cvfit_example_GHK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pylab import*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cvfit import data\n",
    "from cvfit.equations import GHK\n",
    "from cvfit.fitting import SingleFitSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: Example.xlsx\n",
      "\n",
      "X\tY\ts(Y)\tweight\n",
      "5\t-8.42\t0\t1\n",
      "5\t-7.13\t0\t1\n",
      "5\t-9.45\t0\t1\n",
      "5\t-7.01\t0\t1\n",
      "50\t-5.02\t0\t1\n",
      "50\t-6.2\t0\t1\n",
      "50\t-4.96\t0\t1\n",
      "100\t-2.41\t0\t1\n",
      "100\t-1.83\t0\t1\n",
      "100\t-2.16\t0\t1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "filename = \"./Example/Example.xlsx\"\n",
    "set0 = data.read_sets_from_Excel(filename, 2, 0, 3)[0]\n",
    "print(\"Loaded: \" + os.path.split(str(filename))[1])\n",
    "print (str(set0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear equation fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\tFitting session for Set 1 initialised!"
     ]
    }
   ],
   "source": [
    "equation = GHK('GHK', pars=np.array([1.0, 150.0, 145.0, 5.0]))\n",
    "fsession = SingleFitSession(set0, equation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of point fitted = 10\n",
      "Number of parameters estimated = 1\n",
      "Degrees of freedom = 9\n",
      "Residual error SD = 0.776      (variance = 0.602)\n",
      "Parameter 1: r  \t= 0.707792  \t  Approx SD = 0.0102738\t  CV = 1.5\n",
      "Parameter 2: totOut  \t= 150  \t  (fixed)\n",
      "Parameter 3: In1  \t= 145  \t  (fixed)\n",
      "Parameter 4: In2  \t= 5  \t  (fixed)\n",
      "Minimum SSD = 5.422; \n",
      "Max log-likelihood = -11.129\n",
      "Correlation matrix = [!!!! PRINTOUT OF CORRELATION MATRIX NOT IMPLEMENTED YET. SORRY.\n",
      "\n",
      "\n",
      "LIKELIHOOD INTERVALS\n",
      "2.56-unit Likelihood Intervals  (equivalent SD for Gaussian- 2.26)\n",
      "Lmax= -11.1287;   Lcrit= -13.687\n",
      "Parameter 1:   r\t= 0.707792\t  LOWER = 0.682738\t  UPPER = 0.733097\n",
      "Parameter 2:   totOut\t= 150\t  (fixed)\n",
      "Parameter 3:   In1\t= 145\t  (fixed)\n",
      "Parameter 4:   In2\t= 5\t  (fixed)\n"
     ]
    }
   ],
   "source": [
    "fsession.fit()\n",
    "fsession.calculate_errors()\n",
    "print(fsession.string_estimates())\n",
    "print(fsession.string_liklimits())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "plX, plY = equation.calculate_plot(set0.X, equation.pars)\n",
    "rlim = fsession.Llimits[0]\n",
    "plX1, plY1 = equation.calculate_plot(set0.X, [rlim[0], 150.0, 145.0, 5.0])\n",
    "plX2, plY2 = equation.calculate_plot(set0.X, [rlim[1], 150.0, 145.0, 5.0])\n",
    "set0.average_pooled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(set0.X, set0.Y, 'ro') # all data points\n",
    "errorbar(set0.avX, set0.avY, yerr=set0.avS, fmt='o')\n",
    "plot(plX, plY, 'b-') # fit\n",
    "plot(plX1, plY1, 'k--') # lower likelihood limit\n",
    "plot(plX2, plY2, 'k--') # higher likelihood limit\n",
    "xlabel('Kout, mM')\n",
    "ylabel('Erev, mV');"
   ]
  },
  {
   "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",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}