Feature/3 create additional plot examples for monte carlo

README.md

@@ -1,50 +1,38 @@
 # Using VTS in a Jupyter notebook
 
-This is likely to change once `python` scripting is formally supported by `VTS`.  
+This information will likely change as more support for `python` and `.NET` is introduced.  
 
-## macOS installation details
-
-This is a record of how I got `vts` working in `JupyterLab`.
+## Installation details
 
 ### Step 1: Install .NET 6
 
 You will need version 6 of .NET which is available from Microsoft below
 
 https://dotnet.microsoft.com/en-us/download/dotnet/6.0
 
-I saved everything to `$HOME/Documents/Code/dotnet6`
+
+***On MacOS:***
+Save everything to `$HOME/Documents/Code/dotnet6`
 
 Do not forget to update your `~/.bash_profile` or whatever you use so that this directory is in your path.  Also define `DOTNET_ROOT` 
 
     PATH=$PATH:$HOME/Documents/Code/dotnet6
 
     export DOTNET_ROOT=$HOME/Documents/Code/dotnet6
 
-### Step 2: Install VTS
-
-Follow the guidelines at https://github.com/VirtualPhotonics/VTS/wiki/Getting-Started-on-Mac  Briefly, clone the `.git` repo 
+### Step 2: Clone the scripting repository
 
     git clone https://github.com/VirtualPhotonics/vts.git
 
-This will create the directory `vts` that is needed for the next step
-
-Install both `powershell` and `nuget` using [Homebrew](https://brew.sh)
-
-    brew install powershell
-    brew install nuget
+### Step 3: Download the VTS libraries
 
-Now build VTS.  If you don't have `matlab` don't worry, it seemed to work fine without completing the `matlab` tests
+Get the latest VTS libraries for the specific platform from [releases](https://github.com/VirtualPhotonics/Vts.Scripting.Python/releases) and extract them into the **libraries** folder under **scripting**. 
 
-    pwsh
-    cd vts
-    ./BuildTestRelease.ps1 
-    exit
-
-### Step 3: Install pythonnet
+### Step 4: Install pythonnet
 
     pip install pythonnet
 
-Because `pythonnet` under macOS (or linux) defaults to `mono`, two more things need to added to `~/.bash_profile`
+***Note for MacOS and Linux:*** Because `pythonnet` under macOS (or linux) defaults to `mono`, two more things need to added to `~/.bash_profile`
 
     export PYTHONNET_RUNTIME=coreclr
     export PYTHONNET_PYDLL=/usr/local/bin/python3   
@@ -54,15 +42,18 @@ Obviously use the path for python on your system  (`which python3` will tell you
 Next start a `JupyterLab` notebook to verify that things are installed correctly
 
     import clr
-
     clr.AddReference("System")
     from System import Console
     Console.WriteLine("Hello from .NET 6!")
 
 The final test is importing from `Vts.dll`
+
+    from pythonnet import set_runtime
+    set_runtime("coreclr")
 
     import clr
-    clr.AddReference("/path/to/vts/src/Vts/publish/local/Vts.dll")    
+    clr.AddReference("/path/to/libraries/Vts.dll")    
+
     from Vts import *
 
 where, of course, "/path/to" above has been adapted to your system
@@ -71,8 +62,12 @@ where, of course, "/path/to" above has been adapted to your system
 
 To run `VTS` programs in `python` include the following the header
 
+```
+    from pythonnet import set_runtime
+    set_runtime("coreclr")
+
     import clr
-    clr.AddReference("/path/to/vts/src/Vts/publish/local/Vts.dll")    
+    clr.AddReference("/path/to/libraries/Vts.dll")    
 
     from Vts import *
     from Vts.Common import *
@@ -87,4 +82,5 @@ To run `VTS` programs in `python` include the following the header
     from Vts.MonteCarlo.Factories import *
     from Vts.MonteCarlo.PhotonData import *
     from Vts.MonteCarlo.PostProcessing import *
-    from System import Array, Double
+    from System import Array, Double
+```

scripting/libraries/readme.txt

@@ -0,0 +1 @@
+The VTS libraries should be copied into this folder. Get the latest VTS library (VTS.dll) and its dependencies from the releases section of this repository https://github.com/VirtualPhotonics/Vts.Scripting.Python/releases

