def view(self, dtype=None, type=None):
u"""Return view of *data* i.e. new *ValueArray* object but pointing to
same data.
"""
if type is None:
return self.__class__(ndarray.view(self), self.info, copy=False)
else:
return ndarray.view(self, dtype, type)
def view(self, dtype=None, type=None):
u"""Return view of *data* i.e. new *hfarray* object but pointing to
same data.
"""
if type is None:
return self.__class__(ndarray.view(self, dtype=self.dtype),
dims=self.dims, copy=False)
else:
return ndarray.view(self, dtype=self.dtype, type=type)
def view(self, obj):
"""Returns a view of the mrecarray."""
try:
if issubclass(obj, ndarray):
return ndarray.view(self, obj)
except TypeError:
pass
dtype = np.dtype(obj)
if dtype.fields is None:
return self.__array__().view(dtype)
return ndarray.view(self, obj)
def view(self, obj):
"""Returns a view of the mrecarray."""
try:
if issubclass(obj, ndarray):
return ndarray.view(self, obj)
except TypeError:
pass
dtype_ = np.dtype(obj)
if dtype_.fields is None:
return self.__array__().view(dtype_)
return ndarray.view(self, obj)
def test_subtype_view(self):
from numpy import ndarray, array
class matrix(ndarray):
def __new__(subtype, data, dtype=None, copy=True):
if isinstance(data, matrix):
return data
return data.view(subtype)
a = array(range(5))
b = matrix(a)
assert isinstance(b, matrix)
assert b.__array_priority__ == 0.0
assert (b == a).all()
assert isinstance(b.view(), matrix)
a = array(5)[()]
for s in [matrix, ndarray]:
b = a.view(s)
assert b == a
assert type(b) is type(a)
a = matrix(array(range(5)))
for s in [matrix, ndarray]:
b = ndarray.view(a, s)
assert (b == a).all()
assert type(b) is s
def __getitem__(self, indx):
"""Returns all the fields sharing the same fieldname base.
The fieldname base is either `_data` or `_mask`."""
_localdict = self.__dict__
_mask = ndarray.__getattribute__(self, '_mask')
_data = ndarray.view(self, _localdict['_baseclass'])
# We want a field ........
if isinstance(indx, basestring):
#!!!: Make sure _sharedmask is True to propagate back to _fieldmask
#!!!: Don't use _set_mask, there are some copies being made...
#!!!: ...that break propagation
#!!!: Don't force the mask to nomask, that wrecks easy masking
obj = _data[indx].view(MaskedArray)
obj._mask = _mask[indx]
obj._sharedmask = True
fval = _localdict['_fill_value']
if fval is not None:
obj._fill_value = fval[indx]
# Force to masked if the mask is True
if not obj.ndim and obj._mask:
return masked
return obj
# We want some elements ..
# First, the data ........
obj = np.array(_data[indx], copy=False).view(mrecarray)
obj._mask = np.array(_mask[indx], copy=False).view(recarray)
return obj
def __getitem__(self, indx):
"""
Returns all the fields sharing the same fieldname base.
The fieldname base is either `_data` or `_mask`.
"""
_localdict = self.__dict__
_mask = ndarray.__getattribute__(self, '_mask')
_data = ndarray.view(self, _localdict['_baseclass'])
# We want a field
if isinstance(indx, str):
# Make sure _sharedmask is True to propagate back to _fieldmask
# Don't use _set_mask, there are some copies being made that
# break propagation Don't force the mask to nomask, that wreaks
# easy masking
obj = _data[indx].view(MaskedArray)
obj._mask = _mask[indx]
obj._sharedmask = True
fval = _localdict['_fill_value']
if fval is not None:
obj._fill_value = fval[indx]
# Force to masked if the mask is True
if not obj.ndim and obj._mask:
return masked
return obj
# We want some elements.
# First, the data.
obj = np.array(_data[indx], copy=False).view(mrecarray)
obj._mask = np.array(_mask[indx], copy=False).view(recarray)
return obj
def __getitem__(self, indx):
"""Returns all the fields sharing the same fieldname base.
The fieldname base is either `_data` or `_mask`."""
_localdict = self.__dict__
# We want a field ........
if indx in ndarray.__getattribute__(self, 'dtype').names:
obj = self._data[indx].view(TimeSeries)
obj._dates = _localdict['_dates']
obj._mask = make_mask(_localdict['_mask'][indx])
return obj
# We want some elements ..
obj = TimeSeries.__getitem__(self, indx)
if isinstance(obj, MaskedArray) and not isinstance(obj, TimeSeries):
obj = ndarray.view(obj, MaskedRecords)
return obj
def __getitem__(self, indx):
"""Returns all the fields sharing the same fieldname base.
