diff --git a/adtributor.py b/adtributor.py
deleted file mode 100644
index 0f2454f..0000000
--- a/adtributor.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import numpy as np
-import pandas as pd
-
-
-def adtributor(df, dimensions, Teep=0.1, Tep=0.67, k=3):
-    # For each element, compute the explanatory power.
-    F = df['predict'].sum()
-    A = df['real'].sum()
-    df['ep'] = (df['real'] - df['predict']) / (A - F)
-
-    # For each element, compute the surprise.
-    # Ignore any divide by zero.
-    with np.errstate(divide='ignore'):
-        p = df['predict'] / F
-        q = df['real'] / A
-        p_term = np.nan_to_num(p * np.log(2 * p / (p + q)))
-        q_term = np.nan_to_num(q * np.log(2 * q / (p + q)))
-        df['surprise'] = 0.5 * (p_term + q_term)
-        
-    candidate_set = []
-    for d in dimensions:
-        elements = df.groupby(d).sum()
-        elements = elements.sort_values('surprise', ascending=False)
-        cumulative_ep = elements.loc[elements['ep'] > Teep, 'ep'].cumsum()
-        if np.any(cumulative_ep > Tep):
-            idx = (cumulative_ep > Tep).idxmax()
-            candidate = {'elements': cumulative_ep[:idx].index.values.tolist(),
-                         'explanatory_power': cumulative_ep[idx],
-                         'surprise': elements.loc[:idx, 'surprise'].sum(),
-                         'dimension': d}
-            candidate_set.append(candidate)
-
-    # Sort by surprise and return the top k
-    candidate_set = sorted(candidate_set, key=lambda t: t['surprise'], reverse=True)[:k]
-    return candidate_set
-
-
-def adtributor_new(df, dimensions, Teep=0.1, Tep=0.67, k=3):
-    elements = pd.DataFrame(columns=['real', 'predict', 'dimension', 'element'])
-    for d in dimensions:
-        dim = df.groupby(d).sum().reset_index()
-        dim['element'] = dim[d]
-        dim['dimension'] = d
-        dim = dim.drop(columns=d)
-        elements = pd.concat([elements, dim], axis=0, sort=False)
-
-    # For each element, compute the explanatory power.
-    F = df['predict'].sum()
-    A = df['real'].sum()
-    elements['ep'] = (elements['real'] - elements['predict']) / (A - F)
-
-    # For each element, compute the surprise.
-    # Ignore any divide by zero.
-    with np.errstate(divide='ignore'):
-        p = elements['predict'] / F
-        q = elements['real'] / A
-        p_term = np.nan_to_num(p * np.log(2 * p / (p + q)))
-        q_term = np.nan_to_num(q * np.log(2 * q / (p + q)))
-        elements['surprise'] = 0.5 * (p_term + q_term)
-
-    candidate_set = []
-    for d in dimensions:
-        dim_elems = elements.loc[elements['dimension'] == d].set_index('element')
-        dim_elems = dim_elems.sort_values('surprise', ascending=False)
-        cumulative_ep = dim_elems.loc[dim_elems['ep'] > Teep, 'ep'].cumsum()
-        if np.any(cumulative_ep > Tep):
-            idx = (cumulative_ep > Tep).idxmax()
-            candidate = {'elements': cumulative_ep[:idx].index.values.tolist(),
-                         'explanatory_power': cumulative_ep[idx],
-                         'surprise': dim_elems.loc[:idx, 'surprise'].sum(),
-                         'dimension': d}
-            candidate_set.append(candidate)
-
-    # Sort by surprise and return the top k
-    candidate_set = sorted(candidate_set, key=lambda t: t['surprise'], reverse=True)[:k]
-    return candidate_set