trial summary now includes information about agonistic counts and competition duratin ala ana silva. started to adapt trial_summary_eval.py accordingly to evaluate this
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@ -1,6 +1,7 @@
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import itertools
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from event_time_correlations import load_and_converete_boris_events
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from tqdm import tqdm
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import numpy as np
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import pandas as pd
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@ -182,6 +183,7 @@ def main(data_folder=None):
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trial_summary = pd.DataFrame(columns=['recording', 'group', 'win_fish', 'lose_fish', 'win_ID', 'lose_ID',
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'sex_win', 'sex_lose', 'size_win', 'size_lose', 'EODf_win', 'EODf_lose',
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'exp_win', 'exp_lose', 'chirps_win', 'chirps_lose', 'rises_win', 'rises_lose',
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'chase_count', 'contact_count', 'med_chase_dur', 'comp_dur0', 'comp_dur1',
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'draw'])
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trial_summary_row = {f'{s}':None for s in trial_summary.keys()}
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@ -277,15 +279,35 @@ def main(data_folder=None):
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#############################################################################################################
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### physical behavior
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med_chase_dur = contact_n = chase_n = comp_dur0 = comp_dur1 = -1
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if video_eval:
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
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load_and_converete_boris_events(trial_path, recording, sr, video_stated_FPS=video_stated_FPS)
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = load_and_converete_boris_events(trial_path, recording, sr)
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only_contact_mask = np.ones_like(contact_t_GRID, dtype=bool)
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for enu, ct in enumerate(contact_t_GRID):
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for Cag_on_off_t in ag_on_off_t_GRID:
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if Cag_on_off_t[0] <= ct <= Cag_on_off_t[1]:
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only_contact_mask[enu] = 0
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break
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elif ct < Cag_on_off_t[0]:
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break
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contact_t_solely = contact_t_GRID[only_contact_mask]
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ag_offs = np.concatenate((contact_t_GRID, ag_on_off_t_GRID[:, 1]))
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ag_offs = ag_offs[np.argsort(ag_offs)]
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med_chase_dur = np.median(ag_on_off_t_GRID[:,1] - ag_on_off_t_GRID[:,0])
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contact_n = len(contact_t_GRID)
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chase_n = len(ag_on_off_t_GRID)
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comp_dur0 = ag_offs[2]
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comp_dur1 = ag_offs[2] - ag_offs[0]
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win_fish_no = trials_meta['fish1'][trial_idx] if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else trials_meta['fish2'][trial_idx]
