Add Python code

dependencies.txt

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+backcall==0.1.0
+cycler==0.10.0
+decorator==4.4.0
+ipython==7.8.0
+ipython-genutils==0.2.0
+jedi==0.15.1
+kiwisolver==1.1.0
+matplotlib==3.1.1
+numpy==1.17.0
+parso==0.5.1
+pexpect==4.7.0
+pickleshare==0.7.5
+prompt-toolkit==2.0.9
+ptyprocess==0.6.0
+Pygments==2.4.2
+pyparsing==2.4.2
+python-dateutil==2.8.0
+scipy==1.3.1
+six==1.12.0
+traitlets==4.3.2
+wcwidth==0.1.7

kurtosis.py

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+from numpy import *
+
+def kurtosis(x, flag=1, dim=None):
+    # return k
+    #KURTOSIS Kurtosis.
+    #   K = KURTOSIS(X) returns the sample kurtosis of the values in X.  For a
+    #   vector input, K is the fourth central moment of X, divided by fourth
+    #   power of its standard deviation.  For a matrix input, K is a row vector
+    #   containing the sample kurtosis of each column of X.  For N-D arrays,
+    #   KURTOSIS operates along the first non-singleton dimension.
+    #
+    #   KURTOSIS(X,0) adjusts the kurtosis for bias.  KURTOSIS(X,1) is the same
+    #   as KURTOSIS(X), and does not adjust for bias.
+    #
+    #   KURTOSIS(X,FLAG,'all') is the kurtosis of all the elements of X.
+    #
+    #   KURTOSIS(X,FLAG,DIM) takes the kurtosis along dimension DIM of X.
+    #
+    #   KURTOSIS(X,FLAG,VECDIM) finds the kurtosis of the elements of X based
+    #   on the dimensions specified in the vector VECDIM.
+    #
+    #   KURTOSIS treats NaNs as missing values, and removes them.
+    #
+    #   See also MEAN, MOMENT, STD, VAR, SKEWNESS.
+
+    #   Copyright 1993-2018 The MathWorks, Inc.
+
+    # Validate flag
+    if flag not in [0,1]:
+        raise Exception('stats:trimmean:BadFlagReduction')
+
+    if dim is None:
+        if x.size == 0:
+            # The output size for [] is a special case, handle it here.
+            k = empty(x.shape)
+            k[:] = nan
+            return k
+        else:
+            # Figure out which dimension nanmean will work along.
+            # First dimension with length > 1
+            try:
+                dim = [i for i,d in enumerate(x.shape) if d != 1][0]
+            except IndexError: # all dimensions are length 1
+                dim = 0
+
+    # Center X, compute its fourth and second moments, and compute the
+    # uncorrected kurtosis.
+    x0 = x - nanmean(x,dim, keepdims=True)
+    s2 = nanmean(x0**2,dim, keepdims=True) # this is the biased variance estimator
+    m4 = nanmean(x0**4,dim, keepdims=True)
+    k = m4 / s2**2
+
+    # Bias correct the kurtosis.
+    if flag == 0:
+        n = sum(invert(isnan(x)).astype(int), dim, keepdims=True)
+        n[n<4] = nan # bias correction is not defined for n < 4.
+        k = ((n+1)*k - 3*(n-1)) * (n-1)/((n-2)*(n-3)) + 3
+    return k