Merge remote-tracking branch 'origin/master' into copy_columns

fact/VERSION

@@ -1 +1 @@
-0.10.6
+0.11.0

fact/analysis/core.py

@@ -4,23 +4,24 @@
 
 from .statistics import li_ma_significance
 
-default_theta_off_keys = tuple('Theta_Off_{}_deg'.format(i) for i in range(1, 6))
+default_theta_off_keys = tuple('theta_deg_off_{}'.format(i) for i in range(1, 6))
 default_prediction_off_keys = tuple(
-    'background_prediction_{}'.format(i) for i in range(1, 6)
+    'gamma_prediction_off_{}'.format(i) for i in range(1, 6)
 )
 
 
-off_key_re = re.compile('([a-zA-z1-9]+)_Off_([0-9])(_deg)?')
+off_key_re = re.compile('([a-zA-z1-9]+_deg)_off_([0-9])?')
 
 
 def calc_run_summary_source_independent(
         events, runs,
         prediction_threshold,
         theta2_cut,
-        prediction_key='signal_prediction',
-        theta_key='Theta_deg',
+        prediction_key='gamma_prediction',
+        theta_key='theta_deg',
         theta_off_keys=default_theta_off_keys,
         ):
+
     '''
     Calculate run summaries for the given theta^2 and signal prediction cuts.
     This function requires, that no source dependent features,
@@ -82,7 +83,7 @@ def calc_run_summary_source_independent(
 def split_on_off_source_independent(
         events,
         theta2_cut,
-        theta_key='Theta_deg',
+        theta_key='theta_deg',
         theta_off_keys=default_theta_off_keys,
         ):
     '''
@@ -125,7 +126,7 @@ def split_on_off_source_independent(
 def calc_run_summary_source_dependent(
         events, runs,
         prediction_threshold,
-        on_prediction_key='signal_prediction',
+        on_prediction_key='gamma_prediction',
         off_prediction_keys=default_prediction_off_keys,
         ):
     '''
@@ -179,7 +180,7 @@ def calc_run_summary_source_dependent(
 def split_on_off_source_dependent(
         events,
         prediction_threshold,
-        on_prediction_key='signal_prediction',
+        on_prediction_key='gamma_prediction',
         off_prediction_keys=default_prediction_off_keys,
         ):
     '''
@@ -228,20 +229,17 @@ def drop_off_columns(df, off_region, inplace=False):
     '''
     Replace the "On" column with the column
     of the respective off region.
-    For example for `off_region = 1`, `Theta` is replaced by
-    Theta_Off_1 and all Theta_Off_<N> columns are dropped.
-    Same for all other columns, containing the pattern `_Off_<N>`
+    For example for `off_region = 1`, `theta` is replaced by
+    theta_off_1 and all theta_off_<N> columns are dropped.
+    Same for all other columns, containing the pattern `_off_<N>`
     '''
     if inplace is False:
         df = df.copy()
 
     for col in df.columns:
         m = off_key_re.match(col)
         if m:
-            on_key, key_region, deg = m.groups()
-            if deg:
-                on_key += deg
-
+            on_key, key_region = m.groups()
             if int(key_region) == off_region:
                 df.drop(on_key, axis=1, inplace=True)
                 df[on_key] = df[col]