def build(self, expnames):
for exposure in expnames:
blank = np.zeros(self.meta_wcs.array_shape, dtype=np.int16)
exp = fits.open(exposure)
sci_extns = wcs_functions.get_extns(exp)
for sci in sci_extns:
wcs = HSTWCS(exp, ext=sci)
edges_x = [0]*wcs.naxis2 + [wcs.naxis1-1]*wcs.naxis2 + list(range(wcs.naxis1)) * 2
edges_y = list(range(wcs.naxis2)) * 2 + [0]*wcs.naxis1 + [wcs.naxis2-1]*wcs.naxis1
sky_edges = wcs.pixel_to_world_values(np.vstack([edges_x, edges_y]).T)
meta_edges = self.meta_wcs.world_to_pixel_values(sky_edges).astype(np.int32)
# Account for rounding problems with creating meta_wcs
meta_edges[:,1] = np.clip(meta_edges[:,1], 0, self.meta_wcs.array_shape[0]-1)
meta_edges[:,0] = np.clip(meta_edges[:,0], 0, self.meta_wcs.array_shape[1]-1)
# apply meta_edges to blank mask
# Use PIL to create mask
parray = np.array(meta_edges.T)
polygon = list(zip(parray[0], parray[1]))
nx = self.meta_wcs.array_shape[1]
ny = self.meta_wcs.array_shape[0]
img = Image.new('L', (nx, ny) , 0)
ImageDraw.Draw(img).polygon(polygon, outline=1, fill=1)
blank = np.array(img)
self.total_mask += blank.astype(np.int16)
def build(self, expnames):
for exposure in expnames:
blank = np.zeros(self.meta_wcs.array_shape, dtype=np.int16)
exp = fits.open(exposure)
sci_extns = wcs_functions.get_extns(exp)
for sci in sci_extns:
wcs = HSTWCS(exp, ext=sci)
edges_x = [0] * wcs.naxis2 + [wcs.naxis1 - 1
] * wcs.naxis2 + list(
range(wcs.naxis1)) * 2
edges_y = list(range(wcs.naxis2)) * 2 + [0] * wcs.naxis1 + [
wcs.naxis2 - 1
] * wcs.naxis1
sky_edges = wcs.pixel_to_world_values(
np.vstack([edges_x, edges_y]).T)
meta_edges = self.meta_wcs.world_to_pixel_values(
sky_edges).astype(np.int32)
blank[meta_edges[:, 1], meta_edges[:, 0]] = 1
# Fill in outline of each chip
blank = morphology.binary_dilation(blank,
structure=NDIMAGE_STRUCT2)
blank = morphology.binary_fill_holes(blank)
blank = morphology.binary_erosion(blank,
structure=NDIMAGE_STRUCT2)
self.total_mask += blank.astype(np.int16)
def build(self, expnames, scale=False, scale_kw='EXPTIME'):
""" Create mask showing where all input exposures overlap the footprint's WCS
Notes
-----
This method populates the following attributes (initialized as all zeros):
- total_mask : shows number of chips per pixel
- scaled_mask : if computed, shows (by default) exposure time per pixel
Parameters
-----------
expnames : list
List of filenames for all input exposures that overlap the SkyFootprint WCS
scale : bool, optional
If specified, scale each chip by the value of the `scale_kw` keyword from the input exposure.
scale_kw : str, optional
If `scale` is `True`, get the scaling value from this keyword. This keyword is assumed to be
in the PRIMARY header.
"""
for exposure in expnames:
blank = np.zeros(self.meta_wcs.array_shape, dtype=np.int16)
exp = fits.open(exposure)
sci_extns = wcs_functions.get_extns(exp)
for sci in sci_extns:
wcs = HSTWCS(exp, ext=sci)
edges_x = [0] * wcs.naxis2 + [wcs.naxis1 - 1
] * wcs.naxis2 + list(
range(wcs.naxis1)) * 2
edges_y = list(range(wcs.naxis2)) * 2 + [0] * wcs.naxis1 + [
wcs.naxis2 - 1
] * wcs.naxis1
sky_edges = wcs.pixel_to_world_values(
np.vstack([edges_x, edges_y]).T)
meta_edges = self.meta_wcs.world_to_pixel_values(
sky_edges).astype(np.int32)
# Account for rounding problems with creating meta_wcs
meta_edges[:, 1] = np.clip(meta_edges[:, 1], 0,
self.meta_wcs.array_shape[0] - 1)
meta_edges[:, 0] = np.clip(meta_edges[:, 0], 0,
self.meta_wcs.array_shape[1] - 1)
# apply meta_edges to blank mask
# Use PIL to create mask
parray = np.array(meta_edges.T)
polygon = list(zip(parray[0], parray[1]))
nx = self.meta_wcs.array_shape[1]
ny = self.meta_wcs.array_shape[0]
img = Image.new('L', (nx, ny), 0)
ImageDraw.Draw(img).polygon(polygon, outline=1, fill=1)
blank = np.array(img)
self.total_mask += blank.astype(np.int16)
# Compute scaled mask if specified...
if scale:
scale_val = fits.getval(exposure, scale_kw)
self.scaled_mask += blank.astype(np.int16) * scale_val