def test_stack_errors(self):
v = Variable(["x", "y"], [[0, 1], [2, 3]], {"foo": "bar"})
with self.assertRaisesRegexp(ValueError, "invalid existing dim"):
v.stack(z=("x1",))
with self.assertRaisesRegexp(ValueError, "cannot create a new dim"):
v.stack(x=("x",))
def test_items(self):
data = np.random.random((10, 11))
v = Variable(['x', 'y'], data)
# test slicing
self.assertVariableIdentical(v, v[:])
self.assertVariableIdentical(v, v[...])
self.assertVariableIdentical(Variable(['y'], data[0]), v[0])
self.assertVariableIdentical(Variable(['x'], data[:, 0]), v[:, 0])
self.assertVariableIdentical(Variable(['x', 'y'], data[:3, :2]),
v[:3, :2])
# test array indexing
x = Variable(['x'], np.arange(10))
y = Variable(['y'], np.arange(11))
self.assertVariableIdentical(v, v[x.values])
self.assertVariableIdentical(v, v[x])
self.assertVariableIdentical(v[:3], v[x < 3])
self.assertVariableIdentical(v[:, 3:], v[:, y >= 3])
self.assertVariableIdentical(v[:3, 3:], v[x < 3, y >= 3])
self.assertVariableIdentical(v[:3, :2], v[x[:3], y[:2]])
self.assertVariableIdentical(v[:3, :2], v[range(3), range(2)])
# test iteration
for n, item in enumerate(v):
self.assertVariableIdentical(Variable(['y'], data[n]), item)
with self.assertRaisesRegexp(TypeError, 'iteration over a 0-d'):
iter(Variable([], 0))
# test setting
v.values[:] = 0
self.assertTrue(np.all(v.values == 0))
# test orthogonal setting
v[range(10), range(11)] = 1
self.assertArrayEqual(v.values, np.ones((10, 11)))
def test_numpy_same_methods(self):
v = Variable([], np.float32(0.0))
self.assertEqual(v.item(), 0)
self.assertIs(type(v.item()), float)
v = Coordinate('x', np.arange(5))
self.assertEqual(2, v.searchsorted(2))
def test_stack_errors(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'})
with self.assertRaisesRegexp(ValueError, 'invalid existing dim'):
v.stack(z=('x1',))
with self.assertRaisesRegexp(ValueError, 'cannot create a new dim'):
v.stack(x=('x',))
def test_concat(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u, Variable.concat([v[:2], v[2:]], 'x'))
self.assertLazyAndIdentical(u[:2], Variable.concat([v[0], v[1]], 'x'))
self.assertLazyAndIdentical(
u[:3], Variable.concat([v[[0, 2]], v[[1]]], 'x', positions=[[0, 2], [1]]))
def test_outer_indexer_consistency_with_broadcast_indexes_vectorized():
def nonzero(x):
if isinstance(x, np.ndarray) and x.dtype.kind == 'b':
x = x.nonzero()[0]
return x
original = np.random.rand(10, 20, 30)
v = Variable(['i', 'j', 'k'], original)
I = ReturnItem() # noqa: E741 # allow ambiguous name
# test orthogonally applied indexers
indexers = [I[:], 0, -2, I[:3], np.array([0, 1, 2, 3]), np.array([0]),
np.arange(10) < 5]
for i, j, k in itertools.product(indexers, repeat=3):
if isinstance(j, np.ndarray) and j.dtype.kind == 'b': # match size
j = np.arange(20) < 4
if isinstance(k, np.ndarray) and k.dtype.kind == 'b':
k = np.arange(30) < 8
_, expected, new_order = v._broadcast_indexes_vectorized((i, j, k))
expected_data = nputils.NumpyVIndexAdapter(v.data)[expected.tuple]
if new_order:
old_order = range(len(new_order))
expected_data = np.moveaxis(expected_data, old_order,
new_order)
outer_index = indexing.OuterIndexer((nonzero(i), nonzero(j),
nonzero(k)))
actual = indexing._outer_to_numpy_indexer(outer_index, v.shape)
actual_data = v.data[actual]
np.testing.assert_array_equal(actual_data, expected_data)
def test_roll_consistency(self):
v = Variable(('x', 'y'), np.random.randn(5, 6))
for axis, dim in [(0, 'x'), (1, 'y')]:
for shift in [-3, 0, 1, 7, 11]:
expected = np.roll(v.values, shift, axis=axis)
actual = v.roll(**{dim: shift}).values
self.assertArrayEqual(expected, actual)
def test_expand_dims_object_dtype(self):
v = Variable([], ("a", 1))
actual = v.expand_dims(("x",), (3,))
exp_values = np.empty((3,), dtype=object)
for i in range(3):
exp_values[i] = ("a", 1)
expected = Variable(["x"], exp_values)
assert actual.identical(expected)
def test_unstack_2d(self):
v = Variable(["x", "y"], [[0, 1], [2, 3]])
actual = v.unstack(y={"z": 2})
expected = Variable(["x", "z"], v.data)
self.assertVariableIdentical(actual, expected)
actual = v.unstack(x={"z": 2})
expected = Variable(["y", "z"], v.data.T)
self.assertVariableIdentical(actual, expected)
def test_unstack_2d(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]])
actual = v.unstack(y={'z': 2})
expected = Variable(['x', 'z'], v.data)
self.assertVariableIdentical(actual, expected)
actual = v.unstack(x={'z': 2})
expected = Variable(['y', 'z'], v.data.T)
self.assertVariableIdentical(actual, expected)
def test_concat_attrs(self):
# different or conflicting attributes should be removed
v = self.cls('a', np.arange(5), {'foo': 'bar'})
w = self.cls('a', np.ones(5))
expected = self.cls('a', np.concatenate([np.arange(5), np.ones(5)]))
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 2
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
w.attrs['foo'] = 'bar'
expected.attrs['foo'] = 'bar'
self.assertVariableIdentical(expected, Variable.concat([v, w], 'a'))
def test_concat_attrs(self):
# different or conflicting attributes should be removed
v = self.cls("a", np.arange(5), {"foo": "bar"})
w = self.cls("a", np.ones(5))
expected = self.cls("a", np.concatenate([np.arange(5), np.ones(5)]))
self.assertVariableIdentical(expected, Variable.concat([v, w], "a"))
w.attrs["foo"] = 2
self.assertVariableIdentical(expected, Variable.concat([v, w], "a"))
w.attrs["foo"] = "bar"
expected.attrs["foo"] = "bar"
self.assertVariableIdentical(expected, Variable.concat([v, w], "a"))
def test_pickle(self):
# Test that pickling/unpickling does not convert the dask
# backend to numpy
a1 = Variable(['x'], build_dask_array('x'))
a1.compute()
assert not a1._in_memory
assert kernel_call_count == 1
a2 = pickle.loads(pickle.dumps(a1))
assert kernel_call_count == 1
assert_identical(a1, a2)
assert not a1._in_memory
assert not a2._in_memory
def test_variable_pickle(self):
# Test that pickling/unpickling does not convert the dask
# backend to numpy
a1 = Variable(['x'], build_dask_array())
a1.compute()
self.assertFalse(a1._in_memory)
self.assertEquals(kernel_call_count, 1)
a2 = pickle.loads(pickle.dumps(a1))
self.assertEquals(kernel_call_count, 1)
self.assertVariableIdentical(a1, a2)
self.assertFalse(a1._in_memory)
self.assertFalse(a2._in_memory)
def test_transpose(self):
v = Variable(['time', 'x'], self.d)
v2 = Variable(['x', 'time'], self.d.T)
self.assertVariableIdentical(v, v2.transpose())
self.assertVariableIdentical(v.transpose(), v.T)
x = np.random.randn(2, 3, 4, 5)
w = Variable(['a', 'b', 'c', 'd'], x)
w2 = Variable(['d', 'b', 'c', 'a'], np.einsum('abcd->dbca', x))
self.assertEqual(w2.shape, (5, 3, 4, 2))
self.assertVariableIdentical(w2, w.transpose('d', 'b', 'c', 'a'))
self.assertVariableIdentical(w, w2.transpose('a', 'b', 'c', 'd'))
w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x))
self.assertVariableIdentical(w, w3.transpose('a', 'b', 'c', 'd'))
def test_transpose(self):
v = Variable(["time", "x"], self.d)
v2 = Variable(["x", "time"], self.d.T)
self.assertVariableIdentical(v, v2.transpose())
self.assertVariableIdentical(v.transpose(), v.T)
x = np.random.randn(2, 3, 4, 5)
w = Variable(["a", "b", "c", "d"], x)
w2 = Variable(["d", "b", "c", "a"], np.einsum("abcd->dbca", x))
self.assertEqual(w2.shape, (5, 3, 4, 2))
self.assertVariableIdentical(w2, w.transpose("d", "b", "c", "a"))
self.assertVariableIdentical(w, w2.transpose("a", "b", "c", "d"))
w3 = Variable(["b", "c", "d", "a"], np.einsum("abcd->bcda", x))
self.assertVariableIdentical(w, w3.transpose("a", "b", "c", "d"))
def test_unstack(self):
v = Variable('z', [0, 1, 2, 3], {'foo': 'bar'})
actual = v.unstack(z=OrderedDict([('x', 2), ('y', 2)]))
expected = Variable(('x', 'y'), [[0, 1], [2, 3]], v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.unstack(z=OrderedDict([('x', 4), ('y', 1)]))
expected = Variable(('x', 'y'), [[0], [1], [2], [3]], v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.unstack(z=OrderedDict([('x', 4)]))
expected = Variable('x', [0, 1, 2, 3], v.attrs)
self.assertVariableIdentical(actual, expected)
def test_unstack(self):
v = Variable("z", [0, 1, 2, 3], {"foo": "bar"})
actual = v.unstack(z=OrderedDict([("x", 2), ("y", 2)]))
expected = Variable(("x", "y"), [[0, 1], [2, 3]], v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.unstack(z=OrderedDict([("x", 4), ("y", 1)]))
expected = Variable(("x", "y"), [[0], [1], [2], [3]], v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.unstack(z=OrderedDict([("x", 4)]))
expected = Variable("x", [0, 1, 2, 3], v.attrs)
self.assertVariableIdentical(actual, expected)
def test_reduce_keep_attrs(self):
_attrs = {'units': 'test', 'long_name': 'testing'}
v = Variable(['x', 'y'], self.d, _attrs)
# Test dropped attrs
vm = v.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = v.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(_attrs))
self.assertEqual(vm.attrs, _attrs)
def test_reduce_keep_attrs(self):
_attrs = {"units": "test", "long_name": "testing"}
v = Variable(["x", "y"], self.d, _attrs)
# Test dropped attrs
vm = v.mean()
self.assertEqual(len(vm.attrs), 0)
self.assertEqual(vm.attrs, OrderedDict())
# Test kept attrs
vm = v.mean(keep_attrs=True)
self.assertEqual(len(vm.attrs), len(_attrs))
self.assertEqual(vm.attrs, _attrs)
def test_data_and_values(self):
v = Variable(['time', 'x'], self.d)
self.assertArrayEqual(v.data, self.d)
self.assertArrayEqual(v.values, self.d)
self.assertIs(source_ndarray(v.values), self.d)
with self.assertRaises(ValueError):
# wrong size
v.values = np.random.random(5)
d2 = np.random.random((10, 3))
v.values = d2
self.assertIs(source_ndarray(v.values), d2)
