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djgagne/hagelslag | hagelslag/util/munkres.py | Munkres.__clear_covers | def __clear_covers(self):
"""Clear all covered matrix cells"""
for i in range(self.n):
self.row_covered[i] = False
self.col_covered[i] = False | python | def __clear_covers(self):
"""Clear all covered matrix cells"""
for i in range(self.n):
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self.col_covered[i] = False | [
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djgagne/hagelslag | hagelslag/util/munkres.py | Munkres.__erase_primes | def __erase_primes(self):
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self.marked[i][j] = 0 | python | def __erase_primes(self):
"""Erase all prime markings"""
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.update | def update(self, a, b, c, d):
"""
Update contingency table with new values without creating a new object.
"""
self.table.ravel()[:] = [a, b, c, d]
self.N = self.table.sum() | python | def update(self, a, b, c, d):
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.bias | def bias(self):
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Frequency Bias.
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.csi | def csi(self):
"""Gilbert's Score or Threat Score or Critical Success Index a/(a+b+c)"""
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.ets | def ets(self):
"""Equitable Threat Score, Gilbert Skill Score, v, (a - R)/(a + b + c - R), R=(a+b)(a+c)/N"""
r = (self.table[0, 0] + self.table[0, 1]) * (self.table[0, 0] + self.table[1, 0]) / self.N
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r = (self.table[0, 0] + self.table[0, 1]) * (self.table[0, 0] + self.table[1, 0]) / self.N
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.hss | def hss(self):
"""Doolittle (Heidke) Skill Score. 2(ad-bc)/((a+b)(b+d) + (a+c)(c+d))"""
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.pss | def pss(self):
"""Peirce (Hansen-Kuipers, True) Skill Score (ad - bc)/((a+c)(b+d))"""
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return (self.table[0, 0] * self.table[1, 1] - self.table[0, 1] * self.table[1, 0]) / \
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djgagne/hagelslag | hagelslag/evaluation/ContingencyTable.py | ContingencyTable.css | def css(self):
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djgagne/hagelslag | hagelslag/util/output_tree_ensembles.py | load_tree_object | def load_tree_object(filename):
"""
Load scikit-learn decision tree ensemble object from file.
Parameters
----------
filename : str
Name of the pickle file containing the tree object.
Returns
-------
tree ensemble object
"""
with open(filename) as file_obj:
... | python | def load_tree_object(filename):
"""
Load scikit-learn decision tree ensemble object from file.
Parameters
----------
filename : str
Name of the pickle file containing the tree object.
Returns
-------
tree ensemble object
"""
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djgagne/hagelslag | hagelslag/util/output_tree_ensembles.py | output_tree_ensemble | def output_tree_ensemble(tree_ensemble_obj, output_filename, attribute_names=None):
"""
Write each decision tree in an ensemble to a file.
Parameters
----------
tree_ensemble_obj : sklearn.ensemble object
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output_filename : str
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tree_ensemble_obj : sklearn.ensemble object
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output_filename : str
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djgagne/hagelslag | hagelslag/util/output_tree_ensembles.py | print_tree_recursive | def print_tree_recursive(tree_obj, node_index, attribute_names=None):
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Parameters
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tree_obj : sklearn.tree._tree.Tree object
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mgraffg/EvoDAG | EvoDAG/bagging_fitness.py | BaggingFitness.set_classifier_mask | def set_classifier_mask(self, v, base_mask=True):
"""Computes the mask used to create the training and validation set"""
base = self._base
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a = np.unique(v)
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... | python | def set_classifier_mask(self, v, base_mask=True):
"""Computes the mask used to create the training and validation set"""
base = self._base
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mgraffg/EvoDAG | EvoDAG/bagging_fitness.py | BaggingFitness.set_regression_mask | def set_regression_mask(self, v):
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mgraffg/EvoDAG | EvoDAG/bagging_fitness.py | BaggingFitness.fitness | def fitness(self, v):
"Fitness function in the training set"
base = self._base
if base._classifier:
if base._multiple_outputs:
hy = SparseArray.argmax(v.hy)
fit_func = base._fitness_function
if fit_func == 'macro-F1' or fit_func == 'a_F... | python | def fitness(self, v):
"Fitness function in the training set"
base = self._base
if base._classifier:
if base._multiple_outputs:
hy = SparseArray.argmax(v.hy)
fit_func = base._fitness_function
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mgraffg/EvoDAG | EvoDAG/bagging_fitness.py | BaggingFitness.fitness_vs | def fitness_vs(self, v):
"""Fitness function in the validation set
In classification it uses BER and RSE in regression"""
base = self._base
if base._classifier:
if base._multiple_outputs:
v.fitness_vs = v._error
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"""Fitness function in the validation set
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base = self._base
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mgraffg/EvoDAG | EvoDAG/bagging_fitness.py | BaggingFitness.set_fitness | def set_fitness(self, v):
"""Set the fitness to a new node.
