2_analyse_individual_sessions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import + Set paths and params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import functions from .py utils file: \n",
    "from Utilities.single_session_functions import *\n",
    "\n",
    "#Set paths \n",
    "# list of animals to process, can one or multiple animals eg. ['mouse1','mouse2', etc.]\n",
    "Animal_ID = ['EJT100']\n",
    "\n",
    "# input path should be where the data from 1_process_bpod_ouput was saved \n",
    "InputPath = (r'D:\\Behavioural_data_output\\\\')\n",
    "# usually you want this to be the same as the input path\n",
    "OutputPath = (r'D:\\Behavioural_data_output\\\\')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# main script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Processing data for: EJT100</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "c:\\miniconda\\Lib\\site-packages\\numpy\\core\\_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "min() iterable argument is empty",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 65\u001b[0m\n\u001b[0;32m     63\u001b[0m a \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdelete(a, [port1\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,port2\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,port3\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,port4\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,port5\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m     64\u001b[0m new_order \u001b[38;5;241m=\u001b[39m [port1\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m [port2\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m [port3\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m [port4\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m [port5\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(a)\n\u001b[1;32m---> 65\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mplot_poke_proportions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransition_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTransitionLatency_unfilt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mExperiment_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCurrentAnimal\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mInputPathCurrent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mP1alignedtimefiltseqs_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_order\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     67\u001b[0m \u001b[38;5;66;03m##############################################################################\u001b[39;00m\n\u001b[0;32m     69\u001b[0m trial_split_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mtuple\u001b[39m(df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m)))\n",
      "File \u001b[1;32mc:\\Users\\Emmett Thompson\\OneDrive\\Documents\\sequence_behaviour_for_sjlab_github\\Utilities\\single_session_functions.py:343\u001b[0m, in \u001b[0;36mplot_poke_proportions\u001b[1;34m(transition_data, TransitionLatency_unfilt, file, Experiment_type, CurrentAnimal, InputPathCurrent, port1, P1alignedtimefiltseqs_data, new_order)\u001b[0m\n\u001b[0;32m    340\u001b[0m sigma \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m.2\u001b[39m\n\u001b[0;32m    341\u001b[0m f, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m20\u001b[39m))\n\u001b[1;32m--> 343\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstripplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpal\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwhite\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjitter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mzorder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mort\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    345\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime from Initiation\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m)\n\u001b[0;32m    346\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m)\n",
