flim.analysis package¶
Subpackages¶
Submodules¶
flim.analysis.aetraining module¶
- class flim.analysis.aetraining.AETraining(name='Autoencoder: Train', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_description()¶
Returns a description for this analyzing module. This may include instructions on how to use the parameters.
- Returns
the description.
- Return type
str
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- train(data, learning_rate, weight_decay, batch_size, train_loader, val_loader, input_scaler, imputer, label_encoders)¶
- class flim.analysis.aetraining.AETrainingConfigDlg(parent, title, input={}, description=None, selectedgrouping=['None'], selectedfeatures='All', epoches=20, batch_size=200, learning_rate=0.0001, weight_decay=1e-07, timeseries='', model='', modelfile='', device='cpu', rescale=False, checkpoint_interval=20, autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- OnDeselectAll(event)¶
- OnSelectAll(event)¶
- get_option_panels()¶
- class flim.analysis.aetraining.datasets(data, labels=[])¶
Bases:
Generic[torch.utils.data.dataset.T_co]
flim.analysis.boxplots module¶
Created on Thu Dec 17 16:11:37 2020
@author: khs3z
- class flim.analysis.boxplots.BoxPlot(name='Box Plot', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
flim.analysis.freqhisto module¶
Created on Wed Dec 16 14:18:30 2020
@author: khs3z
- class flim.analysis.freqhisto.FreqHisto(name='Frequency Histogram', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- histogram(data, column, title=None, groups=[], normalize=None, titlesuffix=None, **kwargs)¶
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.freqhisto.FreqHistoConfigDlg(parent, title, input=None, selectedgrouping=['None'], selectedfeatures='All', bins=20, stacked=False, cumulative=False, histtype='step', datatable=False, featuresettings={}, settingspecs={}, autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- get_option_panels()¶
flim.analysis.heatmap module¶
- class flim.analysis.heatmap.Heatmap(name='Heatmap', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.heatmap.HeatmapConfigDlg(parent, title, input={}, selectedgrouping=['None'], selectedfeatures='All', corr_type='pearson', numbers=False, autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- get_option_panels()¶
flim.analysis.kde module¶
Created on Wed Dec 16 14:18:30 2020
@author: khs3z
- class flim.analysis.kde.KDE(name='KDE', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin,prefect.core.task.Task- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- grouped_kdeplot(data, column, title=None, ax=None, show_legend=True, return_plot=True, groups=[], dropna=True, linestyles=None, pivot_level=1, **kwargs)¶
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
flim.analysis.lineplots module¶
Created on Wed Dec 16 14:18:30 2020
@author: khs3z
- class flim.analysis.lineplots.LinePlot(name='Line Plot', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- grouped_lineplot(data, feature, title=None, categories=[], dropna=True, ax=None, fig=None, **kwargs)¶
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
flim.analysis.meanbarplots module¶
Created on Wed Dec 16 14:18:30 2020
@author: khs3z
- class flim.analysis.barplots.BarPlot(name='Bar Plot', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.barplots.BarPlotConfigDlg(parent, title, input=None, selectedgrouping=['None'], selectedfeatures='All', orientation='vertical', ordering=[], ebar='+/-', etype='std', dropna=True, bartype='single', autosave=True, working_dir='', legend=True)¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- OnErroBarChange(event)¶
- get_option_panels()¶
- flim.analysis.barplots.grouped_meanbarplot(data, feature, ax=None, title=None, bartype='single', categories=[], dropna=True, pivot_level=1, orientation='horizontal', error_bar='None', error_type='std', legend=True)¶
flim.analysis.pca module¶
Created on Wed Dec 16 14:18:30 2020
@author: khs3z
- class flim.analysis.pca.PCAnalysis(name='PCA', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_description()¶
Returns a description for this analyzing module. This may include instructions on how to use the parameters.
- Returns
the description.
