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illustation of agonistic categories nearly completed. cleaned up associated code

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Till Raab 2023-08-09 15:26:03 +02:00
parent 48314b363f
commit 7e55ba3b09
1 changed files with 142 additions and 96 deletions

238
ethogram.py
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@ -10,6 +10,8 @@ import pandas as pd
import scipy.stats as scp
import networkx as nx
from thunderfish.powerspectrum import decibel
from IPython import embed
from event_time_correlations import load_and_converete_boris_events
glob_colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF', 'k']
@ -90,6 +92,40 @@ def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
ax.set_ylim(-1.3, 1.3)
ax.set_title(title, fontsize=12)
def create_marcov_matrix(individual_event_times, individual_event_labels):
event_times = []
event_labels = []
for ll, t in zip(individual_event_labels, individual_event_times):
event_times.extend(t)
event_labels.extend(np.full(len(t), ll))
time_sorter = np.argsort(event_times)
event_times = np.array(event_times)[time_sorter]
event_labels = np.array(event_labels)[time_sorter]
marcov_matrix = np.zeros((len(individual_event_labels) + 1, len(individual_event_labels) + 1))
for enu_ori, label_ori in enumerate(individual_event_labels):
for enu_tar, label_tar in enumerate(individual_event_labels):
n = len(event_times[:-1][(event_labels[:-1] == label_ori) & (event_labels[1:] == label_tar) & (
np.diff(event_times) <= 5)])
marcov_matrix[enu_ori, enu_tar] = n
for enu_tar, label_tar in enumerate(individual_event_labels):
n = len(event_times[:-1][(event_labels[1:] == label_tar) & (np.diff(event_times) > 5)])
marcov_matrix[-1, enu_tar] = n
marcov_matrix[-1, 5] = 0
individual_event_labels.append('void')
### get those cases where ag_on does not point to event and no event points to corresponding ag_off ... add thise cases in marcov matrix
chase_on_idx = np.where(event_labels == individual_event_labels[4])[0]
chase_off_idx = np.where(event_labels == individual_event_labels[5])[0]
helper_mask = np.ones_like(chase_on_idx)
helper_mask[np.diff(event_times)[chase_on_idx] <= 5] = 0
helper_mask[np.diff(event_times)[chase_off_idx - 1] <= 5] = 0
marcov_matrix[4, 5] += np.sum(helper_mask)
return marcov_matrix
def main(base_path):
if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'markov')):
os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'markov'))
@ -102,24 +138,27 @@ def main(base_path):
all_marcov_matrix = []
all_event_counts = []
all_agonistic_categorie = []
all_chase_durs = []
# agonistic categorie plot
fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0)
ax = fig.add_subplot(gs[1, 0])
ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
plt.setp(ax_spec.get_xticklabels(), visible=False)
for i in range(1, 5):
ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
fill_dots = np.arange(4, 5.1, 0.125)
ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
got_examples = [False, False, False, False]
example_skips = [3, 4, 3, 0]
# fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
# gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0)
# ax = fig.add_subplot(gs[1, 0])
# ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
# plt.setp(ax_spec.get_xticklabels(), visible=False)
#
# for i in range(1, 5):
# ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
# ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
#
# fill_dots = np.arange(4, 5.1, 0.125)
# ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
got_examples = [False, False, False]
example_ag_on_off = [[], [], []]
example_chirp_times = [[], [], []]
example_rise_times = [[], [], []]
example_1_path = ''
example_skips = [3, 4, 3]
for index, trial in trial_summary.iterrows():
trial_path = os.path.join(base_path, trial['recording'])
@ -155,77 +194,39 @@ def main(base_path):
rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
event_times = []
event_labels = []
loop_times = [chirp_times[1], rise_times[1], chirp_times[0], rise_times[0], ag_on_off_t_GRID[:, 0],
ag_on_off_t_GRID[:, 1], contact_t_GRID]
loop_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$', r'chace$_{on}$', r'chace$_{off}$', 'contact']
event_counts = np.array([len(chirp_times[1]), len(rise_times[1]), len(chirp_times[0]), len(rise_times[0]), len(ag_on_off_t_GRID), len(ag_on_off_t_GRID), len(contact_t_GRID)])
for ll, t in zip(loop_labels, loop_times):
event_times.extend(t)
event_labels.extend(np.full(len(t), ll))
time_sorter = np.argsort(event_times)
event_times = np.array(event_times)[time_sorter]
event_labels = np.array(event_labels)[time_sorter]