      "File \u001b[1;32mc:\\miniconda\\Lib\\site-packages\\seaborn\\_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[0;32m     44\u001b[0m     )\n\u001b[0;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\miniconda\\Lib\\site-packages\\seaborn\\categorical.py:2794\u001b[0m, in \u001b[0;36mstripplot\u001b[1;34m(x, y, hue, data, order, hue_order, jitter, dodge, orient, color, palette, size, edgecolor, linewidth, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2791\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe `split` parameter has been renamed to `dodge`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2792\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg, \u001b[38;5;167;01mUserWarning\u001b[39;00m)\n\u001b[1;32m-> 2794\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_StripPlotter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2795\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mjitter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2796\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2797\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n",
      "File \u001b[1;32mc:\\miniconda\\Lib\\site-packages\\seaborn\\categorical.py:1100\u001b[0m, in \u001b[0;36m_StripPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, jitter, dodge, orient, color, palette)\u001b[0m\n\u001b[0;32m   1098\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[0;32m   1099\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_variables(x, y, hue, data, orient, order, hue_order)\n\u001b[1;32m-> 1100\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_colors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1102\u001b[0m \u001b[38;5;66;03m# Set object attributes\u001b[39;00m\n\u001b[0;32m   1103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdodge \u001b[38;5;241m=\u001b[39m dodge\n",
      "File \u001b[1;32mc:\\miniconda\\Lib\\site-packages\\seaborn\\categorical.py:319\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_colors\u001b[1;34m(self, color, palette, saturation)\u001b[0m\n\u001b[0;32m    317\u001b[0m \u001b[38;5;66;03m# Determine the gray color to use for the lines framing the plot\u001b[39;00m\n\u001b[0;32m    318\u001b[0m light_vals \u001b[38;5;241m=\u001b[39m [colorsys\u001b[38;5;241m.\u001b[39mrgb_to_hls(\u001b[38;5;241m*\u001b[39mc)[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m rgb_colors]\n\u001b[1;32m--> 319\u001b[0m lum \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlight_vals\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m.6\u001b[39m\n\u001b[0;32m    320\u001b[0m gray \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39mcolors\u001b[38;5;241m.\u001b[39mrgb2hex((lum, lum, lum))\n\u001b[0;32m    322\u001b[0m \u001b[38;5;66;03m# Assign object attributes\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: min() iterable argument is empty"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# reloads imports (utils) if something changes\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "#### MAIN ####\n",
    "for Animal_index, CurrentAnimal in enumerate(Animal_ID):\n",
    "    display(HTML(f\"<b>Processing data for: {CurrentAnimal}</b>\"))\n",
    "    InputPathCurrent = (InputPath + CurrentAnimal)\n",
    "    if os.path.isdir(InputPathCurrent):\n",
    "        for file in os.listdir(InputPathCurrent+ '\\\\Preprocessed\\\\'):\n",
    "            print(file)\n",
    "            #load in data for given Animal and session:\n",
    "            CurrentInputPath = (InputPathCurrent + '\\\\Preprocessed\\\\'+file+'\\\\')\n",
    "            Session_data =  pd.read_csv((CurrentInputPath+'PreProcessed_SessionInfo.csv'))\n",
    "            poke_data =  pd.read_csv((CurrentInputPath+'PreProcessed_RawPokeData.csv'))\n",
    "            transition_data =  pd.read_csv((CurrentInputPath+'PreProcessed_TransitionData.csv'))\n",
    "            timefiltseqs_data =  pd.read_csv((CurrentInputPath+'PreProcessed_Sequence_timefilteredonly_df.csv'))\n",
    "            P1alignedtimefiltseqs_data =  pd.read_csv((CurrentInputPath+'PreProcessed_Sequence_df_timefiltered_port1aligned.csv'))\n",
    "            P2alignedtimefiltseqs_data =  pd.read_csv((CurrentInputPath+'PreProcessed_Sequence_df_timefiltered_port2aligned.csv'))\n",
    "            P3alignedtimefiltseqs_data =  pd.read_csv((CurrentInputPath+'PreProcessed_Sequence_df_timefiltered_port3aligned.csv'))\n",
