Revision 22583a6977ec026a8dd683a62992ab87f7ac0556 authored by Anne Urai on 07 April 2021, 05:53:12 UTC, committed by Anne Urai on 07 April 2021, 05:53:12 UTC
1 parent 2e61e1d
suppfig_end_session_histogram.py
"""
HISTOGRAM OF SESSION END STATUSES DURING TRAINING
Miles Wells, UCL, 2019
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import datajoint as dj
from ibl_pipeline import acquisition
from paper_behavior_functions import \
(figpath, query_sessions, query_subjects, group_colors, seaborn_style,
FIGURE_HEIGHT, FIGURE_WIDTH)
# Set default figure size.
save_path = figpath() # Our figure save path
colors = group_colors()
seaborn_style()
endcriteria = dj.create_virtual_module('SessionEndCriteriaImplemented',
'group_shared_end_criteria')
sessions = query_sessions().proj(session_start_date='date(session_start_time)')
subj_crit = query_subjects().aggr(
acquisition.Session(),
first_day='min(date(session_start_time))').proj('first_day')
session_num = (sessions * subj_crit).proj(n='DATEDIFF(session_start_date, first_day)')
df = (endcriteria.SessionEndCriteriaImplemented * session_num).fetch(format='frame') # Fetch data
# Convert statuses to numerical
fig, ax = plt.subplots(1, 1, figsize=(FIGURE_WIDTH/4, FIGURE_HEIGHT))
ids = {k: v for v, k in enumerate(df['end_status'].unique())}
df['end_status_id'] = df['end_status'].map(ids)
bins = [0, 6, 13, 20, 27, 34]
ax = df.pivot(columns='end_status_id').n.plot(
kind='hist', color=colors, bins=bins, stacked=True, density=True) # weights=369,
ax.legend(ids.keys())
ax.set_xlabel('Session #')
ax.set_ylabel('Frequency')
plt.gcf().savefig(os.path.join(save_path, "suppfig_end_status_histogram.png"))
# Unity plot
max_n_days = 40
normalize = True
df = df.reset_index()
counts = np.array([[sum(df['end_status_id'].where(df['n'] == n_days) == criterion)
if n_days < max_n_days
else sum(df['end_status_id'].where(df['n'] >= n_days) == criterion)
for n_days in range(max_n_days+1)]
for criterion in np.sort(df['end_status_id'].unique())])
if normalize:
counts = np.stack([n / sum(n) for n in counts.T]).T
# counts = np.stack([n / sum(n) for n in counts])
bar_l = range(1, counts.shape[1]+1)
# bottom = np.zeros_like(bar_l).astype('float')
bottom = np.vstack((np.zeros((1, counts.shape[1])), np.cumsum(counts, axis=0)[:-1, :]))
fig, ax = plt.subplots(1, 1, figsize=(FIGURE_WIDTH / 2, FIGURE_HEIGHT))
for i in range(counts.shape[0]):
ax.bar(bar_l, counts[i, :], bottom=bottom[i, :], width=1, label=list(ids.keys())[i],
color=colors[i])
ax.set_xticks([1] + [i * 7 for i in range(1, round(max_n_days+7/7))])
ax.set_xticks([0, 10, 20, 30, 40])
ax.set_xlim([0, counts.shape[1]+.5])
ax.set_xlabel('Session #')
ax.set_ylabel('Proportion')
ax.legend(loc='upper right')
plt.tight_layout()
sns.despine(trim=False)
plt.gcf().savefig(os.path.join(save_path, "suppfig_end_status_histogram_normalized.png"), dpi=300)
plt.gcf().savefig(os.path.join(save_path, "suppfig_end_status_histogram_normalized.pdf"))
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