Lab-3.ipynb

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    "# Lab 3- Training the Deep Neural Network in your head (aka Hand Scan)\n",
    "\n",
    "DUE: 10 am, 2/10/2017. A snapshot will be taken at this time. We will discuss these problems throughout the following week.\n",
    "\n",
    "## Introduction\n",
    "Before High Energy Physicists used computers with automatic reconstruction to turn raw data into features, they relied on hand scans performed by people. In this lab we will setup a hand scan using the Liquid Argon TPC (LArTPC) data we looked at last time. The task will be to identify the type of particle. You will be the handscanner. The steps are as follows:\n",
    "\n",
    "    * Data Engineering: Load data from various files\n",
    "    * Training: Train the handscanner by presenting images of the data with the labels.\n",
    "    * Validation: Ask the handscanner to classify some randomly selected images, and see how well they do.\n"
   ]
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   "source": [
    "## Data Engineering\n",
    "\n",
    "Our data is stored in a bunch of files. You can see the files by listing the directory using the unix \"ls\" command. You can call shell commands, like \"ls\", from Jupyter:"
   ]
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      "kaonPlus_3-1462432851.h5       photon_10-1466546466.h5\r\n",
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      "kaonPlus_3-1462834335.h5       photon_10-1466626405.h5\r\n",
      "kaonPlus_3-1463161408.h5       photon_10-1466642559.h5\r\n",
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      "kaonPlus_3-1466779756.h5       photon_11-1466805930.h5\r\n",
      "kaonPlus_3-1466806088.h5       photon_1-1462232439.h5\r\n",
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      "kaonPlus_3-1466806973.h5       photon_1-1463161409.h5\r\n",
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      "kaonPlus_4-1463161408.h5       photon_1-1466528457.h5\r\n",
      "kaonPlus_4-1463360629.h5       photon_1-1466605316.h5\r\n",
      "kaonPlus_4-1463590710.h5       photon_1-1466626405.h5\r\n",
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      "kaonPlus_4-1466216930.h5       photon_1-1466805843.h5\r\n",
      "kaonPlus_4-1466387460.h5       photon_12-1462232439.h5\r\n",
      "kaonPlus_4-1466439559.h5       photon_12-1462312144.h5\r\n",
      "kaonPlus_4-1466528762.h5       photon_12-1463161409.h5\r\n",
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      "kaonPlus_4-1466605529.h5       photon_12-1466605316.h5\r\n",
      "kaonPlus_4-1466643136.h5       photon_12-1466642559.h5\r\n",
      "kaonPlus_4-1466643524.h5       photon_12-1466805843.h5\r\n",
      "kaonPlus_4-1466695301.h5       photon_13-1462232439.h5\r\n",
      "kaonPlus_4-1466779756.h5       photon_13-1462312144.h5\r\n",
      "kaonPlus_4-1466806088.h5       photon_13-1463161409.h5\r\n",
      "kaonPlus_4-1466806933.h5       photon_13-1466605316.h5\r\n",
      "kaonPlus_4-1466806973.h5       photon_13-1466642559.h5\r\n",
      "kaonPlus_5-1462146936.h5       photon_13-1466805843.h5\r\n",
      "kaonPlus_5-1462522983.h5       photon_13-1466805930.h5\r\n",
      "kaonPlus_5-1462834335.h5       photon_14-1462232439.h5\r\n",
      "kaonPlus_5-1463161408.h5       photon_14-1462312144.h5\r\n",
      "kaonPlus_5-1463360629.h5       photon_14-1463161409.h5\r\n",
      "kaonPlus_5-1463590710.h5       photon_14-1463360631.h5\r\n",
      "kaonPlus_5-1465961077.h5       photon_14-1463590711.h5\r\n",
      "kaonPlus_5-1466216930.h5       photon_14-1466218433.h5\r\n",
      "kaonPlus_5-1466387460.h5       photon_14-1466439393.h5\r\n",
      "kaonPlus_5-1466439559.h5       photon_14-1466528457.h5\r\n",
      "kaonPlus_5-1466528762.h5       photon_14-1466626405.h5\r\n",
      "kaonPlus_5-1466546624.h5       photon_14-1466642559.h5\r\n",
      "kaonPlus_5-1466605529.h5       photon_14-1466643029.h5\r\n",
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      "kaonPlus_5-1466643136.h5       photon_14-1466805843.h5\r\n",
      "kaonPlus_5-1466643524.h5       photon_14-1466805930.h5\r\n",
      "kaonPlus_5-1466695301.h5       photon_15-1462232439.h5\r\n",
      "kaonPlus_5-1466779756.h5       photon_15-1462312144.h5\r\n",
      "kaonPlus_5-1466806088.h5       photon_15-1463161409.h5\r\n",
      "kaonPlus_5-1466806933.h5       photon_15-1463360631.h5\r\n",
      "kaonPlus_5-1466806973.h5       photon_15-1466218433.h5\r\n",
      "kaonPlus_6-1462146936.h5       photon_15-1466528457.h5\r\n",
      "kaonPlus_6-1462204064.h5       photon_15-1466605316.h5\r\n",
      "kaonPlus_6-1462522983.h5       photon_15-1466626405.h5\r\n",
      "kaonPlus_6-1462834335.h5       photon_15-1466642559.h5\r\n",
      "kaonPlus_6-1463161408.h5       photon_15-1466805843.h5\r\n",
      "kaonPlus_6-1463360629.h5       photon_15-1466805930.h5\r\n",
      "kaonPlus_6-1463590710.h5       photon_16-1462232439.h5\r\n",
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      "kaonPlus_6-1466216930.h5       photon_16-1463161409.h5\r\n",
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      "kaonPlus_6-1466387460.h5       photon_16-1466605316.h5\r\n",
      "kaonPlus_6-1466528762.h5       photon_16-1466642559.h5\r\n",
      "kaonPlus_6-1466546624.h5       photon_16-1466805843.h5\r\n",
      "kaonPlus_6-1466605529.h5       photon_17-1462232439.h5\r\n",
      "kaonPlus_6-1466643136.h5       photon_17-1462312144.h5\r\n",
      "kaonPlus_6-1466643524.h5       photon_17-1463161409.h5\r\n",
      "kaonPlus_6-1466695301.h5       photon_17-1466605316.h5\r\n",
      "kaonPlus_6-1466779756.h5       photon_17-1466642559.h5\r\n",
      "kaonPlus_6-1466806933.h5       photon_17-1466805843.h5\r\n",
      "kaonPlus_6-1466806973.h5       photon_17-1466805930.h5\r\n",
      "kaonPlus_7-1462146936.h5       photon_18-1462232439.h5\r\n",
      "kaonPlus_7-1462204064.h5       photon_18-1462312144.h5\r\n",
      "kaonPlus_7-1462522983.h5       photon_18-1463360631.h5\r\n",
      "kaonPlus_7-1462834335.h5       photon_18-1463590711.h5\r\n",
      "kaonPlus_7-1463161408.h5       photon_18-1466218433.h5\r\n",
      "kaonPlus_7-1463360629.h5       photon_18-1466439393.h5\r\n",
      "kaonPlus_7-1465961077.h5       photon_18-1466605316.h5\r\n",
      "kaonPlus_7-1466216930.h5       photon_18-1466626405.h5\r\n",
      "kaonPlus_7-1466275727.h5       photon_18-1466642559.h5\r\n",
      "kaonPlus_7-1466387460.h5       photon_18-1466643029.h5\r\n",
      "kaonPlus_7-1466528762.h5       photon_18-1466805930.h5\r\n",
      "kaonPlus_7-1466546624.h5       photon_19-1462232439.h5\r\n",
      "kaonPlus_7-1466605529.h5       photon_19-1462312144.h5\r\n",
      "kaonPlus_7-1466626552.h5       photon_19-1463161409.h5\r\n",
      "kaonPlus_7-1466643136.h5       photon_19-1466218433.h5\r\n",
      "kaonPlus_7-1466643524.h5       photon_19-1466605316.h5\r\n",
      "kaonPlus_7-1466695301.h5       photon_19-1466626405.h5\r\n",
      "kaonPlus_7-1466705578.h5       photon_19-1466642559.h5\r\n",
      "kaonPlus_7-1466779756.h5       photon_19-1466805843.h5\r\n",
      "kaonPlus_7-1466806088.h5       photon_19-1466805930.h5\r\n",
      "kaonPlus_7-1466806933.h5       photon_2-1462232439.h5\r\n",
      "kaonPlus_7-1466806973.h5       photon_2-1462312144.h5\r\n",
      "kaonPlus_8-1462146936.h5       photon_2-1463161409.h5\r\n",
      "kaonPlus_8-1462522983.h5       photon_2-1466218433.h5\r\n",
      "kaonPlus_8-1462834335.h5       photon_2-1466605316.h5\r\n",
      "kaonPlus_8-1463161408.h5       photon_2-1466642559.h5\r\n",
      "kaonPlus_8-1463360629.h5       photon_2-1466805843.h5\r\n",
      "kaonPlus_8-1465961077.h5       photon_2-1466805930.h5\r\n",
      "kaonPlus_8-1466216930.h5       photon_3-1462232439.h5\r\n",
      "kaonPlus_8-1466387460.h5       photon_3-1462312144.h5\r\n",
      "kaonPlus_8-1466439559.h5       photon_3-1463161409.h5\r\n",
      "kaonPlus_8-1466528762.h5       photon_3-1466218433.h5\r\n",
      "kaonPlus_8-1466546624.h5       photon_3-1466626405.h5\r\n",
      "kaonPlus_8-1466605529.h5       photon_3-1466642559.h5\r\n",
      "kaonPlus_8-1466643136.h5       photon_3-1466805930.h5\r\n",
      "kaonPlus_8-1466643524.h5       photon_4-1462232439.h5\r\n",
      "kaonPlus_8-1466695301.h5       photon_4-1462312144.h5\r\n",
      "kaonPlus_8-1466779756.h5       photon_4-1463161409.h5\r\n",
      "kaonPlus_8-1466806088.h5       photon_4-1463360631.h5\r\n",
      "kaonPlus_8-1466806933.h5       photon_4-1466528457.h5\r\n",
      "kaonPlus_8-1466806973.h5       photon_4-1466605316.h5\r\n",
      "kaonPlus_9-1462146936.h5       photon_4-1466626405.h5\r\n",
      "kaonPlus_9-1462522983.h5       photon_4-1466642559.h5\r\n",
      "kaonPlus_9-1462834335.h5       photon_4-1466805843.h5\r\n",
      "kaonPlus_9-1463161408.h5       photon_5-1462232439.h5\r\n",
      "kaonPlus_9-1463360629.h5       photon_5-1462312144.h5\r\n",
      "kaonPlus_9-1463590710.h5       photon_5-1463161409.h5\r\n",
      "kaonPlus_9-1465961077.h5       photon_5-1466218433.h5\r\n",
      "kaonPlus_9-1466216930.h5       photon_5-1466439393.h5\r\n",
      "kaonPlus_9-1466387460.h5       photon_5-1466605316.h5\r\n",
      "kaonPlus_9-1466439559.h5       photon_5-1466626405.h5\r\n",
      "kaonPlus_9-1466528762.h5       photon_5-1466642559.h5\r\n",
      "kaonPlus_9-1466546624.h5       photon_5-1466805843.h5\r\n",
      "kaonPlus_9-1466605529.h5       photon_6-1462232439.h5\r\n",
      "kaonPlus_9-1466642679.h5       photon_6-1462312144.h5\r\n",
      "kaonPlus_9-1466643136.h5       photon_6-1463161409.h5\r\n",
      "kaonPlus_9-1466643524.h5       photon_6-1466218433.h5\r\n",
      "kaonPlus_9-1466695301.h5       photon_6-1466605316.h5\r\n",
      "kaonPlus_9-1466779756.h5       photon_6-1466642559.h5\r\n",
      "kaonPlus_9-1466806088.h5       photon_6-1466805843.h5\r\n",
      "kaonPlus_9-1466806933.h5       photon_6-1466805930.h5\r\n",
      "kaonPlus_9-1466806973.h5       photon_7-1462232439.h5\r\n",
      "muon_0-1462146938.h5\t       photon_7-1462312144.h5\r\n",
      "muon_0-1462204067.h5\t       photon_7-1463161409.h5\r\n",
