02_get_netcdf.ipynb

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   "source": [
    "# Reading and visualizing a NetCDF file with SURFRAD solar radiation data\n",
    "\n",
    "*Date: April 29th, 2020*\n",
    "\n",
    "## Overview\n",
    "This tutorial will demonstrate how to open and manipulate a NetCDF file from a thredds data server, containing solar radiation data from the BSRN network.\n",
    "\n",
    "NetCDF is a file format for storing array oriented scientific data, such as solar radiation, temperature or aerosol optical depth. The format has a number of advantages over other common file formats, including the standardized integration of metadata allowing for the data to be self-describin and easy access of a subset of a larger dataset.\n",
    "\n",
    "## Source data\n",
    "The data in this tutorial is from the SURFRAD (https://gml.noaa.gov/grad/surfrad/), which is an American network of high quality stations measuring solar and atmospheric radiation. Currently, there are 7 stations in operation, measuring at a minimum: global horizontal irradiance, diffuse horizontal irradiance and direct normal irradiance with a 1 minute time step.\n",
    "\n",
    "SURFRAD and other data are available as NetCDF files on a thredds server hosted at [webservice-energy.org](http://webservice-energy.org). The naming conventions follow the [climate and forecast conventions](https://www.unidata.ucar.edu/software/netcdf/conventions.html) in order to be interoperable. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Import python modules\n",
    "***\n",
    "First import the necessary modules:"
   ]
  },
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import netCDF4 as nc\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sg2\n",
    "import sys\n",
    "from datetime import datetime, timedelta\n",
    "from IPython import display\n",
    "from lib.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Visualisation of the content of the TDS server\n",
    "\n",
    "Check the content of the catalog at the following URL :\n",
    "\n",
    "[http://tds.webservice-energy.org/thredds/catalog/surfrad-stations/catalog.html](http://tds.webservice-energy.org/thredds/catalog/surfrad-stations/catalog.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Read NetCDF file\n",
    "\n",
    "Here, we access data from the SURFRAD station \"Bondville\" from the thredds server.\n",
    "\n",
    "\n",
    "## Option 1) Manually\n",
    "\n",
    "The first option is to use the NetCDF libary to access the data over the OpenDAP protocol :"
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Dataset'>\n",
      "root group (NETCDF3_CLASSIC data model, file format DAP2):\n",
      "    description: Archive of solar radiation networks worldwide provided by the Webservice-Energy initiative supported by MINES Paris PSL. Files are provided as NetCDF file format with the support of a Thredds Data Server.\n",
      "    title: Timeseries of NOAA's Surface Radiation Budget Network (SURFRAD) (SURFRAD)\n",
      "    keywords: meteorology, station, time, Earth Science > Atmosphere > Atmospheric Radiation > Incoming Solar Radiation, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature, Earth Science > Atmosphere > Atmospheric Pressure > Sea Level Pressure\n",
      "    keywords_vocabulary: GCMD Science Keywords\n",
      "    keywords_vocabulary_url: https://gcmd.earthdata.nasa.gov/static/kms/\n",
      "    contact: Lionel MENARD, Raphael JOLIVET, Yves-Marie SAINT-DRENAN, Philippe BLANC\n",
      "    institution:  MINES Paris PSL\n",
      "    record: Basic measurements (global irradiance, direct irradiance, diffuse irradiance, air temperature, relative humidity, pressure)\n",
      "    Network_ShortName: SURFRAD\n",
      "    Network_Name: NOAA's Surface Radiation Budget Network (SURFRAD)\n",
      "    Network_Region: USA\n",
      "    Network_TimeResolution: 1 min\n",
