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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
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ethogram.py
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@ -10,6 +10,8 @@ import pandas as pd
import scipy.stats as scp import scipy.stats as scp
import networkx as nx import networkx as nx
from thunderfish.powerspectrum import decibel
from IPython import embed from IPython import embed
from event_time_correlations import load_and_converete_boris_events from event_time_correlations import load_and_converete_boris_events
glob_colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF', 'k'] 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_ylim(-1.3, 1.3)
ax.set_title(title, fontsize=12) 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): def main(base_path):
if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'markov')): 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')) os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'markov'))
@ -102,24 +138,27 @@ def main(base_path):
all_marcov_matrix = [] all_marcov_matrix = []
all_event_counts = [] all_event_counts = []
all_agonistic_categorie = [] all_agonistic_categorie = []
all_chase_durs = []
# agonistic categorie plot # agonistic categorie plot
fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54)) # 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) # 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 = fig.add_subplot(gs[1, 0])
ax_spec = fig.add_subplot(gs[0, 0], sharex=ax) # ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
plt.setp(ax_spec.get_xticklabels(), visible=False) # plt.setp(ax_spec.get_xticklabels(), visible=False)
#
for i in range(1, 5): # 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([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') # 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) # fill_dots = np.arange(4, 5.1, 0.125)
ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3) # ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
got_examples = [False, False, False, False] got_examples = [False, False, False]
example_skips = [3, 4, 3, 0] 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(): for index, trial in trial_summary.iterrows():
trial_path = os.path.join(base_path, trial['recording']) 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_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]]] rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
event_times = [] # trial marcov matrix
event_labels = [] individual_event_times = [chirp_times[1], rise_times[1], chirp_times[0], rise_times[0], ag_on_off_t_GRID[:, 0],
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]
ag_on_off_t_GRID[:, 1], contact_t_GRID] individual_event_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$',
loop_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$', r'chace$_{on}$', r'chace$_{off}$', 'contact'] 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)
marcov_matrix = create_marcov_matrix(individual_event_times, individual_event_labels)
all_marcov_matrix.append(marcov_matrix) 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)) 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): for enu, (chase_on_time, chase_off_time) in enumerate(ag_on_off_t_GRID):
chase_dur = chase_off_time - chase_on_time chase_dur = chase_off_time - chase_on_time
chase_durs[enu] = chase_dur
chirp_dt = chase_dur if chase_dur < 5 else 5 chirp_dt = chase_dur if chase_dur < 5 else 5
max_dt = 5 max_dt = 5
# check if rise before chase / chirp at end
rise_before, chirp_arround_end = False, False 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)): 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_times_oi = rise_times[1][((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)]
rise_before = True rise_before = True
if np.any( ((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)): 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_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 chirp_arround_end = True
# define agonistic categorie based on rise/chirp occurance
if rise_before: if rise_before:
if chirp_arround_end: if chirp_arround_end:
agonitic_categorie[enu] = 1 agonitic_categorie[enu] = 1
@ -241,21 +242,18 @@ def main(base_path):
if chase_dur > 10: 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 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: if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
for ct in chirp_time_oi: example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10], example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi)
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='k', lw=2) example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi)
for rt in rise_times_oi: example_1_path = trial_path
ax.plot([rt - chase_on_time, rt - chase_on_time],
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='firebrick', lw=2)
got_examples[0] = True got_examples[0] = True
else: else:
example_skips[int(agonitic_categorie[enu] - 1)] -= 1 example_skips[int(agonitic_categorie[enu] - 1)] -= 1
elif agonitic_categorie[enu] == 2 and not got_examples[1]: elif agonitic_categorie[enu] == 2 and not got_examples[1]:
if chase_dur > 10: if chase_dur > 10:
if example_skips[int(agonitic_categorie[enu] - 1)] == 0: if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
for rt in rise_times_oi: example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
ax.plot([rt - chase_on_time, rt - chase_on_time], example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi)
