def organized(self,spav="fast",non_ill=1):
""" Sort all raw data in a 11 x npos array
structure of all_mean = AH then ZZZ or NN or NZ for instance
mirror positions : ["N","Z","A","H"] """
if spav == "fast":
npix = 2
else:
npix = 4800
all_mean = zeros([11,self.npos,npix]) # all mean frames from raw files are stored in all_mean (11 filter wheel positions, npos pointing mirror positions)
all_std = zeros([11,self.npos,npix]) # all std from raw files are stored in all_std (11 filter wheel positions, npos pointing mirror positions
all_tms = zeros([11,self.npos]) # relative timestamp of each raw file (average through all frames)
real_tms = [] # absolute timestamp of each mirror position (when filter wheel in position 5)
counter_filters = zeros([11]) # number of scene positions already accoiunted for for each filter
for fichier in self.files:
raw_data = FirrRaw(fichier)
nf = min(raw_data.nframes,self.nframes)
raw_data.analyze(nf,spav=spav,illuminated=self.illuminated,non_illuminated=self.non_illuminated,non_ill=non_ill)
# print fichier,raw_data.good
# if raw_data.good: # do not read ill files
correct_pixels = raw_data.correct_pixels
if raw_data.mpos == 2 and all_mean[raw_data.fpos-1,0,:].all() == 0: # keep only first calibration
all_mean[raw_data.fpos-1,0,correct_pixels] = raw_data.mean[correct_pixels]
all_std[raw_data.fpos-1,0,correct_pixels] = raw_data.std[correct_pixels]
all_tms[raw_data.fpos-1,0] = raw_data.tms
elif raw_data.mpos == 3 and all_mean[raw_data.fpos-1,1,:].all() == 0: # keep only first calibration
all_mean[raw_data.fpos-1,1,correct_pixels] = raw_data.mean[correct_pixels]
all_std[raw_data.fpos-1,1,correct_pixels] = raw_data.std[correct_pixels]
all_tms[raw_data.fpos-1,1] = raw_data.tms
elif raw_data.mpos == 0 or raw_data.mpos == 1: # nadir or zenith view
scene = counter_filters[raw_data.fpos-1] + 2
all_mean[raw_data.fpos-1,scene,correct_pixels] = raw_data.mean[correct_pixels]
all_std[raw_data.fpos-1,scene,correct_pixels] = raw_data.std[correct_pixels]
all_tms[raw_data.fpos-1,scene] = raw_data.tms
counter_filters[raw_data.fpos-1]+=1
for np in range(self.npos):
real_tms+=[self.date0+timedelta(seconds=1e-3*all_tms[5,np])]
self.all_mean = ma.masked_equal(all_mean,0)
self.all_std = all_std
self.all_tms = all_tms
self.real_timestamp = real_tms
def organized(self,spav="all"):
""" Sort all raw data in a 11 x npos array
structure of all_mean = AH then ZZZ or NN or NZ for instance
mirror positions : ["N","Z","A","H"] """
if spav == "all":
npix = 2
else:
npix = 4800
all_mean = zeros([11,self.npos,npix]) # all mean frames from raw files are stored in all_mean (11 filter wheel positions, npos pointing mirror positions)
all_std = zeros([11,self.npos,npix]) # all std from raw files are stored in all_std (11 filter wheel positions, npos pointing mirror positions
all_tms = zeros([11,self.npos]) # relative timestamp of each raw file (average through all frames)
real_tms = [] # absolute timestamp of each mirror position (when filter wheel in position 5)
for fichier in self.files:
raw_data = FirrRaw(fichier)
raw_data.analyze(raw_data.nframes,spav=spav)
correct_pixels = raw_data.correct_pixels
if raw_data.mpos == 2 and all_mean[raw_data.fpos-1,0,:].all() == 0: # keep only first calibration
all_mean[raw_data.fpos-1,0,correct_pixels] = raw_data.mean[correct_pixels]
all_std[raw_data.fpos-1,0,correct_pixels] = raw_data.std[correct_pixels]
all_tms[raw_data.fpos-1,0] = raw_data.tms
elif raw_data.mpos == 3 and all_mean[raw_data.fpos-1,1,:].all() == 0: # keep only first calibration
all_mean[raw_data.fpos-1,1,correct_pixels] = raw_data.mean[correct_pixels]
all_std[raw_data.fpos-1,1,correct_pixels] = raw_data.std[correct_pixels]
all_tms[raw_data.fpos-1,1] = raw_data.tms
elif raw_data.mpos == 0 or raw_data.mpos == 1: # nadir or zenith view
scene = 0
while all_mean[raw_data.fpos-1,scene+2,:].any()!=0:
scene+=1
all_mean[raw_data.fpos-1,scene+2,correct_pixels] = raw_data.mean[correct_pixels]
all_std[raw_data.fpos-1,scene+2,correct_pixels] = raw_data.std[correct_pixels]
all_tms[raw_data.fpos-1,scene+2] = raw_data.tms
for np in range(self.npos):
real_tms+=[self.date0+timedelta(seconds=1e-3*all_tms[5,np])]
self.all_mean = all_mean
self.all_std = all_std
self.all_tms = all_tms
self.real_timestamp = real_tms
from FirrRaw import FirrRaw
#-------------Latex Style--------------
rc('font',**{'family':'serif','serif':['Computer Modern'],'size':'17'})
rc('text', usetex=True)
##--------------------------------------
#
rc('xtick',labelsize=22)
rc('ytick',labelsize=22)
""" Read a sample raw file and return the correspondin mirror position, filter position, number of frames, \
number of pixels per frame, average frame and standard deviation of all frames"""
rawfile = "Data/test.raw"
raw_data_nospav = FirrRaw(rawfile) # create a FirrRaw object
raw_data_spav = FirrRaw(rawfile)
raw_data_nospav.analyze() # Analyze the file to extract all necessary information
raw_data_spav.analyze(spav=2)
print "mirror position",raw_data_nospav.mpos
print "filter position",raw_data_nospav.fpos
print "number of frames",raw_data_nospav.nframes
print "number of pixels per frame",raw_data_nospav.npixels
print "number of bad pixels",raw_data_nospav.npixels-shape(raw_data_nospav.correct_pixels)[1]
mean_image_nospav = raw_data_nospav.mean
std_image_nospav = raw_data_nospav.std
mean_image_spav = raw_data_spav.mean