def check_flagged_region(mosaicname, ra, dec, maj, min, pa, plot=False):
mosaicfile = get_mosaic_name(mosaicname)
maj = np.max((maj, 0.001))
lhdu = extract_subim(mosaicfile, ra, dec, 2 * maj, verbose=False)
if lhdu is None:
print mosaicname, ra, dec, maj
return True
region = pyregion.parse('fk5;ellipse({ra},{dec},{a},{b},{pa})'.format(
ra=ra, dec=dec, a=maj, b=min, pa=pa - 90))
data = lhdu[0].data
region_mask = region.get_mask(lhdu[0])
flagged = np.any(np.isnan(data[region_mask]))
if plot:
plt.figure()
plt.subplot(131)
plt.imshow(data)
plt.subplot(132)
plt.imshow(region_mask)
plt.subplot(133)
plt.imshow(data * region_mask)
return flagged
def ellipses_from_coordinates(coordinates):
"""
This function creates a region consisting of ellipses, based on a list of coordinates
:param coordinates: the list of coordinates for the different ellipses
:return: the region of ellipses
"""
# Initialize the region string
region_string = "# Region file format: DS9 version 3.0\n"
region_string += "global color=green\n"
region_string += "image\n"
# Loop over the objects in the coordinates list, adding a line for each one
for object in coordinates:
if type(object).__name__ == "Gaussian2D": line = "ellipse(" + str(object.x_mean.value) + "," + str(object.y_mean.value) + "," + str(object.x_stddev.value) + "," + str(object.y_stddev.value) + ",0.0)\n"
elif type(object).__name__ == "AiryDisk2D":
# see https://en.wikipedia.org/wiki/Airy_disk#Approximation_using_a_Gaussian_profile and
# http://astropy.readthedocs.org/en/latest/api/astropy.modeling.functional_models.AiryDisk2D.html#astropy.modeling.functional_models.AiryDisk2D
sigma = 0.42 * object.radius.value * 0.81989397882
line = "ellipse(" + str(object.x_0.value) + "," + str(object.y_0.value) + "," + str(sigma) + "," + str(sigma) + ",0.0)\n"
else: raise ValueError("Models other than Gaussian2D and AiryDisk2D are not yet supported")
region_string += line
# Parse the region string into a region object
region = pyregion.parse(region_string)
# Return the region
return region
def positions_of_stars_from_ds9(image_name, message, catalogue_name):
# Display image
d = ds9.ds9()
d.set("file " + image_name)
# Tell the user to select the regions
answer = False
while answer == False:
window = myWindow(message)
window.box()
try:
region = pyregion.parse(d.get("region"))
answer = True
except ValueError: # No regions defined
print "\n There is no region loaded in the image! \n"
coords_RADEC = [cc.coord_list[0:2] for cc in region]
#hdr = fits.getheader(image_name)
#w = wcs.WCS(hdr)
#coords_RADEC = w.all_pix2world(xyout,1)
with open(catalogue_name, 'w') as fd:
for reg in coords_RADEC:
fd.write(" {0} {1} \n".format(*reg))
return region
def filterImage(maskFileName, imageFileName, filterType):
imgList = []
imgData, _ = readFitsImage(imageFileName)
xlim, ylim = imgData.shape
if filterType == "REG":
reg = open(maskFileName, 'r').read()
hdulist = pyfits.open(imageFileName)
maskData = pyregion.parse(reg).get_mask(hdu=hdulist[0])
#maskData = writeMaskFile(maskData, 'mask.fits')
if filterType == "FITS":
maskData, _ = readFitsImage(maskFileName)
assert (maskData.shape == imgData.shape
), "Mask shape does not match image shape!"
if filterType == "NONE":
maskData = np.ones((xlim, ylim))
for i in range(xlim):
for j in range(ylim):
if maskData[i][j] != 0:
if (i + j) % 2 == 1:
type = 'v'
else:
type = 'o'
#type = 'v'
imgList.append((j, i, imgData[i][j], type))
return imgList
def __init__(self, X, Y, t, header, regionDefinition):
self.X = X
self.Y = Y
self.t = t
self.regions = [pyregion.parse(regionDefinition).as_imagecoord(header)]
def _parseLines(self, lines, headerWCS):
regDefinitions = filter(lambda x: x != 'fk5\n', lines)
if len(regDefinitions) > 0:
# Extract all circle(ra,dec,radius) expressions
# (catch the warnings generated by pyregion, which are
# harmless)
with warnings.catch_warnings():
warnings.filterwarnings(action="ignore",
category=AstropyDeprecationWarning)
warnings.filterwarnings(action="ignore",
category=FITSFixedWarning)
regions = pyregion.parse(
" ".join(lines)).as_imagecoord(headerWCS)
self.filter = regions
self.n = len(regions)
else:
# There are no regions!
self.filter = None
self.n = 0
def maskFits(regFile, imageFile):
f1 = open(regFile,'r')
reg = f1.read()
hdulist = pyfits.open(imageFile)
h = pyfits.getheader(imageFile)
hdr = h.copy()
matrix =hdulist[0].data
r = pyregion.parse(reg)
Xrange = matrix.shape[0]
Yrange = matrix.shape[1]
mask = r.get_mask(hdu=hdulist[0])
new_matrix = np.zeros((Xrange,Yrange))
mask_matrix = np.ones((Xrange,Yrange))
for i in range(Xrange):
for j in range(Yrange):
if(mask[i,j]!=True):
mask_matrix[i,j] = 0
new_matrix[i,j]=matrix[i,j]
# write out the new fits file.
imageName = imageFile.split('.')
outName = imageName[0] + "_masked." +imageName[1]
hdu=fits.PrimaryHDU(new_matrix)
hdu.writeto(outName,clobber='true')
hdu_mask=fits.PrimaryHDU(mask_matrix)
hdu_mask.writeto("mask.fits",clobber='true')
f1.close()
hdulist.close()
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 pyregion.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 = pyregion.parse(region_string).as_imagecoord(wcs)
assert np.allclose([reg[0].coord_list[-1]], [0.5 / 0.1])
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 pyregion.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 = pyregion.parse(region_string).as_imagecoord(wcs)
assert np.allclose([reg[0].coord_list[-1]], [0.5/0.1])
def load_regions(region_file):
with open(region_file) as f:
region_string = f.read()
# Workaround for bug in pyregion.parse when color is of form '#fff'
region_string = region_string.replace('color=#', 'color=')
regions = pyregion.parse(region_string)
return regions
def pix_val_in_reg(reg_ds9, coord, fits_data, fits_head):
"""
get the pixel values in the region reg_ds9
:param reg_ds9: ds9 region in the format of e.g., 'ellipse(a, b, ...', no header
:param coord: the coordinate system of the region, e.g. 'galactic', 'icrs', etc
:param fits_img: the fits image
:return: an numpy array containing the values of pixels within the region
"""
n_pix_y, n_pix_x = fits_data.shape
# create a filter using the region
r = pyregion.parse(coord+';' + reg_ds9).as_imagecoord(fits_head)
reg_filter = r.get_filter()
# read the numbers in the region
splits = reg_ds9.split(',')
# degree; the Galactic longitude of the center if the region is ellipse, circle or box;
# one point if the region is polygon
head = splits[0].split('(')
region_type = head[0]
if region_type == 'polygon':
coords = []
coords.append(head[1])
n = len(splits)
for i in range(1, n-1):
coords.append(splits[i])
tail = splits[-1].split(')')[0]
coords.append(tail)
coords = [float(i) for i in coords]
glons = coords[0::2]
glats = coords[1::2]
glon = np.mean(glons)
glat = np.mean(glats)
glon_delta = (max(glons) - min(glons)) / 2.
