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 |
|---|---|---|---|---|---|---|---|---|---|
d2eaf851d77ba3ff512ba37c09916e63a2034a53b333172647f38f8af95dc4e4 | def addFilesToCombineArchive(archive_path, file_names, entry_locations, file_formats, master_attributes, out_archive_path):
' Add multiple files to an existing COMBINE archive on disk and save the result as a new archive.\n\n :param archive_path: The path to the archive.\n :param file_names: List of extra fil... | Add multiple files to an existing COMBINE archive on disk and save the result as a new archive.
:param archive_path: The path to the archive.
:param file_names: List of extra files to add.
:param entry_locations: List of destination locations for the files in the output archive.
:param file_format: List of formats for... | tellurium/bombBeetle.py | addFilesToCombineArchive | madfain/BombBeetle | 1 | python | def addFilesToCombineArchive(archive_path, file_names, entry_locations, file_formats, master_attributes, out_archive_path):
' Add multiple files to an existing COMBINE archive on disk and save the result as a new archive.\n\n :param archive_path: The path to the archive.\n :param file_names: List of extra fil... | def addFilesToCombineArchive(archive_path, file_names, entry_locations, file_formats, master_attributes, out_archive_path):
' Add multiple files to an existing COMBINE archive on disk and save the result as a new archive.\n\n :param archive_path: The path to the archive.\n :param file_names: List of extra fil... |
a8353acf36cc3cb3cf31f6ba251a8fa9c00d7993050b4ee94f8f6e46f72e9c9e | def createCombineArchive(archive_path, file_names, entry_locations, file_formats, master_attributes, description=None):
' Create a new COMBINE archive containing the provided entries and locations.\n\n :param archive_path: The path to the archive.\n :param file_names: List of extra files to add.\n :param e... | Create a new COMBINE archive containing the provided entries and locations.
:param archive_path: The path to the archive.
:param file_names: List of extra files to add.
:param entry_locations: List of destination locations for the files in the output archive.
:param file_format: List of formats for the resp. files.
:p... | tellurium/bombBeetle.py | createCombineArchive | madfain/BombBeetle | 1 | python | def createCombineArchive(archive_path, file_names, entry_locations, file_formats, master_attributes, description=None):
' Create a new COMBINE archive containing the provided entries and locations.\n\n :param archive_path: The path to the archive.\n :param file_names: List of extra files to add.\n :param e... | def createCombineArchive(archive_path, file_names, entry_locations, file_formats, master_attributes, description=None):
' Create a new COMBINE archive containing the provided entries and locations.\n\n :param archive_path: The path to the archive.\n :param file_names: List of extra files to add.\n :param e... |
7fcd714539c5e3be8e23bd3c72d72bba2f71a8e2c0eee8de9352f785ed155f7f | def getEigenvalues(m):
' Eigenvalues of matrix.\n\n Convenience method for computing the eigenvalues of a matrix m\n Uses numpy eig to compute the eigenvalues.\n\n :param m: numpy array\n :returns: numpy array containing eigenvalues\n '
from numpy import linalg
(w, v) = linalg.eig(m)
retu... | Eigenvalues of matrix.
Convenience method for computing the eigenvalues of a matrix m
Uses numpy eig to compute the eigenvalues.
:param m: numpy array
:returns: numpy array containing eigenvalues | tellurium/bombBeetle.py | getEigenvalues | madfain/BombBeetle | 1 | python | def getEigenvalues(m):
' Eigenvalues of matrix.\n\n Convenience method for computing the eigenvalues of a matrix m\n Uses numpy eig to compute the eigenvalues.\n\n :param m: numpy array\n :returns: numpy array containing eigenvalues\n '
from numpy import linalg
(w, v) = linalg.eig(m)
retu... | def getEigenvalues(m):
' Eigenvalues of matrix.\n\n Convenience method for computing the eigenvalues of a matrix m\n Uses numpy eig to compute the eigenvalues.\n\n :param m: numpy array\n :returns: numpy array containing eigenvalues\n '
from numpy import linalg
(w, v) = linalg.eig(m)
retu... |
b058dffb0d0d3f6dc3983b473775155af253e12bbc519497727c3ab00b88d818 | def plotArray(result, loc='upper right', show=True, resetColorCycle=True, xlabel=None, ylabel=None, title=None, xlim=None, ylim=None, xscale='linear', yscale='linear', grid=False, labels=None, **kwargs):
' Plot an array.\n\n :param result: Array to plot, first column of the array must be the x-axis and remaining... | Plot an array.
:param result: Array to plot, first column of the array must be the x-axis and remaining columns the y-axis
:param loc: Location of legend box. Valid strings 'best' | upper right' | 'upper left' | 'lower left' | 'lower right' | 'right' | 'center left' | 'center right' | 'lower center' | 'upper center' |... | tellurium/bombBeetle.py | plotArray | madfain/BombBeetle | 1 | python | def plotArray(result, loc='upper right', show=True, resetColorCycle=True, xlabel=None, ylabel=None, title=None, xlim=None, ylim=None, xscale='linear', yscale='linear', grid=False, labels=None, **kwargs):
' Plot an array.\n\n :param result: Array to plot, first column of the array must be the x-axis and remaining... | def plotArray(result, loc='upper right', show=True, resetColorCycle=True, xlabel=None, ylabel=None, title=None, xlim=None, ylim=None, xscale='linear', yscale='linear', grid=False, labels=None, **kwargs):
' Plot an array.\n\n :param result: Array to plot, first column of the array must be the x-axis and remaining... |
272d7f678c44b3ba635e2719ecf5c5a4bfaebca4bb86ff55d98badb0ac00f4d0 | def plotWithLegend(r, result=None, loc='upper right', show=True):
'\n Plot an array and include a legend. The first argument must be a roadrunner variable.\n The second argument must be an array containing data to plot. The first column of the array will\n be the x-axis and remaining columns the y-axis. Re... | Plot an array and include a legend. The first argument must be a roadrunner variable.
The second argument must be an array containing data to plot. The first column of the array will
be the x-axis and remaining columns the y-axis. Returns
a handle to the plotting object.
plotWithLegend (r) | tellurium/bombBeetle.py | plotWithLegend | madfain/BombBeetle | 1 | python | def plotWithLegend(r, result=None, loc='upper right', show=True):
'\n Plot an array and include a legend. The first argument must be a roadrunner variable.\n The second argument must be an array containing data to plot. The first column of the array will\n be the x-axis and remaining columns the y-axis. Re... | def plotWithLegend(r, result=None, loc='upper right', show=True):
'\n Plot an array and include a legend. The first argument must be a roadrunner variable.\n The second argument must be an array containing data to plot. The first column of the array will\n be the x-axis and remaining columns the y-axis. Re... |
00b0422a89f75c3c52b5946dc95d4aaeddac1e34b40f0f49bb4bc04878c65b4f | def loadTestModel(string):
"Loads particular test model into roadrunner.\n ::\n\n rr = te.loadTestModel('feedback.xml')\n\n :returns: RoadRunner instance with test model loaded\n "
import roadrunner.testing
return roadrunner.testing.getRoadRunner(string) | Loads particular test model into roadrunner.
::
rr = te.loadTestModel('feedback.xml')
:returns: RoadRunner instance with test model loaded | tellurium/bombBeetle.py | loadTestModel | madfain/BombBeetle | 1 | python | def loadTestModel(string):
"Loads particular test model into roadrunner.\n ::\n\n rr = te.loadTestModel('feedback.xml')\n\n :returns: RoadRunner instance with test model loaded\n "
import roadrunner.testing
return roadrunner.testing.getRoadRunner(string) | def loadTestModel(string):
"Loads particular test model into roadrunner.\n ::\n\n rr = te.loadTestModel('feedback.xml')\n\n :returns: RoadRunner instance with test model loaded\n "
import roadrunner.testing
return roadrunner.testing.getRoadRunner(string)<|docstring|>Loads particular test mod... |
bc602df0528bc404c02ddd07a3120ce2d3ac305e4daf0001ab5c00760d66a55c | def getTestModel(string):
"SBML of given test model as a string.\n ::\n\n # load test model as SBML\n sbml = te.getTestModel('feedback.xml')\n r = te.loadSBMLModel(sbml)\n # simulate\n r.simulate(0, 100, 20)\n\n :returns: SBML string of test model\n "
import roadrunne... | SBML of given test model as a string.
::
# load test model as SBML
sbml = te.getTestModel('feedback.xml')
r = te.loadSBMLModel(sbml)
# simulate
r.simulate(0, 100, 20)
:returns: SBML string of test model | tellurium/bombBeetle.py | getTestModel | madfain/BombBeetle | 1 | python | def getTestModel(string):
"SBML of given test model as a string.\n ::\n\n # load test model as SBML\n sbml = te.getTestModel('feedback.xml')\n r = te.loadSBMLModel(sbml)\n # simulate\n r.simulate(0, 100, 20)\n\n :returns: SBML string of test model\n "
import roadrunne... | def getTestModel(string):
"SBML of given test model as a string.\n ::\n\n # load test model as SBML\n sbml = te.getTestModel('feedback.xml')\n r = te.loadSBMLModel(sbml)\n # simulate\n r.simulate(0, 100, 20)\n\n :returns: SBML string of test model\n "
import roadrunne... |
8bc338e74a4a62b14d01cc4c7cc1bdca799f6b57c9194151258aaea5dadc289f | def listTestModels():
' List roadrunner SBML test models.\n ::\n\n print(te.listTestModels())\n\n :returns: list of test model paths\n '
import roadrunner.testing
modelList = []
fileList = roadrunner.testing.dir('*.xml')
for pathName in fileList:
modelList.append(os.path.base... | List roadrunner SBML test models.
::
print(te.listTestModels())
:returns: list of test model paths | tellurium/bombBeetle.py | listTestModels | madfain/BombBeetle | 1 | python | def listTestModels():
' List roadrunner SBML test models.\n ::\n\n print(te.listTestModels())\n\n :returns: list of test model paths\n '
import roadrunner.testing
modelList = []
fileList = roadrunner.testing.dir('*.xml')
for pathName in fileList:
modelList.append(os.path.base... | def listTestModels():
' List roadrunner SBML test models.\n ::\n\n print(te.listTestModels())\n\n :returns: list of test model paths\n '
import roadrunner.testing
modelList = []
fileList = roadrunner.testing.dir('*.xml')
for pathName in fileList:
modelList.append(os.path.base... |
46ed15f0dbba070e21e23cfc1448444a728efa37ef5c88be6f42c8f203eaf7be | def _model_function_factory(key):
' Dynamic creation of model functions.\n\n :param key: function key, i.e. the name of the function\n :type key: str\n :return: function object\n :rtype: function\n '
def f(self):
return getattr(self.model, key).__call__()
f.__name__ = key
f.__doc... | Dynamic creation of model functions.
:param key: function key, i.e. the name of the function
:type key: str
:return: function object
:rtype: function | tellurium/bombBeetle.py | _model_function_factory | madfain/BombBeetle | 1 | python | def _model_function_factory(key):
' Dynamic creation of model functions.\n\n :param key: function key, i.e. the name of the function\n :type key: str\n :return: function object\n :rtype: function\n '
def f(self):
return getattr(self.model, key).__call__()
f.__name__ = key
f.__doc... | def _model_function_factory(key):
' Dynamic creation of model functions.\n\n :param key: function key, i.e. the name of the function\n :type key: str\n :return: function object\n :rtype: function\n '
def f(self):
return getattr(self.model, key).__call__()
f.__name__ = key
f.__doc... |
444383f11511e76164aa551db6acd12e0f1d35830be98a952e45c6445a3f3316 | def VersionDict():
'Return dict of version strings.'
import tesbml, tesedml, tecombine
return {'tellurium': getTelluriumVersion(), 'roadrunner': roadrunner.getVersionStr(roadrunner.VERSIONSTR_BASIC), 'antimony': antimony.__version__, 'phrasedml': phrasedml.__version__, 'tesbml': libsbml.getLibSBMLDottedVers... | Return dict of version strings. | tellurium/bombBeetle.py | VersionDict | madfain/BombBeetle | 1 | python | def VersionDict():
import tesbml, tesedml, tecombine
return {'tellurium': getTelluriumVersion(), 'roadrunner': roadrunner.getVersionStr(roadrunner.VERSIONSTR_BASIC), 'antimony': antimony.__version__, 'phrasedml': phrasedml.__version__, 'tesbml': libsbml.getLibSBMLDottedVersion(), 'tesedml': tesedml.__versi... | def VersionDict():
import tesbml, tesedml, tecombine
return {'tellurium': getTelluriumVersion(), 'roadrunner': roadrunner.getVersionStr(roadrunner.VERSIONSTR_BASIC), 'antimony': antimony.__version__, 'phrasedml': phrasedml.__version__, 'tesbml': libsbml.getLibSBMLDottedVersion(), 'tesedml': tesedml.__versi... |
d05b1e641925aec3ee0d0093991b489771654513cdcbacc10817ed4f92a6526f | def DumpJSONInfo():
'Tellurium dist info. Goes into COMBINE archive.'
return json.dumps({'authoring_tool': 'tellurium', 'info': 'Created with Tellurium (tellurium.analogmachine.org).', 'version_info': VersionDict()}) | Tellurium dist info. Goes into COMBINE archive. | tellurium/bombBeetle.py | DumpJSONInfo | madfain/BombBeetle | 1 | python | def DumpJSONInfo():
return json.dumps({'authoring_tool': 'tellurium', 'info': 'Created with Tellurium (tellurium.analogmachine.org).', 'version_info': VersionDict()}) | def DumpJSONInfo():
return json.dumps({'authoring_tool': 'tellurium', 'info': 'Created with Tellurium (tellurium.analogmachine.org).', 'version_info': VersionDict()})<|docstring|>Tellurium dist info. Goes into COMBINE archive.<|endoftext|> |
d75aa109fab436cb3ba19ad3ea3d3d35ed119b940bf27bb56ff6e3037fd9603b | def setLastReport(report):
'Used by SED-ML to save the last report created (for validation).'
global _last_report
_last_report = report | Used by SED-ML to save the last report created (for validation). | tellurium/bombBeetle.py | setLastReport | madfain/BombBeetle | 1 | python | def setLastReport(report):
global _last_report
_last_report = report | def setLastReport(report):
global _last_report
_last_report = report<|docstring|>Used by SED-ML to save the last report created (for validation).<|endoftext|> |
afee1cb5d42c9af24bd144e73c2ac8aff03f104a5e77934c10f1692459e4f48d | def getLastReport():
'Get the last report generated by SED-ML.'
