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def content ( self , ** args ) : self . gist_name = '' if 'name' in args : self . gist_name = args [ 'name' ] self . gist_id = self . getMyID ( self . gist_name ) elif 'id' in args : self . gist_id = args [ 'id' ] else : raise Exception ( 'Either provide authenticated user\'s Unambigious Gistname or any unique Gistid' ...
Doesn t require manual fetching of gistID of a gist passing gistName will return the content of gist . In case names are ambigious provide GistID or it will return the contents of recent ambigious gistname
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def edit ( self , ** args ) : self . gist_name = '' if 'description' in args : self . description = args [ 'description' ] else : self . description = '' if 'name' in args and 'id' in args : self . gist_name = args [ 'name' ] self . gist_id = args [ 'id' ] elif 'name' in args : self . gist_name = args [ 'name' ] self ....
Doesn t require manual fetching of gistID of a gist passing gistName will return edit the gist
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def starred ( self , ** args ) : ids = [ ] r = requests . get ( '%s/gists/starred' % BASE_URL , headers = self . gist . header ) if 'limit' in args : limit = args [ 'limit' ] else : limit = len ( r . json ( ) ) if ( r . status_code == 200 ) : for g in range ( 0 , limit ) : ids . append ( '%s/%s/%s' % ( GIST_URL , r . j...
List the authenticated user s starred gists
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def links ( self , ** args ) : if 'name' in args : self . gist_name = args [ 'name' ] self . gist_id = self . getMyID ( self . gist_name ) elif 'id' in args : self . gist_id = args [ 'id' ] else : raise Exception ( 'Gist Name/ID must be provided' ) if self . gist_id : r = requests . get ( '%s/gists/%s' % ( BASE_URL , s...
Return Gist URL - Link Clone - Link and Script - Link to embed
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def load_forecasts ( self ) : run_date_str = self . run_date . strftime ( "%Y%m%d" ) forecast_file = self . forecast_path + "{0}/{1}_{2}_{3}_consensus_{0}.nc" . format ( run_date_str , self . ensemble_name , self . model_name , self . forecast_variable ) print ( "Forecast file: " + forecast_file ) forecast_data = Datas...
Load neighborhood probability forecasts .
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def load_coordinates ( self ) : coord_file = Dataset ( self . coordinate_file ) if "lon" in coord_file . variables . keys ( ) : self . coordinates [ "lon" ] = coord_file . variables [ "lon" ] [ : ] self . coordinates [ "lat" ] = coord_file . variables [ "lat" ] [ : ] else : self . coordinates [ "lon" ] = coord_file . v...
Loads lat - lon coordinates from a netCDF file .
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def evaluate_hourly_forecasts ( self ) : score_columns = [ "Run_Date" , "Forecast_Hour" , "Ensemble Name" , "Model_Name" , "Forecast_Variable" , "Neighbor_Radius" , "Smoothing_Radius" , "Size_Threshold" , "ROC" , "Reliability" ] all_scores = pd . DataFrame ( columns = score_columns ) for h , hour in enumerate ( range (...
Calculates ROC curves and Reliability scores for each forecast hour .
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def evaluate_period_forecasts ( self ) : score_columns = [ "Run_Date" , "Ensemble Name" , "Model_Name" , "Forecast_Variable" , "Neighbor_Radius" , "Smoothing_Radius" , "Size_Threshold" , "ROC" , "Reliability" ] all_scores = pd . DataFrame ( columns = score_columns ) if self . coordinate_file is not None : coord_mask = ...
Evaluates ROC and Reliability scores for forecasts over the full period from start hour to end hour
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def bootstrap_main ( args ) : version_info = sys . version_info if version_info . major != 3 or version_info . minor < 6 : return None , "python36" main_fn = load_module_as_package ( "nionui_app.nionswift" ) if main_fn : return main_fn ( [ "nionui_app.nionswift" ] + args , { "pyqt" : None } ) , None return None , "main...
Main function explicitly called from the C ++ code . Return the main application object .
