def zsview(im, cmap=pl.cm.gray, figsize=(8,5), contours=False, ccolor='r'):
z1, z2 = zscale(im)
pl.figure(figsize=figsize)
pl.imshow(im, vmin=z1, vmax=z2, origin='lower', cmap=cmap, interpolation='none')
if contours:
pl.contour(im, levels=[z2], origin='lower', colors=ccolor)
pl.tight_layout()
def display1D(self):
self.spec1DView.canvas.ax.clear()
xx=np.arange(self.spec1d.size)
# if(self.lastWLCFile):
# xx=self.wavelen
if (self.wlc):
xx=self.wavelen
#self.spec1DView.canvas.ax.plot(xx,self.spec1d,'b-')
self.spec1DView.canvas.ax.plot(xx,self.spec1d,'b-')
#medskyUnderAp = self.medsky1d*(self.extractWin_x1-self.extractWin_x0)
if (self.imagetype=='science'):
medskyUnderAp = ( self.medsky1d - self.medskylev ) *(self.extractWin_x1-self.extractWin_x0)
self.spec1DView.canvas.ax.plot(xx,medskyUnderAp,'r-')
#self.spec1DView.canvas.ax.plot(xx,medskyUnderAp,'r-')
# print np.shape(xx)
# print np.shape(medskyUnderAp)
self.spec1DView.canvas.ax.set_title(self.latestFrame)
y0,y1=zscale(self.spec1d)
# self.spec1DView.canvas.ax.set_ylim([y0,y1])
if(self.imagetype=='arc'): y1=np.max(self.spec1d)
if(y0<100.0): y0=0.0
if(y1>100.0):
self.spec1DView.canvas.ax.set_ylim([y0,y1])
if (self.wlc):
self.spec1DView.canvas.ax.set_xlabel(r'$\lambda (\AA)$')
else:
self.spec1DView.canvas.ax.set_xlabel('x (pix)')
self.spec1DView.canvas.draw()
def plot_image(self, result, title='ScatterPlot', grid=False):
if ZSCALE == True:
z_min, z_max = zscale.zscale(result.data_array, nsamples=2000, contrast=0.25)
else:
z_min = result.zmin
z_max = result.zmax
fig, graph = plt.subplots()
# define the colormap and norm
#cmap = plt.get_cmap('RdYlBu_r')
#cmap = plt.get_cmap('jet')
cmap = plt.get_cmap('Greys_r')
cmap.set_over('#FFFFFF')
cmap.set_under('#000000')
cmap.set_bad('#00FF00')
image = graph.imshow(result.get_img_array(self.master), cmap=cmap, vmin=z_min, vmax=z_max, origin='lower',
interpolation='nearest',
extent=([result.xmin, result.xmax, result.ymin, result.ymax])
)
graph.set_xlabel(result.x_name, fontsize=20)
graph.set_ylabel(result.y_name, fontsize=20)
graph.set_title(title)
graph.grid(grid)
colorbar = fig.colorbar(image)
colorbar.set_label(result.z_name, fontsize=20)
return fig
def do_image(self,undone_image,contour_file=None,contour_levels=5,contour_color=None):
"""Make an image"""
version = 4
success = False
print("Making an image for "+undone_image)
print("Using files... "+str(self.images[undone_image]))
cube_data = pyfits.getdata(self.filenames[self.images[undone_image][0]])
z1_k,z2_k = zscale.zscale(np.nan_to_num(cube_data[0,...]))
z1_h,z2_h = zscale.zscale(np.nan_to_num(cube_data[1,...]))
z1_j,z2_j = zscale.zscale(np.nan_to_num(cube_data[2,...]))
aplpy.make_rgb_image(self.filenames[self.images[undone_image][0]],
self.filenames[self.images[undone_image][0]].replace('.fits','_2d.png'),
vmin_r = z1_k,vmax_r = z2_k,vmin_g = z1_h, vmax_g = z2_h, vmin_b = z1_j, vmax_b = z2_j)
gc = aplpy.FITSFigure(self.filenames[self.images[undone_image][0]].replace('.fits','_2d.fits'))
gc.show_rgb(self.filenames[self.images[undone_image][0]].replace('.fits','_2d.png'))
gc.tick_labels.set_xformat("dd.dd")
gc.tick_labels.set_yformat("dd.dd")
#out_filename = self.filenames[undone_image].replace('.fits','.pdf')
if contour_file:
#gc.show_contour(contour_file,levels=contour_levels,colors="black",linewidths = 3.,smooth=3.,alpha=0.5)
gc.show_contour(contour_file,levels=contour_levels,colors=contour_color,linewidths = 1.)
#chunks = contour_file.split('_')
#print(chunks)
#linename = chunks[3]
#moment_name = chunks[4][0:-5]
#tag = "_"+linename+"_"+moment_name
#print(tag)
#out_filename = out_filename.replace('.pdf',tag+'.pdf')
#self.add_labels(gc,undone_image,linename,moment_name = moment_name,color=contour_color)
else:
pass
#self.add_labels(gc,undone_image)
#gc.show_rectangles(self.apos,self.bpos,0.0625,0.0625,ec='w',lw=2,facecolor='none',zorder=1)
#gc.show_ellipses(self.apos,self.bpos,self.awin*2,self.bwin*2,angle=self.twin,facecolor='none',ec='green',lw=2)
#print(self.awin,self.bwin)
success = True
if success:
self.report_success(undone_image,version)
return(gc)
def get_offset_center(f, plot=False, interactive=False):
'''
Given a fits image, returns the offset in Ra, DEC, that needs to be applied for the telescope tp go
from the current pointing position, to the coodinates of the object specified in the fits file.
'''
if(not os.path.isfile(f)):
print "File %s does not exist! Returning Zero offsets..."%f
return -1, 0,0
else:
image = pf.open(f)
wcs = pywcs.WCS(image[0].header)
rra, rdec = cc.hour2deg(image[0].header['OBJRA'],image[0].header['OBJDEC'] )
x, y = np.round(wcs.wcs_sky2pix(rra, rdec, 0), 0)
pra, pdec = wcs.wcs_pix2sky(np.array([[1293., 1280.]] , np.float_), 0)[0]
dra, ddec = cc.get_offset(pra, pdec, rra, rdec)
xl, yu = np.round(wcs.wcs_sky2pix(rra+90./3600, rdec-90./3600, 0), 0)
xu, yl = np.round(wcs.wcs_sky2pix(rra-90./3600, rdec+90./3600, 0), 0)
imageloc = image[0].data.T[xl:xu,yl:yu]
if imageloc.shape[0]==0 or imageloc.shape[1]==0:
logger.warn( "Astrometry has FAILED on this! The object is outside the frame! Resending to the numb astrometric solution")
logger.error("Astrometry has FAILED on this! The object is outside the frame! Resending to the numb astrometric solution")
print "Pixels are", xl, xu, yl, yu
try:
code, dra, ddec = get_offset_center_failed_astro(f, plot=plot, interactive=interactive)
return 2, dra, ddec
except:
return -1,0,0
if(plot):
plt.figure(figsize=(8,8))
zmin, zmax = zscale.zscale(imageloc)
#print zmin, zmax, imageloc, (xl,xu,yl,yu)
obj = fitsutils.get_par(f, "OBJECT")
plt.suptitle(obj, fontsize=20)
plt.imshow(imageloc.T, extent=(xl[0],xu[0],yl[0],yu[0]), aspect="equal", interpolation="none", origin="lower", vmin=zmin, vmax=zmax)
plt.plot(1293., 1280., "ws", ms=7, label="Current pointing")
plt.plot(x, y, "b*", ms=10, label="Target pointing")
plt.gca().invert_xaxis()
plt.legend()
if (interactive):
plt.show()
else:
plt.savefig(os.path.join(os.path.dirname(f).replace("raw", "phot"), os.path.basename(f).replace(".fits", "_a.png")))
plt.clf()
return 0, dra, ddec
def updatezScaling(self):
if (str(self.ui.comboBoxScaling.currentText()) == 'ZScale'):
self.z0, self.z1 = zscale(self.data2D)
if (str(self.ui.comboBoxScaling.currentText()) == 'Min/Max'):
self.z0 = np.min(self.data2D)
self.z1 = np.max(self.data2D)
if (str(self.ui.comboBoxScaling.currentText()) == 'Manual'):
self.z0 = float(self.ui.lineEditz0.text())
self.z1 = float(self.ui.lineEditz1.text())
self.logger.debug("updating scaling")
self.ui.lineEditz0.setText("%.3f" % (self.z0))
self.ui.lineEditz1.setText("%.3f" % (self.z1))
# self.displayRaw2D()
try:
self.display2D()
self.plotProfile()
except:
pass
def updatezScaling(self):
if(str(self.ScalingCombo.currentText())=='ZScale'):
self.z0,self.z1=zscale(self.data2D)
if(str(self.ScalingCombo.currentText())=='Min/Max'):
self.z0=np.min(self.data2D)
self.z1=np.max(self.data2D)
if(str(self.ScalingCombo.currentText())=='Manual'):
self.z0=float(self.z0Fill.text())
self.z1=float(self.z1Fill.text())
if (idebugGUI): print "updating scaling"
self.z0Fill.setText("%.3f"%(self.z0))
self.z1Fill.setText("%.3f"%(self.z1))
self.displayRaw2D()
try:
self.displaySS2D()
self.plotProfile()
except:
pass
def plot_image(infile, outfile, ext):
print(infile, outfile, ext)
fig = plt.figure(figsize=(10, 10))
ax = fig.gca()
fig.suptitle('%s' % (outfile), fontsize=15)
try:
hdu = fits.open(infile)
except:
print("cannot open file!!! ")
#print hdu[0].header
image_data = hdu[ext].data
#ax.axes.get_xaxis().set_visible(False)
#ax.axes.get_yaxis().set_visible(False)
image_data[image_data < 0] = 1e-20
clow, chigh = zs.zscale(image_data, nsamples=2000)
# percentage
'''
clow = np.percentile(image_data,50)
chigh = np.percentile(image_data,60)
'''
#plt.imshow(image_data, cmap='afmhot',interpolation='none', norm=LogNorm(),clim=[clow,chigh])
plt.imshow(image_data,
cmap='binary',
interpolation='none',
clim=[clow, chigh],
origin='lower')
#plt.imshow(image_data, cmap='gray',interpolation='nearest', clim=[clow,chigh])
#fig.set_tight_layout(True)
plt.savefig(outfile, pad_inches=0.)