d3 = np.random.random((10, 3))
v.data = d3
self.assertIs(source_ndarray(v.data), d3)
def test_stack(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]], {'foo': 'bar'})
actual = v.stack(z=('x', 'y'))
expected = Variable('z', [0, 1, 2, 3], v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.stack(z=('x',))
expected = Variable(('y', 'z'), v.data.T, v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.stack(z=(),)
self.assertVariableIdentical(actual, v)
actual = v.stack(X=('x',), Y=('y',)).transpose('X', 'Y')
expected = Variable(('X', 'Y'), v.data, v.attrs)
self.assertVariableIdentical(actual, expected)
def test_stack(self):
v = Variable(["x", "y"], [[0, 1], [2, 3]], {"foo": "bar"})
actual = v.stack(z=("x", "y"))
expected = Variable("z", [0, 1, 2, 3], v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.stack(z=("x",))
expected = Variable(("y", "z"), v.data.T, v.attrs)
self.assertVariableIdentical(actual, expected)
actual = v.stack(z=())
self.assertVariableIdentical(actual, v)
actual = v.stack(X=("x",), Y=("y",)).transpose("X", "Y")
expected = Variable(("X", "Y"), v.data, v.attrs)
self.assertVariableIdentical(actual, expected)
def test_broadcast_equals(self):
v1 = Variable((), np.nan)
v2 = Variable(('x'), [np.nan, np.nan])
self.assertTrue(v1.broadcast_equals(v2))
self.assertFalse(v1.equals(v2))
self.assertFalse(v1.identical(v2))
v3 = Variable(('x'), [np.nan])
self.assertTrue(v1.broadcast_equals(v3))
self.assertFalse(v1.equals(v3))
self.assertFalse(v1.identical(v3))
self.assertFalse(v1.broadcast_equals(None))
v4 = Variable(('x'), [np.nan] * 3)
self.assertFalse(v2.broadcast_equals(v4))
def test_count(self):
expected = Variable([], 3)
actual = Variable(["x"], [1, 2, 3, np.nan]).count()
self.assertVariableIdentical(expected, actual)
v = Variable(["x"], np.array(["1", "2", "3", np.nan], dtype=object))
actual = v.count()
self.assertVariableIdentical(expected, actual)
actual = Variable(["x"], [True, False, True]).count()
self.assertVariableIdentical(expected, actual)
self.assertEqual(actual.dtype, int)
expected = Variable(["x"], [2, 3])
actual = Variable(["x", "y"], [[1, 0, np.nan], [1, 1, 1]]).count("y")
self.assertVariableIdentical(expected, actual)
def test_count(self):
expected = Variable([], 3)
actual = Variable(['x'], [1, 2, 3, np.nan]).count()
self.assertVariableIdentical(expected, actual)
v = Variable(['x'], np.array(['1', '2', '3', np.nan], dtype=object))
actual = v.count()
self.assertVariableIdentical(expected, actual)
actual = Variable(['x'], [True, False, True]).count()
self.assertVariableIdentical(expected, actual)
self.assertEqual(actual.dtype, int)
expected = Variable(['x'], [2, 3])
actual = Variable(['x', 'y'], [[1, 0, np.nan], [1, 1, 1]]).count('y')
self.assertVariableIdentical(expected, actual)
def test_roll(self):
v = Variable('x', [1, 2, 3, 4, 5])
self.assertVariableIdentical(v, v.roll(x=0))
self.assertIsNot(v, v.roll(x=0))
expected = Variable('x', [5, 1, 2, 3, 4])
self.assertVariableIdentical(expected, v.roll(x=1))
self.assertVariableIdentical(expected, v.roll(x=-4))
self.assertVariableIdentical(expected, v.roll(x=6))
expected = Variable('x', [4, 5, 1, 2, 3])
self.assertVariableIdentical(expected, v.roll(x=2))
self.assertVariableIdentical(expected, v.roll(x=-3))
with self.assertRaisesRegexp(ValueError, 'dimension'):
v.roll(z=0)
def test_concat_fixed_len_str(self):
# regression test for #217
for kind in ["S", "U"]:
x = self.cls("animal", np.array(["horse"], dtype=kind))
y = self.cls("animal", np.array(["aardvark"], dtype=kind))
actual = Variable.concat([x, y], "animal")
expected = Variable("animal", np.array(["horse", "aardvark"], dtype=kind))
self.assertVariableEqual(expected, actual)
def test_concat_number_strings(self):
# regression test for #305
a = self.cls('x', ['0', '1', '2'])
b = self.cls('x', ['3', '4'])
actual = Variable.concat([a, b], dim='x')
expected = Variable('x', np.arange(5).astype(str).astype(object))
self.assertVariableIdentical(expected, actual)
self.assertEqual(expected.dtype, object)
self.assertEqual(type(expected.values[0]), str)
def test_concat_fixed_len_str(self):
# regression test for #217
for kind in ['S', 'U']:
x = self.cls('animal', np.array(['horse'], dtype=kind))
y = self.cls('animal', np.array(['aardvark'], dtype=kind))
actual = Variable.concat([x, y], 'animal')
expected = Variable(
'animal', np.array(['horse', 'aardvark'], dtype=kind))
self.assertVariableEqual(expected, actual)
class TestVariable(DaskTestCase):
def assertLazyAndIdentical(self, expected, actual):
self.assertLazyAnd(expected, actual, assert_identical)
def assertLazyAndAllClose(self, expected, actual):
self.assertLazyAnd(expected, actual, assert_allclose)
def setUp(self):
self.values = np.random.RandomState(0).randn(4, 6)
self.data = da.from_array(self.values, chunks=(2, 2))
self.eager_var = Variable(('x', 'y'), self.values)
self.lazy_var = Variable(('x', 'y'), self.data)
def test_basics(self):
v = self.lazy_var
assert self.data is v.data
assert self.data.chunks == v.chunks
assert_array_equal(self.values, v)
def test_copy(self):
self.assertLazyAndIdentical(self.eager_var, self.lazy_var.copy())
self.assertLazyAndIdentical(self.eager_var,
self.lazy_var.copy(deep=True))
def test_chunk(self):
for chunks, expected in [(None, ((2, 2), (2, 2, 2))),
(3, ((3, 1), (3, 3))),
({'x': 3, 'y': 3}, ((3, 1), (3, 3))),
({'x': 3}, ((3, 1), (2, 2, 2))),
({'x': (3, 1)}, ((3, 1), (2, 2, 2)))]:
rechunked = self.lazy_var.chunk(chunks)
assert rechunked.chunks == expected
self.assertLazyAndIdentical(self.eager_var, rechunked)
def test_indexing(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u[0], v[0])
self.assertLazyAndIdentical(u[:1], v[:1])
self.assertLazyAndIdentical(u[[0, 1], [0, 1, 2]], v[[0, 1], [0, 1, 2]])
with raises_regex(TypeError, 'stored in a dask array'):
v[:1] = 0
def test_squeeze(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u[0].squeeze(), v[0].squeeze())
def test_equals(self):
v = self.lazy_var
assert v.equals(v)
assert isinstance(v.data, da.Array)
assert v.identical(v)
assert isinstance(v.data, da.Array)
def test_transpose(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u.T, v.T)
def test_shift(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u.shift(x=2), v.shift(x=2))
self.assertLazyAndIdentical(u.shift(x=-2), v.shift(x=-2))
assert v.data.chunks == v.shift(x=1).data.chunks
def test_roll(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u.roll(x=2), v.roll(x=2))
assert v.data.chunks == v.roll(x=1).data.chunks
def test_unary_op(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(-u, -v)
self.assertLazyAndIdentical(abs(u), abs(v))
self.assertLazyAndIdentical(u.round(), v.round())
def test_binary_op(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(2 * u, 2 * v)
self.assertLazyAndIdentical(u + u, v + v)
self.assertLazyAndIdentical(u[0] + u, v[0] + v)
def test_repr(self):
expected = dedent("""\
<xarray.Variable (x: 4, y: 6)>
dask.array<shape=(4, 6), dtype=float64, chunksize=(2, 2)>""")
assert expected == repr(self.lazy_var)
def test_pickle(self):
# Test that pickling/unpickling does not convert the dask
# backend to numpy
a1 = Variable(['x'], build_dask_array('x'))
a1.compute()
assert not a1._in_memory
assert kernel_call_count == 1
a2 = pickle.loads(pickle.dumps(a1))
assert kernel_call_count == 1
assert_identical(a1, a2)
assert not a1._in_memory
assert not a2._in_memory
def test_reduce(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndAllClose(u.mean(), v.mean())
self.assertLazyAndAllClose(u.std(), v.std())
self.assertLazyAndAllClose(u.argmax(dim='x'), v.argmax(dim='x'))
self.assertLazyAndAllClose((u > 1).any(), (v > 1).any())
self.assertLazyAndAllClose((u < 1).all('x'), (v < 1).all('x'))
with raises_regex(NotImplementedError, 'dask'):
v.median()
def test_missing_values(self):
values = np.array([0, 1, np.nan, 3])
data = da.from_array(values, chunks=(2,))
eager_var = Variable('x', values)
lazy_var = Variable('x', data)
self.assertLazyAndIdentical(eager_var, lazy_var.fillna(lazy_var))
self.assertLazyAndIdentical(Variable('x', range(4)),
lazy_var.fillna(2))
self.assertLazyAndIdentical(eager_var.count(), lazy_var.count())
def test_concat(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u, Variable.concat([v[:2], v[2:]], 'x'))
self.assertLazyAndIdentical(u[:2], Variable.concat([v[0], v[1]], 'x'))
self.assertLazyAndIdentical(u[:2], Variable.concat([u[0], v[1]], 'x'))
self.assertLazyAndIdentical(u[:2], Variable.concat([v[0], u[1]], 'x'))
self.assertLazyAndIdentical(
u[:3],
Variable.concat([v[[0, 2]], v[[1]]], 'x', positions=[[0, 2], [1]]))
def test_missing_methods(self):
v = self.lazy_var
try:
v.argsort()
except NotImplementedError as err:
assert 'dask' in str(err)
try:
v[0].item()
except NotImplementedError as err:
assert 'dask' in str(err)
def test_univariate_ufunc(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndAllClose(np.sin(u), xu.sin(v))
def test_bivariate_ufunc(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(v, 0))
self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(0, v))
@pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
reason='Need dask 0.16 for new interface')
def test_compute(self):
u = self.eager_var
v = self.lazy_var
assert dask.is_dask_collection(v)
(v2,) = dask.compute(v + 1)
assert not dask.is_dask_collection(v2)
assert ((u + 1).data == v2.data).all()
@pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4',
reason='Need dask 0.16 for new interface')
def test_persist(self):
u = self.eager_var
v = self.lazy_var + 1
(v2,) = dask.persist(v)
assert v is not v2
assert len(v2.__dask_graph__()) < len(v.__dask_graph__())
