# Reshape the result into two columns
idx.shape = (-1, 2)
return idx
results = {}
for direct in ('forward', 'reverse'):
edict = ds.edict_for_direction(direct)
results[direct] = {}
for I in edict.keys():
#sres = psearch.search(zrange, cvfrange, I, T, update_pbar, direct)
#savetxt(get_fname('{}_I{}.csv'.format(direct, I)), sres, delimiter=',')
sres = genfromtxt(get_fname('{}_I{}.csv'.format(direct, I)), delimiter=',')
results[direct][I] = sres
plot_search_map(sres, (zrange[0], zrange[-1]), (cvfrange[0], cvfrange[-1]), I, direct,
get_fname('{}_I{}.png'.format(direct, I)))
results['combined'] = {}
zs = []
for I in results['forward'].keys():
results['combined'][I] = results['forward'][I] * results['reverse'][I]
savetxt(get_fname('{}_I{}.csv'.format('combined', I)), results['combined'][I], delimiter=',')
plot_search_map(results['combined'][I], (zrange[0], zrange[-1]), (cvfrange[0], cvfrange[-1]), I, 'combined',
get_fname('{}_I{}.png'.format('combined', I)))
r = results['combined'][I]
min_indicies = unravel_index(r.argmin(), r.shape)
min_val = (zrange[min_indicies[0]], cvfrange[min_indicies[1]])
print('I = {}: z* = {}, cvf = {}'.format(I, min_val[0], min_val[1]))
if I != 0:
zs.append(min_val[0])
# do search for forward/back
for direction, result_stash in [("forward", fresults), ("reverse", rresults)]:
search_engine = ParamSearchEngine(accelcs, dstore)
edict = dstore.edict_for_direction(direction)
for I in edict.keys():
print(str.format("{} bias, I = {}", direction, I))
pbar = ProgressBar(
widgets=["Parameter search: ", Percentage(), " ", Bar()], maxval=len(zrange) * len(cvfrange)
)
pbar.start()
search_map = search_engine.search(zrange, cvfrange, I, args.temperature, pbar.update, direction)
print("\n")
result_stash[I] = search_map
outpath = path.join(args.outputdir, str.format("Searchmap_I{}_{}.png", I, direction))
dmplots.plot_search_map(
search_map, (zrange[0], zrange[-1]), (cvfrange[0], cvfrange[-1]), I, direction, outpath
)
outpath = path.join(args.outputdir, str.format("Searchmap_I{}_{}.csv", I, direction))
np.savetxt(outpath, search_map, delimiter=",")
fzaverage = 0
ict = 0
zvalues = []
cresults = {}
# multiply forward/reverse maps to get current-min maps
for I in fresults.keys():
combined_map = fresults[I] * rresults[I]
outpath = path.join(args.outputdir, str.format("Searchmap_I{}_combined.png", I))
dmplots.plot_search_map(combined_map, (zrange[0], zrange[-1]), (cvfrange[0], cvfrange[-1]), I, "combined", outpath)
cresults[I] = combined_map