def search_trails(self):
"""Search star trails in image"""
threshold = detect_threshold(self.data, snr=1)
sigma = 2.0 * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
self.segments = detect_sources(self.data, threshold, npixels=1000, filter_kernel=kernel)
self.segments_properties = segment_properties(self.data, self.segments)
def source_detection_individual(self, psfFWHM, nsigma=3.0, sc_key=''):
'''
Parameters
----------
psfFWHM : float
FWHM of the imaging point spread function
nsigma : float
source detection threshold
'''
data = np.array(self.data.copy())
psfFWHMpix = psfFWHM / self.pixel_scales[0].value
thresholder = detect_threshold(data, nsigma=nsigma)
sigma = psfFWHMpix * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=5, y_size=5)
kernel.normalize()
segm = detect_sources(data,
thresholder,
npixels=5,
filter_kernel=kernel)
props = source_properties(data, segm)
tab = Table(props.to_table())
self.sources_catalog = tab
srcPstradec = self.data.wcs.all_pix2world(tab['xcentroid'],
tab['ycentroid'], 1)
sc = SkyCoord(srcPstradec[0], srcPstradec[1], unit='deg')
sctab = Table([sc, np.arange(len(sc))],
names=['sc', 'sloop_{0}'.format(sc_key)])
self.sources_skycord = sctab
def getsky (data,psfFWHMpix,snr=3.0,dtype=bool) :
threshold = detect_threshold(data, snr=snr)
sigma = psfFWHMpix * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(data, threshold, npixels=5, filter_kernel=kernel)
ny, nx =data.shape
if(dtype == bool):
mask = np.zeros_like(data, dtype=bool)
mask[np.isnan(data)] = True
mask[np.isinf(data)] = True
if (dtype == int) :
mask = np.zeros_like(data, dtype=int)
mask[np.isnan(data)] = 1
mask[np.isinf(data)] = 1
for loopx in range(nx) :
for loopy in range(ny):
if segm.data[loopy][loopx] > 0:
if(dtype == bool):
mask[loopy][loopx]=True
if (dtype == int) :
mask[loopy][loopx]=1
result = {
'mask':mask,
'segmantation':segm
}
return result
def ImageSlice(imgname):
hdu = fits.open(imgname)
img = hdu[0].data
threshold = detect_threshold(img, snr=4.)
#Detection for every sources to find the position of the galaxy.
sigma = 2. * gaussian_fwhm_to_sigma # FWHM = 2.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(img, threshold, npixels=5, filter_kernel=kernel)
segm_areas = segm.areas
segm_areas = np.delete(segm.areas, 0) # deleting background area.
label_gal = segm_areas.argmax() # finding the galaxy by searching
# a source which has largest area.
rslice = segm.slices[label_gal][0] # row slice
cslice = segm.slices[label_gal][1] # column slice
ri = rslice.start
rf = rslice.stop
ci = cslice.start
cf = cslice.stop
b = 0 # expanding constant
s_img = hdu[0].data[ri - b:rf + b, ci - b:cf + b]
return s_img
def get_segmentation_map(
image: torch.Tensor,
npixels=5, ## minimum number of connectied pixels
get_mask=False,
) -> torch.Tensor:
""" compute smoothed segmentation map marking the part of the image that contains the main source
optional: when get_mask=True, also provide a mask map of all additional saurces
"""
# get segmap from red colorband
img = image[0]
# create segmentation map
threshold = detect_threshold(img, 1.5)
segm = detect_sources(img, threshold, npixels)
# Keep only the largest segment
# ## !!! change this to take the central segment
main_label = np.argmax(segm.areas) + 1
segmap = segm.data == main_label
# regularize shape
segmap_float = ndi.uniform_filter(np.float64(segmap), size=10)
segmap = segmap_float > 0.5
if not get_mask:
return segmap
else:
# mask additional objects
mask = np.zeros_like(img).astype("bool")
for obj_label in range(1, len(segm.areas) + 1):
if obj_label == main_label:
continue
mask[segm.data == obj_label] = True
return segmap, mask
def _segmap_base(data,
numpix,
mask=None,
nsigma=2,
contrast=0.4,
nlevels=5,
kernel=None):
"""Returns a generic segmentation map of the input image
INPUTS:
data: image data
numpix: minimum region size in pixels (default: 10)
mask: mask for the image (default: None)
snr: signal-to-noise threshold for detecting objects (default: 2)
contrast: contrast ratio used in deblending (default: 0.4)
nlevels: number of lebels to split image to when deblending (default: 5)
