fig_paper_Random_Static_trajectory_samples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import copy\n",
    "import numpy\n",
    "import modules.io as io\n",
    "import modules.plot_func as pltt\n",
    "import modules.traj_analysis as tran\n",
    "import modules.helper_func_class as misc\n",
    "import modules.process_mouse_trials_lib as plib\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "#import matplotlib.image as mpimg\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "THE SUFFIXES IN THE VARIABLE NAMES\n",
    "\n",
    "*** rt suffix -> relative target experiments\n",
    "                 this means STATIC entrance,\n",
    "                 since the target is always\n",
    "                 positioned in the same spot\n",
    "                 relative to the entrance\n",
    "                 the mouse takes\n",
    "\n",
    "*** ft suffix -> fixed target experiments\n",
    "                 this means RANDOM entrance,\n",
    "                 since the target is in a\n",
    "                 different spot in every trial,\n",
    "                 relative to the entrance the mouse\n",
    "                 takes\n",
    "\n",
    "*** 2t suffix -> two target experiments\n",
    "                 mice are trained in two locations, consecutively\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "mouse_traj_dir_rt      = r'./experiments/relative_target/mouse_*'\n",
    "mouse_traj_dir_rt90    = r'./experiments/relative_target_90deg/mouse_*'\n",
    "mouse_traj_dir_ft      = r'./experiments/fixed_target/mouse_*'\n",
    "mouse_traj_dir_2t      = r'./experiments/two_target_no_cues/mouse_*'\n",
    "mouse_traj_dir_p2_R180 = [r'./experiments/two_targets_rot/mouse_*'       ,\n",
    "                          r'./experiments/two_targets_rot_fem/mouse_*'   ,\n",
    "                          r'./experiments/two_targets_rot_mixsex/mouse_*']\n",
    "\n",
    "selfint_file_variable  = 'selfint_st_rt'\n",
    "\n",
    "output_dir = 'figs/paper/active_sensing'\n",
    "try:\n",
    "    os.makedirs(output_dir)\n",
    "except FileExistsError:\n",
    "    pass\n",
    "\n",
    "colors = pltt.get_gradient(5,'blue2')\n",
    "\n",
    "color_lightblue   = numpy.array((65, 102, 216, 255))/255 #pltt.get_gradient(5,'blue2')[2] #numpy.array((65, 102, 216, 255))/255\n",
    "color_darkblue    = pltt.get_gradient(2,'blue')[1] #numpy.array((224, 53, 53, 255))/255\n",
    "color_lightred    = pltt.get_gradient(2,'red')[0] #numpy.array((65, 102, 216, 255))/255 #pltt.get_gradient(5,'blue2')[2] #numpy.array((65, 102, 216, 255))/255\n",
    "color_darkred     = pltt.get_gradient(2,'red')[1] #numpy.array((224, 53, 53, 255))/255\n",
    "color_lightyellow = numpy.append(pltt.get_gradient(10,'yellow')[2],1.0)\n",
    "color_darkyellow  = numpy.append(pltt.get_gradient(2,'yellow')[::-1][0],1.0)\n",
    "\n",
    "color_h1 = color_lightblue\n",
    "color_h2 = color_darkblue\n",
    "\n",
    "SMALL_FONTSIZE  = 12\n",
    "MEDIUM_FONTSIZE = 14\n",
    "LARGE_FONTSIZE  = 16\n",
    "plt.rc('font',        size=SMALL_FONTSIZE )  # controls default text sizes\n",
    "plt.rc('axes',   titlesize=SMALL_FONTSIZE )  # fontsize of the axes title\n",
    "plt.rc('axes',   labelsize=MEDIUM_FONTSIZE) # fontsize of the x and y labels\n",
    "plt.rc('xtick',  labelsize=SMALL_FONTSIZE ) # fontsize of the tick labels\n",
    "plt.rc('ytick',  labelsize=SMALL_FONTSIZE ) # fontsize of the tick labels\n",
    "plt.rc('legend',  fontsize=SMALL_FONTSIZE ) # legend fontsize\n",
    "plt.rc('figure', titlesize=LARGE_FONTSIZE ) # fontsize of the figure title\n",
    "\n",
    "\n",
    "FIGSIZE_1PANEL = numpy.array((4,3),dtype=float)\n",