      "muon_0-1463161411.h5\t       photon_7-1463590711.h5\r\n",
      "muon_0-1463590714.h5\t       photon_7-1466218433.h5\r\n",
      "muon_0-1465960648.h5\t       photon_7-1466626405.h5\r\n",
      "muon_0-1466216932.h5\t       photon_7-1466642559.h5\r\n",
      "muon_0-1466218429.h5\t       photon_7-1466643029.h5\r\n",
      "muon_0-1466387130.h5\t       photon_7-1466805930.h5\r\n",
      "muon_0-1466439235.h5\t       photon_8-1462232439.h5\r\n",
      "muon_0-1466526554.h5\t       photon_8-1462312144.h5\r\n",
      "muon_0-1466546416.h5\t       photon_8-1463161409.h5\r\n",
      "muon_0-1466605238.h5\t       photon_8-1463360631.h5\r\n",
      "muon_0-1466626321.h5\t       photon_8-1466528457.h5\r\n",
      "muon_0-1466642494.h5\t       photon_8-1466605316.h5\r\n",
      "muon_0-1466642975.h5\t       photon_8-1466626405.h5\r\n",
      "muon_0-1466643298.h5\t       photon_8-1466642559.h5\r\n",
      "muon_10-1462146938.h5\t       photon_8-1466805843.h5\r\n",
      "muon_10-1462204067.h5\t       photon_8-1466805930.h5\r\n",
      "muon_10-1463161411.h5\t       photon_9-1462232439.h5\r\n",
      "muon_10-1463360633.h5\t       photon_9-1462312144.h5\r\n",
      "muon_10-1465960648.h5\t       photon_9-1463161409.h5\r\n",
      "muon_10-1466218429.h5\t       photon_9-1466218433.h5\r\n",
      "muon_10-1466387130.h5\t       photon_9-1466439393.h5\r\n",
      "muon_10-1466526554.h5\t       photon_9-1466528457.h5\r\n",
      "muon_10-1466546416.h5\t       photon_9-1466626405.h5\r\n",
      "muon_10-1466605238.h5\t       photon_9-1466642559.h5\r\n",
      "muon_10-1466642494.h5\t       photon_9-1466805843.h5\r\n",
      "muon_10-1466642975.h5\t       pion_0_0-1462834340.h5\r\n",
      "muon_10-1466643298.h5\t       pion_0_0-1463360635.h5\r\n",
      "muon_11-1462204067.h5\t       pion_0_0-1463722323.h5\r\n",
      "muon_11-1463161411.h5\t       pion_0_0-1463722327.h5\r\n",
      "muon_11-1463590714.h5\t       pion_0_0-1463722334.h5\r\n",
      "muon_11-1465960648.h5\t       pion_0_0-1464738862.h5\r\n",
      "muon_11-1466218429.h5\t       pion_0_0-1464822925.h5\r\n",
      "muon_11-1466387130.h5\t       pion_0_0-1464918770.h5\r\n",
      "muon_11-1466526554.h5\t       pion_0_0-1465113999.h5\r\n",
      "muon_11-1466546416.h5\t       pion_0_0-1465222696.h5\r\n",
      "muon_11-1466605238.h5\t       pion_0_0-1466216927.h5\r\n",
      "muon_11-1466642494.h5\t       pion_0_0-1466218424.h5\r\n",
      "muon_11-1466642975.h5\t       pion_0_10-1462834340.h5\r\n",
      "muon_11-1466643298.h5\t       pion_0_10-1463360635.h5\r\n",
      "muon_1-1462204067.h5\t       pion_0_10-1463722323.h5\r\n",
      "muon_1-1463161411.h5\t       pion_0_10-1463722327.h5\r\n",
      "muon_1-1463590714.h5\t       pion_0_10-1463722334.h5\r\n",
      "muon_1-1466218429.h5\t       pion_0_10-1464738862.h5\r\n",
      "muon_1-1466275452.h5\t       pion_0_10-1464822925.h5\r\n",
      "muon_1-1466387130.h5\t       pion_0_10-1465113999.h5\r\n",
      "muon_1-1466439235.h5\t       pion_0_10-1465222696.h5\r\n",
      "muon_1-1466526554.h5\t       pion_0_10-1466216927.h5\r\n",
      "muon_1-1466546416.h5\t       pion_0_10-1466218424.h5\r\n",
      "muon_1-1466605238.h5\t       pion_0_11-1462834340.h5\r\n",
      "muon_1-1466642494.h5\t       pion_0_11-1463360635.h5\r\n",
      "muon_1-1466642975.h5\t       pion_0_11-1463722323.h5\r\n",
      "muon_1-1466643298.h5\t       pion_0_11-1463722327.h5\r\n",
      "muon_12-1462204067.h5\t       pion_0_11-1463722334.h5\r\n",
      "muon_12-1463161411.h5\t       pion_0_11-1464738862.h5\r\n",
      "muon_12-1463360633.h5\t       pion_0_11-1464822925.h5\r\n",
      "muon_12-1463590714.h5\t       pion_0_11-1465222696.h5\r\n",
      "muon_12-1465960648.h5\t       pion_0_11-1466216927.h5\r\n",
      "muon_12-1466218429.h5\t       pion_0_11-1466218424.h5\r\n",
      "muon_12-1466387130.h5\t       pion_0_1-1463360635.h5\r\n",
      "muon_12-1466439235.h5\t       pion_0_1-1463590716.h5\r\n",
      "muon_12-1466526554.h5\t       pion_0_1-1463722323.h5\r\n",
      "muon_12-1466546416.h5\t       pion_0_1-1463722327.h5\r\n",
      "muon_12-1466605238.h5\t       pion_0_1-1463722334.h5\r\n",
      "muon_12-1466626321.h5\t       pion_0_1-1464738862.h5\r\n",
      "muon_12-1466642494.h5\t       pion_0_1-1464822925.h5\r\n",
      "muon_12-1466642975.h5\t       pion_0_1-1465113999.h5\r\n",
      "muon_12-1466643298.h5\t       pion_0_1-1465222696.h5\r\n",
      "muon_13-1462204067.h5\t       pion_0_1-1466216927.h5\r\n",
      "muon_13-1463161411.h5\t       pion_0_1-1466218424.h5\r\n",
      "muon_13-1463360633.h5\t       pion_0_12-1462834340.h5\r\n",
      "muon_13-1463590714.h5\t       pion_0_12-1463360635.h5\r\n",
      "muon_13-1465960648.h5\t       pion_0_12-1463590716.h5\r\n",
      "muon_13-1466218429.h5\t       pion_0_12-1463722327.h5\r\n",
      "muon_13-1466275452.h5\t       pion_0_12-1463722330.h5\r\n",
      "muon_13-1466387130.h5\t       pion_0_12-1463722334.h5\r\n",
      "muon_13-1466526554.h5\t       pion_0_12-1464738862.h5\r\n",
      "muon_13-1466546416.h5\t       pion_0_12-1464822925.h5\r\n",
      "muon_13-1466605238.h5\t       pion_0_12-1464918770.h5\r\n",
      "muon_13-1466642494.h5\t       pion_0_12-1465113999.h5\r\n",
      "muon_13-1466642975.h5\t       pion_0_12-1465399372.h5\r\n",
      "muon_13-1466643298.h5\t       pion_0_12-1466216927.h5\r\n",
      "muon_14-1462146938.h5\t       pion_0_12-1466218424.h5\r\n",
      "muon_14-1462204067.h5\t       pion_0_13-1462834340.h5\r\n",
      "muon_14-1463161411.h5\t       pion_0_13-1463360635.h5\r\n",
      "muon_14-1463360633.h5\t       pion_0_13-1463590716.h5\r\n",
      "muon_14-1465960648.h5\t       pion_0_13-1463722327.h5\r\n",
      "muon_14-1466218429.h5\t       pion_0_13-1463722330.h5\r\n",
      "muon_14-1466387130.h5\t       pion_0_13-1463722334.h5\r\n",
      "muon_14-1466526554.h5\t       pion_0_13-1464738862.h5\r\n",
      "muon_14-1466546416.h5\t       pion_0_13-1464822925.h5\r\n",
      "muon_14-1466605238.h5\t       pion_0_13-1464918770.h5\r\n",
      "muon_14-1466642494.h5\t       pion_0_13-1465113999.h5\r\n",
      "muon_14-1466642975.h5\t       pion_0_13-1465399372.h5\r\n",
      "muon_14-1466643298.h5\t       pion_0_13-1466216927.h5\r\n",
      "muon_15-1462146938.h5\t       pion_0_13-1466218424.h5\r\n",
      "muon_15-1462204067.h5\t       pion_0_14-1463722323.h5\r\n",
      "muon_15-1463161411.h5\t       pion_0_14-1463722327.h5\r\n",
      "muon_15-1463590714.h5\t       pion_0_14-1463722334.h5\r\n",
      "muon_15-1465960648.h5\t       pion_0_14-1464738862.h5\r\n",
      "muon_15-1466218429.h5\t       pion_0_14-1464822925.h5\r\n",
      "muon_15-1466387130.h5\t       pion_0_14-1465113999.h5\r\n",
      "muon_15-1466526554.h5\t       pion_0_14-1465222696.h5\r\n",
      "muon_15-1466546416.h5\t       pion_0_14-1465960313.h5\r\n",
      "muon_15-1466605238.h5\t       pion_0_14-1466216927.h5\r\n",
      "muon_15-1466642494.h5\t       pion_0_14-1466218424.h5\r\n",
      "muon_15-1466642975.h5\t       pion_0_15-1462834340.h5\r\n",
      "muon_15-1466643298.h5\t       pion_0_15-1463161413.h5\r\n",
      "muon_16-1462204067.h5\t       pion_0_15-1463360635.h5\r\n",
      "muon_16-1463161411.h5\t       pion_0_15-1463590716.h5\r\n",
      "muon_16-1463360633.h5\t       pion_0_15-1463722323.h5\r\n",
      "muon_16-1463590714.h5\t       pion_0_15-1463722327.h5\r\n",
      "muon_16-1465960648.h5\t       pion_0_15-1463722334.h5\r\n",
      "muon_16-1466218429.h5\t       pion_0_15-1464738862.h5\r\n",
      "muon_16-1466387130.h5\t       pion_0_15-1464822925.h5\r\n",
      "muon_16-1466439235.h5\t       pion_0_15-1465222696.h5\r\n",
      "muon_16-1466526554.h5\t       pion_0_15-1466216927.h5\r\n",
      "muon_16-1466546416.h5\t       pion_0_15-1466218424.h5\r\n",
      "muon_16-1466605238.h5\t       pion_0_16-1463360635.h5\r\n",
      "muon_16-1466626321.h5\t       pion_0_16-1463590716.h5\r\n",
      "muon_16-1466642494.h5\t       pion_0_16-1463722327.h5\r\n",
      "muon_16-1466642975.h5\t       pion_0_16-1463722330.h5\r\n",
      "muon_16-1466643298.h5\t       pion_0_16-1463722334.h5\r\n",
      "muon_17-1462204067.h5\t       pion_0_16-1464738862.h5\r\n",
      "muon_17-1462522986.h5\t       pion_0_16-1464822925.h5\r\n",
      "muon_17-1463161411.h5\t       pion_0_16-1464918770.h5\r\n",
      "muon_17-1463360633.h5\t       pion_0_16-1465113999.h5\r\n",
      "muon_17-1463590714.h5\t       pion_0_16-1465399372.h5\r\n",
      "muon_17-1465960648.h5\t       pion_0_16-1466216927.h5\r\n",
      "muon_17-1466218429.h5\t       pion_0_16-1466218424.h5\r\n",
      "muon_17-1466275452.h5\t       pion_0_17-1462834340.h5\r\n",
      "muon_17-1466387130.h5\t       pion_0_17-1463360635.h5\r\n",
      "muon_17-1466526554.h5\t       pion_0_17-1463590716.h5\r\n",
      "muon_17-1466546416.h5\t       pion_0_17-1463722327.h5\r\n",
      "muon_17-1466605238.h5\t       pion_0_17-1463722330.h5\r\n",
      "muon_17-1466626321.h5\t       pion_0_17-1463722334.h5\r\n",
      "muon_17-1466642494.h5\t       pion_0_17-1464738862.h5\r\n",
      "muon_17-1466642975.h5\t       pion_0_17-1464822925.h5\r\n",
      "muon_17-1466643298.h5\t       pion_0_17-1464918770.h5\r\n",
      "muon_18-1462146938.h5\t       pion_0_17-1465113999.h5\r\n",
      "muon_18-1462204067.h5\t       pion_0_17-1465399372.h5\r\n",
      "muon_18-1463161411.h5\t       pion_0_17-1466216927.h5\r\n",
      "muon_18-1463360633.h5\t       pion_0_17-1466218424.h5\r\n",
      "muon_18-1463590714.h5\t       pion_0_18-1462834340.h5\r\n",
      "muon_18-1465960648.h5\t       pion_0_18-1463722323.h5\r\n",
      "muon_18-1466218429.h5\t       pion_0_18-1463722327.h5\r\n",
      "muon_18-1466387130.h5\t       pion_0_18-1463722334.h5\r\n",
      "muon_18-1466526554.h5\t       pion_0_18-1464738862.h5\r\n",
      "muon_18-1466546416.h5\t       pion_0_18-1464822925.h5\r\n",
      "muon_18-1466605238.h5\t       pion_0_18-1465113999.h5\r\n",
      "muon_18-1466642494.h5\t       pion_0_18-1465222696.h5\r\n",
      "muon_18-1466642975.h5\t       pion_0_18-1465399372.h5\r\n",
      "muon_18-1466643298.h5\t       pion_0_18-1465960313.h5\r\n",
      "muon_19-1462204067.h5\t       pion_0_18-1466216927.h5\r\n",
      "muon_19-1463161411.h5\t       pion_0_18-1466218424.h5\r\n",
      "muon_19-1463590714.h5\t       pion_0_19-1462834340.h5\r\n",
      "muon_19-1465960648.h5\t       pion_0_19-1463161413.h5\r\n",
      "muon_19-1466218429.h5\t       pion_0_19-1463360635.h5\r\n",
      "muon_19-1466387130.h5\t       pion_0_19-1463590716.h5\r\n",
      "muon_19-1466526554.h5\t       pion_0_19-1463722323.h5\r\n",
      "muon_19-1466546416.h5\t       pion_0_19-1463722327.h5\r\n",
      "muon_19-1466605238.h5\t       pion_0_19-1463722334.h5\r\n",
      "muon_19-1466642494.h5\t       pion_0_19-1464738862.h5\r\n",
      "muon_19-1466642975.h5\t       pion_0_19-1464822925.h5\r\n",
      "muon_19-1466643298.h5\t       pion_0_19-1465222696.h5\r\n",
      "muon_2-1462204067.h5\t       pion_0_19-1466216927.h5\r\n",
      "muon_2-1463161411.h5\t       pion_0_19-1466218424.h5\r\n",
      "muon_2-1463360633.h5\t       pion_0_2-1462834340.h5\r\n",
      "muon_2-1463590714.h5\t       pion_0_2-1463360635.h5\r\n",
      "muon_2-1465960648.h5\t       pion_0_2-1463590716.h5\r\n",
      "muon_2-1466218429.h5\t       pion_0_2-1463722327.h5\r\n",
      "muon_2-1466387130.h5\t       pion_0_2-1463722330.h5\r\n",
      "muon_2-1466526554.h5\t       pion_0_2-1463722334.h5\r\n",
      "muon_2-1466546416.h5\t       pion_0_2-1464738862.h5\r\n",
      "muon_2-1466605238.h5\t       pion_0_2-1464822925.h5\r\n",
      "muon_2-1466642494.h5\t       pion_0_2-1465113999.h5\r\n",
      "muon_2-1466642975.h5\t       pion_0_2-1465399372.h5\r\n",
      "muon_2-1466643298.h5\t       pion_0_2-1466216927.h5\r\n",
      "muon_3-1462204067.h5\t       pion_0_2-1466218424.h5\r\n",
      "muon_3-1462522986.h5\t       pion_0_3-1462834340.h5\r\n",
      "muon_3-1463161411.h5\t       pion_0_3-1463161413.h5\r\n",
      "muon_3-1463360633.h5\t       pion_0_3-1463360635.h5\r\n",
      "muon_3-1465960648.h5\t       pion_0_3-1463590716.h5\r\n",
      "muon_3-1466218429.h5\t       pion_0_3-1463722327.h5\r\n",
      "muon_3-1466387130.h5\t       pion_0_3-1463722330.h5\r\n",
      "muon_3-1466526554.h5\t       pion_0_3-1463722334.h5\r\n",
      "muon_3-1466546416.h5\t       pion_0_3-1464738862.h5\r\n",
      "muon_3-1466605238.h5\t       pion_0_3-1464822925.h5\r\n",
      "muon_3-1466642494.h5\t       pion_0_3-1465113999.h5\r\n",
      "muon_3-1466642975.h5\t       pion_0_3-1465960313.h5\r\n",
      "muon_3-1466643298.h5\t       pion_0_3-1466216927.h5\r\n",
      "muon_4-1462146938.h5\t       pion_0_3-1466218424.h5\r\n",
      "muon_4-1462204067.h5\t       pion_0_4-1462834340.h5\r\n",
      "muon_4-1463161411.h5\t       pion_0_4-1463360635.h5\r\n",
      "muon_4-1463360633.h5\t       pion_0_4-1463722323.h5\r\n",
      "muon_4-1463590714.h5\t       pion_0_4-1463722327.h5\r\n",
      "muon_4-1465960648.h5\t       pion_0_4-1463722334.h5\r\n",
      "muon_4-1466218429.h5\t       pion_0_4-1464738862.h5\r\n",
      "muon_4-1466387130.h5\t       pion_0_4-1464822925.h5\r\n",
      "muon_4-1466439235.h5\t       pion_0_4-1464918770.h5\r\n",
      "muon_4-1466526554.h5\t       pion_0_4-1465113999.h5\r\n",
      "muon_4-1466546416.h5\t       pion_0_4-1465222696.h5\r\n",
      "muon_4-1466605238.h5\t       pion_0_4-1466216927.h5\r\n",
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      "nuebar_NC_3-1469513472.h5      pionPlus_11-1466780135.h5\r\n",
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      "nuebar_NC_4-1469512962.h5      pionPlus_1-1462204073.h5\r\n",
      "nuebar_NC_4-1469513002.h5      pionPlus_1-1463161417.h5\r\n",
      "nuebar_NC_4-1469513040.h5      pionPlus_1-1463360640.h5\r\n",
      "nuebar_NC_4-1469513081.h5      pionPlus_1-1463590721.h5\r\n",
      "nuebar_NC_4-1469513114.h5      pionPlus_1-1466216924.h5\r\n",
      "nuebar_NC_4-1469513150.h5      pionPlus_1-1466218422.h5\r\n",
      "nuebar_NC_4-1469513218.h5      pionPlus_1-1466705968.h5\r\n",
      "nuebar_NC_4-1469513267.h5      pionPlus_1-1466706291.h5\r\n",
      "nuebar_NC_4-1469513311.h5      pionPlus_1-1466713827.h5\r\n",
      "nuebar_NC_4-1469513349.h5      pionPlus_1-1466779826.h5\r\n",
      "nuebar_NC_4-1469513392.h5      pionPlus_1-1466780135.h5\r\n",
      "nuebar_NC_4-1469513434.h5      pionPlus_1-1466780260.h5\r\n",
      "nuebar_NC_4-1469513472.h5      pionPlus_1-1466806195.h5\r\n",
      "nuebar_NC_5-1469512412.h5      pionPlus_1-1466806367.h5\r\n",
      "nuebar_NC_5-1469512448.h5      pionPlus_1-1466806485.h5\r\n",
      "nuebar_NC_5-1469512487.h5      pionPlus_1-1466877030.h5\r\n",
      "nuebar_NC_5-1469512529.h5      pionPlus_1-1466877083.h5\r\n",
      "nuebar_NC_5-1469512634.h5      pionPlus_1-1466877139.h5\r\n",
      "nuebar_NC_5-1469512704.h5      pionPlus_1-1466877206.h5\r\n",
      "nuebar_NC_5-1469512742.h5      pionPlus_1-1466877240.h5\r\n",
      "nuebar_NC_5-1469512962.h5      pionPlus_12-1462204073.h5\r\n",
      "nuebar_NC_5-1469513002.h5      pionPlus_12-1462522991.h5\r\n",
      "nuebar_NC_5-1469513040.h5      pionPlus_12-1463161417.h5\r\n",
      "nuebar_NC_5-1469513081.h5      pionPlus_12-1463360640.h5\r\n",
      "nuebar_NC_5-1469513114.h5      pionPlus_12-1463590721.h5\r\n",
      "nuebar_NC_5-1469513150.h5      pionPlus_12-1466216924.h5\r\n",
      "nuebar_NC_5-1469513218.h5      pionPlus_12-1466218422.h5\r\n",
      "nuebar_NC_5-1469513267.h5      pionPlus_12-1466705968.h5\r\n",
      "nuebar_NC_5-1469513311.h5      pionPlus_12-1466706136.h5\r\n",
      "nuebar_NC_5-1469513349.h5      pionPlus_12-1466706291.h5\r\n",
      "nuebar_NC_5-1469513392.h5      pionPlus_12-1466713827.h5\r\n",
      "nuebar_NC_5-1469513434.h5      pionPlus_12-1466779826.h5\r\n",
      "nuebar_NC_5-1469513472.h5      pionPlus_12-1466780135.h5\r\n",
      "nuebar_NC_6-1461971809.h5      pionPlus_12-1466780260.h5\r\n",
      "nuebar_NC_6-1469512412.h5      pionPlus_12-1466806195.h5\r\n",
      "nuebar_NC_6-1469512448.h5      pionPlus_12-1466806367.h5\r\n",
      "nuebar_NC_6-1469512487.h5      pionPlus_12-1466806485.h5\r\n",
      "nuebar_NC_6-1469512529.h5      pionPlus_12-1466877030.h5\r\n",
      "nuebar_NC_6-1469512634.h5      pionPlus_12-1466877083.h5\r\n",
      "nuebar_NC_6-1469512704.h5      pionPlus_12-1466877139.h5\r\n",
      "nuebar_NC_6-1469512742.h5      pionPlus_12-1466877240.h5\r\n",
      "nuebar_NC_6-1469512962.h5      pionPlus_13-1462204073.h5\r\n",
      "nuebar_NC_6-1469513002.h5      pionPlus_13-1462522991.h5\r\n",
      "nuebar_NC_6-1469513040.h5      pionPlus_13-1463161417.h5\r\n",
      "nuebar_NC_6-1469513081.h5      pionPlus_13-1463360640.h5\r\n",
      "nuebar_NC_6-1469513114.h5      pionPlus_13-1463590721.h5\r\n",
      "nuebar_NC_6-1469513150.h5      pionPlus_13-1466216924.h5\r\n",
      "nuebar_NC_6-1469513218.h5      pionPlus_13-1466218422.h5\r\n",
      "nuebar_NC_6-1469513267.h5      pionPlus_13-1466705968.h5\r\n",
      "nuebar_NC_6-1469513311.h5      pionPlus_13-1466706136.h5\r\n",
      "nuebar_NC_6-1469513349.h5      pionPlus_13-1466706291.h5\r\n",
      "nuebar_NC_6-1469513392.h5      pionPlus_13-1466713827.h5\r\n",
      "nuebar_NC_6-1469513434.h5      pionPlus_13-1466713982.h5\r\n",
      "nuebar_NC_6-1469513472.h5      pionPlus_13-1466779826.h5\r\n",
      "nuebar_NC_7-1461971809.h5      pionPlus_13-1466780135.h5\r\n",
      "nuebar_NC_7-1469512412.h5      pionPlus_13-1466780260.h5\r\n",
      "nuebar_NC_7-1469512448.h5      pionPlus_13-1466806195.h5\r\n",
      "nuebar_NC_7-1469512487.h5      pionPlus_13-1466806367.h5\r\n",
      "nuebar_NC_7-1469512529.h5      pionPlus_13-1466806485.h5\r\n",
      "nuebar_NC_7-1469512634.h5      pionPlus_13-1466877030.h5\r\n",
      "nuebar_NC_7-1469512704.h5      pionPlus_13-1466877083.h5\r\n",
      "nuebar_NC_7-1469512742.h5      pionPlus_13-1466877139.h5\r\n",
      "nuebar_NC_7-1469512962.h5      pionPlus_13-1466877240.h5\r\n",
      "nuebar_NC_7-1469513002.h5      pionPlus_14-1462204073.h5\r\n",
      "nuebar_NC_7-1469513040.h5      pionPlus_14-1462522991.h5\r\n",
      "nuebar_NC_7-1469513081.h5      pionPlus_14-1463161417.h5\r\n",
      "nuebar_NC_7-1469513114.h5      pionPlus_14-1463360640.h5\r\n",
      "nuebar_NC_7-1469513150.h5      pionPlus_14-1463590721.h5\r\n",
      "nuebar_NC_7-1469513218.h5      pionPlus_14-1466216924.h5\r\n",
      "nuebar_NC_7-1469513267.h5      pionPlus_14-1466218422.h5\r\n",
      "nuebar_NC_7-1469513311.h5      pionPlus_14-1466705968.h5\r\n",
      "nuebar_NC_7-1469513349.h5      pionPlus_14-1466706291.h5\r\n",
      "nuebar_NC_7-1469513392.h5      pionPlus_14-1466713827.h5\r\n",
      "nuebar_NC_7-1469513434.h5      pionPlus_14-1466779826.h5\r\n",
      "nuebar_NC_7-1469513472.h5      pionPlus_14-1466780135.h5\r\n",
      "nuebar_NC_8-1461971809.h5      pionPlus_14-1466780260.h5\r\n",
      "nuebar_NC_8-1469512412.h5      pionPlus_14-1466806195.h5\r\n",
      "nuebar_NC_8-1469512448.h5      pionPlus_14-1466806367.h5\r\n",
      "nuebar_NC_8-1469512487.h5      pionPlus_14-1466877030.h5\r\n",
      "nuebar_NC_8-1469512529.h5      pionPlus_14-1466877083.h5\r\n",
      "nuebar_NC_8-1469512634.h5      pionPlus_14-1466877139.h5\r\n",
      "nuebar_NC_8-1469512704.h5      pionPlus_14-1466877206.h5\r\n",
      "nuebar_NC_8-1469512742.h5      pionPlus_14-1466877240.h5\r\n",
      "nuebar_NC_8-1469512962.h5      pionPlus_15-1462204073.h5\r\n",
      "nuebar_NC_8-1469513002.h5      pionPlus_15-1462522991.h5\r\n",
      "nuebar_NC_8-1469513040.h5      pionPlus_15-1463161417.h5\r\n",
      "nuebar_NC_8-1469513081.h5      pionPlus_15-1463360640.h5\r\n",
      "nuebar_NC_8-1469513114.h5      pionPlus_15-1463590721.h5\r\n",
      "nuebar_NC_8-1469513150.h5      pionPlus_15-1466216924.h5\r\n",
      "nuebar_NC_8-1469513218.h5      pionPlus_15-1466218422.h5\r\n",
      "nuebar_NC_8-1469513267.h5      pionPlus_15-1466705968.h5\r\n",
      "nuebar_NC_8-1469513311.h5      pionPlus_15-1466706291.h5\r\n",
      "nuebar_NC_8-1469513349.h5      pionPlus_15-1466713827.h5\r\n",
      "nuebar_NC_8-1469513392.h5      pionPlus_15-1466779826.h5\r\n",
      "nuebar_NC_8-1469513434.h5      pionPlus_15-1466780135.h5\r\n",
