From e8dfb3cf49d4e29103c89a0c961770783da39d91 Mon Sep 17 00:00:00 2001
From: "Tu, Ethan" <tuethan@msu.edu>
Date: Mon, 13 Apr 2020 15:46:37 -0400
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-</head>
-<body>
-<main>
-<article id="content">
-<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:
- """The pkOptimizer object is an optimizer for parameters in pk models."""
-
- def __init__ (self, wd, Flow = 1/60, Vp = 0.05, Visf = 0.15, PS = 1/60):
- """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.
- """
-
- def getData(self, wd):
- """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)
- """
-
- os.chdir(wd)
- #os.chdir(r"C:\Users\Ethan\OneDrive - Michigan State University\MSU\Classwork\Computational Modeling\Models\Data")
- #create directory of all csv files,
- data = list(csv.reader(open('CTPERF005_stress.csv'), delimiter = '\t'))
-
- t = []
- aorta = []
- myo = []
-
- for i in range(12):
- t.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][0])[0]))
- aorta.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][1])[0]))
- myo.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][2])[0]))
-
- return t, aorta, myo
-
- def gammaFunc(self, time, a, l, s):
- """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.
- """
- rv = gamma(a, loc = l, scale = s) #input function
- return rv.pdf(time)
-
- def curveFit(self, t, aorta, myo, model):
- """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).
- """
-
- def getPlot(self):
- """Plots the original data to the fitted curve."""
- plt.plot(t, aorta, 'bo', label='data')
- #plt.plot(t, y, 'b-', label='data')
- popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = 'trf')
-
- print(f'alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}')
-
- plt.plot(t, gammaFunc(t, *popt), 'r-', label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
- plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), 'b-')
-
-
-# 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:
- """The pkOptimizer object is an optimizer for parameters in pk models."""
-
- def __init__ (self, wd, Flow = 1/60, Vp = 0.05, Visf = 0.15, PS = 1/60):
- """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.
- """
-
- def getData(self, wd):
- """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)
- """
-
- os.chdir(wd)
- #os.chdir(r"C:\Users\Ethan\OneDrive - Michigan State University\MSU\Classwork\Computational Modeling\Models\Data")
- #create directory of all csv files,
- data = list(csv.reader(open('CTPERF005_stress.csv'), delimiter = '\t'))
-
- t = []
- aorta = []
- myo = []
-
- for i in range(12):
- t.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][0])[0]))
- aorta.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][1])[0]))
- myo.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][2])[0]))
-
- return t, aorta, myo
-
- def gammaFunc(self, time, a, l, s):
- """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.
- """
- rv = gamma(a, loc = l, scale = s) #input function
- return rv.pdf(time)
-
- def curveFit(self, t, aorta, myo, model):
- """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).
- """
-
- def getPlot(self):
- """Plots the original data to the fitted curve."""
- plt.plot(t, aorta, 'bo', label='data')
- #plt.plot(t, y, 'b-', label='data')
- popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = 'trf')
-
- print(f'alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}')
-
- plt.plot(t, gammaFunc(t, *popt), 'r-', label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
- plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), 'b-')</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> : <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> : <code>double</code></dt>
-<dd>Vp is the volume of plasma in mL.</dd>
-<dt><strong><code>Visf</code></strong> : <code>double</code></dt>
-<dd>Visf is the volume of interstitial fluid in mL.</dd>
-<dt><strong><code>PS</code></strong> : <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):
- """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).
- """</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):
- """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.
- """
- 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> : <code>double</code>[]</dt>
-<dd>list of all timepoints</dd>
-<dt><strong><code>aorta</code></strong> : <code>double</code>[]</dt>
-<dd>concentration of tracer in aorta (input function)</dd>
-<dt><strong><code>myo</code></strong> : <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> : <code>double</code>[]</dt>
-<dd>list of all timepoints</dd>
-<dt><strong><code>aorta</code></strong> : <code>double</code>[]</dt>
-<dd>concentration of tracer in aorta (input function)</dd>
-<dt><strong><code>myo</code></strong> : <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):
- """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)
- """
-
- os.chdir(wd)
- #os.chdir(r"C:\Users\Ethan\OneDrive - Michigan State University\MSU\Classwork\Computational Modeling\Models\Data")
- #create directory of all csv files,
- data = list(csv.reader(open('CTPERF005_stress.csv'), delimiter = '\t'))
-
- t = []
- aorta = []
- myo = []
-
- for i in range(12):
- t.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][0])[0]))
- aorta.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][1])[0]))
- myo.append(float(re.compile('\d+[.]+\d+|\d+').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):
- """Plots the original data to the fitted curve."""
- plt.plot(t, aorta, 'bo', label='data')
- #plt.plot(t, y, 'b-', label='data')
- popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = 'trf')
-
- print(f'alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}')
-
- plt.plot(t, gammaFunc(t, *popt), 'r-', label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
- plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), 'b-')</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="">
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