AcrossAnimal_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import and set paths\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import functions from .py utils file: \n",
    "from Utilities.across_animals_functions import *\n",
    "\n",
    "\n",
    "#Animals to include in the analysis\n",
    "Animal_ID = ['EJT100','EJT...','EJT...']\n",
    "\n",
    "# name to save this anbalysis under \n",
    "Cohort_name = 'Test_Analysis'\n",
    "\n",
    "#Set paths\n",
    "InputPath = (r'D:\\Behavioural_data_output\\\\')\n",
    "\n",
    "OutputPath = (r'D:\\Behavioural_data_output\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# perfect score data:\n",
    "AA_PerfectScores = []\n",
    "AA_AttemptedSequences = []\n",
    "#Transition score data:\n",
    "AttemptedTs = [[],[],[],[]]\n",
    "AA_TransitionScores = [[],[],[],[]]\n",
    "AA_TrainingLevels= []\n",
    "AA_FilteredTransitionTimes = [] \n",
    "AA_Transitionscore_trials = []\n",
    "AA_TrialsPerSessions = []\n",
    "AA_TransitionTimes_inin = []\n",
    "AA_TransitionTimes_outin = []\n",
    "AA_TrainingLevels = []\n",
    "\n",
    "for Animal_index, CurrentAnimal in enumerate(Animal_ID):\n",
    "\n",
    "    InputPathCurrent = (InputPath + CurrentAnimal+r'\\\\AcrossSessions\\\\AnalysisOutput\\\\')\n",
    "    \n",
    "    #load in data:\n",
    "    if not os.path.isdir(InputPathCurrent):\n",
    "        print('Error, do Across Sessions analsis first')\n",
    "        \n",
    "    # perfect score data\n",
    "    with open (InputPathCurrent + 'PerfectScore_trials' , 'rb') as fp:\n",
    "        PerfectCount = pickle.load(fp)        \n",
    "    AA_PerfectScores = AA_PerfectScores + [PerfectCount]\n",
    "    \n",
    "    # Transition score data\n",
    "    with open (InputPathCurrent + 'Transitionscore_trials' , 'rb') as fp:\n",
    "        Transitionscore_trials = pickle.load(fp)        \n",
    "    AA_Transitionscore_trials = AA_Transitionscore_trials + [Transitionscore_trials]\n",
    "    \n",
    "    # TrialsPerSessions data\n",
    "    with open (InputPathCurrent + 'TrialsPerSessions' , 'rb') as fp:\n",
    "        TrialsPerSessions = pickle.load(fp)        \n",
    "    AA_TrialsPerSessions = AA_TrialsPerSessions + [TrialsPerSessions]#\n",
    "    \n",
    "    # Transition times in in\n",
    "    with open (InputPathCurrent + 'MeanTransitionTimes_bytrial_inin' , 'rb') as fp:\n",
    "        Mean_Transitions_bytrial_inin = pickle.load(fp)        \n",
    "    AA_TransitionTimes_inin = AA_TransitionTimes_inin + [Mean_Transitions_bytrial_inin]\n",
    "    \n",
    "    # Transition times out in\n",
    "    with open (InputPathCurrent + 'MeanTransitionTimes_bytrial_outin' , 'rb') as fp:\n",
    "        Mean_Transitions_bytrial_outin = pickle.load(fp)        \n",
    "    AA_TransitionTimes_outin = AA_TransitionTimes_outin + [Mean_Transitions_bytrial_outin]\n",
    "    \n",
    "    with open (InputPathCurrent + 'Training_levels' , 'rb') as fp:\n",
    "        Training_levels = pickle.load(fp)        \n",
    "    AA_TrainingLevels = AA_TrainingLevels + [Training_levels]\n",
    "\n",
    "    \n",
    "print('Plotting...')\n",
    "    \n",
    "################################################################################################################\n",
    "## Perfect scores \n",
    "#plot mean + all curves Perfect scores (smoothed more)\n",
    "window = 100\n",
    "################################################################################################################\n",
    "\n",
    "TrialbyTrial_Pscores = conactinate_nth_items(AA_PerfectScores)\n",
    "MeanLearningCurve = []\n",
    "for i,item in enumerate(TrialbyTrial_Pscores):\n",
    "    MeanLearningCurve = MeanLearningCurve + [np.mean(item)]\n",
    "\n",
    "fig,ax = plt.subplots(1, 1, figsize=(20, 10))\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_xlim([0, 7000])\n",
    "for i in range(len(AA_PerfectScores)):\n",
