densities_offtarget_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from scipy.interpolate import interp1d\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import random\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import statsmodels.api as sm\n",
    "import opto_util as opto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({\n",
    "    'font.sans-serif': 'arial',\n",
    "    'font.family': 'sans-serif'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_names(sheet_df):\n",
    "    c_names = ['cell','area', 'mean', 'min', 'max', 'x', 'y', 'slice']\n",
    "    \n",
    "    if sheet_df.columns[1] == 'Unnamed: 1':\n",
    "        sheet_df.columns = c_names\n",
    "    return sheet_df\n",
    "\n",
    "def measure_z_stack(stack_df):\n",
    "    count = np.shape(stack_df)[0]\n",
    "    \n",
    "    min_x = np.min(stack_df.x)\n",
    "    max_x = np.max(stack_df.x)\n",
    "    min_y = np.min(stack_df.y)\n",
    "    max_y = np.max(stack_df.y)\n",
    "    min_z = np.min(stack_df.slice)\n",
    "    max_z = np.max(stack_df.slice)\n",
    "    volume = (max_x-min_x)/1000*(max_y-min_y)/1000*(max_z-min_z)/1000\n",
    "    volume = max_x/1000*max_y/1000*max_z/1000\n",
    "    density = count/volume\n",
    "    summary_dict = {'min_x':min_x,'max_x':max_x,'min_y':min_y,'max_y':max_y,'min_z':min_z,'max_z':max_z,\n",
    "                   'volume':volume,'count':count,'density':density}\n",
    "    return summary_dict\n",
    "\n",
    "def Cre_df_from_xls(xls_file):\n",
    "    xls = pd.ExcelFile(xls_file)\n",
    "    dict_list=[]\n",
    "    for sheet_name in xls.sheet_names:\n",
    "    \n",
    "        sheet_df = xls.parse(sheet_name)\n",
    "        sheet_df = add_names(sheet_df)\n",
    "        dic=measure_z_stack(sheet_df)\n",
    "        dic['sheet_name']=sheet_name\n",
    "        dict_list.append(dic)\n",
    "    return pd.DataFrame(dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PenkKv_df = Cre_df_from_xls('cell_locations/penk_kv_z_stacks.xlsx')\n",
    "RorbKv_df = Cre_df_from_xls('cell_locations/rorb_kv_z_stacks.xlsx')\n",
    "RorbAi_df = Cre_df_from_xls('cell_locations/rorb_ai167_z_stacks.xlsx')\n",
    "Tlx3Kv_df = Cre_df_from_xls('cell_locations/tlx3_kv_z_stacks.xlsx')\n",
    "Ntsr1Ai_df = Cre_df_from_xls('cell_locations/ntsr1_ai167_z_stacks.xlsx')\n",
    "PvalbKv_df = Cre_df_from_xls('cell_locations/pvalb_kv_z_stacks.xlsx')\n",
    "SstAi_df = Cre_df_from_xls('cell_locations/sst_ai167_z_stacks.xlsx')\n",
    "SstKv_df = Cre_df_from_xls('cell_locations/sst_kv_z_stacks.xlsx')\n",
    "Scnn1a_df = Cre_df_from_xls('cell_locations/scnn1a_ai167_z_stacks.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_x</th>\n",
       "      <th>max_x</th>\n",
       "      <th>min_y</th>\n",
       "      <th>max_y</th>\n",
       "      <th>min_z</th>\n",
       "      <th>max_z</th>\n",
       "      <th>volume</th>\n",
       "      <th>count</th>\n",
       "      <th>density</th>\n",
       "      <th>sheet_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.583</td>\n",
       "      <td>285.222</td>\n",
       "      <td>7.583</td>\n",
       "      <td>288.139</td>\n",
       "      <td>11.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.011506</td>\n",
       "      <td>115</td>\n",
       "      <td>9995.044665</td>\n",
       "      <td>03232020_049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.416</td>\n",
       "      <td>288.139</td>\n",
       "      <td>16.915</td>\n",
       "      <td>290.472</td>\n",
       "      <td>15.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.010127</td>\n",
       "      <td>49</td>\n",
       "      <td>4838.429196</td>\n",
       "      <td>05282020_113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.333</td>\n",
       "      <td>281.139</td>\n",
       "      <td>22.748</td>\n",
       "      <td>271.807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.010087</td>\n",
       "      <td>90</td>\n",
       "      <td>8922.505931</td>\n",
       "      <td>03232020_050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.500</td>\n",
       "      <td>289.305</td>\n",
       "      <td>106.740</td>\n",
       "      <td>285.222</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.005941</td>\n",
       "      <td>60</td>\n",
       "      <td>10099.033052</td>\n",
       "      <td>05282020_134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.500</td>\n",
       "      <td>273.557</td>\n",
       "      <td>65.910</td>\n",
       "      <td>288.139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.004414</td>\n",
       "      <td>39</td>\n",
       "      <td>8835.409912</td>\n",
       "      <td>05282020_137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.083</td>\n",
       "      <td>274.723</td>\n",
       "      <td>5.833</td>\n",
       "      <td>294.555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.009063</td>\n",
       "      <td>93</td>\n",
       "      <td>10261.326498</td>\n",
       "      <td>05282020_138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.583</td>\n",
       "      <td>296.888</td>\n",
       "      <td>16.915</td>\n",
       "      <td>286.389</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.007992</td>\n",
       "      <td>86</td>\n",
       "      <td>10760.231637</td>\n",
       "      <td>05282020_144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.666</td>\n",
       "      <td>296.304</td>\n",
       "      <td>31.497</td>\n",
       "      <td>289.305</td>\n",
       "      <td>7.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.006172</td>\n",
       "      <td>52</td>\n",
       "      <td>8425.145181</td>\n",
       "      <td>05282020_145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.583</td>\n",
       "      <td>293.388</td>\n",
       "      <td>6.999</td>\n",
       "      <td>242.060</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.006179</td>\n",
       "      <td>67</td>\n",
       "      <td>10844.016620</td>\n",
       "      <td>05282020_146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.750</td>\n",
       "      <td>282.306</td>\n",
       "      <td>2.333</td>\n",
       "      <td>275.890</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.006464</td>\n",
       "      <td>76</td>\n",
       "      <td>11756.537465</td>\n",
       "      <td>05282020_161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.416</td>\n",
       "      <td>292.222</td>\n",
       "      <td>1.750</td>\n",
       "      <td>259.558</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.007206</td>\n",
       "      <td>89</td>\n",
       "      <td>12351.482105</td>\n",
       "      <td>05282020_162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.333</td>\n",
       "      <td>236.810</td>\n",
       "      <td>12.832</td>\n",
       "      <td>288.722</td>\n",
       "      <td>4.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.004239</td>\n",
       "      <td>29</td>\n",
       "      <td>6841.107157</td>\n",
       "      <td>05282020_163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.666</td>\n",
       "      <td>288.722</td>\n",
       "      <td>35.580</td>\n",
       "      <td>249.059</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.003955</td>\n",
       "      <td>40</td>\n",
       "      <td>10113.819169</td>\n",
       "      <td>10302020_235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.999</td>\n",
       "      <td>285.222</td>\n",
       "      <td>69.993</td>\n",
       "      <td>252.559</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.006123</td>\n",
       "      <td>38</td>\n",
       "      <td>6206.100451</td>\n",
       "      <td>10302020_236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.666</td>\n",
       "      <td>294.555</td>\n",
       "      <td>49.578</td>\n",
       "      <td>267.724</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.012144</td>\n",
       "      <td>104</td>\n",
       "      <td>8563.650099</td>\n",
       "      <td>05272020_051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.333</td>\n",
       "      <td>296.888</td>\n",
       "      <td>18.665</td>\n",
       "      <td>287.555</td>\n",
       "      <td>14.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.011013</td>\n",
       "      <td>110</td>\n",
       "      <td>9988.250077</td>\n",
       "      <td>05272020_052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.083</td>\n",
       "      <td>295.721</td>\n",
       "      <td>5.833</td>\n",
       "      <td>297.471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.013107</td>\n",
       "      <td>112</td>\n",
       "      <td>8544.860062</td>\n",
       "      <td>05272020_053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min_x    max_x    min_y    max_y  min_z  max_z    volume  count  \\\n",
       "0   7.583  285.222    7.583  288.139   11.0  140.0  0.011506    115   \n",
       "1   6.416  288.139   16.915  290.472   15.0  121.0  0.010127     49   \n",
       "2   2.333  281.139   22.748  271.807    1.0  132.0  0.010087     90   \n",
       "3   3.500  289.305  106.740  285.222    1.0   72.0  0.005941     60   \n",
       "4   3.500  273.557   65.910  288.139    1.0   56.0  0.004414     39   \n",
       "5   4.083  274.723    5.833  294.555    1.0  112.0  0.009063     93   \n",
       "6   7.583  296.888   16.915  286.389    1.0   94.0  0.007992     86   \n",
       "7   4.666  296.304   31.497  289.305    7.0   72.0  0.006172     52   \n",
       "8   7.583  293.388    6.999  242.060    1.0   87.0  0.006179     67   \n",
       "9   1.750  282.306    2.333  275.890    1.0   83.0  0.006464     76   \n",
       "10  6.416  292.222    1.750  259.558    1.0   95.0  0.007206     89   \n",
       "11  2.333  236.810   12.832  288.722    4.0   62.0  0.004239     29   \n",
       "12  4.666  288.722   35.580  249.059    1.0   55.0  0.003955     40   \n",
       "13  6.999  285.222   69.993  252.559    1.0   85.0  0.006123     38   \n",
       "14  4.666  294.555   49.578  267.724    1.0  154.0  0.012144    104   \n",
       "15  2.333  296.888   18.665  287.555   14.0  129.0  0.011013    110   \n",
       "16  4.083  295.721    5.833  297.471    1.0  149.0  0.013107    112   \n",
       "\n",
       "         density    sheet_name  \n",
       "0    9995.044665  03232020_049  \n",
       "1    4838.429196  05282020_113  \n",
       "2    8922.505931  03232020_050  \n",
       "3   10099.033052  05282020_134  \n",
       "4    8835.409912  05282020_137  \n",
       "5   10261.326498  05282020_138  \n",
       "6   10760.231637  05282020_144  \n",
       "7    8425.145181  05282020_145  \n",
       "8   10844.016620  05282020_146  \n",
       "9   11756.537465  05282020_161  \n",
       "10  12351.482105  05282020_162  \n",
       "11   6841.107157  05282020_163  \n",
       "12  10113.819169  10302020_235  \n",
       "13   6206.100451  10302020_236  \n",
       "14   8563.650099  05272020_051  \n",
       "15   9988.250077  05272020_052  \n",
       "16   8544.860062  05272020_053  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PenkKv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_density_tdT(cell_locs,thickness, res):\n",
    "    min_x=np.min(cell_locs.x)\n",
    "    max_x=np.max(cell_locs.x)\n",
    "    min_y=np.min(cell_locs.y)\n",
    "    max_y=np.max(cell_locs.y)\n",
    "    dx=max_x-min_x\n",
    "    dy=max_y-min_y\n",
    "    dx_m=dx/1000.*res\n",
    "    dy_m=dy/1000.*res\n",
    "    thickness/=1000.\n",
    "    sa=dx_m*dy_m\n",
    "    v=dx_m*dy_m*thickness\n",
    "    count=np.shape(cell_locs)[0]\n",
    "    density=count/v\n",
    "    summary_dict = {'min_x':min_x,'max_x':max_x,'min_y':min_y,'max_y':max_y,'thickness':thickness,\n",
    "                    'volume':v,'count':count,'density':density,'sa':sa}\n",
    "    return summary_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_x</th>\n",
       "      <th>max_x</th>\n",
       "      <th>min_y</th>\n",
       "      <th>max_y</th>\n",
       "      <th>thickness</th>\n",
       "      <th>volume</th>\n",
       "      <th>count</th>\n",
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       "      <th>sa</th>\n",
       "      <th>Cre</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7781.000</td>\n",
       "      <td>9145.000</td>\n",
       "      <td>699.333</td>\n",
       "      <td>1568.667</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.029538</td>\n",
       "      <td>175</td>\n",
       "      <td>5924.638730</td>\n",
       "      <td>1.181507</td>\n",
       "      <td>Sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7598.667</td>\n",
