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poppy-project/pypot | pypot/dynamixel/io/abstract_io.py | AbstractDxlIO.enable_torque | def enable_torque(self, ids):
""" Enables torque of the motors with the specified ids. """
self._set_torque_enable(dict(zip(ids, itertools.repeat(True)))) | python | def enable_torque(self, ids):
""" Enables torque of the motors with the specified ids. """
self._set_torque_enable(dict(zip(ids, itertools.repeat(True)))) | [
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poppy-project/pypot | pypot/dynamixel/io/abstract_io.py | AbstractDxlIO.disable_torque | def disable_torque(self, ids):
""" Disables torque of the motors with the specified ids. """
self._set_torque_enable(dict(zip(ids, itertools.repeat(False)))) | python | def disable_torque(self, ids):
""" Disables torque of the motors with the specified ids. """
self._set_torque_enable(dict(zip(ids, itertools.repeat(False)))) | [
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poppy-project/pypot | pypot/dynamixel/io/abstract_io.py | AbstractDxlIO.get_pid_gain | def get_pid_gain(self, ids, **kwargs):
""" Gets the pid gain for the specified motors. """
return tuple([tuple(reversed(t)) for t in self._get_pid_gain(ids, **kwargs)]) | python | def get_pid_gain(self, ids, **kwargs):
""" Gets the pid gain for the specified motors. """
return tuple([tuple(reversed(t)) for t in self._get_pid_gain(ids, **kwargs)]) | [
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poppy-project/pypot | pypot/dynamixel/io/abstract_io.py | AbstractDxlIO.set_pid_gain | def set_pid_gain(self, pid_for_id, **kwargs):
""" Sets the pid gain to the specified motors. """
pid_for_id = dict(itertools.izip(pid_for_id.iterkeys(),
[tuple(reversed(t)) for t in pid_for_id.values()]))
self._set_pid_gain(pid_for_id, **kwargs) | python | def set_pid_gain(self, pid_for_id, **kwargs):
""" Sets the pid gain to the specified motors. """
pid_for_id = dict(itertools.izip(pid_for_id.iterkeys(),
[tuple(reversed(t)) for t in pid_for_id.values()]))
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poppy-project/pypot | pypot/dynamixel/io/abstract_io.py | AbstractDxlIO.get_control_table | def get_control_table(self, ids, **kwargs):
""" Gets the full control table for the specified motors.
..note:: This function requires the model for each motor to be known. Querring this additional information might add some extra delay.
"""
error_handler = kwargs['error_handler... | python | def get_control_table(self, ids, **kwargs):
""" Gets the full control table for the specified motors.
..note:: This function requires the model for each motor to be known. Querring this additional information might add some extra delay.
"""
error_handler = kwargs['error_handler... | [
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poppy-project/pypot | pypot/robot/config.py | check_motor_eprom_configuration | def check_motor_eprom_configuration(config, dxl_io, motor_names):
""" Change the angles limits depanding on the robot configuration ;
Check if the return delay time is set to 0.
"""
changed_angle_limits = {}
changed_return_delay_time = {}
for name in motor_names:
m = config['motors'... | python | def check_motor_eprom_configuration(config, dxl_io, motor_names):
""" Change the angles limits depanding on the robot configuration ;
Check if the return delay time is set to 0.
"""
changed_angle_limits = {}
changed_return_delay_time = {}
for name in motor_names:
m = config['motors'... | [
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icometrix/dicom2nifti | dicom2nifti/compressed_dicom.py | _get_gdcmconv | def _get_gdcmconv():
"""
Get the full path to gdcmconv.
If not found raise error
"""
gdcmconv_executable = settings.gdcmconv_path
if gdcmconv_executable is None:
gdcmconv_executable = _which('gdcmconv')
if gdcmconv_executable is None:
gdcmconv_executable = _which('gdcmconv.ex... | python | def _get_gdcmconv():
"""
Get the full path to gdcmconv.
If not found raise error
"""
gdcmconv_executable = settings.gdcmconv_path
if gdcmconv_executable is None:
gdcmconv_executable = _which('gdcmconv')
if gdcmconv_executable is None:
gdcmconv_executable = _which('gdcmconv.ex... | [
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icometrix/dicom2nifti | dicom2nifti/compressed_dicom.py | compress_directory | def compress_directory(dicom_directory):
"""
This function can be used to convert a folder of jpeg compressed images to an uncompressed ones
:param dicom_directory: directory of dicom files to compress
"""
if _is_compressed(dicom_directory):
return
logger.info('Compressing dicom files ... | python | def compress_directory(dicom_directory):
"""
This function can be used to convert a folder of jpeg compressed images to an uncompressed ones
:param dicom_directory: directory of dicom files to compress
"""
if _is_compressed(dicom_directory):
return
logger.info('Compressing dicom files ... | [
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icometrix/dicom2nifti | dicom2nifti/compressed_dicom.py | is_dicom_file | def is_dicom_file(filename):
"""
Util function to check if file is a dicom file
the first 128 bytes are preamble
the next 4 bytes should contain DICM otherwise it is not a dicom
:param filename: file to check for the DICM header block
:type filename: six.string_types
:returns: True if it is... | python | def is_dicom_file(filename):
"""
Util function to check if file is a dicom file
the first 128 bytes are preamble
the next 4 bytes should contain DICM otherwise it is not a dicom
:param filename: file to check for the DICM header block
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icometrix/dicom2nifti | dicom2nifti/compressed_dicom.py | _is_compressed | def _is_compressed(dicom_file, force=False):
"""
Check if dicoms are compressed or not
"""
header = pydicom.read_file(dicom_file,
defer_size="1 KB",
stop_before_pixels=True,
force=force)
uncompressed_types ... | python | def _is_compressed(dicom_file, force=False):
"""
Check if dicoms are compressed or not
"""
header = pydicom.read_file(dicom_file,
defer_size="1 KB",
stop_before_pixels=True,
force=force)
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icometrix/dicom2nifti | dicom2nifti/compressed_dicom.py | _decompress_dicom | def _decompress_dicom(dicom_file, output_file):
"""
This function can be used to convert a jpeg compressed image to an uncompressed one for further conversion
:param input_file: single dicom file to decompress
"""
gdcmconv_executable = _get_gdcmconv()
subprocess.check_output([gdcmconv_executab... | python | def _decompress_dicom(dicom_file, output_file):
"""
This function can be used to convert a jpeg compressed image to an uncompressed one for further conversion
:param input_file: single dicom file to decompress
"""
gdcmconv_executable = _get_gdcmconv()
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icometrix/dicom2nifti | scripts/dicomdiff.py | dicom_diff | def dicom_diff(file1, file2):
""" Shows the fields that differ between two DICOM images.
Inspired by https://code.google.com/p/pydicom/source/browse/source/dicom/examples/DicomDiff.py
"""
datasets = compressed_dicom.read_file(file1), compressed_dicom.read_file(file2)
rep = []
for dataset in ... | python | def dicom_diff(file1, file2):
""" Shows the fields that differ between two DICOM images.
