433 lines
21 KiB
Python
433 lines
21 KiB
Python
import os
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import sys
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from matplotlib.patches import Patch
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from matplotlib.lines import Line2D
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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import numpy as np
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import pandas as pd
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import scipy.stats as scp
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import networkx as nx
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from thunderfish.powerspectrum import decibel
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from IPython import embed
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from event_time_correlations import load_and_converete_boris_events
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glob_colors = ['#BA2D22', '#53379B', '#F47F17', '#3673A4', '#AAB71B', '#DC143C', '#1E90FF', 'k']
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def plot_transition_matrix(matrix, labels):
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fig = plt.figure(figsize=(20/2.54, 20/2.54))
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#gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.1, right=0.9, top=0.95, wspace=0.1, width_ratios=[8, 1])
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gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.925, top=0.95)
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ax = fig.add_subplot(gs[0, 0])
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divider = make_axes_locatable(ax)
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cax = divider.append_axes('right', size='5%', pad=0.05)
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# cax = fig.add_subplot(gs[0, 1])
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im = ax.imshow(matrix)
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ax.set_xticks(list(range(len(matrix))))
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ax.set_yticks(list(range(len(matrix))))
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ax.set_xticklabels(labels, rotation=45)
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ax.set_yticklabels(labels)
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fig.colorbar(im, cax=cax, orientation='vertical')
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ax.tick_params(labelsize=10)
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cax.tick_params(labelsize=10)
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'markov', 'event_counts' + '.png'), dpi=300)
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plt.close()
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def plot_transition_diagram(matrix, labels, node_size, ax, threshold=5,
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color_by_origin=False, color_by_target=False, title=''):
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matrix[matrix <= threshold] = 0
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matrix = np.around(matrix, decimals=1)
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Graph = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
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node_labels = dict(zip(Graph, labels))
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# Graph = nx.relabel_nodes(Graph, node_labels)
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edge_labels = nx.get_edge_attributes(Graph, 'weight')
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positions = nx.circular_layout(Graph)
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positions2 = nx.circular_layout(Graph)
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for p in positions:
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positions2[p][0] *= 1.2
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positions2[p][1] *= 1.2
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# ToDo: nodes
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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)])
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nx.draw_networkx_labels(Graph, pos=positions2, labels=node_labels, ax=ax)
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# google networkx drawing to get better graphs with networkx
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# nx.draw(Graph, pos=positions, node_size=node_size, label=labels, with_labels=True, ax=ax)
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# # ToDo: edges
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edge_width = np.array([x / 5 for x in [*edge_labels.values()]])
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if color_by_origin:
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edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 0]]
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elif color_by_target:
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edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 1]]
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else:
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edge_colors = 'k'
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edge_width[edge_width >= 6] = 6
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nx.draw_networkx_edges(Graph, pos=positions, node_size=node_size, width=edge_width,
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arrows=True, arrowsize=20,
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min_target_margin=25, min_source_margin=25, connectionstyle="arc3, rad=0.025",
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ax=ax, edge_color=edge_colors)
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nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.2, edge_labels=edge_labels, ax=ax, rotate=True)
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ax.spines["top"].set_visible(False)
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ax.spines["bottom"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.spines["left"].set_visible(False)
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ax.set_xlim(-1.3, 1.3)
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ax.set_ylim(-1.3, 1.3)
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ax.set_title(title, fontsize=12)
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def create_marcov_matrix(individual_event_times, individual_event_labels):
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event_times = []
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event_labels = []
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for ll, t in zip(individual_event_labels, individual_event_times):
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event_times.extend(t)
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event_labels.extend(np.full(len(t), ll))
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time_sorter = np.argsort(event_times)
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event_times = np.array(event_times)[time_sorter]
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event_labels = np.array(event_labels)[time_sorter]
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marcov_matrix = np.zeros((len(individual_event_labels) + 1, len(individual_event_labels) + 1))
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for enu_ori, label_ori in enumerate(individual_event_labels):
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for enu_tar, label_tar in enumerate(individual_event_labels):
