body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
f0c4de5b1bbf8b36bdcbd18b95ab18f5bb667284594f92a4f2903931aecc081d | def load_keypoints(file_path):
'\n Load keypoints from a specific file as tuples\n\n Parameters\n ----------\n file_path : str\n path to the file with keypoints\n\n Returns\n -------\n keypoints : list of tuples\n list of keypoint tuples in format (x, y, obj_class)\n\n Note\n ... | Load keypoints from a specific file as tuples
Parameters
----------
file_path : str
path to the file with keypoints
Returns
-------
keypoints : list of tuples
list of keypoint tuples in format (x, y, obj_class)
Note
----
This function serves as helper for the pointdet.utils.dataset.PointsDataset class
and pr... | contextnet/utils/utils.py | load_keypoints | ushakovegor/ContextNetwork | 0 | python | def load_keypoints(file_path):
'\n Load keypoints from a specific file as tuples\n\n Parameters\n ----------\n file_path : str\n path to the file with keypoints\n\n Returns\n -------\n keypoints : list of tuples\n list of keypoint tuples in format (x, y, obj_class)\n\n Note\n ... | def load_keypoints(file_path):
'\n Load keypoints from a specific file as tuples\n\n Parameters\n ----------\n file_path : str\n path to the file with keypoints\n\n Returns\n -------\n keypoints : list of tuples\n list of keypoint tuples in format (x, y, obj_class)\n\n Note\n ... |
7ffb869d7eb856815ccd5f71c0d09015a01347e87f26c1aa9ca2e100de8b092a | def parse_master_yaml(yaml_path):
'\n Imports master yaml and converts paths to make the usable from inside the script\n\n Parameters\n ----------\n yaml_path : str\n path to master yaml from the script\n\n Returns\n -------\n lists : dict of list of str\n dict with lists pf conve... | Imports master yaml and converts paths to make the usable from inside the script
Parameters
----------
yaml_path : str
path to master yaml from the script
Returns
-------
lists : dict of list of str
dict with lists pf converted paths | contextnet/utils/utils.py | parse_master_yaml | ushakovegor/ContextNetwork | 0 | python | def parse_master_yaml(yaml_path):
'\n Imports master yaml and converts paths to make the usable from inside the script\n\n Parameters\n ----------\n yaml_path : str\n path to master yaml from the script\n\n Returns\n -------\n lists : dict of list of str\n dict with lists pf conve... | def parse_master_yaml(yaml_path):
'\n Imports master yaml and converts paths to make the usable from inside the script\n\n Parameters\n ----------\n yaml_path : str\n path to master yaml from the script\n\n Returns\n -------\n lists : dict of list of str\n dict with lists pf conve... |
406a98c0a8755fd68c5e292c0f71174f4df3e64d11a37d9fb374e703c0c9a590 | def compute_distances_no_loops(Y, X):
'\n Compute the distance between each test point in X and each training point\n in self.X_train using no explicit loops.\n\n Input / Output: Same as compute_distances_two_loops\n '
dists = np.zeros((Y.shape[0], X.shape[0]))
dists -= ((2 * X) @ Y.T)
dists... | Compute the distance between each test point in X and each training point
in self.X_train using no explicit loops.
Input / Output: Same as compute_distances_two_loops | contextnet/utils/utils.py | compute_distances_no_loops | ushakovegor/ContextNetwork | 0 | python | def compute_distances_no_loops(Y, X):
'\n Compute the distance between each test point in X and each training point\n in self.X_train using no explicit loops.\n\n Input / Output: Same as compute_distances_two_loops\n '
dists = np.zeros((Y.shape[0], X.shape[0]))
dists -= ((2 * X) @ Y.T)
dists... | def compute_distances_no_loops(Y, X):
'\n Compute the distance between each test point in X and each training point\n in self.X_train using no explicit loops.\n\n Input / Output: Same as compute_distances_two_loops\n '
dists = np.zeros((Y.shape[0], X.shape[0]))
dists -= ((2 * X) @ Y.T)
dists... |
2f5b5288e19cc0f3123c05c9a72b089e93ebd3bccf99651fc5ac2cb448764b75 | def measure(self, object1, object2):
'\n Returns the measure value between two objects\n '
return 0 | Returns the measure value between two objects | contextnet/utils/utils.py | measure | ushakovegor/ContextNetwork | 0 | python | def measure(self, object1, object2):
'\n \n '
return 0 | def measure(self, object1, object2):
'\n \n '
return 0<|docstring|>Returns the measure value between two objects<|endoftext|> |
be53767f60000534d3f848e3acafbedf8c66b146fd92a406d9ba5af92e4e8786 | def matrix(self, container1, container2):
'\n Returns the matrix of measure values between two sets of objects\n Sometimes can be implemented in a faster way than making couplewise measurements\n '
matrix = np.zeros((len(container1), len(container2)))
for (i, object1) in enumerate(conta... | Returns the matrix of measure values between two sets of objects
Sometimes can be implemented in a faster way than making couplewise measurements | contextnet/utils/utils.py | matrix | ushakovegor/ContextNetwork | 0 | python | def matrix(self, container1, container2):
'\n Returns the matrix of measure values between two sets of objects\n Sometimes can be implemented in a faster way than making couplewise measurements\n '
matrix = np.zeros((len(container1), len(container2)))
for (i, object1) in enumerate(conta... | def matrix(self, container1, container2):
'\n Returns the matrix of measure values between two sets of objects\n Sometimes can be implemented in a faster way than making couplewise measurements\n '
matrix = np.zeros((len(container1), len(container2)))
for (i, object1) in enumerate(conta... |
41b9ac6e745e94aea266cf5ad488492adb61e200d7a7b89efb7399222547a608 | def pixel_histogram(img, nbits=None, ax=None, log_scale=True):
'\n Plot pixel value histogram.\n\n Parameters\n ----------\n img : py:class:`~numpy.ndarray`\n 2D or 3D image.\n nbits : int, optional\n Bit-depth of camera data.\n ax : :py:class:`~matplotlib.axes.Axes`, optional\n ... | Plot pixel value histogram.
Parameters
----------
img : py:class:`~numpy.ndarray`
2D or 3D image.
nbits : int, optional
Bit-depth of camera data.
ax : :py:class:`~matplotlib.axes.Axes`, optional
`Axes` object to fill, default is to create one.
log_scale : bool, optional
Whether to use log scale in ... | DiffuserCam/diffcam/plot.py | pixel_histogram | WilliamCappelletti/diffusercam-project | 0 | python | def pixel_histogram(img, nbits=None, ax=None, log_scale=True):
'\n Plot pixel value histogram.\n\n Parameters\n ----------\n img : py:class:`~numpy.ndarray`\n 2D or 3D image.\n nbits : int, optional\n Bit-depth of camera data.\n ax : :py:class:`~matplotlib.axes.Axes`, optional\n ... | def pixel_histogram(img, nbits=None, ax=None, log_scale=True):
'\n Plot pixel value histogram.\n\n Parameters\n ----------\n img : py:class:`~numpy.ndarray`\n 2D or 3D image.\n nbits : int, optional\n Bit-depth of camera data.\n ax : :py:class:`~matplotlib.axes.Axes`, optional\n ... |
4ebe2b22f624a50525973beaca2e62fff69c7aaae783aae85f074c8be89ad500 | def plot_cross_section(vals, idx=None, ax=None, dB=True, plot_db_drop=3, min_val=0.0001, max_val=None, plot_width=None, **kwargs):
'\n Plot cross-section of a 2-D image.\n\n Parameters\n ----------\n vals : py:class:`~numpy.ndarray`\n 2-D image data.\n idx : int, optional\n Row for whic... | Plot cross-section of a 2-D image.
Parameters
----------
vals : py:class:`~numpy.ndarray`
2-D image data.
idx : int, optional
Row for which to plot cross-section. Default is to take middle.
ax : :py:class:`~matplotlib.axes.Axes`, optional
`Axes` object to fill, default is to create one.
dB : bool, optional... | DiffuserCam/diffcam/plot.py | plot_cross_section | WilliamCappelletti/diffusercam-project | 0 | python | def plot_cross_section(vals, idx=None, ax=None, dB=True, plot_db_drop=3, min_val=0.0001, max_val=None, plot_width=None, **kwargs):
'\n Plot cross-section of a 2-D image.\n\n Parameters\n ----------\n vals : py:class:`~numpy.ndarray`\n 2-D image data.\n idx : int, optional\n Row for whic... | def plot_cross_section(vals, idx=None, ax=None, dB=True, plot_db_drop=3, min_val=0.0001, max_val=None, plot_width=None, **kwargs):
'\n Plot cross-section of a 2-D image.\n\n Parameters\n ----------\n vals : py:class:`~numpy.ndarray`\n 2-D image data.\n idx : int, optional\n Row for whic... |
51562f2d4f069fb566b15da1e4ed6e9102b550587c3e02c330707424c78df0dc | def plot_autocorr2d(vals, pad_mode='reflect', ax=None):
'\n Plot 2-D autocorrelation of image.\n\n Parameters\n ----------\n vals : py:class:`~numpy.ndarray`\n 2-D image.\n pad_mode : str\n Desired padding. See NumPy documentation: https://numpy.org/doc/stable/reference/generated/numpy.... | Plot 2-D autocorrelation of image.
Parameters
----------
vals : py:class:`~numpy.ndarray`
2-D image.
pad_mode : str
Desired padding. See NumPy documentation: https://numpy.org/doc/stable/reference/generated/numpy.pad.html
ax : :py:class:`~matplotlib.axes.Axes`, optional
`Axes` object to fill, default i... | DiffuserCam/diffcam/plot.py | plot_autocorr2d | WilliamCappelletti/diffusercam-project | 0 | python | def plot_autocorr2d(vals, pad_mode='reflect', ax=None):
'\n Plot 2-D autocorrelation of image.\n\n Parameters\n ----------\n vals : py:class:`~numpy.ndarray`\n 2-D image.\n pad_mode : str\n Desired padding. See NumPy documentation: https://numpy.org/doc/stable/reference/generated/numpy.... | def plot_autocorr2d(vals, pad_mode='reflect', ax=None):
'\n Plot 2-D autocorrelation of image.\n\n Parameters\n ----------\n vals : py:class:`~numpy.ndarray`\n 2-D image.\n pad_mode : str\n Desired padding. See NumPy documentation: https://numpy.org/doc/stable/reference/generated/numpy.... |
0dd9962b22210a6ff737c6802f3f9a89c9a31f58b1c3c56301f073dc174b09ac | def check_pipeline_parameters(self):
'Check pipeline parameters.'
if ('full_width_at_half_maximum' not in self.parameters.keys()):
self.parameters['full_width_at_half_maximum'] = [8, 8, 8]
if ('t1_native_space' not in self.parameters.keys()):
self.parameters['t1_native_space'] = False
if... | Check pipeline parameters. | pipelines/fmri_preprocessing/fmri_preprocessing_pipeline.py | check_pipeline_parameters | aramis-lab/clinica_pipeline_fmri_preprocessing | 1 | python | def check_pipeline_parameters(self):
if ('full_width_at_half_maximum' not in self.parameters.keys()):
self.parameters['full_width_at_half_maximum'] = [8, 8, 8]
if ('t1_native_space' not in self.parameters.keys()):
self.parameters['t1_native_space'] = False
if ('freesurfer_brain_mask' no... | def check_pipeline_parameters(self):
if ('full_width_at_half_maximum' not in self.parameters.keys()):
self.parameters['full_width_at_half_maximum'] = [8, 8, 8]
if ('t1_native_space' not in self.parameters.keys()):
self.parameters['t1_native_space'] = False
if ('freesurfer_brain_mask' no... |
c8fcfaa17224eb15d04c1abe0461f9ea3cdb1ae5c642ab770c92f2088911dbbf | def get_input_fields(self):
'Specify the list of possible inputs of this pipelines.\n\n Returns:\n A list of (string) input fields name.\n '
if (('unwarping' in self.parameters) and self.parameters['unwarping']):
return ['et', 'blipdir', 'tert', 'time_repetition', 'num_slices', ... | Specify the list of possible inputs of this pipelines.
Returns:
A list of (string) input fields name. | pipelines/fmri_preprocessing/fmri_preprocessing_pipeline.py | get_input_fields | aramis-lab/clinica_pipeline_fmri_preprocessing | 1 | python | def get_input_fields(self):
'Specify the list of possible inputs of this pipelines.\n\n Returns:\n A list of (string) input fields name.\n '
if (('unwarping' in self.parameters) and self.parameters['unwarping']):
return ['et', 'blipdir', 'tert', 'time_repetition', 'num_slices', ... | def get_input_fields(self):
'Specify the list of possible inputs of this pipelines.\n\n Returns:\n A list of (string) input fields name.\n '
if (('unwarping' in self.parameters) and self.parameters['unwarping']):
return ['et', 'blipdir', 'tert', 'time_repetition', 'num_slices', ... |
e279c6f2441d20b5ff74ee605ccaf1a051c22278d65e8c3d6c148d17fa875b29 | def get_output_fields(self):
'Specify the list of possible outputs of this pipelines.\n\n Returns:\n A list of (string) output fields name.\n '
if (('t1_native_space' in self.parameters) and self.parameters['t1_native_space']):
return ['t1_brain_mask', 'mc_params', 'native_fmri'... | Specify the list of possible outputs of this pipelines.
Returns:
A list of (string) output fields name. | pipelines/fmri_preprocessing/fmri_preprocessing_pipeline.py | get_output_fields | aramis-lab/clinica_pipeline_fmri_preprocessing | 1 | python | def get_output_fields(self):
'Specify the list of possible outputs of this pipelines.\n\n Returns:\n A list of (string) output fields name.\n '
if (('t1_native_space' in self.parameters) and self.parameters['t1_native_space']):
return ['t1_brain_mask', 'mc_params', 'native_fmri'... | def get_output_fields(self):
'Specify the list of possible outputs of this pipelines.\n\n Returns:\n A list of (string) output fields name.\n '
if (('t1_native_space' in self.parameters) and self.parameters['t1_native_space']):
return ['t1_brain_mask', 'mc_params', 'native_fmri'... |
74ecf0c7fb3d9a1557516837143edee4814d7da3f53710fceb7054e8a37498d8 | def build_input_node(self):
'Build and connect an input node to the pipelines.\n\n References:\n https://lcni.uoregon.edu/kb-articles/kb-0003\n\n '
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
import json
import numpy as np
from clinica.... | Build and connect an input node to the pipelines.