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lose_fish_no = trials_meta['fish2'][trial_idx] if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else trials_meta['fish1'][trial_idx]
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trial_summary.loc[len(trial_summary)] = trial_summary_row
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trial_summary.iloc[-1] = {'recording' : recording,
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trial_summary.iloc[-1] = {'recording': recording,
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'group': trials_meta['group'][trial_idx],
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'win_fish': win_fish_no,
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'lose_fish': lose_fish_no,
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@ -303,7 +325,12 @@ def main(data_folder=None):
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'chirps_lose': len(chirp_times[1]),
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'rises_win': len(rise_idx_int[0]),
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'rises_lose': len(rise_idx_int[1]),
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'draw': 1 if trials_meta['winner'][trial_idx] == -1 else 0
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'draw': 1 if trials_meta['winner'][trial_idx] == -1 else 0,
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'chase_count': chase_n,
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'contact_count': contact_n,
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'med_chase_dur': med_chase_dur,
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'comp_dur0': comp_dur0,
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'comp_dur1': comp_dur1
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}
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# embed()
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@ -1,44 +1,44 @@
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,recording,group,win_fish,lose_fish,win_ID,lose_ID,sex_win,sex_lose,size_win,size_lose,EODf_win,EODf_lose,exp_win,exp_lose,chirps_win,chirps_lose,rises_win,rises_lose,draw
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0,2019-11-25-09_59,3,1,2,10.0,6.0,f,m,13.2,12.0,713.0544113886845,762.0273047058653,1,1,36,2657,22,165,0
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1,2019-11-26-10_00,3,4,3,3.0,4.0,m,m,15.5,17.5,883.141322780704,918.0584506431281,1,1,472,1322,17,481,0
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2,2019-11-27-10_00,3,5,6,1.0,6.0,f,f,14.4,12.65,728.1663791991439,650.6079943890219,1,1,16,2041,14,311,0
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3,2019-11-28-09_58,3,1,3,3.0,1.0,f,m,13.2,17.5,720.9491781126661,888.9029901347602,2,2,370,30,26,37,0
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4,2019-11-29-09_59,3,4,2,11.0,0.0,m,m,15.5,12.0,927.3677808126133,757.8912786740188,2,2,119,1232,56,161,0
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5,2019-12-02-10_00,3,3,5,3.0,0.0,m,f,17.5,14.4,866.5433040990971,719.3596160671729,3,2,2,759,28,165,0
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6,2019-12-03-10_01,3,1,6,3.0,4.0,f,f,13.2,12.65,709.888382193265,645.345316200243,3,2,61,3191,23,230,0
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7,2019-12-04-10_00,3,3,2,11.0,8.0,m,m,17.5,12.0,867.515070390076,732.5567785654214,4,3,2,1005,31,73,0
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8,2019-12-06-10_00,3,4,6,1.0,2.0,m,f,15.5,12.65,912.2881743174412,652.9837532796978,3,3,34,306,58,188,0
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9,2019-12-09-10_00,3,5,2,5.0,9.0,f,m,14.4,12.0,715.5845509704202,726.7506510306094,4,4,446,2345,10,180,0
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10,2019-12-10-10_00,3,3,6,3.0,2.0,m,f,17.5,12.65,853.973867720756,640.9374451096469,5,4,1,205,31,165,0
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11,2019-12-11-10_00,3,4,1,13.0,1.0,m,f,15.5,13.2,909.5564241038855,704.759181051688,4,5,44,260,48,165,0
