class MontageSolverSchema(ArgSchema):
data_dir = InputDir(
required=False,
description="directory containing metafile, images, and matches")
metafile = InputFile(
required=False,
description=("fullpath to metafile. Helps in the case of multiple"
" metafiles in one directory. data_dir will take "
" os.path.dirname(metafile)"))
output_dir = OutputDir(required=False,
missing=None,
default=None,
description="directory for output files")
read_transform_from = Str(
required=False,
missing='metafile',
default='metafile',
validator=mm.validate.OneOf(['metafile', 'reffile', 'dict']),
description="3 possible ways to read in the reference transform")
ref_transform = InputFile(required=False,
missing=None,
default=None,
description="transform json")
ref_transform_dict = Dict(require=False,
missing=None,
description="transform in from memory")
ransacReprojThreshold = Float(
required=False,
missing=10.0,
default=10.0,
description=("passed into cv2.estimateAffinePartial2D()"
"for RANSAC filtering of montage template matches"))
compress_output = Boolean(
required=False,
missing=True,
default=True,
description=("tilespecs will be .json or .json.gz"))
solver_templates = List(
Str,
required=True,
description="input json basenames for the solver args")
solver_template_dir = InputDir(
required=True, description="location of the templates for the solver")
@mm.post_load
def check_solver_inputs(self, data):
for args in data['solver_templates']:
argpath = os.path.join(data['solver_template_dir'], args)
if not os.path.isfile(argpath):
raise mm.ValidationError("solver arg file doesn't exist: %s" %
argpath)
@mm.post_load
def check_metafile(self, data):
if ('data_dir' not in data) & ('metafile' not in data):
raise mm.ValidationError(" must specify either data_dir"
" or metafile")
class AutomergingParams(DefaultSchema):
merge_threshold = Float(
required=True,
default=2.5,
help='Minimum merge score required to perform a merge')
distance_to_compare = Int(
required=True,
default=5,
help='Distance (in channels) to look for potential merges')
class OutputParameters(OutputSchema):
execution_time = Float()
kilosort_commit_hash = String()
kilosort_commit_date = String()
mask_channels = NumpyArray()
nTemplate = Int()
nTot = Int()
class InputParameters(ArgSchema):
stimulus = Nested(StimulusInputParameters,
required=True,
help='Defines the stimulus from which CSD is calculated')
probes = Nested(ProbeInputParameters,
many=True,
required=True,
help='Probewise parameters.')
pre_stimulus_time = Float(
required=True,
help='how much time pre stimulus onset is used for CSD calculation ')
post_stimulus_time = Float(
required=True,
help='how much time post stimulus onset is used for CSD calculation ')
num_trials = Int(
default=None,
allow_none=True,
help='Number of trials after stimulus onset from which to compute CSD')
volts_per_bit = Float(
default=1.0,
help=
'If the data are not in units of volts, they must be converted. In the past, this value was 0.195'
)
memmap = Bool(
default=False,
help=
'whether to memory map the data file on disk or load it directly to main memory'
)
memmap_thresh = Float(
default=np.inf,
help=
'files larger than this threshold (bytes) will be memmapped, regardless of the memmap setting.'
