Revision 76657cfbe9c36d509f270a046c00b2b601077732 authored by Alex Pan on 18 February 2021, 19:01:29 UTC, committed by GitHub on 18 February 2021, 19:01:29 UTC
fixed num_star
1 parent d6f4fde
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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