def generate_neurons_io_handler(input_filename, output_filename,
parameters_filename):
"""
Uses generate_neurons to process a input_filename (HDF5 dataset) and outputs results to an output_filename (HDF5
dataset). Also,
Args:
input_filename HDF5 filename to read from (should be a path to a h5py.Dataset)
output_filename HDF5 filename to write to (should be a path to a h5py.Group)
parameters_filename JSON filename with parameters.
"""
# Extract and validate file extensions.
# Parse parameter filename and validate that the name is acceptable
parameters_filename_details = pathHelpers.PathComponents(
parameters_filename)
# Clean up the extension so it fits the standard.
if (parameters_filename_details.extension.lower().lstrip(os.extsep)
not in ["json"]):
raise Exception(
"Parameter file with filename: \"" + parameters_filename + "\"" +
" provided with an unknown file extension: \"" +
parameters_filename_details.extension +
"\". If it is a supported format, please run the given file through nanshe_converter first before proceeding."
)
# Parse the parameters from the json file.
parameters = xjson.read_parameters(parameters_filename)
if (len(parameters) == 1) and ("generate_neurons_blocks" in parameters):
generate_neurons_blocks(input_filename, output_filename,
**parameters["generate_neurons_blocks"])
else:
generate_neurons_a_block(input_filename, output_filename, **parameters)
def generate_neurons_io_handler(input_filename, output_filename, parameters_filename):
"""
Uses generate_neurons to process a input_filename (HDF5 dataset) and outputs results to an output_filename (HDF5
dataset). Also,
Args:
input_filename HDF5 filename to read from (should be a path to a h5py.Dataset)
output_filename HDF5 filename to write to (should be a path to a h5py.Group)
parameters_filename JSON filename with parameters.
"""
# Extract and validate file extensions.
# Parse parameter filename and validate that the name is acceptable
parameters_filename_details = pathHelpers.PathComponents(parameters_filename)
# Clean up the extension so it fits the standard.
if ( parameters_filename_details.extension.lower().lstrip(os.extsep) not in ["json"] ):
raise Exception("Parameter file with filename: \"" + parameters_filename + "\"" + " provided with an unknown file extension: \"" + parameters_filename_details.extension + "\". If it is a supported format, please run the given file through nanshe_converter first before proceeding.")
# Parse the parameters from the json file.
parameters = xjson.read_parameters(parameters_filename)
if (len(parameters) == 1) and ("generate_neurons_blocks" in parameters):
generate_neurons_blocks(input_filename, output_filename, **parameters["generate_neurons_blocks"])
else:
generate_neurons_a_block(input_filename, output_filename, **parameters)
def main(*argv):
"""
Simple main function (like in C). Takes all arguments (as from sys.argv) and returns an exit status.
Args:
argv(list): arguments (includes command line call).
Returns:
int: exit code (0 if success)
"""
# Only necessary if running main (normally if calling command line). No point in importing otherwise.
import argparse
argv = list(argv)
# Creates command line parser
parser = argparse.ArgumentParser(description = "Parses input from the command line for a batch job.")
parser.add_argument("format",
choices = ["tiff"],
help = "Format to convert from to HDF5.",
)
parser.add_argument("config_filename",
metavar = "CONFIG_FILE",
type = str,
help = "JSON file that provides configuration options for how to import TIFF(s)."
)
parser.add_argument("input_files",
metavar = "INPUT_FILE",
type = str,
nargs = "+",
help = "TIFF file paths (with optional regex e.g. \"./*.tif\")."
)
parser.add_argument("output_file",
metavar = "OUTPUT_FILE",
type = str,
nargs = 1,
help = "HDF5 file to export (this should include a path to where the internal dataset should be stored)."
)
# Results of parsing arguments (ignore the first one as it is the command line call).
parsed_args = parser.parse_args(argv[1:])
# Go ahead and stuff in parameters with the other parsed_args
parsed_args.parameters = xjson.read_parameters(parsed_args.config_filename)
if parsed_args.format == "tiff":
xtiff.convert_tiffs(parsed_args.input_files, parsed_args.output_file[0], **parsed_args.parameters)
return(0)
def main(*argv):
"""
Simple main function (like in C). Takes all arguments (as from sys.argv) and returns an exit status.
