clean

scripts/01_bgdmodel.py

@@ -30,48 +30,27 @@ enkey = sys.argv[1]
 fn="detcnts.{}.fits".format(enkey)
 print("Reading {}".format(datadir+fn))
 
-#d = fits.getdata(datadir+fn)
-#df=pd.DataFrame(np.array(d).byteswap().newbyteorder())
-
 with fits.open(datadir+fn) as data:
     df = pd.DataFrame(data[1].data)
     
-print(df.columns)
+# print(df.columns)
 
-#interp_a = np.interp(50, [0,100], [0, 1])
-#print(interp_a)
-#sys.exit()
-        
 plotme=False
-sigma=3
 ntotal=0
 nrev=0
 bgdmodel={}
 
+# will be populated
 ignored_scw=[]
-ignored_rev=[131,132,133,134,135,136,# flare
-             280,281,282,#flare
-             1057,1102,1150, # flare
-             1452,1459,1472, # flare
-             2303,2304, # flare
-             2549, # flare
-             334,1760] # ignore orbits with pecular slope over phase
-ignored_rev=[131,132,133,136,
-             280,281,282,
-             1057,1102,1150,1452,1459,
-             2303,2304,2305,
-             2483,2549]
+
 
 if not os.path.exists(proddir):
     os.makedirs(proddir)
     
 for rev in range(revmin,revmax):
 
-#    if not (rev==341):
-#        continue
-
     df0 = df.query('CLEAN > 0.0 & REV == {} & ( abs(LAT) > {} | abs(LON) > {}) & PHASE > {} & PHASE < {}'.format(rev,bmax,lmax,phmin,phmax))
-    #df0 = df.query('CLEAN > 0.0 & REV == {} & abs(LAT) > {} & abs(LON) > {} & PHASE > {} & PHASE < {}'.format(rev,bmax,lmax,phmin,phmax))
+
     nobs=len(df0)
     print("*** REV {} *** {} ScWs".format(rev,df0.shape[0]))
     if not(nobs):
@@ -89,7 +68,7 @@ for rev in range(revmin,revmax):
             c = 0
             """ run regression """
             print("*** Run regression for {}".format(rev))
-            #print(df0['CLEAN'].values)
+            
             x = x.reshape((-1, 1))
             # https://machinelearningmastery.com/robust-regression-for-machine-learning-in-python/
             #model = LinearRegression()