NSRDB-Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# notebook based on: https://gist.github.com/PjEdwards/748085ffe2e0e527165891203c633a59#file-nsrdb-data-py-L29-L189"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import h5pyd as h5py\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import os\n",
    "import csv\n",
    "from datetime import datetime\n",
    "from pvlib import solarposition, tracking  # need to pip install this\n",
    "from pandas import DatetimeIndex\n",
    "import math\n",
    "import numbers\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map PSM cloud_type to one of clear = 0, cloudy = 1 where the\n",
    "# array index is the cloud_type id\n",
    "def clear_or_cloudy(cloud_type):\n",
    "    return 0 if cloud_type < 2 else 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# these valus are pased into the from_psm function in the real code\n",
    "year = 2015  # valid years: 1998 - 2017\n",
    "site_hdf5_id=151106\n",
    "equipment_type=32.0  # ? jlr what should this be?\n",
    "phipv=180.0\n",
    "tilt=30.0\n",
    "THREAD_COUNT=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "site_hdf5_id=151106, equipment_type=FIXED_TILT, phipv=180.0, tilt=30.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.utcnow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The attributes we'll include in the output\n",
    "public_psm3_attributes = [\n",
    "            'air_temperature',\n",
    "            'clearsky_dhi',\n",
    "            'clearsky_dni',\n",
    "            'clearsky_ghi',\n",
    "            'cloud_type',\n",
    "            'dew_point',\n",
    "            'dhi',\n",
    "            'dni',\n",
    "            'fill_flag',\n",
    "            'ghi',\n",
    "            'relative_humidity',\n",
    "            'solar_zenith_angle',\n",
    "            'surface_albedo',\n",
    "            'surface_pressure',\n",
    "            'total_precipitable_water',\n",
    "            'wind_direction',\n",
    "            'wind_speed'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The attributes not part of the public list that are needed for\n",
    "# the spectral processing tool\n",
    "private_psm3_attributes = [\n",
    "            'aod',\n",
    "            'cloud_press_acha',\n",
    "            'cld_opd_dcomp',\n",
    "            'cld_reff_dcomp'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "######## Hard coded input attributes #########\n",
    "ATMOS = 'USSA'\n",
    "SOLAR = 1366.1\n",
    "CO_2 = 330.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate this inline below for each time slice using\n",
    "# http://pvlib-python.readthedocs.io/en/latest/generated/pvlib.solarposition.spa_python.html\n",
    "phi0 = solar_azimuth = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "######### Operational Variables #########\n",
    "base_dir = os.path.split(os.getcwd())[0]\n",
    "out_dir = os.path.join(base_dir, \"results\")\n",
    "data_dir = \"/nrel/nsrdb/\"\n",
    "data_file = os.path.join(data_dir, 'nsrdb_%d.h5' % year)\n",
    "is_leap_year = year in range(1980, 2100, 4)\n",
    "public_data = {}\n",
    "private_data = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File(data_file, \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',\n",
       "               '2015-01-01 02:00:00', '2015-01-01 03:00:00',\n",
       "               '2015-01-01 04:00:00', '2015-01-01 05:00:00',\n",
       "               '2015-01-01 06:00:00', '2015-01-01 07:00:00',\n",
       "               '2015-01-01 08:00:00', '2015-01-01 09:00:00',\n",
       "               ...\n",
       "               '2015-12-31 14:00:00', '2015-12-31 15:00:00',\n",
       "               '2015-12-31 16:00:00', '2015-12-31 17:00:00',\n",
       "               '2015-12-31 18:00:00', '2015-12-31 19:00:00',\n",
       "               '2015-12-31 20:00:00', '2015-12-31 21:00:00',\n",
       "               '2015-12-31 22:00:00', '2015-12-31 23:00:00'],\n",
       "              dtype='datetime64[ns]', length=8760, freq=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_index_vals = []\n",
    "arr = f['time_index'][0::2]\n",
    "for e in arr:\n",
    "    time_index_vals.append(e.decode('utf-8'))\n",
    "dti = DatetimeIndex(time_index_vals)\n",
    "dti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab time index, use the map function to turn into arrays of vals,\n",
    "# splat turns the array of arrays into a big bunch of params passed\n",
    "# into the zip method which orients the data into arrays of year, month,\n",
