Graphics

Graphics#

echopop.graphics.plot_age_length_heatmap(data: DataArray, include_filter: dict[str, Any] | None = None, exclude_filter: dict[str, Any] | None = None, replace_value=None, axis_kwargs: dict[str, Any] | None = None, plot_kwargs: dict[str, Any] | None = None, colorbar_kwargs: dict[str, Any] | None = None, imshow_kwargs: dict[str, Any] | None = None, savepath: Path | None = None, savefig_kwargs: dict[str, Any] | None = None) → None#

Plot an age-length heatmap from a DataFrame.

Parameters:

datapandas.DataFrame: DataFrame indexed by 'length_bin' and with columns 'age_bin'.
include_filterdict, optional: Dictionary of filters to include specific data. Passed to echopop.utils.apply_filters().
exclude_filterdict, optional: Dictionary of filters to exclude specific data. Passed to echopop.utils.apply_filters().
replace_valueany, optional: Value to use for missing or filtered data.
axis_kwargsdict, optional: Additional keyword arguments for axis formatting (e.g., labels). Example: axis_kwargs={'xlabel': 'Age', 'ylabel': 'Length'}
plot_kwargsdict, optional: Additional keyword arguments for matplotlib.pyplot.subplots().
colorbar_kwargsdict, optional: Additional keyword arguments for matplotlib.pyplot.colorbar().
imshow_kwargsdict, optional: Additional keyword arguments for matplotlib.axes.Axes.imshow().
save_pathpathlib.Path, optional: Filepath for saving the figure.
savefig_kwargsdict, optional: Keyword arguments used by matplotlib.pyplot.savefig() for saving the figure to the associated save filepath.

Returns:

None

Notes

All keyword argument dictionaries are optional and are passed directly to the underlying plotting functions. If a keyword is present in both a specific kwargs dict and plot_kwargs, the value in plot_kwargs takes precedence.

Examples

>>> plot_age_length_heatmap(df, axis_kwargs={'xlabel': 'Age', 'ylabel': 'Length'})

echopop.graphics.plot_kriged_mesh(data: DataFrame | GeoDataFrame, variable: str, projection: str = 'EPSG:4326', coordinate_names: tuple[str, str] = ('longitude', 'latitude'), plot_type: Literal['hexbin', 'pcolormesh', 'scatter'] = 'hexbin', scatter_kwargs: dict[str, Any] | None = None, hexbin_kwargs: dict[str, Any] | None = None, pseudocolormesh_kwargs: dict[str, Any] | None = None, coast_kwargs: dict[str, Any] | None = None, axis_kwargs: dict[str, Any] | None = None, plot_kwargs: dict[str, Any] | None = None, colorbar_kwargs: dict[str, Any] | None = None, savepath: Path | None = None, savefig_kwargs: dict[str, Any] | None = None) → None#

Plot a kriged mesh or survey data using various plot types.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame

Input data with coordinates and the variable to plot.

variablestr

Name of the column to plot.

projectionstr, default=’EPSG:4326’

CRS for the plot. This should be either a projected or geodetic coordinate system definition compatible with with geopandas.GeoDataFrame.

coordinate_namestuple[str], default=(‘longitude’, ‘latitude’)

Names of the coordinate columns. This is a tuple with an expected order of 'x' and then 'y'.

plot_type{‘hexbin’, ‘pcolormesh’, ‘scatter’}, default=’hexbin’

Type of plot to produce. Options are:

‘hexbin’: Creates a hexagonal binned plot using matplotlib.pyplot.hexbin(), which visualizes a summary statistic defined by the user (via the reduce_C_function argument, which defaults to numpy.mean()) over a two-dimensional grid of hexagons. The plotted hexagonal bins can be configured using keyword arguments supplied to the hexbin_kwargs argument.
‘pcolormesh’: Interpolates the variable onto a regular grid using verde.Chain for spatial interpolation that returns an xarray.DataArray. The resulting grid is then displayed as a pseudocolor mesh using xarray.DataArray.plot.pcolormesh(). The plotted pseudocolor mesh can be configured using keyword arguments supplied to the pseudocolormesh_kwargs argument.
‘scatter’: Plots each data point individually using matplotlib.axes.Axes.scatter() points based on the variable value. The plotted points can be configured using keyword arguments supplied to the scatter_kwargs argument.