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def _migrate_library ( workspace_dir : pathlib . Path , do_logging : bool = True ) -> pathlib . Path : library_path_11 = workspace_dir / "Nion Swift Workspace.nslib" library_path_12 = workspace_dir / "Nion Swift Library 12.nslib" library_path_13 = workspace_dir / "Nion Swift Library 13.nslib" library_paths = ( library_...
Migrate library to latest version .
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def merge_input_csv_forecast_json ( input_csv_file , forecast_json_path , condition_models , dist_models ) : try : run_date = input_csv_file [ : - 4 ] . split ( "_" ) [ - 1 ] print ( run_date ) ens_member = "_" . join ( input_csv_file . split ( "/" ) [ - 1 ] [ : - 4 ] . split ( "_" ) [ 3 : - 1 ] ) ens_name = input_csv_...
Reads forecasts from json files and merges them with the input data from the step csv files .
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def mark_data_dirty ( self ) : self . __cache . set_cached_value_dirty ( self . __display_item , self . __cache_property_name ) self . __initialize_cache ( ) self . __cached_value_dirty = True
Called from item to indicate its data or metadata has changed .
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def recompute_if_necessary ( self , ui ) : self . __initialize_cache ( ) if self . __cached_value_dirty : with self . __is_recomputing_lock : is_recomputing = self . __is_recomputing self . __is_recomputing = True if is_recomputing : pass else : def recompute ( ) : try : if self . __recompute_thread_cancel . wait ( 0.0...
Recompute the data on a thread if necessary .
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def recompute_data ( self , ui ) : self . __initialize_cache ( ) with self . __recompute_lock : if self . __cached_value_dirty : try : calculated_data = self . get_calculated_data ( ui ) except Exception as e : import traceback traceback . print_exc ( ) traceback . print_stack ( ) raise self . __cache . set_cached_valu...
Compute the data associated with this processor .
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def thumbnail_source_for_display_item ( self , ui , display_item : DisplayItem . DisplayItem ) -> ThumbnailSource : with self . __lock : thumbnail_source = self . __thumbnail_sources . get ( display_item ) if not thumbnail_source : thumbnail_source = ThumbnailSource ( ui , display_item ) self . __thumbnail_sources [ di...
Returned ThumbnailSource must be closed .
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def getMyID ( self , gist_name ) : r = requests . get ( '%s' % BASE_URL + '/users/%s/gists' % self . user , headers = self . gist . header ) if ( r . status_code == 200 ) : r_text = json . loads ( r . text ) limit = len ( r . json ( ) ) for g , no in zip ( r_text , range ( 0 , limit ) ) : for ka , va in r . json ( ) [ ...
Getting gistID of a gist in order to make the workflow easy and uninterrupted .
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def close ( self ) : assert self . __closed == False self . __closed = True self . finish_periodic ( ) for weak_dialog in self . __dialogs : dialog = weak_dialog ( ) if dialog : try : dialog . request_close ( ) except Exception as e : pass self . _file_menu = None self . _edit_menu = None self . _processing_menu = None...
Close the document controller .
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def add_periodic ( self , interval : float , listener_fn ) : class PeriodicListener : def __init__ ( self , interval : float , listener_fn ) : self . interval = interval self . __listener_fn = listener_fn if callable ( listener_fn ) : self . call = self . __listener_fn else : def void ( * args , ** kwargs ) : pass self...
Add a listener function and return listener token . Token can be closed or deleted to unlisten .
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def __update_display_items_model ( self , display_items_model : ListModel . FilteredListModel , data_group : typing . Optional [ DataGroup . DataGroup ] , filter_id : typing . Optional [ str ] ) -> None : with display_items_model . changes ( ) : if data_group is not None : display_items_model . container = data_group d...
Update the data item model with a new container filter and sorting .
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def focused_data_item ( self ) -> typing . Optional [ DataItem . DataItem ] : return self . __focused_display_item . data_item if self . __focused_display_item else None
Return the data item with keyboard focus .
