class MergeStacksParameters(RenderParameters):
stack1 = Str(required=True,
metadata={'description': 'first stack to merge'})
stack2 = Str(required=True,
metadata={'description': 'second stack to merge'})
output_stack = Str(required=True,
metadata={'description': 'stack to save answer into'})
zmin = Int(required=False, metadata={'description': 'zvalue to start'})
zmax = Int(required=False, metadata={'description': 'zvalue to end'})
z_intersection = Boolean(
required=False,
default=False,
metadata={
'description':
'only output z values that appears in both stacks\
(default False, output z values in either stack)'
})
pool_size = Int(required=False,
default=20,
metadata={
'description':
'size of pool for parallel processing (default=20)'
})
class FitTransformsByPointMatchParameters(RenderParameters):
dst_stack = Str(required=True,
description= 'stack with tiles in the desired destination space')
src_stack = Str(required=True,
description='stack with tiles in the input space')
output_stack = Str(required=True,
description= 'name to call output version of \
input_stack_src_space with a transform added to bring it \
into the destination space')
matchcollection = Str(required=True,
description = "point match collection expressing point matches between the stacks")
num_local_transforms = Int(required=True,
description="number of local transforms to preserver, \
assumes point matches written down after such local transforms")
transform_type = Str(required = False, default = 'affine',
validate = mm.validate.OneOf(["affine","rigid"]),
description = "type of transformation to fit")
pool_size = Int(required=False, default=20,
description= 'degree of parallelism (default 20)')
setz = Boolean(required=False, default = True,
description="whether to change z's to the destination stack")
class FlipStackParameters(RenderParameters):
input_stack = Str(required=True,
metadata={'description': 'stack to apply affine to'})
output_stack = Str(
required=False,
metadata={
'description':
'stack to save answer into (defaults to overwriting input_stack)'
})
minZ = Int(required=True, metadata={'description': 'minimum Z to flip'})
maxZ = Int(required=True, metadata={'description': 'maximum Z to flip'})
pool_size = Int(required=False,
default=20,
metadata={
'description':
'size of pool for parallel processing (default=20)'
})
delete_after = Boolean(
required=False,
default=False,
metadata={
'description':
'whether to delete the old image files or not after flipping'
})
class BigFetaSchema(ArgSchema):
"""The input schema used by the BigFeta solver
"""
first_section = Int(required=True,
description='first section for matrix assembly')
last_section = Int(required=True,
description='last section for matrix assembly')
n_parallel_jobs = Int(
default=4,
required=False,
description=("number of parallel jobs that will run for "
"retrieving tilespecs, assembly from pointmatches, "
"and import_tilespecs_parallel"))
processing_chunk_size = Int(
default=1,
required=False,
description=("number of pairs per multiprocessing job. can help "
"parallelizing pymongo calls."))
solve_type = String(default='montage',
required=False,
description='Solve type options (montage, 3D)',
validator=mm.validate.OneOf(['montage', '3D']))
close_stack = Boolean(default=True,
required=False,
description='Set output stack to state COMPLETE?')
overwrite_zlayer = Boolean(
default=True,
required=False,
description='delete section before import tilespecs?')
profile_data_load = Boolean(
default=False,
description="module will raise exception after timing tilespec read")
transformation = String(default='AffineModel',
validate=mm.validate.OneOf([
'AffineModel', 'SimilarityModel',
'Polynomial2DTransform', 'affine', 'rigid',
'affine_fullsize', 'RotationModel',
'TranslationModel', 'ThinPlateSplineTransform'
]),
description="transformation to use for the solve")
fullsize_transform = Boolean(default=False,
description='use fullsize affine transform')
poly_order = Int(default=2,
required=False,
description='order of polynomial transform.')
output_mode = String(
default='none',
validate=mm.validate.OneOf(['none', 'hdf5', 'stack']),
description=("none: just solve and show logging output\n"
"hdf5: assemble to hdf5_options.output_dir\n"
"stack: write to output stack"))
assemble_from_file = String(
default='',
description=("path to an hdf5 file for solving from hdf5 output."
