Avocado Training Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avocado Training Demo\n",
    "\n",
    "Avocado is a multi-scale deep tensor factorization model that learns a latent representation of the human epigenome. It does so by modeling epigenomics experiments, such as those contained within the Roadmap compendium, as a tensor with three orthogonal axes---epigenomics assays (such as ChIP-seq, DNase-seq, etc), human cell types, and genomic coordinates. The model then decomposes the tensor such that each component has its own set of latent factors. These factors are jointly trained with a neural network model that combines the factors in a non-linear manner on the task of predicting epigenomic signal from the latent factors. When training is complete, one can either use latent factors and the neural network to impute epigenomic experiments that have not yet been performed, or use the learned latent factors by themselves for downstream genomics analyses in the place of epigenomic data itself. The following schematic describes the model.\n",
    "\n",
    "<img src=\"figures/Avocado-Schematic.png\" style=\"width:66%\">\n",
    "\n",
    "This tutorial will demonstrate how someone can use the provided Avocado code-base in order to train their own models on their own sets of data. The full Avocado model utilized a cluster of GPUs for many hours to perform training, and so we will focus on a scaled-down version of the model restricted to the ENCODE Pilot Regions. Should a user be running this tutorial on a machine that has a GPU, uncomment the following lines in order to enable GPU acceleration. By default it will use the first cuda enabled device, but by changing the number after `cuda` one can specify any GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import os, sys   \n",
    "#os.environ['THEANO_FLAGS'] = \"device=cuda0\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before beginning, let's output the package versions and computer specifics that this tutorial was run with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jmschr/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using Theano backend.\n",
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Oct 05 2018 \n",
      "\n",
      "numpy 1.14.2\n",
      "pandas 0.23.4\n",
      "keras 2.1.6\n",
      "theano 1.0.1\n",
      "sklearn 0.20.0\n",
      "joblib 0.12.4\n",
      "tqdm 4.24.0\n",
      "xgboost 0.7.post3\n",
      "matplotlib 2.1.0\n",
      "seaborn 0.8.0\n",
      "\n",
      "compiler   : GCC 7.2.0\n",
      "system     : Linux\n",
      "release    : 4.15.0-34-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 4\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -m -p numpy,pandas,keras,theano,sklearn,joblib,tqdm,xgboost,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load up some basic utility modules and plotting libraries that we'll use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn; seaborn.set_style('whitegrid')\n",
    "import itertools\n",
    "import numpy; numpy.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using the Avocado code-base, one needs only load the Avocado model from the `model` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from avocado import Avocado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Avocado model is a thin wrapper over a keras defined model that is fairly simple to define. Here is the script that builds the model internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model(n_celltypes, n_celltype_factors, n_assays, n_assay_factors,\n",
    "    n_genomic_positions, n_25bp_factors, n_250bp_factors, n_5kbp_factors,\n",
    "    n_layers, n_nodes, freeze_celltypes=False, freeze_assays=False,\n",
    "    freeze_genome_25bp=False, freeze_genome_250bp=False, \n",
    "    freeze_genome_5kbp=False, freeze_network=False):\n",
    "    \"\"\"This function builds a multi-scale deep tensor factorization model.\"\"\"\n",
    "\n",
    "    celltype_input = Input(shape=(1,), name=\"celltype_input\")\n",
    "    celltype_embedding = Embedding(n_celltypes, n_celltype_factors, \n",
    "        input_length=1, name=\"celltype_embedding\")\n",
    "    celltype_embedding.trainable = not freeze_celltypes\n",
    "    celltype = Flatten()(celltype_embedding(celltype_input))\n",
    "\n",
    "    assay_input = Input(shape=(1,), name=\"assay_input\")\n",
    "    assay_embedding = Embedding(n_assays, n_assay_factors, \n",
    "        input_length=1, name=\"assay_embedding\")\n",
    "    assay_embedding.trainable = not freeze_assays\n",
    "    assay = Flatten()(assay_embedding(assay_input))\n",
    "\n",
    "    genome_25bp_input = Input(shape=(1,), name=\"genome_25bp_input\")\n",
    "    genome_25bp_embedding = Embedding(n_genomic_positions, n_25bp_factors, \n",
    "        input_length=1, name=\"genome_25bp_embedding\")\n",
    "    genome_25bp_embedding.trainable = not freeze_genome_25bp\n",
    "    genome_25bp = Flatten()(genome_25bp_embedding(genome_25bp_input))\n",
    "\n",
    "    genome_250bp_input = Input(shape=(1,), name=\"genome_250bp_input\")\n",
    "    genome_250bp_embedding = Embedding((n_genomic_positions / 10) + 1,\n",
    "        n_250bp_factors, input_length=1, name=\"genome_250bp_embedding\")\n",
    "    genome_250bp_embedding.trainable = not freeze_genome_250bp\n",
    "    genome_250bp = Flatten()(genome_250bp_embedding(genome_250bp_input))\n",
    "\n",
    "    genome_5kbp_input = Input(shape=(1,), name=\"genome_5kbp_input\")\n",
    "    genome_5kbp_embedding = Embedding((n_genomic_positions / 200) + 1, \n",
    "        n_5kbp_factors, input_length=1, name=\"genome_5kbp_embedding\")\n",
    "    genome_5kbp_embedding.trainable = not freeze_genome_5kbp\n",
    "    genome_5kbp = Flatten()(genome_5kbp_embedding(genome_5kbp_input))\n",
    "\n",
    "    layers = [celltype, assay, genome_25bp, genome_250bp, genome_5kbp]\n",
    "    inputs = (celltype_input, assay_input, genome_25bp_input, \n",
    "        genome_250bp_input, genome_5kbp_input)\n",
    "\n",
    "    x = concatenate(layers)\n",
    "    for i in range(n_layers):\n",
    "        layer = Dense(n_nodes, activation='relu', name=\"dense_{}\".format(i))\n",
    "        layer.trainable = not freeze_network\n",
    "        x = layer(x)\n",
    "\n",
    "    layer = Dense(1, name=\"y_pred\")\n",
    "    layer.trainable = not freeze_network\n",
    "    y = layer(x)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=y)\n",
    "    model.compile(optimizer='adam', loss='mse', metrics=['mse'])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of lines correspond to setting up the inputs appropriately since we're using embedding layers as the tensor factorization component.\n",
    "\n",
    "### Training the model\n",
    "\n",
    "Now we're ready to go! The first thing we need to do is define the cell type and assay dimensions. We give the names of the cell types and assays that we want to model and require that each cell type has at least one assay in the training set and that each assay has been performed in at least one cell type in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "celltypes = ['E003', 'E017', 'E065', 'E116', 'E117']\n",
    "assays = ['H3K4me3', 'H3K27me3', 'H3K36me3', 'H3K9me3', 'H3K4me1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to create the data structure that we used to feed the training data into the model. We use a dictionary, `data`, where the keys are a tuple of `(celltype, assay)` and the value is a numpy array that contains the signal values from that epigenomic experiment. These values are assumed to be at 25 bp resolution, though any resolution can be modeled as long as the user is okay with one set of factors being learned at 1/10th the resolution (normally the 250 bp factors) and another set being learned at 1/200th the resolution (normally the 5 kbp factors). We found that the best way to encode these tracks is in a compressed numpy array (ending in .npz) that can be unpacked using `numpy.load(X)['arr_0']` instead of just `numpy.load(X)`. If the user is using uncompressed files (ending in .npy) they should use `numpy.load(X)`.\n",
    "\n",
    "Our example data contains five assays that have been performed in five cell types, for a total of 25 epigenomic experiments. We will train our model on 24 and see how well it can impute the held out experiment, H3K4me3 signal from E065 (Aorta)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {}\n",
    "for celltype, assay in itertools.product(celltypes, assays):\n",
    "    if celltype == 'E065' and assay == 'H3K4me3':\n",
    "        continue\n",
    "        \n",
    "    filename = 'data/{}.{}.pilot.arcsinh.npz'.format(celltype, assay)\n",
    "    data[(celltype, assay)] = numpy.load(filename)['arr_0']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to build our model. Let's first look at the arguments that we can pass in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Avocado in module avocado.model:\n",
      "\n",
      "class Avocado(__builtin__.object)\n",
      " |  An Avocado multi-scale deep tensor factorization model.\n",
      " |  \n",
      " |  The Avocado model is a multi-scale deep tensor factorization model. It is\n",
      " |  multi-scale because it represents the genome axis using three different\n",
      " |  resolutions---25 bp, 250 bp and 5 kbp. It is deep because it replaces the\n",
      " |  dot product component of most linear factorization approaches with a deep\n",
      " |  neural network. The tensor factors and the neural network weights are\n",
      " |  trained jointly to impute the values in the tensor that it is provided.\n",
      " |  \n",
      " |  In this case Avocado is trained on epigenomic data whose dimensions are\n",
      " |  human cell type, epigenomic assay, and genomic coordinate. The trained\n",
      " |  model can impute epigenomic assays that have not yet been performed, and\n",
      " |  the learned factor values can themselves be used to represent genomic\n",
      " |  positions more compactly than the full set of epigenomic measurements\n",
      " |  could.\n",
      " |  \n",
      " |  The default parameters are those used in the manuscript entitled \n",
      " |  \"Multi-scale deep tensor factorization learns a latent representation\n",
      " |  of the human epigenome\". \n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  celltypes : list\n",
      " |          The list of cell type names that will be modeled\n",
      " |  \n",
      " |  assays : list\n",
      " |          The list of assays that will be modeled\n",
      " |  \n",
      " |  n_celltype_factors : int, optional\n",
      " |          The number of factors to use to represent each cell type. Default is 32.\n",
      " |  \n",
      " |  n_assay_factors : int, optional\n",
