pygrass.raster.buffer — GRASS Python Library documentation

# Source code for pygrass.raster.buffer

    from grass.pygrass.raster.raster_type import TYPE as RTYPE
    import ctypes
    import numpy as np

    _CELL = ("int", "intp", "int8", "int16", "int32", "int64")
    CELL = tuple([getattr(np, attr) for attr in _CELL if hasattr(np, attr)])
    _FCELL = "float", "float16", "float32"
    FCELL = tuple([getattr(np, attr) for attr in _FCELL if hasattr(np, attr)])
    _DCELL = "float64", "float128"
    DCELL = tuple([getattr(np, attr) for attr in _DCELL if hasattr(np, attr)])

    class Buffer(np.ndarray):
        """shape, mtype='FCELL', buffer=None, offset=0,
        strides=None, order=None
        """

        @property
        def mtype(self):
            if self.dtype in CELL:
                return "CELL"
            elif self.dtype in FCELL:
                return "FCELL"
            elif self.dtype in DCELL:
                return DCELL
            else:
                err = "Raster type: %r not supported by GRASS."
                raise TypeError(err % self.dtype)

        def __new__(
            cls, shape, mtype="FCELL", buffer=None, offset=0, strides=None, order=None
        ):
            obj = np.ndarray.__new__(
                cls, shape, RTYPE[mtype]["numpy"], buffer, offset, strides, order
            )
            obj.pointer_type = ctypes.POINTER(RTYPE[mtype]["ctypes"])
            obj.p = obj.ctypes.data_as(obj.pointer_type)
            return obj

        def __array_finalize__(self, obj):
            if obj is None:
                return
            self.pointer_type = getattr(obj, "pointer_type", None)
            self.p = getattr(obj, "p", None)

        def __array_wrap__(self, out_arr, context=None):
            """See:
            http://docs.scipy.org/doc/numpy/user/
            basics.subclassing.html#array-wrap-for-ufuncs"""
            if out_arr.dtype == bool:
                # there is not support for boolean maps, so convert into integer
                out_arr = out_arr.astype(np.int32)
            out_arr.p = out_arr.ctypes.data_as(out_arr.pointer_type)
            return np.ndarray.__array_wrap__(self, out_arr, context)

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