Repository: uf3/uf3 (language: python)

Issue:
Symmetry updates
This is a simple fix to address the electronegativity sorting. By converting `reference_X` from a dictionary of element symbols and electronegativity to a dictionary of element symbols and atomic numbers all unit tests pass. Unit test checking for electronegativity sorting has been changed to atomic number sorting


Discussion / hints:

Fix patch:
diff --git a/uf3/data/composition.py b/uf3/data/composition.py
index 4fea4905..54c49377 100644
--- a/uf3/data/composition.py
+++ b/uf3/data/composition.py
@@ -9,24 +9,20 @@
 import ase
 from ase import symbols as ase_symbols
 
-
-reference_X = dict(X=0, H=2.2, He=0, Li=0.98, Be=1.57, B=2.04, C=2.55, N=3.04,
-                   O=3.44, F=3.98, Ne=0, Na=0.93, Mg=1.31, Al=1.61, Si=1.9,
-                   P=2.19, S=2.58, Cl=3.16, Ar=0, K=0.82, Ca=1.0, Sc=1.36,
-                   Ti=1.54, V=1.63, Cr=1.66, Mn=1.55, Fe=1.83, Co=1.88,
-                   Ni=1.91, Cu=1.9, Zn=1.65, Ga=1.81, Ge=2.01, As=2.18,
-                   Se=2.55, Br=2.96, Kr=3.0, Rb=0.82, Sr=0.95, Y=1.22, Zr=1.33,
-                   Nb=1.6, Mo=2.16, Tc=1.9, Ru=2.2, Rh=2.28, Pd=2.2, Ag=1.93,
-                   Cd=1.69, In=1.78, Sn=1.96, Sb=2.05, Te=2.1, I=2.66, Xe=2.6,
-                   Cs=0.79, Ba=0.89, La=1.1, Ce=1.12, Pr=1.13, Nd=1.14,
-                   Pm=1.13, Sm=1.17, Eu=1.2, Gd=1.2, Tb=1.1, Dy=1.22, Ho=1.23,
-                   Er=1.24, Tm=1.25, Yb=1.1, Lu=1.27, Hf=1.3, Ta=1.5, W=2.36,
-                   Re=1.9, Os=2.2, Ir=2.2, Pt=2.28, Au=2.54, Hg=2.0, Tl=1.62,
-                   Pb=2.33, Bi=2.02, Po=2.0, At=2.2, Rn=2.2, Fr=0.7, Ra=0.9,
-                   Ac=1.1, Th=1.3, Pa=1.5, U=1.38, Np=1.36, Pu=1.28, Am=1.3,
-                   Cm=1.3, Bk=1.3, Cf=1.3, Es=1.3, Fm=1.3, Md=1.3, No=1.3,
-                   Lr=1.3, Rf=0, Db=0, Sg=0, Bh=0, Hs=0, Mt=0, Ds=0, Rg=0,
-                   Cn=0, Nh=0, Fl=0, Mc=0, Lv=0, Ts=0, Og=0)
+reference_X = {
+    'H': 1, 'He': 2, 'Li': 3, 'Be': 4, 'B': 5, 'C': 6, 'N': 7, 'O': 8, 'F': 9, 'Ne': 10,
+    'Na': 11, 'Mg': 12, 'Al': 13, 'Si': 14, 'P': 15, 'S': 16, 'Cl': 17, 'Ar': 18, 'K': 19, 'Ca': 20,
+    'Sc': 21, 'Ti': 22, 'V': 23, 'Cr': 24, 'Mn': 25, 'Fe': 26, 'Co': 27, 'Ni': 28, 'Cu': 29, 'Zn': 30,
+    'Ga': 31, 'Ge': 32, 'As': 33, 'Se': 34, 'Br': 35, 'Kr': 36, 'Rb': 37, 'Sr': 38, 'Y': 39, 'Zr': 40,
+    'Nb': 41, 'Mo': 42, 'Tc': 43, 'Ru': 44, 'Rh': 45, 'Pd': 46, 'Ag': 47, 'Cd': 48, 'In': 49, 'Sn': 50,
+    'Sb': 51, 'Te': 52, 'I': 53, 'Xe': 54, 'Cs': 55, 'Ba': 56, 'La': 57, 'Ce': 58, 'Pr': 59, 'Nd': 60,
+    'Pm': 61, 'Sm': 62, 'Eu': 63, 'Gd': 64, 'Tb': 65, 'Dy': 66, 'Ho': 67, 'Er': 68, 'Tm': 69, 'Yb': 70,
+    'Lu': 71, 'Hf': 72, 'Ta': 73, 'W': 74, 'Re': 75, 'Os': 76, 'Ir': 77, 'Pt': 78, 'Au': 79, 'Hg': 80,
+    'Tl': 81, 'Pb': 82, 'Bi': 83, 'Th': 90, 'Pa': 91, 'U': 92, 'Np': 93, 'Pu': 94, 'Am': 95, 'Cm': 96,
+    'Bk': 97, 'Cf': 98, 'Es': 99, 'Fm': 100, 'Md': 101, 'No': 102, 'Lr': 103, 'Rf': 104, 'Db': 105,
+    'Sg': 106, 'Bh': 107, 'Hs': 108, 'Mt': 109, 'Ds': 110, 'Rg': 111, 'Cn': 112, 'Nh': 113, 'Fl': 114,
+    'Mc': 115, 'Lv': 116, 'Ts': 117, 'Og': 118
+}
 