      " |          The number of factors to use to represent each assay. Default is 256.\n",
      " |  \n",
      " |  n_genomic_positions : int, optional\n",
      " |          The number of genomic positions to model. This is typically either\n",
      " |          the size of the pilot regions when performing initial training or\n",
      " |          the size of the chromosome when fitting the genomic latent factors.\n",
      " |          Default is 1126469, the size of the pilot regions in chr1-22.\n",
      " |  \n",
      " |  n_25bp_factors : int, optional\n",
      " |          The number of factors to use to represent the genome at 25 bp\n",
      " |          resolution. Default is 25.\n",
      " |  \n",
      " |  n_250bp_factors : int, optional\n",
      " |          The number of factors to use to represent the genome at 250 bp\n",
      " |          resolution. Default is 40.\n",
      " |  \n",
      " |  n_5kbp_factors : int, optional\n",
      " |          The number of factors to use to represent the genome at 5 kbp\n",
      " |          resolution. Default is 45.\n",
      " |  \n",
      " |  n_layers : int, optional\n",
      " |          The number of hidden layers in the neural model. Default is 2.\n",
      " |  \n",
      " |  n_nodes : int, optional\n",
      " |          The number of nodes per layer. Default is 2048.\n",
      " |  \n",
      " |  batch_size : int, optional\n",
      " |          The size of each batch to use in training. Defaut is 40000.\n",
      " |  \n",
      " |  freeze_celltypes : bool, optional\n",
      " |          Whether to freeze the training of the cell type embedding. Default\n",
      " |          is False.\n",
      " |  \n",
      " |  freeze_assays : bool, optional\n",
      " |          Whether to freeze the training of the assay embeddings. Default\n",
      " |          is False.\n",
      " |  \n",
      " |  freeze_genome_25bp : bool, optional\n",
      " |          Whether to freeze the training of the 25 bp genome factors. Default\n",
      " |          is False.\n",
      " |  \n",
      " |  freeze_genome_250bp : bool, optional\n",
      " |          Whether to freeze the training of the 250 bp genome factors. Default\n",
      " |          is False.\n",
      " |  \n",
      " |  freeze_genome_5kbp : bool, optional\n",
      " |          Whether to freeze the training of the 5 kbp genome factors. Default\n",
      " |          is False.\n",
      " |  \n",
      " |  freeze_network : bool, optional\n",
      " |          Whether to freeze the training of the neural network. Default\n",
      " |          is False.\n",
      " |  \n",
      " |  Example\n",
      " |  -------\n",
      " |  >>> import numpy, itertools\n",
      " |  >>> from avocado import Avocado\n",
      " |  >>>\n",
      " |  >>> celltypes = ['E003', 'E017', 'E065', 'E116', 'E117']\n",
      " |  >>> assays = ['H3K4me3', 'H3K27me3', 'H3K36me3', 'H3K9me3', 'H3K4me1']\n",
      " |  >>> \n",
      " |  >>> data = {}\n",
      " |  >>> for celltype, assay in itertools.product(celltypes, assays):\n",
      " |  >>>    filename = 'data/{}.{}.pilot.arcsinh.npz'.format(celltype, assay)\n",
      " |  >>>    data[(celltype, assay)] = numpy.load(filename)['arr_0']\n",
      " |  >>>\n",
      " |  >>> model = Avocado(celltypes, assays)\n",
      " |  >>> model.fit(data)\n",
      " |  >>>\n",
      " |  >>> track = model.predict(\"E065\", \"H3K27me3\")\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, celltypes, assays, n_celltype_factors=32, n_assay_factors=256, n_genomic_positions=1126469, n_25bp_factors=25, n_250bp_factors=40, n_5kbp_factors=45, n_layers=2, n_nodes=2048, batch_size=40000, freeze_celltypes=False, freeze_assays=False, freeze_genome_25bp=False, freeze_genome_250bp=False, freeze_genome_5kbp=False, freeze_network=False)\n",
      " |  \n",
      " |  fit(self, X_train, X_valid=None, n_epochs=200, epoch_size=120, verbose=1, callbacks=None, **kwargs)\n",
      " |      Fit the model to the given epigenomic tracks.\n",
      " |      \n",
      " |      Pass in a dictionary of training data and an optional dictionary of\n",
      " |      validation data. The keys to this dictionary are a tuple of the format\n",
      " |      (celltype, assay) and the values are the corresponding track in the\n",
      " |      form of a numpy array. The tracks can either be in the form of an array\n",
      " |      that is in memory or as a memory map.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X_train : dict\n",
      " |              A dictionary of training data values, where the keys are a tuple of\n",
      " |              (celltype, assay) and the values are a track.\n",
      " |      \n",
      " |      X_valid : dict or None, optional\n",
      " |              A dictionary of validation data values that are used to calculate\n",
      " |              validation set MSE during the training process. If None, validation\n",
      " |              set statistics are not calculated during the training process.\n",
      " |              Default is None.\n",
      " |      \n",
      " |      n_epochs : int, optional\n",
      " |              The number of epochs to train on before ending training. Default is 120.\n",
      " |      \n",
      " |      epoch_size : int, optional\n",
      " |              The number of batches per epoch. Default is 200.\n",
      " |      \n",
      " |      verbose: int, optional\n",
      " |              The verbosity level of training. Must be one of 0, 1, or 2, where 0\n",
      " |              means silent, 1 means progress bar, and 2 means use only one line\n",
      " |              per epoch.\n",
      " |      \n",
      " |      callbacks : list or None, optional\n",
      " |              A list of keras callback instances to be called during training. \n",
      " |      \n",
      " |      **kwargs : optional\n",
      " |              Any other keyword arguments to be passed into the `fit_generator`\n",
      " |              method.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      history : keras.History.history\n",
      " |              The keras history object that records training loss values and\n",
      " |              metric values.\n",
      " |  \n",
      " |  fit_assays(self, X_train, X_valid=None, n_epochs=200, epoch_size=120, verbose=1, callbacks=None, **kwargs)\n",
      " |      Add a new assay(s) to an otherwise frozen model.\n",
      " |      \n",
      " |      This method will add a new assay to the assay embedding after\n",
      " |      freezing all of the other parameters in the model, including weights\n",
      " |      and the other assay positions. Functionally it will train a new\n",
      " |      assay embedding and return a new model whose assay embedding\n",
      " |      is the concatenation of the old assay embedding and the new one.\n",
      " |      \n",
      " |      Pass in a dictionary of training data and an optional dictionary of\n",
      " |      validation data. The keys to this dictionary are a tuple of the format\n",
      " |      (celltype, assay) and the values are the corresponding track in the\n",
      " |      form of a numpy array. The tracks can either be in the form of an array\n",
      " |      that is in memory or as a memory map. The assays provided should not\n",
      " |      appear in the model.assays attribute, but the cell types should appear\n",
      " |      in the model.celltypes attribute.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X_train : dict\n",
      " |              A dictionary of training data values, where the keys are a tuple of\n",
      " |              (celltype, assay) and the values are a track.\n",
      " |      \n",
      " |      X_valid : dict or None, optional\n",
      " |              A dictionary of validation data values that are used to calculate\n",
      " |              validation set MSE during the training process. If None, validation\n",
      " |              set statistics are not calculated during the training process.\n",
      " |              Default is None.\n",
      " |      \n",
      " |      n_epochs : int, optional\n",
      " |              The number of epochs to train on before ending training. Default is 120.\n",
      " |      \n",
      " |      epoch_size : int, optional\n",
      " |              The number of batches per epoch. Default is 200.\n",
      " |      \n",
      " |      verbose: int, optional\n",
      " |              The verbosity level of training. Must be one of 0, 1, or 2, where 0\n",
      " |              means silent, 1 means progress bar, and 2 means use only one line\n",
      " |              per epoch.\n",
      " |      \n",
      " |      callbacks : list or None, optional\n",
      " |              A list of keras callback instances to be called during training. \n",
      " |      \n",
      " |      **kwargs : optional\n",
      " |              Any other keyword arguments to be passed into the `fit_generator`\n",
      " |              method.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      history : keras.History.history\n",
      " |              The keras history object that records training loss values and\n",
      " |              metric values.\n",
      " |  \n",
      " |  fit_celltypes(self, X_train, X_valid=None, n_epochs=200, epoch_size=120, verbose=1, callbacks=None, **kwargs)\n",
      " |      Add a new cell type(s) to an otherwise frozen model.\n",
      " |      \n",
      " |      This method will add a new cell type to the cell type embedding after\n",
      " |      freezing all of the other parameters in the model, including weights\n",
      " |      and the other cell type positions. Functionally it will train a new\n",
      " |      cell type embedding and return a new model whose cell type embedding\n",
      " |      is the concatenation of the old cell type embedding and the new one.\n",
      " |      \n",
      " |      Pass in a dictionary of training data and an optional dictionary of\n",
      " |      validation data. The keys to this dictionary are a tuple of the format\n",
      " |      (celltype, assay) and the values are the corresponding track in the\n",
      " |      form of a numpy array. The tracks can either be in the form of an array\n",
      " |      that is in memory or as a memory map. The celltypes provided should not\n",
      " |      appear in the model.celltypes attribute but the assays should exclusively\n",
      " |      appear in the model.assays attribute.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X_train : dict\n",
      " |              A dictionary of training data values, where the keys are a tuple of\n",
      " |              (celltype, assay) and the values are a track.\n",
      " |      \n",
      " |      X_valid : dict or None, optional\n",
