Metadata-Version: 2.1
Name: napari-timelapse-processor
Version: 0.0.2
Summary: meta plugin to ease processing timelapse image data
Author: Johannes Soltwedel
Author-email: johannes_richard.soltwedel@tu-dresden.de
License: 
        Copyright (c) 2024, Johannes Soltwedel
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        * Redistributions of source code must retain the above copyright notice, this
          list of conditions and the following disclaimer.
        
        * Redistributions in binary form must reproduce the above copyright notice,
          this list of conditions and the following disclaimer in the documentation
          and/or other materials provided with the distribution.
        
        * Neither the name of copyright holder nor the names of its
          contributors may be used to endorse or promote products derived from
          this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        
Project-URL: Repository, https://github.com/jo-mueller/napari-timelapse-processor.git
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Framework :: napari
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: napari
Requires-Dist: dask
Requires-Dist: distributed
Provides-Extra: testing
Requires-Dist: tox; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"

# napari-timelapse-processor

[![License BSD-3](https://img.shields.io/pypi/l/napari-timelapse-processor.svg?color=green)](https://github.com/jo-mueller/napari-timelapse-processor/raw/main/LICENSE)
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[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-timelapse-processor)](https://napari-hub.org/plugins/napari-timelapse-processor)

meta plugin to ease processing timelapse image data

## API

This plugin exposes two principal funcionalities:

### TimelapseConverter

The `TimelapseConverter` class allows you to stack or unstack any of the supported napari layers from 4D data into a list of 3D layers or vice versa. Currently supported layers are:

- `napari.layers.Image`
- `napari.layers.Labels`
- `napari.layers.Points`
- `napari.layers.Vectors`
- `napari.layers.Surface`

`napari.layers.Tracks` are intrinsically 4D and thus not supported.

**Unstacking example usage:**

```python
from napari_timelapse_processor import TimelapseConverter
import numpy as np

image_4d = np.random.rand(10, 32, 32, 32)  # 10 timepoints of 32x32x32 data
converter = TimelapseConverter()
list_of_images = converter.unstack(image_4d, layertype='napari.types.ImageData')
```

**Stacking example usage:**

```python
from napari_timelapse_processor import TimelapseConverter
import numpy as np

random_points = [np.random.rand(10, 3)  for _ in range(10)]  # 10 timepoints of 10 random 3D points
converter = TimelapseConverter()

# stack the points into a single 4D layer
stacked_points = converter.stack(random_points, layertype='napari.types.PointsData')
```

The `TimeLapseConverter` class also supports (un)stacking the `napari.layers.Layer` type (and its above-listed subclasses). Importantly, `features` that are associated with the respective layer are also (un)stacked.

**Layer example usage**

```python
from napari_timelapse_processor import TimelapseConverter
import numpy as np
from napari.layers import Points
import pandas as pd

random_points = [np.random.rand(10, 3)  for _ in range(10)]  # 10 timepoints of 10 random 3D points
random_features = [pd.DataFrame(np.random.rand(10)) for _ in range(10)]  # 10 timepoints of 10 random feature values

# create a list of 10 Points layers
points = [Points(random_points[i], properties=random_features[i]) for i in range(10)]

converter = TimelapseConverter()
stacked_points = converter.stack(points, layertype='napari.layers.Points')
```

## frame_by_frame

The frame-by-frame functionality provides a decorator that will inspect the decorated function for `TimelapseConverter`-compatible arguments and, if a 4D value is passed as argument, will automatically (un)stack the data before and after the function call. This allows for a more intuitive API when working with timelapse data. Currently supported type annotations are:

- `napari.types.ImageData`
- `napari.types.LabelsData`
- `napari.types.PointsData`
- `napari.types.VectorsData`
- `napari.types.SurfaceData`
- `napari.layers.Layer`
- `napari.layers.Image`
- `napari.layers.Labels`
- `napari.layers.Points`
- `napari.layers.Vectors`
- `napari.layers.Surface`

Additionally, the `frame_by_frame` supports parallelization with [dask.distributed](https://distributed.dask.org/en/latest/). To use it, simply pass the `use_dask=True` argument to the decorated function, even if the function itself does not require this argument. The decorater will then automatically parallelize the function call over the time-axis and remove the `use_dask` argument when calling the function.

**Example interactive code usage:** If you want to use the `frame_by_frame` functionality in, say, a Jupyter notebook, use it like this:

```python

from napari_timelapse_processor import frame_by_frame
import numpy as np

def my_function(image: 'napari.types.ImageData') -> 'napari.types.ImageData':
    return 2 * image

image_4d = np.random.rand(10, 32, 32, 32)  # 10 timepoints of 32x32x32 data

image_4d_processed = frame_by_frame(my_function)(image_4d)  # without dask
image_4d_processed = frame_by_frame(my_function)(image_4d, use_dask=True)  # with dask
```

**Example napari code** If you want to use the `frame_by_frame` functionality in a napari plugin, use it like this:

```python
from napari_timelapse_processor import frame_by_frame

@frame_by_frame
def my_function(image: 'napari.types.ImageData') -> 'napari.types.ImageData':
    return 2 * image
```

**Hint:** The `frame_by_frame` functionality runs under the assumption that input napari-data (e.g., an Image, a Surface, Points, etc) are *always* arguments and any other parameters are *always* keyword arguments. If this is not the case, the decorator will not work as intended.

```python

# This works
frame_by_frame(my_function)(image_4d, some_parameter=2, use_dask=True)

# This does not work
frame_by_frame(my_function)(image=image_4d, some_parameter=2, use_dask=True)
```

----------------------------------

This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.

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Don't miss the full getting started guide to set up your new package:
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and review the napari docs for plugin developers:
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-->

## Installation

You can install `napari-timelapse-processor` via [pip]:

    pip install napari-timelapse-processor




## Contributing

Contributions are very welcome. Tests can be run with [tox], please ensure
the coverage at least stays the same before you submit a pull request.

## License

Distributed under the terms of the [BSD-3] license,
"napari-timelapse-processor" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin

[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