      "    Network_Description: https://gml.noaa.gov/grad/surfrad/sitepage.html \n",
      "    Network_References: https://doi.org/10.1175/1520-0477(1996)077<2857:TNISIS>2.0.CO;2\n",
      "    Network_License:  -\n",
      "    Network_Contact: https://gml.noaa.gov/about/contacts.html\n",
      "    Station_ID: BON\n",
      "    Station_Name: Bondville, Illinois\n",
      "    Station_Latitude: 40.05\n",
      "    Station_Longitude: -88.37\n",
      "    Station_Elevation: 213\n",
      "    Station_LocalTimeZone: UTC-05:00\n",
      "    Station_Address: 1165, County Road 500 East\n",
      "    Station_Country: United States\n",
      "    Station_KoeppenGeigerClimate: Dfa\n",
      "    Station_TimeResolution: 1\n",
      "    featureType: TimeSeries\n",
      "    cdm_data_type: TimeSeries\n",
      "    Conventions: CF-1.6\n",
      "    cdm_timeseries_variables: station_name,latitude,longitude\n",
      "    creation_time: 2022-04-13T15:56:23.468163\n",
      "    update_time: 2022-04-13T15:56:23.468163\n",
      "    Station_DataBegin: 1995-01-01\n",
      "    DODS.strlen: 10\n",
      "    DODS.dimName: ncshort\n",
      "    DODS_EXTRA.Unlimited_Dimension: time\n",
      "    dimensions(sizes): time(14284800), maxStrlen64(64)\n",
      "    variables(dimensions): |S1 station_name(maxStrlen64), float32 latitude(), float32 longitude(), float32 elevation(), int32 Time(time), float32 GHI(time), float32 DHI(time), float32 BNI(time), float32 T2(time), float32 WS(time), float32 WD(time)\n",
      "    groups: \n"
     ]
    }
   ],
   "source": [
    "# URL of the NetCDF file on a Thredds server, accessed via OpenDAP\n",
    "url = 'http://tds.webservice-energy.org/thredds/dodsC/surfrad-stations/SURFRAD-BON.nc'\n",
    "\n",
    "# Open the NetCDF file from the server (without downloading the data yet)\n",
    "data_nc = nc.Dataset(\"[FillMismatch]\"+url) \n",
    "\n",
    "# Show the meta data :\n",
    "print(data_nc)"
   ]
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   "source": [
    "\n",
    "\n",
    "The NetCDF file contains several variables:"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['station_name', 'latitude', 'longitude', 'elevation', 'Time', 'GHI', 'DHI', 'BNI', 'T2', 'WS', 'WD'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nc.variables.keys()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<br>\n",
    "Each variable contains metadata describing the variable. For example, for the variable GHI, the metadata gives the fullname (Global Horizontal Irradiance), the unit (W/m$^2$) and gives a comment that \"GHI is set to 0 for SZA >= 97 deg\"."
   ]
  },
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   "execution_count": 4,
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    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float32 GHI(time)\n",
       "    parameter: Short-wave downward (GLOBAL) radiation\n",
       "    long_name: Global Horizontal Irradiance\n",
       "    standard_name: surface_downwelling_shortwave_flux_in_air\n",
       "    abbreviation: SWD\n",
       "    units: W m-2\n",
       "    Range_LowerBoundary: -10.0\n",
       "    Range_UpperBoundary: 3000\n",
       "    Description: global, mean\n",
       "    least_significant_digit: 1\n",
       "    FirstData: 1997-05-11T00:00\n",
       "    LastData: 2022-02-27T23:59\n",
       "    _ChunkSizes: 1024\n",
       "unlimited dimensions: time\n",
       "current shape = (14284800,)\n",
       "filling off"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nc.variables['GHI']"
   ]
  },
  {
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     "name": "#%% md\n"
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   "source": [
    "<br>\n",
    "\n",
    "Furthermore, this dataset contains three variables related to the location of the BSRN station: latitude, longitude and elevation."
