{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data to analyse in memory\n",
    "\n",
    "It's often quickest and easiest to load data into memory before analysing it.\n",
    "\n",
    "Some types of data, especially from large pixel detectors, may be bigger than the available memory.\n",
    "Other examples show how to work with very large amounts of data.\n",
    "But the [machines in the Maxwell cluster](https://confluence.desy.de/display/MXW/Hardware+in+exfel)\n",
    "have 250–750 GB of memory, so you can use the simple approach for many cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re\n",
    "import xarray as xr\n",
    "\n",
    "from extra_data import open_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabular data (with pandas)\n",
    "\n",
    "We can open a run with EXtra-data on the Maxwell cluster using the proposal & run number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of trains:    3721\n",
      "Duration:       0:06:12.1\n",
      "First train ID: 142844490\n",
      "Last train ID:  142848210\n",
      "\n",
      "0 detector modules ()\n",
      "\n",
      "2 instrument sources (excluding detectors):\n",
      "  - SA1_XTD2_XGM/XGM/DOOCS:output\n",
      "  - SPB_XTD9_XGM/XGM/DOOCS:output\n",
      "\n",
      "2 control sources:\n",
      "  - SA1_XTD2_XGM/XGM/DOOCS\n",
      "  - SPB_XTD9_XGM/XGM/DOOCS\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run = open_run(proposal=900025, run=150)\n",
    "run.info()  # Show overview info about this data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example works with data from two X-Ray Gas Monitors (XGMs).\n",
    "These measure properties of the X-ray beam in different parts of the tunnel.\n",
    "This data refers to one XGM in XTD2 and one in XTD9.\n",
    "\n",
    "[pandas](https://pandas.pydata.org/pandas-docs/stable/) is a popular Python library for working with tabular data.\n",
    "We'll create a pandas dataframe containing the beam x and y position at each XGM, and the photon flux.\n",
    "We can select the columns using 'glob' patterns, like selecting files in the terminal.\n",
    "\n",
    "* `[abc]`: one character, a/b/c\n",
    "* `?`: any one character\n",
    "* `*`: any sequence of characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPB_XTD9_XGM/XGM/DOOCS/beamPosition.iyPos</th>\n",
       "      <th>SPB_XTD9_XGM/XGM/DOOCS/pulseEnergy.photonFlux</th>\n",
       "      <th>SPB_XTD9_XGM/XGM/DOOCS/beamPosition.ixPos</th>\n",
       "      <th>SA1_XTD2_XGM/XGM/DOOCS/beamPosition.iyPos</th>\n",
       "      <th>SA1_XTD2_XGM/XGM/DOOCS/pulseEnergy.photonFlux</th>\n",
       "      <th>SA1_XTD2_XGM/XGM/DOOCS/beamPosition.ixPos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trainId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>142844490</th>\n",
       "      <td>1.717195</td>\n",
       "      <td>1327.06958</td>\n",
       "      <td>-2.277912</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>1410.723755</td>\n",
       "      <td>2.035218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142844491</th>\n",
       "      <td>1.717195</td>\n",
       "      <td>1327.06958</td>\n",
       "      <td>-2.277912</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>1410.137451</td>\n",
       "      <td>2.035218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142844492</th>\n",
       "      <td>1.717195</td>\n",
       "      <td>1327.06958</td>\n",
       "      <td>-2.277912</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>1410.137451</td>\n",
       "      <td>2.035218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142844493</th>\n",
       "      <td>1.717195</td>\n",
       "      <td>1327.06958</td>\n",
       "      <td>-2.277912</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>1410.137451</td>\n",
       "      <td>2.035218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142844494</th>\n",
       "      <td>1.717195</td>\n",
       "      <td>1327.06958</td>\n",
       "      <td>-2.277912</td>\n",
       "      <td>0.161399</td>\n",
       "      <td>1410.137451</td>\n",
       "      <td>2.035218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SPB_XTD9_XGM/XGM/DOOCS/beamPosition.iyPos  \\\n",
       "trainId                                                \n",
       "142844490                                   1.717195   \n",
       "142844491                                   1.717195   \n",
