73 lines
3.3 KiB
Python
73 lines
3.3 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.gridspec as gridspec
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from IPython import embed
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import helper_functions as hf
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from params import *
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import os
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import datetime
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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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save_path = '../../thesis/Figures/Results/'
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kernel_size = 100
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###################################################################################################################
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# load all the data of one day
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# for filename_idx in [1, 4, 6]:
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for filename_idx in [1]:
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filename = sorted(os.listdir('../data/'))[filename_idx]
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all_max_ch_means = np.load('../data/' + filename + '/all_max_ch.npy', allow_pickle=True)
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all_xticks = np.load('../data/' + filename + '/all_xtickses.npy', allow_pickle=True)
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all_ipp = np.load('../data/' + filename + '/all_ipp.npy', allow_pickle=True)
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power_means = np.load('../data/' + filename + '/power_means.npy', allow_pickle=True)
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freq = np.load('../data/' + filename + '/fish_freq_q10.npy', allow_pickle=True)
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###############################################################################################################
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# get fish
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# for fish_number in range(len(power_means)):
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for fish_number in [14]:
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if power_means[fish_number] >= -90.0:
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ipp = all_ipp[fish_number]
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x_tickses = all_xticks[fish_number]
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max_ch_mean = all_max_ch_means[fish_number]
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# smoothing of max channel mean
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kernel = np.ones(kernel_size) / kernel_size
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smooth_mcm = np.convolve(max_ch_mean, kernel, 'valid')
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try:
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smooth_x = x_tickses[int(np.ceil(kernel_size/2)):-int(np.floor(kernel_size/2))]
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except:
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embed()
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quit()
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#####################################################################################################
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# plot traces
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fig1, ax1 = plt.subplots(1, 1, figsize=(13 / inch, 8 / inch))
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fig1.subplots_adjust(left=0.12, bottom=0.15, right=0.99, top=0.99)
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ax1.imshow(ipp[::20].T[::-1], vmin=-100, vmax=-50, aspect='auto', interpolation='gaussian',
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extent=[x_tickses[0], x_tickses[-1], -0.5, 15.5])
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ax1.plot(smooth_x[::20], smooth_mcm[::20], '.', color=color2[4])
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# ax1.set_title('freq: %.1f, power: %.1f' %(freq[:,2][fish_number], power_means[fish_number]), fontsize=fs + 2)
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# ax1.set_title('freq: %.1f, Nr: %.1f' %(freq[:,2][fish_number], fish_number), fontsize=fs + 2)
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ax1.set_xticks(smooth_x[::350])
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ax1.beautimechannelaxis()
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ax1.timeaxis()
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# fig1.autofmt_xdate()
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fig1.savefig(save_path + 'trajectory_'+str(fish_number)+'.pdf')
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# fig1.savefig('../../../goettingen2021_poster/pictures/trajectory_'+ str(fish_number)+'.pdf')
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print(fish_number, freq[fish_number,2])
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plt.show()
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embed()
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