kernel: kernel to use for image smoothing (default: None)
"""
# Convert mask to boolean
if mask is not None and (mask.dtype != "bool"):
mask = np.array(mask, dtype="bool")
threshold = phot.detect_threshold(data, nsigma=nsigma, mask=mask)
segmap = phot.detect_sources(data,
threshold,
numpix,
filter_kernel=kernel,
mask=mask)
segmap = phot.deblend_sources(data,
segmap,
npixels=numpix,
filter_kernel=kernel,
nlevels=nlevels,
contrast=contrast)
return segmap
def get_wcs(pattern):
for filename in pattern:
which_hdu = choose_hdu(filename)
header = fits.getheader(filename, which_hdu)
print(header)
w = WCS(header)
print(w)
data = fits.getdata(filename, ext=0)
threshold = detect_threshold(data, nsigma=2.)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
mean, median, std = sigma_clipped_stats(data, sigma=3)
daofind = DAOStarFinder(fwhm=3.0, threshold=5. * std)
sources = daofind(data - median)
for col in sources.colnames:
sources[col].info.format = '%.8g' # for consistent table output
x_unscaled = np.array(sources['xcentroid'])
y_unscaled = np.array(sources['ycentroid'])
x_min = min(x_unscaled)
y_min = min(y_unscaled)
x_max = max(x_unscaled)
y_max = max(y_unscaled)
x1 = ((x_unscaled - x_min) / x_max) * 90
y1 = ((y_unscaled - y_min) / y_max) * 90
fig, ax = plt.subplots()
ax.plot(x1, y1, 'ok', ms=5)
plt.show()
c1 = SkyCoord(ra=x1 * u.degree, dec=y1 * u.degree)
print(c1)
coo = SkyCoord.from_name('GJ3470')
def segmentation_map(image, extname, get_kernel=False):
# in this context image means a 2D numpy array rather than a GFA_image
# object
par = common.gfa_misc_params()
fwhm_pix = par['nominal_fwhm_asec'] / \
util.nominal_pixel_sidelen_arith()
threshold = detect_threshold(image, snr=2.0)
sigma = fwhm_pix * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma,
x_size=int(np.round(fwhm_pix)),
y_size=int(np.round(fwhm_pix)))
kernel.normalize()
segm = detect_sources(image, threshold, npixels=5, filter_kernel=kernel)
# add my own dilation of segm.array ?
# incorporate masking based on master flat/bias in this analysis ?
if not get_kernel:
return segm
else:
return segm, kernel
def find_all_sources(
image: CCDData,
snr: float = 3., # Threshold SNR for segmentation
fwhm: float = 5., # Kernel FWHM for segmentation
ksize: int = 5, # Kernel size
npixels:
int = 10 # Number of connected pixels required to be considered a source
):
"""
Find extended sources in image with default parameters tuned for expected donut size.
"""
binning = image.header['BINNING']
fwhm = int(fwhm / binning)
ksize = int(ksize / binning)
npixels = int(npixels / binning)
threshold = photutils.detect_threshold(image, nsigma=snr)
sigma = fwhm * stats.gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=ksize, y_size=ksize)
kernel.normalize()
segm = photutils.detect_sources(image.data,
threshold,
npixels=npixels,
filter_kernel=kernel)
cat = photutils.source_properties(image.data, segm, wcs=image.wcs)
return segm, cat
def snr(hduls, name="SCI"):
for hdul in hduls:
data = hdul[name].data
# identify background rms
boxsize=(data.shape)
bkg = Background2D(data, boxsize)
bkg_mean_rms = np.mean(bkg.background_rms)
# subtract bkg from image
new_data = data - bkg.background
# set threshold and detect sources, threshold 5*std above background
threshold = detect_threshold(data=new_data, nsigma=5.0, background=0.0)
SegmentationImage = detect_sources(data=new_data, threshold=threshold, npixels=10)
SourceCatalog = source_properties(new_data, SegmentationImage)
columns = ['id', 'xcentroid', 'ycentroid', 'source_sum']
source_max_values = SourceCatalog.max_value
avg_source_max_values = np.mean(source_max_values)
# calculate signal to noise ratio
signal = avg_source_max_values
noise = bkg_mean_rms
SNR = (signal)/(noise)
hdul["CAT"].header.append(('SNR',SNR,"signal to noise ratio" ))
return (hdul for hdul in hduls)
def make_segmap(filename):
"""
Generates a source segmentation map of the input file.
Parameters
----------
filename : str
The file to make segmentation map for.
Outputs
-------
{filename}_seg.fits
The segmentation map.
