def test_dtw_localconcurrences_short():
with util_numpy.test_uses_numpy() as np:
series = np.array([0, -1, -1, 0, 1, 2, 1, 0, 0, 0, 1, 3, 2, 1, 0, 0, 0, -1, 0])
gamma = 1
threshold_tau = 70
delta = -2 * np.exp(-gamma * np.percentile(series, threshold_tau)) # -len(series)/2 # penalty
delta_factor = 0.5
tau = np.exp(-gamma * np.percentile(series, threshold_tau)) # threshold
# print(f'{tau=}, {delta=}')
buffer = 10
minlen = 3
lc = local_concurrences(series, gamma=gamma, tau=tau, delta=delta, delta_factor=delta_factor, penalty=1)
matches = []
for match in lc.kbest_matches(k=100, minlen=minlen, buffer=buffer):
if match is None:
break
matches.append(match)
assert [(m.row, m.col) for m in matches] == [(10, 17), (4, 19)]
if directory and not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
fn = directory / "test_dtw_localconcurrences_short.png"
fig = plt.figure()
fig, ax = dtwvis.plot_warpingpaths(series, series, lc.wp, path=-1, figure=fig)
for match in matches:
dtwvis.plot_warpingpaths_addpath(ax, match.path)
plt.savefig(fn)
plt.close(fig)
def test_dtw_localconcurrences_eeg():
with util_numpy.test_uses_numpy() as np:
data_fn = Path(__file__).parent / 'rsrc' / 'EEGRat_10_1000.txt'
data = np.loadtxt(data_fn)
series = np.array(data[1500:1700])
gamma = 1
# domain = 2 * np.std(series)
# affinity = np.exp(-gamma * series)
# print(f'Affinity in [{np.min(affinity)}, {np.max(affinity)}]\n'
# f' {np.mean(affinity)} +- {np.std(affinity)}\n'
# f' {np.exp(-gamma * np.mean(series))} +- {np.exp(-gamma * np.std(series))}\n'
# f' {np.exp(-gamma * np.percentile(series, 75))} / {np.exp(-gamma * np.median(series))} / {np.exp(-gamma * np.percentile(series, 25))}\n')
tau_stddev = 0.40
diffp = tau_stddev * np.std(series)
delta = -2 * np.exp(-gamma * diffp**2) # -len(series)/2 # penalty
delta_factor = 0.5
tau = np.exp(-gamma * diffp**2) # threshold
print(f'{tau=}, {delta=}')
# tau=0.8532234738897421, delta=-1.7064469477794841
buffer = 10
minlen = 20
lc = local_concurrences(series,
gamma=gamma,
tau=tau,
delta=delta,
delta_factor=delta_factor)
print(f'{lc.tau=}, {lc.delta=}')
matches = []
for match in lc.kbest_matches(k=100, minlen=minlen, buffer=buffer):
if match is None:
break
matches.append(match)
print([(m.row, m.col) for m in matches])
# assert [(m.row, m.col) for m in matches] == [(84, 95), (65, 93), (50, 117), (117, 200), (32, 180),
# (160, 178), (96, 139), (138, 181), (71, 200), (71, 117),
# (73, 137), (52, 138), (12, 117), (117, 178), (117, 160),
# (30, 160), (32, 52), (30, 117), (117, 135), (160, 200),
# (178, 200), (11, 52), (71, 160), (134, 160), (135, 200),
# (30, 200), (50, 200), (11, 73), (50, 160), (12, 33), (11, 137),
# (36, 143), (11, 179), (88, 160), (66, 178), (11, 93)]
if directory and not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
fn = directory / "test_dtw_localconcurrences.png"
fig = plt.figure()
fig, ax = dtwvis.plot_warpingpaths(series,
series,
lc.wp,
path=-1,
figure=fig)
for match in matches:
dtwvis.plot_warpingpaths_addpath(ax, match.path)
plt.savefig(fn)
plt.close(fig)
def test_dtw_subseq_eeg():
with util_numpy.test_uses_numpy() as np:
data_fn = Path(__file__).parent / 'rsrc' / 'EEGRat_10_1000.txt'
data = np.loadtxt(data_fn)
series = np.array(data[1500:1700])
query = np.array(data[1331:1352])
sa = subsequence_alignment(query, series)
match = sa.best_match()
kmatches = list(sa.kbest_matches(k=15, overlap=0))
segments = [m.segment for m in kmatches]
segments_sol = [[38, 56], [19, 37], [167, 185], [124, 143], [84, 100],
[59, 77], [150, 162], [101, 121], [0, 15]]
assert segments == segments_sol
if directory and not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
fn = directory / "test_dtw_subseq_eeg1.png"
fig = plt.figure(figsize=(20, 30))
dtwvis.plot_warpingpaths(query,
series,
sa.warping_paths(),
match.path,
figure=fig)
plt.savefig(fn)
plt.close(fig)
fn = directory / "test_dtw_subseq_eeg2.png"
startidx, endidx = match.segment
fig = plt.figure()
plt.plot(query, label='query')
plt.plot(series[startidx:endidx], label='best match')
plt.legend()
plt.savefig(fn)
plt.close(fig)
fn = directory / "test_dtw_subseq_eeg3.png"
fig = plt.figure(figsize=(20, 10))
fig, ax = dtwvis.plot_warpingpaths(query,
series,
sa.warping_paths(),
path=-1,
figure=fig)
print(f'plotting {len(kmatches)} matches')
for kmatch in kmatches:
dtwvis.plot_warpingpaths_addpath(ax, kmatch.path)
plt.savefig(fn)
plt.close(fig)