    "    ax.plot(convolve_movmean(AA_PerfectScores[i],window), color = 'teal',alpha = 0.2) \n",
    "\n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "ax.plot(convolve_movmean(MeanLearningCurve,window), color = 'teal')\n",
    "   \n",
    "ax.set_title('Perfect Scores Across Animals',fontsize = 20)\n",
    "ax.set_xlabel('Trials',fontsize = 20)\n",
    "ax.set_ylabel('P_score',fontsize = 20)\n",
    "\n",
    "SaveFig('Perfect_scores_all_curves'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    " \n",
    "\n",
    "################### plot mean curve Perfect scores + stabdard deviation\n",
    "\n",
    "    \n",
    "# calculate standard deviation on pre-convolved p scores (working it out on raw data leads to huge over estimations of st-dev)\n",
    "items = []\n",
    "for i in range(len(AA_PerfectScores)):\n",
    "    items = items + [convolve_movmean(AA_PerfectScores[i],window)]\n",
    "stdLearningCurve= []\n",
    "for item in conactinate_nth_items(items):\n",
    "    stdLearningCurve = stdLearningCurve + [np.std(item)]\n",
    "    \n",
    "    \n",
    "fig,ax = plt.subplots(1, 1, figsize=(20, 10))\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_xlim([0, 7000])\n",
    "ax.plot(convolve_movmean(MeanLearningCurve,50), color = 'teal',alpha = 0.9) \n",
    "# ax.plot(convolve_movmean(upper,50), color = 'yellow',alpha = 0.9) \n",
    "# ax.plot(convolve_movmean(lower,50), color = 'red',alpha = 0.9) \n",
    "\n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "\n",
    "ax.set_xlabel('Trials',fontsize = 20)\n",
    "ax.set_ylabel('P_score',fontsize = 20)\n",
    "\n",
    "upper = np.array(MeanLearningCurve) + np.array(stdLearningCurve)\n",
    "lower = np.array(MeanLearningCurve) - np.array(stdLearningCurve)\n",
    "ax.fill_between(list(range(0,len(upper))),convolve_movmean(lower,window),convolve_movmean(upper,window),\n",
    "    alpha=0.1, edgecolor='None', facecolor='teal',\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "\n",
    "ax.set_title('Mean Perfect Scores Across Animals [+ standard deviation]',fontsize = 20)\n",
    "\n",
    "SaveFig('Perfect_scores_stdev'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    " \n",
    "    \n",
    "################################################################################################################\n",
    "##### plot transition scores:\n",
    "################################################################################################################\n",
    "\n",
    "transitions = [[],[],[],[]]\n",
    "for item in AA_Transitionscore_trials:\n",
    "    for index,transition in enumerate(item):\n",
    "        transitions[index] = transitions[index] + [transition] \n",
    "\n",
    "MeanLearningCurve = [[],[],[],[]]\n",
    "for ind in range(4):\n",
    "    TrialbyTrial_Tscores = conactinate_nth_items(transitions[ind])\n",
    "    for i,item in enumerate(TrialbyTrial_Tscores):\n",
    "        MeanLearningCurve[ind] = MeanLearningCurve[ind] + [np.mean(item)]\n",
    "\n",
    "fig,axs = plt.subplots(2, 2, figsize=(20, 20))\n",
    "for ind, ax in enumerate(fig.axes):\n",
    "    ax.plot(convolve_movmean(MeanLearningCurve[ind],100),color = 'teal')\n",
    "    for item in (transitions[ind]):\n",
    "        ax.plot(convolve_movmean(item,100),color = 'teal', alpha = 0.2)\n",
    "    ax.set_ylim([0, 1])\n",
    "    ax.set_xlim([0, 7000])\n",
    "    \n",
    "    ax.set_title('Transition ' + str(ind+1),fontsize = 20)\n",
    "    \n",
    "    ax.set_xlabel('Trials',fontsize = 20)\n",
    "    ax.set_ylabel('T_score',fontsize = 20)\n",
    "    \n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "\n",
    "SaveFig('Transition_scores_allcurves'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    " \n",
    "    \n",
    "\n",
    "#########transition scores with stdev ##########\n",
    "\n",
    "# calculate standard deviation on pre-convolved p scores (working it out on raw data leads to huge over estimations of st-dev)\n",