"mainly for testing purposes. hdf5 output usually to "
"be solved by external solver"))
ingest_from_file = String(
default='',
description='path to an hdf5 file output from the external solver.')
render_output = String(default='null',
description=("anything besides the default will "
"show all the render stderr/stdout"))
input_stack = Nested(
input_stack, description=("specifies the origin of the tilespecs."))
output_stack = Nested(
output_stack,
description=("specifies the destination of the tilespecs."))
pointmatch = Nested(
pointmatch,
description=("specifies the origin of the point correspondences"))
hdf5_options = Nested(
hdf5_options,
description=("options invoked if output_mode is \"hdf5\""))
matrix_assembly = Nested(
matrix_assembly,
description=("options that control which correspondences are"
" included in the matrix equation and their weights"))
regularization = Nested(
regularization,
description=("options that contol the regularization of different"
" types of variables in the solve"))
transform_apply = List(
Int,
default=[],
missing=[],
description=("tilespec.tforms[i].tform() for i in transform_apply "
"will be performed on the matches before matrix "
"assembly."))
@mm.post_load
def validate_data(self, data):
if (data['regularization']['poly_factors'] is not None) & \
(data['transformation'] == 'Polynomial2DTransform'):
n = len(data['regularization']['poly_factors'])
if n != data['poly_order'] + 1:
raise mm.ValidationError(
"regularization.poly_factors must be a list"
" of length poly_order + 1")
class RemapZsParameters(StackTransitionParameters):
remap_sectionId = Boolean(required=False)
new_zValues = List(Int, required=True)
class ManualSweepState(DefaultSchema):
sweep_number = Integer(description="sweep number", required=True)
passed = Boolean(description="manual override state", required=True)
class FxSweepFeatures(SweepFeatures):
passed = Boolean(description="qc passed or failed", required=True)
class EMA_Schema(ArgSchema):
first_section = Int(
required=True,
description='first section for matrix assembly')
last_section = Int(
required=True,
description='last section for matrix assembly')
n_parallel_jobs = Int(
default=4,
required=False,
description='number of parallel jobs that will run for assembly')
solve_type = String(
default='montage',
required=False,
description='Solve type options (montage, 3D) Default=montage')
close_stack = Boolean(
default=True,
required=False,
description='Close the output stack? - default - True')
overwrite_zlayer = Boolean(
default=True,
required=False,
description='delete section before import tilespecs?')
profile_data_load = Boolean(
default=False)
transformation = String(
default='AffineModel',
validate=lambda x: x in [
'AffineModel', 'SimilarityModel', 'Polynomial2DTransform',
'affine', 'rigid', 'affine_fullsize'])
fullsize_transform = Boolean(
default=False,
description='use fullsize affine transform')
poly_order = Int(
default=3,
required=False,
description='order of polynomial transform')
output_mode = String(
default='hdf5')
assemble_from_file = String(
default='',
description='fullpath to solution_input.h5')
ingest_from_file = String(
default='',
description='fullpath to solution_output.h5')
render_output = String(
default='null',
description=("/path/to/file, null (devnull), or "
"stdout for where to redirect render output"))
input_stack = Nested(stack)
output_stack = Nested(stack)
pointmatch = Nested(pointmatch)
hdf5_options = Nested(hdf5_options)
matrix_assembly = Nested(matrix_assembly)
regularization = Nested(regularization)
showtiming = Int(
default=1,
description='have the routine showhow long each process takes')
log_level = String(
default="INFO",
description='logging level')
@post_load
def validate_data(self, data):
if (data['regularization']['poly_factors'] is not None) & \
(data['transformation'] == 'Polynomial2DTransform'):
n = len(data['regularization']['poly_factors'])
if n != data['poly_order'] + 1:
raise ValidationError(
"regularization.poly_factors must be a list"
" of length poly_order + 1")
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.')
