Exemplify a console usage of crystal structure design

Author: blokhin

mpds_ml_labs/test_design.py

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+
+from __future__ import division
+import random
+import time
+
+from struct_utils import order_disordered
+from knn_sample import knn_sample
+from similar_els import materialize, score
+from common import connect_database, KNN_TABLE, ML_MODELS
+from cif_utils import ase_to_eq_cif
+from prediction import prop_models, load_ml_models
+from prediction_ranges import prediction_ranges, TOL_QUALITY
+
+
+result, error = None, "No results (outside of prediction capabilities)"
+sample = {}
+
+for prop_id in prediction_ranges:
+    dice = random.choice([0, 1])
+    bound = (prediction_ranges[prop_id][0] + prediction_ranges[prop_id][1]) / 3
+    if dice:
+        sample[prop_id + '_min'] = float(bound * 2)
+        sample[prop_id + '_max'] = float(prediction_ranges[prop_id][1])
+    else:
+        sample[prop_id + '_min'] = float(prediction_ranges[prop_id][0])
+        sample[prop_id + '_max'] = float(bound)
+
+active_ml_models = load_ml_models(ML_MODELS, debug=False)
+
+starttime = time.time()
+
+cursor, connection = connect_database()
+
+els_samples = knn_sample(cursor, sample)
+for els_sample in els_samples:
+
+    scoring, error = materialize(els_sample, active_ml_models)
+    if error or not scoring:
+        continue
+
+    result = score(scoring, sample)
+    break
+
+connection.close()
+
+if not result: raise RuntimeError(error)
+
+answer_props = {prop_id: result['prediction'][prop_id]['value'] for prop_id in result['prediction']}
+
+# normalization 10**5
+answer_props['t'] /= 100000
+sample['t_min'] /= 100000
+sample['t_max'] /= 100000
+
+if 'disordered' in result['structure'].info:
+    result['structure'], error = order_disordered(result['structure'])
+    if error: raise RuntimeError(error)
+
+    result['structure'].center(about=0.0)
+
+result_quality, aux_info = 0, []
+for k, v in answer_props.items():
+    aux_info.append([
+        prop_models[k]['name'].replace(' ', '_'),
+        sample[k + '_min'],
+        v,
+        sample[k + '_max'],
+        prop_models[k]['units']
+    ])
+    tol = (sample[k + '_max'] - sample[k + '_min']) * TOL_QUALITY
+    if sample[k + '_min'] - tol < v < sample[k + '_max'] + tol:
+        result_quality += 1
+
+print(ase_to_eq_cif(result['structure'], supply_sg=False, mpds_labs_loop=[result_quality] + aux_info))
+print("Done in %1.2f sc" % (time.time() - starttime))