References:
https://lcni.uoregon.edu/kb-articles/kb-0003 | pipelines/fmri_preprocessing/fmri_preprocessing_pipeline.py | build_input_node | aramis-lab/clinica_pipeline_fmri_preprocessing | 1 | python | def build_input_node(self):
'Build and connect an input node to the pipelines.\n\n References:\n https://lcni.uoregon.edu/kb-articles/kb-0003\n\n '
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
import json
import numpy as np
from clinica.... | def build_input_node(self):
'Build and connect an input node to the pipelines.\n\n References:\n https://lcni.uoregon.edu/kb-articles/kb-0003\n\n '
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
import json
import numpy as np
from clinica.... |
3bfdad0572db2d65299193732e182f939d6aaac40c197d5e903890e67bdfc3ca | def build_output_node(self):
'Build and connect an output node to the pipelines.\n '
import nipype.pipeline.engine as npe
import nipype.interfaces.io as nio
write_node = npe.MapNode(name='WritingCAPS', iterfield=(['container'] + self.get_output_fields()), interface=nio.DataSink(infields=self.get_... | Build and connect an output node to the pipelines. | pipelines/fmri_preprocessing/fmri_preprocessing_pipeline.py | build_output_node | aramis-lab/clinica_pipeline_fmri_preprocessing | 1 | python | def build_output_node(self):
'\n '
import nipype.pipeline.engine as npe
import nipype.interfaces.io as nio
write_node = npe.MapNode(name='WritingCAPS', iterfield=(['container'] + self.get_output_fields()), interface=nio.DataSink(infields=self.get_output_fields()))
write_node.inputs.base_direc... | def build_output_node(self):
'\n '
import nipype.pipeline.engine as npe
import nipype.interfaces.io as nio
write_node = npe.MapNode(name='WritingCAPS', iterfield=(['container'] + self.get_output_fields()), interface=nio.DataSink(infields=self.get_output_fields()))
write_node.inputs.base_direc... |
799f3f87b778833914512edd515e8ba302a97529bc71ce8950f23abaeb367880 | def build_core_nodes(self):
'Build and connect the core nodes of the pipelines.\n '
import fmri_preprocessing_workflows as utils
import nipype.interfaces.utility as nutil
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as npe
from clinica.utils.filemanip import zip_nii, ... | Build and connect the core nodes of the pipelines. | pipelines/fmri_preprocessing/fmri_preprocessing_pipeline.py | build_core_nodes | aramis-lab/clinica_pipeline_fmri_preprocessing | 1 | python | def build_core_nodes(self):
'\n '
import fmri_preprocessing_workflows as utils
import nipype.interfaces.utility as nutil
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as npe
from clinica.utils.filemanip import zip_nii, unzip_nii
unzip_node = npe.MapNode(name='Unzip... | def build_core_nodes(self):
'\n '
import fmri_preprocessing_workflows as utils
import nipype.interfaces.utility as nutil
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as npe
from clinica.utils.filemanip import zip_nii, unzip_nii
unzip_node = npe.MapNode(name='Unzip... |
8a99eb5608796a58569716c3a1f4e27a0d05ecb38bae5dd4d86515ad0cced9d8 | def check_metadata(layer_name, neuron_indices, ideal_activation):
'Checks metadata for errors.\n\n :param layer_name: See doc for `get_saliency_one_neuron`.\n :param neuron_indices: Same.\n :param ideal_activation: Same.\n '
error_checking.assert_is_string(layer_name)
error_checking.assert_is_in... | Checks metadata for errors.
:param layer_name: See doc for `get_saliency_one_neuron`.
:param neuron_indices: Same.
:param ideal_activation: Same. | ml4tc/machine_learning/saliency.py | check_metadata | thunderhoser/ml4tc | 2 | python | def check_metadata(layer_name, neuron_indices, ideal_activation):
'Checks metadata for errors.\n\n :param layer_name: See doc for `get_saliency_one_neuron`.\n :param neuron_indices: Same.\n :param ideal_activation: Same.\n '
error_checking.assert_is_string(layer_name)
error_checking.assert_is_in... | def check_metadata(layer_name, neuron_indices, ideal_activation):
'Checks metadata for errors.\n\n :param layer_name: See doc for `get_saliency_one_neuron`.\n :param neuron_indices: Same.\n :param ideal_activation: Same.\n '
error_checking.assert_is_string(layer_name)
error_checking.assert_is_in... |
bfef33a2a7623f0e87ba9ed2516c0689b93ab42e962746ed2b435eb8e464cf41 | def get_saliency_one_neuron(model_object, three_predictor_matrices, layer_name, neuron_indices, ideal_activation):
'Computes saliency maps with respect to activation of one neuron.\n\n The "relevant neuron" is that whose activation will be used in the numerator\n of the saliency equation. In other words, if ... | Computes saliency maps with respect to activation of one neuron.
The "relevant neuron" is that whose activation will be used in the numerator
of the saliency equation. In other words, if the relevant neuron is n,
the saliency of each predictor x will be d(a_n) / dx, where a_n is the
activation of n.
:param model_obj... | ml4tc/machine_learning/saliency.py | get_saliency_one_neuron | thunderhoser/ml4tc | 2 | python | def get_saliency_one_neuron(model_object, three_predictor_matrices, layer_name, neuron_indices, ideal_activation):
'Computes saliency maps with respect to activation of one neuron.\n\n The "relevant neuron" is that whose activation will be used in the numerator\n of the saliency equation. In other words, if ... | def get_saliency_one_neuron(model_object, three_predictor_matrices, layer_name, neuron_indices, ideal_activation):
'Computes saliency maps with respect to activation of one neuron.\n\n The "relevant neuron" is that whose activation will be used in the numerator\n of the saliency equation. In other words, if ... |
6a4eab573131d9f4234103ca3cf0e87f2feaa7d22eab281eef9f182fe4161314 | def write_composite_file(netcdf_file_name, three_saliency_matrices, three_input_grad_matrices, three_predictor_matrices, model_file_name, use_pmm, pmm_max_percentile_level=None):
'Writes composite saliency map to NetCDF file.\n\n :param netcdf_file_name: Path to output file.\n :param three_saliency_matrices: ... | Writes composite saliency map to NetCDF file.
:param netcdf_file_name: Path to output file.
:param three_saliency_matrices: length-3 list, where each element is either
None or a numpy array of saliency values. three_saliency_matrices[i]
should have the same shape as the [i]th input tensor to the model, but
... | ml4tc/machine_learning/saliency.py | write_composite_file | thunderhoser/ml4tc | 2 | python | def write_composite_file(netcdf_file_name, three_saliency_matrices, three_input_grad_matrices, three_predictor_matrices, model_file_name, use_pmm, pmm_max_percentile_level=None):
'Writes composite saliency map to NetCDF file.\n\n :param netcdf_file_name: Path to output file.\n :param three_saliency_matrices: ... | def write_composite_file(netcdf_file_name, three_saliency_matrices, three_input_grad_matrices, three_predictor_matrices, model_file_name, use_pmm, pmm_max_percentile_level=None):
'Writes composite saliency map to NetCDF file.\n\n :param netcdf_file_name: Path to output file.\n :param three_saliency_matrices: ... |
97ddf8c469e193e8224d38058694692752cf33609e2d7e615a4d350fe6168e5c | def read_composite_file(netcdf_file_name):
"Reads composite saliency map from NetCDF file.\n\n :param netcdf_file_name: Path to input file.\n :return: saliency_dict: Dictionary with the following keys.\n saliency_dict['three_saliency_matrices']: See doc for\n `write_composite_file`.\n saliency_di... | Reads composite saliency map from NetCDF file.
:param netcdf_file_name: Path to input file.
:return: saliency_dict: Dictionary with the following keys.
saliency_dict['three_saliency_matrices']: See doc for
`write_composite_file`.
saliency_dict['three_input_grad_matrices']: Same.
saliency_dict['three_predictor_matr... | ml4tc/machine_learning/saliency.py | read_composite_file | thunderhoser/ml4tc | 2 | python | def read_composite_file(netcdf_file_name):
"Reads composite saliency map from NetCDF file.\n\n :param netcdf_file_name: Path to input file.\n :return: saliency_dict: Dictionary with the following keys.\n saliency_dict['three_saliency_matrices']: See doc for\n `write_composite_file`.\n saliency_di... | def read_composite_file(netcdf_file_name):
"Reads composite saliency map from NetCDF file.\n\n :param netcdf_file_name: Path to input file.\n :return: saliency_dict: Dictionary with the following keys.\n saliency_dict['three_saliency_matrices']: See doc for\n `write_composite_file`.\n saliency_di... |
4b5a4335c6a5182f84976689071b194b803a5e4f7fb0540363d0836439e234ac | def write_file(netcdf_file_name, three_saliency_matrices, three_input_grad_matrices, cyclone_id_strings, init_times_unix_sec, model_file_name, layer_name, neuron_indices, ideal_activation):
'Writes saliency maps to NetCDF file.\n\n E = number of examples\n\n :param netcdf_file_name: Path to output file.\n ... | Writes saliency maps to NetCDF file.
E = number of examples
:param netcdf_file_name: Path to output file.
:param three_saliency_matrices: length-3 list, where each element is either
None or a numpy array of saliency values. three_saliency_matrices[i]
should have the same shape as the [i]th input tensor to th... | ml4tc/machine_learning/saliency.py | write_file | thunderhoser/ml4tc | 2 | python | def write_file(netcdf_file_name, three_saliency_matrices, three_input_grad_matrices, cyclone_id_strings, init_times_unix_sec, model_file_name, layer_name, neuron_indices, ideal_activation):
'Writes saliency maps to NetCDF file.\n\n E = number of examples\n\n :param netcdf_file_name: Path to output file.\n ... | def write_file(netcdf_file_name, three_saliency_matrices, three_input_grad_matrices, cyclone_id_strings, init_times_unix_sec, model_file_name, layer_name, neuron_indices, ideal_activation):
'Writes saliency maps to NetCDF file.\n\n E = number of examples\n\n :param netcdf_file_name: Path to output file.\n ... |
77ec04955554a5fa54ac3d44673ac318b776b7b11190419bf08ee417a8bf4d5d | def read_file(netcdf_file_name):
"Reads saliency maps from NetCDF file.\n\n :param netcdf_file_name: Path to input file.\n :return: saliency_dict: Dictionary with the following keys.\n saliency_dict['three_saliency_matrices']: See doc for `write_file`.\n saliency_dict['three_input_grad_matrices']: Same.... | Reads saliency maps from NetCDF file.
:param netcdf_file_name: Path to input file.
:return: saliency_dict: Dictionary with the following keys.
saliency_dict['three_saliency_matrices']: See doc for `write_file`.
saliency_dict['three_input_grad_matrices']: Same.
saliency_dict['cyclone_id_strings']: Same.
saliency_dict['... | ml4tc/machine_learning/saliency.py | read_file | thunderhoser/ml4tc | 2 | python | def read_file(netcdf_file_name):
"Reads saliency maps from NetCDF file.\n\n :param netcdf_file_name: Path to input file.\n :return: saliency_dict: Dictionary with the following keys.\n saliency_dict['three_saliency_matrices']: See doc for `write_file`.\n saliency_dict['three_input_grad_matrices']: Same.... | def read_file(netcdf_file_name):
"Reads saliency maps from NetCDF file.\n\n :param netcdf_file_name: Path to input file.\n :return: saliency_dict: Dictionary with the following keys.\n saliency_dict['three_saliency_matrices']: See doc for `write_file`.\n saliency_dict['three_input_grad_matrices']: Same.... |
95375aedb115f017568215e9ec0d3721e8fccf4e90b110ae27bb16d75508a3c9 | def fit_eval(self, x, y, validation_data=None, mc=False, verbose=0, epochs=1, metric='mse', **config):
'\n fit_eval will build a model at the first time it is built\n config will be updated for the second or later times with only non-model-arch\n params be functional\n TODO: check the up... | fit_eval will build a model at the first time it is built
config will be updated for the second or later times with only non-model-arch
params be functional
TODO: check the updated params and decide if the model is needed to be rebuilt | pyzoo/zoo/automl/model/base_pytorch_model.py | fit_eval | OpheliaLjh/analytics-zoo | 4 | python | def fit_eval(self, x, y, validation_data=None, mc=False, verbose=0, epochs=1, metric='mse', **config):
'\n fit_eval will build a model at the first time it is built\n config will be updated for the second or later times with only non-model-arch\n params be functional\n TODO: check the up... | def fit_eval(self, x, y, validation_data=None, mc=False, verbose=0, epochs=1, metric='mse', **config):
'\n fit_eval will build a model at the first time it is built\n config will be updated for the second or later times with only non-model-arch\n params be functional\n TODO: check the up... |
d70cd16c2e6f7ab6e2b4037763da3b2125a3dd26721d7475ea0098a78b9419aa | @classmethod
def run(cls, benchmark, _, idfile: str) -> None:
'Generate random results\n\n Args:\n benchmark: the `lib.benchmark.base.Benchmark` object that has\n ordered running a series.\n _: ignored.\n idfile: the output file to store the results.\n '
... | Generate random results
Args:
benchmark: the `lib.benchmark.base.Benchmark` object that has
ordered running a series.
_: ignored.
idfile: the output file to store the results. | tools/perf/lib/benchmark/runner/dummy.py | run | sinkinben/rpma | 2 | python | @classmethod
def run(cls, benchmark, _, idfile: str) -> None:
'Generate random results\n\n Args:\n benchmark: the `lib.benchmark.base.Benchmark` object that has\n ordered running a series.\n _: ignored.\n idfile: the output file to store the results.\n '
... | @classmethod
def run(cls, benchmark, _, idfile: str) -> None:
'Generate random results\n\n Args:\n benchmark: the `lib.benchmark.base.Benchmark` object that has\n ordered running a series.\n _: ignored.\n idfile: the output file to store the results.\n '
... |
1eb303deec444b045491b70fcc3e76194b26b02e62c69f8105b44bb8b1de8003 | def discreteSampling(weights, domain, nrSamples):
'Samples from a discrete probability distribution.\n \n Parameters\n ----------\n weights : 1-D array_like\n Probability mass function.\n domain : 1-D array_like\n Categories or indices.\n nrSamples : int\n Number of samples.\n... | Samples from a discrete probability distribution.
