dataset_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import matplotlib  as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cv2\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "# Optionally, tweak styles.\n",
    "mpl.rc('figure',  figsize=(22*1.5, 12*1.5))\n",
    "mpl.rc('image', cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup paths\n",
    "non_slip_path = '../dataset3/0'\n",
    "slip_path = '../dataset3/1'\n",
    "\n",
    "target_non_slip_path = '../dataset3/processed/0'\n",
    "target_slip_path = '../dataset3/processed/1'\n",
    "\n",
    "if not os.path.exists(target_non_slip_path):\n",
    "    os.makedirs(target_non_slip_path)\n",
    "if not os.path.exists(target_slip_path):\n",
    "    os.makedirs(target_slip_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_names_0 = glob.glob('../dataset3/0/*')\n",
    "frame_names_1 = glob.glob('../dataset3/1/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# readin csv file and transform them into images for visualization\n",
    "\n",
    "\n",
    "Nx, Ny = 30, 30\n",
    "\n",
    "for path in frame_names_0:\n",
    "    frames = pd.read_csv(path)\n",
    "    folder_name = os.path.splitext(os.path.basename(path))[0]\n",
    "    folder_path = os.path.join(target_non_slip_path, folder_name)\n",
    "    \n",
    "    # make folder for each set of frames\n",
    "    if not os.path.exists(folder_path):\n",
    "        os.makedirs(folder_path)\n",
    "    \n",
    "    data = frames.values[:,1:]\n",
    "    print data[:, 1].shape\n",
    "    feature_size = Nx*Ny\n",
    "    for i in range(0, data.shape[1]):\n",
    "        dx_interp = data[:feature_size, i]\n",
    "        dy_interp = data[feature_size:, i]\n",
    "#         print dx_interp.shape, dy_interp.shape\n",
    "        \n",
    "        mag = np.sqrt(dx_interp ** 2 + dy_interp ** 2)\n",
    "\n",
    "        dx_resized = (dx_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        dy_resized = (dy_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        mag_resized = (mag.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        # print '====== ', dx_resized.max(), dx_resized.min()\n",
    "\n",
    "#         # fx, fy = 1, 1\n",
    "#         dx_large = cv2.resize(dx_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         dy_large = cv2.resize(dy_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         mag_large = cv2.resize(mag_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "        # concated = np.concatenate((dx_large, dy_large, mag_large), axis=1)\n",
    "        # concated = np.array(concated, dtype=np.uint8)\n",
    "        dx_large = np.array(dx_resized, dtype=np.uint8)\n",
    "        dy_large = np.array(dy_resized, dtype=np.uint8)\n",
    "        mag_large = np.array(mag_resized, dtype=np.uint8)\n",
    "\n",
    "        dx_large = cv2.applyColorMap(dx_large, cv2.COLORMAP_JET)\n",
    "        dy_large = cv2.applyColorMap(dy_large, cv2.COLORMAP_JET)\n",
    "        mag_large = cv2.applyColorMap(mag_large, cv2.COLORMAP_JET)\n",
    "        \n",
    "#         print dx_large\n",
    "#         print \n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dx.jpg'.format(i)), dx_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dy.jpg'.format(i)), dy_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_mag.jpg'.format(i)), mag_large)\n",
    "        \n",
    "        \n",
    "#     break\n",
    "        \n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for path in frame_names_1:\n",
    "    frames = pd.read_csv(path)\n",
    "    folder_name = os.path.splitext(os.path.basename(path))[0]\n",
    "    folder_path = os.path.join(target_slip_path, folder_name)\n",
    "    \n",
    "    # make folder for each set of frames\n",
    "    if not os.path.exists(folder_path):\n",
    "        os.makedirs(folder_path)\n",
    "    \n",
    "    data = frames.values[:,1:]\n",
    "    print data[:, 1].shape\n",
    "    feature_size = Nx*Ny\n",
    "    for i in range(0, data.shape[1]):\n",
    "        dx_interp = data[:feature_size, i]\n",
