diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 13:16:53 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 13:16:53 +0000 |
| commit | b2215b9b027e7e80492ff6b4f28fc2e1ed1c0cf6 (patch) | |
| tree | 12dd1e017c0eac97a39b791b9955a328cbcfe71c | |
| parent | ac53d35bb0a7ebf56f7ac8443554d82624652c04 (diff) | |
automatic import of python-napari-pyclesperanto-assistantopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-napari-pyclesperanto-assistant.spec | 654 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 656 insertions, 0 deletions
@@ -0,0 +1 @@ +/napari_pyclesperanto_assistant-0.22.1.tar.gz diff --git a/python-napari-pyclesperanto-assistant.spec b/python-napari-pyclesperanto-assistant.spec new file mode 100644 index 0000000..2f6575e --- /dev/null +++ b/python-napari-pyclesperanto-assistant.spec @@ -0,0 +1,654 @@ +%global _empty_manifest_terminate_build 0 +Name: python-napari-pyclesperanto-assistant +Version: 0.22.1 +Release: 1 +Summary: GPU-accelerated image processing in napari using OpenCL +License: BSD-3-Clause +URL: https://github.com/clesperanto/napari_pyclesperanto_assistant +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d2/f4/7cb910f032746576a32a4c601575dede769af02eebbb25f67f4cf2d5d7ec/napari_pyclesperanto_assistant-0.22.1.tar.gz +BuildArch: noarch + +Requires: python3-napari-plugin-engine +Requires: python3-pyopencl +Requires: python3-toolz +Requires: python3-scikit-image +Requires: python3-napari +Requires: python3-pyclesperanto-prototype +Requires: python3-magicgui +Requires: python3-numpy +Requires: python3-pyperclip +Requires: python3-loguru +Requires: python3-jupytext +Requires: python3-jupyter +Requires: python3-pandas +Requires: python3-napari-tools-menu +Requires: python3-napari-time-slicer +Requires: python3-napari-skimage-regionprops +Requires: python3-napari-workflows +Requires: python3-napari-assistant + +%description +# napari-pyclesperanto-assistant
+[](https://forum.image.sc/tag/clesperanto)
+[](http://clesperanto.net)
+[](https://github.com/clesperanto/napari-pyclesperanto-assistant/raw/master/LICENSE)
+[](https://pypi.org/project/napari-pyclesperanto-assistant)
+[](https://python.org)
+[](https://github.com/clesperanto/napari_pyclesperanto_assistant/actions)
+[](https://codecov.io/gh/clesperanto/napari_pyclesperanto_assistant)
+[](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)
+[](https://napari-hub.org/plugins/napari-pyclesperanto-assistant)
+[](https://zenodo.org/badge/latestdoi/322312181)
+
+The py-clEsperanto-assistant is a yet experimental [napari](https://github.com/napari/napari) plugin for building GPU-accelerated image processing workflows.
+It is part of the [clEsperanto](http://clesperanto.net) project and thus, aims at removing programming language related barriers between image processing ecosystems in the life sciences.
+It uses [pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) and with that [pyopencl](https://documen.tician.de/pyopencl/) as backend for processing images.
+
+This napari plugin adds some menu entries to the Tools menu. You can recognize them with their suffix `(clEsperanto)` in brackets.
+Furthermore, it can be used from the [napari-assistant](https://www.napari-hub.org/plugins/napari-assistant) graphical user interface.
+Therefore, just click the menu `Tools > Utilities > Assistant (na)` or run `naparia` from the command line.
+
+
+
+## Usage
+
+### Start up the assistant
+Start up napari, e.g. from the command line:
+```
+napari
+```
+
+Load example data, e.g. from the menu `File > Open Samples > clEsperanto > CalibZAPWfixed` and
+start the assistant from the menu `Tools > Utilities > Assistant (na)`.
+
+
+
+In case of two dimensional timelapse data, an initial conversion step might be necessary depending on your data source.
+Click the menu `Tools > Utilities > Convert to 2d timelapse`. In the dialog, select the dataset and click ok.
+You can delete the original dataset afterwards:
+
+
+
+### Set up a workflow
+
+Choose categories of operations in the top right panel, for example start with denoising using a Gaussian Blur with sigma 1 in x and y.
+
+
+
+Continue with background removal using the top-hat filter with radius 5 in x and y.
