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import numpy as np
from scipy import stats
from itertools import combinations
from statsmodels.stats.multitest import multipletests
from statsmodels.stats.libqsturng import psturng
import warnings


def kw_dunn(groups, to_compare=None, alpha=0.05, method='bonf'):
    """

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from pylab import *
from scipy.stats import uniform, binom, expon, beta

true_gamma = 0.5

N = 600
T = 15
data = zeros((2, N), dtype=float)
event_times = data[0,:]
event_times[:] = uniform(0,15).rvs(N)

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import aesara
import aesara.tensor as at
from aesara.tensor import basic_opt
from aesara.tensor.nnet import sigmoid, softplus
from aesara.tensor.nnet.sigm import _is_1, _skip_mul_1
from aesara import config
from aesara.graph.opt import PatternSub
from aesara.tensor.math import exp, log1p, neg, sub, true_div
from aesara.graph.basic import graph_inputs as at_inputs
from aesara.graph.fg import FunctionGraph

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import aesara
import aesara.tensor as at
from aesara.tensor import basic_opt
from aesara.tensor.nnet import sigmoid, softplus
from aesara import config
from aesara.graph.opt import PatternSub
from aesara.tensor.math import add, exp, mul, log, log1p, neg, sub, true_div
from aesara.graph.basic import graph_inputs as at_inputs
from aesara.graph.fg import FunctionGraph
from aesara.graph.optdb import Query

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