Update examples

.github/workflows/deploy_gh_page.yml

@@ -4,6 +4,7 @@ on :
   push :
     branches :
       - main
+      - update_examples
 
 permissions:
   id-token: write
@@ -23,6 +24,12 @@ jobs :
           python -m pip install --upgrade pip
           pip install sphinx sphinx-gallery sphinx-book-theme
           pip install -r requirements.txt
+          git clone https://gitlab.com/openmcsquare/opentps.git
+          pip install ./opentps
+          pip install --upgrade pip setuptools wheel
+          pip install tigre[cpu]  # CPU-only install if available
+
+
       - name: Build the docs
         run: |
           make html
@@ -40,4 +47,4 @@ jobs :
         uses: actions/deploy-pages@v4
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          artifact_name: github-pages
+          artifact_name: github-pages

.gitignore

@@ -3,5 +3,21 @@ _build
 auto_examples/**
 !auto_examples/**/*.ipynb
 !auto_examples/**/
+examples/**/output/
 
 .idea
+
+auto_community/**
+!auto_community/**/*.ipynb
+!auto_community/**/
+
+
+community/**
+!community/**/*.py
+!community/**/*.rst
+!community/**/
+
+examples/**
+!examples/**/*.py
+!examples/**/*.rst
+!examples/**/

_templates/my_header.html

@@ -64,7 +64,7 @@
   var fileName = pathParts.pop().replace('.html', '.ipynb');
 
   // Check if the forelast word is 'auto_examples'
-  if (foreforelastWord === 'auto_examples'&& fileName !== 'index.ipynb') {
+  if ((foreforelastWord === 'auto_examples'||foreforelastWord === 'auto_community')&& fileName !== 'index.ipynb') {
     // Show the button
     var colabButton = document.getElementById('colab-button');
     colabButton.style.display = 'inline-block';

auto_community/Template/run_template.ipynb

@@ -0,0 +1,104 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Template Example\n\nThis is a minimal example showing how to contribute to the\nCommunity Gallery. It just generates some random data and plots it.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setting up the environment in google collab\n First you need to change the type of execution in the bottom left from processor to GPU. Then you can run the example.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import sys\nif \"google.colab\" in sys.modules:\n    from IPython import get_ipython\n    get_ipython().system('git clone https://gitlab.com/openmcsquare/opentps.git')\n    get_ipython().system('pip install ./opentps')\n    get_ipython().system('pip install scipy==1.10.1')\n    get_ipython().system('pip install cupy-cuda12x')\n    import opentps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "imports\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 1. Generate random data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "x = np.linspace(0, 10, 100)\ny = np.sin(x) + 0.2 * np.random.randn(100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 2. Plot the results\nTo display your plot in the Sphinx gallery, make sure plt.show() is the last line of your code cell.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "plt.figure()\nplt.plot(x, y, label=\"noisy sine\")\nplt.legend()\nplt.title(\"Random sine example\")\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

