Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THIS NOTEBOOK is just a bunch of random junk and test code. Pay it no heed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Compare LSTM implementations on the IMDB sentiment classification task.\n",
    "\n",
    "consume_less='cpu' preprocesses input to the LSTM which typically results in\n",
    "faster computations at the expense of increased peak memory usage as the\n",
    "preprocessed input must be kept in memory.\n",
    "\n",
    "consume_less='mem' does away with the preprocessing, meaning that it might take\n",
    "a little longer, but should require less peak memory.\n",
    "\n",
    "consume_less='gpu' concatenates the input, output and forget gate's weights\n",
    "into one, large matrix, resulting in faster computation time as the GPU can\n",
    "utilize more cores, at the expense of reduced regularization because the same\n",
    "dropout is shared across the gates.\n",
    "\n",
    "Note that the relative performance of the different `consume_less` modes\n",
    "can vary depending on your device, your model and the size of your data.\n",
    "'''\n",
    "%matplotlib inline\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, Dense, LSTM\n",
    "from keras.datasets import imdb\n",
    "\n",
    "\n",
    "max_features = 20000\n",
    "max_length = 80\n",
    "embedding_dim = 256\n",
    "batch_size = 128\n",
    "epochs = 1\n",
    "modes = ['gpu']\n",
    "\n",
    "print('Loading data...')\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features)\n",
    "X_train = sequence.pad_sequences(X_train, max_length)\n",
    "X_test = sequence.pad_sequences(X_test, max_length)\n",
    "\n",
    "# Compile and train different models while meauring performance.\n",
    "results = []\n",
    "for mode in modes:\n",
    "    print('Testing mode: consume_less=\"{}\"'.format(mode))\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(max_features, embedding_dim, input_length=max_length, dropout=0.2))\n",
    "    model.add(LSTM(embedding_dim, dropout_W=0.2, dropout_U=0.2, consume_less=mode))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    start_time = time.time()\n",
    "    history = model.fit(X_train, y_train,\n",
    "                        batch_size=batch_size,\n",
    "                        nb_epoch=epochs,\n",
    "                        validation_data=(X_test, y_test))\n",
    "    average_time_per_epoch = (time.time() - start_time) / epochs\n",
    "\n",
    "    results.append((history, average_time_per_epoch))\n",
    "\n",
    "# Compare models' accuracy, loss and elapsed time per epoch.\n",
    "plt.style.use('ggplot')\n",
    "ax1 = plt.subplot2grid((2, 2), (0, 0))\n",
    "ax1.set_title('Accuracy')\n",
    "ax1.set_ylabel('Validation Accuracy')\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax2 = plt.subplot2grid((2, 2), (1, 0))\n",
    "ax2.set_title('Loss')\n",
    "ax2.set_ylabel('Validation Loss')\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax3 = plt.subplot2grid((2, 2), (0, 1), rowspan=2)\n",
    "ax3.set_title('Time')\n",
    "ax3.set_ylabel('Seconds')\n",
    "for mode, result in zip(modes, results):\n",
    "    ax1.plot(result[0].epoch, result[0].history['val_acc'], label=mode)\n",
    "    ax2.plot(result[0].epoch, result[0].history['val_loss'], label=mode)\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.bar(np.arange(len(results)), [x[1] for x in results],\n",
    "        tick_label=modes, align='center')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('md5', 'sha1', 'sha224', 'sha256', 'sha384', 'sha512')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hashlib\n",
    "hashlib.algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = hashlib.md5()\n",
    "m.update(\"Nobody inspects\")\n",
    "m.update(\" the spammish repetition\")\n",
    "print(m.hexdigest())\n",
    "\n",
    "\n",
    "m = hashlib.md5()\n",
    "m.update(\" the spammish repetition\")\n",
    "m.update(\"Nobody inspects\")\n",
    "print(m.hexdigest())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dfs = [pd.DataFrame(np.random.rand(10, 3), columns=list('abc')) for i in range(3)]\n",
    "DF = pd.DataFrame(np.array(dfs), columns=list('ABC'))\n",
    "\n",
    "print(DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = df[(df.a < df.b) & (df.b < df.c)]\n",
    "print(type(q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('(a < b) & (b < c)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if(storeType == \"hdf5\"):\n",
    "    #If we are reading all the samples use get since it might be faster\n",
    "    #TODO: check if it is actually faster\n",
    "    if(samples_to_read == file_total_entries):\n",
    "        frame = store.get('/'+key)\n",
    "    else:\n",
    "        frame = store.select('/'+key, start=select_start, stop=select_stop)\n",
    "elif(storeType == \"msgpack\"):\n",
    "    frame = frames[key]\n",
    "    frame = frame[select_start:select_stop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from CMS_Deep_Learning.utils.preprocessing import ObjectProfile, \\\n",
    "        preprocessFromPandas_label_dir_pairs, label_dir_pairs_args_decoder, \\\n",
    "        genFrom_label_dir_pairs,maxMutualLength, start_num_fromSplits, resolveProfileMaxes,procsFrom_label_dir_pairs,genFromPPs\n",
    "from CMS_Deep_Learning.utils.archiving import *\n",
    "#The observables taken from the table\n",
    "#observ_types = ['E/c', 'Px', 'Py', 'Pz', 'Charge', \"PT_ET\", \"Eta\", \"Phi\", \"Dxy_Ehad_Eem\"]\n",
    "observ_types =  ['Entry','E/c', 'Px', 'Py', 'Pz', 'PT_ET','Eta', 'Phi', 'Charge', 'X', 'Y', 'Z',\\\n",
    "                     'Dxy', 'Ehad', 'Eem', 'MuIso', 'EleIso', 'ChHadIso','NeuHadIso','GammaIso']\n",
    "vecsize = len(observ_types)\n",
    "epochs = 100\n",
    "batch_size = 100\n",
    "sample_start = 0\n",
    "num_samples = 10000\n",
    "\n",
    "print(\"YAY!\")\n",
    "sys.stdout.flush()\n",
    "\n",
    "object_profiles = [ObjectProfile(\"Electron\",5),\n",
    "                    ObjectProfile(\"MuonTight\", 5),\n",
    "                    ObjectProfile(\"Photon\", 25),\n",
    "                    ObjectProfile(\"MissingET\", 1),\n",
    "                    ObjectProfile(\"EFlowPhoton\",1000, sort_columns=[\"PT_ET\"], sort_ascending=False),  #1300\n",
    "                    ObjectProfile(\"EFlowNeutralHadron\",1000, sort_columns=[\"PT_ET\"], sort_ascending=False),  #1000\n",
    "                    ObjectProfile(\"EFlowTrack\",1000, sort_columns=[\"PT_ET\"], sort_ascending=False)]  #1050\n",
    "\n",
    "\n",
    "\n",
    "label_dir_pairs = \\\n",
    "            [   (\"ttbar\", \"/data/shared/Delphes/ttbar_lepFilter_13TeV/pandas_msg/\"),\n",
    "                (\"wjet\", \"/data/shared/Delphes/wjets_lepFilter_13TeV/pandas_msg/\"),\n",
    "                (\"qcd\", \"/data/shared/Delphes/qcd_lepFilter_13TeV/pandas_msg/\")\n",
    "            ]\n",
    "print(\"RESOLVING MAXES!\")\n",
    "sys.stdout.flush()\n",
    "resolveProfileMaxes(object_profiles, label_dir_pairs)\n",
    "\n",
    "archive_dir = \"MyTestArchiveDir/\"\n",
    "trials = []\n",
    "ldps_pairs = []\n",
    "\n",
    "import socket\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#print(\"FINDING STRIDE!\")\n",
    "#sys.stdout.flush()\n",
    "#print(strideFromTargetSize(object_profiles, label_dir_pairs, observ_types, megabytes=500))\n",
    "#for i, j in [(2,0)]:\n",
    "    #ldps = [label_dir_pairs[i]] + [label_dir_pairs[j]]\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "       \n",
    "    \n",
    "        \n",
    "dps, l = getGensDefaultFormat(archive_dir, (100000,25000,25000), 150000, \\\n",
    "                         object_profiles,label_dir_pairs,observ_types,megabytes=500)\n",
    "train, num_train = l[0]\n",
    "val,   num_val   = l[1]\n",
    "test,  num_test  = l[2]\n",
    "       \n",
    "batchAssertArchived(dps)\n",
    "print(dps)\n",
    "print(train)\n",
    "print(num_train)\n",
    "print(val)\n",
    "print(num_val)\n",
    "print(test)\n",
    "print(num_test)\n",
    "for X,Y in train.getData():\n",
    "    batch_size = X[0].shape[0]\n",
    "    num_read += batch_size\n",
    "    if(num_read >= num_train):\n",
    "        break\n",
    "raise ValueError()\n",
    "    #print(SNs)\n",
    "    #gen_lambda =  lambda s : (DataProcedure(archive_dir,\n",
    "    #                                       False,\n",
    "    #                                       genFrom_label_dir_pairs,\n",
    "    #                                       start=s[0],\n",
    "    #                                       samples_per_label=s[1],\n",
    "    #                                       stride=stride,\n",
    "    #                                        batch_size=batch_size,\n",
    "    #                                       archive_dir=archive_dir,\n",
    "    #                                       label_dir_pairs=label_dir_pairs,\n",
    "    #                                       object_profiles=object_profiles,\n",
    "    #                                       observ_types=observ_types),\n",
    "    #                         len(label_dir_pairs)*s[1])\n",
    "    #l = [gen_lambda(s) for s in SNs]\n",
    "    \n",
    "stride = strideFromTargetSize(object_profiles, label_dir_pairs, observ_types, megabytes=500)\n",
    "SNs = start_num_fromSplits((.7,.15,.15), 200000)\n",
    "print(SNs)\n",
    "gen_lambda =  lambda s : (DataProcedure(archive_dir,\n",
    "                                       False,\n",
    "                                       genFrom_label_dir_pairs,\n",
    "                                       start=s[0],\n",
    "                                       samples_per_label=s[1],\n",
    "                                       stride=stride,\n",
    "                                        batch_size=100,\n",
    "                                       archive_dir=archive_dir,\n",
    "                                       label_dir_pairs=label_dir_pairs,\n",
    "                                       object_profiles=object_profiles,\n",
    "                                       observ_types=observ_types),\n",
    "                         len(label_dir_pairs)*s[1])\n",
    "l = [gen_lambda(s) for s in SNs]\n",
    "train, num_train = l[0]\n",
    "val,   num_val   = l[1]\n",
    "test,  num_test  = l[2]\n",
    "\n",
    "num_read = 0\n",
    "for X,Y in train.getData():\n",
    "    batch_size = X[0].shape[0]\n",
    "    num_read += batch_size\n",
    "    if(num_read >= num_train):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep_clause = \"\"\n",
    "#string = \"Submitted batch job 365288 I AM A PRETTY RAINBOW 38341034  Submitted batch job 42388 Submitted batch job 665245458\"\n",
    "matches = re.findall(\"Submitted batch job [0-9]+\", string) \n",
    "\n",
    "dependencies = [re.findall(\"[0-9]+\", m)[0] for m in matches]\n",
    "if(len(dependencies) > 0):\n",
    "    dep_clause = \"--dependency=afterok:\" + \":\".join(dependencies)\n",
    "print(dep_clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YAY!\n",
      "0.03168\n",
      "[]\n",
      "Generating DataProcedure in range(0,100):\n",
      "   From 2 labels in range(0,100) for 2x100 = 200 Samples\n",
      "Generating DataProcedure in range(100,125):\n",
      "   From 2 labels in range(100,125) for 2x25 = 50 Samples\n",
      "Generating DataProcedure in range(125,150):\n",
      "   From 2 labels in range(125,150) for 2x25 = 50 Samples\n",
      "Failed to load history at /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/history.json\n",
      "Failed to load weights at /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "Epoch 1/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 7.2453 - acc: 0.5200DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00001: acc improved from -inf to 0.52750, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 7.2158 - acc: 0.5275 - val_loss: 8.0151 - val_acc: 0.5000\n",
      "Epoch 2/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 7.6817 - acc: 0.5200DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00002: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 7.6084 - acc: 0.5250 - val_loss: 8.0151 - val_acc: 0.5000\n",
      "Epoch 3/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 7.6191 - acc: 0.5250DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00003: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 7.5214 - acc: 0.5250 - val_loss: 7.5392 - val_acc: 0.5300\n",