truncate_timestamps = Boolean(default=True,
description=('If True, truncate sync '
'timestamps whenever unusually '
'large gapes occur; '
'Default=True'))
class MakeMontageScapeSectionStackParameters(OutputStackParameters):
montage_stack = Str(
required=True,
metadata={'description': 'stack to make a downsample version of'})
image_directory = Str(
required=True,
metadata={'description': 'directory that stores the montage scapes'})
set_new_z = Boolean(
required=False,
default=False,
missing=False,
metadata={
'description':
'set to assign new z values starting from 0 (default - False)'
})
new_z_start = Int(required=False,
default=0,
missing=0,
metadata={'description': 'new starting z index'})
remap_section_ids = Boolean(
required=False,
default=False,
missing=False,
metadata={
'description':
'change section ids to new z values. default = False'
})
imgformat = Str(required=False,
default='tif',
missing='tif',
metadata={
'description':
'image format of the montage scapes (default - tif)'
})
scale = Float(required=True,
metadata={'description': 'scale of montage scapes'})
apply_scale = Boolean(
required=False,
default=False,
missing=False,
metadata={
'description':
'Do you want to scale the downsample to the size of section? Default = False'
})
doFilter = Boolean(
required=False,
default=True,
description=("whether to apply default filtering when generating "
"missing downsamples"))
level = Int(
required=False,
default=1,
description=(
"integer mipMapLevel used to generate missing downsamples"))
fillWithNoise = Boolean(
required=False,
default=False,
description=("Whether to fill the background pixels with noise when "
"generating missing downsamples"))
memGB_materialize = Str(
required=False,
default='12G',
description=("Java heap size in GB for materialization"))
pool_size_materialize = Int(
required=False,
default=1,
description=("number of processes to generate missing downsamples"))
filterListName = Str(
required=False,
description=("Apply specified filter list to all renderings"))
uuid_prefix = Boolean(
required=False,
default=True,
description=(
"Prepend uuid to generated tileIds to prevent collisions"))
uuid_length = Int(
required=False,
default=10,
description=("length of uuid4 string used in uuid prefix"))
@post_load
def validate_data(self, data):
if data['set_new_z'] and data['new_z_start'] < 0:
raise ValidationError('new Z start cannot be less than zero')
elif not data['set_new_z']:
data['new_z_start'] = min(data['zValues'])
# 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 db_params(DefaultSchema):
owner = String(
default='',
required=False,
description='render or mongo owner')
project = String(
default='',
required=False,
description='render or mongo project')
name = List(
String,
cli_as_single_argument=True,
required=False,
many=True,
description='render or mongo collection name')
host = String(
required=False,
description='render host')
port = Int(
default=8080,
required=False,
description='render port')
mongo_host = String(
default='em-131fs',
required=False,
description='mongodb host')
mongo_port = Int(
default=27017,
required=False,
description='mongodb port')
mongo_userName = String(
default='',
required=False,
description='mongo user name')
mongo_authenticationDatabase = String(
default='',
required=False,
description='mongo admin db')
mongo_password = String(
default='',
required=False,
description='mongo pwd')
db_interface = String(
default='mongo',
validator=mm.validate.OneOf(['render', 'mongo', 'file']),
description=("render: read or write via render\n"
"mongo: read or write via pymongo\n"
"file: read or write to file"))
client_scripts = String(
default=("/allen/aibs/pipeline/image_processing/"
"volume_assembly/render-jars/production/scripts"),
required=False,
description='see renderapi.render.RenderClient')
memGB = String(
required=False,
default='5G',
description='see renderapi.render.RenderClient')
validate_client = Boolean(
required=False,
default=False,
description='see renderapi.render.RenderClient')
@mm.pre_load
def tolist(self, data):
if 'name' in data:
if not isinstance(data['name'], list):
data['name'] = [data['name']]