plotter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('paper', font_scale=3.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatanate the data \n",
    "<a id='concat'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Set which param\n",
    "delay_mode_intra = \"null\"\n",
    "delay_mode_inter = \"unimodal\"\n",
    "intra_params = [1.5]\n",
    "inter_params = [1.5]\n",
    "v_stim = 4.0\n",
    "metric = \"training\"  # statistics or classification?\n",
    "networks = [\"topo\", \"random\"]\n",
    "\n",
    "# Control condition\n",
    "dfs = []\n",
    "df = pd.read_csv('data/sum/diff_v_stim/{}_v_stim.csv'.format(metric), keep_default_na=False)\n",
    "\n",
    "df = df[df['v_stim']==v_stim].drop(columns=['v_stim'])  # only those with v_stim=4\n",
    "df[\"intra type\"] = delay_mode_intra\n",
    "df[\"inter type\"] = delay_mode_inter\n",
    "df[\"intra params\"] = \"null\"\n",
    "df[\"inter params\"] = \"null\"\n",
    "df['double conn'] = 'null'\n",
    "df['skip delays'] = 'null'\n",
    "df['skip weights'] = 'null'\n",
    "dfs.append(df)\n",
    "\n",
    "# add all the other datas\n",
    "for network_mode in networks:\n",
    "    for intra_p in intra_params:\n",
    "        for inter_p in inter_params:\n",
    "            # skip connections params\n",
    "            for skip_double in [str(True), str(False)]:\n",
    "                for skip_p in [1.5, 3.0]:\n",
    "                    for skip_w in [1.0, 0.5]:\n",
    "                        df = pd.read_csv('data/sum/{}_{}_intra={}{}_inter={}{}_skip_double={}_d={}_w={}.csv'.\n",
    "                                         format(metric, network_mode, delay_mode_intra, intra_p, delay_mode_inter, inter_p, skip_double, skip_p, skip_w))\n",
    "                        df[\"intra type\"] = delay_mode_intra\n",
    "                        df[\"inter type\"] = delay_mode_inter\n",
    "                        df[\"intra params\"] = intra_p\n",
    "                        df[\"inter params\"] = inter_p\n",
    "                        df['double conn'] = skip_double\n",
    "                        df['skip delays'] = skip_p\n",
    "                        df['skip weights'] = skip_w\n",
    "                        dfs.append(df)\n",
    "\n",
    "# # noise condition does not depend on v_stim\n",
    "# df = pd.read_csv('data/sum/pre/{}_intra={}_inter={}.csv'.format(metric, delay_mode_intra, delay_mode_inter),\n",
    "#                  keep_default_na=False)\n",
    "# df = df[df['network type']=='noise']\n",
    "# dfs.append(df)\n",
    "\n",
    "# concatenate all dataframes into one and save them\n",
    "ultimate = pd.concat(dfs, sort=False)\n",
    "ultimate.to_csv(\"data/sum/{}_intra={}_inter={}.csv\".format(metric, delay_mode_intra, delay_mode_inter), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot\n",
    "<a id='load'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/sum/v_stim=4/measures_intra=null_inter=unimodal.csv', keep_default_na=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) melt them in a necessary format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "melted = df.melt(id_vars=['module index', 'network type', \n",
    "                          'intra type', 'inter type', 'intra params', 'inter params'],\n",
    "#                           'double conn', 'skip delays', 'skip weights'], \n",
    "                 var_name='metric')  # id_vars keep their column, other columns get melted under the column @var_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Select which variables you want to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selected = melted[(melted['module index']>1) & (melted['metric']=='accuracy')]\n",
    "selected = melted[(melted['metric']=='irregularity') &\n",
    "                  (melted['network type']!='noise')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename = 'inter params'\n",
    "name_to = '$\\sigma_{ff}$'\n",
    "selected = selected.rename(columns={rename:name_to}, errors='raise')\n",
    "\n",
    "# selected[name_to] = selected[name_to].replace('null', 0)  # map params to 0\n",
    "# # selected[name_to] = pd.to_numeric(selected[name_to], errors='raise')  # make the hue variable numeric\n",
    "# selected[name_to] = selected[name_to].map({'True':'double', 'False':'single', 'null':'none'})  # skip connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>module index</th>\n",
       "      <th>network type</th>\n",
       "      <th>intra type</th>\n",
       "      <th>inter type</th>\n",
       "      <th>intra params</th>\n",