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self.fitness(v)
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self.del_error(v)
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Returns false in case fitness is not finite"""
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base4sistemas/satcfe | satcfe/resposta/cancelarultimavenda.py | RespostaCancelarUltimaVenda.analisar | def analisar(retorno):
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:param unicode retorno: Retorno da função ``CancelarUltimaVenda``.
"""
resposta = analisar_retorno(forcar_unicode(retorno),
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... | python | def analisar(retorno):
"""Constrói uma :class:`RespostaCancelarUltimaVenda` a partir do
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:param unicode retorno: Retorno da função ``CancelarUltimaVenda``.
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nion-software/nionswift | nion/swift/model/ImportExportManager.py | convert_data_element_to_data_and_metadata_1 | def convert_data_element_to_data_and_metadata_1(data_element) -> DataAndMetadata.DataAndMetadata:
"""Convert a data element to xdata. No data copying occurs.
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data (required)
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djgagne/hagelslag | hagelslag/util/create_sector_grid_data.py | SectorProcessor.output_sector_csv | def output_sector_csv(self,csv_path,file_dict_key,out_path):
"""
Segment forecast tracks to only output data contined within a
region in the CONUS, as defined by the mapfile.
Args:
csv_path(str): Path to the full CONUS csv file.
file_dict_key(str): Dictionary ke... | python | def output_sector_csv(self,csv_path,file_dict_key,out_path):
"""
Segment forecast tracks to only output data contined within a
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csv_path(str): Path to the full CONUS csv file.
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djgagne/hagelslag | hagelslag/util/create_sector_grid_data.py | SectorProcessor.output_sector_netcdf | def output_sector_netcdf(self,netcdf_path,out_path,patch_radius,config):
"""
Segment patches of forecast tracks to only output data contined within a
region in the CONUS, as defined by the mapfile.
Args:
netcdf_path (str): Path to the full CONUS netcdf patch file.
... | python | def output_sector_netcdf(self,netcdf_path,out_path,patch_radius,config):
"""
Segment patches of forecast tracks to only output data contined within a
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nion-software/nionswift | nion/swift/model/Utility.py | clean_dict | def clean_dict(d0, clean_item_fn=None):
"""
Return a json-clean dict. Will log info message for failures.
"""
clean_item_fn = clean_item_fn if clean_item_fn else clean_item
d = dict()
for key in d0:
cleaned_item = clean_item_fn(d0[key])
if cleaned_item is not None:
... | python | def clean_dict(d0, clean_item_fn=None):
"""
Return a json-clean dict. Will log info message for failures.
"""
clean_item_fn = clean_item_fn if clean_item_fn else clean_item
d = dict()
for key in d0:
cleaned_item = clean_item_fn(d0[key])
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nion-software/nionswift | nion/swift/model/Utility.py | clean_list | def clean_list(l0, clean_item_fn=None):
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Return a json-clean list. Will log info message for failures.
"""
clean_item_fn = clean_item_fn if clean_item_fn else clean_item
l = list()
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l.append(cleaned_item)
... | python | def clean_list(l0, clean_item_fn=None):
"""
Return a json-clean list. Will log info message for failures.