Inspired by https://code.google.com/p/pydicom/source/browse/source/dicom/examples/DicomDiff.py
"""
datasets = compressed_dicom.read_file(file1), compressed_dicom.read_file(file2)
rep = []
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icometrix/dicom2nifti | dicom2nifti/image_volume.py | ImageVolume._get_number_of_slices | def _get_number_of_slices(self, slice_type):
"""
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"""
if slice_type == SliceType.AXIAL:
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return self.dimens... | python | def _get_number_of_slices(self, slice_type):
"""
Get the number of slices in a certain direction
"""
if slice_type == SliceType.AXIAL:
return self.dimensions[self.axial_orientation.normal_component]
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icometrix/dicom2nifti | dicom2nifti/convert_dicom.py | _get_first_header | def _get_first_header(dicom_directory):
"""
Function to get the first dicom file form a directory and return the header
Useful to determine the type of data to convert
:param dicom_directory: directory with dicom files
"""
# looping over all files
for root, _, file_names in os.walk(dicom_di... | python | def _get_first_header(dicom_directory):
"""
Function to get the first dicom file form a directory and return the header
Useful to determine the type of data to convert
:param dicom_directory: directory with dicom files
"""
# looping over all files
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icometrix/dicom2nifti | dicom2nifti/image_reorientation.py | _reorient_3d | def _reorient_3d(image):
"""
Reorganize the data for a 3d nifti
"""
# Create empty array where x,y,z correspond to LR (sagittal), PA (coronal), IS (axial) directions and the size
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new_image = numpy.z... | python | def _reorient_3d(image):
"""
Reorganize the data for a 3d nifti
"""
# Create empty array where x,y,z correspond to LR (sagittal), PA (coronal), IS (axial) directions and the size
# of the array in each direction is the same with the corresponding direction of the input image.
new_image = numpy.z... | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | dicom_to_nifti | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for philips images.
As input philips images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: file path to the o... | python | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for philips images.
As input philips images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: file path to the o... | [
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Examples: See unit test
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _assert_explicit_vr | def _assert_explicit_vr(dicom_input):
"""
Assert that explicit vr is used
"""
if settings.validate_multiframe_implicit:
header = dicom_input[0]
if header.file_meta[0x0002, 0x0010].value == '1.2.840.10008.1.2':
raise ConversionError('IMPLICIT_VR_ENHANCED_DICOM') | python | def _assert_explicit_vr(dicom_input):
"""
Assert that explicit vr is used
"""
if settings.validate_multiframe_implicit:
header = dicom_input[0]
if header.file_meta[0x0002, 0x0010].value == '1.2.840.10008.1.2':
raise ConversionError('IMPLICIT_VR_ENHANCED_DICOM') | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _is_multiframe_4d | def _is_multiframe_4d(dicom_input):
"""
Use this function to detect if a dicom series is a philips multiframe 4D dataset
"""
# check if it is multi frame dicom
if not common.is_multiframe_dicom(dicom_input):
return False
header = dicom_input[0]
# check if there are multiple stacks
... | python | def _is_multiframe_4d(dicom_input):
"""
Use this function to detect if a dicom series is a philips multiframe 4D dataset
"""
# check if it is multi frame dicom
if not common.is_multiframe_dicom(dicom_input):
return False
header = dicom_input[0]
# check if there are multiple stacks
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _is_singleframe_4d | def _is_singleframe_4d(dicom_input):
"""
Use this function to detect if a dicom series is a philips singleframe 4D dataset
"""
header = dicom_input[0]
# check if there are stack information
slice_number_mr_tag = Tag(0x2001, 0x100a)
if slice_number_mr_tag not in header:
return False
... | python | def _is_singleframe_4d(dicom_input):
"""
Use this function to detect if a dicom series is a philips singleframe 4D dataset
"""
header = dicom_input[0]
# check if there are stack information
slice_number_mr_tag = Tag(0x2001, 0x100a)
if slice_number_mr_tag not in header:
return False
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _is_bval_type_a | def _is_bval_type_a(grouped_dicoms):
"""
Check if the bvals are stored in the first of 2 currently known ways for single frame dti
"""
bval_tag = Tag(0x2001, 0x1003)
bvec_x_tag = Tag(0x2005, 0x10b0)
bvec_y_tag = Tag(0x2005, 0x10b1)
bvec_z_tag = Tag(0x2005, 0x10b2)
for group in grouped_di... | python | def _is_bval_type_a(grouped_dicoms):
"""
Check if the bvals are stored in the first of 2 currently known ways for single frame dti
"""
bval_tag = Tag(0x2001, 0x1003)
bvec_x_tag = Tag(0x2005, 0x10b0)
bvec_y_tag = Tag(0x2005, 0x10b1)
bvec_z_tag = Tag(0x2005, 0x10b2)
for group in grouped_di... | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _is_bval_type_b | def _is_bval_type_b(grouped_dicoms):
"""
Check if the bvals are stored in the second of 2 currently known ways for single frame dti
"""
bval_tag = Tag(0x0018, 0x9087)
bvec_tag = Tag(0x0018, 0x9089)
for group in grouped_dicoms:
if bvec_tag in group[0] and bval_tag in group[0]:
... | python | def _is_bval_type_b(grouped_dicoms):
"""
Check if the bvals are stored in the second of 2 currently known ways for single frame dti
"""
bval_tag = Tag(0x0018, 0x9087)
bvec_tag = Tag(0x0018, 0x9089)
for group in grouped_dicoms:
if bvec_tag in group[0] and bval_tag in group[0]:
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _multiframe_to_nifti | def _multiframe_to_nifti(dicom_input, output_file):
"""
This function will convert philips 4D or anatomical multiframe series to a nifti
"""
# Read the multiframe dicom file
logger.info('Read dicom file')
multiframe_dicom = dicom_input[0]
# Create mosaic block
logger.info('Creating dat... | python | def _multiframe_to_nifti(dicom_input, output_file):
"""
This function will convert philips 4D or anatomical multiframe series to a nifti
"""
# Read the multiframe dicom file
logger.info('Read dicom file')
multiframe_dicom = dicom_input[0]
# Create mosaic block
logger.info('Creating dat... | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _singleframe_to_nifti | def _singleframe_to_nifti(grouped_dicoms, output_file):
"""
This function will convert a philips singleframe series to a nifti
"""
# Create mosaic block
logger.info('Creating data block')
full_block = _singleframe_to_block(grouped_dicoms)
logger.info('Creating affine')
# Create the nif... | python | def _singleframe_to_nifti(grouped_dicoms, output_file):
"""
This function will convert a philips singleframe series to a nifti
"""
# Create mosaic block
logger.info('Creating data block')
full_block = _singleframe_to_block(grouped_dicoms)
logger.info('Creating affine')
