{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_folder = 'output_control'\n",
    "nmda = h5py.File('./{}/inmda_report.h5'.format(output_folder),'r')\n",
    "v = h5py.File('./{}/v_report.h5'.format(output_folder),'r')\n",
    "hva = h5py.File('./{}/Ca_HVA.ica_report.h5'.format(output_folder),'r')\n",
    "lva = h5py.File('./{}/Ca_LVAst.ica_report.h5'.format(output_folder),'r')\n",
    "ih = h5py.File('./{}/Ih.ihcn_report.h5'.format(output_folder),'r')\n",
    "na = h5py.File('./{}/NaTa_t.gNaTa_t_report.h5'.format(output_folder),'r')\n",
    "\n",
    "spks = h5py.File('./{}/spikes.h5'.format(output_folder),'r')\n",
    "spktimes = spks['spikes']['biophysical']['timestamps'][:]\n",
    "spkinds = np.sort((spktimes*10).astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs = pd.read_csv('/Volumes/TOSHIBA EXT/L5NeuronSimulation_new/L5NeuronSimulation/MorphAnalysis/Segments.csv')\n",
    "segs_degrees = pd.read_csv('SegmentsDegrees.csv').groupby(['Type','Sec ID'])['Degrees'].max().reset_index()\n",
    "segs['segmentID'] = segs.index\n",
    "segs = segs.set_index(['Type','Sec ID']).join(segs_degrees.set_index(['Type','Sec ID'])).reset_index()\n",
    "\n",
    "segs['Sec ID'] = segs['Sec ID'].astype(int)\n",
    "segs['X'] = segs['X'].astype(float)\n",
    "segs['Elec_distanceQ'] = 'None'\n",
    "\n",
    "segs.loc[segs.Type=='dend','Elec_distanceQ'] = pd.qcut(segs.loc[segs.Type=='dend','Elec_distance'], 10, labels=False)\n",
    "segs.loc[segs.Type=='apic','Elec_distanceQ'] = pd.qcut(segs.loc[segs.Type=='apic','Elec_distance'], 10, labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_df = pd.read_csv('na_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.random.choice(na_df[(na_df.na_lower_bound>20) & (na_df.na_lower_bound<1400000)].index,10000):\n",
    "    seg = na_df.loc[i,'segmentID']\n",
    "    if not pd.isnull(na_df.loc[i,'na_lower_bound']):\n",
    "        spkt = int(na_df.loc[i,'na_lower_bound'])\n",
    "        trace = na['report']['biophysical']['data'][spkt-10:spkt+10,seg]\n",
    "        peak_value = np.max(trace)\n",
    "        half_peak = peak_value/2\n",
    "        duration = np.arange(0,20)[trace>half_peak] + spkt - 10\n",
    "        na_df.loc[i,'duration_low'] = duration[0]\n",
    "        na_df.loc[i,'duration_high'] = duration[-1]\n",
    "        na_df.loc[i,'peak_value'] = peak_value\n",
    "    else:\n",
    "        na_df.loc[i,'duration_low'] = np.nan\n",
    "        na_df.loc[i,'duration_high'] = np.nan\n",
    "        na_df.loc[i,'peak_value'] = np.nan\n",
    "        \n",
    "na_df['duration'] = (na_df['duration_high'] - na_df['duration_low'] + 1)/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sns.jointplot(data=na_df[~pd.isnull(na_df.duration_low)], x=\"duration\", y=\"peak_value\",color='red')\n",
    "h.set_axis_labels('duration (ms)', 'peak value (mS/cm^2)', fontsize=16)\n",
    "h.ax_marg_y.set_ylim(0, 0.02)\n",
    "plt.savefig('na_df.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_df_bin = na_df[~pd.isnull(na_df.duration_low)].reset_index(drop=True)\n",
    "\n",
    "na_df_bin['duration_bin'] = pd.cut(na_df_bin['duration'], bins = np.arange(0.15,1.05,0.1), labels=False)\n",
    "na_df_bin['mag_bin'] = pd.cut(na_df_bin['peak_value'], bins = 2*np.logspace(-3,-2,num=15), labels=False)\n",
    "\n",
    "na_df_gb = na_df_bin.groupby(['duration_bin','mag_bin'])['duration'].count().reset_index()\n",
    "\n",
    "na_df_imhist = np.zeros((15,15))\n",
    "for i in np.arange(0,15):\n",
    "    for j in np.arange(0,15):\n",
    "        try:\n",
    "            na_df_imhist[i,j] = na_df_gb[(na_df_gb.duration_bin==j) & (na_df_gb.mag_bin==i)]['duration']\n",
    "        except:\n",
    "            na_df_imhist[i,j] = 0\n",
    "            \n",
    "plt.imshow(100 * na_df_imhist / na_df_imhist.sum(), origin = 'lower')\n",
    "plt.xlabel('duration (ms)', fontsize = 16)\n",
    "plt.ylabel('magnitude (mS/cm^2)', fontsize = 16)\n",
    "plt.xticks(ticks = [0,4,8,12], labels = [0.15, 0.55, 0.95, 1.35])\n",
    "#plt.yticks(ticks = [0,4,8,12], labels = [2e-3, 0.00386, 0.00746, 0.0144])\n",
    "plt.colorbar(label='% of events')\n",
    "plt.savefig('naspkdurations.svg')\n",
    "#plt.xlim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_na_df = na_df.groupby('segmentID')['na_lower_bound'].count().reset_index().rename(columns={'na_lower_bound':'num_na_spikes'})\n",
    "segs_na_df = segs.set_index('segmentID').join(seg_na_df.set_index('segmentID'))\n",
    "segs_na_df.loc[segs_na_df.num_na_spikes>1000,'num_na_spikes'] = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_field = 'num_na_spikes'\n",
    "\n",
    "ax = plt.figure(figsize=(2,5))\n",
    "import matplotlib\n",
    "from matplotlib.colors import Normalize\n",
    "cmap = matplotlib.cm.get_cmap('jet')\n",
    "norm = matplotlib.colors.Normalize(vmin = 0, vmax = 5)\n",
    "\n",
    "for i in segs_na_df[segs_na_df.Type=='apic']['Sec ID'].unique():\n",
    "    section = segs_na_df[(segs_na_df.Type=='apic')&(segs_na_df['Sec ID']==i)]\n",
    "    for j in section.index.tolist()[:-1]:\n",
    "        plt.plot(section.loc[j:j+1,'Coord X'],\n",
    "                 section.loc[j:j+1,'Coord Y'],\n",
    "             color=cmap(norm(section.loc[j:j+1,color_field].mean()/150)),\n",
    "             linewidth = 1*section.loc[j:j+1,'Section_diam'].unique())\n",
    "    \n",
    "for i in segs_na_df[segs_na_df.Type=='dend']['Sec ID'].unique():\n",
    "    section = segs_na_df[(segs_na_df.Type=='dend')&(segs_na_df['Sec ID']==i)]\n",
