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<header>
<h1 class="title">Module <code>pkOptimizer</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">#!/usr/bin/env python
# coding: utf-8
# In[9]:
from scipy.stats import gamma
from scipy.integrate import odeint
from scipy.optimize import minimize
from scipy.optimize import curve_fit
import os
import csv
import re
import math as math
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
class pkOptimizer:
&#34;&#34;&#34;The pkOptimizer object is an optimizer for parameters in pk models.&#34;&#34;&#34;
def __init__ (self, wd, Flow = 1/60, Vp = 0.05, Visf = 0.15, PS = 1/60):
&#34;&#34;&#34;Initializes the model with initial guess parameter values for flow, Vp, Visf, and PS.
Parameters
----------
Flow : double
Flow is the flow of plasma through the blood vessel in mL/(mL*min). Defaults to 1/60.
Vp : double
Vp is the volume of plasma in mL. Defaults to 0.05.
Visf : double
Visf is the volume of interstitial fluid in mL. Defaults to 0.15.
PS : double
PS is the permeability-surface area constant in mL/(g*min). Defaults to 1/60.
&#34;&#34;&#34;
def getData(self, wd):
&#34;&#34;&#34;Imports data from all .csv files in directory.
Parameters
----------
wd : str
wd is the working directory path
Attributes
----------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
Returns
-------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
&#34;&#34;&#34;
os.chdir(wd)
#os.chdir(r&#34;C:\Users\Ethan\OneDrive - Michigan State University\MSU\Classwork\Computational Modeling\Models\Data&#34;)
#create directory of all csv files,
data = list(csv.reader(open(&#39;CTPERF005_stress.csv&#39;), delimiter = &#39;\t&#39;))
t = []
aorta = []
myo = []
for i in range(12):
t.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][0])[0]))
aorta.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][1])[0]))
myo.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][2])[0]))
return t, aorta, myo
def gammaFunc(self, time, a, l, s):
&#34;&#34;&#34;Creates a gamma variate probability density function with given alpha, location, and scale values.
Parameters
----------
time : double[]
array of timepoints
a : double
alpha value of gamma PDF
l : double
location of 50th percentile of function
s : double
scale parameter
Returns
-------
rv.pdf(time)
probability density function of your gamma variate.
&#34;&#34;&#34;
rv = gamma(a, loc = l, scale = s) #input function
return rv.pdf(time)
def curveFit(self, t, aorta, myo, model):
&#34;&#34;&#34;Takes in data and fits gamma curve to aorta and Cisf from model to myo. Returns parameters for best fit.
Parameters
----------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
model : pkModel object
a pk model, either 1Comp or 2Comp
Returns
-------
Flow : double
Flow is the flow of plasma through the blood vessel in mL/(mL*min).
Vp : double
Vp is the volume of plasma in mL.
Visf : double
Visf is the volume of interstitial fluid in mL.
PS : double
PS is the permeability-surface area constant in mL/(g*min).
&#34;&#34;&#34;
def getPlot(self):
&#34;&#34;&#34;Plots the original data to the fitted curve.&#34;&#34;&#34;
plt.plot(t, aorta, &#39;bo&#39;, label=&#39;data&#39;)
#plt.plot(t, y, &#39;b-&#39;, label=&#39;data&#39;)
popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = &#39;trf&#39;)
print(f&#39;alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}&#39;)
plt.plot(t, gammaFunc(t, *popt), &#39;r-&#39;, label=&#39;fit: a=%5.3f, b=%5.3f, c=%5.3f&#39; % tuple(popt))
plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), &#39;b-&#39;)
# In[ ]:</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="pkOptimizer.pkOptimizer"><code class="flex name class">
<span>class <span class="ident">pkOptimizer</span></span>
<span>(</span><span>wd, Flow=0.016666666666666666, Vp=0.05, Visf=0.15, PS=0.016666666666666666)</span>
</code></dt>
<dd>
<section class="desc"><p>The pkOptimizer object is an optimizer for parameters in pk models.</p>
<p>Initializes the model with initial guess parameter values for flow, Vp, Visf, and PS.
Parameters</p>
<hr>
<p>Flow : double
Flow is the flow of plasma through the blood vessel in mL/(mL*min). Defaults to 1/60.</p>
<p>Vp : double
Vp is the volume of plasma in mL. Defaults to 0.05.</p>
<p>Visf : double
Visf is the volume of interstitial fluid in mL. Defaults to 0.15.</p>
<p>PS : double
PS is the permeability-surface area constant in mL/(g*min). Defaults to 1/60.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class pkOptimizer:
&#34;&#34;&#34;The pkOptimizer object is an optimizer for parameters in pk models.&#34;&#34;&#34;
def __init__ (self, wd, Flow = 1/60, Vp = 0.05, Visf = 0.15, PS = 1/60):
&#34;&#34;&#34;Initializes the model with initial guess parameter values for flow, Vp, Visf, and PS.