from astropy.io import fits
from zscale import zscale
'''
Codigo para HDU1
'''
master_bias = fits.getdata("../master/master_bias.fits", 1)
master_flats = fits.getdata("../master/master_flats.fits", 1)
sci1 = fits.getdata("../data/dat.025.fits", 1) # Get HDU1
sci11 = sp.divide(sci1 - master_bias, master_flats)
mn, mx = zscale(sci11)
plt.clf()
plt.imshow(sci11, vmin=mn, vmax=mx)
plt.show()
x = 340#411
y = 543#1838
SR = 45 # 30
"""
# second :
x = 311
y = 1451
SR = 30
def PDF(cutout):
"""
Create a pdf file with the plot of your objects in different filters
Parameters
----------
cutout: list
array of images N filt x N sources
"""
# create the pdf
pdfOut = PdfPages(dirpath + 'stampPDF.pdf')
# it's a sum to derive the good number of stamp
p = 0
# Placement of the Id
sizeId = cutout[0][0].shape
#
while p < np.size(cutout[0]) - 1:
print(p)
if np.size(cutout[0]) - p > 10:
fig, axs = plt.subplots(10, len(cutout), figsize=(8.3, 11.7))
else:
fig, axs = plt.subplots(np.size(cutout[0]) - p,
len(cutout),
figsize=(8.3, 11.7))
# Loop over the 10 sources to be include in one PDF page
for k in range(10):
# id of object
axs[k, 0].text(-sizeId[0], sizeId[0] / 2, tbl['id'][p])
# Loop over the filters
for j in range(len(cutout)):
# Display the image
mappa = axs[k, j].imshow(cutout[j][p].data,
cmap='afmhot',
origin='lower',
interpolation='nearest')
axs[k, j].set_title(filters[j], fontsize=5, pad=2.5)
axs[k, j].get_xaxis().set_visible(False)
axs[k, j].get_yaxis().set_visible(False)
# Plot circular aperture at the coordinate of your object
apertures = SkyCircularAperture(tbl['skycoord'][p],
r=1.5 * u.arcsec)
aperturesPix = apertures.to_pixel(cutout[j][p].wcs)
aperturesPix.plot(color='cyan', ax=axs[k, j], lw=1, alpha=0.5)
# DS9 zscale
zrange = zscale.zscale(cutout[j][p].data)
mappa.set_clim(zrange)
# it's a sum to derive the good number of stamp
if p < np.size(cutout[0]) - 1:
p = p + 1
else:
break
# save the page
plt.savefig(pdfOut, format='pdf')
pdfOut.close()
return
def stack(cutout, radiusStack, gain, zeroPoint, wavelenght):
"""
Print out pdf with stamp stacking and plot graph magnitude
in function of wavelenght
Parameters
---------_
cutout: list
array of images N filt x N sources
radiusStack: float
radius in pixel
zeroPoint: float
Zero point of your image
gain: float
The gain of your image
wavelenght: float
wavelenght of each filters
"""
# define the resulting stacked image as a cutout with the same dimension of the input cutouts
o = 0
# Create pdf where are storing stack stamp and graph
pdfOut = PdfPages(dirpath + 'stacking.pdf')
fig, axs = plt.subplots(1, len(filters), figsize=(5, 5))
# Saving magnitude and error
mag = [[0.0 for i in range(len(filters))] for j in range(2)]
for i in cutout: # i is a list of galaxies
print('Photometry ' + filters[o])
# Assuming that the shape is the same for all galaxies
#this is now a tuple, e.g. (25,25), so the code will work also for rectangular stamps
sizeStack = i[0].shape
#this is now a tuple, e.g. (25,25), so the code will work also for rectangular stamps
print(sizeStack) #just for testing
stackImg = np.zeros(sizeStack)
#LOOP over pixels: (I changed a lot here)
# fait tous les pixel x
for x in range(sizeStack[0]):
# fait tous les pixel y
for y in range(sizeStack[1]):
# for the given pixels, collect the flux from all stamps into a list
pxl = [] #use an empty list, but it can be also np.zeros()
for stamp in i:
pxl.append(stamp.data[x, y])
# caluclate the median:
stackImg[x, y] = np.median(pxl)
axs[o].set_title(filters[o], fontsize=5, pad=2.5)
axs[o].get_xaxis().set_visible(False)
axs[o].get_yaxis().set_visible(False)
mappa = axs[o].imshow(stackImg,
cmap='afmhot',
origin='lower',
interpolation='nearest')
zrange = zscale.zscale(stackImg)
mappa.set_clim(zrange)
# Mask sources
mask = make_source_mask(stackImg, snr=1, npixels=3, dilate_size=3)
# Derive the background and the rms image
bkg = Background2D(
stackImg,
int(sizeStack[0] / 10),
filter_size=1,
sigma_clip=None,
bkg_estimator=SExtractorBackground(SigmaClip(sigma=2.5)),
bkgrms_estimator=StdBackgroundRMS(SigmaClip(sigma=2.5)),
exclude_percentile=90,
mask=mask)
if gain[o] == 0.:
gain[o] = 1000000000000000000000000000.
# Calculate the total error
error = calc_total_error(stackImg, bkg.background_rms, gain[o])
# Define a cicularAperture in the wcs position of your objects
apertures = CircularAperture(
(int(sizeStack[0] / 2), int(sizeStack[1] / 2)), r=radiusStack[o])
# Derive the flux and error flux
photometry = aperture_photometry(stackImg - bkg.background,
apertures,
error=error)
# Saving magnitude and error
mag[0][o] = -2.5 * np.log10(
photometry['aperture_sum'][0]) + zeroPoint[o]
mag[1][o] = 1.0857 * (photometry['aperture_sum_err'][0] /
photometry['aperture_sum'][0])
o = o + 1
plt.savefig(pdfOut, format='pdf')
# Plot
plt.clf()
y = mag[0]
x = wavelenght
plt.plot(x, y, 'ro')
xlimL = wavelenght[0] - 1000
xlimR = wavelenght[len(wavelenght) - 1] + 1000
plt.xlim(xlimL, xlimR)
plt.ylim(32, 22)
plt.errorbar(wavelenght,
mag[0],
yerr=mag[1],
fmt='none',
capsize=10,
ecolor='blue',
zorder=1)
plt.xlabel('wavelenght(Å)')
plt.ylabel('magnitude')
plt.title('')
plt.savefig(pdfOut, format='pdf')
pdfOut.close()
np.savetxt(dirpath + 'fluxStack.txt',
mag,
header='Ligne1: mag, Ligne2: mag_err')
return
def get_offsets_A_B(f, plot=False, interactive=False):
'''
Returns the offsets for A and B, so that when offseting, the images do not overlap.
Example fits:/scr2/nblago/Projects/SEDM/data/finders/f_b_a_rPTF15fks_r.fits
'''
from scipy import stats
image = pf.open(f)
data = image[0].data.T
wcs = pywcs.WCS(image[0].header)
ra, dec = cc.hour2deg(image[0].header['OBJRA'], image[0].header['OBJDEC'])
obj = fitsutils.get_par(f, "OBJECT")
pra, pdec = wcs.wcs_sky2pix(ra, dec, 0)
#Get a local image
xl, yl = np.round(wcs.wcs_sky2pix(ra + 30. / 3600, dec - 30. / 3600, 0), 0)
xu, yu = np.round(wcs.wcs_sky2pix(ra - 30. / 3600, dec + 30. / 3600, 0), 0)
imageloc = image[0].data[xl:xu, yu:yl]
bkg = np.median(imageloc)
perc10 = np.percentile(imageloc, 15)
perc90 = np.percentile(imageloc, 85)
mask = (image[0].data > perc10) * (image[0].data < perc90)
bkg_std = np.std(image[0].data[mask]) # mad(image[0].data)
linestyles = ['solid', 'dashed', 'dashdot', 'dotted', 'solid']
offsets = np.array([[+3, -4], [+3, +4], [+2, +4], [+2, +2]])
Noff = len(offsets)
pvalues = np.zeros((Noff, 2))
if (plot):
fig, axarr = plt.subplots(2,
Noff,
sharey='row',
figsize=(6 * len(offsets), 12))
axarr = np.array(axarr, ndmin=1)
for i, off in enumerate(offsets):
ra_off = ra - off[0] / 3600.
dec_off = dec - off[1] / 3600.
ra_off_op = ra + off[0] / 3600.
dec_off_op = dec + off[1] / 3600.
prao, pdeco = wcs.wcs_sky2pix(ra + off[0] / 3600.,
dec + off[1] / 3600., 0)
praosym, pdecosym = wcs.wcs_sky2pix(ra - 2 * off[0] / 3600.,
dec - 2 * off[1] / 3600., 0)
#Extract sample window to check wether the background matches well.
#number on samples on each side
ns = 7
sample = data[prao - ns:prao + ns, pdeco - ns:pdeco + ns]
sm = np.median(sample)
sstd = np.std(sample[(sample < 6000) * (sample > 0)])
bkg_prob = np.minimum(1 - stats.norm.cdf(sm, bkg, bkg_std),
stats.norm.cdf(sm, bkg, bkg_std))
#Extract sample window to check wether the background matches well.