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _create_affine_multiframe | def _create_affine_multiframe(multiframe_dicom):
"""
Function to create the affine matrix for a siemens mosaic dataset
This will work for siemens dti and 4D if in mosaic format
"""
first_frame = multiframe_dicom[Tag(0x5200, 0x9230)][0]
last_frame = multiframe_dicom[Tag(0x5200, 0x9230)][-1]
#... | python | def _create_affine_multiframe(multiframe_dicom):
"""
Function to create the affine matrix for a siemens mosaic dataset
This will work for siemens dti and 4D if in mosaic format
"""
first_frame = multiframe_dicom[Tag(0x5200, 0x9230)][0]
last_frame = multiframe_dicom[Tag(0x5200, 0x9230)][-1]
#... | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _multiframe_to_block | def _multiframe_to_block(multiframe_dicom):
"""
Generate a full datablock containing all stacks
"""
# Calculate the amount of stacks and slices in the stack
number_of_stack_slices = int(common.get_ss_value(multiframe_dicom[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)]))
number_of_stacks = int(int... | python | def _multiframe_to_block(multiframe_dicom):
"""
Generate a full datablock containing all stacks
"""
# Calculate the amount of stacks and slices in the stack
number_of_stack_slices = int(common.get_ss_value(multiframe_dicom[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)]))
number_of_stacks = int(int... | [
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icometrix/dicom2nifti | dicom2nifti/convert_philips.py | _fix_diffusion_images | def _fix_diffusion_images(bvals, bvecs, nifti, nifti_file):
"""
This function will remove the last timepoint from the nifti, bvals and bvecs if the last vector is 0,0,0
This is sometimes added at the end by philips
"""
# if all zero continue of if the last bvec is not all zero continue
if numpy.... | python | def _fix_diffusion_images(bvals, bvecs, nifti, nifti_file):
"""
This function will remove the last timepoint from the nifti, bvals and bvecs if the last vector is 0,0,0
This is sometimes added at the end by philips
"""
# if all zero continue of if the last bvec is not all zero continue
if numpy.... | [
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icometrix/dicom2nifti | dicom2nifti/convert_generic.py | dicom_to_nifti | def dicom_to_nifti(dicom_input, output_file):
"""
This function will convert an anatomical dicom series to a nifti
Examples: See unit test
:param output_file: filepath to the output nifti
:param dicom_input: directory with the dicom files for a single scan, or list of read in dicoms
"""
if... | python | def dicom_to_nifti(dicom_input, output_file):
"""
This function will convert an anatomical dicom series to a nifti
Examples: See unit test
:param output_file: filepath to the output nifti
:param dicom_input: directory with the dicom files for a single scan, or list of read in dicoms
"""
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icometrix/dicom2nifti | dicom2nifti/convert_generic.py | _convert_slice_incement_inconsistencies | def _convert_slice_incement_inconsistencies(dicom_input):
"""
If there is slice increment inconsistency detected, for the moment CT images, then split the volumes into subvolumes based on the slice increment and process each volume separately using a space constructed based on the highest resolution increment
... | python | def _convert_slice_incement_inconsistencies(dicom_input):
"""
If there is slice increment inconsistency detected, for the moment CT images, then split the volumes into subvolumes based on the slice increment and process each volume separately using a space constructed based on the highest resolution increment
... | [
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icometrix/dicom2nifti | dicom2nifti/common.py | is_hitachi | def is_hitachi(dicom_input):
"""
Use this function to detect if a dicom series is a hitachi dataset
:param dicom_input: directory with dicom files for 1 scan of a dicom_header
"""
# read dicom header
header = dicom_input[0]
if 'Manufacturer' not in header or 'Modality' not in header:
... | python | def is_hitachi(dicom_input):
"""
Use this function to detect if a dicom series is a hitachi dataset
:param dicom_input: directory with dicom files for 1 scan of a dicom_header
"""
# read dicom header
header = dicom_input[0]
if 'Manufacturer' not in header or 'Modality' not in header:
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icometrix/dicom2nifti | dicom2nifti/common.py | is_ge | def is_ge(dicom_input):
"""
Use this function to detect if a dicom series is a GE dataset
:param dicom_input: list with dicom objects
"""
# read dicom header
header = dicom_input[0]
if 'Manufacturer' not in header or 'Modality' not in header:
return False # we try generic conversi... | python | def is_ge(dicom_input):
"""
Use this function to detect if a dicom series is a GE dataset
:param dicom_input: list with dicom objects
"""
# read dicom header
header = dicom_input[0]
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icometrix/dicom2nifti | dicom2nifti/common.py | is_philips | def is_philips(dicom_input):
"""
Use this function to detect if a dicom series is a philips dataset
:param dicom_input: directory with dicom files for 1 scan of a dicom_header
"""
# read dicom header
header = dicom_input[0]
if 'Manufacturer' not in header or 'Modality' not in header:
... | python | def is_philips(dicom_input):
"""
Use this function to detect if a dicom series is a philips dataset
:param dicom_input: directory with dicom files for 1 scan of a dicom_header
"""
# read dicom header
header = dicom_input[0]
if 'Manufacturer' not in header or 'Modality' not in header:
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icometrix/dicom2nifti | dicom2nifti/common.py | is_siemens | def is_siemens(dicom_input):
"""
Use this function to detect if a dicom series is a siemens dataset
:param dicom_input: directory with dicom files for 1 scan
"""
# read dicom header
header = dicom_input[0]
# check if manufacturer is Siemens
if 'Manufacturer' not in header or 'Modality'... | python | def is_siemens(dicom_input):
"""
Use this function to detect if a dicom series is a siemens dataset
:param dicom_input: directory with dicom files for 1 scan
"""
# read dicom header
header = dicom_input[0]
# check if manufacturer is Siemens
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icometrix/dicom2nifti | dicom2nifti/common.py | _get_slice_pixeldata | def _get_slice_pixeldata(dicom_slice):
"""
the slice and intercept calculation can cause the slices to have different dtypes
we should get the correct dtype that can cover all of them
:type dicom_slice: pydicom object
:param dicom_slice: slice to get the pixeldata for
"""
data = dicom_slice... | python | def _get_slice_pixeldata(dicom_slice):
"""
the slice and intercept calculation can cause the slices to have different dtypes
we should get the correct dtype that can cover all of them
:type dicom_slice: pydicom object
:param dicom_slice: slice to get the pixeldata for
"""
data = dicom_slice... | [
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icometrix/dicom2nifti | dicom2nifti/common.py | set_fd_value | def set_fd_value(tag, value):
"""
Setters for data that also work with implicit transfersyntax
:param value: the value to set on the tag
:param tag: the tag to read
