512 lines
22 KiB
Python
512 lines
22 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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def gauss(t, shift, sigma, size, norm = False):
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if not hasattr(shift, '__len__'):
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g = np.exp(-((t - shift) / sigma) ** 2 / 2) * size
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if norm:
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g = g / (np.sum(g) * (t[1] - t[0]))
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return g
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else:
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t = np.array([t, ] * len(shift))
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res = np.exp(-((t.transpose() - shift).transpose() / sigma) ** 2 / 2) * size
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return res
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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 = 2.45
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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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time_bins = np.arange(0, 24 * time_factor + 1, bin_len)
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# percent roaming
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re = []
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roaming_events = []
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roaming_threshold = 2.1
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roaming_merge = 20 # in minutes
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roaming_exclusion = 0.25
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iai = []
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dauer = []
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wann = []
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max_move = []
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distances = []
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percent = []
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speeds = []
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speed_transit = []
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max_move_boxes = [[], [], [], []]
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fish_names = []
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roam_dist = []
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roam_start = []
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conv_arrays = []
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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/cbf15.npy', allow_pickle=True)
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stl = np.load('../data/stl.npy', allow_pickle=True)
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freq = np.load('../data/f.npy', allow_pickle=True)
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names = np.load('../data/n.npy', allow_pickle=True)
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speed_average = np.load('../data/speed.npy', allow_pickle=True)
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trial_dur = np.load('../data/trial_dur.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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movement, re_mov = calc_movement(cbf2, cbf_counter)
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cbf_counter += 1
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up_times, down_times = threshold_crossings(movement, roaming_threshold)
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u, d = merge_events(up_times, down_times, 4 * roaming_merge)
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roam_dur = np.diff(np.array([u, d]), axis=0)[0]
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ausschlag = []
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distance = []
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for a_idx in range(len(u)):
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ausschlag.append(np.nansum(movement[u[a_idx]:u[a_idx] + roam_dur[a_idx] + 1]))
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distance.append(np.max(re_mov[u[a_idx]:u[a_idx] + roam_dur[a_idx] + 1]))
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ausschlag = np.array(ausschlag)
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distance = np.array(distance)
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speed = np.array(ausschlag / (roam_dur / 4))
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# roaming events append only for the roaming fish
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if not stl[i]:
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if np.any(movement > roaming_threshold):
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c += 1
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re.append(np.array([up_times, down_times]))
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roaming_events.append(np.array([u * 3, d * 3])) # * 3 because than in 5 s intervals
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iai.extend(np.diff(sorted(np.hstack([u, d]))))
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# distance
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for dist_i in range(len(u)):
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try:
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roam_traj = trajectories[i][
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(trajec_x[i] >= u[dist_i] * 15) & (trajec_x[i] < d[dist_i] * 15 + 15)]
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di = np.max(roam_traj) - np.min(roam_traj)
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roam_dist.append(di)
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roam_start.append(roam_traj[0])
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except:
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plt.plot(trajec_x[i], trajectories[i])
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plt.plot(np.arange(0, len(movement)) * 5 * 3, movement)
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plt.plot(u * 5 * 3, np.ones_like(u), 'o')
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plt.plot(d * 5 * 3 + 15, np.ones_like(d), 'o')
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embed()
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quit()
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# append
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dauer.extend(roam_dur / 4)
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wann.extend(u * 3) # * 3 because than in 5 s intervals
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max_move.extend(ausschlag)
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distances.append(distance)
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speeds.extend(speed)
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# percent
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trial_dur = np.diff([np.where(~np.isnan(cbf2[2, :, cbf_counter - 1]))[0][0],
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np.where(~np.isnan(cbf2[2, :, cbf_counter - 1]))[0][-1]])[0]
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if names[i] == 'Eigenmannia':
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print(np.round(trial_dur / 4, 2), np.round(np.sum(roam_dur) / 4, 2),
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np.round(np.sum(roam_dur) / trial_dur * 100, 2))
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percent.append(
