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Warning, /analysis/Prototype4/EMCal/ReadExampleTTree.ipynb is written in an unsupported language. File is not indexed. 

0001 {
0002  "cells": [
0003   {
0004    "cell_type": "code",
0005    "execution_count": 1,
0006    "id": "f397bebe-0010-4aa2-964d-6639e1a6bbf7",
0007    "metadata": {},
0008    "outputs": [],
0009    "source": [
0010     "# imports to write dynamic markdown contents\n",
0011     "import os\n",
0012     "from IPython.display import display, Markdown, Latex\n",
0013     "from IPython.display import HTML"
0014    ]
0015   },
0016   {
0017    "cell_type": "code",
0018    "execution_count": 2,
0019    "id": "dcd177c5-86be-4f66-bb1d-5a7c447e9118",
0020    "metadata": {},
0021    "outputs": [
0022     {
0023      "data": {
0024       "text/markdown": [
0025        "*For the result HTML page:* "
0026       ],
0027       "text/plain": [
0028        "<IPython.core.display.Markdown object>"
0029       ]
0030      },
0031      "metadata": {},
0032      "output_type": "display_data"
0033     },
0034     {
0035      "data": {
0036       "text/html": [
0037        "<script>\n",
0038        "code_show=false; \n",
0039        "function code_toggle() {\n",
0040        " if (code_show){\n",
0041        " $('div.input').hide();\n",
0042        " } else {\n",
0043        " $('div.input').show();\n",
0044        " }\n",
0045        " code_show = !code_show\n",
0046        "} \n",
0047        "$( document ).ready(code_toggle);\n",
0048        "</script>\n",
0049        "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
0050       ],
0051       "text/plain": [
0052        "<IPython.core.display.HTML object>"
0053       ]
0054      },
0055      "execution_count": 2,
0056      "metadata": {},
0057      "output_type": "execute_result"
0058     }
0059    ],
0060    "source": [
0061     "# turn off/on code for the result HTML page\n",
0062     "\n",
0063     "display(Markdown('*For the result HTML page:* '))\n",
0064     "    \n",
0065     "HTML('''<script>\n",
0066     "code_show=false; \n",
0067     "function code_toggle() {\n",
0068     " if (code_show){\n",
0069     " $('div.input').hide();\n",
0070     " } else {\n",
0071     " $('div.input').show();\n",
0072     " }\n",
0073     " code_show = !code_show\n",
0074     "} \n",
0075     "$( document ).ready(code_toggle);\n",
0076     "</script>\n",
0077     "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
0078    ]
0079   },
0080   {
0081    "cell_type": "code",
0082    "execution_count": 3,
0083    "id": "d97df479-e3f0-4d8f-96b3-ea1618d1bede",
0084    "metadata": {},
0085    "outputs": [],
0086    "source": [
0087     "import uproot\n",
0088     "\n",
0089     "import pandas as pd\n",
0090     "import os\n",
0091     "import matplotlib.pyplot as plt\n",
0092     "import matplotlib as mpl\n",
0093     "import matplotlib.colors\n",
0094     "import numpy as np\n",
0095     "import glob\n",
0096     "mpl.style.use('seaborn-talk')"
0097    ]
0098   },
0099   {
0100    "cell_type": "markdown",
0101    "id": "a533c964-9597-40aa-ba85-d69b88ffdcb9",
0102    "metadata": {},
0103    "source": [
0104     "# Open an example file"
0105    ]
0106   },
0107   {
0108    "cell_type": "code",
0109    "execution_count": 4,
0110    "id": "c0dade78-94ae-439e-b0b4-8b0be80da6dd",
0111    "metadata": {},
0112    "outputs": [],
0113    "source": [
0114     "file = uproot.open(\"/sphenix/user/trinn/forcsi/testtrainingfile.root\")\n",
0115     "\n",
0116     "n_channel_per_event = 64"
0117    ]
0118   },
0119   {
0120    "cell_type": "markdown",
0121    "id": "55cfd0d3-4ac6-401a-b73a-4df3f814356d",
0122    "metadata": {},
0123    "source": [
0124     "The file contains only one object (`trainingtree` of class `TTree`)"
0125    ]
0126   },
0127   {
0128    "cell_type": "code",
0129    "execution_count": 5,
0130    "id": "e2106013-eba9-42e4-bd20-adca9ff7bd72",
0131    "metadata": {},
0132    "outputs": [
0133     {
0134      "data": {
0135       "text/plain": [
0136        "['trainingtree;1']"
0137       ]
0138      },
0139      "execution_count": 5,
0140      "metadata": {},
0141      "output_type": "execute_result"
0142     }
0143    ],
