# Source code for drizzle.resample

    """
    The `drizzle` module defines the `Drizzle` class, for combining input
    images into a single output image using the drizzle algorithm.
    """
    import numpy as np

    from drizzle import cdrizzle

    __all__ = ["Drizzle", "blot_image"]

    SUPPORTED_DRIZZLE_KERNELS = [
        "square",
        "gaussian",
        "point",
        "turbo",
        "lanczos2",
        "lanczos3",
    ]

    CTX_PLANE_BITS = 32

    class Drizzle:
        """
        A class for managing resampling and co-adding of multiple images onto a
        common output grid. The main method of this class is :py:meth:`add_image`.
        The main functionality of this class is to resample and co-add multiple
        images onto one output image using the "drizzle" algorithm described in
        `Fruchter and Hook, PASP 2002 <https://doi.org/10.1086/338393>`_.
        In the simplest terms, it redistributes flux
        from input pixels to one or more output pixels based on the chosen kernel,
        supplied weights, and input-to-output coordinate transformations as defined
        by the ``pixmap`` argument. For more details, see :ref:`main-user-doc`.

        This class keeps track of the total exposure time of all co-added images
        and also of which input images have contributed to an output (resampled)
        pixel. This is accomplished via *context image*.

        Main outputs of :py:meth:`add_image` can be accessed as class properties
        ``out_img``, ``out_wht``, ``out_ctx``, and ``exptime``.

        .. warning::
            Output arrays (``out_img``, ``out_wht``, and ``out_ctx``) can be
            pre-allocated by the caller and be passed to the initializer or the
            class initializer can allocate these arrays based on other input
            parameters such as ``output_shape``. If caller-supplied output arrays
            have the correct type (`numpy.float32` for ``out_img`` and ``out_wht``
            and `numpy.int32` for the ``out_ctx`` array) and if ``out_ctx`` is
            large enough not to need to be resized, these arrays will be used as is
            and may be modified by the :py:meth:`add_image` method. If not,
            a copy of these arrays will be made when converting to the expected
            type (or expanding the context array).

        Output Science Image
        --------------------

        Output science image is obtained by adding input pixel fluxes according to
        equations (4) and (5) in
        `Fruchter and Hook, PASP 2002 <https://doi.org/10.1086/338393>`_.
        The weights and coefficients in those equations will depend on the chosen
        kernel, input image weights, and pixel overlaps computed from ``pixmap``.

        Output Weight Image
        -------------------

        Output weight image stores the total weight of output science pixels
        according to equation (4) in
        `Fruchter and Hook, PASP 2002 <https://doi.org/10.1086/338393>`_.
        It depends on the chosen kernel, input image weights, and pixel overlaps
        computed from ``pixmap``.

        Output Context Image
        --------------------

        Each pixel in the context image is a bit field that encodes
        information about which input image has contributed to the corresponding
        pixel in the resampled data array. Context image uses 32 bit integers to
        encode this information and hence it can keep track of only 32 input images.
        First bit corresponds to the first input image, second bit corrsponds to the
        second input image, and so on. We call this (0-indexed) order "context ID"
        which is represented by the ``ctx_id`` parameter/property. If the number of
        input images exceeds 32, then it is necessary to have multiple context
        images ("planes") to hold information about all input images with the first
        plane encoding which of the first 32 images contributed to the output data
        pixel, second plane representing next 32 input images (number 33-64), etc.
        For this reason, context array is either a 2D array (if the total number
        of resampled images is less than 33) of the type `numpy.int32` and shape
        ``(ny, nx)`` or a a 3D array of shape ``(np, ny, nx)`` where ``nx`` and
        ``ny`` are dimensions of image's data. ``np`` is the number of "planes"
        equal to ``(number of input images - 1) // 32 + 1``. If a bit at position
        ``k`` in a pixel with coordinates ``(p, y, x)`` is 0 then input image number
        ``32 * p + k`` (0-indexed) did not contribute to the output data pixel
        with array coordinates ``(y, x)`` and if that bit is 1 then input image
        number ``32 * p + k`` did contribute to the pixel ``(y, x)`` in the
        resampled image.

