def __init__(self, **kwargs):
"""Initialization"""
Block.__init__(self, **kwargs)
self.add_input('data', structure='dict')
self.add_input('mads', structure='dict')
self.add_input('peaks', structure='dict')
# The following line is useful to avoid PyCharm warnings.
self.probe_path = self.probe_path
self._dtype = None
self._nb_samples = None
self._nb_channels = None
self._sampling_rate = None
self._params_pipe = Pipe()
self._number_pipe = Pipe()
self._data_pipe = Pipe()
self._mads_pipe = Pipe()
self._peaks_pipe = Pipe()
self._qt_process = GUIProcess(self._params_pipe,
self._number_pipe,
self._data_pipe,
self._mads_pipe,
self._peaks_pipe,
probe_path=self.probe_path)
self._is_mad_reception_blocking = False
self._is_peak_reception_blocking = False
def __init__(self, **kwargs):
"""Initialization of the object.
Parameters:
data_path: string
dtype: string
nb_channels: integer
nb_samples: integer
sampling_rate: float
is_realistic: boolean
speed_factor: float
nb_replay: integer
probe_path: string
See also:
circusort.block.Block
"""
Block.__init__(self, **kwargs)
self.add_output('data', structure='dict')
# Lines useful to remove PyCharm warnings.
self.data_path = self.data_path
self.dtype = self.dtype
self.nb_channels = self.nb_channels
self.nb_samples = self.nb_samples
self.sampling_rate = self.sampling_rate
self.is_realistic = self.is_realistic
self.speed_factor = self.speed_factor
self.nb_replay = self.nb_replay
self.offset = self.offset
self.probe_path = self.probe_path
self.zero_channels = self.zero_channels
self._output_dtype = 'float32'
self._quantum_size = self.gain
self._quantum_offset = float(np.iinfo('int16').min)
self._buffer_rate = float(self.nb_samples) / self.sampling_rate
self._absolute_start_time = None
self._absolute_end_time = None
if self.probe_path is not None:
self.probe = load_probe(self.probe_path, logger=self.log)
# Log info message.
string = "{} reads the probe layout"
message = string.format(self.name)
self.log.info(message)
else:
self.probe = None
if self.zero_channels is not None:
if np.iterable(self.zero_channels):
self.zero_channels = np.array(self.zero_channels)
else:
self.zero_channels = np.array([self.zero_channels])
def __init__(self, **kwargs):
Block.__init__(self, **kwargs)
self.add_input('updater', structure='dict')
# The following line is useful to disable a PyCharm warning.
self.output_directory = self.output_directory
self._output_counter = 0
self._sleep_duration = 0.01 # s
def __init__(self, **kwargs):
"""Initialize block."""
Block.__init__(self, **kwargs)
# The following lines are useful to avoid some PyCharms's warnings.
self.input_specs = self.input_specs
self.degree = self.degree
self.overlap = self.overlap
self.nb_samples = self.nb_samples
self.sampling_rate = self.sampling_rate
assert self.degree >= self.overlap # TODO add docstring.
# Declare input names and structures.
self._input_names = []
self._input_structures = []
self._input_policies = []
self._are_synced_inputs = {}
for input_spec in self.input_specs:
if isinstance(input_spec, (str, unicode)):
name = input_spec
structure = 'array'
policy = 'hard_blocking'
elif isinstance(input_spec, dict):
name = input_spec['name']
if 'structure' in input_spec:
structure = input_spec['structure']
else:
structure = 'array'
if 'policy' in input_spec:
policy = input_spec['policy']
else:
policy = 'hard_blocking'
else:
raise NotImplementedError() # TODO complete.
self._input_names.append(name)
self._input_structures.append(structure)
self._input_policies.append(policy)
self._are_synced_inputs[name] = False
# Declare the inputs.
for name, structure in zip(self._input_names, self._input_structures):
self.add_input(name, structure)
# Declare the outputs.
for name, structure in zip(self._input_names, self._input_structures):
for k in range(0, self.degree):
output_name = self._get_output_name(name, k)
self.add_output(output_name, structure)
self._nb_channels = None
self._nb_samples = None
def __init__(self, **kwargs):
"""Initialize channel dispatcher.
"""
Block.__init__(self, **kwargs)
keys = ['data', 'peaks', 'mads', 'pcs']
for key in keys:
self.add_input(key, structure='dict')
self.add_output(key, structure='dict')
self._nb_samples = None
def __init__(self, **kwargs):
"""Initialize template updater.
