cryodata 0.1.0b4

Cryo-EM data processing tools for deep learning (e.g., cryo-IEF).

Cryo-EM data processing tools for deep learning (e.g., cryo-IEF)

This repository contains tools for processing cryo-EM data, particularly for training deep learning models like cryo-IEF. Raw cryo-EM data, such as particles extracted from cryoSPARC jobs, is normally in MRC format and requires preprocessing before it can be used for training. The tools are designed to handle various tasks such as data augmentation, normalization, and preparation of datasets for training. For more details on implementation and usage, refer to the cryo-IEF repository.

Installation

To install the required packages, run the following command:

pip install cryodata

Usage

To use the tools in this repository, you can import the necessary modules in your Python scripts. For example:

from cryodata.data_preprocess.mrc_preprocess import raw_data_preprocess
from cryodata.cryoemDataset import CryoEMDataset, CryoMetaData
import torch

raw_data_path = 'path/to/cryosparc/particles/job'  # path to the raw cryo-EM data from a cryosparc job (e.g., particles extraction)
processed_data_path = 'path/to/processed/data'  # path to save the processed cryoem data

# preprocess the cryoem data (e.g., particles data from a cryosparc job)
new_cs_data = raw_data_preprocess(raw_data_path, processed_data_path,
                        resize=224,
                        save_raw_data=False,
                        save_FT_data=False,
                        is_to_int8=True)
# create a dataset from the processed data
meta_data = CryoMetaData(processed_data_path=processed_data_path)
cryodataset = CryoEMDataset(metadata=meta_data)
dataloader =torch.utils.data.DataLoader(cryodataset, batch_size=32, shuffle=True)

In this example, we preprocess the raw cryo-EM data and create a dataset that can be used for training deep learning models.