Args:
argv(list): arguments (includes command line call).
Returns:
int: exit code (0 if success)
"""
# Only necessary if running main (normally if calling command line). No point in importing otherwise.
import argparse
argv = list(argv)
# Creates command line parser
parser = argparse.ArgumentParser(description = "Parses input from the command line for a registration job.")
parser.add_argument("config_filename",
metavar = "CONFIG_FILE",
type = str,
help = "JSON file that provides configuration options for how to import TIFF(s)."
)
parser.add_argument("input_filenames",
metavar = "INPUT_FILE",
type = str,
nargs = "+",
help = "HDF5 file to import (this should include a path to where the internal dataset should be stored)."
)
parser.add_argument("output_filenames",
metavar = "OUTPUT_FILE",
type = str,
nargs = 1,
help = "HDF5 file to export (this should include a path to where the internal dataset should be stored)."
)
# Results of parsing arguments (ignore the first one as it is the command line call).
parsed_args = parser.parse_args(argv[1:])
# Go ahead and stuff in parameters with the other parsed_args
parsed_args.parameters = xjson.read_parameters(parsed_args.config_filename)
parsed_args.input_file_components = []
for each_input_filename in parsed_args.input_filenames:
parsed_args.input_file_components.append(PathComponents(each_input_filename))
parsed_args.output_file_components = []
for each_output_filename in parsed_args.output_filenames:
parsed_args.output_file_components.append(PathComponents(each_output_filename))
for each_input_filename_components, each_output_filename_components in itertools.izip(parsed_args.input_file_components, parsed_args.output_file_components):
with h5py.File(each_input_filename_components.externalPath, "r") as input_file:
with h5py.File(each_output_filename_components.externalPath, "a") as output_file:
data = input_file[each_input_filename_components.internalPath]
result_filename = registration.register_mean_offsets(
data, to_truncate=True, **parsed_args.parameters
)
with h5py.File(result_filename, "r") as result_file:
result_file.copy(
"reg_frames",
output_file[each_output_filename_components.internalDirectory],
name=each_output_filename_components.internalDatasetName
)
os.remove(result_filename)
os.removedirs(os.path.dirname(result_filename))
return(0)
def main(*argv):
"""
Simple main function (like in C). Takes all arguments (as from sys.argv) and returns an exit status.
Args:
argv(list): arguments (includes command line call).
Returns:
int: exit code (0 if success)
"""
# Only necessary if running main (normally if calling command line). No point in importing otherwise.
import argparse
argv = list(argv)
# Creates command line parser
parser = argparse.ArgumentParser(
description="Parses input from the command line for a batch job.")
parser.add_argument(
"format",
choices=["tiff"],
help="Format to convert from to HDF5.",
)
parser.add_argument(
"config_filename",
metavar="CONFIG_FILE",
type=str,
help=
"JSON file that provides configuration options for how to import TIFF(s)."
)
parser.add_argument(
"input_files",
metavar="INPUT_FILE",
type=str,
nargs="+",
help="TIFF file paths (with optional regex e.g. \"./*.tif\").")
parser.add_argument(
"output_file",
metavar="OUTPUT_FILE",
type=str,
nargs=1,
help=
"HDF5 file to export (this should include a path to where the internal dataset should be stored)."
)
# Results of parsing arguments (ignore the first one as it is the command line call).
parsed_args = parser.parse_args(argv[1:])
# Go ahead and stuff in parameters with the other parsed_args
parsed_args.parameters = xjson.read_parameters(parsed_args.config_filename)
if parsed_args.format == "tiff":
xtiff.convert_tiffs(parsed_args.input_files,
parsed_args.output_file[0],
**parsed_args.parameters)
return (0)
def main(*argv):
"""
Simple main function (like in C). Takes all arguments (as from
sys.argv) and returns an exit status.
Args:
argv(list): arguments (includes command line call).
Returns:
int: exit code (0 if success)
"""
# Only necessary if running main (normally if calling command line). No
# point in importing otherwise.
import argparse
argv = list(argv)
# Creates command line parser
parser = argparse.ArgumentParser(
description="Parses input from the command line " +
"for a registration job."