    "AX_POS_1PANEL  = numpy.array( [ 0.125, 0.125, 0.9, 0.88 ] ) # [xmin,ymin,width,heigh]\n",
    "AX_POS_2PANELS = numpy.array([   AX_POS_1PANEL/2.0,\n",
    "                               [ 1.5*AX_POS_1PANEL[0]+AX_POS_1PANEL[2]/2,AX_POS_1PANEL[1]/2,AX_POS_1PANEL[2]/2-0.5*AX_POS_1PANEL[0],AX_POS_1PANEL[3]/2 ] ])\n",
    "\n",
    "\n",
    "# linear regression aux function\n",
    "linreg_lin_func = lambda x,lr: lr.intercept + lr.slope * x\n",
    "linreg_pl_func  = lambda x,lr: numpy.exp(lr.intercept + numpy.log(x)*lr.slope)\n",
    "linreg_exp_func = lambda x,lr: numpy.exp(lr.intercept + x*lr.slope)\n",
    "\n",
    "# t-test aux function\n",
    "p_significant = 0.05\n",
    "q_FDR         = 0.05 # FDR-level for thresholding p_values correcting for false-discovery rate in multiple comparisons\n",
    "check_significance = lambda ttest_res: ttest_res.pvalue < p_significant\n",
    "\n",
    "\n",
    "# loads experiment MAT files from file path according to the parameters\n",
    "\n",
    "time_delay_after_food  = 3.0 # sec\n",
    "\n",
    "# all_trials[k][m] -> mouse m in trial k\n",
    "all_trials_rt,trial_labels_rt = io.load_trial_file(mouse_traj_dir_rt,load_only_training_sessions_relative_target=True ,skip_15_relative_target=True ,use_extra_trials_relative_target=False,sort_by_trial=True,fix_nan=True,remove_after_food=False,align_to_top=True,group_by='trial',return_group_by_keys=True)\n",
    "all_trials_ft,trial_labels_ft = io.load_trial_file(mouse_traj_dir_ft,load_only_training_sessions_relative_target=True ,skip_15_relative_target=True ,use_extra_trials_relative_target=False,sort_by_trial=True,fix_nan=True,remove_after_food=False,align_to_top=True,group_by='trial',return_group_by_keys=True,max_trial_number=len(all_trials_rt))\n",
    "all_trials_ft_full,_          = io.load_trial_file(mouse_traj_dir_ft,load_only_training_sessions_relative_target=True ,skip_15_relative_target=False,use_extra_trials_relative_target=True ,sort_by_trial=True,fix_nan=True,remove_after_food=False,align_to_top=True,group_by='trial',return_group_by_keys=True,max_trial_number=18)\n",
    "all_trials_rt_p1_complete     = io.load_trial_file(mouse_traj_dir_rt,file_name_expr='mpos_*Probe_*',load_only_training_sessions_relative_target=False,skip_15_relative_target=False,use_extra_trials_relative_target=True,remove_after_food=False,sort_by_trial=True,fix_nan=True,align_to_top=True)\n",
    "all_trials_ft_p1_complete     = io.load_trial_file(mouse_traj_dir_ft,file_name_expr='mpos_*Probe_*',load_only_training_sessions_relative_target=False,skip_15_relative_target=False,use_extra_trials_relative_target=True,remove_after_food=False,sort_by_trial=True,fix_nan=True,align_to_top=True)\n",
    "all_trials_rt                 = tran.remove_path_after_food(all_trials_rt,r_target=None,return_t_to_food=False,force_main_target=True,hole_horizon=None,time_delay_after_food=time_delay_after_food)\n",
    "all_trials_ft                 = tran.remove_path_after_food(all_trials_ft,r_target=None,return_t_to_food=False,force_main_target=True,hole_horizon=None,time_delay_after_food=time_delay_after_food)\n",
    "all_trials_ft_full            = tran.remove_path_after_food(all_trials_ft_full,r_target=None,return_t_to_food=False,force_main_target=True,hole_horizon=None,time_delay_after_food=time_delay_after_food)\n",
    "all_trials_rt_p1              = tran.remove_path_after_food(all_trials_rt_p1_complete,r_target=None,return_t_to_food=False,force_main_target=True,hole_horizon=None,copy_tracks=True,time_delay_after_food=time_delay_after_food)\n",
    "all_trials_ft_p1              = tran.remove_path_after_food(all_trials_ft_p1_complete,r_target=None,return_t_to_food=False,force_main_target=True,hole_horizon=None,copy_tracks=True,time_delay_after_food=time_delay_after_food)\n",
