def get_date_list(start, stop, days):
ret = []
delta = datetime.timedelta(days=days)
check = start
while check <= stop:
ret.append(check)
try:
check += delta
except TypeError:
# Probably an older netcdftime object
check = datetime.datetime(check.year, check.month, check.day, check.hour, check.second, check.microsecond)
check += delta
check = netcdftime.datetime(check.year, check.month, check.day, check.hour, check.second, check.microsecond)
return ret
def _calculate_structure(self):
# Make value arrays for the vectorisable field elements.
element_definitions = self._field_vector_element_arrays()
# Identify the vertical elements and payload.
blev_array = dict(element_definitions).get('blev')
vertical_elements = ('lblev', 'bhlev', 'bhrlev', 'brsvd1', 'brsvd2',
'brlev')
# Make an ordering copy.
ordering_definitions = element_definitions[:]
# Replace time value tuples with integers and bind the vertical
# elements to the (expected) primary vertical element "blev".
for index, (name, array) in enumerate(ordering_definitions):
if name in ('t1', 't2'):
array = np.array(
[self._time_comparable_int(*tuple(val)) for val in array])
ordering_definitions[index] = (name, array)
if name in vertical_elements and blev_array is not None:
ordering_definitions[index] = (name, blev_array)
# Perform the main analysis: get vector dimensions, elements, arrays.
dims_shape, primary_elements, vector_element_arrays_and_dims = \
optimal_array_structure(ordering_definitions,
element_definitions)
# Replace time tuples in the result with real datetime-like values.
# N.B. so we *don't* do this on the whole (expanded) input arrays.
for name in ('t1', 't2'):
if name in vector_element_arrays_and_dims:
arr, dims = vector_element_arrays_and_dims[name]
arr_shape = arr.shape[:-1]
extra_length = arr.shape[-1]
# Flatten out the array apart from the last dimension,
# convert to netcdftime objects, then reshape back.
arr = np.array([
netcdftime.datetime(*args)
for args in arr.reshape(-1, extra_length)
]).reshape(arr_shape)
vector_element_arrays_and_dims[name] = (arr, dims)
# Write the private cache values, exposed as public properties.
self._vector_dims_shape = dims_shape
self._primary_dimension_elements = primary_elements
self._element_arrays_and_dims = vector_element_arrays_and_dims
# Do all this only once.
self._structure_calculated = True
def _calculate_structure(self):
# Make value arrays for the vectorisable field elements.
element_definitions = self._field_vector_element_arrays()
# Identify the vertical elements and payload.
blev_array = dict(element_definitions).get('blev')
vertical_elements = ('lblev', 'bhlev', 'bhrlev',
'brsvd1', 'brsvd2', 'brlev')
# Make an ordering copy.
ordering_definitions = element_definitions[:]
# Replace time value tuples with integers and bind the vertical
# elements to the (expected) primary vertical element "blev".
for index, (name, array) in enumerate(ordering_definitions):
if name in ('t1', 't2'):
array = np.array(
[self._time_comparable_int(*tuple(val)) for val in array])
ordering_definitions[index] = (name, array)
if name in vertical_elements and blev_array is not None:
ordering_definitions[index] = (name, blev_array)
# Perform the main analysis: get vector dimensions, elements, arrays.
dims_shape, primary_elements, vector_element_arrays_and_dims = \
optimal_array_structure(ordering_definitions,
element_definitions)
# Replace time tuples in the result with real datetime-like values.
# N.B. so we *don't* do this on the whole (expanded) input arrays.
for name in ('t1', 't2'):
if name in vector_element_arrays_and_dims:
arr, dims = vector_element_arrays_and_dims[name]
arr_shape = arr.shape[:-1]
extra_length = arr.shape[-1]
# Flatten out the array apart from the last dimension,
# convert to netcdftime objects, then reshape back.
arr = np.array([netcdftime.datetime(*args)
for args in arr.reshape(-1, extra_length)]
).reshape(arr_shape)
vector_element_arrays_and_dims[name] = (arr, dims)
# Write the private cache values, exposed as public properties.
self._vector_dims_shape = dims_shape
self._primary_dimension_elements = primary_elements
self._element_arrays_and_dims = vector_element_arrays_and_dims
# Do all this only once.
self._structure_calculated = True
