This page was generated from: notebooks/other/hello_world.ipynb

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%config InlineBackend.rc = {'figure.figsize': (10,6)}
%matplotlib inline

Hello world!

This example shows the workflow for generating a mesh from segmented data, and demonstrates a few of the features of Nanomesh. It uses a synthetic binary image with several rounded blob-like objects generated by skimage.

Image data in Nanomesh is stored as an nanomesh.Image type. Calling nanomesh.Image will create the appropriate subtype, nanomesh.Plane for 2D or nanomesh.Volume for 3D data.

from skimage.data import binary_blobs
from nanomesh import Image

blobs = binary_blobs(length=100, volume_fraction=0.25, seed=2102)
plane = Image(blobs)

print(plane)

Plane(shape=(100, 100), range=(False,True), dtype=bool)

nanomesh.Image is essentially a container for a numpy array with some methods for image segmentation and visualization.

plane.show()

<AxesSubplot:xlabel='x', ylabel='y'>

Generating a mesh from image data is simple in Nanomesh using nanomesh.Plane.generate_mesh(). The options opts are passed to the triangulation function (nanomesh.triangulate()). In this example, we use q30 to generate a quality mesh with minimum angles of 30°, and a50 to limit the triangle size to 50 pixels.

The returned mesh is a nanomesh.MeshContainer with the generated triangles and line segments.

mesh = plane.generate_mesh(opts='q30a10')
mesh

<MeshContainer>
  Number of points: 932
  Number of cells:
    triangle: 1754
    line: 2685
  Point data: physical
  Cell data: physical
  Field data: feature, background

In the next cell, we plot the triangles.

mesh.plot('triangle')

<AxesSubplot:title={'center':'triangle mesh'}>

Nanomesh can also calculate cell quality metrics and show them as a colored triangle or histogram plot.

Note that the area shaded in green below highlights the optimal range.

Have a look at the nanomesh.metrics submodule or in the example: Calculate mesh quality metrics.

from nanomesh import metrics

triangle_mesh = mesh.get('triangle')

metrics.histogram(triangle_mesh, metric='radius_ratio')

<AxesSubplot:title={'center':'Histogram of radius ratio'}, xlabel='Radius ratio', ylabel='frequency'>

Nanomesh uses [meshio](https://github.com/nschloe/meshio) to write data to most meshing formats.

mesh.write('mesh.vtk')

Warning: VTK requires 3D points, but 2D points given. Appending 0 third component.

For more practical examples of how to use Nanomesh, check out the how-to guides and notebooks: Overview.

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