      "Epoch 4/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 7.0535 - acc: 0.5300DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00004: acc improved from 0.52750 to 0.53000, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 7.0541 - acc: 0.5300 - val_loss: 8.2976 - val_acc: 0.4800\n",
      "Epoch 5/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 7.1803 - acc: 0.5350DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00005: acc improved from 0.53000 to 0.54500, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 6.9601 - acc: 0.5450 - val_loss: 7.8310 - val_acc: 0.4900\n",
      "Epoch 6/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 6.1355 - acc: 0.5750DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00006: acc improved from 0.54500 to 0.58000, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 6.2133 - acc: 0.5800 - val_loss: 7.0574 - val_acc: 0.5100\n",
      "Epoch 7/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.8022 - acc: 0.6250DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00007: acc improved from 0.58000 to 0.61000, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 5.9244 - acc: 0.6100 - val_loss: 7.7386 - val_acc: 0.4800\n",
      "Epoch 8/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.6336 - acc: 0.6050DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00008: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 5.9918 - acc: 0.5975 - val_loss: 7.1167 - val_acc: 0.5400\n",
      "Epoch 9/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.2672 - acc: 0.6500DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00009: acc improved from 0.61000 to 0.64000, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 5.5011 - acc: 0.6400 - val_loss: 8.0373 - val_acc: 0.4800\n",
      "Epoch 10/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.5399 - acc: 0.6200DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00010: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 5.8331 - acc: 0.5975 - val_loss: 8.0365 - val_acc: 0.4900\n",
      "Epoch 11/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.5929 - acc: 0.6150DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00011: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 5.7756 - acc: 0.6150 - val_loss: 8.0364 - val_acc: 0.4600\n",
      "Epoch 12/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.2848 - acc: 0.6600DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00012: acc improved from 0.64000 to 0.65000, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 5.4446 - acc: 0.6500 - val_loss: 7.7702 - val_acc: 0.4800\n",
      "Epoch 13/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 4.8977 - acc: 0.6900DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00013: acc improved from 0.65000 to 0.66250, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 5.2478 - acc: 0.6625 - val_loss: 8.0422 - val_acc: 0.4900\n",
      "Epoch 14/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.1962 - acc: 0.6700DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00014: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 5.2759 - acc: 0.6625 - val_loss: 7.6886 - val_acc: 0.4900\n",
      "Epoch 15/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.2383 - acc: 0.6650DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00015: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 5.4384 - acc: 0.6500 - val_loss: 8.0408 - val_acc: 0.4800\n",
      "Epoch 16/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 5.1662 - acc: 0.6700DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00016: acc did not improve\n",
      "200/200 [==============================] - 0s - loss: 5.3220 - acc: 0.6625 - val_loss: 7.6023 - val_acc: 0.4700\n",
      "Epoch 17/100\n",
      "DataProcedure results '0b3b7a30bcf059a1ae62dd38fe68b4c2b8565403' read from archive\n",
      "100/200 [==============>...............] - ETA: 0s - loss: 4.8188 - acc: 0.6750DataProcedure results 'fb2f74985aa1a48f85f0555422d8407f3f778ecb' read from archive\n",
      "Epoch 00016: early stopping\n",
      "Epoch 00017: acc improved from 0.66250 to 0.66750, saving model to /data/shared/Delphes/testArchive/blobs/8c1f7/28e3d7a17233d3ba7bd181d38887602459b/weights.h5\n",
      "200/200 [==============================] - 0s - loss: 5.0931 - acc: 0.6675 - val_loss: 8.2241 - val_acc: 0.4600\n",
      "('T@:', <class 'CMS_Deep_Learning.utils.archiving.DataProcedure'>)\n",
      "USING max_q_size: 1\n",
      "DataProcedure results '766588b56e461599f7333e7d45fecad15b860ccb' read from archive\n",
      "Test Complete [7.8014559745788574, 0.4699999988079071]\n"
     ]
    }
   ],
   "source": [
    "import socket\n",
    "import sys, os\n",
    "if(\"daint\" in socket.gethostname()):\n",
    "    DELPHES_DIR = \"/scratch/daint/dweiteka/Delphes/\"\n",
    "    SOFTWAR_DIR = \"/scratch/daint/dweiteka/\"\n",
    "else:\n",
    "    DELPHES_DIR = \"/data/shared/Delphes/\"\n",
    "    SOFTWAR_DIR = \"/data/shared/Software/\"\n",
    "\n",
    "if(not SOFTWAR_DIR in sys.path):\n",
    "    sys.path.append(SOFTWAR_DIR)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ntpath\n",
    "import glob\n",
    "#import deepconfig\n",
    "\n",
    "from CMS_Deep_Learning.utils.preprocessing import *\n",
    "from CMS_Deep_Learning.utils.callbacks import OverfitStopping, SmartCheckpoint\n",
    "from CMS_Deep_Learning.utils.archiving import *\n",
    "from CMS_Deep_Learning.layers.lorentz import Lorentz, _lorentz\n",
    "from CMS_Deep_Learning.layers.slice import Slice\n",
    "\n",
    "from keras.models import Sequential, Model, model_from_json\n",
    "from keras.layers import Dense, Flatten, Reshape, Activation, Dropout, Convolution2D, merge, Input, Flatten, Lambda, LSTM, Masking\n",
    "from keras.engine.topology import Layer\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils.visualize_util import plot\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "\n",
    "\n",
    "#dc = deepconfig.deepconfig(gpu='gpu0', backend='theano')\n",
    "\n",
    "\n",
    "#The observables taken from the table\n",
    "observ_types =  ['Entry','E/c', 'Px', 'Py', 'Pz', 'PT_ET','Eta', 'Phi', 'Charge', 'X', 'Y', 'Z',\\\n",
    "                     'Dxy', 'Ehad', 'Eem', 'MuIso', 'EleIso', 'ChHadIso','NeuHadIso','GammaIso']\n",
    "vecsize = len(observ_types)\n",
    "epochs = 100\n",
    "batch_size = 100\n",
    "sample_start = 0\n",
    "num_samples = 10000\n",
    "\n",
    "print(\"YAY!\")\n",
    "sys.stdout.flush()\n",
    "\n",
    "object_profiles = [ObjectProfile(\"Electron\",5),\n",
    "                    ObjectProfile(\"MuonTight\", 5),\n",
    "                    ObjectProfile(\"Photon\", 25),\n",
    "                    ObjectProfile(\"MissingET\", 1),\n",
    "                    ObjectProfile(\"EFlowPhoton\",10, sort_columns=[\"PT_ET\"], sort_ascending=False),  #1300\n",
    "                    ObjectProfile(\"EFlowNeutralHadron\",10, sort_columns=[\"PT_ET\"], sort_ascending=False),  #1000\n",
    "                    ObjectProfile(\"EFlowTrack\",10, sort_columns=[\"PT_ET\"], sort_ascending=False)]  #1050\n",
    "\n",
    "\n",
    "\n",
    "label_dir_pairs = \\\n",
    "            [   (\"ttbar\", DELPHES_DIR+\"ttbar_lepFilter_13TeV/pandas_msg/\"),\n",
    "                (\"wjet\", DELPHES_DIR+\"wjets_lepFilter_13TeV/pandas_msg/\"),\n",
    "                (\"qcd\", DELPHES_DIR+\"qcd_lepFilter_13TeV/pandas_msg/\")\n",
    "            ]\n",
    "\n",
    "def genModel(depth, width):\n",
    "    inputs = [None] * len(object_profiles)\n",
    "    fist_layer = [None] * len(object_profiles)\n",
    "    #Build Inputs\n",
    "    for i,profile in enumerate(object_profiles):\n",
    "        key = profile.name\n",
    "        max_size = profile.max_size\n",
    "        inp = Input(shape=(max_size, vecsize), name=key)\n",
    "        inputs[i] = inp\n",
    "        fist_layer[i] = Flatten()(inp)\n",
    "    #Merge inputs\n",
    "    merged = merge(fist_layer, mode='concat', name=\"merge\")\n",
    "    a = merged\n",
    "    v = [width] * depth\n",
    "    for j in range(len(v)):\n",
    "        a = Dense(v[j], activation='relu', name=\"dense_2_\"+str(j))(a)\n",
    "    second_dense = a \n",
    "    \n",
    "    #Create dense sigmoid layer for classification\n",
    "    dense_out = Dense(2, activation='sigmoid', name='main_output')(second_dense)\n",
    "    dense = Model(input=inputs, output=dense_out, name=\"dense\")\n",
    "    return dense\n",
    "\n",
    "archive_dir = DELPHES_DIR + \"testArchive/\"\n",
    "earlyStopping = EarlyStopping(verbose=1, patience=10)\n",
    "overfitStopping = OverfitStopping(verbose=1, patience=20)\n",
    "trial_tups = []\n",
    "ldps_pairs = []\n",
    "for i, j in [(2,0)]:\n",
    "    ldps = [label_dir_pairs[i]] + [label_dir_pairs[j]]\n",
    "    dps, l = getGensDefaultFormat(archive_dir, (100,25,25), 150, \\\n",
    "                         object_profiles,ldps,observ_types,megabytes=500)\n",
    "    \n",
    "    dependencies = batchAssertArchived(dps)\n",
    "    \n",
    "    train, num_train = l[0]\n",
    "    val,   num_val   = l[1]\n",
    "    test,  num_test  = l[2]\n",
    "\n",
    "    labels = [x[0] for x in ldps]\n",
    "    for name in ['dense']:\n",
    "        for depth in [1]:\n",
    "            for width in [100]:\n",
    "                for activation in ['tanh']:\n",
    "                    for lstm_dropout in [0.0]:\n",
    "                        for dropout in [0.0]:\n",
    "                            activation_name = activation if isinstance(activation, str) \\\n",
    "                                                else activation.__name__\n",
    "\n",
    "                            model = genModel(depth,width)\n",
    "\n",
    "                            trial = KerasTrial(archive_dir, name=name, model=model)\n",
    "\n",
    "                            trial.setTrain(train_procedure=train,\n",
    "                                           samples_per_epoch=num_train\n",
    "                                          )\n",
    "                            trial.setValidation(val_procedure=val,\n",
    "                                               nb_val_samples=num_val\n",
    "                                               )\n",
    "                            trial.setCompilation(loss='binary_crossentropy',\n",
    "                                      optimizer='rmsprop',\n",
    "                                      metrics=['accuracy']\n",
    "                                          )\n",
    "                            trial.setFit_Generator( \n",
    "                                            nb_epoch=epochs,\n",
    "                                            callbacks=[earlyStopping, overfitStopping],\n",
    "                                            max_q_size = min(int(num_train/batch_size), 1))\n",
    "\n",
    "\n",
    "                            trial.write()\n",
    "                            #print(trial.to_json())\n",
    "\n",
    "\n",
    "                            trial.to_record({\"lables\": labels,\n",
    "                                             \"depth\": depth,\n",
    "                                             #\"width\": width,\n",
    "                                             \"activation\": activation_name,\n",
    "                                             \"dropout\":dropout,\n",
    "                                             \"lstm_dropout\":lstm_dropout\n",
    "                                            })\n",
    "                                       \n",
    "                            trial_tups.append((trial, test, num_test, dependencies))\n",
    "                \n",
