Rename CellSeg into CellMet

TimSaundersLab · Oct 11, 2024 · 0c83190 · 0c83190
1 parent c75206e
commit 0c83190
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diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
@@ -18,7 +18,7 @@ jobs:
     runs-on: ubuntu-latest
     environment:
       name: pypi
-      url: https://pypi.org/p/CellSegAnalysis
+      url: https://pypi.org/p/CellMet
     permissions:
       id-token: write
     steps:

diff --git a/.gitignore b/.gitignore
@@ -5,7 +5,7 @@ build/
 
 notebooks/.ipynb_checkpoints/
 
-CellSeg.egg-info/
+CellMet.egg-info/
 
 notebooks/*/*.png
 
@@ -17,6 +17,7 @@ notebooks/*/*.png
 
 *.eps
 
-CellSeg/__pycache__/
+CellMet/__pycache__/
 
 dist/
+
diff --git a/CellSeg/__init__.py → CellMet/__init__.py b/CellSeg/__init__.py → CellMet/__init__.py
diff --git a/CellMet/_version.py b/CellMet/_version.py
@@ -0,0 +1,10 @@
+
+# THIS FILE IS GENERATED FROM CellMet SETUP.PY
+#
+short_version = '0.9.1'
+version = '0.9.1'
+full_version = '0.9.1.dev0+c75206e'
+git_revision = 'c75206eb2718cd9793b3e57de99a5513ef3d65d5'
+release = False
+if not release:
+    version = full_version
diff --git a/CellSeg/analysis.py → CellMet/analysis.py b/CellSeg/analysis.py → CellMet/analysis.py
diff --git a/CellSeg/image.py → CellMet/image.py b/CellSeg/image.py → CellMet/image.py
diff --git a/CellSeg/io.py → CellMet/io.py b/CellSeg/io.py → CellMet/io.py
diff --git a/CellSeg/plot.py → CellMet/plot.py b/CellSeg/plot.py → CellMet/plot.py
diff --git a/CellSeg/segmentation.py → CellMet/segmentation.py b/CellSeg/segmentation.py → CellMet/segmentation.py
diff --git a/CellSeg/segmentation2D.py → CellMet/segmentation2D.py b/CellSeg/segmentation2D.py → CellMet/segmentation2D.py
diff --git a/CellSeg/utils.py → CellMet/utils.py b/CellSeg/utils.py → CellMet/utils.py
diff --git a/CellSeg/version.py b/CellSeg/version.py
diff --git a/INSTALL.md b/INSTALL.md
@@ -1,9 +1,9 @@
 ## System requirements
 Linux, Windows and Mac OS are supported for running the code. At least 8 GB of RAM is required to run the software. 32GB may be required for larger images. The software has been heavily tested on Ubuntu 22.04 and less-tested on Mac OS and Windows 10. Please open an issue if you have any problems with installation. 
 
-## Install CellSeg using pip
+## Install CellMet using pip
 
-This install a cross-platform, pure python version of CellSeg.
+This install a cross-platform, pure python version of CellMet.
 Some advanced features are not available, namely:
 
 ```sh
@@ -17,21 +17,21 @@ debian-like linux distribution. If you already have a basic
 scientific python stack, use it, don't install anaconda.
 
 
-### Download and install `CellSeg` from source
+### Download and install `CellMet` from source
 
 If you want to do that, I assume you already know how to manage
 dependencies on your platform. The simplest way to manage dependencies is to use [`conda`](https://docs.conda.io/en/latest/miniconda.html) to manage the dependencies (you can use [`mamba`](https://github.com/mamba-org/mamba) as a faster alternative to conda).
 
 
 ```bash
-git clone --recursive https://github.com/TimSaundersLab/CellSeg.git
-cd CellSeg
+git clone --recursive https://github.com/TimSaundersLab/CellMet.git
+cd CellMet
 ```
 
 Then create a virtual environment:
 
