nomad_dos_fingerprints/DOSfingerprint.py

@@ -47,6 +47,9 @@ class DOSFingerprint():
     def get_similarities(self, list_of_fingerprints):
         return np.array([self.similarity_function(self, fp) for fp in list_of_fingerprints])
 
+    def __eq__(self, other):
+        return self.bins == other.bins and self.indices == other.indices and self.stepsize == other.stepsize and self.grid_id == other.grid_id and self.filling_factor == other.filling_factor
+
     def _integrate_to_bins(self, xs, ys):
         """
         Performs stepwise numerical integration of ``ys`` over the range of ``xs``. The stepsize of the generated histogram is controlled by DOSFingerprint().stepsize.
@@ -85,7 +88,10 @@ class DOSFingerprint():
         """
         grid_array = grid.grid()
         # cut the energy and dos to grid size
-        energy, dos = np.transpose([(e,d) for e,d in zip(energy, dos) if (e >= grid_array[0][0] and e <= grid_array[-1][0])])
+        energy_dos = np.transpose([(e,d) for e,d in zip(energy, dos) if (e >= grid_array[0][0] and e <= grid_array[-1][0])])
+        if len(energy_dos) != 2:
+            return [0, 0], ''
+        energy, dos = energy_dos
         # calculate fingerprint
         bin_fp = ''
         grid_index = 0

nomad_dos_fingerprints/grid.py

@@ -27,6 +27,8 @@ class Grid():
         return {'grid_type' : grid_type, 'num_bins' : int(num_bins), 'mu' : float(mu), 'sigma' : float(sigma), 'cutoff' : tuple([float(x) for x in cutoff[1:-1].split(',')])}
 
     def grid(self):
+        if self.grid_type != 'dg_cut':
+            raise NotImplementedError('Currently, only the grid dg_cut is implemented.')
         asc = 0
         desc = 0
         x_grid = [0]