Parameters
----------
weights : 1-D array_like
Probability mass function.
domain : 1-D array_like
Categories or indices.
nrSamples : int
Number of samples.
Returns
-------
domain : 1-D array_like
Sampled categories.
Examples
--------
>>> w = n... | src/pyResampling.py | discreteSampling | can-cs/pyResampling | 0 | python | def discreteSampling(weights, domain, nrSamples):
'Samples from a discrete probability distribution.\n \n Parameters\n ----------\n weights : 1-D array_like\n Probability mass function.\n domain : 1-D array_like\n Categories or indices.\n nrSamples : int\n Number of samples.\n... | def discreteSampling(weights, domain, nrSamples):
'Samples from a discrete probability distribution.\n \n Parameters\n ----------\n weights : 1-D array_like\n Probability mass function.\n domain : 1-D array_like\n Categories or indices.\n nrSamples : int\n Number of samples.\n... |
c9dfaa81f25edf4d3fd2b74d88449b6d9450930b636650e035482bcbc6132a7e | def resampling(w, scheme='mult'):
"Resampling of particle indices.\n \n Parameters\n ----------\n w : 1-D array_like\n Normalized weights\n scheme : string\n Resampling scheme to use:\n \n mult : Multinomial resampling\n \n res : Residual resampling\n ... | Resampling of particle indices.
Parameters
----------
w : 1-D array_like
Normalized weights
scheme : string
Resampling scheme to use:
mult : Multinomial resampling
res : Residual resampling
strat : Stratified resampling
sys : Systematic resampling
Returns
-------
ind : 1-D ... | src/pyResampling.py | resampling | can-cs/pyResampling | 0 | python | def resampling(w, scheme='mult'):
"Resampling of particle indices.\n \n Parameters\n ----------\n w : 1-D array_like\n Normalized weights\n scheme : string\n Resampling scheme to use:\n \n mult : Multinomial resampling\n \n res : Residual resampling\n ... | def resampling(w, scheme='mult'):
"Resampling of particle indices.\n \n Parameters\n ----------\n w : 1-D array_like\n Normalized weights\n scheme : string\n Resampling scheme to use:\n \n mult : Multinomial resampling\n \n res : Residual resampling\n ... |
233df30edca811f1bf3ceef12eb47fe29bcc05c537ad09964e3fe1a506205227 | @click.command()
@click.argument('config_file')
def run(config_file):
'This program is the starting point for every neural_network. It pulls together the configuration and all necessary\n neural_network classes to load\n\n '
config = load_config(config_file)
config_global = config['global']
sess_c... | This program is the starting point for every neural_network. It pulls together the configuration and all necessary
neural_network classes to load | nn/run_experiment.py | run | UKPLab/lsdsem2017-story-cloze | 12 | python | @click.command()
@click.argument('config_file')
def run(config_file):
'This program is the starting point for every neural_network. It pulls together the configuration and all necessary\n neural_network classes to load\n\n '
config = load_config(config_file)
config_global = config['global']
sess_c... | @click.command()
@click.argument('config_file')
def run(config_file):
'This program is the starting point for every neural_network. It pulls together the configuration and all necessary\n neural_network classes to load\n\n '
config = load_config(config_file)
config_global = config['global']
sess_c... |
80a493239328fe84f715c7701a69e7cd1c809a8ebee5aba4b616330b15210f4b | def numJewelsInStones(self, J, S):
'\n :type J: str\n :type S: str\n :rtype: int\n '
count = 0
for jewel in J:
for stone in S:
if (jewel == stone):
count += 1
return count | :type J: str
:type S: str
:rtype: int | algorithm/leetcode/2018-03-25.py | numJewelsInStones | mhoonjeon/problemsolving | 0 | python | def numJewelsInStones(self, J, S):
'\n :type J: str\n :type S: str\n :rtype: int\n '
count = 0
for jewel in J:
for stone in S:
if (jewel == stone):
count += 1
return count | def numJewelsInStones(self, J, S):
'\n :type J: str\n :type S: str\n :rtype: int\n '
count = 0
for jewel in J:
for stone in S:
if (jewel == stone):
count += 1
return count<|docstring|>:type J: str
:type S: str
:rtype: int<|endoftext|> |
0fdfe218951efa08bf7ccc278266528fde7d2ad4a9303e0f8bae060bdb30e292 | def numberOfLines(self, widths, S):
'\n :type widths: List[int]\n :type S: str\n :rtype: List[int]\n '
lines = 1
line_width = 0
for ch in S:
index = (ord(ch) - ord('a'))
if ((line_width + widths[index]) <= 100):
line_width += widths[index]
else:
... | :type widths: List[int]
:type S: str
:rtype: List[int] | algorithm/leetcode/2018-03-25.py | numberOfLines | mhoonjeon/problemsolving | 0 | python | def numberOfLines(self, widths, S):
'\n :type widths: List[int]\n :type S: str\n :rtype: List[int]\n '
lines = 1
line_width = 0
for ch in S:
index = (ord(ch) - ord('a'))
if ((line_width + widths[index]) <= 100):
line_width += widths[index]
else:
... | def numberOfLines(self, widths, S):
'\n :type widths: List[int]\n :type S: str\n :rtype: List[int]\n '
lines = 1
line_width = 0
for ch in S:
index = (ord(ch) - ord('a'))
if ((line_width + widths[index]) <= 100):
line_width += widths[index]
else:
... |
dcf38366be4da12e81a582f9d1a34d5edfd08243f5f93f4cf9c18866fcb621c2 | def uniqueMorseRepresentations(self, words):
'\n :type words: List[str]\n :rtype: int\n '
word_set = []
for word in words:
s = ''
for ch in word:
s += self.alpha_morse[ch]
word_set.append(s)
return len(list(set(word_set))) | :type words: List[str]
:rtype: int | algorithm/leetcode/2018-03-25.py | uniqueMorseRepresentations | mhoonjeon/problemsolving | 0 | python | def uniqueMorseRepresentations(self, words):
'\n :type words: List[str]\n :rtype: int\n '
word_set = []
for word in words:
s =
for ch in word:
s += self.alpha_morse[ch]
word_set.append(s)
return len(list(set(word_set))) | def uniqueMorseRepresentations(self, words):
'\n :type words: List[str]\n :rtype: int\n '
word_set = []
for word in words:
s =
for ch in word:
s += self.alpha_morse[ch]
word_set.append(s)
return len(list(set(word_set)))<|docstring|>:type words: L... |
dd3c81d849de39e6d1754378a3ff733dfb56eff3ccde26ba2c4390103f9fa74a | def _quick_sub_sort_tail(self, start, end):
'循环版本，模拟尾递归，可以大大减少递归栈深度，而且时间复杂度不变'
while (start < end):
pivot = self._rand_partition(start, end)
if ((pivot - start) < (end - pivot)):
self._quick_sub_sort_tail(start, (pivot - 1))
start = (pivot + 1)
else:
s... | 循环版本，模拟尾递归，可以大大减少递归栈深度，而且时间复杂度不变 | algorithms/ch02sort/m05_quick_sort.py | _quick_sub_sort_tail | yidao620c/core-algorithm | 819 | python | def _quick_sub_sort_tail(self, start, end):
while (start < end):
pivot = self._rand_partition(start, end)
if ((pivot - start) < (end - pivot)):
self._quick_sub_sort_tail(start, (pivot - 1))
start = (pivot + 1)
else:
self._quick_sub_sort_tail((pivot + ... | def _quick_sub_sort_tail(self, start, end):
while (start < end):
pivot = self._rand_partition(start, end)
if ((pivot - start) < (end - pivot)):
self._quick_sub_sort_tail(start, (pivot - 1))
start = (pivot + 1)
else:
self._quick_sub_sort_tail((pivot + ... |
39af815b4453e92f45433fcdc89b84d511f02a5dc040abff639a6db948b52c72 | def _rand_partition(self, start, end):
'分解子数组： 随机化版本'
pivot = randint(start, end)
(self.seq[pivot], self.seq[end]) = (self.seq[end], self.seq[pivot])
pivot_value = self.seq[end]
i = (start - 1)
for j in range(start, end):
if (self.seq[j] <= pivot_value):
i += 1
(s... | 分解子数组： 随机化版本 | algorithms/ch02sort/m05_quick_sort.py | _rand_partition | yidao620c/core-algorithm | 819 | python | def _rand_partition(self, start, end):
pivot = randint(start, end)
(self.seq[pivot], self.seq[end]) = (self.seq[end], self.seq[pivot])
pivot_value = self.seq[end]
i = (start - 1)
for j in range(start, end):
if (self.seq[j] <= pivot_value):
i += 1
(self.seq[i], se... | def _rand_partition(self, start, end):
pivot = randint(start, end)
(self.seq[pivot], self.seq[end]) = (self.seq[end], self.seq[pivot])
pivot_value = self.seq[end]
i = (start - 1)
for j in range(start, end):
if (self.seq[j] <= pivot_value):
i += 1
(self.seq[i], se... |
ab4725ac78e7533804e78e555f1bd0f7aa245feab368e07215c190e8f5cc222c | def _quick_sub_sort_recursive(self, start, end):
'递归版本的'
if (start < end):
q = self._rand_partition(start, end)
self._quick_sub_sort_recursive(start, (q - 1))
self._quick_sub_sort_recursive((q + 1), end) | 递归版本的 | algorithms/ch02sort/m05_quick_sort.py | _quick_sub_sort_recursive | yidao620c/core-algorithm | 819 | python | def _quick_sub_sort_recursive(self, start, end):
if (start < end):
q = self._rand_partition(start, end)
self._quick_sub_sort_recursive(start, (q - 1))
self._quick_sub_sort_recursive((q + 1), end) | def _quick_sub_sort_recursive(self, start, end):
if (start < end):
q = self._rand_partition(start, end)
self._quick_sub_sort_recursive(start, (q - 1))
self._quick_sub_sort_recursive((q + 1), end)<|docstring|>递归版本的<|endoftext|> |
c131ad33c0c4049a4069a8ae7ca337712b87112004bb883aba3e6ad1f1453463 | def prepare_data(self, obj, data):
'\n Hook for modifying outgoing data\n '
return data | Hook for modifying outgoing data | flask_peewee/rest/__init__.py | prepare_data | rammie/flask-peewee | 0 | python | def prepare_data(self, obj, data):
'\n \n '
return data | def prepare_data(self, obj, data):
'\n \n '
return data<|docstring|>Hook for modifying outgoing data<|endoftext|> |
b0f0667412debe7948f51880923a2b8876469524f333a44ae9619a7dc319bcd6 | def __init__(self) -> None:
'Initialize the internal data structure.'
self._src = None
warnings.warn('The QuadraticProgramToIsing class is deprecated and will be removed in a future release. Use the .to_ising() method on a QuadraticProgram object instead.', DeprecationWarning) | Initialize the internal data structure. | qiskit/optimization/converters/quadratic_program_to_ising.py | __init__ | MartenSkogh/qiskit-aqua | 15 | python | def __init__(self) -> None:
self._src = None
warnings.warn('The QuadraticProgramToIsing class is deprecated and will be removed in a future release. Use the .to_ising() method on a QuadraticProgram object instead.', DeprecationWarning) | def __init__(self) -> None:
self._src = None
warnings.warn('The QuadraticProgramToIsing class is deprecated and will be removed in a future release. Use the .to_ising() method on a QuadraticProgram object instead.', DeprecationWarning)<|docstring|>Initialize the internal data structure.<|endoftext|> |
00a0eeab66be0627b86242216b4ef472b8edced6eac97e4b489b4f740effea0b | def encode(self, op: QuadraticProgram) -> Tuple[(OperatorBase, float)]:
'Convert a problem into a qubit operator\n\n Args:\n op: The optimization problem to be converted. Must be an unconstrained problem with\n binary variables only.\n Returns:\n The qubit operator... | Convert a problem into a qubit operator
Args:
op: The optimization problem to be converted. Must be an unconstrained problem with
binary variables only.
Returns:
The qubit operator of the problem and the shift value.
Raises:
QiskitOptimizationError: If a variable type is not binary.
