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unused_code.py
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# New plots ONLY if NEEDED!!
def plotradec_sexvsY2Q1(args):
import matplotlib.pyplot as plt
if args.verbose > 0: print args
catlistFile = """D%08d_r%sp%02d_red_catlist.csv""" % (args.expnum, args.reqnum, args.attnum)
are_you_here(catlistFile):
print '%s does not seem to exist... exiting now...' % catlistFile
sys.exit(1)
data = np.genfromtxt(catlistFile, dtype=None, delimiter=',', names=True)
for i in range(data['FILENAME'].size):
ra1 = [];
ra2 = [];
dec1 = [];
dec2 = []
catFilename = os.path.basename(data['FILENAME'][i])
rac = data['RA_CENT'][i];
decc = data['DEC_CENT'][i]
rac1 = data['RAC1'][i];
decc1 = data['DECC1'][i]
rac2 = data['RAC2'][i];
decc2 = data['DECC2'][i]
rac3 = data['RAC3'][i];
decc3 = data['DECC3'][i]
rac4 = data['RAC4'][i];
decc4 = data['DECC4'][i]
CCDpoints = [[rac2, decc2], [rac, decc2], [rac3, decc3], [rac3, decc], [rac4, decc4], [rac, decc4], [rac1, decc1],
[rac1, decc]]
ccdline = plt.Polygon(CCDpoints, fill=None, edgecolor='g')
pnglistout = """%s.png""" % (catFilename)
objlistFile = """%s_Obj.csv""" % (catFilename)
stdlistFile = """%s_std.csv""" % (catFilename)
are_you_here(objlistFile)
are_you_here(stdlistFile)
# Read in the file...
ra1 = np.genfromtxt(stdlistFile, dtype=float, delimiter=',', skiprows=1, usecols=(1))
dec1 = np.genfromtxt(stdlistFile, dtype=float, delimiter=',', skiprows=1, usecols=(2))
ra2 = np.genfromtxt(objlistFile, dtype=float, delimiter=',', skiprows=1, usecols=(1))
dec2 = np.genfromtxt(objlistFile, dtype=float, delimiter=',', skiprows=1, usecols=(2))
plt.axes()
plt.gca().add_patch(ccdline)
plt.scatter(ra1, dec1, marker='.')
plt.scatter(ra2, dec2, c='r', marker='+')
line = plt.Polygon(CCDpoints, fill=None, edgecolor='r')
plt.title(catFilename, color='#afeeee')
plt.savefig(pnglistout, format="png")
ra1 = [];
ra2 = [];
dec1 = [];
dec2 = []
plt.clf()
