def test_distance1():
with util_numpy.test_uses_numpy() as np:
directory = prepare_directory()
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 10, 1, 0, 2, 1, 0, 0, 0])
d, paths = dtw.warping_paths(s1, s2)
# print(d, "\n", paths)
if not dtwvis.test_without_visualization():
dtwvis.plot_warpingpaths(s1,
s2,
paths,
filename=directory / "temp1.png")
weights = np.full((len(s1), 8), np.inf)
weights[:, 2:4] = 0.0
weights[4:7, 2:4] = 10.0
weights[:, 4:6] = 0.0
weights[4:7, 4:6] = 10.0
d, paths = dtww.warping_paths(s1, s2, weights)
# print(d, "\n", paths)
if not dtwvis.test_without_visualization():
dtwvis.plot_warpingpaths(s1,
s2,
paths,
filename=directory / "temp2.png")
def test_psi_dtw_1a():
with util_numpy.test_uses_numpy() as np:
x = np.arange(0, 20, .5)
s1 = np.sin(x)
s2 = np.sin(x - 1)
# Add noise
# random.seed(1)
# for idx in range(len(s2)):
# if random.random() < 0.05:
# s2[idx] += (random.random() - 0.5) / 2
d, paths = dtw.warping_paths(s1, s2, psi=2, window=25)
path = dtw.warping_path(s1, s2, psi=2)
if not dtwvis.test_without_visualization():
if directory:
dtwvis.plot_warpingpaths(s1,
s2,
paths,
path,
filename=str(directory /
"test_psi_dtw_1a.png"))
# print(paths[:,:])
# dtwvis.plot_warping(s1, s2, path, filename=os.path.expanduser("~/Desktop/test_psi_dtw_1_1.png"))
# path = dtw.best_path(paths)
# dtwvis.plot_warpingpaths(s1, s2, paths, path, filename=os.path.expanduser("~/Desktop/test_psi_dtw_1_2.png"))
np.testing.assert_equal(d, 0.0)
def print_dtw_matrix(series_1, series_2, output_matrix):
"""
Function to print dtw distance matrix
:param series_1: First time series to compare
:param series_2: Second time series to compare
:param output_matrix: path / file where output matrix will be stored
"""
contador = 0
len1 = roundup(series_1.__len__())
len2 = roundup(series_2.__len__())
series_1 = series_1[:len1]
series_2 = series_2[:len2]
series_1 = np.split(series_1, int(len1 / 100))
series_2 = np.split(series_2, int(len2 / 100))
for i in range(series_1.__len__()):
d, paths = dtw.warping_paths(series_1[i],
series_2[i],
window=25,
psi=5)
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(series_1[i],
series_2[i],
paths,
best_path,
filename=output_matrix % contador)
contador += 1
def test_dtw_subseq1():
with util_numpy.test_uses_numpy() as np:
query = np.array([1., 2, 0])
series = np.array([1., 0, 1, 2, 1, 0, 2, 0, 3, 0, 0])
sa = subsequence_alignment(query, series)
mf = sa.matching_function()
# print(f'{mf=}')
match = sa.best_match()
# print(match)
# print(f'Segment={match.segment}')
# print(f'Path={match.path}')
if not dtwvis.test_without_visualization():
try:
import matplotlib.pyplot as plt
except ImportError:
raise MatplotlibException("No matplotlib available")
if directory:
plt.plot(mf)
plt.savefig(directory / "subseq_matching.png")
dtwvis.plot_warpingpaths(query,
series,
sa.warping_paths(),
match.path,
filename=directory /
"subseq_warping.png")
plt.close()
best_k = sa.kbest_matches(k=3)
assert match.path == [(0, 2), (1, 3), (2, 4)]
assert [m.segment for m in best_k] == [[2, 4], [5, 7], [0, 1]]
def test_bug2():
with util_numpy.test_uses_numpy() as np:
