def __getitem__(self, indx):
"""x.__getitem__(y) <==> x[y]
Returns the item described by indx. Not a copy.
"""
_data = ndarray.__getattribute__(self, '_data')
_mask = ndarray.__getattribute__(self, '_mask')
_err = ndarray.__getattribute__(self, '_err')
output = _data.__getitem__(indx)
if not getattr(output, 'ndim', False):
if _mask is not nomask and _mask[indx]:
return masked
if _err is not noerr:
return (output, _err[indx])
else:
return (output, None)
newdvect = _data.__getitem__(indx).view(type(self))
newdvect._mask = _mask[indx]
if _err is noerr:
newdvect._err = noerr
else:
newdvect._err = _err[indx]
newdvect._name = self._name
newdvect._units = self._units
return newdvect
def __setattr__(self, attr, val):
"""
Sets the attribute attr to the value val.
"""
# Should we call __setmask__ first ?
if attr in ['mask', 'fieldmask']:
self.__setmask__(val)
return
# Create a shortcut (so that we don't have to call getattr all the time)
_localdict = object.__getattribute__(self, '__dict__')
# Check whether we're creating a new field
newattr = attr not in _localdict
try:
# Is attr a generic attribute ?
ret = object.__setattr__(self, attr, val)
except Exception:
# Not a generic attribute: exit if it's not a valid field
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
optinfo = ndarray.__getattribute__(self, '_optinfo') or {}
if not (attr in fielddict or attr in optinfo):
raise
else:
# Get the list of names
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
# Check the attribute
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except Exception:
return ret
# Let's try to set the field
try:
res = fielddict[attr][:2]
except (TypeError, KeyError) as e:
raise AttributeError(
f'record array has no attribute {attr}') from e
if val is masked:
_fill_value = _localdict['_fill_value']
if _fill_value is not None:
dval = _localdict['_fill_value'][attr]
else:
dval = val
mval = True
else:
dval = filled(val)
mval = getmaskarray(val)
obj = ndarray.__getattribute__(self, '_data').setfield(dval, *res)
_localdict['_mask'].__setitem__(attr, mval)
return obj
def __setattr__(self, attr, val):
"""
Sets the attribute attr to the value val.
"""
# Should we call __setmask__ first ?
if attr in ['mask', 'fieldmask']:
self.__setmask__(val)
return
# Create a shortcut (so that we don't have to call getattr all the time)
_localdict = object.__getattribute__(self, '__dict__')
# Check whether we're creating a new field
newattr = attr not in _localdict
try:
# Is attr a generic attribute ?
ret = object.__setattr__(self, attr, val)
except:
# Not a generic attribute: exit if it's not a valid field
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
optinfo = ndarray.__getattribute__(self, '_optinfo') or {}
if not (attr in fielddict or attr in optinfo):
exctype, value = sys.exc_info()[:2]
raise exctype(value)
else:
# Get the list of names
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
# Check the attribute
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except:
return ret
# Let's try to set the field
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("record array has no attribute %s" % attr)
if val is masked:
_fill_value = _localdict['_fill_value']
if _fill_value is not None:
dval = _localdict['_fill_value'][attr]
else:
dval = val
mval = True
else:
dval = filled(val)
mval = getmaskarray(val)
obj = ndarray.__getattribute__(self, '_data').setfield(dval, *res)
_localdict['_mask'].__setitem__(attr, mval)
return obj
def filled(self, fill_value=None):
"""Returns an array of the same class as the _data part, where masked
values are filled with fill_value.
If fill_value is None, self.fill_value is used instead.
Subclassing is preserved.