    "batchExecuteAndTestTrials(trial_tups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"model\": \"{\"class_name\": \"Model\", \"keras_version\": \"1.0.6\", \"config\": {\"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 10, 20], \"name\": \"EFlowNeutralHadron\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"EFlowNeutralHadron\"}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 10, 20], \"name\": \"EFlowPhoton\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"EFlowPhoton\"}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 10, 20], \"name\": \"EFlowTrack\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"EFlowTrack\"}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 5, 20], \"name\": \"Electron\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"Electron\"}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 1, 20], \"name\": \"MissingET\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"MissingET\"}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 5, 20], \"name\": \"MuonTight\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"MuonTight\"}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 25, 20], \"name\": \"Photon\", \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": \"Photon\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_106\"}, \"inbound_nodes\": [[[\"Electron\", 0, 0]]], \"name\": \"flatten_106\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_107\"}, \"inbound_nodes\": [[[\"MuonTight\", 0, 0]]], \"name\": \"flatten_107\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_108\"}, \"inbound_nodes\": [[[\"Photon\", 0, 0]]], \"name\": \"flatten_108\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_109\"}, \"inbound_nodes\": [[[\"MissingET\", 0, 0]]], \"name\": \"flatten_109\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_110\"}, \"inbound_nodes\": [[[\"EFlowPhoton\", 0, 0]]], \"name\": \"flatten_110\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_111\"}, \"inbound_nodes\": [[[\"EFlowNeutralHadron\", 0, 0]]], \"name\": \"flatten_111\"}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_112\"}, \"inbound_nodes\": [[[\"EFlowTrack\", 0, 0]]], \"name\": \"flatten_112\"}, {\"class_name\": \"Merge\", \"config\": {\"name\": \"merge\", \"concat_axis\": -1, \"mode_type\": \"raw\", \"dot_axes\": [-1, -1], \"mode\": \"concat\", \"output_shape\": null, \"output_shape_type\": \"raw\"}, \"inbound_nodes\": [[[\"flatten_106\", 0, 0], [\"flatten_107\", 0, 0], [\"flatten_108\", 0, 0], [\"flatten_109\", 0, 0], [\"flatten_110\", 0, 0], [\"flatten_111\", 0, 0], [\"flatten_112\", 0, 0]]], \"name\": \"merge\"}, {\"class_name\": \"Dense\", \"config\": {\"W_constraint\": null, \"b_constraint\": null, \"name\": \"dense_2_0\", \"activity_regularizer\": null, \"trainable\": true, \"init\": \"glorot_uniform\", \"bias\": true, \"input_dim\": null, \"b_regularizer\": null, \"W_regularizer\": null, \"activation\": \"relu\", \"output_dim\": 100}, \"inbound_nodes\": [[[\"merge\", 0, 0]]], \"name\": \"dense_2_0\"}, {\"class_name\": \"Dense\", \"config\": {\"W_constraint\": null, \"b_constraint\": null, \"name\": \"main_output\", \"activity_regularizer\": null, \"trainable\": true, \"init\": \"glorot_uniform\", \"bias\": true, \"input_dim\": null, \"b_regularizer\": null, \"W_regularizer\": null, \"activation\": \"sigmoid\", \"output_dim\": 2}, \"inbound_nodes\": [[[\"dense_2_0\", 0, 0]]], \"name\": \"main_output\"}], \"input_layers\": [[\"Electron\", 0, 0], [\"MuonTight\", 0, 0], [\"Photon\", 0, 0], [\"MissingET\", 0, 0], [\"EFlowPhoton\", 0, 0], [\"EFlowNeutralHadron\", 0, 0], [\"EFlowTrack\", 0, 0]], \"output_layers\": [[\"main_output\", 0, 0]], \"name\": \"dense\"}}\"\n",
      "\n",
      "\n",
      "\n",
      "['\"EFlowNeutralHadron\"', '\"EFlowNeutralHadron\"', '\"EFlowPhoton\"', '\"EFlowPhoton\"', '\"EFlowTrack\"', '\"EFlowTrack\"', '\"Electron\"', '\"Electron\"', '\"MissingET\"', '\"MissingET\"', '\"MuonTight\"', '\"MuonTight\"', '\"Photon\"', '\"Photon\"', '\"flatten_106\"', '\"flatten_106\"', '\"flatten_107\"', '\"flatten_107\"', '\"flatten_108\"', '\"flatten_108\"', '\"flatten_109\"', '\"flatten_109\"', '\"flatten_110\"', '\"flatten_110\"', '\"flatten_111\"', '\"flatten_111\"', '\"flatten_112\"', '\"flatten_112\"', '\"merge\"', '\"merge\"', '\"dense_2_0\"', '\"dense_2_0\"', '\"main_output\"', '\"main_output\"', '\"dense\"']\n",
      "\n",
      "\n",
      "\n",
      "\"model\": \"{\"class_name\": \"Model\", , \"config\": {\"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 10, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 10, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 10, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 5, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 1, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 5, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 25, 20], \"name\": @, \"input_dtype\": \"float32\"}, \"inbound_nodes\": [], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": @}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Merge\", \"config\": {\"name\": @, \"concat_axis\": -1, \"mode_type\": \"raw\", \"dot_axes\": [-1, -1], \"mode\": \"concat\", \"output_shape\": null, \"output_shape_type\": \"raw\"}, \"inbound_nodes\": [[[@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0]]], \"name\": @}, {\"class_name\": \"Dense\", \"config\": {\"W_constraint\": null, \"b_constraint\": null, \"name\": @, \"activity_regularizer\": null, \"trainable\": true, \"init\": \"glorot_uniform\", \"bias\": true, \"input_dim\": null, \"b_regularizer\": null, \"W_regularizer\": null, \"activation\": \"relu\", \"output_dim\": 100}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}, {\"class_name\": \"Dense\", \"config\": {\"W_constraint\": null, \"b_constraint\": null, \"name\": @, \"activity_regularizer\": null, \"trainable\": true, \"init\": \"glorot_uniform\", \"bias\": true, \"input_dim\": null, \"b_regularizer\": null, \"W_regularizer\": null, \"activation\": \"sigmoid\", \"output_dim\": 2}, \"inbound_nodes\": [[[@, 0, 0]]], \"name\": @}], \"input_layers\": [[@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0], [@, 0, 0]], \"output_layers\": [[@, 0, 0]], \"name\": @}}\"\n"
     ]
    }
   ],
   "source": [
    "string = \\\n",
    "'\"model\": \"{\\\"class_name\\\": \\\"Model\\\", \\\"keras_version\\\": \\\"1.0.6\\\", \\\"config\\\": {\\\"layers\\\": [{\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 10, 20], \\\"name\\\": \\\"EFlowNeutralHadron\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"EFlowNeutralHadron\\\"}, {\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 10, 20], \\\"name\\\": \\\"EFlowPhoton\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"EFlowPhoton\\\"}, {\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 10, 20], \\\"name\\\": \\\"EFlowTrack\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"EFlowTrack\\\"}, {\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 5, 20], \\\"name\\\": \\\"Electron\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"Electron\\\"}, {\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 1, 20], \\\"name\\\": \\\"MissingET\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"MissingET\\\"}, {\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 5, 20], \\\"name\\\": \\\"MuonTight\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"MuonTight\\\"}, {\\\"class_name\\\": \\\"InputLayer\\\", \\\"config\\\": {\\\"batch_input_shape\\\": [null, 25, 20], \\\"name\\\": \\\"Photon\\\", \\\"input_dtype\\\": \\\"float32\\\"}, \\\"inbound_nodes\\\": [], \\\"name\\\": \\\"Photon\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_106\\\"}, \\\"inbound_nodes\\\": [[[\\\"Electron\\\", 0, 0]]], \\\"name\\\": \\\"flatten_106\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_107\\\"}, \\\"inbound_nodes\\\": [[[\\\"MuonTight\\\", 0, 0]]], \\\"name\\\": \\\"flatten_107\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_108\\\"}, \\\"inbound_nodes\\\": [[[\\\"Photon\\\", 0, 0]]], \\\"name\\\": \\\"flatten_108\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_109\\\"}, \\\"inbound_nodes\\\": [[[\\\"MissingET\\\", 0, 0]]], \\\"name\\\": \\\"flatten_109\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_110\\\"}, \\\"inbound_nodes\\\": [[[\\\"EFlowPhoton\\\", 0, 0]]], \\\"name\\\": \\\"flatten_110\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_111\\\"}, \\\"inbound_nodes\\\": [[[\\\"EFlowNeutralHadron\\\", 0, 0]]], \\\"name\\\": \\\"flatten_111\\\"}, {\\\"class_name\\\": \\\"Flatten\\\", \\\"config\\\": {\\\"trainable\\\": true, \\\"name\\\": \\\"flatten_112\\\"}, \\\"inbound_nodes\\\": [[[\\\"EFlowTrack\\\", 0, 0]]], \\\"name\\\": \\\"flatten_112\\\"}, {\\\"class_name\\\": \\\"Merge\\\", \\\"config\\\": {\\\"name\\\": \\\"merge\\\", \\\"concat_axis\\\": -1, \\\"mode_type\\\": \\\"raw\\\", \\\"dot_axes\\\": [-1, -1], \\\"mode\\\": \\\"concat\\\", \\\"output_shape\\\": null, \\\"output_shape_type\\\": \\\"raw\\\"}, \\\"inbound_nodes\\\": [[[\\\"flatten_106\\\", 0, 0], [\\\"flatten_107\\\", 0, 0], [\\\"flatten_108\\\", 0, 0], [\\\"flatten_109\\\", 0, 0], [\\\"flatten_110\\\", 0, 0], [\\\"flatten_111\\\", 0, 0], [\\\"flatten_112\\\", 0, 0]]], \\\"name\\\": \\\"merge\\\"}, {\\\"class_name\\\": \\\"Dense\\\", \\\"config\\\": {\\\"W_constraint\\\": null, \\\"b_constraint\\\": null, \\\"name\\\": \\\"dense_2_0\\\", \\\"activity_regularizer\\\": null, \\\"trainable\\\": true, \\\"init\\\": \\\"glorot_uniform\\\", \\\"bias\\\": true, \\\"input_dim\\\": null, \\\"b_regularizer\\\": null, \\\"W_regularizer\\\": null, \\\"activation\\\": \\\"relu\\\", \\\"output_dim\\\": 100}, \\\"inbound_nodes\\\": [[[\\\"merge\\\", 0, 0]]], \\\"name\\\": \\\"dense_2_0\\\"}, {\\\"class_name\\\": \\\"Dense\\\", \\\"config\\\": {\\\"W_constraint\\\": null, \\\"b_constraint\\\": null, \\\"name\\\": \\\"main_output\\\", \\\"activity_regularizer\\\": null, \\\"trainable\\\": true, \\\"init\\\": \\\"glorot_uniform\\\", \\\"bias\\\": true, \\\"input_dim\\\": null, \\\"b_regularizer\\\": null, \\\"W_regularizer\\\": null, \\\"activation\\\": \\\"sigmoid\\\", \\\"output_dim\\\": 2}, \\\"inbound_nodes\\\": [[[\\\"dense_2_0\\\", 0, 0]]], \\\"name\\\": \\\"main_output\\\"}], \\\"input_layers\\\": [[\\\"Electron\\\", 0, 0], [\\\"MuonTight\\\", 0, 0], [\\\"Photon\\\", 0, 0], [\\\"MissingET\\\", 0, 0], [\\\"EFlowPhoton\\\", 0, 0], [\\\"EFlowNeutralHadron\\\", 0, 0], [\\\"EFlowTrack\\\", 0, 0]], \\\"output_layers\\\": [[\\\"main_output\\\", 0, 0]], \\\"name\\\": \\\"dense\\\"}}\"'\n",
    "print(string)\n",
    "names = [s.replace('\"name\": ', \"\" ) for s in re.findall(r'\"name\": \"[^\"]*\"', string)]\n",
    "for name in names:\n",
    "    string = string.replace(name, \"@\")\n",
    "print('\\n\\n')\n",
    "print(names)\n",
    "string = re.sub(r'\"keras_version\": \"[^\"]*\"', \"\", string)\n",
    "\n",
    "print('\\n\\n')\n",
    "print(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "from CMS_Deep_Learning.utils.archiving import get_all_trials\n",
    "l= get_all_trials(archive_dir)\n",
    "for x in l:\n",
    "    print(x.remove_from_archive())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "if __package__ is None:\n",
    "    sys.path.append(os.path.realpath(\"../\"))\n",
    "import ROOT \n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "import pandas as pd\n",
    "from itertools import islice\n",
    "import glob\n",
    "import ntpath\n",
    "import getopt\n",
    "from CMS_Deep_Learning.utils.meta import msgpack_assertMeta\n",
    "\n",
    "\n",
    "def DeltaRsq(A_Eta, A_Phi, B_Eta, B_Phi):\n",
    "    '''Computes the \n",
    "        #Arguments\n",
    "            #consider N = # of A, M = # of B\n",
    "            A_Eta -- A numpy array of shape (N,) of all the Eta values of a certain\n",
    "                        type of object in a given sample\n",
    "            A_Phi -- A numpy array of shape (N,) of all the Phi values of a certain\n",
    "                        type of object in a given sample\n",
    "            B_Eta -- A numpy array of shape (M,) of all the Eta values of a certain\n",
    "                        type of object in a given sample\n",
    "            B_Phi -- A numpy array of shape (M,) of all the Phi values of a certain\n",
    "                        type of object in a given sample\n",
    "        #Returns\n",
    "            A numpy ndarray of shape (N,M) containing the pairwise squared angular distances  \n",
    "            between two sets of objects of types A and B\n",
    "    '''\n",
    "\n",
    "    B_Eta = B_Eta.reshape(1,B_Eta.shape[-1])\n",
    "    B_Phi = B_Phi.reshape(1,B_Phi.shape[-1])\n",
    "\n",
    "    A_Eta = A_Eta.reshape(A_Eta.shape[-1],1)\n",
    "    A_Phi = A_Phi.reshape(A_Phi.shape[-1],1)\n",
    "\n",
    "    #print(A_Eta.shape, B_Eta.shape)\n",
    "    DeltaEta = A_Eta - B_Eta\n",
    "    DeltaPhi = A_Phi - B_Phi\n",
    "    \n",
    "    tooLarge = -2.*math.pi*(DeltaPhi > math.pi)\n",