    "            P4alignedtimefiltseqs_data =  pd.read_csv((CurrentInputPath+'PreProcessed_Sequence_df_timefiltered_port4aligned.csv'))\n",
    "            P5alignedtimefiltseqs_data =  pd.read_csv((CurrentInputPath+'PreProcessed_Sequence_df_timefiltered_port5aligned.csv'))\n",
    "            \n",
    "            #experiment type:\n",
    "            Experiment_type = 'Auto'\n",
    "            \n",
    "            #Plot transition times:\n",
    "            #pull data from dfs:\n",
    "            Time_filter = np.array(transition_data.loc[:,'2s_Time_Filter_out_in'])\n",
    "            TransitionTypes_unfilt = np.array(transition_data.loc[:,'Transition_type'])\n",
    "            TransitionLatency_unfilt = np.array(transition_data.loc[:,'out_in_Latency'])\n",
    "            # filter out transitions that are too slow (defined in preprocessing as 2s)\n",
    "            TransitionTypes_Tfilt = list(TransitionTypes_unfilt[Time_filter==1])\n",
    "            TransitionLatency_Tfilt = list(TransitionLatency_unfilt[Time_filter==1])\n",
    "            # determine ports for each transition \n",
    "            Transitions = []\n",
    "            Transitions = Transitions + list(np.array(Session_data.loc[:,'Transition1']))\n",
    "            Transitions = Transitions + list(np.array(Session_data.loc[:,'Transition2']))\n",
    "            Transitions = Transitions + list(np.array(Session_data.loc[:,'Transition3']))\n",
    "            Transitions = Transitions + list(np.array(Session_data.loc[:,'Transition4']))\n",
    "            \n",
    "            # plot_transition_times, call function \n",
    "            plot_transition_times(TransitionLatency_Tfilt, TransitionTypes_Tfilt, Transitions, CurrentAnimal, file, Experiment_type, InputPathCurrent)\n",
    "\n",
    "            # plot transition times plot 2\n",
    "            plot_transition_times2(TransitionLatency_Tfilt, TransitionTypes_Tfilt, Transitions, CurrentAnimal, file, Experiment_type, InputPathCurrent)\n",
    "                \n",
    "            # plot transition trajectories\n",
    "            plot_transition_trajectories(timefiltseqs_data, Session_data, CurrentAnimal, file, Experiment_type, InputPathCurrent)\n",
    "\n",
    "            TimeFiltered_seqs = list(timefiltseqs_data.loc[:, 'Sequence_ids'])\n",
    "            transition_number = determineTransitionNumber(TimeFiltered_seqs)\n",
    "            port1 = list(Session_data.loc[:, 'Port1'])[0]\n",
    "            port2 = list(Session_data.loc[:, 'Port2'])[0]\n",
    "            port3 = list(Session_data.loc[:, 'Port3'])[0]\n",
    "            port4 = list(Session_data.loc[:, 'Port4'])[0]\n",
    "            port5 = list(Session_data.loc[:, 'Port5'])[0]\n",
    "            ## Tanrasition Heatmap Plot:   \n",
    "            plot_transition_heatmap(transition_number, port1, port2, port3, port4, port5, CurrentAnimal, file, Experiment_type, InputPathCurrent)\n",
    "            \n",
    "            ##### Plot poke proportions \n",
    "            a = np.array([0,1,2,3,4,5,6,7])\n",
    "            a = np.delete(a, [port1-1,port2-1,port3-1,port4-1,port5-1])\n",
    "            new_order = [port1-1] + [port2-1] + [port3-1] + [port4-1] + [port5-1] + list(a)\n",
    "            df = plot_poke_proportions(transition_data, TransitionLatency_unfilt, file, Experiment_type, CurrentAnimal, InputPathCurrent, port1, P1alignedtimefiltseqs_data, new_order)\n",
    "\n",
    "            ##############################################################################\n",
    "\n",
    "            trial_split_data = dict(tuple(df.groupby('index')))\n",
    "\n",
    "\n",
    "            from scipy.optimize import curve_fit\n",
    "\n",
    "            def gaussian(x, mean, amplitude, standard_deviation):\n",
    "                return amplitude * np.exp( - (x - mean)**2 / (2*standard_deviation ** 2))\n",
    "\n",
    "            colors = ['#E6BFC4','#285CA6','#219CC1','#A4D3B6','#ECEFB6','#E6BFC4']\n",
    "\n",
    "            f, ax = plt.subplots(figsize=(10, 10))\n",
    "            bins_number=100\n",
    "            for i in trial_split_data:\n",
    "                if i > 0:\n",
    "                    x = list(trial_split_data[i]['Time'])\n",