QiskitOptim... | qiskit/optimization/converters/quadratic_program_to_ising.py | encode | MartenSkogh/qiskit-aqua | 15 | python | def encode(self, op: QuadraticProgram) -> Tuple[(OperatorBase, float)]:
'Convert a problem into a qubit operator\n\n Args:\n op: The optimization problem to be converted. Must be an unconstrained problem with\n binary variables only.\n Returns:\n The qubit operator... | def encode(self, op: QuadraticProgram) -> Tuple[(OperatorBase, float)]:
'Convert a problem into a qubit operator\n\n Args:\n op: The optimization problem to be converted. Must be an unconstrained problem with\n binary variables only.\n Returns:\n The qubit operator... |
acdf5bfd64fb5c3ab557176afbab4616e27ea426d3e195a3342f0ee15ae452ea | def set_fake_reg_key(fake_reg_key: FakeRegistryKey, sub_key: Union[(str, None)]=None, last_modified_ns: Union[(int, None)]=None) -> FakeRegistryKey:
"\n Creates a registry key if it does not exist already\n\n >>> fake_reg_root = FakeRegistryKey()\n >>> assert set_fake_reg_key(fake_reg_key=fake_reg_root, su... | Creates a registry key if it does not exist already
>>> fake_reg_root = FakeRegistryKey()
>>> assert set_fake_reg_key(fake_reg_key=fake_reg_root, sub_key=r'HKEY_LOCAL_MACHINE').full_key == 'HKEY_LOCAL_MACHINE'
>>> assert set_fake_reg_key(fake_reg_key=fake_reg_root,
... sub_key=r'HKEY_LOCAL_MACHINE\SOFTWARE\Microso... | fake_winreg/fake_reg.py | set_fake_reg_key | bitranox/fake_winreg | 2 | python | def set_fake_reg_key(fake_reg_key: FakeRegistryKey, sub_key: Union[(str, None)]=None, last_modified_ns: Union[(int, None)]=None) -> FakeRegistryKey:
"\n Creates a registry key if it does not exist already\n\n >>> fake_reg_root = FakeRegistryKey()\n >>> assert set_fake_reg_key(fake_reg_key=fake_reg_root, su... | def set_fake_reg_key(fake_reg_key: FakeRegistryKey, sub_key: Union[(str, None)]=None, last_modified_ns: Union[(int, None)]=None) -> FakeRegistryKey:
"\n Creates a registry key if it does not exist already\n\n >>> fake_reg_root = FakeRegistryKey()\n >>> assert set_fake_reg_key(fake_reg_key=fake_reg_root, su... |
1196bd53b8923064ebd77c2f4a0fbf49702ed079fbcb5c0f0971b664df377f16 | def get_fake_reg_key(fake_reg_key: FakeRegistryKey, sub_key: str) -> FakeRegistryKey:
'\n >>> # Setup\n >>> fake_reg_root = FakeRegistryKey()\n >>> assert set_fake_reg_key(fake_reg_key=fake_reg_root,\n ... sub_key=r\'HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows NT\').full_key == r\'HKEY_LOCAL_MA... | >>> # Setup
>>> fake_reg_root = FakeRegistryKey()
>>> assert set_fake_reg_key(fake_reg_key=fake_reg_root,
... sub_key=r'HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT').full_key == r'HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT'
>>> # Test existing Key
>>> assert get_fake_reg_key(fake_reg_key=fake_reg_root,
... | fake_winreg/fake_reg.py | get_fake_reg_key | bitranox/fake_winreg | 2 | python | def get_fake_reg_key(fake_reg_key: FakeRegistryKey, sub_key: str) -> FakeRegistryKey:
'\n >>> # Setup\n >>> fake_reg_root = FakeRegistryKey()\n >>> assert set_fake_reg_key(fake_reg_key=fake_reg_root,\n ... sub_key=r\'HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows NT\').full_key == r\'HKEY_LOCAL_MA... | def get_fake_reg_key(fake_reg_key: FakeRegistryKey, sub_key: str) -> FakeRegistryKey:
'\n >>> # Setup\n >>> fake_reg_root = FakeRegistryKey()\n >>> assert set_fake_reg_key(fake_reg_key=fake_reg_root,\n ... sub_key=r\'HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows NT\').full_key == r\'HKEY_LOCAL_MA... |
5d7fa99df2cfd977cc865961b2c062065db57e0e562b70adb479248b79cf6826 | def set_fake_reg_value(fake_reg_key: FakeRegistryKey, sub_key: str, value_name: str, value: Union[(None, bytes, str, List[str], int)], value_type: int=REG_SZ, last_modified_ns: Union[(int, None)]=None) -> FakeRegistryValue:
"\n sets the value of the fake key - we create here keys on the fly, but beware of the la... | sets the value of the fake key - we create here keys on the fly, but beware of the last_modified_ns time !
if You need to have correct last_modified_ns time for each subkey, You need to create those keys first
>>> # Setup
>>> fake_reg_root = FakeRegistryKey()
>>> fake_reg_key = set_fake_reg_key(fake_reg_key=fake_reg_r... | fake_winreg/fake_reg.py | set_fake_reg_value | bitranox/fake_winreg | 2 | python | def set_fake_reg_value(fake_reg_key: FakeRegistryKey, sub_key: str, value_name: str, value: Union[(None, bytes, str, List[str], int)], value_type: int=REG_SZ, last_modified_ns: Union[(int, None)]=None) -> FakeRegistryValue:
"\n sets the value of the fake key - we create here keys on the fly, but beware of the la... | def set_fake_reg_value(fake_reg_key: FakeRegistryKey, sub_key: str, value_name: str, value: Union[(None, bytes, str, List[str], int)], value_type: int=REG_SZ, last_modified_ns: Union[(int, None)]=None) -> FakeRegistryValue:
"\n sets the value of the fake key - we create here keys on the fly, but beware of the la... |
ddee84f411db70d5a35870fe4b3e1d8be582f7861888b75034d20ab4cee127c5 | def get_windows_timestamp_now() -> int:
'\n Windows Timestamp in hundreds of ns since 01.01.1601 – 00:00:00 UTC\n\n >>> assert get_windows_timestamp_now() > 10000\n >>> save_time = get_windows_timestamp_now()\n >>> time.sleep(0.1)\n >>> assert get_windows_timestamp_now() > save_time\n\n '
linu... | Windows Timestamp in hundreds of ns since 01.01.1601 – 00:00:00 UTC
>>> assert get_windows_timestamp_now() > 10000
>>> save_time = get_windows_timestamp_now()
>>> time.sleep(0.1)
>>> assert get_windows_timestamp_now() > save_time | fake_winreg/fake_reg.py | get_windows_timestamp_now | bitranox/fake_winreg | 2 | python | def get_windows_timestamp_now() -> int:
'\n Windows Timestamp in hundreds of ns since 01.01.1601 – 00:00:00 UTC\n\n >>> assert get_windows_timestamp_now() > 10000\n >>> save_time = get_windows_timestamp_now()\n >>> time.sleep(0.1)\n >>> assert get_windows_timestamp_now() > save_time\n\n '
linu... | def get_windows_timestamp_now() -> int:
'\n Windows Timestamp in hundreds of ns since 01.01.1601 – 00:00:00 UTC\n\n >>> assert get_windows_timestamp_now() > 10000\n >>> save_time = get_windows_timestamp_now()\n >>> time.sleep(0.1)\n >>> assert get_windows_timestamp_now() > save_time\n\n '
linu... |
8138cb165b34430b3f2d0c74195497fea3421b51dea0a9f29f22045834f01835 | def __init__(self) -> None:
'\n >>> fake_reg_root = FakeRegistryKey()\n '
self.full_key: str = ''
self.parent_fake_registry_key: Optional[FakeRegistryKey] = None
self.subkeys: Dict[(str, FakeRegistryKey)] = dict()
self.values: Dict[(str, FakeRegistryValue)] = dict()
self.last_modif... | >>> fake_reg_root = FakeRegistryKey() | fake_winreg/fake_reg.py | __init__ | bitranox/fake_winreg | 2 | python | def __init__(self) -> None:
'\n \n '
self.full_key: str =
self.parent_fake_registry_key: Optional[FakeRegistryKey] = None
self.subkeys: Dict[(str, FakeRegistryKey)] = dict()
self.values: Dict[(str, FakeRegistryValue)] = dict()
self.last_modified_ns: int = 0 | def __init__(self) -> None:
'\n \n '
self.full_key: str =
self.parent_fake_registry_key: Optional[FakeRegistryKey] = None
self.subkeys: Dict[(str, FakeRegistryKey)] = dict()
self.values: Dict[(str, FakeRegistryValue)] = dict()
self.last_modified_ns: int = 0<|docstring|>>>> fake_re... |
811d6eb948c6d707f41ba144b12b104aa691e065f073cdd81996a7c874a91a34 | def __init__(self) -> None:
'\n >>> fake_reg_value = FakeRegistryValue()\n '
self.full_key: str = ''
self.value_name: str = ''
self.value: RegData = ''
self.value_type: int = REG_SZ
self.access: int = 0
self.last_modified_ns: Union[(None, int)] = None | >>> fake_reg_value = FakeRegistryValue() | fake_winreg/fake_reg.py | __init__ | bitranox/fake_winreg | 2 | python | def __init__(self) -> None:
'\n \n '
self.full_key: str =
self.value_name: str =
self.value: RegData =
self.value_type: int = REG_SZ
self.access: int = 0
self.last_modified_ns: Union[(None, int)] = None | def __init__(self) -> None:
'\n \n '
self.full_key: str =
self.value_name: str =
self.value: RegData =
self.value_type: int = REG_SZ
self.access: int = 0
self.last_modified_ns: Union[(None, int)] = None<|docstring|>>>> fake_reg_value = FakeRegistryValue()<|endoftext|> |
32fca93c44b1c47f1ac51dd51103c2f700dbac0f1d7688aa749eba3e11545ffd | def restrict2ROI(img, vertices):
'\n Applies an image mask.\n \n Only keeps the region of the image defined by the polygon\n formed from `vertices`. The rest of the image is set to black.\n `vertices` should be a numpy array of integer points.\n '
mask = np.zeros_like(img)
if (len(img.shap... | Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
`vertices` should be a numpy array of integer points. | P2_subroutines.py | restrict2ROI | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def restrict2ROI(img, vertices):
'\n Applies an image mask.\n \n Only keeps the region of the image defined by the polygon\n formed from `vertices`. The rest of the image is set to black.\n `vertices` should be a numpy array of integer points.\n '
mask = np.zeros_like(img)
if (len(img.shap... | def restrict2ROI(img, vertices):
'\n Applies an image mask.\n \n Only keeps the region of the image defined by the polygon\n formed from `vertices`. The rest of the image is set to black.\n `vertices` should be a numpy array of integer points.\n '
mask = np.zeros_like(img)
if (len(img.shap... |
567d2dbe70cd6ec9c6ca1e10d092517c63138d227019b8768ba5c28b85ea5a83 | def gaussian_blur(img, kernel_size):
'Applies a Gaussian Noise kernel'
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0) | Applies a Gaussian Noise kernel | P2_subroutines.py | gaussian_blur | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def gaussian_blur(img, kernel_size):
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0) | def gaussian_blur(img, kernel_size):
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)<|docstring|>Applies a Gaussian Noise kernel<|endoftext|> |
9998f1991fff90f37103d018878f9c7040b21f7500b0a043666dc6a216551545 | def calibrateCamera(FORCE_REDO=False):
' Load images, get the corner positions in image and generate\n the calibration matrix and the distortion coefficients.\n if FORCE_REDO == False; reads previously saved .npz file, if available\n '
if (os.path.isfile('cal_para.npz') and (FORCE_REDO == False... | Load images, get the corner positions in image and generate
the calibration matrix and the distortion coefficients.
if FORCE_REDO == False; reads previously saved .npz file, if available | P2_subroutines.py | calibrateCamera | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def calibrateCamera(FORCE_REDO=False):
' Load images, get the corner positions in image and generate\n the calibration matrix and the distortion coefficients.\n if FORCE_REDO == False; reads previously saved .npz file, if available\n '
if (os.path.isfile('cal_para.npz') and (FORCE_REDO == False... | def calibrateCamera(FORCE_REDO=False):
' Load images, get the corner positions in image and generate\n the calibration matrix and the distortion coefficients.\n if FORCE_REDO == False; reads previously saved .npz file, if available\n '
if (os.path.isfile('cal_para.npz') and (FORCE_REDO == False... |
fedd0a09a146c11034a20609e65120978827438d0914f30f127119408cf0ef8a | def lanepxmask(img_RGB, sobel_kernel=7):
' Take RGB image, perform necessary color transformation /gradient calculations\n and output the detected lane pixels mask, alongside an RGB composition of the 3 sub-masks (added)\n for visualization\n '
MORPH_ENHANCE = True
kernel = cv2.getStructuri... | Take RGB image, perform necessary color transformation /gradient calculations
and output the detected lane pixels mask, alongside an RGB composition of the 3 sub-masks (added)
for visualization | P2_subroutines.py | lanepxmask | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def lanepxmask(img_RGB, sobel_kernel=7):
' Take RGB image, perform necessary color transformation /gradient calculations\n and output the detected lane pixels mask, alongside an RGB composition of the 3 sub-masks (added)\n for visualization\n '
MORPH_ENHANCE = True
kernel = cv2.getStructuri... | def lanepxmask(img_RGB, sobel_kernel=7):
' Take RGB image, perform necessary color transformation /gradient calculations\n and output the detected lane pixels mask, alongside an RGB composition of the 3 sub-masks (added)\n for visualization\n '
MORPH_ENHANCE = True
kernel = cv2.getStructuri... |
6dbe582070fbf432b4eb9d3eaa6d3b2906aab0b565050e4102c2f6480b329004 | def color_preprocessing(img_RGB, GET_BOX=False):
' Apply color-based pre-processing of frames'
box_size = 30
box_ystep = 80
box_vertices = (box_size * np.array([((- 1), (- 1)), ((- 1), 1), (1, 1), (1, (- 1))], dtype=np.int32))
(x_box, y_box) = closePolygon(box_vertices)
image_new = np.copy(img_R... | Apply color-based pre-processing of frames | P2_subroutines.py | color_preprocessing | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def color_preprocessing(img_RGB, GET_BOX=False):
' '
box_size = 30
box_ystep = 80
box_vertices = (box_size * np.array([((- 1), (- 1)), ((- 1), 1), (1, 1), (1, (- 1))], dtype=np.int32))
(x_box, y_box) = closePolygon(box_vertices)
image_new = np.copy(img_RGB)
(Ny, Nx) = np.shape(img_RGB)[0:2]
... | def color_preprocessing(img_RGB, GET_BOX=False):
' '
box_size = 30
box_ystep = 80
box_vertices = (box_size * np.array([((- 1), (- 1)), ((- 1), 1), (1, 1), (1, (- 1))], dtype=np.int32))
(x_box, y_box) = closePolygon(box_vertices)
image_new = np.copy(img_RGB)
(Ny, Nx) = np.shape(img_RGB)[0:2]
... |
d5f0d3348eabedfba5b87b81f232fde45c9d12bfebb40050ee9c9e18b621fe5b | def weight_fit_cfs(left, right):
' judge fit quality, providing weights and a weighted average of the coefficients\n inputs are LaneLine objects '
cfs = np.vstack((left.cf, right.cf))
cf_MSE = np.vstack((left.MSE, right.MSE))
w1 = (np.sum(cf_MSE) / cf_MSE)
w2 = np.reshape((np.array([left.Npix... | judge fit quality, providing weights and a weighted average of the coefficients
inputs are LaneLine objects | P2_subroutines.py | weight_fit_cfs | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def weight_fit_cfs(left, right):
' judge fit quality, providing weights and a weighted average of the coefficients\n inputs are LaneLine objects '
cfs = np.vstack((left.cf, right.cf))
cf_MSE = np.vstack((left.MSE, right.MSE))
w1 = (np.sum(cf_MSE) / cf_MSE)
w2 = np.reshape((np.array([left.Npix... | def weight_fit_cfs(left, right):
' judge fit quality, providing weights and a weighted average of the coefficients\n inputs are LaneLine objects '
cfs = np.vstack((left.cf, right.cf))
cf_MSE = np.vstack((left.MSE, right.MSE))
w1 = (np.sum(cf_MSE) / cf_MSE)
w2 = np.reshape((np.array([left.Npix... |
0f1ea2098a5ed6473a726d508e2dfbfa620cc3409c0bd07c83729221ab0a750b | def find_lane_xy_frommask(mask_input, nwindows=9, margin=100, minpix=50, NO_IMG=False):
' Take the input mask and perform a sliding window search\n Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the\n windows/detections\n **Parameters/Keywords:\n ... | Take the input mask and perform a sliding window search
Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the
windows/detections
**Parameters/Keywords:
nwindows ==> Choose the number of sliding windows
margin ==> Set the width of the windows +/- margin
minpix ==> Se... | P2_subroutines.py | find_lane_xy_frommask | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def find_lane_xy_frommask(mask_input, nwindows=9, margin=100, minpix=50, NO_IMG=False):
' Take the input mask and perform a sliding window search\n Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the\n windows/detections\n **Parameters/Keywords:\n ... | def find_lane_xy_frommask(mask_input, nwindows=9, margin=100, minpix=50, NO_IMG=False):
' Take the input mask and perform a sliding window search\n Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the\n windows/detections\n **Parameters/Keywords:\n ... |
148bd8038e73111cd854f28ce37b9cce55c2ae5fae729aedc8359dacebf4a74e | def find_lane_xy_frompoly(mask_input, polycf_left, polycf_right, margin=80, NO_IMG=False):
' Take the input mask and perform a search around the polynomial-matching area\n Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the\n windows/detections\n *... | Take the input mask and perform a search around the polynomial-matching area
Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the
windows/detections
**Parameters/Keywords:
margin ==> Set the width of the windows +/- margin
NO_IMG ==> do not calculate the diagnose o... | P2_subroutines.py | find_lane_xy_frompoly | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def find_lane_xy_frompoly(mask_input, polycf_left, polycf_right, margin=80, NO_IMG=False):
' Take the input mask and perform a search around the polynomial-matching area\n Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the\n windows/detections\n *... | def find_lane_xy_frompoly(mask_input, polycf_left, polycf_right, margin=80, NO_IMG=False):
' Take the input mask and perform a search around the polynomial-matching area\n Return the coordinates of the located pixels, polynomial coefficients and optionally an image showing the\n windows/detections\n *... |
7328f87317d9a4def5d5ebc605e4e445fb5419288c443fbc6965dd59bea144e9 | def getlane_annotation(mask_shape, polycf_left, polycf_right, img2annotate=[], xmargin=5, ymin=0, PLOT_LINES=False):
' Give the shape and the polynomial coefficients, return byte mask showing the region inside the two curves\n (plus an optional x-margin). Also add the annotations to an image if it is provide... | Give the shape and the polynomial coefficients, return byte mask showing the region inside the two curves
(plus an optional x-margin). Also add the annotations to an image if it is provided.
