def load_acq(filename, chtrig=0):
"""
Populate object phys_input from acq extension files.
Parameters
----------
filename : str
path to the txt labchart file
chtrig : int, optional
index of trigger channel. Default is 0.
Returns
-------
BlueprintInput
Note
----
chtrig is not a 0-based Python index - instead, it's human readable (i.e., 1-based).
This is handy because, when initialising the class, a new channel corresponding
to time is added at the beginning - that is already taken into account!
See Also
--------
physio_obj.BlueprintInput
"""
from bioread import read_file
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
data = read_file(filename).channels
freq = [
data[0].samples_per_second,
]
timeseries = [
data[0].time_index,
]
units = [
's',
]
names = [
'time',
]
for k, ch in enumerate(data):
LGR.info(f'{k:02d}. {ch}')
timeseries.append(ch.data)
freq.append(ch.samples_per_second)
units.append(ch.units)
names.append(ch.name)
return BlueprintInput(timeseries, freq, names, units, chtrig)
def populate_phys_input(filename, chtrig):
"""
Populate object phys_input from acq files.
Parameters
----------
filename: str
path to the txt labchart file
chtrig : int
index of trigger channel
Returns
-------
BlueprintInput
See Also
--------
physio_obj.BlueprintInput
"""
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
data = read_file(filename).channels
freq = [
data[chtrig].samples_per_second,
]
timeseries = [
data[chtrig].time_index,
]
units = [
's',
]
names = [
'time',
]
for k, ch in enumerate(data):
LGR.info(f'{k:02d}. {ch}')
timeseries.append(ch.data)
freq.append(ch.samples_per_second)
units.append(ch.units)
names.append(ch.name)
return BlueprintInput(timeseries, freq, names, units)
def populate_phys_input(filename, chtrig):
"""
Populate object phys_input
"""
data = read_file(filename).channels
freq = [data[chtrig].samples_per_second] * 2
timeseries = [data[chtrig].time_index, data[chtrig].data]
units = ['s', data[chtrig].units]
names = ['time', 'trigger']
for k, ch in enumerate(data):
if k != chtrig:
print(f'{k:02d}. {ch}')
timeseries.append(ch.data)
freq.append(ch.samples_per_second)
units.append(ch.units)
names.append(ch.name)
return BlueprintInput(timeseries, freq, names, units)
def process_labchart(channel_list, chtrig, header=[]):
"""
Process labchart header and channel_list and make a physio_obj.BlueprintInput.
Parameters
----------
channel_list: list
list with channels only
chtrig : int
index of trigger channel, starting in 1 for human readability
header: list
list with that contains file header
Returns
-------
BlueprintInput
Raises
------
ValueError
If len(header) == 0 and therefore there is no header
If sampling is not in ['hr', 'min', 's', 'ms', 'µs'] reference:
https://www.adinstruments.com/support/knowledge-base/how-can-channel-titles-ranges-intervals-etc-text-file-be-imported-labchart
See Also
--------
physio_obj.BlueprintInput
"""
# get frequency
# check header has some length
if len(header) == 0:
raise AttributeError('Files without header are not supported yet')
interval = header[0][1].split(" ")
# check the interval is in some of the correct labchart units
if interval[-1] not in ['hr', 'min', 's', 'ms', 'µs']:
raise AttributeError(
f'Interval unit "{interval[-1]}" is not in a valid LabChart'
'time unit, this probably means your file is not in Labchart format'
)
# check if interval is in seconds, if not change the units to seconds
if interval[-1] != 's':
LGR.warning('Interval is not in seconds. Converting its value.')
if interval[-1] == 'hr':
interval[0] = float(interval[0]) * 3600
interval[-1] = 's'
elif interval[-1] == 'min':
interval[0] = float(interval[0]) * 60
interval[-1] = 's'
elif interval[-1] == 'ms':
interval[0] = float(interval[0]) / 1000
interval[-1] = 's'
elif interval[-1] == 'µs':
interval[0] = float(interval[0]) / 1000000
interval[-1] = 's'
else:
interval[0] = float(interval[0])
# get units
range_list = header[5][1:]
orig_units = []
for item in range_list:
orig_units.append(item.split(' ')[1])
units = [
's',
]
# get names
orig_names = header[4][1:]
orig_names_len = len(orig_names)
names = [
'time',
]
# get channels
# this transposes the channel_list from a list of samples x channels to
# a list of channels x samples
timeseries = list(map(list, zip(*channel_list)))
freq = [1 / interval[0]] * len(timeseries)
timeseries = [np.array(darray) for darray in timeseries]
