python - How to find zero crossings with hysteresis?

Question

In numpy, I would like to detect the points at which the signal crosses from (having been previously) below a certain threshold, to being above a certain other threshold. This is for things like debouncing, or accurate zero crossings in the presence of noise, etc.

Like this:

import numpy

# set up little test problem
N = 1000
values = numpy.sin(numpy.linspace(0, 20, N))
values += 0.4 * numpy.random.random(N) - 0.2
v_high = 0.3
v_low = -0.3

# find transitions from below v_low to above v_high    
transitions = numpy.zeros_like(values, dtype=numpy.bool)

state = "high"

for i in range(N):
    if values[i] > v_high:
        # previous state was low, this is a low-to-high transition
        if state == "low":
            transitions[i] = True
        state = "high"
    if values[i] < v_low:
        state = "low"

I would like a way to do this without looping over the array explicitly: but I can't think of any way, since each state value depends on the previous state. Is it possible to do without a loop?

Answer 1

This can be done like so:

def hyst(x, th_lo, th_hi, initial = False):
    hi = x >= th_hi
    lo_or_hi = (x <= th_lo) | hi
    ind = np.nonzero(lo_or_hi)[0]
    if not ind.size: # prevent index error if ind is empty
        return np.zeros_like(x, dtype=bool) | initial
    cnt = np.cumsum(lo_or_hi) # from 0 to len(x)
    return np.where(cnt, hi[ind[cnt-1]], initial)

Explanation: ind are the indices of all the samples where the signal is below the lower or above the upper threshold, and for which the position of the 'switch' is thus well-defined. With cumsum, you make some sort of counter which points to the index of the last well-defined sample. If the start of the input vector is between the two thresholds, cnt will be 0, so you need to set the the corresponding output to the initial value using the where function.

Credit: this is a trick I found in an old post on some Matlab forum, which I translated to Numpy. This code is a bit hard to understand and also needs to allocate various intermediate arrays. It would be better if Numpy would include a dedicated function, similar to your simple for-loop, but implemented in C for speed.

Quick test:

x = np.linspace(0,20, 1000)
y = np.sin(x)
h1 = hyst(y, -0.5, 0.5)
h2 = hyst(y, -0.5, 0.5, True)
plt.plot(x, y, x, -0.5 + h1, x, -0.5 + h2)
plt.legend(('input', 'output, start=0', 'output, start=1'))
plt.title('Thresholding with hysteresis')
plt.show()

Result: