0.9.Scattering_at_30deg.ipynb

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   "source": [
    "** ** \n",
    "\n",
    "<figure>\n",
    "<div style=\"text-align:center\"><img src=\"./img/compton_setup.png\" /></div>\n",
    "<p><center>The experimental setup.</center></p>\n",
    "</figure>\n",
    "\n",
    "# Compton gamma-ray scattering at a 30-degree angle\n",
    "\n",
    "** **\n",
    "* This project uses the lmfit package. Lmfit provides curve fiting using non-linear least squares minimization and was developed by Matt Newville: https://github.com/newville."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15f0470-4207-414f-9fe6-263f89795674",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d61f1679-b2c8-4f3a-9939-5865c44c5157",
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncomment the following line to install lmfit\n",
    "#!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e53f6d8-c3aa-44c4-a10c-64f76c6e361e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from numpy import sqrt, pi, exp, linspace, loadtxt\n",
    "from lmfit.models import MoffatModel, PolynomialModel\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import matplotlib.lines as mlines\n",
    "import pandas as pd\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.signal import find_peaks\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d286428a-95e4-446b-93de-bc3336f5c66f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "907e6f02-3654-49fb-b3a4-716fe6bd6dd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Load data\n",
    "\n",
    "#Load channel counts of scattering from large aluminum target at 18deg\n",
    "df_30 = pd.read_csv(\"./Data/180912-1310_Cs-137_LgHole_LgAl_030-degrees.csv\")"
   ]
  },
  {
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   "execution_count": 4,
   "id": "a6c9720c-4b60-49e7-9638-b6e399e68f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
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    "df_30.head()"
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       "    Channel  0\n",
       "1         1  0\n",
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   "source": [
    "#force start the index at 1 and add it as a column to match the channel numbers\n",
    "# Set the index to start at 1\n",
    "df_30.index = range(1, len(df_30) + 1)\n",
    "\n",
    "# Add the index as the first column\n",
    "df_30.insert(0, 'Channel', df_30.index)\n",
    "\n",
    "df_30.head(15)"
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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#preview the data \n",
    "\n",
    "#Uncomment the next line to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#rename the second column\n",
    "df_30.rename(columns={\"0\": \"Counts\"}, inplace=True)\n",
    "\n",
    "#remove unnecessary data\n",
    "df_30 = df_30[df_30['Channel'] <= 700]\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#preview the spectrum\n",
    "plt.plot('Channel', 'Counts', data = df_30, marker = \".\")\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27373bb-4aa1-4ac2-ae22-5813ed1bf121",
   "metadata": {},
   "source": [
    "### Use a smoothing algorithm to estimate the range of the gamma peak\n",
    "\n",
    "(As it can be seen in the graph above, the gamma peak of interest is not clearly resolved.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae2adc67-3cad-480f-9f8a-131296759830",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 900x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import filters\n",
    "from scipy.signal import butter, filtfilt\n",
    "\n",
    "#define the filter rack\n",
    "def butter_lowpass(cutoff, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    normal_cutoff = cutoff / nyq\n",
    "    b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
    "    return b, a\n",
    "\n",
    "def butter_lowpass_filtfilt(data, cutoff, fs, order=5):\n",
    "    b, a = butter_lowpass(cutoff, fs, order=order)\n",
    "    y = filtfilt(b, a, data)\n",
    "    return y\n",
    "\n",
    "#set the cuttoff frequency\n",
    "cutoff = 1\n",
    "\n",
    "#set the sampling frequency\n",
    "fs = 7\n",
    "\n",
    "#filter the data\n",