**Parameters/Keywords:
img2annotate ==> RGB image to annotate, if provided (should match the size of mask_shape!)
xmargin ==> ... | P2_subroutines.py | getlane_annotation | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def getlane_annotation(mask_shape, polycf_left, polycf_right, img2annotate=[], xmargin=5, ymin=0, PLOT_LINES=False):
' Give the shape and the polynomial coefficients, return byte mask showing the region inside the two curves\n (plus an optional x-margin). Also add the annotations to an image if it is provide... | def getlane_annotation(mask_shape, polycf_left, polycf_right, img2annotate=[], xmargin=5, ymin=0, PLOT_LINES=False):
' Give the shape and the polynomial coefficients, return byte mask showing the region inside the two curves\n (plus an optional x-margin). Also add the annotations to an image if it is provide... |
368880227f5858458b0b094590c10129580aff9064f54c36417b743182044bc3 | def cf_px2m(poly_cf_px, img_shape):
' Convert from pixel polynomial coefficients (order 2) to m\n x = f(y), with origin at center/bottom of image, positive y upwards!'
(Ny, Nx) = img_shape[0:2]
ym_per_pix = (30 / 720)
xm_per_pix = (3.7 / 700)
(a, b, c) = poly_cf_px
poly_cf_m = np.array([(... | Convert from pixel polynomial coefficients (order 2) to m
x = f(y), with origin at center/bottom of image, positive y upwards! | P2_subroutines.py | cf_px2m | felipeqda/CarND-Advanced-Lane-Lines | 0 | python | def cf_px2m(poly_cf_px, img_shape):
' Convert from pixel polynomial coefficients (order 2) to m\n x = f(y), with origin at center/bottom of image, positive y upwards!'
(Ny, Nx) = img_shape[0:2]
ym_per_pix = (30 / 720)
xm_per_pix = (3.7 / 700)
(a, b, c) = poly_cf_px
poly_cf_m = np.array([(... | def cf_px2m(poly_cf_px, img_shape):
' Convert from pixel polynomial coefficients (order 2) to m\n x = f(y), with origin at center/bottom of image, positive y upwards!'
(Ny, Nx) = img_shape[0:2]
ym_per_pix = (30 / 720)
xm_per_pix = (3.7 / 700)
(a, b, c) = poly_cf_px
poly_cf_m = np.array([(... |
eb25619189b599323e26c3047025f527bdb84d104b2636b2751a67a52dfb20d4 | def get_direction(ball_angle: float) -> int:
'Get direction to navigate robot to face the ball\n\n Args:\n ball_angle (float): Angle between the ball and the robot\n\n Returns:\n int: 0 = forward, -1 = right, 1 = left\n '
if ((ball_angle >= 340) or (ball_angle <= 20)):
return 0
... | Get direction to navigate robot to face the ball
Args:
ball_angle (float): Angle between the ball and the robot
Returns:
int: 0 = forward, -1 = right, 1 = left | controllers/rcj_soccer_team_yellow/utils.py | get_direction | fdmxfarhan/phasemoon | 0 | python | def get_direction(ball_angle: float) -> int:
'Get direction to navigate robot to face the ball\n\n Args:\n ball_angle (float): Angle between the ball and the robot\n\n Returns:\n int: 0 = forward, -1 = right, 1 = left\n '
if ((ball_angle >= 340) or (ball_angle <= 20)):
return 0
... | def get_direction(ball_angle: float) -> int:
'Get direction to navigate robot to face the ball\n\n Args:\n ball_angle (float): Angle between the ball and the robot\n\n Returns:\n int: 0 = forward, -1 = right, 1 = left\n '
if ((ball_angle >= 340) or (ball_angle <= 20)):
return 0
... |
9fe124824fabbed4eb4583dd8ef6aa475cf042ae4b3f2aea60cb666a12556b7c | def set_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set border style.\n\n Args:\n style: border style\n\n Raises:\n InvalidParamError: border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
raise InvalidParamErr... | Set border style.
Args:
style: border style
Raises:
InvalidParamError: border style does not exist | prettyqt/gui/texttablecellformat.py | set_border_style | phil65/PrettyQt | 7 | python | def set_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set border style.\n\n Args:\n style: border style\n\n Raises:\n InvalidParamError: border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
raise InvalidParamErr... | def set_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set border style.\n\n Args:\n style: border style\n\n Raises:\n InvalidParamError: border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
raise InvalidParamErr... |
4e84fecee05e0756a08a21fb210c9741f17f4b342d6ac2f29b97f0ab6f119b94 | def set_bottom_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set bottom border style.\n\n Args:\n style: bottom border style\n\n Raises:\n InvalidParamError: bottom border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
... | Set bottom border style.
Args:
style: bottom border style
Raises:
InvalidParamError: bottom border style does not exist | prettyqt/gui/texttablecellformat.py | set_bottom_border_style | phil65/PrettyQt | 7 | python | def set_bottom_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set bottom border style.\n\n Args:\n style: bottom border style\n\n Raises:\n InvalidParamError: bottom border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
... | def set_bottom_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set bottom border style.\n\n Args:\n style: bottom border style\n\n Raises:\n InvalidParamError: bottom border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
... |
51ecdf491c934285ede2074d9bda3c60cf1946ab28f25518afda9b6b21612f42 | def get_bottom_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current bottom border style.\n\n Returns:\n bottom border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.bottomBorderStyle()] | Get the current bottom border style.
Returns:
bottom border style | prettyqt/gui/texttablecellformat.py | get_bottom_border_style | phil65/PrettyQt | 7 | python | def get_bottom_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current bottom border style.\n\n Returns:\n bottom border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.bottomBorderStyle()] | def get_bottom_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current bottom border style.\n\n Returns:\n bottom border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.bottomBorderStyle()]<|docstring|>Get the current bottom border style.
Returns:
... |
dd263f731def0c5237f79ab0f098194f4b12c9f2edfd75d006e321be6f53dc8a | def set_left_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set left border style.\n\n Args:\n style: left border style\n\n Raises:\n InvalidParamError: left border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
r... | Set left border style.
Args:
style: left border style
Raises:
InvalidParamError: left border style does not exist | prettyqt/gui/texttablecellformat.py | set_left_border_style | phil65/PrettyQt | 7 | python | def set_left_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set left border style.\n\n Args:\n style: left border style\n\n Raises:\n InvalidParamError: left border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
r... | def set_left_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set left border style.\n\n Args:\n style: left border style\n\n Raises:\n InvalidParamError: left border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
r... |
5aa787139d44526882947c14b34393f9408f90eb7fa8376db1fc874bd6d9a5c1 | def get_left_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current left border style.\n\n Returns:\n left border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.leftBorderStyle()] | Get the current left border style.
Returns:
left border style | prettyqt/gui/texttablecellformat.py | get_left_border_style | phil65/PrettyQt | 7 | python | def get_left_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current left border style.\n\n Returns:\n left border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.leftBorderStyle()] | def get_left_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current left border style.\n\n Returns:\n left border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.leftBorderStyle()]<|docstring|>Get the current left border style.
Returns:
left bord... |
c049b04a4cf3ff2b9d4d2e5b999716f09f50d84a82f49264d00ec3d80d222918 | def set_right_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set right border style.\n\n Args:\n style: right border style\n\n Raises:\n InvalidParamError: right border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
... | Set right border style.
Args:
style: right border style
Raises:
InvalidParamError: right border style does not exist | prettyqt/gui/texttablecellformat.py | set_right_border_style | phil65/PrettyQt | 7 | python | def set_right_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set right border style.\n\n Args:\n style: right border style\n\n Raises:\n InvalidParamError: right border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
... | def set_right_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set right border style.\n\n Args:\n style: right border style\n\n Raises:\n InvalidParamError: right border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
... |
3afa918a3ff563c1739f51fe058d0da46b5b40760e6d3a291ec8cd43ad9f0f0f | def get_right_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current right border style.\n\n Returns:\n right border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.rightBorderStyle()] | Get the current right border style.
Returns:
right border style | prettyqt/gui/texttablecellformat.py | get_right_border_style | phil65/PrettyQt | 7 | python | def get_right_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current right border style.\n\n Returns:\n right border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.rightBorderStyle()] | def get_right_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current right border style.\n\n Returns:\n right border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.rightBorderStyle()]<|docstring|>Get the current right border style.
Returns:
righ... |
f369010d2a11e94ab7b9a3a6c55eb75bfb757a20c57142a95f53d0dce3198ab2 | def set_top_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set top border style.\n\n Args:\n style: top border style\n\n Raises:\n InvalidParamError: top border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
raise... | Set top border style.
Args:
style: top border style
Raises:
InvalidParamError: top border style does not exist | prettyqt/gui/texttablecellformat.py | set_top_border_style | phil65/PrettyQt | 7 | python | def set_top_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set top border style.\n\n Args:\n style: top border style\n\n Raises:\n InvalidParamError: top border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
raise... | def set_top_border_style(self, style: gui.textframeformat.BorderStyleStr):
'Set top border style.\n\n Args:\n style: top border style\n\n Raises:\n InvalidParamError: top border style does not exist\n '
if (style not in gui.textframeformat.BORDER_STYLES):
raise... |
7a1fab1c6d1b005c9c9a2d12e7d551eead52abdecbf34a391a011ec91fa789cf | def get_top_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current top border style.\n\n Returns:\n top border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.topBorderStyle()] | Get the current top border style.
Returns:
top border style | prettyqt/gui/texttablecellformat.py | get_top_border_style | phil65/PrettyQt | 7 | python | def get_top_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current top border style.\n\n Returns:\n top border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.topBorderStyle()] | def get_top_border_style(self) -> gui.textframeformat.BorderStyleStr:
'Get the current top border style.\n\n Returns:\n top border style\n '
return gui.textframeformat.BORDER_STYLES.inverse[self.topBorderStyle()]<|docstring|>Get the current top border style.
