def analyze(self,frame_number=1000,spav="None",illuminated=illuminated,non_illuminated=non_illuminated,non_ill=1):
"""Compute average frame and standard deviation
frame_number: defines how many frames should be used (default is all frames)
spav: spatial average to be computed
- "all" to return a single average value for illuminated pixels
- integer n to return spatial moving average with square (1+n)**2
- nothing to compute no spatial average
"""
f = open(self.name,'rb') # open raw file
header = np.fromfile(f, dtype=np.uint32,count=4)
access_table = np.fromfile(f, dtype=np.uint16,count=self.npixels) # id of the used pixels on the 120 x 160 standard grid of the bolometer
tms = []
data_1D = [] # list to save all frames
data_1D_non_ill = []
for k in range(min(frame_number,self.nframes)):
t = np.fromfile(f, dtype=np.uint32,count=1) # frame timestamp
frame = array(np.fromfile(f, dtype=np.uint16,count=self.npixels)).astype(float) # values for all pixels, returned in a 1D array
if len(frame) == self.npixels: # prevent from keeping erroneous frames
tms+= [int(t)]
if spav == "fast": # like "all" without eliminating bad pixels before averaging
frame = mean(frame[illuminated] - non_ill*frame[non_illuminated])
data_1D+=[frame*ones(2)]
elif isinstance(spav, int):
"""spatial mooving average on a spav x spav square"""
data_1D+= [Toolbox.get_av(frame,spav)]
else:
data_1D+=[frame] # append new frame to existing ones
if data_1D == []: # mark bad file to avoid reading in a sequence
self.good = False
elif shape(data_1D)[0] == 1: # mark bad file to avoid reading in a sequence
self.good = False
data_1D = array(data_1D)
self.std = std(data_1D,axis=0)
self.mean = mean(data_1D,axis=0)
# remove bad pixels from analysis
if spav == "fast":
corr = where(self.std<1.)[0]
elif isinstance(spav, int):
corr = where(self.std<1.2)[0]
else:
corr = where(self.std<1.8)[0]
if not corr.size:
print self.name,"No correct pixels"
self.all_tms = array(tms)
self.tms = mean(tms)
self.correct_pixels = corr
def analyze(self,frame_number=1000,spav="None"):
"""Compute average frame and standard deviation
frame_number: defines how many frames should be used (default is all frames)
spav: spatial average to be computed
- "all" to return a single average value for illuminated pixels
- integer n to return spatial moving average with square (1+n)**2
- nothing to compute no spatial average
"""
f = open(self.name,'rb') # open raw file
header = np.fromfile(f, dtype=np.uint32,count=4)
access_table = np.fromfile(f, dtype=np.uint16,count=self.npixels) # id of the used pixels on the 120 x 160 standard grid of the bolometer
tms = []
data_1D = [] # list to save all frames
for k in range(min(frame_number,self.nframes)):
t = np.fromfile(f, dtype=np.uint32,count=1) # frame timestamp
frame = array(np.fromfile(f, dtype=np.uint16,count=self.npixels)).astype(float) # values for all pixels, returned in a 1D array
if len(frame)==self.npixels: # prevent from keeping erroneous frames
tms+= [int(t)]
if spav=="all":
"""average on all illuminated pixels, only one value is saved per frame but an array is created to keep consistency with other options"""
frame = mean(frame[illuminated])
data_1D+= [frame*ones(2)]
if isinstance(spav, int):
"""spatial mooving average on a spav x spav square"""
data_1D+=[Toolbox.get_av(frame,spav)]
else:
"""no spatial average"""
data_1D+=[frame] # append new frame to existing ones
self.mean = mean(array(data_1D),axis=0)
self.std = std(array(data_1D),axis=0)
if spav == "all":
corr=where(self.std<5.)[0] # identify eroneous pixels
elif isinstance(spav, int):
corr = where(self.std<1.2)[0]
else:
corr = where(self.std<2.)
if not corr.size:
print self.name,"No correct pixels",self.std
plot(array(data_1D))[0]
show()
self.all_tms = array(tms)
self.tms = mean(tms)
self.correct_pixels = corr
