Metadata-Version: 2.4
Name: dummy-spatialdata
Version: 0.1.9
Summary: A package for creating arbitrary spatialdata for testing purposes.
Project-URL: Documentation, https://dummy-spatialdata.readthedocs.io/
Project-URL: Homepage, https://github.com/BIMSBBioinfo/dummy-spatialdata
Project-URL: Source, https://github.com/BIMSBBioinfo/dummy-spatialdata
Author-email: Artür Manukyan <amanukyan.umms@gmail.com>
License: MIT
Requires-Python: >=3.12
Requires-Dist: dummy-anndata>=0.0.3
Requires-Dist: geopandas
Requires-Dist: pillow
Requires-Dist: requests
Requires-Dist: shapely
Requires-Dist: spatialdata>=0.5.0
Provides-Extra: dev
Requires-Dist: pre-commit; extra == 'dev'
Requires-Dist: twine>=4.0.2; extra == 'dev'
Provides-Extra: test
Requires-Dist: coverage; extra == 'test'
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

# dummy-spatialdata

Allows generating dummy spatialdata objects, which can be useful for testing purposes.

## Installation

```bash
pip install dummy-spatialdata
```

## Example usage

`dummy-spatialdata` is compatible with both spatialdata == 0.5.0 (zarr v2) and 0.7.2 (zarr v3)

Thus please use 

1. `conda create --name dummy_sd_env python==3.12 spatialdata==0.7.2` or 
2. `conda create --name dummy_sd_env_05 python==3.12 spatialdata==0.5.0 setuptools==75.8.0`

```{python}
from dummy_spatialdata import generate_dataset
import dummy_anndata
import spatialdata_plot as sdp 
import spatialdata as sd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import anndata as ad
import tempfile as tf

# generate spatialdata
sdata = generate_dataset(
    images = [
        {'type': 'rgb', 'scale_factors': [2,2,2], 'shape': {'x': 64, 'y': 64},
         'coordinate_system': ['identity', 'scale', 'mapAxis', 'translation', 'rotation', 'affine', 'sequence']},
    ],
    labels = [
        {'n': 12, 'scale_factors': [2,2,2],  'shape': {'x': 64, 'y': 64},
         'coordinate_system': ['identity', 'scale', 'mapAxis', 'translation', 'rotation', 'affine', 'sequence']},
        {'n': 12, 'scale_factors': [2,2,2],  'shape': {'x': 64, 'y': 64},
         'coordinate_system': ['identity', 'scale', 'mapAxis', 'translation', 'rotation', 'affine', 'sequence']},
    ],
    shapes = [
        {'n': 12, 'type': 'polygon', 'shape': {'x': 64, 'y': 64},
         'coordinate_system': ['identity', 'scale', 'mapAxis', 'translation', 'rotation', 'affine', 'sequence']},
        {'n': 12, 'type': 'circle', 'shape': {'x': 64, 'y': 64},
         'overlapping': False, 'coordinate_system': ['identity', 'scale', 'mapAxis', 'translation', 'rotation', 'affine', 'sequence']}
    ],
    points = [
        {'n': 12, 'coordinate_system': ['identity', 'scale', 'mapAxis', 'translation', 'rotation', 'affine', 'sequence']}
    ],
    tables = [
        {'table': dummy_anndata.generate_dataset(n_obs=12, n_vars=20), 'element': 'shape', 'element_index': 0},
        {'table': dummy_anndata.generate_dataset(n_obs=12, n_vars=20), 'element': 'shape', 'element_index': 1},
        {'table': dummy_anndata.generate_dataset(n_obs=12, n_vars=20), 'element': 'label', 'element_index': 0},
        {'table': dummy_anndata.generate_dataset(n_obs=12, n_vars=20), 'element': 'label', 'element_index': 1},
    ],
    coordinate_systems = {
        'identity': {'transformations': ['identity']},
        'scale': {'transformations': ['scale']},
        'mapAxis': {'transformations': ['mapAxis']},
        'translation': {'transformations': ['translation']},
        'rotation': {'transformations': ['rotation']},
        'affine': {'transformations': ['affine']},
        'sequence': {'transformations': ['scale', 'mapAxis', 'translation', 'rotation', 'affine']}
    },
    SEED=13
)
sdata
```

```
SpatialData object
├── Images
│     └── 'image_0': DataTree[cyx] (3, 64, 64), (3, 32, 32), (3, 16, 16), (3, 8, 8)
├── Labels
│     ├── 'label_0': DataTree[yx] (64, 64), (32, 32), (16, 16), (8, 8)
│     └── 'label_1': DataTree[yx] (64, 64), (32, 32), (16, 16), (8, 8)
├── Points
│     └── 'point_0': DataFrame with shape: (<Delayed>, 2) (2D points)
├── Shapes
│     ├── 'shape_0': GeoDataFrame shape: (12, 1) (2D shapes)
│     └── 'shape_1': GeoDataFrame shape: (12, 2) (2D shapes)
└── Tables
      ├── 'table_0': AnnData (12, 20)
      ├── 'table_1': AnnData (12, 20)
      ├── 'table_2': AnnData (12, 20)
      └── 'table_3': AnnData (12, 20)
with coordinate systems:
    ▸ 'affine', with elements:
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
    ▸ 'identity', with elements:
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
    ▸ 'mapAxis', with elements:
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
    ▸ 'rotation', with elements:
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
...
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
    ▸ 'sequence', with elements:
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
    ▸ 'translation', with elements:
        image_0 (Images), label_0 (Labels), label_1 (Labels), point_0 (Points), shape_0 (Shapes), shape_1 (Shapes)
```

You can plot the demo data now!

```{python}
sdata.pl.render_images('image_0').pl.render_shapes('shape_0', color='Gene001', table_name = 'table_0', table_layer = 'float_matrix').pl.show(coordinate_systems = 'affine')
```