    "    for j in section.index.tolist()[:-1]:\n",
    "        plt.plot(section.loc[j:j+1,'Coord X'],\n",
    "                 section.loc[j:j+1,'Coord Y'],\n",
    "             color=cmap(norm(section.loc[j:j+1,color_field].mean()/150)),\n",
    "             linewidth = 1*section.loc[j:j+1,'Section_diam'].unique())\n",
    "        \n",
    "plt.scatter(segs_na_df[(segs_na_df.Type=='soma')&(segs_na_df['Sec ID']==0)].loc[2,'Coord X'],\n",
    "         segs_na_df[(segs_na_df.Type=='soma')&(segs_na_df['Sec ID']==0)].loc[2,'Coord Y'],color='k',s=100)\n",
    "plt.vlines(110,400,500)\n",
    "plt.text(0,450,'100 um')\n",
    "plt.hlines(400,110,210)\n",
    "plt.text(110,350,'100 um')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "cbar = plt.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap))\n",
    "#cbar.ax.set_ylabel('log(elec_distance)', rotation=270)\n",
    "\n",
    "#ax2.ax.set_title('log(elec_distance)',rotation=270)\n",
    "plt.box(False)\n",
    "plt.savefig('na_spk_segments.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NMDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmda_df = pd.read_csv('nmda_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmda_df['duration'] = (nmda_df['nmda_upper_bound'] - nmda_df['nmda_lower_bound'])/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmda_df['log_duration'] = np.log(nmda_df['duration'])\n",
    "nmda_df['log_mag'] = np.log(np.abs(nmda_df['mag']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sns.jointplot(data=nmda_df[(nmda_df.mag<-0.1)&\n",
    "                               (nmda_df.duration<250)&\n",
    "                               (nmda_df.duration>26)], x=\"duration\", y=\"mag\",alpha=0.02, color='blue')\n",
    "h.set_axis_labels('duration (ms)', 'magnitude (nA ms)', fontsize=16)\n",
    "plt.savefig('nmda_df.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmda_df_bin = nmda_df[(nmda_df.mag<-0.1)&(nmda_df.duration<250)&(nmda_df.duration>26)].copy()\n",
    "\n",
    "nmda_df_bin['duration_bin'] = pd.cut(nmda_df_bin['duration'], bins = 2*np.logspace(1.1,1.8,num=15), labels=False)\n",
    "nmda_df_bin['mag_bin'] = pd.cut(-nmda_df_bin['mag'], bins = np.logspace(-1,0.5,num=15), labels=False)\n",
    "\n",
    "nmda_df_gb = nmda_df_bin.groupby(['duration_bin','mag_bin'])['duration'].count().reset_index()\n",
    "\n",
    "nmda_df_imhist = np.zeros((15,15))\n",
    "for i in np.arange(0,15):\n",
    "    for j in np.arange(0,15):\n",
    "        try:\n",
    "            nmda_df_imhist[i,j] = nmda_df_gb[(nmda_df_gb.duration_bin==j) & (nmda_df_gb.mag_bin==i)]['duration']\n",
    "        except:\n",
    "            nmda_df_imhist[i,j] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(100 * nmda_df_imhist / nmda_df_imhist.sum(), origin = 'lower')\n",
    "plt.xlabel('duration (ms)', fontsize = 16)\n",
    "plt.ylabel('magnitude (nA ms)', fontsize = 16)\n",
    "plt.xticks(ticks = [0,5,10,14], labels = [25, 45, 80, 126])\n",
    "plt.yticks(ticks = [0,4,8,12], labels = [0.10, 0.27, 0.72, 1.93])\n",
    "plt.colorbar(label='% of events')\n",
    "plt.savefig('nmdaspkdurations.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_nmda_df = nmda_df.groupby('segmentID')['nmda_lower_bound'].count().reset_index().rename(columns={'nmda_lower_bound':'num_nmda_spikes'})\n",
    "\n",
    "segs_nmda_df = segs.set_index('segmentID').join(seg_nmda_df.set_index('segmentID'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_field = 'num_nmda_spikes'\n",
    "\n",
    "ax = plt.figure(figsize=(2,5))\n",
    "import matplotlib\n",
    "from matplotlib.colors import Normalize\n",
    "cmap = matplotlib.cm.get_cmap('jet')\n",
    "norm = matplotlib.colors.Normalize(vmin = 0, vmax = 10)\n",
    "\n",
    "for i in segs_nmda_df[segs_nmda_df.Type=='apic']['Sec ID'].unique():\n",
    "    section = segs_nmda_df[(segs_nmda_df.Type=='apic')&(segs_nmda_df['Sec ID']==i)]\n",
    "    for j in section.index.tolist()[:-1]:\n",
    "        plt.plot(section.loc[j:j+1,'Coord X'],\n",
    "                 section.loc[j:j+1,'Coord Y'],\n",
    "             color=cmap(norm(section.loc[j:j+1,color_field].mean()/150)),\n",
    "             linewidth = 1*section.loc[j:j+1,'Section_diam'].unique())\n",
    "    \n",
    "for i in segs_nmda_df[segs_nmda_df.Type=='dend']['Sec ID'].unique():\n",
    "    section = segs_nmda_df[(segs_nmda_df.Type=='dend')&(segs_nmda_df['Sec ID']==i)]\n",
    "    for j in section.index.tolist()[:-1]:\n",
    "        plt.plot(section.loc[j:j+1,'Coord X'],\n",
    "                 section.loc[j:j+1,'Coord Y'],\n",
    "             color=cmap(norm(section.loc[j:j+1,color_field].mean()/150)),\n",
    "             linewidth = 1*section.loc[j:j+1,'Section_diam'].unique())\n",
    "        \n",
    "plt.scatter(segs_nmda_df[(segs_nmda_df.Type=='soma')&(segs_nmda_df['Sec ID']==0)].loc[2,'Coord X'],\n",
    "         segs_nmda_df[(segs_nmda_df.Type=='soma')&(segs_nmda_df['Sec ID']==0)].loc[2,'Coord Y'],color='k',s=100)\n",
    "plt.vlines(110,400,500)\n",
    "plt.text(0,450,'100 um')\n",
    "plt.hlines(400,110,210)\n",
    "plt.text(110,350,'100 um')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "cbar = plt.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap))\n",
    "#cbar.ax.set_ylabel('log(elec_distance)', rotation=270)\n",
    "\n",
    "#ax2.ax.set_title('log(elec_distance)',rotation=270)\n",
    "plt.box(False)\n",