- Return type
str
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.pca.PCAnalysisConfigDlg(parent, title, input=None, description=None, selectedgrouping=['None'], selectedfeatures='All', keeporig=False, keepstd=True, explainedhisto=False, n_components=None, autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- get_option_panels()¶
flim.analysis.randomforest module¶
Created on Wed Dec 16 14:18:30 2020
@author: khs3z
- class flim.analysis.randomforest.RandomForest(name='Random Forest', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.randomforest.RandomForestConfigDlg(parent, title, input=None, selectedgrouping=['None'], selectedfeatures='All', classifier='', importancehisto=True, n_estimators=100, test_size=0.3, autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- get_option_panels()¶
flim.analysis.relativechange module¶
Created on Thu Dec 17 09:50:44 2020
@author: khs3z
- class flim.analysis.relativechange.RelativeChange(name='Relative Change', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.relativechange.RelativeChangeConfigDlg(parent, title, input=None, selectedgrouping=['None'], selectedfeatures='All', method='mean', refgroup='', refvalue='', autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- OnRefGroupChanged(event)¶
- get_option_panels()¶
flim.analysis.aerun module¶
- class flim.analysis.aerun.AERunningConfigDlg(parent, title, input=None, selectedgrouping=['None'], selectedfeatures='All', modelfile='', device='cpu', autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- OnBrowse(event)¶
- get_option_panels()¶
- class flim.analysis.aerun.RunAE(name='Autoencoder: Run', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- run_model(modelfile)¶
flim.analysis.scatterplots module¶
Created on Thu Dec 17 16:11:28 2020
@author: khs3z
- class flim.analysis.scatterplots.ScatterPlot(name='Scatter Plot', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- grouped_scatterplot(data, combination, title=None, categories=[], dropna=True, pivot_level=1, **kwargs)¶
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
flim.analysis.summarystats module¶
Created on Thu Dec 17 09:50:44 2020
@author: khs3z
- class flim.analysis.summarystats.SummaryStats(name='Summarize', **kwargs)¶
Bases:
flim.plugin.AbstractPlugin- agg_functions = {'count': 'count', 'max': 'max', 'mean': 'mean', 'median': 'median', 'min': 'min', 'percentile(25)': <function percentile.<locals>.percentile_>, 'percentile(75)': <function percentile.<locals>.percentile_>, 'sem': 'sem', 'std': 'std', 'sum': 'sum'}¶
- execute()¶
Executes the analysis using the analyzer’s set input and configuration.
- Returns
- Dictionary of pandas.DataFrame and/or matplotlib.pyplot.Figure objects.
Keys represent window titles, values represent the DataFrame or Fugure objects.
- Return type
dict
- get_default_parameters()¶
Provides the plugin’s default parameters.
- Returns
default parameters
- Return type
dict
- get_description()¶
Returns a description for this analyzing module. This may include instructions on how to use the parameters.
- Returns
the description.
- Return type
str
- get_icon()¶
Returns icon for this analysis.
- Returns
The bitmap of the icon.
- Return type
wx.Bitmap
- get_mapped_parameters()¶
Provides a list of the plugins current parameters. Each list item defines a parameters for the smallest independent work unit. The list can be mapped for parallel flow execution.
- Returns
list of current parameters
- Return type
list[dict]
- get_required_categories()¶
Returns the category column names that are required in the input to be analyzed.
Category columns use ‘category’ as dtype in Pandas DataFrame.
- Returns
list of column names.
- Return type
list(str)
- get_required_features()¶
Returns the non-category column names that are required in the input to be analyzed.
Non-category columns are all those columns that do not use ‘category’ as dtype in Pandas dataframe.
- Returns
list of column names.
- Return type
list(str)
- output_definition()¶
Provides type definition of plugin’s execute method.
- Returns
- keys describe output object labels; values represent corresponding
object types
- Return type
dict[type]
- run_configuration_dialog(parent, data_choices={})¶
Executes the plugin’s configuration dialog.
The dialog is initialized with values of the analyzer’s Config object.
- Parameters
parent – parent GUI element
data_choices (dict) – available data tables to choose from. Keys correspond to table names; values correspond to DataFrame objects.
- Returns
The key:value pairs of specified config parameters.
- Return type
dict
- class flim.analysis.summarystats.SummaryStatsConfigDlg(parent, title, input=None, description=None, selectedgrouping=['None'], selectedfeatures='all_features', allaggs=[], selectedaggs='All', singledf=False, autosave=True, working_dir='')¶
Bases:
flim.gui.dialogs.BasicAnalysisConfigDlg- OnDeselectAllAggs(event)¶
- OnSelectAllAggs(event)¶
- get_option_panels()¶
- flim.analysis.summarystats.percentile(n)¶