import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates import matplotlib.colors as mcolors import matplotlib.gridspec as gridspec from mpl_toolkits.axes_grid1.inset_locator import inset_axes from IPython import embed from scipy import stats, optimize import pandas as pd import math import os from IPython import embed from eventdetection import threshold_crossings, merge_events import helper_functions as hf from params import * from statisitic_functions import significance_bar, cohen_d import itertools def get_recording_number_in_time_bins(time_bins): """ Calculates the number of the recordings in the time bins :param time_bins: numpy array with borders of the time bins :return: time_bins_recording: numpy array with the number of recordings to that specific time bin """ # variables time_bins_recordings = np.zeros(len(time_bins) - 1) # load data for index, filename_idx in enumerate([0, 1, 2, 3]): filename = sorted(os.listdir('../data/'))[filename_idx] time_points = np.load('../data/' + filename + '/all_hms.npy', allow_pickle=True) # in which bins is this recording, fill time_bins_recordings unique_time_points = np.unique(np.hstack(time_points)) for idx, tb in enumerate(time_bins[:-1]): if np.any((unique_time_points >= tb) & (unique_time_points <= time_bins[idx + 1])): time_bins_recordings[idx] += 1 return time_bins_recordings def func(x, a, tau, c): return a * np.exp(-x / tau) + c def calc_movement(cbf, i): movement = cbf[0, :, i] + cbf[1, :, i] movement[np.isnan(movement)] = 0 re_mov = cbf[0, :, i] - cbf[1, :, i] re_mov[np.isnan(re_mov)] = 0 return movement, re_mov if __name__ == '__main__': ################################################################################################################### # parameter and variables # plot params inch = 2.54 c = 0 cat_v1 = [0, 0, 750, 0] cat_v2 = [750, 750, 1000, 1000] cat_n = ['Eigenmannia', 'Apteronotus', 'Apteronotus'] # time # time_bins 5 min time_factor = 60 * 60 # tb2 = np.arange(0, 24 * time_factor + 1, 2) tb5 = np.arange(0, 24 * time_factor + 1, 5) # tb10 = np.arange(0, 24 * time_factor + 1, 10) tb15 = np.arange(0, 24 * time_factor + 1, 15) # tb30 = np.arange(0, 24 * time_factor + 1, 30) tb60 = np.arange(0, 24 * time_factor + 1, 60) tb150 = np.arange(0, 24 * time_factor + 1, 150) # tb180 = np.arange(0, 24 * time_factor + 1, 180) tb300 = np.arange(0, 24 * time_factor + 1, 300) # time_edges = np.array([4.5, 6.5, 16.5, 18.5]) * time_factor # day = time_bins[:-1][(time_bins[:-1] >= time_edges[1]) & (time_bins[:-1] <= time_edges[2])] # dusk = time_bins[:-1][(time_bins[:-1] >= time_edges[2]) & (time_bins[:-1] <= time_edges[3])] # night = time_bins[:-1][(time_bins[:-1] <= time_edges[0]) | (time_bins[:-1] >= time_edges[3])] # dawn = time_bins[:-1][(time_bins[:-1] >= time_edges[0]) & (time_bins[:-1] <= time_edges[1])] ################################################################################################################### # load data ################################################################################################################### # load all the data of one day # cbf2 = np.load('../data/cbf2.npy', allow_pickle=True) cbf5 = np.load('../data/cbf5.npy', allow_pickle=True) # cbf10 = np.load('../data/cbf10.npy', allow_pickle=True) cbf15 = np.load('../data/cbf15.npy', allow_pickle=True) # cbf30 = np.load('../data/cbf30.npy', allow_pickle=True) cbf60 = np.load('../data/cbf60.npy', allow_pickle=True) cbf150 = np.load('../data/cbf150.npy', allow_pickle=True) # cbf180 = np.load('../data/cbf180.npy', allow_pickle=True) cbf300 = np.load('../data/cbf300.npy', allow_pickle=True) stl = np.load('../data/stl.npy', allow_pickle=True) names = np.load('../data/n.npy', allow_pickle=True) freq = np.load('../data/f.npy', allow_pickle=True) trajectories = np.load('../data/trajectories.npy', allow_pickle=True) trajec_x = np.load('../data/trajec_x.npy', allow_pickle=True) ############################################################################################################### # variables for index, filename_idx in enumerate([0]): filename = sorted(os.listdir('../data/'))[filename_idx] all_Ctime_v = np.load('../data/' + filename + '/all_Ctime_v.npy', allow_pickle=True) sampling_rate = 1 / np.diff(all_Ctime_v[0])[0] # in sec cbf_counter = 0 ################################################################################################################### # analysis for i in range(len(trajectories)): if names[i] == 'unknown': continue # mov2, re_mov2 = calc_movement(cbf2, cbf_counter) mov5, re_mov5 = calc_movement(cbf5, cbf_counter) # mov10, re_mov10 = calc_movement(cbf10, cbf_counter) mov15, re_mov15 = calc_movement(cbf15, cbf_counter) # mov30, re_mov30 = calc_movement(cbf30, cbf_counter) mov60, re_mov60 = calc_movement(cbf60, cbf_counter) mov150, re_mov150 = calc_movement(cbf150, cbf_counter) # mov180, re_mov180 = calc_movement(cbf180, cbf_counter) mov300, re_mov300 = calc_movement(cbf300, cbf_counter) cbf_counter += 1 trajec = trajectories[i] t_x = trajec_x[i] fig = plt.figure(constrained_layout=True, figsize=[20 / inch, 26 / inch]) gs = gridspec.GridSpec(ncols=2, nrows=6, figure=fig, hspace=0.01, wspace=0.01, height_ratios=[1, 1, 1, 1, 1, 1], width_ratios=[4,1],left=0.1, bottom=0.15, right=0.95, top=0.95) ax0 = fig.add_subplot(gs[0, 0]) ax1 = fig.add_subplot(gs[1, 0], sharex=ax0) ax2 = fig.add_subplot(gs[2, 0], sharex=ax0) ax3 = fig.add_subplot(gs[3, 0], sharex=ax0) ax4 = fig.add_subplot(gs[4, 0], sharex=ax0) ax5 = fig.add_subplot(gs[5, 0], sharex=ax0) # ax6 = fig.add_subplot(gs[6, 0], sharex=ax0) ax11 = fig.add_subplot(gs[1, 1]) ax21 = fig.add_subplot(gs[2, 1]) ax31 = fig.add_subplot(gs[3, 1]) ax41 = fig.add_subplot(gs[4, 1]) ax51 = fig.add_subplot(gs[5, 1]) # ax61 = fig.add_subplot(gs[6, 1]) ax0.plot(t_x/60/60, trajec) # ax1.plot(tb2[:-1]/60/60, mov2) ax1.plot(tb5[:-1]/60/60, mov5) # ax3.plot(tb10[:-1]/60/60, mov10) ax2.plot(tb15[:-1]/60/60, mov15) # ax3.plot(tb30[:-1]/60/60, mov30) ax3.plot(tb60[:-1]/60/60, mov60) ax4.plot(tb150[:-1]/60/60, mov150) ax5.plot(tb300[:-1]/60/60, mov300) # ax11.hist(mov2, bins=np.linspace(1,np.max(mov2),int(np.max(mov2)))) ax11.hist(mov5, bins=np.linspace(1,np.max(mov5)+1,int(np.max(mov5)+1))) # ax31.hist(mov10, bins=np.linspace(1,np.max(mov10),int(np.max(mov10)))) ax21.hist(mov15, bins=np.linspace(1,np.max(mov15)+1,int(np.max(mov15)+1))) # ax31.hist(mov30, bins=np.linspace(1,np.max(mov30),int(np.max(mov30)))) ax31.hist(mov60, bins=np.linspace(1,np.max(mov60)+1,int(np.max(mov60)+1))) ax41.hist(mov150, bins=np.linspace(1,np.max(mov150)+1,int(np.max(mov150)+1))) ax51.hist(mov300, bins=np.linspace(1,np.max(mov300)+1,int(np.max(mov300)+1))) # ax7.hist(mov2) tag = ['trajectory', '5', '15', '60', '150', '300'] for idx, ax in enumerate([ax0, ax1, ax2, ax3, ax4, ax5]): xl_min=np.min(t_x)/60/60 xl_max=np.max(t_x)/60/60 ax.set_xlim([xl_min ,xl_max]) ax.text(0.01, 0.7, tag[idx], transform=ax.transAxes, fontsize='small') if ax != ax0: ax.set_ylabel('n') ax0.set_ylim([0,15]) ax0.invert_yaxis() ax0.set_ylabel('electrode') ax5.set_xlabel('Time [h]') fig.suptitle('EODf '+str(np.round(freq[i],2))+' '+names[i], fontsize=12) # embed() # quit() fig.savefig('../../../jan_plots/trajec'+str(i)+'.pdf') plt.close() # ############################################################################################################### # # roll time axis # start = [] # stop = [] # for j in range(len(roaming_events)): # start.extend(roaming_events[j][0]) # stop.extend(roaming_events[j][1]) # # N_rec_time_bins = get_recording_number_in_time_bins(time_bins[::int((60 / bin_len) * 60)]) # # # rolled time axis for nicer plot midnight in the middle start noon # N_start, bin_edges = np.histogram(np.array(start) * 5, bins=time_bins[::int((60 / bin_len) * 60)]) # N_stop, bin_edges2 = np.histogram(np.array(stop) * 5, bins=time_bins[::int((60 / bin_len) * 60)]) # rolled_start = np.roll(N_start / N_rec_time_bins, int(len(N_start) / 2)) # rolled_stop = np.roll(N_stop / N_rec_time_bins, int(len(N_stop) / 2)) # rolled_bins = (bin_edges[:-1] / time_factor) + 0.5 # # ############################################################################################################### # # figure 1: max_channel_changes per time zone and per duration of the roaming event # fig = plt.figure(constrained_layout=True, figsize=[15 / inch, 14 / inch]) # gs = gridspec.GridSpec(ncols=6, nrows=3, figure=fig, hspace=0.01, wspace=0.01, # height_ratios=[1, 1, 1], width_ratios=[1, 1, 1, 1, 1, 1], left=0.1, bottom=0.15, right=0.95, # top=0.95) # # ax0 = fig.add_subplot(gs[0, :]) # ax1 = fig.add_subplot(gs[1, :3]) # ax2 = fig.add_subplot(gs[1, 3:], sharex=ax1) # ax3 = fig.add_subplot(gs[2, :2], sharey=ax2) # ax4 = fig.add_subplot(gs[2, 2:4], sharey=ax2) # ax5 = fig.add_subplot(gs[2, 4:]) # # # axins = inset_axes(ax1, width='30%', height='60%') # # # bar plot # ax0.bar(rolled_bins, rolled_start, color=color2[4]) # print('bar plot') # print('day: mean ', np.round(np.mean([rolled_start[:6], rolled_start[18:]]), 2), # ' std: ', np.round(np.std([rolled_start[:6], rolled_start[18:]]), 2)) # # print('night: mean ', np.round(np.mean(rolled_start[6:18]), 2), # ' std: ', np.round(np.std(rolled_start[6:18]), 2)) # # ax0.plot([16.5, 6.5], [20, 20], color=color_diffdays[0], lw=7) # ax0.plot([16.5, 18.5], [20, 20], color=color_diffdays[3], lw=7) # ax0.plot([4.5, 6.5], [20, 