   ]
  },
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     "name": "#%%\n"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude = 40.05° N\n",
      "Longitude = -88.37° E\n",
      "Elevation = 213.0m\n"
     ]
    }
   ],
   "source": [
    "lat  = data_nc.variables['latitude'][:]\n",
    "lon  = data_nc.variables['longitude'][:]\n",
    "elev = data_nc.variables['elevation'][:]\n",
    "print(\"Latitude = %s° N\" % lat)\n",
    "print(\"Longitude = %s° E\" % lon) \n",
    "print(\"Elevation = %sm\" % elev)"
   ]
  },
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     "name": "#%% md\n"
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   "source": [
    "<br>\n",
    "Next, the time series data is extracted and saved to individual variables. \n",
    "For demonstration purposes, only measurement data for the first three years are extracted."
   ]
  },
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     "name": "#%%\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.54 s, sys: 82.7 ms, total: 9.62 s\n",
      "Wall time: 10.3 s\n"
     ]
    },
    {
     "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>GHI</th>\n",
       "      <th>BNI</th>\n",
       "      <th>DHI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:01:00</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:02:00</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:03:00</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01 00:04:00</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GHI  BNI  DHI\n",
       "2019-03-01 00:00:00 -1.3  0.4  0.1\n",
       "2019-03-01 00:01:00 -1.3  0.4 -0.2\n",
       "2019-03-01 00:02:00 -1.3  0.4 -0.3\n",
       "2019-03-01 00:03:00 -1.3  0.4 -0.4\n",
       "2019-03-01 00:04:00 -1.3  0.4 -0.4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "NB_SAMPLES=60*24*365*3 # 3 years of data\n",
    "\n",
    "# Loading and convert the time vector \n",
    "time=pd.to_datetime(\n",
    "    data_nc.variables['Time'][-NB_SAMPLES:], unit='s', origin=pd.Timestamp('1970-01-01'))\n",
    "\n",
    "# Download measurements of the three components of the solar irradiance\n",
    "GHI = data_nc.variables['GHI'][-NB_SAMPLES:].filled(np.nan)\n",
    "DHI = data_nc.variables['DHI'][-NB_SAMPLES:].filled(np.nan)\n",
    "BNI = data_nc.variables['BNI'][-NB_SAMPLES:].filled(np.nan)\n",
    "\n",
    "# Build a Pandas Dataframe\n",
    "data = pd.DataFrame({'GHI': GHI,'BNI': BNI,'DHI':DHI}, index=time)\n",
    "\n",
    "# Show first values\n",
    "data.head()"
   ]
  },
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    "pycharm": {
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   "source": [
    "## Option 2) Use `libinsitu`\n",
    "\n",
    "We developped [libinsitu](https://git.sophia.mines-paristech.fr/oie/libinsitu), a library providing several utils to read and manipulate NetCDF files for in situ data.\n",
    "\n",
    "This library provides the function [`nc2df()`](https://git.sophia.mines-paristech.fr/oie/libinsitu#nc2df) that automize all the steps above to read NetCDF data into a Panda Dataframe :"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {
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     "end_time": "2022-05-05T13:48:58.301833Z",
     "start_time": "2022-05-05T13:48:57.126309Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from libinsitu.common import nc2df\n",
    "\n",
    "start = datetime.now() - timedelta(days=365*3)\n",
    "\n",
    "# Load data over OpenDAP into a pandas DataFrame\n",
    "# 'url' is defined in the section above \n",