      "nuebar_NC_8-1469513472.h5      pionPlus_15-1466780260.h5\r\n",
      "nuebar_NC_9-1461971809.h5      pionPlus_15-1466806195.h5\r\n",
      "nuebar_NC_9-1469512412.h5      pionPlus_15-1466806367.h5\r\n",
      "nuebar_NC_9-1469512448.h5      pionPlus_15-1466877030.h5\r\n",
      "nuebar_NC_9-1469512487.h5      pionPlus_15-1466877083.h5\r\n",
      "nuebar_NC_9-1469512529.h5      pionPlus_15-1466877139.h5\r\n",
      "nuebar_NC_9-1469512634.h5      pionPlus_15-1466877206.h5\r\n",
      "nuebar_NC_9-1469512704.h5      pionPlus_15-1466877240.h5\r\n",
      "nuebar_NC_9-1469512742.h5      pionPlus_16-1462204073.h5\r\n",
      "nuebar_NC_9-1469512962.h5      pionPlus_16-1462522991.h5\r\n",
      "nuebar_NC_9-1469513002.h5      pionPlus_16-1463161417.h5\r\n",
      "nuebar_NC_9-1469513040.h5      pionPlus_16-1463360640.h5\r\n",
      "nuebar_NC_9-1469513081.h5      pionPlus_16-1463590721.h5\r\n",
      "nuebar_NC_9-1469513114.h5      pionPlus_16-1466216924.h5\r\n",
      "nuebar_NC_9-1469513150.h5      pionPlus_16-1466218422.h5\r\n",
      "nuebar_NC_9-1469513218.h5      pionPlus_16-1466705968.h5\r\n",
      "nuebar_NC_9-1469513267.h5      pionPlus_16-1466706136.h5\r\n",
      "nuebar_NC_9-1469513311.h5      pionPlus_16-1466706291.h5\r\n",
      "nuebar_NC_9-1469513349.h5      pionPlus_16-1466713827.h5\r\n",
      "nuebar_NC_9-1469513392.h5      pionPlus_16-1466779826.h5\r\n",
      "nuebar_NC_9-1469513434.h5      pionPlus_16-1466780135.h5\r\n",
      "nuebar_NC_9-1469513472.h5      pionPlus_16-1466780260.h5\r\n",
      "nue_CC_0-1469384326.h5\t       pionPlus_16-1466806195.h5\r\n",
      "nue_CC_0-1469384367.h5\t       pionPlus_16-1466806367.h5\r\n",
      "nue_CC_0-1469384383.h5\t       pionPlus_16-1466806485.h5\r\n",
      "nue_CC_0-1469384404.h5\t       pionPlus_16-1466877030.h5\r\n",
      "nue_CC_0-1469384421.h5\t       pionPlus_16-1466877083.h5\r\n",
      "nue_CC_0-1469384441.h5\t       pionPlus_16-1466877139.h5\r\n",
      "nue_CC_0-1469384464.h5\t       pionPlus_16-1466877240.h5\r\n",
      "nue_CC_0-1469384488.h5\t       pionPlus_17-1462204073.h5\r\n",
      "nue_CC_0-1469384511.h5\t       pionPlus_17-1462522991.h5\r\n",
      "nue_CC_0-1469384590.h5\t       pionPlus_17-1463161417.h5\r\n",
      "nue_CC_0-1469384613.h5\t       pionPlus_17-1463360640.h5\r\n",
      "nue_CC_0-1469384652.h5\t       pionPlus_17-1463590721.h5\r\n",
      "nue_CC_0-1469384749.h5\t       pionPlus_17-1466216924.h5\r\n",
      "nue_CC_0-1469568019.h5\t       pionPlus_17-1466218422.h5\r\n",
      "nue_CC_10-1469384326.h5        pionPlus_17-1466705968.h5\r\n",
      "nue_CC_10-1469384367.h5        pionPlus_17-1466706136.h5\r\n",
      "nue_CC_10-1469384383.h5        pionPlus_17-1466706291.h5\r\n",
      "nue_CC_10-1469384404.h5        pionPlus_17-1466713827.h5\r\n",
      "nue_CC_10-1469384421.h5        pionPlus_17-1466779826.h5\r\n",
      "nue_CC_10-1469384464.h5        pionPlus_17-1466780135.h5\r\n",
      "nue_CC_10-1469384488.h5        pionPlus_17-1466780260.h5\r\n",
      "nue_CC_10-1469384511.h5        pionPlus_17-1466806195.h5\r\n",
      "nue_CC_10-1469384613.h5        pionPlus_17-1466806367.h5\r\n",
      "nue_CC_10-1469384652.h5        pionPlus_17-1466877030.h5\r\n",
      "nue_CC_10-1469384749.h5        pionPlus_17-1466877083.h5\r\n",
      "nue_CC_10-1469568019.h5        pionPlus_17-1466877139.h5\r\n",
      "nue_CC_11-1469384326.h5        pionPlus_17-1466877240.h5\r\n",
      "nue_CC_11-1469384367.h5        pionPlus_18-1462204073.h5\r\n",
      "nue_CC_11-1469384383.h5        pionPlus_18-1462522991.h5\r\n",
      "nue_CC_11-1469384404.h5        pionPlus_18-1463161417.h5\r\n",
      "nue_CC_11-1469384421.h5        pionPlus_18-1463360640.h5\r\n",
      "nue_CC_11-1469384464.h5        pionPlus_18-1463590721.h5\r\n",
      "nue_CC_11-1469384488.h5        pionPlus_18-1466216924.h5\r\n",
      "nue_CC_11-1469384511.h5        pionPlus_18-1466218422.h5\r\n",
      "nue_CC_11-1469384590.h5        pionPlus_18-1466705968.h5\r\n",
      "nue_CC_11-1469384613.h5        pionPlus_18-1466706291.h5\r\n",
      "nue_CC_11-1469384631.h5        pionPlus_18-1466713827.h5\r\n",
      "nue_CC_11-1469384652.h5        pionPlus_18-1466779826.h5\r\n",
      "nue_CC_11-1469384693.h5        pionPlus_18-1466780135.h5\r\n",
      "nue_CC_11-1469384749.h5        pionPlus_18-1466780260.h5\r\n",
      "nue_CC_11-1469568019.h5        pionPlus_18-1466806195.h5\r\n",
      "nue_CC_1-1469384326.h5\t       pionPlus_18-1466806367.h5\r\n",
      "nue_CC_1-1469384367.h5\t       pionPlus_18-1466877030.h5\r\n",
      "nue_CC_1-1469384383.h5\t       pionPlus_18-1466877083.h5\r\n",
      "nue_CC_1-1469384404.h5\t       pionPlus_18-1466877139.h5\r\n",
      "nue_CC_1-1469384421.h5\t       pionPlus_18-1466877240.h5\r\n",
      "nue_CC_1-1469384441.h5\t       pionPlus_19-1462204073.h5\r\n",
      "nue_CC_1-1469384464.h5\t       pionPlus_19-1462522991.h5\r\n",
      "nue_CC_1-1469384511.h5\t       pionPlus_19-1463161417.h5\r\n",
      "nue_CC_1-1469384590.h5\t       pionPlus_19-1463360640.h5\r\n",
      "nue_CC_1-1469384652.h5\t       pionPlus_19-1463590721.h5\r\n",
      "nue_CC_1-1469384693.h5\t       pionPlus_19-1466216924.h5\r\n",
      "nue_CC_1-1469384749.h5\t       pionPlus_19-1466218422.h5\r\n",
      "nue_CC_1-1469568019.h5\t       pionPlus_19-1466705968.h5\r\n",
      "nue_CC_12-1469384326.h5        pionPlus_19-1466713827.h5\r\n",
      "nue_CC_12-1469384367.h5        pionPlus_19-1466779826.h5\r\n",
      "nue_CC_12-1469384383.h5        pionPlus_19-1466780135.h5\r\n",
      "nue_CC_12-1469384404.h5        pionPlus_19-1466780260.h5\r\n",
      "nue_CC_12-1469384421.h5        pionPlus_19-1466806195.h5\r\n",
      "nue_CC_12-1469384441.h5        pionPlus_19-1466806367.h5\r\n",
      "nue_CC_12-1469384464.h5        pionPlus_19-1466877030.h5\r\n",
      "nue_CC_12-1469384488.h5        pionPlus_19-1466877083.h5\r\n",
      "nue_CC_12-1469384511.h5        pionPlus_19-1466877139.h5\r\n",
      "nue_CC_12-1469384613.h5        pionPlus_19-1466877206.h5\r\n",
      "nue_CC_12-1469384652.h5        pionPlus_19-1466877240.h5\r\n",
      "nue_CC_12-1469384667.h5        pionPlus_2-1462204073.h5\r\n",
      "nue_CC_12-1469384693.h5        pionPlus_2-1462522991.h5\r\n",
      "nue_CC_12-1469384749.h5        pionPlus_2-1463161417.h5\r\n",
      "nue_CC_12-1469568019.h5        pionPlus_2-1463360640.h5\r\n",
      "nue_CC_13-1469384367.h5        pionPlus_2-1463590721.h5\r\n",
      "nue_CC_13-1469384383.h5        pionPlus_2-1466216924.h5\r\n",
      "nue_CC_13-1469384404.h5        pionPlus_2-1466218422.h5\r\n",
      "nue_CC_13-1469384421.h5        pionPlus_2-1466705968.h5\r\n",
      "nue_CC_13-1469384441.h5        pionPlus_2-1466706136.h5\r\n",
      "nue_CC_13-1469384464.h5        pionPlus_2-1466706291.h5\r\n",
      "nue_CC_13-1469384511.h5        pionPlus_2-1466713827.h5\r\n",
      "nue_CC_13-1469384590.h5        pionPlus_2-1466779826.h5\r\n",
      "nue_CC_13-1469384652.h5        pionPlus_2-1466780135.h5\r\n",
      "nue_CC_13-1469384667.h5        pionPlus_2-1466780260.h5\r\n",
      "nue_CC_13-1469384693.h5        pionPlus_2-1466806195.h5\r\n",
      "nue_CC_13-1469384749.h5        pionPlus_2-1466806367.h5\r\n",
      "nue_CC_13-1469568019.h5        pionPlus_2-1466877030.h5\r\n",
      "nue_CC_14-1469384326.h5        pionPlus_2-1466877083.h5\r\n",
      "nue_CC_14-1469384367.h5        pionPlus_2-1466877139.h5\r\n",
      "nue_CC_14-1469384383.h5        pionPlus_2-1466877240.h5\r\n",
      "nue_CC_14-1469384404.h5        pionPlus_3-1462204073.h5\r\n",
      "nue_CC_14-1469384421.h5        pionPlus_3-1462522991.h5\r\n",
      "nue_CC_14-1469384441.h5        pionPlus_3-1463161417.h5\r\n",
      "nue_CC_14-1469384464.h5        pionPlus_3-1463360640.h5\r\n",
      "nue_CC_14-1469384488.h5        pionPlus_3-1463590721.h5\r\n",
      "nue_CC_14-1469384511.h5        pionPlus_3-1466216924.h5\r\n",
      "nue_CC_14-1469384613.h5        pionPlus_3-1466218422.h5\r\n",
      "nue_CC_14-1469384652.h5        pionPlus_3-1466705968.h5\r\n",
      "nue_CC_14-1469384749.h5        pionPlus_3-1466706136.h5\r\n",
      "nue_CC_14-1469568019.h5        pionPlus_3-1466706291.h5\r\n",
      "nue_CC_15-1469384326.h5        pionPlus_3-1466713827.h5\r\n",
      "nue_CC_15-1469384367.h5        pionPlus_3-1466779826.h5\r\n",
      "nue_CC_15-1469384383.h5        pionPlus_3-1466780135.h5\r\n",
      "nue_CC_15-1469384404.h5        pionPlus_3-1466780260.h5\r\n",
      "nue_CC_15-1469384421.h5        pionPlus_3-1466806195.h5\r\n",
      "nue_CC_15-1469384464.h5        pionPlus_3-1466806367.h5\r\n",
      "nue_CC_15-1469384511.h5        pionPlus_3-1466877030.h5\r\n",
      "nue_CC_15-1469384613.h5        pionPlus_3-1466877083.h5\r\n",
      "nue_CC_15-1469384631.h5        pionPlus_3-1466877139.h5\r\n",
      "nue_CC_15-1469384652.h5        pionPlus_3-1466877240.h5\r\n",
      "nue_CC_15-1469384693.h5        pionPlus_4-1462204073.h5\r\n",
      "nue_CC_15-1469384749.h5        pionPlus_4-1462522991.h5\r\n",
      "nue_CC_15-1469568019.h5        pionPlus_4-1463161417.h5\r\n",
      "nue_CC_16-1469384326.h5        pionPlus_4-1463360640.h5\r\n",
      "nue_CC_16-1469384367.h5        pionPlus_4-1463590721.h5\r\n",
      "nue_CC_16-1469384383.h5        pionPlus_4-1466216924.h5\r\n",
      "nue_CC_16-1469384404.h5        pionPlus_4-1466218422.h5\r\n",
      "nue_CC_16-1469384421.h5        pionPlus_4-1466705968.h5\r\n",
      "nue_CC_16-1469384441.h5        pionPlus_4-1466706291.h5\r\n",
      "nue_CC_16-1469384464.h5        pionPlus_4-1466713827.h5\r\n",
      "nue_CC_16-1469384488.h5        pionPlus_4-1466779826.h5\r\n",
      "nue_CC_16-1469384511.h5        pionPlus_4-1466780135.h5\r\n",
      "nue_CC_16-1469384590.h5        pionPlus_4-1466780260.h5\r\n",
      "nue_CC_16-1469384613.h5        pionPlus_4-1466806195.h5\r\n",
      "nue_CC_16-1469384652.h5        pionPlus_4-1466806367.h5\r\n",
      "nue_CC_16-1469384667.h5        pionPlus_4-1466877030.h5\r\n",
      "nue_CC_16-1469384693.h5        pionPlus_4-1466877083.h5\r\n",
      "nue_CC_16-1469384749.h5        pionPlus_4-1466877139.h5\r\n",
      "nue_CC_16-1469568019.h5        pionPlus_4-1466877240.h5\r\n",