)
filter_cuts = List(Float,
default=[5.0, 150.0],
cli_as_single_argument=True,
help='Cutoff frequencies for bandpass filter')
filter_order = Int(default=5, help='Order for bandpass filter')
reorder_channels = Bool(
default=True,
help='Determines whether LFP channels should be re-ordered')
noisy_channel_threshold = Float(
default=1500.0,
help='Threshold for removing noisy channels from analysis')
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 FeatureExtractionParameters(argschema.schemas.DefaultSchema):
SIFTfdSize = Int(
required=False,
description=(
"SIFT feature descriptor size -- samples per row and column. "
"8 if excluded or None"))
SIFTminScale = Float(
required=False,
description=("SIFT minimum scale -- "
"minSize * minScale < size < maxSize * maxScale. "
"0.5 if excluded or None"))
SIFTmaxScale = Float(
required=False,
description=("SIFT maximum scale -- "
"minSize * minScale < size < maxSize * maxScale. "
"0.85 if excluded or None"))
SIFTsteps = Int(
required=False,
description=("SIFT steps per scale octave. 3 if excluded or None"))
class NoiseWaveformParams(DefaultSchema):
classifier_path = String(required=True,
help='Path to pre-trained waveform classifier')
smoothed_template_amplitude_threshold = Float(
default=0.2, help='Fraction of max amplitude for calculating spread')
template_amplitude_threshold = Float(
default=0.2, help='Fraction of max amplitude for calculating spread')
smoothed_template_filter_width = Int(
default=2, help='Smoothing window for calculating spread')
min_spread_threshold = Int(
default=2,
help='Minimum number of channels for a waveform to be considered good')
mid_spread_threshold = Int(
default=16,
help='Over this channel spread, waveform shape must be considered')
max_spread_threshold = Int(default=25,
help='Maximum channel spread for a good unit')
channel_amplitude_thresh = Float(
default=0.25,
help=
'Fraction of max amplitude for considering channels in spatial peak detection'
)
peak_height_thresh = Float(
default=0.2, help='Minimum height for spatial peak detection')
peak_prominence_thresh = Float(
default=0.2, help='Minimum prominence for spatial peak detection')
peak_channel_range = Int(
default=24, help='Range of channels for detecting spatial peaks')
peak_locs_std_thresh = Float(
default=3.5,
help='Maximum standard deviation of peak locations for good units')
min_temporal_peak_location = Int(default=10,
help='Minimum peak index for good unit')
max_temporal_peak_location = Int(default=30,
help='Maximum peak index for good unit')
template_shape_channel_range = Int(
default=12, help='Range of channels for checking template shape')
wavelet_index = Int(
default=2, help='Wavelet index for noise template shape detection')
min_wavelet_peak_height = Float(
default=0.0, help='Minimum wavelet peak height for good units')
min_wavelet_peak_loc = Int(
default=15, help='Minimum wavelet peak location for good units')
max_wavelet_peak_loc = Int(
default=25, help='Maximum wavelet peak location for good units')
multiprocessing_worker_count = Int(
default=4,
help='Number of workers to use for spatial peak calculation')
use_random_forest = Boolean(default=False,
help='set to false to use heuristic noise id')
use_preclustered = Boolean(
default=False, help='use clusters from cluster_group.tsv or not')
class SessionData(RaisingSchema):
ophys_experiment_id = Int(
required=True, description='unique identifier for this ophys session')
rig_name = String(required=True, description='name of ophys device')
movie_height = Int(required=True,
description='height of field-of-view for 2p movie')
movie_width = Int(required=True,
description='width of field-of-view for 2p movie')
container_id = Int(required=True,
description='container that this experiment is in')
sync_file = String(required=True, description='path to sync file')
segmentation_mask_image_file = String(
required=True, description='path to segmentation_mask_image file')
max_projection_file = String(required=True,
description='path to max_projection file')
behavior_stimulus_file = String(
required=True, description='path to behavior_stimulus file')
dff_file = String(required=True, description='path to dff file')
demix_file = String(required=True, description='path to demix file')
average_intensity_projection_image_file = String(
required=True,
description='path to average_intensity_projection_image file')
rigid_motion_transform_file = String(
required=True, description='path to rigid_motion_transform file')
targeted_structure = String(
required=True,