 ```bash
-conda create --name cellseg
+conda create --name cellmet
 ```
 
 Then install python:
@@ -41,5 +41,5 @@ pip install .
 
 
 
-If all went well, you have successfully installed CellSeg.
+If all went well, you have successfully installed CellMet.
 
diff --git a/README.md b/README.md
@@ -1,4 +1,4 @@
-# CellSeg
+# CellMet
 A generalist algorithm to analyse cell shape from 3D labeled image. 
 
 <p align="center">
@@ -11,12 +11,12 @@ This code is made to analyse cell shape from 3D labeled image. It is divide in t
 - We determine edges and faces associated to each cell. Quality of this analysis is dependant of the quality of the labeled image. There is no segmentation correction.
 - Analysis of cells, faces and edges. 
 
-There is a Python API to allow user to integrate CellSeg into your custom workflow.
+There is a Python API to allow user to integrate CellMet into your custom workflow.
 
 ## Input/Output and datas organisation
 ### Input
 In order to be able to use this project. You first need to segment you image using CellPose (or any other software that gives 3D label image). You need to be satisfied by your label image, since there is no manual correction (only filter can be apply to remove cells that has a volume below a threshold for example).  
-Then, you can perform 3D cell segmentation with CellSeg. 
+Then, you can perform 3D cell segmentation with CellMet. 
 
 ### Output and datas organisation
 You can generate ply/obj file for each cells.  
@@ -27,7 +27,7 @@ Datas are organised as half-edge structures. The vertex, edge, face and cell tab
 </td>
 <td >
 <p align="center">
-<img width="300" alt="image" src="https://github.com/TimSaundersLab/CellSeg/assets/17041165/ee0a6baf-7f74-4ab4-b774-4e394f9d3d36">
+<img width="300" alt="image" src="https://github.com/TimSaundersLab/CellMet/assets/17041165/ee0a6baf-7f74-4ab4-b774-4e394f9d3d36">
 </p>
 </td>
 </tr>
@@ -149,7 +149,7 @@ Prerequisite are necessary to perform the analysis. It performs 3D distance mapp
 
 There are two ways of launching the GUI : 
 - run [GUI.ipynb](https://github.com/sophietheis/Zebrafish-Muscle/blob/main/notebooks/GUI.ipynb) notebook
-- run in command line `python CellSeg/export_gui.py`
+- run in command line `python CellMet/export_gui.py`
 -->
 
 

diff --git a/environment.yml b/environment.yml
@@ -1,4 +1,4 @@
-name: CellSeg
+name: CellMet
 channels:
 - conda-forge
 

diff --git a/notebooks/0-Paper-2DCellSegmentation.ipynb b/notebooks/0-Paper-2DCellSegmentation.ipynb
diff --git a/notebooks/0-Paper-3DCellSegmentation-TimeExecution.ipynb b/notebooks/0-Paper-3DCellSegmentation-TimeExecution.ipynb
@@ -16,8 +16,8 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from CellSeg.segmentation import Segmentation\n",
-    "from CellSeg.analysis import cell_analysis, edge_analysis, face_analysis\n",
+    "from CellMet.segmentation import Segmentation\n",
+    "from CellMet.analysis import cell_analysis, edge_analysis, face_analysis\n",
     "%load_ext snakeviz"
    ]
   },
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path = \"/home/u2175049/Documents/Code/CellSeg/notebooks/\"\n",
+    "path = \"/home/u2175049/Documents/Code/CellMet/notebooks/\"\n",
     "seg_filename = \"artificial_tissue_70cells_1000px_0.tif\"\n",
     "pixel_size = dict(x_size = 0.21,\n",
     "                  y_size = 0.21,\n",

diff --git a/notebooks/0-Paper-DrosophilaEmbryoAnalysis.ipynb b/notebooks/0-Paper-DrosophilaEmbryoAnalysis.ipynb
@@ -16,10 +16,10 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from CellSeg.segmentation import Segmentation\n",
-    "from CellSeg.analysis import cell_analysis, edge_analysis, face_analysis\n",
+    "from CellMet.segmentation import Segmentation\n",
+    "from CellMet.analysis import cell_analysis, edge_analysis, face_analysis\n",
     "\n",
-    "from CellSeg.plot import random_color\n",
+    "from CellMet.plot import random_color\n",
     "\n",
     "import ZebrafishMuscle.plot as ZMplot\n",
     "\n",
@@ -42,7 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# path = \"/home/u2175049/Documents/Code/CellSeg/notebooks/\"\n",
+    "# path = \"/home/u2175049/Documents/Code/CellMet/notebooks/\"\n",
     "# seg_filename = \"artificial_tissue_150cells_5_to70cells.tif\"\n",
     "# pixel_size = dict(x_size = 0.21,\n",
     "#                   y_size = 0.21,\n",