      "nue_CC_17-1469384367.h5        pionPlus_5-1462204073.h5\r\n",
      "nue_CC_17-1469384383.h5        pionPlus_5-1462522991.h5\r\n",
      "nue_CC_17-1469384404.h5        pionPlus_5-1463161417.h5\r\n",
      "nue_CC_17-1469384421.h5        pionPlus_5-1463360640.h5\r\n",
      "nue_CC_17-1469384441.h5        pionPlus_5-1463590721.h5\r\n",
      "nue_CC_17-1469384464.h5        pionPlus_5-1466216924.h5\r\n",
      "nue_CC_17-1469384511.h5        pionPlus_5-1466218422.h5\r\n",
      "nue_CC_17-1469384590.h5        pionPlus_5-1466705968.h5\r\n",
      "nue_CC_17-1469384652.h5        pionPlus_5-1466706291.h5\r\n",
      "nue_CC_17-1469384667.h5        pionPlus_5-1466713827.h5\r\n",
      "nue_CC_17-1469384693.h5        pionPlus_5-1466713982.h5\r\n",
      "nue_CC_17-1469384749.h5        pionPlus_5-1466779826.h5\r\n",
      "nue_CC_17-1469568019.h5        pionPlus_5-1466780135.h5\r\n",
      "nue_CC_18-1469384326.h5        pionPlus_5-1466780260.h5\r\n",
      "nue_CC_18-1469384367.h5        pionPlus_5-1466806195.h5\r\n",
      "nue_CC_18-1469384383.h5        pionPlus_5-1466806367.h5\r\n",
      "nue_CC_18-1469384404.h5        pionPlus_5-1466806485.h5\r\n",
      "nue_CC_18-1469384421.h5        pionPlus_5-1466877030.h5\r\n",
      "nue_CC_18-1469384464.h5        pionPlus_5-1466877083.h5\r\n",
      "nue_CC_18-1469384488.h5        pionPlus_5-1466877139.h5\r\n",
      "nue_CC_18-1469384511.h5        pionPlus_5-1466877206.h5\r\n",
      "nue_CC_18-1469384613.h5        pionPlus_5-1466877240.h5\r\n",
      "nue_CC_18-1469384749.h5        pionPlus_6-1462204073.h5\r\n",
      "nue_CC_18-1469568019.h5        pionPlus_6-1462522991.h5\r\n",
      "nue_CC_19-1469384367.h5        pionPlus_6-1463161417.h5\r\n",
      "nue_CC_19-1469384383.h5        pionPlus_6-1463360640.h5\r\n",
      "nue_CC_19-1469384404.h5        pionPlus_6-1463590721.h5\r\n",
      "nue_CC_19-1469384421.h5        pionPlus_6-1466216924.h5\r\n",
      "nue_CC_19-1469384464.h5        pionPlus_6-1466218422.h5\r\n",
      "nue_CC_19-1469384488.h5        pionPlus_6-1466705968.h5\r\n",
      "nue_CC_19-1469384511.h5        pionPlus_6-1466706136.h5\r\n",
      "nue_CC_19-1469384590.h5        pionPlus_6-1466706291.h5\r\n",
      "nue_CC_19-1469384613.h5        pionPlus_6-1466713827.h5\r\n",
      "nue_CC_19-1469384652.h5        pionPlus_6-1466779826.h5\r\n",
      "nue_CC_19-1469384693.h5        pionPlus_6-1466780135.h5\r\n",
      "nue_CC_19-1469384749.h5        pionPlus_6-1466780260.h5\r\n",
      "nue_CC_19-1469568019.h5        pionPlus_6-1466806195.h5\r\n",
      "nue_CC_2-1469384367.h5\t       pionPlus_6-1466806367.h5\r\n",
      "nue_CC_2-1469384383.h5\t       pionPlus_6-1466877030.h5\r\n",
      "nue_CC_2-1469384404.h5\t       pionPlus_6-1466877083.h5\r\n",
      "nue_CC_2-1469384421.h5\t       pionPlus_6-1466877139.h5\r\n",
      "nue_CC_2-1469384464.h5\t       pionPlus_6-1466877240.h5\r\n",
      "nue_CC_2-1469384488.h5\t       pionPlus_7-1462204073.h5\r\n",
      "nue_CC_2-1469384511.h5\t       pionPlus_7-1462522991.h5\r\n",
      "nue_CC_2-1469384590.h5\t       pionPlus_7-1463161417.h5\r\n",
      "nue_CC_2-1469384613.h5\t       pionPlus_7-1463360640.h5\r\n",
      "nue_CC_2-1469384652.h5\t       pionPlus_7-1463590721.h5\r\n",
      "nue_CC_2-1469384667.h5\t       pionPlus_7-1466218422.h5\r\n",
      "nue_CC_2-1469384693.h5\t       pionPlus_7-1466705968.h5\r\n",
      "nue_CC_2-1469384749.h5\t       pionPlus_7-1466706291.h5\r\n",
      "nue_CC_2-1469568019.h5\t       pionPlus_7-1466713827.h5\r\n",
      "nue_CC_3-1469384326.h5\t       pionPlus_7-1466779826.h5\r\n",
      "nue_CC_3-1469384367.h5\t       pionPlus_7-1466780135.h5\r\n",
      "nue_CC_3-1469384383.h5\t       pionPlus_7-1466780260.h5\r\n",
      "nue_CC_3-1469384404.h5\t       pionPlus_7-1466806195.h5\r\n",
      "nue_CC_3-1469384421.h5\t       pionPlus_7-1466806367.h5\r\n",
      "nue_CC_3-1469384464.h5\t       pionPlus_7-1466877030.h5\r\n",
      "nue_CC_3-1469384511.h5\t       pionPlus_7-1466877083.h5\r\n",
      "nue_CC_3-1469384613.h5\t       pionPlus_7-1466877139.h5\r\n",
      "nue_CC_3-1469384652.h5\t       pionPlus_7-1466877240.h5\r\n",
      "nue_CC_3-1469384667.h5\t       pionPlus_8-1462204073.h5\r\n",
      "nue_CC_3-1469384693.h5\t       pionPlus_8-1462522991.h5\r\n",
      "nue_CC_3-1469384749.h5\t       pionPlus_8-1463161417.h5\r\n",
      "nue_CC_3-1469568019.h5\t       pionPlus_8-1463360640.h5\r\n",
      "nue_CC_4-1469384326.h5\t       pionPlus_8-1463590721.h5\r\n",
      "nue_CC_4-1469384367.h5\t       pionPlus_8-1466216924.h5\r\n",
      "nue_CC_4-1469384383.h5\t       pionPlus_8-1466218422.h5\r\n",
      "nue_CC_4-1469384404.h5\t       pionPlus_8-1466705968.h5\r\n",
      "nue_CC_4-1469384421.h5\t       pionPlus_8-1466706291.h5\r\n",
      "nue_CC_4-1469384441.h5\t       pionPlus_8-1466713827.h5\r\n",
      "nue_CC_4-1469384464.h5\t       pionPlus_8-1466779826.h5\r\n",
      "nue_CC_4-1469384488.h5\t       pionPlus_8-1466780135.h5\r\n",
      "nue_CC_4-1469384511.h5\t       pionPlus_8-1466780260.h5\r\n",
      "nue_CC_4-1469384590.h5\t       pionPlus_8-1466806195.h5\r\n",
      "nue_CC_4-1469384613.h5\t       pionPlus_8-1466806367.h5\r\n",
      "nue_CC_4-1469384631.h5\t       pionPlus_8-1466877030.h5\r\n",
      "nue_CC_4-1469384652.h5\t       pionPlus_8-1466877083.h5\r\n",
      "nue_CC_4-1469384749.h5\t       pionPlus_8-1466877139.h5\r\n",
      "nue_CC_4-1469568019.h5\t       pionPlus_8-1466877240.h5\r\n",
      "nue_CC_5-1469384326.h5\t       pionPlus_9-1462204073.h5\r\n",
      "nue_CC_5-1469384367.h5\t       pionPlus_9-1462522991.h5\r\n",
      "nue_CC_5-1469384383.h5\t       pionPlus_9-1463161417.h5\r\n",
      "nue_CC_5-1469384404.h5\t       pionPlus_9-1463360640.h5\r\n",
      "nue_CC_5-1469384421.h5\t       pionPlus_9-1463590721.h5\r\n",
      "nue_CC_5-1469384441.h5\t       pionPlus_9-1466216924.h5\r\n",
      "nue_CC_5-1469384464.h5\t       pionPlus_9-1466218422.h5\r\n",
      "nue_CC_5-1469384511.h5\t       pionPlus_9-1466705968.h5\r\n",
      "nue_CC_5-1469384590.h5\t       pionPlus_9-1466706291.h5\r\n",
      "nue_CC_5-1469384652.h5\t       pionPlus_9-1466713827.h5\r\n",
      "nue_CC_5-1469384693.h5\t       pionPlus_9-1466713982.h5\r\n",
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      "nue_CC_5-1469568019.h5\t       pionPlus_9-1466780135.h5\r\n",
      "nue_CC_6-1469384367.h5\t       pionPlus_9-1466780260.h5\r\n",
      "nue_CC_6-1469384383.h5\t       pionPlus_9-1466806195.h5\r\n",
      "nue_CC_6-1469384404.h5\t       pionPlus_9-1466806367.h5\r\n",
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      "nue_CC_6-1469384652.h5\t       pionPlus_9-1466877240.h5\r\n",
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      "nue_CC_7-1469384367.h5\t       proton_0-1463360637.h5\r\n",
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      "nue_CC_7-1469384652.h5\t       proton_0-1466779796.h5\r\n",
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      "nue_CC_8-1469568019.h5\t       proton_11-1463360637.h5\r\n",
      "nue_CC_9-1469384326.h5\t       proton_11-1463590717.h5\r\n",
      "nue_CC_9-1469384367.h5\t       proton_11-1466216923.h5\r\n",
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      "nue_CC_9-1469384404.h5\t       proton_11-1466439654.h5\r\n",
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      "nue_CC_9-1469384464.h5\t       proton_11-1466643616.h5\r\n",
      "nue_CC_9-1469384511.h5\t       proton_11-1466705651.h5\r\n",
      "nue_CC_9-1469384590.h5\t       proton_11-1466779796.h5\r\n",
      "nue_CC_9-1469384652.h5\t       proton_1-1462146941.h5\r\n",
      "nue_CC_9-1469384693.h5\t       proton_1-1462204069.h5\r\n",
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      "nue_CC_9-1469568019.h5\t       proton_1-1463360637.h5\r\n",
      "nue_NC_0-1469424406.h5\t       proton_1-1463590717.h5\r\n",
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      "nue_NC_0-1469424508.h5\t       proton_1-1466439654.h5\r\n",
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      "nue_NC_0-1469424546.h5\t       proton_1-1466605645.h5\r\n",
      "nue_NC_0-1469424625.h5\t       proton_1-1466643210.h5\r\n",
      "nue_NC_0-1469424649.h5\t       proton_1-1466643616.h5\r\n",
      "nue_NC_0-1469424674.h5\t       proton_1-1466779796.h5\r\n",
      "nue_NC_0-1469424696.h5\t       proton_12-1462146941.h5\r\n",
      "nue_NC_0-1469424718.h5\t       proton_12-1462204069.h5\r\n",
      "nue_NC_0-1469424737.h5\t       proton_12-1462522988.h5\r\n",
      "nue_NC_0-1469424783.h5\t       proton_12-1463161414.h5\r\n",
      "nue_NC_0-1469424815.h5\t       proton_12-1463360637.h5\r\n",
      "nue_NC_0-1469424840.h5\t       proton_12-1463590717.h5\r\n",
      "nue_NC_0-1469424856.h5\t       proton_12-1466216923.h5\r\n",
      "nue_NC_0-1469424901.h5\t       proton_12-1466439654.h5\r\n",
      "nue_NC_0-1469424927.h5\t       proton_12-1466528869.h5\r\n",
      "nue_NC_10-1461971806.h5        proton_12-1466546736.h5\r\n",
      "nue_NC_10-1469424406.h5        proton_12-1466643210.h5\r\n",
      "nue_NC_10-1469424430.h5        proton_12-1466643616.h5\r\n",
      "nue_NC_10-1469424486.h5        proton_12-1466779796.h5\r\n",
      "nue_NC_10-1469424508.h5        proton_13-1462146941.h5\r\n",
      "nue_NC_10-1469424546.h5        proton_13-1462204069.h5\r\n",
      "nue_NC_10-1469424625.h5        proton_13-1462834341.h5\r\n",
      "nue_NC_10-1469424649.h5        proton_13-1463161414.h5\r\n",
      "nue_NC_10-1469424696.h5        proton_13-1463590717.h5\r\n",
      "nue_NC_10-1469424718.h5        proton_13-1466216923.h5\r\n",
      "nue_NC_10-1469424737.h5        proton_13-1466439654.h5\r\n",
      "nue_NC_10-1469424783.h5        proton_13-1466528869.h5\r\n",
      "nue_NC_10-1469424815.h5        proton_13-1466546736.h5\r\n",
      "nue_NC_10-1469424840.h5        proton_13-1466626624.h5\r\n",
      "nue_NC_10-1469424856.h5        proton_13-1466643210.h5\r\n",
      "nue_NC_10-1469424901.h5        proton_13-1466643616.h5\r\n",
      "nue_NC_10-1469424927.h5        proton_13-1466779796.h5\r\n",
      "nue_NC_11-1469424406.h5        proton_14-1462146941.h5\r\n",
      "nue_NC_11-1469424430.h5        proton_14-1462204069.h5\r\n",
      "nue_NC_11-1469424486.h5        proton_14-1462522988.h5\r\n",
      "nue_NC_11-1469424508.h5        proton_14-1463161414.h5\r\n",
      "nue_NC_11-1469424527.h5        proton_14-1463360637.h5\r\n",
      "nue_NC_11-1469424546.h5        proton_14-1463590717.h5\r\n",
      "nue_NC_11-1469424625.h5        proton_14-1466216923.h5\r\n",
      "nue_NC_11-1469424649.h5        proton_14-1466387525.h5\r\n",
      "nue_NC_11-1469424696.h5        proton_14-1466439654.h5\r\n",
      "nue_NC_11-1469424718.h5        proton_14-1466528869.h5\r\n",
      "nue_NC_11-1469424737.h5        proton_14-1466643210.h5\r\n",
      "nue_NC_11-1469424783.h5        proton_14-1466643616.h5\r\n",
      "nue_NC_11-1469424815.h5        proton_14-1466779796.h5\r\n",
      "nue_NC_11-1469424840.h5        proton_15-1462146941.h5\r\n",
      "nue_NC_11-1469424856.h5        proton_15-1462204069.h5\r\n",
      "nue_NC_11-1469424901.h5        proton_15-1462522988.h5\r\n",
      "nue_NC_11-1469424927.h5        proton_15-1463161414.h5\r\n",
      "nue_NC_1-1469424406.h5\t       proton_15-1463360637.h5\r\n",
      "nue_NC_1-1469424430.h5\t       proton_15-1463590717.h5\r\n",
      "nue_NC_1-1469424486.h5\t       proton_15-1466216923.h5\r\n",
      "nue_NC_1-1469424508.h5\t       proton_15-1466387525.h5\r\n",
      "nue_NC_1-1469424546.h5\t       proton_15-1466439654.h5\r\n",
      "nue_NC_1-1469424625.h5\t       proton_15-1466528869.h5\r\n",
      "nue_NC_1-1469424649.h5\t       proton_15-1466643210.h5\r\n",
      "nue_NC_1-1469424696.h5\t       proton_15-1466643616.h5\r\n",
      "nue_NC_1-1469424718.h5\t       proton_15-1466705651.h5\r\n",
      "nue_NC_1-1469424737.h5\t       proton_15-1466779796.h5\r\n",
      "nue_NC_1-1469424783.h5\t       proton_16-1462146941.h5\r\n",
      "nue_NC_1-1469424815.h5\t       proton_16-1462204069.h5\r\n",
      "nue_NC_1-1469424840.h5\t       proton_16-1462522988.h5\r\n",
      "nue_NC_1-1469424856.h5\t       proton_16-1463161414.h5\r\n",
      "nue_NC_1-1469424901.h5\t       proton_16-1463360637.h5\r\n",
      "nue_NC_1-1469424927.h5\t       proton_16-1463590717.h5\r\n",
      "nue_NC_12-1461971806.h5        proton_16-1466216923.h5\r\n",
      "nue_NC_12-1469424406.h5        proton_16-1466439654.h5\r\n",
      "nue_NC_12-1469424430.h5        proton_16-1466528869.h5\r\n",
      "nue_NC_12-1469424486.h5        proton_16-1466546736.h5\r\n",
      "nue_NC_12-1469424508.h5        proton_16-1466605645.h5\r\n",
      "nue_NC_12-1469424546.h5        proton_16-1466643210.h5\r\n",
      "nue_NC_12-1469424625.h5        proton_16-1466643616.h5\r\n",
      "nue_NC_12-1469424649.h5        proton_16-1466779796.h5\r\n",
      "nue_NC_12-1469424674.h5        proton_17-1462146941.h5\r\n",
      "nue_NC_12-1469424696.h5        proton_17-1462204069.h5\r\n",
      "nue_NC_12-1469424718.h5        proton_17-1463161414.h5\r\n",
      "nue_NC_12-1469424737.h5        proton_17-1463590717.h5\r\n",
      "nue_NC_12-1469424783.h5        proton_17-1466216923.h5\r\n",
      "nue_NC_12-1469424815.h5        proton_17-1466439654.h5\r\n",
      "nue_NC_12-1469424840.h5        proton_17-1466528869.h5\r\n",
      "nue_NC_12-1469424856.h5        proton_17-1466546736.h5\r\n",
      "nue_NC_12-1469424901.h5        proton_17-1466626624.h5\r\n",
      "nue_NC_12-1469424927.h5        proton_17-1466643210.h5\r\n",
      "nue_NC_13-1469424406.h5        proton_17-1466643616.h5\r\n",
      "nue_NC_13-1469424430.h5        proton_17-1466779796.h5\r\n",
      "nue_NC_13-1469424486.h5        proton_18-1462146941.h5\r\n",
      "nue_NC_13-1469424508.h5        proton_18-1462204069.h5\r\n",
      "nue_NC_13-1469424546.h5        proton_18-1462522988.h5\r\n",
      "nue_NC_13-1469424625.h5        proton_18-1463161414.h5\r\n",
      "nue_NC_13-1469424649.h5        proton_18-1463360637.h5\r\n",
      "nue_NC_13-1469424674.h5        proton_18-1463590717.h5\r\n",
      "nue_NC_13-1469424696.h5        proton_18-1466216923.h5\r\n",
      "nue_NC_13-1469424718.h5        proton_18-1466387525.h5\r\n",
      "nue_NC_13-1469424737.h5        proton_18-1466439654.h5\r\n",
      "nue_NC_13-1469424783.h5        proton_18-1466528869.h5\r\n",
      "nue_NC_13-1469424815.h5        proton_18-1466643210.h5\r\n",
      "nue_NC_13-1469424840.h5        proton_18-1466643616.h5\r\n",
      "nue_NC_13-1469424856.h5        proton_18-1466705651.h5\r\n",
      "nue_NC_13-1469424901.h5        proton_18-1466779796.h5\r\n",
      "nue_NC_13-1469424927.h5        proton_19-1462146941.h5\r\n",
      "nue_NC_14-1461971806.h5        proton_19-1462204069.h5\r\n",
      "nue_NC_14-1469424406.h5        proton_19-1462522988.h5\r\n",
      "nue_NC_14-1469424430.h5        proton_19-1463161414.h5\r\n",
      "nue_NC_14-1469424486.h5        proton_19-1463360637.h5\r\n",
      "nue_NC_14-1469424508.h5        proton_19-1463590717.h5\r\n",
      "nue_NC_14-1469424546.h5        proton_19-1466216923.h5\r\n",
      "nue_NC_14-1469424625.h5        proton_19-1466387525.h5\r\n",
      "nue_NC_14-1469424649.h5        proton_19-1466439654.h5\r\n",
      "nue_NC_14-1469424696.h5        proton_19-1466528869.h5\r\n",
      "nue_NC_14-1469424718.h5        proton_19-1466546736.h5\r\n",
      "nue_NC_14-1469424737.h5        proton_19-1466643210.h5\r\n",
      "nue_NC_14-1469424783.h5        proton_19-1466643616.h5\r\n",
      "nue_NC_14-1469424815.h5        proton_19-1466705651.h5\r\n",
      "nue_NC_14-1469424840.h5        proton_19-1466779796.h5\r\n",
      "nue_NC_14-1469424856.h5        proton_2-1462146941.h5\r\n",
      "nue_NC_14-1469424901.h5        proton_2-1462204069.h5\r\n",
      "nue_NC_14-1469424927.h5        proton_2-1462522988.h5\r\n",
      "nue_NC_15-1461971806.h5        proton_2-1463161414.h5\r\n",
      "nue_NC_15-1469424406.h5        proton_2-1463360637.h5\r\n",
      "nue_NC_15-1469424430.h5        proton_2-1463590717.h5\r\n",
      "nue_NC_15-1469424486.h5        proton_2-1466216923.h5\r\n",
      "nue_NC_15-1469424508.h5        proton_2-1466439654.h5\r\n",
      "nue_NC_15-1469424527.h5        proton_2-1466528869.h5\r\n",
      "nue_NC_15-1469424546.h5        proton_2-1466546736.h5\r\n",
      "nue_NC_15-1469424625.h5        proton_2-1466643210.h5\r\n",
      "nue_NC_15-1469424649.h5        proton_2-1466643616.h5\r\n",
      "nue_NC_15-1469424696.h5        proton_2-1466705651.h5\r\n",
      "nue_NC_15-1469424718.h5        proton_2-1466779796.h5\r\n",
      "nue_NC_15-1469424737.h5        proton_3-1462146941.h5\r\n",
      "nue_NC_15-1469424783.h5        proton_3-1462204069.h5\r\n",
      "nue_NC_15-1469424815.h5        proton_3-1462522988.h5\r\n",
      "nue_NC_15-1469424840.h5        proton_3-1463161414.h5\r\n",
      "nue_NC_15-1469424856.h5        proton_3-1463360637.h5\r\n",
      "nue_NC_15-1469424901.h5        proton_3-1463590717.h5\r\n",
      "nue_NC_15-1469424927.h5        proton_3-1466216923.h5\r\n",
      "nue_NC_16-1461971806.h5        proton_3-1466439654.h5\r\n",
      "nue_NC_16-1469424406.h5        proton_3-1466528869.h5\r\n",
      "nue_NC_16-1469424430.h5        proton_3-1466546736.h5\r\n",
      "nue_NC_16-1469424486.h5        proton_3-1466626624.h5\r\n",
      "nue_NC_16-1469424508.h5        proton_3-1466643210.h5\r\n",
      "nue_NC_16-1469424546.h5        proton_3-1466643616.h5\r\n",
      "nue_NC_16-1469424625.h5        proton_3-1466779796.h5\r\n",
      "nue_NC_16-1469424649.h5        proton_4-1462146941.h5\r\n",
      "nue_NC_16-1469424674.h5        proton_4-1462204069.h5\r\n",
      "nue_NC_16-1469424696.h5        proton_4-1463161414.h5\r\n",
      "nue_NC_16-1469424718.h5        proton_4-1463360637.h5\r\n",
      "nue_NC_16-1469424737.h5        proton_4-1463590717.h5\r\n",
      "nue_NC_16-1469424783.h5        proton_4-1466216923.h5\r\n",
      "nue_NC_16-1469424815.h5        proton_4-1466387525.h5\r\n",
      "nue_NC_16-1469424840.h5        proton_4-1466439654.h5\r\n",
      "nue_NC_16-1469424856.h5        proton_4-1466528869.h5\r\n",
      "nue_NC_16-1469424901.h5        proton_4-1466643210.h5\r\n",
      "nue_NC_16-1469424927.h5        proton_4-1466643616.h5\r\n",
      "nue_NC_17-1469424406.h5        proton_4-1466779796.h5\r\n",
      "nue_NC_17-1469424430.h5        proton_5-1462146941.h5\r\n",
      "nue_NC_17-1469424486.h5        proton_5-1462204069.h5\r\n",
      "nue_NC_17-1469424508.h5        proton_5-1463161414.h5\r\n",
      "nue_NC_17-1469424546.h5        proton_5-1463360637.h5\r\n",
      "nue_NC_17-1469424625.h5        proton_5-1466216923.h5\r\n",
      "nue_NC_17-1469424649.h5        proton_5-1466275837.h5\r\n",
      "nue_NC_17-1469424674.h5        proton_5-1466387525.h5\r\n",
      "nue_NC_17-1469424696.h5        proton_5-1466439654.h5\r\n",
      "nue_NC_17-1469424718.h5        proton_5-1466528869.h5\r\n",
      "nue_NC_17-1469424737.h5        proton_5-1466605645.h5\r\n",
      "nue_NC_17-1469424783.h5        proton_5-1466643210.h5\r\n",
      "nue_NC_17-1469424815.h5        proton_5-1466643616.h5\r\n",
      "nue_NC_17-1469424840.h5        proton_5-1466779796.h5\r\n",
      "nue_NC_17-1469424856.h5        proton_6-1462146941.h5\r\n",
      "nue_NC_17-1469424901.h5        proton_6-1462204069.h5\r\n",
      "nue_NC_17-1469424927.h5        proton_6-1462522988.h5\r\n",
      "nue_NC_18-1461971806.h5        proton_6-1463161414.h5\r\n",
      "nue_NC_18-1469424406.h5        proton_6-1463360637.h5\r\n",
      "nue_NC_18-1469424430.h5        proton_6-1463590717.h5\r\n",
      "nue_NC_18-1469424486.h5        proton_6-1466216923.h5\r\n",
      "nue_NC_18-1469424508.h5        proton_6-1466439654.h5\r\n",
      "nue_NC_18-1469424546.h5        proton_6-1466528869.h5\r\n",
      "nue_NC_18-1469424625.h5        proton_6-1466546736.h5\r\n",
      "nue_NC_18-1469424649.h5        proton_6-1466643210.h5\r\n",
      "nue_NC_18-1469424696.h5        proton_6-1466643616.h5\r\n",
      "nue_NC_18-1469424718.h5        proton_6-1466705651.h5\r\n",
      "nue_NC_18-1469424737.h5        proton_6-1466779796.h5\r\n",
      "nue_NC_18-1469424783.h5        proton_7-1462146941.h5\r\n",