The fieldname base is either `_data` or `_mask`."""
_localdict = self.__dict__
# We want a field ........
if indx in ndarray.__getattribute__(self, "dtype").names:
obj = self._data[indx].view(TimeSeries)
obj._dates = _localdict["_dates"]
obj._mask = make_mask(_localdict["_mask"][indx])
return obj
# We want some elements ..
obj = TimeSeries.__getitem__(self, indx)
if isinstance(obj, MaskedArray) and not isinstance(obj, TimeSeries):
obj = ndarray.view(obj, MaskedRecords)
return obj
def __getattribute__(self, attr):
try:
return object.__getattribute__(self, attr)
except AttributeError:
# attr must be a fieldname
pass
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError) as e:
raise AttributeError(
f'record array has no attribute {attr}') from e
# So far, so good
_localdict = ndarray.__getattribute__(self, '__dict__')
_data = ndarray.view(self, _localdict['_baseclass'])
obj = _data.getfield(*res)
if obj.dtype.names is not None:
raise NotImplementedError("MaskedRecords is currently limited to"
"simple records.")
# Get some special attributes
# Reset the object's mask
hasmasked = False
_mask = _localdict.get('_mask', None)
if _mask is not None:
try:
_mask = _mask[attr]
except IndexError:
# Couldn't find a mask: use the default (nomask)
pass
tp_len = len(_mask.dtype)
hasmasked = _mask.view((bool, ((tp_len, ) if tp_len else
()))).any()
if (obj.shape or hasmasked):
obj = obj.view(MaskedArray)
obj._baseclass = ndarray
obj._isfield = True
obj._mask = _mask
# Reset the field values
_fill_value = _localdict.get('_fill_value', None)
if _fill_value is not None:
try:
obj._fill_value = _fill_value[attr]
except ValueError:
obj._fill_value = None
else:
obj = obj.item()
return obj
def __getattribute__(self, attr):
try:
return object.__getattribute__(self, attr)
except AttributeError:
# attr must be a fieldname
pass
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("record array has no attribute %s" % attr)
# So far, so good
_localdict = ndarray.__getattribute__(self, '__dict__')
_data = ndarray.view(self, _localdict['_baseclass'])
obj = _data.getfield(*res)
if obj.dtype.fields:
raise NotImplementedError("MaskedRecords is currently limited to"
"simple records.")
# Get some special attributes
# Reset the object's mask
hasmasked = False
_mask = _localdict.get('_mask', None)
if _mask is not None:
try:
_mask = _mask[attr]
except IndexError:
# Couldn't find a mask: use the default (nomask)
pass
hasmasked = _mask.view((np.bool, (len(_mask.dtype) or 1))).any()
if (obj.shape or hasmasked):
obj = obj.view(MaskedArray)
obj._baseclass = ndarray
obj._isfield = True
obj._mask = _mask
# Reset the field values
_fill_value = _localdict.get('_fill_value', None)
if _fill_value is not None:
try:
obj._fill_value = _fill_value[attr]
except ValueError:
obj._fill_value = None
else:
obj = obj.item()
return obj
def test_subtype_view(self):
from numpy import ndarray, array
class matrix(ndarray):
def __new__(subtype, data, dtype=None, copy=True):
if isinstance(data, matrix):
return data
return data.view(subtype)
a = array(range(5))
b = matrix(a)
assert isinstance(b, matrix)
assert b.__array_priority__ == 0.0
assert (b == a).all()
assert isinstance(b.view(), matrix)
a = array(5)[()]
for s in [matrix, ndarray]:
b = a.view(s)
assert b == a
assert type(b) is type(a)
a = matrix(array(range(5)))
for s in [matrix, ndarray]:
b = ndarray.view(a, s)
assert (b == a).all()
assert type(b) is s
def __getattribute__(self, attr):
try:
return object.__getattribute__(self, attr)
except AttributeError: # attr must be a fieldname
pass
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError, "record array has no attribute %s" % attr
# So far, so good...
_localdict = ndarray.__getattribute__(self, '__dict__')
_data = ndarray.view(self, _localdict['_baseclass'])
obj = _data.getfield(*res)
if obj.dtype.fields:
raise NotImplementedError("MaskedRecords is currently limited to"\
"simple records...")