# That was originally in ZP_outliers()
# #########################################################
# ######### Extra ONLY if the Full Exposure have problem!
# #########################################################
# #FIND OUTLIER via Local Outlier Factor(LOF) see
# #Ref:
# # http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf
# # THIS is a TEST
# #########################################################
# #try
# #mask = ( df1['NewZPFlag3'] < 0 )
# #if ( df1[mask]['x'].size > 0 ):
# #data = df1[['x','y','DiffZP1']]
# ##You can change below value for different MinPts
# ##m=5,10,15,30,35,40,45,50,55 testing
# #m=10
#
# #knndist, knnindices = knn(data,3)
# #reachdist, reachindices = reachDist(data,m,knndist)
#
# #irdMatrix = lrd(m,reachdist)
# #lofScores = lof(irdMatrix,m,reachindices)
# #scores= pd.DataFrame(lofScores,columns=['Score'])
# #mergedData=pd.merge(data,scores,left_index=True,right_index=True)
# #mergedData['flag'] = mergedData.apply(returnFlag,axis=1)
# #Outliers = mergedData[(mergedData['flag']==1)]
# #Normals = mergedData[(mergedData['flag']==0)]
#
# #mergedData1=pd.concat([df1, mergedData], axis=1)
# #mergedData1.to_csv(fout,sep=',',index=False)
#
# #print Outliers
#
# #pnglistout0="""%s_ZP_Warning.png""" % (catlistFile)
# #pnglistout1="""%s_ZP_Score.png""" % (catlistFile)
# ################
# # New plot in X,Y
# # NEED to convert the RA, DEC vs the expCal Zero-point mag
# # New plot SCORE - histogram
# ################
#
#
# #l1=plt.scatter(Normals['x'],Normals['y'],Normals['DiffZP1'],c='b',marker='o')
# #l2=plt.scatter(Outliers['x'],Outliers['y'],Outliers['DiffZP1'],c='r',marker='*')
# #plt.legend((l1,l2),('Regular','Outlier'),scatterpoints=1,loc='upper left',ncol=1, fontsize=9)
# #plt.title('Warning D%08d_r%sp%02d %s-Band' %(args.expnum,args.reqnum,args.attnum,BAND))
# #plt.xlabel(r"$X$", size=18)
# #plt.ylabel(r"$Y$", size=18)
# #plt.xlim([min(data['x']),max(data['x'])])
# #plt.ylim([min(data['y']),max(data['y'])])
# #plt.savefig(pnglistout0, format="png" )
# #plt.clf()
#
# #SCORE - histogram
# #plt.hist(mergedData['Score'],bins=100,facecolor='red')
# #plt.xlabel('LOF Score')
# #plt.ylabel('Frequency')
# #plt.title('Outlier Scores')
# #plt.savefig(pnglistout1, format="png" )
# #plt.clf()
##################################
# New plots for Zero-Points
def plotradec_ZP(args):
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import sys
if args.verbose > 0: print args
catlistFile = """D%08d_r%sp%02d_red_catlist.csv""" % (args.expnum, args.reqnum, args.attnum)
are_you_here(catlistFile)
# ZeroListFile="""Zero_D%08d_r%sp%02d.csv""" % (args.expnum,args.reqnum,args.attnum)
# if not os.path.isfile(catlistFile):
# print '%s does not seem to exist... exiting now...' % ZeroListFile
# sys.exit(1)
# Mergedout="""Merged_D%08d_r%sp%02d.csv""" % (args.expnum,args.reqnum,args.attnum)
# print catlistFile,ZeroListFile,Mergedout
# jointwocsv(catlistFile,ZeroListFile,Mergedout)
# MergedFile="""Merg_allZP_D%08d_r%sp%02d.csv""" % (args.expnum,args.reqnum,args.attnum)
MergedFile = """Merged_D%08d_r%sp%02d.csv""" % (args.expnum, args.reqnum, args.attnum)
