lines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xk roots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_k_4 = [\n",
    "    0.52465, -0.52465,\n",
    "    1.6507, -1.6507\n",
    "]\n",
    "\n",
    "x_k_7 = [\n",
    "    0, 0.81629, -0.81629,\n",
    "    1.6736, -1.6736,\n",
    "    2.6520, -2.6520\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def derivative(f, x, n, dx=5e-2):\n",
    "#     if n==1:\n",
    "#         return (f(x + dx) - f(x - dx)) / (2.0 * dx)\n",
    "#     else:\n",
    "#         return (derivative(f, x+dx, n-1) - derivative(f, x-dx, n-1))/(2.0 * dx)\n",
    "\n",
    "def chebderivative(x, n):\n",
    "    if n == 4:\n",
    "        return 64*x**3 - 96 * x\n",
    "    if n == 7:\n",
    "        return 896*x**6 - 6720*x**4 + 10080*x**2 - 1680"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "chebyshev/hermite polynomials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def chebermit(x, n):\n",
    "#     return (-1)**n * math.exp(x**2) * derivative(lambda x:math.exp(-x**2), x, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ak coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "def akcoeff(x, n):\n",
    "    return (2**(n+1)*math.factorial(n)*math.sqrt(math.pi))/(chebderivative(x, n)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calc ak for the xk for n 4 and 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "n4 = pd.DataFrame(\n",
    "    {\n",
    "        \"x_k\" : x_k_4\n",
    "    }\n",
    ")\n",
    "\n",
    "n7 = pd.DataFrame(\n",
    "    {\n",
    "        \"x_k\" : x_k_7\n",
    "    }\n",
    ")\n",
    "\n",
    "\n",
    "n4['Ak'] = n4['x_k'].apply(akcoeff, args=(4,))\n",
    "n7['Ak'] = n7['x_k'].apply(akcoeff, args=(7,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda0 = 6154.23\n",
    "deltalambda = 0.123\n",
    "\n",
    "def domaintransform(x):\n",
    "    return lambda0 + x * deltalambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "n4['lambdas'] = n4['x_k'].apply(domaintransform)\n",
    "n7['lambdas'] = n7['x_k'].apply(domaintransform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "linedata = pd.read_csv(\n",
    "    \"LINE6154.DAT\", sep='\\s+', \n",
    "    names=[\"wlength\", \"intensity\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "nodelist = []\n",
    "\n",
    "for value in n4['lambdas']:\n",
    "    closest_index = (linedata['wlength'] - value).abs().idxmin()\n",
    "    closest_row = list(linedata.loc[closest_index])\n",
    "    nodelist.append(closest_row)\n",
    "    \n",
    "nodeframe = pd.DataFrame(\n",
    "    nodelist,\n",
    "    columns=['wlength', 'intensity']\n",
    ")\n",
    "\n",
    "nodeframe.reset_index(drop=True,inplace=True)\n",
    "n4 = pd.concat([n4, nodeframe], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodelist = []\n",
    "\n",
    "for value in n7['lambdas']:\n",
    "    closest_index = (linedata['wlength'] - value).abs().idxmin()\n",
    "    closest_row = list(linedata.loc[closest_index])\n",
    "    nodelist.append(closest_row)\n",
    "    \n",
    "nodeframe = pd.DataFrame(\n",
    "    nodelist,\n",
    "    columns=['wlength', 'intensity']\n",
    ")\n",
    "\n",
    "nodeframe.reset_index(drop=True,inplace=True)\n",
    "n7 = pd.concat([n7, nodeframe], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def intensitytransform(x,y):\n",
    "    return (1-x)/(math.exp(-y**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "n4['value'] = n4.apply(lambda row: intensitytransform(row['intensity'], row['x_k']), axis=1)\n",
    "n7['value'] = n7.apply(lambda row: intensitytransform(row['intensity'], row['x_k']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.29036460178282036 0.29128245408207876\n"
     ]
    }
   ],
   "source": [
    "n4['product'] = n4['Ak'] * n4['value']\n",
    "n7['product'] = n7['Ak'] * n7['value']\n",
    "\n",
    "n4result = sum(n4['product']) * deltalambda\n",
    "n7result = sum(n7['product']) * deltalambda\n",
    "print(n4result, n7result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "plt.figure(figsize=(5,5), dpi=150)\n",
    "\n",
    "plt.plot(linedata['wlength'], linedata['intensity'])\n",
    "plt.scatter(n4['lambdas'], n4['intensity'])\n",
    "plt.scatter(n7['lambdas'], n7['intensity'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       x_k        Ak      lambdas   wlength  intensity     value   product\n",
      "0  0.52465  0.804910  6154.294532  6154.294    0.22925  1.014977  0.816966\n",
      "1 -0.52465  0.804910  6154.165468  6154.166    0.29853  0.923745  0.743531\n",
      "2  1.65070  0.081302  6154.433036  6154.434    0.63748  5.529707  0.449577\n",
      "3 -1.65070  0.081302  6154.026964  6154.026    0.71728  4.312476  0.350614\n",
      "0.29036460178282036\n"
     ]
    }
   ],
   "source": [
    "print(n4)\n",
    "print(n4result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       x_k        Ak      lambdas   wlength  intensity      value   product\n",
      "0  0.00000  0.810265  6154.230000  6154.230    0.20006   0.799940  0.648163\n",
      "1  0.81629  0.425604  6154.330404  6154.330    0.30425   1.354679  0.576557\n",
      "2 -0.81629  0.425604  6154.129596  6154.130    0.38139   1.204481  0.512632\n",
      "3  1.67360  0.054498  6154.435853  6154.436    0.64486   5.845626  0.318575\n",
      "4 -1.67360  0.054498  6154.024147  6154.024    0.72451   4.534582  0.247125\n",
      "5  2.65200  0.000971  6154.556196  6154.556    0.96042  44.865659  0.043582\n",
      "6 -2.65200  0.000971  6153.903804  6153.904    0.98046  22.149443  0.021516\n",
      "0.29128245408207876\n"
     ]
    }
   ],
   "source": [
    "print(n7)\n",
    "print(n7result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}