       "      <td>9822.333</td>\n",
       "      <td>893.333</td>\n",
       "      <td>1845.333</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.052733</td>\n",
       "      <td>287</td>\n",
       "      <td>5442.522747</td>\n",
       "      <td>2.109316</td>\n",
       "      <td>Sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7574.000</td>\n",
       "      <td>9339.333</td>\n",
       "      <td>552.000</td>\n",
       "      <td>1493.333</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.041395</td>\n",
       "      <td>131</td>\n",
       "      <td>3164.653985</td>\n",
       "      <td>1.655789</td>\n",
       "      <td>Sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7745.000</td>\n",
       "      <td>8794.000</td>\n",
       "      <td>2562.000</td>\n",
       "      <td>2805.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.008382</td>\n",
       "      <td>126</td>\n",
       "      <td>15032.794214</td>\n",
       "      <td>0.253990</td>\n",
       "      <td>Penk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4820.000</td>\n",
       "      <td>5734.667</td>\n",
       "      <td>2537.333</td>\n",
       "      <td>2716.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.005373</td>\n",
       "      <td>76</td>\n",
       "      <td>14143.508817</td>\n",
       "      <td>0.162833</td>\n",
       "      <td>Penk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4802.000</td>\n",
       "      <td>5892.000</td>\n",
       "      <td>2230.000</td>\n",
       "      <td>2410.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.006451</td>\n",
       "      <td>80</td>\n",
       "      <td>12400.577560</td>\n",
       "      <td>0.195494</td>\n",
       "      <td>Penk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6791.000</td>\n",
       "      <td>7717.000</td>\n",
       "      <td>2777.000</td>\n",
       "      <td>3121.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.010474</td>\n",
       "      <td>205</td>\n",
       "      <td>19572.006930</td>\n",
       "      <td>0.317398</td>\n",
       "      <td>Rorb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3575.333</td>\n",
       "      <td>4515.333</td>\n",
       "      <td>399.667</td>\n",
       "      <td>737.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.010426</td>\n",
       "      <td>213</td>\n",
       "      <td>20428.846738</td>\n",
       "      <td>0.315953</td>\n",
       "      <td>Rorb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6957.000</td>\n",
       "      <td>8179.000</td>\n",
       "      <td>1493.667</td>\n",
       "      <td>1844.333</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.014090</td>\n",
       "      <td>227</td>\n",
       "      <td>16110.607640</td>\n",
       "      <td>0.426973</td>\n",
       "      <td>Rorb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5555.000</td>\n",
       "      <td>6145.000</td>\n",
       "      <td>1318.000</td>\n",
       "      <td>1673.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.006887</td>\n",
       "      <td>165</td>\n",
       "      <td>23958.217794</td>\n",
       "      <td>0.208697</td>\n",
       "      <td>Tlx3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8642.667</td>\n",
       "      <td>9461.333</td>\n",
       "      <td>2986.667</td>\n",
       "      <td>3388.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.010803</td>\n",
       "      <td>280</td>\n",
       "      <td>25917.752294</td>\n",
       "      <td>0.327376</td>\n",
       "      <td>Tlx3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4975.000</td>\n",
       "      <td>5720.000</td>\n",
       "      <td>1028.000</td>\n",
       "      <td>1373.000</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>211</td>\n",
       "      <td>24966.520278</td>\n",
       "      <td>0.256101</td>\n",
       "      <td>Tlx3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7527.000</td>\n",
       "      <td>8073.000</td>\n",
       "      <td>2553.000</td>\n",
       "      <td>2907.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.010634</td>\n",
       "      <td>308</td>\n",
       "      <td>28962.352235</td>\n",
       "      <td>0.212690</td>\n",
       "      <td>Ntsr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8042.333</td>\n",
       "      <td>8662.333</td>\n",
       "      <td>2915.667</td>\n",
       "      <td>3241.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.011098</td>\n",
       "      <td>305</td>\n",
       "      <td>27482.678105</td>\n",
       "      <td>0.221958</td>\n",
       "      <td>Ntsr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6749.000</td>\n",
       "      <td>7389.333</td>\n",
       "      <td>3207.667</td>\n",
       "      <td>3594.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.013611</td>\n",
       "      <td>382</td>\n",
       "      <td>28065.623289</td>\n",
       "      <td>0.272219</td>\n",
       "      <td>Ntsr1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9660.000</td>\n",
       "      <td>10571.000</td>\n",
       "      <td>4135.000</td>\n",
       "      <td>4359.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.011228</td>\n",
       "      <td>184</td>\n",
       "      <td>16388.169494</td>\n",
       "      <td>0.224552</td>\n",
       "      <td>Scnn1a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8769.000</td>\n",
       "      <td>9565.000</td>\n",
       "      <td>2211.000</td>\n",
       "      <td>2514.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.013270</td>\n",
       "      <td>225</td>\n",
       "      <td>16955.314162</td>\n",
       "      <td>0.265404</td>\n",
       "      <td>Scnn1a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8255.000</td>\n",
       "      <td>9291.000</td>\n",
       "      <td>1993.000</td>\n",
       "      <td>2240.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.014079</td>\n",
       "      <td>219</td>\n",
       "      <td>15554.871419</td>\n",
       "      <td>0.281584</td>\n",
       "      <td>Scnn1a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8960.667</td>\n",
       "      <td>10487.333</td>\n",
       "      <td>1514.000</td>\n",
       "      <td>2312.667</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.033543</td>\n",
       "      <td>168</td>\n",
       "      <td>5008.509741</td>\n",
       "      <td>1.341716</td>\n",
       "      <td>Pvalb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4987.000</td>\n",
       "      <td>7156.333</td>\n",
       "      <td>2120.667</td>\n",
       "      <td>3002.000</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.052597</td>\n",
       "      <td>271</td>\n",
       "      <td>5152.429418</td>\n",
       "      <td>2.103862</td>\n",
       "      <td>Pvalb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5776.667</td>\n",
       "      <td>7651.667</td>\n",
       "      <td>2258.000</td>\n",
       "      <td>3092.667</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.043053</td>\n",
       "      <td>156</td>\n",
       "      <td>3623.423511</td>\n",
       "      <td>1.722128</td>\n",
       "      <td>Pvalb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       min_x      max_x     min_y     max_y  thickness    volume  count  \\\n",
       "0   7781.000   9145.000   699.333  1568.667      0.025  0.029538    175   \n",
       "1   7598.667   9822.333   893.333  1845.333      0.025  0.052733    287   \n",
       "2   7574.000   9339.333   552.000  1493.333      0.025  0.041395    131   \n",
       "3   7745.000   8794.000  2562.000  2805.000      0.033  0.008382    126   \n",
       "4   4820.000   5734.667  2537.333  2716.000      0.033  0.005373     76   \n",
       "5   4802.000   5892.000  2230.000  2410.000      0.033  0.006451     80   \n",
       "6   6791.000   7717.000  2777.000  3121.000      0.033  0.010474    205   \n",
       "7   3575.333   4515.333   399.667   737.000      0.033  0.010426    213   \n",
       "8   6957.000   8179.000  1493.667  1844.333      0.033  0.014090    227   \n",
       "9   5555.000   6145.000  1318.000  1673.000      0.033  0.006887    165   \n",
       "10  8642.667   9461.333  2986.667  3388.000      0.033  0.010803    280   \n",
       "11  4975.000   5720.000  1028.000  1373.000      0.033  0.008451    211   \n",
       "12  7527.000   8073.000  2553.000  2907.000      0.050  0.010634    308   \n",
       "13  8042.333   8662.333  2915.667  3241.000      0.050  0.011098    305   \n",
       "14  6749.000   7389.333  3207.667  3594.000      0.050  0.013611    382   \n",
       "15  9660.000  10571.000  4135.000  4359.000      0.050  0.011228    184   \n",
       "16  8769.000   9565.000  2211.000  2514.000      0.050  0.013270    225   \n",
       "17  8255.000   9291.000  1993.000  2240.000      0.050  0.014079    219   \n",
       "18  8960.667  10487.333  1514.000  2312.667      0.025  0.033543    168   \n",
       "19  4987.000   7156.333  2120.667  3002.000      0.025  0.052597    271   \n",
       "20  5776.667   7651.667  2258.000  3092.667      0.025  0.043053    156   \n",
       "\n",
       "         density        sa     Cre  \n",
       "0    5924.638730  1.181507     Sst  \n",
       "1    5442.522747  2.109316     Sst  \n",
       "2    3164.653985  1.655789     Sst  \n",
       "3   15032.794214  0.253990    Penk  \n",
       "4   14143.508817  0.162833    Penk  \n",
       "5   12400.577560  0.195494    Penk  \n",
       "6   19572.006930  0.317398    Rorb  \n",
       "7   20428.846738  0.315953    Rorb  \n",
       "8   16110.607640  0.426973    Rorb  \n",
       "9   23958.217794  0.208697    Tlx3  \n",
       "10  25917.752294  0.327376    Tlx3  \n",
       "11  24966.520278  0.256101    Tlx3  \n",
       "12  28962.352235  0.212690   Ntsr1  \n",
       "13  27482.678105  0.221958   Ntsr1  \n",
       "14  28065.623289  0.272219   Ntsr1  \n",
       "15  16388.169494  0.224552  Scnn1a  \n",
       "16  16955.314162  0.265404  Scnn1a  \n",
       "17  15554.871419  0.281584  Scnn1a  \n",
       "18   5008.509741  1.341716   Pvalb  \n",
       "19   5152.429418  2.103862   Pvalb  \n",
       "20   3623.423511  1.722128   Pvalb  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transgenic_tdT_xls=pd.ExcelFile('cell_locations/transgenic_characterization_links.xlsx')\n",
    "tdT_key=transgenic_tdT_xls.parse('image_info',dtype={'image_id': str})\n",
    "\n",
    "dict_list=[]\n",
    "for i in tdT_key.index:\n",
    "    image_id = tdT_key['image_id'][i]\n",
    "    cell_locs=transgenic_tdT_xls.parse(str(image_id))\n",
    "    thickness=tdT_key['thickness'][i]\n",
    "    \n",
    "    res=tdT_key['resolution'][i]\n",
    "    cre=tdT_key['Cre'][i]\n",
    "    summary_dict=measure_density_tdT(cell_locs,thickness,res)\n",
    "    summary_dict['Cre']=cre\n",
    "    dict_list.append(summary_dict)\n",
    "tdT_dens=pd.DataFrame(dict_list)\n",
    "tdT_dens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4843.938487417326\n",
      "4594.787556620368\n"
     ]
    }
   ],
   "source": [
    "print (np.mean(tdT_dens[tdT_dens.Cre=='Sst'].density))\n",
    "print (np.mean(tdT_dens[tdT_dens.Cre=='Pvalb'].density))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Penk\n",
      "9255.70289868112 cells/mm3\n",
      "Rorb\n",
      "19813.14510734547 cells/mm3\n",
      "Tlx3\n",
      "22330.49893935085 cells/mm3\n",
      "Sst\n",
      "4095.64525514143 cells/mm3\n",
      "Pvalb\n",
      "3349.1725035755426 cells/mm3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fs=10\n",
    "tick_size=4\n",
    "df_list=[PenkKv_df,RorbKv_df,Tlx3Kv_df, SstKv_df, PvalbKv_df]\n",
    "colors=['magenta', 'blue', 'darkorange', 'green', 'purple']\n",
    "styles=['dashed', 'dashed', 'dashed','dashed', 'dashed']\n",
    "cres=['Penk','Rorb','Tlx3','Sst','Pvalb']\n",
    "offs=[0,0,0,0.01,-0.01] ##offsets for marker plotting to avoid Pvalb/Sst overlap\n",
    "fig, ax = plt.subplots()\n",
    "norm_ys=[]\n",
    "tdT_dens_list=[]\n",
    "opto_dens_list=[]\n",
    "csv_file=open('mapping_densities.csv','w')\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    y=df.density\n",
    "    y_sorted= np.sort(y)\n",
    "    cdf=np.linspace(0.,1.,np.size(y_sorted))\n",
    "    color=colors[i]\n",
    "    style=styles[i]\n",
    "    cre=cres[i]\n",
    "    off=offs[i]\n",
    "    ax.plot(y_sorted,cdf,c=color, ls=style, lw=2)\n",
    "    tdT_df=tdT_dens[tdT_dens.Cre==cre]\n",
    "    tdT_mean=np.mean(tdT_df.density)\n",
    "    tdT_dens_list.append(tdT_mean)\n",
    "    tdT_std=np.std(tdT_df.density)\n",