       "142844492                                   1.717195   \n",
       "142844493                                   1.717195   \n",
       "142844494                                   1.717195   \n",
       "\n",
       "           SPB_XTD9_XGM/XGM/DOOCS/pulseEnergy.photonFlux  \\\n",
       "trainId                                                    \n",
       "142844490                                     1327.06958   \n",
       "142844491                                     1327.06958   \n",
       "142844492                                     1327.06958   \n",
       "142844493                                     1327.06958   \n",
       "142844494                                     1327.06958   \n",
       "\n",
       "           SPB_XTD9_XGM/XGM/DOOCS/beamPosition.ixPos  \\\n",
       "trainId                                                \n",
       "142844490                                  -2.277912   \n",
       "142844491                                  -2.277912   \n",
       "142844492                                  -2.277912   \n",
       "142844493                                  -2.277912   \n",
       "142844494                                  -2.277912   \n",
       "\n",
       "           SA1_XTD2_XGM/XGM/DOOCS/beamPosition.iyPos  \\\n",
       "trainId                                                \n",
       "142844490                                   0.161399   \n",
       "142844491                                   0.161399   \n",
       "142844492                                   0.161399   \n",
       "142844493                                   0.161399   \n",
       "142844494                                   0.161399   \n",
       "\n",
       "           SA1_XTD2_XGM/XGM/DOOCS/pulseEnergy.photonFlux  \\\n",
       "trainId                                                    \n",
       "142844490                                    1410.723755   \n",
       "142844491                                    1410.137451   \n",
       "142844492                                    1410.137451   \n",
       "142844493                                    1410.137451   \n",
       "142844494                                    1410.137451   \n",
       "\n",
       "           SA1_XTD2_XGM/XGM/DOOCS/beamPosition.ixPos  \n",
       "trainId                                               \n",
       "142844490                                   2.035218  \n",
       "142844491                                   2.035218  \n",
       "142844492                                   2.035218  \n",
       "142844493                                   2.035218  \n",
       "142844494                                   2.035218  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = run.select([(\"*_XGM/*\", \"*.i[xy]Pos\"), (\"*_XGM/*\", \"*.photonFlux\")]).get_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now make plots to compare the parameters at the two XGM positions.\n",
    "As expected, there's a strong correlation for each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ba7e87f8fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(\n",
    "    x='SA1_XTD2_XGM/XGM/DOOCS/pulseEnergy.photonFlux',\n",
    "    y='SPB_XTD9_XGM/XGM/DOOCS/pulseEnergy.photonFlux',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.scatter(\n",
    "    x='SA1_XTD2_XGM/XGM/DOOCS/beamPosition.ixPos',\n",
    "    y='SPB_XTD9_XGM/XGM/DOOCS/beamPosition.ixPos',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ay = df.plot.scatter(\n",
    "    x='SA1_XTD2_XGM/XGM/DOOCS/beamPosition.iyPos',\n",
    "    y='SPB_XTD9_XGM/XGM/DOOCS/beamPosition.iyPos',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also export the dataframe to a CSV file - or [any other format pandas supports](https://pandas.pydata.org/pandas-docs/stable/io.html) - for further analysis with other tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('xtd2_xtd9_xgm_r150.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As arrays (with xarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll open a different run for this example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of trains:    1475\n",
      "Duration:       0:02:27.5\n",
      "First train ID: 128146446\n",
      "Last train ID:  128147920\n",
      "\n",
      "0 detector modules ()\n",
      "\n",
      "1 instrument sources (excluding detectors):\n",
      "  - SA3_XTD10_PES/ADC/1:network\n",
