simple_example_use.ipynb

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import pandas as pd\n\nimport datatools\nimport gsee",
     "prompt_number": 1,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "sites = ['Sede Boqer', 'Solar Village', 'Tamanrasset']\n\ncoordinates = [(30.8597, 34.7794), (24.91, 46.41), (22.7903, 5.5292)]",
     "prompt_number": 2,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Get AOD data",
     "level": 1
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Not included here (happens in `~/Data/Solar/ImperialDust/read_dust-cleaned.ipynb`)."
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Get MERRA data",
     "level": 1
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "NB: this code only runs with access to MERRA data saved as compressed or uncompressed NetCDF from the MAT1NXRAD table (in `merra_path`) -- ignore if no MERRA data available"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# get CSV files with MERRA data - requires access to the downloaded data\nmerra_path = '/Volumes/data/merra_imperialdust'\ndates = pd.date_range('2008-01-01', '2009-12-31')\ndata = datatools.get_merra(merra_path, coordinates,\n                           ['swtdn', 'swgdn', 'albedo'], dates)",
     "prompt_number": 3,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# This only works if `sites` is a list with the same order as `coordinates`\n\nfor i in range(len(data)):\n    data.ix[i].to_csv('merra ' + sites[i] + '.csv')",
     "prompt_number": 6,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Apply BRL model to MERRA data",
     "level": 1
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Read CSV file with MERRA data\nsite_data = pd.read_csv('merra Solar Village.csv', index_col=0,\n                        parse_dates=True)",
     "prompt_number": 11,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Global horizontal irradiance and clearness index for the BRL model\nghi = site_data['swgdn']\nclearness = site_data['swgdn'] / site_data['swtdn']\n\n# Coordinates for the selected site\nsite_coords = coordinates[1]  # pick the right entry from coordinates list\n\nresult = gsee.processing.apply_brl_model(ghi, clearness, site_coords)",
     "prompt_number": 12,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Get irradiance on any aperture",
     "level": 1
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "result.columns",
     "prompt_number": 14,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "metadata": {},
       "text": "Index([u'global_horizontal', u'diffuse_fraction', u'direct_normal'], dtype='object')"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "dir_horiz = result.global_horizontal * (1- result.diffuse_fraction)\ndiff_horiz = result.global_horizontal * result.diffuse_fraction\n\nplane_irradiance = gsee.trigon.aperture_irradiance(dir_horiz, diff_horiz,\n                                                   coordinates[1], tracking=1,\n                                                   azimuth=1.2)",
     "prompt_number": 15,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Arguments for `aperture_irradiance`:\n\n* direct : a series of direct horizontal irradiance with a datetime index\n* diffuse : a series of diffuse horizontal irradiance with the same datetime index as for direct\n* coords : (lat, lon) tuple of location coordinates\n* tilt : angle of panel relative to the horizontal plane, 0 = flat\n* azimuth : deviation of the tilt direction from the meridian, 0 = towards pole, going clockwise, 3.14 = towards equator\n* tracking : 0 (none, default), 1 (tilt), or 2 (tilt and azimuth). If 1, azimuth is the orientation of the tilt axis, which can be horizontal (tilt=0) or tilted.\n* albedo : reflectance of the surrounding surface\n* dni_only : only calculate and directly return a DNI time series (ignores tilt, azimuth, tracking and albedo arguments)"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "",
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    }
   ],
   "metadata": {}
  }
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