├── .github └── workflows │ └── pytest.yml ├── .gitignore ├── ElMD ├── ElMD.py ├── __init__.py ├── el_lookup │ ├── __init__.py │ ├── atomic.json │ ├── cgcnn.json │ ├── elemnet.json │ ├── jarvis.json │ ├── jarvis_sc.json │ ├── magpie.json │ ├── magpie_sc.json │ ├── mat2vec.json │ ├── matscholar.json │ ├── megnet16.json │ ├── mendeleev.json │ ├── mod_petti.json │ ├── oliynyk.json │ ├── oliynyk_sc.json │ ├── petti.json │ └── random_200.json └── test_ElMD.py ├── LICENSE.txt ├── MANIFEST.in ├── README.md ├── pyproject.toml ├── setup.cfg └── setup.py /.github/workflows/pytest.yml: -------------------------------------------------------------------------------- 1 | name: Install locally with pip and test via Pytest 2 | 3 | on: 4 | push: 5 | paths: 6 | - '**.py' # only run workflow when source files changed 7 | 8 | jobs: 9 | linux-pytest: 10 | runs-on: ubuntu-latest 11 | strategy: 12 | max-parallel: 5 13 | 14 | steps: 15 | - uses: actions/checkout@v2 16 | - name: Set up Python 3.7 17 | uses: actions/setup-python@v2 18 | with: 19 | python-version: '3.7' 20 | 21 | - name: Upgrade pip 22 | shell: bash -l {0} 23 | run: | 24 | python -m pip install --upgrade pip 25 | 26 | - name: Install dependencies 27 | shell: bash -l {0} 28 | run: | 29 | pip install pytest flit 30 | flit install 31 | 32 | - name: Test with pytest 33 | shell: bash -l {0} 34 | run: | 35 | python -m pytest 36 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 98 | __pypackages__/ 99 | 100 | # Celery stuff 101 | celerybeat-schedule 102 | celerybeat.pid 103 | 104 | # SageMath parsed files 105 | *.sage.py 106 | 107 | # Environments 108 | .env 109 | .venv 110 | env/ 111 | venv/ 112 | ENV/ 113 | env.bak/ 114 | venv.bak/ 115 | 116 | # Spyder project settings 117 | .spyderproject 118 | .spyproject 119 | 120 | # Rope project settings 121 | .ropeproject 122 | 123 | # mkdocs documentation 124 | /site 125 | 126 | # mypy 127 | .mypy_cache/ 128 | .dmypy.json 129 | dmypy.json 130 | 131 | # Pyre type checker 132 | .pyre/ 133 | 134 | # pytype static type analyzer 135 | .pytype/ 136 | 137 | # Cython debug symbols 138 | cython_debug/ -------------------------------------------------------------------------------- /ElMD/ElMD.py: -------------------------------------------------------------------------------- 1 | ''' 2 | The Element Movers Distance is an application of the Wasserstein metric between 3 | two compositional vectors 4 | 5 | Copyright (C) 2020 Cameron Hargreaves 6 | This file is part of The Element Movers Distance 7 | 8 | 9 | The Element Movers Distance is free software: you can redistribute it and/or 10 | modify it under the terms of the GNU General Public License as published by 11 | the Free Software Foundation, either version 3 of the License, or 12 | (at your option) any later version. 13 | 14 | The Element Movers Distance is distributed in the hope that it will be useful, 15 | but WITHOUT ANY WARRANTY; without even the implied warranty of 16 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17 | GNU General Public License for more details. 18 | 19 | You should have received a copy of the GNU General Public License 20 | along with dogtag. If not, see . 21 | 22 | __author__ = "Cameron Hargreaves" 23 | __copyright__ = "2019, Cameron Hargreaves" 24 | __credits__ = ["https://github.com/Zapaan", "Loïc Séguin-C. ", "https://github.com/Bowserinator/"] 25 | __license__ = "GPL" 26 | __version__ = "0.5.12" 27 | 28 | ''' 29 | import json 30 | import re 31 | import os 32 | import pkg_resources 33 | 34 | from functools import lru_cache 35 | from site import getsitepackages 36 | from collections import Counter 37 | from copy import deepcopy 38 | 39 | import numpy as np 40 | 41 | try: 42 | from numba import njit 43 | except ImportError: 44 | def njit(*args, **kwargs): 45 | def decorator(func): 46 | return func 47 | return decorator 48 | 49 | def main(): 50 | import time 51 | ts = time.time() 52 | x = ElMD("CaTiO3", metric="mod_petti") 53 | y = ElMD("NaCl", metric="mod_petti") 54 | 55 | print(x.elmd(y)) 56 | print(y.elmd(x)) 57 | 58 | x = ElMD("CaTiO3") 59 | y = ElMD("NaCl") 60 | 61 | print(x.pretty_formula) 62 | print(x.vec_to_formula(x.feature_vector)) 63 | print(y.vec_to_formula(x.feature_vector)) 64 | print(x.vec_to_formula(y.feature_vector)) 65 | 66 | x = ElMD("CaTiO3", metric="fast") 67 | y = ElMD("NaCl", metric="fast") 68 | 69 | print(x.elmd(y)) 70 | print(y.elmd(x)) 71 | 72 | print(x.pretty_formula) 73 | print(x.vec_to_formula(x.feature_vector)) 74 | print(y.vec_to_formula(x.feature_vector)) 75 | print(x.vec_to_formula(y.feature_vector)) 76 | 77 | x = ElMD("CaTiO3", metric="mendeleev") 78 | y = ElMD("NaCl", metric="mendeleev") 79 | 80 | print(x.elmd(y)) 81 | print(y.elmd(x)) 82 | 83 | print(x.pretty_formula) 84 | print(x.vec_to_formula(x.feature_vector)) 85 | print(y.vec_to_formula(x.feature_vector)) 86 | print(x.vec_to_formula(y.feature_vector)) 87 | 88 | print(time.time() - ts) 89 | 90 | @lru_cache(maxsize=16) 91 | def _get_periodic_tab(metric): 92 | """ 93 | Load periodic data from the python site_packages/ElMD folder 94 | """ 95 | if metric == "fast": 96 | metric = "mod_petti" 97 | 98 | paths = getsitepackages() 99 | 100 | python_package_path = "" 101 | 102 | for p in paths: 103 | try: 104 | if "ElMD" in os.listdir(p): 105 | python_package_path = p 106 | except: 107 | pass 108 | 109 | # python_package_path = "" # For local debugging 110 | 111 | local_lookup_folder = os.path.join(python_package_path, "ElMD", "el_lookup") 112 | with open(os.path.join(local_lookup_folder, f"{metric}.json"), 'r') as j: 113 | ElementDict = json.loads(j.read()) 114 | 115 | return ElementDict, local_lookup_folder 116 | 117 | 118 | def elmd(comp1, comp2, metric="mod_petti", return_assignments=False): 119 | if isinstance(comp1, str): 120 | comp1 = ElMD(comp1, metric=metric) 121 | source_demands = comp1.ratio_vector 122 | elif isinstance(comp1, ElMD): 123 | source_demands = comp1.ratio_vector 124 | else: 125 | raise TypeError(f"First composition must be either a string or ElMD object, you input an object of type {type(comp1)}") 126 | 127 | if isinstance(comp2, str): 128 | comp2 = ElMD(comp2, metric=metric) 129 | sink_demands = comp2.ratio_vector 130 | elif isinstance(comp2, ElMD): 131 | sink_demands = comp2.ratio_vector 132 | 133 | else: 134 | raise TypeError(f"Second composition must be either a string or ElMD object, you input an object of type {type(comp2)}") 135 | 136 | if isinstance(comp1, ElMD) and isinstance(comp2, ElMD) and comp1.metric != comp2.metric: 137 | comp2 = ElMD(comp2.formula, metric=comp1.metric) 138 | 139 | if comp1.metric in ['fast', 'mod_petti']: 140 | return simple_emd(comp1.feature_vector, comp2.feature_vector) 141 | 142 | source_labels = np.array([comp1.periodic_tab[comp1.lookup[i]] for i in np.where(source_demands > 0)[0]], dtype=float) 143 | sink_labels = np.array([comp2.periodic_tab[comp2.lookup[i]] for i in np.where(sink_demands > 0)[0]], dtype=float) 144 | 145 | source_demands = source_demands[np.where(source_demands > 0)[0]] 146 | sink_demands = sink_demands[np.where(sink_demands > 0)[0]] 147 | 148 | # Perform a floating point conversion to ints to ensure algorithm terminates 149 | network_costs = np.array([[np.linalg.norm(x - y) for x in sink_labels] for y in source_labels], dtype=np.float64) 150 | 151 | if return_assignments: 152 | return EMD(source_demands, sink_demands, network_costs) 153 | else: 154 | return EMD(source_demands, sink_demands, network_costs)[0] 155 | 156 | def EMD(source_demands, sink_demands, network_costs): 157 | ''' 158 | A numba compiled EMD function from the network simplex algorithm to compare 159 | two distributions with a given distance matrix between node labels 160 | ''' 161 | 162 | if len(network_costs.shape) == 2: 163 | n, m = network_costs.shape 164 | 165 | if len(source_demands) != n or len(sink_demands) != m: 166 | raise ValueError(f"Shape of 2D distance matrix must have n rows and m columns where n is the number of source_demands, and m is the number of sink demands. You have n={len(source_demands)} source_demands and m={len(sink_demands)} sink_demands, but your distance matrix is [{n}, {m}].") 