# check the file has a time channel if not create it and add it
# As the "time" doesn't have a column header, if the number of header names
# is less than the number of timesieries, then "time" is column 0...
# ...otherwise, create the time channel
if not (orig_names_len < len(timeseries)):
duration = (timeseries[0].shape[0] + 1) * interval[0]
t_ch = np.ogrid[0:duration:interval[0]][:-1] # create time channel
timeseries = [
t_ch,
] + timeseries
names = names + orig_names
units = units + orig_units
freq = [1 / interval[0]] * len(timeseries)
freq = check_multifreq(timeseries, freq)
return BlueprintInput(timeseries, freq, names, units, chtrig)
def process_acq(channel_list, chtrig, header=[]):
"""
Process AcqKnowledge header and channel_list to make a physio_obj.BlueprintInput.
Parameters
----------
channel_list: list
list with channels only
chtrig : int
index of trigger channel, starting in 1 for human readability
header: list
list with that contains file header
Returns
-------
BlueprintInput
Raises
------
ValueError
If len(header) == 0 and therefore there is no header
If sampling is not in ['min', 'sec', 'µsec', 'msec','MHz', 'kHz', 'Hz'] reference:
https://www.biopac.com/wp-content/uploads/acqknowledge_software_guide.pdf page 194
See Also
--------
physio_obj.BlueprintInput
"""
# check header is not empty
if len(header) == 0:
raise AttributeError('Files without header are not supported yet')
header.append(channel_list[0])
del channel_list[0] # delete sample size from channel list
# this transposes the channel_list from a list of samples x channels to
# a list of channels x samples
timeseries = list(map(list, zip(*channel_list)))
interval = header[1][0].split()
# check the interval is in some of the correct AcqKnowledge units
if interval[-1].split('/')[0] not in [
'min', 'sec', 'µsec', 'msec', 'MHz', 'kHz', 'Hz'
]:
raise AttributeError(
f'Interval unit "{interval[-1]}" is not in a '
'valid AcqKnowledge format time unit, this probably'
'means your file is not in min, sec, msec, µsec, Mhz, KHz or Hz')
interval[-1] = interval[-1].split('/')[0]
# Check if the header is in frequency or sampling interval
if 'Hz' in interval[-1].split('/')[0]:
print('frequency is given in the header, calculating sample Interval'
' and standarizing to Hz if needed')
freq = float(interval[0])
freq_unit = interval[-1]
if freq_unit == 'MHz':
freq = freq * (1000000)
elif freq_unit == 'kHz':
freq = freq * 1000
interval[0] = 1 / freq
freq = [freq] * (len(timeseries) + 1)
else:
# check if interval is in seconds, if not change the units to seconds and
# calculate frequency
if interval[-1].split('/')[0] != 'sec':
LGR.warning('Interval is not in seconds. Converting its value.')
if interval[-1].split('/')[0] == 'min':
interval[0] = float(interval[0]) * 60
interval[-1] = 's'
elif interval[-1].split('/')[0] == 'msec':
interval[0] = float(interval[0]) / 1000
interval[-1] = 's'
elif interval[-1].split('/')[0] == 'µsec':
interval[0] = float(interval[0]) / 1000000
interval[-1] = 's'
else:
interval[0] = float(interval[0])
interval[-1] = 's'
freq = [1 / interval[0]] * (len(timeseries) + 1)
# get units and names
orig_units = []
orig_names = []
# the for loop starts at index1 at 3 because that's the first line of the header
# with channel name info and ends in 2 + twice the number of channels because
# that should be the last channel name
for index1 in range(3, 3 + len(header[-1]) * 2, 2):
orig_names.append(header[index1][0])
# since units are in the line imediately after we get the units at the same time
orig_units.append(header[index1 + 1][0])
# reorder channels names
names = [
'time',
]
names = names + orig_names
# reoder channels units
units = [
's',
]
units = units + orig_units
# get channels
timeseries = [np.array(darray) for darray in timeseries]
duration = (timeseries[0].shape[0] + 1) * interval[0]
t_ch = np.ogrid[0:duration:interval[0]][:-1] # create time channel
timeseries = [
t_ch,
] + timeseries
freq = check_multifreq(timeseries, freq)
return BlueprintInput(timeseries, freq, names, units, chtrig)
def generate_blueprint(channel_list, chtrig, interval, orig_units, orig_names):
"""
Standarize channel_list, chtrig interval orig_units and orig_names.