data = np.genfromtxt(MergedFile, dtype=None, delimiter=',', names=True)
stdRA = np.std(data['RA_CENT'])
if stdRA > 20:
data['RA_CENT'] = [roundra(x) for x in data['RA_CENT']]
data['RAC1'] = [roundra(x) for x in data['RAC1']]
data['RAC2'] = [roundra(x) for x in data['RAC2']]
data['RAC3'] = [roundra(x) for x in data['RAC3']]
data['RAC4'] = [roundra(x) for x in data['RAC4']]
# print " Unexpected value of the RA spred stdRA=%f \n" % stdRA
# sys.exit(1)
w0 = (data['ZP'] == -999)
w1 = (data['ZP'] > -999)
data0 = data[w0]
data1 = data[w1]
if (len(data1) == 0):
sys.exit(1)
BAND = data1['BAND'][0]
zpmedian = np.median(data1['ZP'])
pnglistout0 = """%s_ZP.png""" % (catlistFile)
pnglistout1 = """%s_deltaZP.png""" % (catlistFile)
pnglistout2 = """%s_NumClipstar.png""" % (catlistFile)
pnglistout3 = """%s_CCDsvsZPs.png""" % (catlistFile)
w2 = (data['NewZPFlag'] == 0)
w3 = (data['NewZPFlag'] == 1)
# w3=(data('NewZPFlag') >0 )
data2 = data[w2]
data3 = data[w3]
pnglistout4 = """%s_NewZP.png""" % (catlistFile)
pnglistout5 = """%s_NewdeltaZP.png""" % (catlistFile)
minra = min(min(data['RA_CENT']), min(data['RAC1']), min(data['RAC2']), min(data['RAC3']), min(data['RAC4'])) - .075
mindec = min(min(data['DEC_CENT']), min(data['DECC1']), min(data['DECC2']), min(data['DECC3']),
min(data['DECC4'])) - .075
maxra = max(max(data['RA_CENT']), max(data['RAC1']), max(data['RAC2']), max(data['RAC3']), max(data['RAC4'])) + .075
maxdec = max(max(data['DEC_CENT']), max(data['DECC1']), max(data['DECC2']), max(data['DECC3']),
max(data['DECC4'])) + .075
################
# New plot the RA, DEC vs the expCal Zero-point mag
################
l1 = plt.scatter(data0['RA_CENT'], data0['DEC_CENT'], c=data0['ZP'], s=15, marker=(25, 0), cmap=mpl.cm.spectral,
vmin=np.min(data1['ZP']), vmax=np.max(data1['ZP']))
l2 = plt.scatter(data1['RA_CENT'], data1['DEC_CENT'], c=data1['ZP'], s=500, marker=(5, 0), cmap=mpl.cm.spectral,
vmin=np.min(data1['ZP']), vmax=np.max(data1['ZP']))
cbar = plt.colorbar(ticks=np.linspace(np.min(data1['ZP']), np.max(data1['ZP']), 4))
cbar.set_label('Zero-Point Mag')
# plt.legend((l1,l2),('No Y2Q1 data','ExpCal'),scatterpoints=1,loc='upper left',ncol=1, fontsize=9)
plt.legend((l1, l2), ('No APASS data', 'ExpCal'), scatterpoints=1, loc='upper left', ncol=1, fontsize=9)
for i in range(data['RA_CENT'].size):
CCDpoints = [[data['RAC2'][i], data['DECC2'][i]], [data['RAC3'][i], data['DECC3'][i]],
[data['RAC4'][i], data['DECC4'][i]], [data['RAC1'][i], data['DECC1'][i]]]
ccdline = plt.Polygon(CCDpoints, fill=None, edgecolor='k')
plt.gca().add_patch(ccdline)
plt.title('D%08d_r%sp%02d %s-Band ZP_Median=%.3f ' % (args.expnum, args.reqnum, args.attnum, BAND, zpmedian))
plt.xlabel(r"$RA$", size=18)
plt.ylabel(r"$DEC$", size=18)
plt.xlim([minra, maxra])
plt.ylim([mindec, maxdec])
plt.savefig(pnglistout0, format="png")
plt.clf()
################
# New plot the RA, DEC vs the expCal Delta Zero-point mag from median
################
