Seaborn Diagnostic

Create arbitrary Seaborn plots.

Description

This diagnostic provides a high-level interface to Seaborn. For this, the input data is arranged into a single pandas.DataFrame, which is then used as input for the Seaborn function defined by the option seaborn_func.

Caveats

All datasets of a given variable must have the same units (e.g., it is not allowed to use datasets with units K and datasets with units °C for the variable tas).

Author

Manuel Schlund (DLR, Germany)

Configuration options in recipe

add_ancillary_variables: bool, optional (default: False)

Add ancillary_variables() to the main data frame. Note that this will assume that ancillary variables are identical across cubes within a group (see option groupby_facet). This equality is not checked!

add_aux_coords: bool, optional (default: False)

Add aux_coords to the main data frame. Note that this will assume that auxiliary coordinates are identical across cubes within a group (see option groupby_facet). This equality is not checked!

add_cell_measures: bool, optional (default: False)

Add cell_measures() to the main data frame. Note that this will assume that cell measures are identical across cubes within a group (see option groupby_facet). This equality is not checked!

data_frame_ops: dict, optional

Perform additional operations on the main data frame. Allowed operations are pandas.DataFrame.query() (dict key query) and pandas.DataFrame.eval() (dict key eval). Operations are defined by strings (dict values). Examples: {'query': 'latitude > 80', 'eval': 'longitude = longitude - 180.0'}.

dropna_kwargs: dict, optional

Optional keyword arguments for pandas.DataFrame.dropna() to drop missing values in the input data. If not given, do not drop NaNs. Note: NaNs are dropped after potential data_frame_ops.

facets_as_columns: list of str, optional

Facets that will be added as a columns to the main data frame. Values for these facets must be identical across all datasets within a group (see option groupby_facet).

groupby_facet: str, optional (default: ‘alias’)

Facet which is used to group input datasets when creating the main data frame. All datasets within a group are expected to have the same index after calling iris.pandas.as_data_frame() on them. These datasets within a group will then get merged (combined along axis 1, i.e., columns) into a single data frame per group. Finally, the data frames for all groups are concatenated (combined along axis 0, i.e., rows) into one main data frame. groupby_facet is also added as a column to this main data frame.

legend_title: str, optional (default: None)

Title for legend. If None, Seaborn will determine the legend title (if possible).

plot_object_methods: dict, optional

Execute methods of the object returned by the plotting function (seaborn_func). This object will either be a matplotlib.axes.Axes (e.g., scatterplot(), lineplot()), a seaborn.FacetGrid (e.g., relplot(), displot()), a seaborn.JointGrid (e.g., jointplot()), or a seaborn.PairGrid (e.g., pairplot()). Dictionary keys are method names, dictionary values function arguments (use a dict to specify keyword arguments). Example (for relplot()): {'set': {'xlabel': 'X [km]'}, 'set_titles': 'Model {col_name}'}.

reset_index: bool, optional (default: False)

Put coordinate information of datasets into columns instead of (multi-) indices. This avoids the deletion of coordinate information if different groups of datasets have different dimensions but increases the memory footprint of this diagnostic.

savefig_kwargs: dict, optional

Optional keyword arguments for matplotlib.pyplot.savefig(). By default, uses bbox_inches: tight, dpi: 300, orientation: landscape.

seaborn_func: str

Function used to plot the data. Must be a function of Seaborn. An overview of Seaborn’s plotting functions is given here.

seaborn_kwargs: dict, optional

Optional keyword arguments for the plotting function given by seaborn_func. Must not include an argument called data. Example: {'x': 'variable_1', 'y': 'variable_2', 'hue': 'coord_1'}. Note: variables (here: variable_1 and variable_2 are identified by their variable_group in the recipe, i.e., the keys that specify variable groups in variables.

seaborn_settings: dict, optional

Options for seaborn.set_theme() (affects all plots).

suptitle: str or None, optional (default: None)

Suptitle for the plot (see matplotlib.pyplot.suptitle()). If None, do not create a suptitle. If the plot shows only a single panel, use plot_object_methods with {'set': {'title': 'TITLE'}} instead.