gistfile1.txt

import numpy as np

POINTS_PER_WF = int(1e4)
X_SPACE = np.linspace(0, 100, POINTS_PER_WF)

def make_waveform_with_noise():
    def add_noise(vec):
        stdev = float(np.random.uniform(0, 0.2))
        return vec + np.random.normal(0, stdev, size=len(vec))

    f = np.random.choice((np.sin, np.cos))
    wf = f(X_SPACE * np.random.normal(scale=5)) * np.random.normal(scale=5) + np.random.normal(scale=50)
    return wf, add_noise(wf)

def generate_training_data(batch_size, rescaling = 1e-3):
    while True:
        x = np.empty((batch_size, POINTS_PER_WF, 1))
        y = np.empty((batch_size, POINTS_PER_WF, 1))

        for sample in range(batch_size):
            valid_wf, noisy_wf = make_waveform_with_noise()
            x[sample, :, :] = noisy_wf.reshape((1, POINTS_PER_WF, 1)) * rescaling
            y[sample, :, :] = valid_wf.reshape((1, POINTS_PER_WF, 1)) * rescaling

        yield x, y