{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples for pytimber"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the CERN logging database and extracting machine parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "import pytimber\n",
    "import datetime\n",
    "log = pytimber.LoggingDB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search for parameters\n",
    "Use the wildcard `%`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LHC.BCTDC.A6R4.B1:BEAM_INTENSITY',\n",
       " 'LHC.BCTDC.A6R4.B1:BEAM_INTENSITY_ADC24BIT',\n",
       " 'LHC.BCTDC.A6R4.B2:BEAM_INTENSITY',\n",
       " 'LHC.BCTDC.A6R4.B2:BEAM_INTENSITY_ADC24BIT',\n",
       " 'LHC.BCTDC.B6R4.B1:BEAM_INTENSITY',\n",
       " 'LHC.BCTDC.B6R4.B1:BEAM_INTENSITY_ADC24BIT',\n",
       " 'LHC.BCTDC.B6R4.B2:BEAM_INTENSITY',\n",
       " 'LHC.BCTDC.B6R4.B2:BEAM_INTENSITY_ADC24BIT']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.search(\"LHC.BCTDC.%:BEAM_INTENSITY%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the parameter tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the ipython autocomplete (`TAB`) to explorer the parameter tree interactively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LHC.BQM.B1:ADC_AQUISITION_MAX',\n",
       " 'LHC.BQM.B1:ADC_AQUISITION_MIN',\n",
       " 'LHC.BQM.B1:ADC_WAVEFORM',\n",
       " 'LHC.BQM.B1:ATTENUATION',\n",
       " 'LHC.BQM.B1:BUNCH_INTENSITIES',\n",
       " 'LHC.BQM.B1:BUNCH_INTENSITY_MAX',\n",
       " 'LHC.BQM.B1:BUNCH_INTENSITY_MEAN',\n",
       " 'LHC.BQM.B1:BUNCH_INTENSITY_MIN',\n",
       " 'LHC.BQM.B1:BUNCH_INTENSITY_STDDEV',\n",
       " 'LHC.BQM.B1:BUNCH_LENGTHS',\n",
       " 'LHC.BQM.B1:BUNCH_LENGTH_MAX',\n",
       " 'LHC.BQM.B1:BUNCH_LENGTH_MEAN',\n",
       " 'LHC.BQM.B1:BUNCH_LENGTH_MIN',\n",
       " 'LHC.BQM.B1:BUNCH_LENGTH_STDDEV',\n",
       " 'LHC.BQM.B1:BUNCH_MEANS',\n",
       " 'LHC.BQM.B1:BUNCH_PEAKS',\n",
       " 'LHC.BQM.B1:BUNCH_PEAK_MAX',\n",
       " 'LHC.BQM.B1:BUNCH_PEAK_MEAN',\n",
       " 'LHC.BQM.B1:BUNCH_PEAK_MIN',\n",
       " 'LHC.BQM.B1:BUNCH_PEAK_STDDEV',\n",
       " 'LHC.BQM.B1:FILLED_BUCKETS',\n",
       " 'LHC.BQM.B1:NO_BUNCHES']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.tree.LHC.Beam_Quality.Beam_1.get_vars()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get\n",
    "get() is the main function to query data from the database. The options are:\n",
    "\n",
    "    +    a list of variables names or a pattern for variable name matching;\n",
    "    +    the start of the time window;\n",
    "    +    the end of the time window (optional). If t2 is `None`, the last available data point before t1 is returned\n",
    "         (search-range is one year);\n",
    "    +    a pattern for fundamental data matching (optional)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the latest datapoint\n",
    "If only one timestamp is given as a parameter, the closest datapoint to it is returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of variables to be queried:\n",
      "LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\n",
      "Retrieved 1 values for LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2015, 12, 3, 15, 50, 39), 3212811170000.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = datetime.datetime.now()\n",
    "log.get(\"LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\", ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timestamps can be specified as python `datetime` object or as string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of variables to be queried:\n",
      "LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\n",
      "Retrieved 1 values for LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2015, 10, 12, 18, 12, 32), 8196013600.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsStr = \"2015-10-12 18:12:32.453255123\"\n",
    "log.get(\"LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\", tsStr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get datapoints in a time range\n",
    "\n",
    "Use datetime.timedelta to define time-offsets.\n",
    "\n",
    "Here we get data from ten minutes in the past over a window of 10 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of variables to be queried:\n",
      "F16.BCT.203:TOTAL_INTENSITY\n",
      "Retrieved 8 values for F16.BCT.203:TOTAL_INTENSITY\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([datetime.datetime(2015, 12, 3, 15, 39, 58, 299999),\n",
       "  datetime.datetime(2015, 12, 3, 15, 39, 59, 500000),\n",