    "        dy_interp = data[feature_size:, i]\n",
    "#         print dx_interp.shape, dy_interp.shape\n",
    "        \n",
    "        mag = np.sqrt(dx_interp ** 2 + dy_interp ** 2)\n",
    "\n",
    "        dx_resized = (dx_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        dy_resized = (dy_interp.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        mag_resized = (mag.reshape(Nx, Ny) + 30) * 256 / 60\n",
    "        # print '====== ', dx_resized.max(), dx_resized.min()\n",
    "\n",
    "#         # fx, fy = 1, 1\n",
    "#         dx_large = cv2.resize(dx_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         dy_large = cv2.resize(dy_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "#         mag_large = cv2.resize(mag_resized, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_LINEAR)\n",
    "\n",
    "        # concated = np.concatenate((dx_large, dy_large, mag_large), axis=1)\n",
    "        # concated = np.array(concated, dtype=np.uint8)\n",
    "        dx_large = np.array(dx_resized, dtype=np.uint8)\n",
    "        dy_large = np.array(dy_resized, dtype=np.uint8)\n",
    "        mag_large = np.array(mag_resized, dtype=np.uint8)\n",
    "\n",
    "        dx_large = cv2.applyColorMap(dx_large, cv2.COLORMAP_JET)\n",
    "        dy_large = cv2.applyColorMap(dy_large, cv2.COLORMAP_JET)\n",
    "        mag_large = cv2.applyColorMap(mag_large, cv2.COLORMAP_JET)\n",
    "        \n",
    "#         print dx_large\n",
    "#         print \n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dx.jpg'.format(i)), dx_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_dy.jpg'.format(i)), dy_large)\n",
    "        cv2.imwrite(os.path.join(folder_path, '{}_mag.jpg'.format(i)), mag_large)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample dataset to obtain 5 times size dataset with striding=1 and sample length being 10\n",
    "\n",
    "# 0-1-2-3-4-5-6-7-8-9-10-11-12-13--------------20\n",
    "#  --->\n",
    "# 0-----------------9\n",
    "#   1-----------------10\n",
    "#     2------------------11\n",
    "#       3-------------------12\n",
    "#         4--------------------13\n",
    "resample_non_slip_path = '../dataset/resample/0'\n",
    "resample_slip_path = '../dataset/resample/1'\n",
    "\n",
    "if not os.path.exists(resample_non_slip_path):\n",
    "        os.makedirs(resample_non_slip_path)\n",
    "if not os.path.exists(resample_slip_path):\n",
    "        os.makedirs(resample_slip_path)\n",
    "        \n",
    "        \n",
    "for path in frame_names_0:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_0_9  = data[:, 0:10]\n",
    "    data_1_10 = data[:, 1:11]\n",
    "    data_2_11 = data[:, 2:12]\n",
    "    data_3_12 = data[:, 3:13]\n",
    "    data_4_13 = data[:, 4:14]\n",
    "    \n",
    "    df1 = pd.DataFrame(data_0_9)\n",
    "    df1.to_csv(os.path.join(resample_non_slip_path, ind + '_0_9.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_1_10)\n",
    "    df2.to_csv(os.path.join(resample_non_slip_path, ind + '_1_10.csv'), index=False)\n",
    "    df3 = pd.DataFrame(data_2_11)\n",
    "    df3.to_csv(os.path.join(resample_non_slip_path, ind + '_2_11.csv'), index=False)\n",
    "    df4 = pd.DataFrame(data_3_12)\n",
    "    df4.to_csv(os.path.join(resample_non_slip_path, ind + '_3_12.csv'), index=False)\n",
    "    df5 = pd.DataFrame(data_4_13)\n",
    "    df5.to_csv(os.path.join(resample_non_slip_path, ind + '_4_13.csv'), index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for path in frame_names_1:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_0_9  = data[:, 0:10]\n",
    "    data_1_10 = data[:, 1:11]\n",
    "    data_2_11 = data[:, 2:12]\n",
    "    data_3_12 = data[:, 3:13]\n",
    "    data_4_13 = data[:, 4:14]\n",
    "    \n",
    "    df1 = pd.DataFrame(data_0_9)\n",
    "    df1.to_csv(os.path.join(resample_slip_path, ind + '_0_9.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_1_10)\n",
    "    df2.to_csv(os.path.join(resample_slip_path, ind + '_1_10.csv'), index=False)\n",
    "    df3 = pd.DataFrame(data_2_11)\n",