       "      <th>$\\sigma_{ff}$</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>topo</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.480663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>241</td>\n",
       "      <td>1.0</td>\n",
       "      <td>topo</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.500693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>topo</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.327287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>243</td>\n",
       "      <td>3.0</td>\n",
       "      <td>topo</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.170548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>244</td>\n",
       "      <td>0.0</td>\n",
       "      <td>topo</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.493682</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>395</td>\n",
       "      <td>3.0</td>\n",
       "      <td>random</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.492969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>random</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.490509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>1.0</td>\n",
       "      <td>random</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.571516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>2.0</td>\n",
       "      <td>random</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.541875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>3.0</td>\n",
       "      <td>random</td>\n",
       "      <td>null</td>\n",
       "      <td>unimodal</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>irregularity</td>\n",
       "      <td>1.492027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     module index network type intra type inter type intra params  \\\n",
       "240           0.0         topo       null   unimodal         null   \n",
       "241           1.0         topo       null   unimodal         null   \n",
       "242           2.0         topo       null   unimodal         null   \n",
       "243           3.0         topo       null   unimodal         null   \n",
       "244           0.0         topo       null   unimodal         null   \n",
       "..            ...          ...        ...        ...          ...   \n",
       "395           3.0       random       null   unimodal          1.5   \n",
       "396           0.0       random       null   unimodal          1.5   \n",
       "397           1.0       random       null   unimodal          1.5   \n",
       "398           2.0       random       null   unimodal          1.5   \n",
       "399           3.0       random       null   unimodal          1.5   \n",
       "\n",
       "    $\\sigma_{ff}$        metric     value  \n",
       "240          null  irregularity  1.480663  \n",
       "241          null  irregularity  1.500693  \n",
       "242          null  irregularity  1.327287  \n",
       "243          null  irregularity  1.170548  \n",
       "244          null  irregularity  1.493682  \n",
       "..            ...           ...       ...  \n",
       "395          10.0  irregularity  1.492969  \n",
       "396          10.0  irregularity  1.490509  \n",
       "397          10.0  irregularity  1.571516  \n",
       "398          10.0  irregularity  1.541875  \n",
       "399          10.0  irregularity  1.492027  \n",
       "\n",
       "[160 rows x 8 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network dynamics\n",
    "<a id='dynamics'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 30})\n",
    "\n",
    "g = sns.FacetGrid(selected, row=\"network type\",\n",
    "                  sharex=True, sharey='col', margin_titles=False, aspect=2)\n",
    "my_pal = sns.color_palette(sns.color_palette(\"PuRd\"), 4)  # Oranges, \"ch:2.5,-.2,dark=.1\"\n",
    "g = g.map_dataframe(sns.lineplot, \"module index\", 'value', hue=name_to, palette=my_pal, legend=False)\n",
    "\n",
    "# # ticks and labels\n",
    "# metrics = df.columns[1:] \n",
    "# xticks = [None, 'M0', 'M1',\"M2\", \"M3\"]\n",
    "# ylabels = [\"Pearson CC\", \"LvR\", \"spikes/sec\", \"Fano factor\"]  # ylabels are units of metrics\n",
    "\n",
    "# # ticks and labels\n",
    "# # https://cduvallet.github.io/posts/2018/11/facetgrid-ylabel-access\n",
    "# for ax in g.axes.flat:\n",
    "#     ax.set(xlabel=None, xticklabels=xticks, ylabel=None)\n",
    "#     if ax.get_title():\n",
    "#         ax.set_title(ax.get_title().split('=')[1])\n",
    "#     if ax.texts:\n",
    "#         txt = ax.texts[0]\n",
    "#         ax.text(txt.get_unitless_position()[0], txt.get_unitless_position()[1],\n",
    "#                 txt.get_text().split('=')[1],\n",
