diff --git a/pk_optimizer/pkOptimizer.ipynb b/pk_optimizer/pkOptimizer.ipynb
deleted file mode 100644
index fa5b9f96be05394b2e3e00ed1a4a0d4c82c29825..0000000000000000000000000000000000000000
--- a/pk_optimizer/pkOptimizer.ipynb
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@@ -1,175 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "from scipy.stats import gamma\n",
- "from scipy.integrate import odeint \n",
- "from scipy.optimize import minimize\n",
- "from scipy.optimize import curve_fit\n",
- "\n",
- "import os\n",
- "import csv\n",
- "import re\n",
- "import math as math\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "#%matplotlib inline\n",
- "\n",
- "class pkOptimizer:\n",
- " \"\"\"The pkOptimizer object is an optimizer for parameters in pk models.\"\"\"\n",
- " \n",
- " def __init__ (self, wd, Flow = 1/60, Vp = 0.05, Visf = 0.15, PS = 1/60):\n",
- " \"\"\"Initializes the model with initial guess parameter values for flow, Vp, Visf, and PS.\n",
- " Parameters\n",
- " ---------- \n",
- " Flow : double\n",
- " Flow is the flow of plasma through the blood vessel in mL/(mL*min). Defaults to 1/60.\n",
- " \n",
- " Vp : double\n",
- " Vp is the volume of plasma in mL. Defaults to 0.05.\n",
- " \n",
- " Visf : double\n",
- " Visf is the volume of interstitial fluid in mL. Defaults to 0.15.\n",
- " \n",
- " PS : double\n",
- " PS is the permeability-surface area constant in mL/(g*min). Defaults to 1/60. \n",
- " \"\"\"\n",
- " \n",
- " def getData(self, wd):\n",
- " \"\"\"Imports data from all .csv files in directory.\n",
- " Parameters\n",
- " ---------- \n",
- " wd : str\n",
- " wd is the working directory path\n",
- " \n",
- " Attributes\n",
- " ----------\n",
- " t : double[]\n",
- " list of all timepoints\n",
- " aorta : double[]\n",
- " concentration of tracer in aorta (input function)\n",
- " myo : double[]\n",
- " concentration of tracer in myocardial tissue (Cisf)\n",
- " \n",
- " Returns\n",
- " -------\n",
- " t : double[]\n",
- " list of all timepoints\n",
- " aorta : double[]\n",
- " concentration of tracer in aorta (input function)\n",
- " myo : double[]\n",
- " concentration of tracer in myocardial tissue (Cisf)\n",
- " \"\"\"\n",
- " \n",
- " os.chdir(wd)\n",
- " #os.chdir(r\"C:\\Users\\Ethan\\OneDrive - Michigan State University\\MSU\\Classwork\\Computational Modeling\\Models\\Data\")\n",
- " #create directory of all csv files,\n",
- " data = list(csv.reader(open('CTPERF005_stress.csv'), delimiter = '\\t'))\n",
- "\n",
- " t = []\n",
- " aorta = []\n",
- " myo = []\n",
- " \n",
- " for i in range(12):\n",
- " t.append(float(re.compile('\\d+[.]+\\d+|\\d+').findall(data[i+1][0])[0]))\n",
- " aorta.append(float(re.compile('\\d+[.]+\\d+|\\d+').findall(data[i+1][1])[0]))\n",
- " myo.append(float(re.compile('\\d+[.]+\\d+|\\d+').findall(data[i+1][2])[0]))\n",
- "\n",
- " return t, aorta, myo\n",
- "\n",
- " def gammaFunc(self, time, a, l, s):\n",
- " \"\"\"Creates a gamma variate probability density function with given alpha, location, and scale values.\n",
- " Parameters\n",
- " ---------- \n",
- " time : double[]\n",
- " array of timepoints\n",
- " a : double\n",
- " alpha value of gamma PDF\n",
- " l : double\n",
- " location of 50th percentile of function\n",
- " s : double\n",
- " scale parameter \n",
- " \n",
- " Returns\n",
- " -------\n",
- " rv.pdf(time)\n",
- " probability density function of your gamma variate.\n",
- " \"\"\"\n",
- " rv = gamma(a, loc = l, scale = s) #input function\n",
- " return rv.pdf(time)\n",
- " \n",
- " def curveFit(self, t, aorta, myo, model):\n",
- " \"\"\"Takes in data and fits gamma curve to aorta and Cisf from model to myo. Returns parameters for best fit.\n",
- " \n",
- " Parameters\n",
- " ---------- \n",
- " t : double[]\n",
- " list of all timepoints\n",
- " aorta : double[]\n",
- " concentration of tracer in aorta (input function)\n",
- " myo : double[]\n",
- " concentration of tracer in myocardial tissue (Cisf)\n",
- " model : pkModel object\n",
- " a pk model, either 1Comp or 2Comp\n",
- " \n",
- " Returns\n",
- " -------\n",
- " Flow : double\n",
- " Flow is the flow of plasma through the blood vessel in mL/(mL*min).\n",
- " \n",
- " Vp : double\n",
- " Vp is the volume of plasma in mL.\n",
- " \n",
- " Visf : double\n",
- " Visf is the volume of interstitial fluid in mL.\n",
- " \n",
- " PS : double\n",
- " PS is the permeability-surface area constant in mL/(g*min).\n",
- " \"\"\"\n",
- " \n",
- " def getPlot(self):\n",
- " \"\"\"Plots the original data to the fitted curve.\"\"\"\n",
- " plt.plot(t, aorta, 'bo', label='data')\n",
- " #plt.plot(t, y, 'b-', label='data')\n",
- " popt, pcov = curve_fit(gammaFunc, t, aorta, p0 = [2, 8, 10000], method = 'trf')\n",
- "\n",
- " print(f'alpha = {popt[0]}, loc = {popt[1]}, scale = {popt[2]}')\n",
- "\n",
- " plt.plot(t, gammaFunc(t, *popt), 'r-', label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))\n",
- " plt.plot(time, gammaFunc(time, .1313, 8.533, 10000), 'b-')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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",
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- "nbformat": 4,
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