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def selected_display_item ( self ) -> typing . Optional [ DisplayItem . DisplayItem ] : display_item = self . focused_display_item if not display_item : selected_display_panel = self . selected_display_panel display_item = selected_display_panel . display_item if selected_display_panel else None return display_item
Return the selected display item .
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def _get_two_data_sources ( self ) : selected_display_items = self . selected_display_items if len ( selected_display_items ) < 2 : selected_display_items = list ( ) display_item = self . selected_display_item if display_item : selected_display_items . append ( display_item ) if len ( selected_display_items ) == 1 : di...
Get two sensible data sources which may be the same .
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def calculate_origin_and_size ( canvas_size , data_shape , image_canvas_mode , image_zoom , image_position ) -> typing . Tuple [ typing . Any , typing . Any ] : if data_shape is None : return None , None if image_canvas_mode == "fill" : data_shape = data_shape scale_h = float ( data_shape [ 1 ] ) / canvas_size [ 1 ] sc...
Calculate origin and size for canvas size data shape and image display parameters .
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def auto_migrate_storage_system ( * , persistent_storage_system = None , new_persistent_storage_system = None , data_item_uuids = None , deletions : typing . List [ uuid . UUID ] = None , utilized_deletions : typing . Set [ uuid . UUID ] = None , ignore_older_files : bool = True ) : storage_handlers = persistent_storag...
Migrate items from the storage system to the object context .
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def from_pypirc ( pypi_repository ) : ret = { } pypirc_locations = PYPIRC_LOCATIONS for pypirc_path in pypirc_locations : pypirc_path = os . path . expanduser ( pypirc_path ) if os . path . isfile ( pypirc_path ) : parser = configparser . SafeConfigParser ( ) parser . read ( pypirc_path ) if 'distutils' not in parser ....
Load configuration from . pypirc file cached to only run once
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def pypirc_temp ( index_url ) : pypirc_file = tempfile . NamedTemporaryFile ( suffix = '.pypirc' , delete = False ) print ( pypirc_file . name ) with open ( pypirc_file . name , 'w' ) as fh : fh . write ( PYPIRC_TEMPLATE . format ( index_name = PYPIRC_TEMP_INDEX_NAME , index_url = index_url ) ) return pypirc_file . nam...
Create a temporary pypirc file for interaction with twine
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def get_api ( version : str , ui_version : str = None ) -> API_1 : ui_version = ui_version if ui_version else "~1.0" return _get_api_with_app ( version , ui_version , ApplicationModule . app )
Get a versioned interface matching the given version and ui_version .
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def mask_xdata_with_shape ( self , shape : DataAndMetadata . ShapeType ) -> DataAndMetadata . DataAndMetadata : mask = self . _graphic . get_mask ( shape ) return DataAndMetadata . DataAndMetadata . from_data ( mask )
Return the mask created by this graphic as extended data .
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def data ( self , data : numpy . ndarray ) -> None : self . __data_item . set_data ( numpy . copy ( data ) )
Set the data .
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def display_xdata ( self ) -> DataAndMetadata . DataAndMetadata : display_data_channel = self . __display_item . display_data_channel return display_data_channel . get_calculated_display_values ( True ) . display_data_and_metadata
Return the extended data of this data item display .
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def set_dimensional_calibrations ( self , dimensional_calibrations : typing . List [ CalibrationModule . Calibration ] ) -> None : self . __data_item . set_dimensional_calibrations ( dimensional_calibrations )
Set the dimensional calibrations .
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def graphics ( self ) -> typing . List [ Graphic ] : return [ Graphic ( graphic ) for graphic in self . __display_item . graphics ]
Return the graphics attached to this data item .
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def add_point_region ( self , y : float , x : float ) -> Graphic : graphic = Graphics . PointGraphic ( ) graphic . position = Geometry . FloatPoint ( y , x ) self . __display_item . add_graphic ( graphic ) return Graphic ( graphic )
Add a point graphic to the data item .