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n = len(event_times[:-1][(event_labels[:-1] == label_ori) & (event_labels[1:] == label_tar) & (
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np.diff(event_times) <= 5)])
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marcov_matrix[enu_ori, enu_tar] = n
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for enu_tar, label_tar in enumerate(individual_event_labels):
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n = len(event_times[:-1][(event_labels[1:] == label_tar) & (np.diff(event_times) > 5)])
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marcov_matrix[-1, enu_tar] = n
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marcov_matrix[-1, 5] = 0
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individual_event_labels.append('void')
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### 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
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chase_on_idx = np.where(event_labels == individual_event_labels[4])[0]
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chase_off_idx = np.where(event_labels == individual_event_labels[5])[0]
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helper_mask = np.ones_like(chase_on_idx)
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helper_mask[np.diff(event_times)[chase_on_idx] <= 5] = 0
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helper_mask[np.diff(event_times)[chase_off_idx - 1] <= 5] = 0
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marcov_matrix[4, 5] += np.sum(helper_mask)
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return marcov_matrix
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def main(base_path):
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if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'markov')):
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os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'markov'))
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trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
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chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
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# trial_summary = trial_summary[chirp_notes['good'] == 1]
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trial_mask = chirp_notes['good'] == 1
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all_marcov_matrix = []
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all_event_counts = []
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all_agonistic_categorie = []
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# agonistic categorie plot
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# fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
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# gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0)
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# ax = fig.add_subplot(gs[1, 0])
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# ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
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# plt.setp(ax_spec.get_xticklabels(), visible=False)
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#
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# for i in range(1, 5):
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# ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
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# ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
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#
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# fill_dots = np.arange(4, 5.1, 0.125)
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# ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
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got_examples = [False, False, False]
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example_ag_on_off = [[], [], []]
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example_chirp_times = [[], [], []]
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example_rise_times = [[], [], []]
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example_1_path = ''
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example_skips = [3, 4, 3]
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for index, trial in trial_summary.iterrows():
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trial_path = os.path.join(base_path, trial['recording'])
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if not trial_mask[index]:
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continue
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if trial['group'] < 5:
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continue
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if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
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continue
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if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
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continue
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if trial['draw'] == 1:
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continue
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ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
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times = np.load(os.path.join(trial_path, 'times.npy'))
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sorter = -1 if trial['win_ID'] != ids[0] else 1
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### event times --> BORIS behavior
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
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load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
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### communication
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if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
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continue
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chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
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chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
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chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
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rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
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rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
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rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
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# trial marcov matrix
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individual_event_times = [chirp_times[1], rise_times[1], chirp_times[0], rise_times[0], ag_on_off_t_GRID[:, 0],
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ag_on_off_t_GRID[:, 1], contact_t_GRID]
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individual_event_labels = [r'chirp$_{lose}$', r'rise$_{lose}$', r'chirp$_{win}$', r'rise$_{win}$',
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r'chace$_{on}$', r'chace$_{off}$', 'contact']
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marcov_matrix = create_marcov_matrix(individual_event_times, individual_event_labels)
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all_marcov_matrix.append(marcov_matrix)
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# compute and store trial event counts
