meta analysis continues... next step would be to load the time series data and calculate cross correlations
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8c1f85f89e
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@ -129,7 +129,7 @@ def frequency_q10_compensation(baseline_freqs : np.ndarray,
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# q10 with same values is useless
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continue
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if baseline_freq_times[i] < light_start_sec or baseline_freq_times[j] < light_start_sec:
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# too much frequency changes due to rises in first part of rec !!!
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# too much frequency changes due to rises in first part of rec !!!
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continue
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# if np.abs(Ctemp[i] - Ctemp[j]) < 0.5:
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# continue
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@ -387,7 +387,7 @@ def main(data_folder=None):
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for f in fish_no:
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fish_EODf25 = np.concatenate((trial_summary['EODf_lose'][(trial_summary['group'] == g) & (trial_summary['lose_fish'] == f)],
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trial_summary['EODf_win'][(trial_summary['group'] == g) & (trial_summary['win_fish'] == f)]))
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if np.nanmedian(fish_EODf25) < 740:
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if np.nanmedian(fish_EODf25) < 730:
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sex = 'f'
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else:
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sex = 'm'
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@ -1,17 +1,17 @@
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,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,rises_lose,draw
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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
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0,2019-11-25-09_59,3,1,2,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,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,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,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,m,f,15.5,12.0,927.3677808126133,757.8912786740188,2,2,119,1232,56,161,0
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4,2019-11-29-09_59,3,4,2,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,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,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,m,f,17.5,12.0,867.515070390076,732.5567785654214,4,3,2,1005,31,73,0
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7,2019-12-04-10_00,3,3,2,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,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,f,f,14.4,12.0,715.5845509704202,726.7506510306094,4,4,446,2345,10,180,0
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9,2019-12-09-10_00,3,5,2,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,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,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,f,f,12.0,12.65,708.2029632781753,649.3215729301896,5,5,55,1489,26,152,0
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12,2019-12-12-10_00,3,2,6,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,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,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,m,f,11.933333333333332,11.299999999999999,852.2318545355058,642.0347177867645,3,3,1304,724,57,154,0
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@ -34,9 +34,9 @@ def plot_rise_vs_chirp_count(trial_summary):
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plt.setp(ax_rises.get_xticklabels(), visible=False)
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def plot_chirp_rise_count_per_pairing(trial_summary,
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beh_key_win=None, beh_key_lose=None,
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ylabel='y'):
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def plot_beh_count_per_pairing(trial_summary,
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beh_key_win=None, beh_key_lose=None,
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ylabel='y'):
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mek = ['k', 'None', 'None', 'k']
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markersize = 12
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@ -48,11 +48,11 @@ def plot_chirp_rise_count_per_pairing(trial_summary,
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for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
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win_count.append(trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0)].to_numpy())
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lose_count.append(trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0)].to_numpy())
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lose_count.append(trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
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(trial_summary["sex_lose"] == lose_sex) &
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(trial_summary["draw"] == 0)].to_numpy())
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fig = plt.figure(figsize=(20/2.54, 12/2.54))
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gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
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@ -70,60 +70,6 @@ def plot_chirp_rise_count_per_pairing(trial_summary,
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ax.set_ylabel(ylabel, fontsize=12)
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plt.tick_params(labelsize=10)
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# win_chirps = []
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# lose_chirps = []
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#
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# win_rises = []
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# lose_rises = []
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#
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# for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