0144    "source": [
0145     "file.keys()"
0146    ]
0147   },
0148   {
0149    "cell_type": "markdown",
0150    "id": "72de7e9c-e331-4bea-a280-76d0a95fd45e",
0151    "metadata": {},
0152    "source": [
0153     "Each row in `trainingtree` is one event containing 64 channels [0...63]. Each channel has a waveform of 32 ADC samples `int32`[0...31], its best amplitude, time and pedestal based on traditional functional form analysis by Tim. Note the last ADC number [31] is always zero. Let's ignore it. "
0154    ]
0155   },
0156   {
0157    "cell_type": "code",
0158    "execution_count": 6,
0159    "id": "0e53ddfa-b302-4f1a-abe2-a8bb5b657ebe",
0160    "metadata": {},
0161    "outputs": [
0162     {
0163      "data": {
0164       "text/plain": [
0165        "['eventnumber', 'train_amplitude', 'train_time', 'train_pedestal', 'waveform']"
0166       ]
0167      },
0168      "execution_count": 6,
0169      "metadata": {},
0170      "output_type": "execute_result"
0171     }
0172    ],
0173    "source": [
0174     "trainingtree = file[\"trainingtree\"]\n",
0175     "trainingtree.keys()"
0176    ]
0177   },
0178   {
0179    "cell_type": "code",
0180    "execution_count": 7,
0181    "id": "ab6b6890-ac2b-436c-9649-81a254fc744f",
0182    "metadata": {},
0183    "outputs": [
0184     {
0185      "name": "stdout",
0186      "output_type": "stream",
0187      "text": [
0188       "name                 | typename                 | interpretation                \n",
0189       "---------------------+--------------------------+-------------------------------\n",
0190       "eventnumber          | int32_t                  | AsDtype('>i4')\n",
0191       "train_amplitude      | float[64]                | AsDtype(\"('>f4', (64,))\")\n",
0192       "train_time           | float[64]                | AsDtype(\"('>f4', (64,))\")\n",
0193       "train_pedestal       | float[64]                | AsDtype(\"('>f4', (64,))\")\n",
0194       "waveform             | int32_t[64][32]          | AsDtype(\"('>i4', (64, 32))\")\n"
0195      ]
0196     }
0197    ],
0198    "source": [
0199     "trainingtree.show()"
0200    ]
0201   },
0202   {
0203    "cell_type": "markdown",
0204    "id": "35d53a2e-b21f-4482-bdab-7d5b2042548e",
0205    "metadata": {},
0206    "source": [
0207     "# Sample ADC content\n",
0208     "\n",
0209     "We can dump the TTree content into a `np.array` or dataframe in various ways using [`uproot`](https://uproot.readthedocs.io/en/latest/basic.html): "
0210    ]
0211   },
0212   {
0213    "cell_type": "code",
0214    "execution_count": 8,
0215    "id": "29fff7f1-0f22-45e5-925c-17c500d2a9a0",
0216    "metadata": {},
0217    "outputs": [
0218     {
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0247        "      <th>[0][8]</th>\n",
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0249        "      <th>...</th>\n",
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0313        "<p>2 rows × 2048 columns</p>\n",
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0315       ],
0316       "text/plain": [
0317        "   [0][0]  [0][1]  [0][2]  [0][3]  [0][4]  [0][5]  [0][6]  [0][7]  [0][8]  \\\n",
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0319        "1    1647    1647    1647    1645    1647    1642    1648    1645    1647   \n",
0320        "\n",
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0328        "\n",
0329        "[2 rows x 2048 columns]"
0330       ]
0331      },
0332      "execution_count": 8,
0333      "metadata": {},
0334      "output_type": "execute_result"
0335     }
0336    ],
0337    "source": [
0338     "df_waveform = trainingtree[\"waveform\"].array(library=\"pd\")\n",
0339     "df_waveform"
0340    ]
0341   },
0342   {
0343    "cell_type": "code",
0344    "execution_count": 9,
0345    "id": "85a64af2-3e00-4f10-b288-ac0d4906ec9b",
0346    "metadata": {},
0347    "outputs": [
0348     {
0349      "data": {
0350       "text/plain": [
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0394        "         3.06474662e+00]], dtype=float32)"
0395       ]
0396      },
0397      "execution_count": 9,
0398      "metadata": {},
0399      "output_type": "execute_result"
0400     }
0401    ],