    "    description=cre+':AAV density (cells/mm^3)'\n",
    "    opto.write_csv(csv_file,y_sorted,description)\n",
    "    ax.scatter([tdT_mean],[1.05+off],edgecolor=color,facecolor='none',marker='v')\n",
    "    norm_y=y/tdT_mean\n",
    "    print(cre)\n",
    "    print(np.mean(y), 'cells/mm3')\n",
    "    opto_dens_list.append(np.mean(y))\n",
    "    norm_ys.extend(norm_y)\n",
    "ax.scatter([164000],[1.05],c='grey',marker='v')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "plt.xscale('log')\n",
    "plt.axis([200,200000,0,1.1])\n",
    "plt.ylabel('cumulative density fraction')\n",
    "plt.xlabel('density (cells/mm$^{3}$)')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)    \n",
    "#plt.savefig('AAV_densities.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "opto.write_csv(csv_file,cres,'Cre:AAV')\n",
    "opto.write_csv(csv_file,tdT_dens_list,'Cre_tdT_density')\n",
    "opto.write_csv(csv_file,opto_dens_list,'AAV_tdT_density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8350665951015351\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(norm_ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rorb\n",
      "4424.813404671313 cells/mm3\n",
      "Scnn1a\n",
      "1730.0018522123257 cells/mm3\n",
      "Ntsr1\n",
      "10094.000592162387 cells/mm3\n",
      "Sst\n",
      "1429.936587938761 cells/mm3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fs=10\n",
    "tick_size=4\n",
    "df_list=[RorbAi_df, Scnn1a_df,Ntsr1Ai_df,SstAi_df]\n",
    "colors=['blue','cyan','red','green']\n",
    "styles=['solid','solid','solid','solid']\n",
    "cres=['Rorb','Scnn1a','Ntsr1','Sst']\n",
    "offs=[0.01,-0.01,0,0]\n",
    "fig, ax = plt.subplots()\n",
    "norm_ys=[]\n",
    "tdT_dens_list=[]\n",
    "opto_dens_list=[]\n",
    "for i in range(len(df_list)):\n",
    "    df=df_list[i]\n",
    "    y=df.density\n",
    "    y_sorted= np.sort(y)\n",
    "    cdf=np.linspace(0.,1.,np.size(y_sorted))\n",
    "    color=colors[i]\n",
    "    style=styles[i]\n",
    "    cre=cres[i]\n",
    "    off=offs[i]\n",
    "    ax.plot(y_sorted,cdf,c=color, ls=style, lw=2)\n",
    "    tdT_df=tdT_dens[tdT_dens.Cre==cre]\n",
    "    tdT_mean=np.mean(tdT_df.density)\n",
    "    tdT_dens_list.append(tdT_mean)\n",
    "    tdT_std=np.std(tdT_df.density)\n",
    "    description=cre+':Ai167 density (cells/mm^3)'\n",
    "    opto_dens_list.append(np.mean(y))\n",
    "    opto.write_csv(csv_file,y_sorted,description)\n",
    "    ax.scatter([tdT_mean],[1.05+off],edgecolor=color,facecolor='none',marker='v')\n",
    "    norm_y=y/tdT_mean\n",
    "    print(cre)\n",
    "    print(np.mean(y), 'cells/mm3')\n",
    "    norm_ys.extend(norm_y)\n",
    "    \n",
    "\n",
    "ax.scatter([164000],[1.05],c='grey',marker='v')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "plt.xscale('log')\n",
    "plt.axis([200,200000,0,1.1])\n",
    "plt.ylabel('cumulative density fraction')\n",
    "plt.xlabel('density (cells/mm$^{3}$)')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)    \n",
    "#plt.savefig('Ai167_densities.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "opto.write_csv(csv_file,cres,'Cre:Ai167')\n",
    "opto.write_csv(csv_file,tdT_dens_list,'Cre_tdT_density')\n",
    "opto.write_csv(csv_file,opto_dens_list,'Ai167_tdT_density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2506456563546914\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(norm_ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x136.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_list=[RorbKv_df,Tlx3Kv_df, RorbAi_df, SstKv_df, PvalbKv_df]\n",
    "tested=[4.,4.,4.,15.,7.]\n",
    "conf=[4.,3.,4.,15.,7.]\n",
    "colors=['blue','darkorange','blue','green','purple']\n",
    "facecolors=['white','white','blue','white','white']\n",
    "dens_l=[]\n",
    "i=0\n",
    "fig, ax = plt.subplots()\n",
    "for df in conf_list:\n",
    "    dens=[np.mean(df.density)]\n",
    "    c=colors[i]\n",
    "    fc=facecolors[i]\n",
    "    ppr=conf[i]/tested[i]\n",
    "    ax.scatter(dens,ppr,ec=c,fc=fc)\n",
    "    dens_l.append(dens)\n",
    "    i+=1\n",
    "dens_dens=np.mean(dens_l[0:2])\n",
    "dens_sparse=np.mean(dens_l[2:])\n",
    "ax.scatter([dens_dens],[7./8],c='black',alpha=0.5,marker='s')\n",
    "CI=sm.stats.proportion_confint([7],[8], method='jeffreys')\n",
    "ax.fill_between([dens_dens],CI[0],CI[1],color='black')\n",
    "ax.scatter([dens_sparse],[26/26.],c='black',alpha=0.5,marker='s')\n",
    "CI=sm.stats.proportion_confint([26],[26], method='jeffreys')\n",
    "ax.fill_between([dens_sparse],CI[0],CI[1],color='black')\n",
    "ax.yaxis.set_label('positive predictive value')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.set_ylim([0,1.05])\n",
    "xl=ax.get_xlim()\n",
    "ax.set_xlim([1000,50000])\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "plt.ylabel('positive predictive value')\n",
    "fig.set_figheight(1.9)\n",
    "fig.set_figwidth(1.5)\n",
    "plt.xlabel('average density (cells/mm$^{3}$)')\n",
    "plt.xscale('log')\n",
    "#plt.savefig('ppv_density.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "##results of Gaussian fits to measured spatial resolutions for precise firing (Figure 1-supp. 2E, Figure 1-suppl. 3D)\n",
    "RorbKv_popt_x=[1.        , 0.84361004, 6.77530826]\n",
    "PenkKv_popt_x=[ 1.        , -0.2978597 ,  5.44462275]\n",
    "Tlx3Kv_popt_x=[1.        , 0.1008984 , 5.33277538]\n",
    "SstKv_popt_x=[0.99999957, 0.05276496, 5.04222882]\n",
    "PvalbKv_popt_x=[ 1.        , -1.        ,  9.57963163]\n",
    "SstAi_popt_x=[1.        , 0.35355629, 6.09053678]\n",
    "RorbAi_popt_x=[ 1.        , -0.61669749,  6.64957942]\n",
    "Ntsr1Ai_popt_x=[ 1.        ,  1.        , 10.06377989]\n",
    "Scnn1aAi_popt_x=[0.99000003, 1.        , 6.47350589]\n",
    "\n",
    "RorbKv_popt_z=[ 1.        ,  1.        , 27.63557715]\n",
    "PenkKv_popt_z=[ 1.        , -1.        , 21.11038258]\n",
    "Tlx3Kv_popt_z=[ 1.        ,  0.66846301, 15.1767489 ]\n",
    "RorbAi_popt_z=[ 1.        ,  1.        , 24.72929232]\n",
    "SstKv_popt_z=[ 1.        , -0.67435284, 15.5338903 ]\n",
    "PvalbKv_popt_z=[ 1.        , -1.        , 23.22440667]\n",
    "SstAi_popt_z=[ 1.        , -0.29772707, 22.76692667]\n",
    "Scnn1aAi_popt_z=[ 1.        , -1.        , 20.55288537]\n",
    "Ntsr1Ai_popt_z=[ 1.        ,  1.        , 28.37021021]\n",
    "\n",
    "SstAi_sens=15./17.\n",
    "SstKv_sens=15./16.\n",
    "RorbAi_sens=17./19.\n",
    "RorbKv_sens=10./10.\n",
    "Tlx3Kv_sens=11./12.\n",
    "Scnn1a_sens=13./15.\n",
    "PenkKv_sens=8./8.\n",
    "PvalbKv_sens=12./14.\n",
    "Ntsr1_sens=10./10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_inter_df(xls_path, popt_x, popt_z):\n",
    "    xls = pd.ExcelFile(xls_path)\n",
    "    df_list=[]\n",
    "    for sheet_name in xls.sheet_names:\n",
    "        sheet_df = xls.parse(sheet_name)\n",
    "        sheet_df = add_names(sheet_df)\n",
    "        sheet_df = add_inter_df2(sheet_df, sheet_name, popt_x, popt_z)\n",
    "        df_list.append(sheet_df)\n",
    "    inter_df = pd.DataFrame()\n",
    "    inter_df = inter_df.append(df_list).reset_index()\n",
    "    return inter_df\n",
    "\n",
    "def add_inter_df2(stack_df,sheet_name, popt_x, popt_z):\n",
    "    xyz_array=np.array(stack_df[['x','y','slice']])\n",
    "    dx=[]\n",
    "    dy=[]\n",
    "    dz=[]\n",
    "    dxy=[]\n",
    "    neigh_probs=[]\n",
    "    tot_prob=[]\n",
    "    max_prob=[]\n",
    "    for i in range(np.shape(xyz_array)[0]):  ##for each cell\n",
    "        cell_i_coact=[] ##a list with Pcoactivation for all j\n",
    "        neigh_dict = neighbors2(xyz_array,i, popt_x, popt_z)\n",
    "        dx.append(neigh_dict['neigh_dx'])\n",
    "        dy.append(neigh_dict['neigh_dy'])\n",
    "        dz.append(neigh_dict['neigh_dz'])\n",
    "        dxy.append(neigh_dict['neigh_dxy'])\n",
    "        neigh_probs.append(neigh_dict['neigh_probs'])\n",
    "        tot_prob.append(np.sum(neigh_dict['neigh_probs']))\n",
    "        max_prob.append(np.max(neigh_dict['neigh_probs']))\n",
    "    stack_df['dx']=dx\n",
    "    stack_df['dy']=dy\n",
    "    stack_df['dz']=dz\n",
    "    stack_df['dxy']=dxy\n",
    "    stack_df['neigh_probs']=neigh_probs\n",
    "    stack_df['tot_prob']=tot_prob\n",
    "    stack_df['max_prob']=max_prob\n",
    "    stack_df['min_x']= np.min(stack_df.x)\n",
    "    stack_df['max_x'] = np.max(stack_df.x)\n",
    "    stack_df['min_y'] = np.min(stack_df.y)\n",
    "    stack_df['max_y'] = np.max(stack_df.y)\n",
    "    stack_df['min_z'] = np.min(stack_df.slice)\n",
    "    stack_df['max_z'] = np.max(stack_df.slice)\n",
    "    stack_df['sheet_name'] = sheet_name\n",
    "    return stack_df \n",
    "\n",
    "def neighbors2(xyz_array,i,popt_x, popt_z):\n",
    "    neigh_dx = []\n",
    "    neigh_dy = []\n",
    "    neigh_dz = []\n",
    "    neigh_dxy = []\n",
    "    neigh_probs = []\n",
    "    for j in range(np.shape(xyz_array)[0]):\n",
    "        if i!=j:\n",
    "            dx = xyz_array[j,0] - xyz_array[i,0]\n",
    "            dy = xyz_array[j,1] - xyz_array[i,1]\n",
    "            dz = xyz_array[j,2] - xyz_array[i,2]\n",
    "            dxy = math.sqrt(dx**2+dy**2)\n",
    "            prob = off_activation_prob2(dxy,dz, popt_x, popt_z)\n",
    "            neigh_dx.append(dx)\n",
    "            neigh_dy.append(dy)\n",
    "            neigh_dz.append(dz)\n",
    "            neigh_dxy.append(dxy)\n",
    "            neigh_probs.append(float(prob))\n",
    "    return {'neigh_dx':neigh_dx, 'neigh_dy':neigh_dy,'neigh_dz':neigh_dz,'neigh_dxy':neigh_dxy,\n",
    "           'neigh_probs':neigh_probs}\n",
    "\n",
    "\n",
    "def off_activation_prob2(dxy,dz, popt_x, popt_z):\n",
    "    max_xy=40\n",
    "    max_z=100\n",
    "    if dxy > max_xy or abs(dz) > max_z: #save some time if large distance between neighbors\n",
    "        return 0\n",
    "    else:\n",
    "        xy_prob = Gauss(dxy, *popt_x)\n",
    "        z_prob = Gauss(dz, *popt_z)\n",
    "        return min([xy_prob,z_prob])\n",
    "    \n",
    "    \n",
    "def Gauss(x, a, x0, sigma):\n",
    "    return a * np.exp(-(x - x0)**2 / (2 * sigma**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "PenkKv_inter=make_inter_df('cell_locations/penk_kv_z_stacks.xlsx',PenkKv_popt_x, PenkKv_popt_z)\n",
    "RorbKv_inter=make_inter_df('cell_locations/rorb_kv_z_stacks.xlsx',RorbKv_popt_x, RorbKv_popt_z)\n",
    "RorbAi_inter=make_inter_df('cell_locations/rorb_ai167_z_stacks.xlsx',RorbAi_popt_x, RorbAi_popt_z)\n",
    "Tlx3Kv_inter=make_inter_df('cell_locations/tlx3_kv_z_stacks.xlsx',Tlx3Kv_popt_x, Tlx3Kv_popt_z)\n",
    "Scnn1a_inter=make_inter_df('cell_locations/scnn1a_ai167_z_stacks.xlsx',Scnn1aAi_popt_x, Scnn1aAi_popt_z)\n",
    "Ntsr1_inter=make_inter_df('cell_locations/ntsr1_ai167_z_stacks.xlsx',Ntsr1Ai_popt_x, Ntsr1Ai_popt_z)\n",
    "PvalbKv_inter=make_inter_df('cell_locations/pvalb_kv_z_stacks.xlsx',PvalbKv_popt_x, PvalbKv_popt_z)\n",
    "SstKv_inter=make_inter_df('cell_locations/sst_kv_z_stacks.xlsx',SstKv_popt_x, SstKv_popt_z)\n",
    "SstAi_inter=make_inter_df('cell_locations/sst_ai167_z_stacks.xlsx',SstAi_popt_x, SstAi_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>slice</th>\n",
       "      <th>dz</th>\n",
       "      <th>dxy</th>\n",
       "      <th>neigh_probs</th>\n",
       "      <th>tot_prob</th>\n",
       "      <th>max_prob</th>\n",
       "      <th>sheet_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>160.984</td>\n",
       "      <td>65.910</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[2.0, 2.0, 8.0, 9.0, 11.0, 12.0, 15.0, 19.0, 2...</td>\n",
       "      <td>[121.93987076014146, 155.0081277385157, 96.875...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>5.149611e-10</td>\n",
       "      <td>5.149611e-10</td>\n",
       "      <td>03232020_049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>158.068</td>\n",
       "      <td>187.815</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[-2.0, 0.0, 6.0, 7.0, 9.0, 10.0, 13.0, 17.0, 1...</td>\n",
       "      <td>[121.93987076014146, 99.2958340012309, 134.047...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 8.037601760233238e-1...</td>\n",
       "      <td>1.004836e-02</td>\n",
       "      <td>4.411090e-03</td>\n",
       "      <td>03232020_049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>58.911</td>\n",
       "      <td>182.566</td>\n",
       "      <td>13.0</td>\n",
       "      <td>[-2.0, 0.0, 6.0, 7.0, 9.0, 10.0, 13.0, 17.0, 1...</td>\n",
       "      <td>[155.0081277385157, 99.2958340012309, 214.0390...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>1.176097e-01</td>\n",
