Lugovtsov/cosmic-rays/Obrabotka.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "kaayLGB4AyPP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('/content/Untitled_angle_09_12.dat',delimiter = '\\t')\n",
    "df.columns=['Count','Time']\n",
    "ns = 0.064\n",
    "df['Time'] = df.index * ns\n",
    "df['Speed'] = 0.46/df['Time']*10**9\n",
    "correct_df = df[(df['Time']>1.6) & (df['Time']<15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "QTTbLpFSBWIG",
    "outputId": "09b8c4f2-764e-40e7-f1cc-fce0589a6d15"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-8adb9e0c-ac81-43d8-84bb-060e6a6df807\" class=\"colab-df-container\">\n",
       "    <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>Count</th>\n",
       "      <th>Time</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31</td>\n",
       "      <td>1.664</td>\n",
       "      <td>2.764423e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>1.728</td>\n",
       "      <td>2.662037e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23</td>\n",
       "      <td>1.792</td>\n",
       "      <td>2.566964e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>26</td>\n",
       "      <td>1.856</td>\n",
       "      <td>2.478448e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>22</td>\n",
       "      <td>1.920</td>\n",
       "      <td>2.395833e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8adb9e0c-ac81-43d8-84bb-060e6a6df807')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-8adb9e0c-ac81-43d8-84bb-060e6a6df807 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-8adb9e0c-ac81-43d8-84bb-060e6a6df807');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-0afaef0d-7249-44f0-8914-0d49d8966d79\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0afaef0d-7249-44f0-8914-0d49d8966d79')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-0afaef0d-7249-44f0-8914-0d49d8966d79 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    Count   Time         Speed\n",
       "26     31  1.664  2.764423e+08\n",
       "27     31  1.728  2.662037e+08\n",
       "28     23  1.792  2.566964e+08\n",
       "29     26  1.856  2.478448e+08\n",
       "30     22  1.920  2.395833e+08"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "5yKqwV9SxLHP",
    "outputId": "735be114-40dd-449f-8f4a-b48be1d2a5b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time, ns')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(correct_df['Time'], correct_df['Count'])\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Time, ns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SCrC88HHpDFt"
   },
   "outputs": [],
   "source": [
    "n = 15\n",
    "df_avg = pd.Series(correct_df['Count']).rolling(window=n).mean().iloc[n-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "11Dml6iApz8n",
    "outputId": "394a6c70-bd3c-4c44-ae3f-2fa0fef081ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ae94ba16f20>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(correct_df['Time'])[7:-7],df_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "y6b3kv0kBtHV"
   },
   "outputs": [],
   "source": [
    "L=0.46\n",
    "me = 9.11 * 10**-31\n",
    "c = 299792458\n",
    "m_mu = 1.883531627 * 10**-28\n",
    "def gamma(u):\n",
    "    return 1/np.sqrt(1-(u/c)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "Eg95ViryBwzG",
    "outputId": "62f8ee4c-7e67-4ec0-8626-90bf84ad290d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((gamma(correct_df['Speed']))*me*c**2/1.6*10**13, correct_df['Count'])\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('E, MeV')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}