with_grad(lam1, lam2):\n grad_logp = auto_grad_logp(poisson_logp)\n grad = np.array([grad_logp[each](lam1, lam2) for each in ['lam1', 'lam2']])\n return poisson_logp(lam1, lam2), grad\n\n###### Linear model ##########\ntrue_b = np.random.randn(5)\nx = np.random.rand(5, 10)\ndata = np.dot(true_b, x)\ndef linear_model_logp(b, sig):\n if smp.outofbounds(sig > 0):\n return -np.inf\n mu = np.dot(b, x)\n n = len(data)\n likelihood = -n*0.5*np.log(2*np.pi) - \\\n n*0.5*np.log(sig**2) - \\\n np.sum((data - mu)**2)/(2*sig**2)\n prior_sig = -np.log(np.abs(sig))\n prior_b = smp.uniform(b, lower=-5, upper=10)\n return likelihood + prior_sig + prior_b\n" }, { "path": "sampyl/tests/test_samplers.py", "content": "from ..core import np\nfrom ..exceptions import *\nfrom .logps import *\nimport sampyl as smp\nimport pytest\n\n#TODO: Make tests to check correctness of samplers\n\nnp_source = np.__package__\n\nn_samples = 100\n\ndef test_logp_with_grad():\n logp = poisson_with_grad\n start = {'lam1':1., 'lam2': 1.}\n nuts = smp.NUTS(logp, start, grad_logp=True)\n chain = nuts.sample(n_samples)\n\n assert(len(chain)==n_samples)\n\ndef test_parallel_lin_model():\n\n logp = linear_model_logp\n start = {'b':np.zeros(5), 'sig': 1.}\n metro = smp.Metropolis(logp, start)\n nuts = smp.NUTS(logp, start)\n\n metro_chains = metro.sample(n_samples, n_chains=2)\n nuts_chains = nuts.sample(n_samples, n_chains=2)\n\n assert(len(metro_chains) == 2)\n assert(len(nuts_chains) == 2)\n\n\ndef test_parallel_2D():\n\n start = {'lam1': 1., 'lam2': 1.}\n metro = smp.Metropolis(poisson_logp, start)\n nuts = smp.NUTS(poisson_logp, start)\n\n metro_chains = metro.sample(n_samples, n_chains=2)\n nuts_chains = nuts.sample(n_samples, n_chains=2)\n\n assert(len(metro_chains) == 2)\n assert(len(nuts_chains) == 2)\n\n\ndef test_sample_chain():\n start = {'lam1': 1., 'lam2': 1.}\n step1 = smp.Metropolis(poisson_logp, start, condition=['lam2'])\n step2 = smp.NUTS(poisson_logp, start, condition=['lam1'])\n\n chain = smp.Chain([step1, step2], start)\n trace = chain.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_conditional_chain():\n\n logp = poisson_logp\n start = {'lam1': 1., 'lam2': 2.}\n metro = smp.Metropolis(logp, start, condition=['lam2'])\n nuts = smp.NUTS(logp, start, condition=['lam1'])\n\n state = metro._conditional_step()\n assert(state['lam2'] == 2.)\n nuts.state.update(state)\n state = nuts._conditional_step()\n assert(len(state) == 2)\n\n\ndef test_conditional():\n logp = poisson_logp\n start = {'lam1': 1., 'lam2': 2.}\n metro = smp.Metropolis(logp, start, condition=['lam2'])\n state = metro._conditional_step()\n assert(len(state) == 2)\n assert(state['lam2'] == 2.)\n\ndef test_metropolis_linear_model():\n logp = linear_model_logp\n start = {'b':np.zeros(5), 'sig': 1.}\n metro = smp.Metropolis(logp, start)\n trace = metro.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_hamiltonian_linear_model():\n logp = linear_model_logp\n start = {'b': np.zeros(5), 'sig': 1.}\n hmc = smp.Hamiltonian(logp, start)\n trace = hmc.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_nuts_linear_model():\n logp = linear_model_logp\n start = {'b': np.zeros(5), 'sig': 1.}\n nuts = smp.NUTS(logp, start)\n trace = nuts.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_metropolis():\n logp = normal_1D_logp\n start = {'x': 1.}\n metro = smp.Metropolis(logp, start)\n trace = metro.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_hmc_autograd():\n logp = normal_1D_logp\n start = {'x': 1.}\n if np_source == 'autograd.numpy':\n hmc = smp.Hamiltonian(logp, start)\n trace = hmc.sample(n_samples)\n assert(trace.shape == (n_samples,))\n elif np_source == 'numpy':\n with pytest.raises(AutogradError):\n hmc = smp.Hamiltonian(logp, start)\n\n\ndef test_hmc_pass_grad_logp():\n logp, grad_logp = normal_1D_logp, normal_1D_grad_logp\n start = {'x': 1.}\n hmc = smp.Hamiltonian(logp, start, grad_logp=grad_logp)\n trace = hmc.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_NUTS_autograd():\n logp = normal_1D_logp\n start = {'x': 1.