190311_3layer-trans-inv-reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version: \n",
    "- 44,100 Hz sample rate \n",
    "- 0.01 regularization\n",
    "- 300,000 iterations\n",
    "- 0.95 learning rate decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Import the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,shutil,mmap\n",
    "import pickle as pickle\n",
    "import numpy as np                                       # fast vectors and matrices\n",
    "import matplotlib.pyplot as plt                          # plotting\n",
    "from scipy.fftpack import fft\n",
    "\n",
    "import time\n",
    "\n",
    "sys.path.insert(0,'lib/')\n",
    "import config\n",
    "\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import mir_eval\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "notebook_reference = str(190311)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "debugging = False\n",
    "delete_ids = [2560, 1792, 2562, 2506, 2564, 2566, 1793, 2568, 2570, 2571, 2572, 2573, 2575, 2576, 2302, 2581, 2582, \n",
    "              2586, 2075, 2076, 2077, 2590, 2079, 2080, 2081, 2594, 2595, 2596, 2603, 2678, 2397, 2608, 2611, 2614, \n",
    "              2104, 2105, 2619, 2620, 2621, 2622, 2112, 2113, 2114, 2627, 2116, 2629, 2118, 2119, 2632, 2633, 2127, \n",
    "              2131, 2138, 2319, 2140, 2405, 2659, 2148, 2149, 2150, 2151, 2154, 2155, 2156, 2157, 2158, 2159, 2160, \n",
    "              2161, 2677, 2166, 2167, 2168, 2169, 2239, 2325, 2177, 2178, 2179, 2180, 1729, 2186, 2242, 2194, 2195, \n",
    "              2196, 1829, 2198, 2200, 2201, 2202, 2203, 2204, 2330, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, \n",
    "              2215, 2588, 2218, 2219, 2220, 2221, 2222, 2224, 2225, 2227, 2228, 2229, 2230, 2231, 2232, 2234, 2591, \n",
    "              2237, 2238, 1727, 1728, 2241, 1730, 2243, 2244, 1733, 1734, 2593, 2248, 1739, 2240, 2082, 1742, 2083, \n",
    "              1749, 1750, 1751, 1752, 1755, 1756, 1757, 1758, 1760, 1763, 1764, 1765, 1766, 1768, 2282, 1771, 1772, \n",
    "              1773, 1775, 1776, 1777, 2292, 2293, 2294, 2295, 2296, 2297, 1919, 1788, 1789, 1790, 2304, 2305, 2307, \n",
    "              2308, 2310, 2313, 2314, 2315, 1805, 2318, 1807, 2320, 2322, 1811, 1812, 1813, 1817, 1818, 2334, 2335, \n",
    "              2336, 1828, 2341, 2342, 2343, 2567, 2345, 2346, 1835, 2348, 2350, 2491, 2357, 2358, 2359, 2364, 2365, \n",
    "              2366, 2368, 2371, 2372, 2373, 2374, 2529, 2376, 2377, 2379, 2443, 2381, 2383, 2384, 1873, 2388, 2389, \n",
    "              2390, 2391, 2392, 2393, 2618, 2398, 2403, 2404, 1893, 2406, 2410, 2411, 2415, 2417, 2147, 2451, 2420, \n",
    "              2422, 2423, 2424, 1916, 2538, 1918, 2431, 2432, 2433, 1922, 1923, 2436, 2441, 2442, 1931, 1932, 2626, \n",
    "              1735, 2285, 1859, 2247, 2117, 2288, 2466, 2444, 2289, 2472, 2473, 2476, 2477, 2478, 2480, 2481, 2482, \n",
    "              2483, 2462, 2486, 2487, 2488, 2490, 2463, 2492, 2494, 2555, 2497, 1822, 2501, 2502, 2504, 2505, 2078, 2507]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the data\n",
    "Import the recording and labels of the 331 recordings from the data base.\n",
    "\n",
    "#### 1.1 Import the labels of the data set.\n",
    "The 331 labels are saved in a dictionary whose\n",
    "- keys are integers (the ids of the recordings)\n",
    "- values are intervaltrees (for the different intervals of the recording it gives the played notes between 1 and 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(config.labels_path, 'rb') as f:\n",
    "    labels = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "331 labels\n"
     ]
    }
   ],
   "source": [
    "print(len(labels), 'labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2239\n",
      "<class 'intervaltree.intervaltree.IntervalTree'>\n"
     ]
    }
   ],
   "source": [
    "for key, value in labels.items() :\n",
    "    if key == 2239:\n",
    "        # id of the recording\n",
    "        print (key)\n",
    "        # intervaltree containing for each note intervals in which it is played\n",
    "        print (type(value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Import recordings\n",
    "\n",
    "Create a dictionary containing the data (recordings). Use Memory-mapped file objects.\n",
    "For each recording data[rec_id] is a tuple:\n",
    "1. data[rec_id][0] is the memory-mapped file object to the recording. It can be accesed by \n",
    "~~~~~~\n",
    "np.frombuffer(data[rec_id][0], dtype=np.float32).copy()\n",
    "~~~~~~\n",
    "2. data[rec_id][1] is its length (float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sz_float = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict()\n",
    "for record in os.listdir(config.records_path):\n",
    "    fd = os.open(config.records_path + record, os.O_RDONLY)\n",
    "    buff = mmap.mmap(fd, 0, mmap.MAP_SHARED, mmap.PROT_READ)\n",
    "    data[int(record[:-4])] = (buff, len(buff)/sz_float)\n",
    "    os.close(fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key: 2494 , Format: <class 'int'>\n",
      "Value: (<mmap.mmap object at 0x7f38d8a542b8>, 13100544.0)\n",
      "1. Entry: Memory-mapped file objects , <mmap.mmap object at 0x7f38d8a4f660>\n",
      "For example:\n",
      "[0.00036621 0.00039673 0.00036621 0.00036621 0.00033569 0.00033569\n",
      " 0.00036621 0.00036621 0.00036621 0.00033569 0.00036621 0.00039673]\n",
      "2. Entry: length of recording 5293440.0 , Type: <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "for key, value in data.items() :\n",
    "    if key == 2494:\n",
    "        # id of the recording\n",
    "        print ('Key:',key,', Format:',type(key))\n",
    "        # its value is a tuple\n",
    "        print ('Value:',value)\n",
    "        # 1. value: reference to recording\n",
    "        print (\"1. Entry: Memory-mapped file objects ,\", data[2239][0])\n",
    "        # to access content use:\n",
    "        x = np.frombuffer(data[2239][0], dtype=np.float32).copy()\n",
    "        print('For example:')\n",
    "        print(x[3005:3017])\n",
    "        print (\"2. Entry: length of recording\",str(data[2239][1]),\", Type:\",type(data[2239][1]))\n",
    "        #print(len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Split up in the test and train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11 samples in the test set\n",
      "There are 320 samples in the train set\n"
     ]
    }
   ],
   "source": [
    "extended_test_set = True\n",
    "use_mirex = True\n",
    "    \n",
    "# use the small or big test set\n",
    "if extended_test_set:\n",
    "    test_ids = config.test_ids_ext\n",
    "else:\n",
    "    test_ids = config.test_ids\n",
    "\n",
    "# include the mirex dev set in test results\n",
    "if use_mirex:\n",
    "    test_ids = test_ids + config.mirex_id\n",
    "\n",
    "train_ids = [rec_id for rec_id in labels.keys() if rec_id not in test_ids]\n",
    "\n",
    "if debugging == True:\n",
    "    train_ids = [rec_id for rec_id in train_ids if rec_id not in delete_ids]\n",
    "\n",
    "\n",
    "print('There are {} samples in the test set'.format(len(test_ids)))\n",
    "print('There are {} samples in the train set'.format(len(train_ids)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Preprocessing\n",
    "\n",
    "The input data (recordings) are normalized and the possible note range is defines to 128 notes. The lowest note is ..., the highest ... ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the recordings\n",
    "normalize = True\n",
    "# Possible notes are from range [base_note, base_note + m)\n",
    "base_note = 0\n",
    "mm = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Data augmentation\n",
    "\n",
    "Always make two small changes to input data by randomly stretching or shrinking our input audio that are not noticable by human hearing, but augment the data set:\n",
    "1. Pitch-shift in the frequency domain: Randomly  shifting  each data points in a minibatch by an integral number of semitones. This reinforces the architectural structure of the translation-invariant network.\n",
    "2. Jittering: continuous shift to each data point. This makes the models more robust to tuning variation between recordings --> regularization\n",
    "\n",
    "Define a function for extracting a sequence and its label as well as applying the data augmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pitching: stretching or shrinking our input audio with linear interpolation\n",
    "pitch_transforms=5 # =/- 5 semitones\n",
    "jitter=.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pitch-shifting corresponds to a multiplication by $2^{1/12}$ (an octave is equal to 12 semitones and corresponds to a factor 2 in the frequency domain) for each semirone.\n",
    "\n",
    "A scaling factor in the frequency domain implies also a scaling factor in the time domain. (speeding up the recording $\\to$ higher frequency $\\to$ sounds higher.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to obtain one data sample from recordings\n",
    "# input:\n",
    "# - rec_id: the id of the recording\n",
    "# - s: frame-number to extract\n",
    "# - window: windowsize\n",
    "# - pitchshift: shift the label by pitchshift halftones\n",
    "# - scaling_factor: scaling factor in frequency domain (includes corrections because of pitch shift and jitter)\n",
    "# - normalize: boolean\n",
    "# output\n",
    "# - x: audio segment of length window\n",
    "# - y: labels, 0-1-vector of length mm\n",
    "def get_data(rec_id,s,window,pitch_shift,scaling_factor, normalize=True):\n",
    "   \n",
    "    x = np.frombuffer(data[rec_id][0][s*sz_float:int(\n",
    "        s+scaling_factor*window)*sz_float], dtype=np.float32).copy()\n",
    "    \n",
    "    if normalize: \n",
    "        x /= np.linalg.norm(x) + config.epsilon\n",
    "\n",
    "    xp = np.arange(window,dtype=np.float32)\n",
    "    # interpolate the discrete function (np.arange(len(x)), x) at the points (scaling_factor*xp)\n",
    "    x = np.interp(scaling_factor*xp,np.arange(len(x),dtype=np.float32),x).astype(np.float32)\n",
    "    # real-valued audio frame with values in the range [−1,1], sampled at 44.1 kHz\n",
    "    # -> x has length window\n",
    "    \n",
    "    y = np.zeros(mm)\n",
    "    # select center of frame\n",
    "    for label in labels[rec_id][s+scaling_factor*window/2]:\n",
    "        y[label.data[1]+pitch_shift-base_note] = 1 # apply pitch shift here (in frequency domain)\n",
    "    \n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Batch-selection\n",
    "Create the training batch applying the above explained methods for Data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create an input batch for one training step\n",
    "# input:\n",
    "# - model_stats: model_results-class containing training_ids\n",
    "# - batch_size (integer)\n",
    "# - window: window-length\n",
    "# - pitch_transforms: integer in [-5,5], number of maximum semitones shifted\n",
    "# - jitter: float in [-0.1,0.1], tuning variation\n",
    "# output:\n",
    "# - xmb: batch for a training step, size: (batch_size,1,window,1)\n",
    "# - ymb: corresponding labels to the batch xmb, size: (batch_size,1,mm)\n",
    "def get_training_batch(model_stats, batch_size, window, pitch_transforms=0, jitter=0):\n",
    "    # arrays for input data (batch)\n",
    "    xmbatch = np.empty([batch_size,1,window,1],dtype=np.float32)\n",
    "    ymbatch = np.empty([batch_size,1,mm],dtype=np.float32)\n",
    "        \n",
    "    # picking randomly batch_size recordings\n",
    "    rec_ids = [model_stats.train_ids[r] for r in \\\n",
    "               np.random.randint(0,len(model_stats.train_ids),batch_size)]\n",
    "        \n",
    "    if pitch_transforms > 0:\n",
    "        transform_type = np.random.randint(-pitch_transforms,\n",
    "                                           pitch_transforms,batch_size)\n",
    "\n",
    "    if jitter > 0:\n",
    "        jitter_amount = np.random.uniform(-jitter,jitter,batch_size)\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        scaling_factor = 1\n",
    "        shift = 0\n",
    "        if pitch_transforms > 0:\n",
    "            shift = transform_type[i]\n",
    "            # half-step has a frequency ratio of 2^(1/12)\n",
    "            # (shift + jitter_amount) halfsteps are shifted\n",
    "            if jitter > 0:\n",
    "                scaling_factor = (2.**((shift+jitter_amount[i])/12.))\n",
    "            else:\n",
    "                # only shift the halftones from pitch_transform\n",
    "                scaling_factor = (2.**(shift/12.))\n",
    "        \n",
    "        # multiply in frequency domain by scaling_factor\n",
    "        \n",
    "        # pick a frame for training randomly\n",
    "        s = np.random.randint(0,data[rec_ids[i]][1]-scaling_factor*window)\n",
    "        xmbatch[i,0,:,0],ymbatch[i,0,:] = get_data(rec_ids[i],s,window,pitch_shift=shift,scaling_factor=scaling_factor)\n",
    "        \n",
    "    return xmbatch, ymbatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Creation of Training and Test set for protocolling\n",
