onstove.OnStove.to_image#

OnStove.to_image(variable: str, name: 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 = {'size': 12, 'title': {'size': 12, 'weight': 'bold'}}, stats: bool = False, stats_kwargs: dict | None = None, scale_bar: dict | None = None, north_arrow: dict | None = None, figsize: tuple[float, float] = (6.4, 4.8), rasterized: bool = True, dpi: float = 150)[source]#

Saves a map from a desired column variable from the gdf into an image file.

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.

name: str

The map will be saved with that name and extension file in the:attr:output_directory as name.pdf, name.png, name.svg, etc.

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. .. code-block:

:caption: ``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 won’t 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

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.