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event_counts = np.array(list(map(lambda x: len(x), individual_event_times)))
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event_counts = np.append(event_counts, marcov_matrix[-1].sum())
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all_event_counts.append(event_counts)
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# agonistic categories
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agonitic_categorie = np.zeros(len(ag_on_off_t_GRID))
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for enu, (chase_on_time, chase_off_time) in enumerate(ag_on_off_t_GRID):
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chase_dur = chase_off_time - chase_on_time
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chirp_dt = chase_dur if chase_dur < 5 else 5
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max_dt = 5
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# check if rise before chase / chirp at end
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rise_before, chirp_arround_end = False, False
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if np.any(((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)):
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rise_times_oi = rise_times[1][((chase_on_time - rise_times[1]) > 0) & ((chase_on_time - rise_times[1]) < max_dt)]
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rise_before = True
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if np.any( ((chase_off_time - chirp_times[1]) < chirp_dt) & ((chirp_times[1] - chase_off_time) < max_dt)):
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# ToDo: check if I realy get all chirps... currently not the case
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chirp_time_oi = chirp_times[1][((chase_off_time - chirp_times[1]) < chase_dur) & ((chirp_times[1] - chase_off_time) < max_dt)]
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chirp_arround_end = True
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# define agonistic categorie based on rise/chirp occurance
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if rise_before:
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if chirp_arround_end:
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agonitic_categorie[enu] = 1
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else:
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agonitic_categorie[enu] = 2
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else:
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if chirp_arround_end:
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agonitic_categorie[enu] = 3
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else:
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agonitic_categorie[enu] = 4
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if agonitic_categorie[enu] == 1 and not got_examples[0]:
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if chase_dur > 10:
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if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0):
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if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
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example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
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example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi)
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example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi)
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example_1_path = trial_path
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got_examples[0] = True
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else:
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example_skips[int(agonitic_categorie[enu] - 1)] -= 1
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elif agonitic_categorie[enu] == 2 and not got_examples[1]:
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if chase_dur > 10:
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if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
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example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
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example_rise_times[int(agonitic_categorie[enu] - 1)].extend(rise_times_oi)
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got_examples[1] = True
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else:
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example_skips[int(agonitic_categorie[enu] - 1)] -= 1
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elif agonitic_categorie[enu] == 3 and not got_examples[2]:
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if chase_dur > 10:
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if np.any((chirp_time_oi - chase_off_time) < 0) and np.any((chirp_time_oi - chase_off_time) > 0):
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if example_skips[int(agonitic_categorie[enu] - 1)] == 0:
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example_ag_on_off[int(agonitic_categorie[enu] - 1)].extend([chase_on_time, chase_off_time])
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example_chirp_times[int(agonitic_categorie[enu] - 1)].extend(chirp_time_oi)
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got_examples[2] = True
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else:
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example_skips[int(agonitic_categorie[enu] - 1)] -= 1
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else:
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pass
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all_agonistic_categorie.append(agonitic_categorie)
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### agonistic categorie example figure
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fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
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gs = gridspec.GridSpec(2, 1, left=0.1, bottom=0.1, right=0.9, top=0.95, height_ratios=[1, 4], hspace=0)
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ax = fig.add_subplot(gs[1, 0])
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ax_spec = fig.add_subplot(gs[0, 0], sharex=ax)
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plt.setp(ax_spec.get_xticklabels(), visible=False)
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for i in range(1, 5):
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ax.fill_between([0, 4], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
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ax.fill_between([5, 10], np.array([-.2, -.2]) + i, np.array([.2, .2]) + i, color='tab:grey')
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fill_dots = np.arange(4, 5.1, 0.125)
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ax.plot(fill_dots, np.ones_like(fill_dots)*i, '.', color='tab:grey', markersize=3)
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for enu, (chirp_time_oi, rise_times_oi, ag_on_off) in enumerate(zip(example_chirp_times, example_rise_times, example_ag_on_off)):
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chase_on_time, chase_off_time = ag_on_off
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for ct in chirp_time_oi:
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ax.plot([ct - chase_off_time + 10, ct - chase_off_time + 10], [enu + .8, enu + 1.2], color='k', lw=2)
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for rt in rise_times_oi:
