def std(self, axis=None, dtype=None, out=None, ddof=1):
"""
Compute unbiased standard deviation of non-null values
"""
nona = remove_na(self.values)
if len(nona) < 2:
return NaN
return ndarray.std(nona, axis, dtype, out, ddof)
def std(self, axis=None, dtype=None, out=None, ddof=1):
"""
Unbiased standard deviation of non-null values
"""
nona = remove_na(self.values)
if len(nona) < 2:
return NaN
return ndarray.std(nona, axis, dtype, out, ddof)
def compute_stats(samples_healthy, samples_unhealthy, samples_s, samples):
samples_healthy = array(samples_healthy)
samples_unhealthy = array(samples_unhealthy)
samples_s = array(samples_s)
healthy_mean = ndarray.mean(samples_healthy,0)
unhealthy_mean = ndarray.mean(samples_unhealthy, 0)
s_mean = ndarray.mean(samples_s, 0)
healthy_std = ndarray.std(samples_healthy, 0, dtype=float64)
unhealthy_std = ndarray.std(samples_unhealthy, 0, dtype=float64)
s_std = ndarray.std(samples_s, 0, dtype=float64)
print "Healthy means: \t", healthy_mean
print "Unhealthy means: \t", unhealthy_mean
print "S means: \t\t", s_mean
print "Healthy standard dev: \t", healthy_std
print "Unhealthy standard dev: \t", unhealthy_std
print "S standard dev: \t", s_std
x1 = range(len(samples_healthy))
x2 = range(len(samples_unhealthy))
x3 = range(len(samples_s))
dur1 = [sample[0] for sample in samples_healthy]
mean1 = [sample[1] for sample in samples_healthy]
var1 = [sample[2] for sample in samples_healthy]
dur2 = [sample[0] for sample in samples_unhealthy]
mean2 = [sample[1] for sample in samples_unhealthy]
var2 = [sample[2] for sample in samples_unhealthy]
dur3 = [sample[0] for sample in samples_s]
mean3 = [sample[1] for sample in samples_s]
var3 = [sample[2] for sample in samples_s]
plt.figure()
plt.hist(mean1, normed=True, bins=100, color='red', range=(0, 50))
plt.figure()
plt.hist(mean2, normed=True, bins=100, color='green', range=(0, 50))
plt.figure()
plt.hist(mean3, normed=True, bins=100, color='blue', range=(0, 50))
plt.show()