"""
# See if segmap already exists
outfile = filename.replace('.fits', '_seg.fits')
# Get the data
data = fits.getdata(filename, 'SCI')
# Detector sources; Make segmap
threshold = detect_threshold(data, 3.0)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(data, threshold, npixels=3, filter_kernel=kernel)
fits.writeto(outfile, segm.data, overwrite=True)
return outfile
def get_wcs(pattern):
for filename in pattern:
which_hdu = choose_hdu(filename)
header = fits.getheader(filename, which_hdu)
data = fits.getdata(filename, ext=0)
threshold = detect_threshold(data, nsigma=2.)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
mean, median, std = sigma_clipped_stats(data, sigma=3)
daofind = DAOStarFinder(fwhm=3.0, threshold=5. * std)
sources = daofind(data - median)
for col in sources.colnames:
sources[col].info.format = '%.8g' # for consistent table output
x = np.array(sources['xcentroid'])
y = np.array(sources['ycentroid'])
OB = header['OBJECT']
cobj = SkyCoord.from_name(OB, parse=True)
cobjx = cobj.ra.degree
cobjy = cobj.dec.degree
refx = x[1]
refy = y[1]
Bin = float(header['CCDSUM'][0])
Res = 0.25 #arcsec/px
f_arcsec = Bin * Res #arcsec/px
f = f_arcsec / 3600
for n in x:
dx = x - refx
xscal = cobjx + (f * dx)
for n in y:
dy = y - refy
yscal = cobjy + (f * dy)
fig1, ax = plt.subplots()
c1 = SkyCoord(ra=xscal * u.degree,
dec=yscal * u.degree) # ra and dec of each source
ax.plot(xscal, yscal, 'ok', ms=5)
result = Vizier.query_object(OB)
interesting_table = result['I/305/out']
x1 = np.array(interesting_table['RAJ2000'])
y1 = np.array(interesting_table['DEJ2000'])
c2 = SkyCoord(ra=x1 * u.degree, dec=y1 * u.degree)
fig2, ax = plt.subplots()
ax.plot(x1, y1, 'or', mfc='none', ms=10)
idx, d2d, d3d = c1.match_to_catalog_sky(c2)
fig3, ax = plt.subplots()
ax.plot(xscal, yscal, 'ok', ms=5)
ax.plot(x1, y1, 'or', mfc='none', ms=10)
plt.show()
dx, d2d, d3d = c1.match_to_catalog_sky(c2)
def make_segmentation_image(data,
fwhm=2.0,
snr=5.0,
x_size=5,
y_size=5,
npixels=7,
nlevels=32,
contrast=0.001,
deblend=True):
"""
Use photutils to create a segmentation image containing detected sources.
data : 2D `~numpy.ndarray`
Image to segment into sources.
fwhm : float (default: 2.0)
FWHM of the kernel used to filter the image.
snr : float (default: 5.0)
Source S/N used to set detection threshold.
x_size : int (default: 5)
X size of the 2D `~astropy.convolution.Gaussian2DKernel` filter.
y_size : int (default: 5)
Y size of the 2D `~astropy.convolution.Gaussian2DKernel` filter.
npixels : int (default: 7)
Number of connected pixels required to be considered a source.
nlevels : int (default: 32)
Number of multi-thresholding levels to use when deblending sources.
contrast : float (default: 0.001)
Fraction of the total blended flux that a local peak must have to be considered a separate object.
deblend : bool (default: True)
If true, deblend sources after creating segmentation image.
"""
sigma = fwhm * stats.gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=x_size, y_size=y_size)
kernel.normalize()
threshold = photutils.detect_threshold(data, nsigma=snr)
segm = photutils.detect_sources(data,
threshold,
npixels=npixels,
filter_kernel=kernel)
if deblend:
segm = photutils.deblend_sources(data,
segm,
npixels=npixels,
filter_kernel=kernel,
nlevels=nlevels,
contrast=contrast)
return segm
def make_tweakreg_catalog(model, kernel_fwhm, snr_threshold, sharplo=0.2,
sharphi=1.0, roundlo=-1.0, roundhi=1.0):
"""
Create a catalog of point-line sources to be used for image
alignment in tweakreg.
Parameters
----------
model : `ImageModel`
The input `ImageModel` of a single image. The input image is
assumed to be background subtracted.
kernel_fwhm : float
The full-width at half-maximum (FWHM) of the 2D Gaussian kernel
used to filter the image before thresholding. Filtering the
image will smooth the noise and maximize detectability of
objects with a shape similar to the kernel.
snr_threshold : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
sharplo : float, optional
The lower bound on sharpness for object detection.
sharphi : float, optional
The upper bound on sharpness for object detection.
roundlo : float, optional
The lower bound on roundess for object detection.
roundhi : float, optional
The upper bound on roundess for object detection.
Returns
-------
catalog : `~astropy.Table`
An astropy Table containing the source catalog.
"""
if not isinstance(model, ImageModel):
raise ValueError('The input model must be a ImageModel.')
# threshold = snr_threshold * model.err # can't input img to daofind
threshold_img = photutils.detect_threshold(model.data, snr=snr_threshold)
threshold = threshold_img[0, 0] # constant image
sources = photutils.daofind(model.data, fwhm=kernel_fwhm,
threshold=threshold, sharplo=sharplo,
sharphi=sharphi, roundlo=roundlo,
roundhi=roundhi)
columns = ['id', 'xcentroid', 'ycentroid', 'flux']
catalog = sources[columns]
return catalog
def maskstarsSEG(image: np.ndarray) -> np.ndarray:
""" 'Cleans' image of external sources.
For example will remove all stars that do not interfere with the object of interest.
Parameters
----------
image : np.ndarray
Image to be cleaned.
Returns
-------
imageClean : np.ndarray
Image cleaned of external sources.
"""
cenpix = np.array([int(image.shape[0]/2) + 1, int(image.shape[1]/2) + 1])
mean, median, std = sigma_clipped_stats(image, sigma=3.)
# create segmentation map
# TODO give user option to specify kernel, size of kernel and threshold?
imageClean = np.copy(image)
threshold = detect_threshold(image, 1.5)
sigma = 3.0 * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(image, threshold, npixels=8, filter_kernel=kernel)
# if no sources return
if segm is None:
return imageClean
# Save potions of segmentation map outwith object of interest
stars = []
for i, segment in enumerate(segm.segments):
if not _inBbox(segment.bbox.extent, cenpix):
stars.append(i)
# clean image of external sources
for i in stars:
masked = segm.segments[i].data_ma
masked = np.where(masked > 0., 1., 0.) * np.random.normal(mean, std, size=segm.segments[i].data.shape)
imageClean[segm.segments[i].bbox.slices] = masked
imageClean = np.where(imageClean == 0, image, imageClean)
return imageClean
def find_center_segment(self, sigma_level, kernel=None, min_pixels=5):
"""
This function ...
:param sigma_level:
:param kernel:
:param min_pixels:
:return:
"""
# If a subtracted box is present, use it to find the center segment
box = self.subtracted if self.has_background else self.cutout
if not np.all(self.mask):
mean, median, stddev = statistics.sigma_clipped_statistics(
box, mask=self.mask)
threshold = mean + stddev * sigma_level
else:
threshold = detect_threshold(box, snr=2.0) #snr=2.0
# Perform the segmentation
segments = detect_sources(box,
threshold,
npixels=min_pixels,
filter_kernel=kernel).data
# To plot the multiple segments that are detected
#if segments.max() > 1: plotting.plot_box(np.ma.masked_array(box, mask=segments.astype(bool)))
# Get the label of the center segment
rel_center = self.cutout.rel_position(self.center)
try:
label = segments[int(round(rel_center.y)),
int(round(rel_center.x))]
except:
try:
label = segments[int(rel_center.y), int(rel_center.x)]
except:
return Mask((segments == label))
# If the center pixel is identified as being part of the background, create an empty mask (the center does not
# correspond to a segment)
if label == 0:
return Mask(np.zeros_like(self.cutout, dtype=bool))
# Create a mask of the center segment
else:
return Mask((segments == label))
def create_seg_map(self):
'''
Creates segmentation map, from original FLT file, that is used in
background subtraction and to fix cosmic rays.
Parameters
----------
self : object
DashData object created from an individual IMA file.
Output
------
Segmentation Image : fits file
Segmentation map
Source List : .dat file
List of sources and their properties
'''
flt = fits.open(self.flt_file_name)
data = flt[1].data
threshold = detect_threshold(data, nsigma=3.)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(data,
threshold,
npixels=10,
filter_kernel=kernel)
hdu = fits.PrimaryHDU(segm.data)
if not os.path.exists('segmentation_maps'):
os.mkdir('segmentation_maps')
hdu.writeto(('segmentation_maps/{}_seg.fits').format(self.root),
overwrite=True)
# Create source list
cat = source_properties(data, segm)
tbl = cat.to_table()
tbl['xcentroid'].info.format = '.2f'
tbl['ycentroid'].info.format = '.2f'
tbl['cxx'].info.format = '.2f'
tbl['cxy'].info.format = '.2f'
tbl['cyy'].info.format = '.2f'
ascii.write(tbl,
'segmentation_maps/{}_source_list.dat'.format(self.root))
def detect_sources(pattern):# extracts the light sources from the image (basing on sigma, FWHM, thrsholds...)
rot = rotate_img(pattern)
threshold = detect_threshold(rot, nsigma=2.)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
mean, median, std = sigma_clipped_stats(rot, sigma=3)
daofind = DAOStarFinder(fwhm=3.0, threshold=5.*std)
sources = daofind(rot - median)
for col in sources.colnames:
sources[col].info.format = '%.8g' # for consistent table output
# Pixel coordinates of the sources
x1 = np.array(sources['xcentroid'])
y1 = np.array(sources['ycentroid'])
return(x1,y1)
def make_segments(image, npixels=None, nsigma=3., fwhm=8., kernel_size=4):
"""
Segment an image.
Parameters
----------
image : array like
Input image
npixels : int
The number of connected pixels, each greater than ``threshold``,
that an object must have to be detected. ``npixels`` must be a
positive integer.
nsigma : float or image array
The number of standard deviations per pixel above the
``background`` for which to consider a pixel as possibly being
part of a source.
fwhm : float
FWHM of smoothing gaussian kernel
kernel_size : int
Size of smoothing kernel
Returns
-------
segment_image : `~photutils.segmentation.SegmentationImage` or `None`
A 2D segmentation image, with the same shape as ``data``, where
sources are marked by different positive integer values. A
value of zero is reserved for the background. If no sources
are found then `None` is returned.
"""
if npixels is None:
npixels = fwhm**2
kernel = make_kernel(fwhm, kernel_size) if kernel_size else None
# Make detection threshold
if isinstance(nsigma, int):
threshold = detect_threshold(image, nsigma=nsigma)
else:
threshold = nsigma
return detect_sources(
image,
threshold,
npixels=npixels,
filter_kernel=kernel,
)
def getsegmentation(image):
'''
pass in image name, array of x coord, array of y coord.
Returns object size
'''
# read in image
data = fits.getdata(image)
threshold = detect_threshold(data, nsigma=2.)
