def __init__(self, lst=[]):
#Store the list, so we can later do interesting data work
# Note that we project the data into radians!
self.original_list = lst
self.list = [radians(i) for i in filter_list(lst)]
if len(lst):
value, self.std, self.n, self.err, self.min, self.max = self.meanstdv(lst)
else:
self.std = float(-9e9)
self.n = 9e9
self.err = 9e9
self.min = 9.e9
self.max = 9e9
def __init__(self, lst=[]):
#Store the list, so we can later do interesting data work
# Note that we project the data into radians!
self.original_list = lst
self.list = [radians(i) for i in filter_list(lst)]
if len(lst):
value, self.std, self.n, self.err, self.min, self.max = self.meanstdv(
lst)
else:
self.std = float(-9e9)
self.n = 9e9
self.err = 9e9
self.min = 9.e9
self.max = 9e9
def meanstdv(self, lst, bad_value=-32768):
"Calculate mean and standard deviation of data in lst:"
if (lst[0] < 1):
pass
lst = [radians(i) for i in filter_list(lst, bad_value)]
if not len(lst):
return tuple([bad_value for i in range(6)])
n, std = len(lst), 0
# The mean of an azimuth is no simple task.
# Some sanity checking
if ((max(lst) > 2 * pi) or (min(lst) < 0)): raise Exception
# Convenience functions
ave = lambda x: sum(x) / len(x)
rotate = lambda x: x + (
2 * pi) if x < 0 else x # bring back to positive angle values
# self.list is already in radians.
sa = ave([sin(i) for i in lst])
ca = ave([cos(i) for i in lst])
# Take the arctan of this, using atan2 to preserve the quadrant information
mean = rotate(atan2(sa, ca))
# In a normal calculation, our standard deviation value would be
#
# for a in lst:
# std += (a - mean)**2
# try: std = sqrt(std / float(n-1))
# except ZeroDivisionError:
# if (n == 1): std = 0
# else: raise ZeroDivisionError
#
# However, we cannot do something so simple because angles may cross 0 (i.e. average
# north could be the list of values [355, 356, 0, 4,5]. Thus, we have to keep
# our mean into radians, and we'll use the list of radians as our sample population
for a in lst:
std += (a - mean)**2
try:
std = sqrt(std / float(n - 1))
except ZeroDivisionError:
if (n == 1):
std = 0 # Only one sample, Standard Deviation is meaningless
else:
raise ZeroDivisionError
err = std / sqrt(n)
return mean, std, n, err, min(lst), max(lst)
def meanstdv(self, lst, bad_value=-32768):
"Calculate mean and standard deviation of data in lst:"
lst = filter_list(lst, bad_value)
if not len(lst):
return tuple([bad_value for i in range(6)])
n, mean, std = len(lst), 0, 0
for a in lst:
mean += a
mean = mean / float(n)
for a in lst:
std += (a - mean)**2
try: std = sqrt(std / float(n-1))
except ZeroDivisionError:
if (n == 1): std = 0
else: raise ZeroDivisionError
err = std/sqrt(n)
print(lst)
return mean, std, n, err, min(lst), max(lst)
def meanstdv(self, lst, bad_value=-32768):
"Calculate mean and standard deviation of data in lst:"
lst = filter_list(lst, bad_value)
if not len(lst):
return tuple([bad_value for i in range(6)])
n, mean, std = len(lst), 0, 0
for a in lst:
mean += a
mean = mean / float(n)
for a in lst:
std += (a - mean)**2
try:
std = sqrt(std / float(n - 1))
except ZeroDivisionError:
if (n == 1): std = 0
else: raise ZeroDivisionError
err = std / sqrt(n)
print(lst)
return mean, std, n, err, min(lst), max(lst)
def meanstdv(self, lst, bad_value=-32768):
"Calculate mean and standard deviation of data in lst:"
if (lst[0]<1):
pass
lst = [radians(i) for i in filter_list(lst, bad_value)]
if not len(lst):
return tuple([bad_value for i in range(6)])
n, std = len(lst),0
# The mean of an azimuth is no simple task.
# Some sanity checking
if ((max(lst)>2*pi) or (min(lst)< 0)): raise Exception
# Convenience functions
ave = lambda x: sum(x)/len(x)
rotate = lambda x: x+(2*pi) if x<0 else x # bring back to positive angle values
# self.list is already in radians.
sa = ave([sin(i) for i in lst])
ca = ave([cos(i) for i in lst])
# Take the arctan of this, using atan2 to preserve the quadrant information
mean = rotate(atan2(sa,ca))
# In a normal calculation, our standard deviation value would be
#
# for a in lst:
# std += (a - mean)**2
# try: std = sqrt(std / float(n-1))
# except ZeroDivisionError:
# if (n == 1): std = 0
# else: raise ZeroDivisionError
#
# However, we cannot do something so simple because angles may cross 0 (i.e. average
# north could be the list of values [355, 356, 0, 4,5]. Thus, we have to keep
# our mean into radians, and we'll use the list of radians as our sample population
for a in lst:
std += (a - mean)**2
try: std = sqrt(std / float(n-1))
except ZeroDivisionError:
if (n == 1): std = 0 # Only one sample, Standard Deviation is meaningless
else: raise ZeroDivisionError
err = std/sqrt(n)
return mean, std, n, err, min(lst), max(lst)