 
 class ChemicalSystem:


Test patch:
diff --git a/tests/test_composition.py b/tests/test_composition.py
index 434e07db..b073c1ea 100644
--- a/tests/test_composition.py
+++ b/tests/test_composition.py
@@ -21,11 +21,11 @@ def test_ternary(self):
         element_list = ['Al', 'Cu', 'Zr']
         handler = ChemicalSystem(element_list)
         assert len(handler.interactions_map[2]) == 6
-        assert handler.numbers == [13, 40, 29]
+        assert handler.numbers == [13, 29, 40]
 
     def test_composition_tuple(self):
         element_list = ['Al', 'Cu', 'Zr']
         handler = ChemicalSystem(element_list)
         geom = ase.Atoms('Al2Zr5')
         comp = handler.get_composition_tuple(geom)
-        assert np.allclose(comp, [2, 5, 0])
+        assert np.allclose(comp, [2, 0, 5])
diff --git a/tests/test_representation.py b/tests/test_representation.py
index 12bcfe06..fcd83019 100644
--- a/tests/test_representation.py
+++ b/tests/test_representation.py
@@ -23,6 +23,14 @@ def simple_water():
                      cell=None)
     yield geom
 
+@pytest.fixture()
+def strained_H2O_molecule():
+    geom = ase.Atoms('H2O',
+                     positions=[[0, 0, 0], [1.5, 0.0, 0.0], [0, 2.0, 0]],
+                     pbc=False,
+                     cell=None)
+    yield geom
+
 @pytest.fixture()
 def simple_molecule_CPtC():
     geom = ase.Atoms('CPtC',
@@ -46,6 +54,18 @@ def atoms_molecule_Yb2La2():
                       pbc = True,
                       cell = [30.0, 30.0, 30.0]) 
     yield geom
+
+@pytest.fixture()
+def methane_structure():
+    geom = ase.Atoms("CH4",
+            positions=[[15.000000000, 15.000000000, 15.000010729],
+                [15.629117489, 15.629117489, 15.629128218],
+                [14.370881617, 14.370881617, 15.629128218],
+                [15.629117489, 14.370881617, 14.370892346],
+                [14.370881617, 15.629117489, 14.370892346]],
+            pbc=True,
+            cell=[30, 30, 30])
+    yield geom
     
