180206_test_minimize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The first step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No starting values entered: Using grid-search\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10365.9        initial\n",
      "    2        1.57082      10193.5        golden\n",
      "    3        1.65836      10160.3        golden\n",
      "    4        1.66641      10159.3        parabolic\n",
      "    5        1.68601      10158.2        parabolic\n",
      "    6        1.68808      10158.1        parabolic\n",
      "    7        1.73083      10159.8        golden\n",
      "    8        1.69885      10157.8        parabolic\n",
      "    9        1.71106      10157.8        golden\n",
      "   10        1.70771      10157.7        parabolic\n",
      "   11        1.70633      10157.7        parabolic\n",
      "   12        1.70698      10157.7        parabolic\n",
      "   13        1.70697      10157.7        parabolic\n",
      "   14        1.70698      10157.7        parabolic\n",
      "   15        1.70699      10157.7        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10890.9        initial\n",
      "    2        1.57082        10743        golden\n",
      "    3        1.65836      10751.5        golden\n",
      "    4        1.60481      10735.8        parabolic\n",
      "    5        1.60615      10735.8        parabolic\n",
      "    6        1.60675      10735.8        parabolic\n",
      "    7        1.60672      10735.8        parabolic\n",
      "    8        1.60671      10735.8        parabolic\n",
      "    9        1.60672      10735.8        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918        11373        initial\n",
      "    2        1.57082      11320.4        golden\n",
      "    3        1.65836        11375        golden\n",
      "    4        1.54275      11320.2        parabolic\n",
      "    5        1.55578      11319.4        parabolic\n",
      "    6          1.556      11319.4        parabolic\n",
      "    7        1.55621      11319.4        parabolic\n",
      "    8         1.5562      11319.4        parabolic\n",
      "    9        1.55619      11319.4        parabolic\n",
      "   10         1.5562      11319.4        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10289.9        initial\n",
      "    2        1.57082      10120.9        golden\n",
      "    3        1.65836      10089.5        golden\n",
      "    4        1.66384        10089        parabolic\n",
      "    5        1.67715      10088.5        parabolic\n",
      "    6        1.67796      10088.5        parabolic\n",
      "    7        1.72457      10086.4        golden\n",
      "    8        1.75338      10091.4        golden\n",
      "    9        1.70677      10087.4        golden\n",
      "   10        1.73558      10086.7        golden\n",
      "   11        1.72547      10086.4        parabolic\n",
      "   12        1.72713      10086.4        parabolic\n",
      "   13        1.73035      10086.4        golden\n",
      "   14        1.72762      10086.4        parabolic\n",
      "   15        1.72753      10086.4        parabolic\n",
      "   16        1.72752      10086.4        parabolic\n",
      "   17        1.72753      10086.4        parabolic\n",
      "   18        1.72752      10086.4        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918        10539        initial\n",
      "    2        1.57082      10389.2        golden\n",
      "    3        1.65836      10398.4        golden\n",
      "    4        1.60419      10383.2        parabolic\n",
      "    5        1.60462      10383.2        parabolic\n",
      "    6        1.60423      10383.2        parabolic\n",
      "    7        1.60423      10383.2        parabolic\n",
      "    8        1.60423      10383.2        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10800.3        initial\n",
      "    2        1.57082      10679.2        golden\n",
      "    3        1.65836        10713        golden\n",
      "    4        1.57895      10678.3        parabolic\n",
      "    5        1.58333      10678.2        parabolic\n",
      "    6        1.58396      10678.2        parabolic\n",
      "    7        1.58393      10678.2        parabolic\n",
      "    8        1.58392      10678.2        parabolic\n",
      "    9        1.58393      10678.2        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10283.3        initial\n",
      "    2        1.57082      10112.3        golden\n",
      "    3        1.65836      10083.5        golden\n",
      "    4        1.65764      10083.5        parabolic\n",
      "    5         1.6588      10083.5        parabolic\n",
      "    6        1.65848      10083.5        parabolic\n",
      "    7        1.65847      10083.5        parabolic\n",
      "    8        1.65848      10083.5        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10438.1        initial\n",
      "    2        1.57082      10295.9        golden\n",
      "    3        1.65836      10309.8        golden\n",
      "    4        1.59893      10292.5        parabolic\n",
      "    5        1.59777      10292.5        parabolic\n",
      "    6          1.597      10292.5        parabolic\n",
      "    7        1.59699      10292.5        parabolic\n",
      "    8        1.59699      10292.5        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10583.1        initial\n",
      "    2        1.57082      10456.9        golden\n",
      "    3        1.65836      10492.7        golden\n",
      "    4        1.57854      10456.1        parabolic\n",
      "    5        1.58255        10456        parabolic\n",
      "    6        1.58313        10456        parabolic\n",
      "    7         1.5831        10456        parabolic\n",
      "    8        1.58309        10456        parabolic\n",
      "    9         1.5831        10456        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10293.9        initial\n",
      "    2        1.57082      10129.5        golden\n",
      "    3        1.65836        10101        golden\n",
      "    4        1.65911        10101        parabolic\n",
      "    5        1.66977      10101.3        parabolic\n",
      "    6        1.66318      10100.9        golden\n",
      "    7        1.66227      10100.9        parabolic\n",
      "    8        1.66231      10100.9        parabolic\n",
