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Switch resample to the new drizzle package API #8866

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9267a3a
Switch resample to the new drizzle package API
mcara Oct 8, 2024
d288e1a
changelog; flake8
mcara Oct 9, 2024
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1 change: 1 addition & 0 deletions changes/8866.resample.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1 @@
Updated resample code to support the new ``drizzle`` API, see https://github.com/spacetelescope/drizzle/pull/134 for more details.
4 changes: 1 addition & 3 deletions jwst/resample/gwcs_drizzle.py
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@@ -1,6 +1,5 @@
import numpy as np

from drizzle import util
from drizzle import cdrizzle
from . import resample_utils

Expand Down Expand Up @@ -338,7 +337,7 @@ def dodrizzle(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon,
"""

# Insure that the fillval parameter gets properly interpreted for use with tdriz
if util.is_blank(str(fillval)):
if str(fillval).strip() == '':
fillval = 'NAN'
else:
fillval = str(fillval)
Expand Down Expand Up @@ -382,7 +381,6 @@ def dodrizzle(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon,
# Compute the mapping between the input and output pixel coordinates
# for use in drizzle.cdrizzle.tdriz
pixmap = resample_utils.calc_gwcs_pixmap(input_wcs, output_wcs, insci.shape)
# inwht[np.isnan(pixmap[:,:,0])] = 0.

log.debug(f"Pixmap shape: {pixmap[:,:,0].shape}")
log.debug(f"Input Sci shape: {insci.shape}")
Expand Down
49 changes: 27 additions & 22 deletions jwst/resample/resample.py
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Expand Up @@ -5,7 +5,7 @@

import numpy as np
import psutil
from drizzle import cdrizzle, util
from drizzle import cdrizzle
from spherical_geometry.polygon import SphericalPolygon

from stdatamodels.jwst import datamodels
Expand Down Expand Up @@ -192,7 +192,6 @@
output_pix_area * np.rad2deg(3600)**2
)


def do_drizzle(self, input_models):
"""Pick the correct drizzling mode based on self.single
"""
Expand Down Expand Up @@ -251,10 +250,10 @@
else:
iscale = 1.0
return iscale

def resample_group(self, input_models, indices):
"""Apply resample_many_to_many for one group

Parameters
----------
input_models : ModelLibrary
Expand Down Expand Up @@ -290,7 +289,7 @@
if isinstance(img, datamodels.SlitModel):
# must call this explicitly to populate area extension
# although the existence of this extension may not be necessary
img.area = img.area
img.area = img.area

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what does this line do? does it break anything to remove it?

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I have no idea. It is a strange line but in Python, sometimes things are magical. Seems to be related to SlitModel and also used (set) in the photom step (multi-slit). Maybe @melanieclarke knows more.

iscale = self._get_intensity_scale(img)
log.debug(f'Using intensity scale iscale={iscale}')

Expand Down Expand Up @@ -339,7 +338,7 @@
"""
output_models = []
for group_id, indices in input_models.group_indices.items():

output_model = self.resample_group(input_models, indices)

if not self.in_memory:
Expand All @@ -357,7 +356,7 @@
# build ModelLibrary as an association from the output files
# this saves memory if there are multiple groups
asn = asn_from_list(output_models, product_name='outlier_i2d')
asn_dict = json.loads(asn.dump()[1]) # serializes the asn and converts to dict
asn_dict = json.loads(asn.dump()[1]) # serializes the asn and converts to dict

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return ModelLibrary(asn_dict, on_disk=True)
# otherwise just build it as a list of in-memory models
return ModelLibrary(output_models, on_disk=False)
Expand Down Expand Up @@ -397,9 +396,11 @@
log.debug(f'Using intensity scale iscale={iscale}')
img.meta.iscale = iscale

inwht = resample_utils.build_driz_weight(img,
weight_type=self.weight_type,
good_bits=self.good_bits)
inwht = resample_utils.build_driz_weight(
img,
weight_type=self.weight_type,
good_bits=self.good_bits,
)
# apply sky subtraction
blevel = img.meta.background.level
if not img.meta.background.subtracted and blevel is not None:
Expand Down Expand Up @@ -446,14 +447,13 @@

return ModelLibrary([output_model,], on_disk=False)


def resample_variance_arrays(self, output_model, input_models):
"""Resample variance arrays from input_models to the output_model.