assert v2.__dask_keys__() == v.__dask_keys__()
assert dask.is_dask_collection(v)
assert dask.is_dask_collection(v2)
self.assertLazyAndAllClose(u + 1, v)
self.assertLazyAndAllClose(u + 1, v2)
def add_original_variables(dataset, height, srf_size=None):
tu.add_geolocation_variables(dataset, SWATH_WIDTH, height)
tu.add_quality_flags(dataset, SWATH_WIDTH, height)
# Temperature_misc_housekeeping
default_array = DefaultData.create_default_array(
height,
NUM_THERMISTORS,
np.float32,
dims_names=["housekeeping", "y"],
fill_value=np.NaN)
variable = Variable(["housekeeping", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["units"] = "TODO"
dataset["Temperature_misc_housekeeping"] = variable
# ancil_data
default_array = DefaultData.create_default_array(
height,
ANCIL_VAL,
np.float64,
dims_names=["ancil_val", "y"],
fill_value=np.NaN)
variable = Variable(["ancil_val", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "Additional per scan information: year, day_of_year, secs_of_day, sat_lat, " \
"sat_long, sat_alt, sat_heading, year, day_of_year, secs_of_day"
dataset["ancil_data"] = variable
# channel_quality_flag
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
dataset["channel_quality_flag"] = variable
# cold_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["cold_counts"] = variable
# counts_to_tb_gain
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["counts_to_tb_gain"] = variable
# counts_to_tb_offset
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["counts_to_tb_offset"] = variable
# gain_control
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["gain_control"] = variable
# tb
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["standard_name"] = "toa_brightness_temperature"
tu.add_units(variable, "K")
dataset["tb"] = variable
# thermal_reference
default_array = DefaultData.create_default_vector(
height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "TODO")
dataset["thermal_reference"] = variable
# warm_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["warm_counts"] = variable
def test_reduce_funcs(self):
v = Variable('x', np.array([1, np.nan, 2, 3]))
self.assertVariableIdentical(v.mean(), Variable([], 2))
self.assertVariableIdentical(v.mean(skipna=True), Variable([], 2))
self.assertVariableIdentical(v.mean(skipna=False), Variable([],
np.nan))
self.assertVariableIdentical(np.mean(v), Variable([], 2))
self.assertVariableIdentical(v.prod(), Variable([], 6))
self.assertVariableIdentical(v.cumsum(axis=0),
Variable('x', np.array([1, 1, 3, 6])))
self.assertVariableIdentical(v.cumprod(axis=0),
Variable('x', np.array([1, 1, 2, 6])))
self.assertVariableIdentical(v.var(), Variable([], 2.0 / 3))
if LooseVersion(np.__version__) < '1.9':
with self.assertRaises(NotImplementedError):
v.median()
else:
self.assertVariableIdentical(v.median(), Variable([], 2))
v = Variable('x', [True, False, False])
self.assertVariableIdentical(v.any(), Variable([], True))
self.assertVariableIdentical(v.all(dim='x'), Variable([], False))
v = Variable('t', pd.date_range('2000-01-01', periods=3))
with self.assertRaises(NotImplementedError):
v.max(skipna=True)
self.assertVariableIdentical(v.max(),
Variable([], pd.Timestamp('2000-01-03')))
def add_full_fcdr_variables(dataset, height):
# height is ignored - supplied just for interface compatibility tb 2017-02-05
# count_vis
default_array = DefaultData.create_default_array(
FULL_SIZE, FULL_SIZE, np.uint8)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.uint8))
variable.attrs["long_name"] = "Image counts"
tu.add_units(variable, "count")
tu.add_chunking(variable, CHUNKSIZES)
dataset["count_vis"] = variable
dataset["u_latitude"] = MVIRI._create_angle_variable_int(
1.5E-05, long_name="Uncertainty in Latitude", unsigned=True)
MVIRI._add_geo_correlation_attributes(dataset["u_latitude"])
dataset["u_longitude"] = MVIRI._create_angle_variable_int(
1.5E-05, long_name="Uncertainty in Longitude", unsigned=True)
MVIRI._add_geo_correlation_attributes(dataset["u_longitude"])
# u_time
default_array = DefaultData.create_default_vector(IR_SIZE,
np.float32,
fill_value=np.NaN)
variable = Variable([IR_Y_DIMENSION], default_array)
variable.attrs["standard_name"] = "Uncertainty in Time"
tu.add_units(variable, "s")
tu.add_encoding(variable, np.uint16,
DefaultData.get_default_fill_value(np.uint16),
0.009155273)
variable.attrs["pdf_shape"] = "rectangle"
dataset["u_time"] = variable
dataset["u_satellite_zenith_angle"] = MVIRI._create_angle_variable_int(
7.62939E-05,
long_name="Uncertainty in Satellite Zenith Angle",
unsigned=True)
dataset[
"u_satellite_azimuth_angle"] = MVIRI._create_angle_variable_int(
7.62939E-05,
long_name="Uncertainty in Satellite Azimuth Angle",
unsigned=True)
dataset["u_solar_zenith_angle"] = MVIRI._create_angle_variable_int(
7.62939E-05,
long_name="Uncertainty in Solar Zenith Angle",
unsigned=True)
dataset["u_solar_azimuth_angle"] = MVIRI._create_angle_variable_int(
7.62939E-05,
long_name="Uncertainty in Solar Azimuth Angle",
unsigned=True)
dataset["a0_vis"] = tu.create_scalar_float_variable(
"Calibration Coefficient at Launch", units="Wm^-2sr^-1/count")
dataset["a1_vis"] = tu.create_scalar_float_variable(
"Time variation of a0", units="Wm^-2sr^-1/count day^-1 10^5")
dataset["a2_vis"] = tu.create_scalar_float_variable(
"Time variation of a0, quadratic term",
units="Wm^-2sr^-1/count year^-2")
dataset["mean_count_space_vis"] = tu.create_scalar_float_variable(
"Space count", units="count")
# u_a0_vis
variable = tu.create_scalar_float_variable("Uncertainty in a0",
units="Wm^-2sr^-1/count")
MVIRI._add_calibration_coeff_correlation_attributes(variable)
dataset["u_a0_vis"] = variable
# u_a1_vis
variable = tu.create_scalar_float_variable(
"Uncertainty in a1", units="Wm^-2sr^-1/count day^-1 10^5")
MVIRI._add_calibration_coeff_correlation_attributes(variable)
dataset["u_a1_vis"] = variable
# u_a2_vis
variable = tu.create_scalar_float_variable(
"Uncertainty in a2", units="Wm^-2sr^-1/count year^-2")
MVIRI._add_calibration_coeff_correlation_attributes(variable)
dataset["u_a2_vis"] = variable
# u_zero_vis
variable = tu.create_scalar_float_variable("Uncertainty zero term",
units="Wm^-2sr^-1/count")
MVIRI._add_calibration_coeff_correlation_attributes(
variable, image_correlation_scale=[-np.inf, np.inf])
dataset["u_zero_vis"] = variable
# covariance_a_vis
variable = tu.create_float_variable(
COV_SIZE,
COV_SIZE,
long_name=
"Covariance of calibration coefficients from fit to calibration runs",
dim_names=["cov_size", "cov_size"],
fill_value=np.NaN)
tu.add_fill_value(variable, np.NaN)
tu.add_units(variable, "Wm^-2sr^-1/count")
MVIRI._add_calibration_coeff_correlation_attributes(
variable, image_correlation_scale=[-np.inf, np.inf])
dataset["covariance_a_vis"] = variable
dataset["u_electronics_counts_vis"] = tu.create_scalar_float_variable(
"Uncertainty due to Electronics noise", units="count")
dataset["u_digitization_counts_vis"] = tu.create_scalar_float_variable(
"Uncertainty due to digitization", units="count")
# allan_deviation_counts_space_vis
variable = tu.create_scalar_float_variable(
"Uncertainty of space count", units="count")
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "digitised_gaussian"
dataset["allan_deviation_counts_space_vis"] = variable
# u_mean_counts_space_vis
variable = tu.create_scalar_float_variable(
"Uncertainty of space count", units="count")
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "digitised_gaussian"
dataset["u_mean_counts_space_vis"] = variable
# sensitivity_solar_irradiance_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) * (a2_vis * years_since_launch * years_since_launch + a1_vis * years_since_launch + a0_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis * solar_irradiance_vis)"
dataset["sensitivity_solar_irradiance_vis"] = variable
# sensitivity_count_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (a2_vis * years_since_launch * years_since_launch + a1_vis * years_since_launch + a0_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_count_vis"] = variable
# sensitivity_count_space
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "-1.0 * distance_sun_earth * distance_sun_earth * PI * (a2_vis * years_since_launch * years_since_launch + a1_vis * years_since_launch + a0_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_count_space"] = variable
# sensitivity_a0_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_a0_vis"] = variable
# sensitivity_a1_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) * years_since_launch / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_a1_vis"] = variable
# sensitivity_a2_vis
variable = tu.create_scalar_float_variable()
variable.attrs["virtual"] = "true"
variable.attrs["dimension"] = "y, x"
variable.attrs[
"expression"] = "distance_sun_earth * distance_sun_earth * PI * (count_vis - mean_count_space_vis) * years_since_launch*years_since_launch / (cos(solar_zenith_angle * PI / 180.0) * solar_irradiance_vis)"
dataset["sensitivity_a2_vis"] = variable
effect_names = [
"u_solar_irradiance_vis", "u_a0_vis", "u_a1_vis", "u_a2_vis",
"u_zero_vis", "u_solar_zenith_angle", "u_mean_count_space_vis"
]
dataset["Ne"] = Coordinate("Ne", effect_names)
num_effects = len(effect_names)
default_array = DefaultData.create_default_array(num_effects,
num_effects,
np.float32,
fill_value=np.NaN)
variable = Variable(["Ne", "Ne"], default_array)
tu.add_encoding(variable, np.int16, -32768, 3.05176E-05)
variable.attrs["valid_min"] = -1
variable.attrs["valid_max"] = 1
variable.attrs[
"long_name"] = "Channel error correlation matrix for structured effects."
