def find_sources_array(image,fwhm,show=False):
if show == True:
print('Obtain the point source locations at the FITS image = \n')
print(image)
print('\n')
# im,hdr = fits.getdata(image,header=True) #reading the fits image (data + header)
# im = np.array(im,dtype='Float64') #transform the data for a matrix
tam = np.shape(image) #dimension of the matrix
mean, median, std = sigma_clipped_stats(image, sigma=fwhm, iters=5)
if show == True:
print('Mean, Median, STD = \n')
print(mean, median, std)
print('\n')
sources = daofind(image - median,fwhm=fwhm, threshold=5.*std)
if show == True:
print('Sources found! \n')
print(sources)
print('\n')
plt.figure()
plt.imshow(image,origin='lower', cmap=plt.cm.gray,vmin=np.mean(image)-np.std(image),
vmax=np.mean(image)+np.std(image))
plt.colorbar()
plt.scatter(sources['xcentroid'],sources['ycentroid'],color='red')
#Transform astropy table in a pandas dataframe
data = []
for i in sources.keys():
data.append(sources[i])
data = DataFrame(data).T
data.columns = sources.keys()
return data
def calc_offset_slow(image_phot):
"Find the offset between the images."
"This uses a painfully slow brute force approach. Definitely not recommended."
"Hasn't been tested as a function"
corr = np.zeros((60,60))
for i in range(60):
for j in range(60):
corr[i,j] = np.sum(np.maximum(im1, np.roll(np.roll(im0,i-30,axis=0),j-30,axis=1)))
# Normalize the correlation array so there is one peak, with range 0 .. 1
corr = corr - np.min(corr)
corr = np.max(corr) - corr
corr = corr / np.max(corr)
# Use DAOfind to locate this peak. Could do it other methods, but this works well, even if overkill.
s2 = daofind(corr, fwhm=10, threshold=0.8)
# Now get the offsets. This is in pixels.
dx = int(np.round(s2['xcentroid'][0]))
dy = int(np.round(s2['ycentroid'][0]))
# And roll the star catalog image
im0_rolled = np.roll(np.roll(image_0,dy-30,axis=0), dx-30, axis=1)
return im0_rolled
def convert_rows_to_wv(direct_file, grism_file, rows):
""" Converts the rows to wavelength bins.
Parameters
----------
direct_file : str
The path to the direct file.
grism_file : str
The path to the grism file.
rows : array
The array of rows that correspond to the spatial scan.
Returns
------
wv : array
The array solution for for wavelength.
"""
# Collect data from FITS headers
with fits.open(grism_file) as hdu:
hdr = hdu[0].header
hdr1 = hdu[1].header
sci_postarg_1 = hdr['POSTARG1']
sci_postarg_2 = hdr['POSTARG2']
sci_crpix_1 = hdr1['CRPIX1'] # this isn't a real keyword...
sci_crpix_2 = hdr1['CRPIX2']
with fits.open(direct_file) as hdu:
hdr = hdu[0].header
hdr1 = hdu[1].header
data = hdu[1].data
cal_postarg_1 = hdr['POSTARG1']
cal_postarg_2 = hdr['POSTARG2']
cal_crpix_1 = hdr1['CRPIX1']
cal_crpix_2 = hdr1['CRPIX2']
# Find the central source
mean, med, std = sigma_clipped_stats(data, sigma=3.0, iters=5)
sources = daofind(data-med, fwhm=3.0, threshold=5.*std)
source = sources[np.where(sources['flux'] == np.max(sources['flux']))]
x_cen, y_cen = source['xcentroid'], source['ycentroid']
# Calculate the offset
x_offset = sci_crpix_1 - cal_crpix_1 + (sci_postarg_1 - cal_postarg_1)/0.135
y_offset = sci_crpix_2 - cal_crpix_2 + (sci_postarg_2 - cal_postarg_2)/0.121
pos_x, pos_y = x_cen + x_offset, y_cen + y_offset
constants_0 = [8.95E3, 9.35925E-2, 0.0, 0.0, 0.0, 0.0]
constants_1 = [4.51423E1, 3.17239E-4, 2.17055E-3, -7.42504E-7, 3.4863E-7, 3.09213E-7]
coords_0 = constants_0[0] + constants_0[1]*pos_x + constants_0[2]*pos_y
coords_1 = constants_1[0] + constants_1[1]*pos_x + constants_1[2]*pos_y + constants_1[3]*pos_x**2 + constants_1[4]*pos_x*pos_y + constants_1[5]*pos_y**2
wv = coords_0 + coords_1*(rows-pos_x) + pos_y
return wv
def source_detection(ccd, fwhm=3.0, sigma=3.0, iters=5, threshold=5.0):
"""
Returns an astropy table containing the position of sources within the image.
Parameters
----------------
ccd : numpy.ndarray
The CCD Image array.
fwhm : float, optional
Full-width half-max of stars in the image.
sigma : float, optional.
The number of standard deviations to use as the lower and upper clipping limit.
iters : int, optional
The number of iterations to perform sigma clipping
threshold : float, optional.
The absolute image value above which to select sources.
Returns
-----------
sources
an astropy table of the positions of sources in the image.
"""
data = ccd.data
mean, median, std = sigma_clipped_stats(data, sigma=sigma, iters=iters)
sources = daofind(data - median, fwhm=fwhm, threshold=threshold*std)
return sources
def do_photometry(hdu, extensions=None, threshold=5, fwhm=2.5):
if extensions is None:
extensions = np.arange(1, len(hdu))
if not isiterable(extensions):
extensions = (extensions, )
output = {}
for ext in extensions:
header = hdu[ext].header
data = hdu[ext].data
image_wcs = WCS(header)
background = mad_std(data)
sources = daofind(data, threshold=threshold * background, fwhm=fwhm)
positions = (sources['xcentroid'], sources['ycentroid'])
sky_positions = pixel_to_skycoord(*positions, wcs=image_wcs)
apertures = CircularAperture(positions, r=2.)
photometry_table = aperture_photometry(data, apertures)
photometry_table['sky_center'] = sky_positions
output[str(ext)] = photometry_table
return output
def find_source_daofind(frame, ellipse, config, track_record, special=False):
"""
This function ...
:param data:
:return:
"""
# TODO: FIX THIS FUNCTION
sigma_level = 5.0
# Calculate the sigma-clipped statistics of the data
mean, median, std = sigma_clipped_stats(data, sigma=3.0)
result_table = daofind(data - median,
fwhm=3.0,
threshold=sigma_level * std)
result_table.rename_column('xcentroid', 'x_peak')
result_table.rename_column('ycentroid', 'y_peak')
# If requested, make a plot with the source(s) indicated
if plot:
plotting.plot_peaks(data,
result_table['x_peak'],
result_table['y_peak'],
radius=4.0)
# Return the list of source positions
#return result_table, median
source = []
return source
def find_source_daofind(frame, ellipse, config, track_record, special=False):
"""
This function ...