 
 @pytest.fixture()
@@ -81,9 +101,157 @@ def binary_chemistry_equal_electronegativity():
 def binary_chemistry_3B():
     element_list = ['C', 'Pt']
     chemistry_config = composition.ChemicalSystem(element_list,degree=3)
-    yield chemistry_config    
+    yield chemistry_config
+
+@pytest.fixture()
+def methane_chem_config():
+    chemistry_config = composition.ChemicalSystem(['H','C'],degree=3)
+    yield chemistry_config
+
+@pytest.fixture()
+def strained_H2O_molecule_feature():
+    two_body = {('H', 'H'): np.array([0.0, 0.40032798833819255, 1.1900510204081631, 
+                    0.40949951409135077, 0.00012147716229348758, 0.0, 0.0, 0.0, 
+                    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
+                ('H', 'O'): np.array([0.0, 0.0, 0.20991253644314867, 1.4571185617103986, 
+                    1.745019436345967, 0.5846695821185617, 0.0032798833819242057, 
+                    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
+                ('O', 'O'): np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
+                    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])}
+    three_body = {('H', 'H', 'H'):{"position":np.array([]),"value":np.array([])}, 
+                ('H', 'H', 'O'):{"position":np.array([9, 10, 11, 12, 16, 17, 18, 
+                    19, 20, 24, 25, 26, 27, 28, 33, 34, 35, 36, 37, 52, 53, 54, 
+                    55, 59, 60, 61, 62, 63, 68, 69, 70, 71, 72, 78, 79, 80, 81, 
+                    82, 97, 98, 99, 100, 105, 106, 107, 108, 109, 115, 116, 117, 
+                    118, 119, 125, 126, 127, 128, 129, 145, 146, 147, 148, 154, 
+                    155, 156, 157, 158, 164, 165, 166, 167, 168, 174, 175, 176, 
+                    177, 178]),
+                    "value":np.array([8.998308318036238e-06, 5.553122679602926e-05, 
+                        2.1162688081307482e-05, 4.16588348057235e-08, 8.998308318036216e-06, 
+                        0.0004546957671957677, 0.0022863479443642424, 0.0008419840681063094, 
+                        1.652467113960362e-06, 0.0002069610913148332, 0.002806441139065861, 
+                        0.005365949534820799, 0.001370294467973358, 2.5828477579548518e-06, 
+                        0.0002069610913148332, 0.0022965370010438087, 0.0022191800163791483, 
+                        0.00017107547669926987, 2.2218045229719143e-07, 0.0007536083216355335, 
+                        0.004650740244167441, 0.0017723751268094983, 3.4889274149793364e-06, 
+                        0.0007536083216355316, 0.038080770502645474, 0.1914816403405049, 
+                        0.07051616570390326, 0.00013839412079418004, 0.017332991397617244, 
+                        0.23503944539676538, 0.44939827354124107, 0.11476216169276852, 
+                        0.00021631349972871838, 0.017332991397617244, 0.19233497383741857, 
+                        0.1858563263717533, 0.01432757117356382, 1.8607612879889746e-05, 
+                        0.0009935632101164991, 0.006131572958728215, 0.0023367134756443622, 
+                        4.599829676465292e-06, 0.0009935632101164967, 0.05020599096119924, 
+                        0.2524509188568845, 0.09296907418673811, 0.00018245991049978955, 
+                        0.022851953832679444, 0.3098778757718547, 0.5924902611364619, 
+                        0.15130334750539126, 0.0002851894399408475, 0.022851953832679444, 
+                        0.2535759605319199, 0.2450344601418637, 0.018889583885544334, 
+                        2.453242494114816e-05, 0.00018746475662575454, 0.0011569005582506068, 
+                        0.00044088933502723824, 8.678923917859043e-07, 0.00018746475662575405, 
+                        0.00947282848324514, 0.04763224884092161, 0.01754133475221474, 
+                        3.442639820750746e-05, 0.004311689402392347, 0.05846752373053862, 