       "      <td>1.031087e-01</td>\n",
       "      <td>03232020_049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>253.725</td>\n",
       "      <td>93.908</td>\n",
       "      <td>19.0</td>\n",
       "      <td>[-8.0, -6.0, -6.0, 1.0, 3.0, 4.0, 7.0, 11.0, 1...</td>\n",
       "      <td>[96.87507979351551, 134.04770157671484, 214.03...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>2.437228e-05</td>\n",
       "      <td>1.070152e-05</td>\n",
       "      <td>03232020_049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>218.729</td>\n",
       "      <td>140.570</td>\n",
       "      <td>20.0</td>\n",
       "      <td>[-9.0, -7.0, -7.0, -1.0, 2.0, 3.0, 6.0, 10.0, ...</td>\n",
       "      <td>[94.38538353474016, 76.88853585548368, 165.243...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.4566259959916...</td>\n",
       "      <td>2.134046e-03</td>\n",
       "      <td>1.818031e-03</td>\n",
       "      <td>03232020_049</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>107</td>\n",
       "      <td>131.820</td>\n",
       "      <td>173.233</td>\n",
       "      <td>149.0</td>\n",
       "      <td>[-148.0, -148.0, -148.0, -144.0, -138.0, -138....</td>\n",
       "      <td>[73.1983070979104, 87.89089839681921, 154.7471...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>1.043849e-01</td>\n",
       "      <td>1.031087e-01</td>\n",
       "      <td>05272020_053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>108</td>\n",
       "      <td>192.481</td>\n",
       "      <td>181.399</td>\n",
       "      <td>149.0</td>\n",
       "      <td>[-148.0, -148.0, -148.0, -144.0, -138.0, -138....</td>\n",
       "      <td>[48.95661598803578, 113.97923453857723, 200.87...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>5.926322e-02</td>\n",
       "      <td>3.002056e-02</td>\n",
       "      <td>05272020_053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>109</td>\n",
       "      <td>197.731</td>\n",
       "      <td>164.484</td>\n",
       "      <td>149.0</td>\n",
       "      <td>[-148.0, -148.0, -148.0, -144.0, -138.0, -138....</td>\n",
       "      <td>[66.6593153580203, 129.52953177171602, 213.668...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>1.020853e-02</td>\n",
       "      <td>4.210182e-03</td>\n",
       "      <td>05272020_053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>110</td>\n",
       "      <td>47.245</td>\n",
       "      <td>134.154</td>\n",
       "      <td>149.0</td>\n",
       "      <td>[-148.0, -148.0, -148.0, -144.0, -138.0, -138....</td>\n",
       "      <td>[162.66277790570282, 137.73187226274098, 143.3...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>1.316992e-04</td>\n",
       "      <td>1.316746e-04</td>\n",
       "      <td>05272020_053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>111</td>\n",
       "      <td>71.743</td>\n",
       "      <td>228.061</td>\n",
       "      <td>149.0</td>\n",
       "      <td>[-148.0, -148.0, -148.0, -144.0, -138.0, -138....</td>\n",
       "      <td>[107.90757491946523, 46.82570424457063, 73.513...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>1.304510e-04</td>\n",
       "      <td>8.817810e-05</td>\n",
       "      <td>05272020_053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1249 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index        x        y  slice  \\\n",
       "0         0  160.984   65.910   11.0   \n",
       "1         1  158.068  187.815   13.0   \n",
       "2         2   58.911  182.566   13.0   \n",
       "3         3  253.725   93.908   19.0   \n",
       "4         4  218.729  140.570   20.0   \n",
       "...     ...      ...      ...    ...   \n",
       "1244    107  131.820  173.233  149.0   \n",
       "1245    108  192.481  181.399  149.0   \n",
       "1246    109  197.731  164.484  149.0   \n",
       "1247    110   47.245  134.154  149.0   \n",
       "1248    111   71.743  228.061  149.0   \n",
       "\n",
       "                                                     dz  \\\n",
       "0     [2.0, 2.0, 8.0, 9.0, 11.0, 12.0, 15.0, 19.0, 2...   \n",
       "1     [-2.0, 0.0, 6.0, 7.0, 9.0, 10.0, 13.0, 17.0, 1...   \n",
       "2     [-2.0, 0.0, 6.0, 7.0, 9.0, 10.0, 13.0, 17.0, 1...   \n",
       "3     [-8.0, -6.0, -6.0, 1.0, 3.0, 4.0, 7.0, 11.0, 1...   \n",
       "4     [-9.0, -7.0, -7.0, -1.0, 2.0, 3.0, 6.0, 10.0, ...   \n",
       "...                                                 ...   \n",
       "1244  [-148.0, -148.0, -148.0, -144.0, -138.0, -138....   \n",
       "1245  [-148.0, -148.0, -148.0, -144.0, -138.0, -138....   \n",
       "1246  [-148.0, -148.0, -148.0, -144.0, -138.0, -138....   \n",
       "1247  [-148.0, -148.0, -148.0, -144.0, -138.0, -138....   \n",
       "1248  [-148.0, -148.0, -148.0, -144.0, -138.0, -138....   \n",
       "\n",
       "                                                    dxy  \\\n",
       "0     [121.93987076014146, 155.0081277385157, 96.875...   \n",
       "1     [121.93987076014146, 99.2958340012309, 134.047...   \n",
       "2     [155.0081277385157, 99.2958340012309, 214.0390...   \n",
       "3     [96.87507979351551, 134.04770157671484, 214.03...   \n",
       "4     [94.38538353474016, 76.88853585548368, 165.243...   \n",
       "...                                                 ...   \n",
       "1244  [73.1983070979104, 87.89089839681921, 154.7471...   \n",
       "1245  [48.95661598803578, 113.97923453857723, 200.87...   \n",
       "1246  [66.6593153580203, 129.52953177171602, 213.668...   \n",
       "1247  [162.66277790570282, 137.73187226274098, 143.3...   \n",
       "1248  [107.90757491946523, 46.82570424457063, 73.513...   \n",
       "\n",
       "                                            neigh_probs      tot_prob  \\\n",
       "0     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  5.149611e-10   \n",
       "1     [0.0, 0.0, 0.0, 0.0, 0.0, 8.037601760233238e-1...  1.004836e-02   \n",
       "2     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  1.176097e-01   \n",
       "3     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  2.437228e-05   \n",
       "4     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.4566259959916...  2.134046e-03   \n",
       "...                                                 ...           ...   \n",
       "1244  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  1.043849e-01   \n",
       "1245  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  5.926322e-02   \n",
       "1246  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  1.020853e-02   \n",
       "1247  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  1.316992e-04   \n",
       "1248  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  1.304510e-04   \n",
       "\n",
       "          max_prob    sheet_name  \n",
       "0     5.149611e-10  03232020_049  \n",
       "1     4.411090e-03  03232020_049  \n",
       "2     1.031087e-01  03232020_049  \n",
       "3     1.070152e-05  03232020_049  \n",
       "4     1.818031e-03  03232020_049  \n",
       "...            ...           ...  \n",
       "1244  1.031087e-01  05272020_053  \n",
       "1245  3.002056e-02  05272020_053  \n",
       "1246  4.210182e-03  05272020_053  \n",
       "1247  1.316746e-04  05272020_053  \n",
       "1248  8.817810e-05  05272020_053  \n",
       "\n",
       "[1249 rows x 10 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##created data frames where each row is cell. dxy, dz each contain an array for each cell with corresponding \n",
    "##distances to all imaged neighbors, neigh_probs contains an array with corresponding off-target estimates\n",
    "c_list=['index','x','y','slice','dz','dxy','neigh_probs','tot_prob','max_prob','sheet_name']\n",
    "PenkKv_inter[c_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_neighbors(sheet_df, i,ax):\n",
    "    dxys=sheet_df.dxy[i]\n",
    "    dzs = sheet_df.dz[i]\n",
    "    probs = sheet_df.neigh_probs[i]\n",
    "    ax.scatter(dxys,dzs,c=probs)\n",
    "\n",
    "def plot_neighbors_3d(sheet_df, i,ax,sens_factor=1):\n",
    "    \n",
    "    dxs=np.array(sheet_df.dx.values[i])\n",
    "    dys = np.array(sheet_df.dy.values[i])\n",
    "    dzs = np.array(sheet_df.dz.values[i])\n",
    "    probs = np.array(sheet_df.neigh_probs.values[i])*sens_factor\n",
    "    neigh_df=pd.DataFrame({'x':dxs,'y':dys,'z':dzs,'prob':probs})\n",
    "    z_p=neigh_df[neigh_df.prob<0.010]\n",
    "    z_p=z_p[(abs(z_p.x)<50) & (abs(z_p.y)<50)]\n",
    "    plot_df=neigh_df[neigh_df.prob>0.01]\n",
    "    count=np.shape(plot_df)[0]\n",
    "    ax.scatter(plot_df.x,plot_df.y,plot_df.z,c=plot_df.prob,cmap='plasma',alpha=0.5,vmin=0,vmax=1,s=7,zorder=1)\n",
    "    ax.scatter(z_p.x,z_p.y,z_p.z,c='black',alpha=0.1,s=1,zorder=0)\n",
    "    return count\n",
    "\n",
    "def overlay_cells(df,n,sens_factor=1,name=''):\n",
    "    df['z_dist']=df['max_z']-df['slice']\n",
    "\n",
    "    sub=df[(df.z_dist>30) & (df.slice>30)] #subset for cells >30 microns from borders\n",
    "    random.seed(10)\n",
    "    random_list=random.sample(list(sub.index),n)\n",
    "    fig = plt.figure(figsize=(2,2))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    count=0\n",
    "    for i in random_list:\n",
    "        i_count=plot_neighbors_3d(df,i,ax,sens_factor)\n",
    "        count=count+i_count\n",
    "    print(count)\n",
    "    xyzlim = np.array([ax.get_xlim3d(),ax.get_ylim3d(),ax.get_zlim3d()]).T\n",
    "    XYZlim = [min(xyzlim[0]),max(xyzlim[1])]\n",
    "    XYZlim = [-50,50]\n",
    "    ax.set_xlim3d(XYZlim)\n",
    "    ax.set_ylim3d(XYZlim)\n",
    "    ax.set_zlim3d(XYZlim)\n",
    "    ax.set_box_aspect(aspect = (1,1,1))\n",
    "    ax.scatter([0],[0],[0],s=12,c='green',zorder=2)\n",
    "    ax.set_xlabel('x (' r'$\\mu$'+'m)',labelpad=0)\n",
    "    ax.set_ylabel('y (' r'$\\mu$'+'m)',labelpad=0)\n",
    "    ax.set_zlabel('z (' r'$\\mu$'+'m)',labelpad=0)\n",
    "    tvs=[-50,-25,0,25,50]    \n",
    "    ax.set(xticks=tvs, yticks=tvs,zticks=tvs)\n",
    "    ax.axes.xaxis.set_ticklabels(['-50','','0','','50'])\n",
    "    ax.axes.yaxis.set_ticklabels([-50,'',0,'',50])\n",
    "    ax.axes.zaxis.set_ticklabels([-50,'',0,'',50])\n",
    "    ax.tick_params(axis='x', which='major', pad=0)\n",
    "    ax.tick_params(axis='y', which='major', pad=0)\n",
    "    ax.tick_params(axis='z', which='major', pad=0)\n",
    "    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label, ax.zaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()):\n",
    "        item.set_fontsize(8)\n",
    "    fig.tight_layout()\n",
    "    if len(name)>0:\n",
    "        plt.savefig(name+'3d.svg')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(PenkKv_inter,100,PenkKv_sens,'Penk_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "p=ax.scatter([0,0.2,0.4,0.6,0.8,1],[1,1,1,1,1,1],c=[0,0.2,0.4,0.6,0.8,1],alpha=1,cmap='plasma')\n",
    "\n",
    "\n",
    "\n",
    "plt.yticks([])\n",
    "plt.xticks([0,0.2,0.4,0.6,0.8,1],fontname='arial')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.set_xlabel('off-target spike prob.',fontsize=8,fontname='arial')\n",
    "fig.set_figheight(0.5)\n",
    "fig.set_figwidth(2)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "plt.tick_params(axis='both', which='major', labelsize=8)\n",