    "\n",
    "Routines that create sample for the protcoling:\n",
    "1. Routine to obtain the training samples from numpy-file.\n",
    "2. Routine to obtain test samples given the recording IDs of the test samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# function to obtain training sample which is already saved in \n",
    "# numpy fromat in file. only normalization is necessary.\n",
    "# input:\n",
    "# - d: window size (integer)\n",
    "# - normalize: boolean\n",
    "# output:\n",
    "# - Xtrain: training sample of size (15000, 1, d, 1)\n",
    "# - Ytrain: corresponding labels of size (15000, 128)\n",
    "def get_training_sample(d=16384, normalize=True):\n",
    "    cachex = config.tmp + 'Xtrain_ext{}.npy'.format(d)\n",
    "    cachey = config.tmp + 'Ytrain_ext{}.npy'.format(d)\n",
    "    #shape Xtrain: (15000, 1, 16384, 1)\n",
    "    #shape Ytrain: (15000, 128)\n",
    "    if os.path.exists(cachex) and os.path.exists(cachey):\n",
    "        Xtrain = np.load(cachex)\n",
    "        Ytrain = np.load(cachey)\n",
    "    else:\n",
    "        print(cachex)\n",
    "        print(cachey)\n",
    "        raise Exception('Unimplemented')\n",
    "\n",
    "    if normalize:\n",
    "        Xtrain = Xtrain.reshape(len(Xtrain),1,d,1)\n",
    "        for i in range(len(Xtrain)):\n",
    "            Xtrain[i] /= np.linalg.norm(Xtrain[i]) + config.epsilon\n",
    "\n",
    "    return Xtrain,Ytrain\n",
    "\n",
    "\n",
    "# function to obtain test sample using get_data(). every sample is split \n",
    "# up in count sequences.\n",
    "# input:\n",
    "# - rec_ids: ids of test samples\n",
    "# - window: size of the input frame\n",
    "# - count: number of sequences to be created for each sample\n",
    "# - fixed_stride: -1 (stride is adapted to sample) or nonnegative integer\n",
    "# - pitch_shift: value by which recording is stretched or shrinked\n",
    "# output:\n",
    "# - Xtest: test sample of size (count*len(rec_ids),1,window,1)\n",
    "# - Ytest: corresponding labels of size (count*len(rec_ids),mm)\n",
    "def get_test_sample(rec_ids, window, count, fixed_stride=-1, pitch_shift=0):\n",
    "    Xtest = np.zeros([count*len(rec_ids),1,window,1])\n",
    "    Ytest = np.zeros([count*len(rec_ids),mm])\n",
    "    offset = 44100\n",
    "    # calculate scaling factor\n",
    "    sf = 2.**(pitch_shift/12.)\n",
    "    # for every sample create 1000 sequences\n",
    "    for i in range(len(rec_ids)):\n",
    "        X = np.zeros([count,1,window,1])\n",
    "        Y = np.zeros([count,mm])\n",
    "        # calculate teh stride in case it is not fixed\n",
    "        stride = (data[rec_ids[i]][1]-offset-int(sf*window))/count if fixed_stride==-1 else fixed_stride\n",
    "        for j in range(count):\n",
    "            X[j,0,:,0],Y[j] = get_data(rec_ids[i],int(offset+j*stride),window,pitch_shift=int(round(pitch_shift)),scaling_factor=sf)\n",
    "        \n",
    "        Xtest[i*count:(i+1)*count] = X\n",
    "        Ytest[i*count:(i+1)*count] = Y\n",
    "    return Xtest, Ytest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the results, losses, precicions and weights at each protocol point inside a class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class model_results():\n",
    "    def __init__(self, test_ids, train_ids):\n",
    "        # weights\n",
    "        self.weights = dict()\n",
    "        # step\n",
    "        self.iter = 0\n",
    "        # result statistics\n",
    "        self.stats = dict()\n",
    "        self.stats['iter'] = [False,'{:<8}',[]]\n",
    "        # time that has passed since last record\n",
    "        self.stats['time'] = [True,'{:<8.0f}',[]]\n",
    "        self.stats['lr'] = [False,'{:<8.6f}',[]]\n",
    "        # mean squared errors\n",
    "        self.stats['mse_train_1'] = [True,'{:<16.6f}',[]]\n",
    "        self.stats['mse_train'] = [True,'{:<16.6f}',[]]\n",
    "        self.stats['mse_test'] = [True,'{:<16.6f}',[]]\n",
    "        # precicions\n",
    "        self.stats['avp_train'] = [True,'{:<16.6f}',[]]\n",
    "        self.stats['avp_test'] = [True,'{:<16.6f}',[]]\n",
    "        \n",
    "        self.averages = []\n",
    "        \n",
    "        self.test_ids = test_ids\n",
    "        self.train_ids = train_ids\n",
    "\n",
    "    # Register the weights inside the list w_list. \n",
    "    # The list name_list contains the corresponding names of the wariables in w_list\n",
    "    def register_weights(self, w_list, name_list):\n",
    "        if len(w_list) != len(name_list):\n",
    "            print(\"error: input lists need to have same length\")\n",
    "            \n",
    "        for ii in range(len(w_list)):\n",
    "            w = w_list[ii]\n",
    "            name = name_list[ii]\n",
    "            # register the norm of the weights\n",
    "            self.stats['n'+name] = [False,'{:<8.3f}',[]] \n",
    "            # register the weights\n",
    "            self.weights[name] = w\n",
    "            \n",
    "        return None\n",
    "        \n",
    "model_stats = model_results(test_ids, train_ids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Error measures\n",
    "Define the error measures for comparing the correct labeling Y with the extimated labeling Y_hat.\n",
    "Both labelings are of size (mm) for each segment. The errors are calculated at each protocol point for the training and the test set.\n",
    "1. Mean Squared Error: When applying the model to a sequence or sequences the mean squared error is calculated directly by applying the tensorflow function.\n",
    "~~~~~~~~~~~~~~~~~~~~~\n",
    "tf.reduce_mean(tf.nn.l2_loss( Y_hat - Y ))\n",
    "~~~~~~~~~~~~~~~~~~~~~\n",
    "2. Average Precision: The average precision is calculated with the metric function from the sklear package.\n",
    "~~~~~~~~~~~~~~~~~~~~~\n",
    "sklearn.metrics.average_precision_score\n",
    "~~~~~~~~~~~~~~~~~~~~~\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Define the graph\n",
    "\n",
    "Define batch size (batch_size segments at each training step). Also define a small factor wscale to scale the weights. Regularization can be avoided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=150\n",
    "if debugging == True:\n",
    "    batch_size = 5\n",
    "out = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Define layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First layer: \n",
    "\n",
    "Lenth of the input (segment) is window=16384. Compute a strided convolution with a d=4096-sample recptive field and a stride=512 sample stride. Since every segment has 16384 samples there are 25 regions for each frame.\n",
    "\n",
    "After the convolution for each region a filterbank computation is calculated. \n",
    "As a filter a log=spaced filterbank is chosen (k=512 sine and cosine filters with logarithmically spaced frequencies from 50 hz to 6000 hz) which is going to be applied to all the regions of the convolution of size d=4096"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for input layer\n",
    "\n",
    "# input size of segment, predict notes played at midpoint of segment\n",
    "window=16384\n",
    "# size of regions that outputs the convolution from layer one (receptive field)\n",
    "dd = 4096\n",
    "#stride for first layer convolution\n",
    "stride=512\n",
    "# --> 25 regions\n",
    "\n",
    "# variables for filterbank (= first layer)\n",
    "# number of nodes in first layer (by filterbank get from dd nodes to kk nodes)\n",
    "kk = 512 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_filters(d,k):\n",
    "    # create k filters\n",
    "    # d is the length of the receptive field (input)\n",
    "    x = np.linspace(0, 2*np.pi, d, endpoint=False)\n",
    "    wsin = np.empty((1,d,1,k), dtype=np.float32)\n",
    "    wcos = np.empty((1,d,1,k), dtype=np.float32)\n",
    "    start_freq = 50.\n",
    "    end_freq = 6000.\n",
    "    num_cycles = start_freq*d/44100.                                          \n",
    "    scaling_ind = np.log(end_freq/start_freq)/k\n",
    "    window_mask = 1.0-1.0*np.cos(x)\n",
    "    for ind in range(k):\n",
    "        wsin[0,:,0,ind] = window_mask*np.sin(np.exp(ind*scaling_ind)*num_cycles*x)\n",
    "        wcos[0,:,0,ind] = window_mask*np.cos(np.exp(ind*scaling_ind)*num_cycles*x)\n",
    "            \n",
    "    return wsin,wcos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for second layer\n",
    "d2_x = 1          # lvl2 input dims_x\n",
    "d2_y = 128          # lvl2 input dims_y\n",
    "k2 = 128        # num lvl2 filters\n",
    "stride_y = 2    # lvl2 stride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for third layer\n",
    "d3_x = 25 # lvl3 input dims_x\n",
    "d3_y = 1 # lvl3 input dims_y (fully connected)\n",
    "k3 = 4096 # num lvl3 filters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the sizes of nodes at each layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First layer regions: (25,512)\n",
      "Second layer regions: (25,193)\n",
      "Third layer regions: (1,193)\n"
     ]
    }
   ],
   "source": [
    "num_regions  = int(1 + (window-dd)/stride)\n",
    "print ('First layer regions: ({},{})'.format(num_regions,kk))\n",
    "num_regions2_x  = int(1 + (num_regions-d2_x)/1)\n",
    "num_regions2_y = int(1 + (kk-d2_y)/stride_y)\n",
    "print ('Second layer regions: ({},{})'.format(num_regions2_x,num_regions2_y))\n",
    "num_regions3_x = int(1 + (num_regions2_x - d3_x)/1)\n",
    "num_regions3_y = int(1 + (num_regions2_y - d3_y)/1)\n",
    "print ('Third layer regions: ({},{})'.format(num_regions3_x,num_regions3_y))\n",
    "\n",
    "# create filters (shape: (1, 4096, 1, 512)) = number of regions x size of region\n",
    "wsin,wcos = create_filters(dd,kk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Define tf.Graph()\n",
    "Set it all together and define the graph.\n",
    "\n",
    "For evaluation not the trained weights, but its moving averages with decay 0.9998 are utilized. The shadow variables are maintained by tf.train.ExponentialMovingAverage. In the block for evaluating the direct model their values are retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Weights ----\n",
      "w <tf.Variable 'parameters/w:0' shape=(1, 128, 1, 128) dtype=float32_ref>\n",
      "w2 <tf.Variable 'parameters/w2:0' shape=(25, 1, 128, 4096) dtype=float32_ref>\n",
      "beta <tf.Variable 'parameters/beta:0' shape=(790528, 128) dtype=float32_ref>\n",
      "---- Layers ----\n",
      "zx Tensor(\"queued_model/add:0\", shape=(?, 1, 25, 512), dtype=float32)\n",
      "z2 Tensor(\"queued_model/Relu:0\", shape=(?, 128, 25, 193), dtype=float32)\n",
      "z3 Tensor(\"queued_model/Relu_1:0\", shape=(?, 4096, 1, 193), dtype=float32)\n",
      "y Tensor(\"queued_model/MatMul:0\", shape=(150, 128), dtype=float32)\n",
      "loss Tensor(\"queued_model/Mean:0\", shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "tf.set_random_seed(999)\n",
    "\n",
    "#end_learning_rate = .00001/3/3/3\n",
    "starter_learning_rate = .00001\n",
    "mom = .95\n",
    "\n",
    "beta_reg = 0.01\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "     \n",
    "    # if the input is a batch\n",
    "    with tf.variable_scope('data_queue'):\n",
    "        xb = tf.placeholder(tf.float32, shape=[None, 1, window+(out-1)*stride, 1])\n",
    "        yb = tf.placeholder(tf.float32, shape=[None, out, mm])\n",
    "\n",
    "        xq, yq = xb, yb\n",
    "        \n",
    "    # if the input is just one segment\n",
    "    with tf.variable_scope('direct_data'):\n",
    "        xd = tf.placeholder(tf.float32, shape=[None,1,window,1])\n",
    "        yd = tf.placeholder(tf.float32, shape=[None, mm])\n",
    "    \n",
    "    print ('---- Weights ----')\n",
    "    with tf.variable_scope('parameters'): #glorot  \n",
    "        w = tf.get_variable('w', [d2_x,d2_y,1,k2]) # 1 x 128 x 1 x 128 (16.384 values)\n",
    "        print ('w',w)\n",
    "        w2 = tf.get_variable('w2', [d3_x,d3_y,k2,k3]) # 25 x 1 x 128 x 4096 (13.107.200 values)\n",
    "        print ('w2',w2)\n",
    "        beta = tf.get_variable('beta', [int(num_regions3_x*num_regions3_y*k3),mm]) # (1*193*4096) x 128 (101.187.584 values)\n",
    "        print ('beta',beta)\n",
    "        model_stats.register_weights([w,w2,beta],['w','w2','beta'])\n",
    "\n",
    "    print ('---- Layers ----')\n",
    "    with tf.variable_scope('queued_model'):\n",
    "        zx = tf.square(tf.nn.conv2d(xq,wsin,strides=[1,1,stride,1],padding='VALID')) \\\n",
    "           + tf.square(tf.nn.conv2d(xq,wcos,strides=[1,1,stride,1],padding='VALID'))\n",
    "        print ('zx',zx)\n",
    "        z2 = tf.nn.relu(tf.nn.conv2d(tf.log(zx+10e-15),w,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "        print ('z2',z2)\n",
    "        z3 = tf.nn.relu(tf.nn.conv2d(z2,w2,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "        print ('z3',z3)\n",
    "        y = tf.matmul(tf.reshape(z3,[batch_size,int(num_regions3_x*num_regions3_y*k3)]),beta)\n",
    "        print ('y',y)\n",