    "\n",
    "                    bin_heights, bin_borders, _ = plt.hist(x, bins=bins_number, label='histogram', color = colors[i], alpha = 0.4)\n",
    "                    bin_centers = bin_borders[:-1] + np.diff(bin_borders) / 2\n",
    "                    try:\n",
    "                        popt, _ = curve_fit(gaussian, bin_centers, bin_heights, p0=[1., 0., 1.])\n",
    "                        x_interval_for_fit = np.linspace(bin_borders[0], bin_borders[-1], 10000)\n",
    "                        plt.plot(x_interval_for_fit, gaussian(x_interval_for_fit, *popt), label='fit', color = colors[i],linewidth = 2)\n",
    "                    except Exception:\n",
    "                        pass\n",
    "\n",
    "            ax.set_xlim(0,2)\n",
    "\n",
    "            #save\n",
    "            SaveFig((CurrentAnimal + '_' + file+'_' + Experiment_type[0][2::] +'_' + 'Port1FittedHistogram_pokeInTimes_Plot.png'),InputPathCurrent + '\\\\SingleSessions\\\\' + file + '\\\\')  \n",
    "\n",
    "            #########################################################################\n",
    "            ## Plot transition proportions. Take all transitions that are time filtered (>2s) and decide if they are correct, errors (seq relevant port to wrong place) or neutral (non relevant port to anywhere or repeated poke)\n",
    "\n",
    "            TrialStartRefs = []\n",
    "            for i in range(len(timefiltseqs_data.loc[:,'SessionTime_Reference'])):\n",
    "                TrialStartRefs = TrialStartRefs + literal_eval(timefiltseqs_data.loc[:,'SessionTime_Reference'][i])\n",
    "\n",
    "            Correct_proportion,Error_proportion,Neutral_proportion,x_refs = return_sequence_accuracy_props(list(timefiltseqs_data.loc[:,'Sequence_ids']),TrialStartRefs)\n",
    "\n",
    "\n",
    "            fig, ax = plt.subplots(1, 1,figsize=(20,10))\n",
    "\n",
    "            ax.set_title(('Transition Proportions'), loc = 'left',fontsize = 20,pad = 30, color = 'red')\n",
    "            ax.plot(x_refs,convolve_movmean(Correct_proportion,30),'o-',linewidth=2,markersize = 7)\n",
    "            ax.plot(x_refs,convolve_movmean(Error_proportion,30),'o-', color = 'red',linewidth=2,markersize = 7)\n",
    "            ax.plot(x_refs,convolve_movmean(Neutral_proportion,30),'o-',color = 'grey',linewidth=2,markersize = 7)\n",
    "\n",
    "\n",
    "            patch1 = mpatches.Patch(color='blue', label=('Correct'))\n",
    "            patch2 = mpatches.Patch(color='red', label=('Error'))\n",
    "            patch3 = mpatches.Patch(color='grey', label=('Neutral'))\n",
    "            ax.legend(handles=[patch1,patch2,patch3],frameon=False)\n",
    "\n",
    "            ax.set_ylim([0, 1])\n",
    "\n",
    "            ax.set_xlabel('Time',fontsize = 15)\n",
    "            ax.set_ylabel('Proportion',fontsize = 15)\n",
    "\n",
    "            SaveFig((CurrentAnimal + '_' + file+'_' + Experiment_type[0][2::] +'_' + 'TransitionProportions.png'),InputPathCurrent + '\\\\SingleSessions\\\\' + file + '\\\\')\n",
    "            \n",
    "            ################################# Repeat pokes included data: ###########################################\n",
    "            \n",
    "            ## Heatmap plots with repeate pokes\n",
    "\n",
    "            Port_transits = determime_heatmapdata(determineTransitionNumber(list(timefiltseqs_data.loc[:,'Sequence_ids'])),port1,port2,port3,port4)\n",
    "\n",
    "            a = np.array([0,1,2,3,4,5,6,7])\n",
    "            a = np.delete(a, [port1-1,port2-1,port3-1,port4-1,port5-1])\n",
    "            new_order = [port1-1] + [port2-1] + [port3-1] + [port4-1] + [port5-1] + list(a)\n",
    "\n",
    "            Port_transits = reversedata(Port_transits,new_order)    \n",
    "            x_axis_labels = ['Port 1','Port 2','Port 3','Port 4'] # labels for x-axis\n",
    "            y_axis_labels = ['Port Z','Port Y','Port X','Port 5','Port 4','Port 3','Port 2','Port 1'] # labels for y-axis\n",
    "            labels =  np.array([['','','',''],\n",
    "                                ['','','',''],\n",
    "                                ['','','',''],\n",
    "                                ['','','','T4'],\n",