variable.attrs[
"description"] = "Matrix_describing correlations between errors of the uncertainty_effects due to spectral response function errors (determined using Monte Carlo approach)"
dataset["effect_correlation_matrix"] = variable
def test_0d_int32_encoding(self):
original = Variable((), np.int32(0), encoding={'dtype': 'int64'})
expected = Variable((), np.int64(0))
actual = conventions.maybe_encode_dtype(original)
self.assertDatasetIdentical(expected, actual)
def test_vectorized_lazily_indexed_array(self):
original = np.random.rand(10, 20, 30)
x = indexing.NumpyIndexingAdapter(original)
v_eager = Variable(["i", "j", "k"], x)
lazy = indexing.LazilyOuterIndexedArray(x)
v_lazy = Variable(["i", "j", "k"], lazy)
arr = ReturnItem()
def check_indexing(v_eager, v_lazy, indexers):
for indexer in indexers:
actual = v_lazy[indexer]
expected = v_eager[indexer]
assert expected.shape == actual.shape
assert isinstance(
actual._data,
(
indexing.LazilyVectorizedIndexedArray,
indexing.LazilyOuterIndexedArray,
),
)
assert_array_equal(expected, actual)
v_eager = expected
v_lazy = actual
# test orthogonal indexing
indexers = [(arr[:], 0, 1), (Variable("i", [0, 1]), )]
check_indexing(v_eager, v_lazy, indexers)
# vectorized indexing
indexers = [
(Variable("i", [0, 1]), Variable("i", [0, 1]), slice(None)),
(slice(1, 3, 2), 0),
]
check_indexing(v_eager, v_lazy, indexers)
indexers = [
(slice(None, None, 2), 0, slice(None, 10)),
(Variable("i", [3, 2, 4, 3]), Variable("i", [3, 2, 1, 0])),
(Variable(["i", "j"], [[0, 1], [1, 2]]), ),
]
check_indexing(v_eager, v_lazy, indexers)
indexers = [
(Variable("i", [3, 2, 4, 3]), Variable("i", [3, 2, 1, 0])),
(Variable(["i", "j"], [[0, 1], [1, 2]]), ),
]
check_indexing(v_eager, v_lazy, indexers)
def test_missing_fillvalue(self):
v = Variable(['x'], np.array([np.nan, 1, 2, 3]))
v.encoding = {'dtype': 'int16'}
with self.assertWarns('floating point data as an integer'):
conventions.encode_cf_variable(v)
def _create_channel_uncertainty_uint16(height):
default_array = DefaultData.create_default_array(NUM_CHANNELS, height, dtype=np.float32, dims_names=["y", "channel"], fill_value=np.NaN)
variable = Variable(["y", "channel"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
return variable
def add_full_fcdr_variables(dataset, height):
# c_earth
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_RAD_CHANNELS, np.uint16, dims_names=["rad_channel", "y", "x"])
variable = Variable(["rad_channel", "y", "x"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
variable.attrs["long_name"] = "counts_earth"
tu.add_units(variable, "count")
variable.attrs["ancilliary_variables"] = "scnlinf quality_scanline_bitmask quality_channel_bitmask mnfrqualflags"
dataset["c_earth"] = variable
# L_earth
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_RAD_CHANNELS, np.float32, np.NaN, ["rad_channel", "y", "x"])
variable = Variable(["rad_channel", "y", "x"], default_array)
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.0001)
variable.attrs["standard_name"] = "toa_outgoing_inband_radiance"
tu.add_units(variable, "W/Hz/m ** 2/sr")
variable.attrs["long_name"] = "Channel radiance, NOAA/EUMETSAT calibrated"
variable.attrs["ancilliary_variables"] = "scnlinf quality_scanline_bitmask quality_channel_bitmask mnfrqualflags"
dataset["L_earth"] = variable
# u_lat
variable = HIRS._create_angle_variable(height, "uncertainty_latitude")
dataset["u_lat"] = variable
# u_lon
variable = HIRS._create_angle_variable(height, "uncertainty_longitude")
dataset["u_lon"] = variable
# u_time
variable = tu.create_float_variable(SWATH_WIDTH, height, "uncertainty_time")
tu.add_encoding(variable, np.uint16, 65535, 0.01)
tu.add_units(variable, "s")
dataset["u_time"] = variable
# u_c_earth
default_array = DefaultData.create_default_array(NUM_CALIBRATION_CYCLE, NUM_CHANNELS, np.uint16, dims_names=["channel", "calibration_cycle"])
variable = Variable(["channel", "calibration_cycle"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
tu.add_units(variable, "count")
variable.attrs["long_name"] = "uncertainty counts for Earth views"
variable.attrs["ancilliary_variables"] = "u_c_earth_chan_corr"
variable.attrs["channels_affected"] = "all"
variable.attrs["parameter"] = "C_E"
variable.attrs["pdf_shape"] = "gaussian"
dataset["u_c_earth"] = variable
# u_L_earth_independent
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_random")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_independent"] = variable
# u_L_earth_structured
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_structured")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_structured"] = variable
# u_L_earth_systematic
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_systematic")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_systematic"] = variable
# u_L_earth_total
variable = HIRS._create_3d_rad_uncertainty_variable(height, "uncertainty_radiance_Earth_total")
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
tu.add_units(variable, "mW m^-2 sr^-1 cm")
dataset["u_L_earth_total"] = variable
# S_u_L_earth
variable = tu.create_float_variable(NUM_RAD_CHANNELS, NUM_RAD_CHANNELS, "covariance_radiance_Earth", dim_names=["rad_channel", "rad_channel"])
tu.add_encoding(variable, np.uint32, DefaultData.get_default_fill_value(np.uint32), 0.01)
dataset["S_u_L_earth"] = variable
# u_bt_random
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_random")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_random"] = variable
# u_bt_structured
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_structured")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_structured"] = variable
# u_bt_systematic
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_systematic")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_systematic"] = variable
# u_bt_total
variable = HIRS._create_3d_bt_uncertainty_variable(height, "uncertainty_bt_total")
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
tu.add_units(variable, "K")
dataset["u_bt_total"] = variable
# S_bt
variable = tu.create_float_variable(NUM_RAD_CHANNELS, NUM_RAD_CHANNELS, "covariance_brightness_temperature", dim_names=["rad_channel", "rad_channel"])
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["S_bt"] = variable
# l1b_calcof
default_array = DefaultData.create_default_array(height, NUM_COEFFS, np.float32, dims_names=["coeffs", "y"])
variable = Variable(["coeffs", "y"], default_array)
tu.add_encoding(variable, np.int32, DefaultData.get_default_fill_value(np.int32), 0.01)
variable.attrs["standard_name"] = "calibration_coefficients"
dataset["l1b_calcof"] = variable
# navigation_status
variable = HIRS._create_int32_vector(height, standard_name="status_flag", long_name="Navigation status bit field", orig_name="hrs_navstat")
dataset["navigation_status"] = variable
# quality_flags
variable = HIRS._create_int32_vector(height, standard_name="status_flag", long_name="Quality indicator bit field", orig_name="hrs_qualind")
dataset["quality_flags"] = variable
variable = HIRS._create_scaled_uint16_vector(height, long_name="Platform altitude", original_name="hrs_scalti")
tu.add_units(variable, "km")
dataset["platform_altitude"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform pitch angle", original_name="hrs_pitchang")
tu.add_units(variable, "degree")
dataset["platform_pitch_angle"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform roll angle", original_name="hrs_rollang")
tu.add_units(variable, "degree")
dataset["platform_roll_angle"] = variable
variable = HIRS._create_scaled_int16_vector(height, long_name="Platform yaw angle", original_name="hrs_yawang")
tu.add_units(variable, "degree")
dataset["platform_yaw_angle"] = variable
# scan_angles
default_array = DefaultData.create_default_array(NUM_SCAN_ANGLES, height, np.float32, dims_names=["y", "num_scan_angles"], fill_value=np.NaN)
variable = Variable(["y", "num_scan_angles"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), scale_factor=0.01)
tu.add_units(variable, "degree")
variable.attrs["long_name"] = "Scan angles"
variable.attrs["orig_name"] = "hrs_ang"
dataset["scan_angles"] = variable
dataset["l1b_scanline_number"] = HIRS._create_int16_vector(height, long_name="scanline number", orig_name="hrs_scnlin")
dataset["scanline_position"] = HIRS._create_int8_vector(height, long_name="Scanline position number in 32 second cycle", orig_name="hrs_scnpos")
# second_original_calibration_coefficients
default_array = DefaultData.create_default_array(WIDTH_TODO, height, np.float32, fill_value=np.NaN)
variable = Variable(["y", "width_todo"], default_array)
tu.add_encoding(variable, np.int32, DefaultData.get_default_fill_value(np.int32), scale_factor=0.01)