"""
clean_item_fn = clean_item_fn if clean_item_fn else clean_item
l = list()
for index, item in enumerate(l0):
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l.append(cleaned_item)
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nion-software/nionswift | nion/swift/model/Utility.py | clean_tuple | def clean_tuple(t0, clean_item_fn=None):
"""
Return a json-clean tuple. Will log info message for failures.
"""
clean_item_fn = clean_item_fn if clean_item_fn else clean_item
l = list()
for index, item in enumerate(t0):
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l.append(cleaned_item... | python | def clean_tuple(t0, clean_item_fn=None):
"""
Return a json-clean tuple. Will log info message for failures.
"""
clean_item_fn = clean_item_fn if clean_item_fn else clean_item
l = list()
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nion-software/nionswift | nion/swift/model/Utility.py | clean_item | def clean_item(i):
"""
Return a json-clean item or None. Will log info message for failure.
"""
itype = type(i)
if itype == dict:
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elif itype == list:
return clean_list(i)
elif itype == tuple:
return clean_tuple(i)
elif itype == numpy.float... | python | def clean_item(i):
"""
Return a json-clean item or None. Will log info message for failure.
"""
itype = type(i)
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nion-software/nionswift | nion/swift/model/Utility.py | clean_item_no_list | def clean_item_no_list(i):
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re... | python | def clean_item_no_list(i):
"""
Return a json-clean item or None. Will log info message for failure.
"""
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if itype == dict:
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nion-software/nionswift | nion/swift/model/Utility.py | sample_stack_all | def sample_stack_all(count=10, interval=0.1):
"""Sample the stack in a thread and print it at regular intervals."""
def print_stack_all(l, ll):
l1 = list()
l1.append("*** STACKTRACE - START ***")
code = []
for threadId, stack in sys._current_frames().items():
sub_cod... | python | def sample_stack_all(count=10, interval=0.1):
"""Sample the stack in a thread and print it at regular intervals."""
def print_stack_all(l, ll):
l1 = list()
l1.append("*** STACKTRACE - START ***")
code = []
for threadId, stack in sys._current_frames().items():
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mgraffg/EvoDAG | EvoDAG/gp.py | Individual.decision_function | def decision_function(self, X):
"Decision function i.e. the raw data of the prediction"
self._X = Model.convert_features(X)
self._eval()
return self._ind[0].hy | python | def decision_function(self, X):
"Decision function i.e. the raw data of the prediction"
self._X = Model.convert_features(X)
self._eval()
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mgraffg/EvoDAG | EvoDAG/gp.py | Individual._eval | def _eval(self):
"Evaluates a individual using recursion and self._pos as pointer"
pos = self._pos
self._pos += 1
node = self._ind[pos]
if isinstance(node, Function):
args = [self._eval() for x in range(node.nargs)]
node.eval(args)
for x in arg... | python | def _eval(self):
"Evaluates a individual using recursion and self._pos as pointer"
pos = self._pos
self._pos += 1
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleMemberProduct.load_data | def load_data(self, num_samples=1000, percentiles=None):
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num_samples: Number of random samples at each grid point
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleMemberProduct.encode_grib2_percentile | def encode_grib2_percentile(self):
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Encodes member percentile data to GRIB2 format.
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Series of GRIB2 messages
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Encodes member percentile data to GRIB2 format.
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleProducts.load_data | def load_data(self):
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleProducts.point_consensus | def point_consensus(self, consensus_type):
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Calculate grid-point statistics across ensemble members.
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consensus_type: mean, std, median, max, or percentile_nn
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EnsembleConsensus containing point statistic
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleProducts.point_probability | def point_probability(self, threshold):
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threshold: If >= threshold assigns a 1 to member, otherwise 0.