"""
if tag.VR == 'OB' or tag.VR == 'UN':
value = struct.pack('d', value)
tag.value = value | python | def set_fd_value(tag, value):
"""
Setters for data that also work with implicit transfersyntax
:param value: the value to set on the tag
:param tag: the tag to read
"""
if tag.VR == 'OB' or tag.VR == 'UN':
value = struct.pack('d', value)
tag.value = value | [
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icometrix/dicom2nifti | dicom2nifti/common.py | set_ss_value | def set_ss_value(tag, value):
"""
Setter for data that also work with implicit transfersyntax
:param value: the value to set on the tag
:param tag: the tag to read
"""
if tag.VR == 'OB' or tag.VR == 'UN':
value = struct.pack('h', value)
tag.value = value | python | def set_ss_value(tag, value):
"""
Setter for data that also work with implicit transfersyntax
:param value: the value to set on the tag
:param tag: the tag to read
"""
if tag.VR == 'OB' or tag.VR == 'UN':
value = struct.pack('h', value)
tag.value = value | [
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icometrix/dicom2nifti | dicom2nifti/common.py | apply_scaling | def apply_scaling(data, dicom_headers):
"""
Rescale the data based on the RescaleSlope and RescaleOffset
Based on the scaling from pydicomseries
:param dicom_headers: dicom headers to use to retreive the scaling factors
:param data: the input data
"""
# Apply the rescaling if needed
pr... | python | def apply_scaling(data, dicom_headers):
"""
Rescale the data based on the RescaleSlope and RescaleOffset
Based on the scaling from pydicomseries
:param dicom_headers: dicom headers to use to retreive the scaling factors
:param data: the input data
"""
# Apply the rescaling if needed
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icometrix/dicom2nifti | dicom2nifti/common.py | write_bvec_file | def write_bvec_file(bvecs, bvec_file):
"""
Write an array of bvecs to a bvec file
:param bvecs: array with the vectors
:param bvec_file: filepath to write to
"""
if bvec_file is None:
return
logger.info('Saving BVEC file: %s' % bvec_file)
with open(bvec_file, 'w') as text_file:
... | python | def write_bvec_file(bvecs, bvec_file):
"""
Write an array of bvecs to a bvec file
:param bvecs: array with the vectors
:param bvec_file: filepath to write to
"""
if bvec_file is None:
return
logger.info('Saving BVEC file: %s' % bvec_file)
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icometrix/dicom2nifti | dicom2nifti/common.py | write_bval_file | def write_bval_file(bvals, bval_file):
"""
Write an array of bvals to a bval file
:param bvals: array with the values
:param bval_file: filepath to write to
"""
if bval_file is None:
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logger.info('Saving BVAL file: %s' % bval_file)
with open(bval_file, 'w') as text_file:
... | python | def write_bval_file(bvals, bval_file):
"""
Write an array of bvals to a bval file
:param bvals: array with the values
:param bval_file: filepath to write to
"""
if bval_file is None:
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logger.info('Saving BVAL file: %s' % bval_file)
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icometrix/dicom2nifti | dicom2nifti/common.py | sort_dicoms | def sort_dicoms(dicoms):
"""
Sort the dicoms based om the image possition patient
:param dicoms: list of dicoms
"""
# find most significant axis to use during sorting
# the original way of sorting (first x than y than z) does not work in certain border situations
# where for exampe the X wi... | python | def sort_dicoms(dicoms):
"""
Sort the dicoms based om the image possition patient
:param dicoms: list of dicoms
"""
# find most significant axis to use during sorting
# the original way of sorting (first x than y than z) does not work in certain border situations
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icometrix/dicom2nifti | dicom2nifti/common.py | validate_orientation | def validate_orientation(dicoms):
"""
Validate that all dicoms have the same orientation
:param dicoms: list of dicoms
"""
first_image_orient1 = numpy.array(dicoms[0].ImageOrientationPatient)[0:3]
first_image_orient2 = numpy.array(dicoms[0].ImageOrientationPatient)[3:6]
for dicom_ in dicoms... | python | def validate_orientation(dicoms):
"""
Validate that all dicoms have the same orientation
:param dicoms: list of dicoms
"""
first_image_orient1 = numpy.array(dicoms[0].ImageOrientationPatient)[0:3]
first_image_orient2 = numpy.array(dicoms[0].ImageOrientationPatient)[3:6]
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icometrix/dicom2nifti | dicom2nifti/common.py | set_tr_te | def set_tr_te(nifti_image, repetition_time, echo_time):
"""
Set the tr and te in the nifti headers
:param echo_time: echo time
:param repetition_time: repetition time
:param nifti_image: nifti image to set the info to
"""
# set the repetition time in pixdim
nifti_image.header.structarr[... | python | def set_tr_te(nifti_image, repetition_time, echo_time):
"""
Set the tr and te in the nifti headers
:param echo_time: echo time
:param repetition_time: repetition time
:param nifti_image: nifti image to set the info to
"""
# set the repetition time in pixdim
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icometrix/dicom2nifti | dicom2nifti/convert_ge.py | dicom_to_nifti | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for ge images.
As input ge images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: the filepath to the output n... | python | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for ge images.
As input ge images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
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icometrix/dicom2nifti | dicom2nifti/convert_ge.py | _4d_to_nifti | def _4d_to_nifti(grouped_dicoms, output_file):
"""
This function will convert ge 4d series to a nifti
"""
# Create mosaic block
logger.info('Creating data block')
full_block = _get_full_block(grouped_dicoms)
logger.info('Creating affine')
# Create the nifti header info
affine, slic... | python | def _4d_to_nifti(grouped_dicoms, output_file):
"""
This function will convert ge 4d series to a nifti
"""
# Create mosaic block
logger.info('Creating data block')
full_block = _get_full_block(grouped_dicoms)
logger.info('Creating affine')
# Create the nifti header info
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icometrix/dicom2nifti | dicom2nifti/convert_hitachi.py | dicom_to_nifti | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for hitachi images.
As input hitachi images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: file path to the o... | python | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for hitachi images.
As input hitachi images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
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icometrix/dicom2nifti | dicom2nifti/convert_siemens.py | dicom_to_nifti | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion function for ge images.
As input ge images are required. It will then determine the type of images and do the correct conversion
:param output_file: filepath to the output nifti
:param dicom_input: dir... | python | def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion function for ge images.
