def rt2dt(array, units_in, units_out=None, dummy1=None):
'''Convert reference times to date-time objects
The returned array is always independent.
:Parameters:
array: numpy array-like
units_in: `Units`
units_out: *optional*
Ignored.
dummy1:
Ignored.
:Returns:
`numpy.ndarray`
An array of `cf.Datetime` objects with the same shape as
*array*.
'''
mask = None
if numpy_ma_isMA(array):
# num2date has issues if the mask is nomask
mask = array.mask
if mask is numpy_ma_nomask or not numpy_ma_is_masked(array):
array = array.view(numpy_ndarray)
units = units_in.units
calendar = getattr(units_in, 'calendar', 'standard')
array = cftime.num2date(array, units, calendar,
only_use_cftime_datetimes=True)
# array = units_in._utime.num2date(array)
if mask is not None:
array = numpy_ma_masked_where(mask, array)
ndim = numpy_ndim(array)
if mask is None:
# There is no missing data
return numpy_array(array, dtype=object)
# return numpy_vectorize(
# partial(dt2Dt, calendar=units_in._calendar),
# otypes=[object])(array)
else:
# There is missing data
if not ndim:
return numpy_ma_masked_all((), dtype=object)
else:
# array = numpy_array(array)
# array = numpy_vectorize(
# partial(dt2Dt, calendar=units_in._calendar),
# otypes=[object])(array)
return numpy_ma_masked_where(mask, array)
def __init__(self, array):
"""**Initialization**
:Parameters:
array: numpy array
The array to be stored on disk in a temporary file.
**Examples:**
>>> f = CachedArray(numpy.array([1, 2, 3, 4, 5]))
>>> f = CachedArray(numpy.ma.array([1, 2, 3, 4, 5]))
"""
super().__init__()
# ------------------------------------------------------------
# Use mkstemp because we want to be responsible for deleting
# the temporary file when done with it.
# ------------------------------------------------------------
_partition_dir = mkdtemp(
prefix="cf_cachedarray_", dir=CONSTANTS["TEMPDIR"]
)
fd, _partition_file = mkstemp(
prefix="cf_cachedarray_", suffix=".npy", dir=_partition_dir
)
close(fd)
# The name of the temporary file storing the array
self._set_component("_partition_dir", _partition_dir)
self._set_component("_partition_file", _partition_file)
# Numpy data type of the array
self._set_component("dtype", array.dtype)
# Tuple of the array's dimension sizes
self._set_component("shape", array.shape)
# Number of elements in the array
self._set_component("size", array.size)
# Number of dimensions in the array
self._set_component("ndim", array.ndim)
if numpy_ma_is_masked(array):
# Array is a masked array. Save it as record array with
# 'data' and 'mask' elements because this seems much
# faster than using numpy.ma.dump.
self._set_component("_masked_as_record", True)
numpy_save(_partition_file, array.toflex())
else:
self._set_component("_masked_as_record", False)
if hasattr(array, "mask"):
# Array is a masked array with no masked elements
numpy_save(_partition_file, array.view(numpy_ndarray))
else:
# Array is not a masked array.
numpy_save(_partition_file, array)