      "nue_NC_18-1469424815.h5        proton_7-1462204069.h5\r\n",
      "nue_NC_18-1469424840.h5        proton_7-1462522988.h5\r\n",
      "nue_NC_18-1469424856.h5        proton_7-1463161414.h5\r\n",
      "nue_NC_18-1469424901.h5        proton_7-1463360637.h5\r\n",
      "nue_NC_18-1469424927.h5        proton_7-1463590717.h5\r\n",
      "nue_NC_19-1469424406.h5        proton_7-1466216923.h5\r\n",
      "nue_NC_19-1469424430.h5        proton_7-1466439654.h5\r\n",
      "nue_NC_19-1469424486.h5        proton_7-1466528869.h5\r\n",
      "nue_NC_19-1469424508.h5        proton_7-1466546736.h5\r\n",
      "nue_NC_19-1469424527.h5        proton_7-1466626624.h5\r\n",
      "nue_NC_19-1469424546.h5        proton_7-1466643210.h5\r\n",
      "nue_NC_19-1469424625.h5        proton_7-1466643616.h5\r\n",
      "nue_NC_19-1469424649.h5        proton_7-1466779796.h5\r\n",
      "nue_NC_19-1469424696.h5        proton_8-1462146941.h5\r\n",
      "nue_NC_19-1469424718.h5        proton_8-1462204069.h5\r\n",
      "nue_NC_19-1469424737.h5        proton_8-1463161414.h5\r\n",
      "nue_NC_19-1469424783.h5        proton_8-1463360637.h5\r\n",
      "nue_NC_19-1469424815.h5        proton_8-1463590717.h5\r\n",
      "nue_NC_19-1469424840.h5        proton_8-1466216923.h5\r\n",
      "nue_NC_19-1469424856.h5        proton_8-1466387525.h5\r\n",
      "nue_NC_19-1469424877.h5        proton_8-1466439654.h5\r\n",
      "nue_NC_19-1469424901.h5        proton_8-1466528869.h5\r\n",
      "nue_NC_19-1469424927.h5        proton_8-1466643210.h5\r\n",
      "nue_NC_2-1461971806.h5\t       proton_8-1466643616.h5\r\n",
      "nue_NC_2-1469424406.h5\t       proton_8-1466779796.h5\r\n",
      "nue_NC_2-1469424430.h5\t       proton_9-1462146941.h5\r\n",
      "nue_NC_2-1469424486.h5\t       proton_9-1462204069.h5\r\n",
      "nue_NC_2-1469424508.h5\t       proton_9-1463161414.h5\r\n",
      "nue_NC_2-1469424546.h5\t       proton_9-1463360637.h5\r\n",
      "nue_NC_2-1469424625.h5\t       proton_9-1463590717.h5\r\n",
      "nue_NC_2-1469424649.h5\t       proton_9-1466216923.h5\r\n",
      "nue_NC_2-1469424674.h5\t       proton_9-1466387525.h5\r\n",
      "nue_NC_2-1469424696.h5\t       proton_9-1466439654.h5\r\n",
      "nue_NC_2-1469424718.h5\t       proton_9-1466528869.h5\r\n",
      "nue_NC_2-1469424737.h5\t       proton_9-1466546736.h5\r\n",
      "nue_NC_2-1469424783.h5\t       proton_9-1466605645.h5\r\n",
      "nue_NC_2-1469424815.h5\t       proton_9-1466643210.h5\r\n",
      "nue_NC_2-1469424840.h5\t       proton_9-1466643616.h5\r\n",
      "nue_NC_2-1469424856.h5\t       proton_9-1466779796.h5\r\n",
      "nue_NC_2-1469424901.h5\r\n"
     ]
    }
   ],
   "source": [
    "!ls /data/LArIAT/h5_files/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a lot of files. Lets count how many... in python. There are a variety of ways of getting back a directory listing in python. Here's one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Files: 6745\n",
      "First Filename: /data/LArIAT/h5_files/electron_9-1463722329.h5\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "Files=glob.glob(\"/data/LArIAT/h5_files/*\")\n",
    "print \"Number of Files:\", len(Files)\n",
    "print \"First Filename:\", Files[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the file names, you notice that they start with the type of particle. Each file contains a samples of \"events\". In each event, we simulated shooting a particle into the detector and stored the response. The name of the file specifies what type of particle was simulated in that file.\n",
    "\n",
    "Let's try to figure out what types. We'll loop over the file names, strip out the first part of the file name, and store it in a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of types of particles: 16\n",
      "----------------------------------------------------------\n",
      "Number of files for each particle type: {'numu': 944, 'kaonPlus': 417, 'antielectron': 48, 'pion': 240, 'numubar': 747, 'nuebar': 631, 'photon': 185, 'antiproton': 360, 'proton': 268, 'electron': 339, 'muon': 277, 'pionPlus': 399, 'kaonMinus': 396, 'nue': 616, 'antimuon': 411, 'pionMinus': 467}\n",
      "----------------------------------------------------------\n",
      "First file of each type:\n",
      "numu : /data/LArIAT/h5_files/numu_CC_7-1468443731.h5\n",
      "kaonPlus : /data/LArIAT/h5_files/kaonPlus_11-1466643524.h5\n",
      "antielectron : /data/LArIAT/h5_files/antielectron_17-1462522993.h5\n",
      "pion : /data/LArIAT/h5_files/pion_0_19-1463161413.h5\n",
      "numubar : /data/LArIAT/h5_files/numubar_CC_1-1469306203.h5\n",
      "nuebar : /data/LArIAT/h5_files/nuebar_CC_3-1469468394.h5\n",
      "photon : /data/LArIAT/h5_files/photon_12-1466805843.h5\n",
      "antiproton : /data/LArIAT/h5_files/antiproton_7-1466998603.h5\n",
      "proton : /data/LArIAT/h5_files/proton_8-1466779796.h5\n",
      "electron : /data/LArIAT/h5_files/electron_9-1463722329.h5\n",
      "muon : /data/LArIAT/h5_files/muon_0-1466643298.h5\n",
      "pionPlus : /data/LArIAT/h5_files/pionPlus_1-1462204073.h5\n",
      "kaonMinus : /data/LArIAT/h5_files/kaonMinus_6-1466695249.h5\n",
      "nue : /data/LArIAT/h5_files/nue_NC_1-1469424927.h5\n",
      "antimuon : /data/LArIAT/h5_files/antimuon_12-1466216931.h5\n",
      "pionMinus : /data/LArIAT/h5_files/pionMinus_13-1466713895.h5\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "FileCount= {}  # Store the count here\n",
    "FileLists= {}  # Organize the files by particle type here.\n",
    "\n",
    "for aFile in Files:\n",
    "    # Lets strip the path (everything before the \"/\"s) and get the filename:\n",
    "    FileName=os.path.basename(aFile)\n",
    "    \n",
    "    # Now use everything before the first \"_\" as the particle name\n",
    "    ParticleName=FileName.split('_')[0]\n",
    "    \n",
    "    if ParticleName in FileCount.keys():\n",
    "        FileCount[ParticleName]+=1\n",
    "        FileLists[ParticleName].append(aFile)\n",
    "    else:\n",
    "        FileCount[ParticleName]=1\n",
    "        FileLists[ParticleName]= [aFile]\n",
    "    \n",
    "print \"Number of types of particles:\", len(FileCount.keys())\n",
    "print \"----------------------------------------------------------\"\n",
    "print \"Number of files for each particle type:\", FileCount\n",
    "print \"----------------------------------------------------------\"\n",
    "print \"First file of each type:\"\n",
    "for aFile in FileLists:\n",
    "    print aFile,\":\",FileLists[aFile][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can count how many examples are in each file by open them up in h5py like we did in the last lab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data: (500, 2, 240, 4096)\n",
      "Number of events in file: 500\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "\n",
    "f=h5py.File(FileLists[\"pionPlus\"][0],\"r\")\n",
    "\n",
    "# Read the First N_Events. Data is stored as float16, lets store it as float32 to avoid overflows later when we sum.\n",
    "print \"Shape of the data:\", f[\"features\"].shape\n",
    "print \"Number of events in file:\", f[\"features\"].shape[0]\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3.1- Basic File Manipulation\n",
    "\n",
    "Create two dictionaries. Store the total number of events of each particle type in one. Store the number events in each file in another. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N_Events_perType = {}\n",
    "N_Events_perFile = {}\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "\n",
    "# Fill in your solution here        \n",
    "    \n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "We will use matplotlib for most of our plotting. There are lots of tutorials and primers out there that you can find searching the web. A good tutorial can be found in the [Scipy Lectures](http://www.scipy-lectures.org/intro/matplotlib/matplotlib.html). Look through these on your own time, it is not necessary for doing these exercise.\n",
    "\n",
    "The raw data from a LArTPC detector looks like an image. The LArIAT detector, which we have simulated, has 2 readout views. The following code gives you an example how to plot these images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (500, 2, 240, 4096)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7Fh75XDhDxGen7s6HdT+5B7Ou4tCfHMvsriMsrF0Le1iaQsmZ5muzK3PqRlY2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\nNKIYEYVCoVAoFBrx/wOCRfeZVef25QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99b08edc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the first pion file\n",
    "f=h5py.File(FileLists[\"pionPlus\"][0],\"r\")\n",
    "\n",
    "# Get the images\n",
    "images=f[\"features\"]\n",
    "\n",
    "print \"Data shape:\", images.shape\n",
    "\n",
    "def PlotEvent(image):\n",
    "    # Make two plots. Create a 1 by 2 grid the plots.\n",
    "    ax1 = plt.subplot(1,2,1)\n",
    "    ax2 = plt.subplot(1,2,2)\n",
    "\n",
    "    # Plot the first view. Note: [EventNumber, View] = [0,0]\n",
    "    ax1.imshow(image[0])\n",
    "\n",
    "    # Plot the second view \n",
    "    ax2.imshow(image[1])\n",
    "\n",
    "    # The data is 240 by 4096. Change the aspect ratio so the plot is not squished. \n",
    "    ax1.set_aspect(16)  \n",
    "    ax2.set_aspect(16) \n",
    "\n",
    "# Plot the 5th Event\n",
    "PlotEvent(images[4])\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3.2.1- Setup Training\n",
    "\n",
    "Write a function that takes a file, and creates a grid of plots showing the first N events. Use this function to plot the first 9 events in the first file of each particle type in a 3 by 3 grid. You only need to show one view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numu :\n",
      "kaonPlus :\n",
      "antielectron :\n",
      "pion :\n",
      "numubar :\n",
      "nuebar :\n",
      "photon :\n",
      "antiproton :\n",
      "proton :\n",
      "electron :\n",
      "muon :\n",
      "pionPlus :\n",
      "kaonMinus :\n",
      "nue :\n",