global _last_report
return _last_report | Get the last report generated by SED-ML. | tellurium/bombBeetle.py | getLastReport | madfain/BombBeetle | 1 | python | def getLastReport():
global _last_report
return _last_report | def getLastReport():
global _last_report
return _last_report<|docstring|>Get the last report generated by SED-ML.<|endoftext|> |
c278e53f72a1fcf501f86f011ebed233b2705b0e13ce34dcbf9ccbb988f11488 | @classmethod
def all(cls, preds, index=0, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Calculates NMS predictions for all object classes\n\n Returns:\n 3 item tuple (\n bbs: 2d torch.Tensor\n scores: 1d torch.Tensor\n cls ids: 1d torch.Tensor\n ... | Calculates NMS predictions for all object classes
Returns:
3 item tuple (
bbs: 2d torch.Tensor
scores: 1d torch.Tensor
cls ids: 1d torch.Tensor
) | ssdmultibox/predict.py | all | aaronlelevier/ssd-pytorch | 0 | python | @classmethod
def all(cls, preds, index=0, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Calculates NMS predictions for all object classes\n\n Returns:\n 3 item tuple (\n bbs: 2d torch.Tensor\n scores: 1d torch.Tensor\n cls ids: 1d torch.Tensor\n ... | @classmethod
def all(cls, preds, index=0, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Calculates NMS predictions for all object classes\n\n Returns:\n 3 item tuple (\n bbs: 2d torch.Tensor\n scores: 1d torch.Tensor\n cls ids: 1d torch.Tensor\n ... |
fc5efc8bb30a8da2e93ec082ca9b789616f62be434c9f36fc80c775ab01ffab1 | @classmethod
def single(cls, cls_id, preds, index=0, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Full predictions for a single class\n\n Args:\n cls_id (int): category_id\n preds: mini-batch preds from model\n index (int): index of batch item to choose\n conf_thre... | Full predictions for a single class
Args:
cls_id (int): category_id
preds: mini-batch preds from model
index (int): index of batch item to choose
conf_thresh (float):
percent confidence threshold to filter detections by
Returns:
tuple(bbs, scores, cls_ids) or None | ssdmultibox/predict.py | single | aaronlelevier/ssd-pytorch | 0 | python | @classmethod
def single(cls, cls_id, preds, index=0, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Full predictions for a single class\n\n Args:\n cls_id (int): category_id\n preds: mini-batch preds from model\n index (int): index of batch item to choose\n conf_thre... | @classmethod
def single(cls, cls_id, preds, index=0, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Full predictions for a single class\n\n Args:\n cls_id (int): category_id\n preds: mini-batch preds from model\n index (int): index of batch item to choose\n conf_thre... |
ec87e67a9ff57f2357ffd068664b7968e13e6225319e72e89fda9e584d9842cb | @classmethod
def single_nms(cls, cls_id, item_bbs, item_cats, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Returns the NMS detections for a single image\n\n Args:\n cls_id (int): category id of object to detect\n item_bbs (2d array): [feature_maps, 4] bbs preds\n item_cats (2... | Returns the NMS detections for a single image
Args:
cls_id (int): category id of object to detect
item_bbs (2d array): [feature_maps, 4] bbs preds
item_cats (2d array):[feature_maps, 20] one-hot cats preds
conf_thresh (float):
percent confidence threshold to filter detections by
Returns:
tu... | ssdmultibox/predict.py | single_nms | aaronlelevier/ssd-pytorch | 0 | python | @classmethod
def single_nms(cls, cls_id, item_bbs, item_cats, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Returns the NMS detections for a single image\n\n Args:\n cls_id (int): category id of object to detect\n item_bbs (2d array): [feature_maps, 4] bbs preds\n item_cats (2... | @classmethod
def single_nms(cls, cls_id, item_bbs, item_cats, conf_thresh=cfg.NMS_CONF_THRESH):
'\n Returns the NMS detections for a single image\n\n Args:\n cls_id (int): category id of object to detect\n item_bbs (2d array): [feature_maps, 4] bbs preds\n item_cats (2... |
7e73047e4937f8d8818550333e850dd1c2b058d9c1ab73e21fd155b5365bc580 | @staticmethod
def nms(boxes, scores, overlap=0.5, top_k=200):
'Apply non-maximum suppression at test time to avoid detecting too many\n overlapping bounding boxes for a given object.\n Args:\n boxes: (tensor) The location preds for the img, Shape: [num_priors,4].\n scores: (tenso... | Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The overlap thresh for su... | ssdmultibox/predict.py | nms | aaronlelevier/ssd-pytorch | 0 | python | @staticmethod
def nms(boxes, scores, overlap=0.5, top_k=200):
'Apply non-maximum suppression at test time to avoid detecting too many\n overlapping bounding boxes for a given object.\n Args:\n boxes: (tensor) The location preds for the img, Shape: [num_priors,4].\n scores: (tenso... | @staticmethod
def nms(boxes, scores, overlap=0.5, top_k=200):
'Apply non-maximum suppression at test time to avoid detecting too many\n overlapping bounding boxes for a given object.\n Args:\n boxes: (tensor) The location preds for the img, Shape: [num_priors,4].\n scores: (tenso... |
bb7afc8f55b6153b449a1d9ed6a75316f9fff51b7cf890ee251bd268a648a1a8 | def estep(X: np.ndarray, mixture: GaussianMixture) -> Tuple[(np.ndarray, float)]:
'E-step: Softly assigns each datapoint to a gaussian component\n\n Args:\n X: (n, d) array holding the data\n mixture: the current gaussian mixture\n\n Returns:\n np.ndarray: (n, K) array holding the soft co... | E-step: Softly assigns each datapoint to a gaussian component
Args:
X: (n, d) array holding the data
mixture: the current gaussian mixture
Returns:
np.ndarray: (n, K) array holding the soft counts
for all components for all examples
float: log-likelihood of the assignment | Statistical Method for Colaborative Filtering/em_simple.py | estep | arifmujib/MIT-Machine-Learning-Projects | 0 | python | def estep(X: np.ndarray, mixture: GaussianMixture) -> Tuple[(np.ndarray, float)]:
'E-step: Softly assigns each datapoint to a gaussian component\n\n Args:\n X: (n, d) array holding the data\n mixture: the current gaussian mixture\n\n Returns:\n np.ndarray: (n, K) array holding the soft co... | def estep(X: np.ndarray, mixture: GaussianMixture) -> Tuple[(np.ndarray, float)]:
'E-step: Softly assigns each datapoint to a gaussian component\n\n Args:\n X: (n, d) array holding the data\n mixture: the current gaussian mixture\n\n Returns:\n np.ndarray: (n, K) array holding the soft co... |
e22b59d74b7d155010597d20f62313aa22cbe16ab44319a6db3828424f2f99fe | def mstep(X: np.ndarray, post: np.ndarray) -> GaussianMixture:
'M-step: Updates the gaussian mixture by maximizing the log-likelihood\n of the weighted dataset\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all exampl... | M-step: Updates the gaussian mixture by maximizing the log-likelihood
of the weighted dataset
Args:
X: (n, d) array holding the data
post: (n, K) array holding the soft counts
for all components for all examples
Returns:
GaussianMixture: the new gaussian mixture | Statistical Method for Colaborative Filtering/em_simple.py | mstep | arifmujib/MIT-Machine-Learning-Projects | 0 | python | def mstep(X: np.ndarray, post: np.ndarray) -> GaussianMixture:
'M-step: Updates the gaussian mixture by maximizing the log-likelihood\n of the weighted dataset\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all exampl... | def mstep(X: np.ndarray, post: np.ndarray) -> GaussianMixture:
'M-step: Updates the gaussian mixture by maximizing the log-likelihood\n of the weighted dataset\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all exampl... |
a80b44ed1d8d7cb7dba5979a5a509f76d2833b9329ff86af92f192a0389d565c | def run(X: np.ndarray, mixture: GaussianMixture, post: np.ndarray) -> Tuple[(GaussianMixture, np.ndarray, float)]:
'Runs the mixture model\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all examples\n\n Returns:\n ... | Runs the mixture model
Args:
X: (n, d) array holding the data
post: (n, K) array holding the soft counts
for all components for all examples
Returns:
GaussianMixture: the new gaussian mixture
np.ndarray: (n, K) array holding the soft counts
for all components for all examples
float... | Statistical Method for Colaborative Filtering/em_simple.py | run | arifmujib/MIT-Machine-Learning-Projects | 0 | python | def run(X: np.ndarray, mixture: GaussianMixture, post: np.ndarray) -> Tuple[(GaussianMixture, np.ndarray, float)]:
'Runs the mixture model\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all examples\n\n Returns:\n ... | def run(X: np.ndarray, mixture: GaussianMixture, post: np.ndarray) -> Tuple[(GaussianMixture, np.ndarray, float)]:
'Runs the mixture model\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all examples\n\n Returns:\n ... |
105ed72bdb04f3dc280d8069b074065171fc22195edce4d891efb90a56b4edf8 | def ksg_cmi(x_data, y_data, z_data, k=5):
'\n KSG Conditional Mutual Information Estimator: I(X;Y|Z)\n See e.g. http://proceedings.mlr.press/v84/runge18a.html\n\n x_data: data with shape (num_samples, x_dim) or (num_samples,)\n y_data: data with shape (num_samples, y_dim) or (num_samples... | KSG Conditional Mutual Information Estimator: I(X;Y|Z)
See e.g. http://proceedings.mlr.press/v84/runge18a.html
x_data: data with shape (num_samples, x_dim) or (num_samples,)
y_data: data with shape (num_samples, y_dim) or (num_samples,)
z_data: conditioning data with shape (num_samples, z_dim) or (num_samples,)
k: num... | pycit/estimators/ksg_cmi.py | ksg_cmi | syanga/pycit | 10 | python | def ksg_cmi(x_data, y_data, z_data, k=5):
'\n KSG Conditional Mutual Information Estimator: I(X;Y|Z)\n See e.g. http://proceedings.mlr.press/v84/runge18a.html\n\n x_data: data with shape (num_samples, x_dim) or (num_samples,)\n y_data: data with shape (num_samples, y_dim) or (num_samples... | def ksg_cmi(x_data, y_data, z_data, k=5):
'\n KSG Conditional Mutual Information Estimator: I(X;Y|Z)\n See e.g. http://proceedings.mlr.press/v84/runge18a.html\n\n x_data: data with shape (num_samples, x_dim) or (num_samples,)\n y_data: data with shape (num_samples, y_dim) or (num_samples... |
baeabc73d3136570af6251ab40da854276dc84ab372f0bc2475578633c8c66ac | def plot_pfilter(time, expected, observed, particles, weights, means):
'Apply a particle filter to a time series, and plot the\n first component of the predictions alongside the expected\n output.'
plt.plot(time, expected, 'C1', lw=3)
plt.plot(time, observed, '+C3', lw=3)
ts = np.tile(time[(:, Non... | Apply a particle filter to a time series, and plot the
first component of the predictions alongside the expected
output. | dynamic/particle_utils.py | plot_pfilter | johnhw/summerschool2017 | 6 | python | def plot_pfilter(time, expected, observed, particles, weights, means):
'Apply a particle filter to a time series, and plot the\n first component of the predictions alongside the expected\n output.'
plt.plot(time, expected, 'C1', lw=3)
plt.plot(time, observed, '+C3', lw=3)
ts = np.tile(time[(:, Non... | def plot_pfilter(time, expected, observed, particles, weights, means):
'Apply a particle filter to a time series, and plot the\n first component of the predictions alongside the expected\n output.'
plt.plot(time, expected, 'C1', lw=3)
plt.plot(time, observed, '+C3', lw=3)
ts = np.tile(time[(:, Non... |
66eb7c8e5c20905fa00e8b271c0774102646afffcbeb206cb2242ed09b661ed2 | def test_mia_run(self):
'"This tests the run method of MIA with a reduced data set'
(traces, keys, plain) = FileLoader.main(CONST_DEFAULT_TRACES_FILE, CONST_DEFAULT_KEYS_FILE, CONST_DEFAULT_PLAIN_FILE)
(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext) = Da... | "This tests the run method of MIA with a reduced data set | tests/attack/test_mia_integration.py | test_mia_run | AISyLab/side-channel-attacks | 14 | python | def test_mia_run(self):
(traces, keys, plain) = FileLoader.main(CONST_DEFAULT_TRACES_FILE, CONST_DEFAULT_KEYS_FILE, CONST_DEFAULT_PLAIN_FILE)
(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext) = DataPartitioner.get_traces(traces, keys, plain, 1000, 0, 0, 1... | def test_mia_run(self):
(traces, keys, plain) = FileLoader.main(CONST_DEFAULT_TRACES_FILE, CONST_DEFAULT_KEYS_FILE, CONST_DEFAULT_PLAIN_FILE)
(profiling_traces, profiling_keys, profiling_plaintext, attack_traces, attack_keys, attack_plaintext) = DataPartitioner.get_traces(traces, keys, plain, 1000, 0, 0, 1... |
ca1149be38092976dd554c734b0958160fd009550b59551e4526c2e9660de017 | @functools.lru_cache()
def get_backend():
'Return current backend.'
return {'file': FileBackend, 's3': S3Backend}[app.config['STORAGE_BACKEND']['name']]() | Return current backend. | fluffy/component/backends.py | get_backend | fawaf/fluffy | 135 | python | @functools.lru_cache()
def get_backend():
return {'file': FileBackend, 's3': S3Backend}[app.config['STORAGE_BACKEND']['name']]() | @functools.lru_cache()
def get_backend():
return {'file': FileBackend, 's3': S3Backend}[app.config['STORAGE_BACKEND']['name']]()<|docstring|>Return current backend.<|endoftext|> |
1a337dae630dac627863a5f8d99be0ba67fc88a39db3683d7db99946ea53dddf | def test_list_forms_data(admin_user):
'Should return the correct fields about the forms.'