    "    tooSmall = 2.*math.pi*(DeltaPhi < -math.pi)\n",
    "    DeltaPhi = DeltaPhi + tooLarge + tooSmall    \n",
    "    delRsq = DeltaEta*DeltaEta+DeltaPhi*DeltaPhi\n",
    "    return delRsq\n",
    "\n",
    "def trackMatch(prtEta, prtPhi, trkEta, trkPhi):\n",
    "    '''Matches a reconstructed particle with its track\n",
    "        #Arguments\n",
    "            #consider N = # of particles, M = # of tracks\n",
    "            prtEta -- A numpy array of shape (N,) of all the Eta values of a certain\n",
    "                        type of reconstructed particle in a given sample\n",
    "            prtPhi -- A numpy array of shape (N,) of all the Phi values of a certain\n",
    "                        type of reconstructed particle in a given sample\n",
    "            trkEta -- A numpy array of shape (M,) of all the Eta values of a track in \n",
    "                        a given sample\n",
    "            trkPhi -- A numpy array of shape (M,) of all the trkPhi values of a track in \n",
    "                        a given sample\n",
    "        #Returns\n",
    "            A numpy array of shape (N,) containing the index of each track corresponding to each\n",
    "            reconstruced particle.\n",
    "    '''\n",
    "    delRsq = DeltaRsq(prtEta, prtPhi, trkEta, trkPhi)\n",
    "    index = np.argmin(delRsq, axis=1)\n",
    "    return index\n",
    "\n",
    "\n",
    "def Iso(A_Eta, A_Phi, A_Pt, B_Eta, B_Phi,maxdist=0.3):\n",
    "    '''Computes the isolation between two object types\n",
    "        #Arguments\n",
    "            #consider N = # of particles in group A, M = # of particles in group B\n",
    "            (A or B)_Eta --  numpy array with shape (N or M,) \n",
    "                                corresponding to the Eta values of the particles\n",
    "            (A or B)_Phi --  numpy array with shape (N or M,) \n",
    "                                corresponding to the Phi values of the particles\n",
    "            (A or B)_Pt --  numpy array with shape (N or M,) \n",
    "                                corresponding to the transverse momentum values of the particles\n",
    "            maxdist -- The maximum cartesian distance between Eta and Phi to be included in the isolation\n",
    "        #Returns\n",
    "            The isolations of each particle in B for each particle in A\n",
    "    '''\n",
    "    DRsq = DeltaRsq(A_Eta, A_Phi, B_Eta, B_Phi)\n",
    "\n",
    "    #Exclude particles in B that are too far away\n",
    "    CloseTracks = DRsq < maxdist*maxdist\n",
    "    DRsq = DRsq * CloseTracks\n",
    "\n",
    "    out = np.sum(DRsq, axis=1, dtype='float64')/A_Pt\n",
    "    return out\n",
    "\n",
    "def fill_object(dicts_by_object,leaves_by_object,entry, start_index,obj, PT_ET_MET, M, others):\n",
    "    '''Fills an object with values for a given entry\n",
    "        #Arguments\n",
    "            dicts_by_object -- A dictionary keyed by object type containing dictionaries of arrays\n",
    "                                keyed by observable type. Arrays are expected to be prefilled with\n",
    "                                zeros\n",
    "            leaves_by_object -- A dictionary keyed by object type, containing dictionaries of tuples \n",
    "                                like (leaf, branch) keyed by observable type. Only valid ROOT \n",
    "                                observables are used as keys. Each (leaf,branch) pair corresponds\n",
    "                                to the leaf and branch of a ROOT observable.\n",
    "            entry -- The entry to in the ROOT file to read from\n",
    "            start_index -- Where to start filling each array in the dictionary dicts_by_object[obj].\n",
    "                            They should all have the same length since each array is a column in a table.\n",
    "            obj -- The type of object we are filling\n",
    "            PT_ET_MET -- The particluar flavor of transverse energy for this kind of object either \n",
    "                        ('PT',  'ET', 'MET')\n",
    "            M -- The mass of this kind of object\n",
    "            others -- Any ROOT observables unique to this kind of object that we should read\n",
    "                                             \n",
    "        #Returns \n",
    "            The number of values filled in for each column of our table\n",
    "            \n",
    "    '''\n",
    "    d = leaves_by_object[obj]\n",
    "    fill_dict = dicts_by_object[obj]\n",
    "    for (leaf, branch) in d.values():\n",
    "        branch.GetEntry(entry)\n",
    "    n_values = d[\"Phi\"][0].GetLen()\n",
    "    # print(obj,n_values)\n",
    "\n",
    "    #start_index = index_by_objects[obj]\n",
    "    lv = ROOT.TLorentzVector()\n",
    "    l_PT = d[PT_ET_MET][0]\n",
    "    l_Eta = d[\"Eta\"][0]\n",
    "    l_Phi = d[\"Phi\"][0]\n",
    "    l_others = [(other, d[other][0]) for other in others]\n",
    "    #lv_leaves = [d[ lorentz_vars[i] ][0] for i in range(lorentz_vars)]\n",
    "    for i in range(n_values):\n",
    "        index = start_index + i\n",
    "        # print(obj,index)\n",
    "        PT = l_PT.GetValue(i)\n",
    "        Eta = l_Eta.GetValue(i)\n",
    "        Phi = l_Phi.GetValue(i)\n",
    "        lv.SetPtEtaPhiM(PT,Eta,Phi, M)\n",
    "        fill_dict[\"Entry\"][index] = entry\n",
    "        fill_dict[\"E/c\"][index] = lv.E()\n",
    "        fill_dict[\"Px\"][index] = lv.Px()\n",
    "        fill_dict[\"Py\"][index] = lv.Py()\n",
    "        fill_dict[\"Pz\"][index] = lv.Pz()\n",
    "        fill_dict[\"PT_ET\"][index] = PT\n",
    "        fill_dict[\"Eta\"][index] = Eta\n",
    "        fill_dict[\"Phi\"][index] = Phi\n",
    "        #print(Phi)\n",
    "        for (other, l_other) in l_others:\n",
    "            #if(obj == \"EFlowTrack\"): print(other, l_other.GetValue(i))\n",
    "            fill_dict[other][index] = l_other.GetValue(i)\n",
    "        # for OUTPUT_OBSERVS\n",
    "    return n_values \n",
    "def fillTrackMatch(dicts_by_object,obj, trackIndicies, prtStart, trackStart):\n",
    "    '''Fills an object with values for a given entry\n",
    "        #Arguments\n",
    "            dicts_by_object -- A dictionary keyed by object type containing dictionaries of arrays\n",
    "                                keyed by observable type. Arrays are expected to be prefilled with\n",
    "                                zeros\n",
    "            obj -- The type of object we are matching with a track\n",
    "            trackIndicies -- The result of trackMatch(). A numpy array of indicies corresponding to\n",
    "                            tracks being matched to particles\n",
    "            prtStart -- Where to start filling in X,Y,Z, and Dxy values for paricles matched to tracks\n",
    "            trackStart -- Where to start reading track values from.\n",
    "                #Note: We need to know where to start because we may not be filling or reading from\n",
    "                        the beginning of each of our table columns.\n",
    "                                             \n",
    "        #Returns(void)\n",
    "            \n",
    "    '''\n",
    "    l = dicts_by_object[obj]\n",
    "    t = dicts_by_object[\"EFlowTrack\"]\n",
    "    lX = l[\"X\"]\n",
    "    lY = l[\"Y\"]\n",
    "    lZ = l[\"Z\"]\n",
    "    lDxy = l[\"Dxy\"]\n",
    "    tX = t[\"X\"]\n",
    "    tY = t[\"Y\"]\n",
    "    tZ = t[\"Z\"]\n",
    "    tDxy = t[\"Dxy\"]\n",
    "    for lepI, trkI in enumerate(trackIndicies):\n",
    "        lepIndex = lepI + prtStart\n",
    "        trkIndex = trkI + trackStart\n",
    "        # print(obj, lepIndex, trkIndex, tX[trkIndex], tY[trkIndex], tZ[trkIndex])\n",
    "        lX[lepIndex] = tX[trkIndex]\n",
    "        lY[lepIndex] = tY[trkIndex]\n",
    "        lZ[lepIndex] = tZ[trkIndex]\n",
    "        lDxy[lepIndex] = tDxy[trkIndex]\n",
    "\n",
    "    \n",
    "def fillIso(dicts_by_object,obj, isoType,  obj_start,iso):\n",
    "    '''Fills an object with values for a given entry\n",
    "        #Arguments\n",
    "            dicts_by_object -- A dictionary keyed by object type containing dictionaries of arrays\n",
    "                                keyed by observable type. Arrays are expected to be prefilled with\n",
    "                                zeros\n",
    "            obj -- The type of object are running isolation on\n",
    "            isoType -- The object type corresponding to the type of isolation we are running\n",
    "            obj_start -- Where to start filling in isolation values\n",
    "                 #Note: We need to know where to start because we may not be filling or reading from\n",
    "                        the beginning of each of our table columns.\n",
    "            iso -- The result of Iso(). A list of isolation values.\n",
    "        #Returns(void)     \n",
    "    '''\n",
    "    isoArr = dicts_by_object[obj][isoType]\n",
    "    # print(type(iso), iso)\n",
    "    for i in range(len(iso)):\n",
    "        isoArr[obj_start+i] = iso[i]\n",
    "\n",
    "def getEtaPhiPTasNumpy(dicts_by_object,obj, start, n_vals):\n",
    "    '''Gets numpy arrays corresponding to all of the Eta, Phi, and Pt values for a given object type\n",
    "            in a given sample. Here start and n_vals specifiy where the sample starts and how long it is. \n",
    "        #Arguments\n",
    "            dicts_by_object -- A dictionary keyed by object type containing dictionaries of arrays\n",
    "                                keyed by observable type. Arrays are expected to be prefilled with\n",
    "                                zeros\n",
    "            obj -- The type of object we are reading from to create our numpy arrays\n",
    "            start -- where to start reading in that object\n",
    "                #Note: We need to know where to start because we may not be filling or reading from\n",
    "                        the beginning of each of our table columns.\n",
    "            n_vals -- How many values to read.\n",
    "        #Returns\n",
    "            Eta, Phi, Pt -- numpy arrays    \n",
    "    '''\n",
    "    d = dicts_by_object[obj]\n",
    "    Eta =  np.array( d[\"Eta\"][start:start+n_vals] )\n",
    "    Phi =  np.array( d[\"Phi\"][start:start+n_vals] )\n",
    "    Pt =  np.array( d[\"PT_ET\"][start:start+n_vals] )\n",
    "    return Eta, Phi, Pt \n",
    "\n",
    "\n",
    "#Masses for electrons and muons\n",
    "mass_of_electron = np.float64(0.0005109989461) #eV/c\n",
    "mass_of_muon = np.float64(0.1056583715) \n",
    "\n",
    "OBJECT_TYPES = ['Electron', 'MuonTight', 'Photon', 'MissingET', 'EFlowPhoton', 'EFlowNeutralHadron', 'EFlowTrack']\n",
    "PT_ET_TYPES  = ['PT',          'PT',       'PT',      'MET',        'ET',           'ET',               'PT', ]\n",
    "EXTRA_FILLS  = [['Charge'], ['Charge'],     [],        [],     ['Ehad', 'Eem'],  ['Ehad', 'Eem'], ['Charge','X', 'Y', 'Z', 'Dxy'],]\n",
    "MASSES =    [mass_of_electron, mass_of_muon, 0,        0,           0,                0,                 0]\n",
    "TRACK_MATCH =   [True,        True,        False,    False,        False,           False,              False]\n",
    "COMPUTE_ISO =   [True,        True,        True,     False,        True,           True,              False]\n",
    "\n",
    "ROOT_OBSERVS =  ['PT', 'ET', 'MET', 'Eta', 'Phi', 'Charge', 'X', 'Y', 'Z', 'Dxy', 'Ehad', 'Eem']\n",
    "OUTPUT_OBSERVS =  ['Entry','E/c', 'Px', 'Py', 'Pz', 'PT_ET','Eta', 'Phi', 'Charge', 'X', 'Y', 'Z',\\\n",
    "                     'Dxy', 'Ehad', 'Eem', 'MuIso', 'EleIso', 'ChHadIso','NeuHadIso','GammaIso']\n",
    "ISO_TYPES = [('MuIso', 'MuonTight'), ('EleIso','Electron'), ('ChHadIso','EFlowTrack') ,('NeuHadIso','EFlowNeutralHadron'),('GammaIso','EFlowPhoton')]\n",
    "\n",
    "def delphes_to_pandas(filepath, verbosity=1):\n",
    "    start_time = time.clock()\n",
    "    fileIN = ROOT.TFile.Open(filepath)\n",
    "    tree = fileIN.Get(\"Delphes\")\n",
    "    n_entries=tree.GetEntries()\n",
    "\n",
    "    tree.SetCacheSize(30*1024*1024)\n",
    "\n",
    "\n",
    "    #Get all the leaves that we need to read and their associated branches\n",
    "    leaves_by_object = {}\n",
    "    for obj in OBJECT_TYPES:\n",