    "# day, etc which is how the csv writing code needs it later\n",
    "ti_csv = list(zip(*(map(lambda d: [d.year, d.month, d.day, d.hour, d.minute], dti))))\n",
    "public_data['year'] = ti_csv[0]\n",
    "public_data['month'] = ti_csv[1]\n",
    "public_data['day'] = ti_csv[2]\n",
    "public_data['hour'] = ti_csv[3]\n",
    "public_data['minute'] = ti_csv[4]\n",
    "# Collect a little site data needed for calcs\n",
    "meta = f['meta'][site_hdf5_id]\n",
    "lat = meta[0]\n",
    "lon = meta[1]\n",
    "elevation = meta[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate solar zenith and azimuth for each timeslice\n",
    "solar_pos = solarposition.spa_python(dti, lat, lon, elevation)\n",
    "azimuth = solar_pos['azimuth'].values.tolist()\n",
    "public_data['solar_azimuth_angle'] = azimuth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jlr - ignoring equipment_type == SINGLE_AXIS\n",
    "# Stretch phipv and tilt vals into full size arrays\n",
    "panel_tilt = np.full(len(azimuth), tilt)\n",
    "panel_azimuth = np.full(len(azimuth), phipv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14. -16. -17. ... -12. -13. -15.]\n",
      "[ 0.  0.  0. ... 60. 48. 30.]\n",
      "[  0.   0.   0. ... 976. 896. 699.]\n",
      "[  0.   0.   0. ... 422. 287. 119.]\n",
      "[1. 0. 0. ... 0. 0. 0.]\n",
      "[-15. -17. -18. ... -14. -15. -18.]\n",
      "[ 0.  0.  0. ... 60. 48. 30.]\n",
      "[  0.   0.   0. ... 976. 896. 699.]\n",
      "[0 0 0 ... 0 0 0]\n",
      "[  0.   0.   0. ... 422. 287. 119.]\n",
      "[86.46 88.82 84.84 ... 81.32 83.45 76.93]\n",
      "<map object at 0x7f85cedb8940>\n",
      "[0.866 0.866 0.866 ... 0.866 0.866 0.866]\n",
      "[730. 730. 730. ... 740. 740. 740.]\n",
      "[0.366 0.348 0.327 ... 0.166 0.166 0.165]\n",
      "[211.7 210.6 206.  ... 246.2 243.4 242.9]\n",
      "[4.6 4.2 4.1 ... 3.4 3.4 2.5]\n"
     ]
    }
   ],
   "source": [
    "# Grab all the public_data PSM data\n",
    "for ds_name in public_psm3_attributes:\n",
    "    ds = f[ds_name]\n",
    "    tmp = ds[0::2, site_hdf5_id]\n",
    "    if 'scale_factor' in ds.attrs.keys():\n",
    "        scale_factor = ds.attrs['scale_factor']\n",
    "        tmp = tmp * scale_factor\n",
    "    elif 'psm_scale_factor' in ds.attrs.keys():\n",
    "        scale_factor = ds.attrs['psm_scale_factor']\n",
    "        tmp = tmp/scale_factor\n",
    "    # special validation for solar zenith angle\n",
    "    if(ds_name == 'solar_zenith_angle'):\n",
    "        # zero out any nightime values (>0 and <90)\n",
    "        tmp = map(lambda z: z if (z < 86 and z > 0) else -9999.0, tmp)\n",
    "        # count valid zenith angles\n",
    "        mask = ma.masked_equal(tmp, -9999.0)\n",
    "        usable_time_slices = len(mask[~mask.mask])\n",
    "    public_data[ds_name] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cloud_press_acha not found\n"
     ]
    }
   ],
   "source": [
    "# Grab all the private_data PSM data\n",
    "for ds_name in private_psm3_attributes:\n",
    "    if ds_name not in f:\n",
    "        print(f\"{ds_name} not found\")\n",
    "        continue\n",
    "    ds = f[ds_name]\n",
    "    scale_factor = None\n",
    "    add_offset = None\n",
    "    tmp = ds[0::2, site_hdf5_id]\n",
    "    if 'psm_scale_factor' in ds.attrs.keys():\n",
    "        scale_factor = ds.attrs['psm_scale_factor']\n",
    "        tmp = tmp/scale_factor\n",
    "    if 'psm_add_offset' in ds.attrs.keys():\n",
    "        add_offset = ds.attrs['psm_add_offset']\n",
    "        tmp = tmp + add_offset\n",
    "    # enforce valid ranges and custom scale and offset for these\n",
    "    if(ds_name == 'cld_opd_dcomp'):\n",
    "        tmp = map(lambda z: 0.0001 if z < 0.0001 else z, tmp)\n",
    "        tmp = map(lambda z: 300.0 if z > 300.0 else z, tmp)\n",
    "    elif(ds_name == 'cld_reff_dcomp'):\n",
    "        tmp = tmp * 2\n",
    "        tmp = map(lambda z: 10.0 if z < 10.0 else z, tmp)\n",
    "        tmp = map(lambda z: 120.0 if z > 120.0 else z, tmp)\n",
    "    elif(ds_name == 'cloud_press_acha'):\n",
    "        tmp = map(lambda z: 500.0 if z < 100.0 else z, tmp)\n",
    "        tmp = map(lambda z: 500.0 if z > 800.0 else z, tmp)\n",
    "\n",
    "    private_data[ds_name] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total rows / timeslices\n",
    "size_y = len(public_data['year'])\n",
    "# Prepare the data index for use in the loop\n",
    "data_idx = 0\n",
    "# Track how many rows of fortran input have been written\n",
    "written_row_count = 0\n",