167 | 168 | network_costs = network_costs.ravel() 169 | 170 | else: 171 | raise ValueError("Must input a 2D distance matrix between the elements of both distributions") 172 | 173 | return network_simplex(source_demands, sink_demands, network_costs) 174 | 175 | @njit() 176 | def simple_emd(dist1, dist2): 177 | return np.sum(np.abs(np.cumsum(dist1 - dist2))) 178 | 179 | class ElMD(): 180 | ATOM_REGEX = r'([A-Z][a-z]*)(\d*\.?\d*[-+]?x?)' 181 | OPENERS = '({[' 182 | CLOSERS = ')}]' 183 | 184 | # As the current optimization solver only takes in ints we must multiply 185 | # all floats to capture the decimal places 186 | FP_MULTIPLIER = 100000000 187 | 188 | def __init__(self, formula="", metric="mod_petti", strict_parsing=False, x=1): 189 | self.metric = metric 190 | 191 | if isinstance(formula, ElMD): 192 | # Copy all attributes from the given instance 193 | self.__dict__.update(vars(formula)) 194 | else: 195 | self.formula = formula.strip() 196 | self.strict_parsing = strict_parsing 197 | self.x = x 198 | 199 | self.periodic_tab, self.el_lookup_folder = _get_periodic_tab(metric) 200 | self.petti_lookup, _ = _get_periodic_tab("mod_petti") 201 | self.lookup = self._gen_lookup() 202 | # self.petti_lookup = self.filter_petti_lookup() 203 | 204 | self.composition = self._parse_formula(self.formula) 205 | self.normed_composition = self._normalise_composition(self.composition) 206 | self.ratio_vector = self._gen_ratio_vector() 207 | self.petti_vector = self._gen_petti_vector() 208 | 209 | self.pretty_formula = self.vec_to_formula() 210 | 211 | self.feature_vector = self._gen_feature_vector() 212 | 213 | def full_feature_vector(self, positional_encode=False, min_freq=1e-4): 214 | feature_dicts = os.listdir(self.el_lookup_folder) 215 | 216 | # Skip unscaled chemical feature vectors 217 | feature_dicts = [f for f in feature_dicts if f[:-5] + "_sc.json" not in feature_dicts] 218 | 219 | # Strip redundant permutations of atomic number (included in other descriptors) 220 | feature_dicts = [f[:-5] for f in feature_dicts if f[:-5] not in ["atomic", "mendeleev", "petti", "mod_petti"]] 221 | 222 | if not positional_encode: 223 | full_features = [] 224 | 225 | for f in feature_dicts: 226 | # Fixes __pycache__ and __init__ cases 227 | if '__' in f: 228 | continue 229 | 230 | d, _ = _get_periodic_tab(f) 231 | vectors = np.array([d[el] for el in self.normed_composition.keys()]) 232 | 233 | # mean of els 234 | full_features.append(np.mean(vectors, axis=0)) 235 | 236 | weighted_features = np.array([r for r in self.normed_composition.values()]) * vectors.T 237 | weighted_features = weighted_features.T 238 | # weighted mean 239 | full_features.append(np.mean(weighted_features, axis=0)) 240 | # min 241 | full_features.append(np.min(weighted_features, axis=0)) 242 | # max 243 | full_features.append(np.max(weighted_features, axis=0)) 244 | # range 245 | full_features.append(np.max(weighted_features, axis=0) - np.min(vectors, axis=0)) 246 | # std 247 | full_features.append(np.std(weighted_features, axis=0)) 248 | 249 | # convert to numpy array if single dimension (redundant?) 250 | full_features = [np.array([x]) if not isinstance(x, np.ndarray) else x for x in full_features] 251 | 252 | return np.concatenate(full_features) 253 | 254 | else: 255 | features = [] 256 | 257 | for f in feature_dicts: 258 | d, _ = _get_periodic_tab(f) 259 | features.append(np.array([d[el] for el in self.normed_composition.keys()])) 260 | 261 | features = np.concatenate([x for x in features if x.ndim != 1], axis=1) 262 | 263 | d_model = features.shape[1] 264 | 265 | # Fractional encoding as described by Anthony Wang: https://www.nature.com/articles/s41524-021-00545-1 266 | # Method taken from https://towardsdatascience.com/master-positional-encoding-part-i-63c05d90a0c3 267 | fraction = np.array([frac for frac in self.normed_composition.values()]) 268 | freqs = min_freq ** (2 * (np.arange(d_model) // 2) / d_model) 269 | 270 | # Take a linear scaling of sin functions and a log 271 | # scaling of sin functions to capture small stoichiometries 272 | lin_pos_enc = fraction.reshape(-1,1) * freqs.reshape(1,-1) 273 | 274 | lin_pos_enc[:, ::2] = np.cos(lin_pos_enc[:, ::2]) 275 | lin_pos_enc[:, 1::2] = np.sin(lin_pos_enc[:, 1::2]) 276 | 277 | log_pos_enc = np.log10(fraction.reshape(-1,1) * freqs.reshape(1,-1)) 278 | 279 | log_pos_enc[:, ::2] = np.cos(log_pos_enc[:, ::2]) 280 | log_pos_enc[:, 1::2] = np.sin(log_pos_enc[:, 1::2]) 281 | 282 | return features + lin_pos_enc + log_pos_enc 283 | 284 | def elmd(self, comp2 = None, comp1 = None, return_assignments=False): 285 | ''' 286 | Calculate the minimal cost flow between two weighted vectors using the 287 | network simplex method. This is overloaded to accept a range of input 288 | types. 289 | ''' 290 | if comp1 is None: 291 | comp1 = self 292 | 293 | if comp2 is None: 294 | raise TypeError("elmd() missing 1 required positional argument") 295 | 296 | if isinstance(comp2, str): 297 | comp2 = ElMD(comp2) 298 | 299 | if self.metric == "fast": 300 | return simple_emd(comp1.ratio_vector, comp2.ratio_vector) 301 | 302 | else: 303 | return elmd(comp1, comp2, metric=self.metric, return_assignments=return_assignments) 304 | 305 | def _gen_ratio_vector(self): 306 | ''' 307 | Create a numpy array from a composition dictionary. 308 | ''' 309 | comp = self.normed_composition 310 | 311 | if isinstance(comp, str): 312 | comp = self._parse_formula(comp) 313 | comp = self._normalise_composition(comp) 314 | 315 | comp_labels = [] 316 | comp_ratios = [] 317 | 318 | for k in sorted(comp.keys()): 319 | comp_labels.append(self.petti_lookup[k]) 320 | comp_ratios.append(comp[k]) 321 | 322 | indices = np.array(comp_labels, dtype=np.int64) 323 | ratios = np.array(comp_ratios, dtype=np.float64) 324 | 325 | numeric = np.zeros(shape=max([x for x in self.petti_lookup.values() if isinstance(x, int)]) + 1, dtype=np.float64) 326 | numeric[indices] = ratios 327 | 328 | return numeric 329 | 330 | def _gen_petti_vector(self): 331 | comp = self.normed_composition 332 | 333 | if isinstance(comp, str): 334 | comp = self._parse_formula(comp) 335 | comp = self._normalise_composition(comp) 336 | 337 | comp_labels = [] 338 | comp_ratios = [] 339 | 340 | for k in sorted(comp.keys()): 341 | comp_labels.append(self.petti_lookup[k]) 342 | comp_ratios.append(comp[k]) 343 | 344 | indices = np.array(comp_labels, dtype=np.int64) 345 | ratios = np.array(comp_ratios, dtype=np.float64) 346 | 347 | numeric = np.zeros(shape=len(self.petti_lookup), dtype=np.float64) 348 | numeric[indices] = ratios 349 | 350 | return numeric 351 | 352 | def _gen_feature_vector(self): 353 | """ 354 | Perform the dot product between the ratio vector and its elemental representation. 355 | """ 356 | # If we only have an integer representation, return the vector as is 357 | if type(self.periodic_tab["H"]) is int: 358 | return self.ratio_vector 359 | 360 | n = int(len(self.normed_composition)) 361 | m = len(self.periodic_tab["H"]) 362 | numeric = np.zeros(shape=(n, m), dtype=float) 363 | 364 | els = list(self.periodic_tab.keys()) 365 | 366 | for i, k in enumerate(self.normed_composition.keys()): 367 | try: 368 | numeric[i] = self.periodic_tab[k] 369 | except: 370 | print(f"Failed to process {self.formula} with {self.metric} due to element {k}, discarding this element.") 