I\'m trying to implement a rectangular pulse train in python.
I searched scipy and there is no signal that implements. http://docs.scipy.org/doc/scipy/reference/sign
If you're looking for just periodic pulse trains, like the example you gave - here's a pulse train that is on for 5 cycles then off for five cycles:
N = 100 # sample count
P = 10 # period
D = 5 # width of pulse
sig = np.arange(N) % P < D
Giving
plot(sig)
You can replace np.arange(N)
with your linspace
here. Note this is not equivalent to your code, as the pulses are not centered.
And here's a fully configurable pulse train:
def rect(T):
"""create a centered rectangular pulse of width $T"""
return lambda t: (-T/2 <= t) & (t < T/2)
def pulse_train(t, at, shape):
"""create a train of pulses over $t at times $at and shape $shape"""
return np.sum(shape(t - at[:,np.newaxis]), axis=0)
sig = pulse_train(
t=np.arange(100), # time domain
at=np.array([0, 10, 40, 80]), # times of pulses
shape=rect(10) # shape of pulse
)
Giving:
I think this is one of those cases where matlab's pulsetran
function is more confusing than the one-line implementation of it in python, which is possibly why scipy does not provide it.
All the answers are nice but I found they are having some problems with scipy.integrate
so I created 3 types specially keeping in mind scipy.integrate
:
def uniform_pulse_function(self, t, start, stop, pulsewidth, period, amplitude):
func = amplitude * np.where((t > start and t < stop and (t % period <(pulsewidth))),
1, 0)
func = (amplitude[int(t//period)])*np.where((t>start and t<stop and (t%period<(pulsewidth))), 1, 0)
return func
def custom_pulse_function(self, t, start, stop, pulsewidth, period, amplitude):
func = (amplitude[int(t//period)]) * np.where((t > start and t < stop and (t % period < (pulsewidth[int(t//period)]))), 1, 0)
return func
You could use the square
function from scipy.signal
:
Verbatim from here:
from scipy import signal
import matplotlib.pyplot as plt
t = np.linspace(0, 1, 500, endpoint=False)
plt.plot(t, signal.square(2 * np.pi * 5 * t))
plt.ylim(-2, 2)
Therefore, for your example, do this:
T=10
D=5
N=10
shift = 1/4 # number of cycles to shift (1/4 cycle in your example)
x = np.linspace(0, T*N, 10000, endpoint=False)
y=signal.square(2 * np.pi * (1/T) * x + 2*shift*np.pi)
plt.plot(x,y)
plt.ylim(-2, 2)
plt.xlim(0, T*N)