form = EventFormFactory()
field = form.fields.first()
option = field.options.first()
client = get_api_client(user=admin_user)
url = (_get_forms_url() + '?all')
response = client.get(url)
response = ... | Should return the correct fields about the forms. | app/tests/forms/test_eventform_integration.py | test_list_forms_data | TIHLDE/Lepton | 7 | python | def test_list_forms_data(admin_user):
form = EventFormFactory()
field = form.fields.first()
option = field.options.first()
client = get_api_client(user=admin_user)
url = (_get_forms_url() + '?all')
response = client.get(url)
response = response.json()
assert (response[0] == {'id': s... | def test_list_forms_data(admin_user):
form = EventFormFactory()
field = form.fields.first()
option = field.options.first()
client = get_api_client(user=admin_user)
url = (_get_forms_url() + '?all')
response = client.get(url)
response = response.json()
assert (response[0] == {'id': s... |
fc60472e02e3df846426609678a97dece5afe7a257ee07ed3bbdfc3e97d86f17 | def test_retrieve_evaluation_event_form_as_member_when_has_attended_event(member):
'\n A member should be able to retrieve an event form of type evaluation if\n they has attended the event.\n '
event = EventFactory(limit=1)
registration = RegistrationFactory(user=member, event=event, is_on_wait=Fal... | A member should be able to retrieve an event form of type evaluation if
they has attended the event. | app/tests/forms/test_eventform_integration.py | test_retrieve_evaluation_event_form_as_member_when_has_attended_event | TIHLDE/Lepton | 7 | python | def test_retrieve_evaluation_event_form_as_member_when_has_attended_event(member):
'\n A member should be able to retrieve an event form of type evaluation if\n they has attended the event.\n '
event = EventFactory(limit=1)
registration = RegistrationFactory(user=member, event=event, is_on_wait=Fal... | def test_retrieve_evaluation_event_form_as_member_when_has_attended_event(member):
'\n A member should be able to retrieve an event form of type evaluation if\n they has attended the event.\n '
event = EventFactory(limit=1)
registration = RegistrationFactory(user=member, event=event, is_on_wait=Fal... |
306a220bae60b0c43005d0e20868255a2420e1ce928c59d4ae0697200f8cbd73 | def test_retrieve_evaluation_event_form_as_member_when_has_not_attended_event(member):
'A member should not be able to retrieve an event evaluation form if they have not attended the event.'
event = EventFactory(limit=1)
registration = RegistrationFactory(user=member, event=event, is_on_wait=False, has_atte... | A member should not be able to retrieve an event evaluation form if they have not attended the event. | app/tests/forms/test_eventform_integration.py | test_retrieve_evaluation_event_form_as_member_when_has_not_attended_event | TIHLDE/Lepton | 7 | python | def test_retrieve_evaluation_event_form_as_member_when_has_not_attended_event(member):
event = EventFactory(limit=1)
registration = RegistrationFactory(user=member, event=event, is_on_wait=False, has_attended=False)
form = EventFormFactory(event=registration.event, type=EventFormType.EVALUATION)
cl... | def test_retrieve_evaluation_event_form_as_member_when_has_not_attended_event(member):
event = EventFactory(limit=1)
registration = RegistrationFactory(user=member, event=event, is_on_wait=False, has_attended=False)
form = EventFormFactory(event=registration.event, type=EventFormType.EVALUATION)
cl... |
0e1145af89e6a8734f46476e43388afc1293f6d8ebfa41b798f1450742177676 | @permission_params
def test_create_event_form_as_admin(permission_test_util):
'An admin should be able to create an event form.'
(member, event, expected_create_status_code, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory.build()
client = get_api_client(user=member)
... | An admin should be able to create an event form. | app/tests/forms/test_eventform_integration.py | test_create_event_form_as_admin | TIHLDE/Lepton | 7 | python | @permission_params
def test_create_event_form_as_admin(permission_test_util):
(member, event, expected_create_status_code, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory.build()
client = get_api_client(user=member)
url = _get_forms_url()
response = client.pos... | @permission_params
def test_create_event_form_as_admin(permission_test_util):
(member, event, expected_create_status_code, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory.build()
client = get_api_client(user=member)
url = _get_forms_url()
response = client.pos... |
e6655269e921a7b3101e578dee4fa7842c67af36c1cb0b3ab61958ffc896a9ad | @permission_params
def test_update_event_form_as_admin(permission_test_util):
'An admin should be able to update an event form.'
(member, event, _, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory(event=event)
client = get_api_client(user=member)
url = _get_form_det... | An admin should be able to update an event form. | app/tests/forms/test_eventform_integration.py | test_update_event_form_as_admin | TIHLDE/Lepton | 7 | python | @permission_params
def test_update_event_form_as_admin(permission_test_util):
(member, event, _, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory(event=event)
client = get_api_client(user=member)
url = _get_form_detail_url(form)
new_title = 'New form title'
... | @permission_params
def test_update_event_form_as_admin(permission_test_util):
(member, event, _, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory(event=event)
client = get_api_client(user=member)
url = _get_form_detail_url(form)
new_title = 'New form title'
... |
5506199c3852019cdc9760aa6c7dae372670b702a2d9cdf974c70c38197b9772 | @permission_params
def test_delete_event_form_as_admin(permission_test_util):
'An admin should be able to delete an event form.'
(member, event, _, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory(event=event)
client = get_api_client(user=member)
url = _get_form_det... | An admin should be able to delete an event form. | app/tests/forms/test_eventform_integration.py | test_delete_event_form_as_admin | TIHLDE/Lepton | 7 | python | @permission_params
def test_delete_event_form_as_admin(permission_test_util):
(member, event, _, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory(event=event)
client = get_api_client(user=member)
url = _get_form_detail_url(form)
response = client.delete(url)
... | @permission_params
def test_delete_event_form_as_admin(permission_test_util):
(member, event, _, expected_update_delete_status_code) = permission_test_util
form = EventFormFactory(event=event)
client = get_api_client(user=member)
url = _get_form_detail_url(form)
response = client.delete(url)
... |
05839a9a07b6b6fd8ecbae81fe12a3ddbbaf4c769fe720d8e6c45f473d5ac409 | def eval(model, data_loader, criterion):
'\n Function for evaluation step\n\n Args:\n model ([]): tranformer model\n data_loader (BucketIterator): data_loader to evaluate\n criterion (Loss Object): criterion to calculate the loss \n '
losses = []
with torch.no_grad():
f... | Function for evaluation step
Args:
model ([]): tranformer model
data_loader (BucketIterator): data_loader to evaluate
criterion (Loss Object): criterion to calculate the loss | notebooks/train.py | eval | macabdul9/transformers | 3 | python | def eval(model, data_loader, criterion):
'\n Function for evaluation step\n\n Args:\n model ([]): tranformer model\n data_loader (BucketIterator): data_loader to evaluate\n criterion (Loss Object): criterion to calculate the loss \n '
losses = []
with torch.no_grad():
f... | def eval(model, data_loader, criterion):
'\n Function for evaluation step\n\n Args:\n model ([]): tranformer model\n data_loader (BucketIterator): data_loader to evaluate\n criterion (Loss Object): criterion to calculate the loss \n '
losses = []
with torch.no_grad():
f... |
d0f99490376cc9cefc08eb0175637e6a3d1962430614d735411303615b4fa2f8 | def train(model, train_loader, val_loader, criterion, optimizer, epochs=10):
'\n \n Function to train the model\n\n Args:\n model (nn.Module): model \n train_loader (B): [description]\n val_loader ([type]): [description]\n criterion ([type]): [description]\n optimizer ([t... | Function to train the model
Args:
model (nn.Module): model
train_loader (B): [description]
val_loader ([type]): [description]
criterion ([type]): [description]
optimizer ([type]): [description]
epochs (int, optional): [description]. Defaults to 10. | notebooks/train.py | train | macabdul9/transformers | 3 | python | def train(model, train_loader, val_loader, criterion, optimizer, epochs=10):
'\n \n Function to train the model\n\n Args:\n model (nn.Module): model \n train_loader (B): [description]\n val_loader ([type]): [description]\n criterion ([type]): [description]\n optimizer ([t... | def train(model, train_loader, val_loader, criterion, optimizer, epochs=10):
'\n \n Function to train the model\n\n Args:\n model (nn.Module): model \n train_loader (B): [description]\n val_loader ([type]): [description]\n criterion ([type]): [description]\n optimizer ([t... |
6d0fbe99c8c7f98221dc0a7c25ea8bb7897d41a867da7fa6589b5b6148ee621a | def poincare_2d_visualization(model, animation, epoch, eval_result, avg_loss, avg_pos_loss, avg_neg_loss, tree, figure_title, num_nodes=50, show_node_labels=()):
'Create a 2-d plot of the nodes and edges of a 2-d poincare embedding.\n\n Parameters\n ----------\n model : :class:`~hyperbolic.dag_emb_model.DA... | Create a 2-d plot of the nodes and edges of a 2-d poincare embedding.
Parameters
----------
model : :class:`~hyperbolic.dag_emb_model.DAGEmbeddingModel`
The model to visualize, model size must be 2.
tree : list
Set of tuples containing the direct edges present in the original dataset.
figure_title : str
Ti... | poincare_viz.py | poincare_2d_visualization | dalab/hyperbolic_cones | 103 | python | def poincare_2d_visualization(model, animation, epoch, eval_result, avg_loss, avg_pos_loss, avg_neg_loss, tree, figure_title, num_nodes=50, show_node_labels=()):
'Create a 2-d plot of the nodes and edges of a 2-d poincare embedding.\n\n Parameters\n ----------\n model : :class:`~hyperbolic.dag_emb_model.DA... | def poincare_2d_visualization(model, animation, epoch, eval_result, avg_loss, avg_pos_loss, avg_neg_loss, tree, figure_title, num_nodes=50, show_node_labels=()):
'Create a 2-d plot of the nodes and edges of a 2-d poincare embedding.\n\n Parameters\n ----------\n model : :class:`~hyperbolic.dag_emb_model.DA... |
0a6d05ec08d1f784d0b8f2bf746cde6ca20b9bf344c02fa6b3be0aac9714f54a | def poincare_distance_heatmap(origin_point, x_range=((- 1.0), 1.0), y_range=((- 1.0), 1.0), num_points=100):
'Create a heatmap of Poincare distances from `origin_point` for each point (x, y),\n where x and y lie in `x_range` and `y_range` respectively, with `num_points` points chosen uniformly in both ranges.\n\... | Create a heatmap of Poincare distances from `origin_point` for each point (x, y),
where x and y lie in `x_range` and `y_range` respectively, with `num_points` points chosen uniformly in both ranges.
Parameters
----------
origin_point : tuple (int, int)
(x, y) from which distances are to be measured and plotted.