0402    "source": [
0403     "array_train_amplitude = trainingtree[\"train_amplitude\"].array(library=\"np\")\n",
0404     "array_train_amplitude"
0405    ]
0406   },
0407   {
0408    "cell_type": "code",
0409    "execution_count": 10,
0410    "id": "949b2767-242e-45f5-84b2-effc7e69cf3e",
0411    "metadata": {},
0412    "outputs": [
0413     {
0414      "data": {
0415       "text/plain": [
0416        "array([[[1646, 1642, 1641, ..., 1648, 1647,    0],\n",
0417        "        [1690, 1685, 1685, ..., 1690, 1687,    0],\n",
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0419        "        ...,\n",
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0423        "\n",
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0431       ]
0432      },
0433      "execution_count": 10,
0434      "metadata": {},
0435      "output_type": "execute_result"
0436     }
0437    ],
0438    "source": [
0439     "array_waveform = trainingtree[\"waveform\"].array(library=\"np\")\n",
0440     "array_waveform"
0441    ]
0442   },
0443   {
0444    "cell_type": "code",
0445    "execution_count": 11,
0446    "id": "4e4fafab-6827-4865-a9ff-4a52a88683ae",
0447    "metadata": {},
0448    "outputs": [
0449     {
0450      "data": {
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0465        "</style>\n",
0466        "<table border=\"1\" class=\"dataframe\">\n",
0467        "  <thead>\n",
0468        "    <tr style=\"text-align: right;\">\n",
0469        "      <th></th>\n",
0470        "      <th>train_amplitude[0]</th>\n",
0471        "      <th>train_amplitude[1]</th>\n",
0472        "      <th>train_amplitude[2]</th>\n",
0473        "      <th>train_amplitude[3]</th>\n",
0474        "      <th>train_amplitude[4]</th>\n",
0475        "      <th>train_amplitude[5]</th>\n",
0476        "      <th>train_amplitude[6]</th>\n",
0477        "      <th>train_amplitude[7]</th>\n",
0478        "      <th>train_amplitude[8]</th>\n",
0479        "      <th>train_amplitude[9]</th>\n",
0480        "      <th>...</th>\n",
0481        "      <th>waveform[63][22]</th>\n",
0482        "      <th>waveform[63][23]</th>\n",
0483        "      <th>waveform[63][24]</th>\n",
0484        "      <th>waveform[63][25]</th>\n",
0485        "      <th>waveform[63][26]</th>\n",
0486        "      <th>waveform[63][27]</th>\n",
0487        "      <th>waveform[63][28]</th>\n",
0488        "      <th>waveform[63][29]</th>\n",
0489        "      <th>waveform[63][30]</th>\n",
0490        "      <th>waveform[63][31]</th>\n",
0491        "    </tr>\n",
0492        "  </thead>\n",
0493        "  <tbody>\n",
0494        "    <tr>\n",
0495        "      <th>0</th>\n",
0496        "      <td>5.251483</td>\n",
0497        "      <td>6.592450</td>\n",
0498        "      <td>3.343703</td>\n",
0499        "      <td>-1.500787</td>\n",
0500        "      <td>1.346111</td>\n",
0501        "      <td>3.842633</td>\n",
0502        "      <td>3.60259</td>\n",
0503        "      <td>6.333031</td>\n",
0504        "      <td>2.132213</td>\n",
0505        "      <td>4.957017</td>\n",
0506        "      <td>...</td>\n",
0507        "      <td>1532</td>\n",
0508        "      <td>1531</td>\n",
0509        "      <td>1534</td>\n",
0510        "      <td>1535</td>\n",
0511        "      <td>1535</td>\n",
0512        "      <td>1533</td>\n",
0513        "      <td>1532</td>\n",
0514        "      <td>1534</td>\n",
0515        "      <td>1526</td>\n",
0516        "      <td>0</td>\n",
0517        "    </tr>\n",
0518        "    <tr>\n",
0519        "      <th>1</th>\n",
0520        "      <td>5.190209</td>\n",
0521        "      <td>4.761475</td>\n",
0522        "      <td>18.613552</td>\n",
0523        "      <td>7.687067</td>\n",
0524        "      <td>4.488912</td>\n",
0525        "      <td>1.461436</td>\n",
0526        "      <td>0.00000</td>\n",
0527        "      <td>6.395168</td>\n",
0528        "      <td>5.430846</td>\n",
0529        "      <td>2.117929</td>\n",
0530        "      <td>...</td>\n",
0531        "      <td>1537</td>\n",
0532        "      <td>1536</td>\n",
0533        "      <td>1531</td>\n",
0534        "      <td>1538</td>\n",
0535        "      <td>1537</td>\n",
