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gen_figure3_recogtask_roc.py
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gen_figure3_recogtask_roc.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Assess and plot behavioral ROC curves for recognition task for the EMU sessions
This code and used helper functions are adapted from:
https://github.com/rutishauserlab/recogmem-release-NWB/tree/master/RutishauserLabtoNWB/events/newolddelay/python/analysis
"""
import os
import numpy as np
from glob import glob
from pynwb import NWBHDF5IO
from ephys_utills import cal_d_prime, cal_auc, check_inclusion, dynamic_split
import matplotlib.pyplot as plt
import argparse
# Set Matplotlib SVG font type
plt.rcParams['svg.fonttype'] = 'none'
def main(nwb_input_dir):
nwb_session_files = sorted(glob(os.path.join(nwb_input_dir, 'sub-*/*.nwb')))
aucAll = []
stats_all_X = np.zeros((len(nwb_session_files), 7))
stats_all_Y = np.zeros((len(nwb_session_files), 7))
accuracies_high = []
accuracies_low = []
index = 0
for session_ii in nwb_session_files:
print(f'processing {os.path.basename(session_ii)}...')
# Open the NWB file and read its content
with NWBHDF5IO(session_ii,'r') as nwb_io:
nwbfile = nwb_io.read()
trials_df = nwbfile.trials.to_dataframe()
recog_new0_old1 = trials_df['stimulus_file'].to_numpy(dtype=str)[1:]
recog_new0_old1 = np.array([ 0 if 'new' in nii.lower() else 1 for nii in recog_new0_old1 ])
recog_resp_correct = trials_df['response_correct'].to_numpy()[1:]
assert len(recog_new0_old1) == len(recog_resp_correct)
actual_resp_button = trials_df['actual_response'].to_numpy()[1:]
assert np.isin(actual_resp_button, np.arange(1,6+1)).all() # responses should be within [1,6]
actual_resp_new0_old1 = [ 0 if aii in [1,2,3] else 1 for aii in actual_resp_button]
# --- just a control - can be skipped ---
actual_resp_new0_old1_altv = []
for gt_newold, resp_tf in zip(recog_new0_old1.astype(bool),recog_resp_correct):
if resp_tf: actual_resp_new0_old1_altv.append(gt_newold)
else: actual_resp_new0_old1_altv.append(np.logical_not(gt_newold))
actual_resp_new0_old1_altv = np.array(actual_resp_new0_old1_altv).astype(int)
assert np.array_equal(actual_resp_new0_old1,actual_resp_new0_old1_altv)
# --- o ---
response_recog = actual_resp_button
new_old_labels = recog_new0_old1 # ground_truth
typecounter = []
for i in range(6, 0, -1):
new_old_labels_selected = new_old_labels[response_recog == i]
nTP = np.sum(new_old_labels_selected == 1)
nFN = 0
nTN = 0
nFP = np.sum(new_old_labels_selected == 0)
typecounter.append([nTP, nFN, nTN, nFP])
typecounter = np.asarray(typecounter).T
n_old = np.sum(typecounter[0, :])
n_new = np.sum(typecounter[3, :])
stats_all = []
for i in range(typecounter.shape[1]):
temp = np.sum(typecounter[:, 0:i+1], axis=1)
d1, zH1, zF1, H, F, se = cal_d_prime(temp, n_old, n_new);
stats_all.append([d1, zH1, zF1, H, F, se])
stats_all = np.asarray(stats_all)
auc = cal_auc(stats_all)
aucAll.append(auc)
x = stats_all[0:5, 4]
y = stats_all[0:5, 3]
x = np.insert(x, 0, 0)
x = np.append(x, 1)
y = np.insert(y, 0, 0)
y = np.append(y, 1)
stats_all_X[index] = x
stats_all_Y[index] = y
index += 1
is_included = check_inclusion(response_recog, auc)
if is_included:
split_status, split_mode, ind_TP_high, ind_TP_low, ind_FP_high, ind_FP_low, ind_TN_high, \
ind_TN_low, ind_FN_high, ind_FN_low, n_response = dynamic_split(response_recog, new_old_labels)
nr_TN_high = len(ind_TN_high[0])
nr_TP_high = len(ind_TP_high[0])
nr_TN_low = len(ind_TP_high[0]) + len(ind_TP_low[0])
nr_TP_low = len(ind_TP_low[0])
nr_TN_low = len(ind_TN_low[0])
nr_high_response = len(ind_TN_high[0]) + len(ind_TP_high[0]) + len(ind_FN_high[0]) + len(ind_FP_high[0])
nr_low_response = len(ind_TN_low[0]) + len(ind_TP_low[0]) + len(ind_FN_low[0]) + len(ind_FP_low[0])
per_accuracy_high = (nr_TN_high + nr_TP_high) / nr_high_response
per_accuracy_low = (nr_TN_low + nr_TP_low) / nr_low_response
accuracies_high.append(per_accuracy_high*100)
accuracies_low.append(per_accuracy_low*100)
# ----- Plot computed values -----
fig, axes = plt.subplots(1,2, figsize = (5.3, 2.2))
mean_X = np.mean(stats_all_X, axis = 0)
mean_Y = np.mean(stats_all_Y, axis = 0)
from scipy.stats import ttest_rel
tval, pval = ttest_rel(accuracies_high,accuracies_low)
print(ttest_rel(accuracies_high,accuracies_low))
fcol = '#006f91'
axes[0].plot(mean_X, mean_Y, color=fcol, alpha=1,
linewidth=1.3, markersize=4, marker='D')
for ii in range(0,len(stats_all_X)):
axes[0].plot(stats_all_X[ii], stats_all_Y[ii],
color='gray', alpha=0.15, marker='.')