x_... | poincare_viz.py | poincare_distance_heatmap | dalab/hyperbolic_cones | 103 | python | def poincare_distance_heatmap(origin_point, x_range=((- 1.0), 1.0), y_range=((- 1.0), 1.0), num_points=100):
'Create a heatmap of Poincare distances from `origin_point` for each point (x, y),\n where x and y lie in `x_range` and `y_range` respectively, with `num_points` points chosen uniformly in both ranges.\n\... | def poincare_distance_heatmap(origin_point, x_range=((- 1.0), 1.0), y_range=((- 1.0), 1.0), num_points=100):
'Create a heatmap of Poincare distances from `origin_point` for each point (x, y),\n where x and y lie in `x_range` and `y_range` respectively, with `num_points` points chosen uniformly in both ranges.\n\... |
547f13775870625b43fa74deac07f9568c673854e409bf32a5eed5dd995239a8 | def __init__(self, *args, **kwds):
'\n Constructor. Any message fields that are implicitly/explicitly\n set to None will be assigned a default value. The recommend\n use is keyword arguments as this is more robust to future message\n changes. You cannot mix in-order arguments and keyword arguments.\n\n... | Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
header,pose
:param args: complete... | TrekBot2_WS/devel/.private/geographic_msgs/lib/python2.7/dist-packages/geographic_msgs/msg/_GeoPoseStamped.py | __init__ | Rafcin/RescueRoboticsLHMV | 1 | python | def __init__(self, *args, **kwds):
'\n Constructor. Any message fields that are implicitly/explicitly\n set to None will be assigned a default value. The recommend\n use is keyword arguments as this is more robust to future message\n changes. You cannot mix in-order arguments and keyword arguments.\n\n... | def __init__(self, *args, **kwds):
'\n Constructor. Any message fields that are implicitly/explicitly\n set to None will be assigned a default value. The recommend\n use is keyword arguments as this is more robust to future message\n changes. You cannot mix in-order arguments and keyword arguments.\n\n... |
1fb6b2b708db1f101aab56633ecd49b6f4087e60f5bbe6926e83ee92f9106530 | def _get_types(self):
'\n internal API method\n '
return self._slot_types | internal API method | TrekBot2_WS/devel/.private/geographic_msgs/lib/python2.7/dist-packages/geographic_msgs/msg/_GeoPoseStamped.py | _get_types | Rafcin/RescueRoboticsLHMV | 1 | python | def _get_types(self):
'\n \n '
return self._slot_types | def _get_types(self):
'\n \n '
return self._slot_types<|docstring|>internal API method<|endoftext|> |
3411963652b50dc80a3ad33fc6ae6284e1ec01a7407c670cfca5ec67a7cf4f07 | def serialize(self, buff):
'\n serialize message into buffer\n :param buff: buffer, ``StringIO``\n '
try:
_x = self
buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))
_x = self.header.frame_id
length = len(_x)
if (python... | serialize message into buffer
:param buff: buffer, ``StringIO`` | TrekBot2_WS/devel/.private/geographic_msgs/lib/python2.7/dist-packages/geographic_msgs/msg/_GeoPoseStamped.py | serialize | Rafcin/RescueRoboticsLHMV | 1 | python | def serialize(self, buff):
'\n serialize message into buffer\n :param buff: buffer, ``StringIO``\n '
try:
_x = self
buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))
_x = self.header.frame_id
length = len(_x)
if (python... | def serialize(self, buff):
'\n serialize message into buffer\n :param buff: buffer, ``StringIO``\n '
try:
_x = self
buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))
_x = self.header.frame_id
length = len(_x)
if (python... |
62db6f4627009d5683ea157115e112419acb1e4094ed957bc46187e2c3d2f790 | def deserialize(self, str):
'\n unpack serialized message in str into this message instance\n :param str: byte array of serialized message, ``str``\n '
try:
if (self.header is None):
self.header = std_msgs.msg.Header()
if (self.pose is None):
self.pose = geograph... | unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str`` | TrekBot2_WS/devel/.private/geographic_msgs/lib/python2.7/dist-packages/geographic_msgs/msg/_GeoPoseStamped.py | deserialize | Rafcin/RescueRoboticsLHMV | 1 | python | def deserialize(self, str):
'\n unpack serialized message in str into this message instance\n :param str: byte array of serialized message, ``str``\n '
try:
if (self.header is None):
self.header = std_msgs.msg.Header()
if (self.pose is None):
self.pose = geograph... | def deserialize(self, str):
'\n unpack serialized message in str into this message instance\n :param str: byte array of serialized message, ``str``\n '
try:
if (self.header is None):
self.header = std_msgs.msg.Header()
if (self.pose is None):
self.pose = geograph... |
51423eb71bd72896fc7f08265580e021e911cbf4a479425187fd4b7f98f3fc4e | def serialize_numpy(self, buff, numpy):
'\n serialize message with numpy array types into buffer\n :param buff: buffer, ``StringIO``\n :param numpy: numpy python module\n '
try:
_x = self
buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))
... | serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module | TrekBot2_WS/devel/.private/geographic_msgs/lib/python2.7/dist-packages/geographic_msgs/msg/_GeoPoseStamped.py | serialize_numpy | Rafcin/RescueRoboticsLHMV | 1 | python | def serialize_numpy(self, buff, numpy):
'\n serialize message with numpy array types into buffer\n :param buff: buffer, ``StringIO``\n :param numpy: numpy python module\n '
try:
_x = self
buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))
... | def serialize_numpy(self, buff, numpy):
'\n serialize message with numpy array types into buffer\n :param buff: buffer, ``StringIO``\n :param numpy: numpy python module\n '
try:
_x = self
buff.write(_get_struct_3I().pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))
... |
38896605d88d39d0f35035f8150e35b77381fef724b12a47444543de878e9402 | def deserialize_numpy(self, str, numpy):
'\n unpack serialized message in str into this message instance using numpy for array types\n :param str: byte array of serialized message, ``str``\n :param numpy: numpy python module\n '
try:
if (self.header is None):
self.header = std_ms... | unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module | TrekBot2_WS/devel/.private/geographic_msgs/lib/python2.7/dist-packages/geographic_msgs/msg/_GeoPoseStamped.py | deserialize_numpy | Rafcin/RescueRoboticsLHMV | 1 | python | def deserialize_numpy(self, str, numpy):
'\n unpack serialized message in str into this message instance using numpy for array types\n :param str: byte array of serialized message, ``str``\n :param numpy: numpy python module\n '
try:
if (self.header is None):
self.header = std_ms... | def deserialize_numpy(self, str, numpy):
'\n unpack serialized message in str into this message instance using numpy for array types\n :param str: byte array of serialized message, ``str``\n :param numpy: numpy python module\n '
try:
if (self.header is None):
self.header = std_ms... |
9d1b6e8267c581e066375e6463351edc4934d18f8d1ee3919229212ecde43dd9 | def utctoweekseconds(utc=datetime.datetime.utcnow(), leapseconds=37):
' Returns the GPS week, the GPS day, and the seconds\n and microseconds since the beginning of the GPS week '
datetimeformat = '%Y-%m-%d %H:%M:%S'
epoch = datetime.datetime.strptime('1980-01-06 00:00:00', datetimeformat)
... | Returns the GPS week, the GPS day, and the seconds
and microseconds since the beginning of the GPS week | Simulation/python/FMU.py | utctoweekseconds | elke0011/OpenFlightSim | 15 | python | def utctoweekseconds(utc=datetime.datetime.utcnow(), leapseconds=37):
' Returns the GPS week, the GPS day, and the seconds\n and microseconds since the beginning of the GPS week '
datetimeformat = '%Y-%m-%d %H:%M:%S'
epoch = datetime.datetime.strptime('1980-01-06 00:00:00', datetimeformat)
... | def utctoweekseconds(utc=datetime.datetime.utcnow(), leapseconds=37):
' Returns the GPS week, the GPS day, and the seconds\n and microseconds since the beginning of the GPS week '
datetimeformat = '%Y-%m-%d %H:%M:%S'
epoch = datetime.datetime.strptime('1980-01-06 00:00:00', datetimeformat)
... |
893688f6a64378c1fe02a0a977328ca65d744f61d9a62c0641e8e8e1e015ff7b | def __init__(self, parent):
'\n Initialize a Resource Layer.\n\n :type parent: CoAP\n :param parent: the CoAP server\n '
self._parent = parent | Initialize a Resource Layer.
:type parent: CoAP
:param parent: the CoAP server | src/Bubot_CoAP/layers/resource_layer.py | __init__ | businka/Bubot_CoAP | 0 | python | def __init__(self, parent):
'\n Initialize a Resource Layer.\n\n :type parent: CoAP\n :param parent: the CoAP server\n '
self._parent = parent | def __init__(self, parent):
'\n Initialize a Resource Layer.\n\n :type parent: CoAP\n :param parent: the CoAP server\n '
self._parent = parent<|docstring|>Initialize a Resource Layer.
:type parent: CoAP
:param parent: the CoAP server<|endoftext|> |
a4e54fdf24aaf5cbf742162a6dc2f6d3c06dd2fcb7cc03b0482e997383ea637a | async def edit_resource(self, transaction, path):
'\n Render a POST on an already created resource.\n\n :param path: the path of the resource\n :param transaction: the transaction\n :return: the transaction\n '
resource_node = self._parent.root[path]
transaction.resource =... | Render a POST on an already created resource.
:param path: the path of the resource
:param transaction: the transaction
:return: the transaction | src/Bubot_CoAP/layers/resource_layer.py | edit_resource | businka/Bubot_CoAP | 0 | python | async def edit_resource(self, transaction, path):
'\n Render a POST on an already created resource.\n\n :param path: the path of the resource\n :param transaction: the transaction\n :return: the transaction\n '
resource_node = self._parent.root[path]
transaction.resource =... | async def edit_resource(self, transaction, path):
'\n Render a POST on an already created resource.\n\n :param path: the path of the resource\n :param transaction: the transaction\n :return: the transaction\n '
resource_node = self._parent.root[path]
transaction.resource =... |
8ba2cb1819e4e08ae69403cb6b8617c201ab47c0415d720f1c9effe5fbd61b35 | async def add_resource(self, transaction, parent_resource, lp):
'\n Render a POST on a new resource.\n\n :param transaction: the transaction\n :param parent_resource: the parent of the resource\n :param lp: the location_path attribute of the resource\n :return: the response\n ... | Render a POST on a new resource.
:param transaction: the transaction
:param parent_resource: the parent of the resource
:param lp: the location_path attribute of the resource
:return: the response | src/Bubot_CoAP/layers/resource_layer.py | add_resource | businka/Bubot_CoAP | 0 | python | async def add_resource(self, transaction, parent_resource, lp):
'\n Render a POST on a new resource.\n\n :param transaction: the transaction\n :param parent_resource: the parent of the resource\n :param lp: the location_path attribute of the resource\n :return: the response\n ... | async def add_resource(self, transaction, parent_resource, lp):
'\n Render a POST on a new resource.\n\n :param transaction: the transaction\n :param parent_resource: the parent of the resource\n :param lp: the location_path attribute of the resource\n :return: the response\n ... |
465c5779d0e1f93e813e8e270b429a8ebeb93ab9952fed46d926ca713e55ec33 | async def create_resource(self, path, transaction):
'\n Render a POST request.\n\n :param path: the path of the request\n :param transaction: the transaction\n :return: the response\n '
t = self._parent.root.with_prefix(path)
max_len = 0
imax = None
for i in t:
... | Render a POST request.
:param path: the path of the request
:param transaction: the transaction
:return: the response | src/Bubot_CoAP/layers/resource_layer.py | create_resource | businka/Bubot_CoAP | 0 | python | async def create_resource(self, path, transaction):
'\n Render a POST request.\n\n :param path: the path of the request\n :param transaction: the transaction\n :return: the response\n '
t = self._parent.root.with_prefix(path)
max_len = 0
imax = None
for i in t:
... | async def create_resource(self, path, transaction):
'\n Render a POST request.\n\n :param path: the path of the request\n :param transaction: the transaction\n :return: the response\n '
t = self._parent.root.with_prefix(path)
max_len = 0
imax = None
for i in t:
... |
671ebdc6eeb186be54b54d58cd54cd90ae2c891ed9260db4f1c6cd13bea353bf | async def update_resource(self, transaction):
'\n Render a PUT request.\n\n :param transaction: the transaction\n :return: the response\n '
if transaction.request.if_match:
if ((None not in transaction.request.if_match) and (str(transaction.resource.etag) not in transaction.r... | Render a PUT request.
:param transaction: the transaction
:return: the response | src/Bubot_CoAP/layers/resource_layer.py | update_resource | businka/Bubot_CoAP | 0 | python | async def update_resource(self, transaction):
'\n Render a PUT request.\n\n :param transaction: the transaction\n :return: the response\n '
if transaction.request.if_match:
if ((None not in transaction.request.if_match) and (str(transaction.resource.etag) not in transaction.r... | async def update_resource(self, transaction):
'\n Render a PUT request.\n\n :param transaction: the transaction\n :return: the response\n '
if transaction.request.if_match:
if ((None not in transaction.request.if_match) and (str(transaction.resource.etag) not in transaction.r... |
93146a3f224279b4de48928eae3642d41d7edb9422ae5882f1b7d5d57613fd84 | async def delete_resource(self, transaction, path):
'\n Render a DELETE request.\n\n :param transaction: the transaction\n :param path: the path\n :return: the response\n '
resource = transaction.resource
method = getattr(resource, 'render_DELETE', None)
try:
r... | Render a DELETE request.
:param transaction: the transaction
:param path: the path
:return: the response | src/Bubot_CoAP/layers/resource_layer.py | delete_resource | businka/Bubot_CoAP | 0 | python | async def delete_resource(self, transaction, path):
'\n Render a DELETE request.\n\n :param transaction: the transaction\n :param path: the path\n :return: the response\n '
resource = transaction.resource
method = getattr(resource, 'render_DELETE', None)
try:
r... | async def delete_resource(self, transaction, path):
'\n Render a DELETE request.\n\n :param transaction: the transaction\n :param path: the path\n :return: the response\n '
resource = transaction.resource
method = getattr(resource, 'render_DELETE', None)
try:
r... |
625e2ca5c81448dcb8f838650cb1cbf2065b561761cea9f6ccbc72bd918f59ea | async def get_resource(self, transaction):
'\n Render a GET request.\n\n :param transaction: the transaction\n :return: the transaction\n '
method = getattr(transaction.resource, 'render_GET', None)
try:
resource = (await method(request=transaction.request))
except No... | Render a GET request.
:param transaction: the transaction
:return: the transaction | src/Bubot_CoAP/layers/resource_layer.py | get_resource | businka/Bubot_CoAP | 0 | python | async def get_resource(self, transaction):
'\n Render a GET request.\n\n :param transaction: the transaction\n :return: the transaction\n '
method = getattr(transaction.resource, 'render_GET', None)
try:
resource = (await method(request=transaction.request))
except No... | async def get_resource(self, transaction):
'\n Render a GET request.\n\n :param transaction: the transaction\n :return: the transaction\n '
method = getattr(transaction.resource, 'render_GET', None)
try:
resource = (await method(request=transaction.request))
except No... |
bd6f53ef40ab041f94c77729a10ea71ba554dde572c94b8230e31996e4f54919 | async def discover(self, transaction):
'\n Render a GET request to the .well-know/core link.\n\n :param transaction: the transaction\n :return: the transaction\n '
transaction.response.code = defines.Codes.CONTENT.number
payload = ''
for i in self._parent.root.dump():
... | Render a GET request to the .well-know/core link.
:param transaction: the transaction
:return: the transaction | src/Bubot_CoAP/layers/resource_layer.py | discover | businka/Bubot_CoAP | 0 | python | async def discover(self, transaction):
'\n Render a GET request to the .well-know/core link.\n\n :param transaction: the transaction\n :return: the transaction\n '
transaction.response.code = defines.Codes.CONTENT.number
payload =
for i in self._parent.root.dump():
i... | async def discover(self, transaction):
'\n Render a GET request to the .well-know/core link.\n\n :param transaction: the transaction\n :return: the transaction\n '
transaction.response.code = defines.Codes.CONTENT.number
payload =
for i in self._parent.root.dump():
i... |
aa6247e0de8ab3e034a7359ac49f0c84e42588aadf4a67fd9b5fcf109c25f2da | @staticmethod
def corelinkformat(resource):
'\n Return a formatted string representation of the corelinkformat in the tree.\n\n :return: the string\n '
msg = (('<' + resource.path) + '>;')
assert isinstance(resource, Resource)
keys = sorted(list(resource.attributes.keys()))
for ... | Return a formatted string representation of the corelinkformat in the tree.