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def mask_xdata ( self ) -> DataAndMetadata . DataAndMetadata : display_data_channel = self . __display_item . display_data_channel shape = display_data_channel . display_data_shape mask = numpy . zeros ( shape ) for graphic in self . __display_item . graphics : if isinstance ( graphic , ( Graphics . SpotGraphic , Graph...
Return the mask by combining any mask graphics on this data item as extended data .
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def data_item ( self ) -> DataItem : display_panel = self . __display_panel if not display_panel : return None data_item = display_panel . data_item return DataItem ( data_item ) if data_item else None
Return the data item associated with this display panel .
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def set_data_item ( self , data_item : DataItem ) -> None : display_panel = self . __display_panel if display_panel : display_item = data_item . _data_item . container . get_display_item_for_data_item ( data_item . _data_item ) if data_item . _data_item . container else None display_panel . set_display_panel_display_it...
Set the data item associated with this display panel .
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def add_data_item ( self , data_item : DataItem ) -> None : display_item = data_item . _data_item . container . get_display_item_for_data_item ( data_item . _data_item ) if data_item . _data_item . container else None if display_item : self . __data_group . append_display_item ( display_item )
Add a data item to the group .
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def close ( self ) -> None : self . __data_channel_buffer . stop ( ) self . __data_channel_buffer . close ( ) self . __data_channel_buffer = None if not self . __was_playing : self . __hardware_source . stop_playing ( )
Close the task .
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def record ( self , frame_parameters : dict = None , channels_enabled : typing . List [ bool ] = None , timeout : float = None ) -> typing . List [ DataAndMetadata . DataAndMetadata ] : if frame_parameters : self . __hardware_source . set_record_frame_parameters ( self . __hardware_source . get_frame_parameters_from_di...
Record data and return a list of data_and_metadata objects .
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def create_record_task ( self , frame_parameters : dict = None , channels_enabled : typing . List [ bool ] = None ) -> RecordTask : return RecordTask ( self . __hardware_source , frame_parameters , channels_enabled )
Create a record task for this hardware source .
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def grab_next_to_finish ( self , timeout : float = None ) -> typing . List [ DataAndMetadata . DataAndMetadata ] : self . start_playing ( ) return self . __hardware_source . get_next_xdatas_to_finish ( timeout )
Grabs the next frame to finish and returns it as data and metadata .
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def set_control_output ( self , name : str , value : float , * , options : dict = None ) -> None : self . __instrument . set_control_output ( name , value , options )
Set the value of a control asynchronously .
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def get_property_as_float ( self , name : str ) -> float : return float ( self . __instrument . get_property ( name ) )
Return the value of a float property .
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def set_property_as_float ( self , name : str , value : float ) -> None : self . __instrument . set_property ( name , float ( value ) )
Set the value of a float property .
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def data_items ( self ) -> typing . List [ DataItem ] : return [ DataItem ( data_item ) for data_item in self . __document_model . data_items ]
Return the list of data items .
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def display_items ( self ) -> typing . List [ Display ] : return [ Display ( display_item ) for display_item in self . __document_model . display_items ]
Return the list of display items .
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def get_source_data_items ( self , data_item : DataItem ) -> typing . List [ DataItem ] : return [ DataItem ( data_item ) for data_item in self . _document_model . get_source_data_items ( data_item . _data_item ) ] if data_item else None
Return the list of data items that are data sources for the data item .
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def get_dependent_data_items ( self , data_item : DataItem ) -> typing . List [ DataItem ] : return [ DataItem ( data_item ) for data_item in self . _document_model . get_dependent_data_items ( data_item . _data_item ) ] if data_item else None
Return the dependent data items the data item argument .
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def create_data_item ( self , title : str = None ) -> DataItem : data_item = DataItemModule . DataItem ( ) data_item . ensure_data_source ( ) if title is not None : data_item . title = title self . __document_model . append_data_item ( data_item ) return DataItem ( data_item )
Create an empty data item in the library .
