invert4geom.plotting

Module Contents

Functions

plot_2_parameter_cv_scores(scores, parameter_pairs[, ...])	plot a scatter plot graph with x axis equal to parameter 1, y axis equal to
plot_cv_scores(scores, parameters[, logx, logy, ...])	plot a graph of cross-validation scores vs hyperparameter values
plot_convergence(results[, iter_times, logy, ...])	plot a graph of misfit and time vs iteration number.
plot_dynamic_convergence(results, l2_norm_tolerance, ...)	plot a graph of misfit and time vs iteration number.
grid_inversion_results(misfits, topos, corrections, ...)	create grids from the various data variables of the supplied gravity dataframe and
plot_inversion_topo_results(prisms_ds[, ...])	plot the initial and final topography grids from the inversion and their difference
plot_inversion_grav_results(grav_results, region, ...)	plot the initial and final misfit grids from the inversion and their difference
plot_inversion_iteration_results(grids, grav_results, ...)	plot the starting misfit, updated topography, and correction grids for a specified
plot_inversion_results(grav_results, topo_results, ...)	plot various results from the inversion
add_light(plotter, prisms)	add a light to a pyvista plotter object
show_prism_layers(prisms[, cmap, color_by])	show prism layers using PyVista

Attributes

clear_output
plt
sns
pyvista

clear_output

plt

sns

pyvista

plot_2_parameter_cv_scores(scores, parameter_pairs, logx=False, logy=False, param_names=('Hyperparameter 1', 'Hyperparameter 2'), figsize=(5, 3.5), cmap='viridis')

plot a scatter plot graph with x axis equal to parameter 1, y axis equal to parameter 2, and points colored by cross-validation scores.

Parameters:

• scores (list[float]) – score values

• parameter_pairs (list[float]) – parameter values

• logx (bool, optional) – make the x or y axes log scale, by default False

• logy (bool, optional) – make the x or y axes log scale, by default False

• param_names (tuple[str, str], optional) – name to give for the parameters, by default “Hyperparameter”

• figsize (tuple[float, float], optional) – size of the figure, by default (5, 3.5)

• cmap (str, optional) – matplotlib colormap for scores, by default “viridis”

Return type:

None

plot_cv_scores(scores, parameters, logx=False, logy=False, param_name='Hyperparameter', figsize=(5, 3.5), plot_title=None)

plot a graph of cross-validation scores vs hyperparameter values

Parameters:

• scores (list[float]) – score values

• parameters (list[float]) – parameter values

• logx (bool, optional) – make the x or y axes log scale, by default False

• logy (bool, optional) – make the x or y axes log scale, by default False

• param_name (str, optional) – name to give for the parameters, by default “Hyperparameter”

• figsize (tuple[float, float], optional) – size of the figure, by default (5, 3.5)

• plot_title (str | None, optional) – title of figure, by default None

Return type:

None

plot_convergence(results, iter_times=None, logy=False, inversion_region=None, figsize=(5, 3.5))

plot a graph of misfit and time vs iteration number.

Parameters:

• results (pd.DataFrame) – gravity result dataframe

• iter_times (list[float] | None, optional) – list of iteration execution times, by default None

• logy (bool, optional) – choose whether to plot y axis in log scale, by default False

• inversion_region (tuple[float, float, float, float] | None, optional) – inside region of inversion, by default None

• figsize (tuple[float, float], optional) – width and height of figure, by default (5, 3.5)

Return type:

None

plot_dynamic_convergence(results, l2_norm_tolerance, starting_misfit, inversion_region=None, figsize=(5, 3.5))

plot a graph of misfit and time vs iteration number.