    "data = nc2df(url, vars=[\"GHI\", \"DHI\", \"BNI\"], start_time=start, drop_duplicates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'description': 'Archive of solar radiation networks worldwide provided by the Webservice-Energy initiative supported by MINES Paris PSL. Files are provided as NetCDF file format with the support of a Thredds Data Server.', 'title': \"Timeseries of NOAA's Surface Radiation Budget Network (SURFRAD) (SURFRAD)\", 'keywords': 'meteorology, station, time, Earth Science > Atmosphere > Atmospheric Radiation > Incoming Solar Radiation, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature, Earth Science > Atmosphere > Atmospheric Pressure > Sea Level Pressure', 'keywords_vocabulary': 'GCMD Science Keywords', 'keywords_vocabulary_url': 'https://gcmd.earthdata.nasa.gov/static/kms/', 'contact': 'Lionel MENARD, Raphael JOLIVET, Yves-Marie SAINT-DRENAN, Philippe BLANC', 'institution': ' MINES Paris PSL', 'record': 'Basic measurements (global irradiance, direct irradiance, diffuse irradiance, air temperature, relative humidity, pressure)', 'Network_ShortName': 'SURFRAD', 'Network_Name': \"NOAA's Surface Radiation Budget Network (SURFRAD)\", 'Network_Region': 'USA', 'Network_TimeResolution': '1 min', 'Network_Description': 'https://gml.noaa.gov/grad/surfrad/sitepage.html ', 'Network_References': 'https://doi.org/10.1175/1520-0477(1996)077<2857:TNISIS>2.0.CO;2', 'Network_License': ' -', 'Network_Contact': 'https://gml.noaa.gov/about/contacts.html', 'Station_ID': 'BON', 'Station_Name': 'Bondville, Illinois', 'Station_Latitude': '40.05', 'Station_Longitude': '-88.37', 'Station_Elevation': '213', 'Station_LocalTimeZone': 'UTC-05:00', 'Station_Address': '1165, County Road 500 East', 'Station_Country': 'United States', 'Station_KoeppenGeigerClimate': 'Dfa', 'Station_TimeResolution': '1', 'featureType': 'TimeSeries', 'cdm_data_type': 'TimeSeries', 'Conventions': 'CF-1.6', 'cdm_timeseries_variables': 'station_name,latitude,longitude', 'creation_time': '2022-04-13T15:56:23.468163', 'update_time': '2022-04-13T15:56:23.468163', 'Station_DataBegin': '1995-01-01', 'DODS.strlen': 10, 'DODS.dimName': 'ncshort', 'DODS_EXTRA.Unlimited_Dimension': 'time'}\n",
      "                            GHI         DHI         BNI\n",
      "2019-05-06 17:21:00  644.500000  490.100006  151.100006\n",
      "2019-05-06 17:22:00  673.400024  490.500000  182.800003\n",
      "2019-05-06 17:23:00         NaN  487.000000  138.199997\n",
      "2019-05-06 17:24:00         NaN  484.500000  107.000000\n",
      "2019-05-06 17:25:00  675.799988  487.399994  190.199997\n"
     ]
    }
   ],
   "source": [
    "# Meta data is attached to the resulting dataframe\n",
    "print(data.attrs)\n",
    "\n",
    "# libinsitu took care of transforming the time series, replacing missing vals by Nans\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 3: Calculation of sun position\n",
    "***\n",
    "\n",
    "In this tutorial, the sun position will be calculated using the wps_SG2 webservice available through the python package *solargeometry*. A request is sent to the webservice, which carries out the calculations and returns the results. For longer time series, this method is significantly faster than calculating the sun position locally using, for example, the pvlib-python library.\n",
    "\n",
    "\n",
    "output\n",
    "    The output is a python list of fields identified by python strings.  Each\n",
    "    given fields will be computed and provided in the output o as follow :\n",
    "    *\"field.name\"* will be *o.field.name*.  Possible fields and there output\n",
    "    type are:\n",
    "\n",
    "    * \"geoc.ut\" **timestamp** as array of datetime64[ms]\n",
    "    * \"geoc.tt\" **terrestrial time** as array of datetime64[ms]\n",
    "    * \"geoc.R\" **Radius Sun-Earth** in astronomical unit (au) as array of double\n",
    "    * \"geoc.L\" **Heliocentric Earth true longitude** in radians as array of double\n",
    "    * \"geoc.delta\" **Geocentric declination** in radians as array of double\n",
    "    * \"geoc.EOT\" **Equation of Time: difference between apparent solar time and mean solar time** in radians as array of double (to be multiplied by pi/12 for hourly values)\n",
