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evaluation of the trial_summary initiated. Now the fun beginns

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Till Raab 2023-05-19 13:17:26 +02:00
parent 3c9bbb6a91
commit 42640c17d9
3 changed files with 190 additions and 8 deletions
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28
complete_analysis.py
View File

@ -177,8 +177,9 @@ def main(data_folder=None):
sr = 20_000 sr = 20_000
light_start_sec = 3*60*60 light_start_sec = 3*60*60
trial_summary = pd.DataFrame(columns=['group', 'win_fish', 'lose_fish', 'sex_win', 'sex_lose', 'size_win', 'size_lose', 'EODf_win', 'EODf_lose', trial_summary = pd.DataFrame(columns=['recording', 'group', 'win_fish', 'lose_fish', 'sex_win', 'sex_lose',
'exp_win', 'exp_lose', 'chirps_win', 'chirps_lose', 'rises_win', 'rise_lose']) 'size_win', 'size_lose', 'EODf_win', 'EODf_lose', 'exp_win', 'exp_lose',
'chirps_win', 'chirps_lose', 'rises_win', 'rise_lose', 'draw'])
trial_summary_row = {f'{s}':None for s in trial_summary.keys()} trial_summary_row = {f'{s}':None for s in trial_summary.keys()}
for trial_idx in tqdm(np.arange(len(trials_meta)), desc='Trials'): for trial_idx in tqdm(np.arange(len(trials_meta)), desc='Trials'):
@ -186,6 +187,7 @@ def main(data_folder=None):
group = trials_meta['group'][trial_idx] group = trials_meta['group'][trial_idx]
recording = trials_meta['recording'][trial_idx][1:-1] recording = trials_meta['recording'][trial_idx][1:-1]
print('') print('')
print(recording) print(recording)
rec_id1 = trials_meta['rec_id1'][trial_idx] rec_id1 = trials_meta['rec_id1'][trial_idx]
@ -209,6 +211,16 @@ def main(data_folder=None):
############################################################################################################# #############################################################################################################
### meta collect ### meta collect
if (winner_fish := trials_meta['winner'][trial_idx]) == -1:
pass
elif np.isnan(winner_fish):
continue
elif winner_fish != trials_meta['fish1'][trial_idx] and winner_fish != trials_meta['fish2'][trial_idx]:
embed()
quit()
print(f'not participating winner in {recording}!!!')
continue
win_id = rec_id1 if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else rec_id2 win_id = rec_id1 if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else rec_id2
lose_id = rec_id2 if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else rec_id1 lose_id = rec_id2 if trials_meta['fish1'][trial_idx] == trials_meta['winner'][trial_idx] else rec_id1
@ -270,7 +282,8 @@ def main(data_folder=None):
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] 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]
trial_summary.loc[len(trial_summary)] = trial_summary_row trial_summary.loc[len(trial_summary)] = trial_summary_row
trial_summary.iloc[-1] = {'group': trials_meta['group'][trial_idx], trial_summary.iloc[-1] = {'recording' : recording,
'group': trials_meta['group'][trial_idx],
'win_fish': win_fish_no, 'win_fish': win_fish_no,
'lose_fish': lose_fish_no, 'lose_fish': lose_fish_no,
'sex_win': 'n', 'sex_win': 'n',
@ -284,7 +297,8 @@ def main(data_folder=None):
'chirps_win': len(chirp_times[0]), 'chirps_win': len(chirp_times[0]),
'chirps_lose': len(chirp_times[1]), 'chirps_lose': len(chirp_times[1]),
'rises_win': len(rise_idx_int[0]), 'rises_win': len(rise_idx_int[0]),
'rise_lose': len(rise_idx_int[1]) 'rise_lose': len(rise_idx_int[1]),
'draw': 1 if trials_meta['winner'][trial_idx] == -1 else 0
} }