      " |              A dictionary of validation data values that are used to calculate\n",
      " |              validation set MSE during the training process. If None, validation\n",
      " |              set statistics are not calculated during the training process.\n",
      " |              Default is None.\n",
      " |      \n",
      " |      n_epochs : int, optional\n",
      " |              The number of epochs to train on before ending training. Default is 120.\n",
      " |      \n",
      " |      epoch_size : int, optional\n",
      " |              The number of batches per epoch. Default is 200.\n",
      " |      \n",
      " |      verbose: int, optional\n",
      " |              The verbosity level of training. Must be one of 0, 1, or 2, where 0\n",
      " |              means silent, 1 means progress bar, and 2 means use only one line\n",
      " |              per epoch.\n",
      " |      \n",
      " |      callbacks : list or None, optional\n",
      " |              A list of keras callback instances to be called during training. \n",
      " |      \n",
      " |      **kwargs : optional\n",
      " |              Any other keyword arguments to be passed into the `fit_generator`\n",
      " |              method.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      history : keras.History.history\n",
      " |              The keras history object that records training loss values and\n",
      " |              metric values.\n",
      " |  \n",
      " |  get_params(self)\n",
      " |  \n",
      " |  load_weights(self, name, verbose=0)\n",
      " |      Load serialized weights on a layer-by-layer case.\n",
      " |      \n",
      " |      Load the weights of a pre-saved model on a layer-by-layer case. This\n",
      " |      method will iterate through the layers of the serialized model and\n",
      " |      this model jointly and set the weights in this model to that of the\n",
      " |      serialized model should the weight matrices be of the same size. Should\n",
      " |      they not be of the same size it will not modify the current weight\n",
      " |      matrix. \n",
      " |      \n",
      " |      A primary use of this function should be after an initial model has been\n",
      " |      trained on the Pilot regions and now one is fitting a model to each of\n",
      " |      the chromosomes. The size of the genome factors will differ but the other\n",
      " |      components will remain the same. Correspondingly, the identically sized\n",
      " |      weight matrices are those that should be held constant while the differing\n",
      " |      size weight matrices should differ.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      name : str\n",
      " |              The suffix of the name of the weights file.\n",
      " |      \n",
      " |      verbose : int, optional\n",
      " |              The verbosity level when loading weights. 0 means silent, 1 means\n",
      " |              notify when a weight matrix has been set, 2 means notify what\n",
      " |              action has been taken on each layer.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      None\n",
      " |  \n",
      " |  predict(self, celltype, assay, verbose=0)\n",
      " |      Predict a track of epigenomic data.\n",
      " |      \n",
      " |      This will predict a track of epigenomic data, resulting in one signal\n",
      " |      value per genomic position modeled. Users pass in the cell type and\n",
      " |      the assay that they wish to impute and receive the track of data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      celltype : str\n",
      " |              The cell type (aka biosample) to be imputed. Must be one of the\n",
      " |              elements from the list of cell types passed in upon model\n",
      " |              initialization.\n",
      " |      \n",
      " |      assay : str\n",
      " |              The assay to be imputed. Must be one of the elements from the list\n",
      " |              of assays passed in upon model initialization.\n",
      " |      \n",
      " |      verbose : int, optional\n",
      " |              The verbosity level of the prediction. Must be 0 or 1.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      track : numpy.ndarray\n",
      " |              A track of epigenomic signal value predictions for the specified\n",
      " |              cell type and assay for the considered genomic positions.\n",
      " |  \n",
      " |  save(self, name='avocado', separators=(',', ' : '), indent=4)\n",
      " |      Serialize the model to disk.\n",
      " |      \n",
      " |      This function produces two files. The first is a json file that has the\n",
      " |      model hyperparameters associated with it. The second is a h5 file that\n",
      " |      contains the architecture of the neural network model, the weights, and\n",
      " |      the optimizer.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      name : str, optional\n",
      " |              The name to use for the json and the h5 file that are stored.\n",
      " |      \n",
      " |      separators : tuple, optional\n",
      " |              The separators to use in the resulting JSON object.\n",
      " |      \n",
      " |      indent : int, optional\n",
      " |              The number of spaces to use in the indent of the JSON.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      None\n",
      " |  \n",
      " |  summary(self)\n",
      " |      A wrapper method for the keras summary method.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  load(self, name, freeze_celltypes=False, freeze_assays=False, freeze_genome_25bp=False, freeze_genome_250bp=False, freeze_genome_5kbp=False, freeze_network=False) from __builtin__.type\n",
      " |      Load a model that has been serialized to disk.\n",
      " |      \n",
      " |      The keras model that is saved to disk does not contain any of the\n",
      " |      wrapper information \n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      name : str\n",
      " |              The name of the file to load. There must be both a .json and a\n",
      " |              .h5 file with this suffix. For example, if \"Avocado\" is passed in,\n",
      " |              there must be both a \"Avocado.json\" and a \"Avocado.h5\" file to\n",
      " |              be loaded in.\n",
      " |      \n",
      " |      freeze_celltypes : bool, optional\n",
      " |              Whether to freeze the training of the cell type embedding. Default\n",
      " |              is False.\n",
      " |      \n",
      " |      freeze_assays : bool, optional\n",
      " |              Whether to freeze the training of the assay embeddings. Default\n",
      " |              is False.\n",
      " |      \n",
      " |      freeze_genome_25bp : bool, optional\n",
      " |              Whether to freeze the training of the 25 bp genome factors. Default\n",
      " |              is False.\n",
      " |      \n",
      " |      freeze_genome_250bp : bool, optional\n",
      " |              Whether to freeze the training of the 250 bp genome factors. Default\n",
      " |              is False.\n",
      " |      \n",
      " |      freeze_genome_5kbp : bool, optional\n",
      " |              Whether to freeze the training of the 5 kbp genome factors. Default\n",
      " |              is False.\n",
      " |      \n",
      " |      freeze_network : bool, optional\n",
      " |              Whether to freeze the training of the neural network. Default\n",
      " |              is False.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      model : Avocado\n",
      " |              An Avocado model.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  assay_embedding\n",
      " |      Returns the learned assay embedding as a numpy array.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      None\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      assay_embedding : numpy.ndarray, shape=(n_assays, n_factors)\n",
      " |              The learned embedding corresponding to the input name\n",
      " |              'assay_embedding'. The assays are ordered according to the order \n",
      " |              defined in self.assays.\n",
      " |  \n",
      " |  celltype_embedding\n",
      " |      Returns the learned cell type embedding as a numpy array.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      None\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      celltype_embedding : numpy.ndarray, shape=(n_celltypes, n_factors)\n",
      " |              The learned embedding corresponding to the input name \n",
      " |              'celltype_embedding'. The cell types are ordered according to the\n",
      " |              order defined in self.celltypes.\n",
      " |  \n",
      " |  genome_embedding\n",
      " |      Returns the learned genomic embedding as a numpy array.\n",
      " |      \n",
      " |      This function will concatenate together the three resolutions of\n",
      " |      genomic factors, such that the first columns correspond to the\n",
      " |      25 bp factors, the next columns correspond to the 250 bp factors,\n",
      " |      and the final columns correspond to the 5 kbp factors. The factors\n",
      " |      that span more than 25 bp will be repeated across several successive\n",
      " |      positions \n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      None\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      genome_embedding : numpy.ndarray, shape=(n_genomic_positions, \n",
      " |              n_25bp_factors + n_250bp_factors + n_5kbp_factors)\n",
      " |              The learned embedding corresponding to the input names\n",
      " |              genome_25bp_embedding, genome_250bp_embedding, and \n",
      " |              genome_5kbp_embedding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(Avocado)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default parameters are the ones that are used in the manuscript. Since we are using a scaled down version, we will use significantly fewer parameters so that a model can be trained on a modest laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Avocado(celltypes, assays, n_layers=1, n_nodes=128, n_assay_factors=24, n_celltype_factors=32,\n",
