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train | MachineLearningEnsembleProducts.write_grib2 | Writes data to grib2 file. Currently, grib codes are set by hand to hail.
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path: Path to directory containing grib2 files.
Returns: | hagelslag/processing/EnsembleProducts.py | def write_grib2(self, path):
"""
Writes data to grib2 file. Currently, grib codes are set by hand to hail.
Args:
path: Path to directory containing grib2 files.
Returns:
"""
if self.percentile is None:
var_type = "mean"
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... | def write_grib2(self, path):
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Writes data to grib2 file. Currently, grib codes are set by hand to hail.
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path: Path to directory containing grib2 files.
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train | EnsembleConsensus.init_file | Initializes netCDF file for writing
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filename: Name of the netCDF file
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Initializes netCDF file for writing
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train | EnsembleConsensus.write_to_file | Outputs data to a netCDF file. If the file does not exist, it will be created. Otherwise, additional variables
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out_data: Full-path and name of output netCDF file | hagelslag/processing/EnsembleProducts.py | def write_to_file(self, out_data):
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train | Workspace.restore | Restore the workspace to the given workspace_uuid.
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"""
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train | Workspace.new_workspace | Create a new workspace, insert into document_model, and return it. | nion/swift/Workspace.py | def new_workspace(self, name=None, layout=None, workspace_id=None, index=None) -> WorkspaceLayout.WorkspaceLayout:
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train | Workspace.ensure_workspace | Looks for a workspace with workspace_id.
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train | Workspace.create_workspace | Pose a dialog to name and create a workspace. | nion/swift/Workspace.py | def create_workspace(self) -> None:
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train | Workspace.rename_workspace | Pose a dialog to rename the workspace. | nion/swift/Workspace.py | def rename_workspace(self) -> None:
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train | Workspace.remove_workspace | Pose a dialog to confirm removal then remove workspace. | nion/swift/Workspace.py | def remove_workspace(self):
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def confirm_clicked():
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train | Workspace.clone_workspace | Pose a dialog to name and clone a workspace. | nion/swift/Workspace.py | def clone_workspace(self) -> None:
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train | Workspace.__replace_displayed_display_item | Used in drag/drop support. | nion/swift/Workspace.py | def __replace_displayed_display_item(self, display_panel, display_item, d=None) -> Undo.UndoableCommand:
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train | bootstrap | Given a set of DistributedROC or DistributedReliability objects, this function performs a
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score_objs: A list of DistributedROC or DistributedReliability objects. Objects must have an __add__ method
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train | DistributedROC.update | Update the ROC curve with a set of forecasts and observations
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train | DistributedROC.merge | Ingest the values of another DistributedROC object into this one and update the statistics inplace.
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train | DistributedROC.roc_curve | Generate a ROC curve from the contingency table by calculating the probability of detection (TP/(TP+FN)) and the
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A pandas.DataFrame containing the POD, POFD, and the corresponding probability thresholds. | hagelslag/evaluation/ProbabilityMetrics.py | def roc_curve(self):
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train | DistributedROC.performance_curve | Calculate the Probability of Detection and False Alarm Ratio in order to output a performance diagram.
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train | DistributedROC.auc | Calculate the Area Under the ROC Curve (AUC). | hagelslag/evaluation/ProbabilityMetrics.py | def auc(self):
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Calculate the maximum Critical Success Index across all probability thresholds
Returns:
The maximum CSI as a float
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Calculate the maximum Critical Success Index across all probability thresholds
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The maximum CSI as a float
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train | DistributedROC.get_contingency_tables | Create an Array of ContingencyTable objects for each probability threshold.
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Create an Array of ContingencyTable objects for each probability threshold.
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Create an Array of ContingencyTable objects for each probability threshold.
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Read the DistributedROC string and parse the contingency table values from it.
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Update the statistics with a set of forecasts and observations.
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train | DistributedReliability.merge | Ingest another DistributedReliability and add its contents to the current object.
