def testScan(self):
argv = 'cdscan -q -d test -x some_junk.xml u_2000.nc u_2001.nc u_2002.nc v_2000.nc v_2001.nc v_2002.nc'.split(
)
pth = cdat_info.get_sampledata_path()
os.chdir(pth)
cdscan(argv)
baseline = ET.parse("test.xml")
new = ET.parse("some_junk.xml")
b_root = baseline.getroot()
new_root = new.getroot()
results = diffElements(b_root, new_root)
self.assertIsNone(results)
os.unlink("some_junk.xml")
def testopenFile(self):
'''
retrieve value from cdscan
'''
argv = 'cdscan -x test_dap.xml https://dataserver.nccs.nasa.gov/thredds/dodsC/bypass/CREATE-IP/reanalysis/MERRA/mon/atmos/zg.ncml'.split(
)
pth = cdat_info.get_sampledata_path()
os.chdir(pth)
cdscan(argv)
f = cdms2.open("test_dap.xml")
s = f['zg']
self.assertAlmostEqual(s[0, 0, 0, 0], -73.563, 3)
os.unlink("test_dap.xml")
def testopenFile(self):
'''
retrieve value from cdscan
'''
data_file1 = "https://dpesgf03.nccs.nasa.gov/thredds/dodsC/CREATE-IP/reanalysis/NASA-GMAO/GEOS-5/MERRA/mon/atmos/cl/cl_Amon_reanalysis_MERRA_197901-197912.nc"
data_file2 = "https://dpesgf03.nccs.nasa.gov/thredds/dodsC/CREATE-IP/reanalysis/NASA-GMAO/GEOS-5/MERRA/mon/atmos/cl/cl_Amon_reanalysis_MERRA_198001-198012.nc"
f1 = cdms2.open(data_file1)
f2 = cdms2.open(data_file2)
cl1 = f1["cl"]
cl2 = f2["cl"]
argv = "cdscan -x test_dap.xml {f1} {f2}".format(
f1=data_file1, f2=data_file2).split()
pth = cdat_info.get_sampledata_path()
os.chdir(pth)
cdscan(argv)
f = cdms2.open("test_dap.xml")
s = f['cl']
self.assertEqual(cl1[5, 20, 80, 140], s[5, 20, 80, 140])
self.assertEqual(cl2[5, 20, 80, 140], s[17, 20, 80, 140])
os.unlink("test_dap.xml")
def testForecast(self):
# Generate some test data, then write it out. This section doesn't
# test anything.
xaxis = cdms2.createAxis([0.1, 0.2], id='x')
taxis = cdms2.createAxis([10.0, 20.0], id='t')
taxis.units = 'seconds' # required!
#... Note that this is seconds since the forecast was generated, i.e. the appropriate
# standard_name is "forecast_period".
taxis.standard_name = 'forecast_period'
taxis.designateTime()
v1 = cdms2.tvariable.TransientVariable([[1.1, 1.2], [2.1, 2.1]],
axes=[taxis, xaxis],
id='var')
v2 = cdms2.tvariable.TransientVariable([[3.1, 3.2], [4.1, 4.1]],
axes=[taxis, xaxis],
id='var')
v3 = cdms2.tvariable.TransientVariable([[5.1, 5.2], [6.1, 6.1]],
axes=[taxis, xaxis],
id='var')
date1 = cdms2.tvariable.TransientVariable(20100825, id='nbdate')
sec1 = cdms2.tvariable.TransientVariable(0, id='nbsec')
date2 = cdms2.tvariable.TransientVariable(20100826, id='nbdate')
sec2 = cdms2.tvariable.TransientVariable(0, id='nbsec')
date3 = cdms2.tvariable.TransientVariable(20100827, id='nbdate')
sec3 = cdms2.tvariable.TransientVariable(0, id='nbsec')
f1 = self.getTempFile('test_fc1', 'w')
f2 = self.getTempFile('test_fc2', 'w')
f3 = self.getTempFile('test_fc3', 'w')
print(("TEMP:", self.tempdir))
f1.write(v1)
f1.write(date1)
f1.write(sec1)
f2.write(v2)
f2.write(date2)
f2.write(sec2)
f3.write(v3)
f3.write(date3)
f3.write(sec3)
f1.close()
f2.close()
f3.close()
os.chdir(self.tempdir)
cdscan(
"cdscan -q --forecast -x test_fc.xml test_fc1 test_fc2 test_fc3".
split())
# Read in the data.
fcs = cdms2.forecast.forecasts('test_fc.xml',
("2010-08-25", "2010-08-27"))
vin = fcs('var')
fcaxis = vin._TransientVariable__domain[0][0]
tinaxis = vin._TransientVariable__domain[1][0]
xinaxis = vin._TransientVariable__domain[2][0]
# Test vin against original data, looking especially at the forecast axis.
# The following should be True, error otherwise:
self.assertEqual(vin.id, v1.id)
self.assertTrue(numpy.alltrue(vin[0, :, :] == v1[:, :]))
self.assertTrue(numpy.alltrue(vin[1, :, :] == v2[:, :]))
self.assertEqual(vin.shape, (2, 2, 2))
self.assertTrue(numpy.allclose(tinaxis._data_, taxis._data_))
self.assertTrue(numpy.allclose(xinaxis._data_, xaxis._data_))
tinaxis = vin.getAxis(1)
# self.assertTrue(tinaxis.isTime())
self.assertFalse(xinaxis.isTime())
self.assertFalse(fcaxis.isTime())
self.assertTrue(fcaxis.isForecast())
self.assertEqual(fcaxis.shape, (3, ))
#...Note that fcaxis is an Axis so its shape comes from len(fcaxis.node) which is the
# number of forecasts described in test_fc.xml; in contrast for TransientAxis the shape
# would be len(fcaxis._data_) which is the number of forecasts that
# have been read.
self.assertEqual(fcaxis._data_, [2010082500000, 2010082600000])
self.assertEqual(fcaxis.id, 'fctau0')