:param data:
:return:
"""
# TODO: FIX THIS FUNCTION
sigma_level = 5.0
# Calculate the sigma-clipped statistics of the data
mean, median, std = sigma_clipped_stats(data, sigma=3.0)
result_table = daofind(data - median, fwhm=3.0, threshold=sigma_level*std)
result_table.rename_column('xcentroid', 'x_peak')
result_table.rename_column('ycentroid', 'y_peak')
# If requested, make a plot with the source(s) indicated
if plot: plotting.plot_peaks(data, result_table['x_peak'], result_table['y_peak'], radius=4.0)
# Return the list of source positions
#return result_table, median
source = []
return source
def try_dao(fitsfile, outfile='out.png'):
hdulist = fits.open(fitsfile)
hdu = hdulist[0]
if len(hdu.data.shape) == 3:
data = hdu.data[0]
else:
data = hdu.data
mean, median, std = sigma_clipped_stats(data[800:900, 800:900],
sigma=3.0,
iters=5)
print(mean, median, std)
#data = hdu.data
sources = daofind(data - median, fwhm=3.0, threshold=5. * std)
print 'Found %i sources' % len(sources)
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r=4.)
norm = ImageNormalize(stretch=SqrtStretch(), vmin=2000, vmax=3000)
plt.imshow(data, cmap='Greys', origin='lower', norm=norm)
plt.title('%i Sources from a single all-sky frame' % len(sources))
apertures.plot(color='blue', lw=1.5, alpha=0.5)
plt.savefig(filename=outfile)
def _detect_sources(self):
from photutils import daofind
fwhm = 3.
detection_threshold = 3.
sources = daofind(self.image,
threshold=(self._med + self._std *
detection_threshold), fwhm=fwhm)
pl.plot(sources['xcentroid'], sources['ycentroid'], 'r.')
def findStars(image):
# In order to use the MAD as a consistent estimator for the estimation
# of the standard deviation σ, one takes σ = k * MAD where k is a constant
# scale factor, which depends on the distribution. For normally distributed
# data k is taken to be k = 1.48[26]
bkg_sigma = 1.48 * mad(image)
stars = daofind(image, fwhm=3.0, threshold=5 * bkg_sigma)
#print stars
return stars
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 getcentroids(imagedata,box):
nx = len(imagedata)
xmin,xmax,ymin,ymax = box #box borders, eye-balled
if nx < 50:
rows = np.arange(0,nx)
centroids = np.zeros((nx,2))
for i in rows:
#Index image data within defined box
image_box = imagedata[i][ymin:ymax, xmin:xmax]
#Using DAOFIND to locate the star within the defined box
sources = daofind(image_box,100.,2.126, exclude_border=True)
#Add xmin and ymin values onto the found sources for true pixel value
centroids[i,:] = [sources['xcentroid']+xmin, sources['ycentroid']+ymin]
else: #Condition applied to part 8
image_box = imagedata[ymin:ymax, xmin:xmax]
sources = daofind(image_box,100.,2.216, exclude_border=True)
centroids = [sources['xcentroid']+xmin,sources['ycentroid']+ymin]
return centroids
def _detect_sources(self):
from photutils import daofind
fwhm = 3.
detection_threshold = 3.
sources = daofind(self.image,
threshold=(self._med +
self._std * detection_threshold),
fwhm=fwhm)
pl.plot(sources['xcentroid'], sources['ycentroid'], 'r.')
def find_stars(im):
"Locate stars in an array, using DAOphot. Returns N x 2 array with xy positions. No magnitudes."
mean, median, std = sigma_clipped_stats(im, sigma=3.0, iters=5)
sources = daofind(im - median, fwhm=3.0, threshold=5.*std)
x_phot = sources['xcentroid']
y_phot = sources['ycentroid']
points_phot = np.transpose((x_phot, y_phot)) # Create an array N x 2
return points_phot
def starbright(fnstar, fnflat, istar, axs, fg):
# %% load data
data = meanstack(fnstar, 100)[0]
# %% flat field
flatnorm = readflat(fnflat, fnstar)
data = (data / flatnorm).round().astype(data.dtype)
# %% background
mean, median, std = sigma_clipped_stats(data, sigma=3.0)
rfact = data.shape[0] // 40
cfact = data.shape[1] // 40
bg = Background(data, (rfact, cfact), interp_order=1, sigclip_sigma=3)
# http://docs.astropy.org/en/stable/units/#module-astropy.units
# dataphot = (data - bg.background)*u.ph/(1e-4*u.m**2 * u.s * u.sr)
# data = (data-0.97*data.min()/bg.background.min()*bg.background) * u.ph/(u.cm**2 * u.s * u.sr)
data = data * u.ph / (u.cm**2 * u.s * u.sr)
# %% source extraction
sources = daofind(data, fwhm=3.0, threshold=5 * std)
# %% star identification and quantification
XY = column_stack((sources["xcentroid"], sources["ycentroid"]))
apertures = CircularAperture(XY, r=4.0)
norm = ImageNormalize(stretch=SqrtStretch())
flux = apertures.do_photometry(data, effective_gain=camgain)[0]
# %% plots
fg.suptitle("{}".format(fnflat.parent), fontsize="x-large")
hi = axs[-3].imshow(flatnorm, interpolation="none", origin="lower")
fg.colorbar(hi, ax=axs[-3])
axs[-3].set_title("flatfield {}".format(fnflat.name))
hi = axs[-2].imshow(bg.background, interpolation="none", origin="lower")
fg.colorbar(hi, ax=axs[-2])
axs[-2].set_title("background {}".format(fnstar.name))
hi = axs[-1].imshow(data.value,
cmap="Greys",
origin="lower",
norm=norm,
interpolation="none")
fg.colorbar(hi, ax=axs[-1])
for i, xy in enumerate(XY):
axs[-1].text(xy[0],
xy[1],
str(i),
ha="center",
va="center",
fontsize=16,
color="w")
apertures.plot(ax=axs[-1], color="blue", lw=1.5, alpha=0.5)
axs[-1].set_title("star {}".format(fnstar.name))
return flux[istar]
def find_sources(imageFile, data, seeing_in_pix, threshold=5.):
# estimate the 1-sigma noise level using the median absolute deviation of the image
print "[*] Estimating 1-sigma noise level."
# generate a mask for 0 pixel counts. These are chip gaps or skycell edges generated by
# np.nan_to_num and will affect noise level estimate.
mask = np.where(data != 0)
bkg_sigma = mad_std(data[mask])
#print np.median(data), mad(data), bkg_sigma
# use daofind to detect sources setting
print "[*] Detecting %d-sigma sources in %s" % (threshold, imageFile)
sources = daofind(data, fwhm=seeing_in_pix, threshold=threshold*bkg_sigma)
print "[*] Source detection successful."
print "\t[i] %d sources detected: " % (len(sources["xcentroid"]))
print
print sources
return sources, bkg_sigma
def get_fiber_flux(file,fwhm=20,threshold=100):
image,header = qi.readimage(file,plot=False)
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
source = daofind(image - median, fwhm=fwhm, threshold=threshold*std)
xcen = np.int(np.round(source['xcentroid'][0]))
ycen = np.int(np.round(source['ycentroid'][0]))
plt.plot([xcen],[ycen],'rx',markersize=20)
params = fitgaussian(image)
y,x = params[1],params[2]
apdict = tp.optimal_aperture(x,y,image,[150,160])
return apdict
def find_sources(imageFile, data, seeing_in_pix, threshold=5.):
# estimate the 1-sigma noise level using the median absolute deviation of the image
print "[*] Estimating 1-sigma noise level."
# generate a mask for 0 pixel counts. These are chip gaps or skycell edges generated by
# np.nan_to_num and will affect noise level estimate.
mask = np.where(data != 0)
bkg_sigma = mad_std(data[mask])
#print np.median(data), mad(data), bkg_sigma
# use daofind to detect sources setting
print "[*] Detecting %d-sigma sources in %s" % (threshold, imageFile)
sources = daofind(data,
fwhm=seeing_in_pix,
threshold=threshold * bkg_sigma)
print "[*] Source detection successful."
print "\t[i] %d sources detected: " % (len(sources["xcentroid"]))
print
print sources
return sources, bkg_sigma
def starbright(fnstar,fnflat,istar,axs,fg):
#%% load data
data = meanstack(fnstar,100)[0]
#%% flat field
flatnorm = readflat(fnflat,fnstar)
data = (data/flatnorm).round().astype(data.dtype)
#%% background
mean, median, std = sigma_clipped_stats(data, sigma=3.0)
rfact=data.shape[0]//40
cfact=data.shape[1]//40
bg = Background(data,(rfact,cfact),interp_order=1, sigclip_sigma=3)
# http://docs.astropy.org/en/stable/units/#module-astropy.units
#dataphot = (data - bg.background)*u.ph/(1e-4*u.m**2 * u.s * u.sr)
# data = (data-0.97*data.min()/bg.background.min()*bg.background) * u.ph/(u.cm**2 * u.s * u.sr)
data = data* u.ph/(u.cm**2 * u.s * u.sr)
#%% source extraction
sources = daofind(data, fwhm=3.0, threshold=5*std)
#%% star identification and quantification
XY = column_stack((sources['xcentroid'], sources['ycentroid']))
apertures = CircularAperture(XY, r=4.)
