This example covers the callable methods of the Python AWIPS DAF when working with gridded data. We start with a connection to an EDEX server, then query data types, then grid names, parameters, levels, and other information. Finally the gridded data is plotted for its domain using Matplotlib and Cartopy.
In [1]:
from awips.dataaccess import DataAccessLayer
DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
dataTypes = DataAccessLayer.getSupportedDatatypes()
list(dataTypes)
Out[1]:
In [2]:
request = DataAccessLayer.newDataRequest()
request.setDatatype("grid")
available_grids = DataAccessLayer.getAvailableLocationNames(request)
available_grids.sort()
list(available_grids)
Out[2]:
In [3]:
request.setLocationNames("RAP13")
availableParms = DataAccessLayer.getAvailableParameters(request)
availableParms.sort()
list(availableParms)
Out[3]:
In [4]:
request.setParameters("T")
availableLevels = DataAccessLayer.getAvailableLevels(request)
for level in availableLevels:
print(level)
request.setLevels()
For this example we will use Surface Temperature
In [5]:
request.setLevels("2.0FHAG")
YYYY-MM-DD HH:MM:SSYYYY-MM-DD HH:MM:SS (F:ff)
In [6]:
cycles = DataAccessLayer.getAvailableTimes(request, True)
times = DataAccessLayer.getAvailableTimes(request)
fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)
list(fcstRun)
Out[6]:
In [7]:
response = DataAccessLayer.getGridData(request, [fcstRun[-1]])
for grid in response:
data = grid.getRawData()
lons, lats = grid.getLatLonCoords()
print('Time :', str(grid.getDataTime()))
print('Model:', str(grid.getLocationName()))
print('Parm :', str(grid.getParameter()))
print('Unit :', str(grid.getUnit()))
print(data.shape)
In [8]:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import numpy as np
import numpy.ma as ma
from scipy.io import loadmat
def make_map(bbox, projection=ccrs.PlateCarree()):
fig, ax = plt.subplots(figsize=(16, 9),
subplot_kw=dict(projection=projection))
ax.set_extent(bbox)
ax.coastlines(resolution='50m')
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
return fig, ax
cmap = plt.get_cmap('rainbow')
bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
fig, ax = make_map(bbox=bbox)
cs = ax.pcolormesh(lons, lats, data, cmap=cmap)
cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')
cbar.set_label(grid.getLocationName().decode('UTF-8') +" " \
+ grid.getLevel().decode('UTF-8') + " " \
+ grid.getParameter().decode('UTF-8') \
+ " (" + grid.getUnit().decode('UTF-8') + ") " \
+ "valid " + str(grid.getDataTime().getRefTime()))
2. contourf
In [9]:
fig2, ax2 = make_map(bbox=bbox)
cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,
vmin=data.min(), vmax=data.max())
cbar2 = fig2.colorbar(cs2, extend='both', shrink=0.5, orientation='horizontal')
cbar2.set_label(grid.getLocationName().decode('UTF-8') +" " \
+ grid.getLevel().decode('UTF-8') + " " \
+ grid.getParameter().decode('UTF-8') \
+ " (" + grid.getUnit().decode('UTF-8') + ") " \
+ "valid " + str(grid.getDataTime().getRefTime()))