def process_run():
folder = os.getcwd()
if os.path.isfile("error.json"):
error = loadfn("error.json")
print("{} ERROR: {}".format(folder, error))
required_files = ['seed_data.pickle']
if not all([os.path.isfile(fn) for fn in required_files]):
print("{} ERROR: no seed data, no analysis to be done")
else:
analyzer = StabilityAnalyzer(hull_distance=0.2, parallel=True)
with open(os.path.join("seed_data.pickle"), "rb") as f:
result_df = pickle.load(f)
unique_structures = loadfn("discovered_unique_structures.json")
all_result_ids = list(unique_structures.keys())
summary = result_df.loc[all_result_ids]
summary = summary[['Composition', 'delta_e']]
analyzer.analyze(result_df,
all_result_ids=all_result_ids,
new_result_ids=all_result_ids)
# Add stabilities
summary['stabilities'] = pd.Series(analyzer.stabilities)
chemsys = os.path.split(folder)[-1]
# Get all DFT data
response = requests.get('{}/synthesis-discovery/{}/dft-results'.format(
API_URL, chemsys))
data = json.loads(response.content.decode('utf-8'))
data = pd.DataFrame(data)
aggregated = {}
for result in data['dft_results']:
aggregated.update(result)
simulation_data = pd.DataFrame.from_dict(aggregated, orient='index')
summary['bandgap'] = simulation_data['bandgap']
# Apply garcia correction
summary['bandgap_garcia_exp'] = 1.358 * summary['bandgap'] + 0.904
summary['structure'] = pd.Series(unique_structures)
summary['chemsys'] = [
'-'.join(sorted(list(Composition(comp).as_dict().keys())))
for comp in summary['Composition']
]
# Add structure data
symmetry_data = {
key: get_structure_data(structure)
for key, structure in unique_structures.items()
}
symmetry_df = pd.DataFrame.from_dict(symmetry_data, orient='index')
summary = pd.concat([summary, symmetry_df], axis=1)
summary['url'] = simulation_data['url']
return summary
def test_analyze(self):
df = pd.read_csv(os.path.join(CAMD_TEST_FILES, "test_df_analysis.csv"),
index_col="id")
df['Composition'] = df['formula']
analyzer = StabilityAnalyzer(hull_distance=0.1)
seed_data = filter_dataframe_by_composition(df, "TiNO")
# TODO: resolve drop_duplicates filtering mp data
seed_data = seed_data.drop_duplicates(keep='last').dropna()
new_exp_indices = ["mp-30998", "mp-572822"]
new_experimental_results = seed_data.loc[new_exp_indices]
seed_data = seed_data.drop(index=new_exp_indices)
summary, seed_data = analyzer.analyze(
new_experimental_results=seed_data, seed_data=pd.DataFrame(),
)
summary, new_seed = analyzer.analyze(
new_experimental_results=new_experimental_results,
seed_data=seed_data
)
self.assertAlmostEqual(new_seed.loc['mp-30998', 'stability'], 0)
self.assertAlmostEqual(new_seed.loc["mp-572822", 'stability'], 0.52784795)
self.assertTrue(new_seed.loc['mp-30998', 'is_stable'])
self.assertFalse(new_seed.loc["mp-572822", 'is_stable'])
