2017_SPD_Portland/2017 SPD Workshop - Creating a timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeSeries - a new object for handling time domain data\n",
    "\n",
    "### NOTE: Internet access is required in order to use this tutorial\n",
    "\n",
    "TimeSeries is a new feature in the SunPy 0.8 release, replacing the LightCurve object which is now deprecated. Similar to LightCurve, TimeSeries handles time domain data from a variety of solar instruments, including every mission that was previously supported by LightCurve. In TimeSeries, the downloading of data has been separated from the object - now, the object operates only on data files, while downloading is done via other tools such as the Unified Downloader (FIDO).\n",
    "\n",
    "In this notebook, we demonstrate how to use TimeSeries and give a brief tour of its features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "import sunpy\n",
    "from sunpy.net import Fido, attrs as a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Download a data file to work with\n",
    "As mentioned above, TimeSeries does not download data itself. That functionality is covered by other SunPy tools. Without going into detail, here we use Fido to download a GOES XRS data file from 2011 June 7. Detailed tutorials on using FIDO may be found elsewhere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                        ]\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b========================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./go1520110607.2.fits']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_results = Fido.search(a.Time('2011-06-07 00:00','2011-06-07 08:00'),a.Instrument('XRS'))\n",
    "Fido.fetch(search_results[0],path='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create a TimeSeries object from a data file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a GOES data file in the local directory, let's load it into a TimeSeries object and take a look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: Config parameter 'enabled_record_valued_keyword_cards' in section [io.fits] of the file '/Users/ainglis/.astropy/config/astropy.cfg' is deprecated. Use 'enable_record_valued_keyword_cards' in section [io.fits] instead. [astropy.config.configuration]\n",
      "/Users/ainglis/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
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IVJHnFUhtHo9hGIYRF5vHYxiGYTQKTPEYhmEYBcUUj2EYhlFQSlLxiEgrEZkiIkeHLYth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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121d8ab50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IVJHnFUhtHo9hGIYRF5vHYxiGYTQKTPEYhmEYBcUUj2EYhlFQSlLxiEgrEZkiIkeHLYth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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121d8ab50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sunpy import timeseries\n",
    "goes = timeseries.TimeSeries('go1520110607.fits')\n",
    "goes.peek()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see a quicklook plot of the data in the object. It shows the two GOES XRS channels, 0.5-4A and 1-8A. We can see that a solar flare occured at ~06:20 UT."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TimeSeries object retains the metadata from the originating file. We can query this as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|-------------------------------------------------------------------------------------------------|\n",
      "|TimeRange                  | Columns         | Meta                                              |\n",
      "|-------------------------------------------------------------------------------------------------|\n",
      "|2011-06-06 23:59:59.961999 | xrsa            | simple: True                                      |\n",
      "|            to             | xrsb            | bitpix: 8                                         |\n",
      "|2011-06-07 23:59:57.631999 |                 | naxis: 0                                          |\n",
      "|                           |                 | extend: True                                      |\n",
      "|                           |                 | date: 26/06/2012                                  |\n",
      "|                           |                 | numext: 3                                         |\n",
      "|                           |                 | telescop: GOES 15                                 |\n",
      "|                           |                 | instrume: X-ray Detector                          |\n",
      "|                           |                 | object: Sun                                       |\n",
      "|                           |                 | origin: SDAC/GSFC                                 |\n",
      "|                           |                 | ...                                               |\n",
      "|-------------------------------------------------------------------------------------------------|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(goes.meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data itself is stored in something called a pandas DataFrame. This DataFrame is designed to hold column-based data. The first column is the index (i.e. the time dimension), while the other columns correspond to the dependent variables, in this case the flux from the two GOES channels. Below, we show what the data looks like, showing only the first 5 rows for brevity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    xrsa          xrsb\n",
      "2011-06-06 23:59:59.961999  1.000000e-09  1.887100e-07\n",
      "2011-06-07 00:00:02.008999  1.000000e-09  1.834600e-07\n",
      "2011-06-07 00:00:04.058999  1.000000e-09  1.860900e-07\n",
      "2011-06-07 00:00:06.104999  1.000000e-09  1.808400e-07\n",
      "2011-06-07 00:00:08.151999  1.000000e-09  1.860900e-07\n"
