mag = np.where((sxflag==0), mag, None) mag = np.where((dqflag==0), mag, None) # Now ditch the values out of the arrays where mag is None # NB do mag last magerr = magerr[np.flatnonzero(mag)] refmag = refmag[np.flatnonzero(mag)] refmagerr = refmagerr[np.flatnonzero(mag)] mag = mag[np.flatnonzero(mag)] if(len(mag) == 0): print "No good sources to plot" sys.exit(1) # Now apply the exposure time and nom_at_ext corrections to mag et = float(ad.exposure_time()) if(ad.is_type('GMOS_NODANDSHUFFLE')): print "Imaging Nod-And-Shuffle. Photometry may be dubious" et /= 2.0 etmag = 2.5*math.log10(et) nom_at_ext = float(ad.nominal_atmospheric_extinction()) mag += etmag mag += nom_at_ext # Can now calculate the zp array zp = refmag - mag zperr = np.sqrt(refmagerr*refmagerr + magerr*magerr) # Trim values out of zp where the zeropoint error is > 0.1
from astrodata import AstroData import urllib2, urllib # This is a GMOS_N imaging science dataset ad = AstroData("/home/callen/SVN-AD/gemini_python/test_data/calsearch/N20110531S0114.fits") desc_dict = {'instrument':ad.instrument().for_db(), 'observation_type': ad.observation_type().for_db(), 'data_label':ad.data_label().for_db(), 'detector_x_bin':ad.detector_x_bin().for_db(), 'detector_y_bin':ad.detector_y_bin().for_db(), 'read_speed_setting':ad.read_speed_setting().for_db(), 'gain_setting':ad.gain_setting().for_db(), 'amp_read_area':ad.amp_read_area().for_db(), 'ut_datetime':ad.ut_datetime().for_db(), 'exposure_time':ad.exposure_time().for_db(), 'object': ad.object().for_db(), 'filter_name':ad.filter_name().for_db(), 'focal_plane_mask':ad.focal_plane_mask().for_db(), } print repr(desc_dict) type_list = ad.types ad.close() sequence = [('descriptors', desc_dict), ('types', type_list)] postdata = urllib.urlencode(sequence) #postdata = urllib.urlencode({"hello":1.}) url = "http://hbffits3.hi.gemini.edu/calmgr/processed_flat/" #url = "http://hbffits1/calmgr/processed_bias/"