s1 = np.array([0, 0, 1, 2, 1, 0, 1, 0, 0], dtype=np.double)
s2 = np.array([0.0, 1, 2, 0, 0, 0, 0, 0, 0])
d1a = dtw.distance_fast(s1, s2, window=2)
d1b = dtw.distance(s1, s2, window=2)
if directory:
fn = directory / "warpingpaths.png"
else:
file = tempfile.NamedTemporaryFile()
fn = Path(file.name + "_warpingpaths.png")
d2, paths = dtw.warping_paths(s1, s2, window=2)
best_path = dtw.best_path(paths)
if not dtwvis.test_without_visualization():
dtwvis.plot_warpingpaths(s1,
s2,
paths,
best_path,
filename=fn,
shownumbers=False)
print("Figure saved to", fn)
assert d1a == pytest.approx(d2)
assert d1b == pytest.approx(d2)
def test_psi_dtw_1d():
with util_numpy.test_uses_numpy() as np:
x = np.arange(0, 20, .5)
s1 = np.sin(x)
s2 = np.sin(x - 1)
random.seed(1)
for idx in range(len(s2)):
if random.random() < 0.05:
s2[idx] += (random.random() - 0.5) / 2
# print(f's1 = [' + ','.join(f'{vv:.2f}' for vv in s1) + ']')
# print(f's2 = [' + ','.join(f'{vv:.2f}' for vv in s2) + ']')
# print('distance_fast')
d1 = dtw.distance_fast(s1, s2, psi=2)
# print(f'{d1=}')
# print('warping_paths')
d2, paths = dtw.warping_paths(s1, s2, window=25, psi=2)
# print(f'{d2=}')
with np.printoptions(threshold=np.inf, linewidth=np.inf):
print(paths)
# print('warping_paths fast')
d3, paths = dtw.warping_paths_fast(s1, s2, window=25, psi=2)
# print(f'{d3=}')
# print(paths)
# print('best_path')
best_path = dtw.best_path(paths)
if not dtwvis.test_without_visualization():
if directory:
dtwvis.plot_warpingpaths(s1, s2, paths, best_path, filename=directory / "test_psi_dtw_1d.png")
np.testing.assert_almost_equal(d1, d2)
np.testing.assert_almost_equal(d1, d3)
def test_twoleadecg_1(directory=None):
with util_numpy.test_uses_numpy() as np:
s1 = np.array([1.8896,-0.23712,-0.23712,-0.20134,-0.16556,-0.20134,-0.16556,-0.12978,-0.058224,0.013335,0.031225,0.10278,0.013335,-0.094004,-0.058224,-0.11189,-0.14767,-0.16556,-0.14767,-0.094004,-0.14767,-0.16556,-0.16556,-0.21923,-0.21923,-0.25501,-0.20134,-0.20134,-0.18345,-0.23712,-0.20134,-0.23712,-0.12978,-0.11189,-0.46969,-1.2747,-2.3481,-2.8133,-2.7775,-2.5986,-2.3839,-2.0082,-1.8651,-1.6146,-1.3463,-1.1495,-0.88115,-0.55914,-0.34446,-0.16556,-0.0045548,0.2459,0.53214,0.65737,0.71104,0.74682,0.76471,0.76471,0.80049,0.81838,0.87204,0.88993,0.97938,0.97938,1.0152,1.0867,1.1583,1.1762,1.212,1.2656,1.2656,1.2477,1.2656,1.1762,1.0867,0.99727,0.88993,0.74682,0.63948,0.58581,0.47847,0.38902])
s2 = np.array([1,0.93163,0.094486,0.094486,0.038006,0.080366,0.080366,0.052126,0.080366,0.12273,0.22157,0.29217,0.41925,0.48985,0.39101,0.39101,0.30629,0.24981,0.19333,0.080366,-0.0043544,-0.018474,-0.089075,-0.11731,-0.14555,-0.17379,-0.21615,-0.27263,-0.20203,-0.315,-0.25851,-0.17379,-0.28675,-0.24439,0.16509,-0.11731,-1.0069,-1.9812,-2.4895,-2.786,-2.9272,-2.4612,-2.0518,-1.8964,-1.8258,-1.7411,-1.6705,-1.2893,-0.99276,-0.65388,-0.37148,-0.30087,-0.046714,0.30629,0.53221,0.65929,0.65929,0.72989,0.74401,0.87109,0.89933,0.95581,0.96993,1.0546,1.1394,1.2523,1.2523,1.2947,1.3088,1.3512,1.2806,1.2806,1.1394,1.097,0.89933,0.72989,0.67341,0.54633,0.37689,0.23569,0.10861,0.080366,-0.074955])