Parameters
----------
Flow : double
Flow is the flow of plasma through the blood vessel in mL/(mL*min). Defaults to 1/60.
Vp : double
Vp is the volume of plasma in mL. Defaults to 0.05.
Visf : double
Visf is the volume of interstitial fluid in mL. Defaults to 0.15.
PS : double
PS is the permeability-surface area constant in mL/(g*min). Defaults to 1/60.
&#34;&#34;&#34;
def getData(self, wd):
&#34;&#34;&#34;Imports data from all .csv files in directory.
Parameters
----------
wd : str
wd is the working directory path
Attributes
----------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
Returns
-------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
&#34;&#34;&#34;
os.chdir(wd)
#os.chdir(r&#34;C:\Users\Ethan\OneDrive - Michigan State University\MSU\Classwork\Computational Modeling\Models\Data&#34;)
#create directory of all csv files,
data = list(csv.reader(open(&#39;CTPERF005_stress.csv&#39;), delimiter = &#39;\t&#39;))
t = []
aorta = []
myo = []
for i in range(12):
t.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][0])[0]))
aorta.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][1])[0]))
myo.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][2])[0]))
return t, aorta, myo
def gammaFunc(self, time, a, l, s):
&#34;&#34;&#34;Creates a gamma variate probability density function with given alpha, location, and scale values.
Parameters
----------
time : double[]
array of timepoints
a : double
alpha value of gamma PDF
l : double
location of 50th percentile of function
s : double
scale parameter
Returns
-------
rv.pdf(time)
probability density function of your gamma variate.
&#34;&#34;&#34;
rv = gamma(a, loc = l, scale = s) #input function
return rv.pdf(time)
def curveFit(self, t, aorta, myo, model):
&#34;&#34;&#34;Takes in data and fits gamma curve to aorta and Cisf from model to myo. Returns parameters for best fit.
Parameters
----------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
model : pkModel object
a pk model, either 1Comp or 2Comp
Returns
-------
Flow : double
Flow is the flow of plasma through the blood vessel in mL/(mL*min).
Vp : double
Vp is the volume of plasma in mL.
Visf : double
Visf is the volume of interstitial fluid in mL.
PS : double
PS is the permeability-surface area constant in mL/(g*min).
&#34;&#34;&#34;
def getPlot(self):
&#34;&#34;&#34;Plots the original data to the fitted curve.&#34;&#34;&#34;
plt.plot(t, aorta, &#39;bo&#39;, label=&#39;data&#39;)
#plt.plot(t, y, &#39;b-&#39;, label=&#39;data&#39;)
popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = &#39;trf&#39;)
print(f&#39;alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}&#39;)
plt.plot(t, gammaFunc(t, *popt), &#39;r-&#39;, label=&#39;fit: a=%5.3f, b=%5.3f, c=%5.3f&#39; % tuple(popt))
plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), &#39;b-&#39;)</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="pkOptimizer.pkOptimizer.curveFit"><code class="name flex">
<span>def <span class="ident">curveFit</span></span>(<span>self, t, aorta, myo, model)</span>
</code></dt>
<dd>
<section class="desc"><p>Takes in data and fits gamma curve to aorta and Cisf from model to myo. Returns parameters for best fit.</p>
<h2 id="parameters">Parameters</h2>
<p>t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
model : pkModel object
a pk model, either 1Comp or 2Comp</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>Flow</code></strong> :&ensp;<code>double</code></dt>
<dd>Flow is the flow of plasma through the blood vessel in mL/(mL*min).</dd>
<dt><strong><code>Vp</code></strong> :&ensp;<code>double</code></dt>
<dd>Vp is the volume of plasma in mL.</dd>
<dt><strong><code>Visf</code></strong> :&ensp;<code>double</code></dt>
<dd>Visf is the volume of interstitial fluid in mL.</dd>
<dt><strong><code>PS</code></strong> :&ensp;<code>double</code></dt>
<dd>PS is the permeability-surface area constant in mL/(g*min).</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def curveFit(self, t, aorta, myo, model):
&#34;&#34;&#34;Takes in data and fits gamma curve to aorta and Cisf from model to myo. Returns parameters for best fit.
Parameters
----------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
model : pkModel object
a pk model, either 1Comp or 2Comp
Returns
-------
Flow : double
Flow is the flow of plasma through the blood vessel in mL/(mL*min).
Vp : double
Vp is the volume of plasma in mL.
Visf : double
Visf is the volume of interstitial fluid in mL.
PS : double
PS is the permeability-surface area constant in mL/(g*min).
&#34;&#34;&#34;</code></pre>
</details>
</dd>
<dt id="pkOptimizer.pkOptimizer.gammaFunc"><code class="name flex">
<span>def <span class="ident">gammaFunc</span></span>(<span>self, time, a, l, s)</span>
</code></dt>
<dd>
<section class="desc"><p>Creates a gamma variate probability density function with given alpha, location, and scale values.
