def run_for_all_spikes(self, file_path, max_channels_per_template=16, peak_sign='neg', **job_kwargs):
"""
This run the PCs on all spike from the sorting.
This is a long computation because waveform need to be extracted from each spikes.
Used mainly for `export_to_phy()`
PCs are exported to a .npy single file.
"""
p = self._params
we = self.waveform_extractor
sorting = we.sorting
recording = we.recording
assert sorting.get_num_segments() == 1
assert p['mode'] in ('by_channel_local', 'by_channel_global')
file_path = Path(file_path)
all_spikes = sorting.get_all_spike_trains(outputs='unit_index')
spike_times, spike_labels = all_spikes[0]
max_channels_per_template = min(max_channels_per_template, we.recording.get_num_channels())
best_channels_index = get_template_best_channels(we, max_channels_per_template,
peak_sign=peak_sign, outputs='index')
unit_channels = [best_channels_index[unit_id] for unit_id in sorting.unit_ids]
if p['mode'] == 'by_channel_local':
all_pca = self._fit_by_channel_local()
elif p['mode'] == 'by_channel_global':
one_pca = self._fit_by_channel_global()
all_pca = [one_pca] * recording.get_num_channels()
# nSpikes, nFeaturesPerChannel, nPCFeatures
# this come from phy template-gui
# https://github.com/kwikteam/phy-contrib/blob/master/docs/template-gui.md#datasets
shape = (spike_times.size, p['n_components'], max_channels_per_template)
all_pcs = np.lib.format.open_memmap(file_path, mode='w+', dtype='float32', shape=shape)
# and run
func = _all_pc_extractor_chunk
init_func = _init_work_all_pc_extractor
n_jobs = ensure_n_jobs(recording, job_kwargs.get('n_jobs', None))
if n_jobs == 1:
init_args = (recording, )
else:
init_args = (recording.to_dict(), )
init_args = init_args + (all_pcs, spike_times, spike_labels, we.nbefore, we.nafter, unit_channels, all_pca)
processor = ChunkRecordingExecutor(recording, func, init_func, init_args, job_name='extract PCs', **job_kwargs)
processor.run()
def get_unit_amplitudes(waveform_extractor,
peak_sign='neg',
outputs='concatenated',
**job_kwargs):
"""
Computes the spike amplitudes from a WaveformExtractor.
Amplitudes can be computed in absolute value (uV) or relative to the template amplitude.
1. The waveform extractor is used to determine the max channel per unit.
2. Then a "peak_shift" is estimated because for some sorter the spike index is not always at the
extremum.
3. Extract all epak chunk by chunk (parallel or not)
"""
we = waveform_extractor
recording = we.recording
sorting = we.sorting
all_spikes = sorting.get_all_spike_trains()
extremum_channels_index = get_template_extremum_channel(
waveform_extractor, peak_sign=peak_sign, outputs='index')
peak_shifts = get_template_extremum_channel_peak_shift(waveform_extractor,
peak_sign='neg')
# and run
func = _unit_amplitudes_chunk
init_func = _init_worker_unit_amplitudes
init_args = (recording.to_dict(), sorting.to_dict(),
extremum_channels_index, peak_shifts)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
job_name='extract amplitudes',
**job_kwargs)
out = processor.run()
amps, segments = zip(*out)
amps = np.concatenate(amps)
segments = np.concatenate(segments)
amplitudes = []
for segment_index in range(recording.get_num_segments()):
mask = segments == segment_index
amplitudes.append(amps[mask])
if outputs == 'concatenated':
return amplitudes
elif outputs == 'by_units':
amplitudes_by_units = []
for segment_index in range(recording.get_num_segments()):
amplitudes_by_units.append({})
for unit_id in sorting.unit_ids:
spike_times, spike_labels = all_spikes[segment_index]
mask = spike_labels == unit_id
amps = amplitudes[segment_index][mask]
amplitudes_by_units[segment_index][unit_id] = amps
return amplitudes_by_units
def localize_peaks(recording, peaks, method='center_of_mass',
local_radius_um=150, ms_before=0.3, ms_after=0.6,
**job_kwargs):
"""
Localize peak (spike) in 2D or 3D depending the probe.ndim of the recording.
