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