Merge branch 'hotfix/Chi2_KS_naming' into develop

benford/benford.py

@@ -13,7 +13,8 @@
     plot_mantissa_arc_test, plot_roll_mse, plot_roll_mad
 from .reports import _inform_, _report_mad_, _report_test_, _deprecate_inform_,\
     _report_mantissa_
-from .stats import Z_score, chi_square, chi_square_2, KS, KS_2
+from .stats import Z_score, chi_sq, chi_sq_2, kolmogorov_smirnov,\
+    kolmogorov_smirnov_2
 
 class Base(DataFrame):
     """Internalizes and prepares the data for Analysis.
@@ -122,8 +123,8 @@ def __init__(self, base, digs, confidence, limit_N=None, sec_order=False):
         self.N = _set_N_(len(base), limit_N)
         self['Z_score'] = Z_score(self, self.N)
         self.ddf = len(self) - 1
-        self.chi_square = chi_square_2(self)
-        self.KS = KS_2(self)
+        self.chi_square = chi_sq_2(self)
+        self.KS = kolmogorov_smirnov_2(self)
         self.MAD = self.AbsDif.mean()
         self.MSE = (self.AbsDif ** 2).mean()
         self.confidence = confidence
@@ -646,12 +647,12 @@ def first_digits(self, digs, confidence=None, high_Z='pos',
 
         # Chi-square statistic
         if chi_square:
-            self.chi_square = chi_square(df, ddf=len(df) - 1,
+            self.chi_square = chi_sq(df, ddf=len(df) - 1,
                                            confidence=confidence,
                                            verbose=self.verbose)
         # KS test
         if KS:
-            self.KS = KS(df, confidence=confidence, N=len(temp),
+            self.KS = kolmogorov_smirnov(df, confidence=confidence, N=len(temp),
                            verbose=self.verbose)
 
         # Plotting the expected frequncies (line) against the found ones(bars)
@@ -722,18 +723,18 @@ def second_digit(self, confidence=None, high_Z='pos',
         if MAD:
             self.MAD = df.AbsDif.mean()
             if self.verbose:
-                _report_mad_(digs, self.MAD)
+                _report_mad_(22, self.MAD)
         # Mean Square Error
         if MSE:
             self.MSE = (df.AbsDif ** 2).mean()
 
         # Chi-square statistic
         if chi_square:
-            self.chi_square = chi_square(df, ddf=9, confidence=confidence,
+            self.chi_square = chi_sq(df, ddf=9, confidence=confidence,
                                            verbose=self.verbose)
         # KS test
         if KS:
-            self.KS = KS(df, confidence=confidence, N=len(temp),
+            self.KS = kolmogorov_smirnov(df, confidence=confidence, N=len(temp),
                            verbose=self.verbose)
 
         # Plotting the expected frequncies (line) against the found ones(bars)
@@ -806,11 +807,11 @@ def last_two_digits(self, confidence=None, high_Z='pos',
 
         # Chi-square statistic
         if chi_square:
-            self.chi_square = chi_square(df, ddf=99, confidence=confidence,
+            self.chi_square = chi_sq(df, ddf=99, confidence=confidence,
                                            verbose=self.verbose)
         # KS test
         if KS:
-            self.KS = KS(df, confidence=confidence, N=len(temp),
+            self.KS = kolmogorov_smirnov(df, confidence=confidence, N=len(temp),
                            verbose=self.verbose)
 
         # Plotting expected frequencies (line) versus found ones (bars)

benford/stats.py

@@ -1,5 +1,5 @@
 from numpy import sqrt
-# from .constants import digs_dict, confs, crit_chi2, KS_crit, mad_dict
+from .constants import crit_chi2, KS_crit
 
 
 def Z_score(frame, N):
@@ -17,7 +17,7 @@ def Z_score(frame, N):
            (frame.Expected * (1. - frame.Expected)) / N)
 
 
-def chi_square(frame, ddf, confidence, verbose=True):
+def chi_sq(frame, ddf, confidence, verbose=True):
     """Comnputes the chi-square statistic of the found distributions and compares
     it with the critical chi-square of such a sample, according to the
     confidence level chosen and the degrees of freedom - len(sample) -1.
@@ -48,7 +48,7 @@ def chi_square(frame, ddf, confidence, verbose=True):
         return (found_chi, crit_chi)
 
 
-def chi_square_2(frame):
+def chi_sq_2(frame):
     """Computes the chi-square statistic of the found distributions
 
     Args:
@@ -62,7 +62,7 @@ def chi_square_2(frame):
     return (dif_counts ** 2 / exp_counts).sum()
 
 
-def KS(frame, confidence, N, verbose=True):
+def kolmogorov_smirnov(frame, confidence, N, verbose=True):
     """Computes the Kolmogorov-Smirnov test of the found distributions
     and compares it with the critical chi-square of such a sample,
     according to the confidence level chosen.
@@ -96,7 +96,7 @@ def KS(frame, confidence, N, verbose=True):
         return (suprem, crit_KS)
 
 
-def KS_2(frame):
+def kolmogorov_smirnov_2(frame):
     """Computes the Kolmogorov-Smirnov test of the found distributions
     
     Args: