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 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_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_distance1_b():
with util_numpy.test_uses_numpy() as np:
s1 = [0, 0, 1, 2, 1, 0, 1, 0, 0]
s2 = [0, 1, 2, 0, 0, 0, 0, 0, 0]
d2, wps = dtw.warping_paths(s1, s2)
print(wps)
assert d2 == pytest.approx(math.sqrt(2))
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 get_lead_DTW_euclid(x, y):
# x = xy[:,0]
# y = xy[:,1]
d, paths = dtw.warping_paths(x, y, window=None, psi=None)
best_path = dtw.best_path(paths)
# # dtwvis.plot_warpingpaths( x, y, paths, best_path)
lead_E = create_lead_from_path(best_path, x, 0)
return (lead_E, d)
def test_psi_dtw_2a():
x = np.arange(0, 20, .5)
s1 = np.sin(x - 1)
s2 = np.sin(x)
d, paths = dtw.warping_paths(s1, s2, psi=2, window=3)
# path = dtw.warping_path(s1, s2, psi=2)
# dtwvis.plot_warping(s1, s2, path, filename=os.path.expanduser("~/Desktop/test_psi_dtw_2_1.png"))
# path = dtw.best_path(paths)
# dtwvis.plot_warpingpaths(s1, s2, paths, path, filename=os.path.expanduser("~/Desktop/test_psi_dtw_2_2.png"))
np.testing.assert_equal(d, 0.0)
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_bug_size():
"""Two series of length 1500 should not trigger a size error.
The warping paths matrix is of size 1501**2 = 2_253_001.
If using 64bit values: 1501**2*64/(8*1024*1024) = 17.2MiB.
"""
with util_numpy.test_uses_numpy() as np:
s1 = np.random.rand(1500)
s2 = np.random.rand(1500)
d1, _ = dtw.warping_paths_fast(s1, s2)
d2, _ = dtw.warping_paths(s1, s2)
assert d1 == pytest.approx(d2)
def test(amplitude, center, width, noise, target_norm, len_a, window):
source_norm = amplitude * norm.pdf(range(0, 400), center, width)
source_norm = np.random.normal(source_norm, scale=noise)
d, paths = dtw.warping_paths(target_norm, source_norm, window, psi=0)
best_path = dtw.best_path(paths)
paths = np.array(best_path)
euclidean = d
init_euclidean = abs(paths[:, 0] - paths[:, 1])
amplitude, center, width, noise = str(amplitude), str(center), str(
width), str(noise)
return euclidean, init_euclidean
def test_distance1_b():
with util_numpy.test_uses_numpy() as np:
dist_opts = {}
s1 = np.array(
[0., 0.01, 0., 0.01, 0., 0., 0., 0.01, 0.01, 0.02, 0., 0.])
s2 = np.array([0., 0.02, 0.02, 0., 0., 0.01, 0.01, 0., 0., 0., 0.])
d1 = dtw.distance(s1, s2, **dist_opts)
d2 = dtw.distance_fast(s1, s2, **dist_opts)
d3, wps = dtw.warping_paths(s1, s2, **dist_opts)
print(np.power(wps, 2))
assert d1 == d2
assert d1 == d3
assert d1 == pytest.approx(0.02)
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 best_match_ts(query, ts_dict):
"""
Applying the brute-force pattern to find the best match for the query from the set of the ts_dict
:param: query list
:param: ts_dict
best_match: (type of dict) {key: time_series_ID, value: time_series_data, distance: similarity_distance(DTW Algorithm),
best_path: side-product from dtw algorithm}
:return: best_match_list [{bm_dict1}, {bm_dict2}, {bm_dict3}], .....]
"""
query_len = len(query)
best_match_list = []
best_of_so_far = float('inf')
start_time = datetime.datetime.now()
for key, value in ts_dict.items():
candidates = slice_list(value, query_len)
candidates.sort(
key=lambda each: get_distance(dtw.warping_paths, each, query))
for i in range(len(candidates)):
distance, paths = dtw.warping_paths(query, candidates[i])
if distance < best_of_so_far:
best_match = dict()
best_of_so_far = distance
best_match['ts_id'] = key
best_match['value'] = candidates[i]
best_match['distance'] = distance
best_match['best_path'] = dtw.best_path(paths)
best_match_list.append(best_match)
else:
break
for match in best_match_list:
if match['distance'] > best_of_so_far:
best_match_list.remove(match)
end_time = datetime.datetime.now()
print("Time period of the execution for the brute_force: " +
str((end_time - start_time).microseconds) + "ms")
return best_match_list
def test_psi_dtw_2a():
with util_numpy.test_uses_numpy() as np:
x = np.arange(0, 20, .5)
s1 = np.sin(x - 1)
s2 = np.sin(x)
d, paths = dtw.warping_paths(s1, s2, psi=2, window=3)
# try:
# path = dtw.warping_path(s1, s2, psi=2)
# dtwvis.plot_warping(s1, s2, path, filename=os.path.expanduser("~/Desktop/test_psi_dtw_2_1.png"))
# path = dtw.best_path(paths)
# dtwvis.plot_warpingpaths(s1, s2, paths, path, filename=os.path.expanduser("~/Desktop/test_psi_dtw_2_2.png"))
# except MatplotlibException:
# pass
np.testing.assert_equal(d, 0.0)
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_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 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 d():