norm = ImageNormalize(stretch=SqrtStretch())
flux = apertures.do_photometry(data,effective_gain=camgain)[0]
#%% plots
fg.suptitle('{}'.format(fnflat.parent),fontsize='x-large')
hi = axs[-3].imshow(flatnorm,interpolation='none',origin='lower')
fg.colorbar(hi,ax=axs[-3])
axs[-3].set_title('flatfield {}'.format(fnflat.name))
hi = axs[-2].imshow(bg.background,interpolation='none',origin='lower')
fg.colorbar(hi,ax=axs[-2])
axs[-2].set_title('background {}'.format(fnstar.name))
hi = axs[-1].imshow(data.value,
cmap='Greys', origin='lower', norm=norm,interpolation='none')
fg.colorbar(hi,ax=axs[-1])
for i,xy in enumerate(XY):
axs[-1].text(xy[0],xy[1], str(i),ha='center',va='center',fontsize=16,color='w')
apertures.plot(ax=axs[-1], color='blue', lw=1.5, alpha=0.5)
axs[-1].set_title('star {}'.format(fnstar.name))
return flux[istar]
def process_file(inpath, file_name, t_constant, sigma, fwhm, r, kernel_size, outpath, plot):
print "Processing " + file_name
hdulist = fits.open(inpath + file_name)
image = hdulist[0].data
if isinstance(sigma, list):
threshold = calc_sigma(image, sigma[0], sigma[1]) * t_constant
else:
threshold = t_constant*sigma
median_out = signal.medfilt(image,kernel_size)
median_sub = np.subtract(image,median_out)
sources = daofind(median_sub, threshold, fwhm)
sources_2 = np.array(sources["id", "xcentroid", "ycentroid", "sharpness", "roundness1", "roundness2", "npix", "sky", "peak", "flux", "mag"])
print_line= (file_name+","+str(sources_2))
base_name = os.path.splitext(file_name)[0]
file = open(outpath + base_name + ".out", "a")
file.write(print_line)
file.close()
positions = (sources['xcentroid'], sources['ycentroid'])
# print positions
apertures = CircularAperture(positions, r)
phot_table = aperture_photometry(median_sub, apertures)
phot_table_2 = np.array(phot_table["aperture_sum", "xcenter", "ycenter"])
print_line= (","+str(phot_table_2)+"\n")
file = open(outpath + base_name + ".out", "a")
file.write(print_line)
file.write("\n")
file.close()
hdulist[0].data = median_sub
file = open(outpath + base_name + ".fits", "w")
hdulist.writeto(file)
file.close()
if plot:
median_sub[median_sub<=0]=0.0001
plt.imshow(median_sub, cmap='gray', origin='lower')
apertures.plot(color='blue', lw=1.5, alpha=0.5)
plt.show()
def star_detection(filename):
''' Detect stars, create scatter plot'''
image_data = open_fits(filename)
mean, median, std = sigma_clipped_stats(image_data, sigma=3.0, iters=5)
print mean, median, std
sources = daofind(image_data - median, fwhm=3.0, threshold=5.*std)
print sources
x_val = [x[1] for x in sources]
y_val = [x[2] for x in sources]
pyplot.imshow(image_data, norm=LogNorm())
pyplot.scatter(x_val, y_val, color='k', marker='o', label='Detected stars')
pyplot.xlim(1800, 2200)
pyplot.ylim(1800, 2200)
pyplot.colorbar()
pyplot.legend()
pyplot.title('Star detection')
pyplot.xlabel('X-axis [pixels]')
pyplot.ylabel('Y-axis [pixels]')
pyplot.savefig('star_detection.pdf')
pyplot.show()
def find_stars(image, plot = False, fwhm = 20.0, threshold=3.):
from astropy.stats import sigma_clipped_stats
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
from photutils import daofind
sources = daofind(image - median, fwhm=fwhm, threshold=threshold*std)
# stars already found accurately, vet_sources will be implemented when working properly
# vet_sources(10.0,10.0)
if plot == True:
# from astropy.visualization import SqrtStretch
# from astropy.visualization.mpl_normalize import ImageNormalize
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r=4.)
#norm = ImageNormalize(stretch=SqrtStretch())
#plt.imshow(image, cmap='Greys', origin='lower', norm=norm)
qi.display_image(image)
apertures.plot(color='blue', lw=1.5, alpha=0.5)
return sources
def try_dao(fitsfile, outfile='out.png'):
hdulist = fits.open(fitsfile)
hdu = hdulist[0]
if len(hdu.data.shape) == 3:
data = hdu.data[0]
else:
data = hdu.data
mean, median, std = sigma_clipped_stats(data[800:900, 800:900], sigma=3.0, iters=5)
print(mean, median, std)
#data = hdu.data
sources = daofind(data - median, fwhm=3.0, threshold=5.*std)
print 'Found %i sources' % len(sources)
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r=4.)
norm = ImageNormalize(stretch=SqrtStretch(), vmin=2000, vmax=3000)
plt.imshow(data, cmap='Greys', origin='lower', norm=norm)
plt.title('%i Sources from a single all-sky frame' % len(sources))
apertures.plot(color='blue', lw=1.5, alpha=0.5)
plt.savefig(filename=outfile)
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
# file.close()
#
# import matplotlib.pylab as plt
# im2 = image
# im2[im2<=0]=0.0001
# plt.imshow(im2, cmap='gray', origin='lower')
# apertures.plot(color='blue', lw=1.5, alpha=0.5)
# plt.show()
hdulist = fits.open(inpath+file_name)
image = hdulist[0].data
#image = image.astype(float) - np.median(image)
from photutils import daofind
from astropy.stats import mad_std
bkg_sigma = mad_std(image)
sources = daofind(image, fwhm, threshold*bkg_sigma)
#print_line= (file_name+","+str(sources_2)+"\n")
sources_2 = np.array(sources["id", "xcentroid", "ycentroid", "sharpness", "roundness1", "roundness2", "npix", "sky", "peak", "flux", "mag"])
print_line= (file_name+","+str(sources_2))
file= open(outpath, "a")
file.write(print_line)
file.close()
from photutils import aperture_photometry, CircularAperture
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r)
phot_table = aperture_photometry(image, apertures)
phot_table_2 = np.array(phot_table["aperture_sum", "xcenter", "ycenter"])
print_line= (","+str(phot_table_2)+"\n")
file= open(outpath, "a")
file.write(print_line)
ax2 = figLook.add_subplot(122)
ax1.imshow(imageShow1, cmap='Greys', norm=LogNorm())
ax1.set_title('%s-band'%hdrShow1['FILTER2'])
ax2.imshow(imageShow2, cmap='Greys', norm=LogNorm())
ax2.set_title('%s-band'%hdrShow2['FILTER2'])
# Move the figure on the screen
thismanager = get_current_fig_manager()
thismanager.window.wm_geometry("+1100+0")
#figLook.canvas.manager.window.SetPosition((500, 0))
#####################################
# Choose how to do source extraction
threshold = thres*std1
#sources = ph.irafstarfind(imageDataRed, threshold=threshold, fwhm=fwhm)#, exclude_border=True)
sources = ph.daofind(image1, threshold=threshold, fwhm=fwhm, sigma_radius=sigma1)#, exclude_border=True)
#print(sources)
# Put a mask here that we might not need anymore
Xs = sources['xcentroid']#[np.where(sources['fwhm']<2)]
Ys = sources['ycentroid']#[np.where(sources['fwhm']<2)]
positions = (Xs.data, Ys.data)
apertures = ph.CircularAperture(positions, r=10.)