description='Anatomical structure that the experiment targeted')
targeted_depth = Int(
required=True,
description='Cortical depth that the experiment targeted')
stimulus_name = String(required=True, description='Stimulus Name')
date_of_acquisition = String(
required=True,
description=
'date of acquisition of experiment, as string (no timezone info but relative ot UTC)'
)
reporter_line = List(String, required=True, description='reporter line')
driver_line = List(String, required=True, description='driver line')
external_specimen_name = Int(required=True,
description='LabTracks ID of the animal')
full_genotype = String(required=True, description='full genotype')
surface_2p_pixel_size_um = Float(
required=True,
description='the spatial extent (in um) of the 2p field-of-view')
ophys_cell_segmentation_run_id = Int(
required=True,
description=
'ID of the active segmentation run used to generate this file')
cell_specimen_table_dict = Nested(
CellSpecimenTable,
required=True,
description='Table of cell specimen info')
sex = String(required=True, description='sex')
age = String(required=True, description='age')
class OutputParameters(DefaultSchema):
inputs = Nested(InputParameters,
description="The parameters argued to this executable",
required=True)
scale_correction = Float(
description=("Z axis scale factor to correct for slice shrinkage"),
required=True)
scale_transform = Nested(
AffineDictSchema,
required=False,
description='Dictionary defining an affine transform')
class InputParameters(ArgSchema):
output_path = String(required=True, help="write outputs to here")
stimulus_pkl_path = String(
required=True,
help="path to pkl file containing raw stimulus information",
)
sync_h5_path = String(
required=True,
help="path to h5 file containing synchronization information",
)
wheel_radius = Float(default=8.255, help="radius, in cm, of running wheel")
subject_position = Float(
default=2 / 3,
help=
"normalized distance of the subject from the center of the running wheel (1 is rim, 0 is center)",
)
use_median_duration = Boolean(
default=True,
help=
"frame timestamps are often too noisy to use as the denominator in the velocity calculation. Can instead use the median frame duration."
)
class ProbeOutputParameters(DefaultSchema):
name = String(required=True, help="Identifier for this probe")
output_paths = Dict(
required=True,
help="Paths of each mappable file written by this run of the module.",
)
total_time_shift = Float(
required=True,
help=
"Translation (in seconds) from master->probe times computed for this probe.",
)
global_probe_sampling_rate = Float(
required=True,
help=
"The sampling rate of this probe in Hz, assessed on the master clock.",
)
global_probe_lfp_sampling_rate = Float(
required=True,
help=
"The sampling rate of LFP collected on this probe in Hz, assessed on the master clock.",
)
class OutputParameters(DefaultSchema):
inputs = Nested(InputParameters,
description="The parameters argued to this executable",
required=True)
tilt_correction = Float(
description=
'Tilt correction about x axis to align with streamlines (radians)',
required=True)
tilt_transform_dict = Nested(
AffineDictSchema,
required=False,
description='Dictionary defining an affine transform')
class InputParameters(ArgSchema):
class Meta:
unknown = RAISE
log_level = LogLevel(default='INFO',
description="set the logging level of the module")
case = String(required=True,
validate=lambda s: s in VALID_CASES,
help='select a use case to run')
sub_images = Nested(SubImage,
required=True,
many=True,
help='Sub images composing this image series')
affine_params = List(
Float,
help='Parameters of affine image stack to reference space transform.')
deformation_field_path = String(
required=True,
help=
'Path to parameters of the deformable local transform from affine-transformed image stack to reference space transform.'
)
image_series_slice_spacing = Float(
required=True,
help='Distance (microns) between successive images in this series.')
target_spacings = List(
Float,
required=True,
help='For each volume produced, downsample to this isometric resolution'
)
reference_spacing = Nested(
ReferenceSpacing,
required=True,
help='Native spacing of reference space (microns).')
reference_dimensions = Nested(ReferenceDimensions,
required=True,
help='Native dimensions of reference space.')
sub_image_count = Int(required=True, help='Expected number of sub images')
grid_prefix = String(required=True, help='Write output grid files here')
accumulator_prefix = String(
required=True,
help='If this run produces accumulators, write them here.')