371 | 372 | element_features = np.nan_to_num(numeric) 373 | 374 | weighted_vector = np.dot(self.ratio_vector[np.where(self.ratio_vector > 0)[0]], element_features) 375 | 376 | return weighted_vector 377 | 378 | def vec_to_formula(self, vector=None): 379 | ''' 380 | Return a normalized formula string ordered by the mod_petti dictionary 381 | ''' 382 | if vector is None: 383 | vector = self.petti_vector 384 | 385 | inds = np.where(vector != 0.0)[0] 386 | pretty_form = "" 387 | 388 | for i, ind in enumerate(inds): 389 | if vector[ind] == 1: 390 | pretty_form = pretty_form + f"{self.lookup[ind]}" 391 | else: 392 | pretty_form = pretty_form + f"{self.lookup[ind]}{vector[ind]:.3f}".strip('0') + ' ' 393 | 394 | return pretty_form.strip() 395 | 396 | def _gen_lookup(self): 397 | lookup = {} 398 | 399 | for i, (k, v) in enumerate(self.petti_lookup.items()): 400 | lookup[k] = v 401 | lookup[int(v)] = k 402 | 403 | return lookup 404 | 405 | def _is_balanced(self, formula): 406 | """Check if all sort of brackets come in pairs.""" 407 | c = Counter(formula) 408 | return c['['] == c[']'] and c['{'] == c['}'] and c['('] == c[')'] 409 | 410 | def _dictify(self, tuples): 411 | """Transform tuples of tuples to a dict of atoms.""" 412 | res = dict() 413 | 414 | for atom, n in tuples: 415 | if atom[-1].lower() == "x": 416 | if self.strict_parsing: 417 | raise ValueError(f"The element {atom} in the composition {self.formula} is undefined. Set strict_parsing=False to read x=1.") 418 | else: 419 | atom = atom[:-1] 420 | n = self.x 421 | 422 | if not isinstance(n, (int, float)) and "x" in n and "-" in n: 423 | n = float(str.split(n, "-")[0]) - self.x 424 | if n < 0: n = 0 425 | 426 | if not isinstance(n, (int, float)) and "x" in n and "+" in n: 427 | n = float(str.split(n, "+")[0]) + self.x 428 | 429 | try: 430 | if atom in self.lookup: 431 | res[atom] += float(n or 1) 432 | elif self.strict_parsing: 433 | raise ValueError(f"The element {atom} in the composition {self.formula} is not in the lookup dictionary for {self.metric}. Set strict_parsing=False to skip this element") 434 | 435 | except KeyError: 436 | res[atom] = float(n or 1) 437 | 438 | except ValueError as e: 439 | raise e 440 | 441 | return res 442 | 443 | def _fuse(self, mol1, mol2, w=1): 444 | """ Fuse 2 dicts representing molecules. Return a new dict. """ 445 | return {atom: (mol1.get(atom, 0) + mol2.get(atom, 0)) * w for atom in set(mol1) | set(mol2)} 446 | 447 | def _parse(self, formula): 448 | """ 449 | Return the molecule dict and length of parsed part. 450 | Recurse on opening brackets to parse the subpart and 451 | return on closing ones because it is the end of said subpart. 452 | """ 453 | q = [] 454 | mol = {} 455 | i = 0 456 | 457 | while i < len(formula): 458 | # Using a classic loop allow for manipulating the cursor 459 | token = formula[i] 460 | 461 | if token in self.CLOSERS: 462 | # Check for an index for this part 463 | m = re.match(r'\d+\.*\d*|\.\d*', formula[i+1:]) 464 | if m: 465 | weight = float(m.group(0)) 466 | i += len(m.group(0)) 467 | else: 468 | weight = 1 469 | 470 | submol = self._dictify(re.findall(self.ATOM_REGEX, ''.join(q))) 471 | return self._fuse(mol, submol, weight), i 472 | 473 | elif token in self.OPENERS: 474 | submol, l = self._parse(formula[i+1:]) 475 | mol = self._fuse(mol, submol) 476 | # skip the already read submol 477 | i += l + 1 478 | else: 479 | q.append(token) 480 | 481 | i += 1 482 | 483 | # Fuse in all that's left at base level 484 | return self._fuse(mol, self._dictify(re.findall(self.ATOM_REGEX, ''.join(q)))), i 485 | 486 | def _parse_formula(self, formula): 487 | """Parse the formula and return a dict with occurences of each atom.""" 488 | if not self._is_balanced(formula): 489 | raise ValueError("Your brackets not matching in pairs ![{]$[&?)]}!]") 490 | 491 | return self._parse(formula)[0] 492 | 493 | def _normalise_composition(self, input_comp): 494 | """ Sum up the numbers in our counter to get total atom count """ 495 | composition = deepcopy(input_comp) 496 | # check it has been processed 497 | if isinstance(composition, str): 498 | composition = self._parse_formula(composition) 499 | 500 | atom_count = sum(composition.values(), 0.0) 501 | 502 | for atom in composition: 503 | composition[atom] /= atom_count 504 | 505 | return composition 506 | 507 | def _get_atomic_num(self, element): 508 | """ Return atomic number from element """ 509 | try: 510 | np.array(self.periodic_tab[element]) 511 | except Exception as e: 512 | if self.strict_parsing: 513 | raise Exception(f"Element, {element} not found in lookup dict {self.metric}, in composition {self.formula}") 514 | else: 515 | return 0 516 | 517 | def _get_position(self, element): 518 | """ 519 | Return either the x, y coordinate of an elements position, or the 520 | x-coordinate on the Pettifor numbering system as a 2-dimensional 521 | """ 522 | keys = list(self.periodic_tab.keys()) 523 | 524 | try: 525 | atomic_num = keys.index(element) 526 | return atomic_num 527 | 528 | except: 529 | if self.strict_parsing: 530 | raise KeyError(f"One of the elements in {self.composition} is not in the {self.metric} dictionary. Try a different representation or use strict_parsing=False") 531 | else: 532 | return -1 533 | 534 | def _return_positions(self, composition): 535 | """ Return a dictionary of associated positions for each element """ 536 | element_pos = {} 537 | 538 | for element in composition: 539 | element_pos[element] = self._get_position(element) 540 | 541 | return element_pos 542 | 543 | def __repr__(self): 544 | return f"ElMD({self.pretty_formula})" 545 | 546 | def __len__(self): 547 | return len(self.normed_composition) 548 | 549 | def __eq__(self, other): 550 | return self.pretty_formula == other.pretty_formula 551 | 552 | def __ne__(self, other): 553 | return self.pretty_formula != other.pretty_formula 554 | 555 | def __lt__(self, other): 556 | # Compute this based on the distance to hydrogen, this does not require 557 | # the network simplex to calculate 558 | return np.dot(self.ratio_vector, np.arange(len(self.ratio_vector))[::-1]) < \ 559 | np.dot(other.ratio_vector, np.arange(len(other.ratio_vector))[::-1]) 560 | 561 | def __gt__(self, other): 562 | return np.dot(self.ratio_vector, np.arange(len(self.ratio_vector))[::-1]) > \ 563 | np.dot(other.ratio_vector, np.arange(len(other.ratio_vector))[::-1]) 564 | 565 | def __hash__(self): 566 | return hash(str(self.ratio_vector)) 567 | 568 | 569 | ''' 570 | This is an implementation of the network simplex algorithm for computing the 571 | minimal flow atomic similarity distance between two compounds 572 | 573 | Copyright (C) 2019 Cameron Hargreaves 574 | ported from networkx to numba/numpy, Copyright (C) 2010 Loïc Séguin-C. 575 | All rights reserved. 576 | BSD license. 577 | ''' 578 | 579 | @njit() 580 | def reduced_cost(i, costs, potentials, tails, heads, flows): 581 | """Return the reduced cost of an edge i. 582 | """ 583 | c = costs[i] - potentials[tails[i]] + potentials[heads[i]] 584 | 585 | if flows[i] == 0: 586 | return c 587 | else: 588 | return -c 589 | 590 | @njit() 591 | def find_entering_edges(e, f, tails, heads, costs, potentials, flows): 592 | """Yield entering edges until none can be found. 593 | """ 594 | # Entering edges are found by combining Dantzig's rule and Bland's 595 | # rule. The edges are cyclically grouped into blocks of size B. Within 596 | # each block, Dantzig's rule is applied to find an entering edge. The 597 | # blocks to search is determined following Bland's rule. 598 | 599 | B = np.int64(np.ceil(np.sqrt(e))) # block size 600 | 601 | M = (e + B - 1) // B # number of blocks needed to cover all edges 602 | m = 0 603 | 604 | while m < M: 605 | # Determine the next block of edges. 