d, paths = dtw.warping_paths(s1, s2, psi=2, window=5)
path = dtw.warping_path(s1, s2, psi=2)
if directory:
dtwvis.plot_warping(s1, s2, path, filename=str(directory / "warping.png"))
path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(s1, s2, paths, path, filename=str(directory / "warpingpaths.png"))
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)
def time_series_dtw_mapping_path(s1,s2,max_shift_rate=0.005,image_output=None):
T0=time.time()
print("find warping paths...",end=" ")
d, paths = dtaidistance.dtw.warping_paths_fast(s1, s2,window=int(max_shift_rate*max(len(s1),len(s2))),max_step=2 ,penalty=0.1,psi=500)
print("done(use time: {0})".format(time.time()-T0))
T0=time.time()
print("\tfind best path...",end=" ")
best_path = dtaidistance.dtw.best_path(paths)
print("done(use time: {0})".format(time.time()-T0))
if(image_output):
dtwvis.plot_warpingpaths(s1, s2, paths, best_path, filename=image_output)
del paths
return (best_path)
def test_normalize2():
with util_numpy.test_uses_numpy() as np:
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 0, 0, 0, 2, 1, 0, 0, 0])
d1, paths1 = dtw.warping_paths(s1, s2, psi=2)
d2, paths2 = dtw.warping_paths_fast(s1, s2, psi=2)
path1 = dtw.best_path(paths1)
path2 = dtw.best_path(paths2)
if directory:
dtwvis.plot_warpingpaths(s1, s2, paths1, path1, filename=directory / "normalize.png")
np.testing.assert_almost_equal(d1, d2, decimal=4)
np.testing.assert_almost_equal(paths1, paths2, decimal=4)
np.testing.assert_almost_equal(path1, path2, decimal=4)
def compute_dtw(county_covid_cases_delta, mobility_type_data, mobility_data_type_index):
print(county_covid_cases_delta.shape)
print(mobility_type_data.shape)
mobility_data_type_name = ['recreation', 'groecry', 'park', 'transit', 'work', 'residential']
distance = dtw_visualize.distance(county_covid_cases_delta, mobility_type_data)
print(distance)
with open('results/confirmed_cases_dtw_distance.csv','a') as csvfile:
csvfile.write(mobility_data_type_name[mobility_data_type_index] + " " + str(distance) + "\n")
d, paths = dtw_visualize.warping_paths(county_covid_cases_delta, mobility_type_data, window=25, psi=2)
best_path = dtw_visualize.best_path(paths)
dtwvis.plot_warpingpaths(county_covid_cases_delta, mobility_type_data, paths, best_path, filename="results/dtw_" + mobility_data_type_name[mobility_data_type_index] +".png")
plt.title("confirmed cases vs " + mobility_data_type_name[mobility_data_type_index] + " DTW")
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_normalize2_prob():
psi = 0
if dtw.dtw_cc is not None:
dtw.dtw_cc.srand(random.randint(1, 100000))
else:
print("WARNING: dtw_cc not found")
with util_numpy.test_uses_numpy() as np:
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 0, 0, 0, 2, 1, 0, 0, 0])
d1, paths1 = dtw.warping_paths(s1, s2, psi=psi)
d2, paths2 = dtw.warping_paths_fast(s1, s2, psi=psi)
# print(np.power(paths1,2))
path1 = dtw.best_path(paths1)
path2 = dtw.best_path(paths2)
prob_paths = []
for i in range(30):
prob_paths.append(dtw.warping_path_prob(s1, s2, d1/len(s1), psi=psi))