fig = plt.figure(10, figsize=(12,12))
ax = fig.add_subplot(111)
mutable = {}
fig.canvas.mpl_connect('button_press_event', onclick)
for i in range(0, target_b.size - edit):
image_b, hd1 = fits.getdata(dir_t + target_b[i], header=True)
hd = hd1['UTC']
hour_b[i] = float(hd[9:11])
minute_b[i] = float((hd[11:13])) / 60.0
second_b[i] = float((hd[13:22])) / 3600.0
time_b[i] = hour_b[i] + minute_b[i] + second_b[i]
image_b = (image_b - darkavg) / normflat
slice_comp1 = image_b[1237:1337, 760:860] #[795:825,1272:1302]
slice_target = image_b[650:750, 620:720] #[660:690,690:720]
slice_comp2 = image_b[1315:1415, 1330:1430] #[1365:1395,1350:1380]
sources_b = daofind(slice_target,
fwhm=FWHM,
threshold=thresh,
exclude_border=True)
positions_b = zip(sources_b['xcentroid'], sources_b['ycentroid'])
apertures_b = CircularAperture(positions_b, r=aperture_r)
phot_table_b = aperture_photometry(slice_target, apertures_b)
mag_b[i] = np.max(phot_table_b['aperture_sum']) / exposure_time
sources_b1 = daofind(slice_comp1,
fwhm=FWHM,
threshold=thresh,
exclude_border=True)
positions_b1 = zip(sources_b1['xcentroid'], sources_b1['ycentroid'])
apertures_b1 = CircularAperture(positions_b1, r=aperture_r1)
phot_table_b1 = aperture_photometry(slice_comp1, apertures_b1)
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))
##################################################################################
# +++HR DIAGRAM!+++ #
##################################################################################
mosaic_area = fits.getdata(dir + '/Mosaic_B/mosaic_area.fits')
#mosaic area plot:
plt.figure(1)
plt.imshow(mosaic_area, origin='lower')
plt.xlabel('Pixels', fontsize=15)
plt.ylabel('Pixels', fontsize=15)
mosaic_area_cropped = mosaic_area[400:1490, 380:1610]
raw_cropped =
sourcesB = daofind(nosky_crop, fwhm=5.0, threshold=1.5*bkg_sigma)
#background subtraction:
#phot_table = aperture_photometry(data - bkg, apertures)
data =
apertures = CircularAperture(positions, r=3)
annulus_apertures = CircularAnnulus(positions, r_in=6, r_out=8)
rawflux_table = aperture_photometry(data, apertures)
bkgflux_table = aperture_photometry(data, annulus_apertures)
phot_table = hstack([rawflux_table, bkgflux_table], table_names=['raw', 'bkg'])
aperture_area = np.pi*3**2
annulus_area = np.pi*(8**2-6**2)
final_sum = (phot_table['aperture_sum_raw']-phot_table['aperture_sum_bkg'] * aperture_area/annulus_area)
phot_table = ['residual_aperture_sum']
def fit_sources(image1d, psfbase, shape, normperim, medianim, mastermask,
threshold=12, **kwargs):
'''find and fit sources in the image
perform PSF subtraction and then find and fit sources
see comments in code for details
Parameters
----------
image1d : ndarray
flattened, normalized image
psfbase : ndarray
2d array of psf templates (PSF library)
threshold : float
Detection threshold. Higher numbers find only the stronger sources.
Experiment to find the right value.
kwargs : dict or names arguments
arguments for daofind (fwmh, min and max roundness, etc.)
Returns
-------
fluxes_gaussian : astropy.table.Table
imag :
PSF subtracted image
scaled_im :
PSF subtracted image in daofind scaling
'''
psf_coeff = psf_from_projection(image1d, psfbase)
im = image1d - np.dot(psfbase, psf_coeff)
bkg_sigma = 1.48 * mad(im)
# Do source detection on 2d, scaled image
scaled_im = remove_normmask(im.reshape((-1, 1)), np.ones(1), np.ones_like(medianim), mastermask).reshape(shape)
imag = remove_normmask(im.reshape((-1, 1)), normperim, medianim, mastermask).reshape(shape)
sources = photutils.daofind(scaled_im, threshold=threshold * bkg_sigma, **kwargs)
if len(sources) == 0:
return None, imag, scaled_im
else:
# insert extra step here to find the brightest source, subtract it and
# redo the PSF fit or add a PSF model to psfbase to improve the PSF fit
# I think 1 level of that is enough, no infinite recursion.
# Idea 1: mask out a region around the source, so that this does not
# influence the PSF fit.
newmask = deepcopy(mastermask).reshape(shape)
for source in sources:
sl, temp = overlap_slices(shape, [9,9], [source['xcentroid'], source['ycentroid']])
newmask[sl[0], sl[1]] = True
newmask = newmask.flatten()
psf_coeff = psf_from_projection(image1d[~(newmask[~mastermask])],
psfbase[~(newmask[~mastermask]), :])
im = image1d - np.dot(psfbase, psf_coeff)
scaled_im = remove_normmask(im.reshape((-1, 1)), np.ones(1), np.ones_like(medianim), mastermask).reshape(shape)
imag = remove_normmask(im.reshape((-1, 1)), normperim, medianim, mastermask).reshape(shape)
# cosmics in the image lead to high points, which means that the
# average area will be overcorrected
imag = imag - np.ma.median(imag)
# do photometry on image in real space
psf_gaussian = photutils.psf.GaussianPSF(1.8) # width measured by hand
# default in photutils is to freeze this stuff, but I disagree
# psf_gaussian.fixed['sigma'] = False
# psf_gaussian.fixed['x_0'] = False
# psf_gaussian.fixed['y_0'] = False
fluxes_gaussian = photutils.psf.psf_photometry(imag, sources['xcentroid', 'ycentroid'], psf_gaussian)
'''Estimate flux of Gaussian PSF from A and sigma.
Should be part of photutils in a more clever (analytic) implementation.
As long as it's missing there, but in this crutch here.
'''
x, y = np.mgrid[-3:3, -4:4]
amp2flux = np.sum(psf_gaussian.evaluate(x, y, 1, 1, 0, 1.8)) # 1.8 hard-coded above
fluxes_gaussian.add_column(MaskedColumn(name='flux_fit', data=amp2flux * fluxes_gaussian['amplitude_fit']))
return fluxes_gaussian, imag, scaled_im
def find_ghost_centers(fnlist, tolerance=3, thresh=4, guess=None):
"""This routine finds the most likely optical center for a series of images
by attempting to match ghost reflections of stars with their stellar
counterparts. It can be rather slow if the fields are very dense with
stars, but is also much more likely to succeed in locating the correct
center in dense fields.
Images should have already been aperture masked at this point. If they
haven't, run 'aperture_mask' on them first.
It is useful if the images have had their seeing FWHMs measured. If they
have, these values (in pixels) should be in the fits headers as "FPFWHM".
If not, the FWHM is assumed to be 5 pixels (and this is not ideal).
The routine creates a number of temporary files while running, which are
deleted at the end of the routine. Interrupting the routine while running
is probably not a great idea.
The routine returns a list of image centers as well as appending these to
the fits headers as "fpxcen" and "fpycen"
Inputs:
fnlist -> List of strings, each one containing the path to a fits image.
The images should be roughly aligned to one another or the
routine will not work.
tolerance -> Optional. How close two pixels can be and be "close enough"
Default is 3 pixels.
thresh -> Optional. Level above sky background variation to look for objs.
Default is 4.5 (times SkySigma). Decrease if center positions
aren't being found accurately. Increase for crowded fields to
decrease computation time.
guess -> Optional. If you already have an idea of where the center should
be, you'd put it here. Should be a 2-long iterable, with guess[0]
being the X center guess, and guess[1] being the y center guess.