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np.array([trial_dur / 4, np.sum(roam_dur) / 4, (np.sum(roam_dur) / 4) / trial_dur * 100]))
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else:
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speed_transit.append(speed_average[i])
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print(c)
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# embed()
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# quit()
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###############################################################################################################
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# correction
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wann = np.hstack(np.array(wann))
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dauer = np.hstack(np.array(dauer))
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max_move = np.hstack(np.array(max_move))
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speeds = np.hstack(np.array(speeds))
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roam_dist = np.array(roam_dist)
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roam_start = np.array(roam_start)
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# all roaming intervals less than 30 seconds excluded
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wann = wann[dauer > roaming_exclusion]
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max_move = max_move[dauer > roaming_exclusion]
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speeds = speeds[dauer > roaming_exclusion]
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roam_dist = roam_dist[dauer > roaming_exclusion]
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roam_start = roam_start[dauer > roaming_exclusion]
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dauer = dauer[dauer > roaming_exclusion]
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print('median dauer: ', np.median(dauer), ' 25, 75: ', np.percentile(dauer, [25, 75]))
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###############################################################################################################
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# statistic
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n, bin_edges = np.histogram(iai, bins=np.arange(0, np.max(iai) + 1, 1))
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b, a, r, p, std = stats.linregress(dauer, max_move)
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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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N_rec_time_bins = get_recording_number_in_time_bins(time_bins[::int((60 / bin_len) * 60)])
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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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# 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, 17 / inch])
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gs = gridspec.GridSpec(ncols=6, nrows=4, figure=fig, hspace=0.01, wspace=0.01,
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height_ratios=[1, 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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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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ax6 = fig.add_subplot(gs[3, :])
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# axins = inset_axes(ax1, width='30%', height='60%')
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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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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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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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# curve_fit: tau, std, n
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curvefit_stat = []
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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], [0, 1, 2, 3], ['day', 'dusk', 'night', 'dawn'], [1, 2, 3, 4]):
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# boxplot ax1
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props_e = dict(linewidth=2, color=color_dadunida[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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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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# 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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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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# plot dauer vs speed
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x_dauer = dauer[dauer <= 100][np.in1d(wann[dauer <= 100] * 5, plot_zone)]
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y_speed = speeds[dauer <= 100][np.in1d(wann[dauer <= 100] * 5, plot_zone)]
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ax2.plot(x_dauer, y_speed, 'o', alpha=0.3, color=color_dadunida[color_zone])
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ax3.plot(x_dauer, y_speed, 'o', alpha=0.3, color=color_dadunida[color_zone])
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# plot curve fit
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ax4.plot(xdata, func(xdata, *popt), '-', color=color_dadunida[color_zone], label=day_zone)
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ax4.set_ylim(ax2.get_ylim())
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# distance
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pdf_x_dist = np.arange(0, 15, 0.1)
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conv_array = np.zeros(len(pdf_x_dist))
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for e in roam_dist[np.in1d(wann * 5, plot_zone)]:
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conv_array += gauss(pdf_x_dist, e, 1, 0.2, norm=True)
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conv_array = conv_array / np.sum(conv_array) / 0.1
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conv_arrays.append(conv_array)
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ax6.plot(pdf_x_dist, conv_array, color=color_dadunida[color_zone], label=day_zone)
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ax6.plot([pdf_x_dist[np.cumsum(conv_array) < 5][-1], pdf_x_dist[np.cumsum(conv_array) < 5][-1]],
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[-1.0, conv_array[np.cumsum(conv_array) < 5][-1]], color=color_dadunida[color_zone])
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# print(day_zone, '50', pdf_x_dist[np.cumsum(conv_array) < 5][-1],
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# '25', pdf_x_dist[np.cumsum(conv_array) < 2.5][-1], '75', pdf_x_dist[np.cumsum(conv_array) < 7.5][-1])
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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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# 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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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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print(np.round(stats.ks_2samp(np.cumsum(conv_arrays[subset[0]]), np.cumsum(conv_arrays[subset[1]])), 3))
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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], 3.)
