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Lisa Malenfant
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Separated the 2 examples into 2 scripts and added documentation
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‎scripting/monte_carlo/demo_01_r_of_rho_simple.ipynb

+11-79
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@@ -4,9 +4,16 @@
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"cell_type": "markdown",
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"metadata": {},
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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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"# Using VTS in Jupyter Notebooks\n",
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"**David Cuccia**\n",
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"\n",
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"Before we can run the Python code we need to download the latest zip file from the downloads and put the dll files into the libraries folder."
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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 [VTS libraries](https://github.com/VirtualPhotonics/VTS) have been downloaded from the zip file in downloads and extracted to the libraries folder"
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]
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},
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{
@@ -15,6 +22,7 @@
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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\")"
@@ -54,7 +62,7 @@
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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",
@@ -123,82 +131,6 @@
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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": "markdown",
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"metadata": {},
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"source": [
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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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"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 detectorResults[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",
190-
"chart.add_trace(go.Scatter(x=detectorMidpoints2, y=logReflectance2, mode='markers', name='log(R(ρ)) [mm-2] - CAW'))\n",
191-
"chart.update_layout( title=\"DAW vs CAW\", xaxis_title=xLabel, yaxis_title=yLabel)\n",
192-
"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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"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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@@ -0,0 +1,187 @@
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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": [
8+
"# Using VTS in Jupyter Notebooks\n",
9+
"**Lisa Malenfant**\n",
10+
"\n",
11+
"**December 2023**\n",
12+
"\n",
13+
"This is a simple example of Jupyter notebook using VTS. It is assumed that\n",
14+
"\n",
15+
"* [.NET 6](https://dotnet.microsoft.com/en-us/download/dotnet/6.0) has been installed\n",
16+
"\n",
17+
"* The [VTS libraries](https://github.com/VirtualPhotonics/VTS) have been downloaded from the zip file in downloads and extracted to the libraries folder"
18+
]
19+
},
20+
{
21+
"cell_type": "code",
22+
"execution_count": null,
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"id": "1c12174d",
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"metadata": {},
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"outputs": [],
26+
"source": [
27+
"#Import the Operating System so we can access the files for the VTS library\n",
28+
"import os\n",
29+
"current_directory = os.getcwd()\n",
30+
"publish_local = current_directory.replace(\"monte_carlo\", \"libraries\\Vts.dll\")"
31+
]
32+
},
33+
{
34+
"cell_type": "markdown",
35+
"id": "7f248374",
36+
"metadata": {},
37+
"source": [
38+
"Use pip to install PythonNet Plotly and Numpy"
39+
]
40+
},
41+
{
42+
"cell_type": "code",
43+
"execution_count": null,
44+
"id": "b08ccbd2",
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"metadata": {},
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"outputs": [],
47+
"source": [
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"pip install pythonnet plotly numpy"
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]
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},
51+
{
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"cell_type": "markdown",
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"id": "82d0ef92",
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"metadata": {},
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"source": [
56+
"Import the Core CLR runtime from PythonNet and add the reference for the VTS library and its dependencies\n",
57+
"\n",
58+
"Import the namespaces from the Python libraries and the VTS library"
59+
]
60+
},
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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": [
68+
"from pythonnet import set_runtime\n",
69+
"set_runtime(\"coreclr\")\n",
70+
"import clr\n",
71+
"clr.AddReference(publish_local)\n",
72+
"import numpy as np\n",
73+
"import plotly.graph_objects as go\n",
74+
"import plotly.express as px\n",
75+
"from Vts import *\n",
76+
"from Vts.Common import *\n",
77+
"from Vts.Extensions import *\n",
78+
"from Vts.Modeling.Optimizers import *\n",
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"from Vts.Modeling.ForwardSolvers import *\n",
80+
"from Vts.SpectralMapping import *\n",
81+
"from Vts.Factories import *\n",
82+
"from Vts.MonteCarlo import *\n",
83+
"from Vts.MonteCarlo.Sources import *\n",
84+
"from Vts.MonteCarlo.Tissues import *\n",
85+
"from Vts.MonteCarlo.Detectors import *\n",
86+
"from Vts.MonteCarlo.Factories import *\n",
87+
"from Vts.MonteCarlo.PhotonData import *\n",
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"from Vts.MonteCarlo.PostProcessing import *\n",
89+
"from System import Array"
90+
]
91+
},
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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": [
97+
"Setup the values for the simulations and plot the results using Plotly\n",
98+
"\n",
99+
"DAW vs CAW"
100+
]
101+
},
102+
{
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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",
110+
"detectorRange = DoubleRange(start=0, stop=40, number=201)\n",
111+
"detectorInput = ROfRhoDetectorInput()\n",
112+
"detectorInput.Rho = detectorRange\n",
113+
"detectorInput.Name = \"ROfRho\"\n",
114+
"detectors = Array.CreateInstance(IDetectorInput,1)\n",
115+
"detectors[0] = detectorInput\n",
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"\n",
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"simulationOptions1 = SimulationOptions()\n",
118+
"simulationOptions1.AbsorptionWeightingType = AbsorptionWeightingType.Discrete\n",
119+
"# create a SimulationInput object to define the simulation\n",
120+
"simulationInput1 = SimulationInput()\n",
121+
"simulationInput1.N=1000\n",
122+
"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",
128+
"# create a SimulationInput object to define the simulation\n",
129+
"simulationInput2 = SimulationInput()\n",
130+
"simulationInput2.N=1000\n",
131+
"simulationInput2.OutputName = \"MonteCarloROfRho-CAW\"\n",
132+
"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",
137+
"simulation2 = MonteCarloSimulation(simulationInput2)\n",
138+
"\n",
139+
"# run the simulations\n",
140+
"simulationOutput1 = simulation1.Run()\n",
141+
"simulationOutput2 = simulation2.Run()\n",
142+
"\n",
143+
"# plot the results using Plotly\n",
144+
"detectorResults1 = Array.CreateInstance(ROfRhoDetector,1)\n",
145+
"detectorResults1[0] = simulationOutput1.ResultsDictionary[\"ROfRho\"]\n",
146+
"logReflectance1 = [r for r in detectorResults1[0].Mean]\n",
147+
"detectorMidpoints1 = [mp for mp in detectorRange.AsEnumerable()]\n",
148+
"\n",
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"detectorResults2 = Array.CreateInstance(ROfRhoDetector,1)\n",
150+
"detectorResults2[0] = simulationOutput2.ResultsDictionary[\"ROfRho\"]\n",
151+
"logReflectance2 = [r for r in detectorResults2[0].Mean]\n",
152+
"detectorMidpoints2 = [mp for mp in detectorRange.AsEnumerable()]\n",
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"\n",
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"xLabel = \"ρ [mm]\"\n",
155+
"yLabel = \"log(R(ρ)) [mm-2]\"\n",
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"\n",
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"chart = go.Figure()\n",
158+
"chart.add_trace(go.Scatter(x=detectorMidpoints1, y=logReflectance1, mode='markers', name='log(R(ρ)) [mm-2] - DAW'))\n",
159+
"chart.add_trace(go.Scatter(x=detectorMidpoints2, y=logReflectance2, mode='markers', name='log(R(ρ)) [mm-2] - CAW'))\n",
160+
"chart.update_layout( title=\"DAW vs CAW\", xaxis_title=xLabel, yaxis_title=yLabel)\n",
161+
"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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