    "\n",
    "    \n",
    "fig,axs = plt.subplots(2, 2, figsize=(20, 20))\n",
    "for ind, ax in enumerate(fig.axes):\n",
    "    ax.plot(convolve_movmean(MeanLearningCurve[ind],100), color = 'teal')\n",
    "    \n",
    "    ax.set_ylim([0, 1])\n",
    "    ax.set_xlim([0, 7000])\n",
    "    \n",
    "    ax.set_xlabel('Trials',fontsize = 20)\n",
    "    ax.set_ylabel('T_score',fontsize = 20)\n",
    "\n",
    "    ### standard deviation:\n",
    "    items = []\n",
    "    for item in transitions[ind]:\n",
    "        items = items + [convolve_movmean(item,window)]\n",
    "    stdLearningCurve= []\n",
    "    for item in conactinate_nth_items(items):\n",
    "        stdLearningCurve = stdLearningCurve + [np.std(item)]\n",
    "        \n",
    "    ax.set_title('Transition ' + str(ind+1),fontsize = 20)\n",
    "\n",
    "    upper = np.array(MeanLearningCurve[ind]) + np.array(stdLearningCurve)\n",
    "    lower = np.array(MeanLearningCurve[ind]) - np.array(stdLearningCurve)\n",
    "    ax.fill_between(list(range(0,len(upper))),convolve_movmean(lower,window),convolve_movmean(upper,window),\n",
    "    alpha=0.1, edgecolor='None', facecolor='teal',\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "    \n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "     \n",
    "SaveFig('Transition_scores_+standardev'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "\n",
    "################################################################################################################\n",
    "##### plot number of trials per session (non cumulative and cumulative) across animals \n",
    "################################################################################################################\n",
    "\n",
    "mean_trials_per_sesh = []\n",
    "std_trials_per_sesh = []\n",
    "for item in conactinate_nth_items(AA_TrialsPerSessions):\n",
    "    mean_trials_per_sesh = mean_trials_per_sesh + [(np.mean(item))]\n",
    "    std_trials_per_sesh = std_trials_per_sesh + [np.std(item)]\n",
    "    \n",
    "upper = np.array(mean_trials_per_sesh) + np.array(std_trials_per_sesh)\n",
    "lower = np.array(mean_trials_per_sesh) - np.array(std_trials_per_sesh)\n",
    "\n",
    "\n",
    "\n",
    "fig,[ax1,ax2] = plt.subplots(2, 1, figsize=(20, 20))\n",
    "\n",
    "ax1.plot(mean_trials_per_sesh, color = 'teal',alpha = 0.9) \n",
    "\n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "\n",
    "ax1.set_xlabel('Session',fontsize = 20)\n",
    "ax1.set_ylabel('Trials',fontsize = 20)\n",
    "\n",
    "ax1.set_title('Trials per session [+ standard deviation]',fontsize = 20)\n",
    "\n",
    "ax1.fill_between(list(range(0,len(upper))),(lower),(upper),\n",
    "    alpha=0.1, edgecolor='None', facecolor='teal',\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "\n",
    "\n",
    "\n",
    "cum_mean_trials_per_sesh = []\n",
    "cum_std_trials_per_sesh = []\n",
    "\n",
    "all_items= []\n",
    "for item in AA_TrialsPerSessions:\n",
    "    all_items = all_items + [np.cumsum(item)]\n",
    "for item in conactinate_nth_items(all_items):\n",
    "    cum_mean_trials_per_sesh = cum_mean_trials_per_sesh + [(np.mean(item))]\n",
    "    cum_std_trials_per_sesh = cum_std_trials_per_sesh + [np.std(item)]\n",
    "    \n",
    "upper = np.array(cum_mean_trials_per_sesh) + np.array(cum_std_trials_per_sesh)\n",
    "lower = np.array(cum_mean_trials_per_sesh) - np.array(cum_std_trials_per_sesh)\n",
    "\n",
    "ax2.plot(cum_mean_trials_per_sesh, color = 'teal',alpha = 0.9) \n",
    "\n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "ax1.legend(handles=[patch1])\n",
    "ax2.legend(handles=[patch1])\n",
    "\n",
    "\n",
    "# for i in range(len(AA_TrialsPerSessions)):\n",
    "#     ax2.plot(np.cumsum(AA_TrialsPerSessions[i]))\n",
    "\n",
    "ax2.set_xlabel('Session',fontsize = 20)\n",
    "ax2.set_ylabel('Trials',fontsize = 20)\n",