    "    df3.to_csv(os.path.join(resample_slip_path, ind + '_2_11.csv'), index=False)\n",
    "    df4 = pd.DataFrame(data_3_12)\n",
    "    df4.to_csv(os.path.join(resample_slip_path, ind + '_3_12.csv'), index=False)\n",
    "    df5 = pd.DataFrame(data_4_13)\n",
    "    df5.to_csv(os.path.join(resample_slip_path, ind + '_4_13.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## resample data  with skipping ---> Halve the size  to increase the dynamic change through the frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-slip case processed=====================\n"
     ]
    }
   ],
   "source": [
    "# resample dataset to obtain 5 times size dataset with striding=1 and sample length being 10\n",
    "\n",
    "# 0-1-2-3-4-5-6-7-8-9-10-11-12-13--------------20\n",
    "#  --->\n",
    "# 0-2-4-6-8-10-12-14-16-18\n",
    "#   1-3-5-7-9-11-13-15-17-19\n",
    "resample_skipping_non_slip_path = '../dataset/resample_skipping/0'\n",
    "resample_skipping_slip_path = '../dataset/resample_skipping/1'\n",
    "\n",
    "if not os.path.exists(resample_skipping_non_slip_path):\n",
    "        os.makedirs(resample_skipping_non_slip_path)\n",
    "if not os.path.exists(resample_skipping_slip_path):\n",
    "        os.makedirs(resample_skipping_slip_path)\n",
    "        \n",
    "        \n",
    "for path in frame_names_0:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_e  = data[:, [0,2,4,6,8,10,12,14,16,18]]\n",
    "    data_o = data[:, [1,3,5,7,9,11,13,15,17,19]]\n",
    "    \n",
    "    \n",
    "    df1 = pd.DataFrame(data_e)\n",
    "    df1.to_csv(os.path.join(resample_skipping_non_slip_path, ind + '_e.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_o)\n",
    "    df2.to_csv(os.path.join(resample_skipping_non_slip_path, ind + '_o.csv'), index=False)\n",
    "\n",
    "print 'non-slip case processed====================='\n",
    "\n",
    "for path in frame_names_1:\n",
    "    ind = os.path.splitext(os.path.basename(path))[0]\n",
    "    frames = pd.read_csv(path)\n",
    "    data = frames.values[:,1:]\n",
    "    \n",
    "    data_e  = data[:, [0,2,4,6,8,10,12,14,16,18]]\n",
    "    data_o = data[:, [1,3,5,7,9,11,13,15,17,19]]\n",
    "    \n",
    "    \n",
    "    df1 = pd.DataFrame(data_e)\n",
    "    df1.to_csv(os.path.join(resample_skipping_slip_path, ind + '_e.csv'), index=False)\n",
    "    df2 = pd.DataFrame(data_o)\n",
    "    df2.to_csv(os.path.join(resample_skipping_slip_path, ind + '_o.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## for raspi cam sensor, with raspi_dataset, do resampling with 1 skipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample dataset to obtain 2 times size dataset with striding=1 and sample length being 15\n",
    "\n",
    "# 0-1-2-3-4-5-6-7-8-9-10-11-12-13--------------29\n",
    "#  --->\n",
    "# 0-2-4-6-8-10-12-14-16-18-20-22-24-26-28\n",
    "# 1-3-5-7-9-11-13-15-17-19-21-23-25-27-29\n",
    "\n",
    "from_dist = {\n",
    "             't_path': glob.glob('../raspi_dataset/0/*'),\n",
    "             'r_path': glob.glob('../raspi_dataset/1/*'), \n",
    "             'l_path': glob.glob('../raspi_dataset/2/*'),\n",
    "             's_path': glob.glob('../raspi_dataset/3/*')\n",
    "            }\n",
    "\n",
    "\n",
    "to_dict = {\n",
    "           't_path': '../raspi_dataset/resample_skipping_stride1/0',\n",
    "           'r_path': '../raspi_dataset/resample_skipping_stride1/1', \n",
    "           'l_path': '../raspi_dataset/resample_skipping_stride1/2',\n",
    "           's_path': '../raspi_dataset/resample_skipping_stride1/1/3'\n",
    "            }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if the dir doesn't exit, make the dir\n",
    "for key, value in to_dict.iteritems():\n",
    "    if not os.path.exists(value):\n",
    "        os.makedirs(value)\n",
    "\n",
    "        \n",
    "        \n",
    "for key, glob_path in from_dist.iteritems():\n",
    "    for path in glob_path:\n",
    "        ind = os.path.splitext(os.path.basename(path))[0]\n",
    "        \n",
    "        frames = pd.read_csv(path)\n",
