Merge pull request #123 from paxtonfitzpatrick/pep396

bug fixes, test updates, other minor fixes

.travis.yml

@@ -3,9 +3,10 @@ sudo: false
 services:
 - xvfb
 python:
-- '2.7'
-- '3.5'
 - '3.6'
+- '3.7'
+- '3.8'
+- '3.9'
 before_script:
 - export DISPLAY=:99.0
 - sleep 3

README.md

@@ -58,7 +58,7 @@ This will work on clean systems, but if you encounter issues you may need to run
 
 <h2>Requirements</h2>
 
-+ python 2.7, 3.4+
++ python>=3.6
 + pandas>=0.18.0
 + seaborn>=0.7.1
 + matplotlib>=1.5.1

quail/analysis/recmat.py

@@ -51,16 +51,16 @@ def recall_matrix(egg, match='exact', distance='euclidean', features=None):
         return _recmat_smooth(egg.pres, egg.rec, features, distance, match)
 
 def _recmat_exact(presented, recalled, features):
-    lists = presented.index.to_numpy()()
+    lists = presented.index.to_numpy()
     cols = max(presented.shape[1], recalled.shape[1])
     result = np.empty((presented.shape[0], cols))*np.nan
     for li, l in enumerate(lists):
         p_list = presented.loc[l]
         r_list = recalled.loc[l]
         for i, feature in enumerate(features):
             get_feature = lambda x: np.array(x[feature]) if not np.array(pd.isnull(x['item'])).any() else np.nan
-            p = np.vstack(p_list.apply(get_feature).to_numpy()())
-            r = r_list.dropna().apply(get_feature).to_numpy()()
+            p = np.vstack(p_list.apply(get_feature).to_numpy())
+            r = r_list.dropna().apply(get_feature).to_numpy()
             r = np.vstack(list(filter(lambda x: x is not np.nan, r)))
             try:
                 m = [np.where((p==x).all(axis=1))[0] for x in r]
@@ -90,15 +90,15 @@ def _recmat_smooth(presented, recalled, features, distance, match):
     return recmat
 
 def _similarity_smooth(presented, recalled, features, distance):
-    lists = presented.index.to_numpy()()
+    lists = presented.index.to_numpy()
     res = np.empty((len(lists), len(features), recalled.iloc[0].shape[0], presented.iloc[0].shape[0]))*np.nan
     for li, l in enumerate(lists):
         p_list = presented.loc[l]
         r_list = recalled.loc[l]
         for i, feature in enumerate(features):
             get_feature = lambda x: np.array(x[feature]) if np.array(pd.notna(x['item'])).any() else np.nan
-            p = np.vstack(p_list.apply(get_feature).to_numpy()())
-            r = r_list.dropna().apply(get_feature).to_numpy()()
+            p = np.vstack(p_list.apply(get_feature).to_numpy())
+            r = r_list.dropna().apply(get_feature).to_numpy()
             r = np.vstack(list(filter(lambda x: x is not np.nan, r)))
             tmp = 1 - cdist(r, p, distance)
             res[li, i, :tmp.shape[0], :] =  tmp

requirements.txt

@@ -2,7 +2,7 @@ numpy>=1.10.4
 scipy>=0.17.1
 matplotlib>=1.5.1
 seaborn>=0.9.0
-pandas>=0.18.1
+pandas>=0.24.0
 future
 six
 deepdish

setup.py

@@ -27,7 +27,7 @@
 
 setup(
     name='quail',
-    version='0.2.0',
+    version='0.2.1',
     description=DESCRIPTION,
     long_description=LONG_DESCRIPTION,
     author='Contextual Dynamics Lab',