+                        0.11179061530876638, 0.02854780141611156, 5.380932829072594e-05, 
+                        0.004311689402392347, 0.047844520855079224, 0.046232917007898805, 
+                        0.00356407243123478, 4.6287594228581435e-06])},
+                ('H', 'O', 'O'):{"position":np.array([]),"value":np.array([])},
+                ('O', 'H', 'H'):{"position":np.array([50, 51, 52, 53, 59, 60, 61, 
+                    62, 69, 70, 71, 72, 79, 80, 81, 82, 89, 90, 91, 92, 99, 100, 
+                    101, 102, 109, 110, 111, 112, 119, 120, 121, 122, 129, 130, 
+                    131, 132]),
+                    "value":np.array([8.998308318036216e-06, 0.00018699609473419023,
+                        0.00016637497150535723, 2.343309457821933e-05, 0.0002069610913148332,
+                        0.0043009101788863795, 0.0038266243446232195, 0.0005389611752990452,
+                        0.0002069610913148332, 0.0043009101788863795, 0.0038266243446232195,
+                        0.0005389611752990452, 0.00035693289661543583, 0.007417511757789529,
+                        0.006599540536379156, 0.0009295127516026982, 0.008767351737873276,
+                        0.1821965283026791, 0.16210468057005284, 0.02283164515071167,
+                        0.008257447599851224, 0.17160008293440834, 0.15267676551808262,
+                        0.021503769791279246, 0.01283158766151963, 0.26665643109095494,
+                        0.2372507093666391, 0.033415592868540726, 0.01393538014853207,
+                        0.28959461871168224, 0.2576593725379629, 0.03629005247013563,
+                        0.0011037924870124407, 0.02293818762072729, 0.020408663171323782,
+                        0.0028744596015948995])},
+                    ('O', 'H', 'O'):{"position":np.array([]),"value":np.array([])},
+                    ('O', 'O', 'O'):{"position":np.array([]),"value":np.array([])}
+                }
+    features = {2:two_body,3:three_body}
+    yield features
+
+@pytest.fixture()
+def methane_feature():
+    two_body = {('H', 'H'):np.array([0.0, 0.10764117873003697, 4.380510760509621,
+                    6.909855011070257, 0.6019930496900838, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+                    0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
+                ('H', 'C'):np.array([4.217956715718236, 3.381599561086582, 0.3909862297136271,
+                    0.009457493481554552, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+                    0.0, 0.0, 0.0, 0.0]),
+                ('C', 'C'):np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+                    0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])}
+
+    three_body = {('H', 'H', 'H'):{"position":np.array([43, 44, 45, 46, 51, 52, 53,
+        54, 60, 61, 62, 63, 70, 71, 72, 73, 80, 81, 82, 83, 90, 91, 92, 93, 100,
+        101, 102, 103, 110, 111, 112, 113, 120, 121, 122, 123, 130, 131, 132, 133]),
+        "value":np.array([0.03100005703780889, 0.10614310151474303, 0.040768935952206535,
+            4.330727647630547e-05, 0.4045895910301375, 1.385300484452129, 0.532085702407565,
+            0.0005652142270837575, 0.25021837004738573, 0.8567388705253303, 0.3290683204255295,
+            0.0003495566513918442, 0.0032085455717557163, 0.01098594682978099, 0.004219637039864313,
+            4.482358532223911e-06, 1.3201002902211445, 4.519976816290672, 1.7360962954640766,
+            0.0018441884856963812, 1.632831640958409, 5.590757927037117, 2.147376971267495,
+            0.0022810761679567374, 0.020937770196598957, 0.07169018640060694, 
+            0.027535775533769652, 2.9250197881750437e-05, 0.5049122380066092, 
+            1.7288016879906885, 0.6640224780125649, 0.0007053656017778708, 
+            0.012948960742367371, 0.04433678470046677, 0.01702949612348602, 
