ModelDataArray#

This is the GeoST extension for xarray.DataArray. Available after importing geost. For example:

import numpy as np
import xarray as xr

import geost

# Create example model data array with 3D data
da = xr.DataArray(
    np.random.rand(3, 3, 4),
    dims=("y", "x", "z"),
    coords={"y": [2, 1, 0], "x": [0, 1, 2], "z": [0, 1, 2, 3]},
)
da.gst
# Output:
# <geost.models.model_array.ModelDataArray at 0x7f8c4e4c0d30>

Analysis#

`ModelDataArray.get_thickness`(condition)	Calculate the thickness where a voxelmodel or layermodel meets a specified condition.
`ModelDataArray.get_top_bottom`(condition)	Derive the top and bottom depths at each x,y-location of a specified condition from a voxelmodel or layermodel.
`ModelDataArray.most_common`([return_thickness])	Determine the "most common" value and corresponding thickness in a voxelmodel or a layermodel.
`ModelDataArray.value_counts`([dim, normalize])	Get the value counts of unique values in a DataArray.
`ModelDataArray.unique`()	Get the unique values in a DataArray without NaNs.

Coordinate Reference System#

ModelDataArray.write_crs(crs[, inplace])

Write the coordinate reference system (CRS) to the model.

Export#

`ModelDataArray.to_collection`()	Create a `Collection` from the model data.
`ModelDataArray.to_geopackage_3d`(filepath)	Write data to geopackage file containing a layer "locations" with point geometries and a layer "3dlines" with 3D line geometries.
`ModelDataArray.to_pyvista_grid`([data_vars, ...])	Convert the model to a PyVista grid.

General#

`ModelDataArray.bounds`()	Determine the bounding box of the model.
`ModelDataArray.resolution`([meters])	Determine the resolution of the model.
`ModelDataArray.vertical_bounds`()	Determine the vertical bounds (depth min, depth max) of the model.

Selection#

`ModelDataArray.slice_depth_interval`([upper, ...])	Slice a specified depth interval from a voxelmodel or layermodel between upper and lower bounds.
`ModelDataArray.slice_xy`(xmin, ymin, xmax, ymax)	Slice the model in the x and y dimensions.

Spatial#

`ModelDataArray.mask_geometries`(geometries[, ...])	Mask 'x','y'-locations that overlap with geometries (points, lines, polygons).
`ModelDataArray.select_points`(points[, crs, ...])	Select model data at specified point locations.
`ModelDataArray.select_along_lines`(lines[, ...])	Select model data at specified distances along given line geometries.
`ModelDataArray.select_within_bbox`(xmin, ...)	Select data within a specified bounding box (xmin, ymin, xmax, ymax).

Attributes#

`ModelDataArray.bottom`	xarray.DataArray with the depths of the bottom of each layer in a layermodel.
`ModelDataArray.crs`	The Coordinate Reference System (CRS) represented as a pyproj.CRS object.
`ModelDataArray.model_type`	The type of the model as a ModelType enum member.
`ModelDataArray.ndims`	The number of dimensions in the Dataset/DataArray
`ModelDataArray.shape`	The shape of the model as a tuple of integers with the size of each dimension.
`ModelDataArray.surface_level`	xarray.DataArray with the surface level elevation of the model.
`ModelDataArray.top`	xarray.DataArray with the depths of the top of each layer in a layermodel.
`ModelDataArray.x`	The x coordinates of the model as an xarray.DataArray.
`ModelDataArray.x_dim`	The name of the x dimension in the Dataset/DataArray
`ModelDataArray.y`	The y coordinates of the model as an xarray.DataArray.
`ModelDataArray.y_dim`	The name of the y dimension in the Dataset/DataArray
`ModelDataArray.z`	The coordinates of the model's vertical dimension as an xarray.DataArray.
`ModelDataArray.z_dim`	The name of the z dimension in the Dataset/DataArray