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Lisa Glover
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Merge pull request #7 from VirtualPhotonics/feature/3-create-additional-plot-examples-for-monte-carlo
Feature/3 create additional plot examples for monte carlo
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‎README.md

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

‎scripting/libraries/readme.txt

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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

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

‎scripting/monte_carlo/demo_02_DAW_vs_CAW.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "075fe817",
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"metadata": {},
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"source": [
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"# Using VTS in Jupyter Notebooks\n",
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"**Lisa Malenfant**\n",
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"\n",
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"**December 2023**\n",
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"\n",
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"This is a simple example of Jupyter notebook using VTS. It is assumed that\n",
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"\n",
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"* [.NET 6](https://dotnet.microsoft.com/en-us/download/dotnet/6.0) has been installed\n",
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"\n",
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"* 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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c12174d",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import the Operating System so we can access the files for the VTS library\n",
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"import os\n",
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"current_directory = os.getcwd()\n",
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"publish_local = current_directory.replace(\"monte_carlo\", \"libraries\\Vts.dll\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7f248374",
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"metadata": {},
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"source": [
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"Use pip to install PythonNet Plotly and Numpy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b08ccbd2",
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install pythonnet plotly numpy"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82d0ef92",
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"metadata": {},
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"source": [
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"Import the Core CLR runtime from PythonNet and add the reference for the VTS library and its dependencies\n",
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"\n",
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"Import the namespaces from the Python libraries and the VTS library"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "38947713",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pythonnet import set_runtime\n",
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"set_runtime(\"coreclr\")\n",
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"import clr\n",
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"clr.AddReference(publish_local)\n",
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"import numpy as np\n",
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"import plotly.graph_objects as go\n",
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"import plotly.express as px\n",
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"from Vts import *\n",
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"from Vts.Common import *\n",
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"from Vts.Extensions import *\n",
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"from Vts.Modeling.Optimizers import *\n",
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"from Vts.Modeling.ForwardSolvers import *\n",
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"from Vts.SpectralMapping import *\n",
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"from Vts.Factories import *\n",
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"from Vts.MonteCarlo import *\n",
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"from Vts.MonteCarlo.Sources import *\n",
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"from Vts.MonteCarlo.Tissues import *\n",
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"from Vts.MonteCarlo.Detectors import *\n",
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"from Vts.MonteCarlo.Factories import *\n",
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"from Vts.MonteCarlo.PhotonData import *\n",
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"from Vts.MonteCarlo.PostProcessing import *\n",
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"from System import Array"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a2674c8b",
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"metadata": {},
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"source": [
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"Setup the values for the simulations and plot the results using Plotly\n",
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"\n",
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"DAW vs CAW"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b16d74a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Setup the detector input for the simulation\n",
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"detectorRange = DoubleRange(start=0, stop=40, number=201)\n",
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"detectorInput = ROfRhoDetectorInput()\n",
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"detectorInput.Rho = detectorRange\n",
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"detectorInput.Name = \"ROfRho\"\n",
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"detectors = Array.CreateInstance(IDetectorInput,1)\n",
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"detectors[0] = detectorInput\n",
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"\n",
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"simulationOptions1 = SimulationOptions()\n",
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"simulationOptions1.AbsorptionWeightingType = AbsorptionWeightingType.Discrete\n",
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"# create a SimulationInput object to define the simulation\n",
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"simulationInput1 = SimulationInput()\n",
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"simulationInput1.N=1000\n",
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"simulationInput1.OutputName = \"MonteCarloROfRho-DAW\"\n",
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"simulationInput1.DetectorInputs= detectors\n",
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"simulationInput1.Options = simulationOptions1\n",
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"\n",
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"simulationOptions2 = SimulationOptions()\n",
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"simulationOptions2.AbsorptionWeightingType = AbsorptionWeightingType.Continuous\n",
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"# create a SimulationInput object to define the simulation\n",
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"simulationInput2 = SimulationInput()\n",
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"simulationInput2.N=1000\n",
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"simulationInput2.OutputName = \"MonteCarloROfRho-CAW\"\n",
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"simulationInput2.DetectorInputs = detectors\n",
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"simulationInput2.Options = simulationOptions2\n",
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"\n",
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"# create the simulations\n",
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"simulation1 = MonteCarloSimulation(simulationInput1)\n",
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"simulation2 = MonteCarloSimulation(simulationInput2)\n",
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"\n",
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"# run the simulations\n",
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"simulationOutput1 = simulation1.Run()\n",
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"simulationOutput2 = simulation2.Run()\n",
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"\n",
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"# plot the results using Plotly\n",
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"detectorResults1 = Array.CreateInstance(ROfRhoDetector,1)\n",
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"detectorResults1[0] = simulationOutput1.ResultsDictionary[\"ROfRho\"]\n",
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"logReflectance1 = [r for r in detectorResults1[0].Mean]\n",
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"detectorMidpoints1 = [mp for mp in detectorRange.AsEnumerable()]\n",
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"\n",
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"detectorResults2 = Array.CreateInstance(ROfRhoDetector,1)\n",
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"detectorResults2[0] = simulationOutput2.ResultsDictionary[\"ROfRho\"]\n",
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"logReflectance2 = [r for r in detectorResults2[0].Mean]\n",
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"detectorMidpoints2 = [mp for mp in detectorRange.AsEnumerable()]\n",
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"\n",
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"xLabel = \"ρ [mm]\"\n",
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"yLabel = \"log(R(ρ)) [mm-2]\"\n",
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"\n",
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"chart = go.Figure()\n",
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"chart.add_trace(go.Scatter(x=detectorMidpoints1, y=logReflectance1, mode='markers', name='log(R(ρ)) [mm-2] - DAW'))\n",
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"chart.add_trace(go.Scatter(x=detectorMidpoints2, y=logReflectance2, mode='markers', name='log(R(ρ)) [mm-2] - CAW'))\n",
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"chart.update_layout( title=\"DAW vs CAW\", xaxis_title=xLabel, yaxis_title=yLabel)\n",
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"chart.update_yaxes(type=\"log\")\n",
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"chart.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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