)
parser.add_argument("config_filename",
metavar="CONFIG_FILE",
type=str,
help="JSON file that provides configuration options " +
"for how to import TIFF(s)."
)
parser.add_argument("input_filenames",
metavar="INPUT_FILE",
type=str,
nargs=1,
help="HDF5 file to import (this should include a " +
"path to where the internal dataset should be " +
"stored)."
)
parser.add_argument("output_filenames",
metavar="OUTPUT_FILE",
type=str,
nargs=1,
help="HDF5 file to export (this should include a " +
"path to where the internal dataset should be " +
"stored)."
)
# Results of parsing arguments
# (ignore the first one as it is the command line call).
parsed_args = parser.parse_args(argv[1:])
# Go ahead and stuff in parameters with the other parsed_args
parsed_args.parameters = xjson.read_parameters(parsed_args.config_filename)
parsed_args.input_file_components = []
for each_input_filename in parsed_args.input_filenames:
parsed_args.input_file_components.append(
hdf5.serializers.split_hdf5_path(each_input_filename)
)
parsed_args.output_file_components = []
for each_output_filename in parsed_args.output_filenames:
parsed_args.output_file_components.append(
hdf5.serializers.split_hdf5_path(each_output_filename)
)
for each_input_filename_components, each_output_filename_components in iters.izip(
parsed_args.input_file_components, parsed_args.output_file_components):
with h5py.File(each_input_filename_components[0], "r") as input_file:
with h5py.File(each_output_filename_components[0], "a") as output_file:
data = input_file[each_input_filename_components[1]]
result_filename = registration.register_mean_offsets(
data, to_truncate=True, **parsed_args.parameters
)
with h5py.File(result_filename, "r") as result_file:
result_file.copy(
"reg_frames",
output_file[os.path.dirname(each_output_filename_components[1])],
name=each_output_filename_components[1]
)
if parsed_args.parameters.get("include_shift", False):
result_file.copy(
"space_shift",
output_file[os.path.dirname(each_output_filename_components[1])],
name=each_output_filename_components[1] + "_shift"
)
# Copy all attributes from raw data to the final result.
output = output_file[
each_output_filename_components[1]
]
for each_attr_name in data.attrs:
output.attrs[each_attr_name] = data.attrs[each_attr_name]
# Only remove the directory if our input or output files are
# not stored there.
os.remove(result_filename)
in_out_dirnames = set(
os.path.dirname(os.path.abspath(_.filename)) for _ in [
input_file, output_file
]
)
result_dirname = os.path.dirname(result_filename)
if result_dirname not in in_out_dirnames:
os.rmdir(result_dirname)
return(0)
def main(*argv):
"""
Simple main function (like in C). Takes all arguments (as from
sys.argv) and returns an exit status.
Args:
argv(list): arguments (includes command line call).
Returns:
int: exit code (0 if success)
"""
# Only necessary if running main (normally if calling command line). No
# point in importing otherwise.
import argparse
argv = list(argv)
# Creates command line parser
parser = argparse.ArgumentParser(
description="Parses input from the command line " +
"for a registration job.")
parser.add_argument("config_filename",
metavar="CONFIG_FILE",
type=str,
help="JSON file that provides configuration options " +
"for how to import TIFF(s).")
parser.add_argument("input_filenames",
metavar="INPUT_FILE",
type=str,
nargs=1,
help="HDF5 file to import (this should include a " +
"path to where the internal dataset should be " +
"stored).")
parser.add_argument("output_filenames",
metavar="OUTPUT_FILE",
type=str,
nargs=1,
help="HDF5 file to export (this should include a " +
"path to where the internal dataset should be " +
"stored).")