variable.attrs["long_name"] = "Second original calibration coefficients (unsorted)"
variable.attrs["orig_name"] = "hrs_scalcof"
dataset["l1b_second_original_calibration_coefficients"] = variable
dataset["Tc_baseplate"] = HIRS._create_counts_vector(height, "temperature_baseplate_counts")
dataset["Tc_ch"] = HIRS._create_counts_vector(height, "temperature_coolerhousing_counts")
dataset["Tc_elec"] = HIRS._create_counts_vector(height, "temperature_electronics_counts")
dataset["Tc_fsr"] = HIRS._create_counts_vector(height, "temperature_first_stage_radiator_counts")
dataset["Tc_fwh"] = HIRS._create_counts_vector(height, "temperature_filter_wheel_housing_counts")
dataset["Tc_fwm"] = HIRS._create_counts_vector(height, "temperature_filter_wheel_monitor_counts")
dataset["Tc_icct"] = HIRS._create_counts_vector(height, "temperature_internal_cold_calibration_target_counts")
dataset["Tc_iwct"] = HIRS._create_counts_vector(height, "temperature_internal_warm_calibration_target_counts")
dataset["Tc_patch_exp"] = HIRS._create_counts_vector(height, "temperature_patch_expanded_scale_counts")
dataset["Tc_patch_full"] = HIRS._create_counts_vector(height, "temperature_patch_full_range_counts")
dataset["Tc_tlscp_prim"] = HIRS._create_counts_vector(height, "temperature_telescope_primary_counts")
dataset["Tc_tlscp_sec"] = HIRS._create_counts_vector(height, "temperature_telescope_secondary_counts")
dataset["Tc_tlscp_tert"] = HIRS._create_counts_vector(height, "temperature_telescope_tertiary_counts")
dataset["Tc_scanmirror"] = HIRS._create_counts_vector(height, "temperature_scanmirror_counts")
dataset["Tc_scanmotor"] = HIRS._create_counts_vector(height, "temperature_scanmotor_counts")
dataset["u_Tc_baseplate"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_baseplate_counts")
dataset["u_Tc_ch"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_coolerhousing_counts")
dataset["u_Tc_elec"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_electronics_counts")
dataset["u_Tc_fsr"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_first_stage_radiator_counts")
dataset["u_Tc_fwh"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_filter_wheel_housing_counts")
dataset["u_Tc_fwm"] = HIRS._create_counts_uncertainty_vector(height, "uncertainty_temperature_filter_wheel_monitor_counts")
dataset["u_Tc_icct"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_internal_cold_calibration_target_counts")
dataset["u_Tc_iwct"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_internal_warm_calibration_target_counts")
dataset["u_Tc_patch_exp"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_patch_expanded_scale_counts")
dataset["u_Tc_patch_full"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_patch_full_range_counts")
dataset["u_Tc_tlscp_prim"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_primary_counts")
dataset["u_Tc_tlscp_sec"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_secondary_counts")
dataset["u_Tc_tlscp_tert"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_telescope_tertiary_counts")
dataset["u_Tc_scanmirror"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_scanmirror_counts")
dataset["u_Tc_scanmotor"] = HIRS._create_counts_uncertainty_vector_uint32(height, "uncertainty_temperature_scanmotor_counts")
dataset["u_sol_za"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_solar_zenith_angle", height, FILL_VALUE)
dataset["u_sol_aa"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_solar_azimuth_angle", height, FILL_VALUE)
dataset["u_sat_za"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_satellite_zenith_angle", height, FILL_VALUE)
dataset["u_sat_aa"] = HIRS._create_geo_angle_uncertainty_variable("uncertainty_local_azimuth_angle", height, FILL_VALUE)
# u_c_earth_chan_corr
dataset["u_c_earth_chan_corr"] = HIRS._create_channel_correlation_variable("u_c_earth channel correlations", np.int16)
# u_c_space
default_array = DefaultData.create_default_array(NUM_CALIBRATION_CYCLE, NUM_CHANNELS, np.uint16, dims_names=["channel", "calibration_cycle"])
variable = Variable(["channel", "calibration_cycle"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.uint16))
tu.add_units(variable, "count")
tu.add_scale_factor(variable, 0.005)
variable.attrs["long_name"] = "uncertainty counts for space views"
variable.attrs["ancilliary_variables"] = "u_c_space_chan_corr"
variable.attrs["channels_affected"] = "all"
variable.attrs["parameter"] = "C_s"
variable.attrs["pdf_shape"] = "gaussian"
dataset["u_c_space"] = variable
# u_c_space_chan_corr
dataset["u_c_space_chan_corr"] = HIRS._create_channel_correlation_variable("u_c_space channel correlations", np.uint16)
# u_Earthshine
dataset["u_Earthshine"] = HIRS._create_channel_uncertainty_uint16(height)
# u_O_Re
dataset["u_O_Re"] = HIRS._create_channel_uncertainty_uint16(height)
# u_O_TIWCT
default_array = DefaultData.create_default_vector(height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["u_O_TIWCT"] = variable
# u_O_TPRT
default_array = DefaultData.create_default_vector(height, np.uint16, DefaultData.get_default_fill_value(np.uint16))
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 65535)
tu.add_scale_factor(variable, 0.01)
tu.add_units(variable, "K")
variable.attrs["channels_affected"] = "all"
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.IMG_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.IMG_CORR_UNIT] = corr.IMG
variable.attrs[corr.IMG_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["parameter"] = "O_TPRT"
variable.attrs["pdf_shape"] = "gaussian"
variable.attrs["short_name"] = "O_TPRT"
variable.attrs["ancilliary_variables"] = "u_O_TPRT_chan_corr"
dataset["u_O_TPRT"] = variable
dataset["u_Rself"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_SRF_calib"] = HIRS._create_channel_uncertainty_uint16(height)
default_array = DefaultData.create_default_array(PRT_NUMBER_IWT, PRT_READING, dtype=np.float32, dims_names=["prt_number_iwt", "prt_reading"], fill_value=np.NaN)
variable = Variable(["prt_number_iwt", "prt_reading"], default_array)
tu.add_encoding(variable, np.uint16, DefaultData.get_default_fill_value(np.uint16), 0.01)
dataset["u_d_PRT"] = variable
dataset["u_electronics"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_periodic_noise"] = HIRS._create_channel_uncertainty_uint16(height)
dataset["u_nonlinearity"] = HIRS._create_scaled_uint16_vector(NUM_CHANNELS, dimension_name=["channel"], scale_factor=0.01)
dataset["emissivity"] = tu.create_scalar_float_variable("emissivity", units="1")
dataset["temp_corr_slope"] = tu.create_scalar_float_variable("Slope for effective temperature correction", units="1")
dataset["temp_corr_offset"] = tu.create_scalar_float_variable("Offset for effective temperature correction", units="1")
# mnfrqualflags
default_array = DefaultData.create_default_array(NUM_MINOR_FRAME, height, np.int32, dims_names=["y", "minor_frame"], fill_value=0)
variable = Variable(["y", "minor_frame"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "minor_frame_quality_flags_bitfield"
dataset["mnfrqualflags"] = variable
# scnlintime
variable = HIRS._create_int32_vector(height, standard_name="time", long_name="Scan line time of day", orig_name="hrs_scnlintime")
tu.add_units(variable, "ms")
dataset["scnlintime"] = variable
# scnlinf
default_array = DefaultData.create_default_vector(height, np.int16, fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "scanline_bitfield"
variable.attrs["flag_masks"] = "16384, 32768"
variable.attrs["flag_meanings"] = "clock_drift_correction southbound_data"
dataset["scnlinf"] = variable
# scantype
default_array = DefaultData.create_default_vector(height, np.int8, fill_value=0)
variable = Variable(["y"], default_array)
variable.attrs["standard_name"] = "status_flag"
variable.attrs["long_name"] = "scantype_bitfield"
variable.attrs["flag_values"] = "0, 1, 2, 3"
variable.attrs["flag_meanings"] = "earth_view space_view cold_bb_view main_bb_view"
dataset["scantype"] = variable
def add_original_variables(dataset, height, srf_size=None):
# height is ignored - supplied just for interface compatibility tb 2017-02-05
tu.add_quality_flags(dataset,
FULL_SIZE,
FULL_SIZE,
chunksizes=CHUNKSIZES)
# time
default_array = DefaultData.create_default_array(
IR_SIZE, IR_SIZE, np.uint32)
variable = Variable([IR_Y_DIMENSION, IR_X_DIMENSION], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.uint32))
variable.attrs["standard_name"] = "time"
variable.attrs["long_name"] = "Acquisition time of pixel"
tu.add_units(variable, "seconds since 1970-01-01 00:00:00")
tu.add_offset(variable, TIME_FILL_VALUE)
tu.add_chunking(variable, CHUNKSIZES)