    "                                ['','','T3',''],\n",
    "                                ['','T2','',''],\n",
    "                                ['T1','','',''],\n",
    "                                ['','','','']])\n",
    "\n",
    "            mask = np.zeros_like(Port_transits)\n",
    "\n",
    "            #plot:\n",
    "            with sns.axes_style(\"white\"):\n",
    "                fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "                h1 = sns.heatmap(Port_transits,xticklabels=x_axis_labels, yticklabels=y_axis_labels, \n",
    "                                linewidths=.5, mask=mask, square=True,cmap=\"YlGnBu\",annot = labels,fmt = '',cbar_kws = dict(use_gridspec=False,location=\"right\"))\n",
    "\n",
    "            #add seprrating line:\n",
    "            ax.hlines([3], *ax.get_ylim())\n",
    "            ax.set_ylabel('End Port',size = 20)\n",
    "            ax.set_xlabel('Start Port',size = 20)\n",
    "\n",
    "            SaveFig((CurrentAnimal + '_' + file+'_' + Experiment_type[0][2::] +'_' + 'HeatMap_RepeatPokesIncluded.png'),InputPathCurrent + '\\\\SingleSessions\\\\' + file + '\\\\Repeat_Poke_Data\\\\')\n",
    "\n",
    "            ############### Plot transition trajectories with repeat pokes ####################\n",
    "\n",
    "            port1 =  2\n",
    "            port2 =  1   \n",
    "            port3 =  6\n",
    "            port4 =  3   \n",
    "            port5 =  7   \n",
    "\n",
    " \n",
    "            #determine numbers of transitons (time filtered sequences + repeat pokes removed)\n",
    "            transition_number = determineTransitionNumber(list(timefiltseqs_data.loc[:,'Sequence_ids']))\n",
    "\n",
    "            # set xy coords of each trajectory:\n",
    "            x = ([1,1], [1,2], [1,3], [1,4], [1,1], [1,2], [1,3], [1,4],\n",
    "                [2,1], [2,2], [2,3], [2,4], [2,1], [2,2], [2,3], [2,4],\n",
    "                [3,1], [3,2], [3,3], [3,4], [3,1], [3,2], [3,3], [3,4],\n",
    "                [4,1], [4,2], [4,3], [4,4], [4,1], [4,2], [4,3], [4,4],\n",
    "                [1,1], [1,2], [1,3], [1,4], [1,1], [1,2], [1,3], [1,4],\n",
    "                [2,1], [2,2], [2,3], [2,4], [2,1], [2,2], [2,3], [2,4],\n",
    "                [3,1], [3,2], [3,3], [3,4], [3,1], [3,2], [3,3], [3,4],\n",
    "                [4,1], [4,2], [4,3], [4,4], [4,1], [4,2], [4,3], [4,4])\n",
    "            y = ([1,0.5], [1,1], [1,1], [1,1], [1,2], [1,2], [1,2], [1,2],\n",
    "                [1,1], [1,0.5], [1,1], [1,1], [1,2], [1,2], [1,2], [1,2],\n",
    "                [1,1], [1,1], [1,0.5], [1,1], [1,2], [1,2], [1,2], [1,2],\n",
    "                [1,1], [1,1], [1,1], [1,0.5], [1,2], [1,2], [1,2], [1,2],\n",
    "                [2,1], [2,1], [2,1], [2,1], [2,2.5], [2,2], [2,2], [2,2],\n",
    "                [2,1], [2,1], [2,1], [2,1], [2,2], [2,2.5], [2,2], [2,2],\n",
    "                [2,1], [2,1], [2,1], [2,1], [2,2], [2,2], [2,2.5], [2,2],\n",
    "                [2,1], [2,1], [2,1], [2,1], [2,2], [2,2], [2,2], [2,2.5])\n",
    "\n",
    "            nrow = 1 \n",
    "            ncol = 5\n",
    "            fig, axs = plt.subplots(nrow, ncol,figsize=(50, 10))\n",
    "\n",
    "            count = 0\n",
    "            for ind, ax in enumerate(fig.axes):\n",
    "                ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "                ax.axis('off')\n",
    "                ax.set_ylim([0, 5])\n",
    "                ax.set_xlim([0, 5])\n",
    "\n",
    "                count = count + 1\n",
    "\n",
    "                Colours = ['black', 'firebrick', 'grey']\n",
    "\n",
    "                #Add port reference circles\n",
    "                circle1 = plt.Circle((1, 1), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle2 = plt.Circle((2, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle3 = plt.Circle((3, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle4 = plt.Circle((4, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle5 = plt.Circle((1, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle6 = plt.Circle((2, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle7 = plt.Circle((3, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle8 = plt.Circle((4, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "\n",