scatter_kwargsdict, optional

Additional keyword arguments passed directly to matplotlib.pyplot.scatter() if plot_type='scatter'. For example, you can control marker size, color, alpha, etc. Example: scatter_kwargs={'s': 20, 'c': 'red', 'alpha': 0.7}

hexbin_kwargsdict, optional

Additional keyword arguments passed directly to matplotlib.pyplot.hexbin() if plot_type='hexbin'. For example, you can control grid size, colormap, etc. Example: hexbin_kwargs={'gridsize': 50, 'cmap': 'viridis'}

pseudocolormesh_kwargsdict, optional

Additional keyword arguments passed to the mesh interpolation and to xarray.DataArray.plot.pcolormesh() if plot_type='pcolormesh'. For example, you can specify mesh spacing, colormap, shading, etc. Example: pseudocolormesh_kwargs={'spacing': 0.01, 'cmap': 'plasma'}

coast_kwargsdict, optional

Additional keyword arguments passed to the coastline plotting function such as cartopy.mpl.geoaxes.GeoAxes.add_feature(). These control the appearance of the coastline overlay. Example: coast_kwargs={'edgecolor': 'black', 'linewidth': 0.5}

axis_kwargsdict, optional

Additional keyword arguments passed to axis formatting functions (e.g., setting axis limits, labels, or grid). These are merged with any defaults and passed to the axis/axes object. Example: axis_kwargs={'xlabel': 'Longitude', 'ylabel': 'Latitude', 'xlim': (-130, -120)}

plot_kwargsdict, optional

Additional keyword arguments passed to the main plotting function, depending on plot_type. These are merged with the specific kwargs above and can override them. Example: plot_kwargs={'alpha': 0.8}

colorbar_kwargsdict, optional

Additional keyword arguments passed to matplotlib.pyplot.colorbar() for customizing the colorbar. These control label, orientation, ticks, etc. Example: colorbar_kwargs={'label': 'Biomass (kg)', 'orientation': 'vertical'}

save_pathPath, optional

Filepath for saving the figure.

savefig_kwargsdict, optional

Keyword arguments used by matplotlib.pyplot.savefig() for saving the figure to the associated save filepath.

Returns:

None

Notes

All keyword argument dictionaries are optional. If provided, they are passed directly to the underlying matplotlib or Cartopy plotting functions. If the same keyword is present in both a specific kwargs dict (e.g., scatter_kwargs) and plot_kwargs, the value in plot_kwargs takes precedence.

Examples

>>> plot_kriged_mesh(df, 'biomass', plot_type='hexbin',
...                  hexbin_kwargs={'gridsize': 40, 'cmap': 'viridis'},
...                  coast_kwargs={'edgecolor': 'gray'},
...                  colorbar_kwargs={'label': 'Biomass (kg)'})

echopop.graphics.plot_transect_map(data: DataFrame | GeoDataFrame, variable: str, projection: str = 'EPSG:4326', coordinate_names: tuple[str, str] = ('longitude', 'latitude'), scatter_kwargs: dict[str, Any] | None = None, transect_kwargs: dict[str, Any] | None = None, coast_kwargs: dict[str, Any] | None = None, axis_kwargs: dict[str, Any] | None = None, plot_kwargs: dict[str, Any] | None = None, colorbar_kwargs: dict[str, Any] | None = None, savepath: Path | None = None, savefig_kwargs: dict[str, Any] | None = None) → None#