    "        leaves_by_object[obj] = {}\n",
    "        for observ in ROOT_OBSERVS:\n",
    "            leaf = tree.GetLeaf(obj + '.' + observ)\n",
    "            if(isinstance(leaf,ROOT.TLeafElement)):\n",
    "                leaves_by_object[obj][observ] = (leaf, leaf.GetBranch())\n",
    "                #print(leaf.GetBranch())\n",
    "\n",
    "\n",
    "    #Allocate the data for the tables by filling arrays with zeros\n",
    "    dicts_by_object = {}\n",
    "    dicts_by_object[\"NumValues\"] = {}\n",
    "    for obj in OBJECT_TYPES:\n",
    "        dicts_by_object[obj] = {}\n",
    "        # print(obj)\n",
    "        (leaf, branch) = leaves_by_object[obj]['Phi']\n",
    "        total_values = 0\n",
    "\n",
    "        #Loop over all the Phi values (since everything has Phi) and accumilate\n",
    "        #   the total number of values for each object type\n",
    "        for entry in range(n_entries):\n",
    "            branch.GetEntry(entry)\n",
    "            total_values += leaf.GetLen()\n",
    "\n",
    "        #Fill arrays with zeros to avoid reallocating data later\n",
    "        for observ in OUTPUT_OBSERVS:\n",
    "            dicts_by_object[obj][observ] = [0] * total_values\n",
    "        dicts_by_object[\"NumValues\"][obj] = [0] * n_entries\n",
    "        # print(total_values)\n",
    "    \n",
    "\n",
    "    index_by_objects = {o:0 for o in OBJECT_TYPES}\n",
    "    last_time = time.clock()\n",
    "    prev_entry = 0\n",
    "    to_ommit = []\n",
    "    for entry in range(n_entries):\n",
    "\n",
    "        #Make a pretty progress bar in the terminal\n",
    "        if(verbosity > 0):\n",
    "            c = time.clock() \n",
    "            if(c > last_time + .25):\n",
    "                percent = float(entry)/float(n_entries)\n",
    "                sys.stdout.write('\\r')\n",
    "                sys.stdout.write(\"[%-20s] %r/%r  %r(Entry/sec)\" % ('='*int(20*percent), entry, int(n_entries), 4 * (entry-prev_entry)))\n",
    "                sys.stdout.flush()\n",
    "                last_time = c\n",
    "                prev_entry = entry\n",
    "\n",
    "        #Initialize some temporary helper variables\n",
    "        number_by_object = {}\n",
    "        Eta_Phi_PT_by_object = {}\n",
    "\n",
    "        #Fill each type of object with everything that is observable in the ROOT file for that object\n",
    "        #   in addition to Energy and the three components of momentum\n",
    "        for obj, PT_ET_type, mass, extra_fills in zip(OBJECT_TYPES, PT_ET_TYPES, MASSES, EXTRA_FILLS):\n",
    "            start = index_by_objects[obj]\n",
    "            n = fill_object(dicts_by_object,leaves_by_object,entry, start, obj, PT_ET_type, mass, extra_fills)\n",
    "            dicts_by_object[\"NumValues\"][obj][entry] = n\n",
    "            number_by_object[obj] = n\n",
    "            Eta_Phi_PT_by_object[obj] = getEtaPhiPTasNumpy(dicts_by_object,obj, start, n)\n",
    "\n",
    "        #Do Track matching for objects with TRACK_MATCH = True\n",
    "        trkEta, trkPhi, dummy = Eta_Phi_PT_by_object[\"EFlowTrack\"]\n",
    "        start_tracks = index_by_objects[\"EFlowTrack\"]\n",
    "        for obj, ok in zip(OBJECT_TYPES, TRACK_MATCH):\n",
    "            if(ok):\n",
    "                start = index_by_objects[obj]\n",
    "                Phi, Eta, PT = Eta_Phi_PT_by_object[obj]\n",
    "                matches = trackMatch(Phi, Eta, trkEta, trkPhi)\n",
    "                #if(len(matches) > 0 ): print(entry, obj,len(matches))\n",
    "                #print(matches, to_ommit)\n",
    "                to_ommit += [start_tracks + x for x in matches.tolist()]\n",
    "                fillTrackMatch(dicts_by_object,obj, matches, start, start_tracks)\n",
    "\n",
    "        #Compute isolation\n",
    "        for obj, ok in zip(OBJECT_TYPES, COMPUTE_ISO):\n",
    "            start = index_by_objects[obj]\n",
    "            if(ok):\n",
    "                objEta, objPhi, objPt = Eta_Phi_PT_by_object[obj]\n",
    "                for iso_type, iso_obj in ISO_TYPES:\n",
    "                    isoEta, isoPhi, isoPt = Eta_Phi_PT_by_object[iso_obj]\n",
    "                    iso_val = Iso(objEta, objPhi, objPt, isoEta, isoPhi) \n",
    "                    iso_val = iso_val - 1.0 if obj == iso_obj else iso_val\n",
    "                    fillIso(dicts_by_object,obj, iso_type,  start, iso_val)\n",
    "\n",
    "        for obj in OBJECT_TYPES:\n",
    "            index_by_objects[obj] += number_by_object[obj]\n",
    "    pandas_out = {}\n",
    "    for obj,d in dicts_by_object.items():\n",
    "        if(obj == \"NumValues\"):\n",
    "            pandas_out[obj] = pd.DataFrame(d, columns=OBJECT_TYPES)\n",
    "            #print(pandas_out[obj])\n",
    "        else:\n",
    "            pandas_out[obj] = pd.DataFrame(d, columns=OUTPUT_OBSERVS)\n",
    "    \n",
    "    #Remove Tracks that correspond to Electrons and Muons\n",
    "    df = pandas_out[\"EFlowTrack\"]\n",
    "    to_drop = df.iloc[to_ommit]\n",
    "    to_subtract = to_drop[['Entry']].groupby('Entry').agg({'Entry':'count'})\n",
    "    to_subtract = to_subtract.rename(columns={'Entry':'EFlowTrack'})\n",
    "   \n",
    "    #Update the numValues so that they are correct\n",
    "    pandas_out[\"NumValues\"] = pandas_out[\"NumValues\"].sub(to_subtract,fill_value=0.0)\n",
    "    \n",
    "    cleaned = df.drop(df.index[to_ommit]).reset_index(drop=True)\n",
    "    pandas_out[\"EFlowTrack\"] = cleaned\n",
    "    #print(pandas_out[\"EFlowTrack\"])\n",
    "\n",
    "        # pandas_out[\"NumValues\"][obj][]\n",
    "        # if(TRACK_MATCH[OBJECT_TYPES.index(obj)] or obj == \"EFlowTrack\"):\n",
    "        # if(COMPUTE_ISO[OBJECT_TYPES.index(obj)]):\n",
    "            # print(obj)\n",
    "            # print(pandas_out[obj][['MuIso', 'EleIso', 'ChHadIso','NeuHadIso','GammaIso']])\n",
    "\n",
    "    print(\"ElapseTime: %.2f\" % float(time.clock()-start_time))\n",
    "\n",
    "    return pandas_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data/shared/Delphes/wjets_lepFilter_13TeV/wjets_lepFilter_13TeV_183.root\n",
      "[=================== ] 9996/10000  48(Entry/sec)         Entry         E/c        Px        Py          Pz     PT_ET  \\\n",
      "0            0   10.596948 -0.192637 -0.375362  -10.588546  0.421907   \n",
      "1            0    6.517591  0.265998  0.011153   -6.512151  0.266232   \n",
      "2            0   41.186101  0.068642  1.506764  -41.158473  1.508327   \n",
      "3            0    7.151819 -0.325622 -0.179988   -7.142135  0.372055   \n",
      "4            0   15.947530 -0.541896 -0.557196  -15.928578  0.777250   \n",
      "5            0   22.512369  0.205594 -1.110438  -22.484025  1.129310   \n",
      "6            0   14.518618  0.554851 -0.372464  -14.503230  0.668273   \n",
      "7            0   20.206680 -0.537209  0.742090  -20.185901  0.916129   \n",
      "8            0   20.342246 -0.880339 -0.691567  -20.311418  1.119492   \n",
      "9            0    3.598565 -0.150266 -0.142240   -3.592612  0.206911   \n",
      "10           0    9.908190 -0.394587 -0.404369   -9.892068  0.564990   \n",
      "11           0   12.022815 -0.148869 -0.660200  -12.003752  0.676776   \n",
      "12           0   14.582874  0.568308 -0.517140  -14.562617  0.768380   \n",
      "13           0    7.469927  0.399225  0.164462   -7.457438  0.431773   \n",
      "14           0   30.747709  0.686048  1.608959  -30.697918  1.749117   \n",
      "15           0   22.024162 -0.222666  1.223567  -21.989021  1.243662   \n",
      "16           0   13.278635  0.101298  0.759467  -13.256512  0.766193   \n",
      "17           0   11.417673 -0.414096  0.507860  -11.398853  0.655284   \n",
      "18           0    4.422678 -0.158819  0.174458   -4.416381  0.235922   \n",
      "19           0   24.244982 -1.215568  0.564861  -24.207902  1.340401   \n",
      "20           0    0.709294  0.245013 -0.152181   -0.648003  0.288427   \n",
      "21           0    8.908760  0.253465 -0.502127   -8.890986  0.562473   \n",
      "22           0    5.250347  0.165345 -0.305151   -5.238864  0.347068   \n",
      "23           0   11.905741  0.700107 -0.301814  -11.881306  0.762392   \n",
      "24           0   10.266682  0.030557  0.776210  -10.237252  0.776812   \n",
      "25           0   25.762997 -1.102190  1.144105  -25.713969  1.588646   \n",
      "26           0    0.679448  0.281641  0.067736   -0.614605  0.289672   \n",
      "27           0   16.094408 -0.638050 -1.070845  -16.046063  1.246522   \n",
      "28           0   15.914077 -0.120754 -1.324426  -15.858410  1.329919   \n",
      "29           0    4.421466  0.264456 -0.232916   -4.407400  0.352402   \n",
      "...        ...         ...       ...       ...         ...       ...   \n",
      "3652464   9999    0.803375 -0.130916  0.180798    0.771741  0.223220   \n",
      "3652465   9999    5.127143  0.273510  0.398212    5.104333  0.483094   \n",
      "3652466   9999    0.421963  0.212377  0.129249    0.340945  0.248615   \n",
      "3652467   9999    3.093460 -0.180535 -0.180964    3.082881  0.255618   \n",
      "3652468   9999   28.342828  0.241201 -2.466172   28.234300  2.477940   \n",
      "3652469   9999   12.841214  0.305182 -0.923463   12.804330  0.972584   \n",
      "3652470   9999    8.699944  0.532006 -0.532434    8.667324  0.752673   \n",
      "3652471   9999    0.749488 -0.075088 -0.184094    0.722636  0.198819   \n",
      "3652472   9999   18.882643  1.004373  1.106784   18.823402  1.494569   \n",
      "3652473   9999    5.687397 -0.392246  0.256960    5.668033  0.468920   \n",
      "3652474   9999    0.765744  0.064296 -0.750098    0.139935  0.752849   \n",
      "3652475   9999    9.610008 -0.121909  0.606932    9.590048  0.619054   \n",
      "3652476   9999    6.500098 -0.374961 -0.109775    6.488346  0.390700   \n",
      "3652477   9999   10.158909 -0.553517 -0.239290   10.140996  0.603027   \n",
      "3652478   9999   12.555609  0.031917 -0.779074   12.531374  0.779727   \n",
      "3652479   9999    8.721985  0.327489 -0.387236    8.707228  0.507150   \n",
      "3652480   9999    4.503749 -0.246996  0.114312    4.495518  0.272166   \n",
      "3652481   9999   28.637346 -1.386425  0.658891   28.596175  1.535028   \n",
      "3652482   9999   10.270759 -0.403428 -0.339140   10.257228  0.527039   \n",
      "3652483   9999  110.807584 -2.454660 -4.304863  110.696718  4.955523   \n",
      "3652484   9999   19.967515  0.751668  0.451561   19.948251  0.876876   \n",
      "3652485   9999   10.596752  0.314483  0.412217   10.584060  0.518481   \n",
      "3652486   9999    6.602267  0.092273  0.280127    6.595676  0.294933   \n",
      "3652487   9999   32.707162 -1.611539  0.309543   32.665969  1.640998   \n",
      "3652488   9999   15.785294 -0.701108  0.278538   15.767257  0.754411   \n",
      "3652489   9999   11.614261 -0.428122 -0.205098   11.604556  0.474714   \n",
      "3652490   9999   13.681770  0.394498 -0.393894   13.670408  0.557477   \n",
      "3652491   9999   15.401284  0.321136  0.590606   15.386604  0.672268   \n",
      "3652492   9999   16.916742 -0.132542  0.680634   16.902524  0.693419   \n",
      "3652493   9999   17.476381 -0.464731  0.508171   17.462809  0.688631   \n",
      "\n",
      "              Eta       Phi  Charge          X           Y            Z  \\\n",
      "0       -3.916287 -2.044950       1  -0.002498    0.001882    23.702751   \n",
      "1       -3.890623  0.041904       1   0.000000    0.000000   -18.776846   \n",
      "2       -3.999912  1.525272      -1   0.000000    0.000000    26.633217   \n",
      "3       -3.648549 -2.636640       1   0.000000    0.000000   -47.712811   \n",
      "4       -3.713850 -2.342275      -1   0.000000    0.000000    43.187889   \n",
      "5       -3.684976 -1.387723      -1   0.000000    0.000000    23.852568   \n",
      "6       -3.771107 -0.591195      -1   0.419270   -0.452859     5.449513   \n",
      "7       -3.786245  2.197392       1   0.000000    0.000000    26.633217   \n",
      "8       -3.592214 -2.475713      -1   0.000000    0.000000    22.033371   \n",
      "9       -3.548322 -2.383629      -1 -42.123569  -55.233223 -1059.964844   \n",
      "10      -3.556643 -2.343951      -1   0.000000    0.000000    25.011456   \n",