    "                circle9 = plt.Circle((1, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle10 = plt.Circle((2, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle11 = plt.Circle((3, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle12 = plt.Circle((4, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle13 = plt.Circle((1, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle14 = plt.Circle((2, 2), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle15 = plt.Circle((3, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle16 = plt.Circle((4, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "\n",
    "                circle17 = plt.Circle((1, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle18 = plt.Circle((2, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle19 = plt.Circle((3, 1), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle20 = plt.Circle((4, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle21 = plt.Circle((1, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle22 = plt.Circle((2, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle23 = plt.Circle((3, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle24 = plt.Circle((4, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "\n",
    "                circle25 = plt.Circle((1, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle26 = plt.Circle((2, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle27 = plt.Circle((3, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle28 = plt.Circle((4, 1), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle29 = plt.Circle((1, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle30 = plt.Circle((2, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "                circle31 = plt.Circle((3, 2), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle32 = plt.Circle((4, 2), 0.3, color=Colours[2],fill=False, linewidth=5)\n",
    "\n",
    "                circle33 = plt.Circle((1, 1), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle34 = plt.Circle((2, 1), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle35 = plt.Circle((3, 1), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle36 = plt.Circle((4, 1), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle37 = plt.Circle((1, 2), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle38 = plt.Circle((2, 2), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle39 = plt.Circle((3, 2), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "                circle40 = plt.Circle((4, 2), 0.3, color=Colours[0],fill=False, linewidth=5)\n",
    "\n",
    "\n",
    "                if count < 5:\n",
    "                    ax.text(0.7,2.7,('Transitions from port ' + str(count)),fontsize = 25, color = 'k')\n",
    "                else: \n",
    "                    ax.text(0.7,2.7,('Transition from all ports'),fontsize = 25, color = 'k')  \n",
    "\n",
    "                if count == 1:\n",
    "                    ax.add_artist(circle1), ax.add_artist(circle2), ax.add_artist(circle3),ax.add_artist(circle4),ax.add_artist(circle5),ax.add_artist(circle6),ax.add_artist(circle7),ax.add_artist(circle8)\n",
    "                elif count == 2:\n",
    "                    ax.add_artist(circle9),ax.add_artist(circle10),ax.add_artist(circle11),ax.add_artist(circle12),ax.add_artist(circle13),ax.add_artist(circle14),ax.add_artist(circle15),ax.add_artist(circle16)\n",
    "                elif count == 3:\n",
    "                    ax.add_artist(circle25),ax.add_artist(circle26),ax.add_artist(circle27),ax.add_artist(circle28),ax.add_artist(circle29),ax.add_artist(circle30),ax.add_artist(circle31),ax.add_artist(circle32)\n",
    "                elif count == 4:\n",
    "                    ax.add_artist(circle17),ax.add_artist(circle18),ax.add_artist(circle19),ax.add_artist(circle20),ax.add_artist(circle21),ax.add_artist(circle22),ax.add_artist(circle23),ax.add_artist(circle24)\n",
    "                elif count == 5:\n",
    "                    ax.add_artist(circle33),ax.add_artist(circle34),ax.add_artist(circle35),ax.add_artist(circle36),ax.add_artist(circle37),ax.add_artist(circle38),ax.add_artist(circle39),ax.add_artist(circle40)\n",