dataset["time"] = variable
dataset["solar_azimuth_angle"] = MVIRI._create_angle_variable_int(
0.005493164, standard_name="solar_azimuth_angle", unsigned=True)
dataset["solar_zenith_angle"] = MVIRI._create_angle_variable_int(
0.005493248, standard_name="solar_zenith_angle")
dataset["satellite_azimuth_angle"] = MVIRI._create_angle_variable_int(
0.01,
standard_name="sensor_azimuth_angle",
long_name="sensor_azimuth_angle",
unsigned=True)
dataset["satellite_zenith_angle"] = MVIRI._create_angle_variable_int(
0.01, standard_name="platform_zenith_angle", unsigned=True)
# count_ir
default_array = DefaultData.create_default_array(
IR_SIZE, IR_SIZE, np.uint8)
variable = Variable([IR_Y_DIMENSION, IR_X_DIMENSION], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.uint8))
variable.attrs["long_name"] = "Infrared Image Counts"
tu.add_units(variable, "count")
tu.add_chunking(variable, CHUNKSIZES)
dataset["count_ir"] = variable
# count_wv
default_array = DefaultData.create_default_array(
IR_SIZE, IR_SIZE, np.uint8)
variable = Variable([IR_Y_DIMENSION, IR_X_DIMENSION], default_array)
tu.add_fill_value(variable,
DefaultData.get_default_fill_value(np.uint8))
variable.attrs["long_name"] = "WV Image Counts"
tu.add_units(variable, "count")
tu.add_chunking(variable, CHUNKSIZES)
dataset["count_wv"] = variable
default_array = DefaultData.create_default_array(FULL_SIZE,
FULL_SIZE,
np.uint8,
fill_value=0)
variable = Variable(["y", "x"], default_array)
variable.attrs["flag_masks"] = "1, 2, 4, 8, 16, 32"
variable.attrs[
"flag_meanings"] = "uncertainty_suspicious uncertainty_too_large space_view_suspicious not_on_earth suspect_time suspect_geo"
variable.attrs["standard_name"] = "status_flag"
tu.add_chunking(variable, CHUNKSIZES)
dataset["data_quality_bitmask"] = variable
# distance_sun_earth
dataset["distance_sun_earth"] = tu.create_scalar_float_variable(
long_name="Sun-Earth distance", units="au")
# solar_irradiance_vis
dataset["solar_irradiance_vis"] = tu.create_scalar_float_variable(
standard_name="solar_irradiance_vis",
long_name="Solar effective Irradiance",
units="W*m-2")
# u_solar_irradiance_vis
default_array = np.full([], np.NaN, np.float32)
variable = Variable([], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs[
"long_name"] = "Uncertainty in Solar effective Irradiance"
tu.add_units(variable, "Wm^-2")
variable.attrs[corr.PIX_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.PIX_CORR_UNIT] = corr.PIXEL
variable.attrs[corr.PIX_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.SCAN_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.SCAN_CORR_UNIT] = corr.LINE
variable.attrs[corr.SCAN_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs[corr.IMG_CORR_FORM] = corr.RECT_ABS
variable.attrs[corr.IMG_CORR_UNIT] = corr.DAYS
variable.attrs[corr.IMG_CORR_SCALE] = [-np.inf, np.inf]
variable.attrs["pdf_shape"] = "rectangle"
dataset["u_solar_irradiance_vis"] = variable
if srf_size is None:
srf_size = SRF_SIZE
default_array = DefaultData.create_default_array(srf_size,
NUM_CHANNELS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function weights'
variable.attrs[
"description"] = 'Per channel: weights for the relative spectral response function'
tu.add_encoding(variable, np.int16, -32768, 0.000033)
dataset['SRF_weights'] = variable
default_array = DefaultData.create_default_array(srf_size,
NUM_CHANNELS,
np.float32,
fill_value=np.NaN)
variable = Variable(["channel", "n_frequencies"], default_array)
variable.attrs["long_name"] = 'Spectral Response Function frequencies'
variable.attrs[
"description"] = 'Per channel: frequencies for the relative spectral response function'
tu.add_encoding(variable, np.int32, -2147483648, 0.0001)
tu.add_units(variable, "nm")
variable.attrs["source"] = "Filename of SRF"
variable.attrs["Valid(YYYYDDD)"] = "datestring"
dataset['SRF_frequencies'] = variable
# srf covariance_
default_array = DefaultData.create_default_array(srf_size,
srf_size,
np.float32,
fill_value=np.NaN)
variable = Variable([SRF_VIS_DIMENSION, SRF_VIS_DIMENSION],
default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs[
"long_name"] = "Covariance of the Visible Band Spectral Response Function"
tu.add_chunking(variable, CHUNKSIZES)
dataset["covariance_spectral_response_function_vis"] = variable
# u_srf_ir
default_array = DefaultData.create_default_vector(srf_size,
np.float32,
fill_value=np.NaN)
variable = Variable([SRF_IR_WV_DIMENSION], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs[
"long_name"] = "Uncertainty in Spectral Response Function for IR channel"
dataset["u_spectral_response_function_ir"] = variable
# u_srf_wv
default_array = DefaultData.create_default_vector(srf_size,
np.float32,
fill_value=np.NaN)
variable = Variable([SRF_IR_WV_DIMENSION], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs[
"long_name"] = "Uncertainty in Spectral Response Function for WV channel"
dataset["u_spectral_response_function_wv"] = variable
dataset["a_ir"] = tu.create_scalar_float_variable(
long_name="Calibration parameter a for IR Band",
units="mWm^-2sr^-1cm^-1")
dataset["b_ir"] = tu.create_scalar_float_variable(
long_name="Calibration parameter b for IR Band",
units="mWm^-2sr^-1cm^-1/DC")
dataset["u_a_ir"] = tu.create_scalar_float_variable(
long_name="Uncertainty of calibration parameter a for IR Band",
units="mWm^-2sr^-1cm^-1")
dataset["u_b_ir"] = tu.create_scalar_float_variable(
long_name="Uncertainty of calibration parameter b for IR Band",
units="mWm^-2sr^-1cm^-1/DC")
dataset["a_wv"] = tu.create_scalar_float_variable(
long_name="Calibration parameter a for WV Band",
units="mWm^-2sr^-1cm^-1")
dataset["b_wv"] = tu.create_scalar_float_variable(
long_name="Calibration parameter b for WV Band",
units="mWm^-2sr^-1cm^-1/DC")
dataset["u_a_wv"] = tu.create_scalar_float_variable(
long_name="Uncertainty of calibration parameter a for WV Band",
units="mWm^-2sr^-1cm^-1")
dataset["u_b_wv"] = tu.create_scalar_float_variable(
long_name="Uncertainty of calibration parameter b for WV Band",
units="mWm^-2sr^-1cm^-1/DC")
dataset["bt_a_ir"] = tu.create_scalar_float_variable(
long_name="IR Band BT conversion parameter A", units="1")
dataset["bt_b_ir"] = tu.create_scalar_float_variable(
long_name="IR Band BT conversion parameter B", units="1")
dataset["bt_a_wv"] = tu.create_scalar_float_variable(
long_name="WV Band BT conversion parameter A", units="1")
dataset["bt_b_wv"] = tu.create_scalar_float_variable(
long_name="WV Band BT conversion parameter B", units="1")
dataset["years_since_launch"] = tu.create_scalar_float_variable(
long_name="Fractional year since launch of satellite",
units="years")
x_ir_wv_dim = dataset.dims["x_ir_wv"]
dataset["x_ir_wv"] = Coordinate(
"x_ir_wv", np.arange(x_ir_wv_dim, dtype=np.uint16))
y_ir_wv_dim = dataset.dims["y_ir_wv"]
dataset["y_ir_wv"] = Coordinate(
"y_ir_wv", np.arange(y_ir_wv_dim, dtype=np.uint16))
srf_size_dim = dataset.dims["srf_size"]
dataset["srf_size"] = Coordinate(
"srf_size", np.arange(srf_size_dim, dtype=np.uint16))
def add_full_fcdr_variables(dataset, height):
# u_Temperature_misc_housekeeping
default_array = DefaultData.create_default_array(
height,
NUM_THERMISTORS,
np.float32,
dims_names=["housekeeping", "y"],
fill_value=np.NaN)
variable = Variable(["housekeeping", "y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
variable.attrs["units"] = "TODO"
dataset["u_Temperature_misc_housekeeping"] = variable
# u_cold_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_cold_counts"] = variable
# u_counts_to_tb_gain
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_counts_to_tb_gain"] = variable
# u_counts_to_tb_offset
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_counts_to_tb_offset"] = variable
# u_gain_control
default_array = DefaultData.create_default_array(
height,
NUM_CHANNELS,
np.float32,
dims_names=["channel", "y"],
fill_value=np.NaN)
variable = Variable([
"channel",
"y",
], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_gain_control"] = variable
# u_tb
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, NUM_CHANNELS, np.float32, np.NaN)
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "K")
dataset["u_tb"] = variable
# u_thermal_reference
default_array = DefaultData.create_default_vector(
height, np.float32, np.NaN)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
tu.add_units(variable, "TODO")
dataset["u_thermal_reference"] = variable
# u_warm_counts
default_array = DefaultData.create_default_array_3d(
SWATH_WIDTH, height, CALIB_NUMBER, np.float32, np.NaN)
variable = Variable(["calib_number", "y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["long_name"] = "TODO"
dataset["u_warm_counts"] = variable
