Note
Go to the end to download the full example code.
Clustering Vectors#
This can be used to segment a 4-D STEM dataset into different clusters based on the diffraction pattern at each real space position.
import pyxem as pxm
from scipy.ndimage import gaussian_filter
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
# Getting the vectors for some dataset
s = pxm.data.mgo_nanocrystals(allow_download=True)
s.data[s.data < 120] = 1
s.filter(gaussian_filter, sigma=(0.5, 0.5, 0, 0), inplace=True) # only in real space
s.template_match_disk(disk_r=3, subtract_min=False, inplace=True)
vectors = s.get_diffraction_vectors(threshold_abs=0.5, min_distance=3)
# Now we can convert the vectors into a 2D array of rows/columns
flat_vectors = (
vectors.flatten_diffraction_vectors()
) # flatten the vectors into a 2D array
scan = DBSCAN(eps=1.0, min_samples=2)
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Clustering the Vectors#
It is very important that we first normalize the real and reciprocal space distances The column scale factors map the real space and reciprocal space distances to the same scale Here this means that the clustering algorithm operates on 10 nm in real space and .1 nm^-1 in reciprocal space based on the units for the vectors.
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vect = clustered.map_vectors(
pxm.utils.vectors.column_mean,
columns=[0, 1],
label_index=-1,
dtype=float,
shape=(2,),
)
plt.figure()
plt.scatter(vect[:, 1], vect[:, 0])
/home/docs/checkouts/readthedocs.org/user_builds/pyxem/envs/v0.19.1/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.
return _methods._mean(a, axis=axis, dtype=dtype,
/home/docs/checkouts/readthedocs.org/user_builds/pyxem/envs/v0.19.1/lib/python3.10/site-packages/numpy/core/_methods.py:121: RuntimeWarning: invalid value encountered in divide
ret = um.true_divide(
<matplotlib.collections.PathCollection object at 0x7f636e52a740>
clusterer = DBSCAN(min_samples=2, eps=20)
clustered2 = clustered.cluster_labeled_vectors(method=clusterer)
m, p = clustered2.to_markers(s, alpha=0.8, get_polygons=True)
7 : Clusters Found!
Visualizing the Clustering#
This clustering is decent. It shows that there might be some small tilt boundaries in the data which segment some of the nano-crystals into different clusters. It also shows the effect of using a phosphor screen which has some pretty severe after glow. This results in a smearing of the features and elongated clusters along the scan direction.
# sphinx_gallery_thumbnail_number = 3
Total running time of the script: (0 minutes 52.123 seconds)