    "plt.savefig('nmda_spk_segments.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df = pd.read_csv('ca_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df['dist_from_soma_spike'] = ca_df['ca_lower_bound'].apply(lambda x: np.min(np.abs(x-spkinds)))\n",
    "ca_df['duration'] = (ca_df['ca_upper_bound'] - ca_df['ca_lower_bound'])/10\n",
    "ca_df['mag_dur'] = ca_df['mag']/ca_df['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df = ca_df[(ca_df.mag<-0.1)&\n",
    "                           (ca_df.duration<250)&\n",
    "                           (ca_df.duration>26)&\n",
    "                           (ca_df.dist_from_soma_spike>50)&\n",
    "                           (ca_df.mag_dur<-0.006)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df_bin = ca_df[(ca_df.mag<-0.1)&\n",
    "                           (ca_df.duration<250)&\n",
    "                           (ca_df.duration>26)&\n",
    "                           (ca_df.dist_from_soma_spike>50)&\n",
    "                           (ca_df.mag_dur<-0.006)].copy()\n",
    "\n",
    "ca_df_bin['duration_bin'] = pd.cut(ca_df_bin['duration'], bins = 2*np.logspace(1.1,1.5,num=15), labels=False)\n",
    "ca_df_bin['mag_bin'] = pd.cut(-ca_df_bin['mag'], bins = np.linspace(0.1,1.4,num=15), labels=False)\n",
    "\n",
    "ca_df_gb = ca_df_bin.groupby(['duration_bin','mag_bin'])['duration'].count().reset_index()\n",
    "\n",
    "ca_df_imhist = np.zeros((15,15))\n",
    "for i in np.arange(0,15):\n",
    "    for j in np.arange(0,15):\n",
    "        try:\n",
    "            ca_df_imhist[i,j] = ca_df_gb[(ca_df_gb.duration_bin==j) & (ca_df_gb.mag_bin==i)]['duration']\n",
    "        except:\n",
    "            ca_df_imhist[i,j] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1719bCOElbfo/1faeRa8d6QkEtwDbSdom63KYxpor1P4deAeApFeRuiOesv1W21NsTwF+ABxfKbiGEDrQ4ESDPNsIk/RySWdKujJ7/eoss1ZNIxpgbfcBnwauAuaQRgvcI+k4SYNzfY8E/kPSHcDPSWkS2/R3WwihUdKyMbW3FjiHFLMGl8a6Hzgiz4UjPpMrW2TsirJ9R5f8fC9p2Ydq9zimKZULIbRO+44imGz7IklHQWooSso1XCumyoYQ2oLa93vq85I2IusllvRGYHGeC+vqIpA0StKOkt6eDZsKIYThy5sLtkYQlrRlNhN0jqR7JB1e4RxJOjmbTXqnpNfXqN2RpGdF20r6X+A84DN53lY9q8p+ipSScKNs1y7AbdlkhGtsnzzkxS2kceMYs/mWtU8s8/i+WxQvc6D4r+JFOxT7mrT+vOJlLt62WJnjFhdPqbfu/GJlTpxfPBXf2o8VS5GolbkSJ1VU9G/FfcMos7+uyUZtomEPsPqAI23fJmld4FZJV5fNFt2HlHZ1O9LopNNZczz+KrZvlfR2Uk5sAfflzU2QqwUr6T+Ak0gryx7C6uNZrwfek+c+IYQwpAa0YG0vGJwAYHsJ6WH65mWnHQCc5+RGYANJmw51z+yB+xeBF23fXU/il7xdBJ8Dvm97BvDrsmNzSZE9hBCKG8i55SRpCvA64KayQ5VmlJYH4VL7k1rGF0m6RdLnJW2Vpw55A+w2pGEKlTxPGvgfQgjF1DcOdrKk2SXbjPLbSVqHNM3/CNvPlR8eogaVq2Y/Yvs7tt8AfAB4LfBQnrdVz6qyU4Y4tj3wWM77hBBCRXWMIlhoe+ehDkoaSwquF9i+pMIpeWaUlt9zCvA+UhdpP6nLoKa8LdjLgKOz1RQHWdJkUhqvS3PeJ4QQKmvMKAKR0gvOsX3CEKfNAj6SjSZ4Iylb1pCLp0m6iZToZTTwXtu72v5+nrdUz6KHewF3k/ozDJwM7AA8CRyX8z4hhNBMbwY+DNwl6fZs31dI+aqx/SPSRKd9gXnAC8DHatzzo7bnFqlM3kUPn5a0M2l62LuAv2XXnkLKcFXexxFCCHVpxEQD2zdQI+9hNvX+U3XcdpGkM4HNbO+TLQSwu+0za11YT7rCJcA3si2EEBrHtPNU2XNIq7X8V/b6ftIiAzUDbCzHHUJoDw3og22SybYvIhskliWtGl4uAknX1FEB235HHeeHEMJqujEXQbUuglGs/vtie2AT4GHgH8DLSUO3FgD31VvjEEJYTfsG2M+xei6CjYGD81xYbdHDPQZ/zhYbPAl4o+2bS/bvRuqLOKlQtUMIYVCbBtgsr0HzchGQHmx9rTS4ZgXfRFqk8Jv5qxtCCKuT82+tYLvP9j315iLIO4pgO+CpIY49Cbwyb4EjbfnksfztY/Vnxvr2B84rXOZfX9i68LV/XFAsrcOiV61VuMyB+9erfVIFay0sXCRrP/5ioevGLFxauEytyP3/YnXLVxQus2hWrOFkxNLoYlnOhpPBqyHadxRBYXlbsA8BHx/i2MdJ/bIhhFBYu7VgJb05+3N80XvkbcEeC1wg6W7gYl56yHUwaTbXB4tWIIQQgHbsgz0ZeAPwF6BWUu6K8s7kulDSQlKgPQoYC6wkrRL7Ltt/LFJ4CCEA0ML+1SpWSjob2FzSGgsK2D6s1g3qmcn1B+APkkYBk0kZbYqliA8hhHLtF2D3A/Ym5WG5tcgN6l70MAuqTxYpLIQQhqI2a67ZXghcKGmO7TuK3CNXgJV0dI1TbDtyFIQQutHTkn5NytRl4AbgcNvza12YtwV7TJVjgw37CLAhhOLar4tg0NnAz4D3Zq8/lO17Z60Lcw3Tsj2qfCOtLjudlCO2bcfBhhA6QHtPNHiZ7bOzyQZ9ts8hTZetqXA2LduLbJ9HSuV1atH7hBAC0M7ZtJ6S9CFJo7PtQ8DTeS5sRLrCO4C3NeA+IYRe1r4B9t9I63E9QUpudXC2r6a6RxFUsB9DT6MNIYSaRPuNIhhk+++kpbvrlncUwVkVdo8DdgReA3y9SOEhhAC060SDYcvbgt2LNRvnLwKPAD8Azm1kpUIIPahXA6ztKU2uRwih1zUowGbfuPcDnrS9Y4XjewC/ISWxArjEdlNWxs7bRfAR4Le213hyJmkSsF82oqDteKxZvkX96eYeXP6yJtSmtrXGFkup9/iCXKNGKpq4qFiauEm3P1O4zIEJYwtd58eeKFwma00oVmYL0hUOx3BSHbZSA7sIziGteF0tJl1ve796bpotFXM8MB74ru1La12TdxTB2cC2QxzbJjseQgjFNWgUge3rgOK//TOSNinb9TnSw66p5JxYlbcPtloTZyLQ4ky9IYSO5rpGEUyWNLvk9UzbM+sscXdJdwCPA5+3fU+Fc34k6VZSa/VF4FngA6TVZZ/LU0i1VWV3YvUciO+WVN6fsRYwDXggT2EhhDCk/F0EC23vPIySbgO2tr1U0r7ApaRVW1avjn2gpHcDl0s6FziCFGDXBg7MU1C1FuwBvDT8ysB/DXHe08CheQoLIYShjNQwLdvPlfx8haTTJE3OsmeVn3uZpCuA/wQuAb5l+/q8ZVXrg/0BqX/1FaQugn/NXpdum5Hm6c7KW2AIIVQ0QjO5JG0iSdnPu5LiYKUH+PtLugG4hpRzZRpwkKSfSxrqmdRqqi3bvRhYnBW0DbDAdvHHqSGEMJQGToOV9HNgD1Jf7XzSN/GxALZ/RJrq+klJfcAyYJrtSqV/E9id1BV6he1dgc9J2g74FingVpV3HOwjec4LIYQiROO6CGy/v8bxU0jDuGpZTAqia1GyyIDtB8gRXKFKF4Gk/qz5jKSB7PVQW4wiCCEMSxumKzyI9ECrj/Rwq27VWrDHAfNLfu7CiWwhhLbRZhEme+j1w+Hco1of7LElPx8znEJCCKGmNguwjdCIdIUhhDA8PZ5NC0mvICWd3Qoon9Rt2zEWNoRQXK8GWEkHAL8kPRR7ElhedkoXfjQhhJHUrgm3hyNvC/abwLXAB2131OoFGm3GrVP/8N3PTXqwcJnHL9y+8LV/u2ezQtdNuqv46j/rPF4sg5deLHYdwJjnXih0Xf+K4mVqbLEeMS9bVrjMlqg4pLP99XIXwSuAIzstuIYQOkTr1ttqqrwBdi5pme4QQmiOLgyweb9XfhH4SvagK4QQGmpwJlebTTQYtrwB9hhSC3aOpLslXVe2/U/eAiVNlXSfpHmSvlzlvIMlWdLO2esPSrq9ZBvIUiqGELqABpxr6yR5uwj6gfuGW5ik0cCpwDtJs8RukTTL9r1l560LHAbcNLjP9gXABdnx1wC/sX37cOsUQmgDvdwHa3uPBpW3KzDP9oMAki4k5Z29t+y8bwDfAT4/xH3eD/y8QXUKIbSBTvv6n0fxsT3FbA48WvJ6frZvFUmvA7a0fXmV+xxCBNgQussI5YMdSXknGrytyuEBUlqvubZrDVKstLbXqo9M0ijgRGB6lbrsBrxg++4q58wAZgCMmbx+jSqFENpBN7Zg8/bBXkvt3x0vSDrZ9lBLy0BqsW5Z8noL0qJjg9YFdgSuzRKObwLMkrS/7cFFzqZRo/WaLYA2E2DCtpt34V9bCF2oC/+n5g2wB5DSdt0BXAz8A3g5KTfBa4GvAbsBX5S0yPb3hrjPLcB22QoJj5GC5ao8i9kqCpMHX0u6lrTi4+zs9SjgvUC1FnUIodPUt6psx8gbYA8Efmf7E2X7z5f0Y2BP2x+T1E9aALFigLXdJ+nTwFXAaOAs2/dIOg6YnWNtr7cB8wcfkoUQukMjVzRoJ3kD7EGkB0uVXAz8Ivv5d2R9n0OxfQVwRdm+o4c4d4+y19cCb6xZ2xBC5+nQHArV5B1FMBoYahXFV2bHIWXZKs+0FUIINTVqJpeksyQ9Kanig3AlJ2eTne6U9PpGv5dBeQPsFcDxkt6TTRZA0mhJB5NWV/xtdt4/AX9rfDVDCF0t7xCtfI3cc4CpVY7vA2yXbTOA04tVura8XQSfAX5NygnbJ2kRsGF2/Q3ZcUjDtY5vdCWHY9TSUax1wzp1X7fnpAMKl/nwvJcXvnaDuaNrn1TBOvOLp/Fbe94aS8LnohdeLFzmwKJnC19buMzni6UddH9/g2sSKmnUQy7b10maUuWUA4DzsqW6b5S0gaRNbS9oTA1ekncm10LgrZL+hTRaYFNgAXCj7atLzju30RUMIfSGOgLsZEmzS17PzIZm5jXUhKfWBNhBtn8P/L7RlQgh9DhTz0OuhbZ3HkZpVSc8NVIsehhCaAsjOEyr1oSnhsmdi0DSDEl/lfSCpP7yrRmVCyH0kJHLRTAL+Eg2muCNwOJm9L9C/lwEHyHN5DoX+GfgLGAssD/wFFkawRBCKKKREw0k/RzYg9RXOx/4OileYftHpFFR+wLzgBeAjzWm5DXl7SI4Avh/pDSC/w6cZvs2SRuS8hQUewwdQggAblwybdvvr3HcwKcaUlgNebsItgOuI2XOGgDGAdheRBoHe3hTahdC6B1dmK4wb4BdBozKIv8TpFVmBy0Fiq01HUIImW5ckytvF8FdpCmxfwCuJy2A+BDQR1qva25TahdC6A0GOmy9rTzyBtiZvNRq/Rop0N6QvV5CyrYVQgjFdV98zT2T6xclP8+T9E/A7sDawJ+zmV4hhFBYp339z6PQRAPbz5NasSGE0BCdtiR3HnUFWElbkmZATCg/ZvuaRlUqhNBjOnCEQB55Jxq8gjSZYNfBXdmfzn42L+WEbStjlpnJd9ef9emhbTYtXOaW1xVPCzR+UbGMT+MeXVS4zIGnCg5jHlN8prVX9hW+tnCZfcUzjoXmShMNui/C5v0fcgawFWnCwVxgRdNqFELoTT28JtcuwHTbv2pmZUIIvauXW7DziVZrCKFZurQPNu9MruOBL0ma2MzKhBB6VcpFkGfrJHnHwZ4vaQfgYUk3AuVPVGz7ow2vXQihd/RqF4Gk6cBRQD/wetbsLui+TyaEMHLcuDW52knePthjSYseHmp75FerCyF0v15twQIbkXLARnANITRH98XX3A+5bgBe1cyKhBB6mwYGcm2dJG8L9nDgIkmLgN+x5kMubHfWOw8htA/T0xMN5mR/njfEcddxrxBCWI1wT080OI6u7CEJIbSNBgVYSVOBk0j5Uc6w/e2y49OB7wKPZbtOsX1GQwovk3cc7DHNKDyEEFZpQICVNBo4FXgnaQbqLZJm2b637NRf2P70sAusIe9DrhBCaJ7BPtg8W3W7AvNsP2h7BXAhcEBzKl1b1/ebakU/4x5bXPd1k+5+WeEy17m3+Crm6usvdF3hlIPAwPMvFLpOo1T7pCG4v9j7HJYu7OPrJg0aIbA58GjJ6/nAbhXOe4+ktwH3A5+1/WiFc4YtWrAhhDbg9AswzwaTJc0u2WaU3KjSb/3y36yXAVNsv5a0Msu5zXlPPdCCDSF0AFPPN4yFtnce4th80qorg7YAHl+tKLv0695PgP/OW3C9ogUbQmgPjemDvQXYTtI2ksYB04BZpSdIKl2uZH9eGobacNGCDSG0hUaMg7XdJ+nTwFWkYVpn2b5H0nHAbNuzgMMk7Q/0Ac8A04dd8BAiwIYQ2kODHkLavgK4omzf0SU/H0XKDth0EWBDCK1nQ3/3zZWNABtCaA9dOIwuAmwIoT1EgA0hhCYw0GHrbeURATaE0AYMXZjxNAJsCKH1TDzkCiGEpok+2BBCaJIIsB2orw+eXFj3ZRv/dknhIvuffKrwtZ30j6wLu8xCy7ij/u3n1f0BNoTQ/gx02IKGeUSADSG0h2jBhhBCM3TnVNm2SVcoaaqk+yTNk/TlCsffJuk2SX2SDm5FHUMITWKwB3JtnaQtAmzJQmX7AK8G3i/p1WWn/Z2UVuxnI1u7EMKIGHC+rYO0SxfBqoXKACQNLlS2aiVI2w9nxzrrV1gIIZ/og22avAuVhRC6kR2jCJooz0Jl+W+WFkGbATBh1MSitwkhjKRowTZNzYXK6mF7JjATYP0xG3ff31oIXcetWcq9ydriIRc5FioLIXSxwXSFXfaQqy0CrO0+YHChsjnARYMLlWWLkyFpF0nzgfcCP5Z0T+tqHEJoOA/k2zpIu3QR5Fmo7BZS10EIocsYcINap5KmAieRVpU9w/a3y2hn2xMAAAmFSURBVI6PB84D3gA8DRwyOEqp0dqiBRtC6HF2Q1qwOcfUHwossv1K4ETgv5vwjoAIsCGENuH+/lxbDavG1NteAQyOqS91AHBu9vPFwDskVRrJNGxt00XQLM/1L1x41aIzHxni8GSg/lyGvSM+n9p65TOq9j63Hu7Nl7Doqj/44sk5T58gaXbJ65nZyCHIN6Z+1Tm2+yQtBjaiCX+PXR9gbW881DFJs23vPJL16STx+dTWK59Rs9+n7akNulWeMfUNHXdfTXQRhBC6SZ4x9avOkTQGWB94phmViQAbQugmecbUzwI+mv18MHCN3ZxpZF3fRVDDzNqn9LT4fGrrlc+oI95n1qc6OKZ+NHDW4Jh6YLbtWcCZwPmS5pFartOaVR81KXCHEELPiy6CEEJokgiwIYTQJD0RYCVtKelPkuZIukfS4dn+YyQ9Jun2bNu31XVtFUkTJN0s6Y7sMzo223+OpIdKPqOdWl3XVpI0WtJfJV2evd4rW8robknnZk+lO5qkDSRdLGlu9n9md0nfkHRn9m/g95I2a3U9O0FP9MFK2hTY1PZtktYFbgUOBN4HLLX9vZZWsA1kM1km2l4qaSxwA3A48AngctsXt7SCbULS54CdgfWA/YFHgHfYvj97kPKI7TNbWcfhknQucL3tM7In8WsDA7afy44fBrza9idaWc9O0BMtWNsLbN+W/byElLFr89bWqr04WZq9HJtt3f/btw6StgD+L3BGtmsjYLnt+7PXVwPvaUXdGkXSesDbSE/asb3C9rODwTUzkfi3kUtPBNhSkqYArwNuynZ9Ovvqc5akDVtWsTaQff29HXgSuNr24Gf0rewzOjHLRNSrfgB8ERjMOLIQGCtpcIbTwaw+yL0TvQJ4Cjg76wo5Q9JEAEnfkvQo8EHg6Go3CUlPBVhJ6wC/Ao7IfiOfDmwL7AQsAL7fwuq1nO1+2zuRZr/sKmlH4ChgB2AXYBLwpRZWsWUk7Qc8afvWwX3Z4PRpwImSbgaWAH0tqmKjjAFeD5xu+3XA88CXAWz/l+0tgQtI+ZtDDT0TYLN+xV8BF9i+BMD2P7KgMgD8hJSJp+fZfha4Fpiada/Y9nLgbHr3M3ozsL+kh0kZmvaS9FPbf7H9Vtu7AtcBD7Sykg0wH5hf8u3lYlLALfUzOrwrZKT0RIDNHuCcCcyxfULJ/k1LTjsIuHuk69YuJG0saYPs57WAvYG5g59R9hkeSI9+RraPsr2F7SmkVus1tj8k6WWwKonzl4AftbCaw2b7CeBRSdtnu94B3Ctpu5LT9gfmjnjlOlDHDynJ6c3Ah4G7sj5GgK+QkvHuROqwfxj4eGuq1xY2Bc7NEhaPIi3bc7mkayRtTMpAdDtpVEF4yRey7oNRpK/V17S6Qg3wGeCCbATBg8DHgDOyoDtAGjkR/w5y6IlhWiGE0Ao90UUQQgitEAE2hBCaJAJsCCE0SQTYEEJokgiwIYTQJBFgu5CkayVd26Ky98iylI0q2z9FkiVNb1G9fijpshEs7yBJT2SzB0OPigAbGm0P4Ous+W9rAbA78NuRrpCkbUljnI8dwWIvBZ4AvjCCZYY2EwE2VJUlgBn2hBTby23faPupRtSrTkcAd9iePVIFZnkKZpKSCU0YqXJDe4kA2+EkTcsSIy/PEmUfVOGc6dnX8yll+4+R5LJ9zrImfVnSQ8AK4DVZQu4Ts8TSS7Ovv5dJ2qH0fqTWK8DK7F7OjlXsIpD0oSzJ94uSFko6v2wKM5IelvTT7L3OkfS8pNmS3pLj8xkPfIg0f750/x5ZfQ6U9GNJz0halL3H0ZJ2kXRDVtY9kt5Vdv0ukq6W9LSkFyQ9KOm0suIvAjYA/rVWPUN36pWpsl1J0t6kwPFb4EhgY+AkUi7X+4Zx6+mkKZKfJ2VTehwYD6wLfJP0dX8S8J/AjZJ2yOawn0HKxHUo8Bagv0b9ZwA/Bn5Bytq1GXA8sJuk15fkpwV4K7A98DXgReAbwOWSpmTJaYbyRlKQu36I4z8ALgEOIeVB/Srp/8XewHeBx7J9l0ja2vbCrF/1KuDm7LNaAkwB3lR64+zcOcBUygJ86BG2Y+vQDfhf4F5gVMm+3Ui5Fa4t2Tc92zel7PpjyL7NluwzKaCuVaPs0aRM90uAz5bfExhTdv6UbP/0kuv/Afyp7Ly3ZOcdVrLvYWARsGHJvp2z8z5Qo55fIs2fH1e2f4/s+rPK9t+W7X9Lyb7XZvs+Wlb2a3P8HZ0P3N/qfyuxtWaLLoIOlSVl2QW42CndIgBOaeYeHubtf2d7WYUy3yfpJknPkvKePg+sQ2pZ1mt74GWk3KKr2L6BlEzk7WXn/8X2opLXd2V/blWjnM2A52yvGOL4lWWv5wLPZ/Uo3QcvJdN+AHgW+HHWxVEtyfZTWR1CD4oA27kmk7oC/lHhWKV99VhQvkPSu0lf5ecAHyC1lHchBZAiD3EmDVUW6en7pLJ9z5S+cMpPS46yJwDLqxxfVPZ6BSl4lpY1GJwnZK8XA3uSWvqnAX/P+qYr5UhdlqOOoUtFgO1cC4GVwMsrHCvf92L257iy/RsNce9KKdamAfNsT7d9he2bgTtYMxDmNRgwN6lwbBPg6YL3Lfc00PClgGzfbvs9pPe/O/A34KJsFYhSk2jcewkdJgJsh7LdD9wCHFw6qF/SbqT+zlKPZH/uWHLeGOBf6ihybdZcDuXDpL7UUoOtxbVq3O8+Ukt7WulOSW8Ctgb+p466VTOXtG7WFg2632ps99m+kfTwbRTwqrJTtmF4DxxDB4tRBJ3t68DvgUsl/Zg0iuBY0lfsUreQWljfzYLxctIIgHoWMPwdcKCkE4HLgTcAh1H2dZr00A3gSElXAv2uMP7Udr+ko0n9mD8Ffkpa6fdbpD7Os+uoWzXXZX/uSloOZdiyBNszSJMJHiKtsnoY6YHfX0rOE6kb5fRGlBs6T7RgO5jtP5BW+NyeNNToC6RB9feVndcHHAA8CpwDnEpaYvqcOor7CSn4HQJcRlq++t3A4rLzLif1S/4nKdjcUqX+M0mt4NcAvwG+k9Xr7V59iFZhth8mDad6dyPul3mA1Lf6NdJDsrNJrft32i4N4m8idRFc2MCyQweJFQ1C18smN5wEbGr7hREs93RgR9tvHakyQ3uJABu6Xjak7S7SmNfvjVCZm5Ama0y1fV2t80N3ii6C0PWyB4L/BoxY65X0oPHICK69LVqwIYTQJNGCDSGEJokAG0IITRIBNoQQmiQCbAghNEkE2BBCaJL/D0cv40k1X5iCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(100 * ca_df_imhist / ca_df_imhist.sum(), origin = 'lower')\n",