glat_delta = (max(glats) - min(glats)) / 2.
l_box = max(glon_delta, glat_delta) * 1.2
# create a box to search pix in the region
w = wcs.WCS(fits_head)
bottom_left = w.wcs_world2pix(glon + l_box, glat - l_box, 1)
up_right = w.wcs_world2pix(glon - l_box, glat + l_box, 1)
# get the mean; avoid the pix number out of bounds
i_min = int(max(bottom_left[0], 0))
i_max = int(min(up_right[0], n_pix_x))
j_min = int(max(bottom_left[1], 0))
j_max = int(min(up_right[1], n_pix_y))
pix_val = []
for i in range(i_min, i_max):
for j in range(j_min, j_max):
if reg_filter.inside1(i, j):
pix_val.append(fits_data[j, i])
return np.asarray(pix_val)
def filterImage(maskFileName, imageFileName, filterType):
imgList = []
imgData, _ = readFitsImage(imageFileName)
xlim, ylim = imgData.shape
if filterType=="REG":
reg = open(maskFileName, 'r').read()
hdulist = pyfits.open(imageFileName)
maskData = pyregion.parse(reg).get_mask(hdu=hdulist[0])
maskData = writeMaskFile(maskData, 'mask.fits')
if filterType=="FITS":
maskData, _= readFitsImage(maskFileName)
assert (maskData.shape==imgData.shape), "Mask shape does not match image shape!"
if filterType=="NONE":
maskData = np.ones((xlim, ylim))
for i in range(xlim):
for j in range(ylim):
if maskData[i][j]!=0:
if (i+j)%2 ==1:
type = 'v'
else:
type = 'o'
#type = 'v'
imgList.append((j,i, imgData[i][j], type))
return imgList
def ds9_region(ds, reg, obj=None, field_parameters=None):
r"""
Create a data container from a ds9 region file. Requires the pyregion
package (https://pyregion.readthedocs.io/en/latest/) to be installed.
Parameters
----------
ds : FITSDataset
The Dataset to create the region from.
reg : string
The filename of the ds9 region, or a region string to be parsed.
obj : data container, optional
The data container that will be used to create the new region.
Defaults to ds.all_data.
field_parameters : dictionary, optional
A set of field parameters to apply to the region.
Examples
--------
>>> ds = yt.load("m33_hi.fits")
>>> circle_region = ds9_region(ds, "circle.reg")
>>> print(circle_region.quantities.extrema("flux"))
"""
import pyregion
from yt.frontends.fits.api import EventsFITSDataset
if os.path.exists(reg):
r = pyregion.open(reg)
else:
r = pyregion.parse(reg)
reg_name = reg
header = ds.wcs_2d.to_header()
# The FITS header only contains WCS-related keywords
header["NAXIS1"] = nx = ds.domain_dimensions[ds.lon_axis]
header["NAXIS2"] = ny = ds.domain_dimensions[ds.lat_axis]
filter = r.get_filter(header=header)
mask = filter.mask((ny, nx)).transpose()
if isinstance(ds, EventsFITSDataset):
prefix = "event_"
else:
prefix = ""
def _reg_field(field, data):
i = data[prefix + "xyz"[ds.lon_axis]].d.astype("int") - 1
j = data[prefix + "xyz"[ds.lat_axis]].d.astype("int") - 1
new_mask = mask[i, j]
ret = np.zeros(data[prefix + "x"].shape)
ret[new_mask] = 1.0
return ret
ds.add_field(("gas", reg_name), sampling_type="cell", function=_reg_field)
if obj is None:
obj = ds.all_data()
if field_parameters is not None:
for k, v in field_parameters.items():
obj.set_field_parameter(k, v)
return obj.cut_region(["obj['%s'] > 0" % (reg_name)])
def subcubes_from_ds9(cube, region_file='../nro_maps/SouthShells.reg', pad_factor=1., shape='exact'):
"""
Extracts subcubes using the ds9 region file.
Parameters
----------
cube : SpectralCube, str
The cube to be chopped. Must be type spectral_cube.SpectralCube or str filename.
region_file : str
Path to a ds9 region file.
pad_factor : float, optional
Expand the subcube around the region by this factor.
shape : {'square', 'exact'}
The shape of the subcube returned. 'square' returns the
smallest square subcube that contains the region.
'exact' returns only the pixels contained within the region.
Returns
-------
subcubes: list of SpectralCube of SpectralCube
"""
from spectral_cube import SpectralCube
import pyregion
try:
#If cube is a str filename, read a SpectralCube.
cube = SpectralCube.read(cube)
except ValueError:
pass
if shape == 'square':
import astropy.units as u
subcube_list = []
region_list = pyregion.open(region_file)
for region in region_list:
half_width = region.coord_list[2] * pad_factor * u.deg
ra_center = region.coord_list[0] * u.deg
dec_center = region.coord_list[1] * u.deg
ra_range = [ra_center - half_width, ra_center + half_width]
dec_range = [dec_center - half_width, dec_center + half_width]
#print(ra_range, dec_range)
subcube_list.append(cube.subcube(ra_range[1], ra_range[0], dec_range[0], dec_range[1]))
if shape == 'exact':
region_list = pyregion.open(region_file)
subcube_list = []
for region in region_list:
if pad_factor != 1.:
new_string = '{};{}({},{},{}")'.format(region.coord_format, region.name,
region.coord_list[0], region.coord_list[1],
region.coord_list[2]*3600.*pad_factor)
region = pyregion.parse(new_string)[0]
subcube_list.append(cube.subcube_from_ds9region(pyregion.ShapeList([region])))
if len(subcube_list) == 1:
return subcube_list[0]
else:
return subcube_list
def cross_match_CSAR(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', CSAR_catalog = '/mnt/scratch-lustre/jkeown/DS9_regions/L1157/CSAR/CEPl1157_CSAR.dat', high_res_coldens_image = '/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', CSAR_core_indices='L1157_matched_CSAR_cores.dat'):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
### Import the CSAR catalog
### ***MAKE SURE FIRST TWO COLUMNS OF "CSAR_catalog" FILE ARE X_POSITION AND Y_POSITION OF SOURCE IN DECIMAL DEGREES***
CSAR_array = numpy.loadtxt(CSAR_catalog,comments='#')
#print CSAR_array
CSAR_positions = numpy.column_stack((CSAR_array[:,0], CSAR_array[:,1]))
#print CSAR_positions
w = wcs.WCS(high_res_coldens_image)
pos_pix = w.wcs_world2pix(CSAR_positions, 1)
