Update cellfinder CLI name to brainmapper

README.md

@@ -21,7 +21,7 @@ You can also find the documentation for the backend BrainGlobe tools these workf
 
 At present, the package offers the following workflows:
 
-- [cellfinder](#cellfinder): Whole-brain detection, registration, and analysis.
+- [brainmapper](#brainmapper): Whole-brain detection, registration, and analysis.
 
 These workflows should be representative of the most common use-cases and are meant to be easy to reuse. They also serve as an example of how to combine several BrainGlobe tools to achieve a goal, such as whole brain cell detection and atlas registration.
 These workflows typically combine several BrainGlobe tools (possibly together with other tools) to achieve a goal,
@@ -30,9 +30,10 @@ such as whole brain cell detection and atlas registration.
 ## Secondary purposes of brainglobe-workflows, for developers
 
 We also use these workflows to support code development. We do this by regularly benchmarking the time they take to complete to ensure performance is stable as the code changes.
-* Developers can install these benchmarks locally via `pip install .[dev]`. By executing `asv run`,  the benchmarks will run with default parameters on a small dataset that is downloaded from [GIN](https://gin.g-node.org/G-Node/info/wiki). See [the asv docs](https://asv.readthedocs.io/en/v0.6.1/using.html#running-benchmarks) for further details on how to run benchmarks.
-* Developers can also run these benchmarks on  data available locally, by specifying the relevant paths in an input configuration file (JSON file).
-* We additionally run and benchmark the workflows locally on a internal desktop machine with large example datasets. These benchmarks are run periodically and the results are made publicly available.
+
+- Developers can install these benchmarks locally via `pip install .[dev]`. By executing `asv run`,  the benchmarks will run with default parameters on a small dataset that is downloaded from [GIN](https://gin.g-node.org/G-Node/info/wiki). See [the asv docs](https://asv.readthedocs.io/en/v0.6.1/using.html#running-benchmarks) for further details on how to run benchmarks.
+- Developers can also run these benchmarks on  data available locally, by specifying the relevant paths in an input configuration file (JSON file).
+- We additionally run and benchmark the workflows locally on a internal desktop machine with large example datasets. These benchmarks are run periodically and the results are made publicly available.
 
 ## Installation
 
@@ -54,33 +55,35 @@ Please see the [developers guide](https://brainglobe.info/developers/index.html)
 
 **If you use any tools in the [brainglobe suite](https://brainglobe.info/documentation/index.html), please [let us know](mailto:[email protected]?subject=cellfinder), and we'd be happy to promote your paper/talk etc.**
 
-If you find [`cellfinder`](#cellfinder) useful, and use it in your research, please cite the paper outlining the cell detection algorithm:
+If you find [`brainmapper`](#brainmapper) useful, and use it in your research, please cite the original paper outlining the cell detection algorithm (`cellfinder`):
 > Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074
 [https://doi.org/10.1371/journal.pcbi.1009074](https://doi.org/10.1371/journal.pcbi.1009074)
 >
-If you use any of the image registration functions in `cellfinder`, please also cite [`brainreg`](https://github.com/brainglobe/brainreg#citing-brainreg).
+If you use any of the image registration functions in `brainmapper`, please also cite [`brainreg`](https://github.com/brainglobe/brainreg#citing-brainreg).
 
 ---
 
-## Cellfinder
+## Brainmapper
 
 Whole-brain cell detection, registration and analysis.
 
-If you want to just use the cell detection part of `cellfinder`, please see the standalone [cellfinder](https://github.com/brainglobe/cellfinder-core) package, or the [cellfinder plugin](https://github.com/brainglobe/cellfinder-napari) for [napari](https://napari.org/).
-
+If you want to just use the cell detection part of `brainmapper`, please see the standalone [cellfinder](https://github.com/brainglobe/cellfinder) package and its [`napari`](https://napari.org/) plugin.
 `cellfinder` is a collection of tools developed by [Adam Tyson](https://github.com/adamltyson), [Charly Rousseau](https://github.com/crousseau) and [Christian Niedworok](https://github.com/cniedwor) in the [Margrie Lab](https://www.sainsburywellcome.org/web/groups/margrie-lab), generously supported by the [Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/).
 
-`cellfinder` is a designed for the analysis of whole-brain imaging data such as [serial-section imaging](https://sainsburywellcomecentre.github.io/OpenSerialSection/) and lightsheet imaging in cleared tissue.
+`brainmapper` is a workflow designed for the analysis of whole-brain imaging data such as [serial-section imaging](https://sainsburywellcomecentre.github.io/OpenSerialSection/) and lightsheet imaging in cleared tissue.
 The aim is to provide a single solution for:
 
-- Cell detection (initial cell candidate detection and refinement using  deep learning) (using the [cellfinder](https://github.com/brainglobe/cellfinder-core) backend package),
+- Cell detection (initial cell candidate detection and refinement using  deep learning) (using the [cellfinder](https://github.com/brainglobe/cellfinder) backend package),
 - Atlas registration (using [brainreg](https://github.com/brainglobe/brainreg)),
 - Analysis of cell positions in a common space.
 
 Basic usage:
 
 ```bash
-cellfinder -s signal_images -b background_images -o output_dir --metadata metadata
+brainmapper -s signal_images -b background_images -o output_dir --metadata metadata
 ```
 
-Full documentation can be found [here](https://brainglobe.info/documentation/cellfinder/index.html).
+Full documentation can be found [here](https://brainglobe.info/documentation/brainglobe-workflows/brainmapper.html).
+
+NOTE: The `brainmapper` workflow previously used the name "cellfinder", but this has been discontinued following the release of the [unified `cellfinder`](https://github.com/brainglobe/cellfinder) backend package to avoid conflation of terms.
+See our [blog post](https://brainglobe.info/blog/version1/cellfinder-core-and-plugin-merge.html) from the release for more information.

pyproject.toml

@@ -64,7 +64,7 @@ napari = ["napari[pyqt5]", "brainglobe-napari-io", "cellfinder[napari]>=1.0.0"]
 
 [project.scripts]
 cellfinder-workflow = "brainglobe_workflows.cellfinder:main_app_wrapper"
-cellfinder = "brainglobe_workflows.main:main"
+brainmapper = "brainglobe_workflows.main:main"
 
 [build-system]
 requires = ["setuptools>=45", "wheel", "setuptools_scm[toml]>=6.2"]