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train | DistributedReliability.reliability_curve | Calculates the reliability diagram statistics. The key columns are Bin_Start and Positive_Relative_Freq
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train | DistributedReliability.brier_score_components | Calculate the components of the Brier score decomposition: reliability, resolution, and uncertainty. | hagelslag/evaluation/ProbabilityMetrics.py | def brier_score_components(self):
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Calculate the components of the Brier score decomposition: reliability, resolution, and uncertainty.
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train | DistributedReliability.brier_score | Calculate the Brier Score | hagelslag/evaluation/ProbabilityMetrics.py | def brier_score(self):
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Calculate the Brier Score
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Calculate the Brier Score
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train | DistributedReliability.brier_skill_score | Calculate the Brier Skill Score | hagelslag/evaluation/ProbabilityMetrics.py | def brier_skill_score(self):
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Calculate the Brier Skill Score
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Update the statistics with forecasts and observations.
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train | DistributedCRPS.crps | Calculates the continuous ranked probability score. | hagelslag/evaluation/ProbabilityMetrics.py | def crps(self):
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train | DistributedCRPS.crps_climo | Calculate the climatological CRPS. | hagelslag/evaluation/ProbabilityMetrics.py | def crps_climo(self):
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Calculate the climatological CRPS.
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train | DistributedCRPS.crpss | Calculate the continous ranked probability skill score from existing data. | hagelslag/evaluation/ProbabilityMetrics.py | def crpss(self):
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então resultar em uma lista dos alertas ativos.
:param cliente_sat: Uma instância de
:class:`satcfe.clientelocal.ClienteSATLocal` ou
:clas... | satcfe/alertas.py | 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... | 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.
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train | has_metadata_value | Return whether the metadata value for the given key exists.
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If using a custom key, we recommend structuring your keys in the '<group... | nion/swift/model/Metadata.py | def has_metadata_value(metadata_source, key: str) -> bool:
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There are a set of predefined keys that, when used, will be type checked and be interoperable with other
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There are a set of predefined keys that, when used, will be type checked and be interoperable with other
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train | set_metadata_value | Set the metadata value for the given key.
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There are a set of predefined keys that, when used, will be type checked and be interoperable with other
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train | delete_metadata_value | Delete the metadata value for the given key.
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train | LineGraphAxes.calculate_y_ticks | Calculate the y-axis items dependent on the plot height. | nion/swift/LineGraphCanvasItem.py | def calculate_y_ticks(self, plot_height):
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train | LineGraphAxes.calculate_x_ticks | Calculate the x-axis items dependent on the plot width. | nion/swift/LineGraphCanvasItem.py | def calculate_x_ticks(self, plot_width):
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x_calibration = self.x_calibration
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train | LineGraphHorizontalAxisLabelCanvasItem.size_to_content | Size the canvas item to the proper height. | nion/swift/LineGraphCanvasItem.py | def size_to_content(self):
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new_sizing = self.copy_sizing()
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train | LineGraphVerticalAxisScaleCanvasItem.size_to_content | Size the canvas item to the proper width, the maximum of any label. | nion/swift/LineGraphCanvasItem.py | def size_to_content(self, get_font_metrics_fn):
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train | LineGraphVerticalAxisLabelCanvasItem.size_to_content | Size the canvas item to the proper width. | nion/swift/LineGraphCanvasItem.py | def size_to_content(self):
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train | get_snippet_content | Load the content from a snippet file which exists in SNIPPETS_ROOT | hatchery/snippets.py | def get_snippet_content(snippet_name, **format_kwargs):
""" Load the content from a snippet file which exists in SNIPPETS_ROOT """
filename = snippet_name + '.snippet'
snippet_file = os.path.join(SNIPPETS_ROOT, filename)
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filename = snippet_name + '.snippet'
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train | LinePlotCanvasItem.update_display_properties | Update the display values. Called from display panel.
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As a layer, this canvas item will respond to the update by calling prepare_render on the layer's rendering
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"""Update the display values. Called from display panel.
