Python ma.make_mask_descr方法代碼示例

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import make_mask_descr [as 別名]
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 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import make_mask_descr [as 別名]
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 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import make_mask_descr [as 別名]
def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None,
                formats=None, names=None, titles=None,
                byteorder=None, aligned=False,
                mask=nomask, hard_mask=False, fill_value=None, keep_mask=True,
                copy=False,
                **options):

        self = recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset,
                                strides=strides, formats=formats, names=names,
                                titles=titles, byteorder=byteorder,
                                aligned=aligned,)

        mdtype = ma.make_mask_descr(self.dtype)
        if mask is nomask or not np.size(mask):
            if not keep_mask:
                self._mask = tuple([False] * len(mdtype))
        else:
            mask = np.array(mask, copy=copy)
            if mask.shape != self.shape:
                (nd, nm) = (self.size, mask.size)
                if nm == 1:
                    mask = np.resize(mask, self.shape)
                elif nm == nd:
                    mask = np.reshape(mask, self.shape)
                else:
                    msg = "Mask and data not compatible: data size is %i, " + \
                          "mask size is %i."
                    raise MAError(msg % (nd, nm))
                copy = True
            if not keep_mask:
                self.__setmask__(mask)
                self._sharedmask = True
            else:
                if mask.dtype == mdtype:
                    _mask = mask
                else:
                    _mask = np.array([tuple([m] * len(mdtype)) for m in mask],
                                     dtype=mdtype)
                self._mask = _mask
        return self 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import make_mask_descr [as 別名]
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 

# 需要導入模塊: from numpy import ma [as 別名]
# 或者: from numpy.ma import make_mask_descr [as 別名]
def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None,
                formats=None, names=None, titles=None,
                byteorder=None, aligned=False,
                mask=nomask, hard_mask=False, fill_value=None, keep_mask=True,
                copy=False,
                **options):
        #
        self = recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset,
                                strides=strides, formats=formats, names=names,
                                titles=titles, byteorder=byteorder,
                                aligned=aligned,)
        #
        mdtype = ma.make_mask_descr(self.dtype)
        if mask is nomask or not np.size(mask):
            if not keep_mask:
                self._mask = tuple([False] * len(mdtype))
        else:
            mask = np.array(mask, copy=copy)
            if mask.shape != self.shape:
                (nd, nm) = (self.size, mask.size)
                if nm == 1:
                    mask = np.resize(mask, self.shape)
                elif nm == nd:
                    mask = np.reshape(mask, self.shape)
                else:
                    msg = "Mask and data not compatible: data size is %i, " + \
                          "mask size is %i."
                    raise MAError(msg % (nd, nm))
                copy = True
            if not keep_mask:
                self.__setmask__(mask)
                self._sharedmask = True
            else:
                if mask.dtype == mdtype:
                    _mask = mask
                else:
                    _mask = np.array([tuple([m] * len(mdtype)) for m in mask],
                                     dtype=mdtype)
                self._mask = _mask
        return self
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