    "#                 transform=ax.transAxes,\n",
    "#                 va='center')\n",
    "#         # Remove the original text\n",
    "#         ax.texts[0].remove()\n",
    "# # ylabel for metrics\n",
    "# for axi,ax in enumerate(g.axes[0]):\n",
    "#     ax.set(ylabel=ylabels[axi])\n",
    "    \n",
    "\n",
    "# ticks, labels...\n",
    "# g.fig.suptitle(name_to)\n",
    "g.set(xlabel=None, xticks=range(4), xticklabels=[], ylabel=None, title=None, ylim=[0.2,1.7])\n",
    "\n",
    "g.fig.text(0, 0.5, 'irregularity', va='center', rotation='vertical')\n",
    "namelist = ['topographic', 'random']\n",
    "for i,ax in enumerate(g.axes):\n",
    "    ax[0].set_ylabel(namelist[i])\n",
    "\n",
    "xticks= ['M0', 'M1', 'M2', 'M3']\n",
    "g.axes[1,0].set(xticks=range(4), xticklabels=xticks)\n",
    "    \n",
    "\n",
    "# # legends\n",
    "# handles, labels = g.axes[0][-1].get_legend_handles_labels()\n",
    "# g.axes[0][-1].legend(handles=handles[1:3], labels=labels[1:3], bbox_to_anchor=(0.1, 0.2))\n",
    "# g.axes[0][-1].legend(handles=handles[1:], labels=labels[1:], bbox_to_anchor=(0.1, 0.2), \n",
    "#                      ncol=2, handlelength=0.5, columnspacing=0.3, borderpad=0.2)\n",
    "\n",
    "# save the figure\n",
    "# plt.tight_layout()\n",
    "plt.savefig(\"irr_nulluni.eps\", bbox_to_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification\n",
    "<a id='classi'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4-1) Plot with FacetGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 40})\n",
    "# create a 2x2 grid with row being the [accuracy, MSE] and column being [topo, random]\n",
    "g = sns.catplot(x=\"module index\", y=\"value\", hue=name_to, data = selected,\n",
    "            kind='bar', row='network type', sharey='row', margin_titles=False,\n",
    "            ci='sd', aspect=2.0, color='magenta', legend=False)\n",
    "\n",
    "# handles, legends = g.axes[0][0].get_legend_handles_labels()  # set legent to 'brief' to get handles and legends than turn off\n",
    "# g.axes[1][0].legend(handles, legends, bbox_to_anchor=(0.6,0.3))\n",
    "\n",
    "# ticks, labels...\n",
    "g.fig.suptitle(name_to)\n",
    "g.set(xlabel=None, xticks=[], ylabel=None, title=None, ylim=[0,1.0])\n",
    "# g.fig.text(0, 0.5,'accuracy', va='center', rotation='vertical')\n",
    "namelist = ['topographic', 'random']\n",
    "for i,ax in enumerate(g.axes):\n",
    "    ax[0].axhline(y=0.1, color='black', linewidth=2.0)\n",
    "    ax[0].set_ylabel(namelist[i])\n",
    "xticks= ['M2', 'M3']\n",
    "g.axes[1,0].set(xticks=range(2), xticklabels=xticks)\n",
    "\n",
    "\n",
    "# https://wckdouglas.github.io/2016/12/seaborn_annoying_title\n",
    "# xticks = [\"M2\", \"M3\"]\n",
    "# g.set(xlabel=None)\n",
    "# for ax in g.axes[0]:\n",
    "#     ax.set(xticks=range(2), xticklabels=xticks)\n",
    "#     ax.axhline(y=0.1, color='black', linewidth=2.0)  # accuracy of 0.1 is at chance level\n",
    "# for ax in g.axes.flat:\n",
    "#     ax.set(xlabel=None, xticklabels=xticks, ylabel=None)\n",
    "#     if ax.get_title():\n",
    "#         ax.set_title(ax.get_title().split('=')[1],\n",
    "#                      fontsize='x-large')\n",
    "#     if ax.texts:\n",
    "#         txt = ax.texts[0]\n",
    "#         ax.text(txt.get_unitless_position()[0], txt.get_unitless_position()[1],\n",
    "#                 txt.get_text().split('=')[1],\n",
    "#                 transform=ax.transAxes,\n",
    "#                 va='center',\n",
    "#                 fontsize='x-large')\n",
    "#         # Remove the original text\n",
    "#         ax.texts[0].remove()\n",
    "\n",
    "# save the figure\n",
    "# g.fig.tight_layout()\n",
    "plt.savefig('acc_nulluni.eps', bbox_to_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4-2) Or only just a single plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "sns.barplot(x='module index', y='accuracy', hue='double conn', data=df[(df['module index'] >1) & (df['network type']=='random')])\n",
    "\n",
    "xticks = [\"M2\", \"M3\"]\n",
    "plt.xticks(range(2), xticks)\n",
    "plt.axhline(y=0.1, color='black', linewidth=2.0)\n",
    "\n",
    "plt.savefig('training_random_double.pdf', bbox_to_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Errthing\n",
    "<a id='compare'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "'''\n",
    "load data, compute average across trials and merge statistics with classification results\n",
    "'''\n",
    "measures = pd.read_csv('data/measures_errthing.csv', keep_default_na=False)\n",