+
+
+
+For labeling the objects, use [Voronoi-Otsu-Labeling](https://nbviewer.jupyter.org/github/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/voronoi_otsu_labeling.ipynb) with both sigma parameters set to 2.
+
+
+
+The labeled objects can be extended using a Voronoi diagram to derive a estimations of cell boundaries.
+
+
+
+You can then configure napari to show the label boundaries on top of the original image:
+
+
+
+When your workflow is set up, click the play button below your dataset:
+
+
+
+### Neighbor statistics
+
+When working with 2D or 3D data you can analyze measurements in relationship with their neighbors.
+For example, you can measure the area of blobs as shown in the example shown below using the menu
+`Tools > Measurements > Statistics of labeled pixels (clesperant)` and visualize it as `area` image by double-clicking on the table column (1).
+Additionally, you can measure the maximum area of the 6 nearest neighbors using the menu `Tools > Measurments > Neighborhood statistics of measurements`.
+The new column will then be called "max_nn6_area..." (2). When visualizing such parametric images next by each other, it is recommended to use
+[napari-brightness-contrast](https://www.napari-hub.org/plugins/napari-brightness-contrast) and visualize the same intensity range to see differences correctly.
+
+
+
+### Code generation
+You can also export your workflow as Python/Jython code or as notebook. See the [napari-assistant documentation](https://www.napari-hub.org/plugins/napari-assistant) for details.
+
+## Features
+[pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) offers various possibilities for processing images. It comes from developers who work in life sciences and thus, it may be focused towards processing two- and three-dimensional microscopy image data showing cells and tissues. A selection of pyclesperanto's functionality is available via the assistant user interface. Typical workflows which can be built with this assistant include
+* image filtering
+ * denoising / noise reduction (mean, median, Gaussian blur)
+ * background subtraction for uneven illumination or out-of-focus light (bottom-hat, top-hat, subtract Gaussian background)
+ * grey value morphology (local minimum, maximum. variance)
+ * gamma correction
+ * Laplace operator
+ * Sobel operator
+* combining images
+ * masking
+ * image math (adding, subtracting, multiplying, dividing images)
+ * absolute / squared difference
+* image transformations
+ * translation
+ * rotation
+ * scale
+ * reduce stack
+ * sub-stacks
+* image projections
+ * minimum / mean / maximum / sum / standard deviation projections
+* image segmentation
+ * binarization (thresholding, local maxima detection)
+ * labeling
+ * regionalization
+ * instance segmentation
+ * semantic segmentation
+ * detect label edges
+ * label spots
+ * connected component labeling
+ * Voronoi-Otsu-labeling
+* post-processing of binary images
+ * dilation
+ * erosion
+ * binary opening
+ * binary closing
+ * binary and / or / xor
+* post-processing of label images
+ * dilation (expansion) of labels
+ * extend labels via Voronoi
+ * exclude labels on edges
+ * exclude labels within / out of size / value range
+ * merge touching labels
+* parametric maps
+ * proximal / touching neighbor count
+ * distance measurements to touching / proximal / n-nearest neighbors
+ * pixel count map
+ * mean / maximum / extension ratio map
+* label measurements / post processing of parametric maps
+ * minimum / mean / maximum / standard deviation intensity maps
+ * minimum / mean / maximum / standard deviation of touching / n-nearest / neighbors
+* neighbor meshes
+ * touching neighbors
+ * n-nearest neighbors
+ * proximal neighbors
+ * distance meshes
+* measurements based on label images
+ * bounding box 2D / 3D
+ * minimum / mean / maximum / sum / standard deviation intensity
+ * center of mass
+ * centroid
+ * mean / maximum distance to centroid (and extension ratio shape descriptor)
+ * mean / maximum distance to center of mass (and extension ratio shape descriptor)
+ * statistics of neighbors (See related [publication](https://www.frontiersin.org/articles/10.3389/fcomp.2021.774396/full))
+* code export
+ * python / Fiji-compatible jython
+ * python jupyter notebooks
+* pyclesperanto scripting
+ * cell segmentation
+ * cell counting
+ * cell differentiation
+ * tissue classification
+
+## Installation
+
+It is recommended to install the assistant using conda. If you have never used conda before, it is recommended to read
+[this blog post](https://biapol.github.io/blog/johannes_mueller/anaconda_getting_started/) first.
