refactor web-app, present settings and another test script

data/settings.ini.sample

@@ -0,0 +1,5 @@
+[mpds_ml_labs]
+serve_ui = true
+ml_models =
+    /path_to_models/model_one.pkl
+    /path_to_models/model_two.pkl

mpds_ml_labs/app.py

@@ -5,14 +5,15 @@
 
 from flask import Flask, Blueprint, Response, request, send_from_directory
 
-from cors import crossdomain
 from struct_utils import detect_format, poscar_to_ase, symmetrize, get_formula
 from cif_utils import cif_to_ase, ase_to_eq_cif
 from prediction import ase_to_ml_model, get_legend, load_ml_model
+from common import SERVE_UI, ML_MODELS
 
 
 app_labs = Blueprint('app_labs', __name__)
-ml_model = None
+static_path = os.path.realpath(os.path.join(os.path.dirname(__file__), '../webassets'))
+active_ml_model = None
 
 def fmt_msg(msg, http_code=400):
     return Response('{"error":"%s"}' % msg, content_type='application/json', status=http_code)
@@ -37,20 +38,23 @@ def html_formula(string):
     if sub: html_formula += '</sub>'
     return html_formula
 
-@app_labs.route('/', methods=['GET'])
-def index():
-    return send_from_directory(os.path.dirname(__file__), 'index.html')
-
-@app_labs.route('/index.css', methods=['GET'])
-def style():
-    return send_from_directory(os.path.dirname(__file__), 'index.css')
-
-@app_labs.route('/player.html', methods=['GET'])
-def player():
-    return send_from_directory(os.path.dirname(__file__), 'player.html')
+if SERVE_UI:
+    @app_labs.route('/', methods=['GET'])
+    def index():
+        return send_from_directory(static_path, 'index.html')
+    @app_labs.route('/index.css', methods=['GET'])
+    def style():
+        return send_from_directory(static_path, 'index.css')
+    @app_labs.route('/player.html', methods=['GET'])
+    def player():
+        return send_from_directory(static_path, 'player.html')
+
+@app_labs.after_request
+def add_cors_header(response):
+    response.headers['Access-Control-Allow-Origin'] = '*'
+    return response
 
 @app_labs.route("/predict", methods=['POST'])
-@crossdomain(origin='*')
 def predict():
     if 'structure' not in request.values:
         return fmt_msg('Invalid request')
@@ -81,7 +85,7 @@ def predict():
     if error:
         return fmt_msg(error)
 
-    prediction, error = ase_to_ml_model(ase_obj, ml_model)
+    prediction, error = ase_to_ml_model(ase_obj, active_ml_model)
     if error:
         return fmt_msg(error)
 
@@ -105,10 +109,15 @@ def predict():
 
 if __name__ == '__main__':
     if sys.argv[1:]:
-        ml_model = load_ml_model(sys.argv[1:])
-        print("Loaded models: " + " ".join(sys.argv[1:]))
+        print("Models to load:\n" + "\n".join(sys.argv[1:]))
+        active_ml_model = load_ml_model(sys.argv[1:])
+
+    elif ML_MODELS:
+        print("Models to load:\n" + "\n".join(ML_MODELS))
+        active_ml_model = load_ml_model(ML_MODELS)
+
     else:
-        print("No model loaded")
+        print("No models to load")
 
     app = Flask(__name__)
     app.debug = False

mpds_ml_labs/cif_utils.py

@@ -45,6 +45,7 @@ def cif_to_ase(cif_string):
         else:
             return None, 'Absent space group info in CIF'
 
+        disordered = False
         try:
             cellpar = (
                 float( parsed_cif['_cell_length_a'][0].split('(')[0] ),
@@ -61,9 +62,13 @@ def cif_to_ase(cif_string):
                     [ char.split('(')[0] for char in parsed_cif['_atom_site_fract_z'] ]
                 ]).astype(np.float)
             )
+            disordered = any([float(occ) != 1 for occ in parsed_cif.get('_atom_site_occupancy', [])])
         except:
             return None, 'Unexpected non-numerical values occured in CIF'
 
+        if disordered:
+            return None, 'This is disordered structure (not yet supported)'
+
     symbols = parsed_cif.get('_atom_site_type_symbol')
     if not symbols:
         symbols = parsed_cif.get('_atom_site_label')

mpds_ml_labs/common.py

@@ -0,0 +1,19 @@
+
+import os
+from ConfigParser import ConfigParser
+
+
+DATA_PATH = os.path.realpath(os.path.join(os.path.dirname(__file__), '../data'))
+config = ConfigParser()
+config_path = os.path.join(DATA_PATH, 'settings.ini')
+
+if os.path.exists(config_path):
+    config.read(config_path)
+    SERVE_UI = config.get('mpds_ml_labs', 'serve_ui')
+    ML_MODELS = [path.strip() for path in filter(
+        None,
+        config.get('mpds_ml_labs', 'ml_models').split()
+    )]
+else:
+    SERVE_UI = True
+    ML_MODELS = []

mpds_ml_labs/struct_utils.py

@@ -1,5 +1,6 @@
 
 import re
+import fractions
 import cStringIO
 
 from ase.atoms import Atoms
@@ -87,30 +88,45 @@ def symmetrize(ase_obj, accuracy=1E-03):
         return None, 'Unrecognized sites or invalid site symmetry in structure'
 
