class InputSchema(ArgSchema):
class Meta:
unknown = RAISE
log_level = LogLevel(default='INFO',
description='set the logging level of the module')
files = Nested(FileToCopy,
many=True,
required=True,
description='files to be copied')
use_rsync = Boolean(
default=True,
description=
'copy files using rsync rather than shutil (this is not likely to work if you are running windows!)'
)
hasher_key = String(
default='sha256',
validate=lambda st: st in available_hashers,
allow_none=True,
description=
'select a hash function to compute over base64-encoded pre- and post-copy files'
)
raise_if_comparison_fails = Boolean(
default=True,
description='if a hash comparison fails, throw an error (vs. a warning)'
)
make_parent_dirs = Boolean(
default=True,
description='build missing parent directories for destination')
chmod = Int(
default=775,
description=
"destination files (and any created parents will have these permissions"
)
class SessionUploadInputSchema(ArgSchema, NonFileParameters):
log_level = LogLevel(default='INFO',
description='set the logging level of the module')
files = Nested(FileToCopy,
many=True,
required=True,
description='files to be copied')
class InputSchema(ArgSchema):
class Meta:
unknown = RAISE
log_level = LogLevel(default='INFO', description='set the logging level of the module')
rule = String(default='run', required=False)
dockerfile = String(required=True, validate=check_read_access, description='Dockerfile for image')
modelfile = String(required=True, validate=check_read_access, description='Zip file for model')
video_input_file = String(required=True, validate=check_read_access, description='Eye tracking movie')
ellipse_output_data_file = String(required=True, validate=check_write_access_overwrite, description='write outputs to here')
ellipse_output_video_file = String(required=False, validate=check_write_access_overwrite, description='write outputs to here')
points_output_video_file = String(required=False, validate=check_write_access_overwrite, description='write outputs to here')
class InputSchema(ArgSchema):
class Meta:
unknown = mm.RAISE
log_level = LogLevel(default='INFO',
description='Logging level of the module')
session_data = Nested(BehaviorSessionData,
required=True,
description='Data pertaining to a behavior session')
output_path = String(required=True,
validate=check_write_access_overwrite,
description='Path of output.json to be written')
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 InputSchema(ArgSchema):
class Meta:
unknown = RAISE
log_level = LogLevel(default='INFO',
description='set the logging level of the module')
session_data = Nested(
SessionData,
required=True,
description='records of the individual probes used for this experiment'
)
output_path = String(required=True,
validate=check_write_access_overwrite,
description='write outputs to here')
class InputSchema(ArgSchema):
class Meta:
unknown = RAISE
log_level = LogLevel(default='INFO',
description='set the logging level of the module')
session_data = Nested(SessionData, required=True,
description='records of the individual probes '
'used for this experiment')
output_path = String(required=True, validate=check_write_access_overwrite,
description='write outputs to here')
skip_eye_tracking = Boolean(
required=True, default=False,
description="Whether or not to skip processing eye tracking data. "
"If True, no eye tracking data will be written to NWB")
class InputSchema(ArgSchema):
class Meta:
unknown = RAISE
log_level = LogLevel(default='INFO',
description='set the logging level of the module')
motion_border = Nested(MotionBorder,
required=True,
description='border widths - pixels outside the '
'border are considered invalid')
storage_directory = String(required=True,
description='used to set output directory')
motion_corrected_stack = String(required=True,
description='path to h5 file containing '
'motion corrected image stack')
rois = Nested(Roi,
many=True,
description='specifications of individual regions of '
'interest')
log_0 = String(required=True,
description='path to motion correction output csv') #
class TraceExtractionInputSchema(ArgSchema):
log_level = LogLevel(default='INFO',
description="set the logging level of the module")
motion_border = Nested(
MotionBorder,
required=True,
description=("border widths - pixels outside the border are "
"considered invalid"))
storage_directory = OutputDir(required=True,
description="used to set output directory")
motion_corrected_stack = H5InputFile(
required=True,
description="path to h5 file containing motion corrected image stack")
rois = Nested(
ExtractROISchema,
many=True,
description="specifications of individual regions of interest")
log_0 = InputFile(
required=False,
description=("path to motion correction output csv. "
"NOTE: not used, but provided by LIMS schema."))
class InputSchema(ArgSchema):
# ============== Required fields ==============
input_file = InputFile(
required=True,
description=('An h5 file containing ellipses fits for '
'eye, pupil, and corneal reflections.'))
