本文整理汇总了Python中pylab.axvline方法的典型用法代码示例。如果您正苦于以下问题:Python pylab.axvline方法的具体用法?Python pylab.axvline怎么用?Python pylab.axvline使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pylab
的用法示例。
在下文中一共展示了pylab.axvline方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: pick_peaks
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def pick_peaks(nc, L=16):
"""Obtain peaks from a novelty curve using an adaptive threshold."""
offset = nc.mean() / 20.
nc = filters.gaussian_filter1d(nc, sigma=4) # Smooth out nc
th = filters.median_filter(nc, size=L) + offset
#th = filters.gaussian_filter(nc, sigma=L/2., mode="nearest") + offset
peaks = []
for i in range(1, nc.shape[0] - 1):
# is it a peak?
if nc[i - 1] < nc[i] and nc[i] > nc[i + 1]:
# is it above the threshold?
if nc[i] > th[i]:
peaks.append(i)
#plt.plot(nc)
#plt.plot(th)
#for peak in peaks:
#plt.axvline(peak)
#plt.show()
return peaks
示例2: plot_signal_and_label
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def plot_signal_and_label(self, title, timestamp, signal, label_timestamp, label):
if not self.to_save and not self.to_show:
return
pylab.figure()
pylab.plot(timestamp, signal, color='m', label='signal')
for i in range(0, len(label_timestamp)):
pylab.axvline(label_timestamp[i], color="k", label="{}: key {}".format(i, label[i]), ls='dashed')
pylab.legend()
pylab.title(title)
pylab.xlabel('Time')
pylab.ylabel('Amplitude')
示例3: plot_sensor_data_and_segment
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def plot_sensor_data_and_segment(self, title, timestamp, x, y, z, segment, label):
if not self.to_save and not self.to_show:
return
self.big_figure()
pylab.plot(timestamp, x, color='r', label='x')
pylab.plot(timestamp, y, color='g', label='y')
pylab.plot(timestamp, z, color='b', label='z')
for i in range(0, len(segment)):
pylab.axvline(segment[i][0], color="c", ls='dashed')
pylab.axvline(segment[i][1], color="k", label="{}: key {}".format(i, label[i]), ls='dashed')
pylab.axvline(segment[i][2], color="m", ls='dashed')
pylab.legend()
pylab.title(title)
pylab.xlabel('Time')
pylab.ylabel('Amplitude')
示例4: plot
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def plot(self):
import pylab as plt
q_ticks_int = [self.q_dist[i] for i in self.q_ticks]
q_ticks_label = self.q_labels
for i, q in enumerate(q_ticks_label):
if q in self.translate_to_pylab:
q_ticks_label[i] = self.translate_to_pylab[q]
plt.plot(self.q_dist, self.ew_list)
plt.xticks(q_ticks_int, q_ticks_label)
for x in q_ticks_int:
plt.axvline(x, color="black")
return plt
示例5: plot_objectivefunction
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def plot_objectivefunction(results,evaluation,limit=None,sort=True, fig_name = 'objective_function.png'):
"""Example Plot as seen in the SPOTPY Documentation"""
import matplotlib.pyplot as plt
likes=calc_like(results,evaluation,spotpy.objectivefunctions.rmse)
data=likes
#Calc confidence Interval
mean = np.average(data)
# evaluate sample variance by setting delta degrees of freedom (ddof) to
# 1. The degree used in calculations is N - ddof
stddev = np.std(data, ddof=1)
from scipy.stats import t
# Get the endpoints of the range that contains 95% of the distribution
t_bounds = t.interval(0.999, len(data) - 1)
# sum mean to the confidence interval
ci = [mean + critval * stddev / np.sqrt(len(data)) for critval in t_bounds]
value="Mean: %f" % mean
print(value)
value="Confidence Interval 95%%: %f, %f" % (ci[0], ci[1])
print(value)
threshold=ci[1]
happend=None
bestlike=[data[0]]
for like in data:
if like<bestlike[-1]:
bestlike.append(like)
if bestlike[-1]<threshold and not happend:
thresholdpos=len(bestlike)
happend=True
else:
bestlike.append(bestlike[-1])
if limit:
plt.plot(bestlike,'k-')#[0:limit])
plt.axvline(x=thresholdpos,color='r')
plt.plot(likes,'b-')
#plt.ylim(ymin=-1,ymax=1.39)
else:
plt.plot(bestlike)
plt.savefig(fig_name)
示例6: plot_sensor_data_and_label
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def plot_sensor_data_and_label(self, title, timestamp, x, y, z, label_timestamp, label=None):
if not self.to_save and not self.to_show:
return
self.big_figure()
pylab.plot(timestamp, x, color='r', label='x')
pylab.plot(timestamp, y, color='g', label='y')
pylab.plot(timestamp, z, color='b', label='z')
for i in range(0, len(label_timestamp)):
if label is not None:
if i != 0:
pylab.axvline(label_timestamp[i], color="k", ls='dashed')
else:
pylab.axvline(label_timestamp[i], color="k", label="keystroke", ls='dashed')
else:
pylab.axvline(label_timestamp[i], color="k", ls='dashed')
pylab.legend()
pylab.title(title)
pylab.xlabel('Time')
pylab.ylabel('Amplitude')
if label:
pylab.xticks(label_timestamp, label)
示例7: processFlat
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import axvline [as 别名]
def processFlat(self):
"""Main process.
Returns
-------
est_idxs : np.array(N)
Estimated indeces the segment boundaries in frames.
est_labels : np.array(N-1)
Estimated labels for the segments.
"""
# Preprocess to obtain features
F = self._preprocess()
# Normalize
F = msaf.utils.normalize(F, norm_type=self.config["bound_norm_feats"])
# Make sure that the M_gaussian is even
if self.config["M_gaussian"] % 2 == 1:
self.config["M_gaussian"] += 1
# Median filter
F = median_filter(F, M=self.config["m_median"])
#plt.imshow(F.T, interpolation="nearest", aspect="auto"); plt.show()
# Self similarity matrix
S = compute_ssm(F)
# Compute gaussian kernel
G = compute_gaussian_krnl(self.config["M_gaussian"])
#plt.imshow(S, interpolation="nearest", aspect="auto"); plt.show()
# Compute the novelty curve
nc = compute_nc(S, G)
# Find peaks in the novelty curve
est_idxs = pick_peaks(nc, L=self.config["L_peaks"])
# Add first and last frames
est_idxs = np.concatenate(([0], est_idxs, [F.shape[0] - 1]))
# Empty labels
est_labels = np.ones(len(est_idxs) - 1) * -1
# Post process estimations
est_idxs, est_labels = self._postprocess(est_idxs, est_labels)
return est_idxs, est_labels
# plt.figure(1)
# plt.plot(nc);
# [plt.axvline(p, color="m") for p in est_bounds]
# [plt.axvline(b, color="g") for b in ann_bounds]
# plt.figure(2)
# plt.imshow(S, interpolation="nearest", aspect="auto")
# [plt.axvline(b, color="g") for b in ann_bounds]
# plt.show()