      "\n",
      "11 control sources:\n",
      "  - SA3_XTD10_PES/ADC/1\n",
      "  - SA3_XTD10_PES/ASENS/MAGN_X\n",
      "  - SA3_XTD10_PES/ASENS/MAGN_Y\n",
      "  - SA3_XTD10_PES/ASENS/MAGN_Z\n",
      "  - SA3_XTD10_PES/DCTRL/V30300S_NITROGEN\n",
      "  - SA3_XTD10_PES/DCTRL/V30310S_NEON\n",
      "  - SA3_XTD10_PES/DCTRL/V30320S_KRYPTON\n",
      "  - SA3_XTD10_PES/DCTRL/V30330S_XENON\n",
      "  - SA3_XTD10_PES/GAUGE/G30300F\n",
      "  - SA3_XTD10_PES/GAUGE/G30310F\n",
      "  - SA3_XTD10_PES/MCPS/MPOD\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run = open_run(proposal=900027, run=67)\n",
    "run.info()  # Show overview info about the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This run includes data from a Photo-Electron Spectrometer (PES),\n",
    "a monitoring device which records energy spectra for each train.\n",
    "Here's the data from one of its 16 spectrometers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;SA3_XTD10_PES/ADC/1:network.digitizers.channel_4_A.raw.samples&#x27; (trainId: 1475, dim_0: 40000)&gt;\n",
       "array([[ -6, -10,  -7, ..., -10,  -8,  -9],\n",
       "       [ -8,  -8,  -7, ...,  -9,  -2, -11],\n",
       "       [ -8, -10,  -7, ...,  -6,  -8, -11],\n",
       "       ...,\n",
       "       [ -7,  -9,  -8, ...,  -9,  -2,  -5],\n",
       "       [ -5, -10,  -8, ...,  -5,  -4, -10],\n",
       "       [ -7,  -8,  -7, ...,  -6,  -5,  -8]], dtype=int16)\n",
       "Coordinates:\n",
       "  * trainId  (trainId) uint64 128146446 128146447 ... 128147919 128147920\n",
       "Dimensions without coordinates: dim_0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'SA3_XTD10_PES/ADC/1:network.digitizers.channel_4_A.raw.samples'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 1475</li><li><span>dim_0</span>: 40000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fd35272f-86bb-4e09-bada-b1d35cd45ddc' class='xr-array-in' type='checkbox' checked><label for='section-fd35272f-86bb-4e09-bada-b1d35cd45ddc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-6 -10 -7 -7 -6 -9 -7 -7 -9 -9 -4 ... -8 -5 -8 -2 -6 -4 -9 -7 -6 -5 -8</span></div><div class='xr-array-data'><pre>array([[ -6, -10,  -7, ..., -10,  -8,  -9],\n",
       "       [ -8,  -8,  -7, ...,  -9,  -2, -11],\n",
       "       [ -8, -10,  -7, ...,  -6,  -8, -11],\n",
       "       ...,\n",
       "       [ -7,  -9,  -8, ...,  -9,  -2,  -5],\n",
       "       [ -5, -10,  -8, ...,  -5,  -4, -10],\n",
       "       [ -7,  -8,  -7, ...,  -6,  -5,  -8]], dtype=int16)</pre></div></div></li><li class='xr-section-item'><input id='section-b4702d26-a269-4bf6-8f4e-4ab65ed59855' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4702d26-a269-4bf6-8f4e-4ab65ed59855' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>128146446 128146447 ... 128147920</div><input id='attrs-3e3c3488-c95d-4ed0-a119-825a45ba8d10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e3c3488-c95d-4ed0-a119-825a45ba8d10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bfa1c09-13eb-4f51-9736-7684e16d6145' class='xr-var-data-in' type='checkbox'><label for='data-5bfa1c09-13eb-4f51-9736-7684e16d6145' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([128146446, 128146447, 128146448, ..., 128147918, 128147919, 128147920],\n",
       "      dtype=uint64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc7f17f6-4da7-484a-8756-25bbc5520259' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc7f17f6-4da7-484a-8756-25bbc5520259' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'SA3_XTD10_PES/ADC/1:network.digitizers.channel_4_A.raw.samples' (trainId: 1475, dim_0: 40000)>\n",
       "array([[ -6, -10,  -7, ..., -10,  -8,  -9],\n",
       "       [ -8,  -8,  -7, ...,  -9,  -2, -11],\n",
       "       [ -8, -10,  -7, ...,  -6,  -8, -11],\n",
       "       ...,\n",
       "       [ -7,  -9,  -8, ...,  -9,  -2,  -5],\n",
       "       [ -5, -10,  -8, ...,  -5,  -4, -10],\n",
       "       [ -7,  -8,  -7, ...,  -6,  -5,  -8]], dtype=int16)\n",
       "Coordinates:\n",
       "  * trainId  (trainId) uint64 128146446 128146447 ... 128147919 128147920\n",
       "Dimensions without coordinates: dim_0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run['SA3_XTD10_PES/ADC/1:network', 'digitizers.channel_4_A.raw.samples'].xarray()"
   ]
  },