### create marcov_matrix for one trial !!!
marcov_matrix = np.zeros((len(loop_labels)+1, len(loop_labels)+1))
for enu_ori, label_ori in enumerate(loop_labels):
for enu_tar, label_tar in enumerate(loop_labels):
n = len(event_times[:-1][(event_labels[:-1] == label_ori) & (event_labels[1:] == label_tar) & (np.diff(event_times) <= 5)])
marcov_matrix[enu_ori, enu_tar] = n
for enu_tar, label_tar in enumerate(loop_labels):
n = len(event_times[:-1][(event_labels[1:] == label_tar) & (np.diff(event_times) > 5)])
marcov_matrix[-1, enu_tar] = n
marcov_matrix[-1, 5] = 0
loop_labels.append('void')
event_counts = np.append(event_counts, marcov_matrix[-1].sum())
### get those cases where ag_on does not point to event and no event points to corresponding ag_off ... add thise cases in marcov matrix
chase_on_idx = np.where(event_labels == loop_labels[4])[0]
chase_off_idx = np.where(event_labels == loop_labels[5])[0]
helper_mask = np.ones_like(chase_on_idx)
helper_mask[np.diff(event_times)[chase_on_idx] <= 5] = 0
helper_mask[np.diff(event_times)[chase_off_idx-1] <= 5] = 0
marcov_matrix[4, 5] += np.sum(helper_mask)
# trial marcov matrix
individual_event_times = [chirp_times[1], rise_times[1], chirp_times[0], rise_times[0], ag_on_off_t_GRID[:, 0],
ag_on_off_t_GRID[:, 1], contact_t_GRID]
individual_event_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$',
r'chace$_{on}$', r'chace$_{off}$', 'contact']
marcov_matrix = create_marcov_matrix(individual_event_times, individual_event_labels)
all_marcov_matrix.append(marcov_matrix)
all_event_counts.append(event_counts)
# plot_transition_matrix(marcov_matrix, loop_labels)
# plot_transition_diagram(marcov_matrix, loop_labels, node_size=event_counts)
# plot_transition_diagram(marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100, loop_labels, node_size=event_counts)+
# compute and store trial event counts
event_counts = np.array(list(map(lambda x: len(x), individual_event_times)))
event_counts = np.append(event_counts, marcov_matrix[-1].sum())
all_event_counts.append(event_counts)
# agonistic categories
agonitic_categorie = np.zeros(len(ag_on_off_t_GRID))
chase_durs = np.zeros_like(agonitic_categorie)
all_agonistic_categorie.append(agonitic_categorie)
all_chase_durs.append(chase_durs)
### plotting
for enu, (chase_on_time, chase_off_time) in enumerate(ag_on_off_t_GRID):
chase_dur = chase_off_time - chase_on_time
chase_durs[enu] = chase_dur
chirp_dt = chase_dur if chase_dur < 5 else 5
max_dt = 5
# check if rise before chase / chirp at end
rise_before, chirp_arround_end = False, False
if np.any(((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)):
rise_times_oi = rise_times[1][((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)]
rise_before = True
if np.any( ((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)):
# chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)]
# ToDo: check if I realy get all chirps... currently not the case
chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chase_dur) & ((chirp_times[1] - chase_off_time) < max_dt)]
chirp_arround_end = True
# define agonistic categorie based on rise/chirp occurance
if rise_before:
if chirp_arround_end:
agonitic_categorie[enu] = 1
@ -241,21 +242,18 @@ def main(base_path):
if chase_dur > 10:
if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0):
if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
for ct in chirp_time_oi:
ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10],
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='k', lw=2)
for rt in rise_times_oi:
ax.plot([rt - chase_on_time, rt - chase_on_time],
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='firebrick', lw=2)
example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi)
example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi)