def _create_channel_correlation_variable(long_name, data_type):
data_array = DefaultData.create_default_array(NUM_CHANNELS, NUM_CHANNELS, np.float32, fill_value=np.NaN)
variable = Variable(["channel", "channel"], data_array)
tu.add_encoding(variable, data_type, DefaultData.get_default_fill_value(data_type), scale_factor=0.01)
variable.attrs["long_name"] = long_name
return variable
def test_shift2d(self):
v = Variable(('x', 'y'), [[1, 2], [3, 4]])
expected = Variable(('x', 'y'), [[np.nan, np.nan], [np.nan, 1]])
self.assertVariableIdentical(expected, v.shift(x=1, y=1))
def _create_3d_bt_uncertainty_variable(height, standard_name):
default_array = DefaultData.create_default_array_3d(SWATH_WIDTH, height, NUM_CHANNELS, np.float32, dims_names=["channel", "y", "x"])
variable = Variable(["channel", "y", "x"], default_array)
tu.add_fill_value(variable, DefaultData.get_default_fill_value(np.float32))
variable.attrs["standard_name"] = standard_name
return variable
def test_big_endian_reduce(self):
# regression test for GH489
data = np.ones(5, dtype='>f4')
v = Variable(['x'], data)
expected = Variable([], 5)
self.assertVariableIdentical(expected, v.sum())
def test_expand_dims(self):
v = Variable(['x'], [0, 1])
actual = v.expand_dims(['x', 'y'])
expected = Variable(['x', 'y'], [[0], [1]])
self.assertVariableIdentical(actual, expected)
actual = v.expand_dims(['y', 'x'])
self.assertVariableIdentical(actual, expected.T)
actual = v.expand_dims(OrderedDict([('x', 2), ('y', 2)]))
expected = Variable(['x', 'y'], [[0, 0], [1, 1]])
self.assertVariableIdentical(actual, expected)
v = Variable(['foo'], [0, 1])
actual = v.expand_dims('foo')
expected = v
self.assertVariableIdentical(actual, expected)
with self.assertRaisesRegexp(ValueError, 'must be a superset'):
v.expand_dims(['z'])
def test_reduce(self):
v = Variable(['x', 'y'], self.d, {'ignored': 'attributes'})
self.assertVariableIdentical(v.reduce(np.std, 'x'),
Variable(['y'], self.d.std(axis=0)))
self.assertVariableIdentical(v.reduce(np.std, axis=0),
v.reduce(np.std, dim='x'))
self.assertVariableIdentical(v.reduce(np.std, ['y', 'x']),
Variable([], self.d.std(axis=(0, 1))))
self.assertVariableIdentical(v.reduce(np.std),
Variable([], self.d.std()))
self.assertVariableIdentical(
v.reduce(np.mean, 'x').reduce(np.std, 'y'),
Variable([],
self.d.mean(axis=0).std()))
self.assertVariableAllClose(v.mean('x'), v.reduce(np.mean, 'x'))
with self.assertRaisesRegexp(ValueError, 'cannot supply both'):
v.mean(dim='x', axis=0)
def test_squeeze(self):
v = Variable(['x', 'y'], [[1]])
self.assertVariableIdentical(Variable([], 1), v.squeeze())
self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze('x'))
self.assertVariableIdentical(Variable(['y'], [1]), v.squeeze(['x']))
self.assertVariableIdentical(Variable(['x'], [1]), v.squeeze('y'))
self.assertVariableIdentical(Variable([], 1), v.squeeze(['x', 'y']))
v = Variable(['x', 'y'], [[1, 2]])
self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze())
self.assertVariableIdentical(Variable(['y'], [1, 2]), v.squeeze('x'))
with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'):
v.squeeze('y')
def test_stack_unstack_consistency(self):
v = Variable(['x', 'y'], [[0, 1], [2, 3]])
actual = (v.stack(z=('x', 'y')).unstack(z=OrderedDict([('x',
2), ('y',
2)])))
self.assertVariableIdentical(actual, v)
def add_easy_fcdr_variables(dataset,
height,
corr_dx=None,
corr_dy=None,
lut_size=None):
# height is ignored - supplied just for interface compatibility tb 2017-02-05
# reflectance
default_array = DefaultData.create_default_array(FULL_SIZE,
FULL_SIZE,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["standard_name"] = "toa_bidirectional_reflectance_vis"
variable.attrs[
"long_name"] = "top of atmosphere bidirectional reflectance factor per pixel of the visible band with central wavelength 0.7"
tu.add_units(variable, "1")
tu.add_encoding(variable,
np.uint16,
DefaultData.get_default_fill_value(np.uint16),
3.05176E-05,
chunksizes=CHUNKSIZES)
dataset["toa_bidirectional_reflectance_vis"] = variable
# u_independent
default_array = DefaultData.create_default_array(FULL_SIZE,
FULL_SIZE,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["long_name"] = "independent uncertainty per pixel"
tu.add_units(variable, "1")
tu.add_encoding(variable,
np.uint16,
DefaultData.get_default_fill_value(np.uint16),
3.05176E-05,
chunksizes=CHUNKSIZES)
dataset["u_independent_toa_bidirectional_reflectance"] = variable
# u_structured
default_array = DefaultData.create_default_array(FULL_SIZE,
FULL_SIZE,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
variable.attrs["long_name"] = "structured uncertainty per pixel"
tu.add_units(variable, "1")
tu.add_encoding(variable,
np.uint16,
DefaultData.get_default_fill_value(np.uint16),
3.05176E-05,
chunksizes=CHUNKSIZES)
dataset["u_structured_toa_bidirectional_reflectance"] = variable
# u_common
dataset[
"u_common_toa_bidirectional_reflectance"] = tu.create_scalar_float_variable(
long_name="common uncertainty per slot", units="1")
dataset[
"sub_satellite_latitude_start"] = tu.create_scalar_float_variable(
long_name="Latitude of the sub satellite point at image start",
units="degrees_north")
dataset[
"sub_satellite_longitude_start"] = tu.create_scalar_float_variable(
long_name="Longitude of the sub satellite point at image start",
units="degrees_east")
dataset[
"sub_satellite_latitude_end"] = tu.create_scalar_float_variable(
long_name="Latitude of the sub satellite point at image end",
units="degrees_north")
dataset[
"sub_satellite_longitude_end"] = tu.create_scalar_float_variable(
long_name="Longitude of the sub satellite point at image end",
units="degrees_east")
tu.add_correlation_matrices(dataset, NUM_CHANNELS)
if lut_size is not None:
tu.add_lookup_tables(dataset, NUM_CHANNELS, lut_size=lut_size)
if corr_dx is not None and corr_dy is not None:
tu.add_correlation_coefficients(dataset, NUM_CHANNELS, corr_dx,
corr_dy)
tu.add_coordinates(dataset, ["vis", "wv", "ir"])
def test_roll(self):
v = Variable('x', [1, 2, 3, 4, 5])
self.assertVariableIdentical(v, v.roll(x=0))
self.assertIsNot(v, v.roll(x=0))
expected = Variable('x', [5, 1, 2, 3, 4])
self.assertVariableIdentical(expected, v.roll(x=1))
self.assertVariableIdentical(expected, v.roll(x=-4))
self.assertVariableIdentical(expected, v.roll(x=6))
expected = Variable('x', [4, 5, 1, 2, 3])
self.assertVariableIdentical(expected, v.roll(x=2))
self.assertVariableIdentical(expected, v.roll(x=-3))
with self.assertRaisesRegexp(ValueError, 'dimension'):
v.roll(z=0)
def test_concat_dim_is_variable(self):
objs = [Dataset({"x": 0}), Dataset({"x": 1})]
coord = Variable("y", [3, 4])
expected = Dataset({"x": ("y", [0, 1]), "y": [3, 4]})
actual = concat(objs, coord)
assert_identical(actual, expected)
def test_lazily_indexed_array(self):
original = np.random.rand(10, 20, 30)
x = indexing.NumpyIndexingAdapter(original)
v = Variable(["i", "j", "k"], original)
lazy = indexing.LazilyOuterIndexedArray(x)
v_lazy = Variable(["i", "j", "k"], lazy)
arr = ReturnItem()
# test orthogonally applied indexers
indexers = [
arr[:], 0, -2, arr[:3], [0, 1, 2, 3], [0],
np.arange(10) < 5
]
for i in indexers:
for j in indexers:
for k in indexers:
if isinstance(j, np.ndarray) and j.dtype.kind == "b":
j = np.arange(20) < 5
if isinstance(k, np.ndarray) and k.dtype.kind == "b":
k = np.arange(30) < 5
expected = np.asarray(v[i, j, k])
for actual in [
v_lazy[i, j, k],
v_lazy[:, j, k][i],
v_lazy[:, :, k][:, j][i],
]:
assert expected.shape == actual.shape
assert_array_equal(expected, actual)
assert isinstance(actual._data,
indexing.LazilyOuterIndexedArray)
# make sure actual.key is appropriate type
if all(
isinstance(k, (int, slice))
for k in v_lazy._data.key.tuple):
assert isinstance(v_lazy._data.key,
indexing.BasicIndexer)
else:
assert isinstance(v_lazy._data.key,
indexing.OuterIndexer)
# test sequentially applied indexers
indexers = [
(3, 2),
(arr[:], 0),
(arr[:2], -1),
(arr[:4], [0]),
([4, 5], 0),
([0, 1, 2], [0, 1]),
([0, 3, 5], arr[:2]),
]
for i, j in indexers:
expected = v[i][j]
actual = v_lazy[i][j]
assert expected.shape == actual.shape
assert_array_equal(expected, actual)
# test transpose
if actual.ndim > 1:
order = np.random.choice(actual.ndim, actual.ndim)
order = np.array(actual.dims)
transposed = actual.transpose(*order)
assert_array_equal(expected.transpose(*order), transposed)
assert isinstance(
actual._data,
(
indexing.LazilyVectorizedIndexedArray,
indexing.LazilyOuterIndexedArray,
),
)
assert isinstance(actual._data, indexing.LazilyOuterIndexedArray)
assert isinstance(actual._data.array,
indexing.NumpyIndexingAdapter)
def test_remap_label_indexers(self):
