Classes
class	SpatOutlier
Functions
def	bias_correction
def	mk_test
def	independant
Variables
tuple	oc = np.random.randn(100)
int	mc = 2
int	mp = 2
tuple	mp_adjusted = bias_correction(oc, mc, mp)
tuple	x = np.random.randn(5,20)
tuple	foo = SpatOutlier(x)
tuple	x1 = foo.fill_with_nan()

Function Documentation

def ambhas.stats.bias_correction	(	oc,
		mc,
		mp
	)

Input:
    oc: observed current
    mc: modeled current
    mp: modeled prediction     

Output:
    mp_adjusted: adjusted modeled prediction

Definition at line 17 of file stats.py.

def ambhas.stats.independant	(	x,
		y,
		alpha = `0.05`
	)

this program calculates check if the joint cdf == multiplication of marginal
distribution or not 
using the chi-squared test 
    
Input:
    x:   a vector of data
    y:   a vector of data
    alpha: significance level

Output:
    ind: True (if independant) False (if dependant)
    p: p value of the significance test
    
Examples
--------
  >>> x = np.random.rand(100)
  >>> y = np.random.rand(100)
  >>> ind,p = independant(x,y,0.05)

Definition at line 108 of file stats.py.

def ambhas.stats.mk_test	(	x,
		alpha = `0.05`
	)

this perform the MK (Mann-Kendall) test to check if there is any trend present in 
data or not

Input:
    x:   a vector of data
    alpha: significance level

Output:
    trend: tells the trend (increasing, decreasing or no trend)
    h: True (if trend is present) or False (if trend is absence)
    p: p value of the sifnificance test
    z: normalized test statistics 
    
Examples
--------
  >>> x = np.random.rand(100)
  >>> trend,h,p,z = mk_test(x,0.05)

Definition at line 47 of file stats.py.