    "        loss = tf.reduce_mean(tf.nn.l2_loss(y-tf.reshape(yq,[batch_size,mm])))\n",
    "        print ('loss',loss)\n",
    "            \n",
    "    # learning rate decay\n",
    "    global_step = tf.Variable(0, trainable=False)\n",
    "    lr = tf.train.exponential_decay(starter_learning_rate, global_step, 10000, 0.95, staircase=True)\n",
    "\n",
    "    reg = tf.nn.l2_loss(w) + tf.nn.l2_loss(w2) + tf.nn.l2_loss(beta)\n",
    "\n",
    "    opt_op = tf.train.MomentumOptimizer(lr,mom).minimize(tf.reduce_mean(loss + beta_reg*reg), global_step=global_step)\n",
    "    \n",
    "    # Create an ExponentialMovingAverage object\n",
    "    ema = tf.train.ExponentialMovingAverage(decay=0.9998)\n",
    "    \n",
    "    with tf.control_dependencies([opt_op]):\n",
    "        training_op = ema.apply([w, w2, beta])\n",
    "    \n",
    "    with tf.variable_scope('direct_model'):\n",
    "        # use ema.average(var) to return the average of variable var\n",
    "        wavg = ema.average(w)\n",
    "        w2avg = ema.average(w2)\n",
    "        betaavg = ema.average(beta)\n",
    "\n",
    "        direct_zx = tf.square(tf.nn.conv2d(xd,wsin,strides=[1,1,stride,1],padding='VALID')) \\\n",
    "                + tf.square(tf.nn.conv2d(xd,wcos,strides=[1,1,stride,1],padding='VALID'))\n",
    "        direct_z2 = tf.nn.relu(tf.nn.conv2d(tf.log(direct_zx+10e-15),wavg,strides=[1,1,1,stride_y],padding='VALID',data_format='NCHW'))\n",
    "        direct_z3 = tf.nn.relu(tf.nn.conv2d(direct_z2,w2avg,strides=[1,1,1,1],padding='VALID',data_format='NCHW'))\n",
    "        direct_y = tf.matmul(tf.reshape(direct_z3,[tf.shape(xd)[0],int(num_regions3_x*num_regions3_y*k3)]),betaavg)\n",
    "        direct_loss = tf.reduce_mean(tf.nn.l2_loss(direct_y-yd))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 Evaluation of the graph\n",
    "\n",
    "For the aplication of the neuronal network a moving average of the weights is used. Therefore, a different function for evaluation is used.\n",
    "\n",
    "Inside the function predict_direct_model the mean squared error is calculated directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for prediction without training that splits up the whole set\n",
    "# in small subsets to prevent errors of type \"ResourceExhaustedError\"\n",
    "# input: \n",
    "# - sess: the actual session\n",
    "# - X: sample, size: (n,1,window,1)\n",
    "# - Y: corresponding labels, size: (n,mm)\n",
    "# output:\n",
    "#  Yhat: prediction, size: (n,mm)\n",
    "#  mse: Mean Squared Error when comparing Y and Yhat\n",
    "def predict_direct_model(sess, X, Y):\n",
    "    Yhat = np.empty((len(X),mm))\n",
    "    subdiv = 100\n",
    "    subset = X.shape[0]/subdiv\n",
    "    mse = 0\n",
    "    for j in range(subdiv):\n",
    "        Yhat[int(subset*j):int(subset*(j+1))], se = sess.run([direct_y,direct_loss], \n",
    "                                                         feed_dict={xd: X[int(subset*j):int(subset*(j+1))],\n",
    "                                                                    yd: Y[int(subset*j):int(subset*(j+1))]})\n",
    "        mse += se\n",
    "\n",
    "    return Yhat, mse/float(subdiv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "if debugging == True:\n",
    "    num_steps = 5\n",
    "    control_step = 1\n",
    "else:\n",
    "    num_steps = 300000\n",
    "    control_step = 1000\n",
    "\n",
    "gpu_memory_growth = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arrays for input data (batch)\n",
    "xmb = np.empty([batch_size,1,window,1],dtype=np.float32)\n",
    "ymb = np.empty([batch_size,1,mm],dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train saver\n",
    "saver = tf.train.Saver({'wavg': wavg, 'w2avg': w2avg, 'betaavg': betaavg})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "iter\t test_squ_loss\t test_avg_prec\t train_squ_loss\t train_avg_prec\t regularization\t time (in s)\n",
      "1 \t 246.55195496 \t 0.0176381 \t 340.18234253 \t 0.0217515 \t 253.11946 \t 31.70181584\n",
      "1001 \t 130.38121849 \t 0.02888717 \t 218.4153186 \t 0.03648819 \t 356.1115 \t 495.70083785\n",
      "2001 \t 128.43350399 \t 0.04476062 \t 215.22413528 \t 0.05922177 \t 355.23907 \t 495.55893731\n",
      "3001 \t 124.43698349 \t 0.17438758 \t 209.18399521 \t 0.22024525 \t 354.27664 \t 493.3543334\n",
      "4001 \t 117.86273445 \t 0.45673067 \t 199.20055649 \t 0.47504747 \t 353.1125 \t 494.32345629\n",
      "5001 \t 110.03727905 \t 0.56663212 \t 187.12594131 \t 0.56663827 \t 352.00272 \t 494.81141233\n",
      "6001 \t 102.09441299 \t 0.60505757 \t 174.78120911 \t 0.60087094 \t 350.87793 \t 494.46966887\n",
      "7001 \t 94.71134552 \t 0.62733442 \t 163.07450668 \t 0.62155302 \t 349.7526 \t 494.98899412\n",
      "8001 \t 88.50901434 \t 0.64563621 \t 153.0508606 \t 0.63853601 \t 348.664 \t 495.2165308\n",
      "9001 \t 83.3354459 \t 0.66143061 \t 144.50317352 \t 0.65306761 \t 347.59204 \t 495.67126298\n",
      "10001 \t 78.93146065 \t 0.67401027 \t 137.04291176 \t 0.66418588 \t 346.6146 \t 495.72882628\n",
      "11001 \t 75.36878296 \t 0.68496112 \t 130.85522484 \t 0.67366069 \t 345.6447 \t 494.98582959\n",
      "12001 \t 72.5716258 \t 0.69378192 \t 125.94771935 \t 0.68143877 \t 344.671 \t 495.32962465\n",
      "13001 \t 70.32074484 \t 0.70088396 \t 122.02915245 \t 0.68791676 \t 343.7174 \t 496.56246758\n",
      "14001 \t 68.50030832 \t 0.70613121 \t 118.92912659 \t 0.69302549 \t 342.77673 \t 495.87011981\n",
      "15001 \t 66.87577972 \t 0.71034242 \t 116.28066841 \t 0.69731647 \t 341.85114 \t 497.37743783\n",
      "16001 \t 65.5724155 \t 0.71383073 \t 114.0590509 \t 0.70094822 \t 340.98572 \t 496.78291774\n",
      "17001 \t 64.62140318 \t 0.71708242 \t 112.33958336 \t 0.70411288 \t 340.07086 \t 494.91741776\n",
      "18001 \t 63.89750841 \t 0.71970737 \t 110.95366119 \t 0.70667922 \t 339.22464 \t 494.82636666\n",
      "19001 \t 63.23140312 \t 0.72256539 \t 109.65339874 \t 0.70951706 \t 338.3554 \t 496.41825175\n",
      "20001 \t 62.56503628 \t 0.7254703 \t 108.37877876 \t 0.71249624 \t 337.4596 \t 496.80455017\n",
      "21001 \t 61.91024279 \t 0.72859422 \t 107.21590942 \t 0.71533416 \t 336.61786 \t 497.12092948\n",
      "22001 \t 61.35009911 \t 0.73141208 \t 106.22892097 \t 0.71795565 \t 335.7853 \t 497.304286\n",
      "23001 \t 60.80783384 \t 0.73433137 \t 105.32457603 \t 0.72051996 \t 334.94373 \t 496.18637776\n",
      "24001 \t 60.31861305 \t 0.737126 \t 104.49754631 \t 0.72291268 \t 334.1167 \t 498.15342474\n",
      "25001 \t 59.95203772 \t 0.73923546 \t 103.84274673 \t 0.72490727 \t 333.32727 \t 497.69402051\n",
      "26001 \t 59.61751692 \t 0.74118089 \t 103.2504052 \t 0.72674119 \t 332.50482 \t 497.51833344\n",
      "27001 \t 59.2782305 \t 0.74321026 \t 102.66677017 \t 0.72860056 \t 331.67014 \t 498.62605691\n",
      "28001 \t 58.96089485 \t 0.74515873 \t 102.13432518 \t 0.73033472 \t 330.8295 \t 498.36165428\n",
      "29001 \t 58.70625647 \t 0.7465867 \t 101.66593658 \t 0.73183368 \t 330.02124 \t 496.73465371\n",
      "30001 \t 58.48038187 \t 0.74782582 \t 101.23572975 \t 0.73323334 \t 329.19766 \t 499.33963823\n",
      "31001 \t 58.25776762 \t 0.7491992 \t 100.83513748 \t 0.73461132 \t 328.37097 \t 496.13631177\n",
      "32001 \t 58.06730465 \t 0.7502414 \t 100.46782349 \t 0.73575965 \t 327.58542 \t 496.62635207\n",
      "33001 \t 57.8546319 \t 0.75151785 \t 100.10430519 \t 0.73691631 \t 326.80148 \t 497.39370799\n",
      "34001 \t 57.69405695 \t 0.75239639 \t 99.79561928 \t 0.73803194 \t 326.00638 \t 499.38193822\n",
      "35001 \t 57.53100555 \t 0.75346851 \t 99.51085342 \t 0.7390645 \t 325.2576 \t 497.73996854\n",
      "36001 \t 57.40066662 \t 0.75424628 \t 99.25035896 \t 0.74002918 \t 324.47034 \t 497.11605167\n",
      "37001 \t 57.28013041 \t 0.75497707 \t 98.97929794 \t 0.74103768 \t 323.6809 \t 497.43667436\n",
      "38001 \t 57.17252964 \t 0.75564266 \t 98.74792953 \t 0.74188294 \t 322.90393 \t 498.50347543\n",
      "39001 \t 57.05792143 \t 0.75630726 \t 98.53968506 \t 0.74268237 \t 322.1114 \t 497.44869089\n",
      "40001 \t 56.97263884 \t 0.7568253 \t 98.36767365 \t 0.74344822 \t 321.37683 \t 497.78876281\n",
      "41001 \t 56.87928091 \t 0.75722183 \t 98.1355249 \t 0.74431938 \t 320.61914 \t 498.57702017\n",
      "42001 \t 56.77849413 \t 0.7578364 \t 97.93939087 \t 0.74508021 \t 319.87732 \t 497.61683345\n",
      "43001 \t 56.69091893 \t 0.75848636 \t 97.7640799 \t 0.74579609 \t 319.13873 \t 499.33294272\n",
      "44001 \t 56.60473678 \t 0.75907132 \t 97.56180458 \t 0.74660171 \t 318.44043 \t 498.59478188\n",
      "45001 \t 56.52159061 \t 0.75962772 \t 97.36084023 \t 0.74739401 \t 317.72845 \t 498.36912847\n",
      "46001 \t 56.43561932 \t 0.76023748 \t 97.19434814 \t 0.74804864 \t 316.99115 \t 498.2035675\n",
      "47001 \t 56.36426052 \t 0.76070003 \t 97.0056958 \t 0.7487941 \t 316.2971 \t 500.0582819\n",
      "48001 \t 56.28431967 \t 0.76129802 \t 96.83394005 \t 0.74946856 \t 315.60724 \t 500.08076549\n",
      "49001 \t 56.22204869 \t 0.76168777 \t 96.66184624 \t 0.75016814 \t 314.89322 \t 503.99719167\n",
      "50001 \t 56.14543067 \t 0.76218795 \t 96.49523125 \t 0.75081701 \t 314.22125 \t 502.51222348\n",
      "51001 \t 56.07111878 \t 0.76268652 \t 96.33191048 \t 0.75146048 \t 313.5348 \t 501.47246575\n",
      "52001 \t 55.97971251 \t 0.76330294 \t 96.14102448 \t 0.75218615 \t 312.87262 \t 578.64142323\n",
      "53001 \t 55.91220591 \t 0.76378311 \t 95.99179977 \t 0.75279686 \t 312.1833 \t 761.73115754\n",
      "54001 \t 55.83745409 \t 0.76436321 \t 95.85429237 \t 0.75342073 \t 311.55225 \t 857.63777733\n",
      "55001 \t 55.7746431 \t 0.76478488 \t 95.71326698 \t 0.7540702 \t 310.89307 \t 938.68342161\n",
      "56001 \t 55.7193177 \t 0.76513857 \t 95.58986229 \t 0.75461222 \t 310.24915 \t 710.75142527\n",
      "57001 \t 55.6843051 \t 0.76533467 \t 95.49441689 \t 0.75510514 \t 309.651 \t 617.91228819\n",
      "58001 \t 55.63346333 \t 0.7656435 \t 95.34758102 \t 0.75571187 \t 309.00348 \t 587.5838213\n",
      "59001 \t 55.58380331 \t 0.76598673 \t 95.2245826 \t 0.75626868 \t 308.36475 \t 576.6695745\n",
      "60001 \t 55.51210869 \t 0.76661247 \t 95.10602119 \t 0.75685043 \t 307.74847 \t 573.58666706\n",
      "61001 \t 55.46744787 \t 0.76694684 \t 94.97857307 \t 0.75741476 \t 307.11365 \t 570.87584138\n",
      "62001 \t 55.42261223 \t 0.76727227 \t 94.85794556 \t 0.75791862 \t 306.48978 \t 572.40749073\n",
      "63001 \t 55.3725808 \t 0.76756326 \t 94.70943871 \t 0.75845351 \t 305.8704 \t 570.79552841\n",
      "64001 \t 55.33320317 \t 0.76774056 \t 94.57967743 \t 0.75898178 \t 305.2563 \t 568.28065777\n",
      "65001 \t 55.28438761 \t 0.76797663 \t 94.46046402 \t 0.75947276 \t 304.68262 \t 570.7196939\n",
      "66001 \t 55.26517346 \t 0.76815652 \t 94.38792313 \t 0.75984514 \t 304.11847 \t 572.90063596\n",
      "67001 \t 55.22275684 \t 0.76842765 \t 94.2959594 \t 0.76022672 \t 303.50772 \t 571.70830703\n",
      "68001 \t 55.17530827 \t 0.76863297 \t 94.16944855 \t 0.76070716 \t 302.91595 \t 571.46261191\n",
      "69001 \t 55.13748577 \t 0.76883642 \t 94.06578712 \t 0.76112139 \t 302.31842 \t 576.61961174\n",
      "70001 \t 55.10879393 \t 0.76891072 \t 93.9732547 \t 0.76153706 \t 301.74286 \t 578.66113853\n",
      "71001 \t 55.07587345 \t 0.76905263 \t 93.88103439 \t 0.76192048 \t 301.1704 \t 573.86162019\n",
      "72001 \t 55.04264762 \t 0.7692292 \t 93.79645584 \t 0.76229161 \t 300.5942 \t 574.99170828\n",
      "73001 \t 54.99350178 \t 0.76947899 \t 93.67438881 \t 0.7627895 \t 300.027 \t 575.83232999\n",
      "74001 \t 54.97056593 \t 0.7695882 \t 93.57582748 \t 0.76318913 \t 299.45166 \t 579.27588654\n",
      "75001 \t 54.93731186 \t 0.76970731 \t 93.47875221 \t 0.76367759 \t 298.8927 \t 593.36881685\n",
      "76001 \t 54.90209528 \t 0.76983801 \t 93.37028023 \t 0.76420625 \t 298.33524 \t 532.3882165\n",
      "77001 \t 54.86631868 \t 0.77008294 \t 93.26874344 \t 0.76472163 \t 297.8009 \t 515.97634697\n",
      "78001 \t 54.82403446 \t 0.77039062 \t 93.16073494 \t 0.76523621 \t 297.27118 \t 509.48114872\n",
      "79001 \t 54.78733541 \t 0.77064379 \t 93.08009621 \t 0.7656258 \t 296.73352 \t 505.56718278\n",
      "80001 \t 54.761234 \t 0.77084391 \t 93.01573067 \t 0.76595343 \t 296.18872 \t 506.33495307\n",
      "81001 \t 54.73462747 \t 0.77098572 \t 92.93052742 \t 0.7663842 \t 295.67133 \t 504.34307456\n",
      "82001 \t 54.69339501 \t 0.77127908 \t 92.82630348 \t 0.76676839 \t 295.13336 \t 500.44479442\n",
      "83001 \t 54.6616264 \t 0.77150957 \t 92.73139938 \t 0.76720197 \t 294.61554 \t 500.64793134\n",
      "84001 \t 54.64900521 \t 0.77154973 \t 92.65265701 \t 0.76757337 \t 294.08502 \t 501.46845055\n",
      "85001 \t 54.62343419 \t 0.77166736 \t 92.56026314 \t 0.76798064 \t 293.5652 \t 500.97690201\n",
      "86001 \t 54.59542435 \t 0.77188358 \t 92.47698914 \t 0.76831785 \t 293.06058 \t 501.19970393\n",
      "87001 \t 54.56783418 \t 0.77208497 \t 92.39322418 \t 0.76868848 \t 292.5614 \t 500.84174895\n",
      "88001 \t 54.55183699 \t 0.77215522 \t 92.31517311 \t 0.76903324 \t 292.05255 \t 500.88130116\n",
      "89001 \t 54.5466993 \t 0.77217194 \t 92.24439377 \t 0.76937167 \t 291.55872 \t 500.84708905\n",