        As an example, let's assume we have 8 input images. Then, when ``out_ctx``
        pixel values are displayed using binary representation (and decimal in
        parenthesis), one could see values like this::

            00000001 (1) - only first input image contributed to this output pixel;
            00000010 (2) - 2nd input image contributed;
            00000100 (4) - 3rd input image contributed;
            10000000 (128) - 8th input image contributed;
            10000100 (132=128+4) - 3rd and 8th input images contributed;
            11001101 (205=1+4+8+64+128) - input images 1, 3, 4, 7, 8 have contributed
            to this output pixel.

        In order to test if a specific input image contributed to an output pixel,
        one needs to use bitwise operations. Using the example above, to test
        whether input images number 4 and 5 have contributed to the output pixel
        whose corresponding ``out_ctx`` value is 205 (11001101 in binary form) we
        can do the following:

        >>> bool(205 & (1 << (5 - 1)))  # (205 & 16) = 0 (== 0 => False): did NOT contribute
        False
        >>> bool(205 & (1 << (4 - 1)))  # (205 & 8) = 8 (!= 0 => True): did contribute
        True

        In general, to get a list of all input images that have contributed to an
        output resampled pixel with image coordinates ``(x, y)``, and given a
        context array ``ctx``, one can do something like this:

        .. doctest-skip::

            >>> import numpy as np
            >>> np.flatnonzero([v & (1 << k) for v in ctx[:, y, x] for k in range(32)])

        For convenience, this functionality was implemented in the
        :py:func:`~drizzle.utils.decode_context` function.

        References
        ----------
        A full description of the drizzling algorithm can be found in
        `Fruchter and Hook, PASP 2002 <https://doi.org/10.1086/338393>`_.

        Examples
        --------
        .. highlight:: python
        .. code-block:: python

            # wcs1 - WCS of the input image usually with distortions (to be resampled)
            # wcs2 - WCS of the output image without distortions

            import numpy as np
            from drizzle.resample import Drizzle
            from drizzle.utils import calc_pixmap

            # simulate some data and a pixel map:
            data = np.ones((240, 570))
            pixmap = calc_pixmap(wcs1, wcs2)
            # or simulate a mapping from input image to output image frame:
            # y, x = np.indices((240, 570), dtype=np.float64)
            # pixmap = np.dstack([x, y])

            # initialize Drizzle object
            d = Drizzle(out_shape=(240, 570))
            d.add_image(data, exptime=15, pixmap=pixmap)

            # access outputs:
            d.out_img
            d.out_ctx
            d.out_wht

        """

        def __init__(self, kernel="square", fillval=None, out_shape=None,
                     out_img=None, out_wht=None, out_ctx=None, exptime=0.0,
                     begin_ctx_id=0, max_ctx_id=None, disable_ctx=False):
            """
            kernel: str, optional
                The name of the kernel used to combine the input. The choice of
                kernel controls the distribution of flux over the kernel. The kernel
                names are: "square", "gaussian", "point", "turbo",
                "lanczos2", and "lanczos3". The square kernel is the default.

                .. warning::
                   The "gaussian" and "lanczos2/3" kernels **DO NOT**
                   conserve flux.

            out_shape : tuple, None, optional
                Shape (`numpy` order ``(Ny, Nx)``) of the output images (context
                image will have a third dimension of size proportional to the number
                of input images). This parameter is helpful when neither
                ``out_img``, ``out_wht``, nor ``out_ctx`` images are provided.

            fillval: float, None, str, optional
                The value of output pixels that did not have contributions from
                input images' pixels. When ``fillval`` is either `None` or
                ``"INDEF"`` and ``out_img`` is provided, the values of ``out_img``
                will not be modified. When ``fillval`` is either `None` or
                ``"INDEF"`` and ``out_img`` is **not provided**, the values of
                ``out_img`` will be initialized to `numpy.nan`. If ``fillval``
                is a string that can be converted to a number, then the output
                pixels with no contributions from input images will be set to this
                ``fillval`` value.

            out_img : 2D array of float32, None, optional
                A 2D numpy array containing the output image produced by
                drizzling. On the first call the array values should be set to zero.
                Subsequent calls it will hold the intermediate results.

            out_wht : 2D array of float32, None, optional
                A 2D numpy array containing the output counts. On the first
                call it should be set to zero. On subsequent calls it will
                hold the intermediate results.

