pv_finding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[RAPID2018](https://rapid2018.org/)\n",
    "\n",
    "\n",
    "# [RAMP](https://www.ramp.studio/problems/mars_craters) on Primary Vertex reconstruction\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Introduction](#Introduction)\n",
    "2. [Preprocessing](#Preprocessing)\n",
    "3. [Workflow](#Workflow)\n",
    "4. [Evaluation](#Scoring)\n",
    "5. [Local testing/exploration](#Local-testing)\n",
    "6. [Submission](#Submitting-to-ramp.studio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The prediction task\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With no further do, let's have a look at the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Required dependencies and downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installation of libraries and `ramp-workflow`\n",
    "\n",
    "To get this notebook running and test your models locally using the `ramp_test_submission`, we recommend that you use the Python distribution from [Anaconda](https://www.anaconda.com/download/) or [Miniconda](https://conda.io/miniconda.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda env create -f environment.yml   # use the environment.yml file to create the 'mars-craters' env\n",
    "# !source activate mars-craters          # activate the virtual environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OR** if you have Python already installed but are **not using Anaconda**, you'll want to use `pip` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download script (optional)\n",
    "\n",
    "If the data has not yet been downloaded locally, uncomment the following cell and run it.\n",
    "\n",
    "There are ~700Mb of images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !python download_data.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The input data\n",
    "The input data consists of a json file for each event (where an event is a bunch crossing).\n",
    "The json file contains the hits in the Velo detector, Velo states from a simplified Kalman filter of the reconstructed Velo tracks, as well as the MC truth information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from matplotlib import pyplot as plt\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EventNumber\n",
      "MCParticles\n",
      "MCVertices\n",
      "RunNumber\n",
      "VPClusters\n",
      "VeloTracks\n"
     ]
    }
   ],
   "source": [
    "jdata = json.load(open('data/train/RapidVPData_6719289_74665.json'))\n",
    "for key in jdata:\n",
    "    print(key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in the reconstructed hits and tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "VeloTracks  = jdata['VeloTracks']\n",
    "VeloHits  = jdata['VPClusters']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hits are made of (x,y,z) coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MCPs': ['3602'],\n",
      " 'key': '2201901426',\n",
      " 'x': 21.856674194335938,\n",
      " 'y': -16.118497848510742,\n",
      " 'z': 749.4190063476562}\n",
      "21.856674194335938 -16.118497848510742 749.4190063476562\n"
     ]
    }
   ],
   "source": [
    "key = '2201901426'\n",
    "hit = VeloHits[key]\n",
    "pprint(hit)\n",
    "print (hit['x'], hit['y'], hit['z'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Velo tracks consist of states (x,y,z,tx,ty,p/q) and a covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ClosestToBeam': [0.004886898594421041,\n",
      "                   0.003085980878339001,\n",
      "                   56.206748962402344,\n",
      "                   0.011977268382906914,\n",
      "                   -0.01913546212017536,\n",
      "                   0.0],\n",
      " 'LHCbIDs': ['2148023507', '2150121437', '2152219111', '2154316785'],\n",
      " 'MCPs': ['2910'],\n",
      " 'errCTBState': [0.009285581751691809,\n",
      "                 0.009285581751691809,\n",
      "                 1.0332852440342322e-07,\n",
      "                 1.0332852440342322e-07,\n",
      "                 3.0836403897841565e-05],\n",
      " 'isBackwards': True,\n",
      " 'key': '0'}\n"
     ]
    }
   ],
   "source": [
    "track = VeloTracks['0']\n",
    "pprint(track)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can, for example, pltos the x-y distribution of all hits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faea023eda0>]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hits_x = [item['x'] for  key, item in VeloHits.items()]\n",
    "hits_y = [item['y'] for  key, item in VeloHits.items()]\n",
    "plt.plot(hits_x,hits_y, 'o', markersize=1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or z distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([308.,  74., 231., 397., 375., 261.,  79.,  73., 117., 189.]),\n",
       " array([-288.08099365, -184.21479492,  -80.34859619,   23.51760254,\n",
       "         127.38380127,  231.25      ,  335.11619873,  438.98239746,\n",
       "         542.84859619,  646.71479492,  750.58099365]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hits_z = [item['z'] for  key, item in VeloHits.items()]\n",