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12,2019-12-12-10_00,3,2,6,3.0,5.0,m,f,12.0,12.65,708.2029632781753,649.3215729301896,5,5,55,1489,26,152,0
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13,2019-12-16-10_00,3,4,5,7.0,1.0,m,f,15.5,14.4,911.4475182245616,734.3463774893517,5,5,52,963,39,123,0
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14,2020-03-13-10_00,4,5,4,0.0,1.0,f,f,12.5,12.266666666666666,726.3470010966499,705.1654694195288,2,2,54,941,70,177,0
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15,2020-03-16-10_00,4,3,1,3.0,2.0,m,f,11.933333333333332,11.299999999999999,852.2318545355058,642.0347177867645,3,3,1304,724,57,154,0
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16,2020-03-18-10_34,4,5,3,0.0,4.0,f,m,12.5,11.933333333333332,725.8257351336636,863.6524533012707,3,4,16557,2089,339,43,1
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17,2020-03-19-10_00,4,1,4,0.0,1.0,f,f,11.299999999999999,12.266666666666666,659.5490944255365,697.5034008357667,4,4,52,1583,36,197,0
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18,2020-03-20-10_00,4,5,2,0.0,1.0,f,f,12.5,12.266666666666666,,,4,4,45,665,75,76,0
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19,2020-03-23-09_58,4,2,1,0.0,2.0,f,f,12.266666666666666,11.299999999999999,699.4914052830558,654.7533296886725,5,5,84,1158,17,67,1
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20,2020-03-24-10_00,4,4,3,2.0,1.0,f,m,12.266666666666666,11.933333333333332,684.578069899078,854.0458114588357,5,5,883,2,184,86,1
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21,2020-03-25-10_00,4,5,1,1.0,0.0,f,f,12.5,11.299999999999999,733.5001619575638,647.9874053272127,5,6,819,1831,48,70,1
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22,2020-03-31-09_59,4,3,2,0.0,3.0,m,f,11.933333333333332,12.266666666666666,860.5459022492297,692.2978867242133,6,6,10,225,26,50,1
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23,2020-05-11-10_00,5,1,2,3.0,4.0,m,f,12.333333333333334,13.166666666666666,834.369973908149,667.9762847453638,1,1,4,631,25,230,0
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24,2020-05-12-10_00,5,5,3,7.0,6.0,f,m,19.0,10.666666666666666,697.6088902440882,818.2108387976053,1,1,1,117,8,429,0
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25,2020-05-13-10_00,5,4,2,4.0,7.0,m,f,17.5,13.166666666666666,885.2957289220773,681.372424868242,1,2,34,614,22,98,0
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26,2020-05-14-10_00,5,5,1,4.0,5.0,f,m,19.0,12.333333333333334,703.5828000211009,840.457519990521,2,2,83,316,10,232,0
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27,2020-05-15-10_00,5,4,3,18.0,19.0,m,m,17.5,10.666666666666666,875.2647282681933,824.4852744512042,2,2,98,745,27,255,0
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28,2020-05-18-10_00,5,2,3,3.0,6.0,f,m,13.166666666666666,10.666666666666666,677.7516154017525,837.794665426305,3,3,338,530,28,270,0
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29,2020-05-19-10_00,5,5,4,6.0,7.0,f,m,19.0,17.5,699.3246023368515,881.0368775083901,3,3,628,1457,2,256,0
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30,2020-05-21-10_00,5,5,2,1434.0,1420.0,f,f,19.0,13.166666666666666,702.20947265625,684.967041015625,4,4,86,671,43,257,0
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31,2020-05-25-10_00,5,4,1,30.0,19.0,m,m,17.5,12.333333333333334,880.891870115058,842.1688052017244,4,4,125,165,37,122,0
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32,2020-05-27-10_00,6,3,1,10.0,17.0,f,m,13.5,9.0,686.4001347696975,815.713300982056,1,1,17,92,8,330,0
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33,2020-05-28-10_00,6,2,4,7.0,12.0,m,f,11.0,11.0,774.6150067187118,728.8412253286924,1,1,69,684,84,342,0
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34,2020-05-29-10_00,6,5,3,10.0,12.0,m,f,17.5,13.5,805.4542233630881,681.7640419584177,1,2,373,478,18,58,0