# Results of parsing arguments
# (ignore the first one as it is the command line call).
parsed_args = parser.parse_args(argv[1:])
# Go ahead and stuff in parameters with the other parsed_args
parsed_args.parameters = xjson.read_parameters(parsed_args.config_filename)
parsed_args.input_file_components = []
for each_input_filename in parsed_args.input_filenames:
parsed_args.input_file_components.append(
hdf5.serializers.split_hdf5_path(each_input_filename))
parsed_args.output_file_components = []
for each_output_filename in parsed_args.output_filenames:
parsed_args.output_file_components.append(
hdf5.serializers.split_hdf5_path(each_output_filename))
for each_input_filename_components, each_output_filename_components in iters.izip(
parsed_args.input_file_components,
parsed_args.output_file_components):
with h5py.File(each_input_filename_components[0], "r") as input_file:
with h5py.File(each_output_filename_components[0],
"a") as output_file:
data = input_file[each_input_filename_components[1]]
result_filename = registration.register_mean_offsets(
data, to_truncate=True, **parsed_args.parameters)
with h5py.File(result_filename, "r") as result_file:
result_file.copy("reg_frames",
output_file[os.path.dirname(
each_output_filename_components[1])],
name=each_output_filename_components[1])
if parsed_args.parameters.get("include_shift", False):
result_file.copy(
"space_shift",
output_file[os.path.dirname(
each_output_filename_components[1])],
name=each_output_filename_components[1] + "_shift")
# Copy all attributes from raw data to the final result.
output = output_file[each_output_filename_components[1]]
for each_attr_name in data.attrs:
output.attrs[each_attr_name] = data.attrs[each_attr_name]
# Only remove the directory if our input or output files are
# not stored there.
os.remove(result_filename)
in_out_dirnames = set(
os.path.dirname(os.path.abspath(_.filename))
for _ in [input_file, output_file])
result_dirname = os.path.dirname(result_filename)
if result_dirname not in in_out_dirnames:
os.rmdir(result_dirname)
return (0)
def main(*argv):
"""
Simple main function (like in C). Takes all arguments (as from sys.argv) and returns an exit status.
Args:
argv(list): arguments (includes command line call).
Returns:
int: exit code (0 if success)
"""
# Only necessary if running main (normally if calling command line). No point in importing otherwise.
import argparse
argv = list(argv)
# Creates command line parser
parser = argparse.ArgumentParser(
description="Parses input from the command line for a registration job."
)
parser.add_argument(
"config_filename",
metavar="CONFIG_FILE",
type=str,
help=
"JSON file that provides configuration options for how to import TIFF(s)."
)
parser.add_argument(
"input_filenames",
metavar="INPUT_FILE",
type=str,
nargs="+",
help=
"HDF5 file to import (this should include a path to where the internal dataset should be stored)."
)
parser.add_argument(
"output_filenames",
metavar="OUTPUT_FILE",
type=str,
nargs=1,
help=
"HDF5 file to export (this should include a path to where the internal dataset should be stored)."
)
# Results of parsing arguments (ignore the first one as it is the command line call).
parsed_args = parser.parse_args(argv[1:])
# Go ahead and stuff in parameters with the other parsed_args
parsed_args.parameters = xjson.read_parameters(parsed_args.config_filename)
parsed_args.input_file_components = []
for each_input_filename in parsed_args.input_filenames:
parsed_args.input_file_components.append(
PathComponents(each_input_filename))
parsed_args.output_file_components = []
for each_output_filename in parsed_args.output_filenames:
parsed_args.output_file_components.append(
PathComponents(each_output_filename))
for each_input_filename_components, each_output_filename_components in itertools.izip(
parsed_args.input_file_components,
parsed_args.output_file_components):
with h5py.File(each_input_filename_components.externalPath,
"r") as input_file:
with h5py.File(each_output_filename_components.externalPath,
"a") as output_file:
data = input_file[each_input_filename_components.internalPath]
result_filename = registration.register_mean_offsets(
data, to_truncate=True, **parsed_args.parameters)
with h5py.File(result_filename, "r") as result_file:
result_file.copy(
"reg_frames",
output_file[
each_output_filename_components.internalDirectory],
name=each_output_filename_components.