    "\n",
    "hole_horizon_rt_R180          = 5.0 # cm\n",
    "get_hole_horiz_rt_R180        = lambda tr: hole_horizon_rt_R180 if int(tr.mouse_number) in [14,16] else None\n",
    "all_trials_rt_R180            = io.load_trial_file(mouse_traj_dir_rt,file_name_expr='mpos_*_R180_1*',load_only_training_sessions_relative_target=False,skip_15_relative_target=True,use_extra_trials_relative_target=True,sort_by_trial=True,fix_nan=True,remove_after_food=False,group_by='none')#,max_trial_number=n_trials_to_use)\n",
    "all_trials_rt_R180            = io.group_track_list([ tran.remove_path_after_food(tr,r_target=tr.r_target_reverse,return_t_to_food=False,force_main_target=False,hole_horizon=get_hole_horiz_rt_R180(tr),time_delay_after_food=0.0) for tr in all_trials_rt_R180 ],group_by='trial',return_group_keys=False)[0]\n",
    "all_trials_rt_R180            = plib.rotate_trial_file(all_trials_rt_R180,(0,-1),return_only_track=True)\n",
    "\n",
    "save_output_figures = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_trials_rt_R90            = io.load_trial_file(mouse_traj_dir_rt90,file_name_expr='mpos_*',\n",
    "                                                  load_only_training_sessions_relative_target=True,\n",
    "                                                  skip_15_relative_target=False,use_extra_trials_relative_target=False,\n",
    "                                                  sort_by_trial=True,fix_nan=True,remove_after_food=True,group_by='trial',max_trial_number=21)\n",
    "#all_trials_rt_R90            = io.group_track_list([ tran.remove_path_after_food(tr,r_target=tr.r_target_reverse,return_t_to_food=False,force_main_target=False,hole_horizon=get_hole_horiz_rt_R180(tr),time_delay_after_food=0.0) for tr in all_trials_rt_R90 ],group_by='trial',return_group_keys=False)\n",
    "all_trials_rt_R90            = plib.rotate_trial_file(all_trials_rt_R90,(0,-1),return_only_track=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_trials_rt_R90_probe      = io.load_trial_file(mouse_traj_dir_rt90,file_name_expr='mpos_*R90*',\n",
    "                                                  load_only_training_sessions_relative_target=False,\n",
    "                                                  skip_15_relative_target=False,use_extra_trials_relative_target=False,\n",
    "                                                  sort_by_trial=True,fix_nan=True,remove_after_food=False,group_by='none')\n",
    "all_trials_rt_R90_probe      = io.group_track_list([ tran.remove_path_after_food(tr,r_target=tr.r_target_reverse,return_t_to_food=False,force_main_target=False,hole_horizon=get_hole_horiz_rt_R180(tr),time_delay_after_food=0.0) for tr in all_trials_rt_R90_probe ],group_by='trial',return_group_keys=False)[0]\n",
    "all_trials_rt_R90_probe      = plib.rotate_trial_file(all_trials_rt_R90_probe,(0,-1),return_only_track=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[trackfile(file_name='Raw data-Hidden Food Maze-04Nov2022-Trial   103.xlsx', day='13', exper_date='04Nov2022', is_reverse=1, keep_between_targets=False, mouse_gender='M', mouse_number='2', remove_after_food=False, start_location='SW', start_quadrant=2, trial='R90', arena_picture='BKGDimage-20221104_cropped.png', arena_picture_extent=[-105.53609805924413, 92.53703779366703, -72.46542805100178, 76.50943533697632]),\n",