      "    9        1.66229      10100.9        parabolic\n",
      "   10        1.66229      10100.9        parabolic\n",
      "   11        1.66229      10100.9        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918        10410        initial\n",
      "    2        1.57082      10274.1        golden\n",
      "    3        1.65836      10301.9        golden\n",
      "    4         1.5861      10273.4        parabolic\n",
      "    5        1.58327      10273.3        parabolic\n",
      "    6        1.58249      10273.3        parabolic\n",
      "    7         1.5825      10273.3        parabolic\n",
      "    8         1.5825      10273.3        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n",
      " \n",
      " Func-count     x          f(x)          Procedure\n",
      "    1        1.42918      10473.8        initial\n",
      "    2        1.57082      10358.8        golden\n",
      "    3        1.65836      10404.5        golden\n",
      "    4        1.56974      10358.7        parabolic\n",
      "    5        1.56368      10358.6        parabolic\n",
      "    6        1.51231      10376.8        golden\n",
      "    7        1.56482      10358.5        parabolic\n",
      "    8        1.56483      10358.5        parabolic\n",
      "    9        1.56483      10358.5        parabolic\n",
      "   10        1.56482      10358.5        parabolic\n",
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import DataFrame as df\n",
    "# import MSM_util\n",
    "from MSM_util import *\n",
    "import sys, scipy, numpy\n",
    "\n",
    "# print(scipy.__version__, numpy.__version__, sys.version_info)\n",
    "\n",
    "GLD = pd.read_excel('data_GVZ_GLD.xlsx')\n",
    "date_GLD = GLD.iloc[:,3]\n",
    "GLD = GLD.loc[:,'GLD']\n",
    "GLD_d = GLD - np.mean(GLD)\n",
    "# plt.plot(date_GLD,GLD_d)\n",
    "data = GLD_d\n",
    "\n",
    "kbar = 5\n",
    "startingvals = []\n",
    "LB = [1, 1, 0.001, 0.0001]\n",
    "UB = [50, 1.99, 0.99999, 5]\n",
    "\n",
    "# set up A_template for a transition matrix\n",
    "A_template = T_mat_temp(kbar)\n",
    "\n",
    "\n",
    "# Grid search for starating values\n",
    "# TODO: (try sklearn.GridsearchCV) or map lambda instead of double for-loop\n",
    "# input_param, LLS = MSM_starting_values(data, startingvals, kbar, A_template)\n",
    "input_param, LLS = MSM_starting_values2(data, startingvals, kbar)\n",
    "input_param[0] = sum(input_param[0])\n",
    "bound = list(map(lambda x,y: (x,y), LB,UB))\n",
    "\n",
    "# input_param = np.asarray(input_param)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## input as list [ , ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "MinimizerException",
     "evalue": "\nparams must be a minimizer.Parameters() instance or list of Parameters()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMinimizerException\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-6858d2c1e564>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m param_op = minimize(MSM_likelihood_new2, input_param, args=(kbar, data),\n\u001b[1;32m----> 3\u001b[1;33m                     method ='tnc', bounds=bound, tol= 1e-5)\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fcn, params, method, args, kws, scale_covar, iter_cb, reduce_fcn, **fit_kws)\u001b[0m\n\u001b[0;32m   1954\u001b[0m                        \u001b[0miter_cb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miter_cb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_covar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscale_covar\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1955\u001b[0m                        reduce_fcn=reduce_fcn, **fit_kws)\n\u001b[1;32m-> 1956\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mfitter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m   1647\u001b[0m                         val.lower().startswith(user_method)):\n\u001b[0;32m   1648\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mscalar_minimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m         \"\"\"\n\u001b[1;32m--> 733\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprepare_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    734\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m         \u001b[0mvars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_vals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mprepare_fit\u001b[1;34m(self, params)\u001b[0m\n\u001b[0;32m    615\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mpar\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    616\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mParameter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 617\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mMinimizerException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_err_nonparam\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    618\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    619\u001b[0m                     \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpar\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMinimizerException\u001b[0m: \nparams must be a minimizer.Parameters() instance or list of Parameters()"
     ]
    }
   ],
   "source": [
    "\n",
    "param_op = minimize(MSM_likelihood_new2, input_param, args=(kbar, data),\n",
    "                    method ='tnc', bounds=bound, tol= 1e-5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## input as array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MinimizerResult' object has no attribute 'params'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-fc08aa0e5e1b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0minput_param2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_param\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMSM_likelihood_new2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_param2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkbar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;34m'L-BFGS-B'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbound\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtol\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;36m1e-5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fcn, params, method, args, kws, scale_covar, iter_cb, reduce_fcn, **fit_kws)\u001b[0m\n\u001b[0;32m   1954\u001b[0m                        \u001b[0miter_cb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miter_cb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_covar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscale_covar\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1955\u001b[0m                        reduce_fcn=reduce_fcn, **fit_kws)\n\u001b[1;32m-> 1956\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mfitter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m   1647\u001b[0m                         val.lower().startswith(user_method)):\n\u001b[0;32m   1648\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mscalar_minimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m         \"\"\"\n\u001b[1;32m--> 733\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprepare_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    734\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    735\u001b[0m         \u001b[0mvars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_vals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mprepare_fit\u001b[1;34m(self, params)\u001b[0m\n\u001b[0;32m    626\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# note that this *does* belong to self...\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 628\u001b[1;33m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_constraints\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    629\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnfev\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    630\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'MinimizerResult' object has no attribute 'params'"
     ]
    }
   ],
   "source": [
    "input_param2 = np.asarray(input_param)\n",
    "minimize(MSM_likelihood_new2, input_param2, args=(kbar, data),method ='L-BFGS-B', bounds=bound, tol= 1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lawof\\Documents\\Fractals\\Thanasarn_MSM\\GIT_MSM\\MSM_util.py:460: RuntimeWarning: invalid value encountered in sqrt\n",
      "  g_m = np.sqrt(g_m1)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The input contains nan values",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-6751ffa58fec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m# do fit, here with leastsq model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mminner\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMinimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMSM_likelihood_new2V2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcn_args\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkbar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;34m'L-BFGS-B'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;31m# # calculate final result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m   1647\u001b[0m                         val.lower().startswith(user_method)):\n\u001b[0;32m   1648\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mscalar_minimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m    774\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdifferential_evolution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_bounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 776\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscipy_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfmin_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    777\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maborted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_abort\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    448\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'l-bfgs-b'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m         return _minimize_lbfgsb(fun, x0, args, jac, bounds,\n\u001b[1;32m--> 450\u001b[1;33m                                 callback=callback, **options)\n\u001b[0m\u001b[0;32m    451\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'tnc'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m         return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[1;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)\u001b[0m\n\u001b[0;32m    326\u001b[0m             \u001b[1;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m             \u001b[1;31m# Overwrite f and g:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    329\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb'NEW_X'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m             \u001b[1;31m# new iteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36mfunc_and_grad\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    271\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mjac\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 273\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    274\u001b[0m             \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_approx_fprime_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[1;34m(*wrapper_args)\u001b[0m\n\u001b[0;32m    290\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mpenalty\u001b[1;34m(self, fvars)\u001b[0m\n\u001b[0;32m    544\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m         \"\"\"\n\u001b[1;32m--> 546\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__residual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfvars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    547\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce_fcn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36m__residual\u001b[1;34m(self, fvars, apply_bounds_transformation)\u001b[0m\n\u001b[0;32m    487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserfcn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserkws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_nan_policy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnan_policy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan_policy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    491\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_cb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36m_nan_policy\u001b[1;34m(a, nan_policy, handle_inf)\u001b[0m\n\u001b[0;32m   1835\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1836\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcontains_nan\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1837\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The input contains nan values\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1838\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The input contains nan values"
     ]
    }
   ],
   "source": [
    "from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "# create a set of Parameters\n",