Parameters
----------
recording: RecordingExtractor
The recording extractor object
peaks: numpy
peak vector given by detect_peaks() in "compact_numpy" way.
method: str
Method to be used ('center_of_mass')
local_radius_um: float
Radius in micrometer to make neihgborhood for channel
around the peak
ms_before: float
The left window before a peak in millisecond
ms_after: float
The left window before a peak in millisecond
{}
Returns
-------
peak_locations: np.array
Array with estimated x-y location for each spike
"""
assert method in ('center_of_mass',)
# find channel neighbours
assert local_radius_um is not None
channel_distance = get_channel_distances(recording)
neighbours_mask = channel_distance < local_radius_um
nbefore = int(ms_before * recording.get_sampling_frequency() / 1000.)
nafter = int(ms_after * recording.get_sampling_frequency() / 1000.)
contact_locations = recording.get_channel_locations()
# TODO
# make a memmap for peaks to avoid serilisation
# and run
func = _localize_peaks_chunk
init_func = _init_worker_localize_peaks
init_args = (recording.to_dict(), peaks, method, nbefore, nafter, neighbours_mask, contact_locations)
processor = ChunkRecordingExecutor(recording, func, init_func, init_args, handle_returns=True,
job_name='localize peaks', **job_kwargs)
peak_locations = processor.run()
peak_locations = np.concatenate(peak_locations)
return peak_locations
def test_ChunkRecordingExecutor():
recording = generate_recording(num_channels=2)
# make dumpable
recording = recording.save()
def func(segment_index, start_frame, end_frame, worker_ctx):
import os, time
# print('func', segment_index, start_frame, end_frame, worker_ctx, os.getpid())
time.sleep(0.010)
# time.sleep(1.0)
return os.getpid()
def init_func(arg1, arg2, arg3):
worker_ctx = {}
worker_ctx['arg1'] = arg1
worker_ctx['arg2'] = arg2
worker_ctx['arg3'] = arg3
return worker_ctx
init_args = 'a', 120, 'yep'
# no chunk
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
verbose=True,
progress_bar=False,
n_jobs=1,
chunk_size=None)
processor.run()
# chunk + loop
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
verbose=True,
progress_bar=False,
n_jobs=1,
chunk_memory="500k")
processor.run()
# chunk + parralel
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
verbose=True,
progress_bar=True,
n_jobs=2,
total_memory="200k",
job_name='job_name')
processor.run()
def find_spike_from_templates(recording,
waveform_extractor,
method='simple',
method_kwargs={},
**job_kwargs):
"""
Find spike from a recording from known given templates.
Template are represented as WaveformExtractor so statistics can be extracted.
Parameters
----------
recording: RecordingExtractor
The recording extractor object
method: 'simple' / ...
peak_detect_kwargs: dict
Params for peak detection
job_kwargs: dict
Parameters for ChunkRecordingExecutor
"""
assert method in ('simple', )
if method == 'simple':
method_kwargs = check_kwargs_simple_matching(recording,
waveform_extractor,
method_kwargs)
# and run
func = _find_spike_chunk
init_func = _init_worker_find_spike
init_args = (recording.to_dict(), method, method_kwargs)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
job_name='find spikes',
**job_kwargs)
spikes = processor.run()
spikes = np.concatenate(spikes)
return spikes
def test_ChunkRecordingExecutor():
recording = generate_recording(num_channels=2)
# make dumpable
recording = recording.save()
init_args = 'a', 120, 'yep'
# no chunk
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
verbose=True,
progress_bar=False,
n_jobs=1,
chunk_size=None)
processor.run()
# chunk + loop
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
verbose=True,
progress_bar=False,
n_jobs=1,
chunk_memory="500k")
processor.run()
# chunk + parralel
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
verbose=True,
progress_bar=True,
n_jobs=2,
total_memory="200k",
job_name='job_name')
processor.run()
def detect_peaks(recording,
method='by_channel',
peak_sign='neg',
detect_threshold=5,
n_shifts=2,
local_radius_um=100,
noise_levels=None,
random_chunk_kwargs={},
outputs='numpy_compact',
**job_kwargs):
"""
Peak detection ported from tridesclous into spikeinterface.
