deploy: 35b5e5d

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 09227e536d4b0057af2317e6c639bd9f
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/13d29bba0b7f4883cb92cda379ddc864/plot_dummy.py

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+"""
+==============
+A dummy script
+==============
+
+Dummy script to illustrate structure of examples folder
+"""
+
+import matplotlib.pyplot as plt
+
+# %%
+# Import necessary packages
+import numpy as np
+import osipi
+
+# %%
+# Generate synthetic AIF with default settings and plot the result.
+
+# Define time points in units of seconds - in this case we use a time
+# resolution of 0.5 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 0.5)
+
+# Create an AIF with default settings
+ca = osipi.aif_parker(t)
+
+# Plot the AIF over the full range
+plt.plot(t, ca, "r-")
+plt.plot(t, 0 * t, "k-")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
+plt.show()
+
+# %%
+# The bolus arrival time (BAT) defaults to 30s. What happens if we
+# change it? Let's try, by changing it in steps of 30s:
+
+ca = osipi.aif_parker(t, BAT=0)
+plt.plot(t, ca, "b-", label="BAT = 0s")
+ca = osipi.aif_parker(t, BAT=30)
+plt.plot(t, ca, "r-", label="BAT = 30s")
+ca = osipi.aif_parker(t, BAT=60)
+plt.plot(t, ca, "g-", label="BAT = 60s")
+ca = osipi.aif_parker(t, BAT=90)
+plt.plot(t, ca, "m-", label="BAT = 90s")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
+plt.legend()
+plt.show()
+
+# Choose the last image as a thumbnail for the gallery
+# sphinx_gallery_thumbnail_number = -1

_downloads/6794ff0fe2ebfd1be519862fb198ceb5/plot_aif_parker.py

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+"""
+======================================
+The Parker AIF - a play with variables
+======================================
+
+Simulating a Parker AIF with different settings.
+
+"""
+
+import matplotlib.pyplot as plt
+
+# %%
+# Import necessary packages
+import numpy as np
+import osipi
+
+# %%
+# Generate synthetic AIF with default settings and plot the result.
+
+# Define time points in units of seconds - in this case we use a time
+# resolution of 0.5 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 0.5)
+
+# Create an AIF with default settings
+ca = osipi.aif_parker(t)
+
+# Plot the AIF over the full range
+plt.plot(t, ca, "r-")
+plt.plot(t, 0 * t, "k-")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
+plt.show()
+
+# %%
+# The bolus arrival time (BAT) defaults to 0s. What happens if we
+# change it? Let's try, by changing it in steps of 30s:
+
+ca = osipi.aif_parker(t, BAT=0)
+plt.plot(t, ca, "b-", label="BAT = 0s")
+ca = osipi.aif_parker(t, BAT=30)
+plt.plot(t, ca, "r-", label="BAT = 30s")
+ca = osipi.aif_parker(t, BAT=60)
+plt.plot(t, ca, "g-", label="BAT = 60s")
+ca = osipi.aif_parker(t, BAT=90)
+plt.plot(t, ca, "m-", label="BAT = 90s")
+plt.xlabel("Time (sec)")
+plt.ylabel("Plasma concentration (mM)")
+plt.legend()
+plt.show()
+
+# Choose the last image as a thumbnail for the gallery
+# sphinx_gallery_thumbnail_number = -1

_downloads/7aabdb582807b10ab82e2dcfaa256c12/plot_extended_tofts.py

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+"""
+====================
+The Extended Tofts model
+====================
+
+Simulating tissue concentrations from extended Tofts model with different settings.
+"""
+
+import matplotlib.pyplot as plt
+
+# %%
+# Import necessary packages
+import numpy as np
+import osipi
+
+# %%
+# Generate Parker AIF with default settings.
+
+# Define time points in units of seconds - in this case we use a time
+# resolution of 1 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 1)
+
+# Create an AIF with default settings
+ca = osipi.aif_parker(t)
+
+# %%
+# Plot the tissue concentrations for an extracellular volume fraction
+# of 0.2 and 3 different plasma volumes of 0.05, 0.2 and 0.6
+Ktrans = 0.2  # in units of 1/min
+ve = 0.2  # volume fraction between 0 and 1
+vp = [0.05, 0.2, 0.6]  # volume fraction between 0 and 1
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0])
+plt.plot(t, ct, "b-", label=f"vp = {vp[0]}")
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[1])
+plt.plot(t, ct, "g-", label=f"vp = {vp[1]}")
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[2])
+plt.plot(t, ct, "m-", label=f"vp = {vp[2]}")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
+plt.legend()
+plt.show()
+
+# %%
+# Comparing different discretization methods for an extracellular
+# volume fraction of 0.2, Ktrans of 0.2 /min and vp of 0.05
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0])  # Defaults to Convolution
+plt.plot(t, ct, "b-", label="Convolution")
+ct = osipi.extended_tofts(t, ca, Ktrans, ve, vp[0], discretization_method="exp")
+plt.plot(t, ct, "g-", label="Exponential Convolution")
+plt.title(f"Ktrans = {Ktrans} /min")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
+plt.legend()
+plt.show()
+
+# Choose the last image as a thumbnail for the gallery
+# sphinx_gallery_thumbnail_number = -1