As input ge images are required. It will then determine the type of images and do the correct conversion
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icometrix/dicom2nifti | dicom2nifti/convert_siemens.py | _get_sorted_mosaics | def _get_sorted_mosaics(dicom_input):
"""
Search all mosaics in the dicom directory, sort and validate them
"""
# Order all dicom files by acquisition number
sorted_mosaics = sorted(dicom_input, key=lambda x: x.AcquisitionNumber)
for index in range(0, len(sorted_mosaics) - 1):
# Validat... | python | def _get_sorted_mosaics(dicom_input):
"""
Search all mosaics in the dicom directory, sort and validate them
"""
# Order all dicom files by acquisition number
sorted_mosaics = sorted(dicom_input, key=lambda x: x.AcquisitionNumber)
for index in range(0, len(sorted_mosaics) - 1):
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icometrix/dicom2nifti | dicom2nifti/convert_siemens.py | _mosaic_to_block | def _mosaic_to_block(mosaic):
"""
Convert a mosaic slice to a block of data by reading the headers, splitting the mosaic and appending
"""
# get the mosaic type
mosaic_type = _get_mosaic_type(mosaic)
# get the size of one tile format is 64p*64 or 80*80 or something similar
matches = re.find... | python | def _mosaic_to_block(mosaic):
"""
Convert a mosaic slice to a block of data by reading the headers, splitting the mosaic and appending
"""
# get the mosaic type
mosaic_type = _get_mosaic_type(mosaic)
# get the size of one tile format is 64p*64 or 80*80 or something similar
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icometrix/dicom2nifti | dicom2nifti/convert_siemens.py | _create_affine_siemens_mosaic | def _create_affine_siemens_mosaic(dicom_input):
"""
Function to create the affine matrix for a siemens mosaic dataset
This will work for siemens dti and 4d if in mosaic format
"""
# read dicom series with pds
dicom_header = dicom_input[0]
# Create affine matrix (http://nipy.sourceforge.net/... | python | def _create_affine_siemens_mosaic(dicom_input):
"""
Function to create the affine matrix for a siemens mosaic dataset
This will work for siemens dti and 4d if in mosaic format
"""
# read dicom series with pds
dicom_header = dicom_input[0]
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icometrix/dicom2nifti | dicom2nifti/resample.py | resample_single_nifti | def resample_single_nifti(input_nifti):
"""
Resample a gantry tilted image in place
"""
# read the input image
input_image = nibabel.load(input_nifti)
output_image = resample_nifti_images([input_image])
output_image.to_filename(input_nifti) | python | def resample_single_nifti(input_nifti):
"""
Resample a gantry tilted image in place
"""
# read the input image
input_image = nibabel.load(input_nifti)
output_image = resample_nifti_images([input_image])
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icometrix/dicom2nifti | dicom2nifti/convert_dir.py | convert_directory | def convert_directory(dicom_directory, output_folder, compression=True, reorient=True):
"""
This function will order all dicom files by series and order them one by one
:param compression: enable or disable gzip compression
:param reorient: reorient the dicoms according to LAS orientation
:param ou... | python | def convert_directory(dicom_directory, output_folder, compression=True, reorient=True):
"""
This function will order all dicom files by series and order them one by one
:param compression: enable or disable gzip compression
:param reorient: reorient the dicoms according to LAS orientation
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aegirhall/console-menu | consolemenu/menu_formatter.py | MenuFormatBuilder.clear_data | def clear_data(self):
"""
Clear menu data from previous menu generation.
"""
self.__header.title = None
self.__header.subtitle = None
self.__prologue.text = None
self.__epilogue.text = None
self.__items_section.items = None | python | def clear_data(self):
"""
Clear menu data from previous menu generation.
"""
self.__header.title = None
self.__header.subtitle = None
self.__prologue.text = None
self.__epilogue.text = None
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aegirhall/console-menu | consolemenu/menu_component.py | MenuComponent.inner_horizontal_border | def inner_horizontal_border(self):
"""
The complete inner horizontal border section, including the left and right border verticals.
Returns:
str: The complete inner horizontal border.
"""
return u"{lm}{lv}{hz}{rv}".format(lm=' ' * self.margins.left,
... | python | def inner_horizontal_border(self):
"""
The complete inner horizontal border section, including the left and right border verticals.
Returns:
str: The complete inner horizontal border.
"""
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aegirhall/console-menu | consolemenu/menu_component.py | MenuComponent.outer_horizontal_border_bottom | def outer_horizontal_border_bottom(self):
"""
The complete outer bottom horizontal border section, including left and right margins.
Returns:
str: The bottom menu border.
"""
return u"{lm}{lv}{hz}{rv}".format(lm=' ' * self.margins.left,
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"""
The complete outer bottom horizontal border section, including left and right margins.
Returns:
str: The bottom menu border.
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aegirhall/console-menu | consolemenu/menu_component.py | MenuComponent.outer_horizontal_border_top | def outer_horizontal_border_top(self):
"""
The complete outer top horizontal border section, including left and right margins.
Returns:
str: The top menu border.
"""
return u"{lm}{lv}{hz}{rv}".format(lm=' ' * self.margins.left,
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"""
The complete outer top horizontal border section, including left and right margins.
Returns:
str: The top menu border.
"""
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aegirhall/console-menu | consolemenu/menu_component.py | MenuComponent.row | def row(self, content='', align='left'):
"""
A row of the menu, which comprises the left and right verticals plus the given content.
Returns:
str: A row of this menu component with the specified content.
"""
return u"{lm}{vert}{cont}{vert}".format(lm=' ' * self.margi... | python | def row(self, content='', align='left'):
"""
A row of the menu, which comprises the left and right verticals plus the given content.
Returns:
str: A row of this menu component with the specified content.
"""
return u"{lm}{vert}{cont}{vert}".format(lm=' ' * self.margi... | [
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aegirhall/console-menu | consolemenu/format/menu_borders.py | MenuBorderStyleFactory.create_border | def create_border(self, border_style_type):
"""
Create a new MenuBorderStyle instance based on the given border style type.
Args:
border_style_type (int): an integer value from :obj:`MenuBorderStyleType`.
Returns:
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"""
Create a new MenuBorderStyle instance based on the given border style type.
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border_style_type (int): an integer value from :obj:`MenuBorderStyleType`.
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aegirhall/console-menu | consolemenu/format/menu_borders.py | MenuBorderStyleFactory.is_win_python35_or_earlier | def is_win_python35_or_earlier():
"""
Convenience method to determine if the current platform is Windows and Python version 3.5 or earlier.
Returns:
bool: True if the current platform is Windows and the Python interpreter is 3.5 or earlier; False otherwise.
"""
retu... | python | def is_win_python35_or_earlier():
"""
Convenience method to determine if the current platform is Windows and Python version 3.5 or earlier.
Returns:
bool: True if the current platform is Windows and the Python interpreter is 3.5 or earlier; False otherwise.
"""
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aegirhall/console-menu | consolemenu/items/submenu_item.py | SubmenuItem.set_menu | def set_menu(self, menu):
"""
Sets the menu of this item.
Should be used instead of directly accessing the menu attribute for this class.
:param ConsoleMenu menu: the menu
"""
self.menu = menu
self.submenu.parent = menu | python | def set_menu(self, menu):
"""
Sets the menu of this item.
Should be used instead of directly accessing the menu attribute for this class.
