def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0):
"""
Resample the output/resampled image to recreate an input image based on
the input image's world coordinate system
Parameters
----------
median_model : ~jwst.datamodels.DataModel
blot_img : datamodel
Datamodel containing header and WCS to define the 'blotted' 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.
"""
blot_wcs = blot_img.meta.wcs
# Compute the mapping between the input and output pixel coordinates
pixmap = calc_gwcs_pixmap(blot_wcs, median_model.meta.wcs,
blot_img.data.shape)
log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape))
log.debug("Sci shape: {}".format(blot_img.data.shape))
pix_ratio = 1
log.info('Blotting {} <-- {}'.format(blot_img.data.shape,
median_model.data.shape))
outsci = np.zeros(blot_img.shape, dtype=np.float32)
# Currently tblot cannot handle nans in the pixmap, so we need to give some
# other value. -1 is not optimal and may have side effects. But this is
# what we've been doing up until now, so more investigation is needed
# before a change is made. Preferably, fix tblot in drizzle.
pixmap[np.isnan(pixmap)] = -1
tblot(median_model.data,
pixmap,
outsci,
scale=pix_ratio,
kscale=1.0,
interp=interp,
exptime=1.0,
misval=0.0,
sinscl=sinscl)
return outsci
def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0):
"""
Resample the output/resampled image to recreate an input image based on
the input image's world coordinate system
Parameters
----------
median_model : ~jwst.datamodels.DataModel
blot_img : datamodel
Datamodel containing header and WCS to define the 'blotted' 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.
"""
blot_wcs = blot_img.meta.wcs
# Compute the mapping between the input and output pixel coordinates
pixmap = calc_gwcs_pixmap(blot_wcs, median_model.meta.wcs,
blot_img.data.shape)
log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape))
log.debug("Sci shape: {}".format(blot_img.data.shape))
# median_model_pscale = median_model.meta.wcsinfo.cdelt1
# blot_pscale = blot_img.meta.wcsinfo.cdelt1
pix_ratio = 1
log.info('Blotting {} <-- {}'.format(blot_img.data.shape,
median_model.data.shape))
outsci = np.zeros(blot_img.shape, dtype=np.float32)
tblot(median_model.data,
pixmap,
outsci,
scale=pix_ratio,
kscale=1.0,
interp=interp,
exptime=1.0,
misval=0.0,
sinscl=sinscl)
return outsci
def extract_image(self, blot_wcs, interp='poly5', sinscl=1.0):
"""
Resample the output/resampled image to recreate an input image based on
the input image's world coordinate system
Parameters
----------
blot_wcs : wcs
The world coordinate system to be used to define the 'blotted' 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.
"""
blot_shape = resample_utils.build_size_from_domain(blot_wcs.domain)
_outsci = np.zeros((blot_shape[1], blot_shape[0]), dtype=np.float32)
# Compute the mapping between the input and output pixel coordinates
#log.info("Creating PIXMAP for blotted image...")
pixmap = resample_utils.calc_gwcs_pixmap(self.source_wcs, blot_wcs)
source_pscale = self.source_wcs.forward_transform[
'cdelt1'].factor.value
blot_pscale = blot_wcs.forward_transform['cdelt1'].factor.value
pix_ratio = source_pscale / blot_pscale
#log.info("Starting blot...")
cdrizzle.tblot(self.source,
pixmap,
_outsci,
scale=pix_ratio,
kscale=1.0,
interp=interp,
exptime=1.0,
misval=0.0,
sinscl=sinscl)
return _outsci
def extract_image(self, blot_img, interp='poly5', sinscl=1.0):
"""
Resample the output/resampled image to recreate an input image based on
the input image's world coordinate system
Parameters
----------
blot_img : datamodel
Datamodel containing header and WCS to define the 'blotted' 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.
"""
blot_wcs = blot_img.meta.wcs
outsci = np.zeros(blot_img.shape, dtype=np.float32)
# Compute the mapping between the input and output pixel coordinates
pixmap = resample_utils.calc_gwcs_pixmap(blot_wcs, self.source_wcs,
outsci.shape)
log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape))
log.debug("Sci shape: {}".format(outsci.shape))
source_pscale = self.source_model.meta.wcsinfo.cdelt1
blot_pscale = blot_img.meta.wcsinfo.cdelt1
pix_ratio = source_pscale / blot_pscale
log.info('Blotting {} <-- {}'.format(outsci.shape, self.source.shape))
tblot(self.source, pixmap, outsci, scale=pix_ratio, kscale=1.0,
interp=interp, exptime=1.0, misval=0.0, sinscl=sinscl)
return outsci
def extract_image(self, blot_img, interp='poly5', sinscl=1.0):
"""
Resample the output/resampled image to recreate an input image based on
the input image's world coordinate system
Parameters
----------
blot_img : datamodel
Datamodel containing header and WCS to define the 'blotted' 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.
"""
blot_wcs = blot_img.meta.wcs
blot_shape = resample_utils.build_size_from_domain(blot_wcs.domain)
outsci = np.zeros((blot_shape[0], blot_shape[1]), dtype=np.float32)
# Compute the mapping between the input and output pixel coordinates
#log.info("Creating PIXMAP for blotted image...")
pixmap = resample_utils.calc_gwcs_pixmap(self.source_wcs, blot_wcs)
source_pscale = self.source_model.meta.wcsinfo.cdelt1
blot_pscale = blot_img.meta.wcsinfo.cdelt1
pix_ratio = source_pscale / blot_pscale
#log.info("Starting blot...")
cdrizzle.tblot(self.source, pixmap, outsci, scale=pix_ratio, kscale=1.0,
interp=interp, exptime=1.0, misval=0.0, sinscl=sinscl)
return outsci