       " trackfile(file_name='Raw data-Hidden Food Maze-04Nov2022-Trial   105.xlsx', day='13', exper_date='04Nov2022', is_reverse=1, keep_between_targets=False, mouse_gender='M', mouse_number='4', remove_after_food=False, start_location='NE', start_quadrant=4, trial='R90', arena_picture='BKGDimage-20221104_cropped.png', arena_picture_extent=[-105.53609805924413, 92.53703779366703, -72.46542805100178, 76.50943533697632])]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trials_rt_R90_probe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[trackfile(file_name='Raw data-Hidden Food Maze-04Nov2022-Trial    98.xlsx', day='12', exper_date='04Nov2022', is_reverse=0, keep_between_targets=False, mouse_gender='M', mouse_number='1', remove_after_food=False, start_location='SW', start_quadrant=2, trial='21', arena_picture='BKGDimage-20221104_cropped.png', arena_picture_extent=[-105.53609805924413, 92.53703779366703, -72.46542805100178, 76.50943533697632]),\n",
       " trackfile(file_name='Raw data-Hidden Food Maze-04Nov2022-Trial    99.xlsx', day='12', exper_date='04Nov2022', is_reverse=0, keep_between_targets=False, mouse_gender='M', mouse_number='2', remove_after_food=False, start_location='SE', start_quadrant=3, trial='21', arena_picture='BKGDimage-20221104_cropped.png', arena_picture_extent=[-105.53609805924413, 92.53703779366703, -72.46542805100178, 76.50943533697632]),\n",
       " trackfile(file_name='Raw data-Hidden Food Maze-04Nov2022-Trial   100.xlsx', day='12', exper_date='04Nov2022', is_reverse=0, keep_between_targets=False, mouse_gender='M', mouse_number='3', remove_after_food=False, start_location='NE', start_quadrant=4, trial='21', arena_picture='BKGDimage-20221104_cropped.png', arena_picture_extent=[-105.53609805924413, 92.53703779366703, -72.46542805100178, 76.50943533697632]),\n",
       " trackfile(file_name='Raw data-Hidden Food Maze-04Nov2022-Trial   101.xlsx', day='12', exper_date='04Nov2022', is_reverse=0, keep_between_targets=False, mouse_gender='M', mouse_number='4', remove_after_food=False, start_location='NW', start_quadrant=1, trial='21', arena_picture='BKGDimage-20221104_cropped.png', arena_picture_extent=[-105.53609805924413, 92.53703779366703, -72.46542805100178, 76.50943533697632])]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trials_rt_R90[-1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rotated trajectories examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sys\n",
    "#if 'modules.traj_analysis' in sys.modules.keys():\n",
    "#    del sys.modules['modules.traj_analysis']\n",
    "#if tran:\n",
    "#    del tran\n",
    "#import modules.traj_analysis as tran\n",
    "\n",
    "#import sys\n",
    "#if 'modules.plot_func' in sys.modules.keys():\n",
    "#    del sys.modules['modules.plot_func']\n",
    "#if pltt:\n",
    "#    del pltt\n",
    "#import modules.plot_func as pltt\n",
    "\n",
    "use_reverse_targets  = True\n",
    "keep_between_targets = False\n",
    "stop_at_food         = True\n",
    "filename_expr        = 'mpos_*Probe2_*'\n",
    "mouse_traj_dir       = r'./experiments/two_targets_rot/mouse_*'\n",
    "\n",
    "hole_horizon          = 10.0 # cm\n",
    "time_delay_after_food = 1.0 # sec\n",
    "\n",
    "input_tracks           = io.load_trial_file(mouse_traj_dir,file_name_expr=filename_expr,align_to_top=True,fix_nan=True,sort_by_trial=True,return_group_by_keys=False,remove_after_food=False)\n",
    "if keep_between_targets:\n",
    "    input_tracks       = tran.keep_path_between_targets(input_tracks,return_t_in_targets=False,hole_horizon=hole_horizon,time_delay_after_food=time_delay_after_food,copy_tracks=True,use_reverse_targets=use_reverse_targets)\n",
    "elif stop_at_food:\n",
    "    input_tracks       = tran.remove_path_after_food(input_tracks,force_main_target=False,return_t_to_food=False,hole_horizon=hole_horizon,time_delay_after_food=time_delay_after_food,copy_tracks=True,use_reverse_targets=use_reverse_targets)\n",
    "\n",
    "trim_trajectories  = True\n",
    "traj_alpha         = 0.5\n",