    "    * \"geoc.Theta_a\" **Geocentric Earth true longitude** in radians as array of double\n",
    "    * \"geoc.epsilon\" **Earth true obliquity** in radians as array of double\n",
    "    * \"geoc.nu\" **Apparent sideral time** in radians as array of double\n",
    "    * \"geoc.r_alpha\" **Geocentric right ascension** in radians as array of double\n",
    "    * \"geoc.Delta_psi\" **Nutation in Geocentric Sun longitude** in radians as array of double\n",
    "    * \"gp.lambda\" **Longitude** in radians as array of double\n",
    "    * \"gp.phi\" **Latitude** in radians as array of double\n",
    "    * \"gp.u\" **phi geocentric**\n",
    "    * \"gp.x\"\n",
    "    * \"gp.y\"\n",
    "    * \"topoc.delta\" **Topocentric sun declination** in radians as array of double\n",
    "    * \"topoc.alpha_S\" **Topocentric sun azimuth** in radians as array of double\n",
    "    * \"topoc.gamma_S0\" **Topocentric sun elevation angle without correction of atm. refraction.** in radians as array of double\n",
    "    * \"topoc.omega\" **Topocentric local hour angle** in radians as array of double\n",
    "    * \"topoc.r_alpha\" **Topocentric right sun ascension** in radians as array of double\n",
    "    * \"topoc.toa_hi\" **Top of atmosphere horizontal irradiance** in W.m^{-2} as array of double\n",
    "    * \"topoc.toa_ni\" **Top of atmosphere normal irradiance** in W.m^{-2} as array of double\n",
    "\n",
    "    The abreviations geoc and topoc stand for respectively geocentric and topocentric.\n",
    "\n",
    "    Moreover the size of arrays of geoc.* fields will be (M,), the size of\n",
    "    arrays of gp.* fields will be (N,) and the size of arrays of topoc.* will\n",
    "    be (N,M).\n",
    "\n",
    "\n",
    "This library can be installed via pip:  \"pip install sg2 -f https://pip.oie-lab.net/python-devel/ \"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T13:49:02.411676Z",
     "start_time": "2022-05-05T13:49:01.698410Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "namespace(geoc=namespace(EOT=array([245.05889599, 245.0588962 , 245.05889642, ..., 263.83887605,\n",
      "       263.8388766 , 263.83887715])), gp=namespace(), topoc=namespace(alpha_S=array([[4.17638793, 4.17200626, 4.16762444, ..., 2.51279869, 2.50844836,\n",
      "        2.50409799]]), delta=array([[ 0.2896983 ,  0.28970169,  0.28970508, ..., -0.1405939 ,\n",
      "        -0.14058932, -0.14058475]]), gamma_S0=array([[-0.30431925, -0.30442921, -0.30453889, ...,  0.11760394,\n",
      "         0.11767259,  0.11774168]]), omega=array([[90542.81454369, 90542.81890723, 90542.82327076, ...,\n",
      "        97022.43689043, 97022.4412543 , 97022.44561817]]), r_alpha=array([[ 0.75810646,  0.75811819,  0.75812993, ..., -0.33265966,\n",
      "        -0.33264826, -0.33263686]]), toa_hi=array([[  0.        ,   0.        ,   0.        , ..., 162.89569924,\n",
      "        162.99029448, 163.08548908]]), toa_ni=array([[   0.        ,    0.        ,    0.        , ..., 1388.31912959,\n",
      "        1388.31865731, 1388.31818502]])))\n"
     ]
    }
   ],
   "source": [
    "gp = [lat, lon, elev]\n",
    "\n",
    "p = sg2.sun_position([gp], data.index.values,\n",
    "                     [\"geoc.EOT\",\n",
    "                      \"topoc.omega\",\n",
    "                      \"topoc.gamma_S0\",\n",
    "                      \"topoc.alpha_S\",\n",
    "                      \"topoc.delta\",\n",
    "                      \"topoc.r_alpha\",\n",
    "                      \"topoc.toa_hi\",\n",
    "                      \"topoc.toa_ni\"])\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Computation of sun rise and sunset with `sg2`.\n",
    "\n",
    "\n",
    "def sun_rise(ggeopoint, timestamp)\n",
    "- input geopoints: (N,3) of double each row is lon in degrees, lat in degrees, altitude in metter.\n",