      "antimuon :\n",
      "pionMinus :\n"
     ]
    }
   ],
   "source": [
    "def PlotEvents(FileName, N_Events):\n",
    "    ### BEGIN SOLUTION\n",
    "\n",
    "    # Fill in your solution here        \n",
    "    \n",
    "    ### END SOLUTION\n",
    "    pass\n",
    "\n",
    "N_Events=9\n",
    "\n",
    "for aFile in FileLists:\n",
    "    FileName=FileLists[aFile][0]\n",
    "    ParticleName=os.path.basename(FileName).split('_')[0]\n",
    "    \n",
    "    print ParticleName,\":\"\n",
    "    PlotEvents(FileName,N_Events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3.2.2- Train Yourself\n",
    "\n",
    "By looking closely at each particle type, identify at least one \"feature\" that would allow you to by eye uniquely identify that particle type. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Type you answer in this box.\n",
    "\n",
    "### BEGIN SOLUTION\n",
    "\n",
    "- muon/antimuon: your description here\n",
    "- electron/antielectron: your description here\n",
    "- pion: your description here\n",
    "- pionPlus/pionMinus: your description here\n",
    "- kaonPlus/kaonMinus: your description here\n",
    "- photon: your description here\n",
    "- nue/nuebar: your description here\n",
    "- numu/numubar: your description here\n",
    "- proton/antiproton: your description here\n",
    "\n",
    "### END SOLUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation (Homework)\n",
    "\n",
    "Now we have to setup a validation process. We will first assign each particle type a unique index. Then we will load some events of each particle type, mix them while keeping track of the indecies. Finally we will present the images to the handscanner, ask them to classify, and keep track of how well they do.\n",
    "\n",
    "Read through and try to understand the following code which setups up 2 dictionaries we will use to uniquely identify particle types. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index map:\n",
      "{'numu': 0, 'nue': 13, 'antielectron': 9, 'pion': 3, 'numubar': 0, 'nuebar': 13, 'photon': 6, 'electron': 9, 'proton': 8, 'antiproton': 8, 'muon': 10, 'pionPlus': 11, 'kaonMinus': 1, 'kaonPlus': 1, 'antimuon': 10, 'pionMinus': 11}\n",
      "Reverse Index map:\n",
      "{0: 'numu', 1: 'kaonMinus', 3: 'pion', 6: 'photon', 8: 'proton', 9: 'antielectron', 10: 'muon', 11: 'pionPlus', 13: 'nue'}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assign index to particle type\n",
    "ParticleTypesIndexMap = {}\n",
    "\n",
    "for i,ParticleType in enumerate(FileLists.keys()):    \n",
    "    ParticleTypesIndexMap[ParticleType]=i\n",
    "    \n",
    "# Merge particle/anti-particle\n",
    "for ParticleName in ParticleTypesIndexMap:\n",
    "    if 'bar' in ParticleName:\n",
    "        ParticleTypesIndexMap[ParticleName]=ParticleTypesIndexMap[ParticleName.split('bar')[0]]\n",
    "        \n",
    "    if 'anti' in ParticleName:\n",
    "        ParticleTypesIndexMap[ParticleName]=ParticleTypesIndexMap[ParticleName.split('anti')[1]]\n",
    "\n",
    "    if 'Minus' in ParticleName:\n",
    "        ParticleTypesIndexMap[ParticleName]=ParticleTypesIndexMap[ParticleName.split('Minus')[0]+\"Plus\"]\n",
    "    \n",
    "print \"Index map:\"\n",
    "print ParticleTypesIndexMap\n",
    "\n",
    "# Reverse Map\n",
    "ParticleTypesIndexMapR={}\n",
    "\n",
    "for p in ParticleTypesIndexMap:\n",
    "    if ParticleTypesIndexMap[p] not in ParticleTypesIndexMapR:\n",
    "        ParticleTypesIndexMapR[ParticleTypesIndexMap[p]]=p\n",
    "\n",
    "print \"Reverse Index map:\"\n",
    "print ParticleTypesIndexMapR\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we load the data and mix them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: /data/LArIAT/h5_files/numu_CC_7-1469162877.h5\n",
      "Opening: /data/LArIAT/h5_files/kaonPlus_5-1466605529.h5\n",
      "Opening: /data/LArIAT/h5_files/antielectron_2-1462204074.h5\n",
      "Opening: /data/LArIAT/h5_files/pion_0_19-1465222696.h5\n",
      "Opening: /data/LArIAT/h5_files/numubar_CC_10-1469306203.h5\n",
      "Opening: /data/LArIAT/h5_files/nuebar_NC_0-1469513434.h5\n",
      "Opening: /data/LArIAT/h5_files/photon_2-1466642559.h5\n",
      "Opening: /data/LArIAT/h5_files/antiproton_7-1466998552.h5\n",
      "Opening: /data/LArIAT/h5_files/proton_15-1466439654.h5\n",
      "Opening: /data/LArIAT/h5_files/electron_19-1464793391.h5\n",
      "Opening: /data/LArIAT/h5_files/muon_14-1466218429.h5\n",
      "Warning: Sample /data/LArIAT/h5_files/muon_14-1466218429.h5 had only 5 events.\n",
      "Opening: /data/LArIAT/h5_files/pionPlus_1-1463590721.h5\n",
      "Opening: /data/LArIAT/h5_files/kaonMinus_14-1463590719.h5\n",
      "Opening: /data/LArIAT/h5_files/nue_NC_14-1461971806.h5\n",
      "Opening: /data/LArIAT/h5_files/antimuon_3-1466967198.h5\n",
      "Opening: /data/LArIAT/h5_files/pionMinus_5-1466714027.h5\n",
      "(155, 2, 240, 4096) (155,)\n"
     ]
    }
   ],
   "source": [
    "Data_X = None\n",
    "Data_Y = None\n",
    "N_Events_perType=10\n",
    "\n",
    "for ParticleType in FileLists:\n",
    "    # Open the first file\n",
    "    FileName=FileLists[ParticleType][1] # we will take the 2nd file so we don't use the training sample for validation\n",
    "    print \"Opening:\",FileName\n",
    "    f=h5py.File(FileName,\"r\")\n",
    "    \n",
    "    # Get the images/features\n",
    "    images=np.array(f[\"features\"][:N_Events_perType])\n",
    "    \n",
    "    # Warn if not enough events\n",
    "    N_Events_read=images.shape[0]\n",
    "    if not N_Events_read==N_Events_perType:\n",
    "        print \"Warning: Sample\", FileName, \"had only\",N_Events_read,\"events.\"\n",
    "    \n",
    "    # Assign labels\n",
    "    labels=np.empty(N_Events_read)\n",
    "    labels.fill(ParticleTypesIndexMap[ParticleType])\n",
    "\n",
    "    # Store some of them\n",
    "    try:\n",
    "        # If we have already read some data, add to it\n",
    "        Data_X=np.concatenate((Data_X,images))\n",
    "        Data_Y=np.concatenate((Data_Y,labels))\n",
    "    except:\n",
    "        # If we haven't read any data yet\n",
    "        Data_X=images\n",
    "        Data_Y=labels\n",
    "    \n",
    "        \n",
    "    f.close()\n",
    "\n",
    "print Data_X.shape, Data_Y.shape\n",
    "\n",
    "def shuffle_in_unison_inplace(a, b):\n",
    "    assert len(a) == len(b)\n",
    "    p = np.random.permutation(len(a))\n",
    "    return a[p], b[p]\n",
    "    \n",
    "Data_X,Data_Y=shuffle_in_unison_inplace(Data_X,Data_Y)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Exercise 3.3.1\n",
    "\n",
    "The following code presents images and asks the handscanner for a type. Read through it carefully. Try it out. Then instrument this code so it keeps track of success and failures. The goal is to create a confusion matrix, a table that keeps track of how often each particle type is correctly identified and how often it is misidentified as any other type.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E+1FFz/lu6f3x+cVF3MRPqspHYm510qxboJmWT+qjhU/EXn9e8ajY/lo9VOViW1fXTZoT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tLtZgVwmn3M1SoYRVxVIrK5Pe5HPTldVpaUK6HQtNV6FP95B4qWD1QfdhXW8ok4ahlm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9998819d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select Type from: {0: 'numu', 1: 'kaonMinus', 3: 'pion', 6: 'photon', 8: 'proton', 9: 'antielectron', 10: 'muon', 11: 'pionPlus', 13: 'nue'}\n",
      "Input:11\n",
      "You selected: pionPlus\n"
     ]
    },
    {
     "data": {
      "image/png": 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K57E4YqaoYb3A440x+SPi8r2r4fDle2r7+g9G0vSaAe0+XOZ5kfVq8ZBFR4mm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99d82d6bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select Type from: {0: 'numu', 1: 'kaonMinus', 3: 'pion', 6: 'photon', 8: 'proton', 9: 'antielectron', 10: 'muon', 11: 'pionPlus', 13: 'nue'}\n",
      "Input:3\n",
      "You selected: pion\n"
     ]
    },
    {
     "data": {
      "image/png": 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fu5e3v/QXYeut8BTg6R18NUQhuDRrsWkQdzfxW/UFFlQTtXZCCivLqcsivYCm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\nb6fbHhoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99a98dd6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select Type from: {0: 'numu', 1: 'kaonMinus', 3: 'pion', 6: 'photon', 8: 'proton', 9: 'antielectron', 10: 'muon', 11: 'pionPlus', 13: 'nue'}\n",
      "Input:5\n",
      "You selected:"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "5",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-5b8cacb2b329>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0;34m\"You selected:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParticleTypesIndexMapR\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 5"
     ]
    }
   ],
   "source": [
    "View=0\n",
    "\n",
    "for image in Data_X:\n",
    "    PlotEvent(image)\n",
    "    plt.show()\n",
    "    \n",
    "    print \"Select Type from:\", ParticleTypesIndexMapR\n",
    "    try:\n",
    "        answer=int(raw_input('Input:'))\n",
    "    except ValueError:\n",
    "        print \"Not a number\"\n",
    "        \n",
    "    # Stop loop\n",
    "    if answer==-1:\n",
    "        break\n",
    "    \n",
    "    print \"You selected:\", ParticleTypesIndexMapR[answer]\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Exercise 3.3.2\n",
    "\n",
    "Make yourself the handscanner. Use above code to go through the full data sample and create a confusion matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Honors Problems:\n",
    "\n",
    "### Exercise 3.4.1\n",
    "Write a function that downsamples all of images by summing samples to reduce the 4096 long dimension of the data.\n",
    "\n",
    "### Exercise 3.4.2\n",
    "Write a function that returns a sub-region in the 4096 long dimention where the total charge is max."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}