    "\n",
    "ax2.fill_between(list(range(0,len(upper))),(lower),(upper),\n",
    "    alpha=0.1, edgecolor='None', facecolor='teal',\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "\n",
    "ax2.set_title('Cumulative Trials per session [+ standard deviation]',fontsize = 20)\n",
    "    \n",
    "SaveFig('Trials_Per_session_'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "    \n",
    "\n",
    "################################################################################################################\n",
    "#### Transition times  in in\n",
    "################################################################################################################\n",
    "\n",
    "transitions = [[],[],[],[]]\n",
    "for item in AA_TransitionTimes_inin:\n",
    "    for index,transition in enumerate(item):\n",
    "        if index < 4:\n",
    "            transitions[index] = transitions[index] + [transition] \n",
    "\n",
    "MeanLearningCurve = [[],[],[],[]]\n",
    "for ind in range(4):\n",
    "    TrialbyTrial_Tscores = conactinate_nth_items(transitions[ind])\n",
    "    for i,item in enumerate(TrialbyTrial_Tscores):\n",
    "        MeanLearningCurve[ind] = MeanLearningCurve[ind] + [np.mean(item)]\n",
    "\n",
    "fig,axs = plt.subplots(4, 1, figsize=(10, 20))\n",
    "for ind, ax in enumerate(fig.axes):\n",
    "    ax.plot(convolve_movmean(MeanLearningCurve[ind],100),color = 'teal')\n",
    "    for item in (transitions[ind]):\n",
    "        ax.plot(convolve_movmean(item,100),color = 'teal', alpha = 0.2)\n",
    "#     ax.set_ylim([0, 2])\n",
    "    ax.set_xlim([0, 7000])\n",
    "    \n",
    "    ax.set_title('Transition ' + str(ind+1),fontsize = 20,loc = 'left')\n",
    "    \n",
    "    ax.set_xlabel('Trials',fontsize = 20)\n",
    "    ax.set_ylabel('Time',fontsize = 20)\n",
    "    \n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "\n",
    "SaveFig('Transition_times_inin_allcurves'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "#########transition scores with stdev ##########\n",
    "\n",
    "# calculate standard deviation on pre-convolved p scores (working it out on raw data leads to huge over estimations of st-dev)\n",
    "    \n",
    "fig,axs = plt.subplots(4, 1, figsize=(10, 20))\n",
    "for ind, ax in enumerate(fig.axes):\n",
    "    ax.plot(convolve_movmean(MeanLearningCurve[ind],100), color = 'teal')\n",
    "    \n",
    "#     ax.set_ylim([0, 2])\n",
    "    ax.set_xlim([0, 7000])\n",
    "    \n",
    "    ax.set_xlabel('Trials',fontsize = 20)\n",
    "    ax.set_ylabel('Time',fontsize = 20)\n",
    "\n",
    "    ### standard deviation:\n",
    "    items = []\n",
    "    for item in transitions[ind]:\n",
    "        items = items + [convolve_movmean(item,window)]\n",
    "    stdLearningCurve= []\n",
    "    for item in conactinate_nth_items(items):\n",
    "        stdLearningCurve = stdLearningCurve + [np.std(item)]\n",
    "        \n",
    "    ax.set_title('Transition ' + str(ind+1),fontsize = 20, loc = 'left')\n",
    "\n",
    "    upper = np.array(MeanLearningCurve[ind]) + np.array(stdLearningCurve)\n",
    "    lower = np.array(MeanLearningCurve[ind]) - np.array(stdLearningCurve)\n",
    "    ax.fill_between(list(range(0,len(upper))),convolve_movmean(lower,window),convolve_movmean(upper,window),\n",
    "    alpha=0.1, edgecolor='None', facecolor='teal',\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "    \n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "     \n",
    "SaveFig('Transition_times_inin +standardev'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "################################################################################################################\n",
    "#### Transition times  out in\n",
    "################################################################################################################\n",
    "\n",
    "transitions = [[],[],[],[]]\n",