 
-def get_formula(ase_obj):
-    formula_sequence = ['Fr','Cs','Rb','K','Na','Li',  'Be','Mg','Ca','Sr','Ba','Ra',  'Sc','Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb',  'Ac','Th','Pa','U','Np','Pu',  'Ti','Zr','Hf',  'V','Nb','Ta',  'Cr','Mo','W',  'Fe','Ru','Os',  'Co','Rh','Ir',  'Mn','Tc','Re',  'Ni','Pd','Pt',  'Cu','Ag','Au',  'Zn','Cd','Hg',  'B','Al','Ga','In','Tl',  'Pb','Sn','Ge','Si','C',   'N','P','As','Sb','Bi',   'H',   'Po','Te','Se','S','O',  'At','I','Br','Cl','F',  'He','Ne','Ar','Kr','Xe','Rn']
+FORMULA_SEQUENCE = ['Fr','Cs','Rb','K','Na','Li',  'Be','Mg','Ca','Sr','Ba','Ra',  'Sc','Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb',  'Ac','Th','Pa','U','Np','Pu',  'Ti','Zr','Hf',  'V','Nb','Ta',  'Cr','Mo','W',  'Fe','Ru','Os',  'Co','Rh','Ir',  'Mn','Tc','Re',  'Ni','Pd','Pt',  'Cu','Ag','Au',  'Zn','Cd','Hg',  'B','Al','Ga','In','Tl',  'Pb','Sn','Ge','Si','C',   'N','P','As','Sb','Bi',   'H',   'Po','Te','Se','S','O',  'At','I','Br','Cl','F',  'He','Ne','Ar','Kr','Xe','Rn']
 
-    labels = {}
-    types = []
-    count = 0
+def get_formula(ase_obj, find_gcd=True):
+    parsed_formula = {}
 
-    for k, label in enumerate(ase_obj.get_chemical_symbols()):
-        if label not in labels:
-            labels[label] = count
-            types.append([k+1])
-            count += 1
+    for label in ase_obj.get_chemical_symbols():
+        if label not in parsed_formula:
+            parsed_formula[label] = 1
         else:
-            types[ labels[label] ].append(k+1)
+            parsed_formula[label] += 1
 
-    atoms = labels.keys()
-    atoms = [x for x in formula_sequence if x in atoms] + [x for x in atoms if x not in formula_sequence]
+    expanded = reduce(fractions.gcd, parsed_formula.values()) if find_gcd else 1
+    if expanded > 1:
+        parsed_formula = {el: int(content / float(expanded))
+                        for el, content in parsed_formula.items()}
+
+    atoms = parsed_formula.keys()
+    atoms = [x for x in FORMULA_SEQUENCE if x in atoms] + [x for x in atoms if x not in FORMULA_SEQUENCE]
     formula = ''
     for atom in atoms:
-        n = len(types[labels[atom]])
-        if n == 1:
-            n = ''
-        else:
-            n = str(n)
-        formula += atom + n
+        index = parsed_formula[atom]
+        index = '' if index == 1 else str(index)
+        formula += atom + index
 
     return formula
+
+
+def sgn_to_crsystem(number):
+    if   195 <= number <= 230:
+        return 'cubic'
+    elif 168 <= number <= 194:
+        return 'hexagonal'
+    elif 143 <= number <= 167:
+        return 'trigonal'
+    elif 75  <= number <= 142:
+        return 'tetragonal'
+    elif 16  <= number <= 74:
+        return 'orthorhombic'
+    elif 3   <= number <= 15:
+        return 'monoclinic'
+    else:
+        return 'triclinic'

mpds_ml_labs/test_ml.py

@@ -0,0 +1,64 @@
+
+import os, sys
+
+from struct_utils import detect_format, poscar_to_ase, symmetrize
+from cif_utils import cif_to_ase
+from prediction import ase_to_ml_model, load_ml_model, human_names
+from common import ML_MODELS, DATA_PATH
+
+
+models, structures = [], []
+
+if sys.argv[1:]:
+    inputs = [f for f in sys.argv[1:] if os.path.isfile(f)]
+    models, structures = \
+        [f for f in inputs if f.endswith('.pkl')], [f for f in inputs if not f.endswith('.pkl')]
+
+if not models:
+    models = ML_MODELS
+
+if not structures:
+    structures = [os.path.join(DATA_PATH, f) for f in os.listdir(DATA_PATH) if os.path.isfile(os.path.join(DATA_PATH, f))]
+
+active_ml_model = load_ml_model(models)
+
+for fname in structures:
+    print
+    print(fname)
+    structure = open(fname).read()
+
+    fmt = detect_format(structure)
+
+    if fmt == 'cif':
+        ase_obj, error = cif_to_ase(structure)
+        if error:
+            print(error)
+            continue
+
+    elif fmt == 'poscar':
+        ase_obj, error = poscar_to_ase(structure)
+        if error:
+            print(error)
+            continue
+
+    else:
+        print('Error: %s is not a crystal structure' % fname)
+        continue
+
+    ase_obj, error = symmetrize(ase_obj)
+    if error:
+        print(error)
+        continue
+
+    prediction, error = ase_to_ml_model(ase_obj, active_ml_model)
+    if error:
+        print(error)
+        continue
+
+    for prop_id, pdata in prediction.items():
+        print("{0:40} = {1:6} (MAE = {2:4}), {3}".format(
+            human_names[prop_id]['name'],
+            pdata['value'],
+            pdata['mae'],
+            human_names[prop_id]['units']
+        ))