Parameters:

• results (pd.DataFrame) – gravity result dataframe

• l2_norm_tolerance (float) – l2 norm tolerance

• starting_misfit (float) – starting misfit rmse

• inversion_region (tuple[float, float, float, float] | None, optional) – inside region of inversion, by default None

• figsize (tuple[float, float], optional) – width and height of figure, by default (5, 3.5)

Return type:

None

grid_inversion_results(misfits, topos, corrections, prisms_ds, grav_results, region)

create grids from the various data variables of the supplied gravity dataframe and prism dataset

Parameters:

• misfits (list[str]) – list of misfit column names in the gravity results dataframe

• topos (list[str]) – list of topography variable names in the prism dataset

• corrections (list[str]) – list of correction variable names in the prism dataset

• prisms_ds (xr.Dataset) – resulting dataset of prism layer from the inversion

• grav_results (pd.DataFrame) – resulting dataframe of gravity data from the inversion

• region (tuple[float, float, float, float]) – region to use for gridding in format (xmin, xmax, ymin, ymax)

Returns:

lists of misfit, topography, and correction grids

Return type:

tuple[list[xr.DataArray], list[xr.DataArray], list[xr.DataArray]]

plot_inversion_topo_results(prisms_ds, topo_cmap_perc=1, region=None)

plot the initial and final topography grids from the inversion and their difference

Parameters:

• prisms_ds (xr.Dataset) – dataset resulting from inversion

• topo_cmap_perc (float, optional) – value to multiple min and max values by for colorscale, by default 1

• region (tuple[float, float, float, float], optional) – clip grids to this region before plotting

Return type:

None

plot_inversion_grav_results(grav_results, region, iterations)

plot the initial and final misfit grids from the inversion and their difference

Parameters:

• grav_results (pd.DataFrame) – resulting dataframe of gravity data from the inversion

• region (tuple[float, float, float, float]) – region to use for gridding in format (xmin, xmax, ymin, ymax)

• iterations (list[int]) – list of all the iteration numbers

Return type:

None

plot_inversion_iteration_results(grids, grav_results, topo_results, parameters, iterations, topo_cmap_perc=1, misfit_cmap_perc=1, corrections_cmap_perc=1)

plot the starting misfit, updated topography, and correction grids for a specified number of the iterations of an inversion

Parameters:

• grids (tuple[list[xr.DataArray], list[xr.DataArray], list[xr.DataArray]]) – lists of misfit, topography, and correction grids

• grav_results (pd.DataFrame) – gravity dataframe resulting from the inversion

• topo_results (pd.DataFrame) – topography dataframe resulting from the inversion

• parameters (dict[str, Any]) – inversion parameters resulting from the inversion

• iterations (list[int]) – list of all the iteration numbers which occurred in the inversion

• topo_cmap_perc (float, optional) – value to multiply the max and min colorscale values by, by default 1

• misfit_cmap_perc (float, optional) – value to multiply the max and min colorscale values by, by default 1

• corrections_cmap_perc (float, optional) – value to multiply the max and min colorscale values by, by default 1

Return type:

None

plot_inversion_results(grav_results, topo_results, parameters, grav_region, iters_to_plot=None, plot_iter_results=True, plot_topo_results=True, plot_grav_results=True, **kwargs)

plot various results from the inversion

Parameters:

• grav_results (pd.DataFrame | str) – gravity results dataframe or filename

• topo_results (pd.DataFrame | str) – topography results dataframe or filename

• parameters (dict[str, Any] | str) – inversion parameters dictionary or filename

• grav_region (tuple[float, float, float, float] | None) – region to use for gridding in format (xmin, xmax, ymin, ymax), by default None

• iters_to_plot (int | None, optional) – number of iterations to plot, including the first and last, by default None

• plot_iter_results (bool, optional) – plot the iteration results, by default True

• plot_topo_results (bool, optional) – plot the topography results, by default True

• plot_grav_results (bool, optional) – plot the gravity results, by default True

• kwargs (Any)

Return type:

None

add_light(plotter, prisms)

add a light to a pyvista plotter object

Parameters:

• plotter (pyvista.Plotter) – pyvista plotter object

• prisms (xr.Dataset) – harmonica prisms layer

Return type:

None

show_prism_layers(prisms, cmap='viridis', color_by='density', **kwargs)

show prism layers using PyVista

Parameters:

• prisms (list | xr.Dataset) – either a single harmonica prism layer of list of layers,

• cmap (str, optional) – matplotlib colorscale to use, by default “viridis”

• color_by (str, optional) – either use a variable of the prism_layer dataset, typically ‘density’ or ‘thickness’, or choose ‘constant’ to have each layer colored by a unique color use kwarg colors to alter these colors, by default is “density”

• kwargs (Any)

Return type:

None