      "11      -3.569575 -1.792578      -1   0.000000    0.000000    26.569407   \n",
      "12      -3.635771 -0.738293      -1   0.000000    0.000000    23.852568   \n",
      "13      -3.543050  0.390768      -1   0.000000    0.000000    23.852568   \n",
      "14      -3.559042  1.167747       1   0.000000    0.000000   -18.776846   \n",
      "15      -3.566429  1.750808       1   0.000000    0.000000    23.852568   \n",
      "16      -3.544791  1.438198      -1   0.000000    0.000000    23.852568   \n",
      "17      -3.550171  2.254842       1   0.000000    0.000000    23.852568   \n",
      "18      -3.623433  2.309304      -1   0.000000    0.000000    22.033371   \n",
      "19      -3.587623  2.706591       1   0.000000    0.000000    23.852568   \n",
      "20      -1.548809 -0.555800       1   0.000000    0.000000    26.633217   \n",
      "21      -3.454597 -1.103330      -1   0.000000    0.000000    23.852568   \n",
      "22      -3.408583 -1.074235      -1   0.000000    0.000000    23.852568   \n",
      "23      -3.440436 -0.407024      -1   0.419270   -0.452859     5.449513   \n",
      "24      -3.273174  1.531450      -1  39.509022  183.616608 -2562.248779   \n",
      "25      -3.478252  2.337537      -1   0.000000    0.000000    23.852568   \n",
      "26      -1.496784  0.236024       1   0.000000    0.000000   -18.776846   \n",
      "27      -3.249759 -2.108150       1   0.000000    0.000000    23.852568   \n",
      "28      -3.173482 -1.661719      -1   0.000000    0.000000    23.852568   \n",
      "29      -3.221010 -0.722070      -1   0.000000    0.000000    14.706533   \n",
      "...           ...       ...     ...        ...         ...          ...   \n",
      "3652464  1.953927  2.197516       1   0.000000    0.000000   -88.228485   \n",
      "3652465  3.053012  0.968954       1   0.000000    0.000000   -34.584671   \n",
      "3652466  1.121232  0.546709      -1   0.000000    0.000000   -88.228485   \n",
      "3652467  3.184795 -2.355007       1   0.000000    0.000000   -31.674393   \n",
      "3652468  3.128177 -1.473302      -1   0.000000    0.000000    39.499233   \n",
      "3652469  3.272168 -1.251620       1   0.000000    0.000000   -12.913421   \n",
      "3652470  3.138711 -0.785800       1   0.000000    0.000000    66.719681   \n",
      "3652471  2.002069 -1.958076      -1   0.000000    0.000000   -31.674393   \n",
      "3652472  3.227983  0.833870      -1   0.000000    0.000000    66.719681   \n",
      "3652473  3.187019  2.561641       1   0.000000    0.000000    66.719681   \n",
      "3652474  0.184820 -1.485288      -1   0.000000    0.000000   -34.584671   \n",
      "3652475  3.434475  1.769020       1   0.000000    0.000000     0.097110   \n",
      "3652476  3.503876 -2.856787       1   0.000000    0.000000   -88.228485   \n",
      "3652477  3.516410 -2.733549      -1   0.000000    0.000000    -4.184634   \n",
      "3652478  3.471160 -1.529852      -1   0.000000    0.000000    39.499233   \n",
      "3652479  3.537096 -0.868798      -1   0.000000    0.000000   -12.913421   \n",
      "3652480  3.498487  2.708138       1   0.000000    0.000000   -12.913421   \n",
      "3652481  3.618591  2.697945      -1   0.000000    0.000000   -88.228485   \n",
      "3652482  3.662270 -2.442556       1   0.000000    0.000000   116.032997   \n",
      "3652483  3.799939 -2.089020       1   0.000000    0.000000     0.097110   \n",
      "3652484  3.818161  0.540967      -1   0.000000    0.000000   -63.990974   \n",
      "3652485  3.709947  0.919086      -1   0.000000    0.000000     0.097110   \n",
      "3652486  3.801069  1.252592      -1   0.000000    0.000000   -63.990974   \n",
      "3652487  3.684807  2.951825       1   0.000000    0.000000   -88.228485   \n",
      "3652488  3.733473  2.763431      -1   0.000000    0.000000    27.222816   \n",
      "3652489  3.890006 -2.694834       1   0.000000    0.000000   -77.140495   \n",
      "3652490  3.893130 -0.784632      -1   0.000000    0.000000   -12.913421   \n",
      "3652491  3.824220  1.072772      -1   0.000000    0.000000     0.097110   \n",
      "3652492  3.887151  1.763122      -1   0.000000    0.000000   -77.140495   \n",
      "3652493  3.926659  2.311574      -1   0.000000    0.000000    39.499233   \n",
      "\n",
      "                  Dxy  Ehad  Eem  MuIso  EleIso  ChHadIso  NeuHadIso  GammaIso  \n",
      "0        3.082202e-03     0    0      0       0         0          0         0  \n",
      "1        0.000000e+00     0    0      0       0         0          0         0  \n",
      "2        2.586684e-13     0    0      0       0         0          0         0  \n",
      "3        2.768631e-14     0    0      0       0         0          0         0  \n",
      "4       -2.222425e-14     0    0      0       0         0          0         0  \n",
      "5        2.713975e-13     0    0      0       0         0          0         0  \n",
      "6        1.419810e-01     0    0      0       0         0          0         0  \n",
      "7       -9.706472e-14     0    0      0       0         0          0         0  \n",
      "8       -3.435068e-14     0    0      0       0         0          0         0  \n",
      "9       -2.166541e+01     0    0      0       0         0          0         0  \n",
      "10       4.115817e-14     0    0      0       0         0          0         0  \n",
      "11       9.617573e-14     0    0      0       0         0          0         0  \n",
      "12      -1.140532e-13     0    0      0       0         0          0         0  \n",
      "13       6.514155e-14     0    0      0       0         0          0         0  \n",
      "14      -3.081865e-13     0    0      0       0         0          0         0  \n",
      "15       5.377074e-14     0    0      0       0         0          0         0  \n",
      "16      -4.992358e-14     0    0      0       0         0          0         0  \n",
      "17       5.109019e-14     0    0      0       0         0          0         0  \n",
      "18       6.877985e-15     0    0      0       0         0          0         0  \n",
      "19      -3.217190e-14     0    0      0       0         0          0         0  \n",
      "20       7.642860e-15     0    0      0       0         0          0         0  \n",
      "21      -8.176166e-14     0    0      0       0         0          0         0  \n",
      "22      -3.356334e-14     0    0      0       0         0          0         0  \n",
      "23       2.496301e-01     0    0      0       0         0          0         0  \n",
      "24       4.819390e+00     0    0      0       0         0          0         0  \n",
      "25      -4.347941e-14     0    0      0       0         0          0         0  \n",
      "26       2.969412e-15     0    0      0       0         0          0         0  \n",
      "27      -4.087088e-14     0    0      0       0         0          0         0  \n",
      "28      -6.210827e-14     0    0      0       0         0          0         0  \n",
      "29      -2.365909e-14     0    0      0       0         0          0         0  \n",
      "...               ...   ...  ...    ...     ...       ...        ...       ...  \n",
      "3652464  1.822283e-14     0    0      0       0         0          0         0  \n",
      "3652465  1.550899e-14     0    0      0       0         0          0         0  \n",
      "3652466 -3.052843e-14     0    0      0       0         0          0         0  \n",
      "3652467 -2.279800e-14     0    0      0       0         0          0         0  \n",
      "3652468 -6.783922e-13     0    0      0       0         0          0         0  \n",
      "3652469  8.915530e-15     0    0      0       0         0          0         0  \n",
      "3652470  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652471  1.270998e-14     0    0      0       0         0          0         0  \n",
      "3652472  1.481048e-13     0    0      0       0         0          0         0  \n",
      "3652473  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652474  6.555353e-14     0    0      0       0         0          0         0  \n",
      "3652475  7.676988e-14     0    0      0       0         0          0         0  \n",
      "3652476  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652477 -1.088735e-14     0    0      0       0         0          0         0  \n",
      "3652478 -4.452436e-14     0    0      0       0         0          0         0  \n",
      "3652479  2.204399e-14     0    0      0       0         0          0         0  \n",
      "3652480  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652481  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652482  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652483  4.714523e-13     0    0      0       0         0          0         0  \n",
      "3652484  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652485  3.870557e-14     0    0      0       0         0          0         0  \n",
      "3652486 -3.057201e-15     0    0      0       0         0          0         0  \n",
      "3652487  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3652488 -1.487456e-14     0    0      0       0         0          0         0  \n",
      "3652489  7.710557e-15     0    0      0       0         0          0         0  \n",
      "3652490 -2.341202e-14     0    0      0       0         0          0         0  \n",
      "3652491 -6.071969e-14     0    0      0       0         0          0         0  \n",
      "3652492  5.080291e-14     0    0      0       0         0          0         0  \n",
      "3652493  0.000000e+00     0    0      0       0         0          0         0  \n",
      "\n",
      "[3652494 rows x 20 columns]\n",
      "         Entry         E/c        Px        Py          Pz     PT_ET  \\\n",
      "0            0   10.596948 -0.192637 -0.375362  -10.588546  0.421907   \n",
      "1            0    6.517591  0.265998  0.011153   -6.512151  0.266232   \n",
      "2            0   41.186101  0.068642  1.506764  -41.158473  1.508327   \n",
      "3            0    7.151819 -0.325622 -0.179988   -7.142135  0.372055   \n",
      "4            0   15.947530 -0.541896 -0.557196  -15.928578  0.777250   \n",
      "5            0   22.512369  0.205594 -1.110438  -22.484025  1.129310   \n",
      "6            0   14.518618  0.554851 -0.372464  -14.503230  0.668273   \n",
      "7            0   20.206680 -0.537209  0.742090  -20.185901  0.916129   \n",
      "8            0   20.342246 -0.880339 -0.691567  -20.311418  1.119492   \n",
      "9            0    3.598565 -0.150266 -0.142240   -3.592612  0.206911   \n",
      "10           0    9.908190 -0.394587 -0.404369   -9.892068  0.564990   \n",
      "11           0   12.022815 -0.148869 -0.660200  -12.003752  0.676776   \n",
      "12           0   14.582874  0.568308 -0.517140  -14.562617  0.768380   \n",
      "13           0    7.469927  0.399225  0.164462   -7.457438  0.431773   \n",
      "14           0   30.747709  0.686048  1.608959  -30.697918  1.749117   \n",
      "15           0   22.024162 -0.222666  1.223567  -21.989021  1.243662   \n",
      "16           0   13.278635  0.101298  0.759467  -13.256512  0.766193   \n",
      "17           0   11.417673 -0.414096  0.507860  -11.398853  0.655284   \n",
      "18           0    4.422678 -0.158819  0.174458   -4.416381  0.235922   \n",
      "19           0   24.244982 -1.215568  0.564861  -24.207902  1.340401   \n",
      "20           0    0.709294  0.245013 -0.152181   -0.648003  0.288427   \n",
      "21           0    8.908760  0.253465 -0.502127   -8.890986  0.562473   \n",
      "22           0    5.250347  0.165345 -0.305151   -5.238864  0.347068   \n",
      "23           0   11.905741  0.700107 -0.301814  -11.881306  0.762392   \n",
      "24           0   10.266682  0.030557  0.776210  -10.237252  0.776812   \n",
      "25           0   25.762997 -1.102190  1.144105  -25.713969  1.588646   \n",
      "26           0    0.679448  0.281641  0.067736   -0.614605  0.289672   \n",
      "27           0   16.094408 -0.638050 -1.070845  -16.046063  1.246522   \n",
      "28           0   15.914077 -0.120754 -1.324426  -15.858410  1.329919   \n",
      "29           0    4.421466  0.264456 -0.232916   -4.407400  0.352402   \n",
      "...        ...         ...       ...       ...         ...       ...   \n",