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ax.plot([rt - chase_on_time, rt - chase_on_time], [enu + .8, enu + 1.2], color='firebrick', lw=2)
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stacked_agonistic_categories = np.hstack(all_agonistic_categorie)
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pct_each_categorie = np.zeros(4)
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for enu, cat in enumerate(range(1, 5)):
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pct_each_categorie[enu] = len(stacked_agonistic_categories[stacked_agonistic_categories == cat]) / len(stacked_agonistic_categories)
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ax.text(15.2, enu + 1, f'{pct_each_categorie[enu] * 100:.1f}' + ' $\%$', clip_on=False, fontsize=14, ha='left', va='center')
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# plot correct spectrogram
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ex1_df_idx = trial_summary[trial_summary['recording'] == os.path.split(example_1_path)[-1]].index.to_numpy()[0]
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lose_id = trial_summary.iloc[ex1_df_idx]['lose_ID']
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# ToDo: use fill_spec
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spec = np.load(os.path.join(example_1_path, 'spec.npy'))
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times = np.load(os.path.join(example_1_path, 'times.npy'))
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fund_v = np.load(os.path.join(example_1_path, 'fund_v.npy'))
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ident_v = np.load(os.path.join(example_1_path, 'ident_v.npy'))
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idx_v = np.load(os.path.join(example_1_path, 'idx_v.npy'))
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artificial_t_axis = np.linspace(times[0], times[-1], spec.shape[1])
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artificial_f_axis = np.linspace(0, 2000, spec.shape[0])
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# plt.pcolormesh(artificial_t_axis, artificial_f_axis, decibel(spec), vmin=-100, vmax=-50)
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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)]
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max_f, min_f = np.max(lose_freq_in_snippet) + 10, np.min(lose_freq_in_snippet) - 10
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t_idx0 = np.where(artificial_t_axis >= example_ag_on_off[0][0] - 5)[0][0]
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t_idx1 = np.where(artificial_t_axis <= example_ag_on_off[0][1] + 5)[0][-1]
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f_idx0 = np.where(artificial_f_axis >= min_f)[0][0]
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f_idx1 = np.where(artificial_f_axis <= max_f)[0][-1]
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# ToDo this does not work. fix it tomorow
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ax_spec.pcolormesh(artificial_t_axis[t_idx0:t_idx1+2] - example_ag_on_off[0][0],
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artificial_f_axis[f_idx0:f_idx1+2],
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decibel(spec[t_idx0:t_idx1+1, f_idx0:f_idx1+1]), vmin=-100, vmax=-50)
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ax.plot([0, 0], [0.8, 5], '--', color='k', lw=1)
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ax.plot([10, 10], [0.8, 5], '--', color='k', lw=1)
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ax.set_ylim(0.25, 4.5)
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ax.set_xlim(-5, 15)
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ax.set_yticks([1, 2, 3, 4])
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# ax.set_yticklabels([r'rise$_{pre}$ $&$ chirp$_{end}$', r'only rise$_{pre}$', r'only chirp$_{end}$', 'no communication'])
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ax.set_yticklabels(['A ', 'B ', 'C ', 'D '])
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ax.invert_yaxis()
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ax.set_xlabel('time [s]', fontsize=12)
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ax.tick_params(axis='y', labelsize=20)
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ax.tick_params(axis = 'x', labelsize=10)
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legend_elements = [Line2D([0], [0], color='firebrick', lw=2, label=r'rise$_{lose}$'),
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Line2D([0], [0], color='k', lw=2, label=r'chirp$_{lose}$'),
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Patch(facecolor='tab:grey', edgecolor='w', label= 'chase event')]
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|
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ax_spec.legend(handles=legend_elements, loc='lower right', ncol=3, bbox_to_anchor=(1, 1), frameon=False, fontsize=10, facecolor='white')
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ax.spines[['right', 'top']].set_visible(False)
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plt.show()
|
|
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embed()
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|
quit()
|
|
|
|
### bar plot - agonistic categories counts/pct #####################################################################
|
|
|
|
fig, ax = plt.subplots(figsize=(20/2.54, 12/2.54))
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|
ax.bar(np.arange(4),
|
|
[len(stacked_agonistic_categories[stacked_agonistic_categories == 1]),
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|
len(stacked_agonistic_categories[stacked_agonistic_categories == 2]),
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|
len(stacked_agonistic_categories[stacked_agonistic_categories == 3]),
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|
len(stacked_agonistic_categories[stacked_agonistic_categories == 4])])
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|
ax.set_xticks(np.arange(4))
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|
ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _'])
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|
plt.show()
|
|
|
|
# pct
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|
pct_agon_categorie = np.zeros(shape=(len(all_agonistic_categorie), 4))
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|
for enu, agonitic_categorie in enumerate(all_agonistic_categorie):
|
|
for cat in np.arange(4):
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|
pct_agon_categorie[enu, cat] = len(agonitic_categorie[agonitic_categorie == cat+1]) / len(agonitic_categorie)
|
|
|
|
fig, ax = plt.subplots(figsize=(20 / 2.54, 12 / 2.54))
|
|
ax.bar(np.arange(4), pct_agon_categorie.mean(0))
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|
ax.errorbar(np.arange(4), pct_agon_categorie.mean(0), yerr=pct_agon_categorie.std(0), fmt='', color='k', linestyle='None')
|
|
ax.set_xticks(np.arange(4))
|
|
ax.set_xticklabels([r'rise$_{pre}$ + chirp$_{end}$', r'rise$_{pre}$ + _', r'_ + chirp$_{end}$', '_ + _'])
|
|
plt.show()
|
|
|
|
|
|
### 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, 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,
|
|
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,
|
|
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()
|
|
|
|
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,
|
|
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)
|
|
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,
|
|
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()
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main(sys.argv[1])
|