sample = data[praosym - 7:praosym + 7, pdecosym - 7:pdecosym + 7]
smo = np.median(sample)
sstdo = np.std(sample)
bkg_probo = np.minimum(1 - stats.norm.cdf(smo, bkg, bkg_std),
stats.norm.cdf(smo, bkg, bkg_std))
pvalues[i] = np.array([bkg_prob, bkg_probo])
if (plot):
#Retrieve the image of the object
xl, yl = wcs.wcs_sky2pix(ra + 20. / 3600, dec - 20. / 3600, 0)
xu, yu = wcs.wcs_sky2pix(ra - 20. / 3600, dec + 20. / 3600, 0)
ifuwin = data[xl:xu, yu:yl]
#Retrieve the offset A image of the object
x0, y0 = wcs.wcs_sky2pix(ra_off + 20. / 3600, dec_off - 20. / 3600,
0)
x1, y1 = wcs.wcs_sky2pix(ra_off - 20. / 3600, dec_off + 20. / 3600,
0)
ifuwin1 = data[x0:x1, y1:y0]
nx, ny = ifuwin1.shape
#print nx,ny, ifuwin1.shape
#Retrieve the offset A image of the object
x0, y0 = wcs.wcs_sky2pix(ra_off_op + 20. / 3600,
dec_off_op - 20. / 3600, 0)
x1, y1 = wcs.wcs_sky2pix(ra_off_op - 20. / 3600,
dec_off_op + 20. / 3600, 0)
ifuwin2 = data[x0:x0 + nx, y1:y1 + ny]
#Plot the A and B
zmin, zmax = zscale.zscale(ifuwin)
zmin1, zmax1 = zscale.zscale(ifuwin1 - ifuwin2)
axarr[0, i].imshow(ifuwin.T,
aspect="auto",
vmin=zmin,
vmax=zmax,
extent=(20, -20, -20, 20),
alpha=0.5)
axarr[1, i].imshow(ifuwin1.T - ifuwin2.T,
aspect="auto",
vmin=zmin1,
vmax=zmax1,
extent=(20, -20, -20, 20),
alpha=0.5) #, cmap=matplotlib.cm.RdBu_r)
#axarr[1, i].imshow(-1 * ifuwin2.T, aspect="auto", vmin=zmin2, vmax=zmax2, extent=(20,-20,-20,20), alpha=0.5)#, cmap=matplotlib.cm.RdBu_r)
axarr[0, i].scatter(0, 0, marker="x", s=20)
axarr[1, i].scatter(0, 0, marker="x", s=20)
axarr[0, i].set_xlabel("OBJRA")
axarr[0, i].set_ylabel("OBJDEC")
axarr[1, i].set_xlabel("OBJRA")
axarr[1, i].set_ylabel("OBJDEC")
axarr[0, i].text(
19,
17,
"Red stats: $\mu=$%.2f, $\sigma=$%.2f, p-value=%.5f" %
(sm, sstd, bkg_prob),
bbox=dict(facecolor='white', alpha=0.5))
axarr[0, i].text(
19,
14,
"Blue stats: $\mu=$%.2f, $\sigma=$%.2f, p-value=%.5f" %
(smo, sstdo, bkg_probo),
bbox=dict(facecolor='white', alpha=0.5))
axarr[0, i].text(19,
11,
"Background stats: $\mu=$%.2f, $\sigma=$%.2f" %
(bkg, bkg_std),
bbox=dict(facecolor='white', alpha=0.5))
#r = plt.Circle((prao-pra, pdeco-pdec), 5, facecolor="none", edgecolor="red", lw=2)
#b = plt.Circle((praosym-pra, pdecosym-pdec), 5, facecolor="none", edgecolor="blue", lw=2)
r = plt.Circle((2 * off[0], +2 * off[1]),
2,
facecolor="none",
edgecolor="red",
lw=3,
ls=linestyles[i])
b = plt.Circle((-2 * off[0], -2 * off[1]),
2,
facecolor="none",
edgecolor="blue",
lw=3,
ls=linestyles[i])
#Plot the true location of the IFU
patches = []
Path = mpath.Path
path_data = [(Path.MOVETO, [20, -12]), (Path.LINETO, [20, 20]),
(Path.LINETO, [-13, 13]), (Path.LINETO, [-20, -18]),
(Path.CLOSEPOLY, [20, 10])]
codes, verts = zip(*path_data)
path = mpath.Path(verts, codes)
patch = mpatches.PathPatch(path)
patches.append(patch)
collection = PatchCollection(patches, cmap=plt.cm.YlGn, alpha=0.2)
colors = np.linspace(0, 1, len(patches))
collection.set_array(np.array(colors))
axarr[0, i].add_artist(r)
axarr[0, i].add_artist(b)
axarr[1, i].add_collection(collection)
plt.suptitle(obj, fontsize=20)
prod = np.prod(pvalues, axis=1)
if (plot and interactive):
axarr[0, np.argmax(prod)].text(0, 0, "WINNER")
plt.show()
elif (plot):
axarr[0, np.argmax(prod)].text(0, 0, "WINNER")
plt.savefig(
os.path.join(
os.path.dirname(f).replace("raw", "phot"),
os.path.basename(f).replace(".fits", "_ab.png")))
plt.clf()
return 0, offsets[np.argmax(prod)], -2 * offsets[np.argmax(prod)]
def get_offset_center(f, plot=False, interactive=False):
'''
Given a fits image, returns the offset in Ra, DEC, that needs to be applied for the telescope tp go
from the current pointing position, to the coodinates of the object specified in the fits file.
'''
if (not os.path.isfile(f)):
print "File %s does not exist! Returning Zero offsets..." % f
return -1, 0, 0
else:
image = pf.open(f)
wcs = pywcs.WCS(image[0].header)
rra, rdec = cc.hour2deg(image[0].header['OBJRA'],
image[0].header['OBJDEC'])
x, y = np.round(wcs.wcs_sky2pix(rra, rdec, 0), 0)
pra, pdec = wcs.wcs_pix2sky(np.array([[1293., 1280.]], np.float_),
0)[0]
dra, ddec = cc.get_offset(pra, pdec, rra, rdec)
xl, yu = np.round(
wcs.wcs_sky2pix(rra + 90. / 3600, rdec - 90. / 3600, 0), 0)
xu, yl = np.round(
wcs.wcs_sky2pix(rra - 90. / 3600, rdec + 90. / 3600, 0), 0)
imageloc = image[0].data.T[xl:xu, yl:yu]
if imageloc.shape[0] == 0 or imageloc.shape[1] == 0:
logger.warn(
"Astrometry has FAILED on this! The object is outside the frame! Resending to the numb astrometric solution"
)
logger.error(
"Astrometry has FAILED on this! The object is outside the frame! Resending to the numb astrometric solution"
)
print "Pixels are", xl, xu, yl, yu
try:
code, dra, ddec = get_offset_center_failed_astro(
f, plot=plot, interactive=interactive)
return 2, dra, ddec
except:
return -1, 0, 0
if (plot):
plt.figure(figsize=(8, 8))
zmin, zmax = zscale.zscale(imageloc)
#print zmin, zmax, imageloc, (xl,xu,yl,yu)
obj = fitsutils.get_par(f, "OBJECT")
plt.suptitle(obj, fontsize=20)
plt.imshow(imageloc.T,
extent=(xl[0], xu[0], yl[0], yu[0]),
aspect="equal",
interpolation="none",
origin="lower",
vmin=zmin,
vmax=zmax)
plt.plot(1293., 1280., "ws", ms=7, label="Current pointing")
plt.plot(x, y, "b*", ms=10, label="Target pointing")
plt.gca().invert_xaxis()
plt.legend()
if (interactive):
plt.show()
else:
plt.savefig(
os.path.join(
os.path.dirname(f).replace("raw", "phot"),
os.path.basename(f).replace(".fits", "_a.png")))
plt.clf()
return 0, dra, ddec
def get_offset_center_failed_astro(f, plot=False, interactive=True):
'''
For fields where astrometry is challenging, there is a simple solution.
Find the brightest peak within the pointing error of the telescope.
As this fields will usually be centered in Standard stars and very short exposure time,
the fit intends to
'''
image = pf.open(f)
data = image[0].data
wcs = pywcs.WCS(image[0].header)
ra, dec = cc.hour2deg(image[0].header['OBJRA'], image[0].header['OBJDEC'])
pra, pdec = wcs.wcs_sky2pix(ra, dec, 0)
#Get a local image
#xl, yl = np.array(wcs.wcs_sky2pix(ra+(60./3600)*np.cos(np.deg2rad(dec)), dec-60./3600, 0), dtype=np.int)
#xu, yu = np.array(wcs.wcs_sky2pix(ra-(60./3600)*np.cos(np.deg2rad(dec)), dec+60./3600, 0), dtype=np.int)
imageloc = image[0].data.T[1293 - 150:1293 + 150, 1280 - 150:1280 + 150]
nx = 300
ny = 300
def_x = np.argmax(np.sum(imageloc, axis=0))
def_y = np.argmax(np.sum(imageloc, axis=1))
newx = pra - nx / 2. + def_x
newy = pdec - ny / 2. + def_y
pra, pdec = wcs.wcs_pix2sky(np.array([[newx[0], newy[0]]], np.float_),
0)[0]
dra, ddec = cc.get_offset(ra, dec, pra, pdec)
print "Offset", dra, ddec, "Position RA,DEC", pra, pdec
x, y, fwhmx, fwhmy, bkg, amp = fit_utils.fit_gauss(imageloc)
if (plot):
plt.figure(figsize=(8, 8))
obj = fitsutils.get_par(f, "OBJECT")
plt.suptitle(obj, fontsize=20)
zmin, zmax = zscale.zscale(imageloc)
plt.imshow(imageloc,
aspect="auto",
interpolation="none",
origin="lower",
vmin=zmin,
vmax=zmax,
extent=(0, +300, 0, +300))
plt.plot(x, y, "go", ms=20, label="Centroid using gaussiuan fit.")
plt.plot(def_x, def_y, "b*", ms=20, label="Centroid using max/min.")
plt.plot(150, 150, "wo", ms=20, label="Initial pointing")
plt.legend()
'''zmin, zmax = zscale.zscale(data)
plt.imshow(data, aspect="auto", interpolation="none", origin="lower", vmin=zmin, vmax=zmax)
plt.plot(newx, newy, "go")
plt.show()'''
if (interactive):
plt.show()
else:
plt.savefig(
os.path.join(
os.path.dirname(f).replace("raw", "phot"),
os.path.basename(f).replace(".fits", "_std.png")))
plt.clf()
return 1, ddec, dra
def display(self, pix, name=None, bufname=None, z1=None, z2=None,
transform=None, zscale=False, contrast=0.25, scale=None,
offset=None, frame=None,quiet=False):
""" Displays byte-scaled (UInt8) n to XIMTOOL device.
This method uses the IIS protocol for displaying the data
to the image display device, which requires the data to be
byte-scaled.
If input is not byte-scaled, it will perform scaling using
set values/defaults.