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='firebrick', lw=2)
got_examples[1] = True got_examples[1] = True
else: else:
example_skips[int(agonitic_categorie[enu] - 1)] -= 1 example_skips[int(agonitic_categorie[enu] - 1)] -= 1
@ -263,27 +261,69 @@ def main(base_path):
if chase_dur > 10: 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 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: if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
for ct in chirp_time_oi: example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10], example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi)
[agonitic_categorie[enu] - .2, agonitic_categorie[enu] + .2], color='k', lw=2)
got_examples[2] = True got_examples[2] = True
else: else:
example_skips[int(agonitic_categorie[enu] - 1)] -= 1 example_skips[int(agonitic_categorie[enu] - 1)] -= 1
else: else:
pass pass
### agonistic categories all_agonistic_categorie.append(agonitic_categorie)
stacked_agonistic_categories = np.hstack(all_agonistic_categorie)
stacked_all_chase_durs = np.hstack(all_chase_durs) ### 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) pct_each_categorie = np.zeros(4)
for enu, cat in enumerate(range(1, 5)): for enu, cat in enumerate(range(1, 5)):
pct_each_categorie[enu] = len(stacked_agonistic_categories[stacked_agonistic_categories == cat]) / len(stacked_agonistic_categories) pct_each_categorie[enu] = len(stacked_agonistic_categories[stacked_agonistic_categories == cat]) / len(stacked_agonistic_categories)
ax.text(15.2, enu + 1, f'{pct_each_categorie[enu] * 100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center')
# example plot
for enu, cat_pct in enumerate(pct_each_categorie): # plot correct spectrogram
ax.text(15.2, enu+1, f'{cat_pct*100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center') 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([0, 0], [0.8, 5], '--', color='k', lw=1)
ax.plot([10, 10], [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='y', labelsize=20)
ax.tick_params(axis = 'x', labelsize=10) ax.tick_params(axis = 'x', labelsize=10)
legend_elements = [Line2D([0], [0], color='firebrick', lw=2, label=r'rise$_{lose}$'), legend_elements = [Line2D([0], [0], color='firebrick', lw=2, label=r'rise$_{lose}$'),
Line2D([0], [0], color='k', lw=2, label=r'chirp$_{lose}$'), Line2D([0], [0], color='k', lw=2, label=r'chirp$_{lose}$'),
Patch(facecolor='tab:grey', edgecolor='w', label= 'chase event')] 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) ax.spines[['right', 'top']].set_visible(False)
plt.show() plt.show()
# bar plot embed()
quit()
### bar plot - agonistic categories counts/pct #####################################################################
fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54)) fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
ax.bar(np.arange(4), ax.bar(np.arange(4),
[len(stacked_agonistic_categories[stacked_agonistic_categories == 1]), [len(stacked_agonistic_categories[stacked_agonistic_categories == 1]),
@ -330,28 +375,28 @@ def main(base_path):
plt.show() plt.show()
### marcov models ### marcov models plots ############################################################################################
all_marcov_matrix = np.array(all_marcov_matrix) all_marcov_matrix = np.array(all_marcov_matrix)
all_event_counts = np.array(all_event_counts) all_event_counts = np.array(all_event_counts)
collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0) collective_marcov_matrix = np.sum(all_marcov_matrix, axis=0)
collective_event_counts = np.sum(all_event_counts, 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, 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) fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
plot_transition_diagram( plot_transition_diagram(
collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100, 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.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_destination' + '.png'), dpi=300)
plt.close() plt.close()
fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54)) 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) 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, 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 [%]') title='target triggered by origin [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300) plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300)
plt.close() plt.close()
@ -362,7 +407,7 @@ def main(base_path):
plot_transition_diagram( plot_transition_diagram(
marcov_matrix / event_counts.reshape(len(event_counts), 1) * 100, 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 [%]') title='origin triggers target [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_destination' + '.png'), plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_destination' + '.png'),
dpi=300) dpi=300)
@ -371,11 +416,12 @@ def main(base_path):
fig, ax = plt.subplots(figsize=(21 / 2.54, 19 / 2.54)) 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) fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
plot_transition_diagram(marcov_matrix / event_counts * 100, 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 [%]') title='target triggered by origin [%]')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_origin' + '.png'), plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', f'markov_{i}_origin' + '.png'),
dpi=300) dpi=300)
plt.close() plt.close()
####################################################################################################################
embed() embed()
quit() quit()