    "\n",
    "                if count == 1: \n",
    "                    port = port1\n",
    "                if count == 2: \n",
    "                    port = port2\n",
    "                if count == 3:\n",
    "                    port = port3\n",
    "                if count == 4: \n",
    "                    port = port4\n",
    "                if port > 4:\n",
    "                    a = port - 4\n",
    "                    b = 2\n",
    "                else: \n",
    "                    a = port\n",
    "                    b = 1\n",
    "                REF_circle = plt.Circle((a, b), 0.3, color=Colours[1],fill=False, linewidth=5)\n",
    "\n",
    "                if count == 1: \n",
    "                    ax.add_artist(REF_circle)\n",
    "                    normalise = max(transition_number[((port1*8)-8):((port1*8)-8)+8])\n",
    "                    for i in range(((port1*8)-8),((port1*8)-8)+8): \n",
    "                        ax.plot(x[i],y[i],markersize = 130 , markeredgewidth=0.001, color= 'firebrick', marker='None',linewidth=(5*(transition_number[i]/normalise)))  \n",
    "                        ax.plot(x[i][1],y[i][1],'o',color = 'firebrick',alpha = 0.3,markersize = (60*(transition_number[i]/normalise)))\n",
    "                if count == 2:\n",
    "                    ax.add_artist(REF_circle)\n",
    "                    normalise = max(transition_number[((port2*8)-8):((port2*8)-8)+8])\n",
    "                    for i in range(((port2*8)-8),((port2*8)-8)+8): \n",
    "                        ax.plot(x[i],y[i],markersize = 130 , markeredgewidth=0.001, color= 'firebrick', marker='None',linewidth=(5*(transition_number[i]/normalise))) \n",
    "                        ax.plot(x[i][1],y[i][1],'o',color = 'firebrick',alpha = 0.3,markersize = (60*(transition_number[i]/normalise)))\n",
    "                if count == 3: \n",
    "                    ax.add_artist(REF_circle)\n",
    "                    normalise = max(transition_number[((port3*8)-8):((port3*8)-8)+8])\n",
    "                    for i in range(((port3*8)-8),((port3*8)-8)+8): \n",
    "                        ax.plot(x[i],y[i],markersize = 130 , markeredgewidth=0.001, color= 'firebrick', marker='None',linewidth=(5*(transition_number[i]/normalise))) \n",
    "                        ax.plot(x[i][1],y[i][1],'o',color = 'firebrick',alpha = 0.3,markersize = (60*(transition_number[i]/normalise)))\n",
    "                if count == 4: \n",
    "                    ax.add_artist(REF_circle)\n",
    "                    normalise = max(transition_number[((port4*8)-8):((port4*8)-8)+8])\n",
    "                    for i in range(((port4*8)-8),((port4*8)-8)+8): \n",
    "                        ax.plot(x[i],y[i],markersize = 130 , markeredgewidth=0.001, color= 'firebrick', marker='None',linewidth=(5*(transition_number[i]/normalise))) \n",
    "                        ax.plot(x[i][1],y[i][1],'o',color = 'firebrick',alpha = 0.3,markersize = (60*(transition_number[i]/normalise)))\n",
    "\n",
    "                if count == 5:\n",
    "                    for i in range(len(transition_number)):\n",
    "                        normalise = max(transition_number)  \n",
    "                        ax.plot(x[i],y[i],markersize = 130 , markeredgewidth=0.001, color= 'firebrick', marker='None',linewidth=(5*(transition_number[i]/normalise))) \n",
    "\n",
    "                if count == 2:\n",
    "                    ax.plot(1,3.5,'o',color = 'firebrick',alpha = 0.3,markersize = 60) \n",
    "                    ax.text(1.3,3.45,('=  ' + str(normalise)+'  transitions'),fontsize = 25, color = 'k') \n",
    "\n",
    "            SaveFig((CurrentAnimal + '_' + file+'_' + Experiment_type[0][2::] +'_' + 'TransitionDiagrams_RepeatPokesIncluded.png'),InputPathCurrent + '\\\\SingleSessions\\\\' + file + '\\\\Repeat_Poke_Data\\\\')\n",
    "            \n",
    "    else:\n",
    "        print('ERROR: No preprocessed files for ' + CurrentAnimal)\n",
    "        \n",
    "print('finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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