internalDatasetName)
os.remove(result_filename)
os.removedirs(os.path.dirname(result_filename))
return (0)
def _import_configuration(self):
from PyQt4.QtGui import QFileDialog
filename = QFileDialog.getOpenFileName(caption="Import Configuration",
filter="*.json")
filename = str(filename)
config_all = read_parameters(filename)
config = config_all
if "generate_neurons_blocks" in config:
config = config["generate_neurons_blocks"]
config = config["generate_neurons"]
preprocess_config = config["preprocess_data"]
dictionary_config = config["generate_dictionary"]["spams.trainDL"]
postprocess_config = config["postprocess_data"]
if "remove_zeroed_lines" in preprocess_config:
local_config = preprocess_config["remove_zeroed_lines"]
erosion_shape = local_config["erosion_shape"]
dilation_shape = local_config["dilation_shape"]
self.nanshePreprocessingApplet.topLevelOperator.ToRemoveZeroedLines.setValue(
True)
self.nanshePreprocessingApplet.topLevelOperator.ErosionShape.setValue(
erosion_shape)
self.nanshePreprocessingApplet.topLevelOperator.DilationShape.setValue(
dilation_shape)
else:
self.nanshePreprocessingApplet.topLevelOperator.ToRemoveZeroedLines.setValue(
False)
if "extract_f0" in preprocess_config:
local_config = preprocess_config["extract_f0"]
bias = local_config.get("bias")
temporal_smoothing_gaussian_filter_stdev = local_config[
"temporal_smoothing_gaussian_filter_stdev"]
temporal_smoothing_gaussian_filter_window_size = local_config[
"temporal_smoothing_gaussian_filter_window_size"]
half_window_size = local_config["half_window_size"]
which_quantile = local_config["which_quantile"]
spatial_smoothing_gaussian_filter_stdev = local_config[
"spatial_smoothing_gaussian_filter_stdev"]
spatial_smoothing_gaussian_filter_window_size = local_config[
"spatial_smoothing_gaussian_filter_window_size"]
self.nanshePreprocessingApplet.topLevelOperator.ToExtractF0.setValue(
True)
if bias is not None:
self.nanshePreprocessingApplet.topLevelOperator.BiasEnabled.setValue(
True)
self.nanshePreprocessingApplet.topLevelOperator.Bias.setValue(
bias)
else:
self.nanshePreprocessingApplet.topLevelOperator.BiasEnabled.setValue(
False)
self.nanshePreprocessingApplet.topLevelOperator.TemporalSmoothingGaussianFilterStdev.setValue(
temporal_smoothing_gaussian_filter_stdev)
self.nanshePreprocessingApplet.topLevelOperator.TemporalSmoothingGaussianFilterWindowSize.setValue(
temporal_smoothing_gaussian_filter_window_size)
self.nanshePreprocessingApplet.topLevelOperator.HalfWindowSize.setValue(
half_window_size)
self.nanshePreprocessingApplet.topLevelOperator.WhichQuantile.setValue(
which_quantile)
self.nanshePreprocessingApplet.topLevelOperator.SpatialSmoothingGaussianFilterStdev.setValue(
spatial_smoothing_gaussian_filter_stdev)
self.nanshePreprocessingApplet.topLevelOperator.SpatialSmoothingGaussianFilterWindowSize.setValue(
spatial_smoothing_gaussian_filter_window_size)
else:
self.nanshePreprocessingApplet.topLevelOperator.ToExtractF0.setValue(
False)
if "wavelet_transform" in preprocess_config:
local_config = preprocess_config["wavelet_transform"]
scale = local_config["scale"]
self.nanshePreprocessingApplet.topLevelOperator.ToWaveletTransform.setValue(
True)
self.nanshePreprocessingApplet.topLevelOperator.Scale.setValue(
local_config["scale"])
else:
self.nanshePreprocessingApplet.topLevelOperator.ToWaveletTransform.setValue(
False)
norm = preprocess_config["normalize_data"]["renormalized_images"].get(
"ord", 2)
self.nansheDictionaryLearningApplet.topLevelOperator.Ord.setValue(norm)
self.nansheDictionaryLearningApplet.topLevelOperator.K.setValue(
dictionary_config["K"])
self.nansheDictionaryLearningApplet.topLevelOperator.Gamma1.setValue(
dictionary_config["gamma1"])
self.nansheDictionaryLearningApplet.topLevelOperator.Gamma2.setValue(
dictionary_config["gamma2"])
self.nansheDictionaryLearningApplet.topLevelOperator.NumThreads.setValue(
dictionary_config["numThreads"])
self.nansheDictionaryLearningApplet.topLevelOperator.Batchsize.setValue(
dictionary_config["batchsize"])
self.nansheDictionaryLearningApplet.topLevelOperator.NumIter.setValue(