+
+```shell
+conda create --name cle_39 python=3.9 napari-pyclesperanto-assistant
+conda activate cle_39
+```
+
+Mac-users please also install this:
+
+ conda install -c conda-forge ocl_icd_wrapper_apple
+
+Linux users please also install this:
+
+ conda install -c conda-forge ocl-icd-system
+
+You can then start the napari-assistant using this command:
+
+```
+naparia
+```
+
+
+## Feedback and contributions welcome!
+clEsperanto is developed in the open because we believe in the open source community. See our [community guidelines](https://clij.github.io/clij2-docs/community_guidelines). Feel free to drop feedback as [github issue](https://github.com/clEsperanto/pyclesperanto_prototype/issues) or via [image.sc](https://image.sc)
+
+## Acknowledgements
+This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden.
+This project has been made possible in part by grant number [2021-240341 (Napari plugin accelerator grant)](https://chanzuckerberg.com/science/programs-resources/imaging/napari/improving-image-processing/) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.
+
+[Imprint](https://clesperanto.github.io/imprint)
+
+ + +%package -n python3-napari-pyclesperanto-assistant +Summary: GPU-accelerated image processing in napari using OpenCL +Provides: python-napari-pyclesperanto-assistant +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-napari-pyclesperanto-assistant +# napari-pyclesperanto-assistant
+[](https://forum.image.sc/tag/clesperanto)
+[](http://clesperanto.net)
+[](https://github.com/clesperanto/napari-pyclesperanto-assistant/raw/master/LICENSE)
+[](https://pypi.org/project/napari-pyclesperanto-assistant)
+[](https://python.org)
+[](https://github.com/clesperanto/napari_pyclesperanto_assistant/actions)
+[](https://codecov.io/gh/clesperanto/napari_pyclesperanto_assistant)
+[](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)
+[](https://napari-hub.org/plugins/napari-pyclesperanto-assistant)
+[](https://zenodo.org/badge/latestdoi/322312181)
+
+The py-clEsperanto-assistant is a yet experimental [napari](https://github.com/napari/napari) plugin for building GPU-accelerated image processing workflows.
+It is part of the [clEsperanto](http://clesperanto.net) project and thus, aims at removing programming language related barriers between image processing ecosystems in the life sciences.
+It uses [pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) and with that [pyopencl](https://documen.tician.de/pyopencl/) as backend for processing images.
+
+This napari plugin adds some menu entries to the Tools menu. You can recognize them with their suffix `(clEsperanto)` in brackets.
+Furthermore, it can be used from the [napari-assistant](https://www.napari-hub.org/plugins/napari-assistant) graphical user interface.
+Therefore, just click the menu `Tools > Utilities > Assistant (na)` or run `naparia` from the command line.
+
+
+
+## Usage
+
+### Start up the assistant
+Start up napari, e.g. from the command line:
+```
+napari
+```
+
+Load example data, e.g. from the menu `File > Open Samples > clEsperanto > CalibZAPWfixed` and
+start the assistant from the menu `Tools > Utilities > Assistant (na)`.
+
+
+
+In case of two dimensional timelapse data, an initial conversion step might be necessary depending on your data source.
+Click the menu `Tools > Utilities > Convert to 2d timelapse`. In the dialog, select the dataset and click ok.
+You can delete the original dataset afterwards:
+
+
+
+### Set up a workflow
+
+Choose categories of operations in the top right panel, for example start with denoising using a Gaussian Blur with sigma 1 in x and y.
+
+
+
+Continue with background removal using the top-hat filter with radius 5 in x and y.
+
+
+
+For labeling the objects, use [Voronoi-Otsu-Labeling](https://nbviewer.jupyter.org/github/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/voronoi_otsu_labeling.ipynb) with both sigma parameters set to 2.
+
+
+
+The labeled objects can be extended using a Voronoi diagram to derive a estimations of cell boundaries.
+
+
+
+You can then configure napari to show the label boundaries on top of the original image:
+
+
+
+When your workflow is set up, click the play button below your dataset:
+
+
+
+### Neighbor statistics
+
+When working with 2D or 3D data you can analyze measurements in relationship with their neighbors.
+For example, you can measure the area of blobs as shown in the example shown below using the menu
+`Tools > Measurements > Statistics of labeled pixels (clesperant)` and visualize it as `area` image by double-clicking on the table column (1).