Arguments:
probe_path: string
radius: none | float (optional)
cc_merge: float (optional)
cc_mixture: none | float (optional)
templates_path: none | string (optional)
overlaps_path: none | string (optional)
precomputed_template_paths: none | list (optional)
sampling_rate: float (optional)
nb_samples: integer (optional)
"""
Block.__init__(self, **kwargs)
# The following lines are useful to avoid some PyCharm's warnings.
self.probe_path = self.probe_path
self.radius = self.radius
self.cc_merge = self.cc_merge
self.cc_mixture = self.cc_mixture
self.templates_path = self.templates_path
self.overlaps_path = self.overlaps_path
self.precomputed_template_paths = self.precomputed_template_paths
self.sampling_rate = self.sampling_rate
self.nb_samples = self.nb_samples
self.skip_overlaps = self.skip_overlaps
# Initialize private attributes.
if self.probe_path is None:
self.probe = None
# Log error message.
string = "{}: the probe file must be specified!"
message = string.format(self.name)
self.log.error(message)
else:
self.probe = load_probe(self.probe_path, radius=self.radius, logger=self.log)
# Log info message.
string = "{} reads the probe layout"
message = string.format(self.name)
self.log.info(message)
self._template_store = None
self._template_dictionary = None
self._overlap_store = None
self._two_components = None
self.add_input('templates', structure='dict')
self.add_output('updater', structure='dict')
def __init__(self, **kwargs):
"""Initialize multiplexer.
Arguments:
output_specs: list (optional)
degree: integer (optional)
The default value is 2.
nb_samples: integer (optional)
The default value is 1024.
sampling_rate: float (optional)
The default value is 20e+3.
"""
Block.__init__(self, **kwargs)
# The following lines are useful to avoid some PyCharms's warnings.
self.output_specs = self.output_specs
self.degree = self.degree
self.nb_samples = self.nb_samples
self.sampling_rate = self.sampling_rate
# Declare output names and structures.
self._output_names = []
self._output_structures = []
for output_spec in self.output_specs:
if isinstance(output_spec, (str, unicode)):
name = output_spec
structure = 'array'
elif isinstance(output_spec, dict):
name = output_spec['name']
if 'structure' in output_spec:
structure = output_spec['structure']
else:
structure = 'array'
else:
raise NotImplementedError() # TODO complete.
self._output_names.append(name)
self._output_structures.append(structure)
# Declare the inputs.
for name, structure in zip(self._output_names,
self._output_structures):
for k in range(0, self.degree):
input_name = self._get_input_name(name, k)
self.add_input(input_name, structure)
# Declare the outputs.
for name, structure in zip(self._output_names,
self._output_structures):
self.add_output(name, structure)
def __init__(self, **kwargs):
Block.__init__(self, **kwargs)
# The following lines are useful to avoid some PyCharm's warning.
self.templates_init_path = self.templates_init_path
self.add_input('updater')
self.add_output('spikes', 'dict')
self._data = {}
self.n_size = 2 * self.max_delay + 1
self.bin_size = int(self.cc_bin * self.sampling_rate * 1e-3)
self.raw_lags = np.linspace(-self.max_delay*self.cc_bin, self.max_delay*self.cc_bin, 2*self.max_delay+1)
self.results = {
'spikes': [],
'templates': []
}
def __init__(self, **kwargs):
"""Initialization.
Arguments:
sampling_rate: float (optional)
The sampling rate used to record the signal [Hz].
The default value is 20e+3.
cut_off: float (optional)
The cutoff frequency used to define the high-pass filter [Hz].
The default value is 500.0.
order: integer (optional)
The order used to define the high-pass filter.
The default value is 1.
remove_median: boolean (optional)
The option to remove the median over all the channels for each time step.
The default value is False.
use_gpu: boolean (optional)
The option to use the GPU.
The default value is False.
"""
Block.__init__(self, **kwargs)
self.add_output('data', structure='dict')
self.add_input('data', structure='dict')
# Lines useful to solve PyCharm warnings.
self.cut_off = self.cut_off
self.order = self.order
self.sampling_rate = self.sampling_rate
self.remove_median = self.remove_median
self.use_gpu = self.use_gpu
# Check that the cut off frequency is at least 0.1 Hz.
if self.cut_off < 0.1:
self.cut_off = 0.1 # Hz
self.dtype = None
self.nb_samples = None
self.nb_channels = None
# self.sampling_rate = None # already defined above
self._b = None
self._a = None
self._z = None
self._filter_engine = None
def __init__(self, **kwargs):
Block.__init__(self, **kwargs)
self.add_output('mads', structure='dict')
self.add_input('data', structure='dict')
# The following lines are useful to avoid some PyCharm warnings.
self.sampling_rate = self.sampling_rate
self.time_constant = self.time_constant
self.epsilon = self.epsilon
self._dtype = None
self._nb_samples = None
self._nb_channels = None
self._n = 0
self._medians = None
self._mads = None
self._last_mads = None
def __init__(self, **kwargs):
"""Initialize peak grouper.
