class DepthEstimationParams(DefaultSchema):
hi_noise_thresh = Float(required=True,
default=50.0,
help='Max RMS noise for including channels')
lo_noise_thresh = Float(required=True,
default=3.0,
help='Min RMS noise for including channels')
save_figure = Bool(required=True, default=True)
figure_location = OutputFile(required=True, default=None)
smoothing_amount = Int(
required=True,
default=5,
help='Gaussian smoothing parameter to reduce channel-to-channel noise')
power_thresh = Float(
required=True,
default=2.5,
help=
'Ignore threshold crossings if power is above this level (indicates channels are in the brain)'
)
diff_thresh = Float(
required=True,
default=-0.07,
help='Threshold to detect large increases is power at brain surface')
freq_range = NumpyArray(
required=True,
default=[0, 10],
help='Frequency band for detecting power increases')
max_freq = Int(required=True,
default=150,
help='Maximum frequency to plot')
channel_range = NumpyArray(
required=True,
default=[370, 380],
help='Channels assumed to be out of brain, but in saline')
n_passes = Int(
required=True,
default=10,
help='Number of times to compute offset and surface channel')
skip_s_per_pass = Int(
required=True,
default=100,
help='Number of seconds between data chunks used on each pass')
start_time = Float(
required=True,
default=0,
help='First time (in seconds) for computing median offset')
time_interval = Float(required=True,
default=5,
help='Number of seconds for computing median offset')
nfft = Int(required=True,
default=4096,
help='Length of FFT used for calculations')
air_gap = Int(
required=True,
default=100,
help='Approximate number of channels between brain surface and air')
class MySchema(ArgSchema):
a = NumpyArray(dtype='float',
description='Test input array schema',
validate=validate.Shape((2, 2)))
b = NumpyArray(dtype='float',
description='Test array',
validate=validate.Shape((2, None)))
class MyNestedParameters(DefaultSchema):
name = Str(required=True, description='name of vector')
increment = Int(required=True, description='value to increment')
array = NumpyArray(dtype=np.float,
required=True,
description='array to increment')
write_output = Boolean(required=False, default=True)
class InputParameters(ArgSchema):
output_dir = OutputDir(
default="./",
description="Directory in which to store data output files")
input_source = InputFile(description="Path to input movie", required=True)
pupil_bounding_box = NumpyArray(dtype="int", default=[])
cr_bounding_box = NumpyArray(dtype="int", default=[])
start_frame = Int(description="Frame of movie to start processing at")
stop_frame = Int(description="Frame of movie to end processing at")
frame_step = Int(description=(
"Interval of frames to process. Used for skipping frames,"
"if 1 it will process every frame between start and stop"))
ransac = Nested(RansacParameters)
annotation = Nested(AnnotationParameters)
starburst = Nested(StarburstParameters)
eye_params = Nested(EyeParameters)
qc = Nested(QCParameters)
class OutputParameters(OutputSchema):
execution_time = Float()
kilosort_commit_hash = String()
kilosort_commit_date = String()
mask_channels = NumpyArray()
nTemplate = Int()
nTot = Int()
class ProbeInputParameters(DefaultSchema):
name = String(required=True, help='Identifier for this probe')
lfp_input_file_path = String(required=True,
description="path to original LFP .dat file")
lfp_timestamps_input_path = String(required=True,
description="path to LFP timestamps")
lfp_data_path = String(required=True,
help="Path to LFP data continuous file")
lfp_timestamps_path = String(
required=True, help="Path to LFP timestamps aligned to master clock")
lfp_channel_info_path = String(required=True,
help="Path to LFP channel info")
total_channels = Int(default=384,
help='Total channel count for this probe.')