+Additionally, you can measure the maximum area of the 6 nearest neighbors using the menu `Tools > Measurments > Neighborhood statistics of measurements`.
+The new column will then be called "max_nn6_area..." (2). When visualizing such parametric images next by each other, it is recommended to use
+[napari-brightness-contrast](https://www.napari-hub.org/plugins/napari-brightness-contrast) and visualize the same intensity range to see differences correctly.
+
+
+
+### Code generation
+You can also export your workflow as Python/Jython code or as notebook. See the [napari-assistant documentation](https://www.napari-hub.org/plugins/napari-assistant) for details.
+
+## Features
+[pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) offers various possibilities for processing images. It comes from developers who work in life sciences and thus, it may be focused towards processing two- and three-dimensional microscopy image data showing cells and tissues. A selection of pyclesperanto's functionality is available via the assistant user interface. Typical workflows which can be built with this assistant include
+* image filtering
+ * denoising / noise reduction (mean, median, Gaussian blur)
+ * background subtraction for uneven illumination or out-of-focus light (bottom-hat, top-hat, subtract Gaussian background)
+ * grey value morphology (local minimum, maximum. variance)
+ * gamma correction
+ * Laplace operator
+ * Sobel operator
+* combining images
+ * masking
+ * image math (adding, subtracting, multiplying, dividing images)
+ * absolute / squared difference
+* image transformations
+ * translation
+ * rotation
+ * scale
+ * reduce stack
+ * sub-stacks
+* image projections
+ * minimum / mean / maximum / sum / standard deviation projections
+* image segmentation
+ * binarization (thresholding, local maxima detection)
+ * labeling
+ * regionalization
+ * instance segmentation
+ * semantic segmentation
+ * detect label edges
+ * label spots
+ * connected component labeling
+ * Voronoi-Otsu-labeling
+* post-processing of binary images
+ * dilation
+ * erosion
+ * binary opening
+ * binary closing
+ * binary and / or / xor
+* post-processing of label images
+ * dilation (expansion) of labels
+ * extend labels via Voronoi
+ * exclude labels on edges
+ * exclude labels within / out of size / value range
+ * merge touching labels
+* parametric maps
+ * proximal / touching neighbor count
+ * distance measurements to touching / proximal / n-nearest neighbors
+ * pixel count map
+ * mean / maximum / extension ratio map
+* label measurements / post processing of parametric maps
+ * minimum / mean / maximum / standard deviation intensity maps
+ * minimum / mean / maximum / standard deviation of touching / n-nearest / neighbors
+* neighbor meshes
+ * touching neighbors
+ * n-nearest neighbors
+ * proximal neighbors
+ * distance meshes
+* measurements based on label images
+ * bounding box 2D / 3D
+ * minimum / mean / maximum / sum / standard deviation intensity
+ * center of mass
+ * centroid
+ * mean / maximum distance to centroid (and extension ratio shape descriptor)
+ * mean / maximum distance to center of mass (and extension ratio shape descriptor)
+ * statistics of neighbors (See related [publication](https://www.frontiersin.org/articles/10.3389/fcomp.2021.774396/full))
+* code export
+ * python / Fiji-compatible jython
+ * python jupyter notebooks
+* pyclesperanto scripting
+ * cell segmentation
+ * cell counting
+ * cell differentiation
+ * tissue classification
+
+## Installation
+
+It is recommended to install the assistant using conda. If you have never used conda before, it is recommended to read
+[this blog post](https://biapol.github.io/blog/johannes_mueller/anaconda_getting_started/) first.
+
+```shell
+conda create --name cle_39 python=3.9 napari-pyclesperanto-assistant
+conda activate cle_39
+```
+
+Mac-users please also install this:
+
+ conda install -c conda-forge ocl_icd_wrapper_apple
+
+Linux users please also install this:
+
+ conda install -c conda-forge ocl-icd-system
+
+You can then start the napari-assistant using this command:
+
+```
+naparia
+```
+
+
+## Feedback and contributions welcome!
+clEsperanto is developed in the open because we believe in the open source community. See our [community guidelines](https://clij.github.io/clij2-docs/community_guidelines). Feel free to drop feedback as [github issue](https://github.com/clEsperanto/pyclesperanto_prototype/issues) or via [image.sc](https://image.sc)
+
+## Acknowledgements
+This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden.