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# win_chirps.append(trial_summary['chirps_win'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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# lose_chirps.append(trial_summary['chirps_lose'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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# win_rises.append(trial_summary['rises_win'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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# lose_rises.append(trial_summary['rises_lose'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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#
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#
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# fig = plt.figure(figsize=(20/2.54, 12/2.54))
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# gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
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# ax = fig.add_subplot(gs[0, 0])
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# ax.boxplot(win_chirps, positions=np.arange(len(win_chirps))-0.15, widths= .2, sym='')
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# ax.boxplot(lose_chirps, positions=np.arange(len(lose_chirps))+0.15, widths= .2, sym='')
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#
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# ax.set_xticks(np.arange(len(win_chirps)))
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# ax.set_xticklabels([u'\u2642\u2642', u'\u2642\u2640', u'\u2640\u2642', u'\u2640\u2640'])
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# # ax.set_xticklabels(['mm', 'mf', 'fm', 'ff'])
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# y0, y1 = ax.get_ylim()
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# for i in range(len(win_chirps)):
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# ax.text(i, y1, f'n={len(win_chirps[i]):.0f}', fontsize=10, ha='center', va='bottom')
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# ax.set_ylim(top = y1*1.1)
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# ax.set_ylabel('chirps [n]', fontsize=12)
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# plt.tick_params(labelsize=10)
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#
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# fig = plt.figure(figsize=(20/2.54, 12/2.54))
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# gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
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# ax = fig.add_subplot(gs[0, 0])
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# ax.boxplot(win_rises, positions=np.arange(len(win_rises))-0.15, widths= .2, sym='')
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# ax.boxplot(lose_rises, positions=np.arange(len(lose_rises))+0.15, widths= .2, sym='')
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#
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# ax.set_xticks(np.arange(len(win_rises)))
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# ax.set_xticklabels([u'\u2642\u2642', u'\u2642\u2640', u'\u2640\u2642', u'\u2640\u2640'])
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# # ax.set_xticklabels(['mm', 'mf', 'fm', 'ff'])
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# y0, y1 = ax.get_ylim()
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# for i in range(len(win_rises)):
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# ax.text(i, y1, f'n={len(win_rises[i]):.0f}', fontsize=10, ha='center', va='bottom')
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# ax.set_ylim(top = y1*1.1)
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# ax.set_ylabel('rises [n]', fontsize=12)
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# plt.tick_params(labelsize=10)
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# # plt.show()
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def plot_beh_count_vs_meta(trial_summary,
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beh_key_win=None, beh_key_lose=None,
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@ -183,151 +129,73 @@ def plot_beh_count_vs_meta(trial_summary,
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plt.setp(ax[3].get_yticklabels(), visible=False)
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plt.tick_params(labelsize=10)
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###############################################################################################################
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# win_chirps = []
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# lose_chirps = []
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#
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# win_rises = []
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# lose_rises = []
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#
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# d_size = []
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# d_EODf = []
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#
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# for win_sex, lose_sex in itertools.product(['m', 'f'], repeat=2):
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# win_chirps.append(trial_summary['chirps_win'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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# lose_chirps.append(trial_summary['chirps_lose'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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# win_rises.append(trial_summary['rises_win'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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# lose_rises.append(trial_summary['rises_lose'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy())
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#
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# w_size = trial_summary['size_win'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy()
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# l_size = trial_summary['size_lose'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy()
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#
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# w_EODf = trial_summary['EODf_win'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy()
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# l_EODf = trial_summary['EODf_lose'][(trial_summary["sex_win"] == win_sex) &
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# (trial_summary["sex_lose"] == lose_sex) &
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# (trial_summary["draw"] == 0)].to_numpy()
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#
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# d_size.append(w_size-l_size)