      "90001 \t 54.52498369 \t 0.77224477 \t 92.15514832 \t 0.76970108 \t 291.0481 \t 498.47363663\n",
      "91001 \t 54.50172812 \t 0.7724694 \t 92.06818962 \t 0.77013987 \t 290.57776 \t 498.34599996\n",
      "92001 \t 54.48518841 \t 0.77262714 \t 91.99410515 \t 0.77048315 \t 290.08112 \t 499.47664237\n",
      "93001 \t 54.4473596 \t 0.77288616 \t 91.91472168 \t 0.77076726 \t 289.59296 \t 499.49569631\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94001 \t 54.43201113 \t 0.77294949 \t 91.81788666 \t 0.7712122 \t 289.08765 \t 499.61610436\n",
      "95001 \t 54.40058547 \t 0.7731064 \t 91.75217361 \t 0.77145645 \t 288.61697 \t 501.80698133\n",
      "96001 \t 54.38962749 \t 0.77312133 \t 91.69533592 \t 0.77172596 \t 288.1338 \t 500.18945241\n",
      "97001 \t 54.37403378 \t 0.7732716 \t 91.64192581 \t 0.77201753 \t 287.68527 \t 500.84734035\n",
      "98001 \t 54.36069569 \t 0.77330108 \t 91.5586644 \t 0.77239556 \t 287.22858 \t 500.74550939\n",
      "99001 \t 54.33510327 \t 0.77340157 \t 91.48211708 \t 0.77267657 \t 286.75342 \t 501.37213588\n",
      "100001 \t 54.31995394 \t 0.77358426 \t 91.45161667 \t 0.77279022 \t 286.31158 \t 501.11437845\n",
      "101001 \t 54.30034754 \t 0.77376791 \t 91.40010712 \t 0.77303322 \t 285.8281 \t 501.22218776\n",
      "102001 \t 54.27506725 \t 0.77401786 \t 91.32048256 \t 0.773356 \t 285.3976 \t 501.08979893\n",
      "103001 \t 54.27105631 \t 0.77398829 \t 91.24708252 \t 0.77370173 \t 284.93335 \t 501.65452576\n",
      "104001 \t 54.24059367 \t 0.77413331 \t 91.15356155 \t 0.77406462 \t 284.48285 \t 502.01194835\n",
      "105001 \t 54.22653979 \t 0.77420943 \t 91.08959267 \t 0.77433299 \t 284.05255 \t 500.82230186\n",
      "106001 \t 54.20102996 \t 0.77432772 \t 91.01228241 \t 0.77465089 \t 283.61993 \t 502.18066359\n",
      "107001 \t 54.22534478 \t 0.77406946 \t 90.99652504 \t 0.77474267 \t 283.18018 \t 501.68220878\n",
      "108001 \t 54.22108779 \t 0.77405103 \t 90.96988083 \t 0.77486433 \t 282.7627 \t 501.6946857\n",
      "109001 \t 54.21117298 \t 0.77403107 \t 90.90479362 \t 0.77512303 \t 282.33572 \t 500.86106062\n",
      "110001 \t 54.17814975 \t 0.77432673 \t 90.85117096 \t 0.77538781 \t 281.90314 \t 501.02012348\n",
      "111001 \t 54.1489115 \t 0.7746811 \t 90.7812014 \t 0.77569486 \t 281.4783 \t 501.54411602\n",
      "112001 \t 54.12615801 \t 0.77494848 \t 90.72142021 \t 0.77601123 \t 281.05502 \t 502.1150229\n",
      "113001 \t 54.11235376 \t 0.77505137 \t 90.66026527 \t 0.77632822 \t 280.62115 \t 502.89606833\n",
      "114001 \t 54.09322341 \t 0.77519462 \t 90.58008202 \t 0.77663035 \t 280.2256 \t 502.18640947\n",
      "115001 \t 54.07738673 \t 0.77530821 \t 90.53512848 \t 0.776833 \t 279.81744 \t 499.78846097\n",
      "116001 \t 54.05008024 \t 0.77550079 \t 90.45493225 \t 0.77711012 \t 279.41675 \t 501.10415435\n",
      "117001 \t 54.04840954 \t 0.77539413 \t 90.38872879 \t 0.77742187 \t 278.99982 \t 501.25244021\n",
      "118001 \t 54.05340064 \t 0.77522917 \t 90.33389626 \t 0.77769509 \t 278.61902 \t 500.66893911\n",
      "119001 \t 54.04795255 \t 0.77517681 \t 90.27970665 \t 0.77798647 \t 278.22964 \t 501.03279042\n",
      "120001 \t 54.02450364 \t 0.77534391 \t 90.20509254 \t 0.7783089 \t 277.82693 \t 499.47701073\n",
      "121001 \t 54.00874528 \t 0.77543607 \t 90.14904228 \t 0.77855213 \t 277.43427 \t 501.00356174\n",
      "122001 \t 54.00917727 \t 0.77539772 \t 90.0874102 \t 0.77881563 \t 277.04834 \t 502.38556266\n",
      "123001 \t 54.00008649 \t 0.77544609 \t 90.04112442 \t 0.77900642 \t 276.66876 \t 501.57355142\n",
      "124001 \t 53.96486225 \t 0.77577595 \t 89.97099045 \t 0.77934111 \t 276.27896 \t 501.42021298\n",
      "125001 \t 53.93911124 \t 0.77606202 \t 89.90852257 \t 0.77966403 \t 275.9105 \t 502.05714965\n",
      "126001 \t 53.91940532 \t 0.77623906 \t 89.85299904 \t 0.77990596 \t 275.54156 \t 502.01427865\n",
      "127001 \t 53.90776165 \t 0.77640377 \t 89.78978363 \t 0.78021363 \t 275.14764 \t 502.45744848\n",
      "128001 \t 53.89141806 \t 0.77656038 \t 89.73197617 \t 0.78049497 \t 274.79608 \t 502.95550513\n",
      "129001 \t 53.87510414 \t 0.7767763 \t 89.69338203 \t 0.78075486 \t 274.42163 \t 501.73326397\n",
      "130001 \t 53.84868133 \t 0.77688685 \t 89.62866066 \t 0.78106047 \t 274.04388 \t 502.84419394\n",
      "131001 \t 53.82502737 \t 0.77709303 \t 89.57129395 \t 0.78131875 \t 273.6677 \t 501.86980987\n",
      "132001 \t 53.79620869 \t 0.77733772 \t 89.5330584 \t 0.78146516 \t 273.2905 \t 501.71500802\n",
      "133001 \t 53.77475067 \t 0.77746001 \t 89.49292633 \t 0.78165346 \t 272.9373 \t 501.79079533\n",
      "134001 \t 53.73600215 \t 0.77781705 \t 89.42898224 \t 0.78190227 \t 272.58398 \t 502.37481833\n",
      "135001 \t 53.69923372 \t 0.77811113 \t 89.3711834 \t 0.78217172 \t 272.24557 \t 501.67586899\n",
      "136001 \t 53.6851227 \t 0.77824745 \t 89.31797661 \t 0.78238279 \t 271.89746 \t 502.03400588\n",
      "137001 \t 53.67233491 \t 0.77833888 \t 89.26157364 \t 0.78257382 \t 271.5473 \t 501.43466353\n",
      "138001 \t 53.65791397 \t 0.77841451 \t 89.21286636 \t 0.78276073 \t 271.19183 \t 501.38749719\n",
      "139001 \t 53.6314275 \t 0.77858289 \t 89.16151871 \t 0.78297959 \t 270.83014 \t 503.21655846\n",
      "140001 \t 53.62888681 \t 0.77856939 \t 89.10822464 \t 0.78324673 \t 270.4795 \t 502.61338902\n",
      "141001 \t 53.6340567 \t 0.7785892 \t 89.07209518 \t 0.78343992 \t 270.16385 \t 503.02960157\n",
      "142001 \t 53.61750817 \t 0.77873779 \t 89.01062935 \t 0.78370716 \t 269.8284 \t 503.4353137\n",
      "143001 \t 53.60657857 \t 0.77879129 \t 88.95310028 \t 0.7840041 \t 269.50793 \t 503.46504116\n",
      "144001 \t 53.61074739 \t 0.77883942 \t 88.90562935 \t 0.78424858 \t 269.19016 \t 503.3097496\n",
      "145001 \t 53.60731833 \t 0.77887703 \t 88.85784134 \t 0.7844562 \t 268.85767 \t 502.70406771\n",
      "146001 \t 53.60744952 \t 0.7788652 \t 88.79465378 \t 0.78467252 \t 268.52045 \t 503.84460568\n",
      "147001 \t 53.60353456 \t 0.77885083 \t 88.75165573 \t 0.7848247 \t 268.19205 \t 502.24889398\n",
      "148001 \t 53.5887116 \t 0.77898761 \t 88.69616219 \t 0.78503606 \t 267.8791 \t 503.1388979\n",
      "149001 \t 53.54828239 \t 0.77921782 \t 88.63006508 \t 0.78523374 \t 267.56088 \t 502.12931132\n",
      "150001 \t 53.51371656 \t 0.77948096 \t 88.57906052 \t 0.78538458 \t 267.234 \t 503.19434595\n",
      "151001 \t 53.48883081 \t 0.77971545 \t 88.52824852 \t 0.78563393 \t 266.91724 \t 503.62165475\n",
      "152001 \t 53.4831238 \t 0.77981724 \t 88.48632133 \t 0.7858471 \t 266.59784 \t 503.68812394\n",
      "153001 \t 53.4724326 \t 0.77988158 \t 88.44570938 \t 0.78600692 \t 266.2975 \t 503.29028249\n",
      "154001 \t 53.45654066 \t 0.77998264 \t 88.40429878 \t 0.78619302 \t 265.98395 \t 503.26338935\n",
      "155001 \t 53.47738012 \t 0.77981348 \t 88.38268105 \t 0.78634531 \t 265.68448 \t 503.85941529\n",
      "156001 \t 53.47832782 \t 0.77979163 \t 88.33406372 \t 0.78657632 \t 265.3757 \t 503.06670189\n",
      "157001 \t 53.47713255 \t 0.77975375 \t 88.28317406 \t 0.78680288 \t 265.08215 \t 503.45454431\n",
      "158001 \t 53.47070321 \t 0.77976897 \t 88.24021141 \t 0.78698319 \t 264.78656 \t 502.65881968\n",
      "159001 \t 53.452882 \t 0.77990984 \t 88.18937515 \t 0.78720867 \t 264.50073 \t 502.28424406\n",
      "160001 \t 53.45856676 \t 0.77982091 \t 88.15480659 \t 0.78739862 \t 264.20026 \t 502.41634059\n",
      "161001 \t 53.45207247 \t 0.77981118 \t 88.09828705 \t 0.7876606 \t 263.8964 \t 503.59230447\n",
      "162001 \t 53.44737287 \t 0.77978811 \t 88.04765083 \t 0.78782572 \t 263.6064 \t 502.98784018\n",
      "163001 \t 53.42795975 \t 0.77991113 \t 88.00960609 \t 0.78798423 \t 263.33405 \t 502.62373066\n",
      "164001 \t 53.43052727 \t 0.77991356 \t 87.98537468 \t 0.78818602 \t 263.0567 \t 502.87918663\n",
      "165001 \t 53.43085031 \t 0.77986249 \t 87.9348542 \t 0.78839106 \t 262.76923 \t 503.39116454\n",
      "166001 \t 53.40789047 \t 0.78003438 \t 87.88100319 \t 0.78865069 \t 262.4709 \t 502.16745043\n",
      "167001 \t 53.40219419 \t 0.78000993 \t 87.82641823 \t 0.78885618 \t 262.1809 \t 503.77153015\n",
      "168001 \t 53.400889 \t 0.78000662 \t 87.79158363 \t 0.78908472 \t 261.90247 \t 503.95570827\n",
      "169001 \t 53.38931615 \t 0.78009441 \t 87.73629967 \t 0.78935006 \t 261.64383 \t 502.75223422\n",
      "170001 \t 53.36616066 \t 0.78029911 \t 87.68780602 \t 0.7895814 \t 261.3553 \t 502.35642385\n",
      "171001 \t 53.37293354 \t 0.78023699 \t 87.66069298 \t 0.78975537 \t 261.08606 \t 502.83029437\n",
      "172001 \t 53.36866921 \t 0.78028286 \t 87.62623619 \t 0.78994359 \t 260.8133 \t 501.4864924\n",
      "173001 \t 53.35484672 \t 0.78035626 \t 87.57323135 \t 0.79015441 \t 260.54236 \t 502.21574306\n",
      "174001 \t 53.34739212 \t 0.78042709 \t 87.5362529 \t 0.79033104 \t 260.26486 \t 502.58262205\n",
      "175001 \t 53.32410197 \t 0.7806463 \t 87.47267426 \t 0.79056156 \t 260.00433 \t 503.16198683\n",
      "176001 \t 53.3164592 \t 0.78078012 \t 87.45980286 \t 0.7906293 \t 259.7307 \t 503.74043512\n",
      "177001 \t 53.30981448 \t 0.78095393 \t 87.42597908 \t 0.79081462 \t 259.467 \t 503.87118387\n",
      "178001 \t 53.28612267 \t 0.78103217 \t 87.36119247 \t 0.79096021 \t 259.20557 \t 503.619174\n",
      "179001 \t 53.28358951 \t 0.78093433 \t 87.33427559 \t 0.79107681 \t 258.95172 \t 503.0706501\n",
      "180001 \t 53.2702708 \t 0.78098363 \t 87.291064 \t 0.7912958 \t 258.69376 \t 502.01573753\n",
      "181001 \t 53.25872778 \t 0.78110079 \t 87.25474945 \t 0.79153441 \t 258.4411 \t 503.73800015\n",
      "182001 \t 53.24586417 \t 0.78123424 \t 87.20987511 \t 0.79172852 \t 258.19095 \t 502.6708827\n",
      "183001 \t 53.23776568 \t 0.78137499 \t 87.14970108 \t 0.79199052 \t 257.94836 \t 502.93123078\n",
      "184001 \t 53.24098498 \t 0.78148899 \t 87.12191963 \t 0.79215835 \t 257.72076 \t 503.54926348\n",
      "185001 \t 53.22861733 \t 0.78165276 \t 87.08275421 \t 0.79231278 \t 257.4821 \t 502.88305235\n",
      "186001 \t 53.22515542 \t 0.78171777 \t 87.06780693 \t 0.79241826 \t 257.23044 \t 502.19760227\n",
      "187001 \t 53.22410106 \t 0.78172027 \t 87.04865242 \t 0.79250408 \t 256.98557 \t 502.70089889\n",
      "188001 \t 53.20338472 \t 0.78187042 \t 86.99731705 \t 0.79266927 \t 256.7362 \t 503.18326664\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "189001 \t 53.1897296 \t 0.78200568 \t 86.95731064 \t 0.79285849 \t 256.49878 \t 502.27405357\n",
      "190001 \t 53.18702877 \t 0.78207447 \t 86.91770607 \t 0.79302122 \t 256.26562 \t 503.50961328\n",
      "191001 \t 53.17835513 \t 0.78217483 \t 86.87982597 \t 0.79317528 \t 256.02698 \t 503.98735046\n",
      "192001 \t 53.15197686 \t 0.7824208 \t 86.82589737 \t 0.79337208 \t 255.78613 \t 502.67641091\n",
      "193001 \t 53.13654026 \t 0.78252385 \t 86.76813446 \t 0.79361454 \t 255.55534 \t 502.20358753\n",
      "194001 \t 53.13488024 \t 0.78255549 \t 86.71613503 \t 0.79386812 \t 255.3287 \t 504.33748436\n",
      "195001 \t 53.12041175 \t 0.78268808 \t 86.65804222 \t 0.79412784 \t 255.1015 \t 503.29979229\n",
      "196001 \t 53.11499281 \t 0.78266407 \t 86.60357712 \t 0.79424735 \t 254.86559 \t 502.58425403\n",
      "197001 \t 53.12366198 \t 0.78258313 \t 86.57005806 \t 0.79439627 \t 254.62779 \t 502.95724607\n",
      "198001 \t 53.12024288 \t 0.78261992 \t 86.53470314 \t 0.79457438 \t 254.42693 \t 502.76631641\n",
      "199001 \t 53.11786787 \t 0.78268866 \t 86.5172538 \t 0.79471677 \t 254.19565 \t 503.46387482\n",
      "200001 \t 53.10583415 \t 0.78278645 \t 86.48181252 \t 0.79488743 \t 253.96712 \t 503.92505074\n",
      "201001 \t 53.1088904 \t 0.78275477 \t 86.44513901 \t 0.79504777 \t 253.74596 \t 503.2113905\n",
      "202001 \t 53.09368499 \t 0.78285745 \t 86.39782257 \t 0.79527828 \t 253.53577 \t 502.84880805\n",
      "203001 \t 53.08603185 \t 0.7828395 \t 86.36267975 \t 0.79545658 \t 253.30643 \t 502.74236584\n",
      "204001 \t 53.05582042 \t 0.78309679 \t 86.31341164 \t 0.79564759 \t 253.0785 \t 503.28291416\n",
      "205001 \t 53.05050046 \t 0.78319786 \t 86.28601265 \t 0.795817 \t 252.87918 \t 502.94009399\n",
      "206001 \t 53.04647196 \t 0.78318056 \t 86.25007286 \t 0.79594833 \t 252.66476 \t 502.80128956\n",
      "207001 \t 53.0385934 \t 0.78317627 \t 86.21893288 \t 0.79601644 \t 252.45079 \t 501.31312108\n",
      "208001 \t 53.03110337 \t 0.78324321 \t 86.19443527 \t 0.7961166 \t 252.23553 \t 503.86274242\n",
      "209001 \t 53.02496031 \t 0.78330561 \t 86.17518692 \t 0.7962003 \t 252.03036 \t 502.85259247\n",
      "210001 \t 53.01883108 \t 0.78341738 \t 86.1290374 \t 0.79640644 \t 251.8292 \t 503.40406919\n",
      "211001 \t 53.02042382 \t 0.78343575 \t 86.07824837 \t 0.79663291 \t 251.62213 \t 502.35544896\n",
      "212001 \t 53.0118826 \t 0.78350261 \t 86.01626694 \t 0.79688098 \t 251.40979 \t 503.51042557\n",
      "213001 \t 53.00493527 \t 0.78346466 \t 85.97092628 \t 0.79701455 \t 251.20009 \t 502.22276664\n",
      "214001 \t 53.00503035 \t 0.78336804 \t 85.92707596 \t 0.79716872 \t 251.00784 \t 502.71145105\n",
      "215001 \t 53.00696624 \t 0.78332456 \t 85.89413574 \t 0.79732187 \t 250.80849 \t 502.96719193\n",