# embed() # embed()
@ -340,9 +354,6 @@ def main(data_folder=None):
plt.savefig(os.path.join(os.path.join(os.path.split(__file__)[0], 'figures', f'{recording}.png')), dpi=300) plt.savefig(os.path.join(os.path.join(os.path.split(__file__)[0], 'figures', f'{recording}.png')), dpi=300)
plt.close() plt.close()
embed()
quit()
fig = plt.figure(figsize=(20/2.54, 20/2.54)) fig = plt.figure(figsize=(20/2.54, 20/2.54))
gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, height_ratios=[1, 3], width_ratios=[3, 1]) gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, height_ratios=[1, 3], width_ratios=[3, 1])
ax = fig.add_subplot(gs[1, 0]) ax = fig.add_subplot(gs[1, 0])
@ -380,7 +391,8 @@ def main(data_folder=None):
sex = 'm' sex = 'm'
trial_summary['sex_win'][(trial_summary['group'] == g) & (trial_summary['win_fish'] == f)] = sex trial_summary['sex_win'][(trial_summary['group'] == g) & (trial_summary['win_fish'] == f)] = sex
trial_summary['sex_lose'][(trial_summary['group'] == g) & (trial_summary['lose_fish'] == f)] = sex trial_summary['sex_lose'][(trial_summary['group'] == g) & (trial_summary['lose_fish'] == f)] = sex
embed()
quit()
pass pass
if __name__ == '__main__': if __name__ == '__main__':

44
trial_summary.csv Normal file
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@ -0,0 +1,44 @@
,recording,group,win_fish,lose_fish,sex_win,sex_lose,size_win,size_lose,EODf_win,EODf_lose,exp_win,exp_lose,chirps_win,chirps_lose,rises_win,rise_lose,draw
0,2019-11-25-09_59,3,1,2,f,f,13.2,12.0,713.0544113886845,762.0273047058653,1,1,36,2657,22,165,0
1,2019-11-26-10_00,3,4,3,m,m,15.5,17.5,883.141322780704,918.0584506431281,1,1,472,1322,17,481,0
2,2019-11-27-10_00,3,5,6,f,f,14.4,12.65,728.1663791991439,650.6079943890219,1,1,16,2041,14,311,0
3,2019-11-28-09_58,3,1,3,f,m,13.2,17.5,720.9491781126661,888.9029901347602,2,2,370,30,26,37,0
4,2019-11-29-09_59,3,4,2,m,f,15.5,12.0,927.3677808126133,757.8912786740188,2,2,119,1232,56,161,0
5,2019-12-02-10_00,3,3,5,m,f,17.5,14.4,866.5433040990971,719.3596160671729,3,2,2,759,28,165,0
6,2019-12-03-10_01,3,1,6,f,f,13.2,12.65,709.888382193265,645.345316200243,3,2,61,3191,23,230,0
7,2019-12-04-10_00,3,3,2,m,f,17.5,12.0,867.515070390076,732.5567785654214,4,3,2,1005,31,73,0
8,2019-12-06-10_00,3,4,6,m,f,15.5,12.65,912.2881743174412,652.9837532796978,3,3,34,306,58,188,0
9,2019-12-09-10_00,3,5,2,f,f,14.4,12.0,715.5845509704202,726.7506510306094,4,4,446,2345,10,180,0
10,2019-12-10-10_00,3,3,6,m,f,17.5,12.65,853.973867720756,640.9374451096469,5,4,1,205,31,165,0
11,2019-12-11-10_00,3,4,1,m,f,15.5,13.2,909.5564241038855,704.759181051688,4,5,44,260,48,165,0
12,2019-12-12-10_00,3,2,6,f,f,12.0,12.65,708.2029632781753,649.3215729301896,5,5,55,1489,26,152,0
13,2019-12-16-10_00,3,4,5,m,f,15.5,14.4,911.4475182245616,734.3463774893517,5,5,52,963,39,123,0
14,2020-03-13-10_00,4,5,4,m,f,12.5,12.266666666666666,726.3470010966499,705.1654694195288,2,2,54,941,70,177,0
15,2020-03-16-10_00,4,3,1,m,f,11.933333333333332,11.299999999999999,852.2318545355058,642.0347177867645,3,3,1304,724,57,154,0
16,2020-03-18-10_34,4,5,3,m,m,12.5,11.933333333333332,725.8257351336636,863.6524533012707,3,4,16557,2089,339,43,1
17,2020-03-19-10_00,4,1,4,f,f,11.299999999999999,12.266666666666666,659.5490944255365,697.5034008357667,4,4,52,1583,36,197,0
18,2020-03-20-10_00,4,5,2,m,f,12.5,12.266666666666666,,,4,4,45,665,75,76,0
19,2020-03-23-09_58,4,2,1,f,f,12.266666666666666,11.299999999999999,699.4914052830558,654.7533296886725,5,5,84,1158,17,67,1
20,2020-03-24-10_00,4,4,3,f,m,12.266666666666666,11.933333333333332,684.578069899078,854.0458114588357,5,5,883,2,184,86,1
