def write_recording(recording: RecordingExtractor,
save_path: PathType,
time_axis: int = 0,
dtype: OptionalDtypeType = None,
**write_binary_kwargs):
"""
Save the traces of a recording extractor in binary .dat format.
Parameters
----------
recording : RecordingExtractor
The recording extractor object to be saved in .dat format.
save_path : str
The path to the file.
time_axis : int, optional
If 0 then traces are transposed to ensure (nb_sample, nb_channel) in the file.
If 1, the traces shape (nb_channel, nb_sample) is kept in the file.
dtype : dtype
Type of the saved data. Default float32.
**write_binary_kwargs: keyword arguments for write_to_binary_dat_format() function
"""
write_to_binary_dat_format(recording,
save_path,
time_axis=time_axis,
dtype=dtype,
**write_binary_kwargs)
def write_recording(recording,
save_path,
time_axis=0,
dtype=None,
chunk_size=None):
'''Saves the traces of a recording extractor in binary .dat format.
Parameters
----------
recording: RecordingExtractor
The recording extractor object to be saved in .dat format
save_path: str
The path to the file.
time_axis: 0 (default) or 1
If 0 then traces are transposed to ensure (nb_sample, nb_channel) in the file.
If 1, the traces shape (nb_channel, nb_sample) is kept in the file.
dtype: dtype
Type of the saved data. Default float32.
chunk_size: None or int
If not None then the copy done by chunk size.
This avoid to much memory consumption for big files.
'''
write_to_binary_dat_format(recording,
save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size)
def write_to_binary_dat_format(self,
save_path,
time_axis=0,
dtype=None,
chunk_size=None,
chunk_mb=500,
verbose=False):
'''Saves the traces of this recording extractor into binary .dat format.
Parameters
----------
save_path: str
The path to the file.
time_axis: 0 (default) or 1
If 0 then traces are transposed to ensure (nb_sample, nb_channel) in the file.
If 1, the traces shape (nb_channel, nb_sample) is kept in the file.
dtype: dtype
Type of the saved data. Default float32
chunk_size: None or int
If not None then the file is saved in chunks.
This avoid to much memory consumption for big files.
If 'auto' the file is saved in chunks of ~ 500Mb
chunk_mb: None or int
Chunk size in Mb (default 500Mb)
verbose: bool
If True, output is verbose
'''
X = DiskReadMda(self._timeseries_path)
header_size = X._header.header_size
if dtype is None or dtype == self.get_dtype():
try:
with open(self._timeseries_path,
'rb') as src, open(save_path, 'wb') as dst:
src.seek(header_size)
shutil.copyfileobj(src, dst)
except Exception as e:
print('Error occurred while copying:', e)
print('Writing to binary')
write_to_binary_dat_format(self,
save_path=save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb,
verbose=verbose)
else:
write_to_binary_dat_format(self,
save_path=save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb,
verbose=verbose)
def write_to_binary_dat_format(self,
save_path,
time_axis=0,
dtype=None,
chunk_size=None,
chunk_mb=500):
'''Saves the traces of this recording extractor into binary .dat format.
Parameters
----------
save_path: str
The path to the file.
time_axis: 0 (default) or 1
If 0 then traces are transposed to ensure (nb_sample, nb_channel) in the file.
If 1, the traces shape (nb_channel, nb_sample) is kept in the file.
dtype: dtype
Type of the saved data. Default float32
chunk_size: None or int
If not None then the file is saved in chunks.
This avoid to much memory consumption for big files.
If 'auto' the file is saved in chunks of ~ 500Mb
chunk_mb: None or int
Chunk size in Mb (default 500Mb)
'''
if dtype is None or dtype == self.get_dtype():
try:
shutil.copy(self._datfile, save_path)
except Exception as e:
print('Error occurred while copying:', e)
print('Writing to binary')
write_to_binary_dat_format(self,
save_path=save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb)
else:
write_to_binary_dat_format(self,
save_path=save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb)
def write_to_binary_dat_format(self, save_path, time_axis=0, dtype=None, chunk_size=None, chunk_mb=500,
n_jobs=1, joblib_backend='loky', verbose=False):
'''Saves the traces of this recording extractor into binary .dat format.
