def main(file_path):
darklist = functions.find_objname(file_path,functions.read_config_file("BIAS_HEADER"))
master_dark = average_darks(darklist)
try:
functions.save_fits(master_dark,file_path+"temp/master_dark.fits")
except IOError:
os.system("mkdir "+file_path+"temp/")
functions.save_fits(master_dark,file_path+"temp/master_dark.fits")
def fistar(fits):
### Use this command to automatically identify stars within an image
### to create a catalogue for matching
### first apply median filter to image
data = pyfits.getdata(fits)
data = ndimage.filters.median_filter(data,10)
functions.save_fits(data,"temp.fits")
command = "fistar "
#command += "-i "+fits+" "
command += "-i temp.fits "
command += "-o "+fits+".fistar "
command += "-t 100 "
#command += "-f 1000 "
command += "-M badpix.fits "
command += "-F id,cx,cy,fwhm"
os.system(command)
### Now plot image
image = pyfits.getdata(fits)
coords = loadtxt(fits+".fistar")
mask = coords[:,3] > 3.
mask *= coords[:,1] > 10
mask *= coords[:,1] < len(image[0])-10
mask *= coords[:,2] > 10
mask *= coords[:,2] < len(image[0])-10
coords = coords[mask]
savetxt(fits+".fistar",coords,fmt="%.5f")
#sigma = 0.01
#stdev = std(image)
lim = 500
med = median(image)
plt.clf()
plt.imshow(image,cmap="gray",origin="lower",vmin=med-0.5*lim,vmax=med+lim,interpolation="nearest")
plt.scatter(coords[:,1],coords[:,2],marker="o",facecolor="None",edgecolor="r",s=200)
plt.savefig(fits+".png")
#plt.show()
plt.close()
return coords
def main(file_path):
darklist = functions.find_objname(file_path,functions.read_config_file("FLAT_HEADER"))
if len(darklist) == 0:
print "Error: no flats found"
raise IOError
master_dark = average_darks(darklist,file_path+"temp/master_dark.fits")
try:
functions.save_fits(master_dark,file_path+"temp/master_flat.fits")
except IOError:
os.system("mkdir "+file_path+"temp/")
functions.save_fits(master_dark,file_path+"temp/master_flat.fits")