     ]
    }
   ],
   "source": [
    "print(goes.data[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for a particular variable can easily be extracted like this (again, extracting only the first 5 rows for convenience):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-06-06 23:59:59.961999    1.000000e-09\n",
      "2011-06-07 00:00:02.008999    1.000000e-09\n",
      "2011-06-07 00:00:04.058999    1.000000e-09\n",
      "2011-06-07 00:00:06.104999    1.000000e-09\n",
      "2011-06-07 00:00:08.151999    1.000000e-09\n",
      "Name: xrsa, dtype: float32\n"
     ]
    }
   ],
   "source": [
    "xrsa = goes.data['xrsa'][0:5]\n",
    "print(xrsa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TimeSeries object also knows the units of the data in each column, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('xrsa', Unit(\"W / m2\")), ('xrsb', Unit(\"W / m2\"))])\n"
     ]
    }
   ],
   "source": [
    "print(goes.units)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Working with TimeSeries objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the entire file contents are converted to a TimeSeries object. A subset of the data can easily be extracted, similar to creating a SubMap from a larger Map object. This is done using the ```truncate()``` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ainglis/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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7tqXLv/TSmIecfLJZEr/7Hfz612mQMYnMmAH33w+vvLJnNekBA+DUU2HwYBtb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\nx3HSyv8DqvXeSAg/PDcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115ac050>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7tqXLv/TSmIecfLJZEr/7Hfz612mQMYnMmAH33w+vvLJnNekBA+DUU2HwYBtb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\nx3HSyv8DqvXeSAg/PDcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115ac050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goes2 = goes.truncate('2011-06-07 06:00','2011-06-07 08:00')\n",
    "goes2.peek()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the maxima in the data\n",
    "We can easily find the maximum value for each channel, and the index location of these maxima, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xrsa    0.000004\n",
      "xrsb    0.000026\n",
      "dtype: float32\n",
      "xrsa   2011-06-07 06:39:00.761999\n",
      "xrsb   2011-06-07 06:41:24.118999\n",
      "dtype: datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "print(goes2.data.max())\n",
    "print(goes2.data.idxmax())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subtracting the moving average of the data\n",
    "The TimeSeries object contains a number of useful tools for common time series operations. For example, to subtract a rolling mean of the data, we can use the built-in function ```rolling()```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "smoothed_goes_data = goes2.data.rolling(60, center=True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object `smoothed_goes_data` contains the rolling mean with a 60-point window for each channel. We can easily subtract it from the original data and plot the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BsFior4TP/yBLqm/7u0+GrZRSSil7aICt/IeVwY5xs0TEkjYDUiZLNtiXSrOlP3doJMy4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\nCNf+TdqMfvKr3u3D39SUtO2EM2aBnNB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j8qdFh+2L60TBmX8Yno+F1C0A3L80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\nRIANAAAAJOj/AUldT1J65942AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124630350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(1,figsize=(12,6))\n",
    "plt.plot(goes2.data.index,goes2.data['xrsa'] - smoothed_goes_data['xrsa'],label='xrsa')\n",
    "plt.plot(goes2.data.index,goes2.data['xrsb'] - smoothed_goes_data['xrsb'],label='xrsb')\n",
    "plt.xlim(datetime.datetime(2011,6,7,6,25),datetime.datetime(2011,6,7,6,45))\n",
    "plt.legend(fontsize=14)\n",
    "\n",
    "#This line just formats the x-axis to look nicer\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resample the TimeSeries\n",
    "It is easy to resample the data in the TimeSeries, for example to reduce the cadence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "goes_resampled = goes2.data.resample('30S').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             xrsa          xrsb\n",
      "2011-06-07 06:00:00  1.000000e-09  2.447357e-07\n",
      "2011-06-07 06:00:30  1.000000e-09  2.399513e-07\n",
      "2011-06-07 06:01:00  1.000000e-09  2.379893e-07\n",
      "2011-06-07 06:01:30  1.000000e-09  2.340040e-07\n",
      "2011-06-07 06:02:00  1.000000e-09  2.268373e-07\n"
     ]
    }
   ],
   "source": [
    "print(goes_resampled[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "goes2.data.diff() # take the difference between each data point\n",
    "goes2.data.cumsum() # return the cumulative sum of the data.\n",
    "goes2.index.duplicated() # returns True for time indexes that appear more than once in the TimeSeries\n",
    "goes2.index.drop_duplicates() # just remove all duplicate rows from the TimeSeries\n",
    "goes2.concatenate() # allows two DataFrames to be joined together into one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and much more!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Summary\n",
    "By the end of this notebook you should have some familarity with the basic functionality of TimeSeries, including:\n",
    "  - how to create a TimeSeries from a data file\n",
    "  - how to access the data and metadata in the TimeSeries object\n",
    "  - how to create a truncated TimeSeries from a longer one\n",
    "  - how to perform simple data manipulation, such as averaging, subtracting a running mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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