      "3647212   9999    0.803375 -0.130916  0.180798    0.771741  0.223220   \n",
      "3647213   9999    5.127143  0.273510  0.398212    5.104333  0.483094   \n",
      "3647214   9999    0.421963  0.212377  0.129249    0.340945  0.248615   \n",
      "3647215   9999    3.093460 -0.180535 -0.180964    3.082881  0.255618   \n",
      "3647216   9999   28.342828  0.241201 -2.466172   28.234300  2.477940   \n",
      "3647217   9999   12.841214  0.305182 -0.923463   12.804330  0.972584   \n",
      "3647218   9999    8.699944  0.532006 -0.532434    8.667324  0.752673   \n",
      "3647219   9999    0.749488 -0.075088 -0.184094    0.722636  0.198819   \n",
      "3647220   9999   18.882643  1.004373  1.106784   18.823402  1.494569   \n",
      "3647221   9999    5.687397 -0.392246  0.256960    5.668033  0.468920   \n",
      "3647222   9999    0.765744  0.064296 -0.750098    0.139935  0.752849   \n",
      "3647223   9999    9.610008 -0.121909  0.606932    9.590048  0.619054   \n",
      "3647224   9999    6.500098 -0.374961 -0.109775    6.488346  0.390700   \n",
      "3647225   9999   10.158909 -0.553517 -0.239290   10.140996  0.603027   \n",
      "3647226   9999   12.555609  0.031917 -0.779074   12.531374  0.779727   \n",
      "3647227   9999    8.721985  0.327489 -0.387236    8.707228  0.507150   \n",
      "3647228   9999    4.503749 -0.246996  0.114312    4.495518  0.272166   \n",
      "3647229   9999   28.637346 -1.386425  0.658891   28.596175  1.535028   \n",
      "3647230   9999   10.270759 -0.403428 -0.339140   10.257228  0.527039   \n",
      "3647231   9999  110.807584 -2.454660 -4.304863  110.696718  4.955523   \n",
      "3647232   9999   19.967515  0.751668  0.451561   19.948251  0.876876   \n",
      "3647233   9999   10.596752  0.314483  0.412217   10.584060  0.518481   \n",
      "3647234   9999    6.602267  0.092273  0.280127    6.595676  0.294933   \n",
      "3647235   9999   32.707162 -1.611539  0.309543   32.665969  1.640998   \n",
      "3647236   9999   15.785294 -0.701108  0.278538   15.767257  0.754411   \n",
      "3647237   9999   11.614261 -0.428122 -0.205098   11.604556  0.474714   \n",
      "3647238   9999   13.681770  0.394498 -0.393894   13.670408  0.557477   \n",
      "3647239   9999   15.401284  0.321136  0.590606   15.386604  0.672268   \n",
      "3647240   9999   16.916742 -0.132542  0.680634   16.902524  0.693419   \n",
      "3647241   9999   17.476381 -0.464731  0.508171   17.462809  0.688631   \n",
      "\n",
      "              Eta       Phi  Charge          X           Y            Z  \\\n",
      "0       -3.916287 -2.044950       1  -0.002498    0.001882    23.702751   \n",
      "1       -3.890623  0.041904       1   0.000000    0.000000   -18.776846   \n",
      "2       -3.999912  1.525272      -1   0.000000    0.000000    26.633217   \n",
      "3       -3.648549 -2.636640       1   0.000000    0.000000   -47.712811   \n",
      "4       -3.713850 -2.342275      -1   0.000000    0.000000    43.187889   \n",
      "5       -3.684976 -1.387723      -1   0.000000    0.000000    23.852568   \n",
      "6       -3.771107 -0.591195      -1   0.419270   -0.452859     5.449513   \n",
      "7       -3.786245  2.197392       1   0.000000    0.000000    26.633217   \n",
      "8       -3.592214 -2.475713      -1   0.000000    0.000000    22.033371   \n",
      "9       -3.548322 -2.383629      -1 -42.123569  -55.233223 -1059.964844   \n",
      "10      -3.556643 -2.343951      -1   0.000000    0.000000    25.011456   \n",
      "11      -3.569575 -1.792578      -1   0.000000    0.000000    26.569407   \n",
      "12      -3.635771 -0.738293      -1   0.000000    0.000000    23.852568   \n",
      "13      -3.543050  0.390768      -1   0.000000    0.000000    23.852568   \n",
      "14      -3.559042  1.167747       1   0.000000    0.000000   -18.776846   \n",
      "15      -3.566429  1.750808       1   0.000000    0.000000    23.852568   \n",
      "16      -3.544791  1.438198      -1   0.000000    0.000000    23.852568   \n",
      "17      -3.550171  2.254842       1   0.000000    0.000000    23.852568   \n",
      "18      -3.623433  2.309304      -1   0.000000    0.000000    22.033371   \n",
      "19      -3.587623  2.706591       1   0.000000    0.000000    23.852568   \n",
      "20      -1.548809 -0.555800       1   0.000000    0.000000    26.633217   \n",
      "21      -3.454597 -1.103330      -1   0.000000    0.000000    23.852568   \n",
      "22      -3.408583 -1.074235      -1   0.000000    0.000000    23.852568   \n",
      "23      -3.440436 -0.407024      -1   0.419270   -0.452859     5.449513   \n",
      "24      -3.273174  1.531450      -1  39.509022  183.616608 -2562.248779   \n",
      "25      -3.478252  2.337537      -1   0.000000    0.000000    23.852568   \n",
      "26      -1.496784  0.236024       1   0.000000    0.000000   -18.776846   \n",
      "27      -3.249759 -2.108150       1   0.000000    0.000000    23.852568   \n",
      "28      -3.173482 -1.661719      -1   0.000000    0.000000    23.852568   \n",
      "29      -3.221010 -0.722070      -1   0.000000    0.000000    14.706533   \n",
      "...           ...       ...     ...        ...         ...          ...   \n",
      "3647212  1.953927  2.197516       1   0.000000    0.000000   -88.228485   \n",
      "3647213  3.053012  0.968954       1   0.000000    0.000000   -34.584671   \n",
      "3647214  1.121232  0.546709      -1   0.000000    0.000000   -88.228485   \n",
      "3647215  3.184795 -2.355007       1   0.000000    0.000000   -31.674393   \n",
      "3647216  3.128177 -1.473302      -1   0.000000    0.000000    39.499233   \n",
      "3647217  3.272168 -1.251620       1   0.000000    0.000000   -12.913421   \n",
      "3647218  3.138711 -0.785800       1   0.000000    0.000000    66.719681   \n",
      "3647219  2.002069 -1.958076      -1   0.000000    0.000000   -31.674393   \n",
      "3647220  3.227983  0.833870      -1   0.000000    0.000000    66.719681   \n",
      "3647221  3.187019  2.561641       1   0.000000    0.000000    66.719681   \n",
      "3647222  0.184820 -1.485288      -1   0.000000    0.000000   -34.584671   \n",
      "3647223  3.434475  1.769020       1   0.000000    0.000000     0.097110   \n",
      "3647224  3.503876 -2.856787       1   0.000000    0.000000   -88.228485   \n",
      "3647225  3.516410 -2.733549      -1   0.000000    0.000000    -4.184634   \n",
      "3647226  3.471160 -1.529852      -1   0.000000    0.000000    39.499233   \n",
      "3647227  3.537096 -0.868798      -1   0.000000    0.000000   -12.913421   \n",
      "3647228  3.498487  2.708138       1   0.000000    0.000000   -12.913421   \n",
      "3647229  3.618591  2.697945      -1   0.000000    0.000000   -88.228485   \n",
      "3647230  3.662270 -2.442556       1   0.000000    0.000000   116.032997   \n",
      "3647231  3.799939 -2.089020       1   0.000000    0.000000     0.097110   \n",
      "3647232  3.818161  0.540967      -1   0.000000    0.000000   -63.990974   \n",
      "3647233  3.709947  0.919086      -1   0.000000    0.000000     0.097110   \n",
      "3647234  3.801069  1.252592      -1   0.000000    0.000000   -63.990974   \n",
      "3647235  3.684807  2.951825       1   0.000000    0.000000   -88.228485   \n",
      "3647236  3.733473  2.763431      -1   0.000000    0.000000    27.222816   \n",
      "3647237  3.890006 -2.694834       1   0.000000    0.000000   -77.140495   \n",
      "3647238  3.893130 -0.784632      -1   0.000000    0.000000   -12.913421   \n",
      "3647239  3.824220  1.072772      -1   0.000000    0.000000     0.097110   \n",
      "3647240  3.887151  1.763122      -1   0.000000    0.000000   -77.140495   \n",
      "3647241  3.926659  2.311574      -1   0.000000    0.000000    39.499233   \n",
      "\n",
      "                  Dxy  Ehad  Eem  MuIso  EleIso  ChHadIso  NeuHadIso  GammaIso  \n",
      "0        3.082202e-03     0    0      0       0         0          0         0  \n",
      "1        0.000000e+00     0    0      0       0         0          0         0  \n",
      "2        2.586684e-13     0    0      0       0         0          0         0  \n",
      "3        2.768631e-14     0    0      0       0         0          0         0  \n",
      "4       -2.222425e-14     0    0      0       0         0          0         0  \n",
      "5        2.713975e-13     0    0      0       0         0          0         0  \n",
      "6        1.419810e-01     0    0      0       0         0          0         0  \n",
      "7       -9.706472e-14     0    0      0       0         0          0         0  \n",
      "8       -3.435068e-14     0    0      0       0         0          0         0  \n",
      "9       -2.166541e+01     0    0      0       0         0          0         0  \n",
      "10       4.115817e-14     0    0      0       0         0          0         0  \n",
      "11       9.617573e-14     0    0      0       0         0          0         0  \n",
      "12      -1.140532e-13     0    0      0       0         0          0         0  \n",
      "13       6.514155e-14     0    0      0       0         0          0         0  \n",
      "14      -3.081865e-13     0    0      0       0         0          0         0  \n",
      "15       5.377074e-14     0    0      0       0         0          0         0  \n",
      "16      -4.992358e-14     0    0      0       0         0          0         0  \n",
      "17       5.109019e-14     0    0      0       0         0          0         0  \n",
      "18       6.877985e-15     0    0      0       0         0          0         0  \n",
      "19      -3.217190e-14     0    0      0       0         0          0         0  \n",
      "20       7.642860e-15     0    0      0       0         0          0         0  \n",
      "21      -8.176166e-14     0    0      0       0         0          0         0  \n",
      "22      -3.356334e-14     0    0      0       0         0          0         0  \n",
      "23       2.496301e-01     0    0      0       0         0          0         0  \n",
      "24       4.819390e+00     0    0      0       0         0          0         0  \n",
      "25      -4.347941e-14     0    0      0       0         0          0         0  \n",
      "26       2.969412e-15     0    0      0       0         0          0         0  \n",
      "27      -4.087088e-14     0    0      0       0         0          0         0  \n",
      "28      -6.210827e-14     0    0      0       0         0          0         0  \n",
      "29      -2.365909e-14     0    0      0       0         0          0         0  \n",
      "...               ...   ...  ...    ...     ...       ...        ...       ...  \n",
      "3647212  1.822283e-14     0    0      0       0         0          0         0  \n",
      "3647213  1.550899e-14     0    0      0       0         0          0         0  \n",
      "3647214 -3.052843e-14     0    0      0       0         0          0         0  \n",
      "3647215 -2.279800e-14     0    0      0       0         0          0         0  \n",
      "3647216 -6.783922e-13     0    0      0       0         0          0         0  \n",
      "3647217  8.915530e-15     0    0      0       0         0          0         0  \n",
      "3647218  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647219  1.270998e-14     0    0      0       0         0          0         0  \n",
      "3647220  1.481048e-13     0    0      0       0         0          0         0  \n",
      "3647221  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647222  6.555353e-14     0    0      0       0         0          0         0  \n",
      "3647223  7.676988e-14     0    0      0       0         0          0         0  \n",
      "3647224  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647225 -1.088735e-14     0    0      0       0         0          0         0  \n",
      "3647226 -4.452436e-14     0    0      0       0         0          0         0  \n",
      "3647227  2.204399e-14     0    0      0       0         0          0         0  \n",
      "3647228  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647229  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647230  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647231  4.714523e-13     0    0      0       0         0          0         0  \n",