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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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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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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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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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ax4.set_xlabel('Duration [min]', fontsize=fs)
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ax4.set_xlim([0, 10])
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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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ax6.set_xlabel('Distance [m]')
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ax6.set_ylabel('PDF')
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ax6.set_xlim([0, 15])
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ax6.set_ylim([-0.01, 0.3])
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tagx = [-0.05, -0.07, -0.07, -0.17, -0.17, -0.17, -0.05]
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for idx, ax in enumerate([ax0, ax1, ax2, ax3, ax4, ax5, ax6]):
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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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# fig.savefig(save_path + 'roaming_events.pdf')
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# fig.savefig(save_path_pres + 'roaming_events.pdf')
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plt.show()
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# df = pd.DataFrame({'duration': dauer, 'speed': speeds, 'distance': roam_dist})
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###############################################################################################################
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# figure supplements
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fig = plt.figure(constrained_layout=True, figsize=[15 / inch, 12 / inch])
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gs = gridspec.GridSpec(ncols=2, nrows=2, figure=fig, hspace=0.01, wspace=0.01,
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height_ratios=[1, 1], width_ratios=[1, 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[0, 1])
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ax2 = fig.add_subplot(gs[1, 0])
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ax3 = fig.add_subplot(gs[1, 1])
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for plot_zone, color_zone, day_zone, pos_zone, ax in \
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zip([day, dusk, night, dawn], [0, 1, 2, 3], ['day', 'dusk', 'night', 'dawn'], [1, 2, 3, 4], [ax0, ax1, ax2, ax3]):
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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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popt, pcov = optimize.curve_fit(func, x_dauer, y_speed)
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|
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# plot dauer vs speed
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ax.plot(xdata, func(xdata, *popt), '-', color=color_dadunida[color_zone], label=day_zone)
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ax.plot(x_dauer, y_speed, 'o', alpha=0.3, color=color_dadunida[color_zone])
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print(len(x_dauer))
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ax.set_ylabel('Speed [m/min]', fontsize=fs)
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ax.set_xlabel('Duration [min]', fontsize=fs)
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ax.make_nice_ax()
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ax.text(-0.218, 0.9, chr(ord('A') + color_zone), transform=ax.transAxes, fontsize='large')
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ax.text(0.8, 0.9, day_zone, transform=ax.transAxes, fontsize='large')
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ax.set_ylim([0, 30])
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# fig.savefig(save_path + 'supplements_roaming.pdf')
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embed()
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quit()
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|
|
|
|
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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, 15 / inch])
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|
gs = gridspec.GridSpec(ncols=2, nrows=3, figure=fig, hspace=0.01, wspace=0.01,
|
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height_ratios=[1, 1, 1], width_ratios=[1, 1], left=0.1, bottom=0.15, right=0.95,
|
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top=0.95)
|
|
|
|
ax0 = fig.add_subplot(gs[0, :])
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|
ax1 = fig.add_subplot(gs[1, 0])
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ax2 = fig.add_subplot(gs[1, 1], sharex=ax1)
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ax6 = fig.add_subplot(gs[2, :])
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|
|
|
# bar plot
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ax0.bar(rolled_bins, rolled_start, color=color2[4])
|
|
|
|
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
|
|
for plot_zone, color_zone, day_zone, pos_zone in \
|
|
zip([day, dusk, night, dawn], [0, 1, 2, 3], ['day', 'dusk', 'night', 'dawn'], [1, 2, 3, 4]):
|
|
# boxplot ax1
|
|
props_e = dict(linewidth=2, color=color_dadunida[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')
|
|
|
|
# plot dauer vs speed
|
|
x_dauer = dauer[dauer <= 100][np.in1d(wann[dauer <= 100] * 5, plot_zone)]
|
|
y_speed = speeds[dauer <= 100][np.in1d(wann[dauer <= 100] * 5, plot_zone)]
|
|
ax2.plot(x_dauer, y_speed, 'o', alpha=0.3, color=color_dadunida[color_zone])
|
|
|
|
pdf_x_dist = np.arange(0, 15, 0.1)
|
|
conv_array = np.zeros(len(pdf_x_dist))
|
|
|
|
for e in roam_dist[np.in1d(wann * 5, plot_zone)]:
|
|
conv_array += gauss(pdf_x_dist, e, 1, 0.2, norm=True)
|
|
|
|
conv_array = conv_array / np.sum(conv_array) / 0.1
|
|
conv_arrays.append(conv_array)
|
|
print(day_zone, 'percentil 25,50,75:', np.round(np.percentile(conv_array, [25,50,75]), 4))
|
|
|
|
ax6.plot(pdf_x_dist, conv_array, color=color_dadunida[color_zone], label=day_zone)
|
|
###############################################################################################################
|
|
# 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])
|
|
|
|
ax6.set_xlabel('Distance [m]')
|
|
ax6.set_ylabel('PDF')
|
|
# ax6.set_xscale('symlog')
|
|
# ax6.set_xlim([0, 15])
|
|
|
|
tagx = [-0.05, -0.1, -0.1, -0.05]
|
|
for idx, ax in enumerate([ax0, ax1, ax2, ax6]):
|
|
ax.make_nice_ax()
|
|
ax.text(tagx[idx], 1.05, chr(ord('A') + idx), transform=ax.transAxes, fontsize='large')
|
|
|
|
fig.savefig('../../../goettingen2021_poster/pictures/roaming_events.pdf')
|
|
|
|
plt.show()
|
|
|
|
embed()
|
|
quit() |