onstove.OnStove.plot#

OnStove.plot(variable: str, metric='mean', labels: dict[str, str] | None = None, cmap: dict[str, str] | str = 'viridis', cumulative_count: tuple[float, float] | None = None, quantiles: tuple[float] | None = None, nodata: float | int = nan, admin_layer: GeoDataFrame | VectorLayer | None = None, title: str | None = None, legend: bool = True, legend_title: str = '', legend_cols: int = 1, legend_position: tuple[float, float] = (1.02, 0.6), legend_prop: dict | None = None, stats: bool = False, stats_kwargs: dict | None = None, scale_bar: dict | None = None, north_arrow: dict | None = None, ax: Axes | None = None, figsize: tuple[float, float] = (6.4, 4.8), rasterized: bool = True, dpi: float = 150, save_as: str | None = None, save_style: bool = False, style_classes: int = 5) → Axes[source]#

Plots a map from a desired column variable from the gdf.

The map can be for categorical or continuous data. If categorical, a legend will be created with the colors of the categories. If continuous, a color bar will be created with the range of the data. For continuous data a metric parameter can be passed indicating the desired statistic to be visualized. Moreover, continuous data can be presented using cumulative_count or quantiles.

Parameters:

variable: str

The column name from the gdf to plot.

metric: str, default ‘mean’

Metric to use to aggregate data. It is only used for continuous data. For available metrics see create_layer().

labels: dictionary of str key-value pairs, optional

Dictionary with the keys-value pairs to use for the data categories. It is only used for categorical data— see create_layer().

cmap: dictionary of str key-value pairs or str, default ‘viridis’

Dictionary with the colors to use for each data category if the data is categorical—see create_layer(). If the data is continuous, then a name of a color scale accepted by matplotlib should be passed.

cumulative_count: array-like of float, optional

List of lower and upper limits to consider for the cumulative count. If defined the map will be displayed with the cumulative count representation of the data.

See also

RasterLayer.quantiles()

nodata: float or int, default ```np.nan`

Defines nodata values to be ignored when plotting.

admin_layer: gpd.GeoDataFrame or VectorLayer, optional

The administrative boundaries to plot as background. If no admin_layer is provided then the :attr:mask_layer will be used if available, if not then no boundaries will be plotted.

title: str, optional

The title of the plot.

legend: bool, default False

Whether to display a legend—only applicable for categorical data.

legend_title: str, default ‘’

Title of the legend.

legend_cols: int, default 1

Number of columns to divide the rows of the legend.

legend_position: array-like of float, default (1.05, 1)

Position of the upper-left corner of the legend measured in fraction of x and y axis.

legend_prop: dict

Dictionary with the font properties of the legend. It can contain any property accepted by the prop parameter from matplotlib.pyplot.legend. It defaults to {'title': {'size': 12, 'weight': 'bold'}, 'size': 12}.

stats: bool, default False

Whether to display the statistics of the analysis in the map.

stats_kwargs: dictionary, optional

Dictionary of arguments to control the position and style of the statistics box.

stats_kwargs default arguments#

{'extra_stats': None, 'stats_position': (1.02, 0.9),
 'pad': 0, 'sep': 6, 'fontsize': 10,
'fontcolor': 'black', 'fontweight': 'normal',
'box_props': dict(boxstyle='round',
                  facecolor='#f1f1f1ff',
                  edgecolor='lightgray')}

stats_kwargs other options#

{'extra_stats': dict('StatA': value),
'fontsize': 10, 'stats_position': (1, 0.9),
'pad': 2, 'sep': 0, 'fontcolor': 'black',
'fontweight': 'normal',
'box_props': dict(facecolor='lightyellow',
                  edgecolor='black',
                  alpha=1,
                  boxstyle="sawtooth")}

scale_bar: dict, optional

Dictionary with the parameters needed to create a ScaleBar. If not defined, no scale bar will be displayed.