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def create_data_item_from_data ( self , data : numpy . ndarray , title : str = None ) -> DataItem : return self . create_data_item_from_data_and_metadata ( DataAndMetadata . DataAndMetadata . from_data ( data ) , title )
Create a data item in the library from an ndarray .
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def create_data_item_from_data_and_metadata ( self , data_and_metadata : DataAndMetadata . DataAndMetadata , title : str = None ) -> DataItem : data_item = DataItemModule . new_data_item ( data_and_metadata ) if title is not None : data_item . title = title self . __document_model . append_data_item ( data_item ) retur...
Create a data item in the library from a data and metadata object .
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def copy_data_item ( self , data_item : DataItem ) -> DataItem : data_item = copy . deepcopy ( data_item . _data_item ) self . __document_model . append_data_item ( data_item ) return DataItem ( data_item )
Copy a data item .
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def snapshot_data_item ( self , data_item : DataItem ) -> DataItem : data_item = data_item . _data_item . snapshot ( ) self . __document_model . append_data_item ( data_item ) return DataItem ( data_item )
Snapshot a data item . Similar to copy but with a data snapshot .
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def get_data_item_by_uuid ( self , data_item_uuid : uuid_module . UUID ) -> DataItem : data_item = self . _document_model . get_data_item_by_uuid ( data_item_uuid ) return DataItem ( data_item ) if data_item else None
Get the data item with the given UUID .
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def get_graphic_by_uuid ( self , graphic_uuid : uuid_module . UUID ) -> Graphic : for display_item in self . _document_model . display_items : for graphic in display_item . graphics : if graphic . uuid == graphic_uuid : return Graphic ( graphic ) return None
Get the graphic with the given UUID .
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def has_library_value ( self , key : str ) -> bool : desc = Metadata . session_key_map . get ( key ) if desc is not None : field_id = desc [ 'path' ] [ - 1 ] return bool ( getattr ( ApplicationData . get_session_metadata_model ( ) , field_id , None ) ) return False
Return whether the library value for the given key exists .
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def get_library_value ( self , key : str ) -> typing . Any : desc = Metadata . session_key_map . get ( key ) if desc is not None : field_id = desc [ 'path' ] [ - 1 ] return getattr ( ApplicationData . get_session_metadata_model ( ) , field_id ) raise KeyError ( )
Get the library value for the given key .
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def set_library_value ( self , key : str , value : typing . Any ) -> None : desc = Metadata . session_key_map . get ( key ) if desc is not None : field_id = desc [ 'path' ] [ - 1 ] setattr ( ApplicationData . get_session_metadata_model ( ) , field_id , value ) return raise KeyError ( )
Set the library value for the given key .
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def delete_library_value ( self , key : str ) -> None : desc = Metadata . session_key_map . get ( key ) if desc is not None : field_id = desc [ 'path' ] [ - 1 ] setattr ( ApplicationData . get_session_metadata_model ( ) , field_id , None ) return raise KeyError ( )
Delete the library value for the given key .
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def all_display_panels ( self ) -> typing . List [ DisplayPanel ] : return [ DisplayPanel ( display_panel ) for display_panel in self . __document_controller . workspace_controller . display_panels ]
Return the list of display panels currently visible .
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def get_display_panel_by_id ( self , identifier : str ) -> DisplayPanel : display_panel = next ( ( display_panel for display_panel in self . __document_controller . workspace_controller . display_panels if display_panel . identifier . lower ( ) == identifier . lower ( ) ) , None ) return DisplayPanel ( display_panel ) ...
Return display panel with the identifier .
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def display_data_item ( self , data_item : DataItem , source_display_panel = None , source_data_item = None ) : for display_panel in self . __document_controller . workspace_controller . display_panels : if display_panel . data_item == data_item . _data_item : display_panel . request_focus ( ) return DisplayPanel ( dis...
Display a new data item and gives it keyboard focus . Uses existing display if it is already displayed .
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def create_data_item_from_data ( self , data : numpy . ndarray , title : str = None ) -> DataItem : return DataItem ( self . __document_controller . add_data ( data , title ) )
Create a data item in the library from data .