            out_ctx : 2D or 3D array of int32, None, optional
                A 2D or 3D numpy array holding a bitmap of which image was an input
                for each output pixel. Should be integer zero on first call.
                Subsequent calls hold intermediate results. This parameter is
                ignored when ``disable_ctx`` is `True`.

            exptime : float, optional
                Exposure time of previously resampled images when provided via
                parameters ``out_img``, ``out_wht``, ``out_ctx``.

            begin_ctx_id : int, optional
                The context ID number (0-based) of the first image that will be
                resampled (using `add_image`). Subsequent images will be asigned
                consecutively increasing ID numbers. This parameter is ignored
                when ``disable_ctx`` is `True`.

            max_ctx_id : int, None, optional
                The largest integer context ID that is *expected* to be used for
                an input image. When it is a non-negative number and ``out_ctx`` is
                `None`, it allows to pre-allocate the necessary array for the output
                context image. If the actual number of input images that will be
                resampled will exceed initial allocation for the context image,
                additional context planes will be added as needed (context array
                will "grow" in the third dimention as new input images are added.)
                The default value of `None` is equivalent to setting ``max_ctx_id``
                equal to ``begin_ctx_id``. This parameter is ignored either when
                ``out_ctx`` is provided or when ``disable_ctx`` is `True`.

            disable_ctx : bool, optional
                Indicates to not create a context image. If ``disable_ctx`` is set
                to `True`, parameters ``out_ctx``, ``begin_ctx_id``, and
                ``max_ctx_id`` will be ignored.

            """
            self._disable_ctx = disable_ctx

            if disable_ctx:
                self._ctx_id = None
                self._max_ctx_id = None
            else:
                if begin_ctx_id < 0:
                    raise ValueError("Invalid context image ID")
                self._ctx_id = begin_ctx_id  # the ID of the *last* image to be resampled
                if max_ctx_id is None:
                    max_ctx_id = begin_ctx_id
                elif max_ctx_id < begin_ctx_id:
                    raise ValueError("'max_ctx_id' cannot be smaller than 'begin_ctx_id'.")
                self._max_ctx_id = max_ctx_id

            if exptime < 0.0:
                raise ValueError("Exposure time must be non-negative.")

            if (exptime > 0.0 and out_img is None and out_ctx is None and out_wht is None):
                raise ValueError(
                    "Exposure time must be 0.0 for the first resampling "
                    "(when no ouput resampled images have been provided)."
                )

            if (
                exptime == 0.0 and
                    (
                        (out_ctx is not None and np.sum(out_ctx) > 0) or
                        (out_wht is not None and np.sum(out_wht) > 0)
                    )
            ):
                raise ValueError(
                    "Inconsistent exposure time and context and/or weight images: "
                    "Exposure time cannot be 0 when context and/or weight arrays "
                    "are non-zero."
                )

            self._texptime = exptime

            if kernel.lower() not in SUPPORTED_DRIZZLE_KERNELS:
                raise ValueError(f"Kernel '{kernel}' is not supported.")
            self._kernel = kernel

            if fillval is None:
                fillval = "INDEF"

            elif isinstance(fillval, str):
                fillval = fillval.strip()
                if fillval.upper() in ["", "INDEF"]:
                    fillval = "INDEF"
                else:
                    float(fillval)
                    fillval = str(fillval)

            else:
                fillval = str(fillval)

            if out_img is None and fillval == "INDEF":
                fillval = "NaN"

            self._fillval = fillval

            # shapes will collect user specified 'out_shape' and shapes of
            # out_* arrays (if provided) in order to check all shapes are the same.
            shapes = set()

            if out_img is not None:
                out_img = np.asarray(out_img, dtype=np.float32)
                shapes.add(out_img.shape)

            if out_wht is not None:
                out_wht = np.asarray(out_wht, dtype=np.float32)
                shapes.add(out_wht.shape)

            if out_ctx is not None:
                out_ctx = np.asarray(out_ctx, dtype=np.int32)
                if out_ctx.ndim == 2:
                    out_ctx = out_ctx[None, :, :]
                elif out_ctx.ndim != 3:
                    raise ValueError("'out_ctx' must be either a 2D or 3D array.")
                shapes.add(out_ctx.shape[1:])

