Commits (4)
 ... ... @@ -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 ... ...
 ... ... @@ -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] ... ...