+This project has been made possible in part by grant number [2021-240341 (Napari plugin accelerator grant)](https://chanzuckerberg.com/science/programs-resources/imaging/napari/improving-image-processing/) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.
+
+[Imprint](https://clesperanto.github.io/imprint)
+
+ + +%package help +Summary: Development documents and examples for napari-pyclesperanto-assistant +Provides: python3-napari-pyclesperanto-assistant-doc +%description help +# napari-pyclesperanto-assistant
+[](https://forum.image.sc/tag/clesperanto)
+[](http://clesperanto.net)
+[](https://github.com/clesperanto/napari-pyclesperanto-assistant/raw/master/LICENSE)
+[](https://pypi.org/project/napari-pyclesperanto-assistant)
+[](https://python.org)
+[](https://github.com/clesperanto/napari_pyclesperanto_assistant/actions)
+[](https://codecov.io/gh/clesperanto/napari_pyclesperanto_assistant)
+[](https://en.wikipedia.org/wiki/Software_release_life_cycle#Alpha)
+[](https://napari-hub.org/plugins/napari-pyclesperanto-assistant)
+[](https://zenodo.org/badge/latestdoi/322312181)
+
+The py-clEsperanto-assistant is a yet experimental [napari](https://github.com/napari/napari) plugin for building GPU-accelerated image processing workflows.
+It is part of the [clEsperanto](http://clesperanto.net) project and thus, aims at removing programming language related barriers between image processing ecosystems in the life sciences.
+It uses [pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) and with that [pyopencl](https://documen.tician.de/pyopencl/) as backend for processing images.
+
+This napari plugin adds some menu entries to the Tools menu. You can recognize them with their suffix `(clEsperanto)` in brackets.
+Furthermore, it can be used from the [napari-assistant](https://www.napari-hub.org/plugins/napari-assistant) graphical user interface.
+Therefore, just click the menu `Tools > Utilities > Assistant (na)` or run `naparia` from the command line.
+
+
+
+## Usage
+
+### Start up the assistant
+Start up napari, e.g. from the command line:
+```
+napari
+```
+
+Load example data, e.g. from the menu `File > Open Samples > clEsperanto > CalibZAPWfixed` and
+start the assistant from the menu `Tools > Utilities > Assistant (na)`.
+
+
+
+In case of two dimensional timelapse data, an initial conversion step might be necessary depending on your data source.
+Click the menu `Tools > Utilities > Convert to 2d timelapse`. In the dialog, select the dataset and click ok.
+You can delete the original dataset afterwards:
+
+
+
+### Set up a workflow
+
+Choose categories of operations in the top right panel, for example start with denoising using a Gaussian Blur with sigma 1 in x and y.
+
+
+
+Continue with background removal using the top-hat filter with radius 5 in x and y.
+
+
+
+For labeling the objects, use [Voronoi-Otsu-Labeling](https://nbviewer.jupyter.org/github/clEsperanto/pyclesperanto_prototype/blob/master/demo/segmentation/voronoi_otsu_labeling.ipynb) with both sigma parameters set to 2.
+
+
+
+The labeled objects can be extended using a Voronoi diagram to derive a estimations of cell boundaries.
+
+
+
+You can then configure napari to show the label boundaries on top of the original image:
+
+
+
+When your workflow is set up, click the play button below your dataset:
+
+
+
+### Neighbor statistics
+
+When working with 2D or 3D data you can analyze measurements in relationship with their neighbors.
+For example, you can measure the area of blobs as shown in the example shown below using the menu
+`Tools > Measurements > Statistics of labeled pixels (clesperant)` and visualize it as `area` image by double-clicking on the table column (1).
+Additionally, you can measure the maximum area of the 6 nearest neighbors using the menu `Tools > Measurments > Neighborhood statistics of measurements`.
+The new column will then be called "max_nn6_area..." (2). When visualizing such parametric images next by each other, it is recommended to use
+[napari-brightness-contrast](https://www.napari-hub.org/plugins/napari-brightness-contrast) and visualize the same intensity range to see differences correctly.
+
+
+
+### Code generation
+You can also export your workflow as Python/Jython code or as notebook. See the [napari-assistant documentation](https://www.napari-hub.org/plugins/napari-assistant) for details.
