discretize.utils.mesh_builder_xyz#
- discretize.utils.mesh_builder_xyz(xyz, h, padding_distance=None, base_mesh=None, depth_core=None, expansion_factor=1.3, mesh_type='tensor')[source]#
Generate a tensor or tree mesh using a cloud of points.
For a cloud of (x,y[,z]) locations and specified minimum cell widths (hx,hy,[hz]), this function creates a tensor or a tree mesh. The lateral extent of the core region is determine by the cloud of points. Other properties of the mesh can be defined automatically or by the user. If base_mesh is an instance of
TensorMesh
orTreeMesh
, the core cells will be centered on the underlying mesh to reduce interpolation errors.- Parameters:
- xyz(
n
,dim
)numpy.ndarray
Location points
- h(
dim
)list
Cell size(s) for the core mesh
- padding_distance
list
,optional
Padding distances [[W,E], [N,S], [Down,Up]], default is no padding.
- base_mesh
discretize.TensorMesh
ordiscretize.TreeMesh
,optional
discretize mesh used to center the core mesh
- depth_core
float
,optional
Depth of core mesh below xyz
- expansion_factorfloat.
optional
Expansion factor for padding cells. Ignored if mesh_type = tree
- mesh_type{‘tensor’, ‘tree’}
Specify output mesh type
- xyz(
- Returns:
discretize.TensorMesh
ordiscretize.TreeMesh
Mesh of type specified by mesh_type
Examples
>>> import discretize >>> import matplotlib.pyplot as plt >>> import numpy as np
>>> xy_loc = np.random.randn(8,2) >>> mesh = discretize.utils.mesh_builder_xyz( ... xy_loc, [0.1, 0.1], depth_core=0.5, ... padding_distance=[[1,2], [1,0]], ... mesh_type='tensor', ... )
>>> axs = plt.subplot() >>> mesh.plot_image(mesh.cell_volumes, grid=True, ax=axs) >>> axs.scatter(xy_loc[:,0], xy_loc[:,1], 15, c='w', zorder=3) >>> axs.set_aspect('equal') >>> plt.show()
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Source code
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