# napari-macrokit
[](https://github.com/hanjinliu/napari-macrokit/raw/main/LICENSE)
[](https://pypi.org/project/napari-macrokit)
[](https://python.org)
[](https://github.com/hanjinliu/napari-macrokit/actions)
[](https://codecov.io/gh/hanjinliu/napari-macrokit)
[](https://napari-hub.org/plugins/napari-macrokit)
Executable script generation for napari plugins.
↑ [Example](https://github.com/hanjinliu/napari-macrokit/blob/main/examples/regionprops.py) showing the real-time recording of GUI operation.
This napari plugin aims at making image analysis reproducible with arbitrary input/output types.
## Usage
Create a macro object, decorate functions with `record` method and run!
```python
from napari_macrokit import get_macro
macro = get_macro("my-plugin-specifier") # get macro object
# define a function
@macro.record
def add(a: float, b: float) -> float:
return a + b
# run
result = add(3.2, 5.4)
add(result, 1.0)
macro
# Out:
# >>> float0 = add(3.2, 5.4)
# >>> float1 = add(float0, 1.0)
```
## Record GUI Operations
You can use recordable functions in your widgets to keep tracks of GUI operations.
More simply, you can double-decorate functions with `record` and `magicgui`.
```python
import numpy as np
from magicgui import magicgui
import napari
from napari.types import ImageData
from napari_macrokit import get_macro
macro = get_macro("my-plugin-specifier") # get macro object
# define recordable magicgui
@magicgui
@macro.record
def add(image: ImageData, b: float) -> ImageData:
return image + b
viewer = napari.Viewer() # launch a viewer
viewer.add_image(np.random.random((100, 100))) # image data
viewer.window.add_dock_widget(add) # add magicgui to the viewer
```
Running add twice in GUI and you'll find macro updated like below.
```python
macro
# Out
# >>> image0 = add(viewer.layers['Image'].data, 0.06)
# >>> image1 = add(image0, 0.12)
```
## Combining Plugins
Suppose you have two modules that use `napari-macrokit`.
```python
# napari_module_0.py
from napari.types import ImageData
from scipy import ndimage as ndi
from napari_macrokit import get_macro
macro = get_macro("napari-module-0")
@macro.record
def gaussian_filter(image: ImageData, sigma: float) -> ImageData:
return ndi.gaussian_filter(image, sigma=sigma)
@macro.record
def threshold(image: ImageData, value: float) -> ImageData:
return image > value
```
```python
# napari_module_1.py
from napari.types import ImageData
import numpy as np
from napari_macrokit import get_macro
macro = get_macro("napari-module-1")
@macro.record
def estimate_background(image: ImageData) -> float:
return np.percentile(image, 10.0)
```
You can use functions from both modules to build an analysis workflow by collecting existing macro objects with `collect_macro` function. All the recordable actions in the modules will also be recorded to the returned macro object.
```python
import numpy as np
from napari_macrokit import collect_macro
from napari_module_0 import gaussian_filter, threshold
from napari_module_1 import estimate_background
# global_macro will record all the macro available at this point
global_macro = collect_macro()