scripting/monte_carlo/demo_01_r_of_rho_simple.ipynb

@@ -1,16 +1,40 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using VTS in Jupyter Notebooks\n",
+    "**David Cuccia**\n",
+    "\n",
+    "**December 2023**\n",
+    "\n",
+    "This is a simple example of Jupyter notebook using VTS. It is assumed that\n",
+    "\n",
+    "* [.NET 6](https://dotnet.microsoft.com/en-us/download/dotnet/6.0) has been installed\n",
+    "\n",
+    "* The latest [VTS libraries](https://github.com/VirtualPhotonics/Vts.Scripting.Python/releases) have been downloaded from the zip file in releases and extracted to the libraries folder"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "#Import the Operating System so we can access the files for the VTS library\n",
     "import os\n",
     "current_directory = os.getcwd()\n",
     "publish_local = current_directory.replace(\"monte_carlo\", \"libraries\\Vts.dll\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use pip to install PythonNet Plotly and Numpy"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -20,6 +44,15 @@
     "pip install pythonnet plotly numpy"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the Core CLR runtime from PythonNet and add the reference for the VTS library and its dependencies\n",
+    "\n",
+    "Import the namespaces from the Python libraries and the VTS library"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -29,7 +62,7 @@
     "from pythonnet import set_runtime\n",
     "set_runtime(\"coreclr\")\n",
     "import clr\n",
-    "clr.AddReference(publish_local) # Copy the VTS dlls into the libraries folders\n",
+    "clr.AddReference(publish_local)\n",
     "import numpy as np\n",
     "import plotly.graph_objects as go\n",
     "import plotly.express as px\n",
@@ -50,6 +83,15 @@
     "from System import Array"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setup the values for the simulations and plot the results using Plotly\n",
+    "\n",
+    "ROfRho"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -84,18 +126,11 @@
     "yLabel = \"log(R(ρ)) [mm-2]\"\n",
     "\n",
     "chart = go.Figure()\n",
-    "chart.add_trace(go.Scatter(x=detectorMidpoints, y=logReflectance, mode='lines+markers'))\n",
+    "chart.add_trace(go.Scatter(x=detectorMidpoints, y=logReflectance, mode='markers'))\n",
     "chart.update_layout( title=\"log(R(ρ)) [mm-2]\", xaxis_title=xLabel, yaxis_title=yLabel)\n",
     "chart.update_yaxes(type=\"log\")\n",
     "chart.show()\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

