added similarity function to DOSFingerprint for direct access

nomad_dos_fingerprints/DOSfingerprint.py

 import numpy as np
 from bitarray import bitarray
+from functools import partial
 
 from .grid import Grid
+from .similarity import tanimoto_similarity
 
 ELECTRON_CHARGE = 1.602176565e-19
 
 class DOSFingerprint():
 
-    def __init__(self, stepsize = 0.05):
+    def __init__(self, stepsize = 0.05, similarity_function = tanimoto_similarity, **kwargs):
         self.bins = ''
         self.indices = []
         self.stepsize = stepsize
         self.filling_factor = 0
         self.grid_id = None
+        self.set_similarity_function(similarity_function, **kwargs)
 
     def calculate(self, dos_energies, dos_values, grid_id = 'dg_cut:56:-2:7:(-10, 5)', unit_cell_volume = 1, n_atoms = 1):
         energy, dos = self._convert_dos(dos_energies, dos_values, unit_cell_volume = unit_cell_volume, n_atoms = n_atoms)
@@ -35,6 +38,15 @@ class DOSFingerprint():
         self.filling_factor = fp_dict['filling_factor']
         return self
 
+    def set_similarity_function(self, similarity_function, **kwargs):
+        self.similarity_function = partial(similarity_function, **kwargs)
+
+    def get_similarity(self, fingerprint):
+        return self.similarity_function(self, fingerprint)
+
+    def get_similarities(self, list_of_fingerprints):
+        return np.array([self.similarity_function(self, fp) for fp in list_of_fingerprints])
+
     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.
@@ -57,7 +69,7 @@ class DOSFingerprint():
         dos_channels = [np.array(values) for values in dos]
         dos = sum(dos_channels) * ELECTRON_CHARGE * unit_cell_volume * n_atoms
         return energy, dos
-    
+
     def _binary_bin(self, dos_value, grid_bins):
         bin_dos = ''
         for grid_bin in grid_bins:

tests/test_DOSfingerprint.py

@@ -2,7 +2,7 @@ import pytest
 import numpy as np
 
 from nomad_dos_fingerprints import DOSFingerprint, tanimoto_similarity
-from nomad_dos_fingerprints.DOSfingerprint import ELECTRON_CHARGE 
+from nomad_dos_fingerprints.DOSfingerprint import ELECTRON_CHARGE
 
 def test_integrate_to_bins():
     test_data_x = np.linspace(0, np.pi, num = 1000)

tests/test_functional.py

@@ -4,9 +4,9 @@ import numpy as np
 
 with open(os.path.join(os.path.dirname(__file__), 'fingerprint_generation_test_data.json'), 'r') as test_data_file:
     test_data = json.load(test_data_file)
-    
+
 def test_fingerprint_values():
-    
+
     for fp, mid in test_data['fingerprints']:
         raw_data  = test_data[mid]
         new_fingerprint = DOSFingerprint().calculate(raw_data['dos_energies'], raw_data['dos_values'])
@@ -15,7 +15,7 @@ def test_fingerprint_values():
         old_fingerprint.indices = json.loads(fp)['indices']
         old_fingerprint.grid_id = new_fingerprint.grid_id
         assert old_fingerprint.indices == new_fingerprint.indices
-        assert np.isclose(tanimoto_similarity(old_fingerprint, new_fingerprint),1, atol=5e-2)
+        assert np.isclose(old_fingerprint.get_similarity(new_fingerprint),1, atol=5e-2)
 
 def test_materials_similarity():
 
@@ -25,10 +25,7 @@ def test_materials_similarity():
     raw_data = [test_data[mid] for mid in mids]
     new_fingerprints = [DOSFingerprint().calculate(entry['dos_energies'], entry['dos_values']) for entry in raw_data]
     matrix = []
-    for fp1 in new_fingerprints:
-        row = []
-        for fp2 in new_fingerprints:
-            row.append(tanimoto_similarity(fp1,fp2))
-        matrix.append(row)
+    for fp in new_fingerprints:
+        matrix.append(fp.get_similarities(new_fingerprints))
     print(matrix - np.array(similarity_matrix))
-    assert np.isclose(similarity_matrix, matrix, atol = 5e-2).all()
\ No newline at end of file
+    assert np.isclose(similarity_matrix, matrix, atol = 5e-2).all()