dictionary_config["iter"])
self.nansheDictionaryLearningApplet.topLevelOperator.Lambda1.setValue(
dictionary_config["lambda1"])
self.nansheDictionaryLearningApplet.topLevelOperator.Lambda2.setValue(
dictionary_config["lambda2"])
self.nansheDictionaryLearningApplet.topLevelOperator.PosAlpha.setValue(
dictionary_config["posAlpha"])
self.nansheDictionaryLearningApplet.topLevelOperator.PosD.setValue(
dictionary_config["posD"])
self.nansheDictionaryLearningApplet.topLevelOperator.Clean.setValue(
dictionary_config["clean"])
self.nansheDictionaryLearningApplet.topLevelOperator.Mode.setValue(
dictionary_config["mode"])
self.nansheDictionaryLearningApplet.topLevelOperator.ModeD.setValue(
dictionary_config["modeD"])
self.nanshePostprocessingApplet.topLevelOperator.SignificanceThreshold.setValue(
postprocess_config["wavelet_denoising"]["estimate_noise"]
["significance_threshold"])
self.nanshePostprocessingApplet.topLevelOperator.WaveletTransformScale.setValue(
postprocess_config["wavelet_denoising"]["wavelet.transform"]
["scale"])
self.nanshePostprocessingApplet.topLevelOperator.NoiseThreshold.setValue(
postprocess_config["wavelet_denoising"]["significant_mask"]
["noise_threshold"])
major_axis_length_config = postprocess_config["wavelet_denoising"][
"accepted_region_shape_constraints"].get("major_axis_length")
if major_axis_length_config is not None:
min = major_axis_length_config.get("min")
max = major_axis_length_config.get("max")
if min is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled.setValue(
True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Min.setValue(
min)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled.setValue(
False)
if max is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled.setValue(
True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Max.setValue(
max)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled.setValue(
False)
self.nanshePostprocessingApplet.topLevelOperator.PercentagePixelsBelowMax.setValue(
postprocess_config["wavelet_denoising"]
["remove_low_intensity_local_maxima"]
["percentage_pixels_below_max"])
self.nanshePostprocessingApplet.topLevelOperator.MinLocalMaxDistance.setValue(
postprocess_config["wavelet_denoising"]
["remove_too_close_local_maxima"]["min_local_max_distance"])
area_config = postprocess_config["wavelet_denoising"][
"accepted_neuron_shape_constraints"].get("area")
if area_config is not None:
min = area_config.get("min")
max = area_config.get("max")
if min is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Min_Enabled.setValue(
True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Min.setValue(
min)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Min_Enabled.setValue(
False)
if max is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Max_Enabled.setValue(
True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Max.setValue(
max)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Max_Enabled.setValue(
False)
eccentricity_config = postprocess_config["wavelet_denoising"][
"accepted_neuron_shape_constraints"].get("eccentricity")
if eccentricity_config is not None:
min = eccentricity_config.get("min")
max = eccentricity_config.get("max")
if min is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled.setValue(
True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Min.setValue(
min)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled.setValue(
False)
if max is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled.setValue(
True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Max.setValue(
max)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled.setValue(
False)
self.nanshePostprocessingApplet.topLevelOperator.AlignmentMinThreshold.setValue(
postprocess_config["merge_neuron_sets"]["alignment_min_threshold"])
self.nanshePostprocessingApplet.topLevelOperator.OverlapMinThreshold.setValue(
postprocess_config["merge_neuron_sets"]["overlap_min_threshold"])
self.nanshePostprocessingApplet.topLevelOperator.Fuse_FractionMeanNeuronMaxThreshold.setValue(
postprocess_config["merge_neuron_sets"]["fuse_neurons"]