surface_channel = Int(required=True, help="Probe surface channel")
reference_channels = NumpyArray(required=False,
help="Probe reference channels")
lfp_sampling_rate = Float(required=True, help="Sampling rate of LFP data")
noisy_channels = NumpyArray(required=False,
help="Noisy channels to remove")
class EphysParams(DefaultSchema):
sample_rate = Float(required=True, default=30000.0, help='Sample rate of Neuropixels AP band continuous data')
lfp_sample_rate = Float(require=True, default=2500.0, help='Sample rate of Neuropixels LFP band continuous data')
bit_volts = Float(required=True, default=0.195, help='Scalar required to convert int16 values into microvolts')
num_channels = Int(required=True, default=384, help='Total number of channels in binary data files')
reference_channels = NumpyArray(required=False, default=[36, 75, 112, 151, 188, 227, 264, 303, 340, 379], help='Reference channels on Neuropixels probe (numbering starts at 0)')
template_zero_padding = Int(required=True, default=21, help='Zero-padding on templates output by Kilosort')
vertical_site_spacing = Float(required=False, default=20e-6, help='Vertical site spacing in meters')
probe_type = String(required=False, default='3A', help='3A, 3B1, or 3B2')
lfp_band_file = String(required=False, help='Location of LFP band binary file')
ap_band_file = String(required=False, help='Location of AP band binary file')
class MySchema(ArgSchema):
array = NumpyArray(default=[[1, 2, 3], [4, 5, 6]],
dtype="uint8",
description="my example array")
string_list = List(List(Str),
default=[["hello", "world"], ["lists!"]],
cli_as_single_argument=True,
description="list of lists of strings")
int_list = List(Int,
default=[1, 2, 3],
cli_as_single_argument=True,
description="list of ints")
nested = Nested(MyNestedSchema, required=True)
class OutputParameters(DefaultSchema):
inputs = Nested(
PiaWmStreamlineSchema,
description="The parameters argued to this executable",
required=True
)
depth_field_file = OutputFile(
required=True,
description='location of depth field xarray')
gradient_field_file = OutputFile(
required=True,
description='location of gradient field xarray')
translation = NumpyArray(
required=False,
description='translation if applied')
class regularization(ArgSchema):
default_lambda = Float(
default=0.005,
description='regularization factor')
translation_factor = Float(
default=0.005,
description='regularization factor')
poly_factors = NumpyArray(
Float,
required=False,
default=None,
missing=None,
cli_as_single_argument=True,
description=("List of regularization factors by order (0, 1, ..., n)"
"will override other settings for Polynomial2DTransform"
"will multiply default_lambda"))
freeze_first_tile = Boolean(
default=False,
required=False)
class EphysParams(DefaultSchema):
sample_rate = Float(
required=True,
default=30000.0,
help='Sample rate of Neuropixels AP band continuous data')
lfp_sample_rate = Float(
require=True,
default=2500.0,
help='Sample rate of Neuropixels LFP band continuous data')
bit_volts = Float(
required=True,
default=0.195,
help='Scalar required to convert int16 values into microvolts')
num_channels = Int(required=True,
default=384,
help='Total number of channels in binary data files')
reference_channels = NumpyArray(
required=False,
default=[36, 75, 112, 151, 188, 227, 264, 303, 340, 379],
help='Reference channels on Neuropixels probe (numbering starts at 0)')
template_zero_padding = Int(
required=True,
default=21,
help='Zero-padding on templates output by Kilosort')
vertical_site_spacing = Float(required=False,
default=20e-6,
help='Vertical site spacing in meters')
probe_type = String(required=False, default='NP1', help='3A, 3B2, NP1')
lfp_band_file = String(required=False,
help='Location of LFP band binary file')
ap_band_file = String(required=False,
help='Location of AP band binary file')
reorder_lfp_channels = Bool(
required=False,
default=True,
help=
'Should we fix the ordering of LFP channels (necessary for 3a probes following extract_from_npx modules)'
)
cluster_group_file_name = String(required=False,
default='cluster_group.tsv')
class NumpyFileuint16(ArgSchema):
a = NumpyArray(dtype='uint16',
required=True,
decription='list of lists representing numpy array')
class MyOutputSchema(DefaultSchema):
a = Str(required=True, description="a simple string")
b = Int(default=5, description="a default integer")
M = NumpyArray(required=True, description="a numpy array of answers")
class MyOutputParams(DefaultSchema):
name = Str(required=True, description='name of vector')
inc_array = NumpyArray(dtype=np.float,
required=True,
description='incremented array')