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train | LinePlotCanvasItem.__view_to_intervals | Change the view to encompass the channels and data represented by the given intervals. | nion/swift/LinePlotCanvasItem.py | def __view_to_intervals(self, data_and_metadata: DataAndMetadata.DataAndMetadata, intervals: typing.List[typing.Tuple[float, float]]) -> None:
"""Change the view to encompass the channels and data represented by the given intervals."""
left = None
right = None
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left = None
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train | LinePlotCanvasItem.__view_to_selected_graphics | Change the view to encompass the selected graphic intervals. | nion/swift/LinePlotCanvasItem.py | def __view_to_selected_graphics(self, data_and_metadata: DataAndMetadata.DataAndMetadata) -> None:
"""Change the view to encompass the selected graphic intervals."""
all_graphics = self.__graphics
graphics = [graphic for graphic_index, graphic in enumerate(all_graphics) if self.__graphic_selecti... | def __view_to_selected_graphics(self, data_and_metadata: DataAndMetadata.DataAndMetadata) -> None:
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train | LinePlotCanvasItem.prepare_display | Prepare the display.
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When data or display parameters change, the internal state of the line plot gets updated. This method takes
that internal state and updates the child canvas items.
... | nion/swift/LinePlotCanvasItem.py | def prepare_display(self):
"""Prepare the display.
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train | LinePlotCanvasItem.__update_cursor_info | Map the mouse to the 1-d position within the line graph. | nion/swift/LinePlotCanvasItem.py | def __update_cursor_info(self):
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train | TrackProcessor.find_model_patch_tracks | Identify storms in gridded model output and extract uniform sized patches around the storm centers of mass.
Returns: | hagelslag/processing/TrackProcessing.py | def find_model_patch_tracks(self):
"""
Identify storms in gridded model output and extract uniform sized patches around the storm centers of mass.
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train | TrackProcessor.find_model_tracks | Identify storms at each model time step and link them together with object matching.
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train | TrackProcessor.find_mrms_tracks | Identify objects from MRMS timesteps and link them together with object matching.
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train | TrackProcessor.match_tracks | Match forecast and observed tracks.
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train | TrackProcessor.extract_model_attributes | Extract model attribute data for each model track. Storm variables are those that describe the model storm
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train | TrackProcessor.calc_track_errors | Calculates spatial and temporal translation errors between matched
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obs_tracks: List of observed track STObjects
track_pairings: List of tuples pairing forecast and observed tracks.
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train | Connection.persistent_object_context_changed | Override from PersistentObject. | nion/swift/model/Connection.py | def persistent_object_context_changed(self):
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train | PropertyConnection.persistent_object_context_changed | Override from PersistentObject. | nion/swift/model/Connection.py | def persistent_object_context_changed(self):
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train | FilterController.__display_for_tree_node | Return the text display for the given tree node. Based on number of keys associated with tree node. | nion/swift/FilterPanel.py | def __display_for_tree_node(self, tree_node):
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train | FilterController.update_all_nodes | Update all tree item displays if needed. Usually for count updates. | nion/swift/FilterPanel.py | def update_all_nodes(self):
""" Update all tree item displays if needed. Usually for count updates. """
item_model_controller = self.item_model_controller
if item_model_controller:
if self.__node_counts_dirty:
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""" Update all tree item displays if needed. Usually for count updates. """
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train | FilterController.date_browser_selection_changed | Called to handle selection changes in the tree widget.
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based on the list of keys. It then sets th... | nion/swift/FilterPanel.py | def date_browser_selection_changed(self, selected_indexes):
"""
Called to handle selection changes in the tree widget.
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Called to handle selection changes in the tree widget.
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train | FilterController.text_filter_changed | Called to handle changes to the text filter.
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"""
Called to handle changes to the text filter.
:param text: The text for the filter.
"""
text = text.strip() if text else None
if text is not None:
self.__text_filter = ListModel.TextFilter("text_for_filter", te... | def text_filter_changed(self, text):
"""
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train | FilterController.__update_filter | Create a combined filter. Set the resulting filter into the document controller. | nion/swift/FilterPanel.py | def __update_filter(self):
"""
Create a combined filter. Set the resulting filter into the document controller.
