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0.1.11

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aicsshparam

Spherical harmonics parametrization for 3D starlike shapes

Published25 Feb 2025

Direct dependencies22

Transitive dependenciesNot calculated

README

Version 0.1.11 documentation

AICS Spherical Harmonics Parametrization

Spherical harmonics parametrization for 3D starlike shapes.

Installation:

Build Dependencies for Python 3.13.*

If you are using Python 3.13.*, you will need to install the following build dependencies:

fftw
openblas
cmake
pkg-config

One convenient way to install these dependencies is by using Conda. You can run the following command:

conda install -c conda-forge fftw openblas cmake pkg-config

Stable Release: pip install aicsshparam

Build from source to make customization:

git clone git@github.com:AllenCell/aics-shparam.git
cd aics-shparam
pip install -e .

How to use

Here we outline an example of how one could use spherical harmonics coefficients as shape descriptors on a synthetic dataset composed by 3 different shapes: spheres, cubes and octahedrons.

# Import required packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from aicsshparam import shtools, shparam
from skimage.morphology import ball, cube, octahedron
np.random.seed(42) # for reproducibility

# Function that returns binary images containing one of the three
# shapes: cubes, spheres octahedrons of random sizes.
def get_random_3d_shape():
    idx = np.random.choice([0, 1, 2], 1)[0]
    element = [ball, cube, octahedron][idx]
    label = ['ball', 'cube', 'octahedron'][idx]
    img = element(10 + int(10 * np.random.rand()))
    img = np.pad(img, ((1, 1), (1, 1), (1, 1)))
    img = img.reshape(1, *img.shape)
    # Rotate shapes to increase dataset variability.
    img = shtools.rotate_image_2d(
        image=img,
        angle=360 * np.random.rand()
    ).squeeze()
    return label, img

# Compute spherical harmonics coefficients of shape and store them
# in a pandas dataframe.
df_coeffs = []
for i in range(30):
    # Get a random shape
    label, img = get_random_3d_shape()
    # Parameterize with L=4, which corresponds to50 coefficients
    # in total
    (coeffs, _), _ = shparam.get_shcoeffs(image=img, lmax=4)
    coeffs.update({'label': label})
    df_coeffs.append(coeffs)
df_coeffs = pd.DataFrame(df_coeffs)

# Vizualize the resulting dataframe
with pd.option_context('display.max_rows', 5, 'display.max_columns', 5):
    display(df_coeffs)

Coefficients dataframe

# Let's use PCA to reduce the dimensionality of the coefficients
# dataframe from 51 down to 2.
pca = PCA(n_components=2)
trans = pca.fit_transform(df_coeffs.drop(columns=['label']))
df_trans = pd.DataFrame(trans)
df_trans.columns = ['PC1', 'PC2']
df_trans['label'] = df_coeffs.label

# Vizualize the resulting dataframe
with pd.option_context('display.max_rows', 5, 'display.max_columns', 5):
    display(df_trans)

PCA dataframe

# Scatter plot to show how similar shapes are grouped together.
fig, ax = plt.subplots(1,1, figsize=(3,3))
for label, df_label in df_trans.groupby('label'):
    ax.scatter(df_label.PC1, df_label.PC2, label=label, s=50)
plt.legend(loc='upper left', bbox_to_anchor=(1.05, 1))
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.show()

PC1 vs. PC2

Reference

For an example of how this package was used to analyse a dataset of over 200k single-cell images at the Allen Institute for Cell Science, please check out our paper in bioaRxiv.

Development

See CONTRIBUTING.md for information related to developing the code.

Questions?

If you have any questions, feel free to leave a comment in our Allen Cell forum: https://forum.allencell.org/.

Free software: Allen Institute Software License

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Declared dependencies 51

Native requirements

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Package details

Ecosystem: PyPI
Registry license: Allen Institute Software License – This software license is the 2-clause BSD license plus a third clause that prohibits redistribution and use for commercial purposes without further permission. Copyright © 2021 Matheus Viana, Allen Institute. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Redistributions and use for commercial purposes are not permitted without the Allen Institute’s written permission. For purposes of this license, commercial purposes are the incorporation of the Allen Institute's software into anything for which you will charge fees or other compensation or use of the software to perform a commercial service for a third party. Contact terms@alleninstitute.org for commercial licensing opportunities. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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Release published: 25 Feb 2025
Source modified: Unknown
First observed: 21 Oct 2026
Last observed: 21 Oct, 10:21 UTC

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