if not dtwvis.test_without_visualization():
if directory:
fig, ax = dtwvis.plot_warpingpaths(s1, s2, paths1, path1)
for p in prob_paths:
py, px = zip(*p)
py = [pyi + (random.random() - 0.5) / 5 for pyi in py]
px = [pxi + (random.random() - 0.5) / 5 for pxi in px]
ax[3].plot(px, py, ".-", color="yellow", alpha=0.25)
fig.savefig(directory / "normalize2_prob.png")
np.testing.assert_almost_equal(d1, d2, decimal=4)
np.testing.assert_almost_equal(paths1, paths2, decimal=4)
np.testing.assert_almost_equal(path1, path2, decimal=4)
def test_twoleadecg_1():
"""Example from http://www.timeseriesclassification.com/description.php?Dataset=TwoLeadECG"""
with util_numpy.test_uses_numpy() as np:
s1 = np.array([1.8896,-0.23712,-0.23712,-0.20134,-0.16556,-0.20134,-0.16556,-0.12978,-0.058224,0.013335,0.031225,0.10278,0.013335,-0.094004,-0.058224,-0.11189,-0.14767,-0.16556,-0.14767,-0.094004,-0.14767,-0.16556,-0.16556,-0.21923,-0.21923,-0.25501,-0.20134,-0.20134,-0.18345,-0.23712,-0.20134,-0.23712,-0.12978,-0.11189,-0.46969,-1.2747,-2.3481,-2.8133,-2.7775,-2.5986,-2.3839,-2.0082,-1.8651,-1.6146,-1.3463,-1.1495,-0.88115,-0.55914,-0.34446,-0.16556,-0.0045548,0.2459,0.53214,0.65737,0.71104,0.74682,0.76471,0.76471,0.80049,0.81838,0.87204,0.88993,0.97938,0.97938,1.0152,1.0867,1.1583,1.1762,1.212,1.2656,1.2656,1.2477,1.2656,1.1762,1.0867,0.99727,0.88993,0.74682,0.63948,0.58581,0.47847,0.38902])
s2 = np.array([1,0.93163,0.094486,0.094486,0.038006,0.080366,0.080366,0.052126,0.080366,0.12273,0.22157,0.29217,0.41925,0.48985,0.39101,0.39101,0.30629,0.24981,0.19333,0.080366,-0.0043544,-0.018474,-0.089075,-0.11731,-0.14555,-0.17379,-0.21615,-0.27263,-0.20203,-0.315,-0.25851,-0.17379,-0.28675,-0.24439,0.16509,-0.11731,-1.0069,-1.9812,-2.4895,-2.786,-2.9272,-2.4612,-2.0518,-1.8964,-1.8258,-1.7411,-1.6705,-1.2893,-0.99276,-0.65388,-0.37148,-0.30087,-0.046714,0.30629,0.53221,0.65929,0.65929,0.72989,0.74401,0.87109,0.89933,0.95581,0.96993,1.0546,1.1394,1.2523,1.2523,1.2947,1.3088,1.3512,1.2806,1.2806,1.1394,1.097,0.89933,0.72989,0.67341,0.54633,0.37689,0.23569,0.10861,0.080366,-0.074955])
d, paths = dtw.warping_paths(s1, s2, psi=2, window=5)
path = dtw.warping_path(s1, s2, psi=2)
if not dtwvis.test_without_visualization():
if directory:
import matplotlib.pyplot as plt
fig, axs = dtwvis.plot_warping(s1, s2, path) # type: plt.Figure, plt.axes.Axes
fig.set_size_inches(12, 10)
fig.set_dpi(100)
fig.savefig(str(directory / "warping.png"))
plt.close(fig)
path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(s1, s2, paths, path, filename=str(directory / "warpingpaths.png"))
def test_distance1():
directory = prepare_directory()
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 10, 1, 0, 2, 1, 0, 0, 0])
d, paths = dtw.warping_paths(s1, s2)
# print(d, "\n", paths)
dtwvis.plot_warpingpaths(s1, s2, paths, filename=directory / "temp1.png")
weights = np.full((len(s1), 8), np.inf)
weights[:, 2:4] = 0.0
weights[4:7, 2:4] = 10.0
weights[:, 4:6] = 0.0
weights[4:7, 4:6] = 10.0
d, paths = dtww.warping_paths(s1, s2, weights)
# print(d, "\n", paths)
dtwvis.plot_warpingpaths(s1, s2, paths, filename=directory / "temp2.png")