storage_directory = String(
required=False,
help='Storage directory for this image series. Not used')
filter_bit = Int(
default=None,
allow_none=True,
help=
'if provided, signals that pixels with this bit high have passed the optional post-filter stage'
)
nprocesses = Int(default=8, help='spawn this many worker subprocesses')
reduce_level = Int(
default=0, help='power of two by which to downsample each input axis')
class ApplyAffineParametersBase(RenderParameters):
transformId = Str(
required=True,
description='transform reference name to use when applying changes')
M00 = Float(required=False,
default=1.0,
description='M00 (x\'=M00*x element of affine (default 1.0)')
M10 = Float(required=False,
default=0.0,
description='M10 (y\'=M10*x element of affine (default 0.0)')
M01 = Float(required=False,
default=0.0,
description='M01 (x\'=M01*y element of affine (default 0.0)')
M11 = Float(required=False,
default=1.0,
description='M11 (y\'=M11*y) element of affine (default 1.0)')
B0 = Float(
required=False,
default=0.0,
description='B0 (x translation) element of affine (defautl 0.0)')
B1 = Float(
required=False,
default=0.0,
description='B1 (y translation) element of affine (default 0.0)')
zmin = Int(required=False, description='zvalue to start')
zmax = Int(required=False, description='zvalue to end')
pool_size = Int(
required=False,
default=20,
description='size of pool for parallel processing (default=20)')
class BaseRegistrationItem(DefaultSchema):
x_offset = Int(
required=True,
description=("X offset from the top-left of the image to the center "
"of the gold reticle in image coordinates (pixel units)"))
y_offset = Int(
required=True,
description=("Y offset from the top-left of the image to the center "
"of the gold reticle in image coordinates (pixel units)"))
rotation = Float(
required=True,
description=("Rotation of the image relative to gold reticle in "
"degrees"))
pixel_size_um = Float(required=True,
description="Linear dimension of a pixel in microns")
acquired_at = DateTime(required=True, description="Acquisition timestamp")
stage_image_rotation = Float(
description="Rotation of stage relative to image")
stage_x = Float(description="X position of stage when image was acquired")
stage_y = Float(description="Y position of stage when image was acquired")
stage_z = Float(description="Z position of stage when image was acquired")
home_offset_x = Int(
description="X offset between reticle home and vasculature home")
home_offset_y = Int(
description="Y offset between reticle home and vasculature home")
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 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 RenderSectionAtScaleParameters(RenderParameters):
input_stack = Str(
required=True,
description='Input stack to make the downsample version of')
image_directory = OutputDir(
required=True,
description='Directory to save the downsampled sections')
imgformat = Str(required=False,
default="png",
missing="png",
description='Image format (default - png)')
doFilter = Boolean(required=False,
default=True,
missing=True,
description='Apply filtering before rendering')
fillWithNoise = Boolean(
required=False,
default=False,
missing=False,
description='Fill image with noise (default - False)')
scale = Float(required=True,
description='scale of the downsampled sections')
minZ = Int(required=False,
default=-1,
missing=-1,
description='min Z to create the downsample section from')
maxZ = Int(required=False,
default=-1,
missing=-1,
description='max Z to create the downsample section from')
filterListName = Str(
required=False,
description=("Apply specified filter list to all renderings"))
bounds = Nested(Bounds, required=False, default=None, missing=None)
use_stack_bounds = Boolean(
required=False,
default=False,
missing=False,
description=
'Do you want to use stack bounds while downsampling?. Default=False')
pool_size = Int(required=False,
default=20,
missing=20,
description='number of parallel threads to use')
@post_load
def validate_data(self, data):
