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    "Dependencies | Version\n",
    "--- | ---\n",
    "SatPy | 0.9.0\n",
    "\n",
    "# Cartopy Plot\n",
    "\n",
    "This example loads multiple VIIRS SDR granules of band I04 data, resamples them to a known `AreaDefinition`, and uses matplotlib to plot the data on a map. Although cartopy is not imported directly, we use pyresample's `to_cartopy_crs()` method to create a cartopy CRS object and use that in the plotting.\n",
    "\n",
    "This example should act as a starting point for plotting data processed with SatPy. Additional information can be found on [XArray's plotting documentation](http://xarray.pydata.org/en/stable/plotting.html#maps) as well as [Cartopy's documentation](https://scitools.org.uk/cartopy/docs/latest/gallery/geostationary.html#sphx-glr-gallery-geostationary-py) for help building more complex plots."
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   "source": [
    "## Load data\n",
    "\n",
    "Using the `glob` module makes it easy to get a series of data files. After creating the Scene object, we load the I04 band data."
   ]
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   "source": [
    "from satpy import Scene\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "filenames = glob('/data/data/viirs/conus_day/*t180*.h5')\n",
    "scn = Scene(reader='viirs_sdr', filenames=filenames)\n",
    "scn.load(['I04'])"
   ]
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   "source": [
    "## Resample to a uniform area\n",
    "\n",
    "Next we put the VIIRS data on a high resolution uniform grid. We could have also used any other area definition. For polar-orbiting satellite data it is best to resample the data first before putting it on a plot due to the size of the lon/lat arrays that would need to be used. For geo-stationary satellite data, which is typically already gridded, the source data's area can be used directly in the plotting steps below."
   ]
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     "text": [
      "/Users/davidh/anaconda/envs/polar2grid_py36/lib/python3.6/site-packages/dask/local.py:271: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  return func(*args2)\n",
      "/Users/davidh/anaconda/envs/polar2grid_py36/lib/python3.6/site-packages/dask/local.py:271: RuntimeWarning: invalid value encountered in less_equal\n",
      "  return func(*args2)\n"
     ]
    }
   ],
   "source": [
    "my_area = scn['I04'].attrs['area'].compute_optimal_bb_area({'proj': 'lcc', 'lon_0': -95., 'lat_0': 25., 'lat_1': 25., 'lat_2': 25.})\n",
    "new_scn = scn.resample(my_area)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Plot data using matplotlib and cartopy\n",
    "\n",
    "To plot the data using cartopy we first convert the pyresample AreaDefinition to a cartopy CRS and then use that to create matplotlib axes."
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "/Users/davidh/repos/git/pyresample/pyresample/_cartopy.py:36: UserWarning: 'cartopy' >= 0.17 required for better 'from_proj' functionality.\n",
      "  warnings.warn(\"'cartopy' >= 0.17 required for better 'from_proj' \"\n"
     ]
    }
   ],
   "source": [
    "crs = new_scn['I04'].attrs['area'].to_cartopy_crs()\n",
    "ax = plt.axes(projection=crs)\n",
    "\n",
    "ax.coastlines()\n",
    "ax.gridlines()\n",
    "ax.set_global()\n",
    "plt.imshow(new_scn['I04'], transform=crs, extent=crs.bounds, origin='upper')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Kelvin\")\n",
    "plt.show()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Cartopy Plot 1.png)"
   ]
  }
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