:param ConsoleMenu menu: the menu
"""
self.menu = menu
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aegirhall/console-menu | consolemenu/validators/regex.py | RegexValidator.validate | def validate(self, input_string):
"""
Validate input_string against a regex pattern
:return: True if match / False otherwise
"""
validation_result = False
try:
validation_result = bool(match(pattern=self.pattern, string=input_string))
except TypeError... | python | def validate(self, input_string):
"""
Validate input_string against a regex pattern
:return: True if match / False otherwise
"""
validation_result = False
try:
validation_result = bool(match(pattern=self.pattern, string=input_string))
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aegirhall/console-menu | consolemenu/multiselect_menu.py | MultiSelectMenu.process_user_input | def process_user_input(self):
"""
This overrides the method in ConsoleMenu to allow for comma-delimited and range inputs.
Examples:
All of the following inputs would have the same result:
* 1,2,3,4
* 1-4
* 1-2,3-4
* 1 -... | python | def process_user_input(self):
"""
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Examples:
All of the following inputs would have the same result:
* 1,2,3,4
* 1-4
* 1-2,3-4
* 1 -... | [
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aegirhall/console-menu | consolemenu/console_menu.py | ConsoleMenu.remove_item | def remove_item(self, item):
"""
Remove the specified item from the menu.
Args:
item (MenuItem): the item to be removed.
Returns:
bool: True if the item was removed; False otherwise.
"""
for idx, _item in enumerate(self.items):
if ite... | python | def remove_item(self, item):
"""
Remove the specified item from the menu.
Args:
item (MenuItem): the item to be removed.
Returns:
bool: True if the item was removed; False otherwise.
"""
for idx, _item in enumerate(self.items):
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aegirhall/console-menu | consolemenu/console_menu.py | ConsoleMenu.remove_exit | def remove_exit(self):
"""
Remove the exit item if necessary. Used to make sure we only remove the exit item, not something else.
Returns:
bool: True if item needed to be removed, False otherwise.
"""
if self.items:
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... | python | def remove_exit(self):
"""
Remove the exit item if necessary. Used to make sure we only remove the exit item, not something else.
Returns:
bool: True if item needed to be removed, False otherwise.
"""
if self.items:
if self.items[-1] is self.exit_item:
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aegirhall/console-menu | consolemenu/console_menu.py | ConsoleMenu.draw | def draw(self):
"""
Refresh the screen and redraw the menu. Should be called whenever something changes that needs to be redrawn.
"""
self.screen.printf(self.formatter.format(title=self.title, subtitle=self.subtitle, items=self.items,
prol... | python | def draw(self):
"""
Refresh the screen and redraw the menu. Should be called whenever something changes that needs to be redrawn.
"""
self.screen.printf(self.formatter.format(title=self.title, subtitle=self.subtitle, items=self.items,
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aegirhall/console-menu | consolemenu/console_menu.py | ConsoleMenu.process_user_input | def process_user_input(self):
"""
Gets the next single character and decides what to do with it
"""
user_input = self.get_input()
try:
num = int(user_input)
except Exception:
return
if 0 < num < len(self.items) + 1:
self.curren... | python | def process_user_input(self):
"""
Gets the next single character and decides what to do with it
"""
user_input = self.get_input()
try:
num = int(user_input)
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return
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aegirhall/console-menu | consolemenu/console_menu.py | ConsoleMenu.go_down | def go_down(self):
"""
Go down one, wrap to beginning if necessary
"""
if self.current_option < len(self.items) - 1:
self.current_option += 1
else:
self.current_option = 0
self.draw() | python | def go_down(self):
"""
Go down one, wrap to beginning if necessary
"""
if self.current_option < len(self.items) - 1:
self.current_option += 1
else:
self.current_option = 0
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aegirhall/console-menu | consolemenu/console_menu.py | ConsoleMenu.go_up | def go_up(self):
"""
Go up one, wrap to end if necessary
"""
if self.current_option > 0:
self.current_option += -1
else:
self.current_option = len(self.items) - 1
self.draw() | python | def go_up(self):
"""
Go up one, wrap to end if necessary
"""
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self.current_option += -1
else:
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aegirhall/console-menu | consolemenu/selection_menu.py | SelectionMenu.get_selection | def get_selection(cls, strings, title="Select an option", subtitle=None, exit_option=True, _menu=None):
"""
Single-method way of getting a selection out of a list of strings.
Args:
strings (:obj:`list` of :obj:`str`): The list of strings this menu should be built from.
... | python | def get_selection(cls, strings, title="Select an option", subtitle=None, exit_option=True, _menu=None):
"""
Single-method way of getting a selection out of a list of strings.
Args:
strings (:obj:`list` of :obj:`str`): The list of strings this menu should be built from.
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_object_is_ordered_dict | def ensure_object_is_ordered_dict(item, title):
"""
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"""
assert isinstance(title, str)
if not isinstance(item, OrderedDict):
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raise TypeError(msg.format(title, type... | python | def ensure_object_is_ordered_dict(item, title):
"""
Checks that the item is an OrderedDict. If not, raises ValueError.
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assert isinstance(title, str)
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_object_is_string | def ensure_object_is_string(item, title):
"""
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"""
assert isinstance(title, str)
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return None | python | def ensure_object_is_string(item, title):
"""
Checks that the item is a string. If not, raises ValueError.
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assert isinstance(title, str)
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_object_is_ndarray | def ensure_object_is_ndarray(item, title):
"""
Ensures that a given mapping matrix is a dense numpy array. Raises a
helpful TypeError if otherwise.
"""
assert isinstance(title, str)
if not isinstance(item, np.ndarray):
msg = "{} must be a np.ndarray. {} passed instead."
raise Ty... | python | def ensure_object_is_ndarray(item, title):
"""
Ensures that a given mapping matrix is a dense numpy array. Raises a
helpful TypeError if otherwise.
"""
assert isinstance(title, str)
if not isinstance(item, np.ndarray):
msg = "{} must be a np.ndarray. {} passed instead."
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_columns_are_in_dataframe | def ensure_columns_are_in_dataframe(columns,
dataframe,
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data_title='data'):
"""
Checks whether each column in `columns` is in `dataframe`. Raises
ValueError if any of the columns are not... | python | def ensure_columns_are_in_dataframe(columns,
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timothyb0912/pylogit | pylogit/choice_tools.py | check_argument_type | def check_argument_type(long_form, specification_dict):
"""
Ensures that long_form is a pandas dataframe and that specification_dict
is an OrderedDict, raising a ValueError otherwise.
Parameters
----------
long_form : pandas dataframe.
Contains one row for each available alternative, fo... | python | def check_argument_type(long_form, specification_dict):
"""
Ensures that long_form is a pandas dataframe and that specification_dict
is an OrderedDict, raising a ValueError otherwise.
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long_form : pandas dataframe.
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_alt_id_in_long_form | def ensure_alt_id_in_long_form(alt_id_col, long_form):
"""
Ensures alt_id_col is in long_form, and raises a ValueError if not.
Parameters
----------
alt_id_col : str.
Column name which denotes the column in `long_form` that contains the
alternative ID for each row in `long_form`.
... | python | def ensure_alt_id_in_long_form(alt_id_col, long_form):
"""
Ensures alt_id_col is in long_form, and raises a ValueError if not.
