rds2py 0.10.0

Parse and save Python objects as **RDS or RData** files

rds2py

Parse and save Python objects as RDS or RData files. rds2py supports various base classes from R, and Bioconductor's SummarizedExperiment and SingleCellExperiment S4 classes. For more details, check out rds2cpp library.

Installation

Package is published to PyPI

pip install rds2py

# or install optional dependencies
pip install rds2py[optional]

By default, the package does not install packages to convert python representations to BiocPy classes. Please consider installing all optional dependencies.

Usage

[!NOTE]

If you do not have an RDS object handy, feel free to download one from single-cell-test-files.

from rds2py import read_rds, read_rda
r_obj = read_rds("path/to/file.rds") # or read_rda("path/to/file.rda")

The returned r_obj either returns an appropriate Python class if a parser is already implemented or returns the dictionary containing the data from the RDS file.

Save RDS/RData files

You can also construct RDS or RData files from Python objects. rds2py supports writing atomic types, generic dictionaries/lists, and BiocPy objects.

from rds2py import write_rds, write_rda
import numpy as np

# Write atomic types
write_rds(np.array([1, 2, 3], dtype=np.int32), "path/to/file.rds")

# Write complex objects
from genomicranges import GenomicRanges
from iranges import IRanges

gr = GenomicRanges(
    seqnames=["chr1", "chr2"],
    ranges=IRanges(start=[1, 2], width=[10, 20]),
    strand=["+", "-"]
)
write_rds(gr, "path/to/granges.rds")

Write-your-own-reader

Reading RDS or RData files as dictionary representations allows users to write their own custom readers into appropriate Python representations.

from rds2py import parse_rds, parse_rda

robject = parse_rds("path/to/file.rds") # or use parse_rda for rdata files
print(robject)

If you know this RDS file contains an GenomicRanges object, you can use the built-in reader or write your own reader to convert this dictionary.

from rds2py.read_granges import read_genomic_ranges

gr = read_genomic_ranges(robject)
print(gr)

Type Conversion Reference

R Type	Python/NumPy Type
numeric	numpy.ndarray (float64)
integer	numpy.ndarray (int32)
character	list of str
logical	numpy.ndarray (bool)
factor	list
data.frame	BiocFrame
matrix	numpy.ndarray or scipy.sparse matrix
dgCMatrix	scipy.sparse.csc_matrix
dgRMatrix	scipy.sparse.csr_matrix

and integration with BiocPy ecosystem for Bioconductor classes

• SummarizedExperiment
• RangedSummarizedExperiment
• SingleCellExperiment
• GenomicRanges
• MultiAssayExperiment

Developer Notes

This project uses pybind11 to provide bindings to the rds2cpp library. Please make sure necessary C++ compiler is installed on your system.

Note

This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.