    "plt.xlabel('duration (ms)', fontsize = 16)\n",
    "plt.ylabel('magnitude (nA ms)', fontsize = 16)\n",
    "plt.xticks(ticks = [0,5,10,14], labels = [25, 35, 49, 63])\n",
    "plt.yticks(ticks = [0,4,8,12], labels = [0.10, 0.47, 0.84, 1.21])\n",
    "plt.colorbar(label='% of events')\n",
    "plt.savefig('caspkdurations.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = sns.jointplot(data=ca_df, x=\"duration\", y=\"mag\",alpha=0.1, color='black')\n",
    "h.set_axis_labels('duration (ms)', 'magnitude (nA ms)', fontsize=16)\n",
    "plt.savefig('ca_df.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ca_df = ca_df[(ca_df.mag<-0.1)&\n",
    "                           (ca_df.duration<250)&\n",
    "                           (ca_df.duration>26)&\n",
    "                           (ca_df.dist_from_soma_spike>50)]\n",
    "\n",
    "seg_ca_df = ca_df.groupby('segmentID')['ca_lower_bound'].count().reset_index().rename(columns={'ca_lower_bound':'num_ca_spikes'})\n",
    "\n",
    "segs_ca_df = segs.set_index('segmentID').join(seg_ca_df.set_index('segmentID')).reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc79240f28>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "segs_ca_df['num_ca_spikes'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs_ca_df_plot = segs_ca_df.copy()\n",
    "#segs_ca_df_plot.loc[(segs_ca_df_plot['Coord Y']<630)|(segs_ca_df_plot['Coord Y']>830),'num_ca_spikes'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segs_ca_df[(segs_ca_df.HVA_gbar>0.0004)&(segs_ca_df.Type=='apic')]['Coord Y'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segs_ca_df[(segs_ca_df.HVA_gbar>0.0004)&(segs_ca_df.Type=='apic')]['Coord Y'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>segmentID</th>\n",
       "      <th>Type</th>\n",
       "      <th>Sec ID</th>\n",
       "      <th>BMTK ID</th>\n",
       "      <th>X</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Section_L</th>\n",
       "      <th>Section_diam</th>\n",
       "      <th>Coord X</th>\n",
       "      <th>Coord Y</th>\n",
       "      <th>Coord Z</th>\n",
       "      <th>Elec_distance</th>\n",
       "      <th>Elec_distance_nexus</th>\n",
       "      <th>HVA_gbar</th>\n",
       "      <th>Degrees</th>\n",
       "      <th>Elec_distanceQ</th>\n",
       "      <th>num_ca_spikes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>0</td>\n",
       "      <td>soma</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.316936</td>\n",
       "      <td>23.169362</td>\n",
       "      <td>17.526643</td>\n",
       "      <td>-9.249733</td>\n",
       "      <td>-0.577674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997705</td>\n",
       "      <td>0.198622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>1</td>\n",
       "      <td>soma</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.950809</td>\n",
       "      <td>23.169362</td>\n",
       "      <td>17.526643</td>\n",
       "      <td>-4.624868</td>\n",
       "      <td>-0.288877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999137</td>\n",
       "      <td>0.198907</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>2</td>\n",
       "      <td>soma</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.584681</td>\n",
       "      <td>23.169362</td>\n",
       "      <td>17.526643</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524</th>\n",
       "      <td>3</td>\n",
       "      <td>soma</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>16.218553</td>\n",
       "      <td>23.169362</td>\n",
       "      <td>17.526643</td>\n",
       "      <td>4.624854</td>\n",
       "      <td>0.288824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999154</td>\n",
       "      <td>0.198911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2525</th>\n",
       "      <td>4</td>\n",
       "      <td>soma</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>20.852426</td>\n",
       "      <td>23.169362</td>\n",
       "      <td>17.526643</td>\n",
       "      <td>9.249712</td>\n",
       "      <td>0.577722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.997674</td>\n",
       "      <td>0.198616</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      segmentID  Type  Sec ID  BMTK ID    X   Distance  Section_L  \\\n",
       "2521          0  soma       0        0  0.1   2.316936  23.169362   \n",
       "2522          1  soma       0        0  0.3   6.950809  23.169362   \n",
       "2523          2  soma       0        0  0.5  11.584681  23.169362   \n",
       "2524          3  soma       0        0  0.7  16.218553  23.169362   \n",
       "2525          4  soma       0        0  0.9  20.852426  23.169362   \n",
       "\n",
       "      Section_diam   Coord X   Coord Y  Coord Z  Elec_distance  \\\n",
       "2521     17.526643 -9.249733 -0.577674      0.0       0.997705   \n",
       "2522     17.526643 -4.624868 -0.288877      0.0       0.999137   \n",
       "2523     17.526643 -0.000010  0.000021      0.0       1.000000   \n",
       "2524     17.526643  4.624854  0.288824      0.0       0.999154   \n",
       "2525     17.526643  9.249712  0.577722      0.0       0.997674   \n",
       "\n",
       "      Elec_distance_nexus  HVA_gbar  Degrees Elec_distanceQ  num_ca_spikes  \n",
       "2521             0.198622       0.0        0           None            NaN  \n",
       "2522             0.198907       0.0        0           None            NaN  \n",
