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msc_and_plot.py
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msc_and_plot.py
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import bids
import enlighten
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.signal import coherence
from os.path import join, exists
sns.set(style='white', context='talk')
mather = '/Users/katherine.b/Dropbox/Data/ds001242'
musser = '/Users/katherine.b/Dropbox/Data/musserk23'
diva = '/home/data/nbc/Laird_DIVA/dset'
datasets = [musser, mather]
manager = enlighten.get_manager()
for dataset in datasets:
dset_name = dataset.split('/')[-1]
dset = bids.BIDSLayout(dataset, derivatives=True)
files = dset.derivatives['PhysioComb'].get(extension='tsv', desc='filtered', suffix='physio', invalid_filters=True)
tocks = manager.counter(total=len(files), desc=dset_name, unit='files')
msc_ecg = pd.DataFrame()
msc_eda = pd.DataFrame()
for file in files:
fs = file.get_metadata()['SamplingFrequency']
dat = pd.read_table(file.path)
cardiac = dat.filter(regex='cardiac.*').columns
nperseg = fs * 4
subject = file.entities['subject']
base_path =file.path.replace(file.filename, '')
try:
session = file.entities['session']
no_mr_path = f'{base_path}sub-{subject}_ses-{session}_desc-noMR_physio.tsv'
except:
no_mr_path = f'{base_path}sub-{subject}_desc-noMR_physio.tsv'
base_path = file.path.replace(file.filename, '')
if exists(no_mr_path):
no_mr = pd.read_table(no_mr_path, header=0)
for col1 in cardiac:
if exists(no_mr_path):
# calculate MSC for no-MR here if dset doesn't contain ds0001242 or whatever
f, Cxy = coherence(dat[col1],
no_mr['cardiac'],
fs=fs,
nperseg=nperseg)
temp = pd.Series(data=Cxy,
index=f,
name=f'{col1}_no_mr')
msc_ecg = pd.concat([msc_ecg, temp],
axis=1)
else:
pass
for col2 in cardiac:
if col1 == col2:
pass
else:
f, Cxy = coherence(dat[col1],
dat[col2],
fs=fs,
nperseg=nperseg)
temp = pd.Series(data=Cxy,
index=f,
name=f'{col1}_{col2}')
msc_ecg = pd.concat([msc_ecg, temp],
axis=1)
eda = dat.filter(regex='scr.*').columns
for col1 in eda:
if exists(no_mr_path):
# calculate MSC for no-MR here if dset doesn't contain ds0001242 or whatever
f, Cxy = coherence(dat[col1],
no_mr['scr'],
fs=fs,
nperseg=nperseg * 10)
temp = pd.Series(data=Cxy,
index=f,
name=f'{col1}_no_mr')
msc_eda = pd.concat([msc_eda, temp],
axis=1)
for col2 in eda:
if col1 == col2:
pass
else:
f, Cxy = coherence(dat[col1],
dat[col2],
fs=fs,
nperseg=nperseg * 10)
temp = pd.Series(data=Cxy,
index=f,
name=f'{col1}_{col2}')
msc_eda = pd.concat([msc_eda, temp],
axis=1)
tocks.update()
new_names = [f.filename.split('_desc')[0] for f in files]
plots = msc_ecg.columns.unique()
for plot in plots:
temp = msc_ecg[plot]
temp.columns = new_names
fig,ax = plt.subplots(figsize=(5,5))
sns.lineplot(data=temp[temp.index < 60], lw=0.9, dashes=False)
ax.get_legend().remove()
ax.set_xlabel('Hz')
ax.set_ylabel('MSC')
ax.set_ylim([-0.1,1.1])
fig.savefig(f'{dset_name}_{plot}_msc-ecg.png', dpi=400, bbox_inches='tight')
plots = msc_eda.columns.unique()
for plot in plots:
temp = msc_eda[plot]
temp.columns = new_names
fig,ax = plt.subplots(figsize=(5,5))
sns.lineplot(data=temp[temp.index <= 1], lw=0.9, dashes=False)
ax.get_legend().remove()
ax.set_xlabel('Hz')
ax.set_ylabel('MSC')
ax.set_ylim([-0.1,1.1])
ax.set_xlim([-0.1,1.1])
fig.savefig(f'{dset_name}_{plot}_msc-eda.png', dpi=400, bbox_inches='tight')