# FIXME will be able to remove with render-python tweak
if data.get('filterListName') is not None:
warnings.warn(
"filterListName not implemented -- will use default behavior",
UserWarning)
class Kilosort2Parameters(DefaultSchema):
trange = String(required=False, default='[0 Inf]', help='Time range in seconds to process')
fproc = String(required=False, default="fullfile('D:\kilosort_datatemp', 'temp_wh.dat')", help='Processed data file on a fast ssd')
KSver = String(required=False, default='2.5', help='kilsort version: 2.0 (tracking) or 2.5(data shift correction)')
remDup = Int(required=False, default=0, help='KS2 remove duplicates')
finalSplits = Int(required=False, default=1, help='KS2 final splits by SVD')
labelGood = Int(required=False, default=1, help='KS2 noise cluster detection')
saveRez = Int(required=False, default=1, help='KS2 save rez.mat file')
copy_fproc = Int(required=False, default=1, help='Copy processed binary to output directory')
chanMap = String(required=False, default="'chanMap.mat'", help='path to channel map .mat file')
fshigh = Int(required=False, default=150, help='frequency for high pass filtering')
minfr_goodchannels = Float(required=False, default=0.1, help='minimum firing rate on a "good" channel (0 to skip)')
Th = String(required=False, default='[10 4]', help='threshold (last pass can be lower')
lam = Int(required=False, default=10, help='how important is the amplitude penalty (like in Kilosort1, 0 means not used, 10 is average, 50 is a lot)')
AUCsplit = Float(required=False, default=0.9, help='splitting a cluster at the end requires at least this much isolation for each sub-cluster (max = 1)')
minFR = Float(required=False, default=1/50., help='minimum spike rate (Hz)')
momentum = String(required=False, default='[20 400]', help='number of samples to average over (annealed)')
sigmaMask = Int(required=False, default=30, help='spatial constant in um for computing residual variance of spike')
ThPre = Int(required=False, default=8, help='threshold crossings for pre-clustering (in PCA projection space)')
spkTh = Int(required=False, default=-6, help='spike threshold is standard deviations')
reorder = Int(required=False, default=1, help='whether to reorder batches for drift correction')
nskip = Int(required=False, default=25, help='how many batches to skip for determining spike PCs')
GPU = Int(required=False, default=1, help='whether to run this code on an Nvidia GPU')
nfilt_factor = Int(required=False, default=4, help='max number of clusters per good channel (even temporary ones)')
ntbuff = Int(required=False, default=64, help='samples of symmetrical buffer for whitening and spike detection')
NT = String(required=False, default='64*1024 + ops.ntbuff', help='this is the batch size; for GPU should be a multiple of 32 + ntbuff)')
whiteningRange = Int(required=False, default=32)
nSkipCov = Int(required=False, default=25, help='compute whitening matrix from every Nth batch')
scaleproc = Int(required=False, default=200, help='int16 scaling of whitenend data')
nPCs = Int(required=False, default=3, help='how many PCs to project the spikes into')
useRAM = Int(required=False, default=0, help='must be 0')
gain = Float(required=False, default=1, help='uV/bit to report amplitudes in uV')
CSBseed = Int(required=False, default=1, help='random seed for clusterSingleBatches')
LTseed = Int(required=False, default=1, help='random seed for learnTemplates')
nNeighbors = Int(required=False, default=32, help='number of channels to include in template')
class regularization(DefaultSchema):
default_lambda = Float(default=0.005,
description='common regularization value')
translation_factor = Float(
default=0.005,
description=("translation regularization factor."
" multiplies default_lambda"))
poly_factors = List(
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. multiplies default_lambda"))
thinplate_factor = Float(
required=False,
default=1e-5,
missing=1e-5,
description=("regularization factor for thin plate spline "
"control points. multiplies default_lambda."))