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_distance3():
s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0,
0]])
w = np.array([[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 1.1, 1., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 1.1, 1., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 2., 2.2, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 1., 1.1, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 1.3, 1.43, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf]])
d, paths = dtww.warping_paths(s[0], s[1], w, window=0)
path = dtw.best_path(paths)
if directory:
wp_fn = directory / "warping_paths.png"
dtwvis.plot_warpingpaths(s[0], s[1], paths, path, filename=wp_fn)
def get_plot_wrapping_paths(data, col1, col2):
'''
input:
data: dataframe 源数据
col1: 列名
col2：列名
output: 输出图像
'''
indicators = [i for i in data.columns if i not in 'date']
array_subset = data[indicators].values
array_subset_zscore = stats.zscore(array_subset)
array_subset_zscore_T = array_subset_zscore.T
x_idx = indicators.index(col1)
y_idx = indicators.index(col2)
x = array_subset_zscore_T[x_idx, :]
y = array_subset_zscore_T[y_idx, :]
d, paths = dtw.warping_paths(x, y, window=25, psi=2)
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(x, y, paths, best_path)
def dtw_(self, length_min, length_max):
path = dtw.warping_path(self.new_real_normal[length_min:length_max],
self.ncsimul_y_normal[length_min:length_max])
distance, paths = dtw.warping_paths(
self.new_real_normal[length_min:length_max],
self.ncsimul_y_normal[length_min:length_max])
dtwvis.plot_warping(self.new_real_normal[length_min:length_max],
self.ncsimul_y_normal[length_min:length_max],
path,
filename="warp" + str(self.test) + ".png")
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(self.new_real_normal[length_min:length_max],
self.ncsimul_y_normal[length_min:length_max],
paths,
best_path,
filename="best_path" + str(self.test) +
".png")
def test_subsequence():
with util_numpy.test_uses_numpy() as np:
s1 = np.array([1., 2, 0])
s2 = np.array([1., 0, 1, 2, 1, 0, 1, 0, 0, 0, 0])
penalty = 0.1
psi = [0, 0, len(s2), len(s2)]
d1, paths1 = dtw.warping_paths(s1, s2, penalty=penalty, psi=psi)
d2, paths2 = dtw.warping_paths_fast(s1, s2, penalty=penalty, psi=psi)
path1 = dtw.best_path(paths1)
print(paths1)
path2 = dtw.best_path(paths2)
print(paths2)
if not dtwvis.test_without_visualization():
if directory:
dtwvis.plot_warpingpaths(s1, s2, paths1, path1, filename=directory / "subseq.png")
np.testing.assert_almost_equal(d1, d2, decimal=4)
np.testing.assert_almost_equal(paths1, paths2, decimal=4)
np.testing.assert_almost_equal(path1, path2, decimal=4)
np.testing.assert_almost_equal(paths1[3:4, 0:12][0],
[np.inf,1.421,1.005,1.421,2.002,1.000,-1,-1,-1,-1,-1,-1], decimal=3)
def prep_dtw(y, y_, min, max, file_):
try:
len(y) >= max and len(y_) >= max
except:
raise NameError('the maximum lengh not respects lenght of inputs')
else:
path = dtw.warping_path(y[min:max], y_[min:max])
distance, paths = dtw.warping_paths(y[min:max], y_[min:max])
dtwvis.plot_warping(y[min:max],
y_[min:max],
path,
