Metadata-Version: 2.1
Name: dolomite-se
Version: 0.1.1
Summary: Save and load summarized experiments in the dolomite framework!
Home-page: https://github.com/ArtifactDB/dolomite-se
Author: LTLA
Author-email: infinite.monkeys.with.keyboards@gmail.com
License: MIT
Project-URL: Documentation, https://github.com/ArtifactDB/dolomite-se
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
License-File: LICENSE.txt
Requires-Dist: importlib-metadata; python_version < "3.8"
Requires-Dist: dolomite-base>=0.2.0
Requires-Dist: dolomite-matrix>=0.1.0
Requires-Dist: dolomite-ranges>=0.1.0
Requires-Dist: summarizedexperiment<0.5.0,>=0.4.2
Provides-Extra: testing
Requires-Dist: setuptools; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"
Requires-Dist: numpy; extra == "testing"

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# Save and load SummarizedExperiments in Python

## Introduction

The **dolomite-se** package is the Python counterpart to the [**alabaster.se**](https://github.com/ArtifactDB/alabaster.se) R package,
providing methods for saving/reading `SummarizedExperiment` or `RangeSummarizedExperiment` objects within the [**dolomite** framework](https://github.com/ArtifactDB/dolomite-base).
All components of the `SummarizedExperiment` - assays, row data and column data - are saved to their respective file representations,
which can be loaded in a new R/Python environment for cross-language analyses.

## Quick start

Let's mock up a `SummarizedExperiment`:

```python
import summarizedexperiment
import biocframe
import numpy

se = summarizedexperiment.SummarizedExperiment(
    assays={ "counts": numpy.random.rand(1000, 200) },
    row_data=biocframe.BiocFrame(
        { "foo": numpy.random.rand(1000) }, 
        row_names = ["gene" + str(i) for i in range(1000)]
    ),
    column_data=biocframe.BiocFrame(
        { "whee": numpy.random.rand(200) },
        row_names = ["cell" + str(i) for i in range(200)]
    )
)
```

Now we can save it:

```python
from dolomite_base import save_object
import dolomite_se
import os
from tempfile import mkdtemp

path = os.path.join(mkdtemp(), "test")
save_object(se, path)
```

And load it again, e,g., in a new session:

```python
from dolomite_base import read_object

roundtrip = read_object(path)
## Class SummarizedExperiment with 1000 features and 200 samples
##   assays: ['counts']
##   row_data: ['foo']
##   column_data: ['whee']
```

This also works for `RangeSummarizedExperiment` objects storing `row_ranges` to the specified path. 