Peak detection based on threhold crossing in term of k x MAD
Ifg the MAD is not provide then it is estimated with random snipet
Several methods:
* 'by_channel' : peak are dettected in each channel independantly
* 'locally_exclusive' : locally given a radius the best peak only is taken but
not neirbhoring channels
Parameters
----------
recording: RecordingExtractor
The recording extractor object
method:
peak_sign='neg'/ 'pos' / 'both'
Signa of the peak.
detect_threshold: float
Threshold in median absolute deviations (MAD) to detect peaks
n_shifts: int
Number of shifts to find peak. E.g. if n_shift is 2, a peak is detected (if detect_sign is 'negative') if
a sample is below the threshold, the two samples before are higher than the sample, and the two samples after
the sample are higher than the sample.
noise_levels: np.array
noise_levels can be provide externally if already computed.
random_chunk_kwargs: dict
A dict that contain option to randomize chunk for get_noise_levels()
Only used if noise_levels is None
numpy_compact: str numpy_compact/numpy_split/sorting
The type of the output. By default "numpy_compact"
give a vector with complex dtype.
"""
assert method in ('by_channel', 'locally_exclusive')
assert peak_sign in ('both', 'neg', 'pos')
assert outputs in ('numpy_compact', 'numpy_split', 'sorting')
if method == 'locally_exclusive' and not HAVE_NUMBA:
raise ModuleNotFoundError(
'"locally_exclusive" need numba which is not installed')
if noise_levels is None:
noise_levels = get_noise_levels(recording, **random_chunk_kwargs)
abs_threholds = noise_levels * detect_threshold
if method == 'locally_exclusive':
assert local_radius_um is not None
channel_distance = get_channel_distances(recording)
neighbours_mask = channel_distance < local_radius_um
else:
neighbours_mask = None
# and run
func = _detect_peaks_chunk
init_func = _init_worker_detect_peaks
init_args = (recording.to_dict(), method, peak_sign, abs_threholds,
n_shifts, neighbours_mask)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
**job_kwargs)
peaks = processor.run()
peak_sample_inds, peak_chan_inds, peak_amplitudes, peak_segments = zip(
*peaks)
peak_sample_inds = np.concatenate(peak_sample_inds)
peak_chan_inds = np.concatenate(peak_chan_inds)
peak_amplitudes = np.concatenate(peak_amplitudes)
peak_segments = np.concatenate(peak_segments)
if outputs == 'numpy_compact':
dtype = [('sample_ind', 'int64'), ('channel_ind', 'int64'),
('amplitude', 'float64'), ('segment_ind', 'int64')]
peaks = np.zeros(peak_sample_inds.size, dtype=dtype)
peaks['sample_ind'] = peak_sample_inds
peaks['channel_ind'] = peak_chan_inds
peaks['amplitude'] = peak_amplitudes
peaks['segment_ind'] = peak_segments
return peaks
elif outputs == 'numpy_split':
return peak_sample_inds, peak_chan_inds, peak_amplitudes, peak_segments
elif outputs == 'sorting':
#@alessio : here we can do what you did in old API
# the output is a sorting where unit_id is in fact one channel
raise NotImplementedError
def detect_peaks(recording,
method='by_channel',
peak_sign='neg',
detect_threshold=5,
n_shifts=2,
local_radius_um=50,
noise_levels=None,
random_chunk_kwargs={},
outputs='numpy_compact',
localization_dict=None,
**job_kwargs):
"""Peak detection based on threshold crossing in term of k x MAD.