+
+## Features
+[pyclesperanto](https://github.com/clEsperanto/pyclesperanto_prototype) offers various possibilities for processing images. It comes from developers who work in life sciences and thus, it may be focused towards processing two- and three-dimensional microscopy image data showing cells and tissues. A selection of pyclesperanto's functionality is available via the assistant user interface. Typical workflows which can be built with this assistant include
+* image filtering
+ * denoising / noise reduction (mean, median, Gaussian blur)
+ * background subtraction for uneven illumination or out-of-focus light (bottom-hat, top-hat, subtract Gaussian background)
+ * grey value morphology (local minimum, maximum. variance)
+ * gamma correction
+ * Laplace operator
+ * Sobel operator
+* combining images
+ * masking
+ * image math (adding, subtracting, multiplying, dividing images)
+ * absolute / squared difference
+* image transformations
+ * translation
+ * rotation
+ * scale
+ * reduce stack
+ * sub-stacks
+* image projections
+ * minimum / mean / maximum / sum / standard deviation projections
+* image segmentation
+ * binarization (thresholding, local maxima detection)
+ * labeling
+ * regionalization
+ * instance segmentation
+ * semantic segmentation
+ * detect label edges
+ * label spots
+ * connected component labeling
+ * Voronoi-Otsu-labeling
+* post-processing of binary images
+ * dilation
+ * erosion
+ * binary opening
+ * binary closing
+ * binary and / or / xor
+* post-processing of label images
+ * dilation (expansion) of labels
+ * extend labels via Voronoi
+ * exclude labels on edges
+ * exclude labels within / out of size / value range
+ * merge touching labels
+* parametric maps
+ * proximal / touching neighbor count
+ * distance measurements to touching / proximal / n-nearest neighbors
+ * pixel count map
+ * mean / maximum / extension ratio map
+* label measurements / post processing of parametric maps
+ * minimum / mean / maximum / standard deviation intensity maps
+ * minimum / mean / maximum / standard deviation of touching / n-nearest / neighbors
+* neighbor meshes
+ * touching neighbors
+ * n-nearest neighbors
+ * proximal neighbors
+ * distance meshes
+* measurements based on label images
+ * bounding box 2D / 3D
+ * minimum / mean / maximum / sum / standard deviation intensity
+ * center of mass
+ * centroid
+ * mean / maximum distance to centroid (and extension ratio shape descriptor)
+ * mean / maximum distance to center of mass (and extension ratio shape descriptor)
+ * statistics of neighbors (See related [publication](https://www.frontiersin.org/articles/10.3389/fcomp.2021.774396/full))
+* code export
+ * python / Fiji-compatible jython
+ * python jupyter notebooks
+* pyclesperanto scripting
+ * cell segmentation
+ * cell counting
+ * cell differentiation
+ * tissue classification
+
+## Installation
+
+It is recommended to install the assistant using conda. If you have never used conda before, it is recommended to read
+[this blog post](https://biapol.github.io/blog/johannes_mueller/anaconda_getting_started/) first.
+
+```shell
+conda create --name cle_39 python=3.9 napari-pyclesperanto-assistant
+conda activate cle_39
+```
+
+Mac-users please also install this:
+
+ conda install -c conda-forge ocl_icd_wrapper_apple
+
+Linux users please also install this:
+
+ conda install -c conda-forge ocl-icd-system
+
+You can then start the napari-assistant using this command:
+
+```
+naparia
+```
+
+
+## Feedback and contributions welcome!
+clEsperanto is developed in the open because we believe in the open source community. See our [community guidelines](https://clij.github.io/clij2-docs/community_guidelines). Feel free to drop feedback as [github issue](https://github.com/clEsperanto/pyclesperanto_prototype/issues) or via [image.sc](https://image.sc)
+
+## Acknowledgements
+This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden.
+This project has been made possible in part by grant number [2021-240341 (Napari plugin accelerator grant)](https://chanzuckerberg.com/science/programs-resources/imaging/napari/improving-image-processing/) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.
+
+[Imprint](https://clesperanto.github.io/imprint)
+
+ + +%prep +%autosetup -n napari-pyclesperanto-assistant-0.22.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-napari-pyclesperanto-assistant -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.22.1-1 +- Package Spec generated @@ -0,0 +1 @@ +b626d484e90aaae4e7180907ce986a2d napari_pyclesperanto_assistant-0.22.1.tar.gz |