Outputs:
xcenlist -> List of image center X coordinates
ycenlist -> List of image center Y coordinates
"""
# Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = FPImage(fnlist[0])
toggle = firstimage.fwhm
axcen = firstimage.axcen
aycen = firstimage.aycen
arad = firstimage.arad
firstimage.close()
if axcen is None:
exit("Error! Images have not yet been aperture-masked! Do this first!")
if toggle is None:
print "Warning: FWHMs have not been measured!"
print "Assuming 5 pixel FWHM for all images."
fwhm = 5.*np.ones(len(fnlist))
else:
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
fwhm[i] = image.fwhm
image.close()
# Get sky background levels
skyavg = np.empty(len(fnlist))
skysig = np.empty(len(fnlist))
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
skyavg[i], skysig[i], _skyvar = image.skybackground()
image.close()
# Identify the stars in each image
xlists = []
ylists = []
maglists = []
print "Identifying stars and ghosts in each image..."
for i in range(len(fnlist)):
xlists.append([])
ylists.append([])
maglists.append([])
image = FPImage(fnlist[i])
axcen = image.axcen
aycen = image.aycen
arad = image.arad
sources = daofind(image.inty-skyavg[i],
fwhm=fwhm[i],
threshold=thresh*skysig[i]).as_array()
for j in range(len(sources)):
# Masks for center and edge of image
cenmask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 > (0.05*arad)**2)
edgemask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 < (0.95*arad)**2)
if np.logical_and(cenmask, edgemask):
xlists[i].append(sources[j][1])
ylists[i].append(sources[j][2])
maglists[i].append(sources[j][-1])
image.close()
if guess is None:
# Use the found stars to come up with a center guess for each image
xcen = np.zeros(len(fnlist))
ycen = np.zeros(len(fnlist))
goodcen = np.zeros(len(fnlist))
for i in range(len(fnlist)):
N = len(xlists[i])
xcenarray = np.zeros(((N*N-N)/2))
ycenarray = np.zeros(((N*N-N)/2))
dist2array = np.zeros(((N*N-N)/2))
sub1array = np.zeros(((N*N-N)/2))
index = 0
# All "possible" centers for all possible pairs of stars
for j in range(N):
for k in range(j+1, N):
xcenarray[index] = xlists[i][j]+xlists[i][k]
ycenarray[index] = ylists[i][j]+ylists[i][k]
index = index+1
xcenarray, ycenarray = 0.5*xcenarray, 0.5*ycenarray
# Cross check the various possible centers against each other
for j in range(len(xcenarray)):
dist2array = ((xcenarray-xcenarray[j])**2 +
(ycenarray-ycenarray[j])**2)
sub1array[j] = np.sum(dist2array < tolerance**2)
# Determine the locations of the "best" centers.
bestcenloc = np.where(sub1array == max(sub1array))[0]
# Now cross check JUST the best ones against each other
sub1array = np.zeros(len(bestcenloc))
xcenarray = xcenarray[bestcenloc]
ycenarray = ycenarray[bestcenloc]
for j in range(len(bestcenloc)):
dist2array = ((xcenarray-xcenarray[j])**2 +
(ycenarray-ycenarray[j])**2)
sub1array[j] = np.sum(dist2array < tolerance**2)
# Again, determine the locations of the "best" centers.
bestcenloc = np.where(sub1array == max(sub1array))[0]
xcen[i] = np.average(xcenarray[bestcenloc])
ycen[i] = np.average(ycenarray[bestcenloc])
# Cross-check the various image's centers against each other
for i in range(len(fnlist)):
dist2array = (xcen-xcen[i])**2 + (ycen-ycen[i])**2
goodcen[i] = np.sum(dist2array < tolerance**2)
# Determine where in the arrays the best fitting centers are
bestcenloc = np.where(goodcen == max(goodcen))[0]
bestxcen = np.average(xcen[bestcenloc])
bestycen = np.average(ycen[bestcenloc])
else:
# Forced guess:
bestxcen, bestycen = guess[0], guess[1]
# Now we want to improve the center for each image using the best guesses
xcenlist = np.zeros(len(fnlist))
ycenlist = np.zeros(len(fnlist))
objxs = []
objys = []
ghoxs = []
ghoys = []
for i in range(len(fnlist)):
# Where would the reflected objects be if they exist
refxlist = 2.*bestxcen - np.array(xlists[i])
refylist = 2.*bestycen - np.array(ylists[i])
# Populate lists of objects and ghosts based on this
objxs.append([])
objys.append([])
ghoxs.append([])
ghoys.append([])
for j in range(len(xlists[i])):
dist2list = ((xlists[i] - refxlist[j])**2 +
(ylists[i] - refylist[j])**2)
matchlist = dist2list < tolerance**2
if np.sum(matchlist) >= 1:
# We found a match! Now we need to know where the match is
matchloc = np.where(matchlist == 1)[0][0]
# Cool, now, is this thing brighter than the ghost?
if maglists[i][matchloc] < maglists[i][j]:
# It is! Record it:
objxs[i].append(xlists[i][matchloc])
objys[i].append(ylists[i][matchloc])
ghoxs[i].append(xlists[i][j])
ghoys[i].append(ylists[i][j])
# Calculate the centers based on the object / ghost coords
if len(objxs[i]) == 0:
xcenlist[i] = 0
ycenlist[i] = 0
else:
xcenlist[i] = 0.5*(np.average(objxs[i])+np.average(ghoxs[i]))
ycenlist[i] = 0.5*(np.average(objys[i])+np.average(ghoys[i]))
# Fill in the blanks with a "best guess"
xcenlist[xcenlist == 0] = np.average(xcenlist[xcenlist != 0])
ycenlist[ycenlist == 0] = np.average(ycenlist[ycenlist != 0])
xcenlist[np.isnan(xcenlist)] = bestxcen
ycenlist[np.isnan(ycenlist)] = bestycen
# Append the values to the image headers
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
image.xcen = xcenlist[i]
image.ycen = ycenlist[i]
image.close()
# Manually verify ghost centers
while True:
yn = raw_input("Manually verify ghost centers? (Recommended) (y/n) ")
if "n" in yn or "N" in yn:
break
elif "y" in yn or "Y" in yn:
goodtog = verify_center(fnlist,
objxs, objys,
ghoxs, ghoys,
xcenlist, ycenlist)
if goodtog:
break
else:
exit("Centers not approved!")
return xcenlist, ycenlist
starCatalog = starCatalog[np.where(theseStars)]
# Form a "catalog" of position entries for matching
ra1 = starCatalog['RAJ2000']
dec1 = starCatalog['DEJ2000']
catalog1 = SkyCoord(ra = ra1, dec = dec1, frame = 'fk5')
# Read in the image and find tars in the image
Ifile = (stokesDir + delim +
'_'.join([thisTarget, thisWaveband, 'I']) + '.fits')
stokesI = Image(Ifile)
mean, median, std = sigma_clipped_stats(stokesI.arr, sigma=3.0, iters=5)
threshold = median + 3.0*std
fwhm = 3.0
sources = daofind(stokesI.arr, threshold, fwhm, ratio=1.0, theta=0.0,
sigma_radius=1.5, sharplo=0.2, sharphi=1.0,
roundlo=-1.0, roundhi=1.0, sky=0.0,
exclude_border=True)
# Convert source positions to RA and Dec
wcs = WCS(stokesI.header)
ADstars = wcs.all_pix2world(sources['xcentroid'], sources['ycentroid'], 0)
catalog2 = SkyCoord(ra = ADstars[0]*u.deg, dec = ADstars[1]*u.deg, frame = 'fk5')
###
### This slow, meat-axe method was useful for verification.
### It produces the same results as the method below.
###
# # Loop through each of the detected sources, and check for possible confusion
# keepStars = []
# numCat1Match = []
### test segmentation import numpy as np from photutils import datasets 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()
def align_norm(fnlist, tolerance=5, thresh=3.5):
"""Aligns a set of images to each other, as well as normalizing the images
to the same average brightness.