Parameters
----------
alt_id_col : str.
Column name which denotes the column in `long_form` that contains the
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_specification_cols_are_in_dataframe | def ensure_specification_cols_are_in_dataframe(specification, dataframe):
"""
Checks whether each column in `specification` is in `dataframe`. Raises
ValueError if any of the columns are not in the dataframe.
Parameters
----------
specification : OrderedDict.
Keys are a proper subset of... | python | def ensure_specification_cols_are_in_dataframe(specification, dataframe):
"""
Checks whether each column in `specification` is in `dataframe`. Raises
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Parameters
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timothyb0912/pylogit | pylogit/choice_tools.py | check_keys_and_values_of_name_dictionary | def check_keys_and_values_of_name_dictionary(names,
specification_dict,
num_alts):
"""
Check the validity of the keys and values in the names dictionary.
Parameters
----------
names : OrderedDict, optional.
... | python | def check_keys_and_values_of_name_dictionary(names,
specification_dict,
num_alts):
"""
Check the validity of the keys and values in the names dictionary.
Parameters
----------
names : OrderedDict, optional.
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_all_columns_are_used | def ensure_all_columns_are_used(num_vars_accounted_for,
dataframe,
data_title='long_data'):
"""
Ensure that all of the columns from dataframe are in the list of used_cols.
Will raise a helpful UserWarning if otherwise.
Parameters
-----... | python | def ensure_all_columns_are_used(num_vars_accounted_for,
dataframe,
data_title='long_data'):
"""
Ensure that all of the columns from dataframe are in the list of used_cols.
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timothyb0912/pylogit | pylogit/choice_tools.py | check_dataframe_for_duplicate_records | def check_dataframe_for_duplicate_records(obs_id_col, alt_id_col, df):
"""
Checks a cross-sectional dataframe of long-format data for duplicate
observations. Duplicate observations are defined as rows with the same
observation id value and the same alternative id value.
Parameters
----------
... | python | def check_dataframe_for_duplicate_records(obs_id_col, alt_id_col, df):
"""
Checks a cross-sectional dataframe of long-format data for duplicate
observations. Duplicate observations are defined as rows with the same
observation id value and the same alternative id value.
Parameters
----------
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_num_chosen_alts_equals_num_obs | def ensure_num_chosen_alts_equals_num_obs(obs_id_col, choice_col, df):
"""
Checks that the total number of recorded choices equals the total number of
observations. If this is not the case, raise helpful ValueError messages.
Parameters
----------
obs_id_col : str.
Denotes the column in ... | python | def ensure_num_chosen_alts_equals_num_obs(obs_id_col, choice_col, df):
"""
Checks that the total number of recorded choices equals the total number of
observations. If this is not the case, raise helpful ValueError messages.
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timothyb0912/pylogit | pylogit/choice_tools.py | check_type_and_values_of_alt_name_dict | def check_type_and_values_of_alt_name_dict(alt_name_dict, alt_id_col, df):
"""
Ensures that `alt_name_dict` is a dictionary and that its keys are in the
alternative id column of `df`. Raises helpful errors if either condition
is not met.
Parameters
----------
alt_name_dict : dict.
A... | python | def check_type_and_values_of_alt_name_dict(alt_name_dict, alt_id_col, df):
"""
Ensures that `alt_name_dict` is a dictionary and that its keys are in the
alternative id column of `df`. Raises helpful errors if either condition
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alt_name_dict : dict.
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_ridge_is_scalar_or_none | def ensure_ridge_is_scalar_or_none(ridge):
"""
Ensures that `ridge` is either None or a scalar value. Raises a helpful
TypeError otherwise.
Parameters
----------
ridge : int, float, long, or None.
Scalar value or None, determining the L2-ridge regression penalty.
Returns
------... | python | def ensure_ridge_is_scalar_or_none(ridge):
"""
Ensures that `ridge` is either None or a scalar value. Raises a helpful
TypeError otherwise.
Parameters
----------
ridge : int, float, long, or None.
Scalar value or None, determining the L2-ridge regression penalty.
Returns
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timothyb0912/pylogit | pylogit/choice_tools.py | get_original_order_unique_ids | def get_original_order_unique_ids(id_array):
"""
Get the unique id's of id_array, in their original order of appearance.
Parameters
----------
id_array : 1D ndarray.
Should contain the ids that we want to extract the unique values from.
Returns
-------
original_order_unique_ids... | python | def get_original_order_unique_ids(id_array):
"""
Get the unique id's of id_array, in their original order of appearance.
Parameters
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id_array : 1D ndarray.
Should contain the ids that we want to extract the unique values from.
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timothyb0912/pylogit | pylogit/choice_tools.py | create_sparse_mapping | def create_sparse_mapping(id_array, unique_ids=None):
"""
Will create a scipy.sparse compressed-sparse-row matrix that maps
each row represented by an element in id_array to the corresponding
value of the unique ids in id_array.
Parameters
----------
id_array : 1D ndarray of ints.
E... | python | def create_sparse_mapping(id_array, unique_ids=None):
"""
Will create a scipy.sparse compressed-sparse-row matrix that maps
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value of the unique ids in id_array.
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id_array : 1D ndarray of ints.
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timothyb0912/pylogit | pylogit/choice_tools.py | check_wide_data_for_blank_choices | def check_wide_data_for_blank_choices(choice_col, wide_data):
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Checks `wide_data` for null values in the choice column, and raises a
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----------
choice_col : str.
Denotes the column in `wide_data` that is used to record each
... | python | def check_wide_data_for_blank_choices(choice_col, wide_data):
"""
Checks `wide_data` for null values in the choice column, and raises a
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_unique_obs_ids_in_wide_data | def ensure_unique_obs_ids_in_wide_data(obs_id_col, wide_data):
"""
Ensures that there is one observation per row in wide_data. Raises a
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Parameters
----------
obs_id_col : str.