21,2020-03-25-10_00,4,5,1,m,f,12.5,11.299999999999999,733.5001619575638,647.9874053272127,5,6,819,1831,48,70,1
22,2020-03-31-09_59,4,3,2,m,f,11.933333333333332,12.266666666666666,860.5459022492297,692.2978867242133,6,6,10,225,26,50,1
23,2020-05-11-10_00,5,1,2,m,f,12.333333333333334,13.166666666666666,834.369973908149,667.9762847453638,1,1,4,631,25,230,0
24,2020-05-12-10_00,5,5,3,f,m,19.0,10.666666666666666,697.6088902440882,818.2108387976053,1,1,1,117,8,429,0
25,2020-05-13-10_00,5,4,2,m,f,17.5,13.166666666666666,885.2957289220773,681.372424868242,1,2,34,614,22,98,0
26,2020-05-14-10_00,5,5,1,f,m,19.0,12.333333333333334,703.5828000211009,840.457519990521,2,2,83,316,10,232,0
27,2020-05-15-10_00,5,4,3,m,m,17.5,10.666666666666666,875.2647282681933,824.4852744512042,2,2,98,745,27,255,0
28,2020-05-18-10_00,5,2,3,f,m,13.166666666666666,10.666666666666666,677.7516154017525,837.794665426305,3,3,338,530,28,270,0
29,2020-05-19-10_00,5,5,4,f,m,19.0,17.5,699.3246023368515,881.0368775083901,3,3,628,1457,2,256,0
30,2020-05-21-10_00,5,5,2,f,f,19.0,13.166666666666666,702.20947265625,684.967041015625,4,4,86,671,43,257,0
31,2020-05-25-10_00,5,4,1,m,m,17.5,12.333333333333334,880.891870115058,842.1688052017244,4,4,125,165,37,122,0
32,2020-05-27-10_00,6,3,1,f,m,13.5,9.0,686.4001347696975,815.713300982056,1,1,17,92,8,330,0
33,2020-05-28-10_00,6,2,4,m,f,11.0,11.0,774.6150067187118,728.8412253286924,1,1,69,684,84,342,0
34,2020-05-29-10_00,6,5,3,m,f,17.5,13.5,805.4542233630881,681.7640419584177,1,2,373,478,18,58,0
35,2020-06-02-10_00,6,1,4,m,f,9.0,11.0,820.4496652837709,723.7667250846596,2,2,485,1253,69,309,0
36,2020-06-03-10_10,6,5,2,m,m,17.5,11.0,810.7042669363011,783.6640529162586,2,2,54,182,16,74,0
37,2020-06-04-10_00,6,3,4,f,f,13.5,11.0,695.5929553333448,714.6541711375795,3,3,44,994,34,291,0
38,2020-06-05-10_00,6,2,1,m,m,11.0,9.0,804.8998142492978,827.5225072258723,3,3,117,425,41,143,0
39,2020-06-08-10_00,6,5,3,m,f,17.5,13.5,816.1812754102803,691.6736840654672,3,4,1087,170,8,14,1
40,2020-06-09-10_00,6,3,2,f,m,13.5,11.0,691.8529359583595,798.4298849024372,5,4,18,632,21,391,0
41,2020-06-10-10_00,6,5,1,m,m,17.5,9.0,815.498890219021,828.5259822280207,4,4,66,269,1,14,0
42,2020-06-11-10_00,6,5,4,m,f,17.5,11.0,817.6355361855158,730.7609124893474,5,4,144,1100,2,54,0
1 recording group win_fish lose_fish sex_win sex_lose size_win size_lose EODf_win EODf_lose exp_win exp_lose chirps_win chirps_lose rises_win rise_lose draw
2 0 2019-11-25-09_59 3 1 2 f f 13.2 12.0 713.0544113886845 762.0273047058653 1 1 36 2657 22 165 0
3 1 2019-11-26-10_00 3 4 3 m m 15.5 17.5 883.141322780704 918.0584506431281 1 1 472 1322 17 481 0
4 2 2019-11-27-10_00 3 5 6 f f 14.4 12.65 728.1663791991439 650.6079943890219 1 1 16 2041 14 311 0
5 3 2019-11-28-09_58 3 1 3 f m 13.2 17.5 720.9491781126661 888.9029901347602 2 2 370 30 26 37 0
6 4 2019-11-29-09_59 3 4 2 m f 15.5 12.0 927.3677808126133 757.8912786740188 2 2 119 1232 56 161 0
7 5 2019-12-02-10_00 3 3 5 m f 17.5 14.4 866.5433040990971 719.3596160671729 3 2 2 759 28 165 0
8 6 2019-12-03-10_01 3 1 6 f f 13.2 12.65 709.888382193265 645.345316200243 3 2 61 3191 23 230 0
9 7 2019-12-04-10_00 3 3 2 m f 17.5 12.0 867.515070390076 732.5567785654214 4 3 2 1005 31 73 0
10 8 2019-12-06-10_00 3 4 6 m f 15.5 12.65 912.2881743174412 652.9837532796978 3 3 34 306 58 188 0
11 9 2019-12-09-10_00 3 5 2 f f 14.4 12.0 715.5845509704202 726.7506510306094 4 4 446 2345 10 180 0
12 10 2019-12-10-10_00 3 3 6 m f 17.5 12.65 853.973867720756 640.9374451096469 5 4 1 205 31 165 0