606 | l = f + B 607 | if l <= e: 608 | edge_inds = np.arange(f, l) 609 | else: 610 | l -= e 611 | edge_inds = np.concatenate((np.arange(f, e), np.arange(l))) 612 | 613 | f = l 614 | 615 | # Find the first edge with the lowest reduced cost. 616 | r_costs = np.empty(edge_inds.shape[0]) 617 | 618 | for y, z in np.ndenumerate(edge_inds): 619 | r_costs[y] = reduced_cost(z, costs, potentials, tails, heads, flows) 620 | 621 | # This takes the first occurrence which should stop cycling 622 | h = np.argmin(r_costs) 623 | 624 | i = edge_inds[h] 625 | c = reduced_cost(i, costs, potentials, tails, heads, flows) 626 | 627 | p = q = -1 628 | 629 | if c >= 0: 630 | m += 1 631 | 632 | # Entering edge found. 633 | else: 634 | if flows[i] == 0: 635 | p = tails[i] 636 | q = heads[i] 637 | else: 638 | p = heads[i] 639 | q = tails[i] 640 | 641 | return i, p, q, f 642 | 643 | # All edges have nonnegative reduced costs. The flow is optimal. 644 | return -1, -1, -1, -1 645 | 646 | @njit() 647 | def find_apex(p, q, size, parent): 648 | """Find the lowest common ancestor of nodes p and q in the spanning 649 | tree. 650 | """ 651 | size_p = size[p] 652 | size_q = size[q] 653 | 654 | while True: 655 | while size_p < size_q: 656 | p = parent[p] 657 | size_p = size[p] 658 | while size_p > size_q: 659 | q = parent[q] 660 | size_q = size[q] 661 | if size_p == size_q: 662 | if p != q: 663 | p = parent[p] 664 | size_p = size[p] 665 | q = parent[q] 666 | size_q = size[q] 667 | else: 668 | return p 669 | 670 | @njit() 671 | def trace_path(p, w, edge, parent): 672 | """Return the nodes and edges on the path from node p to its ancestor 673 | w. 674 | """ 675 | cycle_nodes = [p] 676 | cycle_edges = [] 677 | 678 | while p != w: 679 | cycle_edges.append(edge[p]) 680 | p = parent[p] 681 | cycle_nodes.append(p) 682 | 683 | return cycle_nodes, cycle_edges 684 | 685 | @njit() 686 | def find_cycle(i, p, q, size, edge, parent): 687 | """Return the nodes and edges on the cycle containing edge i == (p, q) 688 | when the latter is added to the spanning tree. 689 | 690 | The cycle is oriented in the direction from p to q. 691 | """ 692 | w = find_apex(p, q, size, parent) 693 | cycle_nodes, cycle_edges = trace_path(p, w, edge, parent) 694 | cycle_nodes = np.array(cycle_nodes[::-1]) 695 | cycle_edges = np.array(cycle_edges[::-1]) 696 | 697 | if cycle_edges.shape[0] < 1: 698 | cycle_edges = np.concatenate((cycle_edges, np.array([i]))) 699 | 700 | elif cycle_edges[0] != i: 701 | cycle_edges = np.concatenate((cycle_edges, np.array([i]))) 702 | 703 | cycle_nodes_rev, cycle_edges_rev = trace_path(q, w, edge, parent) 704 | 705 | cycle_nodes = np.concatenate((cycle_nodes, np.int64(cycle_nodes_rev[:-1]))) 706 | cycle_edges = np.concatenate((cycle_edges, np.int64(cycle_edges_rev))) 707 | 708 | return cycle_nodes, cycle_edges 709 | 710 | @njit() 711 | def residual_capacity(i, p, capac, flows, tails): 712 | """Return the residual capacity of an edge i in the direction away 713 | from its endpoint p. 714 | """ 715 | if tails[np.int64(i)] == np.int64(p): 716 | return capac[np.int64(i)] - flows[np.int64(i)] 717 | 718 | else: 719 | return flows[np.int64(i)] 720 | 721 | @njit() 722 | def find_leaving_edge(cycle_nodes, cycle_edges, capac, flows, tails, heads): 723 | """Return the leaving edge in a cycle represented by cycle_nodes and 724 | cycle_edges. 725 | """ 726 | cyc_edg_rev = cycle_edges[::-1] 727 | cyc_nod_rev = cycle_nodes[::-1] 728 | 729 | res_caps = [] 730 | for i in range(cycle_edges.shape[0]): 731 | res_caps.append(residual_capacity(cyc_edg_rev[i], cyc_nod_rev[i], capac, flows, tails)) 732 | 733 | res_caps = np.array(res_caps) 734 | 735 | j = cyc_edg_rev[np.argmin(res_caps)] 736 | s = cyc_nod_rev[np.argmin(res_caps)] 737 | 738 | t = heads[np.int64(j)] if tails[np.int64(j)] == s else tails[np.int64(j)] 739 | return j, s, t 740 | 741 | @njit() 742 | def augment_flow(cycle_nodes, cycle_edges, f, tails, flows): 743 | """Augment f units of flow along a cycle representing Wn with cycle_edges. 744 | """ 745 | for i, p in zip(cycle_edges, cycle_nodes): 746 | if tails[int(i)] == np.int64(p): 747 | flows[int(i)] += f 748 | else: 749 | flows[int(i)] -= f 750 | 751 | @njit() 752 | def trace_subtree(p, last, next): 753 | """Yield the nodes in the subtree rooted at a node p. 754 | """ 755 | tree = [] 756 | tree.append(p) 757 | 758 | l = last[p] 759 | while p != l: 760 | p = next[p] 761 | tree.append(p) 762 | 763 | return np.array(tree, dtype=np.int64) 764 | 765 | @njit(cache=True) 766 | def remove_edge(s, t, size, prev, last, next, parent, edge): 767 | """Remove an edge (s, t) where parent[t] == s from the spanning tree. 768 | """ 769 | size_t = size[t] 770 | prev_t = prev[t] 771 | last_t = last[t] 772 | next_last_t = next[last_t] 773 | # Remove (s, t). 774 | parent[t] = -2 775 | edge[t] = -2 776 | # Remove the subtree rooted at t from the depth-first thread. 777 | next[prev_t] = next_last_t 778 | prev[next_last_t] = prev_t 779 | next[last_t] = t 780 | prev[t] = last_t 781 | 782 | # Update the subtree sizes and last descendants of the (old) ancestors 783 | # of t. 784 | while s != np.int64(-2): 785 | size[s] -= size_t 786 | if last[s] == last_t: 787 | last[s] = prev_t 788 | s = parent[s] 789 | 790 | @njit() 791 | def make_root(q, parent, size, last, prev, next, edge): 792 | """ 793 | Make a node q the root of its containing subtree. 794 | """ 795 | ancestors = [] 796 | # -2 means node is checked 797 | while q != np.int64(-2): 798 | ancestors.append(q) 799 | q = parent[q] 800 | ancestors.reverse() 801 | 802 | ancestors_min_last = ancestors[:-1] 803 | next_ancs = ancestors[1:] 804 | 805 | for p, q in zip(ancestors_min_last, next_ancs): 806 | size_p = size[p] 807 | last_p = last[p] 808 | prev_q = prev[q] 809 | last_q = last[q] 810 | next_last_q = next[last_q] 811 | 812 | # Make p a child of q. 813 | parent[p] = q 814 | parent[q] = -2 815 | edge[p] = edge[q] 816 | edge[q] = -2 817 | size[p] = size_p - size[q] 818 | size[q] = size_p 819 | 820 | # Remove the subtree rooted at q from the depth-first thread. 821 | next[prev_q] = next_last_q 822 | prev[next_last_q] = prev_q 823 | next[last_q] = q 824 | prev[q] = last_q 825 | 826 | if last_p == last_q: 827 | last[p] = prev_q 828 | last_p = prev_q 829 | 830 | # Add the remaining parts of the subtree rooted at p as a subtree 831 | # of q in the depth-first thread. 832 | prev[p] = last_q 833 | next[last_q] = p 834 | next[last_p] = q 835 | prev[q] = last_p 836 | last[q] = last_p 837 | 838 | @njit() 839 | def add_edge(i, p, q, next, prev, last, size, parent, edge): 840 | """Add an edge (p, q) to the spanning tree where q is the root of a 841 | subtree. 842 | """ 843 | last_p = last[p] 844 | next_last_p = next[last_p] 845 | size_q = size[q] 846 | last_q = last[q] 847 | # Make q a child of p. 848 | parent[q] = p 849 | edge[q] = i 850 | # Insert the subtree rooted at q into the depth-first thread. 851 | next[last_p] = q 852 | prev[q] = last_p 853 | prev[next_last_p] = last_q 854 | next[last_q] = next_last_p 855 | 856 | # Update the subtree sizes and last descendants of the (new) ancestors 857 | # of q. 858 | while p != np.int64(-2): 859 | size[p] += size_q 860 | if last[p] == last_p: 861 | last[p] = last_q 862 | p = parent[p] 863 | 864 | @njit() 865 | def update_potentials(i, p, q, heads, potentials, costs, last, next): 866 | """Update the potentials of the nodes in the subtree rooted at a node 867 | q connected to its parent p by an edge i. 868 | """ 869 | if q == heads[i]: 870 | d = potentials[p] - costs[i] - potentials[q] 871 | else: 872 | d = potentials[p] + costs[i] - potentials[q] 873 | 874 | tree = trace_subtree(q, last, next) 875 | for q in tree: 876 | potentials[q] += d 877 | 878 | @njit() 879 | def occurs_first(array, item1, item2): 880 | for val in array: 881 | if val == item1: 882 | return True 883 | 884 | elif val == item2: 885 | return False 886 | 887 | @njit() 888 | def network_simplex(source_demands, sink_demands, network_costs): 889 | ''' 890 | This is a port of the network simplex algorithm implented by Loïc Séguin-C 891 | for the networkx package to allow acceleration via the numba package 892 | 893 | Copyright (C) 2010 Loïc Séguin-C. 894 | All rights reserved. 