    "plt.hist(hits_z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is transformed from the json format to a (hopefully) more convenient form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rec data\n"
     ]
    }
   ],
   "source": [
    "X, y = problem.get_train_data('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X contains the training/test data, y the truth information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[EventData EventData EventData EventData EventData EventData]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of helper classes to organize the data.\n",
    "The test and train data X is a numpy array of object of type EventData. Let' have a closer look at this helper class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "event = X[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It contains the two members, hits and tracks, which are lists containing the Velo hits and states, respectively, again using helper classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[VeloHit(9.393913269042969, -26.134666442871094, -288.08099365234375),\n",
       " VeloHit(9.0050048828125, -24.579030990600586, -288.08099365234375),\n",
       " VeloHit(-0.38714027404785156, -14.097940444946289, -288.08099365234375),\n",
       " VeloHit(-0.38714027404785156, -13.514577865600586, -288.08099365234375),\n",
       " VeloHit(-7.1735992431640625, -16.59343719482422, -288.08099365234375)]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event.hits[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[VeloState(-0.014058536488221307,-0.024329251519816353,-20.015792846679688,0.0337035171687603,-0.04416502267122269),\n",
       " VeloState(0.04143393940914564,0.03616156003867843,155.3652801513672,0.01934237591922283,-0.017269106581807137),\n",
       " VeloState(0.5647286828943905,-0.37778717743778856,92.0198745727539,0.009444397874176502,0.01475947443395853),\n",
       " VeloState(-0.01761487652828464,0.013645172620613266,67.32335662841797,-0.09499440342187881,0.05916781723499298),\n",
       " VeloState(-0.04450437906444904,0.007529980805301761,-21.103111267089844,0.09429869055747986,-0.06211370974779129)]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event.tracks[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The VeloHit class just holds the x,y,z coordinates of the hit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VeloHit(9.393913269042969, -26.134666442871094, -288.08099365234375)\n",
      "9.393913269042969 -26.134666442871094 -288.08099365234375\n"
     ]
    }
   ],
   "source": [
    "hit = event.hits[0]\n",
    "print (hit)\n",
    "print(hit.x, hit.y, hit.z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The VeloTrack helper class holds the state (x,y,z,tx,ty,p/q) of a simplified Kalman fit of the track, as well as the corresponding covariance matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VeloState(-0.014058536488221307,-0.024329251519816353,-20.015792846679688,0.0337035171687603,-0.04416502267122269)\n",
      "track state:\n",
      "x: -0.014058536488221307\n",
      "y: -0.024329251519816353\n",
      "z: -20.015792846679688\n",
      "tx: 0.0337035171687603\n",
      "ty: -0.04416502267122269\n",
      "pq: 0.0\n",
      "covariance matrix:\n",
      "cov x: 0.00149062710342775\n",
      "cov y: 0.00149062710342775\n",
      "cov tx: 5.936173863574368e-08\n",
      "cov ty: 5.936173863574368e-08\n",
      "cov xtx: 8.931202236391695e-06\n"
     ]
    }
   ],
   "source": [
    "track = event.tracks[0]\n",
    "print(track)\n",
    "print(\"track state:\")\n",
    "print(\"x:\", track.x)\n",
    "print(\"y:\", track.y)\n",
    "print(\"z:\", track.z)\n",
    "print(\"tx:\", track.tx)\n",
    "print(\"ty:\", track.ty)\n",
    "print(\"pq:\", track.pq)\n",
    "\n",
    "print(\"covariance matrix:\")\n",
    "print(\"cov x:\", track.cov_x)\n",
    "print(\"cov y:\", track.cov_y)\n",
    "print(\"cov tx:\", track.cov_tx)\n",
    "print(\"cov ty:\", track.cov_ty)\n",
    "print(\"cov xtx:\", track.cov_xtx)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It should be noted that the p/q is always zero, since we have no momentum estimate from the Velo Kalman fit.\n",
    "The covariance matrix consist of the diagonal elements x, y, tx, ty and the off-diagonal element xtx. The Velo Kalman fit contains some simplifications, resulting that the covariance element for x and y is always equal. The same is true for the xtx and yty elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can make plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "hits_x = [hit.x for hit in event.hits]\n",
    "hits_y = [hit.y for hit in event.hits]\n",
    "hits_z = [hit.z for hit in event.hits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fae9d50def0>]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hits_x, hits_y, 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([188.,  38., 125., 186., 201., 174.,  51.,  52.,  83., 114.]),\n",
       " array([-288.08099365, -184.21479492,  -80.34859619,   23.51760254,\n",
       "         127.38380127,  231.25      ,  335.11619873,  438.98239746,\n",
       "         542.84859619,  646.71479492,  750.58099365]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hits_z)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}