    "classi = pd.read_csv('data/training_errthing.csv', keep_default_na=False)\n",
    "groupy = lambda df:df.groupby(['module index', 'network type', 'intra type', 'inter type', 'intra params', 'inter params']).mean()\n",
    "measures_g = groupy(measures)\n",
    "classi_g = groupy(classi)\n",
    "df_joined = classi_g.join(measures_g, lsuffix='!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "selected = df_joined.loc[(2,'random')]  # M2 of random network\n",
    "selected = selected.reset_index()  # delete multiIndex structure\n",
    "\n",
    "def transform_rows_delay (row):\n",
    "    '''\n",
    "    aux function to use combinations of intra type and inter type to determine the delay type\n",
    "    '''\n",
    "    if (row['intra params']=='null'):\n",
    "        return 'null'\n",
    "    elif (row['intra type']=='null') and (row['inter type']=='unimodal'):\n",
    "        return 'inter'\n",
    "    elif (row['intra type']=='unimodal') and (row['inter type']=='null'):\n",
    "        return 'intra'\n",
    "    else:\n",
    "        return 'both'\n",
    "\n",
    "selected['delay type'] = selected.apply(lambda row:transform_rows_delay(row), axis=1)  # add the delay type column\n",
    "selected = selected.drop(columns=['intra type', 'inter type'])  # drop redundant columns\n",
    "# selected['delay params'] = selected.apply(lambda row:trasform_rows_params(row), axis=1)\n",
    "# selected = selected.drop(columns=['intra params', 'inter params'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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       "<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",
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       "        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>intra params</th>\n",
       "      <th>inter params</th>\n",
       "      <th>Unnamed: 0!</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>synchrony</th>\n",
       "      <th>irregularity</th>\n",
       "      <th>firing rate</th>\n",
       "      <th>variability</th>\n",
       "      <th>delay type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.1005</td>\n",
       "      <td>0.360033</td>\n",
       "      <td>110</td>\n",
       "      <td>0.083175</td>\n",
       "      <td>1.242206</td>\n",
       "      <td>3.170182</td>\n",
       "      <td>8.435313</td>\n",
       "      <td>inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>0.360090</td>\n",
       "      <td>150</td>\n",
       "      <td>0.010910</td>\n",
       "      <td>1.532495</td>\n",
       "      <td>2.916002</td>\n",
       "      <td>1.251371</td>\n",
       "      <td>inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0890</td>\n",
       "      <td>0.360089</td>\n",
       "      <td>130</td>\n",
       "      <td>0.031001</td>\n",
       "      <td>1.436319</td>\n",
       "      <td>2.950268</td>\n",
       "      <td>4.298904</td>\n",
       "      <td>inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.1305</td>\n",
       "      <td>0.359415</td>\n",
       "      <td>30</td>\n",
       "      <td>0.102346</td>\n",
       "      <td>1.192047</td>\n",
       "      <td>3.393392</td>\n",
       "      <td>10.045327</td>\n",
       "      <td>null</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.360152</td>\n",
       "      <td>110</td>\n",
       "      <td>0.074555</td>\n",
       "      <td>1.251966</td>\n",
       "      <td>3.078592</td>\n",
       "      <td>6.742603</td>\n",
       "      <td>intra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.2970</td>\n",
       "      <td>0.343846</td>\n",
       "      <td>150</td>\n",
       "      <td>0.337375</td>\n",
       "      <td>0.632046</td>\n",
       "      <td>7.948538</td>\n",
       "      <td>38.530370</td>\n",
       "      <td>intra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.3120</td>\n",
       "      <td>0.342377</td>\n",
       "      <td>130</td>\n",
       "      <td>0.225493</td>\n",
       "      <td>0.744249</td>\n",
       "      <td>5.934898</td>\n",
       "      <td>25.848010</td>\n",
       "      <td>intra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.1305</td>\n",
       "      <td>0.359415</td>\n",
       "      <td>30</td>\n",
       "      <td>0.102346</td>\n",
       "      <td>1.192047</td>\n",
       "      <td>3.393392</td>\n",
       "      <td>10.045327</td>\n",
       "      <td>null</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>0.360108</td>\n",
       "      <td>110</td>\n",
       "      <td>0.062544</td>\n",
       "      <td>1.260764</td>\n",
       "      <td>3.076788</td>\n",
