1.8.Scattering_at_120deg.ipynb

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    "** ** \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 120-degree angle\n",
    "\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": "e5785ab9-27a9-4454-b8a4-0382e05c2d08",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7add9b10-33a5-4d23-8d9c-a5de51401901",
   "metadata": {
    "tags": []
   },
   "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": "cc59b7ea-4916-4822-abcc-1c6919721404",
   "metadata": {},
   "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_120 = pd.read_csv(\"./Data/180912-1458_Cs-137_LgHole_LgAl_120-degrees.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6c9720c-4b60-49e7-9638-b6e399e68f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "   0\n",
       "0  0\n",
       "1  0\n",
       "2  0\n",
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       "4  0"
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df_120.head()"
   ]
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       "   Channel  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_120.index = range(1, len(df_120) + 1)\n",
    "\n",
    "# Add the index as the first column\n",
    "df_120.insert(0, 'Channel', df_120.index)\n",
    "\n",
    "df_120.head()"
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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",
    "\n",
    "#rename the second column\n",
    "df_120.rename(columns={\"0\": \"Counts\"}, inplace=True)\n",
    "\n",
    "#remove unnecessary data\n",
    "df_120 = df_120[df_120['Channel'] <= 700]\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#preview the spectrum\n",
    "plt.plot('Channel', 'Counts', data = df_120, marker = \".\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>156</th>\n",
       "      <td>156</td>\n",
       "      <td>143</td>\n",
       "      <td>Range 155-186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>157</td>\n",
       "      <td>143</td>\n",
       "      <td>Range 155-186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>158</td>\n",
       "      <td>148</td>\n",
       "      <td>Range 155-186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>159</td>\n",
       "      <td>139</td>\n",
       "      <td>Range 155-186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>160</td>\n",
       "      <td>143</td>\n",
       "      <td>Range 155-186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Counts  Channel Range\n",
       "156      156     143  Range 155-186\n",
       "157      157     143  Range 155-186\n",
       "158      158     148  Range 155-186\n",
       "159      159     139  Range 155-186\n",
       "160      160     143  Range 155-186"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the channel ranges\n",
    "channel_ranges = [0, 155, 186, 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_120['Channel Range'] = pd.cut(df_120['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_120.groupby('Channel Range')}\n",
    "\n",
    "# Access individual DataFrames using keys (e.g., channel_range_dfs['Range 0-40'])\n",
    "channel_range_dfs['Range 155-186'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7765ef4-36a3-4791-b7a9-dde1b1223ec1",
   "metadata": {},
   "source": [
    "## Define the model, fit, and print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "id": "149f5e88-b6b1-48ce-8677-b0b3436fc431",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#initialize the parameters to be optimized\n",
    "pars2  = mod2.make_params(g2_amplitude=207.0, g2_center=170.0, g2_sigma=5.17, g2_beta=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e919907f-a5c5-4dd7-87b8-1c93cef66067",
   "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   = 52\n",
      "    # data points      = 31\n",
      "    # variables        = 4\n",
      "    chi-square         = 27.0015303\n",
      "    reduced chi-square = 1.00005668\n",
      "    Akaike info crit   = 3.71909646\n",
      "    Bayesian info crit = 9.45504528\n",
      "    R-squared          = 0.99843912\n",
      "[[Variables]]\n",
      "    g2_amplitude:  192.359279 +/- 5.11289409 (2.66%) (init = 207)\n",
      "    g2_center:     170.619376 +/- 0.52275792 (0.31%) (init = 170)\n",
      "    g2_sigma:      10.0055653 +/- 6.11832412 (61.15%) (init = 5.17)\n",
      "    g2_beta:       0.32519051 +/- 0.24695235 (75.94%) (init = 1)\n",
      "    g2_fwhm:       54.5375545 +/- 17.8049909 (32.65%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     192.359279 +/- 5.11289410 (2.66%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)      = +0.9889\n",
      "    C(g2_amplitude, g2_sigma) = -0.6404\n",
      "    C(g2_amplitude, g2_beta)  = -0.5477\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit with weights=1./yerr\n",
    "result2 = mod2.fit(channel_range_dfs['Range 155-186']['Counts'], pars2, x = channel_range_dfs['Range 155-186']['Channel'],\n",
    "                   weights = 1 / (channel_range_dfs['Range 155-186'][\"Channel\"] * 0.072732))                   \n",
    "\n",
    "#print the Fit Statistics and parameters\n",
    "print('------------------------------------------------------')\n",
    "print(result2.fit_report())\n",
    "print('------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5046ee-4467-43c6-816d-90d82f40ae33",
   "metadata": {},
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 0-155'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 155-186'], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(channel_range_dfs['Range 155-186']['Channel'], result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 186-700'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Channel')\n",
    "\n",
    "plt.annotate('gamma@661.657keV', xy=(182, 192), xytext=(200, 225),\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 120deg 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": 12,
   "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": 13,
   "id": "cd9b1766-1168-432c-a096-464fea6507a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170.61937623504434\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": 14,
   "id": "95593d9f-fdd4-4e12-b3c7-0f72c9fd9f06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear model estimation\n",
      "----------------------------\n",
      "227.80491628939672\n",
      "\n",
      "Quadratic model estimation\n",
      "----------------------------\n",
      "204.78335015812024\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": "20dbaba8-e2f6-4e60-a13b-6e9f4c1e622d",
   "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": 15,
   "id": "1c996454-df90-4f37-a4c9-18cf779e7384",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 1.4999999999999998, 'y': 4.8832094954392815}\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(120))\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": "4b76531a-c180-447d-9bce-d48c2152cda6",
   "metadata": {},
   "source": [
    "### Append the values to a csv file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dccbf2c-570c-4f96-80f0-4b03f52c7acb",
   "metadata": {},
   "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": "2c5d25dc-663e-4ad6-a050-3f460a99fc3b",
   "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": "347d745a-cf83-480e-b700-5046c7081f8c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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