{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "22bc2a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/chn/repo/SiC-2nd-paper'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib\n",
    "\n",
    "def lorentzian(x, A, x0, gamma, k, b):\n",
    "    return A / (1 + ((x - x0) / gamma)**2) + k * x + b\n",
    "def double_lorentzian(x, A1, x1, gamma1, A2, x2, gamma2, k, b):\n",
    "    return lorentzian(x, A1, x1, gamma1, k, b) + lorentzian(x, A2, x2, gamma2, 0, 0)\n",
    "\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f4aba80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitting_peek(x, y, x_range, function, guess, bound):\n",
    "  mask = (x > x_range[0]) & (x < x_range[1])\n",
    "  x = x[mask]\n",
    "  y = y[mask]\n",
    "  popt, pcov = curve_fit(function, x, y, p0=guess, maxfev=10000, bounds=bound)\n",
    "  return popt\n",
    "\n",
    "def fitting_line_xzyx(x, y):\n",
    "  integration = []\n",
    "  result = fitting_peek(x, y, fitting_range[1], lorentzian, fitting_init_parameter[1], fitting_bound[1])\n",
    "  integration.append(result[0] * result[2] * np.pi)\n",
    "  return integration\n",
    "def fitting_line_xzzx(x, y):\n",
    "  integration = []\n",
    "  result = fitting_peek(x, y, fitting_range[2], lorentzian, fitting_init_parameter[2], fitting_bound[2])\n",
    "  integration.append(result[0] * result[2] * np.pi)\n",
    "  result = fitting_peek(x, y, [830, 847], lorentzian, [1e3, 838, 2, 0, 100], (-np.inf, np.inf))\n",
    "  integration.append(result[0] * result[2] * np.pi)\n",
    "  return integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "591e7dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fitting_init_parameter = [\n",
    "  [300, 196, 1, 2e3, 204, 0.8, 0, 900],\n",
    "  [1000, 266, 1, 0, 100],\n",
    "  [1e3, 611, 5, 0, 100],\n",
    "  [1e4, 776, 1, 1e3, 797, 1, 1, 100]\n",
    "]\n",
    "fitting_range = [\n",
    "  [180, 220],\n",
    "  [250, 280],\n",
    "  [600, 617],\n",
    "  [700, 850]\n",
    "]\n",
    "fitting_bound = [\n",
    "  ([0, 192, 0, 0, 200, 0, -np.inf, -np.inf], [np.inf, 198, np.inf, np.inf, 206, np.inf, np.inf, np.inf]),\n",
    "  ([0, 260, 0, -np.inf, -np.inf], [np.inf, 270, np.inf, np.inf, np.inf]),\n",
    "  ([0, 605, 0, -np.inf, -np.inf], [np.inf, 617, np.inf, np.inf, np.inf]),\n",
    "  (-np.inf, np.inf)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad979c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "xzyx_x = np.loadtxt('画图/拉曼结果拟合/251008/2/xzyx_x.txt')\n",
    "xzyx_y = np.loadtxt('画图/拉曼结果拟合/251008/2/xzyx_y.txt')\n",
    "xzzx_x = np.loadtxt('画图/拉曼结果拟合/251008/2/xzzx_x.txt')\n",
    "xzzx_y = np.loadtxt('画图/拉曼结果拟合/251008/2/xzzx_y.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ea7b0b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[ 6.20863446e+02  8.38299773e+02  9.88636310e-01 -2.75421651e-01\n",
      "  4.37847606e+02]\n",
      "1\n",
      "[ 5.70835585e+02  8.38290687e+02  1.06223999e+00 -4.20011037e-01\n",
      "  5.54691452e+02]\n",
      "2\n",
      "[ 5.67699634e+02  8.38310484e+02  1.09375996e+00 -1.91829315e-01\n",
      "  3.63015294e+02]\n",
      "3\n",
      "[ 5.71587243e+02  8.38312536e+02  1.10515225e+00 -2.35440551e-01\n",
      "  4.04026894e+02]\n",
      "4\n",
      "[ 5.63526465e+02  8.38321133e+02  1.11857378e+00 -1.57440689e-01\n",
      "  3.35431720e+02]\n",
      "5\n",
      "[ 5.58936304e+02  8.38298057e+02  1.11846058e+00 -4.78122075e-01\n",
      "  5.97983191e+02]\n",
      "6\n",
      "[ 5.94881490e+02  8.38293742e+02  1.03933855e+00 -2.20381683e-01\n",
      "  3.93859587e+02]\n",
      "7\n",
      "[ 5.89327016e+02  8.38306592e+02  1.06489366e+00 -3.99797321e-01\n",
      "  5.43370844e+02]\n",
      "8\n",
      "[ 6.07322341e+02  8.38311130e+02  1.05443289e+00 -3.31104222e-01\n",
      "  4.87033118e+02]\n",
      "9\n",
      "[ 6.03524138e+02  8.38298730e+02  1.01543786e+00 -1.65621189e-01\n",
      "  3.44437001e+02]\n"
     ]
    }
   ],
   "source": [
    "# 检查拟合结果\n",
    "for site in range(10):\n",
    "  print(site)\n",
    "  result = fitting_peek(xzzx_x[site], xzzx_y[site], [830, 847], lorentzian, [1e3, 838, 2, 0, 100], (-np.inf, np.inf))\n",
    "  fig = go.Figure()\n",
    "  fig.add_trace(go.Scatter(x=xzzx_x[site], y=np.log10(xzzx_y[site]), mode='lines', name='data'))\n",
    "  fig.add_trace(go.Scatter(x=xzzx_x[site], y=np.log10(lorentzian(xzzx_x[site], *result)), mode='lines', name='fit'))\n",
    "  fig.update_yaxes(range=[1, 4])\n",
    "  # fig.show()\n",
    "  print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e3526b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 收集拟合结果\n",
    "integration_all = []\n",
    "for site in range(10):\n",
    "  integration_xzyx = fitting_line_xzyx(xzyx_x[site], xzyx_y[site])\n",
    "  integration_xzzx = fitting_line_xzzx(xzzx_x[site], xzzx_y[site])\n",
    "  integration_all.append([integration_xzyx[0], integration_xzzx[0], integration_xzzx[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c49bc98d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: [2195.66402216 3117.93160487 1956.38300586]\n",
      "std: [55.69099914 55.3995643  30.57802302]\n"
     ]
    }
   ],
   "source": [
    "# 计算峰面积的平均和标准差\n",
    "integration_all = np.array(integration_all)\n",
    "mean = np.mean(integration_all, axis=0)\n",
    "std = np.std(integration_all, axis=0)\n",
    "print(\"mean:\", mean)\n",
    "print(\"std:\", std)"
   ]
  }
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