       "2523             0.199079       0.0        0           None            NaN  \n",
       "2524             0.198911       0.0        0           None            NaN  \n",
       "2525             0.198616       0.0        0           None            NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segs_ca_df_plot[(segs_ca_df_plot.Type=='soma')&(segs_ca_df_plot['Sec ID']==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_field = 'num_ca_spikes'\n",
    "\n",
    "ax = plt.figure(figsize=(2,5))\n",
    "import matplotlib\n",
    "from matplotlib.colors import Normalize\n",
    "cmap = matplotlib.cm.get_cmap('jet')\n",
    "norm = matplotlib.colors.Normalize(vmin = 0, vmax = 2)\n",
    "\n",
    "for i in segs_ca_df_plot[segs_ca_df_plot.Type=='apic']['Sec ID'].unique():\n",
    "    section = segs_ca_df_plot[(segs_ca_df_plot.Type=='apic')&(segs_ca_df_plot['Sec ID']==i)]\n",
    "    for j in section.index.tolist()[:-1]:\n",
    "        plt.plot(section.loc[j:j+1,'Coord X'],\n",
    "                 section.loc[j:j+1,'Coord Y'],\n",
    "             color=cmap(norm(section.loc[j:j+1,color_field].mean()/150)),\n",
    "             linewidth = 1*section.loc[j:j+1,'Section_diam'].unique())\n",
    "    \n",
    "for i in segs_ca_df_plot[segs_ca_df_plot.Type=='dend']['Sec ID'].unique():\n",
    "    section = segs_ca_df_plot[(segs_ca_df_plot.Type=='dend')&(segs_ca_df_plot['Sec ID']==i)]\n",
    "    for j in section.index.tolist()[:-1]:\n",
    "        plt.plot(section.loc[j:j+1,'Coord X'],\n",
    "                 section.loc[j:j+1,'Coord Y'],\n",
    "             color=cmap(norm(section.loc[j:j+1,color_field].mean()/150)),\n",
    "             linewidth = 1*section.loc[j:j+1,'Section_diam'].unique())\n",
    "        \n",
    "plt.scatter(segs_ca_df_plot[(segs_ca_df_plot.Type=='soma')&(segs_ca_df_plot['Sec ID']==0)].loc[2523,'Coord X'],\n",
    "         segs_ca_df_plot[(segs_ca_df_plot.Type=='soma')&(segs_ca_df_plot['Sec ID']==0)].loc[2523,'Coord Y'],color='k',s=100)\n",
    "plt.vlines(110,400,500)\n",
    "plt.text(0,450,'100 um')\n",
    "plt.hlines(400,110,210)\n",
    "plt.text(110,350,'100 um')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "cbar = plt.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap))\n",
    "#cbar.ax.set_ylabel('log(elec_distance)', rotation=270)\n",
    "\n",
    "#ax2.ax.set_title('log(elec_distance)',rotation=270)\n",
    "plt.box(False)\n",
    "plt.savefig('ca_spk_segments.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs_lt = ca_df[(ca_df.duration<50) & (ca_df.mag<-0.5) & (ca_df.dist_from_soma_spike>50) & (ca_df.mag_dur<-0.006)]['segmentID'].unique()\n",
    "segs_gt = ca_df[(ca_df.duration<50) & (ca_df.mag<-0.5) & (ca_df.dist_from_soma_spike>50) & (ca_df.mag_dur>-0.006)]['segmentID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94a09c8390>]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(segs['Coord X'], segs['Coord Y'])\n",
    "plt.plot(segs.loc[segs.segmentID.isin(segs_lt),'Coord X'],segs.loc[segs.segmentID.isin(segs_lt),'Coord Y'],'m*')\n",
    "plt.scatter(segs['Coord X'], segs['Coord Y'])\n",
    "plt.plot(segs.loc[segs.segmentID.isin(segs_gt),'Coord X'],segs.loc[segs.segmentID.isin(segs_gt),'Coord Y'],'r*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94b45904a8>]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg = 1852\n",
    "low = 1461407\n",
    "high = 1461703\n",
    "plt.plot(hva['report']['biophysical']['data'][low-1000:high+1000,seg]+\\\n",
    "         lva['report']['biophysical']['data'][low-1000:high+1000,seg]+\\\n",
    "         ih['report']['biophysical']['data'][low-1000:high+1000,seg])\n",
    "plt.twinx()\n",
    "plt.plot(v['report']['biophysical']['data'][low-1000:high+1000,seg],color='k')\n",
    "plt.plot(v['report']['biophysical']['data'][low-1000:high+1000,0],color='k',alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94be22e4a8>]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hva['report']['biophysical']['data'][162647-1000:163693+1000,1070]+\\\n",
    "         lva['report']['biophysical']['data'][162647-1000:163693+1000,1070]+\\\n",
    "         ih['report']['biophysical']['data'][162647-1000:163693+1000,1070])\n",
    "plt.twinx()\n",
    "plt.plot(v['report']['biophysical']['data'][162647-1000:163693+1000,1070],color='k')\n",
    "plt.plot(v['report']['biophysical']['data'][162647-1000:163693+1000,0],color='k',alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "import scipy.stats as st\n",
    "\n",
    "levy_dist = partial(st.levy_stable.rvs, alpha=1.37, beta=-1.00, loc=0.92, scale=0.44, size=1)\n",
    "levy_dist2 = partial(st.levy_stable.rvs, alpha=1.37, beta=-1.00, loc=0.92/2, scale=0.44, size=1)\n",
    "levy_dist3 = partial(st.levy_stable.rvs, alpha=1.37, beta=-1.00, loc=0.92/10, scale=0.44, size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "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": [
    "x = levy_dist(size=1000)\n",
    "x[x<0] = 0\n",
    "plt.hist(x[x>0],bins=np.arange(0,5,.1),alpha=0.2)\n",
    "x = levy_dist2(size=1000)\n",
    "x[x<0] = 0\n",
    "plt.hist(x[x>0],bins=np.arange(0,5,.1),alpha=0.2)\n",
    "#x = levy_dist3(size=1000)\n",
    "#x[x<0] = 0\n",
    "#plt.hist(x[x>0],bins=np.arange(0,5,.1),alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File('exc_stim_spikes_control.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "from raster_maker import SonataWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.concatenate((f['spikes']['exc_stim']['node_ids'][:].reshape(-1,1),\n",