### Loop through the potential matched cores identified in the step above.
counter = 0
matched_cores = []
for line in cores_array:
x_coor = str(line[3])
y_coor = str(line[4])
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;ellipse(' + x_coor + ', ' + y_coor + ', ' + str((line[50]/2.0)/3600.) + ', ' +
str((line[51]/2.0)/3600.) + ', ' + str(line[52]+90.0)+')')
r = pyregion.parse(region)
f=fits.open(high_res_coldens_image)
mymask = r.get_mask(hdu=f[0])
f.close()
newmask=mymask
### Set all values outside the core's ellipse to zero,
### all values inside the ellipse are set to one.
newmask=numpy.where(newmask==0,0,1)
mask_shape = numpy.shape(newmask)
### Loop through the CSAR catalog
### If any CSAR cores fall within a getsources core's ellipse,
### store the getsources core's index
match_counter=0
for i in pos_pix:
ypos = int(round(i[1],0))-1
xpos = int(round(i[0],0))-1
if ypos<=mask_shape[0] and xpos<=mask_shape[1]:
if newmask[ypos][xpos]==1 and match_counter==0:
matched_cores.append(counter)
# match_counter prevents counting indices twice
# if two CSAR cores fall within getsources ellipse
match_counter+=1
#print len(matched_cores)
counter += 1
print 'CSAR_matched:total ratio =' + str(round(float(len(matched_cores)) / float(len(cores_array[:,0])),3))
### Save the CSAR matched core indices to a file
#numpy.savetxt(CSAR_core_indices, matched_cores, fmt='%i')
return numpy.array(matched_cores)
def parse(region_string):
"""
This function is a simple wrapper around the pyregion.parse function, to contain
:param region_string:
:return:
"""
# Parse the region string and create a region
return pyregion.parse(region_string)
def draw_ellipse(ax, center, region_string):
header = ax.projection._pywcs.to_header()
cel_ax['fk5'].plot([center.ra()],[center.dec()],'r+', zorder=10, markersize=20)
reg = pyregion.parse(region_string).as_imagecoord(header)
patch_list, artist_list = reg.get_mpl_patches_texts()
for p in patch_list:
p.set_zorder(10)
ax.add_patch(p)
def get_max_intensity(source, pyfits, roi, colorbar_radius=None):
""" Return the maximum value in the pyfits file either
within the extended source's size or otherwise
within the 68% containment radius of the PSF (at
the lowest energy). """
try:
import pyregion
except:
return pyfits[0].data.max()
if source is None and colorbar_radius is None:
return pyfits[0].data.max()
if colorbar_radius is not None:
ra, dec = roi.roi_dir.ra(), roi.roi_dir.dec()
reg = pyregion.parse("fk5; circle(%.4f, %.4f, %.4f)" %
(ra, dec, colorbar_radius))
extensionmask = reg.get_mask(pyfits[0])
elif hasattr(source, 'spatial_model'):
# For extended sources,
# Get the maximum intensity value inside
# the spatial model's extension
extension_string = '\n'.join(
region_writer.unparse_extension(source.spatial_model,
r68=True))
reg = pyregion.parse(extension_string)
extensionmask = reg.get_mask(pyfits[0])
else:
extensionmask = False # no mask
# Get the maximum intensity inside the PSF (in lowest bin)
emin = roi.bin_edges[0]
ra, dec = source.skydir.ra(), source.skydir.dec()
r68 = roi.sa.psf.inverse_integral(emin, 1,
68) # 1=front entering events
reg = pyregion.parse("fk5; circle(%.4f, %.4f, %.4f)" % (ra, dec, r68))
psfmask = reg.get_mask(pyfits[0])
# use whatever region mask is bigger
return pyfits[0].data[psfmask | extensionmask].max()
def ds9_region(ds, reg, obj=None, field_parameters=None):
r"""
Create a data container from a ds9 region file. Requires the pyregion
package (http://leejjoon.github.io/pyregion/) to be installed.
Parameters
----------
ds : FITSDataset
The Dataset to create the region from.
reg : string
The filename of the ds9 region, or a region string to be parsed.
obj : data container, optional
The data container that will be used to create the new region.
Defaults to ds.all_data.
field_parameters : dictionary, optional
A set of field parameters to apply to the region.
Examples
--------
>>> ds = yt.load("m33_hi.fits")
>>> circle_region = ds9_region(ds, "circle.reg")
>>> print circle_region.quantities.extrema("flux")
"""
import pyregion
if os.path.exists(reg):
r = pyregion.open(reg)
else:
r = pyregion.parse(reg)
reg_name = reg
filter = r.get_filter(header=ds.wcs_2d.to_header())
nx = ds.domain_dimensions[ds.lon_axis]
ny = ds.domain_dimensions[ds.lat_axis]
mask = filter.mask((ny, nx)).transpose()
def _reg_field(field, data):
i = data["xyz"[ds.lon_axis]].ndarray_view().astype("int") - 1
j = data["xyz"[ds.lat_axis]].ndarray_view().astype("int") - 1
new_mask = mask[i, j]
ret = data["zeros"].copy()
ret[new_mask] = 1.
return ret
ds.add_field(("gas", reg_name), function=_reg_field)
if obj is None:
obj = ds.all_data()
if field_parameters is not None:
for k, v in field_parameters.items():
obj.set_field_parameter(k, v)
return obj.cut_region(["obj['%s'] > 0" % (reg_name)])
def get_rms_map(infilename,ds9region,outfilename):
polylist = convert_regionfile_to_poly(ds9region)
hdu=fits.open(infilename)
hduflat = flatten(hdu)
map=hdu[0].data
for direction,ds9region in enumerate(polylist):
r = pyregion.parse(ds9region)
manualmask = r.get_mask(hdu=hduflat)
rmsval = get_rms_array(hdu[0].data[0][0][np.where(manualmask == True)])
hdu[0].data[0][0][np.where(manualmask == True)] = rmsval
print 'RMS = %s for direction %i'%(rmsval,direction)
hdu.writeto(outfilename,clobber=True)
def reg2sl(self):
"""Convert a region file to a starlist."""
