Convert patch-master .mat intracellular electrophysiology to NWB. Developed in collaboration with the Tolias and Berens labs under the DANDI project.
author: Ben Dichter, ben.dichter@gmail.com
Text metadata is stored in a YAML file, and must be edited with the correct fields to be added to the NWB file.
pip install tolias-lab-to-nwbText metadata is stored in a YAML file, and must be edited with the correct fields to be added to the NWB file.
Convert all in python:
from tolias_lab_to_nwb.convert import convert_all
convert_all(data_dir='/Volumes/easystore5T/data/Tolias/ephys',
metafile_fpath='metafile.yml',
out_dir='/Volumes/easystore5T/data/Tolias/nwb',
meta_csv_file='/Volumes/easystore5T/data/Tolias/ephys/mini-atlas-meta-data.csv')Convert single session in python:
import os
from dateutil import parser
from ruamel import yaml
from scipy.io import loadmat
from tolias_lab_to_nwb.convert import ToliasNWBConverter
from tolias_lab_to_nwb.data_prep import data_preparation
input_fpath = '/path/to/08 01 2019 sample 1.mat'
output_fpath = 'path/to/dest.nwb'
metafile_fpath = 'path/to/metafile.yml'
fpath_base, fname = os.path.split(input_fpath)
session_id = os.path.splitext(fname)[0]
with open(metafile_fpath) as f:
metadata = yaml.safe_load(f)
metadata['NWBFile']['session_start_time'] = parser.parse(session_id[:10])
metadata['NWBFile']['session_id'] = session_id
tolias_converter = ToliasNWBConverter(metadata)
data = loadmat(input_fpath)
time, current, voltage, curr_index_0 = data_preparation(data)
tolias_converter.add_icephys_data(current, voltage, rate=25e3)
tolias_converter.save(output_fpath)in command line:
usage: convert.py [-h] [-o OUTPUT_FPATH] [-m METAFILE] input_fpath
convert .mat file to NWB
positional arguments:
input_fpath path of .mat file to convert
optional arguments:
-h, --help show this help message and exit
-o OUTPUT_FPATH, --output_fpath OUTPUT_FPATH
path to save NWB file. If not provided, file will
output as input_fname.nwb in the same directory
as the input data.
-m METAFILE, --metafile METAFILE
YAML file that contains metadata for experiment.
If not provided, will look for metafile.yml in the
same directory as the input data.
example usage:
python -m tolias_lab_to_nwb.convert '/path/to/08 01 2019 sample 1.mat'
python -m tolias_lab_to_nwb.convert '/path/to/08 01 2019 sample 1.mat' -m path/to/metafile.yml
python -m tolias_lab_to_nwb.convert '/path/to/08 01 2019 sample 1.mat' -m path/to/metafile.yml -o path/to/dest.nwb
from pynwb import NWBHDF5IO
import numpy as np
import matplotlib.pyplot as plt
fpath = 'path/to/08012019_sample_1.nwb'
io = NWBHDF5IO(fpath, 'r', load_namespaces=True)
nwb = io.read()
def plot_sweep(sweep, ax=None):
if ax is None:
_, ax = plt.subplots()
dat = sweep.data[:]
yy = dat * sweep.conversion
xx = np.arange(len(dat))/sweep.rate
ax.plot(xx, yy)
ax.set_ylabel(sweep.unit)
ax.set_xlabel('time (s)')
def get_stim_and_response(nwb, stim_name):
stimulus = nwb.stimulus[stim_name]
df = nwb.sweep_table.to_dataframe()
stim_select = df['series'].apply(lambda x: x[0].object_id) == stimulus.object_id
sweep_number = df['sweep_number'][stim_select].values[0]
resp_select = df['sweep_number'] == sweep_number - stim_select
response = df['series'][resp_select].values[0][0]
return stimulus, response
stimulus, response = get_stim_and_response(nwb, 'CurrentClampStimulusSeries002')
fig, axs = plt.subplots(2,1, sharex=True)
plot_sweep(stimulus, ax=axs[0])
plot_sweep(response, ax=axs[1])
_ = axs[0].set_xlabel('')
%% read
fpath = '/Volumes/easystore5T/data/Tolias/08 01 2019 sample 1.nwb';
nwb = nwbRead(fpath);
stim_name = 'CurrentClampStimulusSeries002';
stimulus = nwb.stimulus_presentation.get(stim_name);
sweep_table = nwb.general_intracellular_ephys_sweep_table;
sweep_numbers = sweep_table.sweep_number.data.load;
for i = 1:length(sweep_table.series.data)
obj = sweep_table.series.data(i);
if obj.refresh(nwb) == stimulus
sweep_number = sweep_numbers(i);
break;
end
end
ind = find(sweep_numbers == sweep_number);
ind = ind(ind ~= i); % remove stim ind
response = sweep_table.series.data(ind).refresh(nwb);
%% plot
yy = stimulus.data.load * stimulus.data_conversion;
xx = (1:length(yy)) / stimulus.starting_time_rate;
subplot(2,1,1)
plot(xx,yy)
ylabel(stimulus.data_unit)
yy = response.data.load * response.data_conversion;
xx = (1:length(yy)) / response.starting_time_rate;
subplot(2,1,2)
plot(xx,yy)
ylabel(response.data_unit)
xlabel('time (s)')