class QualityMetricsParams(DefaultSchema):
isi_threshold = Float(required=False,
default=0.0015,
help='Maximum time (in seconds) for ISI violation')
min_isi = Float(required=False,
default=0.00,
help='Minimum time (in seconds) for ISI violation')
num_channels_to_compare = Int(
required=False,
default=13,
help='Number of channels to use for computing PC metrics; must be odd')
max_spikes_for_unit = Int(
required=False,
default=500,
help='Number of spikes to subsample for computing PC metrics')
max_spikes_for_nn = Int(
required=False,
default=10000,
help='Further subsampling for NearestNeighbor calculation')
n_neighbors = Int(
required=False,
default=4,
help='Number of neighbors to use for NearestNeighbor calculation')
n_silhouette = Int(
required=False,
default=10000,
help='Number of spikes to use for calculating silhouette score')
drift_metrics_min_spikes_per_interval = Int(
required=False,
default=10,
help='Minimum number of spikes for computing depth')
drift_metrics_interval_s = Float(
required=False,
default=100,
help='Interval length is seconds for computing spike depth')
quality_metrics_output_file = String(
required=True, help='CSV file where metrics will be saved')
class PostprocessingParams(DefaultSchema):
within_unit_overlap_window = Float(
required=False,
default=0.000166,
help='Time window for removing overlapping spikes for one unit.')
between_unit_overlap_window = Float(
required=False,
default=0.000166,
help='Time window for removing overlapping spikes between two units.')
between_unit_dist_um = Int(
required=False,
default=5,
help=
'Number of channels (above and below peak channel) to search for overlapping spikes'
)
deletion_mode = String(required=False,
default='lowAmpCluster',
help='lowAmpCluster or deleteFirst')
include_pcs = Boolean(
required=False,
default=True,
help='Set to false if features were not saved with Phy output')
class QualityMetricsParams(DefaultSchema):
isi_threshold = Float(required=False,
default=0.0015,
help='Maximum time (in seconds) for ISI violation')
min_isi = Float(required=False,
default=0.00,
help='Minimum time (in seconds) for ISI violation')
max_radius_um = Int(required=False,
default=68,
help='Maximum radius for computing PC metrics, in um')
max_spikes_for_unit = Int(
required=False,
default=500,
help='Number of spikes to subsample for computing PC metrics')
max_spikes_for_nn = Int(
required=False,
default=10000,
help='Further subsampling for NearestNeighbor calculation')
n_neighbors = Int(
required=False,
default=4,
help='Number of neighbors to use for NearestNeighbor calculation')
n_silhouette = Int(
required=False,
default=10000,
help='Number of spikes to use for calculating silhouette score')
drift_metrics_min_spikes_per_interval = Int(
required=False,
default=10,
help='Minimum number of spikes for computing depth')
drift_metrics_interval_s = Float(
required=False,
default=100,
help='Interval length is seconds for computing spike depth')
include_pcs = Boolean(
required=False,
default=True,
help='Set to false if features were not saved with Phy output')
class CellFeatures(DefaultSchema):
blowout_mv = Float(description="blash", required=False, allow_none=True)
seal_gohm = Float(description="blash", allow_none=True)
electrode_0_pa = Float(description="blash", allow_none=True)
input_access_resistance_ratio = Float(description="blash", allow_none=True)
input_resistance_mohm = Float(description="blash", allow_none=True)
initial_access_resistance_mohm = Float(description="blash",
allow_none=True)
class ConsolidateTransformsParameters(RenderParameters):
stack = Str(required=True, description='stack to consolidate')
postfix = Str(
required=False,
default="_CONS",
description=
'postfix to add to stack name on saving if no output defined (default _CONS)'
)
transforms_slice = Slice(
required=True,
description="a string representing a slice describing \
the set of transforms to be consolidated (i.e. 1:)"
)
output_stack = Str(
required=False,
description='name of output stack (default to adding postfix to input)'
)
pool_size = Int(
required=False,
default=10,
description='name of output stack (default to adding postfix to input)'
)
minZ = Float(
required=False,
description=
"""minimum z to consolidate in read in from stack and write to output_stack\
default to minimum z in stack""")
maxZ = Float(
required=False,
description=
"""minimaximummum z to consolidate in read in from stack and write to output_stack\
default to maximum z in stack""")
overwrite_zlayer = Boolean(
required=False,
default=False,
description=("whether to remove the existing layer from the "
"target stack before uploading."))