13 11 2019-12-11-10_00 3 4 1 m f 15.5 13.2 909.5564241038855 704.759181051688 4 5 44 260 48 165 0
14 12 2019-12-12-10_00 3 2 6 f f 12.0 12.65 708.2029632781753 649.3215729301896 5 5 55 1489 26 152 0
15 13 2019-12-16-10_00 3 4 5 m f 15.5 14.4 911.4475182245616 734.3463774893517 5 5 52 963 39 123 0
16 14 2020-03-13-10_00 4 5 4 m f 12.5 12.266666666666666 726.3470010966499 705.1654694195288 2 2 54 941 70 177 0
17 15 2020-03-16-10_00 4 3 1 m f 11.933333333333332 11.299999999999999 852.2318545355058 642.0347177867645 3 3 1304 724 57 154 0
18 16 2020-03-18-10_34 4 5 3 m m 12.5 11.933333333333332 725.8257351336636 863.6524533012707 3 4 16557 2089 339 43 1
19 17 2020-03-19-10_00 4 1 4 f f 11.299999999999999 12.266666666666666 659.5490944255365 697.5034008357667 4 4 52 1583 36 197 0
20 18 2020-03-20-10_00 4 5 2 m f 12.5 12.266666666666666 4 4 45 665 75 76 0
21 19 2020-03-23-09_58 4 2 1 f f 12.266666666666666 11.299999999999999 699.4914052830558 654.7533296886725 5 5 84 1158 17 67 1
22 20 2020-03-24-10_00 4 4 3 f m 12.266666666666666 11.933333333333332 684.578069899078 854.0458114588357 5 5 883 2 184 86 1
23 21 2020-03-25-10_00 4 5 1 m f 12.5 11.299999999999999 733.5001619575638 647.9874053272127 5 6 819 1831 48 70 1
24 22 2020-03-31-09_59 4 3 2 m f 11.933333333333332 12.266666666666666 860.5459022492297 692.2978867242133 6 6 10 225 26 50 1
25 23 2020-05-11-10_00 5 1 2 m f 12.333333333333334 13.166666666666666 834.369973908149 667.9762847453638 1 1 4 631 25 230 0
26 24 2020-05-12-10_00 5 5 3 f m 19.0 10.666666666666666 697.6088902440882 818.2108387976053 1 1 1 117 8 429 0
27 25 2020-05-13-10_00 5 4 2 m f 17.5 13.166666666666666 885.2957289220773 681.372424868242 1 2 34 614 22 98 0
28 26 2020-05-14-10_00 5 5 1 f m 19.0 12.333333333333334 703.5828000211009 840.457519990521 2 2 83 316 10 232 0
29 27 2020-05-15-10_00 5 4 3 m m 17.5 10.666666666666666 875.2647282681933 824.4852744512042 2 2 98 745 27 255 0
30 28 2020-05-18-10_00 5 2 3 f m 13.166666666666666 10.666666666666666 677.7516154017525 837.794665426305 3 3 338 530 28 270 0
31 29 2020-05-19-10_00 5 5 4 f m 19.0 17.5 699.3246023368515 881.0368775083901 3 3 628 1457 2 256 0
32 30 2020-05-21-10_00 5 5 2 f f 19.0 13.166666666666666 702.20947265625 684.967041015625 4 4 86 671 43 257 0
33 31 2020-05-25-10_00 5 4 1 m m 17.5 12.333333333333334 880.891870115058 842.1688052017244 4 4 125 165 37 122 0
34 32 2020-05-27-10_00 6 3 1 f m 13.5 9.0 686.4001347696975 815.713300982056 1 1 17 92 8 330 0
35 33 2020-05-28-10_00 6 2 4 m f 11.0 11.0 774.6150067187118 728.8412253286924 1 1 69 684 84 342 0
36 34 2020-05-29-10_00 6 5 3 m f 17.5 13.5 805.4542233630881 681.7640419584177 1 2 373 478 18 58 0
37 35 2020-06-02-10_00 6 1 4 m f 9.0 11.0 820.4496652837709 723.7667250846596 2 2 485 1253 69 309 0
38 36 2020-06-03-10_10 6 5 2 m m 17.5 11.0 810.7042669363011 783.6640529162586 2 2 54 182 16 74 0
39 37 2020-06-04-10_00 6 3 4 f f 13.5 11.0 695.5929553333448 714.6541711375795 3 3 44 994 34 291 0
40 38 2020-06-05-10_00 6 2 1 m m 11.0 9.0 804.8998142492978 827.5225072258723 3 3 117 425 41 143 0
41 39 2020-06-08-10_00 6 5 3 m f 17.5 13.5 816.1812754102803 691.6736840654672 3 4 1087 170 8 14 1
42 40 2020-06-09-10_00 6 3 2 f m 13.5 11.0 691.8529359583595 798.4298849024372 5 4 18 632 21 391 0
43 41 2020-06-10-10_00 6 5 1 m m 17.5 9.0 815.498890219021 828.5259822280207 4 4 66 269 1 14 0
44 42 2020-06-11-10_00 6 5 4 m f 17.5 11.0 817.6355361855158 730.7609124893474 5 4 144 1100 2 54 0

126
trial_summary_eval.py Normal file
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@ -0,0 +1,126 @@