    "#plt.savefig('cbar_h.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "526\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(RorbKv_inter,100,sens_factor=RorbKv_sens)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(RorbAi_inter,100,sens_factor=RorbAi_sens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(Tlx3Kv_inter,100,Tlx3Kv_sens,'Tlx3_')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(SstKv_inter,100,SstKv_sens,'SstKv_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "359\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(Ntsr1_inter,100,Ntsr1_sens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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E/FMZ7C4dRkvVltLqFPzhZ7YRjZTweo2yjaVQKOQ0TEtLi+wI5PN5/H6/nIPU6XR4vd4VGeRSweObShWmUikymQzXXXfdeXNYSqWScrm87I1UKBTk83leeeUV1Go1JpOJUqlEOp2Wi+Y8Ho98czPpIv/7L18hHMyg0Wf5+H/fjtmlxORQkwqJeBoMOGqr7n4ikeCpp56isbGRt7/9lmqgNZRn157aC9o8NfUGPvynmwjNZmjqtKLRVkMlVemqwW5f/J1zuRxqtRqlUrnAEQiHw4TDYVQqFb29vYyMjGCxWHA4HNjtdsxms3w9lUqFTCaDwWB4c6pCs9m8okjycnEqeDVJnUql2LNnj+zKR6NRhoaG6OnpIRwOUygUZAk2N5MmEsrhqdUyNhonGQdBVeb6/2IhG4MtuzvRGatfz2KxsG3bNkaPx3jhsTG23NyA/Trdgs9fjmCTw3Eee2AIlUrgvR/vwmTVUCgUiEajsoqSSmpUKhXFYpGJiYkFJTYSKpUKarWapqYmXC4XBoNBPpfP5yOZTGIwGLDb7Wi1WgKBAM3NzW9OYgmCgEqlOq80glcl1rkoFov09fWh0+kwmUwLgopSGUkymaRUKi0gr6fOiMOlY2J8Dr2pRKkSJZlMolAomAxOIvSnaGhowOFwYDQasalbGX/pJLO6KGPHU3z4q5tRqZe3d/K5Ej/8fj/P/GgMp11LU6uDXzw6yK3vr0MURTnSLwiC3Ewhdd54vV75e0jlQmq1ekGTrCTdJUeltrYWURTJZDJEo1FmZmaIxWJy+CaZTF4RYgmCYAYqoiimL+a4VfEKJWKdD9LMhFAohMViQRRFTp06RSQSoaenh5qaGg4ePLjgGJPJRHd3N3NzcxiNRnQ6nXwOh8PBH/3ZTqan4oSjE4gU5Vb6+vp6vF6vHOBMp9OERxQUSyXcNUbiwSKlQuW8xDp9MszJ/jAWu5rxkQRWi4GGzgrDw8N4PB5qa2sxGAwolUra29sJBAIEg0G6u7vligZRFJmenkaj0WC1WpmYmFjSQ56fspG85IaGBrntbWhoiB//+MdMTU1xww03sGfPntfwlC4OgiC0Ah1Uc4x5QRCOA2OiKK5oRtSqEGs5aXTue7LZLPF4HJVKRTAYZGxsjJtuugmn0yl7l/PLf6FKWulh+Hw+uYbLbDaTz1Xo7++nuaWWA79OMDQ4yd47NbS0VXsFqagoZUy0tehINaWYOXmasUE/zTsVnBg4Sltb24K84nyYLVoUCgFznY5GtZJb7mxh99vrQCihUqmY9kU4cmiOunoTDU0WjEYjgUBArmQF5OS5QqGQv8e5JkMmk8Hn89HY2LgoBSQIAhZL9dy33HILDQ0NbN269aKfz8VCEAQFcCPQBJwE1MDvAS8AP17JOVZFFSqVygsOIpOqR5uamhgaGkKj0bB3794FNe3RaJTJyUna2trk10VRJJFIoNFoiMVisoSYnQnxV1/+NUrBRCp9Bo1BQaFQ4sF/HuGTn9qI3Vrkgb8+QSyUw+bS8fuf3cxHv3a93Pk8NxvDNzqE2SaiNZTx+Xw4nU4MBgP5XJmxE2HanSZc7SY277IjKFMoVSJarYF8rsTjD86gUadRa5T88Z/uwOO1sG7dOtnYhuqPaT5ZDAbDknXyRqPxvGU3lUqFXC6HzWa7nJ7hL0RR9M/774cEQdi40oMvq8RKJpMMDg7S2tpKbW3tgr9Lhm1NTY18k6XO52w2S0NDA/F4nHQ6TSwW49grYygFE63tTg7un6FUEZj2pSnmBb73rVP81ofUxEI5vI1G/FNppifixLMTuN1uPO5GnnkoQyqaZ248TXuLEttdReLxUbLZLCd+nSX6eBJ1WUGmKUvtuxpJ5quFhYVCAf9snFSqzOYtZqanUoSDWWrrqvahIAhMTU0hiiItLS0LvmM+nycUCmE2m7FYqkFbrVZLfX09qVRKTqAvFafL5XKXzcY6W3ITEAThPYARKANRURR/vtJzrEoB9YWIJdkKk5OT9PT0LCIVIFcmRKNVIxyqVQLxeByLxYLX66Wuro7x8XGy2Szbd3Wh1paYmojTvcnFO97VSqUM23a5KJcrhCNZbC4d/qk0NpcOp1dHdCJKbDyOSjBRzKsoJEpkUyUSkRLDLxbYtGkTO3fuRJnWoSpVyGuKxKbjjPdNodfr5SLFdDZEa4eOGV+a2noTre02yuUy4+PjBINB2YbM5/MAMmmKxWL1+PRiO7hQKJBOp5f0nMvlMtls9nIb7w3A7wBJwEC1tHnFuOTGu1Tmks/nsVgsTE5OLmhFNxgM6HQ6DAYDHo9Hjl1JYYzNmzdjMpnI5XIUCgVcLheNjY0UCgU+/T+vJ5NS4K0zodUpODM0zvR4CaVKoOeaGt72zmZmJhOo9DnSg0l0L1mIi1nM70jSut7Gy6M+DFolWqUSg7ka4BQEgXf+9gaePJRCny7TscuJq83B7Oys7L12dXVx67tq6N6wBYNRg0r1arVDNpvFarXKXUdarZYzZ84QiUTwer14PJ4lCeJwOORG2HNxhYhVAg4CA1QFUPRiDr6kqjCVStHX10djYyMajYbh4WHq6+vlli1BEHC73eh0OhQKBUajkba2NvnmRiIRkskk2WyWkZERWWrMzs7KaZzWjga50/ht/8FFLq1Bb1DS0l79gXmbdExNBUkfLaAxaVFoFERPxPivn+lhz61env7XYxTzOW56f5N83XVtFn7v4ZvJ+HMYvDrUBhVe0YNGoyEajaLVaikUCpwePIHZbMZut+N0Osnn88zOzlJTUyOX30QiETo6OmTHxev1ymrwXCyXlZDCEJeZWEZgF9AOWIDTwJcFQVCIorh85/BZrIoqXEpizczMcOLECTZu3EhDQwOZTAa1Wk13dzcWiwWTyURHR8eCVjBAdrsBnE4nhUIBn8/Hzp07ZW9Lsr3y+TypVIp8Pn/WiRBQ69KUKgn5fEajkfb2dhqva6CUKZGP5nHvdKJQCPzq51OMTJc4NZkjEMwsuA61QYW1zYTaUP3tCYJAa2srmzZtkruJpMqNM2fO0NvbSzQaxWAwyN81m80SCoXQ6XS43W5EUXzN7VtXgFg24JAoih8TRfH9oih+Gar210oOXnWJVS6XOXXqFKVSiV27dslBU5fLhU6nQ6vVkk6n5YDquTdaUinFYpH+/n6MRiM9PT1yS35dXR25bJlSwUKNy87srA+z2YzX663aJ2UrlUqZTDxHeq6Atd6AxqjGvc2BqV6PWBIx1Okp5MuMDSfo6Kyh91iaqYkELe22C37Pc6fTSINKampqKBaLzM3NMTY2Rm9vL4VCgZaWFrmqQRTF83bpnA+Xi1iCIAhita4oDtQLgvA+quGGwbPVqSvCqhFLso2kEuTGxsYFrrVOp5ONWcmeKBaL1U6as1n/UqkkV0r09fXR1ta2oD5coVCQThX4+28eJxzO4XTquOu/XYPFWvXGThxKMHA4TKlY5tn8OPUGLY1Njdz0Z+soK4poXdVJyrl0ifBMhk1bajh2eI5KGRqbl1ZPy6FQKDA1NYXH45ElqVqtRq/XYzabaWpqYmxsDL/fj9/vJxqN4na7CYfDuN1uOjo6lj13NpuVwwvSPcxms5dl7JEoiqIgCB7geiBMtVbeCiRgAfHOi1Uz3pPJJL29vfT09CzqpYPFuUKFQkEmkyESiWA2m8lmswwMDMjnk4z2+RAEgdmZJOFwjoZGM0ODcwwOznD9ni4ARgczWGxGkqEcAX+FrW9zkgkWCE8miAsBXC4XJr2NR+47RSKcR2tWYtSqiAsCj/1giE98dqtcpQBVG69UKlFTU7Mo/iTFls41AaRYk16vx2q1kk6n0Wg0cld0LpdjdHSUqakp1Gq1fL/mS+54PE4ymcRisciS7TKP9m4FuoF+wA84OEuslZAKVolYs7OzcgJ5uVaopQx8m82G0WiUB8+aTCamp6e54YYbFpGqWCySSCTo7GykmC/x63+foK5eh9f7qnpo69Jx5MU4gqhgU6eHclCFwanB0WjGUK4GK0OTWRLhPK5GA8Mno6SEIp46LdFwjsBshpaOV38UhUKBfD4vS1GTySQTTKfT0d7evkiVzw+K6nQ6OdJuNpuJRqPo9XoaGxvlTqPZ2VnOnDmDXq+Xm1VrampwOBwolcoFVaeXY6bWWYl0ADggCMJW4Abgw8D3gV8LgqAURfGC6zZWhVh1dXVks9nz9tdJEiuVShGLxaitrUWpVKLRVKsFBgYGaG5upqGhYclfZi6XIxgMotOEq53VjRa0GhVOp5tENMexI6dpXqdi45ZOkqkkRp2CUqxAXaeVSDIEVKscHLUKLE4toakM3nojKoWSsdEY69brcNca8PurwWYpNADVlv65uTl5BsP873QxqFQq1NfXE4vF0Ol0bNu2DajaldlslkgkwpkzZ8jlclitVhwOhzxw93LhrCrcBNQCp0RR/N+CIKSAwtm/r2iHy6oQSxpxvcRFEg6H5ZSFNKhj/iAOabxPZ2cnPp8Pr9e7ZNrCZDKh1WoJBsIolQpq6w2EAll84zG+d9+LFPMVNOYMn/xiO+6KAYfKSzaTo2zKMDQ0RCQcYlOdA7vby3s+s45EsIijVk8FkV/9osTb31FVg9HYq61WkoSwWq2y4yEhnU4zOjpKU1PToqoOqVXt3B+INA48FostSIEJgoDBYMBgMNDQ0EClUiEejxOJRBgfH2f//v3k83mOHj3K9u3bL/g8BEGoB75BdW7DgCiKf3fBgxaiGdgDtAmCEAd2AwcFQTAB5ZUkolfNxloqV1gqlYhGq3E1g8FApVKRe/UEQcDn8zE1NcXWrVsxGAxMT0/LBBVFkVgsJttgbrcbl8tFXV092VSa/hMB3nJrLS+/cJhELENNvY6ZiTLxaJb0UIFjvxikkC/T+Y4a1m/eglVzCMHfR37yCJOu9eTtDZQ01bp0T71Gtq3mV8BKUCqVi8gejRTRa3M0Ni42Ofx+PzMzM3R0dCxIcktpq9bW1vOqNYVCIc/Zqq2tJZfL8YMf/IBDhw6tiFjAHwB/K4rifkEQ9gmC8F1RFBfPL1gefUAK0ACNVIOl/4Xq7IfPAS9cyIi/pJF3tVpNS0sLSqWSVCpFIlGNL0klM8VikZ07dy5osy8Wi0QiEbRaLSMjIwQCAerq6uQZBhaLmff9lyZ23WDg5ZdfprN7A/t/GiMdVVFTpyaZDjP7CowPFcimS5w+GGTLWzw4xSjXfciDppSmy22huK5bbtaYnp7mwIEDWCzVRPKFWuYPvDjOQ/8yQn09JKJ6br29dcHfHQ4H0WhUnkcvQYpjXYytVC6XMZlM2O127rrrrpUe5gWmzv47StWrC634Q8EFbKFKLqktvxd4VBTFCFzYiF8V5X2+GyXNeEomk8zNzZFKpXjllVcwGAxs3rx5gRpRKBTyShJRFNmwYQM333wzLpcLn88HVH/1ExMTTExMsHnTdn79TBS9w4yoz7H5ZiVer4eMMU02XkBdEUgpKijtSuJZF8VIAkQRoa46/qiuro729nbZcQgEAhw7doyjR48yMTGxYAJONl1iajhBNl3i6OEZVOoyVpuSF54fXECgXC6Hz+fD4XCQSqWIRCLy35arVJVmUiy1Dk+qUr3IqoZJqrk+qHp0sYs5WBTFI6IofhMYoUrSA6Iofk8i1UqwKhJrJb/AmpoanE4nvb29bNiwYck6KIVCgU6no62tDa1WK583n8+Tz+fJZDKMj4+j0Wi44YYbmBhJkIxN4WlQ4J9RYtTbsVgsuHeqmPMpKaYFtDMVytEyDTe2on3nNgS1BkH76kOamYqx/xdZolMl3v3BndR4jMz5E4yN+omERykUs2jVRp59ME4+LSAKgAPyWRvRSJ4tO7TkcjnZnqpUKszNzWEwGIjH4+RyOex2+3k9u3K5TDgcxmazLarJeo1lyf8I/I0gCB8BHhNF8fw1TedAStuIovgs8OzFHCth1TuhJe9tfuBQFEVmZ2epVCpyO9dSkKLT5/7d4XBQKBQYHBzE5XLR09MDwMxMgFQ6RTSWxl1rweWpTs1r72xgz/9qZnYijsttQC0qMXq0KJTVh1qulLnvwH08P/k8mrEGnKHdTI0l+NWTE7ztjmb+7hvHyeVKNDaZ+Z0/6GZ6JEEkMINCW6L35QR1Gw3o1Eo+/Ptb6bnGsUB1GgwGdu/eLUfZ56u+5bSHSlUd971UafdrIdbZOqoPrfiAxcdXBEFYL4riIIAgCC6qZTMr3uq56sSSaockI7xcLst7bqRKhuWwXMNFKBTi9OnT2O123G43giBw5OA0D/z9fkRRRVdXG3/0uZ0c731FPkZrAFEXQdQoMJ8zG/6+A/fxrcPfIlvKotHq6Gwu0JBfj1qjwDeZJJstUd9oYuh0gN7jIvV1XuqaHfgnM+hNBRxePcmJFL0nDqLSteHxeBZMVn4tgcz5x1QqFWKxGEaj8bK3188zyj8jCMKzoig+CHwU+CdeHeh2Qax6i73RaKSjo0Oe/jI/xbN///7zHr8UsSYmJnjppZe46aabyOfz8q9+eCiAUq3BYashMJckMDfH9GiS+tpq1YNYVOB0uJZMgzw/+TzZUtWeKZAjXjvIjvpabvmPLeSyJQwGFb7xBN4GDTa7AVEo8t6725mdTHBqwMLJ/hibt5m5dk8nTqeDZDLJ1NQUgiDgcDhwOp0LgqnnQkqaL0fAUqlEMBhcsEThCnXovEsQhFmgBrgYr3L1bKz5fYOCIBAKhRgcHKSnpwebzbai80jEkvYuS7Pe9+zZI+8llIjX2mFk9FshhotF1q238Z0//zUhf4qJIzrqXWUOnY6jtsANf9hEfXs10CkZzzc33czxwHGypSx6lZ63Nu3iPe/trF5EBWq1Jmb8SbqajTQ3V+NU4XAYUROje7uSW/duZnJyknXrOlAqlXKbV6FQIBKJMDk5SSqVkitFbTbbAqL5fD5Z/S0FjUZDS0sLarVaXj51uSTWPG9vDPh74B6qHmL+Ys6zahJLimUplUrGxsYIh8Ps2LFjyeEYy2H+OMj+/n4MBgO1tbVoNBo0Go086qhSqWA2mdm6ZT3eegu+4QTxUAqDtUIsmCN3PEtTj4OUv8xsX4r6dg8zMzPMzc2xceNGPnft5xAQeG7yOW5uuIE7Z73E9z2CbusehoYE/L44dreSkSNF3vUBPVPTVYehq6tLHmcp7UicD41Gg9frxev1ysHhl156iUKhQGtrK/l8nkQigdfrvWA0Xbpvko11BfbuvAAkRFH8b4Ig/DPVWNaKsWrEUiqV5PN5Tp06hV6vZ/v27UvevPM1h0rd0JOTk2zZsoW6ujqZrPDqVL9kMkm2EMTh0hIOZHHVGaiY6pj1zWJsU+LUmhETWkwmBfXrqtIknU7j9/txuVw0NDTw+es/z+ev/zyFvgMM/+JnTBNDcWyGynXvZWI0SWWkwvZr61FrlfIkZq1Wi1arJRQKEQgEZPV3biWHdK3za87sdjuHDx+WG1PNZrOcGzyfTXalJs2IovjivH9/5GKPXzVVWKlUOHHiBB0dHXJN+7kkkiTScrVIuVyO6elpWlpa5ABpfX29fOOl4w0GA61tjfzx/1jPzFSKXz46ylQ4TlkN227TUinmEMIVvK0OXJ1V9729vR2j0SivlJM8sFSsRLlQQWmuEIqlGTwUwNVgJJ8rY6pRo1RWPVW/309DQ4M8/EQaA5DL5chms3IV7HzodDq6u7vlCletVkt3d7f84wiHw/T29pLP56mtraWmpmZBmz0gD0OR8pZvFKwKsaSx0Z2dnQtIJZWGSK8tRSzJJjGbzfj9fmpraymXy6TTaXnovgRJYqnVaux2O6lUilQqxORYGIUmgyAoSESLtHc78e7yEovFOH78OCqVCpfLhcPhwOfzcebMGdavX181jpvb8eW9lNMZZmnDaFZhtmuwKARyxaqUmy8tpqen8fl88vx2o9HI2NgYXq930ZRBadGUzWZb8COTvodKpWL9+vWMjY3JKa5kMonJZJLHIF1BVfi6sGqFfnV1dYvsKbVavUDMSwb+/LhPPB7nyJEjdHV10d7eTi6Xo6mpSZ7KMh/SL19CqVQik48Si0SYHRMQhFc7ivv7+6lUKnR2dmI2m4nH4/T29tLb20tjYyNutxu1Wk0wFUVzWzddLVu52a2mgpKfPzJKpSxyzQ0GFAoFFotFrlNvamrCZrNx/PhxAoEAra2t1NXVySSaX4smlSZLHdPzv0+lUqFcLqPX6+WNsFIcT+rq6e/vl/sGLBbLa6o8vVJYVeN9/kMXBGFRm5ckscrlMn6/X26Xv+6669iwYQPBYFCuF18K0o2XkEgkUKmho70BFQH0ejPlhIvNm7uIxWKMjo6SSqWYnZ2Vx2Zv376d2tpagsGgvOlUZ1Jj8gKKMlazgd/62OJZqRL0ej3lcpna2lp5KYLVamV8fBxBEGRVJqlPvV6PwWCQZ3tJmJ+Mn/99pXOYzWZaWlo4evQoarWa5557jve///20trYudVlXHVbVeJ9PrFwuRyKRwOVyybaHUqkkFovJ/XPDw8Ns375dLj+WbLXlID0wURQ5ffo0+Xyerq4unle+iMloQqvV4fRUbSqbzcbWrVsXlPb6/X4CgQB+vx+bzUZHRzVc0N/fz/79+ykUCrS3t6PT6WhpaZHtsEKhsIAAkmcaj8dxOp0olUoaGxvlPKbUTh8KhTAajTQ3NxOJRJidnWXjxo3yeVaSCpMk5qc//ek3DKngEoQbJGSzWaLRKFarVVaRknrLZrPEYjFuvPHGBa1Q50qkc6FQKCiXyxw7dgyTyUR9fT0vvPACb/3P9WRCdlQa2Ly7hv5DAUZPxti4u4a2DVW7R6XQoC+46GmvQ2dTEg6HGRsbkzuTu7u7USqVBINBJicnmZubk+0cqTCvqenVFjFpfJHVapXVnJTr9Pv9aLXaBXMW1Gr1glb6c/coLocrHCB9zVhViTV/ZYkUFJwv5hUKBeFwmFwux+7duxfZZOeboQXVh+Hz+Vi3bh0AL7/8Mm63G7fXQf32evx+P8cOD/LvD8bRGzScPBLik/dsx2TV8Oy3R/EPJlHrlNz2uU7MdjMTExNUKhW2bdtGOp3m2LFjqFQqurq6cLlccj2ZtAtRoVDIA0wsFgutra2L1JjX60Wr1cpDTqQ1LDqdTm6fL5VKjI+PY7VacbvdFAoFMpmMPOp7PqQBbG9K4x0Wq8JzbYdKpUIkEkGn0y0YATkf5yNWKBTi0KFDWCwWMpkM8Xic7du3U6mIBCYEhg5P0LXdisXoRCGkMFo0RIM5Cvky+VSJuaEUjiY9kYksgZEkaZOP7u5uGhsb5QcqdVhLKRqpq1kKVSQSCQYHB0nE02i0KkZHR7FarQs6iaS0js/nIxqNyhF3aVa7VLjodDrlSoahoSH8fj/XXnvtkgS6Aj2FrxuXjFjzkc/nOX78ODqdbsEWsHOxHLECgQAvv/yyrKpEUZQlR3AKnvinIVQq6H9ljk9+dSe+mwq88usZjDYVM74kG7e5qd1gZvZkEpVOwJ8ZZV1H06JZVVLzg8Viob6+nnK5zOzsLL29vajValwuF70HCxx9OYbRWqJj8yBKlcj27dtxuVw4nU75x+TxeBY0QkgZBb/fj0ajWVA2VF9fL0/4Wwpvaom1XBVpIpGgr6+P9evXk0gkFtlQUmQ9EoksqIqAqpE8MTHB3Nwce/bsob+/nw0bNtDa2ko6nSYYDHL8yDiRSAK9LUt4UkBgFzfd0cjhQzNkciV+9MBpbA4db727jdnhKKPTg1iLNZz4xyCZ6wXa3+5dUNbi8/nQarXoNFYioRxOtwutsh2tRofNauCVl/aj1ecJzIpsv66N625qQ6fTEYvFmJ6eliWWRBSJSFJgt6GhYZEJcKEtYNls9g012BYuofEO1drv0dFRtmzZgtFolDugpenJ+XyeiYkJtFotPp8Pu90uV2NWKhVOnTolhwoGBgbo7OyUI9Bmsxmj0UjNh+ooJAYYHJhg07U6Dh85hEowk07lqG2wEhpJMflSBO0NJaZiQzTXtvLNj71CtlxGu2+cz9fdjLfbJl+zQqEgGsrzyAOvkMuWSeXyVJQgihWa2gQUqiIqhYOaGgXt6+qJRqOkUiksFgsNDQ0YDAZisRhjY2Ok02kymQxer1f2Hi9m65eE82UrrlZcElUont0hmEgk2Llzp7xcSTLe8/m8XNgmBRSlFvnp6Wny+Tx9fX3YbNVFSQcOHOA973nPorSG1HzxsT/bjihukwOooVCInp1hjvxiiro5PWNPTTLy8wLvvn83/pEc6XyJGpeeYDBLYCZDzXozc3Nz8oCPsVMBQv4sLZ1WTvz7LI0dBlQqgZEzEf7ki+/A7ytTW2+krdMuOyypVIpAIMDk5CRqtRqHw0Fzc7M8DGRkZESO1judzhUR7HyOzNWOVVeFpVKJvr4+DAYD27ZtQzy7SV6v18vutVSpIM3clEYYxWIxDAYDR44cobGxEYVCgd/v5wMf+MCSpTcGg0FWrZI6UyqVeDwePvwHHt55fZxn/rKXsjFLKSRw8sgg7rYa6jdamRtOUbfBSse1LlKpFIODg9TX1zMyPMEvfxRgYlDFzEiSrdfXMOUPIAgKPvP522hf56G96pTKcSuj0Uhtba2s0jKZDKFQiNHRUQqFAkqlkkwmQ1NTE8ViUZ5tIalMi8WyZExrvqS6HM2qq4lVJVahUOCVV16hqalJbqOSXHCVSkU8HketVpPL5YjH42g0GsbHx2lpaSEej5NKpRAEgebmZk6dOoVSqeS6665bFImXXHC73b7AERBFkV//+xT7X/Cx+ZoaOruUqJwVTFkX9q0GagQDYqbEO/7AQiSgoaGxhrH+IOlkCTV2KMJMf5xyTsVbbm9heizF29/lRWex0dnZST6/cDOGFH44tzrBYDDQ1NREY2Mjo6OjnDx5Up7ZbrVa8Xg8WCwWkskk09PTnD59WvYUHY5XS52ldXJvNFLBKhIrkUiQSqXYtWvXIukixXKkcdRS86ler6elpQWDwcCZM2eYmZlBr9czNjZGpVKR1/qei0wmw8TEBM3NzfKycYDgXIYnHxvC7tDx+CMnuPNDjdz5jRsJnorR/93TTD+dRmtVs/ML28jmC/zgK0fpe+E0mWyR5k4DreYoRoUWc8RI1J/DZK+gNxfZtn0rsViMWCyGzWYjmSjjn03R0mo9r+EtCAJ+v590Os3tt9+O2WwmkUjg9/uZnJyUvUPJA41EIkxNTaFQKHC5XBiNxhWNPvrqV79KX18fJpOJf/mXf3kX8DzwHapDPeKiKH5ppc9xtbBqvdvSVL7zVYtKkfXa2lqMRqM8bG18fJxSqcRNN90k18VbrVb6+/sZHh4mmUwu8CYNBgMul4twOMzw8LA8elGlVqAQBGamAwiCQHd3Jyq1ktDcHHOJOfR1GgqJIsV0kWSwRD6lZCKVYyyTZXIqhTqqx+jUsateoLO7TE6d52c/STF4KoLdbqe1tZV4rMQ3/uIgD3ynl//9N4fJ5xcmxX0+n9zGNTY2hk6nY9euXbLkkUaGu91uuru70Wq1TExMMDw8TKVSobm5mXXr1iEIAhMTEzz88MOk02kOHDiw7H3dv38/Ho9HSkFJizD3iaL434Cms53RlxWrJrEkopwP0pALCZVKRZ4w09nZSV9fHy0tLa9uSM1m2b9/P319fZjNZmpra6mvr8dqtcp1Sna7XVZFFquanTeo8I072HNTJ00tVpKJAiWTEXezm9xsAe91LnROLXadkkymQDpfQCsoiAkinnYvZpWSqDnGyHSayfE0Vlue7307zp//r7egUqno6x0mEU/R0uZkdiZNLJrDc3YwiTSBRtqCls1m2bp166L7Yjab0Wg0GAwGeaZ7uVwmGo0SCASIx+Po9Xp5eei+ffsIh8PL3tfPf/7z3HzzzQwMDPDAAw/cA4xTrQAFmKY6h2F6pc9yNXBZV3bOJ1axWOT48eNyh3NfXx8bNmxYIPG0Wq0c1ZZ6C6enpzl16pRsk0hTlvP5PL29vdz01q6zwUmRvpeD/N8HTpAux1BrU/z+x3bQurkOQRBITObwoMFl1oJGoHOPiy1/uomhgyc580sdkQkFgSk/2g0GnC6zPEc0FElSLKcYHxXo2eTF6Xq1D1Cj0dDa2srY2Bj5fJ6enh65DGZ+WYw0p+Hce+NyuXC5XHL87ujRo9x7773o9Xre/va3L7qf99xzDwMDA2zbto23vOUt0uJODQsbVuu5jEvGJVxWYklJ5Ewmw/Hjx2ltbUUURQYHB7nmmmsW3WyFQkF3dzdNTU3EYjFcLhd6vV6e6xAIBBgfH5enBK5fvx6Xq+rlnT4Y54nvnsE3mMDTYSRLgWAoh+NMEneLlVQoj1mv4bduW8/ESJz3/+EmpuaG0budQJTtOxykcjHWrbPykU/uocZtpFgsMj4+Tm1dmEg4Q0uLmnA4iNPpRKVSIYoiIyMjlEolOjs7mZmZQaPRyDtxpIqP5ubm88algsEgo6Oj/O3f/i3f+c532LNnz5LhiS99qWo6ff3rX+euu+6STIJ7qbbXf1cQhD3AsCiKl51YwgVa8Fc0ZAuqHtnzzz/P7t27l0yk5nK56tS9EycolUp0dVVrpoLBIBs3blzUAbxSxGIx+vv7cTqdJJNJWbKFB5xMnsxy4kSAcllk421uOnImivEKJpeGt/5hGwe+O0F8JkfnO5wUmqqlx3qNmSfuHyYRLmB1aXn/n/XIy55SqZTsNEi5w2AwSDgcRq2u7irUarVs3LiR2dlZTp8+zTXXXINWq8VgMJBMJkmn0+dtpggGg/T39/O1r32NP/mTP+HOO++82FtyVbiQq0qsF198ka1bty7y5JLJJBMTE6jVaqampti5cydTU1NUKhUcDgeZTIaWlpaLXqgdCAR4+pFehKKT697WzPpNTtLpNJOTkwyfCPHgX85SLIK3y8QffnYn+787gWBTIMYqvOvPunC1G8mmcvSfOoFJ6SUbh4ohRktrI8WkGmedXiYVLFz1dm6YY2BggFQqRaVSQaVSydWzXV1dK46ah0IhBgYGuPfee/nkJz/JBz/4wRXfi3m4Koi1qqpwqdJjeLXqMp1Oo1QqOX78OBaLhY6ODsLhsDwqcnp6Wp52txSKxaLctzg9Pc3hl4c59kIGu0NgbCjJp7+2G6vdyIYNG9CrY7y4s4DFJjA2GuHxpw9ytDdFpajA4TDwHlsP2WyGEwMncBqb2fedqeo4RnOJjfesx+B9VfVkMhn0ev2CrV0SpMk5Go2GtrY2IpEIjY2NJJNJZmZmOHjwIHa7nZqaGgqFAsViUQ7+zkckEuHkyZPcd999fPSjH32tpLpqsKqj4paqcKhUKpw8eRKVSsWGDRvQaDSYzWbGx8fZt2+f7BVqNBpsNtuyZcnZbJbR0VESiQSjo6OEQiHWr9+AIKgYH0/xyv4Z/u1fTlIqVZ0Dd60Rt9dIMgGhiMjgkMhsMY+3R0PekuLHjx3gicefwqA1ERgtUyyU8bZYqGR1FPOv/ugjkQjPP/+83OgwX8KLosjJkyfRaDS0t7fL5S3SzkGj0cj69eupqalhZmaG5557jl/+8peMjIwsmFAjrfn9xje+wYc+9CF+53d+Z9WeyZXCqkms+d3QEgqFAsePH6empgar1cqJEyfYsGEDZrNZ3nYVDocJBoOMjIxgtVpRqVSLIupQJZ7dbpdV6ubNmzn04ix+f4rxkRg7b2hmaCjC1ESC1nYbBqOauz63ncGBMP/0wAnq6k1MT6TIFNUkY0V+/bM5jKYyJ58+hbJgJhkUEcUKW26uw+J4NZIuRdEFQZAXSEkOxMDAAHq9nra2NiqVCmNjY9jtdhobG1Gr1XL1bCqVIhqNcu2112IwGEin0/T29soprdOnT/PDH/6QvXv38rGPfWy1HskVxaqqwvmlM9LNa2trk9XFfM9PqkdqaGiQxyNKnt7Q0JA8xU+qCoDqEF2z2SxPxPv5j0/jqE8wPhdkNmhlncuDxfpqSYrBqOaaHR62n/DSeyzAjbc00t1jYd/Dp9i0pQXfUAqjQkvDJiNTZ6J0vU3EsT7C1JSKmpoa9Ho9Op2Onp4eeSmnNDL81KlT8iYNWLwFTZr1lc1mcbvd1NfXY7PZ5O/d3t4u21T3338/fr+fv/iLv3hDpm+WwqoZ7wB9fX3Y7XaUSiWnTp2iu7ubaDRKMBhk8+bNK57CIp5dJRcIBAiHw2i1WnkD2Py68//7nV4OvDBINlth27Vt7H3feppbF48Cr1RE4rEc2VyCkeFR9v+8QiJWxKBXokupUJQFHF497/+zHlCWmJ6elueEulwu3G43JpOJRCLB5OQkmUwGj8cjOxzSskspvSOp8/kprHMhjS//7ne/y9atW/nUpz6FUqm8qJEEy+CqYOaqEktaxpRIJOjp6WFiYoJyuUx3d/frWvVx/PhxzGaz3Brmdrtxu91UygoGjofQG1R0baquMVkOs7Oz+Hw+tmzZQrEA4yMxHvrhCVIxEY0SPvrfu2nrrKVQKDA+Pi53JYfDYQKBAOl0GovFQigUorGxkYaGBhKJBDabjbm5Ofm6VoJUKkVvby/f//736ejo4J577llNSXVVEGtVVWEkEqFQKHDNNddw+vRpLBYLbW1tr+mmSSUp0hwHqW4rk8kQCARkG8Vd78bttp2XVNI89fXr158NBQiUKjkmp8bx1rqIhkWmfEFySSX7/t8IGkOeD9ztRe1Qy0M+isUiR48exWAwMDc3RyAQoFKpcM011yw5EHc5SNs7fvjDH9LQ0MBXv/rV3xj1Nx+rRiwpflMsFjl27BgtLS1L7j9eKebm5jh06BA33HDDgu5ig8FAS0sLLS0t5PN5AoEAAwMD8nwDqSNbWk4+NjZGIpGgtbWV6elpeax2c4uT2lo34XCJhnoH23Z08sCfnwChSMRfof9AhGhniacfHsbl1rFuS4Gm5uocCWkV78zMjLxqTprqfD51n8lk6O3t5aGHHsJisXDfffddtCSfnJxk7969bNmyhdraWhwOB+Pj48Tjce6//355pNKVxqoRKxaLcdddd+FwOPjd3/1deZ6Dx+NZdg/fcpibm2NqaopbbrkFWH5CjVarpbGxkfr6egYHB+UNrFLdutvtRqlUsmnTJrnmXHrwVpuBL/75bfhn03hrjRiMamwuHcMnY1TKYLSoeeiBATQ6JS8+dwazo40tWx0MDAzg9XppbGyU85rpdHqBFK2pqaGmpmZBPC6bzXL8+HGeeOIJFAoF3/rWt16TefD888/LSfrrr7+e7373uzz55JM8++yzfO973+MLX/jCRZ/zUmDViOVwOPjsZz+Lx+PhyJEj/MM//APt7e3ceuutOBwOVCoVbW1tC5ZgLoXJyUlCoRDbtm0jFovJAdSlJIG0jsRoNFJfXy8vSWpvb+fYsWOkUikUCgVDQ0O43R7sdtsCghqMato6bPJ/3/kHXRx53oLJquWaPW5++bNBJifm0GkN1NV5KBaLWK3WRVLBaDTS2toqz8AKBoP09vbi9/vlHsUzZ86wb98+kskk3//+91+zzblr1y5uueUWPB4Pt9xyi9wd3dDQwMzMZU8JLotVNd4XHCiKTE5O8sgjj/DYY4/hcDi47bbbaG9vx+l04vF45K1f0vuHh4fJ5XL09PTIrWClUmlZ9fLySwPs+8kgXV3r+M/v70JvqObrpHV0bW1t+KdTfOf+Q0RCSa6/xcyWHXU4HC5cLsd5Uy2JRIKHHnyKbLSG1vZauq7R09rWLIcVLoRMJsP09DSlUokjR47wjW98A4Cf//zni3ZFXwz+7d/+jeuuu47m5mZuv/12RFHkpz/9Kc8++ywvv/wyX/jCF64Kg+2SEWvBSc6WgTz++OM88cQTeL1ebrvtNtlGcLvdzM3NodVq5SK3lZzzy3/675TKFbIZuOM/reMttzRy4sQJnE6nHJZ45IenOX5oDpNFQ6Vcoa5Vy4vPT+CtU3Lnh9qor/cuiJUBsqGu1+upra1FrVYTj8dpaGi4qG6ZfD7P4cOHefLJJxkdHeXuu+9m586di5Z/XgyOHDnCfffdR01NDZs2bSKfzzM4OEg0GuU73/kOVqv1zUOsBSc8O5r70Ucf5bHHHsNms7Fx40Z27dpV3TB/drHkhR6gKIp87Ysvkc+VSKeKvOs/tWFzRamrq1vgNDz38wmeenQYpSBQ12xiypekrsHE9FSSD36kA6ujugBcr9fjdrux2WxywaG0VKq1tfWi268KhQLHjh3jpZde4uDBgzz66KOXay3cm5NYC04uinzuc58jHA4zMTGByWTijjvuoL6+HrvdjsfjweVyLXqo8Xi8Wh6T1fKzp0ax2zV4m5KsW9e6oEWsWo2Q4+TxKNlsia6NLr79vw6Ty5VAgE9/fje19SZEUSSdTjMzM4PP55MbS6XPXip/uZxDEQgEKBaLTE9Pc/DgQZ599lmeeOKJ19RP+BqxRqxzEQgEePzxx3n00UfRarXccccdNDU1yfM6GxoaUKlUBAIBUqkULS0tFAoFent76ejokCooZcTjcaanp2ltbZXrvaZ9SU71h2htt9G+7tVGCEnCSEPQAoEAwWBQXoguLUWHqv00MzNDQ0PDIsKMj4/T39/P9PQ0Tz31FE8++eTl7mJeI9b5EAqFeOKJJ3jkkUcolUpcf/31bN++nZqaGjlmVCwW6evro6ura8kmjmKxSCqVWrTB9FxIsyUkcs6XRtKmjUAgQLlcpqamBovFQjqdxuPxLEjBlEoljh07Rm9vL48++ihPP/30gnROIpGQB+ReQrw5iSWKItFoVO7qWQn8fr/8oMrlMnv37qW2thZBEFi3bp1cTbASpNNpksmkXCufy+U4fvw4nZ2dOByOBU2o56ZoisWiTLJcLifnEc1mM5VKhWPHjnHy5El++MMf8vTTTy8I7JZKJUZGRrBYLIsmHa4y3pzEKpfL8g2eP/5npYjFYnz729/m/vvv59prr5VDGBaLBY/HI88WXQ7RaJRwOExzczPlcpnjx4+zfv16OYEsORcXamUrlUqEw2Hm5uZIp9OEQiFOnDjBM888w09/+tMll1DlcjlUKtVFVcm+Brw5iQUrn2a3HFKplNwi9fTTT/PYY4+RSCTYu3cvHR0dC0h2ricmjXmUbLPl1OhymN8JDcjkPHz4MN/+9rfxeDy89NJLVzL/9+Yl1mpAFEVGR0fR6XTU19eTTCZ56qmnePTRRwkGg7znPe+hpaVFXlPndrtl20ba8bNhw4YF6upCCIVCC7psKpUKvb29jI6O8s1vfpOnn34aj8dzpZPKa8R6vQiFQnKl5nykUin27dvHD37wA6anp/ngBz8oV67abDa5M2j+/NOVQMpFSiq8r6+P8fFx/uqv/oqnnnrqoqocLiHemMSaPyfgfe97HzfffDOf+MQncDqdWK1W7rnnnkt3tReJUqlEMpnk2Wef5ZFHHmFoaIitW7eyd+9ezGYzHo8Hj8ez4hhToVAgkUhgtVo5deoUExMT3HvvvfzkJz9ZduHSSjA9Pc1nPvMZHA4HPT093H333a/5XLxRifXOd76Tjo4O8vk8X/va19i3bx86nY4PfehDfOQjH+HrX//6FfvlSkSyWCxLRsq/8IUvUFtby6FDhxgeHmbv3r1s3LhRLoP2eDzn7W+Mx+PMzMzI1Qz33HMPjz/+OO3t7a/rur/0pS9x2223cf3113P77bfz4x//eMVe7hK4Koh10e7J/DkBX/rSl2hpaeHGG28EqrM0Z2dnrxixstms3H281DDYe++9V/53Pp/nl7/8JQ8//DCnTp1i7969bN68eQHJzg2HmM1m8vk8oVCIe+65hx/96Eevm1RQDac0NjYC1bGR8Xj8deUTrwasmFhLzQkoFAo0NTXJi8Cnp6dfV3Hf64XJZKK9vX1FAUgpsn/HHXdQKBT41a9+xcMPP0xfXx+33347mzdvltNKbrcbg8HA4OAgwWCQL3/5yzz44IN0dXWtynVL97CxsZFIJHJRXurViotWhV//+tfx+Xyk02m++MUv0tjYyMc//nF5LPUXv/jFS3e1lwHFYpGHHnqIJ598kuHhYe644w62bdtGqVTi2Wef5ZlnnuHBBx9csBzg9cLv9/PpT38as9nMjh07Xm8L2FWhCt/QXuGlQjabpVQqodfref755/nsZz9LsVhEoVDgcDj48pe/zFve8pYrfZnLYY1YbxQcOHCAbdu2cezYMXbv3n2lL2dZZLNZ9Hr9GrGuBKQlT6816n+1IpPJ8NOf/pT3vve9/xN4CAiLohi5UtfzpiOWtLz7tbalXW2QKjFKpRKiKKLRaP4r1WFrZ4AfV99y/od8KXDV/2ynp6f5wAc+wCc/+Un+7u/+7nWfz2g0LlqP+0aG9D2eeeYZAERR/H/Ar4DdgOFKkAouLLGuOARBuAf4mSiK+wVB2Ae8WxTF4oWOezNBEIT3A+8DjgFPiKI4IAjCh4GoKIpPXolruuolFuClOvoQIAqsPGv8Gwphnrg9S6B3iqL4XqAX+K+CIKwDDgLHr8wVXuYZpK8R0qDWKcABxK7o1VwFkNSbIAi/S/XeBARBuB/4DFACfKIoZq/gJb4hVKEX+BsgCRwWRfF7V/iSrigEQVADf05V7d0E/KMoiscEQbgXUIqi+Lmz7xOulH0FbwBirWExzkoqE2AAslRt0CFBEGyiKMau6MWdxRvBxloDIAiCUhCEL56VWL8GCsDDVM2ZtwmCoBZFMSZcJe7uG8HGuuohCMKXgU1AiurDvhS7bJxAGfg94LtUY4x/ATwCPC95yldS/c3HmsRaHVwPzFE1nFd9l40gCF3AHwHPUCVXI1Uby03V7gy8nvNfCqxJLEAQhCbgJ1Td81kgArRQDW18ShTF4AVO8RdUpVQPsOq7bERRPC0IwimqoRcl8IdUo+t/LIri2JU21JfCmsSq4maqEgdgP3CTKIp3Aw8Ay9awCILwJUEQHgJ2n32wYS7dLpsEVUkVPfv/p0RR7IOrR/3Nx5rEquIQVTUzd/b/x86+7gOWrVwURfEeAEEQ/ocgCP8AGIGvsUq7bARBuBlwiqL4mCiKTwmCcAvVspgx4DZBEIKiKP6f13LuS43LRqyrUVzPw1bgZVEUK4Ig5HiVTA2sQNqIovj1JV7+7VW4rgCwYd7nPAMgCMLzVLXN4nHMVwkuObHOuselq5hUUK0E+GtBEIJUbS2tIAh/D9iBT1zuixEE4dqzn+2iavxbRVGMz3tLSRTFCpC7Wn+wlzxAKgjCJuBdwM+o2gljcHXaBVcLzqrQZmA98Bbgh6Iofu9qJdFSuKTEEgRBKYpiWRCErwJNQB/wK1EUe8/+/UagQtWWCQGZN8qNu1wQBOGdwI5l1O1Vi0vtFWrOSqybgAZRFP9mHqk2ATdSjct8hmqGXhQE4RpBELSCILypPdZ5EfQkVafgDYVL/fC6gP8A/CXwrCAIdoCz/38H1SCfhaoX9ZQgCM3Ap4B1Z20Izr5fuFpSFZcL8yR3ryiKX4CF5TJXOy5bEloQhHcDMVEUnxcEoQ3YQzUF0gL8lGoA8T9T/XV6qCZXDwL7RVHMX5aLXMOq4VLbWIr5kuecv90NbKOaBvkr4BaqEnQG6AYGqKpKAzAEnBBF8VlBELZSJejYUuddw9WBS6oKz1Vn5/y5TDVO9ArwBeBuqlHlZmBCFMWfUJVazwNx4K1nj6sFbr2U172G14/LZiAv4e1NAWOiKP6jKIq/T9UWG6TqJY6efc/1wNNnX+s7+5oLSF/6K17D68EVSemcVZFPC4Jw6Ox/K0VRnKZqZx07a6vXUrWxcsAOqnk7qCZhc4IgqERRLF2J61/DhXFVVZAuFQAUBEFDNeVygmp6471Uy3FHlzjFGq4SXFXEmo9zDf+znuRNVLP6B6/cla1hJbhqibUUBEHQA7m16PzVjzcUsdbwxsGbOm2yhkuHNWKt4ZJgjVhruCRYI9YaLgnWiLWGS4I1Yq3hkmCNWGu4JFgj1houCdaItYZLgv8PkiLlZYb6Sh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(PvalbKv_inter,100,PvalbKv_sens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2526501001.py:57: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlay_cells(SstAi_inter,100,SstAi_sens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def midPoints(x):\n",
    "    x=np.array(x)\n",
    "    x_values=(x[:-1]+x[1:])/2\n",
    "    return x_values\n",
    "\n",
    "def neighbors_tested(df,z_bins=np.arange(-75,76,10),xy_bins=[0,10,20,30,40,50,60],savename='',vmax=0.4):\n",
    "    n_bins=len(z_bins)-1\n",
    "    n_bins_xy=len(xy_bins)-1\n",
    "    z_summary=midPoints(z_bins)\n",
    "    summary_tested=[]\n",
    "    summary_neighs=np.zeros(shape=(n_bins_xy,n_bins))\n",
    "    for i in df.index:\n",
    "        cell=df.loc[i]\n",
    "        probed_down=cell.max_z>=z_summary+cell.slice\n",
    "        probed_up=1<=z_summary+cell.slice\n",
    "        summary_tested.append(probed_down*probed_up)\n",
    "        H_cell, xedges, yedges = np.histogram2d(cell.dxy,cell.dz, bins=(xy_bins, z_bins))\n",
    "        summary_neighs+=H_cell\n",
    "    summary_tested=np.array(summary_tested)\n",
    "    counts=np.sum(summary_tested,axis=0)\n",
    "    out=np.tile(counts,(n_bins_xy,1))\n",
    "    neighbors_heatmap(summary_neighs,xedges,yedges,savename)\n",
    "    corrected_heatmap(summary_neighs,out,xedges,yedges,savename,vmax=vmax)\n",
    "    return {'neighbors':summary_neighs,'tested':out, 'xedges':xedges,'yedges':yedges}\n",
    "\n",
    "def neighbors_heatmap(neighbors, xedges, yedges,savename=''):\n",
    "    fig, ax = plt.subplots()\n",
    "    p=ax.imshow(neighbors.T, interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,cmap='gray')\n",
    "    cb=plt.colorbar(p,fraction=0.046*2.2, pad=0.04)\n",
    "    cb.ax.tick_params(labelsize=8) \n",
    "    cb.set_label('# neighbors', fontsize=8, fontname='arial')\n",
    "    plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "    plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "    fig.set_figwidth(0.8)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "    plt.yticks(fontname = \"arial\")\n",
    "    plt.xticks(fontname= \"arial\")\n",
    "    if len(savename)>0:\n",
    "        plt.savefig(savename+'_neighsHeat.svg')\n",
    "    plt.show()\n",
    "    \n",
    "def corrected_heatmap(neighbors, tested, xedges, yedges,savename='',vmax=0.4):\n",
    "    fig, ax = plt.subplots()\n",
    "    p=ax.imshow(neighbors.T/tested.T, interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,vmax=vmax,cmap='YlGn_r')\n",
    "    cb=plt.colorbar(p, fraction=0.046*2.2, pad=0.04)\n",
    "    cb.set_label('neighbors per cell', fontsize=8, fontname='arial')\n",
    "    cb.ax.tick_params(labelsize=8) \n",
    "    plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "    plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "    fig.set_figwidth(0.8)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "    plt.yticks(fontname = \"arial\")\n",
    "    plt.xticks(fontname= \"arial\")\n",
    "    if len(savename)>0:\n",
    "        plt.savefig(savename+'_psCorr.svg')\n",
    "    plt.show()\n",
    "    \n",
    "def photostim_heatmap_fit(xy_popt,z_popt,xy_d,z_d):\n",
    "    midx=(xy_d[:-1]+xy_d[1:])/2\n",
    "    midz=(z_d[:-1]+z_d[1:])/2\n",
    "    summary_probs=np.zeros(shape=[len(midx),len(midz)])\n",
    "    i=0\n",
    "    for x in midx:\n",
    "        j=0\n",
    "        x_p = Gauss(x, *xy_popt)\n",
    "        for z in midz:\n",
    "            z_p = Gauss(z, *z_popt)\n",
    "            if z_p<x_p:\n",
    "                act_prob = z_p\n",
    "            else:\n",
    "                act_prob = x_p\n",
    "            act_prob=x_p*z_p\n",
    "            act_prob=np.min([x_p,z_p])\n",
    "            summary_probs[i,j]=act_prob\n",
    "            j+=1\n",
    "        i+=1\n",
    "    return summary_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##grey scale heatmap is a 2d hist of the number of neighbors. We find more neighbors within an imaging plane due to\n",
    "##edge effects. This is also observed if objects are randomly placed in a confined space.\n",
    "##green-yellow heatmap normalizes the previous heatmap by the number of cells a sufficient distance from the z-borders\n",
    "##of the imaging volume\n",
    "x_d=np.arange(0,61,6)\n",
    "z_d=np.arange(-69,70,6)\n",
    "\n",
    "Tlx3_neighbors=neighbors_tested(Tlx3Kv_inter,z_bins=z_d,xy_bins=x_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##cleaned version for fig\n",
    "neighbors=Tlx3_neighbors['neighbors']\n",
    "tested=Tlx3_neighbors['tested']\n",
    "xedges=Tlx3_neighbors['xedges']\n",
    "yedges=Tlx3_neighbors['yedges']\n",
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(neighbors.T/tested.T, interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,vmax=0.4,cmap='YlGn_r')\n",
    "fig.set_figwidth(0.8)\n",
    "plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks(fontname= \"arial\")\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "#plt.savefig('Tlx3Kv_normNeigh.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"Tlx3_neighs.csv\", neighbors.T/tested.T, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tlx3Kv_ps_array = photostim_heatmap_fit(Tlx3Kv_popt_x,Tlx3Kv_popt_z,x_d,z_d)\n",
    "Tlx3Kv_ps_array = Tlx3Kv_ps_array\n",
    "xedges=Tlx3_neighbors['xedges']\n",
    "yedges=Tlx3_neighbors['yedges']\n",
    "fig, ax = plt.subplots()\n",
    "p=ax.imshow(Tlx3Kv_ps_array.T,interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmax=1,vmin=0,cmap='plasma')\n",