Returns:
top border sty... |
2213e7f2714da41e313990dd806665c40f74df8ff181e98eaca1360ab1ffa3d9 | def __init__(self, file_name, timeout=30, delay=0.2, stealing=False):
' Prepare the file locker. Specify the file to lock and optionally\n the maximum timeout and the delay between each attempt to lock.\n '
self.is_locked = False
self.lockfile = os.path.join(os.getcwd(), ('%s.lock' % file_... | Prepare the file locker. Specify the file to lock and optionally
the maximum timeout and the delay between each attempt to lock. | useful/filelock.py | __init__ | tuttle/python-useful | 7 | python | def __init__(self, file_name, timeout=30, delay=0.2, stealing=False):
' Prepare the file locker. Specify the file to lock and optionally\n the maximum timeout and the delay between each attempt to lock.\n '
self.is_locked = False
self.lockfile = os.path.join(os.getcwd(), ('%s.lock' % file_... | def __init__(self, file_name, timeout=30, delay=0.2, stealing=False):
' Prepare the file locker. Specify the file to lock and optionally\n the maximum timeout and the delay between each attempt to lock.\n '
self.is_locked = False
self.lockfile = os.path.join(os.getcwd(), ('%s.lock' % file_... |
997995282f9dd2094226f65b499af017127f5064364add750004ef818f71d8b3 | def acquire(self):
' Acquire the lock, if possible. If the lock is in use, it check again\n every `wait` seconds. It does this until it either gets the lock or\n exceeds `timeout` number of seconds, in which case it throws\n an exception.\n '
start_time = time.time()
... | Acquire the lock, if possible. If the lock is in use, it check again
every `wait` seconds. It does this until it either gets the lock or
exceeds `timeout` number of seconds, in which case it throws
an exception. | useful/filelock.py | acquire | tuttle/python-useful | 7 | python | def acquire(self):
' Acquire the lock, if possible. If the lock is in use, it check again\n every `wait` seconds. It does this until it either gets the lock or\n exceeds `timeout` number of seconds, in which case it throws\n an exception.\n '
start_time = time.time()
... | def acquire(self):
' Acquire the lock, if possible. If the lock is in use, it check again\n every `wait` seconds. It does this until it either gets the lock or\n exceeds `timeout` number of seconds, in which case it throws\n an exception.\n '
start_time = time.time()
... |
3cca8d7b23c621f707f5dbc3ca2eaed2c35a3ec6ea85f32633703b9a68257e5a | def release(self):
' Get rid of the lock by deleting the lockfile.\n When working in a `with` statement, this gets automatically\n called at the end.\n '
if self.is_locked:
if (self.fd is not None):
os.close(self.fd)
os.unlink(self.lockfile)
self.... | Get rid of the lock by deleting the lockfile.
When working in a `with` statement, this gets automatically
called at the end. | useful/filelock.py | release | tuttle/python-useful | 7 | python | def release(self):
' Get rid of the lock by deleting the lockfile.\n When working in a `with` statement, this gets automatically\n called at the end.\n '
if self.is_locked:
if (self.fd is not None):
os.close(self.fd)
os.unlink(self.lockfile)
self.... | def release(self):
' Get rid of the lock by deleting the lockfile.\n When working in a `with` statement, this gets automatically\n called at the end.\n '
if self.is_locked:
if (self.fd is not None):
os.close(self.fd)
os.unlink(self.lockfile)
self.... |
940a31c8c8d2897818c23a5d89ad9379a6fa02731e3540af375eb331e3cfa556 | def __enter__(self):
' Activated when used in the with statement.\n Should automatically acquire a lock to be used in the with block.\n '
if (not self.is_locked):
self.acquire()
return self | Activated when used in the with statement.
Should automatically acquire a lock to be used in the with block. | useful/filelock.py | __enter__ | tuttle/python-useful | 7 | python | def __enter__(self):
' Activated when used in the with statement.\n Should automatically acquire a lock to be used in the with block.\n '
if (not self.is_locked):
self.acquire()
return self | def __enter__(self):
' Activated when used in the with statement.\n Should automatically acquire a lock to be used in the with block.\n '
if (not self.is_locked):
self.acquire()
return self<|docstring|>Activated when used in the with statement.
Should automatically acquire a lock t... |
ec81bab2cc1c0341532d83545e67a38c5e3201faf5846d1f5acf0b53f9d3be87 | def __exit__(self, exc_type, exc_value, traceback):
" Activated at the end of the with statement.\n It automatically releases the lock if it isn't locked.\n "
if self.is_locked:
self.release() | Activated at the end of the with statement.
It automatically releases the lock if it isn't locked. | useful/filelock.py | __exit__ | tuttle/python-useful | 7 | python | def __exit__(self, exc_type, exc_value, traceback):
" Activated at the end of the with statement.\n It automatically releases the lock if it isn't locked.\n "
if self.is_locked:
self.release() | def __exit__(self, exc_type, exc_value, traceback):
" Activated at the end of the with statement.\n It automatically releases the lock if it isn't locked.\n "
if self.is_locked:
self.release()<|docstring|>Activated at the end of the with statement.
It automatically releases the lock if... |
5d4311be612c9baababcc02d5aeac6a7c27403cbabf04459950928043657a002 | def __del__(self):
" Make sure that the FileLock instance doesn't leave a lockfile\n lying around.\n "
self.release() | Make sure that the FileLock instance doesn't leave a lockfile
lying around. | useful/filelock.py | __del__ | tuttle/python-useful | 7 | python | def __del__(self):
" Make sure that the FileLock instance doesn't leave a lockfile\n lying around.\n "
self.release() | def __del__(self):
" Make sure that the FileLock instance doesn't leave a lockfile\n lying around.\n "
self.release()<|docstring|>Make sure that the FileLock instance doesn't leave a lockfile
lying around.<|endoftext|> |
ec4f1bbcaaa6be54f018cb9b6a06156fa3af56156b919530288cb5f53620abf4 | def __call__(self, func):
'\n Support for using the instance as decorator. The entire function will be protected.\n '
@wraps(func)
def inner(*args, **kwargs):
with self:
return func(*args, **kwargs)
return inner | Support for using the instance as decorator. The entire function will be protected. | useful/filelock.py | __call__ | tuttle/python-useful | 7 | python | def __call__(self, func):
'\n \n '
@wraps(func)
def inner(*args, **kwargs):
with self:
return func(*args, **kwargs)
return inner | def __call__(self, func):
'\n \n '
@wraps(func)
def inner(*args, **kwargs):
with self:
return func(*args, **kwargs)
return inner<|docstring|>Support for using the instance as decorator. The entire function will be protected.<|endoftext|> |
ce9c3eb48cc54efd9cfd750acb9d6a5cc7c9d4b70546074caec94b232fe06dab | def GetParams(self):
'Testing engine with the same tensor repeated as output via identity.'
input_name = 'input'
input_dims = [100, 32]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtypes.float32, shape=input_dims, name=input_name)
b = self._ConstOp((32, 4))
... | Testing engine with the same tensor repeated as output via identity. | tensorflow/python/compiler/tensorrt/test/identity_output_test.py | GetParams | 2hyan8/tensorflow | 36 | python | def GetParams(self):
input_name = 'input'
input_dims = [100, 32]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtypes.float32, shape=input_dims, name=input_name)
b = self._ConstOp((32, 4))
x1 = math_ops.matmul(x, b)
b = self._ConstOp((1, 4))
... | def GetParams(self):
input_name = 'input'
input_dims = [100, 32]
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtypes.float32, shape=input_dims, name=input_name)
b = self._ConstOp((32, 4))
x1 = math_ops.matmul(x, b)
b = self._ConstOp((1, 4))
... |
b8d6e0955969ca4cf3bbb11a67542acd2c2fe7d505f2994006ea6d6db4ec61b5 | def ExpectedEnginesToBuild(self, run_params):
'Return the expected engines to build.'
return ['TRTEngineOp_0'] | Return the expected engines to build. | tensorflow/python/compiler/tensorrt/test/identity_output_test.py | ExpectedEnginesToBuild | 2hyan8/tensorflow | 36 | python | def ExpectedEnginesToBuild(self, run_params):
return ['TRTEngineOp_0'] | def ExpectedEnginesToBuild(self, run_params):
return ['TRTEngineOp_0']<|docstring|>Return the expected engines to build.<|endoftext|> |
78ee1a6603d0b62b884429280dd209e5b65667d11bfe02c9fb3f8d7f084d9569 | def session_expired(self):
'The session has ended due to session expiration'
if self.expiry_time:
return (self.expiry_time <= timezone.now())
return False | The session has ended due to session expiration | tracking/models.py | session_expired | yassam/django-tracking2 | 0 | python | def session_expired(self):
if self.expiry_time:
return (self.expiry_time <= timezone.now())
return False | def session_expired(self):
if self.expiry_time:
return (self.expiry_time <= timezone.now())
return False<|docstring|>The session has ended due to session expiration<|endoftext|> |
3cd51f3a6b5298db332e6d93d22f5b0ef5fddcd0cdb7b342818bd4a19ea7be5f | def session_ended(self):
'The session has ended due to an explicit logout'
return bool(self.end_time) | The session has ended due to an explicit logout | tracking/models.py | session_ended | yassam/django-tracking2 | 0 | python | def session_ended(self):
return bool(self.end_time) | def session_ended(self):
return bool(self.end_time)<|docstring|>The session has ended due to an explicit logout<|endoftext|> |
c6ac0ffd6d7b640767da5e7ff56c7c15f846ba953b2173b676984fa4d2f9ad94 | @property
def last_time(self):
'datetime of last time visited - start_time + time_on_site'
return (self.start_time + timedelta(seconds=self.time_on_site)) | datetime of last time visited - start_time + time_on_site | tracking/models.py | last_time | yassam/django-tracking2 | 0 | python | @property
def last_time(self):
return (self.start_time + timedelta(seconds=self.time_on_site)) | @property
def last_time(self):
return (self.start_time + timedelta(seconds=self.time_on_site))<|docstring|>datetime of last time visited - start_time + time_on_site<|endoftext|> |
22030fc976f4294f47e1485792c59d46edc20fe83be35d2af00e0efce8947455 | @property
def geoip_data(self):
"Attempts to retrieve MaxMind GeoIP data based upon the visitor's IP"
if ((not HAS_GEOIP) or (not TRACK_USING_GEOIP)):
return
if (not hasattr(self, '_geoip_data')):
self._geoip_data = None
try:
gip = GeoIP(cache=GEOIP_CACHE_TYPE)
... | Attempts to retrieve MaxMind GeoIP data based upon the visitor's IP | tracking/models.py | geoip_data | yassam/django-tracking2 | 0 | python | @property
def geoip_data(self):
if ((not HAS_GEOIP) or (not TRACK_USING_GEOIP)):
return
if (not hasattr(self, '_geoip_data')):
self._geoip_data = None
try:
gip = GeoIP(cache=GEOIP_CACHE_TYPE)
self._geoip_data = gip.city(self.ip_address)
except GeoIPEx... | @property
def geoip_data(self):
if ((not HAS_GEOIP) or (not TRACK_USING_GEOIP)):
return
if (not hasattr(self, '_geoip_data')):
self._geoip_data = None
try:
gip = GeoIP(cache=GEOIP_CACHE_TYPE)
self._geoip_data = gip.city(self.ip_address)
except GeoIPEx... |
ca3b83d46df29f4680ca63ccc927357957e8f6a1511c48b22a882ddf0606ac0d | @property
def platform(self):
'\n Returns string describing browser platform. Falls back to agent\n string if either user_agents module not found, or\n TRACK_PARSE_AGENT is False\n '
if (not user_agents):
return self.user_agent
if (not hasattr(self, '_platform_string')):
... | Returns string describing browser platform. Falls back to agent
string if either user_agents module not found, or
TRACK_PARSE_AGENT is False | tracking/models.py | platform | yassam/django-tracking2 | 0 | python | @property
def platform(self):
'\n Returns string describing browser platform. Falls back to agent\n string if either user_agents module not found, or\n TRACK_PARSE_AGENT is False\n '
if (not user_agents):
return self.user_agent
if (not hasattr(self, '_platform_string')):
... | @property
def platform(self):
'\n Returns string describing browser platform. Falls back to agent\n string if either user_agents module not found, or\n TRACK_PARSE_AGENT is False\n '
if (not user_agents):
return self.user_agent
if (not hasattr(self, '_platform_string')):
... |
bbdb08ad55cf49ed0ebed5f76b89e4b8306d795335be28e3fbcee9c0009c516b | def task_hutch_install():
'\n Hutch: Compile and install the Hutch extension.\n '
return {'actions': [(lambda : os.chdir('cmudb/extensions/hutch/')), 'sudo PYTHONPATH=../../tscout:$PYTHONPATH python3 tscout_feature_gen.py', 'PG_CONFIG=%(pg_config)s make clean -j', 'PG_CONFIG=%(pg_config)s make -j', 'PG_CO... | Hutch: Compile and install the Hutch extension. | dodos/hutch.py | task_hutch_install | 17zhangw/postgres | 0 | python | def task_hutch_install():
'\n \n '
return {'actions': [(lambda : os.chdir('cmudb/extensions/hutch/')), 'sudo PYTHONPATH=../../tscout:$PYTHONPATH python3 tscout_feature_gen.py', 'PG_CONFIG=%(pg_config)s make clean -j', 'PG_CONFIG=%(pg_config)s make -j', 'PG_CONFIG=%(pg_config)s make install -j', (lambda : ... | def task_hutch_install():
'\n \n '
return {'actions': [(lambda : os.chdir('cmudb/extensions/hutch/')), 'sudo PYTHONPATH=../../tscout:$PYTHONPATH python3 tscout_feature_gen.py', 'PG_CONFIG=%(pg_config)s make clean -j', 'PG_CONFIG=%(pg_config)s make -j', 'PG_CONFIG=%(pg_config)s make install -j', (lambda : ... |
3b2bdd5db92d4357c6c6ce2e824e955737da0ee052326a689f0889b92930339f | def logoutfunction(self, line):
' Main logout worker function\n\n :param line: command line input\n :type line: string.\n '
try:
(_, _) = self._parse_arglist(line)
except (InvalidCommandLineErrorOPTS, SystemExit):
if (('-h' in line) or ('--help' in line)):
re... | Main logout worker function
:param line: command line input
:type line: string. | src/extensions/COMMANDS/LogoutCommand.py | logoutfunction | xnox/python-redfish-utility | 0 | python | def logoutfunction(self, line):
' Main logout worker function\n\n :param line: command line input\n :type line: string.\n '
try:
(_, _) = self._parse_arglist(line)
except (InvalidCommandLineErrorOPTS, SystemExit):
if (('-h' in line) or ('--help' in line)):
re... | def logoutfunction(self, line):
' Main logout worker function\n\n :param line: command line input\n :type line: string.\n '
try:
(_, _) = self._parse_arglist(line)
except (InvalidCommandLineErrorOPTS, SystemExit):
if (('-h' in line) or ('--help' in line)):
re... |
51714612da3c83e0bb904a2284a207355b682d7495f4c518b97a27e19b10efab | def run(self, line):
' Wrapper function for main logout function\n\n :param line: command line input\n :type line: string.\n '
sys.stdout.write('Logging session out.\n')
self.logoutfunction(line)
return ReturnCodes.SUCCESS | Wrapper function for main logout function
:param line: command line input
:type line: string. | src/extensions/COMMANDS/LogoutCommand.py | run | xnox/python-redfish-utility | 0 | python | def run(self, line):
' Wrapper function for main logout function\n\n :param line: command line input\n :type line: string.\n '
sys.stdout.write('Logging session out.\n')
self.logoutfunction(line)
return ReturnCodes.SUCCESS | def run(self, line):
' Wrapper function for main logout function\n\n :param line: command line input\n :type line: string.\n '
sys.stdout.write('Logging session out.\n')
self.logoutfunction(line)
return ReturnCodes.SUCCESS<|docstring|>Wrapper function for main logout function
:para... |
64b13e0cdc3f473e0413f9ecb2c06ee0bab7470a7a9ad77faf84250587f6692d | def definearguments(self, customparser):
' Wrapper function for new command main function\n\n :param customparser: command line input\n :type customparser: parser.\n '
if (not customparser):
return
customparser.add_argument('-u', '--user', dest='user', help='Pass this flag along... | Wrapper function for new command main function
:param customparser: command line input
:type customparser: parser. | src/extensions/COMMANDS/LogoutCommand.py | definearguments | xnox/python-redfish-utility | 0 | python | def definearguments(self, customparser):
' Wrapper function for new command main function\n\n :param customparser: command line input\n :type customparser: parser.\n '
if (not customparser):
return
customparser.add_argument('-u', '--user', dest='user', help='Pass this flag along... | def definearguments(self, customparser):
' Wrapper function for new command main function\n\n :param customparser: command line input\n :type customparser: parser.\n '
if (not customparser):
return
customparser.add_argument('-u', '--user', dest='user', help='Pass this flag along... |
baf991d31965b80ba3f4c423e9f65de32ba930c321340b196069420d0fffe4a5 | def __init__(self, game, show_progress=True):
'Build new CFR instance.\n\n Args:\n game (Game): ACPC game definition object.\n '
self.game = game
self.show_progress = show_progress
if (game.get_num_players() != 2):
raise AttributeError('Only games with 2 players are supp... | Build new CFR instance.