    "counts_smooth = butter_lowpass_filtfilt(df_30[\"Counts\"], cutoff, fs)\n",
    "\n",
    "#Plot the smooth signal over the data \n",
    "#%matplotlib widget\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "trace = plt.figure()\n",
    "trace.clf()\n",
    "\n",
    "plt.plot(df_30[\"Channel\"], df_30[\"Counts\"], 'r', linewidth=1.0)\n",
    "plt.plot(df_30[\"Channel\"], counts_smooth, 'b', linewidth=1.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8287b797-0f59-4e3d-bd71-6aac8796ff92",
   "metadata": {},
   "source": [
    "The images below show the filtered data at two sampling frequencies overimposed on the actual data and zoomed in on the peaks.\n",
    "\n",
    "The peak of interest is the shortest one. \n",
    "\n",
    "\n",
    "![](./img/smooth1.png)\n",
    "\n",
    "![](./img/smooth2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55c5918c-620c-4d5a-9869-c27dae6a9805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Channel Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>413</td>\n",
       "      <td>192</td>\n",
       "      <td>Range 412-434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>414</td>\n",
       "      <td>214</td>\n",
       "      <td>Range 412-434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>415</td>\n",
       "      <td>186</td>\n",
       "      <td>Range 412-434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>416</td>\n",
       "      <td>220</td>\n",
       "      <td>Range 412-434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>417</td>\n",
       "      <td>197</td>\n",
       "      <td>Range 412-434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Counts  Channel Range\n",
       "413      413     192  Range 412-434\n",
       "414      414     214  Range 412-434\n",
       "415      415     186  Range 412-434\n",
       "416      416     220  Range 412-434\n",
       "417      417     197  Range 412-434"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the channel ranges\n",
    "channel_ranges = [0, 412, 434, 700]\n",
    "\n",
    "# Create labels for the ranges\n",
    "labels = [f\"Range {i}-{j}\" for i, j in zip(channel_ranges[:-1], channel_ranges[1:])]\n",
    "\n",
    "# Use pd.cut() to categorize the data into the specified ranges\n",
    "df_30['Channel Range'] = pd.cut(df_30['Channel'], bins=channel_ranges, labels=labels, include_lowest=True)\n",
    "\n",
    "# Create a dictionary of DataFrames, one for each channel range\n",
    "channel_range_dfs = {label: group for label, group in df_30.groupby('Channel Range')}\n",
    "\n",
    "# Access individual DataFrames using keys (e.g., channel_range_dfs['Range 0-40'])\n",
    "channel_range_dfs['Range 412-434'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d01501-df0c-45ac-883d-878ca079e1c0",
   "metadata": {},
   "source": [
    "## Fit the peak and print the results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "406ce5e6-7458-4a28-b3cc-72d5cd79a401",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#use builtin NameModel, specify prefix:\n",
    "mod2 = MoffatModel(prefix='g2_') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "149f5e88-b6b1-48ce-8677-b0b3436fc431",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#initialize the parameters to be optimized\n",
    "pars2  = mod2.make_params(g2_amplitude=221.0, g2_center=423.0, g2_sigma=22.0, g2_beta=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33f19a78-7d30-4815-9412-be00f90e426c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g2_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 571\n",
      "    # data points      = 22\n",
      "    # variables        = 4\n",
      "    chi-square         = 18.1128236\n",
      "    reduced chi-square = 1.00626798\n",
      "    Akaike info crit   = 3.72270989\n",
      "    Bayesian info crit = 8.08687971\n",
      "    R-squared          = 0.99616770\n",
      "[[Variables]]\n",
      "    g2_amplitude:  199.777575 +/- 5.63772806 (2.82%) (init = 221)\n",
      "    g2_center:     419.459338 +/- 4.46655106 (1.06%) (init = 423)\n",
      "    g2_sigma:      3097.07646 +/- 3.4382e+08 (11101571.05%) (init = 22)\n",
      "    g2_beta:       4816.92560 +/- 1.0592e+09 (21988979.13%) (init = 1)\n",