}\n if np_source == 'autograd.numpy':\n nuts = smp.NUTS(logp, start)\n trace = nuts.sample(n_samples)\n assert(trace.shape == (n_samples,))\n elif np_source == 'numpy':\n with pytest.raises(AutogradError):\n nuts = smp.NUTS(logp, start)\n\n\ndef test_NUTS_pass_grad_logp():\n logp, grad_logp = normal_1D_logp, normal_1D_grad_logp\n start = {'x': 1.}\n nuts = smp.NUTS(logp, start, grad_logp=grad_logp)\n trace = nuts.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_sampler_num_logp():\n logp = 1.\n start = {'x': None}\n with pytest.raises(TypeError):\n metro = smp.Metropolis(logp, start)\n\n\ndef test_sampler_no_args_logp():\n def logp():\n return x\n start = {'x': None}\n with pytest.raises(ValueError):\n metro = smp.Metropolis(logp, start)\n\n\ndef test_metropolis_two_vars():\n logp = poisson_logp\n start = {'lam1':1., 'lam2':1.}\n metro = smp.Metropolis(logp, start)\n trace = metro.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\ndef test_metropolis_two_vars_start():\n logp = poisson_logp\n start = {'lam1':1., 'lam2':1.}\n metro = smp.Metropolis(logp, start)\n trace = metro.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\ndef test_slice():\n logp = normal_1D_logp\n start = {'x': 1.}\n slice = smp.Slice(logp, start)\n trace = slice.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\ndef test_slice_two_vars():\n logp = poisson_logp\n start = {'lam1': 1., 'lam2': 1.}\n slice = smp.Slice(logp, start)\n trace = slice.sample(n_samples)\n assert(trace.shape == (n_samples,))\n\n\n\n" }, { "path": "sampyl/tests/test_starting.py", "content": "from ..core import np\nfrom .logps import *\nimport sampyl as smp\nimport pytest\n\n\ndef test_1d_MAP():\n logp = normal_1D_logp\n start = {'x': 1.}\n state = smp.find_MAP(logp, start)\n" }, { "path": "sampyl/tests/test_state.py", "content": "from ..core import np\nfrom ..state import State\n\nstate1 = State([('x', 1)])\nstate2 = State([('x', np.array([1, 2, 3]))])\nstate3 = State([('x', np.array([1,2,3])), ('y', 1)])\nstate4 = State([('x', np.array([2.]))])\nstate5 = State([('x', np.array([2,1,4])), ('y', 2)])\n\ndef test_add_states():\n new = state3 + state5\n print(new)\n assert(type(new) == State)\n assert(np.all(new['x'] == np.array([3, 3, 7])))\n assert(new['y'] == 3)\n\ndef test_add_list():\n new = state3 + [np.array([2, 3, 4]), 2]\n assert(type(new) == State)\n assert(np.all(new['x'] == np.array([3, 5, 7])))\n assert(new['y'] == 3)\n\ndef test_add_multi_array():\n new = state2 + np.array([2, 3, 4])\n assert(type(new) == State)\n assert(np.all(new['x'] == np.array([3, 5, 7])))\n\ndef test_add_single_array():\n new = state4 + np.array([1.])\n assert(type(new) == State)\n assert(len(new) == 1)\n assert(new['x'] == np.array([3.]))\n\ndef test_add_int():\n new = state1 + 1\n assert(type(new)==State)\n assert(new['x'] == 2)\n\ndef test_add_float():\n new = state1 + 1.\n assert(type(new)==State)\n assert(new['x'] == 2.)\n\ndef test_radd_int():\n new = 1 + state1\n assert(type(new)==State)\n assert(new['x'] == 2)\n\ndef test_radd_float():\n new = 1. + state1\n assert(type(new)==State)\n assert(new['x'] == 2.)\n\ndef test_mul_int():\n new = state1 * 2\n assert(type(new)==State)\n assert(new['x'] == 2)\n\ndef test_mul_float():\n new = state1 * 2.\n assert(type(new)==State)\n assert(new['x'] == 2.)\n\ndef test_rmul_int():\n new = 2 * state1\n assert(type(new)==State)\n assert(new['x'] == 2)\n\ndef test_rmul_float():\n new = 2. * state1\n assert(type(new)==State)\n assert(new['x'] == 2.)" }, { "path": "setup.py", "content": "#!/usr/bin/env python\n\nimport setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\n\nclassifiers = ['Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.6',\n 'License :: OSI Approved :: MIT License',\n 'Intended Audience :: Science/Research',\n 'Topic :: Scientific/Engineering',\n 'Topic :: Scientific/Engineering :: Mathematics',\n 'Operating System :: OS Independent']\n\n\nsetuptools.setup(name='sampyl-mcmc',\n version='0.3',\n description='MCMC Samplers in Python & Numpy',\n author='Mat Leonard',\n author_email='leonard.mat@gmail.com',\n url='http://matatat.org/sampyl/',\n packages=['sampyl', 'sampyl.samplers'],\n classifiers=classifiers,\n install_requires=['numpy', 'scipy', 'autograd'])\n" } ]