Plot survey transects and variable values on a map.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame: Input data with coordinates and variable to plot.
variablestr: Name of the column to plot.
projectionstr, default=’EPSG:4326’: CRS for the plot. This should be either a projected or geodetic coordinate system definition compatible with geopandas.GeoDataFrame.
coordinate_namestuple[str], default=(‘longitude’, ‘latitude’): Names of the coordinate columns. This is a tuple with an expected order of 'x' and then 'y'.
scatter_kwargsdict, optional: Additional keyword arguments passed directly to matplotlib.axes.Axes.scatter(). For example, you can control marker size, color, alpha, etc., such as: scatter_kwargs={'s': 20, 'c': 'red', 'alpha': 0.7}
transect_kwargsdict, optional: Additional keyword arguments passed to geopandas.GeoDataFrame.plot() for transect lines. For example, you can control line color, width, style, etc. such as: transect_kwargs={'color': 'black', 'linewidth': 1.5}
coast_kwargsdict, optional: Additional keyword arguments passed to the coastline plotting function such as cartopy.mpl.geoaxes.GeoAxes.add_feature(). These control the appearance of the coastline overlay. Example: coast_kwargs={'edgecolor': 'black', 'linewidth': 0.5}
axis_kwargsdict, optional: Additional keyword arguments passed to axis formatting functions (e.g., setting axis limits, labels, or grid). These are merged with any defaults and passed to the axis/axes object. Example: axis_kwargs={'xlabel': 'Longitude', 'ylabel': 'Latitude', 'xlim': (-130, -120)}
plot_kwargsdict, optional: Additional keyword arguments passed to the main plotting function, depending on plot_type. These are merged with the specific kwargs above and can override them. Example: plot_kwargs={'alpha': 0.8}
colorbar_kwargsdict, optional: Additional keyword arguments passed to matplotlib.pyplot.colorbar() for customizing the colorbar. These control label, orientation, ticks, etc. Example: colorbar_kwargs={'label': 'Biomass (kg)', 'orientation': 'vertical'}
save_pathPath, optional: Filepath for saving the figure.
savefig_kwargsdict, optional: Keyword arguments used by matplotlib.pyplot.savefig() for saving the figure to the associated save filepath.

Returns:

None

Notes

All keyword argument dictionaries are optional and are passed directly to the underlying plotting functions. If a keyword is present in both a specific kwargs dict and plot_kwargs, the value in plot_kwargs takes precedence.

Examples

>>> plot_transect_map(df, 'biomass',
...                  scatter_kwargs={'s': 10, 'c': 'blue'},
...                  transect_kwargs={'color': 'black', 'linewidth': 1.5},
...                  coast_kwargs={'edgecolor': 'gray'},
...                  colorbar_kwargs={'label': 'Biomass (kg)'})

class echopop.graphics.Diagnostics(json_theme: dict[str, Any] | None = None)#

Diagnostics plotting utilities for survey and mesh data.

Parameters:

json_themedict, optional: A Bokeh theme dictionary to apply to plots.

Methods

`plot_mesh_cropping`(mesh_data, ...[, projection])	Plot the results of the crop meshing.
`plot_nasc_map`(transect_data[, projection])	Plot NASC (Nautical Area Scattering Coefficient) values for transects.
`plot_stratified_results`(stratum_results)	Plot stratified biomass results with error bars and relative bias.
`plot_transect_mesh_regions`(transect_data, ...)	Plot the mesh region assignment to transects.

class echopop.graphics.VariogramGUI(data: DataFrame, lag_resolution: float, n_lags: int, coordinates: tuple, variogram_parameters: dict[str, Any] | None = None)#

Interactive graphical user interface for variogram analysis and model fitting.

The VariogramGUI class provides an interactive, tabbed interface for computing, visualizing, and optimizing empirical and theoretical variograms from spatial data. It is designed for use in Jupyter environments and leverages ipywidgets, holoviews, and bokeh for dynamic plotting and user input.

Parameters:

datapandas.DataFrame: Input spatial dataset containing coordinate columns and variables for variogram analysis.
lag_resolutionfloat: The distance interval for each lag bin in the variogram calculation.
n_lagsint: Number of lag bins to use for empirical variogram computation.
coordinatestuple of str: Names of the columns in data representing spatial coordinates, e.g. ("longitude", "latitude").
variogram_parametersdict, optional: Dictionary of initial variogram model parameters and their default values.

Attributes:

optimized_parameters: Return the best-fit, optimized variogram parameters.

Notes

All computation and plotting is handled internally; users interact only through the GUI.
For advanced scripting or batch processing, use the echopop.geostatistics.Variogram class directly.

Examples

Instantiate the class in a Jupyter notebook and display the GUI by evaluating the instance:

>>> gui = VariogramGUI(
...     data=survey_df,
...     lag_resolution=5.0,
...     n_lags=15,
...     coordinates=("longitude", "latitude"),
...     variogram_parameters={"sill": {"value": 2.0}, "nugget": {"value": 0.1}}
... )
>>> gui  # Displays the interactive GUI

Graphics

Contents

Graphics#