def __new__(subtype, *args, **kwargs):
'''
Create new object.
No default mapping - use empty dictionary.
'''
values_mapping = kwargs.pop('values_mapping', {})
obj = MaskedArray.__new__(MaskedArray, *args, **kwargs)
obj.__class__ = MappedArray
# Must occur after class change for obj.state to update!
obj.values_mapping = values_mapping
return obj
def __new__(cls, *args, **kwargs):
'''
Create new object.
No default mapping - use empty dictionary.
'''
values_mapping = kwargs.pop('values_mapping', {})
obj = MaskedArray.__new__(MaskedArray, *args, **kwargs)
obj.__class__ = MappedArray
# Must occur after class change for obj.state to update!
obj.values_mapping = values_mapping
return obj
def __new__(
cls,
data,
yorigin,
xorigin,
origin,
cellsize,
proj=None,
fill_value=None,
fobj=None,
mode=None, # mask=None,
*args,
**kwargs):
# The mask will always be calculated, even if its already present or not needed at all...
mask = np.zeros_like(
data, np.bool) if fill_value is None else data == fill_value
if origin not in ORIGINS:
raise TypeError(
"Argument 'origin' must be one of '{:}'".format(ORIGINS))
try:
# Does this work for grids crossing the equator??
origin = "".join(("l" if cellsize[0] > 0 else "u",
"l" if cellsize[1] > 0 else "r"))
# iterable of len < 2, numeric value
except (IndexError, TypeError):
cs = abs(cellsize)
cellsize = (cs if origin[0] == "l" else -cs,
cs if origin[1] == "l" else -cs)
obj = MaskedArray.__new__(cls,
data=data,
fill_value=fill_value,
mask=mask,
*args,
**kwargs)
obj.unshare_mask()
obj._optinfo["yorigin"] = yorigin
obj._optinfo["xorigin"] = xorigin
obj._optinfo["origin"] = origin
obj._optinfo["cellsize"] = tuple(cellsize)
obj._optinfo["_proj"] = _Projection(proj)
obj._optinfo[
"fill_value"] = fill_value #if fill_value is not None else _dtypeInfo(obj.dtype)["min"]
obj._optinfo["mode"] = mode
obj._optinfo["_fobj"] = fobj
return obj
def __new__(cls, data, err=noerr, name='', units='', mask=nomask, dtype=None, edtype=None):
"""
Construct a Dvect from an input array which we try to make into a masked array as far as possible
"""
# Try to make a masked array out of the input
#obj = masked_array(data, dtype=dtype, mask=mask, subok=True).view(cls)
obj = MaskedArray.__new__(cls, data, dtype=dtype, mask=mask)
if not isinstance(obj, Dvect):
obj = obj.view(cls)
# Add attributes
if err is not noerr:
obj._err = array(err, dtype=edtype)
if obj._err.shape != obj.shape:
raise DvectError('Dvect.__new__: errors and data are incompatible')
obj._name = name
obj._units = units
return obj
def __new__(
cls,
data,
geotrans=None,
proj=None,
fill_value=None,
fobj=None,
color_mode=None, # mask=None,
yvalues=None,
xvalues=None,
mode="r",
*args,
**kwargs):
# NOTE: The mask will always be calculated, even if its
# already present or not needed at all...
mask = (np.zeros_like(data, np.bool)
if fill_value is None else data == fill_value)
self = MaskedArray.__new__(cls,
data=data,
fill_value=fill_value,
mask=mask,
*args,
**kwargs)
self.unshare_mask()
self.__dict__["geotrans"] = geotrans
self.__dict__["proj"] = _Projection(proj)
self.__dict__["color_mode"] = color_mode
self.__dict__["mode"] = mode
self.__dict__["_fobj"] = fobj
self.__dict__["_yvalues"] = yvalues
self.__dict__["_xvalues"] = xvalues
return self