"""
_localdict = self.__dict__
d = ndarray.__getattribute__(self, '_data')
fm = _localdict['_fieldmask']
if not np.asarray(fm, dtype=bool_).any():
return d
#
if fill_value is None:
value = _check_fill_value(_localdict['_fill_value'], self.dtype)
else:
value = fill_value
if np.size(value) == 1:
value = [
value,
] * len(self.dtype)
#
if self is masked:
result = np.asanyarray(value)
else:
result = d.copy()
for (n, v) in zip(fm.dtype.names, value):
np.putmask(np.asarray(result[n]), np.asarray(fm[n]), v)
return result
def __array_finalize__(self, obj):
# Make sure we have a _fieldmask by default ..
_fieldmask = getattr(obj, '_fieldmask', None)
if _fieldmask is None:
mdescr = _make_mask_dtype(ndarray.__getattribute__(self, 'dtype'))
_mask = getattr(obj, '_mask', nomask)
if _mask is nomask:
_fieldmask = np.empty(self.shape, dtype=mdescr).view(recarray)
_fieldmask.flat = tuple([False] * len(mdescr))
else:
_fieldmask = narray([tuple([m] * len(mdescr)) for m in _mask],
dtype=mdescr).view(recarray)
# Update some of the attributes
if obj is not None:
_baseclass = getattr(obj, '_baseclass', type(obj))
else:
_baseclass = recarray
attrdict = dict(_fieldmask=_fieldmask,
_hardmask=getattr(obj, '_hardmask', False),
_fill_value=getattr(obj, '_fill_value', None),
_sharedmask=getattr(obj, '_sharedmask', False),
_baseclass=_baseclass)
self.__dict__.update(attrdict)
# Finalize as a regular maskedarray .....
# Update special attributes ...
self._basedict = getattr(obj, '_basedict',
getattr(obj, '__dict__', {}))
self.__dict__.update(self._basedict)
return
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 filled(self, fill_value=None):
"""Returns an array of the same class as the _data part, where masked
values are filled with fill_value.
If fill_value is None, self.fill_value is used instead.
Subclassing is preserved.
"""
_localdict = self.__dict__
d = ndarray.__getattribute__(self, '_data')
fm = _localdict['_fieldmask']
if not np.asarray(fm, dtype=bool_).any():
return d
#
if fill_value is None:
value = _check_fill_value(_localdict['_fill_value'],self.dtype)
else:
value = fill_value
if np.size(value) == 1:
value = [value,] * len(self.dtype)
#
if self is masked:
result = np.asanyarray(value)
else:
result = d.copy()
for (n, v) in zip(fm.dtype.names, value):
np.putmask(np.asarray(result[n]), np.asarray(fm[n]), v)
return result
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 __array_finalize__(self,obj):
# Make sure we have a _fieldmask by default ..
_fieldmask = getattr(obj, '_fieldmask', None)
if _fieldmask is None:
mdescr = _make_mask_dtype(ndarray.__getattribute__(self, 'dtype'))
_mask = getattr(obj, '_mask', nomask)
if _mask is nomask:
_fieldmask = np.empty(self.shape, dtype=mdescr).view(recarray)
_fieldmask.flat = tuple([False]*len(mdescr))
else:
_fieldmask = narray([tuple([m]*len(mdescr)) for m in _mask],
dtype=mdescr).view(recarray)
# Update some of the attributes
if obj is not None:
_baseclass = getattr(obj,'_baseclass',type(obj))
else:
_baseclass = recarray
attrdict = dict(_fieldmask=_fieldmask,
_hardmask=getattr(obj,'_hardmask',False),
_fill_value=getattr(obj,'_fill_value',None),
_sharedmask=getattr(obj,'_sharedmask',False),
_baseclass=_baseclass)
self.__dict__.update(attrdict)