auto_examples/DoseComputation/run_SimpleDoseCalculation.ipynb

@@ -0,0 +1,227 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Simple dose computation\nauthor: OpenTPS team\n\nIn this example we are going to create a generic CT and use the MCsquare dose calculator to compute the dose image\n\nrunning time: ~ 7 minute\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setting up the environment in google collab\n First you need to change the type of execution in the bottom left from processor to GPU. Then you can run the example.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import sys\nif \"google.colab\" in sys.modules:\n    from IPython import get_ipython\n    get_ipython().system('git clone https://gitlab.com/openmcsquare/opentps.git')\n    get_ipython().system('pip install ./opentps')\n    get_ipython().system('pip install scipy==1.10.1')\n    get_ipython().system('pip install cupy-cuda12x')\n    import opentps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "imports\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport os\nfrom matplotlib import pyplot as plt\nimport math"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "import the needed opentps.core packages\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from opentps.core.data.images import CTImage\nfrom opentps.core.data.images import ROIMask\nfrom opentps.core.data.plan import ProtonPlanDesign\nfrom opentps.core.data import DVH\nfrom opentps.core.data import Patient\nfrom opentps.core.io import mcsquareIO\nfrom opentps.core.io.scannerReader import readScanner\nfrom opentps.core.processing.doseCalculation.doseCalculationConfig import DoseCalculationConfig\nfrom opentps.core.processing.doseCalculation.protons.mcsquareDoseCalculator import MCsquareDoseCalculator\nfrom opentps.core.processing.imageProcessing.resampler3D import resampleImage3DOnImage3D,resampleImage3D"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generic CT creation\nwe will first create a generic CT of a box fill with water and air\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ctCalibration = readScanner(DoseCalculationConfig().scannerFolder)\nbdl = mcsquareIO.readBDL(DoseCalculationConfig().bdlFile)\n\npatient = Patient()\npatient.name = 'Patient'\n\nctSize = 150\n\nct = CTImage()\nct.name = 'CT'\nct.patient = patient\n\nhuAir = -1024.\nhuWater = ctCalibration.convertRSP2HU(1.)\ndata = huAir * np.ones((ctSize, ctSize, ctSize))\ndata[:, 50:, :] = huWater\nct.imageArray = data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Region of interest\nwe will now create a region of interest wich is a small 3D box of size 20*20*20\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "roi = ROIMask()\nroi.patient = patient\nroi.name = 'TV'\nroi.color = (255, 0, 0) # red\ndata = np.zeros((ctSize, ctSize, ctSize)).astype(bool)\ndata[65:85, 65:85, 65:85] = True\nroi.imageArray = data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configuration of MCsquare\nto configure Mcsquare we need to calibrate it with the CT calibration obtained above\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mc2 = MCsquareDoseCalculator()\nmc2.beamModel = bdl\nmc2.ctCalibration = ctCalibration\nmc2.nbPrimaries = 1e7"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plan Creation\nwe will now create a plan and create one beam\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Design plan\nbeamNames = [\"Beam1\",\"Beam2\",\"Beam3\"]\ngantryAngles = [0.,90.,270.]\ncouchAngles = [0.,0.,0.]\n\n# Generate new plan\nplanDesign = ProtonPlanDesign()\nplanDesign.ct = ct\nplanDesign.targetMask = roi\nplanDesign.gantryAngles = gantryAngles\nplanDesign.beamNames = beamNames\nplanDesign.couchAngles = couchAngles\nplanDesign.calibration = ctCalibration\nplanDesign.spotSpacing = 5.0\nplanDesign.layerSpacing = 5.0\nplanDesign.targetMargin = 5.0\n\nplan = planDesign.buildPlan()  # Spot placement\nplan.PlanName = \"NewPlan\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Center of mass\nHere we look at the part of the 3D CT image where \"stuff is happening\" by getting the CoM. We use the function resampleImage3DOnImage3D to the same array size for both images.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "roi = resampleImage3DOnImage3D(roi, ct)\nCOM_coord = roi.centerOfMass\nCOM_index = roi.getVoxelIndexFromPosition(COM_coord)\nZ_coord = COM_index[2]\n\nimg_ct = ct.imageArray[:, :, Z_coord].transpose(1, 0)\ncontourTargetMask = roi.getBinaryContourMask()\nimg_mask = contourTargetMask.imageArray[:, :, Z_coord].transpose(1, 0)\n\n#Output path \noutput_path = 'Output'\nif not os.path.exists(output_path):\n    os.makedirs(output_path)\n\nimage = plt.imshow(img_ct,cmap='Blues')\nplt.colorbar(image)\nplt.contour(img_mask,colors=\"red\")\nplt.title(\"Created CT with ROI\")\nplt.text(5,40,\"Air\",color= 'black')\nplt.text(5,100,\"Water\",color = 'white')\nplt.text(71,77,\"TV\",color ='red')\nplt.savefig(os.path.join(output_path, 'SimpleCT.png'),format = 'png')\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Dose Computation\nWe now use the MCsquare dose calculator to compute the dose of the created plan\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "doseImage = mc2.computeDose(ct, plan)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "img_dose = resampleImage3DOnImage3D(doseImage, ct)\nimg_dose = img_dose.imageArray[:, :, Z_coord].transpose(1, 0)\nscoringSpacing = [2, 2, 2]\nscoringGridSize = [int(math.floor(i / j * k)) for i, j, k in zip([150,150,150], scoringSpacing, [1,1,1])]\nroiResampled = resampleImage3D(roi, origin=ct.origin, gridSize=scoringGridSize, spacing=scoringSpacing)\ntarget_DVH = DVH(roiResampled, doseImage)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\nax[0].imshow(img_ct, cmap='gray')\nax[0].imshow(img_mask, alpha=.2, cmap='binary')  # PTV\ndose = ax[0].imshow(img_dose, cmap='jet', alpha=.2)\ncbar = plt.colorbar(dose, ax=ax[0])\ncbar.set_label('Dose(Gy)')\nax[1].plot(target_DVH.histogram[0], target_DVH.histogram[1], label=target_DVH.name)\nax[1].set_xlabel(\"Dose (Gy)\")\nax[1].set_ylabel(\"Volume (%)\")\nplt.grid(True)\nplt.legend()\nplt.savefig(os.path.join(output_path, 'SimpleDose.png'), format = 'png')\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print('D95 = ' + str(target_DVH.D95) + ' Gy')\nprint('D5 = ' + str(target_DVH.D5) + ' Gy')\nprint('D5 - D95 =  {} Gy'.format(target_DVH.D5 - target_DVH.D95))"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}