def test_estimate_cdelt():
l, b = 0, 0
# This is the ra,dec of l,b=0,0
ra, dec = 266.404497776, -28.9364329295
# This is 'almost' the ra,dec of l,b=0,0 - works
# ra,dec=266.40,-28.93
wcs = pywcs.WCS(naxis=2)
wcs.wcs.crpix = [5.5, 5.5]
wcs.wcs.cdelt = [0.1, -0.1]
wcs.wcs.crval = [l, b]
wcs.wcs.ctype = ["GLON-ZEA".encode("ascii"), "GLAT-ZEA".encode("ascii")]
import stregion.wcs_helper as wcs_helper
proj = wcs_helper.get_kapteyn_projection(wcs)
cdelt = wcs_helper.estimate_cdelt(proj, 5.5, 5.5)
assert np.allclose([cdelt], [0.1])
region_string = "fk5; circle(%s, %s, 0.5000)" % (ra, dec)
reg = stregion.parse(region_string).as_imagecoord(wcs)
assert np.allclose([reg[0].coord_list[-1]], [0.5 / 0.1])
def _combine_exclude_mask(self, mask):
# create masks from exclude/include regions and combine it with the
# input DQ mask:
#
regmask = None
if self.src_find_filters is not None and \
'region_file' in self.src_find_filters:
reg_file_name = self.src_find_filters['region_file']
if not os.path.isfile(reg_file_name):
raise IOError(
"The 'exclude' region file '{:s}' does not exist.".format(
reg_file_name))
else:
return mask
# get data image size:
(img_ny, img_nx) = self.source.shape
# find out if user provided a region file or a mask FITS file:
reg_file_ext = os.path.splitext(reg_file_name)[-1]
if reg_file_ext.lower().strip() in ['.fits', '.fit'] and \
basicFITScheck(reg_file_name):
# likely we are dealing with a FITS file.
# check that the file is a simple with 2 axes:
hdulist = fits.open(reg_file_name, memmap=False)
extlist = get_extver_list(hdulist, extname=None)
for ext in extlist:
usermask = hdulist[ext].data
if usermask.shape == (img_ny, img_nx):
regmask = usermask.astype(np.bool)
break
hdulist.close()
if regmask is None:
raise ValueError("None of the image-like extensions in the "
"user-provided exclusion mask '{}' has a "
"correct shape".format(reg_file_name))
else:
# we are dealing with a region file:
reglist = pyregion.open(reg_file_name)
## check that regions are in image-like coordinates:
##TODO: remove the code below once 'pyregion' package can correctly
## (DS9-like) convert sky coordinates to image coordinates for all
## supported shapes.
#if not all([ (x.coord_format == 'image' or \
# x.coord_format == 'physical') for x in reglist]):
# print("WARNING: Some exclusion regions are in sky coordinates.\n"
# " These regions will be ignored.")
# # filter out regions in sky coordinates:
# reglist = pyregion.ShapeList(
# [x for x in reglist if x.coord_format == 'image' or \
# x.coord_format == 'physical']
# )
#TODO: comment out next lines if we do not support region files
# in sky coordinates and uncomment previous block:
# Convert regions from sky coordinates to image coordinates:
auxwcs = _AuxSTWCS(self.wcs)
reglist = reglist.as_imagecoord(auxwcs, rot_wrt_axis=2)
# if all regions are exclude regions, then assume that the entire image
# should be included and that exclude regions exclude from this
# rectangular region representing the entire image:
if all([x.exclude for x in reglist]):
# we slightly widen the box to make sure that
# the entire image is covered:
imreg = pyregion.parse(
"image;box({:.1f},{:.1f},{:d},{:d},0)".format(
(img_nx + 1) / 2.0, (img_ny + 1) / 2.0, img_nx + 1,
img_ny + 1))
reglist = pyregion.ShapeList(imreg + reglist)
# create a mask from regions:
regmask = np.asarray(reglist.get_mask(shape=(img_ny, img_nx)),
dtype=np.bool)
if mask is not None and regmask is not None:
mask = np.logical_and(regmask, mask)
else:
mask = regmask
#DEBUG:
if mask is not None:
fn = os.path.splitext(self.fname)[0] + '_srcfind_mask.fits'
fits.writeto(fn, mask.astype(dtype=np.uint8), overwrite=True)
return mask
def _combine_exclude_mask(self, mask):
# create masks from exclude/include regions and combine it with the
# input DQ mask:
#
regmask = None
if self.src_find_filters is not None and \
'region_file' in self.src_find_filters:
reg_file_name = self.src_find_filters['region_file']
if not os.path.isfile(reg_file_name):
raise IOError("The 'exclude' region file '{:s}' does not exist."