Parameters
----------
recording: RecordingExtractor
The recording extractor object.
method: 'by_channel', 'locally_exclusive'
Method to use. Options:
* 'by_channel' : peak are detected in each channel independently
* 'locally_exclusive' : a single best peak is taken from a set of neighboring channels
peak_sign: 'neg', 'pos', 'both'
Sign of the peak.
detect_threshold: float
Threshold, in median absolute deviations (MAD), to use to detect peaks.
n_shifts: int
Number of shifts to find peak.
For example, if `n_shift` is 2, a peak is detected if a sample crosses the threshold,
and the two samples before and after are above the sample.
local_radius_um: float
The radius to use for detection across local channels.
noise_levels: array, optional
Estimated noise levels to use, if already computed.
If not provide then it is estimated from a random snippet of the data.
random_chunk_kwargs: dict, optional
A dict that contain option to randomize chunk for get_noise_levels().
Only used if noise_levels is None.
outputs: 'numpy_compact', 'numpy_split', 'sorting'
The type of the output. By default, "numpy_compact" returns an array with complex dtype.
In case of 'sorting', each unit corresponds to a recording channel.
localization_dict : dict, optional
Can optionally do peak localization at the same time as detection.
This avoids running `localize_peaks` separately and re-reading the entire dataset.
{}
Returns
-------
peaks: array
Detected peaks.
Notes
-----
This peak detection ported from tridesclous into spikeinterface.
"""
assert method in ('by_channel', 'locally_exclusive')
assert peak_sign in ('both', 'neg', 'pos')
assert outputs in ('numpy_compact', 'numpy_split', 'sorting')
if method == 'locally_exclusive' and not HAVE_NUMBA:
raise ModuleNotFoundError(
'"locally_exclusive" need numba which is not installed')
if noise_levels is None:
noise_levels = get_noise_levels(recording,
return_scaled=False,
**random_chunk_kwargs)
abs_threholds = noise_levels * detect_threshold
if method == 'locally_exclusive':
assert local_radius_um is not None
channel_distance = get_channel_distances(recording)
neighbours_mask = channel_distance < local_radius_um
else:
neighbours_mask = None
# deal with margin
if localization_dict is None:
extra_margin = 0
else:
assert isinstance(localization_dict, dict)
assert localization_dict['method'] in dtype_localize_by_method.keys()
localization_dict = init_kwargs_dict(localization_dict['method'],
localization_dict)
nbefore = int(localization_dict['ms_before'] *
recording.get_sampling_frequency() / 1000.)
nafter = int(localization_dict['ms_after'] *
recording.get_sampling_frequency() / 1000.)
extra_margin = max(nbefore, nafter)
# and run
func = _detect_peaks_chunk
init_func = _init_worker_detect_peaks
init_args = (recording.to_dict(), method, peak_sign, abs_threholds,
n_shifts, neighbours_mask, extra_margin, localization_dict)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
job_name='detect peaks',
**job_kwargs)
peaks = processor.run()
peaks = np.concatenate(peaks)
if outputs == 'numpy_compact':
return peaks
elif outputs == 'sorting':
return NumpySorting.from_peaks(
peaks, sampling_frequency=recording.get_sampling_frequency())
def compute_amplitudes(self, **job_kwargs):
we = self.waveform_extractor
recording = we.recording
sorting = we.sorting
all_spikes = sorting.get_all_spike_trains(outputs='unit_index')
self._all_spikes = all_spikes
peak_sign = self._params['peak_sign']
return_scaled = self._params['return_scaled']
extremum_channels_index = get_template_extremum_channel(
we, peak_sign=peak_sign, outputs='index')
peak_shifts = get_template_extremum_channel_peak_shift(
we, peak_sign=peak_sign)
# put extremum_channels_index and peak_shifts in vector way
extremum_channels_index = np.array(
[extremum_channels_index[unit_id] for unit_id in sorting.unit_ids],
dtype='int64')