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35,2020-06-02-10_00,6,1,4,7.0,8.0,m,f,9.0,11.0,820.4496652837709,723.7667250846596,2,2,485,1253,69,309,0
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36,2020-06-03-10_10,6,5,2,14.0,5.0,m,m,17.5,11.0,810.7042669363011,783.6640529162586,2,2,54,182,16,74,0
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37,2020-06-04-10_00,6,3,4,6.0,7.0,f,f,13.5,11.0,695.5929553333448,714.6541711375795,3,3,44,994,34,291,0
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38,2020-06-05-10_00,6,2,1,10.0,14.0,m,m,11.0,9.0,804.8998142492978,827.5225072258723,3,3,117,425,41,143,0
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39,2020-06-08-10_00,6,5,3,4.0,0.0,m,f,17.5,13.5,816.1812754102803,691.6736840654672,3,4,1087,170,8,14,1
|
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40,2020-06-09-10_00,6,3,2,10.0,12.0,f,m,13.5,11.0,691.8529359583595,798.4298849024372,5,4,18,632,21,391,0
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41,2020-06-10-10_00,6,5,1,5.0,8.0,m,m,17.5,9.0,815.498890219021,828.5259822280207,4,4,66,269,1,14,0
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42,2020-06-11-10_00,6,5,4,10.0,13.0,m,f,17.5,11.0,817.6355361855158,730.7609124893474,5,4,144,1100,2,54,0
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,recording,group,win_fish,lose_fish,win_ID,lose_ID,sex_win,sex_lose,size_win,size_lose,EODf_win,EODf_lose,exp_win,exp_lose,chirps_win,chirps_lose,rises_win,rises_lose,chase_count,contact_count,med_chase_dur,comp_dur0,comp_dur1,draw
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0,2019-11-25-09_59,3,1,2,10.0,6.0,f,m,13.2,12.0,713.0544113886845,762.0273047058653,1,1,36,2657,22,165,-1,-1,-1,-1,-1,0
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1,2019-11-26-10_00,3,4,3,3.0,4.0,m,m,15.5,17.5,883.141322780704,918.0584506431281,1,1,472,1322,17,481,-1,-1,-1,-1,-1,0
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2,2019-11-27-10_00,3,5,6,1.0,6.0,f,f,14.4,12.65,728.1663791991439,650.6079943890219,1,1,16,2041,14,311,-1,-1,-1,-1,-1,0
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3,2019-11-28-09_58,3,1,3,3.0,1.0,f,m,13.2,17.5,720.9491781126661,888.9029901347602,2,2,370,30,26,37,-1,-1,-1,-1,-1,0
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4,2019-11-29-09_59,3,4,2,11.0,0.0,m,m,15.5,12.0,927.3677808126133,757.8912786740188,2,2,119,1232,56,161,-1,-1,-1,-1,-1,0
|
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5,2019-12-02-10_00,3,3,5,3.0,0.0,m,f,17.5,14.4,866.5433040990971,719.3596160671729,3,2,2,759,28,165,-1,-1,-1,-1,-1,0
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6,2019-12-03-10_01,3,1,6,3.0,4.0,f,f,13.2,12.65,709.888382193265,645.345316200243,3,2,61,3191,23,230,-1,-1,-1,-1,-1,0
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7,2019-12-04-10_00,3,3,2,11.0,8.0,m,m,17.5,12.0,867.515070390076,732.5567785654214,4,3,2,1005,31,73,-1,-1,-1,-1,-1,0
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8,2019-12-06-10_00,3,4,6,1.0,2.0,m,f,15.5,12.65,912.2881743174412,652.9837532796978,3,3,34,306,58,188,-1,-1,-1,-1,-1,0
|
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9,2019-12-09-10_00,3,5,2,5.0,9.0,f,m,14.4,12.0,715.5845509704202,726.7506510306094,4,4,446,2345,10,180,-1,-1,-1,-1,-1,0
|
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10,2019-12-10-10_00,3,3,6,3.0,2.0,m,f,17.5,12.65,853.973867720756,640.9374451096469,5,4,1,205,31,165,-1,-1,-1,-1,-1,0
|
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11,2019-12-11-10_00,3,4,1,13.0,1.0,m,f,15.5,13.2,909.5564241038855,704.759181051688,4,5,44,260,48,165,-1,-1,-1,-1,-1,0
|
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12,2019-12-12-10_00,3,2,6,3.0,5.0,m,f,12.0,12.65,708.2029632781753,649.3215729301896,5,5,55,1489,26,152,-1,-1,-1,-1,-1,0
|
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13,2019-12-16-10_00,3,4,5,7.0,1.0,m,f,15.5,14.4,911.4475182245616,734.3463774893517,5,5,52,963,39,123,-1,-1,-1,-1,-1,0
|
