我有一组表示数字输出的CSV值。它是使用模拟示波器收集的,因此它不是一个完美的数字信号。我试图过滤掉数据以获得完美的数字信号来计算周期(可能会有所不同)。 我还想定义从这个过滤得到的最大误差。
这样的事情:
观
应用数据阈值。这是一个伪代码:
for data_point_raw in data_array:
if data_point_raw < 0.8: data_point_perfect = LOW
if data_point_raw > 2 : data_point_perfect = HIGH
else:
#area between thresholds
if previous_data_point_perfect == Low : data_point_perfect = LOW
if previous_data_point_perfect == HIGH: data_point_perfect = HIGH
有两个问题困扰着我。
答案 0 :(得分:7)
这里有一些可能有用的代码。
from __future__ import division
import numpy as np
def find_transition_times(t, y, threshold):
"""
Given the input signal `y` with samples at times `t`,
find the times where `y` increases through the value `threshold`.
`t` and `y` must be 1-D numpy arrays.
Linear interpolation is used to estimate the time `t` between
samples at which the transitions occur.
"""
# Find where y crosses the threshold (increasing).
lower = y < threshold
higher = y >= threshold
transition_indices = np.where(lower[:-1] & higher[1:])[0]
# Linearly interpolate the time values where the transition occurs.
t0 = t[transition_indices]
t1 = t[transition_indices + 1]
y0 = y[transition_indices]
y1 = y[transition_indices + 1]
slope = (y1 - y0) / (t1 - t0)
transition_times = t0 + (threshold - y0) / slope
return transition_times
def periods(t, y, threshold):
"""
Given the input signal `y` with samples at times `t`,
find the time periods between the times at which the
signal `y` increases through the value `threshold`.
`t` and `y` must be 1-D numpy arrays.
"""
transition_times = find_transition_times(t, y, threshold)
deltas = np.diff(transition_times)
return deltas
if __name__ == "__main__":
import matplotlib.pyplot as plt
# Time samples
t = np.linspace(0, 50, 501)
# Use a noisy time to generate a noisy y.
tn = t + 0.05 * np.random.rand(t.size)
y = 0.6 * ( 1 + np.sin(tn) + (1./3) * np.sin(3*tn) + (1./5) * np.sin(5*tn) +
(1./7) * np.sin(7*tn) + (1./9) * np.sin(9*tn))
threshold = 0.5
deltas = periods(t, y, threshold)
print("Measured periods at threshold %g:" % threshold)
print(deltas)
print("Min: %.5g" % deltas.min())
print("Max: %.5g" % deltas.max())
print("Mean: %.5g" % deltas.mean())
print("Std dev: %.5g" % deltas.std())
trans_times = find_transition_times(t, y, threshold)
plt.plot(t, y)
plt.plot(trans_times, threshold * np.ones_like(trans_times), 'ro-')
plt.show()
输出:
Measured periods at threshold 0.5:
[ 6.29283207 6.29118893 6.27425846 6.29580066 6.28310224 6.30335003]
Min: 6.2743
Max: 6.3034
Mean: 6.2901
Std dev: 0.0092793
您可以使用numpy.histogram
和/或matplotlib.pyplot.hist
来进一步分析periods(t, y, threshold)
返回的数组。
答案 1 :(得分:2)
这不是您的问题的答案,只是建议可能有所帮助。我在这里写它是因为我不能把图像放在评论中。
我认为您应该在进行任何处理之前以某种方式规范化数据。
在归一化到0 ... 1范围后,您应该应用过滤器。
答案 2 :(得分:1)
如果你真的只对这段时间感兴趣,你可以绘制傅立叶变换,你会有一个峰值信号的频率出现(所以你有周期)。傅立叶域中的峰值越宽,周期测量中的误差越大
import numpy as np
data = np.asarray(my_data)
np.fft.fft(data)
答案 3 :(得分:1)
你的过滤很好,它与施密特触发器基本相同,但你可能遇到的主要问题是速度。使用Numpy的好处是它可以像C一样快,而你必须在每个元素上迭代一次。
使用SciPy的中值滤波器可以实现类似的功能。以下应达到类似的结果(并且不依赖于任何量级):
filtered = scipy.signal.medfilt(raw)
filtered = numpy.where(filtered > numpy.mean(filtered), 1, 0)
您可以使用medfilt(raw, n_samples)
调整中值过滤的强度,n_samples
默认为3。
至于错误,那将是非常主观的。一种方法是在不过滤的情况下对信号进行离散,然后比较差异。例如:
discrete = numpy.where(raw > numpy.mean(raw), 1, 0)
errors = np.count_nonzero(filtered != discrete)
error_rate = errors / len(discrete)