axes[0].set_ylim(0,1)
axes[0].set_xlim(0,1)
axes[0].set_xlabel('False positive rate', fontsize=8)
axes[0].set_ylabel('True positive rate', fontsize=8)
axes[0].plot([0, 1], [0, 1], color='black', alpha=0.90, linewidth=0.95)
axes[0].tick_params(axis='both', which='major', labelsize=7, length=3, pad=2)
axes[0].set_aspect('equal', 'box')
fcol = '#006f91'
x_axis = ['High', 'Low']
axes[1].plot(x_axis, [accuracies_high, accuracies_low],
marker='o', color=fcol, alpha=0.6, markersize=4,
markerfacecolor=fcol, markeredgecolor=None)
axes[1].set_xlabel('Confidence', fontsize=8)
axes[1].set_ylabel('Accuracy (% correct)', fontsize=8)
axes[1].tick_params(axis='x', which='major', labelsize=8, length=3, pad=2)
axes[1].tick_params(axis='y', which='major', labelsize=7, length=3, pad=2)
[x1, x2] = axes[1].get_xlim()
axes[1].errorbar([x1-0.15, x2+0.15],
[np.mean(accuracies_high), np.mean(accuracies_low)],
yerr=[np.std(accuracies_high), np.std(accuracies_low)],
fmt='s', capsize=3, capthick=1., markersize=4, color=fcol,
)
def label_diff(ax,i,j,x_vals,y_vals,pval,yshift=0.0,h=0.01,text='*'):
if pval < 0.05:
if pval < 0.0001: text = 'p<0.0001'
else: text = r'p=%1.4f'%pval
# else: text = r'p$\approx$%1.3f'%pval
else:
return
x1, x2 = x_vals[i], x_vals[j]
y = max(y_vals[i], y_vals[j]) + yshift
ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=0.8,color='k')
if len(text)>4:
ax.text((x1+x2)*.5+0.1, y+h+0.01, text, ha='center', va='bottom',color='k',
fontsize=7, fontweight='normal',rotation=0)
else:
ax.text((x1+x2)*.5, y+h-0.025, text, ha='center', va='bottom',
color='k',fontweight='normal')
signf_max = 102
# order: pval_MZvsDZ, pval_MZvsRd, pval_DZvsRd
label_diff(axes[1],0,1,[x1-0.15, x2+0.15],[signf_max,signf_max],pval,yshift=-0.008)
axes[1].spines['top'].set_visible(False)
axes[1].spines['right'].set_visible(False)
axes[1].get_xaxis().tick_bottom()
axes[1].get_yaxis().tick_left()
axes[1].margins(0.1)
plt.tight_layout()
plt.savefig('recognition_roc.png', dpi=300)
plt.savefig('recognition_roc.svg', dpi=300)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Load NWB files to plot behavioral ROC curves for recognition task for the EMU sessions.")
parser.add_argument('--nwb_input_dir', type=str, required=True, help='Directory containing NWB files.')
args = parser.parse_args()
main(args.nwb_input_dir)
'''
python gen_figure3_recogtask_roc.py --nwb_input_dir /path/to/nwb_files/
'''