raab/competition_experiments
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add void to ethogram. ethogram is now colorful :D

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Till Raab 2023-06-16 10:52:09 +02:00
parent 725c0b426a
commit e22108cdd7
1 changed files with 23 additions and 13 deletions
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36
ethogram.py
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@ -9,6 +9,8 @@ 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']
def plot_transition_matrix(matrix, labels):
fig, ax = plt.subplots()
@ -21,9 +23,8 @@ def plot_transition_matrix(matrix, labels):
plt.show()
def plot_transition_diagram(matrix, labels, node_size):
matrix[matrix <= 5] = 0
def plot_transition_diagram(matrix, labels, node_size, threshold=5):
matrix[matrix <= threshold] = 0
matrix = np.around(matrix, decimals=1)
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)
@ -36,16 +37,20 @@ def plot_transition_diagram(matrix, labels, node_size):
positions = nx.circular_layout(Graph)
positions2 = nx.circular_layout(Graph)
for p in positions:
positions2[p][0] -= .1
positions2[p][1] -= .1
positions2[p][0] *= 1.2
positions2[p][1] *= 1.2
# ToDo: nodes
nx.draw_networkx_nodes(Graph, pos=positions, node_size=node_size*2, ax=ax, alpha=0.5)
nx.draw_networkx_labels(Graph, pos=positions, labels=node_labels)
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)
# 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 = [x / 10 for x in [*edge_labels.values()]]
edge_width = np.array([x / 5 for x in [*edge_labels.values()]])
edge_colors = np.array(glob_colors)[np.array([*edge_labels.keys()], dtype=int)[:, 0]]
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=10, connectionstyle="arc3, rad=0.0",
@ -54,17 +59,18 @@ def plot_transition_diagram(matrix, labels, node_size):
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=10, connectionstyle="arc3, rad=0.0",
ax=ax)
nx.draw_networkx_edge_labels(Graph, positions, label_pos=0.2, edge_labels=edge_labels, ax=ax, rotate=False)
min_target_margin=25, min_source_margin=10, 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)
# plt.title(title)
plt.show()
def main(base_path):
@ -153,8 +159,12 @@ def main(base_path):
collective_event_counts = np.sum(all_event_counts, axis=0)
plot_transition_matrix(collective_marcov_matrix, loop_labels)
plot_transition_diagram(collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100, loop_labels, collective_event_counts)
plot_transition_diagram(collective_marcov_matrix / collective_event_counts.reshape(len(collective_event_counts), 1) * 100, loop_labels, collective_event_counts, threshold=5)
# for i in range(len(all_marcov_matrix)):
# plot_transition_diagram(
# all_marcov_matrix[i] / all_event_counts[i].reshape(len(all_event_counts[i]), 1) * 100,
# loop_labels, all_event_counts[i], threshold=5)
embed()
quit()
pass