import numpy as np
import itertools
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from IPython import embed
colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF']
female_color, male_color = '#e74c3c', '#3498db'
Wc, Lc = 'darkgreen', '#3673A4'
def plot_chirp_rise_count_per_pairing(trial_summary):
win_chirps = []
lose_chirps = []
win_rises = []
lose_rises = []
for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
win_chirps.append(trial_summary['chirps_win'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
lose_chirps.append(trial_summary['chirps_lose'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
win_rises.append(trial_summary['rises_win'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
lose_rises.append(trial_summary['rise_lose'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
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])
ax.boxplot(win_chirps, positions=np.arange(len(win_chirps))-0.15, widths= .2, sym='')
ax.boxplot(lose_chirps, positions=np.arange(len(lose_chirps))+0.15, widths= .2, sym='')
ax.set_xticks(np.arange(len(win_chirps)))
# ax.set_xticklabels([u'\u2642\u2642', u'\u2642\u2640', u'\u2640\u2642', u'\u2640\u2640'])
ax.set_xticklabels(['mm', 'mf', 'fm', 'ff'])
y0, y1 = ax.get_ylim()
for i in range(len(win_chirps)):
ax.text(i, y1, f'n={len(win_chirps[i]):.0f}', fontsize=10, ha='center', va='bottom')
ax.set_ylim(top = y1*1.1)
ax.set_ylabel('chirps [n]', fontsize=12)
plt.tick_params(labelsize=10)
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])
ax.boxplot(win_rises, positions=np.arange(len(win_rises))-0.15, widths= .2, sym='')
ax.boxplot(lose_rises, positions=np.arange(len(lose_rises))+0.15, widths= .2, sym='')
ax.set_xticks(np.arange(len(win_rises)))
# ax.set_xticklabels([u'\u2642\u2642', u'\u2642\u2640', u'\u2640\u2642', u'\u2640\u2640'])
ax.set_xticklabels(['mm', 'mf', 'fm', 'ff'])
y0, y1 = ax.get_ylim()
for i in range(len(win_rises)):
ax.text(i, y1, f'n={len(win_rises[i]):.0f}', fontsize=10, ha='center', va='bottom')
ax.set_ylim(top = y1*1.1)
ax.set_ylabel('rises [n]', fontsize=12)
plt.tick_params(labelsize=10)
plt.show()
def plot_chirp_rise_count_per_vs_size_diff(trial_summary):
win_chirps = []
lose_chirps = []
win_rises = []
lose_rises = []
d_size = []
for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
win_chirps.append(trial_summary['chirps_win'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
lose_chirps.append(trial_summary['chirps_lose'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
win_rises.append(trial_summary['rises_win'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
lose_rises.append(trial_summary['rise_lose'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy())
w_size = trial_summary['size_win'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
l_size = trial_summary['size_lose'][(trial_summary["sex_win"] == win_sex) &
(trial_summary["sex_lose"] == lose_sex) &
(trial_summary["draw"] == 0)].to_numpy()
d_size.append(w_size-l_size)
embed()
quit()
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])
mek = ['k', 'None', 'None', 'k']
c = [male_color, male_color, female_color, female_color]
for i in range(len(lose_rises)):
ax.plot(d_size[i]*-1, lose_rises[i], 'p', color=c[i], markeredgecolor=mek[i], markersize=8)
ax.set_ylabel('rises [n]', fontsize=12)
plt.tick_params(labelsize=10)
def main():
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
plot_chirp_rise_count_per_pairing(trial_summary)
plot_chirp_rise_count_per_vs_size_diff(trial_summary)
pass
if __name__ == '__main__':
main()