def test_indexer(data, x, expected_pos, expected_idx=None):
pos, idx = indexing.remap_label_indexers(data, {"x": x})
assert_array_equal(pos.get("x"), expected_pos)
assert_array_equal(idx.get("x"), expected_idx)
data = Dataset({"x": ("x", [1, 2, 3])})
mindex = pd.MultiIndex.from_product([["a", "b"], [1, 2], [-1, -2]],
names=("one", "two", "three"))
mdata = DataArray(range(8), [("x", mindex)])
test_indexer(data, 1, 0)
test_indexer(data, np.int32(1), 0)
test_indexer(data, Variable([], 1), 0)
test_indexer(mdata, ("a", 1, -1), 0)
test_indexer(
mdata,
("a", 1),
[True, True, False, False, False, False, False, False],
[-1, -2],
)
test_indexer(
mdata,
"a",
slice(0, 4, None),
pd.MultiIndex.from_product([[1, 2], [-1, -2]]),
)
test_indexer(
mdata,
("a", ),
[True, True, True, True, False, False, False, False],
pd.MultiIndex.from_product([[1, 2], [-1, -2]]),
)
test_indexer(mdata, [("a", 1, -1), ("b", 2, -2)], [0, 7])
test_indexer(mdata, slice("a", "b"), slice(0, 8, None))
test_indexer(mdata, slice(("a", 1), ("b", 1)), slice(0, 6, None))
test_indexer(mdata, {"one": "a", "two": 1, "three": -1}, 0)
test_indexer(
mdata,
{
"one": "a",
"two": 1
},
[True, True, False, False, False, False, False, False],
[-1, -2],
)
test_indexer(
mdata,
{
"one": "a",
"three": -1
},
[True, False, True, False, False, False, False, False],
[1, 2],
)
test_indexer(
mdata,
{"one": "a"},
[True, True, True, True, False, False, False, False],
pd.MultiIndex.from_product([[1, 2], [-1, -2]]),
)
coder = strings.EncodedStringCoder(allows_unicode=True)
raw = Variable(('x',), raw_data, encoding={'dtype': 'S1'})
actual = coder.encode(raw)
expected = Variable(('x',), expected_data, attrs={'_Encoding': 'utf-8'})
assert_identical(actual, expected)
raw = Variable(('x',), raw_data)
assert_identical(coder.encode(raw), raw)
coder = strings.EncodedStringCoder(allows_unicode=False)
assert_identical(coder.encode(raw), expected)
@pytest.mark.parametrize('original', [
Variable(('x',), [b'ab', b'cdef']),
Variable((), b'ab'),
Variable(('x',), [b'a', b'b']),
Variable((), b'a'),
])
def test_CharacterArrayCoder_roundtrip(original):
coder = strings.CharacterArrayCoder()
roundtripped = coder.decode(coder.encode(original))
assert_identical(original, roundtripped)
@pytest.mark.parametrize('data', [
np.array([b'a', b'bc']),
np.array([b'a', b'bc'], dtype=strings.create_vlen_dtype(bytes_type)),
])
def test_CharacterArrayCoder_encode(data):
def test_concat_dim_is_variable(self):
objs = [Dataset({'x': 0}), Dataset({'x': 1})]
coord = Variable('y', [3, 4])
expected = Dataset({'x': ('y', [0, 1]), 'y': [3, 4]})
actual = concat(objs, coord)
assert_identical(actual, expected)
def test_transpose(self):
v = Variable(['time', 'x'], self.d)
v2 = Variable(['x', 'time'], self.d.T)
self.assertVariableIdentical(v, v2.transpose())
self.assertVariableIdentical(v.transpose(), v.T)
x = np.random.randn(2, 3, 4, 5)
w = Variable(['a', 'b', 'c', 'd'], x)
w2 = Variable(['d', 'b', 'c', 'a'], np.einsum('abcd->dbca', x))
self.assertEqual(w2.shape, (5, 3, 4, 2))
self.assertVariableIdentical(w2, w.transpose('d', 'b', 'c', 'a'))
self.assertVariableIdentical(w, w2.transpose('a', 'b', 'c', 'd'))
w3 = Variable(['b', 'c', 'd', 'a'], np.einsum('abcd->bcda', x))
self.assertVariableIdentical(w, w3.transpose('a', 'b', 'c', 'd'))
class TestVariable(DaskTestCase):
def assertLazyAnd(self, expected, actual, test):
expected_copy = expected.copy(deep=False)
actual_copy = actual.copy(deep=False)
with dask.set_options(get=dask.get):
test(actual_copy, expected_copy)
var = getattr(actual, 'variable', actual)
self.assertIsInstance(var.data, da.Array)
def assertLazyAndIdentical(self, expected, actual):
self.assertLazyAnd(expected, actual, self.assertVariableIdentical)
def assertLazyAndAllClose(self, expected, actual):
self.assertLazyAnd(expected, actual, self.assertVariableAllClose)
def setUp(self):
self.values = np.random.randn(4, 6)
self.data = da.from_array(self.values, chunks=(2, 2))
self.eager_var = Variable(('x', 'y'), self.values)
self.lazy_var = Variable(('x', 'y'), self.data)
def test_basics(self):
v = self.lazy_var
self.assertIs(self.data, v.data)
self.assertEqual(self.data.chunks, v.chunks)
self.assertArrayEqual(self.values, v)
def test_copy(self):
self.assertLazyAndIdentical(self.eager_var, self.lazy_var.copy())
self.assertLazyAndIdentical(self.eager_var,
self.lazy_var.copy(deep=True))
def test_chunk(self):
for chunks, expected in [(None, ((2, 2), (2, 2, 2))),
(3, ((3, 1), (3, 3))),
({
'x': 3,
'y': 3
}, ((3, 1), (3, 3))),
({
'x': 3
}, ((3, 1), (2, 2, 2))),
({
'x': (3, 1)
}, ((3, 1), (2, 2, 2)))]:
rechunked = self.lazy_var.chunk(chunks)
self.assertEqual(rechunked.chunks, expected)
self.assertLazyAndIdentical(self.eager_var, rechunked)
def test_indexing(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u[0], v[0])
self.assertLazyAndIdentical(u[:1], v[:1])
self.assertLazyAndIdentical(u[[0, 1], [0, 1, 2]], v[[0, 1], [0, 1, 2]])
with self.assertRaisesRegexp(TypeError, 'stored in a dask array'):
v[:1] = 0
def test_squeeze(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u[0].squeeze(), v[0].squeeze())
def test_equals(self):
v = self.lazy_var
self.assertTrue(v.equals(v))
self.assertIsInstance(v.data, da.Array)
self.assertTrue(v.identical(v))
self.assertIsInstance(v.data, da.Array)
def test_transpose(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u.T, v.T)
def test_shift(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u.shift(x=2), v.shift(x=2))
self.assertLazyAndIdentical(u.shift(x=-2), v.shift(x=-2))
self.assertEqual(v.data.chunks, v.shift(x=1).data.chunks)
def test_roll(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u.roll(x=2), v.roll(x=2))
self.assertEqual(v.data.chunks, v.roll(x=1).data.chunks)
def test_unary_op(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(-u, -v)
self.assertLazyAndIdentical(abs(u), abs(v))
self.assertLazyAndIdentical(u.round(), v.round())
def test_binary_op(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(2 * u, 2 * v)
self.assertLazyAndIdentical(u + u, v + v)
self.assertLazyAndIdentical(u[0] + u, v[0] + v)
def test_reduce(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndAllClose(u.mean(), v.mean())
self.assertLazyAndAllClose(u.std(), v.std())
self.assertLazyAndAllClose(u.argmax(dim='x'), v.argmax(dim='x'))
self.assertLazyAndAllClose((u > 1).any(), (v > 1).any())
self.assertLazyAndAllClose((u < 1).all('x'), (v < 1).all('x'))
with self.assertRaisesRegexp(NotImplementedError, 'dask'):
v.prod()
with self.assertRaisesRegexp(NotImplementedError, 'dask'):
v.median()
def test_missing_values(self):
values = np.array([0, 1, np.nan, 3])
data = da.from_array(values, chunks=(2, ))
eager_var = Variable('x', values)
lazy_var = Variable('x', data)
self.assertLazyAndIdentical(eager_var, lazy_var.fillna(lazy_var))
self.assertLazyAndIdentical(Variable('x', range(4)),
lazy_var.fillna(2))
self.assertLazyAndIdentical(eager_var.count(), lazy_var.count())
def test_concat(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndIdentical(u, Variable.concat([v[:2], v[2:]], 'x'))
self.assertLazyAndIdentical(u[:2], Variable.concat([v[0], v[1]], 'x'))
self.assertLazyAndIdentical(
u[:3],
Variable.concat([v[[0, 2]], v[[1]]], 'x', positions=[[0, 2], [1]]))
def test_missing_methods(self):
v = self.lazy_var
try:
v.argsort()
except NotImplementedError as err:
self.assertIn('dask', str(err))
try:
v[0].item()
except NotImplementedError as err:
self.assertIn('dask', str(err))
def test_ufuncs(self):
u = self.eager_var
v = self.lazy_var
self.assertLazyAndAllClose(np.sin(u), xu.sin(v))
def test_shift(self):
v = Variable('x', [1, 2, 3, 4, 5])
self.assertVariableIdentical(v, v.shift(x=0))
self.assertIsNot(v, v.shift(x=0))
expected = Variable('x', [np.nan, 1, 2, 3, 4])
self.assertVariableIdentical(expected, v.shift(x=1))
expected = Variable('x', [np.nan, np.nan, 1, 2, 3])
self.assertVariableIdentical(expected, v.shift(x=2))
expected = Variable('x', [2, 3, 4, 5, np.nan])
self.assertVariableIdentical(expected, v.shift(x=-1))
expected = Variable('x', [np.nan] * 5)
self.assertVariableIdentical(expected, v.shift(x=5))
self.assertVariableIdentical(expected, v.shift(x=6))
with self.assertRaisesRegexp(ValueError, 'dimension'):
v.shift(z=0)
v = Variable('x', [1, 2, 3, 4, 5], {'foo': 'bar'})
self.assertVariableIdentical(v, v.shift(x=0))
expected = Variable('x', [np.nan, 1, 2, 3, 4], {'foo': 'bar'})
self.assertVariableIdentical(expected, v.shift(x=1))
def setUp(self):
self.values = np.random.RandomState(0).randn(4, 6)
self.data = da.from_array(self.values, chunks=(2, 2))
self.eager_var = Variable(('x', 'y'), self.values)
self.lazy_var = Variable(('x', 'y'), self.data)