"""
#Ensure that the input array 'pix' is a numpy array
pix = n.array(pix)
self.z1 = z1
self.z2 = z2
# If any of the display parameters are specified here, apply them
#if z1 or z2 or transform or scale or offset or frame:
# If zscale=True (like IRAF's display) selected, calculate z1 and z2 from
# the data, and clear any transform specified in the call
# Offset and scale arew applied to the data and z1,z2, so they have no effect
# on the display
if zscale:
if transform != None:
if not quiet:
print "transform disallowed when zscale=True"
transform = None
z1, z2 = _zscale.zscale(pix, contrast=contrast)
self.set(frame=frame, z1=z1, z2=z2,
transform=transform, scale=scale, offset=offset)
# Initialize the display device
if not self.view._display or self.view.checkDisplay() is False:
self.open()
_d = self.view._display
self.handle = _d.getHandle()
# If no user specified values are provided, interrogate the array itself
# for the full range of pixel values
if self.z1 == None:
self.z1 = n.minimum.reduce(n.ravel(pix))
if self.z2 == None:
self.z2 = n.maximum.reduce(n.ravel(pix))
# If the user has not selected a specific buffer for the display,
# select and set the frame buffer size based on input image size.
if bufname == 'iraf':
useiraf = True
bufname = None
else:
useiraf = False
if bufname != None:
_d.setFBconfig(None,bufname=bufname)
else:
_ny,_nx = pix.shape
_d.selectFB(_nx,_ny,reset=1,useiraf=useiraf)
# Initialize the specified frame buffer
_d.setFrame(self.frame)
_d.eraseFrame()
# Apply user specified scaling to image, returns original
# if none are specified.
bpix = self._transformImage(pix)
# Recompute the pixel range of (possibly) transformed array
_z1 = self._transformImage(self.z1)
_z2 = self._transformImage(self.z2)
# If there was a problem in the transformation, then restore the original
# array as the one to be displayed, even though it may not be ideal.
if _z1 == _z2:
if not quiet:
print 'Error encountered during transformation. No transformation applied...'
bpix = pix
self.z1 = n.minimum.reduce(n.ravel(bpix))
self.z2 = n.maximum.reduce(n.ravel(bpix))
# Failsafe in case input image is flat:
if self.z1 == self.z2:
self.z1 -= 1.
self.z2 += 1.
else:
# Reset z1/z2 values now so that image gets displayed with
# correct range. Also, when displaying transformed images
# this allows the input pixel value to be displayed, rather
# than the transformed pixel value.
self.z1 = _z1
self.z2 = _z2
_wcsinfo = displaydev.ImageWCS(bpix,z1=self.z1,z2=self.z2,name=name)
if not quiet:
print 'Image displayed with Z1: ',self.z1,' Z2:',self.z2
bpix = self._fbclipImage(bpix,_d.fbwidth,_d.fbheight)
# Update the WCS to match the frame buffer being used.
_d.syncWCS(_wcsinfo)
# write out WCS to frame buffer, then erase buffer
_d.writeWCS(_wcsinfo)
# Now, send the trimmed image (section) to the display device
_d.writeImage(bpix,_wcsinfo)
def get_offsets_A_B(f, plot=False, interactive=False):
'''
Returns the offsets for A and B, so that when offseting, the images do not overlap.
Example fits:/scr2/nblago/Projects/SEDM/data/finders/f_b_a_rPTF15fks_r.fits
'''
from scipy import stats
image = pf.open(f)
data = image[0].data.T
wcs = pywcs.WCS(image[0].header)
ra, dec = cc.hour2deg(image[0].header['OBJRA'], image[0].header['OBJDEC'] )
obj = fitsutils.get_par(f, "OBJECT")
pra, pdec = wcs.wcs_sky2pix(ra, dec, 0)
#Get a local image
xl, yl = np.round(wcs.wcs_sky2pix(ra+30./3600, dec-30./3600, 0), 0)
xu, yu = np.round(wcs.wcs_sky2pix(ra-30./3600, dec+30./3600, 0), 0)
imageloc = image[0].data[xl:xu,yu:yl]
bkg = np.median(imageloc)
perc10 = np.percentile(imageloc, 15)
perc90 = np.percentile(imageloc, 85)
mask = (image[0].data > perc10) * (image[0].data < perc90)
bkg_std = np.std(image[0].data[mask])# mad(image[0].data)
linestyles = ['solid' , 'dashed' , 'dashdot' , 'dotted', 'solid']
offsets = np.array([[+3, -4], [+3, +4], [+2, +4], [+2, +2]])
Noff = len(offsets)
pvalues = np.zeros((Noff, 2))
if (plot):
fig, axarr = plt.subplots(2, Noff, sharey='row', figsize=(6*len(offsets), 12))
axarr = np.array(axarr, ndmin=1)
for i, off in enumerate(offsets):
ra_off = ra - off[0]/3600.
dec_off = dec - off[1]/3600.
ra_off_op = ra + off[0]/3600.
dec_off_op = dec + off[1]/3600.
prao, pdeco = wcs.wcs_sky2pix(ra+ off[0]/3600., dec + off[1]/3600., 0)
praosym, pdecosym = wcs.wcs_sky2pix(ra- 2*off[0]/3600., dec - 2*off[1]/3600., 0)
#Extract sample window to check wether the background matches well.
#number on samples on each side
ns = 7
sample = data[prao-ns:prao+ns, pdeco-ns:pdeco+ns]
sm = np.median(sample)
sstd = np.std(sample[(sample<6000)*(sample>0)])
bkg_prob = np.minimum(1 - stats.norm.cdf(sm, bkg, bkg_std), stats.norm.cdf(sm, bkg, bkg_std))
#Extract sample window to check wether the background matches well.
sample = data[praosym-7:praosym+7, pdecosym-7:pdecosym+7]
smo = np.median(sample)
sstdo = np.std(sample)
bkg_probo = np.minimum(1 - stats.norm.cdf(smo, bkg, bkg_std), stats.norm.cdf(smo, bkg, bkg_std))
pvalues[i] = np.array([bkg_prob, bkg_probo])
if(plot):
#Retrieve the image of the object
xl, yl = wcs.wcs_sky2pix(ra+20./3600, dec-20./3600, 0)
xu, yu = wcs.wcs_sky2pix(ra-20./3600, dec+20./3600, 0)
ifuwin = data[xl:xu,yu:yl]
#Retrieve the offset A image of the object
x0, y0 = wcs.wcs_sky2pix(ra_off+20./3600, dec_off-20./3600, 0)
x1, y1 = wcs.wcs_sky2pix(ra_off-20./3600, dec_off+20./3600, 0)
ifuwin1 = data[x0:x1,y1:y0]
nx, ny = ifuwin1.shape
#print nx,ny, ifuwin1.shape
#Retrieve the offset A image of the object
x0, y0 = wcs.wcs_sky2pix(ra_off_op+20./3600, dec_off_op-20./3600, 0)
x1, y1 = wcs.wcs_sky2pix(ra_off_op-20./3600, dec_off_op+20./3600, 0)
ifuwin2 = data[x0:x0+nx,y1:y1+ny]
#Plot the A and B
zmin, zmax = zscale.zscale(ifuwin)
zmin1, zmax1 = zscale.zscale(ifuwin1-ifuwin2)
axarr[0, i].imshow(ifuwin.T, aspect="auto", vmin=zmin, vmax=zmax, extent=(20,-20,-20,20), alpha=0.5)
axarr[1, i].imshow(ifuwin1.T-ifuwin2.T, aspect="auto", vmin=zmin1, vmax=zmax1, extent=(20,-20,-20,20), alpha=0.5)#, cmap=matplotlib.cm.RdBu_r)
#axarr[1, i].imshow(-1 * ifuwin2.T, aspect="auto", vmin=zmin2, vmax=zmax2, extent=(20,-20,-20,20), alpha=0.5)#, cmap=matplotlib.cm.RdBu_r)
axarr[0, i].scatter(0, 0, marker="x", s=20)
axarr[1, i].scatter(0, 0, marker="x", s=20)
axarr[0, i].set_xlabel("OBJRA")
axarr[0, i].set_ylabel("OBJDEC")
axarr[1, i].set_xlabel("OBJRA")
axarr[1, i].set_ylabel("OBJDEC")
axarr[0, i].text(19, 17, "Red stats: $\mu=$%.2f, $\sigma=$%.2f, p-value=%.5f"%(sm, sstd, bkg_prob), bbox=dict(facecolor='white', alpha=0.5))
axarr[0, i].text(19, 14, "Blue stats: $\mu=$%.2f, $\sigma=$%.2f, p-value=%.5f"%(smo, sstdo, bkg_probo), bbox=dict(facecolor='white', alpha=0.5))
axarr[0, i].text(19, 11, "Background stats: $\mu=$%.2f, $\sigma=$%.2f"%( bkg, bkg_std), bbox=dict(facecolor='white', alpha=0.5))
#r = plt.Circle((prao-pra, pdeco-pdec), 5, facecolor="none", edgecolor="red", lw=2)
#b = plt.Circle((praosym-pra, pdecosym-pdec), 5, facecolor="none", edgecolor="blue", lw=2)
r = plt.Circle((2*off[0], + 2*off[1]), 2, facecolor="none", edgecolor="red", lw=3, ls=linestyles[i])
b = plt.Circle((-2*off[0], -2*off[1]), 2, facecolor="none", edgecolor="blue", lw=3, ls=linestyles[i])
#Plot the true location of the IFU
patches = []
Path = mpath.Path
path_data = [
(Path.MOVETO, [20, -12]),
(Path.LINETO, [20, 20]),
(Path.LINETO, [-13, 13]),
(Path.LINETO, [-20 , -18]),
(Path.CLOSEPOLY, [20, 10])
]
codes, verts = zip(*path_data)
path = mpath.Path(verts, codes)
patch = mpatches.PathPatch(path)
patches.append(patch)
collection = PatchCollection(patches, cmap=plt.cm.YlGn, alpha=0.2)
colors = np.linspace(0, 1, len(patches))
collection.set_array(np.array(colors))
axarr[0, i].add_artist(r)
axarr[0, i].add_artist(b)
axarr[1, i].add_collection(collection)
plt.suptitle(obj, fontsize=20)
prod = np.prod(pvalues, axis=1)
if (plot and interactive):
axarr[0, np.argmax(prod)].text(0,0,"WINNER")
plt.show()
elif(plot):
axarr[0, np.argmax(prod)].text(0,0,"WINNER")
plt.savefig(os.path.join(os.path.dirname(f).replace("raw", "phot"), os.path.basename(f).replace(".fits", "_ab.png")))
plt.clf()
return 0, offsets[np.argmax(prod)], -2*offsets[np.argmax(prod)]
def show_list(fits_fns, nx=5, ny=3, size_mult=3.2, zoom_lvl=None, fontsize=8):
"""
Written by John Capone ([email protected]).
Purpose: displays images in specified list file.
