def get_dtw_wrapping_path(data, col1, col2):
'''
input:
data: dataframe 源数据
col1: 列名
col2：列名
output: 输出图像，上方是col1，下方是col2
'''
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_for_dtw_zscore_T[col1,:]
# y = array_for_dtw_zscore_T[col2,:]
x = array_subset_zscore_T[x_idx, :]
y = array_subset_zscore_T[y_idx, :]
path = dtw.warping_path(x, y)
outname = col1 + 'vs' + col2
ds_xy = dtw.distance(x, y)
dtwvis.plot_warping(
x,
y,
path,
filename=
"D:/Pythoncode/JD_mart/operation_flow_distribution/DTW_for_business/results/%s.png"
% outname)
print("%s 和 %s 的DTW距离: %2.4f" % (col1, col2, ds_xy))
def print_dtw(series_1, series_2, output_path):
"""
Function to draw the DTW for two different time series
:param series_1: First time serie to compare
:param series_2: Second time serie to compare
:param output_path: Path where the pictures will be stored
"""
len1 = roundup(series_1.__len__())
len2 = roundup(series_2.__len__())
contador = 0
series_1 = series_1
series_2 = series_2
series_1 = series_1[:len1]
series_1 = np.split(series_1, int(len1 / 100))
series_2 = series_2[:len2]
series_2 = np.split(series_2, int(len2 / 100))
for i in range(series_1.__len__()):
path = dtw.warping_path(series_1[i], series_2[i])
print(path)
dtwvis.plot_warping(series_1[i],
series_2[i],
path,
filename=output_path % contador)
contador += 1
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 dtw_visual(x, y): # shape X, Y np.array([0., 0, 1, 2, 1, 0, 2, 1, 0, 0])
"""
Plot to show how dtw works
:param x: time series
:param y: time series
:return:
"""
from dtaidistance import dtw
from dtaidistance import dtw_visualisation as dtwvis
path = dtw.warping_path(x, y)
dtwvis.plot_warping(x, y, path, filename="tmp.png")
def main():
all_calculations = []
for pattern in patterns:
len_template = len(pattern.get_pattern())
window_size = len_template
while window_size < len_list_of_closes:
start_window: int = 0
end_window: int = window_size # not -1 because thts not how array[x:y] works
while end_window < len_list_of_closes:
window = list_of_closes[start_window:end_window]
minimum_close, maximum_close = _get_min_max_close(window)
window_calculations = _calculate_dtw_over_time_window(
window=window,
minimum_close=minimum_close,
maximum_close=maximum_close,
pattern=pattern,
start_window=start_window,
end_window=end_window,
threshold=5)
if window_calculations:
all_calculations = all_calculations + window_calculations
start_window = start_window + 1
end_window = end_window + 1
window_size = window_size + len_template
all_calculations_df = pd.DataFrame(all_calculations).sort_values(
by=['distance'])
csv = all_calculations_df.to_csv('data/output.csv', index=False)
for i, row in all_calculations_df.iterrows():
window = history_price_btc[
(history_price_btc['date'] >= row['start_date'])
& (history_price_btc['date'] <= row['end_date'])]
plot_fig = window.plot(x='date', y='close').get_figure()
plot_fig.savefig(
f'data/imgs/{row["start_date"]}-{row["end_date"]}-{row["pattern_name"]}.png'
)
plt.close(plot_fig)
dtwvis.plot_warping(
row['normalized_data'],
row['pattern'],
row['path'],
filename=
f'data/imgs/{row["start_date"]}-{row["end_date"]}-{row["pattern_name"]}-dtw.png'
)
def test_normalize():
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])
r, path = dtw.warp(s1, s2)
if directory:
dtwvis.plot_warp(s1, s2, r, path, filename=str(directory / "test_normalize1.png"))
r_c = np.array([0., 1., 2., 2., 1., 0.5, 0., 0., 2., 1., 0., 0., 0.])
if directory:
path = dtw.warping_path(s1, s2, psi=2)
dtwvis.plot_warping(s1, s2, path, filename=str(directory / "test_normalize2.png"))
np.testing.assert_almost_equal(r, r_c, decimal=4)
def test_bug4():
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])
path = dtw.warping_path(s1, s2)
if directory:
fn = directory / "bug4.png"
else:
file = tempfile.NamedTemporaryFile()
fn = Path(file.name + "_bug4.png")
dtwvis.plot_warping(s1, s2, path, filename=str(fn))
def compare_34dim(self, new_video_coordinates, reference_coordinates, i, j,
weights):
# new_video_coordinates = self.normalize(new_video_coordinates)
new_video_coordinates = new_video_coordinates.reshape(i, 34)
reference_coordinates = reference_coordinates.reshape(j, 34)
best_path = dtw.best_path(
dtw_ndim.warping_paths(new_video_coordinates,
reference_coordinates)[1])
for body_part in range(17):
dtw_visualisation.plot_warping(
new_video_coordinates[:, 2 * body_part],
reference_coordinates[:, 2 * body_part], best_path,
"warp" + str(2 * body_part) + ".png")
dtw_visualisation.plot_warping(
new_video_coordinates[:, 2 * body_part + 1],
reference_coordinates[:, 2 * body_part + 1], best_path,
"warp" + str(2 * body_part + 1) + ".png")
# Calculating euclidean distance per body part to apply weights
# max_frames = max(i, j)
body_part_scores = []
for body_part_i in range(17):
v1_part, v2_part = [False] * len(best_path) * 2, [
False
] * len(best_path) * 2
print(len(v1_part), len(v2_part), len(best_path))
print(best_path)
for k, t in enumerate(best_path):
new_frame, reference_frame = t
v1_part[k * 2] = new_video_coordinates[new_frame][body_part_i *
2]
v1_part[k * 2 +
1] = new_video_coordinates[new_frame][body_part_i * 2 +
1]
v2_part[k *
2] = reference_coordinates[reference_frame][body_part_i
* 2]
v2_part[k * 2 + 1] = reference_coordinates[reference_frame][
body_part_i * 2 + 1]
v1_part = v1_part / np.linalg.norm(v1_part)
v2_part = v2_part / np.linalg.norm(v2_part)
score = self.percentage_score(ed.distance(v1_part, v2_part))
print(score)
body_part_scores.append(score)
return self.apply_weights(weights, body_part_scores), body_part_scores
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 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_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"))
#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")
from dtaidistance import dtw from dtaidistance import dtw_visualisation as dtwvis import 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]) path = dtw.warping_path(s1, s2) dtwvis.plot_warping(s1, s2, path, filename="img/dynamicwarping.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")
from dtaidistance import dtw from dtaidistance import dtw_visualisation as dtwvis import pandas as pd # 计算股票间的对数收益时间序列间的动态时间规整距离 path1,path2 = "000001.XSHE.csv","000063.XSHE.csv", feature = "rclose" length = 20 window,psi = 10,5 rc1,rc2 = pd.read_csv(path1)[feature][:length],pd.read_csv(path2)[feature][:length] dis, paths = dtw.warping_paths(rc1, rc2, window=window, psi=psi) # 动态时间规整距离 print(dis) # 绘图（输出形式） best_path = dtw.best_path(paths) dtwvis.plot_warpingpaths(rc1, rc2, paths, best_path,shownumbers=True) # 绘图（保存） path = dtw.warping_path(rc1, rc2) dtwvis.plot_warping(rc1, rc2, path, filename="wrapping.png")
