229 lines
10 KiB
Python
229 lines
10 KiB
Python
import os
|
|
import sys
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.gridspec as gridspec
|
|
from mpl_toolkits.axes_grid1 import make_axes_locatable
|
|
import numpy as np
|
|
import pandas as pd
|
|
import scipy.stats as scp
|
|
import networkx as nx
|
|
|
|
from IPython import embed
|
|
from event_time_correlations import load_and_converete_boris_events
|
|
glob_colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF', 'k']
|
|
|
|
|
|
def plot_transition_matrix(matrix, labels):
|
|
fig = plt.figure(figsize=(20/2.54, 20/2.54))
|
|
#gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.1, right=0.9, top=0.95, wspace=0.1, width_ratios=[8, 1])
|
|
gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.925, top=0.95)
|
|
ax = fig.add_subplot(gs[0, 0])
|
|
|
|
divider = make_axes_locatable(ax)
|
|
cax = divider.append_axes('right', size='5%', pad=0.05)
|
|
|
|
# cax = fig.add_subplot(gs[0, 1])
|
|
im = ax.imshow(matrix)
|
|
ax.set_xticks(list(range(len(matrix))))
|
|
ax.set_yticks(list(range(len(matrix))))
|
|
ax.set_xticklabels(labels, rotation=45)
|
|
ax.set_yticklabels(labels)
|
|
|
|
fig.colorbar(im, cax=cax, orientation='vertical')
|
|
|
|
ax.tick_params(labelsize=10)
|
|
cax.tick_params(labelsize=10)
|
|
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'event_counts' + '.png'), dpi=300)
|
|
plt.close()
|
|
|
|
|
|
def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
|
|
color_by_origin=False, color_by_target=False, title=''):
|
|
|
|
|
|
|
|
matrix[matrix <= threshold] = 0
|
|
matrix = np.around(matrix, decimals=1)
|
|
Graph = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
|
|
|
|
node_labels = dict(zip(Graph, labels))
|
|
# Graph = nx.relabel_nodes(Graph, node_labels)
|
|
|
|
edge_labels = nx.get_edge_attributes(Graph, 'weight')
|
|
positions = nx.circular_layout(Graph)
|
|
positions2 = nx.circular_layout(Graph)
|
|
for p in positions:
|
|
positions2[p][0] *= 1.2
|
|
positions2[p][1] *= 1.2
|
|
|
|
# ToDo: nodes
|
|
nx.draw_networkx_nodes(Graph, pos=positions, node_size=node_size, ax=ax, alpha=0.5, node_color=np.array(glob_colors)[:len(node_size)])
|
|
nx.draw_networkx_labels(Graph, pos=positions2, labels=node_labels, ax=ax)
|
|
# google networkx drawing to get better graphs with networkx
|
|
# nx.draw(Graph, pos=positions, node_size=node_size, label=labels, with_labels=True, ax=ax)
|
|
# # ToDo: edges
|
|
edge_width = np.array([x / 5 for x in [*edge_labels.values()]])
|
|
if color_by_origin:
|
|
edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 0]]
|
|
elif color_by_target:
|
|
edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 1]]
|
|
else:
|
|
edge_colors = 'k'
|
|
|
|
|
|
edge_width[edge_width >= 6] = 6
|
|
|
|
nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
|
|
arrows=True, arrowsize=20,
|
|
min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.025",
|
|
ax=ax, edge_color=edge_colors)
|
|
nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.2, edge_labels=edge_labels, ax=ax, rotate=True)
|
|
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["bottom"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
ax.spines["left"].set_visible(False)
|
|
|
|
ax.set_xlim(-1.3, 1.3)
|
|
ax.set_ylim(-1.3, 1.3)
|
|
ax.set_title(title, fontsize=12)
|
|
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'))
|
|
|
|
trial_summary = pd.read_csv(os.path.join(base_path, '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_mask = chirp_notes['good'] == 1
|
|
|
|
all_marcov_matrix = []
|
|
all_event_counts = []
|
|
for index, trial in trial_summary.iterrows():
|
|
trial_path = os.path.join(base_path, trial['recording'])
|
|
|
|
if not trial_mask[index]:
|
|
continue
|
|
if trial['group'] < 5:
|
|
continue
|
|
if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
|
|
continue
|
|
if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
|
|
continue
|
|
if trial['draw'] == 1:
|
|
continue
|
|
|
|
ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
|
|
times = np.load(os.path.join(trial_path, 'times.npy'))
|
|
sorter = -1 if trial['win_ID'] != ids[0] else 1
|
|
|
|
### event times --> BORIS behavior
|
|
contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
|
|
load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
|
|
|
|
### communication
|
|
if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
|
|
continue
|
|
|
|
chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
|
|
chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
|
|
chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
|
|
|
|
rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
|
|
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 1: which beh 2 is triggered by beh. 1 ?
|
|
marcov_matrix = np.zeros((len(loop_labels)+1, len(loop_labels)+1))
|
|
### create marcov_matrix 2: beh 2 is triggered by which beh. 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)
|
|
|
|
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)
|
|
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)
|
|
|
|
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 [%]')
|
|
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, title='target triggered by origin [%]')
|
|
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'markov_origin' + '.png'), dpi=300)
|
|
plt.close()
|
|
|
|
for i, (marcov_matrix, event_counts) in enumerate(zip(all_marcov_matrix, all_event_counts)):
|
|
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.reshape(len(event_counts), 1) * 100,
|
|
loop_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)
|
|
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(marcov_matrix / event_counts * 100,
|
|
loop_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()
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main(sys.argv[1])
|