"""
filters = list()
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train | TreeNode.__get_keys | Return the keys associated with this node by adding its key and then adding parent keys recursively. | nion/swift/FilterPanel.py | def __get_keys(self):
""" Return the keys associated with this node by adding its key and then adding parent keys recursively. """
keys = list()
tree_node = self
while tree_node is not None and tree_node.key is not None:
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train | TreeNode.insert_value | Insert a value (data item) into this tree node and then its
children. This will be called in response to a new data item being
inserted into the document. Also updates the tree node's cumulative
child count. | nion/swift/FilterPanel.py | def insert_value(self, keys, value):
"""
Insert a value (data item) into this tree node and then its
children. This will be called in response to a new data item being
inserted into the document. Also updates the tree node's cumulative
child count.
"""
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"""
Insert a value (data item) into this tree node and then its
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inserted into the document. Also updates the tree node's cumulative
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train | TreeNode.remove_value | Remove a value (data item) from this tree node and its children.
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"""
Remove a value (data item) from this tree node and its children.
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"""
self.count -= 1
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keys = self.__value_reverse_mapping[value]
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Remove a value (data item) from this tree node and its children.
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self.count -= 1
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train | label_storm_objects | From a 2D grid or time series of 2D grids, this method labels storm objects with either the Enhanced Watershed
or Hysteresis methods.
Args:
data: the gridded data to be labeled. Should be a 2D numpy array in (y, x) coordinate order or a 3D numpy array
in (time, y, x) coordinate order
... | hagelslag/processing/tracker.py | def label_storm_objects(data, method, min_intensity, max_intensity, min_area=1, max_area=100, max_range=1,
increment=1, gaussian_sd=0):
"""
From a 2D grid or time series of 2D grids, this method labels storm objects with either the Enhanced Watershed
or Hysteresis methods.
Args:... | def label_storm_objects(data, method, min_intensity, max_intensity, min_area=1, max_area=100, max_range=1,
increment=1, gaussian_sd=0):
"""
From a 2D grid or time series of 2D grids, this method labels storm objects with either the Enhanced Watershed
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train | extract_storm_objects | After storms are labeled, this method extracts the storm objects from the grid and places them into STObjects.
The STObjects contain intensity, location, and shape information about each storm at each timestep.
Args:
label_grid: 2D or 3D array output by label_storm_objects.
data: 2D or 3D array... | hagelslag/processing/tracker.py | def extract_storm_objects(label_grid, data, x_grid, y_grid, times, dx=1, dt=1, obj_buffer=0):
"""
After storms are labeled, this method extracts the storm objects from the grid and places them into STObjects.
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"""
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train | extract_storm_patches | After storms are labeled, this method extracts boxes of equal size centered on each storm from the grid and places
them into STObjects. The STObjects contain intensity, location, and shape information about each storm
at each timestep.
Args:
label_grid: 2D or 3D array output by label_storm_objects.... | hagelslag/processing/tracker.py | def extract_storm_patches(label_grid, data, x_grid, y_grid, times, dx=1, dt=1, patch_radius=16):
"""
After storms are labeled, this method extracts boxes of equal size centered on each storm from the grid and places
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"""
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train | track_storms | Given the output of extract_storm_objects, this method tracks storms through time and merges individual
STObjects into a set of tracks.