Parameters
----------
save_path: str
The path to the file.
time_axis: 0 (default) or 1
If 0 then traces are transposed to ensure (nb_sample, nb_channel) in the file.
If 1, the traces shape (nb_channel, nb_sample) is kept in the file.
dtype: dtype
Type of the saved data. Default float32
chunk_size: None or int
If not None then the file is saved in chunks.
This avoid to much memory consumption for big files.
If 'auto' the file is saved in chunks of ~ 500Mb
chunk_mb: None or int
Chunk size in Mb (default 500Mb)
n_jobs: int
Number of jobs to use (Default 1)
joblib_backend: str
Joblib backend for parallel processing ('loky', 'threading', 'multiprocessing')
'''
if dtype is None or dtype == self.get_dtype():
try:
shutil.copy(self._datfile, save_path)
except Exception as e:
print('Error occurred while copying:', e)
print('Writing to binary')
write_to_binary_dat_format(self, save_path=save_path, time_axis=time_axis, dtype=dtype,
chunk_size=chunk_size, chunk_mb=chunk_mb, n_jobs=n_jobs,
joblib_backend=joblib_backend)
else:
write_to_binary_dat_format(self, save_path=save_path, time_axis=time_axis, dtype=dtype,
chunk_size=chunk_size, chunk_mb=chunk_mb, n_jobs=n_jobs,
joblib_backend=joblib_backend)
def write_recording(recording,
save_path,
params=dict(),
raw_fname='raw.mda',
params_fname='params.json',
geom_fname='geom.csv',
dtype=None,
chunk_size=None,
chunk_mb=500):
'''
Parameters
----------
recording: RecordingExtractor
The recording extractor to be saved
save_path: str or Path
The folder in which the Mda files are saved
params: dictionary
Dictionary with optional parameters to save metadata. Sampling frequency is appended to this dictionary.
raw_fname: str
File name of raw file (default raw.mda)
params_fname: str
File name of params file (default params.json)
geom_fname: str
File name of geom file (default geom.csv)
dtype: dtype
dtype to be used. If None dtype is same as recording traces.
chunk_size: None or int
Number of chunks to save the file in. This avoid to much memory consumption for big files.
If None and 'chunk_mb' is given, the file is saved in chunks of 'chunk_mb' Mb (default 500Mb)
chunk_mb: None or int
Chunk size in Mb (default 500Mb)
'''
save_path = Path(save_path)
if not save_path.exists():
if not save_path.is_dir():
os.makedirs(str(save_path))
save_file_path = save_path / raw_fname
parent_dir = save_path
channel_ids = recording.get_channel_ids()
num_chan = recording.get_num_channels()
num_frames = recording.get_num_frames()
geom = recording.get_channel_locations()
if not save_path.is_dir():
os.mkdir(save_path)
if dtype is None:
dtype = recording.get_dtype()
if dtype == 'float':
dtype = 'float32'
if dtype == 'int':
dtype = 'int16'
with save_file_path.open('wb') as f:
header = MdaHeader(dt0=dtype, dims0=(num_chan, num_frames))
header.write(f)
# takes care of the chunking
write_to_binary_dat_format(recording,
file_handle=f,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb)
params["samplerate"] = recording.get_sampling_frequency()
with (parent_dir / params_fname).open('w') as f:
json.dump(params, f)
np.savetxt(str(parent_dir / geom_fname), geom, delimiter=',')
def write_to_binary_dat_format(self,
save_path,
time_axis=0,
dtype=None,
chunk_size=None,
chunk_mb=500,
n_jobs=1,
joblib_backend='loky',
verbose=False):
"""Saves the traces of this recording extractor into binary .dat format.