    "        n_25bp_factors=10, n_250bp_factors=20, n_5kbp_factors=30, batch_size=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at a summary of the layers in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "celltype_input (InputLayer)     (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "assay_input (InputLayer)        (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "genome_25bp_input (InputLayer)  (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "genome_250bp_input (InputLayer) (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "genome_5kbp_input (InputLayer)  (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "celltype_embedding (Embedding)  (None, 1, 32)        160         celltype_input[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "assay_embedding (Embedding)     (None, 1, 24)        120         assay_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "genome_25bp_embedding (Embeddin (None, 1, 10)        11264690    genome_25bp_input[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "genome_250bp_embedding (Embeddi (None, 1, 20)        2252940     genome_250bp_input[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "genome_5kbp_embedding (Embeddin (None, 1, 30)        168990      genome_5kbp_input[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 32)           0           celltype_embedding[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 24)           0           assay_embedding[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 10)           0           genome_25bp_embedding[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 20)           0           genome_250bp_embedding[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)             (None, 30)           0           genome_5kbp_embedding[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 116)          0           flatten_1[0][0]                  \n",
      "                                                                 flatten_2[0][0]                  \n",
      "                                                                 flatten_3[0][0]                  \n",
      "                                                                 flatten_4[0][0]                  \n",
      "                                                                 flatten_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_0 (Dense)                 (None, 128)          14976       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "y_pred (Dense)                  (None, 1)            129         dense_0[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 13,702,005\n",
      "Trainable params: 13,702,005\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, all we need to do to train the model is to run the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method fit in module avocado.model:\n",
      "\n",
      "fit(self, X_train, X_valid=None, n_epochs=200, epoch_size=120, verbose=1, callbacks=None, **kwargs) method of avocado.model.Avocado instance\n",
      "    Fit the model to the given epigenomic tracks.\n",
      "    \n",
      "    Pass in a dictionary of training data and an optional dictionary of\n",
      "    validation data. The keys to this dictionary are a tuple of the format\n",
      "    (celltype, assay) and the values are the corresponding track in the\n",
      "    form of a numpy array. The tracks can either be in the form of an array\n",
      "    that is in memory or as a memory map.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    X_train : dict\n",
      "            A dictionary of training data values, where the keys are a tuple of\n",
      "            (celltype, assay) and the values are a track.\n",
      "    \n",
      "    X_valid : dict or None, optional\n",
      "            A dictionary of validation data values that are used to calculate\n",
      "            validation set MSE during the training process. If None, validation\n",
      "            set statistics are not calculated during the training process.\n",
      "            Default is None.\n",
      "    \n",
      "    n_epochs : int, optional\n",
      "            The number of epochs to train on before ending training. Default is 120.\n",
      "    \n",
      "    epoch_size : int, optional\n",
      "            The number of batches per epoch. Default is 200.\n",
      "    \n",
      "    verbose: int, optional\n",
      "            The verbosity level of training. Must be one of 0, 1, or 2, where 0\n",
      "            means silent, 1 means progress bar, and 2 means use only one line\n",
      "            per epoch.\n",
      "    \n",
      "    callbacks : list or None, optional\n",
      "            A list of keras callback instances to be called during training. \n",
      "    \n",
      "    **kwargs : optional\n",
      "            Any other keyword arguments to be passed into the `fit_generator`\n",
      "            method.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    history : keras.History.history\n",
      "            The keras history object that records training loss values and\n",
      "            metric values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(model.fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now run training for one epoch. The default training scheme involves sequentially going through all positions in the given genomic region, seeing batch_size * epoch_size positions before saying that the epoch is complete. If one wants to match one epoch with one pass of the genome, they should balance batch_size and epoch_size such that they are roughly equal to n_genomic_positions. Since we are training on the ENCODE Pilot Regions, which have 1,126,469 positions, and we set batch_size to 10,000, we will set epoch_size to 113."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "avocado/model.py:438: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<generator..., 113, 1, callbacks=None, workers=1, verbose=1, use_multiprocessing=True)`\n",
      "  callbacks=callbacks, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "113/113 [==============================] - 19s 168ms/step - loss: 0.2417 - mean_squared_error: 0.2417\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8d4c381250>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data, n_epochs=1, epoch_size=113)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now scanned through the genome approximately once. This means that each 25 bp factor has seen exactly one training sample, the 250 bp factors have each seen 10, the 5 kbp factors 200. The cell type factors, assay factors, and neural network have each seen ~1.2 million samples.\n",
    "\n",
    "Let's now make a prediction. We can do that using the `predict` function, where we pass in the cell type and assay that we want to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_hat  = model.predict(\"E065\", \"H3K4me3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then visually inspect this imputation and compare the MSE of the imputation to that of imputing the average signal value of the track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_true = numpy.load(\"data/E003.H3K4me3.pilot.arcsinh.npz\")['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lYo/Y8dKkSRMoKSnBw8NDqEesqvps1aoVNDU1GVvfR1C7zs7OpqULVlNT45sT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\nhBBCCCHDBk2ACCGEEEIIIcPG/wDl9d+Q7WxXbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d40e58250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start, end = 5000, 22000\n",
    "x = numpy.arange(start*25/1000., end*25/1000., 25/1000.)\n",
    "\n",
    "mse_global = ((y_hat - y_true) ** 2).mean()\n",
    "baseline_mse = ((y_true - y_true.mean()) ** 2).mean()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.subplot(211)\n",
    "plt.title(\"How good is our E065 H3K4me3 Imputation? Global MSE: {:4.4}, Global Baseline MSE: {:4.4}\".format(mse_global, baseline_mse), fontsize=16)\n",
    "plt.fill_between(x, 0, y_true[start:end], color='b', label=\"Roadmap Signal\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.fill_between(x, 0, y_hat[start:end], color='g', label=\"Avocado Imputation\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.xlabel(\"Genomic Coordinate (kb)\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like after one round the model has mostly learned to predict low signal everywhere. What happens if we let the model train for another epoch? Let's also focus on two of the peaks in order to see the behavior of the model better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "113/113 [==============================] - 19s 164ms/step - loss: 0.2312 - mean_squared_error: 0.2312\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8d40e580d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data, n_epochs=1, epoch_size=113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3LrS0tLgpkMtrlBTSqyCt/KWVvTaxxywvgJFlRjJra2toa2sjODgYV65cKbe+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\nnmJl2rRp7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QQ///zzZet/+ukn7W+lFJmZmZfFxeHDh4mKiqpxv4UQ4kYjyY0QQtSBV199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\nB76SRqE/1Dh2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d43006510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat  = model.predict(\"E065\", \"H3K4me3\") # Make new predictions\n",
    "\n",
    "start, end = 12750, 15000\n",
    "x = numpy.arange(start*25/1000., end*25/1000., 25/1000.)\n",
    "\n",
    "mse_global = ((y_hat - y_true) ** 2).mean()\n",
    "baseline_mse = ((y_true - y_true.mean()) ** 2).mean()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.subplot(211)\n",
    "plt.title(\"How good is our E065 H3K4me3 Imputation? Global MSE: {:4.4}, Global Baseline MSE: {:4.4}\".format(mse_global, baseline_mse), fontsize=16)\n",
    "plt.fill_between(x, 0, y_true[start:end], color='b', label=\"Roadmap Signal\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.fill_between(x, 0, y_hat[start:end], color='g', label=\"Avocado Imputation\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.xlabel(\"Genomic Coordinate (kb)\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like after the second epoch we're able to see some blocks of imputed signal raise up. This makes sense, because the lower resolution factors are likely to train faster since they see more samples per epoch. However, the peaks in the imputed signal don't quite match those in the real data quite yet.\n",
    "\n",
    "Let's jump ahead 3 more epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "avocado/model.py:438: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<generator..., 113, 3, callbacks=None, workers=1, verbose=1, use_multiprocessing=True)`\n",
      "  callbacks=callbacks, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "113/113 [==============================] - 18s 160ms/step - loss: 0.2232 - mean_squared_error: 0.2232\n",
      "Epoch 2/3\n",
      "113/113 [==============================] - 20s 176ms/step - loss: 0.2139 - mean_squared_error: 0.2139\n",
      "Epoch 3/3\n",