    "                             f['spikes']['exc_stim']['timestamps'][:].reshape(-1,1)),axis=1),\n",
    "                  columns=['node_id','timestamps'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = 'exc_stim_spikes_50p.h5'\n",
    "writer = SonataWriter(fname, [\"spikes\", \"exc_stim\"], [\"timestamps\", \"node_ids\"], [np.float, np.int])\n",
    "\n",
    "for i in np.unique(f['spikes']['exc_stim']['node_ids'][:]):\n",
    "    #import pdb; pdb.set_trace()\n",
    "    ts = df.loc[df.node_id==i,'timestamps'].values\n",
    "    ts_redux = np.random.choice(ts,int(ts.shape[0]*0.5))\n",
    "    if len(ts_redux)==0:\n",
    "        ts_redux = ts\n",
    "    writer.append_repeat(\"node_ids\", i + 0, len(ts_redux))\n",
    "    writer.append_ds(ts_redux + 0, \"timestamps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File('exc_stim_spikes_control.h5','r')\n",
    "ex = h5py.File('exc_stim_spikes.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.concatenate((f['spikes']['exc_stim']['node_ids'][:].reshape(-1,1), \n",
    "                                  f['spikes']['exc_stim']['timestamps'][:].reshape(-1,1)),axis=1),\n",
    "                  columns=['node_ids','timestamps'])\n",
    "\n",
    "df2 = pd.DataFrame(np.concatenate((ex['spikes']['exc_stim']['node_ids'][:].reshape(-1,1), \n",
    "                                  ex['spikes']['exc_stim']['timestamps'][:].reshape(-1,1)),axis=1),\n",
    "                  columns=['node_ids','timestamps'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 2)"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.node_ids==5135].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99, 2)"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.node_ids==5135].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<HDF5 dataset \"node_ids\": shape (713312,), type \"<i8\">,\n",
       " <HDF5 dataset \"timestamps\": shape (713312,), type \"<f8\">)"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f['spikes']['exc_stim']['node_ids'], f['spikes']['exc_stim']['timestamps']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<HDF5 dataset \"node_ids\": shape (355397,), type \"<i8\">,\n",
       " <HDF5 dataset \"timestamps\": shape (355397,), type \"<f8\">)"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex['spikes']['exc_stim']['node_ids'], ex['spikes']['exc_stim']['timestamps']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = 'exc_stim_spikes_150p.h5'\n",
    "writer = SonataWriter(fname, [\"spikes\", \"exc_stim\"], [\"timestamps\", \"node_ids\"], [np.float, np.int])\n",
    "\n",
    "for i in np.arange(0,np.unique(f['spikes']['exc_stim']['node_ids'][:]).shape[0]):\n",
    "    ts = df.loc[df.node_id==i,'timestamps'].values\n",
    "    extras = np.random.choice(ts,int(ts.shape[0]*0.5)) + np.random.uniform(low=-10,high=10,size=int(ts.shape[0]*0.5))\n",
    "    ts_redux = np.concatenate((ts,extras))\n",
    "\n",
    "    writer.append_repeat(\"node_ids\", i + 0, len(ts_redux))\n",
    "    writer.append_ds(ts_redux + 0, \"timestamps\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "f50 = h5py.File('exc_stim_spikes_50p.h5','r')\n",
    "f150 = h5py.File('exc_stim_spikes_150p.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.333333333333333"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(pd.DataFrame(f['spikes']['exc_stim']['node_ids'][:])[0].value_counts().values/30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1666666666666665"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(pd.DataFrame(f50['spikes']['exc_stim']['node_ids'][:])[0].value_counts().values/30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.5"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(pd.DataFrame(f150['spikes']['exc_stim']['node_ids'][:])[0].value_counts().values/30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "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": [
    "y,e = np.histogram(pd.DataFrame(f['spikes']['exc_stim']['node_ids'][:])[0].value_counts().values/30,\n",
    "         bins=np.arange(0,20,0.2))\n",
    "plt.plot(e[1:],y/np.sum(y))\n",
    "y,e = np.histogram(pd.DataFrame(f50['spikes']['exc_stim']['node_ids'][:])[0].value_counts().values/30,\n",
    "         bins=np.arange(0,20,0.2))\n",
    "plt.plot(e[1:],y/np.sum(y))\n",
    "y,e = np.histogram(pd.DataFrame(f150['spikes']['exc_stim']['node_ids'][:])[0].value_counts().values/30,\n",
    "         bins=np.arange(0,20,0.2))\n",
    "plt.plot(e[1:],y/np.sum(y))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File('./output/spikes.h5','r')\n",
    "g = h5py.File('./output_control/spikes.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.00000e+00, 9.63089e+04, 9.63163e+04, 9.63235e+04, 9.72523e+04,\n",
       "        9.72585e+04, 9.74758e+04, 9.76168e+04, 9.77256e+04, 9.79906e+04]),\n",
       " array([4.00000e+00, 9.63089e+04, 9.63163e+04, 9.63235e+04, 9.72523e+04,\n",
       "        9.72585e+04, 9.74758e+04, 9.76168e+04, 9.77256e+04, 9.79906e+04]))"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f['spikes']['biophysical']['timestamps'][0:10], g['spikes']['biophysical']['timestamps'][0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File('exc_stim_spikes.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<HDF5 dataset \"node_ids\": shape (350277,), type \"<i8\">,\n",
       " <HDF5 dataset \"timestamps\": shape (350277,), type \"<f8\">)"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f['spikes']['exc_stim']['node_ids'], f['spikes']['exc_stim']['timestamps']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = h5py.File('exc_stim_spikes_control.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<HDF5 dataset \"node_ids\": shape (713312,), type \"<i8\">,\n",