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def create_data_item_from_data_and_metadata ( self , data_and_metadata : DataAndMetadata . DataAndMetadata , title : str = None ) -> DataItem : data_item = DataItemModule . new_data_item ( data_and_metadata ) if title is not None : data_item . title = title self . __document_controller . document_model . append_data_it...
Create a data item in the library from the data and metadata .
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def document_windows ( self ) -> typing . List [ DocumentWindow ] : return [ DocumentWindow ( document_controller ) for document_controller in self . __application . document_controllers ]
Return the document windows .
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def create_panel ( self , panel_delegate ) : panel_id = panel_delegate . panel_id panel_name = panel_delegate . panel_name panel_positions = getattr ( panel_delegate , "panel_positions" , [ "left" , "right" ] ) panel_position = getattr ( panel_delegate , "panel_position" , "none" ) properties = getattr ( panel_delegate...
Create a utility panel that can be attached to a window .
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def get_hardware_source_by_id ( self , hardware_source_id : str , version : str ) : actual_version = "1.0.0" if Utility . compare_versions ( version , actual_version ) > 0 : raise NotImplementedError ( "Hardware API requested version %s is greater than %s." % ( version , actual_version ) ) hardware_source = HardwareSou...
Return the hardware source API matching the hardware_source_id and version .
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def library ( self ) -> Library : assert self . __app . document_model return Library ( self . __app . document_model )
Return the library object .
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def pad_matrix ( self , matrix , pad_value = 0 ) : max_columns = 0 total_rows = len ( matrix ) for row in matrix : max_columns = max ( max_columns , len ( row ) ) total_rows = max ( max_columns , total_rows ) new_matrix = [ ] for row in matrix : row_len = len ( row ) new_row = row [ : ] if total_rows > row_len : new_ro...
Pad a possibly non - square matrix to make it square .
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def __step1 ( self ) : C = self . C n = self . n for i in range ( n ) : minval = min ( self . C [ i ] ) for j in range ( n ) : self . C [ i ] [ j ] -= minval return 2
For each row of the matrix find the smallest element and subtract it from every element in its row . Go to Step 2 .
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def __step3 ( self ) : n = self . n count = 0 for i in range ( n ) : for j in range ( n ) : if self . marked [ i ] [ j ] == 1 : self . col_covered [ j ] = True count += 1 if count >= n : step = 7 else : step = 4 return step
Cover each column containing a starred zero . If K columns are covered the starred zeros describe a complete set of unique assignments . In this case Go to DONE otherwise Go to Step 4 .
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def __step4 ( self ) : step = 0 done = False row = - 1 col = - 1 star_col = - 1 while not done : ( row , col ) = self . __find_a_zero ( ) if row < 0 : done = True step = 6 else : self . marked [ row ] [ col ] = 2 star_col = self . __find_star_in_row ( row ) if star_col >= 0 : col = star_col self . row_covered [ row ] =...
Find a noncovered zero and prime it . If there is no starred zero in the row containing this primed zero Go to Step 5 . Otherwise cover this row and uncover the column containing the starred zero . Continue in this manner until there are no uncovered zeros left . Save the smallest uncovered value and Go to Step 6 .
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def __step6 ( self ) : minval = self . __find_smallest ( ) for i in range ( self . n ) : for j in range ( self . n ) : if self . row_covered [ i ] : self . C [ i ] [ j ] += minval if not self . col_covered [ j ] : self . C [ i ] [ j ] -= minval return 4
Add the value found in Step 4 to every element of each covered row and subtract it from every element of each uncovered column . Return to Step 4 without altering any stars primes or covered lines .
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def __find_smallest ( self ) : minval = sys . maxsize for i in range ( self . n ) : for j in range ( self . n ) : if ( not self . row_covered [ i ] ) and ( not self . col_covered [ j ] ) : if minval > self . C [ i ] [ j ] : minval = self . C [ i ] [ j ] return minval
Find the smallest uncovered value in the matrix .
