Exemplo n.º 1
0
    def filter(self, boolarray):
        if boolarray.squeeze().ndim > 1:
            raise ValueError("filter can only use array with one dimension")
        if boolarray.dtype != np.dtype(bool):
            if boolarray.dims[0].name not in self.ivardata:
                out = self.copy()
                msg = "Filter warning: DataBlock does not contain dimension %r"
                msg = msg % boolarray.dims[0]
                warn(msg)
                return out
            localdim = self.ivardata[boolarray.dims[0].name]
            intersection = set(localdim.data).intersection(boolarray)
            barray = boolarray.astype(bool)
            barray[...] = False
            for f in intersection:
                barray[boolarray == f] = True
            boolarray = barray

        out = DataBlock()
        out.blockname = self.blockname
        out.comments = self.comments
        out.xname = self.xname

        olddim = boolarray.dims[0]
        newdim = olddim.__class__(olddim, data=olddim.data[boolarray])

        for v in self.vardata.keys():
            data = self.vardata[v].view()
            try:
                i = data.dims_index(boolarray.dims[0])
            except IndexError:  # This variable does not sweep in boolarray dim
                out[v] = data
                continue
            newinfo = list(data.dims)
            reorder = list(range(data.ndim))
            del newinfo[i]
            del reorder[i]
            reorder = tuple([i] + reorder)
            newinfo = tuple([newdim] + newinfo)
            data = data.transpose(*reorder)
            utdata = hfarray(array(data)[boolarray],
                             dims=newinfo,
                             unit=data.unit,
                             outputformat=data.outputformat)
            dimorder = [x.name for x in self.vardata[v].dims]
            utdata = utdata.reorder_dimensions(*dimorder)
            out[v] = utdata
        out.xname = self.xname
        return out
Exemplo n.º 2
0
    def filter(self, boolarray):
        if boolarray.squeeze().ndim > 1:
            raise ValueError("filter can only use array with one dimension")
        if boolarray.dtype != np.dtype(bool):
            if boolarray.dims[0].name not in self.ivardata:
                out = self.copy()
                msg = "Filter warning: DataBlock does not contain dimension %r"
                msg = msg % boolarray.dims[0]
                warn(msg)
                return out
            localdim = self.ivardata[boolarray.dims[0].name]
            intersection = set(localdim.data).intersection(boolarray)
            barray = boolarray.astype(bool)
            barray[...] = False
            for f in intersection:
                barray[boolarray == f] = True
            boolarray = barray

        out = DataBlock()
        out.blockname = self.blockname
        out.comments = self.comments
        out.xname = self.xname

        olddim = boolarray.dims[0]
        newdim = olddim.__class__(olddim, data=olddim.data[boolarray])

        for v in self.vardata.keys():
            data = self.vardata[v].view()
            try:
                i = data.dims_index(boolarray.dims[0])
            except IndexError:  # This variable does not sweep in boolarray dim
                out[v] = data
                continue
            newinfo = list(data.dims)
            reorder = list(range(data.ndim))
            del newinfo[i]
            del reorder[i]
            reorder = tuple([i] + reorder)
            newinfo = tuple([newdim] + newinfo)
            data = data.transpose(*reorder)
            utdata = hfarray(array(data)[boolarray], dims=newinfo,
                             unit=data.unit,
                             outputformat=data.outputformat)
            dimorder = [x.name for x in self.vardata[v].dims]
            utdata = utdata.reorder_dimensions(*dimorder)
            out[v] = utdata
        out.xname = self.xname
        return out
Exemplo n.º 3
0
    def __new__(subtype,
                data,
                dims=None,
                dtype=None,
                copy=True,
                order=None,
                subok=False,
                ndmin=0,
                unit=None,
                info=None,
                Z0=None):

