Triangle wave shaped array in Python Triangle wave shaped array in Python numpy numpy

Triangle wave shaped array in Python


The simplest way to generate a triangle wave is by using signal.sawtooth. Notice that signal.sawtooth(phi, width) accepts two arguments. The first argument is the phase, the next argument specifies the symmetry. width = 1 gives a right-sided sawtooth, width = 0 gives a left-sided sawtooth and width = 0.5 gives a symmetric triangle. Enjoy!

from scipy import signalimport numpy as npimport matplotlib.pyplot as pltt = np.linspace(0, 1, 500)triangle = signal.sawtooth(2 * np.pi * 5 * t, 0.5)plt.plot(t, triangle)


Use a generator:

def triangle(length, amplitude):     section = length // 4     for direction in (1, -1):         for i in range(section):             yield i * (amplitude / section) * direction         for i in range(section):             yield (amplitude - (i * (amplitude / section))) * direction

This'll work fine for a length divisible by 4, you may miss up to 3 values for other lengths.

>>> list(triangle(100, 0.5))[0.0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.48, 0.46, 0.44, 0.42, 0.4, 0.38, 0.36, 0.33999999999999997, 0.32, 0.3, 0.28, 0.26, 0.24, 0.21999999999999997, 0.2, 0.18, 0.15999999999999998, 0.14, 0.12, 0.09999999999999998, 0.08000000000000002, 0.06, 0.03999999999999998, 0.020000000000000018, -0.0, -0.02, -0.04, -0.06, -0.08, -0.1, -0.12, -0.14, -0.16, -0.18, -0.2, -0.22, -0.24, -0.26, -0.28, -0.3, -0.32, -0.34, -0.36, -0.38, -0.4, -0.42, -0.44, -0.46, -0.48, -0.5, -0.48, -0.46, -0.44, -0.42, -0.4, -0.38, -0.36, -0.33999999999999997, -0.32, -0.3, -0.28, -0.26, -0.24, -0.21999999999999997, -0.2, -0.18, -0.15999999999999998, -0.14, -0.12, -0.09999999999999998, -0.08000000000000002, -0.06, -0.03999999999999998, -0.020000000000000018]


To use numpy:

def triangle2(length, amplitude):    section = length // 4    x = np.linspace(0, amplitude, section+1)    mx = -x    return np.r_[x, x[-2::-1], mx[1:], mx[-2:0:-1]]