example_1_path = trial_path
got_examples[0] = True
else:
example_skips[int(agonitic_categorie[enu] - 1)] -= 1
elif agonitic_categorie[enu] == 2 and not got_examples[1]:
if chase_dur > 10:
if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
for rt in rise_times_oi:
ax.plot([rt - chase_on_time, rt - chase_on_time],
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='firebrick', lw=2)
example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi)
got_examples[1] = True
else:
example_skips[int(agonitic_categorie[enu] - 1)] -= 1
@ -263,27 +261,69 @@ def main(base_path):
if chase_dur > 10:
if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0):
if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
for ct in chirp_time_oi:
ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10],
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='k', lw=2)
example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi)
got_examples[2] = True
else:
example_skips[int(agonitic_categorie[enu] - 1)] -= 1
else:
pass
### agonistic categories
stacked_agonistic_categories = np.hstack(all_agonistic_categorie)
stacked_all_chase_durs = np.hstack(all_chase_durs)
all_agonistic_categorie.append(agonitic_categorie)
### agonistic categorie example figure
fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0)
ax = fig.add_subplot(gs[1, 0])
ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
plt.setp(ax_spec.get_xticklabels(), visible=False)
for i in range(1, 5):
ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
fill_dots = np.arange(4, 5.1, 0.125)
ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
for enu, (chirp_time_oi, rise_times_oi, ag_on_off) in enumerate(zip(example_chirp_times, example_rise_times, example_ag_on_off)):
chase_on_time, chase_off_time = ag_on_off
for ct in chirp_time_oi:
ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10], [enu + .8, enu + 1.2], color='k', lw=2)
for rt in rise_times_oi:
ax.plot([rt - chase_on_time, rt - chase_on_time], [enu + .8, enu + 1.2], color='firebrick', lw=2)
stacked_agonistic_categories = np.hstack(all_agonistic_categorie)
pct_each_categorie = np.zeros(4)
for enu, cat in enumerate(range(1, 5)):
pct_each_categorie[enu] = len(stacked_agonistic_categories[stacked_agonistic_categories == cat]) / len(stacked_agonistic_categories)
# example plot
for enu, cat_pct in enumerate(pct_each_categorie):
ax.text(15.2, enu+1, f'{cat_pct*100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center')
ax.text(15.2, enu + 1, f'{pct_each_categorie[enu] * 100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center')
# plot correct spectrogram
ex1_df_idx = trial_summary[trial_summary['recording'] == os.path.split(example_1_path)[-1]].index.to_numpy()[0]
lose_id = trial_summary.iloc[ex1_df_idx]['lose_ID']
# ToDo: use fill_spec
spec = np.load(os.path.join(example_1_path, 'spec.npy'))
times = np.load(os.path.join(example_1_path, 'times.npy'))
fund_v = np.load(os.path.join(example_1_path, 'fund_v.npy'))
ident_v = np.load(os.path.join(example_1_path, 'ident_v.npy'))
idx_v = np.load(os.path.join(example_1_path, 'idx_v.npy'))
artificial_t_axis = np.linspace(times[0], times[-1], spec.shape[1])
artificial_f_axis = np.linspace(0, 2000, spec.shape[0])
# plt.pcolormesh(artificial_t_axis, artificial_f_axis, decibel(spec), vmin=-100, vmax=-50)
lose_freq_in_snippet = fund_v[(ident_v == lose_id) & (times[idx_v] > example_ag_on_off[0][0]-5) & (times[idx_v] < example_ag_on_off[0][1]+5)]
max_f, min_f = np.max(lose_freq_in_snippet) + 10, np.min(lose_freq_in_snippet) - 10
t_idx0 = np.where(artificial_t_axis >= example_ag_on_off[0][0] - 5)[0][0]
t_idx1 = np.where(artificial_t_axis <= example_ag_on_off[0][1] + 5)[0][-1]
f_idx0 = np.where(artificial_f_axis >= min_f)[0][0]