Both the alignment and normalization are accomplished through stellar
photometry using the IRAF routine 'daophot'. The centroids of a handful
of stars are found and used to run the IRAF routine 'imalign'. The
instrumental magnitudes of the stars are used to determine by how much
each image must be scaled for the photometry to match across images.
The images are simply updated with their rescaled, shifted selves. This
overwrites the previous images and adds the header keyword 'fpphot' to
the images.
A handful of temporary files are created during this process, which should
all be deleted by the routine at the end. But if it is interrupted, they
might not be.
If the uncertainty images exist, this routine also shifts them by the same
amounts as the intensity images, as well as updating the uncertainty values
for both the new normalization and the uncertainties in normalizing the
images.
Inputs:
fnlist -> List of strings, each the path to a fits image.
tolerance -> How close two objects can be and still be considered the same
object. Default is 3 pixels.
thresh -> Optional. Level above sky background variation to look for objs.
Default is 3.5 (times SkySigma). Decrease if center positions
aren't being found accurately. Increase for crowded fields to
decrease computation time.
"""
# Get image FWHMs
fwhm = np.empty(len(fnlist))
firstimage = FPImage(fnlist[0])
toggle = firstimage.fwhm
axcen = firstimage.axcen
aycen = firstimage.aycen
arad = firstimage.arad
firstimage.close()
if axcen is None:
print "Error! Images have not yet been aperture-masked! Do this first!"
crash()
if toggle is None:
print "Warning! FWHMs have not been measured!"
print "Assuming 5 pixel FWHM for all images."
for i in range(len(fnlist)):
fwhm[i] = 5
else:
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
fwhm[i] = image.fwhm
image.close()
# Get sky background levels
skyavg = np.empty(len(fnlist))
skysig = np.empty(len(fnlist))
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
skyavg[i], skysig[i], _skyvar = image.skybackground()
image.close()
# Identify the stars in each image
xlists = []
ylists = []
print "Identifying stars in each image..."
for i in range(len(fnlist)):
xlists.append([])
ylists.append([])
image = FPImage(fnlist[i])
axcen = image.axcen
aycen = image.aycen
arad = image.arad
sources = daofind(image.inty-skyavg[i],
fwhm=fwhm[i],
threshold=thresh*skysig[i]).as_array()
for j in range(len(sources)):
# If the source is not near the center or edge
centermask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 > (0.05*arad)**2)
edgemask = ((sources[j][1]-axcen)**2 +
(sources[j][2]-aycen)**2 < (0.95*arad)**2)
if np.logical_and(centermask, edgemask):
xlists[i].append(sources[j][1])
ylists[i].append(sources[j][2])
image.close()
# Match objects between fields
print "Matching objects between images..."
xcoo = []
ycoo = []
for i in range(len(xlists[0])):
# For each object in the first image
accept = True
for j in range(1, len(fnlist)):
# For each other image
dist2 = ((np.array(xlists[j])-xlists[0][i])**2 +
(np.array(ylists[j])-ylists[0][i])**2)
if (min(dist2) > tolerance**2):
accept = False
break
if accept:
# We found an object at that position in every image
xcoo.append(xlists[0][i])
ycoo.append(ylists[0][i])
# Create coordinate arrays for the photometry and shifting
x = np.zeros((len(fnlist), len(xcoo)))
y = np.zeros_like(x)
for i in range(len(xcoo)):
# For every object found in the first image
for j in range(len(fnlist)):
# Find that object in every image
dist2 = ((np.array(xlists[j])-xcoo[i])**2 +
(np.array(ylists[j])-ycoo[i])**2)
index = np.argmin(dist2)
x[j, i] = xlists[j][index]
y[j, i] = ylists[j][index]
# Do aperture photometry on the matched objects
print "Performing photometry on matched stars..."
counts = np.zeros_like(x)
dcounts = np.zeros_like(x)
for i in range(len(fnlist)):
image = FPImage(fnlist[i])
apertures = CircularAperture((x[i], y[i]), r=2*fwhm[i])
annuli = CircularAnnulus((x[i], y[i]), r_in=3*fwhm[i], r_out=4*fwhm[i])
phot_table = aperture_photometry(image.inty,
apertures, error=np.sqrt(image.vari))
sky_phot_table = aperture_photometry(image.inty, annuli,
error=np.sqrt(image.vari))
counts[i] = phot_table["aperture_sum"] / apertures.area()
counts[i] -= sky_phot_table["aperture_sum"] / annuli.area()
counts[i] *= apertures.area()
dcounts[i] = phot_table["aperture_sum_err"] / apertures.area()
image.close()
# Calculate the shifts and normalizations
norm, dnorm = calc_norm(counts, dcounts)
for i in range(x.shape[1]):
x[:, i] = -(x[:, i] - x[0, i])
y[:, i] = -(y[:, i] - y[0, i])
xshifts = np.average(x, axis=1)
yshifts = np.average(y, axis=1)
# Normalize the images and put shifts in the image headers
for i in range(len(fnlist)):
image = FPImage(fnlist[i], update=True)
image.phottog = "True"
image.dnorm = dnorm[i]
image.inty /= norm[i]
image.vari = image.vari/norm[i]**2
image.xshift = xshifts[i]
image.yshift = yshifts[i]
image.close()
return
(hours, mins, secs) = ultracamutils.timedeltaHoursMinsSeconds(timeLeft)
timeLeftString = str(hours).zfill(2) + ":" + str(mins).zfill(2) + ":" + str(secs).zfill(2)
ccdFrame = rdat()
statusString = "\r%s Frame: [%d/%d]"%(timeLeftString, trueFrameNumber, frameRange)
sys.stdout.write(statusString)
sys.stdout.flush()
windows = ccdFrame[0]
for windowIndex, w in enumerate(windows):
image = w._data
allWindows[windowIndex].addData(image)
bkg_sigma = 1.48 * mad(image)
sources = daofind(image, fwhm=4.0, threshold=2.5*bkg_sigma)
sources.pprint()
filteredSources = []
for index, s in enumerate(sources):
newSource = {}
new = True
newSource = (s['xcentroid'], s['ycentroid'], s['flux'])
if index==0:
filteredSources.append(newSource)
for f in filteredSources:
if f[0]==newSource[0] and \
f[1]==newSource[1] and \
f[2]==newSource[2]:
plt.plot(pixel,xx,marker='.', c='r')
'''
'''
statistics on the image, determines background noise, standard deviation and mean photon counts
to find all objects in the image that are above some threshold, and are NEAR some FWHM value.
typing "sources" without quotes in the spyde terminal will print all of the sources, x,y coords
of their locations in the image, and a peak photon count, and corresponding flux and magnitude values
for the sources as well.
'''
mean, median, std = sigma_clipped_stats(cutdata, sigma =5.0, iters=10)
sources = daofind(cutdata - median, fwhm=targetFWHM, threshold=2.*std)
#sources = sources[(sources['peak'] > 1000.)]