["fraction_mean_neuron_max_threshold"])
def _import_configuration(self):
from PyQt4.QtGui import QFileDialog
filename = QFileDialog.getOpenFileName(caption="Import Configuration", filter="*.json")
filename = str(filename)
config_all = read_parameters(filename)
config = config_all
if "generate_neurons_blocks" in config:
config = config["generate_neurons_blocks"]
config = config["generate_neurons"]
preprocess_config = config["preprocess_data"]
dictionary_config = config["generate_dictionary"]["spams.trainDL"]
postprocess_config = config["postprocess_data"]
if "remove_zeroed_lines" in preprocess_config:
local_config = preprocess_config["remove_zeroed_lines"]
erosion_shape = local_config["erosion_shape"]
dilation_shape = local_config["dilation_shape"]
self.nanshePreprocessingApplet.topLevelOperator.ToRemoveZeroedLines.setValue(True)
self.nanshePreprocessingApplet.topLevelOperator.ErosionShape.setValue(erosion_shape)
self.nanshePreprocessingApplet.topLevelOperator.DilationShape.setValue(dilation_shape)
else:
self.nanshePreprocessingApplet.topLevelOperator.ToRemoveZeroedLines.setValue(False)
if "extract_f0" in preprocess_config:
local_config = preprocess_config["extract_f0"]
bias = local_config.get("bias")
temporal_smoothing_gaussian_filter_stdev = local_config["temporal_smoothing_gaussian_filter_stdev"]
temporal_smoothing_gaussian_filter_window_size = local_config["temporal_smoothing_gaussian_filter_window_size"]
half_window_size = local_config["half_window_size"]
which_quantile = local_config["which_quantile"]
spatial_smoothing_gaussian_filter_stdev = local_config["spatial_smoothing_gaussian_filter_stdev"]
spatial_smoothing_gaussian_filter_window_size = local_config["spatial_smoothing_gaussian_filter_window_size"]
self.nanshePreprocessingApplet.topLevelOperator.ToExtractF0.setValue(True)
if bias is not None:
self.nanshePreprocessingApplet.topLevelOperator.BiasEnabled.setValue(True)
self.nanshePreprocessingApplet.topLevelOperator.Bias.setValue(bias)
else:
self.nanshePreprocessingApplet.topLevelOperator.BiasEnabled.setValue(False)
self.nanshePreprocessingApplet.topLevelOperator.TemporalSmoothingGaussianFilterStdev.setValue(temporal_smoothing_gaussian_filter_stdev)
self.nanshePreprocessingApplet.topLevelOperator.TemporalSmoothingGaussianFilterWindowSize.setValue(temporal_smoothing_gaussian_filter_window_size)
self.nanshePreprocessingApplet.topLevelOperator.HalfWindowSize.setValue(half_window_size)
self.nanshePreprocessingApplet.topLevelOperator.WhichQuantile.setValue(which_quantile)
self.nanshePreprocessingApplet.topLevelOperator.SpatialSmoothingGaussianFilterStdev.setValue(spatial_smoothing_gaussian_filter_stdev)
self.nanshePreprocessingApplet.topLevelOperator.SpatialSmoothingGaussianFilterWindowSize.setValue(spatial_smoothing_gaussian_filter_window_size)
else:
self.nanshePreprocessingApplet.topLevelOperator.ToExtractF0.setValue(False)
if "wavelet_transform" in preprocess_config:
local_config = preprocess_config["wavelet_transform"]
scale = local_config["scale"]
self.nanshePreprocessingApplet.topLevelOperator.ToWaveletTransform.setValue(True)
self.nanshePreprocessingApplet.topLevelOperator.Scale.setValue(local_config["scale"])
else:
self.nanshePreprocessingApplet.topLevelOperator.ToWaveletTransform.setValue(False)
norm = preprocess_config["normalize_data"]["renormalized_images"].get("ord", 2)
self.nansheDictionaryLearningApplet.topLevelOperator.Ord.setValue(norm)
self.nansheDictionaryLearningApplet.topLevelOperator.K.setValue(dictionary_config["K"])
self.nansheDictionaryLearningApplet.topLevelOperator.Gamma1.setValue(dictionary_config["gamma1"])
self.nansheDictionaryLearningApplet.topLevelOperator.Gamma2.setValue(dictionary_config["gamma2"])
self.nansheDictionaryLearningApplet.topLevelOperator.NumThreads.setValue(dictionary_config["numThreads"])
self.nansheDictionaryLearningApplet.topLevelOperator.Batchsize.setValue(dictionary_config["batchsize"])
self.nansheDictionaryLearningApplet.topLevelOperator.NumIter.setValue(dictionary_config["iter"])
self.nansheDictionaryLearningApplet.topLevelOperator.Lambda1.setValue(dictionary_config["lambda1"])