Input:
fits_fns: list of file names
nx: number of images to display simultaneously in x
ny: number of images to display simultaneously in y
size_mult: multiple to determine image sizes
zoom_lvl: amount to decrease image resolution by (to save memory)
fontsize: fontsize for plots
Usage:
1) enter python or ipython environment
2) load function -> 'from rat_preproc import show_list'
3) run function -> 'show_list(fits_fns)'
- decreasing zoom_lvl (i.e. from 0.5 to 0.1) decreases the size of the displayed image, thus decreasing the amount of memory required
4) function will display arrays of images in list file for inspection by user
Notes:
- added escape character
- user can now change font size
"""
nx = int(nx); ny = int(ny) # force parameter types to int
if type(fits_fns) not in [list, dict]:
print_err("Error: fits_fns must be a list or dictionary of fits file names. Exiting...")
return
if type(fits_fns) is dict: # convert dictionary to list
fits_fns = list(itertools.chain.from_iterable(fits_fns.values()))
nfits = len(fits_fns) # number of fits files listed
pl.ion() # pylab in interactive mode
# create figures of subplots for review
nfigs = int(np.ceil(nfits / float(nx * ny)))
fig = pl.figure(figsize=(nx*size_mult,ny*size_mult), tight_layout=True)
for i in range(nfigs):
start_fits = i*nx*ny
if (i + 1)*nx*ny <= nfits:
stop_fits = (i + 1)*nx*ny - 1
nsubplts = nx*ny
else:
stop_fits = nfits
nsubplts = nfits - start_fits
print "Displaying frames {} - {} ({} total).".format(start_fits, stop_fits, nfits)
# display image in each subplot
for j in range(nsubplts):
ax = fig.add_subplot(ny, nx, j+1) # new subplot
fits_fn = fits_fns[start_fits + j]
dpath, fits_fn = os.path.split(fits_fn)
if len(dpath) != 0:
dpath += '/'
temp = fits_fn.split('.')
if len(temp) == 1:
fits_fn += '.fits'
elif len(temp) == 2:
if temp[1].lower() != 'fits':
print_err("Error: invalid \"{}\" file type detected. Should be \"fits\" file type. Exiting...".format(temp[1]))
pl.close('all') # close image to free memory
return
else:
print_err("Error: file names should not include \".\" except for file type extention. Exiting...")
pl.close('all') # close image to free memory
return
fits_id = temp[0] # fits file name with extention removed
# open data
hdulist = pf.open(dpath + fits_fn)
im = hdulist[0].data
h = hdulist[0].header
hdulist.close() # close FITs file
# display
if zoom_lvl is not None:
imdisp = zoom(im, zoom_lvl)
else:
imdisp = im
z1, z2 = zscale(im)
ax.imshow(imdisp, vmin=z1, vmax=z2, origin='lower', cmap=pl.cm.gray, interpolation='none')
ax.set_xticks([])
ax.set_yticks([])
try:
ax.set_title("{} - {} filter".format(fits_id, h['FILTER']), fontsize=fontsize) # title with identifier
except:
ax.set_title("{}".format(fits_id), fontsize=fontsize) # title with identifier
fig.canvas.draw()
usr_select = raw_input("Press any key to continue or \"q\" to quit: ") # prompt user to continue
fig.clear() # clear image
if usr_select.lower() == 'q':
print_bold("Exiting...")
pl.close('all') # close image to free memory
return
pl.close('all') # close image to free memory
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2.5, plotdir=None, box=15, arcsecpix=0.394):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
from pyraf import iraf
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
if (plotdir is None):
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
print "Will create output files", out_name, clean_name
# Read values from .ec file
fwhm_value = fwhm/arcsecpix
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')/arcsecpix
elif (fwhm is None):
fwhm_value=3.5/arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
noise = fitsutils.get_par(image, 'RDNOISE')
print "FWHM: %.1f pixels, %.1f arcsec"%(fwhm_value, fwhm_value*arcsecpix)
aperture_rad = math.ceil(float(fwhm_value)*1.5) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*4
if (not plot_only):
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime",\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
#fwhm = fwhm_value,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), ("stdev","<f4"),\
("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(np.floor(np.sqrt(len(m))))
dimY = int(np.ceil(len(m)*1./dimX))
outerrad = sky_rad+10
cutrad = outerrad + 15
print "Cutrad %.1f"%cutrad
plt.suptitle("FWHM=%.2f arcsec. %d stars"%(fwhm_value*arcsecpix, len(m)))
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( i*dimY + j < len(m)):
k = i*dimY + j
#print dimX, dimY, i, j, k
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
y1 = np.maximum(y1, 0); y2=np.maximum(y2, 0); x1=np.maximum(x1, 0); x2 = np.maximum(x2, 0)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except ValueError:
print y1, y2, x1, x2
print img[x1:x2,y1:y2]
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=aperture_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+20)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
plt.tight_layout()
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
def get_app_phot_target(image, ra=None, dec=None, plot=True, store=True, wcsin="logical", fwhm=None, box=15, arcsecpix=0.394, app=2):
'''
coords: files:
wcsin: can be "world", "logic"
fwhm: in arcsec
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
print "Assuming ra=%.5f, dec=%.5f"%(ra, dec)
pra, pdec = get_xy_coords(image, ra, dec)
else:
if("logic" in wcsin):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as wcs module does not seem to be working well.
try:
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#print "Retrieved the pixel number"
pra, pdec = get_xy_coords(image, ra, dec)
except IndexError:
print "Error with astrometry.net. trying the rudimentary method."
pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
if (not fwhm is None):
fwhm_value = fwhm
elif (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
else:
#Put some default value for Palomar
fwhm_value=1.5
if (wcsin == 'logical'):
fwhm_value = fwhm_value / arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
if (not fitsutils.has_par(image, "EXPTIME")):
if (fitsutils.has_par(image, "ITIME") and fitsutils.has_par(image, "COADDS")):
exptime = fitsutils.get_par(image, "ITIME")*fitsutils.get_par(image, "COADDS")
fitsutils.update_par(image, "EXPTIME", exptime)
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*app) # Set aperture radius to two times the PSF radius
sky_rad= math.ceil(aperture_rad*app*2)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), \
("stdev","<f4"), ("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT") and fitsutils.has_par(image, "ZEROPTU")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
else:
mag = 0
magerr = 0
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
#zmin, zmax = zscale.zscale(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50])
#zmin, zmax = zscale.zscale(impf[0].data)
#im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
impf[0].data[np.isnan(impf[0].data)] = np.nanmedian(impf[0].data)
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
im = plt.imshow(impf[0].data, vmin=np.percentile(impf[0].data, 5), vmax=np.percentile(impf[0].data, 95), origin="bottom")
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"), dpi=200)
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
def extract_star_sequence(imfile, band, plot=True, survey="sdss", debug=False, refstars=None, plotdir="."):
"""
Given a fits image: imfile and a the name of the band which we want to extract the sources from,
it saves the extracted sources into '/tmp/sdss_cat_det.txt' file.
If the band does not match the bands in the survey, a change is performed to adapt to the new band.
If plotting activated, plots the USNOB1 field of stars on top of the star field image.
Red circles are stars identified from the catalogue in the correct magnitude range.
Yellow circles are stars that are isolated.
"""
survey = str.lower(survey)
minmag = 15
maxmag = 21.0
f = pf.open(imfile)
wcs = pywcs.WCS(f[0].header)
img = f[0].data
img[img < 0] = 0
ra, dec = wcs.wcs_pix2sky(np.array([img.shape[0] / 2, img.shape[1] / 2], ndmin=2), 1)[0]
ra0, dec0 = wcs.wcs_pix2sky(np.array([img.shape[0], img.shape[1]], ndmin=2), 1)[0]
sr = 2 * np.abs(dec - dec0)
logger.info("%.4f %.4f %.4f" % (ra, dec, sr))
if not refstars is None:
shutil.copy(refstars, "/tmp/tmp_sdss_%s.cat" % creationdate)
catalog = np.genfromtxt("/tmp/tmp_sdss_%s.cat" % creationdate, names=True, dtype=None, delimiter=",")
cat_ra = catalog["ra"]
cat_dec = catalog["dec"]
try:
mag = catalog["R"]
except:
mag = catalog["r"]
elif str.lower(survey) == "usnob1":
# ra, dec = coordinates_conversor.hour2deg(f[0].header['RA'], f[0].header['DEC'])
# SEDM FoV is 6.5 arcmin, due to uncertainties in the position, 4 arcmin radius assumed.
# Download USNO-B1 catalog for the position
catalog_url = "http://www.nofs.navy.mil/cgi-bin/vo_cone.cgi?CAT=USNO-B1&RA=%.5f&DEC=%.5f&SR=%.4f&VERB=1" % (
ra,
dec,
sr,
)
logger.info("Downloading USNO-B1 catalog...")
urllib.urlretrieve(catalog_url, "/tmp/tmp_%s.cat" % creationdate)
# Read RA, Dec and magnitude from XML format USNO catalog
catalog = parse_single_table("/tmp/tmp_%s.cat" % creationdate)
cat_ra = catalog.array["RA"].data
cat_dec = catalog.array["DEC"].data
cat_R1mag = catalog.array["R1"].data
cat_R2mag = catalog.array["R2"].data
cat_B1mag = catalog.array["B1"].data
cat_B2mag = catalog.array["B2"].data
cat_I2mag = catalog.array["I2"].data
cat_R1mag[cat_R1mag == 0] = np.nan
cat_R2mag[cat_R2mag == 0] = np.nan
cat_B1mag[cat_B1mag == 0] = np.nan
cat_B2mag[cat_B2mag == 0] = np.nan
cat_I2mag[cat_I2mag == 0] = np.nan
Bmag = np.nanmean(np.array([cat_B1mag, cat_B2mag]), axis=0)
Rmag = np.nanmean(np.array([cat_R1mag, cat_R2mag]), axis=0)
Imag = cat_I2mag
mag = Rmag
elif survey == "apass":
# Download USNO-B1 catalog for the position
catalog_url = "https://www.aavso.org/cgi-bin/apass_download.pl?ra=%.5f&dec=%.5f&radius=%.4f8&outtype=1" % (
ra,
dec,
sr,
)
print "Downloading APASS catalog..."
urllib.urlretrieve(catalog_url, "/tmp/tmp_apass_%s.cat" % creationdate)
catalog = np.genfromtxt("/tmp/tmp_apass_%s.cat" % creationdate, delimiter=",", names=True)
if np.ndim(catalog) == 0:
return False
cat_ra = catalog["radeg"]
cat_dec = catalog["decdeg"]
mag = catalog["Sloan_r"]
elif survey == "sdss":
minmag = 15
maxmag = 21.5
catalog_url = (
"http://skyserver.sdss.org/dr9/en/tools/search/x_radial.asp?ra=%.5f&dec=%.5f&check_type=type&type=6&radius=%.4f&check_u=u&min_u=%.2f&max_u=%.2f&check_g=g&min_g=%.2f&max_g=%.2f&check_r=r&min_r=%.2f&max_r=%.2f&check_i=i&min_i=%.2f&max_i=%.2f&check_z=z&min_z=%.2f&max_z=%.2f&entries=top&topnum=500&format=csv"
% (ra, dec, sr * 60, minmag, maxmag, minmag, maxmag, minmag, maxmag, minmag, maxmag, minmag, maxmag)
)
logger.info("Downloading SDSS catalog...")