Scale bar dictionary example#

dict(size=1000000, style='double',
     textprops=dict(size=8), location=(1, 0),
     linekw=dict(lw=1, color='black'),
     extent=0.01)

Note

See onstove.scale_bar() for more details

north_arrow: dict, optional

Dictionary with the parameters needed to create a north arrow icon in the map. If not defined, the north icon wont be displayed.

North arrow dictionary example#

dict(size=30, location=(0.92, 0.92), linewidth=0.5)

Note

See onstove.north_arrow() for more details

ax: matplotlib.axes.Axes, optional

A matplotlib axes instance can be passed in order to overlay layers in the same axes.

figsize: tuple of floats, default (6.4, 4.8)

The size of the figure in inches.

rasterized: bool, default True

Whether to rasterize the output.It converts vector graphics into a raster image (pixels). It can speed up rendering and produce smaller files for large data sets—see more at Rasterization for vector graphics.

dpi: int, default 150

The resolution of the figure in dots per inch.

save_as: str, optional

If a string is passed, then the map will be saved with that name and extension file in the:attr:output_directory as name.pdf, name.png, name.svg, etc.

save_style: bool, default False

Whether to save the style of the plot as a .sld file—see onstove.RasterLayer.save_style().

style_classes: int, default 5

number of classes to include in the .sld style.

Returns:

matplotlib.axes.Axes: The axes of the figure.

See also

create_layer
to_image
to_raster
RasterLayer.plot
VectorLayer.plot

Examples

>>> africa = OnStove('results.pkl')
...
>>> cmap = {'Biomass ICS (ND)': '#6F4070',
...         'LPG': '#66C5CC',
...         'Biomass': '#FFB6C1',
...         'Biomass ICS (FD)': '#af04b3',
...         'Pellets ICS (FD)': '#ef02f5',
...         'Charcoal': '#364135',
...         'Charcoal ICS': '#d4bdc5',
...         'Biogas': '#73AF48',
...         'Biogas and Biomass ICS (ND)': '#F6029E',
...         'Biogas and Biomass ICS (FD)': '#F6029E',
...         'Biogas and Pellets ICS (FD)': '#F6029E',
...         'Biogas and LPG': '#0F8554',
...         'Biogas and Biomass': '#266AA6',
...         'Biogas and Charcoal': '#3B05DF',
...         'Biogas and Charcoal ICS': '#3B59DF',
...         'Electricity': '#CC503E',
...         'Electricity and Biomass ICS (ND)': '#B497E7',
...         'Electricity and Biomass ICS (FD)': '#B497E7',
...         'Electricity and Pellets ICS (FD)': '#B497E7',
...         'Electricity and LPG': '#E17C05',
...         'Electricity and Biomass': '#FFC107',
...         'Electricity and Charcoal ICS': '#660000',
...         'Electricity and Biogas': '#f97b72',
...         'Electricity and Charcoal': '#FF0000'}
...
>>>   labels = {'Biogas and Electricity': 'Electricity and Biogas',
...             'Collected Traditional Biomass': 'Biomass',
...             'Collected Improved Biomass': 'Biomass ICS (ND)',
...             'Traditional Charcoal': 'Charcoal',
...             'Biomass Forced Draft': 'Biomass ICS (FD)',
...             'Pellets Forced Draft': 'Pellets ICS (FD)'}
...
>>> scale_bar_prop = dict(size=1000000, style='double', textprops=dict(size=8),
...                       linekw=dict(lw=1, color='black'), extent=0.01)
>>> north_arow_prop = dict(size=30, location=(0.92, 0.92), linewidth=0.5)
...
>>> africa.plot('max_benefit_tech', labels=labels, cmap=cmap,
...             stats=True,
...             legend=True, legend_position=(0.03, 0.47),
...             legend_title='Maximum benefit cooking technology',
...             legend_prop={'title': {'size': 10, 'weight': 'bold'}, 'size': 10},
...             scale_bar=scale_bar_prop, north_arrow=north_arow_prop,
...             figsize=(16, 9), dpi=300, rasterized=True)

max benefit cooking technology over SSA created with OnStove