# coding: utf-8
#
#    Project: X-ray image reader
#             https://github.com/silx-kit/fabio
#
#    Copyright 2020(C) European Synchrotron Radiation Facility, Grenoble, France
#
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#  and to permit persons to whom the Software is furnished to do so,
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#
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"""Template for FabIO image reader

This is a template for adding new file formats to FabIO

We hope it will be relatively easy to add new file formats to fabio in the
future.
The basic idea is the following:

2) readheader fills in a dictionary of "name":"value" pairs in self.header.
   No one expects to find anything much in there.

3) read fills in self.data with a numpy array holding the image.
   Some info are automatically exposed from data:
   * self.shape is the image dimensions,
   * self.dtype is the numpy.dtype of the data.

4) The member variables "_need_a_seek_to_read" and "_need_a_real_file" are there
   in case you have
   trouble with the transparent handling of bz2 and gz files.

5) Add your new module as an import into fabio.fabioformats.
   Your class will be registered automatically.

6) Fill out the magic numbers for your format in fabio.openimage if you know
   them (the characteristic first few bytes in the file)

7) Upload a testimage to the file release system and create a unittest testcase
   which opens an example of your new format, confirming the image has actually
   been read in successfully (eg check the mean, max, min and esd are all correct,
   perhaps orientation too)

8) Run pylint on your code and then please go clean it up. Have a go at mine
   while you are at it, before requesting a pull-request on github.

9) Bask in the warm glow of appreciation when someone unexpectedly learns they
   don't need to convert their data into another format

"""

__authors__ = ["Jerome Kieffer"]
__contact__ = "jerome.kieffer@esrf.fr"
__license__ = "MIT"
__copyright__ = "2020 ESRF"
__date__ = "25/11/2020"

import logging
logger = logging.getLogger(__name__)
import numpy
try:
    import h5py
except ImportError:
    h5py = None
else:
    try:
        import hdf5plugin
    except:
        pass
from .fabioutils import NotGoodReader
from .fabioimage import FabioImage, OrderedDict
try:
    from .ext import dense as cython_densify
except ImportError:
    cython_densify = None


def densify(mask,
            radius,
            index,
            intensity,
            dummy,
            background,
            background_std=None):
    """Generate a dense image of its sparse representation
    
    :param mask: 2D array with NaNs for mask and pixel radius for the valid pixels
    :param radius: 1D array with the radial distance
    :param index: position of non-background pixels
    :param intensity: intensities of non background pixels (at index position)
    :param dummy: numerical value for masked-out pixels in dense image
    :return: dense frame as 2D array
    :param background: 1D array with the background values at given distance from the center
    :param background_std: 1D array with the background std at given distance from the center
    """
    dense = numpy.interp(mask, radius, background)
    if background_std is not None:
        std = numpy.interp(mask, radius, background_std)
        numpy.maximum(0.0, numpy.random.normal(dense, std), out=dense)
    flat = dense.ravel()
    flat[index] = intensity
    dtype = intensity.dtype
    if numpy.issubdtype(dtype, numpy.integer):
        dense = numpy.round(dense)
    dense = numpy.ascontiguousarray(dense, dtype=dtype)
    dense[numpy.logical_not(numpy.isfinite(mask))] = dummy
    return dense


class SparseImage(FabioImage):
    """FabIO image class for images compressed by sparsification of Bragg peaks 

    While the sparsification requires pyFAI and substential resources, re-densifying the data is easy.
    