:return: the string | src/Bubot_CoAP/layers/resource_layer.py | corelinkformat | businka/Bubot_CoAP | 0 | python | @staticmethod
def corelinkformat(resource):
'\n Return a formatted string representation of the corelinkformat in the tree.\n\n :return: the string\n '
msg = (('<' + resource.path) + '>;')
assert isinstance(resource, Resource)
keys = sorted(list(resource.attributes.keys()))
for ... | @staticmethod
def corelinkformat(resource):
'\n Return a formatted string representation of the corelinkformat in the tree.\n\n :return: the string\n '
msg = (('<' + resource.path) + '>;')
assert isinstance(resource, Resource)
keys = sorted(list(resource.attributes.keys()))
for ... |
f824b27641433887977d4221e74d1400a4aeddad338625e7077a2bf306454119 | def load_data(messages_filepath, categories_filepath):
'Load messages and categories data from the given file paths, process them and merge them\n \n Args:\n messages_filepath: \n categories_filepath:\n\n Returns:\n df: pandas.DataFrame: dataframe containing the messages data combined with their c... | Load messages and categories data from the given file paths, process them and merge them
Args:
messages_filepath:
categories_filepath:
Returns:
df: pandas.DataFrame: dataframe containing the messages data combined with their category classifications | process_data.py | load_data | karthikvijayakumar/Disaster-Response-Text-Classification | 1 | python | def load_data(messages_filepath, categories_filepath):
'Load messages and categories data from the given file paths, process them and merge them\n \n Args:\n messages_filepath: \n categories_filepath:\n\n Returns:\n df: pandas.DataFrame: dataframe containing the messages data combined with their c... | def load_data(messages_filepath, categories_filepath):
'Load messages and categories data from the given file paths, process them and merge them\n \n Args:\n messages_filepath: \n categories_filepath:\n\n Returns:\n df: pandas.DataFrame: dataframe containing the messages data combined with their c... |
24dde391793e88edf9891dd925014d406c668cc82e4ddce35b8165d0756d78f3 | def clean_data(df):
'Removes duplicates from the dataset\n Args:\n df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories\n\n Returns:\n df: pandas.DataFrame: Deduplicated input data \n '
return df.drop_duplicates() | Removes duplicates from the dataset
Args:
df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories
Returns:
df: pandas.DataFrame: Deduplicated input data | process_data.py | clean_data | karthikvijayakumar/Disaster-Response-Text-Classification | 1 | python | def clean_data(df):
'Removes duplicates from the dataset\n Args:\n df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories\n\n Returns:\n df: pandas.DataFrame: Deduplicated input data \n '
return df.drop_duplicates() | def clean_data(df):
'Removes duplicates from the dataset\n Args:\n df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories\n\n Returns:\n df: pandas.DataFrame: Deduplicated input data \n '
return df.drop_duplicates()<|docstring|>Removes duplicate... |
f8875a2ac4aec92143751e8a53d9672e6b833ad00c7d555bd7c463942593513c | def save_data(df, table_name, database_filename):
'Writes the dataframe into a sqlite database at the given location\n \n Args:\n df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories \n table_name: string. Table to write the input data frame to\n ... | Writes the dataframe into a sqlite database at the given location
Args:
df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories
table_name: string. Table to write the input data frame to
database_filename: File location to create and store SQLite database
Returns:
N... | process_data.py | save_data | karthikvijayakumar/Disaster-Response-Text-Classification | 1 | python | def save_data(df, table_name, database_filename):
'Writes the dataframe into a sqlite database at the given location\n \n Args:\n df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories \n table_name: string. Table to write the input data frame to\n ... | def save_data(df, table_name, database_filename):
'Writes the dataframe into a sqlite database at the given location\n \n Args:\n df: pandas.DataFrame: Input data containing messages and their classifications into multiple categories \n table_name: string. Table to write the input data frame to\n ... |
b832c292b18df7c95166f67666622390484393765e78eee3d7d3d0b6b3aa425a | def main():
'Main function for the file. This is entry point of execution\n \n Args:\n None\n\n Returns:\n None\n\n '
if (len(sys.argv) == 4):
(messages_filepath, categories_filepath, database_filepath) = sys.argv[1:]
print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}... | Main function for the file. This is entry point of execution
Args:
None
Returns:
None | process_data.py | main | karthikvijayakumar/Disaster-Response-Text-Classification | 1 | python | def main():
'Main function for the file. This is entry point of execution\n \n Args:\n None\n\n Returns:\n None\n\n '
if (len(sys.argv) == 4):
(messages_filepath, categories_filepath, database_filepath) = sys.argv[1:]
print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}... | def main():
'Main function for the file. This is entry point of execution\n \n Args:\n None\n\n Returns:\n None\n\n '
if (len(sys.argv) == 4):
(messages_filepath, categories_filepath, database_filepath) = sys.argv[1:]
print('Loading data...\n MESSAGES: {}\n CATEGORIES: {}... |
a2d6fcfbd01c97f8b3c33ec88d0f9acd0a47cc173c70c0c4b10e9ef2ccaaa5f8 | def deprecated(func):
'This is a decorator which can be used to mark functions\n as deprecated. It will result in a warning being emitted\n when the function is used.'
@functools.wraps(func)
def new_func(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
warnings.wa... | This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used. | src/helpers/decorators.py | deprecated | Lakoc/bachelor_thesis | 0 | python | def deprecated(func):
'This is a decorator which can be used to mark functions\n as deprecated. It will result in a warning being emitted\n when the function is used.'
@functools.wraps(func)
def new_func(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
warnings.wa... | def deprecated(func):
'This is a decorator which can be used to mark functions\n as deprecated. It will result in a warning being emitted\n when the function is used.'
@functools.wraps(func)
def new_func(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
warnings.wa... |
092ade2784f9fac8b3df89ff37a86faf0e8eb3f3bc2a44068fc1bb33bddd555e | def timeit(func):
'This is a decorator which can be used to measure function time spent.'
@functools.wraps(func)
def new_func(*args, **kwargs):
start_time = time.time()
ret_val = func(*args, **kwargs)
elapsed_time = (time.time() - start_time)
print(f'function [{func.__name__... | This is a decorator which can be used to measure function time spent. | src/helpers/decorators.py | timeit | Lakoc/bachelor_thesis | 0 | python | def timeit(func):
@functools.wraps(func)
def new_func(*args, **kwargs):
start_time = time.time()
ret_val = func(*args, **kwargs)
elapsed_time = (time.time() - start_time)
print(f'function [{func.__name__}] finished in {int((elapsed_time * 1000))} ms')
return ret_val... | def timeit(func):
@functools.wraps(func)
def new_func(*args, **kwargs):
start_time = time.time()
ret_val = func(*args, **kwargs)
elapsed_time = (time.time() - start_time)
print(f'function [{func.__name__}] finished in {int((elapsed_time * 1000))} ms')
return ret_val... |
71a0573133235de5923e716950a1653146e0bdeaf1ed8023f224198c53d0920e | def frankie_angles_from_g(g, verbo=True, energy=50):
"\n Converted from David's code, which converted from Bob's code.\n I9 internal simulation coordinates: x ray direction is positive x direction, positive z direction is upward, y direction can be determined by right hand rule.\n I9 mic file coordinates: ... | Converted from David's code, which converted from Bob's code.
I9 internal simulation coordinates: x ray direction is positive x direction, positive z direction is upward, y direction can be determined by right hand rule.
I9 mic file coordinates: x, y directions are the same as the simulation coordinates.
I9 detector im... | util/Simulation.py | frankie_angles_from_g | Yufeng-shen/StrainRecon | 0 | python | def frankie_angles_from_g(g, verbo=True, energy=50):
"\n Converted from David's code, which converted from Bob's code.\n I9 internal simulation coordinates: x ray direction is positive x direction, positive z direction is upward, y direction can be determined by right hand rule.\n I9 mic file coordinates: ... | def frankie_angles_from_g(g, verbo=True, energy=50):
"\n Converted from David's code, which converted from Bob's code.\n I9 internal simulation coordinates: x ray direction is positive x direction, positive z direction is upward, y direction can be determined by right hand rule.\n I9 mic file coordinates: ... |
1664484bd3026d18e9fb119dcfe468db4cb8bceb0e37727d767150332e30f9d8 | def GetProjectedVertex(Det1, sample, orien, etalimit, grainpos, getPeaksInfo=False, bIdx=True, omegaL=(- 90), omegaU=90, energy=50):
'\n Get the observable projected vertex on a single detector and their G vectors.\n Caution!!! This function only works for traditional nf-HEDM experiment setup.\n\n Paramete... | Get the observable projected vertex on a single detector and their G vectors.
Caution!!! This function only works for traditional nf-HEDM experiment setup.
Parameters
------------
Det1: Detector
Remember to move this detector object to correct position first.
sample: CrystalStr
Must calculated G list
o... | util/Simulation.py | GetProjectedVertex | Yufeng-shen/StrainRecon | 0 | python | def GetProjectedVertex(Det1, sample, orien, etalimit, grainpos, getPeaksInfo=False, bIdx=True, omegaL=(- 90), omegaU=90, energy=50):
'\n Get the observable projected vertex on a single detector and their G vectors.\n Caution!!! This function only works for traditional nf-HEDM experiment setup.\n\n Paramete... | def GetProjectedVertex(Det1, sample, orien, etalimit, grainpos, getPeaksInfo=False, bIdx=True, omegaL=(- 90), omegaU=90, energy=50):
'\n Get the observable projected vertex on a single detector and their G vectors.\n Caution!!! This function only works for traditional nf-HEDM experiment setup.\n\n Paramete... |
c759171fa6e1f200cb71807caac6ce0a6a3e8804222fcd140a1f3163ecda0029 | def digitize(xy):
'\n xy: ndarray shape(4,2)\n J and K indices in float, four points. This digitize method is far from ideal\n\n Returns\n -------------\n f: list\n list of integer tuples (J,K) that is hitted. (filled polygon)\n\n '
p = path.Path(xy)
def line(pixels, x0, y0, x1... | xy: ndarray shape(4,2)
J and K indices in float, four points. This digitize method is far from ideal
Returns
-------------
f: list
list of integer tuples (J,K) that is hitted. (filled polygon) | util/Simulation.py | digitize | Yufeng-shen/StrainRecon | 0 | python | def digitize(xy):
'\n xy: ndarray shape(4,2)\n J and K indices in float, four points. This digitize method is far from ideal\n\n Returns\n -------------\n f: list\n list of integer tuples (J,K) that is hitted. (filled polygon)\n\n '
p = path.Path(xy)
def line(pixels, x0, y0, x1... | def digitize(xy):
'\n xy: ndarray shape(4,2)\n J and K indices in float, four points. This digitize method is far from ideal\n\n Returns\n -------------\n f: list\n list of integer tuples (J,K) that is hitted. (filled polygon)\n\n '
p = path.Path(xy)
def line(pixels, x0, y0, x1... |
3f88052c4f0b2313aceb753633f595b83966c02beae0ced43948add7e15d97db | def BackProj(self, HitPos, omega, TwoTheta, eta):
'\n HitPos: ndarray (3,)\n The position of hitted point on lab coord, unit in mm\n '
scatterdir = np.array([np.cos(TwoTheta), (np.sin(TwoTheta) * np.sin(eta)), (np.sin(TwoTheta) * np.cos(eta))])
t = (HitPos[2] / (np.sin(TwoTheta)... | HitPos: ndarray (3,)
The position of hitted point on lab coord, unit in mm | util/Simulation.py | BackProj | Yufeng-shen/StrainRecon | 0 | python | def BackProj(self, HitPos, omega, TwoTheta, eta):
'\n HitPos: ndarray (3,)\n The position of hitted point on lab coord, unit in mm\n '
scatterdir = np.array([np.cos(TwoTheta), (np.sin(TwoTheta) * np.sin(eta)), (np.sin(TwoTheta) * np.cos(eta))])
t = (HitPos[2] / (np.sin(TwoTheta)... | def BackProj(self, HitPos, omega, TwoTheta, eta):
'\n HitPos: ndarray (3,)\n The position of hitted point on lab coord, unit in mm\n '
scatterdir = np.array([np.cos(TwoTheta), (np.sin(TwoTheta) * np.sin(eta)), (np.sin(TwoTheta) * np.cos(eta))])
t = (HitPos[2] / (np.sin(TwoTheta)... |
5edb27b47b45fe96c4b77cee9fe28542094b5590b7331b29deb0cb22a7dc56cb | def _get_file_as_dict(self, file_path):
'Open file path and return as dict.'
with open(file_path) as f:
return json.load(f) | Open file path and return as dict. | rdsslib/taxonomy/taxonomy_client.py | _get_file_as_dict | JiscSD/rdss-shared-libraries | 0 | python | def _get_file_as_dict(self, file_path):
with open(file_path) as f:
return json.load(f) | def _get_file_as_dict(self, file_path):
with open(file_path) as f:
return json.load(f)<|docstring|>Open file path and return as dict.<|endoftext|> |
0473eeadcf74c9df64e5abdb67293f9791104a52a03b6aa729f0418e698fac2c | def _get_vocab_dict(self, vocab_id):
'Get a vocabulary by ID.'
base_dir = self._get_filedir()
file_name = None
try:
file_name = VOCAB_FILE_LOOKUP[vocab_id]
except KeyError:
raise VocabularyNotFound
path = os.path.join(base_dir, file_name)
return self._get_file_as_dict(path) | Get a vocabulary by ID. | rdsslib/taxonomy/taxonomy_client.py | _get_vocab_dict | JiscSD/rdss-shared-libraries | 0 | python | def _get_vocab_dict(self, vocab_id):
base_dir = self._get_filedir()
file_name = None
try:
file_name = VOCAB_FILE_LOOKUP[vocab_id]
except KeyError:
raise VocabularyNotFound
path = os.path.join(base_dir, file_name)
return self._get_file_as_dict(path) | def _get_vocab_dict(self, vocab_id):
base_dir = self._get_filedir()
file_name = None
try:
file_name = VOCAB_FILE_LOOKUP[vocab_id]
except KeyError:
raise VocabularyNotFound
path = os.path.join(base_dir, file_name)
return self._get_file_as_dict(path)<|docstring|>Get a vocabula... |
7bfff14bad6cee278deaebef796e0056cf80272875c0a9634ec303b631dc9f6e | def get_by_name(self, vocab_id, name):
'Get a vocab item by name.'
values_dict = self._get_vocab_dict(vocab_id)
values = values_dict.get('vocabularyValues', [])
for val in values:
val_name = val['valueName']
if (val_name == name):
return val['valueId']
raise ValueNotFound | Get a vocab item by name. | rdsslib/taxonomy/taxonomy_client.py | get_by_name | JiscSD/rdss-shared-libraries | 0 | python | def get_by_name(self, vocab_id, name):
values_dict = self._get_vocab_dict(vocab_id)
values = values_dict.get('vocabularyValues', [])
for val in values:
val_name = val['valueName']
if (val_name == name):
return val['valueId']
raise ValueNotFound | def get_by_name(self, vocab_id, name):
values_dict = self._get_vocab_dict(vocab_id)
values = values_dict.get('vocabularyValues', [])
for val in values:
val_name = val['valueName']
if (val_name == name):
return val['valueId']
raise ValueNotFound<|docstring|>Get a vocab it... |
7acb5fbf249c945c6faace458b7e5bcc2ca9b48086967d8c269cfecd77667091 | def globus_initFlow():
'\n Retrieve cached/Create a new access token\n and use it to create an OAuth2WebServerFlow\n '
userAndPass = ('%s:%s' % (auth_settings.GLOBUS_OAUTH_ID, auth_settings.GLOBUS_OAUTH_SECRET))
b64_userAndPass = b64encode(userAndPass)
auth_header = ('Basic %s' % b64_userAndPas... | Retrieve cached/Create a new access token
and use it to create an OAuth2WebServerFlow | django_cyverse_auth/protocol/globus.py | globus_initFlow | simpsonw/django-cyverse-auth | 1 | python | def globus_initFlow():
'\n Retrieve cached/Create a new access token\n and use it to create an OAuth2WebServerFlow\n '
userAndPass = ('%s:%s' % (auth_settings.GLOBUS_OAUTH_ID, auth_settings.GLOBUS_OAUTH_SECRET))
b64_userAndPass = b64encode(userAndPass)
auth_header = ('Basic %s' % b64_userAndPas... | def globus_initFlow():
'\n Retrieve cached/Create a new access token\n and use it to create an OAuth2WebServerFlow\n '
userAndPass = ('%s:%s' % (auth_settings.GLOBUS_OAUTH_ID, auth_settings.GLOBUS_OAUTH_SECRET))
b64_userAndPass = b64encode(userAndPass)
auth_header = ('Basic %s' % b64_userAndPas... |
d91225ab7a1147273349dcd31acc7c05d79923a47bfc1f32c0dcab9b33ce6bd4 | def globus_logout(redirect_uri, redirect_name='Jetstream'):
'\n Redirect to logout of globus\n '
flow = globus_initFlow()
auth_uri = flow.auth_uri
web_logout_url = auth_uri.replace('oauth2/authorize', 'web/logout')
web_logout_url += ('?client_id=%s&redirect_name=%s&redirect_uri=%s' % (flow.cli... | Redirect to logout of globus | django_cyverse_auth/protocol/globus.py | globus_logout | simpsonw/django-cyverse-auth | 1 | python | def globus_logout(redirect_uri, redirect_name='Jetstream'):
'\n \n '
flow = globus_initFlow()
auth_uri = flow.auth_uri
web_logout_url = auth_uri.replace('oauth2/authorize', 'web/logout')
web_logout_url += ('?client_id=%s&redirect_name=%s&redirect_uri=%s' % (flow.client_id, redirect_name, redir... | def globus_logout(redirect_uri, redirect_name='Jetstream'):
'\n \n '
flow = globus_initFlow()
auth_uri = flow.auth_uri
web_logout_url = auth_uri.replace('oauth2/authorize', 'web/logout')
web_logout_url += ('?client_id=%s&redirect_name=%s&redirect_uri=%s' % (flow.client_id, redirect_name, redir... |
1f54562ea0b4ffc36f04149dd61fb7fbac0699d8f181c501193be358e416f31b | def globus_authorize(request):
"\n Redirect to the IdP based on 'flow'\n "
flow = globus_initFlow()
auth_uri = flow.step1_get_authorize_url()
auth_uri += '&authentication_hint=36007761-2cf2-4e74-a068-7473afc1d054'
auth_uri = auth_uri.replace('access_type=offline', 'access_type=online')
log... | Redirect to the IdP based on 'flow' | django_cyverse_auth/protocol/globus.py | globus_authorize | simpsonw/django-cyverse-auth | 1 | python | def globus_authorize(request):
"\n \n "
flow = globus_initFlow()
auth_uri = flow.step1_get_authorize_url()
auth_uri += '&authentication_hint=36007761-2cf2-4e74-a068-7473afc1d054'
auth_uri = auth_uri.replace('access_type=offline', 'access_type=online')
logger.warn(auth_uri)
return HttpR... | def globus_authorize(request):
"\n \n "
flow = globus_initFlow()
auth_uri = flow.step1_get_authorize_url()
auth_uri += '&authentication_hint=36007761-2cf2-4e74-a068-7473afc1d054'
auth_uri = auth_uri.replace('access_type=offline', 'access_type=online')
logger.warn(auth_uri)
return HttpR... |
88c4e35a2f8edf664ffb0e321fee605538f164f895036249e4da4e977b279bc2 | def _extract_user_from_email(raw_username):
'\n Usernames come from the globus provider in the form:\n example@example.com\n '
if (not raw_username):
return None
return raw_username.split('@')[0] | Usernames come from the globus provider in the form:
example@example.com | django_cyverse_auth/protocol/globus.py | _extract_user_from_email | simpsonw/django-cyverse-auth | 1 | python | def _extract_user_from_email(raw_username):
'\n Usernames come from the globus provider in the form:\n example@example.com\n '
if (not raw_username):
return None
return raw_username.split('@')[0] | def _extract_user_from_email(raw_username):
'\n Usernames come from the globus provider in the form:\n example@example.com\n '
if (not raw_username):
return None
return raw_username.split('@')[0]<|docstring|>Usernames come from the globus provider in the form:
example@example.com<|endoftext... |
2a8f612b8c2c35bc6eee227d0553c6636f997932ac4bfad3319915f082ca55f5 | def _map_email_to_user(raw_username):
'\n Input: example@example.com\n Output: test\n '
if (not auth_settings.GLOBUS_MAPPING_FILE):
logger.info('GLOBUS_MAPPING_FILE NOT defined. Check your auth settings!!')
return raw_username
if (not os.path.exists(auth_settings.GLOBUS_MAPPING_FIL... | Input: example@example.com
Output: test | django_cyverse_auth/protocol/globus.py | _map_email_to_user | simpsonw/django-cyverse-auth | 1 | python | def _map_email_to_user(raw_username):
'\n Input: example@example.com\n Output: test\n '
if (not auth_settings.GLOBUS_MAPPING_FILE):
logger.info('GLOBUS_MAPPING_FILE NOT defined. Check your auth settings!!')
return raw_username
if (not os.path.exists(auth_settings.GLOBUS_MAPPING_FIL... | def _map_email_to_user(raw_username):
'\n Input: example@example.com\n Output: test\n '
if (not auth_settings.GLOBUS_MAPPING_FILE):
logger.info('GLOBUS_MAPPING_FILE NOT defined. Check your auth settings!!')
return raw_username
if (not os.path.exists(auth_settings.GLOBUS_MAPPING_FIL... |
178258e8b5201fc9603449f53b76b874ab3b0c6cc4e63eeadb94043f909952f9 | def globus_validate_code(request):
"\n This flow is used to create a new Token on behalf of a Service Client\n (Like Troposphere)\n Validates 'code' returned from the IdP\n If valid: Return new AuthToken to be passed to the Resource Provider.\n else: Return None\n "
code = request.GET.get(... | This flow is used to create a new Token on behalf of a Service Client
(Like Troposphere)
Validates 'code' returned from the IdP
If valid: Return new AuthToken to be passed to the Resource Provider.
else: Return None | django_cyverse_auth/protocol/globus.py | globus_validate_code | simpsonw/django-cyverse-auth | 1 | python | def globus_validate_code(request):
"\n This flow is used to create a new Token on behalf of a Service Client\n (Like Troposphere)\n Validates 'code' returned from the IdP\n If valid: Return new AuthToken to be passed to the Resource Provider.\n else: Return None\n "
code = request.GET.get(... | def globus_validate_code(request):
"\n This flow is used to create a new Token on behalf of a Service Client\n (Like Troposphere)\n Validates 'code' returned from the IdP\n If valid: Return new AuthToken to be passed to the Resource Provider.\n else: Return None\n "
code = request.GET.get(... |
d2288a60ac3cf094c3f0c1023f40dd14b44376e9f76f4f7b52e3eead0e87a79f | def create_user_token_from_globus_profile(profile, access_token):
"\n Use this method on your Resource Provider (Like Atmosphere)\n to exchange a profile (that was retrieved via a tokeninfo endpoint)\n for a UserToken that can then be internally validated in an 'authorize' authBackend step..\n "
log... | Use this method on your Resource Provider (Like Atmosphere)
to exchange a profile (that was retrieved via a tokeninfo endpoint)
for a UserToken that can then be internally validated in an 'authorize' authBackend step.. | django_cyverse_auth/protocol/globus.py | create_user_token_from_globus_profile | simpsonw/django-cyverse-auth | 1 | python | def create_user_token_from_globus_profile(profile, access_token):
"\n Use this method on your Resource Provider (Like Atmosphere)\n to exchange a profile (that was retrieved via a tokeninfo endpoint)\n for a UserToken that can then be internally validated in an 'authorize' authBackend step..\n "
log... | def create_user_token_from_globus_profile(profile, access_token):
"\n Use this method on your Resource Provider (Like Atmosphere)\n to exchange a profile (that was retrieved via a tokeninfo endpoint)\n for a UserToken that can then be internally validated in an 'authorize' authBackend step..\n "
log... |
2e5eeeda409a8c07640bed9b353d546016c6b031a8463107ab1f6d2073676277 | def analyze(model, force_warning=False, num_error_samples=1000, pre_equilibrium_approx=False, skip_checks=False, min_time=0.0, max_time=None):
'Perform all the analysis steps at once.'
curdir = os.getcwd()
analysis = Analysis(model, force_warning, num_error_samples)
analysis.extract_data(min_time=min_ti... | Perform all the analysis steps at once. | seekr2/analyze.py | analyze | seekrcentral/seekr2 | 1 | python | def analyze(model, force_warning=False, num_error_samples=1000, pre_equilibrium_approx=False, skip_checks=False, min_time=0.0, max_time=None):
curdir = os.getcwd()
analysis = Analysis(model, force_warning, num_error_samples)
analysis.extract_data(min_time=min_time, max_time=max_time)
if (not skip_c... | def analyze(model, force_warning=False, num_error_samples=1000, pre_equilibrium_approx=False, skip_checks=False, min_time=0.0, max_time=None):
curdir = os.getcwd()
analysis = Analysis(model, force_warning, num_error_samples)
analysis.extract_data(min_time=min_time, max_time=max_time)
if (not skip_c... |
45453bce1550f82eb976ab0029c9bbd313e355d215584423b4a873079d37e28b | def __init__(self, model, force_warning=False, num_error_samples=0):
'\n Creates the Analyze() object, which applies transition \n statistics and times, as well as MMVT theory, to compute \n kinetics and thermodynamics quantities.\n '
self.model = model
self.anchor_stats_list = [... | Creates the Analyze() object, which applies transition
statistics and times, as well as MMVT theory, to compute
kinetics and thermodynamics quantities. | seekr2/analyze.py | __init__ | seekrcentral/seekr2 | 1 | python | def __init__(self, model, force_warning=False, num_error_samples=0):
'\n Creates the Analyze() object, which applies transition \n statistics and times, as well as MMVT theory, to compute \n kinetics and thermodynamics quantities.\n '
self.model = model
self.anchor_stats_list = [... | def __init__(self, model, force_warning=False, num_error_samples=0):
'\n Creates the Analyze() object, which applies transition \n statistics and times, as well as MMVT theory, to compute \n kinetics and thermodynamics quantities.\n '
self.model = model
self.anchor_stats_list = [... |
13b700c2ccf45841ae8a69d7a71e614eba5322a315c47cf8cc053ab7f81b72f4 | def elber_check_anchor_stats(self, silent=False):
'\n Check the anchor statistics to make sure that enough bounces\n have been observed to perform the analysis\n '
anchors_missing_statistics = []
for (i, anchor) in enumerate(self.model.anchors):
if anchor.bulkstate:
... | Check the anchor statistics to make sure that enough bounces
have been observed to perform the analysis | seekr2/analyze.py | elber_check_anchor_stats | seekrcentral/seekr2 | 1 | python | def elber_check_anchor_stats(self, silent=False):
'\n Check the anchor statistics to make sure that enough bounces\n have been observed to perform the analysis\n '
anchors_missing_statistics = []
for (i, anchor) in enumerate(self.model.anchors):
if anchor.bulkstate:
... | def elber_check_anchor_stats(self, silent=False):
'\n Check the anchor statistics to make sure that enough bounces\n have been observed to perform the analysis\n '
anchors_missing_statistics = []
for (i, anchor) in enumerate(self.model.anchors):
if anchor.bulkstate:
... |
76e92edbad392b7b8b71a992b048720fdbc93f7d160be48cd26a7c1f17d3b96d | def mmvt_check_anchor_stats(self, silent=False):
'\n Check the anchor statistics to make sure that enough transitions\n have been observed to perform the analysis\n '
anchors_missing_statistics = []
for (i, anchor) in enumerate(self.model.anchors):
if anchor.bulkstate:
... | Check the anchor statistics to make sure that enough transitions
have been observed to perform the analysis | seekr2/analyze.py | mmvt_check_anchor_stats | seekrcentral/seekr2 | 1 | python | def mmvt_check_anchor_stats(self, silent=False):
'\n Check the anchor statistics to make sure that enough transitions\n have been observed to perform the analysis\n '
anchors_missing_statistics = []
for (i, anchor) in enumerate(self.model.anchors):
if anchor.bulkstate:
... | def mmvt_check_anchor_stats(self, silent=False):
'\n Check the anchor statistics to make sure that enough transitions\n have been observed to perform the analysis\n '
anchors_missing_statistics = []
for (i, anchor) in enumerate(self.model.anchors):
if anchor.bulkstate:
... |
7b8493c4d09a31fc212fd844c2171df2f3c8a99e3ce662266cb1b5fb40e6befc | def extract_data(self, min_time=None, max_time=None, max_step_list=None, silence_errors=True):
'\n Extract the data from simulations used in this analysis.\n '
files_already_read = False
if (len(self.anchor_stats_list) > 0):
files_already_read = True
if (self.model.openmm_settings ... | Extract the data from simulations used in this analysis. | seekr2/analyze.py | extract_data | seekrcentral/seekr2 | 1 | python | def extract_data(self, min_time=None, max_time=None, max_step_list=None, silence_errors=True):
'\n \n '
files_already_read = False
if (len(self.anchor_stats_list) > 0):
files_already_read = True
if (self.model.openmm_settings is not None):
timestep = self.model.openmm_setti... | def extract_data(self, min_time=None, max_time=None, max_step_list=None, silence_errors=True):
'\n \n '
files_already_read = False
if (len(self.anchor_stats_list) > 0):
files_already_read = True
if (self.model.openmm_settings is not None):