    "for item in AA_TransitionTimes_outin:\n",
    "    for index,transition in enumerate(item):\n",
    "        if index < 4:\n",
    "            transitions[index] = transitions[index] + [transition] \n",
    "\n",
    "MeanLearningCurve = [[],[],[],[]]\n",
    "for ind in range(4):\n",
    "    TrialbyTrial_Tscores = conactinate_nth_items(transitions[ind])\n",
    "    for i,item in enumerate(TrialbyTrial_Tscores):\n",
    "        MeanLearningCurve[ind] = MeanLearningCurve[ind] + [np.mean(item)]\n",
    "\n",
    "fig,axs = plt.subplots(4, 1, figsize=(10, 20))\n",
    "for ind, ax in enumerate(fig.axes):\n",
    "    ax.plot(convolve_movmean(MeanLearningCurve[ind],100),color = 'teal')\n",
    "    for item in (transitions[ind]):\n",
    "        ax.plot(convolve_movmean(item,100),color = 'teal', alpha = 0.2)\n",
    "#     ax.set_ylim([0, 2])\n",
    "    ax.set_xlim([0, 7000])\n",
    "    \n",
    "    ax.set_title('Transition ' + str(ind+1),fontsize = 20,loc = 'left')\n",
    "    \n",
    "    ax.set_xlabel('Trials',fontsize = 20)\n",
    "    ax.set_ylabel('Time',fontsize = 20)\n",
    "    \n",
    "patch1 = mpatches.Patch(color='teal', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "\n",
    "SaveFig('Transition_times_outin_allcurves'+Cohort_name+'_'+Animal_ID[0]+'_to_'+Animal_ID[-1]+'.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "\n",
    "################################################################################################################\n",
    "## Training levels  \n",
    "window = 100\n",
    "xlim = 4000\n",
    "################################################################################################################\n",
    "\n",
    "BL_MeanLearningCurve = mean_learning_curve(AA_TrainingLevels,Animal_ID)\n",
    "\n",
    "standard_dev = []\n",
    "for item in conactinate_nth_items(AA_TrainingLevels):\n",
    "    standard_dev = standard_dev+ [np.std(item)]\n",
    "\n",
    "fig,ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "# ax.set_ylim([0, 1])\n",
    "# ax.set_xlim([0, 7000])\n",
    "\n",
    "ax.set_ylim([0, 50])\n",
    "    \n",
    "ax.set_xlim([0, xlim])\n",
    "ax.plot(convolve_movmean(BL_MeanLearningCurve,window), '--',color = 'black',linewidth = 2)\n",
    "\n",
    "upper = np.array(BL_MeanLearningCurve) + np.array(standard_dev)\n",
    "lower = np.array(BL_MeanLearningCurve) - np.array(standard_dev)\n",
    "\n",
    "ax.fill_between(list(range(0,len(upper))),convolve_movmean(lower,window),convolve_movmean(upper,window),\n",
    "alpha=0.1, edgecolor='None', facecolor='grey',\n",
    "linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "\n",
    "   \n",
    "ax.set_title('Training levels across animals',fontsize = 20)\n",
    "ax.set_xlabel('Trials',fontsize = 20)\n",
    "\n",
    "ax.set_ylabel('Training level',fontsize = 20)\n",
    "\n",
    "ax.tick_params(axis=\"y\", labelsize=15)\n",
    "ax.tick_params(axis=\"x\", labelsize=15)\n",
    "\n",
    "# Hide the right and top spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Only show ticks on the left and bottom spines\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax.tick_params(axis=\"y\", labelsize=15)\n",
    "ax.tick_params(axis=\"x\", labelsize=15)\n",
    "    \n",
    "\n",
    "SaveFig('Tlevels.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "\n",
    "transitions = [[],[],[],[]]\n",
    "for item in AA_TransitionTimes_inin:\n",
    "    for index,transition in enumerate(item):\n",
    "        if index < 4:\n",
    "            transitions[index] = transitions[index] + [transition] \n",
    "\n",
    "MeanLearningCurve = [[],[],[],[]]\n",
    "for ind in range(4):\n",
    "    TrialbyTrial_Tscores = conactinate_nth_items(transitions[ind])\n",
    "    for i,item in enumerate(TrialbyTrial_Tscores):\n",
    "        MeanLearningCurve[ind] = MeanLearningCurve[ind] + [np.mean(item)]\n",
    "\n",
    "#########transition times with stdev ##########\n",