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def __find_a_zero ( self ) : row = - 1 col = - 1 i = 0 n = self . n done = False while not done : j = 0 while True : if ( self . C [ i ] [ j ] == 0 ) and ( not self . row_covered [ i ] ) and ( not self . col_covered [ j ] ) : row = i col = j done = True j += 1 if j >= n : break i += 1 if i >= n : done = True return ( r...
Find the first uncovered element with value 0
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def __find_star_in_row ( self , row ) : col = - 1 for j in range ( self . n ) : if self . marked [ row ] [ j ] == 1 : col = j break return col
Find the first starred element in the specified row . Returns the column index or - 1 if no starred element was found .
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def __find_star_in_col ( self , col ) : row = - 1 for i in range ( self . n ) : if self . marked [ i ] [ col ] == 1 : row = i break return row
Find the first starred element in the specified row . Returns the row index or - 1 if no starred element was found .
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def __find_prime_in_row ( self , row ) : col = - 1 for j in range ( self . n ) : if self . marked [ row ] [ j ] == 2 : col = j break return col
Find the first prime element in the specified row . Returns the column index or - 1 if no starred element was found .
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def __clear_covers ( self ) : for i in range ( self . n ) : self . row_covered [ i ] = False self . col_covered [ i ] = False
Clear all covered matrix cells
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def __erase_primes ( self ) : for i in range ( self . n ) : for j in range ( self . n ) : if self . marked [ i ] [ j ] == 2 : self . marked [ i ] [ j ] = 0
Erase all prime markings
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def update ( self , a , b , c , d ) : self . table . ravel ( ) [ : ] = [ a , b , c , d ] self . N = self . table . sum ( )
Update contingency table with new values without creating a new object .
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def output_tree_ensemble ( tree_ensemble_obj , output_filename , attribute_names = None ) : for t , tree in enumerate ( tree_ensemble_obj . estimators_ ) : print ( "Writing Tree {0:d}" . format ( t ) ) out_file = open ( output_filename + ".{0:d}.tree" , "w" ) tree_str = print_tree_recursive ( tree . tree_ , 0 , attribu...
Write each decision tree in an ensemble to a file .
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def print_tree_recursive ( tree_obj , node_index , attribute_names = None ) : tree_str = "" if node_index == 0 : tree_str += "{0:d}\n" . format ( tree_obj . node_count ) if tree_obj . feature [ node_index ] >= 0 : if attribute_names is None : attr_val = "{0:d}" . format ( tree_obj . feature [ node_index ] ) else : attr...
Recursively writes a string representation of a decision tree object .
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def fitness_vs ( self , v ) : base = self . _base if base . _classifier : if base . _multiple_outputs : v . fitness_vs = v . _error else : v . fitness_vs = - ( ( base . y - v . hy . sign ( ) ) . sign ( ) . fabs ( ) * base . _mask_vs ) . sum ( ) else : mask = base . _mask y = base . y hy = v . hy if not isinstance ( mas...
Fitness function in the validation set In classification it uses BER and RSE in regression
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def set_fitness ( self , v ) : base = self . _base self . fitness ( v ) if not np . isfinite ( v . fitness ) : self . del_error ( v ) return False if base . _tr_fraction < 1 : self . fitness_vs ( v ) if not np . isfinite ( v . fitness_vs ) : self . del_error ( v ) return False self . del_error ( v ) return True
Set the fitness to a new node . Returns false in case fitness is not finite
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def output_sector_csv ( self , csv_path , file_dict_key , out_path ) : csv_file = csv_path + "{0}_{1}_{2}_{3}.csv" . format ( file_dict_key , self . ensemble_name , self . member , self . run_date . strftime ( self . date_format ) ) if exists ( csv_file ) : csv_data = pd . read_csv ( csv_file ) if self . inds is None :...
Segment forecast tracks to only output data contined within a region in the CONUS as defined by the mapfile .
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def clean_dict ( d0 , clean_item_fn = None ) : 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 : d [ key ] = cleaned_item return d
Return a json - clean dict . Will log info message for failures .