#positions = SC(ra=targetRAdeg * u.deg, dec = targetDECdeg * u.deg, frame='fk5')
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r=4.0)
norm = ImageNormalize(stretch=SqrtStretch())
'''Uncomment these plot commands to view the image. plt.imshow plots the whole image, and apertures.plot
from photutils import datasets
print "Python version:", sys.version
print "Astropy version:", astropy.__version__
hdu = datasets.load_star_image()
image = hdu.data[500:700, 500:700]
image = hdu.data
print np.median(image)
image -= np.median(image)
from photutils import daofind
from astropy.stats import median_absolute_deviation as mad
bkg_sigma = 1.48 * mad(image)
sources = daofind(image, fwhm=4.0, threshold=3*bkg_sigma)
print sources
for s in sources:
print s
figure = matplotlib.pyplot.figure(figsize=(10, 10))
matplotlib.pyplot.title("Sample image")
matplotlib.pyplot.imshow(image, cmap='gray')
#matplotlib.pyplot.gca().invert_yaxis()
matplotlib.pyplot.show()
ax=figure.add_subplot(1,1,1)
matplotlib.pyplot.axis('off')
extent = ax.get_window_extent().transformed(figure.dpi_scale_trans.inverted())
######################################################################################################### Now let's match to the SDSS Calib Field Field0 = ascii.read(top+'DR10_SDSS_CALIB_FIELD1_4.csv') #Field1 = Field0[np.where(Field0['rmag'] < 18)] Field00 = ascii.read(top+'DR10_SDSS_CALIB_FIELD2_4.csv') #Field00 = ascii.read(top+'DR10_SDSS_CALIB_FIELD3.csv') #Field3 = Field2[np.where(Field2['rmag'] < 18)] ######################################################################################################### Start the show # First let's get the images and the necessary data associated with them image1, median1, ObjName1, band1, std1, hdr1, w1 = GetImage(image0, mask) image2, median2, ObjName2, band2, std2, hdr2, w2 = GetImage(image00, mask) image3, median3, ObjName3, band3, std3, hdr3, w3 = GetImage(image000, mask) # Grab sources in the first image threshold = thres*std1 sources = ph.daofind(image1, threshold=threshold, fwhm=fwhm) # Filter out sources close to the edges sX, sY = sources['xcentroid'][np.where( (sources['xcentroid']>=20) & (sources['xcentroid']<=1000) & (sources['ycentroid']>=20) & (sources['ycentroid']<=1000) )], sources['ycentroid'][np.where( (sources['xcentroid']>=20) & (sources['xcentroid']<=1000) & (sources['ycentroid']>=20) & (sources['ycentroid']<=1000) )] Xs1 = sX Ys1 = sY # Grab sources in the second image threshold = thres*std2 sources = ph.daofind(image2, threshold=threshold, fwhm=fwhm) # Filter out sources close to the edges sX, sY = sources['xcentroid'][np.where( (sources['xcentroid']>=20) & (sources['xcentroid']<=1000) & (sources['ycentroid']>=20) & (sources['ycentroid']<=1000) )], sources['ycentroid'][np.where( (sources['xcentroid']>=20) & (sources['xcentroid']<=1000) & (sources['ycentroid']>=20) & (sources['ycentroid']<=1000) )] Xs2 = sX Ys2 = sY # Grab sources in the third image threshold = thres*std3
def compute(data):
mean, median, std = sigma_clipped_stats(data, sigma=3.0, iters=5)
sources = daofind(data - median, fwhm=3.0, threshold=5.*std)
return sources
def daofind_centroid(img, init=None, daofind_kwargs=None):
"""
Find centroids using photutils.daofind.
If init==None, all centroids found will be returned, along with
a flag indicating how many centroids were found.
If init is a (RA, Dec) pair of PIXEL coords, the closest centroid
will be returned, along with a flag indicating how many centroids were
found.
Inputs
------
img: array-like
a 2-D image
init: array-like, length 2 (optional)
if provided, only return the source closest to the initial position
daofind_kwargs: dict (optional)
keyword arguments for photutils.daofind function
default fwhm=2.5, threshold=1000
Outputs
-------
coords: Table
xcentroid and ycentroid are the relevant columns
num_sources:
number of sources found. Note that if init is supplied,
num_sources may be >1 but only one row of coords is returned
"""
# if no daofind arguments provided, use default fwhm and background
if daofind_kwargs is None:
daofind_kwargs = dict()
daofind_kwargs["fwhm"] = daofind_kwargs.get("fwhm", 2.5)
daofind_kwargs["threshold"] = daofind_kwargs.get("threshold", 1e3)
# find sources
sources = photutils.daofind(img, **daofind_kwargs)
num_sources = len(sources)
logging.debug("%d sources", num_sources)
logging.debug(sources)
# if an initial position is provided, only return the closest source
if (init is not None) and (num_sources>1):
ra, dec = init
sep = np.sqrt((sources["xcentroid"] - ra)**2 +
(sources["ycentroid"] - dec)**2)
loc = np.argmin(sep)
coords = sources[loc]
elif (init is not None) and (num_sources==1):
coords = sources[0]
else:
coords = sources
return coords, num_sources
# blue mosaic b_nan = np.isnan(mosaic_b) b_img_raw = mosaic_b b_img_raw[b_nan] = 1 b_img = b_img_raw[175:1255, 125:1205] sig_scal = 14 FWHM = 4 ap_r = 6.0 ap_v = 6.0 ap_b = 6.0 #background for red filter sig_r = np.std(r_img[165:195, 330:360]) # no stars source_r = daofind(r_img, fwhm=FWHM, threshold=sig_scal * sig_r) #background for vis filter sig_v = np.std(v_img[165:195, 330:360]) # no stars source_v = daofind(v_img, fwhm=FWHM, threshold=sig_scal * sig_v) #background for blue filter sig_b = np.std(b_img[165:195, 330:360]) # no stars source_b = daofind(b_img, fwhm=FWHM, threshold=sig_scal * sig_b) # position of each star r_position = zip(source_r['xcentroid'], source_r['ycentroid']) v_position = zip(source_v['xcentroid'], source_v['ycentroid']) b_position = zip(source_b['xcentroid'], source_b['ycentroid']) # aperatures around stars
ground_truth = make_random_gaussians(num_sources, [min_flux, max_flux],
[min_xmean, max_xmean],
[min_xmean, max_xmean],
[sigma_psf, sigma_psf],
[sigma_psf, sigma_psf],
random_state=123)
shape = (256, 256)
image = (make_gaussian_sources(shape, ground_truth) +
make_noise_image(shape, type='poisson', mean=1., random_state=123))
ground_truth.write('input.html')
# estimate background as the median after sigma clipping the sources
_, bkg, std = sigma_clipped_stats(image, sigma=3.0, iters=5)
# find potential sources with daofind
sources = daofind(image - bkg, threshold=4.0*std,
fwhm=sigma_psf*gaussian_sigma_to_fwhm)
intab = Table(names=['id', 'x_0', 'y_0', 'flux_0'],
data=[sources['id'], sources['xcentroid'],
sources['ycentroid'], sources['flux']])
intab.write('intab.html')
groups = daogroup(intab, crit_separation=2.0*sigma_psf*gaussian_sigma_to_fwhm)
plt.subplot(1, 2, 1)
plt.imshow(image, origin='lower', interpolation='nearest')
plt.title('Simulated data')
plt.xlabel('x-position (pixel units)')
plt.ylabel('y-position (pixel units)')
plt.subplot(1, 2, 2)
for i in range(len(groups)):
from photutils import daofind
from astropy.stats import sigma_clipped_stats
from photutils import find_peaks
DO_TEST = False
if (DO_TEST):
num = 200 # Number of brightest objects to keep
image_s = hbt.remove_sfit(image,4)
mean, median, std = sigma_clipped_stats(image, sigma=3.0, iters=5)
sources = daofind(image, fwhm=2.0, threshold=2.*std)
threshold = median + (10.0 * std) # Ten sigma
tbl = find_peaks(image, threshold, box_size=5)
sources.sort('flux') # Sort in-place
tbl.sort('peak_value')
if (num > 0):
index_start = -num
else:
index_start = 0
x_phot = np.array(sources['xcentroid'][index_start:].data)
y_phot = np.array(sources['ycentroid'][index_start:].data)
pickle.dump(cat, lun)
lun.close()
print("Wrote: " + file_stars_pkl)