       "      <td>6.178627</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.2310</td>\n",
       "      <td>0.354286</td>\n",
       "      <td>150</td>\n",
       "      <td>0.069831</td>\n",
       "      <td>0.997593</td>\n",
       "      <td>4.121390</td>\n",
       "      <td>11.948267</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.2270</td>\n",
       "      <td>0.352874</td>\n",
       "      <td>130</td>\n",
       "      <td>0.071981</td>\n",
       "      <td>0.969589</td>\n",
       "      <td>4.103644</td>\n",
       "      <td>10.721729</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>null</td>\n",
       "      <td>null</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.1305</td>\n",
       "      <td>0.359415</td>\n",
       "      <td>30</td>\n",
       "      <td>0.102346</td>\n",
       "      <td>1.192047</td>\n",
       "      <td>3.393392</td>\n",
       "      <td>10.045327</td>\n",
       "      <td>null</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   intra params inter params  Unnamed: 0!  accuracy       MSE  Unnamed: 0  \\\n",
       "0           1.5          0.5         44.5    0.1005  0.360033         110   \n",
       "1           1.5         10.0         60.5    0.0980  0.360090         150   \n",
       "2           1.5          5.0         52.5    0.0890  0.360089         130   \n",
       "3          null         null         12.5    0.1305  0.359415          30   \n",
       "4           0.5          1.5         44.5    0.1015  0.360152         110   \n",
       "5          10.0          1.5         60.5    0.2970  0.343846         150   \n",
       "6           5.0          1.5         52.5    0.3120  0.342377         130   \n",
       "7          null         null         12.5    0.1305  0.359415          30   \n",
       "8           0.5          0.5         44.5    0.0955  0.360108         110   \n",
       "9          10.0         10.0         60.5    0.2310  0.354286         150   \n",
       "10          5.0          5.0         52.5    0.2270  0.352874         130   \n",
       "11         null         null         12.5    0.1305  0.359415          30   \n",
       "\n",
       "    synchrony  irregularity  firing rate  variability delay type  \n",
       "0    0.083175      1.242206     3.170182     8.435313      inter  \n",
       "1    0.010910      1.532495     2.916002     1.251371      inter  \n",
       "2    0.031001      1.436319     2.950268     4.298904      inter  \n",
       "3    0.102346      1.192047     3.393392    10.045327       null  \n",
       "4    0.074555      1.251966     3.078592     6.742603      intra  \n",
       "5    0.337375      0.632046     7.948538    38.530370      intra  \n",
       "6    0.225493      0.744249     5.934898    25.848010      intra  \n",
       "7    0.102346      1.192047     3.393392    10.045327       null  \n",
       "8    0.062544      1.260764     3.076788     6.178627       both  \n",
       "9    0.069831      0.997593     4.121390    11.948267       both  \n",
       "10   0.071981      0.969589     4.103644    10.721729       both  \n",
       "11   0.102346      1.192047     3.393392    10.045327       null  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
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   "execution_count": 23,
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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3 ,figsize=(30,20))\n",
    "plt.rcParams[\"legend.markerscale\"] = 1.0\n",
    "import itertools\n",
    "metriclist = list(itertools.combinations(['synchrony', 'irregularity', 'firing rate', 'variability'], r=2))\n",
    "for index,pair in enumerate(metriclist):\n",
    "    ri = index//3\n",
    "    ci = index%3\n",
    "    sns.scatterplot(x=pair[0], y=pair[1], size='accuracy', hue='delay type', data=selected,\n",
    "                sizes=(200,3000), palette=['magenta','black','orange','green'], ax=axes[ri][ci], legend='brief')\n",
    "\n",
    "# dirty way to handle legend    \n",
    "h1,l1 = axes[-1][-1].get_legend_handles_labels()\n",
    "for index in range(6):  # remove all legends\n",
    "    ri = index//3\n",
    "    ci = index%3\n",
    "    axes[ri][ci].get_legend().remove()\n",
    "handles = h1[0:6]\n",
    "labels = l1[0:6]\n",
    "handles.append(h1[6])\n",
    "labels.append(l1[6])\n",
    "handles.append(h1[-1])\n",
    "labels.append(l1[-1])\n",
    "axes[-1][0].legend(handles, labels)\n",
    "\n",
    "plt.savefig('dynamic_accuracy_color.eps', bbox_to_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data",
   "language": "python",
   "name": "data"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}