self.nansheDictionaryLearningApplet.topLevelOperator.Lambda2.setValue(dictionary_config["lambda2"])
self.nansheDictionaryLearningApplet.topLevelOperator.PosAlpha.setValue(dictionary_config["posAlpha"])
self.nansheDictionaryLearningApplet.topLevelOperator.PosD.setValue(dictionary_config["posD"])
self.nansheDictionaryLearningApplet.topLevelOperator.Clean.setValue(dictionary_config["clean"])
self.nansheDictionaryLearningApplet.topLevelOperator.Mode.setValue(dictionary_config["mode"])
self.nansheDictionaryLearningApplet.topLevelOperator.ModeD.setValue(dictionary_config["modeD"])
self.nanshePostprocessingApplet.topLevelOperator.SignificanceThreshold.setValue(
postprocess_config["wavelet_denoising"]["estimate_noise"]["significance_threshold"]
)
self.nanshePostprocessingApplet.topLevelOperator.WaveletTransformScale.setValue(
postprocess_config["wavelet_denoising"]["wavelet.transform"]["scale"]
)
self.nanshePostprocessingApplet.topLevelOperator.NoiseThreshold.setValue(
postprocess_config["wavelet_denoising"]["significant_mask"]["noise_threshold"]
)
major_axis_length_config = postprocess_config["wavelet_denoising"]["accepted_region_shape_constraints"].get("major_axis_length")
if major_axis_length_config is not None:
min = major_axis_length_config.get("min")
max = major_axis_length_config.get("max")
if min is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled.setValue(True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Min.setValue(min)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled.setValue(False)
if max is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled.setValue(True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Max.setValue(max)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled.setValue(False)
self.nanshePostprocessingApplet.topLevelOperator.PercentagePixelsBelowMax.setValue(
postprocess_config["wavelet_denoising"]["remove_low_intensity_local_maxima"]["percentage_pixels_below_max"]
)
self.nanshePostprocessingApplet.topLevelOperator.MinLocalMaxDistance.setValue(
postprocess_config["wavelet_denoising"]["remove_too_close_local_maxima"]["min_local_max_distance"]
)
area_config = postprocess_config["wavelet_denoising"]["accepted_neuron_shape_constraints"].get("area")
if area_config is not None:
min = area_config.get("min")
max = area_config.get("max")
if min is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Min_Enabled.setValue(True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Min.setValue(min)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Min_Enabled.setValue(False)
if max is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Max_Enabled.setValue(True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Max.setValue(max)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Area_Max_Enabled.setValue(False)
eccentricity_config = postprocess_config["wavelet_denoising"]["accepted_neuron_shape_constraints"].get("eccentricity")
if eccentricity_config is not None:
min = eccentricity_config.get("min")
max = eccentricity_config.get("max")
if min is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled.setValue(True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Min.setValue(min)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled.setValue(False)
if max is not None:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled.setValue(True)
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Max.setValue(max)
else:
self.nanshePostprocessingApplet.topLevelOperator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled.setValue(False)
self.nanshePostprocessingApplet.topLevelOperator.AlignmentMinThreshold.setValue(
postprocess_config["merge_neuron_sets"]["alignment_min_threshold"]
)
self.nanshePostprocessingApplet.topLevelOperator.OverlapMinThreshold.setValue(
postprocess_config["merge_neuron_sets"]["overlap_min_threshold"]
)
self.nanshePostprocessingApplet.topLevelOperator.Fuse_FractionMeanNeuronMaxThreshold.setValue(
postprocess_config["merge_neuron_sets"]["fuse_neurons"]["fraction_mean_neuron_max_threshold"]
)
