mapgwm.framework module

Functions for preprocessing products from the MAP Framework Team

mapgwm.framework.add_discrete_colors_legend(ax, data, unit_labels=None, cmap='copper', vmin=None, vmax=None, alpha=1, **kwargs)

Add a legend with a colored rectangle and label for each discrete value in data.

Parameters

axmatplotlib axes object

An axes of the current figure.

datanp.array or list-like

Discrete data represented by legend.

unit_labelsdict, optional

Dictionary of unit labels keyed by integer value in data, by default None, in which case all values in data are included with the integer value as the label.

cmapstr, optional

Matplotlib colormap, by default ‘copper’

vminfloat, optional

Minimum value of colormap range, by default None

vmaxfloat, optional

Maximum value of colormap range, , by default None

**kwargskeyword arguments to matplotlib.pyplot.legend()

Return

lgmatplotlib.pyplot.legend() handle

mapgwm.framework.get_layer(botm_array, i, j, elev)

Return the botm_array for elevations at i, j locations.

Parameters

botm_array3D numpy array

layer bottom elevations

iscaler or sequence

row index (zero-based)

jscaler or sequence

column index

elevscaler or sequence

elevation (in same units as model)

Returns

knp.ndarray (1-D) or scalar

zero-based layer index

mapgwm.framework.layers_to_zones(botm_array, model_cell_z_centers)

Map the layers from one grid to zones on another grid. In other words, given a 3D array of model cell bottom elevations (botm_array), and a second 3D array of cell centers at the same i, j locations as botm_array, but with different vertical discretization (model_cell_z_centers), assign each cell in the model_cell_z_centers array a zone value based on its vertical position within botm array. For example, a cell that lies above all of the elevations in botm_array would be assigned a zone number of zero; a cell that lies below the first bottom elevation would be assigned a value of 1.

Parameters

botm_array3D numpy array

Layer bottom elevations

model_cell_z_centers3D numpy array

Cell centers for another grid that is aligned with botm_array along the i and j axis, but has a different vertical discretation.

Returns

zones3D numpy array

Zone numbers indicating the layer in botm_array for each cell in model_cell_z_centers.

mapgwm.framework.plot_cross_sections(layers, out_pdf, property_data=None, voxel_start_layer=0, voxel_zones=None, cmap='copper', voxel_cmap='viridis', unit_labels=None, add_raster_surfaces=None, modelgrid=None)

Generate a multi-page PDF of the layer cross sections.

Parameters

layers3D numpy array

Array of layer elevations, starting with the model top. (Length equal to the number of botm_array + 1)

property_data3D numpy array

Array of zone numbers generated by setup_model_layers.

out_pdfstr (filepath)

Filename of multi-page PDF.

voxel_start_layerint, optional

First layer with voxel data, by default 0

voxel_zonessequence, optional

Zone numbers within property_data that are voxel-based, by default None

cmapstr, optional

Matplotlib colormap for non-voxel zone numbers, by default ‘copper’, to contrast with colormap for voxel-based zone numbers.

voxel_cmapstr, optional

Matplotlib colormap for voxel-based zone numbers, by default ‘viridis’.

unit_labelsdict, optional

Dictionary mapping non-voxel zone numbers to hydrogeologic units, by default None

mapgwm.framework.plot_slice(layer_elevations, property_data=None, row=0, column=slice(None, None, None), voxel_start_layer=0, voxel_zones=None, cmap='copper', voxel_cmap='viridis', unit_labels=None, add_surfaces=None)

Plot a single cross section slice

Parameters

layer_elevations3D numpy array

Array of layer elevations, starting with the model top. (Length equal to the number of botm_array + 1)

property_data3D numpy array

Array of zone numbers generated by setup_model_layers.

rowint or slice instance

If a cross section along a row is desired, row should be a integer, and column should be a slice instance indicating the range of columns to include. by default, 0.

columnint or slice instance

If a cross section along a column is desired, column should be a integer, and row should be a slice instance indicating the range of rows to include. by default, slice(None), which includes all columns.

voxel_start_layerint, optional

First layer with voxel data, by default 0

voxel_zonessequence, optional

Zone numbers within property_data that are voxel-based, by default None

cmapstr, optional

Matplotlib colormap for non-voxel zone numbers, by default ‘copper’, to contrast with colormap for voxel-based zone numbers.

voxel_cmapstr, optional

Matplotlib colormap for voxel-based zone numbers, by default ‘viridis’.

unit_labelsdict, optional

Dictionary mapping non-voxel zone numbers to hydrogeologic units, by default None

Returns

axmatplotlib AxesSubplot instance for figure

mapgwm.framework.plot_zone_maps(layers, out_pdf, zones=None, voxel_cmap='viridis', zones_cmap='copper', voxel_zones=None, unit_labels=None)

Generate a multi-page PDF of the zones in each layer in plan view.

