oscilloscope's Introduction

Oscilloscope

An oscilloscope for python that just works™

Features

Simple to use

import random
from time import sleep

from oscilloscope import Osc

osc = Osc()


@osc.signal
def simple_random_signal(state):
    while True:
        state.draw(random.random())
        sleep(0.1)


osc.start()

Gives you this

Parallel compute

Each osc.signal gets it's own process.

This

import random
from time import sleep

from oscilloscope import Osc


osc = Osc(nrows=2, ncols=3)


@osc.signal
def signal1(state):
    while True:
        state.draw((random.random())
        sleep(0.1)


@osc.signal
def signal2(state):
    while True:
        state.draw(random.random(), row=1, col=2)
        sleep(0.1)


osc.start()

Gives you this

P.S. Don't worry about race conditions, state.draw() is atomic. (See zproc)

Dynamic axis scale

The Y-axis's scale is dynamic, meaning that the graph's y axis scales with your signal.

This

import random
from time import sleep

from oscilloscope import Osc


# adjust window_sec and intensity to improve visibility
osc = Osc(window_sec=10, intensity=1)


@osc.signal
def increasing_signal(state):
    delta = 1

    while True:
        state.draw(random.randint(-delta, delta))
        delta += 5
        sleep(0.01)


osc.start()

Gives you this

Automatic normalization

This

import random
from time import sleep

from oscilloscope import Osc


# turn on normalization
osc = Osc(normalize=True)


@osc.signal
def increasing_signal(state):
    delta = 1

    while True:
        state.draw(random.randint(-delta, delta))
        delta += 5
        sleep(0.01)


osc.start()

Gives you this

This was the same signal as the [earlier](#Automatic normalization) one, but it looks a lot like the simple example, because we turned on normalization!

The Y-axis will now show, % max-amplitude encountered at the time, not the raw value.