    "        data = frames.values[:,1:]\n",
    "\n",
    "        data_e = data[:, [0,2,4,6,8,10,12,14,16,18, 20, 22, 24, 26, 28]]\n",
    "        data_o = data[:, [1,3,5,7,9,11,13,15,17,19, 21, 23, 25, 27, 29]]\n",
    "\n",
    "\n",
    "        df1 = pd.DataFrame(data_e)\n",
    "        df1.to_csv(os.path.join(to_dict[key], ind + '_e.csv'), index=False)\n",
    "        df2 = pd.DataFrame(data_o)\n",
    "        df2.to_csv(os.path.join(to_dict[key], ind + '_o.csv'), index=False)\n",
    "    \n",
    "    print ' case processed ====================='"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "def vec_color_encoding(x, y, encoding='hsv'):\n",
    "    if not x.shape == y.shape:\n",
    "        print '2d vector components should have same shapes.'\n",
    "        return None\n",
    "    hsv = np.zeros((x.shape[0], x.shape[1], 3))\n",
    "    hsv[..., 1] = 255\n",
    "\n",
    "    mag, ang = cv2.cartToPolar(x, y)\n",
    "    \n",
    "    # print np.max(mag), np.min(mag)\n",
    "    hsv[..., 0] = ang*180/np.pi/2\n",
    "    # hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n",
    "    hsv[..., 2] = mag*10\n",
    "    \n",
    "    hsv = np.uint8(hsv)\n",
    "    if encoding == 'hsv':\n",
    "        return hsv\n",
    "    elif encoding == 'rgb':\n",
    "        # print('converted')\n",
    "        bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n",
    "        return bgr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visualization\n",
    "we visualize the frames in each data point with optical flow like vector representation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "831\n",
      "(1800,)\n",
      "(15,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GwyQAAAAAoDMeJgEAAAAAnfEwCQAAAADojIdJAAAAAEBnPEwCAAAAADrjYRIAAAAA0NnSPEw+9dRT8cQTT8QTTzwRjz/+eHz0ox99e3l7e7v3dufzefzBH/xBPP744/Hv//7vjf/31a9+NZ588sn42Mc+Fj/3cz8XW1tbd/o1gM7uRezv7OzEZz7zmXj/+99/p4cP9HYvYv8rX/lKfPzjH48nnngiPvWpT8Vrr712p18D6OVexP9zzz0XH//4x+NjH/tYfOpTn4pXX331Tr8G0Nm9iP3/9uyzz8Zjjz3Wex/3o5V7fQDvlF/5lV95+79/4Ad+IJ555pn48Ic/fMfbfeqpp2I2m8WFCxca61955ZX4tV/7tXj++efj8uXL8fTTT8fnP//5+OVf/uU73ifQxTsd+xERP/VTPxUf/ehH73gfwJ14p2P/a1/7WnzhC1+I559/Pi5duhSf+9zn4rd+67fid37nd+54n0BX73T8/8d//Ef80R/9Ufz5n/95nD17Nn7zN38znn766fj93//9O94n0MW9GPdERLzxxhvxZ3/2Z3e8n/vN0vwyebf82I/9WPz6r/96rK6uNtb/zd/8TXzkIx+Jy5cvR0TEj//4j8df//Vf34tDBO6K28V+RMSv/uqvxk/+5E/eg6MC7r7bxf6FCxfi85//fFy6dCkiIj784Q/H17/+9XtxiMBdc7v4f+CBB+KZZ56Js2fPRkTERz7ykXjhhRfuxSECd8VR456IiN/4jd+In/mZn3mHj+reW5pfJrt4+umn4+///u/T+i996Uvx6KOPNtZ9z/d8j93Giy++GA8//PDbyw8//HBcuXIlbty48XZHC3y7WUTs//f/e+mllxZ8dMDds4jYf+ihh+Khhx56e/kf//Ef44Mf/OAiDxO4KxYR/4888kg88sgjERGxv78ff/mXfxk/+IM/uOhDBRZqUeOef/iHf4jt7e344R/+4fj5n//5BR/ltzceJo3Pfvaz8dnPfvaOtrG3t9f4GXxtbS0Gg0Hs7e3xMIlvW4uIfeB+tOjY/4u/+Iv4p3/6p6V85Qn3n0XG/zPPPBN/+qd/Gt/7vd8bn/70pxeyTeBuWUTs7+/vx+c+97n40pe+tKCjur/wmutdsrm5GYeHh28vHxwcxHw+j83NzXt4VACAu+3LX/5yfPGLX4w/+ZM/iYsXL97rwwHeUb/4i78Y//qv/xrf933fFz/90z99rw8HuOu++MUvxo/8yI803khcJvwyaXT5yft23v3ud8e//du/vb384osvxsWLF+PMmTMLOkpg8RYR+8D9aFGx//zzz8eXv/zlePbZZ+PBBx9c3AECd9Ei4v9rX/tazGaz+NCHPhQrKyvxiU98In77t387bt68ydgH37YWEft/+7d/G9euXYtnn3327XXf//3fH88999zbr34fZzxMGov4yfuHfuiH4gtf+EJ84xvfiPe85z3xx3/8x/Hkk08u6AiBu4PXXLGsFhH7r7/+evzu7/5ufOUrX+FBEveVRcT/N77xjfjDP/zDeO655+L06dPxd3/3d3H58mUeJPFtbRGx/9WvfrWx/Nhjj8U///M/39E27yc8TN6hJ598MiaTSbz++uvxC7/wC7G+vh7PPPNMfOADH4innnoqfvZnfzam02m8//3vj1/6pV+614cLLMztYn80GsVnPvOZmEwmMZ1O44knnoiIoJoxjo3bxf6//Mu/xM7OTnzyk598u+3Kykr81V/91T08WmCxbhf/P/qjPxovvvhi/MRP/ETM5/M4c+ZM/N7v/d69PlxgYY4a8y+zwXw+n9/rgwAAAAAA3F8owAMAAAAA6IyHSQAAAABAZzxMAgAAAAA642ESAAAAANDZkdVcBw9+l1vbXLTle3SlayTPsYOR2ZXsazjIbVaG7W1Ctj0zbXTd1GxmMsvrxrJubD44lTYTcz6kzTAmqclQDmoY+XgGpXPfXDcobSe32Z3fNNs+PgbfWYl/E0vz2dHLERFDiclK/LvQXh0evRwRMR8evRyRv4eNfxNLh9JQr4eIfE2462gqbWb5ADT+R6W4zTTeh4VrxF1Hu/Mbrfu6X5Vif1ao3Tbo2fdrP+769ZGsc7Gv27axL8vu/mDjutD3p+vD3R/aY38gfYiLfR/Hqvm5St/vup1jHfsPPmRWFv7dXWsZuhOnK/U+4PZlxz2ybuRiX9e571C4n7n++rBP7JvtTKSNXgsRMZg1x0IjE+eVcU+O6/bYn5vt7B/j2I+IGLyr0vebJmmcU+j7h+YRpDTu79H3v9Pxn+4PlfjP435d1z/+28c9Of7zMe/fZtzPL5MAAAAAgM54mAQAAAAAdMbDJAAAAACgMx4mAQAAAACdHVmAxyeQF9q4JNa2z5k89Bhqsq