      "216001 \t 52.99710192 \t 0.78334751 \t 85.87106026 \t 0.79744183 \t 250.61037 \t 502.79169703\n",
      "217001 \t 53.00432898 \t 0.78331163 \t 85.83481491 \t 0.79761351 \t 250.42297 \t 503.77060676\n",
      "218001 \t 53.01100865 \t 0.78328388 \t 85.81194267 \t 0.7977161 \t 250.22728 \t 502.68264842\n",
      "219001 \t 53.0189052 \t 0.7831425 \t 85.78862022 \t 0.79783124 \t 250.02934 \t 501.79356813\n",
      "220001 \t 53.00105718 \t 0.78328795 \t 85.74995941 \t 0.79798674 \t 249.83136 \t 500.28214407\n",
      "221001 \t 52.98616487 \t 0.78338899 \t 85.70469101 \t 0.79817726 \t 249.64322 \t 497.71058989\n",
      "222001 \t 52.98121683 \t 0.78344247 \t 85.67463341 \t 0.79829338 \t 249.44183 \t 503.15238953\n",
      "223001 \t 52.97628657 \t 0.78347172 \t 85.63445915 \t 0.79845353 \t 249.26419 \t 503.94471884\n",
      "224001 \t 52.96542713 \t 0.78356109 \t 85.59401161 \t 0.79859301 \t 249.08362 \t 504.45221829\n",
      "225001 \t 52.95649646 \t 0.78365857 \t 85.57324661 \t 0.7987007 \t 248.89142 \t 503.40373278\n",
      "226001 \t 52.94695983 \t 0.78376924 \t 85.54478951 \t 0.79887757 \t 248.70671 \t 503.0568881\n",
      "227001 \t 52.9509199 \t 0.78369739 \t 85.5243232 \t 0.79903535 \t 248.5224 \t 504.02385664\n",
      "228001 \t 52.94376631 \t 0.78372586 \t 85.48346367 \t 0.79922887 \t 248.34929 \t 504.05358267\n",
      "229001 \t 52.93939613 \t 0.78365509 \t 85.4521611 \t 0.7993497 \t 248.16602 \t 504.1516335\n",
      "230001 \t 52.9341159 \t 0.78370469 \t 85.43814331 \t 0.79943566 \t 247.97612 \t 503.62921262\n",
      "231001 \t 52.93379525 \t 0.78372932 \t 85.40924973 \t 0.79950442 \t 247.79068 \t 502.55422449\n",
      "232001 \t 52.94375016 \t 0.78367546 \t 85.37822815 \t 0.79966404 \t 247.61295 \t 502.49894023\n",
      "233001 \t 52.92210932 \t 0.78386043 \t 85.32441292 \t 0.79983938 \t 247.44376 \t 502.41805196\n",
      "234001 \t 52.89367075 \t 0.78399161 \t 85.2753186 \t 0.80002269 \t 247.2746 \t 503.66568804\n",
      "235001 \t 52.90319295 \t 0.78383742 \t 85.25583168 \t 0.80016924 \t 247.09085 \t 502.52595782\n",
      "236001 \t 52.90184596 \t 0.7838172 \t 85.2285347 \t 0.800357 \t 246.91624 \t 503.72203374\n",
      "237001 \t 52.89670599 \t 0.78383729 \t 85.19243179 \t 0.80050211 \t 246.73215 \t 503.79539227\n",
      "238001 \t 52.88816526 \t 0.78396566 \t 85.15211525 \t 0.8006968 \t 246.56595 \t 503.19044352\n",
      "239001 \t 52.89503991 \t 0.78386236 \t 85.14349525 \t 0.80074862 \t 246.38142 \t 502.77501059\n",
      "240001 \t 52.90301782 \t 0.78382013 \t 85.13929817 \t 0.80082886 \t 246.21259 \t 502.73822308\n",
      "241001 \t 52.90701445 \t 0.78375455 \t 85.1297892 \t 0.80092483 \t 246.05084 \t 503.09993219\n",
      "242001 \t 52.90624763 \t 0.78372586 \t 85.09641106 \t 0.801013 \t 245.88652 \t 502.79801965\n",
      "243001 \t 52.89068707 \t 0.78387886 \t 85.0419442 \t 0.80122765 \t 245.7224 \t 501.53161907\n",
      "244001 \t 52.88910616 \t 0.78390676 \t 85.01874733 \t 0.80135869 \t 245.55066 \t 503.60248327\n",
      "245001 \t 52.88724675 \t 0.78385331 \t 84.99977951 \t 0.80145754 \t 245.38235 \t 503.79174471\n",
      "246001 \t 52.86941778 \t 0.78396851 \t 84.96175072 \t 0.80159819 \t 245.21808 \t 502.45291209\n",
      "247001 \t 52.8601356 \t 0.78404635 \t 84.92213326 \t 0.80176447 \t 245.05792 \t 503.39744067\n",
      "248001 \t 52.86117915 \t 0.7839439 \t 84.91918137 \t 0.80180886 \t 244.90138 \t 503.97636366\n",
      "249001 \t 52.87073341 \t 0.78383961 \t 84.90915192 \t 0.80188397 \t 244.74573 \t 503.36052823\n",
      "250001 \t 52.87145051 \t 0.78380373 \t 84.89345772 \t 0.80194116 \t 244.58482 \t 503.53719401\n",
      "251001 \t 52.86682554 \t 0.78383304 \t 84.85589172 \t 0.80203932 \t 244.42847 \t 502.97281575\n",
      "252001 \t 52.86261609 \t 0.78387846 \t 84.81562859 \t 0.80218244 \t 244.27591 \t 503.42640924\n",
      "253001 \t 52.85993626 \t 0.78397694 \t 84.79902245 \t 0.80226573 \t 244.10751 \t 502.51159239\n",
      "254001 \t 52.8465335 \t 0.78417151 \t 84.76569931 \t 0.80238074 \t 243.94073 \t 503.70777678\n",
      "255001 \t 52.83909623 \t 0.7842793 \t 84.73397903 \t 0.80251134 \t 243.7841 \t 504.01508856\n",
      "256001 \t 52.84136898 \t 0.78432041 \t 84.72601753 \t 0.80262139 \t 243.626 \t 504.64049077\n",
      "257001 \t 52.83346439 \t 0.78441402 \t 84.68458328 \t 0.80274239 \t 243.47906 \t 502.69880915\n",
      "258001 \t 52.82762491 \t 0.78447433 \t 84.64675903 \t 0.80288528 \t 243.33609 \t 502.75730968\n",
      "259001 \t 52.81961304 \t 0.78455154 \t 84.60853722 \t 0.80301833 \t 243.17914 \t 503.25511146\n",
      "260001 \t 52.81810222 \t 0.78453138 \t 84.57850426 \t 0.80312402 \t 243.02661 \t 504.25122285\n",
      "261001 \t 52.80511277 \t 0.78462966 \t 84.54417435 \t 0.80326446 \t 242.89 \t 502.8428247\n",
      "262001 \t 52.7903205 \t 0.78472766 \t 84.51524223 \t 0.80339914 \t 242.74028 \t 503.8602047\n",
      "263001 \t 52.7835801 \t 0.78480695 \t 84.47430801 \t 0.80355423 \t 242.59999 \t 504.22823787\n",
      "264001 \t 52.78609014 \t 0.78482702 \t 84.45617432 \t 0.80370132 \t 242.4637 \t 503.43563247\n",
      "265001 \t 52.78564858 \t 0.78471759 \t 84.42544586 \t 0.80384098 \t 242.32213 \t 503.85877204\n",
      "266001 \t 52.78058987 \t 0.78468979 \t 84.39748756 \t 0.80395345 \t 242.17552 \t 504.23115921\n",
      "267001 \t 52.77043877 \t 0.78468889 \t 84.37556885 \t 0.80403945 \t 242.04327 \t 502.82557631\n",
      "268001 \t 52.76483139 \t 0.78461529 \t 84.34207573 \t 0.80415491 \t 241.89328 \t 503.13849735\n",
      "269001 \t 52.77548778 \t 0.7844887 \t 84.31493683 \t 0.80431973 \t 241.7629 \t 502.3970747\n",
      "270001 \t 52.76437016 \t 0.78460517 \t 84.28946098 \t 0.80443612 \t 241.62538 \t 503.12718391\n",
      "271001 \t 52.76426815 \t 0.784608 \t 84.27455391 \t 0.8045526 \t 241.49023 \t 502.72677517\n",
      "272001 \t 52.76059237 \t 0.78467406 \t 84.25310188 \t 0.80472764 \t 241.35066 \t 503.21436691\n",
      "273001 \t 52.75601283 \t 0.78474897 \t 84.23541649 \t 0.80485798 \t 241.21007 \t 504.66273522\n",
      "274001 \t 52.75870579 \t 0.78475034 \t 84.22085709 \t 0.80488445 \t 241.06537 \t 513.00211263\n",
      "275001 \t 52.76414932 \t 0.78470489 \t 84.19223877 \t 0.80498498 \t 240.93442 \t 518.95845008\n",
      "276001 \t 52.76676147 \t 0.78473783 \t 84.16796104 \t 0.80510096 \t 240.80045 \t 517.7324779\n",
      "277001 \t 52.76314877 \t 0.78483151 \t 84.14696533 \t 0.80519143 \t 240.67801 \t 517.31410146\n",
      "278001 \t 52.75987692 \t 0.78489194 \t 84.12651367 \t 0.80531219 \t 240.54341 \t 516.82534838\n",
      "279001 \t 52.76384842 \t 0.78486519 \t 84.1080159 \t 0.80546255 \t 240.41428 \t 518.6573441\n",
      "280001 \t 52.75800867 \t 0.78487936 \t 84.08531013 \t 0.80553644 \t 240.28159 \t 517.66800618\n",
      "281001 \t 52.77554526 \t 0.78476691 \t 84.0927993 \t 0.80555558 \t 240.1524 \t 517.23153663\n",
      "282001 \t 52.77277369 \t 0.78471919 \t 84.0624897 \t 0.80559642 \t 240.01431 \t 517.20434642\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "283001 \t 52.76508385 \t 0.78480545 \t 84.02409714 \t 0.80573793 \t 239.88635 \t 517.70200038\n",
      "284001 \t 52.77173441 \t 0.78470386 \t 84.00095459 \t 0.80578523 \t 239.76695 \t 517.01409245\n",
      "285001 \t 52.77222985 \t 0.78467156 \t 83.97292145 \t 0.80589826 \t 239.63333 \t 516.54485369\n",
      "286001 \t 52.7658812 \t 0.78469038 \t 83.94851517 \t 0.80597753 \t 239.49963 \t 516.74999094\n",
      "287001 \t 52.75530361 \t 0.78474435 \t 83.92104607 \t 0.80608263 \t 239.38431 \t 518.39199495\n",
      "288001 \t 52.75384186 \t 0.78467057 \t 83.89684265 \t 0.80621299 \t 239.26021 \t 517.57649684\n",
      "289001 \t 52.75039518 \t 0.78469239 \t 83.87277351 \t 0.80633557 \t 239.13881 \t 516.32862425\n",
      "290001 \t 52.76936958 \t 0.78449942 \t 83.87452316 \t 0.80637124 \t 239.0116 \t 517.8482511\n",
      "291001 \t 52.76453977 \t 0.78451509 \t 83.85195282 \t 0.80644731 \t 238.88866 \t 516.16029739\n",
      "292001 \t 52.76675048 \t 0.78446919 \t 83.83403214 \t 0.80650854 \t 238.75964 \t 517.26474071\n",
      "293001 \t 52.76254913 \t 0.78451657 \t 83.80745934 \t 0.80664357 \t 238.6329 \t 517.75985599\n",
      "294001 \t 52.75805392 \t 0.78459719 \t 83.78357635 \t 0.80671244 \t 238.51482 \t 517.81100202\n",
      "295001 \t 52.74316425 \t 0.7847733 \t 83.76513786 \t 0.80677578 \t 238.39865 \t 518.28785729\n",
      "296001 \t 52.73811443 \t 0.78487489 \t 83.75351669 \t 0.8069257 \t 238.27989 \t 518.47544265\n",
      "297001 \t 52.71930562 \t 0.78516534 \t 83.72864449 \t 0.80710098 \t 238.16881 \t 521.14677095\n",
      "298001 \t 52.71527153 \t 0.78523176 \t 83.70891495 \t 0.80719793 \t 238.04819 \t 521.83862424\n",
      "299001 \t 52.71558753 \t 0.78522185 \t 83.69635284 \t 0.80725064 \t 237.92505 \t 520.78504205\n",
      "\n",
      "TRAINING DONE\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(graph=graph) as session:\n",
    "    tf.global_variables_initializer().run()\n",
    "    print(\"Initialized\")\n",
    "    ptime = time.time()\n",
    "    tfconfig = tf.ConfigProto()\n",
    "    tfconfig.gpu_options.allow_growth=gpu_memory_growth\n",
    "    init_op = tf.global_variables_initializer()    \n",
    "    sess = tf.Session(config=tfconfig)\n",
    "    sess.run(init_op)\n",
    "    print ('iter\\t test_squ_loss\\t test_avg_prec\\t',\n",
    "           'train_squ_loss\\t train_avg_prec\\t regularization\\t time (in s)')\n",
    "    for step in range(num_steps):\n",
    "        #print('Step',step)\n",
    "        #print('There are {} samples in the test set'.format(len(model_stats.test_ids)))\n",
    "        #print('There are {} samples in the train set'.format(len(model_stats.train_ids)))\n",
    "       \n",
    "        xmb, ymb = get_training_batch(model_stats, batch_size, window, \n",
    "                                      pitch_transforms, jitter)\n",
    "        \n",
    "        # every 1000-nd step save the results           \n",
    "        if step%control_step==0:\n",
    "            # training step \n",
    "            # to check output the loss (save the loss but you cannot compare it with the mse_train)\n",
    "            _, np_loss, np_global_step, np_lr, reg_np = \\\n",
    "            sess.run([training_op, loss, global_step, lr, reg], \n",
    "                                     feed_dict={xb: xmb, yb: ymb})\n",
    "            \n",
    "            # A) run model on test set\n",
    "                        \n",
    "            # 1. get the test set\n",
    "            X,Y = get_test_sample(model_stats.test_ids, window, 1000)\n",
    "            Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "            # shape Xtest: (1000*len(test_ids), 1, 16384, 1)\n",
    "            # shape Ytest: (1000*len(test_ids), 128)\n",
    "            # extended test-set: 11 samples\n",
    "\n",
    "            \n",
    "            # 2. prediction of output and calculation of\n",
    "            # - MSE\n",
    "            # - average precision\n",
    "            Y_p, mse_test = predict_direct_model(sess, X, Y)\n",
    "            avp_test = average_precision_score(Y.flatten(), Y_p.flatten())\n",
    "            del X,Y,Y_p\n",
    "            \n",
    "            # B) run model on training set\n",
    "            \n",
    "            # 1. get training sample\n",
    "            X,Y = get_training_sample(window)\n",
    "            Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "            # shape Xtrain: (15000, 1, 16384, 1)\n",
    "            # shape Ytrain: (15000, 128)\n",
    "            \n",
    "            # 2. prediction of output and calculation of\n",
    "            # - MSE\n",
    "            # - average precision\n",
    "            Y_p, mse_train = predict_direct_model(sess, X, Y)\n",
    "            avp_train = average_precision_score(Y.flatten(), Y_p.flatten())\n",
    "            \n",
    "            del X,Y,Y_p\n",
    "            \n",
    "            # c) Print the temporary results\n",
    "            print (np_global_step,'\\t', round(mse_test,8),\n",
    "                    '\\t', round(avp_test,8),\n",
    "                    '\\t', round(mse_train,8),\n",
    "                    '\\t', round(avp_train,8),\n",
    "                    '\\t', round(reg_np,8),\n",
    "                    '\\t', round(time.time() - ptime,8))\n",
    "            \n",
    "            #D) Save the temporary results\n",
    "            model_stats.stats['iter'][2].append(np_global_step)\n",
    "            # protocol learning rate\n",
    "            model_stats.stats['lr'][2].append(np_lr)\n",
    "            # protocol runtime\n",
    "            model_stats.stats['time'][2].append(time.time() - ptime)\n",
    "            # protocol weights and its norms\n",
    "            weight_norms = dict()\n",
    "            for name, w in model_stats.weights.items():\n",
    "                weight_norms[name] = np.mean(np.linalg.norm(w.eval(session=sess),axis=0))\n",
    "            for name, norm in weight_norms.items():\n",
    "                model_stats.stats['n'+name][2].append(norm)\n",
    "            # protocol training errors     \n",
    "            track = []\n",
    "            model_stats.stats['mse_train_1'][2].append(np_loss)\n",