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    "The PES consists of 16 spectrometers arranged in a circle around the beamline. We'll retrieve the data for two of these, separated by 90&deg;. N and E refer to their positions in the circle, although these are not literally North and East.\n",
    "\n",
    "[xarray](https://xarray.pydata.org/en/stable/) extends numpy arrays with metadata about the dimensions:\n",
    "we use this to annotate the data with the pulse train IDs they relate to.\n",
    "This is important when correlating data from different sources, as each source may be missing data for some\n",
    "pulse trains, so we need to match them up based on train IDs.\n",
    "See the page on [Aligning data from different sources](aligning_trains.ipynb) for more about this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1475, 40000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_n = run['SA3_XTD10_PES/ADC/1:network', 'digitizers.channel_4_A.raw.samples'].xarray()\n",
    "data_e = run['SA3_XTD10_PES/ADC/1:network', 'digitizers.channel_3_A.raw.samples'].xarray()\n",
    "\n",
    "data_n, data_e = xr.align(data_n, data_e)\n",
    "nsamples = data_n.shape[1]\n",
    "data_n.shape"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "We'll get a few other values from the run to annotate the plot.\n",
    "This uses pandas - see the section above for more about that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SA3_XTD10_PES/MCPS/MPOD/channels.U200.measurementSenseVoltage   -0.101792\n",
       "SA3_XTD10_PES/MCPS/MPOD/channels.U201.measurementSenseVoltage   -0.111782\n",
       "SA3_XTD10_PES/MCPS/MPOD/channels.U202.measurementSenseVoltage   -0.106823\n",
       "SA3_XTD10_PES/MCPS/MPOD/channels.U203.measurementSenseVoltage   -0.107910\n",
       "Name: 128146446, dtype: float32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the first values from four channels measuring voltage\n",
    "electr = run.select('SA3_XTD10_PES/MCPS/MPOD', 'channels.U20[0123].measurementSenseVoltage').get_dataframe()\n",
    "electr_voltages = electr.iloc[0].sort_index()\n",
    "electr_voltages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SA3_XTD10_PES/DCTRL/V30300S_NITROGEN/interlock.AActionState    1\n",
      "SA3_XTD10_PES/DCTRL/V30310S_NEON/interlock.AActionState        0\n",
      "SA3_XTD10_PES/DCTRL/V30320S_KRYPTON/interlock.AActionState     1\n",
      "SA3_XTD10_PES/DCTRL/V30330S_XENON/interlock.AActionState       1\n",
      "Name: 128146446, dtype: uint32\n"
     ]
    }
   ],
   "source": [
    "gas_interlocks = run.select('SA3_XTD10_PES/DCTRL/*', 'interlock.AActionState').get_dataframe()\n",
    "\n",
    "# Take the first row of the gas interlock data and find which gas was unlocked\n",
    "row = gas_interlocks.iloc[0].sort_index()\n",
    "print(row)\n",
    "if (row == 0).any():\n",
    "    key = row[row == 0].index[0]\n",
    "    target_gas = re.search(r'(XENON|KRYPTON|NITROGEN|NEON)', key).group(1).title()\n",
    "else:\n",
    "    target_gas = 'No gas'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can average the spectra across the trains in this run, and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 0.0005*nsamples, nsamples, endpoint=False)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "for ax, dataset, start_time in zip(axes, [data_n, data_e], [15.76439411, 15.76289411]):\n",
    "    ax.plot(x, dataset.sum(axis=0))\n",
    "    ax.yaxis.major.formatter.set_powerlimits((0, 0))\n",
    "    ax.set_xlim(15.75, 15.85)\n",
    "    ax.set_xlabel('time ($\\mu$s)')\n",
    "\n",
    "    ax.axvline(start_time, color='red', linestyle='dotted', label='Start time')\n",
    "    ax.axvline(start_time + 0.0079, color='magenta', linestyle='dotted', label='Neon K 1s')\n",
    "    ax.axvline(start_time + 0.041, color='black', label='Auger peak')\n",
    "    ax.legend()\n",
    "\n",
    "axes[0].set_title('Spectrometer 00 N')\n",
    "axes[1].set_title('Spectrometer 04 E')\n",
    "fig.suptitle('{gas};  900 eV; [A B C D] = [{voltages[0]:.1f} {voltages[1]:.1f} {voltages[2]:.1f} {voltages[3]:.1f}] V; run 67'\n",
    "             .format(gas=target_gas, voltages=electr_voltages.values), y=1.05);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectra look different because the beam is horizontally polarised, so the E spectrometer sees a peak that the N spectrometer doesn't."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "xfel",
   "language": "python",
   "name": "xfel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}