# start image analysis!
image = np.random.random((100, 100))
out = gaussian_filter(image, 2.0)
thresh = estimate_background(out)
binary = threshold(out, thresh)
macro
# Out
# >>> image0 = gaussian_filter(arr0, 2.0)
# >>> float0 = estimate_background(image0)
# >>> image1 = threshold(image1, float0)
```
---------------------------------
This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
## Installation
You can install `napari-macrokit` via [pip]:
pip install napari-macrokit
To install latest development version :
pip install git+https://github.com/hanjinliu/napari-macrokit.git
## 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-macrokit" 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
[file an issue]: https://github.com/hanjinliu/napari-macrokit/issues
[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
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"description": "# napari-macrokit\n\n[](https://github.com/hanjinliu/napari-macrokit/raw/main/LICENSE)\n[](https://pypi.org/project/napari-macrokit)\n[](https://python.org)\n[](https://github.com/hanjinliu/napari-macrokit/actions)\n[](https://codecov.io/gh/hanjinliu/napari-macrokit)\n[](https://napari-hub.org/plugins/napari-macrokit)\n\nExecutable script generation for napari plugins.\n\n\n↑ [Example](https://github.com/hanjinliu/napari-macrokit/blob/main/examples/regionprops.py) showing the real-time recording of GUI operation.\n\nThis napari plugin aims at making image analysis reproducible with arbitrary input/output types.\n\n## Usage\n\nCreate a macro object, decorate functions with `record` method and run!\n\n```python\nfrom napari_macrokit import get_macro\n\nmacro = get_macro(\"my-plugin-specifier\") # get macro object\n\n# define a function\n@macro.record\ndef add(a: float, b: float) -> float:\n return a + b\n\n# run\nresult = add(3.2, 5.4)\nadd(result, 1.0)\n\nmacro\n\n# Out:\n# >>> float0 = add(3.2, 5.4)\n# >>> float1 = add(float0, 1.0)\n```\n\n## Record GUI Operations\n\nYou can use recordable functions in your widgets to keep tracks of GUI operations.\nMore simply, you can double-decorate functions with `record` and `magicgui`.\n\n```python\nimport numpy as np\nfrom magicgui import magicgui\nimport napari\nfrom napari.types import ImageData\nfrom napari_macrokit import get_macro\n\nmacro = get_macro(\"my-plugin-specifier\") # get macro object\n\n# define recordable magicgui\n@magicgui\n@macro.record\ndef add(image: ImageData, b: float) -> ImageData:\n return image + b\n\nviewer = napari.Viewer() # launch a viewer\nviewer.add_image(np.random.random((100, 100))) # image data\nviewer.window.add_dock_widget(add) # add magicgui to the viewer\n```\n\nRunning add twice in GUI and you'll find macro updated like below.\n\n```python\nmacro\n# Out\n# >>> image0 = add(viewer.layers['Image'].data, 0.06)\n# >>> image1 = add(image0, 0.12)\n```\n\n## Combining Plugins\n\nSuppose you have two modules that use `napari-macrokit`.\n\n```python\n# napari_module_0.py\n\nfrom napari.types import ImageData\nfrom scipy import ndimage as ndi\nfrom napari_macrokit import get_macro\n\nmacro = get_macro(\"napari-module-0\")\n\n@macro.record\ndef gaussian_filter(image: ImageData, sigma: float) -> ImageData:\n return ndi.gaussian_filter(image, sigma=sigma)\n\n@macro.record\ndef threshold(image: ImageData, value: float) -> ImageData:\n return image > value\n```\n\n```python\n# napari_module_1.py\n\nfrom napari.types import ImageData\nimport numpy as np\nfrom napari_macrokit import get_macro\nmacro = get_macro(\"napari-module-1\")\n\n@macro.record\ndef estimate_background(image: ImageData) -> float:\n return np.percentile(image, 10.0)\n\n```\n\nYou can use functions from both modules to build an analysis workflow by collecting existing macro objects with `collect_macro` function. All the recordable actions in the modules will also be recorded to the returned macro object.\n\n```python\nimport numpy as np\nfrom napari_macrokit import collect_macro\nfrom napari_module_0 import gaussian_filter, threshold\nfrom napari_module_1 import estimate_background\n\n# global_macro will record all the macro available at this point\nglobal_macro = collect_macro()\n\n# start image analysis!\nimage = np.random.random((100, 100))\n\nout = gaussian_filter(image, 2.0)\nthresh = estimate_background(out)\nbinary = threshold(out, thresh)\n\nmacro\n# Out\n# >>> image0 = gaussian_filter(arr0, 2.0)\n# >>> float0 = estimate_background(image0)\n# >>> image1 = threshold(image1, float0)\n```\n\n---------------------------------\n\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n\n## Installation\n\nYou can install `napari-macrokit` via [pip]:\n\n pip install napari-macrokit\n\n\n\nTo install latest development version :\n\n pip install git+https://github.com/hanjinliu/napari-macrokit.git\n\n\n## Contributing\n\nContributions are very welcome. Tests can be run with [tox], please ensure\nthe coverage at least stays the same before you submit a pull request.\n\n## License\n\nDistributed under the terms of the [BSD-3] license,\n\"napari-macrokit\" is free and open source software\n\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[file an issue]: https://github.com/hanjinliu/napari-macrokit/issues\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n",
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