def test_DSG_create_contiguous(self):
# Define the ragged array values
ragged_array = numpy.array([1, 3, 4, 3, 6], dtype="float32")
# Define the count array values
count_array = [2, 3]
# Initialise the count variable
count_variable = cf.Count(data=cf.Data(count_array))
count_variable.set_property(
"long_name", "number of obs for this timeseries"
)
# Initialise the contiguous ragged array object
array = cf.RaggedContiguousArray(
compressed_array=cf.Data(ragged_array),
shape=(2, 3),
size=6,
ndim=2,
count_variable=count_variable,
)
# Initialize the auxiliary coordinate construct with the ragged
# array and set some properties
z = cf.AuxiliaryCoordinate(
data=cf.Data(array),
properties={
"standard_name": "height",
"units": "km",
"positive": "up",
},
)
self.assertTrue(
(
z.data.array
== numpy.ma.masked_array(
data=[[1.0, 3.0, 99], [4.0, 3.0, 6.0]],
mask=[[False, False, True], [False, False, False]],
fill_value=1e20,
dtype="float32",
)
).all()
)
self.assertEqual(z.data.get_compression_type(), "ragged contiguous")
self.assertTrue(
(
z.data.compressed_array
== numpy.array([1.0, 3.0, 4.0, 3.0, 6.0], dtype="float32")
).all()
)
self.assertTrue(
(z.data.get_count().data.array == numpy.array([2, 3])).all()
)
def test_DSG_create_contiguous(self):
if self.test_only and inspect.stack()[0][3] not in self.test_only:
return
# Define the ragged array values
ragged_array = numpy.array([1, 3, 4, 3, 6], dtype='float32')
# Define the count array values
count_array = [2, 3]
# Initialise the count variable
count_variable = cf.Count(data=cf.Data(count_array))
count_variable.set_property('long_name',
'number of obs for this timeseries')
# Initialise the contiguous ragged array object
array = cf.RaggedContiguousArray(
compressed_array=cf.Data(ragged_array),
shape=(2, 3),
size=6,
ndim=2,
count_variable=count_variable)
# Initialize the auxiliary coordinate construct with the ragged
# array and set some properties
z = cf.AuxiliaryCoordinate(data=cf.Data(array),
properties={
'standard_name': 'height',
'units': 'km',
'positive': 'up'
})
self.assertTrue(
(z.data.array == numpy.ma.masked_array(data=[[1.0, 3.0, 99],
[4.0, 3.0, 6.0]],
mask=[[False, False, True],
[False, False,
False]],
fill_value=1e+20,
dtype='float32')).all())
self.assertEqual(z.data.get_compression_type(), 'ragged contiguous')
self.assertTrue(
(z.data.compressed_array == numpy.array([1., 3., 4., 3., 6.],
dtype='float32')).all())
self.assertTrue(
(z.data.get_count().data.array == numpy.array([2, 3])).all())
# Define the ragged array values
ragged_array = cf.Data([280, 281, 279, 278, 279.5])
# Define the count array values
count_array = [1, 4]
# Create the count variable
count_variable = cf.Count(data=cf.Data(count_array))
count_variable.set_property('long_name', 'number of obs for this timeseries')
# Create the contiguous ragged array object, specifying the
# uncompressed shape
array = cf.RaggedContiguousArray(compressed_array=ragged_array,
shape=(2, 4),
size=8,
ndim=2,
count_variable=count_variable)
# Create the field construct with the domain axes and the ragged
# array
T = cf.Field()
T.set_properties({
'standard_name': 'air_temperature',
'units': 'K',
'featureType': 'timeSeries'
})
# Create the domain axis constructs for the uncompressed array
X = T.set_construct(cf.DomainAxis(4))
Y = T.set_construct(cf.DomainAxis(2))
def test_DSG_contiguous(self):
f = cf.read(self.contiguous, verbose=0)
self.assertEqual(len(f), 2)
# Select the specific humidity field
q = [
g
for g in f
if g.get_property("standard_name") == "specific_humidity"
][0]
self.assertTrue(q._equals(q.data.array.mask, self.a.mask))
self.assertTrue(
q._equals(self.a, q.data.array),
"\nself.a=\n" + str(self.a) + "\nq.array=\n" + str(q.array),
)
cf.write(f, tmpfile, verbose=0)
g = cf.read(tmpfile)
self.assertEqual(len(g), len(f))
for i in range(len(f)):
self.assertTrue(g[i].equals(f[i], verbose=2))
# ------------------------------------------------------------
# Test creation
# ------------------------------------------------------------
# Define the ragged array values
ragged_array = numpy.array(
[280, 282.5, 281, 279, 278, 279.5], dtype="float32"
)
# Define the count array values
count_array = [2, 4]
# Create the count variable
count_variable = cf.Count(data=cf.Data(count_array))
count_variable.set_property(
"long_name", "number of obs for this timeseries"
)
# Create the contiguous ragged array object
array = cf.RaggedContiguousArray(
compressed_array=cf.Data(ragged_array),
shape=(2, 4),
size=8,
ndim=2,
count_variable=count_variable,
)
# Create the field construct with the domain axes and the ragged
# array
tas = cf.Field()
tas.set_properties(
{
"standard_name": "air_temperature",
"units": "K",
"featureType": "timeSeries",
}
)
# Create the domain axis constructs for the uncompressed array
X = tas.set_construct(cf.DomainAxis(4))
Y = tas.set_construct(cf.DomainAxis(2))
# Set the data for the field
tas.set_data(cf.Data(array), axes=[Y, X])
cf.write(tas, tmpfile)
def test_DSG_contiguous(self):
if self.test_only and inspect.stack()[0][3] not in self.test_only:
return
f = cf.read(self.contiguous, verbose=0)
self.assertEqual(len(f), 2)
# Select the specific humidity field
q = [g for g in f
if g.get_property('standard_name') == 'specific_humidity'][0]
self.assertTrue(q._equals(q.data.array.mask, self.a.mask))
self.assertTrue(q._equals(self.a, q.data.array),
'\nself.a=\n'+str(self.a)+'\nq.array=\n'+str(q.array))
cf.write(f, tmpfile, verbose=0)
g = cf.read(tmpfile)
self.assertEqual(len(g), len(f))
for i in range(len(f)):
self.assertTrue(g[i].equals(f[i], verbose=2))
# ------------------------------------------------------------
# Test creation
# ------------------------------------------------------------
# Define the ragged array values
ragged_array = numpy.array([280, 282.5, 281, 279, 278, 279.5],
dtype='float32')
# Define the count array values
count_array = [2, 4]
# Create the count variable
count_variable = cf.Count(data=cf.Data(count_array))
count_variable.set_property(
'long_name', 'number of obs for this timeseries')
# Create the contiguous ragged array object
array = cf.RaggedContiguousArray(
compressed_array=cf.Data(ragged_array),
shape=(2, 4), size=8, ndim=2,
count_variable=count_variable)
# Create the field construct with the domain axes and the ragged
# array
tas = cf.Field()
tas.set_properties({'standard_name': 'air_temperature',
'units': 'K',
'featureType': 'timeSeries'})
# Create the domain axis constructs for the uncompressed array
X = tas.set_construct(cf.DomainAxis(4))
Y = tas.set_construct(cf.DomainAxis(2))
# Set the data for the field
tas.set_data(cf.Data(array), axes=[Y, X])
cf.write(tas, tmpfile)