Argument:
nb_groups: integer (optional)
The number of groups from which data will be gathered.
The default value is 1.
"""
Block.__init__(self, **kwargs)
# The following line is useful to avoid some PyCharm's warning.
self.nb_groups = self.nb_groups
for k in range(0, self.nb_groups):
self.add_input('peaks_{}'.format(k), structure='dict')
self.add_output('peaks', structure='dict')
self.nb_samples = None
self.sampling_rate = None
def __init__(self, **kwargs):
Block.__init__(self, **kwargs)
self.add_input('peaks', structure='dict')
# The following lines are useful to avoid some PyCharm warnings.
self.pos_peaks = self.pos_peaks
self.neg_peaks = self.neg_peaks
self.data_path = self.data_path
self.sampling_rate = self.sampling_rate
self.nb_samples = self.nb_samples
if self.pos_peaks is not None or self.neg_peaks is not None:
self._mode = 'raw'
elif self.data_path is not None:
self._mode = 'hdf5'
else:
self._mode = 'raw' # TODO complete.
self._h5_file = None
def __init__(self, **kwargs):
Block.__init__(self, **kwargs)
# The following lines are useful to avoid some PyCharm's warning.
self.data_path = self.data_path
self.spike_times = self.spike_times
self.amplitudes = self.amplitudes
self.templates = self.templates
self.rejected_times = self.rejected_times
self.rejected_amplitudes = self.rejected_amplitudes
self.directory = self.directory
self.sampling_rate = self.sampling_rate
self.nb_samples = self.nb_samples
self.add_input('spikes', structure='dict')
if self.data_path is None:
self._mode = 'raw'
else:
self._mode = 'hdf5'
self._h5_file = None
def __init__(self, **kwargs):
"""Initialization.
Parameter:
data_path: none | string (optional)
The path to the file to use to write the data.
The default value is None.
dataset_name: none | string (optional)
The name to use for the HDF5 dataset.
The default value is None
mode: none | string (optional)
The mode to use to write into the file.
The default value is None.
nb_samples: integer (optional)
The number of sampling times for each buffer.
The default value is 1024.
sampling_rate: float (optional)
The sampling rate used to record the data.
The default value is 20e+3.
"""
Block.__init__(self, **kwargs)
self.add_input('data', structure='dict')
# Lines useful to remove some PyCharm warnings.
self.data_path = self._get_temp_file(
) if self.data_path is None else self.data_path
self.dataset_name = 'dataset' if self.dataset_name is None else self.dataset_name
self.mode = 'default' if self.mode is None else self.mode
self.nb_samples = self.nb_samples
self.sampling_rate = self.sampling_rate
self._dtype = None
self._nb_samples = None
self._nb_channels = None
self._previous_number = None
self._raw_file = None
self._h5_file = None
self._h5_dataset = None
def __init__(self, **kwargs):
"""Initialize channel dispatcher.
Argument:
nb_groups: integer (optional)
The number of groups into which data will be dispatch.
The default value is 1.
"""
Block.__init__(self, **kwargs)
# The following line is useful to disable some PyCharm's warning.
self.nb_groups = self.nb_groups
self.add_input('data', structure='dict')
for k in range(0, self.nb_groups):
output_name = 'data_{}'.format(k)
self.add_output(output_name, structure='dict')
self.dtype = None
self.nb_samples = None
self.nb_channels = None
self.sampling_rate = None
def __init__(self, **kwargs):
"""Initialize fitter
Arguments:
templates_init_path: string (optional)
overlaps_init_path: string (optional)
with_rejected_times: boolean (optional)
sampling_rate: float (optional)
discarding_eoc_from_updater: boolean (optional)
_nb_fitters: integer (optional)
_fitter_id: integer (optional)
"""
Block.__init__(self, **kwargs)
# The following lines are useful to avoid some PyCharm's warnings.
self.templates_init_path = self.templates_init_path
self.overlaps_init_path = self.overlaps_init_path
self.with_rejected_times = self.with_rejected_times
self.sampling_rate = self.sampling_rate
self.discarding_eoc_from_updater = self.discarding_eoc_from_updater
self._nb_fitters = self._nb_fitters
self._fitter_id = self._fitter_id
# Initialize private attributes.
self._template_store = None
self._overlaps_store = None
self.add_input('updater', structure='dict')
self.add_input('data', structure='dict')
self.add_input('peaks', structure='dict')
self.add_output('spikes', structure='dict')
self._nb_channels = None
self._nb_samples = None
self._number = None
def __init__(self, **kwargs):
Block.__init__(self, **kwargs)