obj = obj.view(MaskedArray)
obj._baseclass = ndarray
obj._isfield = True
# Get some special attributes
_fill_value = _localdict.get('_fill_value', None)
_mask = _localdict.get('_mask', None)
# Reset the object's mask
if _mask is not None:
try:
obj._mask = _mask[attr]
except IndexError:
# Couldn't find a mask: use the default (nomask)
pass
# Reset the field values
if _fill_value is not None:
try:
obj._fill_value = _fill_value[attr]
except ValueError:
obj._fill_value = None
return obj
def __getattribute__(self, attr):
try:
return object.__getattribute__(self, attr)
except AttributeError: # attr must be a fieldname
pass
fielddict = ndarray.__getattribute__(self, "dtype").fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError, "record array has no attribute %s" % attr
# So far, so good...
_localdict = ndarray.__getattribute__(self, "__dict__")
_data = ndarray.view(self, _localdict["_baseclass"])
obj = _data.getfield(*res)
if obj.dtype.fields:
raise NotImplementedError("MaskedRecords is currently limited to" "simple records...")
obj = obj.view(MaskedArray)
obj._baseclass = ndarray
obj._isfield = True
# Get some special attributes
_fill_value = _localdict.get("_fill_value", None)
_mask = _localdict.get("_mask", None)
# Reset the object's mask
if _mask is not None:
try:
obj._mask = _mask[attr]
except IndexError:
# Couldn't find a mask: use the default (nomask)
pass
# Reset the field values
if _fill_value is not None:
try:
obj._fill_value = _fill_value[attr]
except ValueError:
obj._fill_value = None
return obj
def view(self, dtype=None, type=None):
"""
Returns a view of the mrecarray.
"""
# OK, basic copy-paste from MaskedArray.view.
if dtype is None:
if type is None:
output = ndarray.view(self)
else:
output = ndarray.view(self, type)
# Here again.
elif type is None:
try:
if issubclass(dtype, ndarray):
output = ndarray.view(self, dtype)
dtype = None
else:
output = ndarray.view(self, dtype)
# OK, there's the change
except TypeError:
dtype = np.dtype(dtype)
# we need to revert to MaskedArray, but keeping the possibility
# of subclasses (eg, TimeSeriesRecords), so we'll force a type
# set to the first parent
if dtype.fields is None:
basetype = self.__class__.__bases__[0]
output = self.__array__().view(dtype, basetype)
output._update_from(self)
else:
output = ndarray.view(self, dtype)
output._fill_value = None
else:
output = ndarray.view(self, dtype, type)
# Update the mask, just like in MaskedArray.view
if (getattr(output, '_mask', nomask) is not nomask):
mdtype = ma.make_mask_descr(output.dtype)
output._mask = self._mask.view(mdtype, ndarray)
output._mask.shape = output.shape
return output
def view(self, dtype=None, type=None):
"""
Returns a view of the mrecarray.
"""
# OK, basic copy-paste from MaskedArray.view.
if dtype is None:
if type is None:
output = ndarray.view(self)
else:
output = ndarray.view(self, type)
# Here again.
elif type is None:
try:
if issubclass(dtype, ndarray):
output = ndarray.view(self, dtype)
dtype = None
else:
output = ndarray.view(self, dtype)
# OK, there's the change
except TypeError:
dtype = np.dtype(dtype)
# we need to revert to MaskedArray, but keeping the possibility
# of subclasses (eg, TimeSeriesRecords), so we'll force a type
# set to the first parent
if dtype.fields is None:
basetype = self.__class__.__bases__[0]
output = self.__array__().view(dtype, basetype)
output._update_from(self)
else:
output = ndarray.view(self, dtype)
output._fill_value = None
else:
output = ndarray.view(self, dtype, type)
# Update the mask, just like in MaskedArray.view
if (getattr(output, '_mask', nomask) is not nomask):
mdtype = ma.make_mask_descr(output.dtype)
output._mask = self._mask.view(mdtype, ndarray)
output._mask.shape = output.shape
return output
def _getdata(self):
"Returns the data as a recarray."
return ndarray.view(self, recarray)
def _data(self):
"""
Returns the data as a recarray.
"""
return ndarray.view(self, recarray)
def _getdata(self):
"Returns the data as a recarray."
return ndarray.view(self, recarray)
def _data(self):
"""
Returns the data as a recarray.
"""
return ndarray.view(self, recarray)