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14,2020-03-13-10_00,4,5,4,0.0,1.0,f,f,12.5,12.266666666666666,726.3470010966499,705.1654694195288,2,2,54,941,70,177,-1,-1,-1,-1,-1,0
|
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15,2020-03-16-10_00,4,3,1,3.0,2.0,m,f,11.933333333333332,11.299999999999999,852.2318545355058,642.0347177867645,3,3,1304,724,57,154,-1,-1,-1,-1,-1,0
|
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16,2020-03-18-10_34,4,5,3,0.0,4.0,f,m,12.5,11.933333333333332,725.8257351336636,863.6524533012707,3,4,16557,2089,339,43,-1,-1,-1,-1,-1,1
|
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17,2020-03-19-10_00,4,1,4,0.0,1.0,f,f,11.299999999999999,12.266666666666666,659.5490944255365,697.5034008357667,4,4,52,1583,36,197,-1,-1,-1,-1,-1,0
|
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18,2020-03-20-10_00,4,5,2,0.0,1.0,f,f,12.5,12.266666666666666,,,4,4,45,665,75,76,-1,-1,-1,-1,-1,0
|
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19,2020-03-23-09_58,4,2,1,0.0,2.0,f,f,12.266666666666666,11.299999999999999,699.4914052830558,654.7533296886725,5,5,84,1158,17,67,-1,-1,-1,-1,-1,1
|
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20,2020-03-24-10_00,4,4,3,2.0,1.0,f,m,12.266666666666666,11.933333333333332,684.578069899078,854.0458114588357,5,5,883,2,184,86,-1,-1,-1,-1,-1,1
|
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21,2020-03-25-10_00,4,5,1,1.0,0.0,f,f,12.5,11.299999999999999,733.5001619575638,647.9874053272127,5,6,819,1831,48,70,-1,-1,-1,-1,-1,1
|
||||
22,2020-03-31-09_59,4,3,2,0.0,3.0,m,f,11.933333333333332,12.266666666666666,860.5459022492297,692.2978867242133,6,6,10,225,26,50,-1,-1,-1,-1,-1,1
|
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23,2020-05-11-10_00,5,1,2,3.0,4.0,m,f,12.333333333333334,13.166666666666666,834.369973908149,667.9762847453638,1,1,4,631,25,230,257,64,8.970694512533555,100.99643135612173,30.044093259648236,0
|
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24,2020-05-12-10_00,5,5,3,7.0,6.0,f,m,19.0,10.666666666666666,697.6088902440882,818.2108387976053,1,1,1,117,8,429,177,41,9.00936129922411,159.3446124742803,92.10428590023439,0
|
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25,2020-05-13-10_00,5,4,2,4.0,7.0,m,f,17.5,13.166666666666666,885.2957289220773,681.372424868242,1,2,34,614,22,98,141,29,5.453177983807791,631.0193675453793,296.98548750114094,0
|
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26,2020-05-14-10_00,5,5,1,4.0,5.0,f,m,19.0,12.333333333333334,703.5828000211009,840.457519990521,2,2,83,316,10,232,110,84,7.984691451887784,120.87115971578609,48.72015123186202,0
|
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27,2020-05-15-10_00,5,4,3,18.0,19.0,m,m,17.5,10.666666666666666,875.2647282681933,824.4852744512042,2,2,98,745,27,255,103,51,3.4800170287107903,611.4764343926292,172.26084292120913,0
|
||||
28,2020-05-18-10_00,5,2,3,3.0,6.0,f,m,13.166666666666666,10.666666666666666,677.7516154017525,837.794665426305,3,3,338,530,28,270,155,43,4.524014042955969,129.84185422831942,71.18555429988729,0
|
||||
29,2020-05-19-10_00,5,5,4,6.0,7.0,f,m,19.0,17.5,699.3246023368515,881.0368775083901,3,3,628,1457,2,256,127,39,4.9493575519445585,116.77268033338163,33.87216574612323,0
|
||||
30,2020-05-21-10_00,5,5,2,1434.0,1420.0,f,f,19.0,13.166666666666666,702.20947265625,684.967041015625,4,4,86,671,43,257,108,28,4.466013862921045,146.42990571916766,114.80168968841137,0
|
||||
31,2020-05-25-10_00,5,4,1,30.0,19.0,m,m,17.5,12.333333333333334,880.891870115058,842.1688052017244,4,4,125,165,37,122,239,81,4.021345815963286,89.89906357553095,49.22281945885742,0
|
||||
32,2020-05-27-10_00,6,3,1,10.0,17.0,f,m,13.5,9.0,686.4001347696975,815.713300982056,1,1,17,92,8,330,75,24,8.429370582688534,992.586747488321,453.67800480131825,0
|
||||
33,2020-05-28-10_00,6,2,4,7.0,12.0,m,f,11.0,11.0,774.6150067187118,728.8412253286924,1,1,69,684,84,342,45,7,5.026682269954108,318.76777400520655,208.22064633617217,0
|
||||