895 | BSD license. 896 | 897 | References 898 | ---------- 899 | .. [1] Z. Kiraly, P. Kovacs. 900 | Efficient implementation of minimum-cost flow algorithms. 901 | Acta Universitatis Sapientiae, Informatica 4(1):67--118. 2012. 902 | .. [2] R. Barr, F. Glover, D. Klingman. 903 | Enhancement of spanning tree labeling procedures for network 904 | optimization. 905 | INFOR 17(1):16--34. 1979. 906 | ''' 907 | # Constant used throughout for conversions from floating point to integer 908 | fp_multiplier = np.array([1000000], dtype=np.int64) 909 | 910 | # Using numerical ordering is nice for indexing 911 | sources = np.arange(source_demands.shape[0]).astype(np.int64) 912 | sinks = np.arange(sink_demands.shape[0]).astype(np.int64) + source_demands.shape[0] 913 | 914 | # Add one additional node for a dummy source and sink 915 | nodes = np.arange(source_demands.shape[0] + sink_demands.shape[0]).astype(np.int64) 916 | 917 | # Multiply by a large number and cast to int to remove floating points 918 | source_d_fp = source_demands * fp_multiplier.astype(np.int64) 919 | source_d_int = source_d_fp.astype(np.int64) 920 | sink_d_fp = sink_demands * fp_multiplier.astype(np.int64) 921 | sink_d_int = sink_d_fp.astype(np.int64) 922 | 923 | # FP conversion error correction 924 | source_sum = np.sum(source_d_int) 925 | sink_sum = np.sum(sink_d_int) 926 | 927 | if source_sum < sink_sum: 928 | source_ind = np.argmax(source_d_int) 929 | source_d_int[source_ind] += sink_sum - source_sum 930 | 931 | elif sink_sum < source_sum: 932 | sink_ind = np.argmax(sink_d_int) 933 | sink_d_int[sink_ind] += source_sum - sink_sum 934 | 935 | # Create demands array 936 | demands = np.concatenate((-source_d_int, sink_d_int)).astype(np.int64) 937 | 938 | # Create fully connected arcs between all sources and sinks 939 | conn_tails = np.array([i for i, x in enumerate(sources) for j, y in enumerate(sinks)], dtype=np.int64) 940 | conn_heads = np.array([j + sources.shape[0] for i, x in enumerate(sources) for j, y in enumerate(sinks)], dtype=np.int64) 941 | 942 | # Add arcs to and from the dummy node 943 | dummy_tails = [] 944 | dummy_heads = [] 945 | 946 | for node, demand in np.ndenumerate(demands): 947 | if demand > 0: 948 | dummy_tails.append(node[0]) 949 | dummy_heads.append(-1) 950 | else: 951 | dummy_tails.append(-1) 952 | dummy_heads.append(node[0]) 953 | 954 | # Concatenate these all together 955 | tails = np.concatenate((conn_tails, np.array(dummy_heads).T)).astype(np.int64) 956 | heads = np.concatenate((conn_heads, np.array(dummy_heads).T)).astype(np.int64) # edge targets 957 | 958 | # Create costs and capacities for the arcs between nodes 959 | network_costs = network_costs * fp_multiplier 960 | network_capac = np.array([np.array([source_demands[i], sink_demands[j]]).min() for i, x in np.ndenumerate(sources) for j, y in np.ndenumerate(sinks)], dtype=np.float64) * fp_multiplier 961 | 962 | # TODO finish? 963 | # If there is only one node on either side we can return capacity and costs 964 | # if sources.shape[0] == 1 or sinks.shape[0] == 1: 965 | # tot_costs = np.array([cost * network_capac[i_ret] for i_ret, cost in np.ndenumerate(network_costs)], dtype=np.float64) 966 | # return np.float64(np.sum(tot_costs)) 967 | 968 | # inf_arr = (np.sum(network_capac.astype(np.int64)), np.sum(np.absolute(network_costs)), np.max(np.absolute(demands))) 969 | 970 | # Set a suitably high integer for infinity 971 | faux_inf = 3 * np.max(np.array((np.sum(network_capac.astype(np.int64)), np.sum(np.absolute(network_costs)), np.max(np.absolute(demands))), dtype=np.int64)) 972 | 973 | # network_costs = network_costs * fp_multiplier 974 | 975 | # Add the costs and capacities to the dummy nodes 976 | costs = np.concatenate((network_costs, np.ones(nodes.shape[0]) * faux_inf)).astype(np.int64) 977 | capac = np.concatenate((network_capac, np.ones(nodes.shape[0]) * fp_multiplier)).astype(np.int64) 978 | 979 | # Construct the initial spanning tree. 980 | e = conn_tails.shape[0] 981 | n = nodes.shape[0] 982 | 983 | # Initialise zero flow in the connected arcs, and full flow to the dummy 984 | flows = np.concatenate((np.zeros(e), np.array([abs(d) for d in demands]))).astype(np.int64) 985 | 986 | # General arrays for the spanning tree 987 | potentials = np.array([faux_inf if d <= 0 else -faux_inf for d in demands]).T 988 | parent = np.concatenate((np.ones(n) * -1, np.array([-2]))).astype(np.int64) 989 | edge = np.arange(e, e+n).astype(np.int64) 990 | size = np.concatenate((np.ones(n), np.array([n + 1]))).astype(np.int64) 991 | next = np.concatenate((np.arange(1, n), np.array([-1, 0]))).astype(np.int64) 992 | prev = np.arange(-1, n) # previous nodes in depth-first thread 993 | last = np.concatenate((np.arange(n), np.array([n - 1]))).astype(np.int64) # last descendants in depth-first thread 994 | 995 | ########################################################################### 996 | # Main Pivot loop 997 | ########################################################################### 998 | 999 | f = 0 1000 | 1001 | while True: 1002 | i, p, q, f = find_entering_edges(e, f, tails, heads, costs, potentials, flows) 1003 | if p == -1: # If no entering edges then the optimal score is found 1004 | break 1005 | 1006 | cycle_nodes, cycle_edges = find_cycle(i, p, q, size, edge, parent) 1007 | j, s, t = find_leaving_edge(cycle_nodes, cycle_edges, capac, flows, tails, heads) 1008 | augment_flow(cycle_nodes, cycle_edges, residual_capacity(j, s, capac, flows, tails), tails, flows) 1009 | 1010 | if i != j: # Do nothing more if the entering edge is the same as the 1011 | # the leaving edge. 1012 | if parent[t] != s: 1013 | # Ensure that s is the parent of t. 1014 | s, t = t, s 1015 | 1016 | if occurs_first(cycle_edges, j, i): 1017 | # Ensure that q is in the subtree rooted at t. 1018 | p, q = q, p 1019 | 1020 | remove_edge(s, t, size, prev, last, next, parent, edge) 1021 | make_root(q, parent, size, last, prev, next, edge) 1022 | add_edge(i, p, q, next, prev, last, size, parent, edge) 1023 | update_potentials(i, p, q, heads, potentials, costs, last, next) 1024 | 1025 | flow_cost = 0 1026 | final_flows = flows[:e].astype(np.float64) / fp_multiplier 1027 | edge_costs = costs[:e].astype(np.float64) 1028 | 1029 | # dot product is returning wrong values for some reason... 1030 | for arc_ind, flow in np.ndenumerate(final_flows): 1031 | flow_cost += flow * edge_costs[arc_ind] 1032 | 1033 | final = flow_cost / fp_multiplier 1034 | final = final.astype(np.float64) 1035 | 1036 | return final[0], final_flows 1037 | 1038 | 1039 | 1040 | if __name__ == "__main__": 1041 | main() 1042 | -------------------------------------------------------------------------------- /ElMD/__init__.py: -------------------------------------------------------------------------------- 1 | """Implementation of Element movers distance for chemical similarity compositions.""" 