Args:
storm_objects: list of list of STObjects that have not been tracked.
times: List of times associated with each set of STObjects
distance_componen... | hagelslag/processing/tracker.py | def track_storms(storm_objects, times, distance_components, distance_maxima, distance_weights, tracked_objects=None):
"""
Given the output of extract_storm_objects, this method tracks storms through time and merges individual
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Args:
storm_objects: list of list of ... | def track_storms(storm_objects, times, distance_components, distance_maxima, distance_weights, tracked_objects=None):
"""
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train | MulticlassContingencyTable.peirce_skill_score | Multiclass Peirce Skill Score (also Hanssen and Kuipers score, True Skill Score) | hagelslag/evaluation/MulticlassContingencyTable.py | def peirce_skill_score(self):
"""
Multiclass Peirce Skill Score (also Hanssen and Kuipers score, True Skill Score)
"""
n = float(self.table.sum())
nf = self.table.sum(axis=1)
no = self.table.sum(axis=0)
correct = float(self.table.trace())
return (correct /... | def peirce_skill_score(self):
"""
Multiclass Peirce Skill Score (also Hanssen and Kuipers score, True Skill Score)
"""
n = float(self.table.sum())
nf = self.table.sum(axis=1)
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train | MulticlassContingencyTable.gerrity_score | Gerrity Score, which weights each cell in the contingency table by its observed relative frequency.
:return: | hagelslag/evaluation/MulticlassContingencyTable.py | def gerrity_score(self):
"""
Gerrity Score, which weights each cell in the contingency table by its observed relative frequency.
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k = self.table.shape[0]
n = float(self.table.sum())
p_o = self.table.sum(axis=0) / n
p_sum = np.cumsum(p_o)[:-1]
... | def gerrity_score(self):
"""
Gerrity Score, which weights each cell in the contingency table by its observed relative frequency.
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"""
k = self.table.shape[0]
n = float(self.table.sum())
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train | centroid_distance | Euclidean distance between the centroids of item_a and item_b.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer being evaluated
max_value: Maximum distan... | hagelslag/processing/ObjectMatcher.py | def centroid_distance(item_a, time_a, item_b, time_b, max_value):
"""
Euclidean distance between the centroids of item_a and item_b.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
... | def centroid_distance(item_a, time_a, item_b, time_b, max_value):
"""
Euclidean distance between the centroids of item_a and item_b.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
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train | shifted_centroid_distance | Centroid distance with motion corrections.
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time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
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"""
Centroid distance with motion corrections.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
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Centroid distance with motion corrections.
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item_a: STObject from the first set in ObjectMatcher
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train | closest_distance | Euclidean distance between the pixels in item_a and item_b closest to each other.
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time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer being evaluated
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"""
Euclidean distance between the pixels in item_a and item_b closest to each other.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set... | def closest_distance(item_a, time_a, item_b, time_b, max_value):
"""
Euclidean distance between the pixels in item_a and item_b closest to each other.
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train | ellipse_distance | Calculate differences in the properties of ellipses fitted to each object.
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item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer being evaluated
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"""
Calculate differences in the properties of ellipses fitted to each object.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
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Calculate differences in the properties of ellipses fitted to each object.
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train | nonoverlap | Percentage of pixels in each object that do not overlap with the other object
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item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer being evaluated
max_value:... | hagelslag/processing/ObjectMatcher.py | def nonoverlap(item_a, time_a, item_b, time_b, max_value):
"""
Percentage of pixels in each object that do not overlap with the other object
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in Object... | def nonoverlap(item_a, time_a, item_b, time_b, max_value):
"""
Percentage of pixels in each object that do not overlap with the other object
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item_a: STObject from the first set in ObjectMatcher
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train | max_intensity | RMS difference in maximum intensity
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer being evaluated
max_value: Maximum distance value used as scaling va... | hagelslag/processing/ObjectMatcher.py | def max_intensity(item_a, time_a, item_b, time_b, max_value):
"""
RMS difference in maximum intensity
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer be... | def max_intensity(item_a, time_a, item_b, time_b, max_value):
"""
RMS difference in maximum intensity
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time_a: Time integer being evaluated
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train | area_difference | RMS Difference in object areas.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer being evaluated
max_value: Maximum distance value used as scaling value ... | hagelslag/processing/ObjectMatcher.py | def area_difference(item_a, time_a, item_b, time_b, max_value):
"""
RMS Difference in object areas.
