Merge pull request #220 from amarquand/dev

Update pyproject

README.md

@@ -23,17 +23,6 @@ conda create -n <env_name> python==3.12
 source activate <env_name>
 ```
 
-### Install nutpie and numba from conda-forge
-
-```
-conda install nutpie numba -c conda-forge
-```
-
-#### Install torch from pytorch.org
-
-Use the command that you get from the command builder here: https://pytorch.org/get-started/locally/. This will ensure you do not install the CUDA version of torch if your pc does not have a GPU. We also recommend that you use the `conda` option. 
-
-
 #### Install PCNtoolkit
 
 Using pip:
@@ -46,6 +35,11 @@ Using a local clone of the repo:
 python -m pip install .
 ```
 
+#### Test installation  
+```
+python -c "import pcntoolkit as pk;print(pk.__file__)"
+```
+
 ## Alternative installation (on a shared resource)
 
 #### Make sure conda is available on the system.
@@ -58,7 +52,7 @@ conda --version
 #### Create a conda environment in a shared location
 
 ```
-conda create -y python==3.12 numpy mkl blas --prefix=/shared/conda/<env_name>
+conda create -y python==3.12 --prefix=/shared/conda/<env_name>
 ```
 
 #### Activate the conda environment 
@@ -67,35 +61,20 @@ conda create -y python==3.12 numpy mkl blas --prefix=/shared/conda/<env_name>
 conda activate /shared/conda/<env_name>
 ```
 
-### Install nutpie using conda 
-
-```
-conda install nutpie numba -c conda-forge
-```
-
-#### install torch 
-
-Using the command that you get from the command builder here:
-
-```
-https://pytorch.org/get-started/locally/
-```
-
-If your shared resource has no GPU, make sure you select the 'CPU' field in the 'Compute Platform' row. Here we also prefer conda over pip.
-
 #### Clone the repo
 
 ```
 git clone https://github.com/amarquand/PCNtoolkit.git
 ```
 
-### Install in the conda environment
+#### Install in the conda environment
 
 ```
 cd PCNtoolkit/
 python -m pip install .
 ```
-### Test 
+
+#### Test installation  
 ```
 python -c "import pcntoolkit as pk;print(pk.__file__)"
 ```

doc/source/pages/BLR_normativemodel_protocol.rst

@@ -41,7 +41,6 @@ your computer).
 
 .. code:: ipython3
 
-    ! pip install nutpie
     ! pip install pcntoolkit
     ! pip install -r requirements.txt
 

doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.ipynb

@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/AuguB/pcntoolkit_doc_notebooks/blob/main/scripts/HBR_NormativeModel_FCONdata_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -60,8 +60,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install pcntoolkit\n",
-        "!pip install nutpie"
+        "!pip install pcntoolkit"
       ]
     },
     {
@@ -596,9 +595,9 @@
   ],
   "metadata": {
     "colab": {
+      "include_colab_link": true,
       "name": "HBR_NormativeModel_FCONdata_Tutorial.ipynb",
-      "provenance": [],
-      "include_colab_link": true
+      "provenance": []
     },
     "kernelspec": {
       "display_name": "Python 3 (ipykernel)",
@@ -625,4 +624,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

doc/source/pages/HBR_NormativeModel_FCONdata_Tutorial.rst

@@ -23,7 +23,6 @@ Step 0: Install necessary libraries & grab data files
 .. code:: ipython3
 
     !pip install pcntoolkit
-    !pip install nutpie
 
 For this tutorial we will use data from the `Functional Connectom
 Project FCON1000 <http://fcon_1000.projects.nitrc.org/>`__ to create a

doc/source/pages/apply_normative_models.ipynb

@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/AuguB/pcntoolkit_doc_notebooks/blob/main/scripts/apply_normative_models_ct.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -26,16 +26,15 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "!pip install pcntoolkit\n",
-        "!pip install nutpie"
-      ],
+      "execution_count": null,
+      "id": "ks50sIbMOGzI",
       "metadata": {
         "id": "ks50sIbMOGzI"
       },
-      "id": "ks50sIbMOGzI",
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "!pip install pcntoolkit\n"
+      ]
     },
     {
       "cell_type": "code",
@@ -761,9 +760,9 @@
   ],
   "metadata": {
     "colab": {
+      "include_colab_link": true,
       "name": "apply_normative_models.ipynb",
-      "provenance": [],
-      "include_colab_link": true
+      "provenance": []
     },
     "kernelspec": {
       "display_name": "braincharts",
@@ -790,4 +789,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

doc/source/pages/apply_normative_models.rst

@@ -17,8 +17,7 @@ at least version 0.27)
 .. code:: ipython3
 
     !pip install pcntoolkit
-    !pip install nutpie
-
+    
 .. code:: ipython3
 
     ! git clone https://github.com/predictive-clinical-neuroscience/braincharts.git

doc/source/pages/installation.rst

@@ -19,20 +19,8 @@ Basic installation (on a local machine)
 .. code-block:: bash
 
 	source activate <env_name>
-
-4. Install torch using the torch instructions.	
-
-.. code-block:: bash
-
-	# Command found on the torch website: https://pytorch.org/get-started/locally/
-
-5. Install required conda packages
 	
-.. code-block:: bash
-
-	conda install numba nutpie -c conda-forge
-	
-6. Install PCNtoolkit (plus dependencies)
+4. Install PCNtoolkit (plus dependencies)
 
 .. code-block:: bash
 
@@ -62,37 +50,29 @@ Alternative installation (on a shared resource)
 	conda activate /shared/conda/<env_name>
 
 
-4. Install other dependencies
-
-.. code-block:: bash
-
-	# Command found on the torch website: https://pytorch.org/get-started/locally/
-	conda install numba nutpie -c conda-forge
-
-	
-5. Install pip dependencies
+4. Install pip dependencies
 
 .. code-block:: bash
 
 	pip --no-cache-dir install nibabel sklearn torch glob3 
 
 	
-6. Clone the repo
+5. Clone the repo
 
 .. code-block:: bash
 
 	git clone https://github.com/amarquand/PCNtoolkit.git
 
 	
-7. Install in the conda environment
+6. Install in the conda environment
 
 .. code-block:: bash
 
 	cd PCNtoolkit/
 	python3 setup.py install
 
 	
-8. Test 
+7. Test 
 
 .. code-block:: bash
 

doc/source/pages/normative_modelling_walkthrough.ipynb

@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/AuguB/pcntoolkit_doc_notebooks/blob/main/scripts/normative_modelling_walkthrough.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -61,8 +61,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install pcntoolkit\n",
-        "!pip install nutpie"
+        "!pip install pcntoolkit"
       ]
     },
     {
@@ -620,10 +619,10 @@
   ],
   "metadata": {
     "colab": {
+      "include_colab_link": true,
       "name": "normative_modelling_walkthrough.ipynb",
       "provenance": [],
-      "toc_visible": true,
-      "include_colab_link": true
+      "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3 (ipykernel)",
@@ -645,4 +644,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

doc/source/pages/normative_modelling_walkthrough.rst

@@ -39,7 +39,6 @@ bad if you cannot complete everything during the tutorial.
 .. code:: ipython3
 
     !pip install pcntoolkit
-    !pip install nutpie
 
 
 **Option 1:** Connect your Google Drive account, and load data from