session_sync_file = InputFile(
required=True,
description=('An h5 file containing timestamps to synchronize '
'eye tracking video frames with rest of ephys '
'session events.'))
output_file = OutputFile(
required=True,
description=('Full save path of output h5 file that '
'will be created by this module.'))
monitor_position_x_mm = Float(required=True,
description=("Monitor center X position in "
"'global' coordinates "
"(millimeters)."))
monitor_position_y_mm = Float(required=True,
description=("Monitor center Y position in "
"'global' coordinates "
"(millimeters)."))
monitor_position_z_mm = Float(required=True,
description=("Monitor center Z position in "
"'global' coordinates "
"(millimeters)."))
monitor_rotation_x_deg = Float(required=True,
description="Monitor X rotation in degrees")
monitor_rotation_y_deg = Float(required=True,
description="Monitor Y rotation in degrees")
monitor_rotation_z_deg = Float(required=True,
description="Monitor Z rotation in degrees")
camera_position_x_mm = Float(required=True,
description=("Camera center X position in "
"'global' coordinates "
"(millimeters)"))
camera_position_y_mm = Float(required=True,
description=("Camera center Y position in "
"'global' coordinates "
"(millimeters)"))
camera_position_z_mm = Float(required=True,
description=("Camera center Z position in "
"'global' coordinates "
"(millimeters)"))
camera_rotation_x_deg = Float(required=True,
description="Camera X rotation in degrees")
camera_rotation_y_deg = Float(required=True,
description="Camera Y rotation in degrees")
camera_rotation_z_deg = Float(required=True,
description="Camera Z rotation in degrees")
led_position_x_mm = Float(required=True,
description=("LED X position in 'global' "
"coordinates (millimeters)"))
led_position_y_mm = Float(required=True,
description=("LED Y position in 'global' "
"coordinates (millimeters)"))
led_position_z_mm = Float(required=True,
description=("LED Z position in 'global' "
"coordinates (millimeters)"))
equipment = String(required=True,
description=('String describing equipment setup used '
'to acquire eye tracking videos.'))
date_of_acquisition = String(required=True,
description='Acquisition datetime string.')
eye_video_file = InputFile(required=True,
description=('Full path to raw eye video '
'file (*.avi).'))
# ============== Optional fields ==============
eye_radius_cm = Float(default=0.1682,
description=('Radius of tracked eye(s) in '
'centimeters.'))
cm_per_pixel = Float(default=(10.2 / 10000.0),
description=('Centimeter per pixel conversion '
'ratio.'))
log_level = LogLevel(default='INFO',
description='Set the logging level of the module.')
class InputSchema(ArgSchema):
class Meta:
unknown = mm.RAISE
log_level = LogLevel(default="INFO",
help="set the logging level of the module")
output_path = String(
required=True,
validate=check_write_access,
help="write outputs to here",
)
session_id = Int(required=True,
help="unique identifier for this ecephys session")
session_start_time = DateTime(
required=True,
help="the date and time (iso8601) at which the session started",
)
stimulus_table_path = String(
required=True,
validate=check_read_access,
help="path to stimulus table file",
)
invalid_epochs = Nested(InvalidEpoch,
many=True,
required=True,
help="epochs with invalid data")
probes = Nested(
Probe,
many=True,
required=True,
help="records of the individual probes used for this experiment",
)
running_speed_path = String(
required=True,
help=
"data collected about the running behavior of the experiment's subject",
)
session_sync_path = String(
required=True,
validate=check_read_access,
help=
"Path to an h5 experiment session sync file (*.sync). This file relates events from different acquisition modalities to one another in time."
)
eye_tracking_rig_geometry = Dict(
required=True,
help=
"Mapping containing information about session rig geometry used for eye gaze mapping."
)
eye_dlc_ellipses_path = String(
required=True,
validate=check_read_access,
help=
"h5 filepath containing raw ellipse fits produced by Deep Lab Cuts of subject eye, pupil, and corneal reflections during experiment"
)
eye_gaze_mapping_path = String(
required=False,
allow_none=True,
help=
"h5 filepath containing eye gaze behavior of the experiment's subject")
pool_size = Int(
default=3,
help="number of child processes used to write probewise lfp files")
optotagging_table_path = String(
required=False,
validate=check_read_access,
help=
"file at this path contains information about the optogenetic stimulation applied during this "
)
session_metadata = Nested(
SessionMetadata,
allow_none=True,
required=False,
help="miscellaneous information describing this session")