Args:
item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
item_b: STObject from the second set in ObjectMatcher
time_b: Time integer bein... | def area_difference(item_a, time_a, item_b, time_b, max_value):
"""
RMS Difference in object areas.
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item_a: STObject from the first set in ObjectMatcher
time_a: Time integer being evaluated
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train | mean_minimum_centroid_distance | RMS difference in the minimum distances from the centroids of one track to the centroids of another track
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constrai... | hagelslag/processing/ObjectMatcher.py | def mean_minimum_centroid_distance(item_a, item_b, max_value):
"""
RMS difference in the minimum distances from the centroids of one track to the centroids of another track
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
... | def mean_minimum_centroid_distance(item_a, item_b, max_value):
"""
RMS difference in the minimum distances from the centroids of one track to the centroids of another track
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
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train | mean_min_time_distance | Calculate the mean time difference among the time steps in each object.
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item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constraint.
Returns:
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"""
Calculate the mean time difference among the time steps in each object.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as... | def mean_min_time_distance(item_a, item_b, max_value):
"""
Calculate the mean time difference among the time steps in each object.
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item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
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train | start_centroid_distance | Distance between the centroids of the first step in each object.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constraint.
Returns:
Distance value ... | hagelslag/processing/ObjectMatcher.py | def start_centroid_distance(item_a, item_b, max_value):
"""
Distance between the centroids of the first step in each object.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scali... | def start_centroid_distance(item_a, item_b, max_value):
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Distance between the centroids of the first step in each object.
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item_b: STObject from the second set in TrackMatcher
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train | start_time_distance | Absolute difference between the starting times of each item.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constraint.
Returns:
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"""
Absolute difference between the starting times of each item.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value... | def start_time_distance(item_a, item_b, max_value):
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Absolute difference between the starting times of each item.
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item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
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train | duration_distance | Absolute difference in the duration of two items
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item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constraint.
Returns:
Distance value between 0 and 1. | hagelslag/processing/ObjectMatcher.py | def duration_distance(item_a, item_b, max_value):
"""
Absolute difference in the duration of two items
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper con... | def duration_distance(item_a, item_b, max_value):
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Absolute difference in the duration of two items
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item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
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train | mean_area_distance | Absolute difference in the means of the areas of each track over time.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constraint.
Returns:
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Absolute difference in the means of the areas of each track over time.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scal... | def mean_area_distance(item_a, item_b, max_value):
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Absolute difference in the means of the areas of each track over time.
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item_a: STObject from the first set in TrackMatcher
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train | ObjectMatcher.match_objects | Match two sets of objects at particular times.
Args:
set_a: list of STObjects
set_b: list of STObjects
time_a: time at which set_a is being evaluated for matching
time_b: time at which set_b is being evaluated for matching
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List of tu... | hagelslag/processing/ObjectMatcher.py | def match_objects(self, set_a, set_b, time_a, time_b):
"""
Match two sets of objects at particular times.
Args:
set_a: list of STObjects
set_b: list of STObjects
time_a: time at which set_a is being evaluated for matching
time_b: time at which set... | def match_objects(self, set_a, set_b, time_a, time_b):
"""
Match two sets of objects at particular times.
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train | ObjectMatcher.cost_matrix | Calculates the costs (distances) between the items in set a and set b at the specified times.
Args:
set_a: List of STObjects
set_b: List of STObjects
time_a: time at which objects in set_a are evaluated
time_b: time at whcih object in set_b are evaluated
... | hagelslag/processing/ObjectMatcher.py | def cost_matrix(self, set_a, set_b, time_a, time_b):
"""
Calculates the costs (distances) between the items in set a and set b at the specified times.
Args:
set_a: List of STObjects
set_b: List of STObjects
time_a: time at which objects in set_a are evaluated... | def cost_matrix(self, set_a, set_b, time_a, time_b):
"""
Calculates the costs (distances) between the items in set a and set b at the specified times.
Args:
set_a: List of STObjects
set_b: List of STObjects
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train | ObjectMatcher.total_cost_function | Calculate total cost function between two items.