Denotes the column in `wide_data` that contains the observation ID
val... | python | def ensure_unique_obs_ids_in_wide_data(obs_id_col, wide_data):
"""
Ensures that there is one observation per row in wide_data. Raises a
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_chosen_alternatives_are_in_user_alt_ids | def ensure_chosen_alternatives_are_in_user_alt_ids(choice_col,
wide_data,
availability_vars):
"""
Ensures that all chosen alternatives in `wide_df` are present in the
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availability_vars):
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_each_wide_obs_chose_an_available_alternative | def ensure_each_wide_obs_chose_an_available_alternative(obs_id_col,
choice_col,
availability_vars,
wide_data):
"""
Checks whether or not each ob... | python | def ensure_each_wide_obs_chose_an_available_alternative(obs_id_col,
choice_col,
availability_vars,
wide_data):
"""
Checks whether or not each ob... | [
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_all_wide_alt_ids_are_chosen | def ensure_all_wide_alt_ids_are_chosen(choice_col,
alt_specific_vars,
availability_vars,
wide_data):
"""
Checks to make sure all user-specified alternative id's, both in
`alt_specific_vars` a... | python | def ensure_all_wide_alt_ids_are_chosen(choice_col,
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wide_data):
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_contiguity_in_observation_rows | def ensure_contiguity_in_observation_rows(obs_id_vector):
"""
Ensures that all rows pertaining to a given choice situation are located
next to one another. Raises a helpful ValueError otherwise. This check is
needed because the hessian calculation function requires the design matrix
to have contigui... | python | def ensure_contiguity_in_observation_rows(obs_id_vector):
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | relate_obs_ids_to_chosen_alts | def relate_obs_ids_to_chosen_alts(obs_id_array,
alt_id_array,
choice_array):
"""
Creates a dictionary that relates each unique alternative id to the set of
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Parameters
----------
... | python | def relate_obs_ids_to_chosen_alts(obs_id_array,
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choice_array):
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Creates a dictionary that relates each unique alternative id to the set of
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_cross_sectional_bootstrap_samples | def create_cross_sectional_bootstrap_samples(obs_id_array,
alt_id_array,
choice_array,
num_samples,
seed=None):
"""
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seed=None):
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_bootstrap_id_array | def create_bootstrap_id_array(obs_id_per_sample):
"""
Creates a 2D ndarray that contains the 'bootstrap ids' for each replication
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Parameters
----------
obs_id_per_sample : 2D ndarray of ints.
Should have one row for ea... | python | def create_bootstrap_id_array(obs_id_per_sample):
"""
Creates a 2D ndarray that contains the 'bootstrap ids' for each replication
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | check_column_existence | def check_column_existence(col_name, df, presence=True):
"""
Checks whether or not `col_name` is in `df` and raises a helpful error msg
if the desired condition is not met.
Parameters
----------
col_name : str.
Should represent a column whose presence in `df` is to be checked.
df : ... | python | def check_column_existence(col_name, df, presence=True):
"""
Checks whether or not `col_name` is in `df` and raises a helpful error msg
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col_name : str.
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | ensure_resampled_obs_ids_in_df | def ensure_resampled_obs_ids_in_df(resampled_obs_ids, orig_obs_id_array):
"""
Checks whether all ids in `resampled_obs_ids` are in `orig_obs_id_array`.
Raises a helpful ValueError if not.
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"""
Checks whether all ids in `resampled_obs_ids` are in `orig_obs_id_array`.
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_bootstrap_dataframe | def create_bootstrap_dataframe(orig_df,
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boot_id_col="bootstrap_id"):
"""
Will create the altered dataframe of data needed to estimate a choi... | python | def create_bootstrap_dataframe(orig_df,
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] | f83b0fd6debaa7358d87c3828428f6d4ead71357 | https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/bootstrap_sampler.py#L301-L360 | train |
timothyb0912/pylogit | pylogit/bootstrap.py | get_param_names | def get_param_names(model_obj):
"""
Extracts all the names to be displayed for the estimated parameters.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names', 'nest_names']`.
R... | python | def get_param_names(model_obj):
"""
Extracts all the names to be displayed for the estimated parameters.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names', 'nest_names']`.
R... | [
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model_obj : an instance of an MNDC object.
Should have the following attributes:
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Returns
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timothyb0912/pylogit | pylogit/bootstrap.py | get_param_list_for_prediction | def get_param_list_for_prediction(model_obj, replicates):
"""
Create the `param_list` argument for use with `model_obj.predict`.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names'... | python | def get_param_list_for_prediction(model_obj, replicates):
"""
Create the `param_list` argument for use with `model_obj.predict`.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names'... | [
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Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names', 'nest_names']`.
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] | f83b0fd6debaa7358d87c3828428f6d4ead71357 | https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/bootstrap.py#L78-L135 | train |
timothyb0912/pylogit | pylogit/bootstrap.py | Boot.generate_bootstrap_replicates | def generate_bootstrap_replicates(self,
num_samples,
mnl_obj=None,
mnl_init_vals=None,
mnl_fit_kwargs=None,
extract_init_vals=None... | python | def generate_bootstrap_replicates(self,
num_samples,
mnl_obj=None,
mnl_init_vals=None,
mnl_fit_kwargs=None,
extract_init_vals=None... | [
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Parameters
----------
num_samples : positive int.
Specifies the number of bootstrap samples that are to be drawn.
mnl_obj : an instance of pylogit.MNL or None, optional.
Should be the MNL model... | [
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] | f83b0fd6debaa7358d87c3828428f6d4ead71357 | https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/bootstrap.py#L183-L356 | train |
timothyb0912/pylogit | pylogit/bootstrap.py | Boot.generate_jackknife_replicates | def generate_jackknife_replicates(self,
mnl_obj=None,
mnl_init_vals=None,
mnl_fit_kwargs=None,
extract_init_vals=None,
print_res=F... | python | def generate_jackknife_replicates(self,
mnl_obj=None,
mnl_init_vals=None,
mnl_fit_kwargs=None,
extract_init_vals=None,
print_res=F... | [
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",",... | Generates the jackknife replicates for one's given model and dataset.
Parameters
----------
mnl_obj : an instance of pylogit.MNL or None, optional.
Should be the MNL model object that is used to provide starting
values for the final model being estimated. If None, then o... | [
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] | f83b0fd6debaa7358d87c3828428f6d4ead71357 | https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/bootstrap.py#L358-L495 | train |
timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_log_likes_for_replicates | def calc_log_likes_for_replicates(self,
replicates='bootstrap',
num_draws=None,
seed=None):
"""
Calculate the log-likelihood value of one's replicates, given one's
dataset.
... | python | def calc_log_likes_for_replicates(self,
replicates='bootstrap',
num_draws=None,
seed=None):
"""
Calculate the log-likelihood value of one's replicates, given one's
dataset.
... | [
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dataset.
Parameters
----------
replicates : str in {'bootstrap', 'jackknife'}.
Denotes which set of replicates should have their log-likelihoods
calculated.
num_draws : int greater than z... | [
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] | f83b0fd6debaa7358d87c3828428f6d4ead71357 | https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/bootstrap.py#L497-L591 | train |
timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_gradient_norm_for_replicates | def calc_gradient_norm_for_replicates(self,
replicates='bootstrap',
ridge=None,
constrained_pos=None,
weights=None):
"""
Calculate the E... | python | def calc_gradient_norm_for_replicates(self,
replicates='bootstrap',
ridge=None,
constrained_pos=None,
weights=None):
"""
Calculate the E... | [
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Parameters
----------
replicates : str in {'bootstrap', 'jackknife'}.
Denotes which set of replicates should have their log-likelihoods
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ridge : float or Non... | [
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] | f83b0fd6debaa7358d87c3828428f6d4ead71357 | https://github.com/timothyb0912/pylogit/blob/f83b0fd6debaa7358d87c3828428f6d4ead71357/pylogit/bootstrap.py#L593-L661 | train |
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