# create segmentation map
segm = detect_sources(data, threshold, npixels=25)
# skipping deblending for now
# create catalog
cat = source_properties(data, segm)
tbl = cat.to_table()
return segm.data, tbl
def detect_obj(img, snr=2.8, exp_sz= 1.2, plt_show = True):
threshold = detect_threshold(img, snr=snr)
center_img = len(img)/2
sigma = 3.0 * gaussian_fwhm_to_sigma# FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=5, y_size=5)
kernel.normalize()
segm = detect_sources(img, threshold, npixels=10, filter_kernel=kernel)
npixels = 20
segm_deblend = deblend_sources(img, segm, npixels=npixels,
filter_kernel=kernel, nlevels=25,
contrast=0.001)
#Number of objects segm_deblend.data.max()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12.5, 10))
import copy, matplotlib
my_cmap = copy.copy(matplotlib.cm.get_cmap('gist_heat')) # copy the default cmap
my_cmap.set_bad('black')
vmin = 1.e-3
vmax = 2.1
ax1.imshow(img, origin='lower', cmap=my_cmap, norm=LogNorm(), vmin=vmin, vmax=vmax)
ax1.set_title('Data')
ax2.imshow(segm_deblend, origin='lower', cmap=segm_deblend.cmap(random_state=12345))
ax2.set_title('Segmentation Image')
plt.show()
columns = ['id', 'xcentroid', 'ycentroid', 'source_sum', 'area']
cat = source_properties(img, segm_deblend)
tbl = cat.to_table(columns=columns)
tbl['xcentroid'].info.format = '.2f' # optional format
tbl['ycentroid'].info.format = '.2f'
print(tbl)
cat = source_properties(img, segm_deblend)
objs = []
for obj in cat:
position = (obj.xcentroid.value-center_img, obj.ycentroid.value-center_img)
a_o = obj.semimajor_axis_sigma.value
b_o = obj.semiminor_axis_sigma.value
Re = np.pi * a_o * b_o /2.
q = 1 - obj.ellipticity.to_value()
objs.append((position,Re,q))
dis_sq = [np.sqrt((objs[i][0][0])**2+(objs[i][0][1])**2) for i in range(len(objs))]
dis_sq = np.array(dis_sq)
c_index= np.where(dis_sq == dis_sq.min())[0][0]
return objs, c_index
def get_segmentation_map(image: np.array, npixels: int = 5):
""" obtain segmentation map of biggest object in image
Parameter
---------
image : numpy.array
contains the b/w image in 2D
npixers : int
minimum number of connected pixels
"""
threshold = photutils.detect_threshold(image, 1.5)
segm = photutils.detect_sources(image, threshold, npixels)
# Keep only the largest segment
label = np.argmax(segm.areas) + 1
segmap = segm.data == label
# regularize
segmap_float = ndi.uniform_filter(np.float64(segmap), size=10)
segmap = segmap_float > 0.5
return segmap
def make_segmap(f, overwrite=True):
"""
Makes a segmentation map for each extension of the input files.
Parameters
----------
f : str
The filename to make segmentation maps for.
overwrite : bool
Option to overwrite existing segmaps if they exist.
Outputs
-------
{f}_seg_ext_1.fits
The segmentation map for SCI extension 1.
{f}_seg_ext_4.fits
The segmentation map for SCI extension 4.
"""
# Make segmaps for each SCI extension
for i in [1, 4]:
# See if segmap already exists
outfile = f.replace('.fits', '_seg_ext_{}.fits'.format(i))
if (os.path.exists(outfile)) & (overwrite is False):
pass
else:
# Get the data
data = fits.getdata(f, i)
# Detector sources; Make segmap
threshold = detect_threshold(data, snr=1.0)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(data,
threshold,
npixels=3,
filter_kernel=kernel)
fits.writeto(outfile, segm.data, overwrite=overwrite)
def find_center_segment(self, sigma_level, kernel=None, min_pixels=5):
"""
This function ...