    "fig.set_figwidth(0.8)\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks(fontname= \"arial\")\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "#plt.savefig('Tlx3Kv_ps_sensAdj.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"Tlx3_photostim.csv\", Tlx3Kv_ps_array.T, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21407267851083908\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFkAAACSCAYAAAAuJGbYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAIyElEQVR4nO2df4wdVRXHP9/ddtuCdNtChG2XWhH6Q6yBtNEGSJHEqiDBxFiiJmIQqU0brQSjVYmgoikiyg+hsFCNRg0JkdYi0tCa1opYSxEFmgI1hbbbbk3t79pu2HaPf9xZ9s3ObLfvMe/svu39JC/vzcy9c0+/e3pn7rlz7sjMiFSXuv424FQgiuxAFNmBKLIDUWQHosgODPFucFbd7Jq7Z1zZ+ZjeTv3oyQ5EkR2IIjsQRXYgiuxAFNmBKLIDUWQH3AcjGZS9z1d9fWrbjh/P1quhOHj0ZAeiyA6U3V1IGg8sB/4JtAF7gQlAI3CTme0u0L5BQSWefDnwn+T3s8BMM5sPLAFu7LO2lPpoyNDsp6Eh9cEs+6khKrnwrQdWEYReBbye7G8FxuZVkDQHmAMwWdNo1nkVNFu7VOLJFwMNZtYJtNMtbDOwM6+CmbWY2XQzm36qCQyVefJm4E5Juwl98zBJDwCjgblFGjdYKFtkM3seuLYKtgxa/AcjSvdQ9e88K1PEGt+R3rFpc/Y8NXTxi/fJDkSRHYgiO+DeJ9c3jkxtt09uypQZtuuwlzkuRE92IIrsQBTZgSiyA/6DkbNGpza3zK7PFJlyb+0MNE6G6MkORJEdqGRm5BJgAXAY2EqYFakHDFhsZn8v0sDBQCV98mjgi2Z2SNLTyfZzQCewsa/KR88bk9p+6MO/yJT56YOzKzBr4FJJqPNJBb4N/AbYamZrJF0NfAX4YdFG1jpl98mSzgAeAdYBS4H3Jof2AA291JkjaYOkDW3bT73epJIL3z3AROB64D5gsqR7CLMii/MqlE4/NZ37wYqNrVUq6S6+UA1DBjPug5H2MekmXzg6IVPGch7dqmXifbIDUWQHosgO+M+MdKSDP6/97+xMmbr2N1PbOQ/O1hTRkx2IIjsQRXYgiuyA+4Vv58z09gXkDDziYCRSLlFkB9xF3jL/Zp657kauuWCyd9P9RiF9sqRxwF2EJJ2NZnZ/b2XrJJpHNnLHFR9l+J46lm49minzrtHpv32t99BFefKXgHvNbB7wcUlD+6owomEoX73ysoKaH9gUJfI5wPbk9z5CutlblM6MtLS0dFcadUZBzQ9sirqF20ZIzNkOjAH2lx40sxagS923ghe79h8qqPmBTVGe/AjwZUkPAY+b2bG+Khx9s4O7n3qmoOYHNoV4spntAj57MmU7zdh5+CA/em4tvz+yiUnn78mUqdt3emq71qNw7iO+dy+507vJficORhyIIjvg3l2MfyI9tJj+/W2ZMv9gipc5LkRPdiCK7EAU2YEosgPuF77DTekckaaG/ZkyNmRw/e0H179mgBJFdiCK7IB7nzxya0dq+8XDzdlCPWera2gBkTwqyX66lRA7PhO4g7A+3PPABqDDzPpetuwUoyyRJQl41cy+K2ka8ClCnshBQkRyXfEm1j599snJ1NEaSWuA1cArks4Gvg78DHgDuM7MbgA+IuncXs4RE3N6I0mq+VDXBzgdWATMN7MdwGS65/T2kfO/IybmlIGkRuBx4E/A3ZJWJ79/LKkVaDOz1090juFt6VVZnn75wkyZKR0HyzFrwFOWyGZ2AMg+tR365kgvxPtkB6LIDrgPRrQzvbzy2KcaeylZWinnQa0aGqBET3YgiuxAFNmBKLID7he+43v2prZHbdiVKWOnDfcyx4XoyQ5EkR2IIjvQ7+9+Ot7altlXN6JHn1xDA488Kp0ZmUpYF+4x4M/Ag4SFng6Y2XcKtXAQUEl3cQnhRS7HCNNO1wJ/NLMFwPgkEypSQp+enLztpvQp+mWEFbUuBL5HmBn5S3JsB9CUfEcSypoZAa4AhpmZ0b0OXFdSDsA4ct6aUzr91GpbCjO+Vig3aG+ShkhaTJiGup2Q8dQi6VLg32aWEbk0+2lW3WwrvZDlvdCw88iR9I4aj8JVsi7cD3J2f64AWwYt8T7ZgSiyA/0+GKEz2ydbZz/YUUWiJzsQRXYgiuxAFNmBKLIDUWQHosgORJEdkDkHWiTNSQJGA4Zq29QfnjynH9rsi6raFLsLB6LIDvSHyAOqP06oqk3uF75TkdhdOOAWTy5n0VQHW8YDywnZtG2JTRMIqXI3mdnuXitXgKcnl71oahW5nPDsCMCzwEwzmw8sAQpPW/acGclbNPW/ju2Xsh5YRRB6FdCVe9gKjC26MU9PLn0+I7NoqjMXAw1m1gm00y1sMznPjbxd3O4uJJ0D/AQ4BGwws4ddGs63ZRrwDWA38BIwDJhEeBXp3CQptLj24i1c9Ym3cA5EkR2IIjsQRXYgiuxAFNkBF5ElDUvWLep3+sOWQkWWdJukGTmHPk14Sr+o83UdnyRptaQFyfc1J3HajC2SbpY0TlKDpGXJvjMl/boP+z4gqc8XJlQjdlEn6VdAB7DPzL4GzAJOk7SJ8KZ2IwyzhxNGWmuBfyXHSuulkHQRsBA4AGwGxifn+ljyPUXSVcBIwtvhj5S2Z2aLSmxZZmbtkiYCo8xsh6SpwCtJc1OBjZJuI+TBdBCyC14DLgU+Y2brJc2VtNzM0osrlVAVkQnRrGagS6hVhBjBNwlBouOELKoXgV+a2QpJ5+fU68lCYJ6Z7ZW0FPg5IdawDpgBHCVkZT0KvAo8UNqepCFdtphZe3LOq4C/Jb/fRxhmQxD5JWA68DvCcm0rzex6SbcDUwiBpo3AZcCKEwlSNLcSljF7gSAsdL8lpx5YbGYLgT8QBNmfHFuQUy/P3q5zGVn7/0rIzJoIfKtne8lLZnrGEYzgpRAyuroig1cDLye/D5pZR4ldx0ra7gBO+ER1NTx5LcFL3w8Ml1RPSEO7hRCUuUvSAYIHlLItp15PFgH3SdqdtNMzkPMe4JOERM4ngN/mtPcGcIukFWa2H3gS+DywkvBHnijpBkJX1uv1oISLCG8M6pUYIAIkzSPMlDxsZleWUW8G0GRmS09YLoockDQCeNTMPlH4uaPI1SeO+ByIIjsQRXYgiuxAFNmBKLIDUWQHosgO/B8c7p72EHa/pQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "p=ax.imshow(neighbors.T/tested.T*Tlx3Kv_ps_array.T,interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,vmax=0.015,cmap='viridis')\n",
    "fig.set_figwidth(0.8)\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks(fontname= \"arial\")\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "ax.scatter([0],[0],c='white',marker='o')\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "print (np.sum(neighbors.T/tested.T*Tlx3Kv_ps_array.T))\n",
    "#plt.savefig('Tlx3Kv_product_heat.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"Tlx3_prod.csv\", neighbors.T/tested.T*Tlx3Kv_ps_array.T, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SstKv_neighbors=neighbors_tested(SstKv_inter,z_bins=z_d,xy_bins=x_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighbors=SstKv_neighbors['neighbors']\n",
    "tested=SstKv_neighbors['tested']\n",
    "xedges=SstKv_neighbors['xedges']\n",
    "yedges=SstKv_neighbors['yedges']\n",
    "fig, ax = plt.subplots()\n",
    "p = ax.imshow(neighbors.T/tested.T, interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,vmax=0.4,cmap='YlGn_r')\n",
    "fig.set_figwidth(0.8)\n",
    "plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks(fontname= \"arial\")\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "#plt.savefig('SstKv_normNeigh.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"Sst_neighs.csv\", neighbors.T/tested.T, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SstKv_ps_array = photostim_heatmap_fit(SstKv_popt_x,SstKv_popt_z,x_d,z_d)\n",
    "fig, ax = plt.subplots()\n",
    "p=ax.imshow(SstKv_ps_array.T,interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmax=1,vmin=0,cmap='plasma')\n",
    "fig.set_figwidth(0.8)\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks(fontname= \"arial\")\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "##plt.savefig('SstKv_ps_sensAdj.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"Sst_photostim.csv\", SstKv_ps_array.T, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.027499047235153753\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "p=ax.imshow(neighbors.T/tested.T*SstKv_ps_array.T,interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,vmax=0.015,cmap='viridis')\n",
    "fig.set_figwidth(0.8)\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.yticks(fontname = \"arial\")\n",
    "plt.xticks(fontname= \"arial\")\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "ax.scatter([0],[0],c='white',marker='o')\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "print (np.sum(neighbors.T/tested.T*SstKv_ps_array.T))\n",
    "##plt.savefig('SstKv_product_heat.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"Sst_prod.csv\", neighbors.T/tested.T*SstKv_ps_array.T, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_offtarget_fit(df, x_d, z_d, xy_popt, z_popt, savename=''):\n",
    "    neighs_dict=neighbors_tested(df,z_bins=z_d,xy_bins=x_d,savename=savename)\n",
    "    xedges=neighs_dict['xedges']\n",
    "    yedges=neighs_dict['yedges']\n",
    "    ps_array=photostim_heatmap_fit(xy_popt,z_popt,x_d,z_d)\n",
    "    plot_heatmap(ps_array,xedges,yedges,savename)\n",
    "    neighs_per_cell=neighs_dict['neighbors']/neighs_dict['tested']\n",
    "    plot_photostim_dens_product(ps_array,neighs_per_cell,xedges,yedges)\n",
    "    \n",
    "def plot_heatmap(summary_probs,xedges,yedges,savename=''):\n",
    "    fig, ax = plt.subplots()\n",
    "    p=ax.imshow(summary_probs.T, interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmax=1,vmin=0,cmap='plasma')\n",
    "    cb=plt.colorbar(p,fraction=0.046*2.2, pad=0.04)\n",
    "    cb.set_label('off-target spike prob.', fontsize=8, fontname='arial')\n",
    "    cb.ax.tick_params(labelsize=8) \n",
    "    plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "    plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "    fig.set_figwidth(0.8)\n",
    "    plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "    plt.yticks(fontname = \"arial\")\n",
    "    plt.xticks(fontname= \"arial\")\n",
    "    if len(savename)>0:\n",
    "        plt.savefig(savename+'_psHeat.svg')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def plot_photostim_dens_product(summary_probs,neighs_per_cell,xedges,yedges,savename=''):\n",
    "    p=plt.imshow(neighs_per_cell.T*summary_probs.T,interpolation='nearest',filterrad=1,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],vmin=0,cmap='plasma')\n",
    "    plt.scatter([0],[0],s=200,c='white')\n",
    "    cb=plt.colorbar(p)\n",
    "    cb.set_label('off-target spike prob. x neighbors/cell', fontsize=10, fontname='arial')\n",
    "    plt.ylabel('axial offset (' r'$\\mu$'+'m)')\n",
    "    plt.xlabel('lateral offset (' r'$\\mu$'+'m)')\n",
    "    if len(savename)>0:\n",
    "        plt.savefig(savename+'_prodHeat.svg')\n",
    "    plt.show()\n",
    "    print(np.nansum(neighs_per_cell.T*summary_probs.T), \"off-target cells/target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12987469691510176 off-target cells/target\n"
     ]
    }
   ],
   "source": [
    "##above process is strung together for Cre lines without polished plots. cmap scale will vary across Cre lines.\n",
    "estimate_offtarget_fit(PenkKv_inter, x_d, z_d, PenkKv_popt_x, PenkKv_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9zV3tnkpnPOaVeGCH7z1h2gzf/Hfn3uwpuwtm+NvMe9r9LbrxE/zi24H25zjxx9mNJyPsajrQmTKBkNsCy8he4Cu4Ef46SacCr5rZBGFP2J9d5yQ4QCuLWpyfncs74u8mXY6q4c5UrnUpcpKaElcaA5KbZ5dFYwo72ifG3GzMnO6rlffvjZ3osz87FRsc9o/W2D3in3Ozf45vprQvZlsW3eYT7b8ZTWt2c+AO0K51KXKSCrviWKLK8XJIhV1p3CEhtS5FThpP2AIip/TFduyyfZffR/f1+1Y93b1ZFWdnbN48t8df34mbJb037M/hl8zIvrszNHFKB2hNRNpnNxPpPLt5cKe4lRw9nymxpI/iHM8KuMvMnppq0RQ3va13JA0BL9WwCP2480jjHG5mczWz21jxvuynj23eVMjgUNJNwKOhKTHwicCU+BHcRspW4D4z+8RUC96INfulItaaVUXSxoL57xr+Nx7bHD3XsWsypsS4ihgerVURm79GFHZdU8RAMBf5TImHAyvTVpyR55RpxGa8cM1qsPwlLcCZBA8BG4HjcS4qVwKfx9n+rTWzjXlfUmpeDSjsi2PNYFPlPxUaTtgpk6cWNmgpNaJhBmiFtrZWOd/DgAdx7kW2BfkvwY2av2hm7+ZNXGc0Us0Ot7ZeAnxcUnuxBBXidJw1LMBTwGlmdinwbeALCZWhIjRMzSb/fLTaPANswAl8A/Ba8PlbwKJ8ieqRRqrZBU8IqiIfxG12H8fNd0MBH0IOS9h6pmFG4/H5qJl9K6F8TwSuwdloP4dz+Xw0bqPjGjPbWSB5XdEwwk6ZOo3UjKdMkVTYTUQq7CYiFXYTkQq7iUiF3UTUrbAldQbnX9SceirLVKi5sCXdEDhZjfNp4MwKvi98frSkJyRdEfz9zRJeO6Eskq6StFhSh6T1wWd9kgoephZ4SajJicL1ohtvkXQv7nyqHWb2JeAsoEfSZtxZoIZTmXbhNFhP4s6XXhNL5yHpBOBaYCfwCnBY8K5Vwd9jJJ2Dcy11B+4knUx+wbnVYVnWm9mwpKXALDPbImk58D9BdsuBFwJXVQuDcvUCLwOnAqvN7BlJayQ9aGZFT7OvJHUjbNwq0iFAKLANOF30n+AWQMZw/lh+AXzXzB4NzrKKp4tzLXCJmW2X9ADOE+BW3PHQK3AHwI8C9+GsVu+M5hc4f90ADJtZaAt2DhA6FDoOp0YFJ+zncMcu/QB4AviRmV0k6SvAMbiFlRdwLsbCoxUToebNeMD1wDKcQ73wHxrqcVtxdtPX4k6LHyW7CHJFjnRxWiLvMiZ+5//E+XhZClwXzy842DSuUzZcrQXnGyZcfTuXrNuwXWZ2IFKu0UjeB3An9iRKvdTsJ3G19nic6W0rzrHOl3GLEGsl7cTViChv5EgX5xbgDknvBvnEFy6OxPn/3A08BHwvR36vA1+W9KiZDeKORPwM8CPcj22ppM/jupi844UIJ+B8liZKuhAySSRdgrNg+ZaZfayMdCuAhWb2QNUKly/vVNiTR1I3FdqtkQSpsJuIehmgpSRAKuwmIhV2E5EKu4lIhd1EpMJuIlJhNxGpsJuI/wctaYfY4AlqlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8399008301350763 off-target cells/target\n"
     ]
    }
   ],
   "source": [
    "estimate_offtarget_fit(RorbKv_inter, x_d, z_d, RorbKv_popt_x, RorbKv_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.060626545658848945 off-target cells/target\n"
     ]
    }
   ],
   "source": [
    "estimate_offtarget_fit(RorbAi_inter, x_d, z_d, RorbAi_popt_x, RorbAi_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vf2sf8CUzWwCswBWyJTk3v0a7LJNS9zL2/j//AK9hcgxwVfZ5ScxV1vdreC8Fr31SWQ2bw57yV88lobUVuXalnj2MlzJuCZ3Sk+/De+kr8RDWPrxgzEfwxYFrJG3Fe0Cax2u0y7IYuF7SxuQ52QWFPwDeipecWg7cUuN5vwE+IulOM9sCfBd4J3A3/kd2jKR346ZkzPFAilfhAXwtIS5QNIikeXjEyJfN7IwC7U4GppnZbaUJl31mVHLjSNoPuNXMzm63LPWISu4BOmXgFSmRqOQeICq5B4hK7gGiknuAqOQeICq5B4hK7gH+H3/nJR9P84jHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.028577764783655365 off-target cells/target\n"
     ]
    }
   ],
   "source": [
    "estimate_offtarget_fit(Scnn1a_inter, x_d, z_d, Scnn1aAi_popt_x, Scnn1aAi_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9429267516052415 off-target cells/target\n"
     ]
    }
   ],
   "source": [
    "estimate_offtarget_fit(Ntsr1_inter, x_d, z_d, Ntsr1Ai_popt_x, Ntsr1Ai_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7nZ4ZpVfPV21fm1G5HwTOBlYCjxM2UOxM2Fx5su3Xs5AjLXpG4Tnp0EtNek4K5ArvM3KF9xm5wvuMXOF9Rq7wPqNrFS5pUFKsg1vaTTfJMlU6rnBJ50vau8ZXxxMCx6Z1v/L3O0u6V9Lp0WuceOOTZJF0pqQtJc2QdFt0bTNJ/9pEvr0kdewk5W6xpRck3UgIfLTG9t8ChwCzJf2CcPapCebVmQRL1/3Az6LvKvNNQNL7gUXA68DTwNbRvQ6PXneVdCSwMbAYeLuyPNsXV8hym+310YmAc22vkLQ74yf77g4sl3Q+IZLFCGEf/FPAfsAJth+RdLKk222/mc6/Lz5do3DC6tNWQFlp9xBs1+cQFk2KhPgwPwdusH2XpB1q5KtmEXCK7dWSbgW+RrCBP0Q4DG4dIXjiLcCTwFWV5UmaVpbFdtm37EjGY9XtRjC5QlD448CewLeBe4G7bX9S0kXAroTFmOWE8Gd3Jf5PTZGON+kR5wG7AI8y7rBXtvkOEPyyFxFCWY0yvnByeo181RQq7mUm/80PEGLO7AT8fXV5tkeZHKmlHI4NQqya8qrdUYyHNHsjcjcuyzVaUfYIdGbHYbfU8PsJtfe9BLfeAUKwn88RFi4ulfQ6oWZU8kKNfNVcDCyWtDIqp3qxY3vgGEIYsTuAm2uU9yvgc5Lusr2WELL6L4C7CQ/cTpI+Tehu6o4fKng/IT5b5uSLJy0i6RSCJ8y1to9IkG9vYAvbt7ZNuEbl5wpvHUmzgFtsp7IrJAtyhfcZ3TJoy8mIXOF9Rq7wPiNXeJ+RK7zPyBXeZ+QK7zNyhfcZ/w8yN6A2nOFHuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 57.6x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12982235881634935 off-target cells/target\n"
     ]
    }
   ],
   "source": [
    "estimate_offtarget_fit(PvalbKv_inter, x_d, z_d, PvalbKv_popt_x, PvalbKv_popt_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cre</th>\n",
       "      <th>effector</th>\n",
       "      <th>avg_dens</th>\n",
       "      <th>xy_sig</th>\n",
       "      <th>z_sig</th>\n",
       "      <th>off_est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RorbKv</td>\n",
       "      <td>AAV</td>\n",
       "      <td>19813.145110</td>\n",