close_stack = Boolean(required=False, default=False)
class SIFTParameters(DefaultSchema):
initialSigma = Float(
required=True,
description="initial sigma value for SIFT's gaussian blur, in pixels")
steps = Int(required=True, description='steps per SIFT scale octave')
minOctaveSize = Int(
required=True,
description='minimum image size used for SIFT octave, in pixels')
maxOctaveSize = Int(
required=True,
description='maximum image size used for SIFT octave, in pixels')
fdSize = Int(required=True, description='SIFT feature descriptor size')
fdBins = Int(required=True,
description='SIFT feature descriptor orientation bins')
class PointMatchFilteringOptions(DefaultSchema):
NumRandomSamplingsMethod = Str(
required=False,
default="Desired confidence",
missing="Desired confidence",
description="Numerical random sampling method")
MaximumRandomSamples = Int(required=False,
default=5000,
missing=5000,
description="Max number of random samples")
DesiredConfidence = Float(
required=False,
default=99.9,
missing=99.9,
description="Desired confidence value for point match filtering")
Transform = Str(required=False,
default="Affine",
missing="Affine",
description="RANSAC model to fit")
PixelDistanceThreshold = Float(required=False,
default=0.1,
missing=0.1,
description="Pixel distance threshold")
class RansacParameters(DefaultSchema):
minimum_points_for_fit = Int(
default=EllipseFitter.DEFAULT_MINIMUM_POINTS_FOR_FIT,
description="Number of points required to fit data")
number_of_close_points = Int(
default=EllipseFitter.DEFAULT_NUMBER_OF_CLOSE_POINTS,
description=("Number of candidate outliers reselected as inliers "
"required to consider a good fit"))
threshold = Float(
default=EllipseFitter.DEFAULT_THRESHOLD,
description=("Error threshold below which data should be considered "
"an inlier"))
iterations = Int(default=EllipseFitter.DEFAULT_ITERATIONS,
description="Number of iterations to run")
class MeanWaveformParams(DefaultSchema):
samples_per_spike = Int(required=True,
default=82,
help='Number of samples to extract for each spike')
pre_samples = Int(
required=True,
default=20,
help='Number of samples between start of spike and the peak')
num_epochs = Int(required=True,
default=1,
help='Number of epochs to compute mean waveforms')
spikes_per_epoch = Int(require=True,
default=100,
help='Max number of spikes per epoch')
upsampling_factor = Float(
require=False,
default=200 / 82,
help='Upsampling factor for calculating waveform metrics')
spread_threshold = Float(
require=False,
default=0.12,
help='Threshold for computing channel spread of 2D waveform')
site_range = Int(require=False,
default=16,
help='Number of sites to use for 2D waveform metrics')
cWaves_path = InputDir(require=False,
help='directory containing the TPrime executable.')
use_C_Waves = Bool(require=False,
default=False,
help='Use faster C routine to calculate mean waveforms')
snr_radius = Int(
require=False,
default=8,
help='disk radius (chans) about pk-chan for snr calculation in C_waves'
)
mean_waveforms_file = String(required=True,
help='Path to mean waveforms file (.npy)')
class PointMatchParameters(DefaultSchema):
NumRandomSamplingsMethod = Str(
required=False,
default="Desired confidence",
missing="Desired confidence",
description='Numerical Random sampling method')
MaximumRandomSamples = Int(required=False,
default=5000,
missing=5000,
description='Maximum number of random samples')
DesiredConfidence = Float(required=False,
default=99.9,
missing=99.9,
description='Desired confidence level')
PixelDistanceThreshold = Float(
required=False,
default=0.1,
missing=0.1,
description='Pixel distance threshold for filtering')
Transform = Str(required=False,
default="AFFINE",
missing="AFFINE",
description="Transformation type parameter for point "
"match filtering (default AFFINE)")