Args:
game (Game): ACPC game definition object. | cfr/main.py | __init__ | JakubPetriska/poker-agent-kit | 19 | python | def __init__(self, game, show_progress=True):
'Build new CFR instance.\n\n Args:\n game (Game): ACPC game definition object.\n '
self.game = game
self.show_progress = show_progress
if (game.get_num_players() != 2):
raise AttributeError('Only games with 2 players are supp... | def __init__(self, game, show_progress=True):
'Build new CFR instance.\n\n Args:\n game (Game): ACPC game definition object.\n '
self.game = game
self.show_progress = show_progress
if (game.get_num_players() != 2):
raise AttributeError('Only games with 2 players are supp... |
f08d17b3d0ecaf6a5f63ca1d48de9c1849fded104d9f5ad28b8ef5871c411370 | def train(self, iterations, weight_delay=700, checkpoint_iterations=None, checkpoint_callback=(lambda *args: None), minimal_action_probability=None):
'Run CFR for given number of iterations.\n\n The trained tree can be found by retrieving the game_tree\n property from this object. The result strategy ... | Run CFR for given number of iterations.
The trained tree can be found by retrieving the game_tree
property from this object. The result strategy is stored
in average_strategy of each ActionNode in game tree.
This method can be called multiple times on one instance
to train more. This can be used for evaluation during... | cfr/main.py | train | JakubPetriska/poker-agent-kit | 19 | python | def train(self, iterations, weight_delay=700, checkpoint_iterations=None, checkpoint_callback=(lambda *args: None), minimal_action_probability=None):
'Run CFR for given number of iterations.\n\n The trained tree can be found by retrieving the game_tree\n property from this object. The result strategy ... | def train(self, iterations, weight_delay=700, checkpoint_iterations=None, checkpoint_callback=(lambda *args: None), minimal_action_probability=None):
'Run CFR for given number of iterations.\n\n The trained tree can be found by retrieving the game_tree\n property from this object. The result strategy ... |
b1b3162eca93c3f5747480406dbee5cb9a720d65f6a7df47ab760fa83fbbe912 | def make_users_me_request(self):
"\n Need to wrap the get in a class method to get 'self' context into timeit\n "
response = self.client.get(self.url, **self.exporter_headers)
self.assertTrue((response.status_code == status.HTTP_200_OK)) | Need to wrap the get in a class method to get 'self' context into timeit | api/users/tests/tests_performance.py | make_users_me_request | code-review-doctor/lite-api | 3 | python | def make_users_me_request(self):
"\n \n "
response = self.client.get(self.url, **self.exporter_headers)
self.assertTrue((response.status_code == status.HTTP_200_OK)) | def make_users_me_request(self):
"\n \n "
response = self.client.get(self.url, **self.exporter_headers)
self.assertTrue((response.status_code == status.HTTP_200_OK))<|docstring|>Need to wrap the get in a class method to get 'self' context into timeit<|endoftext|> |
c0b536aff21d42c65998028e3dbf93a3a18d0d439a892d7293ef6c45439b2498 | @parameterized.expand([(10, 0), (100, 0), (1000, 0)])
def test_users_me_performance_by_organisation(self, org_count, users):
"\n Tests the performance of the 'users/me' endpoint\n "
self.create_organisations_multiple_users(required_user=self.exporter_user, organisations=org_count, users_per_org=us... | Tests the performance of the 'users/me' endpoint | api/users/tests/tests_performance.py | test_users_me_performance_by_organisation | code-review-doctor/lite-api | 3 | python | @parameterized.expand([(10, 0), (100, 0), (1000, 0)])
def test_users_me_performance_by_organisation(self, org_count, users):
"\n \n "
self.create_organisations_multiple_users(required_user=self.exporter_user, organisations=org_count, users_per_org=users)
print(f'organisations: {org_count}')
... | @parameterized.expand([(10, 0), (100, 0), (1000, 0)])
def test_users_me_performance_by_organisation(self, org_count, users):
"\n \n "
self.create_organisations_multiple_users(required_user=self.exporter_user, organisations=org_count, users_per_org=users)
print(f'organisations: {org_count}')
... |
7c3e8832283c92e3af1164983eede6a7766dddaa10bcd07017b9d59c88d00a7c | @parameterized.expand([(10, 0), (100, 0), (1000, 0)])
def test_users_me_performance_by_sites(self, sites, users):
"\n Tests the performance of the 'users/me' endpoint\n "
self.create_multiple_sites_for_an_organisation(organisation=self.organisation, sites_count=sites)
print(f'sites: {sites}')
... | Tests the performance of the 'users/me' endpoint | api/users/tests/tests_performance.py | test_users_me_performance_by_sites | code-review-doctor/lite-api | 3 | python | @parameterized.expand([(10, 0), (100, 0), (1000, 0)])
def test_users_me_performance_by_sites(self, sites, users):
"\n \n "
self.create_multiple_sites_for_an_organisation(organisation=self.organisation, sites_count=sites)
print(f'sites: {sites}')
self.timeit(self.make_users_me_request) | @parameterized.expand([(10, 0), (100, 0), (1000, 0)])
def test_users_me_performance_by_sites(self, sites, users):
"\n \n "
self.create_multiple_sites_for_an_organisation(organisation=self.organisation, sites_count=sites)
print(f'sites: {sites}')
self.timeit(self.make_users_me_request)<|doc... |
031c240b78b9bd87a79d369b06e4f995243bb136ef12ccb538896a8ba0808ce4 | @parameterized.expand([(1, 10), (1, 100), (1, 1000)])
def test_users_me_performance_by_users_per_site(self, sites, users):
"\n Tests the performance of the 'users/me' endpoint\n "
print(f'users: {users}')
self.create_multiple_sites_for_an_organisation(organisation=self.organisation, sites_coun... | Tests the performance of the 'users/me' endpoint | api/users/tests/tests_performance.py | test_users_me_performance_by_users_per_site | code-review-doctor/lite-api | 3 | python | @parameterized.expand([(1, 10), (1, 100), (1, 1000)])
def test_users_me_performance_by_users_per_site(self, sites, users):
"\n \n "
print(f'users: {users}')
self.create_multiple_sites_for_an_organisation(organisation=self.organisation, sites_count=1, users_per_site=users)
self.timeit(self.... | @parameterized.expand([(1, 10), (1, 100), (1, 1000)])
def test_users_me_performance_by_users_per_site(self, sites, users):
"\n \n "
print(f'users: {users}')
self.create_multiple_sites_for_an_organisation(organisation=self.organisation, sites_count=1, users_per_site=users)
self.timeit(self.... |
86204316e7d80ec4ac38865f66a173dae719cb2373c211c02cfaf2b1524ceb80 | def train(X, Y, args):
'\n Train a model given the arguments, the dataset and \n the corresponding labels (ground-truth)\n\n Parameters\n ----------\n\n X : array\n features of the dataset\n Y : array\n corresponding labels\n args : dict\n arguments to prepare the model\n\n... | Train a model given the arguments, the dataset and
the corresponding labels (ground-truth)
Parameters
----------
X : array
features of the dataset
Y : array
corresponding labels
args : dict
arguments to prepare the model
Returns
-------
model : object
trained model | rrgp/algorithm.py | train | patrickaudriaz/mini-project | 4 | python | def train(X, Y, args):
'\n Train a model given the arguments, the dataset and \n the corresponding labels (ground-truth)\n\n Parameters\n ----------\n\n X : array\n features of the dataset\n Y : array\n corresponding labels\n args : dict\n arguments to prepare the model\n\n... | def train(X, Y, args):
'\n Train a model given the arguments, the dataset and \n the corresponding labels (ground-truth)\n\n Parameters\n ----------\n\n X : array\n features of the dataset\n Y : array\n corresponding labels\n args : dict\n arguments to prepare the model\n\n... |
829f9a1974c3f3c7cc794039b5df35a581e19f8953dcc5bff8dc07ffe3b9e24b | def predict(X, model):
'\n Predict labels given the features and the trained model\n\n Parameters\n ----------\n\n X : array\n features to predict on\n model : object\n trained model\n\n Returns\n -------\n\n predictions : array\n Array with the predicted labels\n\n '... | Predict labels given the features and the trained model
Parameters
----------
X : array
features to predict on
model : object
trained model
Returns
-------
predictions : array
Array with the predicted labels | rrgp/algorithm.py | predict | patrickaudriaz/mini-project | 4 | python | def predict(X, model):
'\n Predict labels given the features and the trained model\n\n Parameters\n ----------\n\n X : array\n features to predict on\n model : object\n trained model\n\n Returns\n -------\n\n predictions : array\n Array with the predicted labels\n\n '... | def predict(X, model):
'\n Predict labels given the features and the trained model\n\n Parameters\n ----------\n\n X : array\n features to predict on\n model : object\n trained model\n\n Returns\n -------\n\n predictions : array\n Array with the predicted labels\n\n '... |
4cca8fb29c02e720f7cbf29bc423f83f5f0d0a8ad0ac4f123ea819e7c94489e1 | def uast2graphlets(self, uast):
"\n :param uast: The UAST root node.\n :generate: The nodes which compose the UAST.\n :class: 'Node' is used to access the nodes of the graphlets.\n "
root = self._extract_node(uast, None)
stack = [(root, uast)]
while stack:
(parent... | :param uast: The UAST root node.
:generate: The nodes which compose the UAST.
:class: 'Node' is used to access the nodes of the graphlets. | sourced/ml/algorithms/uast_inttypes_to_graphlets.py | uast2graphlets | vmarkovtsev/ml | 122 | python | def uast2graphlets(self, uast):
"\n :param uast: The UAST root node.\n :generate: The nodes which compose the UAST.\n :class: 'Node' is used to access the nodes of the graphlets.\n "
root = self._extract_node(uast, None)
stack = [(root, uast)]
while stack:
(parent... | def uast2graphlets(self, uast):
"\n :param uast: The UAST root node.\n :generate: The nodes which compose the UAST.\n :class: 'Node' is used to access the nodes of the graphlets.\n "
root = self._extract_node(uast, None)
stack = [(root, uast)]
while stack:
(parent... |
f53f03bbf12da5e39e0b3beb70aa69a218f3033ee79c673fcbb0b55e52e33b41 | def node2key(self, node):
"\n Builds the string joining internal types of all the nodes\n in the node's graphlet in the following order:\n parent_node_child1_child2_child3. The children are sorted by alphabetic order.\n str format is required for BagsExtractor.\n\n :param node: a ... | Builds the string joining internal types of all the nodes
in the node's graphlet in the following order:
parent_node_child1_child2_child3. The children are sorted by alphabetic order.
str format is required for BagsExtractor.
:param node: a node of UAST
:return: The string key of node | sourced/ml/algorithms/uast_inttypes_to_graphlets.py | node2key | vmarkovtsev/ml | 122 | python | def node2key(self, node):
"\n Builds the string joining internal types of all the nodes\n in the node's graphlet in the following order:\n parent_node_child1_child2_child3. The children are sorted by alphabetic order.\n str format is required for BagsExtractor.\n\n :param node: a ... | def node2key(self, node):
"\n Builds the string joining internal types of all the nodes\n in the node's graphlet in the following order:\n parent_node_child1_child2_child3. The children are sorted by alphabetic order.\n str format is required for BagsExtractor.\n\n :param node: a ... |
9da5bb8e3ad9cc1f726071765de4103f9e5e86611c6ee5bc9377a608305056dc | def __call__(self, uast):
'\n Converts a UAST to a weighed bag of graphlets. The weights are graphlets frequencies.\n :param uast: The UAST root node.\n :return: bag of graphlets.\n '
bag = defaultdict(int)
for node in self.uast2graphlets(uast):
bag[self.node2key(node)] +... | Converts a UAST to a weighed bag of graphlets. The weights are graphlets frequencies.
:param uast: The UAST root node.