2 | __version__ = "0.5.14" 3 | from ElMD.ElMD import EMD, elmd, ElMD 4 | -------------------------------------------------------------------------------- /ElMD/el_lookup/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lrcfmd/ElMD/9af5935184f63a6bea154eb3ae0443f95029c38c/ElMD/el_lookup/__init__.py -------------------------------------------------------------------------------- /ElMD/el_lookup/atomic.json: -------------------------------------------------------------------------------- 1 | {"H": 0, "D": 0, "T": 0, "He": 1, "Li": 2, "Be": 3, "B": 4, "C": 5, "N": 6, "O": 7, "F": 8, "Ne": 9, "Na": 10, "Mg": 11, "Al": 12, "Si": 13, "P": 14, "S": 15, "Cl": 16, "Ar": 17, "K": 18, "Ca": 19, "Sc": 20, "Ti": 21, "V": 22, "Cr": 23, "Mn": 24, "Fe": 25, "Co": 26, "Ni": 27, "Cu": 28, "Zn": 29, "Ga": 30, "Ge": 31, "As": 32, "Se": 33, "Br": 34, "Kr": 35, "Rb": 36, "Sr": 37, "Y": 38, "Zr": 39, "Nb": 40, "Mo": 41, "Tc": 42, "Ru": 43, "Rh": 44, "Pd": 45, "Ag": 46, "Cd": 47, "In": 48, "Sn": 49, "Sb": 50, "Te": 51, "I": 52, "Xe": 53, "Cs": 54, "Ba": 55, "La": 56, "Ce": 57, "Pr": 58, "Nd": 59, "Pm": 60, "Sm": 61, "Eu": 62, "Gd": 63, "Tb": 64, "Dy": 65, "Ho": 66, "Er": 67, "Tm": 68, "Yb": 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-------------------------------------------------------------------------------- 1 | {"D": 102, "T": 102, "H": 102, "He": 0, "Li": 11, 2 | "Be": 76, "B": 85, "C": 86, "N": 87, "O": 96, "F": 101, 3 | "Ne": 1, "Na": 10, "Mg": 72, "Al": 77, "Si": 84, "P": 88, 4 | "S": 95, "Cl": 100, "Ar": 2, "K": 9, "Ca": 15, "Sc": 47, 5 | "Ti": 50, "V": 53, "Cr": 54, "Mn": 71, "Fe": 70, "Co": 69, 6 | "Ni": 68, "Cu": 67, "Zn": 73, "Ga": 78, "Ge": 83, "As": 89, 7 | "Se": 94, "Br": 99, "Kr": 3, "Rb": 8, "Sr": 14, "Y": 20, "Zr": 48, 8 | "Nb": 52, "Mo": 55, "Tc": 58, "Ru": 60, "Rh": 62, "Pd": 64, "Ag": 66, 9 | "Cd": 74, "In": 79, "Sn": 82, "Sb": 90, "Te": 93, "I": 98, "Xe": 4, 10 | "Cs": 7, "Ba": 13, "La": 31, "Ce": 30, "Pr": 29, "Nd": 28, "Pm": 27, 11 | "Sm": 26, "Eu": 16, "Gd": 25, "Tb": 24, "Dy": 23, "Ho": 22, "Er": 21, 12 | "Tm": 19, "Yb": 17, "Lu": 18, "Hf": 49, "Ta": 51, "W": 56, "Re": 57, 13 | "Os": 59, "Ir": 61, "Pt": 63, "Au": 65, "Hg": 75, "Tl": 80, "Pb": 81, 14 | "Bi": 91, "Po": 92, "At": 97, "Rn": 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2, 534, 29.6, 1071.15, 3530.15, 6.66, 0.19, 5.46, 414.0, 11.4, 419, 4.32], "Pr": [59, 140.90765, 6, 3, 3, 1, 0, 0, 17, 3, 2.47, 183.0, 1.65, 4.48, 1.85, 1.32, 1.13, 1.1, 4.6395, 3.1, 1.09, 3.1, 3, 3, 2, 0, 0, 3, 0, 6, 10, 11, 2, 527, 28.2, 1204.15, 3290.15, 6.77, 0.19, 6.89, 296.8, 12.5, 356, 3.7], "Nd": [60, 144.24, 6, 3, 3, 1, 0, 0, 19, 3, 2.06, 182.0, 1.84, 3.99, 1.85, 1.3, 1.14, 1.2, 4.7152, 3.1, 1.09, 3.1, 3, 3, 2, 0, 0, 4, 0, 6, 10, 10, 2, 533, 31.4, 1289.15, 3400.15, 7.0, 0.19, 7.14, 273.0, 16.5, 328, 3.4], "Pm": [61, 144.9127, 6, 3, 3, 1, 0, 0, 21, 3, 2.05, 185.0, 1.63, 3.99, 1.85, 1.28, 1.13, 1.15, 4.8006, 3.1, 1.09, 3.0, 3, 3, 2, 0, 0, 5, 0, 6, 10, 9, 2, 535, 30.1, 1315.15, 3273.15, 7.26, 0.18, 7.88, 214.0, 15.0, 301, 0.0], "Sm": [62, 150.36, 6, 3, 3, 1, 0, 0, 23, 3, 2.38, 185.0, 1.62, 4.14, 1.85, 1.1, 1.17, 1.2, 4.8955, 3.1, 1.09, 2.8, 3, 3, 2, 0, 0, 6, 0, 6, 10, 8, 2, 545, 28.8, 1347.15, 2067.15, 7.52, 0.2, 8.63, 166.4, 13.3, 207, 2.14], "Eu": [63, 151.965, 6, 3, 3, 1, 0, 0, 25, 3, 2.31, 196.0, 1.85, 3.94, 1.85, 1.31, 1.2, 1.15, 5.0001, 3.1, 1.09, 2.0, 3, 3, 2, 0, 0, 7, 0, 6, 10, 7, 2, 547, 27.7, 1095.15, 1802.15, 5.24, 0.18, 9.21, 143.5, 13.9, 178, 1.86], "Gd": [64, 157.25, 6, 3, 3, 1, 0, 0, 27, 3, 2.33, 176.0, 1.61, 3.91, 1.8, 1.08, 1.2, 1.1, 5.1142, 3.1, 1.09, 3.0, 3, 3, 2, 0, 1, 7, 0, 6, 9, 7, 2, 593, 23.5, 1586.15, 3546.15, 7.9, 0.23, 10.05, 359.4, 10.6, 398, 4.14], "Tb": [65, 158.92533999999998, 6, 3, 3, 1, 0, 0, 29, 3, 2.25, 176.0, 1.59, 3.89, 1.75, 1.18, 1.2, 1.2, 5.2379999999999995, 3.1, 1.09, 3.0, 3, 3, 2, 0, 0, 9, 0, 6, 10, 5, 2, 569, 25.5, 1638.15, 3503.15, 8.23, 0.18, 10.8, 330.9, 11.1, 389, 4.05], "Dy": [66, 162.5, 6, 3, 3, 1, 0, 0, 31, 3, 2.28, 175.0, 1.59, 3.67, 1.75, 1.05, 1.22, 1.15, 5.3714, 3.1, 1.09, 3.0, 3, 3, 2, 0, 0, 10, 0, 6, 10, 4, 2, 573, 24.5, 1685.15, 2840.15, 8.55, 0.17, 11.06, 230.1, 10.7, 291, 3.04], "Ho": [67, 164.93032, 6, 3, 3, 1, 0, 0, 33, 3, 2.26, 177.0, 1.58, 3.65, 1.75, 1.04, 1.23, 1.2, 5.5143, 3.1, 1.09, 3.0, 3, 3, 2, 0, 0, 11, 0, 6, 10, 3, 2, 581, 23.6, 1747.15, 2973.15, 8.8, 0.16, 12.2, 241.0, 16.2, 301, 3.14], "Er": [68, 167.26, 6, 3, 3, 1, 0, 0, 35, 3, 2.26, 175.0, 1.57, 3.63, 1.75, 1.03, 1.24, 1.2, 5.6669, 3.1, 1.09, 3.0, 3, 3, 2, 0, 0, 12, 0, 6, 10, 2, 2, 589, 22.7, 1802.15, 3141.15, 9.07, 0.17, 19.9, 261.0, 14.3, 317, 3.29], "Tm": [69, 168.93421, 6, 3, 3, 1, 0, 0, 37, 3, 2.22, 175.0, 1.56, 3.6, 1.75, 1.02, 1.25, 1.2, 5.8291, 3.1, 1.09, 3.0, 3, 3, 2, 0, 0, 13, 0, 6, 10, 1, 2, 597, 21.8, 1818.15, 2223.15, 9.32, 0.16, 16.84, 191.0, 16.8, 232, 2.42], "Yb": [70, 173.04, 6, 3, 3, 1, 0, 0, 39, 3, 2.22, 190.0, 1.56, 3.59, 1.75, 1.13, 1.1, 1.1, 6.0009, 3.1, 1.09, 2.0, 3, 3, 2, 0, 0, 14, 0, 6, 10, 0, 2, 603, 21.0, 1092.15, 1469.15, 6.97, 0.15, 7.66, 128.9, 34.9, 152, 1.6], "Lu": [71, 174.967, 6, 3, 3, 1, 0, 0, 41, 2, 2.17, 175.0, 1.6, 3.37, 1.75, 1.0, 1.27, 1.2, 6.1824, 3.1, 1.09, 3.0, 3, 3, 2, 0, 1, 14, 0, 6, 9, 0, 2, 524, 21.9, 1936.15, 3675.15, 9.84, 0.15, 18.6, 355.9, 16.4, 427, 4.43], "Hf": [72, 178.49, 6, 4, 4, 1, 0, 0, 45, 2, 2.08, 158.0, 1.5, 2.91, 1.55, 0.85, 1.3, 1.73, 6.3704, 3.8, 1.16, 4.0, 4, 2, 2, 0, 2, 14, 0, 6, 8, 0, 2, 659, 16.2, 2500.15, 4873.15, 13.3, 0.14, 24.06, 575.0, 23.0, 619, 6.44], "Ta": [73, 180.9479, 6, 5, 4, 1, 0, 0, 48, 2, 2.0, 145.0, 1.38, 2.79, 1.45, 0.78, 1.5, 1.94, 6.5738, 4.11, 1.34, 5.0, 5, 3, 2, 0, 3, 14, 0, 6, 7, 0, 2, 761, 13.1, 3269.15, 5698.15, 16.6, 0.14, 31.6, 743.0, 57.5, 782, 8.1], "W": [74, 183.84, 6, 6, 4, 1, 0, 0, 51, 2, 1.93, 135.0, 1.46, 2.735, 1.35, 0.74, 2.36, 1.79, 6.7841, 4.4, 1.47, 5.78, 6, 4, 2, 0, 4, 14, 0, 6, 6, 0, 2, 770, 11.1, 3683.15, 5933.15, 19.3, 0.13, 35.4, 824.0, 174.0, 849, 8.9], "Re": [75, 186.207, 6, 7, 4, 1, 0, 0, 54, 2, 1.88, 137.0, 1.59, 2.68, 1.35, 0.77, 1.9, 2.06, 7.004, 4.02, 1.6, 5.78, 7, 5, 2, 0, 5, 14, 0, 6, 5, 0, 2, 760, 9.7, 3453.15, 5873.15, 21.0, 0.13, 33.2, 715.0, 47.9, 770, 8.03], "Os": [76, 190.23, 6, 8, 4, 1, 0, 0, 57, 2, 1.85, 135.0, 1.28, 2.65, 1.3, 0.77, 2.2, 1.85, 7.2335, 4.9, 1.65, 5.78, 8, 5, 2, 0, 6, 14, 0, 6, 4, 0, 2, 839, 8.5, 3318.15, 5303.15, 22.6, 0.13, 31.8, 746.0, 87.6, 791, 8.17], "Ir": [77, 192.22, 6, 9, 4, 1, 0, 0, 60, 2, 1.8, 136.0, 1.37, 2.628, 1.35, 0.77, 2.2, 1.87, 7.4345, 5.4, 1.68, 5.78, 9, 4, 2, 0, 7, 14, 0, 6, 3, 0, 2, 878, 7.6, 2683.15, 4403.15, 22.4, 0.13, 26.1, 604.0, 147.0, 665, 6.94], "Pt": [78, 195.08, 6, 10, 4, 1, 0, 0, 63, 2, 1.77, 139.0, 1.28, 2.7, 1.35, 0.74, 2.28, 1.91, 7.7208, 5.6, 1.72, 5.78, 10, 3, 1, 0, 9, 14, 1, 6, 1, 0, 1, 868, 6.5, 2045.15, 4100.15, 21.4, 0.13, 19.6, 510.0, 71.6, 565, 5.84], "Au": [79, 196.96653999999998, 6, 11, 4, 1, 0, 0, 66, 2, 1.74, 143.0, 1.44, 2.66, 1.35, 1.51, 2.54, 1.19, 7.9791, 5.77, 1.92, 5.44, 11, 2, 1, 0, 10, 14, 1, 6, 0, 0, 1, 890, 6.1, 1337.15, 3353.15, 19.3, 0.128, 12.55, 334.4, 317.0, 366, 3.81], "Hg": [80, 200.59, 6, 12, 4, 1, 0, 0, 71, 2, 1.71, 152.0, 1.49, 2.41, 1.5, 0.83, 2.0, 1.49, 8.2469, 4.91, 1.76, 4.44, 12, 1, 2, 0, 10, 14, 0, 6, 0, 0, 2, 1007, 5.4, 234.25, 630.15, 13.5, 0.139, 2.295, 59.229, 8.34, 61, 0.67], "Tl": [81, 204.3833, 6, 13, 5, 1, 0, 0, 76, 1, 1.56, 172.0, 1.48, 2.235, 1.9, 1.03, 1.62, 1.69, 4.6618, 3.2, 1.7890000000000001, 3.44, 3, 3, 2, 1, 10, 14, 0, 5, 0, 0, 3, 589, 7.6, 576.15, 1730.15, 11.9, 0.13, 4.1419999999999995, 164.1, 46.1, 182, 1.88], "Pb": [82, 207.2, 6, 14, 5, 1, 0, 0, 81, 1, 1.54, 174.0, 1.47, 2.09, 1.8, 1.49, 1.87, 1.92, 4.7404, 3.9, 1.854, 2.44, 4, 4, 2, 2, 10, 14, 0, 4, 0, 0, 4, 716, 6.8, 600.65, 2013.15, 11.4, 0.13, 4.7989999999999995, 177.7, 35.3, 196, 2.03], "Bi": [83, 208.98037, 6, 15, 5, 1, 0, 0, 86, 1, 1.43, 162.0, 1.46, 1.9969999999999999, 1.6, 1.17, 