l1 = plt.scatter(data0['RA_CENT'], data0['DEC_CENT'], c=data0['ZP'], s=15, marker=(25, 0), cmap=mpl.cm.spectral,
vmin=np.min(data1['ZP']), vmax=np.max(data1['ZP']))
l2 = plt.scatter(data1['RA_CENT'], data1['DEC_CENT'], c=1000 * (data1['ZP'] - zpmedian), s=500, marker=(5, 0),
cmap=mpl.cm.spectral, vmin=min(1000 * (data1['ZP'] - zpmedian)),
vmax=max(1000 * (data1['ZP'] - zpmedian)))
cbar = plt.colorbar(
ticks=np.linspace(min(1000 * (data1['ZP'] - zpmedian)), max(1000 * (data1['ZP'] - zpmedian)), 4))
cbar.set_label('Delta Zero-Point mili-Mag')
plt.legend((l1, l2), ('No APASS data', 'ExpCal'), scatterpoints=1, loc='upper left', ncol=1, fontsize=9)
for i in range(data['RA_CENT'].size):
CCDpoints = [[data['RAC2'][i], data['DECC2'][i]], [data['RAC3'][i], data['DECC3'][i]],
[data['RAC4'][i], data['DECC4'][i]], [data['RAC1'][i], data['DECC1'][i]]]
ccdline = plt.Polygon(CCDpoints, fill=None, edgecolor='k')
plt.gca().add_patch(ccdline)
plt.title('D%08d_r%sp%02d %s-Band ZP_Median=%.3f ' % (args.expnum, args.reqnum, args.attnum, BAND, zpmedian))
plt.xlabel(r"$RA$", size=18)
plt.ylabel(r"$DEC$", size=18)
plt.xlim([minra, maxra])
plt.ylim([mindec, maxdec])
plt.savefig(pnglistout1, format="png")
plt.clf()
################
# New plot RA DEC vs Number of stars clipped stars from expCal
################
l1 = plt.scatter(data0['RA_CENT'], data0['DEC_CENT'], c=data0['Nclipped'], s=15, marker=(25, 0),
cmap=mpl.cm.spectral)
l2 = plt.scatter(data1['RA_CENT'], data1['DEC_CENT'], c=data1['Nclipped'], s=500, marker=(5, 0),
cmap=mpl.cm.spectral)
cbar = plt.colorbar()
cbar.set_label('No. 3 $\sigma$ clipped Stars')
plt.legend((l1, l2), ('No APASS data', 'expCal'), scatterpoints=1, loc='upper left', ncol=1, fontsize=9)
for i in range(data['RA_CENT'].size):
CCDpoints = [[data['RAC2'][i], data['DECC2'][i]], [data['RAC3'][i], data['DECC3'][i]],
[data['RAC4'][i], data['DECC4'][i]], [data['RAC1'][i], data['DECC1'][i]]]
ccdline = plt.Polygon(CCDpoints, fill=None, edgecolor='k')
plt.gca().add_patch(ccdline)
plt.title('D%08d_r%sp%02d %s-Band ZP_Median=%.3f ' % (args.expnum, args.reqnum, args.attnum, BAND, zpmedian))
plt.xlabel(r"$RA$", size=18)
plt.ylabel(r"$DEC$", size=18)
plt.xlim([minra, maxra])
plt.ylim([mindec, maxdec])
plt.savefig(pnglistout2, format="png")
plt.clf()
################
# New plot CCDs vs ZP from expCal
plt.errorbar(data0['CCDNUM'], data0['ZP'], data0['ZPrms'], fmt='o', label='No APASS data')
plt.errorbar(data1['CCDNUM'], data1['ZP'], data1['ZPrms'], fmt='o', label='expCal')
legend = plt.legend(loc='upper center', shadow=None, fontsize=12)
legend.get_frame().set_facecolor('#00FFCC')
plt.title('D%08d_r%sp%02d %s-Band ZP_Median=%.3f ' % (args.expnum, args.reqnum, args.attnum, BAND, zpmedian))
plt.xlabel(r"$CCDs$", size=18)
plt.ylabel(r"$Zero Points$", size=18)
plt.ylim(min(data1['ZP']) - .01, max(data1['ZP']) + .02)
plt.xlim(min(data1['CCDNUM']) - 1.5, max(data1['CCDNUM']) + 1.5)
plt.savefig(pnglistout3, format="png")
plt.clf()
################