import pyregion
import astropy.coordinates as coord
import astropy.units as u
print(
"Converting '{input:s}' ds9 region file to '{output:s}' starlist.".
format(**vars(self.opts)))
with open(self.opts.input, 'r') as regionfile:
regions = pyregion.parse(regionfile.read())
with open(self.opts.output, 'w') as starlist:
for i, region in enumerate(regions):
if region.coord_format != 'fk5':
self.log.critical("Coordinate system {!r} unkown!".format(
region.coord_format))
break
# We only parse circles!
if region.name == 'circle':
# Parse the location
ra, dec = region.coord_list[0:2]
loc = coord.FK5(ra=ra, dec=dec, unit=(u.degree, u.degree))
if "text" in region.attr[1]:
name = region.attr[1]["text"]
else:
name = "Target%d" % i
# Handle comments
comments = region.comment.split()
for comment in comments:
if comment.startswith("text={"):
comments.remove(comment)
# Write out the starlist line
starlist.write(
"{name:<15s} {ra:s} {dec:s} {epoch:.0f} {keywords:s}".
format(
name=name.strip(),
ra=loc.ra.format(u.hour, sep=" ", pad=True),
dec=loc.dec.format(sep=" ", alwayssign=True),
epoch=loc.equinox.jyear,
keywords=" ".join(comments),
))
starlist.write("\n")
else:
self.log.warning(
"Region {!r} not parsed. It is not a circle.".format(
region))
def get_rms_map(infilename,ds9region,outfilename):
polylist = convert_regionfile_to_poly(ds9region)
hdu=fits.open(infilename)
hduflat = flatten(hdu)
map=hdu[0].data
for direction,ds9region in enumerate(polylist):
print direction,ds9region
r = pyregion.parse(ds9region)
manualmask = r.get_mask(hdu=hduflat)
rmsval = get_rms_array(hdu[0].data[0][0][np.where(manualmask == True)])
hdu[0].data[0][0][np.where(manualmask == True)] = rmsval
print 'RMS = %s for direction %i'%(rmsval,direction)
hdu.writeto(outfilename,clobber=True)
def ds9_region(ds, reg, obj=None, field_parameters=None):
r"""
Create a data container from a ds9 region file. Requires the pyregion
package (http://leejjoon.github.io/pyregion/) to be installed.
Parameters
----------
ds : FITSDataset
The Dataset to create the region from.
reg : string
The filename of the ds9 region, or a region string to be parsed.
obj : data container, optional
The data container that will be used to create the new region.
Defaults to ds.all_data.
field_parameters : dictionary, optional
A set of field parameters to apply to the region.
Examples
--------
>>> ds = yt.load("m33_hi.fits")
>>> circle_region = ds9_region(ds, "circle.reg")
>>> print circle_region.quantities.extrema("flux")
"""
import pyregion
if os.path.exists(reg):
r = pyregion.open(reg)
else:
r = pyregion.parse(reg)
reg_name = reg
filter = r.get_filter(header=ds.wcs_2d.to_header())
nx = ds.domain_dimensions[ds.lon_axis]
ny = ds.domain_dimensions[ds.lat_axis]
mask = filter.mask((ny,nx)).transpose()
def _reg_field(field, data):
i = data["xyz"[ds.lon_axis]].ndarray_view().astype("int")-1
j = data["xyz"[ds.lat_axis]].ndarray_view().astype("int")-1
new_mask = mask[i,j]
ret = data["zeros"].copy()
ret[new_mask] = 1.
return ret
ds.add_field(("gas",reg_name), function=_reg_field)
if obj is None:
obj = ds.all_data()
if field_parameters is not None:
for k,v in field_parameters.items():
obj.set_field_parameter(k,v)
return obj.cut_region(["obj['%s'] > 0" % (reg_name)])
def mask_from_ds9(image_name, message):
# Display image
d = ds9.ds9()
d.set("file " + image_name)
# Tell the user to select the regions
answer = False
while answer == False:
window = myWindow(message)
window.box()
try:
region = pyregion.parse(d.get("region"))
answer = True
except ValueError: # No regions defined
print "\n There is no region loaded in the image! \n"
return region
def ellipses(ra_list, dec_list, height_list, width_list, angle_list):
# Initialize the region string
region_string = "# Region file format: DS9 version 3.0\n"
region_string += "global color=green\n"
region_string += "fk5\n"
for ra, dec, height, width, angle in zip(ra_list, dec_list, height_list, width_list, angle_list):
line = "fk5;ellipse(%s,%s,%.2f\",%.2f\",%s)\n" % (ra, dec, height, width, angle)
region_string += line
region = pyregion.parse(region_string)
# Return the region
return region
def circles(ra_list, dec_list, radius_list):
# Initialize the region string
region_string = "# Region file format: DS9 version 3.0\n"
region_string += "global color=green\n"
region_string += "fk5\n"
for ra, dec, radius in zip(ra_list, dec_list, radius_list):
line = "fk5;circle(%s,%s,%.2f\")\n" % (ra, dec, radius)
region_string += line
region = pyregion.parse(region_string)
# Return the region
return region
def one_ellipse(parameters):
"""
This function ...