      "113/113 [==============================] - 18s 157ms/step - loss: 0.2013 - mean_squared_error: 0.2013\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8d40e58310>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data, n_epochs=3, epoch_size=113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jBPW3Kat8miu6psuun5ubi7t372LHjh2wtLTklQCztm3bhm3btsHFxaXC6uqMjAx4eHhg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d40a53550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat  = model.predict(\"E065\", \"H3K4me3\") # Make new predictions\n",
    "\n",
    "start, end = 12750, 15000\n",
    "x = numpy.arange(start*25/1000., end*25/1000., 25/1000.)\n",
    "\n",
    "mse_global = ((y_hat - y_true) ** 2).mean()\n",
    "baseline_mse = ((y_true - y_true.mean()) ** 2).mean()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.subplot(211)\n",
    "plt.title(\"How good is our E065 H3K4me3 Imputation? Global MSE: {:4.4}, Global Baseline MSE: {:4.4}\".format(mse_global, baseline_mse), fontsize=16)\n",
    "plt.fill_between(x, 0, y_true[start:end], color='b', label=\"Roadmap Signal\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.fill_between(x, 0, y_hat[start:end], color='g', label=\"Avocado Imputation\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.xlabel(\"Genomic Coordinate (kb)\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More defined peaks that seem better centered around the real peaks. Let's jump forward another 5 epochs and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "avocado/model.py:438: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<generator..., 113, 5, callbacks=None, workers=1, verbose=1, use_multiprocessing=True)`\n",
      "  callbacks=callbacks, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "113/113 [==============================] - 21s 181ms/step - loss: 0.1905 - mean_squared_error: 0.1905\n",
      "Epoch 2/5\n",
      "113/113 [==============================] - 19s 167ms/step - loss: 0.1744 - mean_squared_error: 0.1744\n",
      "Epoch 3/5\n",
      "113/113 [==============================] - 18s 161ms/step - loss: 0.1588 - mean_squared_error: 0.1588\n",
      "Epoch 4/5\n",
      "113/113 [==============================] - 17s 154ms/step - loss: 0.1500 - mean_squared_error: 0.1500\n",
      "Epoch 5/5\n",
      "113/113 [==============================] - 18s 155ms/step - loss: 0.1443 - mean_squared_error: 0.1443\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8d40e581d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data, n_epochs=5, epoch_size=113)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BoqIiZzANPszNzdGtWzfcunVLpu1kwf7voqKiOMENey2oTJ+20s8G0dHRyM3N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yeiTuDjsx9j2d/LEDE9As1rNy9VOtRaNQ5HHEZCdgJmH58Ne4V9mdztLy4ZZEZ3boyCG7k9MpQZ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\nL1ylHlxXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d4093a6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat  = model.predict(\"E065\", \"H3K4me3\") # Make new predictions\n",
    "\n",
    "start, end = 12750, 15000\n",
    "x = numpy.arange(start*25/1000., end*25/1000., 25/1000.)\n",
    "\n",
    "mse_global = ((y_hat - y_true) ** 2).mean()\n",
    "baseline_mse = ((y_true - y_true.mean()) ** 2).mean()\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.subplot(211)\n",
    "plt.title(\"How good is our E065 H3K4me3 Imputation? Global MSE: {:4.4}, Global Baseline MSE: {:4.4}\".format(mse_global, baseline_mse), fontsize=16)\n",
    "plt.fill_between(x, 0, y_true[start:end], color='b', label=\"Roadmap Signal\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.fill_between(x, 0, y_hat[start:end], color='g', label=\"Avocado Imputation\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.ylabel(\"Signal Value\", fontsize=14)\n",
    "plt.xlabel(\"Genomic Coordinate (kb)\", fontsize=14)\n",
    "plt.ylim(0, 7)\n",
    "plt.xlim(start*25/1000., end*25/1000.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like now we're starting to impute peaks at the correct positions. In fact, in the peak on the left we're seeing a little valley between the two imputed mini-peaks, just like in the data.\n",
    "\n",
    "When you're done training the model, you can save it easily. Saving a file produces a json file that describes the structure and a h5 file that stores the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"test-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can then be loaded up later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Avocado.load(\"test-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding additional cell types to a model\n",
    "\n",
    "It may be the case that your favorite cell type or assay is not a part of the pre-trained model. In this case, it is easy to add a new cell type or assay to the pre-trained embeddings. This process essentially freezes the remainder of the model, including the other cell type or assay embeddings, and learns the new embeddings by themselves, and then returns a new model whose cell type or assay embedding contains the pretrained embeddings followed by the newly trained ones.\n",
    "\n",
    "If you want to add a new cell type to the model you need to first create a data dictionary of the same format as before except that the cell types are not currently a part of the model. All of the assays must be a part of the model, though. Let's use the data from E003 but pretend that it's E004 for the purpose of this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {}\n",
    "for assay in assays:\n",
    "    filename = 'data/E003.{}.pilot.arcsinh.npz'.format(assay)\n",
    "    data[('E004', assay)] = numpy.load(filename)['arr_0']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to call the `model.fit_celltypes` method, with the same calls as the normal `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "avocado/model.py:584: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<generator..., 120, 5, callbacks=None, workers=1, verbose=1, use_multiprocessing=True)`\n",
      "  callbacks=callbacks, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "120/120 [==============================] - 6s 54ms/step - loss: 0.1824 - mean_squared_error: 0.1824\n",
      "Epoch 2/5\n",
      "120/120 [==============================] - 6s 53ms/step - loss: 0.1695 - mean_squared_error: 0.1695\n",
      "Epoch 3/5\n",
      "120/120 [==============================] - 7s 56ms/step - loss: 0.1767 - mean_squared_error: 0.1767\n",
      "Epoch 4/5\n",
      "120/120 [==============================] - 8s 70ms/step - loss: 0.1829 - mean_squared_error: 0.1829\n",
      "Epoch 5/5\n",
      "120/120 [==============================] - 7s 57ms/step - loss: 0.1674 - mean_squared_error: 0.1674\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f8d3cef9610>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_celltypes(data, n_epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'E003', u'E017', u'E065', u'E116', u'E117', 'E004']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.celltypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the \"E004\" has been added to the list of cell types stores in the model. Let's see whether it's embedding is similar to that of E003."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83619946, 1.890521  , 3.2636106 , 2.4219823 , 3.017827  ,\n",
       "       0.        ], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.abs(model.celltype_embedding[-1] - model.celltype_embedding).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our learned embedding isn't exactly the same as E003, likely because neither the original model nor the new embedding are fully trained on the data.\n",
    "\n",
    "If you want to add an assay to the embedding you can do the same thing except with a call to `fit_assays`, ensuring that all cell types in the data dictionary are already present but that none of the assays in that dictionary are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting latent representations\n",
    "\n",
    "After training the model we can now extract the latent representations from the model. There are built in functions that allow you to extract the layer named \"celltype_embedding\", the layer named \"assay_embedding\", and build the genome embedding from the three scales of genomic embeddings provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.celltype_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 24)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.assay_embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1126469, 60)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_embedding = model.genome_embedding\n",
    "genome_embedding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The genome embedding is comprised first of columns from the 25 bp embedding, then of columns from the 250 bp embedding, and lastly of columns from the 5 kbp embedding. Each row corresponds to the representation from one 25 bp position, and so the value in the 250 bp representation is repeated 10 times in a row, and the value in the 5 kbp representation is repeated 200 times in a row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to see what that looks like, let's visualize the resulting matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L9HzEbk/pZwF+/YRcz/3+oNe7p9AlpjetbaDaFDvk9usQfUq1ycqNENOd/9JcTJ96Y7v6\nTGi0nPfjaXGqTZRpd3+4y3HVZs9G+eiMenWLVJvjpyLF9NwQva/NjyuW8+4SrNosXlFPTF8TkyWm\nl53Wy9C0Qu77gx/T23PXs3K5St267yS0zhHTh9KjVZtLbygX048vj1BtnrpGPuPy/a9iVZubBsv9\n+uWv66k20W45XsS55HKecOrxY0C4XIYGffR+tX65XD+xTj2m5FSEiulIhx4wAgPkd6c6wlWby6Pk\ncn5xpr5qc+ffksT06ee+UG2C67rFdPLWhmI6JkjurwBwSVc57mzdpPe9jCA5PpQ5VBN0dMn1k5h0\nUrWpNVwuQ+aCVNUmqpncJzZtbaTa9Htavvbxk3JMORCkj3O1DBn0yDA9fnxWIvfhLmUu1SYmWC7n\nGi/j/5EA2Q/+UKJXWAXka+nBel+7rf8JMV28X8cTGCaXNXxwoph+90U5ngDAmI5HxPSJ1EjVZplb\nHuOndshQbU4fkP0/OkH3r2d/kn1waIlbtWkamy+mAwP19guJlMv+bXpj1aauW353kxC57Plleixt\nEiuPzYezo1Sb7pPkPpz2jt4ORS55jCj2cop4xXjZd9a9p59VfPkwOS5WZOljxK4f5fjeeZhcfz+t\n0H0yJlzOO671GdXmZLocp+tdUqjalOTIsWDHcT1uxwXKmOvW1mNnebns75dM1MejEx/L/bqsRK/T\nTSXy+DO4leynwV6eaHVipxyDUwr1+WyrYLnsEZF6O9TvJvvpO6v12Bnjln2nobtctWlWT26//Hzd\nL9weOV587dTHkY6lMp6kZvK8IOu47hdRUXLbNByi5/3p+3Jeo//SULUp/uRHMX08Vc8r/na5jWe/\noceCab0yxbQjVI+LgY3k9gpoLs87i5dtU5/5drs8tx8yVvf/I5/LeC55spNq88HMw2J6zMAs1SZn\ni9xW4dF6XIx6aISYXnXbj6pNj3byfCw1Re9rzePkdVfuoudVm1/YfntwVlYWGjT4704SFxeHrCy9\nAomIiIiIiOjiY/tFq7e7kx0OL38CJyIiIiIioouO7TmtP/30E+bPn4+33noLAPD3v/8dAHDHHXdU\ntklOTsa/Rs2tnG5bpkPeEywvdOu79YVvQ5e8HaZBkL5V4rRL3vrbvG6+arOmUN7ed2VormpjvjUo\nM0/fuhTfTN7atfyEvF2sWbm+7WC9vJMJI8r1T/YBDrl+XvRym9lfwuTnAr202Zorb+9rG6xvD25+\nubz9xFOivyd5g7zdpHPPE6rN7O1y2Sc45Xo/fEbfprHKdAdIoKG3eQND3pbRukyv05bhcrned+t5\nPf6EvHWj8H19G8SOn+VyLvVyG/vIUvl3ohDoNh+HyeV4Yahcx/evkLdSA8CLvWRf+mGNvsXHvJx/\n8+hbc0JNf8dyelmn3Uz3VV4Wpfv/kTx5e5q3/Whbvrzn6ccQuW1au/RtVCsccl5/DVFNMLVE3rrU\nM0jfpvpof3lrbeY6Pa8mY2U/2PT38w+VP4XoW/euhVzvGz26f+0KkjE3NOTta5ObHlOfeSND3lrY\nv0yPZ00ayPWena1vcS7zyGX/xMutTHfXkrfvbMzTtzh/5ZS3MTZGqGrzxyi53mvF6NvMXj8g97VG\nLtnfyr38TTPEtGm8bSnzx8aO17ecvvO+3Lcaubzc4mnI164co8fF1z6Xt5DFl+uI2tSS26a8Qq73\nE+V6P19puo121iX6riTz+mvs0ivsWJCMp56X4+