import numpy as np
import lenstronomy.Util.kernel_util as kernel_util
import lenstronomy.Util.util as util
import warnings


class PSF(object):
    """
    Point Spread Function class.
    This class describes and manages products used to perform the PSF modeling (convolution for extended surface
    brightness and painting of PSF's for point sources).
    """

    def __init__(self, psf_type='NONE', fwhm=None, truncation=5, pixel_size=None, kernel_point_source=None,
                 psf_error_map=None, point_source_supersampling_factor=1, kernel_point_source_init=None):
        """

        :param psf_type: string, type of PSF: options are 'NONE', 'PIXEL', 'GAUSSIAN'
        :param fwhm: float, full width at half maximum, only required for 'GAUSSIAN' model
        :param truncation: float, Gaussian truncation (in units of sigma), only required for 'GAUSSIAN' model
        :param pixel_size: width of pixel (required for Gaussian model, not required when using in combination with ImageModel modules)
        :param kernel_point_source: 2d numpy array, odd length, centered PSF of a point source
        :param psf_error_map: uncertainty in the PSF model. Same shape as point source kernel.
        This error will be added to the pixel error around the position of point sources as follows:
        sigma^2_i += 'psf_error_map'_j * (point_source_flux_i)**2
        :param point_source_supersampling_factor: int, supersampling factor of kernel_point_source
        :param kernel_point_source_init: memory of an initial point source kernel that gets passed through the psf iteration
        """
        self.psf_type = psf_type
        self._pixel_size = pixel_size
        self.kernel_point_source_init = kernel_point_source_init
        if self.psf_type == 'GAUSSIAN':
            if fwhm is None:
                raise ValueError('fwhm must be set for GAUSSIAN psf type!')
            self._fwhm = fwhm
            self._sigma_gaussian = util.fwhm2sigma(self._fwhm)
            self._truncation = truncation
            self._point_source_supersampling_factor = 0
        elif self.psf_type == 'PIXEL':
            if kernel_point_source is None:
                raise ValueError('kernel_point_source needs to be specified for PIXEL PSF type!')
            if len(kernel_point_source) % 2 == 0:
                raise ValueError('kernel needs to have odd axis number, not ', np.shape(kernel_point_source))
            if point_source_supersampling_factor > 1:
                self._kernel_point_source_supersampled = kernel_point_source
                self._point_source_supersampling_factor = point_source_supersampling_factor
                kernel_point_source = kernel_util.degrade_kernel(self._kernel_point_source_supersampled, self._point_source_supersampling_factor)
            self._kernel_point_source = kernel_point_source / np.sum(kernel_point_source)

        elif self.psf_type == 'NONE':
            self._kernel_point_source = np.zeros((3, 3))
            self._kernel_point_source[1, 1] = 1
        else:
            raise ValueError("psf_type %s not supported!" % self.psf_type)
        if psf_error_map is not None:
            self._psf_error_map = psf_error_map
            if self.psf_type == 'PIXEL':
                if len(self._psf_error_map) != len(self._kernel_point_source):
                    raise ValueError('psf_error_map must have same size as kernel_point_source!')
            self.psf_error_map_bool = True
        else:
            self.psf_error_map_bool = False

    @property
    def kernel_point_source(self):
        if not hasattr(self, '_kernel_point_source'):
            if self.psf_type == 'GAUSSIAN':
                kernel_numPix = self._truncation * self._fwhm / self._pixel_size
                if kernel_numPix % 2 == 0:
                    kernel_numPix += 1
                self._kernel_point_source = kernel_util.kernel_gaussian(kernel_numPix, self._pixel_size, self._fwhm)
        return self._kernel_point_source

    @property
    def kernel_pixel(self):
        """
        returns the convolution kernel for a uniform surface brightness on a pixel size

        :return: 2d numpy array
        """
        if not hasattr(self, '_kernel_pixel'):
            self._kernel_pixel = kernel_util.pixel_kernel(self.kernel_point_source, subgrid_res=1)
        return self._kernel_pixel

    def kernel_point_source_supersampled(self, supersampling_factor, updata_cache=True):
        """
        generates (if not already available) a supersampled PSF with ood numbers of pixels centered

        :param supersampling_factor: int >=1, supersampling factor relative to pixel resolution
        :param updata_cache: boolean, if True, updates the cached supersampling PSF if generated.
        Attention, this will overwrite a previously used supersampled PSF if the resulution is changing.
        :return: super-sampled PSF as 2d numpy array
        """
        if hasattr(self, '_kernel_point_source_supersampled') and self._point_source_supersampling_factor == supersampling_factor:
            kernel_point_source_supersampled = self._kernel_point_source_supersampled
        else:
            if self.psf_type == 'GAUSSIAN':
                kernel_numPix = self._truncation / self._pixel_size * supersampling_factor
                kernel_numPix = int(round(kernel_numPix))
                if kernel_numPix > 10000:
                    raise ValueError('The pixelized Gaussian kernel has a grid of %s pixels with a truncation at '
                                     '%s times the sigma of the Gaussian, exceeding the limit allowed.' % (kernel_numPix, self._truncation))
                if kernel_numPix % 2 == 0:
                    kernel_numPix += 1
                kernel_point_source_supersampled = kernel_util.kernel_gaussian(kernel_numPix, self._pixel_size / supersampling_factor, self._fwhm)

            elif self.psf_type == 'PIXEL':

                kernel = kernel_util.subgrid_kernel(self.kernel_point_source, supersampling_factor, odd=True, num_iter=5)
                n = len(self.kernel_point_source)
                n_new = n * supersampling_factor
                if n_new % 2 == 0:
                    n_new -= 1
                if hasattr(self, '_kernel_point_source_supersampled'):
                    warnings.warn("Super-sampled point source kernel over-written due to different subsampling"
                                  " size requested.", Warning)
                kernel_point_source_supersampled = kernel_util.cut_psf(kernel, psf_size=n_new)
            elif self.psf_type == 'NONE':
                kernel_point_source_supersampled = self._kernel_point_source
            else:
                raise ValueError('psf_type %s not valid!' % self.psf_type)
            if updata_cache is True:
                self._kernel_point_source_supersampled = kernel_point_source_supersampled
                self._point_source_supersampling_factor = supersampling_factor
        return kernel_point_source_supersampled

    def set_pixel_size(self, deltaPix):
        """
        update pixel size

        :param deltaPix: pixel size in angular units (arc seconds)
        :return: None
        """
        self._pixel_size = deltaPix
        if self.psf_type == 'GAUSSIAN':
            try:
                del self._kernel_point_source
            except:
                pass

    @property
    def psf_error_map(self):
        if not hasattr(self, '_psf_error_map'):
            self._psf_error_map = np.zeros_like(self.kernel_point_source)
        return self._psf_error_map

    @property
    def fwhm(self):
        """

        :return: full width at half maximum of kernel (in units of pixel)
        """
        if self.psf_type == 'GAUSSIAN':
            return self._fwhm
        else:
            return kernel_util.fwhm_kernel(self.kernel_point_source) * self._pixel_size