2.02, 2.14, 4.8288, 4.69, 2.01, 3.0, 5, 5, 2, 3, 10, 14, 0, 3, 0, 0, 5, 703, 7.4, 544.15, 1833.15, 9.75, 0.12, 11.3, 104.8, 7.87, 207, 2.18], "Th": [90, 232.0381, 7, 3, 3, 1, 0, 0, 16, 2, 1.8, 178.0, 1.65, 4.98, 1.8, 1.19, 1.3, 1.3, 2.8784, 3.2, 1.11, 4.0, 2, 2, 2, 0, 2, 0, 0, 6, 8, 14, 2, 587, 32.1, 2023.15, 5063.15, 11.7, 0.12, 16.1, 514.4, 54.0, 576, 6.2], "U": [92, 238.0289, 7, 3, 3, 1, 0, 0, 20, 3, 1.75, 158.0, 1.42, 4.72, 1.75, 0.87, 1.38, 1.7, 3.3168, 3.4, 1.22, 5.78, 3, 3, 2, 0, 1, 3, 0, 6, 9, 11, 2, 598, 27.4, 1405.15, 4091.15, 19.0, 0.12, 14.0, 477.0, 27.6, 490, 5.55]} -------------------------------------------------------------------------------- /ElMD/el_lookup/petti.json: -------------------------------------------------------------------------------- 1 | {"H": 102, "D": 102, "T": 102, "He": 0, "Li": 11, "Be": 76, "B": 85, "C": 94, "N": 99, "O": 100, "F": 101, "Ne": 1, "Na": 10, "Mg": 72, "Al": 79, "Si": 84, "P": 89, "S": 93, "Cl": 98, "Ar": 2, "K": 9, "Ca": 15, "Sc": 19, "Ti": 50, "V": 53, "Cr": 56, "Mn": 59, "Fe": 60, "Co": 63, "Ni": 66, "Cu": 71, "Zn": 75, "Ga": 80, "Ge": 83, "As": 88, "Se": 92, "Br": 97, "Kr": 3, "Rb": 8, "Sr": 14, "Y": 18, "Zr": 48, "Nb": 51, "Mo": 54, "Tc": 57, "Ru": 62, "Rh": 65, "Pd": 68, "Ag": 70, "Cd": 74, "In": 78, "Sn": 82, "Sb": 87, "Te": 91, "I": 96, "Xe": 4, "Cs": 7, "Ba": 13, "La": 32, "Ce": 31, "Pr": 30, "Nd": 29, "Pm": 28, "Sm": 27, "Eu": 17, "Gd": 26, "Tb": 25, "Dy": 24, "Ho": 23, "Er": 22, "Tm": 21, "Yb": 16, "Lu": 20, "Hf": 49, "Ta": 52, "W": 55, "Re": 58, "Os": 61, "Ir": 64, "Pt": 67, "Au": 69, "Hg": 73, "Tl": 77, "Pb": 81, "Bi": 86, "Po": 90, "At": 95, "Rn": 5, "Fr": 6, "Ra": 12, "Ac": 47, "Th": 46, "Pa": 45, "U": 44, "Np": 43, "Pu": 42, "Am": 41, "Cm": 40, "Bk": 39, "Cf": 38, "Es": 37, "Fm": 36, "Md": 35, "No": 34, "Lr": 33, "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, "Uue": 0} -------------------------------------------------------------------------------- /ElMD/test_ElMD.py: -------------------------------------------------------------------------------- 1 | """Basic tests taken from docs (assuming that docs are correct).""" 2 | from numpy.testing import assert_almost_equal, assert_allclose 3 | 4 | from ElMD import ElMD 5 | 6 | def test_ElMD(): 7 | x = ElMD("CaTiO3") 8 | d = x.elmd("SrTiO3") 9 | assert_almost_equal(d, 0.2) 10 | 11 | 12 | def test_atomic(): 13 | x = ElMD("CaTiO3", metric="atomic") 14 | d = x.elmd("SrTiO3") 15 | assert_almost_equal(d, 3.6) 16 | 17 | 18 | def test_magpie_sc(): 19 | elmd = ElMD(metric="magpie_sc").elmd 20 | d = elmd("NaCl", "LiCl") 21 | assert_almost_equal(d, 0.688539) 22 | 23 | 24 | def test_magpie(): 25 | x = ElMD("NaCl", metric="magpie") 26 | d = x.elmd("LiCl") 27 | assert_almost_equal(d, 46.697806) 28 | 29 | 30 | def test_featurizingDict(): 31 | featurizingDict = ElMD(metric="magpie").periodic_tab 32 | check = [ 33 | 2.0, 34 | 22.98976928, 35 | 370.87, 36 | 1.0, 37 | 3.0, 38 | 166.0, 39 | 0.93, 40 | 1.0, 41 | 0.0, 42 | 0.0, 43 | 0.0, 44 | 1.0, 45 | 1.0, 46 | 0.0, 47 | 0.0, 48 | 0.0, 49 | 1.0, 50 | 29.2433333333, 51 | 0.0, 52 | 0.0, 53 | 229.0, 54 | ] 55 | assert_allclose(featurizingDict["Na"], check) 56 | 57 | 58 | if __name__ == "__main__": 59 | test_ElMD() 60 | test_atomic() 61 | test_magpie_sc() 62 | test_magpie() 63 | test_featurizingDict() 64 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include ElMD/el_lookup/atomic.json 2 | include ElMD/el_lookup/atomic.json 3 | include ElMD/el_lookup/elemnet.json 4 | include ElMD/el_lookup/jarvis_sc.json 5 | include ElMD/el_lookup/magpie_sc.json 6 | include ElMD/el_lookup/matscholar.json 7 | include ElMD/el_lookup/mendeleev.json 8 | include ElMD/el_lookup/oliynyk.json 9 | include ElMD/el_lookup/petti.json 10 | include ElMD/el_lookup/cgcnn.json 11 | include ElMD/el_lookup/jarvis.json 12 | include ElMD/el_lookup/magpie.json 13 | include ElMD/el_lookup/mat2vec.json 14 | include ElMD/el_lookup/megnet16.json 15 | include ElMD/el_lookup/mod_petti.json 16 | include ElMD/el_lookup/oliynyk_sc.json 17 | include ElMD/el_lookup/random_200.json 18 | include README.md 19 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ElMD 2 | 3 | ![A drawing of ants moving earth](https://i.imgur.com/fg8Nrma.png) 4 | 5 | The Element Movers Distance (ElMD) is a similarity measure for chemical compositions. This distance between two compositions is calculated from the minimal amount of work taken to transform one distribution of elements to another along the modified Pettifor scale. 6 | 7 | This repository provides the reference implementations as described in our paper "[The Earth Movers Distance as a metric for the space of inorganic compositions](https://chemrxiv.org/articles/preprint/The_Earth_Mover_s_Distance_as_a_Metric_for_the_Space_of_Inorganic_Compositions/12777566)". 8 | 9 | If you wish to compute this metric between lots of compositions, the ElM2D high-performance library may be more useful and can be found at www.github.com/lrcfmd/ElM2D. 10 | 11 | We recommend installation via pip 12 | 13 | ``` 14 | pip install ElMD 15 | ``` 16 | 17 | For python 3.8+, due to [known library conflicts](https://github.com/materialsproject/matbench/issues/172) it is reccomended to install ElMD separate to its dependencies 18 | 19 | ``` 20 | pip install ElMD --no-deps 21 | pip install numpy # if necessary 22 | pip install numba # Gives significant speedup, but can cause dependency issues with other libraries 23 | ``` 24 | 25 | ## Usage 26 | For simple usage initiate an object with its compositional formula 27 | 28 | ```python 29 | > from ElMD import ElMD 30 | > x = ElMD("CaTiO3") 31 | ``` 32 | 33 | Calculate the distance to a second object with the `elmd` method. 34 | 35 | ```python 36 | > x.elmd("SrTiO3") 37 | 0.2 38 | ``` 39 | 40 | If the assignment plan (how each element in the source composition is mapped to the target composition) is required, this may be returned by setting the `return_assignments` flag in the `elmd` method. 41 | 42 | ```python 43 | > x.elmd("SrTiO3", return_assignments=True) 44 | (0.2, array([0.2, 0. , 0. , 0. , 0.2, 0. , 0. , 0. , 0.6])) 45 | ``` 46 | 47 | If the `mod_petti` elemental scale is suitable and no assignment plan is required, a significantly faster EMD algorithm may be used by setting `metric="fast"` 48 | ```python 49 | > x = ElMD("CaTiO3", metric="fast") 50 | > x.elmd("SrTiO3") 51 | 0.2 52 | ``` 53 | 54 | The compositional parser can handle user defined values of `x` when this is applicable. 55 | 56 | ```python 57 | latp_02 = ElMD("Li1+xAlxTi2-x(PO4)3", x=0.2) # Li1.2Al0.2Ti1.8(PO4)3 58 | latp_03 = ElMD("Li1+xAlxTi2-x(PO4)3", x=0.3) # Li1.3Al0.3Ti1.7(PO4)3 59 | ``` 60 | 61 | Alternate chemical scales may be accessed via the "metric" argument, e.g. 62 | 63 | ```python 64 | > x = ElMD("CaTiO3", metric="atomic") 65 | > x.elmd("SrTiO3") 66 | 3.6 67 | ``` 68 | 69 | The `elmd()` method is overloaded to take two strings, and may be imported directly. The choice of metric is specified with `metric` 70 | 71 | ```python 72 | from ElMD import elmd 73 | > elmd("NaCl", "LiCl") 74 | 0.5 75 | > elmd("NaCl", "LiCl", metric="magpie") 76 | 0.688539 77 | ``` 78 | 79 | The `EMD` function can also be called directly, with the input being two vectors of distributions and the associated distance matrix between them. 80 | 81 | ```python 82 | from ElMD import EMD 83 | > EMD([0.5, 0.5], [0.5, 0.5], [[1, 90], [89, 0]]) 84 | 0.5 85 | ``` 86 | 87 | ## Elemental Similarity 88 | You may use either traditional discrete scales or machine learnt representations for each element. In this instance a vector has been generated for each element, and the distance between elements (not compositions!) is the Euclidean distance. 