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time_a: Timestep in item_a at which cost function is evaluated
time_b: Timestep in item_b at which cost function is evaluated
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"""
Calculate total cost function between two items.
Args:
item_a: STObject
item_b: STObject
time_a: Timestep in item_a at which cost function is evaluated
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"""
Calculate total cost function between two items.
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train | TrackMatcher.match_tracks | Find the optimal set of matching assignments between set a and set b. This function supports optimal 1:1
matching using the Munkres method and matching from every object in set a to the closest object in set b.
In this situation set b accepts multiple matches from set a.
Args:
set_a... | hagelslag/processing/ObjectMatcher.py | def match_tracks(self, set_a, set_b, closest_matches=False):
"""
Find the optimal set of matching assignments between set a and set b. This function supports optimal 1:1
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In this situ... | def match_tracks(self, set_a, set_b, closest_matches=False):
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Find the optimal set of matching assignments between set a and set b. This function supports optimal 1:1
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train | TrackStepMatcher.match | For each step in each track from set_a, identify all steps in all tracks from set_b that meet all
cost function criteria
Args:
set_a: List of STObjects
set_b: List of STObjects
Returns:
track_pairings: pandas.DataFrame | hagelslag/processing/ObjectMatcher.py | def match(self, set_a, set_b):
"""
For each step in each track from set_a, identify all steps in all tracks from set_b that meet all
cost function criteria
Args:
set_a: List of STObjects
set_b: List of STObjects
Returns:
track_pairing... | def match(self, set_a, set_b):
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For each step in each track from set_a, identify all steps in all tracks from set_b that meet all
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set_a: List of STObjects
set_b: List of STObjects
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train | ComputationVariable.variable_specifier | Return the variable specifier for this variable.
The specifier can be used to lookup the value of this variable in a computation context. | nion/swift/model/Symbolic.py | def variable_specifier(self) -> dict:
"""Return the variable specifier for this variable.
The specifier can be used to lookup the value of this variable in a computation context.
"""
if self.value_type is not None:
return {"type": "variable", "version": 1, "uuid": str(self.u... | def variable_specifier(self) -> dict:
"""Return the variable specifier for this variable.
The specifier can be used to lookup the value of this variable in a computation context.
"""
if self.value_type is not None:
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train | ComputationVariable.bound_variable | Return an object with a value property and a changed_event.
The value property returns the value of the variable. The changed_event is fired
whenever the value changes. | nion/swift/model/Symbolic.py | def bound_variable(self):
"""Return an object with a value property and a changed_event.
The value property returns the value of the variable. The changed_event is fired
whenever the value changes.
"""
class BoundVariable:
def __init__(self, variable):
... | def bound_variable(self):
"""Return an object with a value property and a changed_event.
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"""
class BoundVariable:
def __init__(self, variable):
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train | ComputationContext.resolve_object_specifier | Resolve the object specifier.
First lookup the object specifier in the enclosing computation. If it's not found,
then lookup in the computation's context. Otherwise it should be a value type variable.
In that case, return the bound variable. | nion/swift/model/Symbolic.py | def resolve_object_specifier(self, object_specifier, secondary_specifier=None, property_name=None, objects_model=None):
"""Resolve the object specifier.
First lookup the object specifier in the enclosing computation. If it's not found,
then lookup in the computation's context. Otherwise it shou... | def resolve_object_specifier(self, object_specifier, secondary_specifier=None, property_name=None, objects_model=None):
"""Resolve the object specifier.
First lookup the object specifier in the enclosing computation. If it's not found,
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train | Computation.parse_names | Return the list of identifiers used in the expression. | nion/swift/model/Symbolic.py | def parse_names(cls, expression):
"""Return the list of identifiers used in the expression."""
names = set()
try:
ast_node = ast.parse(expression, "ast")
class Visitor(ast.NodeVisitor):
def visit_Name(self, node):
names.add(node.id)
... | def parse_names(cls, expression):
"""Return the list of identifiers used in the expression."""
names = set()
try:
ast_node = ast.parse(expression, "ast")
class Visitor(ast.NodeVisitor):
def visit_Name(self, node):
names.add(node.id)
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train | Computation.bind | Bind a context to this computation.