scripting/monte_carlo/demo_02_DAW_vs_CAW.ipynb

@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "075fe817",
+   "metadata": {},
+   "source": [
+    "# Using VTS in Jupyter Notebooks\n",
+    "**Lisa Malenfant**\n",
+    "\n",
+    "**December 2023**\n",
+    "\n",
+    "This is a simple example of Jupyter notebook using VTS. It is assumed that\n",
+    "\n",
+    "* [.NET 6](https://dotnet.microsoft.com/en-us/download/dotnet/6.0) has been installed\n",
+    "\n",
+    "* The latest [VTS libraries](https://github.com/VirtualPhotonics/Vts.Scripting.Python/releases) have been downloaded from the zip file in releases and extracted to the libraries folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c12174d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import the Operating System so we can access the files for the VTS library\n",
+    "import os\n",
+    "current_directory = os.getcwd()\n",
+    "publish_local = current_directory.replace(\"monte_carlo\", \"libraries\\Vts.dll\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f248374",
+   "metadata": {},
+   "source": [
+    "Use pip to install PythonNet Plotly and Numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b08ccbd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install pythonnet plotly numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82d0ef92",
+   "metadata": {},
+   "source": [
+    "Import the Core CLR runtime from PythonNet and add the reference for the VTS library and its dependencies\n",
+    "\n",
+    "Import the namespaces from the Python libraries and the VTS library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38947713",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pythonnet import set_runtime\n",
+    "set_runtime(\"coreclr\")\n",
+    "import clr\n",
+    "clr.AddReference(publish_local)\n",
+    "import numpy as np\n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.express as px\n",
+    "from Vts import *\n",
+    "from Vts.Common import *\n",
+    "from Vts.Extensions import *\n",
+    "from Vts.Modeling.Optimizers import *\n",
+    "from Vts.Modeling.ForwardSolvers import *\n",
+    "from Vts.SpectralMapping import *\n",
+    "from Vts.Factories import *\n",
+    "from Vts.MonteCarlo import *\n",
+    "from Vts.MonteCarlo.Sources import *\n",
+    "from Vts.MonteCarlo.Tissues import *\n",
+    "from Vts.MonteCarlo.Detectors import *\n",
+    "from Vts.MonteCarlo.Factories import *\n",
+    "from Vts.MonteCarlo.PhotonData import *\n",
+    "from Vts.MonteCarlo.PostProcessing import *\n",
+    "from System import Array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2674c8b",
+   "metadata": {},
+   "source": [
+    "Setup the values for the simulations and plot the results using Plotly\n",
+    "\n",
+    "DAW vs CAW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b16d74a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the detector input for the simulation\n",
+    "detectorRange = DoubleRange(start=0, stop=40, number=201)\n",
+    "detectorInput = ROfRhoDetectorInput()\n",
+    "detectorInput.Rho = detectorRange\n",
+    "detectorInput.Name = \"ROfRho\"\n",
+    "detectors = Array.CreateInstance(IDetectorInput,1)\n",
+    "detectors[0] = detectorInput\n",
+    "\n",
+    "simulationOptions1 = SimulationOptions()\n",
+    "simulationOptions1.AbsorptionWeightingType = AbsorptionWeightingType.Discrete\n",
+    "# create a SimulationInput object to define the simulation\n",
+    "simulationInput1 = SimulationInput()\n",
+    "simulationInput1.N=1000\n",
+    "simulationInput1.OutputName = \"MonteCarloROfRho-DAW\"\n",
+    "simulationInput1.DetectorInputs= detectors\n",
+    "simulationInput1.Options = simulationOptions1\n",
+    "\n",
+    "simulationOptions2 = SimulationOptions()\n",
+    "simulationOptions2.AbsorptionWeightingType = AbsorptionWeightingType.Continuous\n",
+    "# create a SimulationInput object to define the simulation\n",
+    "simulationInput2 = SimulationInput()\n",
+    "simulationInput2.N=1000\n",
+    "simulationInput2.OutputName = \"MonteCarloROfRho-CAW\"\n",
+    "simulationInput2.DetectorInputs = detectors\n",
+    "simulationInput2.Options = simulationOptions2\n",
+    "\n",
+    "# create the simulations\n",
+    "simulation1 = MonteCarloSimulation(simulationInput1)\n",
+    "simulation2 = MonteCarloSimulation(simulationInput2)\n",
+    "\n",
+    "# run the simulations\n",
+    "simulationOutput1 = simulation1.Run()\n",
+    "simulationOutput2 = simulation2.Run()\n",
+    "\n",
+    "# plot the results using Plotly\n",
+    "detectorResults1 = Array.CreateInstance(ROfRhoDetector,1)\n",
+    "detectorResults1[0] = simulationOutput1.ResultsDictionary[\"ROfRho\"]\n",
+    "logReflectance1 = [r for r in detectorResults1[0].Mean]\n",
+    "detectorMidpoints1 = [mp for mp in detectorRange.AsEnumerable()]\n",
+    "\n",
+    "detectorResults2 = Array.CreateInstance(ROfRhoDetector,1)\n",
+    "detectorResults2[0] = simulationOutput2.ResultsDictionary[\"ROfRho\"]\n",
+    "logReflectance2 = [r for r in detectorResults2[0].Mean]\n",
+    "detectorMidpoints2 = [mp for mp in detectorRange.AsEnumerable()]\n",
+    "\n",
+    "xLabel = \"ρ [mm]\"\n",
+    "yLabel = \"log(R(ρ)) [mm-2]\"\n",
+    "\n",
+    "chart = go.Figure()\n",
+    "chart.add_trace(go.Scatter(x=detectorMidpoints1, y=logReflectance1, mode='markers', name='log(R(ρ)) [mm-2] - DAW'))\n",
+    "chart.add_trace(go.Scatter(x=detectorMidpoints2, y=logReflectance2, mode='markers', name='log(R(ρ)) [mm-2] - CAW'))\n",
+    "chart.update_layout( title=\"DAW vs CAW\", xaxis_title=xLabel, yaxis_title=yLabel)\n",
+    "chart.update_yaxes(type=\"log\")\n",
+    "chart.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}