            if out_shape is not None:
                shapes.add(tuple(out_shape))

            if len(shapes) == 1:
                self._out_shape = shapes.pop()
                self._alloc_output_arrays(
                    out_shape=self._out_shape,
                    max_ctx_id=max_ctx_id,
                    out_img=out_img,
                    out_wht=out_wht,
                    out_ctx=out_ctx,
                )
            elif len(shapes) > 1:
                raise ValueError(
                    "Inconsistent data shapes specified: 'out_shape' and/or "
                    "out_img, out_wht, out_ctx have different shapes."
                )
            else:
                self._out_shape = None
                self._out_img = None
                self._out_wht = None
                self._out_ctx = None

        @property
        def fillval(self):
            """Fill value for output pixels without contributions from input images."""
            return self._fillval

        @property
        def kernel(self):
            """Resampling kernel."""
            return self._kernel

        @property
        def ctx_id(self):
            """Context image "ID" (0-based ) of the next image to be resampled."""
            return self._ctx_id

        @property
        def out_img(self):
            """Output resampled image."""
            return self._out_img

        @property
        def out_wht(self):
            """Output weight image."""
            return self._out_wht

        @property
        def out_ctx(self):
            """Output "context" image."""
            return self._out_ctx

        @property
        def total_exptime(self):
            """Total exposure time of all resampled images."""
            return self._texptime

        def _alloc_output_arrays(self, out_shape, max_ctx_id, out_img, out_wht,
                                 out_ctx):
            # allocate arrays as needed:
            if out_wht is None:
                self._out_wht = np.zeros(out_shape, dtype=np.float32)
            else:
                self._out_wht = out_wht

            if self._disable_ctx:
                self._out_ctx = None
            else:
                if out_ctx is None:
                    n_ctx_planes = max_ctx_id // CTX_PLANE_BITS + 1
                    ctx_shape = (n_ctx_planes, ) + out_shape
                    self._out_ctx = np.zeros(ctx_shape, dtype=np.int32)
                else:
                    self._out_ctx = out_ctx

                if not (out_wht is None and out_ctx is None):
                    # check that input data make sense: weight of pixels with
                    # non-zero context values must be different from zero:
                    if np.any(
                        np.bitwise_xor(
                            self._out_wht > 0.0,
                            np.sum(self._out_ctx, axis=0) > 0
                        )
                    ):
                        raise ValueError(
                            "Inconsistent values of supplied 'out_wht' and "
                            "'out_ctx' arrays. Pixels with non-zero context "
                            "values must have positive weights and vice-versa."
                        )

            if out_img is None:
                self._out_img = np.zeros(out_shape, dtype=np.float32)
            else:
                self._out_img = out_img

        def _increment_ctx_id(self):
            """
            Returns a pair of the *current* plane number and bit number in that
            plane and increments context image ID
            (after computing the return value).
            """
            if self._disable_ctx:
                return None, 0

            self._plane_no = self._ctx_id // CTX_PLANE_BITS
            depth = self._out_ctx.shape[0]

            if self._plane_no >= depth:
                # Add a new plane to the context image if planeid overflows
                plane = np.zeros((1, ) + self._out_shape, np.int32)
                self._out_ctx = np.append(self._out_ctx, plane, axis=0)

            plane_info = (self._plane_no, self._ctx_id % CTX_PLANE_BITS)
            # increment ID for the *next* image to be added:
            self._ctx_id += 1

            return plane_info

        def add_image(self, data, exptime, pixmap, scale=1.0,
                      weight_map=None, wht_scale=1.0, pixfrac=1.0, in_units='cps',
                      xmin=None, xmax=None, ymin=None, ymax=None):
            """
            Resample and add an image to the cumulative output image. Also, update
            output total weight image and context images.

