Add error handling for missing parameters. Update version to match PyPI.

plumitas/plumitas.py

@@ -74,11 +74,15 @@ def read_hills(filename='HILLS'):
     return read_colvar(filename)
 
 
-def get_frame_weights(df, temp, bias):
+def get_frame_weights(df, temp, static_bias=None):
+    if not static_bias:
+        raise ValueError('You must supply a static bias column with the'
+                         '"static_bias" arg to get frame weights.')
+
     # calculate normalized weight
     k = 8.314e-3
     beta = 1 / (temp * k)
-    w = np.exp(beta * df.loc[:, bias])
+    w = np.exp(beta * df.loc[:, static_bias])
     df['weight'] = w / w.sum()
     return
 
@@ -117,6 +121,10 @@ def make_2d_free_energy_surface(df, x, y, temp, weight=None, bins=20,
     -------
     axes: matplotlib.AxesSubplot
     """
+    if not weight:
+        raise ValueError('You must supply frame weights to generate the FES.'
+                         'Try using plumitas.get_frame_weights first.')
+
     k = 8.314e-3
     beta = 1 / (temp * k)
 
@@ -192,6 +200,10 @@ def potential_of_mean_force(df, collective_variables,
     -------
     axes: matplotlib.AxesSubplot
     """
+    if not weight:
+        raise ValueError('You must supply frame weights to generate the FES.'
+                         'Try using plumitas.get_frame_weights first.')
+
     k = 8.314e-3
     beta = 1 / (temp * k)
 

plumitas/tests/test_plumitas.py

@@ -36,7 +36,8 @@ def test_make_2d_free_energy_surface():
                                       multi=number_of_replicas,
                                       unbiased=True)
     # overwrite weight column above with real, normalized weights
-    plm.get_frame_weights(multi_colvar_df, bias='pb.bias', temp=300)
+    plm.get_frame_weights(multi_colvar_df,
+                          static_bias='pb.bias', temp=300)
 
     # send over to 2D FES method
     axis = plm.make_2d_free_energy_surface(multi_colvar_df,
@@ -59,7 +60,8 @@ def test_potential_of_mean_force():
     colvar_file = op.join(data_path, "COLVAR")
     colvar_df = plm.read_colvar(colvar_file)
     # get normalized frame weights
-    plm.get_frame_weights(colvar_df, bias='pb.bias', temp=300)
+    plm.get_frame_weights(colvar_df,
+                          static_bias='pb.bias', temp=300)
     axis = plm.potential_of_mean_force(colvar_df,
                                        colvar_df.columns,
                                        weight='weight',

plumitas/version.py

@@ -5,7 +5,7 @@
 _version_major = 0
 _version_minor = 0
 _version_micro = 1  # use '' for first of series, number for 1 and above
-_version_extra = 'dev1'
+_version_extra = 'dev2'
 # _version_extra = ''  # Uncomment this for full releases
 
 # Construct full version string from these.