       "  datetime.datetime(2015, 12, 3, 15, 40, 0, 700000),\n",
       "  datetime.datetime(2015, 12, 3, 15, 40, 1, 900000),\n",
       "  datetime.datetime(2015, 12, 3, 15, 40, 4, 299999),\n",
       "  datetime.datetime(2015, 12, 3, 15, 40, 5, 500000),\n",
       "  datetime.datetime(2015, 12, 3, 15, 40, 6, 700000),\n",
       "  datetime.datetime(2015, 12, 3, 15, 40, 7, 900000)],\n",
       " [0.8495, 0.9815, 1.2483, 5.1966, 0.1751, 2.3153, 0.2713, 1.6268])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = datetime.datetime.now() + datetime.timedelta(minutes=-10, seconds=-60)\n",
    "ts2 = datetime.datetime.now() + datetime.timedelta(minutes=-10, seconds=-50)\n",
    "log.get(\"F16.BCT.203:TOTAL_INTENSITY\", ts1, ts2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by fundamental data\n",
    "\n",
    "A pattern can be provided as last argument of get. It is used to find fundamental data (cycle names in most cases) in the given time window. Only data point having a timestamp where one of the fundamentals is present are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of variables to be queried:\n",
      "F16.BCT.203:TOTAL_INTENSITY\n",
      "F16.BCT.212:TOTAL_INTENSITY\n",
      "Querying fundamentals (pattern: CPS:LHC%):\n",
      "CPS:LHC_ION_NOMINAL_PB54:ION3\n",
      "Retrieved 6 values for F16.BCT.203:TOTAL_INTENSITY\n",
      "Retrieved 6 values for F16.BCT.212:TOTAL_INTENSITY\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'F16.BCT.203:TOTAL_INTENSITY': ([datetime.datetime(2015, 12, 3, 10, 30, 4, 299999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 25, 900000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 47, 500000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 9, 99999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 30, 700000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 52, 299999)],\n",
       "  [4.5622, 4.2348, 4.4315, 3.7718, 4.7268, 4.4153]),\n",
       " 'F16.BCT.212:TOTAL_INTENSITY': ([datetime.datetime(2015, 12, 3, 10, 30, 4, 299999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 25, 900000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 47, 500000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 9, 99999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 30, 700000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 52, 299999)],\n",
       "  [6.0375, 5.9465, 5.8345, 5.201, 6.7865, 5.7365])}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = datetime.datetime(2015, 12, 3, 10, 30, 0)\n",
    "ts2 = '2015-12-03 10:32:00'\n",
    "log.get('F16.BCT.2%:TOTAL_INTENSITY', ts1 ,ts2, 'CPS:LHC%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An explicit list of variables can also be provided:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'F16.BCT.126:TOTAL_INTENSITY': ([datetime.datetime(2015, 12, 3, 10, 30, 4, 299999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 25, 900000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 47, 500000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 9, 99999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 30, 700000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 52, 299999)],\n",
       "  [4.676271, 3.9286778, 4.2757745, 3.169642, 4.4016447, 4.5504003]),\n",
       " 'F16.BCT.203:TOTAL_INTENSITY': ([datetime.datetime(2015, 12, 3, 10, 30, 4, 299999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 25, 900000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 30, 47, 500000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 9, 99999),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 30, 700000),\n",
       "   datetime.datetime(2015, 12, 3, 10, 31, 52, 299999)],\n",
       "  [4.5622, 4.2348, 4.4315, 3.7718, 4.7268, 4.4153])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    
    "log.get(['F16.BCT.126:TOTAL_INTENSITY', 'F16.BCT.203:TOTAL_INTENSITY'], ts1, ts2, 'CPS:LHC%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to query the fundamentals directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CPS:MTE_0:SFTPRO1',\n",
       " 'CPS:MTE_1600:SFTPRO2',\n",
       " 'CPS:MTE_2000:MD1',\n",
       " 'CPS:MTE_2000:SFTPRO2',\n",
       " 'CPS:MTE_2500:MD1',\n",
       " 'CPS:MTE_2500:MD4',\n",
       " 'CPS:MTE_2500:SFTPRO2',\n",
       " 'CPS:MTE_OP:MD1']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = datetime.datetime(2015, 10, 1, 0, 0, 0)\n",
    "ts2 = '2015-11-01 00:00:00'\n",
    "# Uses list comprehension to return a Python list\n",