:param sigma_level:
:param kernel:
:param min_pixels:
:return:
"""
# If a subtracted box is present, use it to find the center segment
box = self.subtracted if self.has_background else self.cutout
if not np.all(self.mask):
mean, median, stddev = statistics.sigma_clipped_statistics(box, mask=self.mask)
threshold = mean + stddev * sigma_level
else: threshold = detect_threshold(box, snr=2.0) #snr=2.0
# Perform the segmentation
segments = detect_sources(box, threshold, npixels=min_pixels, filter_kernel=kernel).data
# To plot the multiple segments that are detected
#if segments.max() > 1: plotting.plot_box(np.ma.masked_array(box, mask=segments.astype(bool)))
# Get the label of the center segment
rel_center = self.cutout.rel_position(self.center)
try:
label = segments[int(round(rel_center.y)), int(round(rel_center.x))]
except:
try:
label = segments[int(rel_center.y), int(rel_center.x)]
except:
return Mask((segments == label))
# If the center pixel is identified as being part of the background, create an empty mask (the center does not
# correspond to a segment)
if label == 0: return Mask(np.zeros_like(self.cutout, dtype=bool))
# Create a mask of the center segment
else: return Mask((segments == label))
def get_wcs(pattern):
for filename in pattern:
with fits.open(filename, 'update') as hdul:
hdr = hdul[0].header
key = hdr['OBJECT']
if key == 'wasp16':
w = WCS(filename)
print(filename)
print(key)
print(w)
image_data = fits.getdata(filename, ext=0)
from photutils import detect_threshold
threshold = detect_threshold(image_data, 2)
from astropy.convolution import Gaussian2DKernel
from astropy.stats import gaussian_fwhm_to_sigma
from photutils import detect_sources
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
segm = detect_sources(image_data,
threshold,
npixels=5,
filter_kernel=kernel)
import numpy as np
import matplotlib.pyplot as plt
from astropy.visualization import SqrtStretch
from astropy.visualization.mpl_normalize import ImageNormalize
norm = ImageNormalize(stretch=SqrtStretch())
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12.5))
ax1.imshow(image_data,
origin='lower',
cmap='Greys_r',
norm=norm)
ax1.set_title('Data')
cmap = segm.cmap(random_state=12345)
ax2.imshow(segm, origin='lower', cmap=cmap)
ax2.set_title('Segmentation Image')
plt.show()
def optimize_m(t_ini, f_ini, alpha_ini, sig_curr):
#keeping in mind that minimize requires flattened arrays
grad_fun = lambda tg: -1 * grad_lnpost(tg, f_ini, alpha_ini, sig_curr)
res = scipy.optimize.minimize(
lambda tt: -1 * lnpost(tt, f_ini, alpha_ini, sig_curr),
t_ini, # theta initial
jac=grad_fun,
method='L-BFGS-B',
bounds=[(1e-5, 10)] * len(t_ini))
tt_prime = res['x']
print(res['nit'])
w_final = tt_prime.reshape((n_grid, n_grid))
print(w_final)
#pick out the peaks using photutils
thresh = detect_threshold(w_final, 3)
tbl = find_peaks(w_final, thresh)
positions = np.transpose((tbl['x_peak'], tbl['y_peak']))
w_peaks = np.zeros((n_grid, n_grid))
w_peaks[positions] = w_final[positions]
return w_peaks
def get_sources_coords(target, xobj, yobj, pattern):
for filename in pattern:
data = fits.getdata(filename, ext=0)
threshold = detect_threshold(data, nsigma=2.)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
mean, median, std = sigma_clipped_stats(data, sigma=3)
daofind = DAOStarFinder(fwhm=3.0, threshold=5. * std)
sources = daofind(data - median)
for col in sources.colnames:
sources[col].info.format = '%.8g' # for consistent table output
# Pixel coordinates of the sources
w = wcs(target, xobj, yobj, pattern)
c = dict()
x1 = np.array(sources['xcentroid'])
y1 = np.array(sources['ycentroid'])
world = w.wcs_pix2world(x1, y1, 0)
c['ra_img'] = world[0]
c['dec_img'] = world[1]
return (c)
def get_data(pattern): # Gets file data, rotates the image and extracts sources
for filename in pattern:
which_hdu = choose_hdu(filename)
header = fits.getheader(filename, which_hdu)
# Gets file data and rotates the image
data = fits.getdata(filename, ext=0)
rot = rotate(data,-90,reshape=False)
# extracts the light sources from the image (basing on sigma, FWHM, thrsholds...)
threshold = detect_threshold(rot, nsigma=2.)
sigma = 3.0 * gaussian_fwhm_to_sigma # FWHM = 3.
kernel = Gaussian2DKernel(sigma, x_size=3, y_size=3)
kernel.normalize()
mean, median, std = sigma_clipped_stats(rot, sigma=3)
daofind = DAOStarFinder(fwhm=3.0, threshold=5.*std)
sources = daofind(rot - median)
for col in sources.colnames:
sources[col].info.format = '%.8g' # for consistent table output
# Pixel coordinates of the sources
x1 = np.array(sources['xcentroid'])
y1 = np.array(sources['ycentroid'])
def make_thr(self, nsigma=None, do_mask=None):
"""
make a threshold array for source detection
"""
from photutils import detect_threshold
if nsigma:
self.detect_thr.params.nsigma = nsigma
if not self.detect_thr.params.nsigma:
self.detect_thr.params.nsigma = 2.