    "            model_stats.stats['mse_train'][2].append(mse_train)\n",
    "            track += [tf.summary.scalar('mse_train', mse_train)]\n",
    "            model_stats.stats['avp_train'][2].append(avp_train)\n",
    "            track += [tf.summary.scalar('avp_train', avp_train)]\n",
    "            # protocol test errors\n",
    "            model_stats.stats['mse_test'][2].append(mse_test)\n",
    "            track += [tf.summary.scalar('mse_test', mse_test)]\n",
    "            model_stats.stats['avp_test'][2].append(avp_test)\n",
    "            track += [tf.summary.scalar('avp_test', avp_test)]\n",
    "            ptime = time.time()\n",
    "            \n",
    "                   \n",
    "        else:\n",
    "            #training step \n",
    "            sess.run([training_op], feed_dict={xb: xmb, yb: ymb})\n",
    "        \n",
    "        if step/num_steps==0.5 or (step+1)/num_steps==1:\n",
    "            saver.save(sess, 'models/'+notebook_reference+'model', global_step=step)            \n",
    "        \n",
    "    print ('\\nTRAINING DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Post Processing\n",
    "\n",
    "Define functions to save the model_results diccionary to a file and load it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case diccionary should be printed\n",
    "def print_dict(dictionary):\n",
    "    for key, value in  dictionary.items():\n",
    "        print (key)\n",
    "        print (value)\n",
    "#print_dict(model_stats.stats)\n",
    "    \n",
    "#save diccionary\n",
    "def save_obj(obj, name ):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# load diccionary\n",
    "def load_obj(name ):\n",
    "    with open(name + '.pkl', 'rb') as f:\n",
    "        return pickle.load(f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.1 Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save diccionary to have a look at outputs at other times\n",
    "#save_obj(model_stats.stats, 'results/'+notebook_reference+'_stats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_stats = load_obj('results/'+notebook_reference+'_stats')\n",
    "\n",
    "# size of plots\n",
    "width = 20\n",
    "height = 13\n",
    "fontsize = 24\n",
    "control_step = 300000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Iteration')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iters_res = results_stats['iter'][2]\n",
    "time_res = results_stats['time'][2]\n",
    "lr_res = results_stats['lr'][2]\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.rcParams['xtick.labelsize']=fontsize\n",
    "plt.rcParams['ytick.labelsize']=fontsize\n",
    "\n",
    "#plot time needed for 'control_step' steps\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Runtime and Learning Rate',fontsize=fontsize)\n",
    "plt.plot(iters_res[1:], time_res[1:], c=\"darkblue\")\n",
    "plt.ylabel('Runtime of last '+str(control_step)+' steps (sec)',fontsize=fontsize)\n",
    "plt.ylim( (0.9*np.min(time_res[1:]), 1.1*np.max(time_res[1:])))\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(iters_res[1:], lr_res[1:], c=\"darkblue\")\n",
    "plt.ylabel('Learning rate',fontsize=fontsize)\n",
    "plt.xlabel('Iteration',fontsize=fontsize)\n",
    "\n",
    "#plt.savefig('results/plots/'+notebook_reference+'_runtime_lr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Iteration')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lCGINwYsvvqiGhgbNmTNHZhZ2d6qac06tra168cUXNXfu3LC7AwAAAAAARhkuJxyCjo4ONTU1EcAqg5mpqamJWWsAAAAAAGBQqiaIZWYzzexyM9tgZh1mttnMbjWzY4bY7slmdpuZbTKzlJm1mdkaM7vEzGZUoN9DbWLc4LkCAAAAAACDVRVBLDM7VNKjkj4l6QBJnZKmSjpR0h/N7AuDaDNiZr+QdKOkEyTNlNQuqVbSYZI+L+lxMzu8Ig8iBK2trVqyZImWLFmimTNnap999une7urqKquNs846S0888UTJMt/73vf0y1/+shJdBgAAAAAAGBRzzoXbAbNaSWsl7S/pIUlnOOceM7NGSf9P0mf8osc551YMoN2PSrrK37xc0sXOuVfMLCbpGEk/krSfpA2SFjjnsqXaW7ZsmVu1alWvvLVr12rhwoXldmlYfe1rX1N9fb0++9nP9sp3zsk5p0ikKuKVVfWcAQAAAACAcJnZaufcsnLKVkNk46PyAli7Jb3dOfeYJDnndjnnPivpZr/cNwfY7vv95d3OufOdc6/47aadc3dK+pC//0BJhw7lAVSbp556SosXL9bHPvYxtbS0aNOmTTr33HO1bNkyLVq0SBdeeGF32aOOOkpr1qxROp3WpEmT9IUvfEGHHXaYjjzySL3yyiuSpK985Su67LLLust/4Qtf0PLly3XQQQfp3nvvlSTt2bNHp5xyig477DC9733v07Jly7RmzZqRf/AAAAAAAGBMqoZfJzzdX17nnHupwP7/kHSSpBYzO9g5t67MdnP3u3qwyP7VgfW6Mtss6vz167Vm9+6hNtPLkvp6XTZ//qDqPv7447rmmmt01VXeZLRLLrlEU6ZMUTqd1hvf+EadeuqpOuSQQ3rV2blzp17/+tfrkksu0ac//WldffXV+sIX+l7J6ZzTypUr9bvf/U4XXnih/vCHP+iKK67QzJkzdcMNN+jhhx9WS0vLoPoNAAAAAABQSKhBLDNrkLTU37yzSLH7Je2UNFHSmySVG8R6VtJBkl5dZH/uuJ2SHi+zzVHjwAMP1OGH99zu61e/+pV++tOfKp1Oa+PGjXr88cf7BLFqa2v1tre9TZK0dOlS/e1vfyvY9rve9a7uMs8++6wk6Z577tHnP/95SdJhhx2mRYsWVfohAQAAAABQFuecspIyzinrnDIDWM8E6hZaz1Z4mXFOab/93DIj9awXyf/QzJla2tAwsk9syMKeibVQUu4n6x4rVMA5lzWzJyQtl3RIoTJF/FjScZLeZGaXSvpmgXtiSdKFzrntg+p9wGBnTA2XurqeyWXr16/X5ZdfrpUrV2rSpEn6wAc+oI6Ojj51EolE93o0GlU6nS7Ydk1NTZ8yYd9bDQAAAADGEhcMXki9ghz5AY+wy+QCLcEgUH/b2bx9pYJGwfrZMtdL3vR6FIpKipopZqaon14/aRJBrBE2K7C+sUS53L5ZJcr04py7wcy+LOlCSedLOt/M2iRNkPf6PybpLOfcfw+ox6PQrl271NDQoMbGRm3atEl33nmn3vrWt1b0GEcddZR+85vf6Oijj9Yjjzyixx8fc5PbAAAAAAyCCwRD+kuFgiTFZqH02S8VzQ/mlRUsCbSXP0NnoMGaTIG+lRM0qsYgTKFASsysO787DWA7EciPBPLLWR9UHUmRMtajZt523nok0Gakgsvc85h7boPPcUSSWW7+z/gWdhAreC+q9hLl9vrL+gG2/01Jz0n6oX+sYIiyTtJUM4v098uEo11LS4sOOeQQLV68WAcccIBe97rXVfwYn/zkJ/XBD35Qhx56qFpaWrR48WJNnDix4scBAAAARpuBBHHGYqrWD1ulAhXFgiTlBGdqIpGC+3sFJ9Q3WBGrYJleZStUhkAKqoGFeRmYmZ0u6Rf+Ztw5V/D6NTP7pbxfG1zhnDuuzLYbJP1K0gmSbpN0kbx7XzVJequkiyVNkfRr59z7irRxrqRzJWm//fZb+txzz/Xav3btWi1cuLCc7ox56XRa6XRayWRS69ev11ve8hatX79esVjvOCnPGQAAAArJBXpSfko7p1Q223s7kBcsm8pme2+XUb9Y3kC2R1MQJxYIRlQiRYehzUKBmvyZPoXy++SVmNGSa4dgDFA9zGy1c25ZOWXDnokV/Dm/WkltRcpNKFC+P9+RF8C6yzn39rxj/tDM1kr6X0nvNbOfO+d+n9+Ac+5H8u+dtWzZMm76VMLu3bt1zDHHKJ1OyzmnH/7wh30CWAAAABi6YLAnGIQZaBBnqEGgStfPjPDzGDNT3A9wxHMpEimaFzfThEhEicB2zN8/HMGcSiZm0AAYK8KOMgTvgzVb0hNFys32l5vKadTMGiWd5W9eVqiMc+6vZvagvF8pfKekPkEslG/SpElavXp12N0AAAAoKOsHS7qyWXUVWQb3V0tgp1D9tBvZ71ajUtHgTq/tQFAnF/CJxWIlg0Ol6vcqX4H6sbxyBHUAYPQJO4i1TpKT9wuFi1QgiGVmEUkH+Zvl3i18vrz/t5L0TIlyT8sLYs0ps10AAACo51ezCgaCBhAs6rdchdoaiVk+ucuXBhqESUYiivvBnuEM4gymfu4eQQAAVINQg1jOuTYzWyXpcElvlnRjgWKvkZS7Q/ifymw6eNn5fioe/NrfXxa7jBEAAGDEuDJmC1VTsGi45gPFzPu1qkQkUnAZD6zXR6NKxGIFy8ZLtFGore68QQSBYgR7gFHLOSfnCi+z2fy8Yvneslh+Jdvy8nvWc+Vy6/nbZlIk4s0+jES89WIpVzY/5fpZ6FheUq8yuXJBuVNkcBZk8LTZX355ZcM/Ru616lnP3/aem0wmq0zGKZ3uvcxkskqnc8tSZbI67ri5mj9/ssaTsGdiSdJ18oJYp5vZhc65/EsGP+svVzvnil1umG+dpA5JSXk3Zv9DfgEza5HU4m/+fcC9BgAAo1LWOXVms15yTh259Wy2Z73M/GBAJ1WBYNFwXiZWM4AAT308XjTAU8lgaZyUrwAAIABJREFUUaElwaC+ch8Mcx9cvGWx9cL7vfr91e2/nUwmG+hL32MU6nvfvJ4P4aU+/BZe98r33u6/Tu64xT5MFsrL9T9Xr/d24b/VwXwQzi+Te4yFHlvf56Lw4y5Vt9hrk/+QgvtL7Su8fyBl+z9O/mtUKK9UoKd4ft9ACzCaXHfdCQSxQvBDSefLmxV1m5md4Zx73P91wX+T9C6/3JfyK5pZ7pTz7865r+XynXPtZvZzeQGsk83sx5IudM69YGZJScdJukLe498l6b+H5ZEBAABJ3geFXBCoV1Aot15m/kCDTIXyUxX6xBKVyg7w1EWjmlxgtlDcTHFnijkpLm/2UUymuJPiiiguKSZTzPmXdvnlYrnkvP2WdrKMk9J+Cq6nnTJp71vdVCqjVCrbnbq6vG3vg62TWUZmFliW89p6y4ykdj/lv/bltlF8f7Fv/ksHMPI/5BfLDwaI8r/97vnmO7iv9Dfk6XS2+3EHn8fcerEgUH4AKVeuUHBorDCTPzuk8EyQ3uu9Z5H01OvdRn79Qq9BMNhUOK9wmUJt5BQKxhQL0JTK7/3Yeh6PmRSNmiKRSPf+Qs9VoecluK9Y8Cz3WEtvFw7I9ddW/8fpv93c69I3r+/rlP/a987P31csPziGiuUXar9vfiXbCva12Oyp/L8bqXgguL/zae48VOhvrScVzg++lkP5mxha2ZE4Ru+y5Z53olFTLBbptYxGI33yeu/rXaaxMaHxJvQglh9weqe8SwVbJD1mZrsk1UuKyLtn1peccysG2PRnJB0i6ShJ50g6x8z2yPsVxIhfpk3Sac65rUN/JCOvtbVVxxxzjCTp5ZdfVjQa1bRp0yRJK1euVCJR3oC++uqrdfzxx2vmzJnD1lcAwMhz/qyfoQSNKpXfVU7gKBd4SWX9pQsss1JGsnRWiawpnpUSGVMsK8UyUjzjLaP+djQjxTNOyYwUyThF/KWlJcs4WdrJpbLKdmXlupyyXVllOjPKprJKd2WV6coq1ZlRpssPRmQll7s0wnnr+W/2O7NOHS7/co6emSaFZkegsgp9kA9+GCyWb+Z9SMh9OOj74aHvB4eamnjBDxm58pFIz8wfqfcskUjEuj+Q9F72Xe8pW7rc0Nvpr+2e9b797zlGfgBCKvyreMHnHgCAcoUexJIk59zDZrZY0hclnShpH0mtklZKutQ5V+69sIJt7jazN0j6oKT3Snq1pMnyviB8RtIfJV3unHuuIg8iBE1NTVqzZo0k6Wtf+5rq6+v12c9+tp9afV199dVqaWkhiAUAKjzjw7t8pve9C3Kp97ZTOp1RRyqrvV1ptaez6kil1Z7Kqj2VUUde6sw4daYy6kxn1ZXOKC0pG5WyUVMmImUiTpmIlDZ5v0wm735JnamMujoz6uz0ll1dGaU6vaBLuiujlB+QyXRlvQBQyl9m5X2izvjLbC5PXgDJKZAX2Bf1v9GNmiKBD8MRk8xJEWcyJ1nWKeK8PC9fmpCVJmT8gFHayaW9ZTadVSbllElllU5llO7KlhXUcZI6/TQY0agpHo8qHo+opiaqZDKmmppo93ptTVQ1NQnVNHrbiUSk+8N5qRkihbeLzRop/K116bZ72io0AyMej3Q/rp5U/nY0Guk1/oPLcoIM/RWpRBv93b+FYAgAAGNfVQSxJMk597Kk8/xUbp2S71accxlJ1/hpXPnZz36m733ve+rq6tJrX/taXXnllcpmszrrrLO0Zs0aOed07rnnasaMGVqzZo3e8573qLa2dkAzuACML871XFbjXYrUc1lSfl7Pdk9+ft5g2vG2MyXbSaez6kpl1dmVUVcqo65UVqlAmXQqq0zanwmTdco6J5ftmVkzbHeqHkGRmClWE1UiEVEsEVE8EVU8EVE0FlHU/+Afi0YVjZiiMVM04s9CyU1Vj5jiMW8ZkXoF8IKXOnUHUaLFbxAbiUjRaESJROEASiLh9dNb5q972/G4/1hihYIxhdvtKduTH4t5wSgAAACMTlUTxBrtzj//z1qz5pWKtrlkyXRddtmbBlzv0Ucf1U033aR7771XsVhM5557rn7961/rwAMP1NatW/XII49Iknbs2KFJkybpiiuu0JVXXqklS5ZUtP/AeJdOZ9XZmS4QzHElAzd9gzeVCQANvJ2+dUZSJGqKxEyRWEQW89YVi8iikmIRKSq5mMlFTNmYyeVmMHm/cS8lzVtGo1I05q3nUlTyphH1XUajpljEFIt4QZ1YNKJ4xFvGoqZEzA+UxCJKxCJKxKJKxEw1sagS8Yhq4lEvxSKqiUVU628nY1HVJqJKxqNKxiKaEPfWa+NRTYhHVRuLevc3cqZIVlLgXjzptPfc52bGxGJ9ZxH1pBiBGgAAAIxJBLHGoLvuuksPPPCAli1bJklqb2/Xvvvuq+OOO05PPPGEzjvvPB1//PF6y1veEnJPgZGVzTp1dKTV3l4opdTentbevWm1tXWpra1Lu3bllp1qa0tp796UOjrS6vQv4+rszPTZDuaN1I14zeTPMrE+s1Jisd4zVYKzU+rq4gVnrChqclHzAkR+YCi3zEalTNSUjjhlYt4lb6molIpIqYhTV0TqijilolKnedudkaw6I1JnxCkTzQWRAsGkWCAvsD8bMWUlxc1UG4loQjSqCUWW/e2fEIn0KlMbjSoZiajGzFv6KW5ckgQAAABUK4JYFTKYGVPDxTmnD3/4w7rooov67PvHP/6h3//+9/rud7+rG264QT/60Y9C6CFQnkwm2x1M2rmzUzt2dHYvd+zo0M6dXb2Wu3Z1FQlQeamjIz3gPtTURNXQkFBjY0ITJsR7zX5paEj0mQGTTPas5/JLX+ZUKPjUNy+/TjBPEdPuTEZt6bTaMhntCqy3ZTLaFVjP396WTnvlA3UGMt8qGYmoLhJRXTTanRr97ZIBpX72BwNPsUik/44AAAAAGPMIYo1Bxx57rE499VSdd955mjp1qlpbW7Vnzx7V1tYqmUzqtNNO09y5c/Wxj31MktTQ0KC2traQe42xJJPJFgw89V52dM908lIqsO6l9vb+g07JZEyTJtVo0qQaNTQkNGFCTE1NSdXWxlVbG+s3TZgQ77Pd2JhQQ4OXEoloRZ4T5/9S255sVnsyGe3OZLQnl7JZbQ9uZ7q8/X7ZPV0Z7WnP9M4LtpEtL+wUM1NDNKrGaFQNsZgaolFNjMW0bzKphmjU2+fn59bro9E+Qarc9oRoVFFmLQEAAAAYIQSxxqBXvepV+upXv6pjjz1W2WxW8XhcV111laLRqM4+++zuXxr61re+JUk666yzdM4553Bjd0jygi3t7emiwady8trauvo9Tl1d72BRQ0NCzc31vbaDafLkGk2c6AWrgsuamuE/jaWyWe1Mp7Uzk/GWuVTG9q50Wrv9gNNAZjiZ1B0wqg8EkOqjUc1MJHoFloJBqVLrNZEIl8oBAAAAGLXMlfN71tCyZcvcqlWreuWtXbtWCxcuDKlHoxPP2cjJZp127uxUa2u7tm5tV2tru1pbO7qXubxt2zr6BKVyN5EuJhaL9AokTZyY0KRJSU2cmOjOz217y5ru2VITJ9aosTGheLwyM5zKkcpmtT2d1vZ0WttSqe717f56fgBqR15eexkznWojEU2MxTTRn92US43+rKa6vEBUMAhVnzfDqd6/XxMBJwAAAABjnZmtds4tK6csM7GAUairK6ONG3frxRfb9OKLuaWXXnjBy9u8eY8ymcJB6mjUNGVKUk1NtZoyJalZs+p08MFTCgSl+s58mjjRuzfUSAdYunKBqGAQqszt/i63m5ALQPlBqEmxmPZPJgsGpQptN8ZiSnDfJgAAAAAYVgSxgCqTSmX0/PNtvQJTwUDVCy+0afPmvX3q1dfHte++DWpubtDixVM1a1a9pk6tVVOTF6zyUlJTp9aqsbFGkUh4s3z2ZDLamkppS1eXtqRS3nreMjdjKrfc208gqi4S0eR4XJNjMU2JxXRgba0mx2I9yd+Xvz2JABQAAAAAjAoEsYAQZLNOL7ywS08+uV3r12/vtXzmmZ19ZlBNmlSj5uYGNTfXa8mS6WpubugOWDU316u5uUGNjTXhPBbntD2dLhqQ2tLV1Sev2OV5MTNNjcc1NR5Xkx+IWtbQQCAKAAAAAEAQa6hyN0lH/8br/de2bNmrlSs3aeXKl/XII1v15JPb9NRTO9TZmekuM2FCTAsWTNGrXz1d73nPwTrwwInad9/G7gBVff3I3Wy/M5vtFXjqE5jKC1a1plJFb1heH41qajyuafG4ZiYSWlxXp2nxuKYlEt350/yg1bR4XBNjMf6eAAAAAAAFEcQagmQyqdbWVjU1NfHBux/OObW2tiqZTIbdlWHV1ZXRww+/ovvv36T77tuo++/fpGee2SlJikRMCxZM1oIFk/W2t83VggVTNH/+JC1YMEWzZtUN6xjqymb1cleXNnV1aWNnpzb565v89WCQqi2TKdiGSWoKBJ0OnjBBR+cCUIlEr2BUbj0ZHbmbtwMAAAAAxjaCWEPQ3NysF198UVu2bAm7K6NCMplUc3Nz2N2oqI0bd+v++zfqvvs26r77Nmn16s3q6EhLkvbZp15HHDFLn/jEEi1fPlNLl85QXV1lZ1Q557Q1ldJzHR16vrNTL3V2amMgOJULWrWm033qRiRNTyQ0M5HQ9Hhc82prS86SmhyPK0qwFgAAAAAQEoJYQxCPxzV37tywu4ERtGNHh2677WndfvvTuvfel/T8822SpEQiqqVLZ+jjHz9MRx45W0ceOVvNzQ1DPl7WOb3Y2alnOzr0fEeHnuvs1HMdHd3p+c7OPveXiplpZiKhWYmE5iaTet3EiZrlb89KJDSrpkaz/ZlTMe4jBQAAAAAYJQhiAf3YtGm3brnlKd1443rdffcLSqezmjWrTkcf3ax//VcvYLVkyTTV1AzuzynjB6rW792rp9rbtb69vXv5dHu7OvPuJTYtHtf+yaQW19XphKYm7ZdMav+aGu2fTGqfmhpNjccVYcYUAAAAAGCMIYgFFPDUU9t1003rddNNT+n++zfKOWn+/Mn6zGeW6eST5+vww2cqEhlYoCiVzeqJvXv1yJ49emTPHj22Z4/Wt7drQ3u7ugKBqmQkonm1tTpowgSd0NSkebW1mptMav9kUvvW1GgC95kCAAAAAIxDBLEAefeWevjhLX7gar0eeWSrJKmlZYYuvPB1Ovnk+TrkkPJv4L8zndaqtjatamvTP3bv1iN79mjd3r1K+cGqmJkOqq3VwRMm6O1+oGpeba3m19Zqdk0NM6kAAAAAAMhDEAvjViaT1X33beyecfXMMzsViZiOOmofXXrpG3XSSfM0Z87Efttpz2T00O7deqCtTQ/s2qUH2tr0ZHt79/7mmhodWlen46dM0avq63VoXZ0OmjBBCe5HBQAAAABA2QhiYVzp7Ezr7rtf0E03rdfNNz+lV17Zq0QiqmOP3U9f+tJr9I53HKjp0+tKtrGhvV1/3r5dK/2g1aN79ijj75uVSOjwhgadMWOGDm9s1LKGBjXF48P/wAAAAAAAGOMIYmHM27x5j26//WnddtvTWrHiWe3Zk1J9fVzHH3+ATj55vo4/fq4aG2uK1t+VTuvuHTt057ZtWrFtmzZ0dEiSpsRiWtbQoBObmnR4Y6MOb2jQ7Jri7QAAAAAAgMEjiIUx6ZFHtuiWW57Srbdu0MqVL0uSmpsbdMYZh+jEEw/QMcfsr2Sy8PDPOKcH29q8oNX27bpv1y6lnVNdJKI3TZ6s85ub9eYpU7Sgtrbse2QBAAAAAIChIYiFMWPTpt267rq1+vnPH9c//rFFkrR8+UxddNHrdOKJB+qww6YVDTrtSKV0W2urbmtt1R+3b9e2dFqStLS+Xhfsu6+OmzJFRzY2ch8rAAAAAABCQhALo9revSndcstT+vnPH9OKFc8pm3V6zWtm6corj9EppyzQzJnF72+1qbNTN2/dqpu2btXdO3Yo7ZxmJhJ6e1OTjpsyRcdOnqxpicQIPhoAAAAAAFAMQSyMSk88sU2XXrpa1123Vm1tXdpvvwZ98Yuv0RlnHKKDDppSsI5zTuv27tVtra26aetW3b9rl5yk+bW1+nRzs941bZoOb2hQhEsEAQAAAACoOgSxMKrcd99GffvbK3XLLU8pkYjqfe87WGeeuVhHH92sSKRv8Kkjk9Ffdu7U7a2tur21VU/7N2Vvqa/XhXPm6ORp03TIhAnc2woAAAAAgCpHEAujwlNPbdcFF/xFN9/8lCZPTuorXzlC//Ivr9b06X0vF9yWSummrVt169atumv7du3JZpWMRHTMpEm6YN99dXxTk/ZLJkN4FAAAAAAAYLAIYqGq7drVqYsuuk+XX/6gamqi+sY3jtKnPtWi+vre96rak8no5q1b9etXXtGd27Yp5Zz2q6nRB2fO1AlNTXrjpEmaEI2G9CgAAAAAAMBQEcRC1Xr44Vd0yim/09NP79CZZy7WN75xlGbNqu9V5qG2Nv140yb9cvNm7cpktG9Njc5rbtZ7p09XS309lwkCAAAAADBGEMRCVfrZzx7Vxz52l6ZMSeqvf32vjjqquXtf1jndsnWrvv3CC7p/1y4lIxGdNm2azpk1S0dNnMiN2QEAAAAAGIMIYqGqpNNZfepTf9IPfvCw3vjGffWrX52oGTO8+16ls1ldu3mzvv3CC1q3d68OSCZ1+bx5OmPGDE2Ox0PuOQAAAAAAGE4EsVA1uroyOv3023X99U/qggsO18UXH61YLCLnnO7Ytk0XbNigtXv3akl9vX61cKFOnTZNsUgk7G4DAAAAAIARQBALVaGjI613v/tW3XrrBv3Xf71Bn/70MknSY3v26Lz16/WnHTs0v7ZWNy5apJOmTuVeVwAAAAAAjDMEsRC6vXtTOumkm/XHPz6n73//WH3840uUdU5XvPSSPr9hg+qiUX133jx9bPZsxZl5BQAAAADAuEQQC6HKZp3OOOMO3XXXc7rmmrfqzDMXa2Nnp85at04rtm/XiU1N+ulBB2l6IhF2VwEAAAAAQIgIYiFU//Zv9+jGG9frO995g848c7H+b+dOnfToo9qTyegH8+fro7Nnc+kgAAAAAAAgiIXwXHvtY7r44r/rIx85VOefv1S/3LxZH163Tvslk/rrkiVaWFcXdhcBAAAAAECV4AZDCMW9976kc85ZoTe8YV9dccWb9NVnn9UH1q7VkY2Nur+lhQAWAAAAAADohZlYGHFbtuzVqaf+Tvvu26Df/vbt+vRzT+v7GzfqzJkz9cMFC5Tg5u0AAAAAACAPQSyMqGzW6cwzf69t2zp0xx3v0sU7Nur7Gzfqgn331bcOOID7XwEAAAAAgIIIYmFEffe7D+qOO57Rd7/7Jv2mcY8uff5FfWqffQhgAQAAAACAkrhuCyPmwQc363Of+4ve+c552vX2Kfrm88/ro7Nm6bJ58whgAQAAAACAkpiJhRGxd29K733vbZo+fYLecUmLzn72aX1gxgx9f8ECAlgAAAAAAKBfBLEwIi655O9av367rrrtRP3LK8/qtY2N+slBBylCAAsAAAAAAJSBIBaG3bPP7tR//McqnfzuBfr6lO2aqrhuXLxYNfwKIQAAAAAAKBNBLAy7Cy74iyIR6dkzp2pbKqX/e/WrNSORCLtbAAAAAABgFGEqDIbV3Xc/r+uvf1JHfHSBHqrt0n8ffLCWNDSE3S0AAAAAADDKEMTCsEmnszr//Lu1z/4N+ttba/WeadN02vTpYXcLAAAAAACMQgSxMGx++tNH9I9/bNGkf5mjuglxXTZvXthdAgAAAAAAoxT3xMKw6OhI66KL7tP85dP02NKEfnTAAZpZUxN2twAAAAAAwCjFTCwMi5/+9BG99NJubXr/VB09aZLOnjUr7C4BAAAAAIBRjCAWKq6jI62LL/67Ziydoo7D6vTDBQsUMQu7WwAAAAAAYBQjiIWK+8lP/qGNG3frldOn6lPNzVpYVxd2lwAAAAAAwCjHPbFQUR0daX3zmys1ZelkpZdO0pf23z/sLgEAAAAAgDGAmVioqB//2JuFte30afri/vurKR4Pu0sAAAAAAGAMYCYWKiaVyuiSS1aqoWWSGpY36bzm5rC7BAAAAAAAxghmYqFibr75KW3cuFttp07VhQccoNpoNOwuAQAAAACAMaJqglhmNtPMLjezDWbWYWabzexWMzumAm1PN7NvmNnDZrbTzHab2Xoz+5WZvbMS/Yf0gx+sUWxWjQ5+wyx9aMaMsLsDAAAAAADGkKq4nNDMDpX0Z0lNftYuSVMlnSjpBDP7knPukkG2fbykX0ia7Ge1S8pImuenaZJuGXzvIUlr17bq7rtfkD6yj7524FzFIlUTHwUAAAAAAGNA6JEGM6uV9Dt5AayHJC12zk2UF3T6L0km6Ztm9pZBtH20pJv8tv5H0quccxOccw3+8d4l6Y6KPJBx7qqrHpbFTPu8c7ZOmTo17O4AAAAAAIAxphpmYn1U0v6Sdkt6u3PuJUlyzu2S9FkzO1DSSZK+KWlFuY2aWVLSNZISkn7knPtocL9zbpu8ABeGaM+eLl39s0flXj9Z/3roAczCAgAAAAAAFVcN0YbT/eV1uQBWnv/wly1mdvAA2n2PpAMlbZf0mSH0D/341a/WaffOLtWePFPnzJoVdncAAAAAAMAYFGoQy8waJC31N+8sUux+STv99TcNoPlccOx659zuQXQPZXDO6fLvPSQdUKuPvHmeJsaqYXIfAAAAAAAYa8KeibVQ3j2vJOmxQgWcc1lJT/ibh5TTqJmZpNf4m/eYWYuZ3WhmW/xfPnzKzL5nZvsPpfOQVq16WY+u2SK9Y5rO23ffsLsDAAAAAADGqLCDWMFrzzaWKJfbV+61ajMkNfrri+TN5jpZ0gRJKXmXGX5C0sP+zd8xSLfctkEy6cRT5+uA2tqwuwMAAAAAAMaosINYdYH19hLl9vrL+jLbnRRYv0DSZklvllTv/zLh6yQ9KWmipOvNbHKZ7SLPzXc/Kx1QqwsWzQ27KwAAAAAAYAwLO4hl/RcZlODjMkkfcM7d5ZxzkuScu1fSqZKykqZLOqdg58zONbNVZrZqy5Ytw9TV0SuVymjdqi1KLpmooyZODLs7AAAAAABgDAs7iBW84Xqpa9EmFChfbrsPO+f+kl/AOfeIpLv8zWMLNeKc+5Fzbplzbtm0adPKPPT48eCDm5Vpz6jltbMUseGKRwIAAAAAAIQfxAreB2t2iXK5fZvKbHezpIy//kSJcrl93JF8EK7/0zOSpHe9iUsJAQAAAADA8Ao7iLVOkvPXFxUqYGYRSQf5m4+X06hzrlPShtxmOVXKaRe9/eEvz0uza3TawaXijwAAAAAAAEMXahDLOdcmaZW/+eYixV4j7wbskvSnATSfK3twiTK5fc8NoF1Iymadnlj5ihpfPUn7JZNhdwcAAAAAAIxxYc/EkqTr/OXpZjarwP7P+svVzrlSlwbmu9ZfHmZmb8jfaWavknSMv3nHANqFpEce36rUjpSWvrbQSwYAAAAAAFBZ1RDE+qG8mVANkm4zs0MkycwazOzbkt7ll/tSfkUzc376Wv4+59x9km7yN681s2PMvLuPm9mRkq6X9/ifk3R1ZR/S2HftH5+SJJ16DPfDAgAAAAAAwy8Wdgecc+1m9k55l/+1SHrMzHZJqpcXZHKSvuScWzGI5s+Ud9P2ZfJ+iXCvmWXkBcwk70bx73TO7R3aoxh/7vzL89KUuN7fsl/YXQEAAAAAAONANczEknPuYUmLJX1X0tOSaiS1Srpd0pudc5cMst1dkl4r6TP6/+3deZhcV3ng/++rtVurLVm2ZTtYBBKM7cdsZg8OGPDkB8YwDMsEk4GBsGQISxIg4MkE+EGAmC1AJowhCYRhCQGGxYRgE/Zk2GQwizfAeAEk2bJkqdXqTV39zh/3llRqV/VWJd2rru/nee5TVfeec+5p1atb1W+fcy5cRXHHwmUUC8S/GbhPeW7N00++fTsb7n88xy1fXnVXJEmSJElSH6h8JFZTZu4AXlpuc60TcyhzAHh7uakHfvizXUzsGOeBL3A9LEmSJEmSdHTUYiSWji1/f+VPAXjao3+94p5IkiRJkqR+YRJL8/bFr/8CVi/ldx9yetVdkSRJkiRJfcIklubtF9fvYf0ZaxlcXpvZqJIkSZIkaZEziaV5G909znGbBqvuhiRJkiRJ6iMmsTQvmcnknQfYcIJJLEmSJEmSdPSYxNK83DYyDsMNTjpxVdVdkSRJkiRJfcQklubl2m17ATj1pNUV90SSJEmSJPUTk1ialxu2DwGw5eQ1FfdEkiRJkiT1E5NYmpebdgwDcI/N6yruiSRJkiRJ6icmsTQvt5RJrHttXltxTyRJkiRJUj8xiaV52X77fgBON4klSZIkSZKOIpNYmpfbbx+BpXD88QNVd0WSJEmSJPURk1ialzvvGGP5+hUsWRJVd0WSJEmSJPURk1ial327xli9cWXV3ZAkSZIkSX3GJJbmLDMZ2z3BOpNYkiRJkiTpKDOJpTnbPTlJ7jnAxk2rqu6KJEmSJEnqM10nsSLiyRHx04jYGxFDEbEvIoZ60TnVy7bxcdgzyYmbBqvuiiRJkiRJ6jPLetDGpcATMvO6HrSlGrt1eBSGG5x60uqquyJJkiRJkvpML6YT3mYCqz/8ZHsxwG7LyWsr7okkSZIkSeo3vRiJtTUiPgZ8Ghhv7szM/9ODtlUjP98+DMA9NpvEkiRJkiRJR1cvkljrgBHggpZ9CZjEWmRuvb1IYp3mdEJJkiRJknSUdZ3Eysz/2ouOqP6237YfgBNP9O6EkiRJkiTp6OrF3QlPi4hPRcTtEXFbRHwyIk7rRedULzt3jgKwaZNJLEmSJEmSdHT1YmH39wOfBU4BTgUuL/dpkdm9c5RYGhx//EDVXZEkSZIkSX2mF0msTZn5/sycLLcPAJt60K5qJDMZ3j3OquNXsGRJVN0dSZIkSZLUZ3qV01FxAAAgAElEQVSRxLojIp4ZEUvL7ZnArh60qxrZPTnJ1J0HWLfRUViSJEmSJOno60US6znA04AdwHbgKeU+LSLbxsdhzwE2bhqsuiuSJEmSJKkP9eLuhLcCF/WgL6qxbRMTsGeSE3/TRd0lSZIkSdLRt+AkVkS8MjMvjYh3Azn9eGa+pKueqVa2j4/DnklOPWl11V2RJEmSJEl9qJuRWNeVj1t70RHV263DY7C/wZaT11TdFUmSJEmS1IcWnMTKzMvLx39o7ouIJcCazBzqQd9UIz/fUbylpzgSS5IkSZIkVaDrhd0j4iMRsS4iVgPXAjdExCu675rq5Be37Qdg0ybXxJIkSZIkSUdfL+5OeGY58upJwOeBuwG/14N2VSPbbh8B4MQTTWJJkiRJkqSjrxdJrOURsZwiifWZzDxAm4XedWzbeXtzJNZgxT2RJEmSJEn9qBdJrMuAm4HVwNcj4nTANbEWkczkzjvGAEdiSZIkSZKkanRzd0IAMvNdwLtadt0SEY/qtl3Vx64DB5jac4AlS4PjjhuoujuSJEmSJKkPLTiJFRHPzMwPRcQfdyjy9oW2rXrZPTkJeyZZu2ElS5ZE1d2RJEmSJEl9qJuRWKvLx7W96Ijqa3RqCvZMsv4E18OSJEmSJEnVWHASKzMvKx9f17vuqI5GGw3Yc4DjT1hXdVckSZIkSVKf6nph94j4h4g4ruX18RHx9922q/pojsTacILrYUmSJEmSpGr04u6E52TmnuaLzLwTuF8P2lVNNJNYJ3hnQkmSJEmSVJFeJLGWRMTxzRcRsYEe3PVQ9bF3ZAL2N9i0ySSWJEmSJEmqRi+STW8D/m9EfAJI4GnAX/SgXdXE7XeMAnDSJhd2lyRJkiRJ1eg6iZWZH4yIrcD5QABPzsxru+6ZamPvvgkAjl+3suKeSJIkSZKkftWL6YQAG4D9mfluYGdE3L1H7aoGhscOALBu1YqKeyJJkiRJkvpVL+5O+BrgT4FXl7uWAx/qtl3Vx/BImcQaXF5xTyRJkiRJUr/qxUis/whcBOwHyMxtwNoetKuaGB6dBGD9apNYkiRJkiSpGr1IYk1kZlIs6k5ErO5Bm6qRkTKJNehILEmSJEmSVJFeJLH+KSIuA46LiOcB/wq8rwftqiZGyjWxBgaWVtwTSZIkSZLUr3pxd8K3RsRjgSHgXsCfZ+YXu+6ZauPQSKyuw0WSJEmSJGlBuspKRMRS4IrMfAxg4mqRGh0rklgDAyaxJEmSJElSNbqaTpiZDWAkItb3qD+qobFRk1iSJEmSJKlavchKjAE/iogvUt6hECAzX9KDtlUDY2MNwOmEkiRJkiSpOr3ISvxzuWmRGi+TWC7sLkmSJEmSqtLtmlj3oxh9dU1mXtebLqluJsYnIWDFCpNYkiRJkiSpGgteEysi/hz4GPCfgH+OiOd105GIODki3hkRN0bEWETcFhGXR8Sju2l32jmWRsTWiMhye22v2l7MJsYaLF25hIiouiuSJEmSJKlPdTMS6+nAfTNzJCI2Al8A3reQhiLiHODLwMZy1xBwAnAh8PiIuCQz39xFX5teDDygB+30lQNjDZY6CkuSJEmSJFWom7sTjmXmCEBm7lpoWxExCHyWIoH1feDszFwPHA+8DQjgTRFxQRd9JSJOA14P3ALc1k1b/ebAeINlK7u6kaUkSZIkSVJXuhmJdY+I+Gz5PKa9JjMvmmM7LwBOB4aBJ2Tmr8r6Q8DLI+IewJOANwFXdtHfdwNrgIuBd3XRTt+ZHG8w6KLukiRJkiSpQt0ksZ447fVbF9jOxeXjR5oJrGneQpHEun9EnJGZ18/3BBFxUdnG5zLzsxFhEmseJsemWL7SJJYkSZIkSarOgpNYmfm1bk8eEWs5tEbVFR2KfQvYC6wHzgfmlcSKiNXAXwOjwEsW1tP+1cgkJ6ZY4UgsSZIkSZJUoaoXOro3xVREgGvaFcjMKeCG8uWZCzjH64FfA96YmTctoH5fG200YGKKlQPdDNqTJEmSJEnqTtVJrM0tz7fNUK55bPMMZe4iIu5HMfrqJ8Cl8+uaAEanpmDckViSJEmSJKlaPUtildP25qu1zugM5UbKxzXz6M8S4DJgKfCHmTkx/+5pdGoKJpIBk1iSJEmSJKlCXSexIuJhEXEtcF35+j4R8Tdzrd7t+WfwIuCBwD9l5hcX0kBEPD8itkbE1p07d/a2d8eIIok1xcqVTieUJEmSJEnV6cVIrHcA/wHYBZCZPwDOm2Pd4ZbngzOUW9WmfEcRcQrwhrL8H8+xL3eRme/NzHMz89xNmzYttJlj2ki5JtaqQZNYkiRJkiSpOj2ZTpiZv5i2qzHHqq3rYJ0yQ7nmse1zbPdNwDrgL4G9EbGmdePQCLAVLfvURnM64aBJLEmSJEmSVKFeJLF+EREPAzIiVkTEyymnFs7B9UCWz89qV6Bc2+pe5ctr59ju6eXj64F9bba7lcdf3bJPbTSnEw56d0JJkiRJklShXiSxXkix/tSpwC+B+5avZ5WZ+4Ct5cvHdij2YGB9+fxLC++mFmK00YDxKVY7EkuSJEmSJFWo6yRWZt6RmRdn5kmZeWJmPjMzd82jiY+UjxdHxOY2x19ePl6VmTfMsU+PzMzotAG3lEVf17JPbexvNGAiWT24vOquSJIkSZKkPtb18JqIeFeb3XuBrZn5mTk0cRnwMoopgJ+LiN/LzGsjYi3wP4Anl+UuaXPu5lTE12Xma+fdec1q39gkgCOxJEmSJElSpXoxnXCAYgrhT8vtHGAD8NyI+KvZKmfmKPBEirsb3h+4JiL2AnuAV1CsmfXqzLyyB33VPO0bmQBg3SpHYkmSJEmSpOr0YnjNPYHzM3MSICLeA1xJscbVj+bSQGb+ICLOplho/UKK9bV2Ad8B3pGZroVVkaGRAwCscTqhJEmSJEmqUC+SWKcCqymmEFI+PyUzGxExPtdGMnMH8NJym2udBa1llZlbFlKvHw2PFtMJ15rEkiRJkiRJFepFEutS4OqI+CoQwHnAGyNiNfCvPWhfFdo3WozEWut0QkmSJEmSVKGuk1iZ+XcR8XngQRRJrEsyc1t5+BXdtq9q7S+nEw66sLskSZIkSapQLxZ2BxgDtgO7gXtGxHk9alcV21/enXBgwCSWJEmSJEmqTteZiYj4fYp1rE4DrgYeAnwTOL/btlW9kTKJ5UgsSZIkSZJUpV6MxHop8EDglsx8FHA/YGcP2lUNjJTTCR2JJUmSJEmSqtSLJNZYZo4BRMTKzLweuFcP2lUNjI41ABgYWFpxTyRJkiRJUj/rxfCaX0bEccCngS9GxJ3Atlnq6Bgx5nRCSZIkSZJUA724O+F/LJ++NiK+AqwHvtBtu6qHsVEXdpckSZIkSdXrKjMREUuAH2bm2QCZ+bWe9Eq1Me50QkmSJEmSVANdrYmVmVPADyLibj3qj2pmfLxIYg0OLq+4J5IkSZIkqZ/1Yo7YZuCaiPgOsL+5MzMv6kHbqtj4wemEjsSSJEmSJEnV6UUS63U9aEM1dWC8QSyBZct6cSNLSZIkSZKkhenFwu5fi4jTgd/IzH+NiFWAw3YWiQPjDZYNLCUiqu6KJEmSJEnqY10Pr4mI5wGfAC4rd50KfLrbdlUPk+NTLFtpTlKSJEmSJFWrF3PEXgQ8HBgCyMyfAif2oF1V7MDUFDk+xXKTWJIkSZIkqWK9SGKNZ+ZE80VELAOyB+2qYqNTUzAxxQoXdZckSZIkSRXrRRLraxFxCTAYEY8FPg5c3oN2VbGDSSxHYkmSJEmSpIr1Ion1KmAn8CPgBcDngT/rQbuq2Gij4UgsSZIkSZJUC13fnRB4IvDBzHxfD9pSjRQjsZKBwV6EiSRJkiRJ0sL1YiTWRcBPIuJ/R8TjyzWxtAiMlNMJBwZ8SyVJkiRJUrW6TmJl5n8F7kmxFtYzgBsj4m+7bVfVG200YHyKAacTSpIkSZKkivVkiE1mHoiIf6G4K+EgxRTD3+9F26pOczrhoCOxJEmSJElSxboeiRURvxMRHwB+BjwF+Ftgc7ftqnrNuxOuGlxedVckSZIkSVKf68UQm2cD/wi8IDPHe9CeamJ0agrGp1jlSCxJkiRJklSxrrMTmfmfW19HxMOBZ2Tmi7ptW9UaaTRgIlnt3QklSZIkSVLFepKdiIj7Uizq/jTgJuD/9KJdVas5nXDNKqcTSpIkSZKkai04iRURvwn8Z+B3gV3Ax4DIzEf1qG+q2P7JBhxI1jgSS5IkSZIkVayb7MT1wDeAJ2TmzwAi4o960ivVwvDoAQDWuLC7JEmSJEmqWDd3J/xPwA7gKxHxvoh4NBC96ZbqYF8zieV0QkmSJEmSVLEFJ7Ey81OZ+XTgDOCrwB8BJ0XEeyLigh71TxUaHp0EYMC7E0qSJEmSpIp1MxILgMzcn5kfzswLgdOAq4FXdd0zVW54ZAKAgYGlFfdEkiRJkiT1u66TWK0yc3dmXpaZ5/eyXVVjfzkSa9CF3SVJkiRJUsV6msTS4jIy5nRCSZIkSZJUDyax1NH+kWJhd5NYkiRJkiSpaiax1NHoWANwOqEkSZIkSaqeSSx1NHZwOqELu0uSJEmSpGqZxFJHY66JJUmSJEmSasIkljoaG3U6oSRJkiRJqgeTWOpofNyRWJIkSZIkqR5MYqmjiXJhd9fEkiRJkiRJVTOJpY4mvDuhJEmSJEmqCZNYaiszmRwvklgrV5rEkiRJkiRJ1TKJpbYOZJLjUyxZFixbZphIkiRJkqRqmZ1QWyONBkwky10PS5IkSZIk1YBJLLU1OjUFE1MsX2kSS5IkSZIkVc8kltpqJrFWmMSSJEmSJEk1YBJLbRVJrGSldyaUJEmSJEk1YBJLbY02GjAxxUrXxJIkSZIkSTVgEkttjZTTCQdWOhJLkiRJkiRVzySW2hqdmoLxKQadTihJkiRJkmrAJJbaKqYTJoNOJ5QkSZIkSTVgEkttNe9OODi4vOquSJIkSZIkmcRSe801sVYNOJ1QkiRJkiRVzySW2mpOJ1y9yiSWJEmSJEmqnkkstdWcTrjakViSJEmSJKkGapPEioiTI+KdEXFjRIxFxG0RcXlEPHqB7W2KiBdExMdb2twfEddFxF9HxD17/TMsJs27E652TSxJkiRJklQDtRhmExHnAF8GNpa7hoATgAuBx0fEJZn55nk2u43Df75hYAVwRrk9NyKek5kf7arzi9T+g9MJTWJJkiRJkqTqVT4SKyIGgc9SJLC+D5ydmeuB44G3AQG8KSIumGfTy4CvA88CNmfmWmAV8FvA1cAA8MEygaZp9kxMwmQyMLC06q5IkiRJkiRVn8QCXgCcTjFS6gmZeQ1AZg5l5suBT5fl3jTPdn87M387Mz+YmTvKNhuZ+e/ABcDtFImuP+rFD7HY3Ll/HIAB18SSJEmSJEk1UIck1sXl40cy81dtjr+lfLx/RJwx10Yz8+szHNsJfL58+YC5ttlP9oxMADA4aBJLkiRJkiRVr9IkVkSs5VAS6YoOxb4F7C2fn9/D0+8qH50v18ae0QOAI7EkSZIkSVI9VD0S694Ua14BXNOuQGZOATeUL8/s4bl/u3z8cQ/bXDT2jjSTWOb4JEmSJElS9apOYm1ueb5thnLNY5tnKDNnEfFE4Nzy5ft70eZis2+/0wklSZIkSVJ9VJ3EWt3yfHSGciPl45puTxgRpwLvLV9+NjO/0G2bi9G+sUnA6YSSJEmSJKkeqk5ixexFeniyiDUUdzs8EbgFeO4s5Z8fEVsjYuvOnTuPRhdrITPZ75pYkiRJkiSpRqpOYg23PB+codyqNuXnJSIGgM9QTCPcCfyHzLxjpjqZ+d7MPDczz920adNCT33M2d9okONTgNMJJUmSJElSPVSdxGpdB+uUGco1j21fyEkiYgXwCYq7G+4BLsjMG2au1b/2NhowkYALu0uSJEmSpHqoOol1PZDl87PaFYiIJcC9ypfXzvcEEbEM+CjweIqRXI/LzKvn39X+MTQ5CRPFSCynE0qSJEmSpDqoNImVmfuAreXLx3Yo9mBgffn8S/Npv0yA/QPwZIqF4y/KzG8uoKt9Ze/kJDidUJIkSZIk1UjVI7EAPlI+XhwRm9scf3n5eNV8pgBGRFDchfAZwATw5Mz8Slc97RNDh00nNIklSZIkSZKqV4ck1mUUdwpcC3wuIs4EiIi1EXEpxSgqgEumV4yILLfXtmn37RR3H5wEnpaZXzgSnV+M9h42ndA1sSRJkiRJUvUqH2aTmaMR8USKqYL3B66JiCFgDUWSLYFLMvPKubYZEXcDXtY8BXBZRFw2Qx9OXmj/F6PWJJbTCSVJkiRJUh3UIkORmT+IiLOBVwMXAqcCu4DvAO/IzHmthcXhI8yWAyf1pKN9onU64cqVtQgRSZIkSZLU52qTocjMHcBLy22udaLD/puBtsc0u72TkzDaYGBgGUuW+M8oSZIkSZKqV4c1sVQzQ40Gy4en2LhxoOquSJIkSZIkASax1MbeyUmWDjfYsMEkliRJkiRJqgeTWLqLoclJlgyZxJIkSZIkSfVhEkt3sbfRIIcm2bBhsOquSJIkSZIkASax1MbeyUmmhiYdiSVJkiRJkmrDJJbuYmhyksmhAyaxJEmSJElSbZjE0l3sGZmgMT5lEkuSJEmSJNWGSSzdxd7d4wBs3OiaWJIkSZIkqR5MYukwB6amGNs7AeBILEmSJEmSVBsmsXSYoUYDhiYBk1iSJEmSJKk+TGLpMEOTkzDUAExiSZIkSZKk+jCJpcPsnZyEfY7EkiRJkiRJ9WISS4dxOqEkSZIkSaojk1g6TDESq8HyFUtYtWp51d2RJEmSJEkCTGJpmr2TkzA0yXHHDxARVXdHkiRJkiQJMImlaYYaDdjXYMNGpxJKkiRJkqT6MImlwzQXdj9hw2DVXZEkSZIkSTrIJJYOM9RoEEOTnLDRJJYkSZIkSaoPk1g6zN7JSWJfwzsTSpIkSZKkWjGJpcPsnZwkhyZNYkmSJEmSpFoxiaXD3DkyQY5NmcSSJEmSJEm1YhJLh9m9ewzAJJYkSZIkSaoVk1g6zB6TWJIkSZIkqYZMYukwe/eMA7DRuxNKkiRJkqQaMYmlwwzvLpJYjsSSJEmSJEl1YhJLB2Um+/dMACaxJEmSJElSvZjE0kEjU1PkvkkANmxwOqEkSZIkSaoPk1g6aGhyEoYmWbIsWLNmedXdkSRJkiRJOsgklg7aOzkJ+xqsOW4lEVF1dyRJkiRJkg4yiaWDhhoNGJpk3fErq+6KJEmSJEnSYUxi6aBiJNYkx5nEkiRJkiRJNWMSSwftLdfE2rjRRd0lSZIkSVK9mMTSQUONBuxrsMkkliRJkiRJqhmTWDqoORLrRJNYkiRJkiSpZkxi6aDdowdgdIqTNq6quiuSJEmSJEmHMYmlg27fPQLACRsHKu6JJEmSJEnS4Uxi6aA7do0CsGGD0wklSZIkSVK9mMTSQbt3jwOwYYMjsSRJkiRJUr2YxNJBe+4cA0xiSZIkSZKk+jGJpYOGdpvEkiRJkiRJ9WQSSwcN75kAYONG18SSJEmSJEn1YhJLAGQmw3eOE0uDdetWVN0dSZIkSZKkw5jEEgBf37uXkT0TrF6/goioujuSJEmSJEmHMYklAC7bto3lw1NsPmFV1V2RJEmSJEm6i2VVd0DV2zkxwSd27uTk8SVs3OBUQkmSJEmSVD+OxBIf2LGDA5msG/HOhJIkSZIkqZ5MYvW5qUwu27aNR6xfz/47x9mwwTsTSpIkSZKk+jGJ1ee+fOed3Dg2xqN+uYybbx7irLM2Vt0lSZIkSZKkuzCJ1ecu276dDUuW8rnXfZ9TT13Di198v6q7JEmSJEmSdBcu7N7Hto+P8+k77uDR35zkiqtu50MfehyrV7uwuyRJkiRJqh9HYvWxP7vpJqaGJ7nqHdfz0IeewjOece+quyRJkiRJktSWI7H61Df27OHvd+zgwZ8d5tu3j/L5zz2ZiKi6W5IkSZIkSW05EqsPTUxN8YKf/IRTtjf4/gd+zrOffRYPfODmqrslSZIkSZLUkUmsPvSWX/yC624fYsmf/5x161bwxjc+ououSZIkSZIkzcjphH3mxtFRXv/zmzn50l+x4+Z9fOlLT2Xz5jVVd0uSJEmSJGlGtRiJFREnR8Q7I+LGiBiLiNsi4vKIeHSX7a6LiDdExHURMRIRuyLiSxHxlF71/Vjzhz/9Kfm+X7LjGzt597vP57zzfq3qLkmSJEmSJM2q8iRWRJwD/Bh4CfDrwDhwAnAh8MWIeNUC2z0NuBr478AZQANYB5wPfDwi3tN97489D/r2ASY+up0XvvA+vPCF9626O5IkSZIkSXNSaRIrIgaBzwIbge8DZ2fmeuB44G1AAG+KiAvm2W4AnwDuDtwMPDwz1wJrgVcCU8ALI+J5PfpRjgmZyTVX/JLzzjuNd77z/Kq7I0mSJEmSNGeRmdWdPOJlwDuAYeCMzPzVtOOfAp4EfC8zHzCPdp8EfIoiWfWAzLx62vF3AC8DdgCnZ+bEbG2ee+65uXXr1rl2obYajSmGhw+wfv3KqrsiSZIkSZL6XERclZnnzqVs1dMJLy4fPzI9gVV6S/l4/4g4YwHt/uv0BFbprUACJ1NML+wbS5cuMYElSZIkSZKOOZUlsSJiLdAcXXVFh2LfAvaWz+eTbHrkTO2WCbNrFtCuJEmSJEmSKlDlSKx7U6x5BYcSSofJzCnghvLlmXNpNCJOpFgYvmO7pWvn064kSZIkSZKqU2USa3PL820zlGse2zxDmaPRriRJkiRJkipSZRJrdcvz0RnKjZSPaypuV5IkSZIkSRWpMokVsxfput2ubr0YEc+PiK0RsXXnzp1ddkuSJEmSJEkLVWUSa7jl+eAM5Va1KT/Xdld1LDWHdjPzvZl5bmaeu2nTpjmeXpIkSZIkSb1WZRKrdb2qU2Yo1zy2veJ2JUmSJEmSVJEqk1jXc2i631ntCkTEEuBe5ctr25WZLjN3AnfM1G6peVfCObUrSZIkSZKk6lSWxMrMfcDW8uVjOxR7MLC+fP6leTT/lZnajYhTOZTgmk+7kiRJkiRJqkCVI7EAPlI+XhwRm9scf3n5eFVm3rCAdi+IiPu0Of7HFAvAb+dQwkuSJEmSJEk1VXUS6zLgFmAt8LmIOBMgItZGxKXAk8tyl0yvGBFZbq9t0+5ngG9T/HyfioiHlHVWRsSfAC8ry70mMyd6+QNJkiRJkiSp95ZVefLMHI2IJ1JM6bs/cE1EDAFrKBJQCVySmVfOs92MiKcAXwfuDnwzIoaBAQ79zP8rM9/Xox9FkiRJkiRJR1Bk5uyljnQnIk4GXg1cCJwKDAHfAd6RmW3XrIqIZsdfl5mv7VBmHfCnFCO6tgCjwNXAezLz4/Ps406KUWOLwQkcWvxeqivjVHVnjOpYYJzqWGCcqu6MUR0LjuU4PT0zN82lYC2SWDq6ImJrZp5bdT+kmRinqjtjVMcC41THAuNUdWeM6ljQL3Fa9ZpYkiRJkiRJ0qxMYkmSJEmSJKn2TGL1p/dW3QFpDoxT1Z0xqmOBcapjgXGqujNGdSzoizh1TSxJkiRJkiTVniOxJEmSJEmSVHsmsSRJkiRJklR7JrH6REScHBHvjIgbI2IsIm6LiMsj4tFV9031EBHPjoicZRueoX5ExPMj4psRsSci9kXE9yPiFRGxYg7nPzci/jEitpUxemtE/G1E3HMOdddFxBsi4rqIGImIXRHxpYh4yhx/9qdGxJfLeiNlO2+IiLVzqa/eiYi1EXFRRLw+Iv4lIu5oib8z5lC/L+MwIn4jIv6u7O942f9/jIgHzOXcmp9u4nQO19mcLWaMU80mIu4WES+L4rte8997X0T8ICLeHBGbZ6m/IiJeGRFXR8RweT39Znl9jTmc/zHluW8vY/TGKL6HnjSHugv+zlrlZ4Dmb6FxGhFb5ngtPXeW8xunmlX57/36iPhCRPwsIvaWsfqriPhMRDxplvpeT3sdp5nptsg34BzgDiDLbS/QKJ9PAa+quo9u1W/As8uYmAB2dNhu7FB3OfDPLTE2Doy0vP4OsGaGcz8LONASk3ta6g4D589Q9zTg5y3l97W0lcB7Zvm539tS9kBZv/n6RuCUqt+bftqAJ7X8+0/fzpilbl/GIfBYYH9L+T1l/5ttPbPq93WxbV3GabPczhmutRcap25dxOevtfzbtn73m2x5vRt4VIf664CtLWX3l9fT5uvLgWUznP+/t5RtlOduvr4dOHuGugv+zkqFnwFuRzdOgS0tZTpdR3cA9zFO3XoQq/9rWpzuA0an7fsEsLxNXa+nRyBOKw8KtyO7AYPAzWXAfA84q9y/DnhrSzBdUHVf3SqPlWeXsfDVBdT9y7LuaHnRWgoEcCGwqzz24Q51z6FInCXwIWBTuf904Mpy/53N/dPqBvCtssxNwMPK/QPAK1ou1M/rcO4/aPlQeDmwstz/sJb/N/9W9XvTTxtFcuC28oPztcDzWq5TsyUH+i4OgZM59IXmSuD0cv+J5c/R/OJxVtXv7WLauozTZrktCzivceo2lzjZQvFLw+eApwDHl/tXAP8fhxKZe4GT29T/WHl8F8X1Myiup8/i0C9uf9Hh3I9rifG3AmvL/WcB3+dQwnNlm7pdfWelos8At6Mfp7QksRZ4buPUbT7x8izgZcD9aUncUCRiL215z/9Hm7peT49AnFYeFG5Hdiv/wyVFxvjUNsc/VR6/quq+ulW7scAkFsUvJ2Nl3Ze0Of5EDmXhz2lz/NPl8e8CS6cdWwPcWh5/W5u6zdEQDeC+bY6/ozy+HVgx7dhKil9CE3h7m7r349BfCJ9Q9fvTL1ubGNjS8kHbMTnQr3EI/FV57Fam/UWs/MLR/OvfJ6t+bxfTttA4Lcs2y21ZwHmNU7e5xMl6Zh6BcgaHfnl6TZv3shmjF7Wp+9Ly2AhwYpvjV5fHP9Xm2GkcGr334jbHF/ydlQo/A9wqidOD19wFnnSJk4wAAA4bSURBVNs4devZBvzv8t/9xmn7vZ4eoTit/E13O7JbGTwJXNbh+MNa/nPN+MXbbXFvLDyJ9aKy3h5goEOZG8oyb562/zgOZep/t0PdS8rj24CYduzj5bErOtQ9lUO/WP3OtGOPb7kA3+XiXpZp/rXgH6t+f/p1Y+5JrL6LQ4p1LXeUx17doe4zyuPjwLqq38/Fus01TsuyzXJb5nkO49StlzH7lfLf/LPT9r+l3H99h3qDHJoW8sJpx85qie+HdqjfnJL6rTbHFvydtcrPALdK4vTgNXcBbRqnbr2O05eU/+6j0/Z7PW1ft+s4dWH3RSyKRVabi6Ve0aHYtyiG6QKcf8Q7pcXoUeXj1zNzrEOZK8vH6TH2WxRzrlvLTNeM3c3Avacde+S0MofJzF8B13Q4d7PfPy7LzXRu/2/UXz/G4ZnASdPKTNf8eVZQ/Jw6dhmn6qVd5ePSafub73XbGMvMUeAb5ctOcbIX+HaH8zZj4EERsaa5swffWav8DNCR0ylOu2GcqtceVj7eNG2/19OZ+73gODWJtbjdm2LuKhz6YnqYzJyiyKRC8UVTOisiromI0fIuFD+OiHdExN07lG/GTdsYK11bPt572l04mnV3ZOYu2ru25fnBGI2IE4ET5nHu6fE9n35viogTZiin6vVjHJ7ZpsxhMvMOioU/251b1fqniLizvMPRLyPikxHx+BnKG6fqiYhYBjy8fPnjlv1BMYULuouT68rvlzPVbT0XdP+dtZLPAB05neK0TblvRsRQ+b31poj4UETMlAw3TtW1iFgTEedExP8Enl7u/uuW415Pj2CcmsRa3FpvS7tthnLNYzPebll94wSKi98IxYK/Z1HMq74mIp7RpnwzbuYSY2vKbc51y79S7JlWfvrzhcT3fPrdrr7qpR/jsPl89wx/KZvp3KrWAylGFxygmMr3ZOBzEfFPHW5dbZyqV15EsebJFPDBlv3rgNXl86ri5Gicu5efATpyOsXpdA8py0AxzfBi4BsR8VfTfrluMk61IBFxWkRkRCTFWlM/AP4bxfpRf56Zf9NS3OtpB72IU5NYi9vqluejM5QbKR/XzFBGi9824DXA2RTznzdSxMTjKTLmg8AHI+K8afWacTaXGIPD42wudVvrt6s713NPj+9u+q366cc47Kbfqs4/AL9DcSeudZm5huIPB+8vjz+Vlr/mtjBO1bWIOAd4Y/nyrzOz9S/tdYiTKs9tjNbELHEKRdLgb4DzKO7YdhywimIK1eVlmZcCr27TvHGqhWpQ3OTkNop1nwAmgTdx18/tOsRKlec+onFqEmtxa/fXB6mtzLwyM///zLwmMyfKfeOZ+XmKud4/oxg18OZOTSzgtM0Y7aZuFedWffVTHBrDx6DMfHZmXpGZe1r2XZ+Zz6FYBBbg9yPijGlVjVN1JSI2U9w1ahVwFfCn04u0PD9S73WnY736zmqMHuPmEKdk5o7MfFFmfiMzh8t9mZnfy8yLKG5kAXBJRBw3/RTNZmbohnGqu8jM7Zl5cmaeTPHH/XtRjBJ8HXB1RJzVUtzr6RFkEmtxG255PjhDuVVtyksHZeZeDv1F7CERsanl8P7ycRWdtR4bbvN8prqtx9vVneu5p8f3XM7dqd+qn36Mw276rXp6HcVfL4NiFGwr41QLFhEbKBbZvTvwU+DxbaZ3Ho04aR0h0ClGF/KdtarPAPXQHON0LpqJr9XAo6cdM07VtcycysyfZOZzgbcDdwM+FBHN/IrX05l1FacmsRa31rmop8xQrnls+xHsi459zTtjBMWaA03NOJtLjA1z+MVq1roRMUhxu1Y4PEa7je/59LtdfdVLP8Zhs+6GiBhYwLlVM5m5n0MLGP/6tMPGqRYkItZT3A3qbOBW4DGZeVubokMc+uWlqjg5Gufu5WeAemQecTqrzLwJ2Fm+nPe1FONU8/Pu8vG+wP3K515PO+hFnJrEWtyu59BQvrPaFSizxfcqX7a9a5BU6jQsthk3bWOs1HqHjXZ1T46IjbPUbS1PZu4E7pjHuafH93z6vbO8e5bqqx/jcNa7u5R3iTuxw7lVT52G4hunmreIWA18HjgX2EGRGLi1Xdnyunhd+bKbOLl3y2iETnVbzwXdf2et5DNAvTGfOJ1Ps+Vjp2upcape+VXL83uA11OOcJyaxFrEMnMfsLV8+dgOxR4MrC+ff+mId0rHsge1PL+l5flXysdHzPBX9mb8TY+xf6O4MxfAYzrUvaB83M7hF+jWc7eN74g4lUMX4OnnbtY9KyI6/bWgeW7/b9RfP8bhdRSLi3Y8d8v+CYqfUzVW/iLXjJWbpx02TjUv5V+7L6dY13IXRWLgp7NUmy1OBoBHlC87xcl6irtvttOMk2+XIw+BnnxnrfIzQF1YYJzO1ubdKe62DXe9lhqn6rW7tzxvHZXk9XTmfi88TjPTbRFvwMsoMrFDwOY2xz9ZHt9adV/dqtuAmOX4OuCGMla+Pe3YyRR3iUngD9vUfUJ5bAo4p83xTzfbBZZMO7aaImGWwFvb1H1SeawB3KfN8beVx7cBK6YdW0nxi1Wntu9TtpvAhVW/R/26UUxdzXI7Y4ZyfRmHwF+Vx24BVk87tgT4Tnn8E1W/l4t5m0ecznatfXNLnJ7Z5rhx6jbXmFwB/Ev573oncP851rtfSyy3ey9fXB4bAU5sc/zqTu8lxfSSoRmu0wv+zkqFnwFulcTpbNfSj7bE6YY2x41Tt7nG6NI5xNv7yn/3AxR3Hm7u93p6hOK08sBwO7IbxWJuN5eBchXll2JgLXBpy3+sC6ruq1ulcbIF+BbwXOBuLftXUNwK/kcc+uXn/Db1/7LlIvx7wNJy/+MoprAk8OEO574PxV/fk+IOHyeU++9GsTZC84vNpjZ1o+x3Aj8HHlLuXwn8CYd+qXpeh3P/QcvP9SfAynL/Q8v2Evi3qt+fftso/nra3Fq/ADxk2rHpH4x9F4cUXzT2lmWuaP7/BTaVP0cC48BZVb+vi21bSJxS3DHrLyimzKxo2X8vDn0JTuD9xqlbF7G5FPgEh36Becg863+srHsH8LiWNv8LxfU1gb/oUPdxLXF8KbC23H8mxffQBG5sxs+0ul19Z6WizwC3ox+nwNeAV1Osn9V8n4PiWvypllh5jXHq1mWcbinf5+cAp7XsX0KxBtaHW97zt7ep7/X0CMRp5YHhduS3MpDuaAnWvRz6sjoFvKrqPrpVHiNbWuIjKe6OdUfLBSgpFif8vQ71lwP/3FJ2rCzffP2d5oW3Q/1nUfz1ohmTe1rqDtMmcdZS9zQO/QKVwL6WthJ4zyw/+3tbyk6U9ZuvbwROqfr96bdtWizOtG0xDhOKId2tP+eesv9Z9uGZVb+ni3FbSJwCX23ZP0kxdWZ4WvmP0+YLqXHqNo/YPK/l33mUYo2hTtt329RfRzEVpfXzf6zl9eXAshnO/2fT4nxvy+udwNkz1F3wd1Yq/AxwO7pxyqFfzpvXojs4lBBobu9mhhE0xqnbHON0y7S4Gi3jY2za/ve3uy7i9fSIxGnlgeF2dDaKv4K+k+JL5hhwO/A54NFV982t+o0iW/9iir8WXE/xi9WB8oLzXYopLqfP0sYS4AXAN8uL5DDwfeCVTJua0qH+ueX5t1P8Rf5W4O+Ae86h7jqK0Q3XlR8uu4EvA0+d48//tLL87rL+dcAbZro4ux3ReJwtKdDcthiHB+v+BvD3wC/Kfm8vf44HVP1+LtZtIXFKsQ7Euyi+/G0rP49HKBJLH2GOo6KNU7dZ/p0fOY/4vLlDGyuAP6WYzjJcXk+/CTyfWabWlPUfQ/E9s/nL3o0U30NPmkPdBX9npcLPALejF6fAUymS6ldTJLkmyvf6+vL9evAc+2Ccus32b72C4nPvsvI92kHxO9I+4Brgb4GHz6ENr6c9jNMoTyBJkiRJkiTVlncnlCRJkiRJUu2ZxJIkSZIkSVLtmcSSJEmSJElS7ZnEkiRJkiRJUu2ZxJIkSZIkSVLtmcSSJEmSJElS7ZnEkiRJkiRJUu2ZxJIkSZIkSVLtmcSSJEmqWEQMl49bIuIZPW77kmmv/28v25ckSTpaTGJJkiTVxxZgXkmsiFg6S5HDkliZ+bB59kmSJKkWTGJJkiTVx5uBR0TE1RHxRxGxNCLeEhHfjYgfRsQLACLikRHxlYj4CPCjct+nI+KqiLgmIp5f7nszMFi29+FyX3PUV5Rt/zgifhQRT29p+6sR8YmIuD4iPhwRUcG/hSRJ0mGWVd0BSZIkHfQq4OWZeSFAmYzam5kPjIiVwL9HxJVl2QcBZ2fmTeXr52Tm7ogYBL4bEZ/MzFdFxB9m5n3bnOvJwH2B+wAnlHW+Xh67H3AWsA34d+DhwL/1/seVJEmaO0diSZIk1dcFwH+JiKuBbwMbgd8oj32nJYEF8JKI+AHwLeDXWsp18lvARzOzkZm3AV8DHtjS9i8zcwq4mmKaoyRJUqUciSVJklRfAbw4M684bGfEI4H9014/BnhoZo5ExFeBgTm03cl4y/MGfmeUJEk14EgsSZKk+tgHrG15fQXwBxGxHCAifjMiVreptx64s0xgnQE8pOXYgWb9ab4OPL1cd2sTcB7wnZ78FJIkSUeAf1WTJEmqjx8Ck+W0wA8A76SYyve9cnH1ncCT2tT7AvDCiPghcAPFlMKm9wI/jIjvZebFLfs/BTwU+AGQwCszc0eZBJMkSaqdyMyq+yBJkiRJkiTNyOmEkiRJkiRJqj2TWJIkSZIkSao9k1iSJEmSJEmqPZNYkiRJkiRJqj2TWJIkSZIkSao9k1iSJEmSJEmqPZNYkiRJkiRJqj2TWJIkSZIkSaq9/wfeNGYP6uRd5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_train_res = results_stats['mse_train'][2]\n",
    "mse_test_res = results_stats['mse_test'][2]\n",
    "\n",
    "avp_train_res = results_stats['avp_train'][2]\n",
    "avp_test_res = results_stats['avp_test'][2]\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "#plot mean squared error\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Error and Precision')\n",
    "plt.plot(iters_res, mse_train_res, label='Training', c=\"c\")\n",
    "plt.plot(iters_res, mse_test_res, label='Test', c=\"darkblue\")\n",
    "plt.legend()\n",
    "plt.ylabel('Mean Square Error')\n",
    "\n",
    "#plot average precision\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(iters_res, avp_train_res, label='Training', c=\"c\")\n",
    "plt.plot(iters_res, avp_test_res, label='Test', c=\"darkblue\")\n",
    "plt.legend()\n",
    "plt.ylabel('Average Precision')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "#plt.savefig('results/plots/'+notebook_reference+'_error_prec.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nw = results_stats['nw'][2]\n",
    "nw2 = results_stats['nw2'][2]\n",
    "nbeta = results_stats['nbeta'][2]\n",
    "\n",
    "#navg_w = results_stats['navg_w'][2]\n",
    "#navg_w2 = results_stats['navg_w2'][2]\n",
    "#navg_beta = results_stats['navg_beta'][2]\n",
    "\n",
    "plt.figure(figsize=(width, height))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "#plt.title('Norm of the Weights of Hidden and Output Layer',fontsize=fontsize)\n",
    "plt.plot(iters_res, nw, label='2nd layer', c=\"darkblue\")\n",
    "plt.plot(iters_res, nw2, label='3rd layer', c=\"c\")\n",
    "\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylabel('l2-Norm',fontsize=fontsize)\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "#plt.plot(iters_res, navg_w, label='2nd layer')\n",
    "#plt.plot(iters_res, navg_w2, label='3rd layer')\n",
    "#plt.plot(iters_res, navg_beta, label='Output layer')\n",
    "plt.plot(iters_res, nbeta, label='Output layer', c=\"darkblue\")\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.ylabel('l2-Norm',fontsize=fontsize)\n",
    "plt.xlabel('Iteration',fontsize=fontsize)\n",
    "\n",
    "plt.savefig('results/plots/'+notebook_reference+'_weight_norms.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2 Mirex Statistics\n",
    "\n",
    "Statistics that are necesarry for evaluation. The autuor used a threshold of 0.4 for calculation of error etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mirex_statistics(Y, Y_p, threshold=.5, base_note=0, mm=128):\n",
    "    \n",
    "    avp = average_precision_score(Y.flatten(), Y_p.flatten())\n",
    "\n",
    "    # we want zeros and ones\n",
    "    Yhat_p = Y_p>threshold\n",
    "    \n",
    "    # obtain frequencies\n",
    "    Yp_list = []\n",
    "    Y_list = []\n",
    "    \n",
    "    # for every sample\n",
    "    for i in range(len(Yhat_p)):\n",
    "        fhat = []\n",
    "        f = []\n",
    "        \n",
    "        for note in range(mm):\n",
    "            if Yhat_p[i][note] == 1:\n",
    "                fhat.append(440.*2**(((note+base_note) - 69.)/12.))\n",
    "            if Y[i][note] == 1:\n",
    "                f.append(440.*2**(((note+base_note) - 69.)/12.))\n",
    "\n",
    "        Yp_list.append(np.array(fhat))\n",
    "        Y_list.append(np.array(f))\n",
    "\n",
    "    # Precision, Recall, Accuracy, Substitution, Miss, False Alarm, and Total Error scores \n",
    "    # based both on raw frequency values and values mapped to a single octave (chroma)\n",
    "    \n",
    "    P,R,Acc,Esub,Emiss,Efa,Etot,cP,cR,cAcc,cEsub,cEmiss,cEfa,cEtot = \\\n",
    "    mir_eval.multipitch.metrics(np.arange(len(Y_list))/100., Y_list,\n",
    "                                np.arange(len(Yp_list))/100., Yp_list)\n",
    "\n",
    "\n",
    "    print('{:.2f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}'.format(\n",
    "        threshold,100*avp,100*P,100*R,Acc,Etot,Esub,Emiss,Efa))\n",
    "\n",
    "    return avp,P,R,Acc,Etot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restore the model results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from models_190311/190311model-299999\n",
      "Model restored.\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "num_steps = 300000\n",
    "\n",
    "# Add ops to save and restore all the variables.\n",
    "saver = tf.train.Saver({'wavg': wavg, 'w2avg': w2avg, 'betaavg': betaavg})\n",
    "\n",
    "# Later, launch the model, use the saver to restore variables from disk, and\n",
    "# do some work with the model.\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    # Restore variables from disk.\n",
    "    saver.restore(sess, 'models_'+notebook_reference+'/'+notebook_reference+'model-'+str(num_steps-1))\n",
    "    print(\"Model restored.\")\n",
    "    # Check the values of the variables\n",
    "\n",
    "    # A) run model on test set\n",
    "\n",
    "    # 1. get the test set\n",
    "    X, Y = get_test_sample(model_stats.test_ids, window, 1000)\n",
    "    Y = Y[:,base_note:base_note+mm] # in general without effect\n",
    "    # shape Xtest: (1000*len(test_ids), 1, 16384, 1)\n",
    "    # shape Ytest: (1000*len(test_ids), 128)\n",
    "    # extended test-set: 11 samples\n",
    "\n",
    "\n",
    "    # 2. prediction of output and calculation of\n",
    "    # - MSE\n",
    "    # - average precision\n",
    "    Y_p, mse_test = predict_direct_model(sess, X, Y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the output of the test sample predicted calculate the mirex statistics:\n",
    "\n",
    "Average Precision (not from mireval), Precision, Recall, Accuracy, Total Error, Substitution, Miss, False Alarm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thr\tAvgP\tP\tR\tAcc\tETot\tESub\tEmiss\tEfa\n",
      "0.30\t78.518\t66.382\t82.497\t0.582\t0.493\t0.099\t0.076\t0.318\n",
      "0.35\t78.518\t70.256\t78.851\t0.591\t0.445\t0.100\t0.111\t0.234\n",
      "0.40\t78.518\t73.545\t74.698\t0.589\t0.424\t0.098\t0.155\t0.171\n",
      "0.45\t78.518\t76.537\t70.100\t0.577\t0.423\t0.091\t0.208\t0.124\n",
      "0.50\t78.518\t79.279\t65.099\t0.556\t0.437\t0.082\t0.267\t0.088\n"
     ]
    }
   ],
   "source": [
    "print('Thr\\tAvgP\\tP\\tR\\tAcc\\tETot\\tESub\\tEmiss\\tEfa')\n",
    "for th in [.3, .35,.4, .45, .5]:\n",
    "    #print(\"When using threshold\",th)\n",
    "    avp,P,R,Acc,Etot = mirex_statistics(Y, Y_p, th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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