        if info is not None:
            warn("_MultiPortArray, use dims not info")
            if dims is not None:
                raise ValueError("Can not specify both dims and info")
            dims = info
            info = None
        data = np.asanyarray(data)
        if dims is None:
            if hasattr(data, 'dims'):
                dims = tuple(data.dims)
            elif len(data.shape) <= len(subtype.default_dim) + 2:
                defaultdims = (subtype.default_dim)
                shape = data.shape[:-2]
                dims = tuple(
                    x.__class__(x.name, range(size))
                    for (x, size) in zip(defaultdims, shape))
                dims = dims + (DimMatrix_i(
                    "i", data.shape[-2]), DimMatrix_j("j", data.shape[-1]))
        out = _hfarray.__new__(subtype,
                               data,
                               dims=dims,
                               dtype=dtype,
                               copy=copy,
                               order=order,
                               subok=subok,
                               ndmin=ndmin,
                               unit=unit)
        return out
Exemplo n.º 4
0
    def sort(self, dim):
        dim = hfarray(dim, copy=False).squeeze()
        if dim.ndim > 1:
            msg = "sort can only get sort direction from array with"\
                  " one dimension"
            raise ValueError(msg)
        else:
            dimname = dim.dims[0].name

        if dimname not in self.ivardata:
            msg = "Sort warning: DataBlock does not contain dimension %r"
            msg = msg % dimname
            warn(msg)
            out = self.copy()
            return out

        sortorder = dim.argsort()

        out = DataBlock()
        out.blockname = self.blockname
        out.comments = self.comments
        out.xname = self.xname

        for v in self.vardata.keys()[:]:
            data = self.vardata[v].view()
            try:
                i = data.dims_index(dimname)
                out[v] = data.take(sortorder, i)
            except IndexError:  # This variable does not contain sort direction
                out[v] = data
                continue

        olddim = self.ivardata[dimname]
        out.xname = self.xname
        data = self[dimname][sortorder]
        out.replace_dim(olddim, olddim.__class__(olddim, data=data))
        return out
Exemplo n.º 5
0
    def sort(self, dim):
        dim = hfarray(dim, copy=False).squeeze()
        if dim.ndim > 1:
            msg = "sort can only get sort direction from array with"\
                  " one dimension"
            raise ValueError(msg)
        else:
            dimname = dim.dims[0].name

        if dimname not in self.ivardata:
            msg = "Sort warning: DataBlock does not contain dimension %r"
            msg = msg % dimname
            warn(msg)
            out = self.copy()
            return out

        sortorder = dim.argsort()

        out = DataBlock()
        out.blockname = self.blockname
        out.comments = self.comments
        out.xname = self.xname

        for v in self.vardata.keys()[:]:
            data = self.vardata[v].view()
            try:
                i = data.dims_index(dimname)
                out[v] = data.take(sortorder, i)
            except IndexError:  # This variable does not contain sort direction
                out[v] = data
                continue

        olddim = self.ivardata[dimname]
        out.xname = self.xname
        data = self[dimname][sortorder]
        out.replace_dim(olddim, olddim.__class__(olddim, data=data))
        return out
Exemplo n.º 6
0
    def __new__(subtype, data, dims=None, dtype=None, copy=True, order=None,
                subok=False, ndmin=0, unit=None, info=None, Z0=None):

        if info is not None:
            warn("_MultiPortArray, use dims not info")
            if dims is not None:
                raise ValueError("Can not specify both dims and info")
            dims = info
            info = None
        data = np.asanyarray(data)
        if dims is None:
            if hasattr(data, 'dims'):
                dims = tuple(data.dims)
            elif len(data.shape) <= len(subtype.default_dim) + 2:
                defaultdims = (subtype.default_dim)
                shape = data.shape[:-2]
                dims = tuple(x.__class__(x.name, range(size))
                             for (x, size) in zip(defaultdims, shape))
                dims = dims + (DimMatrix_i("i", data.shape[-2]),
                               DimMatrix_j("j", data.shape[-1]))
        out = _hfarray.__new__(subtype, data, dims=dims, dtype=dtype,
                               copy=copy, order=order, subok=subok,
                               ndmin=ndmin, unit=unit)
        return out