logger.info("%s" % catalog_url)
urllib.urlretrieve(catalog_url, "/tmp/tmp_sdss_%s.cat" % creationdate)
catalog = np.genfromtxt("/tmp/tmp_sdss_%s.cat" % creationdate, delimiter=",", names=True)
if np.ndim(catalog) == 0:
return False
try:
cat_ra = np.array(catalog["ra"], ndmin=1)
cat_dec = np.array(catalog["dec"], ndmin=1)
if band in catalog.dtype.names:
print "SDSS filter detected"
mag = np.array(catalog[band], ndmin=1)
elif band in ["U", "B", "V", "R", "I", "Z"]:
print "Johnson filter detected."
john = transformations.sdss2johnson(
"/tmp/tmp_sdss_%s.cat" % creationdate, savefile="/tmp/tmp_sdss_%s.cat" % creationdate
)
mag = john[band]
catalog = np.genfromtxt("/tmp/tmp_sdss_%s.cat" % creationdate, dtype=None, names=True, delimiter=",")
else:
print "Unknown band!!", band
except IOError:
logger.error("Problems with SDSS image %s" % band)
return False
except ValueError:
logger.error("Problems with the catalogue for the image")
return False
# Convert ra, dec position of all stars to pixels.
star_pix = np.array([0, 0])
for i in range(len(cat_ra)):
# Get pixel coordinates of USNO stars
s = wcs.wcs_sky2pix(np.array([cat_ra[i], cat_dec[i]], ndmin=2), 1)[0]
star_pix = np.row_stack((star_pix, s))
star_pix = star_pix[1:]
pix2ang = 0.394
rad = math.ceil(25.0 / pix2ang)
# Select only the stars within the image.
mask = (
(star_pix[:, 0] > -rad)
* (star_pix[:, 0] < img.shape[1] + rad)
* (star_pix[:, 1] > -rad)
* (star_pix[:, 1] < img.shape[0] + rad)
)
if band == "u":
mask = mask * (mag < 19)
# Select only stars isolated in a radius of ~12 arcsec.
mask2 = np.array(are_isolated(cat_ra[mask], cat_dec[mask], 15.0))
if len(mask2) == 0:
logger.error("No good stars left")
return False
# Select only stars that are within the proper magnitude range
mask3 = (mag[mask][mask2] < maxmag) * (mag[mask][mask2] > minmag)
mask3 = (
mask3
* (star_pix[:, 0][mask][mask2] > rad)
* (star_pix[:, 0][mask][mask2] < img.shape[1] - rad)
* (star_pix[:, 1][mask][mask2] > rad)
* (star_pix[:, 1][mask][mask2] < img.shape[0] - rad)
)
if survey == "usnob1":
output = np.column_stack(
(
star_pix[:, 0][mask][mask2][mask3],
star_pix[:, 0][mask][mask2][mask3],
cat_ra[mask][mask2][mask3],
cat_dec[mask][mask2][mask3],
Bmag[mask][mask2][mask3],
Rmag[mask][mask2][mask3],
Imag[mask][mask2][mask3],
)
)
np.savetxt("/tmp/usnob1_cat.txt", output, fmt="%.5f %.5f %.5f %.5f %.5f %.5f %.5f", header="#X Y ra dec B R I")
print "Saved to", "/tmp/usnob1_cat.txt"
elif survey == "apass":
if not np.any(mask2) and not np.any(mask3):
print star_pix
print "No stars left...", mask, mask2, mask3
return
else:
print catalog[mask][mask2][mask3]
catalog = catalog[mask][mask2][mask3]
s = star_pix[mask][mask2][mask3]
z = np.zeros(len(s), dtype=[("x", "f8"), ("y", "f8")])
z["x"] = s[:, 0]
z["y"] = s[:, 1]
for n in catalog.dtype.names:
z = rfn.append_fields(z, names=n, data=catalog[n], usemask=False)
fmt = "%.5f"
for i in range(len(z[0]) - 1):
fmt += " %.5f"
np.savetxt(
"/tmp/apass_cat.txt",
z,
fmt=fmt,
header="x y radeg raerr decdeg decerr number_of_Obs V dV B dB g dg r dr i di",
)
print "Saved to", "/tmp/apass_cat.txt"
elif survey == "sdss":
if (not np.any(mask) and not np.any(mask2) and not np.any(mask3)) or len(catalog[mask][mask2][mask3]) == 0:
print star_pix
print "No stars left...", mask, mask2, mask3
return False
else:
catalog = catalog[mask][mask2][mask3]
s = star_pix[mask][mask2][mask3]
print "left %d stars" % (len(catalog)), catalog.dtype.names
z = np.zeros(len(s), dtype=[("x", "f8"), ("y", "f8")])
z["x"] = s[:, 0]
z["y"] = s[:, 1]
header = "x y "
for n in catalog.dtype.names:
if n in [
"objid",
"ra",
"dec",
"u",
"g",
"r",
"i",
"z",
"Err_u",
"Err_g",
"Err_r",
"Err_i",
"Err_z",
] or n in ["id", "ra", "dec", "U", "B", "V", "R", "I", "dU", "dB", "dV", "dR", "dI"]:
z = rfn.append_fields(z, names=n, data=catalog[n], usemask=False)
header += n.replace("Err_", "d") + " "
fmt = "%.5f"
for i in range(len(z[0]) - 1):
fmt += " %.5f"
np.savetxt("/tmp/sdss_cat_%s.txt" % creationdate, z, fmt=fmt, header=header)
logger.info("Saved catalogue stars to %s" % ("/tmp/sdss_cat_%s.txt" % creationdate))
# Find FWHM for this image
out = find_fwhm(imfile, star_pix[:, 1][mask][mask2][mask3], star_pix[:, 0][mask][mask2][mask3], plot=debug)
mask_valid_fwhm = (out["detected"]) * (out["e"] > 0.6) * ~np.isnan(out["fwhm"] * (out["fwhm"] < 30))
if ((np.count_nonzero(mask_valid_fwhm) < 3) and (fitsutils.get_par(imfile, "FILTER") != "u")) or (
(np.count_nonzero(mask_valid_fwhm) < 2) and (fitsutils.get_par(imfile, "FILTER") == "u")
):
logger.error(
"ERROR with FWHM!! Too few points for a valid estimation. %d" % np.count_nonzero(mask_valid_fwhm)
+ ") points"
)
logger.error("%s %s" % (out["detected"], out["fwhm"]))
return False
outd = out[mask_valid_fwhm]
logger.info(
"Average FWHM %.3f arcsec, %.3f pixels" % (np.median(outd["fwhm"]), np.median(outd["fwhm"]) * pix2ang)
)
fwhm = np.percentile(outd["fwhm"], 40)
fitsutils.update_par(imfile, "FWHM", np.round(fwhm, 3))
if band in "ugriz":
header = "x y objid ra dec u g r i z du dg dr di dz"
elif band in "UBVRI":
header = "x y objid ra dec U B V R I dU dB dV dR dI"
np.savetxt("/tmp/sdss_cat_det_%s.txt" % creationdate, z[mask_valid_fwhm], fmt=fmt, header=header)
print "Saved to", "/tmp/sdss_cat_det_%s.txt" % creationdate
# Plot results
img = img - np.nanmin(img)
zmin, zmax = zscale.zscale(img)
logger.info(
"Found %d stars in %s. " % (len(cat_dec), survey)
+ "%d of them within the FoV. " % len(cat_ra[mask])
+ "%d of them are isolated." % len(cat_ra[mask][mask2])
+ "%d of them with suitable magnitudes. " % len(cat_ra[mask][mask2][mask3])
+ "%d of them with detected stars." % np.count_nonzero(mask_valid_fwhm)
)
if plot:
im = plt.imshow(
img, aspect="equal", origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax
)
if len(star_pix[:, 0][mask]) > 0:
plt.scatter(
star_pix[:, 0][mask],
star_pix[:, 1][mask],
marker="o",
s=np.minimum(150, 10000 * (10.0 / mag[mask][mask2]) ** 9),
edgecolor="red",
facecolor="none",
label="catalogue",
)
if len(star_pix[:, 0][mask][mask2]) > 0:
plt.scatter(
star_pix[:, 0][mask][mask2],
star_pix[:, 1][mask][mask2],
marker="o",
s=20,
edgecolor="yellow",
facecolor="none",
label="isolated",
)
if len(star_pix[:, 0][mask][mask2][mask3]) > 0:
plt.scatter(
star_pix[:, 0][mask][mask2][mask3],
star_pix[:, 1][mask][mask2][mask3],
marker="o",
s=200,
edgecolor="green",
facecolor="none",
label="wihtin farme and mag",
)
selected = star_pix[:, :][mask][mask2][mask3][mask_valid_fwhm]
if len(selected) > 0:
plt.scatter(selected[:, 0], selected[:, 1], marker="o", s=400, edgecolor="blue", facecolor="none")
for i in np.arange(len(selected)):
plt.text(selected[i, 0] + 10, selected[i, 1] + 10, i + 1)
plt.legend(loc="best", frameon=False, framealpha=0.9)
plt.savefig(
os.path.join(
plotdir, os.path.basename(imfile).replace(".fits", ".seqstars.png").replace(".new", ".seqstars.png")
)
)
logger.info("Saved stars to %s" % imfile.replace(".fits", ".seqstars.png"))
plt.clf()
return True
def get_app_phot_target(image, plot=False, store=True, wcsin="logical", fwhm=2, box=4, ra=None, dec=None):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = pywcs.WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
pra, pdec = get_xy_coords(image, ra, dec)
else:
if(wcsin == "logical"):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as pywcs does not seem to be working well.
pra, pdec = get_xy_coords(image, ra, dec)
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
fwhm_value = fwhm
nsrc, fwhm_value, ellip = sextractor.get_image_pars(image)
if np.isnan(fwhm_value):
fwhm_value=99
fitsutils.update_par(image, 'FWHM', fwhm_value)
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*3) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad*4)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if (plot):
zmin, zmax = zscale.zscale(impf[0].data)
im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
plt.scatter(pra, pdec, marker="o", s=100, facecolor="none")
plt.savefig(os.path.join(plotdir, imname+".png"))
plt.clf()
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 15. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("ph_mag","<f4"), ("stdev","<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
if (ma.size==1):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"))
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
# Get pixel coordinates of SN
wcs = pywcs.WCS(prihdr)
try:
target_pix = wcs.wcs_sky2pix([(np.array([ra,dec], np.float_))], 1)[0]
except:
print "ERROR when converting sky to wcs. Is astrometry in place? Default coordinates assigned."
target_pix = [+nx/2., ny/2.]
print target_pix
else:
target_pix = [+nx/2., ny/2.]