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def clean_list ( l0 , clean_item_fn = None ) : clean_item_fn = clean_item_fn if clean_item_fn else clean_item l = list ( ) for index , item in enumerate ( l0 ) : cleaned_item = clean_item_fn ( item ) l . append ( cleaned_item ) return l
Return a json - clean list . Will log info message for failures .
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def clean_tuple ( t0 , clean_item_fn = None ) : clean_item_fn = clean_item_fn if clean_item_fn else clean_item l = list ( ) for index , item in enumerate ( t0 ) : cleaned_item = clean_item_fn ( item ) l . append ( cleaned_item ) return tuple ( l )
Return a json - clean tuple . Will log info message for failures .
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def sample_stack_all ( count = 10 , interval = 0.1 ) : def print_stack_all ( l , ll ) : l1 = list ( ) l1 . append ( "*** STACKTRACE - START ***" ) code = [ ] for threadId , stack in sys . _current_frames ( ) . items ( ) : sub_code = [ ] sub_code . append ( "# ThreadID: %s" % threadId ) for filename , lineno , name , li...
Sample the stack in a thread and print it at regular intervals .
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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 args : x . hy = None x . hy_test = None else ...
Evaluates a individual using recursion and self . _pos as pointer
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def create_random_ind_full ( self , depth = 0 ) : "Random individual using full method" lst = [ ] self . _create_random_ind_full ( depth = depth , output = lst ) return lst
Random individual using full method
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def grow_use_function ( self , depth = 0 ) : "Select either function or terminal in grow method" if depth == 0 : return False if depth == self . _depth : return True return np . random . random ( ) < 0.5
Select either function or terminal in grow method
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def create_random_ind_grow ( self , depth = 0 ) : "Random individual using grow method" lst = [ ] self . _depth = depth self . _create_random_ind_grow ( depth = depth , output = lst ) return lst
Random individual using grow method
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def create_population ( self , popsize = 1000 , min_depth = 2 , max_depth = 4 , X = None ) : "Creates random population using ramped half-and-half method" import itertools args = [ x for x in itertools . product ( range ( min_depth , max_depth + 1 ) , [ True , False ] ) ] index = 0 output = [ ] while len ( output ) < p...
Creates random population using ramped half - and - half method
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def fitness_vs ( self ) : "Median Fitness in the validation set" l = [ x . fitness_vs for x in self . models ] return np . median ( l )
Median Fitness in the validation set
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def graphviz ( self , directory , ** kwargs ) : "Directory to store the graphviz models" import os if not os . path . isdir ( directory ) : os . mkdir ( directory ) output = os . path . join ( directory , 'evodag-%s' ) for k , m in enumerate ( self . models ) : m . graphviz ( output % k , ** kwargs )
Directory to store the graphviz models
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def neighborhood_probability ( self , threshold , radius ) : weights = disk ( radius , dtype = np . uint8 ) thresh_data = np . zeros ( self . data . shape [ 1 : ] , dtype = np . uint8 ) neighbor_prob = np . zeros ( self . data . shape , dtype = np . float32 ) for t in np . arange ( self . data . shape [ 0 ] ) : thresh_...
Calculate a probability based on the number of grid points in an area that exceed a threshold .
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def load_data ( self ) : for m , member in enumerate ( self . members ) : mo = ModelOutput ( self . ensemble_name , member , self . run_date , self . variable , self . start_date , self . end_date , self . path , self . map_file , self . single_step ) mo . load_data ( ) if self . data is None : self . data = np . zeros...
Loads data from each ensemble member .
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def point_consensus ( self , consensus_type ) : if "mean" in consensus_type : consensus_data = np . mean ( self . data , axis = 0 ) elif "std" in consensus_type : consensus_data = np . std ( self . data , axis = 0 ) elif "median" in consensus_type : consensus_data = np . median ( self . data , axis = 0 ) elif "max" in ...
Calculate grid - point statistics across ensemble members .