WuQHmMCHHov3FPj5HbM/mYvqW/1PRH6MVOvb7q\nO+SOHOqQ6yLK0H2ynseUKlOub2vPDZB9u3s9fbt3SZHc197xMv6bFUDfWjv7Kjk2eYr1saYgXS7H\nwly9vm6OkOv0YI7sS7UD9T7Tboxcds9pfSvrT1/LW1BrB+tzh28d8jzlygr9Pfsd8sDbytBt3jbd\nJv5QbT3+m283rNVGjhd5yXodL8iRtxZeU6r3z0vqyVSB4iI9Jh8qkcvZu7MeX0MS5e2baz/Q+2Pv\n/nL/+/k7nWayI0iur6uCdQrVly45r1tbHxXTe3brY9j6YLnPpDn0+P9yX9MYk6n7TsglcrkCW+hz\nh92vyPH/H059vBxdKre5ed8DgIOmDK7DDh3P5RVye3UIkrfAppfrY1j72nKdflqqt0Ofcrl+vgzR\nJw+DymQ89Wvr9KgG3WW/nblW37t9pyHbREbpbfNdruzLHQw5r+at9XlVSJwcS0/t1n17RYH8XvOt\n3QAQbUpBcKujI5Bh2od7Q4/bJ8tl3/aWulMrTK7TpRX6FvqmpmPUZZ9PO2tOq+2/tF522WU4dOgQ\nMjIyUF5ejpUrV6Jfv352h0VEREREREQ1gO2FmIKCgjBr1ixMnjwZbrcb119/PVq3bm13WERERERE\nRFQD2H7RCgBXXXUVrrrqKrvDICIiIiIiohrG9tuDiYiIiIiIiM7G9kJMvkhOTsaqkf8txLTPoZN9\nHw6TxWrSTutk38S6Mll7d55O1u7cUCbW17+9nWrz/jPye0ZfflS1ObJZJvrH36STvt0nZJK8+Zmw\nW/bq50iZC/eEO3SxArepeE5ooC4I8UOQLMJzLEB/T21D/k3jO5cuoDQ4UMaY7mXbJHlkgYVTAbrL\ntTcV1+rZTM4rN1sXQdheLov99Kytn1m4qET2g1tDdRGEt0tlmz+N1ctQliKLY3y6rZlqc219GXNE\nY11kYOkO+blYL4Vevg2V22KsqdhEdLgufBGXKIsVeHtm4VLTMwtzvPSdlm5ZKWFzgH7G3YOmj70c\npP/21dRUhGeCl2JlDxbLRP+HTc8LrVNLFy/42iW3VYWXkjsxpqIyjV16OXt0l88Oy0rVxR3WmIo5\nbAvU8Uwul9tmYbBeF7eYutMxhx4L9pnqKewy5Hof7Nbb85SpNk2BQ/el9qYueNjL8167mJ5T3Llv\njmqzbK0s+tTYpYvezAuWfbB9gH5W5IxWcr2v3a2fU2m22rQ/1Df0DUKN3HK5fg7S8XWskH37iJfi\nc4WmdRjtpQDQGdMY3Nal21zTWC7nmqN6LP/ZKb8nxFQMI9qjt9U+UxGeWEM/m/ogZD9NNHRBrFDT\nMaKll6J/74TIwiCzHHpf2+CW2/i7AP3cwIllsliHy0vRj+IAuay1PTKe7/RjF3F3HdlPZ53WBZQi\nHbKvPBKr+/aRI3Jc/Guw7jtdIb8738u+NilMHlu8nVnln5HbomFjXdwkekwLMZ35jjy/OJajl9P8\nPNVuXfUzuI/9LLfVjgr9PVfWk88Uzc/TK/54mTwWl3kp0GUuVlbPy9hpmPpBkVsuQ/NYXbwp66Qc\np8ND9LbKKJXnNplBerwoMYWcEqCP+QNNz3339mz29f+SBenCvRToigqR+2xmmT6XMReV7GR6/uvA\nNvoc8/O98pmdozofUW1+2CKLsvUfqvu/4ZEd1SjWy/Cvf8tCmRtCdJuRpiKcl16q19dr+2U8DbwU\ngGtRIbdpfoBcF528FH4sNBXbimusx6F3jstluClGn886TYXu0g/oIksNY+Q+m5ajryMuayQLvsUM\n0d9jnJGFAJctkftnDy/ns5H15H5UUaKPPd+ekMeali7dt5uYnqm7JVcX+tpsKozZpVzPa+RtclsZ\nefp88bkVcnzt4KWSYo/6sq/kLnq+5hZiIiIiIiIiIjobXrQSERERERFRjcWLViIiIiIiIqqx/Can\ndd/Q/+a0rvdyP/3DsfIecneFvm/60HGZD+f0kpdSu5a8/7v5MP09OxbLnLRAL7k+n5se/DyiQud0\nBAXI+YealquwRD84eJ0pF9VbHtsmj7zn/wWH/p5tpgeo/+TUuSHmdRocpdf7qt0yryLUo9dFt8Yy\nT/jzbJ3f1bBCLkfrUJk3UFqu81KSA2R+S5neVGhVLmPO9pLfsidI3rt/ud5UaBsj12mwlz54JFP2\nr3wvuXc5pvnHesm33BQq/5ZkztvUWxxo4JH5BhFeGoWadvVjXmqHdyyT62JrqM5jMK/mtmV6m58J\nlMvQv5HOsVplyjFJccp1MSVUPtAcAB4qlnlGPaHzstYaMresY0Ad1eaoKe9vUqn++11ksMxFWuPQ\n8yox7futdBoz9ph2v6PQOSahDrmeE11yOet42a/Wm3Lvgr38DbJHmfzet4N0nkxioMylMc8bAOqa\nHlC+M1jHs8kt86WCHbrvDDdkbs+3ATqnr41D7teNTfmqmYG6cw80PRT+iSD9vX0DZd6Ol+ECV5TK\n74l06g2a7pZjsDkfEwCcpn1tqZd4Qk35luY88Ed66ZyrgT/IZb/a2Ui1+dG0HUYjTrUJM22+DtC5\nSNkuGc+eYD1gfAs5Li6I0etrX6bc5qcDdL/YECKXK9Y0nmUH6JzbRqY210edVG3yTstkwZBgPd5+\nZMrLbejW2/M7Uz8d5tZjQYBpm2/ykhtr3kdv9RSrNiUVcj3/I1j21Odb6bzE00flcuYW6LzJRg1k\nHtuMUzqP/yZTvmWX5lmqzXem/OzedfV6/1uRzPMbVKLHC3Nuc++B8nsWrdHnCeYUSG/Hues7ytzO\ntGSdr3c4QPbtvU4d332dj4npJVuaqjY9TP3CvO0AYFagzA1fcoXeR57Z2EBMdy6X/STQS+0Gj2n9\nDR6pcz0LtspjzdoMvU7Np8pDe+r82bXr5Thzwqn34cOmcXm0R48p2zxyvzkWpJfr6lIZc51QOb3G\no2sl7A6Qx/NpTr1fLSuX52fty3Tn2Rsil8uXX/Wucejc6z3lsgZFUl193C0xncucLpF98otQfRz+\nP4/sS2Uu3d92B8h9ONalx85uCZli2uXl/KcgV44pTQfq9ZW6TMa8PSBctTHnKCc20uNXiSknuejD\np5nTSkRERERERP6HF61ERERERERUY/GilYiIiIiIiGosXrQSERERERFRjeU3hZhCb5hVOR2XoB8c\nvG+7fMhzkUcnKP8cLJN9J/bNVG0qTsmk4X/ubKba3Gb63J5vI1WbYNOD68+U62JIbdqbikeZKgkd\nStcPLe4wXSaTP/WqTjhPMiXx94zTxRTMxTF6XK8LhRxcKdeht4eRJ0ImhkfU0kUGUgtlIZzL2xxT\nbULi5LIf3CjntdOj5901RBbciY3X/eLZfbKAwN3huliB2Zq8WPVaQ1PBpO1eChRdZ0rINwxd6mWL\nSxYR6OSlAMoWUyGaG7tniOmAUP23ppIMuRuv3a8LtAz5o/zciY/1w78zsmV8200FxQCge4Xsc8dN\nBWQAwOWQy14coNdFzzC5LdaWyv4+IFr3223ZsqjGtV4elu4plvvw7PW6+MTEcFkY4dgp3b+OBMp9\ndrOXwir3BMs+d/pMmGoTHCgLIbR9KkG1+fxPslDVsIdkQYNX58n9DAAmNJf70bEDuuDUMZeMp8RL\n0aABpsIbZw7psdNh+lh4I70uAsPlPlGwT8/rRKYcK9tN0PvRqkWyzy0Okf3ttW66QJe7SI63tYZc\nptq4du4X00e/0/M+fkbue5266mJI7mLZl08e1gVtmg+V08s+0dsmxHTo3WMquHO9U45vAPBNmdxH\nEsp1YaGrH5dj+/Nz9PdMrif3rbB6envu2SH3tY436Ap15Yfl+HV0u96PLhkojwkbl+t10bW7PKYG\nRsk+uOI7vQ8P7ib77cGtdVWbuEbyuJZ+JEa1aXepHAfDOun43Mfk9xzZpAuOhJiK8zWZpMfgt1+R\n6yLGrU+/rmwux4IAU5GgV47odXFToIyvfnMvx5UU+bkr+urxdcsaeezr1EmfIwXHyQIxgQ11YZyC\ntbLP5RzX66tZTzmmbV8jz+FaNtHH6jN58lhTp74+/yk8LceP8Ch9TlJaJPvXrlP1VBtzgaIIjy5E\nYz6qFZsHSgBtasnzgn3Fen31+4Psg2nfyHGyXowe/zNPyjanDH2OuSdExjMhXhdZSkuR+/knYXpc\nbGcqzuetGN4Dl8u+smG1LgDXIlL204ad9PbL2yvnZS5Y9G2ZPi++bZz8HqNEFzs89pWMOTxKt4ls\nI6ezt+p1evC03H5JHfQ+cnC3HGdCgnQxpIbxsl/kZ8pj9Q9ndJ80G9BAz7ukUK6vHQV6fTU1FaJs\n0kQfU+v0lgWdNn2k9+ETgXI/Oh2oz/NuHSj7dulhfRypOCP73IkFf2UhJiIiIiIiIvI/vGglIiIi\nIiKiGosXrURERERERFRj+U1Oq3HdXyqnW/fXuYtrv5b35Wc4dV7WmgD5udEV+h7t1aa8tcGl+nsW\nh8p7sus79H3vLdzyvvKVhs5XeJkFAAAgAElEQVQfXJ+zR0zvad9KTH9zQueu7DPdG39/U31Pe2CY\nvHc//Wd9b3zdyBIxnVqg84HaRcmckux8L7lbjWXuyvOndM7Qk61lXlh5vl6n5vvwnymS85o/QX9m\n+Zvyta6R+gHOq4rlsjeu0LkYSQ3ktll2qoFqY849+ihQ54nVcshlCHPo3JCRZTInp+81OmduwyqZ\nV2TOk3F62WVfcsrczveb6Vy3iE5y3lnf63VxOk/mVTRsrHOdl5+QuVrDGxxXbcpN+80j+TrXs5VD\n7n8DS2Xu0dpQnU87qanM47z7iM6hezFa5rfUaafzSR5YL/M8ZobpnKE/Fsr18+kfVBPs+0ouV+vr\ndP7UQytlXx5aqvvFgWC5vmJd5x+W3ab0kQAvH0kLli82cem/U3Y0ZP5bsVs/1Dw6TI4XKWU6L2uj\nKafvjyH6oeu9jx8Q0+1rN1Vt6gfKfrHwGjnvZ1fr8WxaC9kHv9yvv3fUlbLvlGXp/u8qkevnnuN6\nzLvW9HD769seUW3u3Sv711/j9H507wm5nE+Z6iBE1NY5VxENZf9askvXXLjJlB+XvVE1Ufn2ETF6\nXqGmw09QA3283LNU9pVTLr3POiGXy+nQHTU9UI5N5jGuuUfnQT3rlP3i1Tp6zFubK8fSeLf+nmYN\n5VgeHK7Hi9xMuexFJXof+ThYLkOKW2/zW10yX7bUoXPA8kx5Yf92yO8Z5tZjXi0v+ZZmHWvL43lm\nnq7FsSVEbr8+Lj0uLjSdW1XAy34Euf3c0Nu8OeTYWeqQ33PAo+fd0SFj7qC7LX4ydUGnyjwFCk0x\n1zX0uDjlMllLYtZufV4wvER+9+kAfZ4S5ZH9qdxLPNEOed75eoj8jNPL70sTS+X37AvStSXaueWx\nMCWwlmqzJkiu54EVuk3P2vLc6uMSfZ6X45Ax73TpnOTZhvzuNjfovpOyWB4fKzxy2UMD9f65z2Pa\nP73U0BjVQ27PV5ObqDYbPaZz3gq9D78RKK81zDVIAKBphRyLVoXp/n/UI8evUS7Zt38K1svZoVyu\nm1WmMRAAwiHbtPHoMbmp6TzYXIMEABKd8nrpYy/LeWWp/J6sIN3/+8bKa5R6o3Wu/+pX5Ppq/OWD\nzGklIiIiIiIi/8OLViIiIiIiIqqxeNFKRERERERENRYvWomIiIiIiKjG8ptCTEeGvFg53b6OTvAu\nLpbFkJxOncRsfq3RaF3QwBEjCyW4dh1UbYrSZNLwvnSdlN7qEpm47irTfx+of19XMZ08M11MJ03U\nmyZvtSwaUbuzLgK17wuZeB0aogtUpBbJYiLRhn64fBl0wRizZaFynb5weY5qM3e9rOjRvVQn30c5\nZIxdHpMPpj71Tqr6TNoRmRAfG6ofVG1Wr6F+6PqPh2R8jQJ0cnugqXiItwICAdGyPxnF+nvK0+T6\neW6bTkrvUCH7SrsAGXPCKL09g/pdJabfv2unapMZJJfhjlb6QePqe3W9HWxZLwtSdGitH1J/KF0W\noolvp4tkZe6X6yt+nOzLjlq6eNPf/iGnb6yvC5E9myPn3Rh6H5n+sGyT9txh1aZJgiwk5C7TxQrM\nNTUCgvQ+W5AlC2SsPFNftentltu4VQ+5n5fl6HlXFMv9M6S27hfhPWXRopMrdWGJupfKz32zRheA\n69dJ9hVPuY7H3FcWeCl08UfT94R21QWTSjbIwkYPpMhlmNdJ96XUzbLND8G6KMmU9rIQx+5tsapN\n+66yiFFwc118yDlkgJhOv+db1SanSBYcSQ7RxTDGNZfrYu9euQxZAbrfXn2p/ExZni584TaNHyGR\nXsb/vbIPFkJ/T88OsnDVzzvjVJti0zHCPI4DQK4hixZtC9V95/5uspBWqWlI+eKA7ks3DpNjqadQ\nF0FzmAqyHF2v+0VyiTzm/8HUTwCgNFcuQ/EZvW3qt5XHH2dDPX6Zj83NO+qCfpu2ymNCv6fkeHvs\nZX0sLC6S8Sxy6H57Vy05r3wvxfFqR8pCVdl5+nsSLjsppgP1KkVIRxlz4Vp9jHCbzolC4+QxdfWP\njdVn6nnkeUrtIH3eYi7cE+ylcE+bm2V/L91+UrUJjJTfk/Oz3ocb3SLHyvzP9XHEGSnnHxSpzwVD\n+l0mpj1H5XFtz7t6OUtcchk+CNXna5GGfK2lW7fpZir0dapc94ukzjKez3fqAnA3TJIxFq/XRSbT\nU+S5cmw9fT62P1sWBW0cLtuklOiTEqfpsNu9hZcCpSGyf+Vk6MJCTXrLczajXJ/nuYvka+u36n6a\nWEfua7GX6QJwRUdkPzh0RC63+ZwTAI4bcmcbeJse84wyuR1cGbog4iubZcx3d9Lngpm75PoJCfVy\nflFHzv90li7iFZcgCzp9u10f8xt6ZEW1iC8fYSEmIiIiIiIi8j+8aCUiIiIiIqIaixetRERERERE\nVGPpRJYa6quQ/96nfUmxzic5WC7vv87w8rDoaxwyF3bXW/oe7Ta99ojp7zbo+9WbmnIeG9bV9+WH\nNZT3vYeO6q3a7H98h5j2mO5XT16oc39ax8v73L3l8TS6RN4/v36fzps0P7y9+60672PVOzKPp3ND\nnZdymSmZIP0HnSfcwbSa+1yv77H/fqnMK9o3R+a1lVboPIbdwTK+ZEPPe1Qtmf8274TOyxobKHOR\nWnbXeUY7N8j8N4eXvJTSrTIXasVWfe9+LY/sT5Mi9bz2l8plbdEtT0wfWalzMZp5vhfTrVw6F8mA\nzMn5c7peF90q5L41oPkx1Wafab1/n6FzIP/YRPaViAm6/wf9Webdmvt70jCZXwgAO00PuJ5QR+/D\ng47KYe2zUJ1PUrpKzjurXC/DqZ9lbs86LzlDN4XJMeXpEp3TMf9aOT5E/Es1wfdBcnvt+NGUT+Kl\n9MDgJNnfvveSQ3q5S+YKnjgZrdqcWCOnu8fp9b5hm+y3XeN1vtKXabLNaafeNsEt5APUy37S+YMH\ndsgYr/PIcfH7zXpMbhFUJKajPHrsPLRTfm+xl8PfnJ/ldz8xTOe9li5aLuOp0Ou9T5jcZwsNfcxy\nlcoxpMsdsn8NfkuPt6v3yn3WS5Y1ri6X8+pcqnOA9znlWKC3FDBrl3z1JS9tCgNkzIVe6iBc94jc\n5tvm6rzqn9fJfN59pkTJum6dW3boK7n9GrXXNQ2e2C1zK3uUO1WbrCC5Fk8eiFRtPnTJMbmeR4//\nE2Pk+ipN91ZjQa73DV7y4faEyHXY8/Nt8hvC9DKcyJUxN3fqvn34lFyGJlFnVJvoS0wx65IecNaV\ny/7FOr0MwyLluBPetY5qs3exnG7ZTJ4XmPNXAaBzX5nHfPRHPd6WF8t17PYyFmxdKJchvpleX2+n\nyGPCcIc+b0l/XcYT21z306IsuT+GlevlOvLCITF9qFSO/z3b6Zxbc62EZoX1VJt8h4znihAv5xuF\n8rypfT09XhzdLbdfS1eZanPgXXle3CRJNUG5ab95vUDXg7nSNN2oozx+Fm/W/b9VT7lcrkJ9vJxu\nqhkww6HPnY9vkuu0+SRdfyL3U3nM6nOlzp815yiv/bM+pjoht01suNz31pfLHFcAaFghz9MLvtfb\nKue4+dxPny/e1U4uQ3BLvX8G75HrJ663XqfuPPlaaJy+FgpuK4+hZ3bq/fFaUy2JQ6rFf/GXViIi\nIiIiIqqxeNFKRERERERENRYvWomIiIiIiKjG4kUrERERERER1VgOw/BS4aOGSU5ORvyLD1ZOH92k\nk++fcMniBdd4dFGenEC5qL1KdUL8GYdMyO8UqxPgn8+XScvXlunE8J2m51C3dOm/D3wYIJP432sn\nE7FnpOkk8IecMtk9KEgn/i8ukcntIwPzVJumV8jiNF+uaqDa7A6W6yvfoYs1XV4m19chvSpwRbmc\nV73aukBFVKxcroczZNGUBOgHXtd1y4TuYXE6If7VHJkEPiU8V7UJi5D9IDdb969VHrnN9VoHmlfI\nV+t69PoKhnwtwqn7YFiofC0gQG6Hp8r009xvK5UrvnmMLhphfpi8t4JTL2yTRbsurdD9dqdTLued\ntXUxgJ0nZdGDn0N08n0Z5HJFGLJNoJcyM23K5PozFy0BgNtMBYpCknQhsu9ek9P7vHxPA5eMr6Fb\nF254LUS+tqC13te2/iwLerwVovv/ANN4tdxUNC4+UBeHmdlAFi9YlKmX84pSGd/24BDVppdbFjFa\nF6SLeBWYCnp0KdWHjfWmblnP0Os03SELeFzm1vGEm7569AQ5fvy0UH0E/wqRxU7urKeLGLlNfXln\nli5c0v8WOQ5N/Vj3/2iH3NeuLdH9NCZArndzARIAOBIol/2EqSDQ+Gb6ge+vZ8ht3NTLcWVQU1k8\nLaSuHoeKTshl2HVcH2tSTPvE8DC9n0fUldtzx0Fd3O0z00PpTxm6iMss076+KECOVQ810IVMzPPq\n3vm4anM8Ve5XFS7dJxu1kGPl00d18a0/t5WFxzZt0/tafqD87lOBul/UNh04Cr38bJBnGu/3Q/b/\nrh59LLx1ktzGrv26/5cckjPPztBjyjeGLNZ0ebkeqxaFyKCvrtD7cGGAXPZOniLVJihAxrPDkPG0\ndusCeotC5Lppb3g7L5DTP3spCBdoOtbc7NBFqQ6WyHj+HabHvDLTmUCMoQs6nTadN50y9HHk0UD9\n2v+a59bL2dMlB9whXoomhifKeByhOr6FK+T5YnsvhaLyHbJvt3TqgjvTDTl29gvU+1GXUrkuPgnV\n8xpjOpepZSoTZ3g5L/jIdOyZWKG3eXySHL+2b9XnvIeC5HHkiJfz666mY98/gnVhuV4Oeb54R0dd\ncHDeTlnAzzwUtPAytverL8ehz07pZRgaKa9ZfszTx7nDpm5wLEBvh1tM/SAqQu+P64pk3/G2r9U2\nFcVt6NbLVWx66erPpqFLly6qHcBfWomIiIiIiKgG40UrERERERER1Vi8aCUiIiIiIqIay29yWkNv\nmFU5/UOZfujuzbfKnID1/9D3vXdIkHkeq9L1Q7GvrCdzZ0Jr63u9Q03pLKk/RKs28W3l/fPrUvW8\nrhks7z3f+aXMJ2nTXufThrWXDwpeslSvi1iXvK+8iZccugaXyDyeFfubqjYRHtk1hgzLUW3u/Frm\nf95RqnOGKkx/G4kN0/GY8zab9pF5T2nf6PybH0y5d97+AnNzmyNi+vQRna+6K19uv8uidN7rP0tl\nmylecubKSmSiQL1Lde7W8WQZ8+7iKNWmSz25nqPbyr5t/g4AaDFZ5i0YZ3QO0al/yRzW8jKd37L/\nlOxPEQ6do/CuKYXp4Qid0xEaLj8XGqO/xxEs99FTe2Tezt7T+oHXLcJl7lHLB1qoNkuflXmlZQ49\nFlSYXupbS+fr1W8j12HRcZ2wHTNC5qvmrdR5RRGJcp/I3KC/x5zTHd5OruTNy/W6uLS17IOlBfp7\niwpljo55vwIAZ/8eYjr/jfWqze69Mj+pa389Nhnlch/eu0GPTe3GmPK1G+p8m+I1B8T0V7vk2HTd\nH3R+Y1Ga7F9u8wYGkJ0px5B3g3Ru+KUVch2O6nxEtXHGyc+lfanHlHZTZF9++y0vudePyWV3/ZQm\npv9vpR7RZplygONidS7e+myZ6xnr1vtearBczgFe8lUDTflcL3kZq25zyf50piJYtUkOkTHf3Frn\nd322R27jQab6BKlHdT8JNdUHaHWJl5zbdnKMe2W1zrMz95QOpTqPrWMj2edyTkaoNi5T3nJ0HX2c\n+7ZA5g43cOl5DbhPbpvU+fJYHRUlxwoAiIiR2+H7/fp8o2d9uQzJ2TqPucI0Vjb06FzLSKd87ZLu\nOo+/4IBchv2Z+hyp3HTEdppqHDigT017jJN5dZ8v1ucF0W7ZLyq85ECa5+WtTXaQ3Gf7ROrzn4Iz\nciyICNfj6/58ud9c2lB/z3cnZW7i9f1l/w+6tLn6zN/my+1wlUvnmX7hlGOT08tyHoX8nieb6rF9\neoYcywdV6DFvaG957MvcrtvUjpbbb46XPnhHkBzT6sfL5Tq4S/el5m3kuU1+ps4B3pknPzf0X+NU\nG9cHb4ppz2m9D39vygE27zMA0DpUnhN5vNQ0iL9eTmd9I9dNdBvdl9ZskPt1lKHH9mZ15byz8/T5\nYly0XKcZp3QNoHaJcrz4LF1fI5iXakC0Pi9eZ6orU9utx7xLY+XYnff2c8xpJSIiIiIiIv/Di1Yi\nIiIiIiKqsXjRSkRERERERDXWBbtofeSRR9CrVy8