img = img - np.nanmin(img)
av = np.median(img.flatten())
mi, ma = zscale.zscale(img)
im = plt.imshow(plt.log10(img), aspect="equal", extent=(0, ny, 0, nx), \
origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=np.log10(av), vmax=np.log10(3*av)) #, interpolation="lanczos")
plt.scatter(target_pix[0], target_pix[1], marker="x", s=10, c="red")
plt.colorbar(im)
filename = os.path.basename(f)
plt.savefig(os.path.join(plot_dir, filename.replace("."+filename.split(".")[-1], "_{:}.png".format(i))), dpi=200)
plt.clf()
def move_to_discarded(mydir, myfilter, ra, dec):
import shutil
for f in glob.glob(os.path.join(mydir, myfilter)):
frames_with_target = get_frames_with_target(f, ra, dec)
if len(frames_with_target) == 0:
discarddir = os.path.join(mydir, "discarded")
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2, plotdir=".", box=15):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
# Read values from .ec file
ecfile= image+".ec"
filter_value=''.join(ecfile).split('.',1)[0]
fwhm_value = fwhm
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
try:
with open(''.join(ecfile),'r') as f:
for line in f:
if "airmass" in line:
airmass_value = line.split('=',1)[1]
else:
airmass_value = 1
if "FWHM" in line:
print line
fwhm_value = line.split('FWHM=',1)[1]
fwhm_value = fwhm_value.rsplit("aperture")[0]
except:
pass
print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*2) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*5
print aperture_rad, sky_rad
if (not plot_only):
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
# Check if files in list, otherwise exit
if not ecfile:
print "No .ec files in directory, exiting"
sys.exit()
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 15. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filters" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("ph_mag","<f4"), ("stdev","<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(4)
dimY = int(np.ceil(len(m)*1./4))
outerrad = sky_rad+10
cutrad = outerrad + 15
plt.suptitle("FWHM="+str(fwhm_value))
k = 0
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( k < len(m)):
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except:
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
continue
else:
continue
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=5.)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+10)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
k = k+1
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
def process_query_image():
if request.args['iterative']:
query_text = request.args['query'].replace("@","@"+str(request.args['iteration']))
title = "Iteration " + str(request.args['iteration'])
else:
query_text = str(request.args['query'])
title = query_text
iterative = request.args['iterative']
z_scale = request.args['z_scale']
language = str(request.args['language'])
callback = request.args['callback']
query_lines = query_text.split("\n")
db_interface.connect()
# process all queries except for the last one
for query_line in query_lines[:-1]: # process all lines except the last one
db_interface.executeQuery(str(query_line), str(language))
# process last query
query_result = db_interface.executeQuery(str(query_lines[-1]), str(language))
if query_result is None: # error processing query
return make_response(json.dumps({
'request': request.json,
'status': 'ERROR',
'message': "error processing query",
}))
data_array, array_dim = db_interface.getFirstAttrArrFromQueryInNumPY(query_result)
db_interface.disconnect()
#print >> sys.stdout, response_json
result = SciDB.SciDB_Result(data_array)
# Get the image array
if not iterative:
img_array = result.get_data_array()
elif request.args['iteration'] == 1:
img_array = result.get_data_array()
else:
master = memcache.get(session.pop('previous_data_key', None), None)
img_array = result.get_img_array(master)
# Get z min and max
if z_scale == True:
z_min, z_max = zscale.zscale(result.data_array, nsamples=2000, contrast=0.25)
else:
z_min, z_max = (array_dim[2][0], array_dim[2][1]) #TODO fix this, currently zmin and zmax are unknown
# Make figure
output = scalrr_vis.plot_image(img_array, z_min, z_max)
encoded_image = base64.b64encode(output.getvalue())
# Save session data
memcache[query_text] = result.get_data_array()
session['previous_data_key'] = query_text
# Make response
json_message = {
'request': request.args,
'status': 'OK',
'image': encoded_image,
#"plot": {"title": title, "x_axis": result.x_name, "y_axis": result.y_name, "z_axis": result.z_name},
"dimensions": {
"x":{"min": array_dim[0][0], "max": array_dim[0][1]},
"y":{"min": array_dim[1][0], "max": array_dim[1][1]},
"z":{"min": array_dim[2][0], "max": array_dim[2][1]}
}
}
message = callback + "(" + json.dumps(json_message) + ")"
response = make_response(message)
#print >> sys.stdout, json_message
return response
def extract_star_sequence(imfile, band, plot=True, survey='apass', debug=False, refstars=None):
'''
Plots the USNOB1 field of stars on top of the star field image.
Red circles are stars identified from the catalogue in the correct magnitude range.
Yellow circles are stars that are isolated.
'''
survey = str.lower(survey)
f = pf.open(imfile)
wcs = pywcs.WCS(f[0].header)
img = f[0].data
img[img<0] = 0
ra, dec = wcs.wcs_pix2sky(np.array([img.shape[0]/2, img.shape[1]/2], ndmin=2), 1)[0]
sr = 5.5/60
print ra,dec
if not refstars is None:
shutil.copy(refstars, "/tmp/tmp_sdss.cat")
catalog = np.genfromtxt("/tmp/tmp_sdss.cat", names=True, dtype=None, delimiter=",")
cat_ra = catalog["ra"]
cat_dec = catalog["dec"]
mag = catalog["R"]
elif str.lower(survey) =='usnob1':
#ra, dec = coordinates_conversor.hour2deg(f[0].header['RA'], f[0].header['DEC'])
#SEDM FoV is 6.5 arcmin, due to uncertainties in the position, 4 arcmin radius assumed.
# Download USNO-B1 catalog for the position
catalog_url = 'http://www.nofs.navy.mil/cgi-bin/vo_cone.cgi?CAT=USNO-B1&RA=%.5f&DEC=%.5f&SR=%.4f&VERB=1' % (ra, dec, sr)
print "Downloading USNO-B1 catalog..."
urllib.urlretrieve(catalog_url, '/tmp/tmp.cat')
# Read RA, Dec and magnitude from XML format USNO catalog
catalog = parse_single_table("/tmp/tmp.cat")
cat_ra = catalog.array['RA'].data
cat_dec = catalog.array['DEC'].data
cat_R1mag = catalog.array['R1'].data
cat_R2mag = catalog.array['R2'].data
cat_B1mag = catalog.array['B1'].data
cat_B2mag = catalog.array['B2'].data
cat_I2mag = catalog.array['I2'].data
cat_R1mag[cat_R1mag==0] = np.nan
cat_R2mag[cat_R2mag==0] = np.nan
cat_B1mag[cat_B1mag==0] = np.nan
cat_B2mag[cat_B2mag==0] = np.nan
cat_I2mag[cat_I2mag==0] = np.nan
Bmag = np.nanmean(np.array([cat_B1mag, cat_B2mag]), axis=0)
Rmag = np.nanmean(np.array([cat_R1mag, cat_R2mag]), axis=0)
Imag = cat_I2mag
mag = Rmag
elif survey == "apass":
# Download USNO-B1 catalog for the position
catalog_url = 'https://www.aavso.org/cgi-bin/apass_download.pl?ra=%.5f&dec=%.5f&radius=%.4f8&outtype=1' % (ra, dec, sr)
print "Downloading APASS catalog..."
urllib.urlretrieve(catalog_url, '/tmp/tmp_apass.cat')
catalog = np.genfromtxt("/tmp/tmp_apass.cat", delimiter=",", names=True)
cat_ra = catalog['radeg']
cat_dec = catalog['decdeg']
mag = catalog['Sloan_r']
elif (survey=='sdss'):
minmag = 0
maxmag = 21.0
catalog_url='http://skyserver.sdss.org/dr7/en/tools/search/x_radial.asp?ra=%.5f&dec=%.5f&check_type=type&type=6\
&radius=%.4f&check_u=u&min_u=%.2f&max_u=%.2f&check_g=g&min_g=%.2f&max_g=%.2f&check_r=r&min_r=%.2f&max_r=%.2f&check_i=i&min_i=%.2f&max_i=%.2f&check_z=z&min_z=%.2f&max_z=%.2f&entries=top&topnum=500&format=csv'%(ra, dec, sr*60,minmag,maxmag,minmag,maxmag,minmag,maxmag,minmag,maxmag,minmag,maxmag)
print "Downloading SDSS catalog..."
print catalog_url
urllib.urlretrieve(catalog_url, '/tmp/tmp_sdss.cat')
catalog = np.genfromtxt("/tmp/tmp_sdss.cat", delimiter=",", names=True)
try:
cat_ra = catalog['ra']
cat_dec = catalog['dec']
mag = catalog[band]
except:
print "Problems with SDSS image", band
return False
#Convert ra, dec position of all stars to pixels.
star_pix = np.array([0,0])
for i in range(len(cat_ra)):
# Get pixel coordinates of USNO stars
s = wcs.wcs_sky2pix(np.array([cat_ra[i], cat_dec[i]], ndmin=2), 1)[0]
star_pix = np.row_stack((star_pix, s))
star_pix = star_pix[1:]
pix2ang = 0.394
rad = math.ceil(25./pix2ang)
#Select only the stars within the image.
mask = (star_pix[:,0]>-rad) * (star_pix[:,0]<img.shape[1]+rad)*(star_pix[:,1]>-rad) * (star_pix[:,1]<img.shape[0]+rad)
if (band == 'u'):
mask = mask * (mag < 19)
#Select only stars isolated in a radius of ~12 arcsec.
mask2 = np.array(are_isolated(cat_ra[mask], cat_dec[mask], 30.))