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# d_EODf.append(w_EODf-l_EODf)
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#
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#
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# mek = ['k', 'None', 'None', 'k']
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# markersize = 12
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# win_colors = [male_color, male_color, female_color, female_color]
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# lose_colors = [male_color, female_color, male_color, female_color]
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#
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# ### SIZE
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# ##################################################################################################################
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# fig = plt.figure(figsize=(20/2.54, 20/2.54))
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# gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, hspace=0.1, wspace=0.1)
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# ax = []
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# ax.append(fig.add_subplot(gs[0, 0]))
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# ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
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# ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0]))
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# ax.append(fig.add_subplot(gs[1, 1], sharex=ax[2], sharey=ax[1]))
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#
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# for i in range(len(lose_chirps)):
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# ax[0].plot(d_size[i], win_rises[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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# ax[1].plot(d_size[i], lose_rises[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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#
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# ax[2].plot(d_size[i], win_rises[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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# ax[3].plot(d_size[i], lose_rises[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2 )
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#
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# ax[0].set_ylabel('win rises [n]', fontsize=12)
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# ax[1].set_ylabel('lose rises [n]', fontsize=12)
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# ax[1].set_xlabel(u'$\Delta$size [cm]', fontsize=12)
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# ax[3].set_xlabel(u'$\Delta$size [cm]', fontsize=12)
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#
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# plt.setp(ax[0].get_xticklabels(), visible=False)
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# plt.setp(ax[2].get_xticklabels(), visible=False)
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#
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# plt.setp(ax[2].get_yticklabels(), visible=False)
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# plt.setp(ax[3].get_yticklabels(), visible=False)
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# plt.tick_params(labelsize=10)
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#
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# ##################################################################################################################
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# fig = plt.figure(figsize=(20/2.54, 20/2.54))
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# gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.95, hspace=0.1, wspace=0.1)
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# ax = []
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# ax.append(fig.add_subplot(gs[0, 0]))
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# ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
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# ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0]))
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# ax.append(fig.add_subplot(gs[1, 1], sharex=ax[2], sharey=ax[1]))
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#
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# for i in range(len(lose_chirps)):
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# ax[0].plot(d_size[i], win_chirps[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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# ax[1].plot(d_size[i], lose_chirps[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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#
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# ax[2].plot(d_size[i], win_chirps[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2)
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# ax[3].plot(d_size[i], lose_chirps[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize, markeredgewidth=2 )
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#
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# ax[0].set_ylabel('win chirps [n]', fontsize=12)
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# ax[1].set_ylabel('lose chirps [n]', fontsize=12)
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# ax[1].set_xlabel(u'$\Delta$size [cm]', fontsize=12)
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# ax[3].set_xlabel(u'$\Delta$size [cm]', fontsize=12)
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#
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# plt.setp(ax[0].get_xticklabels(), visible=False)
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# plt.setp(ax[2].get_xticklabels(), visible=False)
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#
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# plt.setp(ax[2].get_yticklabels(), visible=False)
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# plt.setp(ax[3].get_yticklabels(), visible=False)
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# plt.tick_params(labelsize=10)
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#
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# ### EODf
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# ##################################################################################################################
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#