dd, _ = dtw.warping_paths(s1, s2)
return dd
#!/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])
def test_distance1_b():
s1 = [0, 0, 1, 2, 1, 0, 1, 0, 0]
s2 = [0, 1, 2, 0, 0, 0, 0, 0, 0]
d2, _ = dtw.warping_paths(s1, s2)
assert d2 == pytest.approx(math.sqrt(2))
from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter #%% 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)
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")
def test_bug2():
with util_numpy.test_uses_numpy() as np:
s1 = np.array([
5.005335029629605081e-01, 5.157722489130834864e-01,
4.804319657333316340e-01, 4.520537745752661318e-01,
4.867408184050183717e-01, 4.806534229629605415e-01,
4.530552579964135518e-01, 4.667067057333316171e-01,
4.567955137333316040e-01, 4.414902037333315876e-01,
4.240597964014319321e-01, 4.225263829008334970e-01,
4.030970017333316280e-01, 4.404482984865574768e-01,
3.852339312962939077e-01, 3.634947117333316435e-01,
3.861488867383516266e-01, 3.413363679008334928e-01,
3.451913457333316004e-01, 3.695692377333316680e-01,
3.434781337333315809e-01, 3.063217006568062506e-01,
2.845283817333316145e-01, 2.955394357333315791e-01,
3.151374838781335619e-01, 2.561411067352764026e-01,
2.301194263297469400e-01, 2.478605028202762184e-01,
1.972828198566299318e-01, 2.150545617333316228e-01,
2.232865857333316273e-01, 2.492665580680986370e-01,
2.144049374050155388e-01, 2.079081117333316520e-01,
1.879600957333316391e-01, 1.638555197333316227e-01,
1.425566689000865583e-01, 2.016327177333316067e-01,
2.290943870240647606e-01, 1.900932117333316296e-01,
1.503233018025057766e-01, 1.970833717333316248e-01,
1.999393777333316191e-01, 2.018818837333316019e-01,
2.554168153357214144e-01, 2.345002377333316179e-01,
2.407103957333316113e-01, 2.762874997333316096e-01,
3.059693477333316203e-01, 3.328774862341668528e-01,
3.583867537333316200e-01, 3.743879884050183016e-01,
4.266385131705089373e-01, 4.445410410742424712e-01,
4.642271795675002033e-01, 4.402678696630802357e-01,
4.814591396296271641e-01, 5.317886460815400840e-01,
5.548714817383517683e-01, 5.062713000716849709e-01,
5.431524597333317050e-01, 5.537961812962939323e-01,
5.720852595675002261e-01, 5.933977447347652534e-01,
5.845479257333316969e-01, 6.133363017333317568e-01,
6.276481431102108877e-01, 6.132085097333317414e-01,
5.922371597333316862e-01, 5.778388756463566089e-01
])
s2 = np.array([
5.584292601075275808e-01, 5.214504501075275522e-01,
4.877978901075275542e-01, 5.078206201075274873e-01,
4.769738701075275644e-01, 4.478925501075275428e-01,
4.242528301075275676e-01, 4.307546401075275644e-01,
4.370594201075275187e-01, 4.331284101075275617e-01,
4.810766301075275475e-01, 4.250942801075275335e-01,
3.973955801075275684e-01, 4.380910701075275693e-01,
3.786794801075275552e-01, 3.850050201075275180e-01,
3.576176301075275621e-01, 2.987050201075275302e-01,
3.377542001075275468e-01, 3.262601401075275187e-01,
3.278248801075275276e-01, 3.347294101075275474e-01,
3.222199801075275594e-01, 3.372712101075275304e-01,
2.526810801075275448e-01, 1.774206901075275622e-01,
2.384015601075275825e-01, 2.419624201075275816e-01,
1.694136001075275677e-01, 1.983933401075275715e-01,
2.272449101075275646e-01, 1.490059201075275563e-01,
1.416013701075275744e-01, 1.997542401075275698e-01,
1.791462801075275613e-01, 1.712680901075275819e-01,
1.851759601075275707e-01, 1.450854801075275591e-01,
1.041379601075275718e-01, 9.028068310752757064e-02,
1.358144301075275839e-01, 2.006444701075275616e-01,
2.003521501075275768e-01, 2.100136501075275663e-01,
2.521797401075275280e-01, 2.364524601075275734e-01,
2.236850301075275771e-01, 2.873612101075275205e-01,
3.358473801075275156e-01, 3.288144201075275386e-01,
3.195859301075275605e-01, 3.482947201075275445e-01,
4.032929801075275655e-01, 4.566962501075275682e-01,
5.173766201075274962e-01, 5.463256501075275384e-01,
5.172673701075275465e-01, 5.054312901075275200e-01,
5.344046101075274890e-01, 5.389180101075274898e-01,
5.188896901075275014e-01, 5.484243401075274971e-01,
5.899157901075275934e-01, 5.987863201075275255e-01,
6.357147701075275270e-01, 6.277379101075275525e-01,
5.519873201075274904e-01, 5.634240801075275362e-01,
6.307956401075275332e-01, 6.488636001075275272e-01
])
res1 = dtw.distance(s1, s2)
res2 = dtw.distance(s1, s2, max_dist=.20)
res3, _m3 = dtw.warping_paths(s1, s2)
res4, _m4 = dtw.warping_paths(s1, s2, max_dist=.20)
# print(res1)
# print(res2)
# print(res3)
# print(res4)
# np.savetxt('/Users/wannes/Desktop/debug/m3.txt', m3)
# np.savetxt('/Users/wannes/Desktop/debug/m4.txt', m4)
assert res1 == pytest.approx(res2)
assert res1 == pytest.approx(res3)
assert res1 == pytest.approx(res4)