:return: bag of graphlets. | sourced/ml/algorithms/uast_inttypes_to_graphlets.py | __call__ | vmarkovtsev/ml | 122 | python | def __call__(self, uast):
'\n Converts a UAST to a weighed bag of graphlets. The weights are graphlets frequencies.\n :param uast: The UAST root node.\n :return: bag of graphlets.\n '
bag = defaultdict(int)
for node in self.uast2graphlets(uast):
bag[self.node2key(node)] +... | def __call__(self, uast):
'\n Converts a UAST to a weighed bag of graphlets. The weights are graphlets frequencies.\n :param uast: The UAST root node.\n :return: bag of graphlets.\n '
bag = defaultdict(int)
for node in self.uast2graphlets(uast):
bag[self.node2key(node)] +... |
9e722c97904581dec05898eb08559e4f69a4a81befa564a2272d3e7ea067a995 | def scatterplot(self, func=None, xlabel='[Compound] (nM)', ylabel='Anisotropy', palette='viridis_r', baseline_correction=True, invert=False, *args, **kargs):
'Plot and Curve Fit data on a log[x] axis.'
if (self.df_main is None):
self._load_data()
self._prep_data_for_plotting()
if (self.df_pl... | Plot and Curve Fit data on a log[x] axis. | DoseResponse/dose_response_curve.py | scatterplot | Spill-Tea/Rio | 0 | python | def scatterplot(self, func=None, xlabel='[Compound] (nM)', ylabel='Anisotropy', palette='viridis_r', baseline_correction=True, invert=False, *args, **kargs):
if (self.df_main is None):
self._load_data()
self._prep_data_for_plotting()
if (self.df_plot_ready is None):
self._prep_data_... | def scatterplot(self, func=None, xlabel='[Compound] (nM)', ylabel='Anisotropy', palette='viridis_r', baseline_correction=True, invert=False, *args, **kargs):
if (self.df_main is None):
self._load_data()
self._prep_data_for_plotting()
if (self.df_plot_ready is None):
self._prep_data_... |
f9d265a133c1b72a353e29a6107a23f9a53bedd8886744df5b8390e4954f2005 | def data_summary(self):
'This function summarizes the raw Data.'
if (self.df_main is None):
self._load_data()
self.df_summary = self.df_main.copy()
self.df_summary['N'] = self.df_main.count(axis=1)
self.df_summary['MEAN'] = self.df_main.mean(axis=1)
self.df_summary['SD'] = self.df_main.s... | This function summarizes the raw Data. | DoseResponse/dose_response_curve.py | data_summary | Spill-Tea/Rio | 0 | python | def data_summary(self):
if (self.df_main is None):
self._load_data()
self.df_summary = self.df_main.copy()
self.df_summary['N'] = self.df_main.count(axis=1)
self.df_summary['MEAN'] = self.df_main.mean(axis=1)
self.df_summary['SD'] = self.df_main.std(axis=1) | def data_summary(self):
if (self.df_main is None):
self._load_data()
self.df_summary = self.df_main.copy()
self.df_summary['N'] = self.df_main.count(axis=1)
self.df_summary['MEAN'] = self.df_main.mean(axis=1)
self.df_summary['SD'] = self.df_main.std(axis=1)<|docstring|>This function sum... |
0af176e0250f06bd7bead4a4b96d1c52ce9d38e6eceb6f794ccc6ba49bfb4c36 | def _load_data(self):
'Helper Function to Load data from a file.'
self.df_main = pd.read_csv(self.datafile, header=[0, 1], sep='\t').T
self.n_replicates = len(self.df_main.columns) | Helper Function to Load data from a file. | DoseResponse/dose_response_curve.py | _load_data | Spill-Tea/Rio | 0 | python | def _load_data(self):
self.df_main = pd.read_csv(self.datafile, header=[0, 1], sep='\t').T
self.n_replicates = len(self.df_main.columns) | def _load_data(self):
self.df_main = pd.read_csv(self.datafile, header=[0, 1], sep='\t').T
self.n_replicates = len(self.df_main.columns)<|docstring|>Helper Function to Load data from a file.<|endoftext|> |
194d353cd3dc9c218cf545ea0d95fb0f2ddeacc96c06232450c28b1a7c1de39d | def pad_image(img):
'\n Pad image with 0s to make it square\n :param image: HxWx3 numpy array\n :return: AxAx3 numpy array (square image)\n '
(height, width, _) = img.shape
if (width < height):
border_width = ((height - width) // 2)
padded = cv2.copyMakeBorder(img, 0, 0, border_w... | Pad image with 0s to make it square
:param image: HxWx3 numpy array
:return: AxAx3 numpy array (square image) | bodypart_segmentation_predict.py | pad_image | akashsengupta1997/segmentation_models | 0 | python | def pad_image(img):
'\n Pad image with 0s to make it square\n :param image: HxWx3 numpy array\n :return: AxAx3 numpy array (square image)\n '
(height, width, _) = img.shape
if (width < height):
border_width = ((height - width) // 2)
padded = cv2.copyMakeBorder(img, 0, 0, border_w... | def pad_image(img):
'\n Pad image with 0s to make it square\n :param image: HxWx3 numpy array\n :return: AxAx3 numpy array (square image)\n '
(height, width, _) = img.shape
if (width < height):
border_width = ((height - width) // 2)
padded = cv2.copyMakeBorder(img, 0, 0, border_w... |
0e5db6dcf0e6636c5fc48adb6f15123020a73a91b5b9774ab21b1ed0f01fb8f4 | def readWrite(self):
'\n Step through the structure of a PWDINT file and read/write it.\n\n Logic to control which records will be present is here, which\n comes directly off the File specification.\n '
self._rwFileID()
self._rw1DRecord()
self._rw2DRecord() | Step through the structure of a PWDINT file and read/write it.
Logic to control which records will be present is here, which
comes directly off the File specification. | armi/nuclearDataIO/cccc/pwdint.py | readWrite | DennisYelizarov/armi | 162 | python | def readWrite(self):
'\n Step through the structure of a PWDINT file and read/write it.\n\n Logic to control which records will be present is here, which\n comes directly off the File specification.\n '
self._rwFileID()
self._rw1DRecord()
self._rw2DRecord() | def readWrite(self):
'\n Step through the structure of a PWDINT file and read/write it.\n\n Logic to control which records will be present is here, which\n comes directly off the File specification.\n '
self._rwFileID()
self._rw1DRecord()
self._rw2DRecord()<|docstring|>Step t... |
c4ecf571c30ff697e388b970e0af6cf0f20c739b280bfc9e542b23b0c2d589e6 | def _rw1DRecord(self):
'\n Read/write File specifications on 1D record.\n '
with self.createRecord() as record:
self._metadata.update(record.rwImplicitlyTypedMap(FILE_SPEC_1D_KEYS, self._metadata)) | Read/write File specifications on 1D record. | armi/nuclearDataIO/cccc/pwdint.py | _rw1DRecord | DennisYelizarov/armi | 162 | python | def _rw1DRecord(self):
'\n \n '
with self.createRecord() as record:
self._metadata.update(record.rwImplicitlyTypedMap(FILE_SPEC_1D_KEYS, self._metadata)) | def _rw1DRecord(self):
'\n \n '
with self.createRecord() as record:
self._metadata.update(record.rwImplicitlyTypedMap(FILE_SPEC_1D_KEYS, self._metadata))<|docstring|>Read/write File specifications on 1D record.<|endoftext|> |
49e004df0a5ad58b4afe12172f7b351af6583783aaece9cd91ce094ad0702d9d | def _rw2DRecord(self):
'Read/write power density by mesh point.'
imax = self._metadata['NINTI']
jmax = self._metadata['NINTJ']
kmax = self._metadata['NINTK']
nblck = self._metadata['NBLOK']
if (self._data.powerDensity.size == 0):
self._data.powerDensity = numpy.zeros((imax, jmax, kmax), ... | Read/write power density by mesh point. | armi/nuclearDataIO/cccc/pwdint.py | _rw2DRecord | DennisYelizarov/armi | 162 | python | def _rw2DRecord(self):
imax = self._metadata['NINTI']
jmax = self._metadata['NINTJ']
kmax = self._metadata['NINTK']
nblck = self._metadata['NBLOK']
if (self._data.powerDensity.size == 0):
self._data.powerDensity = numpy.zeros((imax, jmax, kmax), dtype=numpy.float32)
for ki in range(... | def _rw2DRecord(self):
imax = self._metadata['NINTI']
jmax = self._metadata['NINTJ']
kmax = self._metadata['NINTK']
nblck = self._metadata['NBLOK']
if (self._data.powerDensity.size == 0):
self._data.powerDensity = numpy.zeros((imax, jmax, kmax), dtype=numpy.float32)
for ki in range(... |
9421d2986aef6901d9007624fa79ff6376e81a930b60c448c4f9ac10262c9829 | def load_traces_optimally(roi_data_handle, roi_ns=None, frame_ns=None, rois_first=True):
'\n load_traces_optimally(roi_data_handle)\n\n Updates indices, possibly reordered, for optimal loading of ROI traces.\n\n Optional args:\n - roi_ns (int or array-like) : ROIs to load (None for all)\n ... | load_traces_optimally(roi_data_handle)
Updates indices, possibly reordered, for optimal loading of ROI traces.
Optional args:
- roi_ns (int or array-like) : ROIs to load (None for all)
default: None
- frame_ns (int or array-like): frames to load (None for all)
... | sess_util/sess_trace_util.py | load_traces_optimally | AllenInstitute/OpenScope_CA_Analysis | 0 | python | def load_traces_optimally(roi_data_handle, roi_ns=None, frame_ns=None, rois_first=True):
'\n load_traces_optimally(roi_data_handle)\n\n Updates indices, possibly reordered, for optimal loading of ROI traces.\n\n Optional args:\n - roi_ns (int or array-like) : ROIs to load (None for all)\n ... | def load_traces_optimally(roi_data_handle, roi_ns=None, frame_ns=None, rois_first=True):
'\n load_traces_optimally(roi_data_handle)\n\n Updates indices, possibly reordered, for optimal loading of ROI traces.\n\n Optional args:\n - roi_ns (int or array-like) : ROIs to load (None for all)\n ... |
cd68a37138f9a2cb8d98a74b9ebad72d6a81070036368d4d51ef19894200f86a | def load_roi_traces_nwb(sess_files, roi_ns=None, frame_ns=None):
'\n load_roi_traces_nwb(sess_files)\n\n Returns ROI traces from NWB files (stored as frames x ROIs). \n\n Required args:\n - sess_files (list): full path names of the session files\n\n Optional args:\n - roi_ns (int or array-... | load_roi_traces_nwb(sess_files)
Returns ROI traces from NWB files (stored as frames x ROIs).
Required args:
- sess_files (list): full path names of the session files
Optional args:
- roi_ns (int or array-like) : ROIs to load (None for all)
default: None
- frame_ns (i... | sess_util/sess_trace_util.py | load_roi_traces_nwb | AllenInstitute/OpenScope_CA_Analysis | 0 | python | def load_roi_traces_nwb(sess_files, roi_ns=None, frame_ns=None):
'\n load_roi_traces_nwb(sess_files)\n\n Returns ROI traces from NWB files (stored as frames x ROIs). \n\n Required args:\n - sess_files (list): full path names of the session files\n\n Optional args:\n - roi_ns (int or array-... | def load_roi_traces_nwb(sess_files, roi_ns=None, frame_ns=None):
'\n load_roi_traces_nwb(sess_files)\n\n Returns ROI traces from NWB files (stored as frames x ROIs). \n\n Required args:\n - sess_files (list): full path names of the session files\n\n Optional args:\n - roi_ns (int or array-... |
a87cc4c9b62237682cc5b81b0c5416801306e6bb9c074cc72e36d73068601643 | def load_roi_traces(roi_trace_path, roi_ns=None, frame_ns=None):
'\n load_roi_traces(roi_trace_path)\n\n Returns ROI traces from ROI data file (stored as ROI x frames). \n\n Required args:\n - roi_trace_path (Path): full path name of the ROI data file\n\n Optional args:\n - roi_ns (int or ... | load_roi_traces(roi_trace_path)
Returns ROI traces from ROI data file (stored as ROI x frames).
Required args:
- roi_trace_path (Path): full path name of the ROI data file
Optional args:
- roi_ns (int or array-like) : ROIs to load (None for all)
default: None
- frame... | sess_util/sess_trace_util.py | load_roi_traces | AllenInstitute/OpenScope_CA_Analysis | 0 | python | def load_roi_traces(roi_trace_path, roi_ns=None, frame_ns=None):
'\n load_roi_traces(roi_trace_path)\n\n Returns ROI traces from ROI data file (stored as ROI x frames). \n\n Required args:\n - roi_trace_path (Path): full path name of the ROI data file\n\n Optional args:\n - roi_ns (int or ... | def load_roi_traces(roi_trace_path, roi_ns=None, frame_ns=None):
'\n load_roi_traces(roi_trace_path)\n\n Returns ROI traces from ROI data file (stored as ROI x frames). \n\n Required args:\n - roi_trace_path (Path): full path name of the ROI data file\n\n Optional args:\n - roi_ns (int or ... |
34927ce5b493a10198563cedf28caf0cb9d53db075bf401e3e3f6c8c20ffc932 | def load_roi_data_nwb(sess_files):
'\n load_roi_data_nwb(sess_files)\n\n Returns ROI data from NWB files. \n\n Required args:\n - sess_files (Path): full path names of the session files\n\n Returns:\n - roi_ids (list) : ROI IDs\n - nrois (int) : total number of ROIs\n ... | load_roi_data_nwb(sess_files)
Returns ROI data from NWB files.
Required args:
- sess_files (Path): full path names of the session files
Returns:
- roi_ids (list) : ROI IDs
- nrois (int) : total number of ROIs
- tot_twop_fr (int): total number of two-photon frames recorded | sess_util/sess_trace_util.py | load_roi_data_nwb | AllenInstitute/OpenScope_CA_Analysis | 0 | python | def load_roi_data_nwb(sess_files):
'\n load_roi_data_nwb(sess_files)\n\n Returns ROI data from NWB files. \n\n Required args:\n - sess_files (Path): full path names of the session files\n\n Returns:\n - roi_ids (list) : ROI IDs\n - nrois (int) : total number of ROIs\n ... | def load_roi_data_nwb(sess_files):
'\n load_roi_data_nwb(sess_files)\n\n Returns ROI data from NWB files. \n\n Required args:\n - sess_files (Path): full path names of the session files\n\n Returns:\n - roi_ids (list) : ROI IDs\n - nrois (int) : total number of ROIs\n ... |
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