Downloading KFTools Example Data

#

Importage

# KF Tools and related imports
from kftools.data import load_info,fetch_file
from mne.io import read_raw_snirf

# Some neuroimaging library things
import nibabel as nib
from nilearn.plotting import plot_stat_map
from nilearn.image import index_img

# Some generic things
from matplotlib import pyplot as plt
import os
import pandas as pd

Specify data download location

Give a specific location data_dir = ‘/external/rprshnas01/netdata_kcni/jglab/Data/kftools_data’ or maybe data_dir = ‘.’

If data_dir=None the default location is used, which is ‘~/.kftools’ data_dir=None

data_dir = '.'

List available files

info = load_info()
info[['fname', 'site','subid', 'task', 'sesid', 'datetime', 'filetype']]

	fname	site	subid	task	sesid	datetime	filetype
0	snic_sub001_ft_ses01_0909-1523_kp-snf-mom.snirf	snic	sub001	ft	ses01	0909-1523	kp-snf-mom
1	snic_sub001_ft_ses02_0917-1313_kp-snf-mom.snirf	snic	sub001	ft	ses02	0917-1313	kp-snf-mom
2	snic_sub001_ft_ses01_0909-1523_kp-snf-hbm.snirf	snic	sub001	ft	ses01	0909-1523	kp-snf-hbm
3	snic_sub001_ft_ses02_0917-1313_kp-snf-hbm.snirf	snic	sub001	ft	ses02	0917-1313	kp-snf-hbm
4	snic_sub001_ft_ses01_0909-1523_kp-nii-hbo.nii.gz	snic	sub001	ft	ses01	0909-1523	kp-nii-hbo
5	snic_sub001_ft_ses02_0917-1313_kp-nii-hbo.nii.gz	snic	sub001	ft	ses02	0917-1313	kp-nii-hbo
6	snic_sub001_ft_ses01_0909-1523_kp-nii-hbr.nii.gz	snic	sub001	ft	ses01	0909-1523	kp-nii-hbr
7	snic_sub001_ft_ses02_0917-1313_kp-nii-hbr.nii.gz	snic	sub001	ft	ses02	0917-1313	kp-nii-hbr
8	snic_sub001_ft_ses01_0909-1523_kp-nii-evs.tsv	snic	sub001	ft	ses01	0909-1523	kp-nii-evs
9	snic_sub001_ft_ses02_0917-1313_kp-nii-evs.tsv	snic	sub001	ft	ses02	0917-1313	kp-nii-evs
10	snic_sub008_rec_ses02_0920-1711_kp-nii-hbo.nii.gz	snic	sub008	rec	ses02	0920-1711	kp-nii-hbo
11	pitch_sub010_ft_ses01_1017-1706_kp-snf-mom.snirf	pitch	sub010	ft	ses01	1017-1706	kp-snf-mom
12	pitch_sub010_ft_ses01_1017-1706_kp-snf-hbm.snirf	pitch	sub010	ft	ses01	1017-1706	kp-snf-hbm
13	pitch_sub010_ft_ses01_1017-1706_kp-nii-hbo.nii.gz	pitch	sub010	ft	ses01	1017-1706	kp-nii-hbo
14	pitch_sub010_ft_ses01_1017-1706_kp-nii-hbr.nii.gz	pitch	sub010	ft	ses01	1017-1706	kp-nii-hbr
15	pitch_sub010_ft_ses01_1017-1706_kp-nii-evs.tsv	pitch	sub010	ft	ses01	1017-1706	kp-nii-evs
16	snic_sub006_reo_ses01_1101-1810_kp-nii-hbo.nii.gz	snic	sub006	reo	ses01	1101-1810	kp-nii-hbo
17	snic_sub006_reo_ses01_1101-1810_kp-nii-hbr.nii.gz	snic	sub006	reo	ses01	1101-1810	kp-nii-hbr
18	snic_sub006_reo_ses02_1116-1655_kp-nii-hbo.nii.gz	snic	sub006	reo	ses02	1116-1655	kp-nii-hbo
19	snic_sub006_reo_ses02_1116-1655_kp-nii-hbr.nii.gz	snic	sub006	reo	ses02	1116-1655	kp-nii-hbr
20	snic_sub006_reo_ses03_1117-1740_kp-nii-hbo.nii.gz	snic	sub006	reo	ses03	1117-1740	kp-nii-hbo
21	snic_sub006_reo_ses03_1117-1740_kp-nii-hbr.nii.gz	snic	sub006	reo	ses03	1117-1740	kp-nii-hbr
22	snic_sub006_reo_ses01_1101-1810_kp-snf-hmb.snirf	snic	sub006	reo	ses01	1101-1810	kp-snf-hmb
23	snic_sub006_reo_ses01_1101-1810_kp-snf-mom.snirf	snic	sub006	reo	ses01	1101-1810	kp-snf-mom
24	snic_sub006_reo_ses02_1116-1655_kp-snf-hmb.snirf	snic	sub006	reo	ses02	1116-1655	kp-snf-hmb
25	snic_sub006_reo_ses02_1116-1655_kp-snf-mom.snirf	snic	sub006	reo	ses02	1116-1655	kp-snf-mom
26	snic_sub006_reo_ses03_1117-1740_kp-snf-hmb.snirf	snic	sub006	reo	ses03	1117-1740	kp-snf-hmb
27	snic_sub006_reo_ses03_1117-1740_kp-snf-mom.snirf	snic	sub006	reo	ses03	1117-1740	kp-snf-mom