    "# Calc how many timeslices to pass each fortran thread based on total usable\n",
    "input_rows_per_thread = int(math.ceil(float(usable_time_slices)/THREAD_COUNT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'PSM_ATTR_DISPLAY_NAME_MAP' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-5e9796a8cfe9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'psm.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf_out\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'month'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'day'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'hour'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'minute'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mpublic_psm3_attributes\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'solar_azimuth_angle'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'panel_tilt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'panel_azimuth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mcsv_header_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPSM_ATTR_DISPLAY_NAME_MAP\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mwriter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv_header_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-70-5e9796a8cfe9>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(k)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'psm.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf_out\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'month'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'day'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'hour'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'minute'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mpublic_psm3_attributes\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'solar_azimuth_angle'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'panel_tilt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'panel_azimuth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mcsv_header_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPSM_ATTR_DISPLAY_NAME_MAP\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mwriter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv_header_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'PSM_ATTR_DISPLAY_NAME_MAP' is not defined"
     ]
    }
   ],
   "source": [
    "# Open the CSV file for PSM data and write out the data\n",
    "with open(os.path.join(out_dir, 'psm.csv'), 'w') as f_out:\n",
    "    keys = ['year', 'month', 'day', 'hour', 'minute']+public_psm3_attributes+['solar_azimuth_angle', 'panel_tilt', 'panel_azimuth']\n",
    "    csv_header_1 = list(map(lambda k: PSM_ATTR_DISPLAY_NAME_MAP[k], keys))\n",
    "    writer = csv.writer(f_out, delimiter=',')\n",
    "    writer.writerow(csv_header_1)\n",
    "    writer.writerows(zip(*[public_data[key] for key in keys]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['air_temperature',\n",
       " 'clearsky_dhi',\n",
       " 'clearsky_dni',\n",
       " 'clearsky_ghi',\n",
       " 'cloud_type',\n",
       " 'coordinates',\n",
       " 'dew_point',\n",
       " 'dhi',\n",
       " 'dni',\n",
       " 'fill_flag',\n",
       " 'ghi',\n",
       " 'meta',\n",
       " 'relative_humidity',\n",
       " 'solar_zenith_angle',\n",
       " 'surface_albedo',\n",
       " 'surface_pressure',\n",
       " 'time_index',\n",
       " 'total_precipitable_water',\n",
       " 'wind_direction',\n",
       " 'wind_speed']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "wind_speed = f[\"wind_speed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17520, 2018392)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_speed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(336, 2976)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_speed.chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wind_speed.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 4 ms, total: 8 ms\n",
      "Wall time: 24 ms\n"
     ]
    }
   ],
   "source": [
    "%time arr = wind_speed[0:512, 0:512]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 137)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min(), arr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f85eea2ccf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(10,10),dpi=72)\n",
    "plt.imshow(arr, origin=\"lower\", vmin=0, vmax=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pong'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = b'pong'\n",
    "b.decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}