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threshold (float): probability of exceeding this threshold
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleProducts.period_max_neighborhood_probability | def period_max_neighborhood_probability(self, threshold, radius, sigmas=None):
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threshold (float): splitting threshold for probability calculatations
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djgagne/hagelslag | hagelslag/processing/EnsembleProducts.py | EnsembleConsensus.init_file | def init_file(self, filename, time_units="seconds since 1970-01-01T00:00"):
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.restore | def restore(self, workspace_uuid):
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.new_workspace | def new_workspace(self, name=None, layout=None, workspace_id=None, index=None) -> WorkspaceLayout.WorkspaceLayout:
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.ensure_workspace | def ensure_workspace(self, name, layout, workspace_id):
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.create_workspace | def create_workspace(self) -> None:
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""" Pose a dialog to name and create a workspace. """
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.rename_workspace | def rename_workspace(self) -> None:
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.remove_workspace | def remove_workspace(self):
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.clone_workspace | def clone_workspace(self) -> None:
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nion-software/nionswift | nion/swift/Workspace.py | Workspace.__replace_displayed_display_item | def __replace_displayed_display_item(self, display_panel, display_item, d=None) -> Undo.UndoableCommand:
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djgagne/hagelslag | hagelslag/evaluation/ProbabilityMetrics.py | bootstrap | def bootstrap(score_objs, n_boot=1000):
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djgagne/hagelslag | hagelslag/evaluation/ProbabilityMetrics.py | DistributedROC.update | def update(self, forecasts, observations):
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Generate a ROC curve from the contingency table by calculating the probability of detection (TP/(TP+FN)) and the
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Returns:
A pandas.DataFrame containing the POD, POFD, and the corresponding probability thre... | python | def roc_curve(self):
"""
Generate a ROC curve from the contingency table by calculating the probability of detection (TP/(TP+FN)) and the
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Calculate the maximum Critical Success Index across all probability thresholds
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Update the statistics with a set of forecasts and observations.
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forecasts (numpy.ndarray): Array of forecast probability values
observations (numpy.ndarray): Array of observation values
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Update the statistics with a set of forecasts and observations.
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Calculate the Brier Score
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base4sistemas/satcfe | satcfe/alertas.py | checar | def checar(cliente_sat):
"""
Checa em sequência os alertas registrados (veja :func:`registrar`) contra os
dados da consulta ao status operacional do equipamento SAT. Este método irá
então resultar em uma lista dos alertas ativos.
:param cliente_sat: Uma instância de
:class:`satcfe.clientelo... | python | def checar(cliente_sat):
"""
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nion-software/nionswift | nion/swift/LineGraphCanvasItem.py | LineGraphAxes.calculate_y_ticks | def calculate_y_ticks(self, plot_height):
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nion-software/nionswift | nion/swift/LineGraphCanvasItem.py | LineGraphAxes.calculate_x_ticks | def calculate_x_ticks(self, plot_width):
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nion-software/nionswift | nion/swift/LineGraphCanvasItem.py | LineGraphHorizontalAxisLabelCanvasItem.size_to_content | def size_to_content(self):
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nion-software/nionswift | nion/swift/LineGraphCanvasItem.py | LineGraphVerticalAxisScaleCanvasItem.size_to_content | def size_to_content(self, get_font_metrics_fn):
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ajk8/hatchery | hatchery/snippets.py | get_snippet_content | def get_snippet_content(snippet_name, **format_kwargs):
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filename = snippet_name + '.snippet'
snippet_file = os.path.join(SNIPPETS_ROOT, filename)
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nion-software/nionswift | nion/swift/LinePlotCanvasItem.py | LinePlotCanvasItem.update_display_properties | def update_display_properties(self, display_calibration_info, display_properties: typing.Mapping, display_layers: typing.Sequence[typing.Mapping]) -> None:
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nion-software/nionswift | nion/swift/LinePlotCanvasItem.py | LinePlotCanvasItem.__view_to_intervals | def __view_to_intervals(self, data_and_metadata: DataAndMetadata.DataAndMetadata, intervals: typing.List[typing.Tuple[float, float]]) -> None:
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left = None
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nion-software/nionswift | nion/swift/LinePlotCanvasItem.py | LinePlotCanvasItem.__view_to_selected_graphics | def __view_to_selected_graphics(self, data_and_metadata: DataAndMetadata.DataAndMetadata) -> None:
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nion-software/nionswift | nion/swift/LinePlotCanvasItem.py | LinePlotCanvasItem.prepare_display | def prepare_display(self):
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nion-software/nionswift | nion/swift/LinePlotCanvasItem.py | LinePlotCanvasItem.__update_cursor_info | def __update_cursor_info(self):
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""" Map the mouse to the 1-d position within the line graph. """
if not self.delegate: # allow display to work without delegate
return
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pos_1d = None
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.find_model_patch_tracks | def find_model_patch_tracks(self):
"""
Identify storms in gridded model output and extract uniform sized patches around the storm centers of mass.
