def calculateNorm(self,brightarray,darkarray,scale=None):
'''
caculating norm array from bright and dark array. A scale
for the dark array can be specified or calculated. For most
case, scale of 1 is good enough if the exposure time of the
bright and dark are equal. If Gram-Schmidt process is used
to calculate dark_scale on the data, normarray need to be
scaled the same by specifying it.
'''
if scale:
dark_scale = scale
else:
dark_scale = 1.0
normarray = brightarray - dark_scale * darkarray
normarray = numpy.asarray(normarray, numpy.float32)
# division may result infinity or zero division
# so make sure there are no zeros in norm
normarray = numpy.clip(normarray, 0.001, sys.maxint)
stats = numextension.allstats(normarray, mean=True)
normavg = stats['mean']
normarray = normavg / normarray
# Avoid over correcting dead pixels
normarray = numpy.ma.masked_greater(normarray,20).filled(1)
return normarray
def calculateDarkScale(self,rawarray,darkarray):
'''
Calculate the scale used for darkarray using
Gram-Schmidt process for very low signal-to-noise
ratio such as DD raw frames as suggested by Niko Grigoreiff.
Need to apply the same factor used in data dark subtraction as
in dark subtraction for the normarray
'''
onedshape = rawarray.shape[0] * rawarray.shape[1]
a = rawarray.reshape(onedshape)
b = darkarray.reshape(onedshape)
a_std = numextension.allstats(a,std=True)['std']
b_std = numextension.allstats(b,std=True)['std']
if b_std == 0:
return 1.0
ab_corr_coef = numpy.corrcoef(a,b)[(0,1)]
dark_scale = ab_corr_coef * a_std / b_std
return dark_scale
def wrapped(a):
# we cannot remove the numextension requirement here, see Bug #1105
# which is unfortunate, because our simple mrc.py requires numextension
try:
import numextension
result = numextension.allstats(a, **kwargs)
except:
b = numpy.asanyarray(a, dtype=numpy.float64)
result = allstats(b, **kwargs)
if stat != 'all':
result = result[stat]
return result
def wrapped(a):
# we cannot remove the numextension requirement here, see Bug #1105
# which is unfortunate, because our simple mrc.py requires numextension
try:
import numextension
result = numextension.allstats(a, **kwargs)
except:
b = numpy.asanyarray(a, dtype=numpy.float64)
result = allstats(b, **kwargs)
if stat != 'all':
result = result[stat]
return result
'''
import numpy
import numextension
import os
print 'Demonstration of how allstats is better:'
print ''
a = 500 * numpy.ones((1000,1000), dtype=numpy.float32)
print 'Here is a type float32 array where all elements are set to 500.0:'
print a
print ''
numpy_mean = a.mean()
allstats_result = numextension.allstats(a, mean=True)
allstats_mean = allstats_result['mean']
print 'mean calculated by numpy: %s' % (numpy_mean,)
print 'mean calculated by allstats: %s' % (allstats_mean,)
print ''
pid = os.getpid()
print 'Now we will test the memory usage issue.'
print 'You should begin monitoring the memory usage of this process (pid=%d).' % (pid,)
print 'For example, in another terminal, run this:'
print ' top -d 0.5 -b -p %d | grep %d' % (pid,pid)
raw_input('Hit enter to create a large array in memory...')
a = numpy.ones((200, 1024, 1024), dtype=numpy.int16)
memsize = a.size * a.itemsize