559k0FeNzzsRYgMTvTJFtNAo7wxRtm8jlb3EeSbAcmWVfzgE3u8lwaze2/BWiBhUoBnnzMlTXH3sLi3yWHy7IWE4kw8V8oQLVqLyQ5PnM8GpJuXy4odf8zl0Auy64oS7re3IntE/+uUoButf0P7QoxLJ0U1oUYcf2oxrpro7HvCoxo7Omy247r+1MBnva4uvU5LZ7gvocs24JE3YuHuD5FiyW4vj9fZZWefsli38W1rnKnpFIwLbVx10ch9le1SIlpM5JYd8VOpvJF3L1h6AYjEvvucxP9rubeoCdykK8z7ed9X3y3xj1LFvu3UxjSpHGPrb+jhaMq4x7X98vnXN9fif8Ubm47bgyhRRbNfUWL6Qxd/Ou2XafRHv86zllc318f9/PLJAAAAACgMx4mAQAAAACd8TAJAAAAAOisJWeykjtQyH+yOTGFd6c1V2DFHK5OVureXdbPTV1egE4wmpv4PEp5D9rlTGrO2MjlIDT3P3fHqB8pTdBuJgFu/RRuK5088zfQ2HZxozQHJsLEv4ttWad5AhE5/l1+y1i+h+a7RNwmcHTCdTfpryzrtRaRc48LOcuVbBYX//mT/Jta4vLGkkrsu23Lsua/ROQ4drnAKbesEPtpIuvI/bPLfXS5jtr3T0xOTJo33p0zzVvLbXSVj/3mFxnYm1jzk8NClsyMvLHIuX2mSWWydeVS3DXWbT5Yj9gfreY22hVX+u+IHPtusnUdrk0KsW/zlXUi9XYuV76SM4bb6DPucee3UitF49/lTOq438a/1pNYz21S/LvcX/NdtQ7K1Iwz9JLQ/OSIPBZy+5d7j3vk0q8xKsW/2ZVZV8UoCgAAAADQGQ+TAAAAAIDOeJgEAAAAAHTGwyQAAAAAoLOWAjxupSaiuwI8smwn1NUEWrOztUIRhhVJKndJ5itrzWWX5Hog32PfJNQe5lUxk1M4M43SxMCmAElh0va5rPMJ5O3J6rXJS7snvS+FSiJ6SjJ3SdWFAlRamMQV6dEk8zVzSa+faC67yXs1bHddjOZVqfDC2BVTkXM0ddvWAjztRUh8snj7JMB+QuvKtpdc5aTo3831/ZXiaxrrtgCPtLGxL0UXVjZyGy2UsO+uaVf1QArcTA7MtvV8mNjXdeb6qE3cXimx0LXFEuoT5+5zfQsPahhXYt+12ZDYXzOxrwWpTAinsVFEjv2p+aDWf3LFp1K/74q2SOzb7ttNLN9d5d5w7Nlh5YLG/aX4r/T9WlzHjPvXZZ2Lfx0LHZrj0eKEERFzCW437tf411iPiBjoc0Z7xToXocNeRaranx+64JdJAAAAAEBnPEwCAAAAADrjYRIAAAAA0NnROZOVfDA786V8zr0XnSZzNtvRXBKXM7Yh71NvmPeiN880l2fmHextOeZVMwnvnsmjnO03l90EpyP5HhOXNyPbdu9Xz/X7196MNhuSFm47um4JcwkqOcMublPuQGHyXpeDoGFaif9NF/+bsp3N3GYs27lh4n/bTcy7J8vme2i827wxnQTY5cDk6dSzStxWriPd9pLFf+WfGSuzHtt+XT9TmMx8xfyt1yQX/qSZlPrkKfnMydxG895vmjjfM/uf7TaXXU69xnrKkYmI0HXupLXHYynVrxD7+X6wZLFfO5FZZUyjsW5zz+Rv7WJfc8Rc7J+S2D9hYn8k+fQ7Zl86NoqImO00l8euLobGvrmnSD5kzN1Ja49HzRnzf0KtOVGJ/WXkYrJwz6yMaSpttM90TynrOu43OZOnZJzj4n9F4n/PPBvsuTHNdnP5cDe30VoRNv51XfuNd1DID/Z1ULr3/T433+OXSQAAAABAZzxMAgAAAAA642ESAAAAANAZD5MAAAAAgM6OLsBTetR0E3FKomdpglOznTQxr0mrPinrNs1XOiuJ6EOTiHtGvuwbpprCwE3MK4m448LEpKUiDK6NyvvS5NxaIm7eV07EXUI2ObxQBEaLClSukcqEzjb+ZV+nTfyfO9tcXjuV20wkWX3k4t9NzHuzubzv4laTzE0ieqo21B7/rliCFmIYmk5MP+cS2on/Quy72cP79P2u2FgqwJObhNaaOmMaXTjXXF4/bTYk94M1nW06Iq6a2N+X/WkRqYiIgW7LFHhIRUfaK8D4mNV1lUoy7cXX/OTWx1jff2JPp6ky7ikUnzIhE+uFfv8B6edPmthfP99cvmn+1q4Y4fh6c9kV7hlJ7KdrIfLk7y7WpPDg0I5X2ouSZO0F3JYs8m+pFB60/Xqh70/bcfuX2F4x2zkhHzy1lttckHg/dSa32ZT7w5bZjivIdnBNlnOTGGpsm2t0Lhe3fTbQMX2/cX8+i4uNf36ZBAAAAAB0xsMkAAAAAKAzHiYBAAAAAJ0dnTNp33lOjdpX2dyzluWIPOmnmZc0TsgHNfcxIuKc5MRsXsxtdnXyXJMnMDcTk+7L53ZNbo2+O+0mL80zeacWfXK9/DvP7ZMA62SlS5c3E1GL/zSZb5j4d5+Tv13KxYwc/+5q3ZQ2Z8x2zkv8nzTxH5pHafJbxjt53bbsb2SSB4ZyTdj413Xt8a/5kbfW5U9let3k6yjH/5Jxsa85kjautY1ppDlhNm9Mliv58mdN339BEitPnc9tVmTd0MTnvun7t2V/Ns9YroeeeWOV2K/1/WnnreuWr+8v5N+5uC7lg+lyJV/Y/I02ZUMuX/i85H+dMzljJx5oLm+s5zaH+3ndNYn91UqNCXNv0JyxnuOeYeGmm3PsCzl9Sxf7URz3O4Wc7crYSEPCxf+65ky6cY/EsubPR0Sckvg/a/KKv2Xi9oQcZCX+dRwUERGybuZyhmWztu/Xc+/atGw48rinC36ZBAAAAAB0xsMkAAAAAKAzHiYBAAAAAJ3xMAkAAAAA6OzoAjyVBOVSAZLKpO2uAIl8zswnGidk+ZR5Pj4p2zltKvlsSnL62Bzzju4sIq7rxPImWX1PEnFtcR9Nzm3P3neFEXIiettWcFu2yMKs0KblMxGmAJW51vTqdPGvE7efNvGv686Y+F+VIiQHJkavm+IMKzLJ7tAUKpkX4l8T0eeugEIltvUaaVcopbF8KhNX20nZJdbs5NaybGNf+1XT5qSsc0UYdN1Z04evSoGqQ3MNXSvE/sgUqBrsNZdnpgCPnfG6qb08m+v72wtU+euj+zV0rFSK69jY10nbK+Mes51K4UEtvKZjnIjc758zsb+5KStMkZ439vK6VZ1c3cS+FiycuTjXe0N7v+/GOPne4MZGbS2WsdiUUer7C21s8TVddsXXJJZdAZ41GUNoMbaI3Pef08FSRJw+K9s9m9u8aeJ2TeJ/sJ3bhPb9rgCVPC+Yc5bjPxvK2kpxTidfRfXrgV8mAQAAAACd8TAJAAAAAOiMh0kAAAAAQGdH50wOzbPmXHPGzOdKk7bL+7sj00jzZDZMXoC+8n/CtFmRPJUT5msPJW/mpjsek7SmE1UPtnIbzSfQ96Qj8rvSNtVI352uTNGuuQ3VHJglz5uJ8Hm8Jqcj0dj2L7nLsotbjX+zHQnb2Mh/7xhJ7sqGOaA1uSZWzc5WzXUzkjyA2c3cZir7n5ocHD2vdh77Su6M5g6Y86G7NutyrsCSZVGWcoEL+ZB2UnZppDliESb2zfFo3+9if+WwvY3G/gmTWzYy10yK/VO5zVj7fpNTrHmmZuLqSt+f11QmrnZTWesk8cR+7fo3sa5KufKybs200bGQi+tVGfdsmFx1vRdsmNjXCeIjIoYyhrGxL3lkU5NXNpf9m/tr7vfvXuxnSxb7ET7+ddxvP6fLlZxhs50U/6bNht4ftOZI5GvCxr+sm5hxz7o5yKkMvMaV+HfPBrLt6aLi3+VM6vJi66vwyyQAAAAAoDMeJgEAAAAAnfEwCQAAAADojIdJAAAAAEBnPQrwSOJrKTHXVtNoGpnP6WS9tgCJTt7rJgGWIgiaPB4RcUompV41qadr5iCHp5vLU60KERFT2fbcnPaZFg5xxXW0KE6/RNxaaroWYVhCNv51ub3AS2ni9koBKlMbITblcyfN8QwlEVwnUo/Ik8S7IlUrlQpAJv4nkkE/N9fRtD22a0VImt+jVkqjvZUr9nOsVYqv2XMibVwRq9T3uyIk8rl1V4RElk8UYn9oYn8knxuZ+FwxVSDGckG64g0a+zNzXaXYd0UYhrKcz4dbp1L3Zdro3on9MOOeQr/vYl+LkrhxT6kAjxYVNBOijyT2R67wnxQg0TFXRMTUHOShxPW+i30Z98zMxqcy7tGCPFHt97vHfm3cs2SxH+EL8GiRMDfurxTe1IJTlfhfNfta1wJUJv5XZNy/atqk+DdxNDZfRGN5bOJ/LPE/6Rf/oxT/+aRp/Lu4nafnh1o5tip+mQQAAAAAdMbDJAAAAACgMx4mAQAAAACdHZ0zaSel7jFpu82ZLExcvSrvM+t70hEmb8Ydj2zH5ozpJKjm1LhJWPV5/NC8Fz3T3IF+OZOVvJk81bprM5A27e9OL+e/OhRyHR2Nk0re2NDElr6/v1aIf5dbphO3h5s4vZA7487HRNYdmtjW3JmJyxtr5gGMTMTpukFhYl6nEv+VSbCPt0Lsax5ZhOn7zaZT3ozpXTT2V02O2prE7JqZuHpF8mTmO7mNxv7IXGcuR2gs6w5MXI8lt6x37GveTPvk1j4fsnvfv3yxb+i4x55ciQc3Vqr0+xp/LvY1j2zVxH7KETOxP5M8yqGZ2H06zusO5Zj2TfLbnsS+uz5k3FPp9zUvPsKNe9rVJm1fwti3p0Xjv3BebN+vy6aRhtKK6/slBtbNPUTvBwMz7pnpNWJifWLWHci6bbP/PRlEub5/3B7/edyf47/W97fLdVnqlvMZAQAAAABwR3iYBAAAAAB0xsMkAAAAAKAzHiYBAAAAAJ0dXYDHFR1IqZ2FyUvdI6sW3BmaJFstzLBiEmE18dzMmR6ruh1TXWRFKvfsmwlONek2ImJXEtZ3zeSl++0FSIYpET3T01hLIO8nF2pYwkR0953TZNWVyXsLxUxcIQa9JoYm/nQyYXetrUlsu0mAdaL2sSnosGuuiR1pt2Mmd5cJrofTHP+aeK4FRyL6TabeJ+n8Fp0EeMmU+v5CXNvY1yI9bpJsLZpmDicVPDFttACPu88oG/tmwvcD2fauuZ2m2M89u8a6i/1UtyLvKSp/H+3H3TW0dLGuUh8f0WvcU+n3bXFCiX0tWBURMdR+3/zVUuyb4jpzifVdU6Rk2/T7W1LE8LqJyN3mOGvVDDe1uIjri/O4J6sUjardC/p86phxfX9aV4ltd+60OKGLbR33mM3o51z8rxaKS2kBnr2buc2e+dxNuU5cAZ4U/7nvH8o6F//6KR+ROl6plaBq204X/DIJAAAAAOiMh0kAAAAAQGc8TAIAAAAAOmvJmTS5A7rOvaus7KTt+m6u2Vd6B9tkEs5l3Zp5v//cJflM+8TRsWuO+arJpbkmn9vJ+ZijSXOd5gncWqeThbZPSj207zfPZalf3gwTVUfEzOWY9Ih/J+XKuBwE2bZL5ZlIDoLLdzh5XpbNvyHNJNdxy+UJ7OV11+QYb+T4X0/dTL6ONd4rk7LX/iXMxX+7fP0t2fVQyRtz+V6qlOBkYi2dfhPX+rGZOeYT55rLGy4PWpZdjswNk0t2TT54Pd9X1iXWXd9fyX3P9wenve9Pt9TWPS8h1+9rrlfvYgR6Dbk2Escu9lPqvmmzLjljOtF7RMRYjmfb5AZf28rrrsi2tvMXOZkmW2/vDFyP0if2K/nCbjv5GJes34+4TfwX4jZ9xOUVV+4ZqWM325YLwG1XnwVc/B/KuhumLsWVQvzrOChc/LsRS/NzrlJBn5zhWe+c4f53BH6ZBAAAAAB0xsMkAAAAAKAzHiYBAAAAAJ3xMAkAAAAA6OzoAjyuoIEmus4LhUNccmxl8vd0PKYIgubLznZym7PSaGa+9jVZ3jdJyG+a5Nw3moVLTk/WUxP9pi4FuTJZbiURNxddyHurbAfh41/j1OaTV8pcaLy7Iguy/4n5t58DWecKp5yT/a9v5DbXZf9XTay/bo5R4v98bhH6/afmpM0KBahqCeTtCf6VEgu5zZIVYqj0/XZSaj1Phdh3hRp0/+5S1OIJ7pjPyP5PmGvohmxny8T+W6b42ivNYlM+9ptcHE17xL4vvlbZ/9Hb9Z9Zsth38dinAIlV6Pd1/+5wxhKjc9Onn5JjPmH+jntyzVw1RdbeNOtebfb7l8xQciTnbGy+iI6y5oXYr5x6X8ik/ZN6/1i62I+oxb/9XOEz6XnBtJnJOnc4lcKDm1JMSgtSRUTsSARec7FunjtebY7zL4UpvCkHPukZ/7nPbi+82fdXwva93x6/TAIAAAAAOuNhEgAAAADQGQ+TAAAAAIDOjs6ZLL3z7N75lzdvZ+Zddc1vcblec3mj2OXNjGXlLE+IHqvyzvPUvBc9kTavahJlRLyUV71rfKqxvBk5Z3NLDvzAfpHK5L36CZd7pu9OVyYKdhOcau5AIaf1uLETt+u7+i62dTs9438qG5qa7Uzlbzcy/z60obkCV3Ob3ZvN5ZdNjtg319Kq75psNpZPxo3UZkcyA/ZM/FfyiisTSldyXDQcI7EnAAAXnElEQVQnzWcgpD9i63aPlUrejOa2RLjZkzOdYN3mTMqGJmZfE5mUemjy3Dekrx+anPoDWfdmbhLfzNfDQ5OzjeWTkSe33pXYd33/ovLGNPZd36/bdveQHPtL1vfbfl9rRZgmlS6jFPva75u41tgfmDzfdY19M6bZu9hcfsVs54V8Pi5PTjaWL+VPpX5/18S+3q3MN01tXB+fYz/L/X77H3Fux2rHnO37C31AIR0yXVu2jca/u0akPx6av/j66ebywPT9e7LuZXM8L+SovLzfHPe7fPlxIf61h65Em4/b9mx47fsXHf/8MgkAAAAA6IyHSQAAAABAZzxMAgAAAAA642ESAAAAANDZ0QV4KkUYbOEQWbYFeCSptZKIPjHp2TMpLjJwhXxebS7vnc5tXpfiIq/kiUov3HxXWvdIaMEfV8ymeUzbpk2tCEN74ZBcqMGVamj+G8LUJtkWCi0dd5X4d8nhym5GJ6Z2sa2J6C62Zd3QFc2SgjsHZoLrNzdlOf870/mbD6Z1j8Y53VnevUT3tDB578zEbS7EkOmWffzrZ/Lx5OT0ZYt/N5m0nINB4ZyYemilIiSp+FSO/dG8WSxkOjJFozT2D9/Kba5I7P9Xjv1zbz2Q1r1Xbp8DEyNvSGQPzXnV8li1SHPXmWovwuBiP29pyYqQuHgcFIpPqVK/b86tbnua43ok25m6cU9IwZ3Dk7nJ9W81l9/MY6PNK6fSuv8pk7SvRS7csyKxXymO5rSXXXPr8jWshaXo929jYQV43FikRwEqXY6I0Uz6fns8Gv+v5jbX5RHorQupyfobJ9K6RyX+T5r4vy7xX+lrTfmrpNJju3GPjqm0WOet7TS37gq03Q6/TAIAAAAAOuNhEgAAAADQGQ+TAAAAAIDOeJgEAAAAAHR2dAEel3xZSaBNxRNMEYyRpMy6Qj6TZjrqqslO1Xojs7iR2ownWoDHbOibO83lF9ZSk/fF+bTucTmF10ziay6uk+3J2kra97RQhMEl4mqSrfsXhZycu4SJ6H3jX0+ozWevFGJoxunQbGdD62HFXmqzN/mv5opxbhOvSBmQl3OxhvdEXve+uNxYPjDxlupx5b2X4j9HZHtyuE9E161XSvksWfy7uK4UD1G9i081tz00ha60h14xt5mtyeuyb1Ok57Wt5vJ/5Th/b2ymdf9DLvQ905PqGXJFSHSNK8KQaluYNrn4VL82+Rj7FU45VlIxEVcMTTtjc3bT/cMVMJSiTSb2tSSI+ztuHUrsr5jYf0sKDb6UxzgPx3vSuvdKAZIVc6GvFAp+6Lqh7a+PXo7IhaX89dFc64qL3EkBkuOjdvdtbWIrJVXGPTI+neb7w5pse2T2tX0oBXjykD7i6kFz+eXvSE0eiofTuvdJZblVcz603I8br+eYzHTLk57j/rbP3FrXf9zPL5MAAAAAgM54mAQAAAAAdMbDJAAAAACgs7uTM6nv2dom06OXI2JFXpXeMKk1Ov26fXd6/Fpz+eBKbvRyM7/gzJXLqckH44Np3WOx3lh+Ix1RxI68v2wyF9IpmvbMvdTcAXfy9b1ozRPAf6tMj+yukZTImKX4N5M+S5NVE/+b8qdbNykIu9PtxvK2LEdEjK81r5G1V3LuwAfiu9O675Yu5C2TmHBT/s3K5VXmvIDM5RwozRXwE1NPZZn4r+mRS1eK/fzXHk6aH3RxvSnbXjd/xhOTZi78znAntdm50bwfDF7Ik7R/d/zvtO4xyRvbkXtBRMSe5NYcmH+/1W/v/oV33COn3k8SP5M2xH5W6NMr9wY3aXuh3x9O22P/hPzZNkyb07LpvdG11ObmfnPd9IU8QfuH4t1p3f+RWD80ffpBIfYlU9/Gvl4flXuDz02m36/pOe4pbbp93D+cNte5+NdngXWTaH5G2uyt5/jfkrzKyQvfSm0+FA+ldY9L/A8k1iMi5jI2cjn1B2lNpl+t8gtgZdyz6DoQ/DIJAAAAAOiMh0kAAAAAQGc8TAIAAAAAOuNhEgAAAADQ2dEFeFwCueZZDwptdIL2iIhZ83MjMzGvFhxZM0m2mmR+2lS3uSjrJpO8ocnszcbyuTQtcMR7TOmchyWpdWgKkGg5BzfBr37qwCQ4DwtFGHSS3amZXHlmU9jVMk7WW5DqDJi/gl4TtgBVc+XItEkFqExsn5YM7k2T0f2ghLubJ3g63GsszyMXqfquuJHWXYrrjeVBKqkQcV7+zeqKia31woTSyrWYpgIjOdZriejLHv+Vvt98TP950k3KLrE/mOV9rcrHNkzfvynXwykT++vy5x+YO97utBn7w1TyIOJivGnWNa+HNXN/OCexds2c193Ur7dPSu0nrtbtVPr+Spy3T4B9rLiigpVTMJBGNvab61zsa+E1V1zkhISa3gciIkbypz5h/tSHcsgHq3upzQOHr6d1Z+KN5ufMUPJk7DaW1811pSWrahO7txd9zH18bhO2DazU95sLQsc9Nv51bNReeNPFv/b9J0386z1jpVBvZn89x//FndfSujPxcmN5Yn6XWy/E/5rEthZai6iVPsrjHhfbuq690FKXURC/TAIAAAAAOuNhEgAAAADQGQ+TAAAAAIDOjs6ZLOXNuDaFyXtlYtKRecVXcyZXTc7YieZryXHupmnTTOuKi+Z1702Zl3Tb5MicjpfSumFsNpankSfF1lyaDfM+s74r7XJiNHcgT5PqJu917073mIB52fJmbkdzBexpKcS/5BMMzfv8mje25vIC5BX/Myb+T0v8f0dOB46zl5rL19d2U5tzh6+mdStxobE8i5xzMI/mQa2Y3DLNHXC5M5MUy+2x7Sem1nVMXp25fl1jv5I30x77Nl9Y/rSu79+Q9NyTW7mN3g/On8ltTl1sLu+fynm257Zz3sxqNCe49vm5zQMYRL6uVuRzIxOPGvuuX8+TslfyxiqW7N+cbVwX2ui5NfmQ2tG72F+Vda5WxKrcC9bysCPOy/VwWRMUI2Kz2X3H9rnc5uJOnux9LeVMuvPRjP25if2RxP7A5JXN07p++ZD9Jm1n3BMREQPpAwaFcaWL/0Gh7y/E/7rE/4nt3OYBWXdxM7c5K/G+fTa3uXT1rbRuI5rrduxovJlTPzfPBiMZC/m+v0/OcKVWxGIt2V0CAAAAALAIPEwCAAAAADrjYRIAAAAA0BkPkwAAAACAzloK8BhaYMHlJ6eiCzlhdCjJua4Az0ASb0d5PvQYSIGFjTzXelySgiP/6ztym9WN5vLN07mQyMpWLsCzE82KDltmYveDuNpYdom4mng+NMmyOfHWJZlXJqXW7bg2JJ57heJFGv+zHP86WbVLRB9K/GvRhYiIkRRZ2Mi1EuKi5I8/djm3ObXWXL76QG6z/mouSrUrRam2TOL3vsS/K1I1l8I9s8hfVmO7UoTEF1moFKBCUilCohO+mwngNfaHpu/XiatdAZ4VKbDgYv98M/TifRu5zRkpTHLjVG6zsZ2LMOxJkbYtU1hK+/6Jjf0DWc7byUVI7mZccz0kldthivVCv+9iX/7UQ1PbY0XCaCMPO+KCFF579LRpI/3+FVOk5GTkQdVBvNJYNrWvUr8/NrGv/byL/Uj3lPYCPL7wGv1+b6nvN20K8a9tXOHBkca/eUpZkeH5CROA5+V+8KgpvqbPBtfM/eFU5KqGh9EsRrgda6nNvlw3Y3OV5Ph3Y5rxkcv+c5Wimpn+WbtcIfwyCQAAAADojIdJAAAAAEBnPEwCAAAAADo7OmfSTjhdyRlrf3d6KI+xLm9G350emJwxzZtZN3kzD8i70mfNtx7JfLprJ82+tnLezLX4ZmP5auTEzoNoJi9MzcTuM3nneWZyByrvV9fyAnQSVNeC/AIb/6V800L8a+qx+VNqG5c7M5RQGl3PbU7LK//nbGxLG7OvUeT4vxrNjV0xuY770bxIJ5FnGJ6mCdcreWMu/iu5A8pfAcvN9eu6wiS8zCS/aeDy5WXZ5cvrsot96bNHJm/snITau0xK1lxTucy+Vk2+y9X4VmM5Xx0Re9L3u5xJjf2ZyYnpkzfWN29m6WPfjntaV/Qb95hLaCB/6lFOQ4+BDDNWTc7YWYn97zTjJ02HPGW+1qoZ01yXnMkrZii5Lf38gbk3TFM+WHu/7/IhF5UzlttQOyIict0H06+X4l9Op8sZ1u5Xa0dEROgQejUPKeKkdLUPmporqxL/ZmgUq2a8fk3y5d8wN42daN6gDm3OsPb9Lrb1mnD5wH3iv33c0yX6+WUSAAAAANAZD5MAAAAAgM54mAQAAAAAdMbDJAAAAACgs5YCPK7AQveJSQdaSSRycRFbT0CyP10i7khyY2d5ft2UPnvttdxms1lHJ9ZM0vs4cnWT69Gc9dRN2p4L8OQvMk2T7poDKE3a7pJz2+S/z0DWzZcxEd3Ff6UAlSYxu/jXfHbz504Fd0ybuSSVD0wRksOXmstvmWtks1lLJE2cHRFxYIojXJOyIzfMQe7LFejiPyeiu9huj/9KAap+E/MuWVGS1M9HxEDOwdwFZPNvMjAVRrQIg439yq6knocWY4uImElVnCumUMma9P1D2/fn2L8eN2Q5x+OeFGGY2MJq2tfmA1hU7PfT555yH7PjnkJA6nlysV/o99OfzbWR2B/m2h4xl37+TXN9rL0sx5NrjcShidmbcbWxfM0W4Gke5Nh8kVx4rb2o2t2Nfb07LFnsR+RYj8gFdyoFeEybUaHwYNq7K9CmIWnif0XGQtf/M7dZa9aRisFubnNoYuma9P1XTQGeLRn3jO24X2Pbxa22qXQalbitFOmpX0f8MgkAAAAA6IyHSQAAAABAZzxMAgAAAAA6Ozpn0r07XZiQfSDvSpuUsfw6tUnJS69pm1eFZ5IzdtO8839VJm1fM68Tn2mmAMSmmfx9xby8fT2aG98y+QUHKW8mb2cmz/Vu4mrNFajkDvh3sPtYwtyBqcsbK0wMLrllmmrmNmP/VUdWzsyfeya5M9tv5jZXJFdmxRzPaYn3TZN7uWInr24mod008b8v7/iPTSzpV5uZvABd52I7r+s7efWSs32//JVcbpkEtot9/fPbqZP1gijE/s7V3OYtvR+YnZ3Wvt/Gfv6uOfZzm/3U9+cTkqeb7jcpe/5c3zi/W7mX9wmXL1wY92jsu3FP5TY6KPT72s3umfHKW9LGXa6nJPZPmNhfNQd9Q+4FLl94V+JmbMc9cow29rWehMunX1SMLnnsR/iASx15exs7pukR/3OXVynD48Obuc2bX5ddmz+lxv+mqSfh+36N/7zxXWnjxj05/tv7dX+N9IlTciYBAAAAAPcYD5MAAAAAgM54mAQAAAAAdMbDJAAAAACgs5YCPC7zu5CILo+otgiDTl5ayXl3k0lLTZDdXP8jrklyrk6cGhGxJ5NZr5t9rUau7qP5ulvmfBzIgU/s5KVagMcl62oBnn6JuNrGf+ZuFfK5j7iKBWld++TursBIWmca6a5cIvpEipDsmfjXolSuAM+eXCPrOURjzRagalb32TJtDuQcuSIkMzkBleI6lWR1pxbLS16IodT35zYaxpXiU47G/tQV4NEiJGbC6esymbWba35H+v5NG/v5g9ekwIKL/Vx8yhUhGclye+y7+0O/uK60WbbYb+/T7b1BK+64zbibgdJdmdifauybwoMa+3q9RETsyjWzadq4AjxbUlhqy8TInvQGlcJrU3tvqMSjFilpt7jr5Zhx8Z+qSbX/Dey4R8f9ZjPzwqU2lT563/TZ1yW23S1tV4oTnjT7cvF/PZoX103zbfdknO/jv/k5/xhUGff06bMXG//8MgkAAAAA6IyHSQAAAABAZzxMAgAAAAA6a8mZLEzabmecliaVyXvddjRnzBzORN6VHrucGM0DOMhtNHdgEKupjZu8VCcm9XkzTROTf5Pfnc4npJYz1v09aJ8xueR5MxE+4NKM6/m8VPIC+pxeG/8SbmOTF7An4XbDtdH4n+Z/Z1o1ub6aE7ZtvkiO//bcmZn5dy7NE/O5Zbrc7+Tna23JuGDTvr9yUtwl1OOfMAcuX1jWTcy+NIdeJ7uOiNiTvBl3fayaPntfonbH5o1pvrCLfY3rSuy7nMmjl6utiP1CrrxN9pImfXevmzaHk8pJuHwwHfeYsdGh5Fpum6+1EjmRcl++3Zb5tvtyQsylZ8Y9fSd276Pv+Om4q9SKaD9PNv57nF6XM6zXyMTVgZDPub7/QPr+HfPVV0ytlD15dNo2fbb2/bWc4XzWClU6Fjju6Y9fJgEAAAAAnfEwCQAAAADojIdJAAAAAEBnPEwCAAAAADo7ugCPS8dM2eHtbC0HzTN1uypM2j6V7YzNvrQAydQV4NFCDTKR9P/bet6/ZLXvmS9y0GPy3kLpl9sk2VYqG1UK8LSvOfZcrKfJqwvb6Tl5ta3/o5uW/bup5g+0doQpxLCnk8Tbf2fKH9RD1KILETn+ze7TZNW1AiN3rwDPUsb7/69HP++3k1fZglQtn7M1UeQa0ntBRMSBXBAzrQYVEfspIHPsD0wRkhz72WGK/fzlc9/frwiJK0iV9Yn9vqVk7lM29vtUDsmrBoV/vu9TgMfdKrTftwVIJPjS3PThY38i14gW5Ilw/X5uo/2+u3/l71r5W7QXMPSWvN+PiFo1QFN4sPtW/KYL457U95udafxPcxjHoXxu5MZqZtyv/biPf9l/3rQZ9+QOovJsMC+d/bvb9/PLJAAAAACgMx4mAQAAAACd8TAJAAAAAOjs6JzJBeUOlCYvreSHmQ2lHMnCfKtucmvNkNSJpCMi5jaXpflGs5uYdyJb1/ekb21H3512k5dqrmNl0t1F5QksYy7BYnKGK5uuXGqzyuTVZjv7mjdmkhb1X5XG5g1/l0es8TY250zza3xejMZ/pnkBtcmr++YOLLvFxH7vbDvN9zIbmknQutiv9P1D2bbLWXRRq/HncsJ0XS0nzLXpHvv+3Pe48S6bnn28nu++sa85xa7f1ziqxP60MO5xPy8MhqbGgxzU2HzbsWzMj2l02Y2NmmqjUvc7SbPVwI7xliw/2Cr0/f3SVku33lSWwrSZyAXgKpxojZWJGfeMpY3LaR6Yjc9kEKW5lxERYzkqP6bRcY/r17Xvd9up6D7uqeUZ38IvkwAAAACAzniYBAAAAAB0xsMkAAAAAKAzHiYBAAAAAJ0dXYDHJuLKspmBOuXduhxOzUY1ybqaQO4SWDURV5NuIyJmmohrtqNFGOamlI4rnqBfzhUgqRRY0ERXX2BBE3HbiyfU8qRrZUuWTyH+TZtSIYZCHYxKurQWpdKE8ohccGdq2miO+cTMXj11lXtSERKz/8LE1Lodf41UVAootBdiGBSS3o+1ngUWBpV+vVIDRv6Z0xZh0CI9rsBIYVJ2/RfVufkn1qneRCJ/NR/7WoSknZ+UvXthEL8Vjf27N/n4sbKoumt9+n1XfEoaHbo+XQJyWOj33c8L7npQY3OBTuWCdAV40r4KYxFfgKQyNmrfkl4PlWM+dhYU6+bRIK8rFI7SWI/I4x4b/1pcx2wnxb/77q66j1wTbtyl9wwXS/l09C0K1Tyge9H388skAAAAAKAzHiYBAAAAAJ3xMAkAAAAA6OzonMnC7Jj2vWhdrkxe6nIH5HNTdzw6IXtOdUw5k/YV6JSjY3IfzcbnhZyYqRykewc650w6i8mRVOTN3EYl/l3eWGE7KbwquQPmcPRVfZc3pvE/NcldI/2cuUgmJiFZv4efcL29TSVrt0/OZC37rDIN8JL9u1vpZC8qkSyv0onaK3ljru/XWB+5nEndtpuk2tzoKtfnTBr5SGvv+yt5M7nPLtx43Q285SPHnv2+hXyjVE8ib6WUM1aYtF17YpfOruuGJkB1O+5+ZnMmdWzmti0HPnPJb7qvQnqYOx+9Jm0n9r1Svny/Wikptnvmy1fiX/t+ly+f4t/FusmH1DiduTx7WZ6ZL5LW2C5b+x7XpL1Ygd5nBqX4r+cML9kICQAAAACwCDxMAgAAAAA642ESAAAAANAZD5MAAAAAgM4Gc1dpBgAAAACAI/DLJAAAAACgMx4mAQAAAACd8TAJAAAAAOiMh0kAAAAAQGc8TAIAAAAAOuNhEgAAAADQ2f8FkVkTjAfnB6kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x648 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Nx, Ny = 30, 30\n",
    "\n",
    "ri = np.random.randint(0, 999)\n",
    "print ri\n",
    "path = glob.glob(to_dict['r_path'] + '/*')[625]\n",
    "\n",
    "frames = pd.read_csv(path)\n",
    "\n",
    "\n",
    "data = frames.values\n",
    "print data[:, 1].shape\n",
    "feature_size = Nx*Ny\n",
    "\n",
    "fig, axs = plt.subplots(nrows=3, ncols=5, figsize=(14, 9),\n",
    "                        subplot_kw={'xticks': [], 'yticks': []})\n",
    "\n",
    "fig.subplots_adjust(left=0.03, right=0.97, hspace=0.3, wspace=0.05)\n",
    "\n",
    "axs = axs.reshape(15, -1).squeeze()\n",
    "print axs.shape\n",
    "\n",
    "for i in range(0, data.shape[1]):\n",
    "    dx_interp = data[:feature_size, i]\n",
    "    dy_interp = data[feature_size:, i]\n",
    "\n",
    "    dx_resized = dx_interp.reshape(Nx, Ny) \n",
    "    dy_resized = dy_interp.reshape(Nx, Ny)\n",
    "    opt_image = vec_color_encoding(dx_resized, dy_resized, encoding='rgb')\n",
    "    \n",
    "    axs[i].imshow(opt_image)\n",
    "    axs[i].set_title('T = ' + str(i))\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-darkgrid',\n",
       " 'Solarize_Light2',\n",
       " 'seaborn-notebook',\n",
       " 'classic',\n",
       " 'seaborn-ticks',\n",
       " 'grayscale',\n",
       " 'bmh',\n",
       " 'seaborn-talk',\n",
       " 'dark_background',\n",
       " 'ggplot',\n",
       " 'fivethirtyeight',\n",
       " '_classic_test',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-white',\n",
       " 'fast',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-dark',\n",
       " 'tableau-colorblind10',\n",
       " 'seaborn',\n",
       " 'seaborn-dark-palette']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}