    "[f for f in log.getFundamentals(log.toTimestamp(ts1), log.toTimestamp(ts2), 'CPS:MTE%')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get 'aligned' datasets\n",
    "\n",
    "When working with more than one variable it is possible to set one variable as the *master* variable and acquire the other variables only for timestamps that are present for the 'master'.\n",
    "\n",
    "This works with and without filtering by fundamentals.\n",
    "\n",
    "By default the first variable (from an explicit list or from pattern matching) is used as the *master*.\n",
    "\n",
    "**Note:** The output is different from the one obtained with a simple get(). The dictionnary that is returned contains only one list of timestamps ('timestamps' entry). The dictionnary contains one entry per variable with a list of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of variables to be queried:\n",
      "F16.BCT.126:TOTAL_INTENSITY (using as master).\n",
      "F16.BCT.203:TOTAL_INTENSITY\n",
      "F16.BCT.212:TOTAL_INTENSITY\n",
      "F16.BCT.372:TOTAL_INTENSITY\n",
      "Querying fundamentals (pattern: CPS:LHC%):\n",
      "CPS:LHC_ION_NOMINAL_PB54:ION3\n",
      "Retrieved 8 values for F16.BCT.126:TOTAL_INTENSITY (master)\n",
      "Retrieved 8 values for F16.BCT.203:TOTAL_INTENSITY\n",
      "Retrieved 8 values for F16.BCT.212:TOTAL_INTENSITY\n",
      "Retrieved 8 values for F16.BCT.372:TOTAL_INTENSITY\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'F16.BCT.126:TOTAL_INTENSITY': [4.5237007,\n",
       "  4.5999856,\n",
       "  4.188047,\n",
       "  4.268146,\n",
       "  4.5046296,\n",
       "  4.2147465,\n",
       "  4.4550443,\n",
       "  4.5046296],\n",
       " 'F16.BCT.203:TOTAL_INTENSITY': [4.8991,\n",
       "  4.8552,\n",
       "  4.8256,\n",
       "  4.8235,\n",
       "  4.7284,\n",
       "  4.8768,\n",
       "  4.7939,\n",
       "  4.8701],\n",
       " 'F16.BCT.212:TOTAL_INTENSITY': [6.86,\n",
       "  6.517,\n",
       "  6.3805,\n",
       "  6.9755,\n",
       "  6.594,\n",
       "  6.7445,\n",
       "  6.272,\n",
       "  6.272],\n",
       " 'F16.BCT.372:TOTAL_INTENSITY': [7.2526,\n",
       "  7.225,\n",
       "  6.9502,\n",
       "  7.0758,\n",
       "  6.8919,\n",
       "  7.1064,\n",
       "  7.2472,\n",
       "  7.3118],\n",
       " 'timestamps': [datetime.datetime(2015, 12, 3, 14, 0, 18, 700000),\n",
       "  datetime.datetime(2015, 12, 3, 14, 0, 40, 299999),\n",
       "  datetime.datetime(2015, 12, 3, 14, 1, 1, 900000),\n",
       "  datetime.datetime(2015, 12, 3, 14, 1, 23, 500000),\n",
       "  datetime.datetime(2015, 12, 3, 14, 1, 45, 99999),\n",
       "  datetime.datetime(2015, 12, 3, 14, 2, 6, 700000),\n",
       "  datetime.datetime(2015, 12, 3, 14, 2, 28, 299999),\n",
       "  datetime.datetime(2015, 12, 3, 14, 2, 49, 900000)]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    
    "log.getAligned('F16.BCT.%:TOTAL_INTENSITY', '2015-12-03 14:00:00', '2015-12-03 14:03:00', 'CPS:LHC%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use cases\n",
    "\n",
    "### Plot timestamped data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N0oQJE/TSSy/piSeesLA6AABQH9xuRk5DlXHhtDatWrXSxRdfrFWrVsntdstWy946ZcoU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9c44614a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts1 = datetime.datetime.now() + datetime.timedelta(hours=-1)\n",
    "ts2 = datetime.datetime.now()\n",
    "tss, dat = log.get(\"LHC.BCTDC.A6R4.B1:BEAM_INTENSITY\", ts1, ts2)\n",
    "plot( tss, dat, \"-\" )\n",
    "xlabel(\"Time\")\n",
    "ylabel(\"Beam intensity\")\n",
    "xfmt = DateFormatter('%H:%M:%S')\n",
    "gca().xaxis.set_major_formatter(xfmt)\n",
    "gcf().autofmt_xdate()\n",
    "display(gcf())\n",
    "close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with arrays\n",
    "get individual bunch intensities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-10-21 21:53:46.628999\n",
      "[ 0.  0.  0. ...,  0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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3ymKxKC4uTn5+fo7vL2TIkCENLhAAAADwlDqF5Tlz5shisWjw4MHy8/PTnDlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9c4407390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tss, dat = log.get(\"LHC.BCTFR.A6R4.B1:BUNCH_INTENSITY\", ts1)\n",
    "print( tss )\n",
    "print( dat )\n",
    "plot( dat, \"o-\" )\n",
    "xlabel(\"Bunch index\")\n",
    "ylabel(\"Bunch intensity\")\n",
    "display(gcf())\n",
    "close()"
   ]
  }
 ],
 "metadata": {
  "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}