    "  - input timespamp: (M,) of double or datetime64. the requested timestamp in ut\n",
    "  - output: (N,M,3) datetime64[ms] with repectively sun rise, sun transit and\n",
    "sun set in ut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T13:49:50.731382Z",
     "start_time": "2022-05-05T13:49:49.627680Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1480719, 1, 3)\n"
     ]
    }
   ],
   "source": [
    "SR=sg2.sun_rise([gp], data.index.values)\n",
    "print(SR.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Merge and save data\n",
    "\n",
    "The final step is to combine the measurement time series and the sun position time series into one Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T13:55:27.665026Z",
     "start_time": "2022-05-05T13:55:27.592230Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>GHI</th>\n",
       "      <th>BNI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>DELTA</th>\n",
       "      <th>OMEGA</th>\n",
       "      <th>EOT</th>\n",
       "      <th>THETA_Z</th>\n",
       "      <th>GAMMA_S0</th>\n",
       "      <th>ALPHA_S</th>\n",
       "      <th>R</th>\n",
       "      <th>TOA</th>\n",
       "      <th>TOANI</th>\n",
       "      <th>SR_h</th>\n",
       "      <th>SS_h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-06 17:21:00</th>\n",
       "      <td>644.500000</td>\n",
       "      <td>151.100006</td>\n",
       "      <td>490.100006</td>\n",
       "      <td>0.289698</td>\n",
       "      <td>90542.814544</td>\n",
       "      <td>245.058896</td>\n",
       "      <td>1.875116</td>\n",
       "      <td>-0.304319</td>\n",
       "      <td>4.176388</td>\n",
       "      <td>0.758106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 17:22:00</th>\n",
       "      <td>673.400024</td>\n",
       "      <td>182.800003</td>\n",
       "      <td>490.500000</td>\n",
       "      <td>0.289702</td>\n",
       "      <td>90542.818907</td>\n",
       "      <td>245.058896</td>\n",
       "      <td>1.875226</td>\n",
       "      <td>-0.304429</td>\n",
       "      <td>4.172006</td>\n",
       "      <td>0.758118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 17:23:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>138.199997</td>\n",
       "      <td>487.000000</td>\n",
       "      <td>0.289705</td>\n",
       "      <td>90542.823271</td>\n",
       "      <td>245.058896</td>\n",
       "      <td>1.875335</td>\n",
       "      <td>-0.304539</td>\n",
       "      <td>4.167624</td>\n",
       "      <td>0.758130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 17:24:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>484.500000</td>\n",
       "      <td>0.289708</td>\n",
       "      <td>90542.827634</td>\n",
       "      <td>245.058897</td>\n",
       "      <td>1.875445</td>\n",
       "      <td>-0.304648</td>\n",
       "      <td>4.163242</td>\n",
       "      <td>0.758142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 17:25:00</th>\n",
       "      <td>675.799988</td>\n",
       "      <td>190.199997</td>\n",
       "      <td>487.399994</td>\n",
       "      <td>0.289712</td>\n",
       "      <td>90542.831998</td>\n",
       "      <td>245.058897</td>\n",
       "      <td>1.875554</td>\n",
       "      <td>-0.304757</td>\n",
       "      <td>4.158860</td>\n",
       "      <td>0.758153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            GHI         BNI         DHI     DELTA  \\\n",
       "2019-05-06 17:21:00  644.500000  151.100006  490.100006  0.289698   \n",
       "2019-05-06 17:22:00  673.400024  182.800003  490.500000  0.289702   \n",
       "2019-05-06 17:23:00         NaN  138.199997  487.000000  0.289705   \n",
       "2019-05-06 17:24:00         NaN  107.000000  484.500000  0.289708   \n",
       "2019-05-06 17:25:00  675.799988  190.199997  487.399994  0.289712   \n",