# Finalize as a regular maskedarray .....
# Update special attributes ...
self._basedict = getattr(obj, '_basedict', getattr(obj,'__dict__',{}))
self.__dict__.update(self._basedict)
return
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 __getattribute__(self, attr):
getattribute = MaskedRecords.__getattribute__
_dict = getattribute(self, "__dict__")
if attr in (ndarray.__getattribute__(self, "dtype").names or []):
obj = getattribute(self, attr).view(ClimateSeries)
obj._dates = _dict["_dates"]
obj._ensoindicator = getattribute(self, "_ensoindicator")
obj._optinfo = getattribute(self, "_optinfo")
return obj
return getattribute(self, attr)
def __getattribute__(self, attr):
getattribute = MaskedRecords.__getattribute__
_dict = getattribute(self, '__dict__')
if attr in (ndarray.__getattribute__(self, 'dtype').names or []):
obj = getattribute(self, attr).view(ClimateSeries)
obj._dates = _dict['_dates']
obj._ensoindicator = getattribute(self, '_ensoindicator')
obj._optinfo = getattribute(self, '_optinfo')
return obj
return getattribute(self, attr)
def __getattribute__(self, attr):
getattribute = MaskedRecords.__getattribute__
_dict = getattribute(self,'__dict__')
if attr == '_dict':
return _dict
_names = ndarray.__getattribute__(self,'dtype').names
if attr in (_names or []):
obj = getattribute(self,attr).view(TimeSeries)
obj._dates = _dict['_dates']
return obj
return getattribute(self,attr)
def __getattribute__(self, attr):
getattribute = MaskedRecords.__getattribute__
_dict = getattribute(self, "__dict__")
if attr == "_dict":
return _dict
_names = ndarray.__getattribute__(self, "dtype").names
if attr in (_names or []):
obj = getattribute(self, attr).view(TimeSeries)
obj._dates = _dict["_dates"]
return obj
return getattribute(self, attr)
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 __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):
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 __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 _getmask(self):
"""Return the mask of the mrecord.
A record is masked when all the fields are masked.
"""
fieldmask = ndarray.__getattribute__(self, '_fieldmask')
if fieldmask.size > 1:
axis = 1
else:
axis=None
try:
return fieldmask.view((bool_, len(self.dtype))).all(axis)
except ValueError:
return np.all([[f[n].all() for n in fieldmask.dtype.names]
for f in fieldmask], axis=axis)
def _getmask(self):
"""Return the mask of the mrecord.
A record is masked when all the fields are masked.
"""
fieldmask = ndarray.__getattribute__(self, '_fieldmask')
if fieldmask.size > 1:
axis = 1
else:
axis = None
try:
return fieldmask.view((bool_, len(self.dtype))).all(axis)
except ValueError:
return np.all([[f[n].all() for n in fieldmask.dtype.names]
for f in fieldmask],
axis=axis)
def __getattribute__(self, name):
if name == "x":
val = self[0]
elif name == "y":
val = self[1]
elif name == "z":
val = self[2]
elif name == "mag":
val = sqrt(self[0]**2 + self[1]**2 + self[2]**2)
elif name == "mag2":
val = self[0]**2 + self[1]**2 + self[2]**2
else:
return ndarray.__getattribute__(self, name)
log.debug("getattr", extra={'class':'vector', 'object':self, 'attr':name, 'value':val})
return val
def __array_finalize__(self, obj):
# Make sure we have a _fieldmask by default ..
_mask = getattr(obj, "_mask", None)
if _mask is None:
objmask = getattr(obj, "_mask", nomask)
_dtype = ndarray.__getattribute__(self, "dtype")
if objmask is nomask:
_mask = ma.make_mask_none(self.shape, dtype=_dtype)
else:
mdescr = ma.make_mask_descr(_dtype)
_mask = narray([tuple([m] * len(mdescr)) for m in objmask], dtype=mdescr).view(recarray)
# Update some of the attributes
_dict = self.__dict__
_dict.update(_mask=_mask, _fieldmask=_mask)
self._update_from(obj)
if _dict["_baseclass"] == ndarray:
_dict["_baseclass"] = recarray
return
def __array_finalize__(self, obj):
# Make sure we have a _fieldmask by default
_mask = getattr(obj, '_mask', None)
if _mask is None:
objmask = getattr(obj, '_mask', nomask)
_dtype = ndarray.__getattribute__(self, 'dtype')
if objmask is nomask:
_mask = ma.make_mask_none(self.shape, dtype=_dtype)
else:
mdescr = ma.make_mask_descr(_dtype)
_mask = narray([tuple([m] * len(mdescr)) for m in objmask],
dtype=mdescr).view(recarray)
# Update some of the attributes
_dict = self.__dict__
_dict.update(_mask=_mask)
self._update_from(obj)
if _dict['_baseclass'] == ndarray:
_dict['_baseclass'] = recarray
return