if do_mask:
self.detect_thr.params.do_mask = do_mask
if not self.detect_thr.params.do_mask:
self.detect_thr.params.do_mask = False
data = self.data
nsigma = self.detect_thr.params.nsigma
mask = None
if self.detect_thr.params.do_mask:
mask = ~self.mask.mask
self.detect_thr.value = detect_threshold(data,
nsigma=nsigma,
mask=mask)
def estimate_background(cutout, nsigma=1, gauss_width=1, npixels=3):
""" Simple background detecting using super-pixel method"""
sigma = gauss_width * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma)
kernel.normalize()
# Find threshold for cutout, and make segmentation map
threshold = detect_threshold(cutout, snr=nsigma)
segments = detect_sources(cutout,
threshold,
npixels=npixels,
filter_kernel=kernel)
segment_array = segments.data
x_step = int(cutout.shape[0] / 10)
y_step = int(cutout.shape[1] / 10)
super_pixel_medians, super_pixel_rms_vals = [], []
for x in range(0, cutout.shape[0] - x_step, x_step):
for y in range(0, cutout.shape[1] - y_step, y_step):
super_pixel = cutout[y:y + y_step, x:x + x_step]
super_pixel_contents = []
for m in range(0, super_pixel.shape[0]):
for n in range(0, super_pixel.shape[1]):
if segment_array[m][n] == 0:
super_pixel_contents.append(super_pixel[m][n])
super_pixel_medians.append(median(super_pixel_contents))
super_pixel_rms_vals.append(
sqrt((mean(super_pixel_contents) -
median(super_pixel_contents))**2))
return median(super_pixel_medians), median(super_pixel_rms_vals)
def mask_cutout(cutout, nsigma=1., gauss_width=2.0, npixels=5):
""" Masks a cutout using segmentation and deblending using watershed"""
mask_data = {}
# Generate a copy of the cutout just to prevent any weirdness with numpy pointers
cutout_copy = copy(cutout)
sigma = gauss_width * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma)
kernel.normalize()
# Find threshold for cutout, and make segmentation map
threshold = detect_threshold(cutout, snr=nsigma)
segments = detect_sources(cutout,
threshold,
npixels=npixels,
filter_kernel=kernel)
# Attempt to deblend. Return original segments upon failure.
try:
deb_segments = deblend_sources(cutout,
segments,
npixels=5,
filter_kernel=kernel)
except ImportError:
print("Skimage not working!")
deb_segments = segments
except:
# Don't do anything if it doesn't work
deb_segments = segments
segment_array = deb_segments.data
# Center pixel values. (Assume that the central segment is the image, which is should be)
c_x, c_y = floor(segment_array.shape[0] / 2), floor(
segment_array.shape[1] / 2)
central = segment_array[int(c_x)][int(c_y)]
# Estimate background with severe cutout
bg_method = 1
bg_est, bg_rms = estimate_background(cutout_copy)
mask_data["BG_EST"] = bg_est
mask_data["BG_RMS"] = bg_rms
mask_data["N_OBJS"] = segments.nlabels
mask_data["BGMETHOD"] = bg_method
# Use alternative method to try and get a estimate or rms value if first method fails
if isnan(bg_est) or isnan(bg_rms):
bg_pixel_array = []
for x in range(0, segment_array.shape[0]):
for y in range(0, segment_array.shape[1]):
if segment_array[x][y] == 0:
bg_pixel_array.append(cutout_copy[x][y])
bg_est = median(bg_pixel_array)
bg_rms = sqrt((mean(bg_pixel_array) - bg_est)**2)
bg_method = 2
# Return input image if no need to mask
if segments.nlabels == 1:
mask_data["N_MASKED"] = 0
return cutout_copy, mask_data
num_masked = 0
# Mask pixels
for x in range(0, segment_array.shape[0]):
for y in range(0, segment_array.shape[1]):
if segment_array[x][y] not in (0, central):
cutout_copy[x][y] = bg_est
num_masked += 1
mask_data["N_MASKED"] = num_masked
return cutout_copy, mask_data
def make_tweakreg_catalog(model, kernel_fwhm, snr_threshold, sharplo=0.2,
sharphi=1.0, roundlo=-1.0, roundhi=1.0,
brightest=None, peakmax=None):
"""
Create a catalog of point-line sources to be used for image
alignment in tweakreg.
Parameters
----------
model : `ImageModel`
The input `ImageModel` of a single image. The input image is
assumed to be background subtracted.
kernel_fwhm : float
The full-width at half-maximum (FWHM) of the 2D Gaussian kernel
used to filter the image before thresholding. Filtering the
image will smooth the noise and maximize detectability of
objects with a shape similar to the kernel.
snr_threshold : float
The signal-to-noise ratio per pixel above the ``background`` for
which to consider a pixel as possibly being part of a source.
sharplo : float, optional
The lower bound on sharpness for object detection.
sharphi : float, optional
The upper bound on sharpness for object detection.
roundlo : float, optional
The lower bound on roundess for object detection.
roundhi : float, optional
The upper bound on roundess for object detection.
brightest : int, None, optional
Number of brightest objects to keep after sorting the full object list.
If ``brightest`` is set to `None`, all objects will be selected.
peakmax : float, None, optional
Maximum peak pixel value in an object. Only objects whose peak pixel
values are *strictly smaller* than ``peakmax`` will be selected.
This may be used to exclude saturated sources. By default, when
``peakmax`` is set to `None`, all objects will be selected.
.. warning::
`DAOStarFinder` automatically excludes objects whose peak
pixel values are negative. Therefore, setting ``peakmax`` to a
non-positive value would result in exclusion of all objects.
Returns
-------
catalog : `~astropy.Table`
An astropy Table containing the source catalog.
