Trajdata method

.slugignore

@@ -1 +1 @@
-/apps/catlearn/models/metals_catlearn_gp.pkl
+

apps/catlearn/atomistic.py

@@ -41,7 +41,7 @@ def predict_catkit_demo(images):
         try:
             initial_state = [model_ref[s] for s in
                              ase.atoms.string2symbols(
-                                     atoms.info['key_value_pairs']['species'])]
+                                 atoms.info['key_value_pairs']['species'])]
         except KeyError:
             return {}
         display_ref.append(

apps/upload/__init__.py

@@ -43,7 +43,7 @@
 log = logging.getLogger(__name__)
 log.setLevel(logging.DEBUG)
 
-ADMIN_EMAILS = ['maxjh@stanford.edu', 'winther@stanford.edu']
+ADMIN_EMAILS = ['winther@stanford.edu', 'bajdich@stanford.edu']
 FRONTEND_URL = 'https://www.catalysis-hub.org'
 
 upload = flask.Blueprint('upload', __name__)

apps/utils/gas_phase_references.py

@@ -3,6 +3,7 @@
 import numpy as np
 from numpy import array
 
+from ase.atoms import string2symbols
 
 def molecules2symbols(molecules, add_hydrogen=True):
     """ 
@@ -12,8 +13,8 @@ def molecules2symbols(molecules, add_hydrogen=True):
 
     symbols = sorted(
         list(set(
-            ase.atoms.string2symbols(''.join(
-                map(lambda _x: ''.join(ase.atoms.string2symbols(_x)), molecules)
+            string2symbols(''.join(
+                map(lambda _x: ''.join(string2symbols(_x)), molecules)
             ))
         )),
         key=lambda _y: ase.data.atomic_numbers[_y])
@@ -38,7 +39,7 @@ def construct_reference_system(symbols, candidates=['H2', 'H2O', 'NH3', 'CH4', '
     for symbol in symbols:
         added_symbols.append(symbol)
         for candidate in candidates:
-            symbols = ase.atoms.string2symbols(candidate)
+            symbols = string2symbols(candidate)
 
             if set(added_symbols) == set(list(references.keys()) + symbols):
                 references[symbol] = candidate
@@ -69,7 +70,7 @@ def get_atomic_stoichiometry(references):
         species = species.split('_')[0]
 
         key_index[key] = i
-        composition = ase.atoms.string2symbols(species)
+        composition = string2symbols(species)
         for j, symbol in enumerate(composition):
             if symbol == key:
                 stoichiometry[i, i] += 1
@@ -97,7 +98,7 @@ def get_stoichiometry_factors(adsorbates, references):
     stoichiometry = get_atomic_stoichiometry(references)
     stoichiometry_factors = {}
     for adsorbate in adsorbates:
-        for symbol in ase.atoms.string2symbols(adsorbate):
+        for symbol in string2symbols(adsorbate):
             symbol_index = list(
                 map(lambda _x: _x[0], references)).index(symbol)
 

models.py

@@ -27,6 +27,7 @@
 import ase.atoms
 from ase.constraints import dict2constraint
 from ase.calculators.singlepoint import SinglePointCalculator
+from ase.calculators.calculator import Calculator
 import ase.db.sqlite
 import ase.db.core
 import ase.io
@@ -240,7 +241,6 @@ def _toatoms(self, include_results=False):
                  cell=self.cell,
                  pbc=(self.pbc & np.array([1, 2, 4])).astype(bool),
              )
-
         if self.constraints:
             constraints = json.loads(self.constraints)
             if len(constraints[0]['kwargs']['indices']) > 0:
@@ -251,38 +251,35 @@ def _toatoms(self, include_results=False):
                                 self.positions,
                                 cell=self.cell,
                                 pbc=(self.pbc & np.array([1, 2, 4])).astype(bool),
-                                magmoms=self.magmoms,
-                                charges=self.charges,
+                                magmoms=self.initial_magmoms,
+                                charges=self.initial_charges,
                                 tags=self.tags,
                                 masses=self.masses,
                                 momenta=self.momenta,
                                 constraint=constraints)
+        atoms.info = {}
+        atoms.info['unique_id'] = self.unique_id
+        atoms.info['key_value_pairs'] = self.key_value_pairs
+
+        data = self.data
+        if data:
+            atoms.info['data'] = data
 
         if not self.calculator == "unknown":
-            params = self.get('calculator_parameters', {})
-            atoms.calc = get_calculator(self.calculator)(**params)
+            params = self.calculator_parameters
+            atoms.calc = Calculator(self.calculator, **params)
+            atoms.calc.name = self.calculator
         else:
             all_properties = ['energy', 'forces', 'stress', 'dipole',
                               'charges', 'magmom', 'magmoms', 'free_energy']
             results = {}
-            #print(getattr(self, 'energy'))
             for prop in all_properties:
                 result = getattr(self, prop, None)
                 if result is not None:
                     results[prop] = result
-               # print(results)
             if results:
                 atoms.calc = SinglePointCalculator(atoms, **results)
                 atoms.calc.name = getattr(self, 'calculator', 'unknown')
-
-        atoms.info = {}
-        atoms.info['unique_id'] = self.unique_id
-        atoms.info['key_value_pairs'] = self.key_value_pairs
-
-        data = self.data
-        if data:
-            atoms.info['data'] = data
-
         return atoms
 
     @hybrid_property

requirements.template

@@ -1,7 +1,7 @@
 # DO NOT EDIT requirements.txt DIRECTLY OR ADD IT TO VERSION CONTROL
 # It will be overwritten in the install progress
 # Add new packages in requirements.template instead.
-ase==3.16.0
+ase==3.16.2
 backports.functools-lru-cache==1.4
 catkit>=0.5.3
 git+https://github.com/SUNCAT-Center/CatLearn.git@06bbfd370306ae6ecbfee50fd48b5f0a6ff5ef31#egg=catlearn
@@ -53,4 +53,4 @@ SQLAlchemy==1.1.14
 typing==3.6.2
 tqdm==4.19.5
 urwid==1.3.1
-Werkzeug==0.12.2
+Werkzeug==0.12.2