peak_shifts = np.array(
[peak_shifts[unit_id] for unit_id in sorting.unit_ids],
dtype='int64')
if return_scaled:
# check if has scaled values:
if not we.recording.has_scaled_traces():
print("Setting 'return_scaled' to False")
return_scaled = False
# and run
func = _spike_amplitudes_chunk
init_func = _init_worker_spike_amplitudes
n_jobs = ensure_n_jobs(recording, job_kwargs.get('n_jobs', None))
if n_jobs == 1:
init_args = (recording, sorting)
else:
init_args = (recording.to_dict(), sorting.to_dict())
init_args = init_args + (extremum_channels_index, peak_shifts,
return_scaled)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
job_name='extract amplitudes',
**job_kwargs)
out = processor.run()
amps, segments = zip(*out)
amps = np.concatenate(amps)
segments = np.concatenate(segments)
self._amplitudes = []
for segment_index in range(recording.get_num_segments()):
mask = segments == segment_index
amps_seg = amps[mask]
self._amplitudes.append(amps_seg)
# save to folder
file_amps = self.extension_folder / f'amplitude_segment_{segment_index}.npy'
np.save(file_amps, amps_seg)
def detect_peaks(recording,
method='by_channel',
peak_sign='neg',
detect_threshold=5,
n_shifts=2,
local_radius_um=50,
noise_levels=None,
random_chunk_kwargs={},
outputs='numpy_compact',
localization_dict=None,
**job_kwargs):
"""
Peak detection ported from tridesclous into spikeinterface.
Peak detection based on threhold crossing in term of k x MAD
If the MAD is not provide then it is estimated with random snipet
Several methods:
* 'by_channel' : peak are dettected in each channel independantly
* 'locally_exclusive' : locally given a radius the best peak only is taken but
not neighboring channels
Parameters
----------
recording: RecordingExtractor
The recording extractor object
method:
peak_sign='neg'/ 'pos' / 'both'
Signa of the peak.
detect_threshold: float
Threshold in median absolute deviations (MAD) to detect peaks
n_shifts: int
Number of shifts to find peak. E.g. if n_shift is 2, a peak is detected (if detect_sign is 'negative') if
a sample is below the threshold, the two samples before are higher than the sample, and the two samples after
the sample are higher than the sample.
noise_levels: np.array
noise_levels can be provide externally if already computed.
random_chunk_kwargs: dict
A dict that contain option to randomize chunk for get_noise_levels()
Only used if noise_levels is None
numpy_compact: str numpy_compact/numpy_split/sorting
The type of the output. By default "numpy_compact"
give a vector with complex dtype.
localization_dict : None or dict
Can optionally do peak localisation at the same time as detection.
This avoids to run localize_peaks separately and re read the entire dataset.
job_kwargs: dict
Parameters for ChunkRecordingExecutor
"""
assert method in ('by_channel', 'locally_exclusive')
assert peak_sign in ('both', 'neg', 'pos')
assert outputs in ('numpy_compact', 'numpy_split', 'sorting')
if method == 'locally_exclusive' and not HAVE_NUMBA:
raise ModuleNotFoundError(
'"locally_exclusive" need numba which is not installed')
if noise_levels is None:
noise_levels = get_noise_levels(recording, **random_chunk_kwargs)
abs_threholds = noise_levels * detect_threshold
if method == 'locally_exclusive':
assert local_radius_um is not None
channel_distance = get_channel_distances(recording)
neighbours_mask = channel_distance < local_radius_um
else:
neighbours_mask = None
# deal with margin
if localization_dict is None:
extra_margin = 0
else:
assert isinstance(localization_dict, dict)
assert localization_dict['method'] in dtype_localize_by_method.keys()
localization_dict = init_kwargs_dict(localization_dict['method'],
localization_dict)
nbefore = int(localization_dict['ms_before'] *
recording.get_sampling_frequency() / 1000.)
nafter = int(localization_dict['ms_after'] *
recording.get_sampling_frequency() / 1000.)
