Metadata-Version: 2.1
Name: dolomite-se
Version: 0.0.1
Summary: Add a short description here!
Home-page: https://github.com/pyscaffold/pyscaffold/
Author: LTLA
Author-email: infinite.monkeys.with.keyboards@gmail.com
License: MIT
Project-URL: Documentation, https://pyscaffold.org/
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
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
Requires-Dist: dolomite_matrix
Requires-Dist: summarizedexperiment>=0.3.7
Provides-Extra: testing
Requires-Dist: setuptools; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"
Requires-Dist: numpy; extra == "testing"
Requires-Dist: filebackedarray; 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` 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)]
    ),
    col_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 stage_object, write_metadata
import dolomite_se

dir = "test"
meta = stage_object(se, dir, "mydata")
write_metadata(meta, dir)
print(meta["path"])
## mydata/experiment.json
```

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

```python
from dolomite_base import acquire_metadata, load_object

meta = acquire_metadata("test", "mydata/experiment.json")
reloaded = load_object(meta, dir)
## Class SummarizedExperiment with 1000 features and 200 samples
##   assays: ['counts']
##   row_data: ['foo']
##   col_data: ['whee']
```