filename=file_ + "warp_results.png")
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(y[min:max],
y_[min:max],
paths,
best_path,
filename=file_ + "best_path_results.png")
return path, distance
def main():
s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0])
s2 = np.array([0., 1, 2, 3, 1, 0, 0, 0, 2, 1, 0, 0, 0])
path = dtw.warping_path(s1, s2)
dtwvis.plot_warping(s1, s2, path)
plt.figure(1)
plt.subplot(211)
plt.title('Timeseries: s1 & s2')
plt.plot(s1)
plt.subplot(212)
plt.plot(s2)
plt.show()
dist = dtw.distance(s1, s2)
print(dist)
plt.figure(2)
d, paths = dtw.warping_paths(s1, s2, window=3, psi=2)
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(s1, s2, paths, best_path)
def test_distance3():
with util_numpy.test_uses_numpy() as np:
s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0],
[0., 1, 2, 0, 0, 0, 0, 0, 0]])
w = np.array([[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 1.1, 1., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 1.1, 1., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 2., 2.2, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 1., 1.1, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 1.3, 1.43, np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf],
[np.inf, np.inf, 0., 0., 0., 0., np.inf, np.inf]])
d, paths = dtww.warping_paths(s[0], s[1], w, window=0)
path = dtw.best_path(paths)
if not dtwvis.test_without_visualization():
if directory:
wp_fn = directory / "warping_paths.png"
dtwvis.plot_warpingpaths(s[0],
s[1],
paths,
path,
filename=wp_fn)
template_deriv.insert(0, 0)
template_deriv.insert(len(template_deriv), 0)
query_deriv, template_deriv = np.asarray(query_deriv), np.asarray(
template_deriv)
plt.figure()
plt.plot(query)
plt.plot(template)
d, paths = dtw.warping_paths_fast(query_deriv,
template_deriv,
window=1000,
psi=0)
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(query, template, paths, best_path)
best_path = np.asarray(best_path)
L = np.arange(0, len(best_path), 20)
x1, x2, y1, y2 = [], [], [], []
for i in L:
x11 = best_path[i, 0]
x22 = best_path[i, 1]
y11 = d1[x11]
y22 = D2[x22]
x1.append(x11)
x2.append(x22)
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 30 16:39:47 2019 @author: sprholst """ from dtaidistance import dtw from dtaidistance import dtw_visualisation as dtwvis import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 20, .5) s1 = np.sin(x) s2 = np.cos(x) d, paths = dtw.warping_paths(s1, s2, window=50, psi=0) best_path = dtw.best_path(paths) dtwvis.plot_warpingpaths(s1, s2, paths, best_path) best_path = np.asarray(best_path) plt.figure(2) plt.plot(best_path[:, 0], best_path[:, 1])
#%% np.random.seed(0) target_norm = norm.pdf(range(0, 400), 200, 40) source_norm = norm.pdf(range(0, 400), 200, 40) noise_t = target_norm.max() / 5.0 noise_s = source_norm.max() / 40.0 target_norm = np.random.normal(target_norm, scale=noise_t) source_norm = np.random.normal(source_norm, scale=noise_s) #target_norm /= noise_t #source_norm /= noise_s d, paths = dtw.warping_paths(target_norm, source_norm, window=1000, psi=0) best_path = dtw.best_path(paths) dtwvis.plot_warpingpaths(target_norm, source_norm, paths, best_path) paths = np.array(best_path) euclidean = pow((target_norm[paths[:, 0]] - source_norm[paths[:, 1]]), 2) init_euclidean = abs(target_norm - source_norm) cov = np.correlate(source_norm, target_norm, mode='same') fig, ax = plt.subplots(3, 1, figsize=(8, 4)) #eucint = np.interp(range(0,1200),paths[:,0],euclidean) #tint = np.interp(range(0,1200),paths[:,0],target_norm[paths[:,0]]) #sint = np.interp(range(0,1200),paths[:,0],source_norm[paths[:,1]]) #ax[0].scatter(range(0, euclidean.shape[0]),euclidean, s=3) #ax[0].scatter(range(0, 1200), eucint, s=3) ax[0].plot(euclidean) #ax[0].plot(range(0,cov.shape[0]), cov) #ax[1].plot(target_norm)
for i in range(len(rshoul_eY)): rightArm_eangles.append(angle3pt(rshoul_eX[i],rshoul_eY[i], relbow_eX[i], relbow_eY[i], rwrist_eX[i], rwrist_eY[i])) for i in range(len(rshoul_bY)): rightArm_bangles.append(angle3pt(rshoul_bX[i],rshoul_bY[i], relbow_bX[i], relbow_bY[i], rwrist_bX[i], rwrist_bY[i])) for i in range(len(rshoul_bdY)): rightArm_bdangles.append(angle3pt(rshoul_bdX[i],rshoul_bdY[i], relbow_bdX[i], relbow_bdY[i], rwrist_bdX[i], rwrist_bdY[i])) for i in range(len(lshoul_eY)): leftArm_eangles.append(angle3pt(lshoul_eX[i],lshoul_eY[i], lelbow_eX[i], lelbow_eY[i], lwrist_eX[i], lwrist_eY[i])) for i in range(len(lshoul_bY)): leftArm_bangles.append(angle3pt(lshoul_bX[i],lshoul_bY[i], lelbow_bX[i], lelbow_bY[i], lwrist_bX[i], lwrist_bY[i])) for i in range(len(lshoul_bdY)): leftArm_bdangles.append(angle3pt(lshoul_bdX[i],lshoul_bdY[i], lelbow_bdX[i], lelbow_bdY[i], lwrist_bdX[i], lwrist_bdY[i])) leftArm_b = np.asarray(leftArm_bangles, dtype = np.float32) leftArm_e = np.asarray(leftArm_eangles, dtype = np.float32) #distance = dtw.distance(leftArm_eangles[:250], leftArm_bangles[:250]) #print(distance) d, paths = dtw.warping_paths(leftArm_e, leftArm_b, window = 100, psi = 5) best_path = dtw.best_path(paths) #dtwvis.plot_warping(leftArm_eangles, leftArm_bangles, path, filename = "test10.png") #print(paths) dtwvis.plot_warpingpaths(leftArm_e, leftArm_b, paths, path = best_path, filename = "Graphs/bestpath_window100+psi5.png")
#Using point DTW
distanceRshoulX = dtw.distance(rshoul_bX[:250], rshoul_eX[:250])
distanceRshoulY = dtw.distance(rshoul_bY[:250], rshoul_eY[:250])
distanceRelbowX = dtw.distance(relbow_bX[:250], relbow_eX[:250])
distanceRelbowY = dtw.distance(relbow_bY[:250], relbow_eY[:250])
distanceRwristX = dtw.distance(rwrist_bX[:250], rwrist_eX[:250])
distanceRwristY = dtw.distance(rwrist_bY[:250], rwrist_eY[:250])
distance = distanceRshoulX + distanceRshoulY + distanceRelbowX + distanceRelbowY + distanceRwristX + distanceRwristY
distX = distanceRshoulX + distanceRelbowX + distanceRwristX
distY = distanceRshoulY + distanceRelbowY + distanceRwristY
print(distance)
print(dtw.distance(bdxy, exy))
d, paths = dtw.warping_paths(bdxy, exy)
best_path = dtw.best_path(paths)
dtwvis.plot_warping(bdxy,
exy,
best_path,
filename="Graphs/test_point_badX.png")
#print(paths)
dtwvis.plot_warpingpaths(bdxy,
exy,
paths,
path=best_path,
filename="Graphs/testXX.png")
#dynamic time warping