       " <HDF5 dataset \"timestamps\": shape (713312,), type \"<f8\">)"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g['spikes']['exc_stim']['node_ids'], g['spikes']['exc_stim']['timestamps']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 5219, 5220, 5221])"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(g['spikes']['exc_stim']['node_ids'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 5132, 5133, 5134])"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(f['spikes']['exc_stim']['node_ids'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File('./output_low/exc_stim_spikes.h5','r')\n",
    "g = h5py.File('./output_lowmed/exc_stim_spikes.h5','r')\n",
    "h = h5py.File('./output_med/exc_stim_spikes.h5','r')\n",
    "i = h5py.File('./output_medhigh/exc_stim_spikes.h5','r')\n",
    "j = h5py.File('./output_high/exc_stim_spikes.h5','r')\n",
    "\n",
    "fo = h5py.File('./output_low/spikes.h5','r')\n",
    "go = h5py.File('./output_lowmed/spikes.h5','r')\n",
    "ho = h5py.File('./output_med/spikes.h5','r')\n",
    "io = h5py.File('./output_medhigh/spikes.h5','r')\n",
    "jo = h5py.File('./output_high/spikes.h5','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fo['spikes']['biophysical']['timestamps'][:].shape[0]/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f945daa8e48>"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(f['spikes']['exc_stim']['node_ids'][:],columns=['node_ids'])\n",
    "y,_ = np.histogram(df['node_ids'].value_counts().values/30,bins=np.arange(0,30,0.5))\n",
    "plt.plot(np.arange(0,30,0.5)[1:],\n",
    "         y/np.sum(y),\n",
    "         label='{x:0.2f}'.format(x=fo['spikes']['biophysical']['timestamps'][:].shape[0]/30))\n",
    "\n",
    "df = pd.DataFrame(g['spikes']['exc_stim']['node_ids'][:],columns=['node_ids'])\n",
    "y,_ = np.histogram(df['node_ids'].value_counts().values/30,bins=np.arange(0,30,0.5))\n",
    "plt.plot(np.arange(0,30,0.5)[1:],\n",
    "         y/np.sum(y),\n",
    "         label='{x:0.2f}'.format(x=go['spikes']['biophysical']['timestamps'][:].shape[0]/30))\n",
    "\n",
    "df = pd.DataFrame(h['spikes']['exc_stim']['node_ids'][:],columns=['node_ids'])\n",
    "y,_ = np.histogram(df['node_ids'].value_counts().values/30,bins=np.arange(0,30,0.5))\n",
    "plt.plot(np.arange(0,30,0.5)[1:],\n",
    "         y/np.sum(y),\n",
    "         label='{x:0.2f}'.format(x=ho['spikes']['biophysical']['timestamps'][:].shape[0]/30))\n",
    "\n",
    "df = pd.DataFrame(i['spikes']['exc_stim']['node_ids'][:],columns=['node_ids'])\n",
    "y,_ = np.histogram(df['node_ids'].value_counts().values/30,bins=np.arange(0,30,0.5))\n",
    "plt.plot(np.arange(0,30,0.5)[1:],\n",
    "         y/np.sum(y),\n",
    "         label='{x:0.2f}'.format(x=io['spikes']['biophysical']['timestamps'][:].shape[0]/30))\n",
    "\n",
    "df = pd.DataFrame(j['spikes']['exc_stim']['node_ids'][:],columns=['node_ids'])\n",
    "y,_ = np.histogram(df['node_ids'].value_counts().values/30,bins=np.arange(0,30,0.5))\n",
    "plt.plot(np.arange(0,30,0.5)[1:],\n",
    "         y/np.sum(y),\n",
    "         label='{x:0.2f}'.format(x=jo['spikes']['biophysical']['timestamps'][:].shape[0]/30))\n",
    "\n",
    "plt.xlabel('input exc. firing rate (Hz)')\n",
    "plt.ylabel('proportion')\n",
    "\n",
    "plt.legend(title='output firing rate (Hz)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9477508cc0>]"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot([0.50,0.73,5.20,12.93,19.23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 0.7333333333333333, 5.2, 12.933333333333334, 19.233333333333334)"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "f['spikes']['biophysical']['timestamps'].shape[0]/30,\\\n",
    "g['spikes']['biophysical']['timestamps'].shape[0]/30,\\\n",
    "h['spikes']['biophysical']['timestamps'].shape[0]/30,\\\n",
    "i['spikes']['biophysical']['timestamps'].shape[0]/30,\\\n",
    "j['spikes']['biophysical']['timestamps'].shape[0]/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.2"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h['spikes']['biophysical']['timestamps'].shape[0]/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Unable to open object (object 'spikes' doesn't exist)\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-479-fc0259470d47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'spikes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'biophysical'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timestamps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.6/site-packages/h5py/_hl/group.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    262\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid HDF5 object reference\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m             \u001b[0moid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_e\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0motype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5i\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/h5o.pyx\u001b[0m in \u001b[0;36mh5py.h5o.open\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Unable to open object (object 'spikes' doesn't exist)\""
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94711530b8>]"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(i['report']['biophysical']['data'][50000:100000,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
