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Calculation of intersection points (intercepts) of a series or DataFrame

I have periodic data with an index that is a floating point number, for example:

time =    [0, 0.1, 0.21, 0.31, 0.40, 0.49, 0.51, 0.6, 0.71, 0.82, 0.93]
voltage = [1,  -1,  1.1, -0.9,    1,   -1,  0.9,-1.2, 0.95, -1.1, 1.11]
df = DataFrame(data=voltage, index=time, columns=['voltage'])
df.plot(marker='o')

I want to create a function that returns an array of times (indices) with all interpolated points where the voltage values ​​are y_val . For 'boost' , only values ​​are returned where the slope is positive; for 'fall' only values ​​with a negative slope are stored; for 'cross' both return. Therefore, if y_val = 0 and direction = 'cross' , then an array with 10 values ​​will be returned with X values ​​of the intersection points (the first of which is about 0.025), cross(df, y_val, direction='rise' | 'fall' | 'cross')

I thought this could be done using an iterator, but it was interesting if there was a better way to do this.

Thank. I love Pandas and the Pandas community.

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def cross(series, cross=0, direction='cross'):
    """
    Given a Series returns all the index values where the data values equal 
    the 'cross' value. 

    Direction can be 'rising' (for rising edge), 'falling' (for only falling 
    edge), or 'cross' for both edges
    """
    # Find if values are above or bellow yvalue crossing:
    above=series.values > cross
    below=np.logical_not(above)
    left_shifted_above = above[1:]
    left_shifted_below = below[1:]
    x_crossings = []
    # Find indexes on left side of crossing point
    if direction == 'rising':
        idxs = (left_shifted_above & below[0:-1]).nonzero()[0]
    elif direction == 'falling':
        idxs = (left_shifted_below & above[0:-1]).nonzero()[0]
    else:
        rising = left_shifted_above & below[0:-1]
        falling = left_shifted_below & above[0:-1]
        idxs = (rising | falling).nonzero()[0]

    # Calculate x crossings with interpolation using formula for a line:
    x1 = series.index.values[idxs]
    x2 = series.index.values[idxs+1]
    y1 = series.values[idxs]
    y2 = series.values[idxs+1]
    x_crossings = (cross-y1)*(x2-x1)/(y2-y1) + x1

    return x_crossings

# Test it out:
time = [0, 0.1, 0.21, 0.31, 0.40, 0.49, 0.51, 0.6, 0.71, 0.82, 0.93]
voltage = [1,  -1,  1.1, -0.9,    1,   -1,  0.9,-1.2, 0.95, -1.1, 1.11]
df = DataFrame(data=voltage, index=time, columns=['voltage'])
x_crossings = cross(df['voltage'])
y_crossings = np.zeros(x_crossings.shape)
plt.plot(time, voltage, '-ob', x_crossings, y_crossings, 'or')
plt.grid(True)

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