    "params = Parameters()\n",
    "params.add('m0',   value= input_param[0],  min=LB[0], max = UB[0])\n",
    "params.add('b', value= input_param[1],  min=LB[1], max = UB[1])\n",
    "params.add('gamma_k', input_param[2],  min=LB[2], max = UB[2])\n",
    "params.add('sigma', value= input_param[3],  min=LB[3], max = UB[3])\n",
    "\n",
    "\n",
    "# minimize(MSM_likelihood_new2, input_param2, args=(kbar, data),method ='L-BFGS-B', bounds=bound, tol= 1e-5)\n",
    "# do fit, here with leastsq model\n",
    "minner = Minimizer(MSM_likelihood_new2V2, params, fcn_args=(kbar, data))\n",
    "result = minner.minimize(method ='L-BFGS-B')\n",
    "\n",
    "# # calculate final result\n",
    "# final = data + result.residual\n",
    "\n",
    "# # write error report\n",
    "# report_fit(result)\n",
    "\n",
    "# # try to plot results\n",
    "# try:\n",
    "#     import pylab\n",
    "#     pylab.plot(x, data, 'k+')\n",
    "#     pylab.plot(x, final, 'r')\n",
    "#     pylab.show()\n",
    "# except:\n",
    "#     pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.706983226320741"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_param[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.706983226320741"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params['m0'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### add.value in function 2V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Objective function must return a scalar",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[1;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[0;32m    367\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 368\u001b[1;33m                 \u001b[0mfx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    369\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[1;34m(*wrapper_args)\u001b[0m\n\u001b[0;32m    291\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mpenalty\u001b[1;34m(self, fvars)\u001b[0m\n\u001b[0;32m    545\u001b[0m         \"\"\"\n\u001b[1;32m--> 546\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__residual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfvars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    547\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36m__residual\u001b[1;34m(self, fvars, apply_bounds_transformation)\u001b[0m\n\u001b[0;32m    488\u001b[0m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserfcn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserkws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 489\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_nan_policy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnan_policy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan_policy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36m_nan_policy\u001b[1;34m(a, nan_policy, handle_inf)\u001b[0m\n\u001b[0;32m   1836\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcontains_nan\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1837\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The input contains nan values\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1838\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The input contains nan values",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-4599fb61f7f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mminner\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMinimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMSM_likelihood_new2V2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcn_args\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkbar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'slsqp'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m   1647\u001b[0m                         val.lower().startswith(user_method)):\n\u001b[0;32m   1648\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\lmfit\\minimizer.py\u001b[0m in \u001b[0;36mscalar_minimize\u001b[1;34m(self, method, params, **kws)\u001b[0m\n\u001b[0;32m    774\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdifferential_evolution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_bounds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 776\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscipy_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfmin_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    777\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m         \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maborted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_abort\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'slsqp'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m         return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[1;32m--> 458\u001b[1;33m                                constraints, callback=callback, **options)\n\u001b[0m\u001b[0;32m    459\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'dogleg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m         return _minimize_dogleg(fun, x0, args, jac, hess,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\slsqp.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[1;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[0;32m    368\u001b[0m                 \u001b[0mfx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 370\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Objective function must return a scalar\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    371\u001b[0m             \u001b[1;31m# Compute the constraints\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mcons\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'eq'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Objective function must return a scalar"
     ]
    }
   ],
   "source": [
    "from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit\n",
    "import numpy as np\n",
    "\n",
    "# create a set of Parameters\n",
    "params = Parameters()\n",
    "params.add('m0',   value= input_param[0],  min=LB[0], max = UB[0])\n",
    "params.add('b', value= input_param[1],  min=LB[1], max = UB[1])\n",
    "params.add('gamma_k', input_param[2],  min=LB[2], max = UB[2])\n",
    "params.add('sigma', value= input_param[3],  min=LB[3], max = UB[3])\n",
    "\n",
    "minner = Minimizer(MSM_likelihood_new2V2, params, fcn_args=(kbar, data))\n",
    "result = minner.minimize(method='slsqp')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
  }
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