89 | 90 | Due to the disparity in magnitudes of some of these values, a select few have additionally been scaled. 91 | 92 | Linear: 93 | - [mendeleev](https://www.sciencedirect.com/science/article/abs/pii/S0925838803008004) 94 | - [petti](https://www.sciencedirect.com/science/article/abs/pii/S0925838803008004) 95 | - [atomic](https://www.sciencedirect.com/science/article/abs/pii/S0925838803008004) 96 | - [mod_petti](https://iopscience.iop.org/article/10.1088/1367-2630/18/9/093011/meta) 97 | 98 | Chemically Derived: 99 | - [oliynyk](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 100 | - [oliynyk_sc](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 101 | - [jarvis](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 102 | - [jarvis_sc](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 103 | - [magpie](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 104 | - [magpie_sc](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 105 | 106 | Machine Learnt: 107 | - [cgcnn](https://github.com/CompRhys/roost/tree/master/data/embeddings) 108 | - [elemnet](https://github.com/CompRhys/roost/tree/master/data/embeddings) 109 | - [mat2vec](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 110 | - [matscholar](https://github.com/CompRhys/roost/tree/master/data/embeddings) 111 | - [megnet16](https://github.com/CompRhys/roost/tree/master/data/embeddings) 112 | 113 | Random Numbers: 114 | - [random_200](https://github.com/anthony-wang/CrabNet/tree/master/data/element_properties) 115 | 116 | The Euclidean distance between these vectors is taken as the measure of elemental similarity. 117 | 118 | ```python 119 | > x = ElMD("NaCl", metric="magpie") 120 | > x.elmd("LiCl") 121 | 46.697806 122 | 123 | > x = ElMD("NaCl", metric="magpie_sc") 124 | > x.elmd("LiCl") 125 | 0.688539 126 | ``` 127 | 128 | The feature dictionary can be accessed through the `periodic_tab` attribute: 129 | 130 | ```python 131 | > featurizingDict = ElMD(metric="magpie).periodic_tab 132 | > featurizingDict["Na"] 133 | [2.0, 22.98976928, 370.87, 1.0, 3.0, 166.0, 0.93, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 29.2433333333, 0.0, 0.0, 229.0] 134 | ``` 135 | 136 | ## Featurizing 137 | Whilst not the initial purpose, a compositional based feature vector may be generated from ElMD objects should you require it. This is a mean pooling of the weighted composition feature matrix. 138 | 139 | Note that this vector representation is not used at any point during the ElMD distance calculation and is provided solely for convenience. 140 | 141 | We construct this by taking the dot product of the ratios of each element with the features of these elements. Pass the argument feature_pooling="mean" to divide by the total number of elements in the compound. 142 | 143 | ```python 144 | feature_vector = np.dot(ratio_vector, element_feature_matrix) 145 | ``` 146 | 147 | This is accessed through the `feature_vector` attribute. 148 | 149 | ```python 150 | # For single element compositions, equivalent to x.periodic_tab["Cl"] 151 | > x = ElMD("Cl", metric="magpie") 152 | > x.feature_vector 153 | array([ 94. , 35.453 , 171.6 , 17. , 3. , 102. , 154 | 3.16 , 2. , 5. , 0. , 0. , 7. , 155 | 0. , 1. , 0. , 0. , 1. , 24.4975, 156 | 2.493 , 0. , 64. ]) 157 | 158 | # Aggregate vector by each elements contribution 159 | > x = ElMD("NaCl", metric="magpie").feature_vector 160 | array([ 48. , 29.22138464, 271.235 , 9. , 161 | 3. , 134. , 2.045 , 1.5 , 162 | 2.5 , 0. , 0. , 4. , 163 | 0.5 , 0.5 , 0. , 0. , 164 | 1. , 26.87041667, 1.2465 , 0. , 165 | 146.5 ]) 166 | 167 | ``` 168 | 169 | A feature vector of length 8076 can be generated by concatenating the weighted mean, min, max, range, and standard deviation across each available elemental feature across all featurizing dictionaries for each element in the composition by calling the `full_feature_vector()` method. 170 | 171 | ```python 172 | > x = ElMD("NaCl").full_feature_vector() 173 | ``` 174 | 175 | When using 1D unpooled elemental vectors, these may be mapped to the associated chemical formula using the `vec_to_formula` method: 176 | 177 | ```python 178 | x = ElMD("CaTiO3") 179 | y = ElMD("NaCl") 180 | 181 | print(x.pretty_formula) 182 | print(x.vec_to_formula(x.feature_vector)) # Same as above 183 | print(y.vec_to_formula(x.feature_vector)) # Same as above 184 | ``` 185 | 186 | ## Citing 187 | 188 | If you would like to cite this code in your work, please use the Chemistry of Materials reference 189 | 190 | ``` 191 | @article{doi:10.1021/acs.chemmater.0c03381, 192 | author = {Hargreaves, Cameron J. and Dyer, Matthew S. and Gaultois, Michael W. and Kurlin, Vitaliy A. and Rosseinsky, Matthew J.}, 193 | title = {The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions}, 194 | journal = {Chemistry of Materials}, 195 | volume = {32}, 196 | number = {24}, 197 | pages = {10610-10620}, 198 | year = {2020}, 199 | doi = {10.1021/acs.chemmater.0c03381}, 200 | URL = { 201 | https://doi.org/10.1021/acs.chemmater.0c03381 202 | }, 203 | eprint = { 204 | https://doi.org/10.1021/acs.chemmater.0c03381 205 | } 206 | } 207 | ``` 208 | 209 | ## Issues 210 | 211 | Please feel free to post any questions or comments as issues on this GitHub page. 212 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["flit_core >=3.2,<4"] 3 | build-backend = "flit_core.buildapi" 4 | 5 | [project] 6 | name = "ElMD" 7 | authors = [{name = "Cameron Hargreaves", email = "cameron.h@rgreaves.me.uk"}] 8 | readme = "README.md" 9 | classifiers = [ 10 | "Development Status :: 5 - Production/Stable", 11 | "Intended Audience :: Developers", 12 | "Topic :: Software Development :: Build Tools", 13 | "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", 14 | "Programming Language :: Python :: 3", 15 | "Programming Language :: Python :: 3.4", 16 | "Programming Language :: Python :: 3.5", 17 | "Programming Language :: Python :: 3.6", 18 | "Programming Language :: Python :: 3.7", 19 | "Programming Language :: Python :: 3.8", 20 | ] 21 | dynamic = ["version", "description"] 22 | 23 | dependencies = [ 24 | "numba", 25 | "numpy", 26 | "scipy", 27 | ] 28 | 29 | keywords = [ 30 | "ChemInformatics", 31 | "Materials Science", 32 | "Machine Learning", 33 | "Materials Representation" 34 | ] 35 | 36 | [project.urls] 37 | Home = "https://github.com/lrcfmd/ElMD" 38 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.md -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name = 'ElMD', 5 | packages = ['ElMD'], 6 | version = '0.5.14', 7 | license='GPL3', 8 | description = 'An implementation of the Element movers distance for chemical similarity of ionic compositions', 9 | author = 'Cameron Hagreaves', 10 | author_email = 'cameron.h@rgreaves.me.uk', 11 | url = 'https://github.com/lrcfmd/ElMD/', 12 | download_url = 'https://github.com/lrcfmd/ElMD/archive/v0.5.12.tar.gz', 13 | keywords = ['ChemInformatics', 'Materials Science', 'Machine Learning', 'Materials Representation'], 14 | package_data={"elementFeatures": ["el_lookup/*.json"]}, 15 | include_package_data=True, 16 | install_requires=[ 17 | 'setuptools', 18 | 'numba', 19 | 'numpy', 20 | 'scipy', 21 | 'flit', 22 | ], 23 | classifiers=[ 24 | 'Development Status :: 5 - Production/Stable', 25 | 'Intended Audience :: Developers', 26 | 'Topic :: Software Development :: Build Tools', 27 | 'License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3) ', 28 | 'Programming Language :: Python :: 3', 29 | 'Programming Language :: Python :: 3.4', 30 | 'Programming Language :: Python :: 3.5', 31 | 'Programming Language :: Python :: 3.6', 32 | 'Programming Language :: Python :: 3.7', 33 | ], 34 | ) 35 | --------------------------------------------------------------------------------