timestep = self.model.openmm_setti... |
a0d8afd58160615446cf60049bb2bb637cc640f5d31f54d96895f17aee96e71b | def check_extraction(self, silent=False):
'\n Check whether sufficient and correct anchor statistics can \n be used for analysis.\n '
if (self.model.get_type() == 'mmvt'):
result = self.mmvt_check_anchor_stats(silent)
if (self.model.get_type() == 'elber'):
result = self.... | Check whether sufficient and correct anchor statistics can
be used for analysis. | seekr2/analyze.py | check_extraction | seekrcentral/seekr2 | 1 | python | def check_extraction(self, silent=False):
'\n Check whether sufficient and correct anchor statistics can \n be used for analysis.\n '
if (self.model.get_type() == 'mmvt'):
result = self.mmvt_check_anchor_stats(silent)
if (self.model.get_type() == 'elber'):
result = self.... | def check_extraction(self, silent=False):
'\n Check whether sufficient and correct anchor statistics can \n be used for analysis.\n '
if (self.model.get_type() == 'mmvt'):
result = self.mmvt_check_anchor_stats(silent)
if (self.model.get_type() == 'elber'):
result = self.... |
fc5043d35a61bf954f5a3b37d3018ec9fa25c219b63aaf1af148665c65c515da | def fill_out_data_samples_mmvt(self):
'\n Now that the statistics for each anchor have been extracted\n from the output files, construct the global transition\n statistics objects. Applies to systems using MMVT milestoning.\n '
N_alpha_beta = defaultdict(int)
k_alpha_beta = defau... | Now that the statistics for each anchor have been extracted
from the output files, construct the global transition
statistics objects. Applies to systems using MMVT milestoning. | seekr2/analyze.py | fill_out_data_samples_mmvt | seekrcentral/seekr2 | 1 | python | def fill_out_data_samples_mmvt(self):
'\n Now that the statistics for each anchor have been extracted\n from the output files, construct the global transition\n statistics objects. Applies to systems using MMVT milestoning.\n '
N_alpha_beta = defaultdict(int)
k_alpha_beta = defau... | def fill_out_data_samples_mmvt(self):
'\n Now that the statistics for each anchor have been extracted\n from the output files, construct the global transition\n statistics objects. Applies to systems using MMVT milestoning.\n '
N_alpha_beta = defaultdict(int)
k_alpha_beta = defau... |
c9ac71397771c10fcb19c6468bac89e794cc743d71b949071aec13b0e3cce755 | def process_data_samples_mmvt(self, pre_equilibrium_approx=False):
'\n Since the global, system-side statistics have been gathered, \n compute the thermodynamic and kinetic quantities and their\n uncertainties. Applies to systems using MMVT milestoning.\n '
self.main_data_sample.calc... | Since the global, system-side statistics have been gathered,
compute the thermodynamic and kinetic quantities and their
uncertainties. Applies to systems using MMVT milestoning. | seekr2/analyze.py | process_data_samples_mmvt | seekrcentral/seekr2 | 1 | python | def process_data_samples_mmvt(self, pre_equilibrium_approx=False):
'\n Since the global, system-side statistics have been gathered, \n compute the thermodynamic and kinetic quantities and their\n uncertainties. Applies to systems using MMVT milestoning.\n '
self.main_data_sample.calc... | def process_data_samples_mmvt(self, pre_equilibrium_approx=False):
'\n Since the global, system-side statistics have been gathered, \n compute the thermodynamic and kinetic quantities and their\n uncertainties. Applies to systems using MMVT milestoning.\n '
self.main_data_sample.calc... |
cbdf63bfc005dcafcf3fae2272dc2c15206591faa99dab590d0c557b7a5e3004 | def fill_out_data_samples_elber(self):
'\n Now that the statistics for each anchor have been extracted\n from the output files, construct the global transition\n statistics objects. Applies to systems using Elber milestoning.\n '
N_i_j_list = []
R_i_total = []
R_i_average = [... | Now that the statistics for each anchor have been extracted
from the output files, construct the global transition
statistics objects. Applies to systems using Elber milestoning. | seekr2/analyze.py | fill_out_data_samples_elber | seekrcentral/seekr2 | 1 | python | def fill_out_data_samples_elber(self):
'\n Now that the statistics for each anchor have been extracted\n from the output files, construct the global transition\n statistics objects. Applies to systems using Elber milestoning.\n '
N_i_j_list = []
R_i_total = []
R_i_average = [... | def fill_out_data_samples_elber(self):
'\n Now that the statistics for each anchor have been extracted\n from the output files, construct the global transition\n statistics objects. Applies to systems using Elber milestoning.\n '
N_i_j_list = []
R_i_total = []
R_i_average = [... |
ab71766e0d9de83ad5210f11a21b36a4873d67ac8e163cc6c7a4619a81fe935d | def process_data_samples_elber(self, pre_equilibrium_approx=False):
'\n Since the global, system-side statistics have been gathered, \n compute the thermodynamic and kinetic quantities and their\n uncertainties. Applies to systems using Elber milestoning.\n '
if (self.model.k_on_info... | Since the global, system-side statistics have been gathered,
compute the thermodynamic and kinetic quantities and their
uncertainties. Applies to systems using Elber milestoning. | seekr2/analyze.py | process_data_samples_elber | seekrcentral/seekr2 | 1 | python | def process_data_samples_elber(self, pre_equilibrium_approx=False):
'\n Since the global, system-side statistics have been gathered, \n compute the thermodynamic and kinetic quantities and their\n uncertainties. Applies to systems using Elber milestoning.\n '
if (self.model.k_on_info... | def process_data_samples_elber(self, pre_equilibrium_approx=False):
'\n Since the global, system-side statistics have been gathered, \n compute the thermodynamic and kinetic quantities and their\n uncertainties. Applies to systems using Elber milestoning.\n '
if (self.model.k_on_info... |
cfe765b37af9e0ecb84861318a9b0d223974c7971440b14228a568cfaac1d167 | def fill_out_data_samples(self):
'\n Based on the type of milestoning, construct the data samples\n and fill out their statistics.\n '
if (self.model.get_type() == 'mmvt'):
self.fill_out_data_samples_mmvt()
elif (self.model.get_type() == 'elber'):
self.fill_out_data_samp... | Based on the type of milestoning, construct the data samples
and fill out their statistics. | seekr2/analyze.py | fill_out_data_samples | seekrcentral/seekr2 | 1 | python | def fill_out_data_samples(self):
'\n Based on the type of milestoning, construct the data samples\n and fill out their statistics.\n '
if (self.model.get_type() == 'mmvt'):
self.fill_out_data_samples_mmvt()
elif (self.model.get_type() == 'elber'):
self.fill_out_data_samp... | def fill_out_data_samples(self):
'\n Based on the type of milestoning, construct the data samples\n and fill out their statistics.\n '
if (self.model.get_type() == 'mmvt'):
self.fill_out_data_samples_mmvt()
elif (self.model.get_type() == 'elber'):
self.fill_out_data_samp... |
4b709d52f970f13a9c96e2b2aff392d7cce490d45ba6cc7687db069798a30865 | def process_data_samples(self, pre_equilibrium_approx=False):
'\n Based on the type of milestoning, use the data samples to \n compute thermo and kinetics quantities and their uncertainties.\n '
if (self.model.get_type() == 'mmvt'):
self.process_data_samples_mmvt(pre_equilibrium_app... | Based on the type of milestoning, use the data samples to
compute thermo and kinetics quantities and their uncertainties. | seekr2/analyze.py | process_data_samples | seekrcentral/seekr2 | 1 | python | def process_data_samples(self, pre_equilibrium_approx=False):
'\n Based on the type of milestoning, use the data samples to \n compute thermo and kinetics quantities and their uncertainties.\n '
if (self.model.get_type() == 'mmvt'):
self.process_data_samples_mmvt(pre_equilibrium_app... | def process_data_samples(self, pre_equilibrium_approx=False):
'\n Based on the type of milestoning, use the data samples to \n compute thermo and kinetics quantities and their uncertainties.\n '
if (self.model.get_type() == 'mmvt'):
self.process_data_samples_mmvt(pre_equilibrium_app... |
376aa3de636f99068f01b66254b97e6a1ed1b31b583177dedd9112de41d5e582 | def resample_k_N_R_T(self, N_alpha_beta, N_i_j_alpha, R_i_alpha_total, R_i_alpha_average, R_i_alpha_std_dev, R_i_alpha_count, T_alpha_total, T_alpha_average, T_alpha_std_dev, T_alpha_count):
'\n Create data samples from a distribution for computing the\n uncertainties of the thermo and kinetics.\n ... | Create data samples from a distribution for computing the
uncertainties of the thermo and kinetics. | seekr2/analyze.py | resample_k_N_R_T | seekrcentral/seekr2 | 1 | python | def resample_k_N_R_T(self, N_alpha_beta, N_i_j_alpha, R_i_alpha_total, R_i_alpha_average, R_i_alpha_std_dev, R_i_alpha_count, T_alpha_total, T_alpha_average, T_alpha_std_dev, T_alpha_count):
'\n Create data samples from a distribution for computing the\n uncertainties of the thermo and kinetics.\n ... | def resample_k_N_R_T(self, N_alpha_beta, N_i_j_alpha, R_i_alpha_total, R_i_alpha_average, R_i_alpha_std_dev, R_i_alpha_count, T_alpha_total, T_alpha_average, T_alpha_std_dev, T_alpha_count):
'\n Create data samples from a distribution for computing the\n uncertainties of the thermo and kinetics.\n ... |
154c86fd1fe266a4ba04a727c84350e93099ad8fa85eff12e03dd80260a14483 | def print_results(self):
'Print all results of the analysis calculation.'
print('Printing results from MMVT SEEKR calculation')
print('k_off (1/s):', common_analyze.pretty_string_value_error(self.k_off, self.k_off_error))
print('k_ons :')
for key in self.k_ons:
k_on = float(self.k_ons[key])
... | Print all results of the analysis calculation. | seekr2/analyze.py | print_results | seekrcentral/seekr2 | 1 | python | def print_results(self):
print('Printing results from MMVT SEEKR calculation')
print('k_off (1/s):', common_analyze.pretty_string_value_error(self.k_off, self.k_off_error))
print('k_ons :')
for key in self.k_ons:
k_on = float(self.k_ons[key])
diss_constant = (self.k_off / k_on)
... | def print_results(self):
print('Printing results from MMVT SEEKR calculation')
print('k_off (1/s):', common_analyze.pretty_string_value_error(self.k_off, self.k_off_error))
print('k_ons :')
for key in self.k_ons:
k_on = float(self.k_ons[key])
diss_constant = (self.k_off / k_on)
... |
b94b22d3bb36da4e3005f7ee3d0d0b7750d7a188865e706f1e135ba8aff697b6 | def save_plots(self, image_directory):
'\n Save a potentially useful series of plots of some quantities\n obtained during the analysis.\n \n TODO: interact with model, because the way these plots are saved\n depends on the structure of the CVs.\n '
anchor_indices = np.z... | Save a potentially useful series of plots of some quantities
obtained during the analysis.
TODO: interact with model, because the way these plots are saved
depends on the structure of the CVs. | seekr2/analyze.py | save_plots | seekrcentral/seekr2 | 1 | python | def save_plots(self, image_directory):
'\n Save a potentially useful series of plots of some quantities\n obtained during the analysis.\n \n TODO: interact with model, because the way these plots are saved\n depends on the structure of the CVs.\n '
anchor_indices = np.z... | def save_plots(self, image_directory):
'\n Save a potentially useful series of plots of some quantities\n obtained during the analysis.\n \n TODO: interact with model, because the way these plots are saved\n depends on the structure of the CVs.\n '
anchor_indices = np.z... |
6f3922edcd56da93c12ba7594f0cf18764fc9dbce02b1be41e11505a9be9e680 | def lowestCommonAncestor(self, root, p, q):
'\n :type root: TreeNode\n :type p: TreeNode\n :type q: TreeNode\n :rtype: TreeNode\n '
self.pre_order(root, p, q)
return self.ret | :type root: TreeNode
:type p: TreeNode
:type q: TreeNode
:rtype: TreeNode | leetcode/python/lca_bt.py | lowestCommonAncestor | haonancool/OnlineJudge | 0 | python | def lowestCommonAncestor(self, root, p, q):
'\n :type root: TreeNode\n :type p: TreeNode\n :type q: TreeNode\n :rtype: TreeNode\n '
self.pre_order(root, p, q)
return self.ret | def lowestCommonAncestor(self, root, p, q):
'\n :type root: TreeNode\n :type p: TreeNode\n :type q: TreeNode\n :rtype: TreeNode\n '
self.pre_order(root, p, q)
return self.ret<|docstring|>:type root: TreeNode
:type p: TreeNode
:type q: TreeNode
:rtype: TreeNode<|endoftext|> |
10c3f85824158a27db612162fca14242efd4600c6bce5e582b8b12cbe31acf37 | def load_calib():
'\n 读取内参矩阵\n '
def intrinsics(date):
calib = open((('dataloaders/' + str(date)) + '.txt'), 'r')
lines = calib.readlines()
P_rect_line = lines[25]
Proj_str = P_rect_line.split(':')[1].split(' ')[1:]
Proj = np.reshape(np.array([float(p) for p in Pro... | 读取内参矩阵 | dataloaders/kitti_loader.py | load_calib | Hansry/Semi-supervised-depth-estimation | 0 | python | def load_calib():
'\n \n '
def intrinsics(date):
calib = open((('dataloaders/' + str(date)) + '.txt'), 'r')
lines = calib.readlines()
P_rect_line = lines[25]
Proj_str = P_rect_line.split(':')[1].split(' ')[1:]
Proj = np.reshape(np.array([float(p) for p in Proj_str]... | def load_calib():
'\n \n '
def intrinsics(date):
calib = open((('dataloaders/' + str(date)) + '.txt'), 'r')
lines = calib.readlines()
P_rect_line = lines[25]
Proj_str = P_rect_line.split(':')[1].split(' ')[1:]
Proj = np.reshape(np.array([float(p) for p in Proj_str]... |
4d10cf9f23350c1b65804207ae46e70314ad4c96f253b28b1bb6afe7891019e3 | def load_transfrom():
'\n 读取左右相机的转换关系\n '
def load_R_t(date):
calib_t = open((('dataloaders/' + str(date)) + '.txt'), 'r')
transforms = calib_t.readlines()
lines_R_0to2 = transforms[21]
R_0to2_str = lines_R_0to2.split(':')[1].split(' ')[1:]
R_0to2 = np.reshape(np.a... | 读取左右相机的转换关系 | dataloaders/kitti_loader.py | load_transfrom | Hansry/Semi-supervised-depth-estimation | 0 | python | def load_transfrom():
'\n \n '
def load_R_t(date):
calib_t = open((('dataloaders/' + str(date)) + '.txt'), 'r')
transforms = calib_t.readlines()
lines_R_0to2 = transforms[21]
R_0to2_str = lines_R_0to2.split(':')[1].split(' ')[1:]
R_0to2 = np.reshape(np.array([float... | def load_transfrom():
'\n \n '
def load_R_t(date):
calib_t = open((('dataloaders/' + str(date)) + '.txt'), 'r')
transforms = calib_t.readlines()
lines_R_0to2 = transforms[21]
R_0to2_str = lines_R_0to2.split(':')[1].split(' ')[1:]
