launch_live¶
Module: run.launch_live¶
Script for running pyspace live controlling
A script for running pyspace live. The script contains a class to control the other related classes needed in the online mode, and several methods that are used for the general startup of the suite.
Inheritance diagram for pySPACE.run.launch_live:
Class Summary¶
LiveController(parameters_[, live_processing]) |
Controlling suite. |
Function Summary¶
parse_arguments() |
Parses the command line arguments to create options object |
read_parameter_file(parameter_file_name) |
Reads and interprets the given parameter file |
create_and_start_rpc_server(controller_instance) |
Creates and starts the server for the remote procedure calls |
create_backup(liveControl, options) |
Create backup files |
Class¶
LiveController¶
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class
pySPACE.run.launch_live.LiveController(parameters_, live_processing=None)[source]¶ Bases:
objectControlling suite.
This class provides a clean interface to the live environment. It provides contains objects of the classes that are used for the online mode and configures them as needed.
The controller uses the config-files for user related configuration, and additional parameter files for scenario/task specific parameterization.
Class Components Summary
adapt_classification_threshold([load_model])Adapts classification threshold on a special function convert_dict_to_defaultdict(dict_to_convert)predict([load_model, online, remote])Classifies new instances based on the trained pyspace flows prewindowed_train()Trains the pyspace flows which have been prewindowed using the prewindower prewindowing([online])Prewindows the pyspace flows on the data streamed from record()start_prewindowing([online])Start the prewindowing process stop_prediction()stop_prewindowing()Create pyspace live processing server train()Trains the pyspace flows on the data streamed from -
prewindowing(online=True)[source]¶ Prewindows the pyspace flows on the data streamed from an external EEG-Server
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prewindowed_train()[source]¶ Trains the pyspace flows which have been prewindowed using the prewindower
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adapt_classification_threshold(load_model=True)[source]¶ Adapts classification threshold on a special function
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predict(load_model=True, online=True, remote=False)[source]¶ Classifies new instances based on the trained pyspace flows
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__weakref__¶ list of weak references to the object (if defined)
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