       "\n",
       "                            OMEGA         EOT   THETA_Z  GAMMA_S0   ALPHA_S  \\\n",
       "2019-05-06 17:21:00  90542.814544  245.058896  1.875116 -0.304319  4.176388   \n",
       "2019-05-06 17:22:00  90542.818907  245.058896  1.875226 -0.304429  4.172006   \n",
       "2019-05-06 17:23:00  90542.823271  245.058896  1.875335 -0.304539  4.167624   \n",
       "2019-05-06 17:24:00  90542.827634  245.058897  1.875445 -0.304648  4.163242   \n",
       "2019-05-06 17:25:00  90542.831998  245.058897  1.875554 -0.304757  4.158860   \n",
       "\n",
       "                            R  TOA  TOANI SR_h SS_h  \n",
       "2019-05-06 17:21:00  0.758106  0.0    0.0  NaT  NaT  \n",
       "2019-05-06 17:22:00  0.758118  0.0    0.0  NaT  NaT  \n",
       "2019-05-06 17:23:00  0.758130  0.0    0.0  NaT  NaT  \n",
       "2019-05-06 17:24:00  0.758142  0.0    0.0  NaT  NaT  \n",
       "2019-05-06 17:25:00  0.758153  0.0    0.0  NaT  NaT  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = pd.DataFrame({'GHI': data.GHI,\n",
    "                   'BNI': data.BNI,\n",
    "                   'DHI': data.DHI,\n",
    "                   'DELTA':np.squeeze(p.topoc.delta),\n",
    "                   'OMEGA':np.squeeze(p.topoc.omega),\n",
    "                   'EOT':np.squeeze(p.geoc.EOT),\n",
    "                   'THETA_Z':np.squeeze(np.pi/2-p.topoc.gamma_S0),\n",
    "                   'GAMMA_S0':np.squeeze(p.topoc.gamma_S0),\n",
    "                   'ALPHA_S':np.squeeze(p.topoc.alpha_S),\n",
    "                   'R':np.squeeze(p.topoc.r_alpha),\n",
    "                   'TOA':np.squeeze(p.topoc.toa_hi),\n",
    "                   'TOANI':np.squeeze(p.topoc.toa_ni),\n",
    "                   'SR_h':SR[:,0,0],\n",
    "                   'SS_h':SR[:,0,2],\n",
    "                  }, index=data.index.values)\n",
    "out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Copy meta data into merged dataframe\n",
    "out.attrs = data.attrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "It is possible to save Dataframes into various formats.\n",
    "Here we choose \"Parquet\" format. A compact and portable format :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T13:55:38.502489Z",
     "start_time": "2022-05-05T13:55:37.951663Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./tmpData/data.parquet\n",
      "./tmpData/data.meta\n"
     ]
    }
   ],
   "source": [
    "# Save data to parquet file\n",
    "print(TMP_FILE)\n",
    "out.to_parquet(TMP_FILE)\n",
    "\n",
    "# Save meta data to json file\n",
    "print(META_FILE)\n",
    "save_meta(out, META_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T13:56:05.102950Z",
     "start_time": "2022-05-05T13:56:05.095498Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Plots\n",
    "\n",
    "Once all the data is combined into one DataFrame, manipulation and plotting become relatively easy tasks.\n",
    "\n",
    "## Raw time series\n",
    "\n",
    "The first month of the GHI, DHI, and BNI is plotted with the original 1 minute resolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T13:59:04.547102Z",
     "start_time": "2022-05-05T13:59:03.707566Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = out.iloc[0:30*60*24][[\"GHI\", \"DHI\", \"BNI\"]].plot(figsize=(12,5), subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Monthly average\n",
    "\n",
    "Here, we plot the monthly average irradiance as a bar chart, can be done in one line as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-05T14:00:20.442944Z",
     "start_time": "2022-05-05T14:00:19.857291Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = out[['GHI','DHI','BNI']].resample('1M').mean().plot(figsize=(12,6), kind='bar', subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
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