def normalisation() :
global first, second, random_seed
first=str(nanoseconds())[for_ref:for_ref+decimal_places:1].zfill(8)
second=str(nanoseconds())[back_ref+decimal_places:back_ref:-1].zfill(8)
random_seed=str(random_seed).zfill(4)[0:4:1] #user seed string, padded to 4 zeros.
return
def find_refs() :
global for_ref, back_ref, decimal_places
samp_zeros=0 ; sample_size=12
decimal_places=8
reference_length=len(str(nanoseconds()))
for samp_loops in range(sample_size) :
sample1=str(nanoseconds()); sample2=str(nanoseconds())
while sample1==sample2 :
sample2=str(nanoseconds())
sample2=sample2.rstrip('0')
samp_zeros=samp_zeros+(len(sample1)-len(sample2))
reference_zeros=int(samp_zeros/sample_size)
for_ref=reference_length-(decimal_places+reference_zeros)
back_ref=-(reference_zeros+decimal_places+1)
return
def store_index(self, time, index) :
"""Store information about:
* Time-satmp
* Total intensity
* Beam center
* Estimated Particle Size
* Estimated particle nr
"""
self.tot_t[index] = time.time()
self.tot_s[index] = time.seconds()
self.tot_ns[index] = time.nanoseconds()
self.tot_fd[index] = time.fiducial()
self.tot_int[index] = float(self.img.sum())
self.tot_cx[index] = self.cent[0]
self.tot_cy[index] = self.cent[1]
self.tot_size[index] = self.radius
self.tot_score[index] = self.score
self.ave += self.img
def store_index(self, time, index, flag = 1) :
"""Store information about:
* Time-stamp
* Total intensity
* Beam center
* Estimated Particle Size
* Estimated particle nr
"""
if not hasattr(self, 'streak_m'):
self.streak_m = 0
if not hasattr(self, 'streak_s'):
self.streak_s = 0
self.tot_t[index] = time.time()
self.tot_s[index] = time.seconds()
self.tot_ns[index] = time.nanoseconds()
self.tot_fd[index] = time.fiducial()
self.tot_int[index] = float(self.img.sum())
if (self.peak is not None):
self.tot_peak1_int[index] = self.peak1
self.tot_peak2_int[index] = self.peak2
self.tot_streak_m[index] = self.streak_m
self.tot_streak_s[index] = self.streak_s
self.tot_cx[index] = self.cent[0]
self.tot_cy[index] = self.cent[1]
self.tot_size[index] = self.radius
self.tot_score[index] = self.score
if flag :
self.ave += self.img
def store_index(self, time, index, flag = 1) :
"""Store information about:
* Time-stamp
* Total intensity
* Beam center
* Estimated Particle Size
* Estimated particle nr
"""
if not hasattr(self, 'streak_m'):
self.streak_m = 0
if not hasattr(self, 'streak_s'):
self.streak_s = 0
self.tot_t[index] = time.time()
self.tot_s[index] = time.seconds()
self.tot_ns[index] = time.nanoseconds()
self.tot_fd[index] = time.fiducial()
self.tot_int[index] = float(self.img.sum())
if (self.peak is not None):
self.tot_peak1_int[index] = self.peak1
self.tot_peak2_int[index] = self.peak2
self.tot_streak_m[index] = self.streak_m
self.tot_streak_s[index] = self.streak_s
self.tot_cx[index] = self.cent[0]
self.tot_cy[index] = self.cent[1]
self.tot_size[index] = self.radius
self.tot_score[index] = self.score
if flag :
self.ave += self.img
def store_index2(self, time, index, flag=1):
"""Store information about:
* Time-stamp
* Total intensity
* Beam center
* Estimated Particle Size
* Estimated particle nr
"""
self.tot_t[index] = time.time()
self.tot_s[index] = time.seconds()
self.tot_ns[index] = time.nanoseconds()
self.tot_fd[index] = time.fiducial()
self.tot_int[index] = float(self.image.sum())
self.tot_cx[index] = self.cent[0]
self.tot_cy[index] = self.cent[1]
self.tot_size[index] = self.radius
self.tot_score[index] = self.score
if flag:
self.ave += self.image
def nano_hop() :
sample1=str(nanoseconds()); sample2=str(nanoseconds())
while sample1==sample2 :
sample2=str(nanoseconds())
return