      "    g2_fwhm:       74.3062444 +/- 79181.7501 (106561.37%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     199.777575 +/- 5.63772806 (2.82%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)       = +1.0000\n",
      "    C(g2_amplitude, g2_sigma)  = -0.6417\n",
      "    C(g2_amplitude, g2_beta)   = -0.6417\n",
      "    C(g2_amplitude, g2_center) = +0.2552\n",
      "    C(g2_center, g2_sigma)     = -0.2219\n",
      "    C(g2_center, g2_beta)      = -0.2219\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit with weights=1./yerr\n",
    "result2 = mod2.fit(channel_range_dfs['Range 412-434']['Counts'], pars2, x = channel_range_dfs['Range 412-434']['Channel'], weights = 1 / (np.std(channel_range_dfs['Range 412-434'][\"Channel\"]) + (channel_range_dfs['Range 412-434'][\"Channel\"] * 0.0197)))\n",
    "\n",
    "#print the Fit Statistics and parameters\n",
    "print('------------------------------------------------------')\n",
    "print(result2.fit_report())\n",
    "print('------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18588541-abfb-4e62-b127-23c93f81b379",
   "metadata": {},
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a6ec5ef-37e4-4acc-8a34-fada8924ac6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#plot data and models\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 0-412'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 412-434'], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(channel_range_dfs['Range 412-434']['Channel'], result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 434-700'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Channel')\n",
    "\n",
    "plt.annotate('gamma@661.657keV', xy=(437, 230), xytext=(450, 400),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "\n",
    "\n",
    "# Create legend entries for the blue and red lines (data and model) and display the legend\n",
    "blue_line = mlines.Line2D([], [], color='#7c8bfd', label='Data')\n",
    "\n",
    "red_line = mlines.Line2D([], [], color='red', label='Moffat Fit', linewidth=2.5)\n",
    "\n",
    "plt.legend([blue_line, red_line], ['Data', 'Moffat Fit'], loc='lower center', bbox_to_anchor= (0.5, 1.005), ncol=2,\n",
    "            borderaxespad=0, frameon=False)\n",
    "\n",
    "plt.title('Cs-137 gamma ray scattered at 30deg from an Al target ', y = 1.07)\n",
    "\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2b1027-4a59-4481-a1c1-3fbcebed488d",
   "metadata": {},
   "source": [
    "# Estimate the measured energy using the calibration curve (prepared in notebook 5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24a2edda-9e8b-4159-ab0a-3abaf36f6a0c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Model Parameters\n",
      "-------------------------------\n",
      "Parameter    Value       Stderr\n",
      "p_r1_c0   -27.70990     4.87254\n",
      "p_r1_c1     1.45191     0.12099\n",
      "\n",
      "Linear Model Fit Stats\n",
      "-------------------------------\n",
      "Reduced Chi-squared =  1.0038505734658223\n",
      "r-squared =  0.9999951130945643\n",
      "\n",
      "Quadratic Model Parameters\n",
      "-------------------------------\n",
      "Parameter    Value       Stderr\n",
      "p_r2_c0   -35.38041     7.32770\n",
      "p_r2_c1     1.71396     0.22612\n",
      "p_r2_c2    -0.00061     0.00046\n",
      "\n",
      "Quadratic Model Fit Stats\n",
      "-------------------------------\n",
      "Reduced Chi-squared =  1.00411106030073\n",
      "r-squared =  0.9999959265220606\n"
     ]
    }
   ],
   "source": [
    "# Data\n",
    "x_r = np.array([29.3812658, 70.4034958, 100.839961, 102.324468, 254.677094, 350.450834, 638.540552, 738.972592])\n",
    "y_r = np.array([14.4136, 80.998, 122.060, 160.605, 356.005, 511.006, 661.6570, 1274.54])\n",
    "yerr_r = np.array([0.0003, 0.008, 0.010, 0.015, 0.017, 0.008, 0.003, 0.008])\n",
    "\n",
    "#use builtin NameModel, specify prefix for each:\n",
    "mod_r1 = PolynomialModel(1, prefix='p_r1_')\n",
    "mod_r2 = PolynomialModel(2, prefix='p_r2_')\n",
    "\n",
    "#set initial parameters\n",
    "pars_r1 = mod_r1.guess(y_r, x=x_r)\n",
    "pars_r2 = mod_r2.guess(y_r, x=x_r)    \n",
    "\n",
    "#fit with weights=1./yerr\n",