0536        "      <td>1537</td>\n",
0537        "      <td>1535</td>\n",
0538        "      <td>1534</td>\n",
0539        "      <td>1534</td>\n",
0540        "      <td>0</td>\n",
0541        "    </tr>\n",
0542        "  </tbody>\n",
0543        "</table>\n",
0544        "<p>2 rows × 2176 columns</p>\n",
0545        "</div>"
0546       ],
0547       "text/plain": [
0548        "   train_amplitude[0]  train_amplitude[1]  train_amplitude[2]  \\\n",
0549        "0            5.251483            6.592450            3.343703   \n",
0550        "1            5.190209            4.761475           18.613552   \n",
0551        "\n",
0552        "   train_amplitude[3]  train_amplitude[4]  train_amplitude[5]  \\\n",
0553        "0           -1.500787            1.346111            3.842633   \n",
0554        "1            7.687067            4.488912            1.461436   \n",
0555        "\n",
0556        "   train_amplitude[6]  train_amplitude[7]  train_amplitude[8]  \\\n",
0557        "0             3.60259            6.333031            2.132213   \n",
0558        "1             0.00000            6.395168            5.430846   \n",
0559        "\n",
0560        "   train_amplitude[9]  ...  waveform[63][22]  waveform[63][23]  \\\n",
0561        "0            4.957017  ...              1532              1531   \n",
0562        "1            2.117929  ...              1537              1536   \n",
0563        "\n",
0564        "   waveform[63][24]  waveform[63][25]  waveform[63][26]  waveform[63][27]  \\\n",
0565        "0              1534              1535              1535              1533   \n",
0566        "1              1531              1538              1537              1537   \n",
0567        "\n",
0568        "   waveform[63][28]  waveform[63][29]  waveform[63][30]  waveform[63][31]  \n",
0569        "0              1532              1534              1526                 0  \n",
0570        "1              1535              1534              1534                 0  \n",
0571        "\n",
0572        "[2 rows x 2176 columns]"
0573       ]
0574      },
0575      "execution_count": 11,
0576      "metadata": {},
0577      "output_type": "execute_result"
0578     }
0579    ],
0580    "source": [
0581     "trainingtree.arrays([\"train_amplitude\", \"train_time\", \"waveform\"], library=\"pd\")"
0582    ]
0583   },
0584   {
0585    "cell_type": "code",
0586    "execution_count": 12,
0587    "id": "b040ebb5-43d4-4f57-a904-e7e181614b60",
0588    "metadata": {},
0589    "outputs": [
0590     {
0591      "data": {
0592       "image/png": 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\n",
0593       "text/plain": [
0594        "<Figure size 748.8x514.8 with 1 Axes>"
0595       ]
0596      },
0597      "metadata": {
0598       "needs_background": "light"
0599      },
0600      "output_type": "display_data"
0601     }
0602    ],
0603    "source": [
0604     "for event_adc_array in array_waveform:\n",
0605     "    for adc_array in event_adc_array:\n",
0606     "#         print (adc_array)\n",
0607     "    \n",
0608     "        # plot lines\n",
0609     "        plt.plot(adc_array[:31])\n",
0610     "        \n",
0611     "\n",
0612     "plt.title(\"Example data file\")\n",
0613     "plt.xlabel(\"ADC sample [0...30]\")\n",
0614     "plt.ylabel(\"ADC value [14 bit integer]\")\n",
0615     "plt.show()"
0616    ]
0617   },
0618   {
0619    "cell_type": "code",
0620    "execution_count": null,
0621    "id": "2a4b85f1-9e49-4dca-b4b9-7f8a34fe663c",
0622    "metadata": {},
0623    "outputs": [],
0624    "source": []
0625   }
0626  ],
0627  "metadata": {
0628   "kernelspec": {
0629    "display_name": "Python (sPHENIX)",
0630    "language": "python",
0631    "name": "sphenix-env"
0632   },
0633   "language_info": {
0634    "codemirror_mode": {
0635     "name": "ipython",
0636     "version": 3
0637    },
0638    "file_extension": ".py",
0639    "mimetype": "text/x-python",
0640    "name": "python",
0641    "nbconvert_exporter": "python",
0642    "pygments_lexer": "ipython3",
0643    "version": "3.8.0"
0644   }
0645  },
0646  "nbformat": 4,
0647  "nbformat_minor": 5
0648 }
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