--- a/trafficintelligence/utils.py	Wed Jul 17 12:28:01 2024 -0400
+++ b/trafficintelligence/utils.py	Sat Jul 20 20:35:21 2024 -0400
@@ -11,7 +11,7 @@
 from scipy.stats import rv_continuous, kruskal, shapiro, lognorm, norm, t, chi2_contingency
 from scipy.spatial import distance
 from scipy.sparse import dok_matrix
-from numpy import zeros, array, exp, sum as npsum, int64 as npint, arange, cumsum, mean, median, percentile, isnan, ones, convolve,  dtype, isnan, NaN, ma, isinf, savez, load as npload, log, polyfit, float64
+from numpy import zeros, array, exp, sum as npsum, int64 as npint, arange, cumsum, mean, median, percentile, ones, convolve,  dtype, isnan, nan, ma, isinf, savez, load as npload, log, polyfit, float64
 from numpy.random import random_sample, permutation as nppermutation
 from pandas import DataFrame, concat, crosstab
 import matplotlib.pyplot as plt
@@ -501,7 +501,7 @@
             if not allVariables:
                 values = values[:-1]
             for val in values:
-                if val is not NaN:
+                if not isnan(val):
                     newVariable = (var+'_{}'.format(val)).replace('.','').replace(' ','').replace('-','')
                     data[newVariable] = (data[var] == val)
                     newVariables.append(newVariable)
@@ -736,7 +736,7 @@
         experiments[var] = pattern*(2**(nIndependentVariables-i-1))
     experiments = DataFrame(experiments)
     experiments['r2adj'] = 0.
-    experiments['condNum'] = NaN
+    experiments['condNum'] = nan
     experiments['shapiroP'] = -1
     experiments['nobs'] = -1
     return experiments