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add flag mask generating methods #90

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Merged
shunt16 merged 1 commit into from
Nov 3, 2020
Merged

add flag mask generating methods #90

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54 changes: 52 additions & 2 deletions hypernets_processor/data_io/dataset_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,8 @@ def create_default_array(dim_sizes, dtype, dim_names=None, fill_value=None):

if dim_names is not None:
default_array = DataArray(empty_array, dims=dim_names)
elif (dim_names is None) and (dim_sizes == []):
default_array = DataArray(empty_array)
else:
default_array = DataArray(empty_array, dims=DEFAULT_DIM_NAMES[-len(dim_sizes):])

Expand Down Expand Up @@ -259,6 +261,50 @@ def unpack_flags(da):

return ds

@staticmethod
def get_flags_mask_or(da, flags=None):
"""
Returns boolean mask for set of flags, defined as logical or of flags

:type da: xarray.DataArray
:param da: dataset

:type flags: list
:param flags: list of flags (if unset all data flags selected)

:return: flag masks
:rtype: numpy.ndarray
"""

flags_ds = DatasetUtil.unpack_flags(da)

flags = flags if flags is not None else flags_ds.variables
mask_flags = [flags_ds[flag].values for flag in flags]

return np.logical_or.reduce(mask_flags)

@staticmethod
def get_flags_mask_and(da, flags=None):
"""
Returns boolean mask for set of flags, defined as logical and of flags

:type da: xarray.DataArray
:param da: dataset

:type flags: list
:param flags: list of flags (if unset all data flags selected)

:return: flag masks
:rtype: numpy.ndarray
"""

flags_ds = DatasetUtil.unpack_flags(da)

flags = flags if flags is not None else flags_ds.variables
mask_flags = [flags_ds[flag].values for flag in flags]

return np.logical_and.reduce(mask_flags)

@staticmethod
def set_flag(da, flag_name, error_if_set=False):
"""
Expand All @@ -281,7 +327,9 @@ def set_flag(da, flag_name, error_if_set=False):
flag_bit = flag_meanings.index(flag_name)
flag_mask = flag_masks[flag_bit]

return da | flag_mask
da.values = da.values | flag_mask

return da

@staticmethod
def unset_flag(da, flag_name, error_if_unset=False):
Expand All @@ -305,7 +353,9 @@ def unset_flag(da, flag_name, error_if_unset=False):
flag_bit = flag_meanings.index(flag_name)
flag_mask = flag_masks[flag_bit]

return da & ~flag_mask
da.values = da.values & ~flag_mask

return da

@staticmethod
def get_set_flags(da):
Expand Down
56 changes: 56 additions & 0 deletions hypernets_processor/data_io/tests/test_dataset_util.py
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Expand Up @@ -288,6 +288,62 @@ def test_unpack_flags(self):
self.assertTrue((flags["flag7"].data == empty).all())
self.assertTrue((flags["flag8"].data == empty).all())

def test_get_flags_mask_or(self):

ds = Dataset()
meanings = ["flag1", "flag2", "flag3", "flag4", "flag5", "flag6", "flag7", "flag8"]
flags_vector_variable = DatasetUtil.create_flags_variable([2,3], meanings, dim_names=["dim1", "dim2"],
attributes={"standard_name": "std"})

ds["flags"] = flags_vector_variable
ds["flags"] = DatasetUtil.set_flag(ds["flags"], "flag4")
ds["flags"][0, 1] = DatasetUtil.set_flag(ds["flags"][0, 1], "flag5")
ds["flags"][1, 1] = DatasetUtil.set_flag(ds["flags"][1, 1], "flag2")
ds["flags"][1, 2] = DatasetUtil.set_flag(ds["flags"][1, 2], "flag7")

flags_mask = DatasetUtil.get_flags_mask_or(ds["flags"], flags=["flag5", "flag2", "flag7"])

expected_flags_mask = np.array([[False, True, False], [False, True, True]], dtype=bool)

np.testing.assert_array_almost_equal(flags_mask, expected_flags_mask)

def test_get_flags_mask_all(self):
ds = Dataset()
meanings = ["flag1", "flag2", "flag3", "flag4", "flag5", "flag6", "flag7", "flag8"]
flags_vector_variable = DatasetUtil.create_flags_variable([2, 3], meanings, dim_names=["dim1", "dim2"],
attributes={"standard_name": "std"})

ds["flags"] = flags_vector_variable
ds["flags"] = DatasetUtil.set_flag(ds["flags"], "flag4")
ds["flags"][0, 1] = DatasetUtil.set_flag(ds["flags"][0, 1], "flag5")
ds["flags"][1, 1] = DatasetUtil.set_flag(ds["flags"][1, 1], "flag2")
ds["flags"][1, 2] = DatasetUtil.set_flag(ds["flags"][1, 2], "flag7")

flags_mask = DatasetUtil.get_flags_mask_or(ds["flags"])

expected_flags_mask = np.array([[True, True, True], [True, True, True]], dtype=bool)

np.testing.assert_array_almost_equal(flags_mask, expected_flags_mask)

def test_get_flags_mask_and(self):

ds = Dataset()
meanings = ["flag1", "flag2", "flag3", "flag4", "flag5", "flag6", "flag7", "flag8"]
flags_vector_variable = DatasetUtil.create_flags_variable([2,3], meanings, dim_names=["dim1", "dim2"],
attributes={"standard_name": "std"})

ds["flags"] = flags_vector_variable
ds["flags"] = DatasetUtil.set_flag(ds["flags"], "flag4")
ds["flags"][0, 1] = DatasetUtil.set_flag(ds["flags"][0, 1], "flag5")
ds["flags"][1, 1] = DatasetUtil.set_flag(ds["flags"][1, 1], "flag2")
ds["flags"][1, 1] = DatasetUtil.set_flag(ds["flags"][1, 1], "flag7")

flags_mask = DatasetUtil.get_flags_mask_and(ds["flags"], flags=["flag2", "flag7"])

expected_flags_mask = np.array([[False, False, False], [False, True, False]], dtype=bool)

np.testing.assert_array_almost_equal(flags_mask, expected_flags_mask)

def test_get_set_flags(self):

ds = Dataset()
Expand Down