    "\n",
    "start_vec_align       = (0,-1)\n",
    "\n",
    "ignore_entrance_positions   = False\n",
    "normalize_by                = 'max'\n",
    "hole_horizon_hole_check     = 3.0 #cm\n",
    "threshold_method            = 'ampv'\n",
    "velocity_amplitude_fraction = 0.2\n",
    "use_velocity_minima         = True\n",
    "velocity_min_prominence     = 5.0 # cm/s\n",
    "\n",
    "#if trim_trajectories:\n",
    "#    tracks_to_plot = []\n",
    "#    for tr,tr_trim in zip(all_trials_p2_complete,all_trials_p2):\n",
    "#        t0 = 0.0 # seconds\n",
    "#        if (int(tr.mouse_number) == 35):\n",
    "#            t0 = 35.0 # the mouse doesn't move in the first 35 seconds\n",
    "#        if (int(tr.mouse_number) == 34):\n",
    "#            t0 = 20.0 # the mouse doesn't move in the first 35 seconds\n",
    "#        tr_new = tran.slice_track_by_time(tr,t0=t0,t1=tr_trim.time[-1]+1,copy_track=True)\n",
    "#        tr_new.time -= tr_new.time[0]\n",
    "#        tracks_to_plot.append(tr_new)\n",
    "#else:\n",
    "#    tracks_to_plot = copy.deepcopy(all_trials_p2_complete)\n",
    "\n",
    "tracks_to_plot    = list(misc.flatten_list(copy.deepcopy(input_tracks),only_lists=True))\n",
    "nrows_ncols_tuple = (1,4)\n",
    "first_trial=0\n",
    "\n",
    "\n",
    "all_trials = plib.rotate_trial_file(tracks_to_plot,start_vec_align,True)\n",
    "k_slow,t_slow,r_slow,v_th = misc.unpack_list_of_tuples([ tran.find_slowing_down_close_to_hole(tr,hole_horizon_hole_check,threshold_method=threshold_method,gamma=velocity_amplitude_fraction,\n",
    "                                                                                                 return_pos_from='hole',ignore_entrance_positions=ignore_entrance_positions,\n",
    "                                                                                                 use_velocity_minima=use_velocity_minima,velocity_min_prominence=velocity_min_prominence) for tr in all_trials ])\n",
    "\n",
    "\n",
    "fig_size           = tuple(numpy.array((30,22))*0.55)\n",
    "color_red          = numpy.array((255, 66, 66,255))/255\n",
    "color_blue         = numpy.array(( 10, 30,211,255))/255\n",
    "color_orange       = pltt.get_gradient(10,'orange')[0]\n",
    "ax,lines,panel_ind = pltt.plot_all_tracks_2targets(all_trials,hole_horizon,time_delay_after_food,\n",
    "                                       traj1Args=dict(color                  = plt.get_cmap('cool'),\n",
    "                                                      line_gradient_variable = 'time',\n",
    "                                                      alpha                  = traj_alpha,\n",
    "                                                      show_colorbar    = False,\n",
    "                                                      show_target=True,show_reverse_target=True,show_alt_target=True,show_reverse_alt_target=True,\n",
    "                                                      startArgs        = dict(marker='s',markeredgewidth=3  ,markersize=10,color='k'       ,fillstyle='full',markerfacecolor='w',label='Start',             labelArgs=dict(fontsize=12,va='top'   ,ha='left' ,color='k',pad=(4,-1)  ) ),\n",
    "                                                      targetArgs       = dict(marker='^',markeredgewidth=2  ,markersize=10,color=color_blue,fillstyle='none',label='REL B',             labelArgs=dict(fontsize=16,va='bottom',ha='left' , fontweight='bold',color=color_blue,pad=( 2, 2))),\n",
    "                                                      targetAltArgs    = dict(marker='^',markeredgewidth=2  ,markersize=10,color=color_red ,fillstyle='none',label='REL A',             labelArgs=dict(fontsize=16,va='bottom',ha='left' , fontweight='bold',color=color_red ,pad=( 2, 2))),\n",