    "\n",
    "# calculate standard deviation on pre-convolved p scores (working it out on raw data leads to huge over estimations of st-dev)\n",
    "colors = ['black','red','green','blue']\n",
    "\n",
    "fig,ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "for ind, item in enumerate(MeanLearningCurve):\n",
    "    ax.plot(convolve_movmean(MeanLearningCurve[ind],100), color = colors[ind])\n",
    "    \n",
    "#     ax.set_ylim([0, 2])\n",
    "    ax.set_xlim([0, 4000])\n",
    "    \n",
    "    ax.set_xlabel('Trials',fontsize = 20)\n",
    "    ax.set_ylabel('Time',fontsize = 20)\n",
    "\n",
    "    ### standard deviation:\n",
    "    items = []\n",
    "    for item in transitions[ind]:\n",
    "        items = items + [convolve_movmean(item,window)]\n",
    "    stdLearningCurve= []\n",
    "    for item in conactinate_nth_items(items):\n",
    "        stdLearningCurve = stdLearningCurve + [np.std(item)]\n",
    "\n",
    "    upper = np.array(MeanLearningCurve[ind]) + np.array(stdLearningCurve)\n",
    "    lower = np.array(MeanLearningCurve[ind]) - np.array(stdLearningCurve)\n",
    "            \n",
    "\n",
    "    ax.fill_between(list(range(0,len(upper))),convolve_movmean(lower,window),convolve_movmean(upper,window),\n",
    "    alpha=0.1, edgecolor='None', facecolor=colors[ind],\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "    \n",
    "# Hide the right and top spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Only show ticks on the left and bottom spines\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax.tick_params(axis=\"y\", labelsize=15)\n",
    "ax.tick_params(axis=\"x\", labelsize=15)\n",
    "    \n",
    "\n",
    "SaveFig('Times.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "\n",
    "TrialbyTrial_Pscores = conactinate_nth_items(AA_PerfectScores)\n",
    "MeanLearningCurve = []\n",
    "for i,item in enumerate(TrialbyTrial_Pscores):\n",
    "    MeanLearningCurve = MeanLearningCurve + [np.mean(item)]\n",
    "\n",
    "# calculate standard deviation on pre-convolved p scores (working it out on raw data leads to huge over estimations of st-dev)\n",
    "items = []\n",
    "for i in range(len(AA_PerfectScores)):\n",
    "    items = items + [convolve_movmean(AA_PerfectScores[i],window)]\n",
    "stdLearningCurve= []\n",
    "for item in conactinate_nth_items(items):\n",
    "    stdLearningCurve = stdLearningCurve + [np.std(item)]\n",
    "    \n",
    "    \n",
    "fig,ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_xlim([0, 4000])\n",
    "ax.plot(convolve_movmean(MeanLearningCurve,50),color = 'black',alpha = 0.9) \n",
    "# ax.plot(convolve_movmean(upper,50), color = 'yellow',alpha = 0.9) \n",
    "# ax.plot(convolve_movmean(lower,50), color = 'red',alpha = 0.9) \n",
    "\n",
    "patch1 = mpatches.Patch(color='black', label=('n = ' + str(len(Animal_ID))))\n",
    "plt.legend(handles=[patch1])\n",
    "\n",
    "ax.set_xlabel('Trials',fontsize = 20)\n",
    "ax.set_ylabel('P_score',fontsize = 20)\n",
    "\n",
    "upper = np.array(MeanLearningCurve) + np.array(stdLearningCurve)\n",
    "lower = np.array(MeanLearningCurve) - np.array(stdLearningCurve)\n",
    "\n",
    "ax.fill_between(list(range(0,len(upper))),convolve_movmean(lower,window),convolve_movmean(upper,window),\n",
    "    alpha=0.1, edgecolor='None', facecolor='grey',\n",
    "    linewidth=1, linestyle='dashdot', antialiased=True)\n",
    "\n",
    "# Hide the right and top spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "# Only show ticks on the left and bottom spines\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "ax.tick_params(axis=\"y\", labelsize=15)\n",
    "ax.tick_params(axis=\"x\", labelsize=15)\n",
    "\n",
    "\n",
    "\n",
    "SaveFig('Performance.png',OutputPath + '\\\\AcrossAnimals\\\\' + Cohort_name + '\\\\')\n",
    "\n",
    "\n",
    "print('Done!')"
   ]
  }
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