Tutorial about nearest neighbor distances#
from pathlib import Path
%matplotlib inline
import matplotlib.pyplot as plt
import locan as lc
lc.show_versions(system=False, dependencies=False, verbose=False)
Locan:
version: 0.20.0.dev41+g755b969
Python:
version: 3.11.6
Load rapidSTORM data file#
Identify some data in the test_data directory and provide a path using pathlib.Path
path = lc.ROOT_DIR / 'tests/test_data/rapidSTORM_dstorm_data.txt'
print(path, '\n')
dat = lc.load_rapidSTORM_file(path=path, nrows=1000)
/home/docs/checkouts/readthedocs.org/user_builds/locan/envs/latest/lib/python3.11/site-packages/locan/tests/test_data/rapidSTORM_dstorm_data.txt
Jupyter environment detected. Enabling Open3D WebVisualizer.
[Open3D INFO] WebRTC GUI backend enabled.
[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.
Print information about the data:
print(dat.data.head(), '\n')
print('Summary:')
dat.print_summary()
print('Properties:')
print(dat.properties)
position_x position_y frame intensity chi_square local_background
0 9657.40 24533.5 0 33290.10 1192250.0 767.732971
1 16754.90 18770.0 0 21275.40 2106810.0 875.460999
2 14457.60 18582.6 0 20748.70 526031.0 703.369995
3 6820.58 16662.8 0 8531.77 3179190.0 852.789001
4 19183.20 22907.2 0 14139.60 448631.0 662.770020
Summary:
identifier: "1"
comment: ""
source: EXPERIMENT
state: RAW
element_count: 14
frame_count: 1
file {
type: RAPIDSTORM
path: "/home/docs/checkouts/readthedocs.org/user_builds/locan/envs/latest/lib/python3.11/site-packages/locan/tests/test_data/rapidSTORM_dstorm_data.txt"
}
creation_time {
2024-03-14T11:07:49.827896Z
}
Properties:
{'localization_count': 14, 'position_x': 15873.847142857145, 'uncertainty_x': 2361.4490857013648, 'position_y': 17403.909285714286, 'uncertainty_y': 1803.9975262697349, 'intensity': 183987.66999999998, 'local_background': 675.0614, 'frame': 0, 'region_measure_bb': 730882123.3259, 'localization_density_bb': 1.915493559521281e-08, 'subregion_measure_bb': 108337.2}
Visualization#
lc.render_2d(dat, bin_size=1000, rescale=(0,100));
Analyze nearest neighbor distances#
The k-nearest neighbor distances can be analyzed for all localizations or a random subset.
nn = lc.NearestNeighborDistances()
nn.compute(dat)
nn.results.head()
nn_distance | nn_index | |
---|---|---|
0 | 4219.766477 | 12 |
1 | 2304.930812 | 2 |
2 | 2291.302095 | 9 |
3 | 3974.974397 | 10 |
4 | 4797.193422 | 1 |
The histogram shows the experimental distribution and for comparision the expectation for a spatial distribution of complete spatial randomness given the experimental localization density (per default relative to the minimum bounding box region).
nn.hist();
The localization_density relative to the minimum bounding box region is retweated by the localization_density attribute.
nn.localization_density
1.915493559521281e-08
Meta data for the analysis procedure#
nn.meta
identifier: "1"
method {
name: "NearestNeighborDistances"
parameter: "{\'k\': 1}"
}
creation_time {
seconds: 1710414470
nanos: 710585000
}