      "3647232  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647233  3.870557e-14     0    0      0       0         0          0         0  \n",
      "3647234 -3.057201e-15     0    0      0       0         0          0         0  \n",
      "3647235  0.000000e+00     0    0      0       0         0          0         0  \n",
      "3647236 -1.487456e-14     0    0      0       0         0          0         0  \n",
      "3647237  7.710557e-15     0    0      0       0         0          0         0  \n",
      "3647238 -2.341202e-14     0    0      0       0         0          0         0  \n",
      "3647239 -6.071969e-14     0    0      0       0         0          0         0  \n",
      "3647240  5.080291e-14     0    0      0       0         0          0         0  \n",
      "3647241  0.000000e+00     0    0      0       0         0          0         0  \n",
      "\n",
      "[3647242 rows x 20 columns]\n",
      "ElapseTime: 233.27\n"
     ]
    }
   ],
   "source": [
    "import sys, os\n",
    "#if __package__ is None:#\n",
    "#    sys.path.append(os.path.realpath(\"../\"))\n",
    "#    sys.path.append(os.path.realpath(\"../../\"))\n",
    "#from utils.delphes_parse import delphes_to_pandas\n",
    "print(os.path.realpath(\"/data/shared/Delphes/wjets_lepFilter_13TeV/wjets_lepFilter_13TeV_183.root\"))\n",
    "df = delphes_to_pandas(os.path.realpath(\"/data/shared/Delphes/wjets_lepFilter_13TeV/wjets_lepFilter_13TeV_183.root\"))\n",
    "#print(df)\n",
    "#print(to_subtract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3652494 > 3647242"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
    "\n",
    "if __package__ is None:\n",
    "    import sys, os\n",
    "    sys.path.append(os.path.realpath(\"../\"))\n",
    "from CMS_Deep_Learning.preprocessing.pandas_to_numpy import pandas_to_numpy,make_datasets\n",
    "import numpy as np\n",
    "from CMS_Deep_Learning.io import simple_grab\n",
    "data_dirs = ['/bigdata/shared/Delphes/REDUCED_IsoLep/qcd_lepFilter_13TeV/',\n",
    "             '/bigdata/shared/Delphes/REDUCED_IsoLep/ttbar_lepFilter_13TeV/',\n",
    "             '/bigdata/shared/Delphes/REDUCED_IsoLep/wjets_lepFilter_13TeV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[=============       ] 2/3  0(Event/sec)"
     ]
    }
   ],
   "source": [
    "\n",
    "particles, _, hlf = pandas_to_numpy(data_dirs,0,1,sort_columns=['MaxLepDeltaR'], hlf_mean=np.arange(1,7),hlf_std=np.arange(1,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    -1.             38.90494156  14281.85660608     -1.68222219     -1.\n",
      "      -1.        ]\n",
      " [   154.21569824      4.31845236   1229.93966712     -1.57948351     -0.6\n",
      "      -0.5       ]\n",
      " [    -1.              6.27732658    442.71069831     -0.50029451     -1.\n",
      "      -1.        ]]\n"
     ]
    }
   ],
   "source": [
    "np.set_printoptions(threshold=1000000,suppress=True)\n",
    "#print(particles)\n",
    "print(hlf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_datasets(data_dirs, '/bigdata/shared/Delphes/testdata', 20000,sort_on='MaxLepDeltaR',standardize=True,force=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = simple_grab('X',input_keys=['Particles','HLF'], data='/bigdata/shared/Delphes/np_datasets/3_way/random/val/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "particles,hlf = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "     -4.93702507    -0.51094699     0.             0.             1.             0.\n",
      "      0.             0.             0.             0.             1.             0.\n",
      "      0.             0.        ]\n",
      " [    3.86593389     0.30148353    -0.95807448     3.73318173\n",
      "      1.00438988     2.0236516     -1.26592982     0.             0.            -1.\n",
      "      0.             0.             0.             1.             0.             0.\n",
      "      0.             0.             0.        ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             1.             0.             0.\n",
      "      0.        ]\n",
      " [   61.11684441     1.34756774    -3.67113163    60.99160208\n",
      "      3.91064525     3.44120717    -1.21899474     0.             0.99653224\n",
      "      1.39102301     0.39102301     0.             0.             0.             0.\n",
      "      1.             0.             0.             0.        ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             1.             0.             0.\n",
      "      0.        ]\n",
      " [   21.07626297     0.24465649    -1.36093455    21.03085498\n",
      "      1.38275075     3.41614199    -1.39292526     1.             1.81835613\n",
      "      3.93404055     3.93404055     1.             0.             0.             0.\n",
      "      0.             0.             0.            84.2771225 ]\n",
      " [    1.50531083     0.77859497     1.28481963    -0.09481074\n",
      "      1.50232208    -0.06306764     1.02598        1.             0.             0.\n",
      "      0.             1.             0.             0.             0.             0.\n",
      "      0.             0.            84.2771225 ]\n",
      " [    5.03865997    -0.68939422    -0.22226368    -4.98632417     0.724338\n",
      "     -2.62757754    -2.82971025    -1.             0.             2.11550302\n",
      "      2.11550302     1.             0.             0.             0.             0.\n",
      "      0.             0.            84.2771225 ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.             1.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [   95.64778095     0.02083093    -2.54196143    95.61399478\n",
      "      2.54204679     4.32067347    -1.56260169     0.             0.\n",
      "      1.40396626     0.40396626     0.             0.             0.             0.\n",
      "      1.             0.             0.             0.        ]\n",
      " [    2.31886667    -0.17689652     0.89198045    -2.13312474\n",
      "      0.90935224    -1.58837426     1.76657486     1.             6.27549745\n",
      "      3.36181166     3.36181166     1.             0.             0.             0.\n",
      "      0.             0.             0.           -84.30102539]\n",
      " [    1.20380133     0.09238026     0.90672146    -0.7864221      0.91141534\n",
      "     -0.78100348     1.46926284    -1.             3.49084318     0.             0.\n",
      "      1.             0.             0.             0.             0.             0.\n",
      "      0.            84.2771225 ]\n",
      " [   59.73416192    -2.18598756    -0.27453108    59.69351884\n",
      "      2.20315886     3.99281883    -3.01665998    -1.             0.\n",
      "      0.52758449     0.52758449     1.             0.             0.             0.\n",
      "      0.           -81.00671387   -11.82653141  2155.50537109]\n",
      " [  124.71008983   -26.63637666    17.54103164   120.56293855\n",
      "     31.89332771     2.03997517     2.55923963     1.             0.12101845\n",
      "      0.             0.             1.             0.             0.             0.\n",
      "      0.            -0.709535       0.47757125   -81.16451263]\n",
      " [    8.07985255    -0.71907422     1.27648779    -7.94591269\n",
      "      1.46509004    -2.39228106     2.08381033     0.             0.             1.\n",
      "      0.             0.             0.             0.             0.             1.\n",
      "      0.             0.             0.        ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.             1.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [   12.66130905    -0.54322392     0.19336243    12.64817243\n",
      "      0.57661188     3.78176498     2.79962397     1.             3.90444785\n",
      "      2.01023988     2.01023988     1.             0.             0.             0.\n",
      "      0.             0.             0.            84.2771225 ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             1.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.             1.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             1.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             1.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [    3.43380885     2.08373557     2.04725824    -1.80494404\n",
      "      2.92116761    -0.58410007     0.77656823     1.             1.29690412\n",
      "      0.             0.             1.             0.             0.             0.\n",
      "      0.             0.             0.            84.2771225 ]\n",
      " [    1.0437239      0.54626126    -0.87347163    -0.16734854\n",
      "      1.03022039    -0.16173354    -1.01191592     0.             9.43912624\n",
      "      2.51350161     3.51350161     0.             0.             1.             0.\n",
      "      0.             0.             0.             0.        ]\n",
      " [   18.87922941     0.50878201     1.03153274    18.84416048\n",
      "      1.15018213     3.49036002     1.11258006    -1.             2.61646474\n",
      "      4.87965945     4.87965945     1.             0.             0.             0.\n",
      "      0.             0.             0.           -76.53231049]\n",
      " [    2.92375102     1.52175067    -2.39792092    -0.69467271\n",
      "      2.84002638    -0.24222516    -1.00531447     1.             2.05383868\n",
      "      0.50462414     0.50462414     1.             0.             0.             0.\n",
      "      0.             0.             0.           -84.30102539]\n",
      " [    1.29158863    -0.95892116    -0.13024298    -0.85539942\n",
      "      0.96772569    -0.79687256    -3.00659633     1.             2.83384801\n",
      "      0.             0.             1.             0.             0.             0.\n",
      "      0.             0.             0.           -42.99913788]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             1.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [    1.32016655     0.2606064     -0.50439776    -1.19185021     0.5677436\n",
      "     -1.48717129    -1.09390306    -1.             0.             0.             0.\n",
      "      1.             0.             0.             0.             0.             0.\n",
      "      0.           -84.30102539]\n",
      " [    0.             0.             0.             0.             0.             0.\n",
      "      0.             0.             0.             0.             0.             1.\n",
      "      0.             0.             0.             0.             0.             0.\n",
      "      0.        ]\n",
      " [    1.40466369    -1.02689562    -0.25891966     0.92278171\n",
      "      1.05903447     0.78741366    -2.89460254     0.             3.22284815\n",
      "      1.             0.             0.             0.             0.             0.\n",
      "      1.             0.             0.             0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(particles[10,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 801, 19)\n",
      "[ 9.04768169 -0.00023385 -0.00090146 -0.23011986  1.01915742 -0.03107507\n",
      " -0.00047436 -0.00032505  1.75145637  0.80933272  0.74589314  0.56179775\n",
      "  0.00073654  0.18726592  0.0005119   0.24968789  0.01563807 -0.00016863\n",
      "  0.19898135]\n",
      "[  25.59106409    2.39369076    2.38528021   26.93122411    3.22189946\n",
      "    1.86000275    1.36696236    0.58335714    7.13259599    2.69346837\n",
      "    2.66599065    0.49616634    0.02712923    0.39012484    0.02261946\n",
      "    0.43283236   18.08905559   18.08310878  101.79933557]\n",
      "[   106.55623129     46.23044871  12330.08442969      0.0129379       0.572\n",
      "      1.25756   ]\n",
      "[   174.74362834     38.77107294  25649.27705414      1.81329722\n",
      "      0.85317603      1.51637161]\n"
     ]
    }
   ],
   "source": [
    "print(particles[:30000,:,:].shape)\n",
    "flat = particles[:30000,:,:].reshape((30000*particles.shape[1],particles.shape[2]))\n",
    "print(np.mean(flat,axis=0))\n",
    "print(np.std(flat,axis=0))\n",
    "print(np.mean(hlf,axis=0))\n",
    "print(np.std(hlf,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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