Parameters
----------
save_path: str
The path to the file.
time_axis: 0 (default) or 1
If 0 then traces are transposed to ensure (nb_sample, nb_channel) in the file.
If 1, the traces shape (nb_channel, nb_sample) is kept in the file.
dtype: dtype
Type of the saved data. Default float32
chunk_size: None or int
Size of each chunk in number of frames.
If None (default) and 'chunk_mb' is given, the file is saved in chunks of 'chunk_mb' Mb (default 500Mb)
chunk_mb: None or int
Chunk size in Mb (default 500Mb)
n_jobs: int
Number of jobs to use (Default 1)
joblib_backend: str
Joblib backend for parallel processing ('loky', 'threading', 'multiprocessing')
verbose: bool
If True, output is verbose
"""
X = DiskReadMda(self._timeseries_path)
header_size = X._header.header_size
if dtype is None or dtype == self.get_dtype():
try:
with open(self._timeseries_path,
'rb') as src, open(save_path, 'wb') as dst:
src.seek(header_size)
shutil.copyfileobj(src, dst)
except Exception as e:
print('Error occurred while copying:', e)
print('Writing to binary')
write_to_binary_dat_format(self,
save_path=save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb,
n_jobs=n_jobs,
joblib_backend=joblib_backend,
verbose=verbose)
else:
write_to_binary_dat_format(self,
save_path=save_path,
time_axis=time_axis,
dtype=dtype,
chunk_size=chunk_size,
chunk_mb=chunk_mb,
n_jobs=n_jobs,
joblib_backend=joblib_backend,
verbose=verbose)
def write_recording(recording,
save_path,
params=dict(),
raw_fname='raw.mda',
params_fname='params.json',
geom_fname='geom.csv',
dtype=None,
chunk_size=None,
n_jobs=None,
chunk_mb=500,
verbose=False):
"""
Writes recording to file in MDA format.
Parameters
----------
recording: RecordingExtractor
The recording extractor to be saved
save_path: str or Path
The folder in which the Mda files are saved
params: dictionary
Dictionary with optional parameters to save metadata. Sampling frequency is appended to this dictionary.
raw_fname: str
File name of raw file (default raw.mda)
params_fname: str
File name of params file (default params.json)
geom_fname: str
File name of geom file (default geom.csv)
dtype: dtype
dtype to be used. If None dtype is same as recording traces.
chunk_size: None or int
Size of each chunk in number of frames.
If None (default) and 'chunk_mb' is given, the file is saved in chunks of 'chunk_mb' Mb (default 500Mb)
n_jobs: int
Number of jobs to use (Default 1)
chunk_mb: None or int
Chunk size in Mb (default 500Mb)
verbose: bool
If True, output is verbose
"""
save_path = Path(save_path)
save_path.mkdir(parents=True, exist_ok=True)
save_file_path = save_path / raw_fname
parent_dir = save_path
channel_ids = recording.get_channel_ids()
num_chan = recording.get_num_channels()
num_frames = recording.get_num_frames()
geom = recording.get_channel_locations()
if dtype is None:
dtype = recording.get_dtype()
if dtype == 'float':
dtype = 'float32'
if dtype == 'int':
dtype = 'int16'
with save_file_path.open('wb') as f:
header = MdaHeader(dt0=dtype, dims0=(num_chan, num_frames))
header.write(f)
# takes care of the chunking
write_to_binary_dat_format(recording,
file_handle=f,
dtype=dtype,
n_jobs=n_jobs,
chunk_size=chunk_size,
chunk_mb=chunk_mb,
verbose=verbose)
params["samplerate"] = float(recording.get_sampling_frequency())
with (parent_dir / params_fname).open('w') as f:
json.dump(params, f)
np.savetxt(str(parent_dir / geom_fname), geom, delimiter=',')