R_0to2 = np.reshape(np.array([float... |
fef4bd5e660cc670bdb3b81896592f31ee4f766bbe594c92a38116e26a9386d7 | def sortby(tree, col, descending):
'sort tree contents when a column header is clicked on'
data = [(tree.set(child, col), child) for child in tree.get_children('')]
data.sort(reverse=descending)
for (ix, item) in enumerate(data):
tree.move(item[1], '', ix)
tree.heading(col, command=(lambda c... | sort tree contents when a column header is clicked on | tkinter/basic/test/test10.py | sortby | sdyz5210/python | 0 | python | def sortby(tree, col, descending):
data = [(tree.set(child, col), child) for child in tree.get_children()]
data.sort(reverse=descending)
for (ix, item) in enumerate(data):
tree.move(item[1], , ix)
tree.heading(col, command=(lambda col=col: sortby(tree, col, int((not descending))))) | def sortby(tree, col, descending):
data = [(tree.set(child, col), child) for child in tree.get_children()]
data.sort(reverse=descending)
for (ix, item) in enumerate(data):
tree.move(item[1], , ix)
tree.heading(col, command=(lambda col=col: sortby(tree, col, int((not descending)))))<|docstri... |
26fc7d8b413b547a0df20a5b9d3140c8888378460af497819b761dbce504ec54 | def create_lat_lon_features(constant_maps):
'\n create latitude and longitude as additional feature for data\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including latitude and longitude\n\n Returns\n ----------\n latitude_arr\n longitude_arr\n '
(londata, lat... | create latitude and longitude as additional feature for data
Parameters
----------
data: xarray dataarray, with dimensions including latitude and longitude
Returns
----------
latitude_arr
longitude_arr | climfill/feature_engineering.py | create_lat_lon_features | climachine/climfill | 10 | python | def create_lat_lon_features(constant_maps):
'\n create latitude and longitude as additional feature for data\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including latitude and longitude\n\n Returns\n ----------\n latitude_arr\n longitude_arr\n '
(londata, lat... | def create_lat_lon_features(constant_maps):
'\n create latitude and longitude as additional feature for data\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including latitude and longitude\n\n Returns\n ----------\n latitude_arr\n longitude_arr\n '
(londata, lat... |
e3cd6388b76f6893be8a2b6430b20a570051e030518fa110112a0c9d3c884e8e | def create_time_feature(data):
'\n create timestep as additional feature for data\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including landpoints, time\n\n Returns\n ----------\n time_arr: xarray with same dimensions as one feature in array describing\n time st... | create timestep as additional feature for data
Parameters
----------
data: xarray dataarray, with dimensions including landpoints, time
Returns
----------
time_arr: xarray with same dimensions as one feature in array describing
time step | climfill/feature_engineering.py | create_time_feature | climachine/climfill | 10 | python | def create_time_feature(data):
'\n create timestep as additional feature for data\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including landpoints, time\n\n Returns\n ----------\n time_arr: xarray with same dimensions as one feature in array describing\n time st... | def create_time_feature(data):
'\n create timestep as additional feature for data\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including landpoints, time\n\n Returns\n ----------\n time_arr: xarray with same dimensions as one feature in array describing\n time st... |
cc878b9b12c28276a295d84da5b7dea0fb4e14e1c490625735bb7e34caca01ec | def create_embedded_feature(data, start=(- 7), end=0, name='lag_7b'):
"\n create moving window mean along time axis from day 'start' until\n day 'end' relative to current day using xr.DataArray.rolling\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including variable, time\n\n ... | create moving window mean along time axis from day 'start' until
day 'end' relative to current day using xr.DataArray.rolling
Parameters
----------
data: xarray dataarray, with dimensions including variable, time
start: int, start of moving average in days from current day
end: int, end of moving average in days fro... | climfill/feature_engineering.py | create_embedded_feature | climachine/climfill | 10 | python | def create_embedded_feature(data, start=(- 7), end=0, name='lag_7b'):
"\n create moving window mean along time axis from day 'start' until\n day 'end' relative to current day using xr.DataArray.rolling\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including variable, time\n\n ... | def create_embedded_feature(data, start=(- 7), end=0, name='lag_7b'):
"\n create moving window mean along time axis from day 'start' until\n day 'end' relative to current day using xr.DataArray.rolling\n\n Parameters\n ----------\n data: xarray dataarray, with dimensions including variable, time\n\n ... |
6e08697447a51412842041aa6ec8313623645ce4c5b2e3406fd02d4c95b8bca6 | def format_submitter_id(node, args):
'\n Generates "submitter_id" for node with additional identificator values.\n Resulting "submitter_id" only contains lowercase letters, digits, underscore and dash.\n\n Args:\n node (str): node name for "submitter_id"\n args (dict): additional arguments to... | Generates "submitter_id" for node with additional identificator values.
Resulting "submitter_id" only contains lowercase letters, digits, underscore and dash.
Args:
node (str): node name for "submitter_id"
args (dict): additional arguments to add to "submitter_id"
Returns:
str: generated "submitter_id" | covid19-etl/utils/format_helper.py | format_submitter_id | uc-cdis/covid19-tools | 2 | python | def format_submitter_id(node, args):
'\n Generates "submitter_id" for node with additional identificator values.\n Resulting "submitter_id" only contains lowercase letters, digits, underscore and dash.\n\n Args:\n node (str): node name for "submitter_id"\n args (dict): additional arguments to... | def format_submitter_id(node, args):
'\n Generates "submitter_id" for node with additional identificator values.\n Resulting "submitter_id" only contains lowercase letters, digits, underscore and dash.\n\n Args:\n node (str): node name for "submitter_id"\n args (dict): additional arguments to... |
ef1a92ed7359445f07c31a6ae92d62174fa0cd788cfac41ed6d733ab675eb2c1 | def derived_submitter_id(submitter_id, original_node, derived_node, args):
'\n Derive "submitter_id" for other node.\n\n Args:\n submitter_id (str): "submitter_id" to derive from\n original_node (str): name of original node\n derived_node (str): name of derived node\n args (dict): ... | Derive "submitter_id" for other node.
Args:
submitter_id (str): "submitter_id" to derive from
original_node (str): name of original node
derived_node (str): name of derived node
args (dict): additional arguments to add to "derived_submitter_id"
Returns:
str: generated "derived_submitter_id" | covid19-etl/utils/format_helper.py | derived_submitter_id | uc-cdis/covid19-tools | 2 | python | def derived_submitter_id(submitter_id, original_node, derived_node, args):
'\n Derive "submitter_id" for other node.\n\n Args:\n submitter_id (str): "submitter_id" to derive from\n original_node (str): name of original node\n derived_node (str): name of derived node\n args (dict): ... | def derived_submitter_id(submitter_id, original_node, derived_node, args):
'\n Derive "submitter_id" for other node.\n\n Args:\n submitter_id (str): "submitter_id" to derive from\n original_node (str): name of original node\n derived_node (str): name of derived node\n args (dict): ... |
a6d018b0d2a770c6b832faedbbd942a0811cb62102a35538e662da26c9dbbbd5 | def idph_get_date(date_json):
'\n Get date from IDPH JSON\n\n Args:\n date_json (dict): JSON date with "year", "month", "date" fields\n\n Returns:\n str: datetime in "%Y-%m-%d" format\n '
date = datetime.date(**date_json)
return date.strftime('%Y-%m-%d') | Get date from IDPH JSON
Args:
date_json (dict): JSON date with "year", "month", "date" fields
Returns:
str: datetime in "%Y-%m-%d" format | covid19-etl/utils/format_helper.py | idph_get_date | uc-cdis/covid19-tools | 2 | python | def idph_get_date(date_json):
'\n Get date from IDPH JSON\n\n Args:\n date_json (dict): JSON date with "year", "month", "date" fields\n\n Returns:\n str: datetime in "%Y-%m-%d" format\n '
date = datetime.date(**date_json)
return date.strftime('%Y-%m-%d') | def idph_get_date(date_json):
'\n Get date from IDPH JSON\n\n Args:\n date_json (dict): JSON date with "year", "month", "date" fields\n\n Returns:\n str: datetime in "%Y-%m-%d" format\n '
date = datetime.date(**date_json)
return date.strftime('%Y-%m-%d')<|docstring|>Get date from I... |
f6d0fcd870212350a665d8680ffe7cc7aea4e0769051c27258e2e2d62fe74146 | def idph_last_reported_date(utilization_records):
'\n Fetches the "ReportDate" value from the last record of the utilization array\n\n Args:\n utilization_records (list) : List of all historical hospital utilization records\n\n Returns:\n str: last reported date of the data in "%Y-%m-%d" form... | Fetches the "ReportDate" value from the last record of the utilization array
Args:
utilization_records (list) : List of all historical hospital utilization records
Returns:
str: last reported date of the data in "%Y-%m-%d" format | covid19-etl/utils/format_helper.py | idph_last_reported_date | uc-cdis/covid19-tools | 2 | python | def idph_last_reported_date(utilization_records):
'\n Fetches the "ReportDate" value from the last record of the utilization array\n\n Args:\n utilization_records (list) : List of all historical hospital utilization records\n\n Returns:\n str: last reported date of the data in "%Y-%m-%d" form... | def idph_last_reported_date(utilization_records):
'\n Fetches the "ReportDate" value from the last record of the utilization array\n\n Args:\n utilization_records (list) : List of all historical hospital utilization records\n\n Returns:\n str: last reported date of the data in "%Y-%m-%d" form... |
c81961d68d820dd5a5944488f1ac56955d7204b5e5d2a86dce14370930e59aad | def get_date_from_str(date_str):
"\n Receives a date string in %Y-%m-%d format and returns a 'datetime.date' object\n "
return datetime.datetime.strptime(remove_time_from_date_time(date_str), '%Y-%m-%d').date() | Receives a date string in %Y-%m-%d format and returns a 'datetime.date' object | covid19-etl/utils/format_helper.py | get_date_from_str | uc-cdis/covid19-tools | 2 | python | def get_date_from_str(date_str):
"\n \n "
return datetime.datetime.strptime(remove_time_from_date_time(date_str), '%Y-%m-%d').date() | def get_date_from_str(date_str):
"\n \n "
return datetime.datetime.strptime(remove_time_from_date_time(date_str), '%Y-%m-%d').date()<|docstring|>Receives a date string in %Y-%m-%d format and returns a 'datetime.date' object<|endoftext|> |
5b6b7fd834d5fb369ab7ffaed3d6efdd04fddfb4507e13cc66169cb22affb1b0 | def __init__(self, file_name='data.csv', transport=True):
'Computes the aircraft center of gravity at operational empty weight'
self.data = load_data(file_name)
if transport:
self.factors = {'wing': 12.2, 'fuselage': 6.8, 'horizontal_tail': 9.8, 'vertical_tail': 9.8, 'nose_gear': 0.009, 'main_gear':... | Computes the aircraft center of gravity at operational empty weight | aircraft/cg_calculation.py | __init__ | iamlucassantos/tutorial-systems-engineering | 1 | python | def __init__(self, file_name='data.csv', transport=True):
self.data = load_data(file_name)
if transport:
self.factors = {'wing': 12.2, 'fuselage': 6.8, 'horizontal_tail': 9.8, 'vertical_tail': 9.8, 'nose_gear': 0.009, 'main_gear': 0.048, 'power_plant': 1.4, 'systems': 0.1}
else:
self.fa... | def __init__(self, file_name='data.csv', transport=True):
self.data = load_data(file_name)
if transport:
self.factors = {'wing': 12.2, 'fuselage': 6.8, 'horizontal_tail': 9.8, 'vertical_tail': 9.8, 'nose_gear': 0.009, 'main_gear': 0.048, 'power_plant': 1.4, 'systems': 0.1}
else:
self.fa... |
5403d4307d2846cbb20192013b25797aa958b581ad655286da2a818b169cd3ad | def get_cr(self):
'Computes the chord length at the rooot for wing, vertica tail and horizontal tail'
self.cr = ((2 * self.data['S']) / ((self.data['taper'] + 1) * self.data['b']))
self.cr_h = ((2 * self.data['S_h']) / ((self.data['taper_h'] + 1) * self.data['b_h']))
self.cr_v = ((2 * self.data['S_v']) ... | Computes the chord length at the rooot for wing, vertica tail and horizontal tail | aircraft/cg_calculation.py | get_cr | iamlucassantos/tutorial-systems-engineering | 1 | python | def get_cr(self):
self.cr = ((2 * self.data['S']) / ((self.data['taper'] + 1) * self.data['b']))
self.cr_h = ((2 * self.data['S_h']) / ((self.data['taper_h'] + 1) * self.data['b_h']))
self.cr_v = ((2 * self.data['S_v']) / (((self.data['taper_v'] + 1) * self.data['b_half_v']) * 2)) | def get_cr(self):
self.cr = ((2 * self.data['S']) / ((self.data['taper'] + 1) * self.data['b']))
self.cr_h = ((2 * self.data['S_h']) / ((self.data['taper_h'] + 1) * self.data['b_h']))
self.cr_v = ((2 * self.data['S_v']) / (((self.data['taper_v'] + 1) * self.data['b_half_v']) * 2))<|docstring|>Computes ... |
e9ec2c67176623b221a25fdd91732c785427c4276cb124a60e1cdb4fe0c1c37c | def get_areas(self):
'Returns the areas of each group to estimate their mass'
areas = {}
S = self.data['S']
d_fus = self.data['l_h']
b = self.data['b']
(chord_fuselage, _) = self.chord_at_pctg((d_fus / b), surface='w')
area_w = (S - (d_fus * chord_fuselage))
areas['wing'] = area_w
ar... | Returns the areas of each group to estimate their mass | aircraft/cg_calculation.py | get_areas | iamlucassantos/tutorial-systems-engineering | 1 | python | def get_areas(self):
areas = {}
S = self.data['S']
d_fus = self.data['l_h']
b = self.data['b']
(chord_fuselage, _) = self.chord_at_pctg((d_fus / b), surface='w')
area_w = (S - (d_fus * chord_fuselage))
areas['wing'] = area_w
area_v = self.data['S_v']
areas['vertical_tail'] = are... | def get_areas(self):
areas = {}
S = self.data['S']
d_fus = self.data['l_h']
b = self.data['b']
(chord_fuselage, _) = self.chord_at_pctg((d_fus / b), surface='w')
area_w = (S - (d_fus * chord_fuselage))
areas['wing'] = area_w
area_v = self.data['S_v']
areas['vertical_tail'] = are... |
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