f_idx1 = np.where(artificial_f_axis <= max_f)[0][-1]
# ToDo this does not work. fix it tomorow
ax_spec.pcolormesh(artificial_t_axis[t_idx0:t_idx1+2] - example_ag_on_off[0][0],
artificial_f_axis[f_idx0:f_idx1+2],
decibel(spec[t_idx0:t_idx1+1, f_idx0:f_idx1+1]), vmin=-100, vmax=-50)
ax.plot([0, 0], [0.8, 5], '--', color='k', lw=1)
ax.plot([10, 10], [0.8, 5], '--', color='k', lw=1)
@ -297,15 +337,20 @@ def main(base_path):
ax.tick_params(axis='y', labelsize=20)
ax.tick_params(axis = 'x', labelsize=10)
legend_elements = [Line2D([0], [0], color='firebrick', lw=2, label=r'rise$_{lose}$'),
Line2D([0], [0], color='k', lw=2, label=r'chirp$_{lose}$'),
Patch(facecolor='tab:grey', edgecolor='w', label= 'chase event')]
ax.legend(handles=legend_elements, loc='upper right', ncol=3, bbox_to_anchor=(1, 1), frameon=False, fontsize=10, facecolor='white')
ax_spec.legend(handles=legend_elements, loc='lower right', ncol=3, bbox_to_anchor=(1, 1), frameon=False, fontsize=10, facecolor='white')
ax.spines[['right', 'top']].set_visible(False)
plt.show()
# bar plot
embed()
quit()
### bar plot - agonistic categories counts/pct #####################################################################
fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
ax.bar(np.arange(4),
[len(stacked_agonistic_categories[stacked_agonistic_categories == 1]),
@ -330,28 +375,28 @@ def main(base_path):
plt.show()
### marcov models
### marcov models plots ############################################################################################
all_marcov_matrix = np.array(all_marcov_matrix)
all_event_counts = np.array(all_event_counts)
collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0)
collective_event_counts = np.sum(all_event_counts, axis=0)
plot_transition_matrix(collective_marcov_matrix, loop_labels)
plot_transition_matrix(collective_marcov_matrix, individual_event_labels)
fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
plot_transition_diagram(
collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100,
loop_labels, collective_event_counts, ax, threshold=5, color_by_origin=True, title='origin triggers target [%]')
individual_event_labels, collective_event_counts, ax, threshold=5, color_by_origin=True, title='origin triggers target [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_destination' + '.png'), dpi=300)
plt.close()
fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
plot_transition_diagram(collective_marcov_matrix / collective_event_counts * 100,
loop_labels, collective_event_counts, ax, threshold=5, color_by_target=True,
individual_event_labels, collective_event_counts, ax, threshold=5, color_by_target=True,
title='target triggered by origin [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300)
plt.close()
@ -362,7 +407,7 @@ def main(base_path):
plot_transition_diagram(
marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100,
loop_labels, event_counts, ax, threshold=5, color_by_origin=True,
individual_event_labels, event_counts, ax, threshold=5, color_by_origin=True,
title='origin triggers target [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_destination' + '.png'),
dpi=300)
@ -371,11 +416,12 @@ def main(base_path):
fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54))
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
plot_transition_diagram(marcov_matrix / event_counts * 100,
loop_labels, event_counts, ax, threshold=5, color_by_target=True,
individual_event_labels, event_counts, ax, threshold=5, color_by_target=True,
title='target triggered by origin [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_origin' + '.png'),
dpi=300)
plt.close()
####################################################################################################################
embed()
quit()