20], color=color_diffdays[3], lw=7) # # ############################################################################################################### # # curve_fit: tau, std, n # curvefit_stat = [] # # xdata = np.linspace(0.0, 10., 500) # y_speeds = [] # for plot_zone, color_zone, day_zone, pos_zone in \ # zip([day, dusk, night, dawn], [6, 1, 4, 0], ['day', 'dusk', 'night', 'dawn'], [1, 2, 3, 4]): # # # boxplot ax1 # props_e = dict(linewidth=2, color=color2[color_zone]) # bp = ax1.boxplot(dauer[np.in1d(wann * 5, plot_zone)], positions=[pos_zone], widths=0.7, # showfliers=False, vert=False, # boxprops=props_e, medianprops=props_e, capprops=props_e, whiskerprops=props_e) # # x_n = [item.get_xdata() for item in bp['whiskers']][1][1] # n = len(dauer[np.in1d(wann * 5, plot_zone)]) # ax1.text(x_n + 2, pos_zone, str(n), ha='left', va='center') # print('dauer: ', day_zone, np.median(dauer[np.in1d(wann * 5, plot_zone)]), # ' 25, 75: ', np.percentile(dauer[np.in1d(wann * 5, plot_zone)], [25, 75])) # # # curve fit # x_dauer = dauer[dauer <= 10][np.in1d(wann[dauer <= 10] * 5, plot_zone)] # y_speed = speeds[dauer <= 10][np.in1d(wann[dauer <= 10] * 5, plot_zone)] # y_speeds.append(y_speed) # # popt, pcov = optimize.curve_fit(func, x_dauer, y_speed) # perr = np.sqrt(np.diag(pcov)) # print(day_zone, popt, 'perr', perr[1]) # curvefit_stat.append(np.array([popt[1], perr[1], n])) # # # plot dauer vs speed # ax2.plot(x_dauer, y_speed, 'o', alpha=0.3, color=color2[color_zone]) # # ax3.plot(x_dauer, y_speed, 'o', alpha=0.3, color=color2[color_zone]) # # # plot curve fit # ax4.plot(xdata, func(xdata, *popt), '-', color=color2[color_zone], label=day_zone) # ax4.set_ylim(ax2.get_ylim()) # # curvefit_stat = np.array(curvefit_stat) # # plot std of tau # ax5.bar([0, 1, 2, 3], curvefit_stat[:, 0], yerr=curvefit_stat[:, 1], color=color2[4]) # # ############################################################################################################### # # statistic # day_group = [day, dusk, night, dawn] # for subset in itertools.combinations([0, 1, 2, 3], 2): # mean1, std1, n1 = curvefit_stat[subset[0]] # mean2, std2, n2 = curvefit_stat[subset[1]] # t, p = stats.ttest_ind_from_stats(mean1, std1, n1, mean2, std2, n2) # d = cohen_d(y_speeds[subset[0]], y_speeds[subset[1]]) # print(['day', 'dusk', 'night', 'dawn'][subset[0]], ['day', 'dusk', 'night', 'dawn'][subset[1]], 't: ', # np.round(t, 2), 'p: ', np.round(p, 4), 'd: ', d) # # print(stats.mannwhitneyu(dauer[dauer <= 100][np.in1d(wann[dauer <= 100] * 5, day_group[subset[0]])], # dauer[dauer <= 100][np.in1d(wann[dauer <= 100] * 5, day_group[subset[1]])])) # if subset[0] == 0 and subset[1] == 2: # significance_bar(ax5, p, None, subset[0], subset[1], 4.) # # ############################################################################################################### # # labels # ax0.set_ylabel('# Roaming Events', fontsize=fs) # ax0.set_xticks([0, 6, 12, 18, 24]) # ax0.set_xticklabels(['12:00', '18:00', '00:00', '06:00', '12:00']) # ax0.set_xlabel('Time', fontsize=fs) # # ax1.set_yticks([1, 2, 3, 4]) # ax1.set_yticklabels(['day', 'dusk', 'night', 'dawn']) # ax1.set_xlabel('Duration [min]', fontsize=fs) # ax1.invert_yaxis() # # ax2.set_xlabel('Duration [min]', fontsize=fs) # ax2.set_ylabel('Speed [m/min]', fontsize=fs) # ax2.set_ylim([0, 27]) # # ax3.set_ylabel('Speed [m/min]', fontsize=fs) # ax3.set_xlabel('Duration [min]', fontsize=fs) # ax3.set_xlim([0, 10]) # # ax4.set_xlabel('Duration [min]', fontsize=fs) # ax4.set_xlim([0, 10]) # # ax5.set_xticks([0, 1, 2, 3]) # ax5.set_xticklabels(['day', 'dusk', 'night', 'dawn'], rotation=45) # ax5.set_ylabel(r'$\tau$') # # tagx = [-0.05, -0.07, -0.07, -0.17, -0.17, -0.17] # for idx, ax in enumerate([ax0, ax1, ax2, ax3, ax4, ax5]): # ax.make_nice_ax() # ax.text(tagx[idx], 1.05, chr(ord('A') + idx), transform=ax.transAxes, fontsize='large') # # # fig.align_ylabels() # # fig.savefig(save_path + 'roaming_events.pdf') # # fig.savefig(save_path_pres + 'roaming_events.pdf') # # ############################################################################################################### # # figure 2: # linregress_stat = [] # fig2 = plt.figure(constrained_layout=True, figsize=[15 / inch, 10 / inch]) # gs = gridspec.GridSpec(ncols=1, nrows=2, figure=fig2, hspace=0.05, wspace=0.0, # height_ratios=[1, 2], left=0.1, bottom=0.15, right=0.95, top=0.95) # # ax21 = fig2.add_subplot(gs[0, 0]) # ax23 = fig2.add_subplot(gs[1, 0]) # # for plot_zone, color_zone, day_zone, bar_pos, pos_zone in \ # zip([day, dusk, night, dawn], [6, 1, 4, 0], ['day', 'dusk', 'night', 'dawn'], [-0.3, -0.1, 0.1, 0.3], # [0, 1, 2, 3]): # # pdf # N_roam, bin_roam = np.histogram(roam_dist[np.in1d(wann * 5, plot_zone)], bins=np.linspace(0, 15, 16)) # N_roam = N_roam / np.sum(N_roam) / (bin_roam[1] - bin_roam[0]) # ax21.plot(bin_roam[:-1], N_roam, color=color2[color_zone], label=day_zone) # ax21.set_xlabel('Distance [m]') # ax21.set_ylabel('PDF') # ax21.set_xlim([1, 15]) # # # duration vs distance # ax23.plot(dauer[np.in1d(wann * 5, plot_zone)], roam_dist[np.in1d(wann * 5, plot_zone)], 'o', # color=color2[color_zone], alpha=0.3) # res = stats.linregress(dauer[np.in1d(wann * 5, plot_zone)], roam_dist[np.in1d(wann * 5, plot_zone)]) # print(day_zone, res.slope) # linregress_stat.append(np.array([res.slope, res.stderr, len(dauer[np.in1d(wann * 5, plot_zone)])])) # ax23.set_xlabel('Duration [min]') # ax23.set_ylabel('Distance [m]') # ax23.set_xlim([0, 100]) # # print('linregress') # for subset in itertools.combinations([0, 1, 2, 3], 2): # mean1, std1, n1 = linregress_stat[subset[0]] # mean2, std2, n2 = linregress_stat[subset[1]] # t, p = stats.ttest_ind_from_stats(mean1, std1, n1, mean2, std2, n2) # d = cohen_d(y_speeds[subset[0]], y_speeds[subset[1]]) # # print(['day', 'dusk', 'night', 'dawn'][subset[0]], ['day', 'dusk', 'night', 'dawn'][subset[1]], 't: ', # # np.round(t, 2), 'p: ', np.round(p, 4), 'd: ', d) # # print(np.round(0.05 / 6, 4)) # # for axis in [ax21, ax23]: # axis.make_nice_ax() # # ax21.legend(loc='best', bbox_to_anchor=(0.5, 0.7, 0.5, 0.5), ncol=2) # # fig2.savefig(save_path_pres + 'roaming_distance.pdf') # fig2.savefig(save_path + 'roaming_distance.pdf') # # plt.show() # # # df = pd.DataFrame({'duration': dauer, 'speed': speeds, 'distance': roam_dist}) # embed() # quit()