extra_margin = max(nbefore, nafter)
# and run
func = _detect_peaks_chunk
init_func = _init_worker_detect_peaks
init_args = (recording.to_dict(), method, peak_sign, abs_threholds,
n_shifts, neighbours_mask, extra_margin, localization_dict)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
job_name='detect peaks',
**job_kwargs)
peaks = processor.run()
peaks = np.concatenate(peaks)
if outputs == 'numpy_compact':
return peaks
elif outputs == 'sorting':
# @alessio : here we can do what you did in old API
# the output is a sorting where unit_id is in fact one channel
raise NotImplementedError
def run_for_all_spikes(self, file_path, max_channels_per_template=16, peak_sign='neg',
**job_kwargs):
"""
Project all spikes from the sorting on the PCA model.
This is a long computation because waveform need to be extracted from each spikes.
Used mainly for `export_to_phy()`
PCs are exported to a .npy single file.
Parameters
----------
file_path : str or Path
Path to npy file that will store the PCA projections
max_channels_per_template : int, optionl
Maximum number of best channels to compute PCA projections on
peak_sign : str, optional
Peak sign to get best channels ('neg', 'pos', 'both'), by default 'neg'
{}
"""
p = self._params
we = self.waveform_extractor
sorting = we.sorting
recording = we.recording
assert sorting.get_num_segments() == 1
assert p['mode'] in ('by_channel_local', 'by_channel_global')
file_path = Path(file_path)
all_spikes = sorting.get_all_spike_trains(outputs='unit_index')
spike_times, spike_labels = all_spikes[0]
max_channels_per_template = min(max_channels_per_template, we.recording.get_num_channels())
best_channels_index = get_template_channel_sparsity(we, outputs="index", peak_sign=peak_sign,
num_channels=max_channels_per_template)
unit_channels = [best_channels_index[unit_id] for unit_id in sorting.unit_ids]
pca_model = self.get_pca_model()
if p['mode'] in ['by_channel_global', 'concatenated']:
pca_model = [pca_model] * recording.get_num_channels()
# nSpikes, nFeaturesPerChannel, nPCFeatures
# this come from phy template-gui
# https://github.com/kwikteam/phy-contrib/blob/master/docs/template-gui.md#datasets
shape = (spike_times.size, p['n_components'], max_channels_per_template)
all_pcs = np.lib.format.open_memmap(filename=file_path, mode='w+', dtype='float32', shape=shape)
all_pcs_args = dict(filename=file_path, mode='r+', dtype='float32', shape=shape)
# and run
func = _all_pc_extractor_chunk
init_func = _init_work_all_pc_extractor
n_jobs = ensure_n_jobs(recording, job_kwargs.get('n_jobs', None))
if n_jobs == 1:
init_args = (recording,)
else:
init_args = (recording.to_dict(),)
init_args = init_args + (all_pcs_args, spike_times, spike_labels, we.nbefore, we.nafter,
unit_channels, pca_model)
processor = ChunkRecordingExecutor(recording, func, init_func, init_args, job_name='extract PCs', **job_kwargs)
processor.run()
def localize_peaks(recording, peaks, ms_before=0.1, ms_after=0.3,
method='center_of_mass', method_kwargs={},
**job_kwargs):
#~ local_radius_um=150,
"""
Localize peak (spike) in 2D or 3D depending the method.
When a probe is 2D then:
* X is axis 0 of the probe
* Y is axis 1 of the probe
* Z is orthogonal to the plane of the probe
Parameters
----------
recording: RecordingExtractor
The recording extractor object
peaks: numpy
peak vector given by detect_peaks() in "compact_numpy" way.