Variance images from each input model are resampled individually and
added to a weighted sum. If weight_type is 'ivm', the inverse of the
added to a weighted sum. If ``weight_type`` is 'ivm', the inverse of the
resampled read noise variance is used as the weight for all the variance
components. If weight_type is 'exptime', the exposure time is used.
components. If ``weight_type`` is 'exptime', the exposure time is used.

The output_model is modified in place.
"""
Expand Down Expand Up @@ -493,8 +493,10 @@
if rn_var is not None:
mask = (rn_var >= 0) & np.isfinite(rn_var) & (weight > 0)
weighted_rn_var[mask] = np.nansum(
[weighted_rn_var[mask],
rn_var[mask] * weight[mask] * weight[mask]],
[
weighted_rn_var[mask],
rn_var[mask] * weight[mask] * weight[mask]
],
axis=0
)
total_weight_rn_var[mask] += weight[mask]
Expand All @@ -506,8 +508,10 @@
if pn_var is not None:
mask = (pn_var >= 0) & np.isfinite(pn_var) & (weight > 0)
weighted_pn_var[mask] = np.nansum(
[weighted_pn_var[mask],
pn_var[mask] * weight[mask] * weight[mask]],
[
weighted_pn_var[mask],
pn_var[mask] * weight[mask] * weight[mask]
],
axis=0
)
total_weight_pn_var[mask] += weight[mask]
Expand All @@ -517,12 +521,14 @@
if flat_var is not None:
mask = (flat_var >= 0) & np.isfinite(flat_var) & (weight > 0)
weighted_flat_var[mask] = np.nansum(
[weighted_flat_var[mask],
flat_var[mask] * weight[mask] * weight[mask]],
[
weighted_flat_var[mask],
flat_var[mask] * weight[mask] * weight[mask]
],
axis=0
)
total_weight_flat_var[mask] += weight[mask]

del model.meta.iscale
del weight
input_models.shelve(model, i)
Expand All @@ -549,7 +555,6 @@
del weighted_rn_var, weighted_pn_var, weighted_flat_var
del total_weight_rn_var, total_weight_pn_var, total_weight_flat_var


def _resample_one_variance_array(self, name, input_model, output_model):
"""Resample one variance image from an input model.

Expand Down Expand Up @@ -753,7 +758,7 @@
"""

# Insure that the fillval parameter gets properly interpreted for use with tdriz
if util.is_blank(str(fillval)):
if str(fillval).strip() == '':
fillval = 'NAN'
else:
fillval = str(fillval)
Expand Down
16 changes: 5 additions & 11 deletions jwst/resample/resample_utils.py
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Expand Up @@ -4,7 +4,7 @@

import numpy as np
from astropy import units as u
import gwcs
from drizzle.utils import calc_pixmap

from stdatamodels.dqflags import interpret_bit_flags
from stdatamodels.jwst.datamodels.dqflags import pixel
Expand Down Expand Up @@ -124,17 +124,11 @@ def shape_from_bounding_box(bounding_box):
def calc_gwcs_pixmap(in_wcs, out_wcs, shape=None):
""" Return a pixel grid map from input frame to output frame.
"""
if shape:
bb = wcs_bbox_from_shape(shape)
log.debug("Bounding box from data shape: {}".format(bb))
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else:
bb = in_wcs.bounding_box
log.debug("Bounding box from WCS: {}".format(in_wcs.bounding_box))

grid = gwcs.wcstools.grid_from_bounding_box(bb)
pixmap = np.dstack(reproject(in_wcs, out_wcs)(grid[0], grid[1]))
if shape is not None and not np.array_equiv(shape, in_wcs.array_shape):
in_wcs = deepcopy(in_wcs)
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Is a deepcopy necessary here?

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I am not sure. It felt to be a safer way because this method sets array_shape property of the WCS object. If there is no drawback in modifying inputs, deepcopy can be omitted.

in_wcs.array_shape = shape

return pixmap
return calc_pixmap(wcs_from=in_wcs, wcs_to=out_wcs)


def reproject(wcs1, wcs2):
Expand Down
2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -22,7 +22,7 @@ dependencies = [
"astropy>=5.3",
"BayesicFitting>=3.0.1",
"crds>=11.17.14",
"drizzle>=1.15.0",
"drizzle @ git+https://github.com/mcara/drizzle.git@redesign-api",
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remember to change to @main before merge

"gwcs>=0.21.0,<0.23.0",
"numpy>=1.22,<2.0",
"opencv-python-headless>=4.6.0.66",
Expand Down
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