import stampProcessor
import numpy as np
mode = 'propagationDelayCorrection'
coarseTau = 10000
shift = -51318536.0
# We define two sequences x, y as numpy array
# where y is actually a sub-sequence from x
timeStampAlice = np.load('../data/aliceBobtimeStampAlice.npy')
timeStampBob = np.load('../data/aliceBobtimeStampBob.npy')
coarseTimebinAlice = stampProcessor.timebin(coarseTau, timeStampAlice)
coarseTimebinBob = stampProcessor.timebin(coarseTau, timeStampBob)
s1 = coarseTimebinAlice[500000:505000]
s2 = coarseTimebinAlice[500000:505000]
print('len(x)', len(s1))
print('len(y)', len(s2))
from dtaidistance import dtw
from dtaidistance import dtw_visualisation as dtwvis
path = dtw.warping_path(s1, s2)
dtwvis.plot_warping(s1, s2, path, filename="warp.png")
d, paths = dtw.warping_paths(s1, s2, window=25, psi=2)
best_path = dtw.best_path(paths)
dtwvis.plot_warpingpaths(s1, s2, paths, best_path, filename="path.png")
def dynamic_time_warping(metrics, output_path, stimulus_pairs,
trial_dictionary):
output_path = os.path.join(output_path, "DynamicTimeWarping")
if not os.path.exists(output_path):
os.makedirs(output_path)
for metric in metrics:
metric_dir = os.path.join(output_path, metric)
if not os.path.exists(metric_dir):
os.makedirs(metric_dir)
for first_stimulus, second_stimulus in stimulus_pairs:
first_values = []
second_values = []
first_text = []
second_text = []
for trial in TRIALS_FOR_STIMULUS[first_stimulus][1:]:
for window in trial_dictionary[trial]:
first_values.append(
float(trial_dictionary[trial][window][metric]))
first_text.append(f'{trial}_{window}')
for trial in TRIALS_FOR_STIMULUS[second_stimulus][1:]:
for window in trial_dictionary[trial]:
second_values.append(
float(trial_dictionary[trial][window][metric]))
second_text.append(f'{trial}_{window}')
if len(first_values) <= len(second_values):
query = first_values
template = second_values
query_text = first_text
template_text = second_text
title = first_stimulus.split(
' ')[0] + "+" + second_stimulus.split(' ')[0]
else:
query = second_values
template = first_values
query_text = second_text
template_text = first_text
title = second_stimulus.split(
' ')[0] + "+" + first_stimulus.split(' ')[0]
query = np.array(query)
template = np.array(template)
query_normalized = (query - query.min()) / (query.max() -
query.min())
template_normalized = (template - template.min()) / (
template.max() - template.min())
_, paths = dtw.warping_paths(query_normalized,
template_normalized,
window=10,
psi=0)
best_path = dtw.best_path(paths)
metric_file = os.path.join(metric_dir, f'{title}.txt')
log(f'Similarity: {1 - paths[best_path[-1][0] + 1][best_path[-1][1] + 1] / len(best_path)}',
file=metric_file)
for pair in best_path:
log(f'\tPair: {pair}. Match: {query_text[pair[0]]} {template_text[pair[1]]}',
file=metric_file)
fig, axes = dtwvis.plot_warpingpaths(query, template, paths,
best_path)
axes[0].texts[0].set_visible(False)
axes[0].text(
0, 0, "Similarity = {:.4f}".format(
1 - paths[best_path[-1][0] + 1][best_path[-1][1] + 1] /
len(best_path)))
plt.savefig(os.path.join(metric_dir, f'{title}.png'))
plt.close()