Spectroscopy Calculations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectroscopy calculations\n",
    "## Leniar trend line graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list assignment index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-92-01659e8f6f10>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minterval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m180\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1.077\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.299\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.277\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.737\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.481\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.825\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.881\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.833\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.016\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.292\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.351\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.566\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.411\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.423\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.549\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.519\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0.578\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.777\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.609\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.605\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.728\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.64\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list assignment index out of range"
     ]
    }
   ],
   "source": [
    "interval = [5, 10, 30, 50, 80, 120, 150, 180, 200]\n",
    "sample = []\n",
    "sample[0] = [1.077, 1.299, 1.277, 0.737, 1.481, 0.825, 0.881, 0.833, 1.016, 1.292]\n",
    "sample[1] = [0.351, 0.566, 0.411, 0.423, 0.549, 0.519]\n",
    "sample[2] = [0.578, 0.777, 0.609, 0.605, 0.728, 0.64]\n",
    "sample[3] = [0.865, 1.084, 0.851, 0.817, 1.038, 0.872]\n",
    "sample[4] = [1.15, 1.338, 1.093, 1.061, 1.31, 1.155]\n",
    "sample[5] = [1.289, 1.425, 1.271, 1.303, 1.298, 1.459]\n",
    "## Content of uniformity calculations : \n",
    "sample_set = sample[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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VcUKV9eth4EAoUwaWL4fGjYPc2BAQQTPHIqcob0W8of4inlJfyZ8DyQd4+uunqTOxDqNW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\n+n9QbWWvq3pvEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f80e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:28: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "A value in x_new is above the interpolation range.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-131-1c01bc274119>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpolate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsorbance\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0mynew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'-----'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mynew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/interpolate/polyint.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \"\"\"\n\u001b[1;32m     78\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    495\u001b[0m         \u001b[0;31m#    The behavior is set by the bounds_error variable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m         \u001b[0mx_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m         \u001b[0mout_of_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    498\u001b[0m         \u001b[0my_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_new\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_check_bounds\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m    525\u001b[0m                 \"range.\")\n\u001b[1;32m    526\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds_error\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mabove_bounds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 527\u001b[0;31m             raise ValueError(\"A value in x_new is above the interpolation \"\n\u001b[0m\u001b[1;32m    528\u001b[0m                 \"range.\")\n\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: A value in x_new is above the interpolation range."
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "interval = [5, 10, 30, 50, 80, 120, 150, 180, 200,]\n",
    "absorbance = [0.023, 0.041, 0.201, 0.364, 0.472, 0.809, 1.161, 1.295, 1.482]\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "axis = fig.add_subplot(111)\n",
    "z = np.polyfit(interval,absorbance,1)\n",
    "p = np.poly1d(z)\n",
    "axis.plot(interval,absorbance)\n",
    "axis.plot(\n",
    "    interval, \n",
    "    p(interval), \n",
    "    #color=\"red\", \n",
    "    linestyle='dashed',\n",
    "    linewidth=2,\n",
    "    #marker='o',\n",
    "    markerfacecolor='blue',\n",
    "    markersize=3,\n",
    ")\n",
    "axis.grid()\n",
    "axis.set_title('Calibration Curve with ')\n",
    "axis.set_xlabel('Concentration(μg/ml)')\n",
    "axis.set_ylabel('Absorption')\n",
    "plt.show()\n",
    "\n",
    "from scipy import interpolate\n",
    "\n",
    "sample_set = np.ndarray([1.077, 1.299, 1.277, 0.737, 1.481, 0.825, 0.881, 0.833, 1.016, 1.292])\n",
    "sample_set = [1.077, 1.299, 1.277, 0.737, 1.481, 0.825, 0.881, 0.833, 1.016, 1.292]\n",
    "f = interpolate.interp1d(absorbance,interval)\n",
    "\n",
    "ynew = f(np.ndarray(sample_set.sort()))\n",
    "print ('-----')\n",
    "print(ynew.flatten())\n",
    "print(sample_set)\n",
    "print ('-----')\n",
    "axis.plot(\n",
    "    sample_set.sort(), \n",
    "    ynew, \n",
    "    #color=\"red\", \n",
    "    linestyle='dashed',\n",
    "    linewidth=2,\n",
    "    #marker='o',\n",
    "    #markerfacecolor='red',\n",
    "    markersize=3,\n",
    ")\n",
    "plt.show()\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Equation calculated from the curve\n",
    "### TODO Set start point of the graph to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.007504x + (-0.038097)\n"
     ]
    }
   ],
   "source": [
    "print(\"y = %.6fx + (%.6f)\"%(z[0],z[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content of uniformity calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "xnew = np.linspace(0, 10, num=41, endpoint=True)\n",
    "xnew.dtype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.73699999999999999,\n",
       " 0.82499999999999996,\n",
       " 0.83299999999999996,\n",
       " 0.88100000000000001,\n",
       " 1.016,\n",
       " 1.077,\n",
       " 1.2769999999999999,\n",
       " 1.292,\n",
       " 1.2989999999999999,\n",
       " 1.4810000000000001]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xnew = sorted(np.array([1.077, 1.299, 1.277, 0.737, 1.481, 0.825, 0.881, 0.833, 1.016, 1.292]))\n",
    "xnew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.array([1,2,3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_x, grid_y = np.mgrid[0:1:100j, 0:1:200j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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