ms_before: float
The left window before a peak in millisecond
ms_after: float
The left window before a peak in millisecond
method: str
Method to be used ('center_of_mass' or 'monopolar_triangulation')
method_kwargs: dict of kwargs method
'center_of_mass':
* local_radius_um: float
For channel sparsity
'monopolar_triangulation' also have:
* local_radius_um: float
For channel sparsity
* max_distance_um: float default 1000
boundary for distance estimation
Returns
-------
peak_locations: np.array
Array with estimated location for each spike
The dtype depend on the method. ('x', 'y') or ('x', 'y', 'z', 'alpha')
"""
assert method in possible_localization_methods, f"Method {method} is not supported. Choose from {possible_localization_methods}"
# handle default method_kwargs
method_kwargs = init_kwargs_dict(method, method_kwargs)
nbefore = int(ms_before * recording.get_sampling_frequency() / 1000.)
nafter = int(ms_after * recording.get_sampling_frequency() / 1000.)
contact_locations = recording.get_channel_locations()
# margin at border for get_trace
margin = max(nbefore, nafter)
# TODO
# make a memmap for peaks to avoid serilisation
# and run
func = _localize_peaks_chunk
init_func = _init_worker_localize_peaks
init_args = (recording.to_dict(), peaks, method, method_kwargs, nbefore, nafter, contact_locations, margin)
processor = ChunkRecordingExecutor(recording, func, init_func, init_args, handle_returns=True,
job_name='localize peaks', **job_kwargs)
peak_locations = processor.run()
peak_locations = np.concatenate(peak_locations)
return peak_locations
def get_spike_amplitudes(waveform_extractor,
peak_sign='neg',
outputs='concatenated',
return_scaled=True,
**job_kwargs):
"""
Computes the spike amplitudes from a WaveformExtractor.
1. The waveform extractor is used to determine the max channel per unit.
2. Then a "peak_shift" is estimated because for some sorters the spike index is not always at the
peak.
3. Amplitudes are extracted in chunks (parallel or not)
Parameters
----------
waveform_extractor: WaveformExtractor
The waveform extractor object
peak_sign: str
The sign to compute maximum channel:
- 'neg'
- 'pos'
- 'both'
return_scaled: bool
If True and recording has gain_to_uV/offset_to_uV properties, amplitudes are converted to uV.
outputs: str
How the output should be returned:
- 'concatenated'
- 'by_unit'
{}
Returns
-------
amplitudes: np.array
The spike amplitudes.
- If 'concatenated' all amplitudes for all spikes and all units are concatenated
- If 'by_unit', amplitudes are returned as a list (for segments) of dictionaries (for units)
"""
we = waveform_extractor
recording = we.recording
sorting = we.sorting
all_spikes = sorting.get_all_spike_trains()
extremum_channels_index = get_template_extremum_channel(
waveform_extractor, peak_sign=peak_sign, outputs='index')
peak_shifts = get_template_extremum_channel_peak_shift(waveform_extractor,
peak_sign='neg')
if return_scaled:
# check if has scaled values:
if not waveform_extractor.recording.has_scaled_traces():
print("Setting 'return_scaled' to False")
return_scaled = False
# and run
func = _spike_amplitudes_chunk
init_func = _init_worker_spike_amplitudes
init_args = (recording.to_dict(), sorting.to_dict(),
extremum_channels_index, peak_shifts, return_scaled)
processor = ChunkRecordingExecutor(recording,
func,
init_func,
init_args,
handle_returns=True,
job_name='extract amplitudes',
**job_kwargs)
out = processor.run()
amps, segments = zip(*out)
amps = np.concatenate(amps)
segments = np.concatenate(segments)
amplitudes = []
for segment_index in range(recording.get_num_segments()):
mask = segments == segment_index
amplitudes.append(amps[mask])
if outputs == 'concatenated':
return amplitudes
elif outputs == 'by_unit':
amplitudes_by_unit = []
for segment_index in range(recording.get_num_segments()):
amplitudes_by_unit.append({})
for unit_id in sorting.unit_ids:
spike_times, spike_labels = all_spikes[segment_index]
mask = spike_labels == unit_id
amps = amplitudes[segment_index][mask]
amplitudes_by_unit[segment_index][unit_id] = amps
return amplitudes_by_unit