HB Moments file

The kernel portal gives two .snirf file options: 1. The ‘raw’ moments file 2. A ‘Hb moments’ file

The Hb moments file has some initial preprocessing applied to it, including optical density and modified beer lambert law calculations.

fetch_file(data_dir=data_dir, filetype='kp-snf-hbm',
           site='snic', task='ft', subid='sub001', sesid='ses01')

raw = read_raw_snirf('snic_sub001_ft_ses01_0909-1523_kp-snf-hbm.snirf')

df_raw = raw.to_data_frame()

fig, ax = plt.subplots(figsize=(12,3))
df_raw[raw.ch_names[0:5]].loc[3000:].plot(ax=ax)

Out:

Downloading snic_sub001_ft_ses01_0909-1523_kp-snf-hbm.snirf

Downloading...
From: https://drive.google.com/uc?id=11Lcv-dczig_SLGHcyl8hstE3IS5R8oXO
To: /home/runner/work/kernel-flow-tools/kernel-flow-tools/examples/snic_sub001_ft_ses01_0909-1523_kp-snf-hbm.snirf

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Loading /home/runner/work/kernel-flow-tools/kernel-flow-tools/examples/snic_sub001_ft_ses01_0909-1523_kp-snf-hbm.snirf

<AxesSubplot:>

Nifti file

In-the-brain, DOT-reconstructed HbO and HbR time series are provided in the form of 4D nifti images.

fetch_file(data_dir=data_dir, filetype='kp-nii-hbo',
           site='snic', task='ft', subid='sub001', sesid='ses01')

f = 'snic_sub001_ft_ses01_0909-1523_kp-nii-hbo.nii.gz'
imgs = nib.load(f)
img = index_img(imgs,0)
disp = plot_stat_map(img)

Out:

Downloading snic_sub001_ft_ses01_0909-1523_kp-nii-hbo.nii.gz

Downloading...
From: https://drive.google.com/uc?id=16F2jq5QT-gzbOMyzUU15bD6Uqd3qbh43
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/opt/hostedtoolcache/Python/3.8.13/x64/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0
  anat_img = load_mni152_template()

Note that for task-based statistical models, there is a separate and modified events file, that contains the same information as can be pulled directly from the .snirf files (see later examples), but with modified timings.

fetch_file(data_dir=data_dir, filetype='kp-nii-evs',
           site='snic', task='ft', subid='sub001', sesid='ses01')

f = 'snic_sub001_ft_ses01_0909-1523_kp-nii-evs.tsv'
df = pd.read_csv(f, sep='\t')
df

Out:

Downloading snic_sub001_ft_ses01_0909-1523_kp-nii-evs.tsv

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From: https://drive.google.com/uc?id=1DXUIQcbVJg3dc4WAiC7xStbweF9v_4V1
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	timestamp	event	duration	experiment_type	block	block_type	trial	trial_type
0	0.000000	start_experiment	482.688649	finger_tapping	NaN	NaN	NaN	NaN
1	0.027455	start_rest	20.016757	finger_tapping	NaN	NaN	NaN	NaN
2	20.044290	start_block	72.990794	finger_tapping	1.0	left	NaN	NaN
3	20.063716	start_ITI	0.744280	finger_tapping	1.0	left	NaN	NaN
4	20.808075	start_trial	5.008220	finger_tapping	1.0	left	1.0	index
...	...	...	...	...	...	...	...	...
145	461.571315	start_ITI	0.442044	finger_tapping	5.0	left	NaN	NaN
146	462.037850	start_rest	20.018416	finger_tapping	NaN	NaN	NaN	NaN
147	482.056380	start_block	0.632269	finger_tapping	6.0	right	NaN	NaN
148	482.075120	start_ITI	0.600871	finger_tapping	6.0	right	NaN	NaN
149	482.676100	start_trial	0.012548	finger_tapping	6.0	right	66.0	index

150 rows × 8 columns