:param parameters:
:return:
"""
# Create a string identifying this ellipse
region_string = "# Region file format: DS9 version 3.0\n"
region_string += "global color=green\n"
region_string += "image\n"
region_string += "ellipse(" + str(parameters[0]) + "," + str(parameters[1]) + "," + str(parameters[2]) + "," + str(parameters[3]) + "," + str(parameters[4]) + ")\n"
# Create a region and return it
return pyregion.parse(region_string)
def ds9_photometry(xpapoint):
D = ds9.ds9(xpapoint)
try:
reg = pyregion.parse(D.get("regions selected -format ds9 -system wcs -sky fk5 -skyformat sexagesimal"))
except Exception as ex:
print ex
raise ex
pf = D.get_pyfits()
ph = pyfits.PrimaryHDU(data=pf[0].data,header=pf[0].header)
mask = reg.get_mask(ph)
arr = pf[0].data
wherenotnan = (arr == arr)
mask = mask*wherenotnan
hdr = pf[0].header
try:
wcs = pywcs.WCS(hdr.tostring())
except AttributeError:
wcs = pywcs.WCS(hdr)
try:
try:
bmaj = float(hdr['BMAJ'])
bmin = float(hdr['BMIN'])
except KeyError:
# VLA imfits
bmin = None; bmaj = None
for k,v in hdr.iteritems():
if numpy.iterable(v) and "BMAJ" in v:
bmaj = float(v.split()[3])
bmin = float(v.split()[5])
if bmin is None or bmaj is None:
raise KeyError("BMIN and BMAJ not found")
try:
cd1 = wcs.wcs.cd[0,0]
cd2 = wcs.wcs.cd[1,1]
except AttributeError:
cd1,cd2 = wcs.wcs.cdelt[:2]
ppbeam = 2*numpy.pi*bmin*bmaj / abs(cd1*cd2) / (8*numpy.log(2))
#print "CD1: %g CD2: %g" % (cd1, cd2)
sys.stdout.write( "BMAJ: %g BMIN: %g PPBEAM: %g SUM/PPBEAM: %g\n" % (bmaj,bmin,ppbeam,arr[mask].sum()/ppbeam) )
except KeyError:
print "ds9_phot failed - check for BMAJ/BMIN in header"
pass
except Exception as inst:
print "ds9_phot failed - not a header KeyError, something else"
print inst.args
pass
return arr[mask].sum(),arr[mask].mean(),numpy.median(arr[mask]),arr[mask].std(),mask.sum()
def mask_bright_stars(imagefile,star_region_strings,outputname): f = pf.open(imagefile) head = f[0].header data = f[0].data r = pyregion.parse(star_region_strings) r2= r.as_imagecoord(head) #create the mask mymask = r2.get_mask(hdu=f[0]) #Reverse the True and False mymask = np.logical_not(mymask) maskint = mymask*1 #Multiply the mask and the data to cut the stars (where False == 1 i.e., no mask) newimage = data * maskint #can write directly if you want #pf.writeto(outputname, newimage, head) #newimage is the data with the stars cut out whereas maskint is the integer mask with a 0 where the stars are masked and 1 everywhere else and head is the appropriate header return data, maskint, newimage
def extract_from_region(cls, specfile, region):
# Each pixel is in Jy/beam
# Sum to get Jy.pixel/beam
# Divide by boxarea to get Jy/beam again
# Multiply by N_beams = boxarea/area to get Jy
# sum(box)/boxarea * (boxarea/area) = sum(box)/area
hdu = fits.open(specfile)
hdr = hdu[0].header
cube = hdu[0].data[0, :, :, :]
vel = get_velocity(hdr)
beam = extract_beam_info(hdr)
trimhdu = get_trimmed_HDU(specfile)
objname = hdr['object']
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
r = pyregion.open(region).as_imagecoord(trimhdu.header)
except IOError:
r = pyregion.parse(region).as_imagecoord(trimhdu.header)
mask = r.get_mask(hdu=hdu[0], shape=cube.shape[-2:]) #hdu=trimhdu)
flux = np.zeros(vel.shape)
for i in range(len(vel)):
im = cube[i, :, :]
flux[i] = np.sum(im[mask]) / beam['area']
pix_asec = abs(hdr['CDELT1']) * 3600
regarea = np.sum(mask) * pix_asec**2
dv = vel[1] - vel[0]
vobs = hdr['RESTFRQ']
spec = dict(velocity=vel, flux=flux)
kwds = dict(delta_v=dv,
area=regarea,
pix_asec=pix_asec,
restfrq=vobs / 1e9,
bmaj=beam['major'],
bmin=beam['minor'],
bpa=beam['pa'],
barea=beam['area'],
objname=objname)
return cls(spec, **kwds)
def get_rms_map2(infilename,ds9region,outfilename):
runcommand = "MakeMask.py --RestoredIm=%s --OutName=%s.rmsmapmask --Th=%s --Box=50,2 --OutNameNoiseMap='%s.rms'"%(infilename,infilename,3.0,infilename)
run(runcommand,log=None)
infilename = '%s.rms.fits.fits'%infilename
polylist = convert_regionfile_to_poly(ds9region)
hdu=fits.open(infilename)
hduflat = flatten(hdu)
map=hdu[0].data
for direction,ds9region in enumerate(polylist):
r = pyregion.parse(ds9region)
manualmask = r.get_mask(hdu=hduflat)
rmsval = np.mean(hdu[0].data[0][0][np.where(manualmask == True)])
hdu[0].data[0][0][np.where(manualmask == True)] = rmsval
print 'RMS = %s for direction %i'%(rmsval,direction)
hdu.writeto(outfilename,clobber=True)
def prior_mask(priorfile, imhdr, struct):
"Create a binary mask with the prior limits."
opthdr = fits.getheader(priorfile, 1)
if opthdr.get('RA_MIN'):
ramin, ramax, decmin, decmax = [
opthdr.get(k) for k in ['RA_MIN', 'RA_MAX', 'DEC_MIN', 'DEC_MAX']
]
opt_limits = [
ramin, decmin, ramin, decmax, ramax, decmax, ramax, decmin, ramin,
decmin
]
opt_limits = ','.join([str(o) for o in opt_limits])
region = pyregion.parse('fk5;polygon({0})'.format(opt_limits))
mask = regions_mask(region, imhdr, struct, dilate=False)
else:
mask = None
return mask
def reg2sl(self):
"""Convert a region file to a starlist."""
import pyregion
import astropy.coordinates as coord
import astropy.units as u
print("Converting '{input:s}' ds9 region file to '{output:s}' starlist.".format(**vars(self.opts)))
with open(self.opts.input,'r') as regionfile:
regions = pyregion.parse(regionfile.read())
with open(self.opts.output,'w') as starlist:
for i,region in enumerate(regions):
if region.coord_format != 'fk5':
self.log.critical("Coordinate system {!r} unkown!".format(region.coord_format))
break
# We only parse circles!
if region.name == 'circle':
# Parse the location
ra,dec = region.coord_list[0:2]
loc = coord.FK5(ra=ra,dec=dec,unit=(u.degree,u.degree))
if "text" in region.attr[1]:
name = region.attr[1]["text"]
else:
name = "Target%d" % i
# Handle comments
comments = region.comment.split()
for comment in comments:
if comment.startswith("text={"):
comments.remove(comment)
# Write out the starlist line
starlist.write("{name:<15s} {ra:s} {dec:s} {epoch:.0f} {keywords:s}".format(
name = name.strip(),
ra = loc.ra.format(u.hour, sep=" ", pad=True),
dec = loc.dec.format(sep=" ", alwayssign=True),
epoch = loc.equinox.jyear,
keywords = " ".join(comments),
))
starlist.write("\n")
else:
self.log.warning("Region {!r} not parsed. It is not a circle.".format(region))
def get_rms_map2(infilename,ds9region,outfilename):
runcommand = "MakeMask.py --RestoredIm=%s --OutName=rmsmapmask --Th=%s --Box=50,2 --OutNameNoiseMap=%s.noise"%(infilename,3.0,infilename)
run(runcommand,log=None)
infilename = '%s.noise.fits'%infilename
polylist = convert_regionfile_to_poly(ds9region)
hdu=fits.open(infilename)
hduflat = flatten(hdu)
map=hdu[0].data
for direction,ds9region in enumerate(polylist):
print direction,ds9region
r = pyregion.parse(ds9region)
manualmask = r.get_mask(hdu=hduflat)
rmsval = np.mean(hdu[0].data[0][0][np.where(manualmask == True)])
hdu[0].data[0][0][np.where(manualmask == True)] = rmsval
print 'RMS = %s for direction %i'%(rmsval,direction)
hdu.writeto(outfilename,clobber=True)
def filter_events(self, region):
"""
Filter events using a ds9 *region*. Requires the `pyregion <http://pyregion.readthedocs.org/en/latest/>`_ package.