MHTq08rW8vDxMnDgRAwYMwMSJE5Gfr58tSURE\nRERERBevC1aIacuWLahVqxYefvhhfPHFFwCA2bNno06dOpgyZQreeOMN5OfnY8aMGeqzycnJaJu+\n8kKEWWWen5F2/kY1yB/K9IODa7os6EIqNd3koq12h2BJk1ox529Uw+S7dPGEmu6P4ZfaHYIlr5zZ\nYXcIll1Vu7XdIVhS4qXIRk33QLkuxFTTnUTI+RvVINdOcZ+/UQ0T0KKZ3SFYlr9wi90hWPJSRsPz\nN6phxrh0UbGart38nnaH8LuX2uxa+wsxdevWDVFRsora6tWrMWLECADAiBEj8O23316ocIiIiIiI\niMgP2JrTeurUKcTG/qcccmxsLHJz9aNGiIiIiIiI6OLFQkxERERERERUYwWdv0n1iYmJQXZ2NmJj\nY5GdnY3oaP3Q4F+Uf7rmAkb229X16Id712RFhvP8jWqYTaEXJB27Sk0J8X6ffk11EP6XcxLo5QHq\nNd3i8sN2h2BJXGgdu0OwrMLQDzWvyZoF6IfC13Tzgv0vn3xqWaDdIVhSkZZtdwiWBWb7X5HNqHv7\n2R2CNTNS7Y7AMrfhf8dqePzrOOJa9YPdIVh3+7VnfcvWX1r79euHZcuWAQCWLVuG/v372xkOERER\nERER1TAX7KL1gQcewNixY3Hw4EH06dMHS5YswZQpU7B+/XoMGDAA69evx5QpUy5UOEREREREROQH\nLtjtwXPnzvX6+jvvvHOhQiAiIiIiIiI/w0JMREREREREVGPZWojJits31LY7BEvuKi+zOwRL2nXI\nsjsEy2LS/K8YTMLNwXaHYEnF3jy7Q7As9AH/SzPw/Ptru0OwpGzLIbtDsCw40b+KzRmFZ+wOwTJ3\nrv8VbnOd9q8x7p8bm9kdgmUFAf5XNDF85V67Q7DkRhTZHYJlee4Qu0OwrO89/nWsHh3U1O4QLOt3\n+9nf4y+tREREREREVGPxopWIiIiIiIhqLF60EhERERERUY113pxWl8uFTZs24dJLL0WdOvblEN5b\n7l/5SC0vybE7BEvC2vlXzjAA/DvN/2IOWXza7hAs+aa8id0hWBY/coXdIVjWLMS/8pFaDq1ldwiW\nnVheYHcIlmw8Xd/uECwbMdruCH4N/8rDjXX5X35orN0B/ArLggvtDsGSW7v51/gGAMe2hdsdgmUr\nLrE7AmtCL8m2OwTL9p3jvfP+0hoUFISpU6eisNC/dmAiIiIiIiLyfz7dHpyQkICMjIzqjoWIiIiI\niIhI8Omi9f7778cLL7yANWvWIDs7G4WFheIfERERERERUXXw6Tmtt9/+n4fmTJ06FQ6HQ72fmppa\ntVERERERERERwceL1kWLFlV3HOfVcah/JZk7QsPsDsGSBZ9F2R2CZROaH7U7BMtOHo2wOwRLJg07\nZXcIlgX16WF3CNblnLQ7Aks8x07YHYJlBQUVdodgSZD/1duBo7b/FVY5s9G/CjE1NsrsDsGypg3y\n7A7Bsg6FwXaHYMmtP/rXuQUA/NkPH2DyXYp/FacsTdM/NNZ0l009+3s+XbT26tWrqmIhIiIiIiIi\n8pnPf+bYv38/nnnmGdx55504efI/vwqsXr0aaWlp1RYcERERERERXdx8umjduHEjRo0ahYyMDKxb\ntw6lpaUAgIMHD+LVV1+t1gCJiIiIiIjo4uXT7cHz5s3DjBkzcPPNNyMpKany9R49euCdd96ptuD+\n1/Or6l2Q+VSV+1oeszsES8Y3KLU7BMsyDtSxOwTL2k0OsTsESwLatLY7BOuK/K+iefmGFLtDsMR1\n2m13CJY17+xfuT1tLvevYx4AnFrsf7nOG443tDsESwz/6sYAgA/zou0OwbK1JUfsDsGSuWhudwiW\n7Qr06RKkRtkX7F/FBja4/ateBgC8cI73fPqldd++fbj66qvV63Xr1kVenv8l2BMREREREZF/8Omi\nNTIysjKP9X+lpKSgQYMGVR4UEREREREREeDjReuQIUMwZ84cnDx5Eg6HAx6PB8nJyZg9ezauu+66\n6o6RiIiIiIiILlIOwzDOe4N2eXk5HnroIXz99dcwDANBQUFwu90YNGgQZs+ejaCg6r0vPTk5GU1n\nTqvWeVS1tbv861lOG0M9dodgWbjhf8/42oNiu0Ow5L5y/1vHfw7yr+cuAsD1nhi7Q7DkqZIddodg\n2WvO9naHYMmGUP/b9+5unGl3CJZFdAq1OwRLTq1z2R2CZXEzutsdgmWujdvsDsGacv+rM4Ag/xvj\nMtfYHYE1Gwv9rzbCZZ9PQ5cuXby+59PVZnBwMF566SUcPHgQKSkp8Hg8aNeuHeLj46s0UCIiIiIi\nIqL/Zekn0ksuuQQNG/6n0l5oqH/9dZKIiIiIiIj8j8+/zb/33nvo378/kpKSkJSUhH79+uGf//xn\ndcZGREREREREFzmffml98cUX8cEHH2DixIno1KkTAGD79u14+eWXkZ2djenTp1drkERERERERHRx\n8qkQU48ePfDEE09g8ODB4vUvv/wSf/7zn7F58+ZqCxD4TyGm2DtnVOs8qprH5V9PAI8Z5n/J2t/9\n3f+S+Ht1OmZ3CJYYftaPASDs0ki7Q7Cs5Oczdodgicf/asFg1T7/Ko7XPtC/+gQANG6Vb3cIljmC\nznsKVKOcOeF/qVk7T/rf+cUXIeV2h2DJpR7/6xeR/lf/ExEe/xovrmrlX+ecAHD8pRfPWojJp7N+\nwzDQtm1b9Xrbtm3hdvthxTIiIiIiIiLyCz5dtA4bNgwfffSRev3jjz/GsGHDqjwoIiIiIiIiIuAc\nOa3PPfdc5f8dDgeWLFmC9evXo2PHjgCAHTt2IDMzEyNGjKj+KImIiIiIiOiidNaL1l27donpxMRE\nAMDBgwcBALVr10bt2rWRlpZWjeH9V0ZGnQsyn6oSXbvE7hAsCYj1v5yTuMDjdodgWa2+Le0OwRLj\ndIHdIVjmiPGvsQIAQs6U2R2CJSHTH7Y7BMuu37vV7hAsyZvztd0hWLY7JdbuECyLcvpX7mKxy2l3\nCJYdD/LD+hMu/8oR/Zv7oN0hWPZhlP/Vn/i4NMbuECz5IcP/xuRJ53jvrBetH3zwQTWEQkRERERE\nROQ7//vzFxEREREREV00fHpOa3l5ORYvXozNmzfj1KlTMD8lx1uRJiIiIiIiIqLfyqeL1ieeeAKr\nVq1C37590alTp+qOiYiIiIiIiAiAjxetq1atwvz589GzZ8/qjuesvg8Os23ev0ajolp2h2BJyBt7\n7Q7BsmVO/ysetfj1UrtDsMRphNgdgmUVjmK7Q7BsXZl/FWJK/uctdodg2UONrrI7BEtcaGB3CJYN\nd/hXUSMAeCrAv541PyDI/4rXpAVV2B2CZUc8RXaHYMkPExvbHYJlL/3T/4qK9Sr1r758JCjY7hCq\nlE85rdHR0ahXz/8uEIiIiIiIiMi/+XTRev/992PevHk4c+ZMdcdDREREREREVMmn24P79OmDpUuX\nolevXoiNjYXTKX/S//pr/3ueHBEREREREdV8Pl20zpw5E3v27MG4ceN+9W3CmZmZeOihh5CTk4OA\ngADccMMNmDBhAvLy8jBt2jQcO3YMjRs3xksvvYSoqCj1+cHwr195m3TKszsES177uandIViWVG6c\nv1ENEx/of/mW/ubjQP/KJweAVx3+9cDy7XHN7A7BslGJR+wOwZIfdjSxOwTLPgwNtDsEy+4uCbc7\nBEu6D8y0OwTLTu902B2CZSGRLrtDsOSh9/zvuPfUpRl2h2BZSbZPl001RlIj/8rZB4BD53jPp7W/\nbt06LFq0CElJSb86iMDAQMycORPt27dHYWEhrr/+elx++eX49NNP0atXL0yZMgVvvPEG3njjDcyY\nMeNXz4eIiIiIiIh+P3zKaW3QoAHCwn5b9d7Y2Fi0b98eABAREYGWLVsiKysLq1evxogRIwAAI0aM\nwLfffvub5kNERERERES/Hz5dtM6cORNz5szB0aNHq2SmR48eRWpqKjp27IhTp04hNjYWwH8ubHNz\nc6tkHkREREREROT/fLo9eMaMGSgpKcG1116L0NBQVYjpxx9/9HmGRUVFuPfee/Hoo48iIiLCWrRE\nRERERER0UfHpovWhhx6qkplVVFTg3nvvxbBhwzBgwAAAQExMDLKzsxEbG4vs7GxER0d7/ezxMv9K\nMm/V5bfdTn2hTSmuml/RL6SdKQ3sDsEyl8OnmxtqjA4fDrc7BMueatTa7hAsK5//nN0hWNLhuj52\nh2DZmnHf2R2CJX+433n+RjXMgOxCu0OwzHPav4rjPfK9fxVtA4BEd7DdIVi2+3SZ3SFY0sEP1/Gb\nu/yvAGhdj38VFUs5WWF3CJbdco73fLpoHTNmzG8OwjAM/OlPf0LLli0xceLEytf79euHZcuWYcqU\nKVi2bBn69+//m+dFREREREREvw8+XbQWFp77r6e+3OabnJyM5cuXIyEhAcOH/+fXmwceeABTpkzB\n/fffj08++QQNGzbEyy+/7EtIREREREREdBHw6aK1a9eucDjO/pN4amqqT9+xZ88er++98847voRB\nREREREREFxmfLloXLVokpisqKpCamorFixfjvvvuq5bAzJwwLsh8qsp97/jXA31PGbXtDsGyWcEl\ndodg2dFy/3qQffKNK+wOwbIGMWfsDsGylBz/ylO7Jind7hAsOxHkXw+F3/mK//XjxSH+VXsCAEqM\nELtDsOT5btl2h2DZgU1Rdodg2Zim+XaHYEntaxvZHYJlO14rtTsEy9pe4V9PONmxtr7dIVQpn47i\nvXr1Uq/16dMHzZo1w1pp06sAACAASURBVGeffVZ5uy8RERERERFRVfpNpUzbt29v6XE3RERERERE\nRFb86ovW0tJSvPvuu4iLi6vKeIiIiIiIiIgq+XR7cLdu3UQhJsMwUFRUhJCQEMyZM6fagiMiIiIi\nIqKLm08XrQ899JCYDggIQHR0NDp27Ijo6OhqCcyswBF4QeZTVYaW+Ve88aH+lxCfUxJqdwiWtY89\nZXcIlkS3Lbc7BMuCGvhXsSsA2PWxfz2w/MBTP9sdgmXHgurYHYIlPYLK7A7BsunReXaHYFlAoH8V\nefzT1li7Q7AsNOg3ZaLZovSofxXocixy2R2CZXdF+N955/Wbgu0OwZIZ8K9rEQCIPMd7Pl20jhkz\npopCISIiIiIiIvKd//35i4iIiIiIiC4a5/yl1ZzL6o3D4cDmzZurNCgiIiIiIiIi4DwXreZc1v91\n4MABfPDBB3C5Lsx99Inh/vWg54YdC+0OwZKwW4bYHYJl8WkpdodgWcDVo+0OwRJjy7/tDsEyo6LC\n7hAsGzi7nt0hWOIcNNnuECy78cq77A7BkkajIuwOwboA/8snT1noX3n7fwwtsjsEy75w+Vc+OQC0\nKvevGxGbBRTbHYJlngD/quUAAD0D/etYnWn4Vz8GgHNl7Z/zotVbLmtOTg5eeeUVLF26FL169cKD\nDz74W+MjIiIiIiIi8sqnQkwAUFRUhDfffBNvv/024uPj8eabb6JXr17VGRsRERERERFd5M570epy\nufDRRx9hwYIFiIiIwNNPP40hQ/zvVlIiIiIiIiLyP+e8aP3yyy/x0ksvobCwEHfddRduvPFGOJ3O\nCxUbERERERERXeQchmGc9cnaiYmJCA0NxZAhQxARcfaiEI888ki1BPeL5ORkuIf9pVrnUdXmO/0r\n+bkx/OtB2gDQtsK/1jEAzHbvtzsES8o9/vfA8htqtbE7BMtC4V8FKXYY/lVoDgACz1MJv6bZUXLM\n7hAs6xbW1O4QLGuNMLtDsGTaBP8bkx21/a+omCcjy+4QLCnc5n9j8qZ9jewOwbJOsSftDsGSupf6\n33iRPv2v6NKli9f3zvlLa1JSEhwOBw4ePHjWNud7JA4RERERERHRr3XOi9YPP/zwQsVBRERERERE\npPjf/ZVERERERER00fD5kTd2+8bpXw8t7+X2r9umJzzuXw9MBoDvZp2wOwTLVrcLtTsESzwu/+rH\nAFD35RvtDsGyw//3mt0hWHJj4FlLIdRYDYf7V16dww9zs898m2F3CJY5/OxP96MX1bI7BMsyy4/Y\nHYJlV4c1szsES2oZkXaHYFmbQD/b+QBce/yU3SFY8s+TLewOwbLg6Wd/z/96DBEREREREV00eNFK\nRERERERENdZ5L1pdLhcWL16M7OzsCxEPERERERERUaXzXrQGBQXh2WefRUVFxYWIh4iIiIiIiKiS\nT4WYOnbsiNTUVDRu3Li64zmrP3Y+atu8f40tGxrYHYI1ISF2R2AZ722vfu4y/1vLFe/8w+4QLNt8\nJsbuECxp5yiyOwTLXvnQb+oOAgAS/e+Z8Lg+3u4IrDuT7V/HvkUtSuwOwbLyQv8rHlVWkm93CJYU\nnPGvIo8AEBXlf315a+c6dodgScnhXLtDsOz4Od7z6Sg+duxYPP/88zhx4gTat2+PsLAw8X5iYuJv\niY+IiIiIiIjIK58uWh944AEAwNNPP135msPhgGEYcDgcSE1NrZ7oiIiIiIiI6KLm00XrN998U91x\nEBERERERESk+XbQ2a2b/Q5ZLMv0rty6+3mm7Q7AmoKXdEVjW5zb/Kw7miG1jdwiWOGL8K9cSAAKS\n+todgmWjWy2zOwRrAqPtjsCyx5bstDsES8J71rM7BMsq0h12h2BZeGf/ygVMWRxudwiW7Yf/5bQm\nBhTaHYIlJW7/ytkHgKBC/8onB4CX1kXZHYIlT7Q+YXcIVcrnXr5//3689dZbSE9Ph8PhQHx8PCZN\nmoT4eD+svEBERERERER+waefL9esWYPhw4fj8OHD6NGjB7p3745Dhw5h+PDh+Pe//13dMRIRERER\nEdFFyqdfWufNm4fJkydj2rRp6vW5c+fiqquuqpbgiIiIiIiI6OLm0y+tBw8exMiRI9XrI0eOxIED\nB6o8KCIiIiIiIiLAx19ao6OjkZKSghYtWojXU1JSEHOBCrXsOehfRSkiAv2rSNBDj/1sdwiWvVin\n1O4QLHOG+leBruib/a+4AwyP3RFYljHfv/741/TpK+wOwTKnfz0THu7MfLtDsGz3D/5XuG2rM+z8\njWqQDu4yu0OwbHuo/43JBe5Iu0OwpE+4f51bAEBmof8VFbvUGWx3CJbs2NnQ7hAsO1eZR5/OSMeM\nGYMnnngCR48eRefOnQEA27Ztwz/+8Q/ceuutVRAiERERERERkebTRetdd92F0NBQLFq0CHPnzgUA\nxMTEYOrUqZg4cWK1BkhEREREREQXL58uWh0OByZPnozJkycjPz8fhmGgTh1r91qVlZXhpptuQnl5\nOdxuNwYOHIh7770XGRkZeOCBB5Cfn4927dph9uzZCA72r5/fiYiIiIiIqHo4DMMwztfotttuw8sv\nv4zISHmPf2FhIe69914sXLjwvDMyDAPFxcUIDw9HRUUFxo0bhz/96U9YtGgRBgwYgCFDhmDWrFlI\nTEzEuHHjxGeTk5ORP/h5i4tGRETknxLjc+wOgYiI6ILKfPWv6NKli9f3fKoevGHDBpSXl6vXy8rK\nsHnzZp+CcDgcCA//T9K1y+WCy+WCw+HApk2bMHDgQAD/qUa8evVqn76PiIiIiIiIfv/OeXtwWlpa\n5f/379+PkydPVk57PB6sXbsWsbGxPs/M7XZj1KhROHLkCMaNG4emTZuidu3aCAr6TxgNGjRAVlaW\n1WUgIiIiIiKi36lzXrSOGDECDocDDocDEyZMUO8HBwfjscce83lmgYGBWL58OQoKCnDXXXd5fcar\nw+Hw+fuIiIiIiIjo9+2cF63ffPMNDMPAwIEDsXjxYtStW7fyPafTiXr16sHpdFqeae3atdGjRw9s\n374dBQUFcLlcCAoKwokTJ876y21MqP89k5OIiOjXqNWSf8AlIiL6xTkvWps1awYA2L17NwIDA3/T\njHJzcxEUFITatWujtLQUGzZswO23344ePXrg66+/xpAhQ/DZZ5+hX79+v2k+RERERERE9Pvh0yNv\nFixYgNjYWIwdO1a8vnjxYpw8eRJ33333eb8jOzsbM2fOhNvthmEY+MMf/oC+ffuiVatWmDZtGl56\n6SW0bdsWY8aM+XVLQkRERERERL87Pl20fvbZZ5g3b556vW3btvj73//u00VrYmIili1bpl5v2rQp\nPvnkE1/CICIiIiIioouMT4+8ycnJQUxMjHq9bt26oqIwERERERERUVXy6ZfWRo0aITk5GU2bNhWv\nJycnIy4urloCM1uCyAsyHyIiIru1WBNldwhEREQXVNK0s7/n00Xr6NGj8dxzz8HtdqNnz54AgI0b\nN2LOnDmYNGlSlQRJREREREREZObTRevkyZORm5uLJ598Ei6X6z8fDArC+PHjMWXKlGoNkIiIiIiI\niC5ePl20OhwOPPzww7jrrruwb98+GIaBhIQEREREVHd8REREREREdBHz6aL1FxEREUhKSqquWM5p\nq+e0LfMlIiK60K51hdsdAhERUY3h80Xrli1bsHLlSmRmZqKiokK8t3DhwioPjIiIiIiIiMinR94s\nW7YMEydORG5uLtavX4+IiAicPHkSO3fuRJMmTao7RiIiIiIiIrpI+XTR+tZbb+Hxxx/HK6+8AqfT\niRkzZmDFihUYMmQI6tSpU90xEhERERER0UXKp4vWjIwMXH755QCA4OBgFBUVAQBuueUWLF26tPqi\nIyIiIiIioouaTzmtUVFRlReqcXFx2L9/PxITE1FQUIDS0tJqDfAX8YGRF2Q+REREdrusd5bdIRAR\nEV1QB8/xnk8XrV26dMGGDRvQpk0bDBw4EM888ww2bdqEDRs2oHfv3lUUJhEREREREZHk00XrY489\nhrKyMgDAnXfeiYCAAGzbtg39+/fH3XffXa0BEhERERER0cXLp4vW6Ojoyv8HBgZi6tSp1RYQERER\nERER0S/OedH63HPP4b777kOtWrUAACdOnEBsbCwCAnyq31Slnmh94oLPk4iIyA4B4T4/Rp2IiOh3\n75xXn++++y5KSkoqpwcPHoxjx45Ve1BEREREREREwHkuWg3DOOc0ERERERERUXW68Pf5EhERERER\nEfnonEkzDocD+fn5CAwMrJwuKChAXl6eaFenTp3qi5CIiIiIiIguWue8aDUMA0OGDBHTo0ePFtMO\nhwOpqanVF+H/l5dRq9rnQUREVBMUpgXbHQIREdGFNe3sb53zovXdd9+t6lCIiIiIiIiIfHbOi9bu\n3btfqDiIiIiIiIiIFBZiIiIiIiIiohrLb55efm+Rw+4QiIiILoiBqG13CERERBdUn3O8x19aiYiI\niIiIqMbiRSsRERERERHVWD5dtM6fPx8lJSXq9dLSUsyfP7/KgyIiIiIiIiICfLxoXbBgAYqLi9Xr\nJSUlWLBgQZUHRURERERERAT4WIjJMAw4HLoQUmpqKqKioqo8KG8+mciiFEREdHHYu6jA7hCIiIgu\nKOMc753zojUpKQkOhwMOhwP9+/cXF64ejwdlZWUYO3ZsVcVJREREREREJJzzonXWrFkwDAOPPvoo\npk2bhsjIyMr3nE4nGjdujKSkpGoPkoiIiIiIiC5O57xoHTlyJACgSZMmSEpKgtPpvCBBERERERER\nEQE+5rR2794dAJCVlYXc3Fx4PB7xfvv27as+MpM+bxyt9nkQERHVBFeHNrU7BCIiogvqpnO859NF\na0pKCmbMmIEDBw7AMGSKrMPhQGpq6m+Jj4iIiIiIiMgrny5aH3/8cTRo0AB/+ctfEBsb67WSMBER\nEREREVFV8+miNT09HZ999hkuueSS6o6HiIiIiIiIqFKAL40SEhKQk5NTJTN0u90YMWIE7rjjDgBA\nRkYGxowZgwEDBuD+++9HeXl5lcyHiIiIiIiI/J9Pv7ROmzYNc+bMwf3334+E/9fenYdVVe1/HP8A\nQZoDKIUDomYFpiiglWGpN0zzipSZeqUUKi3T1Bxy1gbHnMohU0krvZnpRYUkM4dS0zLTq+H8OORP\ncADDCZJAZP3+8PFciemYytng+/U8PA9n7bXX/u6z2Cy+rLX38fXVHXfk3M3Dw8PuAy5YsED33Xef\n0tLSJEmTJ0/Wiy++qNDQUL311luKjo7W888/n2u/IU417D4GAADFmW92mqNDAACgSF0uYJtdM60v\nvfSS4uPj9fLLL6tJkyYKDg5WcHCwHn30UQUHB9sdyKlTp7R+/Xq1b99ekmSM0ZYtW/TUU09JuvIR\nO+vWrbO7PQAAAABAyWbXTOuCBQtuysHGjRungQMH6o8//pAknT17VuXLl7fN3FauXFlJSUk35VgA\nAAAAgOLvuj6n9UZ8//33qlixovz9/fXzzz/nW48nEwMAAAAArrIraZWkAwcOaPHixTp27JjGjRsn\nLy8vrV27VlWrVlWdOnUK3f+///2vvvvuO23cuFEZGRlKS0vT2LFjdeHCBWVlZemOO+7QqVOn5OXl\nlef+lbJ5QBMA4PaQkeXi6BAAAChSBSWmdt3TumnTJrVv315JSUnasmWLMjIyJEnHjh3Thx9+aFcQ\nAwYM0MaNG/Xdd9/p/fff16OPPqopU6aoUaNG+vbbbyVJy5cvV0hIiF3tAQAAAABKPruS1mnTpmnI\nkCGaOXOmXF1dbeWPPPKI4uPjbyiAgQMH6tNPP1WLFi107tw5dejQ4YbaAwAAAACUHHYtDz506JCa\nNWuWq9zd3V3nz5+/7oM2atRIjRo1kiT5+PgoOjr