Parameters

layers3D numpy array

Array of layer elevations, starting with the model top. (Length equal to the number of botm_array + 1)

zones3D numpy array

Array of zone numbers generated by setup_model_layers.

out_pdfstr (filepath)

Filename of multi-page PDF.

voxel_zonessequence, optional

Zone numbers within property_data that are voxel-based, by default None

unit_labelsstr, optional

Matplotlib colormap for non-voxel zone numbers, by default ‘copper’, to contrast with colormap for voxel-based zone numbers.

voxel_cmapstr, optional

Matplotlib colormap for voxel-based zone numbers, by default ‘viridis’.

unit_labelsdict, optional

Dictionary mapping non-voxel zone numbers to hydrogeologic units, by default None

mapgwm.framework.rasters_to_grid(modelgrid, dem, rasters, dem_elevation_units='meters', raster_elevation_units='meters', dest_elevation_units='meters')

Sample a sequence of rasters onto the i, j locations of a modelgrid, returning a 3D numpy array of the sampled elevations. Fill places with nodata using the next valid surface above.

Parameters

modelgridModflow-setup MFsetupGrid instance

Modflow-setup grid instance describing the model grid

demstr (filepath)

Raster representing the land surface, at the highest resolution being contemplated for the model. Usually this is derived by sampling a higher resolution DEM using zonal statistics, taking the mean DEM value for each model cell.

rasterslist of strings (filepaths)

Raster surfaces describing hydrogelogic contacts surrounding the voxel data.

dem_elevation_unitsstr, optional

Elevation units of dem_means_raster, by default ‘meters’

framework_raster_elevation_unitsstr, optional

Elevation units of the framework_rasters, by default ‘meters’

model_length_unitsstr, optional

Length units used in the model, by default ‘meters’

References

See the documentation for the fill_cells_vertically function in Modflow-setup for an explanation of the filling process.

mapgwm.framework.setup_model_layers(dem_means_raster, facies_classes_netcdf, framework_rasters, modelgrid, facies_class_variable='fac_a', dem_elevation_units='meters', framework_raster_elevation_units='meters', model_length_units='meters', output_folder='.', framework_unit_names=None)

Generate model layering and property zones from voxel-based zone numbers at uniform depths and raster surfaces that represent hydrogeologic contacts. The voxel-based zone numbers may represent hydrogeologically different sediments, as determined from an airborne electromagnetic survey, and are assumed to have priority over the raster surfaces. The model top is set at the land surface (DEM). Subsequent cell bottoms beneath the land surface are set based on the voxel depths subtracted from the DEM elevations, as sampled at the grid cell centers. Voxel cells without valid data are filled with zone numbers based on their position within the raster surfaces of hydrogeologic contacts. For example, a voxel cell that lies above all of the raster surfaces would be assigned a zone number equal to the highest voxel-based zone number + 3 (an arbitrary gap in the zone numbering between the voxel data and the raster surfaces). Similarly, beneath the lowest voxel cell at each location, grid cells are assigned zone values based on their vertical position relative to the raster surfaces. Below the voxel data, botm_array and their bottom elevations correspond to the framework_raster surfaces. The lowermost (last) raster surface forms the model bottom.

Parameters

dem_means_rasterstr (filepath)

Raster representing the land surface, at the highest resolution being contemplated for the model. Usually this is derived by sampling a higher resolution DEM using zonal statistics, taking the mean DEM value for each model cell.

facies_classes_netcdfstr (filepath)

NetCDF file with the voxel zone data, with a facies_class_variable that contains the zone numbers.

framework_rasterslist of strings (filepaths)

Raster surfaces describing hydrogelogic contacts surrounding the voxel data.

modelgridmfsetup.MFsetupGrid instance

Modflow-setup grid instance describing the model grid

facies_class_variablestr, optional

Variable in facies_classes_netcdf containing the zone information, by default ‘fac_a’

dem_elevation_unitsstr, optional

Elevation units of dem_means_raster, by default ‘meters’

framework_raster_elevation_unitsstr, optional

Elevation units of the framework_rasters, by default ‘meters’

model_length_unitsstr, optional

Length units used in the model, by default ‘meters’

output_folderstr, optional

Location where results will be saved, by default ‘.’

framework_unit_nameslist, optional

Unit names for the framework_rasters, by default None

Returns

botm_array3D numpy array

Array of layer elevations, starting with the model top. (Length equal to the number of botm_array + 1)

zone_array3D numpy array

Array of zone numbers, including the values from facies_classes_netcdf, followed by a gap of 3 and then the numbers for the framework_rasters. For example, if there are 5 zones in facies_classes_netcdf, and 3 raster surfaces that intersect cells outside of the voxel data, zones 1-5 would correspond to the voxel zones, and zones 8-10 would correspond to hydrogeologic units bounded by the framework_rasters.

Notes

In addition to the variables returned, the results are written to text-array and GeoTiff files within the output_folder.

Two PDFs of figures are also generated- one showing the layering in cross sections at regular intervals throughout the model grid, and one showing the zones within each layer in plan view.