Parameters</p>
<hr>
<p>time : double[]
array of timepoints
a : double
alpha value of gamma PDF
l : double
location of 50th percentile of function
s : double
scale parameter </p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>rv.pdf</code>(<code>time</code>)</dt>
<dd>probability density function of your gamma variate.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def gammaFunc(self, time, a, l, s):
&#34;&#34;&#34;Creates a gamma variate probability density function with given alpha, location, and scale values.
Parameters
----------
time : double[]
array of timepoints
a : double
alpha value of gamma PDF
l : double
location of 50th percentile of function
s : double
scale parameter
Returns
-------
rv.pdf(time)
probability density function of your gamma variate.
&#34;&#34;&#34;
rv = gamma(a, loc = l, scale = s) #input function
return rv.pdf(time)</code></pre>
</details>
</dd>
<dt id="pkOptimizer.pkOptimizer.getData"><code class="name flex">
<span>def <span class="ident">getData</span></span>(<span>self, wd)</span>
</code></dt>
<dd>
<section class="desc"><p>Imports data from all .csv files in directory.
Parameters</p>
<hr>
<p>wd : str
wd is the working directory path</p>
<h2 id="attributes">Attributes</h2>
<dl>
<dt><strong><code>t</code></strong> :&ensp;<code>double</code>[]</dt>
<dd>list of all timepoints</dd>
<dt><strong><code>aorta</code></strong> :&ensp;<code>double</code>[]</dt>
<dd>concentration of tracer in aorta (input function)</dd>
<dt><strong><code>myo</code></strong> :&ensp;<code>double</code>[]</dt>
<dd>concentration of tracer in myocardial tissue (Cisf)</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>t</code></strong> :&ensp;<code>double</code>[]</dt>
<dd>list of all timepoints</dd>
<dt><strong><code>aorta</code></strong> :&ensp;<code>double</code>[]</dt>
<dd>concentration of tracer in aorta (input function)</dd>
<dt><strong><code>myo</code></strong> :&ensp;<code>double</code>[]</dt>
<dd>concentration of tracer in myocardial tissue (Cisf)</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def getData(self, wd):
&#34;&#34;&#34;Imports data from all .csv files in directory.
Parameters
----------
wd : str
wd is the working directory path
Attributes
----------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
Returns
-------
t : double[]
list of all timepoints
aorta : double[]
concentration of tracer in aorta (input function)
myo : double[]
concentration of tracer in myocardial tissue (Cisf)
&#34;&#34;&#34;
os.chdir(wd)
#os.chdir(r&#34;C:\Users\Ethan\OneDrive - Michigan State University\MSU\Classwork\Computational Modeling\Models\Data&#34;)
#create directory of all csv files,
data = list(csv.reader(open(&#39;CTPERF005_stress.csv&#39;), delimiter = &#39;\t&#39;))
t = []
aorta = []
myo = []
for i in range(12):
t.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][0])[0]))
aorta.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][1])[0]))
myo.append(float(re.compile(&#39;\d+[.]+\d+|\d+&#39;).findall(data[i+1][2])[0]))
return t, aorta, myo</code></pre>
</details>
</dd>
<dt id="pkOptimizer.pkOptimizer.getPlot"><code class="name flex">
<span>def <span class="ident">getPlot</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>Plots the original data to the fitted curve.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def getPlot(self):
&#34;&#34;&#34;Plots the original data to the fitted curve.&#34;&#34;&#34;
plt.plot(t, aorta, &#39;bo&#39;, label=&#39;data&#39;)
#plt.plot(t, y, &#39;b-&#39;, label=&#39;data&#39;)
popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = &#39;trf&#39;)
print(f&#39;alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}&#39;)
plt.plot(t, gammaFunc(t, *popt), &#39;r-&#39;, label=&#39;fit: a=%5.3f, b=%5.3f, c=%5.3f&#39; % tuple(popt))
plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), &#39;b-&#39;)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="pkOptimizer.pkOptimizer" href="#pkOptimizer.pkOptimizer">pkOptimizer</a></code></h4>
<ul class="">
<li><code><a title="pkOptimizer.pkOptimizer.curveFit" href="#pkOptimizer.pkOptimizer.curveFit">curveFit</a></code></li>
<li><code><a title="pkOptimizer.pkOptimizer.gammaFunc" href="#pkOptimizer.pkOptimizer.gammaFunc">gammaFunc</a></code></li>
<li><code><a title="pkOptimizer.pkOptimizer.getData" href="#pkOptimizer.pkOptimizer.getData">getData</a></code></li>
<li><code><a title="pkOptimizer.pkOptimizer.getPlot" href="#pkOptimizer.pkOptimizer.getPlot">getPlot</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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