    "result_r1 = mod_r1.fit(y_r, pars_r1, x=x_r, weights = 1. / (y_r * np.mean(yerr_r / y_r) + y_r * 0.198))\n",
    "result_r2 = mod_r2.fit(y_r, pars_r2, x=x_r, weights = 1. / (y_r * np.mean(yerr_r / y_r) + y_r * 0.186))\n",
    "\n",
    "\n",
    "print()\n",
    "print(\"Linear Model Parameters\")\n",
    "print('-------------------------------')\n",
    "print('Parameter    Value       Stderr')\n",
    "for name, param in result_r1.params.items():\n",
    "    print(f'{name:7s} {param.value:11.5f} {param.stderr:11.5f}')\n",
    "\n",
    "print()\n",
    "print(\"Linear Model Fit Stats\")\n",
    "print('-------------------------------')\n",
    "print(\"Reduced Chi-squared = \", result_r1.redchi)\n",
    "print(\"r-squared = \", result_r1.rsquared)\n",
    "    \n",
    "print()\n",
    "print(\"Quadratic Model Parameters\")\n",
    "print('-------------------------------')\n",
    "print('Parameter    Value       Stderr')\n",
    "for name, param in result_r2.params.items():\n",
    "    print(f'{name:7s} {param.value:11.5f} {param.stderr:11.5f}')    \n",
    "\n",
    "print()\n",
    "print(\"Quadratic Model Fit Stats\")\n",
    "print('-------------------------------')\n",
    "print(\"Reduced Chi-squared = \", result_r2.redchi)\n",
    "print(\"r-squared = \", result_r2.rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c913933-dd8f-445d-a5a4-90fd83f4c531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "419.4593381776969\n"
     ]
    }
   ],
   "source": [
    "#print the new position\n",
    "varys = result2.params.valuesdict()\n",
    "\n",
    "position = varys['g2_center']\n",
    "\n",
    "print(position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95593d9f-fdd4-4e12-b3c7-0f72c9fd9f06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear model estimation\n",
      "----------------------------\n",
      "595.0893584128569\n",
      "\n",
      "Quadratic model estimation\n",
      "----------------------------\n",
      "538.4302411393016\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print(\"Linear model estimation\")\n",
    "print('----------------------------')\n",
    "print(result_r1.eval(pars_r1, x = position))\n",
    "\n",
    "print()\n",
    "print(\"Quadratic model estimation\")\n",
    "print('----------------------------')\n",
    "print(result_r2.eval(pars_r2, x = position))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3dd7e2-2e20-487f-a0b6-64b7b74f5329",
   "metadata": {},
   "source": [
    "## Creating a new table row by plugging the result into the Compton formula\n",
    "\n",
    "![](./img/Compton_formula.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b91b3f01-44e4-4995-b938-63e6a9211afc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 0.1339745962155613, 'y': 1.8572508072429794}\n"
     ]
    }
   ],
   "source": [
    "#Extract the energy from the model\n",
    "energy = result_r2.eval(pars_r2, x = position)\n",
    "\n",
    "#Calculate the variables (to be compared in the last step) according to the Compton model\n",
    "x = 1 - np.cos(np.deg2rad(30))\n",
    "y = 1 / (energy / 1000) #convert the energy to MeV\n",
    "\n",
    "#Add the data to a dictionary\n",
    "Compton_data = {\"x\" : x, \"y\" : y}\n",
    "\n",
    "#Preview the created row of data\n",
    "print(Compton_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdb2363-fb8e-4de8-b32e-5517a77df17f",
   "metadata": {},
   "source": [
    "### Append the values to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "60cc34cf-00c8-4adc-9188-ee65382c095e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import csv\n",
    "\n",
    "# with open(r'./Data/Compton_data.csv', 'a', newline='') as csvfile:\n",
    "#     fieldnames = ['x','y']\n",
    "#     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "#     writer.writerow(Compton_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a897d98-29e6-4b6f-99f9-251789de0b15",
   "metadata": {},
   "source": [
    "## Author\n",
    "** **\n",
    "\n",
    "### <a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8db14f5-fa48-4eb6-a81d-c66c9422b50b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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