diff --git a/pk_optimizer/pkOptimizer.py b/pk_optimizer/pkOptimizer.py
deleted file mode 100644
index d5423c7929acc838efb9376d36aa0fe079e73fd6..0000000000000000000000000000000000000000
--- a/pk_optimizer/pkOptimizer.py
+++ /dev/null
@@ -1,216 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-from scipy.stats import gamma
-from scipy.integrate import odeint
-from scipy.optimize import fmin as fmin
-
-import os
-import csv
-import re
-import math as math
-import numpy as np
-import matplotlib.pyplot as plt
-
-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
- ----------
- wd : string
- wd is the working directory path
- 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.
- time : double[]
- list of all timepoints
- aorta : double[]
- concentration of tracer in aorta (input function)
- myo : double[]
- concentration of tracer in myocardial tissue (Cisf)
- opt : float[]
- opt is the optimized parameters to fit curve
- """
- self.wd = wd
- #self.Flow = Flow
- #self.Vp = Vp
- #self.Visf = Visf
- #self.PS = PS
- self.time = []
- self.aorta = []
- self.myo = []
- self.opt = []
-
- def getData(self, filename):
- """Imports data from all .csv files in directory.
- Parameters
- ----------
- wd : str
- wd is the working directory path
-
- Attributes
- ----------
- time : double[]
- list of all timepoints
- aorta : double[]
- concentration of tracer in aorta (input function)
- myo : double[]
- concentration of tracer in myocardial tissue (Cisf)
-
- Returns
- -------
- time : 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(self.wd)
- #File not found error
- if not os.path.isfile(filename):
- raise ValueError("Input file does not exist: {0}. I'll quit now.".format(filename))
-
- data = list(csv.reader(open(filename), delimiter = '\t'))
-
- for i in range(12):
- self.time.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][0])[0]))
- self.aorta.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][1])[0]))
- self.myo.append(float(re.compile('\d+[.]+\d+|\d+').findall(data[i+1][2])[0]))
-
- return self.time, self.aorta, self.myo
-
- #gamma_var distribution curve
- def gamma_var(self, t = np.arange(0,25), ymax = 10, tmax = 10, alpha = 2, delay = 5):
- """Creates a gamma variate probability density function with given alpha, location, and scale values.
- Parameters
- ----------
- t : double[]
- array of timepoints
- ymax : double
- peak y value of gamma distribution
- tmax : double
- location of 50th percentile of function
- alpha : double
- scale parameter
- delay : double
- time delay of which to start gamma distribution
-
- Returns
- -------
- y : double[]
- probability density function of your gamma variate.
- """
- # Following Madsen 1992 simplified parameterization for gamma variate
-
- t = t
- ymax = ymax
- tmax = tmax
- alpha = alpha
- delay = delay
-
- y = np.zeros(np.size(t)); #preallocate output
-
- for i in range(np.size(y)):
- if t[i] < delay:
- y[i] = 0
- else:
- y[i] = round((ymax*tmax**(-alpha)*math.exp(alpha))*(t[i]-delay)**alpha*math.exp(-alpha*(t[i]-delay)/tmax),3)
- return y
-
- #gamma_var_error
- def MSE(self, param = [10, 10, 2, 5]):
- """Calculates Mean squared error (MSE) between data and gamma variate with given parameters values.
- Parameters
- ----------
- param : ndarray[]
- time : double[]
- array of timepoints
- ymax : double
- peak y value of gamma distribution
- tmax : double
- location of 50th percentile of function
- alpha : double
- scale parameter
- delay : double
- time delay of which to start gamma distribution
-
- Returns
- -------
- MSE : double
- Mean squared error
- """
- if len(param) < 1:
- ymax = 10; tmax = 10; alpha = 2,; delay = 5
- elif len(param) < 2:
- ymax = param[0]; tmax = 10; alpha = 2,; delay = 5
- elif len(param) < 3:
- ymax = param[0]; tmax = param[1]; alpha = 2,; delay = 5
- elif len(param) < 4:
- ymax = param[0]; tmax = param[1]; alpha = param[2],; delay = 5
- else:
- # Mean squared error (MSE) between data and gamma variate with given parameters
- ymax = param[0]
- tmax = param[1]
- alpha = param[2]
- delay = param[3]
-
- MSE = 0
-
- if tmax <= 0 or ymax <= 10 or delay < 0 or alpha<0 or alpha>1000 or tmax>1000:
- MSE = 1000000; #just return a big number
- return MSE
-
- else:
- model_vals = self.gamma_var(self.time, ymax, tmax, alpha, delay);
-
- for i in range(len(self.myo)):
- MSE = (self.aorta[i]-model_vals[i])**2 + MSE
- MSE = MSE / len(self.myo)
- return round(MSE,3)
-
- def inputFuncFit(self, initGuesses):
- """Uses fmin algorithm (Nelder-Mead simplex algorithm) to minimize loss function (MSE) of input function.
- Parameters
- ----------
- initGuesses : ndarray[]
- Array of initial guesses containing:
- time : double[]
- array of timepoints
- ymax : double
- peak y value of gamma distribution
- tmax : double
- location of 50th percentile of function
- alpha : double
- scale parameter
- delay : double
- time delay of which to start gamma distribution
- Returns
- -------
- opt : double[]
- optimized parameters
- """
- # Mean squared error (MSE) between data and gamma variate with given parameters
- self.opt = fmin(self.MSE, initGuesses, maxiter = 1000)
- return self.opt.round(2)
-
- def plotOpt(self):
- """Plots the original data to the fitted curve."""
- plt.plot(self.time, self.aorta, 'bo', label='data')
- #plt.plot(t, y, 'b-', label='data')
-
- y = self.gamma_var(np.arange(0,25,0.01), self.opt[0], self.opt[1], self.opt[2], self.opt[3])
-
- plt.plot(np.arange(0,25,0.01), y, 'r-')
-
-