Standarize channel_list, chtrig interval orig_units and orig_names in the correct units and
format and generate a physio_obj.BlueprintInput object.
Parameters
----------
channel_list : list of strings
list with channels only
chtrig : int
index of trigger channel, starting in 1 for human readability
interval : list of strings
maximum sampling frequency or interval value and unit for the recording.
Example: ["400", "Hz"]
orig_units : list of strings
contains original channels units
orig_names : list of strings
contains original channels name
Returns
-------
BlueprintInput
Raises
------
AttributeError
If sampling is not in ['min', 'sec', 'µsec', 'msec', 'MHz', 'kHz', 'Hz', 'hr', 'min', 's',
'ms', 'µs'] reference:
https://www.adinstruments.com/support/knowledge-base/how-can-channel-titles-ranges-intervals-etc-text-file-be-imported-labchart
https://www.biopac.com/wp-content/uploads/acqknowledge_software_guide.pdf page 194
See Also
--------
physio_obj.BlueprintInput
"""
# this transposes the channel_list from a list of samples x channels to
# a list of channels x samples
timeseries = list(map(list, zip(*channel_list)))
if interval[-1] not in [
'min', 'sec', 'µsec', 'msec', 'MHz', 'kHz', 'Hz', 'hr', 'min', 's',
'ms', 'µs'
]:
raise AttributeError(
f'Interval unit "{interval[-1]}" is not in a '
'valid frequency or time unit format, this probably '
'means your file is not in min, sec, msec, µsec, hr, min, s, ms, µs, '
'Mhz, KHz or Hz')
# Check if the header is in frequency or sampling interval
if 'Hz' in interval[-1]:
LGR.info(
'Retrieving frequency from file header, calculating sample interval, '
'and standarizing to Hz if needed')
freq = float(interval[0])
freq_unit = interval[-1]
if freq_unit == 'MHz':
freq = freq * (1000000)
elif freq_unit == 'kHz':
freq = freq * 1000
interval[0] = 1 / freq
freq = [freq] * len(timeseries)
else:
# check if interval is in seconds, if not change the units to seconds and
# calculate frequency
if interval[-1] not in ('s', 'sec'):
LGR.warning('Sampling interval not expressed in seconds. '
'Converting its value and unit.')
if interval[-1] == 'min':
interval[0] = float(interval[0]) * 60
elif interval[-1] == 'msec':
interval[0] = float(interval[0]) / 1000
elif interval[-1] == 'µsec':
interval[0] = float(interval[0]) / 1000000
elif interval[-1] == 'hr':
interval[0] = float(interval[0]) * 3600
elif interval[-1] == 'ms':
interval[0] = float(interval[0]) / 1000
elif interval[-1] == 'µs':
interval[0] = float(interval[0]) / 1000000
else:
interval[0] = float(interval[0])
# get frequency
freq = [1 / interval[0]] * len(timeseries)
# reorder channels names
names = [
'time',
]
names = names + orig_names
# reoder channels units
units = [
's',
]
units = units + orig_units
timeseries = list(map(list, zip(*channel_list)))
freq = [1 / interval[0]] * len(timeseries)
timeseries = [np.array(darray) for darray in timeseries]
# Check if the file has a time channel, otherwise create it.
# As the "time" doesn't have a column header, if the number of header names
# is less than the number of timeseries, then "time" is column 0...
# ...otherwise, create the time channel
if not (len(orig_names) < len(timeseries)):
duration = (timeseries[0].shape[0] + 1) * interval[0]
t_ch = np.ogrid[0:duration:interval[0]][:-1] # create time channel
timeseries = [
t_ch,
] + timeseries
freq = [max(freq)] + freq
freq = check_multifreq(timeseries, freq)
return BlueprintInput(timeseries, freq, names, units, chtrig)
def load_mat(filename, chtrig=0):
"""
Populate object phys_input from MATLAB files.
