def __init__(self, filelist, **kw):
#TODO: Checkout ipython-progress bar??????????
#TODO: MULTIEXT, CUBE, BURST METHODS
'''
Extract frames from list of fits files. Assuming all the files in the input list are
fits complient and have the same basic structure, data extraction can be done ~10**6
times faster than it can with pyfits (even without multiprocessing!!) by skipping unneessary
check and other cuft.
'''
self.start = start = kw.setdefault('start', 0)
self.step = step = kw.setdefault('step', 1)
self.stop = stop = kw.setdefault('stop', None)
keys = kw.setdefault('keygrab', None )
self.headkeys = []
self.filelist = parsetolist(filelist)
self.Nfiles = Nfiles = len(filelist)
self.clobber = kw.setdefault('clobber', True)
#self.outfiles = []
self.data_buffer = BytesIO()
self.text_buffer = []
#keyword extraction setup
if isinstance(keys, (str, type(None))):
keys = keys,
self.match_keys = matchmaker(*keys)
self.keygrab = keys
self.bar = kw.pop('progressbar', ProgressBar()) #initialise progress bar unless False
self.filemem = [] #memory buffer to reconstruct which frame came from which file
self.setup( self.filelist[0] )
def __init__(self, filelist, **kw):
#WARNING: Turns out this sequential extraction thing is SUPER slow!!
#TODO: Checkout ipython-progress bar??????????
'''
Extract frames from list of fits files.
'''
self.start = start = kw.setdefault( 'start', 0 )
self.step = step = kw.setdefault( 'step', 1 )
self.clobber = kw.setdefault( 'clobber', True )
self.keygrab = kw.setdefault( 'keygrab', None )
self.headkeys = []
self.filelist = parsetolist( filelist )
self.outfiles = []
#map from integer extension number to (header, data) element
self.data_element = lambda hdulist, i: ( hdulist[i+1].header, hdulist[i+1].data ) #return header and data for extension i
#read firts file
first = pyfits.open( filelist[0], memmap=True )
pheader = first[0].header
#Assume all files have same number of extensions and calculate total number of frames.
Next = pheader.get( 'nextend' )
self.Nfiles = Nfiles = len(filelist)
self.Ntot = Ntot = (Nfiles*Next - start) // step
self.stop = stop = kw.setdefault( 'stop', Ntot )
self.padwidth = len(str(Ntot)) #for numerical file naming
self.count = 0
self.bar = kw.pop( 'progressbar', ProgressBar() ) #initialise progress bar unless False
#create master output file (HduList with header of first file)
primary = pyfits.PrimaryHDU( header=pheader )
self.master = pyfits.HDUList( primary ) #Use this as the master header for the output file
self.mheader = self.master[0].header
pool.close()
#pool.join()
#Flatten the twice nested list of string matches (this is the fastest way of doing this!!)
results = []
for r in raw:
results.extend(r)
return merger(results, keys, defaults, return_type)
#====================================================================================================
if __name__ =='__main__':
from time import time
saltpath = '/media/Oceanus/UCT/Observing/SALT/V2400_Oph/20140921/product'
filelist = parsetolist( saltpath+'/bxgp*.fits' )
t0 = time()
#print( len(filelist) )
keys = 'utc-obs','date-obs'
q = superheadhunter( filelist[:100], keys )
#q = headhunter( filelist[0], ('date-obs', 'utc-obs', 'deadtime') )
print( 'Took', time()-t0, 's' )
#print()
##print( q )
#for k,v in q.items():
#print( k, len(v) )
#ipshell()