    The program used for the sparsification is `sparsify-Bragg` from the pyFAI suite
    
    Set the noisy attribute to re-generate background noise
    """

    DESCRIPTION = "spasify-Bragg"

    DEFAULT_EXTENSIONS = [".h5", ".hdf5", ".nxs"]

    def __init__(self, *arg, **kwargs):
        """
        Generic constructor
        """
        if not h5py:
            raise RuntimeError("fabio.SparseImage cannot be used without h5py. Please install h5py and restart")

        FabioImage.__init__(self, *arg, **kwargs)
        self.mask = None
        self.radius = None
        self.background_avg = None
        self.background_std = None
        self.frame_ptr = None
        self.index = None
        self.intensity = None
        self.dummy = None
        self.noisy = False
        self.h5 = None

    def close(self):
        if self.h5 is not None:
            self.h5.close()
            self.dataset = None

    def _readheader(self, infile):
        """
        Read and decode the header of an image:

        :param infile: Opened python file (can be stringIO or bzipped file)
        """
        # list of header key to keep the order (when writing)
        self.header = self.check_header()

    def read(self, fname, frame=None):
        """
        Try to read image

        :param fname: name of the file
        :param frame: number of the frame
        """

        self.resetvals()
        self._readheader(fname)
        self.h5 = h5py.File(fname, mode="r")
        default_entry = self.h5.attrs.get("default")
        if default_entry is None or default_entry not in self.h5:
            raise NotGoodReader("HDF5 file does not contain any default entry.")
        entry = self.h5[default_entry]
        default_data = entry.attrs.get("default")
        if default_data is None or default_data not in entry:
            raise NotGoodReader("HDF5 file does not contain any default NXdata.")
        nx_data = entry[default_data]
        self.mask = nx_data["mask"][()]
        self.radius = nx_data["radius"][()]
        self.background_avg = nx_data["background_avg"][()]
        self.background_std = nx_data["background_std"][()]
        self.frame_ptr = nx_data["frame_ptr"][()]
        self.index = nx_data["index"][()]
        self.intensity = nx_data["intensity"][()]
        self.dummy = self.intensity.dtype.type(nx_data["dummy"][()])
        self._nframes = self.frame_ptr.shape[0] - 1

        if frame is not None:
            return self.getframe(int(frame))
        else:
            self.currentframe = 0
            self.data = self._generate_data(self.currentframe)
            self._shape = None
            return self

    def _generate_data(self, index=0):
        "Actually rebuilds the data for one frame"
        if self.h5 is None:
            logger.warning("Not data have been read from disk")
            return
        start, stop = self.frame_ptr[index:index + 2]
        if cython_densify is not None:
            return cython_densify.densify(self.mask,
                                 self.radius,
                                 self.index[start:stop],
                                 self.intensity[start:stop],
                                 self.dummy,
                                 self.intensity.dtype,
                                 self.background_avg[index],
                                 self.background_std[index] if self.noisy else None)
        else:
            # Fall-back on numpy code.
            return densify(self.mask,
                           self.radius,
                           self.index[start:stop],
                           self.intensity[start:stop],
                           self.dummy,
                           self.background_avg[index],
                           self.background_std[index] if self.noisy else None)

    def getframe(self, num):
        """ returns the frame numbered 'num' in the stack if applicable"""
        if self.nframes > 1:
            new_img = None
            if (num >= 0) and num < self.nframes:
                data = self._generate_data(num)
                new_img = self.__class__(data=data, header=self.header)
                new_img.mask = self.mask
                new_img.radius = self.radius
                new_img.background_avg = self.background_avg
                new_img.background_std = self.background_std
                new_img.frame_ptr = self.frame_ptr
                new_img.index = self.index
                new_img.intensity = self.intensity
                new_img.dummy = self.dummy
                new_img.noisy = self.noisy
                new_img.h5 = self.h5
                new_img._nframes = self.nframes
                new_img.currentframe = num
            else:
                raise IOError("getframe %s out of range [%s %s[" % (num, 0, self.nframes))
        else:
            new_img = FabioImage.getframe(self, num)
        return new_img

    def previous(self):
        """ returns the previous frame in the series as a fabioimage """
        return self.getframe(self.currentframe - 1)

    def next(self):
        """ returns the next frame in the series as a fabioimage """
        return self.getframe(self.currentframe + 1)


# This is not compatibility with old code:
sparseimage = SparseImage