<filename>buzzard/_numpy_raster.py
import numpy as np

from buzzard._a_stored_raster import AStoredRaster, ABackStoredRaster

class NumpyRaster(AStoredRaster):
    """Concrete class defining the behavior of a wrapped numpy array

    >>> help(Dataset.wrap_numpy_raster)

    Features Defined
    ----------------
    - Has an `array` property that points to the numpy array provided at construction.
    """

    def __init__(self, ds, fp, array, channels_schema, wkt, mode):
        self._arr_shape = array.shape
        self._arr_address = array.__array_interface__['data'][0]
        back = BackNumpyRaster(
            ds._back, fp, array, channels_schema, wkt, mode
        )
        super(NumpyRaster, self).__init__(ds=ds, back=back)

    @property
    def array(self):
        """Returns the Raster's full input data as a Numpy array"""
        assert (
            self._arr_address == self._back._arr.__array_interface__['data'][0]
        )
        return self._back._arr.reshape(*self._arr_shape)

class BackNumpyRaster(ABackStoredRaster):
    """Implementation of NumpyRaster"""

    def __init__(self, back_ds, fp, array, channels_schema, wkt, mode):
        array = np.atleast_3d(array)
        self._arr = array
        channel_count = array.shape[-1]

        if 'nodata' not in channels_schema:
            channels_schema['nodata'] = [None] * channel_count

        if 'interpretation' not in channels_schema:
            channels_schema['interpretation'] = ['undefined'] * channel_count

        if 'offset' not in channels_schema:
            channels_schema['offset'] = [0.] * channel_count

        if 'scale' not in channels_schema:
            channels_schema['scale'] = [1.] * channel_count

        if 'mask' not in channels_schema:
            channels_schema['mask'] = ['all_valid']

        super(BackNumpyRaster, self).__init__(
            back_ds=back_ds,
            wkt_stored=wkt,
            channels_schema=channels_schema,
            dtype=array.dtype,
            fp_stored=fp,
            mode=mode,
        )

        self._should_tranform = (
            any(v != 0 for v in channels_schema['offset']) or
            any(v != 1 for v in channels_schema['scale'])
        )

    def get_data(self, fp, channel_ids, dst_nodata, interpolation):
        samplefp = self.build_sampling_footprint(fp, interpolation)
        if samplefp is None:
            return np.full(
                np.r_[fp.shape, len(channel_ids)],
                dst_nodata,
                self.dtype
            )
        chans_indexer = self._best_indexers_of_channel_ids(channel_ids)
        key = list(samplefp.slice_in(self.fp)) + [chans_indexer]
        key = tuple(key)
        array = self._arr[key]
        if self._should_tranform:
            array = (
                array *
                np.asarray(self.channels_schema['scale'])[chans_indexer] +
                np.asarray(self.channels_schema['offset'])[chans_indexer]
            )
        array = self.remap(
            samplefp,
            fp,
            array=array,
            mask=None,
            src_nodata=self.nodata,
            dst_nodata=dst_nodata,
            mask_mode='erode',
            interpolation=interpolation,
        )
        array = array.astype(self.dtype, copy=False)
        return array

    def set_data(self, array, fp, channel_ids, interpolation, mask):
        if not fp.share_area(self.fp):
            return
        if not fp.same_grid(self.fp) and mask is None:
            mask = np.ones(fp.shape, bool)

        dstfp = self.fp.intersection(fp)

        # Remap ****************************************************************
        ret = self.remap(
            fp,
            dstfp,
            array=array,
            mask=mask,
            src_nodata=self.nodata,
            dst_nodata=self.nodata or 0,
            mask_mode='erode',
            interpolation=interpolation,
        )
        if mask is not None:
            array, mask = ret
        else:
            array = ret
        del ret
        array = array.astype(self.dtype, copy=False)
        fp = dstfp
        del dstfp

        # Write ****************************************************************
        slices = fp.slice_in(self.fp)
        for i in channel_ids:
            if mask is not None:
                self._arr[slices + (i,)][mask] = array[..., i][mask]
            else:
                self._arr[slices + (i,)] = array[..., i]

    def fill(self, value, channel_ids):
        for i in channel_ids:
            self._arr[..., i] = value

    def close(self):
        super(BackNumpyRaster, self).close()
        del self._arr

    @staticmethod
    def _best_indexers_of_channel_ids(channel_ids):
        """Create an object to pick the channels of the numpy array. Returns either a slice
        object or a list of int to perform fancy-indexing"""
        l = list(channel_ids)

        if np.all(np.diff(l) == 1):
            start, stop = l[0], l[-1] + 1
            l = slice(start, stop)
        elif np.all(np.diff(l) == -1):
            start, stop = l[0], l[-1] - 1
            if stop < 0:
                stop = None
            l = slice(start, stop, -1)
        return l

<filename>buzzard/_numpy_raster.py
import numpy as np

from buzzard._a_stored_raster import AStoredRaster, ABackStoredRaster

class NumpyRaster(AStoredRaster):
    """Concrete class defining the behavior of a wrapped numpy array