            Parameters
            ----------
            data : 2D numpy.ndarray
                A 2D numpy array containing the input image to be drizzled.

            exptime : float
                The exposure time of the input image, a positive number. The
                exposure time is used to scale the image if the units are counts.

            pixmap : 3D array
                A mapping from input image (``data``) coordinates to resampled
                (``out_img``) coordinates. ``pixmap`` must be an array of shape
                ``(Ny, Nx, 2)`` where ``(Ny, Nx)`` is the shape of the input image.
                ``pixmap[..., 0]`` forms a 2D array of X-coordinates of input
                pixels in the ouput frame and ``pixmap[..., 1]`` forms a 2D array of
                Y-coordinates of input pixels in the ouput coordinate frame.

            scale : float, optional
                The pixel scale of the input image. Conceptually, this is the
                linear dimension of a side of a pixel in the input image, but it
                is not limited to this and can be set to change how the drizzling
                algorithm operates.

            weight_map : 2D array, None, optional
                A 2D numpy array containing the pixel by pixel weighting.
                Must have the same dimensions as ``data``.

                When ``weight_map`` is `None`, the weight of input data pixels will
                be assumed to be 1.

            wht_scale : float
                A scaling factor applied to the pixel by pixel weighting.

            pixfrac : float, optional
                The fraction of a pixel that the pixel flux is confined to. The
                default value of 1 has the pixel flux evenly spread across the image.
                A value of 0.5 confines it to half a pixel in the linear dimension,
                so the flux is confined to a quarter of the pixel area when the square
                kernel is used.

            in_units : str
                The units of the input image. The units can either be "counts"
                or "cps" (counts per second.)

            xmin : float, optional
                This and the following three parameters set a bounding rectangle
                on the input image. Only pixels on the input image inside this
                rectangle will have their flux added to the output image. Xmin
                sets the minimum value of the x dimension. The x dimension is the
                dimension that varies quickest on the image. If the value is zero,
                no minimum will be set in the x dimension. All four parameters are
                zero based, counting starts at zero.

            xmax : float, optional
                Sets the maximum value of the x dimension on the bounding box
                of the input image. If the value is zero, no maximum will
                be set in the x dimension, the full x dimension of the output
                image is the bounding box.

            ymin : float, optional
                Sets the minimum value in the y dimension on the bounding box. The
                y dimension varies less rapidly than the x and represents the line
                index on the input image. If the value is zero, no minimum  will be
                set in the y dimension.

            ymax : float, optional
                Sets the maximum value in the y dimension. If the value is zero, no
                maximum will be set in the y dimension,  the full x dimension
                of the output image is the bounding box.

            Returns
            -------
            nskip : float
                The number of lines from the box defined by
                ``((xmin, xmax), (ymin, ymax))`` in the input image that were
                ignored and did not contribute to the output image.

            nmiss : float
                The number of pixels from the box defined by
                ``((xmin, xmax), (ymin, ymax))`` in the input image that were
                ignored and did not contribute to the output image.

            """
            # this enables initializer to not need output image shape at all and
            # set output image shape based on output coordinates from the pixmap.
            #
            if self._out_shape is None:
                pmap_xmin = int(np.floor(np.nanmin(pixmap[:, :, 0])))
                pmap_xmax = int(np.ceil(np.nanmax(pixmap[:, :, 0])))
                pmap_ymin = int(np.floor(np.nanmin(pixmap[:, :, 1])))
                pmap_ymax = int(np.ceil(np.nanmax(pixmap[:, :, 1])))
                pixmap = pixmap.copy()
                pixmap[:, :, 0] -= pmap_xmin
                pixmap[:, :, 1] -= pmap_ymin
                self._out_shape = (
                    pmap_xmax - pmap_xmin + 1,
                    pmap_ymax - pmap_ymin + 1
                )

                self._alloc_output_arrays(
                    out_shape=self._out_shape,
                    max_ctx_id=max(self._max_ctx_id, self._ctx_id),
                    out_img=None,
                    out_wht=None,
                    out_ctx=None,
                )

            plane_no, id_in_plane = self._increment_ctx_id()

            if exptime <= 0.0:
                raise ValueError("'exptime' *must* be a strictly positive number.")