Returns a new :class:`~pyxsim.event_list.EventList`.
"""
import pyregion
import os
if os.path.exists(region):
reg = pyregion.open(region)
else:
reg = pyregion.parse(region)
r = reg.as_imagecoord(header=self.wcs.to_header())
f = r.get_filter()
idxs = f.inside_x_y(self["xpix"], self["ypix"])
if idxs.sum() == 0:
raise RuntimeError("No events are inside this region!")
new_events = {}
for k, v in self.items():
new_events[k] = v[idxs]
return EventList(new_events, self.parameters)
def _sum_flux(self, ds9_string, data, header):
'''
Adds the flux from each pixel within the region in order to get the total flux for the region.
:param ds9_string: The information for a region in a string format
:param data: The data array of the visit file to be examined
:param header: The header of the visit file to be examined
:return sum_flux: The total flux for this region
'''
reg_definition = 'icrs;%s' % ds9_string.replace(" ", "")
reg = pyregion.parse(reg_definition).as_imagecoord(header)
filt = reg.get_filter()
mask = filt.mask(data)
sum_flux = np.sum(data[mask])
return sum_flux
def __init__(self, x, y, r, ds9_string=None):
'''
Constructs a CircularRegion object.
:param x: The horizontal location of the center of the region
:param y: The vertical location of the center of the region
:param r: Radius
of the region if it is a circle (in pixels)
'''
self._x = x
self._y = y
self._radius_pixels = r
self._mask = None
self._ds9_string = ds9_string
if self._ds9_string is not None:
self._pyregion = pyregion.parse(self._ds9_string)
def make_predict_mask(infilename, ds9region, outfilename, npix_out=10000):
"""
Make mask that is `infilename` everywhere except in regions specified by `ds9region` which is zero.
:param infilename:
:param ds9region:
:param outfilename:
:return:
"""
logger.info('Making: {}'.format(outfilename))
npix_in = getimsize(infilename)
s = "image;box({},{},{},{},0)".format(npix_in // 2, npix_in // 2, npix_out, npix_out)
with open(ds9region, 'w') as f:
f.write(s)
with fits.open(infilename) as hdu:
hduflat = flatten(hdu)
r = pyregion.parse(s)
manualmask = r.get_mask(hdu=hduflat)
hdu[0].data[0][0][np.where(manualmask == True)] = 0.0
hdu.writeto(outfilename, overwrite=True)
if not os.path.isfile(outfilename):
raise IOError("Did not successfully create {}".format(outfilename))
def pix_val_in_reg_old(reg_ds9, coord, fits_data, fits_head):
"""
get the pixel values in the region reg_ds9
:param reg_ds9: ds9 region in the format of e.g., 'ellipse(a, b, ...', no header
:param coord: the coordinate system of the region, e.g. 'galactic', 'icrs', etc
:param fits_img: the fits image
:return: an numpy array containing the values of pixels within the region
"""
n_pix_y, n_pix_x = fits_data.shape
# create a filter using the region
r = pyregion.parse(coord+';' + reg_ds9).as_imagecoord(fits_head)
reg_filter = r.get_filter()
# read the numbers in the region
splits = reg_ds9.split(',')
# degree; the Galactic longitude of the center if the region is ellipse, circle or box;
# one point if the region is polygon
glon = float(splits[0].split('(')[1])
glat = float(splits[1]) # degree
l_box = 1.5 # degree, to make sure all pix in a region are included
# create a box to search pix in the region
w = wcs.WCS(fits_head)
bottom_left = w.wcs_world2pix(glon + l_box, glat - l_box, 1)
up_right = w.wcs_world2pix(glon - l_box, glat + l_box, 1)
# get the mean; avoid the pix number out of bounds
i_min = int(max(bottom_left[0], 0))
i_max = int(min(up_right[0], n_pix_x))
j_min = int(max(bottom_left[1], 0))
j_max = int(min(up_right[1], n_pix_y))
pix_val = []
for i in range(i_min, i_max):
for j in range(j_min, j_max):
if reg_filter.inside1(i, j):
pix_val.append(fits_data[j, i])
return np.asarray(pix_val)
def beam_ellipse(ra, dec, image):
"""
Return pyregion ellpse regon coresponding to the image beam at ra, dec
Parameters
----------
ra: float, ra in degrees
dec: float, dec in degrees
image: obj, lib_fits.Image
Returns
-------
beam_ellipse: obj, pyregion region
"""
b = image.get_beam()
ra = Angle(str(ra) + 'd', unit=u.deg).to_string(sep=':', unit=u.hour)
dec = Angle(dec, unit=u.deg).to_string(sep=':')
fact = 1 / np.sqrt(
4 * np.log(2)
) # this factor is needed to make the pixels in the beam area match the beam_area from the lib_fits function. Not sure why...
ell_str = f"fk5;ellipse({ra}, {dec}, {b[0]*3600*fact}\", {b[1]*3600*fact}\", {b[2]})"
reg_obj = pyregion.parse(ell_str)
return reg_obj
except IOError as e:
print 'FATAL ERROR: ',e
sys.exit()
try:
rm=radiomap(f,verbose=True)
except RadioError as e:
print 'FATAL ERROR: ',e
sys.exit()
#print 'Frequency is %g Hz' % rm.frq
# now apply regions
if b_region:
bg_ir=pyregion.parse(b_region).as_imagecoord(rm.headers[0])
bg=applyregion(rm,bg_ir)
print 'Pixels in background region',bg.pixels
for i in range(rm.nchans):
print '%8.4g Hz Background rms is %f Jy/beam' % (rm.frq[i],bg.rms[i])
print ' Background mean is',bg.mean[i],'Jy/beam'
noise=bg.rms
else:
if bgsub:
raise Error('Background subtraction requested but no bg region')
noise=None
fg_ir=pyregion.parse(f_region).as_imagecoord(rm.headers[0])
if bgsub:
fg=applyregion(rm,fg_ir,offsource=noise,background=bg.mean)
else:
def cross_match(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', YSO_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat', high_res_coldens_image = '/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', proto_core_indices='L1157_matched_protostellar_cores.dat'):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07 = numpy.loadtxt(YSO_catalog,comments='!', unpack=True)
good_proto_indices = good_protostars_getsources.get_good_protostars(YSO_catalog)
#print len(good_proto_indices)