34,2020-05-29-10_00,6,5,3,10.0,12.0,m,f,17.5,13.5,805.4542233630881,681.7640419584177,1,2,373,478,18,58,142,31,3.5186775889678756,86.96038778694245,56.10550749002523,0
|
||||
35,2020-06-02-10_00,6,1,4,7.0,8.0,m,f,9.0,11.0,820.4496652837709,723.7667250846596,2,2,485,1253,69,309,74,46,6.225352657404528,81.35370371660912,6.418686590864368,0
|
||||
36,2020-06-03-10_10,6,5,2,14.0,5.0,m,m,17.5,11.0,810.7042669363011,783.6640529162586,2,2,54,182,16,74,69,29,2.5133455145551125,661.6273158182598,627.4083741412721,0
|
||||
37,2020-06-04-10_00,6,3,4,6.0,7.0,f,f,13.5,11.0,695.5929553333448,714.6541711375795,3,3,44,994,34,291,68,14,5.954685150562,1004.7552367071621,540.6776783135924,0
|
||||
38,2020-06-05-10_00,6,2,1,10.0,14.0,m,m,11.0,9.0,804.8998142492978,827.5225072258723,3,3,117,425,41,143,204,46,4.949348696570041,76.28835465996315,39.16945491894939,0
|
||||
39,2020-06-08-10_00,6,5,3,4.0,0.0,m,f,17.5,13.5,816.1812754102803,691.6736840654672,3,4,1087,170,8,14,56,77,2.474674348284452,279.59831908625716,209.26464957685494,1
|
||||
40,2020-06-09-10_00,6,3,2,10.0,12.0,f,m,13.5,11.0,691.8529359583595,798.4298849024372,5,4,18,632,21,391,271,11,5.996116978907594,226.1957395831394,78.99400561888262,0
|
||||
41,2020-06-10-10_00,6,5,1,5.0,8.0,m,m,17.5,9.0,815.498890219021,828.5259822280207,4,4,66,269,1,14,33,44,1.9720051859872,733.7109663747294,577.2948515056091,0
|
||||
42,2020-06-11-10_00,6,5,4,10.0,13.0,m,f,17.5,11.0,817.6355361855158,730.7609124893474,5,4,144,1100,2,54,27,15,5.4906935341878125,1021.6157746541925,913.4271363249653,0
|
||||
|
||||
|
@ -80,10 +80,58 @@ def plot_beh_count_per_pairing(trial_summary,
|
||||
plt.close()
|
||||
|
||||
|
||||
def plot_beh_count_vs_meta(trial_summary,
|
||||
beh_key_win=None, beh_key_lose=None,
|
||||
meta_key_win=None, meta_key_lose=None,
|
||||
xlabel='x', save_str='random_plot_title'):
|
||||
def plot_meta_correlation(trial_summary, key1, key2, key1_name, key2_name, save_str='random_plot_title'):
|
||||
mek = ['k', 'None', 'None', 'k']
|
||||
markersize = 12
|
||||
win_colors = [male_color, male_color, female_color, female_color]
|
||||
lose_colors = [male_color, female_color, male_color, female_color]
|
||||
if 'lose' in key2_name:
|
||||
colors = lose_colors
|
||||
marker = 'o'
|
||||
elif 'win' in key2_name:
|
||||
colors = win_colors
|
||||
marker = 'd'
|
||||
else:
|
||||
colors = win_colors
|
||||
marker = 's'
|
||||
|
||||
key1_collect = []
|
||||
key2_collect = []
|
||||
|
||||
for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
|
||||
k1 = trial_summary[key1][(trial_summary["sex_win"] == win_sex) &
|
||||
(trial_summary["sex_lose"] == lose_sex) &
|
||||
(trial_summary["draw"] == 0)].to_numpy()
|
||||
k2 = trial_summary[key2][(trial_summary["sex_win"] == win_sex) &
|
||||
(trial_summary["sex_lose"] == lose_sex) &
|
||||
(trial_summary["draw"] == 0)].to_numpy()
|
||||
mask = np.ones_like(k1, dtype=bool)
|
||||
mask[(k1 == -1) | (k2 == -1)] = 0
|
||||
k1 = k1[mask]
|
||||
k2 = k2[mask]
|
||||
key1_collect.append(k1)
|
||||
key2_collect.append(k2)
|
||||
|
||||
fig = plt.figure(figsize=(20/2.54, 12/2.54))
|
||||
gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
|
||||
for i in range(len(key1_collect)):
|
||||
ax.plot(key1_collect[i], key2_collect[i], marker = marker, color=colors[i], markeredgecolor=mek[i],
|
||||
markersize=markersize, markeredgewidth=2, linestyle='None')
|
||||
|
||||
ax.set_xlabel(f'{key1_name}', fontsize=12)
|
||||
ax.set_ylabel(f'{key2_name}', fontsize=12)
|
||||
plt.tick_params(labelsize=10)
|
||||
|
||||
plt.show()
|
||||
embed()
|
||||
quit()
|
||||
|
||||
def plot_beh_count_vs_dmeta(trial_summary,
|
||||
beh_key_win=None, beh_key_lose=None,
|
||||
meta_key_win=None, meta_key_lose=None,
|
||||
xlabel='x', save_str='random_plot_title'):
|
||||
mek = ['k', 'None', 'None', 'k']
|
||||
markersize = 12
|
||||
win_colors = [male_color, male_color, female_color, female_color]
|
||||
@ -201,9 +249,10 @@ def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_ke
|
||||
|
||||
def main(base_path):