+            1.8089780359648227e-05, 8.302214310023149e-05, 0.00028426488868429044, 
+            0.00010918445829116121, 1.159824609519897e-07])},
+        ('H', 'H', 'C'):{"position":np.array([42, 43, 44, 45, 50, 51, 52, 53, 58,
+            59, 60, 61, 67, 68, 69, 70, 87, 88, 89, 90, 95, 96, 97, 98, 104, 105, 
+            106, 107, 114, 115, 116, 117, 134, 135, 136, 137, 143, 144, 145, 146, 
+            153, 154, 155, 156, 163, 164, 165, 166, 183, 184, 185, 186, 193, 194,
+            195, 196, 203, 204, 205, 206, 213, 214, 215, 216]),
+        "value":np.array([0.8429228766683844, 0.39945724689618667, 0.028847549005400645,
+            0.00044543852440743863, 0.48446944868436304, 0.2295878277015724, 
+            0.016580112545328155, 0.00025601554105109207, 0.04169744942536497, 
+            0.01976022814266378, 0.0014270216753349406, 2.2034815825280747e-05, 
+            0.0007619495486119261, 0.00036108436177978177, 2.6076379642926395e-05, 
+            4.0264856010784597e-07, 5.500599910594774, 2.606708819255387, 0.18824833193180882, 
+            0.0029067654649678636, 3.1614667010247706, 1.498204425914217, 0.10819562276557199, 
+            0.001670661814023262, 0.2721019833839744, 0.1289478695660432, 0.009312210543850567, 
+            0.0001437909793569346, 0.004972198210514753, 0.0023563017010207734, 0.00017016471554247018, 
+            2.6275341375052783e-06, 3.401845164645613, 1.6121186664281806, 0.11642215179136364, 
+            0.0017976886525983612, 1.9552085926762723, 0.9265642956744039, 0.06691356617883588, 
+            0.0010332205406989095, 0.1682814295732272, 0.07974779000553874, 0.005759135171737034, 
+            8.892750896186398e-05, 0.003075055214890228, 0.0014572544228538549, 
+            0.00010523834203001602, 1.624998081281485e-06, 0.04362179817874775, 
+            0.020672168103937787, 0.0014928791180028496, 2.3051728634462977e-05, 
+            0.02507160393820065, 0.01188131697632776, 0.0008580314323557801, 
+            1.3248968050901788e-05, 0.002157869686241052, 0.001022604449190199, 
+            7.38492847224609e-05, 1.1403158170713884e-06, 3.943137664421636e-05, 
+            1.8686346747777566e-05, 1.3494693304707247e-06, 2.083732060447781e-08])},
+        ('H', 'C', 'C'):{"position":np.array([]),"value":np.array([])},
+        ('C', 'H', 'H') :{"position":np.array([1, 2, 3, 4, 8, 9, 10, 11, 16, 17, 
+            18, 19, 24, 25, 26, 27, 43, 44, 45, 46, 51, 52, 53, 54, 60, 61, 62, 
+            63, 80, 81, 82, 83, 90, 91, 92, 93, 110, 111, 112, 113]),
+            "value":np.array([0.7915185971384979, 2.7101317492548986, 1.0409455360795885, 
+                0.0011057565049189097, 0.9098497359452233, 3.115293393422155, 
+                1.1965657213353516, 0.0012710658570213517, 0.0783091966940646, 
+                0.26812792647763406, 0.10298634678624186, 0.00010939844490385975, 
+                0.001430966591375146, 0.0048995791197187265, 0.0018818992894873746, 
+                1.999069412209704e-06, 0.2614678116790906, 0.895256561755444, 
+                0.34386273724939853, 0.000365272192765014, 0.045008166700019184, 
+                0.15410637474653752, 0.0591913448185388, 6.287669460066086e-05, 
+                0.0008224472425331765, 0.002816030339823197, 0.0010816205568270109, 
+                1.1489640188764265e-06, 0.0019368876198045738, 0.006631834870523742, 
+                0.002547248452547226, 2.7058443006122534e-06, 7.078661490949063e-05, 
+                0.00024237087186837486, 9.30932148285657e-05, 9.888935039597454e-08, 
+                6.467521397549924e-07, 2.214456507004659e-06, 8.505596144699058e-07, 
+                9.035168449480808e-10])},
+        ('C', 'H', 'C'):{"position":np.array([]),"value":np.array([])},
+        ('C', 'C', 'C'):{"position":np.array([]),"value":np.array([])}
+        }
+    features = {2:two_body,3:three_body}
+    yield features
+
+
 