.format(reg_file_name))
else:
return mask
# get data image size:
(img_ny, img_nx) = self.source.shape
# find out if user provided a region file or a mask FITS file:
reg_file_ext = os.path.splitext(reg_file_name)[-1]
if reg_file_ext.lower().strip() in ['.fits', '.fit'] and \
basicFITScheck(reg_file_name):
# likely we are dealing with a FITS file.
# check that the file is a simple with 2 axes:
hdulist = fits.open(reg_file_name, memmap=False)
extlist = get_extver_list(hdulist,extname=None)
for ext in extlist:
usermask = hdulist[ext].data
if usermask.shape == (img_ny, img_nx):
regmask = usermask.astype(np.bool)
break
hdulist.close()
if regmask is None:
raise ValueError("None of the image-like extensions in the "
"user-provided exclusion mask '{}' has a "
"correct shape".format(reg_file_name))
else:
# we are dealing with a region file:
reglist = pyregion.open(reg_file_name)
## check that regions are in image-like coordinates:
##TODO: remove the code below once 'pyregion' package can correctly
## (DS9-like) convert sky coordinates to image coordinates for all
## supported shapes.
#if not all([ (x.coord_format == 'image' or \
# x.coord_format == 'physical') for x in reglist]):
# print("WARNING: Some exclusion regions are in sky coordinates.\n"
# " These regions will be ignored.")
# # filter out regions in sky coordinates:
# reglist = pyregion.ShapeList(
# [x for x in reglist if x.coord_format == 'image' or \
# x.coord_format == 'physical']
# )
#TODO: comment out next lines if we do not support region files
# in sky coordinates and uncomment previous block:
# Convert regions from sky coordinates to image coordinates:
auxwcs = _AuxSTWCS(self.wcs)
reglist = reglist.as_imagecoord(auxwcs, rot_wrt_axis=2)
# if all regions are exclude regions, then assume that the entire image
# should be included and that exclude regions exclude from this
# rectangular region representing the entire image:
if all([x.exclude for x in reglist]):
# we slightly widen the box to make sure that
# the entire image is covered:
imreg = pyregion.parse("image;box({:.1f},{:.1f},{:d},{:d},0)"
.format((img_nx+1)/2.0, (img_ny+1)/2.0,
img_nx+1, img_ny+1)
)
reglist = pyregion.ShapeList(imreg + reglist)
# create a mask from regions:
regmask = np.asarray(
reglist.get_mask(shape=(img_ny, img_nx)),
dtype=np.bool
)
if mask is not None and regmask is not None:
mask = np.logical_and(regmask, mask)
else:
mask = regmask
#DEBUG:
if mask is not None:
fn = os.path.splitext(self.fname)[0] + '_srcfind_mask.fits'
fits.writeto(fn, mask.astype(dtype=np.uint8), overwrite=True)
return mask
return rp.filter_shape(sss3)
def get_mask(region, hdu, origin=1):
"""
f = pyfits.read("test.fits")
reg = read_region_as_imagecoord(s, f[0].header)
mask = get_mask(reg, f[0])
"""
from stregion.region_to_filter import as_region_filter
data = hdu.data
#header = hdu.header
region_filter = as_region_filter(region, origin=origin)
mask = region_filter.mask(data)
return mask
if __name__ == '__main__':
#reg = stregion.open("../mos_fov.reg")
import stregion
proposed_fov = 'fk5;circle(290.96388,14.019167,843.31194")'
reg = stregion.parse(proposed_fov)
reg_imagecoord = reg.as_imagecoord(header)
patches, txts = reg.get_mpl_patches_texts()
m = reg.get_mask(hdu=f[0])
import matplotlib.pyplot as plt import stregion region = """ image circle(100, 100, 80) box(200, 150, 150, 120, 0) """ r = stregion.parse(region) mask_1or2 = r.get_mask(shape=(300,300)) myfilter = r.get_filter() mask_1and2 = (myfilter[0] & myfilter[1]).mask((300,300)) plt.subplot(121).imshow(mask_1or2, origin="lower", interpolation="nearest") plt.subplot(122).imshow(mask_1and2, origin="lower", interpolation="nearest") plt.show()