6utsAAAAAAJR8ds20li9fPs+n+u7du1eVK1e+\n6UEBAAAAACDZOdPapk0bTZo0SVOnTpWTk5OysrK0detWTZgwQe3atbvVMUqSVpS2+5lRAAAUa329\nzjg6BAAAilRBH3xq10xr37595e3trSeeeEIXL15UaGioIiMj1bBhQ/Xo0eMmhQkAAAAAQE52TV+6\nurpqypQp6tu3r/bs2aPs7GzVqVNHNWvWvMXhAQAAAABuZ3bNtH744YdKT0+Xj4+PWrVqpdatW6tm\nzZr6888/7f7IGwAAAAAArpddM60zZ85UeHi4SpcunaM8PT1dM2fOVK9evW5JcNe6N4t7WgEAt4cT\nieUdHQIAAEXKpYBtds20GmPk5OSUq3zfvn1yd3f/u3EBAAAAAFCgAqcvg4KC5OTkJCcnJzVv3jxH\n4pqdna2MjAx16tTplgcJAAAAALg9FZi0vvXWWzLGaNiwYerXr5/KlStn2+bq6ipvb28FBQXd8iAB\nAAAAALenApPWZ599VpJUrVo1BQUFydXVtUiCAgAAAABAsvNBTI888ojt+9OnT+vSpUs5tletWvXm\nRpUHu26+BQCgBFjqWrrwSgAAlCAdC9hmV9Kalpam0aNH65tvvsmVsEpXHsgEAAAAAMDNZtcE5nvv\nvaf9+/dr5syZuvPOOzVlyhQNGjRIlStX1gcffHCrYwQAAAAA3KbsmmnduHGj3n//fT300ENydnZW\n3bp11bp1a91zzz1avHixWrVqdavjBAAAAADchuxKWlNTU233rZYrV07nzp1TjRo1FBgYqBEjRtzS\nAK+KeCW7SI4DAICjTfgk92ejAwBwu7JrebCPj48SEhIkSffdd5++/vprGWO0Zs0aubu739IAAQAA\nAAC3L7uS1nbt2unAgQOSpFdeeUWLFy+Wv7+/Jk6cqG7dut3SAAEAAAAAty+7lge/+OKLtu+Dg4P1\nzTffaPfu3apRo4b8/PxuVWwAAAAAgNucXUnrX1WtWlVVq1bV8ePH9cYbb2jatGk3O65cxn9yyw8B\nAIAlxGUcdXQIAAAUqWcK2GbX8uD8pKamavXq1TfSBAAAAAAA+bqhpBUAAAAAgFuJpBUAAAAAYFkk\nrQAAAAAAyyrwQUyvvfZagTv/8ccfNzWYggx91aXIjgUAgCMNda3t6BAAAChS+wvYVmDSWqFChQIb\nrlChgqpVq/Z3YgIAAAAAoFAFJq3jx48vqjgAAAAAAMiFe1oBAAAAAJZV4EyrlXw2yzg6BAAAisTv\nLhmODgEAgCLVpnn+25hpBQAAAABYFkkrAAAAAMCySFoBAAAAAJZF0goAAAAAsKxi8yCmky7Zjg4B\nAIAicVKZjg4BAADLYKYVAAAAAGBZJK0AAAAAAMsiaQUAAAAAWFaR3tMaEhKiMmXKyNnZWS4uLlq2\nbJnOnTunfv366fjx4/L29tbUqVPl7u6ea98tl38vylABAHCYSi5lHB0CAACWUeQzrfPnz1dsbKyW\nLVsmSYqKilJwcLBWr16t4OBgRUVFFXVIAAAAAACLcvjy4HXr1qlt27aSpLZt22rt2rUOjggAAAAA\nYBVFnrR27dpV7dq10+LFiyVJKSkp8vLykiR5eXnpzJkzRR0SAAAAAMCiivSe1kWLFqlSpUpKSUnR\nSy+9pFq1ahXl4QEAAAAAxUyRJq2VKlWSJHl6eqpFixaKj4+Xp6enkpOT5eXlpeTkZFWsWDHPff/T\nmA9aBwDcHt7ZWsHRIQAAYBlFtjz44sWLSktLs32/efNmPfDAAwoJCVFMTIwkKSYmRs2bNy+qkAAA\nAAAAFldkM60pKSl6/fXXJUmXL19WmzZt1LRpU9WrV099+/ZVdHS0qlSpomnTphVVSAAAAAAAiyuy\npNXHx0dfffVVrvIKFSpo/vz5RRUGAAAAAKAYKdJ7Wm/E3C3VHB0CAABF4q2AY44OAQCAInW0gG0O\n/5xWAAAAAADyQ9IKAAAAALAsklYAAAAAgGWRtAIAAAAALKvYPIjpJ513dAgAABSJ73eWdXQIAAAU\nqdEFbGOmFQAAAABgWSStAAAAAADLImkFAAAAAFhWsbmndULZTEeHAABAkYhJu8fRIQAAYBnMtAIA\nAAAALIukFQAAAABgWSStAAAAAADLImkFAAAAAFhWsXkQ09epPJQCAHB76NYo0dEhAABQpI4UsI2Z\nVgAAAACAZZG0AgAAAAAsi6QVAAAAAGBZxeae1j+djKNDAACgSFxOz3Z0CAAAWAYzrQAAAAAAyyJp\nBQAAAABYFkkrAAAAAMCySFoBAAAAAJZVbB7ENPfiPkeHAABAkaj139qODgEAgCJ1XwHbmGkFAAAA\nAFgWSSsAAAAAwLJIWgEAAAAAllVs7mkt5eLq6BAAACgSfzo5OToEAAAsg5lWAAAAAIBlkbQCAAAA\nACyLpBUAAAAAYFkkrQAAAAAAyyo2D2Iaq1qODgEAgCLxcM0Tjg4BAIAidbKAbcy0AgAAAAAsi6QV\nAAAAAGBZJK0AAAAAAMsqNve0Vlv5pqNDAACgSBR0Xw8AALcbJ2OMcXQQAAAAAADkheXBAAAAAADL\nImkFAAAAAFgWSSsAAAAAwLIs9SCmoUOHav369fL09FRcXJwk6dy5c+rXr5+OHz8ub29vTZ06Ve7u\n7jLGaOzYsdqwYYNKlSql9957T3Xr1nXwGdze8uq/CRMm6Pvvv5erq6uqV6+u8ePHq3z58kpMTFTr\n1q117733SpICAgI0atQoR4Z/W8ur72bMmKElS5aoYsWKkqT+/furWbNmkqQ5c+YoOjpazs7OGjFi\nhJo0aeKw2JF3//Xt21e//fabJCk1NVXlypVTbGws157FnDx5UoMGDdLvv/8uZ2dndezYUZGRkYx9\nxUR+/cfYZ3359R1jX/GQX/8x9pVgxkK2bt1qdu/ebUJDQ21lEyZMMHPmzDHGGDNnzhwzceJEY4wx\n69evN127djXZ2dlmx44dpn379g6JGf+TV//98MMP5tKlS8YYYyZOnGjrv4SEhBz14Fh59d306dPN\n3Llzc9U9ePCgCQsLMxkZGebYsWOmefPmJisrqyjDxV/k1X/XGj9+vJkxY4YxhmvPapKSkszu3buN\nMcakpqaali1bmoMHDzL2FRP59R9jn/Xl13eMfcVDfv13Lca+ksVSy4Mffvhhubu75yhbt26d2rZt\nK0lq27at1q5dm6PcyclJgYGBunDhgpKTk4s8ZvxPXv33+OOP6447rkzoBwYG6tSpU44IDYXIq+/y\ns27dOoWGhsrNzU0+Pj6qUaOG4uPjb3GEKEhB/WeM0TfffKM2bdoUcVSwh5eXl22mtGzZsqpVq5aS\nkpIY+4qJ/PqPsc/68uu7/DD2WUth/cfYV/JYKmnNS0pKiry8vCRd+QE9c+aMJCkpKUmVK1e21atc\nuXKBv2zgeEuXLlXTpk1trxMTE9W2bVt17txZ27Ztc2BkyM/ChQsVFhamoUOH6vz585JyX3uVKlXi\n2rOwbdu2ydPTUzVr1rSVce1ZU2Jiovbt26eAgADGvmLo2v67FmOf9f217xj7ipe8rj3GvpLH8klr\nfkweHy/r5OTkgEhgj1mzZsnFxUVPP/20pCt/hH3//feKiYnRkCFDNGDAAKWlpTk4SlwrPDxca9as\nUWxsrLy8vPTee+9J4torbuLi4nL8p5lrz5r++OMP9enTR8OGDVPZsmXzrcf1Z0359R9jn/X9te8Y\n+4qX/K49xr6Sx/JJq6enp23pU3Jysu3G+MqVK+dYbnPq1Cnbf6VhLcuXL9f69es1efJk2y94Nzc3\nVahQQZLk7++v6tWr226chzXcfffdcnFxkbOzszp06KBdu3ZJyn3tJSUlce1ZVFZWltasWaPWrVvb\nyrj2rOfSpUvq06ePwsLC1LJlS0mMfcVJXv0nMfYVB3n1HWNf8ZHftcfYVzJZPmkNCQlRTEyMJCkm\nJkbNmzfPUW6M0c6dO1WuXDl+eVjQxo0b9fHHH2vWrFkqXbq0rfzMmTO6fPmyJCkhIUFHjx6Vj4+P\no8JEHq69T27t2rV64IEHJF259r7++mtlZmba+q5+/fqOChMF+PHHH1WrVq0cS9q49qzFGKPhw4er\nVq1aeumll2zljH3FQ379x9hnffn1HWNf8ZBf/0mMfSWVk8lrvYOD9O/fX1u3btXZs2fl6emp3r17\n68knn1Tfvn118uRJValSRdOmTZOHh4eMMRo1apR++OEHlS5dWuPGjVO9evUcfQq3tbz6LyoqSpmZ\nmfLw8JD0v0eMf/vtt5o+fbpcXFzk4uKi3r17KyQkxMFncPvKq++2bt2q/fv3S5K8vb01atQo2x/H\ns2bN0tKlS+Xi4qJhw4bZPg4AjpFX/3Xo0EFDhgxRQECAwsPDbXW59qxl27ZteuGFF+Tr6ytn5yv/\nR+7fv7/q16/P2FcM5Nd/Y8aMYeyzuPz6Li4ujrGvGMiv/5o1a8bYV0JZKmkFAAAAAOBall8eDAAA\nAAC4fZG0AgAAAAAsi6QVAAAAAGBZJK0AAAAAAMsiaQUAAAAAWBZJKwDA0n7++Wf5+fnpzJkzjg7l\npurevbuGDBlie92lSxeNGjXKgREV7vz582rcuLGOHTt2y4/l5+enVatW3fR27Xmfr7cvMjMz9Y9/\n/EO7du260fAAAHm4w9EBAABund9//11RUVFav369Tp48qbJly6pGjRoKDQ1Vu3btVKZMGUeHWKig\noCBt2rRJFSpUuKF2Vq9erc8//1x79+5VVlaWfHx8FBISooiICHl6et6kaP++GTNm6I47bu6wvGzZ\nMo0ePVo7duy4Ke3Nnj1bzZo1U/Xq1SVJ+/fvV1RUlLZv366zZ8+qSpUq6tChg15++WXbZycmJiaq\nefPmudr6+OOP1bRp05sS1812vX3h5uamrl27avLkyZo/f/4tjAwAbk8krQBQQiUmJio8PFxly5bV\nG2+8IT8/P2VnZ+vo0aOKiYmRh4eHwsLCHB1modzc3HTPPffcUBsffPCBoqKiFBERoT59+qhKlSo6\nduyYoqOjtWjRIvXq1esmRZtTVlaWXFxc5OTkVGhdDw+PWxLDzZKenq7o6GjNmjXLVrZ7925VrFhR\nEydOVNWqVRUfnDNoFQAADTNJREFUH68RI0YoKytLr732Wo79586dq9q1a9teu7u7F1ns1+vv9EVY\nWJgmTJiggwcP6oEHHrgFUQHA7YvlwQBQQr3zzjtydnbW0qVLFRoaqvvvv1++vr5q2bKlPvroI7Vp\n08ZWNzU1VSNHjlRwcLCCgoLUuXPnHEsdly1bpqCgIP30009q06aNAgMD1aVLFyUkJOQ45pdffqkW\nLVrI399fLVq00JIlS3Js9/Pz0xdffKEePXooICBATz31lLZs2aJTp06pa9euCgwM1DPPPKM9e/bY\n9slrefDOnTsVERGhwMBANWzYUJGRkUpKSsrzfYiPj9fs2bM1aNAgDR06VA899JC8vb0VHBysKVOm\nKCIiwu74T5w4oddff11BQUEKCgpSr169dOrUKdv2GTNmqE2bNlq2bJmefPJJ1atXTxcvXlR6erqG\nDBmioKAgNW7cWLNnz84V51+XpIaEhOijjz7SW2+9pQYNGqhp06aaO3dujn0+/fRThYWFKTAwUE2a\nNNHw4cN14cIF2/s2dOhQXbx4UX5+fvLz89OMGTMkXVnOOmnSJDVt2lSBgYF67rnn9MMPP+T5/l21\nYcMGOTs7q2HDhray9u3ba8SIEWrUqJF8fHwUGhqq8PBwrV69Otf+Hh4euueee2xfbm5uBR5PurJS\n4NVXX1VAQICeeOIJxcbG2rYlJibKz89PK1asUHh4uOrVq6dWrVpp06ZNhbablZWlMWPG6OGHH9bD\nDz+sCRMmKDs727b97/SFh4eHGjRooLi4uEKPDwC4PiStAFACnTt3Tps2bdILL7ygu+66K886V2f/\njDF69dVXlZSUpDlz5igmJkYPPfSQIiMjlZycbKufmZmpOXPmaNy4cfryyy+Vmpqqd955x7Z9zZo1\nGj16tCIjI7VixQpFRETo3Xff1XfffZfjuLNmzVJoaKhiY2Pl7++vAQMGaPjw4QoPD9fy5cvl5eWV\n417Pv9q/f78iIiJUo0YNLVq0SEuWLNE///lPXb58Oc/6X331le666y517tw5z+3ly5e3K35jjF5/\n/XWlpKRo/vz5WrBggZKTk9WzZ08ZY2ztJSYmKi4uTtOmTVNsbKzuvPNOTZgwQZs3b9b06dP12Wef\nae/evfrll1/yPcer5s+fL19fXy1fvlyvvPKKJk2alGOpr5OTk4YNG6a4uDhNmTJF8fHxGj16tKQr\ny6qHDRum0qVLa9OmTdq0aZNefvllSdLQoUP1yy+/aMqUKVqxYoWeffZZ9ejRQ/v37883lm3btqlu\n3bqFzhqnpaXZ3tNr9e7dW8HBwerUqZPd96rOmDFDISEhiomJUceOHTV48OBc941OmjRJXbp0UUxM\njB577DH17Nkz339gXLVixQoZY/Tll1/q3Xff1ZIlSwpd1ltYX0hS/fr17epXAMB1MgCAEmfnzp3G\n19fXrF69Okd5kyZNTGBgoAkMDDQjR440xhjz448/msDAQJOenp6j7tNPP22ioqKMMcYsXbrU+Pr6\nmsOHD9u2x8bGmrp165rLly8bY4z517/+ZYYMGZKjjcGDB5tOnTrZXvv6+prJkyfbXh84cMD4+vqa\nTz75xFa2ZcsW4+vra1JSUvJ83b9/f9OhQwe734tu3bqZsLCwQusVFv+mTZtM7dq1TUJCgm37sWPH\njJ+fn9m8ebMxxpjp06ebOnXqmNOnT9vqpKWlmbp165rY2NgcZQ0bNjSDBw+2lXXu3Nm8++67ttdP\nPPGE6devX454WrRoYWbOnJnvOWzYsCFHnyxdutQEBgbmqPN///d/xs/Pzxw/fjxHeY8ePczbb7+d\nb9s9evQwgwYNyne7Mcbs3r3b+Pv7m1WrVtnKUlJSzLx588yOHTtMfHy8mTp1qqldu7aJiYkpsC1f\nX18zfPjwHGWRkZFmwIABxhhjEhISjK+vr/noo49s2y9fvmxatmxp3n///Xzb7dy5s2nZsqXJzs62\nlc2cOdM0adIkR52/0xfz5883TZs2LfC8AADXj3taAeA2snDhQmVnZ2vkyJHKzMyUJO3Zs0fp6ekK\nDg7OUTcjIyPH8l83NzfVqlXL9trLy0uXLl3ShQsX5OHhoSNHjui5557L0UbDhg1zzbT6+fnZvr/7\n7rslSb6+vrnKzpw5o4oVK+Y6h3379qlFixZ2n7O5Zha0IIXFf/jwYXl5ealatWq27T4+PvLy8tKh\nQ4fUuHFjSVKlSpVs5yBJCQkJunTpkoKCgmxlZcqUyXHO+bn2vZKuvOfXLpP+6aefFBUVpcOHDys1\nNVXZ2dm6dOmSTp8+rUqVKuXZ5p49e2SMUWhoaI7yzMxMPfroo/nGkpGRkeO8/urIkSPq3r27IiMj\n9dRTT9nKK1asaJvhlaR69erp7Nmzmjt3rp555pl825OkwMDAXK83bNiQbx1nZ2fVr19fhw8fLrDd\ngICAHDPGQUFBmjZtmtLS0lS2bNk89ymsLySpVKlS+vPPPws8NgDg+pG0AkAJVL16dTk5OenIkSM5\nyn18fCRJpUuXtpVlZ2fr7rvv1sKFC3O1c+0f8H99murVP/qvvRcwr6Wjfy3L66mseZVd2+617E1C\nr6pZs6a2b9+uzMzMQu+jLCh+Y0y+S2OvLf/rcuzrjfdaeb3nV9+X48ePq3v37urYsaP69OkjDw8P\n7d27V/3799elS5fybfPqeURHR+dqv1SpUvnu5+HhofPnz+e57fDhw4qMjFTr1q315ptvFnpeAQEB\nWrZsWaH1rKSgvrjq3Llzef6jBQBwY7inFQBKoAoVKuixxx7T559/rj/++KPAunXr1tXvv/8uZ2dn\n1ahRI8fX9XwUTK1atbR9+/YcZdu3b9d99933t84hP3Xq1NGWLVvsrh8WFqaLFy/mmZRLsj24qLD4\n77//fiUlJSkxMdG2PSEhQcnJybr//vvzPX716tXl6uqqnTt32souXryogwcP2n0Oedm9e7cuXbqk\noUOHKigoSPfee2+Oe5AlydXVNde9vg8++KCMMTp9+nSu/s5vdla68r7nNYN56NAhRUREqFWrVho2\nbJhdse/bt8+uJ0L/+uuvuV5fO9v/1zrGGMXHxxf6M/frr7/m+GfCzp075eXlle8sq70OHjyoOnXq\n3FAbAIDcSFoBoIR65513ZIxRu3btFBcXp0OHDum3335TXFyc9u/fb/sczcaNG6tBgwbq2bOnNmzY\noISEBO3YsUPTp0/Xtm3b7D5et27d9NVXX2nhwoU6evSo/v3vf2vFihXq1q3bTT2vbt26ae/evRo5\ncqT279+vI0eO6D//+Y9OnDiRZ/2AgAB169ZNEydO1Pjx47V9+3YdP35cP//8swYOHKgFCxbYFX/j\nxo1Vu3Ztvfnmm9q9e7d27dqlN998U3Xq1ClwWW2ZMmX03HPPafLkydq8ebMOHjyoYcOG5fvgKHvV\nqFFD2dnZmj9/vhISEhQXF5frYULe3t7KyMjQ5s2bdebMGaWnp+vee+9VWFiYhg4dqlWrVikhIUG7\ndu3SvHnz8nzq71WPP/64Dh8+rLNnz9rKDh48qIiICD3yyCPq3r27Tp8+bfu6avny5VqxYoUOHz6s\nI0eOaN68efriiy/UpUuXQs9x9erVWrJkiY4ePao5c+bop59+UmRkZI46ixYt0qpVq3TkyBGNHTtW\nJ06cUHh4eIHtJicna+zYsTpy5IhWrVqlefPm6cUXXyw0nsJs375dTZo0ueF2AAA5sTwYAEooHx8f\nLV++XHPmzNG0adN08uRJubq6qlatWnr++ef1wgsvSLqyzDEqKkpTp07VyJEjdebMGXl6eqpBgwZq\n27at3cd78sknNWLECH3yyScaN26cqlatqrffflshISE39bwefPBBffrpp/rggw/UsWNHubm5yd/f\nX82aNct3n4EDB8rf319ffPGFoqOjdfnyZVWrVk3NmzfX888/b1f8Tk5OmjlzpsaMGWNLuBo3bqyR\nI0cW+kTdwYMHKz09Xb169VKpUqXUuXNnpaen39D7ULt2bQ0fPlwff/yxpk6dqqCgIA0aNEj9+vWz\n1WnQoIE6deqk/v3769y5c+rVq5d69+6t8ePHa/bs2Zo0aZKSkpLk7u6uevXqqVGjRvkez8/PT/Xr\n19fKlSttPzurVq1SSkqKVq5cqZUrV+aof+DAAdv3s2bN0okTJ+Ts7KyaNWtq7Nixhd7PKl154vC3\n336rMWPGqGLFiho/frzq16+fo86AAQP02Wefac+ePapatao+/PBDVa5cucB2w8LClJ2drY4dO8rJ\nyUnt27e/4aR1x44dSk1NVatWrW6oHQBAbk7mRm62AQAAt42NGzdq7NixWrlypVxcXBwaS2Jiopo3\nb67o6GjVq1fPobFIUp8+fVSnTh299tprjg4FAEocZloBAIBdmjZtqqNHj+rUqVPy9vZ2dDiWkZmZ\nqdq1a9+UJcYAgNxIWgEAgN0iIiIcHYLluLm5qWfPno4OAwBKLJYHAwAAAAAsi6cHAwAAAAAsi6QV\nAAAAAGBZJK0AAAAAAMsiaQUAAAAAWBZJKwAAAADAsv4flryeMjho2rsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d3cf23f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "plt.title(\"Avocado Latent Factors\", fontsize=14)\n",
    "plt.imshow(genome_embedding[100:300].T)\n",
    "plt.ylabel(\"Latent Factor Number\", fontsize=14)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.xlabel(\"Genomic Coordinate (25 bp bin)\", fontsize=14)\n",
    "plt.xticks(range(0, 200, 25), range(100, 300, 25), fontsize=10)\n",
    "plt.grid(False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we consider this visualization of genomic positions 2500 to 7500, the three scales of latent factors become immediately obvious. There are some factors that differ from one coordinate to the next, the 25 bp factors, some that are the same for 10 consecutive positions, the 250 bp factors, and some that are the same for 200 consecutive positions, the 5 kbp factors. In this case the 5 kbp factors would span from 0 to 200 and then 200 to 400, but we focus on 100 to 300 for display purposes. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}