class SubImage(RaisingSchema):
specimen_tissue_index = Int()
dimensions = Nested(ImageDimensions)
spacing = Nested(ImageSpacing)
segmentation_paths = Dict()
intensity_paths = Dict()
polygons = Dict()
class InputParameters(ArgSchema):
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 syncronization information")
output_stimulus_table_path = String(
required=True,
help="the output stimulus table csv will be written here")
output_frame_times_path = String(required=True,
help="output all frame times here")
minimum_spontaneous_activity_duration = Float(
default=sys.float_info.epsilon,
help=
"detected spontaneous activity sweeps will be rejected if they last fewer that this many seconds",
)
maximum_expected_spontanous_activity_duration = Float(
default=1225.02541,
help=
"validation will fail if a spontanous activity epoch longer than this one is computed.",
)
frame_time_strategy = String(
default="use_photodiode",
help=
"technique used to align frame times. Options are 'use_photodiode', which interpolates frame times between photodiode edge times (preferred when vsync times are unreliable) and 'use_vsyncs', which is preferred when reliable vsync times are available.",
)
stimulus_name_map = Dict(keys=String(),
values=String(),
help="optionally rename stimuli",
default=default_stimulus_renames)
column_name_map = Dict(keys=String(),
values=String(),
help="optionally rename stimulus parameters",
default=default_column_renames)
extract_const_params_from_repr = Bool(default=True)
drop_const_params = List(
String(),
help="columns to be dropped from the stimulus table",
default=["name", "maskParams", "win", "autoLog", "autoDraw"],
)
fail_on_negative_duration = Bool(
default=False,
help=
"Determine if the module should fail if a stimulus epoch has a negative duration."
)
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 TransformParameters(DefaultSchema):
type = Str(
required=True,
validator=OneOf(["leaf", "interpolated", "list", "ref"]),
description=('Transform type as defined in Render Transform Spec. '
'This module currently expects a "leaf"'))
className = Str(required=True, description='mpicbg-compatible className')
dataString = Str(required=True, description='mpicbg-compatible dataString')
metaData = Dict(required=False,
description="in this schema, otherwise will be stripped")
class OutputParameters(DefaultSchema):
inputs = Nested(
InputParameters,
description="The parameters argued to this executable",
required=True
)
results = Dict(
description="The outputs of feature extraction",
required=True
)
class InputParameters(ArgSchema):
opto_pickle_path = String(
required=True, help='path to file containing optotagging information')
sync_h5_path = String(
required=True,
help='path to h5 file containing syncronization information')
output_opto_table_path = String(
required=True,
help='the optotagging stimulation table will be written here')
conditions = Dict(String, Nested(Condition), default=known_conditions)
class SolverSchema(ArgSchema):
data = Nested(DataLoaderSchema)
transform = Dict(required=True,
description="dict containing transform specification")
leave_out_index = Int(required=False,
missing=None,
default=None,
description="index to leave out of data")
output_dir = OutputDir(
required=False,
missing=None,
default=None,
description="path for writing output json of transform")
class OutputParameters(DefaultSchema):
""" Outputs produced by attach_metadata
"""
inputs = Nested(
InputParameters,
description="The parameters argued to this executable",
required=True
)
sinks = Dict(
description="The sinks to which metadata was attached",
required=True,
many=True
)
class MorphologySummaryParameters(ArgSchema):
pia_transform = Dict(description="input pia transform", required=True)
relative_soma_depth = Float(desription="input relative soma depth",
required=False)
soma_depth = Float(description="input soma depth", required=True)
swc_file = InputFile(description="input swc file", required=True)
thumbnail_file = OutputFile(description="output thumbnail file",
required=True)
cortex_thumbnail_file = OutputFile(
description="output cortex thumbnail file", required=True)
normal_depth_thumbnail_file = OutputFile(
description="output normal depth thumbnail file", required=True)
high_resolution_thumbnail_file = OutputFile(
description="output high resolution cortex thumbnail file",
required=True)
class SparkOptions(argschema.schemas.DefaultSchema):
jarfile = Str(required=True, description=(
"spark jar to call java spark command"))
className = Str(required=True, description=(
"spark class to call"))
driverMemory = Str(required=False, default='6g', description=(
"spark driver memory (important for local spark)"))
memory = Str(
required=False,
description="Memory required for spark job")
sparkhome = InputDir(required=True, description=(
"Spark home directory containing bin/spark_submit"))
spark_files = List(InputFile, required=False, description=(
"list of spark files to add to the spark submit command"))
spark_conf = Dict(required=False, description=(
"dictionary of key value pairs to add to spark_submit "
"as --conf key=value"))
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 RenderSchema(DefaultSchema):
protocol = Str(
default="http",
help="Protocol to connect to render with (http or https)",
required=False)
port = Int(default=80, required=False)
encoding = Str(
default="jpg",
help="Encoding option for the neuroglancer render datasource (jpg or raw16)",
required=False)
all_channels = Boolean(default=False,
help="Use Render API to query for and load all channels",
required=False)
alt_render = Str(
default="",
help="Alternate render host to use for vizrelay API calls [to work in Docker]",
required=False)
enable_one_channel = Boolean(default=False,
help="Enable only one of the channels",
required=False)
channel_name_shader_sub = Dict(default={},
help="Dictionary of CHANNEL_NAME : { SUB_NAME : SUB_VALUE }",
required=False)
class SessionData(RaisingSchema):
ophys_experiment_id = Int(required=True,
description='unique identifier for this ophys '
'session')
ophys_session_id = Int(required=True,
description='The ophys session id that the ophys '
'experiment to be written to NWB is '
'from')
behavior_session_id = Int(required=True,
description='The behavior session id that the '
'ophys experiment to be written to '
'written to NWB is from')
foraging_id = String(required=True,
description='The foraging id associated with the '
'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')
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')
eye_tracking_rig_geometry = Dict(
required=True,
description="Mapping containing information about session rig "
"geometry used for eye gaze mapping."
)
eye_tracking_filepath = String(
required=True,
validate=check_read_access,
description="h5 filepath containing eye tracking ellipses"
)
events_file = InputFile(
required=True,
description='h5 filepath to events data'
)
imaging_plane_group = Int(
required=True,
allow_none=True,
description="A numeric index that indicates the order that the "
"frames were acquired when dealing with an imaging plane "
"in a mesoscope experiment. Will be None for Scientifica "
"experiments."
)
plane_group_count = Int(
required=True,
description="The total number of plane groups associated with the "
"ophys session that the experiment belongs to. Will be 0 "
"for Scientifica experiments and nonzero for Mesoscope "
"experiments."
)
class CellSpecimenTable(RaisingSchema):
cell_roi_id = Dict(String, Int, required=True)
cell_specimen_id = Dict(String, Int(allow_none=True), required=True)
x = Dict(String, Int, required=True)
y = Dict(String, Int, required=True)
max_correction_up = Dict(String, Float, required=True)
max_correction_right = Dict(String, Float, required=True)
max_correction_down = Dict(String, Float, required=True)
max_correction_left = Dict(String, Float, required=True)
valid_roi = Dict(String, Boolean, required=True)
height = Dict(String, Int, required=True)
width = Dict(String, Int, required=True)
mask_image_plane = Dict(String, Int, required=True)
roi_mask = Dict(String, List(List(Boolean)), required=True)
class TiffMetadataOutput(DefaultSchema):
input_tif = Str()
roi_metadata = Dict()
scanimage_metadata = Dict()
class OutputParameters(OutputSchema):
output_file_paths = Dict(required=True)
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")