### Loop through entire core catalog to find cores that are close to YSOs.
### This step significantly reduces the time it would take to run through
### entire catalog.
potential_matches = []
count = 0
for line in cores_array:
match_counter=0
for i in good_proto_indices:
distance = ((line[3]-WCS_ACOOR[i])**2 + (line[4]-WCS_DCOOR[i])**2)**0.5
if distance < 200.0/3600. and match_counter==0:
# matched_counter prevents counting indices twice
# if two YSO candidates fall within getsources ellipse
potential_matches.append(count)
match_counter+=1
count += 1
#print len(potential_matches)
### Loop through the potential matched cores identified in the step above.
matched_cores = []
matched_cores_proto_index = []
for value in potential_matches:
line = cores_array[value]
x_coor = str(line[3])
y_coor = str(line[4])
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;ellipse(' + x_coor + ', ' + y_coor + ', ' + str((line[50]/2.0)/3600.) + ', ' +
str((line[51]/2.0)/3600.) + ', ' + str(line[52]+90.0)+')')
r = pyregion.parse(region)
f=fits.open(high_res_coldens_image)
mymask = r.get_mask(hdu=f[0])
f.close()
newmask=mymask
### Set all values outside the core's ellipse to zero,
### all valus inside the ellipse are set to one.
newmask=numpy.where(newmask==0,0,1)
### Loop through the 70 micron point sources
### If any fall within a core's ellipse, store that core's index
match_counter=0
good_protostar_index = 0
for i in good_proto_indices:
if newmask[int(round(YCO_P[i],0))-1][int(round(XCO_P[i],0))-1]!=0 and match_counter==0:
matched_cores.append(value)
matched_cores_proto_index.append(good_protostar_index)
# matched_counter prevents counting indices twice
# if two YSO candidates fall within getsources ellipse
match_counter+=1
good_protostar_index+=1
else:
good_protostar_index+=1
#print len(matched_cores)
#print matched_cores
#print matched_cores_proto_index
### Save the protostellar core indices to a file
#numpy.savetxt(proto_core_indices, matched_cores, fmt='%i')
# Return indices of matched cores and protostars from the "good" lists of each
# i.e., The indices are the index from the "good" lists and not the unprocessed original, total getsources list
return numpy.column_stack((numpy.array(matched_cores), numpy.array(matched_cores_proto_index)))
import pyregion
import webbrowser
import numpy as np
xpa = sys.argv[1]
dd = ds9.ds9(xpa)
if len(sys.argv) > 2:
regionid = int(sys.argv[2])
else:
# use the most recent region
regionid = -1
rstringlist = dd.get('regions -system wcs -prop select 1')
regions = pyregion.parse(rstringlist)
if len(regions) == 0:
sys.exit("No regions found")
reg = regions[regionid]
coord_fmt = 'Gal' if reg.coord_format == 'galactic' else 'fk5'
coord = reg.coord_list[:2]
url = "http://simbad.u-strasbg.fr/simbad/sim-coo?CooFrame={frame}&Coord={coord}".format(frame=coord_fmt,
coord='{0} {1}'.format(*coord))
if reg.name == 'circle':
radius = reg.coord_list[2]
url += "&Radius={radius}&Radius.unit=deg".format(radius=radius)
format='ascii.ipac')
#dendromask = fits.open('dendrograms_min1mJy_diff1mJy_mask_pruned.fits')
for spw in (0,1,2,3):
cube = SpectralCube.read(tmplt.format(spw))
print(cube)
#mask_reproj = FITS_tools.hcongrid.hastrom(dendromask[0].data.astype('float'),
# dendromask[0].header,
# FITS_tools.strip_headers.flatten_header(cube.header))
#rmask = np.round(mask_reproj.data).astype('int')
for row in pruned_ppcat:
name = row['_idx']
print("Extracting {0} from {1}".format(name, spw))
SL = pyregion.parse("fk5; circle({0},{1},0.5\")"
.format(row['x_cen'], row['y_cen']))
#mask = rmask == name
#dend_inds = np.where(mask)
#view = (slice(None), # all spectral channels
# slice(dend_inds[0].min(), dend_inds[0].max()+1),
# slice(dend_inds[1].min(), dend_inds[1].max()+1),
# )
#sc = cube[view].with_mask(mask[view[1:]])
sc = cube.subcube_from_ds9region(SL)
spec = sc.mean(axis=(1,2))
spec.meta['beam'] = radio_beam.Beam(major=np.nanmedian([bm.major.to(u.deg).value for bm in spec.beams]),
minor=np.nanmedian([bm.minor.to(u.deg).value for bm in spec.beams]),
pa=np.nanmedian([bm.pa.to(u.deg).value for bm in spec.beams]),
)
ss = rp.parse(s)
sss1 = rp.convert_attr(ss)
sss2 = _check_wcs(sss1)
sss3 = rp.sky_to_image(sss2, header, rot_wrt_axis=rot_wrt_axis)
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 pyregion.region_to_filter import as_region_filter
data = hdu.data
region_filter = as_region_filter(region, origin=origin)
mask = region_filter.mask(data)
return mask
if __name__ == '__main__':
#reg = pyregion.open("../mos_fov.reg")
import pyregion
proposed_fov = 'fk5;circle(290.96388,14.019167,843.31194")'
reg = pyregion.parse(proposed_fov)
reg_imagecoord = reg.as_imagecoord(header)
patches, txts = reg.get_mpl_patches_texts()
m = reg.get_mask(hdu=f[0])
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)
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), clobber=True)
return mask
def auto_zoomed_cores(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', YSO_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat', core_figure_directory = '/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/core_figures/', Herschel_70um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cepL1157_070_mu_emission.image.resamp.fits', Herschel_160um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cepL1157_160_mu_emission.image.resamp.fits', Herschel_250um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_250_mu.image.resamp.fits', Herschel_350um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_350_mu.image.resamp.fits', Herschel_500um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_500_mu.image.resamp.fits', Herschel_coldens_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', distance=325.):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
core_center_RA = cores_array[:,3]
core_center_Dec = cores_array[:,4]
core_index = 1
for line in cores_array:
x_coor = str(line[3])
y_coor = str(line[4])
AFWHM_array = [line[14], line[23], line[41], line[59], line[68], line[50]] # 70,160,250,350,500,coldens
BFWHM_array = [line[15], line[24], line[42], line[60], line[69], line[51]]
Theta_array = [line[16], line[25], line[43], line[61], line[70], line[52]]
SIG_array = [line[8], line[17], line[35], line[53], line[62], line[44]]
images_array = [Herschel_70um_image, Herschel_160um_image, Herschel_250um_image,
Herschel_350um_image, Herschel_500um_image, Herschel_coldens_image]
wavelengths = ['070', '160', '250', '350', '500', '255']
maximums = numpy.zeros(len(AFWHM_array))
minimums = numpy.zeros(len(AFWHM_array))
counter = 0
for i in wavelengths:
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;box(' + x_coor + ', ' + y_coor + ', ' + str(0.05) + ', ' +
str(0.05) + ', ' + str(0.0)+')')
r = pyregion.parse(region)
f = fits.open(images_array[counter])
header_primary = fits.getheader(images_array[counter])
data = fits.getdata(images_array[counter])
mymask = r.get_mask(hdu=f[0])
newmask=numpy.where(mymask!=0)
maximums[counter] = max(data[newmask])
minimums[counter] = min(data[newmask])
f.close()
newmask2=numpy.where(mymask==0, 0, data)
fits.writeto(i + '_contour_mask.fits', newmask2, header_primary, clobber=True)
counter+=1
microns = ['mu_070', 'mu_160', 'mu_250', 'mu_350', 'mu_500', 'mu_255']
v_max = maximums
v_min = minimums
contour_levels = [(maximums[0]*0.3,maximums[0]*0.5,maximums[0]*0.7,maximums[0]*0.9),
(maximums[1]*0.3,maximums[1]*0.5,maximums[1]*0.7,maximums[1]*0.9),
(maximums[2]*0.3,maximums[2]*0.5,maximums[2]*0.7,maximums[2]*0.9),
(maximums[3]*0.3,maximums[3]*0.5,maximums[3]*0.7,maximums[3]*0.9),
(maximums[4]*0.3,maximums[4]*0.5,maximums[4]*0.7,maximums[4]*0.9),
(maximums[5]*0.3,maximums[5]*0.5,maximums[5]*0.7,maximums[5]*0.9)]
fig = plt.figure(figsize=(8,5.5))
f=0
for i in wavelengths:
microns[f]=aplpy.FITSFigure(i + '_contour_mask.fits', figure=fig, subplot=(2,3,f+1))
microns[f].recenter(line[3],line[4],0.025)
microns[f].show_contour(i + '_contour_mask.fits', colors=('white', 'white', 'white','grey'), levels=contour_levels[f], overlap=True)
microns[f].show_colorscale(cmap='gist_stern', vmin=v_min[f], vmax=v_max[f])
#microns[f].show_regions('/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/L1157_core_SED/255_all_cores_good.reg')
microns[f].add_colorbar()
if f==2 or f==5:
microns[f].colorbar.set_axis_label_text('MJy/sr')
scale = str('{:.2f}'.format(2*(distance*numpy.tan(numpy.radians(0.5*(1.0/60.))))))
if f==0:
microns[f].add_scalebar((1./60.), path_effects=[Patheffects.withStroke(linewidth=3, foreground='white')])
microns[f].scalebar.show(1./60.) # length in degrees (one arcminute)
microns[f].scalebar.set_corner('top left')
microns[f].scalebar.set_label('1' + "'" + ' = ' + scale + ' pc')
if SIG_array[f]>5.:
line_type = 'solid'
else:
line_type = 'dashed'
microns[f].show_ellipses(line[3], line[4], AFWHM_array[f]/3600., BFWHM_array[f]/3600., Theta_array[f]+90., color='#00FF00', zorder=10, linewidth=1.0, linestyle=line_type)
#microns[f].show_markers(line[3], line[4], c='#00FF00', marker='x', zorder=10, linewidths=1.5,s=20)
#microns[f].show_markers(line[3], line[4], c='black', marker='x', zorder=9, linewidths=2.0,s=30)
microns[f].tick_labels.hide()
microns[f].axis_labels.hide()
#microns[f].set_title('L' + name)
if i=='255':
microns[f].add_label(0.3,0.1,'N$_{H2}$',relative=True, path_effects=[Patheffects.withStroke(linewidth=3, foreground='white')])
else:
microns[f].add_label(0.3,0.1,i + ' $\mu$' + 'm',relative=True, path_effects=[Patheffects.withStroke(linewidth=3, foreground='white')])
f=f+1
fig.subplots_adjust(hspace=0.05, wspace=0.35)
fig.canvas.draw()
fig.suptitle('Core Number ' + str(core_index) + ' - RA: ' + str(line[3]) + ' Dec: ' + str(line[4]))
fig.savefig(core_figure_directory + 'core' +str(core_index) + '.pdf')
print 'Core Number = ' + str(core_index)
for i in range(6):
microns[i].close()
core_index = core_index + 1
import pyregion from astropy import units as u from astropy.coordinates import SkyCoord from photutils import CircularAperture from photutils import aperture_photometry fits = sys.argv[1] reg = sys.argv[2] rtn_type = sys.argv[3] hdu = pyfits.open(fits) data = hdu[0].data hdr = hdu[0].header r = pyregion.parse(reg)[0] r_name = r.name r_format = r.coord_format r_coordlist = r.coord_list x_px = r_coordlist[0]-1 # ds9 region is one-indexed y_px = r_coordlist[1]-1 # ds9 region is one-indexed pixscale = hdr['CDELT1'] ap = [] radii = [] last_ap_flux = 0 for i, radius in enumerate(r_coordlist[3:]): apertures = CircularAperture([(x_px, y_px)], r=radius) phot_table = aperture_photometry(data, apertures) this_ap_flux = float(phot_table['aperture_sum'][0])
from astropy.wcs import WCS
from astropy.wcs.utils import pixel_to_skycoord, skycoord_to_pixel
import pyregion
try:
knot_region_file = sys.argv[1]
line_id = sys.argv[2]
region_frame = sys.argv[3]
except IndexError:
sys.exit("Usage: {} KNOT_REGION_FILE (ha|nii) (linear|image)".format(sys.argv[0]))
with open(knot_region_file) as f:
knot_region_string = f.read()
# Workaround for bug in pyregion.parse when color is of form '#fff'
knot_region_string = knot_region_string.replace("color=#", "color=")
knot_regions = pyregion.parse(knot_region_string)
knot_dict = {
knot.attr[1]["text"]: pyregion.ShapeList([knot])
for knot in knot_regions
if "text" in knot.attr[1] and knot.name == "ellipse"
}
tab = Table.read("alba-knots-frompdf.tab", format="ascii", delimiter="\t")
vcol = {"ha": "V(Ha)", "nii": "V([N II])"}
wcol = {"ha": "W(Ha)", "nii": "W([N II])"}
imhdu = fits.open("new-slits-{}-allvels.fits".format(line_id))["scaled"]
imwcs = WCS(imhdu.header)
ny, nx = imhdu.data.shape
import matplotlib.pyplot as plt import pyregion region = """ image circle(100, 100, 80) box(200, 150, 150, 120, 0) """ r = pyregion.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()
