def run(fns_in,
corner,
run_corner,
sub_json=None,
bounds_csv=None,
dedup=True,
massage=False,
outfn=None,
verbose=False,
**kwargs):
print('Loading data')
Ads, b = loadc_Ads_b(fns_in, corner, ico=True)
# Remove duplicate rows
# is this necessary?
# maybe better to just add them into the matrix directly
if dedup:
oldn = len(Ads)
iold = instances(Ads)
Ads, b = simplify_rows(Ads, b, corner=corner)
print('Simplify %u => %u rows' % (oldn, len(Ads)))
print('Simplify %u => %u instances' % (iold, instances(Ads)))
if sub_json:
print('Sub: %u rows' % len(Ads))
iold = instances(Ads)
names_old = index_names(Ads)
run_sub_json(Ads, sub_json, verbose=verbose)
names = index_names(Ads)
print("Sub: %u => %u names" % (len(names_old), len(names)))
print('Sub: %u => %u instances' % (iold, instances(Ads)))
else:
names = index_names(Ads)
'''
Substitution .csv
Special .csv containing one variable per line
Used primarily for multiple optimization passes, such as different algorithms or additional constraints
'''
if bounds_csv:
Ads2, b2 = loadc_Ads_b([bounds_csv], corner, ico=True)
bounds = Ads2bounds(Ads2, b2)
assert len(bounds), 'Failed to load bounds'
rows_old = len(Ads)
Ads, b = filter_bounds(Ads, b, bounds, corner)
print('Filter bounds: %s => %s + %s rows' %
(rows_old, len(Ads), len(Ads2)))
Ads = Ads + Ads2
b = b + b2
assert len(Ads) or allow_zero_eqns()
assert len(Ads) == len(b), 'Ads, b length mismatch'
if verbose:
print
print_eqns(Ads, b, verbose=verbose)
#print
#col_dist(A_ubd, 'final', names)
if massage:
try:
Ads, b = massage_equations(Ads, b, corner=corner)
except SimplifiedToZero:
if not allow_zero_eqns():
raise
print('WARNING: simplified to zero equations')
Ads = []
b = []
print('Converting to numpy...')
names, Anp = A_ds2np(Ads)
run_corner(Anp,
np.asarray(b),
names,
corner,
outfn=outfn,
verbose=verbose,
**kwargs)
def debug(what):
check_cols()
if 1 or verbose:
print('')
print_eqns(Ads, b, verbose=verbose, label=what, lim=20)
col_dist(Ads, what)
def debug(what):
if verbose:
print('')
print_eqns(Ads, b, verbose=verbose, label=what, lim=20)
col_dist(Ads, what)
check_feasible_d(Ads, b)