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# fig = plt.figure(figsize=(20/2.54, 12/2.54))
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# gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.15, right=0.95, top=0.95)
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# ax = fig.add_subplot(gs[0, 0])
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#
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# for i in range(len(lose_rises)):
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# ax.plot(d_EODf[i], lose_rises[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize)
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# # ax.plot(d_EODf[i]*-1, lose_rises[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize)
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#
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# ax.set_ylabel('lose rises [n]', fontsize=12)
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# ax.set_xlabel(u'$\Delta$EODf [Hz]', fontsize=12)
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# plt.tick_params(labelsize=10)
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#
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# ##################################################################################################################
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# fig = plt.figure(figsize=(20/2.54, 12/2.54))
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# gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.15, right=0.95, top=0.95)
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# ax = fig.add_subplot(gs[0, 0])
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#
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# for i in range(len(lose_chirps)):
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# ax.plot(d_EODf[i], lose_chirps[i], 'p', color=win_colors[i], markeredgecolor=mek[i], markersize=markersize)
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# # ax.plot(d_EODf[i]*-1, lose_chirps[i], 'o', color=lose_colors[i], markeredgecolor=mek[i], markersize=markersize)
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#
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# ax.set_ylabel('lose chirps [n]', fontsize=12)
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# ax.set_xlabel(u'$\Delta$EODf [Hz]', fontsize=12)
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# plt.tick_params(labelsize=10)
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||||
def plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose', ylabel='chirps [n]'):
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mek = ['k', 'None', 'None', 'k']
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markersize = 10
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win_colors = [male_color, male_color, female_color, female_color]
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lose_colors = [male_color, female_color, male_color, female_color]
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lose_beh_per_exp = []
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win_beh_per_exp = []
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for i in np.unique(trial_summary['exp_lose']):
|
||||
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||||
lose_beh_per_exp.append(trial_summary[beh_key_lose][(trial_summary['exp_lose'] == i) &
|
||||
(trial_summary["draw"] == 0)].to_numpy())
|
||||
|
||||
win_beh_per_exp.append(trial_summary[beh_key_win][(trial_summary['exp_lose'] == i) &
|
||||
(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=0.15, right=0.95, top=0.95, hspace=0.1, wspace=0.1)
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
|
||||
ax.boxplot(lose_beh_per_exp, positions = np.unique(trial_summary['exp_lose'])-0.15, widths=0.2)
|
||||
ax.boxplot(win_beh_per_exp, positions = np.unique(trial_summary['exp_lose'])+0.15, widths=0.2)
|
||||
|
||||
for enu, (win_sex, lose_sex) in enumerate(itertools.product(['m', 'f'], repeat=2)):
|
||||
lose_beh_count = trial_summary[beh_key_lose][(trial_summary["sex_win"] == win_sex) &
|
||||
(trial_summary["sex_lose"] == lose_sex) &
|
||||
(trial_summary["draw"] == 0)].to_numpy()
|
||||
win_beh_count = trial_summary[beh_key_win][(trial_summary["sex_win"] == win_sex) &
|
||||
(trial_summary["sex_lose"] == lose_sex) &
|
||||
(trial_summary["draw"] == 0)].to_numpy()
|
||||
|
||||
lose_exp = trial_summary['exp_lose'][(trial_summary["sex_win"] == win_sex) &
|
||||
(trial_summary["sex_lose"] == lose_sex) &
|
||||
(trial_summary["draw"] == 0)].to_numpy()
|
||||
|
||||
win_exp = trial_summary['exp_win'][(trial_summary["sex_win"] == win_sex) &
|
||||
(trial_summary["sex_lose"] == lose_sex) &
|
||||
(trial_summary["draw"] == 0)].to_numpy()
|
||||
|
||||
ax.plot(lose_exp-0.15, lose_beh_count, 'o', color=lose_colors[enu], markeredgecolor=mek[enu],
|
||||
markersize=markersize, markeredgewidth=2)
|
||||
|
||||
ax.plot(win_exp+0.15, win_beh_count, 'p', color=win_colors[enu], markeredgecolor=mek[enu],
|
||||
markersize=markersize, markeredgewidth=2)
|
||||
|
||||
ax.set_xticks(np.unique(trial_summary['exp_lose']))
|
||||
ax.set_xticklabels(np.unique(trial_summary['exp_lose']))
|
||||
ax.set_xlabel('experience [trials]', fontsize=12)
|
||||
ax.set_ylabel(ylabel, fontsize=12)
|
||||
ax.tick_params(labelsize=10)
|
||||
def main():
|
||||
beh_per_exp = []
|
||||
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
|
||||
chirp_notes = pd.read_csv('chirp_notes.csv', index_col=0)
|
||||
# trial_summary = trial_summary[chirp_notes['good'] == 1]
|
||||
trial_summary = trial_summary[chirp_notes['good'] == 1]
|
||||
|
||||
plot_rise_vs_chirp_count(trial_summary)
|
||||
|
||||
plot_chirp_rise_count_per_pairing(trial_summary,
|
||||
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
ylabel='chirps [n]')
|
||||
plot_chirp_rise_count_per_pairing(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
ylabel='rises [n]')
|
||||
plot_beh_count_per_pairing(trial_summary,
|
||||
beh_key_win='chirps_win', beh_key_lose='chirps_lose',
|
||||
ylabel='chirps [n]')
|
||||
plot_beh_count_per_pairing(trial_summary,
|
||||
beh_key_win='rises_win', beh_key_lose='rises_lose',
|
||||
ylabel='rises [n]')
|
||||
|
||||
|
||||
|
||||
plot_beh_count_vs_meta(trial_summary,
|
||||
@ -347,6 +215,9 @@ def main():
|
||||
meta_key_win="EODf_win", meta_key_lose='EODf_lose',
|
||||
xlabel=u'$\Delta$EODf [Hz]')
|
||||
|
||||
plot_beh_conut_vs_experience(trial_summary, beh_key_win='chirps_win', beh_key_lose='chirps_lose', ylabel='chirps [n]')
|
||||
plot_beh_conut_vs_experience(trial_summary, beh_key_win='rises_win', beh_key_lose='rises_lose', ylabel='rises [n]')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user