#Select only stars that are within the proper magnitude range
mask3 = (mag[mask][mask2] < 20.) * (mag[mask][mask2] > 12)
mask3 = mask3 * (star_pix[:,0][mask][mask2]>rad) * (star_pix[:,0][mask][mask2]<img.shape[1]-rad)*(star_pix[:,1][mask][mask2]>rad) * (star_pix[:,1][mask][mask2]<img.shape[0]-rad)
if (survey=='usnob1'):
output = np.column_stack((star_pix[:,0][mask][mask2][mask3], star_pix[:,0][mask][mask2][mask3], \
cat_ra[mask][mask2][mask3], cat_dec[mask][mask2][mask3], Bmag[mask][mask2][mask3], Rmag[mask][mask2][mask3], Imag[mask][mask2][mask3]))
np.savetxt('/tmp/usnob1_cat.txt', output, fmt="%.5f %.5f %.5f %.5f %.5f %.5f %.5f", header='#X Y ra dec B R I')
print "Saved to", '/tmp/usnob1_cat.txt'
elif (survey=='apass'):
if not np.any(mask2) and not np.any(mask3):
print star_pix
print "No stars left...", mask, mask2, mask3
return
else:
print catalog[mask][mask2][mask3]
catalog = catalog[mask][mask2][mask3]
s = star_pix[mask][mask2][mask3]
z = np.zeros(len(s), dtype=[('x','f8'), ('y', 'f8')])
z['x'] = s[:,0]
z['y'] = s[:,1]
for n in catalog.dtype.names:
z = rfn.append_fields(z, names=n, data=catalog[n], usemask=False)
fmt = "%.5f"
for i in range(len(z[0])-1):
fmt += " %.5f"
np.savetxt('/tmp/apass_cat.txt', z, fmt=fmt, \
header='x y radeg raerr decdeg decerr number_of_Obs V dV B dB g dg r dr i di')
print "Saved to", '/tmp/apass_cat.txt'
elif survey=='sdss':
if not np.any(mask) and not np.any(mask2) and not np.any(mask3):
print star_pix
print "No stars left...", mask, mask2, mask3
else:
catalog = catalog[mask][mask2][mask3]
s = star_pix[mask][mask2][mask3]
z = np.zeros(len(s), dtype=[('x','f8'), ('y', 'f8')])
z['x'] = s[:,0]
z['y'] = s[:,1]
for n in catalog.dtype.names:
z = rfn.append_fields(z, names=n, data=catalog[n], usemask=False)
fmt = "%.5f"
for i in range(len(z[0])-1):
fmt += " %.5f"
np.savetxt('/tmp/sdss_cat.txt', z, fmt=fmt, \
header='x y objid run rerun camcol field obj type ra dec u g r i z du dg dr di dz')
print "Saved to", '/tmp/sdss_cat.txt'
#Find FWHM for this image
out = find_fwhm(imfile, star_pix[:,1][mask][mask2][mask3], star_pix[:,0][mask][mask2][mask3], plot=debug)
mask_valid_fwhm = (out['detected']) * (out['e']>0.7) * ~np.isnan(out['fwhm']* (out['fwhm'] < 30))
if (np.count_nonzero(mask_valid_fwhm) < 3):
print "ERROR with FWHM!! Too few points for a valid estimation."
return False
outd = out[mask_valid_fwhm]
print 'Average FWHM',np.median(outd['fwhm']), 'arcsec', np.median(outd['fwhm'])*pix2ang, 'pixels'
fwhm = np.median(outd['fwhm'])
fitsutils.update_par(imfile,'FWHM',fwhm)
np.savetxt('/tmp/sdss_cat_det.txt', z[mask_valid_fwhm], fmt=fmt, \
header='x y objid run rerun camcol field obj type ra dec u g r i z du dg dr di dz')
print "Saved to", '/tmp/sdss_cat_det.txt'
#Plot results
img = img - np.nanmin(img)
zmin, zmax = zscale.zscale(img)
print "Found %d stars in %s. "%(len(cat_dec), survey), \
"%d of them within the FoV. "%len(cat_ra[mask]),\
"%d of them are isolated."%len(cat_ra[mask][mask2]),\
"%d of them with suitable magnitudes. "%len(cat_ra[mask][mask2][mask3]),\
"%d of them with detected stars."%np.count_nonzero(mask_valid_fwhm)
if (plot):
im = plt.imshow(img, aspect="equal", origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
if (len(star_pix[:,0][mask]) >0):
plt.scatter(star_pix[:,0][mask], star_pix[:,1][mask], marker="o", s=np.minimum(150, 10000*(10./mag[mask][mask2])**9), edgecolor="red", facecolor="none")
if (len(star_pix[:,0][mask][mask2]) >0):
plt.scatter(star_pix[:,0][mask][mask2], star_pix[:,1][mask][mask2], marker="o", s=20, edgecolor="yellow", facecolor="none")
if (len(star_pix[:,0][mask][mask2][mask3]) >0):
plt.scatter(star_pix[:,0][mask][mask2][mask3], star_pix[:,1][mask][mask2][mask3], marker="o", s=200, edgecolor="green", facecolor="none")
selected = star_pix[:,:][mask][mask2][mask3][mask_valid_fwhm]
if (len(selected) >0):
plt.scatter(selected[:,0], selected[:,1], marker="o", \
s=400, edgecolor="blue", facecolor="none")
for i in np.arange(len(selected)):
plt.text(selected[i,0]+10, selected[i,1]+10, i+1)
plt.savefig(imfile.replace('.fits', '.seqstars.png'))
print "Saved stars to ",imfile.replace('.fits', '.seqstars.png')
plt.clf()
return True
def get_offset_center_failed_astro(f, plot=False, interactive=True):
'''
For fields where astrometry is challenging, there is a simple solution.
Find the brightest peak within the pointing error of the telescope.
As this fields will usually be centered in Standard stars and very short exposure time,
the fit intends to
'''
image = pf.open(f)
data = image[0].data
wcs = pywcs.WCS(image[0].header)
ra, dec = cc.hour2deg(image[0].header['OBJRA'], image[0].header['OBJDEC'] )
pra, pdec = wcs.wcs_sky2pix(ra, dec, 0)
#Get a local image
#xl, yl = np.array(wcs.wcs_sky2pix(ra+(60./3600)*np.cos(np.deg2rad(dec)), dec-60./3600, 0), dtype=np.int)
#xu, yu = np.array(wcs.wcs_sky2pix(ra-(60./3600)*np.cos(np.deg2rad(dec)), dec+60./3600, 0), dtype=np.int)
imageloc = image[0].data.T[1293-150:1293+150,1280-150:1280+150]
nx = 300
ny=300
def_x = np.argmax(np.sum(imageloc, axis=0))
def_y = np.argmax(np.sum(imageloc, axis=1))
newx = pra-nx/2.+def_x
newy = pdec-ny/2.+def_y
pra, pdec = wcs.wcs_pix2sky(np.array([[newx[0], newy[0]]] , np.float_), 0)[0]
dra, ddec = cc.get_offset(ra, dec, pra, pdec)
print "Offset", dra, ddec, "Position RA,DEC", pra, pdec
x,y, fwhmx, fwhmy, bkg, amp = fit_utils.fit_gauss(imageloc)
if (plot):
plt.figure(figsize=(8,8))
obj = fitsutils.get_par(f, "OBJECT")
plt.suptitle(obj, fontsize=20)
zmin, zmax = zscale.zscale(imageloc)
plt.imshow(imageloc, aspect="auto", interpolation="none", origin="lower", vmin=zmin, vmax=zmax, extent=(0,+300,0,+300))
plt.plot(x, y, "go", ms=20, label="Centroid using gaussiuan fit.")
plt.plot(def_x, def_y, "b*", ms=20, label="Centroid using max/min.")
plt.plot(150,150,"wo", ms=20, label="Initial pointing")
plt.legend()
'''zmin, zmax = zscale.zscale(data)
plt.imshow(data, aspect="auto", interpolation="none", origin="lower", vmin=zmin, vmax=zmax)
plt.plot(newx, newy, "go")
plt.show()'''
if (interactive):
plt.show()
else:
plt.savefig(os.path.join(os.path.dirname(f).replace("raw", "phot"), os.path.basename(f).replace(".fits", "_std.png")))
plt.clf()
return 1, ddec, dra
def do_image(self,
undone_image,
contour_file=None,
contour_levels=5,
contour_color=None):
"""Make an image"""
version = 4
success = False
print("Making an image for " + undone_image)
print("Using files... " + str(self.images[undone_image]))
cube_data = pyfits.getdata(
self.filenames[self.images[undone_image][0]])
z1_k, z2_k = zscale.zscale(np.nan_to_num(cube_data[0, ...]))
z1_h, z2_h = zscale.zscale(np.nan_to_num(cube_data[1, ...]))
z1_j, z2_j = zscale.zscale(np.nan_to_num(cube_data[2, ...]))
aplpy.make_rgb_image(
self.filenames[self.images[undone_image][0]],
self.filenames[self.images[undone_image][0]].replace(
'.fits', '_2d.png'),
vmin_r=z1_k,
vmax_r=z2_k,
vmin_g=z1_h,
vmax_g=z2_h,
vmin_b=z1_j,
vmax_b=z2_j)
gc = aplpy.FITSFigure(
self.filenames[self.images[undone_image][0]].replace(
'.fits', '_2d.fits'))
gc.show_rgb(self.filenames[self.images[undone_image][0]].replace(
'.fits', '_2d.png'))
gc.tick_labels.set_xformat("dd.dd")
gc.tick_labels.set_yformat("dd.dd")
#out_filename = self.filenames[undone_image].replace('.fits','.pdf')
if contour_file:
#gc.show_contour(contour_file,levels=contour_levels,colors="black",linewidths = 3.,smooth=3.,alpha=0.5)
gc.show_contour(contour_file,
levels=contour_levels,
colors=contour_color,
linewidths=1.)
#chunks = contour_file.split('_')
#print(chunks)
#linename = chunks[3]
#moment_name = chunks[4][0:-5]
#tag = "_"+linename+"_"+moment_name
#print(tag)
#out_filename = out_filename.replace('.pdf',tag+'.pdf')
#self.add_labels(gc,undone_image,linename,moment_name = moment_name,color=contour_color)
else:
pass
#self.add_labels(gc,undone_image)
#gc.show_rectangles(self.apos,self.bpos,0.0625,0.0625,ec='w',lw=2,facecolor='none',zorder=1)
#gc.show_ellipses(self.apos,self.bpos,self.awin*2,self.bwin*2,angle=self.twin,facecolor='none',ec='green',lw=2)
#print(self.awin,self.bwin)
success = True
if success:
self.report_success(undone_image, version)
return (gc)
# Get pixel coordinates of SN
wcs = pywcs.WCS(prihdr)
try:
target_pix = wcs.wcs_sky2pix(
[(np.array([ra, dec], np.float_))], 1)[0]
except:
print "ERROR when converting sky to wcs. Is astrometry in place? Default coordinates assigned."
target_pix = [+nx / 2., ny / 2.]
print target_pix
else:
target_pix = [+nx / 2., ny / 2.]
img = img - np.nanmin(img)
av = np.median(img.flatten())
mi, ma = zscale.zscale(img)
im = plt.imshow(plt.log10(img), aspect="equal", extent=(0, ny, 0, nx), \
origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=np.log10(av), vmax=np.log10(3*av)) #, interpolation="lanczos")
plt.scatter(target_pix[0],
target_pix[1],
marker="x",
s=10,
c="red")
plt.colorbar(im)
filename = os.path.basename(f)
plt.savefig(os.path.join(
plot_dir,
filename.replace("." + filename.split(".")[-1],
"_{:}.png".format(i))),
dpi=200)
plt.clf()