_downloads/9dd8307460a64f1314f28e1650ea27b2/plot_aif_parker.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# The Parker AIF - a play with variables\n\nSimulating a Parker AIF with different settings.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Import necessary packages\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport osipi"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Generate synthetic AIF with default settings and plot the result.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Define time points in units of seconds - in this case we use a time\n# resolution of 0.5 sec and a total duration of 6 minutes.\nt = np.arange(0, 6 * 60, 0.5)\n\n# Create an AIF with default settings\nca = osipi.aif_parker(t)\n\n# Plot the AIF over the full range\nplt.plot(t, ca, \"r-\")\nplt.plot(t, 0 * t, \"k-\")\nplt.xlabel(\"Time (sec)\")\nplt.ylabel(\"Plasma concentration (mM)\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The bolus arrival time (BAT) defaults to 0s. What happens if we\nchange it? Let's try, by changing it in steps of 30s:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ca = osipi.aif_parker(t, BAT=0)\nplt.plot(t, ca, \"b-\", label=\"BAT = 0s\")\nca = osipi.aif_parker(t, BAT=30)\nplt.plot(t, ca, \"r-\", label=\"BAT = 30s\")\nca = osipi.aif_parker(t, BAT=60)\nplt.plot(t, ca, \"g-\", label=\"BAT = 60s\")\nca = osipi.aif_parker(t, BAT=90)\nplt.plot(t, ca, \"m-\", label=\"BAT = 90s\")\nplt.xlabel(\"Time (sec)\")\nplt.ylabel(\"Plasma concentration (mM)\")\nplt.legend()\nplt.show()\n\n# Choose the last image as a thumbnail for the gallery\n# sphinx_gallery_thumbnail_number = -1"
+      ]
+    }
+  ],
+  "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": "3.10.14"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/a33e2b5538ac861c3439d2b7ac57fc73/plot_tofts.py

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+"""
+====================
+The Tofts model
+====================
+
+Simulating tissue concentrations from Tofts model with different settings.
+"""
+
+import matplotlib.pyplot as plt
+
+# %%
+# Import necessary packages
+import numpy as np
+import osipi
+
+# %%
+# Generate Parker AIF with default settings.
+
+# Define time points in units of seconds - in this case we use a time
+# resolution of 1 sec and a total duration of 6 minutes.
+t = np.arange(0, 6 * 60, 1)
+
+# Create an AIF with default settings
+ca = osipi.aif_parker(t)
+
+# %%
+# Plot the tissue concentrations for an extracellular volume fraction
+# of 0.2 and 3 different transfer rate constants of 0.05, 0.2 and 0.6
+# /min
+Ktrans = [0.05, 0.2, 0.6]  # in units of 1/min
+ve = 0.2  # volume fraction between 0 and 1
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[0], ve=ve)
+plt.plot(t, ct, "b-", label=f"Ktrans = {Ktrans[0]} /min")
+ct = osipi.tofts(t, ca, Ktrans[1], ve)
+plt.plot(t, ct, "g-", label=f"Ktrans = {Ktrans[1]} /min")
+ct = osipi.tofts(t, ca, Ktrans[2], ve)
+plt.plot(t, ct, "m-", label=f"Ktrans = {Ktrans[2]} /min")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
+plt.legend()
+plt.show()
+
+# %%
+# Comparing different discretization methods for an extracellular
+# volume fraction of 0.2 and Ktrans of 0.2 /min
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[1], ve=ve)  # Defaults to Convolution
+plt.plot(t, ct, "b-", label="Convolution")
+ct = osipi.tofts(t, ca, Ktrans=Ktrans[1], ve=ve, discretization_method="exp")
+plt.plot(t, ct, "g-", label="Exponential Convolution")
+plt.title(f"Ktrans = {Ktrans[1]} /min")
+plt.xlabel("Time (sec)")
+plt.ylabel("Tissue concentration (mM)")
+plt.legend()
+plt.show()
+
+# Choose the last image as a thumbnail for the gallery
+# sphinx_gallery_thumbnail_number = -1