    "                                                      targetRevArgs    = dict(marker='o',markeredgewidth=2  ,markersize= 9,color=color_blue,fillstyle='none',label='B'    ,             labelArgs=dict(fontsize=16,va='bottom',ha='left' , fontweight='bold',color=color_blue,pad=( 2, 2))),\n",
    "                                                      targetAltRevArgs = dict(marker='o',markeredgewidth=2  ,markersize= 9,color=color_red ,fillstyle='none',label='A'    ,zorder=10000,labelArgs=dict(fontsize=16,va='bottom',ha='left' , fontweight='bold',color=color_red ,pad=( 2, 2)))),\n",
    "                                       traj2Args=dict(color=(0,0,0),alpha=traj_alpha),start_align_vector=start_vec_align,\n",
    "                                       trim_trajectories=False,return_panel_ind=True,show_arena_holes=True,\n",
    "                                       probe_title='',fig_size=fig_size,nrows_ncols_tuple=nrows_ncols_tuple,show_probetitle=False,fix_mouse_order_in_probe=False)\n",
    "\n",
    "for a,tr in zip(ax.flatten(),all_trials):\n",
    "    r    = tr.r_start.copy()\n",
    "    r[1] = -r[1]\n",
    "    pltt.plot_point(r,'Start on training',ax=a,fmt='d',color=0.6*numpy.ones(3),pointArgs=dict(markeredgewidth=3,markersize=10,markerfacecolor='w',fillstyle='full'),pad=(5,4),ha='left',fontsize=12,labelcolor=0.5*numpy.ones(3))\n",
    "text_h = [tt for tt in misc.flatten_list(lines) if (('text' in str(type(tt))) and (tt.get_text() != 'Start'))]\n",
    "#text_h[11].set(va='top')   # A,i=0,j=3\n",
    "#text_h[3].set(ha='center') # A,i=1,j=1\n",
    "#text_h[-3].set(va='top')   # A,i=1,j=2\n",
    "#text_h[-1].set(ha='right') # A,i=1,j=3\n",
    "#text_h[6].set(va='top')    # B,i=0,j=1\n",
    "#text_h[4].set(va='top',ha='center',position=numpy.array(text_h[4].get_position())-numpy.array((2,6)))    # B,i=0,j=0\n",
    "\n",
    "\n",
    "ax = pltt.tight_arena_panels(ax,adjust_title_position=False,dy_amid_panels=0.02)\n",
    "\n",
    "cax = []\n",
    "for k,(ind,r,tr) in enumerate(zip(k_slow,r_slow,tracks_to_plot)):\n",
    "    t_seq = tr.time[ind]/tr.time[-1]\n",
    "    i,j = numpy.unravel_index(k,nrows_ncols_tuple)\n",
    "    pts=pltt.plot_trajectory_points(r,ax=ax[i,j],use_scatter=True,s=1e2*t_seq,marker='o',linewidth=1,edgecolor=plt.get_cmap('cool')(t_seq),zorder=9000,alpha=0.8)\n",
    "    pts.set_facecolor('none')\n",
    "    if j == 0:\n",
    "        ax[i,j].text(-75,-50,'Probe B-A 180$^\\\\circ$',rotation=90,va='bottom',ha='center',fontsize=20,fontweight='bold')\n",
    "        x_lim = ax[i,j].get_xlim()\n",
    "        y_lim = ax[i,j].get_xlim()\n",
    "        lh = pltt.plot_horizontal_lines(-70,ax[i,j],xMin=-55,xMax=120*8,color='k',linewidth=1.5,linestyle='-')\n",
    "        lh.set_clip_on(False)\n",
    "        ax[i,j].set_xlim(x_lim)\n",
    "        ax[i,j].set_ylim(y_lim)\n",
    "    if j == (nrows_ncols_tuple[1]-1):\n",
    "        cax.append(pltt._add_colorbar(ax[i,j],'cool',w_fraction_of_ax=0.03,h_fraction_of_ax=0.2,p0=(1.01,1.1),minmax_tick_labels=('start','end'),title='Time\\ncourse',titleArgs=dict(color='k',fontsize=12),ticklabelsArgs=dict(color='k')))\n",
    "\n",
    "\n",
    "if save_output_figures:\n",
    "    detection_label = 'vmin' if use_velocity_minima else 'slowdown'\n",
    "    fileName        = f'{output_dir}/twotargets_ROTPr2_trajectories_ALL_TRIALS_PROBE_A-B_{detection_label}.png'\n",
    "    print(' ... saving ', fileName)\n",
    "    plt.savefig(  fileName,format='png',dpi=300,facecolor=(1,1,1,1),bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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