       "      <td>6.775308</td>\n",
       "      <td>27.635577</td>\n",
       "      <td>0.839901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SstKv</td>\n",
       "      <td>AAV</td>\n",
       "      <td>4095.645255</td>\n",
       "      <td>5.042229</td>\n",
       "      <td>15.533890</td>\n",
       "      <td>0.027499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tlx3Kv</td>\n",
       "      <td>AAV</td>\n",
       "      <td>22330.498940</td>\n",
       "      <td>5.332775</td>\n",
       "      <td>15.176749</td>\n",
       "      <td>0.214073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PenkKv</td>\n",
       "      <td>AAV</td>\n",
       "      <td>9255.702899</td>\n",
       "      <td>5.444623</td>\n",
       "      <td>21.110383</td>\n",
       "      <td>0.129875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PvalbKv</td>\n",
       "      <td>AAV</td>\n",
       "      <td>3349.172504</td>\n",
       "      <td>9.579632</td>\n",
       "      <td>23.224407</td>\n",
       "      <td>0.129822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RorbAi</td>\n",
       "      <td>Ai167</td>\n",
       "      <td>4424.813405</td>\n",
       "      <td>6.649579</td>\n",
       "      <td>24.729292</td>\n",
       "      <td>0.060627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ntsr1Ai</td>\n",
       "      <td>Ai167</td>\n",
       "      <td>10094.000590</td>\n",
       "      <td>10.063780</td>\n",
       "      <td>28.370210</td>\n",
       "      <td>0.942927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scnn1aAi</td>\n",
       "      <td>Ai167</td>\n",
       "      <td>1730.001852</td>\n",
       "      <td>6.473506</td>\n",
       "      <td>20.552885</td>\n",
       "      <td>0.028578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SstAi</td>\n",
       "      <td>Ai167</td>\n",
       "      <td>1429.936588</td>\n",
       "      <td>6.090537</td>\n",
       "      <td>22.766927</td>\n",
       "      <td>0.012495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cre effector      avg_dens     xy_sig      z_sig   off_est\n",
       "0    RorbKv      AAV  19813.145110   6.775308  27.635577  0.839901\n",
       "1     SstKv      AAV   4095.645255   5.042229  15.533890  0.027499\n",
       "2    Tlx3Kv      AAV  22330.498940   5.332775  15.176749  0.214073\n",
       "3    PenkKv      AAV   9255.702899   5.444623  21.110383  0.129875\n",
       "4   PvalbKv      AAV   3349.172504   9.579632  23.224407  0.129822\n",
       "5    RorbAi    Ai167   4424.813405   6.649579  24.729292  0.060627\n",
       "6   Ntsr1Ai    Ai167  10094.000590  10.063780  28.370210  0.942927\n",
       "7  Scnn1aAi    Ai167   1730.001852   6.473506  20.552885  0.028578\n",
       "8     SstAi    Ai167   1429.936588   6.090537  22.766927  0.012495"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "off_ests=pd.read_csv('data/offtarget_ests.csv')\n",
    "off_ests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "##for estimating off-target with homogenous distribution of cells (i.e. # cells proportional to volume)\n",
    "def neighs_given_dens(density, x_d,z_d):\n",
    "    i=-1\n",
    "    z_height=abs(z_d[1]-z_d[0])\n",
    "    out_array=np.zeros([len(x_d)-1,len(z_d)-1])\n",
    "    for x in x_d:\n",
    "        if x!=0:\n",
    "            sa=math.pi*x**2-math.pi*x_d[i]**2 ##area of outer radius - area of inner radius\n",
    "            v=sa*z_height/1e9 ##cubic um to cubic mm\n",
    "            n_cells=v*density\n",
    "            out_array[i,:]=n_cells\n",
    "        i+=1\n",
    "    return out_array     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## draw effective point spread functions based on best Cre line/AAV/Ai167 combos\n",
    "popt=np.array([1,0,5.096]) ##FWHM = 12 um\n",
    "popt_z=np.array([1,0,14.863]) ##FWHM = 35 um\n",
    "bs=1\n",
    "x_d=np.arange(0,61,bs)\n",
    "z_d=np.arange(-69,70,bs)\n",
    "ps_sim1=photostim_heatmap_fit(popt,popt_z,x_d,z_d)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.imshow(ps_sim1.T,extent=[x_d[0], xedges[-1], z_d[0], z_d[-1]],vmax=1,vmin=0,cmap='plasma')\n",
    "\n",
    "fig.set_figwidth(0.8)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "#plt.savefig('ps_sim1.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 57.6x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## draw effective point spread functions based on worst Cre line/AAV/Ai167 combos\n",
    "popt=np.array([1,0,10.192]) #FWHM = 24 um\n",
    "popt_z=np.array([1,0,28.028]) #FWHM = 66 um \n",
    "x_d=np.arange(0,61,bs)\n",
    "z_d=np.arange(-69,70,bs)\n",
    "\n",
    "ps_sim2=photostim_heatmap_fit(popt,popt_z,x_d,z_d)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "p=ax.imshow(ps_sim2.T,extent=[x_d[0], xedges[-1], z_d[0], z_d[-1]],vmax=1,vmin=0,cmap='plasma')\n",
    "fig.set_figwidth(0.8)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "plt.tick_params(axis='both', which='major', labelsize=8, direction='out')\n",
    "plt.ylabel('axial offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "plt.xlabel('lateral offset (' r'$\\mu$'+'m)',fontsize=8,fontname='arial')\n",
    "#plt.savefig('ps_sim2.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv_file=open('homogenous_dist_offT.csv','w')\n",
    "\n",
    "d_list=np.arange(1000,164000,1000) ##range of cell densities\n",
    "off_ts=[]\n",
    "fig, ax = plt.subplots()\n",
    "for density in d_list:\n",
    "    out=neighs_given_dens(density,x_d,z_d)\n",
    "    prod=ps_sim1*out\n",
    "    off_ts.append(np.sum(prod))\n",
    "ax.plot(d_list,off_ts,c='black')\n",
    "opto.write_csv(csv_file,d_list,'densities(cell/mm3)')\n",
    "opto.write_csv(csv_file,off_ts,'high_res_offT')\n",
    "\n",
    "off_ts=[]\n",
    "for density in d_list:\n",
    "    out=neighs_given_dens(density,x_d,z_d)\n",
    "    prod=ps_sim2*out\n",
    "    off_ts.append(np.sum(prod))\n",
    "ax.plot(d_list,off_ts,c='grey',zorder=0)\n",
    "opto.write_csv(csv_file,off_ts,'low_res_offT')\n",
    "\n",
    "colors=['blue','green','darkorange','magenta','purple','blue','red','cyan','green']\n",
    "facecolors=['none','white','white','white','white','blue','red','cyan','green']\n",
    "zs=[1,2,1,1,1,1,1,1,1]\n",
    "for i in off_ests.index:\n",
    "    c=colors[i]\n",
    "    f=facecolors[i]\n",
    "    z=zs[i]\n",
    "    avg_d=off_ests.avg_dens[i]\n",
    "    off=off_ests.off_est[i]\n",
    "    ax.scatter(avg_d, off,edgecolors=c,facecolors=f,zorder=z,alpha=0.8)\n",
    "\n",
    "ax.scatter([68000],[4.7],facecolor='grey', marker='*', edgecolor='black')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "plt.xscale('log')\n",
    "#plt.yscale('log')\n",
    "plt.axis([200,200000,-0.2,5])\n",
    "plt.ylabel('est. off-target cells/stim.')\n",
    "plt.xlabel('density (cells/mm$^{3}$)')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)  \n",
    "csv_file.close()\n",
    "#plt.savefig('off_target_v_density.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv_file=open('Presponse_v_Pcnx.csv','w')\n",
    "ns=[1.05,1.3,2,5]\n",
    "cs=['green','darkorange','red','grey']\n",
    "Pcnx=np.arange(0,1.01,0.01)\n",
    "opto.write_csv(csv_file,Pcnx,'connection probability')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for n, c in zip(ns,cs):\n",
    "    \n",
    "    Pnocnx=(1-Pcnx)**n\n",
    "    Patleast1=1-Pnocnx\n",
    "    ax.plot(Pcnx,Patleast1,c=c)\n",
    "    description=\"n=\"+str(n)\n",
    "    opto.write_csv(csv_file, Patleast1, \"response prob \"+description)\n",
    "ax.plot([0,1],[0,1],c='black',ls='dashed')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "plt.ylabel('response probability')\n",
    "plt.xlabel('connection probability')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)    \n",
    "plt.savefig('rf_vs_PCnx.svg')\n",
    "csv_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/644726190.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/644726190.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/644726190.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/644726190.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for n, c in zip(ns,cs):\n",
    "    \n",
    "    Pnocnx=(1-Pcnx)**n\n",
    "    Patleast1=1-Pnocnx\n",
    "    PoffCnx=1-(1-Pcnx)**(n-1)\n",
    "    Pfp=(1-Pcnx)*PoffCnx\n",
    "    ppv=Pcnx/Patleast1\n",
    "    ax.plot(Pcnx,Pfp,c=c)\n",
    "ax.plot([0,1],[0,1],c='black',ls='dashed')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "plt.ylabel('false positive rate')\n",
    "plt.xlabel('connection probability')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2461507170.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2461507170.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2461507170.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n",
      "/var/folders/r_/1r34q4315614mp9bkdvjcjph0000gp/T/ipykernel_16502/2461507170.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ppv=Pcnx/Patleast1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for n, c in zip(ns,cs):\n",
    "    \n",
    "    Pnocnx=(1-Pcnx)**n\n",
    "    Patleast1=1-Pnocnx\n",
    "    PoffCnx=1-(1-Pcnx)**(n-1)\n",
    "    Pfp=(1-Pcnx)*PoffCnx\n",
    "    ppv=Pcnx/Patleast1\n",
    "    ax.plot(Pcnx,ppv,c=c)\n",
    "ax.plot([0,1],[0,1],c='black',ls='dashed')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_tick_params(size=tick_size,labelsize=fs, direction='out')\n",
    "ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%g'))\n",
    "\n",
    "plt.ylabel('positive predictive value')\n",
    "plt.xlabel('connection probability')\n",
    "fig.set_figheight(2)\n",
    "fig.set_figwidth(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def data_for_cylinder_along_z(center_x,center_y,radius,height_z):\n",
    "    z = np.linspace(0, height_z, 50)\n",
    "    theta = np.linspace(0, 2*np.pi, 50)\n",
    "    theta_grid, z_grid=np.meshgrid(theta, z)\n",
    "    x_grid = radius*np.cos(theta_grid) + center_x\n",
    "    y_grid = radius*np.sin(theta_grid) + center_y\n",
    "    return x_grid,y_grid,z_grid\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "center_x, center_y = 0, 0\n",
    "r_step=6\n",
    "h=12\n",
    "\n",
    "rs=25\n",
    "\n",
    "Xc,Yc,Zc = data_for_cylinder_along_z(center_x,center_y,r_step*1,h)\n",
    "ax.plot_wireframe(Xc, Yc, Zc, alpha=0.5,color='black',rstride=rs, cstride=0)\n",
    "ax.plot([0,0],[r_step*1,r_step*1],[0,h],c='black')\n",
    "ax.plot([0,0],[r_step*-1,r_step*-1],[0,h],c='black')\n",
    "Xc,Yc,Zc = data_for_cylinder_along_z(center_x,center_y,r_step*2,h)\n",
    "ax.plot_wireframe(Xc, Yc, Zc, alpha=0.5,color='red',rstride=rs, cstride=0)\n",
    "ax.plot([0,0],[r_step*2,r_step*2],[0,h],c='red')\n",
    "ax.plot([0,0],[r_step*-2,r_step*-2],[0,h],c='red')\n",
    "\n",
    "Xc,Yc,Zc = data_for_cylinder_along_z(center_x,center_y,r_step*3,h)\n",
    "ax.plot_wireframe(Xc, Yc, Zc, alpha=0.5,color='blue',rstride=rs, cstride=0)\n",
    "ax.plot([0,0],[r_step*3,r_step*3],[0,h],c='blue')\n",
    "ax.plot([0,0],[r_step*-3,r_step*-3],[0,h],c='blue')\n",
    "\n",
    "Xc,Yc,Zc = data_for_cylinder_along_z(center_x,center_y,r_step*4,h)\n",
    "ax.plot_wireframe(Xc, Yc, Zc, alpha=0.5,color='green',rstride=rs, cstride=0)\n",
    "ax.plot([0,0],[r_step*4,r_step*4],[0,h],c='green')\n",
    "ax.plot([0,0],[r_step*-4,r_step*-4],[0,h],c='green')\n",
    "\n",
    "ax.set_ylabel('lateral offset (' r'$\\mu$'+'m)',fontsize=10,fontname='arial')\n",
    "ax.set_zlabel('axial offset (' r'$\\mu$'+'m)',fontsize=10,fontname='arial')\n",
    "ax.set_zticks([0,h/2,h])\n",
    "\n",
    "\n",
    "ax.view_init(15,0)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uXie3uHmd3PL/+fxEtq/55y0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=4\n",
    "colors=['black', 'red', 'blue', 'green']\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(n):\n",
    "    c=colors[i]\n",
    "    inner=r_step*i\n",
    "    outer=r_step*(i+1)\n",
    "    sa=math.pi*outer**2-math.pi*inner**2\n",
    "    v=sa*r_step \n",
    "    plt.scatter(outer,v,c=c)\n",
    "    \n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "x_lim=ax.get_xlim()\n",
    "y_lim=ax.get_ylim()\n",
    "ax.set_xlim([0,x_lim[1]])\n",
    "ax.set_ylim([0,y_lim[1]])\n",
    "ax.set_xlabel('lateral offset (' r'$\\mu$'+'m)', fontsize=10, fontname='arial')\n",
    "ax.set_ylabel('volume  (' r'$\\mu$'+'m'+'$^{3}$)', fontsize=10, fontname='arial')\n",
    "ax.tick_params(axis='both', which='both', labelsize=10, direction='out')\n",
    "fig.set_figwidth(2)\n",
    "fig.set_figheight(2)\n",
    "#plt.savefig('volume_vs_lateral_r.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}