The context allows the computation to convert object specifiers to actual objects. | nion/swift/model/Symbolic.py | def bind(self, context) -> None:
"""Bind a context to this computation.
The context allows the computation to convert object specifiers to actual objects.
"""
# make a computation context based on the enclosing context.
self.__computation_context = ComputationContext(self, cont... | def bind(self, context) -> None:
"""Bind a context to this computation.
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# make a computation context based on the enclosing context.
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train | Computation.unbind | Unlisten and close each bound item. | nion/swift/model/Symbolic.py | def unbind(self):
"""Unlisten and close each bound item."""
for variable in self.variables:
self.__unbind_variable(variable)
for result in self.results:
self.__unbind_result(result) | def unbind(self):
"""Unlisten and close each bound item."""
for variable in self.variables:
self.__unbind_variable(variable)
for result in self.results:
self.__unbind_result(result) | [
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train | ClienteSATLocal.ativar_sat | Sobrepõe :meth:`~satcfe.base.FuncoesSAT.ativar_sat`.
:return: Uma resposta SAT especilizada em ``AtivarSAT``.
:rtype: satcfe.resposta.ativarsat.RespostaAtivarSAT | satcfe/clientelocal.py | def ativar_sat(self, tipo_certificado, cnpj, codigo_uf):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.ativar_sat`.
:return: Uma resposta SAT especilizada em ``AtivarSAT``.
:rtype: satcfe.resposta.ativarsat.RespostaAtivarSAT
"""
retorno = super(ClienteSATLocal, self).ativar_sat(
... | def ativar_sat(self, tipo_certificado, cnpj, codigo_uf):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.ativar_sat`.
:return: Uma resposta SAT especilizada em ``AtivarSAT``.
:rtype: satcfe.resposta.ativarsat.RespostaAtivarSAT
"""
retorno = super(ClienteSATLocal, self).ativar_sat(
... | [
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train | ClienteSATLocal.comunicar_certificado_icpbrasil | Sobrepõe :meth:`~satcfe.base.FuncoesSAT.comunicar_certificado_icpbrasil`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT | satcfe/clientelocal.py | def comunicar_certificado_icpbrasil(self, certificado):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.comunicar_certificado_icpbrasil`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
retorno = super(ClienteSATLocal, self).\
comunica... | def comunicar_certificado_icpbrasil(self, certificado):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.comunicar_certificado_icpbrasil`.
:return: Uma resposta SAT padrão.
:rtype: satcfe.resposta.padrao.RespostaSAT
"""
retorno = super(ClienteSATLocal, self).\
comunica... | [
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train | ClienteSATLocal.enviar_dados_venda | Sobrepõe :meth:`~satcfe.base.FuncoesSAT.enviar_dados_venda`.
:return: Uma resposta SAT especializada em ``EnviarDadosVenda``.
:rtype: satcfe.resposta.enviardadosvenda.RespostaEnviarDadosVenda | satcfe/clientelocal.py | def enviar_dados_venda(self, dados_venda):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.enviar_dados_venda`.
:return: Uma resposta SAT especializada em ``EnviarDadosVenda``.
:rtype: satcfe.resposta.enviardadosvenda.RespostaEnviarDadosVenda
"""
retorno = super(ClienteSATLocal, self... | def enviar_dados_venda(self, dados_venda):
"""Sobrepõe :meth:`~satcfe.base.FuncoesSAT.enviar_dados_venda`.
:return: Uma resposta SAT especializada em ``EnviarDadosVenda``.
:rtype: satcfe.resposta.enviardadosvenda.RespostaEnviarDadosVenda
"""
retorno = super(ClienteSATLocal, self... | [
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... | cb8e8815f4133d3e3d94cf526fa86767b4521ed9 |
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