    >>> help(Dataset.wrap_numpy_raster)

    Features Defined
    ----------------
    - Has an `array` property that points to the numpy array provided at construction.
    """

    def __init__(self, ds, fp, array, channels_schema, wkt, mode):
        self._arr_shape = array.shape
        self._arr_address = array.__array_interface__['data'][0]
        back = BackNumpyRaster(
            ds._back, fp, array, channels_schema, wkt, mode
        )
        super(NumpyRaster, self).__init__(ds=ds, back=back)

    @property
    def array(self):
        """Returns the Raster's full input data as a Numpy array"""
        assert (
            self._arr_address == self._back._arr.__array_interface__['data'][0]
        )
        return self._back._arr.reshape(*self._arr_shape)

class BackNumpyRaster(ABackStoredRaster):
    """Implementation of NumpyRaster"""

    def __init__(self, back_ds, fp, array, channels_schema, wkt, mode):
        array = np.atleast_3d(array)
        self._arr = array
        channel_count = array.shape[-1]

        if 'nodata' not in channels_schema:
            channels_schema['nodata'] = [None] * channel_count

        if 'interpretation' not in channels_schema:
            channels_schema['interpretation'] = ['undefined'] * channel_count

        if 'offset' not in channels_schema:
            channels_schema['offset'] = [0.] * channel_count

        if 'scale' not in channels_schema:
            channels_schema['scale'] = [1.] * channel_count

        if 'mask' not in channels_schema:
            channels_schema['mask'] = ['all_valid']

        super(BackNumpyRaster, self).__init__(
            back_ds=back_ds,
            wkt_stored=wkt,
            channels_schema=channels_schema,
            dtype=array.dtype,
            fp_stored=fp,
            mode=mode,
        )

        self._should_tranform = (
            any(v != 0 for v in channels_schema['offset']) or
            any(v != 1 for v in channels_schema['scale'])
        )

    def get_data(self, fp, channel_ids, dst_nodata, interpolation):
        samplefp = self.build_sampling_footprint(fp, interpolation)
        if samplefp is None:
            return np.full(
                np.r_[fp.shape, len(channel_ids)],
                dst_nodata,
                self.dtype
            )
        chans_indexer = self._best_indexers_of_channel_ids(channel_ids)
        key = list(samplefp.slice_in(self.fp)) + [chans_indexer]
        key = tuple(key)
        array = self._arr[key]
        if self._should_tranform:
            array = (
                array *
                np.asarray(self.channels_schema['scale'])[chans_indexer] +
                np.asarray(self.channels_schema['offset'])[chans_indexer]
            )
        array = self.remap(
            samplefp,
            fp,
            array=array,
            mask=None,
            src_nodata=self.nodata,
            dst_nodata=dst_nodata,
            mask_mode='erode',
            interpolation=interpolation,
        )
        array = array.astype(self.dtype, copy=False)
        return array

    def set_data(self, array, fp, channel_ids, interpolation, mask):
        if not fp.share_area(self.fp):
            return
        if not fp.same_grid(self.fp) and mask is None:
            mask = np.ones(fp.shape, bool)

        dstfp = self.fp.intersection(fp)

        # Remap ****************************************************************
        ret = self.remap(
            fp,
            dstfp,
            array=array,
            mask=mask,
            src_nodata=self.nodata,
            dst_nodata=self.nodata or 0,
            mask_mode='erode',
            interpolation=interpolation,
        )
        if mask is not None:
            array, mask = ret
        else:
            array = ret
        del ret
        array = array.astype(self.dtype, copy=False)
        fp = dstfp
        del dstfp

        # Write ****************************************************************
        slices = fp.slice_in(self.fp)
        for i in channel_ids:
            if mask is not None:
                self._arr[slices + (i,)][mask] = array[..., i][mask]
            else:
                self._arr[slices + (i,)] = array[..., i]

    def fill(self, value, channel_ids):
        for i in channel_ids:
            self._arr[..., i] = value

    def close(self):
        super(BackNumpyRaster, self).close()
        del self._arr

    @staticmethod
    def _best_indexers_of_channel_ids(channel_ids):
        """Create an object to pick the channels of the numpy array. Returns either a slice
        object or a list of int to perform fancy-indexing"""
        l = list(channel_ids)

        if np.all(np.diff(l) == 1):
            start, stop = l[0], l[-1] + 1
            l = slice(start, stop)
        elif np.all(np.diff(l) == -1):
            start, stop = l[0], l[-1] - 1
            if stop < 0:
                stop = None
            l = slice(start, stop, -1)
        return l

Concrete class defining the behavior of a wrapped numpy array      >>> help(Dataset.wrap_numpy_raster)      Features Defined     ----------------     - Has an `array` property that points to the numpy array provided at construction. Returns the Raster's full input data as a Numpy array Implementation of NumpyRaster # Remap **************************************************************** # Write **************************************************************** Create an object to pick the channels of the numpy array. Returns either a slice         object or a list of int to perform fancy-indexing