Parameters
----------
filename: str
path to the txt labchart file
chtrig : int
index of trigger channel.
!!! ATTENTION: IT'S MEANT TO REPRESENT AN INDEX STARTING FROM 1 !!!
Returns
-------
BlueprintInput
Note
----
chtrig is not a 0-based Python index - instead, it's human readable (i.e., 1-based).
This is handy because, when initialising the class, a new channel corresponding
to time is added at the beginning - that is already taken into account!
See Also
--------
physio_obj.BlueprintInput
"""
# Load MATLAB file into dictionary.
from pymatreader import read_mat
mat_dict = read_mat(filename)
if '__header__' in mat_dict:
orig_names = list(mat_dict['labels'])
orig_units = list(mat_dict['units'])
interval = [mat_dict['isi'], mat_dict['isi_units']]
channel_list = mat_dict['data']
return generate_blueprint(channel_list, chtrig, interval, orig_units,
orig_names)
else:
# Convert data into 1d numpy array for easier indexing.
data = np.squeeze(np.asarray(mat_dict['data']))
# Extract number of channels and tick rate.
n_channels = len(mat_dict['titles'])
t_freq = mat_dict['tickrate']
# Stores MATLAB data into lists.
timeseries = []
freq = [
t_freq,
]
units = [
's',
]
names = [
'time',
]
for ch in range(n_channels):
units.append(mat_dict['unittext'][int(mat_dict['unittextmap'][ch] -
1)].strip())
names.append(mat_dict['titles'][ch].strip())
freq.append(mat_dict['samplerate'][ch])
idx_start = int(mat_dict['datastart'][ch])
idx_end = int(mat_dict['dataend'][ch])
timeseries.append(data[idx_start:idx_end])
# Calculate duration based on frequency and create time channel.
interval = 1 / t_freq
duration = (timeseries[0].shape[0] + 1) * interval
t_ch = np.ogrid[0:duration:interval][:-1]
timeseries = [
t_ch,
] + timeseries
return BlueprintInput(timeseries, freq, names, units, chtrig)
def populate_phys_input(filename, chtrig):
"""
Populate object phys_input
for now this works only with labchart files
Input (Properties)
------------------
filename: str
path to the txt labchart file
chtrig : int
index of trigger channel
Output
------------------
BlueprintInput object for more see BlueprintInput docs
"""
header = []
channel_list = []
with open(filename, 'r') as f:
header_l = 0
for line in f:
line = line.rstrip('\n').split('\t')
try:
float(line[0])
except ValueError:
header.append(line)
continue
line = [float(i) for i in line]
channel_list.append(line)
# get frequency
interval = header[0][1].split(" ")
if interval[-1] not in ['hr', 'min', 's', 'ms', 'µs']:
raise AttributeError(
f'Interval unit "{interval[-1]}" is not in a valid LabChart time unit, '
'this probably means your file is not in labchart format')
if interval[-1] != 's':
print('Interval is not in seconds. Converting its value.')
if interval[-1] == 'hr':
interval[0] = float(interval)[0] * 3600
interval[-1] = 's'
elif interval[-1] == 'min':
interval[0] = float(interval[0]) * 60
interval[-1] = 's'
elif interval[-1] == 'ms':
interval[0] = float(interval[0]) / 1000
interval[-1] = 's'
elif interval[-1] == 'µs':
interval[0] = float(interval[0]) / 1000000
interval[-1] = 's'
else:
interval[0] = float(interval[0])
# get units
range_list = header[5][1:]
units = []
for item in range_list:
units.append(item.split(' ')[1])
# get names
orig_names = header[4][1:]
names = ['time', 'trigger']
orig_names.pop(chtrig)
names = names + orig_names
# get channels
timeseries = np.matrix(channel_list).T.tolist()
freq = [1 / interval[0]] * len(timeseries)
timeseries = [np.array(darray) for darray in timeseries]
ordered_timeseries = [timeseries[0], timeseries[chtrig]]
timeseries.pop(chtrig)
timeseries.pop(0)
ordered_timeseries = ordered_timeseries + timeseries
return BlueprintInput(ordered_timeseries, freq, names, units)