|
||||
# ToDo: for chirp and rise analysis different datasets!!!
|
||||
trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
|
||||
# trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
|
||||
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
|
||||
chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
|
||||
trial_summary = trial_summary[chirp_notes['good'] == 1]
|
||||
# trial_summary = trial_summary[chirp_notes['good'] == 1]
|
||||
|
||||
if True:
|
||||
print('')
|
||||
@ -255,22 +304,26 @@ def main(base_path):
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
ylabel='rises [n]', save_str='rises_per_pairing')
|
||||
|
||||
plot_beh_count_vs_meta(trial_summary,
|
||||
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
meta_key_win="size_win", meta_key_lose='size_lose',
|
||||
xlabel=u'$\Delta$size [cm]', save_str='chirps_vs_dSize')
|
||||
plot_beh_count_vs_meta(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
meta_key_win="size_win", meta_key_lose='size_lose',
|
||||
xlabel=u'$\Delta$size [cm]', save_str='rises_vs_dSize')
|
||||
plot_beh_count_vs_meta(trial_summary,
|
||||
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
|
||||
xlabel=u'$\Delta$EODf [Hz]', save_str='chirps_vs_dEODf')
|
||||
plot_beh_count_vs_meta(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
|
||||
xlabel=u'$\Delta$EODf [Hz]', save_str='rises_vs_dEODf')
|
||||
plot_beh_count_vs_dmeta(trial_summary,
|
||||
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
meta_key_win="size_win", meta_key_lose='size_lose',
|
||||
xlabel=u'$\Delta$size [cm]', save_str='chirps_vs_dSize')
|
||||
plot_beh_count_vs_dmeta(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
meta_key_win="size_win", meta_key_lose='size_lose',
|
||||
xlabel=u'$\Delta$size [cm]', save_str='rises_vs_dSize')
|
||||
plot_beh_count_vs_dmeta(trial_summary,
|
||||
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
|
||||
xlabel=u'$\Delta$EODf [Hz]', save_str='chirps_vs_dEODf')
|
||||
plot_beh_count_vs_dmeta(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
|
||||
xlabel=u'$\Delta$EODf [Hz]', save_str='rises_vs_dEODf')
|
||||
|
||||
plot_meta_correlation(trial_summary, key1='med_chase_dur', key2='chirps_lose',
|
||||
key1_name=r'chase duration$_{median}$ [s]', key2_name=r'chirps$_{lose}$')
|
||||
|
||||
if True:
|
||||
### chirp count vs. dSize ###
|
||||
for key in ['chirps_lose', 'chirps_win', 'rises_win', 'rises_lose']:
|
||||
@ -279,7 +332,7 @@ def main(base_path):
|
||||
lose_size_male_win = trial_summary['size_lose'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
win_size_male_win = trial_summary['size_win'][(trial_summary['sex_win'] == 'm') & (trial_summary["draw"] == 0)]
|
||||
|
||||
r, p = scp.pearsonr(lose_chirps_male_win, lose_size_male_win - win_size_male_win)
|
||||
r, p = scp.pearsonr((lose_size_male_win - win_size_male_win)*-1, lose_chirps_male_win)
|
||||
print(f'(Male win) {key} - dSize: Pearson-r={r:.2f} p={p:.3f}')
|
||||
|
||||
lose_chirps_female_win = trial_summary[key][(trial_summary['sex_win'] == 'f') & (trial_summary["draw"] == 0)]
|
||||
@ -313,7 +366,7 @@ def main(base_path):
|
||||
plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
ylabel='chirps [n]', save_str='chirps_by_experince')
|
||||
plot_beh_conut_vs_experience(trial_summary, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]',
|
||||
save_str='chirps_by_experince')
|
||||
save_str='rises_by_experince')
|
||||
|
||||
if True:
|
||||
for key in ['chirps_lose', 'chirps_win', 'rises_lose', 'rises_win']:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user