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() |
def ambhas.stats.bias_correction | ( | oc, | |
mc, | |||
mp | |||
) |
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)
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)
tuple ambhas::stats::foo = SpatOutlier(x) |
int ambhas::stats::mc = 2 |
int ambhas::stats::mp = 2 |
tuple ambhas::stats::mp_adjusted = bias_correction(oc, mc, mp) |
tuple ambhas::stats::oc = np.random.randn(100) |
tuple ambhas::stats::x = np.random.randn(5,20) |
tuple ambhas::stats::x1 = foo.fill_with_nan() |