#==============================================================================
# Do photometry on the NH image
#==============================================================================
# Use DAOphot to search the image for stars. It works really well.
# However, it returns position only -- no magnitudes.
#if (sequence == 'MVIC_D211'):
# im = im[:,0:350]
mean, median, std = sigma_clipped_stats(im, sigma=3.0, iters=5)
sources = daofind(im - median, fwhm=4.0, threshold=2.*std)
x_phot = sources['xcentroid']
y_phot = sources['ycentroid']
flux = sources['flux']
points_phot = np.transpose((y_phot, x_phot, flux)) # Create an array N x 2
# Sort them, bright to faint
order = (np.argsort(points_phot[:,2]))[::-1] # Sort from brightest to faintest
points_phot = points_phot[order]
#==============================================================================
# For each star in the catalog, go and see if there is a star we found with a center within, say, 2 pixels.
#==============================================================================
dec1 = starCatalog['DEJ2000']
catalog1 = SkyCoord(ra=ra1, dec=dec1, frame='fk5')
# Read in the image and find tars in the image
Ifile = (stokesDir + delim + '_'.join([thisTarget, thisWaveband, 'I']) +
'.fits')
stokesI = Image(Ifile)
mean, median, std = sigma_clipped_stats(stokesI.arr, sigma=3.0, iters=5)
threshold = median + 3.0 * std
fwhm = 3.0
sources = daofind(stokesI.arr,
threshold,
fwhm,
ratio=1.0,
theta=0.0,
sigma_radius=1.5,
sharplo=0.2,
sharphi=1.0,
roundlo=-1.0,
roundhi=1.0,
sky=0.0,
exclude_border=True)
# Convert source positions to RA and Dec
wcs = WCS(stokesI.header)
ADstars = wcs.all_pix2world(sources['xcentroid'], sources['ycentroid'], 0)
catalog2 = SkyCoord(ra=ADstars[0] * u.deg,
dec=ADstars[1] * u.deg,
frame='fk5')
###
### This slow, meat-axe method was useful for verification.
where_are_NaNs = np.isnan(image)
image[where_are_NaNs] = 0.
header = hdu[0]
RA[i] = header.header['RA']
DEC[i] = header.header['DEC']
MJD[i] = header.header['MJD-OBS']
wcs = pywcs.WCS(hdu[0].header)
hdu_tbl = pyfits.open(tables[i])
header_tbl = hdu_tbl[0]
MJD_tbl[i] = header_tbl.header['MJD-OBS']
thr = 15.
junk = daofind(image, fwhm=5.0, threshold=thr)
len_junk = len(junk)
while len_junk > 80:
thr=thr+5.
junk = daofind(image, fwhm=5.0, threshold=thr)
len_junk = len(junk)
if len(junk) <= 5:
junk = daofind(image, fwhm=5.0, threshold=10.)
if len(junk) <= 5:
junk = daofind(image, fwhm=5.0, threshold=5.)
try:
x=np.array(junk['xcentroid'])
y=np.array(junk['ycentroid'])
id_=np.array(junk['id'])
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))
srcs = sep.extract(sim.bkg_sub_img, thresh=12*sim.bkg.globalrms)
posflux = srcs[['x','y', 'flux']]
psf_guess = psf.IntegratedGaussianPRF(flux=1, sigma=8)
psf_guess.flux.fixed = False
psf_guess.x_0.fixed = False
psf_guess.y_0.fixed = False
psf_guess.x_0.sigma = True
fitshape = (64,64)
intab = Table(names=['x_0', 'y_0', 'flux_0'], data=posflux)
#subimi = psf.subtract_psf(sim.bkg_sub_img, psf_guess, posflux)
outtabi = psf.psf_photometry(sim.bkg_sub_img, intab, psf_guess, fitshape,
store_fit_info=True)
outtabi['flux_input'] = intab['flux_0']
# with daofind there are lots of garbage
found = daofind(sim.bkg_sub_img, threshold=5*sim.bkg.globalrms, fwhm=10,
exclude_border=True)
intab2 = Table(names=['x_0', 'y_0', 'flux_0'], data=[found['xcentroid'],
found['ycentroid'], found['flux']])
outtabi2 = psf.psf_photometry(sim.bkg_sub_img, intab2, psf_guess, fitshape,
store_fit_info=True)
outtabi2['flux_input'] = intab2['flux_0']
def daofind(imagename, ext=1, outname='default', threshold=3., fwhm=1.5, \
ratio=1.0, theta=0.0, sigma_radius=1.5, sharplo=0.2, \
sharphi=1.0, roundlo=-1.0, roundhi=1.0, sky=0.0, \
exclude_border=True, use_bkgrd=True):
""" Extends `photutils.daofind`, so that outputs a coordinate file
with my formatting from `photutils_plus.phot_tools.write_out_photfile`.
(Later, if I'm clever, use inheritance.)
Parameters
----------
imagename : string
Name of the FITS file.
ext : int
The extension of the FITS file to read.
outname : string
Name of the output coordinate file. If "default", becomes,
"<imagename>.coo."
threshold : int
Threshold for search. Default of "3."
fwhm : float
The full width half max. Default of "1.5."
ratio : float
Default of "1.0."
theta : float
Default of "0.0."
sigma_radius : float
Default of 1.5."
sharplo : float
Default of "0.2."
sharphi : float
Default of "1.0."
roundlo : float
Default of "-1.0."
roundhi : float
Default of "1.0."
sky : float
Default of "0.0."
exclude_border : {True, Flase}
When True, masks out a 10-pixel border on image from search.
use_bkgrd : {True, False}
Setting on will multiply the background by the threshold to
obtain a new threshold value.
Returns
-------
coo_tab : astropy.Table
Table containing the coordinates.
"""
# Fetch function metadata.
current_params = locals()
func_name = sys._getframe().f_code.co_name #daofind.__name__
# Read in FITS file.
hdulist = fits.open(imagename)
data = hdulist[ext].data
# Mask 10 pixels from border. I find the `exclude_border` in
# photutils.daofind inadiquate.
if exclude_border:
data[:,0:10] = -99999.0
data[:,-10:] = -99999.0
data[0:10,:] = -99999.0
data[-10:,:] = -99999.0
# Get the background.
if use_bkgrd:
bkg_sigma = mad_std(data)
threshold = threshold * bkg_sigma
coo_tab = photutils.daofind(data=data,
threshold=threshold,
fwhm=fwhm,
ratio=ratio,
theta=theta,
sigma_radius=sigma_radius,
sharplo=sharplo,
sharphi=sharphi,
roundlo=roundlo,
roundhi=roundhi,
sky=sky,
exclude_border=exclude_border)
# ??Insert background value into coo_tab??
# Create basename for out file.
if outname == 'default':
baseoutname = imagename + '.coo'
else:
baseoutname = outname
# Check whether file already exists. If yes, append number.
fileoutname = baseoutname
i=1
while os.path.isfile(fileoutname):
fileoutname = baseoutname
fileoutname += ('.' + str(i))
i += 1
write_out_photfile(fileoutname, coo_tab, current_params, func_name)
return coo_tab
for i in tqdm(range(len(stardata) - 2)):
xx = stardata[i,:]
plt.plot(pixel,xx,marker='.', c='r')
'''
'''
statistics on the image, determines background noise, standard deviation and mean photon counts
to find all objects in the image that are above some threshold, and are NEAR some FWHM value.
typing "sources" without quotes in the spyde terminal will print all of the sources, x,y coords
of their locations in the image, and a peak photon count, and corresponding flux and magnitude values
for the sources as well.
'''
mean, median, std = sigma_clipped_stats(cutdata, sigma=5.0, iters=10)
sources = daofind(cutdata - median, fwhm=targetFWHM, threshold=2. * std)
#sources = sources[(sources['peak'] > 1000.)]
#positions = SC(ra=targetRAdeg * u.deg, dec = targetDECdeg * u.deg, frame='fk5')
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r=4.0)
norm = ImageNormalize(stretch=SqrtStretch())
'''Uncomment these plot commands to view the image. plt.imshow plots the whole image, and apertures.plot
plots circles around all of the things in the image that have been determined to be sources.'''
#plt.imshow(cutdata, cmap='Spectral', origin='lower', norm=norm)
#apertures.plot(color='blue',lw=1.5, alpha=0.5)