Particle orientation details

Scripting
1

Hello,

I want to compare particle orientations of a set of particles from three different local refinements (different masks).

I tried using the CryoSPARC tools (pd.DataFrame(particles.rows()) to inspect the .cs file. The command spit out the details on the terminal with most of it missing. Is there a way to output the details as a .txt file ? Also which (slot/field) columns should I focus on if I am interested in the particle orientations.

Below are the commands I used and the output on the terminal.

In [ 1 ]: from cryosparc_compute import dataset

In [ 2 ]: dataset_path = ‘/group/jalettsgrp/EM_processing/apadavannil/**********/J897/J897_004_particles.cs’

In [ 3 ]: particle_dataset=dataset.Dataset.load(dataset_path)

In [ 4 ]: import pandas as pd

In [6]: particles = particle_dataset

In [7]: pd.DataFrame(particles.rows())

Out[7]:
alignments3D/alpha alignments3D/alpha_min alignments3D/class … ctf/trefoil_A ctf/type uid
0 0.842109 1.239762 0 … [0.0, 0.0] imported 16330657266949328703
1 1.058801 1.269102 0 … [0.0, 0.0] imported 1200041246811580293
2 0.951611 1.072977 0 … [0.0, 0.0] imported 15347905135533906074
3 0.855065 1.170871 0 … [0.0, 0.0] imported 2366007879522036950
4 0.876978 1.388490 0 … [0.0, 0.0] imported 13119521319855043586
… … … … … … … …
85049 0.995728 1.408782 0 … [0.0, 0.0] imported 3697773154761530906
85050 1.137893 1.307672 0 … [0.0, 0.0] imported 3546904563437995970
85051 0.881422 1.401369 0 … [0.0, 0.0] imported 8781339295885262670
85052 0.816533 1.286479 0 … [0.0, 0.0] imported 16672985918634965055
85053 0.960181 1.187863 0 … [0.0, 0.0] imported 5436120960517091484

[85054 rows x 47 columns]

Highly appreciate any suggestions.

Thanks

Abhilash.

2

Did you try writing the data frame to a csv file, like

pd.DataFrame(particles.rows()).to_csv("/path/to/particles.csv")

?

3

@wtempel Thanks. That worked with writing the output to a .csv file.
Do you have any suggestions as to which (slot/field) I should compare to get an idea of the particle orientations across different local refinements.

Thanks

Abhilash.