test / timing removed

classical/test_iln_3p_3d.py

-""" Analysis of the terms of three-particle (iL)^n in three dimensions """
-
-from itertools import combinations
-import numpy as np
-from mpliouville import MPLPDR, get_stats_expr, get_stats_repl
-from systems import MPPS3D
-import yaml
-
-nprtcls = 3
-numeric = {'tool': 'subs'}
-
-symbols = [str(i)+'x:z' for i in range(nprtcls)]
-indic = list(range(3*nprtcls))
-pairs = list(combinations(zip(indic[0::3], indic[1::3], indic[2::3]), 2))
-ffparams = [{'class': 'FFLennardJones', 'pair': p, 'params': {}} for p in pairs]
-masses = np.full((nprtcls*3, ), 1)
-qval = np.full((nprtcls*3, ), 1) # these are not valid
-pval = np.full((nprtcls*3, ), 0)
-system = MPPS3D(ffparams, symbols, masses, qval, pval, numeric=numeric)
-
-nterms = 3
-
-iln = MPLPDR(nterms, system, numeric=numeric)
-print(yaml.dump(get_stats_expr(iln.ilnqexpr, details=True)))
-print(yaml.dump(get_stats_expr(iln.ilnpexpr, details=True)))
-print(yaml.dump(get_stats_repl(iln.repl, details=True)))
-print(iln.get_val_float(qval, pval))

classical/time_iln_np_3d.py

-""" Scalability measurement of the construction of (iL)^n f expressions for a
-    N-particle 3D system """
-
-from itertools import combinations
-import numpy as np
-from mpliouville import MPLPQP, MPLPAQ, MPLPVQ, MPLPDR, MPLPDV
-from mpliouville import get_stats_expr, get_stats_repl
-from pandas.io.json import json_normalize
-from systems import MPPS3D
-import time
-
-def stats_summary(mplpobj):
-    stats = {}
-    stats['ilnqexpr'] = get_stats_expr(mplpobj.ilnqexpr)
-    stats['ilnpexpr'] = get_stats_expr(mplpobj.ilnpexpr)
-    if hasattr(mplpobj, 'repl'):
-        stats['ilnrepl'] = get_stats_repl(mplpobj.repl)
-    syms = sum(stats[key]['symbols'] for key in stats)
-    adds = sum(stats[key]['adds'] for key in stats)
-    muls = sum(stats[key]['multiplies'] for key in stats)
-    ops = sum(stats[key]['operations'] for key in stats)
-    stats['totals'] = {'symbols': syms, 'adds': adds, 'mutiplies': muls,
-                       'storage': syms + adds + muls, 'operations': ops}
-    return stats
-
-numeric = {'tool': 'subs'}
-
-statistics = []
-for nprtcls in range(2, 5):
-    print('nprtcls:', nprtcls)
-    symbols = [str(i)+'x:z' for i in range(nprtcls)]
-    indic = list(range(3*nprtcls))
-    pairs = list(combinations(zip(indic[0::3], indic[1::3], indic[2::3]), 2))
-    ffparams = [{'class': 'FFLennardJones', 'pair': p, 'params': {}} for p in pairs]
-    masses = np.full((nprtcls*3, ), 1)
-    qval = np.full((nprtcls*3, ), 1)
-    pval = np.full((nprtcls*3, ), 0)
-    for nterms in range(1, 6):
-        print('nterms:', nterms)
-        for mplp in (MPLPQP, MPLPAQ, MPLPVQ, MPLPDR, MPLPDV):
-            system = MPPS3D(ffparams, symbols, masses, qval, pval,
-                            numeric=numeric)
-            print('class:', mplp.__name__)
-            ts = time.time()
-            mplpobj = mplp(nterms, system, numeric=numeric)
-            te = time.time()
-            stats = stats_summary(mplpobj)
-            stats['class'] = mplp.__name__
-            stats['variant'] = 'default'
-            stats['nterms'] = nterms
-            stats['nparticles'] = len(symbols)
-            stats['preparation time'] = te - ts
-            statistics.append(stats)
-
-        for mplp, var in zip((MPLPDR, MPLPDV), ('r', 'r')):
-            system = MPPS3D(ffparams, symbols, masses, qval, pval,
-                            numeric=numeric)
-            print('class:', mplp.__name__)
-            ts = time.time()
-            mplpobj = mplp(nterms, system, numeric=numeric, variant=var)
-            te = time.time()
-            stats = stats_summary(mplpobj)
-            stats['class'] = mplp.__name__
-            stats['variant'] = var
-            stats['nterms'] = nterms
-            stats['nparticles'] = len(symbols)
-            stats['preparation time'] = te - ts
-            statistics.append(stats)
-
-df = json_normalize(statistics)
-df.to_json('exprstats.json')