def get_timing_vs_ndata_at_const_nfreq(nvals,
nharmonics,
max_freq,
filename=None,
overwrite=True,
time_slow=True):
#if template is None:
template = get_default_template(nharmonics=nharmonics)
template.precompute()
# load saved results
results = {}
if filename is None:
filename = './saved_results/timing_results_nh%d_maxfrq%.1f.pkl' % (
nharmonics, max_freq)
if not filename is None and os.path.exists(filename):
old_results = pickle.load(open(filename, 'rb'))
results.update(old_results)
for n in nvals:
if n in results:
continue
x, y, dy = generate_random_signal(n)
x[0] = 0
x[-1] = 1
# time FTP
print("timing: n = %d, h = %d, ftp" % (n, nharmonics))
model = FastTemplatePeriodogram(template=template)
model.fit(x, y, dy)
t0 = time()
frq, p = model.autopower(maximum_frequency=max_freq)
tftp = time() - t0
print(" done in %.4f seconds" % (tftp))
if time_slow:
print("timing: n = %d, h = %d, slow" % (n, nharmonics))
model = SlowTemplatePeriodogram(template=template)
model.fit(x, y, dy)
t0 = time()
p = model.power(frq)
tslow = time() - t0
print(" done in %.4f seconds" % (tslow))
else:
tslow = -1
results[n] = (tftp, tslow)
# save
if not filename is None and overwrite:
pickle.dump(results, open(filename, 'wb'))
return zip(*[results[n] for n in nvals])
def get_timing_vs_ndata(nvals,
nharmonics,
filename=None,
overwrite=True,
time_gatspy=True,
only_use_saved_data=False,
time_lomb_scargle=False):
#if template is None:
template = get_default_template(nharmonics=nharmonics)
#template.precompute()
# load saved results
results = {}
if not filename is None and os.path.exists(filename):
old_results = pickle.load(open(filename, 'rb'))
results.update(old_results)
else:
results = {name: [] for name in ['nfreqs', 'ndata', 'tftp', 'tgats']}
if only_use_saved_data:
return select_from_dict(results, nvals)
# return if nothing to do
if all([n in results for n in nvals]):
return [results[n] for n in nvals]
for n in nvals:
if n in results['ndata']:
continue
results['ndata'].append(n)
x, y, dy = generate_random_signal(n)
# time FTP
print("timing: n = %d, h = %d, ftp" % (n, nharmonics))
model = FastTemplatePeriodogram(template=template)
model.fit(x, y, dy)
t0 = time()
frq, p = model.autopower()
results['tftp'].append(time() - t0)
print(" done in %.4f seconds" % (results['tftp'][-1]))
results['nfreqs'].append(len(frq))
if time_gatspy:
# time GATSPY
print("timing: n = %d, gatspy" % (n))
model = SlowTemplatePeriodogram(template=template)
model.fit(x, y, dy)
t0 = time()
p = model.power(frq)
results['tgats'].append(time() - t0)
print(" done in %.4f seconds" % (results['tgats'][-1]))
else:
results['tgats'].append(-1)
# SORT results
results = sort_timing_dict(results)
# save
if not filename is None and overwrite:
pickle.dump(results, open(filename, 'wb'))
return select_from_dict(results, nvals)