pairs_plotting
¶
This module provides R style pairs plotting functionality.

ema_workbench.analysis.pairs_plotting.
pairs_scatter
(experiments, outcomes, outcomes_to_show=[], group_by=None, grouping_specifiers=None, ylabels={}, legend=True, point_in_time=1, filter_scalar=False, **kwargs)¶ Generate a R style pairs scatter multiplot. In case of timeseries data, the end states are used.
Parameters:  experiments (DataFrame) –
 outcomes (dict) –
 outcomes_to_show (list of str, optional) – list of outcome of interest you want to plot.
 group_by (str, optional) – name of the column in the cases array to group results by. Alternatively, index can be used to use indexing arrays as the basis for grouping.
 grouping_specifiers (dict, optional) – dict of categories to be used as a basis for grouping by. Grouping_specifiers is only meaningful if group_by is provided as well. In case of grouping by index, the grouping specifiers should be in a dictionary where the key denotes the name of the group.
 ylabels (dict, optional) – ylabels is a dictionary with the outcome names as keys, the specified values will be used as labels for the y axis.
 legend (bool, optional) – if true, and group_by is given, show a legend.
 point_in_time (float, optional) – the point in time at which the scatter is to be made. If None is provided (default), the end states are used. point_in_time should be a valid value on time
 filter_scalar (bool, optional) – remove the nontimeseries outcomes. Defaults to True.
Returns:  fig (Figure instance) – the figure instance
 axes (dict) – key is tuple of names of outcomes, value is associated axes instance
 .. note:: the current implementation is limited to seven different – categories in case of column, categories, and/or discretesize. This limit is due to the colors specified in COLOR_LIST.

ema_workbench.analysis.pairs_plotting.
pairs_lines
(experiments, outcomes, outcomes_to_show=[], group_by=None, grouping_specifiers=None, ylabels={}, legend=True, **kwargs)¶ Generate a R style pairs lines multiplot. It shows the behavior of two outcomes over time against each other. The origin is denoted with a circle and the end is denoted with a ‘+’.
Parameters:  experiments (DataFrame) –
 outcomes (dict) –
 outcomes_to_show (list of str, optional) – list of outcome of interest you want to plot.
 group_by (str, optional) – name of the column in the cases array to group results by. Alternatively, index can be used to use indexing arrays as the basis for grouping.
 grouping_specifiers (dict, optional) – dict of categories to be used as a basis for grouping by. Grouping_specifiers is only meaningful if group_by is provided as well. In case of grouping by index, the grouping specifiers should be in a dictionary where the key denotes the name of the group.
 ylabels (dict, optional) – ylabels is a dictionary with the outcome names as keys, the specified values will be used as labels for the y axis.
 legend (bool, optional) – if true, and group_by is given, show a legend.
 point_in_time (float, optional) – the point in time at which the scatter is to be made. If None is provided (default), the end states are used. point_in_time should be a valid value on time
Returns:  fig – the figure instance
 dict – key is tuple of names of outcomes, value is associated axes instance

ema_workbench.analysis.pairs_plotting.
pairs_density
(experiments, outcomes, outcomes_to_show=[], group_by=None, grouping_specifiers=None, ylabels={}, point_in_time=1, log=True, gridsize=50, colormap='coolwarm', filter_scalar=True)¶ Generate a R style pairs hexbin density multiplot. In case of timeseries data, the end states are used.
hexbin makes hexagonal binning plot of x versus y, where x, y are 1D sequences of the same length, N. If C is None (the default), this is a histogram of the number of occurences of the observations at (x[i],y[i]). For further detail see matplotlib on hexbin
Parameters:  experiments (DataFrame) –
 outcomes (dict) –
 outcomes_to_show (list of str, optional) – list of outcome of interest you want to plot.
 group_by (str, optional) – name of the column in the cases array to group results by. Alternatively, index can be used to use indexing arrays as the basis for grouping.
 grouping_specifiers (dict, optional) – dict of categories to be used as a basis for grouping by. Grouping_specifiers is only meaningful if group_by is provided as well. In case of grouping by index, the grouping specifiers should be in a dictionary where the key denotes the name of the group.
 ylabels (dict, optional) – ylabels is a dictionary with the outcome names as keys, the specified values will be used as labels for the y axis.
 point_in_time (float, optional) – the point in time at which the scatter is to be made. If None is provided (default), the end states are used. point_in_time should be a valid value on time
 log (bool, optional) – indicating whether density should be log scaled. Defaults to True.
 gridsize (int, optional) – controls the gridsize for the hexagonal bining. (default = 50)
 cmap (str) – color map that is to be used in generating the hexbin. For details on the available maps, see pylab. (Defaults = coolwarm)
 filter_scalar (bool, optional) – remove the nontimeseries outcomes. Defaults to True.
Returns:  fig – the figure instance
 dict – key is tuple of names of outcomes, value is associated axes instance