            # Ensure that the fillval parameter gets properly interpreted
            # for use with tdriz
            if in_units == 'cps':
                expscale = 1.0
            else:
                expscale = exptime

            self._texptime += exptime

            data = np.asarray(data, dtype=np.float32)
            pixmap = np.asarray(pixmap, dtype=np.float64)
            in_ymax, in_xmax = data.shape

            if pixmap.shape[:2] != data.shape:
                raise ValueError(
                    "'pixmap' shape is not consistent with 'data' shape."
                )

            if xmin is None or xmin < 0:
                xmin = 0

            if ymin is None or ymin < 0:
                ymin = 0

            if xmax is None or xmax > in_xmax - 1:
                xmax = in_xmax - 1

            if ymax is None or ymax > in_ymax - 1:
                ymax = in_ymax - 1

            if weight_map is not None:
                weight_map = np.asarray(weight_map, dtype=np.float32)
            else:  # TODO: this should not be needed after C code modifications
                weight_map = np.ones_like(data)

            pixmap = np.asarray(pixmap, dtype=np.float64)

            if self._disable_ctx:
                ctx_plane = None
            else:
                if self._out_ctx.ndim == 2:
                    raise AssertionError("Context image is expected to be 3D")
                ctx_plane = self._out_ctx[plane_no]

            # TODO: probably tdriz should be modified to not return version.
            #       we should not have git, Python, C, ... versions

            # TODO: While drizzle code in cdrizzlebox.c supports weight_map=None,
            #       cdrizzleapi.c does not. It should be modified to support this
            #       for performance reasons.

            _vers, nmiss, nskip = cdrizzle.tdriz(
                input=data,
                weights=weight_map,
                pixmap=pixmap,
                output=self._out_img,
                counts=self._out_wht,
                context=ctx_plane,
                uniqid=id_in_plane + 1,
                xmin=xmin,
                xmax=xmax,
                ymin=ymin,
                ymax=ymax,
                scale=scale,  # scales image intensity. usually equal to pixel scale
                pixfrac=pixfrac,
                kernel=self._kernel,
                in_units=in_units,
                expscale=expscale,
                wtscale=wht_scale,
                fillstr=self._fillval,
            )
            self._cversion = _vers  # TODO: probably not needed

            return nmiss, nskip

    def blot_image(data, pixmap, pix_ratio, exptime, output_pixel_shape,
                   interp='poly5', sinscl=1.0):
        """
        Resample the ``data`` input image onto an output grid defined by
        the ``pixmap`` array. ``blot_image`` performs resampling using one of
        the several interpolation algorithms and, unlike the "drizzle" algorithm
        with 'square', 'turbo', and 'point' kernels, this resampling is not
        flux-conserving.

        This method works best for with well sampled images and thus it is
        typically used to resample the output of :py:class:`Drizzle` back to the
        coordinate grids of input images of :py:meth:`Drizzle.add_image`.
        The output of :py:class:`Drizzle` are usually well sampled images especially
        if it was created from a set of dithered images.

        Parameters
        ----------
        data : 2D array
            Input numpy array of the source image in units of 'cps'.

        pixmap : 3D array
            A mapping from input image (``data``) coordinates to resampled
            (``out_img``) coordinates. ``pixmap`` must be an array of shape
            ``(Ny, Nx, 2)`` where ``(Ny, Nx)`` is the shape of the input image.
            ``pixmap[..., 0]`` forms a 2D array of X-coordinates of input
            pixels in the ouput frame and ``pixmap[..., 1]`` forms a 2D array of
            Y-coordinates of input pixels in the ouput coordinate frame.

        output_pixel_shape : tuple of int
            A tuple of two integer numbers indicating the dimensions of the output
            image ``(Nx, Ny)``.

        pix_ratio : float
            Ratio of the input image pixel scale to the ouput image pixel scale.

        exptime : float
            The exposure time of the input image.

        interp : str, optional
            The type of interpolation used in the resampling. The
            possible values are:

                - "nearest" (nearest neighbor interpolation);
                - "linear" (bilinear interpolation);
                - "poly3" (cubic polynomial interpolation);
                - "poly5" (quintic polynomial interpolation);
                - "sinc" (sinc interpolation);
                - "lan3" (3rd order Lanczos interpolation); and
                - "lan5" (5th order Lanczos interpolation).

        sincscl : float, optional
            The scaling factor for "sinc" interpolation.

        Returns
        -------
        out_img : 2D numpy.ndarray
            A 2D numpy array containing the resampled image data.

        """
        out_img = np.zeros(output_pixel_shape[::-1], dtype=np.float32)

        cdrizzle.tblot(data, pixmap, out_img, scale=pix_ratio, kscale=1.0,
                       interp=interp, exptime=exptime, misval=0.0, sinscl=sinscl)

        return out_img