"""
if not isinstance(model, ImageModel):
raise ValueError('The input model must be a ImageModel.')
threshold_img = detect_threshold(model.data, snr=snr_threshold)
# TODO: use threshold image based on error array
threshold = threshold_img[0, 0] # constant image
daofind = DAOStarFinder(fwhm=kernel_fwhm, threshold=threshold,
sharplo=sharplo, sharphi=sharphi, roundlo=roundlo,
roundhi=roundhi, brightest=brightest,
peakmax=peakmax)
sources = daofind(model.data)
columns = ['id', 'xcentroid', 'ycentroid', 'flux']
if sources:
catalog = sources[columns]
else:
catalog = Table(names=columns, dtype=(np.int_, np.float_, np.float_,
np.float_))
return catalog
thresh = sky_th * sky_s
print('sky_s x sky_th = threshold')
print('{0:8.6f} x {1:4d} = {2:8.6f}\n'.format(sky_s, sky_th, thresh))
# What if we do "sigma-clip" than MAD?
sky_a, sky_m, sky_s_sc = sigma_clipped_stats(
img) # default is 3-sigma, 5 iters
thresh_sc = sky_th * sky_s_sc
thresh = sky_th * sky_s_sc
print('3 sigma 5 iters clipped case:')
print('{0:8.6f} x {1:4d} = {2:8.6f}\n'.format(sky_s_sc, sky_th, thresh_sc))
#%%
from photutils import detect_threshold
thresh_snr = detect_threshold(data=img.data, snr=3)
thresh_snr = thresh_snr[0][0]
# This will give you 3*bkg_std.
print('detect_threshold', thresh_snr)
#%%
import matplotlib.pyplot as plt
from photutils import DAOStarFinder
from photutils import CircularAperture as CircAp
DAOfind = DAOStarFinder(
fwhm=FWHM,
threshold=thresh_snr,
sharplo=0.2,
sharphi=3.0, # default values: sharplo=0.2, sharphi=1.0,
roundlo=-1.0,
hdu = datasets.load_star_image() data = hdu.data[0:400, 0:400] image = hdu.data.astype(float) image -= np.median(image) from photutils import daofind from astropy.stats import mad_std from astropy.stats import sigma_clipped_stats bkg_sigma = mad_std(image) mean, median, std = sigma_clipped_stats(data, sigma=3.0, iters=5) print(mean, median, std) sources = daofind(image, fwhm=4.0, threshold=3.0*bkg_sigma) print(sources) from photutils import CircularAperture from astropy.visualization import SqrtStretch from astropy.visualization.mpl_normalize import ImageNormalize import matplotlib.pylab as plt positions = (sources['xcentroid'], sources['ycentroid']) apertures = CircularAperture(positions, r=4.) norm = ImageNormalize(stretch=SqrtStretch()) plt.imshow(data, cmap='Greys', origin='lower', norm=norm) apertures.plot(color='blue', lw=1.5, alpha=0.5) # from photutils.datasets import make_100gaussians_image data = make_100gaussians_image() from photutils import detect_threshold threshold = detect_threshold(data, snr=3.)
def extension(self, extension_idx, threshold='', FWHM=3.0, sigma=3.0, snr=50., plot=False):
'''
A method to run aperatue photometry routines on an individual extension and save the results to the exposure class
Parameters
----------
extension_idx: int
Index of the extension
threshold: float (optional)
The absolute image value above which to select sources
FWHM: float
The full width at half maximum
sigma: float
Number of standard deviations to use for background estimation
snr: float
The signal-to-noise ratio to use in the threshold detection
plot: bool
Plot the field with identified sources circled
Returns
-------
source_list: table
A source list for the image
'''
# Define the data array
data = self.hdulist[extension_idx].data.astype(np.float)
# Extract the header and create a WCS object
hdr = self.hdulist[extension_idx].header
wcs = WCS(hdr)
# Estimate the background and background noise
mean, median, std = sigma_clipped_stats(data, sigma=sigma, iters=5)
# Calculate the detection threshold and FWHM if not provided
if not threshold: threshold = np.mean(detect_threshold(data, snr=snr))
# Print the parameters being used
for p,v in zip(['mean','median','std','threshold','FWHM'],[mean,median,std,threshold,FWHM]): print '{!s:10}: {:.3f}'.format(p,v)
# Subtract background and generate sources list of all detections
sources = daofind(data-median, threshold, FWHM)
# Map RA and Dec to pixels
positions = (sources['xcentroid'], sources['ycentroid'])
skycoords = pixel_to_skycoord(*positions, wcs=wcs)
# Calculate magnitudes at given source positions
apertures = CircularAperture(positions, r=2.)
photometry_table = aperture_photometry(data, apertures)
# 'skycoords' IRCS object is problematic for stacking tables so for now we'll just add the ra and dec
# photometry_table['sky_center'] = skycoords
photometry_table['ra'], photometry_table['dec'] = skycoords.ra, skycoords.dec
# Update data in the exposure object
self.source_table = vstack([self.source_table,photometry_table], join_type='inner')
# Plot the sources
if plot:
norm = ImageNormalize(stretch=SqrtStretch())
plt.imshow(data, cmap='Greys', origin='lower', norm=norm)
apertures.plot(color='blue', lw=1.5, alpha=0.5)
print '{!s:10}: {}'.format('sources',len(sources))