# New plot the RA, DEC vs the NEW Zero-point mag
################
l1 = plt.scatter(data2['RA_CENT'], data2['DEC_CENT'], c=data2['NewZP'], s=500, marker=(5, 0), cmap=mpl.cm.spectral,
vmin=np.min(data2['NewZP']), vmax=np.max(data2['NewZP']))
l2 = plt.scatter(data3['RA_CENT'], data3['DEC_CENT'], c=data3['NewZP'], s=25, marker=(25, 0), cmap=mpl.cm.spectral,
vmin=np.min(data2['NewZP']), vmax=np.max(data2['NewZP']))
cbar = plt.colorbar(ticks=np.linspace(np.min(data2['NewZP']), np.max(data2['NewZP']), 4))
cbar.set_label('Zero-Point Mag')
# CHANGE
plt.legend((l1, l2), ('CCD', 'allEXP'), scatterpoints=1, loc='upper left', ncol=1, fontsize=9)
for i in range(data['RA_CENT'].size):
CCDpoints = [[data['RAC2'][i], data['DECC2'][i]], [data['RAC3'][i], data['DECC3'][i]],
[data['RAC4'][i], data['DECC4'][i]], [data['RAC1'][i], data['DECC1'][i]]]
ccdline = plt.Polygon(CCDpoints, fill=None, edgecolor='k')
plt.gca().add_patch(ccdline)
plt.title('D%08d_r%sp%02d %s-Band ' % (args.expnum, args.reqnum, args.attnum, BAND))
plt.xlabel(r"$RA$", size=18)
plt.ylabel(r"$DEC$", size=18)
plt.xlim([minra, maxra])
plt.ylim([mindec, maxdec])
plt.savefig(pnglistout4, format="png")
plt.clf()
################
# New plot the RA, DEC vs the NEW Delta Zero-point mag from median
################
l1 = plt.scatter(data2['RA_CENT'], data2['DEC_CENT'], c=data2['DiffZP1'], s=500, marker=(5, 0),
cmap=mpl.cm.spectral, vmin=np.min(data2['DiffZP1']), vmax=np.max(data2['DiffZP1']))
l2 = plt.scatter(data3['RA_CENT'], data3['DEC_CENT'], c=data3['DiffZP1'], s=25, marker=(25, 0),
cmap=mpl.cm.spectral, vmin=min(data2['DiffZP1']), vmax=max(data2['DiffZP1']))
cbar = plt.colorbar(ticks=np.linspace(min(data2['DiffZP1']), max(data2['DiffZP1']), 4))
cbar.set_label('Delta Zero-Point mili-Mag')
plt.legend((l1, l2), ('CDD', 'allExP'), scatterpoints=1, loc='upper left', ncol=1, fontsize=9)
for i in range(data['RA_CENT'].size):
CCDpoints = [[data['RAC2'][i], data['DECC2'][i]], [data['RAC3'][i], data['DECC3'][i]],
[data['RAC4'][i], data['DECC4'][i]], [data['RAC1'][i], data['DECC1'][i]]]
ccdline = plt.Polygon(CCDpoints, fill=None, edgecolor='k')
plt.gca().add_patch(ccdline)
plt.title('D%08d_r%sp%02d %s-Band ' % (args.expnum, args.reqnum, args.attnum, BAND))
plt.xlabel(r"$RA$", size=18)
plt.ylabel(r"$DEC$", size=18)
plt.xlim([minra, maxra])
plt.ylim([mindec, maxdec])
plt.savefig(pnglistout5, format="png")
plt.clf()
#
##################################
# get_data_home for NOW it is for all CCDs:
#
def Wget_data_home(args):
import csv
import glob
import sys
catname = """D%08d_%s_%s_r%sp%02d_fullcat.fits""" % (args.expnum, "%", "%", args.reqnum, args.attnum)
myfile = """D%08d_*_r%sp%02d_fullcat.fits""" % (args.expnum, args.reqnum, args.attnum)
# Check first if file exists...
if glob.glob(myfile):
# Print '%s does seem to exist... exiting now...' % catname
print "relevant cat files already exist in the current directory... no need to wget..."
else:
print "relevant cat files are not in directory... wgetting them from archive..."
sys.exit(1)