-    
 class TestBasis:
     
     def test_equal_electronegativity(self,binary_chemistry_equal_electronegativity, atoms_molecule_Yb2La2):
@@ -120,6 +288,76 @@ def test_energy_features(self, unary_chemistry, simple_molecule):
                                                      simple_molecule)
         assert len(vector) == 18  # 23 features
 
+    def test_energy_features_strained_H2O_molecule(self, strained_H2O_molecule, \
+            strained_H2O_molecule_feature):
+        element_list = ['H', 'O']
+        chemistry_config = composition.ChemicalSystem(element_list,degree=3)
+        bspline_config = bspline.BSplineBasis(chemistry_config)
+        bspline_handler = BasisFeaturizer(bspline_config)
+        
+        features_2B = bspline_handler.featurize_energy_2B(strained_H2O_molecule)
+        features_3B = bspline_handler.featurize_energy_3B(strained_H2O_molecule)
+        features_con = np.concatenate((features_2B,features_3B))
+
+        features = {}
+        features = {key:[] for key in bspline_config.interactions_map[2]}
+        features.update({key:[] for key in bspline_config.interactions_map[3]})
+        
+        for i in features.keys():
+            start = bspline_config.get_interaction_partitions()[1][i]-2
+            end = bspline_config.get_interaction_partitions()[1][i]-2 + \
+                    bspline_config.get_interaction_partitions()[0][i]
+            features[i] = features_con[start:end]
+
+        for i in bspline_config.interactions_map[2]:
+            assert np.allclose(strained_H2O_molecule_feature[2][i],features[i])
+
+        for i in bspline_config.interactions_map[3]:
+            print(i)
+            feature = features[i]
+            feature_position = np.where(feature!=0)[0]
+            feature_value = feature[feature_position]
+
+            assert np.allclose(strained_H2O_molecule_feature[3][i]["position"],feature_position)
+            #In my hard-coded features I double counted every interaction,
+            #so it needs to be divided by two
+            assert np.allclose(strained_H2O_molecule_feature[3][i]["value"]/2,feature_value)
+
+
+    def test_energy_features_methane(self, methane_chem_config, \
+            methane_structure, methane_feature):
+        bspline_config = bspline.BSplineBasis(methane_chem_config)
+        bspline_handler = BasisFeaturizer(bspline_config)
+
+        features_2B = bspline_handler.featurize_energy_2B(methane_structure)
+        features_3B = bspline_handler.featurize_energy_3B(methane_structure)
+        features_con = np.concatenate((features_2B,features_3B))
+
+        features = {}
+        features = {key:[] for key in bspline_config.interactions_map[2]}
+        features.update({key:[] for key in bspline_config.interactions_map[3]})
+        
+        for i in features.keys():
+            start = bspline_config.get_interaction_partitions()[1][i]-2
+            end = bspline_config.get_interaction_partitions()[1][i]-2 + \
+                    bspline_config.get_interaction_partitions()[0][i]
+            features[i] = features_con[start:end]
+
+        for i in bspline_config.interactions_map[2]:
+            assert np.allclose(methane_feature[2][i],features[i])
+
+        for i in bspline_config.interactions_map[3]:
+            print(i)
+            feature = features[i]
+            feature_position = np.where(feature!=0)[0]
+            feature_value = feature[feature_position]
+
+            assert np.allclose(methane_feature[3][i]["position"],feature_position)
+            #In my hard-coded features I double counted every interaction,
+            #so it needs to be divided by two
+            assert np.allclose(methane_feature[3][i]["value"]/2,feature_value)
+
+
     def test_force_features(self, unary_chemistry, simple_molecule):
         bspline_config = bspline.BSplineBasis(unary_chemistry)
         bspline_handler = BasisFeaturizer(bspline_config)