Returns:
"""
self.model_grid.load_data()
tracked_model_objects = []
model_objects = []
if self.model_grid.d... | python | def find_model_patch_tracks(self):
"""
Identify storms in gridded model output and extract uniform sized patches around the storm centers of mass.
Returns:
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.find_model_tracks | def find_model_tracks(self):
"""
Identify storms at each model time step and link them together with object matching.
Returns:
List of STObjects containing model track information.
"""
self.model_grid.load_data()
model_objects = []
tracked_model_objec... | python | def find_model_tracks(self):
"""
Identify storms at each model time step and link them together with object matching.
Returns:
List of STObjects containing model track information.
"""
self.model_grid.load_data()
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.find_mrms_tracks | def find_mrms_tracks(self):
"""
Identify objects from MRMS timesteps and link them together with object matching.
Returns:
List of STObjects containing MESH track information.
"""
obs_objects = []
tracked_obs_objects = []
if self.mrms_ew is not None:
... | python | def find_mrms_tracks(self):
"""
Identify objects from MRMS timesteps and link them together with object matching.
Returns:
List of STObjects containing MESH track information.
"""
obs_objects = []
tracked_obs_objects = []
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.match_tracks | def match_tracks(self, model_tracks, obs_tracks, unique_matches=True, closest_matches=False):
"""
Match forecast and observed tracks.
Args:
model_tracks:
obs_tracks:
unique_matches:
closest_matches:
Returns:
"""
if unique... | python | def match_tracks(self, model_tracks, obs_tracks, unique_matches=True, closest_matches=False):
"""
Match forecast and observed tracks.
Args:
model_tracks:
obs_tracks:
unique_matches:
closest_matches:
Returns:
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.extract_model_attributes | def extract_model_attributes(self, tracked_model_objects, storm_variables, potential_variables,
tendency_variables=None, future_variables=None):
"""
Extract model attribute data for each model track. Storm variables are those that describe the model storm
directl... | python | def extract_model_attributes(self, tracked_model_objects, storm_variables, potential_variables,
tendency_variables=None, future_variables=None):
"""
Extract model attribute data for each model track. Storm variables are those that describe the model storm
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.match_hail_sizes | def match_hail_sizes(model_tracks, obs_tracks, track_pairings):
"""
Given forecast and observed track pairings, maximum hail sizes are associated with each paired forecast storm
track timestep. If the duration of the forecast and observed tracks differ, then interpolation is used for the
... | python | def match_hail_sizes(model_tracks, obs_tracks, track_pairings):
"""
Given forecast and observed track pairings, maximum hail sizes are associated with each paired forecast storm
track timestep. If the duration of the forecast and observed tracks differ, then interpolation is used for the
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.match_hail_size_step_distributions | def match_hail_size_step_distributions(self, model_tracks, obs_tracks, track_pairings):
"""
Given a matching set of observed tracks for each model track,
Args:
model_tracks:
obs_tracks:
track_pairings:
Returns:
"""
label_... | python | def match_hail_size_step_distributions(self, model_tracks, obs_tracks, track_pairings):
"""
Given a matching set of observed tracks for each model track,
Args:
model_tracks:
obs_tracks:
track_pairings:
Returns:
"""
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djgagne/hagelslag | hagelslag/processing/TrackProcessing.py | TrackProcessor.calc_track_errors | def calc_track_errors(model_tracks, obs_tracks, track_pairings):
"""
Calculates spatial and temporal translation errors between matched
forecast and observed tracks.
Args:
model_tracks: List of model track STObjects
obs_tracks: List of observed track STObjects
... | python | def calc_track_errors(model_tracks, obs_tracks, track_pairings):
"""
Calculates spatial and temporal translation errors between matched
forecast and observed tracks.
Args:
model_tracks: List of model track STObjects
obs_tracks: List of observed track STObjects
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