Coverage for ase / optimize / test / analyze.py: 10.81%

1# fmt: off 

2 

3from collections import defaultdict 

4 

5from numpy import inf 

6 

7import ase.db 

8 

9 

10def analyze(filename, tag='results'): 

11 energies = defaultdict(list) 

12 mintimes = defaultdict(lambda: 999999) 

13 formulas = [] 

14 db = ase.db.connect(filename) 

15 for row in db.select(sort='formula'): 

16 if row.formula not in formulas: 

17 formulas.append(row.formula) 

18 energies[row.formula].append(row.get('energy', inf)) 

19 emin = {formula: min(energies[formula]) for formula in energies} 

20 

21 data = defaultdict(list) 

22 for row in db.select(sort='formula'): 

23 if row.get('energy', inf) - emin[row.formula] < 0.01: 

24 t = row.t 

25 if row.n < 100: 

26 nsteps = row.n 

27 mintimes[row.formula] = min(mintimes[row.formula], t) 

28 else: 

29 nsteps = 9999 

30 t = inf 

31 else: 

32 nsteps = 9999 

33 t = inf 

34 data[row.optimizer].append((nsteps, t)) 

35 

36 print(formulas) 

37 

38 D = sorted(data.items(), key=lambda x: sum(y[0] for y in x[1])) 

39 with open(tag + '-iterations.csv', 'w') as fd: 

40 print('optimizer,' + ','.join(formulas), file=fd) 

41 for o, d in D: 

42 print('{:18},{}' 

43 .format(o, ','.join(f'{x[0]:3}' 

44 if x[0] < 100 else ' ' 

45 for x in d)), 

46 file=fd) 

47 

48 data = {opt: [(n, t / mintimes[f]) for (n, t), f in zip(x, formulas)] 

49 for opt, x in data.items()} 

50 D = sorted(data.items(), key=lambda x: sum(min(y[1], 999) for y in x[1])) 

51 with open(tag + '-time.csv', 'w') as fd: 

52 print('optimizer,' + ','.join(formulas), file=fd) 

53 for o, d in D: 

54 print('{:18},{}' 

55 .format(o, ','.join(f'{x[1]:8.1f}' 

56 if x[0] < 100 else ' ' 

57 for x in d)), 

58 file=fd) 

59 

60 

61if __name__ == '__main__': 

62 analyze('results.db')